Problems 59 - 62 refer to the following experiment: 2 balls are drawn in succession out of a box containing 2 red and 5 white balls. Let R i , be the event that the i t h ball is red, and let W i , be the event that the i t h ball is white. Find the probability that at least 1 ball was red, given that the first ball was (A) Replaced before the second draw (B) Not replaced before the second draw
Problems 59 - 62 refer to the following experiment: 2 balls are drawn in succession out of a box containing 2 red and 5 white balls. Let R i , be the event that the i t h ball is red, and let W i , be the event that the i t h ball is white. Find the probability that at least 1 ball was red, given that the first ball was (A) Replaced before the second draw (B) Not replaced before the second draw
Solution Summary: The author calculates the probability of at least one ball being red when the first ball was replaced before the second draw.
Problems
59
-
62
refer to the following experiment:
2
balls are drawn in succession out of a box containing
2
red and
5
white balls. Let
R
i
, be the event that the
i
t
h
ball is red, and let
W
i
, be the event that the
i
t
h
ball is white.
Find the probability that at least
1
ball was red, given that the first ball was
Is it possible to show me how to come up with an exponential equation by showing all the steps work and including at least one mistake that me as a person can make. Like a calculation mistake and high light what the mistake is. Thanks so much.
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1. The CLT provides an approximate sampling distribution for the arithmetic average Ỹ of a
random sample Y₁, . . ., Yn f(y). The parameters of the approximate sampling distribution
depend on the mean and variance of the underlying random variables (i.e., the population
mean and variance). The approximation can be written to emphasize this, using the expec-
tation and variance of one of the random variables in the sample instead of the parameters
μ, 02:
YNEY,
· (1
(EY,, varyi
n
For the following population distributions f, write the approximate distribution of the sample
mean.
(a) Exponential with rate ẞ: f(y) = ß exp{−ßy}
1
(b) Chi-square with degrees of freedom: f(y) = ( 4 ) 2 y = exp { — ½/ }
г(
(c) Poisson with rate λ: P(Y = y) = exp(-\}
>
y!
y²
2. Let Y₁,……., Y be a random sample with common mean μ and common variance σ². Use the
CLT to write an expression approximating the CDF P(Ỹ ≤ x) in terms of µ, σ² and n, and
the standard normal CDF Fz(·).
Chapter 8 Solutions
Pearson eText for Finite Mathematics for Business, Economics, Life Sciences, and Social Sciences -- Instant Access (Pearson+)
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