Statistics/Basic

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Revision as of 10:12, 2 August 2012 by Walterpachl (talk | contribs) (→‎{{header|REXX}}: 1 typo corrected)
Statistics/Basic is a draft programming task. It is not yet considered ready to be promoted as a complete task, for reasons that should be found in its talk page.

Statistics is all about large groups of numbers. When talking about a set of sampled data, most frequently used is their mean value and standard deviation (stddev). If you have set of data where , the mean is , while the stddev is .

When examining a large quantity of data, one often uses a histogram, which shows the counts of data samples falling into a prechosen set of intervals (or bins). When plotted, often as bar graphs, it visually indicates how often each data value occurs.

Task Using your language's random number routine, generate real numbers in the range of [0, 1]. It doesn't matter if you chose to use open or closed range. Create 100 of such numbers (i.e. sample size 100) and calculate their mean and stddev. Do so for sample size of 1,000 and 10,000, maybe even higher if you feel like. Show a histogram of any of these sets. Do you notice some patterns about the standard deviation?

Extra Sometimes so much data need to be processed that it's impossible to keep all of them at once. Can you calculate the mean, stddev and histogram of a trillion numbers? (You don't really need to do a trillion numbers, just show how it can be done.)

Hint

For a finite population with equal probabilities at all points, one can derive:

Ada

A plain solution for moderate sample sizes

<lang Ada>with Ada.Text_IO, Ada.Command_Line, Ada.Numerics.Float_Random, 

 Ada.Numerics.Generic_Elementary_Functions;

procedure Basic_Stat is 

  package FRG renames Ada.Numerics.Float_Random;
  package TIO renames Ada.Text_IO;

  type Counter is range 0 .. 2**31-1;
  type Result_Array is array(Natural range <>) of Counter;

  package FIO is new TIO.Float_IO(Float);

  procedure Put_Histogram(R: Result_Array; Scale, Full: Counter) is
  begin
     for I in R'Range loop
        FIO.Put(Float'Max(0.0, Float(I)/10.0 - 0.05),
                Fore => 1, Aft => 2, Exp => 0);       TIO.Put("..");
        FIO.Put(Float'Min(1.0, Float(I)/10.0 + 0.05),
                Fore => 1, Aft => 2, Exp => 0);       TIO.Put(": ");
        for J in 1 .. (R(I)* Scale)/Full loop
           Ada.Text_IO.Put("X");
        end loop;
        Ada.Text_IO.New_Line;
     end loop;
  end Put_Histogram;

  procedure Put_Mean_Et_Al(Sample_Size: Counter;
                           Val_Sum, Square_Sum: Float) is
     Mean: constant Float := Val_Sum / Float(Sample_Size);
     package Math is new Ada.Numerics.Generic_Elementary_Functions(Float);
  begin
     TIO.Put("Mean: ");
     FIO.Put(Mean,  Fore => 1, Aft => 5, Exp => 0);
     TIO.Put(",  Standard Deviation: ");
     FIO.Put(Math.Sqrt(abs(Square_Sum / Float(Sample_Size)
                          - (Mean * Mean))), Fore => 1, Aft => 5, Exp => 0);
     TIO.New_Line;
  end Put_Mean_Et_Al;

  N: Counter := Counter'Value(Ada.Command_Line.Argument(1));
  Gen: FRG.Generator;
  Results: Result_Array(0 .. 10) := (others => 0);
  X: Float;
  Val_Sum, Squ_Sum: Float := 0.0;

begin 

  FRG.Reset(Gen);
  for I in 1 .. N loop
     X := FRG.Random(Gen);
     Val_Sum   := Val_Sum + X;
     Squ_Sum := Squ_Sum + X*X;
     declare
        Index: Integer := Integer(X*10.0);
     begin
        Results(Index) := Results(Index) + 1;
     end;
  end loop;
  TIO.Put_Line("After sampling" & Counter'Image(N) & " random numnbers: ");
  Put_Histogram(Results, Scale => 600, Full => N);
  TIO.New_Line;
  Put_Mean_Et_Al(Sample_Size => N, Val_Sum => Val_Sum, Square_Sum => Squ_Sum);

end Basic_Stat;</lang>

Output from a few sample runs: 

> ./basic_stat 1000
After sampling 1000 random numnbers: 
0.00..0.05: XXXXXXXXXXXXXXXXXXXXXXX
0.05..0.15: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.15..0.25: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.25..0.35: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.35..0.45: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.45..0.55: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.55..0.65: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.65..0.75: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.75..0.85: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.85..0.95: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.95..1.00: XXXXXXXXXXXXXXXXXXXXXXXXXXXX

Mean: 0.48727,  Standard Deviation: 0.28502



> ./basic_stat 10_000
After sampling 10000 random numnbers: 
0.00..0.05: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.05..0.15: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.15..0.25: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.25..0.35: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.35..0.45: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.45..0.55: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.55..0.65: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.65..0.75: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.75..0.85: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.85..0.95: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.95..1.00: XXXXXXXXXXXXXXXXXXXXXXXXXXXXX

Mean: 0.50096,  Standard Deviation: 0.28869



> ./basic_stat 100_000
After sampling 100000 random numnbers: 
0.00..0.05: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.05..0.15: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.15..0.25: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.25..0.35: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.35..0.45: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.45..0.55: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.55..0.65: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.65..0.75: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.75..0.85: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.85..0.95: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.95..1.00: XXXXXXXXXXXXXXXXXXXXXXXXXXXXX

Mean: 0.50178,  Standard Deviation: 0.28805

Making the solution ready for one trillion samples

Depending on where you live, one trillion is either 10^12 or 10^18 [1]. Below, I'll assume 10^12, which implies a number of operations I can still perform on my PC.

The above program will fail with such large inputs for two reasons:

1. The type Counter cannot hold such large numbers.

2. The variables Val_Sum and Squ_Sum will numerically fail, because the type Float only provides about six decimal digits of accuracy. I.e., at some point, Val_Sum and (a little bit later) Squ_Sum are so large that adding a value below 1 has no effect, any more.

To make the program ready for sample size 10^12, we modify it as follows.

1. Change the type Counter to hold such large numbers.

2. Define a type High_Precision, that will hold (at least) 15 decimal digits. Define Val_Sum and Squ_Sum as being from that type. Include the neccessary type conversions.

