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

This preview shows pages 1–2. Sign up to view the full content.

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-

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 2

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

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online