An automatic coffee machine dispenses cups of coffee whose volume per cup varies normally with the mean mu = 10 oz. A quality-control researcher randomly selects 8 cups of coffee from the machine and finds that in this sample the mean volume is 9.92 oz. and the standard deviation is 0.23 oz. a. Do these data provide enough evidence to conclude that the mean volume per cup is below the target level? Which of the following two outputs represents the correct way to conduct this test? One-Sample T Test of mu = 10 vs < 10 95% Upper SE Me an 0.08132 N Mean 9.92000 StDev Bound T 8 0.23000 10.07406 -0.98 0.179 Output A: One-Sample Z Test of mu = 10 vs < 10 The assumed standard deviation = 0.23 95% Upper Bound 10.05376 Mean SE Me an Output B: 8 0.163 9.92000 0.08132 -0.98 O Output A O Output B b. Which of the following represents the correct conclusion we can make based on the output you selected in the previous problem (and at the usual significance level of .05)? O The data provide enough evidence to reject H, and to conclude that the mean volume per cup is lower than the target level of 10 oz. O The data provide enough evidence to accept H, and to conclude that the mean volume per cup is at the target level of 10 oz. O The data do not provide enough evidence to reject Ho, so we accept it, and conclude that the mean volume per cup is at the target level of 10 oz. O The data do not provide enough evidence to reject Ho, nor to conclude that the mean volume per cup is lower than the target level of 10 oz.

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Contingency Table

A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.

Binomial Distribution

Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.