3. Provide some progress report, during the running time.

This is the modified program

<lang Ada>with Ada.Text_IO, Ada.Command_Line, Ada.Numerics.Float_Random, 

 Ada.Numerics.Generic_Elementary_Functions;

procedure Long_Basic_Stat is 

  package FRG renames Ada.Numerics.Float_Random;
  package TIO renames Ada.Text_IO;

  type Counter is range 0 .. 2**63-1;
  type Result_Array is array(Natural range <>) of Counter;
  type High_Precision is digits 15;

  package FIO is new TIO.Float_IO(Float);

  procedure Put_Histogram(R: Result_Array; Scale, Full: Counter) is
  begin
     for I in R'Range loop
        FIO.Put(Float'Max(0.0, Float(I)/10.0 - 0.05),
                Fore => 1, Aft => 2, Exp => 0);       TIO.Put("..");
        FIO.Put(Float'Min(1.0, Float(I)/10.0 + 0.05),
                Fore => 1, Aft => 2, Exp => 0);       TIO.Put(": ");
        for J in 1 .. (R(I)* Scale)/Full loop
           Ada.Text_IO.Put("X");
        end loop;
        Ada.Text_IO.New_Line;
     end loop;
  end Put_Histogram;

  procedure Put_Mean_Et_Al(Sample_Size: Counter;
                           Val_Sum, Square_Sum: Float) is
     Mean: constant Float := Val_Sum / Float(Sample_Size);
     package Math is new Ada.Numerics.Generic_Elementary_Functions(Float);
  begin
     TIO.Put("Mean: ");
     FIO.Put(Mean,  Fore => 1, Aft => 5, Exp => 0);
     TIO.Put(",  Standard Deviation: ");
     FIO.Put(Math.Sqrt(abs(Square_Sum / Float(Sample_Size)
                          - (Mean * Mean))), Fore => 1, Aft => 5, Exp => 0);
     TIO.New_Line;
  end Put_Mean_Et_Al;

  N: Counter := Counter'Value(Ada.Command_Line.Argument(1));
  Gen: FRG.Generator;
  Results: Result_Array(0 .. 10) := (others => 0);
  X: Float;
  Val_Sum, Squ_Sum: High_Precision := 0.0;

begin 

  FRG.Reset(Gen);
  for Outer in 1 .. 1000 loop
     for I in 1 .. N/1000 loop
        X := FRG.Random(Gen);
        Val_Sum   := Val_Sum + High_Precision(X);
        Squ_Sum := Squ_Sum + High_Precision(X)*High_Precision(X);
        declare
           Index: Integer := Integer(X*10.0);
        begin
           Results(Index) := Results(Index) + 1;
        end;
     end loop;
     if Outer mod 50 = 0 then
        TIO.New_Line(1);
        TIO.Put_Line(Integer'Image(Outer/10) &"% done; current results:");
        Put_Mean_Et_Al(Sample_Size => (Counter(Outer)*N)/1000,
                       Val_Sum     => Float(Val_Sum),
                       Square_Sum  => Float(Squ_Sum));
     else
        Ada.Text_IO.Put(".");
     end if;
  end loop;
  TIO.New_Line(4);
  TIO.Put_Line("After sampling" & Counter'Image(N) & " random numnbers: ");
  Put_Histogram(Results, Scale => 600, Full => N);
  TIO.New_Line;
  Put_Mean_Et_Al(Sample_Size => N,
                 Val_Sum => Float(Val_Sum), Square_Sum => Float(Squ_Sum));

end Long_Basic_Stat;</lang>

Generating the output for sample size 10^12 took one night on my PC: 

.................................................
 5% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28867
.................................................
 10% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28867
.................................................
 15% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 20% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 25% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 30% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 35% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 40% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 45% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 50% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 55% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 60% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 65% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 70% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 75% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 80% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 85% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 90% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 95% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868
.................................................
 100% done; current results:
Mean: 0.50000,  Standard Deviation: 0.28868




After sampling 1000000000000 random numnbers: 
0.00..0.05: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.05..0.15: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.15..0.25: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.25..0.35: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.35..0.45: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.45..0.55: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.55..0.65: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.65..0.75: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.75..0.85: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.85..0.95: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0.95..1.00: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

Mean: 0.50000,  Standard Deviation: 0.28868

The same program should still work fine for sample size 10^18, but I'll need my PC in the meantime. ;-)

C

Sample code. <lang C>#include <stdio.h>

  1. include <stdlib.h>
  2. include <math.h>
  3. include <stdint.h>
  1. define n_bins 10

double rand01() { return rand() / (RAND_MAX + 1.0); }

double avg(int count, double *stddev, int *hist) { double x[count]; double m = 0, s = 0;

for (int i = 0; i < n_bins; i++) hist[i] = 0; for (int i = 0; i < count; i++) { m += (x[i] = rand01()); hist[(int)(x[i] * n_bins)] ++; }

m /= count; for (int i = 0; i < count; i++) s += x[i] * x[i]; *stddev = sqrt(s / count - m * m); return m; }

void hist_plot(int *hist) { int max = 0, step = 1; double inc = 1.0 / n_bins;

for (int i = 0; i < n_bins; i++) if (hist[i] > max) max = hist[i];

/* scale if numbers are too big */ if (max >= 60) step = (max + 59) / 60;

for (int i = 0; i < n_bins; i++) { printf("[%5.2g,%5.2g]%5d ", i * inc, (i + 1) * inc, hist[i]); for (int j = 0; j < hist[i]; j += step) printf("#"); printf("\n"); } }

/* record for moving average and stddev. Values kept are sums and sum data^2 

*  to avoid excessive precision loss due to divisions, but some loss is inevitable
*/

typedef struct { uint64_t size; double sum, x2; uint64_t hist[n_bins]; } moving_rec;

void moving_avg(moving_rec *rec, double *data, int count) { double sum = 0, x2 = 0; /* not adding data directly to the sum in case both recorded sum and * count of this batch are large; slightly less likely to lose precision*/ for (int i = 0; i < count; i++) { sum += data[i]; x2 += data[i] * data[i]; rec->hist[(int)(data[i] * n_bins)]++; }

rec->sum += sum; rec->x2 += x2; rec->size += count; }

int main() { double m, stddev; int hist[n_bins], samples = 10;

while (samples <= 10000) { m = avg(samples, &stddev, hist); printf("size %5d: %g %g\n", samples, m, stddev); samples *= 10; }

printf("\nHistograph:\n"); hist_plot(hist);

printf("\nMoving average:\n N Mean Sigma\n"); moving_rec rec = { 0, 0, 0, {0} }; double data[100]; for (int i = 0; i < 10000; i++) { for (int j = 0; j < 100; j++) data[j] = rand01();

moving_avg(&rec, data, 100);

if ((i % 1000) == 999) { printf("%4lluk %f %f\n", rec.size/1000, rec.sum / rec.size, sqrt(rec.x2 * rec.size - rec.sum * rec.sum)/rec.size ); } } }</lang>

