## Tag: data analysis

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Flip your intro stats course using "Practical Data Analysis with JMP"

Helping students to reason statistically is challenging enough without also having to provide in-class software instruction. “Practical Data Analysis with JMP, Second Edition” walks students through the process of analysis with JMP at their own speed at home, allowing faculty to devote class time to crucial or subtle statistical concepts

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Solving the analyst’s toughest problems with SAS

If you’re an analyst, you know discovery in a complicated data set is one of the toughest problems to solve. But did you know the Business Knowledge Series course, Exploratory Analysis for Large and Complex Problems Using SAS Enterprise Miner, can help you solve those issues by tackling real-world problems?

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Who Ate My Lunch? Discriminant Thresholds to Reduce False Accusations

Lunch. For some workers, it’s the sweetest part of an otherwise bitter day at the grindstone. Nothing can turn that sweetness sour like going into the breakroom to discover that someone has taken your lunch and eaten it themselves. Nothing like that ever happens here at SAS. But if it

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The Punchline: MANOVA or a Mixed Model?

Edited to add: Thanks for Larry Madger for noticing an important omission in my code below. I have updated the programs to include the response variables, which enables the responses to have different means. So, if you were reading last week, we talked about how to structure your data for

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Data Structure for Repeated Measures Analysis... A Teaser

Next week's blog entry will build on this one, so I want you to take notes, OK? It's not headline news that in most cases, the best way to handle a repeated measures analysis is with a mixed models approach, especially for Normal reponses (for other distributions in the exponential

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Discriminant Analysis, Priors, and Fairy-Selection

A student in my multivariate class last month asked a question about prior probability specifications in discriminant function analysis: What if I don't know what the probabilities are in my population? Is it best to just use the default in PROC DISCRIM? First, a quick refresher of priors in discriminant