Researchers and line personnel in industry and government will find this book useful in self-study. This book is intended primarily for advanced undergraduate and graduate students in the mathematical, physical, and engineering sciences, as well as in economics, business, and related areas. The concluding chapters review the elements of single- and two-factor analysis of variance and the design of analysis of variance experiments. Other chapters explore the distribution of several important statistics statistical tests of hypotheses point and interval estimation and simple linear regression. These topics are followed by discussions of the fundamental principles of probability, the concepts of sample spaces, outcomes, events, probability, independence of events, and the characterization of discrete and continuous random variables. The next chapters introduce the methods of data summarization, including frequency distributions, cumulative frequency distributions, and measures of central tendency and variability. The introductory chapter illustrates the importance of statistical analysis. This 14-chapter text presents the topics covered in a manner that stresses clarity of understanding, interpretation, and method of application. In keeping with the mission of Azusa Pacific University, this program encourages an active conversation about the role of a Christian perspective in the field of applied statistics, particularly in terms of ethical issues prevalent in data analytics.Applied Statistical Methods covers the fundamental understanding of statistical methods necessary to deal with a wide variety of practical problems. Expertise in the use of statistical software packages is developed. The program includes required courses providing a foundation in statistical methods and theory, and electives that allow students to emphasize biostatistics or business analytics and explore a variety of statistical models and techniques for analyzing data. The MS in Applied Statistics and Analytics program provides professional preparation for careers involving the use of data analysis to inform decisions.
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