Home » date » 2007 » Nov » 15 » attachments

Question 1: How many people are saved from death or serious injury by the seatbelt law?

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Thu, 15 Nov 2007 05:13:11 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w.htm/, Retrieved Thu, 15 Nov 2007 13:23:52 +0100
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
1687 0 1508 0 1507 0 1385 0 1632 0 1511 0 1559 0 1630 0 1579 0 1653 0 2152 0 2148 0 1752 0 1765 0 1717 0 1558 0 1575 0 1520 0 1805 0 1800 0 1719 0 2008 0 2242 0 2478 0 2030 0 1655 0 1693 0 1623 0 1805 0 1746 0 1795 0 1926 0 1619 0 1992 0 2233 0 2192 0 2080 0 1768 0 1835 0 1569 0 1976 0 1853 0 1965 0 1689 0 1778 0 1976 0 2397 0 2654 0 2097 0 1963 0 1677 0 1941 0 2003 0 1813 0 2012 0 1912 0 2084 0 2080 0 2118 0 2150 0 1608 0 1503 0 1548 0 1382 0 1731 0 1798 0 1779 0 1887 0 2004 0 2077 0 2092 0 2051 0 1577 0 1356 0 1652 0 1382 0 1519 0 1421 0 1442 0 1543 0 1656 0 1561 0 1905 0 2199 0 1473 0 1655 0 1407 0 1395 0 1530 0 1309 0 1526 0 1327 0 1627 0 1748 0 1958 0 2274 0 1648 0 1401 0 1411 0 1403 0 1394 0 1520 0 1528 0 1643 0 1515 0 1685 0 2000 0 2215 0 1956 0 1462 0 1563 0 1459 0 1446 0 1622 0 1657 0 1638 0 1643 0 1683 0 2050 0 2262 0 1813 0 1445 0 1762 0 1461 0 1556 0 1431 0 1427 0 1554 0 1645 0 1653 0 2016 0 2207 0 1665 0 1361 0 1506 0 1360 0 1453 0 1522 0 1460 0 1552 0 1548 0 1827 0 1737 0 1941 0 1474 0 1458 0 1542 0 1404 0 1522 0 1385 0 1641 0 1510 0 1681 0 1938 0 1868 0 1726 0 1456 0 1445 0 1456 0 1365 0 1487 0 1558 0 1488 0 1684 0 1594 0 1850 0 1998 0 2079 0 1494 0 1057 1 1218 1 1168 1 1236 1 1076 1 1174 1 1139 1 1427 1 1487 1 1483 1 1513 1 1357 1 1165 1 1282 1 1110 1 1297 1 1185 1 1222 1 1284 1 1444 1 1575 1 1737 1 1763 1
 
Text written by user:
 
Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!


Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2324.06337310277 -226.385033602657X[t] -451.374973256309M1[t] -635.461053323771M2[t] -583.133697991392M3[t] -694.556342659014M4[t] -555.478987326639M5[t] -609.464131994259M6[t] -532.074276661885M7[t] -515.434421329508M8[t] -460.85706599713M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237686t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)2324.0633731027744.02993952.783700
X-226.38503360265741.037226-5.516600
M1-451.37497325630953.942919-8.367600
M2-635.46105332377153.941479-11.780600
M3-583.13369799139253.931287-10.812500
M4-694.55634265901453.922166-12.880700
M5-555.47898732663953.914117-10.30300
M6-609.46413199425953.907141-11.305800
M7-532.07427666188553.901237-9.871300
M8-515.43442132950853.896405-9.563400
M9-460.8570659971353.892648-8.551400
M10-319.71721066475453.889963-5.932800
M11-118.38985533237753.888353-2.19690.0293160.014658
t-1.764855332376860.240551-7.336700


Multiple Linear Regression - Regression Statistics
Multiple R0.861322441473346
R-squared0.741876348185605
Adjusted R-squared0.723024620805902
F-TEST (value)39.3532291891914
F-TEST (DF numerator)13
F-TEST (DF denominator)178
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation152.417759557721
Sum Squared Residuals4135148.87028996


