A Factor is a dimension underlying several variables.

Analytical, it is a linear combination of the variables: F1=W1X1+W2X2+... Where: F1 - factor1, Xj - the variables of the study (5 in our example), Wj - weights used to combine the individual scores. The various methods of factor analysis are distinguished by the manner in which the weights Wj are determined.

A Factor score: The score of a respondent on a factor. If we decide to settle with two factors we will have two factor scores for each of the 500 respondents.

A Factor loading: The correlation between a factor and a variable

Labeling Factors: The art of segmentation; consists of selecting a term which best describes all the variables that load highly a factor. Factor #1 may be labeled as “price conscious”: and factor #2 as “ fashion conscious”.

The proportion of total variance of a certain variable accounted for by a factor may be obtained by squaring the loading. In our example factor #1 explains .92342=86.94% of the variance in variable 4.

proc transpose  data =event_transaction  out=result prefix=event;
by branch_cust_ip;
id Event_Costing_Activity_Type_Co;
var count;
run;

data result;
set result;

array events{*} _NUMERIC_ ;
do i = 1 to dim(events);
if events{i} = . then events{i} = 0;
end;
drop i;
run;


proc factor score data=result method=p rotate=orthomax nfactors=10  outstat=fact_events;
var  event: ;
run;

proc score data=personal score=fact_events  out=scores_events;
var  event: ;
run;

data scores_events;
set  scores_events;
max=max(Factor1,
Factor2,
Factor3,
Factor4,
Factor5,
Factor6,
Factor7,
Factor8,
Factor9,
Factor10)
;

min=min(Factor1,
Factor2,
Factor3,
Factor4,
Factor5,
Factor6,
Factor7,
Factor8,
Factor9,
Factor10)
;
run;

data scores_events;
set  scores_events;
array factor Factor1-factor10;

do i=1 to dim(factor);
if max=factor [i] then factor_max=i;
if min=factor [i] then factor_min=i;
end;
run;