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學生對 约翰霍普金斯大学 提供的 回归模型 的評價和反饋

4.4
3,255 個評分
559 條評論

課程概述

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....

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KA
2017年12月16日

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
2017年1月31日

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.

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126 - 回归模型 的 150 個評論(共 541 個)

創建者 hyunwoo j

2016年3月16日

easy to understand and full of new idea about using R.

especially 'manipulate' package is very useful

創建者 Thomas A

2019年10月12日

A good review of regression that allows the student to apply practical implementations in R Studio

創建者 Кирилл С

2019年9月30日

It was rather hard and time consuming, but I learned a lot about poisson and binomial regressions.

創建者 Carlos A D V

2018年7月26日

The best course of the Data Science Specilization until now and by far. Very practical and useful!

創建者 Ahmed M K

2017年6月20日

One of the best courses on Coursera for sure. Thank you so much. Regression has never been easier.

創建者 Muzaffar H

2017年10月14日

A very good data analysis course, highly useful for quantitative method and empirical findings.

創建者 Laura N M

2020年8月27日

The course present a good overview for linear models, including the generalized linear models.

創建者 Ignacio O

2018年7月30日

Excellent course!

I learned a lot of techniques and understood the basics of Regression Models.

創建者 Guilherme B F

2018年3月22日

Really good. Easy to follow and great even if you just need a refresher in regression models.

創建者 Arcenis R

2016年1月18日

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

創建者 ric j n

2017年8月6日

The course is comprehensive in its presentation. Ideas can be easily grasp and replicated.

創建者 Georgios P

2019年3月7日

Great course for beginners, but definitely not for people with no mathematical background!

創建者 sneha

2019年1月23日

Amazing course ! finally I have learned how to implement regression in real world analysis

創建者 Carlos A R C

2020年9月23日

Excellent course. Best of all the Data Science specialization. Good, very good professor.

創建者 Bruno R S

2019年3月4日

A deep review on linear, logistic and regression models. The critical tool for modelling.

創建者 Walter T

2016年12月8日

A well defined learning path to understand the fundation of machine learning techniques.

創建者 Purificación V

2019年11月13日

Es un gran curso para aprender, junto con el resto de los cursos de la especialización.

創建者 Channaveer P

2019年10月12日

Amazing course... good learning experience. Very useful for my role in my Organization.

創建者 Juan P L R

2020年11月26日

Great introduction to regression models, and its application in R. Highly recommended.

創建者 Andrew V

2017年5月14日

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

創建者 BAUYRJAN J

2017年1月31日

Excellent course, but you have to use other materials from different courses as well.

創建者 Johan V M

2020年8月9日

Excellent course! I am totally looking forward to learn a lot more on this subject.

創建者 Sergio A

2017年12月31日

We learn some basic econometrics in this class and how to do basic regression mdels

創建者 Sandhya A

2018年6月2日

Learned a lot about various regression model, concept like fitting and overfitting

創建者 Christian H

2017年8月22日

Great course; practical introduction to regression models at the university level.