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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 computationTue, 22 Nov 2011 06:00:17 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321959627s450aa6ggainbax.htm/, Retrieved Thu, 31 Oct 2024 23:11:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146122, Retrieved Thu, 31 Oct 2024 23:11:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2011-11-21 18:13:43] [86f7284edee3dbb8ea5c7e2dec87d892]
-   P     [Multiple Regression] [] [2011-11-21 18:17:49] [86f7284edee3dbb8ea5c7e2dec87d892]
-    D        [Multiple Regression] [] [2011-11-22 11:00:17] [79818163420d1233b8d9d93d595e6c9e] [Current]
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Dataseries X:
9	1167	333	70
9	669	223	44
9	1053	371	35
9	1939	873	119
9	678	186	30
9	321	111	23
10	2667	1277	46
10	345	102	39
10	1367	580	58
10	1158	420	51
11	1385	521	65
11	1155	358	40
9	1120	435	41
9	1703	690	76
9	1189	393	31
10	3083	1149	82
10	1357	486	36
10	1892	767	62
11	883	338	28
11	1627	485	38
11	1412	465	70
11	1900	816	76
9	777	265	33
9	904	307	40
9	2115	850	126
10	1858	704	56
10	1781	693	63
10	1286	387	46
10	1035	406	35
10	1557	573	108
11	1527	595	34
11	1220	394	54
11	1368	521	35
9	564	172	23
9	1990	835	46
9	1557	669	49
10	2057	749	56
10	1111	368	38
11	686	216	19
10	2011	772	29
10	2232	1084	26
9	1032	445	52
9	1166	451	54
9	1020	300	45
10	1735	836	56
10	3623	1417	596
10	918	330	57
10	1579	477	55
11	2790	1028	99
11	1496	646	51
10	1108	342	21
10	496	218	20
10	1750	591	58
10	744	255	21
10	1101	434	66
9	1612	654	47
9	1805	478	55
9	2460	753	158
9	1653	689	46
9	1234	470	45




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

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146122&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146122&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146122&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' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
TotalNrPV[t] = + 8.01709159766136 + 21.792624199423Month[t] + 2.04610860164739TotalNrCC[t] + 0.974878915341512TotalNrPRV[t] + 1.69908151382348t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TotalNrPV[t] =  +  8.01709159766136 +  21.792624199423Month[t] +  2.04610860164739TotalNrCC[t] +  0.974878915341512TotalNrPRV[t] +  1.69908151382348t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146122&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TotalNrPV[t] =  +  8.01709159766136 +  21.792624199423Month[t] +  2.04610860164739TotalNrCC[t] +  0.974878915341512TotalNrPRV[t] +  1.69908151382348t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146122&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146122&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
TotalNrPV[t] = + 8.01709159766136 + 21.792624199423Month[t] + 2.04610860164739TotalNrCC[t] + 0.974878915341512TotalNrPRV[t] + 1.69908151382348t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.01709159766136307.7260440.02610.979310.489655
Month21.79262419942331.3247690.69570.4895460.244773
TotalNrCC2.046108601647390.09947520.56900
TotalNrPRV0.9748789153415120.3690462.64160.0107220.005361
t1.699081513823481.3408261.26720.2104270.105213

\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) & 8.01709159766136 & 307.726044 & 0.0261 & 0.97931 & 0.489655 \tabularnewline
Month & 21.792624199423 & 31.324769 & 0.6957 & 0.489546 & 0.244773 \tabularnewline
TotalNrCC & 2.04610860164739 & 0.099475 & 20.569 & 0 & 0 \tabularnewline
TotalNrPRV & 0.974878915341512 & 0.369046 & 2.6416 & 0.010722 & 0.005361 \tabularnewline
t & 1.69908151382348 & 1.340826 & 1.2672 & 0.210427 & 0.105213 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146122&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]8.01709159766136[/C][C]307.726044[/C][C]0.0261[/C][C]0.97931[/C][C]0.489655[/C][/ROW]
[ROW][C]Month[/C][C]21.792624199423[/C][C]31.324769[/C][C]0.6957[/C][C]0.489546[/C][C]0.244773[/C][/ROW]
[ROW][C]TotalNrCC[/C][C]2.04610860164739[/C][C]0.099475[/C][C]20.569[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]TotalNrPRV[/C][C]0.974878915341512[/C][C]0.369046[/C][C]2.6416[/C][C]0.010722[/C][C]0.005361[/C][/ROW]
