Relative risk: The ratio of the rate of the disease (usually incidence or mortality) among those exposed to the rate among those not exposed.

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1 Risk management process: The systematic application of management policies, procedures and practices to the tasks of establishing the context, identifying, analysing, evaluating, treating, monitoring and communicating risk. Qualitative and quantitative risk assessment: The term "qualitative" currently covers two distinct types of risk assessment. The first is descriptive in nature and the second is one which uses formal qualitative methods and techniques. It is, therefore, more appropriate to talk about three types of risk assessments, all of which may have an appropriate function. All three types have elements which may be combined appropriately in any one import commodity risk analysis. Descriptive risk assessments rely upon a strong narrative relating of principle events and factors. This form of assessment may contain scientific references, references to other risk assessments, significant quantitative data, etc. Generally these present a salient description of the situation of a particular commodity vis a vis a particular country or region. The end product of this form of risk assessment is an opinion on the categorization of a level of risk ("high, medium, low, minimal, etc."). Qualitative risk assessments utilize a formal model and a large variety of methodologies found in the scientific literature. In a qualitative study, categorical, and sometimes quantitative, data are moved through the model in a calculated manner. Qualitative risk assessments may include both parametric and non-parametric statistics. The techniques of a qualitative risk assessment are transparent. The end product of a qualitative risk assessment is a calculated measure of risk. It is expressed as categorization of a level of risk. But unlike descriptive risk assessments, this end product is more than a judicious "opinion." It is rather a measure which can be replicated under other circumstances. Quantitative risk assessments rely heavily upon quantitative techniques, such as parametric and non-parametric statistics, probability distributions, analysis of variance, sensitivity analysis, and other statistical methodologies. At the same time, they frequently are linked to narrative and qualitative techniques within the whole of their risk analysis. The end product of a quantitative risk assessment is frequently presented as a Relative risk: The ratio of the rate of the disease (usually incidence or mortality) among those exposed to the rate among those not exposed. Secondary risks: Risks which arise from actions taken to mitigate other risks or from extensions to the original scope of the project. Secondary risks can sometimes be important and always need to be analyzed in their own right. Reliability: Evaluating the inherent quality of a test report or publication relating to preferably standardized methodology and the way the experimental procedure and results are described to give evidence of the clarity and plausibility of the findings. It is the

2 probability a system performs a specified function or mission under given conditions for a prescribed time Risk estimation: The scientific determination of the characteristics of risks, usually in as quantitative a way as possible. These include the magnitude, spatial scale, duration and intensity of adverse consequences and their associated probabilities as well as a description of the cause and effect links. Risk evaluation: A component of risk assessment in which judgments are made about the significance and acceptability of risk. Risk identification: Recognizing that a hazard exists and trying to define its characteristics. Often risks exist and are even measured for some time before their adverse consequences are recognized. In other cases, risk identification is a deliberate procedure to review, and it is hoped, anticipate possible hazards. Risk checklist: A checklist of risk mitigation techniques that is used by project evaluators to manage and reduce the potential for loss in a project. Risk diversification: The process of distributing risk to all contractual parties in a construction project; risk diversification is normally accomplished through use of contingency amounts, or risk premiums. Risk measurement: The process of objectively and accurately assessing the amount of potential loss in a construction project. Risk measurement can be either deterministic (a number) or probabilistic (a percent associated with a number). Risk mitigation: The process of removing or reducing risk. Risk mitigation may include risk analysis, or other activities designed to assess the results of risk mitigation initiatives. Risk premium: Contingency amount(s) included in a construction contract to allocate or compensate for funding/cost and schedule uncertainties, which are perceived by the contracting parties to be present in the project. Risk variable: A critical or highly variable cost or schedule (duration) element of a construction project. Residual risks: Those risks which are not avoided, eliminated or transferred in the risk mitigation strategy. Risk analyst: An individual whose primary task is the identification and evaluation of risks during the risk review. Risk assessment tables: Tables that may be used to allocate scores to risks, to help in prioritizing them.

