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The Complete Idiot's Guide to Statistics, 2nd Edition
by Robert Donnelly
Publisher: Alpha
Release Date: May 2007
ISBN: 9781592576340
Topics:
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Table of contents
Book Description
Not a numbers person? No problem!
Table of Contents
Title Page
Dedication
Copyright Page
Foreword
Introduction
Acknowledgements
Part 1 - The Basics
Chapter 1 - Let’s Get Started
Where Is This Stuff Used?
Who Thought of This Stuff?
The Field of Statistics Today
Ethics and Statistics—It’s a Dangerous World Out There
Your Turn
Chapter 2 - Data, Data Everywhere and Not a Drop to Drink
The Importance of Data
The Sources of Data—Where Does All This Stuff Come From?
Types of Data
Types of Measurement Scales—a Weighty Topic
Computers to the Rescue
Your Turn
Chapter 3 - Displaying Descriptive Statistics
Frequency Distributions
Statistical Flower Power—the Stem and Leaf Display
Charting Your Course
Your Turn
Chapter 4 - Calculating Descriptive Statistics: Measures of Central Tendency ...
Measures of Central Tendency
Using Excel to Calculate Central Tendency
Your Turn
Chapter 5 - Calculating Descriptive Statistics: Measures of Dispersion
Range
Variance
Standard Deviation
Calculating the Standard Deviation of Grouped Data
The Empirical Rule: Working the Standard Deviation
Chebyshev’s Theorem
Measures of Relative Position
Using Excel to Calculate Measures of Dispersion
Your Turn
Part 2 - Probability Topics
Chapter 6 - Introduction to Probability
What Is Probability?
Basic Properties of Probability
The Intersection of Events
The Union of Events: A Marriage Made in Heaven
Your Turn
Chapter 7 - More Probability Stuff
Conditional Probability
Independent Versus Dependent Events
Multiplication Rule of Probabilities
Mutually Exclusive Events
Addition Rule of Probabilities
Summarizing Our Findings
Bayes’ Theorem
Your Turn
Chapter 8 - Counting Principles and Probability Distributions
Counting Principles
Probability Distributions
Your Turn
Chapter 9 - The Binomial Probability Distribution
Characteristics of a Binomial Experiment
The Binomial Probability Distribution
Binomial Probability Tables
Using Excel to Calculate Binomial Probabilities
The Mean and Standard Deviation for the Binomial Distribution
Your Turn
Chapter 10 - The Poisson Probability Distribution
Characteristics of a Poisson Process
The Poisson Probability Distribution
Poisson Probability Tables
Using Excel to Calculate Poisson Probabilities
Using the Poisson Distribution as an Approximation to the Binomial Distribution
Your Turn
Chapter 11 - The Normal Probability Distribution
Characteristics of the Normal Probability Distribution
Calculating Probabilities for the Normal Distribution
Using the Normal Distribution as an Approximation to the Binomial Distribution
Your Turn
Part 3 - Inferential Statistics
Chapter 12 - Sampling
Why Sample?
Random Sampling
Sampling Errors
Examples of Poor Sampling Techniques
Your Turn
Chapter 13 - Sampling Distributions
What Is a Sampling Distribution?
Sampling Distribution of the Mean
The Central Limit Theorem
Standard Error of the Mean
Why Does the Central Limit Theorem Work?
