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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 05:24:28 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258720091oudhlwqjrfn36ja.htm/, Retrieved Thu, 28 Mar 2024 11:02:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58079, Retrieved Thu, 28 Mar 2024 11:02:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWorkshop 7 Model 3
Estimated Impact134
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS7 No seasonal D...] [2009-11-18 15:26:28] [445b292c553470d9fed8bc2796fd3a00]
- R PD        [Multiple Regression] [shw-ws7] [2009-11-20 12:24:28] [5b5bced41faf164488f2c271c918b21f] [Current]
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Dataseries X:
2529	314
2196	318
3202	320
2718	323
2728	325
2354	327
2697	330
2651	331
2067	332
2641	334
2539	334
2294	334
2712	339
2314	345
3092	346
2677	352
2813	355
2668	358
2939	361
2617	363
2231	364
2481	365
2421	366
2408	370
2560	371
2100	371
3315	372
2801	373
2403	373
3024	374
2507	375
2980	375
2211	376
2471	376
2594	377
2452	377
2232	378
2373	379
3127	380
2802	384
2641	389
2787	390
2619	391
2806	392
2193	393
2323	394
2529	394
2412	395
2262	396
2154	397
3230	398
2295	399
2715	400
2733	400
2317	401
2730	401
1913	406
2390	407
2484	423
1960	427




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58079&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58079&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58079&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1219.03242808287 + 3.60792597062968X[t] + 141.869234895728M1[t] -90.4173137531013M2[t] + 879.025648762825M3[t] + 341.574344531617M4[t] + 343.009381076913M5[t] + 399.130758398712M6[t] + 303.20896533226M7[t] + 449.295098236438M8[t] -183.026694830014M9[t] + 159.537852880037M10[t] + 206.721793066452M11[t] -7.97247368068132t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  1219.03242808287 +  3.60792597062968X[t] +  141.869234895728M1[t] -90.4173137531013M2[t] +  879.025648762825M3[t] +  341.574344531617M4[t] +  343.009381076913M5[t] +  399.130758398712M6[t] +  303.20896533226M7[t] +  449.295098236438M8[t] -183.026694830014M9[t] +  159.537852880037M10[t] +  206.721793066452M11[t] -7.97247368068132t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58079&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  1219.03242808287 +  3.60792597062968X[t] +  141.869234895728M1[t] -90.4173137531013M2[t] +  879.025648762825M3[t] +  341.574344531617M4[t] +  343.009381076913M5[t] +  399.130758398712M6[t] +  303.20896533226M7[t] +  449.295098236438M8[t] -183.026694830014M9[t] +  159.537852880037M10[t] +  206.721793066452M11[t] -7.97247368068132t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58079&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58079&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1219.03242808287 + 3.60792597062968X[t] + 141.869234895728M1[t] -90.4173137531013M2[t] + 879.025648762825M3[t] + 341.574344531617M4[t] + 343.009381076913M5[t] + 399.130758398712M6[t] + 303.20896533226M7[t] + 449.295098236438M8[t] -183.026694830014M9[t] + 159.537852880037M10[t] + 206.721793066452M11[t] -7.97247368068132t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1219.032428082871238.8751090.9840.3302710.165136
X3.607925970629683.8076620.94750.3483130.174157
M1141.869234895728105.9164841.33940.1870070.093503
M2-90.4173137531013105.36043-0.85820.3952470.197624
M3879.025648762825105.3770428.341700
M4341.574344531617104.7180853.26180.0020890.001045
M5343.009381076913104.460643.28360.0019630.000981
M6399.130758398712104.3933183.82330.0003940.000197
M7303.20896533226104.2654552.9080.0055820.002791
M8449.295098236438104.3658284.3058.7e-054.3e-05
M9-183.026694830014104.250564-1.75560.0858070.042903
M10159.537852880037104.3684331.52860.1332110.066605
M11206.721793066452103.9859341.9880.0527850.026392
t-7.972473680681326.056208-1.31640.1945570.097279

