Using the sleep3ED.sav dataset, I ran a multiple regression predicting daytime sleepiness (totSAS) from sex, age, physical fitness (fitrate), and depression (depress). ...
Using the sleep3ED.sav dataset, I ran a multiple regression predicting daytime sleepiness (totSAS) from sex, age, physical fitness (fitrate), and depression (depress). Based on the unstandardized coefficients, the regression equation is:
totSAS = 12.47 + 0.18(sex) + 0.05(age) – 0.32(fitrate) + 0.41(depress)
Interpreting one standardized coefficient, the Beta for depression (β = .45) indicates that depression is the strongest predictor in the model: as depression scores increase, daytime sleepiness increases more than with any other variable. In practical terms, this suggests that individuals with higher depression symptoms are more likely to feel tired and sleepy during the day, highlighting the importance of addressing mental health when evaluating sleep-related problems.