Introduction to R.pdf - Tools for Business Analytics MSBA 320 Dr Siamak Zadeh Adjunct Professor Ageno School of Business Golden Gate University

Introduction to R.pdf - Tools for Business Analytics MSBA...

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Tools for Business Analytics MSBA 320 Dr. Siamak Zadeh Adjunct Professor Ageno School of Business Golden Gate University
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Introduction to R: R Basics
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Applied Statistical Computing and Graphics 3 Outline Why R, and R Paradigm References, Tutorials and links R Overview R Interface R Workspace Help R Packages Input/Output Reusing Results
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Applied Statistical Computing and Graphics 4 Why R? It's free! It runs on a variety of platforms including Windows, Unix and MacOS. It provides an unparalleled platform for programming new statistical methods in an easy and straightforward manner. It contains advanced statistical routines not yet available in other packages. It has state-of-the-art graphics capabilities.
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Applied Statistical Computing and Graphics 5 R has a Steep Learning Curve (steeper for those that knew SAS or other software before) First , while there are many introductory tutorials (covering data types, basic commands, the interface), none alone are comprehensive. In part, this is because much of the advanced functionality of R comes from hundreds of user contributed packages. Hunting for what you want can be time consuming, and it can be hard to get a clear overview of what procedures are available.
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Applied Statistical Computing and Graphics 6 R has a Learning Curve (steeper for those that knew SAS or other software before) The second reason is more transient. As users of statistical packages, we tend to run one controlled procedure for each type of analysis. Think of PROC GLM in SAS. We can carefully set up the run with all the parameters and options that we need. When we run the procedure, the resulting output may be a hundred pages long. We then sift through this output pulling out what we need and discarding the rest.
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Applied Statistical Computing and Graphics 7 R paradigm is different Rather than setting up a complete analysis at once, the process is highly interactive. You run a command (say fit a model), take the results and process it through another command (say a set of diagnostic plots), take those results and process it through another command (say cross-validation), etc. The cycle may include transforming the data, and looping back through the whole process again. You stop when you feel that you have fully analyzed the data.
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Applied Statistical Computing and Graphics 8 How to download? Google it using R or CRAN (Comprehensive R Archive Network)
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Applied Statistical Computing and Graphics 9 Tutorials Each of the following tutorials are in PDF format. P. Kuhnert & B. Venables, An Introduction to R: Software for Statistical Modeling & Computing J.H. Maindonald, Using R for Data Analysis and Graphics B. Muenchen, R for SAS and SPSS Users W.J. Owen, The R Guide D. Rossiter, Introduction to the R Project for Statistical Computing for Use at the ITC W.N. Venebles & D. M. Smith, An Introduction to R
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