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  3. Monte Carlo Simulations in Python
  • 1

    Introduction to Monte Carlo Simulations

    Free

    What are Monte Carlo simulations and when are they useful? After covering these foundational questions, you’ll learn how to perform simple simulations such as estimating the value of pi. You’ll also learn about resampling, a special type of Monte Carlo Simulation.

  • 2

    Foundations for Monte Carlo

    Now that you can run your own simple simulations, you’re ready to explore real-world application of Monte Carlo simulations across various industries. Then, you’ll dive into the heart of what makes a good simulation work: sampling from the correct probability distribution. You’ll learn about probability distributions for discrete, continuous, and multivariate random variables.

  • 3

    Principled Monte Carlo Simulation

    Once you’re comfortable with your choice of probability distribution, you’re ready to follow a principled Monte Carlo simulation workflow using a dataset of diabetes patient characteristics and outcomes. You will explore the data, perform a simulation, and generate summary statistics to communicate your simulation results.

  • 4

    Model Checking and Results Interpretation

    Discover how to evaluate your Monte Carlo models and communicate the results with easy-to-read visualizations in Seaborn. Finally, use sensitivity analysis to understand how changes to model inputs will impact your results, and practice this concept by simulating how business profits are impacted by changes to sales and inflation!

Initializing

Exercise

Sampling with replacement

Bootstrapping is great for calculating confidence intervals for means; you'll now practice doing just that!

nba_weights contains the weights of a group of NBA players in kilograms: 

nba_weights = [96.7, 101.1, 97.9, 98.1, 98.1, 
               100.3, 101.0, 98.0, 97.4]

You are interested in calculating the 95% confidence interval of the mean weight of NBA players using this list.

The following have been imported for you: random, and numpy as np.

Instructions

100 XP
  • Use random.choices() to sample nine heights from the list 1,000 times, with replacement.
  • Calculate the mean and 95% confidence interval for your simulation results, assigning the lower end of the confidence interval to lower and the upper end to upper.