## Free Statistics

of Irreproducible Research!

Author's title
Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 25 Nov 2020 15:26:41 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Nov/25/t1606314422mmcghyfgu7ffwj5.htm/, Retrieved Sun, 09 May 2021 01:10:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319300, Retrieved Sun, 09 May 2021 01:10:04 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact25
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [women] [2020-11-25 14:26:41] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
11.455804
2.9332886
6.2191311
6.9153776
4.9465626
5.1345641
3.0622244
5.5872164
7.655561
16.656451
3.2168822
6.8287604
1.03288
3.4909358
6.130999
4.6384438
7.9617834
8.9458241
2.4497795
2.6329406
6.1893723
2.0414829
1.0927938
0.73493386
3.5031847
9.033562
8.7579618
4.899559
6.3482659
2.9117379
3.2663727
4.3329434
3.2527249
10.564674
7.944759
6.1244488
3.2393779
2.5294579
2.9625241
5.7379961
9.7991181
6.1244488
15.716767
7.464172
2.3590469
6.1197413
12.20489
5.5732484
4.423213
10.968499
2.0029644
7.8392945
4.8253233
3.3406084
6.2782903
2.0747318
3.6746693
5.4109208
6.6348195
3.3684468
6.3553039
4.899559
4.749134
2.4655845
7.0431161
2.5922086
4.899559
4.9865907
3.075532
4.3940856
7.7248869
14.382577
9.634427
5.7382223
4.3388466
4.4113344
10.634978
7.6963907
11.660951
1.384658
1.4861996
5.0862936
9.0346479
2.4497795
7.1962273
5.5120039
3.2143846
1.031706
6.1244488
6.4555001
2.3437285
4.899559
3.9808917
0.86855819
6.7368937
0.83195229
4.5495905
6.0088932
8.6855819
3.0622244
1.5250024
5.345367
7.0431161
6.1244488
3.5956441
3.676112
2.0489884
2.4497795
3.0013006
5.0752404
4.4855118
8.9530095
1.0734989
14.290381
4.899559
8.2680059
3.4344948
7.3493386
4.8510486
4.7283954
5.5120039
6.8086976
2.6175726
4.0256208
5.4373155
12.283894
3.9808917
4.0483645
11.092494
5.2057815
3.122268
3.9808917
7.2640521
5.8301389
3.1919678
3.9156312
3.3328396
4.6044049
2.8467856
1.0881709
1.4329641
3.8277805
8.1148947
7.464172
5.8168281
6.4337644
2.5320159
4.4960659
1.7978221
5.2057815
0.31847134
5.9144677
10.1985
4.7464478
5.4284887
4.2540191
6.1244488
4.899559
7.9617834
5.8519878
4.1904123
2.3382624
7.9617834
2.756002
5.9429339
2.1149176
5.0526703
3.1847134
1.6472655
5.2057815
10.615711
4.8131688
9.933416
5.678538
3.5783296
4.6161699
8.0394371
7.1451903
7.4332435
2.008016
2.0029644
3.2812198
1.9935608
9.3755581
5.9713376
9.7531847
4.5933366
7.0431161
5.5975028
6.255687
2.7613122
4.899559
10.105341
2.5697342
7.7351672
2.756002
9.353908
5.7758026
5.0065363
5.709151
0.52581949
2.1435571
7.655561
8.5742283
3.1120978
3.9769108
3.7659204
7.3493386
1.0411563
5.8182264
0.22116065
4.899559
3.1070374
3.8370041
4.3866575
3.7284449
4.5933366
6.3220117
6.0509554
4.3927081
6.4306712
5.7324841
4.5933366
3.6860109
7.1560455
7.0431161
4.7770701
9.4928956
3.7913254
3.3684468
3.9808917
5.7324841
2.2058621
1.9053841
4.7770701
2.3810941
4.5569882
7.9617834
5.538632
9.6630192
5.2057815
6.407874
5.6962353
39.808917
4.5933366
9.5694512
8.5742283
0.76253701
5.7253274
18.473419
3.3641338
4.732115
2.1435571
4.1340029
7.6846912
3.7052915
5.4075133
3.9676662
6.0983873
3.3174098
11.722871
3.7454626
7.9617834
2.5409947
4.2200266
4.4585987
7.655561
5.3841308
5.6653097
5.2057815
5.4491294
4.1141335
11.889597
3.0622244
4.5627144
4.899559
0.82612539
1.8373346
6.27756
4.7464478
5.2057815
3.2094011
5.2057815
5.8182264
6.2394385
11.811437
5.8182264
2.9047966
4.1033807
7.3493386
0.50470893
0.46972174
4.6315084
3.5985462
6.4306712
6.3694268
4.3517823
4.6848373
3.9704432
10.707113
4.7464478
9.8436595
4.5062611
5.463969
4.199622
3.1847134
5.6267021
2.1435571
3.6746693
5.7406851
7.9617834
1.378001
5.1328714
3.0622244
6.8243858
1.3132839
5.5120039
5.9395187
6.5867908
4.0946315
6.6097825
5.3524595
2.0603506
3.6396724
6.5787506
2.5142474
5.3287527
7.3493386
9.0991811
8.6660951
2.1713955
9.2993631
2.8152162
7.0431161
7.4630507
5.3928785
3.2519961
4.2871142
5.9151437
7.1349829
11.636453
5.1778246
4.6540697
0.77675936
5.3841308
11.198992
7.4535845
7.8115611
8.2680059
1.8515775
4.512271
3.7898089
3.5713791
10.615711
5.5120039
12.086732
4.899559
2.5875796
3.1531816
2.4419527
3.8781215
6.0776973
5.47977
8.7172811
5.6543214
7.6007479
8.1065431
2.3645204
4.1904123
5.6617127
7.9617834
5.8460648
4.1218217
5.0616149
5.2488066
4.099136
12.327923
25.076734
5.8494735
5.5120039
3.0622244
8.3204223
0.89124441
7.8283457
4.3987754
8.4503174
0.23144719
2.6724867
3.5812038
10.411563
7.0063694
9.5823234
4.5933366
6.7572332
7.3493386
10.394821
6.5109696
18.373346
3.5649777
9.0239812
6.2101911
6.4306712
10.105341
11.785314
3.4702176
4.2462845
6.2554875
13.609886
6.4306712
6.7368937
5.8877054
1.7416401
10.436795
5.1297397
7.0117979
10.564674
9.0991811
3.3075658
3.558339
7.3493386
5.4927119
1.561134
12.15153
4.7961201
5.4140127
6.6348195
12.489072
3.0622244
5.984347
7.5081651
12.853238
