21. A data professional confirms that no two independent variables are highly correlated with each other. Which assumption are they testing for?
- No multicollinearity assumption
- No linearity assumption
- No normality assumption
- No homoscedasticity assumption
22. Which of the following statements accurately describe forward selection and backward elimination? Select all that apply.
- Backward selection begins with the full model with all possible independent variables.
- Forward selection begins with the full model with all possible dependent variables.
- Forward selection begins with the full model with all possible independent variables.
- Forward selection begins with the null model and zero independent variables.
23. A data professional uses a regression technique that estimates the linear relationship between one continuous dependent variable and two or more independent variables. What technique are they using?
- Coefficient regression
- Multiple linear regression
- Simple linear regression
- Interaction regression
24. Which of the following is true regarding variance inflation factors? Select all that apply.
- The larger the variable inflation factor, the more multicollinearity in the model.
- The minimum value is 0.
- The minimum value is 1.
- The larger the variable inflation factor, the less multicollinearity in the model.
25. A data professional reviews model predictions. During the review, they notice a model that oversimplifies the relationship and underfits the observed data, which generates inaccurate estimates. What quality does this model have?
- Variance
- Elimination
- Bias
- Selection
26. What term represents the relationship for how two variables’ values affect each other?
- Feature selection term
- Linearity term
- Underfitting term
- Interaction term
27. Which of the following statements accurately describe adjusted R squared? Select all that apply.
- It is a regression evaluation metric.
- It penalizes unnecessary explanatory variables.
- It is greater than 1.
- It can vary from 0 to 1.
28. What regularization technique is recommended when working with large datasets and when there is uncertainty as to whether variables should drop out of the model?
- Backward regression
- Elastic net regression
- Ridge regression
- Lasso regression