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Complete Team Assignment 1 R-Code Guide for Statistical Learning in Marketing for MADS (Code, Output, Explanation, Interpretation and Extra Tips) CA$12.30   Add to cart
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Complete Team Assignment 1 R-Code Guide for Statistical Learning in Marketing for MADS (Code, Output, Explanation, Interpretation and Extra Tips)
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  • Course
  • Statistical Learning in Marketing (EBM214A05)
  • Institution
  • Rijksuniversiteit Groningen (RuG)
  • Book
  • R for Marketing Research and Analytics

The best extensive step-by-step guide for Team Assignment 1 for Statistical Learning in Marketing for MADS. It includes: Code, Output, Explanation, Interpretation and Extra Tips. 100% of profit from this guide is donated to local Groningen NGOs, as well as national ones.

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Document information

  • February 6, 2023
  • 36
  • 2022/2023
  • Case
  • Maarten gijsenberg
  • A

Subjects

  • slm
  • ebm214a05
  • mads
  • team assignment guide
  • team assignment summary
  • university of groningen
  • mads mad
  • statistical learning in marketing
  • marketing analytics and data science
  • rijksuniversiteit groningen
  • rug

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  • Rijksuniversiteit Groningen (RuG)
  • Marketing Analytics & Data Science
  • Statistical Learning in Marketing (EBM214A05)
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EXTENSIVE STEP -BY-STEP GUIDE FOR TEAM ASSIGNMENT 1

CODE, OUTPUT, EXPLANATION, INTERPRETATION & EXTRA TIPS
Note: Original assignment text available at the end.

, MADS MADLAD |2




Note from MADS MADLAD:

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wishes you g ood luck & persev erance.




Grades Testimony:

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Before we start:

- Clean workspace (optional):



- Load all necessary external packages (using librarian package!):




- Load custom functions (KMO, Bartlett.sphere, a_bic, center_scale):




- Load Dataset & check out the first few rows, get summary and structure:

, MADS MADLAD |5


- Before starting, we also rename our questionnaire variables to more descriptive names
related to the content of the question:




Question 1: What are the factors that can be derived from the many lifestyle and sustainable
clothing perception variables?

- Reduce these two sets of variables into a smaller number of factors, using Factor Analysis.
- Make sure to use standardized variables
- Apply an orthogonal rotation (varimax)
- Include some insights on how the different age categories score on the obtained factors
- Save the factor scores for the rest of your assignment



First, we handle the lifestyle questions (columns 11 to 23 in fashion):

1. Standardize the variables for lifestyle (columns 11 to 23)




2. Before doing checks related to FA, we check if our lifestyle question variables are correlated:




3. Although we are told to use FA, we can check whether it is appropriate by running KMO and
Bartlett (note: the teacher provided the KMO and Bartlett function code):




a. KMO (range: 0 – 1): Our KMO is very close to 1, which is what we want.

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