In this paper, a bivariate Lindley distribution using Morgenstern approach is proposed which can be used for modeling bivariate life time data. Some characteristics of the distribution like moment generating function, joint moments, Pearson correlation coefficient, survival function, hazard rate function, mean residual life function, vitality function and stress-strength parameter R = P r(Y < X), are derived. The conditions under which the proposed distribution is an increasing (decreasing) failure rate distribution and positive (negative) quadrant dependent is discussed. Also, the method of estimating model parameters and stress-strength parameter by maximum likelihood is elucidated. Numerical illustration using simulated data is carried out to access the estimates in terms of mean squared error and relative absolute bias.Keywords Farlie-Gumbel-Morgenstern family, maximum likelihood estimation, mean residual life, mean squared error, positive quadrant dependence, relative absolute bias, stress-strength parameter, vector hazard rate, vitality function AMS 2010 subject classifications 60E05
In literature, Lindley distribution is considered as an alternate to the exponential distribution. In the present work, a methodology is developed to discriminate between exponential and Lindley distributions based on the ratio of the maximum likelihoods. Asymptotic distribution of the test statistic under the null hypothesis is derived and the minimum sample size required to discriminate between the two distributions for a user specified probability of correct selection is obtained. Numerical illustrations of the methodology are given through simulated and real life data sets.
Game-based learning is an exciting and interactive tool used by many teachers across the globe. This research aims to check whether any significant change is found in the learning of the student before and after introducing game-based learning in classroom teaching. MBA students were identified as the target group for this research. The production dice game was used for this experiment. The teacher engaged the first session traditionally and later with the production dice game. Student learning was captured through a Google form before and after the game. The Google form had questions ranging from understanding to analyzing to application-level to capture exactly the effectiveness of game-based learning, Paired sample t-test was applied to check the before and after test results, and it was found that there was a significant change in the learning among the identified target group. Through this study, the authors conclude that game-based learning provides better results in student learning as compared to regular classroom teaching.
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