Regression Analysis - Fundamentals & Practical Applications from Coursera

Linear regression analysis is critical for understanding and defining the strength of the relationship between variables. This analysis can be used to make predictions for a variable given the value of another known variable.

This course provides an overview of linear regression. You will learn how linear regression works, how to build effective linear regression models and how to use and interpret the information these models give us. In addition to the theory, we will perform linear regression on real data using both Excel and Python. The practical cases you will work through will be similar to those you might encounter in a business setting.

Upon completing this course, you will be able to:

• Define linear regression and its applications

• Perform simple “pen and paper” regression calculations in Excel

• Apply Excel’s RegressIt plugin to solve advanced regression calculations

• Construct linear regression models in Python using both statsmodels and sklearn modules

• Explain the implicit assumptions behind linear regression

• Interpret regression outputs such as coefficients and p-values

• Recommend various regression techniques when appropriate

Regression is the critical tool used for making inferences or predictions based on the relationships between variables. Whether you’re working as a business leader or data analyst, the theory and practical toolsets taught in this course will serve you throughout your career. No background in coding with Python is required for this course.

Common career paths for students who take the BIDA™ program are Business Intelligence, Asset Management, Data Analyst, Quantitative Analyst, and other finance careers.

What's inside

Syllabus

Getting Started

In this course, we will learn how linear regression works, how to build effective linear regression models and how to use and interpret the information these models give us. In addition to the theory, we will perform linear regression on real data using both Excel and Python.

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Provides practical cases similar to those encountered in a business setting, making it highly relevant for professionals in various industries

Covers the use of both Excel and Python for performing linear regression, which are essential tools in the field of data analysis

Requires no prior coding experience with Python, making it accessible to beginners interested in learning regression analysis

Applicable to common career paths such as Business Intelligence, Asset Management, Data Analyst, Quantitative Analyst, and other finance careers

Explains the implicit assumptions behind linear regression, which is crucial for understanding the limitations and applicability of the models

Relies on Excel's RegressIt plugin, which may require learners to use older versions of Excel to ensure compatibility and full functionality

Reviews summary

Regression fundamentals: excel and python

According to learners, this course provides a strong fundamental overview of regression analysis. Students particularly appreciate the clear explanations of theory and the practical application using Excel, finding these sections very helpful for building a solid base. Many mention the relevant business problems and real-world examples provided. However, a recurring point of feedback is that the Python part of the course can feel rushed and may require additional resources or prior knowledge for some learners, suggesting it's more of an introduction than an in-depth guide. While excellent for beginners establishing a foundation, it may not be challenging enough for more intermediate learners.

Ideal for fundamentals, less for intermediate.

"Solid introduction to regression. Covers the basics well. Some areas could be more challenging for intermediate learners..."

"Fantastic course for building a foundation in regression analysis."

"Good fundamental overview... Overall, a solid course."

Relevant, real-world examples.

"The practical examples were very relevant to real-world business problems."

"One of the best stats courses I've taken online. Clear explanations, practical examples..."

"We will perform linear regression on real data... The practical cases you will work through will be similar to those you might encounter in a business setting."

Strong fundamentals and Excel use.

"Excellent course! The combination of theory and practical application in both Excel and Python was perfect. It solidified my understanding..."

"Loved this course. The instructor explained complex topics clearly."

"Fantastic course for building a foundation in regression analysis. The step-by-step approach in Excel was particularly helpful."

"Provides a strong understanding of the theory and how to apply it."

Python part may lack depth/clarity.

"Found the Python part confusing. It felt like it assumed more prior knowledge than stated."

"Decent content, but the Python section felt a bit rushed. I had to look up additional resources to fully understand some concepts."

"Mixed feelings... the Python part is too basic and not well-integrated. Felt like two different courses."

"Python sections were useful but could use more depth for total beginners."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Regression Analysis - Fundamentals & Practical Applications with these activities:

Review Basic Statistics Concepts

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Reinforce your understanding of fundamental statistical concepts like p-values and hypothesis testing to better grasp the underlying principles of regression analysis.

Browse courses on Statistical Significance

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Review key statistical terms and definitions.
Work through practice problems on hypothesis testing.
Summarize the main concepts in your own words.

