# 1.) An insurance company is reviewing its current policy rates. When originally setting the rates they believed that the average claim amount was $1,800. They are concerned that the true mean is actually higher than this, because they could potentially lose a lot of money. They randomly select 40 claims, and calculate a sample mean of $1,950. Assuming that the standard deviation of all claims is $500, and using a = 0.05, test to see if the insurance company should be concerned. Use the classical method. graph: Ho: H₁: -tailed test n: x-bar: 0: S: a= _- distribution test statistic: critical value(s): decision: possible error: interpretation:
# 1.) An insurance company is reviewing its current policy rates. When originally setting the rates they believed that the average claim amount was $1,800. They are concerned that the true mean is actually higher than this, because they could potentially lose a lot of money. They randomly select 40 claims, and calculate a sample mean of $1,950. Assuming that the standard deviation of all claims is $500, and using a = 0.05, test to see if the insurance company should be concerned. Use the classical method. graph: Ho: H₁: -tailed test n: x-bar: 0: S: a= _- distribution test statistic: critical value(s): decision: possible error: interpretation:
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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Transcribed Image Text:# 1.) An insurance company is reviewing its current policy rates. When originally setting the rates they
believed that the average claim amount was $1,800. They are concerned that the true mean is actually
higher than this, because they could potentially lose a lot of money. They randomly select 40 claims, and
calculate a sample mean of $1,950. Assuming that the standard deviation of all claims is $500, and using a = 0.05,
test to see if the insurance company should be concerned. Use the classical method.
test statistic:
Ho:
H₁:
-tailed test
n:
x-bar:
0:
S:
α =
distribution
critical value(s):
decision:
possible error:
interpretation:
graph:
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