Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_vle_software_design_tests.wasp

Title produced by software

VLE Software Design (tests)

Date of computation

Sun, 27 Nov 2011 12:21:36 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/27/t13224145054ttbbzox88sqtki.htm/, Retrieved Fri, 26 Apr 2024 17:30:26 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147573, Retrieved Fri, 26 Apr 2024 17:30:26 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

106

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [ws7 Tutorial Popu...] [2010-11-22 11:00:33] [afe9379cca749d06b3d6872e02cc47ed]
-   PD    [Multiple Regression] [] [2010-12-02 19:29:24] [94f4aa1c01e87d8321fffb341ed4df07]
- R         [Multiple Regression] [] [2011-11-25 00:52:53] [74be16979710d4c4e7c6647856088456]
- RMPD          [VLE Software Design (tests) ] [] [2011-11-27 17:21:36] [5f9ad3d6882448a3cbf5628cc61fe2a1] [Current]

Feedback Forum

Post a new message

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147573&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147573&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147573&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Statistical Hypothesis Tests
> t1 Welch Two Sample t-test data: z[z$Year == 0, "WSTOT"] and z[z$Year == 1, "WSTOT"] t = -0.2128, df = 378.371, p-value = 0.8316 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -51.53062 41.46391 sample estimates: mean of x mean of y 554.6824 559.7157
> w1 Asymptotic Wilcoxon Mann-Whitney Rank Sum Test data: z$WSTOT by as.factor(z$Year) (0, 1) Z = -0.3289, p-value = 0.7423 alternative hypothesis: true mu is not equal to 0
> t2 Welch Two Sample t-test data: z[z$Year == 0, "Relevance"] and z[z$Year == 1, "Relevance"] t = -0.3483, df = 410.06, p-value = 0.7278 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.9854203 0.6888020 sample estimates: mean of x mean of y 30.61836 30.76667
> w2 Asymptotic Wilcoxon Mann-Whitney Rank Sum Test data: z$Relevance by as.factor(z$Year) (0, 1) Z = 0.1173, p-value = 0.9066 alternative hypothesis: true mu is not equal to 0
> t3 Welch Two Sample t-test data: z[z$Year == 0, "CriticalThinking"] and z[z$Year == 1, "CriticalThinking"] t = 1.2576, df = 411.987, p-value = 0.2092 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3134206 1.4267401 sample estimates: mean of x mean of y 30.63285 30.07619
> w3 Asymptotic Wilcoxon Mann-Whitney Rank Sum Test data: z$CriticalThinking by as.factor(z$Year) (0, 1) Z = 1.1916, p-value = 0.2334 alternative hypothesis: true mu is not equal to 0
> t4 Welch Two Sample t-test data: z[z$Year == 0, "CognitiveDemand"] and z[z$Year == 1, "CognitiveDemand"] t = 0.9616, df = 413.381, p-value = 0.3368 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4468054 1.3025680 sample estimates: mean of x mean of y 31.61836 31.19048
> w4 Asymptotic Wilcoxon Mann-Whitney Rank Sum Test data: z$CognitiveDemand by as.factor(z$Year) (0, 1) Z = 0.6577, p-value = 0.5107 alternative hypothesis: true mu is not equal to 0

