Statistics, 9th Edition
September 2009, ©2010
This new 9th edition continues to emphasize the importance of understanding statistics in today’s world.
1.1 Why Study Statistics?
1.2 What Is Statistics?
1.3 More About Inferential Statistics.
1.4 Three Types of Data.
1.5 Levels of Measurement.
1.6 Types of Variables.
1.7 How to Use This Book.
Part I: Descriptive Statistics.
2. Describing Data with Tables and Graphs.
Tables (Frequency Distributions).
2.1 Frequency Distributions for Quantitative Data.
2.4 Relative Frequency Distributions.
2.5 Cumulative Frequency Distributions.
2.6 Frequency Distributions for Qualitative (Nominal) Data.
2.7 Interpreting Distributions Constructed by Others.
2.8 Graphs for Quantitative Data.
2.9 Typical Shapes.
2.10 A Graph for Qualitative (Nominal) Data.
2.11 Misleading Graphs.
2.12 Doing It Yourself.
3. Describing Data with Averages.
3.4 Which Average?
3.5 Averages for Qualitative and Ranked Data.
Summary. Important Terms.
4. Describing Variability.
4.1 Intuitive Approach.
4.4 Standard Deviation.
4.5 DETAILS: Standard Deviation.
4.6 Degrees of Freedom (df).
4.7 Interquartile Range (IQR).
4.8 Measures of Variability for Qualitative and Ranked Data.
5. Normal Distributions and Standard Scores (z).
5.1 The Normal Curve.
5.2 z Scores.
5.3 Standard Normal Curve.
5.4 Solving Normal Curve Problems.
5.5 Finding Proportions.
5.6 Finding Scores.
5.7 More about z Scores.
6. Describing Relationships: Correlation.
6.1 An Intuitive Approach.
6.3 A Correlation Coefficient for Quantitative Data: r.
6.4 DETAILS: z Score Formula for r.
6.5 DETAILS: Computation Formula for r.
6.6 Outliers Again.
6.7 Other Types of Correlation Coefficients.
6.8 Computer Output.
7.1 Two Rough Predictions.
7.2 A Regression Line.
7.3 Least Squares Regression Line.
7.4 Standard Error of Estimate, Sy¦x.
7.6 Multiple Regression Equations.
7.7 Regression Toward The Mean.
Part II: Inferential Statistics.
8. Populations, Samples, and Probability.
Populations and Samples.
8.3 Random Sampling.
8.4 Tables of Random Numbers.
8.5 Random Assignment of Subjects.
Surveys or Experiments?
8.8 Addition Rule.
8.9 Multiplication Rule.
8.10 Probability and Statistics.
9. Sampling Distribution of the Mean.
9.1 What is a Sampling Distribution?
9.2 Creating a Sampling Distribution from Scratch.
9.3 Some Important Symbols.
9.4 Mean of All Sample Means
9.5 Standard Error of the Means
9.6 Shape of the Sampling Distribution.
9.7 Other Sampling Distributions.
10. Introduction to Hypothesis Testing: the z Test.
10.1 Testing a Hypothesis about SAT Scores.
10.2 z Test for a Population Mean.
10.3 Step-by-Step Procedure.
10.4 Statement of the Research Problem.
10.5 Null Hypothesis (H0).
10.6 Alternative Hypothesis (H1).
10.7 Decision Rule.
11. More about Hypothesis Testing.
11.1 Why Hypothesis Tests?
11.2 Strong or Weak Decisions.
11.3 One-Tailed and Two-Tailed Tests.
11.4 Choosing a Level of Significance (a).
11.5 Testing a Hypothesis about Vitamin C.
11.6 Four Possible Outcomes.
11.7 If H0 Really Is True.
11.8 If H0 Really Is False Because of a Large Effect.
11.9 If H0 Really Is False Because of a Small Effect.
11.10 Influence of Sample Size.
11.11 Power and Sample Size.
12. Estimation (Confidence Intervals).
12.1 Point Estimate for m.
12.2 Confidence Interval (CI) for m.
12.3 Interpretation of a Confidence Interval.
12.4 Level of Confidence.
12.5 Effect of Sample Size.
12.6 Hypothesis Tests or Confidence Intervals?
12.7 Confidence Interval for Population Percent.
13. tTest for One Sample.
13.1 Gas Mileage Investigation.
13.2 Sampling Distribution of t.
13.4 Common Theme of Hypothesis Tests.
13.5 Reminder about Degrees of Freedom.
13.6 DETAILS: Estimating the Standard Error.
13.7 DETAILS: Calculations for t Test.
13.8 Confidence Intervals for m Based on t.
14. t Test for Two Independent Samples.
14.1 EPO Experiment.
14.2 Statistical Hypotheses.
14.3 Sampling Distribution
14.4 t Test.
14.5 DETAILS: Calculations for t Test.
14.7 Statistically Significant Results.
14.8 Estimating Effect Size: Point Estimates and Confidence Intervals.
14.9 Estimating Effect Size: Cohen's d.
14.10 Reports in the Literature.
14.12 Computer Output.
15. t Test for Two Related Samples (Repeated Measures).
