Discovering Statistics and Data, 3rd Edition

2.1 The Lords of Data

• Examples
• Example 2.1.1
• Example 2.1.2

2.2 Data Classification

• Examples
• Example 2.2.1

2.3 Time Series Data vs. Cross-Sectional Data

• Examples
• Example 2.3.1
• Example 2.3.2
• Example 2.3.3
• Example 2.3.4

3.1 Frequency Distributions

• Examples
• Example 3.1.1

3.2 Displaying Qualitative Data Graphically

3.3 Constructing Frequency Distributions for Quantitative Data

• Examples
• Example 3.3.1

3.4 Histograms and Other Graphical Displays of Quantitative Data

• Examples
• Example 3.4.1
• Example 3.4.2
• Example 3.4.3

3.5 Analyzing Graphs

4.1 Measures of Location

• Examples
• Example 4.1.1
• Example 4.1.2
• Example 4.1.3
• Example 4.1.4
• Example 4.1.5
• Example 4.1.6
• Example 4.1.7

4.2 Measures of Dispersion

• Examples
• Example 4.2.1
• Example 4.2.2
• Example 4.2.3
• Example 4.2.4
• Example 4.2.5
• Example 4.2.6

4.3 Measures of Relative Position, Box Plots, and Outliers

• Examples
• Example 4.3.1
• Example 4.3.2
• Example 4.3.3
• Example 4.3.4

4.4 Data Subsetting

• Examples
• Example 4.4.1

4.5 Analyzing Grouped Data

• Examples
• Example 4.5.1

4.6 Proportions and Percentages

• Examples
• Example 4.6.1
• Example 4.6.2

5.1 Scatterplots and Correlation

5.2 Fitting a Linear Model

5.3 Evaluating the Fit of a Linear Model

• Examples
• Example 5.3.1

5.4 Fitting a Linear Time Trend

• Examples
• Example 5.4.1

5.5 Scatterplots for More Than Two Variables

6.1 Introduction to Probability

• Examples
• Example 6.1.1
• Example 6.1.2
• Example 6.1.3
• Example 6.1.4

6.2 Addition Rules for Probability

• Examples
• Example 6.2.1
• Example 6.2.2
• Example 6.2.3
• Example 6.2.4
• Example 6.2.5

6.3 Multiplication Rules for Probability

• Examples
• Example 6.3.1
• Example 6.3.2
• Example 6.3.3
• Example 6.3.4
• Example 6.3.5
• Example 6.3.6
• Example 6.3.7
• Example 6.3.8
• Example 6.3.9
• Example 6.3.10

6.4 Combinations and Permutations

• Examples
• Example 6.4.1
• Example 6.4.2
• Example 6.4.3
• Example 6.4.4
• Example 6.4.5
• Example 6.4.6
• Example 6.4.7
• Example 6.4.8

6.5 Bayes Theorem

• Examples
• Example 6.5.1
• Example 6.5.2

7.1 Types of Random Variables

• Examples
• Example 7.1.1
• Example 7.1.2
• Example 7.1.3
• Example 7.1.4

7.2 Discrete Random Variables

• Examples
• Example 7.2.1
• Example 7.2.2
• Example 7.2.3
• Example 7.2.4
• Example 7.2.5
• Example 7.2.6

7.3 The Discrete Uniform Distribution

• Examples
• Example 7.3.1
• Example 7.3.2

7.4 The Binomial Distribution

• Examples
• Example 7.4.1
• Example 7.4.2
• Example 7.4.3
• Example 7.4.4
• Example 7.4.5

7.5 The Poisson Distribution

• Examples
• Example 7.5.1
• Example 7.5.2

7.6 The Hypergeometric Distribution

• Examples
• Example 7.6.1
• Example 7.6.2

8.1 The Uniform Distribution

• Examples
• Example 8.1.1

8.2 The Normal Distribution

8.3 The Standard Normal Distribution

• Examples
• Example 8.3.1
• Example 8.3.2
• Example 8.3.3
• Example 8.3.4
• Example 8.3.5
• Example 8.3.6

8.4 Applications of the Normal Distribution

• Examples
• Example 8.4.1
• Example 8.4.2
• Example 8.4.3
• Example 8.4.4
• Example 8.4.5
• Example 8.4.6

8.5 Assessing Normality

• Examples
• Example 8.5.1
• Example 8.5.2
• Example 8.5.3

8.6 Approximation to the Binomial Distribution

• Examples
• Example 8.6.1
• Example 8.6.2
• Example 8.6.3

9.1 Random Samples

• Examples
• Example 9.1.1

9.2 Introduction to Sampling Distributions

9.3 The Distribution of the Sample Mean and the Central Limit Theorem

• Examples
• Example 9.3.1
• Example 9.3.2

9.4 The Distribution of the Sample Proportion

• Examples
• Example 9.5
• Example 9.4.2

9.5 Other Forms of Sampling

10.1 Point Estimation of the Population Mean

10.2 Interval Estimation of the Population Mean

• Examples
• Example 10.2.1
• Example 10.2.3
• Example 10.2.4
• Example 10.2.5
• Example 10.2.6
• Example 10.2.7

