INTRODUCTION – Multiple Linear Regression

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: UNDERSTAND MULTIPLE LINEAR REGRESSION

1. Fill in the blank: _____ is a technique that estimates the linear relationship between one continuous dependent variable and two or more independent variables.

2. What concept refers to how two independent variables together affect the y dependent variable?

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: MODEL ASSUMPTIONS REVISITED

1. Which of the following statements is true? Select all that apply.

2. What is the definition of the no multicollinearity assumption?

3. In what ways might a data professional handle data with multicollinearity? Select all that apply.

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: MODEL INTERPRETATION

1. Fill in the blank: An interaction term represents how the relationship between two independent variables is associated with the changes in the _____ of the dependent variable.

2. Which of the following relevant statistics can be found by using statsmodel’s OLS function? Select all that apply.

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: VARIABLE SELECTION AND MODEL EVALUATION

1. Fill in the blank: Adjusted R squared is a variation of the R squared regression evaluation metric that _____ unnecessary explanatory variables.

2. Which of the following statements accurately describe the differences between adjusted R squared and R squared? Select all that apply.

3. What variable selection process begins with the full model that has all possible independent variables?

4. Which of the following are regularized regression techniques? Select all that apply.

QUIZ: MODULE 3 CHALLENGE

1. A data team working for an online magazine uses a regression technique to learn about advertising sales in different sections of the publication. They estimate the linear relationship between one continuous dependent variable and four independent variables. What technique are they using?

2.What technique turns one categorical variable into several binary variables?

3. Which of the following is true regarding variance inflation factors? Select all that apply.

4. What term represents how the relationship between two independent variables is associated with changes in the mean of the dependent variable?

5. Which of the following statements accurately describe adjusted R squared? Select all that apply.

6. Which of the following statements accurately describe forward selection and backward elimination? Select all that apply.

7. A data professional reviews model predictions for a human resources project. They discover that the model performs poorly on both the training data and the test holdout data, consistently predicting figures that are too low. This leads to inaccurate estimates about employee retention. What quality does this model have too much of?

8. What regularization technique completely removes variables that are less important to predicting the y variable of interest?

9. A data team with a restaurant group uses a regression technique to learn about customer loyalty and ratings. They estimate the linear relationship between one continuous dependent variable and two independent variables. What technique are they using?

10. A data professional confirms that no two independent variables are highly correlated with each other. Which assumption are they testing for?

11. What term represents the relationship for how two variables’ values affect each other?

12. Which regression evaluation metric penalizes unnecessary explanatory variables?

13. A data professional tells you that their model fails to adequately capture the relationship between the target variable and independent variables because it has too much bias. What is the most likely cause of the bias?

14. What regularization technique minimizes the impact of less relevant variables, but drops none of the variables from the equation?

15. Fill in the blank: The no multicollinearity assumption states that no two _____ variables can be highly correlated with each other.

16. Fill in the blank: An interaction term represents how the relationship between two independent variables is associated with changes in the _____ of the dependent variable.

17. A data professional uses an evaluation metric that penalizes unnecessary explanatory variables. Which metric are they using?

18. What stepwise variable selection process begins with the full model with all possible independent variables?

19. A data analytics team creates a model for a project supporting their company’s sales department. The model performs very well on the training data, but it scores much worse when used to predict on new, unseen data. What does this model have too much of?

20. A data professional at a car rental agency uses a regression technique to learn about how customers engage with various sections of the company website. They estimate the linear relationship between one continuous dependent variable and three independent variables. What technique are they using?

21. Which of the following are examples of categorical variables? Select all that apply.

22. Fill in the blank: One hot encoding is a data transformation technique that turns one categorical variable into several _____ variables.

23. What stepwise variable selection process begins with the null model and zero independent variables?

24. What data transformation technique turns one categorical variable into several binary variables?

25. Fill in the blank: The _____ states that no two independent variables (X−i  and X−j) can be highly correlated with each other.

26. Fill in the blank: An interaction term represents the relationship between two independent variables and the change in the mean of the _____ variable.

27. What is the process of determining which variables or features to include in a given model?

CONCLUSION – Multiple Linear Regression