Download BIOS Instant Notes in Analytical Chemistry by David Kealey, P J Haines PDF

By David Kealey, P J Haines

Quick Notes titles specialize in center info and are designed to assist undergraduate scholars come to grips with an issue speedy and easily.Instant Notes in Analytical Chemistry offers scholars with a radical comprehension of analytical chemistry and its purposes. This ebook helps the training of rules and perform of analytical strategies. It additionally comprises analytical concepts standard in laboratories this present day.

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Variance The square of the standard deviation, s2, or estimated standard deviation, s2, is used in a number of statistical computations and tests, such as for calculating accumulated (propagated) errors (Topic B1 and below) or when comparing the precisions of two sets of data (Topic B3). Overall precision Random errors accumulated within an analytical procedure contribute to the overall precision. e. soverall = ͙΂s 2 2 2ෆ ෆ ෆ 1 + s2 + s3 + . . ΃ Example In a titrimetric procedure, the buret must be read twice, and the error associated with each reading must be taken into account in estimating the overall precision.

Proportions of a population within defined limits of the mean. The value of the population standard deviation, s, is given by the formula s= Ί Α (x − m) ๶ i=N i 2 i=1 ᎏᎏ N (1) where xi represents any individual value in the population and N is the total number of values, strictly infinite. The summation symbol, S, is used to show that the numerator of the equation is the sum for i = 1 to i = N of the squares of the deviations of the individual x values from the population mean, m. , when N >50), it may be justifiable to use this formula as the difference between s and s will then be negligible.

The frequency of occurrence of each experimental value is plotted as a function of the magnitude of the error or deviation from the average or mean value. For analytical data, the values are often distributed symmetrically about the mean value, the most common being the normal error or Gaussian distribution curve. The curve (Fig. 4) shows that ● small errors are more probable than large ones, ● positive and negative errors are equally probable, and ● the maximum of the curve corresponds to the mean value.

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