A consulting firm tracks performance metrics for eight regional office

Question

A consulting firm tracks performance metrics for eight regional offices, four in each of two geographic regions (East and West). The following table contains these metrics for calendar year 2019. This firm hires consultants for its regional offices on a calendar-year basis, so the number of consultants employed at each office listed did not change at any point during 2019.

2019 Regional Office Performance Data

Office  
  Region  
  Consultants  
  Projects   Completed  
  Avg Project   Revenue ($k)  
  Total   Costs ($M)  
  Client   Satisfaction (%)   
 
  A 
 West 
 42 
 115 
 86 
 7.9 
 91 
 
  B 
 East 
 35 
 104 
 93 
 7.4 
 88 
 
  C 
 West 
 51 
 135 
 79 
 9.8 
 94 
 
  D 
 East 
 28 
 96 
 102 
 7.1 
 85 
 
  E 
 East 
 47 
 122 
 89 
 8.6 
 92 
 
  F 
 East 
 39 
 110 
 90 
 8.1 
 90 
 
  G 
 West 
 33 
 89 
 95 
 6.2 
 87 
 
  H 
 West 
 44 
 120 
 84 
 8.5 
 93

Each of the following statements, if true, predicts the existence of a correlation between two of the metrics in the table. For each statement, select  Consistent  if the predicted correlation holds for the data in the table. Otherwise, select  Inconsistent .

Bunuel · Answer

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Gmat860sanskar · Answer

RonPurewal   Bunuel  I think there is some typo in the first statement. It starts with "Inconsistent"; I think you may want to remove that word. By the way, thanks for this question. I am quite confused about the third statement. I can see the negative correlation between revenue and client satisfaction; however, the statement "Professional-development work generates no revenue, but nearly always leaves the client completely satisfied with the project overall" confused me. It says that the revenue is minimal, so I got a little confused there because I don’t see much difference between all the revenues.

Bunuel · Answer

Fixed the typo. Thank you!

Edskore · Answer

All three statements are Consistent — here's a walkthrough of each, plus a specific note on the Statement 3 confusion.

Key concept: Graphs and Tables "Consistent/Inconsistent" questions ask whether the data supports the direction of the claimed correlation. You don't need a perfect relationship — just a general trend.

Statement 1: "Higher total expenditures → clients more biased toward reporting complete satisfaction"
Predicted direction: Higher Total Costs → Higher Client Satisfaction.
Check the data (sorted by Total Costs $M):
G(6.2M, 87%) → D(7.1M, 85%) → B(7.4M, 88%) → A(7.9M, 91%) → F(8.1M, 90%) → H(8.5M, 93%) → E(8.6M, 92%) → C(9.8M, 94%)
There's a clear general upward trend. The two small reversals (D slightly below G, F slightly below H) are noise. CONSISTENT.

Statement 2: "More consultants → more likely projects are staffed with competent people → higher satisfaction"
Predicted direction: More Consultants → Higher Satisfaction.
Sorted by consultants: D(28,85%) → G(33,87%) → B(35,88%) → F(39,90%) → A(42,91%) → H(44,93%) → E(47,92%) → C(51,94%)
Again, a strong upward trend with one minor reversal (E vs H). CONSISTENT.

Statement 3 — addressing Gmat860sanskar's confusion directly:
The statement introduces an indirect/two-step logic, which is what trips people up.

The logic chain is: Some projects = professional-development work → these projects generate ZERO revenue → but they nearly always produce complete client satisfaction.

Now think about what this means at the OFFICE level: if an office does a mix of regular projects (which generate normal revenue) AND professional-dev projects (which generate zero revenue), then that office's AVERAGE project revenue gets pulled DOWN by those zero-revenue projects. At the same time, because those zero-revenue projects tend to result in complete client satisfaction, the office's overall satisfaction goes up.

Predicted direction: Lower Average Project Revenue → Higher Client Satisfaction (because the lower avg revenue signals more professional-dev work).
Check: C($79k avg rev, 94%) → H($84k, 93%) → A($86k, 91%) → E($89k, 92%) → F($90k, 90%) → B($93k, 88%) → G($95k, 87%) → D($102k, 85%)
Clear downward trend in revenue paired with an upward trend in satisfaction. CONSISTENT.

The reason you "don't see much difference" in the revenues is that you're looking at them as absolute numbers. The key is the relative ranking — C consistently has the lowest avg revenue AND the highest satisfaction, while D has the highest avg revenue AND the lowest satisfaction. That direction match is what matters.

