Podcast
Questions and Answers
Which assumption of the multiple linear regression model is violated when important variables are omitted from the model?
Which assumption of the multiple linear regression model is violated when important variables are omitted from the model?
- Zero conditional mean (correct)
- Homoskedasticity
- Normality of error terms
- No multicollinearity
In the population regression function $E(y|x) = \beta_0 + \beta_1 x$, what does $\beta_1$ represent?
In the population regression function $E(y|x) = \beta_0 + \beta_1 x$, what does $\beta_1$ represent?
- The error term of the regression model
- The intercept of the regression line
- The slope of the regression line (correct)
- The coefficient of determination
In the Ordinary Least Squares (OLS) estimation, what is the objective function that is minimized?
In the Ordinary Least Squares (OLS) estimation, what is the objective function that is minimized?
- The sum of squared residuals (correct)
- The sum of absolute residuals
- The sum of cubed residuals
- The maximum absolute residual
If the slope coefficient $\beta_1$ in the simple linear regression model is positive, what can be inferred about the relationship between the dependent and independent variables?
If the slope coefficient $\beta_1$ in the simple linear regression model is positive, what can be inferred about the relationship between the dependent and independent variables?
In the regression equation $y_i = \hat{\beta}_0 + \hat{\beta}_1 x_i + \hat{u}_i$, what does $\hat{u}_i$ represent?
In the regression equation $y_i = \hat{\beta}_0 + \hat{\beta}_1 x_i + \hat{u}_i$, what does $\hat{u}_i$ represent?
If the coefficient of determination ($R^2$) in a multiple linear regression model is close to 1, what can be inferred about the model's fit?
If the coefficient of determination ($R^2$) in a multiple linear regression model is close to 1, what can be inferred about the model's fit?
If the residuals in a linear regression model exhibit a pattern when plotted against the fitted values, what assumption is likely violated?
If the residuals in a linear regression model exhibit a pattern when plotted against the fitted values, what assumption is likely violated?
In a multiple linear regression model, what does multicollinearity refer to?
In a multiple linear regression model, what does multicollinearity refer to?
What is the interpretation of the intercept coefficient ($\beta_0$) in a linear regression model?
What is the interpretation of the intercept coefficient ($\beta_0$) in a linear regression model?
If the slope coefficient $\beta_1$ in a simple linear regression model is negative, what can be inferred about the relationship between the dependent and independent variables?
If the slope coefficient $\beta_1$ in a simple linear regression model is negative, what can be inferred about the relationship between the dependent and independent variables?
