Highland, AR, United States
Highland, AR, United States

Lyon College is an independent, residential, co-educational, undergraduate liberal arts college affiliated with the Presbyterian Church . Founded in 1872, it is the oldest independent college in Arkansas. Wikipedia.

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News Article | April 17, 2017
Site: www.prweb.com

LearnHowToBecome.org, a leading resource provider for higher education and career information, has announced its list of the best colleges and universities in Arkansas for 2017. 20 four-year schools made the list, with John Brown University, Hendrix College, Ouachita Baptist University, Harding University and University of Arkansas taking the lead as the top five. Of the 26 two-year schools that were also included, North Arkansas College, Arkansas State University Mountain Home, Black River Technical College, Pulaski Technical College and Arkansas Northeastern College were the top five. A full list of winning schools is included below. “Arkansas is seeing a record low for unemployment in 2017, which is great news for college grads entering the job market,” said Wes Ricketts, senior vice president of LearnHowToBecome.org. “The schools on our list have demonstrated value for not only providing a strong education, but also helping students fulfill career goals after they graduate.” To be included on Arkansas “Best Colleges” list, schools must be regionally accredited, not-for-profit institutions. Each college is also scored on additional data that includes annual alumni earnings 10 years after entering college, career services offered, availability of financial aid and base metrics such as student/teacher ratios and graduation rates. Complete details on each college, their individual scores and the data and methodology used to determine the LearnHowToBecome.org “Best Colleges in Arkansas” list, visit: The Best Four-Year Colleges in Arkansas for 2017 include: Arkansas State University-Main Campus Arkansas Tech University Central Baptist College Harding University Henderson State University Hendrix College John Brown University Lyon College Ouachita Baptist University Philander Smith College Southern Arkansas University Main Campus University of Arkansas University of Arkansas at Little Rock University of Arkansas at Monticello University of Arkansas at Pine Bluff University of Arkansas for Medical Sciences University of Arkansas-Fort Smith University of Central Arkansas University of the Ozarks Williams Baptist College The Best Two-Year Colleges in Arkansas for 2017 include: Arkansas Northeastern College Arkansas State University - Beebe Arkansas State University - Mountain Home Arkansas State University - Newport Baptist Health Schools-Little Rock Black River Technical College College of the Ouachitas Cossatot Community College of the University of Arkansas Crowley's Ridge Technical Institute East Arkansas Community College Mid-South Community College National Park College North Arkansas College NorthWest Arkansas Community College Northwest Technical Institute Ozarka College Phillips Community College Pulaski Technical College Remington College-Little Rock Campus Rich Mountain Community College South Arkansas Community College Southeast Arkansas College Southern Arkansas University Tech University of Arkansas Community College - Batesville University of Arkansas Community College - Morrilton University of Arkansas Hope - Texarkana Arkansas Northeastern College About Us: LearnHowtoBecome.org was founded in 2013 to provide data and expert driven information about employment opportunities and the education needed to land the perfect career. Our materials cover a wide range of professions, industries and degree programs, and are designed for people who want to choose, change or advance their careers. We also provide helpful resources and guides that address social issues, financial aid and other special interest in higher education. Information from LearnHowtoBecome.org has proudly been featured by more than 700 educational institutions.


Cleophas T.J.,Lyon College
Journal of Pharmaceutical Sciences and Research | Year: 2012

Background: Traditional statistical tests are unable to handle a large number of variables. The simplest method to reduce large numbers of variables is the use of add-up scores. But add-up scores do not account for the relative importance of the separate variables, their interactions and differences in units. Principal components analysis and partial least square analysis account all of that, but are virtually unused in clinical trials. Objective: To assess the performance of either of the two methods. Methods: A simulated example of 250 patients' gene expression data as predictor and drug efficacy scores as outcome will be used. For principal components analysis SPSS' s Data Dimension Reduction module was used, for partial least square analysis R Partial Least Squares, a free statistics and forecasting software was used. Results: Of 27 variables three novel predictor variables were constructed. With principal components analysis the 3 were very significant predictors of the add-up outcome score with t-values of 10.2, 21.6, and 6.7 (p<0.000, p<0.000, p<0.000). Partial least squares included the outcome variables in its program, and was also able to predict the outcome variables although at a lower level of significance with t-values of 6.8, 16.2, and 3.5 (p<0.000, p<0.000, p<0.001). Traditional multiple linear regression with the novel predictors in the form of add-up scores as independent variables produced a consistent further reduction of significance with t-values of 3.4, 11.2 and 2.4 (p<0.002, p<0.001, p<0.02). Conclusions: 1. Principal components analysis and partial least squares can handle many more variables than the standard covariance methods like MANOVA and MANCOVA can, and is more sensitive than add-up scores. 2.The methods account the relative importance of the separate variables, their interactions and differences in units. They are also very flexible, to the extent that manifest variables can be applied twice, first in the form of clusters for prediction and second unclusteredly as manifest outcome variables. Partial least squares method is parsimonious to principal components analysis, because it can include outcome variables in the model.


