# InfoCoBuild

## Linear Regression Analysis and Forecasting

Linear Regression Analysis and Forecasting. Instructor: Prof. Shalabh, Department of Mathematics and Statistics, IIT Kanpur. Forecasting is an important aspect of any experimental study. The forecasting can be done by finding the model between the input and output variables. The tools of linear regression analysis help in finding out a statistical model between input variables and output variable which in turn provides forecasting. For example, the yield of a crop depends upon the area of crop, quantity of seeds, rainfall etc. The statistical relation between yield and area of crop, quantity of seeds, rainfall etc. can be determined by the regression analysis and forecasting can be done to know the yield in future. The accuracy of forecasting depends upon the goodness of obtained model. What are its steps and checks required to obtain a good model and in turn, how to do forecasting is being aimed to be taught in this course. (from nptel.ac.in)

 Lecture 20 - Software Implementation of Multiple Linear Regression Model using MINITAB (cont.)

This lecture includes the following topics: 1. How to check normality assumption in multiple linear regression model; 2. Detecting outliers, leverage points and influential points in multiple linear regression model using MINITAB.

Go to the Course Home or watch other lectures:

 Lecture 01 - Basic Fundamental Concepts of Modeling Lecture 02 - Regression Model - A Statistical Tool Lecture 03 - Simple Linear Regression Analysis Lecture 04 - Estimation of Parameters in Simple Linear Regression Model Lecture 05 - Estimation of Parameters in Simple Linear Regression Model (cont.) Lecture 06 - Estimation of Parameters in Simple Linear Regression Model (cont.) Lecture 07 - Maximum Likelihood Estimation of Parameters in Simple Linear Regression Model Lecture 08 - Testing of Hypothesis and Confidence Interval Estimation in Simple Linear Regression Model Lecture 09 - Testing of Hypothesis and Confidence Interval Estimation in Simple Linear Regression Model (cont.) Lecture 10 - Software Implementation in Simple Linear Regression Model using MINITAB Lecture 11 - Multiple Linear Regression Model Lecture 12 - Estimation of Model Parameters in Multiple Linear Regression Model Lecture 13 - Estimation of Model Parameters in Multiple Linear Regression Model (cont.) Lecture 14 - Standardized Regression Coefficients and Testing of Hypothesis Lecture 15 - Testing of Hypothesis (cont.), Goodness of Fit of the Model Lecture 16 - Diagnostics in Multiple Linear Regression Model Lecture 17 - Diagnostics in Multiple Linear Regression Model (cont.) Lecture 18 - Diagnostics in Multiple Linear Regression Model (cont.) Lecture 19 - Software Implementation of Multiple Linear Regression Model using MINITAB Lecture 20 - Software Implementation of Multiple Linear Regression Model using MINITAB (cont.) Lecture 21 - Forecasting in Multiple Linear Regression Model Lecture 22 - Within Sample Forecasting Lecture 23 - Outside Sample Forecasting Lecture 24 - Software Implementation of Forecasting using MINITAB