Learn R for applied statistics : with data visualizations, regressions, and statistics /: with data visualizations, regressions, and statistics. (2019)

• Record Type: Book
• Further Information: Note: Eric Goh Ming Hui.
• Authors: Hui, Eric Goh Ming
• Contents: Chapter 1: Introduction Chapter Goal: To understand what is R, why use R, statistics in data mining and data scienceNo of pages 15Sub -Topics1. What is R?2. High Level and Low Level Language3. What is Statistics?4. What is Data Science?5. What is Data Mining?6. What is Text Mining?7. Three Types of Analytics8. Big Data9. Why R?10. ConclusionChapter 2: Getting StartedChapter Goal: To set up the computer for R ProgrammingNo of pages: 15Sub - Topics 1. What is R and RStudio?2. Installation of R and RStudio3. Integrated Development Environment4. RStudio – The IDE for R. 5. ConclusionChapter 3: Basic SyntaxChapter Goal: To learn R programming basicsNo of pages : 30Sub - Topics: 1. Writing in R Console2. Using Code Editor3. Variables and Data Types4. Vectors5. Lists6. Data Frame7. Logical Statements8. Loops9. Functions10. ConclusionChapter 4: Descriptive StatisticsChapter Goal: To learn Descriptive Statistics in RNo of pages: 20Sub - Topics: 1. Reading Data Files2. Mean, Median, Min, Max, …3. Percentile, Standard Deviations4. The Summary() and Str() functions5. Distributions6. ConclusionChapter 5: Data VisualizationsChapter Goal: To learn Data Visualizations in R No of pages: 20Sub - Topics: 1. What is Data Visualizations?2. Bar Chart, Histogram3. Line Chart, Pie Chart4. Scatterplot and Box Plot5. Scatterplot Matrix6. Decision Trees7. ConclusionChapter 6: Inferential Statistics and RegressionsChapter Goal: To learn inferential statistics and regressions in RNo of pages: 20Sub - Topics: 1. Correlations2. T Test, Chi Square, ANOVA3. Non Parametric Test4. Linear Regressions5. Multiple Linear Regressions.
• Publisher Details: Singapore : Springer Nature Apress
• Publication Date: 2019
• Extent: 1 online resource, illustrations
• Subjects: 519.502855362; R (Computer program language); Machine learning; MATHEMATICS / Applied; MATHEMATICS / Probability & Statistics / General; Machine learning; R (Computer program language); Electronic books
• Languages: English
• ISBNs: 9781484242001; 1484242009
• Related ISBNs: 9781484241998
• Notes: Note: Includes bibliographical references and index.; Note: Online resource; title from PDF title page (EBSCO, viewed December 5, 2018).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.