Linear Regression

Linear Regression

This course is part of Advanced Statistical Techniques for Data Science Specialization

Instructor: Kiah Ong

2,804 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

30 reviews

Intermediate level

Recommended experience

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

30 reviews

Intermediate level

Recommended experience

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

What you'll learn

Describe the assumptions of the linear regression models.
Use R to fit a linear regression model to a given data set.
Interpret and draw conclusions on the linear regression model.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

17 assignments

Taught in English

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This course is part of the Advanced Statistical Techniques for Data Science Specialization

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

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless.

This course is part of the Performance Based Admission courses for the Data Science program. This course will focus on getting you acquainted with the basic ideas behind regression, it provides you with an overview of the basic techniques in regression such as simple and multiple linear regression, and the use of categorical variables. Software Requirements: R Upon successful completion of this course, you will be able to: - Describe the assumptions of the linear regression models. - Compute the least squares estimators using R. - Describe the properties of the least squares estimators. - Use R to fit a linear regression model to a given data set. - Interpret and draw conclusions on the linear regression model. - Use R to perform statistical inference based on the regression models.

Welcome to Linear Regression! In this course, we will cover the following topics: Simple Linear Regression, Multiple Linear Regression, and Regression Models with Qualitative Predictors. In Module 1, we will focus on defining the problem and setting up the simple linear regression model. Additionally, you will be introduced to the least square method as well as performing statistical inferences and predictions using R. There is a lot to read, watch, and consume in this module so, let’s get started!

What's included

15 videos11 readings8 assignments1 discussion prompt1 ungraded lab

15 videosTotal 108 minutes

Instructor Welcome and Course Overview 1 minute
Module 1 Introduction1 minute
Video 1 - Simple Linear Regression Introduction7 minutes
Video 2 - Least Squares Method12 minutes
Video 3 -R12 minutes
How to Use R in Coursera5 minutes
Video 4 - Properties of the Least Squares Estimators Part 1 of 2 10 minutes
Video 4 - Properties of the Least Squares Estimators Part 2 of 2 9 minutes
Video 5 - Part 1 of 36 minutes
Video 5 - Part 2 of 38 minutes
Video 5 - Part 3 of 38 minutes
Video 6 - Part 1 of 211 minutes
Video 6 - Part 2 of 23 minutes
Video 7 - 1 of 28 minutes
Video 7 - Part 2 of 27 minutes

11 readingsTotal 110 minutes

Syllabus10 minutes
Video 1 Slides - Simple Linear Regression Introduction (pdf)10 minutes
Video 2 Slides - Least Squares Method (pdf)10 minutes
Video 3 Slides - R (pdf)10 minutes
First Exercise in R Instructions10 minutes
Video 4 Slides - SLR Properties of betahat (pdf)10 minutes
Video 5 Slides - Inference in the Least Squares Estimators (pdf)10 minutes
Video 6 Slides - OLS Inference in R (pdf)10 minutes
Quiz 6 - OLS Inference in R Instructions10 minutes
Video 7 Slides - Prediction Interval (pdf)10 minutes
Module 1 Summary10 minutes

8 assignmentsTotal 390 minutes

Introduction to Simple Linear Regression30 minutes
Least Squares Method30 minutes
Exercise in R30 minutes
Properties of the Least Squares Estimators30 minutes
Inference in the Least Squares Estimators30 minutes
OLS Inference in R30 minutes
Prediction Interval30 minutes
Module 1 Summative Assessment180 minutes

1 discussion promptTotal 10 minutes

Meet and Greet Discussion10 minutes

1 ungraded labTotal 60 minutes

Exercise in R Lab60 minutes

Welcome to Module 2 - Multiple linear Regression. This module will focus on deriving parameter estimation using matrices as well as using R to do prediction and inference. There is a lot to read, watch, and consume in this module so, let’s get started!

What's included

6 videos4 readings4 assignments

6 videosTotal 45 minutes

Module 2 Introduction1 minute
Video 8 - MLR Intro11 minutes
Video 9 - MLR Least Squares Method11 minutes
Video 10 - MLR Properties of LS Estimators Part 1 of 39 minutes
Video 10 - MLR Properties of LS Estimators Part 2 of 39 minutes
Video 10 - MLR Properties of LS Estimators Part 3 of 35 minutes

4 readingsTotal 40 minutes

Video 8 Slides - MLR Intro.pdf10 minutes
Video 9 Slides - MLR Ordinary Least Squares (pdf)10 minutes
Video 10 Slides - MLR Properties of the LS Estimators (pdf)10 minutes
Module 2 Summary10 minutes

4 assignmentsTotal 270 minutes

Multiple Linear Regression Intro30 minutes
MLR Ordinary Least Squares30 minutes
MLR Properties of LS Estimators30 minutes
Module 2 Summative Assessment180 minutes

Welcome to Module 3 – Regression Models with Qualitative Predictors. This module will focus on setting up a linear regression model that involves qualitative predictors. Additionally, we will use R to help us perform statistical inferences and Predictions. There is a lot to read, watch, and consume in this module so, let’s get started!

What's included

11 videos5 readings4 assignments

11 videosTotal 90 minutes

Module 3 Introduction1 minute
Video 11 - Inference in Multiple Linear Regression Part 1 of 58 minutes
Video 11 - Inference in Multiple Linear Regression Part 2 of 58 minutes
Video 11 - Inference in Multiple Linear Regression Part 3 of 56 minutes
Video 11 - Inference in Multiple Linear Regression Part 4 of 511 minutes
Video 11 - Inference in Multiple Linear Regression Part 5 of 510 minutes
Video 12 - General Concepts on Categorical Variables as Predictors Part 1 of 28 minutes
Video 12 - General Concepts on Categorical Variables as Predictors Part 2 of 211 minutes
Video 13 - Qualitative Predictor with Two or More Classes 1 of 37 minutes
Video 13 - Qualitative Predictor with Two or More Classes 2 of 39 minutes
Video 13 - Qualitative Predictor with Two or More Classes 3 of 311 minutes

5 readingsTotal 50 minutes

Video 11 Slides - Inference in Multiple Linear Regression (pdf)10 minutes
Video 12 Slides - General Concepts on Categorical Variables as Predictors (pdf)10 minutes
Video 13 Slides - Qualitative Predictor with Two or More Classes (pdf)10 minutes
Module 3 Summary10 minutes
Insights from an Industry Leader: Learn More About Our Program10 minutes

4 assignmentsTotal 270 minutes

Inference in Multiple Linear Regression30 minutes
General Concepts of Categorical Variable30 minutes
Qualitative Predictor with Two or More Classes30 minutes
Module 3 Summative Assessment180 minutes

This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course. Be sure to review the course material thoroughly before taking the assessment.

What's included

1 assignment

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Build toward a degree

This course is part of the following degree program(s) offered by Illinois Tech. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructor

Instructor ratings

(11 ratings)

Kiah Ong

Illinois Tech

3 Courses3,546 learners

Offered by

Illinois Tech

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Reviewed on May 12, 2024

The Course has good in-depth explanation on the different regression and assumptions

Reviewed on Aug 17, 2025

Excellent intro, gets the math-intuition-application ratio bang on.

Reviewed on Sep 30, 2023

It is a good course, but I think the video lecture duration should be more.

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