Second Course in Statistics, A: Regression Analysis

Pearson New International Edition

Häftad, Engelska, 2013

1 054 kr

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Beskrivning

The Second Course in Statistics is an increasingly important offering since more students are arriving at college having taken AP Statistics in high school.

Mendenhall/Sincich’s A Second Course in Statistics is the perfect book for courses that build on the knowledge students gain in AP Statistics, or the freshman Introductory Statistics course.

A Second Course in Statistics: Regression Analysis, 7th Edition, focuses on building linear statistical models and developing skills for implementing regression analysis in real situations. This text offers applications for engineering, sociology, psychology, science, and business. The authors use real data and scenarios extracted from news articles, journals, and actual consulting problems to show how to apply the concepts. In addition, seven case studies, now located throughout the text after applicable chapters, invite students to focus on specific problems, and are suitable for class discussion.

Produktinformation

Utgivningsdatum:2013-11-01
Mått:213 x 274 x 25 mm
Vikt:1 334 g
Format:Häftad
Språk:Engelska
Antal sidor:752
Upplaga:7
Förlag:Pearson Education
ISBN:9781292042909

Utforska kategorier

Innehållsförteckning

1. A Review of Basic Concepts (Optional)1.1 Statistics and Data1.2 Populations, Samples, and Random Sampling1.3 Describing Qualitative Data1.4 Describing Quantitative Data Graphically1.5 Describing Quantitative Data Numerically1.6 The Normal Probability Distribution1.7 Sampling Distributions and the Central Limit Theorem1.8 Estimating a Population Mean1.9 Testing a Hypothesis About a Population Mean1.10 Inferences About the Difference Between Two Population Means1.11 Comparing Two Population Variances 2. Introduction to Regression Analysis2.1 Modeling a Response2.2 Overview of Regression Analysis2.3 Regression Applications2.4 Collecting the Data for Regression 3. Simple Linear Regression3.1 Introduction3.2 The Straight-Line Probabilistic Model3.3 Fitting the Model: The Method of Least Squares3.4 Model Assumptions3.5 An Estimator of s23.6 Assessing the Utility of the Model: Making Inferences About the Slope ß13.7 The Coefficient of Correlation3.8 The Coefficient of Determination3.9 Using the Model for Estimation and Prediction3.10 A Complete Example3.11 Regression Through the Origin (Optional) Case Study 1: Legal Advertising--Does It Pay? 4. Multiple Regression Models4.1 General Form of a Multiple Regression Model4.2 Model Assumptions4.3 A First-Order Model with Quantitative Predictors4.4 Fitting the Model: The Method of Least Squares4.5 Estimation of s2, the Variance of e4.6 Testing the Utility of a Model: The Analysis of Variance F-Test4.7 Inferences About the Individual ß Parameters4.8 Multiple Coefficients of Determination: R2 and R2adj4.9 Using the Model for Estimation and Prediction4.10 An Interaction Model with Quantitative Predictors4.11 A Quadratic (Second-Order) Model with a Quantitative Predictor4.12 More Complex Multiple Regression Models (Optional)4.13 A Test for Comparing Nested Models4.14 A Complete Example Case Study 2: Modeling the Sale Prices of Residential Properties in Four Neighborhoods 5. Principles of Model Building5.1 Introduction: Why Model Building is Important5.2 The Two Types of Independent Variables: Quantitative and Qualitative5.3 Models with a Single Quantitative Independent Variable5.4 First-Order Models with Two or More Quantitative Independent Variables5.5 Second-Order Models with Two or More Quantitative Independent Variables5.6 Coding Quantitative Independent Variables (Optional)5.7 Models with One Qualitative Independent Variable5.8 Models with Two Qualitative Independent Variables5.9 Models with Three or More Qualitative Independent Variables5.10 Models with Both Quantitative and Qualitative Independent Variables5.11 External Model Validation 6. Variable Screening Methods6.1 Introduction: Why Use a Variable-Screening Method?6.2 Stepwise Regression6.3 All-Possible-Regressions Selection Procedure6.4 Caveats Case Study 3: Deregulation of the Intrastate Trucking Industry 7. Some Regression Pitfalls7.1 Introduction7.2 Observational Data Versus Designed Experiments7.3 Parameter Estimability and Interpretation7.4 Multicollinearity7.5 Extrapolation: Predicting Outside the Experimental Region7.6 Variable Transformations 8. Residual Analysis8.1 Introduction8.2 Plotting Residuals8.3 Detecting Lack of Fit8.4 Detecting Unequal Variances8.5 Checking the Normality Assumption8.6 Detecting Out