Components coming off an assembly line are either free of defects (S, for success) or defective (F, for failure). Suppose that 80% of all such components are defect-free. Components are independently selected and tested one by one. Let y denote the number of components that must be tested until a defect-free component is obtained. (a) What is the smallest possible y value, and what experimental outcome gives this y value? What is the second smallest y value, and what is the corresponding outcome? The smallest value of y is The outcome corresponding to this is ? v The second smallest value of y is The outcome that corresponds to this is ? v S F (b) What is the set of all possible y values? O The set of nonnegative integers. O The set of all integers. FF FS SF SS O The set of positive integers. O The set of all real numbers. O The set of positive real numbers.

Heights (cm) and weights (kg) are measured for 100 randomly selected adult males, and range from heights of 139 to 194 cm and weights of 39 to 150 kg. Let the predictor variable x be the first variable given. The 100 paired measurements yield x= 167.63 cm, y = 81.47 kg, r=0.106, P-value = 0.294, and y = - 108 + 1.04x. Find the best predicted value of y (weight) given an adult male who is 172 cm tall. Use a 0.05 significance level. The best predicted value of y for an adult male who is 172 cm tall is kg. (Round to two decimal places as needed.)

A weight-loss program wants to test how well their program is working. The company selects a simple random sample of 51 individual that have been using their program for 15 months. For each individual person, the company records the individual's weight when they started the program 15 months ago as an x-value. The subject's current weight is recorded as a y-value. Therefore, a data point such as (205, 190) would be for a specific person and it would indicate that the individual started the program weighing 205 pounds and currently weighs 190 pounds. In other words, they lost 15 pounds. When the company performed a regression analysis, they found a correlation coefficient of r = 0.707. This clearly shows there is strong correlation, which got the company excited. However, when they showed their data to a statistics professor, the professor pointed out that correlation was not the right tool to show that their program was effective. Correlation will NOT show whether or not there is…

A snack food manufacturer estimates that the variance of the number of grams of carbohydrates in servings of its tortilla chips is 1.34. A dietician is asked to test this claim and finds that a random sample of 16 servings has a variance of 1.22. At α=0.05​, is there enough evidence to reject the​ manufacturer's claim? Assume the population is normally distributed. Complete parts​ (a) through​ (e) below.