Babies Weights (Example 2) Some sources report that the weights of full-term newborn babies have a mean of 7 pounds and a standard deviation of 0.6 pound and are Normally distributed. a. What is the probability that one newborn baby will have a weight within 0.6 pound of the mean − that is, between 6.4 and 7.6 pounds, or within one standard deviation of the mean? b. What is the probability the average of four babies’ weights will be within 0.6 pound of the mean − that is, between 6.4 and 7.6 pounds? c. Explain the difference between a and b.
Babies Weights (Example 2) Some sources report that the weights of full-term newborn babies have a mean of 7 pounds and a standard deviation of 0.6 pound and are Normally distributed. a. What is the probability that one newborn baby will have a weight within 0.6 pound of the mean − that is, between 6.4 and 7.6 pounds, or within one standard deviation of the mean? b. What is the probability the average of four babies’ weights will be within 0.6 pound of the mean − that is, between 6.4 and 7.6 pounds? c. Explain the difference between a and b.
Babies Weights (Example 2) Some sources report that the weights of full-term newborn babies have a mean of 7 pounds and a standard deviation of 0.6 pound and are Normally distributed.
a. What is the probability that one newborn baby will have a weight within
0.6
pound of the
mean
−
that
is, between
6.4
and
7.6
pounds, or within one standard deviation of the mean?
b. What is the probability the average of four babies’ weights will be within
0.6
pound of the
mean
−
that
is, between
6.4
and
7.6
pounds?
c. Explain the difference between a and b.
Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.
Throughout, A, B, (An, n≥ 1), and (Bn, n≥ 1) are subsets of 2.
1. Show that
AAB (ANB) U (BA) = (AUB) (AB),
Α' Δ Β = Α Δ Β,
{A₁ U A2} A {B₁ U B2) C (A1 A B₁}U{A2 A B2).
16. Show that, if X and Y are independent random variables, such that E|X|< ∞,
and B is an arbitrary Borel set, then
EXI{Y B} = EX P(YE B).
Proposition 1.1 Suppose that X1, X2,... are random variables. The following
quantities are random variables:
(a) max{X1, X2) and min(X1, X2);
(b) sup, Xn and inf, Xn;
(c) lim sup∞ X
and lim inf∞ Xn-
(d) If Xn(w) converges for (almost) every w as n→ ∞, then lim-
random variable.
→ Xn is a
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