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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, 18 Dec 2012 13:31:06 -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/2012/Dec/18/t1355855492hdrykodnisdhjof.htm/, Retrieved Thu, 31 Oct 2024 23:46:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201549, Retrieved Thu, 31 Oct 2024 23:46:08 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact195
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Multiple Regression] [Unemployment] [2010-11-30 13:40:15] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [multi regress dum...] [2012-12-18 18:31:06] [164264d60f8cebcb0f9613bc1a0dee58] [Current]
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Dataseries X:
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201549&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201549&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201549&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 time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 862212.191666666 -197694.30813492M1[t] + 25198.6289682542M2[t] + 255262.232738095M3[t] + 681812.003174603M4[t] + 720838.606944444M5[t] + 663835.877380952M6[t] + 2108698.81448413M7[t] + 1807278.2515873M8[t] + 519487.855357143M9[t] + 495689.292460317M10[t] + 93400.3962301587M11[t] -42.10376984127t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  862212.191666666 -197694.30813492M1[t] +  25198.6289682542M2[t] +  255262.232738095M3[t] +  681812.003174603M4[t] +  720838.606944444M5[t] +  663835.877380952M6[t] +  2108698.81448413M7[t] +  1807278.2515873M8[t] +  519487.855357143M9[t] +  495689.292460317M10[t] +  93400.3962301587M11[t] -42.10376984127t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201549&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  862212.191666666 -197694.30813492M1[t] +  25198.6289682542M2[t] +  255262.232738095M3[t] +  681812.003174603M4[t] +  720838.606944444M5[t] +  663835.877380952M6[t] +  2108698.81448413M7[t] +  1807278.2515873M8[t] +  519487.855357143M9[t] +  495689.292460317M10[t] +  93400.3962301587M11[t] -42.10376984127t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201549&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201549&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] = + 862212.191666666 -197694.30813492M1[t] + 25198.6289682542M2[t] + 255262.232738095M3[t] + 681812.003174603M4[t] + 720838.606944444M5[t] + 663835.877380952M6[t] + 2108698.81448413M7[t] + 1807278.2515873M8[t] + 519487.855357143M9[t] + 495689.292460317M10[t] + 93400.3962301587M11[t] -42.10376984127t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)862212.19166666637852.28860222.778300
M1-197694.3081349246348.039027-4.26547.3e-053.7e-05
M225198.628968254246300.307910.54420.5883250.294163
M3255262.23273809546257.0801745.51831e-060
M4681812.00317460346218.36845514.75200
M5720838.60694444446184.1841115.607900
M6663835.87738095246154.53719714.382900
M72108698.8144841346129.43646545.712700
M81807278.251587346108.8893439.195900
M9519487.85535714346092.90190911.270500
M10495689.29246031746081.4789210.756800
M1193400.396230158746074.6237682.02720.0471720.023586
t-42.10376984127458.891476-0.09180.9272070.463603

\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) & 862212.191666666 & 37852.288602 & 22.7783 & 0 & 0 \tabularnewline
M1 & -197694.30813492 & 46348.039027 & -4.2654 & 7.3e-05 & 3.7e-05 \tabularnewline
M2 & 25198.6289682542 & 46300.30791 & 0.5442 & 0.588325 & 0.294163 \tabularnewline
M3 & 255262.232738095 & 46257.080174 & 5.5183 & 1e-06 & 0 \tabularnewline
M4 & 681812.003174603 & 46218.368455 & 14.752 & 0 & 0 \tabularnewline
M5 & 720838.606944444 & 46184.18411 & 15.6079 & 0 & 0 \tabularnewline
M6 & 663835.877380952 & 46154.537197 & 14.3829 & 0 & 0 \tabularnewline
M7 & 2108698.81448413 & 46129.436465 & 45.7127 & 0 & 0 \tabularnewline
M8 & 1807278.2515873 & 46108.88934 & 39.1959 & 0 & 0 \tabularnewline
M9 & 519487.855357143 & 46092.901909 & 11.2705 & 0 & 0 \tabularnewline
M10 & 495689.292460317 & 46081.47892 & 10.7568 & 0 & 0 \tabularnewline
M11 & 93400.3962301587 & 46074.623768 & 2.0272 & 0.047172 & 0.023586 \tabularnewline
t & -42.10376984127 & 458.891476 & -0.0918 & 0.927207 & 0.463603 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201549&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]862212.191666666[/C][C]37852.288602[/C][C]22.7783[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-197694.30813492[/C][C]46348.039027[/C][C]-4.2654[/C][C]7.3e-05[/C][C]3.7e-05[/C][/ROW]
[ROW][C]M2[/C][C]25198.6289682542[/C][C]46300.30791[/C][C]0.5442[/C][C]0.588325[/C][C]0.294163[/C][/ROW]
[ROW][C]M3[/C][C]255262.232738095[/C][C]46257.080174[/C][C]5.5183[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]681812.003174603[/C][C]46218.368455[/C][C]14.752[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]720838.606944444[/C][C]46184.18411[/C][C]15.6079[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]663835.877380952[/C][C]46154.537197[/C][C]14.3829[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]2108698.81448413[/C][C]46129.436465[/C][C]45.7127[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]1807278.2515873[/C][C]46108.88934[/C][C]39.1959[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]519487.855357143[/C][C]46092.901909[/C][C]11.2705[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]495689.292460317[/C][C]46081.47892[/C][C]10.7568[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]93400.3962301587[/C][C]46074.623768[/C][C]2.0272[/C][C]0.047172[/C][C]0.023586[/C][/ROW]
[ROW][C]t[/C][C]-42.10376984127[/C][C]458.891476[/C][C]-0.0918[/C][C]0.927207[/C][C]0.463603[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201549&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201549&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)862212.19166666637852.28860222.778300
M1-197694.3081349246348.039027-4.26547.3e-053.7e-05
M225198.628968254246300.307910.54420.5883250.294163
M3255262.23273809546257.0801745.51831e-060
M4681812.00317460346218.36845514.75200
M5720838.60694444446184.1841115.607900
M6663835.87738095246154.53719714.382900
M72108698.8144841346129.43646545.712700
M81807278.251587346108.8893439.195900
M9519487.85535714346092.90190911.270500
M10495689.29246031746081.4789210.756800
M1193400.396230158746074.6237682.02720.0471720.023586
t-42.10376984127458.891476-0.09180.9272070.463603







