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Multiple Linear regression

*Unverified author*
R Software Module: rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Wed, 10 Dec 2008 08:29:56 -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/10/t122892324364tgl09kln49twk.htm/, Retrieved Wed, 10 Dec 2008 15:34:15 +0000
 
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/10/t122892324364tgl09kln49twk.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},
}
 
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
 
Feedback Forum:
2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc] [reply] 
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Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
No linear trend No seasonality
 
Dataseries X:
» Textbox « » Textfile « » CSV «
358.59 122.36 362.96 123.33 362.42 123.04 364.97 124.53 364.04 125.13 361.06 125.85 358.48 126.50 352.96 126.53 359.59 127.07 360.39 124.55 357.40 124.90 362.93 124.32 364.55 122.84 365.73 123.31 364.70 123.31 364.65 124.87 359.43 124.64 362.14 124.73 356.97 124.90 354.82 124.04 353.17 123.28 357.06 123.86 356.18 122.29 355.01 124.09 355.65 124.54 357.31 125.65 357.07 125.70 357.91 125.53 358.48 125.61 358.97 125.55 351.77 125.41 352.16 127.60 359.08 124.68 360.35 124.41 359.53 126.43 359.30 126.38 358.41 125.78 359.68 124.70 355.31 125.07 357.08 125.25 349.71 126.58 354.13 127.13 345.49 125.82 341.69 123.70 344.25 124.39 340.17 123.70 342.47 124.42 344.43 121.05 333.23 121.02 339.72 123.23 342.61 121.32 346.36 120.91 339.09 120.72 339.73 123.31 341.12 119.58 335.94 119.53 333.46 120.59 335.66 118.63 341.12 118.47 342.21 111.81 342.62 114.71 346.06 117.34 344.43 115.77 346.65 118.38 343.74 117.84 335.67 118.83 342.75 120.02 341.77 116.21 345.84 117.08 346.52 120.20 350.79 119.83 345.44 118.92 345.87 118.03 338.48 117.71 337.21 119.55 340.81 116.13 339.86 115.97 342.86 115.99 343.33 114.96 341.73 116.46 351.38 116.55 351.13 113.05 345.99 117.44 347.55 118.84 346.02 117.06 345.29 117.54 347.03 119.31 348.01 118.72 345.48 121.55 349.40 122.61 351.05 121.53 349.70 123.31 350.86 124.07 354.45 123.59 355.30 122.97 357.48 123.22 355.24 123.04 351.79 122.96 355.22 122.81 351.02 122.81 350.28 122.62 350.17 120.82 348.16 119.41 340.30 121.56 343.75 121.59 344.71 118.50 344.13 118.77 342.14 118.86 345.04 117.60 346.02 119.90 346.43 121.83 347.07 121.84 339.33 122.12 339.10 122.12 337.19 121.36 339.58 119.66 327.85 119.32 326.81 120.36 321.73 117.06 320.45 117.48 327.69 115.60 323.95 113.86 320.47 116.92 322.13 117.75 316.34 117.75 314.78 115.31 308.90 116.28 308.62 115.22 314.41 115.65 306.88 115.11 310.60 118.67 321.60 118.04 321.50 116.50 325.68 119.78 324.35 119.95 320.01 120.37 326.88 119.79 332.39 119.43 331.48 121.06 332.62 121.74 324.79 121.09 327.12 122.97 328.91 120.50 328.37 117.18 324.83 115.03 325.90 113.36 326.18 112.59 328.94 111.65 333.78 111.98 328.06 114.87 325.87 114.67 325.41 114.09 318.86 114.77 319.13 117.05 310.16 117.22 311.73 113.18 306.54 110.95 311.16 112.14 311.98 112.72 306.72 110.01 308.05 110.29 300.76 110.74 301.90 110.32 293.09 105.89 292.76 108.97 294.58 109.34 289.90 106.57 296.69 99.49 297.21 101.81 293.31 104.29 296.25 109.73 298.60 105.06 296.87 107.97 301.02 108.13 304.73 109.86 301.92 108.95 295.72 111.20 293.18 110.69 298.35 106.10 297.99 105.68 299.85 104.12 299.85 104.71 304.45 104.30 299.45 103.52 298.14 107.76 298.78 107.80 297.02 107.30 301.33 108.64 294.96 105.03 296.69 108.30 300.73 107.21 301.96 109.27 297.38 109.50 293.87 111.68 285.96 111.80 285.41 111.75 283.70 106.68 284.76 106.37 277.11 105.76 274.73 109.01 274.73 109.01 274.73 109.01 274.73 109.01 274.69 107.69 275.42 105.19 264.15 105.48 276.24 102.22 268.88 100.54 277.97 105.00 280.49 105.44 281.09 107.89 276.16 108.64 272.58 106.70 270.94 109.10 284.31 105.23 283.94 108.41 284.18 108.80 282.83 110.39 283.84 110.22 282.71 110.86 279.29 108.58 280.70 107.70 274.47 106.62 273.44 109.84 275.49 107.16 279.46 107.26 280.19 108.70 288.21 109.85 284.80 109.41 281.41 112.36 283.39 111.03 287.97 110.67 290.77 109.21 290.60 113.58 289.67 113.88 289.84 114.08 298.55 112.33 296.07 113.92 297.14 114.41 295.34 114.57 296.25 115.35 294.30 113.13 296.15 113.29 296.49 112.56 298.05 113.06 301.03 113.46 300.52 115.39 301.50 116.62 296.93 117.04 289.84 117.42 291.44 115.62 286.88 115.16 286.74 115.69 288.93 112.85 292.19 114.05 295.39 112.00 295.86 113.74 293.36 116.26 292.86 118.63 292.73 116.49 296.73 118.23 285.02 116.83 285.24 118.82 288.62 114.36 283.36 112.02 285.84 113.24 291.48 109.75 291.41 110.33 287.77 112.86 284.97 113.04 286.05 113.80 278.19 110.90 281.21 109.96 277.92 108.69 280.08 108.84 269.24 108.47 268.48 108.07 268.83 107.94 269.54 108.11 262.37 108.11 265.12 106.81 265.34 105.58 263.32 105.61 267.18 106.52 260.75 103.86 261.78 104.60 257.27 104.73 255.63 105.12 251.39 104.76 259.49 103.85 261.18 103.83 261.65 103.22 262.01 101.64 265.23 102.13 268.10 104.33 262.27 104.92 263.59 107.78 257.85 104.49 265.69 102.80 271.15 102.86 266.69 104.51 265.77 104.73 262.32 102.58 270.48 99.93 273.03 101.41 269.13 101.05 280.65 99.86 282.75 101.11 281.44 100.89 281.99 101.09 282.86 98.31 287.21 98.08 283.11 99.55 280.66 99.62 282.39 97.37 280.83 98.16 284.71 97.98 279.99 98.15 283.50 97.10 284.88 97.24 288.60 96.70 284.80 96.64 287.20 100.65 286.22 96.75 286.54 97.74 279.58 97.92 283.08 98.34 288.88 93.84 280.18 97.80 284.16 96.20 290.57 95.99 286.82 95.18 273.00 95.95 278.69 92.23 264.54 91.78 271.92 92.97 283.60 89.76 269.25 92.88 263.58 96.23 264.16 95.79 268.85 93.97 269.67 93.90 249.41 93.60 268.99 93.96 268.65 88.69 260.16 88.57 256.55 85.62 251.47 86.25 234.93 85.33 232.96 83.33 215.49 77.78 213.68 78.70 236.07 72.05 235.41 80.75 214.77 81.41 225.85 82.65 224.64 75.85 238.26 75.70 232.44 78.25 222.50 77.41 225.28 76.84 220.49 74.25 216.86 74.95 234.70 68.78 230.06 73.21 238.27 73.26 238.56 78.67 242.70 75.63 249.14 74.99 234.89 83.87 227.78 79.62 234.04 80.13 230.70 79.76 230.17 78.20 218.23 78.05 232.20 79.05 220.76 73.32 215.60 75.17 217.69 73.26 204.35 73.72 191.44 73.57 203.84 70.60 211.86 71.25 210.57 74.22 219.57 73.32 219.98 73.01 226.01 74.21 207.04 75.32 212.52 71.73 217.92 71.94 210.45 72.94 218.53 72.47 223.32 71.94 218.76 74.30 217.63 74.30
 
