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Summary: This reader-friendly book focuses on building linear statistical models and developing skills for implementing regression analysis in real-life situations. It includes applications for a range of fields including engineering, sociology, and psychology, as well as traditional business applications. The authors use the latest material available from news articles, magazines, professional journals, the Internet, and actual consulting problems to illustrate real business ...show more situations and how to solve them using the tools of regression analysis. In addition, this book emphasizes model building and multiple regression models and pays special attention to model validation and spline regression. For professionals in any number of fields, including engineering, sociology, and psychology, who would benefit from learning how to use regression analysis to solve problems. --This text refers to an alternate Hardcover edition. ...show lessEdition/Copyright: 7TH 11
Table of Contents
1. A Review of Basic Concepts (Optional)
1.1 Statistics and Data
1.2 Populations, Samples, and Random Sampling
1.3 Describing Qualitative Data
1.4 Describing Quantitative Data Graphically
1.5 Describing Quantitative Data Numerically
1.6 The Normal Probability Distribution
1.7 Sampling Distributions and the Central Limit Theorem
1.8 Estimating a Population Mean
1.9 Testing a Hypothesis About a Population Mean
1.10 Inferences About the Difference Between Two Population Means
1.11 Comparing Two Population Variances
2. Introduction to Regression Analysis
2.1 Modeling a Response
2.2 Overview of Regression Analysis
2.3 Regression Applications
2.4 Collecting the Data for Regression
3. Simple Linear Regression
3.2 The Straight-Line Probabilistic Model
3.3 Fitting the Model: The Method of Least Squares
3.4 Model Assumptions
3.5 An Estimator of ?2
3.6 Assessing the Utility of the Model: Making Inferences About the Slope ?1
3.7 The Coefficient of Correlation
3.8 The Coefficient of Determination
3.9 Using the Model for Estimation and Prediction
3.10 A Complete Example
3.11 Regression Through the Origin (Optional)
Case Study 1: Legal Advertising--Does It Pay?
4. Multiple Regression Models
4.1 General Form of a Multiple Regression Model
4.2 Model Assumptions
4.3 A First-Order Model with Quantitative Predictors
4.4 Fitting the Model: The Method of Least Squares
4.5 Estimation of ?2, the Variance of ?
4.6 Testing the Utility of a Model: The Analysis of Variance F-Test
4.7 Inferences About the Individual ? Parameters
4.8 Multiple Coefficients of Determination: R2 and R2adj
4.9 Using the Model for Estimation and Prediction
4.10 An Interaction Model with Quantitative Predictors
4.11 A Quadratic (Second-Order) Model with a Quantitative Predictor
4.12 More Complex Multiple Regression Models (Optional)
4.13 A Test for Comparing Nested Models
4.14 A Complete Example
Case Study 2: Modeling the Sale Prices of Residential Properties in Four Neighborhoods
