In a study, 249 mice identified with SCC tumor growth were treated through a variety of drug regimens. Over the course of 45 days, tumor development was observed and measured. The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. The task is to generate all of the tables and figures needed for the technical report of the study.
- Before beginning the analysis, checked the data for duplicate mice and remove any data associated with that mouse ID.
- Generated 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 and Matplotlib's pyplot that shows the number of mice per time point for each treatment regimen throughout the course of the study.
- Generated a pie plot using both Pandas's 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. Calculated the quartiles and IQR and quantitatively to determine if there are any potential outliers across all four treatment regimens.
- Using 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.
- Generated a line plot of time point versus tumor volume for a single mouse treated with Capomulin.
- 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. Plotted the linear regression model on top of the previous scatter plot.
- Used proper labeling of your plots, to include properties such as: plot titles, axis labels, legend labels, x-axis and y-axis limits, etc.
- Used this Matplotlib documentation page for help with changing the style of the outliers.
Pandas, Matplotlib, JupyterNotebook, GitHub