UGBA 104: Business Analytics Actual Exam Questions With
Reviewed 100% Correct Detailed Answers Guaranteed Pass!!
1. Simulation technique that measures and describes various characteristics of the
bottom line performance measure of a model when one of more
values for the independent variables are uncertain
2. Random Variable any variable whose value cannot be predicted or set with certainty
3. Risk chance/uncertainty that a decision will not produce the intended
results; also implies potential for loss
4. Positives + Negatives with simply and easy to do
Best Case/Worst Case
Analysis tedious, error prone, time consuming, not as thorough as simulation
(doesn't tell anything about the shape of a distribution
Best Case
Worst Case best case: most optimistic option
worst case: most pessimistic
5. Flaws with What-If Analysis bias, thousands of what-if scenarios may be required, insight gained
is of little value
6. Discrete Random Variables can only assume one distinct value
7. Continuous Random Vari- can assume any value between zero and the maximum capacity
able
8. Probability Distribution represents the range of possible values as well as the relative likeli-
hood of various levels of demand
9. Discrete Probability Distri- variables can only take on specific values, not continuous
bution
10. Uniform Distribution all values for variables are possible equally likely to occur
11. variables can have only values within a specified range
1/3
Reviewed 100% Correct Detailed Answers Guaranteed Pass!!
1. Simulation technique that measures and describes various characteristics of the
bottom line performance measure of a model when one of more
values for the independent variables are uncertain
2. Random Variable any variable whose value cannot be predicted or set with certainty
3. Risk chance/uncertainty that a decision will not produce the intended
results; also implies potential for loss
4. Positives + Negatives with simply and easy to do
Best Case/Worst Case
Analysis tedious, error prone, time consuming, not as thorough as simulation
(doesn't tell anything about the shape of a distribution
Best Case
Worst Case best case: most optimistic option
worst case: most pessimistic
5. Flaws with What-If Analysis bias, thousands of what-if scenarios may be required, insight gained
is of little value
6. Discrete Random Variables can only assume one distinct value
7. Continuous Random Vari- can assume any value between zero and the maximum capacity
able
8. Probability Distribution represents the range of possible values as well as the relative likeli-
hood of various levels of demand
9. Discrete Probability Distri- variables can only take on specific values, not continuous
bution
10. Uniform Distribution all values for variables are possible equally likely to occur
11. variables can have only values within a specified range
1/3
