# InfoCoBuild

## Regression Analysis

Regression Analysis. Instructor: Dr. Soumen Maity, Department of Mathematics, IIT Kharagpur. This course discusses topics in regression analysis: simple linear regression, multiple linear regression, selecting the best regression model, multicollinearity, model adequacy checking, test for influential observations, transformations and weighting to correct model inadequacies, dummy variables, polynomial regression models, generalized linear models, nonlinear estimation, regression models with autocorrelated errors, measurement errors and calibration problem. (from nptel.ac.in)

 Lecture 33 - Source and Effect of Autocorrelation, Detecting the Presence of Autocorrelation

Go to the Course Home or watch other lectures:

 Simple Linear Regression Lecture 01 - Course Introduction, Simple Linear Regression Lecture 02 - Useful Properties of Least Squares Fit, Statistical Properties of Least Squares Estimators Lecture 03 - Estimation of σ2, Confidence Intervals and Tests for β0 and β1 Lecture 04 - Analysis of Variance (ANOVA), Coefficient of Determination Lecture 05 - Confidence Interval of β1, Interval Estimation of the Mean Response, Prediction of New Observation Multiple Linear Regression Lecture 06 - Estimation of Model Parameters, Properties of Least Squares Estimators Lecture 07 - Hypothesis Testing in Multiple Linear Regression Lecture 08 - Example on Multiple Linear Regression Lecture 09 - Extra Sum of Squares Method, Confidence Intervals in Multiple Regression Selecting the Best Regression Model Lecture 10 - All Possible Regression Approach Lecture 11 - All Possible Regression Approach (cont.) Lecture 12 - Sequential Selection: Backward Elimination, Forward Selection Lecture 13 - Sequential Selection: Forward Selection (cont.), Stepwise Selection Multicollinearity Lecture 14 - Multicollinearity Lecture 15 - Effects of Multicollinearity (cont.), Multicollinearity Diagnostics Lecture 16 - Multicollinearity Diagnostics (cont.), Methods for Dealing with Multicollinearity Model Adequacy Checking Lecture 17 - Residuals: Regular Residuals, Standardized Residuals, Studentized Residuals Lecture 18 - PRESS Residuals, Residual Plots Lecture 19 - The Plot of Residual against the Regressor, Partial Residual Plot Test for Influential Observations Lecture 20 - Test for Influential Observations Transformation and Weighting to Correct Model Inadequacies Lecture 21 - Variance-stabilizing Transformations, Transformations to Linearize the Model Lecture 22 - Generalized and Weighted Least Square Lecture 23 - Analytic Models to Select a Transformation Dummy Variables Lecture 24 - Dummy Variables to Separate Blocks of Data Lecture 25 - Interaction Terms Involving Dummy Variables Lecture 26 - Three Sets of Data and Straight Line Models Polynomial Regression Models Lecture 27 - Polynomial Models in One Variable and Orthogonal Polynomials Lecture 28 - Piecewise Polynomial Fitting Lecture 29 - Polynomial Models in Two or More Variables Generalized Linear Models Lecture 30 - The Exponential Family of Distributions, Fitting Generalized Linear Models Lecture 31 - Generalized Linear Models (cont.) Nonlinear Estimation Lecture 32 - Nonlinear Estimation: Nonlinear Models, Least Square in Nonlinear Case Regression Models with Autocorrelated Errors Lecture 33 - Source and Effect of Autocorrelation, Detecting the Presence of Autocorrelation Lecture 34 - Parameter Estimation in the Presence of Autocorrelation Model Measurement Errors and Calibration Problem Lecture 35 - Measurement Errors and Calibration Problem Tutorials Lecture 36 - Solving Problems from Simple Linear Regression Model Lecture 37 - Solving Problems from Linear Regression Models Lecture 38 - Solving Problems Lecture 39 - Solving Problems: Coefficient of Determination, Autocorrelated Errors Lecture 40 - Nonlinear Estimation, Generalized Linear Models, Dummy Variables