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ジョンズ・ホプキンズ大学(Johns Hopkins University) による Regression Models の受講者のレビューおよびフィードバック

4.4
2,796件の評価
470件のレビュー

コースについて

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....

人気のレビュー

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.

フィルター:

Regression Models: 251 - 275 / 450 レビュー

by Shivanand R K

Jun 21, 2016

Great and Excellent thoughts and course material.

by William A

May 17, 2018

Loved it

by YANAN D

May 24, 2019

Really Helpful

by Illich M

Jun 16, 2017

Tough course! I had to take it a couple of times to understand it.

by Nathan M

Jun 11, 2016

Quite useful!

by Deliang L

Nov 02, 2016

Very helpful!

by Yadder A G

Mar 28, 2019

The course was incredible. You can learn a lot of skills about regression models and even more. It would be incredible if the course could have more examples or little excercises.

by Shashwat K

Oct 15, 2018

goof course.

by 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.

by Lowell R

Oct 07, 2016

Excellent overview of a very broad and complex topic with plenty of useful applications within R. The course project does an outstanding job at teaching the pitfalls of omitted variable bias.

by Wesley E

Feb 15, 2016

Great introduction and plenty of resources for more in depth study.

by Carlos M

Jul 11, 2017

I learned a lot of theory and practical applications of residuals. The swirl assignments were great too!

by gerson d o

Nov 24, 2019

Wonderful!!!!

by Arcenis R

Jan 18, 2016

This course is packed with great lessons and Prof. Caffo puts it all together very cogently.

by Tine M

May 11, 2018

Definitely a difficult course but a very interesting one.

by Daniel J R

Dec 19, 2018

Quite practical. It does encourage one to follow-up with a more advanced course.

by David J B

Feb 19, 2019

Probably the most conceptually challenging and practically useful course in the JH data science certification series (so far... I have a few more courses to complete).

by Manny R

Mar 22, 2019

Really Fun Course. There is a lot to learn in this topic and this could be studied for a lifetime. I feel like I could apply this to discover solutions for issues at work.

by Dora M

Mar 30, 2019

Good class.

by Paul R

Mar 13, 2019

Relatively, this is one of the best courses and lecturers of the specialization, Brian delivers clear, thorough and well-paced lectures. These lectures on statistics, regression and machine learning are where the rubber hits the road after a lot of prep work to learn R and principles/tools of data science taught in earlier classes.

by Alzum S M

Jan 08, 2019

Very much thank you for teaching me such an awesome course

by Nathalie D

Mar 11, 2019

Great course, but please check those subtitles that are occasionally completely off!

by Roopak M

Sep 10, 2018

Nice course that helps make your foundations in regression modelling strong. The complexity of the course project can be increased to a more difficult level.

by Pooia L

Sep 13, 2018

This is a very nice course provided you study a lot for it

by Daniiar B

Sep 27, 2018

Very hard to understand