Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperature each day during the summer. the data are plotted with temperature, in degrees fahrenheit (°f), as the explanatory variable and the number of ice bags sold that day as the response variable. the least squares regression (lsr) line for the data is ˆ=−125.30+2. on one of the observed days, the temperature was 82 °f and 68 bags of ice were sold. determine the number of bags of ice predicted to be sold by the lsr line, ˆ , when the temperature is 82 °f. enter your answer as a whole number, rounding if necessary.

the answer   is 200

step-by-step explanation:

this is a problem about poisson probabilities.   i don't know if you've

already studied poisson distributions, or if you have a place to look

to read more about the subject.  

here's how you can see a poisson distribution coming.   when there are

a lot of events that may happen at any time (or any place), and when

you're counting how many of them happen at a particular time (or a

particular place), and each event is independent of the others, then

the result is a poisson distribution.   this is roughly true for the

chocolate chips; one chip being in a cookie has only a small effect on

the probability that another chip will be in the cookie.

once you know you have a poisson distribution, you know a lot.   there

is only one parameter to a poisson distribution, so if you know the

mean, you know everything.   the form of the distribution is

  probability of n chips = {[e^(-x)]*(x^n)} / (n! )

answer: are you

step-by-step explanation: