The use of The Three Stooges' films as a source of data in an introductory statistics class is described. The Stooges' films are separated into three populations. Using these populations, students may conduct hypothesis tests with data they collect.
The use of The Three Stooges' films as a source of data in an introductory statistics class is described. The Stooges' films are separated into three populations. Using these populations, students may conduct hypothesis tests with data they collect.
This article considers prototype data sets that attain lower and upper bounds on the standard deviation in terms of the range.
While bootstrapping is a computationally intensive procedure, teaching about the concept does not necessarily require any more technology than a simple calculator. This article describes an interactive teaching approach for introducing bootstrapping without using a statistics program or a computer.
The aim of this study was to investigate students' achievement in introductory statistics courses taking into account the relationships between cognitive and non-cognitive factors. It was hypothesised that achievement was related to background in mathematics (a cognitive variable), as well as to attitudes toward statistics and anxiety (non-cognitive variables). Students were presented with measures assessing their attitudes, mathematical competence, and anxiety toward courses and examinations at the beginning and at the end of their statistics course. Achievement was assessed by tasks assigned during the course, as well as by students' final grades and the number of exam failures. The results reveal the relationships between cognitive and non-cognitive factors, their changes during the course, and how both interact in predicting achievement.
This study investigated elementary school teachers' comprehension of data displays. Assessment, interview, and observation data were analyzed to determine their level of comprehension. Results revealed that the teachers were proficient at "reading the data" and computation types of "reading between the data" questions, but were unsuccessful with questions that assessed higher levels of graphical comprehension. Many of the difficulties exhibited by the teachers appear to be attributable to a lack of exposure to the content. Implications for teacher preparation, professional development, and curricula development are discussed.
In this empirical study we compare student performance using two different teaching methods in introductory business statistics course. Two groups were taught in the computer lab with software available at students' fingertips while one was taught in the regular classroom with only a computer workstation for the instructor. VISA (Visual Interactive Statistical Analysis), an Excel-based analysis software package was used in classroom to perform computational analysis of the data in all three groups. Exam data and final course grades indicate that student performance between the two methods was not affected by presence of the software in classroom for use by students. This leads us to conclude that VISA is an intuitive enough tool, which does not require a major learning curve, and can be mastered by students with minimal supervision. Second, we conclude that if the software used for statistics instruction is "teaching-friendly", then technology availability in the classroom does not affect learning efficiency. This allows instructors to concentrate more efforts in class teaching conceptually important material.
Although variability and structure are often considered as antonyms in many<br>everyday settings, a mathematically disciplined view contradicts this<br>opposition. To initiate fifth- (10 years old) and sixth-grade (11 years old)<br>students in this disciplinary view, we engaged students in practices of<br>modeling data. These practices included inventing and revising data displays,<br>inventing and revising measures of centre and variability, and inventing and<br>revising models of chance to account for variability. Here we focus on<br>prospective correspondences between students' invented measures (statistics)<br>of variability and those favoured by the discipline. We suggest that inventing<br>measures positions students to transform their vision of variability from mere<br>difference to more structured forms, some of which coordinate centre and<br>spread. By tracing interactions among an inventor, her classmates, and the<br>teacher, we trace how structuring variability and constituting its measure co-<br>originated during the course of negotiations about the meaning of the measure.<br>Consideration of the coherency, transparency and generalisability of a statistic,<br>all of which are valued by the discipline of statistics, emerged during the course<br>of invention.
A probability exploration built on shooting basketball free throws leads unexpectedly to the golden ratio.
Statistics uses scientific tools but also requires the art of flexible and creative reasoning.
Investigative laboratory modules (labs) can introduce undergraduates to relatively advanced statistical methods from a variety of disciplines. The labs described in this article encourage students early in their undergraduate studies to experience the role of a research scientist and to understand how statistics help advance scientific knowledge. By making students grapple with intriguing real-world problems that demonstrate the intellectual content and broad applicability of statistics as a discipline, these labs encourage students to consider a career in statistics or to incorporate statistical thinking into any career. These materials offer many potential uses: they can be combined to form a second statistics course; they can be incorporated as a final project in an introductory statistics course; or they can be used individually to demonstrate to students and researchers in other disciplines how statisticians approach the scientific process.