Bayesian contour detection in a time series of ultrasound by Hansen M.B.

By Hansen M.B.

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6 = P  Z ≥ 0 . 63 − 0 . 4 )  500  = P (Z ≥ 1 . 37 ) = 1 − P ( Z < 1 . 37 ) = 0 . 196) ≅ 0. ( )   .    Section 9-2 9-21. a) 1) The parameter of interest is the true mean yield, µ. 05. 71884 ( c) n = z α / 2 + z β δ n ≅ 5. 05 )2 3 2 (85 − 90 )2 = (1 . 96 + 1 . 65 )2 9 (− 5 )2 = 4 . 68054. 96 . 11%. 9-25. a) 1) The parameter of interest is the true mean tensile strength, µ. 01. 43) ]= 2[1 − 1] = 0 The smallest level of significance at which we are willing to reject the null hypothesis is 0.

2 is the best “unbiased” estimator. ) The average of the 26 observations provided can be used as an estimator of the mean pull force since we know it is unbiased. 427 pounds. ) The median of the sample can be used as an estimate of the point that divides the population into a “weak” and “strong” half. 1 pounds. 214 square pounds. 488 pounds. 292 pounds. This value is the standard deviation, not of the pull force, but of the mean pull force of the population. ) Only one connector in the sample has a pull force measurement under 73 pounds.

96 solve for n. 78  3  Therefore, n=267. ) The data appear to be normally distributed based on examination of the normal probability plot below. Therefore, there is evidence to support that the level of polyunsaturated fatty acid is normally distributed. ) 99% CI on the mean level of polyunsaturated fatty acid. 023 mm. 51). 8-67 90% lower tolerance bound on bottle wall thickness that has confidence level 90%. 91, ∞) The lower tolerance bound is of interest if we want to make sure the wall thickness is at least a certain value so that the bottle will not break.

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