# A Foundation in the Science of Statistics

Author(s): Michael McKenna

Edition: 3

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ISBN 9781792448669

Details KHPContent 180 days

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

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.

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

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.