(PARTICIPANTS MUST BE WITH THEIR COMPUTERS)
How can you put data to work for you?
How can numbers be used to tell you about present and future of business activities?
This course answers these questions with quantitative models. Candidates will understand the fundamentals of this critical business skill.
Through a series of short lectures, demonstrations, candidates will learn the key ideas with which they can begin to create their own models for an enterprise. Candidates will handle a variety of building blocks that will allow them to start structuring own models.
Introduction to Models
- Course Introduction
- Definition and Uses of Models, Common Functions
- How Models Are Used in Practice
- Key Steps in the Modeling Process
- A Vocabulary for Modeling
- Mathematical Functions.
Linear Models and Optimization
- Introduction to Linear Models and Optimization
- Growth in Discrete Time
- Constant Proportionate Growth
- Present and Future Value
- Introduction to Probabilistic Models
- Examples of Probabilistic Models
- Probability Trees
- Monte Carlo Simulations
- Markov Chain Models
Building Blocks of Probability Models
- The Bernoulli Distribution
- The Binomial Distribution
- The Normal Distribution
- The Empirical Rule.
- Introduction to Regression Model
- Use of Regression Models
- Interpretation of Regression Coefficients
- R-squared and Root Mean Squared Error (RMSE)
- Fitting Curves to Data
- Multiple Regression