What does B, SE, Wald mean in statistical multivariate survival analysis

b is the regression coefficient, se is the standard error, and wald is the statistic of the test.

The b-value refers to the regression coefficient and intercept (constant term), which can be negative (negative value of the regression coefficient when the correlation is negative); OR refers to the definition of odds ratio, which can range from 0 to positive infinity, and cannot be negative; Wald is a chi-square value equal to b divided by its standard error (squared, so it can't be a negative number either. Wald is used to test the b-value to see if the b-value is equal to 0. If the b value is equal to 0, the corresponding or [exp(b)] is 1, indicating that there is no significant difference between the two groups.

or is equal to the anti-natural logarithm of the b value. The higher the WALD value, the less likely it is that the B value is equal to 0.

This is logarithmic regression, wald, I forgot what it means, exp(b) represents the exponent of the b coefficient.

: In the results of the logit analysis.

The one with Wald's **.

It's a test of Wald.

The following SIG is the criterion for judging whether the Wald test is significant, and it is a test of whether the overall regression coefficient is significant.

As stated above.

It's just a reference value.

In statistics, there are only two manifestations of alternating signs, and the proportion of units with or without a certain sign to the total number of units is called a number. It is equivalent to a percentage, an achievement is 10%, you can't say 1%, 30 out of 5 is 35%, and 8 out of 5 is 85%.

One number is the number of the other, which generally refers to the ratio: the standard labor force should be found in the production group, compared with each other, and the number should be evaluated. It is usually used in industrial and agricultural production to indicate the growth of production. A few achievements are a few tenths.

For example, grain production increased by "20 percent". "Twenty percent" is two-tenths, that is, grain production has increased by 20 percent.

Percentage, which means that one number is a number of tenths of another number. It is equivalent to a percentage, an achievement is 10%, you can't say 1%, 30 out of 5 is 35%, and 8 out of 5 is 85%.

For example, grain production increased by "20 percent".

"Twenty percent" is two-tenths, that is, grain production has increased by 20 percent.

When calculating the number, there are two numbers, A and B, find the ratio of the number B to the number A, and turn the ratio into a pure decimal number, then the obtained pure decimal is called the number of B to the number A. The first decimal place is called "cheng" or "minute", and the second digit is called "centi".

For example, the planned grain output was 50,000 catties, but the actual output was 10,000 catties.

Then the percentage of grain production increase is 1 5=, that is, grain production has increased by 20%.

Component means that one number is a number of tenths of another number, which is called a fraction of a number. It is usually used in industrial and agricultural production to indicate the growth of production. A few achievements are a few tenths.

For example, this year's grain output is "20 percent" higher than last year's. "Twenty percent" is two tenths, that is, today's output has increased compared with last year.

The mathematical concept number is the number of percentages of another number, which generally refers to the ratio: the standard labor force should be found in the production group, compared with each other, and the number should be evaluated.

For example, this year's grain output is 20 percent higher than last year's, that is, 20 percent more than last year's, that is, this year's grain output is 20 percent higher than last year's

I'm scared right now that the college entrance examination is over, it's over.

Logistic regression analysis is sufficient.

These independent variables are continuous.

First of all, let's explain the symbols, b is beta, which represents the regression coefficient, and the standardized regression coefficient represents the correlation between the independent variable, that is, the ** variable and the dependent variable, why standardize, because the units of each independent variable and the dependent variable can be unified when standardizing, so that the results are more accurate and the error caused by different units is reduced. The t value is the result of the t test of the regression coefficient, the larger the absolute value, the smaller the sig, the sig represents the significance of the t test, statistically, sig is generally considered to be significant in the coefficient test, significant means that the absolute value of your regression coefficient is significantly greater than 0, indicating that the independent variable can be effective ** the variation of the dependent variable, making this conclusion you are 5% likely to make a mistake, that is, 95% sure that the conclusion is correct.

The test of regression first looks at the anova table, that is, the f test, that table represents a general test of the regression coefficients of all the independent variables you regress, if the sig is <, it means that at least one independent variable can be valid ** dependent variable, which can generally not be reported when writing data analysis results.

Then look at the coefficient table to see if the standardized regression coefficients are significant, and each independent variable has a corresponding regression coefficient and a significance test.

Finally, look at the table of the model summary, the r side is called the coefficient of determination, which is the proportion of the variability that can be explained by the independent variable to the total variation of the dependent variable, representing the degree of interpretation of the regression equation to the dependent variable, and the adjusted r side is reported when reporting, this value is a correction for the increase in independent variables that will continue to enhance the ** force (because even if there are useless independent variables, as long as a few more are added, the r side will also become larger, and the adjusted r side is a penalty for more independent variables), r can be left alone, In the case of standardization, r is also related to the independent variable and the dependent variable.

Hope it works for you.