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### 84 Cards in this Set

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 The situation in which the occurrence of one event affects the probability of occurrence of some other event. Dependent Events A probability distribution with a discrete random variable. Discrete Probability Distribution A random variable that can only assume a finite or limited set of values. Discrete Random Variable The (weighted) average of a probability distribution. Expected Value The situation in which the occurrence of one event has no effect on the probability of occurrence of a second event. Independent Events The probability of events occurring together (or one after the other). Joint Probability The simple probability of an event occurring. Marginal Probability A situation in which only one event can occur on any given trial or experiment. Mutually Exclusive Events A continuous probability distribution that describes the time between customer arrivals n a queuing situation. Negative Exponential Distribution A continuous bell-shaped distribution that is a function of two parameters, the mean and standard deviation of the distribution. Normal distribution A discrete probability distribution used in queuing theory. Poisson Distribution A probability value determined before new or additional information is obtained. It is sometimes called an a priori probability estimate. Prior Probability A statement about the likelihood of an event occurring. It is expressed as a numerical value between 0 and 1, inclusive. Probability The mathematical function that describes a continuous probability distribution. It is represented by f(X). Probability Density Function The set of all possible values of a random variable and their associated probabilities. Probability Distribution A variable that assigns a number to every possible outcome of an experiment. Random Variable An objective way of determining probabilities based on observing frequencies over a number of trials. Relative Frequency Approach A probability value that results from new or revised information and prior probabilities. Revised or Posterior Probability The square root of the variance. Standard Deviation A method of determining probability values based on experience or judgement. Subjective Approach A measure of dispersion or spread of the probability distribution. Variance A measure of the explanatory power of a regression model that takes into consideration the number of independent variables in the model. Adjusted r2 See Dummy Variable. Binary Variable A measure of the strength of the relationshop between two variables. Coefficient of Correlation(r) The percent of the variability in the dependent variable (Y) that is explained by the regression equation. Coefficient of Determination (r2) A condition that exists when one independent bariable is correlated with another independent variable. Collinearity The Y-variable in a regression model. This is what is being predicted. Dependent Variable A variable used to represent a qualitative factor or condition. Dummy variables have values of 0 or 1. This is also called a binary variable or an indicator variable. Dummy Variable The difference between the actual value (Y) and he predicted value (^Y).(The symbol beside the Y is supposed to be above it) Error THe independent variable in a regression equation. Explanatory Variable The X-variable in a regression equation. This is used to help predict the dependent variable. Independent Variable. A reference to the criterion used to select the regression line, to minimize the squared distances between the estimated straight line and the observed values. Least Squares An estimate of the error variance. Mean Squared Error (MSE) A condition that exists when one independent variable is correlated with other independent variables. Multicollineraity A regression model that has more than one independent variable. Multiple Regression Model Another name for p-value. Observed Significance Level A probability value that is used when testing a hypothesis. The hypothesis is rejected when this is low. p-value Another name for explanatory variable. Predictor Variable A forecasting procedure that uses the least squares approach on one or more independent variables to develop a forecasting model. Regression Analysis Another term for error Residual The dependent variable in a regression equation. Response Variable Diagrams of the variable to be forecasted, plotted against another variable, such as time. Also called scatter plots. Scatter Diagrams An estimate of the standard deviation of the errors and is sometimes called the standard deviation of the regression. Standard error of the estimate The total sum of the squared differences between each observation (Y) and the predicted value (^Y). (the symbol beside the Y is supposed to be above it) Sum of Squares Error (SSE) The total sum of the squared differences between each predicted value (^Y) and the mean (Y)(there should be a line over the Y). Sum of Squares Regression (SSR) The total sum of the squared differences between each observation (Y) and the mean (Y) ( there should be a line over the last Y) Sum of Squares Total (SST) A course of action or a strategy that may be chosen by a decision maker. Alternative A number from 0 to 1. When the coefficient is close to 1, the decision criterion is optimistic. When the coefficient is close to zero, the decision criterion is pessimistic. Coefficient of Realism A consequence, normally expressed in a monetary value, that occurs as a result of a particular alternative and state of nature. Conditional Value or Payoff A posterior probability. Conditional Probability A decision-making criterion that uses a weighted average of the best and worst possible payoffs for each alernative. Criterion of Realism A decision-making environment in which the future outcomes or states of nature are known. Decision Making under Certainty A decision-making environment in which several outcomes or states of nature may occur as a result of a decision or alternative. The probabilities of the outcomes or states of nature are known. Decision Making under Risk A decision-making environment in which several outcomes or states of nature may occur. The probabilities of these outcomes, however, are not known. Decision Making under Uncertainty In a decision tree, this is a point where the best of the available alternatives is chosen. The branches represent the alternatives. Decision Node (point) A payoff table Decision Table An analytic and systematic approach to decision making. Decision Theory A graphical representation of a decision making situation. Decision Tree. A decision criterion that places an equal weight on all states of nature. Equally Likely The average value of a decision if it can be repeated many times. THis is determined by multiplying the monetary values by their respective probabilities. The results are then added to arrive at the EMV. Expected Monetary Value (EMV) The average or expected value of information if it were completely accurate. The increase in EMV that results from having perfect information. Expected Value of Perfect Information (EVPI) The increase in EMV that results from having sample or imperfect information. Expected Value of Sample Information (EVSI) The average or expected value of the decision if you knew what would happen ahead of time. You have perfect knowledge. Expected Value with Perfect Information (EVwPI) The criterion of realism Hurwicz Criterion The equally likely criterion Laplace Criterion An optimistic decision-making criterion. This alternative maximizes the minimum payoff. It selects the alternative with the best of the worst possible payoffs. Maximin A criterion that minimizes the maximum opportunity loss. Minimax Regret The amount you would lose by not picking the best alternative. For any state of nature, his is the difference between the consequences of any alternative and the best possible alternative. Opportunity loss The maximax criterion Optimistic Criterion A table that lists the alternatives, states of nature, and payoffs in a decision-making situation. Payoff Table A conditional probability of a state of nature that has been adjusted based on sample information. This is found using Bayes Theorem. Posterior Probability The initial probability of a state of nature before sample information is used with Bayes Theorem to obtain the posterior probability. Prior Probability Opportunity loss Regret A person who seeks risk. On the utility curve, as the monetary value increases, the utility increases at an increasing rate. This decision maker gets more pleasure for a greater risk and higher potential returns. Risk Seeker A person who avoids risk. On the utility curve, as the monetary value, the utility increases at a decreasing rate. This decision maker gets less utility for a greater risk and higher potential returns Risk Avoider Decisions in which the outcome of one decision influences other decisions. Sequential Decisions An outcome or occurrence over which the decision maker has little or no control. State of Nature The process used to determine utility values Standard Gamble In a decision tree, this is a point where the EMV is computed. The branches coming from this node represent states of nature. State of Nature Node. The overall value or worth of a particular outcome. Utility The process of determining the utility of various outcomes. This is normally done using a standard gamble between the worst and best outcomes. Utility Assessment A graph or curve that reveals the relationship between utility and monetary values. When this curve has been constructed, utility values from the curve can be used in the decision-making process. Utility Curve A theory that allows decision makers to incorporate their risk preference and other factors into the decision making process. Utility Theory Another name for the criterion of realism. Weighted Average Criterion