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學生對 莱斯大学 提供的 Linear Regression for Business Statistics 的評價和反饋

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1,273 個評分
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206 條評論

課程概述

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...

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BB

2020年4月21日

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

2017年12月20日

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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126 - Linear Regression for Business Statistics 的 150 個評論（共 202 個）

創建者 Lalit G

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2019年8月5日

Awesome course...Very interesting to learn.

創建者 Solicia X

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2019年11月21日

Had a better understanding on regression.

創建者 Muhammad H B R

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2020年7月5日

ONE OF THE BEST COURSE I HAVE EVER DONE

創建者 lanjun l

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2020年5月31日

This is a very good and useful course.

創建者 Vanshika G

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2020年5月6日

great content. really enjoyed learning

創建者 Achyut D U

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2018年10月10日

Very nicely structured and implemented

創建者 Nazmus S S

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2019年1月29日

VERY GOOD COURSE. Professor is great

創建者 Jesus V

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2020年4月12日

Excellent course! best of the best!

創建者 Aman G

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2020年7月26日

Awesome Faculty and Course Content

創建者 Mahipal G

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2020年6月6日

Best course to learn regression

創建者 Ayush B

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2019年8月12日

Excellent course for beginners

創建者 Andras F

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2018年2月21日

Very useful course, thank you!

創建者 Elmer P

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2020年10月12日

Great educational experience!

創建者 Fadumo L A

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2020年5月7日

It was a very useful course.

創建者 Andrew B

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2017年9月17日

Easy to understand and apply

創建者 vinay b

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2017年9月10日

Well structured course work

創建者 Abeythunga, S

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2020年9月25日

Great learning experience.

創建者 Olivia B

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2018年3月26日

Very well explained and ea

創建者 EJIKE D U

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2020年9月15日

Excellent course content.

創建者 Dr. M R P

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2020年5月20日

VERY INTERESTING COURSE

創建者 Taruraj A

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2018年4月17日

Excellently explained!

創建者 Christo M

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2020年2月21日

Enjoyed this course.

創建者 Victor K

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2018年10月24日

Great explanations!!

創建者 SHIVAM A

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2020年4月13日

Very useful Course!

創建者 Antonio R d G F

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2017年10月29日

Amazing Professor !

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