A certain company would like to determine the amount of time employees waste at work each day. A random sample of 10 of its employees shows a mean time of 121.80 minutes wasted per day with a standard deviation of 9.45 minutes per day. Does the data provide evidence that the mean amount of time wasted by employees each day is more than 120 minutes? Test at α=.05. Assume the population is at least approximately normally distributed.

Name: A certain company would like to determine the amount of time employees
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Description: A certain company would like to determine the amount of time employees waste at work each day. A random sample of 10 of its employees shows a mean time of 121.80 minutes wasted per day with a standard deviation of 9.45 minutes per day. Does the data

Given Data: x¯=121.80s=9.45n=10 Null and Alternate Hypothesis: H0: μ=120HA: μ>120

Answered: A certain company would like to…

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 certain company would like to determine the amount of time employees waste at work each day. A random sample of 10 of its employees shows a mean time of 121.80 minutes wasted per day with a standard deviation of 9.45 minutes per day. Does the data provide evidence that the mean amount of time wasted by employees each day is more than 120 minutes? Test at α=.05. Assume the population is at least 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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