Base and extended logistic models for hypertension meet on likelihood ratio, information criteria, calibration and discrimination, and PHLT 8500 tracks the exposure estimate across both. Searches like "phlt 8500 week 9 assignment example", "phlt8500 week 9 sample" and "phlt 8500 week 9 example" land here.
What a finished PHLT 8500 Week 9 model comparison looks like
Three pages whose centerpiece is a two-column table, one model per column, with each term's odds ratio and interval, then rows for the log likelihood, the likelihood ratio test comparing them, AIC and BIC, the Hosmer and Lemeshow result, and the area under the ROC curve. The prose reads the table in order of what each measure answers. The likelihood ratio test asks whether the added terms together improve fit beyond chance, and they do modestly. The information criteria disagree, AIC favoring the larger model and BIC the smaller, and a paragraph explains why their penalties differ. Discrimination barely moves. The night shift odds ratio shifts only slightly between models. The conclusion keeps smoking, drops overtime hours, and explains the decision in terms of the question rather than the statistics alone.
How a PHLT 8500 Week 9 example is structured
The comparison is framed by its purpose before any number appears: the models are candidates for estimating the night shift association, so the criterion that matters most is whether the added terms change that estimate or improve adjustment, not whether they raise predictive accuracy. Measures are then taken in turn, each introduced by the question it answers. The likelihood ratio test comes first, valid because the models are nested. Information criteria follow, with the disagreement between them explained rather than resolved by preference. Calibration and discrimination come next. The exposure-tracking paragraph is the hinge of the argument, since a term that changes nothing about the estimate of interest and adds little fit is hard to defend. The decision paragraph keeps one added term and drops the other, each for a stated reason.
Purpose before measures
The models compete as tools for estimating the night shift association. That framing decides which comparison measures matter most and prevents a model being chosen for predictive gains the question never asked for.
Nested, so testable
The larger model contains every term of the smaller, which makes the likelihood ratio test valid. The comparison reports the test for the two added terms together and then for each alone.
Two criteria disagree
AIC favors the larger model and BIC the smaller, because the BIC penalty grows with sample size. The comparison explains the difference and treats the disagreement as a signal that the gain is marginal.
Tracking the exposure
The night shift odds ratio is reported under both models. Its small shift shows that neither added term confounds the association much, which weighs against keeping a term that also adds little fit.
One kept, one dropped
Smoking stays, supported by prior evidence and a modest improvement. Overtime hours go, since they overlap with shift assignment and blur the exposure's meaning. Each decision is stated with its reason.
Where marks go in PHLT 8500 Week 9
Graders weigh the decision rule more than any single statistic. A comparison choosing the model with the lower AIC, with no link to the question, has let a criterion decide what the author was asked to argue, and it loses the central criterion. Validity of each comparison is checked: a likelihood ratio test applied to non-nested models, or R squared quoted for a logistic model, marks a misunderstanding. The disagreement between information criteria earns credit when explained. Exposure tracking is valued in doctoral sections, because it ties the comparison to the model's purpose. Calibration and discrimination are expected with their limits. Decisions are scored on stated reasons, and a model kept merely because it is larger draws comment. The side-by-side table's clarity takes the presentation share.
Get a PHLT 8500 Week 9 example written to your instructions
Attach the Week 9 prompt and rubric and describe the two specifications your section is comparing. A comparison reading each measure for the question it answers, tracking the exposure estimate and justifying the final choice returns within 24-48h, the first without charge. Its fit statistics describe a fictional cohort and illustrate reasoning only.
PHLT 8500 Week 9 questions, answered
When can a likelihood ratio test compare two models?
When one model is nested within the other, meaning the smaller model's terms are a subset of the larger's, and both are fit to the same cases. Missing values can quietly break the second condition, since adding a variable with gaps drops cases from the larger model. The comparison confirms both models use the same sample before reporting the test.
What should be done when AIC and BIC disagree?
Both should be reported, with the disagreement explained; it usually means the added terms improve fit modestly. AIC penalizes complexity less, so it tends toward larger models; the BIC penalty grows with sample size, so it favors parsimony in big datasets. Neither is automatically right. The decision should rest on the question the model serves, with the criteria treated as evidence rather than a verdict.
Is a higher area under the ROC curve always better?
For prediction, higher discrimination is generally desirable. For estimating an exposure's effect it matters less, since a model can adjust well for confounding without discriminating sharply between cases and non-cases. A variable that raises discrimination may even be a mediator that should stay out. The comparison reports discrimination and explains how much weight it deserves for this purpose.