QUESTION 16 1. Four assumptions for the multiple linear regression equation are:

Question

QUESTION 16

1.         Four assumptions for the multiple linear regression equation are: linearity, errors with constant variance, means zero, and normally distributed. To obtain estimates of the parameters, which assumptions are needed?

a.Linearity, errors with means zero, and normally distributed

b.Errors with constant variance, means zero, and normally distributed

c.Linearity, errors with constant variance and normally distributed

d.Linearity, errors with constant variance, means zero

QUESTION 17

1.         A failure of the linearity assumption is best detected by what plot?

a.Normal probability plots of the residuals

b.Residuals versus predicted

c.y versus each x separately

d.Plot of residuals in time sequence

QUESTION 18

1.         A failure of the nonconstant variance assumption is best detected by what plots?

a.Normal probability plot

b.Residuals versus predicted

c.Residuals versus independent variables

d.Both b and c

QUESTION 19

1.         If a row of data affects the prediction of its own y, but does not change other predictors very much, what influence measure would be most sensitive?

a.COOK’S D

b.DFFITS

c.DFBETAS

d.Both b and c

QUESTION 20

1.         Given that the following is the covariance matrix for the parameters in a multiple regression model with 3 parameters (one intercept and two slopes), what is the estimated standard error of 1 hat?

a.1.41

b.3

c.1.75

d.0.9

Explanation / Answer

16 ans c.Linearity, errors with constant variance and normally distributed

17 ans a ) normal probability plot

18 ans) d

19 ans) a cooks d

2oa ns)0.9

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