# Deriving monthly study schedule best correlated with exam outcomes

I've recently conducted a survey of students in a class who had recently taken a difficult examination. Part of the output data is like so:

• x_Jan=Hours spent studying weekly in Jan [0 ... n hrs/wk]
• x_Feb=Hours spent studying weekly in Feb [0 ... n hrs/wk]
• (and so on to x_Dec)
• y=Exam score

Here's an example month:

How can I analyze this data to know what amount of time spent studying weekly per month is best correlated with examination outcomes? For the sake of simplicity, I think the question & answer can be simplified to a single month (x), and the solution to multiple months can be easily extrapolated.

On the surface, one might guess those who spend the most time studying perform the best, but, looking at the data qualitatively, it is evident that it isn't quite the case--for example, perhaps some students spend too much time studying too early, or too much time studying too late. I hope to use the data to help students prepare an effective study plan and schedule.

I considered doing linear regression comparing 0 hours to each of X hours vs test score, but sample size quickly becomes a limiting factor. I'd have to bin the data fairly wide to get any significance. (i.e. 0 to 20, 21 to 40, etc.).

Any suggestions? I appreciate your insight!

• Are you able to add any predictor of academic performance to your study? I ask as the results would be affected by both intelligence (somewhat measurable) and academic laziness (not so measurable), with a proxy somewhat combining both factors being the individual's past academic performance. Nov 3, 2018 at 10:00
• Yes. I plan to stratify the results by other factors, such as pre-test GPA, as well. Might dig out some effect modifiers / confounders. Nov 3, 2018 at 11:06
• It seems that exam score is a continue variable, so fit a linear model, and try to select useful covariates and build a final model. Nov 3, 2018 at 19:20
• @a_statistician I can't say for certain whether there's a linear relationship, the plot in the OP is just a piece of a larger dataset. I'd like to leave room for other possibilities, for example, that those who study most may perform poorer than those who study a moderate amount. Also, which component of the linear model would answer the question? It could help determine whether there is a linear relationship between hours studied and exam outcomes, but I'm not sure whether it could say which number of hours studied is most correlated with exam outcome unless you apply wide bins to the hours. Nov 13, 2018 at 1:30
• Linear model does not mean linear relationship. $Y=\beta_0+\beta_1 X_1 +beta_2 X_2...\beta_kX_k +\epsilon$, $X$s can be anything you can derived from the variables (excerpt Y), such as hours spending on the study. They can be square of the hours, spending less than 4 hours, spending 4 or more house, average of hours 3 months before last exam ... Nov 13, 2018 at 2:04