\begin{tabular}{lllllllll}
\hline
Statistical Hypothesis Tests \tabularnewline
> t1
	Welch Two Sample t-test
data:  z[z$Year == 0, "WSTOT"] and z[z$Year == 1, "WSTOT"] 
t = -0.2128, df = 378.371, p-value = 0.8316
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -51.53062  41.46391 
sample estimates:
mean of x mean of y 
 554.6824  559.7157 
 \tabularnewline
> w1
	Asymptotic Wilcoxon Mann-Whitney Rank Sum Test
data:  z$WSTOT by as.factor(z$Year) (0, 1) 
Z = -0.3289, p-value = 0.7423
alternative hypothesis: true mu is not equal to 0 
 \tabularnewline
> t2
	Welch Two Sample t-test
data:  z[z$Year == 0, "Relevance"] and z[z$Year == 1, "Relevance"] 
t = -0.3483, df = 410.06, p-value = 0.7278
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -0.9854203  0.6888020 
sample estimates:
mean of x mean of y 
 30.61836  30.76667 
 \tabularnewline
> w2
	Asymptotic Wilcoxon Mann-Whitney Rank Sum Test
data:  z$Relevance by as.factor(z$Year) (0, 1) 
Z = 0.1173, p-value = 0.9066
alternative hypothesis: true mu is not equal to 0 
 \tabularnewline
> t3
	Welch Two Sample t-test
data:  z[z$Year == 0, "CriticalThinking"] and z[z$Year == 1, "CriticalThinking"] 
t = 1.2576, df = 411.987, p-value = 0.2092
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -0.3134206  1.4267401 
sample estimates:
mean of x mean of y 
 30.63285  30.07619 
 \tabularnewline
> w3
	Asymptotic Wilcoxon Mann-Whitney Rank Sum Test
data:  z$CriticalThinking by as.factor(z$Year) (0, 1) 
Z = 1.1916, p-value = 0.2334
alternative hypothesis: true mu is not equal to 0 
 \tabularnewline
> t4
	Welch Two Sample t-test
data:  z[z$Year == 0, "CognitiveDemand"] and z[z$Year == 1, "CognitiveDemand"] 
t = 0.9616, df = 413.381, p-value = 0.3368
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -0.4468054  1.3025680 
sample estimates:
mean of x mean of y 
 31.61836  31.19048 
 \tabularnewline
> w4
	Asymptotic Wilcoxon Mann-Whitney Rank Sum Test
data:  z$CognitiveDemand by as.factor(z$Year) (0, 1) 
Z = 0.6577, p-value = 0.5107
alternative hypothesis: true mu is not equal to 0 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147573&T=1

[TABLE]
[ROW][C]Statistical Hypothesis Tests[/C][/ROW]
[ROW][C]> t1
	Welch Two Sample t-test
data:  z[z$Year == 0, "WSTOT"] and z[z$Year == 1, "WSTOT"] 
t = -0.2128, df = 378.371, p-value = 0.8316
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -51.53062  41.46391 
sample estimates:
mean of x mean of y 
 554.6824  559.7157 
[/C][/ROW]
[ROW][C]> w1
	Asymptotic Wilcoxon Mann-Whitney Rank Sum Test
data:  z$WSTOT by as.factor(z$Year) (0, 1) 
Z = -0.3289, p-value = 0.7423
alternative hypothesis: true mu is not equal to 0 
[/C][/ROW]
[ROW][C]> t2
	Welch Two Sample t-test
data:  z[z$Year == 0, "Relevance"] and z[z$Year == 1, "Relevance"] 
t = -0.3483, df = 410.06, p-value = 0.7278
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -0.9854203  0.6888020 
sample estimates:
mean of x mean of y 
 30.61836  30.76667 
[/C][/ROW]
[ROW][C]> w2
	Asymptotic Wilcoxon Mann-Whitney Rank Sum Test
data:  z$Relevance by as.factor(z$Year) (0, 1) 
Z = 0.1173, p-value = 0.9066
alternative hypothesis: true mu is not equal to 0 
[/C][/ROW]
[ROW][C]> t3
	Welch Two Sample t-test
data:  z[z$Year == 0, "CriticalThinking"] and z[z$Year == 1, "CriticalThinking"] 
t = 1.2576, df = 411.987, p-value = 0.2092
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -0.3134206  1.4267401 
sample estimates:
mean of x mean of y 
 30.63285  30.07619 
[/C][/ROW]
[ROW][C]> w3
	Asymptotic Wilcoxon Mann-Whitney Rank Sum Test
data:  z$CriticalThinking by as.factor(z$Year) (0, 1) 
Z = 1.1916, p-value = 0.2334
alternative hypothesis: true mu is not equal to 0 
[/C][/ROW]
[ROW][C]> t4
	Welch Two Sample t-test
data:  z[z$Year == 0, "CognitiveDemand"] and z[z$Year == 1, "CognitiveDemand"] 
t = 0.9616, df = 413.381, p-value = 0.3368
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -0.4468054  1.3025680 
sample estimates:
mean of x mean of y 
 31.61836  31.19048 
[/C][/ROW]
[ROW][C]> w4
	Asymptotic Wilcoxon Mann-Whitney Rank Sum Test
data:  z$CognitiveDemand by as.factor(z$Year) (0, 1) 
Z = 0.6577, p-value = 0.5107
alternative hypothesis: true mu is not equal to 0 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147573&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147573&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Statistical Hypothesis Tests
> t1 Welch Two Sample t-test data: z[z$Year == 0, "WSTOT"] and z[z$Year == 1, "WSTOT"] t = -0.2128, df = 378.371, p-value = 0.8316 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -51.53062 41.46391 sample estimates: mean of x mean of y 554.6824 559.7157
> w1 Asymptotic Wilcoxon Mann-Whitney Rank Sum Test data: z$WSTOT by as.factor(z$Year) (0, 1) Z = -0.3289, p-value = 0.7423 alternative hypothesis: true mu is not equal to 0
> t2 Welch Two Sample t-test data: z[z$Year == 0, "Relevance"] and z[z$Year == 1, "Relevance"] t = -0.3483, df = 410.06, p-value = 0.7278 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.9854203 0.6888020 sample estimates: mean of x mean of y 30.61836 30.76667
> w2 Asymptotic Wilcoxon Mann-Whitney Rank Sum Test data: z$Relevance by as.factor(z$Year) (0, 1) Z = 0.1173, p-value = 0.9066 alternative hypothesis: true mu is not equal to 0
> t3 Welch Two Sample t-test data: z[z$Year == 0, "CriticalThinking"] and z[z$Year == 1, "CriticalThinking"] t = 1.2576, df = 411.987, p-value = 0.2092 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3134206 1.4267401 sample estimates: mean of x mean of y 30.63285 30.07619
> w3 Asymptotic Wilcoxon Mann-Whitney Rank Sum Test data: z$CriticalThinking by as.factor(z$Year) (0, 1) Z = 1.1916, p-value = 0.2334 alternative hypothesis: true mu is not equal to 0
> t4 Welch Two Sample t-test data: z[z$Year == 0, "CognitiveDemand"] and z[z$Year == 1, "CognitiveDemand"] t = 0.9616, df = 413.381, p-value = 0.3368 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4468054 1.3025680 sample estimates: mean of x mean of y 31.61836 31.19048
> w4 Asymptotic Wilcoxon Mann-Whitney Rank Sum Test data: z$CognitiveDemand by as.factor(z$Year) (0, 1) Z = 0.6577, p-value = 0.5107 alternative hypothesis: true mu is not equal to 0

