When Y is a linear function of X then the correlation coefficient between X and Y is?

The size of |r| indicates the strength of the linear relationship between x and y: If |r| is near 1 (that is, if r is near either 1 or −1) then the linear relationship between x and y is strong. If |r| is near 0 (that is, if r is near 0 and of either sign) then the linear relationship between x and y is weak.

What are the 5 types of correlation?

Types of Correlation:

• Positive, Negative or Zero Correlation:
• Linear or Curvilinear Correlation:
• Scatter Diagram Method:
• Pearson’s Product Moment Co-efficient of Correlation:
• Spearman’s Rank Correlation Coefficient:

When there is no correlation between X and Y then?

A negative correlation implies a negative relationship between X and Y: as X increases, Y decreases. A correlation of zero implies that there is no linear relationship between X and Y (see below for details).

What is the correlation between X and Y in regression?

By design, we have perfect correlation of x and y: However, when we do a regression, we are looking for a function that relates x and y so the results of the regression coefficients depend on which one we use as the dependent variable, and which we use as the independent variable.

What is the formula for linear correlation coefficient?

Linear Correlation Coefficient Formula. The linear correlation coefficient formula is given by the following formula. Sample Correlation Coefficient Formula [latex]large r_{xy}=frac{S_{xy}}{S_{x}S_{y}}latex] Here, S x and S y are the sample standard deviations, and S xy is the sample covariance. Population Correlation Coefficient Formula

Is the Pearson correlation coefficient of X and Y the same?

The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x). This suggests that doing a linear regression of y given x or x given y should be the Stack Exchange Network

What does the covariance of X and y necessarily reflect?

The covariance of X and Y necessarily reflects the units of both random variables. It is helpful instead to have a dimensionless measure of dependency, such as the correlation coefficient does. Let X and Y be any two random variables (discrete or continuous!) with standard deviations σ X and σ Y, respectively.