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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

3,206 個評分
541 條評論


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....



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.


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.


101 - 回归模型 的 125 個評論(共 521 個)

創建者 Daniel A S


Very good and complete, the professor is very clear in his explanations and very helpful for data science applications.

創建者 Ekaterina S


It was a very usefull course. It is a very good approach to the theme - the main essence without much math difficulty.

創建者 Ivana L


One of the most valuable course in series. Also one of the hardest, expecially if you are newbie to regression models.

創建者 Marco B


very useful! it provides both theoretical framework and practical skills!

it helped me improve my daily data analysis!

創建者 weitinglin


nice and practical class! I think if provide some recommend reading may create more deeper insight in regression

創建者 Joseph R


A very well organized course with nice simple explanations and introductions into the world of regression models

創建者 Gregorio A A P


Excellent, but I would be grateful if you could translate all your courses of absolute quality into Spanish.

創建者 marcelo G


Outstanding material with different levels of difficulty and depth on the subject. Great source material.

創建者 Greg A


I thought I understood regression, but this course help me gain new insights and really sharpen my skills

創建者 Nilrey J D C


Very concise and informative. This gives me a good review in my college statistics regression subjects


創建者 Erika G


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

創建者 Hewan D


I am so happy taking this course. This will open loads of doors for my data analysis. Thank you so much.

創建者 Carlos M


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

創建者 Robert W S


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

創建者 André C L


very good practical approach, with good theoretical coverage of most important principles of regression

創建者 Irene R


Very good course, i learnt a lot. Looking forward to take more advanced courses on regression models.

創建者 Berthold J


Very good lecture and also decent level of difficulties that requires to think/read additional stuff.

創建者 Ewa W


Very Challenging Course

Very well presented

Great material/source of information for study

Loved It !

創建者 hyunwoo j


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

especially 'manipulate' package is very useful

創建者 Thomas A


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

創建者 Сетдеков К Р


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

創建者 Carlos A D V


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

創建者 Ahmed M K


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

創建者 Muzaffar H


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

創建者 Laura N M


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