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Parameters (Session):

Parameters (R input):

R code (references can be found in the software module):

library(coin)
z <- as.data.frame(read.table(file='https://automated.biganalytics.eu/download/PLoS_ONE/t1.csv',sep=',',header=T))
(t1 <- t.test(z[z$Year==0,'WSTOT'],z[z$Year==1,'WSTOT']))
(w1 <- wilcox_test(z$WSTOT ~ as.factor(z$Year)))
bitmap(file='wstot.png')
boxplot(cbind(z[z$Year==0,'WSTOT'], z[z$Year==1,'WSTOT']), notch=T, main='Difference between Workshop Scores?')
dev.off()
z <- as.data.frame(read.table(file='https://automated.biganalytics.eu/download/PLoS_ONE/t2.csv',sep=',',header=T))
(t2 <- t.test(z[z$Year==0,'Relevance'],z[z$Year==1,'Relevance']))
(w2 <- wilcox_test(z$Relevance ~ as.factor(z$Year)))
bitmap(file='relevance.png')
boxplot(cbind(z[z$Year==0,'Relevance'], z[z$Year==1,'Relevance']), notch=T, main='Difference between Perceived Relevance (COLLES construct)?')
dev.off()
(t3 <- t.test(z[z$Year==0,'CriticalThinking'],z[z$Year==1,'CriticalThinking']))
(w3 <- wilcox_test(z$CriticalThinking ~ as.factor(z$Year)))
bitmap(file='criticalthinking.png')
boxplot(cbind(z[z$Year==0,'CriticalThinking'], z[z$Year==1,'CriticalThinking']), notch=T, main='Difference between Critical Thinking (COLLES construct)?')
dev.off()
(t4 <- t.test(z[z$Year==0,'CognitiveDemand'],z[z$Year==1,'CognitiveDemand']))
(w4 <- wilcox_test(z$CognitiveDemand ~ as.factor(z$Year)))
bitmap(file='cognitivedemand.png')
boxplot(cbind(z[z$Year==0,'CognitiveDemand'], z[z$Year==1,'CognitiveDemand']), notch=T, main='Difference between Cognitive Demand (COLLES construct)?')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Statistical Hypothesis Tests',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('t1'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('w1'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('t2'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('w2'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('t3'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('w3'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('t4'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('w4'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code