A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an
important predictor of appraised value in the number of rooms in the house. Consequently, the
appraiser decided to fit a simple linear regression model, where y = appraised value of the house (in
$thousands) and x = number of rooms. Using data collected for a sample of n = 74 houses in East
Meadow, the following results were obtained: û = 17.80x + 74.80
Give a practical interpretation of the estimate of the slope of the least squares line.

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?