Aufgabe 1: Koffeinkonsum
Eine Forschungsgruppe will die Konzentrationsfähigkeit in Abhängigkeit vom Alter (\(a_1\): < Median, \(a_2\): > Median) und von Koffeinkonsum (\(b_1\): kein Koffeinkonsum, \(b_2\): Koffeinkonsum) testen, \(N = 12\).
(a) Vervollständige die Ergebnistabelle für die zweifaktorielle Varianzanalyse und führe die Signifikanztestung durch (Modell mit festen Effekten, \(\alpha = 0.05\))!
Quelle
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QS
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df
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MQ
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F
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Alter (A)
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3
|
|
|
|
Koffeinkonsum (B)
|
27
|
|
|
|
Interaktion (AB)
|
|
|
|
|
Fehler
|
16
|
|
|
|
Total
|
58
|
|
|
|
Lösung
Hypothesen aufstellen:
- Bemerkung: wir haben in dieser Aufgabe ein Modell mit festen Effekten angenommen. Deswegen beziehen sich jetzt alle unsere Hypothesen auf die konkreten Unterschiede zwischen den gewählten Faktorstufen und Faktorstufenkombinationen.
- Alter (A): \(H_0:\bar{\mu}_{1\cdot} = \bar{\mu}_{2\cdot}\)
- Die Populationsmittelwerte der Stufen des Faktors „Alter“ sind gleich. D.h. es gibt keinen Unterschied zwischen den Stufen des Faktors \(A\) und somit auch kein Haupteffekt \(A\).
- Koffeinkonsum (B): \(H_0:\bar{\mu}_{\cdot 1} = \bar{\mu}_{\cdot 2}\)
- Die Populationsmittelwerte der Stufen des Faktors „Koffeinkonsum“ sind gleich. D.h. es gibt keinen Unterschied zwischen den Stufen des Faktors \(B\) und somit auch kein Haupteffekt \(B\).
- Interaktion (AB) \(H_0: \mu_{ij} - \bar{\mu}_{i \cdot} - \bar{\mu}_{\cdot j} + \bar{\mu}_{\cdot \cdot} = 0\)
- Die Zellenmittelwerte der Faktorstufenkombination setzen sich additiv aus den Haupteffekten zusammen. Es gibt keinen Effekt über die beiden Haupteffekte \(A\) und \(B\) hinaus.
Prüfgrößen F berechen:
- Als Erstes können wir die fehlende Quadratsumme \(QS_{AB}\) berechnen, da sich alle QS additiv zu der totalen Quadratsumme \(QS_{tot}\) zusammensetzen. \[QS_{AB} = QS_{tot} - QS_A - QS_B - QS_e \\
= 58 - 3 - 27 - 16 \\
= \underline{\underline{12}}\]
- Dann können wir die Freiheitsgrade bestimmen:
- die totalen Freiheitsgrade \(df_{tot}\) ergeben sich aus der Stichprobengröße: \(df_{tot} = n \cdot p \cdot q - 1 = N - 1 = 12 -1 = \underline{\underline{11}}\)
- die Freiheitsgrade des Faktors \(A\) ergeben sich aus der Anzahl \(p\) der Stufen dieses Faktors (\(a_1\): < Median, \(a_2\): > Median): \(df_A = p -1 = 2 - 1 = \underline{\underline{1}}\)
- die Freiheitsgrade des Faktors \(B\) ergeben sich aus der Anzahl \(q\) der Stufen dieses Faktors (\(b_1\): kein Koffeinkonsum, \(b_2\): Koffeinkonsum): \(df_B = q -1= 2-1= \underline{\underline{1}}\)
- die Freiheitsgrade der Interaktion \(AB\): \(df_{AB} = (p-1)\cdot (q-1) = (2-1)\cdot (2-1) = \underline{\underline{1}}\)
- die Fehlerfreiheitsgrade können wir jetzt als: \(df_e = df_{tot}-df_A - df_B-df_{AB} = 11-1-1-1= \underline{\underline{8}}\) berechen, da alle Freiheitsgrade sich additiv zu \(df_{tot}\) zusammensetzen.
- Jetzt können wir die mittleren Quadrate (MQ) berechen:
- dabei teilen wir die jeweilige Quadratsumme durch die zugehörigen Freiheitsgrade: \[MQ_A = \frac{QS_A}{df_A} = \frac{3}{1} = \underline{\underline{3}}\] \[MQ_B = \frac{QS_B}{df_B} = \frac{27}{1} = \underline{\underline{27}}\] \[MQ_{AB} = \frac{QS_{AB}}{df_{AB}} = \frac{12}{1} = \underline{\underline{12}}\] \[MQ_e = \frac{QS_e}{df_e} = \frac{16}{8} = \underline{\underline{2}}\]
- Jetzt berechen wir die empirischen \(F\)-Werte:
- wir haben in dieser Aufgaben ein Modell mit festen Effekten angenommen, deswegen muss jetzt bei uns im Nenner immer \(MQ_e\) stehen. \[F_A = \frac{MQ_A}{MQ_e} = \frac{3}{2}= \underline{\underline{1.5}}\] \[F_B = \frac{MQ_B}{MQ_e} = \frac{27}{2}= \underline{\underline{13.5}}\] \[F_{AB} = \frac{MQ_{AB}}{MQ_e} = \frac{12}{2}= \underline{\underline{6.0}}\]
Quelle
|
QS
|
df
|
MQ
|
F
|
Alter (A)
|
3
|
1
|
3
|
1.5
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Koffeinkonsum (B)
|
27
|
1
|
27
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13.5
|
Interaktion (AB)
|
12
|
1
|
12
|
6.0
|
Fehler
|
16
|
8
|
2
|
|
Total
|
58
|
11
|
|
|
Kritische F-Werte berechnen:
- das \(\alpha\)-Niveau ist auf \(5\%\) festgelegt, in unserer Tabelle der \(F\)-Werte finden wir genau die Werte für \(\alpha = 0.05\).
- jetzt brauchen wir die Freiheitsgrade für unsere \(F_{krit}\)
- dafür betrachten wir die Formeln für jeden berechneten empirischen \(F\)-Wert und nehmen als
- Zählerfreiheitsgrade den Wert, der der MQ im Zähler entspricht,
- Nennerfreiheitsgrade den Wert, der der MQ im Nenner entspricht (in unserem Fall ist es immer \(df_e\))
- dann ergeben sich folgende Werte \(F_{krit}\): \[F_{df_A, df_e, 95\%} = F_{1, 8, 95\%} = \underline{\underline{5.318}}\] \[F_{df_B, df_e, 95\%} = F_{1, 8, 95\%} = \underline{\underline{5.318}}\] \[F_{df_{AB}, df_e, 95\%} = F_{1, 8, 95\%} = \underline{\underline{5.318}}\]
- in unserem Fall ist es derselbe Wert für alle drei kritischen Werte.
Testentscheidung:
- Jetzt vergleichen wir die Prüfgrößen mit den kritischen Werten:
- Haupteffekt \(A\): \(F_A = 1.5 < 5.318 = F_{krit}\). D.h. die \(H_0\) wird beibehalten. Möglicher Fehler: \(\beta\)-Fehler.
- Haupteffekt \(B\): \(F_B = 13.5 > 5.318 = F_{krit}\). D.h. die \(H_0\) wird abgelehnt. Möglicher Fehler: \(\alpha\)-Fehler.
- Interaktion \(AB\): \(F_{AB} = 6.0 > 5.318 = F_{krit}\). D.h. die \(H_0\) wird abgelehnt. Möglicher Fehler: \(\alpha\)-Fehler.
(b) Es sind die Mittelwerte für jede der \(4\) Zellen des zweifaktoriellen Versuchsplans gegeben. Zeichne die zugehörigen Interaktionsdiagramme! Welche Art von Interaktion liegt vor? Welche Auswirkungen hat dies auf die Interpretierbarkeit der Haupteffekte?
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\(a_1\)
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\(a_2\)
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\(b_1\)
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3
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2
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\(b_2\)
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4
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7
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Lösung
- Erst wollen wir das Diagramm für den Haupteffekt \(A\) zeichnen.
- Wir zeichnen ein Koordinatensystem: auf der \(x\)-Achse tragen wir die beiden Stufen des Faktors \(B\) \(b_1\) und \(b_2\) im beliebigen Abstand voneinander ab. Auf der \(y\)-Achse markieren wir die Werte von \(1\) bis \(7\).
- Wir schauen uns die Spalte \(a_1\) an und tragen die beiden Werte aus dieser Spalte wie folgt in das Diagramm ein: erster Wert \((b_1, 3)\), zweiter Wert \((b_2, 4)\). Danach verbinden wir die beiden Punkte minteinander.
- Jetzt schauen wir uns die Spalte \(a_2\) an und tragen die beiden Werte aus dieser Spalte wie folgt in das Diagramm ein: erster Wert \((b_1, 2)\), zweiter Wert \((b_2, 7)\). Danach verbinden wir auch die beiden Punkte minteinander.
- Das Diagramm sieht folgendermaßen aus:
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAacAAAG7CAYAAABn+1p4AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAADJTSURBVHhe7d0JdFN1+vDxV3FhFBBc/oo44zbKMCAqiMoooqigoIIrMDjKIouKgAiyu4CDgiDCyI6y77IvIiCLCFT2lrUtlLJIy75UKFDxee9ze1OSNGmTNk1uku/nnN9pe3NvOacl+Sa5T9L/JyHyySefyP/7fyH75wEANkacAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtuNXHdLS0mTMmDHSsmVLady4sXTo0EHmz59vXeof4gQA8MavOvTs2VMqVqwoDRo0kI4dO5qBeuCBB8xg+Ys4AQC88bkOe/fulVKlSplhio2NldTUVPNjrVq1pEyZMnLo0CFrT98QJwCANz7XISYmxozJgAEDrC2ZevToYW7funWrtcU3xAkA4I3PddBHSrfddpvUqVNHEhISzG36sWrVqub2o0ePmtt8RZwAAN74VYfx48fLPffcYz6V16RJE/Pj3XffbW7PzeHDh2XWrFlZq379+sQJAOCRz3XQSb2uXbvKrbfeKs2aNTMn9V5++WXz688//9zay7u1a9dKhQoVslbJkiWJEwDAI5/rMHPmTLn++uulW7dusmvXrqyBiPbt28stt9wiy5Yts/b07MCBAzJ8+PCs9cILLxAnAIBHPtehUaNGctVVV5lRcqZTfBqZ5s2bW1t8wzknAIA3PtehRo0aUqJECesrVxoZvdwfxAkA4I3PddAX3V555ZWSmJhobcmkX2tk9HJ/ECcAgDc+10EHGnT4oW7durJ8+XIzSvpRv9bterk/iBMAwBuf65Ceni5jx46V6tWrm0/h6UCDftTXPel2vdwfxAkA4I1fddAArVixQkaNGiVDhw41P65Zs8bvMCniBADwJmR1IE4AAG+IEwDAdogTAMB2iBMAwHaIEwDAdogTAMB2iBMAwHaIEwDAdogTAMB2iBMAwHaIEwDAdogTAMB2iBMAwHaIEwDAdogTAMB2iBMAwHaIEwDkwaK4ozJ00X7pNnmndBifYK7PpifJqGW/SdLBM9ZeyCviBAB+GrX8gFT/73q5p/1qY62S+zrESJm2K+XWd1dIxY4x0mzYNjmSdt7aG3lBnADAD/qISQNU6u2f5f0x8TJ1darMXHvI/Khf6/abWyyXiStTrCOQF8QJAPzQdfJOuaPVCnnt61hJPpRubc2kXz/7+Qa5oekyeefb7dZW5AVxAgA/rNh+XMb/kiIxiSesLa7eG7nDjNMLvTdZW5AXxAkA8mHtrpOyYsdxmb7moAxbvF+e7LHOjNNTn6239kBeECcA8JPGqM+cZOk0MVFe6hsrz/XeKFU+Xivl26+W21quIE4BQJwAwA+LNx81Y3Rnq1/MCT2N0It9N0mLEduk16zdUvvLTcQpAIgTAPjhBSNMGp8X+2ySyatSZcmWoxKTcEJ2HDgtJ89kSNsx8cQpAIgTAPhBx8Q1Pt/8uNfa4qrmF5nTep7ipI+69LjOExPN9cnUXTJ6+QHZfYgX7bojTgDgB30dk8any6Sd1pZMGpjhP+03X4jrKU4aoRo9N8iDXX41L9OIVer8q1TsFCMthm8jUG6IEwD44YUvM5/W06h8PnO3DPxxn3w1N1kaD9kqD3T61RyM0Mv1o8PWfb+b+//j/ZXS/4c98tOWo/Lz9swJvzp9NpmPxnpMS7L2hiJOAOAHjUrd/nFmaG5tuULKtF0l93dYbcbo4yk7ZcIvKWac/t76F0k/d8E85tslv5nb6hnH6XkpZ6sTTpiPxnTSDxcRJwDw07qkUzJpVar5uiZd41YckAWbjkjK8bNmfBzbz2Vkxkn31681bJ6UNkKn8cJFxAkAQmhd0knzEZg+EsNFxAkAQuRo2nlpPnyb+ahJ38kcFxEnAAgBDdMn3+8yz009+vFaWbfrpHUJFHECgCDTMH3qFKYfNh6xLoEDcQIAQ0JCgixZskTGjBljLv1ctwXatv2/S5dJiYQpF8QJQFRLT0+XiRMnyssvvyxVqlSRcvc/LGXve8j8XLfpZbpPIGiYGg/eag5A6OubCJN3xAlA1Dpz5ox07tJVypYtK1f89SG5+oEmUvzxjnLN4x3Mz3WbXqb75DdQZpiGbDVfcPva13GyKt7zWDkyEScAUWvUqFFSpGhRufKOalKi9mC5vvFCc3JOl36u2/Qy3Wf06NHWUf7Tc0yOMDUdts38G1DIGXECEJX0fJLjEVOJV0ZmRcl96WWOR1B5PQc1Ysl+M0z6ThCtR8XL4EX7PC5cRJwARCV9JKS3QdfU6ucxSs6r+HP9zX3z+ujpla9ize+jgfrnB6u8LlxEnABEpVatWsllfynu8lSe19VksRS6qoS0adPGOto/U2MOyqCF+3JduCjodRg0aJB88MEHUrlyZeIEIGTq1asnV5b4q+cYeViXl/ib1K9f3zoaBS3odWjXrp1Ur15d7rzzTuIEIGT0tqhQ4aJyfaMFHmPkvHSfS68sah6D4Ah6HTZv3iwrV66UJk2aECcAITNhwgTzNuiaGp97DJLz0n10Xz0GwRGyOnDOCUAo7dmzR8rfX1Euv+keKVFniMco6dLLdJ8KFR8wj0FwECcAUelsxgVp0HGoXFa4qFxxy4NSvGZfl6f49HPdppddc01xmTZ9hnUkgoE4AYg6GqY56w/Li73Xyt0120mxW8qaj47+UvZFKfJQc3Pp545HTP36fS1nz561jkYwECcAUWf5tmPy5sAt8tRn66XaJzHyUJOBUrtuQ6lVq5ZUeKiKufTz5s2by7x58wlTCBAnAFElbm+avP3tdjNMjtV9WpKs35Ioa9askTlz5phLP+ccU+gQJwBR40jaeekyaadLmNqMjpf4A6etPWAXxAlAVDiXcUG+XfKb1Px8Q1aY6n4dJzGJJ6w9YCfECUDE0zDNXX9YXukXmxWmml9slAkrU4zL/rT2gp0QJwARb/m24/LmoMwBCEeY+s3bI8d/z7D2gN0QJwARbfPeNHnHfQDi+12y/ygTeHZGnABELAYgwhdxAhCR9DzTd0uzD0CsTmAAIhwQJwARR8M0b0P2AYjxvzAAES6IE4CI8/P27AMQX81NZgAijBAnABHF0wDEp+YARLq1B8IBcQIQMY6mnZeubgMQrUfFy47fGIAIN8QJQETwNADxGgMQYYs4AQh7jgGIVxmAiBjECUDY0wGIhm4DEH3nJssxBiDCFnECENa27EuTd79zHYDoPHGn7GMAIqwRJwBhyxyAmOw6APH2iG0StyfN2gPhijgBCEt6nmnksuwDEPpXbhH+iBOAsKNhmr8x+wDEuBUMQEQK4gQg7MQknmQAIsIRJwBhJTHltLw/Jj4rTLo6T0xkACLCECcAYUMHIHpMS3IJU4sR2ySWAYiIQ5wAhAU9zzTKwwDEMgYgIhJxAmB7GqYfNh3JNgAx9ucDDEBEKOIEwPZ+1QGIwa4DEH3mMAARyYgTAFvzNADRSQcgjjAAEcmIEwDbYgAiehEnALak55lGLz+QbQBi6VYGIKIBcQJgOxqmBR4GIMYwABE1iBMA2/l150lp5DYA8SUDEFGFOAGwlZ2pp6Wt+wDEhETZywBEVCFOAGxDByA+m+46ANF8+DbZlMwARLQhTgBsgQEIOCNOAELO4wCEESmNlV6G6EOcAITcGvcBCCNMvWfvNp/mQ3QiTgBCytMAREcGIKIecQIQMjoa/l+PAxCnrD0QrfyuQ0JCggwbNsyMS69eveSnn36SM2fOWJf6jjgB0U1fTKsvqtXXMDnCpOeclmw5au2BaOZXHdatWycNGjSQp59+Who1aiTPP/+8VKlSRcaMGWPt4TviBEQvDdOPsUfMaTxHmBiAgDOf63Ds2DEzRmXLlpUpU6bInj17ZMWKFfLoo49KuXLlzMv9QZyA6LXWHIDYmhUmXb1nMQCBi3yuw6JFi8yYDB061NqS6bvvvjO3//zzz9YW3xAnIDrtSj0jH4xNcAmTDkQwAAFnPtehY8eOUrhw4WyPkPQR1EcffSRxcXHWFt8QJyD6mAMQM1wHIJoN3yYbGYCAG5/rULt2bfMpvdTUVJk2bZp0797dXPPnz/dpIOL48eOyfPnyrKXnrIgTED30PJP+WXX3AYifGICABz7XoUaNGlKpUiXp2rWrtG7dWt5880156aWX5LHHHpMBAwbkGqiNGzfKE088kbVuv/124gREkYUeBiBGMQABL/yK07XXXitNmjQxByGSk5PNp/JatGghpUqVMrflJCkpSbp165a1NGrECYgO+pdrG7sNQPRiAAI58CtOGhP3c0saKd3epk0ba4tvOOcERIddB89IO7cBiPfHxMseBiCQA5/r0LBhQzMmnp6+0+16TsofxAmIfDoA0dN9AGLYNtm4mwEI5MznOowbN86MifvTd4mJieZ2ndjzB3ECIps5ALHCwwDEZgYgkDuf65CSkiIVK1aUF154QVatWiV79+6VLVu2SLNmzaRkyZKMkgNwsTDOwwDEst8YgIBP/KrDrFmzpFq1alKvXj1p2rSpObH3yCOPSJ8+faw9fEecgMgVpwMQQ1wHIL6YyQAEfOd3HfSdIPr27StdunSRHj16mME6ffq0danviBMQmcwBiHFuAxCj42XPYQYg4LuQ1YE4AZFHByA+n7HbJUw6ALGBAQj4iTgBCAgdgBi3IiXbAMRiBiCQB8QJQEAsijuabQBiJAMQyCPiBCDfGIBAoBEnAPmSdPCMtHcbgGgzOl6SGYBAPhAnAHlmDkAYj5Ccw9R02DZZn8QABPKHOAHIEx2AGP8LAxAoGMQJQJ5ohOq6DUB8t5QBCAQGcQLgt81706SJ2wCEPr13hAEIBAhxAuAX7wMQuf9FbMBXxAmAz47/nmGOiDuHqenQrQxAIOCIEwCf6ADEBLcBiFf6xcqiuCPWHkDgECcAPtG/w+Q+APEtAxAoIMQJQK48DUDoX7hlAAIFhTgByNHuQ2fkw/GuAxDvfLudAQgUKOIEwCsdgOg1y3UA4q2hW2VVwglrD6BgECcAHpkDECuzD0Don1+HYe1akalTRb7+WqRXr8w1eLDI7NkiqanWTsgr4gTAoyVbXAcgnv18g4xY8pucZQAiM0AvvCBSurRIqVKZ6/rrRYoXF7n3XpEPPxQ5xXh9fhAnANkkHDgtTYa6DkD8lwGITOvWZQboiitEXn1VpE8fkYEDMz/q17q9cGGR+fOtA5AXxAmACx2A6DZ5p0uY3tYBCGM7DN27ixQtKvLkk0bFE6yNFv36wQeNW1bjtq1VK2sj8oI4Achy/LSXAYh4BiCy6FN6PXqILFxobXDTsmVmnGrWtDYgL4gTANP5jD9l4soUqeU8APFVrCyMZQAiR/HxIps3iyxbJvL99yJPP50Zp0cesXZAXhAnACZzAKK/+wDEfgYgPNEYjRwp8uWXIm+8IfLaayJVq2YOSBQrRpwCgDgBkISU0+bTd44w6frv9CQ5cooBiGzWr8+M0U03iVx3nUj58iL/+pfI88+LNGuWGSXilG/ECYhyOujw0RS3AYgR283BCHigE3l626VB6t0787VOev4pNlbk+HGRDz4gTgFAnIAopgMQvWe7DkDoCPlKBiC80zFxve365htrg5vnnvMtTsnJmePn7hN/MBEnIErpAMSkVanZBiB+ZAAiZ/o6Jr3t6tnT2mA5eFBk+vTMF+LmFid9gW6XLpn76RAFsiFOQJRauvVYtgGI4T8xAJErxzml++4TGTZMZNIkkdGjRdq2zTz/pEMRevkDD1gHuNGI9e+fec6KOHlFnIAo5GkA4jMGIHwza5bI449nDkPoo6Tbbxe5667MKDVpkvluEXrbpvHREDnTYYqOHS8OUxAnr4gTEGWSD6fLR1N2uYSJAQg/LV+e+Uav+mhJl75rxJgxIvv2ZQ5FOLbr1w56bql27cxRc53sq1WLOOWAOAFR5MTpDPlydrJLmPSPCP6yw7hBRcHSCN19d+Y7SOhkn2Oqjzh5RJyAKOFpAOLlr2JlwSYGIIJCg6TnpvSRlSJOOSJOQJTQAYh6DEDYB3HKEXECokBiymlp6jYA0WNakhxmACJ0iFOOiBMQ4XQA4uOprgMQLUZslyQGILJkZGRIUlKSxMTEyA8//CDr16+Xfc7DDAWBOOWIOAERjAGI3CUkJMigQYOkadOm8uyzz0q1SpWkdu3a0rJlS5k6daocdB8HDxTilCPiBESo83/8KZNXMwCRk1WrVkmjRo3knyVKSDXj9ugdY3W/9FJpanx8xljl77pL2rVrJ4mJidYRAUScckScgAi1bFv2AYhhi/fL2fMMQKj9+/dL9erVpbxxO9TPCNJK4+M5vU0yVpqx1hurXaFCUtK4rNV778nZs2etIwOEOOWIOAERKDH1tDQd5j4AsYsBCCfdP/1UihvxmaS3Q17W78bqYMSp+DXXyNy5c60jA4Q45cj4yYQGcQIKxp7D6fKJ2wBE8+HbJOkgAxAO+ijolptvljqXXSYZVoi8rd3GustYL+i7OgQSccqR8ZMJDeIEBJ4OQPSZ4zoA0XjIVlnBAISL+Ph48/bnO70N8mG1NFapkiWtowNkxgyRVq1ENm2yNsCZ8ZMPrmnTpkn//v3NqRjiBASODkBM8TAA8cOmw9YecFiwYIF5+7PUik9u60tj6f7p6enWd0BBM37ywdWiRQt58MEHpVSpUuYvG0BgLPcwADGUAYjszp2TmBEjzNuf6Xob5MNqZ6zixYrJ6dOnrW+Cgmb85INLX+Q2f/58adCggfE7D/o/D0SknalnpOmwbVlh0tX9ewYgXBhRkg0bRCZMkBNvvCFFCxWSVk4B8rZOGevhSy6RRytXtr4RgsH46YcG55yAwDAHIIwQOYeJAQgnTlGSZs1E/vUvkYoVpV6JEvJ34zZorRUhb2u8sf5y6aXSr18/6xsiGIyffmgQJyD/vA5AbGcAwluUHGtdmTJSxgjUc8bt0CJj6SMk5ygdMtY0I0r3GR8fr1pVUlNTrW+MYCBOQJjSAYipMdkHIOZvjPIBiPPnM6M0cWJmlPTPqjtFyVzPPivSqZOM695dHn74YXnIuC361Fj6KGmesUZdcol0MD6Wv+oq84W6y5Yts745goU4AWHq5+3ZByCGLNoXvQMQfkTJ3Md6zzyd3Hv11VelcuXK8sidd8oTxqOpyv/8pzz22GPSplUrWbdunbkfgos4AWFIByCauQ1AfBqtAxB5jJKz5ORkWbhwoYwYMUJ69ughY8eONR8tpaWlWXsg2IgTEGb2Hkk3Q+QcpmbDt8muaBuA0Cht3CgyaZJI8+aeo/TMMyIdO2aedyqodxdHgSBOQBjRAYi+c10HIBoN3io/R9MABFGKCsQJCBOZAxAHo3cAgihFFeIEhAkdD6/vNgAxOBoGIPyJ0vjxRClCECcgDOj5JPcBCH3n8UORPABBlKIacQJsTgcg9K2InMOkoYrYAQiiBANxAmzs5JkM+WruHpcwNRq8xXyT14ijUdI/HzF5cs5R6tCBKEUB4gTYlA5AfO82APFS31iZtyHCBiCIEjwgToBNmQMQAyJ4AIIoIQfECbAhfUdxfWGtI0y6zAGIk8YNerhzRGnKFP0Db96j9OGHIuPGifCGq1GJOAE2s08HIKYluYSpqQ5ApIb5H7ojSvADcQJsxByAmOc6ANFw8BZZvPmotUcYIkrIA+IE2IQOQEz7NfsAxNwNh8PzPJNGKTaWKCFPiBNgE7/syD4AMWhhGA5AECUEAHECbEAHIPRPqzvCpOvjKTvDawCCKCGAiBMQYjoA8cXM3S5h0gGIneEyAEGUUACIExBCOgDRb94el/NMDQdtkUVxYTAA4YjS1Kkib7/tOUo1aoi0by8ydixRgl+IExAiXgcg1tt8AIIoIQiIExAiYTcAQZQQRMQJCIHdh7IPQHxk1wEIjVJcnMj33+ccpXbtRMaMIUoICOIEBNm+o+nyxSzXAYi3htpwAIIoIYSIExBEOgDxtd0HIIgSbIA4AUGiAxDT12QfgJiz/pA9zjMRJdgIcQKCZGV89gGIgT/uDX2YiBJsiDgBQeBxAGJyiAcgiBJsjDgBBWz/0bPSy8MARGKoBiA0Sps3i0ybJvLOO96j9MEHIqNHEyWEBHECCtDJM3/I1/NdByDeHLRFFoZiAIIoIYwQJ6CA6ADEjDWH3AYgNgV/AIIoIQwRJ6CArEo4If8esDkrTDoA8c2CIA5AOKI0fTpRQtghTkABSD6ULi3cBiC6Td4pB0+es/YoQEQJEYA4AQHmaQCiydCtBT8AQZQQQYgTEECnzvwh/T0NQMQW4AAEUUIEIk5AgOgAxMy1rgMQz/feKLPXFdAAhCNKM2Z4j1L16iJt24qMGiWSkmIdCNgfcQICZLWHAYg+c5IDHyaihChAnIAASD6cLi1GuA5AdA30AARRQhTJVx0WLlwoI0eOtL7yD3FCpNAA9Z6d7BKmJkO2SmJKgAYgNEpbtmRG6d13vUfp/ffFuEISJUSEPNdhs3EPrly5cnkODHFCJDhz7oIMXbzP5TyTPrX3Y+wRa498IEqIYnmqQ2pqqrz22mtyxRVXECdELR2AWLDpSLYBiCmrUyU9P+eZiBLgf5zOnj0rX3zxhdxzzz1y1113ESdErRi3AYhnPt8gX85JznuYHFGaOZMoIer5XYd58+ZJmTJlZOrUqVKjRg3ihKi0x8MARJdJiXkbgCBKQDZ+1SE+Pl6qVKkiH+iL+Qz+xCnFuEKNGjUqa9WpU4c4ISx5GoBonJcBCKIEeOVzHY4dOyYNGzaUZ555Rg4ePGhu8ydOa9eulXvvvTdr3XjjjcQJYSf93AUZtni/POd0nqn+gM3muSefaZS2bhWZNSvnKLVpI/Ldd0QJUcmnOuh5psGDB0vp0qVl9erV1lb/4nT48GGZMWNG1qpbty5xQljJ+ONPcwrPOUzP9d4ok1f5OABBlACf+VSHXbt2ye233y7VqlWT0aNHZy0ditDA6OeLFi2y9vYN55wQbmIST8i//+c6AKFP7+UaJqIE+M2nOsTFxZmPktzXddddZwZGP+/UqZO1t2+IE8KJDkC8PWJ7Vph0dZmYKAdP5DAA4Rylli29R6l1a6IEuPGpDmlpaeYwhPt6xLiyaWD08z179lh7+4Y4IVwcOnlOvvQwAJHgbQCCKAH5lq866COmvAaGOCEc6FN2w35yH4CI8zwAQZSAgCFOgBc6ALHQfQCi10aZtCrF9TyTRmnbNpHZs71H6emnM6P07bdECfBBvurQq1cvef31162v/EOcYHe/ehiA6OU8AEGUgAITsjoQJ9jZ3iPZByA6OwYgiBJQ4IgT4EYHIPSPBDqHqdHgrZKw9wRRAoKEOAFO9Cm74ToA0evieaYGX22QHyf9IjJnTs5RatVKZMQIogQEAHECLOYARNzRrDDV6P6rNGs7TWZ89K2cf4coAcFEnADLrztPSoP/bTaj1Lj199LtP1/Liqdflwv/IkpAsBEnwKADEO8N22xEaZp0MaI04+F6knR3RTl/X4XsUXrvvcwoHThgHQ0g0IgTot6RY6dl0rdLs6K05dZ7Jflv/5T08vcTJSBEiBOi1/nzcn7LNlk3YLzM/VdmlBJuLiNJf/2nnCxzL1ECQog4Ifro65S2b5c/58yVI41ayNbb7jOjpCuxVBk5Wrq8/PnkU5lRGj6cKAEhQJwQPawoydy5ZnhOV6osu41HSc5h2lehivz5bkuiBIQYcULkc4uSjoSfu7eC7Lu9XFaY1t/5kGyu00jS/jeEKAE2QJwQuTIyMqM0b15mlB591DyPlHFfBUn9+z1ZUZr5cF2Z0uQz2bFup3UggFAjTog8XqKk60KFinL47vKy4a6HzSj1rdNV3vlsiczf6OFPYAAIGeKEyJFDlHT9aYQp9cGqMqtyZpRe6/Cj+W4QE35x+xMYAEKOOCH8aZR27BCZPz/zXRvcomSup56Sna80lmH1PjajpG9P9EzPDdJjWhJhAmyIOCF8+Rglefdd2ffF/+Sj/iuy3sxVV7uxCZJ8+Iz1zQDYCXFC+PEjSjJsmByOT5a+c/e4hKnhoC0Su+eU9Q0B2A1xQvjwM0o6Eq5P2X27xPVPYNTrHyfzNx62vikAOyJOsD9/ojR0aNbrlPRPYCzfdswlTPr5eAYgANsjTrCvPEbJYVPyKWkydGtWmHQAovu0XYQJCAPECfaTzyipfUfPStux8Vlh0vWB8XXyIQYggHBAnGAfGqX4eJEffsg5Su+84zVK6sip8/KV2wDEm4O2mI+kAIQH4oTQC1CUlD5l993S31zOM9XtHyfzNjAAAYQT4oTQCWCUFAMQQOQgTgg+R5QWLBBp3dp7lN5+W2TIEJHffrMOzFls8il5y20A4tPvGYAAwhFxQvAUUJTU/qNnzYEHR5h0tR0TL7sZgADCEnFCwSvAKKkjaeflq3kMQACRhDih4GiUEhIKLErq7PkLMnKZ2wDE13EylwEIIKwRJwReEKKkdADi5+3HXcJUy/h83IrMty0CEL6IEwInSFFyiN2TxgAEEKGIE/LPEaUff/QepSefFGnRQmTw4HxHSf127Ky0cx+AGM0ABBApiBPyLgRRUjoA0c9tAOKNgVtkIwMQQMQgTvBfiKKkdABilNsAxMtfxTIAAUQY4gTfhTBKSgcgVngYgBixZD/nmYAIQ5yi2PHjx2X37t2ya9cuOXLkiPzxxx/WJW40SomJIgsXirRpk3OUBg0KeJQc4jwMQHwylQEIIBIRpyi0Z88emTN7tnz55Zfy9ttvy1tvvSU9e/aUyZMnGw0yIuRgkygpTwMQ7zMAAUQs4hRl1q5dK++9957cUaSIPGj8/Ksb6xljVTbWLcZq2LChrPzlF9tESZ04nSFfMwABRBXiFEWSkpKkatWqUtb4uX9cqJCsND6mGuuosdYaq6+x7tdVvrxsad7ce5T0siBESekAxKSVKdkGIOasZwACiGTEKUqcOXPGaEpzKXnppTJDf+5e1mJj3WmsOsWLy+F77w1ZlJQ5ALEj+wDE8J/2S/o5zjMBkcy4RQoN4hRcmzZtkkLGo6WOxnIPkvsaZiz93Uy7446QRMnB6wAEYQIinnFrFFx6Mn7Hjh3SsmVL8wYQwTF06FC59JJLZJcVoJzWOWNdY6z2//iHyMCBQY+SOqADEONcByDaMAABRA3j1ii4unfvLvXq1ZNy5coZt4NB/+ejVuvWraWYD4+aHKuSEbKaTzxhHR1c5gDEfNcBiP/oAMRuBiCAaGHcEgVXly5dpHbt2lK6dGnjNjDo/3zU0gm9Gy67TM66Rcjb0jg9W726dXTwmAMQq1IZgACinHFLFBqccwqukSNHmj/vjU4B8rYOGqvopZeadySCKePCn/JLfPYBiGEMQABRx7g1Cg3iFFxbt26VG2+8UVoaj4h+dwqR+/rTWL2NfYoVLSqLFy+2jg6OzXvTpOkw1wGIj6fsJExAFDJukUKDOAVfp06d5C+XXSZjjPgccAqSYx0x1hLjslLG+ne9etZRwXHg+FlpPy4hK0y62oyKl6SDDEAA0ShkdSBOwZeSkiJPPP643H3dddLJ+NkvM5Y+zRdrrJ+NIH1hfCx/9dVSqVIliY2NtY4qeCfPZEh/9wGIb7bIBgYggKhFnKLM9u3b5YMPPpB//OMf8vBNN0mtwoWl9uWXy79uvFHuuusuadywocTExFh7FzwdgJjsNgDxUt9Ymb2OAQggmhGnKDVlyhT573//K43ffFP+U7++fNKtm4wdO1bOnTtn7VHwdABipYcBiKGLGYAAoh1xQshs2Zd9AOIjBiAAGIgTQiLFwwBE61E7GIAAYCJOCDpzAOKHvS5hep0BCABOiBOC6mzGBZmy2tMAxCFrDwAgTggiHYBYFX/CdQDii40yZPE+zjMBcEGcEDSeBiC6MQABwAPihKBIOX5O2o9nAAKAb4gTCpwOQAzINgCxWdYnnbT2AABXxAkFSgcgpq5OleddBiA2ySwGIADkgDihwPyhAxAJJ1zCZA5ALNonZzjPBCAHxAkFZuu+36XZsG1ZYarRc4N0nbyTMAHIFXFCgUg9cU4+dBuAaDVyh+xiAAKAD4gTAu6UhwGIBv9jAAKA74gTAupcxgX5PsZ1AOLFvptk5loGIAD4jjghYHQAYrXbAETNLzbKYAYgAPiJOCFgtu5nAAJAYBAnBIQOQHRwG4B47zsGIADkDXFCvukAxP8WZB+AWMcABIA8Ik7Il8wBiIMMQAAIKOKEPNMBiJjE7AMQgxYyAAEgf4gT8mybDkAMdx2A6DIpkTAByDfihDw56G0AIpUBCAD5R5zgt1PpGfKN2wDEvxmAABBAxAl+0QGIab+6DUD02SQz1jAAASBwiBN85m0AYuDCvZxnAhBQxAk+284ABIAgIU7wycGT56TDBNcBiJbfbZedqaetPQAgcIgTcpWW/od886PbAMSAzbJuFwMQAAoGcUKOdABiutsARB0GIAAUMOIEr3QA4tedJ7MNQOijKM4zAShIxAleJaacluZuAxCdJzIAAaDgESd4dOjkOfn0+11ZYdLVYjgDEACCgzghGx2AGPjjPpcw1R+wWX7eftzaAwAKFnGCCx2AmLEm+wDEdGMbAAQLcYpi6enpcujQIUlNTZXTp0+bAxBrGIAAYAPEKQodPnxYVq1aJRMmTDB/D507d5ZRo0bJDwuXSsOvfs4Kkw5AdGIAAkAI+FWH8+fPS3x8vKxYsUKWLFliftSvdbu/iFNo6O+rR48ecvOtf5dC1/xVLru+tFx2wz+kUPG/SeHiN8mtj74hlVtNNePUYvg2BiAAhIRfdfjpp5/k5ZdfloceekgqVKggFStWNL/W7f4iTsGXkpIiderUkUJFbpSi5WpLseo95dpXx8i19SbKNc/2lqvu/bcUKlZKStzxgFTvPJ8BCAAh43MdDh48KKVLl5ZHHnnEjFFcXJzMnj3bDJVu18v9QZyCSx/ddu7cRQoVLiZFq7SXG5ou87iKPfmpXHrVdVK5+qty8iRvTwQgNHyuw8iRI82YrF692tqSSUOl2/VyfxCn4EpMTJSrri4iV5d+xmOUnNfVld4yfzcLFiywjgaA4PK5Dnry/PHHH7e+uujIkSPmDVnfvn2tLb4hTsGlAw/68y5eq5/HIDmv6/4zWy65vLB06dLFOhoAgitfddCnigYOHCiXXXaZTJs2zdrq2fHjx2Xp0qVZq2HDhsQpiNq2bSuFrigsN7y11GOQ3NcVN9wtzz//vHU0AARXnuugYdJpPT3f9OCDD+Z6fkKfDrz11luzVvHixYlTEDVr1kyuLHaD3NBksccYua8rSt4jVR9/wjoaAIIrT3XQMOmjH41SmTJlfJrW00kxPS/lWDo1RpyCZ8iQIebP+9rXxnqMkfO6/s25clnhotKuXTvraAAILr/rkJaWZoapbNmyPofJE845BdeGDRvMR6tXV2iY66OnYlU7yJWFC8usWbOsowEguPyqw++//y5jxoyRcuXKSdWqVWXx4sXWJf4jTsHXtGlTKXRlEbmmZh/z0ZF7lK5vtECufeU7ubzE36Tak0/LuXPnrCMBILh8roM+ladhKlGihBkmHU3OD+IUfDt37jTvWBS56e9SpHJLKfHyt3Jt3Qlybb1JxucjpWjVjnL1zeXM84grV660jgKA4PO5DuvWrZOiRYvKHXfcYT6tt3fvXpd14sQJa0/fEKfQiImJkfr1/y233HKLXHdHRfnL35+SwndVlxJ3PCglS5aUp55+mtc3AQg5n+vwyiuvmDGpVq2atGjRItuaN2+etadviFPoZGRkyDfffCPvvvuuVHvqKXnMeCTc5K2m0qdPH/N1awAQaj7XoWbNmlKlShWva/z48daeviFO9nHhAu86DsBeQlYH4gQA8IY4AQBshzgBAGyHOAEAbIc4AQBshzgBAGyHOAEAbIc4AQBshzgBAGyHOAEAbIc4AQBshzgBAGyHOAEAbIc4AQBshzgBAGyHOAEAbIc4AQBshzgBAGyHOAEAbIc4AQBshzgBAGyHOAEAbIc4AQBshzgBAGyHOAEAbIc4AQBshzgBAGyHOAEAbIc4AQBshzgBAGyHOAEAbIc4AQBshzgBAGyHOAEAbIc4AQBshzgBAGyHOAEAbIc4AQBshzgBAGwn6HVISEiQDRs2SPPmzYkTAMCjoNfhpZdekptuukmKFClCnAAAHgW9DrNmzZIhQ4bIc889R5wAAB6FrA6ccwIAeEOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOcAAC2Q5wAALZDnAAAtkOckKvt27eb68yZM9YWIHodPXrUvD7s3bvX2oKCQJyQK/096VqxYoW1BYhevXr1Mq8PNWrUsLagIBAn5Io4ARcRp+AgTsgVcQIuIk7BQZyQK+c46fPtSUlJEh8fLzt37pSDBw/K+fPnrT2ByOccp7Nnz8qBAwfM64OuPXv2yKlTp6w9kR/ECblyxGn69OnSvn17ufPOO+Xqq6+WEiVKSP369WXp0qXmlRSIBo44VatWTebOnSs1a9Y0rw+FCxeWcuXKyVdffWXeaUP+ECfkyhEnveLdeOON8tRTT0njxo2lQoUKUrx4cSlWrJjMnz9f/vjjD+sIIHI54vS3v/1N/u///k9uv/128/rw0ksvyV//+lcpVKiQeaftxIkT1hHIC+KEXDnipCGaOHGitVXk+PHj8vHHH8vll19uXlEZrUU0cMRJ1/PPP+/y/37dunVSunRp87LPP//c2oq8IE7IleOK+OGHH1pbXOkjKL18+PDh1hYgcjnidNVVV3m8Q6ZP9enleoeNp7vzjjghV/p70qX3Cj3p16+fefmrr75qbQEilyNOL774orUlOz3/pPts2LDB2gJ/ESfkSn9POf2ufvjhB/NyRmsRDRxx0o/ePPTQQ+Y+vPwi74gTcqW/p5x+V8uXLzcvJ06IBr7ESYeGdB/ilHfECbnS35Mub8+fT5s2zbz8ueees7YAkcsRp+7du1tbXOnU6j333GPus3r1amsr/EWckCv9PelatWqVteUivSK2atXKvLxt27bWVs9SUlLkt99+s74CwpMjTjqp5+nlE8nJyeblOsXqPjCRlpZmXgcSExPNpS9oP3ToEC9k94A4IVf6e9Kl03p65XLQK+aOHTvMqaQrr7xS5syZY12SnR5Xq1YtnvpD2HPE6ZprrpHNmze7BOr06dPSs2dP83I97+RMI/T111+bL97VF7Bfd911ctNNN0mDBg3Mp8YJlCvihFw54qQvttUrl75Fi75li04iaXAuvfRSefbZZ629szt58qR5nH4P4oRw54hTkSJF5N577zWnWPX6sG/fPhkxYoQZLb2u6DunOJw7d86MkN6Je+yxx6RPnz4ydOhQadiwodxwww3mMWvXrrX2hvK7DvrCS71x0vdV0496w5MXxCl8OOL00Ucfmff29O2LqlSpYt770yuhXkG3bdtm7X2R3hNMTU2VHj16mFdK4oRI4IjTG2+8IWXLljUjpdcHPc+k/89LlixpXlecrVmzxjzm8ccfN29DnU2YMMG8g6d39HCRX3XQN/3s0KGD3H///XLzzTebHz/77LNsP2xfEKfwcccdd5hL6b09vYLp1/r779atm9ff/6+//mpe4TRgOixBnBAJ9Dqg///1o94pe++997KuI7Vr13Z5xOSgT3k7jvFE7/Rxe+jK55+G3gt+55135NprrzV/wPoQVD/qDU+nTp08nhjMCXGyB326oaBexa5B0ufUHQEjTrC7jIwM8y8+X7hwwdpS8NLT07PO2+Iin+uwZcsW88blm2++sbZk0nep1udL9flWfxCn0NITtzpJpC+gnTVrluzevdtl2CEQNErO9yKJE+xK71zrU9ArV66U8ePHy8aNG81nigqa/rszZ840rxtPPvmktRXK5zoMGDDA/AHqeSZn+ghKt48dO9ba4hviFDp673D06NHm0wz6O9Clb+qqb/Wv9+IKiv47xAl2tHXrVvNPXziuD7patGghx44ds/YIPH10pnf69VyVPgM1e/Zs6xIon+vQtGlT8xfmiW7Xk97+cMRJn+5hBXfpHQp900p9HYbjiuhYM2bM8HhMXpfzozH9/s5x0j8p4OkYFiuY68iRI/L0009nuy7oatOmjcdj8rKcR8Xdw9S1a1dz+++//+7x2GhcPsdJb1T0l+WJbvf3TT8//fRTc0KldevWrCCvypUru1wBHeuSSy6RMmXKeDwmr2vw4MHWbzx7nDp37uzxGBYrmEtHvN2vC451xRVXeDzGn/X++++bo+OOZ530PG9cXFy2MB0+fFj69+/v8XtE4wpYnPwdg+zdu7d5713/UBcruEtHwJ2vgM5Lx2I9HZPXpeO2Dvr9neOk036ejonWpT8ffXrV02WsglulSpVyuQ44L70D7ekYf5ZGSEfJlYZJz/PqAMT111+fFSbVrl07j8dH6/I5Thof/WV5ott1nNIfnHMKHX1BrF7pHFdA5+Xv79Ef+v2d4wRX+vPR6wWCS9/lRO8oO18PHOuWW26x9so/DdOCBQvMATJ94a0OXsA7n+tgPswyfll6Mt2ZnkDX7X379rW2+IY4hc7Bgwfl7rvvznphrC59+kKvMAX592f03yFO3unPhziFhg4/6LMGjuuDLn0UO3DgQGuP/NFzTI4w6csrGH7Inc910Oku/YXpc6XO9Aeu98J1HNIf+uJd/YNcCI2YmBh55plnzEDp03yVKlWSefPmWZcWDP3/Q5y80+uDXi8QfDq4o29crK/j1Dtqt956q/lHNANF3+BVw6S3lfqCXB1b97Rwkc9x0pN5+sPt2LGj+ZZF+sJN/fj666/Lbbfd5vfI5dSpU6Vu3brWVwiVOnXqZHvtWkEhTjnT64NeLxA6eidcT2EE+k1Y9XyS/v/X21B9bz1vCxf59byaDjHovWydstIrkb7IUu9pOE9kAd7ovdH//Oc/1ldA9ND/9/r/P7eFi/w+6aMhKl++vPmD1NKPGzfOugQAgMBgIgEAYDvECQBgO8QJAGA7xAkAYDvECQBgO8QJAGA7xAkAYDvECQBgO8QJAGA7xAkAYDvECQBgMyL/H1ddUPLbJ604AAAAAElFTkSuQmCC)
- Jetzt wollen wir das Diagramm für den Haupteffekt \(B\) zeichnen.
- Wir zeichnen wieder ein Koordinatensystem: auf der \(x\)-Achse tragen wir die beiden Stufen des Faktors \(A\) \(a_1\) und \(a_2\) im beliebigen Abstand voneinander an. Auf der \(y\)-Achse markieren wir die Werte von \(1\) bis \(7\).
- Wir schauen uns die Zeile \(b_1\) an und tragen die beiden Werte aus dieser Zeile wie folgt in das Diagramm ein: erster Wert \((a_1, 3)\), zweiter Wert \((a_2, 2)\). Danach verbinden wir die beiden Punkte minteinander.
- Jetzt schauen wir uns die Zeile \(b_2\) an und tragen die beiden Werte aus dieser Zeile wie folgt in das Diagramm ein: erster Wert \((a_1, 4)\), zweiter Wert \((a_2, 7)\). Danach verbinden wir auch die beiden Punkte minteinander.
- Das Diagramm sieht folgendermaßen aus:
![](data:image/png;base64,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)
- So haben wir die beiden Diagramme erstellt und können die Art der Interaktion bestimmen.
- Diese ist nämlich hybrid, da wir ein Diagramm haben, wo sich die beiden Linien überkreuzen und ein Diagramm, wo es nicht passiert.
- Dies hat folgende Auswirkungen auf die Interpretierbarkeit der Effekte:
- die beiden \(a\)-Linien überschneiden sich (erstes Diagramm). Deswegen darf der Haupteffekt \(A\) auch bei Signifikanz nicht interpretiert werden!
- die beiden \(b\)-Linien überschneiden sich nicht (zweites Diagramm). Deswegen darf der Haupteffekt \(B\) bei Signifikanz interpretiert werden!
(c) Vervollständige die Ergebnistabelle einer einfaktoriellen Varianzanalyse unter der Annahme, dass der Faktor \(A\) vernachlässigt wurde, und führe die Signifikanztestung durch.
Quelle
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QS
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df
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MQ
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F
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Koffeinkonsum (B)
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Fehler
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Total
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Lösung
Hypothesen aufstellen:
- Faktor \(B\) (Koffeinkonsum): \(H_0: \mu_1 = \mu_2 = \mu_3\), \(H_1: \mu_i \neq \mu_j\)
- Nullhypothese: Die Populationsmittelwerte der Stufen des Faktors „Koffeinkonsum“ sind gleich. D.h. es gibt keinen Unterschied zwischen den Stufen des Faktors \(B\).
- Alternativhypothese: Mindestens zwei Faktorstufen unterscheiden sich.
Prüfgröße \(F\) berechen (Tabelle vervollständigen):
- Als Erstes bestimmen wir die Quadratsummen:
- Dabei wissen wir, dass die totale Quadratsumme sich zwischen der einfaktoriellen und multifaktoriellen ANOVA nicht unterscheidet. D.h. wir können die \(QS_{tot} = \underline{\underline{58}}\) aus der Aufgabenstellung zu (a) übernehmen.
- Die \(QS_B = \underline{\underline{27}}\) bleibt auch ohne Änderungen, da der Faktor \(B\) immer noch die gleiche Varianz aufklärt, wie zuvor.
