Course Contents

Logic of Regression and correlation, scatter diagram, simple linear regression model, least square estimators of parameters and their properties for simple linear regression model, standard error of estimate. Inference about parameters. Multiple linear regression with two regressors. Simple, multiple and partial correlation up to three variables and their testing. Coefficient of multiple determination. Meaning, derivation and application of rank correlation, tied ranks. Concept of analysis of variance. Testing the equality of several means by analysis of variances for one-way classification (equal and unequal number of observations) and two-way classifications. Partitioning of Sum of Squares and Degrees of freedom. Inference about means after ANOVA: The LSD test, Duncan’s test, Newman Keul’s test, Tuckey’s test, Scheffe’s test, Orthogonal contrast test. Basic concept, planning and principles of experimental design, Completely Randomized, Randomized Complete Block and Latin Square Designs: their descriptions, layout, statistical analysis, advantages and disadvantages. Covariance analysis for completely randomized, randomized complete block and Latin square designs; single covariate (Application only). Case Study: Analyze the real data and present the work in the form of report.

Course Synopsis

Introduction to simple and multiple regression and correlation. Introduction to ANOVA and Experimental Designs.

Course Learning Outcomes

1. The students would be able to handle linear and multiple linear modeling on real life data 2. They can easily understand how to form structure of dependence through modeling 3. Students would be able to handle multiple treatments or can check the efficiency of multiple treatments for experimental data 4. They would be able to check the effectiveness of uncontrolled variables in study.