Lies, Statistics, Mathematics and the Truth

Recognizing a key distinction between mathematics and statistics is helpful in understanding how we know if a statement is true. Posting about deductive and inductive reasoning­­­­­­­­ from In All Things - an online hub committed to the claim that the life, death, and resurrection of Jesus Christ has implications for the entire world. http://inallthings.org/lies-statistics-mathematics-and-the-truth

Data from an international multi-centre study of statistics and mathematics anxieties and related variables in university students (the smarvus dataset)

This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts.2-s2.0-85176239315Mayı

Data from an International Multi-Centre Study of Statistics and Mathematics Anxieties and Related Variables in University Students (the SMARVUS Dataset)

This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts. Data and metadata are stored on the Open Science Framework website [https://osf.io/mhg94/]

Using Interactive Data Tools in Mathematics Professional Development Project Report

The goal of the Using Interactive Data Tools in Mathematics Professional Development Project was to develop, conduct, and test professional development for middle level educators to support the effective use of interactive technology for learning targeted data and statistics mathematics standards and practices as outlined in the Common Core State Standards. The professional development included embedded research-­‐based instructional strategies and approaches, as well as activities that used example student lesson materials. Project resources include brief pre/post assessments, interactive applets, student activities, teacher aids, and other materials. The assessments were designed to measure key aspects of the targeted data and statistics mathematics standards and practices within the student lessons. Student performance on the assessments was scored and analyzed by project staff

On the parallel lines for nondegenerate conics

Computation of parallel lines (envelopes) to parabolas, ellipses, and hyperbolas is of importance in structure engineering and theory of mechanisms. Homogeneous polynomials that implicitly define parallel lines for the given offset to a conic are found by computing Groebner bases for an elimination ideal of a suitably defined affine variety. Singularity of the lines is discussed and their singular points are explicitly found as functions of the offset and the parameters of the conic. Critical values of the offset are linked to the maximum curvature of each conic. Application to a finite element analysis is shown. Keywords: Affine variety, elimination ideal, Groebner basis, homogeneous polynomial, singularity, family of curves, envelope, pitch curve, undercutting, cam surfaceComment: 40 pages, 10 figures, TOC, 3 appendices, short version of this paper was presented at the 5th Annual Hawaii International Conference on Statistics, Mathematics and Related Fields, January 16 - 18, 2006, Honolulu Hawaii, US

Statistics, Mathematics, and Teaching

In discussing our teaching, we may focus on content, what we want our students to learn, or on pedagogy, what we do to help them learn. These two topics are of course related. In particular, changes in pedagogy are often driven in part by changing priorities for what kinds of things we want students to learn. It is nonetheless convenient to address content and pedagogy separately. Pedagogy, certainly the less specific of the two, is the topic of my second paper. This paper concerns content, and in particular contains one side of a conversation between a statistician and mathematicians who may find themselves teaching statistics

Bioinformatics as a Tool to Identify Infectious Disease Pathogen Peptide Sequences as Targets for Antibody Engineering

Bioinformatics is an interdisciplinary field of information technology for understanding biological data from genome to protein. It includes a combination of fields of science, computer science, statistics, mathematics, and engineering to analyze, interpret and derive biological data. This chapter describes how to use Bioinformatics to identify pathogen virulence factor peptide sequence similarities in human nerve tissue proteins and for evaluation as antibody engineering target peptides

Determination of Some Optimal Experimental Designs

Statistics (Mathematics

An Overview of Path Analysis: Mediation Analysis Concept in Structural Equation Modeling

This paper provides a tutorial discussion on path analysis structure with concept of structural equation modelling (SEM). The paper delivers an introduction to path analysis technique and explain to how to deal with analyzing the data with this kind of statistical methodology especially with a mediator in the research model. The intended audience is statisticians, mathematicians, or methodologists who either know about SEM or simple basic statistics especially in regression and linear/nonlinear modeling, and Ph.D. students in statistics, mathematics, management, psychology, and even computer science.Comment: 12 page