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Today, I will talk to you about the example analysis of JASP correlation coefficient matrix and thermal map, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following contents for you. I hope you can get something according to this article.

Starting from today, we add a new case data, crime data. This is a set of data on crime rates from 50 states in mei, including population, area, income, illiteracy rate, high school graduation rate, frost days, and crime rates. now we want to look at the relationship between state crime rates and other factors.

The data view is as follows:

Now our research goal is the state crime rate (dependent variable), the possible factors (independent variable) are population, area, income, illiteracy rate, high school graduation rate, frost days. Dependent variable crime rate continuous numerical variable, there are several independent variables, from the research objectives and data types, multiple linear regression analysis can be considered.

Today, we first use JASP to examine the correlation between dependent variables and independent variables in this group of data, which is shown by the correlation coefficient matrix.

JASP menu operation

After reading the data, the menu [classical] → [correction].

Move the dependent variable and independent variable into the [variables] box

Because the data do not obey the characteristics of multivariate normal distribution, we choose to output the Spelman correlation coefficient, and command JASP to mark the statistical significance of each correlation coefficient.

correlation matrix

Let's take a look at the results of the correlation coefficient matrix shown in real time:

There is a statistically significant correlation between illiteracy rate, frost days, high school graduation rate, population and crime rate, and the correlation coefficients are 0.672,-0.544,-0.437 and 0.346, respectively.

At the same time, we also found that there are some statistically significant correlations between independent variables, such as the coefficient between frost and illiteracy rate is-0.683, income and high school graduation rate is 0.510, the visual correlation coefficient is not more than 0.70.

Correlation coefficient thermal diagram

In addition to providing us with a crisp correlation coefficient matrix, the JASP correlation analysis menu also provides a highly visual statistical graph based on the R language.

It is a famous thermal map based on correlation coefficient. Check [heatmap] in the [plot] option.

The light blue block corresponds to the positive correlation coefficient, the khaki block corresponds to the negative correlation coefficient, the depth of the color indicates the size of the correlation coefficient, and the correlation coefficient number is also marked on the color block. in this way, we can quickly see the correlation between dependent variables and independent variables. The above correlation coefficient matrix and correlation coefficient heat map, the results show that the effect is better than SPSS. After reading the above, do you have any further understanding of the JASP correlation coefficient matrix and the example analysis of the thermal map? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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