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Activity-based costing was first clearly defined in 1987 by Robert S. Kaplan and W. Bruns as a chapter in their book Accounting and Management: A Field Study Perspective. [8] They initially focused on manufacturing industry where increasing technology and productivity improvements have reduced the relative proportion of the direct costs of ...
The probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions. The term is motivated by the fact that the probability mass function or probability density function of a sum of independent random variables is the convolution of their corresponding probability mass functions or probability density functions respectively.
A Binomial distributed random variable X ~ B(n, p) can be considered as the sum of n Bernoulli distributed random variables. So the sum of two Binomial distributed random variables X ~ B(n, p) and Y ~ B(m, p) is equivalent to the sum of n + m Bernoulli distributed random variables, which means Z = X + Y ~ B(n + m, p). This can also be proven ...
The sum of n Bernoulli (p) random variables is a binomial (n, p) random variable. The sum of n geometric random variables with probability of success p is a negative binomial random variable with parameters n and p. The sum of n exponential (β) random variables is a gamma (n, β) random variable.
Activity-based management focuses on managing activities to reduce costs and improve customer value. Kaplan and Cooper [1] divide ABM into operational and strategic: Operational ABM is about doing things right, using ABC information to improve efficiency. Those activities which add value to the product can be identified and improved.
A product distribution is a probability distribution constructed as the distribution of the product of random variables having two other known distributions. Given two statistically independent random variables X and Y, the distribution of the random variable Z that is formed as the product = is a product distribution.
Remember that an estimator for the price of a derivative is a random variable, and in the framework of a risk-management activity, uncertainty on the price of a portfolio of derivatives and/or on its risks can lead to suboptimal risk-management decisions. This state of affairs can be mitigated by variance reduction techniques.
Consider the sum, Z, of two independent binomial random variables, X ~ B(m 0, p 0) and Y ~ B(m 1, p 1), where Z = X + Y.Then, the variance of Z is less than or equal to its variance under the assumption that p 0 = p 1 = ¯, that is, if Z had a binomial distribution with the success probability equal to the average of X and Y 's probabilities. [8]