Partial Multiple Correlation

The correlation and regression coefficient measures the degree and nature of the effect of one variable on another. It is very useful to know how one phenomenon is influenced by another. It is also very important to know how one phenomenon is affected by some other variables. Its nature relationship tends to be complex instead of simple. One variable is related to a very number of others. Many of which might be interrelated among themselves. For e.g. , yield of wheat is affected by the type of soil, amount of rainfall, temperature, etc. whether phenomena be physical, biological, chemical or economic, they are affected by a multiplicity of causal factors. It is part of the statistician's task to determine the effect of one cause or more causes acting individually or simultaneously or one cause when the effect of others is estimated. This is for with the help of multiple and partial correlation analysis.

In the problems of multiple correlations we are dealing with situations that include three of more variables. For e.g., we may consider the association between the yield of wheat per acre and both the average daily temperature & the amount of rainfall. We are trying to make estimates of the value of one of the variables based on the values of all the others. The variables whose value we are trying to estimate is termed as the dependent variable and the other variables.

Some of its main important topics are:

1. Partial correlation

2. Multiple correlation

3. Estimates reliability

4. Multiple correlation advantages

5. Ratial correlation analysis