salllym / pymaceuticals_analysis

Generated a summary statistics table, plots using both Pandas's DataFrame.plot() and Matplotlib's pyplot, and calculated the correlation coefficient and linear regression model

Pymaceuticals Inc.

In this study, 249 mice identified with SCC tumor growth were treated with 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 following visualizations were created:

• Summary statistics dataframe

• Bar plot showing the total number of timepoints for all mice tested for each drug regimen throughout the course of the study

• Pie chart showing the distribution of female or male mice in the study

• Box plot of the final tumor volume for all four treatment regimens

• Line plot of volume versus timepoint for Capomulin treatmeant of mouse l509

• Scatter plot of tumor volume versus mouse weight for the Capomulin treatment regimen

• Linear regression model between mouse weight and average tumor volume for the Capomulin treatment plotted on top of the scatter plot

Observations and Insights

• Capomulin and Ramicane reduces the tumor size better.

• There is a positive correlation between mouse weight and average tumor volume, it is 0.84. As the mouse weight increases, the average tumor volume increases too.

• The bar graph shows that Capomulin and Ramicane had the most number of mice tested.