Data Distribution Features

Bata distribution: a random proportion, just as the proportion of defective products in tasks completed within a period of time. Binary: the number of results that appear within the specified number of tests; often used to indicate the success rate or failure rate of the test results, for example, number of defective products in a batch of products to be delivered or the number of special types of customers to be delivered. TIPS: the deviation from the center is a long extension to both sides. Tips are usually used to simulate highly divergent data, which are distributed around the average center, however, the deviation is large. X Distribution: when the standard normal distribution of independent variable N is square and sum, the X distribution result is square. It is often used in statistical experiments. Constant distribution: random numbers are not generated, and constant values are not changed. In the early stages of model building, it is often used to reduce the impact of random factors or to indicate the same number of times and numbers that have been determined. Distribution of experience: If you are familiar with the probability of an event, you often define or define a specific distribution type. Erlang: frequency is mainly based on the arrangement theory, indicating the number of times of services in various activities, used for telephone communication and other modeling. Exponential: exponential distribution, which is the most widely used in industrial and commercial service processes. It is mainly used to define the time interval when an event occurs, such as the time interval between the customer and the supermarket for shopping and the period during which the device is updated and maintained; it is also used for the average telephone conversation time and the number of maintenance times in a certain stage. Extreme 1a: Describes the distribution range of the maximum values of many types of instances. The maximum value is often used in parameters of systematic models such as astronomy, human life, radiation system, material strength, flood and seismic analysis to predict rainfall. Extreme 1b: describes the distribution range of the minimum values of many types of instances. The minimum value is often used in parameters of systematic models such as astronomy, human life, radiation system, material strength, flood and seismic analysis with rainfall prediction. GAMMA: usually used to indicate the time required to complete a task. When the value of the distribution parameter is between 0 and 1, it is similar to a decreasing exponential distribution curve. If the parameter value is greater than 1, the distribution is tilted from the peak value to the minimum value like a pendulum clock. Geometric: In a series of independent benuli experiments with a certain success rate, the number of failure events before the first test is successful is output. It is usually used to represent the number of products checked before the first item is checked, the number of random entities in a batch, or the number of entities required in an order. Hypereponential: hyper exponential distribution is usually used in telephone communication and queuing theory. Inverse gaussin: it is usually used to simulate the spread process of the Brown motion and boundary conditions. It can also simulate the distribution of specific dimensions in the total number, reliability, validity period, and maintenance time. Inverse Weibo: In general, the distribution is definite, but when it reaches the pole, there is a large deviation in the data; this distribution is used to describe several practical processes in the life distribution; it is also used to fit the abnormal data of the deviation zone on one side of the vertex. Johnson SB: This distribution is a transformation of normal distribution. Johnson distribution has been used in the quality control process to describe non-normal processes and can then be converted to normal distribution for use in standard tests. Johnson su: like Johnson Sb, this Division is also normal distribution and can be used to describe non-normal processes in quality control processes. In addition, this can be used for the well-known unstable Pearson IV Distribution, and its value range is quite credible. Laplace (exponential distribution): This distribution has a vertex in the middle to distinguish it from the normal distribution. Laplace Distribution can be used to describe two independent but exponential distribution. It is often used for error analysis. Logarithmic (logarithm distribution): the logarithm distribution can be used to describe a sample type, that is, the exact number of different types in a specified sample. For example, the distribution has been used in the number of persons with certain characteristics in a group of mosquitoes, or the number of certain types of goods in a group of inventory lists. Logistic (mathematical distribution): the mathematical distribution is very similar to the normal distribution and has a larger deviation. The mathematical distribution function is mainly used in the development modes of some problems, such as population problems, commercial benefits, and enterprise collapse. Log logistic (mathematical logarithm): When the parameter S is 1, it is like an exponential distribution. When the parameter S is <1, it tends to be infinitely large at a certain position, the value decreases with the increase of X. When the parameter S> 1, it has a minimum value of 0 at a certain position, then reaches the vertex and gradually decreases. Lognormal (Standard Logarithm): This distribution is often used to describe the time required for an activity (especially when there are multiple affiliated activities), the interval between failed activities or the duration of manual activities; it is also widely used to protect other commercial property insurance, such as stock return or house return on investment. Negative Binomial (negative binary distribution): The negative binary distribution is used to describe the number of tests that failed before the first event succeeds. P represents the probability of success. Normal (Normal Distribution): It is a famous Gaussian curve or a pendulum curve. When an event is caused by objective rather than human factors, it is the most widely used; for example, describe the total distribution or error distribution composed of the sum of many numbers. * (Negative exponential distribution): it is defined as an exponential distribution opposite to an exponential distribution. The left side has a common hop point and the right side has the characteristics of an exponential extension line; this distribution is often used to simulate many empirical phenomena with very long extension curves, such as income distribution problems in a society, urban population, natural resources, stock price fluctuations, and company size, brightness of huixing and congestion in a series of traffic lines. Pearson Type V (Pearson V Distribution): The Pearson V Distribution is usually used to describe the time required to complete some tasks. The distribution density looks similar to the shape of lognormal, however, there is a big pole when X is close to zero. Pearson Type VI (Pearson VI Distribution): The Pearson V Distribution is usually used to describe the time required to complete some tasks; on the left side of the zero, the distribution is continuous and determined; the distribution on the right side of zero is uncertain. Poisson distribution: Poisson distribution mainly refers to the rate of simulated events. For example, the number of calls per minute, the number of error characters on each page, or the number of events in the system within a certain period of time. Note: In queuing theory, the event arrival rate usually defines the Poisson arrival per unit time. This distribution principle is similar to the exponential distribution. Power function: function functions exist on both sides, and the contained values cannot be negative. Even distribution is a special case of function distribution. Rayleight often represents a life cycle because its risk rate increases over time; for example, the life of a vacuum tube. It jumps on the left side and has a long extension line. Triangular: It is generally better suited to represent a business process than a standard distribution, because it provides the most accurate initial assessment of actual value. It is often used when only three feature information (maximum, minimum, and most likely average) is known in the processing process. Uniform Distribution (integer or constant): an even distribution (integer or real number) is used to describe all values within a specific value range. If there is little information about the task, it is usually used to describe the duration of a task activity. Weber Distribution: Weibo is mainly used to describe the product life cycle and project reliability problems, such as the time interval between mechanical equipment damage (TBF) and maintenance cycle (TTR ).