Overview

This package provides a tour of the topics covered in most behavioral science statistics textbooks: descriptive statistics, the logic of hypothesis testing, tests, power analysis, confidence intervals, analysis of variance, correlation/regression, and non-parametric inferential statistics. Yet, it employs a radically different pedagogical approach. Without wholly abandoning the tradition of using a printed textbook to supplement classroom or online instruction, this system has at its core an interactive set of components that run through Web browsers such as Internet Explorer or Netscape Navigator. Working through these components, students create their own customized learning experience, rather than passively reading a printed text. The end result is students who can better master and perhaps even enjoy a subject that many approach with trepidation.

Full Product Details

Author:   Pepper Williams
Publisher:   Sinauer Associates Inc.,U.S.
Imprint:   Sinauer Associates Inc.,U.S.
Edition:   illustrated edition
Dimensions:   Width: 21.50cm , Height: 1.60cm , Length: 27.50cm
Weight:   0.934kg
ISBN:  

9780878939305

ISBN 10:   087893930
Pages:   100
Publication Date:   01 January 2004
Audience:   College/higher education ,  Undergraduate
Format:   Digital
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

PART 1: Introduction to Statistics - Statistics in Society and Science - Descriptive and Inferential Statistics - Populations and Samples - Statistical Notation - Chapter Summary/Review - PART 2: Looking at Data: Frequency Distributions - Organizing Data - Frequency Distribution Tables - Frequency Distribution Histograms - Describing Distributions - Chapter Summary/Review - PART 3: Describing Data: Measuring Center and Spread - The Need to Describe - Measuring Center: The Mean - Measuring Spread: The Standard Deviation - Resistant Measures of Center and Spread - Correlation - z-Scores - Chapter Summary/Review - PART 4: Preparing To Test Hypotheses - Probability - Normal Distributions - The Central Limit Theorem - Chapter Summary/Review - PART 5: From Samples to Populations: Hypothesis Testing with z - What Counts as a Significant Increase? - Logic of Hypothesis Testing - Hypothesis Testing with z-Scores - Directional Hypothesis Tests - Hypothesis Test Assumptions - Evaluating Evidence - Chapter Summary/Review - PART 6: Practical Hypothesis Testing with t - The Problem with z - The t Statistic - Hypothesis Testing for Single Samples with t - Assumptions for t Tests - Reporting Hypothesis Tests - When Should You Use t vs. z? - What You Can Test with t - Chapter Summary/Review - PART 7: Making and Avoiding Hypothesis Test Errors - When Hypothesis Tests Fail - Fishing for Significance - Reporting Failed Hypothesis Tests - Power - Chapter Summary/Review - PART 8: t Tests for Two Means - Two-Condition Experimental Designs - Repeated Measures - Independent Samples - Assumptions for Two-Condition t Tests - Comparing Two-Condition Designs - Chapter Summary/Review - PART 9: Confidence Intervals - Estimation - Constructing Confidence Intervals - Confidence Intervals - Chapter Summary/Review - PART 10: Inference for Three or More Means: Analysis of Variance (ANOVA) - Introduction to ANOVA - The Logic of ANOVA - Testing Hypotheses with Independent-Samples ANOVA - ANOVA for Related Samples - Further Analysis of Multicondition Datasets - F vs. t - Multiple-Factor ANOVA - Chapter Summary/Review - PART 11: Describing Relationships: Correlation and Regression - Covariability - Characteristics of Relationships - The Pearson Correlation - Linear Regression - Using Correlation and Regression in Research - Multiple Regression/Correlation - Chapter Summary/Review - PART 12: Inference for Categorical Data: Chi-Square Tests - Categorical Data - Testing One Sample: The Chi-Square Goodness-of-Fit Test - Testing Two Related Samples: The Sign Test - Testing Independent Samples: The Test for Independence - Testing Relationships between Categorical Variables - Opinion Polls and the Binomial Test - Chapter Summary/Review

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Author Information

PEPPER WILLIAMS is Lecturer at Portland State University, USA.

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