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UitvoerAfrika

*Unverified author*
R Software Module: rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Tue, 23 Dec 2008 05:50:25 -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/23/t1230036750cf7py50nx5ow6ph.htm/, Retrieved Tue, 23 Dec 2008 13:52:40 +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/23/t1230036750cf7py50nx5ow6ph.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 «
184 0 155 0 201,8 0 224,6 0 204,9 0 190,8 0 199 0 179,9 0 211,9 0 200,1 0 208,6 0 232,6 0 199,5 0 169,1 0 194,4 0 227,9 0 224 0 258,1 0 207,6 0 228 0 221 0 247,3 0 214,3 0 252,5 0 256,7 0 194,9 0 264,6 0 277,1 0 236,6 0 271,6 0 216,3 0 241,1 0 265,8 0 280,6 0 276,8 0 263,7 0 231,3 0 190,9 0 250,9 0 252,8 0 214,4 0 268,2 0 178 0 215,6 0 241,3 0 228,3 0 236,5 0 263,5 0 238,8 0 215,1 0 244,6 0 263,5 0 242,7 0 253,4 0 197,3 0 250,5 0 290,8 0 245,9 0 299,5 0 295,8 0 264,1 0 262,7 0 297,1 0 345,1 0 293,9 0 269,4 0 244,9 0 274,2 0 312,5 0 279 0 327,3 0 289,2 0 285,4 0 248,9 0 240,6 0 308,5 0 285,6 0 284,4 0 253,6 0 286,3 0 302,2 0 278 0 304,3 0 304,6 0 283,7 0 253,8 0 266,6 0 345,7 0 287 0 282,1 0 268,1 0 274,6 0 275,9 0 287,5 0 276 0 270,8 0 295,3 0 246,5 0 271,8 0 335,2 0 253,3 0 297,2 0 245,4 0 271,6 0 316,1 0 304,4 0 289,1 0 370,6 0 300 0 269,6 0 346,3 0 348,2 0 317,9 0 365,8 0 260,4 0 292,8 0 404,3 1 341,4 1 351,1 1 384,7 1 358,8 1 332,8 1 381,1 1 340,8 1 348,6 1 356,9 1 321,7 1 360,1 1 399,4 1 340,4 1 430,4 1 463,1 1 423 1 416,1 1 364 1 379,9 1 395,8 1 418,8 1 396,4 1 407,9 1 487,9 1 458,2 1 432,1 1 498,5 1 448,3 1 410,8 1 406 1 441 1 388,9 1 390,5 1 427,8 1 442,1 1 427 1 526,7 1 464,4 1 574,4 1 727 1 506 1 581,2 1
 
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 computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ 72.249.76.132


Multiple Linear Regression - Estimated Regression Equation
Uitvoer[t] = + 226.347932301439 + 71.2389481707318X[t] -15.0214833396616M1[t] -60.7038286577922M2[t] -30.4933168330653M3[t] -14.2074722110545M4[t] -45.8552021445696M5[t] -30.5567782319306M6[t] -69.4352773962149M7[t] -46.859930406653M8[t] -20.2875794302242M9[t] -32.0583862868161M10[t] -26.0676546818696M11[t] + 1.13234531813038t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)226.34793230143913.05244217.341400
X71.238948170731811.2440816.335700
M1-15.021483339661615.451604-0.97220.3325880.166294
M2-60.703828657792215.449504-3.92920.0001316.6e-05
M3-30.493316833065315.448168-1.97390.0502920.025146
M4-14.207472211054515.741228-0.90260.3682540.184127
M5-45.855202144569615.740198-2.91330.0041440.002072
M6-30.556778231930615.739919-1.94140.0541540.027077
M7-69.435277396214915.740389-4.41132e-051e-05
M8-46.85993040665315.741608-2.97680.0034140.001707
M9-20.287579430224215.733591-1.28940.1992970.099648
M10-32.058386286816115.731716-2.03780.0433860.021693
M11-26.067654681869615.730591-1.65710.0996550.049828
t1.132345318130380.10862710.424200


