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03612nam a2200541 4500 |
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978-1-4842-4200-1 |
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DE-He213 |
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20191025231411.0 |
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cr nn 008mamaa |
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181130s2019 xxu| s |||| 0|eng d |
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|a 9781484242001
|9 978-1-4842-4200-1
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|a 10.1007/978-1-4842-4200-1
|2 doi
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|d GrThAP
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|a QA76.7-76.73
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|a 005.13
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|a Hui, Eric Goh Ming.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Learn R for Applied Statistics
|h [electronic resource] :
|b With Data Visualizations, Regressions, and Statistics /
|c by Eric Goh Ming Hui.
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| 250 |
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|a 1st ed. 2019.
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| 264 |
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1 |
|a Berkeley, CA :
|b Apress :
|b Imprint: Apress,
|c 2019.
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| 300 |
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|a XV, 243 p. 111 illus.
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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0 |
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|a Chapter 1: Introduction -- Chapter 2: Getting Started -- Chapter 3: Basic -- Chapter 4: Descriptive Statistics -- Chapter 5: Data Visualizations -- Chapter 6: Inferential Statistics and Regressions.
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| 520 |
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|a Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R's syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. You will: Discover R, statistics, data science, data mining, and big data Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions Work with descriptive statistics Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions.
|
| 650 |
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0 |
|a Programming languages (Electronic computers).
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| 650 |
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0 |
|a Big data.
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| 650 |
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|a Mathematical statistics.
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| 650 |
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|a Open source software.
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| 650 |
|
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|a Computer programming.
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| 650 |
1 |
4 |
|a Programming Languages, Compilers, Interpreters.
|0 http://scigraph.springernature.com/things/product-market-codes/I14037
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| 650 |
2 |
4 |
|a Big Data.
|0 http://scigraph.springernature.com/things/product-market-codes/I29120
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| 650 |
2 |
4 |
|a Probability and Statistics in Computer Science.
|0 http://scigraph.springernature.com/things/product-market-codes/I17036
|
| 650 |
2 |
4 |
|a Open Source.
|0 http://scigraph.springernature.com/things/product-market-codes/I29090
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
0 |
8 |
|i Printed edition:
|z 9781484241998
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| 776 |
0 |
8 |
|i Printed edition:
|z 9781484242018
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| 776 |
0 |
8 |
|i Printed edition:
|z 9781484246344
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-4842-4200-1
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-CWD
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| 950 |
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|a Professional and Applied Computing (Springer-12059)
|
