| werkloosheid - Afrika | *The author of this computation has been verified* | R Software Module: rwasp_multipleregression.wasp (opens new window with default values) | Title produced by software: Multiple Regression | Date of computation: Fri, 19 Dec 2008 16:36:52 -0700 | | Cite this page as follows: | Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q.htm/, Retrieved Sat, 20 Dec 2008 00:39:29 +0100 | | BibTeX entries for LaTeX users: | @Manual{KEY,
author = {{YOUR NAME}},
publisher = {Office for Research Development and Education},
title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q.htm/},
year = {2008},
}
@Manual{R,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2008},
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org},
}
| | Original text written by user: | | | IsPrivate? | No (this computation is public) | | User-defined keywords: | | | Dataseries X: | » Textbox « » Textfile « » CSV « | 180144 356,4
173666 394,3
165688 410,9
161570 385,9
156145 523,7
153730 439,1
182698 399,3
200765 372,9
176512 483,2
166618 468,7
158644 498,3
159585 434,4
163095 371,6
159044 408,7
155511 444,5
153745 383
150569 388,9
150605 385,1
179612 347,2
194690 315,6
189917 300,9
184128 371,2
175335 340,3
179566 301,9
181140 327,4
177876 398,6
175041 379,9
169292 379,7
166070 418,4
166972 367,9
206348 362,5
215706 296,7
202108 343
195411 488,3
193111 402,5
195198 500,7
198770 412,8
194163 385,9
190420 461,9
189733 357,4
186029 316,9
191531 339,2
232571 372,3
243477 264,8
227247 325,9
217859 324,1
208679 324,3
213188 318,2
216234 323,4
213586 295,9
209465 425
204045 337,8
200237 322,7
203666 430,2
241476 403,8
260307 333,7
243324 358,1
244460 426,7
233575 376
237217 312
235243 349,3
230354 340,3
227184 455,7
221678 352,3
217142 481,4
219452 731,9
256446 382,2
265845 392,8
248624 351,6
241114 276,5
229245 371,3
231805 439
219277 394,4
219313 445,5
212610 560
214771 331,8
211142 404,2
211457 489,8
240048 323,9
240636 269,4
230580 319,2
208795 337,6
197922 399,5
194596 316,7 | | 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!
Multiple Linear Regression - Estimated Regression Equation | werkloosheid[t] = + 154551.076702475 + 6.65258468743316Afrika[t] + 7828.00349363026M1[t] + 3072.48596086521M2[t] -2884.3580509503M3[t] -6244.89590528947M4[t] -11413.5937899456M5[t] -11212.6276231597M6[t] + 22962.727876162M7[t] + 34109.4844265709M8[t] + 18226.5056228648M9[t] + 8536.66740490845M10[t] -1248.74773898274M11[t] + 928.120234244064t + e[t] |
Multiple Linear Regression - Ordinary Least Squares | Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value | (Intercept) | 154551.076702475 | 11196.033243 | 13.8041 | 0 | 0 | Afrika | 6.65258468743316 | 24.485203 | 0.2717 | 0.786654 | 0.393327 | M1 | 7828.00349363026 | 7411.556117 | 1.0562 | 0.294514 | 0.147257 | M2 | 3072.48596086521 | 7397.627529 | 0.4153 | 0.679168 | 0.339584 | M3 | -2884.3580509503 | 7596.470358 | -0.3797 | 0.70532 | 0.35266 | M4 | -6244.89590528947 | 7396.894397 | -0.8443 | 0.401401 | 0.2007 | M5 | -11413.5937899456 | 7424.093429 | -1.5374 | 0.12871 | 0.064355 | M6 | -11212.6276231597 | 7627.163287 | -1.4701 | 0.146018 | 0.073009 | M7 | 22962.727876162 | 7378.217301 | 3.1122 | 0.002688 | 0.001344 | M8 | 34109.4844265709 | 7495.437091 | 4.5507 | 2.2e-05 | 1.1e-05 | M9 | 18226.5056228648 | 7390.261041 | 2.4663 | 0.016104 | 0.008052 | M10 | 8536.66740490845 | 7375.046326 | 1.1575 | 0.251 | 0.1255 | M11 | -1248.74773898274 | 7376.902282 | -0.1693 | 0.866066 | 0.433033 | t | 928.120234244064 | 62.930956 | 14.7482 | 0 | 0 |
