Calculate the correlation between variables while eliminating the influence of other related factors. It is best to study it together with correlation analysis. Partial correlation analysis is also called net correlation analysis. It analyzes the relationship between two variables while controlling the linear influence of other variables. For linear correlation, the tool used is the partial correlation coefficient.
1. First call up the window interface of partial correlation analysis, use "permanent population" as a control variable, and eliminate its correlation with "household income" and "planned area" Analytical impact.
2. Check the "zero-order correlation coefficient" in the "Statistics" option bar, which is the correlation coefficient. In the partial correlation, When the number of control variables is one, the partial correlation coefficient is called the first-order partial correlation coefficient; when the number of control variables is two, it is called the second-order partial correlation coefficient; when it is zero, it is called the zero-order correlation coefficient, which is the correlation coefficient. "Mean and standard deviation' are only descriptive statistics, which will be displayed in the final results.
3. Result analysis: There are also two null hypotheses tested by net correlation analysis. The overall partial correlation coefficient is not significantly different from zero." The obtained partial correlation coefficient is 0.335, which is greater than the correlation coefficient of 0.323, indicating that there is a linear relationship. Then observe that the probability value of the partial correlation coefficient is 0, which is less than the significance level of 0.05. Or 0.01, so after excluding the influence of "permanent population", "household income" has a linear relationship with "planned area", but the linear relationship is relatively low.
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