5. Principles of Model Building
5.1 Introduction: Why Model Building is Important
5.2 The Two Types of Independent Variables: Quantitative and Qualitative
5.3 Models with a Single Quantitative Independent Variable
5.4 First-Order Models with Two or More Quantitative Independent Variables
5.5 Second-Order Models with Two or More Quantitative Independent Variables
5.6 Coding Quantitative Independent Variables (Optional)
5.7 Models with One Qualitative Independent Variable
5.8 Models with Two Qualitative Independent Variables
5.9 Models with Three or More Qualitative Independent Variables
5.10 Models with Both Quantitative and Qualitative Independent Variables
5.11 External Model Validation
6. Variable Screening Methods
6.1 Introduction: Why Use a Variable-Screening Method?
6.2 Stepwise Regression
6.3 All-Possible-Regressions Selection Procedure
Case Study 3: Deregulation of the Intrastate Trucking Industry
7. Some Regression Pitfalls
7.2 Observational Data Versus Designed Experiments
7.3 Parameter Estimability and Interpretation
7.5 Extrapolation: Predicting Outside the Experimental Region
7.6 Variable Transformations
8. Residual Analysis
8.2 Plotting Residuals
8.3 Detecting Lack of Fit
8.4 Detecting Unequal Variances
8.5 Checking the Normality Assumption
8.6 Detecting Outliers and Identifying Influential Observations
8.7 Detection of Residual Correlation: The Durbin-Watson Test
Case Study 4: An Analysis of Rain Levels in California
Case Study 5: An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction
9. Special Topics in Regression (Optional)
9.2 Piecewise Linear Regression
9.3 Inverse Prediction
9.4 Weighted Least Squares
9.5 Modeling Qualitative Dependent Variables
9.6 Logistic Regression
9.7 Ridge Regression
9.8 Robust Regression
9.9 Nonparametric Regression Models
10. Introduction to Time Series Modeling and Forecasting
10.1 What is a Time Series?
10.2 Time Series Components
10.3 Forecasting Using Smoothing Techniques (Optional)
10.4 Forecasting: The Regression Approach
10.5 Autocorrelation and Autoregressive Error Models
10.6 Other Models for Autocorrelated Errors (Optional)
10.7 Constructing Time Series Models
10.8 Fitting Time Series Models with Autoregressive Errors
10.9 Forecasting with Time Series Autoregressive Models
10.10 Seasonal Time Series Models: An Example
10.11 Forecasting Using Lagged Values of the Dependent Variable (Optional)
Case Study 6: Modeling Daily Peak Electricity Demands
11. Principles of Experimental Design
11.2 Experimental Design Terminology
11.3 Controlling the Information in an Experiment
11.4 Noise-Reducing Designs
11.5 Volume-Increasing Designs
11.6 Selecting the Sample Size
11.7 The Importance of Randomization
12. The Analysis of Variance for Designed Experiments
12.2 The Logic Behind an Analysis of Variance
12.3 One-Factor Completely Randomized Designs
12.4 Randomized Block Designs
12.5 Two-Factor Factorial Experiments
12.6 More Complex Factorial Designs (Optional)
12.7 Follow-Up Analysis: Tukey's Multiple Comparisons of Means
12.8 Other Multiple Comparisons Methods (Optional)
12.9 Checking ANOVA Assumptions
Case Study 7: Reluctance to Transmit Bad News: The MUM Effect
Appendix A: Derivation of the Least Squares Estimates of ?0 and ?1 in Simple Linear Regression
Appendix B: The Mechanics of a Multiple Regression Analysis
B.2 Matrices and Matrix Multiplication
B.3 Identity Matrices and Matrix Inversion
B.4 Solving Systems of Simultaneous Linear Equations
B.5 The Least Squares Equations and Their Solution
B.6 Calculating SSE and s2
B.7 Standard Errors of Estimators, Test Statistics, and Confidence Intervals for ?0, ?1, ... , ?k
B.8 A Confidence Interval for a Linear Function of the ? Parameters; A Confidence Interval for E(y)
B.9 A Prediction Interval for Some Value of y to be Observed in the Future
Appendix C: A Procedure for Inverting a Matrix
Appendix D: Statistical Tables
Table D.1: Normal Curve Areas
Table D.2: Critical Values for Student's t
Table D.3: Critical Values for the F Statistic: F.10
Table D.4: Critical Values for the F Statistic: F.05
Table D.5: Critical Values for the F Statistic: F.025
Table D.6: Critical Values for the F Statistic: F.01
Table D.7: Random Numbers
Table D.8: Critical Values for the Durbin-Watson d Statistic (? =.05)
Table D.9: Critical Values for the Durbin-Watson d Statistic (? =.01)
Table D.10: Critical Values for the X2-Statistic
Table D.11: Percentage Points of the Studentized Range, q(p,v), Upper 5%
Table D.12: Percentage Points of the Studentized Range, q(p,v), Upper 1%
Appendix E: File Layouts for Case Study Data Sets
Answers to Selected Odd Numbered Exercises
Technology Tutorials: SAS, SPSS, MINITAB, and R (on CD)
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