Find the probability and interpret the results. If convenient, use technology to find the probability. The population mean annual salary for environmental compliance specialists is about $60,500. A random sample of 38 specialists is drawn from this population. What is the probability that the mean salary of the sample is less than $58 500? Assume a = $5,800. The probability that the mean salary of the sample is less than $58,500 is (Round to four decimal places as needed.)
Find the probability and interpret the results. If convenient, use technology to find the probability. The population mean annual salary for environmental compliance specialists is about $60,500. A random sample of 38 specialists is drawn from this population. What is the probability that the mean salary of the sample is less than $58 500? Assume a = $5,800. The probability that the mean salary of the sample is less than $58,500 is (Round to four decimal places as needed.)
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:**Title: Calculating Probability in Salary Statistics**
**Problem:**
Find the probability and interpret the results. If convenient, use technology to find the probability.
The population mean annual salary for environmental compliance specialists is about $60,500. A random sample of 38 specialists is drawn from this population. What is the probability that the mean salary of the sample is less than $58,500? Assume σ = $5,800.
The probability that the mean salary of the sample is less than $58,500 is _____ (Round to four decimal places as needed.)
**Explanation:**
This problem involves calculating the probability that the sample mean salary is less than a specified amount using the normal distribution. Given the population mean, the sample size, and the standard deviation, we can use statistical methods to find the likelihood of observing such a sample mean. This exercise demonstrates important concepts in inferential statistics, including the application of the Central Limit Theorem.
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