A positive correlation coefficient means that as the value of one variable increases, the value of the other variable increases; as one decreases the other decreases. A negative correlation coefficient indicates that as one variable increases, the other decreases, and vice-versa.
1 or -1
That as one variable increases so does the other
Yes.The Pearson correlation coefficient ranges from -1 to 1 inclusive.The sign of the coefficient tells you the kind of correlation:positive: as one variable increases the other also increases (like y = x)negative: as one variable increases the other decreases (like y = -x)0 means no correlation |r| = 1 means perfect correlation
The correlation coefficient ranges from 0 to ±1. The sign of the correlation coefficient shows the correlation as positive (as one increases so does the other) or negative (as one increases the other decreases). 0 represent no correlation and ±1 represents perfect correlation. The further from 0 towards ±1, the stronger the correlation, ie the greater the absolute value* of the correlation coefficient the stronger the correlation. To have a stronger correlation than -0.54 the absolute value must be greater than 0.54; ie all correlation coefficients that are less than -0.54 (eg -0.6, -0.9) and all those greater than +0.54 (eg 0.7, 0.95) are stronger correlations. Mathematically speaking, all those with a correlation coefficient r such that |r| > 0.54 *The absolute value of a number is the number ignoring its sign (ie how far it is away from 0 ignoring the direction along the number line), eg |56| = 56 |-45| = 45 |-56| = 56 Thus |-56| = |56| = 56.
the R value in the calculator also known as the amount of correlation the data points fit
A serious error. The maximum magnitude for a correlation coefficient is 1.The Correlation coefficient is lies between -1 to 1 if it is 0 mean there is no correlation between them. Here they are given less than -1 value so it is not a value of correlation coefficient.
Why the value of correlation coefficient is always between -1 and 1?
A mistake in calculations! ;) If the calculations are done correctly then the sample correlation must lie within the closed interval [-1, 1].
1.
Let r be the correlation coefficient of a sample of n (x,y) observations. Then the statistic t = r sqrt(n-2) / sqrt(1-r^2) is computed. It is compared with a t-distribution critical value with n-1 degrees of freedom. If the calculated t value exceeds the critical t value, the correlation coefficient is considered significantly different from 0.
The product-moment correlation coefficient or PMCC should have a value between -1 and 1. A positive value shows a positive linear correlation, and a negative value shows a negative linear correlation. At zero, there is no linear correlation, and the correlation becomes stronger as the value moves further from 0.
No. The units of the two variables in a correlation will not change the value of the correlation coefficient.
If the correlation coefficient is 0, then the two tings vary separately. They are not related.
There is no such term. The regression (or correlation) coefficient changes as the sample size increases - towards its "true" value. There is no measure of association that is independent of sample size.
The further the correlation coefficient is from 0 (ie the closer to ±1) the stronger the correlation.Therefore -0.75 is a stronger correlation than 0.25The strength of the correlation is dependant on the absolute value of the correlation coefficient; the sign of the correlation coefficient gives the "relative" slope of correlation line:+ve (0 to +1) means that as one variable increases the other also increases;-ve (0 to -1) means that as one variable increases the other decreases.
The correlation can be anything between +1 (strong positive correlation), passing through zero (no correlation), to -1 (strong negative correlation).