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Multicollinearity occurs when... Select one: independent variables are perfectly correlated dependent variables are perfectly correlated an...
Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple...
Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity among the independent variables is often a concern. What is the main problem caused by high multicollinearity among the independent variables in a multiple regression equation? Can you still achieve a high r for your regression equation if multicollinearity is present in your data? Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity...
When two explanatory variables are highly correlated, should you remove one of the correlated explanatory variables...
When two explanatory variables are highly correlated, should you remove one of the correlated explanatory variables to reduce the multicollinearity problem. A. Yes, it will reduce the standard errors on the coefficients and increase the t statistics. B. No, it will not affect the t statistics on the coefficients. C. No, it will cause the coefficient on the remaining variable to be biased. D. Yes, it will improve the fit of the regression model.
When testing for multicollinearity, a regression can be run in which one of the suspected independent...
When testing for multicollinearity, a regression can be run in which one of the suspected independent variables becomes the dependent variable and the other is the independent variable. True False
Good model ____ is found when the independent variables accurately explain or predict the value of...
Good model ____ is found when the independent variables accurately explain or predict the value of the dependent variable. If a correlation is ____ significant, we are confident that the correlation in the sample would also be observed in the population. To determine if a correlation is ____ significant, we examine the regression coefficient to see if it is large enough to make a meaningful impact on the dependent variable. In multiple regression analysis we conduct an ANOVA test of...
7. (4pt) A term used to describe the case when the independent variables in a multiple...
7. (4pt) A term used to describe the case when the independent variables in a multiple regression model are correlated is a. regression b. correlation c. multicollinearity d. None of the above answers is correct 8. (4pt) A variable that cannot be measured in numerical terms is called a. a nonmeasurable random variable b. a constant variable c. a dependent variable d. a categorical variable 9. The following regression model has been proposed to predict sales at a computer store....
Question 5 1 pts Which of the following statements about multicollinearity are correct? Select all that...
Question 5 1 pts Which of the following statements about multicollinearity are correct? Select all that are correct. If you cannot justify dropping an explanatory variable the best solution to multicollinearity may be to do nothing Multicollinearity is a much more serious problem than omitted variable bias Getting better data is a good solution to multicollinearity Perfect multicollinearity can be detected in Minitab by attempting to run a regression. If the data is perfectly collinear the regression will not run...
A researcher would like to predict the dependent variable Y from the two independent variables X1...
A researcher would like to predict the dependent variable Y from the two independent variables X1 and X2 for a sample of N=10 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test statistics to assess the significance of the regression model and partial slopes. Use a significance level α=0.02. X1 X2 Y 40.5 62.9 21.8 16.4 51.3 31.8 62.5 44.4 29.6 60.4 53.6 40.6 50.2 54 33.7 39.2 51.5 37 80.9 16.9 58.1 41.6 52.6...
Consider a multiple regression model of the dependent variable y on independent variables x1, X2, X3, and x4: Using data with n 60 observations for each of the variables, a student obtains the follow...
Consider a multiple regression model of the dependent variable y on independent variables x1, X2, X3, and x4: Using data with n 60 observations for each of the variables, a student obtains the following estimated regression equation for the model given: y0.35 0.58x1 + 0.45x2-0.25x3 - 0.10x4 He would like to conduct significance tests for a multiple regression relationship. He uses the F test to determine whether a significant relationship exists between the dependent variable and He uses the t...
When two or more independent variables in the same regression model can predict each other better...
When two or more independent variables in the same regression model can predict each other better than the dependent variable, the condition is referred to as ____. Autocorrelation Multicollinearity Heteroscedasticity Homoscedasticity
7. (4pt) A term used to describe the case when the independent variables in a multiple regression model are correlated is a. regression b. correlation c. multicollinearity d. None of the above answers is correct 8. (4pt) A variable that cannot be measured in numerical terms is called a. a nonmeasurable random variable b. a constant variable c. a dependent variable d. a categorical variable 9. The following regression model has been proposed to predict sales at a computer store....
Question 5 1 pts Which of the following statements about multicollinearity are correct? Select all that are correct. If you cannot justify dropping an explanatory variable the best solution to multicollinearity may be to do nothing Multicollinearity is a much more serious problem than omitted variable bias Getting better data is a good solution to multicollinearity Perfect multicollinearity can be detected in Minitab by attempting to run a regression. If the data is perfectly collinear the regression will not run...
Consider a multiple regression model of the dependent variable y on independent variables x1, X2, X3, and x4: Using data with n 60 observations for each of the variables, a student obtains the following estimated regression equation for the model given: y0.35 0.58x1 + 0.45x2-0.25x3 - 0.10x4 He would like to conduct significance tests for a multiple regression relationship. He uses the F test to determine whether a significant relationship exists between the dependent variable and He uses the t...
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