The slope of the regression line expressing the linear relation between the current data node and the 窶徃oal node窶 of the simulation. This attribute is only available if you have selected a particular goal node for the simulation, and run a criticality simulation. You can, however, compute the slope for any arbitrary pair of data nodes by making a regression line plot for the pair. This is done by using the 窶彝egression line窶 command in the 窶廨allery窶 menu.
Intercept
The intercept of the regression line expressing the linear relation between the current data node and the 窶徃oal node窶 of the simulation. This attribute is only available if you have selected a particular goal node for the simulation, and run a criticality simulation. You can, however, compute the intercept for any arbitrary pair of data nodes by making a regression line plot for the pair. This is done by using the 窶彝egression line窶 command in the 窶廨allery窶 menu.
Covar.
The covariance between the current data node and the 窶徃oal node窶 of the simulation. This attribute is only available if you have selected a particular goal node for the simulation, and run a criticality simulation. You can, however, compute the covariance between any arbitrary pair of data nodes by making a regression line plot for the pair. This is done by using the 窶彝egression line窶 command in the 窶廨allery窶 menu.
Correl.
The correlation between the current data node and the 窶徃oal node窶 of the simulation. This attribute is only available if you have selected a particular goal node for the simulation, and run a criticality simulation. You can, however, compute the correlation between any arbitrary pair of data nodes by making a regression line plot for the pair. This is done by using the 窶彝egression line窶 command in the 窶廨allery窶 menu.
Cr.mean
The criticality mean of the current data node with respect to the 窶徃oal node窶. The criticality mean is calculated by using the following formula:
[M(G) - M(G | X = M(X))]/M(G)
where G is the goal node, X is the current node, M(G) and M(X) denotes the mean values of G and X respectively, and M(G | X = M(X)) denotes the conditional mean of G given that X is equal to its mean value. Thus, the criticality mean measures the effect of the uncertainty in X on the mean value of the goal node. [All mean values are estimated based the current simulation data.]
This attribute is only available if you have selected a particular goal node for the simulation, and run a criticality simulation. Moreover, the attribute is only available for a data node if the 窶廚rit.窶 attribute of the corresponding model node were checked during the simulation.
Cr.var.
The criticality variance of the current data node with respect to the 窶徃oal node窶. The criticality variance is calculated by using the following formula:
[V(G) - V(G | X = M(X))]/V(G)
where G is the goal node, X is the current node, V(G) and V(X) denotes the variance values of G and X respectively, and V(G | X = M(X)) denotes the conditional variance of G given that X is equal to its mean value. Thus, the criticality variance measures the effect of the uncertainty in X on the variance of the goal node. [All variance values are estimated based on the current simulation data.]
This attribute is only available if you have selected a particular goal node for the simulation, and run a criticality simulation. Moreover, the attribute is only available for a data node if the 窶廚rit.窶 attribute of the corresponding model node were checked during the simulation.
Sn.mean
The sensitivity mean of the current data node with respect to the 窶徃oal node窶. The sensitivity mean is calculated by using the following formula:
[M(G | S*X) - M(G)]/M(G)
where G is the goal node, X is the current node, S is the specified sensitivity factor, M(G) and M(X) denotes the mean values of G and X respectively, and M(G | S*X) denotes the conditional mean of G given that X is replaced by S*X in all calculations. Thus, the sensitivity mean measures the effect of a change in X on the mean value of the goal node. [All mean values are estimated based the current simulation data.]
This attribute is only available if you have selected a particular goal node for the simulation, and run a criticality simulation. Moreover, the attribute is only available for a data node if the 窶彜ens.窶 attribute of the corresponding model node were checked during the simulation.
Sn.var.
The sensitivity variance of the current data node with respect to the 窶徃oal node窶. The sensitivity variance is calculated by using the following formula:
[V(G | S*X) - V(G)]/V(G)
where G is the goal node, X is the current node, S is the specified sensitivity factor, V(G) and V(X) denotes the variance values of G and X respectively, and V(G | S*X) denotes the conditional variance of G given that X is replaced by S*X in all calculations. Thus, the sensitivity variance measures the effect of a change in X on the variance value of the goal node. [All variance values are estimated based the current simulation data.]
This attribute is only available if you have selected a particular goal node for the simulation, and run a criticality simulation. Moreover, the attribute is only available for a data node if the 窶彜ens.窶 attribute of the corresponding model node were checked during the simulation.
Lo.fract.
The lower fractile value of the data node. Which fractile depends on the chosen fractile set. To choose a particular fractile set, you use the 窶廡ractiles窶ｦ窶 command in the 窶彜et窶 submenu of the 窶廨allery窶 menu. If e.g., the 10% fractile is chosen as the lower fractile, the 窶廰o.fract.窶 attribute is such that 10% of the result values for the data node is less than this value, and 90% is greater than this value.
Md.fract.
The medium fractile value of the data node. The medium fractile is always the 50% fractile. Thus, the 窶廴d.fract.窶 attribute is such that 50% of the result values for the data node is less than this value, and 50% is greater than this value.
Hi.fract.
The higher fractile value of the data node. Which fractile depends on the chosen fractile set. To choose a particular fractile set, you use the 窶廡ractiles窶ｦ窶 command in the 窶彜et窶 submenu of the 窶廨allery窶 menu. If e.g., the 90% fractile is chosen as the higher fractile, the 窶廩i.fract.窶 attribute is such that 90% of the result values for the data node is less than this value, and 10% is greater than this value.
Lo.cont.
The lower contingency value of the data node. The rules for calculating contingencies depend on the chosen contingency settings. To change these rules, you use the 窶廚ontingency窶ｦ窶 command in the 窶彜et窶 submenu of the 窶廨allery窶 menu. If e.g., you have chosen to calculate contingency with respect to base values, using 窶廴ean ﾂｱ st.dev窶 as levels, and 窶%窶 as calculation method, the 窶廰o.cont.窶 attribute is calculated according to the following formula:
100 * [M(X) - SD(X)]/M(X)
Md.cont.
The medium contingency value of the data node. The rules for calculating contingencies depend on the chosen contingency settings. To change these rules, you use the 窶廚ontingency窶ｦ窶 command in the 窶彜et窶 submenu of the 窶廨allery窶 menu. If e.g., you have chosen to calculate contingency with respect to base values, using 窶廴ean ﾂｱ st.dev窶 as levels, and 窶%窶 as calculation method, the 窶廰o.cont.窶 attribute is calculated according to the following formula:
100 * M(X)/M(X) = 100
Thus, for this particular setting, the 窶廴d.cont.窶 attribute is always equal to 100%.
Hi.cont.
The higher contingency value of the data node. The rules for calculating contingencies depend on the chosen contingency settings. To change these rules, you use the 窶廚ontingency窶ｦ窶 command in the 窶彜et窶 submenu of the 窶廨allery窶 menu. If e.g., you have chosen to calculate contingency with respect to base values, using 窶廴ean ﾂｱ st.dev窶 as levels, and 窶%窶 as calculation method, the 窶廰o.cont.窶 attribute is calculated according to the following formula:
