You calculate a correlation coefficient and test to see if it is statistically different from 0.
The answer depends on what aspect you wish to compare: If you wish to find out if the two variables are correlated one statistical technique is the chi-square test.
Correlated query has a subquery in it which accesses the column name of a table alias which is outside the subquery.
Yes. Factor pairs are always repeated across pairs since factor pairs are certain kinds of pairs.
There are three types of correlation: positive, negative, and none (no correlation).Positive Correlation: as one variable increases so does the other. Height and shoe size are an example; as one's height increases so does the shoe size.Negative Correlation: as one variable increases, the other decreases. Time spent studying and time spent on video games are negatively correlated; as the your time studying increases, time spent on video games decreases.No Correlation: there is no apparent relationship between the variables. Video game scores and shoe size appear to have no correlation; as one increases, the other has no effect. A No Correlation graph would show this.
As the population increases, the living space per capita of the country decreases.
Two variables are negatively correlated when the slope of the best-fit line that is drawn on the scatter plot with the independent variable on the x-axis and the dependent variable on the y-axis is negative.
Not necessarily. They must decrease together (the question does not say so). Also, the decreases may not be sufficient for the to be correlated. It is less likely that they are negatively correlated, but with the amount of information in the question that is about all that can be said.
Height and Weight.
No, they do not.
The number of TV sets in the UK and my age.
Yes, they are negatively correlated.
Negatively
If two variables are highly correlated, the Pearson correlation will be close to -1.0 or +1.0. A correlation of zero shows no relationship.
1 or -1
The preposition "with" should follow the word "correlated." For example: "The data suggests that these two variables are strongly correlated with each other."
Those are called outliers, in the data, they are bythemselves