Linear Regression

Linear Regression

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

Instructor: Kiah Ong

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2,646 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

30 reviews

Intermediate level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

30 reviews

Intermediate level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

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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.

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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

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
Module 1 Summative Assessment 180 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

Multiple Linear Regression Intro 30 minutes
MLR Ordinary Least Squares 30 minutes
MLR Properties of LS Estimators 30 minutes
Module 2 Summative Assessment 180 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

Inference in Multiple Linear Regression 30 minutes
General Concepts of Categorical Variable 30 minutes
Qualitative Predictor with Two or More Classes 30 minutes
Module 3 Summative Assessment 180 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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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