Overlap between age and years of service is measured, its effect on coefficient stability shown, and a remedy chosen in the PHLT 8500 diagnostic week, judged against the model's purpose. Searches like "phlt 8500 week 7 assignment example", "phlt8500 week 7 sample" and "phlt 8500 week 7 example" land here.
What a finished PHLT 8500 Week 7 diagnostic check looks like
A collinearity table, a small resampling figure and a residual panel fill a little under three pages. The table reports variance inflation factors for every term in the systolic model after years of service is added beside age, and the two stand well above the rest. The figure shows what that means in practice: across bootstrap resamples, the age and service coefficients swing widely and sometimes trade signs, while their combined contribution stays steady. The check then asks the question that decides the remedy. Night shift is the exposure, and age and service are there only as adjustments, so collinearity between them inflates their own standard errors without harming the night shift estimate, which remains stable across resamples. A short residual section closes the check, covering linearity and spread against fitted values.
How a PHLT 8500 Week 7 example is structured
The check moves from detection to consequence to decision, because a collinearity statistic without a consequence is only a number. Detection comes first: the correlation between age and service, then variance inflation factors across all terms. Consequence follows, shown with the resampling figure rather than described, since unstable coefficients are easier to see than to imagine. The decision section is the argument. It sets out the usual remedies, dropping one variable, combining them, or reexpressing service as age at hire, and weighs each against the model's purpose. Because both variables are adjustment terms, the check keeps them and states that their individual coefficients should not be interpreted. A note explains when the decision would change: if years of service were itself the exposure. Residual diagnostics follow briefly.
Overlap, measured
The correlation between age and years of service is reported, then variance inflation factors for every term. The two overlapping predictors stand out, and the check names them before discussing anything else.
Coefficients that swing
Across bootstrap resamples, the age and service coefficients move widely and occasionally reverse sign. Their combined contribution barely changes, which the figure shows by plotting both alongside their sum.
The exposure, untouched
The night shift coefficient is stable across the same resamples. Collinearity between adjustment terms inflates their own uncertainty without degrading the estimate the model was built to produce, and the check says so.
Remedies weighed against purpose
Dropping service, combining the two, or reexpressing service as age at hire are each considered. Because neither variable is the target, the check keeps both and declines to interpret their separate coefficients.
Residuals, briefly
A plot of residuals against fitted values checks linearity and constant spread. The check reports what it shows in two sentences and notes one region of wider spread among the oldest workers.
Where marks go in PHLT 8500 Week 7
The link between the diagnostic and the model's purpose is where this rubric places its heaviest weight. A check that reports high variance inflation factors and drops a variable by reflex has treated a statistic as a rule, and graders read that as a missed judgment. Detection is expected to be accurate and complete across terms. Consequences must be shown or clearly explained; saying collinearity is bad without describing what it does to standard errors or sign stability earns little. Doctoral credit goes to recognizing that collinearity among adjustment terms need not harm the exposure estimate. Remedies are scored on whether alternatives were weighed. The residual section carries a smaller share, and figures without axis labels or captions lose presentation points.
Get a PHLT 8500 Week 7 example written to your instructions
Upload the Week 7 prompt and rubric and name the predictors you suspect of overlapping. A diagnostic check that measures the overlap, shows its effect on stability and ties the remedy to the model's purpose arrives within 24-48h, and a first is not billed. Its bootstrap figure comes from invented data and shows the pattern only.
PHLT 8500 Week 7 questions, answered
Is there a variance inflation factor above which a predictor must be removed?
Rules of thumb circulate, but no threshold settles the question on its own. What matters is what the overlap does to the estimates you need. If the overlapping variables are adjustment terms and the exposure estimate is stable, removal may be unnecessary. If the exposure itself is involved, the problem is serious at lower values. The check states its reasoning instead of citing a number.
Does collinearity bias the coefficients?
No, in the sense that the estimates remain unbiased on average. It inflates their variance, so individual estimates become imprecise and sensitive to small changes in the data. That is why coefficients can swing or reverse sign across resamples while the model's overall predictions hardly move. The check describes it as a precision problem, which points toward the right remedies.
What if the overlapping variables are both of interest?
Then the data may simply not contain enough independent variation to separate their effects, and the honest report says so. Options include estimating their joint contribution, collecting data where they vary more independently, or reframing the question around one of them with a stated reason. Tabachnick and Fidell treat such overlap as part of screening, and the check cites the approach it chose.