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作者简介：Dr. Jim McClave is currently President and CEO of Info Tech, Inc., a statistical consulting and software development firm with an international clientele. He is also currently an Adjunct Professor of Statistics at the University of Florida, where he was a full-time member of the faculty for twenty years.

内容简介

The Eleventh Edition of this highly-regarded introductory text emphasizes inference and sound decision-making through its extensive coverage of data collection and analysis. McClave develops statistical thinking and teaches students to properly assess the credibility of inferences–from the vantage point of both the consumer and the producer. This edition incorporates more exercises and more visual features, such as redesigned end-of-chapter summaries and an increased use of applets. This text assumes a mathematical background of basic algebra.
　　Datasets and other resources (where applicable) for this book are available here.

目　　录

Preface
Applications Index
CHAPTER 1. Statistics, Data, and Statistical Thinking
　1.1 The Science of Statistics
　1.2 Types of Statistical Applications
　1.3 Fundamental Elements of Statistics
　1.4 Types of Data
　1.5 Collecting Data
　1.6 The Role of Statistics in Critical Thinking
　Statistics in Action: USA Weekend Teen Surveys — Are Boys Really from Mars and Girls from Venus?
　Using Technology: Creating and Listing Data in MINITAB
CHAPTER 2. Methods for Describing Sets of Data
　2.1 Describing Qualitative Data
　2.2 Graphical Methods for Describing Quantitative Data

