# 学生对 莱斯大学 提供的 Linear Regression for Business Statistics 的评价和反馈

4.8
587 个评分
93 个审阅

## 课程概述

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...

## 热门审阅

##### 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.

##### SD

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

## 26 - Linear Regression for Business Statistics 的 50 个评论（共 88 个）

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.

Jul 22, 2017

Great source to learn regression with excel. Hard to find elsewhere !

Dec 09, 2017

Really useful for understanding regressions, the meaning of the coefficients. Also very helpful to do more analysis than what most people usually do to data.

Jan 02, 2018

It was great!

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.

Nov 22, 2017

Very Thankful to the Professor for explaining each and every concept in detail.

Mar 26, 2018

Very well explained and ea

May 21, 2018

This is a fantastic course and the teacher is excellent!

May 16, 2017

Well designed and business friendly explanation. Thanks to Sharad Borle I gained much deeper knowledge on linear regression.

Sep 09, 2017

Excellent!

Apr 10, 2017

Well taught and extremely useful information. I was able to take knowledge from this course and apply it directly to analytical reports I write at work.

Apr 29, 2017

Excellent course! Very clear explanations of concepts and lots of great examples.

Recommended!

Oct 11, 2017

Very well designed and good examples illustrate the Regression model. Thanks for the Opportunity,

Sep 10, 2017

Well structured course work

Jan 24, 2018

extemely lucid and connecting course with ample real time excel hands on and example

Oct 30, 2017

Amazing Professor !

Jan 06, 2018

Amazing !! The concepts were explained with clarity which have immediate applicability. Can't wait to apply them into my organisation.

Jul 25, 2018

interesting course

Apr 21, 2019

Very good course for people of all backgrounds and experience levels in the topic! If you are new to regression or familiar with it I highly recommend it.

Apr 26, 2019

practical

May 16, 2019

great course

Jun 19, 2019

I like this course. You need to study this course if you want basic understanding of Statistics because Statistics is base need of analytical field. And instructor explained each and every team in a very simple way. Thanks a lot Professor.

Aug 05, 2019

Awesome course...Very interesting to learn.

May 31, 2019

Nice course!

Aug 12, 2019

Excellent course for beginners