Takeaway: On G&T Consistent/Inconsistent items, always ask "what direction does this statement predict?" then check whether the data moves in that direction across most offices — you don't need a perfect monotone relationship.

RonPurewal · Answer

Official Explanation:

A consulting firm tracks performance metrics for eight regional offices, four in each of two geographic regions (East and West). The following table contains these metrics for calendar year 2019. This firm hires consultants for its regional offices on a calendar-year basis, so the number of consultants employed at each office listed did not change at any point during 2019.

2019 Regional Office Performance Data

Office	Region	Consultants	Projects Completed	Avg Project Revenue ($k)	Total Costs ($M)	Client Satisfaction (%)
A	West	42	115	86	7.9	91
B	East	35	104	93	7.4	88
C	West	51	135	79	9.8	94
D	East	28	96	102	7.1	85
E	East	47	122	89	8.6	92
F	East	39	110	90	8.1	90
G	West	33	89	95	6.2	87
H	West	44	120	84	8.5	93

Each of the following statements, if true, predicts the existence of a correlation between two of the metrics in the table. For each statement, select Consistent if the predicted correlation holds for the data in the table. Otherwise, select Inconsistent.

Part I: As total project expenditures increase, clients tend to become more and more strongly biased toward reporting complete satisfaction with the consultants’ work and the project outcomes:

In this statement there’s no possible confusion about the correlation being asserted: “as total project expenditures increase, clients will become more and more strongly biased toward reporting complete satisfaction.” So, we need to look for a positive correlation between Total Costs and Client Satisfaction Rate.

We can sort by either one of these two variables (for the same reason why it doesn’t matter which variable goes on the x-axis of a scatterplot, if we’re looking only for correlation). If we sort by Total Costs, the two columns of interest are as follows:

The positive correlation is obvious. (Note how all the percentages in the 80s come first, followed by all the ones in the 90s.) So this statement is Consistent with the table.

Part II: The more consultants who work in a given office, the greater the likelihood that each of the office’s projects will be staffed with consultants with the necessary competence and background to perform the work to the complete satisfaction of the client:

The correlation ultimately being predicted here is between Number of Consultants (“the more consultants who work in a given office...”) and Client Satisfaction Rating (“...to the complete satisfaction of the client”). Sort by either of these and then check for the necessary (positive) correlation.

If we sort by Number of Consultants, we get a correlation that’s perfect except for the 93% and 92% values being reversed. So this statement is also Consistent with the table.

Part III: Some of the company’s projects aim exclusively at cash flow and profitability goals; the rest have these as well as intangible professional-development goals. Professional-development work generates no revenue, but nearly always leaves the client completely satisfied with the project overall:

We have no information whatsoever about project goals, so the relevant correlation is between the other two metrics mentioned here: Average Project Revenue—which will be lower for projects that include professional development work—and Client Satisfaction Percentage, which is virtually perfect for those projects. So, the more professional-development projects a given office had, the lower its average revenue ought to be but the higher its overall client satisfaction percentage.

Accordingly, if this statement explains anything in the table, it will explain a negative correlation between Average Project Revenue and Client Satisfaction Percentage.

Sorting the rows by one of these variables (it doesn’t particularly matter which one) reveals a nearly perfect anti-correlation: the orders are exactly the reverse of each other except for one pair of figures (the 92% and 91% client satisfaction figures). So this statement, too, is Consistent with the table.

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RonPurewal · Answer

The statement says that   some   of the projects at certain offices   included   these professional-development objectives, in addition to revenue-generating goals. In other words, this non-revenue-generating but completely-satisfying work formed   a fraction of a fraction of   the total project load at those offices.

That means you're looking for the total revenue per project to be pushed  down to a lower average , and the total client satisfaction  pushed up closer to 100% , at those offices relative to the others—but you're certainly not looking for revenues of zero or satisfaction ratings of literally 100% (or even anywhere particularly close) anywhere.

Consistent	Inconsistent
		As total project expenditures increase, clients tend to become more and more strongly biased toward reporting complete satisfaction with the consultants’ work and the project outcomes.
		The more consultants who work in a given office, the greater the likelihood that each of the office’s projects will be staffed with consultants with the necessary competence and background to perform the work to the complete satisfaction of the client.
		Some of the company’s projects aim exclusively at cash flow and profitability goals; the rest have these as well as intangible professional-development goals. Professional-development work generates no revenue, but nearly always leaves the client completely satisfied with the project overall.

A consulting firm tracks performance metrics for eight regional office

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GMAT Graphs and Tables (G&T) Questions

A consulting firm tracks performance metrics for eight regional office

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