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Regression Analysis: Statistical Modeling of a Response Variable
 
 
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Regression Analysis: Statistical Modeling of a Response Variable [Hardcover]

William J. Wilson (Author)
5.0 out of 5 stars  See all reviews (1 customer review)


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Book Description

0122674758 978-0122674754 January 13, 1998 1st
Regression Analysis provides students with the skills and techniques necessary for the intelligent statistical analysis of a response variable. The book underscores statistical concepts, de-emphasizes formulas, and incorporates real data from the most popular package at this level, SAS. Regression Analysis also covers other linear models such as analysis of variance, analysis of covariance, analysis of a binary response variable, as well as an introduction to nonlinear regression.


* Discusses the analysis of data including estimation, diagnostics, and remedial actions
* Includes examples and exercises which contain both real and simulated data
* Emphasizes computer implementation and methods
* Provides many references for related and more advanced methods
* Supported by instructor's manual containing data sets on disk

Product Details

  • Hardcover: 496 pages
  • Publisher: Academic Press; 1st edition (January 13, 1998)
  • Language: English
  • ISBN-10: 0122674758
  • ISBN-13: 978-0122674754
  • Product Dimensions: 9.6 x 7.8 x 1.1 inches
  • Shipping Weight: 2.3 pounds
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #2,750,542 in Books (See Top 100 in Books)

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5 of 7 people found the following review helpful:
5.0 out of 5 stars 90's Update to a Classic Procedure, March 4, 1999
By A Customer
This review is from: Regression Analysis: Statistical Modeling of a Response Variable (Hardcover)
Freund & Wilson have taken a classical statistical procedure and breathed new life into it. Covering the basics at an understandable level, the text covers all current and traditional topic areas. From Multipe Regression to PCA regression with a tinge of Ridge Regression. Traditional trouble areas are adequately dealt with. The novice reader will have no trouble in getting up to speed and not have any of the formula hemmoraging normally associated with MRA. For you mathematicians take a break and go do a derivative elsewhere. A Dummies book for the rest of us. 5 stars and 6 bottles of beer for this one. Good job fellas!
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Inside This Book (learn more)
First Sentence:
In this chapter we review the statistical methods for inferences on means using samples from one, two, and several populations. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
incomplete principal component regression, model error mean square, overall model statistics, restricted error sum, model error sum, total regression coefficients, outlier scenarios, unequal cell frequencies, segmented polynomials, leverage plots, deterministic portion, principal component variables, dummy variable approach, categorical response variable, resulting mean square, dummy variable model, general linear hypothesis, unrestricted model, individual independent variables, remedial methods, partial coefficient, influential observations, variable selection procedures, variance proportions, residual standard deviation
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Value Prob, Adj R-sq, Dep Mean, Parameter Estimate Standard Error, Parameter Estimates Variable, Var Prop, Estimate Error, Parameter Estimates Parameter Standard, Squares Square, District of Columbia, Sum of Mean Source, Appendix Table, Statistical Abstract of the United States, Value Model, Parameter Estimate Asymptotic Std, Std Error of Estimate, Chi-Square Prob, Two Means Using Independent Samples, Adi R-sq, Confidence Interval Lower Upper, Optional Topic, Predict Value Std Err Predict, Total Normal
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