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 time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
Amerika[t] = + 20.3785932363761 + 2.57998966746182Japan[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)20.37859323637616.5131623.12880.0018860.000943
Japan2.579989667461820.05965743.247400


Multiple Linear Regression - Regression Statistics
Multiple R0.909044788056128
R-squared0.82636242669201
Adjusted R-squared0.82592060080573
F-TEST (value)1870.33501737505
F-TEST (DF numerator)1
F-TEST (DF denominator)393
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation17.4428987902785
Sum Squared Residuals119572.104255705


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1358.59336.06612894699822.5238710530023
2362.96338.56871892444224.3912810755581
3362.42337.82052192087824.5994780791222
4364.97341.66470652539623.3052934746042
5364.04343.21270032587320.8272996741271
6361.06345.07029288644515.9897071135545
7358.48346.74728617029611.7327138297044
8352.96346.8246858603206.13531413968047
9359.59348.21788028074911.3721197192511
10360.39341.71630631874518.6736936812549
11357.4342.61930270235714.7806972976432
12362.93341.12290869522921.8070913047711
13364.55337.30452398738527.2454760126146
14365.73338.51711913109227.2128808689076
15364.7338.51711913109226.1828808689075
16364.65342.54190301233322.1080969876671
17359.43341.94850538881717.4814946111833
18362.14342.18070445888819.9592955411117
19356.97342.61930270235714.3506972976433
20354.82340.4005115883414.4194884116604
21353.17338.43971944106914.7302805589314
22357.06339.93611344819617.1238865518036
23356.18335.88552967028120.2944703297186
24355.01340.52951107171314.4804889282873
25355.65341.69050642207013.9594935779295
26357.31344.55429495295312.7557050470469
27357.07344.68329443632612.3867055636738
28357.91344.24469619285813.6653038071423
29358.48344.45109536625514.0289046337454
30358.97344.29629598620714.6737040137931
31351.77343.9350974327627.83490256723772
32352.16349.5852748045042.57472519549639
33359.08342.05170497551517.0282950244848
34360.35341.35510776530018.9948922346996
35359.53346.56668689357312.9633131064266
36359.3346.437687410212.8623125897998
37358.41344.88969360972313.5203063902769
38359.68342.10330476886417.5766952311356
39355.31343.05790094582512.2520990541748
40357.08343.52229908596813.5577009140316
41349.71346.9536853436932.75631465630739
42354.13348.3726796607975.75732033920341
43345.49344.9928931964220.497106803578408
44341.69339.5233151014032.16668489859743
45344.25341.3035079719512.94649202804878
46340.17339.5233151014030.646684898597446
47342.47341.3809076619751.08909233802495
48344.43332.68634248262911.7436575173713
49333.23332.6089427926050.621057207395138
50339.72338.3107199576961.40928004230451
51342.61333.3829396928439.2270603071566
52346.36332.32514392918414.0348560708159
53339.09331.8349458923667.25505410763363
54339.73338.5171191310921.21288086890756
55341.12328.8937576714612.2262423285401
56335.94328.7647581880877.17524181191322
57333.46331.4995472355961.96045276440366
58335.66326.4427674873719.21723251262889
59341.12326.02996914057715.0900308594228
60342.21308.84723795528233.3627620447184
61342.62316.32920799092126.2907920090792
62346.06323.11458081634522.9454191836546
63344.43319.0639970384325.3660029615697
64346.65325.79777007050620.8522299294943
65343.74324.40457565007619.3354243499237
66335.67326.9587654208648.71123457913651
67342.75330.02895312514312.7210468748569
68341.77320.19919249211421.5708075078865
69345.84322.44378350280523.3962164971947
70346.52330.49335126528616.0266487347138
71350.79329.53875508832521.2512449116747
72345.44327.19096449093518.2490355090649
73345.87324.89477368689420.9752263131059
74338.48324.06917699330614.4108230066938
75337.21328.8163579814368.39364201856397
76340.81319.99279331871720.8172066812834
77339.86319.57999497192320.2800050280773
78342.86319.63159476527223.2284052347281
79343.33316.97420540778626.3557945922137
80341.73320.84418990897920.8858100910210
81351.38321.07638897905130.3036110209494
82351.13312.04642514293439.0835748570658
83345.99323.37257978309222.6174202169084
84347.55326.98456531753820.5654346824619
85346.02322.39218370945623.6278162905439
86345.29323.63057874983821.6594212501622
87347.03328.19716046124518.8328395387548
88348.01326.67496655744321.3350334425573
89345.48333.97633731636011.5036626836404
90349.4336.71112636386912.6888736361308
91351.05333.92473752301017.1252624769896
92349.7338.51711913109211.1828808689075
93350.86340.47791127836310.3820887216366
94354.45339.23951623798215.2104837620182
95355.3337.63992264415517.6600773558446
96357.48338.28492006102119.1950799389791
97355.24337.82052192087817.4194780791222
98351.79337.61412274748114.1758772525192
99355.22337.22712429736217.9928757026385
100351.02337.22712429736213.7928757026384
101350.28336.73692626054413.5430737394562
102350.17332.09294485911218.0770551408875
103348.16328.45515942799119.7048405720087
104340.3334.0021372130346.29786278696574
105343.75334.0795369030589.67046309694187
106344.71326.10736883060118.6026311693989
107344.13326.80396604081617.3260339591842
108342.14327.03616511088715.1038348891126
109345.04323.78537812988521.2546218701146
110346.02329.71935436504816.3006456349523
111346.43334.69873442324911.7312655767510
112347.07334.72453431992412.3454656800764
113339.33335.4469314268133.88306857318709
114339.1335.4469314268133.65306857318713
