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" for maximum efficiency.

This table shows month-to-month percentage changes in the Consumer Price Index for different categories over a 7-month period (March-September 2010). Looking at the structure:

• Rows: Different consumer goods/services categories (All items, Food, Energy, etc.)
• Columns: Monthly percentage changes
• Values: Month-to-month percent changes (like \(0.1\%\) or \(-0.2\%\))

Key insights we need to recognize immediately:

• "All items" represents the overall CPI (total inflation)
• "All items less food and energy" represents core inflation (more stable measure)
• Positive values indicate price increases, negative values indicate decreases
• Zero values indicate no change from previous month

Looking at just one row example, "All items" shows the pattern: \(0.1, -0.1, -0.2, -0.1, 0.3, 0.3, 0.1\)
This tells us prices fluctuated both up and down over these months.

Now let's analyze each statement in the most efficient order.

ANALYZING STATEMENT 2

Statement 2 Translation:
Original: "The electricity price change in August 2010 was \(0.5\%\)"
What we're looking for:

• Find the "Electricity" row in the table
• Check the August 2010 column value
• Compare it to the claimed \(0.5\%\)

In other words: We need to verify if electricity prices rose by exactly \(0.5\%\) in August.

This is the perfect statement to check first - it's a simple direct lookup that requires no calculations!

Looking at the table, we find the "Electricity" row and move across to the August column. The value shown is \(0.2\%\), not the \(0.5\%\) claimed in the statement.

Since \(0.2\% \neq 0.5\%\), Statement 2 is NO.

Teaching Note: Notice how we prioritized this statement first because it requires a single data point check. Always look for statements that can be verified with direct lookups before moving to those requiring calculations or comparisons.

ANALYZING STATEMENT 3

Statement 3 Translation:
Original: "The cumulative change for 'All items less food and energy' from June to September was zero."
What we're looking for:

• Find the row "All items less food and energy"
• Look at values from June through September
• Determine if their combined effect equals zero

In other words: Did the core inflation values from June-September cancel each other out perfectly?

Instead of performing a complex compound calculation, let's use a much faster approach - binary change detection:

Looking at "All items less food and energy" row for June through September:

• June: \(0.2\%\) (positive)
• July: \(0.1\%\) (positive)
• August: \(0\%\) (no change)
• September: \(0\%\) (no change)

Here's why this approach is brilliant: If ANY monthly value is positive and NONE are negative, the cumulative change CANNOT be zero - it must be positive.

Since we see positive changes in June (\(0.2\%\)) and July (\(0.1\%\)) with no offsetting negative changes, the cumulative effect must be positive.

Therefore, Statement 3 is NO.

Teaching Note: We avoided a complex compound calculation by recognizing that when all changes are either positive or zero, the cumulative effect cannot be zero. This binary thinking (is there at least one non-zero value in a specific direction?) saves tremendous time.

ANALYZING STATEMENT 1

Statement 1 Translation:
Original: "The 'All items' category showed greater variability than the 'All items less food and energy' category."
What we're looking for:

• Compare how much the values fluctuate in both categories
• Determine which category has greater variability in its values

In other words: Which category's prices bounced around more during this period?

While we could calculate standard deviations, a much faster approach is to compare the ranges and patterns visually:

For "All items" row:

• Values: \(0.1, -0.1, -0.2, -0.1, 0.3, 0.3, 0.1\)
• Range: From \(-0.2\) to \(0.3\) (total range of \(0.5\))
• Pattern: Mix of positive and negative values

For "All items less food and energy" row:

• Values: \(0, 0, 0.1, 0.2, 0.1, 0, 0\)
• Range: From \(0\) to \(0.2\) (total range of \(0.2\))
• Pattern: Only zeros and small positive values

We can see immediately that "All items" has:

1. A wider range (\(0.5\) vs \(0.2\))
2. Both positive and negative values (more fluctuation)
3. Larger absolute values

Therefore, "All items" clearly shows greater variability, and Statement 1 is YES.

Teaching Note: When comparing variability, look for the category with the larger range and mix of positive/negative values. This visual pattern recognition is much faster than calculating standard deviations and works reliably on the GMAT.

FINAL ANSWER COMPILATION

After analyzing all three statements:

• Statement 1: YES
• Statement 2: NO
• Statement 3: NO

This corresponds to answer choice A: I only.

LEARNING SUMMARY

Skills We Used

• Pattern Recognition over Calculation: We compared variability visually instead of calculating standard deviations
• Binary Logic: For Statement 3, we recognized that any positive changes without offsetting negative changes means the cumulative change can't be zero
• Strategic Statement Ordering: We tackled the simplest verification task first (Statement 2), then moved to increasingly complex analyses

Strategic Insights

• Start with Direct Lookups: Statement 2 was easiest to verify, so we checked it first
• Use Range Assessment: For variability comparisons, the range and presence of positive/negative values provides immediate insight
• Check for Contradictory Evidence: For Statement 3, we only needed to find evidence that contradicted the claim (positive changes) rather than doing the full calculation

Common Mistakes We Avoided

• Unnecessary Standard Deviation Calculations: We didn't need to calculate exact measures of variability when visual comparison was sufficient
• Complex Compound Interest Formulas: For Statement 3, we avoided the compound calculation by using logical reasoning
• Inefficient Statement Order: By starting with the simplest statement, we built momentum and confidence

Remember, on table analysis problems, always look for the most direct verification method first. Sorting, visual pattern recognition, and range comparison are typically faster than calculations. When a statement claims something is equal to a specific value (like zero), look for any evidence that would make this impossible before doing full calculations.