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Direct linkLijin Zhang; Xueyang Li; Zhiyong Zhang – Grantee Submission, 2023
The thriving developer community has a significant impact on the widespread use of R software. To better understand this community, we conducted a study analyzing all R packages available on CRAN. We identified the most popular topics of R packages by text mining the package descriptions. Additionally, using network centrality measures, we…
Descriptors: Computer Software, Programming Languages, Data Analysis, Visual Aids
Download full textAcar, Tülin – International Journal of Assessment Tools in Education, 2019
The purpose of this study was to write programs to define sampling sizes and observation units by probability sampling methods and to provide an idea for software developers. The algorithms of the programs were written in Python 3. The programs may be run by double-clicking on the Windows operating system or by the command prompt of the DOS…
Descriptors: Sample Size, Computer Software, Probability, Statistical Analysis
Soltys, Michael; Dang, Hung D.; Reyes Reilly, Ginger; Soltys, Katharine – Strategic Enrollment Management Quarterly, 2021
A Machine Learning framework for predicting enrollment is proposed. The framework consists of Amazon Web Services SageMaker together with standard Python tools for data analytics, including Pandas, NumPy, MatPlotLib, and ScikitLearn. The tools are deployed with Jupyter Notebooks running on AWS SageMaker. Based on three years of enrollment history,…
Descriptors: Enrollment Management, Strategic Planning, Prediction, Computer Software
Curley, Brenna; Peterson, Anna – Journal of Statistics and Data Science Education, 2022
In this article, we outline several activities revolving around soccer players who participated in the 2018 FIFA World Cup and 2019 FIFA Women's World Cup. Classroom activities are described from different perspectives, useful for a range of different statistics courses. In a first semester probability theory course, students investigate the…
Descriptors: Team Sports, Competition, Teaching Methods, Data Analysis
Seebut, Supot; Wongsason, Patcharee; Kim, Dojin; Putjuso, Thanin; Boonpok, Chawalit – EURASIA Journal of Mathematics, Science and Technology Education, 2022
Simulation modeling is an effective tool for solving problems that cannot be explained analytically or when data cannot be collected. This is done by simulating the observed behavior of a problem under study using a computer program. In math education, this can develop knowledge and fundamental competencies of simulation modeling at a higher level…
Descriptors: Programming Languages, Mathematics Instruction, Grade 12, Secondary School Students
Enhancement of the Command-Line Environment for Use in the Introductory Statistics Course and Beyond
Gerbing, David W. – Journal of Statistics and Data Science Education, 2021
R and Python are commonly used software languages for data analytics. Using these languages as the course software for the introductory course gives students practical skills for applying statistical concepts to data analysis. However, the reliance upon the command line is perceived by the typical nontechnical introductory student as sufficiently…
Descriptors: Statistics Education, Teaching Methods, Introductory Courses, Programming Languages
Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D.; Lee, Daniel; Goodrich, Ben; Betancourt, Michael; Brubaker, Marcus A.; Guo, Jiqiang; Li, Peter; Riddell, Allen – Grantee Submission, 2017
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the…
Descriptors: Programming Languages, Probability, Bayesian Statistics, Monte Carlo Methods
Andrew Gelman; Daniel Lee; Jiqiang Guo – Journal of Educational and Behavioral Statistics, 2015
Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers'…
Descriptors: Programming Languages, Bayesian Statistics, Inferences, Monte Carlo Methods
