They are related but one might not be causing the other
When comparing large data sets.
ANOVA test null hypothesis is the means among two or more data sets are equal.
When you are talking about data that is not continous. When you are talking about data that is not continous.
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.
They are related but one might not be causing the other
they are related, but one might not be causing the other
Correlation.
You can't average means with standard deviations. What are you trying to do with the two sets of data?
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.
The preposition "with" should follow the word "correlated." For example: "The data suggests that these two variables are strongly correlated with each other."
When comparing large data sets.
The answer will depend on what you wish to compare. There are different methods to compare the means, variances as well as other characteristics of the two sets.
The line and the bar graph is used to describe a graph that compares two sets of data.
ANOVA test null hypothesis is the means among two or more data sets are equal.
If the skewness is different, then the data sets are different.Incidentally, there is one [largely obsolete] definition of skewness which is in terms of the mean and median. Under that definition, it would be impossible for two data sets to have equal means and equal medians but opposite skewness.
You can see where the data is clustered