Easy Statistics for Food Science with R

Easy Statistics for Food Science with R presents the application of statistical techniques to assist students and researchers who work in food science and food engineering in choosing the appropriate statistical technique. The book focuses on the use of univariate and multivariate statistical methods in the field of food science. The techniques are presented in a simplified form without relying on complex mathematical proofs. This book was written to help researchers from different fields to analyze their data and make valid decisions. The development of modern statistical packages makes the analysis of data easier than before. The book focuses on the application of statistics and correct methods for the analysis and interpretation of data. R statistical software is used throughout the book to analyze the data. Contains numerous step-by-step tutorials help the reader to learn quickly Covers the theory and application of the statistical techniques Shows how to analyze data using R software Provides R scripts for all examples and figures

Produk Detail:

  • Author : Abbas F. M. Alkarkhi
  • Publisher : Academic Press
  • Pages : 228 pages
  • ISBN : 0128142634
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKEasy Statistics for Food Science with R

Easy Statistics for Food Science with R

Easy Statistics for Food Science with R
  • Author : Abbas F. M. Alkarkhi,Wasin A. A. Alqaraghuli
  • Publisher : Academic Press
  • Release : 18 September 2018
GET THIS BOOKEasy Statistics for Food Science with R

Easy Statistics for Food Science with R presents the application of statistical techniques to assist students and researchers who work in food science and food engineering in choosing the appropriate statistical technique. The book focuses on the use of univariate and multivariate statistical methods in the field of food science. The techniques are presented in a simplified form without relying on complex mathematical proofs. This book was written to help researchers from different fields to analyze their data and make

Applied Statistics for Environmental Science with R

Applied Statistics for Environmental Science with R
  • Author : Abbas F. M. Al-Karkhi,Wasin A. A. Alqaraghuli
  • Publisher : Elsevier
  • Release : 13 September 2019
GET THIS BOOKApplied Statistics for Environmental Science with R

Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing statistical analysis in environmental science and engineering will find this book

Applications of Hypothesis Testing for Environmental Science

Applications of Hypothesis Testing for Environmental Science
  • Author : Abbas F.M. Alkarkhi
  • Publisher : Elsevier
  • Release : 01 December 2020
GET THIS BOOKApplications of Hypothesis Testing for Environmental Science

Applications of Hypothesis Testing for Environmental Science presents the theory and application of hypothesis testing in environmental science, allowing researchers to carry out suitable tests for decision-making on a variety of issues. This book works as a step-by-step resource to provide understanding of the concepts and applications of hypothesis testing in the field of environmental science. The tests are presented in simplified form without relying on complex mathematical proofs to allow researchers to easily locate the most appropriate test and

Contouring Human Development

Contouring Human Development
  • Author : Mukunda Mishra,Soumendu Chatterjee
  • Publisher : Springer Nature
  • Release : 11 May 2020
GET THIS BOOKContouring Human Development

This book acquaints readers with a range of techniques to help them effectively identify, record, map, analyze and report on patterns in various dimensions of human development (HD) with spatial scales down to the village level. It is impossible to capture HD at the local and global scale with only a single index, because differences in HD at the international scale are caused by ‘general’ factors, whereas local-scale differences are influenced by ‘specific’ factors. This book offers a variety of

Handbook of Research on Mixed Methods Research in Information Science

Handbook of Research on Mixed Methods Research in Information Science
  • Author : Ngulube, Patrick
  • Publisher : IGI Global
  • Release : 26 November 2021
GET THIS BOOKHandbook of Research on Mixed Methods Research in Information Science

Mixed methods research is becoming prevalent in many fields, yet little has been done to elevate mixed methods research in information science. A comprehensive picture of information science and its problems is needed to further understand and address the issues associated with it as well as how mixed methods research can be adapted and used. The Handbook of Research on Mixed Methods Research in Information Science discusses the quality of mixed methods studies and methodological transparency, sampling in mixed methods

Modern Data Science with R

Modern Data Science with R
  • Author : Benjamin S. Baumer,Daniel T. Kaplan,Nicholas J. Horton
  • Publisher : CRC Press
  • Release : 13 April 2021
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From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case

Introduction to Data Science

Introduction to Data Science
  • Author : Rafael A. Irizarry
  • Publisher : CRC Press
  • Release : 20 November 2019
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Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data

Graphics for Statistics and Data Analysis with R

Graphics for Statistics and Data Analysis with R
  • Author : Kevin J. Keen
  • Publisher : CRC Press
  • Release : 26 April 2010
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Graphics for Statistics and Data Analysis with R presents the basic principles of sound graphical design and applies these principles to engaging examples using the graphical functions available in R. It offers a wide array of graphical displays for the presentation of data, including modern tools for data visualization and representation. The book considers graphical displays of a single discrete variable, a single continuous variable, and then two or more of each of these. It includes displays and the R

Foundations of Statistics for Data Scientists

Foundations of Statistics for Data Scientists
  • Author : Alan Agresti,Maria Kateri
  • Publisher : CRC Press
  • Release : 22 November 2021
GET THIS BOOKFoundations of Statistics for Data Scientists

Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "

R for Health Data Science

R for Health Data Science
  • Author : Ewen Harrison,Riinu Pius
  • Publisher : CRC Press
  • Release : 31 December 2020
GET THIS BOOKR for Health Data Science

In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this

Analyzing Sensory Data with R

Analyzing Sensory Data with R
  • Author : Sebastien Le,Thierry Worch
  • Publisher : CRC Press
  • Release : 14 December 2018
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Choose the Proper Statistical Method for Your Sensory Data Issue Analyzing Sensory Data with R gives you the foundation to analyze and interpret sensory data. The book helps you find the most appropriate statistical method to tackle your sensory data issue. Covering quantitative, qualitative, and affective approaches, the book presents the big picture of sensory evaluation. Through an integrated approach that connects the different dimensions of sensory evaluation, you’ll understand: The reasons why sensory data are collected The ways

Introduction to Data Analysis with R for Forensic Scientists

Introduction to Data Analysis with R for Forensic Scientists
  • Author : James Michael Curran
  • Publisher : CRC Press
  • Release : 30 July 2010
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Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack the statistical skills or resources that would allow them to explore their data to its full potential. Introduction to Data Analysis with R for Forensic Sciences minimizes theory and mathematics and focuses on the application and practice of statistics to provide researchers with the dexterity necessary to systematically analyze data discovered from the fruits of their research. Using traditional techniques

Statistical Methods for Food Science

Statistical Methods for Food Science
  • Author : John A. Bower
  • Publisher : John Wiley & Sons
  • Release : 09 November 2009
GET THIS BOOKStatistical Methods for Food Science

The recording and analysis of food data are becoming increasingly sophisticated. Consequently, the food scientist in industry or at study faces the task of using and understanding statistical methods. Statistics is often viewed as a difficult subject and is often avoided because of its complexity and a lack of specific application to the requirements of food science. This situation is changing – there is now much material on multivariate applications for the more advanced reader, but a case exists for a

R for Data Science

R for Data Science
  • Author : Hadley Wickham,Garrett Grolemund
  • Publisher : "O'Reilly Media, Inc."
  • Release : 12 December 2016
GET THIS BOOKR for Data Science

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring,