Syllabus
Simple Linear Regression: Linear Regression Model, Least Square Estimation of the Parameters, Hypothesis Testing on the Slope and Intercept, Interval Estimation in Simple Linear Regression, Prediction of New Observations and Coefficient of Determination. Estimation by Maximum Likelihood. Model with Random Regressor.
Multiple Linear Regression: Multiple Linear Regression Models, Estimation of the Model Parameters, Hypothesis testing in Multiple Linear Regression, Confidence Interval on the Regression and Prediction of New observations.
Model Adequacy Checking: Introduction, Residual Analysis, PRESS Statistic, Lack of fit of the Regression Model. Influential Observations, Leverage, Measure of Influence, Cook’s D.
Polynomial Regression Models: Polynomial Models in one variable, Piecewise Polynomial Fitting-Splines, Polynomial Models in two or more variables,
Generalized Regression Models: Logistic Regression Models, Poisson Regression.
Regression Analysis of Time Series Data: Detecting Autocorrelation, The Durbin-Watson Test, Estimating the Parameters in Time Series Regression Models.
Case studies with different data sets.