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Reading Title:
Reading Author(s):
 
 
Book Title:
Book Author(s):
Chapter:
8
Page Range:
Total Pages:
38
 
 
Publisher:
Publication Year:
2006
Language:
English
 
 
 
 
FRM Paid Candidate Price:         US$10.00
Reading Price:
GARP Member (Non-Affiliate):   US$10.00
 
Affiliate & Non-Member:             US$12.00
 
* Order print copy for an additional US$2.66 + shipping & handling (select at checkout)
 
 
 
 
Quantitative Level:
Basic
 
 
Keywords:
 
 
Topics Covered:
Quantitative analysis, statistics, estimation, multiple regression model, partial regression coefficients, collinearity, parameter estimation, ordinary least squares (OLS) estimators, goodness of fit, multiple coefficient of determination R^2, hypothesis testing, test of significance approach, confidence interval approach, analysis of variance (ANOVA), specification bias, adjusted R^2, restricted least squares
 
 
Reading Abstract:
*** From the book *** Chapter 8 extends the idea of the two-variable linear regression model developed in the previous two chapters to multiple regression models, that is, models having more than one explanatory variable. Although in many ways the multiple regression model is an extension of the two-variable model, there are differences when it comes to interpreting the coefficients of the model and in the hypothesis-testing procedure.
 
 
Reading Contents:
8.1 The three-variable linear regression model
8.2 Assumptions of multiple linear regression model
8.3 Estimation of parameters of multiple regression
8.4 Goodness of fit of estimated multiple regression: multiple coefficient of determination, R^2
8.5 Antique clock auction prices revisited
8.6 Hypothesis testing in a multiple regression: general comments
8.7 Testing hypotheses about individual partial regression coefficients
8.8 Testing the joint hypothesis that B_2 = B_3 = 0 or R^2 =0
8.9 Two-variable regression in the context of multiple regression: introduction to specification bias
8.10 Comparing two R^2 values: the adjusted R^2
8.11 When to add an additional explanatory variable to a model
8.12 Restricted least squares
8.13 Illustrative examples
8.14 Summary
Key terms and concepts
Questions
Problems
Appendix 8A: Derivations of OLS estimators given in equations (8.20) and (8.22)
 
 
 
 
Book Review:
*** From the publisher ***
This text provides a simple and straightforward introduction to econometrics for the beginner. The author`s intent is to provide the student with a "user friendly," non-intimidating introduction to econometric theory and techniques. The book motivates students to understand econometric techniques through extensive examples, careful explanations, and a wide variety of problem material. The audience is undergraduate economics, agricultural economics, and business administration majors, MBA students and others in the social and behavioral sciences where econometric techniques, especially the techniques of linear regression analysis, are used.
 



 
   
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