# Linear Regression for Business Statistics

Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction.

Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction.

This is the fourth course in the specialization, "Business Statistics and Analysis". The course introduces you to the very important tool known as Linear Regression. You will learn to apply various procedures such as dummy variable regressions, transforming variables, and interaction effects. All these are introduced and explained using easy to understand examples in Microsoft Excel.

The focus of the course is on understanding and application, rather than detailed mathematical derivations.

Note: This course uses the ‘Data Analysis’ tool box which is standard with the Windows version of Microsoft Excel. It is also standard with the 2016 or later Mac version of Excel. However, it is not standard with earlier versions of Excel for Mac.

WEEK 1

Module 1: Regression Analysis: An Introduction

In this module you will get introduced to the Linear Regression Model. We will build a regression model and estimate it using Excel. We will use the estimated model to infer relationships between various variables and use the model to make predictions. The module also introduces the notion of errors, residuals and R-square in a regression model.

Topics covered include:

• Introducing the Linear Regression

• Building a Regression Model and estimating it using Excel

• Making inferences using the estimated model

• Using the Regression model to make predictions

• Errors, Residuals and R-square

WEEK 2

Module 2: Regression Analysis: Hypothesis Testing and Goodness of Fit

This module presents different hypothesis tests you could do using the Regression output. These tests are an important part of inference and the module introduces them using Excel based examples. The p-values are introduced along with goodness of fit measures R-square and the adjusted R-square. Towards the end of module we introduce the ‘Dummy variable regression’ which is used to incorporate categorical variables in a regression.

Topics covered include:

• Hypothesis testing in a Linear Regression

• ‘Goodness of Fit’ measures (R-square, adjusted R-square)

• Dummy variable Regression (using Categorical variables in a Regression)

WEEK 3

Module 3: Regression Analysis: Dummy Variables, Multicollinearity

This module continues with the application of Dummy variable Regression. You get to understand the interpretation of Regression output in the presence of categorical variables. Examples are worked out to re-inforce various concepts introduced. The module also explains what is Multicollinearity and how to deal with it.

Topics covered include:

• Dummy variable Regression (using Categorical variables in a Regression)

• Interpretation of coefficients and p-values in the presence of Dummy variables

• Multicollinearity in Regression Models

WEEK 4

Module 4: Regression Analysis: Various Extensions

The module extends your understanding of the Linear Regression, introducing techniques such as mean-centering of variables and building confidence bounds for predictions using the Regression model. A powerful regression extension known as ‘Interaction variables’ is introduced and explained using examples. We also study the transformation of variables in a regression and in that context introduce the log-log and the semi-log regression models.

Topics covered include:

• Mean centering of variables in a Regression model

• Building confidence bounds for predictions using a Regression model

• Interaction effects in a Regression

• Transformation of variables

• The log-log and semi-log regression models

Enroll now

## What's inside

### Syllabus

Regression Analysis: An Introduction
Regression Analysis: Hypothesis Testing and Goodness of Fit
Regression Analysis: Dummy Variables, Multicollinearity
Regression Analysis: Various Extensions

## Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Provides tools and knowledge that are standard in the industry
Develops core analytical skills for business statistics and analysis
Taught by Sharad Borle, an expert in the field
Introduces practical methods for building and interpreting regression models
Suitable for learners with some background in statistics and data analysis
Focuses on hands-on application and examples in Microsoft Excel

## Save this course

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## Reviews summary

### Highly recommended linear regression course

Learners say this is a fantastic course with an excellent teacher.

