Relative And Cumulative Frequency Distributions

2013 ◽  
Keyword(s):  
Cumulative Frequency ◽  
Frequency Distributions

Effect of Ethanol on Drug Levels in Blood in Fatal Cases

1982 ◽  
Vol 22 (4) ◽  
pp. 232-234 ◽  
Author(s):  
L. A. King
Keyword(s):  
Blood Levels ◽  
Cumulative Frequency ◽  
Frequency Distributions ◽  
Drug Levels ◽  
Postmortem Blood

The cumulative frequency distributions of postmortem blood levels of four centrally-acting drugs (pentobarbitone, amitriptyline, chlormethiazole and dextropropoxyphene) have been derived from fatalities involving either the named drug only or the drug in association with ethanol only. For each drug, the presence of ethanol markedly reduced the concentration at which a given proportion of fatalities occurred.

Cumulative Frequency Distributions for Hourly Global Illuminance

2011 ◽  
Author(s):  
V. V. Satyamurty ◽  
P. RaviKumar
Keyword(s):  
Present Article ◽  
Cumulative Frequency ◽  
Frequency Distributions

In the present article, cumulative frequency distributions for hourly global horizontal illuminance based on the modeled data of 56 US locations have been presented. Measured illuminance information from other sources also has been utilized. Suitable normalized variables have been introduced to depict the generalized cumulative frequency distributions for hourly global illuminance. The cumulative distributions developed based on representative set locations have been shown to be applicable for another set of locations. In addition, the present article brings out, the major differences between daily and hourly cumulative frequency distributions and, a method to generate hourly (synthetic) global illuminance data from the frequency distributions.

Twister Generator of Poisson Random Numbers with the Use of Cumulative Frequency Technology

2020 ◽  
pp. 101-123
Author(s):  
A.F. Deon ◽  
D.D. Dmitriev ◽  
Yu.A. Menyaev
Keyword(s):  
Poisson Distribution ◽  
Probability Distributions ◽  
Random Variables ◽  
Convergence In Probability ◽  
Random Numbers ◽  
Cumulative Frequency ◽  
New Approach ◽  
Frequency Distributions ◽  
Simulation Results

The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete Poisson probability distributions and skipping in the generated numbers. This paper offers a new approach for creating Poisson random variables based on the complete twister generator of uniform random variables, using cumulative frequency technology. The simulation results confirm that probabilistic and frequency distributions of the obtained stochastic numbers completely coincide with the theoretical Poisson distribution. Moreover, combining this new approach with the tuning algorithm of basic twister generation allows for a significant increase in length of the created sequences without using additional RAM of the computer

Cumulative Frequency Distributions for Daily Global Illuminance

Solar Energy ◽  
2004 ◽  
Author(s):  
V. V. Satyamurty ◽  
P. RaviKumar
Keyword(s):  
Standard Deviation ◽  
Data Base ◽  
Linear Form ◽  
Large Data ◽  
Cumulative Frequency ◽  
Clearness Index ◽  
Frequency Distributions ◽  
Large Data Base ◽  
Average Value ◽  
Monthly Average

The present article describes the development of cumulative frequency distributions (similar to clearness index distributions) for daily global horizontal illuminance based on the modeled data of 56 US locations. Normalized variables have been employed to account for seasonal and location dependencies. The normalized variables contain the maximum and minimum illuminance values in the array of daily values in addition to the daily illuminance (the variable to be related to cumulative frequency) and the monthly average daily illuminance. The distributions thus developed discrete in terms of the normalized monthly average daily global illuminance have been presented in a convenient linear form which accommodates continuous values for the parameter. When data are not available, the maximum and minimum of the daily values needed in estimating the parameter first and eventually the daily illuminance values have been correlated to other more readily available predictors. Present distributions have been validated by comparing against the daily illuminance values available in the large data base employed. Daily global illuminance has been predicted with a standard deviation of 23.44 klux and rms difference of 4.64%. The present correlations can be conveniently applied to obtain the 30 or 31 daily illuminance values in a month from the monthly average value.

scholarly journals Poisson Twister Generator by Cumulative Frequency Technology

Algorithms ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 114 ◽  
Author(s):  
Aleksei F. Deon ◽  
Yulian A. Menyaev
Keyword(s):  
Poisson Distribution ◽  
Probability Distributions ◽  
Random Variables ◽  
Convergence In Probability ◽  
Cumulative Frequency ◽  
New Approach ◽  
Frequency Distributions ◽  
Simulation Results

The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete Poisson probability distributions and skipping in the generated numbers. This article offers a new approach for creating Poisson random variables based on the complete twister generator of uniform random variables, using cumulative frequency technology. The simulation results confirm that probabilistic and frequency distributions of the obtained stochastic numbers completely coincide with the theoretical Poisson distribution. Moreover, combining this new approach with the tuning algorithm of basic twister generation allows for a significant increase in length of the created sequences without using additional RAM of the computer.

Cumulative Frequency Distributions for Hourly Clearness Indices Through Generalized Parameters

2010 ◽  
Author(s):  
V. V. Satyamurty ◽  
P. RaviKumar
Keyword(s):  
Time Scale ◽  
Cumulative Frequency ◽  
Clearness Index ◽  
Frequency Distributions ◽  
The Difference ◽  
Generalized Parameters ◽  
Different Climates

The objective of the present investigation is to develop generalized cumulative frequency distributions for hourly clearness indices applicable for different climates. The distributions have been developed in terms of suitable normalized variables relevant on hourly time scale, and include the swing (the difference between the maximum and minimum values of the hourly clearness index for a particular hour over the days in a month) in the hourly clearness index. The adequacy of the distributions developed in predicting the hourly clearness index has been established by comparing with the data values of the 183 secondary locations in addition to the 56 primary locations in USA for which the data are available.

Generation of Hourly Ambient Temperature from Generalized Cumulative Frequency Distributions

2004 ◽  
Vol 126 (2) ◽  
pp. 683-695 ◽  
Author(s):  
V. V. Satyamurty ◽  
P. Ravikumar
Keyword(s):  
Standard Deviation ◽  
Ambient Temperature ◽  
Data Base ◽  
Present Article ◽  
Large Data ◽  
Cumulative Frequency ◽  
Frequency Distributions ◽  
Large Data Base ◽  
Ambient Temperatures ◽  
Monthly Average

The present article is devoted to make available a method to generate hourly ambient temperature values when the monthly average hourly ambient temperature, T¯ah is known. Cumulative frequency distributions for hourly ambient temperature have been developed employing normalized variables. The normalized variables used in the present study contain not only the monthly average hourly ambient temperature but also the maximum, Tah,max, and minimum, Tah,min. In order that the frequency distributions developed in the present study become available for the purpose of calculating hourly ambient temperature when T¯ah only is known, correlations to estimate Tah,max and Tah,min from known parameters have been presented. The ability of the present distributions to generate hourly ambient temperatures during a day has also been established. A large data base of 239 locations has been employed in developing and validating the distributions and the needed additional correlations. The predicted hourly ambient temperature values have been found to be in agreement with data values within a % rms difference of 0.30 (when Tah is in K) and the standard deviation has been found to be 0.85 K. The agreement between the predicted hourly ambient temperatures during a day employing the present distributions and the data values has been excellent.

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