Evaluating Regression Models Quiz 11

The post is about Evaluating Regression Models Quiz with answers. There are 20 multiple-choice questions about regression models and their evaluation, covering regression analysis, assumptions of regression, coefficient of determination, predicted and predictor variables, etc. Let us start with the Evaluating Regression Models Quiz now.

Evaluating Regression Models Quiz

Online MCQs about Evaluating Regression Models

1. An underfit model is said to have which of the following?

 
 
 
 

2. Regression coefficients may have the wrong sign for the following reasons

 
 
 
 

3. What does regularization introduce into a model that results in a drop in variance?

 
 
 
 

4. A third-order polynomial regression model is described as which of the following?

 
 
 
 

5. When we fit a linear regression model we make strong assumptions about the relationships between variables and variance. These assumptions need to be assessed to be valid if we are to be confident in estimated model parameters. The questions below will help ascertain that you know what assumptions are made and how to verify these.

Which of these is not assumed when fitting a linear regression model?

 
 
 
 

6. A training set is ————–.

 
 
 
 

7. How can the following plot be used to see if residuals satisfy the requirements for a linear regression?

Evaluating Regression Models Quiz 11

 
 
 
 

8. When tuning a model, a grid search attempts to find the value of a parameter that has the smallest —————-.

 
 
 
 

9. The test used to test the individual partial coefficient in the multiple regression is

 
 
 
 

10. What is the difference between Ridge and Lasso regression?

 
 
 
 

11. Which situations are helped by using the cross-validation method to train your model?

 
 
 
 

12. A testing set is —————.

 
 
 
 

13. One cannot apply test of significance if $\varepsilon_i$ in the model $y_i = \alpha + \beta X_i+\varepsilon_i$ are

 
 
 
 

14. Parveen previously fitted a linear regression model to quantify the relationship between age and lung function measured by FEV1. After she fitted her linear regression model she decided to assess the validity of the linear regression assumptions. She knew she could do this by assessing the residuals and so produced the following plot known as a QQ plot.

QQ Plot Regression model residuals

How can she use this plot to see if her residuals satisfy the requirements for a linear regression?

 
 
 
 

15. The residuals are the distance between the observed values and the fitted regression line. If the assumptions of linear regression hold how would we expect the residuals to behave?

 
 
 
 

16. When evaluating models, what is the term used to describe a situation where a model fits the training data very well but performs poorly when predicting new data?

 
 
 
 

17. Let the value of the $R^2$ for a model is 0.0104. What does this tell?

 
 
 

18. The ratio of explained variation to the total variation of the following regression model is called $y_i = \beta_0 + \beta_1 x_{1i} + \beta_2x_{2i} + \varepsilon_i, \quad i=1,2,\cdots, n$.

 
 
 
 

19. What is a strategy you can employ to address an underfit model?

 
 
 
 

20. When using the poly() function to fit a polynomial regression model, you must specify “raw = FALSE” so you can get the expected coefficients.

 
 

MCQs Evaluating Regression Models Quiz with Answers

  • When using the poly() function to fit a polynomial regression model, you must specify “raw = FALSE” so you can get the expected coefficients.
  • A third-order polynomial regression model is described as which of the following?
  • When evaluating models, what is the term used to describe a situation where a model fits the training data very well but performs poorly when predicting new data?
  • An underfit model is said to have which of the following?
  • What does regularization introduce into a model that results in a drop in variance?
  • When tuning a model, a grid search attempts to find the value of a parameter that has the smallest —————-.
  • Which situations are helped by using the cross-validation method to train your model?
  • What is a strategy you can employ to address an underfit model?
  • What is the difference between Ridge and Lasso regression?
  • A training set is ————–.
  • A testing set is —————.
  • Regression coefficients may have the wrong sign for the following reasons
  • The ratio of explained variation to the total variation of the following regression model is called $y_i = \beta_0 + \beta_1 x_{1i} + \beta_2x_{2i} + \varepsilon_i, \quad i=1,2,\cdots, n$.
  • One cannot apply test of significance if $\varepsilon_i$ in the model $y_i = \alpha + \beta X_i+\varepsilon_i$ are
  • The test used to test the individual partial coefficient in the multiple regression is
  • When we fit a linear regression model we make strong assumptions about the relationships between variables and variance. These assumptions need to be assessed to be valid if we are to be confident in estimated model parameters. The questions below will help ascertain that you know what assumptions are made and how to verify these. Which of these is not assumed when fitting a linear regression model?
  • Parveen previously fitted a linear regression model to quantify the relationship between age and lung function measured by FEV1. After she fitted her linear regression model she decided to assess the validity of the linear regression assumptions. She knew she could do this by assessing the residuals and so produced the following plot known as a QQ plot. How can she use this plot to see if her residuals satisfy the requirements for a linear regression?
  • How can the following plot be used to see if residuals satisfy the requirements for a linear regression?
  • Let the value of the $R^2$ for a model is 0.0104. What does this tell?
  • The residuals are the distance between the observed values and the fitted regression line. If the assumptions of linear regression hold how would we expect the residuals to behave?
Evaluating Regression Models Quiz

Performing Statistical Models in R

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