Cover: Introductory Statistics: A Problem-Solving Approach, 3rd Edition by Stephen Kokoska

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Introductory Statistics: A Problem-Solving Approach

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Third  Edition©2020  Stephen Kokoska

ISBN:9781319056049

ISBN:9781319401221

ISBN:9781319056100

ISBN:9781319049621

ISBN:9781319411398

ISBN:9781319411374

About

In almost every occupation and in ordinary daily life, you will have to make data-driven decisions and inferences, as well as assess risk. In addition, you must be able to translate complex problems into manageable pieces, recognize patterns, and, most importantly, solve problems. With a generous collection and variety of exercises there’s ample opportunity for practice and review in a variety of contexts. This text will help you develop the fundamental lifelong skill of solving problems and interpreting solutions in real-world terms.

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Table of Contents

Students can download the following resources from the Student Site: Module datasets: The folders containing the GIS datasets used in each module can be downloaded as compressed zip files and unzipped to access the data. Each module in this book requires you to download a folder for use within ArcGIS Pro.

Chapter 0 Why Study Statistics
Chapter 1 An Introduction to Statistics and Statistical Inference
Chapter 2 Tables and Graphs for Summarizing Data
Chapter 3 Numerical Summary Measures
Chapter 4 Probability
Chapter 5 Random Variables and Discrete Probability Distributions
Chapter 6 Continuous Probability Distributions
Chapter 7 Sampling Distributions
Chapter 8 Confidence Intervals Based on a Single Sample
Chapter 9 Hypothesis Tests Based on a Single Sample
Chapter 10 Confidence Intervals and Hypothesis Tests Based on Two Samples or Treatments
Chapter 11 The Analysis of Variance
Chapter 12 Correlation and Linear Regression
Chapter 13 Categorical Data and Frequency Tables
Chapter 14 Nonparametric Statistics

Optional Sections
(available online at macmillanlearning.com/introstats3e):
Section 6.5 The Normal Approximation to the Binomial Distribution
Section 12.6 The Polynomial and Qualitative Predictor Models
Section 12.7 Model Selection Procedures

Stephen Kokoska

Steve Kokoska received his undergraduate degree from Boston College, and his M.S and Ph.D. from the University of New Hampshire. His initial research interests included the statistical analysis of cancer chemoprevention experiments. He has published a number of research papers in mathematics journals, including: Biometrics, Anticancer Research, and Computer Methods and Programs in Biomedicine. He has also presented results at national conferences, written several books, and been awarded grants from the National Science Foundation, the Center for Rural Pennsylvania, and the Ben Franklin Program.

Steve is a long-time consultant for the College Board and conducted workshops in Brazil, the Dominican Republic, and China. He was the AP Calculus Chief Reader for four years, and has been involved with calculus reform and the use of technology in the classroom. He has been teaching at Bloomsburg University for 25years and recently served as Director of the Honors Program.

Steve has been teaching introductory statistics classes throughout his academic career, and there is no doubt that this is his favorite course. This class (and text) provides students with basic, life-long, quantitative skills that they will use in almost any job and teaches them how to think and reason logically. Steve believes very strongly in data-driven decisions and conceptual understanding through problem solving.