Вернуться к Linear Regression for Business Statistics

4.8
звезд
Оценки: 1,273
Рецензии: 206

## О курсе

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...

## Лучшие рецензии

BB

21 апр. 2020 г.

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.

WB

20 дек. 2017 г.

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.

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## 176–200 из 202 отзывов о курсе Linear Regression for Business Statistics

автор: M

6 июня 2018 г.

Phenomenal course. A little more in-depth explanations and more examples for the concepts introduced in the last two weeks would have been nice though. In week 3 and 4, I found it challenging to go so quickly over so many new concepts all of a sudden. But still, I would really recommend taking this course, I found it useful.

автор: Yaron K

13 апр. 2017 г.

An in depth explanation of how to use Excel for Linear Regression and what the Output values in Excel's Regression mean. Note that the transcripts/subtitles contain many errors, which can be problematical for the hard of hearing or non English speakers, which is why I gave the course only 4 points.

автор: Deleted A

27 дек. 2017 г.

I thought it was very well done but I felt like the material was kind of rushed and some subjects were brushed over. I would like a little more in depth coverage of this subject.

автор: Liu P

16 окт. 2019 г.

content, insightfulness, logics are very comprehensive and carefully designed, however, certain exam questions are questionally and arguably disgned.

автор: Andrew A

2 июля 2018 г.

Overall a good course that cultivates skills in precise use of regression, data handling and understanding of applied business modelling problems.

автор: Jose A A C

15 апр. 2019 г.

I'd like to have more examples regarding Log-Log and the Semi-Log Regression Models and also Interaction Variables interpretations. Thanks a lot!

автор: U I L

18 дек. 2017 г.

That would be better if the correct answer if being shown after passing the exam, because I can't able to learn from my mistakes

Great course !!!

автор: Jacob C

8 апр. 2017 г.

The exercises included help a lot in practically understanding the matter. I did not find that in other courses and it was a miss.

автор: Abigail P

25 июля 2020 г.

Really great overall; would have liked to see more coverage of natural logs. Excellent application!

автор: Rajat S

23 окт. 2020 г.

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

автор: Romanenko Y A

2 февр. 2020 г.

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

автор: Ridhi G

17 янв. 2018 г.

The explanations of a lot of interpretations are repetitive.

автор: Prince N X

7 июня 2017 г.

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

автор: Ekambaram D k

22 нояб. 2019 г.

Good course to know about basics of Linear regression

автор: VAIBHAV A J

5 июня 2020 г.

The course is very good but needs more practice sums

автор: Kim K

8 авг. 2018 г.

Rigorous and rewarding when you put the work in.

автор: Aakash G

16 мая 2020 г.

Theory need to be increase a little

автор: dipak p

2 июня 2019 г.

Very good course for regression

автор: Jean-Philippe M

11 авг. 2019 г.

Great course, great teacher!

автор: Matteo D

6 мая 2020 г.

Well and clear explanation.

автор: Suriya N

1 апр. 2018 г.

Really liked the course!!!

автор: Wesley B

1 дек. 2019 г.

Material is kind of dry

автор: Mihir M

10 окт. 2019 г.

nicely explained

автор: خالد م

13 апр. 2020 г.

Great!

автор: James P W

17 февр. 2019 г.

Needs more worked examples... good luck trying to get any useful feedback from the instructors/discussion board. Your definitely on your own...