Consider coin toss experiment using bayesian analysis with beta prior: g(e) = tr)「(s) where θ represents the probability o heads. in an experiment n con tosses res it n k heads. based on this experiment andus ng ba es theorem e os e ir rot abl density for 8 is given by n rs) a) (2pt) plot prior and posterior distributions for all 4 cases defined by the following values for parameters: (r, s) (1,1),4,4) & (n,,,11) b) (2pt) in each one of these 4 cases, determine the probability that the coin is biased towards heads (ie p(e> 0.5) (you can use beta distribution integration functionality of r)

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step-by-step explanation:

just for that im not answering

answer: a video game has three difficulty levels: easy, medium, and expert. the game's scoring works in such a way that a player has to score a fixed number of points to advance to the next level, and then the scoring starts over. the total score needed to clear all three difficulty levels is 7,500. two times the number of points needed to clear the easy level is 500 more than the number of points needed to clear the medium level. twice the number of points needed to clear the medium difficulty level is 1,500 more than the number of points needed to clear the expert level. if x, y, and z represent the easy, medium, and expert levels, respectively, and a is the coefficient matrix of the system of equations modeling this situation, identify the inverse matrix, a-1, and the solution matrix, x, of the system of equations.

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step-by-step explanation:

graph them on a graphing calculator, press 2nd trace. select intersect. hit enter until it says answer. ti 84 works best

step-by-step explanation: