Regression

hey guys,

Ive got a data set with 3 seemingly unusual observations (given their standard residuals are greater than 2) and i decided to remove these 3 points to see if significance and goodness of fit would increase. However another unusual observation appeared out of nowhere when a regression analysis was performed.
What does this mean and what should i do about it?

Thanks in advance

The reason you are getting another unusual point is because as you keep removing points, and since these may be outliers if I'm not wrong they have a large impact on the mean and hence once removed, there is a shift in the mean and you'll keep getting outliers.

R^2 only tells you how much of the correlation is accounted for by your statistical model, hence you are right and it should move nearer to 1.

Remember that R^2 is coefficient of determination and NOT your correlation coefficient.

Now with regards to your research, a good predictor would be a combination of body fat.

I'm not sure how statistical you want to be but, to break this down, you have
body fat % = forearm+abdomen+height+weight+hip. Thats a linear equation.
I would suggest you do a step wise formulation by adding and removing variables to see when you get a significant effect. Remember when it comes to weight and body fat, there is usually a combination of predictors.

Transforming data does help depending on how much variation you have between the measurements.
Does this help or have I deviated from the main topic?!