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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_chi_squared_tests.wasp
Title produced by softwareChi-Squared and McNemar Tests
Date of computationMon, 15 Nov 2010 14:04:03 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/15/t12898297590aa4k91edrmhcbs.htm/, Retrieved Sun, 28 Apr 2024 16:42:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=94837, Retrieved Sun, 28 Apr 2024 16:42:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact200
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ECEL 2008] [ECEL2008 hypothes...] [2008-06-14 21:17:24] [74be16979710d4c4e7c6647856088456]
F RMPD  [Chi-Squared and McNemar Tests] [Happiness versus ...] [2010-11-11 11:54:28] [6bc4f9343b7ea3ef5a59412d1f72bb2b]
-         [Chi-Squared and McNemar Tests] [Q1 Happiness vs. ...] [2010-11-14 21:13:30] [2843717cd92615903379c14ebee3c5df]
F   P       [Chi-Squared and McNemar Tests] [Chi-Squared Tests...] [2010-11-15 10:22:14] [6ca0fc48dd5333d51a15728999009c83]
F   P           [Chi-Squared and McNemar Tests] [Chi-Squared Tests...] [2010-11-15 14:04:03] [b4ba846736d082ffaee409a197f454c7] [Current]
Feedback Forum
2010-11-23 15:46:53 [411b43619fc9db329bbcdbf7261c55fb] [reply
Hier zien we dat de cell count te laag is (sommige waarden lager dan 5), daarom gebruiken we de “Exact Pearson Chi-squared by simulation” (de student gebruikt deze niet). Bekijk http://www.freestatistics.org/blog/index.php?v=date/2010/Nov/20/t1290249701wxdndkd58pu5t4s.htm/ voor A/B/C/D. De student blijft hier zeer vaag bij zijn antwoord. Als we hier kijken naar de p-waarde (tabel “Statistical Results”) dan zien we dat deze hoger is dan de vooropgestelde type 1 fout (5%), namelijk 6% (het is voor discussie vatbaar of het al dan niet significant verschillend is, gezien de lage P-waarde). Bijgevolg is er hier een mogelijk verband tussen de reële en verwachte waarde. Als we vervolgens naar de grafiek kijken, dan zien we 1 significant verschil bij C-C (donker grijs gekleurd positief balkje). Toch kunnen we hier nog twijfelen over het verband. Opmerking: doordat de student niet de juiste test heeft gebruikt, merk ik op dat hij een andere p-waarde uitkomt.

Aangezien de cell count (zoals eerder aangehaald) niet OK is, gaan we ook nog even de vergelijking making op basis van de groffe indeling HI/LO. (bekijk http://www.freestatistics.org/blog/index.php?v=date/2010/Nov/20/t1290248317hdutnii5c00439w.htm/ berekening met “Pearson Chi-squared test”) Als we hier kijken naar de p-waarde (tabel “Statiscal Results”) dan zien we dat deze hier lager ligt dan de vooropgestelde type 1 fout (5%), namelijk 2%. Hier kunnen we met meer zekerheid zeggen dat er een verband is. Wel is het verschil hier niet significant (dit zien we duidelijk op de grafiek), want er is geen balkje dat gekleurd (rood of blauw) is. Verder zien we deze blokjes op de hoofddiagonaal (van linksboven naar rechtsonder) boven de stippellijn (de reële frequentie ligt hoger dan de verwachte frequentie) liggen. En op de andere diagonaal (van linksonder naar rechtsboven) onder de stippellijn liggen. Dit wijst bijgevolg op een positief verband.

De conclusie hier is dus dat seperate learners happier zijn. Maar deze berekening zegt niets over oorzaak en gevolg. Bij deze beoordeling hangt er niet vanaf of je de Chi-squared test gebruikt, maar wel hoe je de gegevens bekomen bent. Het gaat hier over een experiment, niet gerandomiseerd, … We kunnen dus eigenlijk niet zeggen dat seperate learning u een gelukkig gevoel geeft.

