Quantitative methods in ethnobotany and ethnopharmacology: Considering the overall flora—Hypothesis testing for over- and underused plant families with the Bayesian approach

2011 ◽  
Vol 137 (1) ◽  
pp. 837-843 ◽  
Author(s):  
Caroline S. Weckerle ◽  
Stefano Cabras ◽  
Maria Eugenia Castellanos ◽  
Marco Leonti
Keyword(s):  
Hypothesis Testing ◽  
Bayesian Approach ◽  
Quantitative Methods ◽  
Plant Families ◽  
The Bayesian Approach

Bayesian Hypothesis Testing for Hospitality and Tourism Research

2020 ◽  
pp. 109634802094732
Author(s):  
A. George Assaf ◽  
Mike Tsionas
Keyword(s):  
Hypothesis Testing ◽  
Bayesian Approach ◽  
Prior Distributions ◽  
P Values ◽  
Tourism Research ◽  
Bayesian Hypothesis Testing ◽  
Main Challenge ◽  
Hospitality And Tourism ◽  
Practical Recommendations ◽  
The Bayesian Approach

In hospitality and tourism research, p-values continue to be the most common approach to hypothesis testing. In this article, we elaborate on some of the misconceptions associated with p-values. We discuss the advantages of the Bayesian approach and provide several important practical recommendations and considerations for Bayesian hypothesis testing. With the main challenge of Bayesian hypothesis testing being the sensitivity of the results to prior distributions, we present in this article several priors that can be used for that purpose and illustrate their performance in a regression context.

scholarly journals Beyond P values and Hypothesis Testing: Using the Minimum Bayes Factor to Teach Statistical Inference in Undergraduate Introductory Statistics Courses

2017 ◽  
Vol 6 (4) ◽  
pp. 254 ◽  
Author(s):  
Robert Page ◽  
Eiki Satake
Keyword(s):  
Hypothesis Testing ◽  
Bayesian Approach ◽  
Bayesian Methods ◽  
Bayes Factor ◽  
Statistics Education ◽  
Introductory Statistics ◽  
P Value ◽  
Test Results ◽  
Statistics Course ◽  
The Bayesian Approach

While interest in Bayesian statistics has been growing in statistics education, the treatment of the topic is still inadequate in both textbooks and the classroom. Because so many fields of study lead to careers that involve a decision-making process requiring an understanding of Bayesian methods, it is becoming increasingly clear that Bayesian methods should be included in classes that cover the P value and Hypothesis Testing. We discuss several fallacies associated with the P value and Hypothesis Testing, including why Fisher’s P value and Neyman-Pearson’s Hypothesis Tests are incompatible with each other and cannot be combined to answer the question “What is the probability of the truth of one’s belief based on the evidence?” We go on to explain how the Minimum Bayes Factor can be used as an alternative to frequentist methods, and why the Bayesian approach results in more accurate, credible, and relevant test results when measuring the strength of the evidence. We conclude that educators must realize the importance of teaching the correct interpretation of Fisher’s P value and its alternative, the Bayesian approach, to students in an introductory statistics course.

PENGARUH AKTIVA TETAP DAN AKTIVA LANCAR TERHADAP PENDAPATAN PADA SIMPAN PINJAM PEREMPUAN UPK GERBANG SARI KECAMATAN RENGAT BARAT

2019 ◽  
Vol 7 (2) ◽  
pp. 101-108
Author(s):  
Angga Hapsila
Keyword(s):  
Hypothesis Testing ◽  
Significant Influence ◽  
Quantitative Methods ◽  
Correlation Coefficients ◽  
Multiple Correlation ◽  
Savings And Loans ◽  
Savings And Loan ◽  
Fixed Assets ◽  
Multiple Determination ◽  
Regression Correlation

This research is about savings and loans for women UPK Gerbang Sari, Rengat Barat District. The research lasted for 5 (five) months. This research try to determine the effect of whether there was a significant influence between fixed assets and current assets on income on women's savings and loans UPK Gerbang Sari, Rengat Barat District. The formulation of the problem to be investigated is whether there is a simultaneous and partial effect between fixed assets and current assets on women's savings and loan income UPK Gerbang Sari, Rengat Barat District. This research used quantitative methods, that illustrates whether there is a significant influence between fixed assets and current assets on income on savings and loans for women UPK Gerbang Sari, Rengat Barat District. To analyzed the data, this research used multiple regression, correlation coefficients, and Hypothesis testing which will be assisted using SPSS version 21. From the results of the study with the regression equation Y = 1,379,231-1,075 X1 + 0,214X2. This means: a = if the fixed assets and current assets are equal to Zero (0), then the value of income is equal to 1,379,231. The coefficient of multiple correlation R is 0.686. and furthermore tested with multiple determination Keofisie (R2 is 0.471. This indicates that (X1) fixed assets and (X2) current assets have together an influence of 47.10% and the remaining 52.90% is influenced by variables other than ( X1) and (X2). Hypothesis testing in which F arithmetic (0.891) <Ftable (3.00), then there is no significant effect simultaneously between fixed assets and current assets to income on savings and loans for women in UPK Gerbang Sari, Rengat Barat District.

scholarly journals The Bayesian Approach and the Results of the ISAR-REACT 5 Trial

2021 ◽  
Vol 14 (2) ◽  
pp. 231-232
Author(s):  
Adnan Kastrati ◽  
Alexander Hapfelmeier
Keyword(s):  
Bayesian Approach ◽  
The Bayesian Approach

A new Bayesian approach for QTL mapping of family data

2021 ◽  
Author(s):  
Daiane Aparecida Zuanetti ◽  
Luis Aparecido Milan
Keyword(s):  
Qtl Mapping ◽  
Random Effects ◽  
Bayesian Approach ◽  
Variance Component ◽  
Mendelian Inheritance ◽  
Family Data ◽  
Data Sets ◽  
Mendelian Segregation ◽  
Gaw17 Data ◽  
The Bayesian Approach

In this paper, we propose a new Bayesian approach for QTL mapping of family data. The main purpose is to model a phenotype as a function of QTLs’ effects. The model considers the detailed familiar dependence and it does not rely on random effects. It combines the probability for Mendelian inheritance of parents’ genotype and the correlation between flanking markers and QTLs. This is an advance when compared with models which use only Mendelian segregation or only the correlation between markers and QTLs to estimate transmission probabilities. We use the Bayesian approach to estimate the number of QTLs, their location and the additive and dominance effects. We compare the performance of the proposed method with variance component and LASSO models using simulated and GAW17 data sets. Under tested conditions, the proposed method outperforms other methods in aspects such as estimating the number of QTLs, the accuracy of the QTLs’ position and the estimate of their effects. The results of the application of the proposed method to data sets exceeded all of our expectations.

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