115337.19333.4861392795423.70386072045809
116339.58329.10015684485710.4798431551432
117327.85328.22296035792-0.372960357919756
118326.81330.90614961208-4.09614961208008
119321.73322.392183709456-0.662183709456079
120320.45323.47577936979-3.02577936979007
121327.69318.6253987949629.06460120503817
122323.95314.1362167735789.81378322642172
123320.47322.030985156011-1.56098515601141
124322.13324.172376580005-2.04237658000475
125316.34324.172376580005-7.83237658000477
126314.78317.877201791398-3.09720179139794
127308.9320.379791768836-11.4797917688359
128308.62317.645002721326-9.02500272132634
129314.41318.754398278335-4.34439827833491
130306.88317.361203857906-10.4812038579055
131310.6326.54596707407-15.9459670740696
132321.6324.920573583569-3.32057358356866
133321.5320.9473894956770.55261050432253
134325.68329.409755604952-3.72975560495223
135324.35329.848353848421-5.49835384842073
136320.01330.931949508755-10.9219495087547
137326.88329.435555501627-2.55555550162687
138332.39328.5067592213413.88324077865937
139331.48332.712142379303-1.23214237930335
140332.62334.466535353177-1.84653535317738
141324.79332.789542069327-7.9995420693272
142327.12337.639922644155-10.5199226441554
143328.91331.267348165525-2.35734816552472
144328.37322.7017824695525.66821753044848
145324.83317.1548046845097.67519531549138
146325.9312.84622193984713.0537780601526
147326.18310.85962989590215.3203701040982
148328.94308.43443960848820.5055603915123
149333.78309.2858361987524.4941638012499
150328.06316.74200633771511.3179936622853
151325.87316.2260084042229.64399159577765
152325.41314.72961439709510.6803856029055
153318.86316.4840073709692.3759926290315
154319.13322.366383812781-3.23638381278147
155310.16322.80498205625-12.6449820562500
156311.73312.381823799704-0.651823799704235
157306.54306.628446841264-0.0884468412643684
158311.16309.6986345455441.46136545445608
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160306.72304.203256553852.51674344614974
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170293.31289.4457156559693.86428434403131
171296.25303.480859446961-7.23085944696097
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173296.87298.940077632228-2.07007763222816
174301.02299.3528759790221.66712402097794
175304.73303.8162581037310.91374189626902
176301.92301.4684675063410.451532493659264
177295.72307.27344425813-11.5534442581298
178293.18305.957649527724-12.7776495277243
179298.35294.1154969540754.23450304592547
180297.99293.0319012937414.95809870625939
181299.85289.007117412510.8428825874998
182299.85290.5293113163039.3206886836974
183304.45289.47151555264314.9784844473567
184299.45287.45912361202311.9908763879769
185298.14298.398279802061-0.258279802061208
186298.78298.5014793887600.278520611240326
187297.02297.211484555029-0.191484555028755
188301.33300.6686707094280.661329290572401
189294.96291.3549080098903.60509199010956
190296.69299.791474222491-3.10147422249056
191300.73296.9792854849573.75071451504285
192301.96302.294064199929-0.334064199928537
193297.38302.887461823445-5.50746182344475
194293.87308.511839298512-14.6418392985115
195285.96308.821438058607-22.8614380586069
196285.41308.692438575234-23.2824385752338
197283.7295.611890961202-11.9118909612024
198284.76294.812094164289-10.0520941642893
199277.11293.238300467138-16.1283004671375
200274.73301.623266886388-26.8932668863884
201274.73301.623266886388-26.8932668863884
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217284.18301.081469056221-16.9014690562215
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219283.84304.745054384017-20.9050543840173
220282.71306.396247771193-23.6862477711928
221279.29300.51387132938-21.2238713293799
222280.7298.243480422013-17.5434804220135
223274.47295.457091581155-20.9870915811547
224273.44303.764658310382-30.3246583103818
225275.49296.850286001584-21.3602860015841
226279.46297.108284968330-17.6482849683303
227280.19300.823470089475-20.6334700894753
228288.21303.790458207056-15.5804582070564
229284.8302.655262753373-17.8552627533732
230281.41310.266232272386-28.8562322723855
231283.39306.834846014661-23.4448460146613
232287.97305.906049734375-17.9360497343751
233290.77302.139264819881-11.3692648198808
234290.6313.413819666689-22.8138196666889
235289.67314.187816566927-24.5178165669275
236289.84314.70381450042-24.8638145004199
237298.55310.188832582362-11.6388325823617
238296.07314.291016153626-18.221016153626
239297.14315.555211090682-18.4152110906823
240295.34315.968009437476-20.6280094374762
241296.25317.980401378096-21.7304013780964
242294.3312.252824316331-17.9528243163311
243296.15312.665622663125-16.5156226631251
244296.49310.782230205878-14.2922302058779
245298.05312.072225039609-14.0222250396088
246301.03313.104220906594-12.0742209065936
247300.52318.083600964795-17.5636009647949
248301.5321.256988255773-19.7569882557729
249296.93322.340583916107-25.4105839161069
250289.84323.320979989742-33.4809799897424
251291.44318.676998588311-27.2369985883111
252286.88317.490203341279-30.6102033412786
253286.74318.857597865033-32.1175978650334
254288.93311.530427209442-22.6004272094418
255292.19314.626414810396-22.436414810396
256295.39309.337435992099-13.9474359920993
257295.86313.826618013483-17.9666180134828
258293.36320.328191975487-26.9681919754866
259292.86326.442767487371-33.5827674873711
260292.73320.921589599003-28.1915895990028
261296.73325.410771620386-28.6807716203864
262285.02321.79878608594-36.7787860859399
263285.24326.932965524189-41.6929655241889
264288.62315.426211607309-26.8062116073092
265283.36309.389035785449-26.0290357854485
266285.84312.536623179752-26.6966231797520
267291.48303.53245924031-12.0524592403102
268291.41305.028853247438-13.6188532474380
269287.77311.556227106116-23.7862271061165
270284.97312.020625246260-27.0506252462596
