A study of obesity risk in children in a head start program used a food score calculated from a 45-question food survey to predict body mass index (BMI) percentile in these children 18 months after the initial survey. The study enrolled 20 children. The researchers used a linear regression model for the prediction of BMI percentile. The food scores ranged from 45 to 245. The regression line that is calculated by standard regression programs or by hand is called the least-squares line because it:
A)minimizes the distances of actual BMI percentiles from the regression line.
B)minimizes the squared distances of the actual BMI percentiles from the regression line.
C)minimizes sum of the squared distances of the actual BMI percentiles from the regression line.
D)minimizes the sum of the distances of the actual BMI percentiles from the regression line.

Question

Quizplus · Accepted Answer

The least-squares regression line minimizes the sum of the squared distances of the actual BMI percentiles from the regression line, which is a method used to find the best-fitting line through the data points by minimizing the sum of the squares of the vertical distances of the points from the line.

A Study of Obesity Risk in Children in a Head