Applied Regression Modeling

ISBN-10: 1118097289
ISBN-13: 9781118097281
Edition: 2nd 2012
Authors: Iain Pardoe
List price: $136.95 Buy it from $85.12 Rent it from $40.12
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Description: This book offers a practical, concise introduction to regression analysis for upper-level undergraduate students of diverse disciplines including, but not limited to statistics, the social and behavioral sciences, MBA, and vocational studies. The  More...

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Book details

List price: $136.95
Edition: 2nd
Copyright year: 2012
Publisher: John Wiley & Sons Canada, Limited
Publication date: 7/23/2012
Binding: Hardcover
Pages: 346
Size: 7.25" wide x 10.00" long x 1.00" tall
Weight: 1.694
Language: English

This book offers a practical, concise introduction to regression analysis for upper-level undergraduate students of diverse disciplines including, but not limited to statistics, the social and behavioral sciences, MBA, and vocational studies. The book's overall approach is strongly based on an abundant use of illustrations, examples, case studies, and graphics. It emphasizes major statistical software packages, including SPSS®, Minitab®, SAS®, R, and R/S-PLUS®. Detailed instructions for use of these packages, as well as for Microsoft Office Excel®, are provided on a specially prepared and maintained author web site. Select software output appears throughout the text. To help readers understand, analyze, and interpret data and make informed decisions in uncertain settings, many of the examples and problems use real-life situations and settings. The book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series and forecasting. New to this edition are more exercises, simplification of tedious topics (such as checking regression assumptions and model building), elimination of repetition, and inclusion of additional topics (such as variable selection methods, further regression diagnostic tests, and autocorrelation tests).

Preface
Acknowledgments
Introduction
Statistics in practice
Learning statistics
Foundations
Identifying and summarizing data
Population distributions
Selecting individuals at random-probability
Random sampling
Central limit theorem-normal version
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<sup>2</sup>
Slope parameter
Model assumptions
Checking the model assumptions
Testing 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<sub>1</sub>, X<sub>2</sub>,...) and Y
Least squares criterion
Model evaluation
Regression standard error
Coefficient of determination-R<sup>2</sup>
Regression parameters-global usefulness test
Regression parameters-nested model test
Regression parameters-individuals tests
Model assumptions
Checking the model assumptions
Testing 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
Nonconstant variance
Autocorrelation
Multicollinearity
Excluding important predictor varibales
Overfitting
Extrapolations
Missing data
Power and sample size
Model building guidelines
Model selection
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
Pharmaceutical patches
Data description
Exploratory data analysis
Regression model building
Model diagnostics
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
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
Answers for selected problems
References
Glossary
Index

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