Putting the Central Limit Theorem to Work
Sampling Distribution of the Proportion
Your Turn
Chapter 14 - Confidence Intervals
Confidence Intervals for the Mean with Large Samples
Confidence Intervals for the Mean with Small Samples
Confidence Intervals for the Proportion with Large Samples
Your Turn
Chapter 15 - Introduction to Hypothesis Testing
Hypothesis Testing—the Basics
Type I and Type II Errors
Example of a Two-Tail Hypothesis Test
Example of a One-Tail Hypothesis Test
Your Turn
Chapter 16 - Hypothesis Testing with One Sample
Hypothesis Testing for the Mean with Large Samples
The Role of Alpha in Hypothesis Testing
Introducing the p-Value
Hypothesis Testing for the Mean with Small Samples
Hypothesis Testing for the Proportion with Large Samples
Your Turn
Chapter 17 - Hypothesis Testing with Two Samples
The Concept of Testing Two Populations
Sampling Distribution for the Difference in Means
Testing for Differences Between Means with Large Sample Sizes
Testing a Difference Other Than Zero
Testing for Differences Between Means with Small Sample Sizes and Unknown Sigma
Letting Excel Do the Grunt Work
Testing for Differences Between Means with Dependent Samples
Testing for Differences Between Proportions with Independent Samples
Your Turn
Part 4 - Advanced Inferential Statistics
Chapter 18 - The Chi-Square Probability Distribution
Review of Data Measurement Scales
The Chi-Square Goodness-of-Fit Test
Characteristics of a Chi-Square Distribution
A Goodness-of-Fit Test with the Binomial Distribution
Chi-Square Test for Independence
Your Turn
Chapter 19 - Analysis of Variance
One-Way Analysis of Variance
Completely Randomized ANOVA
Using Excel to Perform One-Way ANOVA
Pairwise Comparisons
Completely Randomized Block ANOVA
Your Turn
Chapter 20 - Correlation and Simple Regression
Independent Versus Dependent Variables
Correlation
Your Turn
Appendix A - Solutions to “Your Turn”
Chapter 1
1. Inferential statistics, because it would not be feasible to survey every Asian American household in the country. These results would be based on a sample of the population and used to make an inference on the entire population.2. Inferential statistics, because it would not be feasible to survey every household in the country. These results would be based on a sample of the population and used to make an inference on the entire population.3. Descriptive statistics, because Hank Aaron’s home run total is based on the entire population, which is every at-bat in his career.4. Descriptive statistics, because the average SAT score would be based on the entire population, which is the incoming freshman class.5. Inferential statistics, because it would not be feasible to survey every American in the country. These results would be based on a sample of the population and used to make an inference on the entire population.Chapter 2
1. Interval data, because temperature in degrees Fahrenheit does not contain a true zero point.2. Ratio data, because monthly rainfall does have a true zero point.3. Ordinal data, because a Master’s degree is a higher level of education than a Bachelor’s or high school degree. However, we cannot claim that a Master’s degree is two or three times higher than the others.4. Nominal data, because we cannot place the categories in any type of order.5. Ratio data, because age does have a true zero point.6. Definitely nominal data, unless you want to get into an argument about which is the lesser gender!7. Interval data, because the difference between years is meaningful but a true zero point does not exist.8. Nominal data, because I am not prepared to name one political party superior to another.9. Nominal data, because these are simply unordered categories.10. Ordinal data, because we can specify that “Above Expectation” is higher on the performance scale than the other two but we cannot comment on the differences between the categories.11. Nominal data, because we cannot claim a person wearing the number “10” is any better then a person wearing the number “4.”12. Ordinal data, because we cannot comment on the difference in performance between students. The top two students may be very far apart grade-wise, whereas the second and third students could be very close.13. Ratio data, because these exam scores have a true zero point.14. Nominal data, because there is no order in the states’ categories.Chapter 3
Chapter 4
1. Mean = 15.9, Median = 17, Mode = 242. Mean = 81.7, Median = 82, Mode = 823. Mean = 32.7, Median = 32.5, Mode = 36 and 274. Mean = 7.2, Median = 6, Mode = 65. 6. 7. Chapter 5
Chapter 6
Chapter 7
Chapter 8
10. The number of three-of-a-kind combinations is (13)4C3 = 52. The number of remaining pairs is (12)4C 2 = 72. The total number of full house hands is (52)(72) = 3,744. P[Full House] =Chapter 9
Chapter 10
Chapter 11
Chapter 12
2. If every employee belonged to a particular department, certain departments could be chosen for the survey, with every individual in those departments asked to participate. Other answers are also possible.3. If each employee can be classified as either a manager or a nonmanager, ensure that the sample proportion for each type is similar to the proportion of managers and nonmanagers in the company. Other answers are also possible.Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 18
Chapter 19
Chapter 20
Appendix B - Statistical Tables
Binomial Probability Tables
Poisson Probability Tables
Normal Probability Tables
Student’s t-Distribution
Chi-Square Probability Distribution
F-Distribution
Appendix C - Glossary
Index