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Looking for 1-2 paragraphs per question with explanations and an example to help illustrate them. I get the basic idea of each question, but I would like something more illustrative to nail down the topics
1. Describe the types of decisions that do not involve uncertainty and risk and common approaches used for analyzing them.
2. Describe how to model decisions involving uncertainty.
3. Explain the components of a decision tree and how optimal decisions are computed.
4. Explain the following terms:
a) calling population
b) reneging
c) jockeying
d) balking
e) queue discipline
5. List and define the major types of performance measures used in queuing analysis.
Suppose you want to test the hypothesis that students who study one week before score different from students who study the night before. A 2 sample t-test test is run based on your data and a p-value is calculated to be 0.0219. What is the approp..
How large a sample should be taken if the desired margin of error is?
The number of degrees of freedom for the appropriate chi-square distribution in a test of independence is:
Explain these results to a person who understands the t test for a single sample but knows nothing about the t test for independent means.
Does there seem to be any difference in the effect of the antibiotics on clearance of otitis media? Express your results in terms of relative risk (RR). Consider separate analyses for unilateral and bilateral cases. Also consider an analysis combi..
Construct a 90 percent confidence interval for the proportion of all kernels that would not pop.
The sample proportion of large gloves for each location is and . (Round your answers to 4 decimal places.)
Ten pairs of points yielded a correlation coefficient y of 0.79. If a=0.05, which of the following statements is correct if H 0:P=0? (Do not calculate a t-value.)
Given what you've learned in this module about the meaning of "statistics", choose one of the examples from Part I (A-F), and raise a relevant question of your own that could be answered by a statistician.
Assume that we will randomly select a sample of 64 measurements from a population having a mean equal to 20 and a standard deviation equal to 4.
A research analyst regresses the number of calls for service (Y) on the Fahrenheit temperature at 4:00 in the afternoon (X). The regression equation finds: Y = 62 + 2.4 X with an r 2 of .67
Reynolds Electronics accepts a lot from a particular supplier if the defective components in the lot do not exceed 1%. Suppose a random sample of five items from a recent shipment is tested.
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