Graph showing relationship between two variables

Choosing the Best Graph Type

graph showing relationship between two variables

produced. C) an inverse relationship between time and the . C) relationship between two variables. C) a scatter diagram showing that there is no clear. A typical example is the relation between price of a commodity and the The relation can be described algebraically by a two variable function or equation. Scatter plots are similar to line graphs in that they use horizontal and vertical axes to plot data points. Scatter plots show how much one variable is affected by another. The relationship between two variables is called their correlation. Scatter.

So something that seems to kind of correlate in a positive direction might have a value of 0. An example of a situation where you might find a perfect positive correlation, as we have in the graph on the left above, would be when you compare the total amount of money spent on tickets at the movie theater with the number of people who go.

graph showing relationship between two variables

This means that every time that "x" number of people go, "y" amount of money is spent on tickets without variation. An example of a situation where you might find a perfect negative correlation, as in the graph on the right above, would be if you were comparing the amount of time it takes to reach a destination with the distance of a car traveling at constant speed from that destination. On the other hand, a situation where you might find a strong but not perfect positive correlation would be if you examined the number of hours students spent studying for an exam versus the grade received.

This won't be a perfect correlation because two people could spend the same amount of time studying and get different grades. But in general the rule will hold true that as the amount of time studying increases so does the grade received.

Scatter Plots

Let's take a look at some examples. The graphs that were shown above each had a perfect correlation, so their values were 1 and The graphs below obviously do not have perfect correlations.

Which graph would have a correlation of 0? Click on Answers when you think that you have them all matched up. After determining the correlation between the variables, you can then predict the behavior of the dependent variable based on the measure of the independent variable. This chart is very useful when one variable is easy to measure and the other is not.

Example You are analyzing the pattern of accidents on a highway. You select the two variables: Once the diagram is completed, you notice that as the speed of vehicle increases, the number of accidents also goes up. This shows that there is a relationship between the speed of vehicles and accidents happening on the highway.

graph showing relationship between two variables

Scatter diagrams can demonstrate a relationship between any element of a process, environment, or activity on one axis and a quality defect on the other axis. Usually the independent variable is plotted along the horizontal axis x-axis and the dependent variable is plotted on the vertical axis y-axis.

The independent variable is also known as the control parameter because it influences the behavior of the dependent variable.

What is a Scatter Diagram (Correlation Chart)?

It is not necessary for one parameter to be a controlling parameter. You can draw the scatter diagram with both variables independent to each other.

In this case you can draw any variable on any axis. Please note that the scatter diagram is different than the Ishikawa or fishbone diagram. With the Ishikawa diagram you see the effect of a cause, and in the scatter diagram you analyze the relationship between the two variables. Type of Scatter Diagram The scatter diagram can be categorized into several types; however, I will discuss the two types that will cover most scatter diagrams used in project management.

The first type is based on the type of correlation, and the second type is based on the slope of trend. According to the type of correlation, scatter diagrams can be divided into following categories: In this type of scatter diagram, data points are spread so randomly that you cannot draw any line through them. In this case you can say that there is no relation between these two variables. Here, the data points are little closer together and you can feel that some kind of relation exists between these two variables.

In this diagram, data points are grouped very close to each other such that you can draw a line by following their pattern.

What is a Scatter Diagram (Correlation Chart)?

In this case you will say that the variables are closely related to each other. As discussed earlier, you can also divide the scatter diagram according to the slope, or trend, of the data points: Scatter Diagram with Strong Positive Correlation Scatter Diagram with Weak Positive Correlation Scatter Diagram with Strong Negative Correlation Scatter Diagram with Weak Negative Correlation Scatter Diagram with Weakest or no Correlation Strong positive correlation means there is a clearly visible upward trend from left to right; a strong negative correlation means there is a clearly visible downward trend from left to right.

A weak correlation means the trend, up of down, is less clear. A flat line from left to right is the weakest correlation, as it is neither positive nor negative and indicates the independent variable does not affect the dependent variable. In positive slant, the correlation will be positive, i. You can say that the slope of straight line drawn along the data points will go up. The pattern will resemble the straight line. For example, if the temperature goes up, cold drink sales will also go up.

Scatter Diagram with Weak Positive Correlation Here as the value of x increases the value of y will also tend to increase, but the pattern will not closely resemble a straight line.

Dependent and Independent Variables - X or Y - Science & Math - Linear, Inverse, Quadratic

In negative slant, the correlation will be negative, i. The slope of a straight line drawn along the data points will go down.