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.

About the course
  • 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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