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Just to drop in an opinion here in late 2025, but this is an ideal use case for AI. I had a summary table in my PDF of Workshop Statistics 3e (Rossman & Chance). I imply snapped a screen grab of the table and told Chat GPT to make the raw data representing it. I had to answer a couple of formatting questions, but the flat CSV file it produced worked flawlessly.
I really like the ability to add visual confidence intervals to a regression line. Of course, these are confidence bands on the slope, and are essentially showing the lines that would be plotted if the slope ranged over its entire 95% confidence interval.
There are also confidence bands for the individual points. It also is a hyperbola like the one CODAP shows for the slope, but it’s wider. It would illustrate the confidence interval for individual predictions.
This is *fantastic* Dan. I’m so new to CODAP that I haven’t really explored all the plugins yet, so using the Sampler is fantastic.
In fact, I’d say it’s *better*. I don’t really care that students get into the nitty-gritty details of counting cases, just that they get the point that we can generate a sampling distribution from the data itself without resorting to the recipes they learn for parametric tests.
I used to do this kind of thing in JMP, but it required scripting back in my day. And, of course, a rank() function!
The test is equivalent to a Mann-Whitney, but my question is more about finding rankings and computing the distribution of ranks. The PDF explains why there are 35 arrangements for this small data set, which give a probability distribution for the test statistic. I doubt there’s a way to produce the exact probability distribution, but *maybe* there’s a way to generate ranks and use the scrambler to make one.
I mean, I know this is pushing the limits.
Sure thing! Here’s the CODAP shared link:
<span style=”caret-color: #1a202c; color: #1a202c; font-family: Roboto, sans-serif; font-size: medium; white-space: pre;”>https://codap3.concord.org/beta#shared=https%3A%2F%2Fcfm-shared.concord.org%2FtrUDBOSELzjrtQohaW2I%2Ffile.json</span>
And a link to the PDF:
