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No.
The strongest correlation coefficient is +1 (positive correlation) and -1 (negative correlation).
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- The correlation coefficient is symmetrical with respect to X and Y i.e.
- The correlation coefficient is the geometric mean of the two regression coefficients. or .
- The correlation coefficient lies between -1 and 1. i.e. .
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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.
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the correlation coefficient range is -1 to +1
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coefficient of determination
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A coefficient of zero means there is no correlation between two variables. A coefficient of -1 indicates strong negative correlation, while +1 suggests strong positive correlation.
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- The correlation coefficient is symmetrical with respect to X and Y i.e.
- The correlation coefficient is the geometric mean of the two regression coefficients. or .
- The correlation coefficient lies between -1 and 1. i.e. .
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The correlation coefficient must lie between -1 and +1 and so a correlation coefficient of 35 is a strong indication of a calculation error.
If you meant 0.35, then it is a weak correlation.
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correlation is a difference in statistics
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A coefficient of correlation of 0.70 infers that there is an overall correlation between the trends being compared. The correlation is not perfect, but enough to be acknowledged and researched further.
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partial correlation is the relation between two variable after controlling for other variables and multiple correlation is correlation between dependent and group of independent variables.
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A correlation coefficient of zero means that two things are not correlated to each other.
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34.32245
Correlation coefficient is less than -1 and greater than 1.
Note: The Correlation coefficient is lies between -1 to 1 if it is 0 mean there is no correlation between them.
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No. Correlation coefficient is measured from +1 to -1. In addition, if the two sets of exam are exactly same, their correlation coefficient is +1.
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Why the value of correlation coefficient is always between -1 and 1?
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Correlation coefficient is a measure of the strength and direction of a relationship between two variables. It quantifies how closely the two variables are related and ranges from -1 (perfect negative correlation) to 1 (perfect positive correlation), with 0 indicating no correlation.
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No.
The units of the two variables in a correlation will not change the value of the correlation coefficient.
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It will be invaluable if (when) you need to calculate sample correlation coefficient, but otherwise, it has pretty much no value.
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Assume that you are correlating two variables x and y. If there is an increasing relationship between x and y, (that is , the graph of y=a+bx, slopes upward), the correlation coefficient is positive. Similarly, if there is a decreasing relationship, the correlation coefficient is negative. The correlation coefficient can assume values only between -1 and 1.
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A correlation coefficient of 1 (r=1) is a perfect positive correlation.
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No, it depends upon the size of the coefficient of correlation: the closer to ±1 the stronger the correlation.
When the correlation coefficient is positive, one variable increases as the other increases; when negative one increases as the other decreases.
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The correlation coefficient, plus graphical methods to verify the validity of a linear relationship (which is what the correlation coefficient measures), and the appropriate tests of the statisitical significance of the correlation coefficient.
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If the correlation coefficient is 0, then the two tings vary separately. They are not related.
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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 0.25
The 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.
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A correlation coefficient is a value between -1 and 1 that shows how close of a good fit the regression line is. For example a regular line has a correlation coefficient of 1. A regression is a best fit and therefore has a correlation coefficient close to one. the closer to one the more accurate the line is to a non regression line.
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The Matthew correlation coefficient considers true and false positives and negatives. The specificity correlation only considers the true classes or rejections.
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Positive correlation = positive association Negative correlation = negative association
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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.
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The answer is r.
Actually 'r' is the usual symbol for the correlation coefficient statistic calculated for a sampleof paired values. The correlation coefficient for a population of pairs of random variables distributed according to a binomial normal distribution is usually denoted by the Greek letter 'rho'.
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The correlation coefficient is a statistical measure of the extent to which two variables change. A correlation coefficient of -0.80 indicated that, on average, an increase of 1 unit in variable X is accompanied by a decrease of 0.8 units in variable Y.
Note that correlation does not imply causation.
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The graph follows a very strong downward trend. Would have helped if you specified which correlation coefficient; there are different types.
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