Multiple Linear Regression - Regression Statistics
Multiple R0.9054085080308
R-squared0.819764566414558
Adjusted R-squared0.80360552754138
F-TEST (value)50.73102260898
F-TEST (DF numerator)13
F-TEST (DF denominator)145
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation40.1043387128193
Sum Squared Residuals233211.907620918


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1184212.458794279906-28.458794279906
2155167.908794279907-12.9087942799073
3201.8199.2516514227642.54834857723553
4224.6216.6698413629057.93015863709493
5204.9186.15445674752118.7455432524791
6190.8202.58522597829-11.7852259782900
7199164.83907213213634.1609278678640
8179.9188.546764439829-8.64676443982875
9211.9216.251460734387-4.35146073438737
10200.1205.612999195926-5.512999195926
11208.6212.736076119003-4.13607611900298
12232.6239.936076119003-7.33607611900307
13199.5226.046938097472-26.5469380974718
14169.1181.496938097472-12.3969380974716
15194.4212.839795240329-18.4397952403288
16227.9230.25798518047-2.35798518046997
17224199.74260056508524.2573994349147
18258.1216.17336979585541.9266302041454
19207.6178.42721594970129.1727840502992
20228202.13490825739325.865091742607
21221229.839604551952-8.83960455195214
22247.3219.20114301349128.0988569865094
23214.3226.324219936568-12.0242199365675
24252.5253.524219936568-1.02421993656750
25256.7239.63508191503617.0649180849637
26194.9195.085081915036-0.185081915036196
27264.6226.42793905789338.1720609421067
28277.1243.84612899803533.2538710019655
29236.6213.3307443826523.2692556173501
30271.6229.76151361341941.8384863865809
31216.3192.01535976726524.2846402327347
32241.1215.72305207495825.3769479250424
33265.8243.42774836951722.3722516304833
34280.6232.78928683105547.8107131689449
35276.8239.91236375413236.8876362458679
36263.7267.112363754132-3.41236375413206
37231.3253.223225732601-21.9232257326008
38190.9208.673225732601-17.7732257326007
39250.9240.01608287545810.8839171245421
40252.8257.434272815599-4.63427281559907
41214.4226.918888200214-12.5188882002144
42268.2243.34965743098424.8503425690163
43178205.603503584830-27.6035035848298
44215.6229.311195892522-13.7111958925221
45241.3257.015892187081-15.7158921870812
46228.3246.377430648620-18.0774306486197
47236.5253.500507571697-17.0005075716966
48263.5280.700507571697-17.2005075716966
49238.8266.811369550165-28.0113695501654
50215.1222.261369550165-7.16136955016528
51244.6253.604226693022-9.00422669302244
52263.5271.022416633164-7.52241663316362
53242.7240.5070320177792.19296798222103
54253.4256.937801248548-3.53780124854821
55197.3219.191647402394-21.8916474023944
56250.5242.8993397100877.60066028991335
57290.8270.60403600464620.1959639953542
58245.9259.965574466184-14.0655744661842
59299.5267.08865138926132.4113486107388
60295.8294.2886513892611.51134861073888
61264.1280.39951336773-16.2995133677299
62262.7235.8495133677326.8504866322702
63297.1267.19237051058729.9076294894130
64345.1284.61056045072860.4894395492718
65293.9254.09517583534439.8048241646565
66269.4270.525945066113-1.12594506611277
67244.9232.77979121995912.1202087800411
68274.2256.48748352765117.7125164723488
69312.5284.19217982221028.3078201777897
70279273.5537182837495.44628171625122
71327.3280.67679520682646.6232047931743
72289.2307.876795206826-18.6767952068257
73285.4293.987657185294-8.58765718529447
74248.9249.437657185294-0.537657185294361
75240.6280.780514328152-40.1805143281515
76308.5298.19870426829310.3012957317073
