Thirty-one hospitalists, one scatter plot and a coefficient near minus 0.30 that could plausibly be zero: HA535's Unit 6 correlation analysis, read for what leadership may conclude. Searches like "ha 535 unit 6 assignment example", "ha535 unit 6 sample" and "ha535 unit 6 example" land here.
What a finished HA535 Unit 6 correlation analysis looks like
Five pages with a scatter plot, a funnel plot and a short results table. Each point is one hospitalist: horizontal position is the ratio of observed to expected length of stay, from 0.79 to 1.14; vertical position is the 30-day readmission rate across that physician's 157 to 415 discharges, pooled at 14.8 percent. Pearson's r is minus 0.296, so r squared says length of stay accounts for under 9 percent of the variation in readmissions. The 95 percent confidence interval runs from minus 0.59 to 0.07, and a permutation test gives p near 0.11. Spearman's rho, minus 0.22, keeps the direction without the pull of extreme points. The funnel plot shows two physicians outside 95 percent limits around the pooled rate, about what chance alone would produce among thirty-one.
How a HA535 Unit 6 example is structured
Leadership's question is stated as asked, then restated as something a correlation can address. A data section explains the unit of analysis, physicians rather than patients, and the attribution rule assigning each discharge to one hospitalist. Assumptions are checked before results: the scatter is inspected for linearity and outliers, which is why a rank correlation accompanies Pearson's. Results arrive in the order a manager needs them, direction, strength, uncertainty. Interpretation carries the most weight. A weak negative association fits a small real effect, no effect at all, or confounding by patient mix the expected-stay model misses. The funnel plot then answers a separate question, whether any single physician's readmission rate is unusual, and finds two at most. The closing paragraph advises against tying individual length-of-stay targets to readmission rates.
The question, restated
Leadership asked whether faster discharges cause readmissions. A correlation across physicians can only show whether the two move together, and the paper says so before computing anything.
One point per hospitalist
Discharges are attributed to the physician who wrote the discharge order, 8,208 in all. The index uses the expected stay for each discharge's MS-DRG, so a heavier caseload does not read as slowness.
Minus 0.296, with a wide interval
Pearson's r, its square, a confidence interval by Fisher's transformation and a permutation p-value, reported together. The interval crosses zero, and the paper states exactly what that permits and forbids.
Two outside the funnel
Readmission rates plotted against each physician's discharge count, with 95 percent limits around the pooled 14.8 percent. Two points fall outside, roughly what chance predicts, so no one is singled out.
What the evidence cannot support
Individual length-of-stay targets linked to readmission penalties. A patient-level model with severity, discharge destination and follow-up timing is proposed as the analysis able to decide the question.
Where marks go in HA535 Unit 6
Reading a correlation as proof that one variable drives the other is the mistake HA535 graders watch for first, and it is expensive. The expected minimum is the coefficient reported with direction, strength and some measure of uncertainty, and a plot that lets a reader judge linearity and outliers. A p-value stated without a confidence interval tends to earn partial credit at best. Choosing the wrong unit of analysis also costs marks, particularly when physician-level rates are treated as patient-level evidence. Interpretation written for managers, with confounders named in plain language and the supported decision stated, is where marks accumulate. The opposite overreach costs too: a weak association is not proof of no effect, and a paper that says so shows it understood its own numbers.
Get a HA535 Unit 6 example written to your instructions
What two variables does your Unit 6 assignment pair, and what question sits behind them? Pass that along with the dataset, the directions and rubric. We compute the correlation from your data, report its uncertainty and write the manager's reading of it; the opening sample carries no charge and lands within 24-48h.
HA535 Unit 6 questions, answered
Pearson or Spearman: which correlation should the analysis use?
Pearson measures straight-line association and suits roughly linear data without extreme points. Spearman works on ranks, so it tolerates skew, outliers and curved but steady relationships. Reporting both is common when the scatter plot raises doubts, as in the example. Whichever you lead with, show the plot so a reader can judge whether the choice was sound.
What does r squared mean in plain terms?
The share of variation in one variable that a straight-line relationship with the other accounts for. An r of minus 0.30 gives about 0.09, so length of stay explains under a tenth of the differences in readmission rates between physicians. That translation is often the most useful sentence in the paper for a manager reading it.
Can a correlation analysis show causation if the data are good?
Not on its own. Good data make the association more trustworthy, but the causal question depends on design: confounders, timing and how patients reach each provider. A correlation can justify a closer study, rule out a strong relationship or lend support to a hunch. Say plainly which of those your result does, and stop there.