Using MATLAB, Please show codes for exponential distribution histogram, GoodFit

Question

Using MATLAB, Please show codes for exponential distribution histogram, GoodFit Test, qqplot. If possible please use starter code below.

Make charts in color

clear
clc
close all

% myData=xlsread('examplevalues.xls');
% If xlsread is giving problems, save/export values to a csv file then use
% the function below to read in the data. Comment line above and remove the
% comment marker "%"

myData=csvread('examplevalues.csv');

% First lets fit the distribution. Replace myDistribution srting (i.e.
% "Normal" with whatever distribution you want to try to fit your data to)
% Option include the following:
%-----------------------------------------------------------
%-----------------------------------------------------------
% Uniform Requires min and max bounds
% Triangular Requires min, mode, and max
%-----------------------------------------------------------
%-----------------------------------------------------------
myDistributionType='Triangular'
if isequal(myDistributionType,'Uniform')
minVal=0; % Change this values as needed
maxVal=1; % Change this values as needed
myPDBestFit=makedist(myDistributionType,'lower',minVal,'upper',maxVal)
elseif isequal(myDistributionType,'Triangular')
minVal=0; % Change this values as needed
modeVal=.5
maxVal=1; % Change this values as needed
myPDBestFit=makedist(myDistributionType,'a',minVal,'b',modeVal,'c',maxVal)
end


figure(1)
subplot(1,2,1)
xPDFplot=linspace(0,max(myData),1000);
yPDFplot = pdf(myPDBestFit,xPDFplot);
yCDFplot = cdf(myPDBestFit,xPDFplot);
myxCDFplot=[0;sort(myData)];
myyCDFplot=[1:numel(myData)]/numel(myData);
myyCDFplot=[0;myyCDFplot'];



numBins=numel(myData)/10;
binEdges=linspace(min(myData),max(myData),numBins);
binCounts=histc(myData,binEdges);
binProbs=binCounts/max(binCounts)*max(yPDFplot);
bar(binEdges,binProbs), hold on

plot(xPDFplot,yPDFplot,'Color',[1 0 0],'LineWidth',2); hold on
legend('Sampled Data PDF','Best-Fit PDF')
title(['Best Fit:' myDistributionType])


subplot(1,2,2)
plot(myxCDFplot,myyCDFplot,'Color',[0 0 1]), hold on
plot(xPDFplot,yCDFplot,'Color',[1 0 0],'LineWidth',2); hold on
legend('Sampled Data CDF','Best-Fit CDF')
title(['Best Fit:' myDistributionType])

% Next KS-test on data with best-fit distribution
[h,p] = kstest(myData,'cdf',myPDBestFit)
if h==0
fprintf('UNABLE TO REJECT THE NULL HYPOTHESIS ')
else
fprintf('WE REJECT THE NULL HYPOTHESIS ')
end

% from "help kstest"
% H = 0 => Do not reject the null hypothesis at the 5% significance level.
% H = 1 => Reject the null hypothesis at the 5% significance level.

figure
qqplot(myData,myPDBestFit)

Service Time 02:05 03:02 02:07 00:20 00:22 02:04 01:00 01:04 00:56 01:51 01:14 01:09 00:57 01:16 01:12 00:53 00:20 00:50 00:57 01:22 00:22 00:44 00:02 02:13 00:43 01:21 01:04 01:31 00:50 01:22 00:40 00:34 01:53 02:10 01:12 03:20 00:19 00:03 00:18 01:42 01:35 02:56 00:53 01:36 01:16 00:13 00:19 02:28 03:09 01:15 01:53 01:03 01:09 00:43 01:18 00:21 00:20 01:20 02:50 00:05 00:12 00:55 00:04 00:24 03:00 01:02 00:36 00:58 01:29 00:25 00:31 01:23 00:56 01:07 00:49 00:42 00:44 00:31 01:09 00:55 01:50 01:08 00:46 01:13 01:07 00:29 00:49

Explanation / Answer

myDistributionType='Triangular'
if isequal(myDistributionType,'Uniform')
minVal=0; % Change this values as needed
maxVal=1; % Change this values as needed
myPDBestFit=makedist(myDistributionType,'lower',minVal,'upper',maxVal)
elseif isequal(myDistributionType,'Triangular')
minVal=0; % Change this values as needed
modeVal=.5
maxVal=1; % Change this values as needed
myPDBestFit=makedist(myDistributionType,'a',minVal,'b',modeVal,'c',maxVal)
end


figure(1)
subplot(1,2,1)
xPDFplot=linspace(0,max(myData),1000);
yPDFplot = pdf(myPDBestFit,xPDFplot);
yCDFplot = cdf(myPDBestFit,xPDFplot);
myxCDFplot=[0;sort(myData)];
myyCDFplot=[1:numel(myData)]/numel(myData);
myyCDFplot=[0;myyCDFplot'];



numBins=numel(myData)/10;
binEdges=linspace(min(myData),max(myData),numBins);
binCounts=histc(myData,binEdges);
binProbs=binCounts/max(binCounts)*max(yPDFplot);
bar(binEdges,binProbs), hold on

plot(xPDFplot,yPDFplot,'Color',[1 0 0],'LineWidth',2); hold on
legend('Sampled Data PDF','Best-Fit PDF')
title(['Best Fit:' myDistributionType])


subplot(1,2,2)
plot(myxCDFplot,myyCDFplot,'Color',[0 0 1]), hold on
plot(xPDFplot,yCDFplot,'Color',[1 0 0],'LineWidth',2); hold on
legend('Sampled Data CDF','Best-Fit CDF')
title(['Best Fit:' myDistributionType])

% Next KS-test on data with best-fit distribution
[h,p] = kstest(myData,'cdf',myPDBestFit)
if h==0
fprintf('UNABLE TO REJECT THE NULL HYPOTHESIS ')
else
fprintf('WE REJECT THE NULL HYPOTHESIS ')
end

% from "help kstest"
% H = 0 => Do not reject the null hypothesis at the 5% significance level.
% H = 1 => Reject the null hypothesis at the 5% significance level.

figure
qqplot(myData,myPDBestFit)

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