I have a \'file.dat\' with 24 (rows) x 16 (columns) data.
I have already tested the following awk script that computes de average of each column.
t
Your script should somehow be in this form instead:
awk '{
sum = 0
for (i=1; i<=NF; i++) {
sum += $i
}
avg = sum / NF
avga[NR] = avg
sum = 0
for (i=1; i<=NF; i++) {
sum += ($i - avg) ^ 2
}
stda[NR] = sqrt(sum / NF)
}
END { for (i = 1; i in stda; ++i) { printf "%f %f \n", avga[i], stda[i] } }' file.dat >> aver-std.dat
Standard deviation is
stdev = sqrt((1/N)*(sum of (value - mean)^2))
But there is another form of the formula which does not require you to know the mean beforehand. It is:
stdev = sqrt((1/N)*((sum of squares) - (((sum)^2)/N)))
(A quick web search for "sum of squares" formula for standard deviation will give you the derivation if you are interested)
To use this formula, you need to keep track of both the sum and the sum of squares of the values. So your awk script will change to:
awk '{for(i=1;i<=NF;i++) {sum[i] += $i; sumsq[i] += ($i)^2}}
END {for (i=1;i<=NF;i++) {
printf "%f %f \n", sum[i]/NR, sqrt((sumsq[i]-sum[i]^2/NR)/NR)}
}' file.dat >> aver-std.dat
To simply calculate the population standard deviation of a list of numbers, you can use a command like this:
awk '{x+=$0;y+=$0^2}END{print sqrt(y/NR-(x/NR)^2)}'
Or this calculates the sample standard deviation:
awk '{sum+=$0;a[NR]=$0}END{for(i in a)y+=(a[i]-(sum/NR))^2;print sqrt(y/(NR-1))}'
^
is in POSIX. **
is supported by gawk
and nawk
but not by mawk
.
Here is some calculation I've made on a grinder data output file for a long soak test which had to be interrupted:
Standard deviation(biased) + average:
cat <grinder_data_file> | grep -v "1$" | awk -F ', ' '{ sum=sum+$5 ; sumX2+=(($5)^2)} END { printf "Average: %f. Standard Deviation: %f \n", sum/NR, sqrt(sumX2/(NR) - ((sum/NR)^2) )}'
Standard deviation(non-biased) + average:
cat <grinder_data_file> | grep -v "1$" | awk -F ', ' '{ sum=sum+$5 ; sumX2+=(($5)^2)} END { avg=sum/NR; printf "Average: %f. Standard Deviation: %f \n", avg, sqrt(sumX2/(NR-1) - 2*avg*(sum/(NR-1)) + ((NR*(avg^2))/(NR-1)))}'