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I am a flexible, self-motivated researcher with a passion for open-source software and community contribution. My research interests are varied and I can join any lab that requires technical expertise in a bit of everything. Be it analysis, development, design, or whatever is necessary.
Sports officials, players, and fans are concerned about overseas player rankings for the IPL auction. These rankings are becoming progressively essential to investors when premium leagues are commercialized. The decision-makers of the Indian Premier League choose cricketers based on their own experience in sports and based on performance statistics...
Several firms have become increasingly concerned with sustainability in recent decades and are thus implementing environmental and social changes in their businesses and supply networks. This article aims to assess suppliers based on green design, corporate social responsibility, energy consumption, and other sustainability factors that might aid t...
In this paper, we seek to find out the probability of obtaining real roots of a quadratic equation AX^2+ BX + C = 0, with A≠0, when the coefficients are independent, identically distributed uniform variates. The exact value of the roots can be obtained from the coefficients and the discriminant indicates if the roots are real or imaginary. Here, we...
Update: The following statement for CLT is incorrect. Please refer to the accepted answer for context.
The Central Limit Theorem (CLT) essentially states that for a sufficiently large sample size, and assuming that the underlying population has a finite positive variance, the sample appears to be normally distributed.
My guess is that most measurable quantities occur with a finite amount of variance. Is there any other reason why we generally assume Random Variables (RVs) to be normally distributed?
Conversely, when should we not assume a RV to be normally distributed? Only when the variance does not converge to a finite limit or any other fundamental reason?