(Example 3) Income in Maryland According to a 2018 Money magazine article, Maryland has one of the highest per capita incomes in the United States, with an average income of $75,847 . Suppose the standard deviation is $ 32 , 000 and the distribution is right-skewed. A random sample of 100 Maryland residents is taken. a. Is the sample size large enough to use the Central Limit Theorem for means? Explain. b. What would the mean and standard error for the sampling distribution? c. What is the probability that the sample mean will be more than $ 32 , 000 away from the population mean?
(Example 3) Income in Maryland According to a 2018 Money magazine article, Maryland has one of the highest per capita incomes in the United States, with an average income of $75,847 . Suppose the standard deviation is $ 32 , 000 and the distribution is right-skewed. A random sample of 100 Maryland residents is taken. a. Is the sample size large enough to use the Central Limit Theorem for means? Explain. b. What would the mean and standard error for the sampling distribution? c. What is the probability that the sample mean will be more than $ 32 , 000 away from the population mean?
Solution Summary: The author analyzes whether the sample size is large enough to use the central limit theorem.
(Example 3) Income in Maryland According to a 2018 Money magazine article, Maryland has one of the highest per capita incomes in the United States, with an average income of
$75,847
. Suppose the standard deviation is
$
32
,
000
and the distribution is right-skewed. A random sample of 100 Maryland residents is taken.
a. Is the sample size large enough to use the Central Limit Theorem for means? Explain.
b. What would the mean and standard error for the sampling distribution?
c. What is the probability that the sample mean will be more than
$
32
,
000
away from the population mean?
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
Chapter 9 Solutions
Pearson eText Introductory Statistics: Exploring the World Through Data -- Instant Access (Pearson+)
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