Health Care

Question 1:

 The model summary table indicates that R value is 0.521 which is a moderate correlation. The R square value 27% (R2) of the variation in son’s height is explained by the variation in the two independent variables “Father’s height in inches” and “Mother’s height in inches”. The adjusted R square value can be generalize to the population and indicates that 26.8% of the variation in son’s height is explained by the variation in father’s height and mother’s height which are predictor variables.

The ANOVA table illustrates that the two predictor variables (Father’s height and Mother’s height) significantly predict son’s height (F2, 397= 73.954, p <0.001)

The Co efficient table shows  the unstandardized coefficient, b, is 0.507 for the father’s height and (-0.089) for the mother’s height, which means the one unit increase in the Father’s height, the son’s height rises almost by 0.507. For one unit increase in Mother’s height, the son’s height decreased by 0.089. However, the standardized score is 0.547 which means for every one standard deviation increase in father’s height, the son’s height increases by 0.547 of a standard deviation.  The t- value= 11.701 and is statistically significant (p<0.001).  Although, b= 0.507, in the general population we would expect, with a 95% likelihood, that a true value of b would fall somewhere between 0.422 and 0.592. The strongest predictor based on the standardized beta value is the father’s height (0.547).