CoffeeScript

<lang coffeescript> generate_statistics = (n) -> 

 hist = {}

 update_hist = (r) ->
   hist[Math.floor 10*r] ||= 0
   hist[Math.floor 10*r] += 1

 sum = 0
 sum_squares = 0.0

 for i in [1..n]
   r = Math.random()
   sum += r
   sum_squares += r*r
   update_hist r
 mean = sum / n
 stddev = Math.sqrt((sum_squares / n) - mean*mean)

 [n, mean, stddev, hist]

display_statistics = (n, mean, stddev, hist) -> 

 console.log "-- Stats for sample size #{n}"
 console.log "mean: #{mean}"
 console.log "sdev: #{stddev}"
 for x, cnt of hist
   bars = repeat "=", Math.floor(cnt*300/n) 
   console.log "#{x/10}: #{bars} #{cnt}"

repeat = (c, n) -> 

 s = 
 s += c for i in [1..n]
 s

for n in [100, 1000, 10000, 1000000] 

 [n, mean, stddev, hist] = generate_statistics n
 display_statistics n, mean, stddev, hist

</lang>

output <lang> > coffee stats.coffee -- Stats for sample size 100 mean: 0.5058459933893755 sdev: 0.2752669422150894 0: ================== 6 0.1: ============================================= 15 0.2: =========================== 9 0.3: ===================== 7 0.4: ============================================= 15 0.5: ======================== 8 0.6: ================================= 11 0.7: ========================================== 14 0.8: ===================== 7 0.9: ======================== 8 -- Stats for sample size 1000 mean: 0.49664502244861797 sdev: 0.2942483939245344 0: ========================== 89 0.1: ===================================== 126 0.2: =========================== 93 0.3: ==================================== 121 0.4: =========================== 93 0.5: ====================== 75 0.6: ================================ 108 0.7: ======================== 82 0.8: ============================== 101 0.9: ================================= 112 -- Stats for sample size 10000 mean: 0.4985696110446239 sdev: 0.29007446138438986 0: ============================== 1005 0.1: ============================== 1016 0.2: ============================== 1022 0.3: ============================== 1012 0.4: ============================ 958 0.5: =============================== 1035 0.6: ============================= 974 0.7: ============================= 968 0.8: ============================= 973 0.9: =============================== 1037 -- Stats for sample size 1000000 mean: 0.5001718024678293 sdev: 0.2887130780006248 0: ============================== 100113 0.1: ============================= 99830 0.2: ============================== 100029 0.3: ============================= 99732 0.4: ============================= 99911 0.5: ============================= 99722 0.6: ============================== 100780 0.7: ============================= 99812 0.8: ============================= 99875 0.9: ============================== 100196 </lang>

D

Translation of: Python

<lang d>import std.stdio, std.algorithm, std.array, std.typecons, 

      std.range, std.exception;

auto meanStdDev(R)(R numbers) /*pure nothrow*/ { 

   if (numbers.empty)
       return tuple(0.0L, 0.0L);

   real sx=0.0, sxx=0.0;
   ulong n;
   foreach (x; numbers) {
       sx += x;
       sxx += x ^^ 2;
       n++;
   }
   return tuple(sx / n, (n * sxx - sx ^^ 2) ^^ 0.5L / n);

}

void showHistogram(R)(R numbers) { 

   enum maxWidth = 50; // characters
   ulong[10] bins;
   foreach (x; numbers) {
       enforce(x >= 0.0 && x <= 1.0);
       bins[cast(size_t)(x * 10)]++;
   }
   immutable real maxFreq = reduce!max(bins);

   alias std.array.replicate R;
   foreach (n, i; bins)
       writefln(" %3.1f: %s", n / 10.0,
                R("*", cast(int)(i / maxFreq * maxWidth)));
   writeln();

}

void main() { 

   import std.random;
   foreach (p; 1 .. 7) {
       auto n = iota(10L ^^ p).map!(_ => uniform(0.0L, 1.0L))();
       writeln(10L ^^ p, " numbers:");
       writefln(" Mean: %8.6f, SD: %8.6f", meanStdDev(n).tupleof);
       showHistogram(n);
   }

}</lang>

Output:
10 numbers:
 Mean: 0.651336, SD: 0.220208
 0.0: *************************
 0.1: **************************************************
 0.2: 
 0.3: **************************************************
 0.4: 
 0.5: *************************
 0.6: *************************
 0.7: *************************
 0.8: *************************
 0.9: *************************

100 numbers:
 Mean: 0.470756, SD: 0.291080
 0.0: *************************************
 0.1: *******************************************
 0.2: *******************************
 0.3: *******************************
 0.4: ******************
 0.5: *********************
 0.6: ****************************
 0.7: **************************************************
 0.8: *******************************
 0.9: ******************

1000 numbers:
 Mean: 0.519127, SD: 0.287775
 0.0: ***************************************
 0.1: *******************************************
 0.2: ****************************************
 0.3: ****************************************
 0.4: ************************************
 0.5: ******************************************
 0.6: **************************************************
 0.7: **************************************
 0.8: ********************************************
 0.9: **********************************

10000 numbers:
 Mean: 0.503266, SD: 0.289198
 0.0: **********************************************
 0.1: **********************************************
 0.2: **************************************************
 0.3: ************************************************
 0.4: ***********************************************
 0.5: *********************************************
 0.6: ***********************************************
 0.7: ************************************************
 0.8: **********************************************
 0.9: **********************************************

100000 numbers:
 Mean: 0.500945, SD: 0.289076
 0.0: *************************************************
 0.1: *************************************************
 0.2: *************************************************
 0.3: *************************************************
 0.4: *************************************************
 0.5: *************************************************
 0.6: *************************************************
 0.7: *************************************************
 0.8: **************************************************
 0.9: *************************************************

1000000 numbers:
 Mean: 0.499970, SD: 0.288635
 0.0: *************************************************
 0.1: *************************************************
 0.2: *************************************************
 0.3: *************************************************
 0.4: *************************************************
 0.5: **************************************************
 0.6: *************************************************
 0.7: *************************************************
 0.8: *************************************************
 0.9: *************************************************

Go

<lang go>package main

import ( 

   "fmt"
   "math"
   "math/rand"
   "strings"

)

func main() { 

   sample(100)
   sample(1000)
   sample(10000)

