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Statistics for the Behavioral Sciences / Edition 7

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Overview

Frederick J. Gravetter and Larry B. Wallnau combine an intuitive, easy-to-understand writing style with a wide variety of built-in learning aids and real-world examples. Applications are integrated to ensure that even students with a weak background in mathematics can achieve mastery of basic statistical concepts. Students using the book go beyond thinking of statistics as just a part of a course they have to take, instead coming to appreciate it as a growing field that helps us make sense of data in our world. The authors skillfully demonstrate to students that having an understanding of a variety of statistical procedures will help them understand published findings, as well as become savvy consumers of information.

Covers frequency distributions/central tendency/ probability/hypothesis tests w/2 independent samples/etc.

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Editorial Reviews

Booknews
Teaches methods of statistics and how to apply them appropriately. This fifth edition contains revised problems and learning checks, and is divided into sections on descriptive statistics, foundations of inferential statistics, inferential statistics for sample mean differences, and non-parametric statistics. Each section is preceded by a brief synopsis of goals and direction of material to be covered. Includes chapter summaries, problem solving tips, worked examples, and problems. The authors are affiliated with the State University of New York College at Brockport. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780495095200
  • Publisher: Cengage Learning
  • Publication date: 5/12/2006
  • Edition description: 7TH
  • Edition number: 7
  • Pages: 792
  • Product dimensions: 8.10 (w) x 10.32 (h) x 1.36 (d)

Meet the Author

Frederick J Gravetter is Professor Emeritus of Psychology at The College at Brockport, State University of New York College. While teaching at Brockport, he specialized in statistics, research design, and cognitive psychology. Dr. Gravetter received his Bachelor's degree in mathematics from M.I.T. and his Ph.D. in psychology from Duke University. In addition to publishing several research articles, Dr. Gravetter is co-author of the best-selling STATISTICS FOR THE BEHAVIORAL SCIENCES, 9th Edition, ESSENTIALS OF STATISTICS FOR THE BEHAVIORAL SCIENCES, 8th Edition, and (with Lori-Ann B. Forzano) RESEARCH METHODS FOR THE BEHAVIORAL SCIENCES, 5th Edition.

Larry B. Wallnau is Professor Emeritus of Psychology at the State University of New York College at Brockport. The recipient of grants and awards in both research and teaching, Dr. Wallnau has published numerous research articles primarily on the effect of psychotropic drugs. With Frederick J Gravetter, he has co-authored previous editions of STATISTICS FOR THE BEHAVIORAL SCIENCES, now in its Ninth Edition, and ESSENTIALS OF STATISTICS FOR THE BEHAVIORAL SCIENCES, now in its Sixth Edition.

