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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 04:58:04 -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/t1258718438am78zu3bpd29wqm.htm/, Retrieved Thu, 28 Mar 2024 14:47:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58055, Retrieved Thu, 28 Mar 2024 14:47:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWorkshop 7 Model 1
Estimated Impact239
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  D        [Multiple Regression] [shw-ws7] [2009-11-20 11:58:04] [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 time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58055&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]3 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=58055&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58055&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 3255.2677926153 -1.88573019166673X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  3255.2677926153 -1.88573019166673X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58055&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  3255.2677926153 -1.88573019166673X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58055&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58055&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] = + 3255.2677926153 -1.88573019166673X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3255.2677926153535.8495476.07500
X-1.885730191666731.443548-1.30630.1966020.098301

\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) & 3255.2677926153 & 535.849547 & 6.075 & 0 & 0 \tabularnewline
X & -1.88573019166673 & 1.443548 & -1.3063 & 0.196602 & 0.098301 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58055&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]3255.2677926153[/C][C]535.849547[/C][C]6.075[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-1.88573019166673[/C][C]1.443548[/C][C]-1.3063[/C][C]0.196602[/C][C]0.098301[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58055&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58055&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)3255.2677926153535.8495476.07500
X-1.885730191666731.443548-1.30630.1966020.098301







Multiple Linear Regression - Regression Statistics
Multiple R0.169058749276525
R-squared0.0285808607069431
Adjusted R-squared0.0118322548570629
F-TEST (value)1.70646207589555
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.196601812988957
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation309.957469882705
Sum Squared Residuals5572270.7218931

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.169058749276525 \tabularnewline
R-squared & 0.0285808607069431 \tabularnewline
Adjusted R-squared & 0.0118322548570629 \tabularnewline
F-TEST (value) & 1.70646207589555 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.196601812988957 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 309.957469882705 \tabularnewline
Sum Squared Residuals & 5572270.7218931 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58055&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.169058749276525[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0285808607069431[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0118322548570629[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.70646207589555[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.196601812988957[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]309.957469882705[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5572270.7218931[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58055&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58055&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 R0.169058749276525
R-squared0.0285808607069431
Adjusted R-squared0.0118322548570629
F-TEST (value)1.70646207589555
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.196601812988957
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation309.957469882705
Sum Squared Residuals5572270.7218931







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
