In matrix terms, the formula that calculates the vector of coefficients in the model is:

| Term | Description | 
|---|---|
| X | design matrix | 
| Y | response vector | 
The standard errors of the coefficients for multiple regression are the square roots of the diagonal elements of this matrix:

| Term | Description | 
|---|---|
| X | design matrix | 
| X' | transpose of the design matrix | 
| s2 | mean square error | 

| Term | Description | 
|---|---|
|  | test statistic for the  coefficient | 
|  |  estimated coefficient | 
|  | standard error of the  estimated coefficient | 
The two-sided p-value for the null hypothesis that a regression coefficient equals 0 is:

The degrees of freedom are the degrees of freedom for error, as follows:
n – p – 1
| Term | Description | 
|---|---|
|  | The cumulative distribution function of the t distribution with degrees of freedom equal to the degrees of freedom for error. | 
| tj | The t statistic for the jth coefficient. | 
| n | The number of observations in the data set. | 
| p | The sum of the degrees of freedom for the terms. The terms do not include the constant. |