Linear Regression for Business Statistics(으)로 돌아가기

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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...

Dec 21, 2017

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.

Jul 12, 2019

I learned a lot.I gain confidence in analyzing data in Excel.I am happy that I have successfully completed it with simple understanding given on each topic.It was great help.Thank you very much

필터링 기준:

교육 기관: Shady N S T

•Dec 27, 2018

I love this Specialization, and look forward to completing it! It's an amazing journey in Statistics with Excel! If you're a beginner in Statistics, you might see the whole Specialization a bit difficult and will need to look for a Statistics course. The instructor is also a huge plus!

교육 기관: Rishav k

•Dec 26, 2017

Could have been more challenging. Moderate courses are easy to pass but doesn't bring about extreme competitive spirit. Some peer graded assignments could be better.

교육 기관: Jordi M

•Nov 17, 2018

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

교육 기관: Padmapriyadarshini

•Jan 03, 2019

Excellent course! added a lot to my understanding

교육 기관: Nazmus S S

•Jan 30, 2019

VERY GOOD COURSE. Professor is great

교육 기관: Ketevani A

•Nov 13, 2018

Excellent course, perfectly planned and explained. Great mentor. Thank you so much.

교육 기관: ARVIND K S

•Mar 16, 2019

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

교육 기관: shubhangi P M

•Mar 20, 2019

Thanks S

교육 기관: Karen S Z

•Aug 01, 2018

Excellent introduction to Linear Regression. As you progress, you learn how to use dummy (category) variables as well as interaction variables. Examples are explained in detail so you can understand how it works. This course isn't about understanding all the detailed math & theory, but explains enough to you understand (at a high level) what you're doing and why. Then, you learn how to do it in Excel. I really enjoyed this class!

교육 기관: Sofia L

•Aug 01, 2018

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

교육 기관: Esther K

•Aug 13, 2018

Excellent course!

교육 기관: Achyut D U

•Oct 10, 2018

Very nicely structured and implemented

교육 기관: Abdullatif A

•Oct 18, 2018

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

교육 기관: Ramasubramaniyam S

•Oct 20, 2018

Completion of the four courses in the specialization makes me feel more interested and confident in the vast art of Business Statistics and Analytics

교육 기관: Scott L

•Sep 16, 2018

Though I was briefly introduced to linear regression in my graduate studies, I found the structure and presentation of this material to be more helpful to learning and understanding the material AND it's use cases.

교육 기관: John D I

•Oct 01, 2018

Great course, very thorough with very good examples and explanations.

교육 기관: Siddharth S

•Jan 18, 2018

Very well structured course. Sharad is an excellent teacher. Learnt a lot from this course.

교육 기관: William B

•Dec 21, 2017

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.

교육 기관: Avi G

•Sep 20, 2017

A very good course on Regression statistics with examples from the business sector that can be used later in work or life. Prof. Borle explained all topics slowly and clearly. i would extend the course to more Regression topics (residuals@ more)

Thank you prof. Borle.

교육 기관: jorge l

•Jun 21, 2018

Good course, examples are very constructive and instructor presentations are vey good

교육 기관: Parul

•Sep 17, 2017

excellent content.

교육 기관: Songly H

•Nov 23, 2017

Great course, easy-to-understand teaching approaches!

교육 기관: Olivia Z

•Jul 05, 2018

very east to understand and quick to learn. strong recommendation!

교육 기관: GAYATHRI S

•Jan 02, 2018

It was great!

교육 기관: Akshay H

•May 05, 2017

Best Course to understand Linear Regression.Thank you team Rice University for simple yet effective course on Linear Regression.Do enroll for this course if you want to understand linear regression thoroughly.