3.5. Regressions
fits
REGRESSION
Logistic regression was performed to determine the effects of gender, age, BMI, fat mass, lean mass, knowledge of PA-related injuries, SB, and level of PA on the likelihood of having PA-related injuries in general and the likelihood that the injury will require emergency medical attention. The logistic regression model was statistically significant, χThe logistic regression model was also statistically significant for the need for medical care after PA-related injury, χAnalysis of dependent variables with more than two categories (questions 2, 3, and 4) was performed using multinomial logistic regression with gender, age, BMI, fat mass, lean mass, knowledge of PA-related injuries, SB, and level of PA as independent variables and “Not injured” as the reference category. Pearson’s chi-square statistic indicated that the model fitted well with data on the number of PA-related injuries sustained, χRegarding types of PA-related injuries, Pearson’s chi-square statistic indicated that the model fits the data well, χRegarding the injured body parts, fit testing indicated that the model fit the data well, χ