How to get accurate normal and binomial distribution plots

I am trying to get a graphical representation of my binomalpdf and normalpdf, however only get flat lines instead, and am not sure why. The code used is listed below.

%the main data set; it is the number of threads %run by my CPU at 3 percent load, collected every other %second for 100 seconds, resulting in 50 data points.

data = [1103, 1101, 1100, 1097, 1099, 1095, 1096, 1096, 1098, 1097, 1096, 1094, 1096, 1097, 1087, 1085, 1084, 1087, 1086, 1082, 1079, 1081, 1078, 1080, 1081, 1077, 1079, 1074, 1073, 1069, 1068, 1064, 1066, 1065, 1066, 1066, 1065, 1065, 1063, 1066, 1067, 1065, 1058, 1060, 1061, 1061, 1060, 1059, 1058, 1057];

%calculates the mean(mu), the mode(md), %the median,(mdn), the standard deviation(sigma), %and the range(rng).

mu = mean(data)

md = mode(data)

mdn = median(data)

sigma = std(data)

rng = range(data)

%closes any open windows and sets the screen size

close all;

scrsz = get(0,'ScreenSize');

%Data Histogram

hist(data,65);

annotation('textbox', [.35, .7, .35, .2]);

%makes them variables in the command window

m = 10;

n = 1;

Array = zeros(m,n);

assignin('base', 'm', m);

assignin('base', 'n', n);

assignin('base', 'Array', Array);

%figure normal distribution plot;

xx = [0:.1:49];

figure('Position',[200 scrsz(4)/2 scrsz(4)/2 scrsz(4)/2-100])

plot(xx,normpdf(data,mu,sigma));

title('Normal data');

xlabel('');

ylabel('Value pdf');

%figure binomial distribution plot;

P= .5;

K = 50;

dnormal=binopdf(data,K,P);

figure('Position',[300 scrsz(4)/2 scrsz(4)/2 scrsz(4)/2-100])

plot(data,dnormal,'--rs','LineWidth',2,... 'MarkerEdgeColor','k',... 'MarkerFaceColor','g',... v b 'MarkerSize',10)

title('Binomial pdf data');

xlabel('data');

ylabel(['pdf for N = ',num2str(50),' and P = ',... num2str(P)]);

[Image Analyst]

I don't have range(). What does it do? (Please list below what toolbox it is in also.)

[the cyclist]

@Peter, I could not get your code to execute. The first plot() command gives and error due to the vectors being different sizes.

@Image Analyst, range() is in the Statistics Toolbox. range(data) is effectively max(data)-min(data) here.