[ROW][C]t[/C][C]1.69908151382348[/C][C]1.340826[/C][C]1.2672[/C][C]0.210427[/C][C]0.105213[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146122&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146122&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)8.01709159766136307.7260440.02610.979310.489655
Month21.79262419942331.3247690.69570.4895460.244773
TotalNrCC2.046108601647390.09947520.56900
TotalNrPRV0.9748789153415120.3690462.64160.0107220.005361
t1.699081513823481.3408261.26720.2104270.105213







Multiple Linear Regression - Regression Statistics
Multiple R0.963980656356215
R-squared0.929258705828958
Adjusted R-squared0.924113884434701
F-TEST (value)180.620207120534
F-TEST (DF numerator)4
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation178.154712272598
Sum Squared Residuals1745650.58277127

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.963980656356215 \tabularnewline
R-squared & 0.929258705828958 \tabularnewline
Adjusted R-squared & 0.924113884434701 \tabularnewline
F-TEST (value) & 180.620207120534 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 178.154712272598 \tabularnewline
Sum Squared Residuals & 1745650.58277127 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146122&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.963980656356215[/C][/ROW]
[ROW][C]R-squared[/C][C]0.929258705828958[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.924113884434701[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]180.620207120534[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]178.154712272598[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1745650.58277127[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146122&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146122&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 R0.963980656356215
R-squared0.929258705828958
Adjusted R-squared0.924113884434701
F-TEST (value)180.620207120534
F-TEST (DF numerator)4
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation178.154712272598
Sum Squared Residuals1745650.58277127







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11167955.445479328781211.554520671219
2669706.725762862511-37.7257628625107
310531002.4750071820750.5249928179255
419392113.21043561158-174.210435611578
5678622.46868432824655.5313156717536
6321463.885468311125-142.885468311125
726672895.56201859809-228.562018598089
8345486.259340768832-141.259340768832
913671484.5210332616-117.521033261599
1011581152.018586104455.98141389555102
1113851395.81556539886-10.8155653988635
1211551039.62697196062115.373028039376
1311201156.26604631779-36.2660463177923
1417031713.84358328865-10.8435832886544
1511891063.97885892283125.021141077167
1630832684.04749216393398.952507836072
1713571284.3321406798272.6678593201813
1818921886.334591055445.66540894456048
19883998.899823540342-115.899823540342
2016271311.12565864975315.874341350252
2114121303.09869342155108.901306578448
2219002028.83116760566-128.83116760566
23777817.619367853238-40.6193678532378
24904912.079163043642-8.07916304364244
2521152108.654801971376.3451980286285
2618581765.1731277701992.8268722298076
2717811751.1891670732929.8108329267149
2812861110.2060749222175.7939250778
2910351140.05755179857-105.057551798567
3015571554.622930607442.37706939256371
3115271550.98798582165-23.9879858216535
3212201160.9168167111859.0831832888191
3313681403.94899124273-35.9489912427348
34564636.272375398673-72.2723753986733
3519902016.96367485757-26.9636748575744
3615571681.93336524395-124.933365243955
3720571875.93791149638181.062088503617
3811111080.521795306430.4782046935977
39686774.482294177756-88.482294177756
4020111901.77392316152109.226076838477
4122322538.93425164331-306.934251643309
4210321236.7241643039-204.724164303904
4311661252.64965525829-86.6496552582948
441020936.61242768528883.387572314712
4517352067.54201195029-332.542011950295
4636233784.46480530567-161.464805305671
479181036.5841014597-118.584101459701
4815791337.11138958501241.888610414991
4927902530.903607081259.096392919004
5014961704.19501482912-208.195014829123
5111081032.8380897824775.1619102175303
52496779.844825776675-283.844825776675
5317501581.78781448795168.212185512046
54744859.923885980617-115.923885980617
5511011271.74595837969-170.745958379692
5616121683.27360866503-71.2736086650307
5718051332.65660761164472.343392388355