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
116871870.92354451406-183.923544514060
215081685.07260911425-177.072609114252
315071735.63510911425-228.635109114254
413851622.44760911424-237.447609114242
516321759.76010911425-127.760109114251
615111704.01010911425-193.010109114250
715591779.63510911425-220.635109114248
816301794.51010911426-164.510109114256
915791847.32260911425-268.322609114246
1016531986.69760911425-333.697609114249
1121522186.26010911425-34.2601091142503
1221482302.88510911425-154.885109114249
1317521849.74528052556-97.7452805255622
1417651663.89434512573101.105654874272
1517171714.456845125732.54315487427317
1615581601.26934512573-43.2693451257275
1715751738.58184512573-163.581845125727
1815201682.83184512573-162.831845125727
1918051758.4568451257346.5431548742728
2018001773.3318451257326.6681548742733
2117191826.14434512573-107.144345125727
2220081965.5193451257342.4806548742729
2322422165.0818451257376.918154874273
2424782281.70684512573196.293154874273
2520301828.56701653704201.43298346296
2616551642.7160811372012.2839188627955
2716931693.27858113720-0.278581137204542
2816231580.0910811372142.9089188627948
2918051717.4035811372087.5964188627952
3017461661.6535811372084.3464188627952
3117951737.2785811372157.721418862795
3219261752.15358113720173.846418862796
3316191804.96608113721-185.966081137205
3419921944.3410811372047.6589188627952
3522332143.9035811372089.0964188627953
3621922260.52858113721-68.5285811372049
3720801807.38875254852272.611247451482
3817681621.53781714868146.462182851318
3918351672.10031714868162.899682851318
4015691558.9128171486810.0871828513171
4119761696.22531714868279.774682851318
4218531640.47531714868212.524682851317
4319651716.10031714868248.899682851317
4416891730.97531714868-41.9753171486821
4517781783.78781714868-5.78781714868267
4619761923.1628171486852.8371828513174
4723972122.72531714868274.274682851318
4826542239.35031714868414.649682851317
4920971786.21048856000310.789511440004
5019631600.35955316016362.64044683984
5116771650.9220531601626.0779468398400
5219411537.73455316016403.265446839839
5320031675.04705316016327.95294683984
5418131619.29705316016193.702946839840
5520121694.92205316016317.07794683984
5619121709.79705316016202.20294683984
5720841762.60955316016321.39044683984
5820801901.98455316016178.015446839840
5921182101.5470531601616.4529468398398
6021502218.17205316016-68.1720531601603
6116081765.03222457147-157.032224571473
6215031579.18128917164-76.1812891716376
6315481629.74378917164-81.7437891716377
6413821516.55628917164-134.556289171638
6517311653.8687891716477.1312108283621
6617981598.11878917164199.881210828362
6717791673.74378917164105.256210828362
6818871688.61878917164198.381210828362
6920041741.43128917164262.568710828362
7020771880.80628917164196.193710828362
7120922080.3687891716411.6312108283621
7220512196.99378917164-145.993789171638
7315771743.85396058295-166.853960582951
7413561558.00302518312-202.003025183115
7516521608.5655251831243.4344748168846
7613821495.37802518312-113.378025183116
7715191632.69052518312-113.690525183116
7814211576.94052518312-155.940525183116
7914421652.56552518312-210.565525183116
8015431667.44052518312-124.440525183115
8116561720.25302518312-64.2530251831158
8215611859.62802518312-298.628025183116
8319052059.19052518312-154.190525183116
8421992175.8155251831223.1844748168843
8514731722.67569659443-249.675696594429
8616551536.82476119459118.175238805407
8714071587.38726119459-180.387261194593
8813951474.19976119459-79.1997611945937
8915301611.51226119459-81.5122611945933
9013091555.76226119459-246.762261194593
9115261631.38726119459-105.387261194593
9213271646.26226119459-319.262261194593
9316271699.07476119459-72.0747611945935
9417481838.44976119459-90.4497611945934
9519582038.01226119459-80.0122611945933
9622742154.63726119459119.362738805407
9716481701.49743260591-53.4974326059064
9814011515.64649720607-114.646497206071
9914111566.20899720607-155.208997206071
10014031453.02149720607-50.0214972060714
10113941590.33399720607-196.333997206071
10215201534.58399720607-14.5839972060711
10315281610.20899720607-82.2089972060712
10416431625.0839972060717.9160027939294
10515151677.89649720607-162.896497206071
10616851817.27149720607-132.271497206071
10720002016.83399720607-16.8339972060710
10822152133.4589972060781.5410027939288
10919561680.31916861738275.680831382616
11014621494.46823321755-32.4682332175485
11115631545.0307332175517.9692667824515
11214591431.8432332175527.1567667824508
11314461569.15573321755-123.155733217549
11416221513.40573321755108.594266782451
11516571589.0307332175567.9692667824511
11616381603.9057332175534.0942667824517
11716431656.71823321755-13.7182332175489
11816831796.09323321755-113.093233217549