125292663.14851243195-134.148512431948
221962655.60559166528-459.605591665281
332022651.83413128195550.165868718053
427182646.1769407069571.8230592930527
527282642.4054803236185.5945196763861
623542638.63401994028-284.634019940280
726972632.9768293652864.0231706347198
826512631.0910991736119.9089008263865
920672629.20536898195-562.205368981947
1026412625.4339085986115.5660914013867
1125392625.43390859861-86.4339085986133
1222942625.43390859861-331.433908598613
1327122616.0052576402895.9947423597203
1423142604.69087649028-290.690876490279
1530922602.80514629861489.194853701387
1626772591.4907651486185.5092348513878
1728132585.83357457361227.166425426388
1826682580.1763839986187.8236160013881
1929392574.51919342361364.480806576388
2026172570.7477330402846.2522669597218
2122312568.86200284861-337.862002848612
2224812566.97627265694-85.9762726569448
2324212565.09054246528-144.090542465278
2424082557.54762169861-149.547621698611
2525602555.661891506944.33810849305561
2621002555.66189150694-455.661891506944
2733152553.77616131528761.223838684722
2828012551.89043112361249.109568876389
2924032551.89043112361-148.890431123611
3030242550.00470093194473.995299068056
3125072548.11897074028-41.1189707402775
3229802548.11897074028431.881029259722
3322112546.23324054861-335.233240548611
3424712546.23324054861-75.2332405486108
3525942544.3475103569449.652489643056
3624522544.34751035694-92.347510356944
3722322542.46178016528-310.461780165277
3823732540.57604997361-167.576049973611
3931272538.69031978194588.309680218056
4028022531.14739901528270.852600984723
4126412521.71874805694119.281251943057
4227872519.83301786528267.166982134723
4326192517.94728767361101.052712326390
4428062516.06155748194289.938442518057
4521932514.17582729028-321.175827290276
4623232512.29009709861-189.290097098610
4725292512.2900970986116.7099029013903
4824122510.40436690694-98.4043669069429
4922622508.51863671528-246.518636715276
5021542506.63290652361-352.632906523609
5132302504.74717633194725.252823668057
5222952502.86144614028-207.861446140276
5327152500.97571594861214.024284051391
5427332500.97571594861232.024284051391
5523172499.08998575694-182.089985756943
5627302499.08998575694230.910014243057
5719132489.66133479861-576.661334798609
5823902487.77560460694-97.7756046069422
5924842457.6039215402726.3960784597255
6019602450.06100077361-490.061000773608

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2529 & 2663.14851243195 & -134.148512431948 \tabularnewline
2 & 2196 & 2655.60559166528 & -459.605591665281 \tabularnewline
3 & 3202 & 2651.83413128195 & 550.165868718053 \tabularnewline
4 & 2718 & 2646.17694070695 & 71.8230592930527 \tabularnewline
5 & 2728 & 2642.40548032361 & 85.5945196763861 \tabularnewline
6 & 2354 & 2638.63401994028 & -284.634019940280 \tabularnewline
7 & 2697 & 2632.97682936528 & 64.0231706347198 \tabularnewline
8 & 2651 & 2631.09109917361 & 19.9089008263865 \tabularnewline
9 & 2067 & 2629.20536898195 & -562.205368981947 \tabularnewline
10 & 2641 & 2625.43390859861 & 15.5660914013867 \tabularnewline
11 & 2539 & 2625.43390859861 & -86.4339085986133 \tabularnewline
12 & 2294 & 2625.43390859861 & -331.433908598613 \tabularnewline
13 & 2712 & 2616.00525764028 & 95.9947423597203 \tabularnewline
14 & 2314 & 2604.69087649028 & -290.690876490279 \tabularnewline
15 & 3092 & 2602.80514629861 & 489.194853701387 \tabularnewline
16 & 2677 & 2591.49076514861 & 85.5092348513878 \tabularnewline
17 & 2813 & 2585.83357457361 & 227.166425426388 \tabularnewline
18 & 2668 & 2580.17638399861 & 87.8236160013881 \tabularnewline
19 & 2939 & 2574.51919342361 & 364.480806576388 \tabularnewline
20 & 2617 & 2570.74773304028 & 46.2522669597218 \tabularnewline
21 & 2231 & 2568.86200284861 & -337.862002848612 \tabularnewline
22 & 2481 & 2566.97627265694 & -85.9762726569448 \tabularnewline
23 & 2421 & 2565.09054246528 & -144.090542465278 \tabularnewline
24 & 2408 & 2557.54762169861 & -149.547621698611 \tabularnewline
25 & 2560 & 2555.66189150694 & 4.33810849305561 \tabularnewline
26 & 2100 & 2555.66189150694 & -455.661891506944 \tabularnewline
