Introductory Regression Analysis

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A01=Allen Webster
applied statistics
Author_Allen Webster
Auxiliary Equation
business data analytics
business math
business statistics
Category=GPS
Category=JHB
Category=KCH
Category=KJMV5
Category=KJT
Category=PBT
Category=PBW
Cochrane Orcutt Method
Coefficients Standard Error
Data Set
decision sciences
Distributed Lag Models
Double Exponential Smoothing
Double Log Model
Dummy Variables
Durbin Watson Statistic
Durbin Wu Hausman Test
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Error Term
Exponential Smoothing
Glejser Test
LAGRANGE Multiplier Test
LPM
Minitab Output
model diagnostics
multivariate analysis
OLS Estimate
Predictor Coef SE Coef
quantitative methods
Reduced Form Equations
regression analysis
regression methods for economic research
Regression Model
residual plots
RHS Variable
simultaneous equations modeling
Single Exponential Smoothing
SS SS
Successive Error Terms
Time Series

Product details

  • ISBN 9780415899321
  • Weight: 1080g
  • Dimensions: 203 x 254mm
  • Publication Date: 03 Dec 2012
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Regression analysis is arguably the single most powerful and widely applicable tool in any effective examination of common business issues. Every day, decision-makers face problems that require constructive actions with significant consequences, and regression procedures can prove a meaningful and valuable asset in the decision-making process. This text is designed to help students achieve a full understanding of regression and the many ways it can be used.

Taking into consideration current statistical technology, Introductory Regression Analysis focuses on the use and interpretation of software, while also demonstrating the logic, reasoning, and calculations that lie behind any statistical analysis. Furthermore, the text emphasizes the application of regression tools to real-life business concerns. This multilayered, yet pragmatic approach fully equips students to derive the benefit and meaning of a regression analysis.

This text is designed to serve in a second undergraduate course in statistics, focusing on regression and its component features. The material presented in this text will build from a foundation of the principles of data analysis. Although previous exposure to statistical concepts would prove helpful, all the material needed for an examination of regression analysis is presented here in a clear and complete form.

Allen Webster is a Professor at Bradley University. He gained his Ph.D. in Economics from Florida State University, and both an M.S. and B.S. in Economics from Fort Hays State University.

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