**how to use the Wald value to calculate significance**
The Wald test (also known as the Wald Chi-Squared Test) is a method of determining the significance of explanatory variables in a model. Variables that add nothing to the model can be removed without influencing the model in any manner. The test can be used for a wide range of models, including those with binary and continuous variables.

The test's null hypothesis is that some parameter equals some value. For example, you might be researching whether consuming junk food twice a week affects weight. Your criterion would be “weight.” The answer might be 0 (showing that you don't believe consuming junk food affects your weight). If the null hypothesis is rejected, it means the variables in the issue can be removed without significantly affecting the model fit.

If the Wald test reveals that the parameters for any explanatory variables are zero, the variables can be removed from the model.

If the test indicates that the parameters are not zero, the variables should be included in the model.

The Wald test is usually talked about in terms of chi-squared because the sampling distribution (as n approaches infinity) is usually known. This variant of the test is sometimes called the Wald Chi-Squared Test to differentiate it from the Wald Log-Linear

**Chi-Square Test**, which is a non-parametric variant based on the log odds ratios.