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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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.
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