You are previewing The Complete Idiot's Guide to Statistics, 2nd Edition.
O'Reilly logo
The Complete Idiot's Guide to Statistics, 2nd Edition

Book Description

Not a numbers person? No problem!

Table of Contents

  1. Title Page
  2. Dedication
  3. Copyright Page
  4. Foreword
  5. Introduction
  6. Acknowledgements
  7. Part 1 - The Basics
    1. Chapter 1 - Let’s Get Started
      1. Where Is This Stuff Used?
      2. Who Thought of This Stuff?
      3. The Field of Statistics Today
      4. Ethics and Statistics—It’s a Dangerous World Out There
      5. Your Turn
    2. Chapter 2 - Data, Data Everywhere and Not a Drop to Drink
      1. The Importance of Data
      2. The Sources of Data—Where Does All This Stuff Come From?
      3. Types of Data
      4. Types of Measurement Scales—a Weighty Topic
      5. Computers to the Rescue
      6. Your Turn
    3. Chapter 3 - Displaying Descriptive Statistics
      1. Frequency Distributions
      2. Statistical Flower Power—the Stem and Leaf Display
      3. Charting Your Course
      4. Your Turn
    4. Chapter 4 - Calculating Descriptive Statistics: Measures of Central Tendency ...
      1. Measures of Central Tendency
      2. Using Excel to Calculate Central Tendency
      3. Your Turn
    5. Chapter 5 - Calculating Descriptive Statistics: Measures of Dispersion
      1. Range
      2. Variance
      3. Standard Deviation
      4. Calculating the Standard Deviation of Grouped Data
      5. The Empirical Rule: Working the Standard Deviation
      6. Chebyshev’s Theorem
      7. Measures of Relative Position
      8. Using Excel to Calculate Measures of Dispersion
      9. Your Turn
  8. Part 2 - Probability Topics
    1. Chapter 6 - Introduction to Probability
      1. What Is Probability?
      2. Basic Properties of Probability
      3. The Intersection of Events
      4. The Union of Events: A Marriage Made in Heaven
      5. Your Turn
    2. Chapter 7 - More Probability Stuff
      1. Conditional Probability
      2. Independent Versus Dependent Events
      3. Multiplication Rule of Probabilities
      4. Mutually Exclusive Events
      5. Addition Rule of Probabilities
      6. Summarizing Our Findings
      7. Bayes’ Theorem
      8. Your Turn
    3. Chapter 8 - Counting Principles and Probability Distributions
      1. Counting Principles
      2. Probability Distributions
      3. Your Turn
    4. Chapter 9 - The Binomial Probability Distribution
      1. Characteristics of a Binomial Experiment
      2. The Binomial Probability Distribution
      3. Binomial Probability Tables
      4. Using Excel to Calculate Binomial Probabilities
      5. The Mean and Standard Deviation for the Binomial Distribution
      6. Your Turn
    5. Chapter 10 - The Poisson Probability Distribution
      1. Characteristics of a Poisson Process
      2. The Poisson Probability Distribution
      3. Poisson Probability Tables
      4. Using Excel to Calculate Poisson Probabilities
      5. Using the Poisson Distribution as an Approximation to the Binomial Distribution
      6. Your Turn
    6. Chapter 11 - The Normal Probability Distribution
      1. Characteristics of the Normal Probability Distribution
      2. Calculating Probabilities for the Normal Distribution
      3. Using the Normal Distribution as an Approximation to the Binomial Distribution
      4. Your Turn
  9. Part 3 - Inferential Statistics
    1. Chapter 12 - Sampling
      1. Why Sample?
      2. Random Sampling
      3. Sampling Errors
      4. Examples of Poor Sampling Techniques
      5. Your Turn
    2. Chapter 13 - Sampling Distributions
      1. What Is a Sampling Distribution?
      2. Sampling Distribution of the Mean
      3. The Central Limit Theorem
      4. Standard Error of the Mean
      5. Why Does the Central Limit Theorem Work?
      6. Putting the Central Limit Theorem to Work
      7. Sampling Distribution of the Proportion
      8. Your Turn
    3. Chapter 14 - Confidence Intervals
      1. Confidence Intervals for the Mean with Large Samples
      2. Confidence Intervals for the Mean with Small Samples
      3. Confidence Intervals for the Proportion with Large Samples
      4. Your Turn
    4. Chapter 15 - Introduction to Hypothesis Testing
      1. Hypothesis Testing—the Basics
      2. Type I and Type II Errors
      3. Example of a Two-Tail Hypothesis Test
      4. Example of a One-Tail Hypothesis Test
      5. Your Turn
    5. Chapter 16 - Hypothesis Testing with One Sample
      1. Hypothesis Testing for the Mean with Large Samples
      2. The Role of Alpha in Hypothesis Testing
      3. Introducing the p-Value
      4. Hypothesis Testing for the Mean with Small Samples
      5. Hypothesis Testing for the Proportion with Large Samples
      6. Your Turn
    6. Chapter 17 - Hypothesis Testing with Two Samples
      1. The Concept of Testing Two Populations
      2. Sampling Distribution for the Difference in Means
      3. Testing for Differences Between Means with Large Sample Sizes
      4. Testing a Difference Other Than Zero
      5. Testing for Differences Between Means with Small Sample Sizes and Unknown Sigma
      6. Letting Excel Do the Grunt Work
      7. Testing for Differences Between Means with Dependent Samples
      8. Testing for Differences Between Proportions with Independent Samples
      9. Your Turn
  10. Part 4 - Advanced Inferential Statistics
    1. Chapter 18 - The Chi-Square Probability Distribution
      1. Review of Data Measurement Scales
      2. The Chi-Square Goodness-of-Fit Test
      3. Characteristics of a Chi-Square Distribution
      4. A Goodness-of-Fit Test with the Binomial Distribution
      5. Chi-Square Test for Independence
      6. Your Turn
    2. Chapter 19 - Analysis of Variance
      1. One-Way Analysis of Variance
      2. Completely Randomized ANOVA
      3. Using Excel to Perform One-Way ANOVA
      4. Pairwise Comparisons
      5. Completely Randomized Block ANOVA
      6. Your Turn
    3. Chapter 20 - Correlation and Simple Regression
      1. Independent Versus Dependent Variables
      2. Correlation
      3. Your Turn
  11. Appendix A - Solutions to “Your Turn”
    1. Chapter 1
    2. 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
    3. 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
    4. Chapter 4
    5. 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
    6. Chapter 6
    7. Chapter 7
    8. Chapter 8
    9. 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
    10. Chapter 10
    11. Chapter 11
    12. Chapter 12
    13. 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
    14. Chapter 14
    15. Chapter 15
    16. Chapter 16
    17. Chapter 17
    18. Chapter 18
    19. Chapter 19
    20. Chapter 20
  12. Appendix B - Statistical Tables
    1. Binomial Probability Tables
    2. Poisson Probability Tables
    3. Normal Probability Tables
    4. Student’s t-Distribution
    5. Chi-Square Probability Distribution
    6. F-Distribution
  13. Appendix C - Glossary
  14. Index