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Tables of Contents for Statistics
Chapter/Section Title
Page #
Page Count
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
1.4 Types of Data
8
2
1.5 Collecting Data
10
2
CASE STUDY 1.1 The Latest Hite Report-Controversy over the Numbers
12
2
CASE STUDY 1.2 A "20/20" View of Survey Results-Fact or Fiction?
14
1
1.6 The Role of Statistics in Critical Thinking
14
2
Quick Review
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
2.3 Summation Notation
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
Quick Review
82
9
Chapter 3 Probability
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
3.3 Complementary Events
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
3.8 Random Sampling
131
5
3.9 Some Counting Rules (Optional)
136
8
CASE STUDY 3.3 Lottery Buster!
144
3
Quick Review
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
Quick Review
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
Quick Review
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
Quick Review
247
6
Chapter 7 Inferences Based on a Single Sample: Estimation with Confidence Intervals
253
36
7.1 Large-Sample Confidence Interval for a Population Mean
254
7
7.2 Small-Sample 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 Jails-Revisited
272
4
7.4 Determining the Sample Size
276
4
CASE STUDY 7.2 Is Caffeine Addictive?
280
2
Quick Review
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 Large-Sample Test of Hypothesis About a Population Mean
296
7
8.3 Observed Significance Levels: p-Values
303
5
8.4 Small-Sample Test of Hypothesis About a Population Mean
308
6
8.5 Large-Sample Test of Hypothesis About a Population Proportion
314
4
CASE STUDY 8.2 Verifying Petitions-How 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
Quick Review
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
Quick Overview
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
Quick Review
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
11.3 Model Assumptions
488
1
11.4 An Estimator of o-2
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
Quick Review
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
12.3 Model Assumptions
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 F-Test
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
Quick Review
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
13.8 Stepwise Regression
670
8
Quick Review
678
7
Chapter 14 The Chi-Square Test and the Analysis of Contingency Tables
685
30
14.1 One-Dimensional Count Data: The Multinomial Distribution
686
7
14.2 Contingency Tables
693
7
CASE STUDY 14.1 Lifestyles of the Married (and Not Famous)
700
6
14.3 A Word of Caution About Chi-Square Tests
706
1
Quick Review
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 Kruskal-Wallis H-Test for a Completely Randomized Design
739
5
15.5 The Friedman Fr-Test for a Randomized Block Design
744
6
15.6 Spearman's Rank Correlation Coefficient
750
8
Quick Review
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