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Friday, April 16, 2010

Regression Project Debrief

These are intended to be notes for me for next time. Though if they help you to deliver this experience better than I did, awesome.

Here is the project description. The students' work is published to the class blog.

Percent of students who were able to:
  • Transcribe the data into an accurate, useful table of values: 100%
  • Model the scatterplot with an appropriate regression equation: 77%
  • Correctly use the equation to make predictions about future values: 60%
  • Interpret the meaning of the variables in the regression equation (or would-be equation): 94%
  • Interpret the meaning of the constants in the regression equation in the context of effect on the graph: 31%
  • Interpret the meaning of the constants in the regression equation in the context of the data: 20%
Those last two kind of bug me. I understand that it was a difficult thing to ask them to do, but I can't decide how important it is. If they know what x and y represent, they can use the equation to make predictions and solve useful problems. Is it really important that they know that in y = (0.44)(1.1)x, that the 1.1 means 10% growth? I don't have a good answer for that.


Changes for next time to the project description:
  • Write more explicit instructions for uploading images and making and editing blog posts.
  • Add a template for each problem that they can copy and paste.
  • Add more implicit instructions to try several types of regressions and compare r-values to find the best one.
  • Delete 'A Piece of Cake' or consider curmudgeon's comment below to just delete the 'cake' part and add a known data point that allows them to check their model.
  • Delete interpreting the equation constants from the quadratics problems.
  • Started out requiring four problems - one from each group - amended that to two problems. Four class periods were only enough time for them to reasonably complete two.
  • Make the easier tasks (with high percentage of success based on this year) worth most of the points, so you won't have to fudge the scoring such that everyone wouldn't fail.

Examples of Excellent Work:

Jarrod and Stephen: Sinusoidal Regression

Harry and Matt: Exponential Regression

And...I'm on goin' on break.