scholarly journals Bayesian regions of evidence (for normal distributions)

2021 ◽  
Author(s):  
Michael Höfler
Keyword(s):  
Data Analysis ◽  
Standard Error ◽  
Prior Distribution ◽  
Normal Distributions ◽  
Bayesian Data Analysis ◽  
Personal Belief ◽  
Bayes Analysis ◽  
Negative Effect ◽  
Normally Distributed

Bayesian data analysis allows a researcher to assess whether a claim about an effect (e.g. effect > 0, effect > Δ, |effect| < Δ)) is justified given the data and a prior distribution, expressing her or his personal belief before seeing the data. However, the recipients of the analysis might use different priors, so it remains unclear whether they would share the claim. "Reverse Bayes" analysis and the "sufficiently sceptical prior" address this problem by asking how strongly one may believe in the absence of an effect in order to be convinced otherwise by the data. A method called "Region of Evidence" is presented that takes this idea and extends it for any normal prior (and a normally distributed estimate). It visualises all the priors that, if they had been used, would support the claim, including those that favour a positive or negative effect. Since the method depends only on an estimate and its standard error, it can be easily applied to previously published results. The paper describes the method and its implementation in a new Stata command called arevi, which can be freely used and modified.

A Bayesian Data Analysis System for the Evaluation of Social Programs

1977 ◽  
Vol 72 (360) ◽  
pp. 711 ◽  
Author(s):  
Ming-Mei Wang ◽  
Melvin R. Novick ◽  
Gerald L. Isaacs ◽  
Dan Ozenne
Keyword(s):  
Data Analysis ◽  
Social Programs ◽  
Bayesian Data Analysis ◽  
Analysis System ◽  
Data Analysis System

scholarly journals THE STUDENT’S LEARNING STYLE AND THEIR WRITING SKILL AT SMK TIARA AKSARA

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Eka Sari ◽  
Ikhfi Imaniah ◽  
Immtihan Hanim
Keyword(s):  
Data Analysis ◽  
Learning Style ◽  
Quantitative Method ◽  
Writing Test ◽  
Writing Skill ◽  
Parametric Statistics ◽  
Non Parametric ◽  
Normally Distributed

Writing is an activity that not only need physical act but also mental act. Students can put their statements, idea and opinion in composing writing. Moreover, students learn with visually, auditory and kinesthetic. Students with multi learning style usually better in learning and understand lessons, particularly in writing. Due to the statement, this study is aimed to find out the correlation between students’ learning style and their writing skill. This study use survey quantitative method and analyze the data by using statitisctical hypotheses. Based on the data analysis, it was found that student’s learning style was not normally distributed, whereas the data of writing test was normally distributed. Therefore, the researchers used non-parametric statistics to analyze the hypotheses. Kendall Tau calculation was used and obtained that Zcount ((0,35) <  Ztable (1,96) with significant 0,05. Thus, H0 is accepted and Hi is rejected. It means that there is no correlation between students’ learning style and their writing skill. In this case, learning style were not dominat factors affected writing skill.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI PENGGUNAAN INFORMASI AKUNTANSI DI UKM WIG KABUPATEN PURBALINGGA

2020 ◽  
Vol 16 (2) ◽  
pp. 143-170
Author(s):  
Aditya Rahmawan ◽  
Eliada Herwiyanti ◽  
Siti Maghfiroh
Keyword(s):  
Data Analysis ◽  
Sampling Method ◽  
Accounting Information ◽  
Educational Background ◽  
Significant Positive Effect ◽  
Purposive Sampling ◽  
Significant Negative Effect ◽  
Negative Effect ◽  
Positive Effect ◽  
Education Business

This study aimed to examine the factors that influence the use of accounting information in SMEs. The object of research is Wig UKM in Purbalingga Regency. From this research, it can be seen how education, business scale, business age, and accounting knowledge of the use of UKM Wig accounting information in Purbalingga. The number of respondents involved in this study was 82 people. Sampling using a purposive sampling method. Based on the results of research and data analysis using SPSS it was found that: (1) educational background did not have a significant negative effect on the use of accounting information, (2) the business scale had no significant positive effect on the use of accounting information, (3) the age of the business had no significant negative effect on the use of accounting information, and (4) accounting knowledge has a significant positive effect on the use of accounting information.

scholarly journals Analysis of Financial Ratios for Predicting Bankruptcy in SMEs Listed on PEFINDO25

2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Vitalia Fina Carla Rettobjaan
Keyword(s):  
Logistic Regression ◽  
Data Analysis ◽  
Activity Ratio ◽  
Significant Positive Effect ◽  
Debt Structure ◽  
Financial Ratio ◽  
Independent Variables ◽  
Negative Effect ◽  
And Control ◽  
Positive Effect

This study aims to analyze the Financial Ratio for Predicting Bankruptcy. The sample used in this study are SMEs according PEFINDO25 period 2013 to 2017. The independent variables in this study is liquidity, profitability, debt structure, solvency and activity ratio; and control variables is size and age, as well as the dependent variable is bankruptcy. The amount of sample in this study 32 companies PEFINDO25 by using purposive sampling. The method of data analysis is done by using logistic regression with SPSS version 23. The result of this research showed that liquidity, profitability and age has significant negative effect on bankruptcy. Debt structure has significant positive effect on bankruptcy. While solvency, activity ratio and size does not significantly effect on bankruptcy

scholarly journals Bayesian data analysis in the phonetic sciences: A tutorial introduction

2018 ◽  
Vol 71 ◽  
pp. 147-161 ◽  
Author(s):  
Shravan Vasishth ◽  
Bruno Nicenboim ◽  
Mary E. Beckman ◽  
Fangfang Li ◽  
Eun Jong Kong
Keyword(s):  
Data Analysis ◽  
Bayesian Data Analysis

Mathematics and Statistics in Anesthesiology

2018 ◽  
Author(s):  
Daniel Mortlock
Keyword(s):  
Probability Theory ◽  
Data Analysis ◽  
Survey Design ◽  
Probability Distributions ◽  
Statistical Tests ◽  
Real World Data ◽  
Mathematical Functions ◽  
P Values ◽  
Bayesian Data Analysis ◽  
Probability And Statistics

Mathematics is the language of quantitative science, and probability and statistics are the extension of classical logic to real world data analysis and experimental design. The basics of mathematical functions and probability theory are summarized here, providing the tools for statistical modeling and assessment of experimental results. There is a focus on the Bayesian approach to such problems (ie, Bayesian data analysis); therefore, the basic laws of probability are stated, along with several standard probability distributions (eg, binomial, Poisson, Gaussian). A number of standard classical tests (eg, p values, the t-test) are also defined and, to the degree possible, linked to the underlying principles of probability theory. This review contains 5 figures, 1 table, and 15 references. Keywords: Bayesian data analysis, mathematical models, power analysis, probability, p values, statistical tests, statistics, survey design

Sign in / Sign up

Export Citation Format

Share Document