Statistics is confusing, even for smart, technically competent people. And many students and professionals find that existing books and web resources don’t give them an intuitive understanding of confusing statistical concepts. That is why this book is needed. Some of the unique qualities of this book are:

• Easy to Understand: Uses unique “graphics that teach” such as concept flow diagrams, compare-and-contrast tables, and even cartoons to enhance “rememberability.”

• Easy to Use: Alphabetically arranged, like a mini-encyclopedia, for easy lookup on the job, while studying, or during an open-book exam.

• Wider Scope: Covers Statistics I and Statistics II and Six Sigma Black Belt, adding such topics as control charts and statistical process control, process capability analysis, and design of experiments. As a result, this book will be useful for business professionals and industrial engineers in addition to students and professionals in the social and physical sciences.

In addition, each of the 60+ concepts is covered in one or more articles. The 75 articles in the book are usually 5–7 pages long, ensuring that things are presented in “bite-sized chunks.” The first page of each article typically lists five “Keys to Understanding” which tell the reader everything they need to know on one page. This book also contains an article on “Which Statistical Tool to Use to Solve Some Common Problems”, additional “Which to Use When” articles on Control Charts, Distributions, and Charts/Graphs/Plots, as well as articles explaining how different concepts work together (e.g., how Alpha, p, Critical Value, and Test Statistic interrelate).

Table of Contents

ALPHA, 𝜶

ALPHA AND BETA ERRORS

ALPHA, p, CRITICAL VALUE, AND TEST STATISTIC – HOW THEY WORK TOGETHER

ALTERNATIVE HYPOTHESIS

ANALYSIS OF MEANS (ANOM)

ANOVA – PART 1: WHAT IT DOES

ANOVA – PART 2: HOW IT DOES IT

ANOVA – PART 3: 1-WAY (AKA SINGLE FACTOR)

ANOVA – PART 4: 2-WAY (AKA 2-FACTOR)

ANOVA vs. REGRESSION

BINOMIAL DISTRIBUTION

CHARTS/GRAPHS/PLOTS – WHICH TO USE WHEN

CHI-SQUARE – THE TEST STATISTIC AND ITS

DISTRIBUTIONS

CHI-SQUARE TEST FOR GOODNESS OF FIT

CHI-SQUARE TEST FOR INDEPENDENCE

CHI-SQUARE TEST FOR THE VARIANCE

CONFIDENCE INTERVALS – PART 1: GENERAL CONCEPTS

CONFIDENCE INTERVALS – PART 2: SOME SPECIFICS

CONTROL CHARTS – PART 1: GENERAL CONCEPTS AND PRINCIPLES

CONTROL CHARTS – PART 2: WHICH TO USE WHEN

CORRELATION – PART 1

CORRELATION – PART 2

CRITICAL VALUE

DEGREES OF FREEDOM

DESIGN OF EXPERIMENTS (DOE) – PART 1

DESIGN OF EXPERIMENTS (DOE) – PART 2

DESIGN OF EXPERIMENTS (DOE) – PART 3

DISTRIBUTIONS – PART 1: WHAT THEY ARE

DISTRIBUTIONS – PART 2: HOW THEY ARE USED

DISTRIBUTIONS – PART 3: WHICH TO USE WHEN

ERRORS – TYPES, USES, AND INTERRELATIONSHIPS

EXPONENTIAL DISTRIBUTION

F

FAIL TO REJECT THE NULL HYPOTHESIS

HYPERGEOMETRIC DISTRIBUTION

HYPOTHESIS TESTING – PART 1: OVERVIEW

HYPOTHESIS TESTING – PART 2: HOW TO

INFERENTIAL STATISTICS

MARGIN OF ERROR

NONPARAMETRIC

NORMAL DISTRIBUTION

NULL HYPOTHESIS

p, p-VALUE

p, t, AND F: “>”OR “<”?

POISSON DISTRIBUTION

POWER

PROCESS CAPABILITY ANALYSIS (PCA)

PROPORTION

r, MULTIPLE R, r2, R2, R SQUARE, R2 ADJUSTED

REGRESSION – PART 1: SUMS OF SQUARES

REGRESSION – PART 2: SIMPLE LINEAR

REGRESSION – PART 3: ANALYSIS BASICS

REGRESSION – PART 4: MULTIPLE LINEAR

REGRESSION – PART 5: SIMPLE NONLINEAR

REJECT THE NULL HYPOTHESIS

RESIDUALS

SAMPLE, SAMPLING

SAMPLE SIZE – PART 1: PROPORTIONS FOR COUNT DATA

SAMPLE SIZE – PART 2: FOR MEASUREMENT/

CONTINUOUS DATA

SAMPLING DISTRIBUTION

SIGMA

SKEW, SKEWNESS

STANDARD DEVIATION

STANDARD ERROR

STATISTICALLY SIGNIFICANT

SUMS OF SQUARES

t – THE TEST STATISTIC AND ITS DISTRIBUTIONS

t-TESTS – PART 1: OVERVIEW

t-TESTS – PART 2: CALCULATIONS AND ANALYSIS

TEST STATISTIC

VARIABLES

VARIANCE

VARIATION/VARIABILITY/DISPERSION/SPREAD

WHICH STATISTICAL TOOL TO USE TO SOLVE SOME

COMMON PROBLEMS

Z

About the Author

Andrew A. Jawlik received his B.S. in Mathematics and his M.S. in Mathematics and Computer Science from the University of Michigan. He held jobs with IBM in marketing, sales, finance, and information technology, as well as a position as Process Executive. In these jobs, he learned how to communicate difficult technical concepts in easy - to - understand terms. He completed Lean Six Sigma Black Belt coursework at the IASSC - accredited Pyzdek Institute. In order to understand the confusing statistics involved, he wrote explanations in his own words and graphics. Using this material, he passed the certification exam with a perfect score. Those statistical explanations then became the starting point for this book.