Chevron Left
Volver a Linear Regression for Business Statistics

Opiniones y comentarios de aprendices correspondientes a Linear Regression for Business Statistics por parte de Universidad Rice

1,273 calificaciones

Acerca del Curso

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...

Principales reseñas


20 de dic. de 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.


21 de abr. de 2020

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.

Filtrar por:

151 - 175 de 202 revisiones para Linear Regression for Business Statistics

por Rajan M

21 de jul. de 2017

Very well explained

por Hénock M

25 de jul. de 2018

interesting course

por Parul K

17 de sep. de 2017

excellent content.

por Esther K

13 de ago. de 2018

Excellent course!

por Yusui T

13 de jul. de 2020

Excellent lesson

por harshit s

5 de jul. de 2020

Great content!!!

por Esohe I G

16 de ago. de 2020

great lecture

por gayathri s

2 de ene. de 2018

It was great!

por Tom B

3 de oct. de 2017

Great Course.

por pooja s

2 de ago. de 2020

nice concept

por Edilson S

30 de may. de 2019

Nice course!

por Jittu S

15 de may. de 2019

great course

por Faizan u H

28 de dic. de 2021


por Kiko S

8 de sep. de 2017


por Deepali D

30 de may. de 2022


por Jonathan J

18 de dic. de 2020


por pandiripalli n c r

18 de oct. de 2020

i love it

por Deep S

15 de sep. de 2020

Great one

por Pulkit S

27 de jul. de 2020


por Vitalii S

26 de abr. de 2019


por Majid A

29 de may. de 2022


por shubhangi m

20 de mar. de 2019

Thanks S

por Bartlomiej B

26 de ene. de 2020


por Colin P

2 de may. de 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.

por Brian B

14 de may. de 2020

Great class. The material was challenging. I was able to work through the various models and equation. I wish still I had a better understanding of interpreting some of the modeling techniques, such as using Mean centered variables and interaction variables. But all and all, I really enjoyed the class and as usual the instructor did a great job.