# stats::uniformCDF

Cumulative distribution function of the uniform distribution

### Use only in the MuPAD Notebook Interface.

This functionality does not run in MATLAB.

## Syntax

```stats::uniformCDF(`a`, `b`)
```

## Description

`stats::uniformCDF(a, b)` returns a procedure representing the cumulative distribution function

of the uniform distribution on the interval [a, b].

The procedure `f := stats::uniformCDF(a, b)` can be called in the form `f(x)` with an arithmetical expression `x`. The return value of `f(x)` is either a floating-point number or a symbolic expression:

If x < a can be decided, then `f(x)` returns 0. If x > b can be decided, then `f(x)` returns the value 1. If ax and xb can be decided, then `f(x)` returns the value ```(x - a)/(b - a)```.

If x is a real floating-point number and both a and b can be converted to real floating-point numbers, then these values are returned as floating-point numbers. Otherwise, symbolic expressions are returned.

The function `f` reacts to properties of identifiers set via `assume`. If x is a symbolic expression with the property xa, or xb, or ax and xb, then the corresponding values are returned.

`f(x)` returns the symbolic call ```stats::uniformCDF(a, b)(x)``` if it cannot be decided whether x lies in the interval [a, b].

Numerical values for `a` and `b` are only accepted if they are real and ab.

## Environment Interactions

The function is sensitive to the environment variable `DIGITS` which determines the numerical working precision.

## Examples

### Example 1

We evaluate the cumulative distribution function on the interval [- 3, 2 π] at various points:

```f := stats::uniformCDF(-3, 2*PI): f(-infinity), f(-3), f(0.5), f(2/3), f(3.0), f(PI), f(infinity)```

`delete f:`

### Example 2

If `x` is a symbolic object without properties, then it cannot be decided whether axb holds. A symbolic function call is returned:

`f := stats::uniformCDF(a, b): f(x)`

With suitable properties, it can be decided whether axb holds. An explicit expression is returned:

`assume(x < a): f(x)`

Note that `assume(x < a)` attached properties both to `a` and `x`. With the next call, we overwrite the property attached to x. However, the property attached to a has to be 'unassumed' as well to avoid inconsistent assumptions x < a and x > b:

`unassume(a): assume(x > b): f(x)`

`assume(a <= x <= b): f(x)`

`assume(b > a): f(a + (b - a)/3)`

`unassume(x): unassume(a): unassume(b): delete f:`

### Example 3

We use symbolic arguments:

`f := stats::uniformCDF(a, b): f(3), f(3.0)`

When numerical values are assigned to a and b, the function `f` starts to produce numerical values:

`a := 0: b := PI: f(3), f(3.0)`

`delete f, a, b:`

## Parameters

 `a`, `b` arithmetical expressions representing real values; a ≤ b is assumed.