Decision Models Flashcards
Decision model
A logical or mathematical representation of a problem or business situation that can be used to understand, analyze, or facilitate making a decision.
Model
An abstraction or representation of a real system, idea, or object. Models capture the most important features of a problem and present them in a form that is easy to interpret.
Influence diagram
A simple descriptive model that describes how various elements of the model influence, or relate to, others.
Uncertainty
Imperfect knowledge of what happened
Risk
The probability of occurrence of an undesirable outcome.
Optimization
The process of finding a set of values for decision variables that minimize or maximize some quantity of interest - profit, revenue, cost, time, and so on - called the objective function.
Optimal solution
Any set of decision variables that optimizes the objective function.
Constraints
Limitations, requirements, or other restrictions that are imposed on any solution, such as “do not exceed the allowable budget” or “ensure that all demand is met.”
Algorithm
A systematic procedure that finds a solution to a problem.
Search algorithms
Solution procedures that generally find good solutions without guarantees of finding the best one.
Deterministic model
A model in which all model input information is either known or assumed to be known with certainty.
Stochastic model
A model in which some of the model input information is uncertain.
Problem-solving
The activity associated with defining, analyzing, and solving a problem and selecting an appropriate solution that solves a problem.
Regression analysis
A tool for building statistical models that characterize relationships among a dependent variable and one or more independent variables, all of which are numerical.
Simple linear regression
A linear relationship between one independent variable, X, and one dependent variable, Y.
Least-squares regression
The mathematical basis for the best-fitting regression line
Residuals
The observed errors associated with estimating the value of the dependent variable using the regression line.
Population
All items of interest for a particular decision or investigation.
Sample
A subset of a population.
Median
The measure of location that specifies the middle value when the data are arranged from least to greatest.
Mode
The observation that occurs most frequently.
Midrange
The average of the greatest and least values in the data set.
Range
The difference between the maximum value and the minimum value in the data set.
Variance
The average of the squared deviations of the observations from the mean.
Dispersion
The degree of variation in the data, that is, the numerical spread (or compactness) of the data.
Standard deviation
The square root of the variance. The standard deviation measures the tendency of a fund’s monthly returns to vary from their long-term average.
Standardized value, z-score
A relative measure the distance and observation is from the mean, which is independent of the units of measurement.
Coefficient of variation
A relative measure of the dispersion in data relative to the mean, a relative measure of risk to return. Standard deviation divided by the mean.
Return to risk
The reciprocal of the coefficient of variation. To maximize return, a higher return to risk ratio is considered better.
Covariance
A measure of the linear association between two variables, X and Y. A positive covariant means that asset returns move together.
Correlation
A measure of the linear relationship between two variables, X and Y.
Correlation coefficient
A measure of the linear correlation between two variables X and Y, giving a value between +1 and -1, where 1 is total positive correlation, 0 is no correlation, and -1 is total negative correlation.
Coefficient of determination, R^2
A measure of how well the regression line fits the data. The proportion of variation in the dependent variable that is explained by the independent variable of the regression model.
Standard error of the estimate
The variability of the observed Y values from the predicted values.
Significance of regression
A hypothesis test of whether the regression coefficient is zero.
Standard residuals
How far each residual is from its mean in units of standard deviations. Residuals divided by their standard deviation.
Multiple linear regression
A wienie or regression model with more than one independent variable.
Multicollinearity
A condition occurring when two or more independent variables in the same regression model contain high levels of the same information and, consequently, or strongly correlated with one another can predict each other better than the dependent variable.
Parsimony
A principal which uses the fewest number of explanatory variables that will provide an adequate interpretation of the dependent variable.
Interaction
Independence between two variables X1 and X2.
Validity
How well a model represents reality.
Data table
Tool for summarizing the impact of one or two inputs on a specified output.
One-way data table
Data table that evaluates and output variable over a range of values for a single input variable
Two-way data table
Data table that evaluates an output variable over a range of values for two different input variables.
What-if analysis
Evaluating what–if questions – how specific combinations of inputs that reflect key assumptions will affect model outputs.
Scenarios
Sets of values that are saved and can be substituted automatically on your worksheet.
Parametric sensitivity analysis
Systematic methods of what-if analysis.
Tornado chart
A graph showing the impact that variation in a model input has on some output while holding all other inputs constant.
Risk analysis
An approach for developing a comprehensive understanding and awareness of the risk associated with a particular variable of interest.
Monte Carlo simulation
The process of generating random values for uncertain inputs in a model, computing the output variables of interest, and repeating this process for many trials in order to understand the distribution of the output results.
