You notice that the coefficient for temperature is different in the model which has mood as a predictor (m2) compared to the model which doesn't (m1). Why is this?单项选择题

A

Adding a new predictor increases complexity, which means the coefficients for all of the variables incur a penalty.

B

The new predictor must have a non-linear relationship with either the old predictor or the outcome, thus making the estimate of the coefficient inaccurate.

C

The new predictor must be capturing some of the variance in the outcome that previously was captured by the old predictor.

D

The two predictors are completely uncorrelated with each other, so the model randomly assigns a coefficient to each

E

This should never happen and indicates that something is wrong with the code.

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类似问题

Finally, Gladly and Super Size run a model called m3 which has two predictor variables (temperature and mood) and the outcome variable meatballs as well as an interaction. The output is shown below. Coefficients:                   Estimate Std. Error t value Pr(>|t|)     (Intercept)      128.04578    2.43944  52.490   <2e-16 *** temperature        7.07231    0.16109  43.903   <2e-16 *** mood              14.62052    1.62318   9.007   <2e-16 *** temperature:mood   1.36555    0.09708  14.066   <2e-16 *** --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 19.76 on 361 degrees of freedom Multiple R-squared:  0.9416,    Adjusted R-squared:  0.9411  F-statistic:  1941 on 3 and 361 DF,  p-value: < 2.2e-16 How would you interpret this model?

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In Model 2, which coefficient is (are) statistically significant? Check all you mark as correct.  

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