FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_meanplot.wasp
Title produced by softwareMean Plot
Date of computationSat, 01 Nov 2008 07:19:15 -0600
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/01/t1225545606g2rfojctt46wvsf.htm/, Retrieved Sun, 19 May 2024 10:06:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20386, Retrieved Sun, 19 May 2024 10:06:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact249

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Notched Boxplots] [workshop 3] [2007-10-26 13:31:48] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F RMPD  [Mean Plot] [workshop 4 deel 1...] [2008-10-31 09:40:26] [077ffec662d24c06be4c491541a44245]
F           [Mean Plot] [] [2008-11-01 13:19:15] [6d40a467de0f28bd2350f82ac9522c51] [Current]
F R           [Mean Plot] [] [2008-11-01 13:59:33] [4c8dfb519edec2da3492d7e6be9a5685]
F               [Mean Plot] [Task 4 - Bob Leysen] [2008-11-02 15:49:09] [57850c80fd59ccfb28f882be994e814e]
F               [Mean Plot] [Task 4] [2008-11-02 16:04:40] [73d6180dc45497329efd1b6934a84aba]
F                 [Mean Plot] [Task 4] [2008-11-02 19:16:07] [6816386b1f3c2f6c0c9f2aa1e5bc9362]
F    D        [Mean Plot] [] [2008-11-01 14:24:03] [4c8dfb519edec2da3492d7e6be9a5685]
F    D          [Mean Plot] [Task 5 - Bob Leysen] [2008-11-02 16:03:09] [57850c80fd59ccfb28f882be994e814e]
F    D            [Mean Plot] [opdracht 4 task 5] [2008-11-03 17:56:26] [077ffec662d24c06be4c491541a44245]
-    D          [Mean Plot] [task 5] [2008-11-02 16:22:21] [73d6180dc45497329efd1b6934a84aba]
F RMPD          [Star Plot] [Star Plot - Bob L...] [2008-11-02 16:44:14] [57850c80fd59ccfb28f882be994e814e]
F   P             [Star Plot] [Q2 -part 2] [2008-11-02 18:39:32] [73d6180dc45497329efd1b6934a84aba]
F   P             [Star Plot] [Star plot - Stefa...] [2008-11-03 19:45:53] [393f8bd7ec1141df13b2cdc1ba8ed059]
-    D              [Star Plot] [Verbetering Q2] [2008-11-05 17:50:39] [2d4aec5ed1856c4828162be37be304d9]
-                     [Star Plot] [Verbetering] [2008-11-09 11:11:25] [79c17183721a40a589db5f9f561947d8]
-   P             [Star Plot] [Part 2 - Q2] [2008-11-03 19:55:23] [547636b63517c1c2916a747d66b36ebf]
-   P             [Star Plot] [Q2- Jens Peeters] [2008-11-11 10:44:11] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Mean with known Variance - Critical Value] [Q1] [2008-11-11 12:00:37] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Mean with known Variance - p-value] [Q2] [2008-11-11 12:25:49] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Mean with known Variance - Type II Error] [Q3] [2008-11-11 12:43:52] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Mean with known Variance - Sample Size] [Q4] [2008-11-11 12:54:50] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Population Mean with known Variance - Confidence Interval] [Q5] [2008-11-11 13:03:28] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Testing Sample Mean with known Variance - Confidence Interval] [Q6] [2008-11-11 13:11:57] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Bivariate Kernel Density Estimation] [Q1-1] [2008-11-11 14:13:42] [b47fceb71c9525e79a89b5fc6d023d0e]
F RM D            [Partial Correlation] [Q1-2] [2008-11-11 14:17:22] [b47fceb71c9525e79a89b5fc6d023d0e]
F RMPD            [Trivariate Scatterplots] [Q1-3] [2008-11-11 14:19:10] [b47fceb71c9525e79a89b5fc6d023d0e]
F    D          [Mean Plot] [task 5] [2008-11-02 17:55:34] [73d6180dc45497329efd1b6934a84aba]
F RM D        [Kendall tau Correlation Matrix] [] [2008-11-01 14:54:43] [4c8dfb519edec2da3492d7e6be9a5685]
F RM D          [Star Plot] [] [2008-11-01 15:07:20] [4c8dfb519edec2da3492d7e6be9a5685]
F RM D            [Notched Boxplots] [] [2008-11-01 15:20:06] [4c8dfb519edec2da3492d7e6be9a5685]
F   P               [Notched Boxplots] [] [2008-11-02 21:57:12] [077ffec662d24c06be4c491541a44245]
F   P             [Star Plot] [] [2008-11-02 21:55:44] [077ffec662d24c06be4c491541a44245]
F   PD          [Kendall tau Correlation Matrix] [] [2008-11-02 21:54:30] [077ffec662d24c06be4c491541a44245]
F             [Mean Plot] [Task 1 - Bob Leysen] [2008-11-02 15:25:34] [57850c80fd59ccfb28f882be994e814e]
F   P           [Mean Plot] [] [2008-11-06 19:08:36] [072bb89749ef40809573ea0372b43d78]
F             [Mean Plot] [task 4 ] [2008-11-02 16:02:26] [73d6180dc45497329efd1b6934a84aba]
-               [Mean Plot] [Task 4] [2008-11-02 19:13:48] [6816386b1f3c2f6c0c9f2aa1e5bc9362]
-   P         [Mean Plot] [EDA part 1 - Q4 -...] [2008-11-11 08:41:43] [33f4701c7363e8b81858dafbf0350eed]
Feedback Forum
2008-11-05 17:24:34 [Ken Van den Heuvel] [reply] 
Q2:
Je stelt: 'Er kan inderdaad besloten worden dat er duidelijke sprake is van invloed van seizonaliteit. Dit stellen we ondermeer vast in de maanden 7 en 9. '

