Preface 

ix  

Chapter 1 Statistics, Data, and Statistical Thinking 


1  (20) 

1.1 The Science of Statistics 


2  (1) 

1.2 Types of Statistical Applications 


2  (2) 

1.3 Fundamental Elements of Statistics 


4  (4) 


8  (2) 


10  (2) 

CASE STUDY 1.1 The Latest Hite ReportControversy over the Numbers 


12  (2) 

CASE STUDY 1.2 A "20/20" View of Survey ResultsFact or Fiction? 


14  (1) 

1.6 The Role of Statistics in Critical Thinking 


14  (2) 


16  (5) 

Chapter 2 Methods for Describing Sets of Data 


21  (70) 

2.1 Describing Qualitative Data 


22  (7) 

2.2 Graphical Methods for Describing Quantitative Data 


29  (6) 

CASE STUDY 2.1 The "Eye Cue" Test: Does Experience Improve Performance? 


35  (7) 


42  (1) 

2.4 Numerical Measures of Central Tendency 


43  (8) 

2.5 Numerical Measures of Variability 


51  (5) 

2.6 Interpreting the Standard Deviation 


56  (8) 

2.7 Numerical Measures of Relative Standing 


64  (3) 

CASE STUDY 2.2 Computer Phobia and Secondary Education Teachers 


67  (3) 

2.8 Quartiles and Box Plots (Optional) 


70  (7) 

CASE STUDY 2.3 Suicide in Urban Jails 


77  (1) 

2.9 Distorting the Truth with Descriptive Techniques 


77  (5) 


82  (9) 


91  (64) 

3.1 Events, Sample Spaces, and Probability 


92  (8) 

CASE STUDY 3.1 Game Show Strategy: To Switch or Not to Switch 


100  (4) 

3.2 Unions and Intersections 


104  (3) 


107  (2) 

3.4 The Additive Rule and Mutually Exclusive Events 


109  (5) 

3.5 Conditional Probability 


114  (6) 

3.6 The Multiplicative Rule and Independent Events 


120  (6) 

CASE STUDY 3.2 O.J., Spousal Abuse, and Murder 


126  (4) 

3.7 Probability and Statistics: An Example 


130  (1) 


131  (5) 

3.9 Some Counting Rules (Optional) 


136  (8) 

CASE STUDY 3.3 Lottery Buster! 


144  (3) 


147  (8) 

Chapter 4 Discrete Random Variables 


155  (38) 

4.1 Two Types of Random Variables 


156  (3) 

4.2 Probability Distributions for Discrete Random Variables 


159  (4) 

4.3 Expected Values of Discrete Random Variables 


163  (3) 

CASE STUDY 4.1 "The Showcase Showdown" 


166  (4) 

4.4 The Binomial Random Variable 


170  (7) 

CASE STUDY 4.2 The Space Shuttle Challenger: Catastrophe in Space 


177  (3) 

4.5 The Poisson Random Variable (Optional) 


180  (5) 

4.6 The Hypergeometric Random Variable (Optional) 


185  (2) 

CASE STUDY 4.3 Probability in a Reverse Cocaine Sting 


187  (1) 


188  (5) 

Chapter 5 Continuous Random Variables 


193  (34) 

5.1 Continuous Probability Distributions 


194  (1) 

5.2 The Uniform Distribution 


195  (3) 

5.3 The Normal Distribution 


198  (11) 

CASE STUDY 5.1 IQ and the Bell Curve 


209  (3) 

5.4 Approximating a Binomial Distribution with a Normal Distribution 


212  (6) 

5.5 The Exponential Distribution (Optional) 


218  (3) 

CASE STUDY 5.2 Is New Always Better Than Used? 


221  (2) 


223  (4) 

Chapter 6 Sampling Distributions 


227  (26) 

6.1 What Is a Sampling Distribution? 


228  (6) 

6.2 Properties of a Sampling Distribution: Unbiasedness and Minimum Variance 


234  (4) 

6.3 The Central Limit Theorem 


238  (8) 

CASE STUDY 6.1 The Insomnia Pill 


246  (1) 


247  (6) 

Chapter 7 Inferences Based on a Single Sample: Estimation with Confidence Intervals 


253  (36) 

7.1 LargeSample Confidence Interval for a Population Mean 


254  (7) 

7.2 SmallSample Confidence Interval for a Population Mean 


261  (8) 

7.3 Large Sample Confidence Interval for a Population Proportion 


269  (3) 

CASE STUDY 7.1 Suicide in Urban JailsRevisited 


272  (4) 

7.4 Determining the Sample Size 


276  (4) 

CASE STUDY 7.2 Is Caffeine Addictive? 


280  (2) 


282  (7) 

Chapter 8 Inferences Based on a Single Sample: Tests of Hypotheses 


289  (50) 

8.1 The Elements of a Test of Hypothesis 


290  (4) 

CASE STUDY 8.1 Statistics Is Murder! 


294  (2) 

8.2 LargeSample Test of Hypothesis About a Population Mean 


296  (7) 

8.3 Observed Significance Levels: pValues 


303  (5) 

8.4 SmallSample Test of Hypothesis About a Population Mean 


308  (6) 

8.5 LargeSample Test of Hypothesis About a Population Proportion 


314  (4) 

CASE STUDY 8.2 Verifying PetitionsHow Many to Check? 


