Let X be the number of material anomalies occurring in a particular region of an aircraft gas-turbine disk. The article "Methodology for Probabilistic Life Prediction of Multiple-Anomaly Materials"+ proposes a Poissondistribution for X. Suppose that μ = 4. (Round your answers to three decimal places.)(a) Compute both P(X ≤ 4) and P(X < 4).P(X ≤ 4) =P(X < 4) =(b) Compute P(4 ≤ x ≤ 9).(c) Compute P(9 ≤ X).(d) What is the probability that the number of anomalies does not exceed the mean value by more than one standard deviation?

Let X represents the number of material anomalies occurring in a particular region of an aircraft…

Answered: Let X be the number of material…

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 31EQ

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Repeat Example 5 when microphone A receives the sound 4 seconds before microphone B.
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
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