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1219.03242808287 & 1238.875109 & 0.984 & 0.330271 & 0.165136 \tabularnewline
X & 3.60792597062968 & 3.807662 & 0.9475 & 0.348313 & 0.174157 \tabularnewline
M1 & 141.869234895728 & 105.916484 & 1.3394 & 0.187007 & 0.093503 \tabularnewline
M2 & -90.4173137531013 & 105.36043 & -0.8582 & 0.395247 & 0.197624 \tabularnewline
M3 & 879.025648762825 & 105.377042 & 8.3417 & 0 & 0 \tabularnewline
M4 & 341.574344531617 & 104.718085 & 3.2618 & 0.002089 & 0.001045 \tabularnewline
M5 & 343.009381076913 & 104.46064 & 3.2836 & 0.001963 & 0.000981 \tabularnewline
M6 & 399.130758398712 & 104.393318 & 3.8233 & 0.000394 & 0.000197 \tabularnewline
M7 & 303.20896533226 & 104.265455 & 2.908 & 0.005582 & 0.002791 \tabularnewline
M8 & 449.295098236438 & 104.365828 & 4.305 & 8.7e-05 & 4.3e-05 \tabularnewline
M9 & -183.026694830014 & 104.250564 & -1.7556 & 0.085807 & 0.042903 \tabularnewline
M10 & 159.537852880037 & 104.368433 & 1.5286 & 0.133211 & 0.066605 \tabularnewline
M11 & 206.721793066452 & 103.985934 & 1.988 & 0.052785 & 0.026392 \tabularnewline
t & -7.97247368068132 & 6.056208 & -1.3164 & 0.194557 & 0.097279 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58079&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1219.03242808287[/C][C]1238.875109[/C][C]0.984[/C][C]0.330271[/C][C]0.165136[/C][/ROW]
[ROW][C]X[/C][C]3.60792597062968[/C][C]3.807662[/C][C]0.9475[/C][C]0.348313[/C][C]0.174157[/C][/ROW]
[ROW][C]M1[/C][C]141.869234895728[/C][C]105.916484[/C][C]1.3394[/C][C]0.187007[/C][C]0.093503[/C][/ROW]
[ROW][C]M2[/C][C]-90.4173137531013[/C][C]105.36043[/C][C]-0.8582[/C][C]0.395247[/C][C]0.197624[/C][/ROW]
[ROW][C]M3[/C][C]879.025648762825[/C][C]105.377042[/C][C]8.3417[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]341.574344531617[/C][C]104.718085[/C][C]3.2618[/C][C]0.002089[/C][C]0.001045[/C][/ROW]
[ROW][C]M5[/C][C]343.009381076913[/C][C]104.46064[/C][C]3.2836[/C][C]0.001963[/C][C]0.000981[/C][/ROW]
[ROW][C]M6[/C][C]399.130758398712[/C][C]104.393318[/C][C]3.8233[/C][C]0.000394[/C][C]0.000197[/C][/ROW]
[ROW][C]M7[/C][C]303.20896533226[/C][C]104.265455[/C][C]2.908[/C][C]0.005582[/C][C]0.002791[/C][/ROW]
[ROW][C]M8[/C][C]449.295098236438[/C][C]104.365828[/C][C]4.305[/C][C]8.7e-05[/C][C]4.3e-05[/C][/ROW]
[ROW][C]M9[/C][C]-183.026694830014[/C][C]104.250564[/C][C]-1.7556[/C][C]0.085807[/C][C]0.042903[/C][/ROW]
[ROW][C]M10[/C][C]159.537852880037[/C][C]104.368433[/C][C]1.5286[/C][C]0.133211[/C][C]0.066605[/C][/ROW]
[ROW][C]M11[/C][C]206.721793066452[/C][C]103.985934[/C][C]1.988[/C][C]0.052785[/C][C]0.026392[/C][/ROW]
[ROW][C]t[/C][C]-7.97247368068132[/C][C]6.056208[/C][C]-1.3164[/C][C]0.194557[/C][C]0.097279[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58079&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58079&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1219.032428082871238.8751090.9840.3302710.165136
X3.607925970629683.8076620.94750.3483130.174157
M1141.869234895728105.9164841.33940.1870070.093503
M2-90.4173137531013105.36043-0.85820.3952470.197624
M3879.025648762825105.3770428.341700
M4341.574344531617104.7180853.26180.0020890.001045
M5343.009381076913104.460643.28360.0019630.000981
M6399.130758398712104.3933183.82330.0003940.000197
M7303.20896533226104.2654552.9080.0055820.002791
M8449.295098236438104.3658284.3058.7e-054.3e-05
M9-183.026694830014104.250564-1.75560.0858070.042903
M10159.537852880037104.3684331.52860.1332110.066605
M11206.721793066452103.9859341.9880.0527850.026392
t-7.972473680681326.056208-1.31640.1945570.097279







Multiple Linear Regression - Regression Statistics
Multiple R0.88502400689593
R-squared0.783267492782127
Adjusted R-squared0.722017001611859
F-TEST (value)12.7879381506467
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value3.25499627251702e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation164.397756950770
