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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.

Q: What do positive values of covariance indicate?

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No. Probability values always have to be positive.

If the points have both positive y-values and x-values it is quadrant 1 If the points have a negative x-value and a positive y-value it is quadrant 2 If the points have both negative y-values and x-values it is quadrant 3 If the points have a positive x-values and a negative y-value it is quadrant 4

Positive for nonmetals and negative for metals

Filipino people have positive and negative aspects of their values. It is important to recognize the positive aspects of these values in order for them achieve their national development goals.

The top right one... it is the first because it is where both the x-value and y-values are positive. The second quadrant is the top left. The x-values are negative and the y-values are postive. The third quadrant is the bottom left. The x-values are negative and the y-values are negative. The fourth quadrant is the bottom right. The x-values are positive and the y-values are negative.

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The covariance between two variables is simply the average product of the values of two variables that have been expressed as deviations from their respective means. ------------------------------------------------------------------------------------------------- A worked example may be referenced at: http://math.info/Statistics/Covariance

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,

variance - covariance - how to calculate and its uses

Covariance - 2011 was released on: USA: 20 September 2011

[ -2n ] is positive for all negative values of 'n' .

) Distinguish clearly between analysis of variance and analysis of covariance.

It is not possible to answer the question because there is no information to indicate if b is positive or negative.It is not possible to answer the question because there is no information to indicate if b is positive or negative.It is not possible to answer the question because there is no information to indicate if b is positive or negative.It is not possible to answer the question because there is no information to indicate if b is positive or negative.

Skew is a statistical function. It is used to indicate the asymmetry of a set of values around the mean of those numbers.Skew is a statistical function. It is used to indicate the asymmetry of a set of values around the mean of those numbers.Skew is a statistical function. It is used to indicate the asymmetry of a set of values around the mean of those numbers.Skew is a statistical function. It is used to indicate the asymmetry of a set of values around the mean of those numbers.Skew is a statistical function. It is used to indicate the asymmetry of a set of values around the mean of those numbers.Skew is a statistical function. It is used to indicate the asymmetry of a set of values around the mean of those numbers.Skew is a statistical function. It is used to indicate the asymmetry of a set of values around the mean of those numbers.Skew is a statistical function. It is used to indicate the asymmetry of a set of values around the mean of those numbers.Skew is a statistical function. It is used to indicate the asymmetry of a set of values around the mean of those numbers.Skew is a statistical function. It is used to indicate the asymmetry of a set of values around the mean of those numbers.Skew is a statistical function. It is used to indicate the asymmetry of a set of values around the mean of those numbers.

[N*(N-1)]/2 N=1700 (1700*1699)/2 = 1,444,150 Covariance

Positive values : 1, 2, 3, 4, 5 Positive is a plus. Negative values:-1,-2,-3,-4,-5 Negative is minus.

No. Probability values always have to be positive.

A mix of linear regression and analysis of variance. analysis of covariance is responsible for intergroup variance when analysis of variance is performed.