The Resource Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language, Tony Fischetti
Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language, Tony Fischetti
Resource Information
The item Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language, Tony Fischetti represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language, Tony Fischetti represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
 Extent
 1 online resource (1 volume)
 Note
 Includes index
 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 threesigma rule and using ztables; 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 twotailed tests; When things go wrong; A warning about significance; A warning about pvalues; Testing the mean of one sample; Assumptions of the one sample ttest; Testing two means; Don't be fooled!
 Assumptions of the independent samples ttestTesting 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 ttest; 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 nonbinary predictor; Kitchen sink regression; The biasvariance tradeoff; Crossvalidation; Striking a balance; Linear regression diagnostics; Second Anscombe relationship; Third Anscombe relationship; Fourth Anscombe relationship; Advanced topics; Exercises; Summary; Chapter 9: Predicting Categorical Variables; kNearest Neighbors; Using kNN in R; Confusion matrices; Limitations of kNN; Logistic regression; Using logistic regression in R; Decision trees; Random forests; Choosing a classifier; The vertical decision boundary
 Isbn
 9781785288142
 Label
 Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language
 Title
 Data analysis with R
 Title remainder
 load, wrangle, and analyze your data using the world's most powerful statistical programming language
 Statement of responsibility
 Tony Fischetti
 Language
 eng
 Summary
 Annotation
 Cataloging source
 UMI
 http://library.link/vocab/creatorName
 Fischetti, Tony
 Dewey number
 519.50285
 Illustrations
 illustrations
 Index
 index present
 LC call number
 QA276.45.R3
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 Community experience distilled
 http://library.link/vocab/subjectName

 R (Computer program language)
 Data mining
 Mathematical statistics
 MATHEMATICS
 MATHEMATICS
 Mathematical statistics
 R (Computer program language)
 Summary expansion
 Load, wrangle, and analyze your data using the world's most powerful statistical programming languageAbout This Book Load, manipulate and analyze data from different sources Gain a deeper understanding of fundamentals of applied statistics A practical guide to performing data analysis in practiceWho This Book Is ForWhether you are learning data analysis for the first time, or you want to deepen the understanding you already have, this book will prove to an invaluable resource. If you are looking for a book to bring you all the way through the fundamentals to the application of advanced and effective analytics methodologies, and have some prior programming experience and a mathematical background, then this is for you. What You Will Learn Navigate the R environment Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Employ hypothesis tests to draw inferences from your data Learn Bayesian methods for estimating parameters Perform regression to predict continuous variables Apply powerful classification methods to predict categorical data Handle missing data gracefully using multiple imputation Identify and manage problematic data points Employ parallelization and Rcpp to scale your analyses to larger data Put best practices into effect to make your job easier and facilitate reproducibilityIn DetailFrequently the tool of choice for academics, 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. The power and domainspecificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it's easy to find support for the latest and greatest algorithms and techniques. Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to realworld data though with realworld examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst. Style and approachLearn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learnbydoing" approach
 Label
 Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language, Tony Fischetti
 Note
 Includes index
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 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 threesigma rule and using ztables; 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 twotailed tests; When things go wrong; A warning about significance; A warning about pvalues; Testing the mean of one sample; Assumptions of the one sample ttest; Testing two means; Don't be fooled!
 Assumptions of the independent samples ttestTesting 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 ttest; 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 nonbinary predictor; Kitchen sink regression; The biasvariance tradeoff; Crossvalidation; Striking a balance; Linear regression diagnostics; Second Anscombe relationship; Third Anscombe relationship; Fourth Anscombe relationship; Advanced topics; Exercises; Summary; Chapter 9: Predicting Categorical Variables; kNearest Neighbors; Using kNN in R; Confusion matrices; Limitations of kNN; Logistic regression; Using logistic regression in R; Decision trees; Random forests; Choosing a classifier; The vertical decision boundary
 Control code
 935744749
 Dimensions
 unknown
 Extent
 1 online resource (1 volume)
 Form of item
 online
 Governing access note
 Owing to Legal Deposit regulations this resource may only be accessed from within National Library of Scotland. For more information contact enquiries@nls.uk.
 Isbn
 9781785288142
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 9781785288142
 Other physical details
 illustrations.
 http://library.link/vocab/ext/overdrive/overdriveId
 cl0500000706
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)935744749
 Label
 Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language, Tony Fischetti
 Note
 Includes index
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 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 threesigma rule and using ztables; 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 twotailed tests; When things go wrong; A warning about significance; A warning about pvalues; Testing the mean of one sample; Assumptions of the one sample ttest; Testing two means; Don't be fooled!
 Assumptions of the independent samples ttestTesting 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 ttest; 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 nonbinary predictor; Kitchen sink regression; The biasvariance tradeoff; Crossvalidation; Striking a balance; Linear regression diagnostics; Second Anscombe relationship; Third Anscombe relationship; Fourth Anscombe relationship; Advanced topics; Exercises; Summary; Chapter 9: Predicting Categorical Variables; kNearest Neighbors; Using kNN in R; Confusion matrices; Limitations of kNN; Logistic regression; Using logistic regression in R; Decision trees; Random forests; Choosing a classifier; The vertical decision boundary
 Control code
 935744749
 Dimensions
 unknown
 Extent
 1 online resource (1 volume)
 Form of item
 online
 Governing access note
 Owing to Legal Deposit regulations this resource may only be accessed from within National Library of Scotland. For more information contact enquiries@nls.uk.
 Isbn
 9781785288142
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 9781785288142
 Other physical details
 illustrations.
 http://library.link/vocab/ext/overdrive/overdriveId
 cl0500000706
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)935744749
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