By L. Kazmier
Boiled-down necessities of the top-selling Schaum's define sequence, for the scholar with restricted time
What might be greater than the bestselling Schaum's define sequence? for college kids trying to find a brief nuts-and-bolts review, it is going to must be Schaum's effortless define sequence. each e-book during this sequence is a pared-down, simplified, and tightly targeted model of its higher predecessor. With an emphasis on readability and brevity, each one new name contains a streamlined and up-to-date structure and absolutely the essence of the topic, offered in a concise and conveniently comprehensible shape. image components resembling sidebars, reader-alert icons, and boxed highlights function chosen issues from the textual content, light up keys to studying, and provides scholars speedy tips that could the essentials.
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Extra resources for Schaum’s Easy outline of business statistics
The Poisson Distribution The Poisson distribution can be used to determine the probability of a designated number of events occurring when the events occur in a continuum of time or space. Such a process is called a Poisson process; it is similar to the Bernoulli process except that the events occur over a continuum and there are no trials as such. An example of such a process is the arrival of incoming calls at a telephone switchboard. As was the case for the Bernoulli process, it is assumed that the events are independent and that the process is stationary.
Now refer to the formula for population variance and note that the variance is in fact a type of arithmetic mean, in that it is the sum of squared deviations divided by the number of such values. From this standpoint alone, the variance is thereby associated with the least-squares criterion. Note also that the sum of the squared deviations in the numerator of the variance formula is precisely the sum that is minimized when the arithmetic mean is used as the measure of location. Therefore, the variance and its square root, the standard deviation, have a close mathematical relationship with the mean, and both are used in statistical inference with sample data.
The Rules of Multiplication The rules of multiplication are concerned with determining the probability of the joint occurrence of A and B. This concerns the intersection of A and B: P(A ∩ B). There are two variations of the rule of multiplication, according to whether the two events are independent or dependent. The rule of multiplication for independent events is: P(A and B) = P(A ∩ B) = P(A)P(B) For dependent events the probability of the joint occurrence of A and B is the probability of A multiplied by the conditional probability of B given A.