5824601997.44808285868462.551917141322
5916531759.00977534882-106.009775348818
6012341311.63619418652-77.6361941865209

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1167 & 955.445479328781 & 211.554520671219 \tabularnewline
2 & 669 & 706.725762862511 & -37.7257628625107 \tabularnewline
3 & 1053 & 1002.47500718207 & 50.5249928179255 \tabularnewline
4 & 1939 & 2113.21043561158 & -174.210435611578 \tabularnewline
5 & 678 & 622.468684328246 & 55.5313156717536 \tabularnewline
6 & 321 & 463.885468311125 & -142.885468311125 \tabularnewline
7 & 2667 & 2895.56201859809 & -228.562018598089 \tabularnewline
8 & 345 & 486.259340768832 & -141.259340768832 \tabularnewline
9 & 1367 & 1484.5210332616 & -117.521033261599 \tabularnewline
10 & 1158 & 1152.01858610445 & 5.98141389555102 \tabularnewline
11 & 1385 & 1395.81556539886 & -10.8155653988635 \tabularnewline
12 & 1155 & 1039.62697196062 & 115.373028039376 \tabularnewline
13 & 1120 & 1156.26604631779 & -36.2660463177923 \tabularnewline
14 & 1703 & 1713.84358328865 & -10.8435832886544 \tabularnewline
15 & 1189 & 1063.97885892283 & 125.021141077167 \tabularnewline
16 & 3083 & 2684.04749216393 & 398.952507836072 \tabularnewline
17 & 1357 & 1284.33214067982 & 72.6678593201813 \tabularnewline
18 & 1892 & 1886.33459105544 & 5.66540894456048 \tabularnewline
19 & 883 & 998.899823540342 & -115.899823540342 \tabularnewline
20 & 1627 & 1311.12565864975 & 315.874341350252 \tabularnewline
21 & 1412 & 1303.09869342155 & 108.901306578448 \tabularnewline
22 & 1900 & 2028.83116760566 & -128.83116760566 \tabularnewline
23 & 777 & 817.619367853238 & -40.6193678532378 \tabularnewline
24 & 904 & 912.079163043642 & -8.07916304364244 \tabularnewline
25 & 2115 & 2108.65480197137 & 6.3451980286285 \tabularnewline
26 & 1858 & 1765.17312777019 & 92.8268722298076 \tabularnewline
27 & 1781 & 1751.18916707329 & 29.8108329267149 \tabularnewline
28 & 1286 & 1110.2060749222 & 175.7939250778 \tabularnewline
29 & 1035 & 1140.05755179857 & -105.057551798567 \tabularnewline
30 & 1557 & 1554.62293060744 & 2.37706939256371 \tabularnewline
31 & 1527 & 1550.98798582165 & -23.9879858216535 \tabularnewline
32 & 1220 & 1160.91681671118 & 59.0831832888191 \tabularnewline
33 & 1368 & 1403.94899124273 & -35.9489912427348 \tabularnewline
34 & 564 & 636.272375398673 & -72.2723753986733 \tabularnewline
35 & 1990 & 2016.96367485757 & -26.9636748575744 \tabularnewline
36 & 1557 & 1681.93336524395 & -124.933365243955 \tabularnewline
37 & 2057 & 1875.93791149638 & 181.062088503617 \tabularnewline
38 & 1111 & 1080.5217953064 & 30.4782046935977 \tabularnewline
39 & 686 & 774.482294177756 & -88.482294177756 \tabularnewline
40 & 2011 & 1901.77392316152 & 109.226076838477 \tabularnewline
41 & 2232 & 2538.93425164331 & -306.934251643309 \tabularnewline
42 & 1032 & 1236.7241643039 & -204.724164303904 \tabularnewline
43 & 1166 & 1252.64965525829 & -86.6496552582948 \tabularnewline
44 & 1020 & 936.612427685288 & 83.387572314712 \tabularnewline
45 & 1735 & 2067.54201195029 & -332.542011950295 \tabularnewline
46 & 3623 & 3784.46480530567 & -161.464805305671 \tabularnewline
47 & 918 & 1036.5841014597 & -118.584101459701 \tabularnewline
48 & 1579 & 1337.11138958501 & 241.888610414991 \tabularnewline
49 & 2790 & 2530.903607081 & 259.096392919004 \tabularnewline
50 & 1496 & 1704.19501482912 & -208.195014829123 \tabularnewline
51 & 1108 & 1032.83808978247 & 75.1619102175303 \tabularnewline
52 & 496 & 779.844825776675 & -283.844825776675 \tabularnewline
53 & 1750 & 1581.78781448795 & 168.212185512046 \tabularnewline
54 & 744 & 859.923885980617 & -115.923885980617 \tabularnewline
55 & 1101 & 1271.74595837969 & -170.745958379692 \tabularnewline
56 & 1612 & 1683.27360866503 & -71.2736086650307 \tabularnewline
57 & 1805 & 1332.65660761164 & 472.343392388355 \tabularnewline
58 & 2460 & 1997.44808285868 & 462.551917141322 \tabularnewline
59 & 1653 & 1759.00977534882 & -106.009775348818 \tabularnewline
60 & 1234 & 1311.63619418652 & -77.6361941865209 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146122&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]1167[/C][C]955.445479328781[/C][C]211.554520671219[/C][/ROW]
[ROW][C]2[/C][C]669[/C][C]706.725762862511[/C][C]-37.7257628625107[/C][/ROW]
