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Applied Regression Modeling A Business Approach

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ISBN-10: 0471970336

ISBN-13: 9780471970330

Edition: 2006

Authors: Iain Pardoe

List price: $140.00
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Description:

Iain Pardoe's text offers a thorough introduction to regression modelling & constructs a general framework for building multiple regression models. SPSS, Excel & R software discussions are incorporated.
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Book details

List price: $140.00
Copyright year: 2006
Publisher: John Wiley & Sons, Incorporated
Publication date: 7/21/2006
Binding: Hardcover
Pages: 324
Size: 7.25" wide x 10.25" long x 0.75" tall
Weight: 1.584
Language: English

Preface
Acknowledgments
Introduction
Statistics in business
Learning statistics
Foundations
Identifying and summarizing data
Population distributions
Selecting individuals at random-probability
Random sampling
Central limit theorem-normal version
Student's t-distribution
Central limit theorem-t version
Interval estimation
Hypothesis testing
The rejection region method
The p-value method
Hypothesis test errors
Random errors and prediction
Chapter summary
Problems
Simple linear regression
Probability model for X and Y
Least squares criterion
Model evaluation
Regression standard error
Coefficient of determination-R[superscript 2]
Slope parameter
Model assumptions
Checking the model assumptions
Model interpretation
Estimation and prediction
Confidence interval for the population mean, E(Y)
Prediction interval for an individual Y-value
Chapter summary
Review example
Problems
Multiple linear regression
Probability model for (X[subscript 1],X[subscript 2],...) and Y
Least squares criterion
Model evaluation
Regression standard error
Coefficient of determination-R[superscript 2]
Regression parameters-global usefulness test
Regression parameters-nested model test
Regression parameters-individual tests
Model assumptions
Checking the model assumptions
Model interpretation
Estimation and prediction
Confidence interval for the population mean, E(Y)
Prediction interval for an individual Y-value
Chapter summary
Problems
Regression model building I
Transformations
Natural logarithm transformation for predictors
Polynomial transformation for predictors
Reciprocal transformation for predictors
Natural logarithm transformation for the response
Transformations for the response and predictors
Interactions
Qualitative predictors
Qualitative predictors with two levels
Qualitative predictors with three or more levels
Chapter summary
Problems
Regression model building II
Influential points
Outliers
Leverage
Cook's distance
Regression pitfalls
Autocorrelation
Multicollinearity
Excluding important predictor variables
Overfitting
Extrapolation
Missing Data
Model building guidelines
Model interpretation using graphics
Chapter summary
Problems
Case studies
Home prices
Data description
Exploratory data analysis
Regression model building
Results and conclusions
Further questions
Vehicle fuel efficiency
Data description
Exploratory data analysis
Regression model building
Results and conclusions
Further questions
Extensions
Generalized linear models
Logistic regression
Poisson regression
Discrete choice models
Multilevel models
Bayesian modeling
Frequentist inference
Bayesian inference
Computer software help
SPSS
Getting started and summarizing univariate data
Simple linear regression
Multiple linear regression
Minitab
Getting started and summarizing univariate data
Simple linear regression
Multiple linear regression
SAS
Getting started and summarizing univariate data
Simple linear regression
Multiple linear regression
R and S-PLUS
Getting started and summarizing univariate data
Simple linear regression
Multiple linear regression
Excel
Getting started and summarizing univariate data
Simple linear regression
Multiple linear regression
Problems
Critical values for t-distributions
Notation and formulas
Univariate data
Simple linear regression
Multiple linear regression
Mathematics refresher
The natural logarithm and exponential functions
Rounding and accuracy
Brief answers to selected problems
References
Glossary
Index