Statistics Workbook for Dummies

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  • Edition: 2nd
  • Format: Paperback
  • Copyright: 2019-04-23
  • Publisher: For Dummies

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Supplemental Materials

What is included with this book?


Practice your way to a higher statistics score 

The adage that "practice makes perfect" is never truer than with math problems. Statistics Workbook For Dummies with Online Practice provides succinct content reviews for every topic, with plenty of examples and practice problems for each concept, in the book and online.  

Every lesson begins with a concept review, followed by a few example problems and plenty of practice problems. There's a step-by-step solution for every problem, with tips and tricks to help with comprehension and retention. New for this edition, free online practice quizzes for each chapter provide extra opportunities to test your knowledge and understanding. 

  • Get FREE access to chapter quizzes in an online test bank
  • Work along with each chapter or use the test bank for final exam review
  • Discover which statistical measures are most meaningful

Scoring high in your Statistics class has never been easier! 

Author Biography

Deborah J. Rumsey, PhD is a longtime statistics professor at The Ohio State University specializing in statistics education. She authored Statistics For Dummies, Statistics II For Dummies, and Probability For Dummies.

Table of Contents

Introduction 1

About This Book 1

Foolish Assumptions 2

Icons Used in This Book 3

Beyond the Book 3

Where to Go from Here 4

Part 1: Getting Off to a Statistically Significant Start 5

Chapter 1: Summarizing Categorical Data: Counts and Percents 7

Counting On the Frequency 7

Relating with Percentages 9

Interpreting Counts and Percents with Caution 11

Answers to Problems in Summarizing Categorical Data 13

Chapter 2: Summarizing Quantitative Data: Means, Medians, and More 17

Finding and Interpreting Measures of Center 18

Finding and Interpreting Measures of Spread 20

Using Percentiles and the Interquartile Range 22

Answers to Problems in Summarizing Quantitative Data 24

Chapter 3: Organizing Categorical Data: Charts and Graphs 27

Making, Interpreting, and Evaluating Pie Charts 27

Making, Interpreting, and Evaluating Bar Graphs 32

Answers to Problems in Organizing Categorical Data 37

Chapter 4: Organizing Quantitative Data: Charts and Graphs 43

Creating a Histogram 44

Making Sense of Histograms 47

Straightening Out Skewed Data 51

Spotting a Misleading Histogram 53

Making Box Plots 55

Interpreting Box Plots 56

Looking at Line Graphs 58

Understanding the Empirical Rule 60

Answers to Problems in Organizing Quantitative Data 63

Part 2: Probability, Distributions, and the Central Limit Theorem (Are You Having Fun Yet?) 73

