Quiz: Linear Regression & F-Statistics

Linear Regression is one of the most widely used statistical methods for predictive modeling in various fields such as finance, marketing, and engineering. It involves fitting a linear equation to a set of data points, which can be used to make predictions about new data. One important aspect of linear regression is the use of F-Statistics, which is a statistical test used to determine the significance of the regression model.

If you’re looking to test your knowledge of Linear Regression and F-Statistics, you’ve come to the right place! It will also be helpful if you are preparing for data science interviews. In this capsule quiz, we’ve compiled 10 questions that cover the key concepts and assumptions of linear regression and F-Statistics. By taking this quiz, you can assess your understanding of the topic and identify areas that you may need to review further. So, whether you’re a student of statistics, data science, or simply interested in learning more about Linear Regression and F-Statistics, this quiz is a great way to test your knowledge and enhance your understanding of this important statistical method.

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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog, Revive-n-Thrive.com

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Latest posts by Ajitesh Kumar (see all)

• MSE vs RMSE vs MAE vs MAPE vs R-Squared: When to Use? - April 22, 2024
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• Author
• Recent Posts

Follow me

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog, Revive-n-Thrive.com

Follow me

Latest posts by Ajitesh Kumar (see all)

• MSE vs RMSE vs MAE vs MAPE vs R-Squared: When to Use? - April 22, 2024
• Gradient Descent in Machine Learning: Python Examples - April 22, 2024
• Loss Function vs Cost Function vs Objective Function: Examples - April 19, 2024

#1. What is the purpose of linear regression model?

To classify data

To classify data

To find patterns in data

To find patterns in data

To predict a continuous variable

To predict a continuous variable

To visualize data

To visualize data

#2. Which of the following is not an assumption of linear regression model?

Linearity

Linearity

Normality

Normality

Homoscedasticity

Homoscedasticity

Independence of observations

Independence of observations

#3. What is the objective of F-statistics in linear regression?

To test the significance of the intercept term

To test the significance of the intercept term

To test the significance of the coefficients

To test the significance of the coefficients

To test the overall significance of the model

To test the overall significance of the model

To test the goodness of fit

To test the goodness of fit

#4. Which of the following statements about R-squared is true?

It always increases when a new predictor is added to the model

It always increases when a new predictor is added to the model

It always decreases when a new predictor is added to the model

It always decreases when a new predictor is added to the model

It can increase or decrease when a new predictor is added to the model

It can increase or decrease when a new predictor is added to the model

It is not affected by the addition of a new predictor

It is not affected by the addition of a new predictor

#5. Q5. What does a p-value less than the significance level indicate in hypothesis testing?

There is evidence to reject the null hypothesis

There is evidence to reject the null hypothesis

There is not enough evidence to reject the null hypothesis

There is not enough evidence to reject the null hypothesis

The null hypothesis is true

The null hypothesis is true

The alternative hypothesis is true

The alternative hypothesis is true

#6. What is the range of F-statistic in linear regression?

0 to 1

0 to 1

-1 to 1

-1 to 1

0 to infinity

0 to infinity

-infinity to infinity

-infinity to infinity

#7. What is multicollinearity in linear regression?

When the model is too simple

When the model is too simple

When the predictors are not independent of each other

When the predictors are not independent of each other

When the predictors are not related to the response variable

When the predictors are not related to the response variable

When the model is overfitting the data

When the model is overfitting the data

#8. Which of the following is not a method for variable selection in linear regression?

Forward selection

Forward selection

Backward elimination

Backward elimination

Stepwise selection

Stepwise selection

Random selection

Random selection

#9. Which of the following is not a type of residuals in linear regression?

Standardized residuals

Standardized residuals

Studentized residuals

Studentized residuals

Cook's error

Cook's error

#10. Which of the following is not a limitation of linear regression model?

Assumes a linear relationship between predictors and response

Assumes a linear relationship between predictors and response

Cannot handle non-linear relationships

Cannot handle non-linear relationships

Assumes homoscedasticity

Assumes homoscedasticity

Can only handle a limited number of predictors

Can only handle a limited number of predictors

Finish

• Author
• Recent Posts

Follow me

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog, Revive-n-Thrive.com

Follow me

Latest posts by Ajitesh Kumar (see all)

• MSE vs RMSE vs MAE vs MAPE vs R-Squared: When to Use? - April 22, 2024
• Gradient Descent in Machine Learning: Python Examples - April 22, 2024
• Loss Function vs Cost Function vs Objective Function: Examples - April 19, 2024