Вернуться к Linear Regression for Business Statistics

4.8
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Оценки: 1,273
Рецензии: 206

## О курсе

Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. This is the fourth course in the specialization, "Business Statistics and Analysis". The course introduces you to the very important tool known as Linear Regression. You will learn to apply various procedures such as dummy variable regressions, transforming variables, and interaction effects. All these are introduced and explained using easy to understand examples in Microsoft Excel. The focus of the course is on understanding and application, rather than detailed mathematical derivations. Note: This course uses the ‘Data Analysis’ tool box which is standard with the Windows version of Microsoft Excel. It is also standard with the 2016 or later Mac version of Excel. However, it is not standard with earlier versions of Excel for Mac. WEEK 1 Module 1: Regression Analysis: An Introduction In this module you will get introduced to the Linear Regression Model. We will build a regression model and estimate it using Excel. We will use the estimated model to infer relationships between various variables and use the model to make predictions. The module also introduces the notion of errors, residuals and R-square in a regression model. Topics covered include: • Introducing the Linear Regression • Building a Regression Model and estimating it using Excel • Making inferences using the estimated model • Using the Regression model to make predictions • Errors, Residuals and R-square WEEK 2 Module 2: Regression Analysis: Hypothesis Testing and Goodness of Fit This module presents different hypothesis tests you could do using the Regression output. These tests are an important part of inference and the module introduces them using Excel based examples. The p-values are introduced along with goodness of fit measures R-square and the adjusted R-square. Towards the end of module we introduce the ‘Dummy variable regression’ which is used to incorporate categorical variables in a regression. Topics covered include: • Hypothesis testing in a Linear Regression • ‘Goodness of Fit’ measures (R-square, adjusted R-square) • Dummy variable Regression (using Categorical variables in a Regression) WEEK 3 Module 3: Regression Analysis: Dummy Variables, Multicollinearity This module continues with the application of Dummy variable Regression. You get to understand the interpretation of Regression output in the presence of categorical variables. Examples are worked out to re-inforce various concepts introduced. The module also explains what is Multicollinearity and how to deal with it. Topics covered include: • Dummy variable Regression (using Categorical variables in a Regression) • Interpretation of coefficients and p-values in the presence of Dummy variables • Multicollinearity in Regression Models WEEK 4 Module 4: Regression Analysis: Various Extensions The module extends your understanding of the Linear Regression, introducing techniques such as mean-centering of variables and building confidence bounds for predictions using the Regression model. A powerful regression extension known as ‘Interaction variables’ is introduced and explained using examples. We also study the transformation of variables in a regression and in that context introduce the log-log and the semi-log regression models. Topics covered include: • Mean centering of variables in a Regression model • Building confidence bounds for predictions using a Regression model • Interaction effects in a Regression • Transformation of variables • The log-log and semi-log regression models...

## Лучшие рецензии

BB

21 апр. 2020 г.

Wonderful Course having in depth knowledge about all the topics of regression analysis. Instructor is very much clear about the topic and having good teaching skill. Method of teaching also very good.

WB

20 дек. 2017 г.

I have found Course 3 and 4 of this specialization to be challenging, but rewarding. It has helped me build confidence that I can do just about anything with data provided to increase positive impact.

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## 51–75 из 202 отзывов о курсе Linear Regression for Business Statistics

автор: Michael H

17 нояб. 2019 г.

This was the most useful and insightful class in the specialization so far, at least for me and what I was looking to get out of these courses

автор: Priyank S ( - M S

29 авг. 2020 г.

An excellent explanation of linear regression with plenty of examples. I would recommend to anyone who is interested to learning regression.

автор: DOMINIC I B

30 мар. 2021 г.

Simply the best teaching and practicing approaches to gaining useful and beneficial lessons in Linear Regression for Business Statistics

автор: Srishti k

8 окт. 2020 г.

A very informative, easy to understand, exceptionally well taught statistics. It will be really helpful for all my future assignments.

автор: Tori G

5 окт. 2019 г.

I thoroughly enjoyed this course. The instructors were very clear and concise thereby making the course easy to follow and understand.

автор: Deleted A

6 янв. 2018 г.

Amazing !! The concepts were explained with clarity which have immediate applicability. Can't wait to apply them into my organisation.

автор: Jordi M

17 нояб. 2018 г.

Excelent course to gain a deep and solid understanding about linear regressions. The course is very focused on this, which is great!!

автор: Sagun R

15 мая 2020 г.

Lots of eye openers information with deep insights, really a great course. Will help in business to implement in real life scenario.

автор: Sofia L

1 авг. 2018 г.

I loved this course and the videos and lecture were clearly explained. Doing the Regression model was a whole new experience for me!

автор: Anar S

16 мая 2017 г.

Well designed and business friendly explanation. Thanks to Sharad Borle I gained much deeper knowledge on linear regression.

автор: Andrew A

14 сент. 2019 г.

Mr. Bodle presents the material in a very organized and understandable fashion. Well worth the time taking this class.

автор: Carlos A M V

13 окт. 2020 г.

Amazing, I learn how to use Microsoft Excel and Linear Regression, that is a useful subject in business. Thanks a lot.

автор: Abdullatif A

18 окт. 2018 г.

The course is essential for those who have no background in linear regression. The Lecturer of this course is amazing.

автор: Pratyush A

9 авг. 2020 г.

Excellent course. Gives great insight about regression and it's application. Must do course for any business analyst.

автор: Abosede G E

14 авг. 2021 г.

I really enjoyed the course. the tutor made it so easy for beginners to understand with real world relevant examples

автор: LynchWong

10 авг. 2017 г.

give a glimpse of regression without math/stats , suit for those who purely focus on LR application in business area

автор: Joaquin M D

13 окт. 2020 г.

Very good teacher. I've learned a lot. The quizzes were just at the right difficulty to measure what you've learned

автор: Abhijit S

15 янв. 2020 г.

Good to learn and gain understanding on Linear regression model , dummy variable as well as log transformation.

автор: Ghazi T A

18 апр. 2020 г.

Excellent course for anyone aiming to build a strong understanding of regression and building relevant models.

автор: Sara A

26 мая 2020 г.

it as a great course and the main point the simplification for all business cases and how to learn the tricks

автор: ARVIND K S

16 мар. 2019 г.

Marvellous course! Gives a very good idea of linear regression. A must for students and practicing managers.

автор: Gregorio A A P

26 авг. 2017 г.

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

автор: Rig B

23 сент. 2020 г.

the course is worth it!! this data analytics can be made so comfortable, was commendable. full points

автор: Yi-Hsuan L

10 сент. 2021 г.

Great course! Would be good to elaborate more on the derivation of log-log/ semi-log transformation.

автор: Panneer S

11 окт. 2017 г.

Very well designed and good examples illustrate the Regression model. Thanks for the Opportunity,