By David Spiegelhalter, Michael Blastland

ISBN-10: 1847658296

ISBN-13: 9781847658296

Meet Norm. He's 31, 5'9", simply over thirteen stone, and works a 39 hour week. He likes a drink, doesn't do sufficient workout and infrequently treats himself to a bar of chocolate (milk). He's typical form of man. in truth, he's the typical man during this smart and weird tackle statistical threat, likelihood, and the way those elements have an effect on our daily offerings. Watch as Norm (who, like every ordinary specimens, feels himself to be uniquely special), and his neighbors cautious Prudence and reckless Kelvin, turns to stats to assist him in life's unending sequence of decisions - should still I fly or take the educate? Have a child? one other drink? Or one other sausage? Do a charity skydive or get a boost on a motorbike?Because likelihood and chance aren't as regards to numbers - it's approximately what we think, who we belief and the way we believe in regards to the global round us. What we do, or don't do, has as a lot do with intestine intuition as difficult proof, with leisure as knowing. If you've ever questioned what the facts in tabloid scare tales particularly suggest, how risky horse-riding is in comparison to class-A medicinal drugs, or what governs accident, you'll find all of it here.From a global specialist in probability and the bestselling writer of The Tiger That Isn't (and author of BBC Radio 4's extra or Less), it is a common-sense (and wildly exciting) consultant to non-public hazard and deciphering the information that characterize it.

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**Additional info for The Norm Chronicles: Stories and Numbers About Danger**

**Sample text**

The object here is to determine ij(x) so that η(α^) = 0, j = 1, 2 , . . , n. 5). 6) where pn(x) = Π ? 6) is known as a Lagrange polynomial. ,n. 1. The Lagrange polynomials of degree 2 are If any function g(y) is to be approximated by /I(Î/), denoted by we can express h(y) in terms of Lagrange polynomials as where g(yj) = Xi-j+i at ith step of a numerical evaluation. 7) 58 Optimization Techniques in Statistics We can obtain the approximation for #(0), say, by /i(0). (0) = J 2 ^ (% - 2 / 1 ) . .

10) Jensen Inequality Let φ be a real valued and convex function defined on the interval (a, 6). 12) when φ is convex and E(X) denotes the expectation of X. Chebychev Inequality Let X be a random variable with mean μ and finite variance σ2. 13) Markov Inequality Let X > Y > 0 be nonnegative random variables. 14) Kolmogorov Inequality Let Xi, i = 1, 2 , . . , n, be independent random variables with E{X{) = 0 having finite second order moments. 15) 3 45 Optimization and Inequalities Chernoff Inequality Let X be a normally distributed random variable with mean zero and unit variance.

For an extensive study, the reader may consult books by Hardy, Littlewood, and Polya (1952), Shisha (1972), Marshall and Olkin (1979), and Tong (1980) with statistical applications. 2. CLASSICAL INEQUALITIES Some of the inequalities that are commonly used in statistical practice are stated here without proof. Cauchy Inequality Let ai, 0 2 , . . , α η and 61, 6 2 , . . , bn be two sequences of real numbers. 1) The integral form of Cauchy inequality can be obtained if we assume that functions f(x) and g(x) are square integrable.

### The Norm Chronicles: Stories and Numbers About Danger by David Spiegelhalter, Michael Blastland

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