- Die Fehlerquadratsumme ergibt sich dementsprechend als \(QS_e = QS_{tot}-QS_B = 58 -27 = \underline{\underline{31}}\)
- Wir können uns diese Fehlerquadratsumme inhaltlich auch so vorstellen: dazu gehört alles, was wir an nicht aufgeklärter Varianz haben. Jetzt haben wir Faktor \(A\) vernachlässigt. Somit gehört jetzt die Varianz, die durch diesen Faktor und durch die Interaktion \(AB\) aufgeklärt werden könnte, auch zur nicht aufgeklärten Varianz. \[QS_{e(einfaktoriell)}= QS_{e(mehrfaktoriell)} + QS_A + QS_{AB} \\
= 16 + 3+ 12 = \underline{\underline{31}}\]
- Hier nochmal die Veranschaulichung der Varianzzerlegung im mehr- vs. einfaktoriellen Fall:
![](data:image/png;base64,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)
- Zu den Freiheitsgraden:
- hier wenden wir den gleichen Ansatz an: die \(df_{tot} = \underline{\underline{11}}\) bleibt erhalten
- genauso auch die \(df_B = \underline{\underline{1}}\)
- die restlichen Freiheitsgrade sind jetzt die Fehlerfreiheitsgrade, die sich wie folgt berechen lassen: \[df_{e (einfaktoriell)} = df_{e (mehrfaktoriell)} + df_A + df_{AB} \\
= 1 + 1 + 8 = \underline{\underline{10}}\]
- Zu den mittleren Quadraten:
- jetzt können wir die MQ wie gewohnt berechnen: \[MQ_B = \frac{QS_B}{df_B} = \frac{27}{1} = \underline{\underline{27}}\] \[MQ_e = \frac{QS_e}{df_e} = \frac{31}{10} = \underline{\underline{3.1}}\]
- Zum empirischen \(F\)-Wert: \[F_B = \frac{MQ_B}{MQ_e} = \frac{27}{3.1}= \underline{\underline{8.71}}\]
Quelle
|
QS
|
df
|
MQ
|
F
|
Koffeinkonsum (B)
|
27
|
1
|
27
|
8.71
|
Fehler
|
31
|
10
|
3.1
|
|
Total
|
58
|
11
|
|
|
Wird der Faktor \(A\) vernachlässigt, geht „systematische Variationsinformation“ verloren. Die Quadratsummen und Freiheitsgrade des Haupteffekts \(A\), der Interaktion und des Fehlers „verschmelzen/addieren sich“ zu einem, unaufgeklärten (Varianz-)Anteil. Die \(MQ_e\) und der \(F\)-Wert sind neu zu berechnen!
kritischer Wert und Testentscheidung
\[F_{df_B, df_e, 95\%} = F_{1, 10, 95\%} = \underline{\underline{4.965}}\] \[F_B = 8.71 > 4.965 = F_{krit}\] D.h. die \(H_0\) wird abgelehnt. Möglicher Fehler: \(\alpha\)-Fehler.
(d) Vervollständige die Ergebnistabelle der einfaktoriellen Varianzanalyse unter der Annahme, dass die vier Zellen des zweifaktoriellen Versuchsplans als die vier Stufen eines neuen Faktors \(C\) betrachtet werden!
Quelle
|
QS
|
df
|
MQ
|
F
|
C
|
|
|
|
|
Fehler
|
|
|
|
|
Total
|
|
|
|
|
Lösung
- Zu Quadratsummen:
- die totale Quadratsumme bleibt wieder erhalten, da die Gesamtvarianz, die zu erklären ist, sich nicht verändert. \(QS_{tot} = \underline{\underline{58}}\)
- Der neue Faktor \(C\) fasst jetzt die ganze systematische Variazionsinformation zusammen, die in \(A\), \(B\) und Interaktion \(AB\) enthalten war. D.h. wir verlieren nichts an aufgeklärter Varianz, sie wird jetzt lediglich durch \(C\) allein aufgeklärt.
\[QS_C = QS_A + QS_B + QS_{AB} = 3 + 27 + 12 = \underline{\underline{42}}\]
- die Fehlervarianz bleibt in diesem Fall unverändert wie in Aufgabe (a): \(QS_e = \underline{\underline{16}}\)
- Hier nochmal die Veranschaulichung der Varianzzerlegung im mehr- vs. einfaktoriellen Fall:
![](data:image/png;base64,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)
- Zu den Freiheitsgraden:
- die \(df_{tot} = \underline{\underline{11}}\) bleiben unverändert
- die \(df_e = \underline{\underline{8}}\) bleiben auch wie im mehrfaktoriellen Fall aus Aufgabe (a)
- die Freiheitsgrade des Faktors \(C\) ergeben sich aus den \(df\) von \(A\), \(B\) und \(AB\):
\[df_C = df_A + df_B + df_{AB} = 1 +1 +1 = \underline{\underline{3}} \]
- Zu den mittleren Quadraten:
- jetzt können wir die MQ wie gewohnt berechnen: \[MQ_C = \frac{QS_C}{df_C} = \frac{42}{3} = \underline{\underline{14}}\] \[MQ_e = \frac{QS_e}{df_e} = \frac{16}{8} = \underline{\underline{2}}\]
- Zum empirischen \(F\)-Wert: \[F_C = \frac{MQ_C}{MQ_e} = \frac{14}{2}= \underline{\underline{7}}\]
Quelle
|
QS
|
df
|
MQ
|
F
|
C
|
42
|
3
|
14
|
7
|
Fehler
|
16
|
8
|
2
|
|
Total
|
58
|
11
|
|
|
Werden die \(4\) Zellen des zweifaktoriellen Designs als \(4\) Stufen eines neuen Faktors \(C\) definiert, geht keine „systematische Variationsinformation“ verloren. Sie kann lediglich nicht mehr auf die einzelnen Quellen zurückgeführt werden. Die Quadratsummen und Freiheitsgrade der beiden Haupteffekte und der Interaktion „verschmelzen/addieren sich“ zu einem, aufgeklärten (Varianz-)Anteil. Die \(MQ_A\) und der \(F\)-Wert sind neu zu berechnen!
Aufgabe 2: Freizeitinteressen
In einem balancierten \(3\) x \(2\)-Design (\(n = 6\)) soll überprüft werden, inwiefern bestimmte Freizeitinteressen (\(a_1\): Sport, \(a_2\): Musik, \(a_3\): Technik) und das Vorhandensein eines Nebenjobs (\(b_1\): vorhanden, \(b_2\): nicht vorhanden) den Studienerfolg (aufsteigend geratet auf einer Skala von \(1\) bis \(10\)) beeinflussen. Die Gruppenmittelwerte sind in nachfolgender Tabelle zusammengetragen:
|
\(a_1\)
|
\(a_2\)
|
\(a_3\)
|
\(b_1\)
|
4
|
3
|
6
|
\(b_2\)
|
3
|
5
|
7
|
(a) Wie groß sind die Werte \(p\), \(q\), \(N\)?
Lösung
- \(p\) ist die Stufenanzahl des Faktors \(A\). Somit ergibt sich \(p= \underline{\underline{3}}\)
- \(q\) ist die Stufenanzahl des Faktors \(B\). Somit ergibt sich \(q= \underline{\underline{2}}\)
- \(N\) ist die Größe der Stichprobe. Wie berechen sie als \(N=p \cdot q \cdot n\), wobei \(p\) und \(q\) gerade definiert wurden und \(n\) sich auf die Personenazahl in jeder Faktorstufenkombination bezieht.
- In unserem Fall ist \(n= 6\).
- Somit können wir \(N\) berechen: \(N=p \cdot q \cdot n = 3 \cdot 2 \cdot 6 = \underline{\underline{36}}\)
(b) Vervollständige die Ergebnistabelle für die zweifaktorielle ANOVA für Modell I (beide Faktoren sind fest) und Modell II (Faktor \(A\) ist ein Faktor mit zufälligen Effekten)!
Quelle
|
QS
|
df
|
MQ
|
F Modell I
|
F Modell II
|
Freizeitinteressen (A)
|
62
|
|
|
|
|
Nebenjob (B)
|
|
|
4.0
|
|
|
Interaktion (AB)
|
|
|
|
|
|
Fehler
|
168
|
|
|
|
|
Total
|
248
|
|
|
|
|
Lösung
Modell I (feste Effekte)
- Freiheitsgrade bestimmen:
- \(df_A = p-1 = 3-1 = \underline{\underline{2}}\)
- \(df_B = q-1 = 2-1 = \underline{\underline{1}}\)
- \(df_{AB} = (p-1) \cdot(q-1) = (3-1) \cdot (2-1) = \underline{\underline{2}}\)
- \(df_{tot} = N-1 = 36-1 = \underline{\underline{35}}\)
- \(df_e = df_{tot}-df_A -df_B -df_{AB} = 36-2-1-2 = \underline{\underline{30}}\)
- die fehlenden Quadratsummen berechnen:
- Die \(QS_B\) können wir mit Hilfe der bereits vorgegebenen \(MQ_B\) berechen:
\[MQ_B = \frac{QS_B}{df_B} \rightarrow QS_B = MQ_B \cdot df_B = 4.0 \cdot 1 = \underline{\underline{4.0}}\]
- die \(QS_{AB}\) können wir jetzt auch berechen, da sich alle \(QS\) additiv zu der totalen Quadratsumme \(QS_{tot}\) zusammensetzen.
\[QS_{AB} = QS_{tot} - QS_A - QS_B - QS_e \\
= 248 - 62 - 4 - 168 \\
= \underline{\underline{14}}\]
- Jetzt können wir die mittleren Quadrate (MQ) berechen:
- dabei teilen wir die jeweilige Quadratsumme durch die zugehörigen Freiheitsgrade: \[MQ_A = \frac{QS_A}{df_A} = \frac{62}{2} = \underline{\underline{31}}\] \[MQ_{AB} = \frac{QS_{AB}}{df_{AB}} = \frac{14}{2} = \underline{\underline{7}}\] \[MQ_e = \frac{QS_e}{df_e} = \frac{168}{30} = \underline{\underline{5.6}}\]
- Jetzt berechen wir die empirischen \(F\)-Werte:
- wir haben in dieser Aufgabe ein Modell mit festen Effekten angenommen, weswegen jetzt bei uns im Nenner immer \(MQ_e\) steht. \[F_A = \frac{MQ_A}{MQ_e} = \frac{31}{5.6}= \underline{\underline{5.54}}\] \[F_B = \frac{MQ_B}{MQ_e} = \frac{4}{5.6}= \underline{\underline{0.71}}\] \[F_{AB} = \frac{MQ_{AB}}{MQ_e} = \frac{7}{5.6}= \underline{\underline{1.25}}\]
Modell II (Faktor \(A\) zufällig)
- Hierfür müssen wir lediglich die \(F\)-Werte anpassen.
- Jetzt sind Faktor \(A\) und die Interaktion \(AB\) zufällig.
- Wenn wir die \(F\)-Werte für die Faktoren \(\underline{\underline{A}}\) \(\underline{\underline{und}}\) \(\underline{\underline{B}}\) berechnen, müssen wir bei beiden Faktoren ihre \(MQ\) durch \(MQ_{AB}\) statt \(MQ_e\) teilen, obwohl nur Faktor A zufällig ist.:
\[F_A = \frac{MQ_A}{MQ_{AB}} = \frac{31}{7}= \underline{\underline{4.43}}\] \[F_B = \frac{MQ_B}{MQ_{AB}} = \frac{4}{7}= \underline{\underline{0.57}}\] * \(F_{AB}\) bleibt unverändert im Vergleich zum Modell mit festen Effekten.
\[F_{AB} = \frac{MQ_{AB}}{MQ_e} = \frac{7}{5.6}= \underline{\underline{1.25}}\]
Quelle
|
QS
|
df
|
MQ
|
F Modell I
|
F Modell II
|
Freizeitinteressen (A)
|
62
|
2
|
31
|
5.54
|
4.43
|
Nebenjob (B)
|
4
|
1
|
4
|
0.71
|
0.57
|
Interaktion (AB)
|
14
|
2
|
7
|
1.25
|
1.25
|
Fehler
|
168
|
30
|
5.6
|
|
|
Total
|
248
|
35
|
|
|
|
(c) Formuliere die statistischen Nullhypothesen für Modell I und Modell II!
Modell I feste Effekte
|
Modell II gemischte Effekte
|
Haupteffekt \(A\) statistische \(H_0\): _______________
|
Haupteffekt \(A\) statistische \(H_0\): _______________
|
Haupteffekt \(B\) statistische \(H_0\): _______________
|
Haupteffekt \(B\) statistische \(H_0\): _______________
|
Interaktionseffekt \(AB\) statistische \(H_0\): _______________
|
Interaktionseffekt \(AB\) statistische \(H_0\): _______________
|
Lösung
Modell I (feste Effekte)
- Hier stellen wir die gewöhnlichen Hypothesen bezüglich der Mittelwertsunterschiede für den mehrfaktoriellen Fall auf.
- Lösung wird der Übersichtlichkeit halber in der Tabelle unten aufgeführt.
Modell I (gemischte Effekte)
- Jetzt ist der Faktor \(A\) zufällig. Das heißt, wir interessieren uns nicht mehr für die Unterschiede zwischen den bestimmten Freizeitinteressen (Sport, Musik und Technik) im Bezug auf Studienerfolg. Jetzt wollen wir wissen, ob die Freizeitinteressen allgemein Studienerfolg beeinflussen. Deswegen stellen wir die Hypothese bezüglich der Varianz dieses zufälligen Faktors. \(H_0\): \(\sigma_A^2=0\)
- Anmerkung: unsere \(H_1\) würden wir folgendermaßen formulieren: \(H_1\): \(\sigma_A^2 >0\).
- Die Hypothesen scheinen nicht erschöpfend formuliert zu sein. Die Varianz kann aber nicht kleiner \(0\) sein. Somit berücksichtigen wir diese Option auch nicht in unseren Hypothesen.
- Da der Faktor \(A\) zufällig ist, ist jetzt auch dessen Interaktion \(AB\) mit dem Faktor \(B\) zufällig. Darum stellen wir auch dafür eine Hypothese bezüglich der Varianz auf. \(H_0\): \(\sigma_{AB}^2=0\)
- Da der Faktor \(B\) weiterhin fest ist, verändert sich die dazugehörige Hypothese nicht.
Modell I feste Effekte
|
Modell II gemischte Effekte
|
Haupteffekt \(A\) statistische \(H_0\): \(\underline{\bar{\mu}_{1\cdot} = \bar{\mu}_{2\cdot} = \bar{\mu}_{3\cdot}}\)
|
Haupteffekt \(A\) statistische \(H_0\): \(\underline{\sigma_A^2=0}\)
|
Haupteffekt \(B\) statistische \(H_0\): \(\underline{\bar{\mu}_{\cdot 1} = \bar{\mu}_{\cdot 2}}\)
|
Haupteffekt \(B\) statistische \(H_0\): \(\underline{\bar{\mu}_{\cdot 1} = \bar{\mu}_{\cdot 2}}\)
|
Interaktionseffekt \(AB\) statistische \(H_0\): \(\underline{\mu_{ij} - \bar{\mu}_{i \cdot} - \bar{\mu}_{\cdot j} + \bar{\mu}_{\cdot \cdot} = 0}\)
|
Interaktionseffekt \(AB\) statistische \(H_0\): \(\underline{\sigma_{AB}^2=0}\)
|
(d) Faktor \(B\) „Nebenjob“ wurde vernachlässigt. Vervollständige die Ergebnistabelle der einfaktoriellen ANOVA!
Quelle
|
QS
|
df
|
MQ
|
F
|
Freizeitinteressen (A)
|
|
|
|
|
Fehler
|
|
|
|
|
Total
|
|
|
|
|
Lösungsansatz
- Eine ähnliche Aufgabe haben wir in Aufgabe 1 (c) schon gelöst. Deswegen stehen hier nur ein paar Tipps.
- Wir überlegen, wie jetzt das Verhältnis der aufgeklärten und unaufgeklärten Varianz ist im Vergleich zu dem zweifaktoriellen Fall:
