In this lesson, Maeve quickly understood the distinction between numerical and categorical data with real-world examples. She also actively answered the questions with me, including identifying biased sampling techniques.

She could improve on distinguishing between ordinal categorical data and discrete numerical data and explaining why a specific sampling method is representative using precise math terminology.

Maeve was able to navigate through the CAS confidently and answer the following questions with little doubt. She quickly identified the five-number summary values and calculated the IQR. She used these numbers to interpret what the data was heading towards.

She can improve on analyzing data skewness and the impacts on outliers. If she can work on becoming quicker at doing it by hand, rather than the cas, she can work through questions much faster!

Maeve was able to execute the multi-step CAS commands to generate the regression line equation accurately and apply standard deviation concepts correctly to compare the spread of different data sets.

She can improve on explaining standard deviation conceptually as a measure of average distance from the mean, rather than just reading off the raw number and interpolating and extrapolating values using the line of best fit while recognizing the limitations of predictions.

Maeve was successfully able to understand the relationship between revenue, expenses, and final profit smoothly. She could also correctly calculate total earnings from different hourly rates and shift lengths

To further improve, she can continue practicing these questions to understand the theory.