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
In statistics, multicollinearity is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy.
Multicollinearity generally occurs when there are high correlations between two or more predictor variables. In other words, one predictor variable can be used to predict the other.
Hence answer here is Multicollinearity
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 Multicollinearity
When two or more independent variables in the same regression model can predict each other better...
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 there is an overlap in the way two or more independent variables influence the dependent variable, you will have ____________________. Multiple Choice multicollinearity heteroscedasticity negative serial correlation overfitting
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
A multiple regression model has _____. a. at least two dependent variables b. more than one dependent variable c. more than one independent variable d. only one independent variable
Multicollinearity occurs when... Select one: independent variables are perfectly correlated dependent variables are perfectly correlated an independent variable is perfectly correlated with the dependent variable the error term is perfectly correlated with the intercept All/Any of the above. Which of the following statements is true regarding an F-Test? Select one: It is a joint hypothesis test. The null hypothesis states the all slope coefficients in the population regresion model are equal to zero. It tests whether or not one's regression...
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...
Heteroscedasticity, in the context of regression, a. leads to more accurate estimates of the standard deviations of the estimated parameters than when homoscedasticity is present. b. occurs when the X variables are correlated with one another. c. can be corrected by removing all X variables from the model. d. occurs when the error terms, εi, do not have constant variance for all values of the predictor (or X) variables. e. is an assumption of the Gauss-Markov theorem.
How does a bivariate regression model differ from a multiple regression model? Multiple Choice A bivariate regression has only one dependent and independent variable but a multiple regression has one dependent variable and may have many independent variables. A bivariate regression has more than one dependent variable and only one independent variable where a multiple regression has one dependent variable and may have many independent variables. A bivariate regression has only one dependent and many independent variables but a multiple...
1. In multivariate regression: a) More than one independent variable is used to predict a single dependent variable b) The value of r gives you the slope c) More than one dependent variable is predicted by a single independent variable d) More regressions are necessary
11. Multiple regression analysis is used when one independent variable is used to predict values of two or more dependent variables. True or False 13. For a two-tailed null hypothesis, the test statistic Z=1.96. Therefore, the p-value is 0.05. True False