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By Sanford Bolton, Charles Bon

The fourth variation of this article contains a disk with specified courses to help in challenge research. The textual content provides clean fabric on thought conformity and unlock pursuits and describes linear regression and correlation, the research of variance and crossover designs.

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Read or Download Pharmaceutical Statistics: Practical and Clinical Applications, Fourth Edition, Revised and Expanded (Drugs and the Pharmaceutical Sciences) PDF

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Extra resources for Pharmaceutical Statistics: Practical and Clinical Applications, Fourth Edition, Revised and Expanded (Drugs and the Pharmaceutical Sciences)

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This concept is elucidated and expanded in the remaining sections of this chapter. 1 The Average Probably the most familiar statistical term in popular use is the average, denoted by X¯ (X bar). The average is also commonly known as the mean or arithmetic average. The average is a summarizing statistic and is a measure of the center of a distribution, particularly meaningful if the data are symmetrically distributed below and above the average. 1) i =1 N N the sum of the observations divided by the number of observations.

Ormsby, E. , and Pikc, R. , Pharm. , 12, 1380, 1995. 2 DATA GRAPHICS ‘‘The preliminary examination of most data is facilitated by the use of diagrams. Diagrams prove nothing, but bring outstanding features readily to the eye; they are therefore no substitute for such critical tests as may be applied to the data, but are valuable in suggesting such tests, and in explaining the conclusions founded upon them’’ This quote is from Ronald A. Fisher, the father of modern statistical methodology [1].

As the sample size increases, the standard deviation of the mean becomes smaller and smaller. d. of the mean, Sx¯, to a very small value by increasing N. Thus means of very large samples hardly vary at all. The concept of the standard deviation of the mean is important, and the student will find it well worth the extra effort made to understand the meaning and implications of Sx¯. 10) i where s 2i is the variance of the ith observation. To compute the variance of the weighted mean, we would need to have an estimate of the variance of each observation.

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