Create a two-sample Z-test single-precision floating-point results object.
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> Create a two-sample Z-test single-precision floating-point results object.
``bash`
npm install @stdlib/stats-base-ztest-two-sample-results-float32
`javascript`
var Float32Results = require( '@stdlib/stats-base-ztest-two-sample-results-float32' );
#### Float32Results( \[arg\[, byteOffset\[, byteLength]]] )
Returns a two-sample Z-test single-precision floating-point results object.
`javascript`
var results = new Float32Results();
// returns {...}
The function supports the following parameters:
- arg: an [ArrayBuffer][@stdlib/array/buffer] or a data object (_optional_).
- byteOffset: byte offset (_optional_).
- byteLength: maximum byte length (_optional_).
A data object argument is an object having one or more of the following properties:
- rejected: boolean indicating whether the null hypothesis was rejected.
- alternative: the alternative hypothesis (e.g., 'two-sided', 'less', or 'greater').Float32Array
- alpha: significance level.
- pValue: p-value.
- statistic: test statistic.
- ci: confidence interval as a [][@stdlib/array/float32].x
- nullValue: difference in means under the null hypothesis.
- xmean: sample mean of .y
- ymean: sample mean of .
#### Float32Results.prototype.rejected
Boolean indicating whether the null hypothesis was rejected.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.rejected;
// returns
`
#### Float32Results.prototype.alternative
The alternative hypothesis.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.alternative;
// returns
`
#### Float32Results.prototype.alpha
Significance level.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.alpha;
// returns
`
#### Float32Results.prototype.pValue
The test p-value.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.pValue;
// returns
`
#### Float32Results.prototype.statistic
The test statistic.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.statistic;
// returns
`
#### Float32Results.prototype.ci
Confidence interval.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.ci;
// returns
`
#### Float32Results.prototype.nullValue
Difference in means under the null hypothesis.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.nullValue;
// returns
`
#### Float32Results.prototype.xmean
Sample mean of x.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.xmean;
// returns
`
#### Float32Results.prototype.ymean
Sample mean of y.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.ymean;
// returns
`
#### Float32Results.prototype.toString( \[options] )
Serializes a results object to a formatted string.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.toString();
// returns
`
The method supports the following options:
- digits: number of digits to display after decimal points. Default: 4.true
- decision: boolean indicating whether to show the test decision. Default: .
Example output:
`text
Two-sample Z-test
Alternative hypothesis: True difference in means is less than 1.0
pValue: 0.0406
statistic: 9.9901
95% confidence interval: [9.7821, 10.4451]
Test Decision: Reject null in favor of alternative at 5% significance level
`
#### Float32Results.prototype.toJSON( \[options] )
Serializes a results object as a JSON object.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.toJSON();
// returns {...}
`
JSON.stringify() implicitly calls this method when stringifying a results instance.
#### Float32Results.prototype.toDataView()
Returns a [DataView][@stdlib/array/dataview] of a results object.
`javascript
var results = new Float32Results();
// returns {...}
// ...
var v = results.toDataView();
// returns
`
- A results object is a [struct][@stdlib/dstructs/struct] providing a fixed-width composite data structure for storing two-sample Z-test results and providing an ABI-stable data layout for JavaScript-C interoperation.
`javascript
var Float32Array = require( '@stdlib/array-float32' );
var Results = require( '@stdlib/stats-base-ztest-two-sample-results-float32' );
var results = new Results({
'rejected': true,
'alpha': 0.05,
'pValue': 0.0132,
'statistic': 2.4773,
'nullValue': 0.0,
'xmean': 3.7561,
'ymean': 3.0129,
'ci': new Float32Array( [ 0.1552, 1.3311 ] ),
'alternative': 'two-sided'
});
var str = results.toString({
'format': 'linear'
});
console.log( str );
`
*
`c`
#include "stdlib/stats/base/ztest/two-sample/results/float32.h"
#### stdlib_stats_ztest_two_sample_float32_results
Structure for holding single-precision floating-point test results.
`c
#include
#include
struct stdlib_stats_ztest_two_sample_float32_results {
// Boolean indicating whether the null hypothesis was rejected:
bool rejected;
// Alternative hypothesis:
int8_t alternative;
// Significance level:
float alpha;
// p-value:
float pValue;
// Test statistic:
float statistic;
// Confidence interval:
float ci[ 2 ];
// Difference in means under the null hypothesis:
float nullValue;
// Sample mean of x:
float xmean;
// Sample mean of y:`
float ymean;
};
*
This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].
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