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|a UAMI
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1 |
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|a Fischetti, Tony,
|e author.
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245 |
1 |
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|a Data analysis with R :
|b a comprehensive guide to manipulating, analyzing, and visualizing data in R /
|c Tony Fischetti.
|
250 |
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|a Second edition.
|
264 |
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1 |
|a Birmingham, UK :
|b Packt Publishing,
|c 2018.
|
300 |
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|a 1 online resource ((570 pages) :
|b illustrations
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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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|a online resource
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|a Online resource; title from title page (Safari, viewed April 19, 2018).
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|a Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; 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; Summary.
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505 |
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|a Chapter 3: Describing RelationshipsMultivariate data; Relationships between a categorical and continuous variable; Relationships between two categorical variables; The relationship between two continuous variables; Covariance; Correlation coefficients; Comparing multiple correlations; Visualization methods; Categorical and continuous variables; Two categorical variables; Two continuous variables; More than two continuous variables; Exercises; Summary; Chapter 4: Probability; Basic probability; A tale of two interpretations; Sampling from distributions; Parameters; The binomial distribution.
|
505 |
8 |
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|a The normal distributionThe 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; The null hypothesis significance testing framework; One and two-tailed tests; Errors in NHST; 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; Assumptions of the independent samples t-test.
|
505 |
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|a Testing more than two meansAssumptions 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: The Bootstrap; What's ... uhhh ... the deal with the bootstrap?; Performing the bootstrap in R (more elegantly); Confidence intervals; A one-sample test of means.
|
505 |
8 |
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|a Bootstrapping statistics other than the meanBusting bootstrap myths; What have we left out?; Exercises; Summary; Chapter 9: Predicting Continuous Variables; Linear models; Simple linear regression; Simple linear regression with a binary predictor; A word of warning; Multiple 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.
|
520 |
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|a R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.
|
542 |
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|f Copyright © 2018 Packt Publishing
|g 2018
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504 |
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|a Includes bibliographical references.
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590 |
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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
|
590 |
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
|
650 |
|
0 |
|a R (Computer program language)
|
650 |
|
0 |
|a Data mining
|x Mathematics.
|
650 |
|
0 |
|a Mathematical statistics
|x Data processing.
|
650 |
|
6 |
|a R (Langage de programmation)
|
650 |
|
6 |
|a Exploration de données (Informatique)
|x Mathématiques.
|
650 |
|
6 |
|a Statistique mathématique
|x Informatique.
|
650 |
|
7 |
|a Database design & theory.
|2 bicssc
|
650 |
|
7 |
|a Information visualization.
|2 bicssc
|
650 |
|
7 |
|a Information architecture.
|2 bicssc
|
650 |
|
7 |
|a Data capture & analysis.
|2 bicssc
|
650 |
|
7 |
|a Computers
|x Data Modeling & Design.
|2 bisacsh
|
650 |
|
7 |
|a Computers
|x Data Processing.
|2 bisacsh
|
650 |
|
7 |
|a REFERENCE
|x Questions & Answers.
|2 bisacsh
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650 |
|
7 |
|a Data mining
|x Mathematics.
|2 fast
|0 (OCoLC)fst02013374
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650 |
|
7 |
|a Mathematical statistics
|x Data processing.
|2 fast
|0 (OCoLC)fst01012133
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650 |
|
7 |
|a R (Computer program language)
|2 fast
|0 (OCoLC)fst01086207
|
776 |
0 |
8 |
|i Print version:
|a Fischetti, Anthony.
|t Data Analysis with R, Second Edition : A comprehensive guide to manipulating, analyzing, and visualizing data in R, 2nd Edition.
|d Birmingham : Packt Publishing, ©2018
|
856 |
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