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Data Science for Lingerie Design
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Module 2
Identifying Trends and Style Relationships
1
Pearson Correlation: Design Features & Performance
Compute and interpret Pearson correlation between a continuous design feature (e.g., neckline depth) and a performance metric (e.g., photoshoot engagement score).
2
Spearman Correlation for Lingerie Design Analysis
Apply Spearman rank correlation to assess the monotonic relationship between model preference rankings and a lingerie's design attributes.
3
Measuring Inter-Rater Reliability with Kendall's W
Use Kendall's W to measure inter-rater reliability among a photographer, stylist, and model who are ranking a set of lingerie styles.
4
Lingerie Trend Lifespan: Exponential Modeling
Model the lifespan of a lingerie trend using an exponential distribution and compute its survival function and hazard rate for a given numerical example.
5
Pearson vs. Spearman: Non-Linear Relationships
Solve a numerical problem comparing Pearson and Spearman correlation on a dataset with a non-linear relationship between a design attribute and appeal.
Previous module
Foundations for Lingerie Data Analysis
Next module
Segmenting Lingerie Styles with Unsupervised Learning