[ROW][C]3[/C][C]1053[/C][C]1002.47500718207[/C][C]50.5249928179255[/C][/ROW]
[ROW][C]4[/C][C]1939[/C][C]2113.21043561158[/C][C]-174.210435611578[/C][/ROW]
[ROW][C]5[/C][C]678[/C][C]622.468684328246[/C][C]55.5313156717536[/C][/ROW]
[ROW][C]6[/C][C]321[/C][C]463.885468311125[/C][C]-142.885468311125[/C][/ROW]
[ROW][C]7[/C][C]2667[/C][C]2895.56201859809[/C][C]-228.562018598089[/C][/ROW]
[ROW][C]8[/C][C]345[/C][C]486.259340768832[/C][C]-141.259340768832[/C][/ROW]
[ROW][C]9[/C][C]1367[/C][C]1484.5210332616[/C][C]-117.521033261599[/C][/ROW]
[ROW][C]10[/C][C]1158[/C][C]1152.01858610445[/C][C]5.98141389555102[/C][/ROW]
[ROW][C]11[/C][C]1385[/C][C]1395.81556539886[/C][C]-10.8155653988635[/C][/ROW]
[ROW][C]12[/C][C]1155[/C][C]1039.62697196062[/C][C]115.373028039376[/C][/ROW]
[ROW][C]13[/C][C]1120[/C][C]1156.26604631779[/C][C]-36.2660463177923[/C][/ROW]
[ROW][C]14[/C][C]1703[/C][C]1713.84358328865[/C][C]-10.8435832886544[/C][/ROW]
[ROW][C]15[/C][C]1189[/C][C]1063.97885892283[/C][C]125.021141077167[/C][/ROW]
[ROW][C]16[/C][C]3083[/C][C]2684.04749216393[/C][C]398.952507836072[/C][/ROW]
[ROW][C]17[/C][C]1357[/C][C]1284.33214067982[/C][C]72.6678593201813[/C][/ROW]
[ROW][C]18[/C][C]1892[/C][C]1886.33459105544[/C][C]5.66540894456048[/C][/ROW]
[ROW][C]19[/C][C]883[/C][C]998.899823540342[/C][C]-115.899823540342[/C][/ROW]
[ROW][C]20[/C][C]1627[/C][C]1311.12565864975[/C][C]315.874341350252[/C][/ROW]
[ROW][C]21[/C][C]1412[/C][C]1303.09869342155[/C][C]108.901306578448[/C][/ROW]
[ROW][C]22[/C][C]1900[/C][C]2028.83116760566[/C][C]-128.83116760566[/C][/ROW]
[ROW][C]23[/C][C]777[/C][C]817.619367853238[/C][C]-40.6193678532378[/C][/ROW]
[ROW][C]24[/C][C]904[/C][C]912.079163043642[/C][C]-8.07916304364244[/C][/ROW]
[ROW][C]25[/C][C]2115[/C][C]2108.65480197137[/C][C]6.3451980286285[/C][/ROW]
[ROW][C]26[/C][C]1858[/C][C]1765.17312777019[/C][C]92.8268722298076[/C][/ROW]
[ROW][C]27[/C][C]1781[/C][C]1751.18916707329[/C][C]29.8108329267149[/C][/ROW]
[ROW][C]28[/C][C]1286[/C][C]1110.2060749222[/C][C]175.7939250778[/C][/ROW]
[ROW][C]29[/C][C]1035[/C][C]1140.05755179857[/C][C]-105.057551798567[/C][/ROW]
[ROW][C]30[/C][C]1557[/C][C]1554.62293060744[/C][C]2.37706939256371[/C][/ROW]
[ROW][C]31[/C][C]1527[/C][C]1550.98798582165[/C][C]-23.9879858216535[/C][/ROW]
[ROW][C]32[/C][C]1220[/C][C]1160.91681671118[/C][C]59.0831832888191[/C][/ROW]
[ROW][C]33[/C][C]1368[/C][C]1403.94899124273[/C][C]-35.9489912427348[/C][/ROW]
[ROW][C]34[/C][C]564[/C][C]636.272375398673[/C][C]-72.2723753986733[/C][/ROW]
[ROW][C]35[/C][C]1990[/C][C]2016.96367485757[/C][C]-26.9636748575744[/C][/ROW]
[ROW][C]36[/C][C]1557[/C][C]1681.93336524395[/C][C]-124.933365243955[/C][/ROW]
[ROW][C]37[/C][C]2057[/C][C]1875.93791149638[/C][C]181.062088503617[/C][/ROW]
[ROW][C]38[/C][C]1111[/C][C]1080.5217953064[/C][C]30.4782046935977[/C][/ROW]
[ROW][C]39[/C][C]686[/C][C]774.482294177756[/C][C]-88.482294177756[/C][/ROW]
[ROW][C]40[/C][C]2011[/C][C]1901.77392316152[/C][C]109.226076838477[/C][/ROW]
[ROW][C]41[/C][C]2232[/C][C]2538.93425164331[/C][C]-306.934251643309[/C][/ROW]
[ROW][C]42[/C][C]1032[/C][C]1236.7241643039[/C][C]-204.724164303904[/C][/ROW]
[ROW][C]43[/C][C]1166[/C][C]1252.64965525829[/C][C]-86.6496552582948[/C][/ROW]
[ROW][C]44[/C][C]1020[/C][C]936.612427685288[/C][C]83.387572314712[/C][/ROW]
[ROW][C]45[/C][C]1735[/C][C]2067.54201195029[/C][C]-332.542011950295[/C][/ROW]
[ROW][C]46[/C][C]3623[/C][C]3784.46480530567[/C][C]-161.464805305671[/C][/ROW]
[ROW][C]47[/C][C]918[/C][C]1036.5841014597[/C][C]-118.584101459701[/C][/ROW]
[ROW][C]48[/C][C]1579[/C][C]1337.11138958501[/C][C]241.888610414991[/C][/ROW]
[ROW][C]49[/C][C]2790[/C][C]2530.903607081[/C][C]259.096392919004[/C][/ROW]
[ROW][C]50[/C][C]1496[/C][C]1704.19501482912[/C][C]-208.195014829123[/C][/ROW]
[ROW][C]51[/C][C]1108[/C][C]1032.83808978247[/C][C]75.1619102175303[/C][/ROW]
[ROW][C]52[/C][C]496[/C][C]779.844825776675[/C][C]-283.844825776675[/C][/ROW]
[ROW][C]53[/C][C]1750[/C][C]1581.78781448795[/C][C]168.212185512046[/C][/ROW]
[ROW][C]54[/C][C]744[/C][C]859.923885980617[/C][C]-115.923885980617[/C][/ROW]
[ROW][C]55[/C][C]1101[/C][C]1271.74595837969[/C][C]-170.745958379692[/C][/ROW]
[ROW][C]56[/C][C]1612[/C][C]1683.27360866503[/C][C]-71.2736086650307[/C][/ROW]
[ROW][C]57[/C][C]1805[/C][C]1332.65660761164[/C][C]472.343392388355[/C][/ROW]
