A Second Course in Statistics: Regression Analysis: United States Edition
Autor William Mendenhall, Terry L. Sincichen Limba Engleză Mixed media product – 3 mar 2003
This very applied text focuses on building linear statistical models and developing skills for implementing regression analysis in real-life situations. It includes applications for engineering, sociology, psychology, etc., as well as traditional business applications. The authors use material from news articles, magazines, professional journals, and actual consulting problems to illustrate real-world problems and how to solve them using the tools of regression analysis.
Datasets and other resources (where applicable) for this book are available here.
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Specificații
ISBN-13: 9780130223234
ISBN-10: 0130223239
Pagini: 852
Greutate: 1.61 kg
Ediția:6Nouă
Editura: Pearson Education
Colecția Pearson Education
Locul publicării:Upper Saddle River, United States
ISBN-10: 0130223239
Pagini: 852
Greutate: 1.61 kg
Ediția:6Nouă
Editura: Pearson Education
Colecția Pearson Education
Locul publicării:Upper Saddle River, United States
Cuprins
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
3.1 Introduction
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 s2
3.6 Assessing the Utility of the Model: Making Inferences About the Slope ß1
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)
3.12 A Summary of the Steps to Follow in a Simple Linear Regression Analysis
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 s2 , the variance of e
4.6 Inferences about the ß parameters
4.7 The Multiple Coefficient of Determination, R2
4.8 Testing the Utility of a Model: The Analysis of Variance F test
4.9 An Interaction Model with Quantitative Predictors
4.10 A Quadratic (Second-Order) Model with a Quantitative Predictor
4.11 Using the model for Estimation and Prediction
4.12 More Complex Multiple Regression Models (Optional)
4.13 A Test for Comparing Nested Models
4.14 A Complete Example
4.15 A Summary of the Steps to Follow in a Multiple Regression Analysis
5. 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 (Optional)
5.12 Model Building: An Example
6. Variable Screening Methods
6.1 Introduction: Why Use a Variable Screening Method?
6.2 Stepwise Regression
6.3 All-Posssible-Regressions Selection Procedure
6.4 Caveats
7. Some Regression Pitfalls
7.1 Introduction
7.2 Observational DataVersus Designed Experiments
7.3 Deviating from the Assumptions
7.4 Parameter Estimability and Interpretation
7.5 Multicollinearity
7.6 Extrapolation: Predicting Outside the Experimental Region
7.7 Data Transformations
8. Residual Analysis
8.1 Introduction
8.2 Plotting Residuals and Detecting Lack of Fit
8.3 Detecting Unequal Variances
8.4 Checking the Normality Assumption
8.5 Detecting Outliers and Identifying Influential Observations
8.6 Detecting Residual Correlation: The Durbin-Watson Test
9. Special Topics in Regression (Optional)
9.1 Introduction
9.2 Piecewise Linear Regression
9.3 Inverse Prediction
9.4 Weighted Least Squares
9.5 Modeling Qualitative Dependent Variable
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)
11. Principles of Experimental Design
11.1 Introduction
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
12.1 Introduction
12.2 The Logic Behind 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
13. CASE STUDY: Modeling the Sale Prices of Residential Properties in Four Neighborhoods
13.1 The Problem
13.2 The Data
13.3 The Theoretical Model
13.4 The Hypothesized Regression Models
13.5 Model Comparisons
13.6 Interpreting the Prediction Equation
13.7 Predicting the Sale Price of a Property
13.8 Conclusions
14. CASE STUDY: An Analysis of Rain Levels in California
14.1 The Problem
14.2 The Data
14.3 A Model for Average Annual Precipitation
14.4 A Residual Analysis of the Model
14.5 Adjustments to the Model
14.6 Conclusions
15. CASE STUDY: Reluctance to Transmit Bad News: the MUM Effect
15.1 The Problem
15.2 The Design
15.3 Analysis of Variance Models and Results
15.4 Follow up Analysis
15.5 Conclusions
16. CASE STUDY: An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction
16.1 The Problem
16.2 The Data
16.3 The Models
16.4 The Regression Analyses
16.5 An Analysis of the Residuals form Model 3
16.6 What the Model 3 Regression Analysis Tells Us
16.7 Comparing the Mean Sale Price for Two Types of Units (Optional)
16.8 Conclusions
17. CASE STUDY: Modeling Daily Peak Electricity Demands
17.1 The Problem
17.2 The Data
17.3 The Models
17.4 The Regression and Autoregression Analyses
17.5 Forecasting Daily Peak Electricity Demand
17.6 Conclusions
Appendix A: The Mechanics of a Multiple Regression Analysis.