Chapter 5: Understanding Probability Basics 75

Grasping the Rules of Probability 75

Avoiding Probability Misconceptions 78

Making Predictions Using Probability 79

Answers to Problems in Probability 81

Chapter 6: Measures of Relative Standing and the Normal Distribution 83

Mastering the Normal Distribution 83

Finding and Interpreting Standard (Z) Scores 86

Knowing Where You Stand with Percentiles 88

Finding Probabilities for a Normal Distribution 90

Finding the Percentile (Backwards Normal) 92

Answers to Problems in Normal Distribution 95

Chapter 7: The Binomial Distribution 105

Characterizing the Binomial Distribution 105

Finding Probabilities Using the Binomial Formula for small n 107

Finding Probabilities Using the Binomial Table for Medium-Sized n 109

Calculating the Mean and Variance of the Binomial 110

Estimating Probabilities in Large Cases — the Normal Approximation 112

Answers to Problems in the Binomial Distribution 114

Chapter 8: The t-Distribution 117

Getting to Know the t-Distribution 117

Working with the t-Table and Degrees of Freedom 120

Answers to Problems in the t-Distribution 122

Chapter 9: Demystifying Sampling Distributions and the Central Limit Theorem 123

Exactly What is a Sampling Distribution? 124

Clearing Up the Central Limit Theorem (Once and for All) 126

Finding Probabilities with the Central Limit Theorem 129

When Your Sample’s Too Small: Employing the t-Distribution 131

Answers to Problems in Sampling Distributions and the Central Limit Theorem 133

Part 3: Guesstimating and Hypothesizing with Confidence 137

Chapter 10: Making Sense of Margin of Error 139

Reviewing Margin of Error 139

Calculating the Margin of Error for Means and Proportions 142

Increasing and Decreasing Margin of Error 144

Interpreting Margin of Error Correctly 146

Answers to Problems in Making Sense of Margin of Error 148

Chapter 11: Calculating Confidence Intervals 151

Walking through a Confidence Interval 151

Deriving a Confidence Interval for a Population Mean 154

Figuring a Confidence Interval for a Population Proportion 156

Calculating a Confidence Interval for the Difference of Two Means 158

Computing a Confidence Interval for the Difference of Two Proportions 160

Answers to Problems in Calculating Confidence Intervals 163

Chapter 12: Deciphering Your Confidence Interval 169

Interpreting Confidence Intervals the Right Way 169

Evaluating Confidence Interval Results: What the Formulas Don’t Tell You 173

Answers to Problems in Confidence Intervals 175

Chapter 13: Testing Hypotheses 177

Walking Through a Hypothesis Test 177

Testing a Hypothesis about a Population Mean 181

Testing a Hypothesis about a Population Proportion 183

Testing for a Difference between Two Population Means 185

Testing for a Mean Difference (Paired t-Test) 188

Testing a Hypothesis about Two Population Proportions 190

Answers to Problems in Testing Hypotheses 192

Chapter 14: Taking the Guesswork Out of p-Values and Type I and II Errors 197

Understanding What p-Values Measure 198

Test (Statistic) Time: Figuring Out p-Values 199

The Value Breakdown: Interpreting p-Values Properly 201

Deciphering Type I Errors 204

Deciphering and Distinguishing Type II Errors 205

Answers to Problems in p-Values and Type I and II Errors 208

Part 4: Statistical Studies and the Hunt for a Meaningful Relationship 211

Chapter 15: Examining Polls and Surveys 213

Planning and Designing a Survey 214

Selecting a Random Sample 215

Carrying Out a Survey Properly 217

Interpreting and Evaluating Survey Results 218

Answers to Problems in Polls and Surveys 220

Chapter 16: Evaluating Experiments 223

Distinguishing Experiments from Observational Studies 223

Designing a Good Experiment 225

Looking for Cause and Effect: Interpreting Experiment Results 228

Answers to Problems in Evaluating Experiments 230

Chapter 17: Looking for Links in Categorical Data: Two-Way Tables 233

Understanding Two-Way Tables Inside and Out 234

Working with Intersection, Unions, and the Addition Rule 237

Figuring Marginal Probabilities 240

Nailing Down Conditional Probabilities and the Multiplication Rule 242

Inspecting the Independence of Categorical Variables 246

Answers to Problems in Two-Way Tables 250

Chapter 18: Searching for Links in Quantitative Data: Correlation and Regression 259

Relating X and Y with a Scatterplot 259

Toeing the Line of Correlation 262

Picking Out the Best Fitting Regression Line 265

Interpreting the Regression Line and Making Predictions 267

Checking the Fit of the Regression Line 269

Answers to Problems in Correlation and Regression 272

Part 5: The Part of Tens 277

Chapter 19: Math Review: Ten Steps to a Better Grade 279

Know Your Math Symbols 279

Uproot Roots and Powers 280

Treat Fractions with Extra Care 280

Obey the Order of Operations 281

Avoid Rounding Errors 282

Get Comfortable with Formulas 283

Stay Calm When Formulas Get Tough 283

Feel Fine about Functions 285

Know When Your Answer is Wrong 286

Show Your Work 287

Chapter 20: Top Ten Statistical Formulas 289

Mean (or Average) 289

Median 290

Sample Standard Deviation 291

Correlation 292

Margin of Error for the Sample Mean 293

Sample Size Needed for Estimating μ 294

Test Statistic for the Mean 295

Margin of Error for the Sample Proportion 296

Sample Size Needed for Estimating p 297

Test Statistic for the Proportion 298

Chapter 21: Ten Ways to Spot Common Statistical Mistakes 301

Scrutinizing Graphs 301

Searching for and Specifying Bias 302

Marking the Margin of Error 303

Scanning for Sample Size 303

Studying Sample Selection (Gotta Be Random) 304

Checking for Confounding Variables 305

Considering Correlation 305

Doing the Math 306

Detecting Selective Reporting 306

Avoiding the Anecdote 307

Appendix: Tables for Reference 309

Index 319

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