Cartoon Guide to Statistics by Larry Gonick

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By Larry Gonick

When you have ever searched for P-values by means of buying at P mart, attempted to monitor the Bernoulli Trials on "People's Court," or imagine that the traditional deviation is a crime in six states, you then desire The caricature consultant to Statistics to place you at the highway to statistical literacy.

The comic strip advisor to Statistics covers the entire valuable principles of contemporary facts: the precis and demonstrate of knowledge, likelihood in playing and medication, random variables, Bernoulli Trails, the principal restrict Theorem, speculation trying out, self belief period estimation, and lots more and plenty more--all defined in uncomplicated, transparent, and definite, humorous illustrations. by no means back will you order the Poisson Distribution in a French eating place!

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L. Anderson and T. A. , New York, 1952. T. '\V. , New York, 1958. S. S. , 1943. C. R. , New York, 1952. S. Kolodziejczyk: On an Important Class of Statistical Hypotheses, Biometrika, vol. 27, 1935. D. Blackwell: Conditional Expectation and U nbiased Sequential Estimation, Ann. JJlath. , vol. 18, pp. 105-110, 1947. E. L. Lehmann and H. Scheffé: Completeness, Similar Regions and Unbiased Estimation, Sankhyii, vol. 10, pp. 305-340, 1950. R. A. , London, 1946. R. A. Fisher: Applications of "Student's" Distribution, 111etron, vol.

In this into the entire Z space, as indicmted by the limits on the integrals on section we shall investigate the multivariate normal distribution and the right side of the equation. 31 that there exists an orthogonal matrix P such that ing work. In this chapter we shall not discuss the estimating of parameters or the testing of hypotheses about parameters in the multiP'RP = D variate normal. We shall reserve this discussion for a later chapter. i the characteristic roots di of R (which are We shall now define the multivariate normal distribution.

23 If a vector Y is distributed \\ and if Z = PY, where P is an orthogonal matrix, then Z is distributed N(O,a21), also. That is to say, if Y is distributed N(0,CT2 1), any orthogonal transformation leaves the distribution unchanged. 22 we know that Z = PY is normal; so we need only find the mean and covariance matrix of Z, and the proof is complete. 1 Example. ), is given by Q = 3yr + 2y~ -1- 2yi + y¡ + 2y 1Y + 2y3y 4 - Oy 1 2 - 2y 2 - 6y3 - 2y 4 +8 We shall find the following: (1) f(y 1 1y 2 , y 3 , y 4 ), (2) f 1 (y 1 ), (3) Pu> (4) p 1n, and (5) the multiple-correlation coefficient p 1 of y 1 on y 2 , Ya' and Y4· First we shall find R and V.

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