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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_edauni.wasp
Title produced by softwareUnivariate Explorative Data Analysis
Date of computationWed, 19 Nov 2008 07:06:25 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/19/t1227103620q54x9nc6wvfta50.htm/, Retrieved Sun, 19 May 2024 11:31:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25034, Retrieved Sun, 19 May 2024 11:31:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact238

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Multiple Regression] [Taak 6 - Q1 (2)] [2008-11-16 10:42:33] [46c5a5fbda57fdfa1d4ef48658f82a0c]
F RMPD    [Univariate Explorative Data Analysis] [Taak 6 Q 2] [2008-11-19 14:06:25] [bda7fba231d49184c6a1b627868bbb81] [Current]
F   PD      [Univariate Explorative Data Analysis] [Q2 task 6] [2008-11-20 17:26:46] [8eb83367d7ce233bbf617141d324189b]
F   PD        [Univariate Explorative Data Analysis] [Seatbelt Law Q2] [2008-11-23 13:04:24] [3548296885df7a66ea8efc200c4aca50]
-           [Univariate Explorative Data Analysis] [Seatbelt Law Q2] [2008-11-23 12:29:32] [3548296885df7a66ea8efc200c4aca50]
-   PD      [Univariate Explorative Data Analysis] [Q2 Seatbelt law] [2008-11-30 13:42:21] [7d3039e6253bb5fb3b26df1537d500b4]
Feedback Forum
2008-11-30 14:38:28 [Stéphanie Claes] [reply] 
De student heeft correcte gegevens gebruikt maar is vergeten de lags in te stellen wat belangrijk is om de seizonaliteit te beoordelen.

=> http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/30/t1228052614yr9ency1rcom5to.htm (met lags).

We moeten controleren of de assumpties voldaan zijn, hierbij gaan we opnieuw de geblogde gegevens uit Q1 bekijken.

- Residuals grafiek is voor een perfect model gelijk aan 0, hier is dat niet het geval;

- Als we naar het histogram, density-plot en qq-plot kijken dan zien we dat er een vrij duidelijke normale verdeling af te lezen is (dit is goed);

- Maar we zien dat er nog een hoge autocorrelatie aanwezig is, er komt nog veel boven het 95% betrouwbaarheidsinterval.

Conclusie: Er wordt niet aan alle assumpties voldaan. Het model is niet perfect maar weet wel 74% van de gevallen te voorspellen.

Een andere mogelijkheid is om de 4 assumpties te testen door Central Tendency te gebruiken. => http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/30/t1228055528apyfy9a1dsogm4f.htm
De winsorized mean moet 0 zijn (gaat hier buiten betrouwbaarheidsinterval vallen).
Trimmed mean valt wel binnen het betrouwbaarheidsinterval.
Rekenkundig gemiddelde = -8.0729257479187e-10 = zeer klein
We gaan hierop de T-test uitvoeren: (-8.0729257479187e-10 - 0) / 10.61 (zie tabel). De uitkomst is kleiner dan 2 in absolute waarden wat betekent dat -8.0729257479187e-10 niet significant verschillend is van nul (wat goed is want het moet nul zijn).

