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Chapter Contents
Chapter Introduction
NAG Toolbox

# NAG Toolbox: nag_specfun_compcdf_normal (s15ac)

## Purpose

nag_specfun_compcdf_normal (s15ac) returns the value of the complement of the cumulative Normal distribution function, $Q\left(x\right)$, via the function name.

## Syntax

[result, ifail] = s15ac(x)
[result, ifail] = nag_specfun_compcdf_normal(x)

## Description

nag_specfun_compcdf_normal (s15ac) evaluates an approximate value for the complement of the cumulative Normal distribution function
 $Qx=12π∫x∞e-u2/2du.$
The function is based on the fact that
 $Qx=12erfcx2$
and it calls nag_specfun_erfc_real (s15ad) to obtain the necessary value of $\mathit{erfc}$, the complementary error function.

## References

Abramowitz M and Stegun I A (1972) Handbook of Mathematical Functions (3rd Edition) Dover Publications

## Parameters

### Compulsory Input Parameters

1:     $\mathrm{x}$ – double scalar
The argument $x$ of the function.

None.

### Output Parameters

1:     $\mathrm{result}$ – double scalar
The result of the function.
2:     $\mathrm{ifail}$int64int32nag_int scalar
${\mathbf{ifail}}={\mathbf{0}}$ unless the function detects an error (see Error Indicators and Warnings).

## Error Indicators and Warnings

There are no failure exits from this function. The argument ifail is included for consistency with other functions in this chapter.

## Accuracy

Because of its close relationship with $\mathit{erfc}$ the accuracy of this function is very similar to that in nag_specfun_erfc_real (s15ad). If $\epsilon$ and $\delta$ are the relative errors in result and argument, respectively, then in principle they are related by
 $ε≃ x e -x2/2 2πQx δ .$
For $x$ negative or small positive this factor is always less than one and accuracy is mainly limited by machine precision. For large positive $x$ we find $\epsilon \sim {x}^{2}\delta$ and hence to a certain extent relative accuracy is unavoidably lost. However the absolute error in the result, $E$, is given by
 $E≃ x e -x2/2 2π δ$
and since this factor is always less than one absolute accuracy can be guaranteed for all $x$.

None.

## Example

This example reads values of the argument $x$ from a file, evaluates the function at each value of $x$ and prints the results.
```function s15ac_example

fprintf('s15ac example results\n\n');

x = [-20   -1     0    1    2    20];
n = size(x,2);
result = x;

for j=1:n
[result(j), ifail] = s15ac(x(j));
end

disp('      x          Q(x)');
fprintf('%12.3e%12.3e\n',[x; result]);

```
```s15ac example results

x          Q(x)
-2.000e+01   1.000e+00
-1.000e+00   8.413e-01
0.000e+00   5.000e-01
1.000e+00   1.587e-01
2.000e+00   2.275e-02
2.000e+01   2.754e-89
```