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From Wikipedia
A calculation is a deliberate process for transforming one or more inputs into one or more results, with variable change.
The term is used in a variety of senses, from the very definite arithmetical calculation of using an algorithm to the vague heuristics of calculating a strategy in a competition or calculating the chance of a successful relationship between two people.
Multiplying 7 by 8 is a simple algorithmic calculation.
Estimating the fair price for financial instruments using the BlackScholes model is a complex algorithmic calculation.
Statistical estimations of the likely election results from opinion polls also involve algorithmic calculations, but provide results made up of ranges of possibilities rather than exact answers.
To calculate means to ascertain by computing. The English word derives from the Latincalculus, which originally meant a small stone in the gallbladder (from Latin calx). It also meant a pebble used for calculating, or a small stone used as a counter in an abacus (Latin abacus, Greekabax). The abacus was an instrument used by Greeks and Romans for arithmetic calculations, preceding the sliderule and the electronic calculator, and consisted of perforated pebbles sliding on an iron bars.
In probability and statistics, a random variable or stochastic variable is a variable whose value is not known. Its possible values might represent the possible outcomes of a yettobeperformed experiment, or the potential values of a quantity whose alreadyexisting value is uncertain (e.g., as a result of incomplete information or imprecise measurements). Intuitively, a random variable can be thought of as a quantity whose value is not fixed, but which can take on different values; a probability distribution is used to describe the probabilities of different values occurring. Realizations of a random variable are called random variates.
Random variables are usually realvalued, but one can consider arbitrary types such as boolean values, complex numbers, vectors, matrices, sequences, trees, sets, shapes, manifolds, functions, and processes. The term random elementis used to encompass all such related concepts. A related concept is thestochastic process, a set of indexed random variables (typically indexed by time or space).
Introduction
Realvalued random variables (those whose range is the real numbers) are used in the sciences to make predictions based on data obtained from scientific experiments. In addition to scientific applications, random variables were developed for the analysis of games of chance and stochastic events. In such instances, the function that maps the outcome to a real number is often the identity function or similarly trivial function, and not explicitly described. In many cases, however, it is useful to consider random variables that are functions of other random variables, and then the mapping function included in the definition of a random variable becomes important. As an example, the square of a random variable distributed according to a standard normal distribution is itself a random variable, with a chisquare distribution. One way to think of this is to imagine generating a large number of samples from a standard normal distribution, squaring each one, and plotting a histogram of the values observed. With enough samples, the graph of the histogram will approximate the density function of a chisquare distribution with one degree of freedom.
Another example is the sample mean, which is the average of a number of samples. When these samples are independent observations of the same random event they can be called independent identically distributed random variables. Since each sample is a random variable, the sample mean is a function of random variables and hence a random variable itself, whose distribution can be computed and properties determined.
One of the reasons that realvalued random variables are so commonly considered is that the expected value (a type of average) and variance (a measure of the "spread", or extent to which the values are dispersed) of the variable can be computed.
There are two types of random variables: discrete and continuous. A discrete random variable maps outcomes to values of a countable set (e.g., the integers), with each value in the range having probability greater than or equal to zero. A continuous random variable maps outcomes to values of an uncountable set (e.g., the real numbers). For a continuous random variable, the probability of any specific value is zero, whereas the probability of some infinite set of values (such as an interval of nonzero length) may be positive. A random variable can be "mixed", with part of its probability spread out over an interval like a typical continuous variable, and part of it concentrated on particular values like a discrete variable. These classifications are equivalent to the categorization of probability distributions.
The expected value of random vectors, random matrices, and similar aggregates of fixed structure is defined as the aggregation of the expected value computed over each individual element. The concept of "variance of a random vector" is normally expressed through a covariance matrix. No generallyagreedupon definition of expected value or variance exists for cases other than just discussed.
Examples
The possible outcomes for one coin toss can be described by the state space \Omega = {heads, tails}. We can introduce a realvalued random variable Y as follows:
Y(\omega) = \begin{cases} 1, & \text{if} \ \ \omega = \text{heads} ,\\ 0, & \text{if} \ \ \omega = \text{tails} . \end{cases}
If the coin is equally likely to land on either side then it has a probability mass function given by:
 \rho_Y(y) = \begin{cases}\frac{1}{2},& \text{if }y=1,\\
\frac{1}{2},& \text{if }y=0.\end{cases}
From Yahoo Answers
Answers:Determinants are tricky to do. I'll just give the formula for the determinant of a 3x3 matrix. Given the matrix:  a11 a12 a13   a21 a22 a23   a31 a32 a33  The determinant D is given by: D = a11(a22a33a32a23) +a12(a23a31a33a21) +a13(a21a32a31a22). To solve simultaneous equations, first make a matrix of coefficients of the variables:  1 2 3   2 1 1   3 3 2  Using the above formula, the determinant of this matrix is 15. We can call this determinant D. To solve for any of the three individual variables, first make a matrix consisting of the first matrix, with the column of the variable of interest replaced by the values on the right side of the equal signs. In this case, for y,  1 3 3   2 6 1   3 11 2  Again using the formula, the determinant of this matrix is 10, as you were told. We can call this determinant Dy. The answer for y is then Dy/D. In this case, y = Dy/D = 10/15 = 2/3. Similarly, x = Dx/D, and z = Dz/D. Determinants can be a pain, so I usually use the MDETERM() function of MS Excel to quickly evaluate them. I believe most scientific calculators can do determinants too. By the way, x = 3 and z = 2/3.
Answers:yes. but if you do ot know what exart you will be putting it is hard to find it out. but I do have one on my website. www.superdavemortgage.com
Answers:If A1 has Amount Borrowed B1 has Annual Interest Rate C1 has the Term of loan in Years This calculates the monthly payment =PMT(B1/12, C1*12, A1) .
Answers:You've given all the instructions for doing this in Excel, but you haven't completed your question. It can't be answered as posted. "A chef has determined that the cost of preparing a certain menu item is calculated by multiplying the number o..."
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