bonem97 / Matplotlib-Challenge

Pharmaceutical data is analyzed in order to produce graphs of treatment performance

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Matplotlib and Pymaceuticals

In this study, 249 mice identified with SCC tumor growth were treated through a variety of drug regimens administered over the course of 45 days.

-----PROCESS------ Generate a summary statistics table consisting of the mean, median, variance, standard deviation, and SEM of the tumor volume for each drug regimen. Generate a bar plot that shows the number of mice per time point for each treatment regimen throughout the course of the study. Generate a pie plot that shows the gender distribution for mice in the study. Calculate 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. Using Matplotlib, generate 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. Generate a line plot of time point versus tumor volume for a single mouse treated with Capomulin. Generate a scatter plot of mouse weight versus average tumor volume for the Capomulin treatment regimen. Calculate the correlation coefficient for the data in this figure as well.

-----DATA SOURCE----- Sourced from Trilogy Education Services and the University of North Carolina at Chapel Hill

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Pharmaceutical data is analyzed in order to produce graphs of treatment performance


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