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Table of Contents

1. INTRODUCTION TO STATISTICS. Preview. Statistics, Science, and Observations. Populations and Samples. The Scientific Method and the Design of Research Studies. Scales of Measurement. Discrete and Continuous Variables. Statistical Notation. Summary. Focus on Problem Solving. Demonstrations. Problem. 2. FREQUENCY DISTRIBUTIONS. Preview. Overview. Frequency Distribution Tables. Frequency Distribution Graphs. The Shape of a Frequency Distribution. Percentiles, Percentile Ranks, and Interpolation. Stem and Leaf Displays. Summary. Focus on Problem Solving. Demonstrations. Problem. 3. CENTRAL TENDENCY. Preview. Overview. The Mean. The Median. The Mode. Selecting a Measure of Central Tendency. Central Tendency and the Shape of the Distribution. Summary. Focus on Problem Solving. Demonstrations. Problem. 4. VARIABILITY. Preview. Overview. The Range. The Interquartile Range and Semi-Interquartile Range. Standard Deviation and Variance for a Population. Standard Deviation and Variance for Samples. Properties of the Standard Deviation. Comparing Measures of Variability. The Role of Variability in Descriptive and Inferential Statistics. Summary. Focus on Problem Solving. Demonstrations. Problem. 5. Z-SCORES: LOCATION OF SCORES AND STANDARDIZED DISTRIBUTIONS. Introduction to z-Scores. z-Scores and Location in a Distribution. Using z-Scores to Standardize a Distribution. Other Standardized Distributions Based on z-Scores. Summary. Focus on Problem Solving. Demonstrations. Problems. 6. PROBABILITY. Preview. Overview. Introduction to Probability. Probability and the Normal Distribution. Percentiles and Percentile Ranks. Probability and the Binomial Distribution. Summary. Focus on Problem Solving.Demonstrations. Problems. 7. PROBABILITY AND SAMPLES: THE DISTRIBUTION OF SAMPLE MEANS. Preview. Overview. The Distribution of Sample Means. Probability and the Distribution of Sample Means. More About Standard Error. Summary. Focus on Problem Solving. Demonstrations. Problems. 8. INTRODUCTION TO HYPOTHESIS TESTING. The Logic of Hypothesis Testing. Uncertainty and Errors in Hypothesis Testing. An Example of a Hypothesis Test. Directional (One-Tailed) Hypothesis Tests. The General Elements of Hypothesis Testing: A Review. Statistical Power. Summary. Focus on Problem Solving. Problems. 9. INTRODUCTION TO THE t STATISTIC. Preview. Overview. The t Statistic—A Substitute for z. Hypothesis Tests with the t Statistic. Summary. Focus on Problem Solving. Demonstrations. Problems. 10. HYPOTHESIS TESTS WITH TWO INDEPENDENT SAMPLES. Preview. Overview. The t Statistic for an Independent-Measures Research Design. Hypothesis Tests with the Independent-Measures t Statistic. Assumptions Underlying the Independent-Measures t Formula. Summary. Focus on Problem Solving. Demonstrations. Problems. 11. HYPOTHESIS TESTS WITH RELATED SAMPLES. Preview. Overview. The t Statistic for Related Samples. Hypothesis Tests for the Repeated-Measures Design. Hypothesis Testing with a Matched-Subjects Design. Uses and Assumptions for Related-Samples t Tests. Summary. Focus on Problem Solving. Demonstrations. Problems. 12. ESTIMATION. Preview. An Overview of Estimation. Estimation with the z-Score. Estimation with the t Statistic. Factors Affecting the Width of a Confidence Interval. Summary. Focus on Problem Solving. Problems. 13. INTRODUCTION TO ANALYSIS OF VARIANCE. Preview. Introduction. The Logic of Analysis of Variance. ANOVA Vocabulary, Notation, and Formulas. The Distribution of F-Ratios. Examples of Hypothesis Testing with ANOVA. Post Hoc Tests. The Relationship Between ANOVA and t Tests. Summary. Focus on Problem Solving. Demonstrations. Problems. 14. REPEATED-MEASURES ANALYSIS OF VARIANCE (ANOVA). Preview. Overview. Notation and Formulas for Repeated-Measures ANOVA. Testing Hypotheses with the Repeated-Measures ANOVA. Advantages of the Repeated-Measures Design. Assumptions of the Repeated-Measures ANOVA. Summary. Focus on Problem Solving. Demonstrations. Problems. 15. TWO-FACTOR ANALYSIS OF VARIANCE (INDEPENDENT MEASURES). Preview. Overview. Main Effects and Interactions. Notation and Formulas. Examples of the Two-Factor ANOVA. Assumptions for the Two-Factor ANOVA. Summary. Focus on Problem Solving. Demonstrations. Problems. 16. CORRELATION AND REGRESSION. Preview. Overview. The Pearson Correlation. Understanding and Interpreting the Pearson Correlation. Hypothesis Tests with the Pearson Correlation. The Spearman Correlation. Other Measures of Relationship. Introduction to Regression. Summary. Focus on Problem Solving. Demonstrations. Problems. Summary. Focus on Problem Solving. Demonstrations. Problems. 17. THE CHI-SQUARE STATISTIC: TESTS FOR GOODNESS OF FIT AND INDEPENDENCE. Preview. Parametric and Non-Parametric Statistical Tests. The Chi-Square Test for Goodness of Fit. The Chi-Square Test for Independence. Assumptions and Restrictions for Chi-Square Tests. Special Applications of the Chi-Square Tests. Summary. Focus on Problem Solving. Demonstrations. Problems. 18. THE BINOMIAL TEST. Preview. Overview. The Binomial Test. The Relationship Between Chi-Square and the Binomial Test. The Sign Test. Summary. Focus on Problem Solving. Demonstrations. Problems. 19. STATISTICAL TECHNIQUES FOR ORDINAL DATA: MANN-WHITNEY, WILCOXON, AND KRUSKAL-WALLIS TESTS. Preview. Data from an Ordinal Scale. The Mann-Whitney U-Test. The Wilcoxon Signed-Ranks Test. Kruskal-Wallis Test. Summary. Focus on Problem Solving. Demonstrations. Problems. APPENDICES. STATISTICS ORGANIZER. REFERENCES. INDEX.
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