If measurements are taken for two (or more) variable for a sample , then the correlation between the variables are the sample correlation. If the sample is representative then the sample correlation will be a good estimate of the true population correlation.
Evidence that there is no correlation.
They can be positive correlation, negative correlation or no correlation depending on 'line of best fit'
Yes it can be a correlation coefficient.
No, it cannot be a correlation coefficient.
A very loose correlation can be made between breaking down gelatin and the ability to breakdown tissue. The scientific process can only be replicated within a lap environment but with the constant evolution of bacterium it is possible at some point for the gelatin and tissue correlation to be much stronger.
Auto correlation is the correlation of one signal with itself. Cross correlation is the correlation of one signal with a different signal.
positive correlation-negative correlation and no correlation
No. The strongest correlation coefficient is +1 (positive correlation) and -1 (negative correlation).
The correlation can be anything between +1 (strong positive correlation), passing through zero (no correlation), to -1 (strong negative correlation).
If measurements are taken for two (or more) variable for a sample , then the correlation between the variables are the sample correlation. If the sample is representative then the sample correlation will be a good estimate of the true population correlation.
No.
Indentation rhymes with correlation
Evidence that there is no correlation.
No, The correlation can not be over 1. An example of a strong correlation would be .99
No. The units of the two variables in a correlation will not change the value of the correlation coefficient.
A correlation coefficient is a statistic that measures the strength and direction of a relationship between two variables. It ranges from -1 to 1, with 1 indicating a perfect positive relationship, -1 indicating a perfect negative relationship, and 0 indicating no relationship between the variables.