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学生对 Rice University 提供的 Linear Regression for Business Statistics 的评价和反馈

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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

HB

Jun 19, 2020

Great learning with examples from real life, great approach to understand the concept without need to deep dive into the mathematical complexities. A great base to get into Data/Business Analytics.

SR

Aug 12, 2020

A very complex last quiz in comparison with the others, truly serves as a skill-checker, without directly asking about a lot of topics. Loved the course, thank you!

筛选依据：

201 - Linear Regression for Business Statistics 的 218 个评论（共 218 个）

创建者 Rajat S

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Oct 23, 2020

the content was good but above all the way it was taught was amazing.

创建者 Eugene R

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Feb 2, 2020

Perfect for beginners, so I agree it can be not like a challenge.

创建者 Ridhi G

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Jan 16, 2018

The explanations of a lot of interpretations are repetitive.

创建者 Prince i y

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Jun 7, 2017

The course was very informative and I have learnt a lot.

创建者 Ekambaram D k

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Nov 22, 2019

Good course to know about basics of Linear regression

创建者 VAIBHAV A J

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Jun 5, 2020

The course is very good but needs more practice sums

创建者 Kim K

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Aug 8, 2018

Rigorous and rewarding when you put the work in.

创建者 Aakash G

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May 16, 2020

Theory need to be increase a little

创建者 dipak p

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Jun 2, 2019

Very good course for regression

创建者 Jean-Philippe M

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Aug 11, 2019

Great course, great teacher!

创建者 Matteo D

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May 5, 2020

Well and clear explanation.

创建者 Suriya N

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Apr 1, 2018

Really liked the course!!!

创建者 Wesley B

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Dec 1, 2019

Material is kind of dry

创建者 Mihir M

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Oct 10, 2019

nicely explained

创建者 Khalid M B

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Apr 13, 2020

Great!

创建者 Dhawal M

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Jan 15, 2020

There are few issues in week 2 quiz. The content is up to the mark. Overall Course is useful.

创建者 DHRUV S

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May 16, 2020

should have a project component at end of course

concepts explained with clarity

创建者 Akshata B

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May 17, 2023

its good

the videos were usefull