In todays class we learnt that linear model fit to data is one that successfully captures the relationship between two variables, even when those variables exhibit characteristics that does not follow traditional assumptions. Here both of the variables involved in the model are non-normally distributed, meaning their data points do not follow the familiar bell-shaped curve. Furthermore, these variables may be skewed, indicating an asymmetry in their distribution, and they could exhibit high variance, implying that data points are spread widely across the range. Additionally, high kurtosis suggests that the data has heavy tails or outliers.
The crab molt model relates to the process of molting in crabs, particularly focusing on the sizes of a crab’s shell before and after molting. “Pre-molt” indicates the size of a crab’s shell before it undergoes molting. “Post-molt” indicates the size of the crab’s shell after the molting. Also a t-test is performed to determine whether there is a difference between the means of two groups.