[ROW][C]58[/C][C]2460[/C][C]1997.44808285868[/C][C]462.551917141322[/C][/ROW]
[ROW][C]59[/C][C]1653[/C][C]1759.00977534882[/C][C]-106.009775348818[/C][/ROW]
[ROW][C]60[/C][C]1234[/C][C]1311.63619418652[/C][C]-77.6361941865209[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146122&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146122&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
11167955.445479328781211.554520671219
2669706.725762862511-37.7257628625107
310531002.4750071820750.5249928179255
419392113.21043561158-174.210435611578
5678622.46868432824655.5313156717536
6321463.885468311125-142.885468311125
726672895.56201859809-228.562018598089
8345486.259340768832-141.259340768832
913671484.5210332616-117.521033261599
1011581152.018586104455.98141389555102
1113851395.81556539886-10.8155653988635
1211551039.62697196062115.373028039376
1311201156.26604631779-36.2660463177923
1417031713.84358328865-10.8435832886544
1511891063.97885892283125.021141077167
1630832684.04749216393398.952507836072
1713571284.3321406798272.6678593201813
1818921886.334591055445.66540894456048
19883998.899823540342-115.899823540342
2016271311.12565864975315.874341350252
2114121303.09869342155108.901306578448
2219002028.83116760566-128.83116760566
23777817.619367853238-40.6193678532378
24904912.079163043642-8.07916304364244
2521152108.654801971376.3451980286285
2618581765.1731277701992.8268722298076
2717811751.1891670732929.8108329267149
2812861110.2060749222175.7939250778
2910351140.05755179857-105.057551798567
3015571554.622930607442.37706939256371
3115271550.98798582165-23.9879858216535
3212201160.9168167111859.0831832888191
3313681403.94899124273-35.9489912427348
34564636.272375398673-72.2723753986733
3519902016.96367485757-26.9636748575744
3615571681.93336524395-124.933365243955
3720571875.93791149638181.062088503617
3811111080.521795306430.4782046935977
39686774.482294177756-88.482294177756
4020111901.77392316152109.226076838477
4122322538.93425164331-306.934251643309
4210321236.7241643039-204.724164303904
4311661252.64965525829-86.6496552582948
441020936.61242768528883.387572314712
4517352067.54201195029-332.542011950295
4636233784.46480530567-161.464805305671
479181036.5841014597-118.584101459701
4815791337.11138958501241.888610414991
4927902530.903607081259.096392919004
5014961704.19501482912-208.195014829123
5111081032.8380897824775.1619102175303
52496779.844825776675-283.844825776675
5317501581.78781448795168.212185512046
54744859.923885980617-115.923885980617
5511011271.74595837969-170.745958379692
5616121683.27360866503-71.2736086650307
5718051332.65660761164472.343392388355
5824601997.44808285868462.551917141322
5916531759.00977534882-106.009775348818
6012341311.63619418652-77.6361941865209







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.2637779456339680.5275558912679350.736222054366032
90.2022183427107830.4044366854215660.797781657289217
100.2384814929115060.4769629858230120.761518507088494
110.1464660527451730.2929321054903470.853533947254827
120.1239484121381320.2478968242762640.876051587861868
130.1314251641220940.2628503282441870.868574835877907
140.09552308202877430.1910461640575490.904476917971226
150.08441453755736220.1688290751147240.915585462442638
160.410246648009270.820493296018540.58975335199073
170.3202963052096970.6405926104193940.679703694790303
180.2547465729113120.5094931458226250.745253427088688
190.2360161096401340.4720322192802670.763983890359866
200.3325945405216030.6651890810432050.667405459478397
210.2709440420974550.5418880841949090.729055957902546
220.2890650298620970.5781300597241940.710934970137903
230.2469873994345160.4939747988690310.753012600565484
240.1897351574748810.3794703149497620.810264842525119
250.1399915139229740.2799830278459480.860008486077026
260.1047775973462810.2095551946925610.895222402653719
270.0735072182372650.147014436474530.926492781762735
280.06686416018585270.1337283203717050.933135839814147
290.05822510651662050.1164502130332410.94177489348338
300.0401256604698560.0802513209397120.959874339530144
310.02751665062925740.05503330125851480.972483349370743
320.01938218765320870.03876437530641740.980617812346791
330.0130770811708880.02615416234177590.986922918829112