Sum Squared Residuals1243224.63456045

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.88502400689593 \tabularnewline
R-squared & 0.783267492782127 \tabularnewline
Adjusted R-squared & 0.722017001611859 \tabularnewline
F-TEST (value) & 12.7879381506467 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 3.25499627251702e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 164.397756950770 \tabularnewline
Sum Squared Residuals & 1243224.63456045 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58079&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.88502400689593[/C][/ROW]
[ROW][C]R-squared[/C][C]0.783267492782127[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.722017001611859[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.7879381506467[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]3.25499627251702e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]164.397756950770[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1243224.63456045[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58079&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58079&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.88502400689593
R-squared0.783267492782127
Adjusted R-squared0.722017001611859
F-TEST (value)12.7879381506467
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value3.25499627251702e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation164.397756950770
Sum Squared Residuals1243224.63456045







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
125292485.8179440756443.182055924359
221962259.99062562865-63.9906256286457
332023228.67696640515-26.6769664051491
427182694.0769664051523.92303359485
527282694.7553812110233.2446187889761
623542750.1201367934-396.120136793402
726972657.0496479581639.950352041843
826512798.77123315228-147.771233152283
920672162.08489237578-95.084892375779
1026412503.89281834641137.107181653591
1125392543.10428485214-4.10428485214211
1222942328.41001810501-34.4100181050085
1327122480.34640917320231.653590826796
1423142261.7349426674752.2650573325285
1530923226.81335747335-134.813357473346
1626772703.03713538523-26.0371353852344
1728132707.32347616174105.676523838262
1826682766.29615771475-98.2961577147456
1929392673.2256688795265.774331120499
2026172818.55518004426-201.555180044257
2122312181.8688392677549.1311607322468
2224812520.06883926775-39.0688392677533
2324212562.88823174412-141.888231744116
2424082362.625668879545.3743311204986
2525602500.1303560651859.8696439348223
2621002259.87133373567-159.871333735667
2733153224.9497485415490.0502514584587
2828012683.13389660028117.866103399718
2924032676.59645946490-273.596459464896
3030242728.35328907664295.646710923355
3125072628.06694830014-121.066948300141
3229802766.18060752364213.819392476363
3322112129.4942667471381.5057332528665
3424712464.086340776506.9136592234963
3525942506.9057332528787.0942667471334
3624522292.21146650573159.788533494267
3722322429.71615369141-197.716153691409
3823732193.06505733253179.934942667471
3931273158.1434721384-31.1434721384029
4028022627.15139810903174.848601890968
4126412638.653590826802.34640917320456
4227872690.4104204385496.5895795614564
4326192590.1240796620428.8759203379601
4428062731.8456648561774.1543351438341
4521932095.1593240796697.840675920338
4623232433.35932407966-110.359324079662
4725292472.5707905854056.4292094146048
4824122261.48444980889150.515550191108
4922622398.98913699457-136.989136994568
5021542162.33804063569-8.338040635687
5132303127.41645544156102.583544558439
5222952585.6006035003-290.600603500302
5327152582.67109233555132.328907664454
5427332630.81999597666102.180004023336
5523172530.53365520016-213.533655200161
5627302668.6473144236661.352685576343
5719132046.39267752967-133.392677529672
5823902384.592677529675.407322470328
5924842481.530959565482.46904043451999
6019602281.26839670087-321.268396700865

