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Introduction to the Practice of Statistics

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Efnisyfirlit

  • Title
  • Copyright
  • Brief Contents
  • Contents
  • To Teachers: About This Book
  • To Students: What Is Statistics?
  • About the Authors
  • Data Table Index
  • Beyond the Basics Index
  • 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
        • Data analysis in action: Don’t hang up on me
        • 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
        • Mean versus 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
        • Least-squares regression
        • Interpreting the regression line
        • Facts about least-squares regression
        • Correlation and regression
        • Another view of r2
      • Section 2.4 Summary
      • Section 2.4 Exercises
      • 2.5 Cautions about Correlation and Regression
        • 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
  • 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
      • 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
      • 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 Power and Inference as a Decision
      • Power
      • Increasing the power
      • Inference as 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
        • The one-sample t confidence interval
        • The one-sample t test
        • Matched pairs t procedures
        • Robustness of the t procedures
        • 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
        • Software approximation for the degrees of freedom
        • The pooled two-sample t procedures
      • Section 7.2 Summary
      • Section 7.2 Exercises
      • 7.3 Additional Topics on Inference
        • Choosing the sample size
        • Inference for non-Normal populations
      • 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: The 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: The 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
  • CHAPTER 9 Inference for Categorical Data
    • Introduction
      • 9.1 Inference for Two-Way Tables
        • 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
        • Estimating the regression parameters
        • Checking model assumptions
        • 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
        • Regression on high school grades
        • Interpretation of results
        • Examining the residuals
        • Refining the model
        • Regression on SAT scores
        • Regression using all variables
        • Test for a collection of regression coefficients
      • Beyond the Basics: Multiple logistic regression
      • 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
        • Data for one-way ANOVA
        • 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
        • Power
      • 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 Model
        • Advantages of two-way ANOVA
        • The two-way ANOVA model
        • Main effects and interactions
      • 13.2 Inference for Two-Way ANOVA
        • The ANOVA table for two-way ANOVA
      • Chapter 13 Summary
      • Chapter 13 Exercises
  • Tables
  • Answers to Odd-Numbered Exercises
  • Notes and Data Sources
  • Index

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Vörumerki: Macmillan
Tilboði lýkur 04.05.2019
Vörunúmer: 9781319183073
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Introduction to the Practice of Statistics

Vörumerki: Macmillan
Tilboði lýkur 04.05.2019
Vörunúmer: 9781319183073
Rafbók

Veldu vöru

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Get the product now
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5.690 kr. 4.836 kr.