A survey prevalence and a small-count death rate, each reported with its 95 percent interval, show in this PU525 Unit 5 example how precision decides what a county can claim. Searches like "pu 525 unit 5 assignment example", "pu525 unit 5 sample" and "pu525 unit 5 example" land here.
What a finished PU525 Unit 5 confidence interval exercise looks like
The exercise runs about four pages with two worked intervals and a short discussion. Part one estimates current smoking at 16.8 percent from 193 of 1,150 respondents and builds a 95 percent interval of 14.6 to 19.0 percent, with the standard error, critical value and margin of error laid out in a small table. The state's prevalence of 13 percent sits below the whole interval, and the example says what that permits. Part one then revisits the interval using a design effect of 1.6 supplied with the survey, which widens it to roughly 14.1 to 19.5. Part two reports 14 overdose deaths in a population of 38,000, a rate of 36.8 per 100,000, and an exact Poisson interval of 20.1 to 61.8. A closing paragraph compares the two intervals' widths and what each can support.
How a PU525 Unit 5 example is structured
The two parts are ordered from the familiar to the less familiar, and the contrast between them carries the lesson. A proportion interval comes first because its formula is the one most readers know, which lets the table of inputs serve as a check on method. The comparison with the state follows directly, since an interval matters only when set against something a decision depends on. The design effect revision comes next and makes a point software users often miss: survey weights and clustering widen intervals, so treating a complex sample as a simple random one overstates precision. Part two changes the kind of estimate, a rate from a small count, where the normal approximation fails and an exact method is needed. The width comparison closes the exercise because it answers the practical question of which figure a county report can responsibly highlight.
Inputs before the interval
Count, sample size, proportion, standard error, z of 1.96 and margin of error in six rows. Anyone can recompute the interval from the table without opening the dataset.
Against the state figure
The state's 13 percent lies below the lower limit, and the example states that the county's prevalence is plausibly higher. Its wording describes the method's long-run coverage, not a probability attached to this single interval.
What the survey design adds
A design effect of 1.6 multiplies the variance, so the standard error grows by its square root. The widened interval still excludes the state figure, and the example reports both versions.
Fourteen deaths, one wide range
The exact Poisson interval runs from about 20 to 62 per 100,000. The example notes that federal reporting practice flags rates resting on fewer than 20 deaths as unreliable, and explains why.
Which estimate a report can lead with
Prevalence supports a firm comparison; the death rate supports only a statement that deaths occurred at a level worth monitoring. Pooling three years is suggested as the remedy.
Where marks go in PU525 Unit 5
Interval exercises are commonly graded on method, correct calculation, interpretation and practical judgment. Method marks require the right interval for the estimate: a normal approximation for a proportion with enough successes and failures, and an exact or adjusted method for a rate built on few events. Calculation marks depend on the standard error being right and the inputs being visible. Interpretation marks are where many papers lose ground, most often by reading the interval as a probability statement about this one county's true prevalence, a wording many sections deduct for wherever it appears. Judgment marks go to the width comparison and the pooling suggestion. Deductions also follow intervals built without the survey's design effect when one was supplied, rates reported without the population they were scaled to, and limits rounded inconsistently.
Get a PU525 Unit 5 example written to your instructions
Send the estimates your PU525 Unit 5 exercise requires, whether proportions, means or rates, with the dataset or summary figures and the rubric. Returned free within 24-48 hours, the first custom sample builds each interval with the method its data call for and compares every result with the benchmark your scenario sets.
PU525 Unit 5 questions, answered
When is an exact interval needed instead of the usual formula?
When counts are small. The familiar plus-or-minus formula relies on a normal approximation that breaks down with few events or with proportions near zero or one. Rates from fewer than about 20 events, and proportions with fewer than ten successes or failures, call for exact Poisson or binomial methods, or an adjusted method such as Wilson's. The example uses an exact Poisson interval for its 14 deaths.
What is a design effect?
It is the ratio of an estimate's variance under a complex survey design to its variance under simple random sampling. Weighting and cluster sampling usually push it above one. Many public health surveys publish design effects or require software that accounts for the design, and an interval that ignores it looks more precise than the data allow.
Why does the example compare the county with the state instead of testing the difference?
Because the state figure comes from a far larger sample and is treated as a fixed benchmark, so asking whether it falls outside the county's interval is a reasonable shortcut. Where both estimates carry meaningful uncertainty, a formal comparison or an interval for the difference is better, and overlapping intervals do not prove two groups are equal. The example notes this limitation in a sentence.