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Dataseries X:
'A'	'A'	'B'	'B'
'A'	'C'	'A'	'C'
'D'	'B'	'D'	'A'
'D'	'C'	'D'	'B'
'B'	'A'	'A'	'A'
'B'	'D'	'A'	'B'
'A'	'D'	'B'	'A'
'B'	'B'	'B'	'C'
'B'	'B'	'B'	'D'
'A'	'A'	'B'	'B'
'A'	'D'	'A'	'D'
'B'	'B'	'A'	'D'
'A'	'B'	'D'	'A'
'A'	'A'	'A'	'A'
'C'	'A'	'A'	'D'
'C'	'C'	'B'	'A'
'B'	'C'	'B'	'A'
'A'	'A'	'A'	'C'
'A'	'A'	'B'	'D'
'C'	'C'	'A'	'B'
'C'	'C'	'A'	'D'
'D'	'D'	'D'	'A'
'A'	'A'	'B'	'B'
'A'	'A'	'D'	'A'
'A'	'C'	'A'	'C'
'A'	'C'	'D'	'D'
'B'	'B'	'A'	'C'
'C'	'A'	'B'	'A'
'C'	'C'	'B'	'A'
'B'	'C'	'D'	'B'
'D'	'D'	'A'	'D'
'D'	'C'	'C'	'A'
'A'	'D'	'A'	'D'
'B'	'A'	'B'	'C'
'B'	'D'	'B'	'B'
'C'	'C'	'A'	'D'
'D'	'D'	'D'	'A'
'B'	'C'	'B'	'B'
'D'	'D'	'A'	'D'
'B'	'C'	'C'	'D'
'A'	'C'	'B'	'D'
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'A'	'C'	'A'	'D'
'B'	'C'	'D'	'A'
'A'	'D'	'D'	'C'
'A'	'B'	'D'	'B'
'A'	'A'	'B'	'D'
'C'	'B'	'B'	'C'
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'A'	'C'	'C'	'C'
'C'	'A'	'A'	'D'
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'B'	'A'	'D'	'A'
'C'	'C'	'D'	'C'
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'A'	'B'	'A'	'B'
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'D'	'B'	'B'	'A'
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'B'	'A'	'D'	'C'
'B'	'B'	'D'	'A'
'D'	'D'	'D'	'B'
'A'	'A'	'B'	'B'
'B'	'A'	'B'	'A'
'A'	'B'	'B'	'B'
'D'	'A'	'A'	'A'
'B'	'A'	'B'	'B'
'A'	'A'	'B'	'D'
'B'	'D'	'C'	'C'
'A'	'D'	'D'	'A'
'A'	'A'	'A'	'B'
'B'	'D'	'C'	'A'
'D'	'D'	'B'	'B'
'A'	'D'	'C'	'D'
'C'	'B'	'B'	'C'
'A'	'A'	'A'	'D'
'B'	'A'	'B'	'D'
'A'	'C'	'A'	'B'
'C'	'B'	'B'	'B'
'C'	'D'	'B'	'B'
'A'	'A'	'B'	'B'
'C'	'B'	'B'	'B'
'D'	'C'	'C'	'A'
'C'	'C'	'D'	'A'
'D'	'B'	'A'	'B'
'B'	'C'	'C'	'A'
'B'	'A'	'B'	'B'
'C'	'D'	'B'	'A'
'D'	'D'	'C'	'D'
'A'	'B'	'A'	'C'
'A'	'B'	'D'	'A'
'B'	'D'	'B'	'C'
'A'	'B'	'A'	'C'
'C'	'B'	'B'	'D'
'A'	'B'	'A'	'D'
'A'	'B'	'D'	'A'
'B'	'A'	'A'	'B'
'C'	'D'	'D'	'C'
'C'	'A'	'B'	'A'
'A'	'C'	'D'	'A'
'A'	'B'	'A'	'C'
'A'	'A'	'B'	'A'
'B'	'B'	'D'	'A'
'A'	'A'	'D'	'A'
'D'	'B'	'B'	'A'
'D'	'C'	'D'	'A'
'C'	'A'	'C'	'C'
'C'	'C'	'B'	'A'
'A'	'B'	'A'	'B'
'A'	'C'	'A'	'D'
'C'	'A'	'B'	'A'
'B'	'A'	'B'	'B'
'C'	'B'	'B'	'D'
'A'	'B'	'B'	'A'
'C'	'A'	'D'	'A'
'D'	'D'	'B'	'A'
'A'	'A'	'B'	'C'
'A'	'A'	'B'	'D'
'C'	'D'	'B'	'C'
'D'	'C'	'C'	'C'
'A'	'C'	'A'	'D'
'A'	'A'	'A'	'D'
'C'	'B'	'A'	'C'
'B'	'C'	'B'	'B'
'C'	'C'	'C'	'C'
'D'	'C'	'D'	'A'
'A'	'A'	'B'	'D'
'D'	'D'	'B'	'A'
'A'	'A'	'B'	'B'
'C'	'D'	'A'	'A'
'D'	'D'	'D'	'A'
'B'	'B'	'B'	'A'
'D'	'B'	'D'	'A'
'B'	'B'	'B'	'C'
'D'	'B'	'C'	'B'
'C'	'B'	'B'	'D'
'D'	'B'	'A'	'A'
'D'	'B'	'B'	'B'
'D'	'D'	'B'	'B'
'A'	'D'	'A'	'C'
'C'	'D'	'A'	'B'
'B'	'B'	'D'	'B'
'C'	'D'	'D'	'C'
'B'	'C'	'D'	'A'
'A'	'A'	'A'	'B'
'A'	'A'	'C'	'A'
'B'	'A'	'A'	'D'
'B'	'A'	'D'	'B'
'B'	'B'	'D'	'A'
'C'	'D'	'C'	'A'
'B'	'C'	'C'	'A'
'C'	'A'	'B'	'A'
'C'	'B'	'A'	'C'
'B'	'C'	'C'	'D'
'C'	'B'	'D'	'A'
'B'	'B'	'C'	'A'