27 & 3315 & 2553.77616131528 & 761.223838684722 \tabularnewline
28 & 2801 & 2551.89043112361 & 249.109568876389 \tabularnewline
29 & 2403 & 2551.89043112361 & -148.890431123611 \tabularnewline
30 & 3024 & 2550.00470093194 & 473.995299068056 \tabularnewline
31 & 2507 & 2548.11897074028 & -41.1189707402775 \tabularnewline
32 & 2980 & 2548.11897074028 & 431.881029259722 \tabularnewline
33 & 2211 & 2546.23324054861 & -335.233240548611 \tabularnewline
34 & 2471 & 2546.23324054861 & -75.2332405486108 \tabularnewline
35 & 2594 & 2544.34751035694 & 49.652489643056 \tabularnewline
36 & 2452 & 2544.34751035694 & -92.347510356944 \tabularnewline
37 & 2232 & 2542.46178016528 & -310.461780165277 \tabularnewline
38 & 2373 & 2540.57604997361 & -167.576049973611 \tabularnewline
39 & 3127 & 2538.69031978194 & 588.309680218056 \tabularnewline
40 & 2802 & 2531.14739901528 & 270.852600984723 \tabularnewline
41 & 2641 & 2521.71874805694 & 119.281251943057 \tabularnewline
42 & 2787 & 2519.83301786528 & 267.166982134723 \tabularnewline
43 & 2619 & 2517.94728767361 & 101.052712326390 \tabularnewline
44 & 2806 & 2516.06155748194 & 289.938442518057 \tabularnewline
45 & 2193 & 2514.17582729028 & -321.175827290276 \tabularnewline
46 & 2323 & 2512.29009709861 & -189.290097098610 \tabularnewline
47 & 2529 & 2512.29009709861 & 16.7099029013903 \tabularnewline
48 & 2412 & 2510.40436690694 & -98.4043669069429 \tabularnewline
49 & 2262 & 2508.51863671528 & -246.518636715276 \tabularnewline
50 & 2154 & 2506.63290652361 & -352.632906523609 \tabularnewline
51 & 3230 & 2504.74717633194 & 725.252823668057 \tabularnewline
52 & 2295 & 2502.86144614028 & -207.861446140276 \tabularnewline
53 & 2715 & 2500.97571594861 & 214.024284051391 \tabularnewline
54 & 2733 & 2500.97571594861 & 232.024284051391 \tabularnewline
55 & 2317 & 2499.08998575694 & -182.089985756943 \tabularnewline
56 & 2730 & 2499.08998575694 & 230.910014243057 \tabularnewline
57 & 1913 & 2489.66133479861 & -576.661334798609 \tabularnewline
58 & 2390 & 2487.77560460694 & -97.7756046069422 \tabularnewline
59 & 2484 & 2457.60392154027 & 26.3960784597255 \tabularnewline
60 & 1960 & 2450.06100077361 & -490.061000773608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58055&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]2663.14851243195[/C][C]-134.148512431948[/C][/ROW]
[ROW][C]2[/C][C]2196[/C][C]2655.60559166528[/C][C]-459.605591665281[/C][/ROW]
[ROW][C]3[/C][C]3202[/C][C]2651.83413128195[/C][C]550.165868718053[/C][/ROW]
[ROW][C]4[/C][C]2718[/C][C]2646.17694070695[/C][C]71.8230592930527[/C][/ROW]
[ROW][C]5[/C][C]2728[/C][C]2642.40548032361[/C][C]85.5945196763861[/C][/ROW]
[ROW][C]6[/C][C]2354[/C][C]2638.63401994028[/C][C]-284.634019940280[/C][/ROW]
[ROW][C]7[/C][C]2697[/C][C]2632.97682936528[/C][C]64.0231706347198[/C][/ROW]
[ROW][C]8[/C][C]2651[/C][C]2631.09109917361[/C][C]19.9089008263865[/C][/ROW]
[ROW][C]9[/C][C]2067[/C][C]2629.20536898195[/C][C]-562.205368981947[/C][/ROW]
[ROW][C]10[/C][C]2641[/C][C]2625.43390859861[/C][C]15.5660914013867[/C][/ROW]
[ROW][C]11[/C][C]2539[/C][C]2625.43390859861[/C][C]-86.4339085986133[/C][/ROW]
[ROW][C]12[/C][C]2294[/C][C]2625.43390859861[/C][C]-331.433908598613[/C][/ROW]
[ROW][C]13[/C][C]2712[/C][C]2616.00525764028[/C][C]95.9947423597203[/C][/ROW]
[ROW][C]14[/C][C]2314[/C][C]2604.69087649028[/C][C]-290.690876490279[/C][/ROW]
[ROW][C]15[/C][C]3092[/C][C]2602.80514629861[/C][C]489.194853701387[/C][/ROW]
[ROW][C]16[/C][C]2677[/C][C]2591.49076514861[/C][C]85.5092348513878[/C][/ROW]
[ROW][C]17[/C][C]2813[/C][C]2585.83357457361[/C][C]227.166425426388[/C][/ROW]
[ROW][C]18[/C][C]2668[/C][C]2580.17638399861[/C][C]87.8236160013881[/C][/ROW]
[ROW][C]19[/C][C]2939[/C][C]2574.51919342361[/C][C]364.480806576388[/C][/ROW]
[ROW][C]20[/C][C]2617[/C][C]2570.74773304028[/C][C]46.2522669597218[/C][/ROW]
[ROW][C]21[/C][C]2231[/C][C]2568.86200284861[/C][C]-337.862002848612[/C][/ROW]
[ROW][C]22[/C][C]2481[/C][C]2566.97627265694[/C][C]-85.9762726569448[/C][/ROW]
[ROW][C]23[/C][C]2421[/C][C]2565.09054246528[/C][C]-144.090542465278[/C][/ROW]
