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R for Microsoft Excel Users : Making the Transition for Statistical Analysis by Conrad Carlberg (2016, Trade Paperback)
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R FOR MICROSOFT EXCEL USERS: MAKING THE TRANSITION FOR STATISTICAL ANALYSIS By Conrad Carlberg **BRAND NEW**.
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About this product
Product Identifiers
PublisherPearson Education
ISBN-100789757850
ISBN-139780789757852
eBay Product ID (ePID)227611412
Product Key Features
Number of Pages272 Pages
LanguageEnglish
Publication NameR for Microsoft Excel Users : Making the Transition for Statistical Analysis
Publication Year2016
SubjectDesktop Applications / Spreadsheets, Statistics
TypeTextbook
Subject AreaComputers, Business & Economics
AuthorConrad Carlberg
FormatTrade Paperback
Dimensions
Item Height0.5 in
Item Weight13.6 Oz
Item Length9.1 in
Item Width7 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2016-955450
Dewey Edition23
IllustratedYes
Dewey Decimal005.54
Table Of Content1 Making the Transition 2 Descriptive Statistics 3 Regression Analysis in Excel and R 4 Analysis of Variance and Covariance in Excel and R 5 Logistic Regression in Excel and R 6 Principal Components Analysis
SynopsisMicrosoft Excel can perform many statistical analyses, but thousands of business users and analysts are now reaching its limits. R, in contrast, can perform virtually any imaginable analysis--if you can get over its learning curve. In R for Microsoft ® Excel Users , Conrad Carlberg shows exactly how to get the most from both programs. Drawing on his immense experience helping organizations apply statistical methods, Carlberg reviews how to perform key tasks in Excel, and then guides readers through reaching the same outcome in R--including which packages to install and how to access them. Carlberg offers expert advice on when and how to use Excel, when and how to use R instead, and the strengths and weaknesses of each tool., Microsoft Excel can perform many statistical analyses, but thousands of business users and analysts are now reaching its limits. R, in contrast, can perform virtually any imaginable analysis--if you can get over its learning curve. In R for Microsoft (R) Excel Users , Conrad Carlberg shows exactly how to get the most from both programs. Drawing on his immense experience helping organizations apply statistical methods, Carlberg reviews how to perform key tasks in Excel, and then guides you through reaching the same outcome in R--including which packages to install and how to access them. Carlberg offers expert advice on when and how to use Excel, when and how to use R instead, and the strengths and weaknesses of each tool. Writing in clear, understandable English, Carlberg combines essential statistical theory with hands-on examples reflecting real-world challenges. By the time you've finished, you'll be comfortable using R to solve a wide spectrum of problems--including many you just couldn't handle with Excel. - Smoothly transition to R and its radically different user interface - Leverage the R community's immense library of packages - Efficiently move data between Excel and R - Use R's DescTools for descriptive statistics, including bivariate analyses - Perform regression analysis and statistical inference in R and Excel - Analyze variance and covariance, including single-factor and factorial ANOVA - Use R's mlogit package and glm function for Solver-style logistic regression - Analyze time series and principal components with R and Excel, Microsoft Excel can perform many statistical analyses, but thousands of business users and analysts are now reaching its limits. R, in contrast, can perform virtually any imaginable analysis--if you can get over its learning curve. In R for Microsoft ® Excel Users , Conrad Carlberg shows exactly how to get the most from both programs. Drawing on his immense experience helping organizations apply statistical methods, Carlberg reviews how to perform key tasks in Excel, and then guides you through reaching the same outcome in R--including which packages to install and how to access them. Carlberg offers expert advice on when and how to use Excel, when and how to use R instead, and the strengths and weaknesses of each tool. Writing in clear, understandable English, Carlberg combines essential statistical theory with hands-on examples reflecting real-world challenges. By the time you've finished, you'll be comfortable using R to solve a wide spectrum of problems--including many you just couldn't handle with Excel. * Smoothly transition to R and its radically different user interface * Leverage the R community's immense library of packages * Efficiently move data between Excel and R * Use R's DescTools for descriptive statistics, including bivariate analyses * Perform regression analysis and statistical inference in R and Excel * Analyze variance and covariance, including single-factor and factorial ANOVA * Use R's mlogit package and glm function for Solver-style logistic regression * Analyze time series and principal components with R and Excel
LC Classification NumberQA276.45.R3
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