10 tips for making analytics converts out of your colleagues

From the Analytically Speaking panel discussion this morning — a live webcast I watched with hundreds of other people — I picked up a multitude of strategies for helping co-workers, managers and executives become comfortable with using analytics to inform decision-making. Here are 10 of my favorite tips from that discussion: Start [...]

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Making better predictive models quickly with JMP

There are many ways of generating a model such as basic linear regression, decision trees, neural nets, and generalized linear models. JMP Pro can be a great tool for data miners, those who want to get more information out of their data and build more accurate predictive models. It incorporates [...]

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Business case studies that teach statistics

JMP users may remember UC Denver Professor Marlene Smith for the lively discussion around the poster she presented at JMP Discovery Summit last year: Using JMP to Teach Business Statistics: Cases and Applications. Now you can access 12 of her business case studies. Professor Smith includes a description of each [...]

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How to create an experiment design that is robust to a linear time trend in the response

It is fairly common that experiments run in a time sequence experience linear drift in the response over the course of the experiment. If you randomize the order of the runs, then the effect of this drift will not tend to bias the estimated factor effects, but it will increase [...]

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New Distribution options in JMP 10: Custom Quantiles

My previous blog post discussed two new options in the JMP 10 Distribution platform for customizing the summary statistics and default quantiles reports. This blog post discusses another new JMP 10 feature for the Distribution platform for continuous data: Custom Quantiles. This feature gives the user even more customization options for studying [...]

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Revised in JMP 10: Power Analysis in Custom Design

In my previous post, I talked about the fundamental quantities that affect the ability of a designed experiment to detect non-negligible effects of the factors. These are: 1)      The size of the effect 2)      The root mean squared error (RMSE) of the fitted model 3)      The significance level of the [...]

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Fundamentals of power analysis in experiment design

When I took my first course in linear models and design of experiments, my professor told the class that the most common question that he encountered in his statistical consulting was, “How many samples do I need [for my results to be statistically significant]?” This question comes out of a [...]

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New in JMP 10 DOE: Discrete Numeric Factors

Among other kinds of factors, the Custom Designer in JMP has facilities for continuous factors and for categorical factors with an arbitrary number of levels. The designer assumes that continuous factors can take any value within the specified range from low to high. Sometimes, though, there are practical restrictions to [...]

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Reliability Growth platform in JMP 10: Video preview

In a previous post, I gave a list of 10 great things about the new JMP 10 software that is being released on March 20. As part of the preview for this release, we have created three videos to give you an early look at some of these new features. In the third video, [...]

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Dresden loves statistics

Telling a story with your data is key. With high-speed computers, statistical discovery now happens faster than ever before, and John Sall showed what this can look like during his keynote speech at the 16th Conference of SAS Users in Research and Development (KSFE) this week in Dresden, Germany. Graphics and animation were at the [...]

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