Start a new question. Make sure to highlight how it differs and what doesn't work from current solution.  ... View more

You still need a loop to handle multiple variables with a value of 2 in the array. But instead of iterative DO loop you can use a DO WHILE loop. data want; set test; array m x:; do until (w=0); w = whichn(2, of m(*)); if w then m(w) = .; end; mean = mean(of m(*)); drop w; run; ... View more

Thanks, Reeza. It is a survey data I'm working on. For each measurement, there are multiple items. For example, a1-a4 is to measure individuals' mental health, and b1-b5 is to measure physical health. The sum of a1-a4 is the final score for a person. Therefore, if one person only completed one item out of four, the person cannot be counted as a valid observation. ... View more

Hello,   this is enormous. I really had allready implemented it via a %macro %Do Loop. The amount of time if do save is astonishing. Thank you so much for this - I need this very procedure "over an over" so this really changes the game.   As I do have to implement it for more than one variable, would you mind considering if I have transferred it correctly to a setting more closely to the actual one?   data want; set have; retain x_end y_end z_end; if _n_ ne 1 then x=x_end; if _n_ ne 1 then y=y_end; if _n_ ne 1 then z=z_end; x_end = intercept1 + a*x + b*y + c*z ; y_end = intercept2 + d*x + e*y + f*z ; z_end = intercept3 + g*x + h*y + i*z ; run; So, former y is now "x_end"; Especially, the three ifs are a concern of mine - could I achieve a better solution?   But, nonetheless, your very answer means a huge leap indeed for me. Once again, thank you so much.   ... View more

Yours alongwith Ballardw's suggestion helped me.   Thanks ... View more

Here is my two cents: a) At 95% confidence interval we will reject the null hypothesis when P value is less than .05. How will you explain this statement to a layman? when P value is less than .05, you can reject H0. For example: H0: Model fit data When P=0.01, you can say H0 is not right or reject H0. Assuming when H0 is true, the corresponding statistic is D0(which measure the distance from H0). P value is the probability of greater than D0 . i.e. P=P( D > D0) . So when P<0.05 ,it is to say there are few D could be greater than D0 , In other words, D0 is very large , no other D is greater than it . Therefore you are far away from H0. Notice: statistic D measure the distance from H0. b) How will you determine that errors are normally distributed for regression analysis? There is a Normal test in proc univariate . c) Difference between covariate and covariance Covariance is the variance between two variables, It measures the relationship between these two variables. Covariate , I think it is believed that it has correlation with response/independent variable Y .There is a so called covariate analysis in ANOVA. proc glm; class C; model Y=X C /solution; quit; ... View more

Cynthia_sas, can you give an example of later case? Thanks ! ... View more