A company tests two new products to make sure they last for more than a year. Product 1 had 950 out of 1,000 test items last for more than a year. Product 2 had 150 out of 200 last for more than a year. If you had to choose one of these two products to use for
more than a year, which one is more likely to last?

Product 1
Product 2

High school seniors with strong academic records apply to the nation’s most selective colleges in greater numbers each year. because the number of slots remains relatively stable, some colleges reject more early applicants. suppose that for a recent admissions class, an ivy league college received 2851 applications for early admission. of this group, it admitted 1033 students early, rejected 854 outright, and deferred 964 to the regular admission pool for further consideration. in the past, this school has admitted 18% of the deferred early admission applicants during the regular admission process. counting the students admitted early and the students admitted during the regular admission process, the total class size was 2375. let e, r, and d represent the events that a student who applies for early admission is admitted early, rejected outright, or deferred to the regular admissions pool.suppose a student applies for early admission. what is the probability that the student will be admitted for early admission or be deferred and later admitted during the regular admission process?

Anut store normally sells cashews for? $4.00 per pound and peanuts for? $1.50 per pound. but at the end of the month the peanuts had not sold? well, so, in order to sell 40 pounds of? peanuts, the manager decided to mix the 40 pounds of peanuts with some cashews and sell the mixture for $2.00 per pound. how many pounds of cashews should be mixed with the peanuts to ensure no change in the? profit?

Consider the linear model for a two-stage nested design with b nested in a as given below. yijk=\small \mu + \small \taui + \small \betaj(i) + \small \varepsilon(ij)k , for i=1,; j= ; k=1, assumption: \small \varepsilon(ij)k ~ iid n (0, \small \sigma2) ; \small \taui ~ iid n(0, \small \sigmat2 ); \tiny \sum_{j=1}^{b} \small \betaj(i) =0; \small \varepsilon(ij)k and \small \taui are independent. using only the given information, derive the least square estimator of \small \betaj(i) using the appropriate constraints (sum to zero constraints) and derive e(msb(a) ).