How to think about statistics

書誌事項

How to think about statistics

John L. Phillips, Jr

(A series of books in psychology)

W.H. Freeman, c1992

Rev. ed

  • : hard
  • : pbk

大学図書館所蔵 件 / 19

この図書・雑誌をさがす

注記

Includes bibliographical references (p. [193]-194) and index

内容説明・目次

巻冊次

: pbk ISBN 9780716722878

内容説明

Modern life is inundated with statistical data-polls, surveys, economic indicators, advertising claims, and research findings. You can't avoid the numbers, but you can learn to determine what they really mean - even if you suffer from "math-phobia." This revised edition offers a common sense method for understanding the statistics that affect your decisions and performance in business, in school, as a consumer, and as a voter. Rather than focus on mathematics and computations, this concise volume familiarizes the reader with the underlying logic of statistical analysis and problem-solving. It reveals how empirical studies are conceived, gathered, reported, interpreted-and sometimes obscured and distorted. The revised edition introduces fundamental concepts, using familiar, concrete examples; develops clearly the implications of those concepts; moves logically from one concept to the next, building a solid framework for interpreting statistical data; Includes numerous sample applications drawn from the fields of education, political science, psychology, social work, and sociology. With dozens of refinements and a new organization, this revised edition should help the reader think critically about the statistical claims and arguments you encounter every day.

目次

  • 1 Introduction: The Task
  • The Basic Ideas
  • Facing Mathphobia
  • 2 Frequency Distributions: Normal Distributions
  • Skewed Distributions
  • Other Configurations
  • Summary
  • 3 Measures of Central Tendency: The Mean (X)
  • The Median (Mdn)
  • The Mode
  • Summary
  • Sample Applications
  • 4 Measures of Variability: The Standard Deviation (S)
  • The Interquartile Range
  • The Range
  • Degrees of Freedom
  • Summary
  • Sample Applications
  • 5 Interpreting Individual Measures: Standard Scores: The z Scale
  • Other Standard Scores
  • Centile (or Percentile) Scores
  • Age and Grade Norms
  • Summary
  • Sample Applications
  • 6 Precision of Measurement
  • Standard Errors
  • Confidence Intervals and Levels of Confidence
  • Effect of N on Standard Error
  • Summary
  • Sample Applications
  • 7 Significance of a Difference between Two Means: An Example
  • Test of Significance: The z Ratio
  • Test of Significance: The t Ratio
  • Significance Levels
  • A Common Misinterpretation
  • One- versus Two-Tail Tests
  • Statistical versus Practical Significance
  • Summary
  • Sample Applications
  • Contents 8 More on the Testing of Hypotheses: Comparison of Frequencies: Chi-Square
  • Multimean Comparisons: Analysis of Variance
  • Summary
  • Sample Applications
  • 9 Correlation: The Rank-Difference Coefficient (p)
  • The Product-Moment Coefficient (r)
  • Effect of Restricted Variability
  • Standard Scores in Correlation
  • A Matrix of Correlations
  • Two Ways of Quantifying Reliability
  • Expectancy Tables and Predictive Validity
  • Reliability and Validity
  • Summary
  • Sample Applications
  • 10 Correlation and Causation: Correlational versus Experimental Studies
  • Continuous versus Discontinuous Variables and Measurements
  • Correlation as an Index of Causation
  • Summary
  • 11 Summary: Appendix 1: Tests of Significance. Appendix II: List of Symbols.
巻冊次

: hard ISBN 9780716722885

内容説明

This edition of "How to Think About Statistics" offers a commonsense method for understanding the statistics that affect the reader's decisions and performance in business, in school, as a consumer, and as a voter. Rather than focus on mathematics and computations, this concise volume familiarizes the reader with the underlying logic of statistical analysis and problem-solving. It reveals how empirical studies are conceived, gathered, reported, interpreted - and sometimes obscured and distorted. This edition of the book introduces fundamental concepts, using familiar, concrete examples; develops clearly the implications of those concepts; moves logically from one concept to the next, building a solid framework for interpreting statistical data; includes numerous sample applications drawn from the fields of education, political science, psychology, social work, and sociology.

目次

  • 1 Introduction: The Task
  • The Basic Ideas
  • Facing Mathphobia
  • 2 Frequency Distributions: Normal Distributions
  • Skewed Distributions
  • Other Configurations
  • Summary
  • 3 Measures of Central Tendency: The Mean (X)
  • The Median (Mdn)
  • The Mode
  • Summary
  • Sample Applications
  • 4 Measures of Variability: The Standard Deviation (S)
  • The Interquartile Range
  • The Range
  • Degrees of Freedom
  • Summary
  • Sample Applications
  • 5 Interpreting Individual Measures: Standard Scores: The z Scale
  • Other Standard Scores
  • Centile (or Percentile) Scores
  • Age and Grade Norms
  • Summary
  • Sample Applications
  • 6 Precision of Measurement
  • Standard Errors
  • Confidence Intervals and Levels of Confidence
  • Effect of N on Standard Error
  • Summary
  • Sample Applications
  • 7 Significance of a Difference between Two Means: An Example
  • Test of Significance: The z Ratio
  • Test of Significance: The t Ratio
  • Significance Levels
  • A Common Misinterpretation
  • One- versus Two-Tail Tests
  • Statistical versus Practical Significance
  • Summary
  • Sample Applications
  • Contents 8 More on the Testing of Hypotheses: Comparison of Frequencies: Chi-Square
  • Multimean Comparisons: Analysis of Variance
  • Summary
  • Sample Applications
  • 9 Correlation: The Rank-Difference Coefficient (p)
  • The Product-Moment Coefficient (r)
  • Effect of Restricted Variability
  • Standard Scores in Correlation
  • A Matrix of Correlations
  • Two Ways of Quantifying Reliability
  • Expectancy Tables and Predictive Validity
  • Reliability and Validity
  • Summary
  • Sample Applications
  • 10 Correlation and Causation: Correlational versus Experimental Studies
  • Continuous versus Discontinuous Variables and Measurements
  • Correlation as an Index of Causation
  • Summary
  • 11 Summary: Appendix 1: Tests of Significance. Appendix II: List of Symbols.

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