The cost and customer rating of 13 televisions is shown on the scatterplot. The televisions are rated on a scale of 0 to 10. Scatterplot with x axis labeled Television Price going from 0 to 1,400 and y axis labeled Rating going from 0 to 12. Values at 61, 1; 185, 3; 221, 10; 291, 2; 462, 4; 666, 5; 686, 4; 730, 5; 862, 7; 997, 10; 1,055, 9; 1,077, 8; 1,263, 7.

Part A: Describe the association shown in the scatterplot. (4 points).

Part B: Give an example of a data point that affects the appropriateness of using a linear regression model to fit all the data. Explain. (4 points)

Part C: Give an example of a television that is cost effective and rated highly by customers. (2 points)

step-by-step explanation:

3024 rhe base is what up in worry about

i think it is 18

step-by-step explanation:

85.560%

step-by-step explanation:

conditional probability is defined as:

p(a|b) = p(a∩b) / p(b)

in other words, the probability that a occurs, given that b has occurred = the probability that both a and b occur / the probability that b occurs.

in this example:

p(turns 64 | turns 44) = p(turns 64 & turns 44) / p(turns 44).

if someone turns 64, they must have already turned 44, so:

p(turns 64 | turns 44) = p(turns 64) / p(turns 44)

on page 42, we see that of the original 100,000, 94944 turn 44, and 81234 turn 64.   therefore:

p(turns 64 | turns 44) = 81234 / 94944

p(turns 64 | turns 44) = 0.85560