diff --git a/+PrognosticsMetrics/plotAlphaLambda.m b/+PrognosticsMetrics/plotAlphaLambda.m
index 4f5f0b5..777f872 100644
--- a/+PrognosticsMetrics/plotAlphaLambda.m
+++ b/+PrognosticsMetrics/plotAlphaLambda.m
@@ -37,9 +37,9 @@
 
 % set up default options
 options = struct('boxFill',[.4 .5 .6],'coneFill',[.95 .95 .95],'width',1,...
-	'timeUnits','s','showResults',1,'xR',-3,'yR',10,'xB',-3,'yB',5,...
-	'printSummary',0,'FontName','Times','textColor',[0 0 0],'lineColor',[0 0 0],...
-	'style','tufte','MarkerSize',3,'FontSize',10,'UTParams',[],'completeLegend',0,...
+	'timeUnits','s','showResults',1,'xR',-0.15,'yR',0.5,'xB',-0.15,'yB',0.9,...
+	'printSummary',1,'FontName','Times','textColor',[0 0 0],'lineColor',[0 0 0],...
+	'style','tukey','MarkerSize',3,'FontSize',10,'UTParams',[],'completeLegend',0,...
 	'name','RUL');
 
 if nargin>3
@@ -184,21 +184,21 @@
 	case 'tukey'
 		
 		% make the box
-		h = patch([k-width/2 k+width/2 k+width/2 k-width/2],[Q(1) Q(1) Q(3) Q(3)],fillColor);
+		h = patch([k-width/6 k+width/6 k+width/6 k-width/6],[Q(1) Q(1) Q(3) Q(3)],fillColor);
 		boxhandles = h;
-		h = line([k-width/2 k+width/2],[Q(2) Q(2)]);
+		h = line([k-width/6 k+width/6],[Q(2) Q(2)]);
 		set(h,'Color',options.lineColor);
 		
 		% mark the mean
 		h = line(k,mean(Y));
-		set(h,'LineStyle','.','Marker','o','Color',options.lineColor,'MarkerFaceColor',options.lineColor);
+		set(h,'LineStyle','-','Marker','o','Color',options.lineColor,'MarkerFaceColor',options.lineColor);
 		boxhandles(end+1) = h;
 		
 		% plot confidence line (whiskers)
 		h = line([k k],[percentile(Y,.05) percentile(Y,.95)]);
 		set(h,'Color',options.lineColor);
 		h = line([k k],[percentile(Y,.05) percentile(Y,.95)]);
-		set(h,'LineStyle','.','Marker','+','Color',options.lineColor);
+		set(h,'LineStyle','-','Marker','+','Color',options.lineColor);
 		boxhandles(end+1) = h;
 		upperMark = [k percentile(Y,.95)];
 		
@@ -239,4 +239,3 @@
 
 
 
-
diff --git a/Tester.m b/Tester.m
index 65132f2..5c4feb6 100644
--- a/Tester.m
+++ b/Tester.m
@@ -26,7 +26,12 @@
     prognosisData.RUL.true=prognosisData.EOL.true - prognosisData.time;
 %		.RUL.values = (N x t) RUL prediction values at each prediction
 %			time.
-    prognosisData.RUL.values=prognosisData.EOL.values - prognosisData.time;
+    prognosisData.RUL.values=ones(N_sam,Time_Max)
+    for i = 1:N_sam
+        for j=1:Time_Max
+            prognosisData.RUL.values(i,j)=prognosisData.EOL.values(i,j) - prognosisData.time(j);
+        end
+    end
 %		.RUL.weights = (N x t) RUL prediction weights at each prediction
 %			time.
     prognosisData.RUL.weights=prognosisData.EOL.weights;
@@ -53,4 +58,4 @@
 
 M = computePrognosisMetrics(prognosisData,alphaBeta,sigma);
 plotAlphaLambda(prognosisData,alphaBeta(1),alphaBeta(2))
-%
\ No newline at end of file
+%