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Correlation is scaled to be between -1 and +1 depending on whether there is positive or negative correlation, and is dimensionless. The covariance however, ranges from zero, in the case of two independent variables, to Var(X), in the case where the two sets of data are equal. The units of COV(X,Y) are the units of X times the units of Y. correlation is the expected value of two random variables (E[XY]),whereas covariance is expected value of variations of two random variable from their expected values,
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How is the coefficient of correlation interpreted?
correlation is a difference in statistics
What is the difference between correlation coefficient and chi square?
correlation is used when there is metric data and chi square is used when there is categorized data. sayan chakrabortty
What can you say about the correlation coefficient and the correlation description when the points lie exactly on vertical or horizontal line?
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.
What is the correlation coefficient of -2?
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?
Why the value of correlation coefficient is always between -1 and 1?
Statical messure of the extent in which two factors vary together?
covariance or correlation
What do positive values of covariance indicate?
as the covariance of the two random variables (X and Y) is used for calculating the correlation coeffitient of those variables it indicates that the relation between those (X and Y) is positive, so they are positively correlated.
What is difference between covariance and variance?
Covariance: An Overview. Variance refers to the spread of a data set around its mean value, while a covariance refers to the measure of the directional relationship between two random variables.
What is the difference between positve and negative correlation?
Positive correlation has a positive slope and negative correlation has a negative slope.
Distinguish between analysis of variance and analysis of covariance?
) Distinguish clearly between analysis of variance and analysis of covariance.
What is the difference between Interrelation and Correlation?
by all means of
What is the difference between Multicollinearity and Autocorrelation?
The difference between multicollinearity and auto correlation is that multicollinearity is a linear relationship between 2 or more explanatory variables in a multiple regression while while auto-correlation is a type of correlation between values of a process at different points in time, as a function of the two times or of the time difference.
What is the difference between direct and indirect correlation in statistics?
GJVG
The goal of the scientist is to design an experiment that will be able to distinguish the difference between?
Correlation and causation.
How is the coefficient of correlation interpreted?
correlation is a difference in statistics
What is the difference between correlation coefficient and chi square?
correlation is used when there is metric data and chi square is used when there is categorized data. sayan chakrabortty
What is the difference between correlation and regression?
correlation we can do to find the strength of the variables. but regression helps to fit the best line
