Linear Regression for Business Statistics(으)로 돌아가기

# 라이스 대학교의 Linear Regression for Business Statistics 학습자 리뷰 및 피드백

4.8
별점
962개의 평가
157개의 리뷰

## 강좌 소개

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...

## 최상위 리뷰

##### 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.

##### BB

Apr 22, 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.

필터링 기준:

## Linear Regression for Business Statistics의 153개 리뷰 중 101~125

교육 기관: vinay b

Sep 10, 2017

Well structured course work

교육 기관: Olivia B

Mar 26, 2018

Very well explained and ea

교육 기관: Dr. M R P

May 20, 2020

VERY INTERESTING COURSE

교육 기관: Taruraj A

Apr 17, 2018

Excellently explained!

교육 기관: Christo M

Feb 21, 2020

Enjoyed this course.

교육 기관: Josefina K S

Oct 24, 2018

Great explanations!!

교육 기관: SHIVAM A

Apr 13, 2020

Very useful Course!

교육 기관: Antonio R d G F

Oct 30, 2017

Amazing Professor !

교육 기관: Rajan M

Jul 21, 2017

Very well explained

교육 기관: MONTCHO H M

Jul 25, 2018

interesting course

교육 기관: Parul

Sep 17, 2017

excellent content.

교육 기관: Esther K

Aug 13, 2018

Excellent course!

교육 기관: Yusui T

Jul 13, 2020

Excellent lesson

교육 기관: harshit s

Jul 06, 2020

Great content!!!

교육 기관: GAYATHRI S

Jan 02, 2018

It was great!

교육 기관: Tom B

Oct 03, 2017

Great Course.

교육 기관: pooja s

Aug 02, 2020

nice concept

교육 기관: EDILSON S S O J

May 31, 2019

Nice course!

교육 기관: jittu s

May 16, 2019

great course

교육 기관: Cristiano S

Sep 09, 2017

Excellent!

교육 기관: Pulkit S

Jul 27, 2020

Excellent

교육 기관: Vitalii S

Apr 26, 2019

practical

교육 기관: shubhangi P M

Mar 20, 2019

Thanks S

교육 기관: Bartlomiej B

Jan 26, 2020

V

교육 기관: 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.