Multiple Linear Regression - Regression Statistics | Multiple R | 0.90285260272637 | R-squared | 0.81514282224978 | Adjusted R-squared | 0.780812203524738 | F-TEST (value) | 23.7439012905179 | F-TEST (DF numerator) | 13 | F-TEST (DF denominator) | 70 | p-value | 0 | Multiple Linear Regression - Residual Statistics | Residual Standard Deviation | 13788.4806232388 | Sum Squared Residuals | 13308553852.8202 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error | 1 | 180144 | 165678.181612951 | 14465.8183870491 | 2 | 173666 | 162102.917274083 | 11563.0827259167 | 3 | 165688 | 157184.626402323 | 8503.37359767666 | 4 | 161570 | 154585.894165042 | 6984.10583495759 | 5 | 156145 | 151262.042684559 | 4882.95731544138 | 6 | 153730 | 151828.320421032 | 1901.67957896827 | 7 | 182698 | 186667.023284038 | -3969.02328403767 | 8 | 200765 | 198566.271832942 | 2198.72816705766 | 9 | 176512 | 184345.193354504 | -7833.19335450421 | 10 | 166618 | 175487.012892824 | -8869.01289282416 | 11 | 158644 | 166826.634489925 | -8182.63448992503 | 12 | 159585 | 168578.402301625 | -8993.40230162488 | 13 | 163095 | 176916.743711128 | -13821.7437111284 | 14 | 159044 | 173336.157304511 | -14292.1573045111 | 15 | 155511 | 168545.59605875 | -13034.5960587499 | 16 | 153745 | 165704.044480378 | -11959.0444803776 | 17 | 150569 | 161502.717079621 | -10933.7170796214 | 18 | 150605 | 162606.523658839 | -12001.5236588391 | 19 | 179612 | 197457.866432751 | -17845.8664327512 | 20 | 194690 | 209322.521541281 | -14632.5215412812 | 21 | 189917 | 194269.869976914 | -4352.86997691393 | 22 | 184128 | 185975.828696728 | -1847.82869672820 | 23 | 175335 | 176912.968920239 | -1577.96892023938 | 24 | 179566 | 178834.377641469 | 731.622358531221 | 25 | 181140 | 187760.142278873 | -6620.14227887264 | 26 | 177876 | 184406.409010097 | -6530.40901009688 | 27 | 175041 | 179253.281898870 | -4212.28189887045 | 28 | 169292 | 176819.533761838 | -7527.53376183785 | 29 | 166070 | 172836.411138829 | -6766.41113882948 | 30 | 166972 | 173629.542013144 | -6657.54201314401 | 31 | 206348 | 208697.093789398 | -2349.09378939764 | 32 | 215706 | 220334.230501618 | -4628.2305016175 | 33 | 202108 | 205687.386603184 | -3579.38660318362 | 34 | 195411 | 197892.289174555 | -2481.28917455537 | 35 | 193111 | 188464.202498726 | 4646.79750127352 | 36 | 195198 | 191294.354288259 | 3903.64571174077 | 37 | 198770 | 199465.715822108 | -695.71582210819 | 38 | 194163 | 195459.363995495 | -1296.36399549524 | 39 | 190420 | 190936.236654169 | -516.236654168734 | 40 | 189733 | 187808.623934237 | 1924.37606576314 | 41 | 186029 | 183298.616603984 | 2730.38339601621 | 42 | 191531 | 184576.055643543 | 6954.94435645655 | 43 | 232571 | 219899.731930263 | 12671.2680697368 | 44 | 243477 | 231259.455861017 | 12217.5441389829 | 45 | 227247 | 216711.070215957 | 10535.9297840427 | 46 | 217859 | 207937.377579808 | 9921.62242019237 | 47 | 208679 | 199081.413187098 | 9597.58681290202 | 48 | 213188 | 201217.700393731 | 11970.2996062685 | 49 | 216234 | 210008.417561980 | 6225.58243801956 | 50 | 213586 | 205998.074184555 | 7587.92581544497 | 51 | 209465 | 201828.199090131 | 7636.80090986879 | 52 | 204045 | 198815.676085292 | 5229.32391470807 | 53 | 200237 | 194474.644406100 | 5762.35559390034 | 54 | 203666 | 196318.883661029 | 7347.11633897138 | 55 | 241476 | 231246.731158846 | 10229.2688411538 | 56 | 260307 | 242855.26175691 | 17451.7382430900 | 57 | 243324 | 228062.726253821 | 15261.2737461786 | 58 | 244460 | 219757.375579667 | 24702.6244203330 | 59 | 233575 | 210562.794626367 | 23012.2053736330 | 60 | 237217 | 212313.897179598 | 24903.1028204018 | 61 | 235243 | 221318.162316314 | 13924.8376836863 | 62 | 230354 | 217430.891755606 | 12923.1082443942 | 63 | 227184 | 213169.876250964 | 14014.1237490358 | 64 | 221678 | 210049.581374188 | 11628.4186258115 | 65 | 217142 | 206667.852406924 | 10474.1475930760 | 66 | 219452 | 209463.411272156 | 9988.58872784406 | 67 | 256446 | 242240.478140526 | 14205.5218594736 | 