Statistics/Normal distribution: Difference between revisions
Added Kotlin
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0.8: *******
0.9: **</pre>
=={{header|Kotlin}}==
{{trans|FreeBASIC}}
<lang scala>// version 1.1.2
val rand = java.util.Random()
fun normalStats(sampleSize: Int) {
if (sampleSize < 1) return
val r = DoubleArray(sampleSize)
val h = IntArray(12) // all zero by default
/*
Generate 'sampleSize' normally distributed random numbers with mean 0.5 and SD 0.25
and calculate in which box they will fall when drawing the histogram
*/
for (i in 0 until sampleSize) {
r[i] = 0.5 + rand.nextGaussian() / 4.0
when {
r[i] < 0.0 -> h[0]++
r[i] >= 1.0 -> h[11]++
else -> h[1 + (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..11) {
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 > 300 then normalize histogram to 300
val scale = if (sampleSize <= 300) 1.0 else 300.0 / sampleSize
println("Sample size $sampleSize\n")
println(" Mean ${"%1.6f".format(mean)} SD ${"%1.6f".format(sd)}\n")
for (i in 0..11) {
when (i) {
0 -> print("< 0.00 : ")
11 -> print(">=1.00 : ")
else -> 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) normalStats(sampleSize)
}</lang>
{{out}}
<pre>
Sample size 100
Mean 0.525211 SD 0.266316
< 0.00 : 3 ***
0.10 : 1 *
0.20 : 3 ***
0.30 : 11 ***********
0.40 : 14 **************
0.50 : 13 *************
0.60 : 15 ***************
0.70 : 13 *************
0.80 : 10 **********
0.90 : 11 ***********
1.00 : 4 ****
>=1.00 : 2 **
Sample size 1000
Mean 0.500948 SD 0.255757
< 0.00 : 29 *********
0.10 : 35 ***********
0.20 : 70 *********************
0.30 : 71 *********************
0.40 : 138 *****************************************
0.50 : 139 ******************************************
0.60 : 168 **************************************************
0.70 : 123 *************************************
0.80 : 110 *********************************
0.90 : 62 *******************
1.00 : 32 **********
>=1.00 : 23 *******
Sample size 10000
Mean 0.501376 SD 0.248317
< 0.00 : 240 *******
0.10 : 305 *********
0.20 : 617 *******************
0.30 : 927 ****************************
0.40 : 1291 ***************************************
0.50 : 1554 ***********************************************
0.60 : 1609 ************************************************
0.70 : 1319 ****************************************
0.80 : 983 *****************************
0.90 : 609 ******************
1.00 : 324 **********
>=1.00 : 222 *******
Sample size 100000
Mean 0.499427 SD 0.250533
< 0.00 : 2341 *******
0.10 : 3246 **********
0.20 : 6005 ******************
0.30 : 9718 *****************************
0.40 : 13247 ****************************************
0.50 : 15595 ***********************************************
0.60 : 15271 **********************************************
0.70 : 13405 ****************************************
0.80 : 9653 *****************************
0.90 : 5990 ******************
1.00 : 3257 **********
>=1.00 : 2272 *******
</pre>
=={{header|Lasso}}==
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