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-183,9235445
-177,0726091
-228,6351091
-237,4476091
-127,7601091
-193,0101091
-220,6351091
-164,5101091
-268,3226091
-333,6976091
-34,26010911
-154,8851091
-97,74528053
101,1056549
2,543154874
-43,26934513
-163,5818451
-162,8318451
46,54315487
26,66815487
-107,1443451
42,48065487
76,91815487
196,2931549
201,4329835
12,28391886
-0,278581137
42,90891886
87,59641886
84,34641886
57,72141886
173,8464189
-185,9660811
47,65891886
89,09641886
-68,52858114
272,6112475
146,4621829
162,8996829
10,08718285
279,7746829
212,5246829
248,8996829
-41,97531715
-5,787817149
52,83718285
274,2746829
414,6496829
310,7895114
362,6404468
26,07794684
403,2654468
327,9529468
193,7029468
317,0779468
202,2029468
321,3904468
178,0154468
16,45294684
-68,17205316
-157,0322246
-76,18128917
-81,74378917
-134,5562892
77,13121083
199,8812108
105,2562108
198,3812108
262,5687108
196,1937108
11,63121083
-145,9937892
-166,8539606
-202,0030252
43,43447482
-113,3780252
-113,6905252
-155,9405252
-210,5655252
-124,4405252
-64,25302518
-298,6280252
-154,1905252
23,18447482
-249,6756966
118,1752388
-180,3872612
-79,19976119
-81,51226119
-246,7622612
-105,3872612
-319,2622612
-72,07476119
-90,44976119
-80,01226119
119,3627388
-53,49743261
-114,6464972
-155,2089972
-50,02149721
-196,3339972
-14,58399721
-82,20899721
17,91600279
-162,8964972
-132,2714972
-16,83399721
81,54100279
275,6808314
-32,46823322
17,96926678
27,15676678
-123,1557332
108,5942668
67,96926678
34,09426678
-13,71823322
-113,0932332
54,34426678
149,7192668
153,8590954
-28,28996923
238,1475308
50,33503077
8,022530771
-61,22746923
-140,8524692
-28,72746923
9,460030771
-121,9149692
41,52253077
115,8975308
27,03735936
-91,11170524
3,325794759
-29,48670524
-73,79920524
50,95079476
-86,67420524
-9,54920524
-66,36170524
73,26329476
-216,2992052
-128,9242052
-142,7843767
27,06655875
60,50405875
35,69155875
16,37905875
-64,87094125
115,5040587
-30,37094125
87,81655875
205,4415587
-64,12094125
-322,7459413
-139,6061127
35,24482274
-4,317677263
17,86982274
2,557322737
129,3073227
-16,31767726
164,8073227
21,99482274
138,6198227
87,05732274
51,43232274
-80,42784867
-105,1918797
5,245620328
68,43312033
-0,879379672
-105,1293797
-82,75437967
-132,6293797
102,5581203
23,18312033
-180,3793797
-267,0043797
30,13544892
23,98638432
90,42388432
31,61138432
81,29888432
25,04888432
-13,57611568
33,54888432
140,7363843
132,3613843
94,79888432
4,173884316

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25034&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25034&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25034&T=0

As an alternative you can also use a QR Code:  
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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Descriptive Statistics
# observations192
minimum-333.6976091
Q1-105.826532175
median4.709752322
mean-8.0729257479187e-10
Q385.02414483
maximum414.6496829

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 192 \tabularnewline
minimum & -333.6976091 \tabularnewline
Q1 & -105.826532175 \tabularnewline
median & 4.709752322 \tabularnewline
mean & -8.0729257479187e-10 \tabularnewline
Q3 & 85.02414483 \tabularnewline
maximum & 414.6496829 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25034&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]192[/C][/ROW]
[ROW][C]minimum[/C][C]-333.6976091[/C][/ROW]
[ROW][C]Q1[/C][C]-105.826532175[/C][/ROW]
[ROW][C]median[/C][C]4.709752322[/C][/ROW]
[ROW][C]mean[/C][C]-8.0729257479187e-10[/C][/ROW]
[ROW][C]Q3[/C][C]85.02414483[/C][/ROW]
[ROW][C]maximum[/C][C]414.6496829[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25034&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25034&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Descriptive Statistics
# observations192
minimum-333.6976091
Q1-105.826532175
median4.709752322
mean-8.0729257479187e-10
Q385.02414483
maximum414.6496829


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 0 ;
Parameters (R input):
par1 = 0 ; par2 = 0 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
x <- as.ts(x)
library(lattice)
bitmap(file='pic1.png')
plot(x,type='l',main='Run Sequence Plot',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic2.png')
hist(x)
grid()
dev.off()
bitmap(file='pic3.png')
if (par1 > 0)
{
densityplot(~x,col='black',main=paste('Density Plot   bw = ',par1),bw=par1)
} else {
densityplot(~x,col='black',main='Density Plot')
}
dev.off()
bitmap(file='pic4.png')
qqnorm(x)
qqline(x)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='lagplot1.png')
dum <- cbind(lag(x,k=1),x)
dum
dum1 <- dum[2:length(x),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main='Lag plot (k=1), lowess, and regression line')
lines(lowess(z))
abline(lm(z))
dev.off()
if (par2 > 1) {
bitmap(file='lagplotpar2.png')
dum <- cbind(lag(x,k=par2),x)
dum
dum1 <- dum[(par2+1):length(x),]
dum1
z <- as.data.frame(dum1)
z
mylagtitle <- 'Lag plot (k='
mylagtitle <- paste(mylagtitle,par2,sep='')
mylagtitle <- paste(mylagtitle,'), and lowess',sep='')
plot(z,main=mylagtitle)
lines(lowess(z))
dev.off()
}
bitmap(file='pic5.png')
acf(x,lag.max=par2,main='Autocorrelation Function')
grid()
dev.off()
}
summary(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Descriptive Statistics',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'# observations',header=TRUE)
a<-table.element(a,length(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'minimum',header=TRUE)
a<-table.element(a,min(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,quantile(x,0.25))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
a<-table.element(a,median(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,mean(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,quantile(x,0.75))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'maximum',header=TRUE)
a<-table.element(a,max(x))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')