regression analysis: simplify complex data relationships coursera weekly challenges 3 answers
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.
- Singular curved regression
- Multiple curved regression
- Singular linear regression
- Multiple linear regression
2. What are ways to ethically communicate multiple regression results as clearly as possible? Select all that apply.
- Ordinary least squares
- One hot encoding
- Interaction terms
- Confidence band
Test your knowledge: Model assumptions revisited
3. Which of the following statements is true? Select all that apply.
- One hot encoding is a data transformation technique.
- One hot encoding is a categorical transformation technique.
- One hot encoding allows data professionals to turn several categorical variables into one binary variable.
- One hot encoding allows data professionals to turn one categorical variable into several binary variables.
4. What is the definition of the no multicollinearity assumption?
- No two independent variables can be highly correlated with each other.
- No observation in the dataset can be independent.
- No variation of the residential can be constant or similar across the model.
- No predictor variable can be linearly related to the outcome variable.
5. In what ways might a data professional handle data with multicollinearity? Select all that apply.
- Create new variables using existing data.
- Square the variables that have high multicollinearity.
- Turn one categorical variable into several binary variables.
- Drop one or more variables that have high multicollinearity.
Test your knowledge: Model interpretation
6. 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.
- category
- multicollinearity
- rate of change
- mean
7. Which of the following relevant statistics can be found by using statsmodel’s OLS function? Select all that apply.
- P-values
- Variance inflation factors
- Coefficients
- Standard errors
Test your knowledge: Variable selection and model evaluation
8. Fill in the blank: Adjusted R squared is a variation of the R squared regression evaluation metric that _____ unnecessary explanatory variables.
- adds
- rewards
- eliminates
- penalizes
Shuffle Q/A 1
9. Which of the following statements accurately describe the differences between adjusted R squared and R squared? Select all that apply.
- Adjusted R squared is easily interpretable.
- R squared is more easily interpretable.
- R squared is used to compare models of varying complexity.
- Adjusted R squared is used to compare models of varying complexity.
10. What variable section process begins with the full model that has all possible independent variables?
- Backward elimination
- Forward selection
- F-test
- Extra-sum-of Squares
