Q.Give an example of data comparison where we can use the scatter plot.

Scatter Plot Usage – A First Look

Imagine you are trying to figure out whether there is a connection between two things in everyday life. For example, does the number of hours you study relate to your exam score? Or does the price of a product relate to how many units people buy? You have a list of pairs—for each student, you have study hours and the score they got. How do you even begin to see if there is a pattern?

This is exactly where a scatter plot comes in. It is a simple but powerful visual tool. You take a graph with two axes. On the horizontal axis (x-axis), you put one variable—say, hours studied. On the vertical axis (y-axis), you put the other variable—say, exam score. Then, for each student, you plot a single dot at the point where their hours and their score meet. If you have 30 students, you get 30 dots scattered across the graph. That is why it is called a scatter plot.

What the Scatter Plot Actually Shows

The key idea is that the pattern of these dots tells you whether the two variables are related, and if so, what kind of relationship they have. In a commerce or humanities context, you are not calculating any formula—you are simply looking at the picture.

• Positive relationship: If the dots generally move upward from left to right (as one variable increases, the other also increases), you have a positive association. Example: more years of education tends to go with higher income.
• Negative relationship: If the dots move downward from left to right (as one increases, the other decreases), you have a negative association. Example: higher price of a product often goes with lower quantity demanded.
• No relationship: If the dots are scattered randomly with no clear upward or downward trend, then the two variables are probably not related. Example: shoe size and exam scores—no pattern at all.

Why It Matters in Commerce and Humanities

In subjects like economics, business studies, psychology, or sociology, you often deal with real-world data where you want to explore possible links. A scatter plot is the first step before any deeper analysis. It helps you:

• Spot whether a relationship exists at all
• See if the relationship is roughly linear (a straight-line pattern) or curved
• Identify outliers—dots that lie far away from the general pattern, which might be errors or special cases worth investigating
• Decide whether it is worth doing further statistical work (like correlation or regression)

For example, a marketing manager might plot advertising spend against sales for the past 12 months. If the dots show a clear upward trend, that is useful evidence. If they are all over the place, then advertising may not be the main driver.

What the NCERT Textbook Says …