CiNii 図書 - How to think about statistics

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.

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.

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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How to think about statistics

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