scholarly journals Verification of normal distribution of welding distortions

2019 ◽  
Vol 91 (3) ◽  
Author(s):  
Damian Grzesiak ◽  
Jarosław Plichta
Keyword(s):  
Statistical Analysis ◽  
Standard Deviation ◽  
Normal Distribution ◽  
Sheet Metal ◽  
Welding Process ◽  
Experiment Planning ◽  
Geometrical Parameters ◽  
Butt Welds ◽  
Planning Methods ◽  
Smirnov Test

The aim of this paper is to answer the question of the distribution of welding distortions. The MIG method was used to make 31 butt welds of 0H18N9 sheet metal, of 6 mm thickness and dimensions 150x350 mm. All joints are made with constant parameters of the welding process. Statistical analysis of the distribution and Kolomogorov-Smirnov test were used in this paper. On the grounds of the analysis it was proved that the distribution of welding deformations is a normal distribution. This justifies the use of experiment planning methods and the use of average values. The relatively high value of the standard deviation makes it necessary to take into account the geometrical parameters of the joint.

scholarly journals Statistical Analysis of Basketball Teams Through Normal Distribution and R Programming

2020 ◽  
Vol 4 (9) ◽  
Author(s):  
Megan Wang
Keyword(s):  
Statistical Analysis ◽  
Standard Deviation ◽  
Probability Density Function ◽  
Normal Distribution ◽  
Sports Analytics ◽  
R Programming Language ◽  
General Improvement ◽  
The Mean ◽  
R Programming ◽  
Two Parameters

Basketball has existed for almost 130 years, becoming one of the most famous sports worldwide by affecting millions of lives and having national and global tournaments. With the general improvement of people's concern and love for sports competition, sports analytics’ role will become more prominent. Hence, this paper combines the relevant knowledge of statistics and typical basketball competition cases from NBA, expounding the application of statistics in sports competition. The paper first examines the importance of normal distribution (also called Gaussian distribution) in statistics through its probability density function and the function's graph. The function has two parameters: the mean for the maximum and standard deviation for the distance away from the mean[1]. By compiling datasets of past teams and individuals for their basketball performances and making simple calculations of their standard deviation and mean, the paper constructs normal distribution graphs using the R programming language. Finally, the paper examines the Real Plus-Minus value and its importance in basketball.

scholarly journals The variability of the flatfoot frequency depending on the diagnostic criteria and the method of statistical analysis

2019 ◽  
Vol 7 (2) ◽  
pp. 41-50 ◽  
Author(s):  
Vladimir M. Kenis ◽  
Alyona J. Dimitrieva ◽  
Andrei V. Sapogovskiy
Keyword(s):  
Statistical Analysis ◽  
Standard Deviation ◽  
Normal Distribution ◽  
Diagnostic Criteria ◽  
Assessment Method ◽  
School Age Children ◽  
Range Data ◽  
Wide Range ◽  
Data Definition ◽  
The Law

Background. Flatfoot frequency in children varies from 0.6% to 77.9%. This wide-range data is associated with lack of uniform diagnostic criteria and method of statistical analysis. Aim. This study aimed to demonstrate the variability in flatfoot frequency in the same population using different indices of footprint and methods of statistical analysis. Material and methods. This study included 317 school-age children. Children with orthopedic and foot pathology were excluded. The main evaluation methods were clinical examination, computer plantography with footprint index calculation (Staheli index, Chippaux–Smirak index, Clarke’s angle, podometric index, arch height index), and statistical analysis (descriptive statistics methods with Kolmogorov–Smirnov and Shapiro–Wilk criteria, data definition according to the law of normal distribution with standard deviation and quartile assessment). Results. According to the law of normal distribution (with a double standard deviation), our study demonstrated that the flatfoot frequency using the plantar footprint indices varies from 1.6% to 4.8% in 7–17-year-old children and using the medial footprint indices, from 1.28% to 2.8% in the same age. Quartile assessment method showed a flatfoot frequency of 5.85%–28.33% with plantar foot indices and 5.7%–15.43% with medial footprint indices. Conclusion. The different plantographic indices and methods of statistical analysis demonstrated that the frequency of a flattened longitudinal arch of the feet in a population may differ significantly. Thus, the frequency of flatfoot determined on the basis of indices calculated on the medial footprint is 1.7–1.8 times lower than that determined on the plantar footprint. In addition, the frequency of flatfoot is 5.5–5.9 times lower than that determined by the quartile assessment.

scholarly journals Statistical analysis of the phytocoenose homogeneity. VI. Frequency distribution of the total species diversity and evenness indices as a function of the area size. Summing-up discussion

2014 ◽  
Vol 55 (1) ◽  
pp. 129-140
Author(s):  
Anna J. Kwiatkowska ◽  
Ewa Symonides
Keyword(s):  
Statistical Analysis ◽  
Standard Deviation ◽  
Species Diversity ◽  
Normal Distribution ◽  
Coefficient Of Variation ◽  
Spatial Differentiation ◽  
Linear Functions ◽  
Frequency Distributions ◽  
Evenness Indices ◽  
Total Species

Homogeneity of the <em>Leucobryo-Pinetum</em> phytocoenose was assessed on the grounds of the agreement of frequency distributions of the total species diversity (A) and evenness (e) indices with the normal distribution. It was confirmed that: 1) empirical frequency distributions of H and e fitted the normal distribution only at some quadrat sizes; 2) values of mean, standard deviation and coefficient of variation were non-linear functions of the area size; 3) mean H and e values calculated for small quadrats (1 and 2 m<sup>2</sup>) differed from those calculated for average (4 and 8 m<sup>2</sup>) and large (16 and 32 m<sup>2</sup>) quadrats: 4) the area size at which frequency distributions of both indices were symmetrical determined the scale of spatial differentiation of the phytocoenose, under which it was homogeneous.

