Comprehending Behavioral Statistics
Russell T. Hurlburt(Author)
Brooks/Cole (Publisher)
2nd Edition
Published on 12. August 1997
Book
Hardback
544 pages
978-0-534-34889-2 (ISBN)
Article exhausted; check for reprint
Description
Hurlburt's eyeball-estimating approach to comprehending behavioural statistics trains students to think critically and discriminate among various statistical techniques. Using Hurlburt's methods, students learn to "read" a graph of data and quickly predict what the statistic will show. Hurlburt also effectively uses repetition and a progressive, cumulative integration of concepts, so students build upon what they learn as they progress through the course rather than learning individual topics in isolation. Hurlburt's approach not only motivates students, but instills in them a true conceptual understanding of what's going on behind the mechanical calculations of statistical procedures. This edition incorporates more "traditional" content and computer exercises that can be worked with standard statistical packages such as SPSS, SAS, or Minitab.
More details
Series
Edition
2nd Revised edition
Language
English
Place of publication
CA
United States
Publishing group
Cengage Learning, Inc
Target group
College/higher education
Edition type
Revised edition
Dimensions
Height: 286 mm
Width: 229 mm
Weight
1452 gr
ISBN-13
978-0-534-34889-2 (9780534348892)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Russell T. Hurlburt
Comprehending Behavioral Statistics
Book
08/2002
3rd Edition
Wadsworth Publishing Co Inc
€74.29
Article not available
Previous edition
Russell T. Hurlburt
Comprehending Behavioral Statistics
Software
06/2005
4th Edition
Wadsworth Publishing Co Inc
€74.76
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Content
Introduction: inductive statements; statistical reasoning; rational decision making; a classic example; samples from populations; probability; a note to the student; exercises for Chapter 1. Mathematical concepts: levels of measurement; continuous and discrete variables; summation; exercises for Chapter 2. Frequency distributions: distributions as tables; distributions as graphs; eyeball-estimation; the shape of distributions; eyeball-calibration; bar graphs of nominal and ordinal variables; connections; exercises for Chapter 3. Stem and leaf displays (Optional). Measures of central tendency: mode; median; mean; comparing the mode, median, and Mean; computing the population mean; connections; exercises for Chapter 4. Computing means from frequency distributions (Optional); Measures of variation: range; standard deviation; variance; eyeball-calibration for distributions; connections; exercises for Chapter 5. Computing the standard deviation from frequency distributions (Optional); Using frequency distributions: points in distributions; areas under normal distributions; other standardized distributions based on Z scores; relative frequencies of real-world normal variables; percentiles and percentile rank in normal distributions; connections; exercises for Chapter 6. Linear interpolation (Optional). Samples and the sampling distribution of the means: random samples; the sampling distribution of the means; connections; exercises for Chapter 7. Parameter estimation: point-estimation; distribution of sample means. confidence intervals; connections; exercises for Chapter 8. Evaluating hypotheses: descriptive versus inferential statistics; evaluating hypotheses; the procedure for evaluating hypotheses; connections; exercises for Chapter 9. Why statistical significance alone is not enough (Optional). Inferences about means of single samples: evaluating hypotheses about means; the relationship between hypothesis testing and confidence intervals; one-sample t test eyeball-calibration; statistical significance is not necessarily practical significance; connections; exercises for Chapter 10. eyeball-estimating one-sample t tests; (Optional). Inferences about means of two independent samples: hypotheses with two independent samples; the test statistic; standard error of the difference between two means; testing hypotheses about means of two independent samples; practical significance versus statistical significance revisited; two-sample t test eyeball-calibration; connections; exercises for Chapter 11. eyeball-estimating two-Independent-sample t tests (Optional); Inferences about means of two dependent samples and statistical power of t tests; dependent-sample tests; testing hypotheses about means of two dependent samples; comparing dependent- and independent-sample t tests; dependent-sample t test eyeball-calibration; statistical power; factors that increase power; using power to determine sample size.