Test: Graph Interpretation - 1 - GMAT MCQ

The investor has 20 Large Cap Growth stocks, 8 Small Cap Growth stocks, and 6 Mid Cap Growth stocks, for a total of 34 Growth stocks. In total, the investor has 96 stocks in her portfolio. Therefore, the percentage of growth stocks in the portfolio is:

The correct answer is (C).
The investor has 20 Large Cap Growth, 17 Large Cap Value, and 11 Large Cap Core stocks, for a total of 48 Large Cap stocks. She has 12 Small Cap Value, 9 Small Cap Core, and 8 Small Cap Growth stocks, for a total of 29 Small Cap stocks. If the investor has n times as many Large Cap stocks in her portfolio as Small Cap stocks, then:
48 = 29n
n = 1.66
The correct answer is (C).

The total number of 7th-grade students who do not read on grade level is 43% + 28% = 0.71 * 200 = 142

If a Springfield middle school student is selected at random, there is a 0.28 probability that he or she reads on grade level.

Probability = Favorable options/Total options

Total options = 600
Favorable options = 0.24*200 + 0.29*200 + 0.32*200 = 48 + 58 + 64 = 170

Probability = 170/600 = 0.28

1. Four companies with stock price over 60$:
A, B, C and D.
A quick check gives us companies B and C that have P/E > 40
Ans: 2
2. Only 1 company (N) seems to have P/E less than 10. (Invest in this, folks)
Ans: 1

Statement 1:
The family’s electricity consumption in January 2011 was approximately ___ % of its consumption in December 2010.
=> Electricity consumption in (Jan 2011 / Dec 2010) x 100 % Approximation - (29/22.5) x 100
= 126%

Statement 2: If the family paid a constant rate of $x per kWh between June 2010 and November 2010, inclusive, then it paid ____ as much between June 2010 and August 2010, inclusive, as it did between September 2010 and November 2010, inclusive.
=> Electricity consumption in the months of (June 2010 + July 2010 + Aug 2010) / (Sep 2010 + Oct 2010 + Nov 2010) Approximation - ( 70 / 47 )
= 1.45 times

Drawing curves that best fit the data show that for species A there is a clear trend towards greater light intensity producing greater leaf area hence we have a positive correlation between the two parameters.

The difference between the two cures is greatest at 2.5 units of light intensity. If we extrapolate the curves it looks as though there would be an even bigger difference at 6 units, but we cannot be sure where the cures will go at levels above 5 as no data is provided. It is possible, for example, that curve A will plateau at 5.

Ans: Positive and 2.5

The correct answer choice is the fourth one - cannot be determined. Don’t be tricked into just adding up the percentages. The charts give the relative portions of the rural economy made up by these 4 items, but nowhere are we told absolute amounts - what was the overall size of the economy in 2000 and in 2010? This is necessary to then calculate the overall earnings in these particular areas in 2000 versus in 2010. Since we don’t know in which year the overall economy was larger, or if they were the same, we cannot make this comparison.

The charts give %s of the rural economy. This question asks about percent change in those percentages. The % of the economy taken up by small businesses nearly doubled in these 10 years, for a percent change of nearly 100%. The % of the economy taken up by remittances increased by nearly 6x in these 10 years, for a percent change of around 467%. 

So we can quickly eliminate those two. But agriculture and non-farming jobs both decreased by proportionally similar amounts. Which experienced the smaller percent change? Non-farming jobs decreased by 6/24, or 25%. Agriculture decreased by 20/60, or 1/3, or 33%. 

Put in these terms, clearly non-farming jobs experienced a smaller % change than non-farming jobs, since 25% is less than 33%. Select the second option: non-farming jobs.

Taking a close look at our curve, we can see that it’s not exactly a bell-curve (with equal rates of decline on either side of the peak) - the rise on the left hand side of the curve is slower than the decline on the right-hand side of the curve. The peak takes place around 100 workers per supervisor, yet 40 workers per supervisor (60 away from the peak value) are just about equally productive as 140 workers per supervisor (40 away from the peak value). If we were to extend a line on the left side of the graph, we will find that at about 30 workers per supervisor, per worker daily production is zero. If we were to extend the trend on the right side of the graph, we will find that at about 155 workers per supervisor,per worker daily production is zero. This suggests that at 160 workers per supervisor, per worker daily production will be negative. Therefore, the company will likely be more productive at 30 workers per supervisor than at 160 workers per supervisor. Choose (B). 

Note that extrapolating beyond the range of the data is generally not a good idea and could lead to some nonsensical conclusions. However, since you were asked to assume that the trend stays the same over the range of workers per supervisor values, we can infer that the company will be more productive at 30 workers per supervisor than at 160 workers per supervisor.

At 115 workers per supervisor, we are on the sloping down side of the curve, suggesting that we need to have slightly fewer workers per supervisor. The question asks about which option would increase productivity (not necessarily maximize it, just increase it), and since only one option can be right, it must be that three of the options will not increase productivity and only one will. 

First option - Adding more workers will mean moving to the right on the curve - more workers per supervisor - which clearly has lower productivity. 

Second option - halving the number of supervisors would mean doubling the number of workers per supervisor, to 230, and as per the trend of the graph, this would presumably have lower productivity. 

Third option - doubling the number of supervisors would mean that the ratio of workers to supervisor would be halved, to around 57.5 workers per supervisor, which has a lower productivity rate than 115 workers per supervisor. 

Fourth option - with 25% fewer workers, the company would have around 87 workers per supervisor. Looking at the curve, although the difference is not huge, this is clearly a more productive ratio than 115 workers per supervisor. Select this final option.

If there is a negative linear relationship, that means that when one variable goes up, the other goes down, and vice versa. The new wrinkle introduced by this question is that there can be a one-month lag in seeing this effect. B and D are opposites - when one goes up, the other goes down. If we were to introduce a one-month lag then it’s possible they could correlate directly, not indirectly. With a one-month lag, B and A now seems to be have a negative relationship - when one goes up, the other goes down the following month, and vice versa. But is it a linear relationship - can it be expressed in the form A = mB +c, for some constant values m and c, given a month’s lagtime? You don’t have to do the math, just look at the graph - in the first month, B went up a lot, and in the second month, A went down a little. In the second month, B went down a little, yet in the third month A went up a lot. This is inconsistent - if there’s a linear relationship, then the relative sizes of the changes need to be consistent with each other. Even if you look at the lag in the reverse direction, you’ll notice this same inconsistency.