Print Package includes a printed copy of the handbook and an access code for the accompanying website that hosts the electronic textbook.
eBook Package includes an access code for the accompanying website that hosts the electronic textbook and handbook.
The Joy of Statistics takes the reader on an important, interdisciplinary, and joyful ride in learning statistics….
The Joy of Statistics introduces the reader to important and basic questions on a broad spectrum of problems facing global society in environmental studies, medicine, education, engineering, business, sociology, and sports. These questions are derived from real-world data and emphasize the joy of learning basic statistics to answer these important and relevant questions.
By integrating real-world data and issues, the reader will be full of joy in learning statistics, which is vitally important and relevant to his/her personal well-being in a complex society.
Successfully fusing a textbook, accompanying workbook, and technology, The Joy of Statistics:
- Introduces the reader to technological tools that allow for access to even more real-world data.
- Features a rigorous, clear, and concise step-by-step development of statistical methodology that is easy for the reader to follow and comprehend.
- Presents the reader with a better understanding of the applicability of statistics and how it can be used to address relevant questions facing today’s society will help make viable public policies and set long-term goals for improving our quality of life more consistently.
- Includes a large number of problems (classified as basic, critical thinking, and real-world) – more than any other book on the subject.
- Provides instructors with a well-organized instructor’s resource library. The library includes sample tests, test solvers, interactive Excel documents, an instructor’s manual with a solutions manual, and ready-to-use Power Point slides for each chapter with animated illustrations of many of the topics.
- Enhances the learning of basic aspects of probability and its usefulness through a review of set theory using Venn diagrams.
Learn More about the Online Content!
Part One: Exploratory Data Analysis
Chapter One: Terminology & Sampling Techniques
1.1 Introduction to Statistics
1.2 Data Types
1.3 Representative Samples
1.4 Levels of Measure
1.5 Random Samples
1.6 Experimental Design
1.7 Additional Terminology
Summary and Terminology
Chapter Two: Graphical Representations of Data
2.1 Data Organization
2.2 Graphic Representation: Qualitative
2.3 Graphic Representation: Quantitative
2.4 Characteristics of Distributions
2.5 Comparison of Two Variables
Chapter Three: Descriptive Statistics
3.1 Introduction to Descriptive Statistics
3.2 Central Tendencies
Part Two: Probability & Discrete Probabilities
Chapter Four: Basic Probability
4.1 Introduction to Set Theory
4.2 Basic Probability
4.3 Sample Space
4.4 Computing Probabilities
4.5 Basic Properties of Probabilities
4.6 Conditional Probability
4.7 Counting Techniques
Chapter Five: Discrete Probability Distributions
5.1 Discrete Random Variables
5.2 Discrete Probability Distribution
5.3 Expected Value and Variance
5.4 Binomial Probability Distribution
5.5 Expected Value and Variance in a Binomial
5.6 Poisson Probability Distribution
5.7 Geometric Probability Distribution
Part Three: Continuous Probabilities
Chapter Six: Continuous Probability Distributions
6.1 Continuous Random Variable Distribution
6.2 Normal Probability Distribution
6.3 Standard Normal PDF
6.4 Central Limit Theorem (CLT)
6.5 Chebyshev & Empirical Rule
6.6 Normal Approximation to a Binomial
6.7 Testing for Normality & Dispersion
Chapter Seven: Point Estimates and Interval Estimates
7.2 Point Interval Estimates
(Confidence Intervals: CI)
7.3 CI: Case I: Normal Data with s-Known
7.4 CI: Case II: Unknown with s-Known
7.5 CI: Case III: Unknown PDF and s-Unknown
7.6 CI: Case IV: Estimating Proportions
7.7 Estimating Sample Size
Chapter Eight: Hypothesis Testing: One Population
8.2 Hypothesis Testing (HT)
8.3 HT: Case I: Normal Dats with s-Known
8.4 HT: Case II: Unknown PDF with s-Known
8.5 HT: Case III: Unknown PDF and s-Unknown
8.6 HT: Case IV: Proportions
8.7 Determining the -value
Chapter Nine: Two Populations
9.1 Introduction to Two Populations
9.2 Point and Interval Estimates: Independent with Known Probability Distribution
9.3 Case II: CI & HT for µ1 – µ2: Independent with Unknown Probability Distribution, s-known
9.4 Case III: CI & HT for µ1 & µ2: Independent with Unknown Probability Distribution, s-unknown
9.5 Case IV: CI & HT for µ1 & µ2: Dependent with Unknown Probability Distribution, s-unknown
9.6 Case V: CI & HT for 1 & 2: Independent with Unknown Probability Distribution, s-unknown
Part Five: Additional Hypothesis Testing & Regression
Chapter Ten: Chi-square Distribution
10.2 Chi-Square Probability Distribution
10.3 CI: Variance and Standard Deviation
10.4 HT: Variance
10.5 HT: Goodness-of-Fit
10.6 HT: Independence
Chapter Eleven: Regression
11.2 The Correlation Coefficient
11.3 The Linear Regression Model y · · x
11.4 CI & HT for
Learning Statistics with Real World Data
A Real World Data
B Random Digital Chart
C Standard Normal PD
D Student t-Distribution PD
E Chi-Square PD