Bock, David: Student Solutions Manual for Stats: Data and Models

Name: Bock, David: Student Solutions Manual for Stats: Data and Models
Brand: Pearson Academic
SKU: 978-0-13-516397-9
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I: EXPLORING AND UNDERSTANDING DATA

1. Stats Starts Here
- 1.1 What Is Statistics?
- 1.2 Data
- 1.3 Variables
- 1.4 Models
2. Displaying and Describing Data
- 2.1 Summarizing and Displaying a Categorical Variable
- 2.2 Displaying a Quantitative Variable
- 2.3 Shape
- 2.4 Center
- 2.5 Spread
3. Relationships Between Categorical VariablesContingency Tables
- 3.1 Contingency Tables
- 3.2 Conditional Distributions
- 3.3 Displaying Contingency Tables
- 3.4 Three Categorical Variables
4. Understanding and Comparing Distributions
- 4.1 Displays for Comparing Groups
- 4.2 Outliers
- 4.3 Re-Expressing Data: A First Look
5. The Standard Deviation as a Ruler and the Normal Model
- 5.1 Using the Standard Deviation to Standardize Values
- 5.2 Shifting and Scaling
- 5.3 Normal Models
- 5.4 Working with Normal Percentiles
- 5.5 Normal Probability Plots
- Review of Part I: Exploring and Understanding Data

II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES

6. Scatterplots, Association, and Correlation
- 6.1 Scatterplots
- 6.2 Correlation
- 6.3 Warning: Correlation Causation
- 6.4 Straightening Scatterplots
7. Linear Regression
- 7.1 Least Squares: The Line of Best Fit
- 7.2 The Linear Model
- 7.3 Finding the Least Squares Line
- 7.4 Regression to the Mean
- 7.5 Examining the Residuals
- 7.6 R²: The Variation Accounted for by the Model
- 7.7 Regression Assumptions and Conditions
8. Regression Wisdom
- 8.1 Examining Residuals
- 8.2 Extrapolation: Reaching Beyond the Data
- 8.3 Outliers, Leverage, and Influence
- 8.4 Lurking Variables and Causation
- 8.5 Working with Summary Values
- 8.6 Straightening Scatterplots: The Three Goals
- 8.7 Finding a Good Re-Expression
9. Multiple Regression
- 9.1 What Is Multiple Regression?
- 9.2 Interpreting Multiple Regression Coefficients
- 9.3 The Multiple Regression Model: Assumptions and Conditions
- 9.4 Partial Regression Plots
- 9.5 Indicator Variables
- Review of Part II: Exploring Relationships Between Variables

III. GATHERING DATA

10. Sample Surveys
- 10.1 The Three Big Ideas of Sampling
- 10.2 Populations and Parameters
- 10.3 Simple Random Samples
- 10.4 Other Sampling Designs
- 10.5 From the Population to the Sample: You Can't Always Get What You Want
- 10.6 The Valid Survey
- 10.7 Common Sampling Mistakes, or How to Sample Badly
11. Experiments and Observational Studies
- 11.1 Observational Studies
- 11.2 Randomized, Comparative Experiments
- 11.3 The Four Principles of Experimental Design
- 11.4 Control Groups
- 11.5 Blocking
- 11.6 Confounding
- Review of Part III: Gathering Data

IV. RANDOMNESS AND PROBABILITY

12. From Randomness to Probability
- 12.1 Random Phenomena
- 12.2 Modeling Probability
- 12.3 Formal Probability
13. Probability Rules!
- 13.1 The General Addition Rule
- 13.2 Conditional Probability and the General Multiplication Rule
- 13.3 Independence
- 13.4 Picturing Probability: Tables, Venn Diagrams, and Trees
- 13.5 Reversing the Conditioning and Bayes' Rule
14. Random Variables
- 14.1 Center: The Expected Value
- 14.2 Spread: The Standard Deviation
- 14.3 Shifting and Combining Random Variables
- 14.4 Continuous Random Variables
15. Probability Models
- 15.1 Bernoulli Trials
- 15.2 The Geometric Model
- 15.3 The Binomial Model
- 15.4 Approximating the Binomial with a Normal Model
- 15.5 The Continuity Correction
- 15.6 The Poisson Model
- 15.7 Other Continuous Random Variables: The Uniform and the Exponential
- Review of Part IV: Randomness and Probability

