Using jack knife to estimation logistic regression model for Breast cancer disease

  • Naba' a Jaafar Abd
  • Mahmoud Mahdi Al-Bayati
  • Muhammad Jassim Muhammad
Keywords: binary logistic regression, greatest possibility method, logistic regression method, jackknife method

Abstract

 

It is considered as one of the statistical methods used to describe and estimate the relationship between randomness (Y) and explanatory variables (X). The second is the homogeneity of the variance, in which the dependent variable is a binary response takes two values  (One when a specific event occurred and zero when that event did not happen) such as (injured and uninjured, married and unmarried) and that a large number of explanatory variables led to the emergence of the problem of linear multiplicity that makes the estimates inaccurate, and the method of greatest possibility and the method of declination of the letter was used in estimating A double-response logistic regression model by adopting the Jacknaev method and comparing the capabilities according to the information standard (AIC)

The Jackknife method and the aforementioned statistical capabilities were applied to study the relationship between the response variable (incidence and absence of breast cancer) for a sample size of (100) samples for the year (2020) and the explanatory variables (the percentage of haemoglobin present in red cells in the blood, red blood cells, white blood cells, Platelets, the percentage of haemoglobin in the blood, the percentage of lymphocytes in the blood, the percentage of monocytes, the percentage of eosinophils, the percentage of basophils) And it was evident through comparison that the character regression method in estimating the two-response logistic regression model is the best in estimating the parameters of the logistic regression model in the case of a problem of linearity

Published
2021-02-28
How to Cite
Abd, N., Al-Bayati, M. and Muhammad, M. (2021) “Using jack knife to estimation logistic regression model for Breast cancer disease”, Journal of Economics and Administrative Sciences, 27(126), pp. 571-582. doi: 10.33095/jeas.v27i126.2125.
Section
Statistical Researches

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