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R per utenti Microsoft Excel: fare la transizione per l'analisi statistica-
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Condizione:
“Almost new. Excellent condition. (shelf 16)”
Come Nuovo
Libro che sembra nuovo anche se è già stato letto. La copertina non presenta segni di usura visibili ed è inclusa la sovraccoperta(se applicabile) per le copertine rigide. Nessuna pagina mancante o danneggiata, piegata o strappata, nessuna sottolineatura/evidenziazione di testo né scritte ai margini. Potrebbe presentare minimi segni identificativi sulla copertina interna. Mostra piccolissimi segni di usura. Per maggiori dettagli e la descrizione di eventuali imperfezioni, consulta l'inserzione del venditore.
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Oggetto che si trova a: Hot Springs Village, Arkansas, Stati Uniti
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Consegna prevista tra il sab 28 set e il mar 1 ott a 43230
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Numero oggetto eBay:185871018264
Specifiche dell'oggetto
- Condizione
- Come Nuovo
- Note del venditore
- “Almost new. Excellent condition. (shelf 16)”
- MPN
- 9780789757852
- Brand
- Que
- Style
- ABIS_BOOK
- ISBN
- 9780789757852
- Subject Area
- Computers, Business & Economics
- Publication Name
- R for Microsoft Excel Users : Making the Transition for Statistical Analysis
- Publisher
- Pearson Education
- Item Length
- 9.1 in
- Subject
- Desktop Applications / Spreadsheets, Statistics
- Publication Year
- 2016
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Height
- 0.5 in
- Item Weight
- 13.6 Oz
- Item Width
- 7 in
- Number of Pages
- 272 Pages
Informazioni su questo prodotto
Product Identifiers
Publisher
Pearson Education
ISBN-10
0789757850
ISBN-13
9780789757852
eBay Product ID (ePID)
227611412
Product Key Features
Number of Pages
272 Pages
Publication Name
R for Microsoft Excel Users : Making the Transition for Statistical Analysis
Language
English
Subject
Desktop Applications / Spreadsheets, Statistics
Publication Year
2016
Type
Textbook
Subject Area
Computers, Business & Economics
Format
Trade Paperback
Dimensions
Item Height
0.5 in
Item Weight
13.6 Oz
Item Length
9.1 in
Item Width
7 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2016-955450
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
005.54
Table Of Content
1 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
Synopsis
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 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 Number
QA276.45.R3
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