340.008222777093303950.01644555418660790.991777222906696
350.004660595474677670.009321190949355340.995339404525322
360.003075866368796280.006151732737592560.996924133631204
370.00393271113845560.00786542227691120.996067288861544
380.002530620679184330.005061241358368670.997469379320816
390.001694582366737330.003389164733474670.998305417633263
400.001779456749260790.003558913498521580.998220543250739
410.003097793645491720.006195587290983440.996902206354508
420.002610403434147870.005220806868295730.997389596565852
430.001395863060706680.002791726121413360.998604136939293
440.0009623416401292540.001924683280258510.999037658359871
450.007560953655255050.01512190731051010.992439046344745
460.05449838483446760.1089967696689350.945501615165532
470.117567433719860.2351348674397190.88243256628014
480.1066664097749170.2133328195498340.893333590225083
490.1099687733105570.2199375466211150.890031226689443
500.07424987361402550.1484997472280510.925750126385975
510.04660975078080940.09321950156161890.953390249219191
520.09838896669076370.1967779333815270.901611033309236

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.263777945633968 & 0.527555891267935 & 0.736222054366032 \tabularnewline
9 & 0.202218342710783 & 0.404436685421566 & 0.797781657289217 \tabularnewline
10 & 0.238481492911506 & 0.476962985823012 & 0.761518507088494 \tabularnewline
11 & 0.146466052745173 & 0.292932105490347 & 0.853533947254827 \tabularnewline
12 & 0.123948412138132 & 0.247896824276264 & 0.876051587861868 \tabularnewline
13 & 0.131425164122094 & 0.262850328244187 & 0.868574835877907 \tabularnewline
14 & 0.0955230820287743 & 0.191046164057549 & 0.904476917971226 \tabularnewline
15 & 0.0844145375573622 & 0.168829075114724 & 0.915585462442638 \tabularnewline
16 & 0.41024664800927 & 0.82049329601854 & 0.58975335199073 \tabularnewline
17 & 0.320296305209697 & 0.640592610419394 & 0.679703694790303 \tabularnewline
18 & 0.254746572911312 & 0.509493145822625 & 0.745253427088688 \tabularnewline
19 & 0.236016109640134 & 0.472032219280267 & 0.763983890359866 \tabularnewline
20 & 0.332594540521603 & 0.665189081043205 & 0.667405459478397 \tabularnewline
21 & 0.270944042097455 & 0.541888084194909 & 0.729055957902546 \tabularnewline
22 & 0.289065029862097 & 0.578130059724194 & 0.710934970137903 \tabularnewline
23 & 0.246987399434516 & 0.493974798869031 & 0.753012600565484 \tabularnewline
24 & 0.189735157474881 & 0.379470314949762 & 0.810264842525119 \tabularnewline
25 & 0.139991513922974 & 0.279983027845948 & 0.860008486077026 \tabularnewline
26 & 0.104777597346281 & 0.209555194692561 & 0.895222402653719 \tabularnewline
27 & 0.073507218237265 & 0.14701443647453 & 0.926492781762735 \tabularnewline
28 & 0.0668641601858527 & 0.133728320371705 & 0.933135839814147 \tabularnewline
29 & 0.0582251065166205 & 0.116450213033241 & 0.94177489348338 \tabularnewline
30 & 0.040125660469856 & 0.080251320939712 & 0.959874339530144 \tabularnewline
31 & 0.0275166506292574 & 0.0550333012585148 & 0.972483349370743 \tabularnewline
32 & 0.0193821876532087 & 0.0387643753064174 & 0.980617812346791 \tabularnewline
33 & 0.013077081170888 & 0.0261541623417759 & 0.986922918829112 \tabularnewline
34 & 0.00822277709330395 & 0.0164455541866079 & 0.991777222906696 \tabularnewline
35 & 0.00466059547467767 & 0.00932119094935534 & 0.995339404525322 \tabularnewline
36 & 0.00307586636879628 & 0.00615173273759256 & 0.996924133631204 \tabularnewline
37 & 0.0039327111384556 & 0.0078654222769112 & 0.996067288861544 \tabularnewline
38 & 0.00253062067918433 & 0.00506124135836867 & 0.997469379320816 \tabularnewline
39 & 0.00169458236673733 & 0.00338916473347467 & 0.998305417633263 \tabularnewline
40 & 0.00177945674926079 & 0.00355891349852158 & 0.998220543250739 \tabularnewline
41 & 0.00309779364549172 & 0.00619558729098344 & 0.996902206354508 \tabularnewline
42 & 0.00261040343414787 & 0.00522080686829573 & 0.997389596565852 \tabularnewline
43 & 0.00139586306070668 & 0.00279172612141336 & 0.998604136939293 \tabularnewline
44 & 0.000962341640129254 & 0.00192468328025851 & 0.999037658359871 \tabularnewline
45 & 0.00756095365525505 & 0.0151219073105101 & 0.992439046344745 \tabularnewline
46 & 0.0544983848344676 & 0.108996769668935 & 0.945501615165532 \tabularnewline
