This course allows learners to explore Regression Models in order to utilise these models for business forecasting. Unlike Time Series Models, Regression Models are causal models, where we identify certain variables in our business that influence other variables. Regressions model this causality, and then we can use these models in order to forecast, and then plan for our business' needs. We will explore simple regression models, multiple regression models, dummy variable regressions, seasonal variable regressions, as well as autoregressions. Each of these are different forms of regression models, tailored to unique business scenarios, in order to forecast and generate business intelligence for organisations.
Este curso forma parte de Programa especializado: Excel Skills for Business Forecasting
ofrecido por
Acerca de este Curso
Habilidades que obtendrás
- Microsoft Excel
- Business Forecasting
- regression models
ofrecido por

Universidad de Macquarie
Macquarie is ranked among the top one per cent of universities in the world, and with a 5-star QS rating, we are recognised for producing graduates who are among the most sought-after professionals in the world. Since our foundation 54 years ago, we have aspired to be a different type of university: one focused on fostering collaboration between students, academics, industry and society.
Programa - Qué aprenderás en este curso
Welcome and Critical Information
Regression Models
In this module, we explore the context and purpose of business forecasting and the three types of business forecasting using regression models. We will learn the theoretical underpinning for a regression model, and understand the relationship between explanatory variables and dependent variables. We will first focus on single variable or simple regression, and learn how to critically evaluate the model using regression diagnostic tools and then use our models for forecasting to suit our organisation's needs.
Multiple Variable Regression
In this module, we extend the simple regression model to take in multiple explanatory variables. We will extend the theoretical underpinning for a regression model by involving multiple dependent variables. We will learn how to critically evaluate the multiple regression models using regression diagnostic tools and then use our models for forecasting to suit our organisation's needs.
Dummy Variable Regression
In this module, we extend the multiple regression model to take in qualitative binary explanatory variables. We will extend the theoretical underpinning for a multiple regression model by creating dummy variables for binary qualitative data. We will learn how to critically evaluate the dummy variable regression models using regression diagnostic tools and then use our models for forecasting to suit our organisation's needs.
Seasonal Dummy Regression
In this module, we extend the binary dummary variable regression model to take in seasonal variables. We will extend the theoretical underpinning for a binary dummy variable regression model by creating a series of dummy variables to capture seasonality. We will learn how to critically evaluate the seasonal dummy regression models using regression diagnostic tools and then use our models for forecasting to suit our organisation's needs.
Reseñas
- 5 stars97,77 %
- 4 stars2,22 %
Principales reseñas sobre EXCEL REGRESSION MODELS FOR BUSINESS FORECASTING
A step-by-step guide for business forecasting! Very easy to follow and the course brush up my statistics on linear regression as well.
The lecturer did great demonstrating how equation looks like on business application.
Learning this course was fantastic, it really improved my knowledge for excel and regression
I think this is one of the best online course you can take. Awesome.
Acerca de Programa especializado: Excel Skills for Business Forecasting
The current state of the world makes business forecasting even more fundamental to the operation of institutions. In this Specialization we focus on Excel Skills for Business Forecasting in three courses — Time Series Models, Regression Models, and Judgmental Forecasting.

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