By Mark P. Silverman

ISBN-10: 1107032814

ISBN-13: 9781107032811

Established round a chain of real-life situations, this attractive creation to statistical reasoning will educate you ways to use robust statistical, qualitative and probabilistic instruments in a technical context. From research of electrical energy accounts, baseball information, and inventory marketplace fluctuations, via to profound questions on physics of fermions and bosons, decaying nuclei, and weather switch, every one bankruptcy introduces proper actual, statistical and mathematical ideas step by step in an enticing narrative type, aiding to increase functional talent within the use of chance and statistical reasoning. With a number of illustrations making it effortless to target crucial details, this insightful publication is ideal for college students and researchers of any self-discipline drawn to the interwoven tapestry of chance, data, and physics.

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**Extra resources for A Certain Uncertainty: Nature's Random Ways**

**Example text**

X¼0 x¼0 ¼ 4 Àμ2 eÀðμ1 þμ2 Þ ðμ1 þ μ2 Þy : y! Hypergeometric functions occur in the solution of second-order differential equations that describe a variety of physical system. e. the Coulomb problem). 12 Poisson moment-generating function 23 The first step is in effect a statement of the sought-for probability by means of Bayes’ theorem. The transition from the first to the second is permitted because the Poisson variates X1 and X2 are assumed independent. In the third step the explicit form of the Poisson probability function is employed.

For example, consider the traditional experiment in introductory physics labs to measure the acceleration g of freefall at the surface of the Earth. This requires timing a vertically falling object and marking the intermediate locations as a function of time. The data comprise measurements of time intervals and spatial intervals with random experimental errors of measurement whose distributions are not a priori known. The standard statistical procedure of error propagation analysis lets one estimate a mean value and standard deviation of g, but, without knowledge of the underlying probability distribution, it is not possible to interpret the significance of these statistical quantities.

The binomial distribution, where p2 ¼ 1 À p1, provides an illustrative special case; Eq. e. 100% anti-correlation, as would be expected. A multinomial distribution can arise sometimes in unexpected ways. Consider the following situation, which will be of interest to us later when we examine means of judging the credibility of models (also referred to as hypothesis testing) with particular focus on examining the properties of radioactive decay. Suppose a random 26 Tools of the trade process has generated K independent Poisson variates fNk ¼ Poi (μk) k ¼ 1 .

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