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Research Paper | Statistics | India | Volume 10 Issue 7, July 2021 | Popularity: 5.3 / 10
Dowry Deaths as Compared to Literacy Rates in different States of India
Dr. Firdous Ansari
Abstract: Background: Dowry death is old wicked tradition. Death by burning of married women was reported to be 452 for the year 1985, 478 for the year 1986 and 300 for the first initial six months of 1987, of women belonging to age16 to 30 years. [1, 2]. 1, 319cases were also reported nationwide in 1986 [1, 3]. Police records did not match hospital records for third degree burn cases among younger married women; far more violence occurs than the crime reports indicate [1, 4]. Methodology: Pub Med and Google search engines were searched to search previously done studies. Data was taken from NCBR, India. Census of India and data was taken for the year 2019 and 2017 respectively. Data was made worth for the study, so that proper conclusions could be made. Analysis was performed after implementation of correlation coefficient, regression coefficient and t test. Results and conclusions were made thereafter. Results: The statistically significant correlation coefficient was found to be -0.329.The ANOVA table was also found indulged significantly. Study concluded enhancement in literacy may pullout dowry deaths. t test reflected a significant value 1.8 (p<0.5). Discussion: Results were showing significant values, those could be interpreted, dowry deaths may fall if literacy rates are increased.
Keywords: Dowry deaths, Literacy rates
Edition: Volume 10 Issue 7, July 2021
Pages: 822 - 823
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Statistics, Nepal, Volume 3 Issue 6, June 2014
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Statistics, India, Volume 8 Issue 5, May 2019
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Statistics, Kenya, Volume 8 Issue 5, May 2019
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Statistics, India, Volume 9 Issue 3, March 2020
Pages: 1201 - 1204Customer Relationship Management (CRM) Viewed by and Analysed by Probability Distribution
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Statistics, Congo, Volume 8 Issue 4, April 2019
Pages: 85 - 95Modeling Tax Revenues Using Kernel Approach Case Study: North Kivu Province (Democratic Republic of Congo) Tax Revenues Time Series Forecasting
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