90<\/td> 180<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n <\/strong>The above illustration shows that as price increases the supply also increases and vice-versa.<\/p>\n\n\n\nDiagrammatically, it can be shown as follows:<\/p>\n\n\n\nPositive correlation<\/figcaption><\/figure>\n\n\n\n<\/span>Negative or Inverse Correlation: <\/span><\/h4>\n\n\n\nIf two variables move in opposite direction i.e. with the increase in one variable, the other variable falls or with the fall in one variable, the other variable rises, the correlation is said to be negative or inverse. For example, the law of demand shows inverse relation between price and demand.<\/p>\n\n\n\nPrice<\/strong><\/td>Demand<\/strong><\/td><\/tr>50<\/td> 180<\/td><\/tr> 60<\/td> 160<\/td><\/tr> 70<\/td> 140<\/td><\/tr> 80<\/td> 120<\/td><\/tr> 90<\/td> 100<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n <\/strong>The above illustration shows that as price increases the demand decreases and vice-versa.<\/p>\n\n\n\nnegative Correlation<\/figcaption><\/figure>\n\n\n\n<\/span>ON THE BASIS OF NUMBER OF VARIABLES<\/span><\/h3>\n\n\n\n<\/span>Simple Correlation<\/span><\/h4>\n\n\n\nWhen there are only two variables and the relationship is studied between those two variables, it is a case of simple correlation Relationships between height and weight, price and demand or income and consumption \u00e9tc. are examples of simple correlation<\/p>\n\n\n\n
<\/span>Multiple Correlation<\/span><\/h4>\n\n\n\nWhen there are more than two variables and we study the relationship between one variable and all the other variables taken together then it is a case of multiple correlation. Suppose there are three variables 1, 2, 3 we can study the multiple correlation between A and B & C taken together or between B and A & C together etc. It can be denoted as r 1.23<\/sub> or r<\/em>2.13 <\/sub>or r<\/em>3.12<\/sub>.<\/p>\n\n\n\n<\/span>Partial Correlation<\/span><\/h4>\n\n\n\nWhen there are more than two variables and the relationship between any two of the variables is studied assuming other variables as constant it is a case of partial correlation. This, in fact, is an extension of multiple correlation. Suppose we study the relationship between rainfall and crop, without taking into consideration the effects of other inputs like fertilizers, seeds and pesticides etc., this technique will be known as partial correlation. Symbolically if x, y, z are the three variables then partial correlation between x and y excluding z will be given by rxy.z<\/sub>, rxz.y<\/sub> or ryz.x<\/sub><\/p>\n\n\n\n<\/span>Total Correlation<\/span><\/h4>\n\n\n\nWhen the correlation between the variables under study taken together at a time, is worked out, it is called total correlation.<\/p>\n\n\n\n
The main point worth consideration is that this line will indicate positive relation if ‘a’ is positive and in case ‘a’ is negative the correlation will also be negative. In such type of correlation, the value of the coefficient of correlation is always + 1 or 1 depending on the sign of ‘a’ in the equation of y = ax + b. Correlation will be + 1 if ‘a’ is +ve and -1 if ‘a’ is -ve.<\/p>\n\n\n\n
<\/span>ON THE BASIS OF CHANGE IN PROPORTION<\/span><\/h3>\n\n\n\n<\/span>Linear Correlation<\/span><\/h4>\n\n\n\nLinear correlation is said to exist if the amount of change in one variable tends to bear a constant ratio to the amount of change in the other variable.<\/p>\n\n\n\nX<\/strong><\/td>Y<\/strong><\/td><\/tr>5<\/td> 10<\/td><\/tr> 10<\/td> 20<\/td><\/tr> 15<\/td> 30<\/td><\/tr> 20<\/td> 40<\/td><\/tr> 25<\/td> 50<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nThus, it is clear from the above that the ratio of change between the two variables is the same. If such variables are plotted on a graph paper all the plotted points would fall on a straight line.<\/p>\n\n\n\n
Diagrammatically, it can be shown as follows:<\/p>\n\n\n\nLinear Correlation<\/figcaption><\/figure>\n\n\n\nThis type of relation does not exist in economics and other social sciences. This type of relation can exist in only physical sciences. However, it has great theoretical importance in economics and other social sciences.<\/p>\n\n\n\n
<\/span>Non-Linear Correlation<\/span><\/h4>\n\n\n\nNon-linear correlation is said to be curvi-linear correlation when the proportion of change in two variables is not proportional. For example, if we double the amount of rainfall, the proportion of rice and wheat etc. would not necessarily be proportional.<\/p>\n\n\n\nX<\/strong><\/td>Y<\/strong><\/td><\/tr>5<\/td> 10<\/td><\/tr> 10<\/td> 13<\/td><\/tr> 15<\/td> 17<\/td><\/tr> 20<\/td> 18<\/td><\/tr> 25<\/td> 21<\/td><\/tr> 30<\/td> 29<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nThus, from the above example it is clear that the ratio of change between two variables is not the same. Now, if we plot all these variables on a graph, they would not fall on a straight line.<\/p>\n\n\n\n
Diagrammatically, it can be shown as follows:<\/p>\n\n\n\nNon-linear correlation<\/figcaption><\/figure>\n\n\n\nSuch types of correlations are found very commonly in the fields of economics and the other social sciences. As such these are very important in the study of social sciences.<\/p>\n\n\n\n
<\/span>ON THE BASIS OF LOGIC<\/span><\/h3>\n\n\n\n<\/span>Logical Correlation<\/span><\/h4>\n\n\n\nWhen the correlation between two variables is not only mathematically defined but also logically sound too, it is called logical correlation. For example, correlation between income and consumption, price and demand, age and playing habits etc. are logical correlations. These correlations are determined by both ways mathematically as well as logically. In other words, it can be said that the correlations in the above cases whether negative or positive can be confirmed by logic or by applying the requisite statistical tools of correlation. There exists functional relationship between the variables.<\/p>\n\n\n\n
<\/span>Illogical Correlation<\/span><\/h4>\n\n\n\nIn certain cases we come across such cases of relationship of variables which are though well defined and established by statistical method of correlation coefficient, yet when tested on the logical point of view they fail to justify their relationship with each other. For example relationship between rainfall and the number of babies born. production of cycles and the death rate. etc. These variables are not connected with each other in any way. But their correlations can be established by applying the statistical methods of correlation. Such type of correlation is known as Non-Sense Correlation or Spurious correlation.<\/p>\n\n\n\n
<\/span>DEGREES OF CORRELATION<\/span><\/h2>\n\n\n\nAccording to Karl Pearson, the coefficient of correlation lies between two limits i.e. +1 and -1. It implies that there is perfect positive relationship between two variables, the value of correlation would be positive one. On the contrary, if there is perfect negative relationship between two variables the value of the correlation will be negative one. It means r <\/em>lies between +1 and -1. Within these limits the value of correlation can be interpreted as:<\/p>\n\n\n\nr<\/em>=+1<\/td>Perfect Positive Correlation<\/td><\/tr> r>+0.75 but < +1<\/em><\/td>High degree of positive correlation<\/td><\/tr> r>+0.5 but < +0.75<\/em><\/td>Moderate degree of positive correlation<\/td><\/tr> r> +0 but <+0.5<\/em><\/td>Low degree of positive correlation<\/td><\/tr> r=0<\/em><\/td>No correlation at all<\/td><\/tr>