repl.txt (4222B)
1 2 {{alias}}( N, correction, x, stride ) 3 Computes the standard error of the mean for a double-precision floating- 4 point strided array using a one-pass algorithm proposed by Youngs and 5 Cramer. 6 7 The `N` and `stride` parameters determine which elements in `x` are accessed 8 at runtime. 9 10 Indexing is relative to the first index. To introduce an offset, use a typed 11 array view. 12 13 If `N <= 0`, the function returns `NaN`. 14 15 Parameters 16 ---------- 17 N: integer 18 Number of indexed elements. 19 20 correction: number 21 Degrees of freedom adjustment. Setting this parameter to a value other 22 than `0` has the effect of adjusting the divisor during the calculation 23 of the standard deviation according to `N - c` where `c` corresponds to 24 the provided degrees of freedom adjustment. When computing the standard 25 deviation of a population, setting this parameter to `0` is the standard 26 choice (i.e., the provided array contains data constituting an entire 27 population). When computing the corrected sample standard deviation, 28 setting this parameter to `1` is the standard choice (i.e., the provided 29 array contains data sampled from a larger population; this is commonly 30 referred to as Bessel's correction). 31 32 x: Float64Array 33 Input array. 34 35 stride: integer 36 Index increment. 37 38 Returns 39 ------- 40 out: number 41 Standard error of the mean. 42 43 Examples 44 -------- 45 // Standard Usage: 46 > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] ); 47 > {{alias}}( x.length, 1, x, 1 ) 48 ~1.20185 49 50 // Using `N` and `stride` parameters: 51 > x = new {{alias:@stdlib/array/float64}}( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] ); 52 > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 ); 53 > var stride = 2; 54 > {{alias}}( N, 1, x, stride ) 55 ~1.20185 56 57 // Using view offsets: 58 > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] ); 59 > var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); 60 > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 ); 61 > stride = 2; 62 > {{alias}}( N, 1, x1, stride ) 63 ~1.20185 64 65 {{alias}}.ndarray( N, correction, x, stride, offset ) 66 Computes the standard error of the mean for a double-precision floating- 67 point strided array using a one-pass algorithm proposed by Youngs and Cramer 68 and alternative indexing semantics. 69 70 While typed array views mandate a view offset based on the underlying 71 buffer, the `offset` parameter supports indexing semantics based on a 72 starting index. 73 74 Parameters 75 ---------- 76 N: integer 77 Number of indexed elements. 78 79 correction: number 80 Degrees of freedom adjustment. Setting this parameter to a value other 81 than `0` has the effect of adjusting the divisor during the calculation 82 of the standard deviation according to `N - c` where `c` corresponds to 83 the provided degrees of freedom adjustment. When computing the standard 84 deviation of a population, setting this parameter to `0` is the standard 85 choice (i.e., the provided array contains data constituting an entire 86 population). When computing the corrected sample standard deviation, 87 setting this parameter to `1` is the standard choice (i.e., the provided 88 array contains data sampled from a larger population; this is commonly 89 referred to as Bessel's correction). 90 91 x: Float64Array 92 Input array. 93 94 stride: integer 95 Index increment. 96 97 offset: integer 98 Starting index. 99 100 Returns 101 ------- 102 out: number 103 Standard error of the mean. 104 105 Examples 106 -------- 107 // Standard Usage: 108 > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] ); 109 > {{alias}}.ndarray( x.length, 1, x, 1, 0 ) 110 ~1.20185 111 112 // Using offset parameter: 113 > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] ); 114 > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 ); 115 > {{alias}}.ndarray( N, 1, x, 2, 1 ) 116 ~1.20185 117 118 See Also 119 -------- 120