When you want the data to be very highly correlated. You may want a very high degree of agreement because you are prepared to allow only a very low probability that the observations were obtained by pure chance. This may be because there is a very high cost associated with making the wrong decision based on the results.
The correlation coefficient gives a measure of the degree to which changes in the variables are related. However, the relationship need not be causal.
A coefficient is a number before a variable. For example, in 2x, the 2 would be the coefficient
Since I am a constant, not a variable, there can be no correlation. The calculation would entail division by zero, which is not permitted.
Since y=14x is a perfect linear relation, the correlation would be 1.
they are the same. +1.00 and -1.00 are the strongest correlations. If you have +.92 and -.92 then that's a strong correlation but if you have -.15 and +.15 then that would be a weak correlation. There for + 1 or - 1 makes no difference
impossible
The graph follows a very strong downward trend. Would have helped if you specified which correlation coefficient; there are different types.
Zero.
Pearson's Product Moment Correlation Coefficient indicates how strong the relationship between variables is. A PMCC of zero or very close would mean a very weak correlation. A PMCC of around 1 means a strong correlation.
I would use Spearman and Kendall
If you remove certain data points from a dataset, the correlation coefficient may be affected depending on the nature of the relationship between the removed data points and the remaining data points. If the removed data points have a strong relationship with the remaining data, the correlation coefficient may change significantly. However, if the removed data points have a weak or no relationship with the remaining data, the impact on the correlation coefficient may be minimal.
The strength of the relationship between 2 variables. Ex. -.78
The correlation coefficient gives a measure of the degree to which changes in the variables are related. However, the relationship need not be causal.
Correlation coefficient My understanding is: two variables as they relate to one another and how accurately you can predict their behavior to one another when together. Basically the strength of the linear association between two variables. When the variables have a tendency to go up and down together, this is a positive correlation coefficient. Variables with a tendency to go up and down in opposition, (one ends up with a high value and the other a low value) this is negatiove correlation coefficient. An example would be the amount of weight a mom gains during pregnancy and the birth weight of the baby
Let me rephrase: Case 1: You have x and y variables, but the values for x is a constant (vertical line) Case 1: You have x and y variables, but the values for y is a constant (horizontal line) Result is that you have zero covariance, so a correlation coefficient can not be calculated because that would cause a division by zero. If one of your x value (Case 1) or y value (case 2) is not exactly the same as the others, then a correlation coefficient can be calculated, but does it mean anything? The correlation coefficient indicates a linear relationship between two random variables, not between a constant and a random variable.
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.
False. Correlation coefficient as denoted by r, ranges from -1 to 1. Coefficient of determination, or r squared ranges from 0 to 1. I note that x,y data points that have a high negative correlation would plot with a negative trend or a negatively sloped line if a best fit regression line is determined. I note also that x,y data points with a high positive correlation would plot with a positive trend or positively sloped line if a best fit regression line is determined. The coefficient of determination for r = 0.9 and r= -0.9 would be 0.81.