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Linear Regression for Business Statistics
  • Level Foundation
  • المدة 28 ساعات hours
  • الطبع بواسطة Rice University
  • Offered by

عن

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

الوحدات

Meet the Professor

    • 1 Videos
    • 2 Readings
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1 Videos

  • Meet the Professor

2 Readings

  • Course FAQs
  • Pre-Course Survey

Lesson 1 - Introducing Linear Regression: Building the Model

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Introducing Linear Regression: Building a Model

2 Readings

  • Toy Sales.xlsx
  • Slides, Lesson 1

Lesson 2 - Introducing Linear Regression: Estimating the Model

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Introducing Linear Regression: Estimating the Model

2 Readings

  • Toy Sales.xlsx
  • Slides, Lesson 2

Lesson 3 - Introducing Linear Regression: Interpreting the Model

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Introducing Linear Regression: Interpreting the Model

2 Readings

  • Toy Sales.xlsx
  • Slides, Lesson 3

Lesson 4 - Introducing Linear Regression: Predictions using the Model

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Introducing Linear Regression: Predictions using the Model

2 Readings

  • Toy Sales.xlsx
  • Slides, Lesson 4

Lesson 5 - Errors, Residuals and R-square

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Errors, Residuals and R-square

2 Readings

  • Toy Sales2.xlsx
  • Slides, Lesson 5

Lesson 6 - Normality Assumption on the Errors

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Normality Assumption on the Errors

1 Readings

  • Slides, Lesson 6

Regression Analysis: An Introduction: Quiz

    • 1 Assignment
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1 Assignment

  • Regression Analysis: An Introduction

Lesson 1 - Hypothesis Testing in a Linear Regression

    • 1 Videos
    • 5 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Hypothesis Testing in a Linear Regression

5 Readings

  • Toy Sales.xlsx
  • Toy Sales (with regression).xlsx
  • Toy Sales (with regression, t-statistic).xlsx
  • Toy Sales (with regression, t-cutoff)
  • Slides, Lesson 1

Lesson 2 - Hypothesis Testing in a Linear Regression: 'p-values'

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

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

2 Readings

  • Toy Sales.xlsx
  • Slides, Lesson 2

Lesson 3 - Hypothesis Testing in a Linear Regression: Confidence Intervals

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Hypothesis Testing in a Linear Regression: Confidence Intervals

2 Readings

  • Toy Sales.xlsx
  • Slides, Lesson 3

Lesson 4 - A Regression Application Using Housing Data

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • A Regression Application Using Housing Data

2 Readings

  • Home Prices.xlsx
  • Slides, Lesson 4

Lesson 5 - 'Goodness of Fit' measures: R-square and Adjusted R-square

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

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

2 Readings

  • Home Prices.xlsx
  • Slides, Lesson 5

Lesson 6 - Categorical Variables in a Regression: Dummy Variables

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Categorical Variables in a Regression: Dummy Variables

2 Readings

  • deliveries1.xlsx
  • Slides, Lesson 6

Regression Analysis: Hypothesis Testing and Goodness of Fit: Quiz

    • 1 Assignment
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1 Assignment

  • Regression Analysis: Hypothesis Testing and Goodness of Fit

Lesson 1 - Dummy Variable Regression: Extension to Multiple Categories

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Dummy Variable Regression: Extension to Multiple Categories

2 Readings

  • deliveries2.xlsx
  • Slides, Lesson 1

Lesson 2 - Dummy Variable Regression: Interpretation of Coefficients

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Dummy Variable Regression: Interpretation of Coefficients

1 Readings

  • Slides, Lesson 2

Lesson 3 - Dummy Variable Regression: Estimation, Interpretation of p-values

    • 1 Videos
    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Dummy Variable Regression: Estimation, Interpretation of p-values

3 Readings

  • deliveries2.xlsx
  • deliveries2 (for prediction).xlsx
  • Slides, Lesson 3

Lesson 4 - A Regression Application Using Refrigerator data

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • A Regression Application Using Refrigerator data

2 Readings

  • Refrigerators.xlsx
  • Slides, Lesson 4

Lesson 5 - A Regression Application Using Refrigerator data (continued...)

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • A Regression Application Using Refrigerator data (continued...)

2 Readings

  • Cars.xlsx
  • Slides, Lesson 5

Lesson 6 - Multicollinearity in Regression Models: What it is and How to Deal with it

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

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

2 Readings

  • Cars.xlsx
  • Slides, Lesson 6

Regression Analysis: Model Application and Multicollinearity: Quiz

    • 1 Assignment
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1 Assignment

  • Regression Analysis: Model Application and Multicollinearity

Lesson 1 - Mean Centering Variables in a Regression Model

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Mean Centering Variables in a Regression Model

2 Readings

  • Height and Weight.xlsx
  • Slides, Lesson 1

Lesson 2 - Building Confidence Bounds for Prediction Using a Regression Model

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Building Confidence Bounds for Prediction Using a Regression Model

2 Readings

  • Height and Weight.xlsx
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Lesson 3 - Interaction Effects in a Regression: An Introduction

    • 1 Videos
    • 1 Readings
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1 Assignment

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1 Videos

  • Interaction Effects in a Regression: An Introduction

1 Readings

  • Slides, Lesson 3

Lesson 4 - Interaction Effects in a Regression: An Application

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • Interaction Effects in a Regression: An Application

2 Readings

  • Height and Weight.xlsx
  • Slides, Lesson 4

Lesson 5 - Transformation of Variables in a Regression: Improving Linearity

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

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1 Videos

  • Transformation of Variables in a Regression: Improving Linearity

1 Readings

  • Slides, Lesson 5

Lesson 6 - The Log-Log and the Semi-Log Regression Models

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Videos

  • The Log-Log and the Semi-Log Regression Models

2 Readings

  • Cocoa.xlsx
  • Slides, Lesson 6

Course 4 Recap

    • 1 Videos
    • 1 Readings
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1 Videos

  • Course 4 Recap

1 Readings

  • End-of-Course Survey

Regression Analysis: Various Extensions: Quiz

    • 1 Assignment
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1 Assignment

  • Regression Analysis: Various Extensions

Auto Summary

"Linear Regression for Business Statistics" is a comprehensive course designed for those in Data Science & AI, focusing on regression analysis as a critical tool for business forecasting and prediction. Taught by expert instructors on Coursera, the course emphasizes practical application over complex mathematics, with hands-on examples using Microsoft Excel. Spanning four weeks, learners will explore topics such as hypothesis testing, dummy variables, multicollinearity, and advanced regression techniques. Ideal for foundation-level learners, the course offers flexible subscription options, including Starter, Professional, and Paid plans.

Sharad Borle
Instructor

Sharad Borle