The following information was obtained from two independent samples selected from two normally distributed populations with unknown but **equal** standard deviations.n1=43; x1¯=74.17 s1=7.42n2=65; x2¯=70.88; s2=8.19Find a point estimate and a 95% confidence interval for μ1−μ2For the following, round all answers to no fewer than 4 decimal places.The point estimate of μ1−μ2 is:AnswerThe lower limit of the confidence interval is:AnswerThe upper limit of the confidence interval is:AnswerThe margin of error for this estimate is:AnswerTest at the 0.05 significance level if μ1 is different than μ2 μ1is AnswerGreater thanNot significantly different thanLess than μ2

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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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The following information was obtained from two independent samples selected from two normally distributed populations with unknown but **equal** standard deviations.

n1=43; x1¯=74.17 s1=7.42
n2=65; x2¯=70.88; s2=8.19
Find a point estimate and a 95% confidence interval for μ1−μ2

For the following, round all answers to no fewer than 4 decimal places.

The point estimate of μ1−μ2 is:	Answer
The lower limit of the confidence interval is:	Answer
The upper limit of the confidence interval is:	Answer
The margin of error for this estimate is:	Answer
Test at the 0.05 significance level if μ1 is different than μ2
μ1is AnswerGreater thanNot significantly different thanLess than μ2

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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