To test Ho: μ = 105 versus H₁: μ#105 a simple random sample of size n = 35 is obtained. Complete parts a through e below. Click here to view the t-Distribution Area in Right Tail. (a) Does the population have to be normally distributed to test this hypothesis? Why? O A. No, because the test is two-tailed. OB. No, because n ≥ 30. OC. Yes, because n ≥ 30. OD. Yes, because the sample is random.
To test Ho: μ = 105 versus H₁: μ#105 a simple random sample of size n = 35 is obtained. Complete parts a through e below. Click here to view the t-Distribution Area in Right Tail. (a) Does the population have to be normally distributed to test this hypothesis? Why? O A. No, because the test is two-tailed. OB. No, because n ≥ 30. OC. Yes, because n ≥ 30. OD. Yes, because the sample is random.
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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Step 1: Define the concept central limit theorem
Central limit theorem:
If the size of the samples taken from the population are sufficiently large (>30) then the mean of all the samples taken would be approximately same as the population mean.
The conditions are,
- The population is normal.
- The sample size is≥ 30.
- If the original population doesn’t’ follows normality, for the large n, the sample mean also follows normality.
- The formula of the standard deviation of sampling distribution is σ/√n if n is large even though population does not follows normality.
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