1. Clone repository to a folder on computer

2. Navigate to MatPlot Lib HW.ipynb in jupyter lab

3. Run all cells

• Gather statistics on effectiveness of tumor treatment regimens using data from a recent mouse study:

• Mouse_metadata.csv and Study_results.csv

• 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 using both Pandas's DataFrame.plot() and Matplotlib's pyplot that shows the number of data points for each treatment regimen.

• Generate 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.

• 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 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.

1. Tumor_Volume

Displays the final tumor volume for each mouse after being treated with different medications.

2. Tumor_time

Lineplot of time point versus tumor volume for a mouse treated with Capomulin.