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Seeing Through Statistics

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ISBN-10: 0534394027

ISBN-13: 9780534394028

Edition: 3rd 2005 (Revised)

Authors: Jessica M. Utts

List price: $178.95
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This third edition of Jessica Utts' popular book develops statistical literacy and critical thinking through real-world applications, with an emphasis on ideas, not calculations. This text focuses on the key concepts that educated citizens need to know about statistics. These ideas are introduced in interesting applied and real contexts, without using an abundance of technicalities and calculations that only serve to confuse students.
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Book details

List price: $178.95
Edition: 3rd
Copyright year: 2005
Publisher: Brooks/Cole
Publication date: 6/23/2004
Binding: Paperback
Pages: 584
Size: 7.25" wide x 9.00" long x 1.00" tall
Weight: 2.046

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…    

Finding Data in Life
The Benefits and Risks of Using Statistics
Detecting Patterns and Relationships
Don't Be Deceived by Improper Use of Statistics
Summary and Conclusions
Reading the News
The Educated Consumer of Data
Origins of News Stories
How to be a Statistics Sleuth: Seven Critical Components
Four Hypothetical Examples of Bad Reports
Planning Your Own Study: Defining the Components in Advance
Measurements, Mistakes, and Misunderstandings
Simple Measures Don't Exist
It's All in the Wording
Open or Closed Questions: Should Choices Be Given? Defining What is Being Measured
Defining a Common Language
How to Get a Good Sample
Common Research Strategies
Defining a Common Language
The Beauty of Sampling
Simple Random Sampling
Other Sampling Methods
Difficulties and Disasters in Sampling
Experiments and Observational Studies
Defining a Common Language
Designing a Good Experiment
Difficulties and Disasters in Experiments
Designing a Good Observational Experiment
Difficulties and Disasters in Observational Studies
Random Sample versus Random Assignment
Getting the Big Picture
Final Questions
Case Studies
Finding Life in Data
Summarizing and Displaying Measurement Data
Turning Data into Information
Picturing Data: Stemplots and Histograms
Five Useful Numbers: A Summary
Traditional Measures: Mean, Variance, and Standard Deviation
Caution: Being Average Isn't Normal
Bell-Shaped Curves and Other Shapes
Populations, Frequency Curves, and Proportions
The Pervasiveness of Normal Curves
Percentiles and Standardized Scores
z-Scores and Familiar Intervals
Plots, Graphs, and Pictures
Well-Designed Statistical Pictures
Pictures of Categorical Data
Pictures of Measurement Variables
Difficulties and Disasters in Plots, Graphs, and Pictures
A Checklist for Statistical Procedures
Relationships Between Measurement Variables
Statistical Relationships
Strength versus Statistical Significance
Measuring Strength Through Correlation
Specifying Linear Relationships through Regression
Relationships Can Be Deceiving
Illegitimate Correlations
Legitimate Correlation Does Not Imply Causation
Some Reasons for Relationships Between Variables
Confirming Causation
Relationships Between Categorical Variables
Displaying Relationships Between Categorical Variables
Relative Risk, Increased Risk, and Odds
Misleading Statistics about Risk
Simpson's Paradox: The Missing Third Variable
Statistical Significance for 2 x 2 Tables
Measuring the Strength of a Relationship
Steps for Assessing Statistical Significance
The Chi-Square Test
Practical versus Statistical Significance
Reading the Economic News
Cost of Living: The Consumer Price Index
Uses of the Consumer Price Index
Criticisms of the Consumer Price Index
Economic Indicators
Understanding and Reporting Trends over Time
Time Series
Components of Time Series
Seasonal Adjustments: Reporting the Consumer Price Index
Cautions and Checklist
Understanding Uncertainty in Life
Understanding Probability and Long-Term Expectations
The Relative-Frequency Interpretation
The Personal-Probability Interpretation
Applying Some Simple Probability Rules
When Will It Happen? Long-Term Gains, Losses, and Expectations
Psychological Influences on Personal Probability
Revisiting Personal Probability
Equivalent Probabilities: Different Decisions
How Personal Probabilities can Be Distorted
Optimism, Reluctance to Change, and Overconfidence
Calibrating Personal Probabilities of Experts
Tips for Improving Your Personal Probabilities and Judgments
When Intuition Differs from Relative Frequency
Revisiting Relative Frequency
The Gambler's Fallacy
Confusion of the Inverse
Using Expected Values to Make Wise Decisions
Making Judgments From Surveys and Experiments
The Diversity of Samples from the Same Population
Setting the Stage
What to Expect of Sample Proportions
What to Expect of Sample Means
What to Expect in Other Situations
Estimating Proportions with Confidence
Confidence Intervals
Three Examples of Confidence Intervals from the Media
Constructing a Confidence Interval for a Proportion
The Role of Confidence Intervals in Research
Confidence Intervals for Population Means
Confidence Intervals for the Difference Between Two Means
Revisiting Case Studies: How Journals Present Confidence Intervals
Understanding Any Confidence Interval
Rejecting Chance--Testing Hypotheses in Research
Using Data to Make Decisions
The Basic Steps for Testing Hypotheses
Testing Hypotheses for Proportions
What Can Go Wrong: The Two Types of Errors
Hypothesis Testing--Examples and Case Studies
How Hypothesis Tests are Reported in the News
Testing Hypotheses about Proportions and Means
Revisiting Case Studies: How Journals Present Hypothesis Tests
Significance, Importance, and Undetected Differences
Real Importance versus Statistical Significance
The Role of Sample Size in Statistical Significance
No Difference versus No Statistically Significant Difference
A Summary of Warnings
Meta-Analysis: Resolving Inconsistencies across Studies
The Need for Meta-Analysis
Two Important Decisions for the Analyst
Some Benefits of Meta-Analysis
Criticisms of Meta-Analysis
Ethics in Statistical Studies
Ethical Treatment of Human and Animal Participants
Assurance of Data Quality
Appropriate Statistical Analyses
Fair Reporting of Results
Putting What You Have Learned to the Test
Case Studies
Solutions to Selected Exercises