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Beginning R 4from beginner to pro /

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Beginning R 4from beginner to pro /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Beginning R 4by Matt Wiley, Joshua F. Wiley.
其他題名:	from beginner to pro /
作者:	Wiley, Matt.
其他作者:	Wiley, Joshua F.
出版者:	Berkeley, CA :Apress :2020.
面頁冊數:	xx, 467 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	R (Computer program language)
電子資源:	https://doi.org/10.1007/978-1-4842-6053-1
ISBN:	9781484260531$q(electronic bk.)

Beginning R 4from beginner to pro /
Wiley, Matt.

Beginning R 4from beginner to pro /[electronic resource] :by Matt Wiley, Joshua F. Wiley. - Berkeley, CA :Apress :2020. - xx, 467 p. :ill., digital ;24 cm.

1: Installing R -- 2: Installing Packages and Using Libraries -- 3: Data Input and Output -- 4: Working with Data -- 5: Data and Samples -- 6: Descriptive Statistics -- 7: Understanding Probability and Distribution -- 8: Correlation and Regression -- 9: Confidence Intervals -- 10: Hypothesis Testing -- 11: Multiple Regression -- 12: Moderated Regression -- 13: Analysts of Variance -- Bibliography.

Learn how to use R 4, write and save R scripts, read in and write out data files, use built-in functions, and understand common statistical methods. This in-depth tutorial includes key R 4 features including a new color palette for charts, an enhanced reference counting system (useful for big data), and new data import settings for text (as well as the statistical methods to model text-based, categorical data) Each chapter starts with a list of learning outcomes and concludes with a summary of any R functions introduced in that chapter, along with exercises to test your new knowledge. The text opens with a hands-on installation of R and CRAN packages for both Windows and macOS. The bulk of the book is an introduction to statistical methods (non-proof-based, applied statistics) that relies heavily on R (and R visualizations) to understand, motivate, and conduct statistical tests and modeling. Beginning R 4 shows the use of R in specific cases such as ANOVA analysis, multiple and moderated regression, data visualization, hypothesis testing, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. You will: Acquire and install R and RStudio Import and export data from multiple file formats Analyze data and generate graphics (including confidence intervals) Interactively conduct hypothesis testing Code multiple and moderated regression solutions.

ISBN: 9781484260531$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-6053-1doiSubjects--Topical Terms:

210846
R (Computer program language)

LC Class. No.: QA276.45.R3 / W55 2020

Dewey Class. No.: 005.262

Beginning R 4from beginner to pro /
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505 0 $a 1: Installing R -- 2: Installing Packages and Using Libraries -- 3: Data Input and Output -- 4: Working with Data -- 5: Data and Samples -- 6: Descriptive Statistics -- 7: Understanding Probability and Distribution -- 8: Correlation and Regression -- 9: Confidence Intervals -- 10: Hypothesis Testing -- 11: Multiple Regression -- 12: Moderated Regression -- 13: Analysts of Variance -- Bibliography.
520 $a Learn how to use R 4, write and save R scripts, read in and write out data files, use built-in functions, and understand common statistical methods. This in-depth tutorial includes key R 4 features including a new color palette for charts, an enhanced reference counting system (useful for big data), and new data import settings for text (as well as the statistical methods to model text-based, categorical data) Each chapter starts with a list of learning outcomes and concludes with a summary of any R functions introduced in that chapter, along with exercises to test your new knowledge. The text opens with a hands-on installation of R and CRAN packages for both Windows and macOS. The bulk of the book is an introduction to statistical methods (non-proof-based, applied statistics) that relies heavily on R (and R visualizations) to understand, motivate, and conduct statistical tests and modeling. Beginning R 4 shows the use of R in specific cases such as ANOVA analysis, multiple and moderated regression, data visualization, hypothesis testing, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. You will: Acquire and install R and RStudio Import and export data from multiple file formats Analyze data and generate graphics (including confidence intervals) Interactively conduct hypothesis testing Code multiple and moderated regression solutions.
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