Question

1)    If an interaction term is significant, one should attempt to interpret the significance of main parameter effects by themselves/in isolation.

True or False

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2) If you observe high levels of correlation between interaction terms and main parameter effects, what should you do?

a) Remove the interaction terms.

b) Include more interaction terms.

c) Test for non-normality and sources of residual error.

d) Remove some of the main parameters.

e) Nothing - high levels of correlation between interaction terms and main effects are expected.

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3) What conclusion can we reach if a dummy variable is significant?

a) We accept the null hypothesis and conclude that the variable does not add to the model's explanatory power.

b) We reject the null hypothesis and conclude that the variable does not add to the model's explanatory power.

c) We reject the null hypothesis and conclude that the variable does add to the model's explanatory power.

d) We accept the null hypothesis and conclude that the variable does add to the model's explanatory power.

e) None of the above

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4) What happens to standard error of coefficients in a linear regression model if we increase the sample size (no. of data points) keeping the structure of the model and the predictors same?

a) Decreases

b) Increases

c) Remains the same

d) Need more information

Answer 1

1.If an interaction term is significant, one should attempt to interpret the significance of main parameter effects by themselves/in isolation

ans-> False

2. If you observe high levels of correlation between interaction terms and main parameter effects, what should you do

ans-> a) Remove the interaction terms.

3. The conclusion that we can reach if a dummy variable is significant

c) We reject the null hypothesis and conclude that the variable does add to the model's explanatory power.

4. The standard error of coefficients in a linear regression model if we increase the sample size (no. of data points) keeping the structure of the model and the predictors same

ans-> a) Decreases