Problem : The life testing data of 1000 specimens of a particular device are givenbelow. Determine the MTTF of the device for an operating period of 10 hours.Time interval inNumber of failureshoursduring the intervalT< 10000.1000 < T< 10501751050 < T< 1100400ldemour 0011100 < T<11503001150 < T< 1200125

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Answered: Problem : The life testing data of 1000…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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tion question Figure 2 shows the relationship in Africa between annual all-cause mortality rates in children ages 0-4 and P. falciparum malaria transmission intensity. Regions are classified into one of four categories based on childhood malaria prevalence: 1 childhood infection prevalence ranges from 0-24% 2-childhood infection prevalence ranges from 25-50% 3-childhood infection prevalence ranges from 51-74% 4-childhood infection prevalence ranges from 75-100% Annual mortality rate/1000 80 60 40 20 10 1 2 3 4 Intensity of P. falciparum transmission
Existing data (year: 1995) (Observed values) * Age group of drivers Accident Severity 18-30 30-50 50+ Fatal 27 22 40 Grievous 27 15 44 injured Minor 82 60 120 97 Total 89 86 262 injury Total 136 204 437 Source: Office of the Dy. Commissioner of Police (Tra
Question 2
te Partial Critical Value Table ects Level of Confidence, Critical Value, Za/2 (1-a) • 100% Area in Each Tail, 2 90% 0.05 1.645 95% 0.025 1.96 99% 0.005 2.575 Print Done
1. Model 1: OLS, using observations 1-706 Dependent variable: RST Coefficient Std. Error t-ratio p-value const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 *** Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 RSTi =3586.38−0.150746 x TOTWRKi , R2=0.103287,SER=421.1357 (38.9124) (0.0167403) Question? could you please help with this question below. 3) By observing the GRETL output in Part (1) above, provide a detailed explanation of the coefficient of determination. Based on your analysis, is this a good model? Why or why not?
Assignment: Analyze Filling Line Problems Using Control Charting Attached Files: File Filling line example for X-bar & control chart.xlsxOpen this document with ReadSpeaker docReader (13.354 KB) Using the provided data, calculate the control limits (X-bar and r-chart, Capability Index, create a control chart, and calculate the percentage of units that are expected to be out of spec (z-value). Show your work A quick way to do the percents part of the filling line problem (you know, the percent underfilled, the percent overfilled, etc.), is to use Excel's norm.dist function rather than looking it up on a printed Z table. Use the norm.dist excel function with the LSL in the first position in the parameter list. Norm.dist gives you the left side return from the Z table. "Excel offers you the NORM.DIST statistical function for working with normal distributions. The NORM.DIST function calculates the probability that variable X falls below or at a specified value"…
I need help with part b please
Can you answer #9?

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