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Avis et commentaires pour d'étudiants pour Linear Regression for Business Statistics par Université de Rice

4.8
étoiles
1,103 évaluations
184 avis

À propos du cours

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...

Meilleurs avis

WB
20 déc. 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
21 avr. 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.

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1 - 25 sur 179 Avis pour Linear Regression for Business Statistics

par Rishav k

26 déc. 2017

Could have been more challenging. Moderate courses are easy to pass but doesn't bring about extreme competitive spirit. Some peer graded assignments could be better.

par Susannah

16 juin 2020

Very good class. Appreciated that it was hands-on, the quizzes were relatively challenging but not unrealistically so, the text on the slides was large (I like to take screenshot and save them to my notes), and the Excel screens were easy to see (unlike some other classes involving Excel I've taken on Coursera, in which you cannot see the calculations and they do not show up legibly in my screenshots/notes), and it was well-organized. I might have liked more reinforcement and explanation of some of the trickier concepts, such as introducing an interaction effect.

par Shady N S

27 déc. 2018

I love this Specialization, and look forward to completing it! It's an amazing journey in Statistics with Excel! If you're a beginner in Statistics, you might see the whole Specialization a bit difficult and will need to look for a Statistics course. The instructor is also a huge plus!

par Lawrence H

7 oct. 2020

This Linear regression for business statistics course had given me the simple ideas and insights on how to use a set of data or information to obtain a Linear co-relation in a linear equation and obtain the value of the coefficient to extrapolate unknown variables. The presenter had presented the course in a simple and clear English cum with lecturer notes and MS Excel files. Mostly important is the feedback in Discussion forum from teaching staff to answer our questions to assist in my learning of this course. I gave 5 stars rating for a well organised and good quality course. Area for improvement is teaching staff should answer questions posted in the discussion forum in a timely manner and this will also help other students to study the same issues raised in the future.

par JOHN S

11 oct. 2020

Content of the course is exceptionally useful. I have taken a few econometrics courses before and this course makes me feel much more confident about using regression for analysis than the other courses. The quizzes have an acceptable level of difficulty - not too easy, not too difficult as well.

This course itself will allow you to understand the basic concepts of linear regression and gives you the confidence to use it for actual real life analysis.

par Karen S Z

31 juil. 2018

Excellent introduction to Linear Regression. As you progress, you learn how to use dummy (category) variables as well as interaction variables. Examples are explained in detail so you can understand how it works. This course isn't about understanding all the detailed math & theory, but explains enough to you understand (at a high level) what you're doing and why. Then, you learn how to do it in Excel. I really enjoyed this class!

par Ashis G

17 mai 2020

This specialization in business analytics with MS Excel is one of the most comprehensive introductions to Linear Regression as well as its applications to business statistics. Dr. Borle's lectures are engaging in their accords, and coupled with the depiction of practical implementations in MS Excel makes this course an enriching experience.

par Faisal n K

17 août 2020

Professor explained all hard and tough concepts in simple and plain language. This course is a nice gift from coursera. definitely, it has improved my overall knowledge and skills. the practical examples used by professor has helped a lot to develop greater understanding of regression and its practical application.

par Anirban G

26 juin 2020

Dear Professor, I started learning this course just few days back and able to complete just because of you, the way you each and every topics is just outstanding. I am very grateful to you as the way you teach any layman can understand the topics.

Thanks,

Anirban Gupta

par Avi G

20 sept. 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.

par shwetamehna

18 juin 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.

par Kirtana S

6 mai 2017

I thoroughly enjoyed this course. This is an excellent course for all those who wish to understand and apply regression at work. Professor Borle explains every concept in detail and ensures he interprets each aspect as simple as possible.

par Anchal R

10 mai 2020

Loved the course structure.

The way each concept is introduced and taught using basic simple example is awesome. Could be easily understood by someone with zero knowledge in statistics. Perfectly minds the knowledge gap for all levels.

par ALONE L R

11 avr. 2020

It 's best course to online learning to the business analysis tool plus software knowledge. It's really help full me . Thank You So Much Coursera. I am lucky to financial help me, Thank you so much......

And Respective Sir Thank you

par praval p

2 mai 2020

The course provides a good understanding of the linear regression process and hands on exercises. The course can however be expanded and more lectures can be provided on topics such as transformation and mean centered regression.

par Michael I

31 juil. 2020

This course is interesting and the Instructor ensured the advertised skills were thoroughly imparted in such easy to understand lessons. I highly recommend this course to others who would like to understand business statistics

par Veerendra A

4 mai 2020

The course was extremely useful and specially the instructor was ver good with interpratation of all kind of analysis.

Thank You sir for such a wonderful session and for all your sharing of knowledge of super statistical skills

par Lori-Ann B

14 nov. 2020

This course examines new ways in which business can analyze data to promote growth, Again, this is a course you need to take if you want to understand "how" and "why" business leaders make decisions. Highly recommend it.

par Scott L

15 sept. 2018

Though I was briefly introduced to linear regression in my graduate studies, I found the structure and presentation of this material to be more helpful to learning and understanding the material AND it's use cases.

par Akshay H

4 mai 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.

par Gareth W

1 mars 2020

A good introduction into the practical uses of regression. It starts with the basics (that you probably learnt at school) and then adds more sophistication that takes the subject up to the current day.

par William B

21 déc. 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.

par Bhavin B

22 avr. 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.

par Himanshu B

20 juin 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.

par Shanmugapriya R

20 sept. 2020

It was a very interesting course with a clear explanation of the concepts with practical examples in videos and ppt. This course helped me in understanding the linear regression concepts clearly.