The objective of the present paper is to provide a simple approach to statistical
inference using the method of transformations of variables.
We demonstrate performance of this powerful tool on examples of constructions
of various estimation procedures, hypothesis testing, Bayes analysis and statistical inference for the stress-strength systems.
We argue that the tool of transformations not only should be used more widely in statistical research
but should become a routine part of calculus-based courses of statistics.
Finally, we provide sample problems for such a course as well as possible undergraduate reserach projects
which utilize transformations of variables.

**Key Words:** Transformations of variables; Estimation; Testing; Stress-strength model; Bayesian inference.

Students often enter an introductory statistics class with less than positive attitudes about the subject.
They tend to believe statistics is difficult and irrelevant to their lives. Observational evidence from previous studies suggests including
projects in a statistics course may enhance students' attitudes toward
statistics. This study examines the relationship between inclusion of a student-designed data collection project in an introductory statistics course and
6 components comprising students' attitudes toward statistics. The sample consisted of 42 college students enrolled in an introductory statistics course.
Comparisons of those who completed the student-designed data collection project (n = 24) and those who did not complete the project (n = 18) suggest that
inclusion of a project may not significantly impact students' attitudes toward statistics. However, these findings must be viewed as only a preliminary step
in the study of the effect of projects on attitudes toward statistics.

**Key Words:** Attitudinal scale; Statistics education; Project-based learning; Comparative study.

The field of Data Mining like Statistics concerns itself with "learning from data" or "turning data into information". For statisticians
the term "Data mining" has a pejorative meaning. Instead of finding useful patterns in large volumes of data as in the case of
Statistics, data mining has the connotation of searching for data to fit preconceived ideas. Here we try to discuss the similarities
and differences as well as the relationships between statisticians and data miners. This article is intended to bridge some of the
gap between the people of these two communities.

**Key Words:** Censored data; Databases; Data dredging; Data fishing; Data mining; Exploratory data analysis; Knowledge discover in data mining; Truncated data.

As part of many universities' Business degrees, students will undertake an introductory statistics course. Lecturers need to help
these students appreciate and recognise the value of possessing quantitative skills and to learn and apply such skills. Three
components to teaching that address these aims as well as the interdependence of these components as part of a process which
enhances the teaching environment and student outcomes are described. Methods and examples to perform the techniques and ideas
are provided along with a discussion of their implementation and effectiveness after delivery in a large first year course.

**Key Words:** Teaching introductory statistics; Pedagogy, Increasing student confidence; Improved learning methods; Teaching materials.

This article shows a concrete and easy recognizable view of a cumulative distribution function(cdf). Photograph views of the search tabs
on dictionaries are used to increase students’ understanding and facility with the concept of a cumulative distribution function. Projects
for student investigations are also given. This motivation and view helps the cdf become a bit more tangible and understandable.

**Key Words:** Alphabet; Kolmogorov; Probability distribution function(pdf); Scrabble; Smirnov; Student projects.

Although graduate students in education are frequently required to write papers throughout their coursework, they typically have limited
experience in communicating in the language of statistics, both verbally and in written form. To succeed in their future careers,
students must be provided with opportunities to develop deep understandings of concepts, develop reasoning skills, and become familiar
with verbalizing and writing about statistics. The instructional approach described here spans the entire semester of a statistics course
and consists of several aspects including cognitively- rich individual assignments, small group activities, and a student-led scoring activity.
To demonstrate the impact of this approach on student learning, qualitative and quantitative data were collected from students in two
statistics courses. Several major areas of evidence are described, including studentassessments indicate improvements in students'
reasoning and understanding, written and verbal communication, and confidence.

**Key Words:** Conceptual understanding; Confidence; Interpretation of results; Verbal communication; Written communication.

The objective of the study is to determine if there is a significant difference in the effects of the treatment and control groups on achievement
as well as on attitude as measured by the posttest. A class of 38 sophomore college students in
the basic statistics taught with the use of computer-assisted instruction and another class of 15 students with the
use of the traditional method from the University of the East, Manila (SY 2003-2004) were the focus of this study. The
research method used was the quasi-experimental, non-equivalent control group design. The statistical tool was the Multiple Analysis
of Covariance. The researcher made use of the CD-ROM prepared by Math Advantage (1997) to serve as the teaching medium for the
experimental group. The following summarizes the findings of the study. The achievement posttest of the treatment group has
higher estimated marginal means than the control group and it is reversed in the attitude posttest. Using Hotelling's Trace for the multivariate test,
the achievement pretest, attitude pretest, and the two groups have a significant effect on the dependent variables, achievement posttest and
attitude posttest. Using covariates to control for the effects of additional variables that might affect performance the attitude pretest
accounts for about 56% of the variability in the two groups while achievement pretest about 15%. Levene’s test shows that the homogeneity
of variances assumption between the two groups is met for achievement posttest but not for attitude posttest. The univariate effects for
achievement posttest that are significant are achievement pretest, college entrance test overall score, and groups. The univariate effects
that are significant for attitude posttest are attitude pretest and high school general weighted average.

**Key Words:** Descriptive statistics; Multimedia; Learning.

This paper provides practical examples of how statistics educators may apply a cooperative framework to
classroom teaching and teacher collaboration. Building on the premise that statistics instruction ought to resemble
statistical practice, an inherently cooperative enterprise, our purpose is to highlight specific ways in which
cooperative methods may translate to statistics education. So doing, we hope to address the concerns of those statistics
educators who are reluctant to adopt more student-centered teaching strategies, as well as those educators who have
tried these methods but ultimately returned to more traditional, teacher-centered instruction.

**Key Words:** Collaboration; Cooperative learning; Collaborative teaching; Statistics education.

The pmg add-on package for the open source statistics software R is
described. This package provides a simple to use graphical user
interface (GUI) that allows introductory statistics students, without
advanced computing skills, to quickly create the graphical and numeric
summaries expected of them.

**Key Words:** Statistics software; Statistical computing; R; Introductory statistics; EDA; Exploratory data analysis.