# Summary

• In a bivariate frequency distribution, the direct or indirect cause and effect relationship between the two variables is called correlation
• The statistical technique used to measure the degree and direction of correlation is known as correlation analysis.
• There are two types of correlation:
• Positive correlation
• Negative correlation
• A numerical measure which shows degree and direction of the relationship between the variables is called correlation coefficient.
• Scatter diagram is a graphical representation of bivariate data.
• If the plotted points lie from lower left corner to upper right corner, then it is a positive correlation.
• If the plotted points lie from the upper left corner to lower right corner, then it is negative correlation.
• If the points are equally distributed without showing any patterns, then there is zero correlation.
• A ratio between the co-variance between two variables to the product of their standard deviations is called Karl Pearsonâ€™s correlation coefficient.
• The ratio of explained variance to total variance is known as coefficient of determination.
• The ratio of unexplained variance to total variance is known as coefficient of non-determination.
• The square root of the coefficient of determination is known as the coefficient of alienation.
• In a bivariate data, if the values of the variables are ranked in the decreasing (or increasing) order, the correlation between these ranks is called rank correlation.
• Regression analysis is concerned with the estimation of value of one variable (or one set of variables) for a given value of the other variable (or other set of variables) on the basis of an average mathematical relationship between the two variables.
• The regression equation of x on y can also be expressed as  and that of y on x can be expressed as
• Graphical representation of regression equation is called regression line.