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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)$8.68
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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.
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
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Before we start:
- Clean workspace (optional):
- Load all necessary external packages (using librarian package!):
- 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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