}

func sample(n int) { 

   // generate data
   d := make([]float64, n)
   for i := range d {
       d[i] = rand.Float64()
   }
   // show mean, standard deviation
   var sum, ssq float64
   for _, s := range d {
       sum += s
       ssq += s * s
   }
   fmt.Println(n, "numbers")
   m := sum / float64(n)
   fmt.Println("Mean:  ", m)
   fmt.Println("Stddev:", math.Sqrt(ssq/float64(n)-m*m))
   // show histogram
   h := make([]int, 10)
   for _, s := range d {
       h[int(s*10)]++
   }
   for _, c := range h {
       fmt.Println(strings.Repeat("*", c*205/int(n)))
   }
   fmt.Println()

}</lang> Output: 

100 numbers
Mean:   0.5231064889267764
Stddev: 0.292668237816841
****************
****************
************************
**********************
******************
******************
****************
**************************
************************
********************

1000 numbers
Mean:   0.496026080160094
Stddev: 0.2880988956436907
*********************
********************
*****************
***********************
******************
**********************
********************
*********************
******************
*******************

10000 numbers
Mean:   0.5009091903581223
Stddev: 0.289269693719711
*******************
********************
********************
********************
*********************
********************
*******************
*******************
********************
*********************

The usual approach to the extra problem is sampling. That is, to not do it.

To show really show how computations could be done a trillion numbers however, here is an outline of a map reduce strategy. The main task indicated that numbers should be generated before doing any computations on them. Consistent with that, The function getSegment returns data based on a starting and ending index, as if it were accessing some large data store.

The following runs comfortably on a simulated data size of 10 million. To scale to a trillion, and to use real data, you would want to use a technique like Distributed_programming#Go to distribute work across multiple computers, and on each computer, use a technique like Parallel_calculations#Go to distribute work across multiple cores within each computer. You would tune parameters like the constant threshold in the code below to optimize cache performance. <lang go>package main

import ( 

   "fmt"
   "math"
   "math/rand"
   "strings"

)

func main() { 

   bigSample(1e7)

}

func bigSample(n int64) { 

   sum, ssq, h := reduce(0, n)
   // compute final statistics and output as above
   fmt.Println(n, "numbers")
   m := sum / float64(n)
   fmt.Println("Mean:  ", m)
   fmt.Println("Stddev:", math.Sqrt(ssq/float64(n)-m*m))
   for _, c := range h {
       fmt.Println(strings.Repeat("*", c*205/int(n)))
   }
   fmt.Println()

}

const threshold = 1e6

func reduce(start, end int64) (sum, ssq float64, h []int) { 

   n := end - start
   if n < threshold {
       d := getSegment(start, end)
       return computeSegment(d)
   }
   // map to two sub problems
   half := (start + end) / 2
   sum1, ssq1, h1 := reduce(start, half)
   sum2, ssq2, h2 := reduce(half, end)
   // combine results
   for i, c := range h2 {
       h1[i] += c
   }
   return sum1 + sum2, ssq1 + ssq2, h1

}

func getSegment(start, end int64) []float64 { 

   d := make([]float64, end-start)
   for i := range d {
       d[i] = rand.Float64()
   }
   return d

}

func computeSegment(d []float64) (sum, ssq float64, h []int) { 

   for _, s := range d {
       sum += s
       ssq += s * s
   }
   h = make([]int, 10)
   for _, s := range d {
       h[int(s*10)]++
   }
   return

}</lang> Output: 

10000000 numbers
Mean:   0.4999673191148989
Stddev: 0.2886663876567514
********************
********************
********************
********************
********************
********************
********************
********************
********************
********************

Icon and Unicon

The following uses the stddev procedure from the Standard_deviation task. In this example,

<lang Icon>procedure main(A)

W := 50 # avg width for histogram bar B := 10 # histogram bins if *A = 0 then put(A,100) # 100 if none specified

while N := get(A) do { # once per argument 

  write("\nN=",N)

  N := 0 < integer(N) | next   # skip if invalid 
  
  stddev() # reset
  m := 0.
  H := list(B,0)               # Histogram of 
  every i := 1 to N do {       # calc running ...
     s := stddev(r := ?0)      # ... std dev 
     m +:= r/N                 # ... mean
     H[integer(*H*r)+1] +:= 1  # ... histogram
     }

  write("mean=",m)
  write("stddev=",s)
  every i := 1 to *H do        # show histogram 
     write(right(real(i)/*H,5)," : ",repl("*",integer(*H*50./N*H[i]))) 
  }

end</lang>

Output:

N=100
mean=0.4941076275054806
stddev=0.2812938788216594
  0.1 : ****************************************
  0.2 : *******************************************************
  0.3 : *******************************************************
  0.4 : **********************************************************************
  0.5 : ****************************************
  0.6 : *********************************************
  0.7 : ****************************************
  0.8 : *****************************************************************
  0.9 : ****************************************
  1.0 : **************************************************

N=10000
mean=0.4935428224375008
stddev=0.2884171825227816
  0.1 : ***************************************************
  0.2 : ***************************************************
  0.3 : ***************************************************
  0.4 : **************************************************
  0.5 : ****************************************************
  0.6 : *************************************************
  0.7 : ***********************************************
  0.8 : ************************************************
  0.9 : **************************************************
  1.0 : ***********************************************

N=1000000
mean=0.4997503773607869
stddev=0.2886322440610256
  0.1 : *************************************************
  0.2 : **************************************************
  0.3 : **************************************************
  0.4 : **************************************************
  0.5 : *************************************************
  0.6 : **************************************************
  0.7 : *************************************************
  0.8 : *************************************************
  0.9 : **************************************************
  1.0 : *************************************************

J

J has library routines to compute mean and standard deviation: <lang j> require'statfns' 

  (mean,stddev) ?1000#0

0.484669 0.287482 

  (mean,stddev) ?10000#0

0.503642 0.290777 

  (mean,stddev) ?100000#0

0.499677 0.288726</lang>

And, for a histogram:

<lang j>histogram=: <: @ (#/.~) @ (i.@#@[ , I.) require'plot' plot ((%*1+i.)100) ([;histogram) ?10000#0</lang>

but these are not quite what is being asked for here.

