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The Joy of Statistics: Learning with Real World Data

Author(s): Chris Tsokos, Rebecca Wooten

Edition: 2

Copyright: 2016

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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
Review Exercises

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
Summary
Review Exercises

Chapter Three: Descriptive Statistics
3.1 Introduction to Descriptive Statistics
3.2 Central Tendencies
3.3 Deviations
Summary
Review Exercises

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
Summary
Review Exercises

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
Summary
Review Exercises

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
Summary
Review Exercises

Chapter Seven: Point Estimates and Interval Estimates
7.1 Introduction
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
Summary
Review Exercises

Chapter Eight: Hypothesis Testing: One Population
8.1 Introduction
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
Summary
Review Exercise

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
Summary
Review Exercises

Part Five: Additional Hypothesis Testing & Regression
Chapter Ten: Chi-square Distribution
10.1 Introduction
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
Summary
Review Exercises

Chapter Eleven: Regression
11.1 Introduction
11.2 The Correlation Coefficient
11.3 The Linear Regression Model y · · x
11.4 CI & HT for
Summary
Review Exercises

Appendix
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
I Index

Chris Tsokos

Chris P. Tsokos is Distinguished University Professor of Mathematics and Statistics at the University of South Florida. Dr. Tsokos received his B.S. in Engineering Sciences/Mathematics, his M.A. in Mathematics from the University of Rhode Island, and his Ph.D. in Statistics and Probability from the University of Connecticut. Prof. Tsokos has served on the faculties at Virginia Polytechnic Institute and State University and the University of Rhode Island.

Dr. Tsokos’ research has extended into a variety of areas, including stochastic systems, statistical models, reliability analysis, ecological systems, operations research, time series, Bayesian analysis, and mathematical and statistical modeling of global warming, and both parametric and nonparametric survival analysis, among others. He has authored more than 300 research publications in these areas.

For the past four years Prof. Tsokos’ research efforts have focused on developing probabilistic models, parametric and nonparametric statistical models for cancer and global warming data. Specifically, his research aims are data driven and are oriented toward understanding the behavior of breast, lung, brain, and colon cancers. Information on the subject matters can be found on his website http://shell.cas.usf.edu/~ctsokos/.

Prof. Tsokos is the author of several publications, research monographs and books, including Random Integral Equations with Applications to Life Sciences and Engineering, Probability Distribution: An Introduction to Probability Theory with Applications, Mainstreams of Finite Mathematics with Applications, Probability with the Essential Analysis, Applied Probability Bayesian Statistical Methods with Applications to Reliability, and Mathematical Statistics with Applications, among others.

Dr. Tsokos is the recipient of many distinguished awards and honors, including Fellow of the American Statistical Association, USF Distinguished Scholar Award, Sigma Xi Outstanding Research Award, USF Outstanding Undergraduate Teaching Award, USF Professional Excellence Award, URI Alumni Excellence Award in Science and Technology, Pi Mu Epsilon, election to the International Statistical Institute, Sigma Pi Sigma, USF Teaching Incentive Program, and several humanitarian and philanthropic recognitions and awards.

Prof. Tsokos is a member of several academic and professional societies. He serves as honorary editor, chief editor, editor, or associate editor of more than 12 academic research journals.

Rebecca Wooten

Rebecca D. Wooten is Assistant Professor of Mathematics and Statistics at the University of South Florida. She received her M.A./B.A. in Mathematics and her Ph.D. in Statistics from the University of South Florida. She has worked for 15 years teaching and has been recognized for her excellence in teaching; teaching courses such as Liberal Arts Math, Basic Statistics, Introduction to Statistics, and Applied Statistics Methods.

Prof. Wooten’s research interests are concentrated in applied statistics with an emphasis on environmental studies. Her research publications span a variety of areas such as global warming (carbon dioxide and temperature), atmospheric sciences and geography (hurricanes), geology (volcanic ash fall), and marine biology (red tide), among others.

Prof. Wooten is extensively involved in activities to improve education not only in mathematics and Statistics, but in education in general. She is the academic coordinator for two free educational assistance programs that offer opportunities for students to volunteer in the local community to get the assistance in their studies that they would have otherwise been unable to afford.

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