In regression analysis, R represents the degree of correlation, and F is used to evaluate whether the regression analysis has statistical significance. Using IBM SPSS Statistics for regression analysis can quickly calculate R and F and provide the regression curve equation. So, what is the f value in SPSS regression analysis? What is the appropriate range for F-value in SPSS regression analysis? This article provides a brief explanation with examples.
1、 What is the f-value in SPSS regression analysis
The F-value is the ratio of the mean square between groups to the mean square within groups in analysis of variance, and the mean square between groups is the ratio of variance to degrees of freedom. Therefore, the F-value is closely related to degrees of freedom, and the shape of the F-distribution curve is also related to degrees of freedom.
F calculation formula
By calculating the ratio of the mean square between groups to the mean square within groups, at a certain confidence level, it is possible to evaluate whether the differences between two groups of data are due to systematic differences between groups or random errors.
In regression analysis, SPSS can not only calculate the F-value, but also provide the relevant significance level P based on the confidence level. If P<0.05, the regression analysis is considered statistically significant. If P>0.05, the regression analysis is considered not statistically significant.
Figure 2 shows the results of linear regression analysis of two sets of variables, with F-values and significance levels visible. In this example, the significance level is 0, indicating statistical significance of the regression results.
F-test data
This section introduces the meaning of F value, the relationship between F value and P value in practical analysis, the range of F value, and the ability to obtain a good P value. We will discuss this in the second section.
2、 What is the appropriate range for F-value in SPSS regression analysis
We observe the variance calculation formula in Figure 1. Assuming that all data comes from a population, that is, the differences between groups and within groups come from random errors, then the F-value is 1, which means that the mean square of within group and between group data is consistent. If the data of each group does not come from a population, the larger the inter group differences and F-values, the greater the differences.
Higher F-value
Therefore, at a certain confidence level, the closer F is to 1, the smaller the difference, and the larger F, the larger the difference. For regression analysis, a larger F value represents statistical significance.
F value close to 1
Generally speaking, based on a certain level of confidence, we use the method described in the first section to evaluate the statistical significance of regression analysis through the significance level corresponding to the F-value. If the significance level is less than 0.05, the regression analysis has statistical significance. If the significance level is higher than 0.05, the regression analysis does not have statistical significance.。
3、 SPSS regression analysis steps
After entering the data, click on Analyze, Regression, Linear, as shown in Figure 5.
Enter the linear regression analysis interface
Add the first column as the independent variable and the second column as the dependent variable, as shown in Figure 6, and click OK.
Specify variables
Figure 7 shows the linear regression results, with an R of 0.404, an F of 1.563, and a significance level of 0.247. This example does not have statistical significance.
Regression analysis results
The above has introduced the steps of regression analysis, the significance and range of F-values. We believe that everyone has a certain understanding of what F-value is in SPSS regression analysis and what range is suitable for F-value in SPSS regression analysis. By calculating the F-value, it is possible to assess whether the differences are due to systematic or random factors between groups, and thus evaluate whether linear regression has statistical significance.
