In each of the following situations, the sampling frame does not match the population, resulting in undercoverage. Give examples
1 answer:
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Answer:
Probability that the sample proportion will be less than 0.03 is 0.10204.
Step-by-step explanation:
We are given that a courier service company wishes to estimate the proportion of people in various states that will use its services. Suppose the true proportion is 0.04.
Also, 469 are sampled.
<em>Let </em><em> = sample proportion</em>
The z-score probability distribution for sample proportion is given by;
Z = ~ N(0,1)
where, = sample proportion
p = true proportion = 0.04
n = sample size = 469
The Z-score measures how many standard deviations the measure is away from the mean. After finding the Z-score, we look at the z-score table and find the p-value (area) associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X.
So, probability that the sample proportion will be less than 0.03 is given by = P( < 0.03)
P( < 0.03) = P(
<
) = P(Z < -1.27) = 1 - P(Z
1.27)
= 1 - 0.89796 = 0.10204
Now, in the z table the P(Z x) or P(Z < x) is given. So, the above probability is calculated by looking at the value of x = 1.27 in the z table which has an area of 0.89796.
Therefore, probability that the sample proportion will be less than 0.03 is 0.10204.