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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Beginner

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3.75 Hrs

Learning hours

29K+
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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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634.7K+ 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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Regression Analysis Using R

3.75 Learning Hours . Beginner

Why upskill with us?

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700+ free courses
In-demand skills & tools
access time
Free life time Access

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