271286.05313.981417393531-27.9314173935305
272278.19306.499447357891-28.3094473578913
273281.21304.074257070477-22.8642570704772
274277.92300.797670192801-22.8776701928007
275280.08301.18466864292-21.1046686429200
276269.24300.230072465959-30.9900724659591
277268.48299.198076598974-30.7180765989743
278268.83298.862677942204-30.0326779422043
279269.54299.301276185673-29.7612761856728
280262.37299.301276185673-36.9312761856728
281265.12295.947289617972-30.8272896179725
282265.34292.773902326994-27.4339023269944
283263.32292.851302017018-29.5313020170183
284267.18295.199092614409-28.0190926144085
285260.75288.33632009896-27.5863200989601
286261.78290.245512452882-28.4655124528818
287257.27290.580911109652-33.3109111096519
288255.63291.587107079962-35.957107079962
289251.39290.658310799676-39.2683107996758
290259.49288.310520202285-28.8205202022854
291261.18288.258920408936-27.0789204089362
292261.65286.685126711785-25.0351267117845
293262.01282.608743037195-20.5987430371949
294265.23283.872937974251-18.6429379742511
295268.1289.548915242667-21.4489152426671
296262.27291.071109146470-28.8011091464696
297263.59298.449879595410-34.8598795954104
298257.85289.961713589461-32.111713589461
299265.69285.601531051451-19.9115310514506
300271.15285.756330431498-14.6063304314983
301266.69290.013313382810-23.3233133828103
302265.77290.580911109652-24.8109111096519
303262.32285.033933324609-22.7139333246090
304270.48278.196960705835-7.71696070583515
305273.03282.015345413679-8.98534541367865
306269.13281.086549133392-11.9565491333924
307280.65278.0163614291132.63363857088716
308282.75281.241348513441.50865148655991
309281.44280.6737507865980.766249213401507
310281.99281.1897487200910.800251279909144
311282.86274.0173774445478.84262255545301
312287.21273.42397982103113.7860201789692
313283.11277.2165646322005.89343536780036
314280.66277.3971639089223.26283609107803
315282.39271.59218715713310.7978128428671
316280.83273.6303789944287.19962100557226
317284.71273.16598085428511.5440191457154
318279.99273.6045790977536.38542090224689
319283.5270.89558994691812.6044100530818
320284.88271.25678850036313.6232114996372
321288.6269.86359407993318.7364059200665
322284.8269.70879469988615.0912053001142
323287.2280.0545532664087.14544673359232
324286.22269.99259356330716.2274064366935
325286.54272.54678333409413.9932166659063
326279.58273.0111814742376.56881852576309
327283.08274.0947771345718.98522286542912
328288.88262.48482363099326.3951763690073
329280.18272.7015827141417.47841728585854
330284.16268.57359924620315.5864007537975
331290.57268.03180141603622.5381985839644
332286.82265.94200978539220.8779902146085
333273267.9286018293375.07139817066288
334278.69258.33104026637920.3589597336208
335264.54257.1700449160217.36995508397869
336271.92260.24023262030111.6797673796991
337283.6251.95846578774831.6415342122515
338269.25260.0080335502299.24196644977068
339263.58268.650998936226-5.07099893622644
340264.16267.515803482543-3.35580348254321
341268.85262.8202222877636.02977771223732
342269.67262.6396230110407.03037698895962
343249.41261.865626110802-12.4556261108018
344268.99262.7944223910886.19557760891194
345268.65249.19787684356419.4521231564357
346260.16248.88827808346911.2717219165311
347256.55241.27730856445715.2726914355435
348251.47242.9027020549578.56729794504253
349234.93240.529111560893-5.5991115608926
350232.96235.369132225969-2.40913222596895
351215.49221.050189571556-5.56018957155587
352213.68223.423780065621-9.74378006562075
353236.07206.26684877700029.8031512230003
354235.41228.7127588839176.69724111608252
355214.77230.415552064442-15.6455520644423
356225.85233.614739252095-7.76473925209496
357224.64216.0708095133558.56919048664543
358238.26215.68381106323522.5761889367647
359232.44222.26278471526310.1772152847371
360222.5220.0955933945952.40440660540500
361225.28218.6249992841426.65500071585823
362220.49211.9428260454168.54717395458436
363216.86213.7488188126393.11118118736107
364234.7197.83028256440036.8697174356005
365230.06209.25963679125520.8003632087447
366238.27209.38863627462828.8813637253715
367238.56223.34638037559715.2136196244031
368242.7215.50321178651327.196788213487
369249.14213.85201839933735.2879816006626
370234.89236.762326646398-1.87232664639837
371227.78225.7973705596861.98262944031437
372234.04227.1131652900916.92683470990886
373230.7226.1585691131304.54143088686969
374230.17222.1337852318908.03621476811014
375218.23221.746786781771-3.51678678177056
376232.2224.3267764492327.8732235507676
377220.76209.54343565467611.2165643453238
378215.6214.3164165394811.28358346051946
379217.69209.3886362746288.30136372537152
380204.35210.575431521661-6.22543152166091
381191.44210.188433071542-18.7484330715416
382203.84202.525863759181.31413624082000
383211.86204.2028570430307.6571429569698
384210.57211.865426355392-1.29542635539181
385219.57209.54343565467610.0265643453238
386219.98208.74363885776311.2363611422370
387226.01211.83962645871714.1703735412828
388207.04214.703414989600-7.66341498959979
389212.52205.4412520834127.07874791658812
390217.92205.98304991357911.9369500864211
391210.45208.5630395810411.88696041895932
392218.53207.35044443733411.1795555626664
393223.32205.98304991357917.3369500864211
394218.76212.0718255287896.68817447121125
395217.63212.0718255287895.55817447121126


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.0008711008199944560.001742201639988910.999128899180006
60.0006007550724600830.001201510144920170.99939924492754
70.0002989528941864160.0005979057883728330.999701047105814
80.0004832699411449370.0009665398822898740.999516730058855
99.97715727559756e-050.0001995431455119510.999900228427244
101.89887504589062e-053.79775009178124e-050.99998101124954
115.1083031427626e-061.02166062855252e-050.999994891696857
121.12847433810323e-062.25694867620647e-060.999998871525662
