返回到 Linear Regression for Business Statistics

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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

筛选依据：

创建者 Achyut D U

•Oct 10, 2018

Very nicely structured and implemented

创建者 Nazmus S S

•Jan 30, 2019

VERY GOOD COURSE. Professor is great

创建者 Ayush B

•Aug 12, 2019

Excellent course for beginners

创建者 Andras F

•Feb 21, 2018

Very useful course, thank you!

创建者 Andrew B

•Sep 17, 2017

Easy to understand and apply

创建者 vinay b

•Sep 10, 2017

Well structured course work

创建者 Olivia B

•Mar 26, 2018

Very well explained and ea

创建者 Taruraj A

•Apr 17, 2018

Excellently explained!

创建者 Josefina K S

•Oct 24, 2018

Great explanations!!

创建者 Antonio R d G F

•Oct 30, 2017

Amazing Professor !

创建者 Rajan M

•Jul 21, 2017

Very well explained

创建者 MONTCHO H M

•Jul 25, 2018

interesting course

创建者 Parul

•Sep 17, 2017

excellent content.

创建者 Esther K

•Aug 13, 2018

Excellent course!

创建者 GAYATHRI S

•Jan 02, 2018

It was great!

创建者 Tom B

•Oct 03, 2017

Great Course.

创建者 EDILSON S S O J

•May 31, 2019

Nice course!

创建者 jittu s

•May 16, 2019

great course

创建者 Cristiano S

•Sep 09, 2017

Excellent!

创建者 Vitalii S

•Apr 26, 2019

practical

创建者 shubhangi P M

•Mar 20, 2019

Thanks S

创建者 Colin P

•May 03, 2018

I found this course the most challenging of the courses in this certificate program, but also the most interesting b/c it the info. can be applied to real world scenarios. Though I do feel I know "enough to be dangerous". There is a lot of depth to linear regression techniques, which this course doesn't cover. But it did open my eyes to the power and possibilities of using linear regression techniques on real world problems.

创建者 Marcos P

•Jun 06, 2018

Phenomenal course. A little more in-depth explanations and more examples for the concepts introduced in the last two weeks would have been nice though. In week 3 and 4, I found it challenging to go so quickly over so many new concepts all of a sudden. But still, I would really recommend taking this course, I found it useful.

创建者 Yaron K

•Apr 13, 2017

An in depth explanation of how to use Excel for Linear Regression and what the Output values in Excel's Regression mean. Note that the transcripts/subtitles contain many errors, which can be problematical for the hard of hearing or non English speakers, which is why I gave the course only 4 points.

创建者 Deleted A

•Dec 28, 2017

I thought it was very well done but I felt like the material was kind of rushed and some subjects were brushed over. I would like a little more in depth coverage of this subject.