Read 'Naked Statistics: Stripping the Dread from the Data'

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Gain a more intuitive understanding of statistical concepts by reading a book that explains them in a clear and engaging manner.

View Naked Statistics: Stripping the Dread from the... on Amazon

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Read a chapter each week.
Take notes on key concepts.
Relate the concepts to real-world examples.

Practice Regression Calculations in Excel

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Solidify your understanding of regression calculations by working through practice problems in Excel.

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Find datasets online.
Perform regression analysis using Excel's built-in functions.
Interpret the results and compare with textbook examples.

Four other activities

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Create a Regression Analysis Blog Post

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Deepen your understanding of regression analysis by explaining a specific concept or application in a blog post.

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Choose a specific regression topic.
Research the topic and gather relevant information.
Write a clear and concise blog post explaining the topic.
Include examples and visualizations to illustrate the concepts.

Build a Predictive Model for a Real-World Dataset

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Apply your regression skills by building a predictive model for a real-world dataset using Python.

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Find a suitable dataset online.
Clean and preprocess the data.
Build a regression model using statsmodels or sklearn.
Evaluate the model's performance and interpret the results.

Read 'Applied Regression Analysis' by Draper and Smith

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Deepen your understanding of regression analysis by studying a comprehensive textbook on the subject.

View Applied Regression Analysis (Wiley Series in... on Amazon

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Read a chapter each week.
Work through the examples and exercises.
Relate the concepts to your own data analysis projects.

Help Others on Regression Analysis Forums

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Reinforce your understanding of regression analysis by helping others on online forums.

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Find online forums related to regression analysis.
Answer questions and provide guidance to other learners.
Explain concepts in your own words and provide examples.

Career center

Learners who complete Regression Analysis - Fundamentals & Practical Applications will develop knowledge and skills that may be useful to these careers:

Data Analyst

A data analyst uses statistical techniques to interpret data and produce reports. Regression analysis is a fundamental tool for any data analyst, and this course provides a strong foundation in both the theory and application of linear regression. This course teaches how to build effective models, interpret results, and work with real-world data using both Excel and Python. These are all critical skills for effectively making sense of data. This course specifically covers simple and multiple linear regression, interpreting regression outputs, and constructing models with statsmodels and sklearn.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

A business intelligence analyst uses data to provide insights that help businesses make better decisions. This role requires a strong understanding of regression, which is a core topic of this course. This course teaches the theory behind linear regression but also provides practical applications using Excel and Python. A business intelligence analyst would benefit from the course's focus on model building, interpretation of results, and working with real-world data sets. This course shows how to use linear regression to define variable relationships and make predictions, which are both important for a business intelligence analyst.

See salaries and explore the career path for Business Intelligence Analyst

Quantitative Analyst

Quantitative analysts develop and implement mathematical and statistical models for financial applications. This role often requires a strong understanding of regression techniques, as taught in this course. This course will help a quantitative analyst learn how to build predictive models using linear regression. In particular, this course teaches how to perform regression and how to use Excel and Python to construct regression models. This course also covers how to interpret outputs to make data-driven decisions, a key skill for any quantitative analyst. The course also includes advanced regression techniques, which may be useful for this role. An advanced degree is typically required for this role.

See salaries and explore the career path for Quantitative Analyst

Statistician

Statisticians use statistical methods to analyze and solve problems with data. This role is deeply involved in the theory and practice of regression analysis, which is a central focus of this course. A statistician could use the skills and knowledge learned in this course to build sophisticated statistical models. This course teaches the fundamentals as well as advanced regression techniques. The course also covers how to use both Excel and Python, which are both tools which a statistician would likely use. A statistician typically requires an advanced degree.

See salaries and explore the career path for Statistician

Market Research Analyst

A market research analyst studies market trends and consumer behavior to advise companies. Regression modeling can be used to analyze trends and predict consumer behavior, and this course will give a market research analyst a solid foundation in building and interpreting such models. The course teaches how to perform calculations, build models, and interpret results. It also incorporates practical, real-world exercises, and uses Excel and Python, both of which are useful tools for a market research analyst. The course also helps a market research analyst understand the assumptions of linear regression.