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2529 & 2485.81794407564 & 43.182055924359 \tabularnewline
2 & 2196 & 2259.99062562865 & -63.9906256286457 \tabularnewline
3 & 3202 & 3228.67696640515 & -26.6769664051491 \tabularnewline
4 & 2718 & 2694.07696640515 & 23.92303359485 \tabularnewline
5 & 2728 & 2694.75538121102 & 33.2446187889761 \tabularnewline
6 & 2354 & 2750.1201367934 & -396.120136793402 \tabularnewline
7 & 2697 & 2657.04964795816 & 39.950352041843 \tabularnewline
8 & 2651 & 2798.77123315228 & -147.771233152283 \tabularnewline
9 & 2067 & 2162.08489237578 & -95.084892375779 \tabularnewline
10 & 2641 & 2503.89281834641 & 137.107181653591 \tabularnewline
11 & 2539 & 2543.10428485214 & -4.10428485214211 \tabularnewline
12 & 2294 & 2328.41001810501 & -34.4100181050085 \tabularnewline
13 & 2712 & 2480.34640917320 & 231.653590826796 \tabularnewline
14 & 2314 & 2261.73494266747 & 52.2650573325285 \tabularnewline
15 & 3092 & 3226.81335747335 & -134.813357473346 \tabularnewline
16 & 2677 & 2703.03713538523 & -26.0371353852344 \tabularnewline
17 & 2813 & 2707.32347616174 & 105.676523838262 \tabularnewline
18 & 2668 & 2766.29615771475 & -98.2961577147456 \tabularnewline
19 & 2939 & 2673.2256688795 & 265.774331120499 \tabularnewline
20 & 2617 & 2818.55518004426 & -201.555180044257 \tabularnewline
21 & 2231 & 2181.86883926775 & 49.1311607322468 \tabularnewline
22 & 2481 & 2520.06883926775 & -39.0688392677533 \tabularnewline
23 & 2421 & 2562.88823174412 & -141.888231744116 \tabularnewline
24 & 2408 & 2362.6256688795 & 45.3743311204986 \tabularnewline
25 & 2560 & 2500.13035606518 & 59.8696439348223 \tabularnewline
26 & 2100 & 2259.87133373567 & -159.871333735667 \tabularnewline
27 & 3315 & 3224.94974854154 & 90.0502514584587 \tabularnewline
28 & 2801 & 2683.13389660028 & 117.866103399718 \tabularnewline
29 & 2403 & 2676.59645946490 & -273.596459464896 \tabularnewline
30 & 3024 & 2728.35328907664 & 295.646710923355 \tabularnewline
31 & 2507 & 2628.06694830014 & -121.066948300141 \tabularnewline
32 & 2980 & 2766.18060752364 & 213.819392476363 \tabularnewline
33 & 2211 & 2129.49426674713 & 81.5057332528665 \tabularnewline
34 & 2471 & 2464.08634077650 & 6.9136592234963 \tabularnewline
35 & 2594 & 2506.90573325287 & 87.0942667471334 \tabularnewline
36 & 2452 & 2292.21146650573 & 159.788533494267 \tabularnewline
37 & 2232 & 2429.71615369141 & -197.716153691409 \tabularnewline
38 & 2373 & 2193.06505733253 & 179.934942667471 \tabularnewline
39 & 3127 & 3158.1434721384 & -31.1434721384029 \tabularnewline
40 & 2802 & 2627.15139810903 & 174.848601890968 \tabularnewline
41 & 2641 & 2638.65359082680 & 2.34640917320456 \tabularnewline
42 & 2787 & 2690.41042043854 & 96.5895795614564 \tabularnewline
43 & 2619 & 2590.12407966204 & 28.8759203379601 \tabularnewline
44 & 2806 & 2731.84566485617 & 74.1543351438341 \tabularnewline
45 & 2193 & 2095.15932407966 & 97.840675920338 \tabularnewline
46 & 2323 & 2433.35932407966 & -110.359324079662 \tabularnewline
47 & 2529 & 2472.57079058540 & 56.4292094146048 \tabularnewline
48 & 2412 & 2261.48444980889 & 150.515550191108 \tabularnewline
49 & 2262 & 2398.98913699457 & -136.989136994568 \tabularnewline
50 & 2154 & 2162.33804063569 & -8.338040635687 \tabularnewline
51 & 3230 & 3127.41645544156 & 102.583544558439 \tabularnewline
52 & 2295 & 2585.6006035003 & -290.600603500302 \tabularnewline
53 & 2715 & 2582.67109233555 & 132.328907664454 \tabularnewline
54 & 2733 & 2630.81999597666 & 102.180004023336 \tabularnewline
55 & 2317 & 2530.53365520016 & -213.533655200161 \tabularnewline
56 & 2730 & 2668.64731442366 & 61.352685576343 \tabularnewline
57 & 1913 & 2046.39267752967 & -133.392677529672 \tabularnewline
58 & 2390 & 2384.59267752967 & 5.407322470328 \tabularnewline
59 & 2484 & 2481.53095956548 & 2.46904043451999 \tabularnewline