3 Risk custodian: An individual who has responsibility for monitoring, controlling and minimizing the project s residual risks. Risk event: The occurrence of an event which has the potential to affect the viability of a project. The manifestation of risk into Consequences. Otherwise, Risk is only a potential. Risk matrix: The presentation of information about risks in a matrix format, enabling each risk to be presented as the cell of a matrix whose rows are usually the stages in the investment life-cycle and whose columns are different causes of risk. A risk matrix is useful as a checklist of different types of risk which might arise over the life of a project but it must always be supplemented by other ways of discovering risks. Risk matrix is a form of Risk Measurement and Risk Prioritization in one step that uses risks on the horizontal axis and system components or audit steps on the left axis. Both axes are sorted to the left corner (High), creating a matrix with quadrants of High, Medium and Low groups of elements and risks. Risk mitigation strategy: An overall plan for mitigating the risks in the investment activity. Risk register: A list of risks identified in the risk review process, including full descriptive detail and cross-references. Risk response plan: A plan (prepared towards the end of the risk review) for controlling the risks once implementation begins. Risk review: An overall assessment of the risks involved in a project, their magnitude and their optimal management. Risk reviews can in principle be held at any stage in the life of a project with each review building on the results of previous ones. Each risk review should be preceded by a risk review plan. Risk reviews should generate information for inclusion in the risk register, risk mitigation strategy and risk response plan. The results of a risk review should be set out in a risk review report. Risk acceptance: An informed decision to suffer the Consequences of likely Events. Risk avoidance: An informed decision not to become involved in a risk situation. Risk adjusted value: In portfolio analysis, this is the (Upside minus Risk-Aversion) muliplied by the Downside or Regret. Risk classification: The categorization of risk, typically into High, Medium, Low and intermediate values. Risk factors: Measurable or observable manifestations or characteristics of a process that either indicates the presence of Risk or tends to increase Exposure.

4 Risk framework: A Model of risks in the organization. Risk frameworks typically enumerate the various classes of risk and the degree of Risk Management expected. Risk model: A mathematical, graphical or verbal description of risk for a particular environment and set of activities within that environment. Useful in Risk Assessment for consistency, training and documentation of the assessment. Risk prioritization: The relation of acceptable levels of risks among alternatives. Risk ranking: The ordinal or cardinal rank prioritization of the risks in various alternatives, projects or units. Risk reduction: Application of Risk Management principles to reduce the Likelihood or Consequences of an Event, or both. Risk response: Management's decisions and actions when risks are revealed. Risk retention: Intentional (or unintentional) retaining the responsibility for loss or Risk Financing within the organization. Risk scenarios: A method of identifying and classifying risks through creative application of Probabilistic events and their Consequences. Typically a Brainstorming or other creative technique is used to stimulate "what might happen." Risk transfer: Shifting the responsibility or Risk Financing burden to another party. Risk treatment: Another term for Risk Management. Expert opinion: Use of experts and expert opinion are intended to be a methodological tool, rather than simply a supportive personal communication. Examples of how expert opinion has been methodologically accessed include (1) requesting and evaluating independent analyses from multiple experts in a given field; or (2) convening a panel of experts who present their analyses on a given topic, followed by interactive panel discussion and recommendation of preferred methods. Quality assurance: 1) The process of evaluating overall project performance on a regular basis to provide confidence that the project will satisfy the relevant quality standards. 2) The organizational unit that is assigned responsibility for quality assurance. Quality control: 1) The process of monitoring specific project results to determine if the comply with relevant quality standards and identifying ways to eliminate causes of unsatisfactory performance. 2) The organizational unit that is assigned responsibility for quality control. Quality planning: Identifying which quality standards are relevant to the project, and determining how to satisfy them.

