| dc.description.abstract | Logistic regression is widely used as a popular model for the analysis of binary data with the areas
of applications including physical, biomedical and behavioral sciences. In this study, the logistic regression
model, as well as the maximum likelihood procedure for the estimation of its parameters, are introduced in
detail. The study has been necessited with the fact that authors looked at the simulation studies of the logistic
models but did not test sensitivity of the normal plots. The fundamental assumption underlying classical results
on the properties of MLE is that the stochastic law which determines the behaviour of the phenomenon
investigated is known to lie within a specified parameter family of probability distribution (the model). This
study focuses on investigating the asymptotic properties of maximum likelihood estimators for logistic
regression models. More precisely, we show that the maximum likelihood estimators converge under conditions
of fixed number of predictor variables to the real value of the parameters as the number of observations tends to
infinity.We also show that the parameters estimates are normal in distribution by plotting the quantile plots and
undertaking the Kolmogorov -Smirnov an the Shapiro-Wilks test for normality,where the result shows that the
null hypothesis is to reject at 0.05% and conclude that parameters came from a normal distribution.
Key Words: Logistic, Asymptotic, Normality, MRA(Multiple Regression Analysis) | en_US |
