Level placement accounts for most of a 9.5 percent raw gap in this GB545 regression study of Tellwater engineers; the 2.2 percent surviving every control is priced and remedied. Searches like "gb 545 unit 8 assignment example", "gb545 unit 8 sample" and "gb545 unit 8 example" land here.
What a finished GB545 Unit 8 pay equity analysis looks like
Seven pages with a population table, three regression models and a remedy table. The population is 520 engineers, 126 women and 394 men, with mean pay of $123,782 and $136,748. Model one regresses the logarithm of salary on sex alone and reproduces the raw gap. Model two adds five engineering levels and narrows it to 2.9 percent. Model three adds tenure, three work locations and hardware, firmware or software function, reaching an R-squared of 0.959 and a coefficient on female of negative 0.0223, a t-statistic of negative 4.63, or 2.2 percent. A distribution table carries the second finding: 49.2 percent of women sit in the two lowest levels against 40.1 percent of men. The remedy table lists 91 women paid below predicted pay and the $478,989 needed to close each gap.
How a GB545 Unit 8 example is structured
Method precedes results. The analysis defines the population, the pay measure, base salary on a fixed date, and the controls, justifying each as a legitimate pay factor rather than a proxy for sex. Models are presented in sequence so a reader can watch the gap shrink and see which variable absorbs how much. Diagnostics follow briefly: sample sizes by cell, why the logarithm of pay is used, and what a coefficient of negative 0.0223 means in dollars. The level distribution receives its own section because controlling for level can conceal a promotion problem, and the paper says so directly. The remedy compares two approaches, a uniform 2.2 percent raise for all women at $352,287 or individual adjustments to predicted pay at $478,989, and recommends the second, with a sentence explaining why the dearer option is fairer.
Population and pay measure
Five hundred twenty engineers, base salary on one date, and the reason bonus and equity were kept out of the dependent variable.
Three models in sequence
Sex alone, then level, then tenure, location and function, with the female coefficient and the variance explained reported at every step.
Negative 0.0223 in dollars
What a log coefficient means for a $120,000 salary, and the standard error that lets the 2.2 percent figure stand as a finding.
Level as a finding of its own
Women at 49.2 percent in the two lowest levels against 40.1 percent of men, raised as a promotion question no regression can settle.
Two remedies, two prices
A uniform raise at $352,287 against individual adjustments to predicted pay at $478,989, and why the costlier option wins.
Where marks go in GB545 Unit 8
A study stopping at the unadjusted average has reported a fact without testing it, and graders in this unit expect the test. Controls chosen without justification weaken everything after them; each needs a sentence explaining why it is a legitimate reason for pay to differ. The best papers show models in sequence so the reader sees what each control absorbs. A significant coefficient reported with no remedy leaves the finance question unanswered, and a remedy with no cost is half of one. Treating level as a neutral control, without asking whether women are placed or promoted into lower levels, misses the second finding many sections care about most. Coefficients from a log model misread as dollar amounts, or significance claimed without a standard error, cost credit quickly and visibly.
Get a GB545 Unit 8 example written to your instructions
The analysis runs on employee-level pay data, or on the Unit 8 dataset exactly as issued, plus the rubric; strip names before sending anything real. Within 24-48h it comes back with models in sequence, controls justified, coefficients translated into dollars and a costed remedy. The first sample is free.
GB545 Unit 8 questions, answered
Why use the logarithm of salary?
Because pay differences tend to be proportional: a gap between senior engineers is larger in dollars than one between junior engineers, yet similar in percentage. Regressing the log of salary lets a single coefficient describe that percentage. The example converts its coefficient back into a percentage and a dollar figure so a non-specialist can follow it. A dollar model is acceptable if your section prefers one.
Should the analysis also examine race and ethnicity?
Many employers run both, and the example notes that a parallel model was run separately and is not reported. Your case may supply only one protected characteristic; work with what is given. Where groups are small, say so, because coefficients estimated from a handful of employees are unstable. Studies like this are often commissioned through counsel, which the example mentions without advising on it.
Is software required to run the regression?
Some tool is, whether a spreadsheet's regression function, a statistics package or a short script. The example's figures were produced by script from a composite dataset, and the paper states its model specification so the results could be reproduced by anyone. Sections differ on whether they supply data; if yours does, report in a sentence which tool produced the estimates.