How to Identify the Relationship in Scatter Plots

TenMarks teaches you how to identify the relationship shown by the scatter plots.

We are going to talk about correlations. In the problem, we need to identify the relationship shown by the scatter plots. Let’s look at our first scatter plot. Remember that a scatter plot shows the core relation between two data sets and then the correlation is the relationship between two data sets. Let’s look at our first scatter plot here. We have our x-axis and our y-axis. As x increases, y increases, so there is a positive correlation. However, since the data points are very close to each other, this indicates that there is a strong positive correlation. Let’s look at our second scatter plat here. As x increases, so does y. Y increases also. That tells us that there is a positive correlation. However, since the data points are far from each other. This indicates that there is a week positive correlation. Let’s look at our third scatter plot here. Our third scatter plot, as x increases, the y is actually decreasing; it’s going down. That tells us there is a negative correlation. However, if you look at our data points near the best fit line, the data points are far from each other. That tells us that it’s a weak negative correlation. Let’s look at our fourth problem here. As our x increases, our y is decreasing again. Our y is going down as the x is increasing. That tells us again that this is another negative correlation. However, if you look at this data points, we have our best fit line. If you look at those data points running the best fit line, they are very close to each other. When those data sets are very close to each other; that means it is a strong correlation. But this means it is a strong negative correlation since the y is going down and the x is going down. Let’s look at five. Here, there is no obvious pattern. The changes in one data set do not affect the other data sets. Here, we have no correlation. Remember that the correlation is between two sets of data. There can be positive, negative or no correlations. When both the data sets are increasing together, it is a positive correlation just like we had in one and two. If an increase in one data set results in the decrease of another data set like that happened in numbers three and four, the x increased, the y decrease. The increase of one data set results in the decrease in the other, it indicates a negative correlation just like we got in three and four. If changes in one data set do not affect the other data sets just like that happened in five; that means there is no correlation.