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAusAAAEJCAYAAAAtnWkpAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAHnPSURBVHhe7b0HlFvXlSWqH8c/+4f57Znp6VH3dE/r99htte22ZdqWaMmyoiVaVpYtK7GYyWLOpCjmXMw5ZzFVschizmKsYs4s5hyLWZJtSee/fYGDeni4KKCqEB6Avdfaiyzg4eEBeHfffc8999yHhCAIgiBi4LPPPjMkCIIgUguadYIgCCImaNYJgiDSA5p1giAIIiZo1gmCINIDmnWCIAgiJmjWCYIg0gOadYIgCCImaNYJgiDSA5p1giAIIiZo1gmCINIDmnWCIAgiJmjWCYIg0gOadYIgCCImaNYJgiDSA5p1giAIIiZo1gmCINIDmnWCIAgiJmjWCYIg0gOadYIgCCImaNYJgiDSA5p1giAIIiZo1gmCINIDmnWCIAgiJmjWCYIg0gOadYIgCCImaNYJgiDSA5p1giAIIiZo1gmCINIDmnWCIAgiJmjWCYIg0gOadYIgCCImaNYJgiDSA5p1giAIIiZo1gmCINIDmnXg6y9F/nI3NlOEkryH5KE6BVIe/JsgCCLdoFknCIJID2jWgc9PiFSsis04UV5QRx56yDHcHtYpiM9++8usl0tBncD155UEH6ouygukTozvItp35n7PsGMs3w8HOQSRPNCsE0Tq8M033ySERHaAZh24f0Dk6pzYjBMBU5knNfW2MU1nSZ5z/joSp/evHdxGu0ZuPWj2Q5+nRPKC54sw4lV9ZnMd+pkD5wi7HPOd1Pw7JyJhE/5Y/Prrr62PxyLhf9CsE0Tt4NU6rw5CP2Pxq6++MvzLX/4S+j9oO9ZN73t5YXuM8A9o1oE720UuDI/NOJFNZl1NdEktP1MYdADgctsxzTo+s+t5fEeV0fmAeY935oKIbsRtIu/uEJToKJT6d7TH3X97aXs/23WBRHpBs04Q1YNNx7x6Bx1Ujfzzn/8cxj/96U+GX375peEXX3xRJXGMvsZ7LtVg9/vq/23XCRL+Ac06cGu9yOnusRknqjbrlZHlACNNd4RZN+Y8YEbNc2Gv95jU4LGVjLwOcw7HKAeu035MAIGouDl/0GDXKLjuRYLNesT3RVjhFWIVahVub6ehom/rFD7//PMw4rEHDx5Yn1O6X68dj3Yu2ploh6KM1pEQqQfNOkHEhlerbBqrBhr6p/oI/bx//77cu3fP8M6dO3L3zk15cPeK3Lt9Se7eumj44NYZOVJ+Woo3npKdBy6G8eyF63L79m3zWvP6u3dD58P5VYdVd1Vz3bpLzfUnaNaBG8tEjjeJzTgR3awHU0JCJlXzwcOPDTOfFmNrjKstsq5GXY/V19rObx6H+cY1RDHr5vX6Pt5rD8I1kIgbwde4TxXTrIddiysNJtp3QRh4BdfWaUC01TxrpwFxh9DfuV0R6iQ+v3XC8EFFueEXt47In+8cCGPPSeEsmHNYZi0vD3H2ihOyZc9507GcOHPVdCzaodg6E1yfXm+0joRIDWjWCcIO1SK3MVcTrBqr+qqG/P6da3L/NnS1XL68fVj+dHu/yN2dcXHPvj3yXs/SuPjJxP0ydPYhmVlyXDbvOiv7j12SioqKkPbiejTQ4jXxqrle3SVSD5p14NqnIodej804URmxdjOKoQwa6nAv7hxvjGswCu81sVaDGsVMB8/vNtNq1r2HeuGNWNsHIbjG6pnlwPuHn8f+nUUfZAQ+J9NfbPB2HKCKr9uYh5vyG3K34oLcv3lcPq9wOo5b++Tr207HcGdHtWjrLGKxx4R9MmLeYdOZbN93Xo6cuCK3bt0KM/HejoSdSOpBs04QVc9QQps0Yg7NghG+f/e6PLh9Tr64XS5/vn1A/nJ7j6OV22vFHaXb5Y+OdtaG7UfukV6T98uMZcdk1dbTcrj8stFdRORx3RhYxKO5IJF80KwDV6aJ7HsqNuNEYsx6nuTBfNuizTazbjlPAJEm3mvC7bAY4ajvUQ1YoupWBI+LGHy4UPk5dIYiwFwz717hjNZxwPTCAN++XSH3Ks7KA2PK98g3t9EBbEsIbZ1CTdll7D4Z7ph4dCQXLl0PGXi3edfIOzuR5INmnchluHUFWgPdAd2Rc2jT/Xu35MGt0/Ll7UPy9W0EMbYmnJu3bLRqZm3ZZNAuKZhzUJZuPCGnzl4xEXiNvrs1123e3d8LkTzQrAMXx4iUfT8244Q9Aq0IRss9dHvSsAhyusx6daL38SJ4jfG+PvA9xJGig2vVz2Mej/bdZxfcIqmdB0RUo+eIjCAybXIYKy7J/ZtH5cuKPSK3tySNf/zEEf0kEZGgyUVHZdvec6YT8UaA2IkkFzTrRC7CrbGqs2rQA2ktdwOR81uH5SvMRt522kiSuW7daqtGJpqth+2WcQsPy9rtp+TylWsmYKKzndBcjbrrd0PNTR5o1oFzg0W2/ofYjBPRzXrQqLvNqsVkh8x08LmISHEKzHrYgCGCNTHDwc8ea5DgQvTvMfCZ9Hsx1xr6fHgf7yAju+AWRHfnAfHU6Pmd29fl3s0T8kXFXvnq1lZH4DenhDbBTwY/7F0m/aYekMJ15XLo+CUzIEEn4jXu7EASB5p1Ilfg1gyvQTcR9Pt35PPbJ+Uvt8sc3duUcq5cVSLvOjqYanYas1cWrT0uZ89Xpiq6jbsGSqi7iQfNOnCmp8imfxObcSKqybQZ6qrMuvN/TakJe4016m0ZCAAxzm9H4FzWdBLz3rZBQVWovlF3LjwwyLC8xnwnrsez3ayr2KnwaeehUXR3BP2uY9C/rEAHsjEttAl8KohOZMmGcjl3IbBoVY27Rn60E2HnUXPQrBPZDNUGt8aCatBN/vmtc4HZyVsb0srlK5ZYdTCVHDh9v6zbcVKuXw+kKLqDJZzhTDxo1pOA+CPrwb895jfcTAdNq/t8USLukcZezx9+LTHNepWG3PMZolxLJSzX74U5h+Uabec1x4ab8TDzbjlXLJw+fVrWr19vJZ5LJ9ydh3YgiPBo54FUkIobl+T+jf3yVcUmR8jXp5U2UU8lP+hdJgWzD8qOvWfkxo0bxrhr5Affm3Yg7ESqD5p1Ilvh1gLVWA2E3L97Q764dcjR182Oxq3zBZeVFFr1Lx1sNniXTFh0SA4fP2+Mu1dzvWkyRM1As54ERDfr+lzAiAbMaIExv9HNOhA0yBEGXs/hOjJocisZeR2xzHrgHNENb9jzMc165YAkkmq67cdEDhYCx0W+V/jro1+LHUOHDpW6deta+Z3vfCd4VGrh7Tw0ig6TjgjGrYqbcuf6cfnixg6rmKeLNjFPF1sN2y0L1xwLTdnie8P3p9F2GvbqgWadyCa42z60QHUWJvP+vbvy+a2T8qebO0Uq1vqOS5cutmpeuvnJhL1StP64XLwUWJxKzU0caNaJWqGqgUk24JFHHpEjR44E/0o+3ELmNumai37r5lW5f32PfH3TMccVa3zHPziC7Te+37tMhsw6ILsOnpObN2+a79E9XcsOJD7QrBPZAHdbV5OOCDD0ALnoX1Qckm8qoK+rfculSxdatc4vbDigTOYsPyLnL1wK01ya9pqDZp2oBQIRbXu6THbgvffekylTpgT/Sg7cogVCyNSkQ+ACqS6XHZO+2+lEINarfEubcPuJvSfvk72HzpoUGXyv2oGgw2YHUjVo1olMh7t9azAEkd+AST8o39xcI3Jzpe+5dOkCq775jTDts5YdknPnLxrNdZt2r+YSVYNmnag5kNuezU7dAVJk8vPzg38lBypWXpMOYbtx/Yrcu1rmdCIQ6RW+5x96OCKdARw8c78cPBow7exA4gPNOpGpcGusRtIDJv22Y9IPOPq62tGv5RnDpUvnW3XNr2zY3zHtSw/J2XMXQpprS48hooNmnSCqwJ49e+TRRx8N/pVYeDsQt0m/eeOK3L26S76+AZPuDIgyhDah9jPVtLunanUhqv4+7EQCoFknMg3uNqzBkEC6y2358hZMOvR1WcaxeOmn8ntHvzKNDRzTPtMx7Rpp15x21VwGSqKDZp0gqgCE5Nvf/rb5N5HQzsMd5YFZrLh5Ve5d3S1fX18hcsMR5gyjTaD9zvd6lcno+Qfl+MkLxrRrB4KO3d2B5Dpo1olMgpo+dzDEVCm5dVC+uQF9XZqxLC6eZ9WyTKGa9kuXAjnt0FxdQ+QOlBCVoFkniBh47LHHZNu2bcG/ag5v5wFhgimEUKFaScWVo/LV9eWOGBdnLG3CnCmEaZ+25JBcuRKoHoPSmLZFqLkKX5j1r+6LPDgUmziOyFm4tRbBEMyYPbhzQf5yY72jU0synsXFc60almlsP3K3bC4tN5qrJR+ZjmgHzTpBxABy1pG7Xhu4Ow8IkXYgEKgb1y7KF9fQiRRlPG2CnGlEB1K2/5Rcu3YttAiV07Q+Met/vuHcZ/ZoYxhxXIoQqxQukRq4NRbEIBvGD2UYv7i5W+R6YdawuHiOVbsylWPm75dTp8+aOu2az07NDQfNOkHEwJw5c+TNN98M/lV9uDsRCBCitYimY/rv1uV98vW1JY4AL84KvuMIbzbw3Z6lMqXwgFkQhd9Jo+z4/XK18/CFWf/TZZEr02ITx8UB774XNdmrIbZZL5G8lJl5571q8BkyHdoeQXcw5MGtU/LV9WWONi3KKhYvmS3vfOxoVRax8YAyWbHpiEmNQY123ViJM5sB0KwTRAygzjrqrdcEEBcIjXYgECBE069fPS+fX1ntCO/CrKJNhDOZzQeXyaadx+Xq1asRUXbtPHKlA/GFWf/ynMi5wbGJ4+JAIvaJiGnWzY7Q4bsuJw3mvfIkL8ei/aqzMHYwePfv3ZIvr28RubYgK1lcNMuqV9nA/lP3yLHyM6GZTUTZvakxuQiadYKIA1hkijzmeKEmzt2BaDT99qVS+frqIkd052cdbeKbDRz96f5QlN29GCqXIj6+MOtfnBI51Tk2cVwciNuswwRHMb92s14u5fpATLMe2JE6ViS8vCAvpuE314JyuqkcIKQJ2u5AdzT984rD8s016OunWcvioplWncoWftC7VOavPCQXL140UXb3+iH3755LoFkniDjw7LPPyvLly4N/VQ1bB4IIAaLpX15Z5ojtvKzl247QZiubDS6TbbtPRETZc8Ww+8KsPzgmcqxhbOK4OFC1WQ+YaG+KjNeYh5t1fU3AKJvnPK+vNOWVKSthdO1dETDfea7jqhhYlBdIHeeYwMsD544YABgTnx0pMmhvaHvQ2UBu+h350/UNIlfnZj2Li2ZYNSrb2HvyHjl5KjzKnqtpMTTrBBEHunfvbhgLER3I/fsmMnD90lH5y+X5jtDOyWraBDebiF35Fq46GIr42KoXZCt8YdbvHxQ59Fps4rg4UO00GEvE2m3WA+bcE9GOEuUOHOt576CZVr+uZj8ec+39LO7rCiELzLqaNLQ3GDcYuAd3LstfrhY7GjQ7J1hcNN2qT9nI/KFlsm3XMbl8+XJYxZhcM+w06wQRBxYvXiz16tUL/hUJbweiaS9Y3X7zwnb5xpjZWVnPt7s7ApsDHDZrr5w7d85s7KFTtG7Dno0diC/M+r29InufjE0cFwcCBjdgiN2MujFzWPQ6ADXFJeZcltQTq1mPEvkOPq7RdavhtiIY0Q+/MPv1ZDhUZ3XW8kHFUUdf5zn6MzNnWFw0zapL2cr3e5VK4ZoDcv78+dCCfxh2d1pMtoNmnSDiAPLVkbdug4qFpr0g0oPyU9euXpa7F1aKXJmRM7QJbbayw4gy2X/4pEmLwe+d7XnsvjDrd0tFyr4fmzguDsQTWdfodjQzH/a8zeXbTLPF9AcQbrrjNuvW84Ub/0yGtie0LdVZGLbPr21zdGd6zrG4cKq85WhQrnH0vD1y5kwgLQbBsFxKi6FZJ4g48fDDD8vp06eDfwXg7kTQgWi1l2uXT8mXlxY4wmopK5fFtAlsNvOjPqWybtsRM0WLAZ2tWky2wBdm/c5WkW1/HZs4Lg7El7Puer6KyHp5tBSTFJj1aDMEAVY9GMkEaFtCuwrkp1fIn5H2cmVqTrK4cIpVj3KB7UeUycHD5aG0GBh2aK4a9mwFzTpBxAnUWkfNdYW7A1Gjjim66xcPyFeXpotcnpJztIlrthObesxcuj8sjz0bDbsvzPrtjSKb/qfYxHFxoEqzbjPUVZl1/b/XmFcnDSZ4fn08PrNuSYFRBAcQtqcyBWg/MGKh/PTbZ+Sry44OX56csywunGzVolxhg36lsmZLZbWYbNVcN2jWCSJOYBdT7GYKaAcSivTcv2/ylyvOb3bEdFLO0iasucKB0/aYPHZ3TiXuj2yJ+PjCrCcY8Zj1SkOtkfboZr3ymKqj8UDA2Lsfj3xtXGa9SkPuSYWJ+EzxATOK69evt3LPnj3BoxILNV1q1DFrdf/mUfnGGNaJOc3iQkdruzm6k8P8/celsmTN/og89mw17DTrBBEntm3bJo899lhYB4I8ZZOffu2a3Dq/1hHSCTnNNx0RzWX2mbzb5FRiYbG7Hns2dB45Z9YdeNNL6hQUGPMb3awD3lzxSpMfOIfryKBhr2T4tcRj1gPniP4Zwp6voVlHJay6deta+eijj8p3vvMdM/M4ZsyYiFTBmkJ1FhFTs5D0+h755pJjVi+Nz3kuWTzRqj+5RtRkn7kkkMcOzc3GIImCZp0g4gSmYLHIFB0HxEArvmCBYcXZ1VZRzTXaBDXX2HvSLjl16lRoEZRWisl0w56NZj3XEGtwUlMgfxgpgg0bNjRre0D8H49VZzM5hduoI8Xh/rVdjr6MJYNcspiBETenF+4yg0R3kCTbIuw06wQRJ9DoEUUqKysLRdSvXLniGPVVVkHNRdqENBfZcVSZHDse2EBJK8Vo56H3UqaBZj3TEYj429NlEgsYJ0TZEW1HgAO6iRRCbCyHoEc0qLlyG/UHV7eJXBxDukizHskJ83fJyZMnsy5IoqBZJ4g4oB1IgwYNZPjw4SGjfvPMCkc8R5NBvuGIJhkgDPvRY+XmPnEb9kydnqVZz3CU5FU7/SVRQF471vxgJ+hvfetbJn0GqTVILVSoqUIbUaP++dWNjq6MIj1csni8VXNyncNnlcmJE+FBkmxJQ6RZJ4gYQCOHwUKjnzBhgrzzzjvGgN0+UyRyYSTpok1Ac5kw7EeOHo8w7JnYedCsE4kCFqbCrGMNEMw7NpwbMmSI7Nq1yxh15B5/cXWdoykjSAuXLBpn1RuyVIbMCDfsmMnJhgg7zTpBVAE16mjsaPQwK9/97nflztmFjmgOJz18o6sjmGQYO4woNYbdlhKTSZ0HzTqRDGAN0KJFi6RFixby/e9/X/7qr/5Kzu6bIXJ+GBmFSxaNlde7lpJR+Mn4UikvD8xq3rlzJyzCnqmgWSeIKFAzBWOFxo5Gj4VU3/unh0XOF5AW2oSTxEYeAcOeyRF2mnUiGcD9785Rv3Jsgcj5oWQVXLJojFVnyEr2n1Iqx49nx6wmQLNOEBZoB+Iuz4hGf/1kicj5IWQU2kSTDLDb2EC0RyPsmVYTmGadSDTcOmuqvlzZInJuMBmDSxaOtmoMGc4J83eGUmKw6DST67DTrBOEB9qQ0ajRuGGs0Ngvn9rsCOUgsgraBJOsZMHMUtN5aMUCd+fhd9CsE4kE7nk16kiFuXd9v6MhA8k4uGThKHnN0RMyNj9dFqm5uO9o1gkiw+E26ljohNqt50/ukq/PwpAOIKugTSzJcM4oKjN12N2beGRC50GzTiQKbqOOtUD3bp6Ub6CvZ/uTcXDJwpFWbSEj+Wb3UilZG665SLnKtAg7zTpBBKENF50IGjOiPTdu3JALZw7Jn04Pc0SyHxmDNrEkw4nOY9nawE6n2CYb9xlMixp2v3YeNOtEIqD3uBr1uxXn5etzBY5+9CXj5JKFI6zaQtr5x547ZcOW3YL6/+jTkXKFPj6TIuw06wQRBBqtRntgoCoqKuT8udPy+alRVsEkI/mqI4xkbL7rdB6bt++Tc+fOmfvMa9j9CJr1GCjJq93uoOUFUuehh5xzRNm4yDyf+N1HUwk16ohqmrVAt6/JX846+nqmD1kNFs0fLq92cbSEjJv1++6QHWWZpblu0KwTRBBosBrtwRbZ58+flzsnJjni2JuMkzaRJO1s2H+HlO05KBcuXJDbt2+b+w73nxoav4Fm3Y6SvIDBDme4qQ5s8x95XKUpx+6idcTsWWRMefD/IZRLQZ2H0rapUaIAYwQi9eve3Vvyp3PQ115kNVk0f5hVU8iq2WLwdtm770DGaK4bNOsE4QANVaM9KNF48eJFuXFiviOMPclq0CaQZHS2LtghR48eNSVBtR6wXxec0qzbUZJnN+ZuY20eq1PgWO4oQFQ+9HykMY/5+gwA7mkY9dCmRxcWipz+hKwBi+YXyO8c/SCrzx7jd8jhw4fl0qVLxrC7F/n72bDTrBM5DW2gaKxotFqi8fKJ1Y4o9iCrSZs4klVzyPSdoZKOqFbgXvzkJ9CsxwlNZ3HlslTPrAei9SGzXtv0Gh8A9zKMOqKYSD+4f8W5j05/TNaQRfOHWrWEjI/jP90mx44dM319ppR0pFknchraicAgYdEJVoufOXlQ/nyyjyOK3clq0iaMZGwWrSr1/eInmvU4ETTr1YqMh+WjByLrAa+P9JgoOewZAjVAMENIO7hz86x8fbqXoxfdyBqyaP4Qq46Q8fGt7jtk9cadcvLkybAKMX7TXDdo1omchnYiuqD07Nmzcuf4SJFTXcka0CaMZGy+33un7Nq9N7T4yY+5lDTr8SGQBhOecx54LFq+egBhue/BJ81jmezUHajGmpnLOxXy5zPQ1y5kLVj06WB5xdENsuZsNGC7lO3aY6pyuTXXr4adZp3ISaAxolFqJ4LcNeSpXzpa6IhhZ7KGtIkiGR+7jNkuhw4dMrmUyF/HfYl71C+dB816HLCkwEQiEDmHKa9ywagr/aXS7HsXnvobqrM6c/ngwhJHJzqRtWTRp4OsGkJWj/0mbZGDBw+avt+7Zshvhp1mncg5aENUo46cNeSunSkvla9PInJhF0gyNm2CSMbP2Uu2y/Hjx81ue36bmqVZj4VAykq8C0EDkfRoueiu9Bd3znoG5a+rzmqe+t2re0VOdiATwKJPB8ornR3NIGvNuUu2mkX+7vx1v2iuGzTrRM7B24lgY5rTp8rl/vGBVmEk46dNDMn4+c7HO2TT1l0mfx33JaZmMaj0Q+dBs14VNFoev5EORMttx4dXgwlPhXGVePQxVGNx7wbqqV+Vr072cDSiPZkA0qwnjkZzt+w0O5zqmiE/psPQrBM5B3cngvQX1Fy9cmSGI4LtyFryt474kbVjm2HbZf/+/ea+9FM5R5r1aKi+UQeiRda9i1Ejzbr/F5yqxmJmCNHKL8+MEznRlkwQi+YNiNANsuZsNXSr7N0bWDOEPVb8uGaIZp3IKaDhaQ6lpr+cPbbJEcA2ZAJoE0Ky+hw7d6spLYZyjpoOo4Y9XZ0HzbodVaezOEAeuyc1RnPQI3LWTc67ZXGqvj6saow/ofco7lnMXN67tM7RhtZkAlk0r7/RCTJxHDNni6m/jj0vUMLZb+kwNOtEzgCNTo26O/3ly+NdHQFsRSaANhEkq89Xu5bKhs8C5Rxxn+J+TXekh2bdhmCeupVquu3HRC4uDRwX7fHor/MPVGMxsDRlGisuyNflHZxBRksygSya18/oBJk4vtFth6zduF1OnDhhyjn6LR2GZp3ICajJ8aa/XDw02xG/fDJBtIkgWTO2G741lA6D+zXd6TA060QsqFnXhfufn53m6EILMsEsnNdX6jkaQSaWnUd+Jnv27AlLh0mn5rpBs07kDDSqjrQCpL+cOLZb/nKstSN+zckE0SaAZM25qGSLSYfxVodJB2jWiViAqdGF+7evHnQ0oRmZBBbO62PVC7L2XLB0c1g6jGpuug07zTqR9UAjc3ci2AAB6QVXDo4WOd6UTCDrdXIEj0wYG/TdJrt27TIbd3gjPanuPGjWiaqA+1Gj6mafgBN9HE1oQiaBhXN7W/WCrD3z+myVnTsD1WG0IpcfFpvSrBNZD3cngpEyNp05cWiDI3qNyQTTJn5k7Thx3iazWRIiPUgtSFekh2adiAbVWA2I3Lmw1tGDRmSSWDi3l7zsaAOZHI6aucGkIGKzJHgGdwpiugw7zTqR1dBOBA0NnQjqqGIByc0DfUSONSQTTJvwkbXjWx/vkC3bdsjJkydDi03T0XHQrBM26H2Ie9KsB7p1Xb463srRgwZkklg4t6dVK8jE8M3u22XTZ1ulvLw8bLFpqjXXDZp1IquBhgWzrotKz58/L6cPLHYEL49MAm3CR9ae/SdtlgMHDli3xU4VaNYJG9TA6Hqg+2dmOFpQn0wiC+d+YtUJMnH8eMymsMWm6V7gT7NOZC3UqGNEjJExFumVHz8s9w+1cwTvIzIJtIkeWXu+0nmnrFqX3kgPzTphg85cIrf39vUT8s3RBiJHPySTyMI5Paw6QSaO0NziFZvkyJEjpiCFe4F/Ogw7zTqRtUCD0qlZjIzPnj0rFw5MccTuAzJJfMkROTI57DgiUFYMs0OYJUr1ph0064QXuPdwD8LEYD3FFycHOjrwPplkFs75WF7q6OgCmVQ2H/iZlJaWmgX+KEyRzlKONOtEVgKNCdSpWewEefTIAfnT4UaO2L1HJok2wSMTx6IVlaUcEV1PZaSHZp3wAvedLiq9fWW/yJE/kilg4ZzuVn0gE895RRvMAn8UpkjnzqY060TWQY26O6qOkfG5vRMcoXuXTCJtYkcmjl1GbpLdu3ebPMpUb5REs064oRqrVbbunxzpaMAfyBSwcE43qz6QiWe7go2yY0flAv90lXKkWSeyDtqI3FF15J3dO9DCEbrfk0mkTezIxPG3nXbK8tWbrdH1ZINmnVCoxmpUveLaafnGRH3fIVPAwjld5TeOHpDJZz1Hc5eUrJODBw+a6Hq6yufSrBNZBzQgb1T9xJ65IoffJpNMm9iRiWW/CevTEl2nWScUuNdgViqj6mOc9v8WmSIWzu5i1QYyOewwbL2JrutGSe7yualCVpl1LCDcvHlzTGIaIxUoyXtIHqpTIOXBv4nUAA1IK8CYXPWjR+Xm3nZW0SMTy9902Ekmma933SbrNmwKi67rtGwyQbNOKHCvwaygL624eVW+PvK+0/7fJFPEwlmdrdpAJoevdN4mK1evtW5OlypklVk/fPiwLFy4MCbRucWD8oI68tBDjuH2sE5BfPbbD2bd/hnypCT4fLbBHfFB1BHRx8O7nU976A0yBbQJHZl4Dpm8NlQZxmzt7tzvyY700KwTgGoszApMy51Tc5y2/zqZQi6e1UledHSATB17jV0tO3fulNOnT5sZ+1RXhskqs45Rz/z582MSeczxIGB0a25sY5r1kjzn/HUkTu9fI0R8hvICqZOlhl07EY2qI+pooo97Ojs3x2tkCmgTOTLxfPeTgHE+fvy4qbuOadlkL3qiWScA3F8wKSbNsOKafHX4faftv0qmkItndbTqApk8vtZ1m6xas84EhdNRdz2rzDoWAMybNy8mc9qsAyl433RAzTqijIg2XrhwQQ7u/Uy+PohoxO/IFNAmcmRyOH7OOtm3b5/Z1RR5w8nuOGjWCdXYUFT9bKHT8b5CppiLZ3awagKZXA6auFrKyspMynWqq3FllVnHdtxz5syJycSY9RLJC0stiTS/EWbdmORAGo15Luz1nvSa4LGVDL8O8/q8kuA1Rj6viG7WHxLn5VkDNBYQDQdRxhs3bpjdHi/t7u2I22/JFNEmcGRy2Lj/Rtm6dWtYSbFkdhw067kNt8bq4v0/HW7itPt6ZIq5eGZ7ebG9owNkSvmHjzfLxo0bTXU5rIfTalzJ0lw3ssqs79+/X2bNmhWTtTfr5VJQJ2CWw/62GWo165p+4nbI0SLcatT1WEvqitvs55Xg/eM364HX2o/PVKCxuCM+iDbu37dL/rT/HavYkcnhC46gkanjp0WBkmKpWPREs57bULOuaYa3LmwWOfAymQbCrNv0gEw+J8xeHarGlar1QkBWmfW9e/fK9OnTYxKdWjyojFq7GSV9JGiow724c7wx68EovDclxmrWvQOBIILn1+i7mnXvYV7YP0N2GXUADQUNxlQnqKgwi0BO7JriCNtLZAppEzcyeew2cm3YoidEPJOVCkOzntvAPYV7S9MM7x8f6LT535Bp4OKZ7ax6QCafrQatNVUFMXOP9UIYuNKsVxOojjBlypSYTK1Zz5M8mG9b7rrNrFvOE0C4ia8cCFQNb2Td/ZliGf1MAjoRRHwwa4LFH1gEcrWsjSNsL5IppE3cyOTxrW6fyfr16820bLLLONKs5zZUY5FmePPGZfnq4FtOm3+BTAMXz2xr1QMy+fxd562ybPlKk8mBTZJSsV4IyCqzvmvXLpk4cWJMVs+sR4tCe3PWIw2wRr8NfWLWAwieK47XZwLcER8t17hn9w75et9LIvufJ1NIm7iRyeWkOStNoAILqnVaNhkdB816bgP3lKYZVpxd7bT358g0cfGMNvK80/bJ9HD45BLZvn272XARM/mpKOOYVWa9tLRUxo4dG5MYCcWD6GY9aNTdjrqqNBhPCksIaTPrVX22zAMaCBqKe2Hp0e3jHFF7lkwxbcJGJpfth65OyUJTmvXchTsggnSre0c+dtr7M2SauHhGa6sWkKlh035rUjajqcgqs47tYEeOHBmTtTbrNkNdlVl3/h84l+c11px1y0AA8Jy/tmY93tf7HdqJIOKDFBgstENVoBulDUT2/ZpMMZ9v54gZmVK+1nmLrFmzxiw01fq/2nEksvOgWc9d4D7CPQVTcuP6Jfl6/8tOe3+aTBMXz2hl1QIyNXy54zYpKl4WNqMJD5JozXUjq8y61taORUSd4kH06LPXUFemxEQz687ZApFx9/miRNwjjb2ev/K1NTfreh2WSH8GAg1DIz5IgUH90107N8rXe591RO1XZIppEzYy+Zwwa7mp/4sUMLSDZKTC0KznJtSAwIwg0FVxaqHT1p8i08jFM1rKc067J9PH/uOWhWY0U5EKk1VmPdE4P7e+fOtbTVxGtxJqqB999NGg8S2QQXXrWs36heDfMN09cbzz2Anz93ljnL/1rW/JI488Emae8dpvf/vb8vDDD5vzP/xwR1kffA5Ynf9tc55zwb+jQa/TS/d1xgtUnLCdC8RnwLRQKqGdCBoIGgpWZmNHx2PbhjuC9iSZBtpEjUw+8wdWVihAKlgyOg6a9dwE7iEM/LS2+v3DHUT2/pJMIxdPz7fqAJk6Nuy9VtauXRva0TTZqTA0614cOQJXGvj/4sXiqFPg/2PGVP5/zhxxesPA/4cOrfz/lCmBf/E3Htf/43gAr8d59P84P4D302PwfzW9e/aILF9e+X8QwGP6fxyr16uPpQFIP8HABf+mCtqJ6KInrMzGCu3rOz9wBK0umQbaRI1MPn/baausWrXKpIChDWoqDNpHokCznpuAzmoKzLXLp+QbzKLtfYJMIxdPb2HVATJ1fLH9DllUWBJRcz2RmusGzboXMNf5+QGzDb73nkj37gEzjcfxfxjuhg0Df8Nwd+gQ+D/MOv7Fc3gcx+L/OB7/x7lwHj0v/u3bt/IYPS+OxXM4F57X/+t74DH8H4+730MHC2nCcmcQ8eyzzwb/Sj7QiSB66E6B2VO61hGzx8k00SZqZGo4ZsaKsFQYrbmeKNCs5ybcAZGKE7NE9vyCTDMXT29u1QAytew9usRo4okTJ5K+izTNug3uqDf+r5Fud9Qb/9dItjvqjX810u2OeuP/Gpl3R+wRJdfIvDtir+8PuP/vNuT6uDt6n2bkO4OIoRhEpABq1lEFpjIFpsD5DX5OponPOgJGpoetB60JdRxIhUG7gNFKVMdBs557UI3VFJgHB1s47fxnZJq5aHozqwaQqWWjPqvM4v5Dhw7J1atXk5oKQ7NOJBQYWdatW1e2bdsWfCR5cEd8NAXm4rbGjpjVIdNEm6CRqeFb3TbJ6tWrTceBHEq0i0SmwtCs5x7UrJsUmGvX5Os9T4ns/imZZi6a1lSebeu0ezKtfKXjZ7J06VKTCnP+/HmzADsZi/sBmnUi4cBCVCyYhXFPFtAQ0CA0BQZT/6iz/2DnM46YPUamiTZBI1PHhYuXmc3h0HEkeoMkmvXcgmqsBkSunXV++90/IX3ARdOaWNs/mXpOmbU4rCoMZqGSkQpDs04kBVOmTJH3kJefJKAhoEFgQIBcMUz9l25ZYhU2MnW0iRmZOg4avzRpHQfNem5BzTruIQREKo6Octr4v5I+4KJpjeUZp72T6WeP4Utk3bp1cvToUTP7hPTDZKTC0KwTSQPMOkx7MoCGoBUKkCuG8kmHNw8Q2eWIGZk22sSMTB3zB6wS98567o6jtp0HzXpuAfeLBkSwBuL+/kZOG/8R6QMumtbI2v7J1DOv5wpTXGPfvn0mHReVuDAblQjNdYNmnUgaIPIo5wjjkEhoI0CDQI4YNrrau3evnP/sI0fIfkimkTYxI1PHN7psDOs4kL6gHUdtQbOeO1CN1Xx1rIH4atfPnTb+A9IHXDStobX9k6nnbzt+JoWFhSYNFxXpEp1+qKBZJ5IKLDTFgtNE5q+jAbjz1dFAduzYIQ92/FKk7F/INNImZmRqOXd+UVI6Dpr13IGadQ2IXD29yWnfj5I+4aKpDeTXTlsn/cFJMxbKli1bTDpusvLWadaJpAOlHFHSMVFAA4jMVy9yROz7ZJppEzIytRwwrth0HInOW6dZzx3gXtGACEo2VhwZLlL6z6RPuGhKnvy6jdPeSV+w+7Aik7fuTj+kWScyEvXq1ZPF7nrxtQAagDdf/dDGPo6IfY9MM21CRqaWzfutjJq3XhvQrOcOcK+4AyL3933otO/vkj7hoin1rW2fTA8b9FoZkbee6EWmNOtESoAt0JG/jn9rC0R8tJzYxYsXA/nqm//oiNh/JdPMpx3hItPL1zpvlBUrVph9B9wdB9pNbUCznjvQgAgGehjwfbXzRyI7/4n0CRdN/sja9sn08OVg3rruIO1OP0wUaNaJlAHRPuSv1xY6PYsGgbzcnTt3yt0tjzsi9v+RaaZNyMjUc+HiItNx6EYdGNzSrBPxwh0QuXput9O2HyF9xEWTP7S2ezJ9nDJzQSj9EKljSD9EO0pUdJ1mnUgpunfvblgboAGgISAfFw0