5.8182264
7.556947
2.3161552
4.2871142
11.33023
3.2563531
4.8253233
9.9102912
5.6068897
4.153974
4.8745613
1.6634305
3.2587765
6.9665605
10.227235
2.8141797
5.2057815
10.49425
9.7991181
9.7640676
7.9617834
4.2871142
7.0431161
3.6419848
6.5639131
13.771734
4.1513955
2.5477707
6.7368937
4.2871142
9.1866732
5.8182264
7.0431161
4.1596256
4.9761146
5.1309919
5.3588927
2.6423386
3.8216561
4.5933366
4.5924179
5.5120039
5.7555061
4.0943318
3.1800023
6.9563705
10.555395
3.613127
9.7644514
2.7936082
5.2057815
4.6044049
9.1866732
8.1570315
10.110201
12.16923
5.9883499
16.229789
7.5687886
10.184616
7.0035302
4.1226063
3.3843925
4.8149014
7.1202941
3.6746693
9.1866732
6.130999
2.756002
5.8182264
11.33023
1.6003585
1.3099078
2.4430678
4.0904575
4.3498524
11.12634
0.68326578
7.3162334
4.899559
6.7368937
6.2650099
3.3649802
4.1690793
5.2423265
2.8204104
8.5742283
9.1866732
4.1728932
10.133179
5.846666
7.655561
3.2497075
5.6729184
4.0946315
7.3493386
8.2009787
5.2057815
9.9522293
2.9694297
5.5120039
5.5168263
14.392455
5.0526703
10.42566
6.2605477
2.756002
4.069356
3.7671769
2.2500692
4.5987089
1.5833092
8.9214234
20.845397
4.899559
4.0180588
6.2520415
3.4351965
5.7449731
5.9551551
4.7842464
4.0355397
8.9419598
9.1866732
5.4595086
5.8159123
2.6407242
2.4497795
2.78953
7.9123314
5.9250481
7.9617834
4.0300385
4.6115952
5.1537484
5.3078556
7.6128607
8.8804508
3.3684468
5.1644001
4.7996567
3.8026428
2.5578013
5.5120039
13.469397
7.0431161
5.9713376
11.071436
3.1154805
4.899559
5.3588927
3.7688916
5.5501229
2.1055956
3.8680729
7.3493386
10.702018
5.790388
6.8898338
6.5837825
5.3323723
3.4920103
5.5547326
9.6282032
4.0880379
2.3451498
0.81381091
3.7988629
1.6116971
9.6460069
3.993371
3.4663547
9.9029957
4.899559
2.4880573
3.9277041
3.0622244
4.9847688
2.756002
8.7334929
2.1435571
5.6714074
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3.9808917
10.411563
5.2429237
0.98293623
3.9808917
11.595623
6.9993701
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6.1244488
6.4873791
3.0399537
2.388535
8.4744901
6.3517011
5.2057815
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3.9808917
1.0049056
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6.1244488
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14.345556
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8.492569
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6.3088617
6.7472741
5.2727043
12.358589
4.9488233
11.769593
5.1037073
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4.5933366
11.636453
0.33823707
8.1242688
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2.769316
3.3641338
4.4541446
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4.6155266
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4.9902916
8.8192063
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3.9291918
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6.4306712
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6.0661207
8.5742283
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11.99623
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6.3042108
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20.414829
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14.698677
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22.123968
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18.462291
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12.243292
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11.636453
5.199532
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6.7254817
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12.397911
14.297569
2.6028907
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7.0431161
3.5985462
10.887909
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4.3768213
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10.717785
2.0445074
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4.2462845
6.5919035
9.9522293
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7.6215363
3.3188502
6.5327454
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3.694112
8.4628747
8.9825249
8.336946
2.2966683
15.923567
5.3674945
6.1175285
4.2816375
11.024008
7.655561
5.6533374
11.90391
5.3343949
5.4630561
6.4306712
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3.3121019
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4.0829659
4.7782646
13.52581
4.5150367
5.1518685
6.902827
11.082336
8.2325924
8.632942
5.8244268
4.6584903
4.6809435
3.9808917
13.859401
6.7254817
6.6450269
9.9105113
9.7991181
3.5840826
5.0526703
3.3116604
3.9512573
16.854051
4.4293649
10.110201
6.6668718
3.5102051
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6.7368937
3.3684468
2.5267318
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3.9057806
5.4140127
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5.3078556
2.9091132
