Statistical Ideas and Methods (with CD-ROM and Internet Companion for Statistics)

ISBN-10: 0495122505
ISBN-13: 9780495122500
Edition: 2006
List price: $211.95
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Description: Emphasizing the conceptual development of statistical ideas, STATISTICAL IDEAS AND METHODS actively engages students and explains topics in the context of excellent examples and case studies. This text balances the spirit of statistical literacy  More...

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Book details

List price: $211.95
Copyright year: 2006
Publisher: Brooks/Cole
Publication date: 1/26/2005
Binding: Mixed Media
Pages: 668
Size: 8.00" wide x 10.25" long x 1.25" tall
Weight: 3.586
Language: English

Emphasizing the conceptual development of statistical ideas, STATISTICAL IDEAS AND METHODS actively engages students and explains topics in the context of excellent examples and case studies. This text balances the spirit of statistical literacy with statistical methodology taught in the introductory statistics course. Jessica Utts and Robert Heckard built the book on two learning premises: (1) New material is much easier to learn and remember if it is related to something interesting or previously known; (2) New material is easier to learn if you actively ask questions and answer them for yourself. More than any other text available, STATISTICAL IDEAS AND METHODS motivates students to develop their statistical intuition by focusing on analyzing data and interpreting results as opposed to focusing on mathematical formulation. STATISTICAL IDEAS AND METHODS provides the exciting coverage from the authors' acclaimed MIND ON STATISTICS along with coverage of additional discrete random variables, nonparametric tests of hypotheses, multiple regression, two-way analysis of variance, and ethics.

Jessica M. Utts is interested in applying statistics to issues of public interest and in educating students and others to be critical consumers of statistical information. Her research has focused on applications ranging from parapsychology to the impacts of transportation on air quality. In the area of statistics education, Professor Utts is the author of SEEING THROUGH STATISTICS (3rd Edition, 2005, Duxbury Press) and the co-author of MIND ON STATISTICS (2nd Edition, 2004, Duxbury Press). She is also the Editor-in-Chief for CYBERSTATS, a Web-based statistics course currently being used in classrooms across the United States. A recipient of two distinguished teaching awards, she frequently presents lectures and workshops on innovative methods for teaching statistics. She was a member of the College Board's Advanced Placement Statistics Development Committee from 1996-2002, serving as Chair from 1999-2002. Professor Utts has appeared on numerous television programs, including ABC's "20/20," "Nightline," "Larry King Live" and "CNN Morning News," discussing topics ranging from parapsychology to luck and lotteries. She has also served on numerous panels and advisory boards in government and industry. She is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science.

Robert F. Heckard is a senior lecturer in statistics at the Pennsylvania State University, where he has taught for over 30 years. He has taught introductory and intermediate applied statistics to more than 15,000 college students. Heckard has been awarded several grants to develop multimedia and web-based instructional materials for teaching statistical concepts, and is a co-author of CyberStats, a Web-based introductory course. 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.

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
Probability
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 Regress

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