R is used to determine whether the relationship is positive or negative based upon the sign of R. R^2 is the predictive percent of behavior in the output that can be explained by the input. R^2 <30% are considered to have no correlation and behavior is explained by chance R^2 of 30% to 49.99% are considered to be a mild relationsip R^2 of 50% to 69.99% are considered to be a moderate relationship R^2 of 70% to 100% are considered to be a strong relationship
the correlation coefficient range is -1 to +1
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 computed from the sample data measures the strength and direction of a linear relationship between two variables. The symbol for the sample correlation coefficient is r. The symbol for the population correlation is p (Greek letter rho).
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.
No, it's a small enough value that it doesn't suggest any correlation at all. There's no hard-and-fast rule for interpreting the correlation coefficient: a very strong correlation in one discipline might be considered weak in others, and the correlation coefficient might be misleading in some cases. But most of the time, you want r to be at least plus or minus 0.9 before even thinking about any relation between the data.
The coefficient of nondetermination is found by 1.00-r squared so 1.00-0.35X0.35 1.00-0.1225 0.8772 round it to 0.88
the correlation coefficient range is -1 to +1
No, r is a coefficient.
1.
It's not quite possible for the coefficient of determination to be negative at all, because of its definition as r2 (coefficient of correlation squared). The coefficient of determination is useful since tells us how accurate the regression line's predictions will be but it cannot tell us which direction the line is going since it will always be a positive quantity even if the correlation is negative. On the other hand, r (the coefficient of correlation) gives the strength and direction of the correlation but says nothing about the regression line equation. Both r and r2 are found similarly but they are typically used to tell us different things.
A correlation coefficient of 1 (r=1) is a perfect positive correlation.
no
It is r.
lower case "r"
It is r.
"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".
The strength of the linear relationship between the two variables in the regression equation is the correlation coefficient, r, and is always a value between -1 and 1, inclusive. The regression coefficient is the slope of the line of the regression equation.