clc % clears the command window clear all % clears all variables as well as well as break points close all % close all previously run figures % Throughout this code ";" will be used at the end of individual lines of % code, this is done to stop that line of code appearing in the command window SCR= xlsread('assignment_data.xlsx', 'B1:P1') % this imports excel data from the service call request field Numdays= xlsread('assignment_data.xlsx', 'B2:P2') %this will import excel data from the number of days field LB=SCR(1); % this is your lower boundary for class widths span=3; % this is the size of your class width HB= LB+span; % this is the higher boundary of the first class [rN,cN]=size(SCR); daycount=0; % daycount is required to allow us to count up through each class group K=1; % K is a counter that is initialised at 1, that will be used to defined different location within vector GNOD etc for i = 1:cN % this tells matlab the number of times to repeate the for loop if SCR(i) < HB % this informs Matlab which class to look in and daycount=Numdays(i)+daycount; % this ensures each individual piece of %information within each section of the given data is analysed else % Else makes the code act in a different way if the if statement is not % met, if it is not met it searches the next boundary class GNOD(K)=daycount; K=K+1; %This daycount=Numdays(i); HB=HB+span; % this defines new higher boundaries end % This ends the else section of the loop end % This ends the for statement completely GNOD(K)=daycount; %without this line of code it will not display the last class grouped GNOD %This is the grouped number of data in each class Midclass_Value=(LB+span/2:3:HB)% this specifies the midpoint values of each specified data group figure(1) % this titles and creates the first plot of the work histogram ('BinEdges',LB:span:HB,'Bincounts',GNOD,'facecolor',[ 0 0 1]); %This sets the parameters of the histogram %Binedges and Bincounts manually specifies bin edges and associated bin counts %These are set by the LB:span:HB and GNOD %Facecolor is set to blue by the [ 0 0 1] hold on% Hold on allows us to keep or save the first graph plot and allow for other charts to plot on top plot(Midclass_Value,GNOD,'color',[1 0 0 ],'LineWidth',1.5) %The above line of code plots the Midclass_Value as a line graph %The line color is set by [1 0 0] and Line width sets the line size as 1.5 yticks(0:1:25) % this sets the incremental step on the Y axis xticks([15:1.5:33]) % this sets the incremental steps on the X axis title('Histogram and Polygon distribution of data set') % This titles the chart xlabel('Number of Request Classes') %This labels the X axis ylabel('Number of days') % This labels the Y axis LOC=find(GNOD==max(GNOD)) % this finds the located where 'GNOD' is equal to the % max of 'GNOD' modal=Midclass_Value(LOC) % This uses previous lines of information to find modal value %of given data text(modal,1, ['Modal Value==',num2str(modal)] ,'FontSize',13,'FontWeight','bold','HorizontalAlignment','center','color',[1 1 0]) % This above line of code inputs "Modal value onto the graph using the above formulas and functions %, the 24-2 sets the % location on graph, 'FontSize' dictates the size of text, 'FontWeigth' and % 'HorizontalAllightment' set the bold style of text and positioning on % cordinate. 'Color' then sets the colour of the text to yellow FrequencyDenc=sum(Numdays) % This line of code establishes the total number of individual enteries of data %which will allow you to work out the occurrence frequency of each group Frequ = GNOD/FrequencyDenc% Connected to the above line of code this calculates the frequency of each group Cum_frequ= cumsum (Frequ); % the cumsum function is the cumulative sum of the frequency Cum_frequ1= 0.00 + [ 0 Cum_frequ]% this displays the cumulative frequency data the 0.00 at the start adds the 0 figure to the data Inv_cum = 1 - [ 0 Cum_frequ ]% This line displays the Inverse frequency. The initial 0 value at the start of the square brackets adds the first figure of 1.0 to the result Inv2=Inv_cum(:, [1:1:6])% This ensures the vector array will display the %correct six figures figure(2)% This starts a a new graph plot plot ([15:3:33],Cum_frequ1,'LineStyle', 'none','Color',[0 0 1],'Marker','o'), grid on %The above code plots the Cum_frequ1 data, linestyle is set to no line %the color is set to blue %The style of data markers is set by the "o" %grid on displays the major grid lines on the plot hold on %this holds the above plot on the figure allowing for multiple plots to be charted on the same graph Smooth=interp1([15:3:33],Cum_frequ1,15:0.1:33,'pchip'); % this above line of code maps multiple plots on the Ogive chart creating % the smooth appearance of the Cum-Frequ1 plot plot(15:0.1:33,Smooth,'LineStyle', ' - - ','Color',[0 0 1],'LineWidth',1.5) %The above code plots the Cum_frequ1 data, linestyle is set to '- - ' or %dashed line %the color is set to blue and the line width sets the size of the line to %1.5 %The style of data markers is set by the "o" %grid on displayes the major grid lines on the plot hold on %this holds the above plot on the figure allowing for multiple plots to be charted on the same graph plot ( [15:3:33],Inv_cum,'LineStyle', 'none', 'Color',[ 1 0 0],'LineWidth',1.5,'Marker','o'), %The above code plots the Inv_cum data, linestyle is set to no line %dot and dash style %the color is set to red using [1 0 0] and the line width sets the size of the line to %1.5 %The style of data markers is set by the "o" Smooth2=interp1([15:3:33],Inv_cum,15:0.1:33,'pchip'); % this above line of code maps multiple plots on the Ogive chart creating % the smooth appearance of the Inv_cum line plot(15:0.1:33,Smooth2,'LineStyle', '-.', 'Color',[ 1 0 0],'LineWidth',1.5) %The above code plots the Cum_frequ1 data, linestyle is set to ' -.' or %dash dot %the color is set to blue and the line width sets the size of the line to %1.5 %The style of data markers is set by the "o" %grid on displays the major grid lines on the plot hold on %this holds the above plot on the figure allowing for multiple plots to be charted on the same graph grid minor % This inputs the minor grid markings on Figure 2 xticks(15:1:33)%Xticks sets the range on the X axis yticks(0.10:0.10:1) %Yticks sets the range on the Y axis xlabel('Xi') % this labels the X axis on Figure 2 ylabel('Frequencies') % this labels the Y axis on Figure 2 title('Ogive Distribution Frequencies') % This inserts the the title on Figure 2 difference=abs(Cum_frequ1 - Inv_cum) % This calculates the difference by subtracting the Inv_cum data % from the Cum_freq1 index=find(difference==min(difference)); % here find is used to find where %difference is equal to the minimum difference XR=15:3:33; % this sets the parameters of the search XR(index); % looks at index with in parameters of XR disp('The Median value on the Ogive plot is') % This displays the line of text % 'The Median value on the Ogive plot is' disp(index) % This displays the index result Num_midclass= Midclass_Value .* GNOD % This multiplies GNOD by midclass value % to create a line of date required for later calculations involving the %mean variance and standard deviation of the grouped data format shortG % this formates the information to display in short form Num_midclasssquare= Num_midclass .* Midclass_Value % This multiplies the %Num_midclass vale by the Midclass_value again to create information %required for later calculating the mean variance and standard deviation of %the grouped data TABLE=table([{'Number of Requests xi'},{'[15,18['},{'[18,21['},{'[21,24['},{'[24,27['},{'[27,30['},{'[30,33]'};... {'Mid Class value Ci'},num2cell(Midclass_Value); ... {'Number of Days Ni'},num2cell(GNOD); ... {'Frequency'},num2cell(Frequ); ... {'Cumulative Frequency'},num2cell(Cum_frequ); ... {'Inverse Cumulated Frequency'},num2cell(Inv2); ... {'Ni* Ci'},num2cell(Num_midclass); ... {'Ni * Ci ^ 2'},num2cell(Num_midclasssquare)]); % This uses the previously created functions through the project to create % a table TABLE=splitvars(TABLE) % 'Splitvars' takes the grouped vector data % from the functions created earlier to display the information in % individual cells, as seen in the above table writetable(TABLE,'Assignment_Data.xlsx','Sheet',2) % This writes the table % created to the excel document the 'Sheet' and '2' dictates which page of % the spreadsheet the table will be written to. In this case sheet 2 Mean_midclass1 = sum (Num_midclass); % This adds the total sum of the %Num_Midclas Mean_group = Mean_midclass1 / FrequencyDenc; % This divides the Mean-midclasss % by FrequenyDenc which in this case is 60 disp('The Mean value of the grouped data is') % This displays the text %'The Mean value of the grouped data is' disp(Mean_group) % This displays the figure for the Mean of the grouped data Var_1= sum(Num_midclasssquare); % this calculates the total sum of the % Ni * Ci ^2 vector Var_2= Mean_midclass1 ^2;% this caluclates the squared value of Mean_midclass1 Var_3= Var_2 / FrequencyDenc ; % This calculates Var_3 divided by frequency density Var_4= Var_1 - Var_3; % this subtracts Var_3 from Var_1 Var_5= Var_4 / FrequencyDenc; % this divides the total of 'Var_4' by % the total number of occurrences across the grouped data( in this case 60) disp('The Variance of the grouped data is') % this displays the text %'The Variance of the grouped data is' disp(Var_5) % This displays the variance of the grouped data stnd_dev= sqrt(Var_5); % This calculates the standard deviation of the %grouped data by finding the square root using the 'squrt' function on the variance disp('The Standard Deviation of the grouped data is') % this displays the text 'The Standard Deviation of the grouped data is' disp(stnd_dev) % this displays the Standard Deviation figure