[ROW][C]24[/C][C]2408[/C][C]2557.54762169861[/C][C]-149.547621698611[/C][/ROW]
[ROW][C]25[/C][C]2560[/C][C]2555.66189150694[/C][C]4.33810849305561[/C][/ROW]
[ROW][C]26[/C][C]2100[/C][C]2555.66189150694[/C][C]-455.661891506944[/C][/ROW]
[ROW][C]27[/C][C]3315[/C][C]2553.77616131528[/C][C]761.223838684722[/C][/ROW]
[ROW][C]28[/C][C]2801[/C][C]2551.89043112361[/C][C]249.109568876389[/C][/ROW]
[ROW][C]29[/C][C]2403[/C][C]2551.89043112361[/C][C]-148.890431123611[/C][/ROW]
[ROW][C]30[/C][C]3024[/C][C]2550.00470093194[/C][C]473.995299068056[/C][/ROW]
[ROW][C]31[/C][C]2507[/C][C]2548.11897074028[/C][C]-41.1189707402775[/C][/ROW]
[ROW][C]32[/C][C]2980[/C][C]2548.11897074028[/C][C]431.881029259722[/C][/ROW]
[ROW][C]33[/C][C]2211[/C][C]2546.23324054861[/C][C]-335.233240548611[/C][/ROW]
[ROW][C]34[/C][C]2471[/C][C]2546.23324054861[/C][C]-75.2332405486108[/C][/ROW]
[ROW][C]35[/C][C]2594[/C][C]2544.34751035694[/C][C]49.652489643056[/C][/ROW]
[ROW][C]36[/C][C]2452[/C][C]2544.34751035694[/C][C]-92.347510356944[/C][/ROW]
[ROW][C]37[/C][C]2232[/C][C]2542.46178016528[/C][C]-310.461780165277[/C][/ROW]
[ROW][C]38[/C][C]2373[/C][C]2540.57604997361[/C][C]-167.576049973611[/C][/ROW]
[ROW][C]39[/C][C]3127[/C][C]2538.69031978194[/C][C]588.309680218056[/C][/ROW]
[ROW][C]40[/C][C]2802[/C][C]2531.14739901528[/C][C]270.852600984723[/C][/ROW]
[ROW][C]41[/C][C]2641[/C][C]2521.71874805694[/C][C]119.281251943057[/C][/ROW]
[ROW][C]42[/C][C]2787[/C][C]2519.83301786528[/C][C]267.166982134723[/C][/ROW]
[ROW][C]43[/C][C]2619[/C][C]2517.94728767361[/C][C]101.052712326390[/C][/ROW]
[ROW][C]44[/C][C]2806[/C][C]2516.06155748194[/C][C]289.938442518057[/C][/ROW]
[ROW][C]45[/C][C]2193[/C][C]2514.17582729028[/C][C]-321.175827290276[/C][/ROW]
[ROW][C]46[/C][C]2323[/C][C]2512.29009709861[/C][C]-189.290097098610[/C][/ROW]
[ROW][C]47[/C][C]2529[/C][C]2512.29009709861[/C][C]16.7099029013903[/C][/ROW]
[ROW][C]48[/C][C]2412[/C][C]2510.40436690694[/C][C]-98.4043669069429[/C][/ROW]
[ROW][C]49[/C][C]2262[/C][C]2508.51863671528[/C][C]-246.518636715276[/C][/ROW]
[ROW][C]50[/C][C]2154[/C][C]2506.63290652361[/C][C]-352.632906523609[/C][/ROW]
[ROW][C]51[/C][C]3230[/C][C]2504.74717633194[/C][C]725.252823668057[/C][/ROW]
[ROW][C]52[/C][C]2295[/C][C]2502.86144614028[/C][C]-207.861446140276[/C][/ROW]
[ROW][C]53[/C][C]2715[/C][C]2500.97571594861[/C][C]214.024284051391[/C][/ROW]
[ROW][C]54[/C][C]2733[/C][C]2500.97571594861[/C][C]232.024284051391[/C][/ROW]
[ROW][C]55[/C][C]2317[/C][C]2499.08998575694[/C][C]-182.089985756943[/C][/ROW]
[ROW][C]56[/C][C]2730[/C][C]2499.08998575694[/C][C]230.910014243057[/C][/ROW]
[ROW][C]57[/C][C]1913[/C][C]2489.66133479861[/C][C]-576.661334798609[/C][/ROW]
[ROW][C]58[/C][C]2390[/C][C]2487.77560460694[/C][C]-97.7756046069422[/C][/ROW]
[ROW][C]59[/C][C]2484[/C][C]2457.60392154027[/C][C]26.3960784597255[/C][/ROW]
[ROW][C]60[/C][C]1960[/C][C]2450.06100077361[/C][C]-490.061000773608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58055&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58055&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
125292663.14851243195-134.148512431948
221962655.60559166528-459.605591665281
332022651.83413128195550.165868718053
427182646.1769407069571.8230592930527
527282642.4054803236185.5945196763861
623542638.63401994028-284.634019940280
726972632.9768293652864.0231706347198
826512631.0910991736119.9089008263865
920672629.20536898195-562.205368981947
1026412625.4339085986115.5660914013867
1125392625.43390859861-86.4339085986133
1222942625.43390859861-331.433908598613
1327122616.0052576402895.9947423597203
1423142604.69087649028-290.690876490279
1530922602.80514629861489.194853701387
1626772591.4907651486185.5092348513878
1728132585.83357457361227.166425426388
1826682580.1763839986187.8236160013881
1929392574.51919342361364.480806576388
2026172570.7477330402846.2522669597218
2122312568.86200284861-337.862002848612
2224812566.97627265694-85.9762726569448
2324212565.09054246528-144.090542465278
2424082557.54762169861-149.547621698611
2525602555.661891506944.33810849305561
2621002555.66189150694-455.661891506944
