scholarly journals Multi-Unit Assignment Problem: FCFS Course Allocation System Data Analysis

2016 ◽  
Vol 55 (1) ◽  
pp. 70-80
Author(s):  
Gediminas Murauskas ◽  
Marijus Radavičius
Keyword(s):  
Data Analysis ◽  
Regression Model ◽  
Empirical Data ◽  
Assignment Problem ◽  
Simulated Data ◽  
Allocation System ◽  
Censored Regression Model ◽  
System Data ◽  
Unit Demand ◽  
Vilnius University

We study the allocation of courses to students with multi-unit demand. Exploratory analysis of courseallocation practices at Vilnius University (VU) is performed. The allocation of students to courses is done by a simplefirst-come first-served (FCFS) procedure. We show that FCFS procedure does not deliver desirable outcomes. Data withstudent preferences is needed in order to compare assignment algorithms. We use censored regression model to constructstudent preferences generation mechanism based on empirical data collected at VU. Using simulated data several multiunitassignment algorithms are compared.

scholarly journals Intragenic Conflict in Phylogenomic Data Sets

2020 ◽  
Vol 37 (11) ◽  
pp. 3380-3388
Author(s):  
Stephen A Smith ◽  
Nathanael Walker-Hale ◽  
Joseph F Walker
Keyword(s):  
Data Analysis ◽  
Empirical Data ◽  
Evolutionary History ◽  
Phylogenetic Signal ◽  
Phylogenetic Analyses ◽  
Simulated Data ◽  
Data Sets ◽  
Alignment Error ◽  
Biological Processes ◽  
Simple Method

Abstract Most phylogenetic analyses assume that a single evolutionary history underlies one gene. However, both biological processes and errors can cause intragenic conflict. The extent to which this conflict is present in empirical data sets is not well documented, but if common, could have far-reaching implications for phylogenetic analyses. We examined several large phylogenomic data sets from diverse taxa using a fast and simple method to identify well-supported intragenic conflict. We found conflict to be highly variable between data sets, from 1% to >92% of genes investigated. We analyzed four exemplar genes in detail and analyzed simulated data under several scenarios. Our results suggest that alignment error may be one major source of conflict, but other conflicts remain unexplained and may represent biological signal or other errors. Whether as part of data analysis pipelines or to explore biologically processes, analyses of within-gene phylogenetic signal should become common.

THE EFFECT BETWEEN COOPERATIVE LERANING AND LEARNING STYLE OF THE GERMANY WRITTING OUTCOM

Jurnal KATA ◽  
2017 ◽  
Vol 1 (2) ◽  
pp. 172
Author(s):  
Fauzan Adhima
Keyword(s):  
Data Analysis ◽  
Cooperative Learning ◽  
Learning Styles ◽  
Factorial Design ◽  
Learning Style ◽  
Interaction Effect ◽  
Empirical Data ◽  
Visual Learning ◽  
Design Data ◽  
Auditory Learning

