regression analysis, Mcqs

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  1. What type of statistical analysis is used to examine the relationship between two or more variables?
    A. T-test
    B. ANOVA
    C. Regression analysis
    D. Chi-square test
    Answer: C
  2. In simple linear regression, what is the term used to describe the dependent variable?
    A. Predictor variable
    B. Response variable
    C. Independent variable
    D. Explanatory variable
    Answer: B
  3. What does the coefficient of determination (R-squared) measure in regression analysis?
    A. Goodness of fit of the regression model
    B. Significance of the regression coefficients
    C. Standard error of the regression
    D. Adjusted R-squared value
    Answer: A
  4. What is the purpose of the residual plot in regression analysis?
    A. To determine the correlation coefficient
    B. To check for outliers in the data
    C. To assess the normality of residuals
    D. To evaluate the linearity assumption
    Answer: D
  5. What does a p-value less than the significance level (e.g., α = 0.05) indicate in regression analysis?
    A. The regression model is significant
    B. There is no relationship between the variables
    C. The residuals are normally distributed
    D. The coefficient estimates are unbiased
    Answer: A
  6. What is the purpose of the Durbin-Watson statistic in regression analysis?
    A. To test for multicollinearity
    B. To assess heteroscedasticity
    C. To check for autocorrelation in residuals
    D. To evaluate the goodness of fit
    Answer: C
  7. Which assumption of linear regression states that there is a linear relationship between the independent and dependent variables?
    A. Normality of residuals
    B. Independence of residuals
    C. Homoscedasticity
    D. Linearity
    Answer: D
  8. What does multicollinearity refer to in regression analysis?
    A. Non-constant variance of residuals
    B. High correlation between independent variables
    C. Outliers in the dependent variable
    D. Non-linear relationship between variables
    Answer: B
  9. Which regression model would be appropriate if the dependent variable is binary (e.g., yes/no)?
    A. Simple linear regression
    B. Multiple regression
    C. Logistic regression
    D. Polynomial regression
    Answer: C
  10. What is the purpose of residual analysis in regression?
    A. To check for normality of the independent variables
    B. To check for normality of the dependent variable
    C. To check the linearity of the relationship
    D. To check the assumptions of the regression model
    Answer: D
  11. What does the F-statistic test in regression analysis?
    A. Overall significance of the regression model
    B. Individual significance of each independent variable
    C. Goodness of fit of the model
    D. Normality of residuals
    Answer: A
  12. In multiple regression analysis, what does the adjusted R-squared measure?
    A. Overall significance of the regression model
    B. Amount of variance explained by the independent variables
    C. Linearity of the relationship between variables
    D. Adjustments for the number of predictors in the model
    Answer: D
  13. What is the primary goal of regression analysis?
    A. To predict future outcomes based on historical data
    B. To determine the mean of a population
    C. To test hypotheses about population parameters
    D. To summarize the distribution of data
    Answer: A
  14. What does a high VIF (Variance Inflation Factor) indicate in regression analysis?
    A. High multicollinearity among independent variables
    B. High normality of residuals
    C. Low correlation between independent variables
    D. Low correlation between dependent and independent variables
    Answer: A
  15. In regression analysis, what does heteroscedasticity refer to?
    A. Non-normality of residuals
    B. Non-linearity of the relationship
    C. Non-constant variance of residuals
    D. Non-independence of residuals
    Answer: C
  16. Which type of regression analysis is suitable when the dependent variable is categorical with more than two categories?
    A. Simple linear regression
    B. Multiple regression
    C. Logistic regression
    D. Polynomial regression
    Answer: C
  17. What is the purpose of the Akaike Information Criterion (AIC) in regression analysis?
    A. To test for multicollinearity
    B. To assess the normality of residuals
    C. To compare the goodness of fit of different models
    D. To evaluate the significance of regression coefficients
    Answer: C
  18. What is the purpose of a scatter plot matrix in regression analysis?
    A. To visualize the relationship between independent and dependent variables
    B. To check for outliers in the data
    C. To assess the normality of residuals
    D. To calculate the correlation coefficient
    Answer: A
  19. What does the term “regression to the mean” imply in regression analysis?
    A. The tendency of independent variables to predict the dependent variable
    B. The tendency of extreme observations to move closer to the mean in subsequent measurements
    C. The relationship between the correlation coefficient and the coefficient of determination
    D. The regression model’s ability to fit the data accurately
    Answer: B
  20. Which regression technique is used to predict a continuous dependent variable based on multiple independent variables?
