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The modern world is awash with data. The R Project is a statistical environment and programming language that can help to make sense of it all. This clear and methodical book will help you learn how to use R from the ground up, giving you a start in the world of data science.
This book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). It focuses on graphic... Læs mere
R for Quantitative Chemistry is an exploration of how the R language can be applied to a wide variety of problems in what is typically termed "Quantitative Chemistry" or sometimes "Analytical Chemistry". This book will be based upon, in large part, actual experimental data.
There has been considerable attention to making the methodologies of structural equation modeling available to... Læs mere
Explores emerging and advanced techniques in food processing, such as ultrasound, microwave... Læs mere
Covering basic univariate and bivariate statistics and regression models for nominal, ordinal, and interval outcomes, this textbook provides... Læs mere
This book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows.
Applied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems.
The book is intended to serve as an introductory course in group theory geared towards second-year university students. The second part is the core of the book... Læs mere
This book covers computational aspects of psychometric methods involved in developing measurement instruments and analyzing measurement data in social sciences. It covers main topics of psychometrics such as validity, reliability, item analysis, and item response theory models.
The book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, using... Læs mere
An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R... Læs mere