Spiral Binomial and Related Distributions for Obsession to Abortion
- 1 Texas State University-San Marcos, United States
Abstract
Repeated abortion is considered an important health concern by the pregnant women and the governments. Educating the public about the impact of abortion becomes the government’s responsibility. For this purpose, the government agencies turn to health and medical researchers for opinion about the impact of abortion. The public health researchers turn to data for clues and seek pertinent data information to answer several questions. One question is: Do repeated pregnancy-abortion data contain clue about a level of obsession? To answer this question based on data-clue, in this article, a new probability distribution is introduced and named it as Spiral Binomial Probability Distribution (SBPD). Various properties of SBPD and its connection to other spiral distributions are established and then utilized to assess whether the estimated obsession to abortion in a given data is significant and how much the obsession impacts likelihood of future pregnancy. When a woman experiences two or more pregnancies, her obsession to abortion is estimated using spiral binomial distribution of this article. The data about pregnancy versus abortion among Romans reveal that under repeated pregnancies (that is, y = 2, 3, 4), their obsession level to abortion changed to 0.02, 0.06 and 0.02. Furthermore, the correlation between the number of pregnancies and the number of abortions increased with an increased obsession level.
DOI: https://doi.org/10.3844/ijrnsp.2012.21.29
Copyright: © 2012 Ramalingam Shanmugam. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- 3,644 Views
- 2,076 Downloads
- 0 Citations
Download
Keywords
- Likelihood Ratio Test
- Correlation
- Regression
- Conditional Probability Distribution
- Weighted Sampling
- Count Distribution
- Marginal Distribution