132.46794361876396e-074.93588723752793e-070.999999753205638
147.46524484285116e-081.49304896857023e-070.999999925347552
151.59933866679504e-083.19867733359008e-080.999999984006613
165.53773981401388e-091.10754796280278e-080.99999999446226
171.18624612631020e-092.37249225262040e-090.999999998813754
182.15116860407289e-104.30233720814578e-100.999999999784883
198.69257173159363e-111.73851434631873e-100.999999999913074
201.54798486093965e-103.09596972187930e-100.999999999845201
215.73208814786082e-101.14641762957216e-090.999999999426791
222.10737896831541e-104.21475793663082e-100.999999999789262
231.22256848743909e-102.44513697487817e-100.999999999877743
246.41566600974497e-111.28313320194899e-100.999999999935843
252.51522151305194e-115.03044302610387e-110.999999999974848
266.4786210668233e-121.29572421336466e-110.999999999993521
271.66242092014782e-123.32484184029564e-120.999999999998338
283.90460535027077e-137.80921070054153e-130.99999999999961
298.75870214445613e-141.75174042889123e-130.999999999999912
301.92871495665461e-143.85742991330921e-140.99999999999998
312.25648912697816e-144.51297825395632e-140.999999999999977
329.30232937560358e-151.86046587512072e-140.99999999999999
332.15474316468337e-154.30948632936674e-150.999999999999998
345.1703665353664e-161.03407330707328e-151
351.33949193550109e-162.67898387100217e-161
363.26154631829766e-176.52309263659532e-171
377.14846719873162e-181.42969343974632e-171
381.60278152815301e-183.20556305630603e-181
395.44243368060719e-191.08848673612144e-181
401.27586683535106e-192.55173367070213e-191
411.95890875354905e-193.91781750709810e-191
424.80711463568103e-209.61422927136207e-201
439.64510766684482e-191.92902153336896e-181
444.57334211634897e-169.14668423269793e-161
454.45113446905278e-158.90226893810556e-150.999999999999996
461.66461356123582e-133.32922712247164e-130.999999999999833
478.0889415335233e-131.61778830670466e-120.999999999999191
481.97382163980817e-123.94764327961635e-120.999999999998026
494.79705153605167e-119.59410307210333e-110.99999999995203
501.10607683494826e-102.21215366989652e-100.999999999889392
518.65981543042106e-111.73196308608421e-100.999999999913402
524.30734859804021e-118.61469719608042e-110.999999999956926
533.84819508793050e-117.69639017586099e-110.999999999961518
545.70230068061979e-111.14046013612396e-100.999999999942977
552.89793840255058e-115.79587680510116e-110.99999999997102
562.05129043619727e-114.10258087239454e-110.999999999979487
572.41763232495737e-114.83526464991473e-110.999999999975824
581.21336307721259e-112.42672615442518e-110.999999999987866
595.79127537692348e-121.15825507538470e-110.999999999994209
601.86543953585674e-113.73087907171348e-110.999999999981346
611.35314106874775e-112.70628213749549e-110.999999999986469
628.11736690740434e-121.62347338148087e-110.999999999991883
635.25335422653175e-121.05067084530635e-110.999999999994747
642.83817283400668e-125.67634566801336e-120.999999999997162
651.44379314202158e-122.88758628404316e-120.999999999998556
661.19127852146462e-122.38255704292924e-120.999999999998809
676.32000682183606e-131.26400136436721e-120.999999999999368
683.40029183399046e-136.80058366798092e-130.99999999999966
692.09715110985055e-134.19430221970111e-130.99999999999979
701.04404709126599e-132.08809418253198e-130.999999999999896
716.39514217318912e-141.27902843463782e-130.999999999999936
723.27916622536518e-146.55833245073035e-140.999999999999967
731.82137454704942e-143.64274909409884e-140.999999999999982
741.03587614848499e-142.07175229696997e-140.99999999999999
758.52094232164232e-151.70418846432846e-140.999999999999992
764.60371672565257e-159.20743345130513e-150.999999999999995
772.45780914054353e-154.91561828108706e-150.999999999999998
781.50314256227648e-153.00628512455296e-150.999999999999998
791.12329883857140e-152.24659767714281e-150.999999999999999
806.25345687577999e-161.25069137515600e-151
819.85564547358438e-161.97112909471688e-150.999999999999999
824.74037514221536e-159.48075028443072e-150.999999999999995
833.07470987113522e-156.14941974227044e-150.999999999999997
841.91141024837359e-153.82282049674718e-150.999999999999998
851.32317277247790e-152.64634554495579e-150.999999999999999
868.48180852464742e-161.69636170492948e-151
875.15643827259351e-161.03128765451870e-151
883.48208261561436e-166.96416523122872e-161
892.21083301166609e-164.42166602333217e-161
901.28183433709555e-162.56366867419111e-161
918.0221465173243e-171.60442930346486e-161
924.74378754488896e-179.48757508977792e-171
932.78489785792134e-175.56979571584267e-171
941.7674795984098e-173.5349591968196e-171
951.30278466743138e-172.60556933486276e-171
961.17185759561335e-172.34371519122671e-171
978.84987164446593e-181.76997432889319e-171
985.70695142869896e-181.14139028573979e-171
994.64283860824871e-189.28567721649742e-181
1003.08418587017205e-186.1683717403441e-181
1012.08100757409882e-184.16201514819765e-181
1021.61672180665896e-183.23344361331792e-181
1031.33812142688722e-182.67624285377444e-181
1041.77612024265946e-183.55224048531891e-181
1051.5803218442808e-183.1606436885616e-181
1061.30914351059219e-182.61828702118439e-181
1071.08667599730385e-182.17335199460771e-181
1089.4155390898251e-191.88310781796502e-181
1099.40199081527728e-191.88039816305546e-181
1108.29404395422613e-191.65880879084523e-181
1117.32407221604517e-191.46481444320903e-181
1126.52688098050607e-191.30537619610121e-181
1131.28836135177388e-182.57672270354775e-181
1142.56441255761906e-185.12882511523811e-181
1155.72031199712371e-181.14406239942474e-171
1166.91834316109817e-181.38366863221963e-171
1175.73566797365583e-171.14713359473117e-161
1187.36025214657585e-161.47205042931517e-151
1196.86186383722187e-151.37237276744437e-140.999999999999993
1207.29542457742854e-141.45908491548571e-130.999999999999927
1211.14149307281533e-132.28298614563066e-130.999999999999886
