There is a significant difference in how SPSS is calculated

It is necessary to calculate the significance of the difference between the infection rate of each variety and the total infection rate. How do you calculate? The chi-square test is used to input the data into the SPSS, and if it is an independent sample, then a column of input groups is used, and the first group of variables is defined as 1

The ANOVA f-test in this graph is not significant. There are two ways to look at the significance test results.

1. Judge according to the F-value.

The "f" in the ** output of SPSS is the calculated result of the sample. Then, considering the threshold of the significance test and the degrees of freedom of the f-statistic, find the cut-off value of f in the f-test table (the following table is the f-cut-off table of =, if set to or the corresponding f-test table should be found). Finally, the F-value calculated by SPSS is compared with the F-critical value, if it is greater than the critical value, it can be said that the result is significant in the sense of , otherwise it is not significant.

2. According to SIGJudgment.

sig. of the SPSS outputAs a result, the calculated f-value is converted to p-value according to the degrees of freedom, which can be directly based on sigJudge whether it is significant, if sig< is significant, otherwise it is not, and this method is more convenient.

Expand on thatThe Z-test, T-test, Chi-Square test, etc., are similar in judging significance or hypothesis testing, either according to the corresponding test table or according to the p-value. If judged according to the inspection table, it can be divided into three steps:

In the first step, the observed value of the statistic, such as the f-value here, is calculated, and in this step, the SPSS will be directly output;

The second step is to look up the table and find the critical value according to the degrees of freedom;

In the third step, the statistical observations output by SPSS are compared with the critical values obtained by the lookup table to obtain the results.

In contrast, judging from the p-value is very simple, SPSS has calculated and output the p-value from the sample, just compare the p-value with .

In addition, in some cases, SPSS will also automatically mark whether it is significant with the number of asterisks (*), for example, when doing correlation coefficient analysis, the level correlation is significantly marked with "** is marked with "*" in the level relevance, etc.

As long as it is used to indicate whether it is due to sampling error or if there is indeed a difference between brands, if p is less than it, it can be considered to be caused by the brand.

Significant difference is a statistical term. It is a statistical evaluation of the discrepancy of data. Usually, the experimental results are at a level or level before it can be said that there is a significant or extremely significant difference between the data.

Principle. When there is a significant difference between the data, it means that the data participating in the comparison are not from the same population, but from two different populations with differences, and this difference may be due to the fact that the data participating in the comparison are from different experimental subjects.

For example, in some general aptitude tests, there will be a significant difference between the results of the group of subjects with a university degree and the group of subjects with a primary school degree. It may also be due to the fact that the experimental treatment has caused a fundamental change in the characteristics of the test subject, so that there will be significant differences in the data from the pre-test and the post-test.

It can be proved that there is indeed a significant difference between the two sets of data you entered, and if it is an experimental group and a control group, it means that there is indeed a significant difference between the two treatments or two materials.

A significant difference indicates that your H1 hypothesis is more correct, rather than indicating that your H1 hypothesis is more correct, and the two must be distinguished.

Before performing the independent samples t-test, the data should be tested for normality. Only when normality is satisfied can further analysis be performed, and if it is not satisfied, data transformation or non-parametric rank sum test can be used.

Hierarchical data and continuous data do not need to set dummy variables. Multi-categorical variables require setting up dummy variables. There are four types of dummy variables ABCD, taking A as a reference, then the explanation is whether B has an effect with respect to A, C has no effect with respect to A, and D has no effect with respect to A.

t-test

It is suitable for comparing small samples between two groups with homogeneous continuous data, normal distribution and variance, and to test whether the difference between the mean of the two treatments is significant.

There are 3 forms of t-test provided by SPSS, which are one-sample t test, independent-sample t teat, and paired-sample t test.

Refer to the above content: Encyclopedia - Difference Significance Test.

1, First of all, set these two sets of data to X and Y respectively, open SPSS, click the Variable View tab in the lower left corner, enter Y in the first row and X in the second row in the Name column, return to the Data View tab, and enter the corresponding data.

2, Then, analyze the data, select Y and X into their respective dialog boxes, then press OK, see the table coefficients in the output window, and then look at the SIG column on the far right, see the SIG value corresponding to X, if this SIG value is larger than the A value you set before, then there is no significant difference between the two sets of numbers, if this SIG value is smaller than the A value you set before, then there is a significant difference between the two sets of numbers.

3. For example, if you preset a= and find sig=, then <, so you should reject the null hypothesis (the null hypothesis is generally assumed that there is no difference between them) and consider that there is a significant difference between the two sets of numbers.

Inspection method:The results of the two groups of 10 tasters on a batch of white wine and a batch of red wine, as well as the corresponding physical and chemical indexes of these two batches of wine and the corresponding composition index of wine grapes are presented. The T-test was used to analyze the results of the two groups of tasters to determine the difference between the two groups.

According to the paired double-sample mean analysis, the average value of each group of tasters' scores on a certain wine was analyzed by t-test, and the two-tailed probability p of the two groups of tasters on a certain wine was obtained, and the significance difference between the two groups of tasters on the same wine was judged by its magnitude and significance probability.

1, First of all, set these two sets of data to X and Y respectively, open SPSS, click the Variable View tab in the lower left corner, enter Y in the first row and X in the second row in the Name column, return to the Data View tab, and enter the corresponding data.

2, Then, analyze the data, select Y and X into their respective dialog boxes, then press OK, see the table coefficients in the output window, and then look at the SIG column on the far right, see the SIG value corresponding to X, if this SIG value is larger than the A value you set before, then there is no significant difference between the two sets of numbers, if this SIG value is smaller than the A value you set before, then there is a significant difference between the two sets of numbers.

3. For example, if you preset a= and find sig=, then <, so you should reject the null hypothesis (the null hypothesis is generally assumed that there is no difference between them) and consider that there is a significant difference between the two sets of numbers.