Introduction to statistics and computer programming by Kossack, Carl Fredrick, 1915-

Jan 1, 1975 · Introduction to statistics and computer programming [Kossack, Carl Fredrick] on Amazon.com. *FREE* shipping on qualifying offers.

We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation.

At the end of this course students should have a broad grasp of R and its capabilities, and a basic understanding of key computational methods in statistics such as optimization, numerical analysis, MCMC techniques, and simulation methods.

Students will learn how to organize computations to access, transform, explore, analyze data and produce results. The primary focus is on teaching the concepts and vocabulary of statistical/scientific computing, rather than providing a tutorial in a particular language.

Prior exposure to statistics and Python is helpful but not required—concepts are introduced gradually, and every code example is accompanied by detailed explanations. The focus throughout is not just on how to use Python to perform analysis, but why the techniques work, when they apply, and how to interpret results with rigor and confidence.

This book aim to provide an accessible though technically solid introduction to the logic of systematical analyses of statistical data to both undergraduate and postgraduate students, in particular in the Social Sciences, Economics, and the Financial Services.

Aug 8, 2023 · In anticipation of such an approach, this textbook has been written with an emphasis on the role of data analysis in statistics, and has coupled such analyses with the use of decision theory so as to assist individuals in resolving practical problems.

The series concentrates on applications of computational methods in statistics to fields of bioinformatics, genomics, epidemiology, business, engineering, finance and applied statistics.

A practical introduction to statistical programming focusing on the R programming language. Students will engage with the programming challenges inherent in the various stages of modern statistical analyses including everything from data collection/aggregation/cleaning to visualization and exploratory analysis to statistical model building and ...