Regression Analysis Using R

Boost your career in Data Analysis by comprehending regression and classification skills. Take up this free course to learn linear regression, multicollinearity, fit-R square and variables concepts for modeling and analyzing data.

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3.75 Hrs

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29.5K+
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Regression Analysis Using R

3.75 Learning Hours . Beginner

Skills you’ll Learn

About this course

Predictive Modeling and Analytics - Regression is designed to help you better understand the basic concepts and cater to your queries on the subject. The course introduces you to predictive modeling and takes you through simple linear regression, best fit line, multiple linear regression, linear regression assumptions, multicollinearity, fit-R square, and variables concepts. It then discusses predictive modeling classification techniques in the latter part of the course. You will understand each of these concepts with the demonstrated examples and solved problems. You will also be able to judge your gains and test your skills with designed subject-oriented assessments at the end of the course. The course also provides you with materials you can refer to at any time after enrolling. 

After completing this free, self-paced, intermediate's guide to Predictive Modeling and Analysis - Regression, you can embark on your Data Science and Business Analytics career with a professional Post Graduate certificate and learn various concepts with millions of aspirants across the globe!

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Course Outline

Introduction to Predictive Modelling

This module shall define predicting, modeling and analyzing and discuss predictive modeling in the first half. The course then speaks about how these predictions can prescribe what tasks to perform to drive the motive of an organization.

Simple Linear Regression

This section begins by telling you what a model is. It then continues by helping you understand how simple linear regression is performed with assumption and simplification steps. You will also learn to model an equation based on the given scenario.

Best Fit Line

This section tells you how one dataset influences the other depending upon the requirements and time. It also talks about how the estimated data can predict the progress and output in the future, through testing and training, by describing a real-life business scenario.

Multiple Linear Regression

This section describes a supervised learning technique where an outcome results from multiple predictions. You will understand how the result is obtained by deriving the solution to the scenario through multiple linear regression methods.

Linear Regression Assumptions

Here in this module, you will understand some linear regression assumptions, such as the assumption of linearity and the assumption of normality of the error distribution. Lastly, you will understand various dimensions to plot the linear regression models. 
 

Concepts of Multicollinearity

At the beginning of this section, you will understand the concepts of structural and data multicollinearity. You will then learn how to solve the problems where the independent variables are not independent but correlated by working on sample problems.

Concept of R-squared

You will begin with understanding different linear regression models and continue learning how to work with gradient descent to find the best model. You will then understand how to find errors with the given data points and learn the concept of determinant coefficient later in this section.

Goodness of Fit - R-squared

In this section, you will learn how to use the R square method to solve the multiple linear regression problems by dividing the given dataset into training and testing data. You will also learn to model equations and judge if it is fit to describe the solution of a given problem.

Significance of Variables

This section tells you how a variable represents a particular part of the problem statement and how all other scenarios affect that variable. You will also learn how variables change depending upon the situation and how they are used to model an equation later in this section.

Our course instructor

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Dr. Bappaditya Mukhopadyay

Professor, Analytics & Finance

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646.6K+ Learners
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2 Courses

With an MA in Economics from Delhi School of Economics and PHD from ISI, Dr. Mukhopadhyay is currently the professor and chairperson of the PGPBA program at Great Lakes Institute of Management. He is also the visiting professor of the University of Ulm, Germany, and distinguished Professorial Associate, Decision Sciences and Modelling Program, Victoria University, Australia. His areas of interest and expertise include applied economic theory, game theory, analytics, statistics, econometrics, derivatives and financial risk management, survey design, execution, and others.

 

Noteworthy achievements:

  • Ranked 4th Amongst the "20 Most Prominent Analytics & Data Science Academicians In India: 2018".
  • Prominent Credentials: He has various research papers published in national as well as international journals. He is currently working on a book titled Measuring and Managing Credit Risk. He has been the Managing Editor at Journal of Emerging Market Finance and Journal of Infrastructure and Development, member of Index Committee, member of Research Advisory Committee, Research Advisory Committee, NICR, Expert member in Faculty Selection committees at various Business schools, among others.
  • Research Interest: Information economics and contract theory, financial risk management, credit risk and agency theory, microfinance institutions, financial Inclusion, analytics in public policy.
  • Teaching Experience: He has more than 20 years of teaching experience in economics, finance.

