# Regression Analysis/Predictive Modelling

Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is a powerful statistical method that allows you to examine the influence of one or more independent variables on a dependent variable.

• As part of the Regression Analysis Program, we are going to teach on
1. Determine the independent and dependent variables
2. Assumptions of the Dependent variables are satisfied like
(Linearity, Normality, Equal Variance, Independence)
3. How to obtained a good model using different statistical techniques.
4. Testing the Model using F-test
5. Testing of the slope that it is significantly different from 0
6. Mean Estimation and Individual Prediction.
• Eligibility : MS/M.Tech/B.E/B.Tech/M.C.A/M.B.A/M.sc in Science/M.com
• Prerequisite: Knowledge in Descriptive & Inferential Statistics
• Duration of the Course : 60 Hours
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Course Details

Tools/Languages :  Course will be explained using R & Minitab

 Fundamentals of R programming for Data Science. Introduction to Regression Analysis Simple Linear Regression Least Square Estimation of Parameters Coefficient Determination & Problems Introduction to Multiple Linear Regression Estimation of Model Parameters Hypothesis testing in MLR About Multi-Collinearity  &  Problems Introduction to Model Adequacy Checking Residual Analysis & PRESS Statistic Detection of Treatment of Outliers & Problems Introduction to Transformations Variance Stabilizing Transformations Transformations to Linearize the Model Box-Cox  & Tidwell Transformations Generalized & Least Squares Methods & Problems Diagnosis for Leverage and influential Points Treatment of Influential Observations Introduction to Polynomial Regression Polynomial Model with One, Two & More Variables Forward Selection, Backward Elimination, Stepwise  Regression Problem solving techniques

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Updated Thu, 29-Oct-2020
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