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ge and if one decreases the other increases on average the two are said to be negatively or inversely correlated.
Simple, partial and multiple correlation:- when only two variables are studied it is a problem of simple correlation. When three or more variables are studied it is a problem of either partial or multiple correlation. In multiple correlation three or more variables are studied simultaneously. For example, when we study the relationship between the amount of fee paid to a plastic surgeon, the complexity of the operation and the quality of their work (in terms of results etc.) then it is a problem of multiple correlation. If we consider only two variables, say, the quality of work and the fee paid to be influencing each other and the effect of the other influencing variable is kept constant then it is a problem of partial correlation.
Linear and non-linear (curvilinear)correlation:- if the amount of change in one variable tends to bear a constant ratio with the amount of change in the other then the correlation is said to be linear. If we draw a graph with one variable on X-axis and the other on Y-axis then almost all the point will approximately fall on a line. If amount of change in one variable does not bear a constant ratio with the amount of change in the other then the correlation is said to be non-linear. In most of the practical situations we find a non-linear relationship between the variables. But the techniques of analysis for measuring non-linear correlation are far more complicated than those for linear correlation. Therefore, we generally make an assumption that the relation between the variables is of linear type.
　　The various methods of ascertaining whether two variables are correlated or not are:-
Scatter diagram method: - This is the simplest method. Here we take one variable on X-axis,the other on Y-axis and plot the points. The greater the scatter of the plotted points lesser is the relationship between the two variables. The more closely the points come to a straight line, the higher the degree of linearrelationship. The correlation is positive or negative depending upon the signof the slope of this line. Merits of the method are that it is simple, easy tounderstand and the rough idea can be easily formed as to whether or not thevariables are related. It is not influenced by the size of extreme itemswhereas most of the mathematical methods of finding correlation are influencedby extreme items. While investigating the correlation we usually first draw thescatter diagram. Its drawback is that the exact degree of correlation cannot beestablished as it is done with mathematical methods.
Graphic method: -in this method we obtain two curves for X and Y variable respectively. Byexamining the direction and closeness of the two curves we can infer whether ornot the two variables are related. Merits and demerits are same as those forscatter diagram.
Karl Pearson's correlation coefficient :- Also called Pearsonian correlation coefficient denoted(universally)by r is given by r = (Sxy) / Nsxsy where x and y are the deviations ofthe variable values from their respective means, N is the number of the pairsof observations and sx, sy are the standard deviations of the variables X and Y respectively.The above formula can also be written in simplified form as r = (Sxy) / (Sx2. Sy2)1/2.Note that this method is to be applied only where the deviations of items, xand y, are taken from t本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。