The requirements for performing a one-sample t-test been met for the following reasons. The sample was not known to be random. The sample data contain no outliers. The population is not normal or cannot be assumed to be normal. The sample data contain at least one outlier. The sample was random. The sample size is too small. The population is normal or can reasonably be assumed to be normal. The sample size is large enough.

MATLAB: An Introduction with Applications
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Chapter1: Starting With Matlab
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Suppose that Rob is a quality control specialist at a toothpaste factory. Part of his job is to verify that the filling machines are
dispensing the proper amount of toothpaste into each tube. It is his first day working on a new machine that fills travel size
toothpaste tubes. The machine is calibrated with an average fill rate of 0.75 ounces of toothpaste per tube.
Rob randomly samples 16 of the 1000 travel size tubes of toothpaste that the machine produces that day. The weight, in
ounces, of Rob's sample data is presented in the stem-and-leaf plot below. The stems are tenths of an ounce and the leaves are
hundredths of an ounce.
5 9
6
6
7
7
8 3 4
8
5
2 3
5 6 8
0 0 0 2
6 6 6
Rob does not know the population standard deviation, so he decides to perform a one-sample t-test to determine if the
machine is filling the tubes with the proper amount of toothpaste. Rob knows from past experience that the amount of
toothpaste dispensed by similar machines follows a normal distribution very closely. Repeated measurements taken by the
same device are usually normally distributed. He will assume that the amount of toothpaste dispensed by this machine is
normally distributed and will examine the sample data for evidence that this assumption might not be correct.
His null and alternative hypotheses are Ho μ = 0.75 and H₁ µ ‡ 0.75, respectively.
Is Rob's decision to use a t-test justified? Why or why not? Select all correct statements from the lists below.
The requirements for performing a one-sample t-test
The sample was not known to be random.
The sample data contain no outliers.
The population is not normal or cannot be assumed to be normal.
been met for the following reasons.
The sample data contain at least one outlier.
The sample was random.
The sample size is too small.
The population is normal or can reasonably be assumed to be normal.
The sample size is large enough.
Transcribed Image Text:Suppose that Rob is a quality control specialist at a toothpaste factory. Part of his job is to verify that the filling machines are dispensing the proper amount of toothpaste into each tube. It is his first day working on a new machine that fills travel size toothpaste tubes. The machine is calibrated with an average fill rate of 0.75 ounces of toothpaste per tube. Rob randomly samples 16 of the 1000 travel size tubes of toothpaste that the machine produces that day. The weight, in ounces, of Rob's sample data is presented in the stem-and-leaf plot below. The stems are tenths of an ounce and the leaves are hundredths of an ounce. 5 9 6 6 7 7 8 3 4 8 5 2 3 5 6 8 0 0 0 2 6 6 6 Rob does not know the population standard deviation, so he decides to perform a one-sample t-test to determine if the machine is filling the tubes with the proper amount of toothpaste. Rob knows from past experience that the amount of toothpaste dispensed by similar machines follows a normal distribution very closely. Repeated measurements taken by the same device are usually normally distributed. He will assume that the amount of toothpaste dispensed by this machine is normally distributed and will examine the sample data for evidence that this assumption might not be correct. His null and alternative hypotheses are Ho μ = 0.75 and H₁ µ ‡ 0.75, respectively. Is Rob's decision to use a t-test justified? Why or why not? Select all correct statements from the lists below. The requirements for performing a one-sample t-test The sample was not known to be random. The sample data contain no outliers. The population is not normal or cannot be assumed to be normal. been met for the following reasons. The sample data contain at least one outlier. The sample was random. The sample size is too small. The population is normal or can reasonably be assumed to be normal. The sample size is large enough.
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