**A Foundation in the Science of Statistics** provides a foundation in the science of statistics to a large sophomore level course. Its intended audience is students of all areas of study with at least basic skills in college algebra. It covers all the topics in a typical introductory course in statistics and provides both book learning and hands-on learning. Emphasis is on the concepts of statistics so tedious, repetitive numerical calculations are not used.

This eBook is meant to be a standalone courseware project rather than a traditional text. The text portion is divided into Units, Chapters, and Lessons to present topics of descriptive statistics, probability (without mathematics), confidence intervals, hypothesis tests, two-sample t-tests, ANOVA, correlation, regression, and binomial data. Each lesson covers one topic and has practice questions to reinforce learning the material. Quizzes and exams can be written from these practice questions directly, or can be randomly selected from these practice questions.

The text of each lesson is written to present its topic in its simplest case, giving the basic concepts in simple form and emphasizing the flow of the knowledge of statistics. In addition, there are hyperlinks to an extensive set of Aids that explain the concepts in more detail. This structure allows a student to get enough information to learn the topic, but find it easy to scan the text on review to prepare for an exam.

Six labs are included in this etext using the relatively easy to use software package JMP to give students a hands-on experience in manipulating data, understanding probability, and doing common statistical using a computer. JMP might be free to most educational institutions.

**Unit 01: Sample & Probability Information**

**Chapter 01:** The Science of Statistics

Lesson 01.1: Concepts About Data

Lesson 01.2: Concepts About Statistics

**Chapter 02: **Sample Information

Lesson 02.1: Distribution of Data

Lesson 02.2: Graphical Summaries of Data

Lesson 02.3: Efficient Statistics

Lesson 02.4: Resistant Statistics

**Chapter 03:** Probability Information

Lesson 03.1: Properties of the Normal Distribution

Lesson 03.2: Probability with the *Standard* Normal Distribution

Lesson 03.3: Probability with *Any* Normal Distribution

**Unit 02: Population Information**

**Chapter 04****: **Statistical Inference

Lesson 04.1: Process of Inference

Lesson 04.2: Probability with Sample Average

Lesson 04.3: Probability with the *t*-Distribution

**Chapter 05****:** Method of Confidence Intervals

Lesson 05.1: Logic of Confidence Intervals

Lesson 05.2: Confidence Intervals with *z*-Scores

Lesson 05.3: Confidence Intervals with *t*-Values

**Chapter 06: **Method of Hypothesis Testing

Lesson 06.1: Logic of Hypothesis Testing

Lesson 06.2: Hypothesis Testing with *z*-Scores

Lesson 06.3: Hypothesis Testing with *t*-Values

**Unit 03: Analysis of Continuous Data**

**Chapter 07: **Method of Two-Sample *t*-Test

Lesson 07.1: Logic of Two-Sample Hypothesis Testing

Lesson 07.2: Hypothesis Tests with Dependent Samples

Lesson 07.3: Hypothesis Tests with Independent Sample

**Chapter 08:** Method of ANOVA

Lesson 08.1: Logic of ANOVA

Lesson 08.2: ANOVA by Hand

Lesson 08.3: ANOVA by Computer

**Chapter 09:** Methods of Linear Relationship

Lesson 09.1: Why a New Method

Lesson 09.2: What is a Scatterplot

Lesson 09.3: What is Regression

Lesson 09.4: Least Squares Regression

**Unit 04: Analysis of Categorical Data**

**Chapter 10: **Methods for Binomial Data

Lesson 10.1: Probability with Binomial Data

Lesson 10.2: Inference Methods for Proportion

Lesson 10.3: Chi-Square Test for Independence

**Chapter 11:** Methods for Categorical Data

Lesson 11.1: One Column of Categorical Data

Lesson 11.2: Two Columns of Categorical Data

**Appendix**

**Preface**

**Symbol Glossary**

**Review Guide**

**
Michael
McKenna
**

Michael McKenna is a senior instructor at Louisiana State University. He has taught a sophomore level introductory course in statistics for over twenty years. He has a Master of Science degree from Texas A&M University, a Master of Business Administration degree from Louisiana State University, and a Masters of Applied Statistics degree from Louisiana State University.