Appendix B: A Procedure for Inverting a Matrix.
Appendix C: Statistical Tables.
Appendix D: SAS for Windows Tutorial.
Appendix E: SPSS for Windows Tutorial.
Appendix F: MINITAB for Windows Tutorial.
Appendix G: Sealed Bid Data for Fixed and Competitive Highway Construction Contracts.
Appendix H: Real Estate Appraisals and Sales Data for Six Neighborhoods in Tampa, Florida.
Appendix I: Condominium Sales Data.
Answers to Odd-Numbered Exercises.
Index.
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
3.1 Introduction
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 s2
3.6 Assessing the Utility of the Model: Making Inferences About the Slope ß1
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)
3.12 A Summary of the Steps to Follow in a Simple Linear Regression Analysis
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 s2 , the variance of e
4.6 Inferences about the ß parameters
4.7 The Multiple Coefficient of Determination, R2
4.8 Testing the Utility of a Model: The Analysis of Variance F test
4.9 An Interaction Model with Quantitative Predictors
4.10 A Quadratic (Second-Order) Model with a Quantitative Predictor
4.11 Using the model for Estimation and Prediction
4.12 More Complex Multiple Regression Models (Optional)
4.13 A Test for Comparing Nested Models
4.14 A Complete Example
4.15 A Summary of the Steps to Follow in a Multiple Regression Analysis
5. 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 (Optional)
5.12 Model Building: An Example
6. Variable Screening Methods
6.1 Introduction: Why Use a Variable Screening Method?
6.2 Stepwise Regression
6.3 All-Posssible-Regressions Selection Procedure
6.4 Caveats
7. Some Regression Pitfalls
7.1 Introduction
7.2 Observational DataVersus Designed Experiments
7.3 Deviating from the Assumptions
7.4 Parameter Estimability and Interpretation
7.5 Multicollinearity
7.6 Extrapolation: Predicting Outside the Experimental Region
7.7 Data Transformations
8. Residual Analysis
8.1 Introduction
8.2 Plotting Residuals and Detecting Lack of Fit
8.3 Detecting Unequal Variances
8.4 Checking the Normality Assumption
8.5 Detecting Outliers and Identifying Influential Observations
8.6 Detecting Residual Correlation: The Durbin-Watson Test
9. Special Topics in Regression (Optional)
9.1 Introduction
9.2 Piecewise Linear Regression
9.3 Inverse Prediction
9.4 Weighted Least Squares
9.5 Modeling Qualitative Dependent Variable
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)
11. Principles of Experimental Design
11.1 Introduction
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
12.1 Introduction
12.2 The Logic Behind 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
13. CASE STUDY: Modeling the Sale Prices of Residential Properties in Four Neighborhoods
13.1 The Problem
13.2 The Data
13.3 The Theoretical Model
13.4 The Hypothesized Regression Models
13.5 Model Comparisons
13.6 Interpreting the Prediction Equation
13.7 Predicting the Sale Price of a Property
13.8 Conclusions
14. CASE STUDY: An Analysis of Rain Levels in California
14.1 The Problem
14.2 The Data
14.3 A Model for Average Annual Precipitation
14.4 A Residual Analysis of the Model
14.5 Adjustments to the Model
14.6 Conclusions
15. CASE STUDY: Reluctance to Transmit Bad News: the MUM Effect
15.1 The Problem
15.2 The Design
15.3 Analysis of Variance Models and Results
15.4 Follow up Analysis
15.5 Conclusions
16. CASE STUDY: An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction
16.1 The Problem
16.2 The Data
16.3 The Models
16.4 The Regression Analyses
16.5 An Analysis of the Residuals form Model 3
16.6 What the Model 3 Regression Analysis Tells Us
16.7 Comparing the Mean Sale Price for Two Types of Units (Optional)
16.8 Conclusions
17. CASE STUDY: Modeling Daily Peak Electricity Demands
17.1 The Problem
17.2 The Data
17.3 The Models
17.4 The Regression and Autoregression Analyses
17.5 Forecasting Daily Peak Electricity Demand
17.6 Conclusions
Appendix A: The Mechanics of a Multiple Regression Analysis.
Appendix B: A Procedure for Inverting a Matrix.
Appendix C: Statistical Tables.
Appendix D: SAS for Windows Tutorial.
Appendix E: SPSS for Windows Tutorial.
Appendix F: MINITAB for Windows Tutorial.
Appendix G: Sealed Bid Data for Fixed and Competitive Highway Construction Contracts.
Appendix H: Real Estate Appraisals and Sales Data for Six Neighborhoods in Tampa, Florida.
