Note: Supplemental materials are not guaranteed with Rental or Used book purchases.
Purchase Benefits
What is included with this book?
PHILLIP I. GOOD, PhD, is Operations Manager of Information Research, a consulting firm specializing in statistical solutions for private and public organizations. He has published over thirty scholarly works, more than 600 articles, and forty-four books, including Common Errors in Statistics (and How to Avoid Them) and A Manager's Guide to the Design and Conduct of Clinical Trials, both published by Wiley.
Preface
1. Variation
1.1. Variation
1.2. Collecting Data
1.2.1 A worked through example
1.3. Summarizing Your Data
1.3.1 Learning to Use R
1.4. Reporting Your Results
1.4.1 Picturing Data
1.4.2. Better Graphics
1.5. Types of Data
1.5.1. Depicting Categorical Data
1.6. Displaying Multiple Variables
1.6.1. From Observations to Questions
1.7. Measures of Location
1.7.1. Which Measure of Location?
1.7.2 Estimating Precision
1.7.3. Estimating with the Bootstrap
1.8. Samples and Populations
1.8.1. Drawing a Random Sample
1.8.2. Ensuring the Sample is Representative
1.9. Summary and Review
2. Probability
2.1. Probability
2.1.1.Events and Outcomes
2.1.2 Venn Diagrams
2.2. Binomial Trials
2.2.1. Permutations and Rearrangements
2.2.2Programming Your Own Functions in R
2.2.3.Back to The Binomial
2.2. 4. Problem Jury
2.3. Conditional Probability
2.3.1. Market Basket Analysis
2.3.2 Negative Results
2.4. Independence
2.5. Applications to Genetics
2.6. Summary and Review
3. Two Natural Distributions
Distribution of Values
1. Cumulative Distribution Function
2. Empirical Distribution Function
Discrete Distributions
Binomial Distribution
1. Properties of the Binomial
Variance and Standard Deviation
Events Rare in Time and Space
1. Applying the Poisson
2. Comparing Observed and Theoretical Distributions
3. Comparing Two Poisson Processes Continuous Distributions
1. Exponential Distribution
Summary and Review
4. Estimation and the Normal Distribution
Point Estimates
Properties of the Normal Distribution
Student's t
Mixtures of Normal Distributions
Using Confidence Intervals to Test Hypotheses
Should we have used the bootstrap
The Parametric Bootstrap
Properties of Independent Observations
Summary and Review
5. Testing Hypotheses
Analyzing an Experiment
Two Types of Errors
Estimating Effect Size
Using Confidence Intervals to Test Hypotheses
Applying the t-test to Measurements
One-sample Problem
Two-sample Problem
Paired Comparison
Permutation Monte Carlo
Which Test Should We Use
0. One-sided vs. Two-sided
1. p-values and Significance Levels
2. Test Assumptions
3. Robustness
4. Power of a Test Procedure
Summary and Review
6. Designing an Experiment or Survey
The Hawthorne Effect
Crafting an experiment.
Designing an Experiment or Survey
Objectives
Sample from the right population
Coping with variation
Matched pairs
Experimental unit
Formulate your hypotheses
What are you going to measure?
Random, representative samples
Treatment allocation
Choosing a random sample
Ensuring your observations are independent
How Large a Sample
Samples of fixed size
Known distribution
Almost normal data
Bootstrap
Sequential Sampling
Adaptive sampling
Meta-Analysis
Summary and Review
7. Guide to Entering, Saving, and Retrieving Large Quantities of Data Using R
Creating and Editing a Data File
Saving and Retrieving a Data File
Retrieving and Using Data Created by Other Programs
Example: Using R to Draw a Random Sample
8. Analyzing Complex Experiments
A. Changes Measured in Percentages
B. Comparing More Than Two Samples
1. Programming the Multi-sample Comparison in R
2. Reusing Your R Functions
3. What Is the Alternative?
4. Testing for a Dose Response or Other Ordered Alternative
C. Equalizing Variances
D. Categorical Data
a. One-Sided Fisher's Exact Test
b. The Two-Sided Test
c. Testing for Goodness of Fit
d. Multnomial Tables
E. Multivariate Analysis
Manipulating Multivariate Data in R
Hotelling's Statistic
Pesarin-Fisher Omnibus Statistic
F. R Programming Guidelines
G. Summary and Review
9. Developing Models
Why Build Models?
Caveats
Classification and Regression Tree
1. How Trees are Grown
2. Examples
3. Incorporating existing knowledge
a) Prior probabilities
b) Misclassification costs
Regression
1. Linear Regression
2. Nonlinear Regression
3. Survival Analysis
Fitting a Regression Equation
a. Ordinary least squares
b. Least absolute deviation
c. Errors in both variables
d. Assumptions
Problems with Regression
a. Goodness-of-fit versus prediction
b. Which model?
Multiple Regression
Quantile Regression
Validation
a. Independent validation
b. Splitting the sample
c. Cross-validation with the bootstrap
Summary and Review
10. Reporting Your Findings
What to Report
Text, Table, or Graph?
R Graphic Packages
Summarizing Your Results
a. Center of the distribution
b. Dispersion
Reporting Analysis Results
a. p-Values or Confidence Intervals?
Exceptions Are the Real Story
n. Non-Responders
o. The Missing Holes
p. Missing Data
q. Recognize and Report Bias
Summary and Review
11. Problem Solving
Real Life Problems
Solving Practical Problems
a. Data Provenance
b. Inspect the Data
c. Validate Data Collection Methods
d. Formulate Hypotheses
Choose a Statistical Methodology
Be Aware of What You Don't Know
Qualify Your Conclusions
Answers to Selected Exercises
Subject Index
Index to R Functions