Before I began the analysis, I checked the data for any mouse ID with duplicate time points and removed any data associated with that mouse ID.
Used the cleaned data to generate a summary statistics table consisting of the mean, median, variance, standard deviation, and SEM of the tumor volume for each drug regimen.
Generated a bar plot using both Pandas's DataFrame.plot() and Matplotlib's pyplot that shows the number of total mice for each treatment regimen throughout the course of the study.
Generated a pie plot using both Pandas's DataFrame.plot() and Matplotlib's pyplot that shows the distribution of female or male mice in the study.
Calculated the final tumor volume of each mouse across four of the most promising treatment regimens: Capomulin, Ramicane, Infubinol, and Ceftamin. Calculate the quartiles and IQR and quantitatively determine if there are any potential outliers across all four treatment regimens.
Used Matplotlib, generated a box and whisker plot of the final tumor volume for all four treatment regimens and highlight any potential outliers in the plot by changing their color and style.
I then selected a mouse that was treated with Capomulin and generate a line plot of time point versus tumor volume for that mouse.
Generated a scatter plot of mouse weight versus average tumor volume for the Capomulin treatment regimen.
Calculated the correlation coefficient and linear regression model between mouse weight and average tumor volume for the Capomulin treatment. Plot the linear regression model on top of the previous scatter plot.