Textbook in PDF format

This book introduces the topics of Big Data, data analytics and data science and features the use of open source data. Among the statistical topics described in this book are: data visualization, descriptive measures, probability, probability distributions, the concept of mathematical expectation, confidence intervals, and hypothesis testing. Also covered are analysis of variance, simple linear regression, multiple linear regression and diagnostics, extensions to multiple linear regression models, contingency tables, Chi-square tests, non-parametric methods, and time series method. Chapters include multiple examples showing the application of the theoretical aspects presented. In addition, practice problems are designed to ensure that the reader understands the concepts and can apply them using real data. Most data will come from regions throughout the U.S. though some datasets come from Europe and countries around the world. Moreover, open portal data will be the basis for many of the examples and problems, allowing the instructor to adapt the application to local data with which students can identify. An appendix will include solutions to some of these practice problems.
Contents
Statistics Suck; SoWhy Do I Need to Learn About It?
Concepts in Statistics
DataVisualization
Descriptive Statistics
Introduction to Probability
Discrete Random Variables
Continuous Random Variables
Properties of Sample Statistics
Interval Estimation for One Population Parameter
Hypothesis Testing for One Population
Statistical Inference to Compare Parameters from Two Populations
Analysis of Variance (ANOVA)
Simple Linear Regression
Multiple Linear Regression
Inference on Association of Categorical Variables
Nonparametric Testing
Forecasting
Appendixes
Math Notation and Symbols
Standard Normal Cumulative Distribution Function
t Distribution Critical Values
Solutions to Odd-Numbered Problems