68 | 265845 | 254385.872322866 | 11459.1276771339 | 69 | 248624 | 239156.927264282 | 9467.0727357182 | 70 | 241114 | 229895.600170543 | 11218.3998294567 | 71 | 229245 | 221668.970289265 | 7576.02971073515 | 72 | 231805 | 224296.218245831 | 7508.7817541691 | 73 | 219277 | 232755.636696646 | -13478.6366966457 | 74 | 219313 | 229268.186475653 | -9955.18647565255 | 75 | 212610 | 225001.183644792 | -12391.1836447922 | 76 | 214771 | 221050.646199025 | -6279.64619902485 | 77 | 211142 | 217291.715679983 | -6149.71567998298 | 78 | 211457 | 218990.263330257 | -7533.26333025715 | 79 | 240048 | 252990.075264178 | -12942.0752641778 | 80 | 240636 | 264702.386183366 | -24066.3861833656 | 81 | 230580 | 250078.826331338 | -19498.8263313378 | 82 | 208795 | 241439.515905874 | -32644.5159058743 | 83 | 197922 | 232994.015988379 | -35072.0159883792 | 84 | 194596 | 234620.049949487 | -40024.0499494866 |
Goldfeld-Quandt test for Heteroskedasticity | p-values | Alternative Hypothesis | breakpoint index | greater | 2-sided | less | 17 | 0.0103127041680709 | 0.0206254083361419 | 0.98968729583193 | 18 | 0.00410413581241847 | 0.00820827162483693 | 0.995895864187581 | 19 | 0.00142525501116088 | 0.00285051002232176 | 0.99857474498884 | 20 | 0.000302382265132678 | 0.000604764530265356 | 0.999697617734867 | 21 | 0.000313984576618663 | 0.000627969153237326 | 0.999686015423381 | 22 | 0.00275379096311824 | 0.00550758192623648 | 0.997246209036882 | 23 | 0.00131901078163512 | 0.00263802156327023 | 0.998680989218365 | 24 | 0.00118040471675036 | 0.00236080943350073 | 0.99881959528325 | 25 | 0.00358379437136542 | 0.00716758874273084 | 0.996416205628635 | 26 | 0.00842055470738744 | 0.0168411094147749 | 0.991579445292613 | 27 | 0.00819714670422539 | 0.0163942934084508 | 0.991802853295775 | 28 | 0.00795181952219257 | 0.0159036390443851 | 0.992048180477807 | 29 | 0.00621862301398852 | 0.0124372460279770 | 0.993781376986012 | 30 | 0.0050807982940351 | 0.0101615965880702 | 0.994919201705965 | 31 | 0.0120445181184406 | 0.0240890362368812 | 0.98795548188156 | 32 | 0.0107952566627065 | 0.0215905133254130 | 0.989204743337293 | 33 | 0.0100520184237925 | 0.020104036847585 | 0.989947981576208 | 34 | 0.0148615388555599 | 0.0297230777111199 | 0.98513846114444 | 35 | 0.0178001178268254 | 0.0356002356536509 | 0.982199882173175 | 36 | 0.0254050553803895 | 0.0508101107607789 | 0.97459494461961 | 37 | 0.0247822338884495 | 0.0495644677768991 | 0.97521776611155 | 38 | 0.0267270725573906 | 0.0534541451147812 | 0.97327292744261 | 39 | 0.0274710106152768 | 0.0549420212305536 | 0.972528989384723 | 40 | 0.0354841240610045 | 0.070968248122009 | 0.964515875938995 | 41 | 0.0362506022209216 | 0.0725012044418433 | 0.963749397779078 | 42 | 0.0349055638850357 | 0.0698111277700715 | 0.965094436114964 | 43 | 0.0627244465700045 | 0.125448893140009 | 0.937275553429995 | 44 | 0.0708274955405098 | 0.141654991081020 | 0.92917250445949 | 45 | 0.0817969383495532 | 0.163593876699106 | 0.918203061650447 | 46 | 0.0903134731058692 | 0.180626946211738 | 0.90968652689413 | 47 | 0.0853825969031017 | 0.170765193806203 | 0.914617403096898 | 48 | 0.082717199932402 | 0.165434399864804 | 0.917282800067598 | 49 | 0.0755927127831872 | 0.151185425566374 | 0.924407287216813 | 50 | 0.0664156987162004 | 0.132831397432401 | 0.9335843012838 | 51 | 0.063610364164897 | 0.127220728329794 | 0.936389635835103 | 52 | 0.102307507811606 | 0.204615015623212 | 0.897692492188394 | 53 | 0.144213873803082 | 0.288427747606164 | 0.855786126196918 | 54 | 0.261578484176966 | 0.523156968353933 | 0.738421515823034 | 55 | 0.503347495041684 | 0.993305009916632 | 0.496652504958316 | 56 | 0.573231759452212 | 0.853536481095575 | 0.426768240547788 | 57 | 0.760482283386947 | 0.479035433226106 | 0.239517716613053 | 