
Discovering Statistics and Data, 4th Edition
by James S. Hawkes
Discovering Statistics and Data is intended for an introductory statistics course and is written in a relaxed, conversational style, with occasional remarks that are intended to both engage and bring a smile to the reader. The fourth edition of Discovering Statistics and Data by James Hawkes presents important concepts and techniques in a straightforward, step-by-step manner. Used on its own, or with the following supplementary materials, this text provides a solid foundation in statistics for both STEM and non-STEM majors.
Companion Website
A curated list of resources for this title is available at stat.hawkeslearning.com. This companion website features:
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Free, real-world data sets
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Step-by-step instructions for TI calculators, Excel, Minitab®, SPSS, JMP, and Rguroo
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Chapter projects that apply lessons to real-life scenarios
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Links to data visualization resources
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External videos explaining key topics
Bundling options include Minitab®, SPSS, JMP, and Rguroo.
Formats: Software, Textbook, eBook
Product | ISBN |
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Software + eBook | 978‑1‑64277‑715‑4 |
Software + eBook + Textbook | 978‑1‑64277‑888‑5 |
Table of Contents
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Chapter 1: Statistics: Thinking with Data
- 1.1 Introduction to Statistical Thinking
- 1.2 Basic Statistical Concepts
- 1.3 Descriptive versus Inferential Statistics
- 1.4 The Value of Statistical Literacy
- 1.5 Statistics and Related Fields as a Career
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Chapter 2: Foundations of Data Analysis
- 2.1 The Evolution of Data
- 2.2 Empirical Foundations: Measurement and Scales
- 2.3 Empiricism at Work: Data Collection and The Scientific Method
- 2.4 Data Classification
- 2.5 Time Series Data versus Cross-Sectional Data
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Chapter 3: Visualizing Data
- 3.1 Frequency Distributions
- 3.2 Displaying Qualitative Data Graphically
- 3.3 Displaying Quantitative Data Graphically
- 3.4 Analyzing Graphs
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Chapter 4: Describing and Summarizing Data from One Variable
- 4.1 Measures of Location
- 4.2 Measures of Dispersion
- 4.3 Measures of Relative Position, Box Plots, and Outliers
- 4.4 Data Subsetting
- 4.5 Analyzing Grouped Data
- 4.6 Proportions and Percentages
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Chapter 5: Discovering Relationships
- 5.1 Scatterplots and Correlation
- 5.2 Fitting a Linear Model
- 5.3 Evaluating the Fit of a Linear Model
- 5.4 Fitting a Linear Time Trend
- 5.5 Scatterplots for More Than Two Variables
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Chapter 6: Probability, Randomness, and Uncertainty
- 6.1 Introduction to Probability
- 6.2 Addition Rules for Probability
- 6.3 Multiplication Rules for Probability
- 6.4 Combinations and Permutations
- 6.5 Bayes' Theorem
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Chapter 7: Discrete Probability Distributions
- 7.1 Types of Random Variables and Probability Distributions
- 7.2 Expected Value and Variance
- 7.3 The Discrete Uniform Distribution
- 7.4 The Binomial Distribution
- 7.5 The Poisson Distribution
- 7.6 The Hypergeometric Distribution
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Chapter 8: Continuous Probability Distributions
- 8.1 The Uniform Distribution
- 8.2 The Normal Distribution
- 8.3 The Standard Normal Distribution
- 8.4 Applications of the Normal Distribution
- 8.5 Assessing Normality
- 8.6 Approximation to the Binomial Distribution
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Chapter 9: Samples and Sampling Distributions
- 9.1 Random Samples and Sampling Distributions
- 9.2 The Distribution of the Sample Mean and the Central Limit Theorem
- 9.3 The Distribution of the Sample Proportion
- 9.4 Other Forms of Sampling
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Chapter 10: Estimation: Single Samples
- 10.1 Point Estimation of Population Parameters
- 10.2 Estimating the Population Mean, σ Known
- 10.3 Estimating the Population Mean, σ Unknown
- 10.4 Estimating the Population Proportion
- 10.5 Estimating the Population Standard Deviation or Variance
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Chapter 11: Hypothesis Testing: Single Samples
- 11.1 Introduction to Hypothesis Testing
- 11.2 Testing a Hypothesis about a Population Mean, σ Known
- 11.3 Testing a Hypothesis about a Population Mean, σ Unknown
- 11.4 The Relationship Between Confidence Interval Estimation and Hypothesis Testing
- 11.5 Testing a Hypothesis about a Population Proportion
- 11.6 Testing a Hypothesis about a Population Standard Deviation or Variance
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Chapter 12: Inferences about Two Samples
- 12.1 Inferences about Two Population Means: Independent Samples, σ1 and σ2 Known
- 12.2 Inferences about Two Population Means: Independent Samples, σ1 and σ2 Unknown
- 12.3 Inference about Two Population Means: Dependent Samples (Paired Difference)
- 12.4 Inference about Two Population Proportions
- 12.5 Inference about Two Population Variances
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Chapter 13: Regression, Inference, and Model Building
- 13.1 Assumptions of the Simple Linear Model
- 13.2 Inference Concerning β1
- 13.3 Inference Concerning the Model's Prediction
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Chapter 14: Multiple Regression
- 14.1 The Multiple Regression Model
- 14.2 The Coefficient of Determination and Adjusted R2
- 14.3 Inference Concerning the Multiple Regression Model and Its Coefficients
- 14.4 Inference Concerning the Model's Prediction
- 14.5 Multiple Regression Models with Qualitative Independent Variables
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Chapter 15: Analysis of Variance (ANOVA)
- 15.1 One-Way ANOVA
- 15.2 Multiple Comparison Procedures
- 15.3 Two-Way ANOVA: The Randomized Block Design
- 15.4 Two-Way ANOVA: The Factorial Design
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Chapter 16: Looking for Relationships in Qualitative Data
- 16.1 The Chi-Square Distribution
- 16.2 The Chi-Square Test for Goodness of Fit
- 16.3 The Chi-Square Test for Association
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Chapter 17: Nonparametric Tests
- 17.1 The Sign Test
- 17.2 The Wilcoxon Signed-Rank Test
- 17.3 The Wilcoxon Rank-Sum Test
- 17.4 The Rank Correlation Test
- 17.5 The Runs Test for Randomness
- 17.6 The Kruskal-Wallis Test