Correlation is a measure of the direction and strength of the linear (straight-line) association between two quantitative variables. the analysis of data from a study found that the scatter plot between two variables, x and y, appeared to show a straight-line relationship and the correlation (r) was calculated to be -0.84. this tells us that

a. there is little reason to believe that the two variables have a linear association relationship
b. all of the data values for the two variables lie on a straight line.
c. there is a strong linear relationship between the two variables with larger values of x tending to be associated with larger values of the y variable.
d. there is a strong linear relationship between x and y with smaller x values tending to be associated with larger values of the y variable.
e. there is a weak linear relationship between x and y with smaller x values tending to be associated with smaller values of the y variable

Amachine that produces a special type of transistor (a component of computers) has a 2% defective rate. the production is considered a random process where each transistor is independent of the others. (a) what is the probability that the 10th transistor produced is the first with a defect? (b) what is the probability that the machine produces no defective transistors in a batch of 100? (c) on average, how many transistors would you expect to be produced before the first with a defect? what is the standard deviation? (d) another machine that also produces transistors has a 5% defective rate where each transistor is produced independent of the others. on average how many transistors would you expect to be produced with this machine before the first with a defect? what is the standard deviation? (e) based on your answers to parts (c) and (d), how does increasing the probability of an event a↵ect the mean and standard deviation of the wait time until success?