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# Price and demand yield of crop and price are examples

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Unformatted text preview: yield and rainfall, are some examples of positive correlation. If the two variables tend to move together in opposite directions so that increase (or) decrease in the value of one variable is accompanied by a decrease or increase in the value of the other variable, then the correlation is called negative (or) inverse correlation. Price and demand, yield of crop and price, are examples of negative correlation. 194 Linear and Non-linear correlation: If the ratio of change between the two variables is a constant then there will be linear correlation between them. Consider the following. X 2 4 6 8 10 12 Y 3 6 9 12 15 18 Here the ratio of change between the two variables is the same. If we plot these points on a graph we get a straight line. If the amount of change in one variable does not bear a constant ratio of the amount of change in the other. Then the relation is called Curvi-linear (or) non-linear correlation. The graph will be a curve. Simple and Multiple correlation: When we study only two variables, the relationship is simple correlation. For example, quantity of money and price level, demand and price. But in a multiple correlation we study more than two variables simultaneously. The relationship of price, demand and supply of a commodity are an example for multiple correlation. Partial and total correlation: The study of two variables excluding some other variable is called Partial correlation. For example, we study price and demand eliminating supply side. In total correlation all facts are taken into account. Computation of correlation: When there exists some relationship between two variables, we have to measure the degree of relationship. This measure is called the measure of correlation (or) correlation coefficient and it is denoted by ‘ r’ . Co-variation: The covariation between the variables x and y is defined as ∑( x − x)( y − y ) Cov( x,y) = where x, y are respectively means of n x and y and ‘ n’ is the number of pairs of observations. 195 Karl pearson’ s coefficient of correlation: Karl pe...
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