1221.83685335787751e-133.67370671575503e-130.999999999999816
1239.3907242756087e-131.87814485512174e-120.999999999999061
1243.86146234174370e-127.72292468348739e-120.999999999996139
1253.84709121469755e-117.6941824293951e-110.999999999961529
1261.56122530737487e-103.12245061474974e-100.999999999843878
1271.93958141955134e-093.87916283910267e-090.999999998060419
1281.10033526087076e-082.20067052174152e-080.999999988996647
1292.59156711038821e-085.18313422077642e-080.999999974084329
1301.13377261383672e-072.26754522767344e-070.999999886622739
1317.54791215376719e-071.50958243075344e-060.999999245208785
1321.12149912675651e-062.24299825351301e-060.999998878500873
1331.31714945514989e-062.63429891029978e-060.999998682850545
1341.92538656623678e-063.85077313247356e-060.999998074613434
1353.06794077952469e-066.13588155904938e-060.99999693205922
1367.08984301338291e-061.41796860267658e-050.999992910156987
1379.04197165985301e-061.80839433197060e-050.99999095802834
1389.71404415891902e-061.94280883178380e-050.99999028595584
1391.19056151583828e-052.38112303167655e-050.999988094384842
1401.49670115073140e-052.99340230146280e-050.999985032988493
1412.45365077048795e-054.9073015409759e-050.999975463492295
1424.55679966770062e-059.11359933540125e-050.999954432003323
1435.53532937359106e-050.0001107065874718210.999944646706264
1445.85355987237933e-050.0001170711974475870.999941464401276
1456.11243998669897e-050.0001222487997339790.999938875600133
1467.08762329757142e-050.0001417524659514280.999929123767024
1478.96885191305325e-050.0001793770382610650.99991031148087
1480.0001488926763173640.0002977853526347280.999851107323683
1490.0003341353587620160.0006682707175240330.999665864641238
1500.0004103562003804980.0008207124007609960.99958964379962
1510.0004887290442520640.0009774580885041280.999511270955748
1520.0005976182477604730.001195236495520950.99940238175224
1530.0006855221798235080.001371044359647020.999314477820177
1540.0008758072918411760.001751614583682350.99912419270816
1550.001574720102256100.003149440204512200.998425279897744
1560.001760099821165940.003520199642331880.998239900178834
1570.001871477405688470.003742954811376940.998128522594312
1580.001993951009839620.003987902019679240.99800604899016
1590.002147149924581760.004294299849163520.997852850075418
1600.002187650968363430.004375301936726870.997812349031637
1610.002245386296469580.004490772592939150.99775461370353
1620.002480334959518980.004960669919037960.997519665040481
1630.002570074255675430.005140148511350860.997429925744325
1640.002380031387469380.004760062774938760.99761996861253
1650.002633790724521440.005267581449042880.997366209275478
1660.002791729844752010.005583459689504030.997208270155248
1670.002624808548604450.005249617097208890.997375191451396
1680.004314341052109010.008628682104218010.99568565894789
1690.00518667064092020.01037334128184040.99481332935908
1700.004829030093770180.009658060187540350.99517096990623
1710.00513921284191430.01027842568382860.994860787158086
1720.00518808686331860.01037617372663720.994811913136681
1730.005007472870914890.01001494574182980.994992527129085
1740.004904952030539870.009809904061079750.99509504796946
1750.004967389481738130.009934778963476260.995032610518262
1760.004925473410201650.00985094682040330.995074526589798
1770.005858079822365630.01171615964473130.994141920177634
1780.007001504572795460.01400300914559090.992998495427205
1790.006923827837131260.01384765567426250.993076172162869
1800.00693516512237650.0138703302447530.993064834877623
1810.008193601661480260.01638720332296050.99180639833852
1820.009262257657149670.01852451531429930.99073774234285
1830.01330345746304400.02660691492608790.986696542536956
1840.01633026069932760.03266052139865520.983669739300672
1850.01632742218621180.03265484437242360.983672577813788
1860.01644994960956260.03289989921912530.983550050390437
1870.01637215029881990.03274430059763980.98362784970118
1880.01697299448210040.03394598896420070.9830270055179
1890.017144238893320.034288477786640.98285576110668
1900.01728705928384890.03457411856769790.982712940716151
1910.0185135941980350.037027188396070.981486405801965
1920.01957298243268070.03914596486536150.98042701756732
1930.02039277010311890.04078554020623780.979607229896881
1940.02534276781759940.05068553563519880.9746572321824
1950.04063236102352680.08126472204705360.959367638976473
1960.06166290425471610.1233258085094320.938337095745284
1970.06216698434844750.1243339686968950.937833015651553
1980.06069872642335660.1213974528467130.939301273576643
1990.06424577065999120.1284915413199820.93575422934001
2000.09649657332914760.1929931466582950.903503426670852
2010.135546345489290.271092690978580.86445365451071
2020.1801782624026430.3603565248052860.819821737597357
2030.2288317381233150.4576634762466310.771168261876685
2040.2579902869769800.5159805739539600.74200971302302
2050.2537518234823260.5075036469646530.746248176517674
2060.3053710850629460.6107421701258920.694628914937054
2070.2831812635832340.5663625271664670.716818736416766
2080.2614419467240930.5228838934481870.738558053275907
2090.2477698012674490.4955396025348990.75223019873255
2100.2331823949476090.4663647898952190.76681760505239
2110.2330412320030990.4660824640061980.766958767996901
2120.2586006115853820.5172012231707640.741399388414618
2130.2729230521738750.5458461043477490.727076947826125
2140.3372084067887660.6744168135775310.662791593211234
2150.3169114859954380.6338229719908760.683088514004562
2160.3092160170457550.618432034091510.690783982954245
2170.3036466311863200.6072932623726390.69635336881368
2180.3186902221972170.6373804443944330.681309777802783
2190.3267172895493750.653434579098750.673282710450625
2200.3466860064569130.6933720129138260.653313993543087
2210.3495325940293930.6990651880587860.650467405970607
