By Stephen M. Stigler

ISBN-10: 0674836014

ISBN-13: 9780674836013

This vigorous selection of essays examines in witty aspect the historical past of a few of the suggestions eager about bringing statistical argument "to the table," and a few of the pitfalls which have been encountered. the themes variety from seventeenth-century medication and the flow of blood, to the reason for the nice melancholy and the impact of the California gold discoveries of 1848 upon rate degrees, to the determinations of the form of the Earth and the rate of sunshine, to the meter of Virgil's poetry and the prediction of the second one Coming of Christ. The identify essay tells how the statistician Karl Pearson got here to factor the problem to place "statistics at the desk" to the economists Marshall, Keynes, and Pigou in 1911. The 1911 dispute concerned the influence of parental alcoholism upon youngsters, however the problem is basic and undying: very important arguments require proof, and quantitative facts calls for statistical review. a few essays learn deep and sophisticated statistical principles corresponding to the aggregation and regression paradoxes; others inform of the foundation of the typical guy and the evaluate of fingerprints as a forerunner of using DNA in forensic technology. numerous of the essays are solely nontechnical; all research statistical principles with an ironic eye for his or her essence and what their historical past can let us know approximately present disputes.

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**Example text**

1, the matrix of V is as construc ted. An elegant and efficient way of construc tion follows (vuv '] t ! 1+Vz1V~~V1z When tion can 1 Vh. 1 is construc ted be applied recursively. inverted -Wishar t matrices can an upper as triangul ar An algorithm be construc ted for generati ng this using the matrix, decompo si- square roots of decompo sition, see generate the Zellner, Bauwens and Van Dijk (1988). It square is course of roots of the also possible inverted -Wishar t as suggeste d random earlier, matrices as to inverses of square 40 of roots distributed random be calculated efficiently.

BAUWENS CORE, Universite Catholique de Louvain, 34 Voie du Roman Pays, 1348 Louvain-La-Neuve, BELGIUM A. RASOUERO GREQE, Ecole des Hautes Etudes en Sciences Sociales, 2 Rue de la Charite, 13002 Marseille, FRANCE Key Words: Residual Autocorrelation, Regression Model, Bayesian Inference. HPD Region, Power, Augmented Regressions, Abstract We evaluate two tests of residual autocorrelation in the linear regression model in a Bayesian framework. Each test checks if an approximate highest posterior density region of the parameters of the autoregressive process of the error contains the null hypothesis.

I) Since o is a non-linear function of e, the posterior density of o is not known analytically. 4) and calculate for each drawing the corresponding value of o. By simple averaging of these N values and of functions of them we can obtain (if N is large enough) a very good approximation of the posterior expectation E(o I y) and of the variance-covaria nce matrix V(o I y). The covariance matrix Cov(o,o: I y) can be computed likewise. E(o: I y) and V(o: I y) being known analytically, this step provides us with E(

### Statistics on the table: the history of statistical concepts and methods by Stephen M. Stigler

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