Predicting Photoshoot Appeal with Regression Models

Develop and interpret an OLS multiple linear regression model to quantify the effect of design features (e.g., lace percentage, color saturation) on a lingerie's appeal score.

Diagnose the validity of an OLS model by testing for multicollinearity (VIF), heteroscedasticity (Breusch-Pagan test), and non-normality of residuals from model output.

Formulate a logistic regression model to predict the probability of a lingerie piece being a 'top pick' (binary outcome) based on its attributes.

Interpret logistic regression coefficients as odds ratios to explain how each design feature influences the odds of a lingerie piece being selected.

Solve a numerical problem: given the output from a logistic regression, calculate the predicted probability of selection for a new lingerie style.