A marketing research company is estimating the average total compensation of CEOs in the service industry. Data were randomly collected from 18 CEOs and the 95% confidence interval for the mean was calculated to be What additional assumption is necessary for this confidence interval to be valid? The sample standard deviation is less than the degrees of freedom. The distribution of the sample means is approximately normal. None. The Central Limit Theorem applies. The population of total compensations of CEOs in the service industry is approximately normally distributed.

Answer: The additional assumption that is necessary for this confidence interval to be valid is, The…

Answered: the 95% confidence interval for the…

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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A marketing research company is estimating the average total compensation of CEOs in the service industry. Data were randomly collected from 18 CEOs and the 95% confidence interval for the mean was calculated to be What additional assumption is necessary for this confidence interval to be valid?

		The sample standard deviation is less than the degrees of freedom.
		The distribution of the sample means is approximately normal.
		None. The Central Limit Theorem applies.
		The population of total compensations of CEOs in the service industry is approximately normally distributed.

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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