5 Software engineering: A discipline that encompasses the process associated with software development, the methods used to analyze, design and test computer software, the management techniques associated with the control and monitoring of software projects and the tools used to support process, methods, and software: Computer software designed to perform probabilistic risk analysis on a personal is suitable for spreadsheet or schedule applications. The software allows the user to specify probability distribution type and ranges of variation for activities within the project (critical variables), and then conducts a Monte Carlo random simulation on the specified cost and schedule variables. Sampling: Is the process by which values are randomly drawn from input probability distributions. Two methods of sampling used Monte Carlo sampling and Latin Hypercube sampling. Simulation: It is a technique whereby a model, such as Excel worksheet, is calculated many times with different input values with the intent of getting a complete representation of all possible scenarios that might occur in an uncertain situation. Monte Carlo simulation: A computerized technique which is the basis for probabilistic risk analysis, and which replicates real life occurrences by mathematically modeling a projected event. Monte Carlo simulation uses pre-defined probability distributions of risk variables to perform random modeling over many "simulations" or computer trials. The results are probabilistic (they form a probability distribution) and therefore yield an expected value (mean) and a standard deviation, as well as cumulative probabilities (zero to 100 percent) which express total likelihood (probability) at any level of variable outcome. Latin hypercube: It is a relatively new stratified sampling technique used in simulation modeling. Stratified sampling techniques, as opposed to Monte Carlo type techniques, tend to force convergence of a sampled distribution in fewer samples. Iteration: An iteration is one recalculation of user s model during a simulation. A simulation consists of many recalculations or iterations. During each iteration, all uncertain variables are sampled once according to their probability distributions, and the model is recalculated using these sampled values. Uncertainty: A source of risk derived from a lack of sufficient knowledge about the underlying probabilities of adverse events and/or their consequences. Consequence: The outcome of an event expressed qualitatively or quantitatively, being a loss, injury, disadvantage or gain. There may be a range of possible outcomes associated with an event. Event: An incident or situation, which occurs in a particular place during a particular interval of time.

6 Probability: The likelihood or degree of certainty of a particular occurrence taking place during a specified time period. Independent probabilities relate to events which do not depend on other events which have occurred previously. Dependent probabilities are the probabilities of occurrence once previous specified events have occurred. Probability distribution: A distribution, input or output, of data point probabilities (can be discrete or continuous), which describe the probability of occurrence of all data points in the distribution. Probability distributions take many various shapes, and are each characterized by a mean (average) and a standard deviation (measure of internal variation). Probability distribution is a distribution which relates a range of particular outcomes to their likelihood. The most common probability distribution is the normal distribution which is shaped like the cross-section of a bell. Probability density function: A relative frequency curve which shows the total area (100 percent) of all data points contained in the distribution. Cumulative distribution function: The zero to 100 percent successive probability for each observed value in a probability distribution. Cumulative probability functions (CDFs) are normally used to express the total probability (zero to 100 percent) for a specified level of output variables (cost and schedule variables) following the probabilistic simulation analysis. Uniform probability distribution: A "flat curve" probability distribution which is characterized by only two points: a lower bound and an upper bound. Triangular distribution: A statistical distribution, which requires the identification of high, low, and most likely values for each selected variable. The resultant data points form the basis for the triangular or three-point distribution. Random variables: Computer-generated "y" axis values which, depending on a userdefined probability distribution, randomly generate new "x" values for each trial in a simulation. Probabilistic estimate: The result of a probabilistic risk analysis; a forecast for modeled cost or schedule events, which is the result of probabilistic or random simulation. Probabilistic risk analysis: An analysis based on computer simulation, which uses predefined probability distributions to model input variables for project cost and schedule. The input variables are cost and schedule variables, which possess a high degree of uncertainty. This uncertainty is expressed through "ranging" the variables, or defining their bounds according to the data points required by the input distributions. For example, triangular distribution requires high, low, and most likely values. Output variables for cost and schedule duration result from the computer simulation, and are also characterized by probability distributions having means (averages) and standard deviations (measures of internal dispersion). A cumulative distribution function describes the total probability or likelihood of occurrence at any level of output variable

