A fashion student was interested in factors that predicted the salaries of catwalk models. She collected data from 231 models. For each model she asked them their salary per day on days when they were working (salary), their age (age), how many years they had worked as a model (years), and then got a panel of experts from modeling agencies to rate the attractiveness of each model as a percentage with 100% being perfectly attractive (beauty). The most appropriate statistical analysis would be:

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Social Studies

17 September, 00:12

A fashion student was interested in factors that predicted the salaries of catwalk models. She collected data from 231 models. For each model she asked them their salary per day on days when they were working (salary), their age (age), how many years they had worked as a model (years), and then got a panel of experts from modeling agencies to rate the attractiveness of each model as a percentage with 100% being perfectly attractive (beauty). The most appropriate statistical analysis would be:

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Paul Mendez · Answer 1

The most appropriate statistical analysis would be multiple regression.

Explanation:

Multiple regression is used to predict the value of a variable (dependent variable) based on the values of two or more other variables (independent variables).

In the given scenario, the student wants to predict the salaries of catwalk models. Hence, salary is the dependent variable. As per the student's analysis, the salary is dependent on three other variables: age, years, and attractiveness. Hence, these are the independent variables.

A multiple regression model can thus effectively analyse how a model's age, years, and attractiveness affect her salary, and how these three factors account for the differences in the salary levels of catwalk models.