2733152553.77616131528761.223838684722
2828012551.89043112361249.109568876389
2924032551.89043112361-148.890431123611
3030242550.00470093194473.995299068056
3125072548.11897074028-41.1189707402775
3229802548.11897074028431.881029259722
3322112546.23324054861-335.233240548611
3424712546.23324054861-75.2332405486108
3525942544.3475103569449.652489643056
3624522544.34751035694-92.347510356944
3722322542.46178016528-310.461780165277
3823732540.57604997361-167.576049973611
3931272538.69031978194588.309680218056
4028022531.14739901528270.852600984723
4126412521.71874805694119.281251943057
4227872519.83301786528267.166982134723
4326192517.94728767361101.052712326390
4428062516.06155748194289.938442518057
4521932514.17582729028-321.175827290276
4623232512.29009709861-189.290097098610
4725292512.2900970986116.7099029013903
4824122510.40436690694-98.4043669069429
4922622508.51863671528-246.518636715276
5021542506.63290652361-352.632906523609
5132302504.74717633194725.252823668057
5222952502.86144614028-207.861446140276
5327152500.97571594861214.024284051391
5427332500.97571594861232.024284051391
5523172499.08998575694-182.089985756943
5627302499.08998575694230.910014243057
5719132489.66133479861-576.661334798609
5823902487.77560460694-97.7756046069422
5924842457.6039215402726.3960784597255
6019602450.06100077361-490.061000773608







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.8083562012735150.383287597452970.191643798726485
60.8179567004105020.3640865991789950.182043299589498
70.7120789305239460.5758421389521090.287921069476054
80.591851408632060.8162971827358790.408148591367940
90.7487928361869140.5024143276261720.251207163813086
100.6680697345789130.6638605308421750.331930265421087
110.5709491245582320.8581017508835370.429050875441768
120.5431696118063430.9136607763873140.456830388193657
130.4910472609248850.982094521849770.508952739075115
140.4502642950464520.9005285900929040.549735704953548
150.6231667395088280.7536665209823430.376833260491172
160.5378660527435060.9242678945129880.462133947256494
170.4675052478571870.9350104957143750.532494752142813
180.3830419730923490.7660839461846990.61695802690765
190.3478286697551780.6956573395103560.652171330244822
200.2838564547384770.5677129094769540.716143545261523
210.3673854995353210.7347709990706410.632614500464679
220.3136774620338960.6273549240677920.686322537966104
230.2768946656338760.5537893312677520.723105334366124
240.2414456005536410.4828912011072820.758554399446359
250.1867324592223450.3734649184446890.813267540777655
260.2924062853682880.5848125707365760.707593714631712
270.6076927340934740.7846145318130530.392307265906526
280.554873886286990.890252227426020.44512611371301
290.521659710194150.956680579611700.47834028980585
300.5639170465252570.8721659069494860.436082953474743
310.5001395392450880.9997209215098240.499860460754912
320.5196376345230070.9607247309539860.480362365476993
330.5793711812690680.8412576374618640.420628818730932
340.5245908055881270.9508183888237470.475409194411873
350.4491371312631510.8982742625263030.550862868736849
360.4036083748835010.8072167497670010.5963916251165
370.4844258192820470.9688516385640950.515574180717953
380.5095183999896630.9809632000206740.490481600010337
390.5856673866392260.8286652267215490.414332613360774
400.5240654242723980.9518691514552040.475934575727602
410.4424487978779120.8848975957558230.557551202122088
420.3930191655344580.7860383310689160.606980834465542
430.316547905692960.633095811385920.68345209430704
440.2916787421265690.5833574842531390.708321257873431
450.3107683717141500.6215367434283010.68923162828585
460.272976102028060.545952204056120.72702389797194
470.2020172216269080.4040344432538160.797982778373092
480.1521245738337100.3042491476674210.84787542616629
490.1428746229816900.2857492459633810.85712537701831
500.1942759915730680.3885519831461370.805724008426931
510.5049970639891810.9900058720216380.495002936010819
520.4515264027576580.9030528055153160.548473597242342
530.3715016084404730.7430032168809460.628498391559527
540.3321019595527650.664203919105530.667898040447235