60 & 1960 & 2281.26839670087 & -321.268396700865 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58079&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]2529[/C][C]2485.81794407564[/C][C]43.182055924359[/C][/ROW]
[ROW][C]2[/C][C]2196[/C][C]2259.99062562865[/C][C]-63.9906256286457[/C][/ROW]
[ROW][C]3[/C][C]3202[/C][C]3228.67696640515[/C][C]-26.6769664051491[/C][/ROW]
[ROW][C]4[/C][C]2718[/C][C]2694.07696640515[/C][C]23.92303359485[/C][/ROW]
[ROW][C]5[/C][C]2728[/C][C]2694.75538121102[/C][C]33.2446187889761[/C][/ROW]
[ROW][C]6[/C][C]2354[/C][C]2750.1201367934[/C][C]-396.120136793402[/C][/ROW]
[ROW][C]7[/C][C]2697[/C][C]2657.04964795816[/C][C]39.950352041843[/C][/ROW]
[ROW][C]8[/C][C]2651[/C][C]2798.77123315228[/C][C]-147.771233152283[/C][/ROW]
[ROW][C]9[/C][C]2067[/C][C]2162.08489237578[/C][C]-95.084892375779[/C][/ROW]
[ROW][C]10[/C][C]2641[/C][C]2503.89281834641[/C][C]137.107181653591[/C][/ROW]
[ROW][C]11[/C][C]2539[/C][C]2543.10428485214[/C][C]-4.10428485214211[/C][/ROW]
[ROW][C]12[/C][C]2294[/C][C]2328.41001810501[/C][C]-34.4100181050085[/C][/ROW]
[ROW][C]13[/C][C]2712[/C][C]2480.34640917320[/C][C]231.653590826796[/C][/ROW]
[ROW][C]14[/C][C]2314[/C][C]2261.73494266747[/C][C]52.2650573325285[/C][/ROW]
[ROW][C]15[/C][C]3092[/C][C]3226.81335747335[/C][C]-134.813357473346[/C][/ROW]
[ROW][C]16[/C][C]2677[/C][C]2703.03713538523[/C][C]-26.0371353852344[/C][/ROW]
[ROW][C]17[/C][C]2813[/C][C]2707.32347616174[/C][C]105.676523838262[/C][/ROW]
[ROW][C]18[/C][C]2668[/C][C]2766.29615771475[/C][C]-98.2961577147456[/C][/ROW]
[ROW][C]19[/C][C]2939[/C][C]2673.2256688795[/C][C]265.774331120499[/C][/ROW]
[ROW][C]20[/C][C]2617[/C][C]2818.55518004426[/C][C]-201.555180044257[/C][/ROW]
[ROW][C]21[/C][C]2231[/C][C]2181.86883926775[/C][C]49.1311607322468[/C][/ROW]
[ROW][C]22[/C][C]2481[/C][C]2520.06883926775[/C][C]-39.0688392677533[/C][/ROW]
[ROW][C]23[/C][C]2421[/C][C]2562.88823174412[/C][C]-141.888231744116[/C][/ROW]
[ROW][C]24[/C][C]2408[/C][C]2362.6256688795[/C][C]45.3743311204986[/C][/ROW]
[ROW][C]25[/C][C]2560[/C][C]2500.13035606518[/C][C]59.8696439348223[/C][/ROW]
[ROW][C]26[/C][C]2100[/C][C]2259.87133373567[/C][C]-159.871333735667[/C][/ROW]
[ROW][C]27[/C][C]3315[/C][C]3224.94974854154[/C][C]90.0502514584587[/C][/ROW]
[ROW][C]28[/C][C]2801[/C][C]2683.13389660028[/C][C]117.866103399718[/C][/ROW]
[ROW][C]29[/C][C]2403[/C][C]2676.59645946490[/C][C]-273.596459464896[/C][/ROW]
[ROW][C]30[/C][C]3024[/C][C]2728.35328907664[/C][C]295.646710923355[/C][/ROW]
[ROW][C]31[/C][C]2507[/C][C]2628.06694830014[/C][C]-121.066948300141[/C][/ROW]
[ROW][C]32[/C][C]2980[/C][C]2766.18060752364[/C][C]213.819392476363[/C][/ROW]
[ROW][C]33[/C][C]2211[/C][C]2129.49426674713[/C][C]81.5057332528665[/C][/ROW]
[ROW][C]34[/C][C]2471[/C][C]2464.08634077650[/C][C]6.9136592234963[/C][/ROW]
[ROW][C]35[/C][C]2594[/C][C]2506.90573325287[/C][C]87.0942667471334[/C][/ROW]
[ROW][C]36[/C][C]2452[/C][C]2292.21146650573[/C][C]159.788533494267[/C][/ROW]
[ROW][C]37[/C][C]2232[/C][C]2429.71615369141[/C][C]-197.716153691409[/C][/ROW]
[ROW][C]38[/C][C]2373[/C][C]2193.06505733253[/C][C]179.934942667471[/C][/ROW]
[ROW][C]39[/C][C]3127[/C][C]3158.1434721384[/C][C]-31.1434721384029[/C][/ROW]
[ROW][C]40[/C][C]2802[/C][C]2627.15139810903[/C][C]174.848601890968[/C][/ROW]
[ROW][C]41[/C][C]2641[/C][C]2638.65359082680[/C][C]2.34640917320456[/C][/ROW]
[ROW][C]42[/C][C]2787[/C][C]2690.41042043854[/C][C]96.5895795614564[/C][/ROW]
[ROW][C]43[/C][C]2619[/C][C]2590.12407966204[/C][C]28.8759203379601[/C][/ROW]
[ROW][C]44[/C][C]2806[/C][C]2731.84566485617[/C][C]74.1543351438341[/C][/ROW]
[ROW][C]45[/C][C]2193[/C][C]2095.15932407966[/C][C]97.840675920338[/C][/ROW]
[ROW][C]46[/C][C]2323[/C][C]2433.35932407966[/C][C]-110.359324079662[/C][/ROW]
[ROW][C]47[/C][C]2529[/C][C]2472.57079058540[/C][C]56.4292094146048[/C][/ROW]
[ROW][C]48[/C][C]2412[/C][C]2261.48444980889[/C][C]150.515550191108[/C][/ROW]
[ROW][C]49[/C][C]2262[/C][C]2398.98913699457[/C][C]-136.989136994568[/C][/ROW]
