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University of Colorado Boulder - Regression Modeling for Marketers 

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Regression Modeling for Marketers
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Coursera 
Overview

Regression modeling helps marketers understand the relationships between various marketing variables and consumer behavior

Duration

19 hours

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Total fee

Free

Mode of learning

Online

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Credential

Certificate

Regression Modeling for Marketers
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Coursera 
Highlights

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Regression Modeling for Marketers
 at 
Coursera 
Course details

What are the course deliverables?
  • Apply regression analysis to understand & predict marketing outcomes
  • Interpret market data & refine statistical models for real-world application
More about this course
  • "Regression Modeling for Marketers" is a specialized course designed to elevate marketing professionals' analytical skills
  • Focusing on regression analysis, the course enables learners to quantify, explain, and predict marketing outcomes using both simple and multiple linear regression models
  • This course stands out by not only teaching the creation and interpretation of market data visualizations but also showing the use of advanced statistical software for gaining marketing insights
  • Learners will explore sophisticated analytical techniques like ANOVA, ANCOVA, and MANCOVA, enhancing their ability to dissect the impact of marketing strategies
  • The course also covers logistic regression and multivariate testing, key tools for anticipating market shifts and consumer choices
  • Additionally, it emphasizes the application of uplift modeling for targeted and personalized marketing campaigns, making it an essential resource for contemporary marketers
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Regression Modeling for Marketers
 at 
Coursera 
Curriculum

Understanding Simple Linear Regression

The Main Ideas of Simple Linear Regression (SLR)

Describing Lines and Normal Distributions

Residuals, SSE, and Ordinary Least Squares Regression

Confidence Bands for Regression Lines: Homoscedasticity

Predicting With SLR: Obtaining Regression Coefficients

A Note About Readings & R-Scripts

Simple Linear Regression

R-Scripts - Simple Linear Regression (SLR), Normal Distribution, and Prediction Intervals

The Main Ideas of Simple Linear Regression (SLR) Quiz

Describing Lines and Normal Distributions Quiz

Residuals, SSE, and Ordinary Least Squares Regression Quiz

Confidence Bands for Regression Lines: Homoscedasticity Quiz

Predicting With SLR: Obtaining Regression Coefficients Quiz

Module 1 Graded Quiz

Interpreting SLR Output

Testing for Normality of Residuals

Standard Errors for Regression Coefficients

R-Squared

Summary of Regression Outputs

Difference of Means, Part 1: Two-Sample T-Test

Difference of Means, Part 2: SLR with Dummy Variables and A/B Testing

Q-Q Plots

R-Scripts - SLR & Q-Q Plots

Adjusted R-Squared

AB Testing

Testing for Normality of Residuals Quiz

Standard Errors for Regression Coefficients Quiz

R-Squared Quiz

Summary of Regression Outputs Quiz

Difference of Means, Part 1: Two-Sample T-Test Quiz

Difference of Means, Part 2: SLR with Dummy Variables and A/B Testing Quiz

Module 2 Graded Quiz

Beyond Simple Linear Regression (SLR)

Checking SLR Assumptions

Assessing SLR Assumptions Visually: Anscombe's Quartet

Non-Parametric Smoothing Regression Using LOESS: Part 1

Non-Parametric Smoothing Regression Using LOESS: Part 2

Comparing SLR and LOESS

Introduction to Multiple Linear Regression (MLR)

R-Squared for MLR

MLR with Discrete Independent Variables: One-Hot Encoding

Interpreting MLR Coefficients for Dummy Variables

Wrap-up on One-Hot Encoding in MLR

R-Scripts - SLR & Comparing Prediction Intervals

Non-Parametric Smoothing Regression

R-Scripts - Smoothing Regression LOESS

Multiple Linear Regression (MLR)

R-Scripts - Multiple Linear Regression (MLR)

One-Hot Encoding with 0-1 Dummy Variables

Checking SLR Assumptions Quiz

Assessing SLR Assumptions Visually: Anscombe's Quartet Quiz

Non-Parametric Smoothing Regression Using LOESS: Part 1 Quiz

Non-Parametric Smoothing Regression Using LOESS: Part 2 Quiz

Comparing SLR and LOESS Quiz

Introduction to Multiple Linear Regression (MLR) Quiz

R-Squared for MLR Quiz

MLR with Discrete Independent Variables: One-Hot Encoding Quiz

Interpreting MLR Coefficients for Dummy Variables Quiz

Wrap-up on One-Hot Encoding in MLR Quiz

Module 3 Graded Quiz

Applying and Generalizing Multiple Linear Regression (MLR)

Analysis of Variance (ANOVA)

Checking Anova Assumptions

Analysis of Covariance (ANCOVA)

Beyond ANCOVA

Logistic Regression & Generalized Linear Models

MLE and Multivariate Logistic Regression

Causal Evaluation with Regression Models 1: Improved A/B Testing with MLR

Causal Evaluation with Regression Models 2: Multivariate Testing and Uplift Modeling

Causal Evaluation with Regression Models 3: Difference-in-Differences (DID)

Causal Evaluation Methods

Analysis of Variance (ANOVA)

R-Scripts - Analysis of Variance (ANOVA) and Beyond

Kruskal-Wallis Non-Parametric ANOVA

ANCOVA & Multicollinearity Testing and VIF Factors

MANOVA & Stepwise Variable Selection

Logistic Regression and Generalized Linear Models

R-Scripts - Logistic Regression

Multinomial Logistic Regression and GAM

A/B Testing

Multivariate Testing & Uplift Modeling

Difference-in-Differences (DID)

Analysis of Variance (ANOVA) Quiz

Checking Anova Assumptions Quiz

Analysis of Covariance (ANCOVA) Quiz

Beyond ANCOVA Quiz

Logistic Regression & Generalized Linear Models Quiz

MLE and Multivariate Logistic Regression Quiz

Causal Evaluation with Regression Models 1: Improved A/B Testing with MLR Quiz

Causal Evaluation with Regression Models 2: Multivariate Testing and Uplift Modeling Quiz

Causal Evaluation with Regression Models 3: Difference-in-Differences (DID) Quiz

Causal Evaluation Methods Quiz

Module 4 Graded Quiz

Regression Modeling for Marketers
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Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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