Excel Regression Models for Business Forecasting from Coursera

What's inside

Syllabus

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. 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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores regression models, which is standard in business forecasting

Teaches skills, knowledge, and tools that are highly relevant to industry

Develops skills and knowledge that are core for business forecasting

Taught by Dr Prashan S. M. Karunaratne, who is recognized for their work in business forecasting

Provides hands-on labs and interactive materials

Requires learners to come in with some background knowledge

Reviews summary

Excel regression for business forecasting

Learners say this Excle Regression for Business Forecasting course offers clear instruction and practical applications for improving business forecasting skills. The learnings are well-suited for beginners to the subject matter and provide step-by-step guidance. Students report the course is largely positive with only minor quality concerns. The material is relevant and useful with engaging assignments to reinforce concepts.

Clear and engaging instruction

"His skills in teaching is very clear and concise."

"the lecturer is very good at explaining"

Clear instruction for beginners

"Dr Prashan taught the course with so much ease and he made me felt like he is sitting right next to me and going through with me with a tone like a older uncle who just tell me exactly what I need to know."

"The lessons are easy to follow. Perfect for beginners."

Relevant assignments with practical applications

"The hands-on practice and case studies were incredibly helpful in reinforcing the concepts."

"I think this is one of the best online course you can take."

Minor quality concerns with materials

"Poor quality control on course materials (power points, worksheets, and tests)"

Career center

Learners who complete Excel Regression Models for Business Forecasting will develop knowledge and skills that may be useful to these careers:

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment recommendations. To be successful in this role, it is crucial to have a strong understanding of data analysis techniques. This course explores regression models, a fundamental technique for analyzing financial data and forecasting trends.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

A Data Scientist uses data to solve business problems and make predictions. To be successful in this role, it is critical to have a strong foundation in data analysis and modeling techniques. This course explores regression models, a key technique for data analysis and forecasting, which may be useful for your work as a Data Scientist.

See salaries and explore the career path for Data Scientist

Actuary

An Actuary assesses financial risks and develops strategies to mitigate those risks. To be successful in this role, it is critical to have a strong foundation in data analysis techniques. This course explores regression models, a key data analysis technique for evaluating and forecasting risks.

See salaries and explore the career path for Actuary

Statistician

A Statistician collects, analyzes, and interprets data. To be successful in this role, it is critical to have a strong foundation in data analysis techniques. This course explores regression models, a key data analysis technique, and how they can be used to make forecasts.

See salaries and explore the career path for Statistician

Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models to solve business problems. To be successful in this role, it is critical to have a strong foundation in data analysis and modeling techniques. This course explores regression models, a key technique for data analysis and forecasting, which may be useful for your work as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Market Researcher

A Market Researcher studies market trends and customer behavior to help businesses make informed decisions. To be successful in this role, it is essential to be able to analyze data and make recommendations based on that data. This course introduces key data analysis techniques, including regression models, which may be useful for your work as a Market Researcher.

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Business Analyst

A Business Analyst identifies and solves problems within a business, examining how a business operates and making recommendations for improvements. As a Business Analyst, you will likely need to be able to analyze data and make recommendations for change. This course can help build a foundation in data analysis techniques, as well as in making recommendations based on data.

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Data Analyst

A Data Analyst studies and interprets data to help businesses understand their customers and make better decisions. To be successful in this role, it is helpful to have a strong foundation in data analysis techniques. This course provides a foundation in regression models, a useful technique for data analysis.

See salaries and explore the career path for Data Analyst

Financial Analyst

A Financial Analyst helps businesses make financial decisions by investigating and interpreting financial data. To be successful as a Financial Analyst, you may need to be able to understand and use a variety of data analysis techniques. This course provides an introduction to regression models and explores how those models can be used to make forecasts. This is a skill that may be useful for your work as a Financial Analyst.

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Consultant

A Consultant provides expert advice to organizations on various topics. To be successful in this role, it is important to be able to analyze data and provide insights. This course explores regression models, a powerful tool for analyzing data and making forecasts, which could be a valuable skill for a Consultant.

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Risk Manager

A Risk Manager identifies, analyzes, and mitigates risks for an organization. To be successful in this role, it is important to have a strong foundation in data analysis techniques. This course provides an introduction to regression models, a helpful tool for analyzing data and making forecasts.

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Sales Manager

A Sales Manager leads and motivates a sales team to achieve sales targets. To be successful in this role, it is often helpful to have a strong understanding of data analysis techniques. This course explores regression models, a key technique for data analysis, and how they can be used to make forecasts, which could be a useful skill for a Sales Manager.

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Project Manager

A Project Manager plans, organizes, and manages projects from start to finish. To be successful, Project Managers often need to be able to analyze data and make decisions based on that data. This course may be useful for learning how to use regression models, a key data analysis technique, to make forecasts.

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Operations Manager

An Operations Manager oversees the day-to-day operations of a business. To be successful in this role, it is helpful to have a strong foundation in data analysis. This course explores regression models, a powerful tool for data analysis, and how those models can be used to make forecasts.

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Marketing Manager

A Marketing Manager develops and executes marketing campaigns to promote a product or service. To be successful in this role, it is important to be able to understand and interpret data. This course introduces regression models, an important data analysis technique for making forecasts, which may be useful in this role.

See salaries and explore the career path for Marketing Manager