Efnisyfirlit

• About this Book
  • Cover Page
  • Title Page
  • Copyright
  • Brief Contents
  • About the Authors
  • Contents
  • To Teachers: About This Book
  • Preface
  • To Students: What Is Statistics?
  • Applications
  • Data Table Index
  • Beyond the Basics Index
• Part I Looking at Data
  • Chapter 1 Looking at Data—Distributions
    • Introduction
    • 1.1 Data
      • Key characteristics of a data set
    • Section 1.1 Summary
    • Section 1.1 Exercises
    • 1.2 Displaying Distributions with Graphs
      • Categorical variables: Bar graphs and pie charts
      • Quantitative variables: Stemplots and histograms
      • Histograms
      • Examining distributions
      • Dealing with outliers
      • Time plots
    • Section 1.2 Summary
    • Section 1.2 Exercises
    • 1.3 Describing Distributions with Numbers
      • Measuring center: The mean
      • Measuring center: The median
      • Comparing the mean and the median
      • Measuring spread: The quartiles
      • The five-number summary and boxplots
      • The 1.5 × IQR rule for suspected outliers
      • Measuring spread: The standard deviation
      • Properties of the standard deviation
      • Choosing measures of center and spread
      • Changing the unit of measurement
    • Section 1.3 Summary
    • Section 1.3 Exercises
    • 1.4 Density Curves and Normal Distributions
      • Density curves
      • Measuring center and spread for density curves
      • Normal distributions
      • The 68–95–99.7 rule
      • Standardizing observations
      • Normal distribution calculations
      • Using the standard Normal table
      • Inverse Normal calculations
      • Normal quantile plots
    • Beyond the Basics: Density estimation
    • Section 1.4 Summary
    • Section 1.4 Exercises
    • Chapter 1 Exercises
  • Chapter 2 Looking at Data—Relationships
    • Introduction
    • 2.1 Relationships
      • Examining relationships
    • Section 2.1 Summary
    • Section 2.1 Exercises
    • 2.2 Scatterplots
      • Interpreting scatterplots
      • The log transformation
      • Adding categorical variables to scatterplots
      • Scatterplot smoothers
      • Categorical explanatory variables
    • Section 2.2 Summary
    • Section 2.2 Exercises
    • 2.3 Correlation
      • The correlation r
      • Properties of correlation
    • Section 2.3 Summary
    • Section 2.3 Exercises
    • 2.4 Least-Squares Regression
      • Fitting a line to data
      • Prediction
      • The least-squares regression line
      • Facts about least-squares regression
      • Correlation and regression
      • Interpretation of r2
    • Section 2.4 Summary
    • Section 2.4 Exercises
    • 2.5 Cautions about Correlation and Regression
      • Extrapolation
      • Residuals
      • The distribution of the residuals
      • Outliers and influential observations
      • Beware of the lurking variable
      • Beware of correlations based on averaged data
      • Beware of restricted ranges
    • Beyond the Basics: Data mining
    • Section 2.5 Summary
    • Section 2.5 Exercises
    • 2.6 Data Analysis for Two-Way Tables
      • The two-way table
      • Joint distribution
      • Marginal distributions
      • Describing relations in two-way tables
      • Conditional distributions
      • Simpson’s paradox
    • Section 2.6 Summary
    • Section 2.6 Exercises
    • 2.7 The Question of Causation
      • Explaining association
      • Establishing causation
    • Section 2.7 Summary
    • Section 2.7 Exercises
    • Chapter 2 Exercises
  • Chapter 3 Producing Data
    • Introduction
    • 3.1 Sources of Data
      • Anecdotal data
      • Available data
      • Sample surveys and experiments
    • Section 3.1 Summary
    • Section 3.1 Exercises
    • 3.2 Design of Experiments
      • Comparative experiments
      • Randomization
      • Randomized comparative experiments
      • How to randomize
      • Randomization using software
      • Randomization using random digits
      • Cautions about experimentation
      • Matched pairs designs
      • Block designs
    • Section 3.2 Summary
    • Section 3.2 Exercises
    • 3.3 Sampling Design
      • Simple random samples
      • How to select a simple random sample
      • Stratified random samples
      • Multistage random samples
      • Cautions about sample surveys
    • Beyond the Basics: Capture-recapture sampling
    • Section 3.3 Summary
    • Section 3.3 Exercises
    • 3.4 Ethics
      • Institutional review boards
      • Informed consent
      • Confidentiality
      • Clinical trials
      • Behavioral and social science experiments
    • Section 3.4 Summary
    • Section 3.4 Exercises
    • Chapter 3 Exercises
• Part II Probability and Inference
  • Chapter 4 Probability: The Study of Randomness
    • Introduction
    • 4.1 Randomness
      • The language of probability
      • Thinking about randomness
      • The uses of probability
    • Section 4.1 Summary
    • Section 4.1 Exercises
    • 4.2 Probability Models
