Rhodora

v.82 (1980)

Rhodora

v.83 (1981)

Rhodora

v.84 (1982)

Rhodora

v.85 (1983)

Rhodora

v.86 (1984)

Rhodora

v.87 (1985)

Rhodora

v.88 (1986)

Rhodora

v.89 (1987)

Rhodora

v.90 (1988)

A methodology for improving computed individual regressions predictions.

This research proposes a methodology to improve computed individual prediction values provided by an existing regression model without having to change either its parameters or its architecture. In other words, we are interested in achieving more accurate results by adjusting the calculated regression prediction values, without modifying or rebuilding the original regression model. Our proposition is to adjust the regression prediction values using individual reliability estimates that indicate if a single regression prediction is likely to produce an error considered critical by the user of the regression. The proposed method was tested in three sets of experiments using three different types of data. The first set of experiments worked with synthetically produced data, the second with cross sectional data from the public data source UCI Machine Learning Repository and the third with time series data from ISO-NE (Independent System Operator in New England). The experiments with synthetic data were performed to verify how the method behaves in controlled situations. In this case, the outcomes of the experiments produced superior results with respect to predictions improvement for artificially produced cleaner datasets with progressive worsening with the addition of increased random elements. The experiments with real data extracted from UCI and ISO-NE were done to investigate the applicability of the methodology in the real world. The proposed method was able to improve regression prediction values by about 95% of the experiments with real data.

"i Went To The Woods Because I Wished To Live Deliberately": How A College-Level Academic Course Can Influence A Lifetime

This study, using the portraiture methodology, provides an analysis of the lifelong significance of an undergraduate program that integrates literature with an outdoor experiential platform. With limited research on long-term effects of an academic outdoor experiential course on one's life, there is space to wonder about the prospect and nature of the long-term significance of an academic course that may offer technical skill, intrapersonal and interpersonal development, and also the delivery of subject matter related to a traditional or mainstream academic area of study. Utilizing an academic skills-oriented lens as well as a character strengths lens, portraits were crafted of four former participants of the University of Michigan's New England Literature Program (NELP) to shed light on the long-term influence of this type of course, crucial participant characteristics that contribute to the program's impact, and specific components of the program that are particularly integral to the course's efficacy. Since 1975, each spring term a small contingent of students and educators has lived in the woods in the New England region as a community of learners, artists and explorers. NELP is an exemplar of a longstanding undergraduate academic English course that integrates the literature of New England writers, exploratory writing and student experiences relating to regional literature and the land. Emergent themes of this course's long-term influence on former participants include increased collaborative skills, increased self-confidence and self-knowledge, a reinforcement of lifelong relationships with the outdoors, and nurtured creativity. For participants to reap benefit from this course, it was important for them to enter with maturity to conduct themselves with openness to new experiences, relationships, and extensive reflection. Findings relating to the integral components of such a program include that of being place-based, oriented towards process, and being an intentional, collaborative community.

Lowell--an industrial dream come true

by H. C. Meserve.

The development of spindles: a paper

by William F. Draper.