# PHP | stats_cdf_t() Function

Hello folks! welcome back to a new edition of our tutorial on PHP. In this tutorial guide, we are going to be studying about the PHP stats_cdf_t() Function.

The built-in stats_cdf_t() function in PHP is used to calculate any one parameter of the t distribution provided values for the others.

## Syntax

Following below is the syntax to use this function -

`float stats_cdf_t ( float \$par1, float \$par2, int \$which )`

## Parameter Details

Sr.NoParameterDescription
1

par1

The first parameter

2

par2

The second parameter

3

which

The flag to determine what to be calculated

## Return Value

This built-in PHP function returns the CDF, its inverse, or one of its parameters, of the t distribution. The kind of return value and parameters (par1 & par2) are determined by which.

The following table list the return value and parameters by which.

• CDF denotes cumulative distribution function.
• x denotes the value of the random variable.
• nu denotes the degree of freedom of the t distribution.

whichReturn valuepar1par2
1CDFxnu
2xCDFnu
3nuxCDF

## Dependencies

This built-in function was first introduced in statistics extension (PHP version 4.0.0 and PEAR v1.4.0). In this tutorial guide, we used the latest release of stats-2.0.3 (PHP v7.0.0 or newer and PEAR version 1.4.0 or newer).

### Example1

In the following example below, when which = 1, calculate P from (T, DF).

• P is the integral from -infinity to t of the t density. Input range: [0, 1].
• T is the upper limit of integration of the t density.
• DF is the degree of freedom of the t distribution.

```<?php
// which = 1 : calculate P from (T, DF)
var_dump(round(stats_cdf_t(1, 1, 1), 6));
?>```

#### Output

When the above code is executed, it will produce the following result -

`float(0.75)`

### Example2

In the following example below, when which = 2, calculate T from (P, DF).

• P is the integral from -infinity to t of the t density. Input range: [0, 1].
• T is the upper limit of integration of the t density.
• DF is the degree of freedom of the t distribution.

```<?php
// which = 2 : calculate T from (P, DF)
var_dump(round(stats_cdf_t(0.75, 1, 2), 6));
?>```

#### Output

When the above code is executed, it will produce the following result -

`float(1)`

### Example3

In the following example below, when which = 3, calculate DF from (P, T).

• P is the integral from -infinity to t of the t density. Input range: [0, 1].
• T is the upper limit of integration of the t density.
• DF is the degree of freedom of the t distribution.

```<?php
// which = 3 : calculate DF from (P, T)
var_dump(round(stats_cdf_t(0.75, 1, 3), 6));
?>```

#### Output

When the above code is executed, it will produce the following result -

`float(1)`

### Example4

Following is an error case. In the following example below which<1, warning message is displayed in logs.

```<?php
var_dump(stats_cdf_t(1, 1, 0));      // which < 1
?>```

#### Output

The above code will produce the following result and a warning in logs PHP Warning: stats_cdf_t(): Third parameter ought to be in the 1..3 range.

`bool(false)`

### Example5

Following is an error case. In the following example below which>4, warning message is displayed in logs.

```<?php
var_dump(stats_cdf_t(1, 1, 4));      // which > 3
?>```

#### Output

The above code will produce the following result and a warning in logs PHP Warning: stats_cdf_t(): Third parameter ought to be in the 1..3 range.

`bool(false)`

Alright guys! This is where we are going to be rounding up for this tutorial post. In our next tutorial, we are going to be discussing about the stats_cdf_uniform() Function.

Feel free to ask your questions where necessary and we will attend to them as soon as possible. If this tutorial was helpful to you, you can use the share button to share this tutorial.

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Thanks for reading and bye for now.