Instead:

<lang j>histogram=: <: @ (#/.~) @ (i.@#@[ , I.)

meanstddevP=:3 :0 

 NB. compute mean and std dev of y random numbers 
 NB. picked from even distribution between 0 and 1
 NB. and display a normalized ascii histogram for this sample
 NB. note: should use population mean, not sample mean, for stddev
 NB.       given the equation specified for this task.
 h=.s=.t=. 0
 buckets=. (%~1+i.)10
 for_n.i.<.y%1e6 do.
   data=. ?1e6#0
   h=.h+ buckets histogram data
   s=.s+ +/ data
   t=.t+ +/(data-0.5)^2
 end.
 data=. ?(1e6|y)#0
 h=.h+ buckets histogram data
 s=.s+ +/ data
 t=.t++/(data-0.5)^2
 smoutput (<.300*h%y)#"0'#'
 (s%y),%:t%y

)</lang>

Example use:

<lang j> meanstddevP 1000

0.488441 0.289744 

  meanstddevP 10000

0.49697 0.289433 

  meanstddevP 100000

0.500872 0.288241</lang>

(That said, note that these numbers are random, so reported standard deviation will vary with the random sample being tested.)

This could handle a trillion random numbers on a bog-standard computer, but I am not inclined to wait that long.

Liberty BASIC

Be aware that the PRNG in LB has a SLIGHT bias. <lang lb> call sample 100 call sample 1000 call sample 10000

end

sub sample n 

   dim dat( n)
   for i =1 to n
       dat( i) =rnd( 1)
   next i

   '// show mean, standard deviation
   sum =0
   sSq =0
   for i =1 to n
       sum =sum +dat( i)
       sSq =sSq +dat( i)^2
   next i
   print n; " data terms used."

   mean =sum / n
   print "Mean ="; mean

   print "Stddev ="; ( sSq /n -mean^2)^0.5

   '// show histogram
   nBins =10
   dim bins( nBins)
   for i =1 to n
       z =int( nBins *dat( i))
       bins( z) =bins( z) +1
   next i
   for b =0 to nBins -1
       for j =1 to int( nBins *bins( b)) /n *70)
           print "#";
       next j
       print
   next b
   print

end sub </lang> 

100000 data terms used.
Mean =0.49870232
Stddev =0.28926563
######################################################################
######################################################################
######################################################################
######################################################################
#####################################################################
#####################################################################
#####################################################################
#####################################################################
######################################################################
#####################################################################


Mathematica

<lang Mathematica>Sample[n_]:= (Print[#//Length," numbers, Mean : ",#//Mean,", StandardDeviation : ",#//StandardDeviation ]; 

       BarChart[BinCounts[#,{0,1,.1}], Axes->False, BarOrigin->Left])&[(RandomReal[1,#])&[ n ]]

Sample/@{100,1 000,10 000,1 000 000} </lang>

Output:

100 numbers, Mean : 0.478899, StandardDeviation : 0.322265
1000 numbers, Mean : 0.503383, StandardDeviation : 0.278352
10000 numbers, Mean : 0.498278, StandardDeviation : 0.28925
1000000 numbers, Mean : 0.500248, StandardDeviation : 0.288713

MATLAB / Octave

<lang Matlab>  % Initialize 

 N = 0; S=0; S2 = 0; 
 binlist = 0:.1:1;	
 h = zeros(1,length(binlist));  % initialize histogram

 % read data and perform computation
 while (1)

% read next sample x 

       if (no_data_available) break; end; 
       N = N + 1;
       S = S + x;
       S2= S2+ x*x;
       ix= sum(x < binlist);
       h(ix) = h(ix)+1; 
 end 	

 % generate output
 m  = S/N;   % mean  
 sd = sqrt(S2/N-mean*mean);  % standard deviation 
 bar(binlist,h)</lang>

PARI/GP

Works with: PARI/GP version 2.4.3 and above

<lang parigp>mean(v)={ 

 sum(i=1,#v,v[i])/#v

}; stdev(v,mu="")={ 

 if(mu=="",mu=mean(v));
 sqrt(sum(i=1,#v,(v[i]-mu)^2))/#v

}; histogram(v,bins=16,low=0,high=1)={ 

 my(u=vector(bins),width=(high-low)/bins);
 for(i=1,#v,u[(v[i]-low)\width+1]++);
 u

}; show(n)={ 

 my(v=vector(n,i,random(1.)),mu=mean(v),s=stdev(v,mu),h=histogram(v),sz=ceil(n/50/16));
 for(i=1,16,for(j=1,h[i]\sz,print1("#"));print());
 print("Mean: "mu);
 print("Stdev: "s);

}; show(100); show(1000); show(10000);</lang>

For versions before 2.4.3, define <lang parigp>rreal()={ 

 my(pr=32*ceil(default(realprecision)*log(10)/log(4294967296))); \\ Current precision
 random(2^pr)*1.>>pr

};</lang> and use rreal() in place of random(1.).

Perl

<lang perl>my @histogram = (0) x 10; my $sum = 0; my $sum_squares = 0; my $n = $ARGV[0];

for (1..$n) { 

 my $current = rand();
 $sum+= $current;
 $sum_squares+= $current ** 2;
 $histogram[$current * @histogram]+= 1;

}

my $mean = $sum / $n;

print "$n numbers\n", 

     "Mean:   $mean\n",
     "Stddev: ", sqrt(($sum_squares / $n) - ($mean ** 2)), "\n";

for my $i (0..$#histogram) { 

 printf "%.1f - %.1f : ", $i/@histogram, (1 + $i)/@histogram;

 print "*" x (30 * $histogram[$i] * @histogram/$n); # 30 stars expected per row
 print "\n";

}</lang>

Usage: 

perl rand_statistics.pl (number of values)
$ perl rand_statistics.pl 100
100 numbers
Mean:   0.531591369804339
Stddev: 0.28440375340793
0.0 - 0.1 : ***************************
0.1 - 0.2 : ************************
0.2 - 0.3 : ***************************
0.3 - 0.4 : ************************
0.4 - 0.5 : *********************************
0.5 - 0.6 : ************************************
0.6 - 0.7 : ************************************
0.7 - 0.8 : ******************
0.8 - 0.9 : ***************************************
0.9 - 1.0 : ************************************

$ perl rand_statistics.pl 1000
1000 numbers
Mean:   0.51011452684812
Stddev: 0.29490201218115
0.0 - 0.1 : ******************************
0.1 - 0.2 : *******************************
0.2 - 0.3 : ***************************
0.3 - 0.4 : *****************************
0.4 - 0.5 : **********************************
0.5 - 0.6 : ****************************
0.6 - 0.7 : ************************
0.7 - 0.8 : *************************************
0.8 - 0.9 : ********************************
0.9 - 1.0 : *********************************

