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Articles by S.K. Sarkar
Total Records ( 4 ) for S.K. Sarkar
  S.K. Sarkar and Habshah Midi
  Logistic regression is a sophisticated statistical tool for data analysis in both control experimentation and observational studies. The goal of logistic regression is to correctly predict the category of outcome for individual cases using the most parsimonious model. To accomplish this goal, a model is created that includes all predictor variables that are useful in predicting the response variable. The logistic regression model is being used with increasing rate in various fields in data analysis. In spite of such increase, there has been no commensurate increase in the use of commonly available methods for assessing the model adequacy. Failure to address model adequacy may lead to misleading or incorrect inferences. Therefore, the goal of this study is to present an overview of a few easily employed methods for assessing the fit of logistic regression models. The summary measures of goodness-of-fit as Likelihood Ratio Test, Hosmer-Lemeshow goodness-of-fit test, Osius-Rojek large sample approximation test, Stukel test and area under Receiver Operating Characteristic curve indicate that the logistic regression model fits the data quite well. However, recommendations are made for the use of methods for assessing the model adequacy in different aspects before proceed to present the results from a fitted logistic regression model.
  S.K. Sarkar , Habshah Midi and Sohel Rana
  Logistic regression is one of the most frequently used statistical methods as a standard method of data analysis in many fields over the last decade. However, analysis of residuals and identification of influential outliers are not studied so frequently to check the adequacy of the fitted logistic regression model. Detection of outliers and influential cases and corresponding treatment is very crucial task of any modeling exercise. A failure to detect influential cases can have severe distortion on the validity of the inferences drawn from such modeling. The aim of this study is to evaluate different measures of standardized residuals and diagnostic statistics by graphical methods to identify potential outliers. Evaluation of diagnostic statistics and their graphical display detected 25 cases as outliers but they did not play notable effect on parameter estimates and summary measures of fits. It is recommended to use residual analysis and note outlying cases that can frequently lead to valuable insights for strengthening the model.
  S.K. Sarkar and Habshah Midi
  Problem statement: The population problem is the biggest problem in the world. In the global and regional context, Bangladesh population has drawn considerable attention of the social scientists, policy makers and international organizations. Bangladesh is now world’s 10th populous country having about 140 million people. The recent experience of Bangladesh shows that fertility can sustain impressive declines even when women’s lives remain severely constrained. Recent statistics also suggest that, despite a continuing increase in contraceptive prevalence rate (56%), the expected fertility decline in Bangladesh has stalled. Approach: The purpose of this study was to explore the possibility of further fertility decline in Bangladesh with special attention to identify some social and demographic factors as predictors which are responsible to desire for more children using stepwise and best subsets logistic regression approaches. The study had compared two approaches to determine an optimum model for prediction of the outcome. Results: It had been found, excess desire for children is solely responsible for the stalled fertility. Conclusion: To overcome the situation, the policy makers of Bangladesh should pay their attention to eliminate the regional variations of desire for more children and introduce awareness programs among rural women about the positive impact of smaller family.
  Habshah Midi , S.K. Sarkar and Sohel Rana
  The aim of this study was to fit a multinomial logit model and check whether any gain achieved by this complicated model over binary logit model. It is quite common in practice, the categorical response have more than two levels. Multinomial logit model is a straightforward extension of binary logit model. When response variable is nominal with more than two levels and the explanatory variables are mixed of interval and nominal scale, multinomial logit analysis is appropriate than binary logit model. The maximum likelihood method of estimation is employed to obtain the estimates and consequently Wald test and likelihood ratio test have been used. The findings suggest that parameter estimates under two logits were similar since neither Wald statistic was significant. Thus, it can be concluded that complicated multinomial logit model was no better than the simpler binary logit model. In case of response variable having more than two levels in categorical data analysis, it is strongly recommended that the adequacy of the multinomial logit model over binary logit model should be justified in its fitting process.
 
 
 
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