SMHS LinearModeling StatsSoftware - Revision history

Dinov: /* SMHS Linear Modeling - Statistical Software */

2016-05-21T17:19:16Z

‎SMHS Linear Modeling - Statistical Software

Dinov: /* SMHS Linear Modeling - Statistical Software */

2016-05-21T17:18:13Z

‎SMHS Linear Modeling - Statistical Software

Dinov: /* SMHS Linear Modeling - Statistical Software */

2016-05-21T17:17:27Z

‎SMHS Linear Modeling - Statistical Software

Dinov: /* SMHS Linear Modeling - Statistical Software */

2016-05-21T17:16:23Z

‎SMHS Linear Modeling - Statistical Software

Pineaumi: /* SMHS Linear Modeling - Statistical Software */

2016-05-19T12:55:25Z

‎SMHS Linear Modeling - Statistical Software

Pineaumi: /* SMHS Linear Modeling - Statistical Software */

2016-05-19T12:53:28Z

‎SMHS Linear Modeling - Statistical Software

Pineaumi: /* SMHS Linear Modeling - Statistical Software */

2016-05-19T12:52:26Z

‎SMHS Linear Modeling - Statistical Software

Imoubara at 14:39, 16 May 2016

2016-05-16T14:39:58Z

Dinov at 15:56, 1 February 2016

2016-02-01T15:56:03Z

Dinov at 15:54, 1 February 2016

2016-02-01T15:54:23Z

@@ Line 6: / Line 6: @@
 {| class="wikitable" style="text-align:left; width:99%" border="1"
 |-
-|Statistical Software||Advantages||Disadvantages
+!Statistical Software||Advantages||Disadvantages
 |-
 | [http://r-project.org R]||
@@ Line 26: / Line 26: @@
 * Steeper learning curve
 |-
-| [http:/www.sas.com SAS] ||
+| [http://www.sas.com SAS] ||
 * Large datasets
 * Commonly used in business & Government

@@ Line 8: / Line 8: @@
 |Statistical Software||Advantages||Disadvantages
 |-
-! [http://r-project.org R]||
+| [http://r-project.org R]||
 * R is actively maintained (100,000 developers, 15K packages)
 * Excellent connectivity to various types of data and other systems
@@ Line 26: / Line 26: @@
 * Steeper learning curve
 |-
-! [http:/www.sas.com SAS] ||
+| [http:/www.sas.com SAS] ||
 * Large datasets
 * Commonly used in business & Government
@@ Line 34: / Line 34: @@
 * Expensive/proprietary
 |-
-! [http://www.stata.com Stata] ||
+| [http://www.stata.com Stata] ||
 * Easy statistical analyses
 ||
 * Mostly classical stats
 |-
-! [http://www.ibm.com/analytics/us/en/technology/spss SPSS] ||
+| [http://www.ibm.com/analytics/us/en/technology/spss SPSS] ||
 * Appropriate for beginners
 * Simple interfaces

@@ Line 34: / Line 34: @@
 * Expensive/proprietary
 |-
-! [http://www.stata.com Stata] ||<li>Easy statistical analyses</li>||<li>Mostly classical stats</li>
+! [http://www.stata.com Stata] ||
 |-
 ! [http://www.ibm.com/analytics/us/en/technology/spss SPSS] ||

