Macquarie University
Excel Regression Models for Business Forecasting
Macquarie University

Excel Regression Models for Business Forecasting

This course is part of Excel Skills for Business Forecasting Specialization

Taught in English

Some content may not be translated

4,884 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.9

(91 reviews)

Intermediate level
Some related experience required
9 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

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Assessments

16 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.9

(91 reviews)

Intermediate level
Some related experience required
9 hours (approximately)
Flexible schedule
Learn at your own pace

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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 quizzes

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 quizzes

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 quizzes

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 quizzes

Instructor

Instructor ratings
5.0 (25 ratings)
Dr Prashan S. M. Karunaratne
Macquarie University
28 Courses1,302,878 learners

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