Option '4' is correct
Multicollinearity.
Multi-collinearity is a situation in which the dependent variable is highly correlated with two or more of the independent variables in a multiple regression.
Which of the following means that two or more independent variables are highly correlated with each...
Which of the following means that two or more independent variables are highly correlated with each other? Multiple Choice value Correlation Standard error Multicollinearity R-Squared < Prev 20 of 50 Next >
TRUE OR FALSE: We cannot avoid multicollinearity in a multiple regression as the independent variables are always correlated with each other to some extent? Perfect multicollinearity means independent variables are - perfectly correlated - positively correlated - highly correlated - not correlated Near multicollinearity means independent variables are - perfectly correlated - positively correlated - highly correlated - not correlated
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 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
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...
5) In a regression model developed to estimate cruise vacation prices, two recorded independent variables were: ship Size (sqft) and ship Capacity (number of people). A correlation analysis yielded the following table: Size Capacity Size Capacity 0.88091 a. There is a high positive correlation between ship Size and ship Capacity. b. Each variable is perfectly correlated with itself. c. One of these two variables should be excluded from the regression model to avoid multicollinearity. d. All of the above statements...
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 a simple linear regression study between two variables x ( the independent variable) and y (the dependent variable), a random large sample is collected and the coefficient of correlation r = −.98 is calculated. A)Which of the following conclusion may be made? Group of answer choices x and y are almost perfectly correlated, and y increases as x is increased. x and y are almost perfectly correlated, and y decreases as x is increased. x and y are moderately...
The covariance of two variables is: a) how they deviate from their means together b) how they deviate from their standard deviations together c) the total variance of both variables d) the percent of variance in one variable explained by another e) the squared differences from time 1 to time 2 If a researcher concludes that “decreases in self-esteem are strongly associated with decreases in social interaction” then what correlation coefficient describes her findings? a) .08 b) .87 c) -.87...
The following ANOVA model is for a multiple regression model with two independent variables: Degrees of Sum of Mean Source Freedom Squares Squares F Regression 2 60 Error 18 120 Total 20 180 Determine the Regression Mean Square (MSR): Determine the Mean Square Error (MSE): Compute the overall Fstat test statistic. Is the Fstat significant at the 0.05 level? A linear regression was run on auto sales relative to consumer income. The Regression Sum of Squares (SSR) was 360 and...