(c) The chip is ready for production when reliability reaches 95%. When does the logistic model predict the chip will be ready for production?Round your answer to the nearest month. (Use the model found in part (a).) A company is developing a new computer chip. Each month a collection of prototypes is tested and the percentage Pof chips that operate successfully is recorded. Here are the results.5 6m = monthP = % successful1234723293545526370(a) Find a logistic model for P versus m.114.32O P=1+ 5.54e 0.313m57.16O P=1+ 6.65e-0.157m124.82OP=-D.104m1+ 1.11e38.11O P =1+ 13.85e-0.078m22.86OP =1+ 2.77e-0.063m(b) A reliability curve is a graph of percentage of successes versus time. Make a reliability curve for this computer chip. Include both the data and the model in your graph, and cover monthsfrom 0 to 15.P.12012010010080806060404020202101214481012141201201001008080606040402020m14141012246.1012(c) The chip is ready for production when reliability reaches 95%. When does the logistic model predict the chip will be ready for production? Round your answer to the nearest month. (Usethe model found in part (a).)

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c) From the given information, the logistic model for the given data is P=114.321+5.54e-0.313m.

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The chip is ready for production when reliability reaches 95%. When does the logistic model predict the chip will be ready for production? Round your answer to the nearest month. (Use the model found in part (a).)

The chip is ready for production when reliability reaches 95%. When does the logistic model predict the chip will be ready for production? Round your answer to the nearest month. (Use the model found in part (a).)

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

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Types of Bias

Statistics is the study of data collection, organization, analysis, interpretation, and presentation. Statistical bias is a characteristic of a statistical technique or its findings in which the expected value deviates from the actual root quantitative parameter being estimated. According to the actual definition of bias, it refers to the tendency of a statistic to overestimate or underestimate a parameter.

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Question

(c) The chip is ready for production when reliability reaches 95%. When does the logistic model predict the chip will be ready for production?

Round your answer to the nearest month. (Use the model found in part (a).)

A company is developing a new computer chip. Each month a collection of prototypes is tested and the percentage Pof chips that operate successfully is recorded. Here are the results.
5 6
m = month
P = % successful
1
2
3
4
7
23
29
35
45
52
63
70
(a) Find a logistic model for P versus m.
114.32
O P=
1+ 5.54e 0.313m
57.16
O P=
1+ 6.65e-0.157m
124.82
OP=
-D.104m
1+ 1.11e
38.11
O P =
1+ 13.85e-0.078m
22.86
OP =
1+ 2.77e-0.063m
(b) A reliability curve is a graph of percentage of successes versus time. Make a reliability curve for this computer chip. Include both the data and the model in your graph, and cover months
from 0 to 15.
P.
120
120
100
100
80
80
60
60
40
40
20
20
2
10
12
14
4
8
10
12
14
120
120
100
100
80
80
60
60
40
40
20
20
m
14
14
10
12
2
4
6.
10
12
(c) The chip is ready for production when reliability reaches 95%. When does the logistic model predict the chip will be ready for production? Round your answer to the nearest month. (Use
the model found in part (a).)

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