Tutorial 7, lessons 1 and 2 #1
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| ## Exercise 1 | ||
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| First, load the `icecream.csv` data file into a DataFrame variable and create scatterplots to examine the relationships between the varables. In this analysis, we are interested in determining what factors influence ice cream consumption from an ice cream truck with a route that travels across different neighborhoods. |
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Make sure to state what the variable names are (in this case for consumption, temperature, and price) since the students won't immediately be able to see that information
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I think an example with an indicator variable makes it easier to motivate why you would want interactions
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Maybe walk them through the indicator variable example, ask some questions about interpretation, then have them try the same approach on the current (non-indicator) example
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| ## Exercise 5 | ||
| Suppose we were to run an experiment where 24 bean plants are randomized into one of four groups: |
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Hmm...this example is already in the online supplement. It would be nice to have a different one.
Also, I think there's reason in the supplement they had this exercise at the beginning: it helps to motivate why we care about interactions.
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| ## Exercise 1 | ||
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Start with a work-out example, then have them apply the same approach to a different dataset? It would be nice to show a couple different transformations we might apply. Maybe a log transform example when then response in monetary (since often it is closer to normal on a log scale)
…ials into tutorial-7-lv
…orials into tutorial-7-lv
…ill need to compute SE
… formula in bootstrap section
Drafted tutorials for the lessons on interaction terms and nonlinear relationships.