N. | Name | Chart | What it does? |
1 | mmBETA(Scale, Shape) | | Probability of an event occurring. For instance, the probability that the next client will buy. |
2 | mmBETAGEN (Scale, Shape, Optional: lower, Optional: upper) | | Like Beta with lower and upper bounds. |
3 | mmBINOMIAL(Trials, Successes) | | The number of events that occur. For instance, the entrance of a new competitor in the market. |
4 | mmCHI2(Degrees) | | The amount of mutually exclusive events. For instance, experience, in years of the use of the PC. |
5 | mmDISCRETE(InputRange, Probabilities) | - | Occurrence of a given number of events only. For instance, the lights of a semaphore. |
6 | mmERF(Mean) | | Returns extreme values. For instance, forecast errors as computed with Forecast Manager tool of MM4XL software. |
7 | mmERLANG(Mean, Phases) | | Amount of time between events. For instance, the client flow in a fast-food restaurant. |
8 | mmEXPON(Mean) | | Amount of time between events. For instance, how long it takes between client arrival and departure. |
9 | mmEXTVALUE(ModalValue, StDeviation) | | Simulates extreme values. For instance, the maximum time taken to serve a client. |
10 | mmGAMMA(Mean, StDeviation) | | Amount of time between events. For instance, the time to issue an order for sodas at a retail store. |
11 | mmGAUSSINV(Mean, Lambda) | | Response time in sequential patterns. For instance, web surfing behavior of car buyers searching for information. |
12 | mmGEO(Trials) | | The number of trials before a positive event. For instance, number of cold calls before we reach a potential buyer. |
13 | mmHYPERGEO(Sample, Defects, BatchSize) | | The number of expected defects in a sample of a given size according to the number of defects expected in the whole batch. |
14 | mmINTUNI(Min, Max) | | Numbers with equal probability within a Lower and a Upper bound. For instance, the preference of clients ordering one of three kinds of pizza. |
15 | mmLOGISTIC(Mean, StDeviation) | | Returns values more spread in the tails of the distribution. For instance, the response of demand to advertising investments. |
16 | mmLOGNORMAL(Mean, StDeviation) | | The product of several independent events. For instance, the monthly value of a market. |
17 | mmNEGBIN(Failures, Successes) | | Number of trials before reaching a certain number of successes. For instance, the number of pedestrians exposed to a billboard to obtain 10 visits. |
18 | mmNORMAL(Mean, StDeviation) | | A normally distributed value around the mean. For instance, the growth of a given market for successive years. |
19 | mmPARETO(Location, ModalValue) | | Can return extreme values. For instance, the spread of income among social classes. |
20 | mmPARETO2(Location, ModalValue) | | Can return extreme values starting from zero. For instance, the mean number of active sessions at a website. |
21 | mmPERT(Location, ModalValue) | | Like mmTRIANGULAR but with smoothed tails. It models events for which the distribution is unknown and thought to be asymmetric. |
22 | mmPOISSON(Rate) | | Number of events occurring given a mean occurrence value. For instance, the population size at different points in time. |
23 | mmRANDBETWEEN(Min, Max) | - | Values in a given interval range. For instance, the pedestrian flow on a sidewalk. |
24 | mmRAYLEIGH(ModalValue) | | Simulates time to perform. For instance, wind speed over a year to estimate the energy recovery from a wind turbine. |
25 | mmSTUDENT(Degrees) | | Events for which we have a mean but not a standard deviation. For instance, the weight of biscuit boxes. |
26 | mmTRIANGULAR(Min, ModalValue, Max) | | Events for which the distribution is unknown and thought to be asymmetric. For instance, the long-term sales of a new product. |
27 | mmUNIFORM(Low, High) | | Bounded by a min and max value, and all values in between have equal likelihood. For instance, the price of a series of products. |
28 | mmWEIBULL(Shape, Spread) | | Models failure time. For instance, the time when a machine enters the critical time for maintenance. |