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94837&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94837&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94837&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132







Tabulation of Results
Happiness x Separate
ABCD
A1312108
B2317812
C2395
D614119

\begin{tabular}{lllllllll}
\hline
Tabulation of Results \tabularnewline
Happiness  x  Separate \tabularnewline
  & A & B & C & D \tabularnewline
A & 13 & 12 & 10 & 8 \tabularnewline
B & 23 & 17 & 8 & 12 \tabularnewline
C & 2 & 3 & 9 & 5 \tabularnewline
D & 6 & 14 & 11 & 9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94837&T=1

[TABLE]
[ROW][C]Tabulation of Results[/C][/ROW]
[ROW][C]Happiness  x  Separate[/C][/ROW]
[ROW][C] [/C][C]A[/C][C]B[/C][C]C[/C][C]D[/C][/ROW]
[C]A[/C][C]13[/C][C]12[/C][C]10[/C][C]8[/C][/ROW]
[C]B[/C][C]23[/C][C]17[/C][C]8[/C][C]12[/C][/ROW]
[C]C[/C][C]2[/C][C]3[/C][C]9[/C][C]5[/C][/ROW]
[C]D[/C][C]6[/C][C]14[/C][C]11[/C][C]9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94837&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94837&T=1

As an alternative you can also use a QR Code:  

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

Tabulation of Results
Happiness x Separate
ABCD
A1312108
B2317812
C2395
D614119







Tabulation of Expected Results
Happiness x Separate
ABCD
A11.6812.2110.099.02
B16.317.0414.0712.59
C5.165.44.463.99
D10.8611.369.388.4

\begin{tabular}{lllllllll}
\hline
Tabulation of Expected Results \tabularnewline
Happiness  x  Separate \tabularnewline
  & A & B & C & D \tabularnewline
A & 11.68 & 12.21 & 10.09 & 9.02 \tabularnewline
B & 16.3 & 17.04 & 14.07 & 12.59 \tabularnewline
C & 5.16 & 5.4 & 4.46 & 3.99 \tabularnewline
D & 10.86 & 11.36 & 9.38 & 8.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94837&T=2

