Statistical Data Treatment and Decision Making in TNDT

2020 ◽  
pp. 397-414
Author(s):  
Vladimir Vavilov ◽  
Douglas Burleigh
Keyword(s):  
Decision Making ◽  
Statistical Data ◽  
Data Treatment

Anecdotal Bias in Capital Allocation Depends on Anecdote Similarity

2021 ◽  
Author(s):  
Shir Dekel ◽  
Micah Goldwater ◽  
Dan Lovallo ◽  
Bruce Burns
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Statistical Data ◽  
Capital Allocation ◽  
Medical Decision ◽  
Target Problem ◽  
Bias Effect ◽  
Business Decision ◽  
Statistical Measures ◽  
Allocation Decisions

Previous research found that anecdotes are more persuasive than statistical data—the anecdotal bias effect. Separate research found that anecdotes that are similar to a target problem are more influential on decision-making than dissimilar anecdotes. Further, previous investigations on anecdotal bias primarily focused on medical decision-making with very little focus on business decision-making. Therefore, we investigated the effect of anecdote similarity on anecdotal bias in capital allocation decisions. Participants were asked to allocate a hypothetical budget between two business projects. One of the projects (the target project) was clearly superior in terms of the provided statistical measures, but some of the participants also saw a description of a project with a conflicting outcome (the anecdotal project). This anecdotal project was always from the same industry as the target project. The anecdote description, however, either contained substantive connections to the target or not. Further, the anecdote conflicted with the statistical measures because it was either successful (positive anecdote) or unsuccessful (negative anecdote). The results showed that participants’ decisions were influenced by anecdotes only when they believed that they were actually relevant to the target project. Further, they still incorporated the statistical measures into their decision. This was found for both positive and negative anecdotes. Further, participants were given information about the way that the anecdotes were sampled that suggested that the statistical information should have been used in all cases. Participants did not use this information in their decisions and still showed an anecdotal bias effect. Therefore, people seem to appropriately use anecdotes based on their relevance, but do not understand the implications of certain statistical concepts.

Value Proposition of Network Companies Providing Restaurant Services in Russia

2020 ◽  
pp. 137-158
Author(s):  
Elizaveta Fainshtein ◽  
Elena Serova
Keyword(s):  
Decision Making ◽  
Empirical Research ◽  
Statistical Data ◽  
Level Of Service ◽  
Value Proposition ◽  
Core Formation ◽  
Industry Data ◽  
Statistical Tool ◽  
Saint Petersburg ◽  
Production Resources

The purpose of the chapter is to recognize how customers perceive the value proposition content of network companies providing restaurant services. This is theoretical and empirical research in equal measure. The chapter presents the results of the restaurants value proposition study in Saint Petersburg (Russia) according to industry data and identifies the target audience key needs and industry characteristics in the market, semantic core formation, and clustering in order to identify the main criteria affecting the value proposition formation. The study was conducted among 16 chain restaurant holdings located in St. Petersburg, using statistical data based on the marketing statistical tool Google KeyWord Planner analytic database. The results made it possible to highlight key criteria of the value proposition by which consumers make their choice. This approach will allow standardizing decision-making systems on the possibility of identifying the innovative potential of the company's transition to a qualitatively new level of service and use of production resources.

scholarly journals A First Global Oceanic Compilation of Observational Dissolved Aluminum Data With Regional Statistical Data Treatment

2020 ◽  
Vol 7 ◽  
Author(s):  
Jan-Lukas Menzel Barraqueta ◽  
Saumik Samanta ◽  
Eric P. Achterberg ◽  
Andrew R. Bowie ◽  
Peter Croot ◽  
...  
Keyword(s):  
Statistical Data ◽  
Data Treatment ◽  
Dissolved Aluminum

Taking librarians off the desk: one library changes its reference desk staffing model

2015 ◽  
Vol 16 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Timothy Peters
Keyword(s):  
Decision Making ◽  
Design Methodology ◽  
Statistical Data ◽  
Decision Making Process ◽  
Service Model ◽  
Content Type ◽  
Staffing Model ◽  
Central Michigan University ◽  
Reference Service

