Mind on Statistics, Australian & New Zealand Version 2nd edition

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• Chapter 1: Thinking Statistically
  • 1.1: What is statistics?
  • 1.2: Some statistical stories in real and complex problems
  • 1.3: Statistics starts before data are collected
  • 1.4: The discovery of knowledge
  • 1: Active Examples
  
  
• Chapter 2: Gathering and Preparing Useful Data
  • 2.1: Datasets and types of investigations
  • 2.2: Some practicalities and challenges in planning data investigations
  • 2.3: Types of data and variables
  • 2.4: Surveys
  • 2.5: Designing experiments
  • 2.6: Some types of observational studies
  • 2.7: Some cautions in experiments and observational studies
  • 2: Active Examples
  
  
• Chapter 3: Turning Data into Graphical Information
  • 3.1: Exploratory data analysis (EDA)
  • 3.2: Categorical data
  • 3.3: Graphs and plots for one continuous variable
  • 3.4: Continuous and categorical data
  • 3.5: More than one continuous variable
  • 3.6: Outlying observations
  • 3.7: Good graphs and bad graphs
  • 3: Active Examples
  
  
• Chapter 4: Data Summaries and Inferential Concepts
  • 4.1: Features of quantitative data
  • 4.2: Measures of location
  • 4.3: Measures of spread or dispersion
  • 4.4: Shape
  • 4.5: Estimates and interval estimation: confidence intervals
  • 4.6: Answering questions
  • 4.7: Bell-shaped distributions
  • 4: Active Examples
  
  
• Chapter 5: Investigating Categorical Variables and Their Relationships
  • 5.1: Background for categorical data
  • 5.2: More than two categorical variables
  • 5.3: One categorical variable: testing a set of proportions
  • 5.4: p-values and testing statistical hypotheses
  • 5.5: Testing independence of two categorical variables
  • 5.6: Risk, relative risk and misleading statistics about risk
  • 5: Active Examples
  
  
• Chapter 6: Probability Essentials for Data Analysis
  • 6.1: What is probability?
  • 6.2: Where do values of probabilities come from?
  • 6.3: What is a random variable?
  • 6.4: Expected values, standard deviations, medians, quartiles and percentiles of random variables
  • 6.5: Parameters and estimates
  • 6.6: Three special distributions
  • 6.7: Normal probability plots
  • 6: Active Examples
  
  
• Chapter 7: Estimating Proportions with Confidence
  • 7.1: Percentages and proportions abound
  • 7.2: Confidence intervals for proportions
  • 7.3: Confidence intervals for the difference in two proportions
  • 7.4: Sample size to estimate a proportion
  • 7.5: Background for confidence intervals for proportions
  • 7.6: Confidence intervals and decisions
  • 7: Active Examples
  
  
• Chapter 8: Analysis of Variance: Categorical Predictors, Continuous Response
  • 8.1: Examples of data investigations that include continuous responses and possible categorical explanatory variables
  • 8.2: One-way ANOVA
  • 8.3: Assumptions and diagnostics for ANOVA
  • 8.4: Multiple comparisons
  • 8.5: Two-way ANOVA
  • 8.6: More on continuous response and categorical explanatory variables
  • 8.7: Other methods for investigating effects of a categorical variable on a continuous variable
  • 8.8: Models, notation and calculations for ANOVA
  • 8: Active Examples
  
  
• Chapter 9: Regression: Investigating Relationships Between Quantitative Variables
  • 9.1: Some examples of data investigations that include continuous responses and quantitative explanatory variables
  • 9.2: Simple linear regression
  • 9.3: Messages from residuals
  • 9.4: Multiple regression
  • 9.5: Some formulae in regression
  • 9: Active Examples
  
  
• Chapter 10: Interval Estimation and Quantitative Variables
  • 10.1: Sample statistics as estimates
  • 10.2: Confidence interval for the mean of a quantitative variable
  • 10.3: Interval estimates for the median
  • 10.4: Confidence interval for difference between two means
  • 10.5: Tolerance intervals for individual values
  • 10.6: Confidence interval for a standard deviation
  • 10.7: Sample size required to estimate a mean with a desired precision
  • 10: Active Examples
  
  
• Chapter 11: Testing Hypotheses in One and Two Samples
  • 11.1: Overview of statistical hypothesis testing
  • 11.2: Testing hypotheses about a proportion
  • 11.3: Testing hypotheses about the difference in two proportions
  • 11.4: Connection with testing independence in contingency tables
  • 11.5: Testing hypotheses about one mean
  • 11.6: Testing hypotheses about the mean of paired differences
  • 11.7: Testing hypotheses about the difference between two means
  • 11.8: Non-parametric tests and medians
  • 11.9: Tests for one or two standard deviations
  • 11.10: The relationship between tests and confidence intervals
  • 11.11: t-tests and ANOVA: correspondences and pitfalls of t-testing in real investigations
  • 11.12: The rejection region approach to hypothesis testing
  • 11.13: Sample size, statistical significance, practical importance and effect size
  • 11: Active Examples
  
  
• Chapter 12: More on Probability, Random Variables and Distributions
  • 12.1: Foundations for probability
  • 12.2: Probability rules
  • 12.3: Independence and conditional probability
  • 12.4: Using conditional probabilities
  • 12.5: Bayes' theorem
  • 12.6: Continuous distributions
  • 12.7: A very special process: the Poisson
  • 12: Active Examples
  
  
• Chapter 13: Sums and Differences of Random Variables
  • 13.1: Examples for which this chapter is needed
  • 13.2: Sums and differences of two random variables
  • 13.3: Means and variances of linear combinations of random variables
  • 13.4: Linear combinations of normal random variables
  • 13.5: Sums of some other independent random variables
  • 13.6: The sample mean and the central limit theorem
  • 13.7: Combining 'errors'
  • 13: Active Examples
  
  
• Chapter 14: Some Further Data Situations
  • 14.1: Binary logistic regression
  • 14.2: Failure and survival data
  • 14: Active Examples