Setup You have credit terms of 3/30 net 60. Your cost per unit is 95, and the price per unit is 115. The monthly discount rate is 1.5%. Simulate 10,000 hvpothetical customers. Each customer will have a random (but known) probability of default between 1% and 20%. = pdefault [0.01, 0.20] - Use the uniform method from the random package to generate the probability for each customer. If the customer does not default, then their probability of paying early (i.e., on day 30 instead of 60) is given by the following:

Process Assume that customers not granted credit will not make the purchase with cash. Assume 30 days in a month. For each customer, calculate their expected revenue and NPV if you sell to them on credit. Print out the number and percent of customers that will be allowed to purchase on credit. Since this is a simulation, your numbers should change slightly every time you run the program.

Results Use plotly and plot a histogram showing the distribution of your expected NPV results. The following example code might be helpful:

pday30pday60=1pday30=0.750.400.20ifififpdefault0.050.050.15 \( \begin{aligned} 1 & \text { \# import packages at the top } \\ 2 & \text { import plotly.express as px } \\ 3 & \\ 4 & \text { \# all of the rest of your code } \\ 5 & \text { \# for your main analysis } \\ 6 & \\ 7 & \text { \# plot the results } \\ 8 & \text { exp_npv_fig = px.histogram( } \\ 9 & \text { data_frame=df, } \\ 10 & \text { x='expected_NpV', } \\ 11 & \text { ) } \\ 12 & \text { exp_npv_fig } \end{aligned} \) pday30pday60=1pday30=0.750.400.20ifififpdefault0.050.050.15 \( \begin{aligned} 1 & \text { \# import packages at the top } \\ 2 & \text { import plotly.express as px } \\ 3 & \\ 4 & \text { \# all of the rest of your code } \\ 5 & \text { \# for your main analysis } \\ 6 & \\ 7 & \text { \# plot the results } \\ 8 & \text { exp_npv_fig = px.histogram( } \\ 9 & \text { data_frame=df, } \\ 10 & \text { x='expected_NpV', } \\ 11 & \text { ) } \\ 12 & \text { exp_npv_fig } \end{aligned} \)