# Trikaal

Past - Present - Future via Data Science

Past - Present - Future via Data Science

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Let us say I want to have prediction and linear model plots of data table in Oracle having data of height and weight using ORE, to use server memory execution rather than client memory execution and show the result in OBIEE reporting tool.

Prerequisite:

1. I have ORE in 12c pdb configured with a user, say RUSER2 with RQROLE and RQADMIN roles.

2. OBIEE 11.1.1.7.0 is configured.

3. Table PRED_TBL is created with height, weight and ID columns.

4. I know R,ORE and OBIEE

Let us start with scripts:

#olm is a ORE script which returns predicted table.

begin

sys.rqScriptDrop('olm');

sys.rqScriptCreate('olm', 'function(dat) {

raw<-dat

library(ORE)

lmdl<-lm(WEIGHT~.,raw)

lpred<-predict(lmdl,raw)

pred_tbl<-cbind(raw,lpred)

pred_tbl

}');

end;

#Creating view which calls olm script with PRED_TBL as input

create or replace view R_PREDICT as

select HEIGHT, WEIGHT, ID,LPRED from table( rqTableEval(

cursor(select * from PRED_TBL),cursor(select 1 as "ore.con…

Prerequisite:

1. I have ORE in 12c pdb configured with a user, say RUSER2 with RQROLE and RQADMIN roles.

2. OBIEE 11.1.1.7.0 is configured.

3. Table PRED_TBL is created with height, weight and ID columns.

4. I know R,ORE and OBIEE

Let us start with scripts:

#olm is a ORE script which returns predicted table.

begin

sys.rqScriptDrop('olm');

sys.rqScriptCreate('olm', 'function(dat) {

raw<-dat

library(ORE)

lmdl<-lm(WEIGHT~.,raw)

lpred<-predict(lmdl,raw)

pred_tbl<-cbind(raw,lpred)

pred_tbl

}');

end;

#Creating view which calls olm script with PRED_TBL as input

create or replace view R_PREDICT as

select HEIGHT, WEIGHT, ID,LPRED from table( rqTableEval(

cursor(select * from PRED_TBL),cursor(select 1 as "ore.con…

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To which question Chi square distribution answers?

Suppose you have a data population. It is

1. Normally distributed

2. You know the standard deviation of population.

Now you want to conduct an experiment out of the sample of data. You will get standard deviation of that sample too.

Now what is the probability that next sample of same size you pick will have less than or equal to standard deviation of earlier sample standard deviation computed? (Greater than case is nothing but 1- (less than case). By default it is left tail test.)

First Step: Find chisquare critical value.

Χ2 = [ ( n - 1 ) * s2 ] / σ2

R function:

chisqcv <- function(samplesize,samplestandarddeviation,populationstandarddeviation){

result<-((samplesize-1)*(samplestandarddeviation*samplestandarddeviation)/(populationstandarddeviation*populationstandarddeviation))

return(result)

}

example:

chisquarecriticalvalue<-chisqcv(7,6,4)

Once you find critical value, find out cumulative probability distribution:

pchisq(chis…

Suppose you have a data population. It is

1. Normally distributed

2. You know the standard deviation of population.

Now you want to conduct an experiment out of the sample of data. You will get standard deviation of that sample too.

Now what is the probability that next sample of same size you pick will have less than or equal to standard deviation of earlier sample standard deviation computed? (Greater than case is nothing but 1- (less than case). By default it is left tail test.)

First Step: Find chisquare critical value.

Χ2 = [ ( n - 1 ) * s2 ] / σ2

R function:

chisqcv <- function(samplesize,samplestandarddeviation,populationstandarddeviation){

result<-((samplesize-1)*(samplestandarddeviation*samplestandarddeviation)/(populationstandarddeviation*populationstandarddeviation))

return(result)

}

example:

chisquarecriticalvalue<-chisqcv(7,6,4)

Once you find critical value, find out cumulative probability distribution:

pchisq(chis…

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- Get link
- Google+
- Other Apps

- Get link
- Google+
- Other Apps