Quadratic Error in Estimating Multidimensional Normal Distribution Densities from Sample Data

1968 ◽  
Vol 13 (2) ◽  
pp. 341-343 ◽  
Author(s):  
G. M. Maniya
Keyword(s):  
Normal Distribution ◽  
Quadratic Error ◽  
Sample Data ◽  
Multidimensional Normal Distribution

Estimating the Parameters of a Cube Root Normal Distribution from Reduced Sample Data

1985 ◽  
Vol 21 (1) ◽  
pp. 89-90
Author(s):  
C. J. Potratz ◽  
R. K. Steinhorst ◽  
C. L. Hanson
Keyword(s):  
Normal Distribution ◽  
Cube Root ◽  
Sample Data

scholarly journals Identification of Statistical Distribution Type by Sample Data

2021 ◽  
Vol 5 (1) ◽  
pp. 19-24
Author(s):  
Ventsislav Georgiev Nikolov ◽  
Aleksandar Krastev ◽  
Snezhina Yanakieva
Keyword(s):  
Normal Distribution ◽  
Statistical Distribution ◽  
Theoretical Distribution ◽  
Software Implementation ◽  
Full Description ◽  
Statistical Distributions ◽  
Software Library ◽  
Sample Data ◽  
Distribution Type

The present paper provides a full description of a software implementation of a system for statistical distributions. Such a system is almost indispensable in many simulation applications where the factors incorporated adhere to a specific non-normal distribution. The realization is developed as a software library that can be integrated in different other applications. There is also the possibility for additional theoretical distribution types to be added.

scholarly journals The t Distribution: A Transformation of the Employee of the Brewery

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Jorge Mario Insignares Movilla U Insignares Movilla
Keyword(s):  
Hypothesis Testing ◽  
Sample Size ◽  
Normal Distribution ◽  
Detailed Analysis ◽  
Small Sample Size ◽  
Small Sample ◽  
The Other ◽  
Data Set ◽  
Sample Data ◽  
T Distribution

In situations where the size of the sample data set is relatively small, toassume a normal distribution. some uncertainties exist. A mistake is to userandom sampling and the other the small sample size. That is why, beginningwith the story of the generalities that solved the problem t distribution, thenabout topics that support, and …nally, a detailed analysis with some relationshipswith other distributions. While ignore the importance for hypothesis testing instatistical inference when means were contrasted

NUANCES OF IDENTIFICATION FOR NORMAL DISTRIBUTION

2020 ◽  
Vol 11 (3) ◽  
pp. 176-183
Author(s):  
Vyacheslav M. Duplyakin
Keyword(s):  
Statistical Analysis ◽  
Normal Distribution ◽  
Numerical Procedure ◽  
Critical Conditions ◽  
Pearson Criterion ◽  
Statistical Series ◽  
Sample Data ◽  
Low Probability ◽  
Normal Law ◽  
The Given

Statistical analysis of sample data is an effective tool for researching trends in economic processes and their critical conditions. The techniques in statistical analysis that are widely used in practice are based on the assumption that the sample data being considered follows a normal distribution. In the article the author reveals that the application of the popular K. Pearson criterion of agreement in such problems to confirm normality distributions of sample data can lead to false conclusions, in cases where the original general population is distributed according to the normal law, and the criterion indicates a low probability of implementing the normality hypothesis. The author proposes a numerical procedure for studying the nuances of identifying the normality in sample data; it uses a novel technique that is based on reference statistical series which correspond to samples of a certain size with the given, fixed estimates of the expected value and standard deviation. The author presents a numerical modeling method and the results of studying the characteristics of sample data that affect the errors in the identification of the normality of the sampled populations. The performed numerical experiments allowed us to obtain statistical data for investigating the reliability of the identification of the sampled distributions. The author presented recommendations that can help to avoid errors in identifying normality.

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