10.3 Estimating the Population Proportion

• Examples
• Example 10.3.1
• Example 10.3.2
• Example 10.3.3
• Example 10.3.4

10.4 Estimating the Population Standard Deviation or Variance

• Examples
• Example 10.4.1

11.1 Introduction to Hypothesis Testing

• Examples
• Example 11.1.1
• Example 11.1.2
• Example 11.1.3
• Example 11.1.4

11.2a Testing a Hypothesis about a Population Mean with Sigma Known

• Examples
• Example 11.2.1
• Example 11.2.1: Continued
• Example 11.2.1: Continued
• Example 11.2.2
• Example 11.2.3
• Example 11.2.4

11.2b Testing a Hypothesis about a Population Mean with Sigma Unknown

• Examples
• Example 11.2.1
• Example 11.2.1: Continued
• Example 11.2.1: Continued
• Example 11.2.2
• Example 11.2.3
• Example 11.2.4

11.2c Testing a Hypothesis about a Population Mean using P-values

• Examples
• Example 11.2.1
• Example 11.2.1: Continued
• Example 11.2.1: Continued
• Example 11.2.2
• Example 11.2.3
• Example 11.2.4

11.3 The Relationship Between Confidence Interval Estimation and Hypothesis Testing

11.4a Testing a Hypothesis about a Population Proportion

• Examples
• Example 11.4.1
• Example 11.4.2

11.4b Testing a Hypothesis about a Population Proportion using P-values

• Examples
• Example 11.4.1
• Example 11.4.2

11.5 Testing a Hypothesis about a Population Standard Deviation or Variance

• Examples
• Example 11.5.1

11.6 Practical Significance vs. Statistical Significance

• Examples
• Example 11.6.1

12.1a Inference about Two Means: Independent Samples with Sigma Known

• Examples
• Example 12.1.1
• Example 12.1.2
• Example 12.1.3
• Example 12.1.4
• Example 12.1.5
• Example 12.1.6

12.1b Inference about Two Means: Independent Samples with Sigma Unknown

• Examples
• Example 12.1.1
• Example 12.1.2
• Example 12.1.3
• Example 12.1.4
• Example 12.1.5
• Example 12.1.6

12.2 Inference about Two Means: Dependent Samples (Paired Difference)

• Examples
• Example 12.2.1
• Example 12.2.2

12.3 Inference about Two Population Proportions

• Examples
• Example 12.3.1

13.1 Assumptions of the Simple Linear Model

13.2 Inference Concerning the Slope

• Examples
• Example 13.2.1
• Example 13.2.2
• Example 13.2.3
• Example 13.2.4

13.3 Inference Concerning the Model's Prediction

• Examples
• Example 13.3.1
• Example 13.3.2

14.1 The Multiple Regression Model

14.2 The Coefficient of Determination and Adjusted R-Squared

14.3 Interpreting the Coefficients of the Multiple Regression Model

14.4 Inference Concerning the Multiple Regression Model and Its Coefficients

• Examples
• Example 14.4.1
• Example 14.4.2
• Example 14.4.3

14.5 Inference Concerning the Model's Prediction

14.6 Multiple Regression Models with Qualitative Independent Variables

• Examples
• Example 14.6.1
• Example 14.6.2

15.1 One-Way ANOVA

• Examples
• Example 15.1.1

15.2 Two-Way ANOVA: The Randomized Block Design

15.3 Two-Way ANOVA: The Factorial Design

16.1 The Chi-Square Distribution

16.2 The Chi-Square Test for Goodness of Fit

• Examples
• Example 16.2.1
• Example 16.2.2

16.3 The Chi-Square Test for Association

• Examples
• Example 16.3.1

17.1 The Sign Test

• Examples
• Example 17.1.1
• Example 17.1.2

17.2 The Wilcoxon Signed-Rank Test

• Examples
• Example 17.2.1
• Example 17.2.2

17.3 The Wilcoxon Rank-Sum Test

• Examples
• Example 17.3.1

17.4 The Rank Correlation Test

• Examples
• Example 17.4.1
• Example 17.4.2

17.5 The Runs Test for Randomness

• Examples
• Example 17.5.1
• Example 17.5.2
• Example 17.5.3

17.6 The Kruskal-Wallis Test

• Examples
• Example 17.6.1
• Example 17.6.2

A.1 Name that Distribution

A.2 Direct Mail

A.3 Type II Errors

A.4 Games of Chance

• Examples
• Example A4.1: Expected Value
• Example A4.2: Expected Value
• Example A4.3: Expected Value

A.5 Comparing Two Population Variances

• Examples
• Example A5.1
• Example A5.2: Performing a Hypothesis Test for Two Population Variances (Right-Tailed)
• Example A5.3: Performing a Hypothesis Test for Two Population Variances (Two-Tailed)

A.6 Statistical Process Control

• Examples
• Example A6.1
• Example A6.2
• Example A6.3
• Example A6.4: Mean Charts Using s
• Example A6.5: p-Charts
• Example A6.6: c-Charts