topic2a - Statistics 512: Applied Linear Models Topic 2a...

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Statistics 512: Applied Linear Models Topic 2a Topic Overview This topic will cover Visual Inference Resampling in R Introduction We spend a lot of time looking at graphs deciding if our data violates the underlying model assumptions. It can be difficult to decide if there is a problem without a point of reference. Recent work in statistical graphics has focused on methods of visual inference . While visual inference can be done in SAS, most implementations use R, so for this section, we will use R code. R is freely available for download at http://www.r-project.org/ . This section, since it uses R and is not covered in the book, is not required ;itis simply presented as a tool to help with your data analysis projects. Visual Inference (Steps) Before you look at your diagnostic plots, 1. Have the program choose a random number between 1 and m . You should not find out the number until the end. The value of m is typically a function of the number of graphs, so somewhere around 12 to 20. 2. Generate m graphs. One graph (the th one) will be your (residual/qq/scatter/etc.) plot.
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This note was uploaded on 03/15/2012 for the course STAT 512 taught by Professor Staff during the Fall '08 term at Purdue.

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topic2a - Statistics 512: Applied Linear Models Topic 2a...

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