# To use scatter diagrams to visualize the relationship

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To use scatter diagrams to visualize the relationship between two variables. To learn how correlation analysis describe the degree to which two variables are linearly related to each other. 17.3. CONTENTS 17.3.1. Types of Correlation 17.3.2. Properties of Correlation Coefficient 17.3.3. Methods of Studying Correlation 17.3.3.1. Scatter Diagram 17.3.3.2. Karl Pearson’s Coefficient of Correlation 17.3.3.3. Rank Correlation Coefficient 17.3.3.4. Concurrent Deviation Method 17.3.1. TYPES OF CORRELATION Correlation is classified into many types, but the important are i. Positive and Negative ii. Simple, Multiple and Partial iii. Linear and Non-Linear i) Positive and Negative Correlation Positive and negative correlation depends upon the direction of change of the variables. If two variables tend to move together in the same direction i.e., an increase in the value of one variable is accompanied by an increase in the value of the other variable; or a decrease in the value of one variable is accompanied by a decrease in the value of the other variable then the correlation is called positive correlation. Height and weight, rainfall and yield of crops are example of positive correlation. If two variables tend to move together in opposite direction so that an increase or decrease in the values of one variable is accompanied by a decrease or increase in the value of the other variable then the correlation is called negative correlation. The pest incidence and crop yield of crops and price are example of negative correlation. ii) Simple, Multiple and Partial Correlation When we study two variables, the relationship is described as simple correlation example age and height; weight and age etc. That is a simple correlation measure the relation between a dependent and an independent variable.

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150 When three or more variables are studied it is a problem of either multiple or partial correlation. In multiple correlations three or more variables are studied simultaneously. For example, we study the relationship between the yield of rice and both the amount of rainfall and the amount of fertilizers used. It is a problem of multiple correlation. On the other hand, in partial correlation we recognize more than two variables to be influencing each other the effect of influencing variable being kept constant. For example, if the above rice problem if we limit our correlation analysis of yield and rainfall to periods when the amount of fertilizers used is constant over periods, it becomes a problem of partial correlation. iii) Linear and Non-linear Correlation Correlation may be linear or non-linear. When the amount of change in one variable leads to a constant ratio of change in other variable, correlation is said to be linear. For examples observe the following two variables X and Y.
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