Statisticians, interests

The most common question posed to statisticians In environmental sampling Is How many samples do I need to take (or "How many replicates, "How many analyses," etc.). The statistical models Introduced previously provide a framework for addressing these questions after the first four steps In a sampling study are completed (l.e. the objectives, populations of Interest, characteristics to be determined, and required precision are stated). The methods In this section are applicable when the objective Is to estimate the average of a characteristic In the population. 

Finally, and of most interest, is the data in Table 25-1C. Here we have taken the same errors as in Table 25-IB and applied them to the X variable rather than the Y variable. By symmetry arguments, we might expect that we should find the same results as in Table 25-1B. In fact, however, the results are different, in several notable ways. In the first place, we arrive at the wrong model. We know that this model is not correct because we know what the right model is, since we predetermined it. This is the first place that what the statisticians have told us about the results are seen. In statistical parlance, the presence of error in the X variable biases the coefficient toward zero , and so we find the slope is decreased (always decreased) from the correct value (of unity, with this data) to 0.96+. So the first problem is that we obtain the wrong model. 

For example, the distribution from which the samples are drawn is assumed to be the true distribution of the parameter of interest. To the degree that the sample distribution differs from the actual distribution (which is generally assumed unknown by the classical statistician), the confidence in the Monte Carlo results is decreased. Just how close these distributions must be is a complicated statistical issue that is frequently unclear. In a practical sense, if misspecification of a sampling distribution occurs for a very sensitive parameter in a multiparameter model, then the confidence in the Monte Carlo results is greatly diminished because the model prediction is greatly influenced by that parameter. 

We commonly refer to the level of effect to be detected as the cliniMlly relevant difference (crd) what level of effect is an important effect from a clinical standpoint. Note also that crd stands for commercially relevant difference it could well be that the decision is based on commercial interests. Finally crd stands for cynically relevant difference It does happen from time to time that a statistician is asked to do a sample size calculation, oh and by the way, we want 200 patients The issue here of course is budget and the question really is what level of effect are we able to detect with a sample size of 200 ... 

In stock market investment, the investor follows trends, cycles, ratio between dividends and interest on savings accounts, etc., to help increase his monetary value. In betting on horses, deciding whether to carry an umbrella, when it is safe to cross the street -all the elements of a statistical decision are present, and all of us, at one time or another, weigh the evidence, apply the rule of thumb, and make the decision. We are all statisticians so why all the fuss 1... 

This series of monographs focuses on mathematic and its applications to problems of current concern to industry, government, and society. These monographs will be of interest to applied mathematicians, numerical analysts, statisticians, engineers and scientists who have an active need to learn useful methodology. 

All these people have some interest in data analysis or chemometrics, but approach the subject in radically different ways. Writing a text that is supposed to appeal to a broad church of scientists must take this into account. The average statistician likes to build on concepts such as significance tests, matrix least squares and so on. A... 

From his graph he was able to tell us (we thought this rather interesting) that there is a maximum point, or rather a minimum point, of incineration time below which it is impossible to go, and our statistician defined this as a thermal barrier that, because of the make, the nature of human tissues, you cannot incinerate them at a rate which is below round about 63 minutes. Now some people will come up with readings of 60, 59, 58, they are the lower ends of this scatter of readings, and that this thermal barrier s optimum temperature is round about 800-900 °C. ... 

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