550.2131821875251700.4263643750503400.78681781247483

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.808356201273515 & 0.38328759745297 & 0.191643798726485 \tabularnewline
6 & 0.817956700410502 & 0.364086599178995 & 0.182043299589498 \tabularnewline
7 & 0.712078930523946 & 0.575842138952109 & 0.287921069476054 \tabularnewline
8 & 0.59185140863206 & 0.816297182735879 & 0.408148591367940 \tabularnewline
9 & 0.748792836186914 & 0.502414327626172 & 0.251207163813086 \tabularnewline
10 & 0.668069734578913 & 0.663860530842175 & 0.331930265421087 \tabularnewline
11 & 0.570949124558232 & 0.858101750883537 & 0.429050875441768 \tabularnewline
12 & 0.543169611806343 & 0.913660776387314 & 0.456830388193657 \tabularnewline
13 & 0.491047260924885 & 0.98209452184977 & 0.508952739075115 \tabularnewline
14 & 0.450264295046452 & 0.900528590092904 & 0.549735704953548 \tabularnewline
15 & 0.623166739508828 & 0.753666520982343 & 0.376833260491172 \tabularnewline
16 & 0.537866052743506 & 0.924267894512988 & 0.462133947256494 \tabularnewline
17 & 0.467505247857187 & 0.935010495714375 & 0.532494752142813 \tabularnewline
18 & 0.383041973092349 & 0.766083946184699 & 0.61695802690765 \tabularnewline
19 & 0.347828669755178 & 0.695657339510356 & 0.652171330244822 \tabularnewline
20 & 0.283856454738477 & 0.567712909476954 & 0.716143545261523 \tabularnewline
21 & 0.367385499535321 & 0.734770999070641 & 0.632614500464679 \tabularnewline
22 & 0.313677462033896 & 0.627354924067792 & 0.686322537966104 \tabularnewline
23 & 0.276894665633876 & 0.553789331267752 & 0.723105334366124 \tabularnewline
24 & 0.241445600553641 & 0.482891201107282 & 0.758554399446359 \tabularnewline
25 & 0.186732459222345 & 0.373464918444689 & 0.813267540777655 \tabularnewline
26 & 0.292406285368288 & 0.584812570736576 & 0.707593714631712 \tabularnewline
27 & 0.607692734093474 & 0.784614531813053 & 0.392307265906526 \tabularnewline
28 & 0.55487388628699 & 0.89025222742602 & 0.44512611371301 \tabularnewline
29 & 0.52165971019415 & 0.95668057961170 & 0.47834028980585 \tabularnewline
30 & 0.563917046525257 & 0.872165906949486 & 0.436082953474743 \tabularnewline
31 & 0.500139539245088 & 0.999720921509824 & 0.499860460754912 \tabularnewline
32 & 0.519637634523007 & 0.960724730953986 & 0.480362365476993 \tabularnewline
33 & 0.579371181269068 & 0.841257637461864 & 0.420628818730932 \tabularnewline
34 & 0.524590805588127 & 0.950818388823747 & 0.475409194411873 \tabularnewline
35 & 0.449137131263151 & 0.898274262526303 & 0.550862868736849 \tabularnewline
36 & 0.403608374883501 & 0.807216749767001 & 0.5963916251165 \tabularnewline
37 & 0.484425819282047 & 0.968851638564095 & 0.515574180717953 \tabularnewline
38 & 0.509518399989663 & 0.980963200020674 & 0.490481600010337 \tabularnewline
39 & 0.585667386639226 & 0.828665226721549 & 0.414332613360774 \tabularnewline
40 & 0.524065424272398 & 0.951869151455204 & 0.475934575727602 \tabularnewline
41 & 0.442448797877912 & 0.884897595755823 & 0.557551202122088 \tabularnewline
42 & 0.393019165534458 & 0.786038331068916 & 0.606980834465542 \tabularnewline
43 & 0.31654790569296 & 0.63309581138592 & 0.68345209430704 \tabularnewline
44 & 0.291678742126569 & 0.583357484253139 & 0.708321257873431 \tabularnewline
45 & 0.310768371714150 & 0.621536743428301 & 0.68923162828585 \tabularnewline
46 & 0.27297610202806 & 0.54595220405612 & 0.72702389797194 \tabularnewline
47 & 0.202017221626908 & 0.404034443253816 & 0.797982778373092 \tabularnewline
48 & 0.152124573833710 & 0.304249147667421 & 0.84787542616629 \tabularnewline
49 & 0.142874622981690 & 0.285749245963381 & 0.85712537701831 \tabularnewline
50 & 0.194275991573068 & 0.388551983146137 & 0.805724008426931 \tabularnewline
51 & 0.504997063989181 & 0.990005872021638 & 0.495002936010819 \tabularnewline
52 & 0.451526402757658 & 0.903052805515316 & 0.548473597242342 \tabularnewline
53 & 0.371501608440473 & 0.743003216880946 & 0.628498391559527 \tabularnewline
54 & 0.332101959552765 & 0.66420391910553 & 0.667898040447235 \tabularnewline
