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Comparison of Convenience Sampling and Purposive Sampling
Ilker Etikan,
Sulaiman Abubakar Musa,
Rukayya Sunusi Alkassim
Issue:
Volume 5, Issue 1, January 2016
Pages:
1-4
Received:
23 November 2015
Accepted:
6 December 2015
Published:
22 December 2015
Abstract: This article studied and compared the two nonprobability sampling techniques namely, Convenience Sampling and Purposive Sampling. Convenience Sampling and Purposive Sampling are Nonprobability Sampling Techniques that a researcher uses to choose a sample of subjects/units from a population. Although, Nonprobability sampling has a lot of limitations due to the subjective nature in choosing the sample and thus it is not good representative of the population, but it is useful especially when randomization is impossible like when the population is very large. It can be useful when the researcher has limited resources, time and workforce. It can also be used when the research does not aim to generate results that will be used to create generalizations pertaining to the entire population. Therefore, there is a need to use nonprobability sampling techniques. The aim of this study is to compare among the two nonrandom sampling techniques in order to know whether one technique is better or useful than the other. Different articles were reviewed to compare between Convenience Sampling and Purposive Sampling and it is concluded that the choice of the techniques (Convenience Sampling and Purposive Sampling) depends on the nature and type of the research.
Abstract: This article studied and compared the two nonprobability sampling techniques namely, Convenience Sampling and Purposive Sampling. Convenience Sampling and Purposive Sampling are Nonprobability Sampling Techniques that a researcher uses to choose a sample of subjects/units from a population. Although, Nonprobability sampling has a lot of limitations...
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A Brief Review of Tests for Normality
Keya Rani Das,
A. H. M. Rahmatullah Imon
Issue:
Volume 5, Issue 1, January 2016
Pages:
5-12
Received:
24 December 2015
Accepted:
5 January 2016
Published:
27 January 2016
Abstract: In statistics it is conventional to assume that the observations are normal. The entire statistical framework is grounded on this assumption and if this assumption is violated the inference breaks down. For this reason it is essential to check or test this assumption before any statistical analysis of data. In this paper we provide a brief review of commonly used tests for normality. We present both graphical and analytical tests here. Normality tests in regression and experimental design suffer from supernormality. We also address this issue in this paper and present some tests which can successfully handle this problem.
Abstract: In statistics it is conventional to assume that the observations are normal. The entire statistical framework is grounded on this assumption and if this assumption is violated the inference breaks down. For this reason it is essential to check or test this assumption before any statistical analysis of data. In this paper we provide a brief review o...
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Kernel-Type Estimators of Divergence Measures and Its Strong Uniform Consistency
Hamza Dhaker,
Papa Ngom,
El Hadji Deme,
Pierre Mendy
Issue:
Volume 5, Issue 1, January 2016
Pages:
13-22
Received:
8 January 2016
Accepted:
23 January 2016
Published:
16 February 2016
Abstract: Nonparametric density estimation, based on kernel-type estimators, is a very popular method in statistical research, especially when we want to model the probabilistic or stochastic structure of a data set. In this paper, we investigate the asymptotic confidence bands for the distribution with kernel-estimators for some types of divergence measures (Rényi-α and Tsallis-α divergence). Our aim is to use the method based on empirical process techniques, in order to derive some asymptotic results. Under different assumptions, we establish a variety of fundamental and theoretical properties, such as the strong consistency of an uniform-in-bandwidth of the divergence estimators. We further apply the previous results in simulated examples, including the kernel-type estimator for Hellinger, Bhattacharyya and Kullback-Leibler divergence, to illustrate this approach, and we show that that the method performs competitively.
Abstract: Nonparametric density estimation, based on kernel-type estimators, is a very popular method in statistical research, especially when we want to model the probabilistic or stochastic structure of a data set. In this paper, we investigate the asymptotic confidence bands for the distribution with kernel-estimators for some types of divergence measures...
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A Review on ‘Probability and Stochastic Processes’
Issue:
Volume 5, Issue 1, January 2016
Pages:
23-26
Received:
17 January 2016
Accepted:
26 January 2016
Published:
16 February 2016
Abstract: This paper is a commentary on the book ‘Probability and Stochastic Processes’ from Ionut Florescu. The book is an excellent introduction to both probability theory and stochastic processes. It provides a comprehensive discussion of the main statistical concepts including the theorems and proofs. The introduction to probability theory is easy accessible and a perfect starting point for undergraduate students even with majors in other subjects than science, such as business or engineering. The book is also up-to-date because it includes programming code for simulations. However, the book has some weaknesses. It is less convincing in more advanced topics of stochastic theory and it does not include solutions to excises and recent research trends.
Abstract: This paper is a commentary on the book ‘Probability and Stochastic Processes’ from Ionut Florescu. The book is an excellent introduction to both probability theory and stochastic processes. It provides a comprehensive discussion of the main statistical concepts including the theorems and proofs. The introduction to probability theory is easy access...
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Tempering Process Optimization in Sae 9254 Wires Through Generalized Reduced Gradient, Genetic Algorithms and Simulated Annealing
Cristie Diego Pimenta,
Messias Borges Silva,
Rosinei Batista Ribeiro,
Rose Lima de Morais Campos,
Walfredo Ribeiro de Campos Junior,
Jorge Luiz Rosa
Issue:
Volume 5, Issue 1, January 2016
Pages:
27-38
Received:
15 January 2016
Accepted:
3 February 2016
Published:
23 February 2016
Abstract: The purpose of this work was the creation of a statistical modeling able to replace the process used to setup of the ovens of the quench hardening and tempering that is traditionally accomplished through adjustments made based on the results of mechanical properties as tested in laboratory and required in customer specifications. We sought to understand the influence of the input variables (factors) on the mechanical properties tensile strength and hardness, in SAE 9254 draw steel wires, with diameters 2.00 mm and 6.50 mm, used in the manufacture of valve springs and clutch for automobile tracking. Were investigated the input variables of the process speed and tempering temperature. Design of Experiments with block Analysis, Quadratic Multiple Regression, Analysis of Variance (ANOVA) and Response Surface Methodology (RSM). For the optimization of statistical models were used the Generalized Reduced Gradient methods (GRG), Genetic Algorithm (AG) and the Meta-heuristics Simulated Annealing (SA). The results revealed that all variables considered have significant influence and models obtained were validated using appropriate statistical methods. This new modeling and its optimization, if properly implemented and enforced, could lead scientific advances which would provide the automation of this process, and consequently cause great impact on increasing productivity and product quality.
Abstract: The purpose of this work was the creation of a statistical modeling able to replace the process used to setup of the ovens of the quench hardening and tempering that is traditionally accomplished through adjustments made based on the results of mechanical properties as tested in laboratory and required in customer specifications. We sought to under...
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