DDUR2/CPpA9pEjEw9x04rMou7T506ZTqORCwypVnPHeBewT2DBfxXjy9w2vZ/IX3ERZM/sLZ7Mn3sN3qRCUgeO3ZMrl+/nvC8dZp1IuV47LHHjJGoCXDjowHo9Cw2ItixYY4jYP9A+oC/ar2T9AH7jCqUjRs3yvHjx03Hgdzj2nYcNOupB34vL5MNvIc7IHL90HCnbf896SMunPy+td2T6WPHQYtl5cqVcvDgQVPqFGvqaNaJjMbp06dN/joiftUFbnxdXIoGgYaxa+1Qke3/mfQBbSJGpp5tBhTLmjVrzOJrtJNERHlo1lMH/E5qmkH8dvp/fS5Z0PfFAA+bIVXsbeW07b8jfcSFk96ztnsyfWzUa5ksXbrUrKHDWjrd4wJtKRGgWSfSAlSGQYWY6gIdiXtx6Z49e+TAmo5WQSNTT5uIkalng57LpaSkxLqrXk1Bs54aqFmGQUdgAsRvp/9X454sw47z4n0wwEO1rXtlrzpt+29JHxFm/SmnnZP+4e+7rjS+BntcYJGpe61QItoqzTqRNjRs2FDGjBkT/Cs2tBNDLiUaAhoEGsaJtX8U2fYw6QPaRIxMPd/ovFaWLFkiu3btMjv8uncyrWnHQbOefKjGwZDj90Iqyv3dK+XBretmNhEGGo+paa/N7xkNeH+8N94P1bu+3PEzp23/J9JHXDjpj9Z2T6aPL7ffIPPnz5etW7ea7AGs90jkIlOadSJtwDQr0mFQDzoeaEcGs44UGjSI7du3y8X1LzgC9jekD/hUK0e4yLTzhbZbZNGiRaZ9oGISOo7aLjKlWU8+8NuoUYcxxyDrztqpcmdMfbl19aL5HfEYtBPHqGFPJNSsIyCCgZ5s+4+kz7hw0rvWdk+ml7Nnz5bNmzebjRqxP0Ei1gopaNaJtAJGHYYdN3UsqFnHsbpzKczDnU0/cATsr0kf8ElHsEh/cPbcTyN2MqVZ9y9U3xA1h8ahNC1yxm+unCB3B7wst0e9L5fPnjQLhmHaEfnGACyRhl2vAefF+18+tVlk638gfcaFE/9gbfNkejliwjxTEQaFLxJdEYZmnUg7pkyZIu+9917wr+jADY8bHx0ZGgIaBBrGV1v+xhGwf0/6gDYBI9PDkRM/lU2bNpmKMDB9aDc06/6F6huMMtYY4DfDupwrxaPkbv8XDW+NeFfOlh8x6SkYgCXasOMcatYxe3n1yDSnXf870mdcOPH31jZPppe9hn0qq1evlkOHDpn1HmifGHwnom3SrBO+ABabYnFGVcANjxsfDQANAZUuPls7xxGv75A+oU3AyPSw5/CFsnbtWjN7hTKniPLE23HgGC8xvQvaniNrT5hkpJ9gUIXIORYGY1bk/KKhcrfvcyFWFLwtJw7tN0Yehh3GPlGGHa/HeTALg9nLG/v7iGz5K9JnXDjxHWubJ9PLNv0Xy/Lly83u0RhQ6+7R2sZrA5p1whdAFAfpMMhDjwbc7Ljx0QDQEA4cOCClq/o74vX/kj7hLx3BIv3BNgMKI+r+VtVx6OMwjUoYNyWi9KD7MTIxxO8CwiQjJx1R9TNnzpjf7uScvnKvzzNhrCh4U44f2GNyymGqE2XY8VqcAwMGXMPt3XnWdk6mlwsnvm1t82R6Wf+TEikuLpbdu3eHFvbTrBNZB2yUVLduXdNReKE3OyJPaABatnHvqg6OeP1b0ie0CRiZHub1XG6iPCjfiPZSVZQHf6tBxzFoZzB/MI8g2uS6desM8X8yscSshy4oRbQcvxd2QkS1qyPTPnYM+q8iWDHktZBhT1RKDF6H1+NaMHt5u+xdp13/P6TPuHDiW9Y2T6aX73VbJoWFhaHyjVj3AS2Frta0TSpo1glfoXv37oZe4EYHtUrB+fPnpaysTPauyBf57P8mfcJftnREi/QF3+9eYso3uqM8aD/eTgN/axQdz8M8wtijnWHGC0YQ0dtly5YZIuJKJp5Yh4MZEPxW5eXl5nfDLrT7J3SSe72ftLJi8O/k6L5AeU5bhN37W1cFPR6DNZ29vL/9Kadd/1+kz7hwwpvWNk+ml691XCMLFiyQHTt2mJmxRFThUtCsE74DoutYOOoGbnTc8Ljx0QBQjm7nzp1yeMUfHfH6P0mf0CZgZHqIKA/KN1YV5VGDpkYdEVVj0g9skht718vVXWvkcukqubRzpaybPszw4o4VZBJ4YftyObd1mZzevESOrpkve4qny5Z5Y+VwQX251+uJqKwYVC8hEXa9F2DWMbBDzvy9bXWddv1t0mdcMOENqeu0cdJf/F2H1TJv3jxTa/3UqVMm2EGzTmQtENFB/jr+VeBGd5t15LYjbebQ8j+IbP4/SJ/QJmBkeviqJcpjM+v4W406jkH6w/2ePycziBWDXpJj+3ebGceaRtj1ONwLMOsw//e3POa06/+d9BkXTHjd2ubJ9PK37dfKrFmzQrXWMXhGGmF1B8420KwTvgRybZ999tngX+FmHQ0AVRJQRu7o8nqOeP1vpE9oEzAyPYRZ//TTT82g1h3lQceh0HalCxtRNQYD4fuf1CEzjBUDXzQRdq0Sg8GXd3BWFXAMiHsEszAw/n/a8g9Ou/5fSZ9xwYTXrG2eTC+fa7NJZsyYYdLX3CVzadaJrEZ+fr4MHTrU/N9tKtARYdSKyhQnVzztiNf/QvqENgEj00NEeebOnWs2RoJZd0d5FGhT+BvGDs/DoKFG8P0ePyEzkBUDnpdj+3fVKMKuz6tZR+rUl5v/TmTT/0z6jAvGv2pt82R6CbM+ffp0k8aLBeI060ROADc58tcRGcSNjhsej6EBYAEWRq8nl//KEa//ifQJn3AEi/QHn3U6Dvf219HMuuYoI6qO45Dj/uDjfyUzlLf6PyPHD+wOi7DDgMeKsOM5HINjdV3QV5v/rdOuv0X6jAvG/87a5sn08qlWW2TatGlmfwvdxZRmncgJYEr+kUceMR2O26xjimnDhg1yceUPHfH6N6RPaBMwMj2EWUf+JGagMLhFpFU7DgXMGVIlsKgUlUjQwSAS/6D7D8kM5q1+T4ci7DDsiLDjd67KNHjNOtY5yMb/kfQhF45/RZ7Id9o56TtOnTo1YjM6mnUiJzBlyhR57733wsw6pphQ8/nKqu854vU/kD6hTbzI9PBXrbbGzJ/E/2HikPaA6h/YhAdTuA+6PUpmOG/1fSoiwl5VDrvbrGN9g9mgbuN/T/qQC8f/1trmyfRz8uTJsnr1apNOiMX6qM5Es07kDGDWJ02aZDocjFYxaoVZv7zynxzx+u9In/BxR6xI/9CbP+mN8qhZRyQVpg7bZCMq9KDr98gs4K0+v5Tyg3vMQAwDMv39Ycq9ULOOVCmY9bPlO0Q2/LekD7lgXD1reyfTzxFjp8qqVauMWdedo2nWiZwBOpnvf//7ZtdSt1m/t+bfOuL135A+oU28yPQR+ZNoJ5o/WZVZR6k+7HaKqNDnXf4rmSW80+cJOXVkv/n9ke4ULR3GbdYRiT9xZJtcWfMDOb/8e3JqySNSvvgf5OiCv5Mj8/82yIfJlPJvne8f/DsZObSxtb2T6WfB6GmyYsUKM0sJs44UNN05ujagWScyArjRUarx8ccfNwufYNYRAVy7uLesWdBFVs7rKCvmdpDlc9pLyex2ZJK5bGozKZnUUNZM/Ei2T/y9lE56VwpHNrGKF5k+xsqfVLOOSCrM+t69e01U6PPOj5BZxIrhr5roOn5nmHGbecDfuB+QKgWzvmPPces9RZJkdA4dNdWUnsYspdkFOGjWbbNZ1QHNOpERwI0OUzFgwADJy8szU0yY3semLwsXLiQTyDnTp8icqZNk/vgCKRzdT5aN/ES2D21heGfgKxFc2aeVvN52lVW4yPQS6z1qYtYfdPpHMktY0bOOHNqy2pRi1HUL+M1tZt0dWcdeFrZ7iiTJ6Bw/frwx65ilxACZZp3IKeBGh7GA2XjuuedkxIgRxqxj0xcyPmIb5NlTJxouGDvYMeJ9ZfXwLlJa0EJ2D2kidwfUqxY3924s77QttgoW6Q/CrCOt5fDhw6HFTu6oajSzfr/jfyGzgNe6/1g2Fc+T3bt3m+oumgplM+vQWFAj6yjjabunSJKMznHjxklJSQnNOpGbULMOs4HpJZRzRBQYBpR0TPjs2TJrygSZ7RAmHFw3vIPsHNpMDg/6wDHXLyWM2/vkSeP2c+UXLXaQPuavW200Zn3NmjXVNuv3OvwDmeG80uWHUjxzvFmzgN8VkXUt3xktso77AZF1HIfI+i9b2e8tkiQj+UT+trDIOtNgiJwDbnTc8DAbWLSBXFwsOIVJnTNnTlZzxrSpMmPiWJkxYbQsGtnb8LMhLWTnkGZyZsBbcrf/b1LC/X3+KC07TreKFOk/2sx6rDQYdDAw63fb/z2ZwbzY8V9kztghUlhYaDbFQtogqv3Ek7OuaTDY9falTvZ7iyTJSD7dcoMx61hgeuDAgTCz7m1v1QXNOpERwI2uZh2mA+bj3Xfflffff99s/JKpnDppokwbP1pmjB4qC4Z9IosKusu2gY1k+6BGcrH/K3K33wtp5/E+b0r7DhOt4kT6l8+2Xh/3AlNvNZg77f4zmaG80PFRmTlqgEl9w2+PFBhEyatbDYZmnSSrR5j1CRMmsBoMkbvAjY4ORs06IkXoiCZOnGhqSfuRk8eOksljRsj0YX1l3pDusnhIR9k6oIHh3X7P+Z4ne/9OenYssIoS6X/Wa7vS3IfVKd2IFDNE4m+3+zsyA3m+w/dl6rA+ZkYOhmHHjh2mxj6i6viN8fvDONim5N1mHRF4bIr0Rvft1nuLJMlIvtB6jfEkK1euDJl1eBbb4Li6oFknMgJus6511mHWsVtYOjh+xFDD6YN7yJxBXaS4f75s6V9fdvZ7X+71fSajeb7XSzKkY2+rGJGZQzXruikSzLp3B1OYM5h17w6mt9r+LZlhPNf+n2Xa8L4mNRA5s9u3bzc6iUEYctChndidFL+5zTioWccxMPZYkPpeb/u9RZJkJF9us8L4A13Ub5vNrClo1omMgJp13PgwHeiEEAHErqaYdkoUx44dI2MKBhnO6t9RZg3oJMv6NpHP+n4gB/q8Iff6PJ21vNTzeRnXqaM8nb/ZKkRkZvG19stkxowZsmHDBjl+/HiobF80s478SrQr5DhXtHmYzCCebfc9mVrQx2rUkdKCqfho6S8KmnWSrB0RINF1Qmh/NOtEzkHNOswGGgCm9REBhFkfM8Yx2HFwxPACGTm4v4wa3E9m9GtvuLbXB7K5z3tyvM/vHMP6VE7yeu9fy+ROrWnSs4xvd1xi1kVs2rRJysvLjWlDigPakQLmDGkRMHMYBCO3uaysTLbNGymrJvaVohHd5NNB7WVW31YysNmbMqDpGzK9VwsyCZzWs7lM/aSZTOnRVCZ1byzjuzSQMR0/kvWtH5ebrf9TVJ5p912ZNqwyor5t2zYT1Tt//rwZoOG31d8dv3c0eM06Np9rPnSn9d4iSTKSb3QojrlrdE1Bs05kBNxmHVO6iBTChGAUi5rrwwuGyPCBfWVUv49lWp+2hpt6vWt4qfdzcr/3L0kPb/V6UmZ2ai7Pt1gtP2++ncwyvt2xyOQuY+df1My2mXVtV+hQ8DzK+2E9CAwf8i5RHhUmEAtVO3bsaIgBMplYIs8VDMzujZWRI0fKkCFDpE+fPrKsw0tyo9XfWHmm7X+tVkS9KsOgz8OsY6YFZr/VsB3We4skyUi+1aEw5mxmTUGzTmQEcKMj6qOVChAB3LJli2yaMkDu96pLVoO3ev5SCjvlySstl1oFh8wOftBlgcydO9e0E7SXaGZd2xUqhWDxNhYWInd9586dsnHjRpN/CSM4cOBAs4Pw0qVLySRxyZIlsnjxYrN3AgZImBFc3fVVud7yP0bwdBvHqFsi6rpTqTeiHsss4HkQ5h73Asx678ml1nuLJMlIftB5rsycOdPoJmYzdV8DmnUiZ6CmQqdoYShgJtbNmyj3ez5OxsnVnX8vb+cvtAoNmV3M/2SmKd8HE4cyfFpj22bWYdAQXUfbgmGH4UNngyg7yjmi/B8WTiEKjDQZMjlE9RYMrjCNXlxcbGZGNvZ6W67nOwbdxVOt/0mmDe9njajDIMCoQyvVJMRjFPQ43Av37t0zFWQmLdplvbdIkoxkfo+ppk1i3Y93NjOeNlgVaNaJjABudDXrOkW7Z88eWVWyVO5/8nMyBrd0flXeazXHKjBkdrJrv4myYMECYwCxWBBGHEbMHWVVg4bOBG0LFUPQvhCZxYJTtDPkLmNwPH/+fGP+EaUnk0NMnSM6vmvXLpPmh2j7tv7vybUWfx3iaceoTx/Rzxj5RETUFXovYA0DzDqqAy1bv9d6b5EkGclu/caZWbGtW7eaAAk0F7panXYYDTTrRMZAI4DojNCRYIcwrLq+0etXcr9HHdLCnV3qSYOWU63CQmY3Bw4da1IqSktLjZHTDXG8HQf+j8dg7vA8pm1h2mHYYNwRkUeEqKSkxBCRWzI5xOJ5RLQRlUMwAtPpZUPry5Xm/94QEfV544caQ4A1BRiI6WJSvF4j6lpLvboGQc06fn/UiC7dfdB6b5EkGclBQ0dFBEho1omcA2547UgwVY9pXyzkODvwVceYPka6uKfLC9Km9QiroJDZz7otPpPRo0ebVAqksMDMRTPrgNuwo43hOHQyiNDCvCNFBvsagPg/mXhC1/AbwXTDsKOaBAZa+0c1kcvN/p3JUV88dZQUFRXJqlWrjCGABmIghtdgcFWTiLobatZxPbpT9LPttlnvMZIkK/lk/kZT7KKwsNCktKFdItgRTXOrC5p1ImOAmx0dEToSTPcipxa5YYeH1ZcHH/+YdHisy1PSo00/q5iQucPnWq6WcePGmUg4cs4xEwUzh46jqk4Dz6FjUaK9KZGWAbofIxNDGGQQZhu/E0q+ITKHhb5HxufL2fbfkw1Fs82upPgNYOJh5pGj7k59wTlqYwxUYzFAw3mxmdab3bfIz5ptI0myCj7fcrlZEI7UNcyKYcCtARK0q5q2SQXNOpFRQEeCTglT88gJw8Kq3WNay4PuP8ppnuvyC+nTpodVRMjc4yttlpjFoIjAure9hpmraceBEpAgkXjob4LfB0YZ0+cYYCEd5tj8QbJrbbHROhCDL+S2u8szYhYE2ljbCB5ei3NAY7XqVoshW633GEmSlXy97UJTehUDaqToYs0P2qZqbm1Bs05kFLQjQWeGhW+oCLNtSi/HsP4gJ3ml62Myom1bq3iQucs/dvo0tDmHeye92nQcNOvJBX4XGG4Yb0TXEdmGIYdhxm8IIpqOxb4w8pqjnoiIukLNOq4BGovofp9J9nuMJMlKftBxRmj3UqSPIY3MHSCpLWjWiYwCOhJMK2kdYFRNWDd3nDzo9mhO8UqXn8jYNi3kqRYbrcJB5jYbd5sRtnspjF1t6/3SrCcXapShbxhYId8V6TBalQfGHVPreAxGGkYgURF1hdusa9WtyYt2Wu8xkiQrmf/xRFNjPRkbIgE060RGATc9RqqIKCG6hCnh1UsXy4Ou/5wTvNHlBzKr7UfyXPOVVsEgSbBj74mmzKKWEEPaGAxYbUwdzXpyoUZZo+sw7AhK4LfDYAtpKfg/HsNziTbqCpwPAwZE9zE4WLp2t/UeI0mykl37jDGb0EEj3TXWE9U+adaJjIKada0Ig3xc7LB4seeT8qDLd7OWtzv/sxS2e5smnYyLA4aMkUWLFkWUEEP7qWnHQbOefOC3UcMOw4zOHsYceod/EanD7wgN1IhdTX/PaMD5VGMR1d+9Z788kW+/z0iS3Ca/aL5FBg8ZZso2Yl0JUtVUc2nWiZwEbnp0Uui4MB2MagXr16+Xg4Pekc+7/FNWsqTdq/Lb/CKrSJCkl79qsc6UbcTW9UgTQ/qEViWoTcdBs5584LcB1bDDNON3Q6ePf0F3NL2mv2VVwDnVrGOtA3Ll3+nxmfVeI0lym7zQcqmMGhUoq6plGxOhuW7QrBMZBdz06Ky0WgGmm1C+8bPR7eXzzo9kFTe0e17eaj7PKg4kGY2oSjB+/HizuyXSxLQqATqO2nQaNOupA34nEB29Uh9TJgv6vlq+EWseuo35TOo03UqSpIV/aDfDaK67VG4iK8EANOu1REneQ/JQnQIpD/5NJBfakSDSpNUKMNW/YtZYedDpH7OCW9o9LfVbTLCKAknGYoPOU8KqEmglGE2bqClo1lML/FY2pgIaEEGOPKb0ZxVtt95rJEluldbdR8vkyZNNSu6hQ4cSXgkGqLVZ/3xuZ7k74OUqiWPiRXlBHXnooTwpCf7td9TMrJdLQR3ndSn7nPp+D0lelDcMfO+BYypZk+urfK8QXd9P2PtYvrdY36d2WIgSYgEUpvixAQF2arzV+Z+t5jdTuLf949K65VCrGJBkvOzca5SpBIOt6hNZlYBmPXeAgAg0FhVhMKW/fUeZPN7Cfr+RZC7z580+k/4DBkdobiICJG7U3qzPaiv3+v66SuKYeFFjs16S57yujhSkOMQd06xHuy7n8TqputjyAqmjBjmKW4/43kOvqc5vYR+EYBo18B+cU7+LEslzzh92Oea7qvr91KxjxIppJmz2gg0IVq5cKcd6vSD3O/6XjOPRdj+W1vk06WTt+XizTVJQUCDz58+Xbdu2hSrBJKIqAc167kDNOgIiWnXr7e6brfccSeYyX8hfmjTNdaPWZv2L6S3kfs/HqySOiRc5Y9ZTCPOdOtdYUsV3a/3eq3vtsY7H867vCt9d5YAlYN7jGcCoWfcuMi0d/JHc6/APGcMT7X4g3Vv2sAoASdaE9VoVmsWlWOiExaWok42FTlqVoDagWc8dQGMRFXQvMu0yapP1niPJXObv204PW9CfSM11o9Zm/cspDcS2/bmbOCZe2EyjMcQIwbojxK5jzPOhxwMMM33GREZ5zoGeP/DeOEbPHTCQla+NNKIRZj34XniPqq4r4nWA5zptxlqvNdp3EYlAtNu8b/A1tuB6dLMePXXGi9C1RUMVZt36fUQBOhI0Al1kih3+UE96zaR+VlPsN55t930Z1KqdteGTZG34YcdpMnHiROuW1zTrRLxQs+5eyD990RbrPUeSucxW3UbJpEmTzOx+ojXXjdqb9UnvW+tCu4lj4kVUsx40psYLqlGNMMmWqK7LPFv/dhB+fphbvH8wpSNkPu0pHmEmU6/LbVijXFeEOQ1eV+i1ITNeg+/CDfO8vr/3M1Ui+vfueszy3VUijsh42LW40mCifEfRoGYdI1fNqSwrK5OlC+bI3fZ/71tebPddGdOykTzZfL210ZNkbdmp50iZMWOGmWnC1vSYeUpU7iTNeu7AprHbtpcyb50kXUS+et9+A0M7l2KWP5Ga60bt02Am/MGah+smjokX0U1juJmLOM5q+Ozm1PtaNcAWDxuOoDF2H1dpuoNReK9pjsusRzHRwfeLHFjE+C5cCH+f6McGHse53fQeh88YzVR7ZyEqGW1gFPi8cZh8C9CRIKcSI1jNqUSpuhMf/1LutPvPvuKVdo/I9Jbvyi+brZOfNtlCkknhL5pulCFDhkTsXIroKM06UR3gXgERHXTnrb/VbZP13iPJXOTzLYpl6NChYZqb6J1LFbU26w/GvW01KG7imHhhM5New2ngNcE2U2wx1wbmWI9Z957fhqhmPU/yYLZt54jHrEe7TudZr4mP67sIwWKEo7yX93t3m/fI67LBPlgJnCeawXd/Hp25CDCWeUcjgAHBCFZzKtetWyebhzSy3oPp4I22/2BM+jNNSqwNnSQTyZdbLQ5tzIHcSURDERXFoDYRHQfNem5Bzbo3b91275FkLhL56mPGjAnLV4fmar66r8z6vdFvSkXrh6skjokXyTDragDDGY9Zt0eLI8y6Pmc7RzrNuvXx+GYbAggea/1uvIgSIY/62RyY54LXh2sN+z681xIONesYwd68edPkVMJILJkxVm63/bu08kabv5eFLV+Ves0WWxs4SSaD9TtONvnqmjvp3ZiDZp2oDlRjUfYTGou1QYuX2+89ksxFduwx1NRXV81FdTp3ffXaaq4btTbrt0e+Jtfy/7pK4ph4kQyzbjWKLtjNetCou19sOV/otcHnIsxqFCOdCrNujsVnsDL8O7ab9eiPR8LyfQExPpt+X+ZaQwfhXJGfxwt0JBjBYnOks2fPSmlpqRQWFsrZjj+UW23+Ni0safm8vN5sjrVhk2Qy+UnfoSZfHbmTyFfXzZC046gtaNZzC7hnvHnrZWVl8mI7RtdJ8qlmK2XQoEGh+uqar56IPS1sqLVZvzn8d3K52b+rkjgmXiTUrEczkB5Yz28zmVWZdef/gWv3vCYes14NoxvXd2EQOKc1ncQcH37eaKbc+n5REP+1Bd/Pdax5beiC4jPr3rx1jGxRBWNHv99bjXQyubHVk/KHZlOtjZokk80X84vCav0iCqq5k4nqOGjWcw+4bzDYU43dv3+/dB3JtTck+Yc2k2XEiBGyaNEis4s6dvrFGqFkpMAAtTbrfz5/UP50fEuVxDHxosZmPWhsvebUaqBhBl0P2A2p10AH//acK/y1wUi4+/qjXJf3PSOvU9+vBt8FYDHklfB+Nn1/93vpZ3Fde5TPEkLwPb3HR1yveTz8esPMu3k+cuDghXYk7nrriCouG9/Pmo6VDJa2fEw+bD7O2phJMlWs32G8jB071uzku3v37ohavzTrRE3g1ljsyojdGQtLGFknyY7dB8r48eOlpKRE9u7dKxcvXjSLsRO1RsiLWpv1RKPGZt1lLsMMowPz+uDjIbqMqvX8DtRAK+sUFBiTG92sA0EjHHrMfl2294y8zkjDar1Wi1kPnCu64fU+7/2syjCzH8usO4j4DBHfa+D7iTxH5WAIrOo9FGgMaBSIHmJEi5Ht9u3bZeHChXKx7T/JzVb/KWnc2fInkt98kLURk2Qq+bOmm6Vnn4EydepUWbNmjRw8eDAidzIRoFnPPajGYtAHjT1z5ozs3LlTXum4Xh5r/BlJ5iR/3axYBgwYYEo2okwuFl9r2mEyUmAA35l1wr+wDaTSCTQIECNZRBExskV5MYx0d/f4jdxo9TcJ5/6W35euzbtZGzBJpoPPN19syofNmzdPtmzZYhZbIwqa6NxJmvXcBMw6NFZLOCIVpve4ddZ7kSRzge+3HW9SYBAYRNqhu2QjzTqRZgQi3/a0mvQBjQLRQ0QRr169GirhuGJ0N7ne8j8mjOX5j8gnzdtbGy5JppOaAoPyYWVlZaGSjYnOnaRZz02oxrrTDZet2mS9F0kyF9ilR3+ZMGGC2dsFKTAXLlwwAUNNgUkGaNaJ+IBUG785dQfoSNA4EEXEyBYjXIx0F82fK+fa/KO1OlF1eCr/72Vw88bWBkuS6WadJptMCsy0adPCUmDcJRsTBZr13IRqLKKGqLyFVBhU3nq7G1NhyNwjZjIHDhxoqsC4K28lOu3QC5p1IuOBjgRRRIxsMcLFSHfZsmWyqfc7VgMeD8/m/62MaF5fnmi61tpgSdIPfDl/vqkCgx30NAUGNbGTUT6MZj03oWbdnQqDylv9JlAbydzjh+3GysiRIyOqwCQzBQagWScyHuhIvKkwWPQxf8pYudriP1SLZ1o8LJObvS3PNF5mbagk6Sc26DAmVAUm2Tvo0aznLnAfwYhgEIj1EOXl5bJm/WdmcbPtviTJbKSZyezV12w+hzLRWCOHwatWgUE7oVkniCjQjsRbFQYj392d6sqV5v8+Ji80/2uZ16weTTqZMXy8yVrp03eATJ8+XdauXSuHDx82g9VE7lrqBs167gL3kc5g6iZ0WB/RfMAa671JktnI11rOkMGDB8ucOXNk06ZNoY2QErn5XDTQrBMZD+1ItCoMRroY8WLku6Sgg9Wcu7mw2fPyUpOF1sZJkn7l79tMlmHDhpmNkLZu3Wo2QkpWCgxAs57b0BlMDAaxLuLQoUNSuGyt1Gm6SX7SeDNJZjV/2mSjdOzWR8aMGRNazI/1G5oCk+iZTC9o1omMBxoIiI5EKxZg8w6MfOfOnSsnWj5i3Vl3ebO68rums6wNkyT9THQcXT8ZYKZjUap0z549oRQYrUhAs04kErifdAYTi/kxOMRi/g97rrbeoySZTXyp+Wzp379/2GL+y5cvJ2Uxvw0060RWAA3FPU2L8nXI4UUu7+re78ulpt8JcXXTn8v7TUZbGyRJZgJfbTlDBg0aJLNnz5aNGzeGKhLodCzaQqJBs57bUI3VGUzd12LKvFXWe5Qks4n5nQaa2uoLFixIyUymFzTrRFZAOxIYFV1oihxeLDRdNH28yUnfkY9dRwdaGyJJZhLbdRtopmOLiopMGT3kECe7IgHNem4D9xSI+8s9g7l582Z5p+tq+Ukj594kySzk800/lT59+sjkyZNNeq23tjrNOkFUA2rY3QtNsTX20qVLpcPHU62NkCQzjS80m2+2up4xY4ZZWOqejtUUmGSAZp1QjdUZTKRe7d69W0ZPX2m9V0kyG9isw6BQiVxoIAapGKy6a6vTrBNEnNCOBIZF6wEjuo7c9dFTi6yNkCQzjS06DZFRo0aZake61TWmYxHtTGaEh2adUI3VGUykXiEFC6lYL7ZZa71fSTKT+VSTpdK7d++I9UEYrKZiYamCZp3IKmhHAuOCRVBID8CUFRaEcKqWzHSi4+jXr59MnTpVVq5cKfv37ze5w+6trmnWiWQC9xcGhTAq7jKOgyausN6zJJnJrN92mAwdOtQUq9Byje4dS2nWCaIG0MgPpmmRFoDcdWzggZ3GJs9dIT9utIkkM5YN2o+Q4cOHm3KNumMpNqlJdlQdoFknFNBYDA5VY3UjuhfarLXetySZifxl42Lp2bOnjB8/3qTTomgFilekOqoO0KwTWQU0HNAd+UHkETWBMVX7TrfV1kZJkn7nk02WmkVOkyZNClvklKwdS72gWScUqrGohIEZTNSbxkLngROWW+9dksxEIqo+ZMgQU3Vrw4YNJuXLvfEcNDdVoFknsg7oSDTyg6kq5PNisSmmaifOLrE2SpL0O/PaDTdR9Xnz5hnTrIuctFxjMo06QLNOKNwai/VB2CRJq2+93mmV9f4lyUziM03mh6LqKAEN/5CKqlvRQLNOZCXcnQnyeVEtQ6dq3+qy0to4SdKvfLbpAunbt68pHbZ8+fKwRU6IqqPjSDZo1gk3oLEaXXdX3xo/c6n1HibJTGLzDv1MrvqcOXNMVB3+wR1Vx/1Ps04QCQDMunuxqU7Vzpi31OwAaWugJOlHtujYX4YNG2ZKh6GuNaLq7kVOqeg0aNYJN9wBERgYBEQ0ut6gF2cwyczlb5tPNSmHqACzbNkyk6uezqg6QLNOZDXQmSDyqNF15K6jM2nej9EfMjNYr/mMUFQdueqIqusiJ3euerI7D5p1wgvcczAuMDAwMgiIIF1g7sJlUrf5euv9TJJ+5s8arZJ2nXua4AhSDrUCjDdXnWadIBIId2fijq4vLiyWJ5qtszZWkvQLf9JovbTv0ktGjhxptrlGBRhvVD1VHQfNOmED7j/ch7gfYWi07nq7QcXWe5ok/cy388eY8riacoiF/Omoq+4FzTqR1UCjQuPSqVoshELuGXLQeo1cLP/acCNJ+pZvtRxvdiudNm2arFq1Svbt2xeqAJPqjoNmnbDBHRCBocGsD2Z/ipcul+dar7be1yTpR9ZtvES6df/EBEcWLlwYNTiSDtCsE1kPd2eiGyUhBw25aC+3XWFttCSZbmI6tvvHPWX06NGyePFis1sp6qq7K8CksuOgWSdsgL6CGl2HscHeFjA6A8ctsd7bJOlH5rUZLIMGDZIZM2bI6tWrzaZz7uBIOnLVFTTrRNYDjQumBp0JouuYqkUOGhbqjZqyWB5rvMHacEkynfygzXAZPHiwzJw5U9auXSsHDx6US5cumfUXWgEmlR0HzTphg5p1DYjA2GBvCxgdGJ43OjMgQvqfzzadZ0o1jh07VoqKisxGiidPngxtOgf/oPd6OkCzTuQE3J0JpmqRg4ZcNGzZ3rBnsbXxkmS6+HSTRdKrVy9T43fJkiVmnQVK42HPAJTKU6Oeyo6DZp2oCrgXkW4IY4P79NSpU6aU4+z5xfJ407XW+5wk/cA6DVeaRaUFBQWhUo2obISiFNhHIBWbzsUCzTqRE1Bjg9ExOhOMlpFSgNSCBQsL5el85laS/uBPG60xi0pRjWDu3LmhGr9Yb4GOA4YoHR0HzTpRFXA/4r7E/Yn7VEs54v4dNI7rg0j/Mq91/9Ci0pKSEtm9e3eoVCOCIxpVTydo1omcARobIpJayhEpBUgtQCnHIeMWMB2G9AXfbz3MLCqdMmWKqUbg7jjSmTdJs07EgmqsbpSE+xaLTXEff9SDM5ik/4ia6j169DBrg7yLStNZqtELmnUiZ4DGprnr6Eyw2BSpBagLjMWmjXsWWhszSaaKLzadafImx4wZI4sWLZKtW7eaxXpajQBRS9zHNOuEH6Eai/sU9ysWQ+P+1RnMZ1qulB812ECSvuATjYqkc5fuZqfS2bNnm7VBBw4cSFvFrapAs07kHLQzcS82hQn5dP4ieb7lcmujJslk86cNVkjHLp+E8iZ1USkW6/mh46BZJ+KBGnb3DCY2o0M6zOjJC8zeAbb7nyRTyX9tsFaatvlE+vfvH0p/QZU47MWCNRd+WFTqBs06kXNAw9PFpjBBGEVjNL1mzRoZMWGe/LjhOmvjJslkskGbAWHpL5jx8VPHQbNOxAO9R90zmFp7HQv6OwxcYL3/STKV/H2LArOIX2cxNf1Fdyr1w6JSN2jWiZyDuzOBCdKdTWGOMLrO77PQ2rhJMlms12yK6TjGjRtnaqoj/cWdN5muRaVu0KwT8QL3qc5gajoMFvRv377dVDd6rcMyazsgyVTwmcZzpHv37mYRP2Yx161bZ2Z/dBbTXRo3nZrrBs06kZNwdyYwQzBFSIfB6Bpm6fUOS6yNnCQTzccbLZWu3XrI8OHDTfUXLHjWjsNbUz2dHQfNOhEv9F7FfQuN1eowqGqE/S1mzF4gv2iyxtoeSDKZ/HnDEmnboZsMHDjQ7Ay9YsWK0CJ+BO7cpXH9BJp1ImehnYmmw8AcaXWYWbPnydMtmL9OJpfIm2zRvrfZNW/69OkmTcDbcWj6S7pBs05UB27DDo3V/S327dtnUg4Lxn3KlEMypYTeNmnTW/r27SsTJkwwmx9htkd3htZZTL13/QSadSKngQbpTYdBbiVG26MmzJY6jddaGz1JJoIftRpoOo6JEyea9ADsmufXjoNmnagu9N5VjcX6C3cFrk8K5lnbBUkmgx/k9zfVtkaNGiULFiwwszyY7cGsj59mMW2gWSdyGmiQ7nQYmCRsMQzTVFxcLN2HzJEf5q0nyYTzjeaj5JNPPjH1fTOh46BZJ2oCr8ZiAR/KOWJdBiKbLXt/am0fJJlIvtVsuHz88ccm3VDz1FFYArM9uvmRH9NfFDTrRE5DjRAaKcwRTBLM0tGjR40xwSYJDT9mZ0Imli82mRK2wAllGvfv3x/WcSAame5FpW7QrBM1gVtjNeVQdzfFABUa+0HXBdZ2QpKJYL0m46Vr166mnvqMGTNC6YaYScdu5lpty0966wXNOkE4QANFY4VJQm6lO3993rx58mq7QqsIkGR1+XSjeREdh9b31Y4DUUh0HH4CzTpRU0BfcT/DsKvGomQuBqjUWDKZfKbRDOncubNZFzR16lRT8a20tNTMoLurbflpFtMGmnWCcKCdiRp25K9jkd/evXtl9erVMm36LPlNyyKrGJBkvPx5gyXSrmN3U4kA9dSXLl0aylO3lWn0U8dBs07UBm6N1fx1DFBVY2fMnCPP5i+zthuSrAnrNlwoHTp2MRsfTZo0yawLwm66Wk8dVYrc6YZ+Bs06QThQY4TOBGZJawOfOnXKLIbCJjXjJ82QXzUvsYoCScbiY3nLJb/9J9KvX79QJQLk7aJk6JUrVyI6Dr91HjTrRG0RTWMxs4QZpolTZskTTVZa2w9JVoc/a7BMWrfrEqr8UlhYaEozI8UVu+p666n7HTTrBBGEGiR0JmjEME+6GArlnRAFHTlumjzeeIVVHEgyGn+Ut0Yatu4rffr0MRsfIU8X+brI29WOA/m8iDrqfeg30KwTtYVXYzGThBklpCQgNQEpCiPG07CTtSOMevO2Hxu9HTt2rNHbTZs2hfavQBoW9BZGHfeiH/XWC5p1gvAADReNGJ2Je8GpVi8YNHK6/LTRGqtIkKSXMOoftBxgdihF5ZdPP/1UNmzYYNZE+HlBqRc060Si4NZYd1AEKWEIigwbO4OGnawRYdSbteke0tv58+cbvXVXftEFpbgPlX4HzTpBeKCNF50JTBSinoh+avUC7HDap2CaPNZwtfyg/jqSjMof1l9tavuiROPIkSNDlV+wMYxufJQJlQgAmnUikVCNdVeIQUqYBkUKxkyXuk1KrO2KJG38WV6RMepaSx0Ll1GiEXp77ty5jNJbL2jWCcICNGI0ZjXsGI1jVI5oKKbTMK3We+hU+UmDVVbRIMkf562UvJa9pUePHjJixAiZPXu2WUiHTbewMQwW2CFv168LSr2gWScSCbfGqmFHUAR7DSC3GIZ99Lgp8lTTpdb2RZJu/iJvkbRq29kYdQRG5s6dawIjWMBsq7SVSUYdoFknCAvUOKFRYxSORo5ROUbnmE7DtBo2suk1ZIr8otFyq3iQuUsY9YatepqOA5twzJo1K1SiEQvq3DuUZkrHQbNOJBqqs9BYLemInGIadrI6hFHHYtLevXubiDqM+po1a0KBERj1TCnRGA006wRRBdCgYabQyGHYEQ2FYUd9YBh25MMNHD6Jhp0MUY06Ul80or5q1Spj1LW2L/J0dYFTpnQcNOtEMqAaq4Yds5iowY60QzXsEyZOkRdbFlnbG5nbfKrhXGnbrqMx6shRR+oLIupeo55JlV9soFkniCqgRkoNO9IW0PiRb4w8OGzooYb9ySbFVjEhc4deo46Iuhp11FLHQjosWs40ow7QrBPJgFtjdRYThh0RdjXsqI89cdJUGnYyjDDq7Tt0NlVfxowZYxbvI0ddU1/cM5iZbNQBmnWCiAHtTNDYoxl2pMQMHzVBnm62xCoqZPbzJ3kl0qh1L1OFQBeTIvUFdfoRUddNODKtZJiCZp1IFlRj3RF2d0oMFp0WFxfLpMnT5NU2C6ztj8wtvtBostnwCPtWoBwugmboi9Ena466O6KeaXrrBc06QcQJt2GHCLgNO1JisOh0zNgJUq8lO5Nc4xMNFkjLtt1COZMw6lhM6k19cU/FZlrHQbNOJBtq2KGxbsOO0rnYeXLZsmUybdo0adRtuqm09C8frSVzjD/4aJW83mSodO7c2exMOn78eNP3og9Geir6ZPTNunhf9TbTQbNOEHFCDRY6E5guGHbksEMcIBIbN240ZR2xW9r7HWdYhYbMPiLCo1OxyJnUxU27d+82i0lh1JH6kslGHaBZJ5INt8ZqhF2rxKCsI+qwYzfpmTNnStd+E+RnDZdZ2ySZnXys/hKpn99TunXrJoMGDZKJEyfKokWLTN+Lwg9YT+auspXJeusFzTpBVAPa8DXCDlHQRaco64g67NjWePLkyZL/8URGf7KYiPC80bQgFOHBVCzSoZAzicVNatQzOfXFDZp1IhVQjVXDrmUdUYcdGydhp1Okl2H2aujwsfJMs0Jr+ySzi3Xz5pnSjCiFO3ToUJk6dapZywBNws6kWkc9G406QLNOENWEuzNBtFQNO+qwQzSwIAr5lZiu7dZvrDzWYLlVfMjM5Y/rL7NGeDAVi7QoVCGIlvqSqZ0HzTqRSqjGuiPsWPeBhdqYtULFDwyOkQbxbvuZ1nZKZgfrNRotHTp0MOuBsHAfMyslJSUmNQprGlA9SDc8glHP5KBINNCsE0QNoKZLI+zuCgYQD52uRTWQgUPHynPNF1lFiMw8Pp63QFq07mIqvgwbNiwU4cGsCmZXNGcy03PUvaBZJ1INt84iwo42hUEwBsMYFCP9QWcyW/aYZKox2dosmZn81/or5A/N+pvZSywkHTt2rCnNiApbmGE5fvy4SZFC34s+GAM7NeqZrrde0KwTRA3h7khg2HVBFKZrISIQEywyRDkpiMx7HbgoKtP5fMNJJj8dC0lR8QWDMY3woMwcZleyNWeSZp1IB9B2NMLuXiuEtAedycTCU0Rb+w4ZJ081KbK2XTKziKBI09bdpHv37mb2ctKkSWb2EhVfkGaoFbYw44K+N5uNOkCzThC1gAqDbboWYgJRgbhAZCA2HXuNkV80LLaKE+lfIu3lveZ9pUuXLiY/HYMvLCTVCA8Wv2FWRSM82ZgzSbNOpAvajtCmNPUQaQ9If0Db27lzp2mLiLoyMJLZxFqgVxqPNGkv2AEas5fTp083qaXQHywk1RrqOnuZ7UYdoFkniAQAAqGGHdO1qP4BMYGoQFwgMhAbiM7ggtFSL3+uPPrhGjID+EyDKWZh08cffyxDhgwJi/AgdxaDsitXrlgjPNkEmnUinVAjhralqYeYyfQGRlCRC2kxfQeOlGebLrC2adKffDJvtjRv1Vm6du1qgiLY6AhBkRUrVsj27dvD8tPdNdT13shm0KwTRIKgguGO/ujCU81jh+hAfFA5pHm38fJYXolVtMj088cfLZU/Nutj8iVRlhFpLzNmzAhFeFCu0x3hwSAtmyM8NOuEH4B2pYERDI7R9tAGsVYEgRFNi0GKGkqpNug0kTrrc0Jr32wyOBRNLygoMAMuBEVQXQv7VaASEFJMMUDL1tnLqkCzThAJhAoHRARiAsOO1AgsgoHYIBIL8YEIQYwGDB4hb7aebhUwMn18sdEEad2ui4mmI18StfOx9gBT7Rh0YfCl+enZtEteVchls/7gi69k8YaLMXnkzN3gK4hkQTVWdRZtD21Q02KwgRJS07DXAarFYCas/6Dh8nKLOda2TqaXmLnMb93RVNYaMGCAiabPnj3brAXCwEvTXrCwGLOXGhRx3we5AJp1gkgwVEA0+oMogOaxo4qBpsUg+gNRgji16zFSnmy02CpmZOpYp36hKcmIaHrfvn1NNB2pS0VFRabyhC5sQoTHm5+ezUYdyGWzfu3Wl/JW99KYXLD+YvAVRLKhOquBEU2LQUoa9jjAzJdXZ1t2H2vWDNnaPplaQmuxDqhTp05mwf7w4cNNZS2kMaEsZ1lZmSnU4E578c5e5hJo1gkiSXAbdkR/MF2Lkn6IyGJRFMQIooQoO0QKC2neazfJlKuyiRuZPP7oo+VSr/FoaduuoynJiNx01E6fP3++iaYjXxLVXjDVrhEezU+HWciFjiOXzfrVii/llc6lMTl3Lc16KqGGXXVWyztCZ3XxqVdnhxaMMDrL1Jj08CcfLTEpL+3btzcbHGHmErXysTgY5Y63bt1qSuAisIUAV7S0l1zQXDdo1gkiiVBR0egP0mI0+gMxQvQHU30QKezIB9HqO6BA3mw1maY9BfzBhyvlpUZjzQJSTMNiURPyXFEGDrnpmzZtMtF0bMSCVCZvhCeXOo1cN+svdtgZk7NWXwi+gkgVtA3CsHt11j2bCROINUMwhUhrGzS4QN5tM0F+Un+ZVRvIxBIm/Y3Gg6Rdu3amHCO0dtSoUWYdEPapwIZySBNFNB2VtZBi6F0LlIsRdQXNOkEkEdqRgNqR6KIod/QHC2hQyQDpFhAviFivvoPl9ZZT5YcfrbKKH1lzwqS/0HC82dwIlQeQ8oJp2ClTpsjChQtNfXzkpnuj6bkc4clls37FMeu/brMzJqevpFlPF9w6q1F2DKxh+mD+sGYIA2+ks2EgjtQYlHkcOGgoTXsS6TbpCIhgcyPsQgqtxWyHai1q5qvWajQ9l6q9xALNOkGkACo27rQYd/QHC2ggVki3QNoFDCOmbJEz3b3XYPlt86nGYH7/g9VkLfl8g3HSrFVnUzMduZJIP0LKi07DwpBid0TNTUc0Hb8VfjON8ORi55HLZv3yzS/lifydMTllOc16uuHWWrRZmD4MtGECYQaxOBx12bHQH7ufYhYNpr3/wCHGtKMyiU03yOrxxx8WOSZ9YMika0AEhRWQXohZDswqY3bZvQ6IWmsHzTpBpBAqPO7oD6LsGv1BugWMIkQMYoYKJBA3iBxM+xv5k9iZ1JCoOtC0VVezeBQmfejQoWY6HOlHS5cuNRE3TMNipgPrClAODvXyMROCaDo6Du08chG5bNYvOWb9Z822xeSkkvPBVxDpgmosiPaqwRFE2WEGYQqxABV50YjoIp8dixoxo4lFqDDtf2wzVn7RYLFVR8iqWeejBfJmk8pIupp0VOVBf4YqL5s3b5a9e/ea2Q7MLnu1NhdnLmOBZp0gUgx3RwJRgjhp9AeiBaOIvD1M2ULUEO1V047pw959B8gHbUfLz/KKrGJJVhLRnd82GiHNW3cJRdLdJh3T4ciVxCI0RNwQedNFTYzwhCOnzfqNL+WnTbbE5MRl54KvININba+qtd7gCNagIKKLfHbMaKLUozvSPnjwYGnXbZC81HSa/PDDEqu+kAHi+3muwURp0KKrdOzY0ZS8RU66prsgkq4m3R0Q0ZQX1Vr0h/itVG+JStCsE0Qa4O1IIFIa/YF4QcTOnTtnagZD3LDQEWIH0YP4QQRRk7ZZpwL5daM5VgHNVf7LByvk6bxp8l6LfmaTDUR3sKkRNtrAAl7kqsKkY40ApsORl45FaFj0aysRph1HrnceuW3Wv5AfN9oUk+OXngm+gvALbDqL4AgiuW7Tjkg7TDsi7Vg7BNMOvYBuIDr8YdsR8quGc62ak6us+9FMeatJf2nbtq0JhmBDI1R3wZorpHFqugtMOoJPMOno19SkQ2sZEIkPNOsEkUaoOGln4p2yhYFEtFdNu0basdnHtGnTTOUSRIC69Bggf2g1Wn6et8gqqrnAn360WF5rPERatulkUl3QcQwcONAMbDArgZ1jke6CSDo2TcEaAZh0TItr5QFvOUZ2HJXIZbN+0THrP8xbH5PjlpwOvoLwE9w6i7atUXY17VjsjzREd6QdOe0Y1EM3oB8woNCTjt0HyJv54+Wx+sVWHcp26mxlk5ZdTI10lF/EolEMarAzNwY5WDi6cuVKoxduk45ZS81L14CIRtOptVWDZp0gfIBonQkMpNu0Q/QgfihDhoWoyLXUCBAWSkI023ftJ681n2A2nbCJbTYRHQemX+vn9zLTrygJhig6Ul0wlY0NjTCwwQAHOekw6RpJR0TNbdI1V5IRHjty2qxf/8Ja6cLLMUUng68g/Aht12jj7uCI27RDF5DTDp1AehxmNREdhgGF1iKFDlqLNI9WnQfJGy3GyVMNsnt2ExF0GPQG+T1Cs5VIKUSgCAEjzPZigT6CIRjkYLCDtVdI5/RG0qPNWlJvqwbNOkH4CO7ORE07jKRG2iF6yPXDwhyIoU7bQiQhlph6xCIpmFWY1padBspbLUabziQbqskgN/KpvJkmgo7IDgy6u+NA9AsLmRANw1Q28lCxWBcDHOSkeyPp6KRtJp0dRyRy2ax/+eevpfRoRUxevP558BWE3+HVWTXt0AWkw0EnUKULs5rQ2m3btoW0FmuIMLOJgAAiygiSYDO1Bm2HSL2mE+Rn9RfKP7+/KmP50w/ny4sNx8gfm/UxC0URQUceOvoUpLmgShl0FimFyPNH4AizvhjcYMYSgx3MVGANlm39j1trifhAs04QPoOKmBKdCQylmnaIH0QQq+g11xJ12iGWEE2IJxZPYuoWUQ/sxonOBNOVjdoOMp3JL+p/ahVpP/LnH80zHcdHLXqaXe+QG4nPgo4DBh0dB0ovzpo1y0S/NIqOfHRMaWNgg1kJzUnXSDo6D5r0+JHLZj0XUJL3kDxUp0DKg39HQ7zH+QrlBVLnIee6HeaVBB9zEGr35UPlpw/Vl2KX1uqsJgIkqrWItkNr0Q4QCEBAQIMkSAFB1ROkyiB40KlrT3m/1TB5sckUqZs3z6ptfuHjH82RZxtMkHeaDjRphDDnCIIglRAzCAj+oC+BQXfrLFIKUVEH5RcRRYfOajAEMxXedBfqbM1Bs04QPoYKm0aA0JFA/CCCqB4DUdQIEFJkEAGCeMKsImcQaTIw7pimRMQd07famSBSAvP+dvMR8kLjyWnvUH70QbHpNLBZ0e8aFciHLfpI63aBvEikt+CasagWkSx8FgxGtOPA4lssGEX0yx1Fx5S2Rncw0FGTrhEedhzxg2bdvygvqGOMqJd1CuK31L4z6y6DbaOa7mifvdKUl0jeQ3XEfBXmnMH/h1AuQ3/6kPx0aHlMrdV0RBhTBEkQSUaQBBv7IL8dKXfQJA2UQG+hWaq3rTv2kgatB8irTUbJbxpPNJr7gw+WWfUw0cT7qL6+0miEvN+slylli7QWrPGBxsKcI7CDAA+CIEivxAwCZioRBFKDjhldlF7ErAN0FlF0TXXRGctokXTqbc1As04QGQAVOe1IQHQkEEWYUESMIZYQTUxBwqx6jTu2dMb0LfIuESFR845pTVQ7gFCjQ2nevp/pUF5pOlZ+22SM/DJvtjxRf67864dLrZ1AdYjOAsT5EC1/vfHQgClv28GktCBqjmvAlDIi52rOkd6C6DlqIeMzIKKFPFK3QUfUC3XqkSaEhUzuVBd8V0x3qR1o1v2LgGHNc2xpzZEpkXXz/q7Paj57VddTkud6vlwK6oQPYryvd2stzCbojrbrzCYCAQiSqHFHxB1riWBmoU3QKFRDcestIu+IUiNgAs3t1auX0btOXbpLwzYDJa9Vf2Pkob31Go82fKnxOKOXsfibRmPl5UajQq/7XeORRls/yu8rbdp1DOkrTDn0FQMIRM0xO6kai3x8NecI9Cxbtsyk/qDdY6YSfQr6FvQxmG2AzqLvcUfR3TOWqrVE7UGzThAZBO1IQAiiuyOBWEI0IZ4QUYipGndMU2JxJUQXC4AQIUFnAuOLSBAi74iiIBqEDgWRFRhl7VCQdgKRx9b8iMK07dDNGHp0LmD9lv1Mp6DE383bdDcdBIjoOF6HzgLnwBQrOikMENSUo9PA4AGdBqaUEZ2COcc0MzoORM/dHQciO/hsmJ6GQUfUCwYdUTBvFJ0mvfagWfcv4jfrriizB+Em3DkuigGObtajn9uN8oK8mMdEB96jarMdgTCzHrj+0OvxXJTvza0V0A5oiEbbNbcdaTIw7pjdhAZBi9x6i/Uy0FuYd0TesTM1jDAMPFJnEICAiUfFKmgfNBBaCBMN7YWhhv5CJ2GwocPQTWgxNBT/gngMz+EYHI/XgYiS41wIyGCQAG2HxkProa9YgI+8cwwqEDmHxiK9B4tqEehBEAQDEaQSYmCCAQqCQuhjoLPoc7yzldTZ5IBmnSAyDG4hVHHUjgSiCfGEWdWpWxh3RNwhtog8IwINowvDi84E0WnkuiOKAsGGcCN1BkYZJh4dChZSwURD7BGFgfDD0KMTQJRIOxgl/sbjeB4dEI7H69AhoWPCudBJodPAe+C98J54bxhzLOLCbAA6OhhE7TiweAmRLEy9amQHn9Gb5uKOorPzSAxo1v2L2GY9YHLDU0XCjXXAhNeJSD3xptJEmvU4z52X5zoueK3GLMefrhP4nOHnjmnWTeqLfjeByHogRSZw3e4cdhvc2qF6okESt3FXvYWZhalFuozqLSLSiLwjfQRpM9BcpM4gaIIZT6TyIXACDYSRR3QbuoiIPPQXOokABnQYZhuEhur/oaV4DschOo7XwYxjQABthSFH0AODBQRpoPXQfOgrjDlmBNAfqMYipRIaq0EQVMnBwATBIJ2ppM6mFjTrBJHhcHckEE4QnYkad424Q2whuhBfiDDMOyImGglCHXc18EidgZBD0NGhICoEE42cTHQqiA6hY0FUHp0LOgREaZT4G4/jeXQUOB6vgxlHh4FzoZNCpwFTjog53lM7DXRs6ODQ0ak5xzUjgoVIlnYcatDdaS7aebDTSCxo1v2LWGbdFg33Pmb+9hjnwGPh57W+rgbnNqiWWQ8OCjzuOvDZA+dXeg24vr9h8EnzWCyn7oJqiVtvQWitV2816g691WAJShhCx1RzEbGGxpWVlZlgBLRPjTz0EGYe2oioPCLeCGBAh6GbMNxK/I3H8Tz0FHqN10C7ESXH+WDIoesYLCDiD1OOxfeqr5gRwOACARD0D9BYjZ5DY2HQ1Zzjs3oNOpF80KwTRJZAOxGl27jbokDuzgSRIO1MEA1CZAVCDkFHFB7mGSYa+eEQfXQq6ACQn4nOAMYeHYwSf+NxPA8TjuNh9NAh4RzonNBJodPAQEGjOXhvXAOuBR1ctI5Dzbl2HF6DDhKJBc26f2EzrGDAi0aJIHtSQGymO3CMLQKvx9Xi3NWF5VoiEYic47NXOQBwXV/ldxfr3OFwaw3oNu5uvdVgiVtzEX1H4EFNvM56wjhj0SbMPDQR+gt9hAZDKxFQgRZDO5X4G4+DOAYDALwGgwFoOM6F8+L8SItUU473h/bjeqCvmJ3UAIhbY0GvOXeTSA1o1gkiy+AVU1CFFqIL8dUoEEQZ4uw28JjKhUFWEw9h1w4FERjtVEB0BEp0DG7iMXQ8eixepx0GzoVOA9FyvIeacrw3rgHX4u443Okt6Az187DzSB1o1v2LgOGMElmvsqpKpUGtkVmvzbmrhaAJj/Mc5v2ifR/uAYZ7UOEZYFQHXg1SrQXVvKvmunUXGgeT7NZdGHloL8w0qBqshBZDO5X4Gzqqz2MAgNfgtdBVnE+11W3KofkYSKi+wpx7o+eg97OBROpBs04QWQwVVrfQqsm1dSbaoUDE1cS7OxSIvXYq7o4lGrWj0M5COwycC+eEKcd7gNppgLZOw2vOFe7/E8kDzbp/EY9Zj5XxURuzXqNzVwfmOmK/jyL69xFeDcZcV+ikMPHVi657odrkpuqWW3NB6JvqLoyyRuJBt/6qBiuhmTbq8/oavB7niWbIo+kraPscRHpBs54l+EOP0rh4+PTd4CuSj3gEutYiTtQINjFWoVYBVxMPamcCQvS1Q3ETnYOb7ufcRtx9Lm/Hoe/NDsN/oFn3L6o068aExs4Lt2pxLLNem3NXA+b11Yh6RzvefE+u6zDHhZn1+AcE1YVXy1Rv3VQNVKo21oTec7kNufv9vddF+BM061mC17uWxsVDp+Iz6wHxh+CFM76FQAHEI9C1FfHqozKnMcSw97c87xH9sO/Gcu2p/0y1h1ewlSroGn1x09sZVEXb68FoHYabhD9As+5fVG3Wg5oUETV2tK6g8hVW3Ypp1mtxbiAYma+yX6nqGDznOa/qc8Tx5jzh1xlm3s3z8Q8Iagub1kWj6mQ8tL0+GonMAc16luDlTqVx8UC1zHrthCuqQLsQzzGJgxpxj/kur3z3yI4nEG0JF3R93hKJqUXeo19gE/VotHUWbtpeUxUJ/4Jm3b+IR68D2uahS7ysWhyHWQdqdG6gKiMeRODc0T5bUJ89jDxftBmA8NdXdR2pgk0XE0Eis0GzniV4rt3OuLjvZA6bdUvHE46gmffMg4Z9FziH63px/ZUCH61DIIjMB806kWgkop8hiFwAzXqW4MlWO+Pi3hMJNOvG/EaPSoSMeDB6Ejgu/JxRzXoV5zavcQx14BojzxkN+rroCJp1z/WEXWMVZj3qZyGILADNOpFYWGYmCYKwgmY9S/CLFjvi4u7yO8FXVI2YZj1opkMm2vu3A2NejZkOCrKado/ZjTC4Mc4dfl4Y7OB1Wq6hEnFGvYPnUFOvA4JQhxItDca8rqqoPUFkNmjWiYTC0cyYekwQhAHNepagTtOtcXH38eqY9UpTHKCa0TjSRRwETHW4gbUeE2bWY59bzbrnEAcwz9EMc8BYR34mi4FXw24YeT73YCFwnXEOBAgig0GzThAEkR7QrGcJftxoU1zcdex28BVVo0qzHoyQR5hlY3KrMuIOPBHoiGPiOLf1vDERNOue1wU+Z7ghDzPjhpWfyYbK6wkONIKvo3knsgk06wRBEOkBzXqW4Id56+Ni2bFbwVdUDW8EPAxhOeheJsasV3Xu2pj1CAMdNjhQs+3+3LbHXDCvD34efDa9LvN4lNcQRAaCZp0gCCI9oFnPEjz64Zq4WHq0IviKqhGPWY9MQwlHbcx6VeeujVmPWGDqfr9o7x31mgJGXgcA5rpCB+H9wiP2BJHJoFknCIJID2jWswQw4fHw7oO/BF9RNao069GMrwc1MutxnLtmZj2O6zH/j9+sm+/IdT6adSKbQbNOEASRHtCsE1ZUbdb1ea+BdQyq64GamfXY545q1oOmOmqueNCMh54PHl95ruBAIezcUdJgzGvDzXiYeTfPMw2GyB7QrBMEQaQHNOuEFbHMOmBMM8ytmwkw60BV566xWXcQcV7vedTAh9EbIQ+Y+sj3CZr9IKu6DoLINNCsEwRBpAc060TWIJ4BBkEQNQPNOkEQRHpAs05kCQJR7fDUGYIgEgWadYIgiPSAZp3IDiC9hk6dIJIGmnWCIIj0gGadIAiCiAmadYIgiPSAZp0gCIKICZp1giCI9IBmnSAIgogJmnWCIIj0gGadIAiCiAmadYIgiPSAZp0gCIKICZp1giCI9IBmnSAIgogJmnWCIIh0QOT/B1l30+vARx9cAAAAAElFTkSuQmCC)
- Daraus können wir erschließen, wie die neuen \(QS\) und \(df\) aussehen müssen.