Ik neem aan dat je maand 7 en 10/11 bedoel gezien er in maand 9 gewoonweg sprak is van een gemiddelde verderzetteing van de neerwaartse trend.

Je had nog mogelijksverder een verklaring hiervoor kunnen zoeken. Data 6 komt ongeveer overeen met het begin van de zomer, de periode waarin mensen volop zomerkledij aankopen. M.a.w. de productie komt hier op gang om in de behoeften van de mensen te voorzien. Deze trend zet zich dan voort tot augustus om vervolgens weer af te bouwen (logisch, zomer loopt op zijn einde, mensen zullen minder geneigd zijn zomerkledij te kopen, productie volgt deze tendens en daalt. Ongeveer hetzelfde fenomeen doet zich voor in oktober, maar dan ditmaal omwille van de winter en de bijhorende winterkledij.

Het gemiddelde van de productie ligt dus hoger in deze periodes, wat we kunnen afleiden uit het stijgende verloop op de grafiek, waardoor we inderdaad kunnen stellen dat seizoenaliteit een effect heeft op de kledijproductie.

Hetzelfde vinden we terug bij Periodic subseries waar de betrouwbaarheidsintervallen bij de boxes van 6 en 10 duidelijk buiten deze van hun voorgangers liggen.
2008-11-09 12:14:33 [2df1bcd103d52957f4a39bd4617794c8] [reply] 
De student besluit correct seizonaliteit uit de data reeksen.

De Mean Plot geeft de periodieke gemiddelden van elke maand grafisch weer, het jaarlijks gemiddelde wordt weergegeven door de horizontale lijn op de grafiek.

De gevonden gemiddelden liggen in de zomer- en wintermaanden hoger dan in de rest van het jaar. Mensen kopen meer kleding en de productie volgt deze trend.
2008-11-09 13:37:09 [Bob Leysen] [reply] 
Het gaat hier ook om periodieke gemiddelden en geen waarden van een dataset.
Er is een significant verschil voor de periode 7 (juni-juli).

