Section 8.1

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Section 8.1. Inference for a Single Proportion. Recall: Population Proportion. Let p be the proportion of “successes” in a population. A random sample of size n is selected, and X is the count of successes in the sample.

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Section 8.1

Inference for a Single Proportion

Recall: Population Proportion
• Let p be the proportion of “successes” in a population. A random sample of size n is selected, and X is the count of successes in the sample.
• Suppose n is small relative to the population size, so that X can be regarded as a binomial random variable with
Recall: Population Proportion
• We use the sample proportion as an estimator of the population proportion p.
• is an unbiased estimator of p, with mean and SD:
• When n is large, is approximately normal. Thus

is approximately standard normal.

CI for a Population Proportion
• Since is normal.
• The standard error of is
• An approximate level C confidence interval for p:

where P(Z ≥ z*) = (1 – C)/2.

CI for a Population Proportion
• The margin of error is
• Use this interval when the successes and failures are both at least 15
• Use Table A or last row of Table D to find z*.
Example:
• A news program constructs a call-in poll about a proposed city ban on handguns. 2372 people call in to the show. Of these, 1921 oppose the ban.
• Construct a 95% confidence interval for the true proportion of people who oppose the ban.
• What are the possible problems with the study design?
Solution:
• Note: Since p is a proportion, if you ever get an upper limit value of > 1 or lower <0 while calculating the CI, replace by 1 and 0 (respectively).
Choosing a Sample Size
• If we want to estimate the proportion p within a specified margin of error m, the required sample size is (at least):
Choosing a Sample Size
• Since is unknown before the data is collected, we use any prior information we have to get a rough known estimate, p*.
• This is especially important if you believe p is close to 0 or 1.
• Where might we find previous information about p?
• If you have no information, we may replace p*, above, with 0.5 to obtain the most conservative sample size.
Example (handguns revisited):
• Assume that we plan to ask randomly chosen people from the phone book.
• We would like to have a margin of error of 0.03=3%. How big a sample size should we have now?
Another example:
• Suppose that the results of a survey of 2,000 television viewers at 11:40p.m. on Monday September 28, 1998 were recorded, and it was determined that 226 viewers watched “The Tonight Show.”
• Estimate with 95% confidence the number of TVs tuned to “The Tonight Show” if there are 100 million potential television sets.
Testing for a single population proportion
• When n is large, is approximately normal, so

is approximately standard normal.

• We may test H0:p = p0 against one of these:
• Ha: p > p0
• Ha: p < p0
• Ha: p ≠ p0
Large-sample Significance Test for a Population Proportion
• The null hypothesis, H0: p = p0
• The test statistic is
Large-sample Significance Test for a Population Proportion
• How big does the sample size need to be?
• The general rule of thumb to use here, as before for approximation of binomial distribution by normal distribution, is
Example:
• A claim is made that only 34% of all college students have part-time jobs. You are a little skeptical of this result and decide to conduct an experiment to show more students work. You get a sample of 100 college students and find that 47 of these students have part-time jobs.
• Conduct a hypothesis test with  = 0.05 to determine whether more than 34% of college students have part-time jobs.
SAS Programs
• proportion.doc

But mostly hand computations.

Section 8.2

Comparing Two Proportions

Comparing Two Proportions
• Before we begin…
• Intuitively, how do you think we will be comparing two proportions?
• Think in terms of two means, what did we do there?
Comparing Two Proportions
• SRS of size n1 from a large population having proportion p1 of successes and an independent SRS of size n2 from another large population having proportion p2 of successes.
• is an estimator of p1
• is an estimator of p2:
Comparing Two Proportions: approximate normality
• Compare p1 and p2 means to examine p1–p2.
• This can be done by studying the difference
• We obtain an approximate standard normal variable
Significance Test – Comparing Two Population Proportions
• When p1 and p2 are unknown, we want to carry out hypothesis testing for
• H0: p1 = p2 (same as p1 – p2=0)
• against one of the following alternatives:
• Ha: p1 > p2
• Ha: p1 < p2
• Ha: p1≠ p2
Comparing Two Population Proportions: Significance Test
• Under the null hypothesis H0: p1 = p2, we view all the data as coming from a single population with proportion p1=p2=p (p unknown). The z-statistic becomes
• Since p is unknown, we use the pooled sample proportion to estimate it:
Comparing Two Population Proportions- Significance Test
• Null hypothesis: H0: p1 = p2
• The test statistic:
Example:
• In a highly-publicized study, doctors confirmed earlier observations that aspirin seems to help prevent heart attacks. The research project employed 21,996 male American physicians. Half of these took an aspirin tablet every other day, while the other half took a placebo on the same schedule. After 3 years, researchers determined that 139 of those who took aspirin and 239 of those who took placebo had had heart attacks. Determine whether these results indicate that aspirin is effective in reducing the incidence of heart attacks at significance level 0.05.
Confidence Interval:
• We are interested in constructing a confidence interval for p1 – p2. The estimate for this is
• The standard error of is defined as
Confidence Interval - Formula:
• An approximate level C confidence interval for p1 – p2is given by

where P(Z≥ z*) = (1 – C)/2

Confidence Interval-margin of error:
• The margin of error is given by
• Use this interval when the number of successes and the number of failures in each sample are at least 10.
Example (Aspirin and Heart Attacks):
• Estimate with 95% confidence the difference in proportion of men risking a heart attack (in the next 3 years) among aspirin takers and non-takers.
Relative risk:
• Example: Calculate the relative risk for the aspirin example:
• Software gives confidence intervals (based on data) for population relative risk: p1 / p2
• This is another way of comparing the two population proportions.