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What is included with this book?
1. INTRODUCTION | |
The Importance of Context: An Example | |
Basic Terminology | |
Selection Among Statistical Procedures | |
Using Computers | |
Summary | |
Exercises | |
2. BASIC CONCEPTS | |
Scales of Measurement | |
Variables | |
Random Sampling | |
Notation | |
Summary | |
Exercises | |
3. DISPLAYING DATA | |
Plotting Data | |
Stem-and-Leaf Displays | |
Histograms | |
Reading Graphs | |
Alternative Methods of Planning Data | |
Describing Distributions | |
Using Computer Programs to Display Data | |
Summary | |
Exercises | |
4. MEASURES OF CENTRAL TENDENCY | |
The Mode | |
The Median | |
The Mean | |
Advantages and Disadvantages of the Mode, the Median and the Mean | |
Obtaining Measures of Central Tendency Using MINITAB | |
A Simple Demonstrationâ€”Seeing Statistics | |
Summary | |
Exercises | |
5. MEASURES OF VARIABILITY | |
Range | |
Interquartile Range and Other Range Statistics | |
The Average Deviation | |
The Variance | |
The Standard Deviation | |
Computational Formulae for the Variance and the Standard Deviation | |
The Mean and the Variance as Estimators | |
Boxplots: Graphical Representations of Dispersion and Extreme Scores | |
Obtaining Measures of Dispersion Using JMP | |
A Final Worked Example | |
Seeing Statistics | |
Summary | |
Exercises | |
6. THE NORMAL DISTRIBUTION | |
The Normal Distribution | |
The Standard Normal Distribution | |
Setting Probable Limits on an Observation | |
Measures Related to z | |
Summary | |
Exercises | |
7. BASIC CONCEPTS OF PROBABILITY | |
Probability | |
Basic Terminology and Rules | |
Discrete versus Continuous Variables | |
Probability Distributions for Discrete Variables | |
Probability Distributions for Continuous Variables | |
Summary | |
Exercises | |
8. SAMPLING DISTRIBUTIONS AND HYPOTHESIS TESTING | |
Two Simple Examples Involving Course Evaluations and Rude Motorists | |
Sampling Distributions | |
Hypothesis Testing | |
The Null Hypothesis | |
Test Statistics and Their Sampling Distributions | |
Using the Normal Distribution to Test Hypotheses | |
Type I and Type II Errors | |
One- and Two-Tailed Tests | |
Seeing Statistics | |
A Final Worked Example | |
Back to Course Evaluations and Rude Motorists | |
Summary | |
Exercises | |
9. CORRELATION | |
Scatter Diagrams | |
The Relationship Between Speed and Accuracy | |
The Covariance | |
The Pearson Productâ€“Moment Correlation Coefficient (r) | |
Correlations with Ranked Data | |
Factors That Affect the Correlation | |
If Something Looks Too Good To Be True, Perhaps It Is | |
Testing the Significance of a Correlation Coefficient | |
Intercorrelation Matrices | |
Other Correlation Coefficients | |
Using MINITAB and SPSS to Obtain Correlation Coefficients | |
Seeing Statistics | |
A Final Worked Example | |
Summary | |
Exercises | |
10. REGRESSION | |
The Relationship Between Stress and Health | |
The Basic Data | |
The Regression Line | |
The Accuracy of Prediction | |
The Influence of Extreme Values | |
Hypothesis Testing in Regression | |
Computer Solution Using SPSS | |
Seeing Statistics | |
A Final Worked Example | |
Summary | |
Exercises | |
11. MULTIPLE REGRESSION | |
Overview | |
Course Evaluations Again | |
Residuals | |
The Visual Representation of Multiple Regression | |
Hypothesis Testing | |
Refining the Regression Equation | |
A Second Example: Height and Weight | |
A Third Example: Psychological Symptoms in Cancer Patients | |
Summary | |
Exercises | |
12. HYPOTHESIS TESTS APPLIED TO MEANS: ONE SAMPLE | |
Sampling Distribution of the Mean | |
Testing Hypotheses About Means When σ Is Known | |
