height, linear regression model, correlation analysis
Abstract
Height is a subject of interest in understanding familial relationships and growth patterns. A sample of 20 boys and 20 females, ages 15 to 19, together with their parents, were gathered for the study, which examines the link between parents’ height and that of their offspring. By treating the parents’ height as the independent variable (X) and the children’s height as the dependent variable (Y), regression models were developed to predict height differences across genders. The models were also cross-tested to compare their predictive power. The optimal regression equations yielded relatively low R² values: 0.096 for the height of father and son, 0.122 for mother and son, 0.078 for father and daughter, and 0.091 for mother and daughter. Despite the relatively low R² values, these findings underscore the complex nature of height inheritance, suggesting that multiple genetic and environmental factors contribute to height variations. To give a more thorough picture of the factors influencing height, subsequent research might examine these extra variables.
