The Classical Linear Regression Model from Coursera

In this course, you will discover the type of questions that econometrics can answer, and the different types of data you might use: time series, cross-sectional, and longitudinal data.

During the course you will:

– Learn to use the Classical Linear Regression Model (CLRM) as well as the Ordinary Least Squares (OLS) estimator, as you discuss the assumptions needed for the OLS to deliver true regression parameters.

– Look at cases with only one independent variable for one dependent variable, before progressing to regression analysis by generalising the bivariate model to multiple regression.

– Explore different model-building philosophies, with particular focus on the general-to-specific approach, and learn how to use goodness-of-fit statistics as the measures of “how well your model explains variations in the dependent variable”.

Throughout this course, you will see examples to help clarify which kind of relationship is of interest, and how we can interpret it. You will also have the opportunity to apply your learning to estimating the Capital Asset Pricing Model using real data with R.

The course is for beginners, so little prior knowledge is required, but you will benefit from an ability to graph two variables in the xy framework, an understanding of basic algebra and taking derivatives. Knowledge of matrix algebra is not a requirement but will also provide you with an advantage.

By the end of this course, you will be able to:

– Describe the problems that econometrics can help addressing and the type of data that should be used

– Explain why some hypotheses are needed for the approach to produce an estimate

– Calculate the coefficients of interest in the classical linear regression model

– Interpret the estimated parameters and goodness of fit statistics

– Estimate single and multiple linear regression models with R.

What's inside

Syllabus

Aims and Uses of Econometrics

Welcome to Coursera and Queen Mary University of London, we are excited to have you studying with us. We are going to help you prepare for your studies by ensuring you know exactly what is expected of you throughout your course and how to most effectively engage with the platform. We will look at how the platform works as well as how you will interact with your peers. You will be introduced to the university you are studying with and we will share some top tips on how to succeed with Coursera. This week we shall start by getting to know Coursera as you will be introduced to the platform and explore how to use the various functions which will support your learning journey. You will see how you can make the most of your learning experience which will enable you to succeed on this course. This week we are going to explore the aims and uses of econometrics for economists and finance professionals and consider some of the questions that econometrics can address. We will also look at the types of data we can work with, and discuss the transformation and manipulation of this data. This week will be focussing on the single regression model.

The Classical Linear Regression Model

This week we shall be focussing on the Classical Linear Regression Model as well as the classical linear regression model. We will explore the assumptions of the OLS approach and see why we need those assumptions. We shall also discuss the Multiple Linear Regression Model and consider why we use linear algebra.

Interpretation of the Ordinary Least Squares Parameters

This week we are going to discuss the interpretation of the Ordinary Least Squares parameters as well as the goodness of fit statistics: R-squared and the adjusted R-squared. We will also consider some CAPM introductory results, model building and determinants of bus driving in the USA.

Capital Asset Pricing Model

This week we are going to focus on a real example of estimating and interpreting the Capital Asset Pricing Model with R. We are also going to look at data description, manipulation, estimations of the CAPM and interpretations of the estimated parameters. We shall discuss expanding the model using the three factors Fama and French (1993) model.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for students looking to explore econometrics and financial modeling with R

Provides a solid foundation suitable for quantitative analysts and finance professionals

Taught by an experienced and esteemed instructor in econometrics

Provides an understanding of econometric methodology and statistical tools necessary for data analysis

Builds a strong understanding of the classical linear regression model, multiple regression, and the capital asset pricing model

Offers hands-on practice with real-world data and R, enhancing the applicability of the learned concepts

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 The Classical Linear Regression Model with these activities:

Read Introductory Econometrics: A Modern Approach

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Gain a solid foundation in econometrics by reviewing a comprehensive textbook.

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Read the relevant chapters for each topic covered in the course.
Highlight key concepts and make notes.
Complete the end-of-chapter exercises.

Follow online tutorials on econometrics software

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Increase your proficiency in econometrics software by following online tutorials.

Browse courses on R

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Identify the software you want to learn.
Find online tutorials for the software.
Follow the tutorials step-by-step.
Practice using the software on your own.

Solve practice econometrics problems

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Reinforce your understanding of econometric concepts and techniques by working through practice problems.

Browse courses on Econometrics

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Identify the relevant concepts and techniques for the problem.
Set up the problem and solve it using the appropriate methods.
Interpret your results and draw conclusions.

Five other activities

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Show all eight activities

Create a presentation on a specific econometric topic

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Deepen your understanding of an econometric topic by researching and presenting on it.

Browse courses on Time Series Analysis

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Choose a topic and research it thoroughly.
Organize your thoughts and create an outline.
Develop visual aids and prepare your presentation.
Practice your presentation and deliver it to an audience.

Attend an econometrics conference or workshop

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Expand your knowledge and network by attending an econometrics conference or workshop.

Browse courses on Econometrics

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Identify an econometrics conference or workshop.
Register for the event.
Attend the sessions and participate in discussions.
Network with other attendees.

Participate in an econometrics workshop

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Develop your econometric skills by participating in a hands-on workshop.

Browse courses on Econometrics

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Identify an econometrics workshop.
Register for the workshop.
Attend the workshop and participate in the activities.
Apply what you learned in the workshop to your own work.

Estimate an econometric model using real data

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Apply your econometric skills to a real-world problem by estimating an econometric model.