318  (2) 

8.6 Calculating Type II Error Probabilities: More About Beta (Optional) 


320  (7) 

8.7 Inferences About a Population Variance (Optional) 


327  (6) 


333  (6) 

Chapter 9 Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses 


339  (62) 

9.1 Comparing Two Population Means: Independent Sampling 


340  (8) 

CASE STUDY 9.1 Detection of Rigged Milk Price 


348  (11) 

9.2 Comparing Two Population Means: Paired Difference Experiments 


359  (7) 

CASE STUDY 9.2 An IQ Comparison of Identical Twins Reared Apart 


366  (5) 

9.3 Comparing Two Population Propertions: Independent Sampling 


371  (7) 

9.4 Determining the Sample Size 


378  (3) 

9.5 Comparing Two Population Variances: Independent Sampling (Optional) 


381  (11) 


392  (9) 

Chapter 10 Analysis of Variance: Comparing More Than Two Means 


401  (72) 

10.1 Elements of a Designed Experiment 


402  (5) 

10.2 The Completely Randomized Design 


407  (15) 

10.3 Multiple Comparisons of Means 


422  (3) 

CASE STUDY 10.1 Can Therapy Help Binge Eaters? 


425  (4) 

10.4 The Randomized Block Design 


429  (15) 

10.5 Factorial Experiments 


444  (10) 

CASE STUDY 10.2 Anxiety Levels and Mathematical Achievement 


454  (7) 


461  (12) 

Chapter 11 Simple Linear Regression 


473  (62) 

11.1 Probabilistic Models 


474  (4) 

11.2 Fitting the Model: The Least Squares Approach 


478  (10) 


488  (1) 


489  (4) 

11.5 Assessing the Utility of the Model: Making Inferences About the Slope Beta 1 


493  (7) 

11.6 The Coefficient of Correlation 


500  (4) 

11.7 The Coefficient of Determination 


504  (5) 

11.8 Using the Model For Estimation and Prediction 


509  (5) 

CASE STUDY 11.1 Statistical Assessment of Damage to Bronx Bricks 


514  (7) 

11.9 Simple Linear Regression: An Example 


521  (3) 


524  (11) 

Chapter 12 Multiple Regression 


535  (70) 

12.1 Multiple Regression: The Model and the Procedure 


536  (1) 

12.2 Fitting the Model: The Least Squares Approach 


537  (4) 


541  (2) 

12.4 Inferences About the Beta Parameters 


543  (10) 

12.5 Checking the Usefulness of a Model: R(2) and the Analysis of Variance FTest 


553  (12) 

12.6 Using the Model for Estimation and Prediction 


565  (2) 

12.7 Multiple Regression: An Example 


567  (4) 

CASE STUDY 12.1 Predicting the Price of Vintage Red Bordeaux Wine 


571  (2) 

12.8 Residual Analysis: Checking the Regression Assumptions 


573  (10) 

CASE STUDY 12.2 Analyzing Water/Oil Mixtures in High Electric Fields 


583  (2) 

12.9 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation 


585  (8) 


593  (12) 

Chapter 13 Model Building 


605  (80) 

13.1 The Two Types of Independent Variables: Quantitative and Qualitative 


607  (2) 

13.2 Models with a Single Quantitative Independent Variable 


609  (7) 

13.3 Models with Two or More Quantitative Independent Variables 


616  (8) 

13.4 Testing Portions of a Model 


624  (10) 

13.5 Models with One Qualitative Independent Variable 


634  (8) 

13.6 Comparing the Slopes of Two or More Lines 


642  (12) 

13.7 Comparing Two or More Response Curves 


654  (2) 

CASE STUDY 13.1 Building a Model for Condo Sale Prices 


656  (14) 


670  (8) 


678  (7) 

Chapter 14 The ChiSquare Test and the Analysis of Contingency Tables 


685  (30) 

14.1 OneDimensional Count Data: The Multinomial Distribution 


686  (7) 


693  (7) 

CASE STUDY 14.1 Lifestyles of the Married (and Not Famous) 


700  (6) 

14.3 A Word of Caution About ChiSquare Tests 


706  (1) 


707  (8) 

Chapter 15 Nonparametric Statistics 


715  (50) 

15.1 Single Population Inferences: The Sign Test 


717  (5) 

15.2 Comparing Two Populations: The Wilcoxon Rank Sum Test for Independent Samples 


722  (8) 

15.3 Comparing Two Populations: The Wilcoxon Signed Rank Test for the Paired Difference Experiment 


730  (5) 

CASE STUDY 15.1 Deadly Exposure: Agent Orange and Vietnam Vets 


735  (4) 

15.4 The KruskalWallis HTest for a Completely Randomized Design 


739  (5) 

15.5 The Friedman FrTest for a Randomized Block Design 


744  (6) 

15.6 Spearman's Rank Correlation Coefficient 


750  (8) 


758  (7) 
Appendix A Tables 

765  (30) 
Appendix B Data Sets 

795  (6) 
Appendix C Calculation Formulas for Analysis of Variance 

801  (4) 
Answers to Selected Exercises 

805  (10) 
References 

815  (4) 
Index 

819  