Linear Regression and Modeling

Linear Regression and Modeling

This course is part of Data Analysis with R Specialization

Instructor: Mine Çetinkaya-Rundel

Access provided by Barbados NTI

103,956 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

1,785 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.

1,785 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

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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 videos Total 47 minutes

Introduction 1 minute
Correlation 9 minutes
Residuals 2 minutes
Least Squares Line 12 minutes
Prediction and Extrapolation 4 minutes
Conditions for Linear Regression 10 minutes
R Squared 4 minutes
Regression with Categorical Explanatory Variables 6 minutes

3 readings Total 30 minutes

Lesson Learning Objectives 10 minutes
Lesson Learning Objectives 10 minutes
Week 1 Suggested Readings and Practice 10 minutes

2 assignments Total 48 minutes

Week 1 Practice Quiz 30 minutes
Week 1 Quiz 18 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 videos Total 24 minutes

Outliers in Regression 7 minutes
Inference for Linear Regression 12 minutes
Variability Partitioning 6 minutes

5 readings Total 50 minutes

Lesson Learning Objectives 10 minutes
Week 2 Suggested Readings and Exercises 10 minutes
About Lab Choices 10 minutes
Week 1 & 2 Lab Instructions (RStudio) 10 minutes
Week 1 & 2 Lab Instructions (RStudio Cloud) 10 minutes

3 assignments Total 90 minutes

Week 2 Practice Quiz 30 minutes
Week 2 Quiz 30 minutes
Week 1 & 2 Lab 30 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 videos Total 57 minutes

Introduction 2 minutes
Multiple Predictors 11 minutes
Adjusted R Squared 10 minutes
Collinearity and Parsimony 4 minutes
Inference for MLR 12 minutes
Model Selection 11 minutes
Diagnostics for MLR 7 minutes

7 readings Total 180 minutes

Lesson Learning Objectives 10 minutes
Lesson Learning Objectives 10 minutes
Week 3 Suggested Readings and Exercises 10 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 experience 10 minutes

3 assignments Total 80 minutes

Week 3 Practice Quiz 30 minutes
Week 3 Quiz 20 minutes
Week 3 Lab 30 minutes

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Instructor

Instructor ratings

(160 ratings)

Mine Çetinkaya-Rundel

Duke University

11 Courses 428,458 learners

Offered by

Duke University

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Reviewed on Sep 26, 2021

linear regression is well taught throughout the course, but I think learning other types of regression modeling would be useful as well and adding them to the course materials is really good.

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 May 14, 2020

It has been a great adventure so far. I still greatly appreciate how final projects are constructed that gives us freedom to choose our approach to the problems within the data set.

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