How do you answer the Python code for questions $1$ and $2?$Assignment $2-$ Continuous Visualization

between courses and come up with insights regarding how to enhance residental education across different subjects.

Question $1$: Grade Distribution Comparison $(55\%)$

STATS$250\_$grade stores the letter grades for those who taok the STATS $250$ course

DATASC$1386\_$grade stares the letter grades for thase who took the DATASCI $305$ course

MATH$217\_$grade stores the lecter grades for those who took the MATH $217$ course

ENGLISH$125\_$grade stores the letter grades for thase who took the ENGLISH $125$ course

ECON$1$e$1\_$grade stores the lecter grades for those who took the ECON $101$ course

EECS$545\_$grade stores the lester grades for those who took the EECS $545$ course

You are asked to:

Make a $3^{*}2$ tigure $($so $6$ subplots$)$ such that for each course you have a histogram using the student grade samples respectively

Remove the gaps between the bars in the histograms if any

For each probability plat, you should overiay a normal distribution with the same mean and standard devilation parameters as you see in the samples $($you can caloulate this!$)$

Hnts:

To make subplots, one good way to start with is to use $f1g,ax=$ plt$.$ subplot $()$

To remare the gaps that might show up in histograms, you can customize the bins parameter

If you want to make histograms using the distplat function in seaborn paciage, you need to specity the parameters kde $=$ False and norn$\_$h$1$st $=$ Trueimport pandas as pd

import seaborn as snd

def grade$\_$distribution$()$:grade$\_$distribution$()$Question $2$: Grade Distribution Normality Check $(35\%)$

noemal distribution, a sutficient condition rendering the design of statistical models valid for those courses. You suggest that a QQ$-$plot is a great method to determine how similar a distribution is to another. Great idea:

Make a $3*2$ figure $($again$,6$ subplots$)$ so that for each course you hare a $QQ$ plot using the student grade samples versus the normal distribution with the same mean and standard deriation

side. Eriefty describe the figure discussing the courses and whether they seem to be normally distributed.

Hint: You may find using fig$=plt.$figure$()$ and fig.add$\_$subplot$()$ functions heiptul to create subplats. Yau don't have to use these functions though.

def grade$\_$norwality$(1$:

return Mone

grade$\_$nornality$()$