Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language /: load, wrangle, and analyze your data using the world's most powerful statistical programming language. (2015)

• Record Type: Book
• Further Information: Note: Tony Fischetti.
• Authors: Fischetti, Tony
• Contents: Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: RefresheR; Navigating the basics; Arithmetic and assignment; Logicals and characters; Flow of control; Getting help in R; Vectors; Subsetting; Vectorized functions; Advanced subsetting; Recycling; Functions; Matrices; Loading data into R; Working with packages; Exercises; Summary; Chapter 2: The Shape of Data; Univariate data; Frequency distributions; Central tendency; Spread; Populations, samples, and estimation; Probability distributions; Visualization methods; Exercises. The binomial distributionThe normal distribution; The three-sigma rule and using z-tables; Exercises; Summary; Chapter 5: Using Data to Reason About the World; Estimating means; The sampling distribution; Interval estimation; How did we get 1.96?; Smaller samples; Exercises; Summary; Chapter 6: Testing Hypotheses; Null Hypothesis Significance Testing; One and two-tailed tests; When things go wrong; A warning about significance; A warning about p-values; Testing the mean of one sample; Assumptions of the one sample t-test; Testing two means; Don't be fooled! Assumptions of the independent samples t-testTesting more than two means; Assumptions of ANOVA; Testing independence of proportions; What if my assumptions are unfounded?; Exercises; Summary; Chapter 7: Bayesian Methods; The big idea behind Bayesian analysis; Choosing a prior; Who cares about coin flips; Enter MCMC -- stage left; Using JAGS and runjags; Fitting distributions the Bayesian way; The Bayesian independent samples t-test; Exercises; Summary; Chapter 8: Predicting Continuous Variables; Linear models; Simple linear regression; Simple linear regression with a binary predictor. A word of warningMultiple regression; Regression with a non-binary predictor; Kitchen sink regression; The bias-variance trade-off; Cross-validation; Striking a balance; Linear regression diagnostics; Second Anscombe relationship; Third Anscombe relationship; Fourth Anscombe relationship; Advanced topics; Exercises; Summary; Chapter 9: Predicting Categorical Variables; k-Nearest Neighbors; Using k-NN in R; Confusion matrices; Limitations of k-NN; Logistic regression; Using logistic regression in R; Decision trees; Random forests; Choosing a classifier; The vertical decision boundary.
• Publisher Details: Birmingham, UK : Packt Publishing
• Publication Date: 2015
• Extent: 1 online resource (1 volume), illustrations
• Subjects: 519.50285; COMPUTERS -- Databases -- Data Mining; R (Computer program language); Data mining -- Mathematics; Mathematical statistics -- Data processing; MATHEMATICS / Applied; MATHEMATICS / Probability & Statistics / General; COMPUTERS -- Databases -- General; Electronic books
• Languages: English
• ISBNs: 9781785286445; 1785286447; 1785288148; 9781785288142
• Related ISBNs: 9781785288142
• Notes: Note: Description based on online resource; title from cover page (Safari, viewed January 21, 2016).
• 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.