15.1 EPO Experiment with Repeated Measures.
15.2 Statistical Hypotheses.
15.3 Sampling Distribution.
15.4 t Test.
15.5 DETAILS: Calculations for t Test.
15.6 Estimating Effect Size.
15.8 Overview: Three t Tests for Population Means.
15.9 t Test for the Population Correlation Coefficient,
16. Analysis of Variance (One Factor).
16.1 Testing a Hypothesis about Aggression and Sleep Deprivation.
16.2 Two Sources of Variability.
16.3 F Test.
16.4 DETAILS: Variance Estimates.
16.5 DETAILS: Mean Squares (MS) and the F Ratio.
16.6 Table for F Distribution.
16.7 ANOVA Summary Tables.
16.8 F Test Is Nondirectional.
16.9 Estimating Effect Size.
16.10 Multiple Comparisons.
16.11 Overview: Flow Chart for ANOVA.
16.12 Reports in the Literature.
16.14 Computer Output.
17. Analysis of Variance (Repeated Measures).
17.1 Sleep Deprivation Experiment with Repeated Measures.
17. 2 F Test.
17.3 Two Complications.
17.4 DETAILS: Variance Estimates.
17.5 DETAILS: Mean Square (MS) and the F Ratio.
17.6 Table for F Distribution.
17.7 ANOVA Summary Tables.
17.8 Estimating Effect Size.
17.9 Multiple Comparisons.
17.10 Reports in the Literature.
18. Analysis of Variance (Two Factors).
18.1 A Two-Factor Experiment: Responsibility in Crowds.
18.2 Three F Tests.
18.4 DETAILS: Variance Estimates.
18.5 DETAILS: Mean Squares (MS) and F Ratios.
18.6 Table for F Distribution.
18.7 Estimating Effect Size.
18.8 Multiple Comparisons.
18.9 Simple Effects.
18.10 OVERVIEW: Flow Chart for Two-Factor ANOVA.
18.11 Reports in the Literature.
18.13 Other Types of ANOVA.
19. Chi-Square (c²) Test for Qualitative (Nominal) Data.
One-variable c² Test.
19.1 Survey of Blood Types.
19.2 Statistical Hypotheses.
19.3 DETAILS: Calculation of c².
19.4 Table for c² Distribution.
19.5 c² Test.
Two-variable c² Test.
19.6 Lost Letter Study.
9.7 Statistical Hypotheses.
9.8 DETAILS: Calculation ofc².
19.9 Table for c² Distribution.
19.10 c² Test.
19.11 Estimating Effect Size.
19.12 Odds Ratios.
19.13 Reports in the Literature.
19.14 Some Precautions.
19.15 Computer Output.
20. Tests for Ranked (Ordinal) Data.
20.1 Use Only When Appropriate.
20.2 A Note on Terminology.
20.3 Mann-Whitney U Test (Two Independent Samples).
20.4 Wilcoxon T Test (Two Related Samples).
20.5 Kruskal-Wallis H Test (Three or More Independent Samples).
20.6 General Comment: Ties.
21. Postscript: Which Test?
21.1 Descriptive or Inferential Statistics?
21.2 Hypothesis Tests or Confidence Intervals?
21.3 Quantitative or Qualitative Data?
21.4 Distinguishing between the Two Types of Data.
21.5 One, Two, or More Groups?21.6 Concluding Comments.
A. Math Review.
B. Answers to Selected Questions.
- A new section (11.11) on using power curves to find an appropriate sample size.
- Expanded discussion of Cohen's guidelines for effective size, with new figure (14.4) that illustrates the separation between pairs of normal curves for selected effect sizes.
- A new section (19.12) on using the odds ratio to understand the importance of statistically significant chi-square tests.
- Chapter summaries have been expanded, whenever appropriate, to include a list of key equations.
- Questions, examples, and computer outputs have been updated.
- Basic concepts and procedures are explained in plain English, and special effort has been made to clarify topics in statistics that are often seen as "mystifying."
- Unnecessary math, computational busy work, and subtle technical distinctions are avoided without sacrificing either accuracy or realism.
- Single examples permeate entire chapters, or even several related chapters, serving as handy frames of reference for new concepts and procedures.
- Each chapter begins with a preview and ends with a summary, lists of important terms and key equations, and review questions.
- The two-color format highlights topic headings and important formulas, keys step-by-step computational instructions to actual computations, and adds an extra dimension to illustrations.
- Key statements appear in bold type, and step-by-step summaries of important procedures, such as solving normal curve problems, appear in boxes.
- Important definitions and reminders about key points appear in page margins.
- Scattered throughout the book are examples of computer outputs for three of the most prevalent programs, Minitab, SPSS, and SAS.
- Progress Checks are introduced within chapter sections and are designed to minimize cumulative confusion. Each chapter ends with Review Questions.
- Questions have been selected to appeal to student interests and provide real-world examples such as a t-test analysis of global temperatures to evaluate a possible greenhouse effect (13.7).
- Appendix B supplies answers to questions marked with an asterisks while other appendices provide a practical math review complete with self-tests, a glossary, and tables of statistical distribution.
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