|
|
|
|
| LEADER |
03179nmm a2200397 u 4500 |
| 001 |
EB001910910 |
| 003 |
EBX01000000000000001073812 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
210123 ||| eng |
| 020 |
|
|
|a 9781484260531
|
| 050 |
|
4 |
|a QA276.45.R3
|
| 100 |
1 |
|
|a Wiley, Matt
|
| 245 |
0 |
0 |
|a Beginning R 4
|b from beginner to pro
|c Matt Wiley, Joshua F. Wiley
|
| 260 |
|
|
|a [Berkeley, CA]
|b Apress
|c 2020
|
| 300 |
|
|
|a 481 pages
|
| 505 |
0 |
|
|a Includes bibliographical references and index
|
| 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
|
| 653 |
|
|
|a R (Langage de programmation)
|
| 653 |
|
|
|a R (Computer program language) / fast
|
| 653 |
|
|
|a Statistique / Informatique
|
| 653 |
|
|
|a Statistics / Data processing / fast
|
| 653 |
|
|
|a R (Computer program language) / http://id.loc.gov/authorities/subjects/sh2002004407
|
| 653 |
|
|
|a Statistics / Data processing / http://id.loc.gov/authorities/subjects/sh85127583
|
| 700 |
1 |
|
|a Wiley, Joshua F.
|e author
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b OREILLY
|a O'Reilly
|
| 028 |
5 |
0 |
|a 10.1007/978-1-4842-6053-1
|
| 776 |
|
|
|z 9781484260524
|
| 776 |
|
|
|z 9781484260531
|
| 776 |
|
|
|z 148426052X
|
| 776 |
|
|
|z 1484260538
|
| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781484260531/?ar
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 005.26/2
|
| 082 |
0 |
|
|a 519.5
|
| 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
|