      • Sample spaces
      • Probability rules
      • Assigning probabilities: Finite number of outcomes
      • Assigning probabilities: Equally likely outcomes
      • Independence and the multiplication rule
      • Applying the probability rules
    • Section 4.2 Summary
    • Section 4.2 Exercises
    • 4.3 Random Variables
      • Discrete random variables
      • Continuous random variables
      • Normal distributions as probability distributions
    • Section 4.3 Summary
    • Section 4.3 Exercises
    • 4.4 Means and Variances of Random Variables
      • The mean of a random variable
      • Statistical estimation and the law of large numbers
      • Thinking about the law of large numbers
    • Beyond the Basics: More laws of large numbers
      • Rules for means
      • The variance of a random variable
      • Rules for variances and standard deviations
    • Section 4.4 Summary
    • Section 4.4 Exercises
    • 4.5 General Probability Rules
      • General addition rules
      • Conditional probability
      • General multiplication rules
      • Tree diagrams
      • Bayes’s rule
      • Independence again
    • Section 4.5 Summary
    • Section 4.5 Exercises
    • Chapter 4 Exercises
  • Chapter 5 Sampling Distributions
    • Introduction
    • 5.1 Toward Statistical Inference
      • Sampling variability
      • Sampling distributions
      • Bias and variability
      • Sampling from large populations
      • Why randomize?
    • Section 5.1 Summary
    • Section 5.1 Exercises
    • 5.2 The Sampling Distribution of a Sample Mean
      • The mean and standard deviation of x
      • The central limit theorem
      • A few more facts related to the sampling distribution of x
    • Beyond the Basics: Weibull distributions
    • Section 5.2 Summary
    • Section 5.2 Exercises
    • 5.3 Sampling Distributions for Counts and Proportions
      • The binomial distributions for sample counts
      • Binomial distributions in statistical sampling
      • Finding binomial probabilities
      • Binomial mean and standard deviation
      • Sample proportions
      • Normal approximation for counts and proportions
      • The continuity correction
      • Binomial formula
      • The Poisson distributions for sample counts
    • Section 5.3 Summary
    • Section 5.3 Exercises
    • Chapter 5 Exercises
  • Chapter 6 Introduction to Inference
    • Introduction
      • Overview of inference
    • 6.1 Estimating with Confidence
      • Statistical confidence
      • Confidence intervals
      • Confidence interval for a population mean
      • How confidence intervals behave
      • Choosing the sample size
      • Some cautions
    • Section 6.1 Summary
    • Section 6.1 Exercises
    • 6.2 Tests of Significance
      • The reasoning of significance tests
      • Stating hypotheses
      • Test statistics
      • P-values
      • Statistical significance
      • Tests for a population mean
      • Two-sided significance tests and confidence intervals
      • The P-value versus a statement of significance
    • Section 6.2 Summary
    • Section 6.2 Exercises
    • 6.3 Use and Abuse of Tests
      • Choosing a level of significance
      • What statistical significance does not mean
      • Don’t ignore lack of significance
      • Statistical inference is not valid for all sets of data
      • Beware of searching for significance
    • Section 6.3 Summary
    • Section 6.3 Exercises
    • 6.4 Inference as a Decision
      • Two types of error
      • Error probabilities
      • The common practice of testing hypotheses
    • Section 6.4 Summary
    • Section 6.4 Exercises
    • Chapter 6 Exercises
  • Chapter 7 Inference for Means
    • Introduction
    • 7.1 Inference for the Mean of a Population
      • The t distributions
      • One-sample t confidence interval
      • The one-sample t test
      • Using software
      • Matched pairs t procedures
      • Robustness of the t procedures
      • Inference for non-normal populations
    • Beyond the Basics: The bootstrap
    • Section 7.1 Summary
    • Section 7.1 Exercises
    • 7.2 Comparing Two Means
      • The two-sample z statistic
      • The two-sample t procedures
      • The two-sample t confidence interval
      • The two-sample t significance test
      • Robustness of the two-sample procedures
      • Inference for small samples
      • The pooled two-sample t procedures
    • Section 7.2 Summary
    • Section 7.2 Exercises
    • 7.3 Sample Size Calculations
      • Sample size for confidence intervals
      • Power of a significance test
    • Section 7.3 Summary
    • Section 7.3 Exercises
    • Chapter 7 Exercises
  • Chapter 8 Inference for Proportions
    • Introduction
    • 8.1 Inference for a Single Proportion
      • Large-sample confidence interval for a single proportion
      • Beyond the Basics: Plus four confidence interval for a single proportion
      • Significance test for a single proportion
      • Choosing a sample size for a confidence interval
