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| Question | Answer |
|---|---|
| BPMS | Business process management system. |
| Utility function | Utility is value. PU + (1-P) L > u; U is value of good outcome; L is value of poor outcome; u is value of present source (no change) |
| stratified random sampling | selecting elements from an accessible population that has been divided into groups or strata |
| Design of experiments | Design of any information gathering exercise where variation is present, whether under the control of the experimenter or not. |
| Response surface modeling | (Regression) specify the value of interest as a function of the covariates. |
| ratio scale | Any scale of measurement possessing magnitude, equal intervals, and an absolute zeros. |
| Discrete event simulation | A simulation methodology that is often used to understand bottlenecks, handles cases not handled by queuing theory, uses multistage process modeling with variations in arrival and service time and utilizing shared resources to perform multiple operations. |
| Queuing model | Designed to identify the most efficient pathway to solution. Ie number of bank tellers to satisfy customers in given amount of time in a waiting queue. |
| Monte Carlo Simulation | Queuing model is not needed. Used to estimate dependent variable randomness. Necessary when distributions of the input variables are not necessarily normally distributed and the relationship to estimate the dependent variable is not simple. |
| Agent based modeling (Abm) | A system modeled (simulated) as a collection of autonomous decision-making entities called agents that are used to discover emergent behavior that is hard to predict without simulating it. |
| System dynamics | A simulation approach used to understanding the interactions of a complex system over time. |
| Game theory | Study of strategic decision-making processes through competition and collaboration |
| Probabilities | The likelihood of a particular event occurring expressed as a percentage to make decisions under chosen risk or tolerance. Bayesian and conditional probabilities are widely used in analytics. |
| confidence interval | the range of values within which a population parameter is estimated to lie. |
| Conjoint Analysis | Determines how consumers value different components (attributes) of a good/service |
| Utility function | A person's willingness to accept risk. |
| uncertainty principle | it is impossible to know variables precisely in the quantum world |
| subjective probability | an estimated probability as determined by the decision maker |
| soft data | data based on an educated guess |
| hard data | statistics gathered through valid research |
| Parametric Analysis | Analysis of the effect of different values of a variable |
| sample design | describes exactly how to choose a sample from the population |
| sampling plan | the explicit strategy used for recruiting participants from the population |
| 5 steps to data collection | 1) how to identify subjects (sample design) 2) how many subjects (sampling plan) 3) determine questions 4) determine possible answers 5) determine control group |
| Box-cox transformation | Normalization process on ratio scale fields |
| time series analysis | a forecast in which past demand data is used to predict future demand. Typically corrects seasonality. |
| reduce uncertainty? | Quadruple the sample number halves the uncertainty. |
| Measure of uncertainty | Commonly measured by standard deviation. |
| rank order scale | a measurement scale in which the respondent compares two or more items and ranks them |
| Likert Scale | This type of question measures the respondents level of agreement with a statement. |
| Individuals, entities, cases, objects, or records | Another name for Rows of Data |
| Variables, features, attributes or fields. | Another name for columns of data (measurements) |
| observed data set (matrix) | Data set, training data, sample, or database. |
| temporal data | denotes the evolution of an object characteristic over a period of time |
| A pattern (local) | Describes a structure relating to a relatively small part of the data or the space in which data could occur. |
| Model structure | Is a global summary of a data set; it makes statements about any point in the full measurement space. |
| Dimensionality | Number of variables |
| Coxcomb chart | Pie chart where the radii of the wedges differ. |
| Density estimation | Model for the overall probability distribution of the data |
| Descriptive vs prediction | prediction has as its objective a unique variable (the markets value, the disease class, etc), while in descriptive problems no single variable is central to the mode |
| Classification vs Regression | In classification modeling, the variable being predicted is categorical. In regression, the variable is quantitative. |
| Score function | Judges the quality of a fitted model. Allows you to compare models and pick the best. |
| Fitted model | Once parameters have been assigned values, we refer to a particular model as a "fitted model" or just "model" |
| Common score functions | Likelihood, sum of squared errors, and miss classification rate. |
| nominal scale | classifies data into distinct categories in which no ranking is implied. |
| Logit transformation formula | F(p) = p/(1-p) |
| data matrix | a grid presenting collected data |
| Multirelational data | Data sets consisting of several matrices or tables. |
| Precision (or reliability) | how close a measured value is to repeated measurements of the same sample. Small variability. A measurement may be precise but inaccurate. Eg. a poor tool may measure same result repeatedly but may be inaccurate. |
| accurate (validity) | conforming exactly or almost exactly to fact or to a standard or performing with total accuracy. Small variability and also true. Accurate procedure has both small bias and small variance. |
| validity | accuracy. The extent to which a measurement procedure measures what it's supposed to measure. |
| bias measurement | In statistical terms, the difference between the mean of repeated measurement and true value is the bias of a measurement procedure. |
| Parameter | Descriptive summaries of populations or distributions of objects. Or, a parameter is a value that indexes a family of mathematical functions. |
| statistics | Values computed from a sample of objects and appropriately chosen statistics can be used as estimates of parameters. |
| population drift | a gradual movement of people that lowers the population in one area and increases it in another. This leads to sample distortion. |
Created by:
ijadajackson