Correlation Coefficient and Linear Regression Model

  • 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.
    Hint: All four box plots should be within the same figure. Use this Matplotlib documentation page for help with changing the style of the outliers.
  • Select a mouse that was treated with Capomulin and generate a line plot of time point versus tumor volume for that mouse.
  • 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.
  • Look across all previously generated figures and tables and write at least three observations or inferences that can be made from the data. Include these observations at the top of notebook.

Here are some final considerations:

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Correlation Coefficient and Linear Regression Model
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  • You must use proper labeling of your plots, to include properties such as: plot titles, axis labels, legend labels, x-axis and y-axis limits, etc.
  • See the starter workbook for help on what modules to import and expected format of the notebook.

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