      • Choosing a sample size for a significance test
    • Section 8.1 Summary
    • Section 8.1 Exercises
    • 8.2 Comparing Two Proportions
      • Large-sample confidence interval for a difference in proportions
      • Beyond the Basics: Plus four confidence interval for a difference in proportions
      • Significance test for a difference in proportions
      • Choosing a sample size for two sample proportions
    • Beyond the Basics: Relative risk
    • Section 8.2 Summary
    • Section 8.2 Exercises
    • Chapter 8 Exercises
• Part III Topics in Inference
  • Chapter 9 Inference for Categorical Data
    • Introduction
    • 9.1 Sources of Data
      • The hypothesis: No association
      • Expected cell counts
      • The chi-square test
      • Computations
      • Computing conditional distributions
      • The chi-square test and the z test
    • Beyond the Basics: Meta-analysis
    • Section 9.1 Summary
    • Section 9.1 Exercises
    • 9.2 Goodness of Fit
    • Section 9.2 Summary
    • Section 9.2 Exercises
    • Chapter 9 Exercises
  • Chapter 10 Inference for Regression
    • Introduction
    • 10.1 Simple Linear Regression
      • Statistical model for linear regression
      • Preliminary data analysis and inference considerations
      • Revisiting the simple linear regression model
      • Estimating the regression parameters
      • Estimating the regression parameters
      • Confidence intervals and significance tests
      • Confidence intervals for mean response
      • Prediction intervals
      • Transforming variables
    • Beyond the Basics: Nonlinear regression
    • Section 10.1 Summary
    • Section 10.1 Exercises
    • 10.2 More Detail about Simple Linear Regression
      • Analysis of variance for regression
      • The ANOVA F test
      • Calculations for regression inference
      • Inference for correlation
    • Section 10.2 Summary
    • Section 10.2 Exercises
    • Chapter 10 Exercises
  • Chapter 11 Multiple Regression
    • Introduction
    • 11.1 Inference for Multiple Regression
      • Population multiple regression equation
      • Data for multiple regression
      • Multiple linear regression model
      • Estimation of the multiple regression parameters
      • Confidence intervals and significance tests for regression coefficients
      • ANOVA table for multiple regression
      • Squared multiple correlation R2
    • Section 11.1 Summary
    • Section 11.1 Exercises
    • 11.2 A Case Study
      • Preliminary analysis
      • Relationships between pairs of variables
      • Fitting a multiple regression model
      • Interpretation of results
      • Examining the residuals
      • Refining the model
      • Considering other sets of explanatory variables
      • Test for a collection of regression coefficients
    • Beyond the Basics: Regression trees
    • Section 11.2 Summary
    • Section 11.2 Exercises
    • Chapter 11 Exercises
  • Chapter 12 One-Way Analysis of Variance
    • Introduction
    • 12.1 Inference for One-Way Analysis of Variance
      • The one-way ANOVA setting
      • Comparing means
      • The two-sample t statistic
      • An overview of ANOVA
      • The ANOVA model
      • Estimates of population parameters
      • Testing hypotheses in one-way ANOVA
      • The ANOVA table
      • The F test
      • Software
    • Beyond the Basics: Testing the equality of spread
    • Section 12.1 Summary
    • Section 12.1 Exercises
    • 12.2 Comparing the Means
      • Contrasts
      • Multiple comparisons
      • Simultaneous confidence intervals
      • Power of the one-way ANOVA F test
    • Section 12.2 Summary
    • Section 12.2 Exercises
    • Chapter 12 Exercises
  • Chapter 13 Two-Way Analysis of Variance
    • Introduction
    • 13.1 The Two-Way ANOVA Modell
      • Advantages of two-way ANOVA
      • The two-way ANOVA model
      • Main effects and interactions
    • Section 13.1 Summary
    • Section 13.1 Exercises
    • 13.2 Inference for Two-Way ANOVA
      • The two-way ANOVA table
      • Carrying out a two-way ANOVA
    • Section 13.2 Summary
    • Section 13.2 Exercises
    • Chapter 13 Exercises
• Companion Chapters
  • Chapter 14 Logistic Regression
    • Introduction
    • 14.1 The Logistic Regression Model
      • Binomial distributions and odds
      • Odds for two groups
      • Model for logistic regression
      • Fitting and interpreting the logistic regression model
    • Section 14.1 Summary
    • Section 14.1 Exercises
    • 14.2 A Case Study
      • Confidence intervals and significance tests
      • Inference for multiple logistic regression
    • Section 14.2 Summary
    • Section 14.2 Exercises
    • Chapter 14 Exercises
    • Notes and Data Sources
  • Chapter 15 Nonparametric Rank Tests
    • Introduction
    • 15.1 Inference for One-Way Analysis of Variance
      • The rank transformation
      • The Wilcoxon rank sum test
      • The Normal approximation
      • What hypotheses does Wilcoxon test?
