time-to-botec

README.md (9499B)

---

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 # dsemch
 
 > Calculate the [standard error of the mean][standard-error] of a double-precision floating-point strided array using a one-pass trial mean algorithm.
 
 <section class="intro">
 
 The [standard error of the mean][standard-error] of a finite size sample of size `n` is given by
 
 <!-- <equation class="equation" label="eq:standard_error_of_the_mean" align="center" raw="\sigma_{\bar{x}} = \frac{\sigma}{\sqrt{n}}" alt="Equation for the standard error of the mean."> -->
 
 <div class="equation" align="center" data-raw-text="\sigma_{\bar{x}} = \frac{\sigma}{\sqrt{n}}" data-equation="eq:standard_error_of_the_mean">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@3ebbbfc49c54971356c0cf8f6282e6720cb07755/lib/node_modules/@stdlib/stats/base/dsemch/docs/img/equation_standard_error_of_the_mean.svg" alt="Equation for the standard error of the mean.">
     <br>
 </div>
 
 <!-- </equation> -->
 
 where `σ` is the population [standard deviation][standard-deviation].
 
 Often in the analysis of data, the true population [standard deviation][standard-deviation] is not known _a priori_ and must be estimated from a sample drawn from the population distribution. In this scenario, one must use a sample [standard deviation][standard-deviation] to compute an estimate for the [standard error of the mean][standard-error]
 
 <!-- <equation class="equation" label="eq:standard_error_of_the_mean_estimate" align="center" raw="\sigma_{\bar{x}} \approx \frac{s}{\sqrt{n}}" alt="Equation for estimating the standard error of the mean."> -->
 
 <div class="equation" align="center" data-raw-text="\sigma_{\bar{x}} \approx \frac{s}{\sqrt{n}}" data-equation="eq:standard_error_of_the_mean_estimate">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@3ebbbfc49c54971356c0cf8f6282e6720cb07755/lib/node_modules/@stdlib/stats/base/dsemch/docs/img/equation_standard_error_of_the_mean_estimate.svg" alt="Equation for estimating the standard error of the mean.">
     <br>
 </div>
 
 <!-- </equation> -->
 
 where `s` is the sample [standard deviation][standard-deviation].
 
 </section>
 
 <!-- /.intro -->
 
 <section class="usage">
 
 ## Usage
 
 ```javascript
 var dsemch = require( '@stdlib/stats/base/dsemch' );
 ```
 
 #### dsemch( N, correction, x, stride )
 
 Computes the [standard error of the mean][standard-error] of a double-precision floating-point strided array `x` using a one-pass trial mean algorithm.
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 
 var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
 var N = x.length;
 
 var v = dsemch( N, 1, x, 1 );
 // returns ~1.20185
 ```
 
 The function has the following parameters:
 
 -   **N**: number of indexed elements.
 -   **correction**: degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the [standard deviation][standard-deviation] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the [standard deviation][standard-deviation] of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the corrected sample [standard deviation][standard-deviation], setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
 -   **x**: input [`Float64Array`][@stdlib/array/float64].
 -   **stride**: index increment for `x`.
 
 The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [standard error of the mean][standard-error] of every other element in `x`,
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
 var N = floor( x.length / 2 );
 
 var v = dsemch( N, 1, x, 2 );
 // returns 1.25
 ```
 
 Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
 
 <!-- eslint-disable stdlib/capitalized-comments -->
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 var floor = require( '@stdlib/math/base/special/floor' );
 
 var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
 var N = floor( x0.length / 2 );
 
 var v = dsemch( N, 1, x1, 2 );
 // returns 1.25
 ```
 
 #### dsemch.ndarray( N, correction, x, stride, offset )
 
 Computes the [standard error of the mean][standard-error] of a double-precision floating-point strided array using a one-pass trial mean algorithm and alternative indexing semantics.
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 
 var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
 var N = x.length;
 
 var v = dsemch.ndarray( N, 1, x, 1, 0 );
 // returns ~1.20185
 ```
 
 The function has the following additional parameters:
 
 -   **offset**: starting index for `x`.
 
 While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the [standard error of the mean][standard-error] for every other value in `x` starting from the second value
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
 var N = floor( x.length / 2 );
 
 var v = dsemch.ndarray( N, 1, x, 2, 1 );
 // returns 1.25
 ```
 
 </section>
 
 <!-- /.usage -->
 
 <section class="notes">
 
 ## Notes
 
 -   If `N <= 0`, both functions return `NaN`.
 -   If `N - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment), both functions return `NaN`.
 -   The underlying algorithm is a specialized case of Neely's two-pass algorithm. As the standard deviation is invariant with respect to changes in the location parameter, the underlying algorithm uses the first strided array element as a trial mean to shift subsequent data values and thus mitigate catastrophic cancellation. Accordingly, the algorithm's accuracy is best when data is **unordered** (i.e., the data is **not** sorted in either ascending or descending order such that the first value is an "extreme" value).
 
 </section>
 
 <!-- /.notes -->
 
 <section class="examples">
 
 ## Examples
 
 <!-- eslint no-undef: "error" -->
 
 ```javascript
 var randu = require( '@stdlib/random/base/randu' );
 var round = require( '@stdlib/math/base/special/round' );
 var Float64Array = require( '@stdlib/array/float64' );
 var dsemch = require( '@stdlib/stats/base/dsemch' );
 
 var x;
 var i;
 
 x = new Float64Array( 10 );
 for ( i = 0; i < x.length; i++ ) {
     x[ i ] = round( (randu()*100.0) - 50.0 );
 }
 console.log( x );
 
 var v = dsemch( x.length, 1, x, 1 );
 console.log( v );
 ```
 
 </section>
 
 <!-- /.examples -->
 
 * * *
 
 <section class="references">
 
 ## References
 
 -   Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." _Communications of the ACM_ 9 (7). Association for Computing Machinery: 496–99. doi:[10.1145/365719.365958][@neely:1966a].
 -   Ling, Robert F. 1974. "Comparison of Several Algorithms for Computing Sample Means and Variances." _Journal of the American Statistical Association_ 69 (348). American Statistical Association, Taylor & Francis, Ltd.: 859–66. doi:[10.2307/2286154][@ling:1974a].
 -   Chan, Tony F., Gene H. Golub, and Randall J. LeVeque. 1983. "Algorithms for Computing the Sample Variance: Analysis and Recommendations." _The American Statistician_ 37 (3). American Statistical Association, Taylor & Francis, Ltd.: 242–47. doi:[10.1080/00031305.1983.10483115][@chan:1983a].
 -   Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036][@schubert:2018a].
 
 </section>
 
 <!-- /.references -->
 
 <section class="links">
 
 [standard-error]: https://en.wikipedia.org/wiki/Standard_error
 
 [standard-deviation]: https://en.wikipedia.org/wiki/Standard_deviation
 
 [@stdlib/array/float64]: https://www.npmjs.com/package/@stdlib/array-float64
 
 [mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
 
 [@neely:1966a]: https://doi.org/10.1145/365719.365958
 
 [@ling:1974a]: https://doi.org/10.2307/2286154
 
 [@chan:1983a]: https://doi.org/10.1080/00031305.1983.10483115
 
 [@schubert:2018a]: https://doi.org/10.1145/3221269.3223036
 
 </section>
 
 <!-- /.links -->