58 | 0.750646629194667 | 0.498706741610667 | 0.249353370805333 | 59 | 0.721603472982503 | 0.556793054034994 | 0.278396527017497 | 60 | 0.668133085373901 | 0.663733829252198 | 0.331866914626099 | 61 | 0.574010617075255 | 0.85197876584949 | 0.425989382924745 | 62 | 0.530881485849598 | 0.938237028300803 | 0.469118514150401 | 63 | 0.488851590975841 | 0.977703181951682 | 0.511148409024159 | 64 | 0.566358921646307 | 0.867282156707386 | 0.433641078353693 | 65 | 0.684474397110313 | 0.631051205779375 | 0.315525602889687 | 66 | 0.666313910247633 | 0.667372179504733 | 0.333686089752367 | 67 | 0.674936098122169 | 0.650127803755662 | 0.325063901877831 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | Description | # significant tests | % significant tests | OK/NOK | 1% type I error level | 8 | 0.156862745098039 | NOK | 5% type I error level | 20 | 0.392156862745098 | NOK | 10% type I error level | 26 | 0.509803921568627 | NOK |
| Charts produced by software: | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/1034sh1229729806.png (open in new window) | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/1034sh1229729806.ps (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/1q2pk1229729806.png (open in new window) | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/1q2pk1229729806.ps (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/2mtud1229729806.png (open in new window) | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/2mtud1229729806.ps (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/319vb1229729806.png (open in new window) | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/319vb1229729806.ps (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/4f8pi1229729806.png (open in new window) | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/4f8pi1229729806.ps (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/54gaf1229729806.png (open in new window) | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/54gaf1229729806.ps (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/6twag1229729806.png (open in new window) | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/6twag1229729806.ps (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/7lwhe1229729806.png (open in new window) | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/7lwhe1229729806.ps (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/8g8kw1229729806.png (open in new window) | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/8g8kw1229729806.ps (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/911lq1229729806.png (open in new window) | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t122972995934fg0z5og2ywk0q/911lq1229729806.ps (open in new window) |
| | Parameters (Session): | par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ; | | Parameters (R input): | par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ; | | R code (references can be found in the software module): | library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
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))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$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')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$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()
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}
| |
Copyright
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:
- personalize online software applications according to your needs
- enforce strict security rules with respect to the data that you upload (e.g. statistical data)
- manage user sessions of online applications
- alert you about important changes or upgrades in resources or applications
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
|
|