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A 'concept test'.  3 multiple choice questions about a graphed normal distribution curve modelling IQ data.

A 'concept test' (no calculations) with 4 multiple-choice parts.

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These questions are designed to help you verify that you can successfully apply the techniques related to maximum-likelihood estimation we have learned in class, e.g., numerical and analytical derivation of estimators, construction of large-sample confidence intervals, and likelihood-ratio tests.

These multiple-choice questions help you verify your understanding of key concepts from the lectures.

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When saving the data necessary to resume an attempt, Numbas only saves the values of variables which use the random number generator.

If you naively generate a large random sample in one variable, it'll be marked as a source of randomness so Numbas will save every value in the sample. Loading this value when you resume the attempt can be very slow.

Instead, you can generate a seed value in one variable and then use it with the seedrandom function in the variable that stores your random list. Only the value of the seed needs to be saved.

A custom marking algorithm on the spreadsheet part changes it to interpret quantities in cells, so they can be compared to the expected answer.

In real use, you'd probably want more sophisticated logic, to give better feedback or to allow partial marks for different units, like the "quantity with units" part type does.