Topic: Algorithm of an estimation of coincidence 2
I welcome! Saw in this branch a subject "algorithm of an estimation of coincidence", I look, answer! Cool! I Will try to ask too! The task: 1) In an advertizing campaign there is a Conversion index. It in this case (number of orders) / (Number of shows of banners) = 0.1 or 10 % Advertising was shown N days. N - it is not known!! We tell in 1 day (either hour, or minute, or second and ) 100 shows and 10 orders, and can and 117 shows and 0 orders or 30 shows and 30 orders. Conversion of 10 % this averaging for all time of show of advertizing. The dispersion is not set and unknown and not . Access to the day statistics is not present. Only to total - all N shows, To - orders, To / N = 0.1 or 10 % however N not small, i.e. such that most likely in an average of 0.1 or 10 % allocation is close to normal, but it is not known. On statements of the problem it is not known, but only my assumptions (like reasonable!) 2) the Manager made certain changes to adjustments of advertizing which could affect conversion. 3) a question: to Find minimum 1 and N1 such, what with probability of X % we can define that Conversion on 1 % i.e. there were> =11 % 4) 2, N2 Y % - Conversion <= 9 % 5) 3, N3 Z % - Conversion did not change almost, i.e. 9 % <To <11 % test value X = Y = Z = 95 % still figure In statements of the problem a certain parameter Capacity [samplings?] 80 % the Dispersion is not known, but it did not change after advertizing campaign modification. About confidential intervals read, but did not understand as them to apply to the answer on 3) 4) 5) P.S. In the Internet a certain formula for calculation N - however its substantiation and an output is not present, so its value zero, the decision should be justified. Results in it turn out too big, the order of 10 thousand conversion and 100. Shows. Intuitively it should seems difference to be visible on the smaller statistics? And to depend on a dispersion or not?