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Contents
Front Matter
Introduction
1.
Linear Algebra I
2.
Linear Algebra II
3.
Model Assumptions of Linear Regression
4.
Multiple Regression - Illustration Using Gala DataSet
5.
Polynomial Regression Fitting Using Poly And Loess in R
6.
General Linear Model
7.
Least Square Estimation, Normal Equations , Error sum of squares
8.
The Algebra of Least Squares
9.
The Geometry of Least Squares
10.
Estimability,Gauss Markov I
11.
Estimability,Gauss Markov II
12.
Variance-Covariance of OLS Estimates
13.
Properties of OLS Estimators
14.
General Linear Hypotheses
15.
Distribution of Test Statistic
16.
Testing General Linear Hypothesis Using R
17.
Theory of ANOVA
18.
One - Way ANOVA Intro I
19.
One -Way ANOVA Intro II
20.
One -Way ANOVA Fixed Effects
21.
One-Way ANOVA Random Effects
22.
One-Way ANOVA Using R
23.
Two-Way ANOVA
24.
Two-Way ANOVA Random and Mixed Effects
25.
Two-Way ANOVA Random Effects
26.
Two-Way ANOVA Using R
27.
ANCOVA I
28.
ANCOVA II
29.
ANCOVA 2 Level Using R
30.
ANCOVA 3 Level Using R
31.
Scheffe's Method of Multiple Comparison
32.
Scheffe's Method using R
33.
Comparision Scheffe's and Tukey's Method of Comparision
34.
Simple Regression Introduction I
35.
Simple Regression Introduction II
Back Matter
Appendix
Regression Analysis I
7
Least Square Estimation, Normal Equations , Error sum of squares
Prof Pooja Sengupta
