Linear regression

Sandra Rogne

For this part, you are able to test your skills and knowledge on linear regression. Below is a quiz with various question on the concepts and important knowledge linear regression such as preparations, simple linear regression, multiple linear regression, model diagnostics, and interaction analysis. After completing the quiz, the correct answers will appear. At the bottom of the page are links to the sections referred to in the quiz for more information and further review.

Test your skills

Linear regression

1 / 10

What is the primary purpose of conducting a linear regression analysis?

To estimate the median value of the dependent variable

To determine the variance of the independent variable

To assess the relationship between the dependent and independent variables

To calculate the odds ratio of the independent variable

2 / 10

In a simple linear regression analysis, what does the “contrast” command allow you to test?

The interaction effects between independent variables

The overall significance of the independent variable coefficients

The correlation between dependent and independent variables

The variance of the residuals

3 / 10

In linear regression analysis, the effect that x has on y is reflected by a B coefficient (B). When interpreting the coefficient of the independent variable (x), what does a coefficient of 0.75 mean?

For each unit increase in x, y increases by 0.75 units

For each unit increase in x, y decreases by 0.75 units

There is no relationship between x and y

x is not a significant predictor of y

4 / 10

If the R-squared value in a linear regression model is 0.75, what does this indicate?

The model explains 75% of the variation in the dependent variable

The model has a 75% chance of predicting the dependent variable accurately

The model is not a good fit for the data

The model has 75 independent variables

5 / 10

What is the primary distinction between simple linear regression and multiple linear regression?

Simple linear regression can only model a linear relationship, while multiple linear regression can model non-linear relationships as well

Simple linear regression uses categorical independent variables, while multiple linear regression uses continuous independent variables

Simple linear regression has a single independent variable, while multiple linear regression has two or more independent variables

Simple linear regression uses continuous independent variables, while multiple linear regression uses categorical independent variables

6 / 10

In multiple linear regression, what does a vif-value near 10 or higher suggest?

The model is well-specified and free from multicollinearity

The model is overfit and should be simplified

Multicollinearity is not a concern in the model

Multicollinearity is present and may affect the accuracy of results

7 / 10

With the command linktest, we can assess whether our model is correctly specified. What is the expectation of the Link test?

We expect _hat to be statistically significant, and _hatsq to be statistically non-significant

We expect _hat to be statistically non-significant, and _hatsq to be statistically significant

We expect both _hat and _hatsq to be statistically significant

We expect both _hat and _hatsq to be statistically non-significant

8 / 10

In linear regression analysis, what does an interaction term represent?

The strength of the association between the main independent variable and the dependent variable

It represents the addition of a new dependent variable to the model

How two independent variables interact in their effect on the dependent variable

Stratifying the analysis based on the values of a third variable

9 / 10

We are doing a linear regression between age and income, with gender (0=Man, 1=Woman) as a moderator. The p-value for the interaction term is 0.01. What does this p-value suggest?

There is no statistically significant effect of age on income

Age and gender does not interact in their effect on income

Men have higher income than women

Age and gender interacts in their effect on income

10 / 10

A significant p-value associated with an interaction term in linear regression analysis indicate that there is a statistically significant change in the relationship between two or more variables based on the moderator.

True

False

Your score is

The average score is 56%

Review of the quiz

Q1 – See more on Linear regression
Q2 – See more on Simple linear regression
Q3 – See more on Linear regression in short
Q4 – See more on Linear regression in short
Q5 – See more on Multiple linear regression
Q6 – See more on Variance inflation factor and correlation matrix
Q7 – See more on Link test
Q8 – See more on Primary approaches to interaction analysis
Q9 – See more on Interaction analysis
Q10 – See more on Interaction analysis