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5.9470638
4.6044049
13.167565
4.899559
5.199532
5.8182264
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 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 0 seconds R Server Big Analytics Cloud Computing Center R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. R Engine error message Error in array(list(11.455804, 2.9332886, 6.2191311, 6.9153776, 4.9465626, : length of 'dimnames' [1] not equal to array extent Execution halted 

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
deleted and are NOT treated as missing values. \tabularnewline
R Engine error message & Error in array(list(11.455804, 2.9332886, 6.2191311, 6.9153776, 4.9465626,  :
length of 'dimnames' [1] not equal to array extent
Execution halted
\tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319300&T=0

[TABLE]
[ROW]
 Summary of computational transaction[/C][/ROW] [ROW] Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW] Raw Output[/C] view raw output of R engine [/C][/ROW] [ROW] Computing time[/C] 0 seconds[/C][/ROW] [ROW] R Server[/C] Big Analytics Cloud Computing Center[/C][/ROW] [ROW] R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.[/C][/ROW] [ROW] R Engine error message[/C][C]Error in array(list(11.455804, 2.9332886, 6.2191311, 6.9153776, 4.9465626, : length of 'dimnames' [1] not equal to array extent Execution halted [/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319300&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319300&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 0 seconds R Server Big Analytics Cloud Computing Center R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. R Engine error message Error in array(list(11.455804, 2.9332886, 6.2191311, 6.9153776, 4.9465626, : length of 'dimnames' [1] not equal to array extent Execution halted 

Parameters (Session):
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
R code (references can be found in the software module):
library(lattice)library(lmtest)library(car)library(MASS)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testmywarning <- ''par6 <- as.numeric(par6)if(is.na(par6)) {par6 <- 12mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'}par1 <- as.numeric(par1)if(is.na(par1)) {par1 <- 1mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'}if (par4=='') par4 <- 0par4 <- as.numeric(par4)if (!is.numeric(par4)) par4 <- 0if (par5=='') par5 <- 0par5 <- as.numeric(par5)if (!is.numeric(par5)) par5 <- 0x <- na.omit(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 <- x1if (par3 == 'First Differences'){(n <- n -1)x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))for (i in 1:n) {for (j in 1:k) {x2[i,j] <- x[i+1,j] - x[i,j]}}x <- x2}if (par3 == 'Seasonal Differences (s)'){(n <- n - par6)x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))for (i in 1:n) {for (j in 1:k) {x2[i,j] <- x[i+par6,j] - x[i,j]}}x <- x2}if (par3 == 'First and Seasonal Differences (s)'){(n <- n -1)x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))for (i in 1:n) {for (j in 1:k) {x2[i,j] <- x[i+1,j] - x[i,j]}}x <- x2(n <- n - par6)x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))for (i in 1:n) {for (j in 1:k) {x2[i,j] <- x[i+par6,j] - x[i,j]}}x <- x2}if(par4 > 0) {x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))for (i in 1:(n-par4)) {for (j in 1:par4) {x2[i,j] <- x[i+par4-j,par1]}}x <- cbind(x[(par4+1):n,], x2)n <- n - par4}if(par5 > 0) {x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))for (i in 1:(n-par5*par6)) {for (j in 1:par5) {x2[i,j] <- x[i+par5*par6-j*par6,par1]}}x <- cbind(x[(par5*par6+1):n,], x2)n <- n - par5*par6}if (par2 == 'Include Seasonal Dummies'){x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))for (i in 1:(par6-1)){x2[seq(i,n,par6),i] <- 1}x <- cbind(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[n,]))if (par3 == 'Linear Trend'){x <- cbind(x, c(1:n))colnames(x)[k+1] <- 't'}print(x)(k <- length(x[n,]))head(x)df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$resid)grid()dev.off()bitmap(file='test1.png')plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')grid()dev.off()bitmap(file='test2.png')sresid <- studres(mylm)hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')xfit<-seq(min(sresid),max(sresid),length=40)yfit<-dnorm(xfit)lines(xfit, yfit)grid()dev.off()bitmap(file='test3.png')densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')dev.off()bitmap(file='test4.png')qqPlot(mylm, main='QQ Plot')grid()dev.off()(myerror <- as.ts(mysum$resid))bitmap(file='test5.png')dum <- cbind(lag(myerror,k=1),myerror)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)print(z)plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')lines(lowess(z))abline(lm(z))grid()dev.off()bitmap(file='test6.png')acf(mysum$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$resid)/2, main='Residual