55 & 0.213182187525170 & 0.426364375050340 & 0.78681781247483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58055&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]5[/C][C]0.808356201273515[/C][C]0.38328759745297[/C][C]0.191643798726485[/C][/ROW]
[ROW][C]6[/C][C]0.817956700410502[/C][C]0.364086599178995[/C][C]0.182043299589498[/C][/ROW]
[ROW][C]7[/C][C]0.712078930523946[/C][C]0.575842138952109[/C][C]0.287921069476054[/C][/ROW]
[ROW][C]8[/C][C]0.59185140863206[/C][C]0.816297182735879[/C][C]0.408148591367940[/C][/ROW]
[ROW][C]9[/C][C]0.748792836186914[/C][C]0.502414327626172[/C][C]0.251207163813086[/C][/ROW]
[ROW][C]10[/C][C]0.668069734578913[/C][C]0.663860530842175[/C][C]0.331930265421087[/C][/ROW]
[ROW][C]11[/C][C]0.570949124558232[/C][C]0.858101750883537[/C][C]0.429050875441768[/C][/ROW]
[ROW][C]12[/C][C]0.543169611806343[/C][C]0.913660776387314[/C][C]0.456830388193657[/C][/ROW]
[ROW][C]13[/C][C]0.491047260924885[/C][C]0.98209452184977[/C][C]0.508952739075115[/C][/ROW]
[ROW][C]14[/C][C]0.450264295046452[/C][C]0.900528590092904[/C][C]0.549735704953548[/C][/ROW]
[ROW][C]15[/C][C]0.623166739508828[/C][C]0.753666520982343[/C][C]0.376833260491172[/C][/ROW]
[ROW][C]16[/C][C]0.537866052743506[/C][C]0.924267894512988[/C][C]0.462133947256494[/C][/ROW]
[ROW][C]17[/C][C]0.467505247857187[/C][C]0.935010495714375[/C][C]0.532494752142813[/C][/ROW]
[ROW][C]18[/C][C]0.383041973092349[/C][C]0.766083946184699[/C][C]0.61695802690765[/C][/ROW]
[ROW][C]19[/C][C]0.347828669755178[/C][C]0.695657339510356[/C][C]0.652171330244822[/C][/ROW]
[ROW][C]20[/C][C]0.283856454738477[/C][C]0.567712909476954[/C][C]0.716143545261523[/C][/ROW]
[ROW][C]21[/C][C]0.367385499535321[/C][C]0.734770999070641[/C][C]0.632614500464679[/C][/ROW]
[ROW][C]22[/C][C]0.313677462033896[/C][C]0.627354924067792[/C][C]0.686322537966104[/C][/ROW]
[ROW][C]23[/C][C]0.276894665633876[/C][C]0.553789331267752[/C][C]0.723105334366124[/C][/ROW]
[ROW][C]24[/C][C]0.241445600553641[/C][C]0.482891201107282[/C][C]0.758554399446359[/C][/ROW]
[ROW][C]25[/C][C]0.186732459222345[/C][C]0.373464918444689[/C][C]0.813267540777655[/C][/ROW]
[ROW][C]26[/C][C]0.292406285368288[/C][C]0.584812570736576[/C][C]0.707593714631712[/C][/ROW]
[ROW][C]27[/C][C]0.607692734093474[/C][C]0.784614531813053[/C][C]0.392307265906526[/C][/ROW]
[ROW][C]28[/C][C]0.55487388628699[/C][C]0.89025222742602[/C][C]0.44512611371301[/C][/ROW]
[ROW][C]29[/C][C]0.52165971019415[/C][C]0.95668057961170[/C][C]0.47834028980585[/C][/ROW]
[ROW][C]30[/C][C]0.563917046525257[/C][C]0.872165906949486[/C][C]0.436082953474743[/C][/ROW]
[ROW][C]31[/C][C]0.500139539245088[/C][C]0.999720921509824[/C][C]0.499860460754912[/C][/ROW]
[ROW][C]32[/C][C]0.519637634523007[/C][C]0.960724730953986[/C][C]0.480362365476993[/C][/ROW]
[ROW][C]33[/C][C]0.579371181269068[/C][C]0.841257637461864[/C][C]0.420628818730932[/C][/ROW]
[ROW][C]34[/C][C]0.524590805588127[/C][C]0.950818388823747[/C][C]0.475409194411873[/C][/ROW]
[ROW][C]35[/C][C]0.449137131263151[/C][C]0.898274262526303[/C][C]0.550862868736849[/C][/ROW]
[ROW][C]36[/C][C]0.403608374883501[/C][C]0.807216749767001[/C][C]0.5963916251165[/C][/ROW]
[ROW][C]37[/C][C]0.484425819282047[/C][C]0.968851638564095[/C][C]0.515574180717953[/C][/ROW]
[ROW][C]38[/C][C]0.509518399989663[/C][C]0.980963200020674[/C][C]0.490481600010337[/C][/ROW]
[ROW][C]39[/C][C]0.585667386639226[/C][C]0.828665226721549[/C][C]0.414332613360774[/C][/ROW]
[ROW][C]40[/C][C]0.524065424272398[/C][C]0.951869151455204[/C][C]0.475934575727602[/C][/ROW]
[ROW][C]41[/C][C]0.442448797877912[/C][C]0.884897595755823[/C][C]0.557551202122088[/C][/ROW]
[ROW][C]42[/C][C]0.393019165534458[/C][C]0.786038331068916[/C][C]0.606980834465542[/C][/ROW]
[ROW][C]43[/C][C]0.31654790569296[/C][C]0.63309581138592[/C][C]0.68345209430704[/C][/ROW]
[ROW][C]44[/C][C]0.291678742126569[/C][C]0.583357484253139[/C][C]0.708321257873431[/C][/ROW]
[ROW][C]45[/C][C]0.310768371714150[/C][C]0.621536743428301[/C][C]0.68923162828585[/C][/ROW]
[ROW][C]46[/C][C]0.27297610202806[/C][C]0.54595220405612[/C][C]0.72702389797194[/C][/ROW]
[ROW][C]47[/C][C]0.202017221626908[/C][C]0.404034443253816[/C][C]0.797982778373092[/C][/ROW]
