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1.7 Summary

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Fundamentals of Quantitative Modeling

Universidade da Pensilvânia

4.6 (4,544 classificações) | 94K alunos inscritos

Curso 1 de 4 no Finance & Quantitative Modeling for Analysts Programa de cursos integrados

How can you put data to work for you? Specifically, how can numbers in a spreadsheet tell us about present and past business activities, and how can we use them to forecast the future? The answer is in building quantitative models, and this course is designed to help you understand the fundamentals of this critical, foundational, business skill. Through a series of short lectures, demonstrations, and assignments, you’ll learn the key ideas and process of quantitative modeling so that you can begin to create your own models for your own business or enterprise. By the end of this course, you will have seen a variety of practical commonly used quantitative models as well as the building blocks that will allow you to start structuring your own models. These building blocks will be put to use in the other courses in this Specialization.

Visualizar o programa do curso

Habilidades que você aprenderá

Modeling, Linear Regression, Probabilistic Models, Regression Analysis

Avaliações

4.6 (4,544 classificações)

5 stars
3,247 ratings
4 stars
1,024 ratings
3 stars
215 ratings
2 stars
38 ratings
1 star
20 ratings

SC

Jun 04, 2018

Course is having ultimate content regarding the understanding of Quantitative modeling and its applications. Having great explanation with examples of linear, power, exponential and log functions.

NM

Jul 23, 2017

Very good background to quantitative modelling. It gets a bit heavy on the mathematical formulas in places, but if you follow through, it helps cement understanding. Good speed/pace of material.

Na lição

Module 1: Introduction to Models

In this module, you will learn how to define a model, and how models are commonly used. You’ll examine the central steps in the modeling process, the four key mathematical functions used in models, and the essential vocabulary used to describe models. By the end of this module, you’ll be able to identify the four most common types of models, and how and when they should be used. You’ll also be able to define and correctly use the key terms of modeling, giving you not only a foundation for further study, but also the ability to ask questions and participate in conversations about quantitative models.

1.1 Course Introduction4:50

1.2 Definition and Uses of Models, Common Functions14:58

1.3 How Models Are Used in Practice10:42

1.4 Key Steps in the Modeling Process7:46

1.5 A Vocabulary for Modeling8:49

1.6 Mathematical Functions20:27

1.7 Summary4:00

Ministrado por

Richard Waterman
Professor of Statistics

Transcrição

So summarizing what we've done in this first module, we started off talking about uses for models. The two key uses for these quantitative models tend to be in predictive analytics, in making predictions, and forecasts, and also in doing optimization problems. We've seen the steps involved in the modelling process. It starts off by defining some variables, identifying the scope of the model. There's a formulation stage. But you must not forget that there is an entire validation and sensitivity analysis phase as well. If your model works well, that's good. But typically it's not going to work well the first time around. That's why I put a feedback loop in the modeling process. So the model never in my experience works perfectly straight out of the box the first time around. We're very iterative, we go back and we revisit some of the assumptions behind the model. We might look for additional terms to put into the model. We might reformulate the model. There's an iterative process there. Ultimately, we feel that the model validates well and we performed our sensitivity analysis. We then ask ourself the key question, is the model fit for purpose? And I've used that language very purposely there because I have not said, is the model right or is it wrong. Because models are never absolutely right because they are almost by definition, simplifications of a much more complicated real world. The key question is, is the model fit for purpose, is it useful at helping me answer questions? In other words, is it going to be a useful decision support tool. So don't forget to validate your model. We've discussed various types of models. That was the language that I introduced in modeling. We talked about deterministic and stochastic. We talked about discrete and continuous. We talked about static and dynamic models. So those terms are important to understand because you might at some point have a conversation as you create more of these models with someone and say, well did you create a discrete time or continuous time model? And being able to understand those words is very helpful if you want to be able to participate in those conversations. The final part of the module was reviewing some essential mathematics in particular seeing the functions that we're going to be using as we create our quantitative models there were four key functions. Linear, power, exponential, and log. And from a modeling point of view, what you want to understand about these functions is how they relate changes in the input to changes in the output and whether or not those changes are being thought of in absolute terms or relative terms. So recall, a straight line is characterized by its constant slope. And that tells us that absolute changes in X are always accompanied with the same absolute change in Y of M. That's what the slope of M equals. Whereas if we had a power function, then we would have a 1% change in X is associated with an approximate M% change in Y. So percent change in X is percent change in Y. And you simply have to think about and understand your business process and ask yourself, well, on which type of change is this process most readily modeled? In terms of absolute change or percent change. And by doing that, you're able to think which of these functions should be used in the model.

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