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- Author: Sarah Baldock
- Publisher: Apress
- ISBN-10: 148420140X
- ISBN-13: 9781484201404
- Format: 19.1 x 23.5 x 1.3 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
Using R for Statistics will get you the answers to most of the problems you are likely to encounter when using a variety of statistics. This book is a problem-solution primer for using R to set up your data, pose your problems and get answers using a wide array of statistical tests. The book walks you through R basics and how to use R to accomplish a wide variety statistical operations.
You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background.
After reading and using this guide, you'll be comfortable using and applying R to your specific statistical analyses or hypothesis tests. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics.
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- Author: Sarah Baldock
- Publisher: Apress
- ISBN-10: 148420140X
- ISBN-13: 9781484201404
- Format: 19.1 x 23.5 x 1.3 cm, softcover
- Language: English English
Using R for Statistics will get you the answers to most of the problems you are likely to encounter when using a variety of statistics. This book is a problem-solution primer for using R to set up your data, pose your problems and get answers using a wide array of statistical tests. The book walks you through R basics and how to use R to accomplish a wide variety statistical operations.
You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background.
After reading and using this guide, you'll be comfortable using and applying R to your specific statistical analyses or hypothesis tests. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics.
Reviews