Specific requirements for "tell a story on airquality"

[padasch]

@stineb I still feel that this report exercise is too loosely defined. Should we add some specific requirements such as, number of plots to be created, number of tables to be created, number of hypothesis to be setup and tested? Else, I guess, we will get a large range of how detailed students solved this exercise, making it difficult to grade in a fair manner.

@khufkens @pepaaran any thoughts on this?

[padasch]

I have not pushed my changes yet, but the exercise formulation right now ist:

In the previous exercises and tutorials, you have learned how to wrangle data, fit simple linear regression models and identify outliers, create figures for temporal patterns, and develop and test hypotheses. Use these skills to tell a story about the airquality dataset (directly available in R, just type airquality into the console).

[khufkens]

Specify what metrics they need to report. Failure to do so will result in loss of points, if hitting everything good execution can give extra credit, if not making up for other bits (not sure if we have this leeway)?

[pepaaran]

Shall we take a look at the data ourselves and write down what are the most important takeaways? Usually, failing to see very obvious patterns is a sign that they don't get the concepts. To get full points, they should be able to identify those (report it in the framework of coming up and testing a hypothesis), provide a plot that shows the main patterns (an outlier plot, a temporal plot, a scatterplot, minimum), provide 2-3 metrics that are relevant for the problem (with their reason for why they chose them) and compare them. I guess those points are the bare minimum and should be included. Thoughts?

[padasch]

Completely agree!

[padasch]

I updated the description with #96 as such:

In the previous exercises and tutorials, you have learned how to wrangle data, fit simple linear regression models and identify outliers, create figures for temporal patterns, and develop and test hypotheses. Use these skills to tell a story about the airquality dataset (directly available in R, just type datasets::airquality into the console).

Your solution has to include:

• At least three publishable figures that show important patterns (e.g., outliers, temporal patterns, scatterplots of correlations, etc.)
• A description of the data that includes at least three statistical metrics that are relevant for the problem (argue for why you chose these metrics).
• Interpretation and discussion of your results and hypotheses. The text alone should not exceed one A4 page (max. 400 words).

Hint: To get more background information on the data, use the help functionalities in RStudio.

[khufkens]

closing as merged