Abstract:
This thesis considered statistical calibration models. It focused on
Eiesinhart s calibration model for two tests X & Y where X is an exact
but expensive and slow test and Y is less expensive but quick and cheap
test.
The objective was to investigate through a simulation experiment the
effect of the degree of linear dependency between X & Y as well as
sample size on confidence interval estimation of the forecasted value of
X .It is shown that changes on these factors have no significant effect on
the degree of confidence.
Also this thesis hosts a comparison between logistic regression model
and calibration linear model . Both models were applied on random
sample of 120 people , 100 are infected with blood cancer and 20 are fit .
And we have 3 independent variables , age , pcv , mch .
When applying both models we discovered that the values of standard
errors in calibration regression model are less than the value of standard
errors in logistic regression model , meaning that calibration regression
method was better . Some other results were reached , like when applying
logistic all variables mentioned above have significant influence on
cancer infection , we also found that pcv variable is the most influential in
cancer infection , followed by the rest age and msh.