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What our learners say about the course

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4.53
Course Rating
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Ratings & Reviews of this Course

Reviewer Profile

5.0

Engaging Content, Practical Applications, and Insightful Discussions
I appreciate the course's engaging content, practical applications, and insightful discussions. It offers real-world examples and interactive activities that enhance understanding. The well-structured lessons and supportive community make learning enjoyable and effective.
Reviewer Profile

5.0

Comprehensive and Insightful Predictive Modeling Course
I enjoyed the detailed explanations of linear and logistic regression techniques along with the practical case studies. The curriculum was well-structured, and the quizzes and assignments reinforced my understanding. The course was easy to follow, and the instructor did an excellent job covering the topic in depth with relevant real-world examples.
Reviewer Profile

5.0

Very Good Course, the Instructors Are the Best
First, the course helps to understand the basics. Afterward, I liked the opportunity to practice.

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Regression Analysis Using R

3.75 Learning Hours . Beginner

Frequently Asked Questions

What are the prerequisites required to learn this Predictive Modeling and Analytics - Regression course?

This is an intermediate-level course. So before you learn Predictive Modeling and Analytics for Regression modeling, you will need to learn the basics of Data Science to understand the basic terms such as modeling and prediction. Regression is a mathematical concept that solves big data set problems such as pattern matching and prediction.

How long does it take to complete this free Predictive Modeling and Analytics - Regression course?

The Predictive Modeling and Analytics - Regression is 2.5 hours long course but is self-paced. However, once you enroll, you can take your own time to complete the course.

Will I have lifetime access to the free course?

Once you enroll in the course, you will have lifetime access to any of the Great Learning Academy's free courses. You can log in and learn whenever you want to.

What are my following learning options after this Predictive Modeling and Analytics - Regression course?

After completing the Predictive Modeling and Analytics - Regression course, you can learn other concepts in Data Science and apply the subject to solve Business Analytics problems in real life. You can learn various algorithms used to develop and train a model and understand its applications.

Why learn Predictive Modeling and Analytics - Regression?

Predictive Modeling and Analytics - Regression is one of the essential concepts in both Machine Learning and Data Science techniques. It is the procedure to map the patterns to understand the similarities and automate the process. Regression is one of the techniques used to model and analyze the data set.

What is Predictive Modeling and Analytics - Regression used for?

Modeling and Analytics make necessary procedures in machine learning and data science. Regression modeling for data analytics uses cumulative knowledge to apply and solve similar problems without or less human intervention. Hence, it is used in various applications such as pattern matching (fingerprint recognition), recognition of forecast events, customer behavior, and financial, economic, and market risks.

Why is Predictive Modeling and Analytics - Regression so popular?

Predictive Modeling and Analytics - Regression is popular since it is used to automate the processes, making it less burden on the maintaining and coding teams. It also reduces errors and makes the system more efficient.

What jobs demand that you learn Predictive Modeling and Analytics - Regression?

With a good hold on Predictive Modeling and Analytics in Regression, you will be able to crack any Data Science and Business Analytics related jobs. You will suit profiles requiring coding skills, Data Scientists, Machine Learning experts, and Business Analysts.

After completing this Predictive Modeling and Analytics - Regression course, will I get a certificate?

Yes, you will get a certificate of completion after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.

What knowledge and skills will I gain upon completing this Predictive Modeling and Analytics - Regression course?

Predictive modeling course gives you an understanding of simple linear regression, best fit line, multiple linear regression, linear regression assumptions, multicollinearity, fit-R square, and variables concepts. You will basket classification skills to work with any vast data set and derive its output from solving any ML and Data Science related problems.

How much does this Predictive Modeling and Analytics - Regression course cost?

The Predictive Modeling and Analytics - Regression is a free course. You can enrol and learn it for free online.

Is there a limit on how many times I can take this Predictive Modeling and Analytics - Regression course?

Once you enroll in the Predictive Modeling and Analytics - Regression course, you have lifetime access to it. So, you can log in anytime and learn it for free online.

Can I sign up for multiple courses from Great Learning Academy simultaneously?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.

Why choose Great Learning Academy for this Predictive Modeling and Analytics - Regression course?

Great Learning Academy provides a free Predictive Modeling and Analytics - Regression course online. The course is self-paced and helps you understand various topics under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts.

 

Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 4 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.

Who is eligible to take this Predictive Modeling and Analytics - Regression course?

Anybody with basic knowledge of computer science and interested in learning Data Modeling and Analytics can take up the course. You do not need any prerequisites to learn the course, so enroll today and learn it for free online.

What are the steps to enroll in this course?

Enrolling in any of the Great Learning Academy's courses is just a one-step process. Sign-up for the courses you are interested in learning through your E-mail ID and start learning them for free online.

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