V. INFERENCE FOR ONE PARAMETER

16. Sampling Distribution Models and Confidence Intervals for Proportions
- 16.1 The Sampling Distribution Model for a Proportion
- 16.2 When Does the Normal Model Work? Assumptions and Conditions
- 16.3 A Confidence Interval for a Proportion
- 16.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?
- 16.5 Margin of Error: Certainty vs. Precision
- 16.6 Choosing the Sample Size
17. Confidence Intervals for Means
- 17.1 The Central Limit Theorem
- 17.2 A Confidence Interval for the Mean
- 17.3 Interpreting Confidence Intervals
- 17.4 Picking Our Interval up by Our Bootstraps
- 17.5 Thoughts About Confidence Intervals
18. Testing Hypotheses
- 18.1 Hypotheses
- 18.2 P-Values
- 18.3 The Reasoning of Hypothesis Testing
- 18.4 A Hypothesis Test for the Mean
- 18.5 Intervals and Tests
- 18.6 P-Values and Decisions: What to Tell About a Hypothesis Test
19. More About Tests and Intervals
- 19.1 Interpreting P-Values
- 19.2 Alpha Levels and Critical Values
- 19.3 Practical vs. Statistical Significance
- 19.4 Errors
- Review of Part V: Inference for One Parameter

VI. INFERENCE FOR RELATIONSHIPS

20. Comparing Groups
- 20.1 A Confidence Interval for the Difference Between Two Proportions
- 20.2 Assumptions and Conditions for Comparing Proportions
- 20.3 The Two-Sample z-Test: Testing for the Difference Between Proportions
- 20.4 A Confidence Interval for the Difference Between Two Means
- 20.5 The Two-Sample t-Test: Testing for the Difference Between Two Means
- 20.6 Randomization Tests and Confidence Intervals for Two Means
- 20.7 Pooling
- 20.8 The Standard Deviation of a Difference
21. Paired Samples and Blocks
- 21.1 Paired Data
- 21.2 The Paired t-Test
- 21.3 Confidence Intervals for Matched Pairs
- 21.4 Blocking
22. Comparing Counts
- 22.1 Goodness-of-Fit Tests
- 22.2 Chi-Square Test of Homogeneity
- 22.3 Examining the Residuals
- 22.4 Chi-Square Test of Independence
23. Inferences for Regression
- 23.1 The Regression Model
- 23.2 Assumptions and Conditions
- 23.3 Regression Inference and Intuition
- 23.4 The Regression Table
- 23.5 Multiple Regression Inference
- 23.6 Confidence and Prediction Intervals
- 23.7 Logistic Regression
- 23.8 More About Regression
- Review of Part VI: Inference for Relationships

VII. INFERENCE WHEN VARIABLES ARE RELATED

24. Multiple Regression Wisdom
- 24.1 Multiple Regression Inference
- 24.2 Comparing Multiple Regression Model
- 24.3 Indicators
- 24.4 Diagnosing Regression Models: Looking at the Cases
- 24.5 Building Multiple Regression Models
25. Analysis of Variance
- 25.1 Testing Whether the Means of Several Groups Are Equal
- 25.2 The ANOVA Table
- 25.3 Assumptions and Conditions
- 25.4 Comparing Means
- 25.5 ANOVA on Observational Data
26. Multifactor Analysis of Variance
- 26.1 A Two Factor ANOVA Model
- 26.2 Assumptions and Conditions
- 26.3 Interactions
27. Statistics and Data Science
- 27.1 Introduction to Data Mining
- Review of Part VII: Inference When Variables Are Related