Browse courses on Regression Analysis

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Define the research question and collect the necessary data.
Choose an appropriate econometric model.
Estimate the model parameters and interpret the results.
Write a report summarizing your findings.

Volunteer as a research assistant for an econometrician

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Gain practical experience and make valuable connections by volunteering for an econometrician.

Browse courses on Econometrics

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Identify an econometrician who is looking for volunteers.
Contact the econometrician and express your interest.
Assist the econometrician with their research projects.
Attend meetings and presentations with the econometrician.

Career center

Learners who complete The Classical Linear Regression Model will develop knowledge and skills that may be useful to these careers:

Statistician

A Statistician uses statistical models to analyze data and make recommendations. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course builds a strong understanding of regression models, which are essential for analyzing data and making sound recommendations. Additionally, the course's focus on data transformation and manipulation will be useful for cleaning and preparing data for analysis.

See salaries and explore the career path for Statistician

Economist

An Economist uses statistical models to analyze economic data and make recommendations on economic policy. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course builds a strong understanding of regression models, which are essential for analyzing economic data and making sound economic policy decisions. Additionally, the course's focus on data transformation and manipulation will be useful for cleaning and preparing economic data for analysis.

See salaries and explore the career path for Economist

Data Scientist

A Data Scientist uses statistical models to analyze data and make recommendations. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course builds a strong understanding of regression models, which are essential for analyzing data and making sound recommendations. Additionally, the course's focus on data transformation and manipulation will be useful for cleaning and preparing data for analysis.

See salaries and explore the career path for Data Scientist

Financial Modeler

A Financial Modeler uses statistical models to build financial models. The Classical Linear Regression Model course at Queen Mary University of London will be useful. The course builds a strong understanding of regression models, which are essential for building financial models. Additionally, the course's focus on data transformation and manipulation will be useful for cleaning and preparing financial data for modeling.

See salaries and explore the career path for Financial Modeler

Business Analyst

A Business Analyst uses statistical models to analyze business data and make recommendations on business strategies. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course builds a strong understanding of regression models, which are essential for analyzing data and making sound business strategy decisions. Additionally, the course's focus on data transformation and manipulation will be useful for cleaning and preparing business data for analysis.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

A Quantitative Analyst uses statistical models to analyze data and develop trading strategies. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course helps build a strong understanding of regression models, which are essential for analyzing data and developing trading strategies. Additionally, the course's focus on interpreting results will be useful for communicating the findings of quantitative analysis.

See salaries and explore the career path for Quantitative Analyst

Data Analyst

A Data Analyst uses statistical models to analyze data and extract insights. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course builds a strong understanding of regression models, which are essential for analyzing data and extracting insights. Additionally, the course's focus on data transformation and manipulation is essential for cleaning and preparing data for analysis.

See salaries and explore the career path for Data Analyst

Operations Research Analyst

An Operations Research Analyst uses statistical models to improve operational efficiency. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course builds a strong understanding of regression models, which are essential for analyzing operational data and making recommendations on improving operational efficiency. Additionally, the course's focus on data transformation and manipulation will be useful for cleaning and preparing operational data for analysis.

See salaries and explore the career path for Operations Research Analyst

Actuary

An Actuary uses statistical models to assess risk and make recommendations on insurance premiums. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course helps build a strong understanding of regression models, which are essential for assessing risk and making sound insurance premium decisions. Additionally, the course's focus on data transformation and manipulation will be useful for cleaning and preparing risk data for analysis.

See salaries and explore the career path for Actuary

Risk Manager

A Risk Manager uses statistical models to assess risk and make recommendations on risk management strategies. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course builds a strong understanding of regression models, which are essential for assessing risk and making sound risk management decisions. Additionally, the course's focus on data transformation and manipulation will be useful for cleaning and preparing risk data for analysis.

See salaries and explore the career path for Risk Manager

Market Researcher

A Market Researcher uses statistical models to analyze market data and make recommendations on marketing strategies. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course builds a strong understanding of regression models, which are essential for analyzing market data and making sound marketing strategy decisions. Additionally, the course's focus on data transformation and manipulation will be useful for cleaning and preparing market data for analysis.

See salaries and explore the career path for Market Researcher

Financial Analyst

A Financial Analyst uses statistical models to analyze financial data and make recommendations on investments. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course helps build a strong understanding of regression models, which are essential for analyzing financial data and understanding investment decisions. Additionally, the course's focus on interpreting results is essential for communicating the findings of financial analysis.

See salaries and explore the career path for Financial Analyst

Investment Analyst

An Investment Analyst uses statistical models to evaluate companies' financial data and make investment recommendations. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course helps build a strong understanding of regression models, which are essential for analyzing financial data and making sound investment decisions. Additionally, the course's focus on data transformation and manipulation will be useful for cleaning and preparing financial data for analysis.

See salaries and explore the career path for Investment Analyst

Software Engineer

A Software Engineer uses statistical models to develop software. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course helps build a foundational understanding of regression models, which are used in machine learning algorithms inside software.

See salaries and explore the career path for Software Engineer

Web Developer

A Web Developer uses statistical models to develop websites. The Classical Linear Regression Model course at Queen Mary University of London may be useful. The course helps build a foundational understanding of regression models, which are used in machine learning algorithms inside websites for search functionality and ad targeting.

See salaries and explore the career path for Web Developer

The Classical Linear Regression Model

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