$ perl rand_statistics.pl 10000
10000 numbers
Mean:   0.495329167703333
Stddev: 0.285944419431566
0.0 - 0.1 : *****************************
0.1 - 0.2 : *******************************
0.2 - 0.3 : *********************************
0.3 - 0.4 : *******************************
0.4 - 0.5 : ******************************
0.5 - 0.6 : *******************************
0.6 - 0.7 : ******************************
0.7 - 0.8 : ******************************
0.8 - 0.9 : *****************************
0.9 - 1.0 : ******************************

$ perl rand_statistics.pl 10000000
10000000 numbers
Mean:   0.499973935749229
Stddev: 0.2887231680817
0.0 - 0.1 : ******************************
0.1 - 0.2 : *******************************
0.2 - 0.3 : ******************************
0.3 - 0.4 : *******************************
0.4 - 0.5 : ******************************
0.5 - 0.6 : *******************************
0.6 - 0.7 : ******************************
0.7 - 0.8 : ******************************
0.8 - 0.9 : *******************************
0.9 - 1.0 : *******************************

Perl 6

<lang perl6>sub generate_statistics($n = 100) { 

   my $then = time;
   my $sum = 0;
   my $sum2 = 0;
   my @hist = 0 xx 10;
   for ^$n {
       my $r = rand;
       $sum += $r;
       $sum2 += $r ** 2;
       ++@hist[10 * $r];
   }
   my $mean = $sum / $n;
   my $stddev = sqrt($sum2 / $n - $mean * $mean);

   say "size: $n";
   say "mean: $mean";
   say "stddev: $stddev";
   for @hist.kv -> $i, $bin {
       say "0.$i\t", '=' x (500 * $bin / $n);
   }
   say "(elapsed: {time - $then})";
   say "";

}

  1. This doesn't terminate, so ^C when you get bored.

for 100, 1_000, 10_000 ... * -> $n { 

   generate_statistics $n;

}</lang>

Output:
size: 100
mean: 0.52518699464629726
stddev: 0.28484207464779548
0.0	==============================
0.1	======================================================================
0.2	===================================
0.3	==================================================
0.4	============================================================
0.5	=============================================
0.6	====================
0.7	===========================================================================
0.8	======================================================================
0.9	=============================================
(elapsed: 0.023430109024047852)

size: 1000
mean: 0.51043974182914975
stddev: 0.29146336553431618
0.0	==============================================
0.1	==================================================
0.2	===========================================
0.3	========================================================
0.4	===================================================
0.5	=======================================
0.6	===========================================================
0.7	====================================================
0.8	==============================================
0.9	========================================================
(elapsed: 0.083790063858032227)

size: 10000
mean: 0.50371817503544458
stddev: 0.2900716333092252
0.0	===================================================
0.1	=================================================
0.2	=============================================
0.3	====================================================
0.4	==============================================
0.5	====================================================
0.6	================================================
0.7	===================================================
0.8	====================================================
0.9	==================================================
(elapsed: 0.5168910026550293)

size: 100000
mean: 0.50030554161684448
stddev: 0.28825035821818784
0.0	=================================================
0.1	==================================================
0.2	=================================================
0.3	=================================================
0.4	==================================================
0.5	==================================================
0.6	==================================================
0.7	==================================================
0.8	=================================================
0.9	=================================================
(elapsed: 4.7263731956481934)

size: 1000000
mean: 0.49960329115026614
stddev: 0.28855491011720108
0.0	==================================================
0.1	=================================================
0.2	==================================================
0.3	==================================================
0.4	==================================================
0.5	=================================================
0.6	==================================================
0.7	=================================================
0.8	=================================================
0.9	=================================================
(elapsed: 47.345431089401245)

PicoLisp

The following has no limit on the number of samples. The 'statistics' function accepts an executable body 'Prg', which it calls repeatedly to get the samples. <lang PicoLisp>(scl 6)

(de statistics (Cnt . Prg) 

  (prinl Cnt " numbers")
  (let (Sum 0  Sqr 0  Hist (need 10 NIL 0))
     (do Cnt
        (let N (run Prg 1)  # Get next number
           (inc 'Sum N)
           (inc 'Sqr (*/ N N 1.0))
           (inc (nth Hist (inc (/ N 0.1)))) ) )
     (let M (*/ Sum Cnt)
        (prinl "Mean:   " (round M))
        (prinl "StdDev: "
           (round
              (sqrt
                 (* 1.0
                    (- (*/ Sqr Cnt) (*/ M M 1.0)) ) ) ) ) )
     (for (I . H) Hist
        (prin (format I 1) " ")
        (do (*/ H 400 Cnt) (prin '=))
        (prinl) ) ) )</lang>

Test: <lang PicoLisp>(statistics 100 

  (rand 0 (dec 1.0)) )

(prinl)

(statistics 10000 

  (rand 0 (dec 1.0)) )

(prinl)

(statistics 1000000 

  (rand 0 (dec 1.0)) )

(prinl)</lang> Output: 

100 numbers
Mean:   0.501
StdDev: 0.284
0.1 ========================================
0.2 ====================================
0.3 ====================================================
0.4 ========================
0.5 ========================
0.6 ================================================================
0.7 ========================================================
0.8 ====================================
0.9 ========================
1.0 ============================================

10000 numbers
Mean:   0.501
StdDev: 0.288
0.1 =======================================
0.2 ========================================
0.3 =======================================
0.4 =========================================
0.5 =========================================
0.6 ========================================
0.7 =========================================
0.8 ========================================
0.9 ========================================
1.0 ========================================

1000000 numbers
Mean:   0.500
StdDev: 0.289
0.1 ========================================
0.2 ========================================
0.3 ========================================
0.4 ========================================
0.5 ========================================
0.6 ========================================
0.7 ========================================
0.8 ========================================
0.9 ========================================
1.0 ========================================

PureBasic

Translation of: Liberty BASIC

Changes were made from the Liberty BASIC version to normalize the histogram as well as implement a random float function. <lang purebasic>Procedure.f randomf() 

 #RNG_max_resolution = 2147483647
 ProcedureReturn Random(#RNG_max_resolution) / #RNG_max_resolution

EndProcedure

Procedure sample(n) 

 Protected i, nBins, binNumber, tickMarks, maxBinValue
 Protected.f sum, sumSq, mean
 
 Dim dat.f(n)
 For i = 1 To n
   dat(i) = randomf()
 Next
 
 ;show mean, standard deviation
 For i = 1 To n
   sum + dat(i)
   sumSq + dat(i) * dat(i)
 Next i
 
