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Statistical Ideas and Methods

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ISBN-10: 0534402844

ISBN-13: 9780534402846

Edition: 2006

Authors: Robert F. Heckard, Jessica M. Utts

List price: $158.95
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Book details

List price: $158.95
Copyright year: 2006
Publisher: Brooks/Cole
Publication date: 1/26/2005
Binding: Mixed Media
Pages: 668
Size: 8.07" wide x 10.08" long x 1.38" tall
Weight: 3.806

Robert F. Heckard is a senior lecturer in statistics at the Pennsylvania State University, where he has taught for more than 30 years. He has taught introductory and intermediate applied statistics to more than 15,000 college students. Bob has been awarded several grants to develop multimedia and web-based instructional materials for teaching statistical concepts. Aside from MIND ON STATISTICS, he is the co-author of STATISTICAL IDEAS AND METHODS (first edition, 2006, Cengage Learning). As a consultant, he is active in the statistical analysis and design of highway safety research and has frequently been a consultant in cancer treatment clinical trials.

Jessica Utts is Professor of Statistics at the University of California at Irvine. She received her B.A. in math and psychology at SUNY Binghamton, and her M.A. and Ph.D. in statistics at Penn State University. Aside from MIND ON STATISTICS, she is the author of SEEING THROUGH STATISTICS and the co-author with Robert Heckard of STATISTICAL IDEAS AND METHODS both published by Cengage Learning. Jessica has been active in the Statistics Education community at the high school and college level. She served as a member and then chaired the Advanced Placement Statistics Development Committee for six years, and was a member of the American Statistical Association task force that produced the GAISE…    

Statistics Success Stories and Cautionary Tales
What is Statistics?
Seven Statistical Stories with Morals
The Common Elements in the Seven Stories
Turning Data Into Information
Raw Data
Types of Data
Summarizing One or Two Categorical Variables
Finding Information in Quantitative Data
Pictures for Quantitative Data
Numerical Summaries of Quantitative Variables
Bell-Shaped Distributions of Numbers
Gathering Useful Information
Description or Decision?
Using Data Wisely
Speaking the Language of Research Studies
Designing a Good Experiment
Designing a Good Observational Study
Difficulties and Disasters in Experiments and Observational Studies
Sampling: Surveys and How to Ask Questions
The Beauty of Sampling
Sampling Methods
Difficulties and Disasters in Sampling
How to Ask Survey Questions
Relationships Between Quantitative Variables
Looking for Patterns with Scatterplots
Describing Linear Patterns with a Regression Line
Measuring Strength and Direction with a Regression Line
Why Answers May Not Make Sense
Correlation Does Not Prove Causation
Relationships Between Categorical Variables
Displaying Relationships between Categorical Variables
Risk, Relative Risk, Odds Ratio, and Increased Risk
Misleading Statistics about Risk
The Effect of a Third Variable and Simpson's Paradox
Assessing the Statistical Significance of a 2 x 2 Table
Random Circumstances
Interpretations of Probability
Probability Definitions and Relationships
Basic Rules for Finding Probabilities
Strategies for Finding Complicated Probabilities
Using Simulation to Estimate Probabilities
Coincidences and Intuitive Judgments about Probability
Random Varaibles
What is a Random Variable?
Discrete Random Variables
Expectations for Random Variables
Binomial Random Variables
Continuous Random Variables
Normal Random Variables
Approximating Binominal Distribution Probabilities
Sums, Differences, and Combinations of Random Variables
Means and Proportions as Random Variables
Understanding Dissimilarity among Samples
Sampling Distributions for Sample Proportions
What to Expect of Sample Means
What to Expect in Other Situations: Central Limit Theorem
Sampling Distribution for Any Statistic
Standardized Statistics
Student's t-Distribution: Replacing ? with s
Statistical Inference
Estimating Proportions with Confidence
The Language and Notation of Estimation
Margin of Error
Confidence Intervals
Calculating a Margin of Error for 95% Confidence
General Theory of Confidence Intervals for a Proportion
Choosing a Sample Size for a Survey
Using Confidence Intervals to Guide Decisions
Testing Hypotheses About Proportions
Formulating Hypothesis Statements
The Logic of Hypothesis Testing: What if the Null is True?
Reaching a Conclusion about the Two Hypotheses
Testing Hypotheses about a Proportion
The Role of Sample Size in Statistical Significance
Real Importance versus Statistical Significance
What Can Go Wrong: The Two Types of Errors
More About Confidence Intervals
Examples of Different Estimation Situations
Standard Errors
Approximate 95% Confidence Intervals
General Confidence Intervals for One Mean or Paired Data
General Confidence Intervals for the Difference Between Two Means (Independent Samples)
The Difference between Two Proportions (Independent Samples)
Understanding Any Confidence Interval
More About Significance Tests
The General Ideas of Significance Testing
Testing Hypotheses about One Mean or Paired Data
Testing the Difference Between Two Means (Independent Samples)
Testing the Difference between Two Population Proportions
The Relationship between Significance Tests and Confidence Intervals
The Two Types of Errors and Their Probabilities
Evaluating Significance in Research Reports
More About Regression
Sample and Population Regression Models
Estimating the Standard Deviation for Regression
Inference about the Linear Regression Relationship
Predicting the Value y for an Individual
Estimating the Mean y at a Specified x
Checking for Conditions for Using regression Models for Inference
More About Categorical Variables
The Chi-Square Test for Two-Way Tables
Analyzing 2 x 2 Tables
Testing Hypotheses about One Categorical Variable: Goodness of Fit
Analysis of Variance
Comparing Means with the ANOVA F-Test
Details of One-Way Analysis of Variance
Other Methods for Comparing Populations
Two-Way Analysis of Variance
Additional Discrete Random Variables
Hypergeometric Distribution
Poisson Distribution
Multinomial Distribution
Nonparametric Tests of Hypotheses
The Sign Test
The Two-Sample Rank-Sum Test
The Wilcoxon Signed-Rank Test
The Kruskal-Wallis Test
Multiple Regression
The Multiple Linear Regression Model
Inference about Multiple Regression Models
Checking Conditions for Multiple Linear Regression
Two-Way Analysis of Variance
Assumptions and Models for Two-Way ANOVA
Testing for Main Effects and Interactions
Ethical Treatment of Human and Animal Participants
Assurance of Data Quality
Appropriate Statistical Analysis
Fair Reporting of Results
Turning Information into Wisdom
Beyond the Data
Transforming Uncertainty into Wisdom
Making Personal Decisions
Control of Societal Risks
Understanding Our World
Getting to Know You
Words to the Wise