%0 Journal Article
%T An Application of Bootstrapping in Logistic Regression Model
%A Isaac Akpor Adjei
%A Rezaul Karim
%J Open Access Library Journal
%V 3
%N 9
%P 1-9
%@ 2333-9721
%D 2016
%I Open Access Library
%R 10.4236/oalib.1103049
%X <div style="text-align:justify;">
	<span style="line-height:1.5;">Computer intensive methods have recently been
intensively studied in the field of mathematics, statistics, physics,
engineering, behavioral and life sciences. Bootstrap is a computer intensive
method that can be used to estimate variability of estimators, estimate
probabilities and quantile related to test statistics or to construct
confidence intervals, explore the shape of distribution of estimators or test
statistics and to construct predictive distributions to show their asymptotic
behaviors. In this paper, we fitted the classical logistic regression model, and performed both parametric and non-parametric bootstrap for estimating
confidence interval of param</span><span style="line-height:1.5;">eters for logistic model and odds ratio. We also
conducted test of hypothesis that the prevalence does not depend on age.
Conclusions from both bootstrap methods were similar to those of
classical logistic regression.</span>
</div>
%K Nonparametric Bootstrap
%K Parametric Bootstrap
%K Logistic Regression
%K Confidence  Interval
%K Test of Hypothesis
%U http://www.oalib.com/paper/5273593