Purpose – This paper is a case study of the decision at Central Michigan University to take librarians off the reference desk. Departmental data on reference desk traffic and other ancillary functions of the reference department was used to make the case for removing the librarians from the desk. Data collected since the decision was made has supported the decision to move to an on-call reference desk staffing model. The paper aims to discuss these issues. Design/methodology/approach – The department reviewed and analyzed data on reference desk transactions of all types from previous years to inform its decision making. Data trends and an analysis of the nature of the questions asked at the reference desk were instrumental in the analysis. Findings – The department determined that the statistical data justified the removal of the librarians from the reference desk. Data collected since moving to the on-call model supports the earlier decision. Originality/value – This paper provides libraries considering their own desk staffing models with a discussion of another library’s decision-making process and evidence of a successful migration to a new reference service model.

From Statistical Data to Spatial Knowledge — informing decision-making in Vietnam

2007 ◽  
Vol 23 (2-3) ◽  
pp. 193-204 ◽  
Author(s):  
Michael Epprecht ◽  
Andreas Heinimann ◽  
Nicholas Minot ◽  
Daniel Müller ◽  
Tim Robinson
Keyword(s):  
Decision Making ◽  
Statistical Data ◽  
Spatial Knowledge

scholarly journals KECEMASAN STATISTIK PADA MAHASISWA CALON GURU

2019 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Surya Sari Faradiba ◽  
Sikky El Walida
Keyword(s):  
Social Sciences ◽  
Decision Making ◽  
Mathematics Education ◽  
Statistical Data ◽  
Science And Technology ◽  
Natural Sciences ◽  
Statistics Anxiety ◽  
Social Sciences And Humanities ◽  
The Impact

Peranan Statistika sebagai salah satu bidang ilmu yang berfungsi untuk merencanakan, mengumpulkan, menganalisis, menginterpretasi, dan merepresentasikan data sebagai dasar untuk pengambilan keputusan sangat penting bagi perkembangan ilmu pengetahuan dan teknologi. Oleh karena itu, tidak mengherankan jika Statistika banyak digunakan dalam berbagai disiplin ilmu lain, antara lain ilmu alam, ilmu sosial, maupun ilmu humaniora. Mengingat tidak semua pengguna statistika memiliki latar belakang pendidikan Matematika, maka penggunaan alat bantu program SPSS menjadi alternatif yang patut dipertimbangkan. Sayangnya, dalam aplikasinya, pengguna SPSS lebih banyak sekedar mengikuti langkah-langkah prosedural tanpa memahami mengapa mereka melakukan hal tersebut. Dampaknya, pengguna SPSS banyak yang merasa kesulitan dalam melakukan analisis data statistik dan semakin tidak menyukai statistika. Penelitian ini bertujuan untuk mengetahui kondisi kecemasan statistik pada mahasiswa yang menggunakan SPSS. Hasil penelitian ini menunjukkan mayoritas mahasiswa dalam penelitian ini (n = 105, 73,4%) tidak menunjukkan kecemasan terhadap statistik melalui empat domain utama yang diukur. Tiga puluh satu siswa (21,7%) menunjukkan kecemasan dalam satu domain, empat siswa (2,8%) menunjukkan kecemasan dalam dua domain dan tiga siswa (2,1%) menunjukkan kecemasan dalam tiga domain. Tidak ada siswa dalam penelitian ini yang menunjukkan kecemasan pada keempat domain sekaligus yang diukur. The role of Statistics as one of the fields of science that functions to plan, collect, analyze, interpret, and represent data as a basis for decision making is very important for the development of science and technology. Therefore, it is not surprising that Statistics is widely used in various other disciplines, including natural sciences, social sciences, and humanities. Given that not all statistical users have a Mathematics education background, the use of SPSS program tools is an alternative that should be considered. Unfortunately, in the application, SPSS users are more just following procedural steps without understanding why they are doing it. The impact is that many SPSS users find it difficult to analyze statistical data and increasingly dislike statistics. This study aims to determine statistical anxiety conditions in students using SPSS. The results of this study indicate that the majority of students in this study (n = 105, 73.4%) did not show statistics anxiety through the four main domains measured. Thirty-one students (21.7%) showed anxiety in one domain, four students (2.8%) showed anxiety in two domains and three students (2.1%) showed anxiety in three domains. There were no students in this study who showed anxiety in all four domains as well as being measured.

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