<p><em>The aim of the study is to reveal the empirical data about the effect of cooperative learning and learning styles on the outcome of germany writting of SMA Negeri 42 Jakarta. The methodology used is the experimental method with a 2x2 factorial design. Data analysis performed by using two way variance analysis. The findings of the study demonstrated: 1)   The teaching cooperative leraning type STAD was higher than students who take cooperative leraning type Pair Cheks teaching. 2) The visual learning style was higher than students who have auditory learning style. 3) The students who take the teaching  cooperative leraning type Pair Cheks and have a visual learning style higher than students who take cooperative leraning type STAD and have a visual learning style . 4) The cooperative leraning type STAD and auditory learning style is higher than  cooperative leraning type Pair Cheks and have auditory learning styles. 5) The teaching cooperative leraning type STAD and have a auditory learning style was higher than the teaching cooperative leraning type STAD and have visual learning styles. 6) The cooperative leraning type Pair Cheks and have a visual learning style is higher than cooperative leraning type Pair Cheks and have auditory learning styles. 7) There is an interaction effect between  cooperative leraning  and learning style of the germany writting outcome.</em></p><p><em><br /></em></p><p> </p><p>Tujuan dari penelitian ini adalah untuk mengetahui pengaruh pembelajaran kooperatif (tipe STAD dan tipe <em>Pair Cheks</em>) dan gaya belajar (visual dan auditori) terhadap keterampilan menulis bahasa Jerman Siswa SMA N 42 Jakarta. Penelitian ini merupakan penelitian kuantitatif dengan metode eksperimen menggunakan desain faktorial 2 X 2. Pengukuran keterampilan menulis bahasa Jerman menggunakan tes tulis sedangkan gaya belajar menggunakan kuesioner. Teknik analisis data dalam penelitian ini adalah teknkik analisis variansi (ANAVA) 2 jalur dan dilanjutkan dengan uji Tuckey untuk melihat interaksi antar kelompok.  Hasil penelitian menunjukan bahwa: (1) keterampilan menulis tipe STAD lebih baik dari pada tipe <em>Pair Cheks</em>, (2) gaya belajar visual lebih baik dari auditori, (3) gaya belajar auditori lebih baik menggunakan pembelajaran kooperatif tipe STAD dari pada tipe <em>Pair Cheks</em>, (4) keterampilan menulis bahasa Jerman kelompok siswa yang memiliki gaya belajar visual lebih baik menggunakan pembelajaran kooperatif tipe <em>Pair Cheks</em> dari pada tipe STAD, (5) Belajar dengan pembelajaran kooperatif tipe STAD lebih baik diterapkan pada siswa yang memiliki gaya belajar auditori dari pada gaya belajar visual, (6) belajar dengan pembelajaran kooperatif tipe <em>Pair Cheks</em> lebih baik diterapkan pada siswa yang memiliki gaya belajar visual dari pada auditori, dan (7) terdapat pengaruh interaksi antara pembelajaran kooperatif dan gaya belajar terhadap keterampilan menulis bahasa Jerman.</p><p>Tujuan dari penelitian ini adalah untuk mengetahui pengaruh pembelajaran kooperatif (tipe STAD dan tipe <em>Pair Cheks</em>) dan gaya belajar (visual dan auditori) terhadap keterampilan menulis bahasa Jerman Siswa SMA N 42 Jakarta. Penelitian ini merupakan penelitian kuantitatif dengan metode eksperimen menggunakan desain faktorial 2 X 2. Pengukuran keterampilan menulis bahasa Jerman menggunakan tes tulis sedangkan gaya belajar menggunakan kuesioner. Teknik analisis data dalam penelitian ini adalah teknkik analisis variansi (ANAVA) 2 jalur dan dilanjutkan dengan uji Tuckey untuk melihat interaksi antar kelompok.  Hasil penelitian menunjukan bahwa: (1) keterampilan menulis tipe STAD lebih baik dari pada tipe <em>Pair Cheks</em>, (2) gaya belajar visual lebih baik dari auditori, (3) gaya belajar auditori lebih baik menggunakan pembelajaran kooperatif tipe STAD dari pada tipe <em>Pair Cheks</em>, (4) keterampilan menulis bahasa Jerman kelompok siswa yang memiliki gaya belajar visual lebih baik menggunakan pembelajaran kooperatif tipe <em>Pair Cheks</em> dari pada tipe STAD, (5) Belajar dengan pembelajaran kooperatif tipe STAD lebih baik diterapkan pada siswa yang memiliki gaya belajar auditori dari pada gaya belajar visual, (6) belajar dengan pembelajaran kooperatif tipe <em>Pair Cheks</em> lebih baik diterapkan pada siswa yang memiliki gaya belajar visual dari pada auditori, dan (7) terdapat pengaruh interaksi antara pembelajaran kooperatif dan gaya belajar terhadap keterampilan menulis bahasa Jerman.</p>

scholarly journals Bayesian modeling and significant features exploration in wavelet power spectra

2007 ◽  
Vol 14 (1) ◽  
pp. 79-88 ◽  
Author(s):  
D. V. Divine ◽  
F. Godtliebsen
Keyword(s):  
Data Analysis ◽  
Traditional Approach ◽  
Statistical Significance ◽  
Power Spectra ◽  
Simulated Data ◽  
Scale Space ◽  
Independent Random Variables ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Testing Procedures

Abstract. This study proposes and justifies a Bayesian approach to modeling wavelet coefficients and finding statistically significant features in wavelet power spectra. The approach utilizes ideas elaborated in scale-space smoothing methods and wavelet data analysis. We treat each scale of the discrete wavelet decomposition as a sequence of independent random variables and then apply Bayes' rule for constructing the posterior distribution of the smoothed wavelet coefficients. Samples drawn from the posterior are subsequently used for finding the estimate of the true wavelet spectrum at each scale. The method offers two different significance testing procedures for wavelet spectra. A traditional approach assesses the statistical significance against a red noise background. The second procedure tests for homoscedasticity of the wavelet power assessing whether the spectrum derivative significantly differs from zero at each particular point of the spectrum. Case studies with simulated data and climatic time-series prove the method to be a potentially useful tool in data analysis.

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

Vegetation Canopy Coverage Estimation Using Physical Models

2013 ◽  
Vol 726-731 ◽  
pp. 4709-4713
Author(s):  
Lin Jing Zhang ◽  
Hong Zhang Ma ◽  
Zhu Bo Zhou ◽  
Zhong Liang Ren ◽  
Xiao Bo Zhu ◽  
...  
Keyword(s):  
Regression Model ◽  
Simulated Data ◽  
Hyperspectral Data ◽  
Physical Models ◽  
Vegetation Canopy ◽  
Canopy Coverage ◽  
Vegetation Indexes ◽  
Ground Measurement ◽  
Highly Correlated ◽  
Coverage Estimation

Based on the physical models of PROSPECT, SAIL and porosity model, hyperspectral data and canopy coverage data of different combined scenes were simulated. According to the simulated data, we chose four sensitive bands and four sensitive vegetation indexes highly correlated to vegetation canopy coverage, and analyzed the correlation between sensitive bands, sensitive vegetation indexes and canopy coverage. Then we built a regression model of canopy coverage with EVI highly correlated with canopy coverage. At last, we verified this model by experimental data from ground measurement experiment. It shows that there is a high correlation between EVI and canopy coverage and the regression model built by EVI can produce an effective result and the RMSE is less than 0.09.