Cleophas T.J.,Lyon College
American Journal of Therapeutics | Year: 2016

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used. The correlation coefficient between the 12 predictor and 4 outcome variables was 0.87 (P 0.0001) meaning that 76% of the variability in the outcome variables was explained by the 12 covariates. Repeated testing after the removal of 5 unimportant predictor and 1 outcome variable produced virtually the same overall result. The MANCOVA identified identical unimportant variables, but it was unable to provide overall statistics. (1) Canonical analysis is remarkable, because it can handle many more variables than traditional multivariate methods such as MANCOVA can. (2) At the same time, it accounts for the relative importance of the separate variables, their interactions and differences in units. (3) Canonical analysis provides overall statistics of the effects of sets of variables, whereas traditional multivariate methods only provide the statistics of the separate variables. (4) Unlike other methods for combining the effects of multiple variables such as factor analysis/partial least squares, canonical analysis is scientifically entirely rigorous. (5) Limitations include that it is less flexible than factor analysis/partial least squares, because only 2 sets of variables are used and because multiple solutions instead of one is offered. We do hope that this article will stimulate clinical investigators to start using this remarkable method. © 2013 Wolters Kluwer Health, Inc.


Cleophas T.J.,Lyon College
American Journal of Therapeutics | Year: 2016

Traditionally, nonlinear relationships like the smooth shapes of airplanes, boats, and motor cars were constructed from scale models using stretched thin wooden strips, otherwise called splines. In the past decades, mechanical spline methods have been replaced with their mathematical counterparts. The objective of the study was to study whether spline modeling can adequately assess the relationships between exposure and outcome variables in a clinical trial and also to study whether it can detect patterns in a trial that are relevant but go unobserved with simpler regression models. A clinical trial assessing the effect of quantity of care on quality of care was used as an example. Spline curves consistent of 4 or 5 cubic functions were applied. SPSS statistical software was used for analysis. The spline curves of our data outperformed the traditional curves because (1) unlike the traditional curves, they did not miss the top quality of care given in either subgroup, (2) unlike the traditional curves, they, rightly, did not produce sinusoidal patterns, and (3) unlike the traditional curves, they provided a virtually 100% match of the original values. We conclude that (1) spline modeling can adequately assess the relationships between exposure and outcome variables in a clinical trial; (2) spline modeling can detect patterns in a trial that are relevant but may go unobserved with simpler regression models; (3) in clinical research, spline modeling has great potential given the presence of many nonlinear effects in this field of research and given its sophisticated mathematical refinement to fit any nonlinear effect in the mostly accurate way; and (4) spline modeling should enable to improve making predictions from clinical research for the benefit of health decisions and health care. We hope that this brief introduction to spline modeling will stimulate clinical investigators to start using this wonderful method. © 2013 Wolters Kluwer Health, Inc.


Coleman M.T.,FutureFuel Chemical Company | Leblanc G.,Lyon College
Organic Process Research and Development | Year: 2010

The effectiveness of diethoxymethane (DEM) as a solvent for O-alkylation of a variety of phenols under phase transfer conditions has been examined and evaluated. The reaction between 4-methoxy phenol and benzyl chloride was selected to compare reaction rates in various solvents and the efficiency of various PTCs. This reaction was further studied to develop a commercially amenable process complete with recycle streams and efficient product isolation. DEM is a good solvent for these types of phase transfer-catalyzed reactions and can be considered as an alternative solvent for dichloromethane and toluene. © 2010 American Chemical Society.


Trademark
Lyon College | Date: 2016-08-22

Binders; Notebook covers; Notebooks; Pens. Cups; Glass beverageware; Glass mugs; Mugs; Tumblers for use as drinking glasses; Water bottles sold empty; Coffee mugs; Cups and mugs; Drinking glasses, namely, tumblers; Glass mugs.


Trademark
Lyon College | Date: 2016-08-22

Decals; Folders; Notebook covers; Notebooks.


Trademark
Lyon College | Date: 2016-07-20

Hats; Shirts; Shirts and short-sleeved shirts; Shorts; Sweat pants; Athletic shirts; Body shirts; Collared shirts; Hooded sweat shirts; Knit shirts; Long-sleeved shirts; Open-necked shirts; Short-sleeve shirts; Short-sleeved or long-sleeved t-shirts; Sport shirts; Sports shirts; Sports shirts with short sleeves; Sweat shirts; T-shirts; Tee shirts; Tee-shirts.


Trademark
Lyon College | Date: 2016-07-20

Hats; Pullovers; Shirts; Shirts and short-sleeved shirts; Sweat pants; Hooded pullovers; Hooded sweat shirts; Knit shirts; Long-sleeved shirts; Short-sleeve shirts; Short-sleeved shirts; Sports shirts; Sweat shirts; T-shirts; Tee shirts; Tee-shirts.


L

Trademark
Lyon College | Date: 2016-08-23

Caps; Gloves; Hats; Pants; Shirts; Baseball caps and hats; Gym pants; Knit tops; Sports pants; Sports shirts; Sports caps and hats; Sweat shirts; T-shirts; Tank-tops; Yoga pants.

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