## Career center

Learners who complete Linear Regression for Business Statistics will develop knowledge and skills that may be useful to these careers:
Statistician
Statisticians collect, analyze, interpret, and present statistical data to inform decision-making in various fields. The Linear Regression for Business Statistics course provides a solid foundation for Statisticians, as it introduces the principles of regression analysis, a fundamental technique in statistical modeling. By learning how to build and interpret regression models, Statisticians can gain insights into complex data, make predictions, and support evidence-based decision-making.
Business Analysts bridge the gap between business and technology, helping organizations understand their business needs and develop solutions that meet those needs. The Linear Regression for Business Statistics course is highly relevant to Business Analysts, as it provides a strong foundation in regression analysis, a valuable technique for analyzing and interpreting business data. By learning how to build and interpret regression models, Business Analysts can gain insights into customer behavior, market trends, and financial performance, enabling them to make informed recommendations and support data-driven decision-making.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment recommendations. The Linear Regression for Business Statistics course provides a strong foundation for Quantitative Analysts, as it introduces the principles of regression analysis, a core technique in financial modeling. By understanding how to build and interpret regression models, Quantitative Analysts can gain insights into financial markets, identify investment opportunities, and develop trading strategies.
Financial Analyst
Financial Analysts evaluate and interpret financial data to make sound investment and business decisions. The Linear Regression for Business Statistics course is highly relevant to Financial Analysts, as it provides a strong foundation in regression analysis, a technique commonly used to analyze and interpret financial data. By understanding how to build and interpret regression models, Financial Analysts can gain insights into financial performance, identify trends and risks, and make informed investment recommendations.
Data Analyst
Data Analysts provide the foundation for effective decision-making in many organizations. They mine and interpret large datasets, extract meaningful insights, and visualize data in understandable formats. The Linear Regression for Business Statistics course provides a strong foundation for Data Analysts, as it introduces the fundamentals of regression analysis, a technique commonly used to analyze and interpret data. By understanding how to build and interpret regression models, Data Analysts can gain valuable insights from complex data, identify trends and patterns, and support evidence-based decision-making.
Market Researcher
Market Researchers conduct research to understand consumer behavior, market trends, and industry dynamics. The Linear Regression for Business Statistics course provides a solid foundation for Market Researchers, as it introduces the principles of regression analysis, a powerful technique for analyzing and interpreting market data. By learning how to build and interpret regression models, Market Researchers can gain valuable insights into consumer preferences, market segmentation, and competitive landscapes, enabling them to develop effective marketing strategies and make informed business decisions.
Operations Research Analyst
Operations Research Analysts use analytical methods to improve the efficiency and effectiveness of business operations. The Linear Regression for Business Statistics course provides a solid foundation for Operations Research Analysts, as it introduces the principles of regression analysis, a valuable technique for analyzing and interpreting operational data. By learning how to build and interpret regression models, Operations Research Analysts can gain insights into process performance, identify bottlenecks, and develop optimization strategies.
Actuary
Actuaries use mathematical and statistical techniques to assess and manage risks in various fields, including insurance, finance, and healthcare. The Linear Regression for Business Statistics course provides a good foundation for Actuaries, as it introduces the principles of regression analysis, a valuable technique for analyzing and interpreting data. By learning how to build and interpret regression models, Actuaries can gain insights into risk factors, develop pricing models, and make informed risk management decisions.
Consultant
Consultants provide advice and expertise to organizations on various business and management issues. The Linear Regression for Business Statistics course may be useful for Consultants, as it provides an introduction to regression analysis, a valuable technique for analyzing and interpreting business data. By understanding the fundamentals of regression, Consultants can enhance their ability to identify trends, make recommendations, and support evidence-based decision-making for their clients.
Economist
Economists study the production, distribution, and consumption of goods and services. The Linear Regression for Business Statistics course may be useful for Economists, as it provides an introduction to regression analysis, a valuable technique for analyzing and interpreting economic data. By understanding the fundamentals of regression, Economists can gain insights into economic trends, forecast economic outcomes, and support policy development.
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. The Linear Regression for Business Statistics course may be useful for Data Scientists, as it provides an introduction to regression analysis, a valuable technique for analyzing and interpreting data. By understanding the fundamentals of regression, Data Scientists can enhance their ability to build and evaluate models, extract meaningful insights, and support data-driven decision-making.
Marketing Manager
Marketing Managers develop and execute marketing strategies to promote products or services. The Linear Regression for Business Statistics course may be useful for Marketing Managers, as it provides an introduction to regression analysis, a valuable technique for analyzing and interpreting marketing data. By understanding the fundamentals of regression, Marketing Managers can gain insights into customer behavior, market trends, and campaign effectiveness, enabling them to make informed marketing decisions.
Data Engineer
Data Engineers design and build systems to manage and process large datasets. The Linear Regression for Business Statistics course may be useful for Data Engineers, as it provides an introduction to regression analysis, a valuable technique for analyzing and interpreting data quality and performance. By understanding the fundamentals of regression, Data Engineers can gain insights into data quality issues, identify data patterns, and develop strategies for data optimization.
Product Manager
Product Managers are responsible for the development, launch, and success of products or services. The Linear Regression for Business Statistics course may be useful for Product Managers, as it provides an introduction to regression analysis, a valuable technique for analyzing and interpreting product data. By understanding the fundamentals of regression, Product Managers can gain insights into customer feedback, market trends, and product performance, enabling them to make informed product decisions.
Software Engineer
Software Engineers design, develop, and maintain software systems. The Linear Regression for Business Statistics course may be useful for Software Engineers, as it provides an introduction to regression analysis, a valuable technique for analyzing and interpreting software performance data. By understanding the fundamentals of regression, Software Engineers can gain insights into software performance, identify bottlenecks, and develop optimization strategies.

