Includes bibliographical references and index.

Statistics suck; so why do I need to learn about it? -- Concepts in statistics -- Data visualization -- 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.

"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"-- Provided by publisher.

Description based on online resource; title from digital title page (viewed on March 02, 2020).