[ROW][C]48[/C][C]0.152124573833710[/C][C]0.304249147667421[/C][C]0.84787542616629[/C][/ROW]
[ROW][C]49[/C][C]0.142874622981690[/C][C]0.285749245963381[/C][C]0.85712537701831[/C][/ROW]
[ROW][C]50[/C][C]0.194275991573068[/C][C]0.388551983146137[/C][C]0.805724008426931[/C][/ROW]
[ROW][C]51[/C][C]0.504997063989181[/C][C]0.990005872021638[/C][C]0.495002936010819[/C][/ROW]
[ROW][C]52[/C][C]0.451526402757658[/C][C]0.903052805515316[/C][C]0.548473597242342[/C][/ROW]
[ROW][C]53[/C][C]0.371501608440473[/C][C]0.743003216880946[/C][C]0.628498391559527[/C][/ROW]
[ROW][C]54[/C][C]0.332101959552765[/C][C]0.66420391910553[/C][C]0.667898040447235[/C][/ROW]
[ROW][C]55[/C][C]0.213182187525170[/C][C]0.426364375050340[/C][C]0.78681781247483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58055&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58055&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
50.8083562012735150.383287597452970.191643798726485
60.8179567004105020.3640865991789950.182043299589498
70.7120789305239460.5758421389521090.287921069476054
80.591851408632060.8162971827358790.408148591367940
90.7487928361869140.5024143276261720.251207163813086
100.6680697345789130.6638605308421750.331930265421087
110.5709491245582320.8581017508835370.429050875441768
120.5431696118063430.9136607763873140.456830388193657
130.4910472609248850.982094521849770.508952739075115
140.4502642950464520.9005285900929040.549735704953548
150.6231667395088280.7536665209823430.376833260491172
160.5378660527435060.9242678945129880.462133947256494
170.4675052478571870.9350104957143750.532494752142813
180.3830419730923490.7660839461846990.61695802690765
190.3478286697551780.6956573395103560.652171330244822
200.2838564547384770.5677129094769540.716143545261523
210.3673854995353210.7347709990706410.632614500464679
220.3136774620338960.6273549240677920.686322537966104
230.2768946656338760.5537893312677520.723105334366124
240.2414456005536410.4828912011072820.758554399446359
250.1867324592223450.3734649184446890.813267540777655
260.2924062853682880.5848125707365760.707593714631712
270.6076927340934740.7846145318130530.392307265906526
280.554873886286990.890252227426020.44512611371301
290.521659710194150.956680579611700.47834028980585
300.5639170465252570.8721659069494860.436082953474743
310.5001395392450880.9997209215098240.499860460754912
320.5196376345230070.9607247309539860.480362365476993
330.5793711812690680.8412576374618640.420628818730932
340.5245908055881270.9508183888237470.475409194411873
350.4491371312631510.8982742625263030.550862868736849
360.4036083748835010.8072167497670010.5963916251165
370.4844258192820470.9688516385640950.515574180717953
380.5095183999896630.9809632000206740.490481600010337
390.5856673866392260.8286652267215490.414332613360774
400.5240654242723980.9518691514552040.475934575727602
410.4424487978779120.8848975957558230.557551202122088
420.3930191655344580.7860383310689160.606980834465542
430.316547905692960.633095811385920.68345209430704
440.2916787421265690.5833574842531390.708321257873431
450.3107683717141500.6215367434283010.68923162828585
460.272976102028060.545952204056120.72702389797194
470.2020172216269080.4040344432538160.797982778373092
480.1521245738337100.3042491476674210.84787542616629
490.1428746229816900.2857492459633810.85712537701831
500.1942759915730680.3885519831461370.805724008426931
510.5049970639891810.9900058720216380.495002936010819
520.4515264027576580.9030528055153160.548473597242342
530.3715016084404730.7430032168809460.628498391559527
540.3321019595527650.664203919105530.667898040447235
550.2131821875251700.4263643750503400.78681781247483







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=58055&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=58055&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58055&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 = 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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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')
}