      • Ties
      • Nonparametric rank and t procedures
    • Section 15.1 Summary
    • Section 15.1 Exercises
    • 15.2 The Wilcoxon Signed Rank Test
      • The Normal approximation
      • Ties
      • Testing a hypothesis about the median of a distribution
    • Section 15.2 Summary
    • Section 15.2 Exercises
    • 15.3 The Kruskal-Wallis Test*
      • Hypotheses and assumptions
      • The Kruskal-Wallis test
    • Section 15.3 Summary
    • Section 15.3 Exercises
    • Chapter 15 Exercises
    • Notes and Data Sources
  • Chapter 16 Bootstrap Methods and Permutation Tests
    • Introduction
    • Software
    • 16.1 The Bootstrap Idea
      • The big idea: Resampling and the bootstrap distribution
      • Thinking about the bootstrap idea
      • Using software
    • Section 16.1 Summary
    • Section 16.1 Exercises
    • 16.2 First Steps in Using the Bootstrap
      • Bootstrap t confidence intervals
      • Bootstrapping to compare two groups
    • Beyond the Basics: The bootstrap for a scatterplot smoother
    • Section 16.2 Summary
    • Section 16.2 Exercises
    • 16.3 How Accurate Is a Bootstrap Distribution?
      • Bootstrapping small samples
      • Bootstrapping a sample median
    • Section 16.3 Summary
    • Section 16.3 Exercises
    • 16.4 Bootstrap Confidence Intervals
      • Bootstrap percentile confidence intervals
      • A more accurate bootstrap confidence interval: BCa
      • Confidence intervals for the correlation
    • Section 16.4 Summary
    • Section 16.4 Exercises
    • 16.5 Significance Testing Using Permutation Tests
      • Using software
      • Permutation tests in practice
      • Permutation tests in other settings
    • Section 16.5 Summary
    • Section 16.5 Exercises
    • Chapter 16 Exercises
    • Notes and Data Sources
  • Chapter 17 Statistics for Quality: Control and Capability
    • Introduction
    • 17.1 Processes and Statistical Process Control
      • Describing processes
      • Statistical process control
      • x charts for process monitoring
      • s charts for process monitoring
    • Section 17.1 Summary
    • Section 17.1 Exercises
    • 17.2 Using Control Charts
      • x and R charts
      • Additional out-of-control rules
      • Setting up control charts
      • Comments on statistical control
      • Don’t confuse control with capability!
    • Section 17.2 Summary
    • Section 17.2 Exercises
    • 17.3 Process Capability Indexes
      • The capability indexes Cp and Cpk
      • Cautions about capability indexes
    • Section 17.3 Summary
    • Section 17.3 Exercises
    • 17.4 Control Charts for Sample Proportions
      • Control limits for p charts
    • Section 17.4 Summary
    • Section 17.4 Exercises
    • Chapter 17 Exercises
    • Notes and Data Sources
• Tables
• Answers to Odd-Numbered Exercises
  • Chapter 1
  • Chapter 2
  • Chapter 3
  • Chapter 4
  • Chapter 5
  • Chapter 6
  • Chapter 7
  • Chapter 8
  • Chapter 9
  • Chapter 10
  • Chapter 11
  • Chapter 12
  • Chapter 13
  • Chapter 14
  • Chapter 15
  • Chapter 16
  • Chapter 17
• Notes and Data Sources
• Index
• Formulas and Key Ideas
• Back Cover

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