77285.6267.68331965290817.9166803470920
78284.4284.1140888836770.285911116322680
79253.6246.3679350375237.23206496247652
80286.3270.07562734521616.2243726547843
81302.2297.7803236397754.41967636022513
82278287.141862101313-9.14186210131332
83304.3294.2649390243910.0350609756098
84304.6321.46493902439-16.8649390243902
85283.7307.575801002859-23.875801002859
86253.8263.025801002859-9.2258010028589
87266.6294.368658145716-27.7686581457160
88345.7311.78684808585733.9131519141427
89287281.2714634704735.7285365295274
90282.1297.702232701242-15.6022327012418
91268.1259.9560788550888.143921144912
92274.6283.663771162780-9.06377116278026
93275.9311.368467457339-35.4684674573394
94287.5300.730005918878-13.2300059188779
95276307.853082841955-31.8530828419548
96270.8335.053082841955-64.2530828419548
97295.3321.163944820424-25.8639448204235
98246.5276.613944820423-30.1139448204235
99271.8307.956801963281-36.1568019632806
100335.2325.3749919034229.8250080965782
101253.3294.859607288037-41.5596072880371
102297.2311.290376518806-14.0903765188064
103245.4273.544222672653-28.1442226726526
104271.6297.251914980345-25.6519149803448
105316.1324.956611274904-8.85661127490394
106304.4314.318149736442-9.91814973644245
107289.1321.441226659519-32.3412266595193
108370.6348.64122665951921.9587733404807
109300334.752088637988-34.7520886379881
110269.6290.202088637988-20.602088637988
111346.3321.54494578084524.7550542191549
112348.2338.9631357209869.23686427901363
113317.9308.4477511056029.45224889439829
114365.8324.87852033637140.9214796636291
115260.4287.132366490217-26.7323664902171
116292.8310.840058797909-18.0400587979094
117404.3409.7837032632-5.48370326320023
118341.4399.145241724739-57.7452417247387
119351.1406.268318647816-55.1683186478156
120384.7433.468318647816-48.7683186478156
121358.8419.579180626284-60.7791806262844
122332.8375.029180626284-42.2291806262843
123381.1406.372037769141-25.2720377691414
124340.8423.790227709283-82.9902277092826
125348.6393.274843093898-44.6748430938979
126356.9409.705612324667-52.8056123246672
127321.7371.959458478513-50.2594584785134
128360.1395.667150786206-35.5671507862056
129399.4423.371847080765-23.9718470807648
130340.4412.733385542303-72.3333855423032
131430.4419.8564624653810.5435375346198
132463.1447.0564624653816.0435375346199
133423433.167324443849-10.1673244438489
134416.1388.61732444384927.4826755561512
135364419.960181586706-55.960181586706
136379.9437.378371526847-57.4783715268472
137395.8406.862986911463-11.0629869114625
138418.8423.293756142232-4.49375614223174
139396.4385.54760229607810.8523977039220
140407.9409.25529460377-1.35529460377022
141487.9436.95999089832950.9400091016707
142458.2426.32152935986831.8784706401322
143432.1433.444606282945-1.34460628294468
144498.5460.64460628294537.8553937170553
145448.3446.7554682614131.54453173858655
146410.8402.2054682614138.59453173858664
147406433.54832540427-27.5483254042705
148441450.966515344412-9.96651534441173
149388.9420.451130729027-31.5511307290271
150390.5436.881899959796-46.3818999597963
151427.8399.13574611364228.6642538863575
152442.1422.84343842133519.2565615786653
153427450.548134715894-23.5481347158939
154526.7439.90967317743286.7903268225677
155464.4447.03275010050917.3672498994907
156574.4474.232750100509100.167249899491
157727460.343612078978266.656387921022
158506415.79361207897890.2063879210221
159581.2447.136469221835134.063530778165