47 & 0.11756743371986 & 0.235134867439719 & 0.88243256628014 \tabularnewline
48 & 0.106666409774917 & 0.213332819549834 & 0.893333590225083 \tabularnewline
49 & 0.109968773310557 & 0.219937546621115 & 0.890031226689443 \tabularnewline
50 & 0.0742498736140255 & 0.148499747228051 & 0.925750126385975 \tabularnewline
51 & 0.0466097507808094 & 0.0932195015616189 & 0.953390249219191 \tabularnewline
52 & 0.0983889666907637 & 0.196777933381527 & 0.901611033309236 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146122&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]8[/C][C]0.263777945633968[/C][C]0.527555891267935[/C][C]0.736222054366032[/C][/ROW]
[ROW][C]9[/C][C]0.202218342710783[/C][C]0.404436685421566[/C][C]0.797781657289217[/C][/ROW]
[ROW][C]10[/C][C]0.238481492911506[/C][C]0.476962985823012[/C][C]0.761518507088494[/C][/ROW]
[ROW][C]11[/C][C]0.146466052745173[/C][C]0.292932105490347[/C][C]0.853533947254827[/C][/ROW]
[ROW][C]12[/C][C]0.123948412138132[/C][C]0.247896824276264[/C][C]0.876051587861868[/C][/ROW]
[ROW][C]13[/C][C]0.131425164122094[/C][C]0.262850328244187[/C][C]0.868574835877907[/C][/ROW]
[ROW][C]14[/C][C]0.0955230820287743[/C][C]0.191046164057549[/C][C]0.904476917971226[/C][/ROW]
[ROW][C]15[/C][C]0.0844145375573622[/C][C]0.168829075114724[/C][C]0.915585462442638[/C][/ROW]
[ROW][C]16[/C][C]0.41024664800927[/C][C]0.82049329601854[/C][C]0.58975335199073[/C][/ROW]
[ROW][C]17[/C][C]0.320296305209697[/C][C]0.640592610419394[/C][C]0.679703694790303[/C][/ROW]
[ROW][C]18[/C][C]0.254746572911312[/C][C]0.509493145822625[/C][C]0.745253427088688[/C][/ROW]
[ROW][C]19[/C][C]0.236016109640134[/C][C]0.472032219280267[/C][C]0.763983890359866[/C][/ROW]
[ROW][C]20[/C][C]0.332594540521603[/C][C]0.665189081043205[/C][C]0.667405459478397[/C][/ROW]
[ROW][C]21[/C][C]0.270944042097455[/C][C]0.541888084194909[/C][C]0.729055957902546[/C][/ROW]
[ROW][C]22[/C][C]0.289065029862097[/C][C]0.578130059724194[/C][C]0.710934970137903[/C][/ROW]
[ROW][C]23[/C][C]0.246987399434516[/C][C]0.493974798869031[/C][C]0.753012600565484[/C][/ROW]
[ROW][C]24[/C][C]0.189735157474881[/C][C]0.379470314949762[/C][C]0.810264842525119[/C][/ROW]
[ROW][C]25[/C][C]0.139991513922974[/C][C]0.279983027845948[/C][C]0.860008486077026[/C][/ROW]
[ROW][C]26[/C][C]0.104777597346281[/C][C]0.209555194692561[/C][C]0.895222402653719[/C][/ROW]
[ROW][C]27[/C][C]0.073507218237265[/C][C]0.14701443647453[/C][C]0.926492781762735[/C][/ROW]
[ROW][C]28[/C][C]0.0668641601858527[/C][C]0.133728320371705[/C][C]0.933135839814147[/C][/ROW]
[ROW][C]29[/C][C]0.0582251065166205[/C][C]0.116450213033241[/C][C]0.94177489348338[/C][/ROW]
[ROW][C]30[/C][C]0.040125660469856[/C][C]0.080251320939712[/C][C]0.959874339530144[/C][/ROW]
[ROW][C]31[/C][C]0.0275166506292574[/C][C]0.0550333012585148[/C][C]0.972483349370743[/C][/ROW]
[ROW][C]32[/C][C]0.0193821876532087[/C][C]0.0387643753064174[/C][C]0.980617812346791[/C][/ROW]
[ROW][C]33[/C][C]0.013077081170888[/C][C]0.0261541623417759[/C][C]0.986922918829112[/C][/ROW]
[ROW][C]34[/C][C]0.00822277709330395[/C][C]0.0164455541866079[/C][C]0.991777222906696[/C][/ROW]
[ROW][C]35[/C][C]0.00466059547467767[/C][C]0.00932119094935534[/C][C]0.995339404525322[/C][/ROW]
[ROW][C]36[/C][C]0.00307586636879628[/C][C]0.00615173273759256[/C][C]0.996924133631204[/C][/ROW]
[ROW][C]37[/C][C]0.0039327111384556[/C][C]0.0078654222769112[/C][C]0.996067288861544[/C][/ROW]
[ROW][C]38[/C][C]0.00253062067918433[/C][C]0.00506124135836867[/C][C]0.997469379320816[/C][/ROW]
[ROW][C]39[/C][C]0.00169458236673733[/C][C]0.00338916473347467[/C][C]0.998305417633263[/C][/ROW]
[ROW][C]40[/C][C]0.00177945674926079[/C][C]0.00355891349852158[/C][C]0.998220543250739[/C][/ROW]
[ROW][C]41[/C][C]0.00309779364549172[/C][C]0.00619558729098344[/C][C]0.996902206354508[/C][/ROW]
[ROW][C]42[/C][C]0.00261040343414787[/C][C]0.00522080686829573[/C][C]0.997389596565852[/C][/ROW]
[ROW][C]43[/C][C]0.00139586306070668[/C][C]0.00279172612141336[/C][C]0.998604136939293[/C][/ROW]
[ROW][C]44[/C][C]0.000962341640129254[/C][C]0.00192468328025851[/C][C]0.999037658359871[/C][/ROW]
[ROW][C]45[/C][C]0.00756095365525505[/C][C]0.0151219073105101[/C][C]0.992439046344745[/C][/ROW]
[ROW][C]46[/C][C]0.0544983848344676[/C][C]0.108996769668935[/C][C]0.945501615165532[/C][/ROW]
