From experimental results to a prediction

In the previous exercise, you were directed to follow a common strategy: establish a baseline, then change one input at a time to estimate the effect of that input on the output.

In this exercise, you'll be working with a new (and different) test_scores() mathematical model. You'll follow the same procedure as before to estimate the effect of each variable.

Make a baseline of a "public" school with academic and religious motivation both set to zero.
Make the following changes one at a time, leaving the other inputs at their baseline values:
Change school to "private".
Change academic motivation to 1.
Change religious motivation to 1.
Using just the results you get from these four model runs (be honest now!), make a prediction of what the model output will be for a "private" school where the student has religious motivation of 2 and academic motivation of 2. Save your prediction in the variable my_prediction. Use this very common linear strategy for making your prediction:
If the change in output going from 0 to 1 is X, then the change in output going from 1 to 2 will also be X.
Find the change in output for each of the inputs in turn. Add up the individual changes to get the anticipated change when more than one input is changed.
Once you have made your prediction, test it out by making a new run of test_scores() with those new inputs.

Exercise

From experimental results to a prediction

Instructions