- Danach bleibt uns wie gewohnt die \(MQ\) und den \(F\)-Wert zu berechnen.
Lösung
- Wird der Faktor \(B\) „Nebenjob“ vernachlässigt, geht „systematische Variationsinformation“ verloren.
- Die Quadratsummen und Freiheitsgrade des Haupteffekts \(B\), der Interaktion \(AB\) und des Fehlers „verschmelzen/addieren sich“ zu einem, unaufgeklärten (Varianz-)Anteil.
- Die Quadratsumme, die Freiheitsgrade und die mittlere Quadratsumme des Haupteffekts \(A\) bleiben konstant.
- Die \(MQ_e\) und der \(F\)-Wert sind neu zu berechnen!
Quelle
|
QS
|
df
|
MQ
|
F
|
Freizeitinteressen (A)
|
62
|
2
|
31
|
5.50
|
Fehler
|
186
|
33
|
5.64
|
|
Total
|
248
|
35
|
|
|
(e) Die sechs Zellen des zweifaktoriellen Versuchsplans werden als die sechs Stufen eines neuen Faktors \(C\) betrachtet. Vervollständige die Ergebnistabelle der einfaktoriellen ANOVA!
Quelle
|
QS
|
df
|
MQ
|
F
|
C
|
|
|
|
|
Fehler
|
|
|
|
|
Total
|
|
|
|
|
Lösungsansatz
- Auch hier haben wir eine ähnliche Aufgabe in Aufgabe 1 (d) schon gelöst.
- Wir überlegen, wie das Verhältnis der aufgeklärten und unaufgeklärten Varianz ist im Vergleich zu dem zweifaktoriellen Fall:
![](data:image/png;base64,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)
- Daraus können wir erschließen, wie die neuen \(QS\) und \(df\) aussehen müssen.
- Danach bleibt uns wie gewohnt die \(MQ\) und den \(F\)-Wert zu berechnen.
Lösung
- Werden die \(6\) Zellen des zweifaktoriellen Designs als \(6\) Stufen eines neuen Faktors \(C\) definiert, geht keine „systematische Variationsinformation“ verloren.
- Die Quadratsummen und Freiheitsgrade der beiden Haupteffekte und der Interaktion „verschmelzen/addieren sich“ zu einem, aufgeklärten (Varianz-)Anteil.
- Die Quadratsumme, die Freitsgrade und die mittlere Quadratsumme des Fehlers bleiben konstant.
- Die \(MQ_A\) und der \(F\)-Wert sind neu zu berechnen!
Quelle
|
QS
|
df
|
MQ
|
F
|
C
|
80
|
5
|
16
|
2.86
|
Fehler
|
168
|
30
|
5.60
|
|
Total
|
248
|
35
|
|
|
(f) Zeichne mithilfe der Zellenmittelwerte die zugehörigen Interaktionsdiagramme! Welche Interaktionsart liegt vor und welche Auswirkungen hat dies auf die Interpretierbarkeit der beiden Haupteffekte?
|
\(a_1\)
|
\(a_2\)
|
\(a_3\)
|
\(b_1\)
|
4
|
3
|
6
|
\(b_2\)
|
3
|
5
|
7
|
Lösung
- Eine ähnliche Aufgabe haben wir bereits gelöst. Deswegen sind hier gleich die Interaktionsdiagramme gezeigt:
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbMAAAGzCAYAAACl7fmHAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAC/YSURBVHhe7d0HeBTl+rDxJHQQIWABRFRQkI4UkaPYAengpyJFFGzIAawoiOABqYpSBOkCKqKCKCi9SW+C9I5UKdKbFPX/fO8zO5vGbthNMpud5P4913sBk9l4TpLNnZmd3USIS0RERMj//vc/+18AAMQiZgAA1yNmAADXI2YAANcjZgAA1yNmAADXI2YAANcjZgAA1yNmAADXI2YAANcjZgAA1yNmAADXI2YAANcjZgAA1yNmAADXI2YAANcjZgAA1yNmAADXI2YAANcjZgAA1yNmAADXI2YAANdzNGbr1q2Tbt26ScuWLa01ZMgQ2bdvn/3W4BAzAIA/jsVs+/btUq1aNXnwwQfltddes9ZDDz0kTZo0SVLQiBkAwB/HYta1a1fJnj27TJw40YqXrnHjxkmWLFlkwIAB9l6BI2YAAH8ci1ndunUlOjpaLl68aG8R6+8apebNm9tbAkfMAAD+OBazDz/80DoK+/777+Xs2bPW+vLLL60ocWQGAEhJjsVMTyu+9NJLUrZsWWnatKm1ihcvbm0L5DGzxYsXy+TJk2MWMQMA+ONYzFasWGFd8FGpUiV55513pF27dlKyZEmpU6eOrF+/3t7LvxdffFHKly8fs4gZAMAfR2KmpxQ1WhqvuXPnyuHDh62jsenTp0vRokWlYcOG9p7+/fzzzzJixIiYRcwAAP44ErMNGzZYj5d16NDB3hKrVatW1tv00v1gEDMAgD+OxGzRokVWfIYOHWpvidWnTx/rbbpPMIgZAMAfR2J26NAh6zlmL7/8sr0lVosWLay36T7BIGYAAH8cuwCkTZs2UrBgQRk2bJh1wYcu/btu07cFi5gBAPxxLGY7d+6Utm3bysMPPyyPPfaYtfTv7du3t94WLGIGAPDHsZgpvYJx0qRJ1hGZLv17sKcXvYgZAMAfR2OWkogZAMAfYgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAA7bZWaMmQ/MtIkzH5qZZgbJR8wAwEHrzDQxU9RMfjO32JPVzA1m7jYz3gySh5gBgIOamslsprSZoWa+tUf/rtsizFQwg+QhZgDgED29WMJMFjM/m0lorBmNmcZO90XSETMAcMhBM96jMF9Om9GY6Uwyg6QjZgAQInr0tczMD2b0gpDuZrwx+9wMko6YAYCDjpkZbqa9mSfMPGrmLjPFzOgFIcQsZRAzAHCIhuwdM4XN5DSjVzT+x0xDMy+YGWKGmKUMYgYADtELPPKYyW1GTynq42Jzzawx473gw1/MvEd0Xcy0M6NHdvpvLhTxjZgBgEP0idEaKr2iUS/2SGi6GV8xi3tEp7fVo7nyZvTfzcysN4P4iBkAOKStGQ1VITMJA6Qhe8CMr5j1NaNHdBoxfUK1Hs1NMdPYjF7G39oM4iNmAOCQH83oc8w0QHXMfGzP22Yqm7ndzI1mNGbDzHjVM6O3+85MXJvM6L56G8RHzADAIXpq8S0zpcxonDRCOkXMPGZGj7qqmdFA6VGclx6FDTaT8NSknn7UffUxOMRHzADAQX+Y8capnz0aMX2+mdLTjbpN90nMfDMdzGjM9BJ/xEfMACCMacReM3O/GX0crYoZbwgRi5gBQBjraUYDplcz6qlKfXHikWYQHzEDgDCmR2GzzejvPetmRh8v00v0Z5hBLGIGAEFavXq1TJ48WQYPHiwjR46UGTNmyKZNm+y3OueUGX28TB83e8YMYhEzAAjQwYMHZdCQQVKrVi2pVKWS3Hr/rVKkahGpUqWKNGjQQL799lu5ePGivbczBpjRmN1rBrGIGQAE4MCBA9K8eXMpdGchiWgSIZEDIiXqmyiJ+MKkpY9Zj0VI6dKl5YMeH9i3SLreZrqa8UUfQ9OY1TKDWMQMAALQ+f3OkiFnBonqYgK2WXMSZ/42a5lZ9SMkz3V55Mcff7RvlTTlzFxjZrKZuDaY0VcF0f+mXuqPWMQMAK5i+fLlctPNN0mGFzNIxDErX75ntVkPRkiFChXkyJEj9q2D19GMXrlY0Ux/MxPM6MtdPWVGX02kupkDZhCLmAHAVej3ngzZTci2xGTL/0w0y3y/mjNnjn3r4O0084aZkmZuMVPGjL5qiL7ocCMzS80gvrCO2c6dO+XNN9+0FjEDkBoumqler7pkLJsxNliJzYkIicwSKX379rXfQ9LokZf+RuqBZvSFh3X0tRr1VCOuFNYxW7dunVSvbg6ozSJmAEJB46VHRjPNfGWmj5n89+aXyIcjY4N1lYnMGikdOnSw3yNCIaxjdurUKVmyZIm1iBkAJ3jjNcuMxutDM3qKTx+fetRMBTPRjaMl4va4uUpkDpgVFSGDBg2y/wsIBR4zA5CuBBKvhFOwb0GJyGwitdgOVmIz0Czz/WrpUh7XCiViBiBNS0q8vKOXwdc38/ym56Vo6aISWS1SItbFZOvK+dGsUhHy2GOPWWeWEDrEDECaEjde48xovN40E0y89Dc56y/RHGtGHztba2b4mOFyTe5rJKKeCdY8s05Z+fLMEbMmmFUhQm4tfCtHZamAmAFwNafidcLMJTNep0+flo8+/sh6DlnE/SZcrSIk6oMoieocJREtzb/LREi1atVk3Phx9i0QSsQMgKuEKl6+XLhwQaZOnSpt2rSxXoux8sOVperDVaVRo0bWU4j0YjWkDmIGIKx546W/BuVrMx+ZecuMxquambixSjgar3pmNF76PK0xZvRXp2i8jpu5Wrz8+eOPP2TNmjUyffp068nRGzZskKNHj9pvRWogZgDCSjjGC+GPmAFIVcQLKYGYAQgpjdcuM954aYQ0Xvqag8HGa7QZ4gVFzAA4inghFIgZgBRFvJAaiBmAZPHGa46Z8Wb0kvf2ZoKJ1ytm9LEyjdd0M7+ZIV4IBjEDEBTihXBEzAAkinjBDYgZgHiIF9yImAHpXErG63MzxAupgZgB6QzxQlpEzIA0jnghPSBmQBrjjddcM9+Y+cTM22aeNhNovFqZ0VejH2Vmmpk1ZogXwhkxA1yOeAHEDHAd4gVciZgBYU7j9bsZjde3ZvqZCSZedc1ovPqYGWlmqhnihbSGmAFhhngBwSNmQCojXkDyETMgxLzxmmdG49XfzDtmiBeQdMQMcBjxApxHzIAURryA0CNmQDLFjdd3Zrzxamymupm4sUo43ni9bKa3mRFmfjaz2swxM8QLCAwxA4JEvIDwQ8yAqyBeQPgjZkACxAtwH2KGdM8br/lmJpgZYKaDmWDj1cvMcDPECwg9YoZ0h3gBaQ8xQ5pHvIC0j5ghzdF47TbjjddAM8HG6yUz3nj9ZOZXM8QLCF/EDK5HvAAQM7gO8QKQEDFD2Isbr4lmNF4dzQQSrypm6pjRePU0M8zMFDPeeOn7BuB+xAxhh3gBCBYxQ6ojXgCSi5jhqv7991/ZtGmTzJkzR7788kuZOXOmrFu3Ts6ePWvvERx9XErj9YuZ7818auZdM03MBBOvHmaGmiFeAByN2V9//SVTp06VPn36WCEaPny47N27135rcIhZ6tixY4cMGDhAnnrqKan6QFW544E75N4H7pXHH39c3uvynqxdu9be0z/iBcBpjsVMQ9a7d2+pWrWqNG3aVJo0aSIVK1aUli1byuHDh+29AkfMQm/VqlXSrFkzuaHkDRJRO0Iiu0ZK1IgoieoRJRGNIiRryazSsGFDmTt3rn0LD+IFINQci9nEiRMlT5488uqrr8r69etl27Zt0q9fPytKo0aNsvcKHDELrRMnTlihiioaJRkGZ5CIVRHmiyXObDZrjFmVIqwfWJbtXhYTr0Fmgo3Xi2a88ZpsZpUZ4gUgUI7F7P7777eOxA4dOmRvETl//rwUKVJEatSoYW8JHDELrWHDhkmmbJkkw6cmZInNFLPyRkjdjnWJF4BU40jM9DRitmzZfMbniy++sI7QgkXMQuue/9wjkeUjJeJ4TLZ8zz9mNYqQTPkzxUlW7BAvAKHgSMxWrlxpxef777+XJUuWyKBBg6Rbt27Wn3pBQSD0woIFCxbELGIWOufOnZNrc18rEe1jkpX4jDPLfH7KHCoTL17dzQwxQ7wAOM2RmC1atMj65ta5c2d55ZVXpHXr1vLss8/KfffdJ88884xs3LjR3tO/119/XR566KGYRcxCR08N68c7or8dq6vNHLPM/i+teIl4AUgVjsasVKlSMnbsWOvijz179si3335rPWbWvHlze0//9CIRjaF3EbPQOXPmjFybyxyZdYzJVeIzyizz+fl1+6/EC0CqcDRmTz75pL0l1ssvv2w9nrZv3z57S2CIWWjdXfluibw7UiJOxSTL70Q+HSnX3XCdXLrEi/QCSB2OxGzLli1WfPRxsoT0CdT6thUrVthbAkPMQuvTTz+VDNkySMSIuNnyMTMjJOqGKHn1jVftWwJA6DkSM70Ev0SJEtKgQQPrydNxPffccxIdHR3vkv1AELPQOnr0qNSqVUsiSphg6WnE9Xa8vLPLRGx8lET8J0KqVKkS8IU9AOAER2KmBg4cKAUKFLB+wtdvdHpacdKkSXLTTTdZF4QEi5iF3vLly61TxblK5pKIJyIksk+kRHxhIvaxidizEZKlVBapU6eOzJgxw74FAKQOx2KmR15t27aVatWqWRd8vPjii1K9enVp1KiRdUFIsIhZ6ti8ebP07NVT6tevL5WrVpZC9xeSSlUrSe3ateX1N1+XX3/91d4TAFKPYzFTGrRvvvlGOnXqZK3PPvssSSFTxCz1XL582Xren75o9IgRI2TKlClWxE6ePGnvAQCpy9GYpSRiBgDwh5gBAFyPmAEAXI+YAQBcj5gBAFyPmAEAXI+YAQBcj5gBAFyPmAEAXI+YAQBcj5gBAFyPmAEAXI+YAQBcj5gBAFyPmAEAXI+YAQBcj5gBgNMOHxaZMUNk1CiRPn1i17hxIqtW2TshOYgZADhpzx6Rt98WqVxZpFAhkZtu8qzMmUVuvVWkXj2R+fPtnZFUxAwAnPTmmyJZs4rceafIe++JDB7sWfp33Wa+t0m1avbOSCpiBgBO0dOLJUt6jsK++sreGMeQIZ6Yaex0XyQZMQMApxw4IDJokEinTvaGBE6e9MRM17x59kYkBTEDgFDRo69t20SWLhWZNUtk9OjYmE2caO+EpCBmAOCkM2dEfvpJZOBAkddeE2neXKRGDZF77vFcEELMUgQxAwCnaMj69xcpV07k2ms9Vy9WrOiJ2RNPiLz7LjFLIcQMAJwybZpIvnwiOXOKtG0rMny4yJQpIitWiGzf7tmHmKUIYgYATtELPzRUJUp4LvZIaPVq/zE7ckTk559Fhg0T6dvXs8aM8dwGVyBmAOCUjh09oSpWTGTvXnujTaP01FO+Y6b7dujgOT2pq0IFkdKlPY+x1a8vMnu2vSO8iBkAOEUDpc8x0+eRtWol8s03nqWX62uUChcWyZvXE7Ovv7ZvZPTu7bnNgw96jswmTxb57juRNm08pyyrVLkyjukcMQMAp+ipxeef9xyZadRuu82zNGIaKr3CUV/9Q2P2ySf2jYxKlTzbFiywN9j0/dWq5XnbiBH2RihiBgBO2r9f5IsvPKcc33jDszRi3lDpEZdu04tFvLp182zzpVcvT8z0ZbIQg5gBgJu0bu2JWc+e9gYoYgYAbrFmjec0pT5utny5vRGKmAGAG2jIGjTwPPamj8MhHmIGAOEubsiaNPG8viPiIWYAEKQjR47Ihg0bZO7cubJw4ULZunWrnPT1pOiUMGcOIQsAMQOAAF2+fFlmz54tb775pjRq1Egeve8+qfbII9K8eXPp1KmTrFy50t4zhUyd6nlOmT4XrWVLQpYIYgYAATh37pwM+ewz05Yqco/5fvSsWe9GRclbZjUxfy9uVu3atWXSpEn2LZJJL9W/6y5PyPQFifUSf/hFzAAgAOPHj5c8uXNLDfO9aIZZR80y35isdcCsr8wqY1axYsVk1apV9q2SaOFCT8gKFPA8r8ypU5hpCDEDgKvQx8TuMnF5wHwfWmWWN2IJ13dm6RFanTp15PTp0/atg6S/NuahhzzvU39djD6p2tfSi0IQw3y03IGYAUgt/fv3l0xRUTJfA3OV1c8s/X6V5MfP9KjO+/7y5/e89JWv9cEH9g2gzEfLHYgZgNTSrFkzuU2vJvRGJpH1h1lRZg3X312WFGvXirRrd/X1ww/2DaDMRz987d+/XwYMGGAtYgYgtdz7n//Iw5kyXREufytrZKR01NdiRMiYj3z4Wr16tdx9993WImYAUsWRI1K7dGm5K0Gw/K3jZmUxMevJayeGlPnoh6/jx4/LtGnTrEXMAISU/qbnGTNEPvpIuhQuLDlMoLbHiZa/9b1Z+v1qht4WIWM++u5AzACERJyISc2a1m95XnrnnVIgUyZpa74PnTIrbrzirvVmVTOrTJkycujQIfsdIhTMZ8AdiBkAR/mIWNzVsWBByW2Ozvr4OULTS/afMis6Vy759ttv7XeKUCFmANI3jdjMmX4jJvfcI/LCC7K3e3d56qmn5M6bbpIW5vvRcBO1H8yf35k/B5s/65p1Z9Gi0qlDB/n333/td45QIWYA0qcAI2a9/ddfRS5ckH379km/fv3kkUcekcrlyskD+fNL1UKFpFLFiuZd1JQvRo+W8+fP2/8BhBIxA5C+JCFiCS1fvlwmTJggn3zyiQweOFCmTJkia/X5YUg1xAxA+vDnnyKzZon07es/YvpLLz/80PMqHD4ihvBFzACkbUQsXSBmANImIpauEDMAaYs3Yh9/TMTSEWIGIG0gYukaMQPgbkQMBjED4E4asdmzE49Yy5YiffoQsXSAmAFwFyIGH4gZAHcgYkgEMQMQ3jRic+aIfPKJSK1a/iPWu7fIypVELJ0iZgDCExFDEIgZgPBCxJAExAxAeAgkYi1aEDH4RMwApC4ihhRAzACkDiKGFETMAIQWEYMDiBmA0Dh6VGTuXJF+/RKPWK9eIitWEDEEhZgBcBYRQwgQMwDOIGIIIWIGIGVpxObNE+nf33/EnntOpGdPIoYUQ8wApAwihlREzAAkDxFDGCBmAJKGiCGMEDMAwSFiCEPEDEBgiBjCGDEDkDiN2Pz5IgMG+I/Ys8+K9Oghsnw5EUOqIGYAfCNicBFiBiA+IgYXImYAPAKNWPfuRAxhh5gB6R0RQxpAzID0ioghDSFmQHpDxJAGETMgvTh2TOSXX0QGDhSpXdt/xD74gIjBdYgZkNYRMaQDxAwBOXHihOzYscN8r1suW7dulaN6qgrhjYghHQlZzA4fPiyjR4+WDRs22FuCQ8xSx6lTp2TmrFnm+90H8vzzz0v1GjWlefPm0qVLF/npp5+szyvCjEZswQKRTz/1HzHzObQitmwZEUOaEJKYXbx40QqRBqlPnz721uAQs9Dbu3ev9OrVS0qVKScZct0sWW6tKjlKNpQstz0gGfMUlsJFS0rHjh1l06ZN9i2QqogY0rGQxGzSpEkSHR1NzFzk8uXL0qFDB8mUI49kL1pDcj7QQfI2myTXv/iL5H1mslz7yP8kR5mnJEPOfNK4cWM5efKkfUuEHBEDnI/Ztm3bzP2pgtSsWZOYucjcuXMlT968kqPU43LdM1OsiCVc17WcIzkqvyKRGTJZp5ARYoFGrFs3IoY0z9GY6UUDzz33nNSrV08mT55MzFykbt261lFXnkbjfIbMu65r/pNkzldSSpcubd8SjiNiwBUci5k+Tta/f3/rm9zatWtl0aJFQcVsxowZMmbMmJhFzELn0qVLcmO+/JK1eD2fAUu4rrn3NYmKirIuFoGDiBjgl2Mxmzp1qhQvXly++uor69/BxkyvnCtbtmzMImah8+eff1of72uq/NdnvBKuXLU+sfYfPmGerN1zRnYe/ktOnLtsvzckm0Zs4UKRQYP8R+yZZ0S6diViSLccidnu3bulatWq0rZtWzl79qy1LdiYLTR33h9++CFmEbPQ0eeQZc2aVXLc/aLPeCVc1z7a1fr8VG49Tl4ZuVnen7BLRs07ILPWHyduyUHEgIA5ErPu3btLjhw5rD/Hjh1rrU6dOlnf8Bo1amT9+8iRI/begSFmoaVH1VlvvVeuf2Guz4DFXdlKPSEZsuSQh7oslke7r463iFsSEDEgaI7ETJ97VKNGjXircuXKVpCKFi1q/Xv9+vX23oEhZqHVvn17yZg9WnLXHegzYN4V/cRoyRh9ixSv/Jg07Lv2ipglXK+M3OKJ2/w/ZLYdt13EzUMjtmhRYBFbupSIAXE4EjN9sq1ekh93ffPNN1aQ9Juk/tt7+jFQxCy09CWr9OKdzAXKm6ANkOtazIwfshfmSXSDIZKl8INy622FZfg3s+RzE6juk36XV8dslZZDNxG3QBExINkcuwAkoWAfM0uImIXexIkTpWLFipLxhuJyTZV2kuux3pKrek/JXfMjyflgR8l8UwUpVaqUDB4y1L6F+T57+V9Zv++szNlwnLhdDREDUgwxQ6KmT58uderUsY7Sbi5WQbLnvVkKFqskpUqXkWrVqsl3331n7+lbwrh98P3v0s7ErcWQwOP2PxM3va2+j3UatyMuj9vx456IDR7sP2LNmon5gidiQIBCFjM9tdjM3EH1xWmTgpilnv3791tHaR999JG80rq1dO/RU8aPHy9btmyx9whcuo4bEQMcE7KYJRcxS5vSRdyIGOA4YoawkqbiRsSAkCFmCGtW3PaeldkmTHpRSDeN22hP3BoEGbe5Gjfzvn53Om4ascWLRT77LPGIvf8+EQNSCDGDq4R13IgYkGqIGVwtLOJGxIBUR8yQpsTEbf1xGTnvgHSb+Lu0DSJurUeZuE3cJaN/MXHbeNx6X37jphFbssQTsTp1fEesaVNPxHQ/IgY4hpghTXMibn/sOCiXFiwiYkAYIWZIV5ITt4ad5sugLt/IgrY95fB91eVMibLyV+m75GLZu+Sfu8oTMSAVETOkaxo3fZzMG7eu5igsYdw0Yu1eGi3963WU5cWqyvYCxWPW3ltKyr47yslv99aTBU+2k/XjZ8rve0/IydR4KgCQjhEzII54cZu4Qbq/9ZUMfaKTrC75gOwsWCJeyLYULCVzytaULx560cTuc6n1/lLrtKQGUU9Lztt4IuYxN+IGOIuYAQl5L+wYMkT+qVVb/ipdTk6XKCtHi5aRg4VLyd4iZa0jsUk1W8VELOEpSe8ibkBoEDPASyOml86biPm8sKNyZfm/xk3kbPt3rdOJY2bssi4OafP5VnkuiAtKNG5jTNzmbzohG/adld1/atz+tv9HAEgKYgYEEDHrwo7OnT1HbH/9Zd8w9rSk/hbtEXMPEDcglRAzpF/JiJg/xA1IHcQM6c+JEyLLlokMHeo/Yk2aiLz3nueVPQKImD+euJ2RWes8cdNfPGrF7bONwcVtQdy4XSBuQALEDOlHCCPmD3EDnEHMkPaFQcT8IW7pw7Ezl2XVrtPWUz5+WPVnzNLP2ZY/ztl7ITmIGdKuMI6YP8Qt7Tl44qJ8MnWv1O7zm5R9e5nc8dpia+V/eYFU7LhCWg7dJL/tPmPvjaQiZkh7vBEbNsw1EfPHitueMzJz3TEZbsftv59vCThu/zVx098kMHbBQfll8wnZ6I3beeIWKr1+3C0FWy+U0m8ttT4f+gtndenfddv1L/4iT/Vfb++NpCJmSDvSUMT8IW7ucuzsZXP0tdw6Chs4fZ+9NZaGTmOmsdN9kXTEDO6XDiLmD3ELb9sPno85CvPlz9OXrJjpmrPhuL0VSUHM4F4aseXLE49Y48YinTqluYj5o3Fba8dt2JwD0uU7T9yeDTJuX5i4LdC47T8re4hbitGjr71HL1gf19W/n7Z+Iaw3ZuMWH7L3QlIQM7gPEQsYcUt95y7+Iyt2nJKpa45K35/2SPuvtkvTTzdIHfuCEGKWMogZ3IOIJRtxCy0N2fglh6yP3a1tFsmdbyyxHkN7sNuvUqv3GuvqU2KWMogZwp9GbMUKkeHDRerW9R+xd98VWbSIiAXBG7cZVtz2S+fvdlrfYDVu9QOJmwmhFbeFB2XhlpOyaf852XP0gpwibpb5m45LqbeWys2tF8qzgzdKn8m7radN6A8Tq3/3XI4faMyWbDtlfYzhGzFD+CJiIUfcUta743dYodKjsSOnL9lbY207eD6gmOl+972/yvoYwzdihvBDxMIGcUuet8dtt0J1z3sr5dDJi/ZWDw3UyyM2XzVm+jF7Ydgmax/9mMI3YobwEUjEnn6aiKWimLitPSZDZ5u4fRsnbh8FFje9VP3LOHHTq/vSatxGzT9gPcdMn0emr8yi/591Tf/tmAnUZusUpD6WpqEas+CgfSuP42cvy9Jtp6TZoA0x++jHEL4RM6Q+IuZaxC1xempRX91Do6VRq/TuCmvpv+/tstIKXI0eq61QdZ/0u30rj1Hz/rBOLeq+bUdvJWZXQcyQeohYmkPcrqSnE/tP2yutRm6WFkM2Wksj9uOqP62368dJt+k+celrOT7QdZW1XaNIzBJHzBB6J0+KrFwpMmJE4hHr2JGIuZzG7bc4cXvPxO0VE7fmg4OM26KDsmjrSdl8IG2floxLI6aPVXoRs8QRM4QOEUv3iFvSEbPEETM4j4jBD+IWOGKWOGIG5xAxBIm4+UfMEkfMEJALFy7IoUOHZMuWLXLgwAE5dy6R347rjdjIkf4j1qiRSIcOIgsXEjH45Y3bdBO3ISZunb4xcRsZeNzamLjpVYJfmbgtTsG4/fPPP3Ls2DHZuXOn7N6923zJm695hxGzxBEzJOrixYumSytl1KhR8u6775oDqaelffv2MmTIEFm8eLGcPn3a3tMgYnBYOMRt69atMn78eOnRo4e0bNlSWrduLR9//LFMnTpN9u/fb++V8ohZ4ogZ/Dpy5IiMGzdOqlatKvnNx/9+s+qZ9ZBZhcyqWLas9O/fX3Zv2kTEkCpCGbf/+7//M1++C6Vp06aSMeeNkunGkpL55sqSuWBFyXj9nZL7xlvk9ddfl9WrnQkOMUscMYNffT/6SG7KnduKVy+ztph1yaydZg0064moKLnR/Nn2ySflfO3aRAyp7sIlE7fdnrh9Nit5cVuy7aRsMXHbd8wTtzVr1kiJkiUl43XFJPd9r0p0w2Fy3bM/S96m30vuep9K9ruaSVS2aHnkkUdkz5499v+ilEPMEkfM4NO6devk5ptuksaZMslu87E3n4Ar1nGzOpiVLTJSptx+OxFD2EmpuH0+faPUqlVLMuQqKLnrDLDC4mvlqNDSCtprr70mly5d+cLCyaHvX/83wTfzXckdiFloNWnc2Dq1uNashBGLuw6aVcKsKjlyEDGEvaTGreQTH5gv90jJ+eC7VwQs4cpW6gnJmi277Nixw/6vpgz936i/fw6+me9I4ev8+fPWg626iFno/P3331KoYEFpYQfrautjszKZo7NzbdqILFhAxOAa3rhN+80TN/2VLa1M3J5JELeClZ+UqCy55PoX5vsMWNyV56kvzd0iQr7++mv7v4JQMN+NwtemTZusq+d0EbPQ0UuO9ePd1yxvsBJbM83S/X9btsx+D4A7+Yvb9bdXlMz5y/qMl68VlSGTdO7c2X6vCAXz3Sh8bdiwQerXr28tYhY6Bw8elMyZM1tHXHGj5W9NNks/P0uXLrXfA5A2eONWrtJ/TMzK+QxXwnVdixmSIUsO6aCn2xEy5ruROxCz0CpcuLA8HRUVL1r+1rtmv+zZssmpU6fsWwNpi17QkTFbLsn7zGSfAYu7ctcbZO4WETJhwgT71ggF893IHYhZaLVq1UpuzZhRNsaJlq+1y6xKGTJI9UcesW8JpD1TpkyRHDlySM57X/MZMO+67rlpkrVYLcmfv4Ds2sXFGqFkviO5AzELLX3iZ5EiRaS++bjrFY3/mJUwZJvNetGsAvnyyYwZM+xbAmmPXhRVv0FDyZSrgOSu+6lc1/znK0KWt8lEyXn/W5IxUyZ5//2u9i0RKsQMfo0cOVKKFi0qNczHfnJkpCwzfy42a4VZs8y/nzR/3lqokPTsyh0Xad+qVaukQoUKkiNfMbmmcmsratH/73OJfnyE5K7TX7KXeVquy3+rVH+sphw/fty+FUKFmCFR+nJW9957r5QxR2kPRUdLSROxR3PmlHK33CKVKlWSIZ9+av3UCqQH+nqkjZs0ldtvv13y3VFechSpKtcW+Y8UvKOsFC9eXNq//U6KP78MgSFmuKrNmzdbLyz85ptvSoP69aVd69YyYMAAWcal+EiH9PmvY8eOlS5dukjjxk3kuRYt5YPuPWTixIn2HkgNxAwA4HrEDADgesQMAOB6xAwA4HrEDADgesQMAOB6xAwA4HrEDADgesQMAOB6xAwA4HrEDADgesQMAOB6xAwA4HrEDADgesQMAOB6xAwA4HrEDADgesQMAOB6xAwA4HrEDADgesQMAOB6xAwA4HrEDADgesQMAOB6xAwA4HrEDADgesQMAOB6xAwA4HrEDADgesQMAOB6xAwA4HrEDADgesQMAOB6xAwA4HrEDADgesQMAOB6jsbs2LFjsmrVKpk3b5619O+6LSmIGQDAH8diptHq1auXVK1aVcqXLy9lypSx/q7bkhI0YgYA8MexmPXv319y5swp3bp1k/Xr18vy5cutv2fLls16W7CIGQDAH0didvnyZSlUqJDUrFnT3hLrvvvukwIFCtj/ChwxAwD440jMTp8+LXXq1JGvv/7a3hLrrbfekowZM9r/ChwxAwD44+gFIAkdOXJEihUrJmXLlrW3+Pfbb7/J/PnzYxYxAwD4E7KYHT9+XLp06WI9jjZ27Fh7q3+NGjWSW265JWYRMwCAPyGJmR6R9e7d2wpZixYtrNOQVzN9+nQZPXp0zCJmAAB/HI/Zvn37rCOy6OjogEPmCzEDAPjjaMx27NghrVq1kvz581sXfiQ1ZIqYAQD8cSxmekTWoEEDK2Qff/yxvTXpiBkAwB9HYnbhwgXriEwvwX/99detsCVcwSJmAAB/HInZ6tWrrfjoBR8aNV8rWMQMAOCPIzGbPHmy9TqMia1gETMAgD+OXgCSkogZAMAfYgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwvbCO2dmzZ2XNmjXWImYAAH/COmZLliyRfPnyWYuYAQD8CeuY/fHHHzJ06FBrETMAgD88ZgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcD1ihhR1/Phx2bJli+zbt8/eAqRven/Q9ddff9lb4ARihhTVp08f63NVo0YNewuQvun9QdeiRYvsLXACMUOKImZAfMQsNIgZUhQxA+IjZqFBzJCi4sbs4sWLcvDgQdm2bZu19u7dK2fOnLH3BNKHuDHTx5R///136/6wc+dOOXLkiFy+fNneE8lBzJCivDF7+OGH5eeff5ZatWpJjhw5JGvWrFKqVCn55JNPrDswkF54YzZp0iRp3769FClSxLpPREdHS+PGjWX+/PnWD35IHmKGFOWNWaFCheSGG26Q2267TVq2bCmPP/643HzzzZIhQwbrDnzq1Cn7FkDa5o2Z/jB34403yqOPPmrdJ8qXLy+5c+eWa6+9VqZNmyb//POPfQskBTFDivLGTFfdunXjXaL/66+/SrFixay39erVy94KpG3e+4OGa/z48fZWkZMnT8r7778vmTJlsn744+ksyUPMkKK8McuePbvPO6eeetS3652XUytID7wxe/vtt+0t8ekRmr59xIgR9hYkBTFDivLGrGHDhvaWK+njZ7rPmjVr7C1A2qVf67r0zIQv/fr1s97+5JNP2luQFMQMKcobM/3Tn8qVK1v7cKky0gP9Wtflz/Tp062383SW5CFmSFGBxEwfANd9iBnSA/1a1+XPggULrLcTs+QhZkhR3ph169bN3hKfXrFVunRpa59ly5bZW4G0S7/Wdfl7jPj777+33l6nTh17C5KCmCFFeWOmVzL6utR4z5491tv1Cq7Ert7SO/6OHTvkwoUL9hbAnfTrXdfSpUvtLbH0PtKuXTvr7W+88Ya91UOfTP3nn39a9xm9L+jS+8zZs2ftPRAXMUOK8sYsV65csmHDhnhBO3/+vPTs2dN6uz5u5o/eZvbs2dZ+nIqE2+nXsS69mjFuiPTrfOvWrdaVvVmyZJGffvrJfovIpUuXrNOPTZs2lRIlSkjevHmtJ1mXLVtW+vfvb72SCOIjZkhR3phdc8011h1Pr+DSl7Tav3+/jBw50oqcPklUX/XAF/1pVCPoPRVJzOB23pjp172GSF/WTe8TejVv7dq1JSoqSmrWrGnv7aH3D72v6JOs9cht2LBh0rdvX+s+pWc1XnnlFSt4iOVozPSDffjwYes1yHTp35P6OmTEzB28MWvevLmULFnSilrVqlWtOOlPn/nz55cuXbrYe8enP7XqT6PefYkZ0gJvzPTrXo+w9OWs9D6hR1oaOA3U5s2b7b09GjRoYN0m7tGa0idaV6hQwXqbr9OW6ZljMdNo6SWn+jJG+fLls5b+feHChT4fS7ka/eQRs/CnP0EWLlzY+lPvoG3btrX+rat+/fp+j8j0sbEBAwZYd/ACBQrIAw88YH3OiRnczvv1r/R+8eCDD1r/vuuuu6Rz585WoBLy7uPL8OHDrfuG/uCIWI7FTA+h9RuTXqGj38B06YvO6qHz7t277b0CR8xSn/4Qor8tNyk/jFyNRkt/Si1TpoysWrUq5giPmCGc6YVKoT7d542ZPtkasRyL2TPPPGO9sGzcnzr0Spxs2bJJp06d7C2BI2ap69ixY7J69WoZN26cdUm9vvL9v//+a781+TRgcX9KJWYIZ/qrjPShkx9++EFmzpxpPSasP+g5TU/F33///dbjZv7OcqRXjsTs77//to7A9BRTQnouWK/eCRYxSz165ZS+yrd+DrxLTxnu2rXL3iPlETOEK41W7969rV/j4r0/FC9e3HoRYSfOWnidO3fOOhWvF4xUqVLF3govR2K2fft26xPs65yu95tUsIfmepsOHTpYP7mzQrtatWoVc6eNu/S5ZBo6X7dJ6tIfhFTCmOnpHF/7s1ihXl9//bX1tZkxY8aY+4IGRh9W2bhxo8/bBLv0aSxxxQ2ZXqqvj0frdQm+bptelyMx029A+glOLGbBvsisHlbfc8898uqrr7JCvPQO5L3TJlx6xObrNkld+lt4VcKY6YVDvvZnsUK97rjjDomMjLzivqBLTwH6uk0wS0+3z5o1y/q6VydOnLgiZHp0+Msvv/i8fXpdqRazYC8rveWWW6zfB6S/7JEV2uXvjqurYMGCPm+T1OV9ZfGEMRszZozP/dPr0vuCfnx8vY3l7Ip7ejHhypMnj8/bBLM0Zl76WHXr1q2tp6roU128l/Dr/aJo0aI+b59elyMx01DpJzaxmOkTB4Oht+Exs9ShvzHae2eNu/ROHeznMVAJY4b49L6gHx+Enp6N8PUDnv4W9ZT8nWQasmbNmknmzJmtJ1c7dV9LKxy5N+gHXQ+Je/ToYW+J1bVrV+tcc7D0i4WYpQ595QG9oCfuHVefDN2mTRt7j5RHzBJHzFKP/rB+/fXXxzv9rr+jT5/sf/r0aXuv5Dl16pQVMr36Wx+b1lONSJxj9wY9/aSvAhH38m19cF/PKZcrV87eEjj9Yunevbv9L4Tahx9+aL16gf6UqFej6ouiJvXVXAJBzBKn9wW9TyB1/Pjjj9b3MT39pxd+aHD0qSspQb9n6sVu+vVfrVo165WTfC29KASxHIvZO++8Y32SvQ9W6lqxYoX108zgwYPtvQLXqFEjmTBhgv0vpAa9iktPd6TUT5+JIWaJ0/uC3ieQujRi+qoeKenQoUMxR3z6IgJ6AOBrffnll/YtoByLmZ5qvPPOO+Xuu++W0aNHW0uvRtSrcThkxtUMGTLEuuhn5cqV9hYgfdCvef3av9rS+whiOXrSXY/K9JVAvB/8//73vzyICQBIcTyCDABwPWIGAHA9YgYAcD1iBgBwPWIGAHA9YgYAcDmR/w+ctiy3m2JetgAAAABJRU5ErkJggg==)
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)
- Interpretierbarkeit der Effekte:
- \(b\)-Linien überschneiden sich \(\rightarrow\) Haupteffekt \(B\) darf auch bei Signifikanz nicht interpretiert werden.
- \(a\)-Linien überschneiden sich \(\rightarrow\) Haupteffekt \(A\) darf auch bei Signifikanz nicht interpretiert werden.
- Art der Interaktion: disordinal.
(g) Fülle die Tabelle der sechs Zellmittelwerte unter der Annahme neu aus, dass es keine Interaktion zwischen Faktor \(A\) und \(B\) gibt!
Lösung
- Als Erstes bestimmen wir die Faktorstufenmittelwerte (Randmittelwerte), indem wir jeweils die Werte (Mittelwerte der Faktorstufenkombinationen) in einer Zeile bzw. Spalte (= Faktorstufe) addieren und durch die Anzahl der Werte in dieser Zeile bzw. Spalte teilen.
|
\(a_1\)
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\(a_2\)
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\(a_3\)
|
|
\(b_1\)
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4
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3
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6
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\(\bar{B}_1 =\) 4.333
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\(b_2\)
|
3
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5
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7
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\(\bar{B}_2 =\) 5.000
|
|
\(\bar{A}_1 =\) 3.500
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\(\bar{A}_2 =\) 4.000
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\(\bar{A}_3 =\) 6.500
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\(\bar{G} =\) 4.667
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- Diese Faktorstufenmittelwerte sind fest, unabhängig davon, wie sich die Werte innerhalb der Faktorstufenkombinationen verändern. Dies wird dadurch begründet, dass die Effekte \(A\) und \(B\) unabhängig davon existieren, wie groß die Interaktion \(AB\) zwischen ihnen ist (und ob sie überhaupt existiert).
- Jetzt erinnern wir uns an die \(H_0\) des Interaktionseffekts (feste Effekte):
\[\mu_{ij} - \bar{\mu}_{i \cdot} - \bar{\mu}_{\cdot j} + \bar{\mu}_{\cdot \cdot} = 0\]
- In Stichprobenkennwerte übersetzt können wir schreiben:
\[(1) \bar{AB}_{ij} - \bar{A}_{i \cdot} - \bar{B}_{\cdot j} + \bar{G} = 0 \\
bzw. \\
(2) \bar{AB}_{ij} = \bar{A}_{i \cdot} + \bar{B}_{\cdot j} - \bar{G}\]
- Diese Formel (2) beschreibt die Mittelwerte der Faktorstufenkombinationen \(\bar{AB}_{ij}\) für den Fall, dass keine Interaktion vorliegt, das heißt, wenn es keine weiteren Effekte gibt, außer \(A\) und \(B\).
- Dies erkennen wir daran, dass die \(\bar{AB}_{ij}\) sich in diesem Fall aus \(\bar{G}\), \(\bar{A}_{i \cdot}\) und \(\bar{B}_{\cdot j}\) zusammensetzen.
- D.h. mit Formel (2) lassen sich die Zellenmittelwerte berechnen unter der Annahme, dass keine Interaktion vorliegt:
\[ \bar{AB}_{11} = \bar{A}_{1 \cdot} + \bar{B}_{\cdot 1} - \bar{G} = 3.500 + 4.333 - 4.667 = 3.167 \\
\bar{AB}_{12} = \bar{A}_{1 \cdot} + \bar{B}_{\cdot 2} - \bar{G} = 3.500 + 5.000 - 4.667 = 3.833 \\
\bar{AB}_{21} = \bar{A}_{2 \cdot} + \bar{B}_{\cdot 1} - \bar{G} = 4.000 + 4.333 - 4.667 = 3.667 \\
\bar{AB}_{22} = \bar{A}_{2 \cdot} + \bar{B}_{\cdot 2} - \bar{G} = 4.000 + 5.000 - 4.667 = 4.333 \\
\bar{AB}_{31} = \bar{A}_{3 \cdot} + \bar{B}_{\cdot 1} - \bar{G} = 6.500 + 4.333 - 4.667 = 6.167 \\
\bar{AB}_{32} = \bar{A}_{3 \cdot} + \bar{B}_{\cdot 2} - \bar{G} = 6.500 + 5.000 - 4.667 = 6.833 \\\]
- Hier sind nochmal diese Werte in der Tabelle dargestellt:
|
\(a_1\)
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\(a_2\)
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\(a_3\)
|
|
\(b_1\)
|
3.167
|
3.667
|
6.167
|
\(\bar{B}_1 =\) 4.333
|
\(b_2\)
|
3.833
|
4.333
|
6.833
|
\(\bar{B}_2 =\) 5.000
|
|
\(\bar{A}_1 =\) 3.500
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\(\bar{A}_2 =\) 4.000
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\(\bar{A}_3 =\) 6.500
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\(\bar{G} =\) 4.667
|