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Foreword by George Cobb | |
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Preface | |
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To the Instructor | |
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To the Student | |
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Acknowledgments | |
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Introduction: Why Data Matters | |
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Statistics in the News | |
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Basic Concepts of Statistical Thinking Presented in the Context of Categorical Data | |
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Proportions in Samples, Proportions in Populations | |
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The Most Popular News Statistics | |
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Percentage, Proportions, Raw Counts, Pie Charts, Bar Charts | |
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How Many People Are There? | |
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U.S. and World Populations, Population Growth, Proportional Changes, The Unemployment Rate, X-Y Plots | |
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Things Vary, and Small Samples Vary the Most | |
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The Law of Large Numbers | |
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The Pattern in Random Sample Proportions | |
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Taking a Good Sample of a Population | |
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Representative and Biased Samples, Random Sampling, Self-Selected Samples | |
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How Samples Vary | |
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Histograms, Bell Curves | |
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How Widely Samples Vary | |
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The Standard Error of a Proportion, The Normal Distribution | |
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Making Inferences | |
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Forecasting the Future | |
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Prediction Intervals for Sample Proportions | |
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What a Sample Reveals About a Population | |
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Confidence Intervals for Proportions in Populations | |
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The Story of Statistical Inference | |
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Null Hypothesis, P-Value, Alpha, Significance | |
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Testing Locations and Differences of Proportions | |
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Testing Where a Proportion Is | |
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The Z-Test | |
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How to Look for Differences in Chances | |
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Cross-Tabulations, Correlation, The Null Hypothesis of the Chi-Square Test | |
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Checking for No Correlation with the Chi-Square Test | |
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Chi-Square Distribution, Degrees of Freedom, Correlation Is Not Causation | |
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Averages and Other Number Line Statistics in the News | |
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Incomes and Other Quantities | |
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Medians, Number Line Observations,Means | |
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Which Tells the Truth-The Mean, the Median . . . or 281 the Weighted Mean? | |
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Standard Deviation, Spread,Weighted Means | |
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Inflation and the Consumer Price Index | |
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Adjusting for Inflation | |
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Statistics in Science | |
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Descriptive and Inferential Statistics for Continuous Data | |
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What Sample Data Distribution Reveals About the Population | |
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Exploratory Data Analysis | |
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Histograms, Stem-and-Leaf Plots, Box Plots | |
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Describing Number Line Variation | |
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Standard Deviation of Samples,Variance | |
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How to See the Future | |
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Prediction Intervals for Number Line Observations and Sample Means, Standard Error of Means, Confidence | |
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Intervals for Population Means, Central Limit Theorem | |
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Testing Treatments | |
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A Cautionary Tale | |
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William Gosset's Troubles with the Z-Test and the T-Distribution, The T-Test | |
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How to Test Whether a Treatment Works | |
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The Logic of Experiments, Correlational Studies | |
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Variances Between and Within | |
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Estimating the Population Variance from Variation Within Groups and from Variation Between Group Means | |
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Analysis of Variance | |
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Fisher's Analysis of Variance | |
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Calculating Fisher's F-Value | |
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What If the Data Are Not Normally Distributed? | |
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The Effect of Nonconstant Variances and Non-normality on ANOVA, Nonparametric Tests | |
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American Counties | |
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Correlation, Scatter Plots | |
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Best Lines | |
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Lines | |
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How to Calculate the Equation of a Line | |
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Finding Best-Fitting Lines | |
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The Least Squares Line | |
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An Excellent Line | |
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Calculating the Slope of the Regression Equation, Standard Error and Confidence Interval for the Regression Equation | |
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Tests of Regression | |
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How to Test the Regression Models | |
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R2, Pearson's r, A T-Test for the Regression Slope | |
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What | |