A researcher is interested in estimating the average amount of sleep obtained by first-year students at MacEwan University. The researcher obtains a random sample of 100 first-year students from MacEwan from which she obtains an average of 7.1 hours of sleepSuppose the researcher obtains a 95% confidence interval of (6.9, 7.3). What is the margin of error?Is it necessary for the population of interest to be normally distributed for the interval in (1) to be valid? Explain.Briefly explain why the interval estimate from (1) is superior to the point estimate

Answered: Image /qna-images/answer/c1a8174f-1c85-4789-b6a6-2944e3e71a24.jpg

Answered: Suppose the researcher obtains a 95%…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Related questions

Question

A researcher is interested in estimating the average amount of sleep obtained by first-year students at MacEwan University. The researcher obtains a random sample of 100 first-year students from MacEwan from which she obtains an average of 7.1 hours of sleep

Suppose the researcher obtains a 95% confidence interval of (6.9, 7.3). What is the margin of error?
Is it necessary for the population of interest to be normally distributed for the interval in (1) to be valid? Explain.
Briefly explain why the interval estimate from (1) is superior to the point estimate

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.

Expert Solution