The Analysis of Significance Difference in Writing Achievement Among the Students Who Are Introvert, Extrovert, and Ambivert

The objective of this research was to investigate significant difference among personality types in students\u27 writing achievement. The samples of the research comprised the third semester students of English Department of IAIN Bengkulu, academic year 2015/2016. The research design was ex - post facto. The result showed that the comparison result of writing achievement between introvert and extrovert types showed that Sig ?, thus the alternative hypothesis was rejected. In conclusion, there was significance difference in writing achievement between introvert and both of extrovert and ambivert types. Meanwhile there was no significant difference in writing achievement between extrovert and ambivert types. This means that introvert students were stronger than extrovert and ambivert students in writing achievement of narrative tex

Significance Analysis for Pairwise Variable Selection in Classification

The goal of this article is to select important variables that can distinguish one class of data from another. A marginal variable selection method ranks the marginal effects for classification of individual variables, and is a useful and efficient approach for variable selection. Our focus here is to consider the bivariate effect, in addition to the marginal effect. In particular, we are interested in those pairs of variables that can lead to accurate classification predictions when they are viewed jointly. To accomplish this, we propose a permutation test called Significance test of Joint Effect (SigJEff). In the absence of joint effect in the data, SigJEff is similar or equivalent to many marginal methods. However, when joint effects exist, our method can significantly boost the performance of variable selection. Such joint effects can help to provide additional, and sometimes dominating, advantage for classification. We illustrate and validate our approach using both simulated example and a real glioblastoma multiforme data set, which provide promising results.Comment: 28 pages, 7 figure

Significance analysis and statistical mechanics: an application to clustering

This paper addresses the statistical significance of structures in random data: Given a set of vectors and a measure of mutual similarity, how likely does a subset of these vectors form a cluster with enhanced similarity among its elements? The computation of this cluster p-value for randomly distributed vectors is mapped onto a well-defined problem of statistical mechanics. We solve this problem analytically, establishing a connection between the physics of quenched disorder and multiple testing statistics in clustering and related problems. In an application to gene expression data, we find a remarkable link between the statistical significance of a cluster and the functional relationships between its genes.Comment: to appear in Phys. Rev. Let

Foreign Direct Investment - An Analysis of its Significance

This article reviews the concepts and problems associated with Foreign Direct Investment , and the subsequent implications for Irish FDI data.

The Influence of Job Satisfaction and Work Experience on Lecturer Performance of Pamulang University

The purpose of this research is to know the effect of job satisfaction and work experience on lecturer performance of Pamulang University. The research design used is quantitative with descriptive method. The analysis method used is multiple linear regression analysis with a sample of 150 and the sampling technique used is proporsionate random sampling. Furthermore, the research is done by testing the stages of analysis that includes descriptive analysis of questionnaires, validity test, reliability test, linear regression test, correlation coefficient test, partial test (t test), simultaneous test and determination test. Regression analysis results proved, Job satisfaction has a significant positive effect on the performance of 0.557, tcount 6.751 and a significance value of 0.000 smaller than 0.05. Work experience has a significant effect on performance of 0.451, the tcount of 5.467 and the significance value of 0.000 is smaller than 0.05. Simultaneous analysis proved job satisfaction and work experience have positive and significant effect on performance value of F arithmetic of 72.201, significance value of 0.000 and determination coefficient of 0.744.  It's means, job satisfaction and work experience able to explain the performance of 74.4% while the rest of 25.6% is explained by other variable

Sound and Fury: McCloskey and Significance Testing in Economics

For about twenty years, Deidre McCloskey has campaigned to convince the economics profession that it is hopelessly confused about statistical significance. She argues that many practices associated with significance testing are bad science and that most economists routinely employ these bad practices: “Though to a child they look like science, with all that really hard math, no science is being done in these and 96 percent of the best empirical economics. . .” (McCloskey 1999). McCloskey’s charges are analyzed and rejected. That statistical significance is not economic significance is a jejune and uncontroversial claim, and there is no convincing evidence that economists systematically mistake the two. Other elements of McCloskey’s analysis of statistical significance are shown to be ill-founded, and her criticisms of practices of economists are found to be based in inaccurate readings and tendentious interpretations of their work. Properly used, significance tests are a valuable tool for assessing signal strength, for assisting in model specification, and for determining causal structure.statistical significance, economic significance, significance testing, regression analysis, econometric methodology, Deirdre McCloskey, Neyman-Pearson testing

Significance Tests for Periodogram Peaks

We discuss methods currently in use for determining the significance of peaks in the periodograms of time series. We discuss some general methods for constructing significance tests, false alarm probability functions, and the role played in these by independent random variables and by empirical and theoretical cumulative distribution functions. We also discuss the concept of "independent frequencies" in periodogram analysis. We propose a practical method for estimating the significance of periodogram peaks, applicable to all time series irrespective of the spacing of the data. This method, based on Monte Carlo simulations, produces significance tests that are tailor-made for any given astronomical time series.Comment: 22 pages, 11 Encapsulated Postscript figures, AAS LaTeX v5.2 Submitted to Ap