Вернуться к Linear Regression for Business Statistics

4.8

Оценки: 400

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Рецензии: 63

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...

автор: WB

•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.

автор: MW

•May 01, 2018

Well structured course with clear modules and helpful exercises to reinforce the material. Professor Borle does a great job and is very responsive to questions.

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Рецензии: 59

автор: Jordi Montserrat

•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!!

автор: Ketevani Aliaidze

•Nov 13, 2018

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

автор: Josefina Koetter Samvelyan

•Oct 24, 2018

Great explanations!!

автор: Ramasubramaniyam Sureshram

•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

автор: Abdullatif Alrasheed

•Oct 18, 2018

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

автор: Achyut Dhananjay Utpat

•Oct 10, 2018

Very nicely structured and implemented

автор: John David Inmon

•Oct 01, 2018

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

автор: Scott Leppla

•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.

автор: Esther Klein

•Aug 13, 2018

Excellent course!

автор: Kim K

•Aug 08, 2018

Rigorous and rewarding when you put the work in.

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