[ROW][C]47[/C][C]0.11756743371986[/C][C]0.235134867439719[/C][C]0.88243256628014[/C][/ROW]
[ROW][C]48[/C][C]0.106666409774917[/C][C]0.213332819549834[/C][C]0.893333590225083[/C][/ROW]
[ROW][C]49[/C][C]0.109968773310557[/C][C]0.219937546621115[/C][C]0.890031226689443[/C][/ROW]
[ROW][C]50[/C][C]0.0742498736140255[/C][C]0.148499747228051[/C][C]0.925750126385975[/C][/ROW]
[ROW][C]51[/C][C]0.0466097507808094[/C][C]0.0932195015616189[/C][C]0.953390249219191[/C][/ROW]
[ROW][C]52[/C][C]0.0983889666907637[/C][C]0.196777933381527[/C][C]0.901611033309236[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146122&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146122&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
80.2637779456339680.5275558912679350.736222054366032
90.2022183427107830.4044366854215660.797781657289217
100.2384814929115060.4769629858230120.761518507088494
110.1464660527451730.2929321054903470.853533947254827
120.1239484121381320.2478968242762640.876051587861868
130.1314251641220940.2628503282441870.868574835877907
140.09552308202877430.1910461640575490.904476917971226
150.08441453755736220.1688290751147240.915585462442638
160.410246648009270.820493296018540.58975335199073
170.3202963052096970.6405926104193940.679703694790303
180.2547465729113120.5094931458226250.745253427088688
190.2360161096401340.4720322192802670.763983890359866
200.3325945405216030.6651890810432050.667405459478397
210.2709440420974550.5418880841949090.729055957902546
220.2890650298620970.5781300597241940.710934970137903
230.2469873994345160.4939747988690310.753012600565484
240.1897351574748810.3794703149497620.810264842525119
250.1399915139229740.2799830278459480.860008486077026
260.1047775973462810.2095551946925610.895222402653719
270.0735072182372650.147014436474530.926492781762735
280.06686416018585270.1337283203717050.933135839814147
290.05822510651662050.1164502130332410.94177489348338
300.0401256604698560.0802513209397120.959874339530144
310.02751665062925740.05503330125851480.972483349370743
320.01938218765320870.03876437530641740.980617812346791
330.0130770811708880.02615416234177590.986922918829112
340.008222777093303950.01644555418660790.991777222906696
350.004660595474677670.009321190949355340.995339404525322
360.003075866368796280.006151732737592560.996924133631204
370.00393271113845560.00786542227691120.996067288861544
380.002530620679184330.005061241358368670.997469379320816
390.001694582366737330.003389164733474670.998305417633263
400.001779456749260790.003558913498521580.998220543250739
410.003097793645491720.006195587290983440.996902206354508
420.002610403434147870.005220806868295730.997389596565852
430.001395863060706680.002791726121413360.998604136939293
440.0009623416401292540.001924683280258510.999037658359871
450.007560953655255050.01512190731051010.992439046344745
460.05449838483446760.1089967696689350.945501615165532
470.117567433719860.2351348674397190.88243256628014
480.1066664097749170.2133328195498340.893333590225083
490.1099687733105570.2199375466211150.890031226689443
500.07424987361402550.1484997472280510.925750126385975
510.04660975078080940.09321950156161890.953390249219191
520.09838896669076370.1967779333815270.901611033309236







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.222222222222222NOK
5% type I error level140.311111111111111NOK
10% type I error level170.377777777777778NOK

\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 & 10 & 0.222222222222222 & NOK \tabularnewline
5% type I error level & 14 & 0.311111111111111 & NOK \tabularnewline
10% type I error level & 17 & 0.377777777777778 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146122&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]10[/C][C]0.222222222222222[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.311111111111111[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.377777777777778[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146122&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146122&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 level100.222222222222222NOK
5% type I error level140.311111111111111NOK
10% type I error level170.377777777777778NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal 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')
}