@@ Line 8: / Line 8: @@
 |Statistical Software||Advantages||Disadvantages
 |-
-|R||<li>R is actively maintained (100,000 developers, 15K packages)</li>
+! [http://r-project.org R]||
+* R is actively maintained (100,000 developers, 15K packages)
-<li>Excellent connectivity to various types of data and other systems</li>
+* Excellent connectivity to various types of data and other systems
+* Versatile for solving problems in many domains
-<li>Versatile for solving problems in many domains</li>
+* It’s free, open-source code
+* Anybody can access/review/extend the source code
-<li>It’s free, open-source code</li>
+* R is very stable and reliable
+* If you change or redistribute the R source code, you have to make those changes available for anybody else to use
-<li>Anybody can access/review/extend the source code</li>
+* R runs anywhere (platform agnostic)
+* Extensibility: R supports extensions, e.g., for data manipulation, statistical modeling, and graphics
-<li>R is very stable and reliable</li>
+* Active and engaged community supports R
+* Unparalleled question-and-answer (Q&A) websites
-<li>If you change or redistribute the R source code, you have to make those changes available for anybody else to use</li>
+* R connects with other languages(Java/C/JavaScript/Python/Fortran) & database systems, and other programs, SAS, SPSS, etc.
+* Other packages have add-ons to connect with R. SPSS has incorporated a link to R, and SAS has protocols to move data and graphics between the two packages
-<li>R runs anywhere (platform agnostic)</li>
+||
+* Mostly scripting language
-<li>Extensibility: R supports extensions, e.g., fordata manipulation, statistical modeling, and graphics</li>
+* Steeper learning curve
 |-
+! [http:/www.sas.com SAS] ||
-|SAS||<li>Large datasets</li>
+* Large datasets
+* Commonly used in business & Government
-<li>Commonly used in business & Government</li>
+||
+* Expensive
-||<li>Expensive</li>
+* Somewhat dated programming language
+* Expensive/proprietary
 |-
+! [http://www.stata.com Stata] ||<li>Easy statistical analyses</li>||<li>Mostly classical stats</li>
 |-
+! [http://www.ibm.com/analytics/us/en/technology/spss SPSS] ||
-|SPAA||<li>Appropriate for beginners Simple interfaces</li>||<li>weak in more cutting edge statistical procedures lacking in robust methods and survey methods</li>
+* Appropriate for beginners
+* Simple interfaces
 |-
+| colspan=3| More comparisons are available online: [http://www.ats.ucla.edu/stat/mult_pkg/compare_packages.htm UCLA/ATS] and [https://en.wikipedia.org/wiki/Comparison_of_statistical_packages Wikipedia].
 |}
 </center>

@@ Line 68: / Line 68: @@
 <center>
-<B>GoogleScholar Research Article Pubs</B>
+GoogleScholar Research Article Pubs
 {| class="wikitable" style="text-align:center; " border="1"
 |-

@@ Line 107: / Line 107: @@
 |2011||14600||32100||32000
 |}
   require(ggplot2)
   require(reshape)
@@ Line 115: / Line 116: @@
   ggplot(data=df, aes(x=Year, y=value, colour=variable, group = variable)) +  geom_line() + geom_line(size=4) + labs(x='Year', y='Citations')
+<CENTER>
 [[Image:SMHS_LinearModeling_Fig002.png|500px]]
 </center>

@@ Line 3: / Line 3: @@
 This section briefly describes the pros and cons of different statistical software platforms.
-[[Image:SMHS_LinearModeling_Fig001.png|500px]]
+<center>
 <center>

← Older revision		Revision as of 15:56, 1 February 2016
Line 58:		Line 58:
	</center>		</center>

		+	===Next see===
		+	[[SMHS_LinearModeling_QC\|Quality Control section ]] for a discussion of data Quality Control (QC) and Quality Assurance (QA) which represent important components of data-driven modeling, analytics and visualization.

← Older revision		Revision as of 15:54, 1 February 2016
Line 3:		Line 3:
	This section briefly describes the pros and cons of different statistical software platforms.		This section briefly describes the pros and cons of different statistical software platforms.

		+	[[Image:SMHS_LinearModeling_Fig001.png\|500px]]

		+	<center>
		+	GoogleScholar Research Article Pubs
		+	{\| class="wikitable" style="text-align:center; " border="1"
		+	\|-
		+	!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')
		+
		+
		+	[[Image:SMHS_LinearModeling_Fig002.png\|500px]]
		+
		+	</center>