Hasil untuk "Analysis"

Kelloway E. Kevin

Increasing unemployment rate and creation of new jobs are most important issues around the world recently. Then many developed countries, including Republic of Korea, establish and enforce a variety of start-up activation policies to increase employment rate and boom up the national economy. Establishing linkage of entrepreneurship motivation, entrepreneurship, entrepreneurial intention and firm performance, focusing on potential entrepreneurs and entrepreneur, it could provide personalized and targeted entrepreneurial policy programs to increase entrepreneurship, because entrepreneurship is the most important factor to activate startups. On this study, it established factors of entrepreneurial motivation on potential entrepreneurs and entrepreneurs, and analyzed the linkage of factors of entrepreneurial motivation, entrepreneurship, entrepreneurial intention(potential entrepreneurs) and firm performance(entrepreneurs). For analysis, this study conducted descriptive statistics, reliability analysis, factor analysis to verify validity, correlation analysis, and regression to analyze influence between factors. Potential entrepreneurs group has 202 samples, and findings show self-efficacy, social network, economic status and government policy influence on entrepreneurship positively. And self-efficacy, startup education, economic status and government policy have a positive effect on entrepreneurial intention, too. Entrepreneurs group has 212 samples, and findings show self-efficacy, social network and economic status influence on entrepreneurship. And each linkage has a positive effect, that self-efficacy - financial and non-financial performance, startup education financial and technological performance, social network - financial performance, economic status - financial and non-financial performance, and government policy - financial and technological performance.

Virginia Braun, Victoria Clarke

Z. Denkowski, S. Migórski, N. Papageorgiou

D. F. Gray

This textbook describes the equipment, observational techniques, and analysis used in the investigation of stellar photospheres. Now in its fourth edition, the text has been thoroughly updated and revised to be more accessible to students. New figures have been added to illustrate key concepts, while diagrams have been redrawn and refreshed throughout. The book starts by developing the tools of analysis, and then demonstrates how they can be applied. Topics covered include radiation transfer, models of stellar photospheres, spectroscopic equipment, how to observe stellar spectra, and techniques for measuring stellar temperatures, radii, surface gravities, chemical composition, velocity fields, and rotation rates. Up-to-date results for real stars are included. Written for starting graduate students or advanced undergraduates, this textbook also includes a wealth of reference material useful to researchers. eBook formats include color imagery while print formats are greyscale only; a wide selection of the color images are available online.

M. Weisz

Paul J. McMurdie, S. Holmes

Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.

Andrew F. Hayes

K. Tamura, G. Stecher, D. Peterson et al.

J. Hair

Kelly B. Haskard Zolnierek, M. Dimatteo

P. Librado, J. Rozas

Ziheng Yang

K. Livak, Thomas D. Schmittgen

M. Eisen, P. Spellman, P. Brown et al.

H. Hsieh, S. Shannon

U. H. Graneheim, B. Lundman

P. Bentler, D. Bonett

Jacob Cohen, P. Cohen, S. West et al.

M. P. Cummings

D. Kahneman, A. Tversky