4. Hospital personnel routinely examine patient records for error, such as incomplete insurance information, on incomplete patient history, or missing/incomplete medical records. On average, about 250 new patients are admitted each day. Historically, about 5% of these records have contained errors. If a random sample of 50 new patient records is checked each day, what is the probability that this sample will contain at least one patient record with missing information?
4. Hospital personnel routinely examine patient records for error, such as incomplete insurance information, on incomplete patient history, or missing/incomplete medical records. On average, about 250 new patients are admitted each day. Historically, about 5% of these records have contained errors. If a random sample of 50 new patient records is checked each day, what is the probability that this sample will contain at least one patient record with missing information?
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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Please answer Q.4

Transcribed Image Text:HW -3
1. An article in Solid State Technology ("Orthogonal Design for Process Optimization and Its Application in Plasma
Etching," May 1987, pp. 127–132) describes the application of factorial designs in developing a nitride etch
process on a single-wafer plasma etcher. The process uses C2F6 as the reactant gas. It is possible to vary the gas
flow, the power applied to the cathode, the pressure in the reactor chamber, and the spacing between the
anode and the cathode (gap). Several response variables would usually be of interest in this process, but in this
example we will concentrate on etch rate for silicon nitride. Perform an appropriate experiment to characterize
the performance of this etching process with respect to the four process variables.
Design
Factor
Gap
A
Pressure
C;F, Flow Power
Level
(сm)
(m Torr)
(SCCM)
(W)
Low (-) 0.80
High (+) 1.20
275
325
450
125
550
200
2. To illustrate the use of a one-half fraction, consider the plasma etch experiment. An article in Solid State
Technology ("Orthogonal Design for Process Optimization and Its Application in Plasma Etching," May 1987, pp.
127-132) describes the application of factorial designs in developing a nitride etch process on a single-wafer
plasma etcher. The process uses C2F6 as the reactant gas. It is possible to vary the gas flow, the power applied
to the cathode, the pressure in the reactor chamber, and the spacing between the anode and the cathode (gap).
Several response variables would usually be of interest in this process, but in this example, we will concentrate
on etch rate for silicon nitride. Perform an appropriate experiment to characterize the performance of this
etching process with respect to the four process variables. Suppose we had decided to use a 24-1 design with I =
ABCD to investigate the four factors gap (A), pressure (B), C2F6 flow rate (C), and power setting (D). Set up this
design and analyze it using only the data from the full factorial that corresponds to the runs in the fraction.
C„F, Flow
Design
Factor
Level
Gap
Pressure
Power
D
(cm)
(m Torr)
(SCCM)
(W)
Low (-) 0.80
450
125
275
High (+) 1.20
550
200
325
3. An accounting firm uses sampling methods in its client auditing processes. Accounts of a particular type are
grouped together in a batch size of 25. The auditor is concerned about erroneous accounts escaping the auditing
process. Sampling and auditing the accounts is time consuming and very expensive, and a random sample of size
n= 5 is about the largest sample that can practically be used. Suppose that the batch of accounts contains one
erroneous account. What is the probability that the sample that is selected contains the erroneous account?
4. Hospital personnel routinely examine patient records for error, such as incomplete insurance information, on
incomplete patient history, or missing/incomplete medical records. On average, about 250 new patients are
admitted each day. Historically, about 5% of these records have contained errors. If a random sample of 50 new
patient records is checked each day, what is the probability that this sample will contain at least one patient
record with missing information?
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