Five predictors of days to pay, each read in its own units, and one confounded comparison corrected by adjustment make up GB701's Unit 8 regression model report. Searches like "gb 701 unit 8 assignment example", "gb701 unit 8 sample" and "gb701 unit 8 example" land here.
What a finished GB701 Unit 8 regression model report looks like
Six pages, a variable table, a correlation matrix and a coefficient table. The variable table defines the outcome, days to pay, and five predictors: e-invoicing, net-45 terms, disputed invoices, tenure in years and annual purchases in hundreds of thousands of dollars. A note explains that credit limit was dropped before fitting because it correlates at .97 with purchases. The model output follows: R-squared of .221, adjusted .202, F of 11.90 on 5 and 210 degrees of freedom, p below .001, and a residual standard error of 12.82 days. Coefficients read -6.44 for e-invoicing, 95 percent interval -10.34 to -2.54; 14.44 for net-45 terms; 2.04 days per disputed invoice; -0.40 per year of tenure; and 0.59 for purchases, whose interval spans zero. Standardized coefficients and the e-invoicing increment, a change in R-squared of .039, complete the table.
How a GB701 Unit 8 example is structured
One question organizes the report, why the Unit 7 comparison and this model disagree, and every section serves the answer. The correlation matrix supplies it: e-invoicing correlates .37 with net-45 terms, and net-45 terms correlate .36 with days to pay, so adopters carried a handicap the simple comparison never removed. Adjusting for terms uncovers the association the raw means concealed. Interpretation stays in business units. Net-45 accounts pay 14.44 days later than comparable net-30 accounts, close to the fifteen extra days their contracts allow; each disputed invoice goes with about two more days, and each year of tenure with about 0.40 fewer. A worked prediction, 33.2 days for an eight-year net-30 adopter with two disputes and $60,000 in annual purchases, shows the equation in use. The e-invoicing increment, an f-squared of .050, is compared with the .04 the Unit 4 memo planned around.
Choosing predictors before fitting
Each predictor enters for a stated reason drawn from credit practice, and the list was fixed in the Unit 4 memo. Credit limit was considered and set aside because it carries almost the same information as purchases, a decision recorded before any model was run.
Reading coefficients in days
Every coefficient becomes a sentence with units: comparable accounts on net-45 terms average 14.44 more days, and each additional disputed invoice goes with about two more. Purchases show no detectable association, with an interval from -1.79 to 2.97 days per $100,000.
Why adjustment changed the answer
Adopters were concentrated among net-45 accounts, which pay later by contract. Once terms are held constant, the adopter gap widens from -0.82 to -6.44 days. The report traces this through the correlation matrix instead of presenting it as a surprise.
How much the model explains
An R-squared of .221 leaves most variation in days to pay unexplained, and the report says so directly. Customer cash position, industry and payment habits are absent from the file, which bounds what any coefficient here can be taken to mean.
Association, stated carefully
No coefficient is given a causal verb. E-invoicing accounts pay sooner, other things equal; whether e-invoicing makes them pay sooner would require a design this archival file cannot provide, and the report names that gap.
Handed on to diagnostics
Standard errors here assume constant variance and approximately normal residuals. The report flags both as untested and passes them to the Unit 9 memo rather than burying them in a footnote.
Where marks go in GB701 Unit 8
Regression reports in GB701 draw their sharpest comments when output is pasted whole and summarized in a line. Coefficients interpreted without units, or without the idea of holding the other predictors constant, show the model was run but not read. Causal language for observational coefficients is corrected in most doctoral sections. Readers also expect an explanation when a simple comparison and a model disagree; silence on the move from -0.82 to -6.44 would look like either confusion or luck. Predictors added without a stated reason, or removed because their p-values were large, suggest the model was searched for rather than specified. An R-squared described as good or poor with no reference to the question invites challenge. Reports that state what the model leaves unexplained, and route untested assumptions forward, tend to read as the most mature work.
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GB701 Unit 8 questions, answered
Should nonsignificant predictors be removed from a GB701 model?
Usually not, when they were chosen in advance for a stated reason. Removing predictors because their p-values are large turns a planned model into a searched one, and the reported p-values for the remaining terms no longer mean what they claim. Keeping purchases in this model, with its interval spanning zero, is itself informative about the business question.
What do standardized coefficients add?
They put predictors measured in different units on a common scale, which helps when comparing how strongly each is associated with the outcome. They do not replace unstandardized coefficients, because a manager needs to know how many days a disputed invoice is worth, not how many standard deviations. Reporting both, with the unstandardized version in the narrative, is common practice.
How much R-squared does a business model need?
There is no threshold. A model built to test whether one predictor matters can be useful with a modest R-squared, as long as the coefficient of interest is estimated precisely. A model built to forecast individual accounts would need far more. Connect the figure to the purpose of the model rather than labeling it high or low.