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Category > Information Systems Posted 16 May 2017 My Price 7.00

Character recognition

 

Character recognition, again. The automatic character recognition device discussed in Exercise 2 successfully reads about 85% of handwritten credit card applications. In Exercise 2 you looked at the histograms showing distributions of sample proportions from 1000 simulated samples of size 20, 50, 75, and 100. The sample statistics from each simulation were as follows:

a) According to the Central Limit Theorem, what should the theoretical mean and standard deviations be for these sample sizes?

b) How close are those theoretical values to what was observed in these simulations?

c) Looking at the histograms in Exercise 2, at what sample size would you be comfortable using the Normal model as an approximation for the sampling distribution?

d) What does the Success/Failure Condition say about the choice you made in part c?

Exercise 2:

Character recognition. An automatic character recognition device can successfully read about 85% of handwritten credit card applications. To estimate what might happen when this device reads a stack of applications, the company did a simulation using samples of size 20, 50, 75, and 100. For each sample size, they simulated 1000 samples with success rate p = 0.85 and constructed the histogram of the 1000 sample proportions, shown here. Explain how these histograms demonstrate what the Central Limit Theorem says about the sampling distribution model for sample proportions. Be sure to talk about shape, center, and spread.

 

 

 
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Answers

Alpha Geek
(8)
Status NEW Posted 16 May 2017 07:05 AM My Price 7.00

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file 1494919308-Answer.docx preview (272 words )
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