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Contents
Front Matter
Introduction
1.
Multiple Regression Introduction I
2.
Multiple Regression Introduction II
3.
Exercise on Multiple Linear Regression I
4.
Exercise on Multiple Linear Regression II
5.
Regression With Dummy Variables I
6.
Regression With Dummy Variables II
7.
Piece-Wise regression-Spline Functions
8.
Comparing Two Regression Models Using Dummy Variables
9.
Box Cox Transformation
10.
Detection of Outliers I
11.
Detection of Outliers II
12.
Detection of Outliers Using R
13.
OLS Versus GLS
14.
Heteroscedasticity 1
15.
Heteroscedasticity 2
16.
Autocorrelation in Regression I
17.
Autocorrelation in Regression II
18.
Exercise on Autocorrelation
19.
Multicollinearity 1
20.
Multicollinearity 2
21.
Multicollinearity 3
22.
Multicollinearity 4
23.
Exercise on Multicollinearity
24.
Normality of Errors 1
25.
Normality of Errors 2
26.
Variable Selection I
27.
Variable Selection II
28.
Lag Variable Models I
29.
Lag Variable Models II
30.
Unobservable Variable 1
31.
Unobservable Variable 2
32.
Unobservable Variable 3
33.
Tobit Model
34.
Tobit Model Using R
35.
Application of GLM
36.
Regression Analysis II
Back Matter
Appendix
Regression Analysis II
4
Exercise on Multiple Linear Regression II
Prof Pooja Sengupta
