New technologies involve a reformulation of statistical teaching contents and methodology and, at the same time, reinforce the need for a deeper training of students, including developing students' statistical reasoning. In this work we evaluate the statistical reasoning ability acquired by health sciences students from the analysis of their final undergraduate projects.
Most institutions of higher learning in the United States offer introductory statistics courses in a variety of flavors. Integration of the subject-specific concepts with the basic applied statistical techniques should be the primary goal of these flavored courses. Solely lecture-based traditional instruction method is not suitable to satisfying this objective. We argue for the incorporation of business-style cases into the introductory statistics curriculum using Constructivist learning theory and the notion of the "liberal arts" education. A typical business case setup is presented and its compatibility with an introductory statistics course is assessed. Finally, a sample business-style case for the application of the simple linear regression is provided.
Using exclusively open-source software, we have developed and implemented an online testing system dubbed YStatTest. This system allows instructors to create homework, quizzes, and exams by querying a database containing question templates. A template is selected based on such elements as keywords, categories, and historical difficulty. Numerical values, data, and correct answers for each question template are randomly generated upon exam creation. Thus, although two students may receive identical templates, they will likely differ in placement on the exam and most assuredly in the data and numerical values associated with the question. We discuss the impact this testing system has had upon student learning and highlight future planned development for our software.
In this age of information technology vast amounts of data are generated from many different processes which necessitate the practice of statistics in some form or other. Many third world students grapple with understanding the subject of Statistics and the success of teaching statistics depends on finding a satisfactory answer to many of the questions asked by the majority of students. This paper highlights the misconceptions that students have about statistics and shows that dispelling the myths and prejudices eases the teaching of the subject matter and the acceptance of statistics as a rewarding career.
Periodic regression is seldom included in syllabus of statistical courses. However, data following periodic or cyclic behavior are often encountered, especially in agriculture. Therefore, we think that this type of regression should be taught to students of agriculture, even in basic courses of statistics. In this paper we propose the way of teaching periodic regression through examples usually encountered in practice. The analysis of data will be based on the graphical interpretation, which would provide the visual display of the investigated problems as well.
Supervised classification or pattern recognition is a method to solve decision problems in Social Sciences. It is organized on the basis of specific sets of predictor variables and the existence of classes known a priori. Based on a training sample, its main objective is to construct a classification rule in order to predict the class to which a new object belongs. Nowadays, the availability and efficacy of powerful computers have made possible many advances in this field, both in Statistics and Computer Sciences. In this section, different methods will be discussed and illustrated with the results obtained in several applications. The following topics will be dealt with: Parametric Discriminant Analysis, Non-parametric Discriminant Analysis, Logistic Discriminant Analysis, Neuronal Networks, Recursive Partitioning and Estimation of Error Rates.
Ever since its founding, the Pedagogy Department at the Complutense University of Madrid has considered statistics to be fundamental instrument in the training of educational researchers. For this reason, the department has made every effort for the teaching of statistics to keep pace with the field itself. However, the results so far have been unsatisfactory. These negative results, combined with Pedagogy students' initial limited ability in statistical processing, point out the need for new techniques that can be used to teach research methods in education.
In this report we present a proposal to work in the classroom starting from some theoretical and conceptual elements that might be used for teachers when facing the problem of teaching the main probability and statistics concepts in the Colombian context. We first reflect on the knowledge that is offered in the country and then outline a didactic work approach from exploratory data analysis.
A recurrent problem in teaching is to evaluate what the students will retain from the contents discussed in class. For the students who completed two basic courses on statistics, we applied a questionnaire with a test, 3 months after the end of the course. The test was composed of 50 true-false questions. The results revealed that students could satisfactorily answer the questions directly related with definitions. However, there was no such performance when the questions required additional procedures related.
We discuss our practice related to classical hypothesis testing about unknown parameters of a normal population offered to undergraduates in the University of São Paulo. We consider the tests for the population mean and variance when the sample size is "large" and "small" as well as the well-known tests comparing the means and variances for independent samples. We suggest an algorithmic approach, which our students appreciate