2220.3387711812706110.6775423625412220.661228818729389
2230.3353745405324310.6707490810648610.66462545946757
2240.3846814344422040.7693628688844080.615318565557796
2250.3814020582297780.7628041164595570.618597941770222
2260.3670239232397250.7340478464794490.632976076760275
2270.3632335548349190.7264671096698390.636766445165081
2280.3503401485241260.7006802970482510.649659851475874
2290.3400760732383860.6801521464767710.659923926761614
2300.3803062869598900.7606125739197810.61969371304011
2310.3893153218759080.7786306437518160.610684678124092
2320.3801623232639830.7603246465279660.619837676736017
2330.3611527331471990.7223054662943990.6388472668528
2340.3739697436346410.7479394872692830.626030256365359
2350.3932885715779880.7865771431559760.606711428422012
2360.413651353408170.827302706816340.58634864659183
2370.4020820612049830.8041641224099650.597917938795017
2380.400982214602580.801964429205160.59901778539742
2390.4016143457945590.8032286915891180.598385654205441
2400.4069483331715290.8138966663430590.593051666828471
2410.4174411798016440.8348823596032880.582558820198356
2420.4109297493892610.8218594987785220.589070250610739
2430.4028821083985050.805764216797010.597117891601495
2440.3908886389472220.7817772778944450.609111361052778
2450.3806087615628210.7612175231256430.619391238437179
2460.3723961088202940.7447922176405880.627603891179706
2470.3730281611509010.7460563223018020.626971838849099
2480.3814311057944530.7628622115889070.618568894205547
2490.4047067683266540.8094135366533080.595293231673346
2500.4649294634392020.9298589268784030.535070536560798
2510.4859846996094250.971969399218850.514015300390575
2520.5187748556982320.9624502886035350.481225144301768
2530.558785598036770.882428803926460.44121440196323
2540.5531126734056340.8937746531887330.446887326594366
2550.5498299954992960.9003400090014090.450170004500704
2560.5320042727781680.9359914544436640.467995727221832
2570.5203473944188180.9593052111623630.479652605581182
2580.5352117678833770.9295764642332450.464788232116623
2590.5841669589211430.8316660821577150.415833041078857
2600.6004110060662760.7991779878674480.399588993933724
2610.6222304152583170.7555391694833660.377769584741683
2620.6736459183887330.6527081632225350.326354081611267
2630.7500259005763720.4999481988472560.249974099423628
2640.750988901831150.4980221963377010.249011098168850
2650.746617035241070.506765929517860.25338296475893
2660.7449853827457520.5100292345084950.255014617254248
2670.723685964529960.552628070940080.27631403547004
2680.7016936394070.5966127211859990.298306360592999
2690.6914784449454620.6170431101090770.308521555054538
2700.6891594416272970.6216811167454050.310840558372703
2710.6900907508428260.6198184983143490.309909249157174
2720.6893487556871970.6213024886256060.310651244312803
2730.6730624055671840.6538751888656310.326937594432816
2740.6558045213168030.6883909573663950.344195478683197
2750.6345609439892440.7308781120215130.365439056010756
2760.6446191737992520.7107616524014950.355380826200748
2770.6542750940679520.6914498118640960.345724905932048
2780.6617461568687650.6765076862624710.338253843131235
2790.6688093917872040.6623812164255920.331190608212796
2800.7137193064003480.5725613871993030.286280693599652
2810.7285253666202730.5429492667594550.271474633379727
2820.7317932998977990.5364134002044030.268206700102201
2830.7447300820204160.5105398359591680.255269917979584
2840.7519401306045030.4961197387909940.248059869395497
2850.7620069914898270.4759860170203460.237993008510173
2860.7761151027426470.4477697945147070.223884897257353
2870.8131493156055340.3737013687889320.186850684394466
2880.8607814599204370.2784370801591260.139218540079563
2890.9158137119909430.1683725760181150.0841862880090574
2900.9310074279672760.1379851440654480.068992572032724
2910.942166904829640.1156661903407180.0578330951703588
2920.9501201474917830.09975970501643440.0498798525082172
2930.9531645989855140.09367080202897220.0468354010144861
2940.9542473943172410.09150521136551730.0457526056827586
2950.9581007241291040.08379855174179220.0418992758708961
2960.971995433329210.05600913334158160.0280045666707908
2970.9883180995492530.02336380090149320.0116819004507466
2980.9955269539508550.008946092098289170.00447304604914459
2990.9967687382982830.006462523403433320.00323126170171666
3000.9971369760354030.005726047929194570.00286302396459728
3010.9985420240376950.002915951924610790.00145797596230540
3020.9994621249224130.001075750155173290.000537875077586643
3030.9998162663634410.0003674672731175980.000183733636558799
3040.9998383479049160.0003233041901680710.000161652095084035
3050.9998672877784140.0002654244431715630.000132712221585782
3060.9999155563768470.0001688872463057548.44436231528769e-05
3070.999907047550310.0001859048993803539.29524496901766e-05
3080.9998959078787270.0002081842425468970.000104092121273449
3090.9998855667878120.0002288664243764440.000114433212188222
3100.9998742001344460.0002515997311081650.000125799865554082
3110.9998654641653210.0002690716693574500.000134535834678725
3120.999872267023590.000255465952820490.000127732976410245
3130.9998508908948250.0002982182103500130.000149109105175006
3140.9998272776757280.0003454446485438480.000172722324271924
3150.9998133684343460.0003732631313080990.000186631565654050
3160.9997817456095130.0004365087809737240.000218254390486862
3170.9997579214242990.0004841571514022330.000242078575701117
3180.9997101277134710.000579744573057470.000289872286528735
3190.9996831357802890.0006337284394225960.000316864219711298
3200.999658009485620.0006839810287593170.000341990514379659
3210.999700481411170.0005990371776585190.000299518588829260
3220.999688038064130.0006239238717396770.000311961935869838
3230.99959403756650.0008119248670002650.000405962433500133
3240.9995878717049950.0008242565900101430.000412128295005072
