Linear Regression MCQs

What is the primary goal of linear regression?

• To classify data into categories.

• To predict a continuous dependent variable based on independent variables.

• To find clusters in data.

• To reduce the dimensionality of data.

If the Variance Inflation Factor (VIF) of a variable is 13, what issue does this indicate?

• Heteroscedasticity.

• Normality of residuals.

• Multicollinearity.

• Autocorrelation.

Which statistical test is commonly used to check for homoscedasticity in linear regression?

• Durbin-Watson test.

• Breusch-Pagan test.

• Jarque-Bera test.

• T-test.

What does a Durbin-Watson test value close to 2 indicate in linear regression?

• Strong positive autocorrelation.

• Strong negative autocorrelation.

• No significant autocorrelation.

• Heteroscedasticity.

Which test is used to assess the normality of residuals in linear regression?

• Breusch-Pagan test.

• Durbin-Watson test.

• Jarque-Bera test.

• F-test.

For the Jarque-Bera test, at what significance level (p-value) is the null hypothesis of normality generally verified?

• When the p-value is close to 0.

• When the p-value is greater than the chosen significance level (e.g., 0.05).

• When the Jarque-Bera statistic is exactly 0.

• When the Jarque-Bera statistic is extremely large.

In a simple linear regression model (y = mx + c), what does 'm' represent?

• The intercept.

• The slope of the line.

• The error term.

• The R-squared value.

Which of the following is NOT an assumption of linear regression?

• Linearity between independent and dependent variables.

• Independence of errors.

• Heteroscedasticity of errors.

• Normality of errors.

What does the R-squared value indicate in linear regression?

• The statistical significance of the model.

• The strength and direction of the relationship between variables.

• The proportion of variance in the dependent variable explained by the independent variables.

• The presence of multicollinearity.

If a linear regression model has a high R-squared value but the p-values for individual coefficients are not statistically significant, what might this indicate?

• The model is perfect and reliable.

• Multicollinearity might be present.

• Heteroscedasticity is affecting the model.

• The model is overfitting the data.