Linear Regression
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Linear Regression
This course is part of Advanced Statistical Techniques for Data Science Specialization
Instructor: Kiah Ong
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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
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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 videos•Total 108 minutes
- Instructor Welcome and Course Overview •1 minute
- Module 1 Introduction•1 minute
- Video 1 - Simple Linear Regression Introduction•7 minutes
- Video 2 - Least Squares Method•12 minutes
- Video 3 -R•12 minutes
- How to Use R in Coursera•5 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 3•6 minutes
- Video 5 - Part 2 of 3•8 minutes
- Video 5 - Part 3 of 3•8 minutes
- Video 6 - Part 1 of 2•11 minutes
- Video 6 - Part 2 of 2•3 minutes
- Video 7 - 1 of 2•8 minutes
- Video 7 - Part 2 of 2•7 minutes
11 readings•Total 110 minutes
- Syllabus•10 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 Instructions•10 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 Instructions•10 minutes
- Video 7 Slides - Prediction Interval (pdf)•10 minutes
- Module 1 Summary•10 minutes
8 assignments•Total 390 minutes
- Module 1 Summative Assessment•180 minutes
- Introduction to Simple Linear Regression•30 minutes
- Least Squares Method•30 minutes
- Exercise in R•30 minutes
- Properties of the Least Squares Estimators•30 minutes
- Inference in the Least Squares Estimators•30 minutes
- OLS Inference in R•30 minutes
- Prediction Interval•30 minutes
1 discussion prompt•Total 10 minutes
- Meet and Greet Discussion•10 minutes
1 ungraded lab•Total 60 minutes
- Exercise in R Lab•60 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 videos•Total 45 minutes
- Module 2 Introduction•1 minute
- Video 8 - MLR Intro•11 minutes
- Video 9 - MLR Least Squares Method•11 minutes
- Video 10 - MLR Properties of LS Estimators Part 1 of 3•9 minutes
- Video 10 - MLR Properties of LS Estimators Part 2 of 3•9 minutes
- Video 10 - MLR Properties of LS Estimators Part 3 of 3•5 minutes
4 readings•Total 40 minutes
- Video 8 Slides - MLR Intro.pdf•10 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 Summary•10 minutes
4 assignments•Total 270 minutes
- Module 2 Summative Assessment•180 minutes
- Multiple Linear Regression Intro•30 minutes
- MLR Ordinary Least Squares•30 minutes
- MLR Properties of LS Estimators•30 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 videos•Total 90 minutes
- Module 3 Introduction•1 minute
- Video 11 - Inference in Multiple Linear Regression Part 1 of 5•8 minutes
- Video 11 - Inference in Multiple Linear Regression Part 2 of 5•8 minutes
- Video 11 - Inference in Multiple Linear Regression Part 3 of 5•6 minutes
- Video 11 - Inference in Multiple Linear Regression Part 4 of 5•11 minutes
- Video 11 - Inference in Multiple Linear Regression Part 5 of 5•10 minutes
- Video 12 - General Concepts on Categorical Variables as Predictors Part 1 of 2•8 minutes
- Video 12 - General Concepts on Categorical Variables as Predictors Part 2 of 2•11 minutes
- Video 13 - Qualitative Predictor with Two or More Classes 1 of 3•7 minutes
- Video 13 - Qualitative Predictor with Two or More Classes 2 of 3•9 minutes
- Video 13 - Qualitative Predictor with Two or More Classes 3 of 3•11 minutes
5 readings•Total 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 Summary•10 minutes
- Insights from an Industry Leader: Learn More About Our Program•10 minutes
4 assignments•Total 270 minutes
- Module 3 Summative Assessment•180 minutes
- Inference in Multiple Linear Regression•30 minutes
- General Concepts of Categorical Variable•30 minutes
- Qualitative Predictor with Two or More Classes•30 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
1 assignment•Total 180 minutes
- Summative Course Assessment •180 minutes
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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
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Reviewed on May 11, 2024
The Course has good in-depth explanation on the different regression and assumptions
Reviewed on Aug 16, 2025
Excellent intro, gets the math-intuition-application ratio bang on.
Reviewed on Sep 29, 2023
It is a good course, but I think the video lecture duration should be more.
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