Een mogelijke verklaring is dat data 6 ongeveer overeen komt met het begin van de zomer, de periode waarin mensen volop zomerkledij aankopen. We zien dat de productie hier op gang komt. Deze trend zet zich dan voort tot augustus om vervolgens weer af te bouwen (logisch, zomer loopt op zijn einde, mensen zullen minder geneigd zijn zomerkledij te kopen, productie volgt deze tendens en daalt). Ongeveer hetzelfde fenomeen doet zich voor in oktober, maar dan ditmaal omwille van de winter en de bijhorende winterkledij.
2008-11-10 11:26:18 [Steffi Van Isveldt] [reply] 
Wanneer er uitspraken gedaan worden over seizonaliteit, moet er bij deze reeks op gelet worden dat maand 1 NIET januari is, maar wel MAART!

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109.20
88.60
94.30
98.30
86.40
80.60
104.10
108.20
93.40
71.90
94.10
94.90
96.40
91.10
84.40
86.40
88.00
75.10
109.70
103.00
82.10
68.00
96.40
94.30
90.00
88.00
76.10
82.50
81.40
66.50
97.20
94.10
80.70
70.50
87.80
89.50
99.60
84.20
75.10
92.00
80.80
73.10
99.80
90.00
83.10
72.40
78.80
87.30
91.00
80.10
73.60
86.40
74.50
71.20
92.40
81.50
85.30
69.90
84.20
90.70
100.30

Tables (Output of Computation)





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=20386&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=20386&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20386&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 time2 seconds
R Server'George Udny Yule' @ 72.249.76.132


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
(n <- length(x))
(np <- floor(n / par1))
arr <- array(NA,dim=c(par1,np+1))
darr <- array(NA,dim=c(par1,np+1))
ari <- array(0,dim=par1)
dx <- diff(x)
j <- 0
for (i in 1:n)
{
j = j + 1
ari[j] = ari[j] + 1
arr[j,ari[j]] <- x[i]
darr[j,ari[j]] <- dx[i]
if (j == par1) j = 0
}
ari
arr
darr
arr.mean <- array(NA,dim=par1)
arr.median <- array(NA,dim=par1)
arr.midrange <- array(NA,dim=par1)
for (j in 1:par1)
{
arr.mean[j] <- mean(arr[j,],na.rm=TRUE)
arr.median[j] <- median(arr[j,],na.rm=TRUE)
arr.midrange[j] <- (quantile(arr[j,],0.75,na.rm=TRUE) + quantile(arr[j,],0.25,na.rm=TRUE)) / 2
}
overall.mean <- mean(x)
overall.median <- median(x)
overall.midrange <- (quantile(x,0.75) + quantile(x,0.25)) / 2
bitmap(file='plot1.png')
plot(arr.mean,type='b',ylab='mean',main='Mean Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.mean,0)
dev.off()
bitmap(file='plot2.png')
plot(arr.median,type='b',ylab='median',main='Median Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.median,0)
dev.off()
bitmap(file='plot3.png')
plot(arr.midrange,type='b',ylab='midrange',main='Midrange Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.midrange,0)
dev.off()
bitmap(file='plot4.png')
z <- data.frame(t(arr))
names(z) <- c(1:par1)
(boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Periodic Subseries'))
dev.off()
bitmap(file='plot4b.png')
z <- data.frame(t(darr))
names(z) <- c(1:par1)
(boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Differenced Periodic Subseries'))
dev.off()
bitmap(file='plot5.png')
z <- data.frame(arr)
names(z) <- c(1:np)
(boxplot(z,notch=TRUE,col='grey',xlab='Block Index',ylab='Value',main='Notched Box Plots - Sequential Blocks'))
dev.off()
bitmap(file='plot6.png')
z <- data.frame(cbind(arr.mean,arr.median,arr.midrange))
names(z) <- list('mean','median','midrange')
(boxplot(z,notch=TRUE,col='grey',ylab='Overall Central Tendency',main='Notched Box Plots'))
dev.off()