[ROW][C]50[/C][C]2154[/C][C]2162.33804063569[/C][C]-8.338040635687[/C][/ROW]
[ROW][C]51[/C][C]3230[/C][C]3127.41645544156[/C][C]102.583544558439[/C][/ROW]
[ROW][C]52[/C][C]2295[/C][C]2585.6006035003[/C][C]-290.600603500302[/C][/ROW]
[ROW][C]53[/C][C]2715[/C][C]2582.67109233555[/C][C]132.328907664454[/C][/ROW]
[ROW][C]54[/C][C]2733[/C][C]2630.81999597666[/C][C]102.180004023336[/C][/ROW]
[ROW][C]55[/C][C]2317[/C][C]2530.53365520016[/C][C]-213.533655200161[/C][/ROW]
[ROW][C]56[/C][C]2730[/C][C]2668.64731442366[/C][C]61.352685576343[/C][/ROW]
[ROW][C]57[/C][C]1913[/C][C]2046.39267752967[/C][C]-133.392677529672[/C][/ROW]
[ROW][C]58[/C][C]2390[/C][C]2384.59267752967[/C][C]5.407322470328[/C][/ROW]
[ROW][C]59[/C][C]2484[/C][C]2481.53095956548[/C][C]2.46904043451999[/C][/ROW]
[ROW][C]60[/C][C]1960[/C][C]2281.26839670087[/C][C]-321.268396700865[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58079&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58079&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
125292485.8179440756443.182055924359
221962259.99062562865-63.9906256286457
332023228.67696640515-26.6769664051491
427182694.0769664051523.92303359485
527282694.7553812110233.2446187889761
623542750.1201367934-396.120136793402
726972657.0496479581639.950352041843
826512798.77123315228-147.771233152283
920672162.08489237578-95.084892375779
1026412503.89281834641137.107181653591
1125392543.10428485214-4.10428485214211
1222942328.41001810501-34.4100181050085
1327122480.34640917320231.653590826796
1423142261.7349426674752.2650573325285
1530923226.81335747335-134.813357473346
1626772703.03713538523-26.0371353852344
1728132707.32347616174105.676523838262
1826682766.29615771475-98.2961577147456
1929392673.2256688795265.774331120499
2026172818.55518004426-201.555180044257
2122312181.8688392677549.1311607322468
2224812520.06883926775-39.0688392677533
2324212562.88823174412-141.888231744116
2424082362.625668879545.3743311204986
2525602500.1303560651859.8696439348223
2621002259.87133373567-159.871333735667
2733153224.9497485415490.0502514584587
2828012683.13389660028117.866103399718
2924032676.59645946490-273.596459464896
3030242728.35328907664295.646710923355
3125072628.06694830014-121.066948300141
3229802766.18060752364213.819392476363
3322112129.4942667471381.5057332528665
3424712464.086340776506.9136592234963
3525942506.9057332528787.0942667471334
3624522292.21146650573159.788533494267
3722322429.71615369141-197.716153691409
3823732193.06505733253179.934942667471
3931273158.1434721384-31.1434721384029
4028022627.15139810903174.848601890968
4126412638.653590826802.34640917320456
4227872690.4104204385496.5895795614564
4326192590.1240796620428.8759203379601
4428062731.8456648561774.1543351438341
4521932095.1593240796697.840675920338
4623232433.35932407966-110.359324079662
4725292472.5707905854056.4292094146048
4824122261.48444980889150.515550191108
4922622398.98913699457-136.989136994568
5021542162.33804063569-8.338040635687
5132303127.41645544156102.583544558439
5222952585.6006035003-290.600603500302
5327152582.67109233555132.328907664454
5427332630.81999597666102.180004023336
5523172530.53365520016-213.533655200161
5627302668.6473144236661.352685576343
5719132046.39267752967-133.392677529672
5823902384.592677529675.407322470328
5924842481.530959565482.46904043451999
6019602281.26839670087-321.268396700865







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.2071569933909450.4143139867818910.792843006609055
180.2716578183982310.5433156367964630.728342181601769
190.2050386523272960.4100773046545920.794961347672704
200.2721013427884800.5442026855769610.72789865721152
210.1722538503210080.3445077006420170.827746149678992
220.2433787757211280.4867575514422560.756621224278872
230.289262924815790.578525849631580.71073707518421
240.2049076965836170.4098153931672350.795092303416383
250.2075546499555120.4151092999110240.792445350044488
