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Linear Regression and Modeling

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Linear Regression and Modeling

This course is part of Data Analysis with R Specialization

Instructor: Mine Çetinkaya-Rundel

104,344 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

1,787 reviews

Beginner level

No prior experience required

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

1,787 reviews

Beginner level

No prior experience required

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments

Taught in English

91%

of learners achieved a positive career outcome

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This course is part of the Data Analysis with R Specialization

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There are 4 modules in this course

This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.

Module details

This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Linear Regression and Modeling. Please take several minutes to browse them through. Thanks for joining us in this course!

What's included

1 video3 readings

In this week we’ll introduce linear regression. Many of you may be familiar with regression from reading the news, where graphs with straight lines are overlaid on scatterplots. Linear models can be used for prediction or to evaluate whether there is a linear relationship between two numerical variables.

What's included

8 videos3 readings2 assignments

8 videosTotal 47 minutes

Introduction1 minute
Correlation9 minutes
Residuals2 minutes
Least Squares Line12 minutes
Prediction and Extrapolation4 minutes
Conditions for Linear Regression10 minutes
R Squared4 minutes
Regression with Categorical Explanatory Variables6 minutes

3 readingsTotal 30 minutes

Lesson Learning Objectives10 minutes
Lesson Learning Objectives10 minutes
Week 1 Suggested Readings and Practice10 minutes

2 assignmentsTotal 48 minutes

Week 1 Practice Quiz30 minutes
Week 1 Quiz18 minutes

Welcome to week 2! In this week, we will look at outliers, inference in linear regression and variability partitioning. Please use this week to strengthen your understanding on linear regression. Don't forget to post your questions, concerns and suggestions in the discussion forum!

What's included

3 videos5 readings3 assignments

3 videosTotal 24 minutes

Outliers in Regression7 minutes
Inference for Linear Regression12 minutes
Variability Partitioning6 minutes

5 readingsTotal 50 minutes

Lesson Learning Objectives10 minutes
Week 2 Suggested Readings and Exercises10 minutes
About Lab Choices10 minutes
Week 1 & 2 Lab Instructions (RStudio)10 minutes
Week 1 & 2 Lab Instructions (RStudio Cloud)10 minutes

3 assignmentsTotal 90 minutes

Week 2 Practice Quiz30 minutes
Week 2 Quiz30 minutes
Week 1 & 2 Lab30 minutes

In this week, we’ll explore multiple regression, which allows us to model numerical response variables using multiple predictors (numerical and categorical). We will also cover inference for multiple linear regression, model selection, and model diagnostics. There is also a final project included in this week. You will use the data set provided to complete and report on a data analysis question. Please read the project instructions to complete this self-assessment.

What's included

7 videos7 readings3 assignments

7 videosTotal 57 minutes

Introduction2 minutes
Multiple Predictors11 minutes
Adjusted R Squared10 minutes
Collinearity and Parsimony4 minutes
Inference for MLR12 minutes
Model Selection11 minutes
Diagnostics for MLR7 minutes

7 readingsTotal 180 minutes

Lesson Learning Objectives10 minutes
Lesson Learning Objectives10 minutes
Week 3 Suggested Readings and Exercises10 minutes
Week 3 Lab Instructions (RStudio)10 minutes
Week 3 Lab Instructions (RStudio Cloud)10 minutes
Project Instructions, Data files, and Checklist 120 minutes
Share your learning experience10 minutes

3 assignmentsTotal 80 minutes

Week 3 Practice Quiz30 minutes
Week 3 Quiz20 minutes
Week 3 Lab30 minutes

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Instructor

Instructor ratings

(162 ratings)

Mine Çetinkaya-Rundel

Duke University

11 Courses430,707 learners

Offered by

Duke University

Learner reviews

5 stars
80.47%
4 stars
15.83%
3 stars
2.90%
2 stars
0.27%
1 star
0.50%

Showing 3 of 1787

Reviewed on Dec 11, 2018

Files for this course were broken and I faced a lot of trouble to find good one. This course may be made more comprehensive and not assuming that reader have also understanding.

Reviewed on Jun 20, 2018

This was the first course where I started noticing that I'm really learning and was able to apply some of the earned knowledge at work.Totally recommended.

Reviewed on Dec 13, 2020

I learn a lot. It added more lessons beyond my graduate school. Especially that the course is based on R, this course is very helpful for my journey towards using R.

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