3250.9995377005597980.0009245988804045060.000462299440202253
3260.9993995549536310.001200890092738140.000600445046369068
3270.9992332799198140.001533440160370920.00076672008018546
3280.999591926157850.0008161476842993840.000408073842149692
3290.9994584007432670.001083198513465080.000541599256732542
3300.9994299407329760.001140118534047670.000570059267023836
3310.9996143687511250.0007712624977501470.000385631248875074
3320.9997260254636460.0005479490727087570.000273974536354378
3330.9996190769571910.000761846085618180.00038092304280909
3340.9997287570809270.0005424858381464030.000271242919073201
3350.999639148831960.0007217023360811680.000360851168040584
3360.9995876203004390.0008247593991218520.000412379699560926
3370.9999235270773580.0001529458452831897.64729226415946e-05
3380.9999085761115930.0001828477768140429.14238884070208e-05
3390.9998601193676960.000279761264608460.00013988063230423
3400.9997859722987580.0004280554024849060.000214027701242453
3410.9997150096877520.0005699806244952160.000284990312247608
3420.9996491083604530.0007017832790938830.000350891639546941
3430.9995760165278520.0008479669442954340.000423983472147717
3440.9994529696799330.001094060640133140.000547030320066572
3450.9997023294485570.0005953411028858710.000297670551442935
3460.999743468302660.0005130633946785230.000256531697339261
3470.9998415650483970.0003168699032061420.000158434951603071
3480.99986601861090.0002679627781991810.000133981389099590
3490.9997842511336950.0004314977326095470.000215748866304774
3500.9996603632778860.0006792734442288740.000339636722114437
3510.9995752176647170.0008495646705668280.000424782335283414
3520.9995526686709210.0008946626581584210.000447331329079211
3530.999728512007540.0005429759849181860.000271487992459093
3540.999607315180320.0007853696393595190.000392684819679760
3550.9996741822394430.0006516355211133680.000325817760556684
3560.9995580080264420.0008839839471155640.000441991973557782
3570.9993103389076470.001379322184707000.000689661092353501
3580.999418452675710.001163094648580140.000581547324290069
3590.9991391854967250.001721629006549350.000860814503274676
3600.9986613543372570.002677291325485900.00133864566274295
3610.9979013680961460.004197263807707670.00209863190385384
3620.9967732741389020.006453451722196370.00322672586109819
3630.9953257462167730.009348507566453630.00467425378322682
3640.9983304896191060.003339020761788880.00166951038089444
3650.998236674231530.003526651536941380.00176332576847069
3660.99922990620450.001540187591001110.000770093795500557
3670.999036526371090.001926947257821710.000963473628910855
3680.999663956604810.0006720867903791070.000336043395189554
3690.9999937189541221.25620917560440e-056.28104587802201e-06
3700.9999858013100042.8397379991731e-051.41986899958655e-05
3710.9999675693365256.4861326950797e-053.24306634753985e-05
3720.9999369758316390.0001260483367227886.30241683613938e-05
3730.9998701155633360.0002597688733271950.000129884436663597
3740.999790655822680.0004186883546398020.000209344177319901
3750.999583782522670.0008324349546621160.000416217477331058
3760.9995475289530440.0009049420939121440.000452471046956072
3770.999243111746430.001513776507142180.000756888253571091
3780.9984029487694010.003194102461197750.00159705123059888
3790.996957253779620.00608549244076140.0030427462203807
3800.9958786293421010.008242741315797140.00412137065789857
3810.9998032181972420.0003935636055148930.000196781802757447
3820.9998794463353760.0002411073292476980.000120553664623849
3830.9997789099848660.0004421800302673740.000221090015133687
3840.9995322198722680.0009355602554646520.000467780127732326
3850.998607512157310.00278497568537970.00139248784268985
3860.9960961731548530.007807653690293320.00390382684514666
3870.9975111230380380.004977753923924790.00248887696196240
3880.996430035483720.00713992903256160.0035699645162808
3890.9918256746685040.01634865066299160.0081743253314958
3900.9665187008333070.06696259833338630.0334812991666932


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level2590.670984455958549NOK
5% type I error level2820.730569948186529NOK
10% type I error level2900.751295336787565NOK
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/10aam61228922984.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/10aam61228922984.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/17i9y1228922983.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/17i9y1228922983.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/2riyr1228922983.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/2riyr1228922983.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/37wfx1228922983.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/37wfx1228922983.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/4jwq91228922983.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/4jwq91228922983.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/5cx221228922983.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/5cx221228922983.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/6mq2g1228922983.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/6mq2g1228922983.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/7a8vc1228922984.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/7a8vc1228922984.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/8az4m1228922984.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/8az4m1228922984.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/938k31228922984.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t122892324364tgl09kln49twk/938k31228922984.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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

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Software written by Ed van Stee & Patrick Wessa


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