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What does a correlation coefficient of r -0.01 imply about the graph of a data set?
It suggests that there is very little evidence of a linear relationship between the variables.
Which of these correlation numbers shows the strongest relationship?
A correlation coefficient of 1 or -1 would be the highest possible statistical relationship. However, the calculation of correlation coefficients between non independent values or small sets of data may show high coefficients when no relationship exists.
What is the strongest linear correlation?
The relationship between two random independently distributed variables is considered to be theoretically the weakest when the correlation coefficient is zero and theoretically the strongest when the correlation coefficient is one, indicating a positive relationship between two variables and negative one, indicating a negative relationationship between two variables. I state that this is a theoretical result as if variables are not random, independently distributed, then a high correlation coefficient can result. For example, let us say that we obtained the following data on age and frequency of accidents: Age 18 1 in 18 people have accidents in a year Age 25 1 in 25 people have accident in a year Age 30 1 in 30 people have accidents in a year Age 35 1 in 6 people have accidents. Age 40 1 in 400 people have accidents If I selectively calculated a correlation coefficient this data including only the three groups ages 18, 25 and 30, you can see I will have a correlation coefficient of 1, however the data was not a random sample of all ages. See related link.
What is a measure of strength and direction of the relationship between two variable or data sets?
Correlation
Why is there a 6 in spearman rho formula?
The number 6 in the Spearman's rank correlation coefficient formula is a constant used to standardize the formula and make it more interpretable. It helps to scale the formula so that the resulting correlation coefficient falls within the range of -1 to 1, which indicates the strength and direction of the relationship between the ranked variables. Essentially, the 6 in the formula is a mathematical adjustment that ensures the correlation coefficient is properly calculated and comparable across different data sets.
