tutorial 4 - Tutorial 4 1. An output of a simple linear...

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Tutorial 4 1. An output of a simple linear regression model Y i = β 0 + β 1 X i + ε i ,i =1 , .., 10 is as follows Coefficients: Estimate Std. Error t value P-value (Intercept) -0.07727 0.12005 -0.644 0.537814 x 0.97295 0.14345 6.783 0.000140 Residual standard error: 0.3761 on 8 degrees of freedom Multiple R-squared: 0.8519, Adjusted R-squared: 0.8333 F-statistic: 46 on 1 and 8 DF, p-value: 0.0001403 (a) ±nd b 0 ,b 1 ,s ( b 0 ) ( b 1 ) ,t ( b 0 ) ( b 1 ) ,R 2 , ˆ σ, r XY and the F-statistic (or F-value) (b) based on the output (alone), set up the ANOVA table (c) Using t-statistic, test H 0 : β 1 =1with α =0 . 05 2. Sales Growth i 1 23456789 1 0 X i : Y e a r 0 123456789 Y i : Sales 98 135 162 178 221 232 283 300 374 395 (a) prepare a scatter plot of the data. does a linear relation appear adequate here? (b) Use the transformation Z = Y and obtain the estimated linear regression for the transformed data. (c) Does the regression line appear to be good to the transformed data? (d) obtain the residuals and plot them against the ±tted values. Also plot the his-
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tutorial 4 - Tutorial 4 1. An output of a simple linear...

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