    A. Simple linear regression
    B. Multiple regression
    C. Logistic regression
    D. Polynomial regression
    Answer: B
  21. What does the term “residual” refer to in regression analysis?
    A. The difference between observed and predicted values
    B. The standard error of the regression
    C. The coefficient of determination (R-squared)
    D. The correlation coefficient
    Answer: A
  22. What does the term “collinearity” refer to in regression analysis?
    A. The relationship between dependent and independent variables
    B. The correlation between residuals and the dependent variable
    C. The correlation between independent variables
    D. The variance inflation factor (VIF)
    Answer: C
  23. In regression analysis, what is the purpose of transforming variables?
    A. To reduce the number of predictors
    B. To standardize the variables
    C. To improve the interpretability of the model
    D. To meet the assumptions of the regression model
    Answer: D
  24. What does the term “interaction effect” refer to in regression analysis?
    A. The effect of outliers on the regression coefficients
    B. The combined effect of two or more independent variables on the dependent variable
    C. The relationship between the dependent variable and the residuals
    D. The correlation between the dependent variable and the intercept
    Answer: B
  25. Which regression technique is used when the dependent variable is a count or rate?
    A. Simple linear regression
    B. Multiple regression
    C. Poisson regression
    D. Logistic regression
    Answer: C
  26. What does the term “adjusted R-squared” measure in regression analysis?
    A. Overall significance of the regression model
    B. Amount of variance explained by the independent variables
    C. Adjustments for the number of predictors in the model
    D. The normality of residuals
    Answer: C
  27. What does a scatter plot show in the context of regression analysis?
    A. The relationship between independent and dependent variables
    B. The correlation coefficient
    C. The residual plots
    D. The distribution of data points
    Answer: A
  28. Which regression technique is used when the dependent variable is binary or dichotomous?
    A. Simple linear regression
    B. Multiple regression
    C. Logistic regression
    D. Polynomial regression
    Answer: C
  29. What is the purpose of the Omnibus Test in regression analysis?
    A. To test for multicollinearity
    B. To assess the overall significance of the regression model
    C. To check for outliers in the data
    D. To evaluate the normality of residuals
    Answer: B
  30. Which of the following is an assumption of linear regression?
    A. Normality of residuals
    B. Independence of residuals
    C. Multicollinearity among independent variables
    D. Non-linear relationship between variables
    Answer: B
  31. What does the term “heteroscedasticity” indicate in regression analysis?
    A. The constant variance of residuals
    B. The non-normality of residuals
    C. The non-linearity of the relationship
    D. The non-constant variance of residuals
    Answer: D
  32. What does a scatter plot matrix help visualize in regression analysis?
    A. The correlation between independent and dependent variables
    B. The interaction effects among independent variables
    C. The normality of residuals
    D. The multicollinearity among independent variables
    Answer: D
  33. What is the purpose of the residual plot in regression analysis?
    A. To visualize the relationship between independent and dependent variables
    B. To assess the normality of residuals
    C. To identify outliers and influential points in the data
    D. To calculate the coefficient of determination (R-squared)
    Answer: C
  34. What does a high VIF (Variance Inflation Factor) indicate in regression analysis?
    A. Low multicollinearity among independent variables
    B. High correlation between independent and dependent variables
    C. High correlation among independent variables
    D. Low correlation between independent variables
    Answer: C
  35. In regression analysis, what does the term “overfitting” refer to?
    A. The model fits the training data well but fails to generalize to new data
    B. The model has a high R-squared value
    C. The model has a low p-value
    D. The model is unbiased
    Answer: A
  36. Which of the following regression techniques is used when the dependent variable is continuous and the relationship is non-linear?
    A. Simple linear regression
    B. Multiple regression
    C. Polynomial regression
    D. Logistic regression
    Answer: C
  37. What does the residual plot in regression analysis help identify?
    A. Outliers and influential points in the data
    B. The correlation coefficient
    C. The adjusted R-squared value
    D. The coefficient of determination (R-squared)
    Answer: A
  38. What does the term “regression coefficient” represent in regression analysis?
    A. The intercept of the regression line
    B. The slope of the regression line
    C. The correlation between independent and dependent variables
    D. The p-value of the regression model
    Answer: B
  39. Which assumption of regression analysis states that the residuals should be normally distributed?
    A. Linearity
    B. Homoscedasticity
    C. Independence
    D. Normality
    Answer: D
  40. What is the purpose of residual diagnostics in regression analysis?
    A. To determine the linearity of the relationship between variables
    B. To identify outliers and influential points in the data
    C. To calculate the correlation coefficient
    D. To assess the multicollinearity among independent variables
    Answer: B
  41. What does the term “collinearity” refer to in regression analysis?
    A. The relationship between dependent and independent variables
    B. The correlation between residuals and the dependent variable
    C. The correlation between independent variables
    D. The variance inflation factor (VIF)
    Answer: C
  42. In regression analysis, what is the purpose of transforming variables?
    A. To reduce the number of predictors
    B. To standardize the variables
    C. To improve the interpretability of the model
    D. To meet the assumptions of the regression model
    Answer: D
  43. What does the term “interaction effect” refer to in regression analysis?
    A. The effect of outliers on the regression coefficients
    B. The combined effect of two or more independent variables on the dependent variable
    C. The relationship between the dependent variable and the residuals
    D. The correlation between the dependent variable and the intercept
    Answer: B
  44. Which regression technique is used when the dependent variable is a count or rate?
    A. Simple linear regression
    B. Multiple regression
    C. Poisson regression
    D. Logistic regression
    Answer: C

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