Research Bulletin
Author:
Publisher:
Published: 1909
Total Pages: 790
ISBN-13:
DOWNLOAD EBOOKRead and Download eBook Full
Author: University of Wisconsin. College of Agricultural and Life Sciences. Research Division
Publisher:
Published: 1911
Total Pages: 1080
ISBN-13:
DOWNLOAD EBOOKAuthor:
Publisher:
Published: 1993
Total Pages: 212
ISBN-13:
DOWNLOAD EBOOKAuthor: National Education Association of the United States. Research Division
Publisher:
Published: 1927
Total Pages: 368
ISBN-13:
DOWNLOAD EBOOKAuthor: U.S. Fish and Wildlife Service
Publisher:
Published: 1940
Total Pages: 238
ISBN-13:
DOWNLOAD EBOOKAuthor: Jacob Cohen
Publisher: Routledge
Published: 2013-05-13
Total Pages: 625
ISBN-13: 1134742770
DOWNLOAD EBOOKStatistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.
Author:
Publisher:
Published: 1940
Total Pages: 224
ISBN-13:
DOWNLOAD EBOOKAuthor: University of Wisconsin--Madison. Agricultural Experiment Station
Publisher:
Published: 1911
Total Pages: 1026
ISBN-13:
DOWNLOAD EBOOKAuthor: Randall E. Schumacker
Publisher: SAGE Publications
Published: 2014-01-28
Total Pages: 648
ISBN-13: 148332477X
DOWNLOAD EBOOKProviding easy-to-use R script programs that teach descriptive statistics, graphing, and other statistical methods, Learning Statistics Using R shows readers how to run and utilize R, a free integrated statistical suite that has an extensive library of functions. Randall E. Schumacker’s comprehensive book describes in detail the processing of variables in statistical procedures. Covering a wide range of topics, from probability and sampling distribution to statistical theorems and chi-square, this introductory book helps readers learn not only how to use formulae to calculate statistics, but also how specific statistics fit into the overall research process. Learning Statistics Using R covers data input from vectors, arrays, matrices and data frames, as well as the input of data sets from SPSS, SAS, STATA and other software packages. Schumacker’s text provides the freedom to effectively calculate, manipulate, and graphically display data, using R, on different computer operating systems without the expense of commercial software. Learning Statistics Using R places statistics within the framework of conducting research, where statistical research hypotheses can be directly addressed. Each chapter includes discussion and explanations, tables and graphs, and R functions and outputs to enrich readers′ understanding of statistics through statistical computing and modeling.