We've selected 44 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Linear Regression for Business Statistics.
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Provides a comprehensive treatment of regression analysis and includes a number of case studies. It is more advanced than some of the other books on this list and useful reference for someone who wants to learn more about the theory of regression analysis.
This textbook provides a more in-depth coverage of business statistics, including topics such as time series analysis, forecasting, and marketing research. It good reference for those who want to learn more about the subject.
Provides a comprehensive treatment of linear models, including regression analysis. It is more advanced than some of the other books on this list and useful reference for someone who wants to learn more about the theory of linear models.
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Comprehensive treatment of linear regression analysis. It is more advanced than the previous two books and useful reference for someone who wants to learn more about the theory of regression analysis.
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Provides a comprehensive treatment of regression analysis using Python. It useful reference for someone who wants to learn more about regression analysis and how to use Python.
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Provides a comprehensive treatment of regression analysis using Stata. It useful reference for someone who wants to learn more about regression analysis and how to use Stata.
Provides a solid foundation in linear regression and covers a variety of topics in regression analysis. It useful reference text for someone who wants to learn more about regression analysis.
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Covers a variety of topics in regression analysis and includes several examples. It useful reference text for someone who wants to learn more about regression analysis.
Provides a comprehensive overview of regression methods, including both linear and nonlinear models.
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Popular textbook for undergraduate and graduate students in statistics and econometrics. It provides a clear and intuitive introduction to the concepts of linear regression and their applications. The book is well-written and contains numerous examples and exercises.
Valuable resource for anyone interested in using regression analysis for applied linguistics research. It provides a clear and comprehensive overview of the subject and includes numerous examples and exercises.
Valuable resource for anyone interested in using regression analysis for finance research. It provides a clear and comprehensive overview of the subject and includes numerous examples and exercises.
Valuable resource for anyone interested in using regression analysis for psychology research. It provides a clear and comprehensive overview of the subject and includes numerous examples and exercises.
Provides a comprehensive overview of business statistics, covering topics such as data collection, probability, hypothesis testing, and regression analysis. It good starting point for those who want to learn more about the subject.
Provides a hands-on guide to deep learning using Python. It good resource for those who want to learn how to use deep learning to solve real-world problems.
Provides a practical guide to linear regression using the R statistical software.
Covers a variety of statistical methods, including regression analysis. It useful reference text for someone who wants to learn more about regression analysis and how it is used in psychology.
Provides a more applied perspective on regression analysis. It includes a number of case studies and examples that illustrate how regression analysis can be used to solve real-world problems.
Provides a data-oriented approach to regression analysis, with a focus on the practical applications of the techniques.
Provides a gentle introduction to linear regression, with a focus on the intuitive understanding of the concepts.
Provides a concise introduction to linear regression, with a focus on the geometric interpretation of the model.
Provides a comprehensive overview of statistical methods used in the atmospheric sciences. It covers a wide range of topics, including linear regression, time series analysis, and spatial statistics. The book is well-written and contains numerous examples and exercises.
Practical guide to using regression analysis for business problems. It covers a wide range of topics, including data management, model selection, and hypothesis testing. The book is well-written and contains numerous examples.
Provides a comprehensive overview of linear regression analysis, with a focus on applications in the social and behavioral sciences.
Provides a theoretical foundation for linear regression, with a focus on statistical inference.
This textbook provides a comprehensive introduction to linear regression analysis. It covers a wide range of topics, including simple linear regression, multiple regression, and ANOVA.
Provides a comprehensive introduction to multiple regression analysis. It covers a wide range of topics, including simple linear regression, multiple regression, and ANOVA.
Provides a comprehensive introduction to regression analysis by example. It covers a wide range of topics, including simple linear regression, multiple regression, and ANOVA.
This textbook provides a comprehensive introduction to regression analysis and generalized linear models. It covers a wide range of topics, including simple linear regression, multiple regression, and ANOVA.
This textbook provides a comprehensive introduction to regression analysis from a mathematical perspective. It covers a wide range of topics, including simple linear regression, multiple regression, and ANOVA.

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