3. (2 points) Look at the below residual plot from a fitted linear regression model: mod$residuals...

 

 

Transcribed Image Text:

3. (2 points) Look at the below residual plot from a fitted linear regression model: mod$residuals -2 -1 0 1 2 3 T 0 2 6 8 10 modSfitted values A. (1 point) Do you trust estimates of the expected value of Y given X (E[YIX]) from this model? Why or why not? B. (1 point) Do you trust standard errors generated by Im() for this model? Why or why not? 4. (2 points) Look at the below residual plot from a fitted linear regression model: mod$residuals 4 -2 0 T 2 4 00 00 Cood 0 5 modSfitted values 10 A. (1 point) Do you trust estimates of the expected value of Y given X (E[Y[X]) from this model? Why or why not? B. (1 point) Do you trust standard errors generated by Im() for this model? Why or why not? 3. (2 points) Look at the below residual plot from a fitted linear regression model: mod$residuals -2 -1 0 1 2 3 T 0 2 6 8 10 modSfitted values A. (1 point) Do you trust estimates of the expected value of Y given X (E[YIX]) from this model? Why or why not? B. (1 point) Do you trust standard errors generated by Im() for this model? Why or why not? 4. (2 points) Look at the below residual plot from a fitted linear regression model: mod$residuals 4 -2 0 T 2 4 00 00 Cood 0 5 modSfitted values 10 A. (1 point) Do you trust estimates of the expected value of Y given X (E[Y[X]) from this model? Why or why not? B. (1 point) Do you trust standard errors generated by Im() for this model? Why or why not?