A company that makes robotic vacuums claims that their newest model of vacuum lasts, on average, two hours when starting on a full charge. To investigate this claim, a consumer group purchases a random sample of five vacuums of this model. They charge each unit fully and then measure the amount of time each unit runs. Here are the data (in hours): 2.2, 1.85, 2.15, 1.95, and 1.90. They would like to know if the data provide convincing evidence that the true mean run time differs from two hours. The consumer group plans to test the hypotheses H0: μ = 2 versus Ha: μ ≠ 2, where μ = the true mean run time for all vacuums of this model. The conditions for inference are met. The test statistic is t = 0.14 and the P-value is greater than 0.25. What conclusion should be made at the significance level, ?Reject H0. There is convincing evidence that the true mean run time for all vacuums of this model is different from 2 hours.Reject H0. There is not convincing evidence that the true mean run time for all vacuums of this model is different from 2 hours.Fail to reject H0. There is convincing evidence that the true mean run time for all vacuums of this model is different from 2 hours.Fail to reject H0. There is not convincing evidence that the true mean run time for all vacuums of this model is different from 2 hours. A company that makes robotic vacuums claims that their newest model of vacuum lasts, on average, two hourswhen starting on a full charge. To investigate this claim, a consumer group purchases a random sample of fivevacuums of this model. They charge each unit fully and then measure the amount of time each unit runs. Here arethe data (in hours): 2.2, 1.85, 2.15, 1.95, and 1.90. They would like to know if the data provide convincing evidencethat the true mean run time differs from two hours. The consumer group plans to test the hypotheses Ho: μ = 2versus H₂: μ # 2, where u the true mean run time for all vacuums of this model. The conditions for inference aremet. The test statistic is t = 0.14 and the P-value is greater than 0.25. What conclusion should be made at thesignificance level, α = 0.01?=Reject Ho. There is convincing evidence that the true mean run time for all vacuums of this model is different from2 hours.O Reject Ho. There is not convincing evidence that the true mean run time for all vacuums of this model is differentfrom 2 hours.O Fail to reject Ho. There is convincing evidence that the true mean run time for all vacuums of this model is differentfrom 2 hours.Fail to reject Ho. There is not convincing evidence that the true mean run time for all vacuums of this model isdifferent from 2 hours.

Let μ be the population mean run time for all vacuums of this model. Given that, Population mean…

Answered: conditions for inference are met. 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 company that makes robotic vacuums claims that their newest model of vacuum lasts, on average, two hours when starting on a full charge. To investigate this claim, a consumer group purchases a random sample of five vacuums of this model. They charge each unit fully and then measure the amount of time each unit runs. Here are the data (in hours): 2.2, 1.85, 2.15, 1.95, and 1.90. They would like to know if the data provide convincing evidence that the true mean run time differs from two hours. The consumer group plans to test the hypotheses H₀: μ = 2 versus H_a: μ ≠ 2, where μ = the true mean run time for all vacuums of this model. The conditions for inference are met. The test statistic is t = 0.14 and the P-value is greater than 0.25. What conclusion should be made at the significance level, ?

Reject H₀. There is convincing evidence that the true mean run time for all vacuums of this model is different from 2 hours.

Reject H₀. There is not convincing evidence that the true mean run time for all vacuums of this model is different from 2 hours.

Fail to reject H₀. There is convincing evidence that the true mean run time for all vacuums of this model is different from 2 hours.

Fail to reject H₀. There is not convincing evidence that the true mean run time for all vacuums of this model is different from 2 hours.

A company that makes robotic vacuums claims that their newest model of vacuum lasts, on average, two hours
when starting on a full charge. To investigate this claim, a consumer group purchases a random sample of five
vacuums of this model. They charge each unit fully and then measure the amount of time each unit runs. Here are
the data (in hours): 2.2, 1.85, 2.15, 1.95, and 1.90. They would like to know if the data provide convincing evidence
that the true mean run time differs from two hours. The consumer group plans to test the hypotheses Ho: μ = 2
versus H₂: μ # 2, where u the true mean run time for all vacuums of this model. The conditions for inference are
met. The test statistic is t = 0.14 and the P-value is greater than 0.25. What conclusion should be made at the
significance level, α = 0.01?
=
Reject Ho. There is convincing evidence that the true mean run time for all vacuums of this model is different from
2 hours.
O Reject Ho. There is not convincing evidence that the true mean run time for all vacuums of this model is different
from 2 hours.
O Fail to reject Ho. There is convincing evidence that the true mean run time for all vacuums of this model is different
from 2 hours.
Fail to reject Ho. There is not convincing evidence that the true mean run time for all vacuums of this model is
different from 2 hours.

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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