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Linear Regression and Modeling

Linear Regression and Modeling

This course is part of Data Analysis with R Specialization

Instructor: Mine Çetinkaya-Rundel

104,358 already enrolled

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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

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This course is part of the Data Analysis with R Specialization

When you enroll in this course, you'll also be enrolled in this 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.

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 Quiz18 minutes
Week 1 Practice Quiz30 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 Quiz30 minutes
Week 1 & 2 Lab30 minutes
Week 2 Practice Quiz30 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 Quiz20 minutes
Week 3 Lab30 minutes
Week 3 Practice Quiz30 minutes

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Instructor

Instructor ratings

(162 ratings)

Mine Çetinkaya-Rundel

Duke University

11 Courses430,832 learners

Offered by

Duke University

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Reviewed on Jan 1, 2017

Again like the first two courses, this course was great. The Lectures were excellent and the assignments very helpful in solidifying understanding. Thanks a lot Dr Mine

Reviewed on Jul 21, 2020

A great primer on linear regression with labs that help to establish understanding and a project that is focused enough not to be overwhelming, and allows the learner to play around with the concepts

Reviewed on Sep 14, 2017

fantastic course on linear regression, concepts are well explained followed by quiz and practical exercises.though you need to complete the prior courses to understand this.

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Frequently asked questions

No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.