11920501995.6557332175554.3442667824512
12022622112.28073321755149.719266782451
12118131659.14090462886153.859095371138
12214451473.28996922903-28.2899692290262
12317621523.85246922903238.147530770974
12414611410.6649692290350.3350307709731
12515561547.977469229038.02253077097358
12614311492.22746922903-61.2274692290265
12714271567.85246922903-140.852469229027
12815541582.72746922903-28.7274692290261
12916451635.539969229039.46003077097336
13016531774.91496922903-121.914969229026
13120161974.4774692290341.5225307709736
13222072091.10246922903115.897530770973
13316651637.9626406403427.0373593596605
13413611452.11170524050-91.111705240504
13515061502.674205240503.32579475949605
13613601389.48670524050-29.4867052405046
13714531526.79920524050-73.7992052405041
13815221471.0492052405050.9507947594957
13914601546.67420524050-86.6742052405044
14015521561.54920524050-9.54920524050376
14115481614.36170524050-66.3617052405043
14218271753.7367052405073.2632947594957
14317371953.29920524050-216.299205240504
14419412069.92420524050-128.924205240504
14514741616.78437665182-142.784376651817
14614581430.9334412519827.0665587480184
14715421481.4959412519860.5040587480183
14814041368.3084412519835.6915587480177
14915221505.6209412519816.3790587480182
15013851449.87094125198-64.870941251982
15116411525.49594125198115.504058748018
15215101540.37094125198-30.3709412519815
15316811593.1834412519887.816558748018
15419381732.55844125198205.441558748018
15518681932.12094125198-64.1209412519819
15617262048.74594125198-322.745941251982
15714561595.60611266329-139.606112663295
15814451409.7551772634635.2448227365406
15914561460.31767726346-4.31767726345942
16013651347.1301772634617.8698227365400
16114871484.442677263462.55732273654045
16215581428.69267726346129.307322736540
16314881504.31767726346-16.3176772634598
16416841519.19267726346164.807322736541
16515941572.0051772634621.9948227365402
16618501711.38017726346138.619822736540
16719981910.9426772634687.0573227365404
16820792027.5676772634651.4323227365403
16914941574.42784867477-80.4278486747727
17010571162.19187967228-105.191879672279
17112181212.754379672285.24562032772056
17211681099.5668796722868.4331203277199
17312361236.87937967228-0.879379672279542
17410761181.12937967228-105.129379672280
17511741256.75437967228-82.7543796722797
17611391271.62937967228-132.629379672279
17714271324.44187967228102.558120327720
17814871463.8168796722823.1831203277203
17914831663.37937967228-180.379379672280
18015131780.00437967228-267.00437967228
18113571326.8645510835930.1354489164073
18211651141.0136156837623.986384316243
18312821191.5761156837690.4238843162428
18411101078.3886156837631.6113843162422
18512971215.7011156837681.2988843162427
18611851159.9511156837625.0488843162426
18712221235.57611568376-13.5761156837575
18812841250.4511156837633.548884316243
18914441303.26361568376140.736384316242
19015751442.63861568376132.361384316243
19117371642.2011156837694.7988843162427
19217631758.826115683764.17388431624253
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/1a2lm1195128784.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/1a2lm1195128784.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/2jusr1195128784.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/2jusr1195128784.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/3cvzc1195128784.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/3cvzc1195128784.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/430oj1195128784.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/430oj1195128784.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/5jz5s1195128784.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/5jz5s1195128784.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/6ehza1195128784.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/6ehza1195128784.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/7rt4r1195128784.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/7rt4r1195128784.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/8wy7v1195128784.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/8wy7v1195128784.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/9rse91195128784.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195129421xwvo03m3ia1eb4w/9rse91195128784.ps (open in new window)


 
Parameters:
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
 
R code (references can be found in the software module):
library(lattice)
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)
a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
 





Copyright

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.


Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.


FreeStatistics.org is powered by