scholarly journals Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model

2021 ◽  
Vol 29 ◽  
pp. 287-295
Author(s):  
Zhiming Zhou ◽  
Haihui Huang ◽  
Yong Liang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Gene Selection ◽  
Simulated Data ◽  
Biological Data ◽  
Cancer Classification ◽  
High Dimensional ◽  
Data Set ◽  
Biomarker Selection

BACKGROUND: In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear statistical explanation and obtain the classification probability of classification label information. However, it is unable to fulfill biomarker selection. OBJECTIVE: The aim of this paper is to give the model efficient gene selection capability. METHODS: In this paper, we propose a new penalized logsum network-based regularization logistic regression model for gene selection and cancer classification. RESULTS: Experimental results on simulated data sets show that our method is effective in the analysis of high-dimensional data. For a large data set, the proposed method has achieved 89.66% (training) and 90.02% (testing) AUC performances, which are, on average, 5.17% (training) and 4.49% (testing) better than mainstream methods. CONCLUSIONS: The proposed method can be considered a promising tool for gene selection and cancer classification of high-dimensional biological data.

PENGARUH BUYING IMPULSIVENESS TERHADAP E-IMPULSIVE BUYING BEHAVIOR DENGAN HEDONIC VALUE DAN UTILITARIAN VALUE SEBAGAI VARIABEL PEMEDIASI

2019 ◽  
Vol 14 (1) ◽  
pp. 61
Author(s):  
Melani Dwi Ratnasari
Keyword(s):  
Data Analysis ◽  
Regression Model ◽  
T Test ◽  
Buying Behavior ◽  
Hedonic Value ◽  
Utilitarian Value ◽  
Impulsive Buying ◽  
The Relationship

The purpose of this study was to examine the hedonic value mediation and utilitarian value variables on the relationship between buying impulsiveness and e-impulsive buying behavior. In this study the number of samples was 123 students using Shopee at Sarjanawiyata Tamansiswa University. Data analysis using a regression model with t test and sobel test. The results showed, 1) impulsiveness buying does not affect hedonic value 2) impulsiveness buying does not affect utilitarian value 3) hedonic value influences e-impulsive buying behavior 4) utilitarian value influences e-impulsive buying behavior 5) impulsiveness buying affects e-impulsive buying behavior 6) hedonic value is not supported as the mediating variable of the relationship between buying impulsiveness and e-impulsive buying behavior 7) supported utilitarian value as the mediating variable of the relationship between buying impulsiveness and e-impulsive buying behavior. Keywords: Shopee, Buying Impulsiveness, E-impulsive Buying Behavior, Hedonic Value, Utilitarian Value ABSTRAK  Tujuan penelitian ini adalah untuk menguji variabel mediasi hedonic value dan utilitarian value pada hubungan antara  buying impulsiveness dan e-impulsive buying behavior. Dalam penelitian ini jumlah sampel adalah 123 orang mahasiswa pengguna Shopee di Universitas Sarjanawiyata Tamansiswa. Analisis data menggunakan model regresi dengan uji t dan sobel test. Hasil penelitian menunjukkan, 1) buying impulsiveness tidak berpengaruh terhadap hedonic value 2) buying impulsiveness tidak berpengaruh terhadap utilitarian value 3) hedonic value berpengaruh terhadap e-impulsive buying behavior 4) utilitarian value berpengaruh terhadap e-impulsive buying behavior 5) buying impulsiveness berpengaruh terhadap e-impulsive buying behavior 6) hedonic value tidak terdukung sebagai variabel pemediasi hubungan antara buying impulsiveness dengan e-impulsive buying behavior 7) utilitarian value terdukung sebagai variabel pemediasi hubungan antara buying impulsiveness dengan e-impulsive buying behavior. Kata-kata Kunci: Shopee, Pembelian tidak terduga, pembelian tidak terencana, nilai kesenangan, nilai manfaat

Retailer Use of Permission-Based Mobile Advertising

2005 ◽  
pp. 139-156 ◽  
Author(s):  
Jari Salo ◽  
Jaana Tähtinen
Keyword(s):  
Content Analysis ◽  
Data Analysis ◽  
New Media ◽  
Empirical Data ◽  
Analysis Data ◽  
Previous Experience ◽  
Advertising Agencies ◽  
Mobile Advertising ◽  
Or Education ◽  
Or Groups

This chapter shows how retailers – without any previous experience or education – utilize a new mobile advertising channel. The chapter focuses on permission-based mobile advertising and the specific features that should be considered when designing and targeting mobile advertising. The empirical part of the chapter analyzes data from a field trial where Finnish retailers were able to use mobile advertising. The empirical data is obtained through the use of content analysis. Data analysis explores whether the retailers positioned the m-adverts to target either individuals or groups and whether the content of the m-adverts reflected and utilized the medium’s specific features. The results of this analysis suggest that both retailers and advertising agencies have to learn how to use m-advertising as a new media. Conclusions of the chapter suggest ways to fully utilize the potential of mobile advertising.

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