Second Course in Statistics, A: Regression Analysis, 8th edition
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Overview
A Second Course in Statistics: Regression Analysis, 8th Edition gives you the background and confidence to apply regression analysis techniques. The authors focus on readability to provide a learning experience rather than a reference, explaining concepts in a logical, intuitive manner with workedout examples. Applications to engineering, sociology, psychology, science and business are demonstrated throughout. Real data and scenarios extracted from news articles, journals and actual consulting problems are used to apply the concepts, helping you utilize the techniques outlined in the text. Case studies throughout focus on specific problems and are suitable for class discussion. This text is ideal for the second half of a 2semester introductory statistics sequence, or a graduate course in applied regression analysis.
Published by Pearson (August 1st 2021)  Copyright © 2020
ISBN13: 9780137515264
Subject: Introductory Statistics
Category: Second Course in Statistics, A: Regression Analysis
Overview
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 StraightLine Probabilistic Model
 3.3 Fitting the Model: The Method of Least Squares
 3.4 Model Assumptions
 3.5 An Estimator of σ^{2}
 3.6 Assessing the Utility of the Model: Making Inferences About the Slope β_{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 FirstOrder Model with Quantitative Predictors
 4.4 Fitting the Model: The Method of Least Squares
 4.5 Estimation of σ^{2}, the Variance of ε
 4.6 Testing Overall Model Utility: The Analysis of Variance FTest
 4.7 Inferences About the Individual β Parameters
 4.8 Multiple Coefficients of Determination: R^{2} and R^{2}_{adj}
 4.9 Using the Model for Estimation and Prediction
 4.10 An Interaction Model with Quantitative Predictors
 4.11 A Quadratic (SecondOrder) 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 ModelBuilding
 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 FirstOrder Models with Two or More Quantitative Independent Variables
 5.5 SecondOrder 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 VariableScreening Method?
 6.2 Stepwise Regression
 6.3 AllPossibleRegressionsSelection Procedure
 6.4 Caveats
 Case Study 3: Deregulation of the Intrastate Trucking Industry
7. Some Regression Pitfalls
 7.1 Introduction
 7.2 Observational Data Versus Designed Experiments
 7.3 Parameter Estimability and Interpretation
 7.4 Multicollinearity
 7.5 Extrapolation: Predicting Outside the Experimental Region
 7.6 Variable 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 Detection of Residual Correlation: The DurbinWatson Test
 Case Study 4: An Analysis of Rain Levels in California
 Case Study 5: Factors Affecting the Sale Price of Condominium Units Sold at Public Auction
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 Variables
 9.6 Logistic Regression
 9.7 Poisson Regression
 9.8 Ridge and Lasso Regression
 9.9 Robust Regression
 9.10 Nonparametric Regression Models
10. 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
 11.1 Introduction
 11.2 Experimental Design Terminology
 11.3 Controlling the Information in an Experiment
 11.4 NoiseReducing Designs
 11.5 VolumeIncreasing 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 an Analysis of Variance
 12.3 OneFactor Completely Randomized Designs
 12.4 Randomized Block Designs
 12.5 TwoFactor Factorial Experiments
 12.6 More Complex Factorial Designs (Optional)
 12.7 FollowUp Analysis: Tukey's Multiple Comparisons of Means
 12.8 Other Multiple Comparisons Methods (Optional)
 12.9 Checking ANOVA Assumptions
 Case Study 7: Voice Versus Face Recognition  Does One Follow the Other?
Appendices
 A: Derivation of the Least Squares Estimates of β_{0} and β_{1} in Simple Linear Regression
 A.1 Introduction
 A.2 Matrices and Matrix Multiplication
 A.3 Identity Matrices and Matrix Inversion
 A.4 Solving Systems of Simultaneous Linear Equations
 A.5 The Least Squares Equations and Their Solution
 A.6 Calculating SSE and s^{2}
 A.7 Standard Errors of Estimators, Test Statistics, and Confidence Intervals for β_{0}, β_{1}, ... , β_{k}
 A.8 A Confidence Interval for a Linear Function of the β Parameters and for E(y)
 A.9 A Prediction Interval for Some Value of y to be Observed in the Future
 B: The Mechanics of a Multiple Regression Analysis
 C: A Procedure for Inverting a Matrix
 D: Statistical Tables
 Table 1: Normal Curve Areas
 Table 2: Critical Values for Student's t
 Table 3: Critical Values for the F Statistic: F_{.10}
 Table 4: Critical Values for the F Statistic: F_{.05}
 Table 5: Critical Values for the F Statistic: F_{.025}
 Table 6: Critical Values for the F Statistic: F_{.01}
 Table 7: Critical Values for the DurbinWatson d Statistic (α=.05)
 Table 8: Critical Values for the DurbinWatson d Statistic (α=.01)
 Table 9: Critical Values for the X^{2}Statistic
 Table 10: Percentage Points of the Studentized Range, q(p,v), Upper 5%
 Table 11: Percentage Points of the Studentized Range, q(p,v), Upper 1%
 E: File Layouts for Case Study Data Sets
Answers to Odd Numbered Exercises
Index
Online:
 SAS Tutorial
 SPSS Tutorial
 MINITAB Tutorial
 R Tutorial
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