À propos de ce cours
4.7
371 notes
57 avis
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...
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Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Calendar

Dates limites flexibles

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Clock

Recommandé : 4 weeks of study

Approx. 12 heures pour terminer
Comment Dots

English

Sous-titres : English

Compétences que vous acquerrez

Log–Log PlotInteraction (Statistics)Linear RegressionRegression Analysis
Globe

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Calendar

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Clock

Recommandé : 4 weeks of study

Approx. 12 heures pour terminer
Comment Dots

English

Sous-titres : English

Programme du cours : ce que vous apprendrez dans ce cours

1

Section
Clock
5 heures pour terminer

Regression Analysis: An Introduction

...
Reading
7 vidéos (Total 65 min), 13 lectures, 7 quiz
Video7 vidéos
Introducing Linear Regression: Building a Model8 min
Introducing Linear Regression: Estimating the Model10 min
Introducing Linear Regression: Estimating the Model12 min
Introducing Linear Regression: Predictions using the Model9 min
Errors, Residuals and R-square14 min
Normality Assumption on the Errors7 min
Reading13 lectures
Course FAQs10 min
Pre-Course Survey10 min
Toy Sales.xlsx10 min
Slides, Lesson 110 min
Toy Sales.xlsx10 min
Slides, Lesson 210 min
Toy Sales.xlsx10 min
Slides, Lesson 310 min
Toy Sales.xlsx10 min
Slides, Lesson 410 min
Toy Sales2.xlsx10 min
Slides, Lesson 510 min
Slides, Lesson 610 min
Quiz7 exercices pour s'entraîner
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Regression Analysis: An Introduction min

2

Section
Clock
5 heures pour terminer

Regression Analysis: Hypothesis Testing and Goodness of Fit

...
Reading
6 vidéos (Total 74 min), 15 lectures, 7 quiz
Video6 vidéos
Hypothesis Testing in a Linear Regression: using 'p-values'7 min
Hypothesis Testing in a Linear Regression: Confidence Intervals9 min
A Regression Application Using Housing Data15 min
'Goodness of Fit' measures: R-square and Adjusted R-square11 min
Categorical Variables in a Regression: Dummy Variables18 min
Reading15 lectures
Toy Sales.xlsx10 min
Toy Sales (with regression).xlsx10 min
Toy Sales (with regression, t-statistic).xlsx10 min
Toy Sales (with regression, t-cutoff)10 min
Slides, Lesson 110 min
Toy Sales.xlsx10 min
Slides, Lesson 210 min
Toy Sales.xlsx10 min
Slides, Lesson 310 min
Home Prices.xlsx10 min
Slides, Lesson 410 min
Home Prices.xlsx10 min
Slides, Lesson 510 min
deliveries1.xlsx10 min
Slides, Lesson 610 min
Quiz7 exercices pour s'entraîner
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Regression Analysis: Hypothesis Testing and Goodness of Fit min

3

Section
Clock
4 heures pour terminer

Regression Analysis: Dummy Variables, Multicollinearity

...
Reading
6 vidéos (Total 62 min), 12 lectures, 7 quiz
Video6 vidéos
Dummy Variable Regression: Interpretation of Coefficients6 min
Dummy Variable Regression: Estimation, Interpretation of p-values17 min
A Regression Application Using Refrigerator data12 min
A Regression Application Using Refrigerator data (continued...)7 min
Multicollinearity in Regression Models: What it is and How to Deal with it10 min
Reading12 lectures
deliveries2.xlsx10 min
Slides, Lesson 110 min
Slides, Lesson 210 min
deliveries2.xlsx10 min
deliveries2 (for prediction).xlsx10 min
Slides, Lesson 310 min
Refrigerators.xlsx10 min
Slides, Lesson 410 min
Cars.xlsx10 min
Slides, Lesson 510 min
Cars.xlsx10 min
Slides, Lesson 610 min
Quiz7 exercices pour s'entraîner
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Regression Analysis: Model Application and Multicollinearity20 min

4

Section
Clock
4 heures pour terminer

Regression Analysis: Various Extensions

...
Reading
7 vidéos (Total 63 min), 11 lectures, 7 quiz
Video7 vidéos
Building Confidence Bounds for Prediction Using a Regression Model9 min
Interaction Effects in a Regression: An Introduction6 min
Interaction Effects in a Regression: An Application8 min
Transformation of Variables in a Regression: Improving Linearity7 min
The Log-Log and the Semi-Log Regression Models17 min
Course 4 Recap1 min
Reading11 lectures
Height and Weight.xlsx10 min
Slides, Lesson 110 min
Height and Weight.xlsx10 min
Slides, Lesson 210 min
Slides, Lesson 310 min
Height and Weight.xlsx10 min
Slides, Lesson 410 min
Slides, Lesson 510 min
Cocoa.xlsx10 min
Slides, Lesson 610 min
End-of-Course Survey10 min
Quiz7 exercices pour s'entraîner
Practice Quiz6 min
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz4 min
Practice Quiz6 min
Practice Quiz6 min
Regression Analysis: Various Extensions22 min
4.7
Direction Signs

50%

a commencé une nouvelle carrière après avoir terminé ces cours
Briefcase

83%

a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Money

25%

a obtenu une augmentation de salaire ou une promotion

Meilleurs avis

par WBDec 21st 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 MWMay 1st 2018

Well structured course with clear modules and helpful exercises to reinforce the material. Professor Borle does a great job and is very responsive to questions.

Enseignants

Sharad Borle

Associate Professor of Management
Jones Graduate School of Business

À propos de Rice University

Rice University is consistently ranked among the top 20 universities in the U.S. and the top 100 in the world. Rice has highly respected schools of Architecture, Business, Continuing Studies, Engineering, Humanities, Music, Natural Sciences and Social Sciences and is home to the Baker Institute for Public Policy....

À propos de la Spécialisation Business Statistics and Analysis

The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. You’ll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You’ll also explore basic probability concepts, including measuring and modeling uncertainty, and you’ll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions. The Specialization culminates with a Capstone Project in which you’ll apply the skills and knowledge you’ve gained to an actual business problem. To successfully complete all course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later. To see an overview video for this Specialization, click here!...
Business Statistics and Analysis

Foire Aux Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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