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Partial Multiple Correlation

The correlation and regression coefficients discussed earlier measure the degree and nature of the effect of one variable on another. While it is useful to know how one phenomenon is influenced by another? It is also important to know how one phenomenon is affected by several other variables. Its nature relationship tends to be complex rather than simple. One variable is related to a great number of others. Many of which may be interrelated among themselves. For example, yield of rice is affected by the type of soil. Temperature, amount of rainfall, etc. whether phenomena be biological physical, 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 two or more causes acting separately or simultaneously or one cause or two or more causes acting separately or simultaneously or one cause when the effect of others is estimated. This is foe with the help of multiple and partial correlation analysis.

In problems of multiple correlations we are dealing with situations that involve three of more variables. For example, we may consider the association between the yield of wheat per acre and both the amount of rainfall and the average daily temperature. 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 called the dependent variable and the other variables. Thus in the height age and weight problem, if we are trying to estimate men’s weight (that is if weight be a dependent variables) we might denote.

Some of its main topics are:

In problems of multiple correlations we are dealing with situations that involve three of more variables. For example, we may consider the association between the yield of wheat per acre and both the amount of rainfall and the average daily temperature. 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 called the dependent variable and the other variables. Thus in the height age and weight problem, if we are trying to estimate men’s weight (that is if weight be a dependent variables) we might denote.

Some of its main topics are:

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Business Forecasting
Central Value Measures
Correlation Regression Analysis
Data Tabulation
Diagrammatic Graph Presentation
Dispersion Measures
Experimental Designs
F Test Variance Analysis
Index Numbers
Partial Multiple Correlation
Probability Expected Value
Sampling Designs
Skewness Moments Kurtosis
Statistical Survey
Decision Theory
Statistical Fallacies
Statistical Quality Control
Statistics What Why
Theoretical Distribution
Vital Statistics
F2 Test
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