Testing a Sample Mean When σ Is Unknown (The One-Sample t Test) | |
Factors That Affect the Magnitude of t and the Decision About H0. A Second Example: The Moon Illusion | |
How Large Is Our Effect? Confidence Limits on the Mean | |
Using JMP to Run One-Sample t Tests | |
A Final Worked Example | |
Seeing Statistics | |
Summary | |
Exercises | |
13. HYPOTHESIS TESTS APPLIED TO MEANS: TWO RELATED SAMPLES | |
Related Samples | |
An Example: Student's t Applied to Difference Scores | |
A Second Example: The Moon Illusion Again | |
Advantages and Disadvantages of Using Related Samples | |
How Large an Effect Have We Found? Using SPSS for t Tests on Related Samples | |
Summary | |
Exercises | |
14. HYPOTHESIS TESTS APPLIED TO MEANS: TWO INDEPENDENT SAMPLES | |
Distribution of Differences Between Means | |
Heterogeneity of Variance | |
Nonnormality of Distributions | |
A Second Example with Two Independent Samples | |
Effect Size Again | |
Confidence Limits on μ1â€“μ2. Use of Computer Programs for Analysis of Two Independent Sample Means | |
A Final Worked Example | |
Seeing Statistics | |
Summary | |
Exercises | |
15. POWER | |
The Basic Concept | |
Factors That Affect the Power of a Test | |
Effect Size | |
Power Calculations for the One-Sample t Test | |
Power Calculations for Differences Between Two Independent Means | |
Power Calculations for the t Test for Related Samples | |
Power Considerations in Terms of Sample Size | |
You Don't Have to Do It by Hand | |
Seeing Statistics | |
Summary | |
Exercises | |
16. ONE WAY ANALYSIS OF VARIANCE | |
The General Approach | |
The Logic of the Analysis of Variance | |
Calculations for the Analysis of Variance | |
Unequal Sample Sizes | |
Multiple Comparison Procedures | |
Violations of Assumptions | |
Magnitude of Effect | |
Use of JMP for a One-Way Analysis of Variance | |
A Final Worked Example | |
Seeing Statistics | |
Summary | |
Exercises | |
17. FACTORIAL ANALYSIS OF VARIANCE | |
Factorial Designs | |
The Extension of the Eysenck Study | |
Interactions | |
Simple Effects | |
Unequal Sample Sizes | |
Measures of Effect Size | |
A Second Example: Maternal Adaptation Revisited | |
Using SPSS for Factorial Analysis of Variance | |
Seeing Statistics | |
Summary | |
Exercises | |
18. REPEATED-MEASURES ANALYSIS OF VARIANCE | |
An Example: The Treatment of Migraine Headaches | |
Multiple Comparisons | |
Effect Size | |
Assumptions Involved in Repeated-Measures Designs | |
Advantages and Disadvantages of Repeated-Measures Designs | |
Using SPSS to Analyze Data in a Repeated-Measures Design | |
A Final Worked Example | |
Summary | |
Exercises | |
19. CHI-SQUARE | |
One Classification Variable: The Chi-Square Goodness-of-Fit Test | |
Two Classification Variables: Contingency Table Analysis | |
Correction for Continuity | |
Chi-Square for Larger Contingency Tables | |
The Problem of Small Expected Frequencies | |
The Use of Chi-Square as a Test on Proportions | |
Non-Independent Observations | |
MINITAB Analysis of Contingency Tables | |
A Final Worked Example | |
Effect Size | |
Seeing Statistics | |
Summary | |
Exercises | |
20. NONPARAMETRIC AND DISTRIBUTIONÂˇVFREE STATISTICAL TESTS | |
The Mannâ€“Whitney Test | |
Wilcoxon's Matched-Pairs Signed-Ranks Test | |
Kruskalâ€“Wallis One-Way Analysis of Variance | |
Friedman's Rank Test for k Correlated Samples | |
Summary | |
Exercises | |
21. CHOOSING THE APPROPRIATE ANALYSIS | |
Exercises and Examples | |
Appendix A: Arithmetic Review | |
Appendix B: Symbols and Notation | |
Appendix C: Basic Statistical Formulae | |
Appendix D: Dataset | |
Appendix E: Statistical Tables | |
Glossary | |
References | |
Answers to Selected Exercises | |
Index |