Basics of Statistics

2018 ◽  
pp. 151-180
Author(s):  
Claudia Kimie Suemoto ◽  
Catherine Lee ◽  
Felipe Fregni
Keyword(s):  
Statistical Analysis ◽  
Standard Deviation ◽  
Missing Data ◽  
Normal Distribution ◽  
Data Classification ◽  
Statistical Test ◽  
Descriptive Statistics ◽  
Critical Step ◽  
Summary Measures ◽  
First Contact

This chapter discusses the first step in statistical analysis: the descriptive statistics and data classification. It is the first contact of the researcher with the data, and it is very important to understand the characteristics of the sample, including the presence of missing data and outliers. To perform this step, the investigator needs to learn the types of variables (i.e. numerical and categorical), and the measures to summarize them. In the case of numerical variables, it is also important to check whether the data follow a normal distribution. Researchers usually use mean and standard deviation for numerical variables, and absolute and relative frequencies for categorical ones. In addition to summary measures, different graphs are used to represent the data. Understanding the data is a critical step in choosing the statistical test, as is discussed further in the next chapters.

ИССЛЕДОВАНИЕ ТОЧНОСТИ ДЕТАЛЕЙ, ПОЛУЧАЕМЫХ ПРИ РАЗДЕЛИТЕЛЬНЫХ ОПЕРАЦИЯХ В ПЕРЕНАЛАЖИВАЕМЫХ ШТАМПАХ

2018 ◽  
pp. 52-63
Author(s):  
Е. А. Фролов ◽  
В. В. Агарков ◽  
С. И. Кравченко ◽  
С. Г. Ясько
Keyword(s):  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Balance Method ◽  
Experiment Planning ◽  
Practical Recommendations

To determine the accuracy of the readjustable punches for separating operations (perforation + punching out) of sheet-metal forming, the accuracy parameters were analyzed using the random balance method using the method of experiment planning. Analytical dependencies are obtained to determine the values of deviation of the outer and inner contour dimensions of perforated and punched out sheet parts. From the dependencies obtained, it is possible to estimate and predict the value of deviation in the dimensions of the resulting part at any time during the operation of the punch. Practical recommendations on the calculation of the actuating dimensions of the working elements (stamping punch, matrix) of readjustable punches are offered.

scholarly journals Design of a New Synthetic Acceptance Sampling Plan

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 653 ◽  
Author(s):  
Saeed Dobbah ◽  
Muhammad Aslam ◽  
Khushnoor Khan
Keyword(s):  
Standard Deviation ◽  
Normal Distribution ◽  
Operating Characteristic ◽  
Linear Optimization ◽  
Sampling Plan ◽  
Quality Characteristic ◽  
Acceptance Sampling ◽  
Acceptance Sampling Plan ◽  
Non Linear

In this paper, we propose a new synthetic sampling plan assuming that the quality characteristic follows the normal distribution with known and unknown standard deviation. The proposed plan is given and the operating characteristic (OC) function is derived to measure the performance of the proposed sampling plan for some fixed parameters. The parameters of the proposed sampling plan are determined using non-linear optimization solution. A real example is added to explain the use of the proposed plan by industry.

scholarly journals Statistical analysis of solid waste composition data: Arithmetic mean, standard deviation and correlation coefficients

2017 ◽  
Vol 69 ◽  
pp. 13-23 ◽  
Author(s):  
Maklawe Essonanawe Edjabou ◽  
Josep Antoni Martín-Fernández ◽  
Charlotte Scheutz ◽  
Thomas Fruergaard Astrup
Keyword(s):  
Statistical Analysis ◽  
Standard Deviation ◽  
Solid Waste ◽  
Correlation Coefficients ◽  
Arithmetic Mean ◽  
Composition Data ◽  
Waste Composition

scholarly journals Application of descriptive statistics in analysis of experimental data

2008 ◽  
Vol 62 (1-2) ◽  
pp. 85-95
Author(s):  
Milorad Mirilovic ◽  
Ivana Pejin
Keyword(s):  
Experimental Data ◽  
Statistical Analysis ◽  
Standard Deviation ◽  
Arithmetic Mean ◽  
Descriptive Statistics ◽  
Qualitative Investigation ◽  
Scientific Methods ◽  
The Subject ◽  
Variation Interval ◽  
Descriptive Statistical Analysis

Statistics today represent a group of scientific methods for the quantitative and qualitative investigation of variations in mass appearances. In fact, statistics present a group of methods that are used for the accumulation, analysis, presentation and interpretation of data necessary for reaching certain conclusions. Statistical analysis is divided into descriptive statistical analysis and inferential statistics. The values which represent the results of an experiment, and which are the subject of observation of a certain occurrence, are called parameters and they are divided into descriptive and numerical. All numerical parameters are divided into non-continuous and continuous. The graphic presentation of the distribution of frequencies can be by poligon or histogram. The most frequently applied descriptive statistical methods are: arithmetic mean, standard deviation, standard error of arithmetic mean, variation coefficient, and variation interval.

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