SMHS LinearModeling StatsSoftware

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SMHS Linear Modeling - Statistical Software

This section briefly describes the pros and cons of different statistical software platforms.

SMHS LinearModeling Fig001.png

GoogleScholar Research Article Pubs

Year R SAS SPSS
1995 8 8620 6450
1996 2 8670 7600
1997 6 10100 9930
1998 13 10900 14300
1999 26 12500 24300
2000 51 16800 42300
2001 133 22700 68400
2002 286 28100 88400
2003 627 40300 78600
2004 1180 51400 137000
2005 2180 58500 147000
2006 3430 64400 142000
2007 5060 62700 131000
2008 6960 59800 116000
2009 9220 52800 61400
2010 11300 43000 44500
2011 14600 32100 32000
require(ggplot2)
require(reshape)
Data_R_SAS_SPSS_Pubs <-read.csv('https://umich.instructure.com/files/522067/download?download_frd=1', header=T)
df <- data.frame(Data_R_SAS_SPSS_Pubs) 
# convert to long format
df <- melt(df ,  id.vars = 'Year', variable.name = 'Time') 
ggplot(data=df, aes(x=Year, y=value, colour=variable, group = variable)) +  geom_line() + geom_line(size=4) + labs(x='Year', y='Citations')


SMHS LinearModeling Fig002.png





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