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.
Excel Regression Models for Business Forecasting
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Excel Regression Models for Business Forecasting
This course is part of Excel Skills for Business Forecasting Specialization
Instructor: Dr Prashan S. M. Karunaratne
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4.9
(102 reviews)
Intermediate level
Some related experience required
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
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Assessments
16 assignments
Taught in English
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This course is part of the Excel Skills for Business Forecasting Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
What's included
2 videos3 readings1 discussion prompt
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.
What's included
4 videos3 readings4 assignments
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.
What's included
4 videos2 readings4 assignments
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.
What's included
4 videos2 readings4 assignments
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. In this module we will also explore autoregressions - their theoretical underpinning, creating an autoregression, critically evaluating this, and utilising our model for business forecasting. We will end the module by learning how to create a composite forecast by combining two forecasts across this course and the first course in this specialisation.
What's included
5 videos2 readings4 assignments
Instructor
Instructor ratings
5.0 (28 ratings)Offered by
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University of Pennsylvania
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