I want to know how age, obesity (0 for no and 1 for yes), smoking status (0 for no smoking and 1 for having smoking) affect the serum Vitamin D level using regression with SPSS.
The best-fit curve for age reported: Linear (R2=0.108), Quadratic (R2=0.109), Cubic (R2=0.106)
So I assumed that the E(VitaminD)= b0 + b1*Age +b2*Age^2
But if want to put this into a multiple regression analysis with age, obesity, and smoking status as independent variables, which one should I use? Age+obesity+smokingStatus or Age^2+obesity+smokingStatus?
Ho Nguyen Tuong -
Are you sure you just want "yes" or "no" for obesity? How about a BMI value? Also, how much smoking? At any rate, you can compare model results for a given sample using a "graphical residual analysis." (You can research that online.) Also, you probably want to consider a "cross-validation," as you do not want to overfit a model to a particular sample when that may mean your model won't fit so well to other data which you wanted to cover. Trying more than one sample could help.
Best wishes - Jim
The R squared value does not seem to increase much after adding the quadratic term. Are you sure the relationship really is curvilinear? If not, it's probably best to go with the linear model. I would also recommend examining bivariate scatterplots and/or residual plots to check for non-linearity and other potential issues.
You said (emphasis added): "I want to know how age, obesity (0 for no and 1 for yes), smoking status (0 for no smoking and 1 for having smoking) affect the serum Vitamin D level using regression with SPSS." Do you mean this, or do you mean associated (and associated after conditioning on ...)? If you mean what you wrote, your approach is questionable.
Obesity 0/1 and smoking 0/1??? That seems like a big oversimplification to me!
And what are your theoretical assumptions about the vit.D level and age? You need to use a model that corresponds to those assumptions, not those simplistic linear and quadratic things that don't explain anything.
Do you have a graph of your vit.D vs age measurements? Or the raw data?
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