See salaries and explore the career path for Market Research Analyst

Financial Analyst

A financial analyst interprets financial data and provides recommendations to clients or organizations. Regression analysis is an important tool in a financial analyst's toolkit, and this course provides a comprehensive understanding of how linear regression works. This course uses both Excel and Python for model building, enabling a financial analyst to apply these techniques in real-world settings. This course also helps financial analysts to understand the relationships between financial variables and to make predictions based on regression models. This course also teaches how to interpret regression outputs, which is essential for a financial analyst.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use statistical methods to assess and manage risk, especially in the insurance and finance sectors. Linear regression is an important tool for analyzing risk, and this course teaches how to build effective regression models. This course will allow an actuary to understand the fundamental assumptions underpinning models, which will help their work be more accurate and reliable. This course uses both Python and Excel, common tools in the actuarial field. An actuary typically requires relevant certifications and, in many cases, an advanced degree.

See salaries and explore the career path for Actuary

Business Consultant

A business consultant advises clients on how to improve business performance using data-driven insights. Regression is a useful tool to understand the relationships between variables in a business context. In this course, a consultant will learn how to build a model, use techniques to make predictions, and interpret the output of regression models, which is a critical skill for a business consultant. This course covers the theory and also includes practical exercises with real-world data, which are both helpful for consultants who work with real client data.

See salaries and explore the career path for Business Consultant

Economist

Economists analyze economic data to understand and predict economic trends. Understanding regression analysis is critical for any economist, and this course will provide hands-on experience in building and interpreting regression models. This course covers the theory, but also gives practical applications with Excel and Python. This course will enable the economist to accurately make inferences and predictions using regression analysis. An economist typically requires an advanced degree in economics.

See salaries and explore the career path for Economist

Risk Analyst

A risk analyst identifies and assesses potential risks to an organization. This role also uses regression techniques when modeling risk. This course provides a strong foundation in regression, which helps risk analysts understand the relationships among variables and predict key risks. This course teaches how to build and interpret regression models, and also covers the use of Excel and Python for applying these techniques. This course covers practical applications with real data, which is helpful for any risk analyst.

See salaries and explore the career path for Risk Analyst

Operations Research Analyst

An operations research analyst uses mathematical and analytical methods to help organizations make better decisions. Regression modeling helps operations research analysts understand how different variables are related and to make predictions based on these. This course provides strong training in building and interpreting regression models, and uses Excel and Python, which are both useful tools for an operations research analyst. The course covers both simple and multiple linear regression, and provides a solid foundation which may be useful.

See salaries and explore the career path for Operations Research Analyst

Marketing Analyst

A marketing analyst analyzes market data to optimize the effectiveness of marketing campaigns. Regression analysis can be used to understand variable relationships and predict outcomes. This course provides an understanding of regression, and may be useful for a marketing analyst. This course uses Excel and Python, which could be useful tools for a marketing analyst. This course may help a marketing analyst use regression to improve marketing decisions. This course may be useful.

See salaries and explore the career path for Marketing Analyst

Pricing Analyst

A pricing analyst uses data to determine the optimal price for products or services. This role often uses regression analysis to understand how different factors affect price. This course provides practical training in building and interpreting regression models. This course teaches how to use Excel and Python to construct models. A pricing analyst benefits from the course's emphasis on understanding regressions output and its assumptions. This course may be useful.

See salaries and explore the career path for Pricing Analyst

Supply Chain Analyst

A supply chain analyst optimizes the efficiency of the supply chain. Regression analysis can be used to understand the relationship between variables, such as inventory and delivery times. This course provides a theoretical and practical overview of linear regression that may be useful for a supply chain analyst. This course teaches how to build regression models and interpret their outputs using both Excel and Python. This course may be useful.

See salaries and explore the career path for Supply Chain Analyst

Project Manager

A project manager is responsible for planning and executing projects, often using data to understand project performance. While not a core skill, understanding regression may be useful to a project manager when making decisions. This course provides a comprehensive introduction to regression analysis. The course may broaden the analytical skills of a project manager. This course may be useful.

See salaries and explore the career path for Project Manager

Regression Analysis - Fundamentals & Practical Applications

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Reviews summary

Regression fundamentals: excel and python

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