Chevron Left
Voltar para Modelos Regressivos

Comentários e feedback de alunos de Modelos Regressivos da instituição Universidade Johns Hopkins

4.4
2,704 classificações
456 avaliações

Sobre o curso

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing....

Melhores avaliações

KA

Dec 17, 2017

Excellent course that is jam-packed with useful material! It is quite challenging and gives a thorough grounding in how to approach the process of selecting a linear regression model for a data set.

BA

Feb 01, 2017

It really helped me to have a better understanding of these Regression Models. However, I've noticed that there is a video recording repeated: Week 3, Model Selection. Part 3 is included in Part 2.

Filtrar por:

51 — 75 de {totalReviews} Avaliações para o Modelos Regressivos

por Do H L

Jun 17, 2016

This course gives a very thorough and rigorous treatment to the topic of regression models.

It teaches you how to derive from the ground, how regression models are made and how to interpret every information available through regression models.

Although the lectures are very lengthy and dry, the course offers a very rich well of information that is not readily available else where.

Thanks to Brian Caffo for the wealth of information about regression models taught through this course!

por Pragya k

May 22, 2017

Exceptional Learning!

por Andrew V

May 15, 2017

Nicely presented and understandable course with a challenging an interesting project.

por Fernando M

Sep 04, 2017

Love it

por Joe B

Jan 30, 2016

Great course with a thorough introduction to regression and linear model.

por Erika G

Jun 28, 2016

I had a lot of fun in this course. The exercises in the text and quizzes help me understand the concepts

por Dan K H

Mar 03, 2017

Once again an excellent course, thank you!

por Shivanand R K

Jun 21, 2016

Great and Excellent thoughts and course material.

por Emanuele M

Aug 11, 2016

It's a great course and tought very well. It required effort, you apply many of previously teach concept and requires a lot of excercise

por Divvya.T

Nov 02, 2017

goood course

por Sandra Y M B

Oct 09, 2016

Everything you need to know to have a clear understanding of regression models and learn how to use their basic functions in R.

por Aisha H

Feb 02, 2016

Loved the course and the content. Only critique is that I would have liked to have a lecture about transformations, and interpretation of transformed data coefficients.

por Henrique d S A

Feb 27, 2018

Nice course it will help me a lot ! Thank you!

por Robert W S

Nov 22, 2016

Excellent course. Might be difficult to get full value of information without prior exposure/background.

por Carlos R O

Dec 26, 2016

Very Nice!

por Yi-Yang L

May 10, 2017

Good

por UDBODH

May 14, 2016

Had fun

por Sindre F

Aug 01, 2016

Interesting and important course!

I don't think this course is suitable for beginners. You need to know this stuff before you take the course. Works well as a refresher.

por Rodrigo P

Aug 04, 2017

Very interesting topics and discussions, but not easy to understand.

por Rafael L G

Jun 01, 2017

Great

por Tiberiu D O

Sep 22, 2017

A very good course!

por yefu w

May 27, 2017

Great course!

por Govind N

Jun 14, 2016

I learnt regression models from this course.

por Edgar I

Jan 01, 2017

Muy intuitivo y con excelentes ejemplos!

por Christian B

May 27, 2017

One of the better classes of the specialization. I found the quizzes quizzes (in particular week 3 and 4) quite challenging. I took the ML class before, which I do not recommend. Take this class before the ML class.