Ebook Statistics 4e Agresti, Franklin, Klingenberg (Global Edition)

Statistics: The Art and Science of Learning from Data
Global Edition, 4th Edition
by Alan Agresti, Christine A. Franklin & Bernhard Klingenberg
Copyright © 2018 by Pearson Education

Table of Content:
1. Statistics: The Art and Science of Learning from Data
1.1 Using Data to Answer Statistical Questions
1.2 Sample Versus Population
1.3 Using Calculators and Computers 

2. Exploring Data with Graphs and Numerical Summaries
2.1 Different Types of Data
2.2 Graphical Summaries of Data
2.3 Measuring the Center of Quantitative Data
2.4 Measuring the Variability of Quantitative Data
2.5 Using Measures of Position to Describe Variability
2.6 Recognizing and Avoiding Misuses of Graphical Summaries 

3. Association: Contingency, Correlation, and Regression
3.1 The Association Between Two Categorical Variables
3.2 The Association Between Two Quantitative Variables
3.3 Predicting the Outcome of a Variable
3.4 Cautions in Analyzing Associations 

4. Gathering Data
4.1 Experimental and Observational Studies
4.2 Good and Poor Ways to Sample
4.3 Good and Poor Ways to Experiment
4.4 Other Ways to Conduct Experimental and Nonexperimental Studies 

Part Review 1 (ONLINE)

5. Probability in Our Daily Lives
5.1 How Probability Quantifies Randomness
5.2 Finding Probabilities
5.3 Conditional Probability
5.4 Applying the Probability Rules 

6. Probability Distributions
6.1 Summarizing Possible Outcomes and Their Probabilities
6.2 Probabilities for Bell-Shaped Distributions
6.3 Probabilities When Each Observation Has Two Possible Outcomes 

7. Sampling Distributions
7.1 How Sample Proportions Vary Around the Population Proportion
7.2 How Sample Means Vary Around the Population Mean 

8. Statistical Inference: Confidence Intervals
8.1 Point and Interval Estimates of Population Parameters
8.2 Constructing a Confidence Interval to Estimate a Population Proportion
8.3 Constructing a Confidence Interval to Estimate a Population Mean
8.4 Choosing the Sample Size for a Study
8.5 Using Computers to Make New Estimation Methods Possible 

9. Statistical Inference: Significance Tests About Hypotheses
9.1 Steps for Performing a Significance Test
9.2 Significance Tests About Proportions
9.3 Significance Tests About Means
9.4 Decisions and Types of Errors in Significance Tests
9.5 Limitations of Significance Tests
9.6 The Likelihood of a Type II Error 

10. Comparing Two Groups
10.1 Categorical Response: Comparing Two Proportions
10.2 Quantitative Response: Comparing Two Means
10.3 Other Ways of Comparing Means and Comparing Proportions
10.4 Analyzing Dependent Samples
10.5 Adjusting for the Effects of Other Variables 

11. Analyzing the Association Between Categorical Variables
11.1 Independence and Dependence (Association)
11.2 Testing Categorical Variables for Independence
11.3 Determining the Strength of the Association
11.4 Using Residuals to Reveal the Pattern of Association
11.5 Fisher’s Exact and Permutation Tests 

12. Analyzing the Association Between Quantitative Variables: Regression Analysis
12.1 Modeling How Two Variables Are Related
12.2 Inference About Model Parameters and the Association
12.3 Describing the Strength of Association
12.4 How the Data Vary Around the Regression Line
12.5 Exponential Regression: A Model for Nonlinearity 

13. Multiple Regression
13.1 Using Several Variables to Predict a Response
13.2 Extending the Correlation and R2 for Multiple Regression
13.3 Using Multiple Regression to Make Inferences
13.4 Checking a Regression Model Using Residual Plots
13.5 Regression and Categorical Predictors
13.6 Modeling a Categorical Response 

14. Comparing Groups: Analysis of Variance Methods
14.1 One-Way ANOVA: Comparing Several Means
14.2 Estimating Differences in Groups for a Single Factor
14.3 Two-Way ANOVA 

15. Nonparametric Statistics
15.1 Compare Two Groups by Ranking
15.2 Nonparametric Methods for Several Groups and for Matched Pairs 

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