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Avis et commentaires pour d'étudiants pour The Classical Linear Regression Model par Queen Mary University of London

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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....
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1 - 3 sur 3 Avis pour The Classical Linear Regression Model

par Ivana P

28 juin 2022

When I started this course, I started it with the idea that I already had some theoretical knowledge of linear regression, and that was I think one of the reasons I passed this course, rather than the course itself. The lectures and the requirements were not compatible most of the time, where the requirements were on a much higher level than the lectures we were given to read and practice. The lectures in this course and the given material were also incompatible - somewhere the reading material was too general, where for instance the matrices parts were too hard and without proper explanation.

Also, the Lab part with the R studio.... Without any explanation and tutorial, we were given the console and expected to "run" and perform the given tasks. This was the place where I came without previous knowledge of R, so a small link to a tutorial or something similar (like explanation what exactly to do) would be much helpful.

par Yngrid C

7 nov. 2022

The structure and content of the course is very good. However, it has administrative problems. One depends on the qualification of the companions, which is not quick or guaranteed. And the instructor's signature does not appear on the certificate.

par Travis D A

4 oct. 2022

there is a Peer-Reviewed Assignment at the end of the course. You can submit your project a week early, and still be listed as "Overdue" because your assignment hasn't been graded yet. Expect to cancel your Specialization Subscription while you wait for the grading to eventually get around to you.