[TABLE]
[ROW][C]Tabulation of Expected Results[/C][/ROW]
[ROW][C]Happiness  x  Separate[/C][/ROW]
[ROW][C] [/C][C]A[/C][C]B[/C][C]C[/C][C]D[/C][/ROW]
[C]A[/C][C]11.68[/C][C]12.21[/C][C]10.09[/C][C]9.02[/C][/ROW]
[C]B[/C][C]16.3[/C][C]17.04[/C][C]14.07[/C][C]12.59[/C][/ROW]
[C]C[/C][C]5.16[/C][C]5.4[/C][C]4.46[/C][C]3.99[/C][/ROW]
[C]D[/C][C]10.86[/C][C]11.36[/C][C]9.38[/C][C]8.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94837&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94837&T=2

As an alternative you can also use a QR Code:  

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

Tabulation of Expected Results
Happiness x Separate
ABCD
A11.6812.2110.099.02
B16.317.0414.0712.59
C5.165.44.463.99
D10.8611.369.388.4







Statistical Results
Pearson's Chi-squared test
Chi Square Statistic16.68
Degrees of Freedom9
P value0.05

\begin{tabular}{lllllllll}
\hline
Statistical Results \tabularnewline
Pearson's Chi-squared test \tabularnewline
Chi Square Statistic & 16.68 \tabularnewline
Degrees of Freedom & 9 \tabularnewline
P value & 0.05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94837&T=3

[TABLE]
[ROW][C]Statistical Results[/C][/ROW]
[ROW][C]Pearson's Chi-squared test[/C][/ROW]
[ROW][C]Chi Square Statistic[/C][C]16.68[/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]9[/C][/ROW]
[ROW][C]P value[/C][C]0.05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94837&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94837&T=3

As an alternative you can also use a QR Code:  

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

Statistical Results
Pearson's Chi-squared test
Chi Square Statistic16.68
Degrees of Freedom9
P value0.05



Parameters (Session):
par1 = 3 ; par2 = 2 ; par3 = Pearson Chi-Squared ;
Parameters (R input):
par1 = 3 ; par2 = 2 ; par3 = Pearson Chi-Squared ;
R code (references can be found in the software module):
library(vcd)
cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
simulate.p.value=FALSE
if (par3 == 'Exact Pearson Chi-Squared by Simulation') simulate.p.value=TRUE
x <- t(x)
(z <- array(unlist(x),dim=c(length(x[,1]),length(x[1,]))))
(table1 <- table(z[,cat1],z[,cat2]))
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
bitmap(file='pic1.png')
assoc(ftable(z[,cat1],z[,cat2],row.vars=1,dnn=c(V1,V2)),shade=T)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Tabulation of Results',ncol(table1)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste(V1,' x ', V2),ncol(table1)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ', 1,TRUE)
for(nc in 1:ncol(table1)){
a<-table.element(a, colnames(table1)[nc], 1, TRUE)
}
a<-table.row.end(a)
for(nr in 1:nrow(table1) ){
a<-table.element(a, rownames(table1)[nr], 1, TRUE)
for(nc in 1:ncol(table1) ){
a<-table.element(a, table1[nr, nc], 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
(cst<-chisq.test(table1, simulate.p.value=simulate.p.value) )
if (par3 == 'McNemar Chi-Squared') {
(cst <- mcnemar.test(table1))
}
if (par3 != 'McNemar Chi-Squared') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Tabulation of Expected Results',ncol(table1)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste(V1,' x ', V2),ncol(table1)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ', 1,TRUE)
for(nc in 1:ncol(table1)){
a<-table.element(a, colnames(table1)[nc], 1, TRUE)
}
a<-table.row.end(a)
for(nr in 1:nrow(table1) ){
a<-table.element(a, rownames(table1)[nr], 1, TRUE)
for(nc in 1:ncol(table1) ){
a<-table.element(a, round(cst$expected[nr, nc], digits=2), 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Statistical Results',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, cst$method, 2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Chi Square Statistic', 1, TRUE)
a<-table.element(a, round(cst$statistic, digits=2), 1,FALSE)
a<-table.row.end(a)
if(!simulate.p.value){
a<-table.row.start(a)
a<-table.element(a, 'Degrees of Freedom', 1, TRUE)
a<-table.element(a, cst$parameter, 1,FALSE)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, 'P value', 1, TRUE)
a<-table.element(a, round(cst$p.value, digits=2), 1,FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')