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Regression Models

This course is a part of Data Science, a 11-course Specialization series from Coursera.

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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Johns Hopkins University

Rating 4.0 based on 463 ratings
Length 5 weeks
Starts Jun 29 (6 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brian Caffo, PhD, Jeff Leek, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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What people are saying

According to other learners, here's what you need to know

data science in 27 reviews

Regression Models is the seventh course in the Data Science specialization.

The knowledge gained in this course has tremendous value in the data science workplace.

Good Course The best course in my mind, but I am chocked about how Data Science people approach regression type of problems, it is almost 100% data mining and no theory!!

The Regression Models is an excellent course for a beginner.I would recommend the enthusiastic students for a great start in Data science.

Extremely valuable content to my pursuit of a career in data science.

Very good for anyone wanting to get into the field of Data Science using R Love it I like Brian Caffo's lectures.

One of the most required skill set in the field of data science.

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statistical inference in 25 reviews

As with Statistical Inference, it is taught by Brian Caffo and suffers from the same issues as the preceding course.

Just like the previous course in the specialization path (Statistical Inference) the course delves into some relevant topics however it doesn't feel as properly structured.

Very detailed and exhaustive course If you thought that the previous course (Statistical Inference) with Brian Caffo was a horrible experience -- think twice and get ready for Regression Models.

Once again, this and Statistical Inference courses are very challenging to truly completed with insightful understanding.

I was optimistic about this class because it started out fixing some of the pedagogical mistakes the professor made in Statistical Inference, but by the time we got to week 3, it was pretty clear that the course was trying to accomplish too much in 4 weeks, and instead of focusing on the most important parts of regression and making sure they were taught well and understood clearly, I feel the course tried to do far too much.

With the first few videos, I was concerned I would be re-living the nightmare that was the Statistical Inference course.

To summarize Statistical Inference: I hated it.

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brian caffo in 19 reviews

Another problem was the instructor, Brian Caffo, who seems like a good guy and good researcher, but not an effective teacher.

Brian Caffo is an excellent professor.

Thank you Brian Caffo!

Brian Caffo's lectures in Statistical Inference were good; in this course they seem to veer left and right rather than get straight to the essence of whatever subject he is lecturing about.

I loved studying Regression Models taught by Prof. Brian Caffo.

Once again the concepts are simple and the math not so hard, yet I had to do a lot of research outside the course to be able to understand these simple concepts and derive the not so hard mathematics.Brian Caffo is clearly brilliant and, I would say, seem to be a good lad too, but something is missing.

The quality of the lectures was very high and the information interesting, so compliments to Dr. Brian Caffo on that.

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highly recommend in 9 reviews

I highly recommend avoid this course, and instead go through the R guide on linear regression; in the end, I used those to get through this course.

I highly recommend new people for this course Expects a level of statistical knowledge already.

I highly recommend it!

Highly recommended course and specialization,There are so many unanswered questions, so many new relationships to uncover.

I highly recommend both of those courses.

Highly recommended.

I highly recommend this course.

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difficult to follow in 7 reviews

He is very unengaging, difficult to follow, and rushes through lectures.

I did find it difficult to follow and understand some of the materials.

I am no used to this educational system so I find difficult to follow without any proof or demonstration of the mathematical tools.

I again found many of the lectures to be difficult to follow along, there seems to be lots of different styles of videos in the way that the person was superimposed on the slides.

Videos were very difficult to follow along with.

The instructor gives too much information and is difficult to follow, some information is even trivial.

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machine learning in 7 reviews

Also it would benefit if there was a clear message coming through, like Machine Learning course where things follow a order.If it was not by the book of Mr.Field with Statistics in r, I would never be able to understand what was really being said in this course.

I feel like I only began to understand the material once I finished the course project, and even then I have no idea how regression models work.I'm now going to be taking a month or 2 off from the courses to read more about statistical inference and regression models on my own, since I feel completely unprepared for the upcoming Machine Learning course.

I have the opportunity to explore all the plotting concept and apply them in regression models arena.Good to take this course to step in the concept of machine learning.

Good foundation in the Data Science Certification for Practical Machine Learning.

There are 3 areas that I would like to dig deeper so far: Statistical Inference, Regression Models and Practical Machine Learning (perhaps + Deep Learning).

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

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.

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Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Counseling Theories & Models Part-Time Faculty $17k

Trainer of Evidence Based Models $54k

Federal - Regression Tester $55k

REGRESSION TESTER $61k

Regression Analyst $69k

Senior Functional and Regression QA Analyst $92k

Assistant Adjunct Professor Statistical Models $122k

Risk Analytics Tools and Models Program Manager $136k

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Coursera

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Johns Hopkins University

Rating 4.0 based on 463 ratings
Length 5 weeks
Starts Jun 29 (6 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brian Caffo, PhD, Jeff Leek, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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