Why Linear Regression Fails? : 5 Critical Assumptions
#linearregression #tutorial #numericals #statistics #businessanalytics #statisticstutorials #mlmodels
The tutorial outlines the five critical assumptions for running a successful simple linear regression model. The assumptions help in choosing a linear regression model to predict usable outcomes.
Without the assumptions being validated, a simple linear regression model created for your data will not produce accurate results. As a result, it renders the model unusable.
Testing these assumptions is the only second most important step in creating a usable linear regression model.
The tutorial is the second part of the previous tutorial on Linear Regression. Hence, it is critical that viewers watch and understand the first part before viewing this tutorial. It will aid the viewer to better comprehend the concept.
The tutorial also requires basic to moderate level of understanding of certain concepts in advanced statistics like hypothesis testing. A beginner in statistics should get themselves aware and comfortable on these concepts before viewing this video.
Chapters:
00:00 Introduction
01:16 Recap Linear Regression
01:39 Need for Assumptions
02:08 Assumptions of Linear Regression
11:18 Revisit 5 steps of Linear Regression
11:41 Assess Regression Model
11:59 Prediction using Regression Model
12:57 Caution on Prediction Model
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