Learn R for applied Statistics : wih data visualizations, regressions, and Statistics / Eric Goh Ming Hui
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9781484246344
- 519.5028 HUI-E
Item type | Current library | Collection | Shelving location | Call number | Status | Date due | Barcode | Item holds |
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BITS Pilani Hyderabad | 510 | General Stack (For lending) | 519.5028 HUI-E (Browse shelf(Opens below)) | Available | 40641 |
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519.5028 CHA-W R graphics cookbook : | 519.5028 EFR-B Computer age statistical inference : | 519.5028 EFR-B Computer age statistical inference : algorithms, evidence, and data science / | 519.5028 HUI-E Learn R for applied Statistics : | 519.5028 JOH-W First course in statistical programming with R / | 519.5028 LAD-L Survey sampling methods / | 519.5028 SCH-J Statistical analysis with excel for dummies / |
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.
What You Will LearnDiscover 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
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