Statistics/Basic: Difference between revisions

=={{header|Kotlin}}==

{{trans|FreeBASIC}}

<lang scala>// version 1.1.2

val rand = java.util.Random()

fun basicStats(sampleSize: Int) {

if (sampleSize < 1) return

val r = DoubleArray(sampleSize)

val h = IntArray(10) // all zero by default

/*

Generate 'sampleSize' random numbers in the interval [0, 1)

and calculate in which box they will fall when drawing the histogram

*/

for (i in 0 until sampleSize) {

r[i] = rand.nextDouble()

h[(r[i] * 10).toInt()]++

}

// adjust one of the h[] values if necessary to ensure they sum to sampleSize

val adj = sampleSize - h.sum()

if (adj != 0) {

for (i in 0..9) {

h[i] += adj

if (h[i] >= 0) break

h[i] -= adj

}

}

val mean = r.average()

val sd = Math.sqrt(r.map { (it - mean) * (it - mean) }.average())

// Draw a histogram of the data with interval 0.1

var numStars: Int

// If sample size > 500 then normalize histogram to 500

val scale = if (sampleSize <= 500) 1.0 else 500.0 / sampleSize

println("Sample size $sampleSize\n")

println(" Mean ${"%1.6f".format(mean)} SD ${"%1.6f".format(sd)}\n")

for (i in 0..9) {

print(" %1.2f : ".format(i / 10.0))

print("%5d ".format(h[i]))

numStars = (h[i] * scale + 0.5).toInt()

println("*".repeat(numStars))

}

println()

}

fun main(args: Array<String>) {

val sampleSizes = intArrayOf(100, 1_000, 10_000, 100_000)

for (sampleSize in sampleSizes) basicStats(sampleSize)

}</lang>

Sample run:

{{out}}

<pre>

Sample size 100

Mean 0.489679 SD 0.286151

0.00 : 12 ************

0.10 : 7 *******

0.20 : 13 *************

0.30 : 9 *********

0.40 : 10 **********

0.50 : 8 ********

0.60 : 14 **************

0.70 : 10 **********

0.80 : 8 ********

0.90 : 9 *********

Sample size 1000

Mean 0.497003 SD 0.290002

0.00 : 104 ****************************************************

0.10 : 92 **********************************************

0.20 : 107 ******************************************************

0.30 : 109 *******************************************************

0.40 : 96 ************************************************

0.50 : 111 ********************************************************

0.60 : 87 ********************************************

0.70 : 79 ****************************************

0.80 : 117 ***********************************************************

0.90 : 98 *************************************************

Sample size 10000

Mean 0.505243 SD 0.288944

0.00 : 991 **************************************************

0.10 : 938 ***********************************************

0.20 : 1034 ****************************************************

0.30 : 958 ************************************************

0.40 : 963 ************************************************

0.50 : 1003 **************************************************

0.60 : 1081 ******************************************************

0.70 : 995 **************************************************

0.80 : 1001 **************************************************

0.90 : 1036 ****************************************************

Sample size 100000

Mean 0.500501 SD 0.288766

0.00 : 10015 **************************************************

0.10 : 9844 *************************************************

0.20 : 10012 **************************************************

0.30 : 10160 ***************************************************

0.40 : 10051 **************************************************

0.50 : 9938 **************************************************

0.60 : 9934 **************************************************

0.70 : 9914 **************************************************

0.80 : 10057 **************************************************

0.90 : 10075 **************************************************

</pre>