 PrintN(Str(n) + " data terms used.")
 mean = sum / n
 PrintN("Mean =" + StrF(mean))
 PrintN("Stddev =" + StrF((sumSq / n) - Sqr(mean * mean)))
 
 ;show histogram
 nBins = 10
 Dim bins(nBins)
 For i = 1 To n
   binNumber = Int(nBins * dat(i))
   bins(binNumber) + 1
 Next
 
 maxBinValue = 1
 For i = 0 To nBins
   If bins(i) > maxBinValue
     maxBinValue = bins(i)
   EndIf
 Next
 
 #normalizedMaxValue = 70
 For binNumber = 0 To nBins
   tickMarks = Int(bins(binNumber) * #normalizedMaxValue / maxBinValue)
   PrintN(ReplaceString(Space(tickMarks), " ", "#"))
 Next
 PrintN("")

EndProcedure

If OpenConsole() 

 sample(100)
 sample(1000)
 sample(10000)
  
 Print(#CRLF$ + #CRLF$ + "Press ENTER to exit"): Input()
 CloseConsole()

EndIf</lang> Sample output: 

100 data terms used.
Mean =0.4349198639
Stddev =-0.1744846404
#########################################################
#########################################
################################
#################################################################
################################
#####################################################
######################################################################
################
########################
################


1000 data terms used.
Mean =0.4960154891
Stddev =-0.1691310555
###############################################################
#######################################################
#############################################################
######################################################################
##########################################################
##############################################################
####################################################################
###############################################################
#############################################################
#####################################################


10000 data terms used.
Mean =0.5042046309
Stddev =-0.1668083966
##################################################################
################################################################
##################################################################
####################################################################
################################################################
######################################################################
####################################################################
###################################################################
####################################################################
####################################################################

Python

The second function, sd2 only needs to go once through the numbers and so can more efficiently handle large streams of numbers. <lang python>def sd1(numbers): 

   if numbers:
       mean = sum(numbers) / len(numbers)
       sd = (sum((n - mean)**2 for n in numbers) / len(numbers))**0.5
       return sd, mean
   else:
       return 0, 0

def sd2(numbers): 

   if numbers:
       sx = sxx = n = 0
       for x in numbers:
           sx += x
           sxx += x*x
           n += 1
       sd = (n * sxx - sx*sx)**0.5 / n
       return sd, sx / n
   else:
       return 0, 0

def histogram(numbers): 

   h = [0] * 10
   maxwidth = 50 # characters
   for n in numbers:
       h[int(n*10)] += 1
   mx = max(h)
   print()
   for n, i in enumerate(h):
       print('%3.1f: %s' % (n / 10, '+' * int(i / mx * maxwidth)))
   print()

if __name__ == '__main__': 

   import random
   for i in range(1,8):
       n = [random.random() for i in range(10**i)]
       print("\n##\n## %i numbers\n##" % 10**i)
       print('  Naive  method: sd: %8.6f, mean: %8.6f' % sd1(n))
       print('  Second method: sd: %8.6f, mean: %8.6f' % sd2(n))
       histogram(n)</lang>
Section of output

for larger sets of random numbers, the distribution of numbers between the bins of the histogram evens out. 

...
##
## 100 numbers
##
  Naive  method: sd: 0.288911, mean: 0.508686
  Second method: sd: 0.288911, mean: 0.508686

0.0: +++++++++++++++++++++++++++++++
0.1: ++++++++++++++++++++++++++++
0.2: +++++++++++++++++++++++++
0.3: ++++++++++++++++++++++++++++++++++++++++++++++++++
0.4: ++++++++++++++++++
0.5: +++++++++++++++++++++++++++++++
0.6: ++++++++++++++++++
0.7: +++++++++++++++++++++++++++++++++++++
0.8: ++++++++++++++++++++++++++++++++++++++++
0.9: +++++++++++++++++++++++++++++++

...

##
## 10000000 numbers
##
  Naive  method: sd: 0.288750, mean: 0.499839
  Second method: sd: 0.288750, mean: 0.499839

0.0: ++++++++++++++++++++++++++++++++++++++++++++++++++
0.1: +++++++++++++++++++++++++++++++++++++++++++++++++
0.2: +++++++++++++++++++++++++++++++++++++++++++++++++
0.3: +++++++++++++++++++++++++++++++++++++++++++++++++
0.4: +++++++++++++++++++++++++++++++++++++++++++++++++
0.5: +++++++++++++++++++++++++++++++++++++++++++++++++
0.6: +++++++++++++++++++++++++++++++++++++++++++++++++
0.7: +++++++++++++++++++++++++++++++++++++++++++++++++
0.8: +++++++++++++++++++++++++++++++++++++++++++++++++
0.9: +++++++++++++++++++++++++++++++++++++++++++++++++

REXX

<lang rexx>/*REXX pgm gens some random #s, shows bin histogram, finds mean & stdDev*/ numeric digits 20 /*use twenty digits precision, */ showDigs=digits()%2 /* ... but only show ten digits. */ parse arg size . /*allow specification of size. */ if size== then size=100 /*if not specified, use 100. */

  1. .=0; do j=1 for size /*generate some random numbers. */
               @.j=random(0,99999)/100000
               _=substr(@.j'00',3,1)  /*determine which bin it's in,   */
               #._=#._+1              /* ... and bump its count.       */
               end

       do k=0 for 10                  /*show a histogram of the bins.  */
       lr='0.'k      ;  if k==0 then lr='0  '   /*adjust for low range.*/
       hr='0.'||(k+1);  if k==9 then hr='1  '   /*   "    " high range.*/
       range=lr"-►"hr                           /*construct the range. */
       bar=#.k/size
       barPC=right(strip(left(format(100*#.k/size,,2),5)),5)   /*comp %*/
       say range barPC copies('─',format(barPC*1,,0))          /*histo.*/
       end

say say 'sample size = ' size say avg=mean(size) say ' mean = ' format(avg,,showDigs) stddev=stddev(size) say ' stddev = ' format(stddev,,showDigs) exit /*stick a fork in it, we're done.*/ /*──────────────────────────────────MEAN subroutine─────────────────────*/ mean: parse arg N .; $=0; do m=1 for N 

                                $=$+@.m
                                end   /*m*/

return $/n /*──────────────────────────────────STDDEV subroutine───────────────────*/ stddev: parse arg N .; $=0; do s=1 for N 