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.01239554697211230.02479109394422470.987604453027888
180.08039653107499530.1607930621499910.919603468925005
190.03423030221192300.06846060442384590.965769697788077
200.02277877301769370.04555754603538740.977221226982306
210.009646444594389380.01929288918877880.99035355540561
220.005953802652203070.01190760530440610.994046197347797
230.002709740890499660.005419481780999310.9972902591095
240.0009877734099941130.001975546819988230.999012226590006
250.0009216873924426750.001843374784885350.999078312607557
260.0003469603599407190.0006939207198814380.99965303964006
270.0002819194309352820.0005638388618705640.999718080569065
280.0001169691629738100.0002339383259476200.999883030837026
296.47553550029429e-050.0001295107100058860.999935244644997
302.64911947308862e-055.29823894617724e-050.99997350880527
312.18244809492493e-054.36489618984986e-050.99997817551905
328.22868599182393e-061.64573719836479e-050.999991771314008
333.40705404911257e-066.81410809822514e-060.99999659294595
341.97034048321217e-063.94068096642434e-060.999998029659517
351.51947175024243e-063.03894350048487e-060.99999848052825
368.64012066826462e-071.72802413365292e-060.999999135987933
371.31849008637047e-062.63698017274095e-060.999998681509914
381.40170216835007e-062.80340433670013e-060.999998598297832
397.18129162510149e-071.43625832502030e-060.999999281870837
409.414904702979e-071.8829809405958e-060.99999905850953
412.99797561788762e-065.99595123577525e-060.999997002024382
421.56066463650045e-063.1213292730009e-060.999998439335363
431.45513204177407e-052.91026408354813e-050.999985448679582
441.32018063097371e-052.64036126194742e-050.99998679819369
458.2809073594492e-061.65618147188984e-050.99999171909264
461.1345229583336e-052.2690459166672e-050.999988654770417
477.34640710043507e-061.46928142008701e-050.9999926535929
483.75433510319506e-067.50867020639013e-060.999996245664897
491.92025204059957e-063.84050408119914e-060.99999807974796
508.9767269339415e-071.7953453867883e-060.999999102327307
514.64863925088462e-079.29727850176924e-070.999999535136075
522.29763940768812e-074.59527881537623e-070.999999770236059
531.07792672642021e-072.15585345284042e-070.999999892207327
546.939938471579e-081.3879876943158e-070.999999930600615
555.67057873681817e-081.13411574736363e-070.999999943294213
562.70232233359152e-085.40464466718305e-080.999999972976777
572.24266805684833e-084.48533611369667e-080.99999997757332
581.30649548801104e-082.61299097602209e-080.999999986935045
591.82972061651936e-083.65944123303873e-080.999999981702794
609.12959400506072e-091.82591880101214e-080.999999990870406
614.02020384463824e-098.04040768927647e-090.999999995979796
626.48288602201318e-091.29657720440264e-080.999999993517114
636.19841115269662e-091.23968223053932e-080.99999999380159
644.13462763152188e-088.26925526304376e-080.999999958653724
655.15617504277932e-081.03123500855586e-070.99999994843825
663.78709737060963e-087.57419474121927e-080.999999962129026
672.28555817544014e-084.57111635088028e-080.999999977144418
681.58885872361139e-083.17771744722279e-080.999999984111413
691.55762461577165e-083.1152492315433e-080.999999984423754
709.1029615659998e-091.82059231319996e-080.999999990897039
712.43832299861037e-084.87664599722075e-080.99999997561677
721.39459368133706e-082.78918736267412e-080.999999986054063
736.72688345149991e-091.34537669029998e-080.999999993273117
743.66708820813735e-097.3341764162747e-090.999999996332912
758.99213841416243e-091.79842768283249e-080.999999991007862
767.1961142905551e-091.43922285811102e-080.999999992803886
777.33071086463068e-091.46614217292614e-080.999999992669289
786.59321359974295e-091.31864271994859e-080.999999993406786
795.51627098658086e-091.10325419731617e-080.999999994483729
806.52063355798542e-091.30412671159708e-080.999999993479366
814.79789334045308e-099.59578668090617e-090.999999995202107
823.49378036460267e-096.98756072920535e-090.99999999650622
833.75398389140752e-097.50796778281504e-090.999999996246016
842.00287034107802e-094.00574068215605e-090.99999999799713
859.57460198865469e-101.91492039773094e-090.99999999904254