Partial Autocorrelation Function')grid()dev.off()bitmap(file='test8.png')opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))plot(mylm, las = 1, sub='Residual Diagnostics')par(opar)dev.off()if (n > n25) {bitmap(file='test9.png')plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')grid()dev.off()}load(file='createtable')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)a<-table.row.end(a)myeq <- colnames(x)[1]myeq <- paste(myeq, '[t] = ', sep='')for (i in 1:k){if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')if (rownames(mysum$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.row.start(a)a<-table.element(a, mywarning)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,'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-STATH0: 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$coefficients)[i],header=TRUE)a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'R-squared',1,TRUE)a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Adjusted R-squared',1,TRUE)a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (value)',1,TRUE)a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)a<-table.element(a, signif(mysum$fstatistic[2],6))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)a<-table.element(a, signif(mysum$fstatistic[3],6))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'p-value',1,TRUE)a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Residual Standard Deviation',1,TRUE)a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')myr <- as.numeric(mysum$resid)myra <-table.start()a <- table.row.start(a)a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <- table.element(a,'Description',1,TRUE)a <- table.element(a,'Link',1,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Histogram',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Central Tendency',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'QQ Plot',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Kernel Density Plot',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Spectral Analysis',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a <- table.row.start(a)a <- table.element(a,'Summary Statistics',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a<-table.end(a)table.save(a,file='mytable7.tab')if(n < 200) {a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))a<-table.element(a,formatC(signif(mysum\$resid[i],6),format='g',flag=' '))a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable4.tab')if (n > n25) {a<-table.start()a<-table.row.start(a)a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'p-values',header=TRUE)a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'breakpoint index',header=TRUE)a<-table.element(a,'greater',header=TRUE)a<-table.element(a,'2-sided',header=TRUE)a<-table.element(a,'less',header=TRUE)a<-table.row.end(a)for (mypoint in kp3:nmkm3) {a<-table.row.start(a)a<-table.element(a,mypoint,header=TRUE)a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable5.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Description',header=TRUE)a<-table.element(a,'# significant tests',header=TRUE)a<-table.element(a,'% significant tests',header=TRUE)a<-table.element(a,'OK/NOK',header=TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'1% type I error level',header=TRUE)a<-table.element(a,signif(numsignificant1,6))a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'a<-table.element(a,dum)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'5% type I error level',header=TRUE)a<-table.element(a,signif(numsignificant5,6))a<-table.element(a,signif(numsignificant5/numgqtests,6))if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'a<-table.element(a,dum)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'10% type I error level',header=TRUE)a<-table.element(a,signif(numsignificant10,6))a<-table.element(a,signif(numsignificant10/numgqtests,6))if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'a<-table.element(a,dum)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable6.tab')}}a<-table.start()a<-table.row.start(a)a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)a<-table.row.end(a)a<-table.row.start(a)reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'',sep=''))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)a<-table.row.end(a)a<-table.row.start(a)reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'',sep=''))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)a<-table.row.end(a)a<-table.row.start(a)reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'',sep=''))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable8.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)a<-table.row.end(a)a<-table.row.start(a)vif <- vif(mylm)a<-table.element(a,paste('',RC.texteval('vif'),'',sep=''))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable9.tab')