Model 1: X is distributed as Poiss(λ) for some unknown λ>0. Model 2: X takes values in {1,2,3,4,5,6,≥7}, and for i=1,2,…7, we let pi denote the (unknown) probability that X=i. Here "≥7" is a placeholder for when the number of siblings is at least 7. For example, we do not distinguish between an individual having 7 siblings or 10 siblings in this model. Which one of the following best describes an advantage of using a Poisson distribution (Model 1) over the distribution in Model 2 to model X? A. It allows us to model the data continuously.
B. It allows individuals to have an arbitrarily large number of siblings.
C. It reduces the amount of unknowns needed for modeling.

step-by-step explanation:

there is a formula i can give you:

it is r^2*n*sin(360/n) divided by 2 where r represents the "radius" and n is the number of sides.

plug them in:

(10sqrt(3))^2*6*sin(360/6) divided by 2

100*3*6*sin(60) divided by 2

900*sin(60)

900*sqrt(3)/2

450*sqrt(3)

approx is 779.4 cm^2

to find the total number of combinations, multiply the quantity of each item by each other:

2 pairs of shoes x 3 pairs of shorts = 6 different combinations.

step-by-step explanation:

sorry i just needed points for a quiz plz for give me

"standard form:

3x2 + 14x − 10 = 0

solutions based on quadratic formula:

x1   = −14 − √ 142 − 4×3×(−10) /   2×3   = −14 − 2 × √ 79 /   6   ≈ −5.296

x2   = −14 + √ 142 − 4×3×(−10) /   2×3   = −14 + 2 × √ 79 /   6   ≈ 0.629