One of the consequences of collinearity in multiple regression is inflated standard errors in some
or all of the estimated slope coefficients.
True
False
One of the consequences of collinearity in multiple regression is inflated standard errors in some or...
Mark the statement true or false. If you believe that the statement is false, briefly explain why you think it is false If VIF(X2) = 1, then we can be sure that collinearity has not inflated the standard error of the estimated partial slope for X, Choose the correct answer below. O A. False. Only NIF(X) = 0 can we be sure that collinearity has not inflated the standard error of the estimated partial slope for X, OB. False. Only...
(b) (1 mark) In the multiple regression model, the assumption of no perfect collinearity is best described as: i. The explanatory variables will not be correlated at all. ii. The explanatory variables will have correlation coefficients close to one. iii. None of the explanatory variables will be an exact linear combination of the other explanatory variables. iv. The dependent variable will not be correlated with the explanatory variables.
(Answer True or False) One of the consequences of the non-normality of the errors is that the estimates become biased in the regression equation.
Question 4 3 pts Consider the estimated multiple regression model using OLS, with the standard errors in parentheses below each estimated coefficient. There are 1,576 observations in the sample: Y = 10 + 2X2i - 5Xzi (3) (1.5) (2) Suppose that the sample mean of Y is 30. For the 18th observation (i=18) in the sample, the value of X2 is 50, the value of X3 is 16, and the value of Y is 20. The residual associated with the...
Question 12 3 pts Consider the estimated multiple regression model using OLS, with the standard errors in parentheses below each estimated coefficient. There are 1,576 observations in the sample: Ỹ = 10 + 2x - 5X36 (3) (1.5) (2) Suppose the null hypothesis is that the true coefficient (population parameter) for X3 is equal to 1. The test statistic associated with this null hypothesis is: -3 0-2 O 2
The model assumptions for multiple regression analysis are : 1. Normally distributed errors 2. Constant variance of the errors 3. Independent errors True False
2 pts Question 4 In the classical regression model we maximize the sum of the squared errors. O True False 2 pts D Question 5 The terms coefficients of determination and R-square are synonyms, measuring how well a regression model fits the data. O True False 2 pts Question 6 Student's t-statistic is calculated as the ratio of an estimated coefficient divided by its standard error. True False
In regression, dividing the sum of square residual by its degrees of freedom provides: Standard errors for regression coefficients Residual (error) variance Standardized residual Studentized residual
Logistic regression is like simple linear or multiple regression in that there is only one DV. a. True b. False
1. In a multiple regression model, changing the scale of one of the independent variables (a) changes the standard error of its own OLS slope estimator (b) changes the standard error of all OLS slope estimators (c) changes the own t-statistic for testing its statistical significance (d) makes its confidence interval larger (e) All of the above