Answer to Question #222097 in Statistics and Probability for Darryl

Question #222097

a) The Ghana Economic Management Association wishes to have information on the mean income of store managers in the retail industry. A random sample of 256 managers reveals a sample mean of Ghc45,420. The standard deviation of this population is Ghc2,050. What is a reasonable range of values for the population mean? Interpret your results. 

b) i. Briefly outline the steps involved in hypothesis testing 

ii. Use a table to illustrate how Type I and Type II error can occur


1
Expert's answer
2021-08-13T08:50:38-0400

Part a

90% CI for μ\mu using Normal dist

Sample Mean xˉ=45420| \bar{x} = 45420

Since the sample size is large, sample standard deviation can be used to approximate population standard deviation.

Population Standard deviation =σ=2050=|\sigma =2050

Sample Size =n=256=| n = 256

Significance level =α=1Confidence=10.9=0.1= \alpha =1— Confidence =1-0.9 = 0.1

The Critical Value=zα/2=z0.06=1.645= z_{\alpha/2} =z_{0.06} =1.645 (From z table , using interpolation 12\frac{1}{2} the distance between 164 and 1.65 )

Critical Values , ±zα/2=±1.645± z_{\alpha/ 2}=±1.645

Margin of Error =E=zα/2σn=1.6452050256=210.765625= E = z_{\alpha/2}*\frac{\sigma}{\sqrt{n}} =1.645 *\frac{2050}{\sqrt{256}}=210.765625

Margin of Error, E=210.766E= 210.766

Limits of 90% confidence interval are given by

Lower limit =xˉE=45420210.365625=45209.234375=45209.234= \bar{x} - E = 45420 — 210.365625 = 45209.234375 = 45 209.234

Upper limit =xˉ+E=45420+210.765625=45630.365625=45630.766=\bar{x}+ E = 45420 +210.765625 = 45630.365625 = 45 630.766  

90% confidence interval is xˉ±E=45420±210.765625=(45209.234375,45630.765625)\bar{x}±E = 45420± 210.765625 = (45209.234375, 45630.765625)

90% CI using normal dist: (45202.234)<μ<(45630.766)(45 202.234) < \mu < (45 630.766)


95% CI for μ\mu using Normal dist

Sample Mean xˉ=45420| \bar{x} = 45420

Since the sample size is large, sample standard deviation can be used to approximate population standard deviation.

Population Standard deviation =σ=2050=|\sigma =2050

Sample Size =n=256=| n = 256

Significance level =α=1Confidence=10.95=0.05= \alpha =1— Confidence =1-0.95 = 0.05

The Critical Value=zα/2=z0.025=1.96= z_{\alpha/2} =z_{0.025} =1.96

Critical Values , ±zα/2=±1.96± z_{\alpha/ 2}=±1.96

Margin of Error =E=zα/2σn=1.962050256=251.125= E = z_{\alpha/2}*\frac{\sigma}{\sqrt{n}} =1.96 *\frac{2050}{\sqrt{256}}=251.125

Margin of Error, E=251.125E= 251.125

Limits of 95% confidence interval are given by

Lower limit =xˉE=45420251.125=45168.875= \bar{x} - E = 45420 - 251.125 = 45168.875

Upper limit =xˉ+E=45420+251.125=45671.125=\bar{x}+ E = 45420 +251.125 = 45671.125  

95% confidence interval is xˉ±E=45420±251.125=(45168.875,45671.125)\bar{x}±E = 45420± 251.125 = (45168.875,45671.125)

90% CI using normal dist: (45168.875)<μ<(45671.125)(45168.875) < \mu < (45671.125)


99% CI for μ\mu using Normal dist

Sample Mean xˉ=45420| \bar{x} = 45420

Population Standard deviation =σ=2050=|\sigma =2050

Sample Size =n=256=| n = 256

Significance level =α=1Confidence=10.99=0.01= \alpha =1— Confidence =1-0.99 = 0.01

The Critical Value=zα/2=z0.005=2.58= z_{\alpha/2} =z_{0.005} =2.58

Critical Values , ±zα/2=±2.58± z_{\alpha/ 2}=±2.58

Margin of Error =E=zα/2σn=2.582050256=330.5625= E = z_{\alpha/2}*\frac{\sigma}{\sqrt{n}} =2.58 *\frac{2050}{\sqrt{256}}=330.5625

Margin of Error, E=330.5625E= 330.5625

Limits of 99% confidence interval are given by

Lower limit =xˉE=45420303.563=45089.438= \bar{x} - E = 45420 - 303.563 = 45089.438

Upper limit =xˉ+E=45420+303.5625=45750.563=\bar{x}+ E = 45420 +303.5625= 45750.563  

99% confidence interval is xˉ±E=45420±303.5625=(45089.438,45750.503)\bar{x}±E = 45420± 303.5625 = (45089.438,45750.503)

99% CI using normal dist: (45089.438)<μ<(45750.503)(45089.438) < \mu < (45750.503)

The widest CI is the one with highest confidence level. 

99% CI is the widest. 


Part b

i)

Enter the Null Hypothesis.

Describe the Alternative Hypothesis.

Determine the Significance Level (a)

Determine the Test Statistic and the Corresponding P-Value.

Making a decision.

ii)

In statistics, a Type I error means rejecting the null hypothesis when it's actually true. In contrast, a Type II error means failing to reject the null hypothesis when it's actually false.


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