                                $=$+(@.s-avg)**2
                                end   /*s*/

return sqrt($/n) /*──────────────────────────────────SQRT subroutine─────────────────────*/ sqrt: procedure; parse arg x; if x=0 then return 0; d=digits() 

       numeric digits 11; g=.sqrtGuess(); do j=0 while p>9;m.j=p; p=p%2+1
       end;     do k=j+5 to 0 by -1;  if m.k>11 then numeric digits m.k
       g=.5*(g+x/g);  end;      numeric digits d;  return g/1

.sqrtGuess: numeric form; m.=11; p=d+d%4+2 

       parse value format(x,2,1,,0) 'E0' with g 'E' _ .;return g*.5'E'_%2

</lang> output when using the default input of: 100 

0  -►0.1 10.00 ──────────
0.1-►0.2  5.00 ─────
0.2-►0.3  7.00 ───────
0.3-►0.4 10.00 ──────────
0.4-►0.5 14.00 ──────────────
0.5-►0.6 10.00 ──────────
0.6-►0.7 10.00 ──────────
0.7-►0.8  6.00 ──────
0.8-►0.9 15.00 ───────────────
0.9-►1   13.00 ─────────────

sample size =  100

       mean =  0.5431318000
     stddev =  0.2865226404

output when using the input of: 1000 

0  -►0.1  9.20 ─────────
0.1-►0.2  9.70 ──────────
0.2-►0.3  9.60 ──────────
0.3-►0.4 11.30 ───────────
0.4-►0.5 10.10 ──────────
0.5-►0.6 11.20 ───────────
0.6-►0.7 10.80 ───────────
0.7-►0.8  9.10 ─────────
0.8-►0.9  9.90 ──────────
0.9-►1    9.10 ─────────

sample size =  1000

       mean =  0.4990093600
     stddev =  0.2825247484

output when using the input of: 10000 

0  -►0.1  9.73 ──────────
0.1-►0.2 10.15 ──────────
0.2-►0.3  9.53 ──────────
0.3-►0.4  9.77 ──────────
0.4-►0.5 10.68 ───────────
0.5-►0.6 10.23 ──────────
0.6-►0.7  9.88 ──────────
0.7-►0.8  9.49 ─────────
0.8-►0.9 10.28 ──────────
0.9-►1   10.26 ──────────

sample size =  10000

       mean =  0.5029963800
     stddev =  0.2880832558

output when using the input of: 100000 

0  -►0.1 10.03 ──────────
0.1-►0.2  9.78 ──────────
0.2-►0.3 10.15 ──────────
0.3-►0.4 10.05 ──────────
0.4-►0.5 10.00 ──────────
0.5-►0.6  9.92 ──────────
0.6-►0.7 10.05 ──────────
0.7-►0.8  9.84 ──────────
0.8-►0.9 10.12 ──────────
0.9-►1   10.06 ──────────

sample size =  100000

       mean =  0.5006211184
     stddev =  0.2888953207

output when using the input of: 1000000 

0  -►0.1  9.97 ──────────
0.1-►0.2 10.00 ──────────
0.2-►0.3  9.96 ──────────
0.3-►0.4 10.05 ──────────
0.4-►0.5 10.05 ──────────
0.5-►0.6  9.98 ──────────
0.6-►0.7  9.98 ──────────
0.7-►0.8 10.02 ──────────
0.8-►0.9  9.99 ──────────
0.9-►1   10.01 ──────────

sample size =  1000000

       mean =  0.5002108791
     stddev =  0.2885573057

Ruby

<lang ruby>def generate_statistics(n) 

 t = Time.now 
 sum = sum2 = 0.0
 hist = Array.new(10, 0)
 n.times do
   r = rand
   sum += r
   sum2 += r**2
   hist[(10*r).to_i] += 1
 end
 mean = sum / n
 stddev = Math::sqrt((sum2 / n) - mean**2)

 puts "size: #{n}"
 puts "mean: #{mean}"
 puts "stddev: #{stddev}"
 hist.each {|x| puts "=" * (75*x/hist.max)}
 puts "(elapsed: #{Time.now.to_i - t.to_i})"
 puts ""

end

[1_000, 1_000_000, 1_000_000_000].each {|n| generate_statistics n}</lang>

output 

size: 1000
mean: 0.49480969251917134
stddev: 0.2866407835263572
=====================================================================
======================================================================
===============================================================
===========================================================
=============================================================
===========================================================================
=======================================================================
=====================================================================
==================================================================
========================================================
(elapsed: 0)

size: 1000000
mean: 0.49965649774638726
stddev: 0.288787703478284
===========================================================================
==========================================================================
==========================================================================
==========================================================================
==========================================================================
==========================================================================
==========================================================================
==========================================================================
==========================================================================
==========================================================================
(elapsed: 14)

size: 1000000000
mean: 0.49999951488216027
stddev: 0.2886774344977368
==========================================================================
==========================================================================
===========================================================================
==========================================================================
==========================================================================
==========================================================================
==========================================================================
==========================================================================
==========================================================================
==========================================================================
(elapsed: 7880)

Tcl

<lang tcl>package require Tcl 8.5 proc stats {size} { 

   set sum 0.0
   set sum2 0.0
   for {set i 0} {$i < $size} {incr i} {

set r [expr {rand()}]

incr histo([expr {int(floor($r*10))}]) set sum [expr {$sum + $r}] set sum2 [expr {$sum2 + $r**2}] 

   }
   set mean [expr {$sum / $size}]
   set stddev [expr {sqrt($sum2/$size - $mean**2)}]
   puts "$size numbers"
   puts "Mean:   $mean"
   puts "StdDev: $stddev"
   foreach i {0 1 2 3 4 5 6 7 8 9} {

# The 205 is a magic factor stolen from the Go solution puts [string repeat "*" [expr {$histo($i)*205/int($size)}]] 

   }

}

stats 100 puts "" stats 1000 puts "" stats 10000</lang> Output: 

100 numbers
Mean:   0.4801193240797704
StdDev: 0.28697057708153784
**************
**********************************
********************
**************
****************************
****************
**************
****************************
****************
****************

1000 numbers
Mean:   0.49478823525495275
StdDev: 0.2821543810265757
*******************
******************
************************
********************
*******************
**********************
*********************
********************
******************
******************

10000 numbers
Mean:   0.49928563715870816
StdDev: 0.2888258479070212
********************
*********************
********************
********************
*******************
*********************
*******************
********************
*********************
********************

As can be seen, increasing the sample size reduces the variation between the buckets, showing that the rand() function at least approximates a uniform distribution. (Because Tcl 8.5 supports arbitrary precision integer arithmetic there is no reason in principle why the details for a trillion numbers couldn't be calculated, but it would take quite a while.)

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