Xcos examples from textbooks and for blocksHEADmaster

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diff --git a/Working_Examples/83/CH14/EX14.2/example_14_2.sce b/Working_Examples/83/CH14/EX14.2/example_14_2.sce
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+++ b/Working_Examples/83/CH14/EX14.2/example_14_2.sce
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+//Chapter 14
+//Example 14.2
+//page 534
+//To estimate the values of the random variables x1 and x2 using WLSE
+clear;clc;
+
+w=diag([0.1;1;0.1]); //assumed matrix
+H=[1 0;0 1;1 1];  //given matrix
+k=inv(H'*w*H)*H'*w; // from eq 14.12b
+y=['y1';'y2';'y3'];
+Px=k*k';
+k=string(k);
+x=[k(1,1)+y(1,1)+k(1,2)+y(2,1)+"+"+k(1,3)+y(3,1) ;k(2,1)+y(1,1)+"+"+k(2,2)+y(2,1)+"+"+k(2,3)+y(3,1)];
+printf('The weighted least square s estimate of the vector x =\n');
+disp(x);
+printf('\n\nThe matrix k is in this case found to be \n');
+disp(k);
+//covariance of measurement is assumed is assumed to be unit matrix
+printf('\n\nThe covariance of the estimation error is obtained as Px=\n');
+disp(Px);
+
+printf('\n\n\n Now choosing W=1\n');
+w=diag([1;1;1]); //assumed matrix
+H=[1 0;0 1;1 1];  //given matrix
+k=inv(H'*w*H)*H'*w; // from eq 14.12b
+Px=k*k';
+printf('\n\nThe matrix k is in this case found to be \n');
+disp(k);
+printf('\n\nThe covariance of the estimation error is obtained as Px=\n');
+disp(Px);
\ No newline at end of file
diff --git a/Working_Examples/83/CH14/EX14.2/result_example_14_2.txt b/Working_Examples/83/CH14/EX14.2/result_example_14_2.txt
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+
+The weighted least square s estimate of the vector x =
+ 
+!0.5238095y1-0.4761905y2+0.4761905y3   !
+!                                      !
+!-0.0476190y1+0.9523810y2+0.0476190y3  !
+
+
+The matrix k is in this case found to be 
+ 
+!0.5238095   -0.4761905  0.4761905  !
+!                                   !
+!-0.0476190  0.9523810   0.0476190  !
+
+
+The covariance of the estimation error is obtained as Px=
+ 
+    0.7278912  - 0.4557823  
+  - 0.4557823    0.9115646  
+
+
+
+ Now choosing W=1
+
+
+The matrix k is in this case found to be 
+ 
+    0.6666667  - 0.3333333    0.3333333  
+  - 0.3333333    0.6666667    0.3333333  
+
+
+The covariance of the estimation error is obtained as Px=
+ 
+    0.6666667  - 0.3333333  
+  - 0.3333333    0.6666667  
+