If two sets of data are correlated, it means that there is a statistical relationship between them, indicating that changes in one set are associated with changes in the other. This relationship can be positive (both sets increase together) or negative (one set increases while the other decreases). However, correlation does not imply causation; it does not mean that one set directly causes the changes in the other.
The line and the bar graph is used to describe a graph that compares two sets of data.
A scatter plot displays two sets of data as ordered pairs. Each point on the graph represents an individual pair of values, typically corresponding to two different variables. This visual representation helps to identify relationships, trends, or correlations between the two sets of data.
If you have 2 sets of data, one that is independent and one that is dependent (I will assume this because relating two sets of unrelated data is useless), then you plot the independent on the x and the dependent on the y and assess how y changes in relation to x
There is no correlation.
they are related, but one might not be causing the other
They are related but one might not be causing the other
they are related, but one might not be causing the other.
If two sets of data are correlated, it means that there is a statistical relationship between them, indicating that changes in one set are associated with changes in the other. This relationship can be positive (both sets increase together) or negative (one set increases while the other decreases). However, correlation does not imply causation; it does not mean that one set directly causes the changes in the other.
Comparing the relationship of two data sets is needed to see which of the two sets have more life distribution. Two data sets involve the use of simple plotting and contour plots.
co-related to or co- related with
The number of TV sets in the UK and my age.
When comparing large data sets.
The line and the bar graph is used to describe a graph that compares two sets of data.
a set of techniques used for analysis of data sets that contain more than one variable, and the techniques are especially valuable when working with correlated variables.
You can see where the data is clustered
In statistics. a confounding variable is one that is not under examination but which is correlated with the independent and dependent variable. Any association (correlation) between these two variables is hidden (confounded) by their correlation with the extraneous variable. A simple example: The proportion of black-and-white TV sets in the UK and the greyness of my hair are negatively correlated. But that is not because the TV sets are becoming colour sets and so my hair is loosing colour, nor the other way around. It is simply that both are correlated with the passage of time. Time is the confounding variable in this example.