Consider the following to answer the question(s) below:
A regression equation that predicts the price of homes in thousands of dollars is = 24.6 + 0.055x1, where x1 is a variable that represents the assessed value of the house and x2 is an indicator variable that represents whether the house is on a busy street (1 = yes, 0 = no).
-The regression model mentioned above resulted in a high R2 and large F, but individual t-statistics were not significant. Which of the following statements is true?
A) We would suspect homoscedasticity in the model.
B) We would suspect autocorrelation in the model.
C) We would suspect collinearity in the model.
D) We would suspect that the model contains influential points.
E) We would suspect that the model contains high-leverage points.

Question

Consider the following to answer the question(s) below:
A regression equation that predicts the price of homes in thousands of dollars is  = 24.6 + 0.055x1, where x1 is a variable that represents the assessed value of the house and x2 is an indicator variable that represents whether the house is on a busy street (1 = yes, 0 = no).
-The regression model mentioned above resulted in a high R2 and large F, but individual t-statistics were not significant. Which of the following statements is true?
A) We would suspect homoscedasticity in the model. 
B) We would suspect autocorrelation in the model. 
C) We would suspect collinearity in the model. 
D) We would suspect that the model contains influential points. 
E) We would suspect that the model contains high-leverage points.

Quizplus · Accepted Answer

If the model has a high R2 and large F, it suggests that the overall model is a good fit. However, if individual t-statistics are not significant, it suggests that there is a problem of collinearity in the model. This means that one or more of the variables is highly correlated with another variable, making it difficult for the model to distinguish the effects of each variable separately.

Consider the Following to Answer the Question(s) Below