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STA2023  Chapter 19 (Confidence Intervals for Proportions)
Skip:
A Confidence Interval for Small Samples (pg. 498)
Statistical Inference
is the process of using sample results to draw a conclusion (make an inference) about the
entire population from which the sample was selected.
Chapters 1925 cover statistical inference for various
types of scenarios.
An underlying theme to any type of statistical inference is that the sample data is unbiased
(representative of the population from which it was collected).
Calculations made from sample data are called sample statistics
(
_
^
,
,
y
s
p
)
The corresponding population measures are called population parameters
(
,
,
p
μ
σ
)
Statistical Inference – using sample statistics to make an inference about a population parameter
General Types of Statistical Inference: (chapters 1925)
1.
Confidence Intervals for Proportion Settings (chapter 19)
2.
Hypothesis Testing for Proportion Settings (chapter 20)
Activity 1:
(see page 505 #18 for a similar question)
Direct mail advertisers send solicitations (a.k.a. “junk mail”) to thousands of potential customers in the hope
that some will buy the company’s product.
The response rate is usually quite low.
Suppose a company wants
to test the response to a new flyer, and sends it to 1000 people randomly selected from their mailing list of
over 200,000 people.
They get orders from 125 of the recipients.
Consider a customer who places an order to
be a success and a customer who does not to be a failure.
A. What is the proportion of successes (
^
p
) in this sample of 1000 customers?
^
p
=
B. Let
p
represent the proportion of successes in the entire population
(the 200,000 people on the mailing list).
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 Spring '08
 Ripol
 Statistics, Mailing list, entire population

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