How to find the correlation coefficient in SPSS

Analysis--Regression--Linearity, Pick the dependent and independent variables.

Statistics - Select "Estimate" and "Confidence Interval, default is 95%".

Corresponds to the "correlation coefficient and correlation coefficient t-test" and "confidence interval 95%", respectively.

OK, and the results are in the "Coefficient A" table.

What do you think about the correlation coefficient?

Correlation analysis is used to study the relationship between quantitative data, including whether there is a relationship and how closely the relationship is. This analysis method is often used before regression analysis; The logical relationship between correlation analysis and regression analysis is as follows: there is a correlation before there is a regression relationship.

How do I analyze the correlation coefficient between the independent and dependent variables?

The SPSSAU operation is as follows:

Here are the results: <>

Correlation analysis is used to study the relationship between quantitative data, whether there is a relationship, how closely the relationship is, etc.;

First, look at whether there is a significant relationship between y and x;

Third: summarize the analysis.

Before correlation analysis, you can use a scatter plot to observe and display the correlation between data, and you can also use a normal graph to observe and display the normal distribution of data.

First of all, look at the significance value, that is, the SIG value or P value, it is to judge the r value, that is, whether the correlation coefficient is statistically significant, the judgment standard is generally, as can be seen from the table, the correlation coefficient between the two variables r = , and its p value is", so the correlation coefficient is not statistically significant.

Regardless of the size of the r-value, it indicates that there is no correlation between the two, and if the p-value is <, then Xiaoyou guess that there is a correlation between the two.

Then look at the r-value, |r|The higher the value, the better the correlation, with a positive number indicating a positive correlation and a negative number touching indicating a negative correlation.

1. Open the SPSS software.

Enter two columns of data, as shown in the figure below. <>

3. Enter the variable to be analyzed, select both variables, and the correlation coefficient.

Pearson was chosen, significance test.

The two-sided test was selected to mark the significance correlation, as shown in the figure below.

4. Select other relevant needs, such as mean and standard deviation.

For the selection of missing values, Chalu and click Continue, as shown in the figure below;

5. Put a tick in the bootstrap menu to trust the interval.

Select the percentile, the sampling selection is simple, and then click OK, as shown in the figure below;

6. Wait for the software analysis to be completed, and then you can get the descriptive analysis and correlation analysis.

The data is not imitated, as shown in the figure below.

The operation path [analysis related bivariate].

Place the variable in the analysis box, check Pearson for Correction and Two-Sided Testing, and click OK.

Result: <>

SPSSAU correlation analysis.

Operation Path: [General Method Related (Pearson Related)] Drag and drop the data into the analysis box on the right. Click Start Analysis;

Result: <>

As can be seen from the above table, the correlation coefficient between the two is about , and the p-value is less than that, so it is clear that there is a correlation between salary and purchase intention.

At the same time, it was found that the results were completely consistent with SPSS, but SPSSAU was more convenient to operate, and the excavation results were richer and easier to understand.

How to perform correlation analysis in SPSS, correlation analysis should first look at the situation of two variables, conform to normal distribution, sample size greater than 30-50, linear relationship, and continuous variables, you can use Pearson distribution.

Tools Raw materials: Dell Inspiron 5000, win10, spss24

1. If the sample data can be correlated with Pearson, this is the most accurate, at the beginning, the sample normality is first distributed, and the k-s test is used.

2. After normality, click Analysis-Relevance-Bivariate, then select Pearson, and check the significance correlation at the same time.

3. After that, move the sample data to the variable, and then click OK in the lower left corner.

4. After the determination, the analysis results appear, first look at the significance, the significance is displayed, indicating that there is a linear relationship between p <.

5. After that, greater than, is highly correlated, if it is a moderate correlation, it is a low correlation, less than uncorrelated.

6. This can be analyzed in batches, as long as each variable is moved to its own column.

The absolute value of the correlation coefficient is generally between 0 and 1, negative numbers represent negative correlations, positive numbers represent positive correlations, and the closer to 0 represents the lower the degree of correlation; The closer it is to 1 or -1, the lower the correlation. In the SPSS software, the lead spike is generally the lead spike that calculates the correlation coefficient and gives the p-value of the correlation coefficient. The correlation coefficient is that the closer it is to 1 or -1, it is correlated.

In the case of p-value, the smaller the p-value, which is generally less than or less than the required strict point, it indicates that the correlation between the variables is significant, that is, there is a significant correlation between the two variables.

The results of the analysis include the residual height, mean and standard deviation.

and correlation coefficients.

and p-value. <>

The first two columns are the mean value and standard gross error of each variable, and the third column starts with the correlation coefficient between the two variables.

The asterisk in the upper right corner of the value represents the p-value. For correlation analysis, the general specification is as follows: p values are represented by an * sign, and p < is represented by 2 * signs; p < is denoted by 1 *.

Summary. 1. First of all, the variables that we usually understand are unidimensional, not multi-dimensional, as you say. Therefore, for SPSS, x1, x2, x3, y1, y2, and y3 are 6 variables, respectively.

2. In the correlation analysis of SPSS, the correlation between these six variables can be counted separately. By calculating the correlation between them, you may be able to get the correlation between x and y that you are talking about, but this correlation is only a qualitative description of your speculation, not a quantitative description.

How to analyze the correlation coefficients (x1, x2, y) between three variables using SPSS?

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1. First of all, the variables that we usually understand are unidimensional, not multi-dimensional, as you say. Therefore, for SPSS, x1, x2, x3, y1, y2, and y3 are 6 variables, respectively. 2. In the correlation analysis of SPSS, the correlation between these six variables can be counted separately.

You may be able to get the correlation between x and y that you are talking about, but this correlation is just a qualitative description of what you speculate, not quantitatively.

1. Use SPSS to enter relevant data and return to the linearity below by analyzing where clicks.

2. A dialog box will pop up in the next step, and you need to determine the corresponding dependent variable and independent variable.

3. At this time, open the statistics window and check the collinearity diagnosis, if there is no problem, continue directly.

4. In this way, after the corresponding results are obtained, the correlation coefficient matrix can be calculated.

1. Start the SPSS software and do the following:

2. Change the file type to xls and find the data you want to open**.

3. Select the default attribute and click OK.

4. Principal component analysis (SPSS) is performed on the imported data. Follow the figure below to reduce the dimensionality.

5. Originally, the yellow quantities on the right side are all in the left column, and you only need to select the variables (note that they are variables, excluding regions) to the right side of the import. Continue to modify the description, extract, and score points.

6. Modify the description: select the coefficient (c), this calculation result is necessary. Click Continue.

7. Extraction: The gravel map is selected, this result is more intuitive, and it can be put into **. Click Continue.

8. After the modification, click OK at the beginning to calculate the relevant precipitation matrix.

In the correlation analysis, you can add me if you need SPSS analysis, thank you.