CHAPTER 6 : CORRELATION  REGRESSION
6.1 Introduction
So far we have considered only univariate
distributions. By the averages, dispersion and skewness of distribution,
we get a complete idea about the structure of the distribution.
Many a time, we come across problems which involve two or more variables.
If we carefully study the figures of rain fall and production of
paddy, figures of accidents and motor cars in a city, of demand
and supply of a commodity, of sales and profit, we may find that
there is some relationship between the two variables. On the other
hand, if we compare the figures of rainfall in America and the production
of cars in Japan, we may find that there is no relationship between
the two variables. If there is any relation between two variables
i.e. when one variable changes the other also changes in the same
or in the opposite direction, we say that the two variables are
correlated.
W. J. King : If it is proved that
in a large number of instances two variables, tend always to fluctuate
in the same or in the opposite direction then it is established that a
relationship exists between the variables. This is called a "Correlation."

Index
6. 1 Introduction
6. 2 Correlation
6. 3 Types of Correlation
6. 4 Degrees of Correlation
6. 5 Methods of determining correlation
6. 6 Coefficients of Correlation for Bivariate
Grouped Data
6. 7 Probable Error
6. 8 Rank Correlation Coefficient
6. 9 Linear Regression
Chapter 7
