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Unformatted text preview: Linear Regression Normality of Errors Assumption plot. y probabilit normal the using assessed is residuals the of normality The residuals. the be will so d distribute normally are errors the If d.f. 2 n on with distributi a follows Further, n. any for d distribute normally be to and have then we assumed is errors the of normality If d distribute normally ely approximat are and large, is n if seen that already have We ). , x N( ~ Y model) x fixed (in the implies This ). N(0, i.i.d. are errors that the assumed often is it model regression linear simple In the 2 2 i i i + Hypothesis Test about Regression Coefficients 2 / , 2 1  t  if ce significan of level at H reject We . ) ( e s t is statistic test The useful. not is regression linear s, other word In variable. response on the impact any has y variable explanator that the conclude to evidence enough us give not does data that the conclude then we rejected not is H If : H against : H is useful is which hypothesis of test a regression linear simple In  = = n t Residual Percent 3.0 1.5 0.01.53.0 99 90 50 10 1 Fitted Value Residual 10.0 7.5 5.0 2 112 Residual Frequency 2 112 4.8 3.6 2.4 1.2 0.0 Observation Order Residual 11 10 9 8 7 6 5 4 3 2 1 2 112 Normal Probabilit y Plot of t he Residuals...
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This note was uploaded on 05/07/2009 for the course FIN AMDA taught by Professor Proflaha during the Spring '09 term at Indian Institute Of Management, Ahmedabad.
 Spring '09
 ProfLaha

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