Appendix I: Condominium Sales Data.
Answers to Odd-Numbered Exercises.
Index.
Caracteristici
- NEW - More computer printouts—An SAS, SPSS, or MINITAB printout now accompanies every statistical technique presented.
- Allows the instructor to emphasize interpretations of the statistical results rather than the calculations required to obtain the results.
- Allows the instructor to emphasize interpretations of the statistical results rather than the calculations required to obtain the results.
- NEW - Statistical software tutorials—Includes basic instructions on how to use the Windows versions of SAS, SPSS, and MINTAB in the appendix.
- Gives students step-by-step instructions and screen shots for each method presented.
- Gives students step-by-step instructions and screen shots for each method presented.
- NEW - A new section in Chapter 1 on graphical and numerical methods of describing qualitative data.
- Provides students with tools for exploring categorical data.
- Provides students with tools for exploring categorical data.
- NEW - New material in Chapter 1 on comparing two population means using a paired difference experiment.
- Presents situations where paired design is more appropriate than independent sampling.
- Presents situations where paired design is more appropriate than independent sampling.
- NEW - Reorganization of topics—Including new placement of multiple regression models, model validation, and spline regression.
- Presents these topics to students in a more logical fashion.
- Presents these topics to students in a more logical fashion.
- NEW - Accompanying data CD-ROM—Contains files for all data sets marked with a CD icon in the text.
- Gives students the data they need, ready for analysis.
- Gives students the data they need, ready for analysis.
- NEW - Updated case study chapters with accompanying data sets.
- Shows students how regression is used to answer a real-life, practical question using the latest data available.
- Shows students how regression is used to answer a real-life, practical question using the latest data available.
- NEW - Updated real-data based examples and exercises.
- Shows students the relevance of the material being studied by using the latest data extracted from news articles, journal articles, magazines, and the Internet.
- Shows students the relevance of the material being studied by using the latest data extracted from news articles, journal articles, magazines, and the Internet.
- NEW - 50% new real-data problems.
- Gives students ample opportunity to practice the concepts learned using the most current data available.
- Gives students ample opportunity to practice the concepts learned using the most current data available.
- Readability.
- Explains concepts to students in a logical, intuitive manner.
- Explains concepts to students in a logical, intuitive manner.
- Emphasis on model building.
- Gives students an in-depth treatment of this fundamental topic throughout the text.
- Gives students an in-depth treatment of this fundamental topic throughout the text.
- Emphasis on developing skills to use regression analysis.
- Teaches students how to use regression analysis as a problem-solving tool and how to apply regression analysis to real-life situations.
- Teaches students how to use regression analysis as a problem-solving tool and how to apply regression analysis to real-life situations.
Caracteristici noi
- More computer printouts—An SAS, SPSS, or MINITAB printout now accompanies every statistical technique presented.
- Allows the instructor to emphasize interpretations of the statistical results rather than the calculations required to obtain the results.
- Allows the instructor to emphasize interpretations of the statistical results rather than the calculations required to obtain the results.
- Statistical software tutorials—Includes basic instructions on how to use the Windows versions of SAS, SPSS, and MINTAB in the appendix.
- Gives students step-by-step instructions and screen shots for each method presented.
- Gives students step-by-step instructions and screen shots for each method presented.
- A new section in Chapter 1 on graphical and numerical methods of describing qualitative data.
- Provides students with tools for exploring categorical data.
- Provides students with tools for exploring categorical data.
- New material in Chapter 1 on comparing two population means using a paired difference experiment.
- Presents situations where paired design is more appropriate than independent sampling.
- Presents situations where paired design is more appropriate than independent sampling.
- Reorganization of topics—Including new placement of multiple regression models, model validation, and spline regression.
- Presents these topics to students in a more logical fashion.
- Presents these topics to students in a more logical fashion.
- Accompanying data CD-ROM—Contains files for all data sets marked with a CD icon in the text.
- Gives students the data they need, ready for analysis.
- Gives students the data they need, ready for analysis.
- Updated case study chapters with accompanying data sets.
- Shows students how regression is used to answer a real-life, practical question using the latest data available.
- Shows students how regression is used to answer a real-life, practical question using the latest data available.
- Updated real-data based examples and exercises.
- Shows students the relevance of the material being studied by using the latest data extracted from news articles, journal articles, magazines, and the Internet.
- Shows students the relevance of the material being studied by using the latest data extracted from news articles, journal articles, magazines, and the Internet.
- 50% new real-data problems.
- Gives students ample opportunity to practice the concepts learned using the most current data available.
- Gives students ample opportunity to practice the concepts learned using the most current data available.