Aluminum contamination in the manufacture of polyethylene teraphthalate (a biomedical polymer) is limited to an average of 220 ppm (parts per million). Below are 26 samples collected from manufacture lots and the amount of aluminum contamination measured. Aluminum contamination in ppm: 291, 222, 125, 79, 145, 119, 244, 118, 182, 63, 30, 140, 101, 102, 87, 183, 60, 191, 119, 311, 120, 172, 70, 30, 90, 115 Perform a t-test to determine if test samples have Al contamination less than 220 ppm at the α = 0.05 level of significance. a. State your H0 and Ha and if this is 1-tailed or 2-tailed b. Check the assumption that the data are normally distributed (show appropriate plots and discuss CLT rules) c. State your p-value from your t-test and your conclusion d. Calculate 95% confidence interval and state conclusion this time based on confidence interval

Name: Aluminum contamination in the manufacture of polyethylene teraphthalat
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Description: Aluminum contamination in the manufacture of polyethylene teraphthalate (a biomedical polymer) is limited to an average of 220 ppm (parts per million). Below are 26 samples collected from manufacture lots and the amount of aluminum contamination meas

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MATLAB: An Introduction with Applications

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Aluminum contamination in the manufacture of polyethylene teraphthalate (a biomedical polymer) is limited to an average of
220 ppm (parts per million). Below are 26 samples collected from manufacture lots and the amount of aluminum contamination measured.

Aluminum contamination in ppm:
291, 222, 125, 79, 145, 119, 244, 118, 182, 63, 30, 140, 101, 102, 87, 183, 60, 191, 119, 311, 120, 172, 70, 30, 90, 115
Perform a t-test to determine if test samples have Al contamination less than 220 ppm at the α = 0.05 level of significance.
	a. State your H0 and Ha and if this is 1-tailed or 2-tailed
	b. Check the assumption that the data are normally distributed (show appropriate plots and discuss CLT rules)
	c. State your p-value from your t-test and your conclusion
	d. Calculate 95% confidence interval and state conclusion this time based on confidence interval

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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