Este curso faz parte do Programa de cursos integrados Business Statistics and Analysis

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Informações sobre o curso

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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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Redefinir os prazos de acordo com sua programação.

Sugerido: 4 weeks of study...

Legendas: English...

Log–Log PlotInteraction (Statistics)Linear RegressionRegression Analysis

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

Sugerido: 4 weeks of study...

Legendas: English...

Week

1...

7 vídeos (Total de 65 min), 13 leituras, 7 testes

Introducing Linear Regression: Building a Model8min

Introducing Linear Regression: Estimating the Model10min

Introducing Linear Regression: Estimating the Model12min

Introducing Linear Regression: Predictions using the Model9min

Errors, Residuals and R-square14min

Normality Assumption on the Errors7min

Course FAQs10min

Pre-Course Survey10min

Toy Sales.xlsx10min

Slides, Lesson 110min

Toy Sales.xlsx10min

Slides, Lesson 210min

Toy Sales.xlsx10min

Slides, Lesson 310min

Toy Sales.xlsx10min

Slides, Lesson 410min

Toy Sales2.xlsx10min

Slides, Lesson 510min

Slides, Lesson 610min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Regression Analysis: An Introductionmin

Week

2...

6 vídeos (Total de 74 min), 15 leituras, 7 testes

Hypothesis Testing in a Linear Regression: using 'p-values'7min

Hypothesis Testing in a Linear Regression: Confidence Intervals9min

A Regression Application Using Housing Data15min

'Goodness of Fit' measures: R-square and Adjusted R-square11min

Categorical Variables in a Regression: Dummy Variables18min

Toy Sales.xlsx10min

Toy Sales (with regression).xlsx10min

Toy Sales (with regression, t-statistic).xlsx10min

Toy Sales (with regression, t-cutoff)10min

Slides, Lesson 110min

Toy Sales.xlsx10min

Slides, Lesson 210min

Toy Sales.xlsx10min

Slides, Lesson 310min

Home Prices.xlsx10min

Slides, Lesson 410min

Home Prices.xlsx10min

Slides, Lesson 510min

deliveries1.xlsx10min

Slides, Lesson 610min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Regression Analysis: Hypothesis Testing and Goodness of Fitmin

Week

3...

6 vídeos (Total de 62 min), 12 leituras, 7 testes

Dummy Variable Regression: Interpretation of Coefficients6min

Dummy Variable Regression: Estimation, Interpretation of p-values17min

A Regression Application Using Refrigerator data12min

A Regression Application Using Refrigerator data (continued...)7min

Multicollinearity in Regression Models: What it is and How to Deal with it10min

deliveries2.xlsx10min

Slides, Lesson 110min

Slides, Lesson 210min

deliveries2.xlsx10min

deliveries2 (for prediction).xlsx10min

Slides, Lesson 310min

Refrigerators.xlsx10min

Slides, Lesson 410min

Cars.xlsx10min

Slides, Lesson 510min

Cars.xlsx10min

Slides, Lesson 610min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Regression Analysis: Model Application and Multicollinearity20min

Week

4...

7 vídeos (Total de 63 min), 11 leituras, 7 testes

Building Confidence Bounds for Prediction Using a Regression Model9min

Interaction Effects in a Regression: An Introduction6min

Interaction Effects in a Regression: An Application8min

Transformation of Variables in a Regression: Improving Linearity7min

The Log-Log and the Semi-Log Regression Models17min

Course 4 Recap1min

Height and Weight.xlsx10min

Slides, Lesson 110min

Height and Weight.xlsx10min

Slides, Lesson 210min

Slides, Lesson 310min

Height and Weight.xlsx10min

Slides, Lesson 410min

Slides, Lesson 510min

Cocoa.xlsx10min

Slides, Lesson 610min

End-of-Course Survey10min

Practice Quiz6min

Practice Quiz4min

Practice Quiz4min

Practice Quiz4min

Practice Quiz6min

Practice Quiz6min

Regression Analysis: Various Extensions22min

4.7

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por WB•Dec 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.

por MW•May 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.

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....

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!...

When will I have access to the lectures and assignments?

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.

What will I get if I subscribe to this Specialization?

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.

What is the refund policy?

Is financial aid available?

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