The Consumer Price Index (CPI) measures the average prices of goods and services purchased by consumers. In the United States,...

OWNING THE DATASET

Let's start by understanding this Consumer Price Index (CPI) table with the intention of "owning the dataset" completely.

The table shows monthly percentage changes in consumer prices across different categories. Right away, we notice three critical features that will make our analysis more efficient:

1. Data structure insight: The first 7 columns show monthly percentage changes (Jan 2010 through Jul 2010) that are seasonally adjusted by default
2. Column labeling insight: Only the last column is explicitly labeled "Unadjusted" and shows 12-month changes
3. Row organization: "All items" appears as a summary row, with specific categories like "Used cars and trucks" and "New vehicles" as separate rows

This table structure immediately tells us something valuable: we don't have monthly unadjusted data at all - only seasonally adjusted monthly changes and unadjusted 12-month changes.

Let's be strategic about the order we tackle the statements to maximize our efficiency.

ANALYZING STATEMENT 2

Statement 2 Translation:
Original: "The Consumer Price Index for All Urban Consumers (CPI-U) for all items increased in March 2010."
What we're looking for:

• The value in the "All items" row under the March 2010 column
• Whether this value is positive (increased) or not

In other words: Did the CPI-U show a positive change in March 2010?

This is a straightforward data lookup. We need to find the "All items" row (typically the first row in CPI tables) and check the March 2010 column.

Looking at the "All items" row for March 2010, we see: +0.1%

Since the value is positive (+0.1%), this tells us the CPI-U for all items did indeed increase in March 2010.

Statement 2 is Yes.

Teaching note: Notice how quickly we can verify this statement with a single data point lookup. Always tackle the simplest verifications first to build momentum and possibly gain insights that help with more complex statements.

ANALYZING STATEMENT 3

Statement 3 Translation:
Original: "The seasonally unadjusted monthly change in the price of used cars and trucks was greater than the seasonally unadjusted monthly change in the price of new vehicles for the majority of the months shown."
What we're looking for:

• Seasonally unadjusted monthly changes for both categories
• Comparison of these changes across multiple months
• Whether used cars greater than new vehicles in most months

In other words: Were the unadjusted monthly price changes for used cars larger than for new vehicles in most months?

Here's where our initial dataset understanding pays off immediately. Remember our key insight: the table only contains seasonally adjusted data for monthly changes. The only unadjusted data is for the 12-month period in the last column.

Since the statement asks us to compare seasonally unadjusted monthly changes, and our table doesn't contain this data, we cannot verify this statement.

Statement 3 is No.

Teaching note: This is why "owning the dataset" is so crucial - understanding what data is and isn't available can sometimes give you an immediate answer without any calculations. Always check the labels and structure of your data before diving into calculations.

ANALYZING STATEMENT 1

Statement 1 Translation:
Original: "The monthly change in the price of used cars and trucks was less than the monthly change in the price of new vehicles for most of the months shown."
What we're looking for:

• Monthly changes for used cars and trucks
• Monthly changes for new vehicles
• Whether used cars less than new vehicles in most months (at least 4 out of 7)

In other words: Were the monthly price changes for used cars smaller than for new vehicles in most months?

This statement requires us to compare the magnitude of price changes between two categories across multiple months. Since the statement doesn't specify "seasonally unadjusted," we'll use the default seasonally adjusted monthly data.

Let's make a quick comparison of the monthly changes:

We've only checked 4 months so far, but we already have our answer. For a statement about "most months" to be true, we would need at least 4 out of 7 months where used car price changes were LESS than new vehicle price changes.

However, we've found 4 months where the opposite is true - used car price changes were GREATER than new vehicle price changes. Therefore, it's impossible for "most months" to show used car prices changing less than new vehicle prices.

Statement 1 is No.

Teaching note: Notice how we stopped our comparison after just 4 months. Once we established a pattern that contradicted the statement in a majority of months, further checking became unnecessary. This visual magnitude comparison is much faster than calculating exact differences.

FINAL ANSWER COMPILATION

Reviewing our findings:

• Statement 1: No - Used car price changes were not less than new vehicle price changes in most months
• Statement 2: Yes - The CPI-U for all items did increase in March 2010
• Statement 3: No - We cannot determine anything about seasonally unadjusted monthly changes because that data isn't provided

The correct answer is: Statement 2 only is Yes.

LEARNING SUMMARY

Skills We Used

1. Dataset Assessment: We immediately identified the structure of the data (seasonally adjusted monthly, unadjusted 12-month) which gave us an instant answer to Statement 3
2. Strategic Statement Ordering: We tackled Statement 2 first (simplest verification), then Statement 3 (data limitation check), and finally Statement 1 (pattern recognition)
3. Visual Comparison: For Statement 1, we didn't need to calculate exact differences - simply comparing the magnitude of changes was sufficient

Strategic Insights

1. Data limitations are powerful: Sometimes recognizing what data you don't have is the fastest path to an answer
2. "Most" statements can be disproven efficiently: Once you find a majority pattern in the opposite direction, you can stop checking
3. Read statements carefully: Note how Statement 3 specified "seasonally unadjusted" while Statement 1 didn't - this subtle difference completely changed the approach

Common Mistakes We Avoided

1. Calculating unnecessary values: We didn't need to compare all 7 months for Statement 1
2. Overlooking data structure: By carefully noting which data was seasonally adjusted vs. unadjusted, we saved time on Statement 3
3. Following a rigid 1-2-3 order: By tackling the simplest verification first, we built momentum and confidence

Remember: In GMAT table analysis questions, understanding what the data shows (and doesn't show) is often more powerful than any calculation you might perform. Always start with a thorough assessment of the table structure before diving into the statements.