Discovering Statistics and Data

by James S. Hawkes

This text introduces students to the modern-day data revolution and future career opportunities surrounding data analysis and processing. Students learn how to measure data, where it comes from, how to visualize it, and how it applies to their everyday lives.

New features include expanded exercises and examples; virtual simulations and games; over 15 large, real data sets for download; modernized pedagogy, including GAISE guidelines; and more.

Bundling options include Minitab®, SPSS, and JMP.

Formats: Software, Textbook, eBook

Product ISBN
Courseware + eBook 978‑1‑64277‑015‑5
Courseware + eBook + Textbook 978‑1‑64277‑011‑7

Table of Contents

  1. Chapter 1: Statistics and Problem Solving
    1. 1.1 The Meaning of Data
    2. 1.2 Statistics as a Career
    3. 1.3 The Data Explosion
    4. 1.4 The Fusion of Data, Computing, and Statistics
    5. 1.5 Big Data
    6. 1.6 Introduction to Statistical Thinking
    7. 1.7 Descriptive vs. Inferential Statistics
    8. 1.8 The Consequences of Statistical Illiteracy
  2. Chapter 2: Data, Reality, and Problem Solving
    1. 2.1 The Lords of Data
    2. 2.2 Data Classification
    3. 2.3 Time Series Data vs. Cross-Sectional Data
  3. Chapter 3: Visualizing Data
    1. 3.1 Frequency Distributions
    2. 3.2 Displaying Qualitative Data Graphically
    3. 3.3 Constructing Frequency Distributions for Quantitative Data
    4. 3.4 Histograms and Other Graphical Displays of Quantitative Data
    5. 3.5 Analyzing Graphs
  4. Chapter 4: Describing and Summarizing Data from One Variable
    1. 4.1 Measures of Location
    2. 4.2 Measures of Dispersion
    3. 4.3 Measures of Relative Position, Box Plots, and Outliers
    4. 4.4 Data Subsetting
    5. 4.5 Analyzing Grouped Data
    6. 4.6 Proportions and Percentages
  5. Chapter 5: Discovering Relationships
    1. 5.1 Scatterplots and Correlation
    2. 5.2 Fitting a Linear Model
    3. 5.3 Evaluating the Fit of a Linear Model
    4. 5.4 Fitting a Linear Time Trend
    5. 5.5 Scatterplots for More Than Two Variables
  6. Chapter 6: Probability, Randomness, and Uncertainty
    1. 6.1 Introduction to Probability
    2. 6.2 Addition Rules for Probability
    3. 6.3 Multiplication Rules for Probability
    4. 6.4 Combinations and Permutations
    5. 6.5 Bayes’ Theorem
  7. Chapter 7: Discrete Probability Distributions
    1. 7.1 Types of Random Variables
    2. 7.2 Discrete Random Variables
    3. 7.3 The Discrete Uniform Distribution
    4. 7.4 The Binomial Distribution
    5. 7.5 The Poisson Distribution
    6. 7.6 The Hypergeometric Distribution
  8. Chapter 8: Continuous Probability Distributions
    1. 8.1 The Uniform Distribution
    2. 8.2 The Normal Distribution
    3. 8.3 The Standard Normal Distribution
    4. 8.4 Applications of the Normal Distribution
    5. 8.5 Assessing Normality
    6. 8.6 Approximation to the Binomial Distribution
  9. Chapter 9: Samples and Sampling Distributions
    1. 9.1 Random Samples
    2. 9.2 Introduction to Sampling Distributions
    3. 9.3 The Distribution of the Sample Mean and the
    4. Central Limit Theorem
    5. 9.4 The Distribution of the Sample Proportion
    6. 9.5 Other Forms of Sampling
  10. Chapter 10: Estimation: Single Samples
    1. 10.1 Point Estimation of the Population Mean
    2. 10.2 Interval Estimation of the Population Mean
    3. 10.3 Estimating the Population Proportion
    4. 10.4 Estimating the Population Standard Deviation
    5. or Variance
  11. Chapter 11: Hypothesis Testing: Single Samples
    1. 11.1 Introduction to Hypothesis Testing
    2. 11.2 Testing a Hypothesis about a Population Mean with σ Known and Unknown
    3. 11.3 The Relationship Between Confidence Interval Estimation and Hypothesis Testing
    4. 11.4 Testing a Hypothesis about a Population Proportion
    5. 11.5 Testing a Hypothesis about a Population Standard Deviation or Variance
    6. 11.6 Practical Significance vs. Statistical Significance
  12. Chapter 12: Inferences about Two Samples
    1. 12.1 Inference about Two Means: Independent Samples
    2. 12.2 Inference about Two Means: Dependent Samples (Paired Difference)
    3. 12.3 Inference about Two Population Proportions
  13. Chapter 13: Regression, Inference, and Model Building
    1. 13.1 Assumptions of the Simple Linear Model
    2. 13.2 Inference Concerning β1
    3. 13.3 Inference Concerning the Model’s Prediction
  14. Chapter 14: Multiple Regression
    1. 14.1 The Multiple Regression Model
    2. 14.2 The Coefficient of Determination and Adjusted R2
    3. 14.3 Interpreting the Coefficients of the Multiple Regression Model
    4. 14.4 Inference Concerning the Multiple Regression Model and Its Coefficients
    5. 14.5 Inference Concerning the Model’s Prediction
    6. 14.6 Multiple Regression Models with Qualitative Independent Variables
  15. Chapter 15: Analysis of Variance (ANOVA)
    1. 15.1 One-Way ANOVA
    2. 15.2 Two-Way ANOVA: The Randomized Block Design
    3. 15.3 Two-Way ANOVA: The Factorial Design
  16. Chapter 16: Looking for Relationships in Qualitative Data
    1. 16.1 The Chi-Square Distribution
    2. 16.2 The Chi-Square Test for Goodness of Fit
    3. 16.3 The Chi-Square Test for Association
  17. Chapter 17: Nonparametric Tests
    1. 17.1 The Sign Test
    2. 17.2 The Wilcoxon Signed-Rank Test
    3. 17.3 The Wilcoxon Rank-Sum Test
    4. 17.4 The Rank Correlation Test
    5. 17.5 The Runs Test for Randomness
    6. 17.6 The Kruskal-Wallis Test