7 (cost or schedule). This technique -- probabilistic risk analysis -- requires effective user facilitation, but is a model for collaborative decision-making and risk mitigation. Project management control system: Any method, process, or system, which exists to manage project resources, document project activity, or authorize project events. Beta distribution: A unimodal distribution with confined lower and upper bounds; shape can be asymmetrical, and depends on the particular distribution. Coefficient of variation: A measure of relative dispersion within a probability distribution. The coefficient of variation is the standard deviation of the probability distribution divided by its expected value (mean). This coefficient serves as a measure of relative risk. Confidence interval: The probability (zero to 100 percent) that an observed value is the true or actual value. The confidence interval, expressed as a percent, is used to interpret the output or results of a probabilistic analysis. Contingency: A risk premium factor or amount that is added to the project budget and/or the schedule, by any party to the contract, to allow/compensate for uncertainty or risk in project implementation. Construction risk: Risk associated with the physical construction phase of project development; for example, construction risk is differentiated from economic risk (loss of project income due to unpredictably low ridership or poor tax base) and political risk (project may be shelved due to new constituent representation). Cost-benefit analysis: Economic analysis used to forecast the net value, usually over time, for a series of capital payments or revenue/cash flow related to project implementation. Cost escalation factor: An inflation-adjustment factor applied to base year costs. Cost index: An inflation-adjustment factor applied to non-base year costs. Critical path: The longest path in a schedule of duration-defined activities. Deterministic method: Cost estimation method, which allows for successive iteration of projected or estimated values, each yielding or "determining" a new bottom line. Histogram: A relative frequency polygon, or bar-chart, which shows discrete noncumulative probabilities for all points in a probability distribution. Lognormal distribution: A unimodal distribution that can take only positive values, and is skewed or "slanted" to the right.

8 Multivariate: An analytical technique that considers or solves for multiple (more than one) decision variables. Ogive: A cumulative frequency polygon (distribution curve), which begins at zero and ends at 100 percent probability for the data points in the distribution. PERT method: Program Evaluation and Review Technique, a probabilistic networkbased scheduling technique in which a beta distribution is used to model activity durations. The total project duration is computed along the network's critical path (the longest path) by adding the means of the activities on the critical path. Tornado graph: A graph, which describes the calculated sensitivities of critical variables resulting from a Monte Carlo simulation. Sensitivity analysis: A technique used to discover how sensitive the results from economic and financial models are to changes in the input values of the variables used to calculate the results. A high degree of sensitivity is a warning to interpret the results of the model with care and circumspection; especially because many of the input variables will themselves have been estimated and therefore be subject to error. Use of econometric models must not obscure awareness of their limitations and possible pitfalls, especially when they are being used for forecasting. Scenario analysis: It identifies combination of inputs which lead to output target values. Scenario analysis attempts to identify groupings of inputs which cause certain output values. Significant: Is to be interpreted as implying a risk the potential consequence of which could have a significant effect on one of the objectives, parameters or 'deliverables', even if it has only a small probability of occurrence. Regression: A mathematical technique used to explain and/or predict. The general form is Y = a + bx + u, where Y is the variable that we are trying to predict; X is the variable that we are using to predict Y, a is the intercept; b is the slope, and u is the regression residual. The and b are chosen in a way to minimize the squared sum of the residuals. The ability to fit or explain is measured by the R-square. Regression analysis: A statistical technique that can be used to estimate relationships between variables. Regression coefficient: Term yielded by regression analysis that indicates the sensitivity of the dependent variable to a particular independent variable. Regression equation: An equation that describes the average relationship between a dependent variable and a set of explanatory variables.

9 Modeling: Is a catch-all phrase that usually means any type of activity where one tries to create a representation of a real life situation so one can analyze it. Predictive microbiology: Predictive microbiology involves knowledge of microbial growth responses to environmental factors summarized as equations or mathematical models. The raw data and models may be stored in a database from which the information can be retrieved and used to interpret the effect of processing and distribution practices on microbial proliferation. Coupled with information on environmental history during processing and storage, predictive microbiology provides precision in making decisions on the microbiologic safety and quality of foods.