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- The estimated regression models having a different number of explanatory variables are compared on the basis of _____. Select one: a. Chi squared -statistic b. Adjusted R squared-statistic c. R squared-statistic d. None of the aboveWhat are the measures of fit that are commonly used for multiple regressions? How can an adjusted R2 take on negative values?(2)What would the consequence be for a regression model if theerrors were not homoscedastic?
- Define Interpretation of coefficients in polynomial regression models?In multiple regression model: what is it means for a variable to be significant? Explain the meaning of the significant variable.In a multiple linear regression, which of the following can cause the OLS estimators to be biased? A sample correlation coefficient of .85 independent variables. The presence of heteroskedasticity. Omitting an important variable i. between two ii. iii. Explain briefly.
- What is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?The OLS estimators of the coefficients in multiple regression will have omitted variable bias: a. i only if an omitted determinant of b. if an omitted variable is correlated with at least one of the regressors, even though it is not a determinant of the dependent variable. C. only if the omitted variable is not normally distributed. d. if an omitted determinant of is a continuous variable. Y; i is correlated with at least one of the regressors. e. if the degree of freedom is less than 50.Which of the following is a consequence of severe multicollinearity in a regression model? A. High standard errors for the estimated coefficientsB. Lower standard errors for the estimated coefficientsC. The OLS estimator becomes biasedD. The dependent variable becomes constant
- Discuss the FIVE (5) importance of adding error term in the regression model.What assumption is violated when multicollinearity is present in the regression model?In regression analysis, a common metric used in assessing the quality of the model being used to fit the data is known as the R-squared coefficient. Explain the R-squared coefficient. What is the difference between the R-squared and adjusted R-squared coefficients?