2. Review of Literature
Peng et al (2002) applied the logistic regression technique to compare the
sample data of gender and recommendation for remedial reading instruction. David et.
al. (2001) used logistic regression analysis to determine whether grade point average
and hours of education is significant predictor of performance on the national athletic
trainers’ association board of certificate examinatio n. E. L. Dey and Astin A. W. (1993)
studied the focus on the practical implications of applying logistic regression, probit
analysis and linear regression to the problem of predicting the college student retention.
Jason et. al. (2001) analyzed the logistic regression method to predict the probability of
passing a course based on the scores on California chemistry diagnostic test at two
different institutions with two different instructors over multiple years. Robert B. and
Vaughan B. (2006) checked which factors were key in enabling or constraining a
students’ ability to close the achievement gap during the school results. Erin et al.
(2010) used multilevel logistic regression analyses, to explore the school and student
level characteristics associated with moderate and high levels of physical activity
among school students. In series of articles Sarma and Sarmah (1999), Saha and
Sarmah (2010) and Saha and Sarmah (2011) discussed the probabilistic analysis and
testing of some important hypothesis using Markov chain.