260.2425059235796270.4850118471592530.757494076420373
270.2266667412547220.4533334825094440.773333258745278
280.182988082714420.365976165428840.81701191728558
290.5626671346315990.8746657307368030.437332865368401
300.8567525562013630.2864948875972750.143247443798637
310.8766010594243750.2467978811512500.123398940575625
320.8790614203444760.2418771593110470.120938579655524
330.8173204708766780.3653590582466440.182679529123322
340.756067025702070.487865948595860.24393297429793
350.7070564029456140.5858871941087710.292943597054386
360.6069214115576870.7861571768846260.393078588442313
370.6649268864260220.6701462271479570.335073113573978
380.5761963257615790.8476073484768420.423803674238421
390.5884049602701170.8231900794597660.411595039729883
400.7242148018140220.5515703963719570.275785198185979
410.6753171127767420.6493657744465160.324682887223258
420.5406771831150280.9186456337699440.459322816884972
430.4838893164785530.9677786329571070.516110683521447

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.207156993390945 & 0.414313986781891 & 0.792843006609055 \tabularnewline
18 & 0.271657818398231 & 0.543315636796463 & 0.728342181601769 \tabularnewline
19 & 0.205038652327296 & 0.410077304654592 & 0.794961347672704 \tabularnewline
20 & 0.272101342788480 & 0.544202685576961 & 0.72789865721152 \tabularnewline
21 & 0.172253850321008 & 0.344507700642017 & 0.827746149678992 \tabularnewline
22 & 0.243378775721128 & 0.486757551442256 & 0.756621224278872 \tabularnewline
23 & 0.28926292481579 & 0.57852584963158 & 0.71073707518421 \tabularnewline
24 & 0.204907696583617 & 0.409815393167235 & 0.795092303416383 \tabularnewline
25 & 0.207554649955512 & 0.415109299911024 & 0.792445350044488 \tabularnewline
26 & 0.242505923579627 & 0.485011847159253 & 0.757494076420373 \tabularnewline
27 & 0.226666741254722 & 0.453333482509444 & 0.773333258745278 \tabularnewline
28 & 0.18298808271442 & 0.36597616542884 & 0.81701191728558 \tabularnewline
29 & 0.562667134631599 & 0.874665730736803 & 0.437332865368401 \tabularnewline
30 & 0.856752556201363 & 0.286494887597275 & 0.143247443798637 \tabularnewline
31 & 0.876601059424375 & 0.246797881151250 & 0.123398940575625 \tabularnewline
32 & 0.879061420344476 & 0.241877159311047 & 0.120938579655524 \tabularnewline
33 & 0.817320470876678 & 0.365359058246644 & 0.182679529123322 \tabularnewline
34 & 0.75606702570207 & 0.48786594859586 & 0.24393297429793 \tabularnewline
35 & 0.707056402945614 & 0.585887194108771 & 0.292943597054386 \tabularnewline
36 & 0.606921411557687 & 0.786157176884626 & 0.393078588442313 \tabularnewline
37 & 0.664926886426022 & 0.670146227147957 & 0.335073113573978 \tabularnewline
38 & 0.576196325761579 & 0.847607348476842 & 0.423803674238421 \tabularnewline
39 & 0.588404960270117 & 0.823190079459766 & 0.411595039729883 \tabularnewline
40 & 0.724214801814022 & 0.551570396371957 & 0.275785198185979 \tabularnewline
41 & 0.675317112776742 & 0.649365774446516 & 0.324682887223258 \tabularnewline
42 & 0.540677183115028 & 0.918645633769944 & 0.459322816884972 \tabularnewline
43 & 0.483889316478553 & 0.967778632957107 & 0.516110683521447 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58079&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]17[/C][C]0.207156993390945[/C][C]0.414313986781891[/C][C]0.792843006609055[/C][/ROW]
[ROW][C]18[/C][C]0.271657818398231[/C][C]0.543315636796463[/C][C]0.728342181601769[/C][/ROW]
[ROW][C]19[/C][C]0.205038652327296[/C][C]0.410077304654592[/C][C]0.794961347672704[/C][/ROW]
[ROW][C]20[/C][C]0.272101342788480[/C][C]0.544202685576961[/C][C]0.72789865721152[/C][/ROW]
[ROW][C]21[/C][C]0.172253850321008[/C][C]0.344507700642017[/C][C]0.827746149678992[/C][/ROW]
[ROW][C]22[/C][C]0.243378775721128[/C][C]0.486757551442256[/C][C]0.756621224278872[/C][/ROW]
[ROW][C]23[/C][C]0.28926292481579[/C][C]0.57852584963158[/C][C]0.71073707518421[/C][/ROW]
