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Description: When an audit must be conducted that involves a tedious examination of a large inventory, the audit may be very costly and time consuming if each item in the inventory must be examined. In such situations, the auditor frequently obtains a random samp

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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When an audit must be conducted that involves a tedious examination of a large inventory, the audit may be very costly and time consuming if each item in the inventory must be examined. In such situations, the auditor frequently obtains a random sample of items from the complete inventory and uses the results of an audit of the sampled items to check the validity of the company's financial statement. A large company’s financial statement claims an inventory that averages $600 per item. The following data are the auditor’s assessment of a random sample of 75 items from the company’s inventory. The values resulting from the audit are rounded to the nearest dollar.

Estimate the mean value of an item in the inventory using a 95% confidence interval.
Is there substantial evidence (a 5 .01) that the mean value of an item in the inventory is less than $600?
What is the target population for the above inferences?
Would normal distribution–based procedures be appropriate for answering the above questions?

303
547
1,368
493
984
507
148 2,546
738
83
2
135
274
74
1,472
399
1,784 71
751
136
571
147
282
2,039
1,909
748
188
548
1
280
102
618
129
1,324 1,428 469
102 454
1,059
939
303
600
234
514
17
1,290 685
3,215 | 3
551
293
1,395
7
28
973
506
511
812
447
11
35
252
1,526
464
67
99
67
259
7
67
248
33
41

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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