Probability and Power
The part of the statistical lesson that I went through last week that has left a profound impact on me is the Poisson Distribution of numbers and the finding out the probability based on this distribution theory and formula. The French mathematician Simeon Denis Poisson developed the Poisson Distribution in 1837 and is named after him. This distribution is used when it is safely assumed that the outcome is the number of times of an event that occurs. This form of distribution formula and theory is used to for determination of the probability of events that are rare events and it helps in giving the probability that an outcome occurs a specified number of times and when the number of trials is generally large and there is a small chance of probability of any one occurrence (Hulley, 2007).
The Poisson distribution can be used in a number of scenarios like in the planning of the number of beds that a hospital would need in the intensive care unit or the number of ambulances that would be needed on call or ascertaining the number of operators that would be required on a switchboard so that an adequate number of resources is always made available. Other...
Standard deviation is a measure of how far the data is spread from the mean, and can be used to assess the accuracy of the sample mean. Type I errors are improper rejections of the null hypothesis. Type II errors are failures to reject the null hypothesis hen it should be rejected. Level of Significance measures the likelihood of committing a Type I error. Power analysis measures the risk of committing a Type II
In order to efficiently predict customer levels based on frequency, "data on customer arrivals must be collected and a statistical test conducted to see whether an assumption of a Poisson distribution for arrivals is reasonable," (Anderson 2008:472). Business such as liquor marts can use such a process to determine if customer frequency is sufficient enough for them to stay open later. For instance, if a store of such nature
Gall states sampling strategies like these may be incompletely accurate due to the verbal data given and artifacts researched. it's never 100% accurate, the sampling, due to the fact the data is gathered through verbal, at times, unproven means. Levin names certain strategies that could easily be devoid of accuracy due to researcher's lack of funds. "View that data-analysis strategies such as cross-validation, boot-strapping, and jackknifing "indicate the likelihood of
Statistics of Oil Peak In the present times, the depletion of oil all over the world has become a major topic of concern all over, and this subject is indeed an extremely important one because of the fact that a world without oil is a frightening prospect and will hurt mankind and the very lifestyle of human beings everywhere. Since it is well-known that everywhere it is conventional oil that is
76). As automation increasingly assumes the more mundane and routine aspects of work of all types, Drucker was visionary in his assessment of how decisions would be made in the years to come. "In the future," said Drucker, "it was possible that all employment would be managerial in nature, and we would then have progressed from a society of labor to a society of management" (Witzel, p. 76). The
Reliability and Validity Reliability Reliability refers to the capacity of an instrument to capture the most accurate and "truest" score of an individual. A reliable test enables us to distinguish one individual from another with confidence that errors will primarily be generated by individual differences, and to a lesser extent, to the imperfections of the instrument. Indicators that a test is reliable include test-retest reliability that is accounted for by the internal
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