About stat_sas

stat_sas · ‎07-23-2014

Normalization is done because PROC FACTOR assumes all factors/components to have variance of 1, while PROC PRINCOMP creates components that have variances equal to the eigenvalues.

stat_sas · ‎07-23-2014

%let month =%str( in July);

stat_sas · ‎07-22-2014

proc sql; select *, MONOTONIC() as CONSNUMBER from have; group by var1-var5; quit;

stat_sas · ‎07-22-2014

Hi, I also consulted documentation and found nothing about P and Q. Sorry about that.

stat_sas · ‎07-22-2014

data a; input prd_id name & $20. prod_qty; cards; 101 pencil 57 102 parker pen 24 103 hp studion shuffle n 10 104 samsung galexy ace 29 ; run;

stat_sas · ‎07-21-2014

It will provide total number of observations for each level of patient_id. So if each patient_id has one frequency then we can conclude that patient_id is unique.

stat_sas · ‎07-21-2014

I think this is happening because of exogenous variable time_period that contains equally spaced values introducing a linearly dependency for higher lags.

stat_sas · ‎07-21-2014

Hi, Not sure about the exact positioning of the variables in text file, this is based on datalines may be helpful in importing text file. data WANT; infile datalines; input ID $7. Date1 8-9 Date2 date9. Date3 date9. date4 date9. @39 Flag1 $1. @40 Location $6. @46 Flag2 $1.; datalines; XXXXXXX0101APR201431MAR201515OCT2012 1H0H0H0A XXXXXXX0201APR201431MAR201525MAY2009 0H0H0H0B XXXXXXX0301APR201431MAR201510SEP1945 1H0H0H0C XXXXXXX0401APR201431MAR201515SEP1953 0H0H0H0B ; run;

stat_sas · ‎07-21-2014

Hi, I think minic can produce the desired info in identify statement of proc arima. Thanks,

stat_sas · ‎07-21-2014

Thanks for the detailed explaination on AIC. As you mentioned adding a new variable in the model will reduce -2 log-likelihood. So if reduction in -2 log-likelihood by adding a new variable is higer than 2P then AIC will be on lower side right?

stat_sas · ‎07-20-2014

I don't think there is a direct relation between AIC and significance of interaction term. If you add more predictors in the model, even some of them are irrelevant to the study you will get lower AIC.

stat_sas · ‎07-19-2014

proc sql; create table file1 as select Age,Count(Distinct Q1) as Uniq, Count(Q2) as Count from test group by Age; quit; proc sql; select age,uniq,count,(uniq/(select sum(uniq) from file1)) as unique_percent,(count/(select sum(count) from file1)) as cnt_percent from file1; quit;

stat_sas · ‎07-19-2014

These are goodness of fit measure which are used to compare one model to another.

stat_sas · ‎07-18-2014

This is happening because your second data set contains variables that don't have missing values. If at least one of the variables in your second dataset has missing values then you will get 5 columns in nlevelsds.

stat_sas · ‎07-18-2014

Not sure, found something seems close to your problem, where Tom has suggested a solution. May be helpful. https://communities.sas.com/message/218009#218009

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