[ROW][C]24[/C][C]0.204907696583617[/C][C]0.409815393167235[/C][C]0.795092303416383[/C][/ROW]
[ROW][C]25[/C][C]0.207554649955512[/C][C]0.415109299911024[/C][C]0.792445350044488[/C][/ROW]
[ROW][C]26[/C][C]0.242505923579627[/C][C]0.485011847159253[/C][C]0.757494076420373[/C][/ROW]
[ROW][C]27[/C][C]0.226666741254722[/C][C]0.453333482509444[/C][C]0.773333258745278[/C][/ROW]
[ROW][C]28[/C][C]0.18298808271442[/C][C]0.36597616542884[/C][C]0.81701191728558[/C][/ROW]
[ROW][C]29[/C][C]0.562667134631599[/C][C]0.874665730736803[/C][C]0.437332865368401[/C][/ROW]
[ROW][C]30[/C][C]0.856752556201363[/C][C]0.286494887597275[/C][C]0.143247443798637[/C][/ROW]
[ROW][C]31[/C][C]0.876601059424375[/C][C]0.246797881151250[/C][C]0.123398940575625[/C][/ROW]
[ROW][C]32[/C][C]0.879061420344476[/C][C]0.241877159311047[/C][C]0.120938579655524[/C][/ROW]
[ROW][C]33[/C][C]0.817320470876678[/C][C]0.365359058246644[/C][C]0.182679529123322[/C][/ROW]
[ROW][C]34[/C][C]0.75606702570207[/C][C]0.48786594859586[/C][C]0.24393297429793[/C][/ROW]
[ROW][C]35[/C][C]0.707056402945614[/C][C]0.585887194108771[/C][C]0.292943597054386[/C][/ROW]
[ROW][C]36[/C][C]0.606921411557687[/C][C]0.786157176884626[/C][C]0.393078588442313[/C][/ROW]
[ROW][C]37[/C][C]0.664926886426022[/C][C]0.670146227147957[/C][C]0.335073113573978[/C][/ROW]
[ROW][C]38[/C][C]0.576196325761579[/C][C]0.847607348476842[/C][C]0.423803674238421[/C][/ROW]
[ROW][C]39[/C][C]0.588404960270117[/C][C]0.823190079459766[/C][C]0.411595039729883[/C][/ROW]
[ROW][C]40[/C][C]0.724214801814022[/C][C]0.551570396371957[/C][C]0.275785198185979[/C][/ROW]
[ROW][C]41[/C][C]0.675317112776742[/C][C]0.649365774446516[/C][C]0.324682887223258[/C][/ROW]
[ROW][C]42[/C][C]0.540677183115028[/C][C]0.918645633769944[/C][C]0.459322816884972[/C][/ROW]
[ROW][C]43[/C][C]0.483889316478553[/C][C]0.967778632957107[/C][C]0.516110683521447[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58079&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58079&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.2071569933909450.4143139867818910.792843006609055
180.2716578183982310.5433156367964630.728342181601769
190.2050386523272960.4100773046545920.794961347672704
200.2721013427884800.5442026855769610.72789865721152
210.1722538503210080.3445077006420170.827746149678992
220.2433787757211280.4867575514422560.756621224278872
230.289262924815790.578525849631580.71073707518421
240.2049076965836170.4098153931672350.795092303416383
250.2075546499555120.4151092999110240.792445350044488
260.2425059235796270.4850118471592530.757494076420373
270.2266667412547220.4533334825094440.773333258745278
280.182988082714420.365976165428840.81701191728558
290.5626671346315990.8746657307368030.437332865368401
300.8567525562013630.2864948875972750.143247443798637
310.8766010594243750.2467978811512500.123398940575625
320.8790614203444760.2418771593110470.120938579655524
330.8173204708766780.3653590582466440.182679529123322
340.756067025702070.487865948595860.24393297429793
350.7070564029456140.5858871941087710.292943597054386
360.6069214115576870.7861571768846260.393078588442313
370.6649268864260220.6701462271479570.335073113573978
380.5761963257615790.8476073484768420.423803674238421
390.5884049602701170.8231900794597660.411595039729883
400.7242148018140220.5515703963719570.275785198185979
410.6753171127767420.6493657744465160.324682887223258
420.5406771831150280.9186456337699440.459322816884972
430.4838893164785530.9677786329571070.516110683521447







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58079&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58079&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58079&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK



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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$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')
}