Monte Carlo Simulations Using Spreadsheets

No coding experience required.

What you'll learn

Monte Carlo simulations

Confidence intervals

Probabilistic simulations

Risk management

A little game theory

Requirements

No programming skills needed.

Only basic spreadsheet knowledge.

Some mathematical (arithmetics and probability) knowledge may be handy but not required.

A spreadhseet software (Microsoft excel, google sheets, etc)

Description

Monte Carlo simulations, named after the world capital of gambling, consist on simulating individual random events belonging to a greater probabilistic system to acquire statistical trends as to how the whole system behaves based on the behaviour of its components and the relation that these components have with one another.More and more, the complexity of engineering systems and business models grow in size and complexity, making it incrisingly harder to analyze via deterministic methods. Monte Carlo simulations come into play to offer a robust and rigorous methodology to understand large and complex systems that would be, otherwise, simple impossible.During this course, we are going to learn the very basic concepts that we need in order to build our own Monte Carlo simulations for any system. Furthermore, we will be able to deliver key results that will let us get insights on to the systems likely behaviour via the confidence intervals.This course is intended to any person who wishes to add this valuable skill to their toolbet with no previous experience or knowledge whatsoever. You will only need to know the very basics of spreadsheets (either Microsoft Excel or Google Sheets) and some basic arithmetic and algebraic knowledge.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Basic concepts

Lecture 2 Introduction

Lecture 3 How to set up a monte carlo simulation

Lecture 4 Practical session (first cases of study)

Lecture 5 Convergeance of the variable of interest with regards to the number of sims

Lecture 6 The confidence intervals

Lecture 7 Calculating the condifence interval (95%) to our practical session

Section 3: The Monty Hall problem

Lecture 8 Introduction

Lecture 9 Simulating the Monty Hall problem (Stay Strategy)

Lecture 10 Simulating the Monty Hall problem (Switch Strategy)

Lecture 11 The Monty Hall problem explained (why does the switch strategy works?)

Section 4: Estimating the value of pi (method 1)

Lecture 12 Introduction

Lecture 13 Creating a uniform random distribution in 2D.

Lecture 14 Estimating the value of pi (iteration by iteration)

Section 5: Estimating the value of pi (method 2)

Lecture 15 Introduction

Lecture 16 Analytical solution (Conditions)

Lecture 17 Analytical solution (Continuation)

Lecture 18 The buffon experiment (practical session 1)

Lecture 19 The value of Pi is independent of the geometry of the system

Lecture 20 CI applied to the buffon experiment

Lecture 21 Final comparison between the 2 methodologies

Section 6: Basic BlackJack strategies

Lecture 22 Can we beat the house in BlackJack?

Lecture 23 BlackJack rules

Lecture 24 Implementing the rules into the spreadsheet

Lecture 25 Monte Carlo simulation to optimize BlackJack's outcome.

Lecture 26 Comparing all the possible scenarios together (final conclusions)

Section 7: Congratulations and good bye :D

Lecture 27 Congratulations and good bye :D

Beginner level students interested in probabilistic simulations,Beginner level students looking to learn how to simulate risk management,Anyone in general who's interested in probabilistic simulations