Preface Applications Index CHAPTER 1. Statistics, Data, and Statistical Thinking 　1.1 The Science of Statistics 　1.2 Types of Statistical Applications 　1.3 Fundamental Elements of Statistics 　1.4 Types of Data 　1.5 Collecting Data 　1.6 The Role of Statistics in Critical Thinking 　Statistics in Action: USA Weekend Teen Surveys — Are Boys Really from Mars and Girls from Venus? 　Using Technology: Creating and Listing Data in MINITAB CHAPTER 2. Methods for Describing Sets of Data 　2.1 Describing Qualitative Data 　2.2 Graphical Methods for Describing Quantitative Data 　2.3 Summation Notation 　2.4 Numerical Measures of Central Tendency 　2.5 Numerical Measures of Variability 　2.6 Interpreting the Standard Deviation 　2.7 Numerical Measures of Relative Standing 　2.8 Methods for Detecting Outliers (Optional) 　2.9 Graphing Bivariate Relationships (Optional) 　2.10 Distorting the Truth with Descriptive Techniques 　Statistics In Action: The "Eye Cue" Test: Does Experience Improve Performance? 　Using Technology: Describing Data in MINITAB CHAPTER 3. Probability 　3.1 Events, Sample Spaces, and Probability 　3.2 Unions and Intersections 　3.3 Complementary Events 　3.4 The Additive Rule and Mutually Exclusive Events 　3.5 Conditional Probability 　3.6 The Multiplicative Rule and Independent Events 　3.7 Random Sampling 　3.8 Some Counting Rules (Optional) 　3.9 Bayes’ Rule (Optional) 　Statistics In Action: Lotto Buster! — Can You Improve Your Chances of Winning the Lottery? 　Using Technology: Generating a Random Sample in MINITAB CHAPTER 4. Discrete Random Variables 　4.1 Two Types of Random Variables 　4.2 Probability Distributions for Discrete Random Variables 　4.3 Expected Values of Discrete Random Variables 　4.4 The Binomial Random Variable 　4.5 The Poisson Random Variable (Optional) 　4.6 The Hypergeometric Random Variable (Optional) 　Statistics in Action: Probability in a Reverse Cocaine Sting — Was Cocaine Really Sold? 　Using Technology: Binomial, Poisson, and Hypergeometric Probabilities in MINITAB CHAPTER 5. Continuous Random Variables 　5.1 Continuous Probability Distributions 　5.2 The Uniform Distribution 　5.3 The Normal Distribution 　5.4 Descriptive Methods for Assessing Normality 　5.5 Approximating a Binomial Distribution with a Normal Distribution (Optional) 　5.6 The Exponential Distribution (Optional) 　Statistics in Action: Super Weapons Development — Is the Hit Ratio Optimized? 　Using Technology: Normal Probability Plots in MINITAB CHAPTER 6. Sampling Distributions 　6.1 What is a Sampling Distribution? 　6.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance (Optional) 　6.3 The Central Limit Theorem 　Statistics in Action: The Insomnia Pill — Will It Take Less Time to Fall Asleep? 　Using Technology: Simulating a Sampling Distribution in MINITAB CHAPTER 7. Inferences Based on a Single Sample: Estimation with Confidence Intervals 　7.1 Identifying the Target Parameter 　7.2 Large-Sample Confidence Interval for a Population Mean 　7.3 Small-Sample Confidence Interval for a Population Mean 　7.4 Large-Sample Confidence Interval for a Population Proportion 　7.5 Determining the Sample Size 　Statistics in Action: Speed–Can a High School Football Player Improve His Sprint Time? 　Using Technology: Confidence Intervals in MINITAB CHAPTER 8. Inferences Based on a Single Sample: Tests of Hypothesis 　8.1 The Elements of a Test of Hypothesis 　8.2 Large-Sample Test of Hypothesis About a Population Mean 　8.3 Observed Significance Levels: p-Values 　8.4 Small-Sample Test of Hypothesis About a Population Mean 　8.5 Large-Sample Test of Hypothesis About a Population Proportion 　8.6 Calculating Type II Error Probabilities: More About β (Optional) 　8.7 Test of Hypothesis About a Population Variance (Optional) 　Statistics in Action: Diary of a Kleenex User — How Many Tissues in a Box? 　Using Technology: Tests of Hypothesis in MINITAB CHAPTER 9. Inferences Based on a Two Samples: Confidence Intervals and Tests of Hypotheses 　9.1 Identifying the Target Parameter 　9.2 Comparing Two Population Means: Independent Sampling 　9.3 Comparing Two Population Means: Paired Difference Experiments 　9.4 Comparing Two Population Proportions: Independent Sampling 　9.5 Determining the Sample Size 　9.6 Comparing Two Population Variances: Independent Sampling (Optional) 　Statistics in Action: Do Homework Assignments Designed to Involve Family Members Really Work? 　Using Technology: Two-Sample Inferences in MINITAB CHAPTER 10. Analysis of Variance: Comparing More Than Two Means 　10.1 Elements of a Designed Experiment 　10.2 The Completely Randomized Design 　10.3 Multiple Comparisons of Means 　10.4 The Randomized Block Design 　10.5 Factorial Experiments 　Statistics in Action: On the Trail of the Cockroach: Do Roaches Travel at Random? 　Using Technology: Analysis of Variance in MINITAB CHAPTER 11. Simple Linear Regression 　11.1 Probabilistic Models 　11.2 Fitting the Model: The Least Squares Approach 　11.3 Model Assumptions 　11.4 Assessing the Utility of the Model: Making Inferences About the Slope β1 　11.5 The Coefficients of Correlation and Determination 　11.6 Using the Model for Estimation and Prediction 　11.7 A Complete Example 　Statistics in Action: Can "Dowsers" Really Detect Water? 　Using Technology: Simple Linear Regression in MINITAB CHAPTER 12. Multiple Regression and Model Building 　12.1 Multiple Regression Models 　12.2 The First-Order Model: Inferences About the Individual β-Parameters 　12.3 Evaluating the Overall Utility of a Model 　12.4 Using the Model for Estimation and Prediction 　12.5 Model Building: Interaction Models 　12.6 Model Building: Quadratic and other Higher-Order Models 　12.7 Model Building: Qualitative (Dummy) Variable Models 　12.8 Model Building: Models with both Quantitative and Qualitative Variables 　12.9 Model Building: Comparing Nested Models (Optional) 　12.10 Model Building: Stepwise Regression (Optional) 　12.11 Residual Analysis: Checking the Regression Assumptions 　12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation 　Statistics in Action: Modeling Condo Sales: Are There Differences in Auction Prices? 　Using Technology: Multiple Regression in MINITAB CHAPTER 13. Categorical Data Analysis 　13.1 Categorical Data and the Multinomial Distribution 　13.2 Testing Categorical Probabilities: One-Way Table 　13.3 Testing Categorical Probabilities: Two-Way (Contingency) Table 　13.4 A Word of Caution About Chi-Square Tests 　Statistics in Action: College Students and Alcohol — Is Drinking Frequency Related to Amount? 　Using Technology: Chi-Square Analyses in MINITAB CHAPTER 14. Nonparametric Statistics 　14.1 Introduction: Distribution-Free Tests 　14.2 Single Population Inferences 　14.3 Comparing Two Populations: Independent Samples 　14.4 Comparing Two Populations: Paired Difference Experiment 　14.5 Comparing Three or More Populations: Completely Randomized Design 　14.6 Comparing Three or More Populations: Randomized Block Design 　14.7 Rank Correlation 　Statistics in Action: How Vulnerable are Wells to Groundwater Contamination? 　Using Technology: Nonparametric Analyses in MINITAB Appendix A: Tables 　Table I Random Numbers 　Table II Binomial Probabilities 　Table III Poisson Probabilities 　Table IV Normal Curve Areas 　Table V Exponentials 　Table VI Critical Values of t 　Table VII Critical Values of χ2 　Table VIII Percentage Points of the F Distribution, α=.10 　Table IX Percentage Points of the F Distribution, α=.05 　Table X Percentage Points of the F Distribution, α=.025 　Table XI Percentage Points of the F Distribution, α=.01 　Table XII Critical Values of TL and TU for the Wilcoxon Rank Sum Test 　Table XIII Critical Values of T0 in the Wilcoxon Signed Rank Test 　Table XIV Critical Values of Spearman's Rank Correlation Coefficient Appendix B: Calculation Formulas for Analysis of Variance Short Answers to Selected Odd-Numbered Exercises Index Photo Credits

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