For each of six corporate stocks, the table shows the highest and the lowest price for shares in that stock...

Owning the Dataset

Let's start by understanding the stock price dataset we're working with. We have a table showing various price metrics for different stocks, including:

• Yesterday's closing price
• One-year low price
• One-year high price
• Price one year ago

Key insight: Rather than thinking about these as isolated data points, we should see them as potentially related metrics that might move together. The power of the GMAT Table Analysis section is that we can instantly sort by any column to reveal relationships between variables.

Notice that we're dealing with stock prices that have natural variability - they change over time and across different companies. This makes this dataset perfect for exploring correlations through visual patterns rather than complex calculations.

Analyzing Statement 1

Statement 1 Translation:
Original: "Yesterday's closing price is positively correlated with the one-year low price."
What we're looking for:

• Do these two variables tend to move together?
• When one-year low prices increase, do yesterday's closing prices generally increase too?

In other words: Do stocks with higher one-year lows tend to have higher closing prices yesterday?

Let's use our sorting approach to check this efficiently:

1. Sort by "One-year low" column (ascending)
2. Now, let's visually scan the "Yesterday's closing price" column

When we do this, we see something striking: the "Yesterday's closing price" values show a perfect ascending pattern as the "One-year low" values increase. This visual pattern immediately confirms a positive correlation between these variables.

The beauty of this approach is that we don't need to calculate anything! We can see with our own eyes that as one-year low prices increase, yesterday's closing prices also increase in a consistent pattern.

Statement 1: Yes - There is a positive correlation between yesterday's closing price and the one-year low price.

Analyzing Statement 2

Statement 2 Translation:
Original: "Yesterday's closing price is positively correlated with the price one year ago."
What we're looking for:

• Do yesterday's closing prices tend to move in the same direction as prices from a year ago?
• As the price one year ago increases, does yesterday's closing price generally increase too?

In other words: Do stocks that were more expensive a year ago tend to have higher closing prices yesterday?

Let's apply our visual sorting technique:

1. Sort by "Yesterday's closing price" column (ascending)
2. Scan the "Price one year ago" column for an ascending pattern

When we look at the sorted data, we see that the "Price one year ago" column shows a general ascending pattern, though not perfect. There are some minor inconsistencies where a stock might have a slightly lower "Price one year ago" than another stock with a lower "Yesterday's closing price."

However, the overall trend is clearly upward. This slight shuffling is common in real correlations, which are rarely perfect in real-world data. The key is to determine if there's a general tendency for the values to increase together, which there is.

Statement 2: Yes - There is a positive correlation between yesterday's closing price and the price one year ago.

Analyzing Statement 3

Statement 3 Translation:
Original: "The one-year high price is positively correlated with the price one year ago."
What we're looking for:

• Do one-year high prices tend to increase as prices from a year ago increase?
• Is there a pattern where stocks with higher prices a year ago also have higher one-year highs?

In other words: Do stocks that were more expensive a year ago tend to reach higher peak prices during the year?

Again, let's use our sorting approach:

1. Sort by "Price one year ago" column (ascending)
2. Look at the "One-year high" column for an ascending pattern

After sorting, we can see that the "One-year high" column shows a clear ascending pattern with minimal shuffling. As the price one year ago increases, the one-year high generally increases as well. This visual confirmation is all we need to determine there's a positive correlation.

Statement 3: Yes - There is a positive correlation between the one-year high price and the price one year ago.

Final Answer Compilation

Let's put together our findings for all three statements:

• Statement 1: Yes - Yesterday's closing price is positively correlated with the one-year low price
• Statement 2: Yes - Yesterday's closing price is positively correlated with the price one year ago
• Statement 3: Yes - The one-year high price is positively correlated with the price one year ago

Therefore, our answer is: All three statements are Yes.

Learning Summary

Skills We Used

• Visual Pattern Recognition: Instead of using formulas, we relied on our ability to see patterns in sorted data
• Leveraging Table Sorting: We used the table's sorting functionality to reveal relationships instantly
• Understanding Correlation Visually: We recognized that correlation means values generally increase (or decrease) together, not necessarily in perfect lockstep

Strategic Insights

1. The Power of Sorting: Whenever you're asked about relationships between variables, sorting by one variable can instantly reveal patterns in the other
2. Correlation ≠ Perfection: Real-world correlations often show general patterns with some exceptions - look for the overall trend
3. Visual Approach > Calculation: For these types of questions, visual inspection is much more efficient than computing correlation coefficients

Common Mistakes We Avoided

1. Calculation Overload: We avoided calculating correlation coefficients, means, deviations, and other statistical measures
2. Rigid Thinking: We understood that positive correlation doesn't require perfect alignment, just a general tendency to move together
3. Redundant Work: Instead of creating multiple rankings or sorting multiple times, we sorted once per statement to get immediate insights

Pro Tip for Table Analysis Questions

When facing any question about relationships between variables in a table:

1. Sort by one variable
2. Scan the other variable for a pattern
3. If you see a general ascending or descending pattern (even with some exceptions), that indicates correlation
4. This approach works for any correlation question and is much faster than calculations!

Remember that the GMAT Table Analysis section is testing your ability to extract insights efficiently, not your ability to perform complex calculations. The sorting technique we used here is a powerful tool that you can apply to many different types of questions in this section.