865.55959799241847e-101.11191959848369e-090.99999999944404
874.73994756275293e-109.47989512550586e-100.999999999526005
881.93280057241483e-093.86560114482965e-090.9999999980672
892.94681210681275e-095.8936242136255e-090.999999997053188
903.64286771303317e-097.28573542606634e-090.999999996357132
915.30209455778704e-091.06041891155741e-080.999999994697905
926.41762390440854e-091.28352478088171e-080.999999993582376
937.65996487689103e-091.53199297537821e-080.999999992340035
945.17374166162653e-091.03474833232531e-080.999999994826258
957.0674765264833e-091.41349530529666e-080.999999992932523
961.96728877366352e-083.93457754732704e-080.999999980327112
971.01662676406886e-082.03325352813772e-080.999999989833732
985.82287639577802e-091.16457527915560e-080.999999994177124
993.98613861008433e-097.97227722016867e-090.999999996013861
1005.80559843015992e-091.16111968603198e-080.999999994194402
1018.7067289067435e-091.7413457813487e-080.999999991293271
1025.65855742927123e-091.13171148585425e-080.999999994341443
1033.82309578835103e-097.64619157670206e-090.999999996176904
1042.38575859309685e-094.7715171861937e-090.999999997614241
1051.09974587087580e-092.19949174175160e-090.999999998900254
1064.98170800788724e-109.96341601577448e-100.99999999950183
1073.50661742445704e-107.01323484891408e-100.999999999649338
1085.53301225266948e-101.10660245053390e-090.999999999446699
1091.18444587942257e-092.36889175884515e-090.999999998815554
1109.34620079544237e-101.86924015908847e-090.99999999906538
1111.02411648737560e-092.04823297475119e-090.999999998975883
1126.24378388193776e-101.24875677638755e-090.999999999375622
1133.29362663354719e-106.58725326709438e-100.999999999670637
1141.56316681585214e-093.12633363170428e-090.999999998436833
1158.49568955932882e-101.69913791186576e-090.999999999150431
1163.90149995255521e-107.80299990511043e-100.99999999960985
1177.65219314948252e-101.53043862989650e-090.99999999923478
1186.41886970274076e-101.28377394054815e-090.999999999358113
1193.79718603681417e-107.59437207362833e-100.999999999620281
1201.66980834795889e-103.33961669591777e-100.99999999983302
1212.25179898196904e-104.50359796393809e-100.99999999977482
1229.33864867581162e-111.86772973516232e-100.999999999906614
1236.65557224285444e-111.33111444857089e-100.999999999933444
1241.13739903243043e-102.27479806486086e-100.99999999988626
1257.33849649100286e-111.46769929820057e-100.999999999926615
1265.00291827762165e-111.00058365552433e-100.99999999994997
1271.86333273238057e-113.72666546476114e-110.999999999981367
1288.58493114464421e-121.71698622892884e-110.999999999991415
1295.21383351390902e-121.04276670278180e-110.999999999994786
1306.84468115698517e-121.36893623139703e-110.999999999993155
1315.07028786499612e-111.01405757299922e-100.999999999949297
1321.33542344567665e-102.67084689135330e-100.999999999866458
1334.41377353471818e-108.82754706943637e-100.999999999558623
1341.85747865107771e-093.71495730215542e-090.999999998142521
1359.4971038511258e-101.89942077022516e-090.99999999905029
1363.97510537000819e-107.95021074001637e-100.99999999960249
1375.03790593335161e-101.00758118667032e-090.99999999949621
1382.76178259797784e-095.52356519595569e-090.999999997238217
1392.80285068082968e-095.60570136165935e-090.99999999719715
1402.23808012207086e-094.47616024414173e-090.99999999776192
1416.78100058087918e-061.35620011617584e-050.99999321899942
1429.081345475371e-061.8162690950742e-050.999990918654525


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1200.952380952380952NOK
5% type I error level1240.984126984126984NOK
10% type I error level1250.992063492063492NOK
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/23/t1230036750cf7py50nx5ow6ph/106uj21230036616.png (open in new window)
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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/23/t1230036750cf7py50nx5ow6ph/87ujt1230036616.ps (open in new window)


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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/23/t1230036750cf7py50nx5ow6ph/9o3hy1230036616.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}
 




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