Multiple Linear Regression - Regression Statistics
Multiple R0.994333318229864
R-squared0.988698747742012
Adjusted R-squared0.986400187960726
F-TEST (value)430.138365680866
F-TEST (DF numerator)12
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation79799.6310883679
Sum Squared Residuals375710886188.537

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.994333318229864 \tabularnewline
R-squared & 0.988698747742012 \tabularnewline
Adjusted R-squared & 0.986400187960726 \tabularnewline
F-TEST (value) & 430.138365680866 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 79799.6310883679 \tabularnewline
Sum Squared Residuals & 375710886188.537 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201549&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.994333318229864[/C][/ROW]
[ROW][C]R-squared[/C][C]0.988698747742012[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.986400187960726[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]430.138365680866[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/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]79799.6310883679[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]375710886188.537[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201549&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201549&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 R0.994333318229864
R-squared0.988698747742012
Adjusted R-squared0.986400187960726
F-TEST (value)430.138365680866
F-TEST (DF numerator)12
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation79799.6310883679
Sum Squared Residuals375710886188.537







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1655362664475.779761905-9113.77976190497
2873127887326.613095238-14199.6130952381
311078971117348.11309524-9451.11309523815
415559641543855.779761912108.2202380951
516711591582840.279761988318.720238095
614933081525795.44642857-32487.4464285713
729577962970616.2797619-12820.2797619045
826386912669153.61309524-30462.6130952377
913056691381321.11309524-75652.1130952381
1012804961357480.44642857-76984.4464285713
11921900955149.446428571-33249.4464285715
12867888861706.9464285716181.05357142854
13652586663970.534523809-11384.5345238095
14913831886821.36785714327009.6321428571
1511085441116842.86785714-8298.86785714284
1615558271543350.5345238112476.4654761905
1716992831582335.03452381116947.96547619
1815094581525290.20119048-15832.2011904762
1932689752970111.03452381298863.96547619
2024250162668648.36785714-243632.367857143
2113127031380815.86785714-68112.8678571429
2213654981356975.201190488522.79880952377
23934453954644.201190476-20191.2011904762
24775019861201.701190476-86182.7011904761
25651142663465.289285714-12323.2892857142
26843192886316.122619048-43124.1226190475
2711467661116337.6226190530428.3773809524
2816526011542845.28928571109755.710714286
2914659061581829.78928571-115923.789285714
3016527341524784.95595238127949.044047619
3129223342969605.78928571-47271.7892857143
3227028052668143.1226190534661.8773809522
3314589561380310.6226190578645.3773809524
3414103631356469.9559523853893.0440476191
351019279954138.95595238165140.044047619
36936574860696.45595238175877.544047619
37708917662960.04404761945956.955952381
38885295885810.877380952-515.877380952416
3910996631115832.37738095-16169.3773809524
4015762201542340.0440476233879.955952381
4114878701581324.54404762-93454.544047619
4214886351524279.71071429-35644.7107142857
4328825302969100.54404762-86570.5440476191
4426770262667637.877380959388.12261904743
4514043981379805.3773809524592.6226190476
4613443701355964.71071429-11594.7107142857
47936865953633.710714286-16768.7107142857
48872705860191.21071428612513.7892857143
49628151662454.798809524-34303.7988095238
50953712885305.63214285768406.3678571428
5111603841115327.1321428645056.8678571429
5214006181541834.79880952-141216.798809524
5316615111580819.2988095280691.7011904762
5414953471523774.46547619-28427.4654761905
5529187862968595.29880952-49809.2988095238
5627756772667132.63214286108544.367857143
5714070261379300.1321428627725.8678571429
5813701991355459.4654761914739.5345238095
59964526953128.4654761911397.5345238095
60850851859685.96547619-8834.96547619048
61683118661949.55357142921168.4464285715
62847224884800.386904762-37576.3869047618
6310732561114821.88690476-41565.8869047619
6415143261541329.55357143-27003.5535714285
6515037341580314.05357143-76580.0535714285
6615077121523269.2202381-15557.2202380953
6728656982968090.05357143-102392.053571429
6827881282666627.38690476121500.613095238
6913915961378794.8869047612801.1130952381
7013663781354954.220238111423.7797619047
71946295952623.220238095-6328.22023809521
72859626859180.720238095445.279761904761

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 655362 & 664475.779761905 & -9113.77976190497 \tabularnewline
2 & 873127 & 887326.613095238 & -14199.6130952381 \tabularnewline
3 & 1107897 & 1117348.11309524 & -9451.11309523815 \tabularnewline
4 & 1555964 & 1543855.7797619 & 12108.2202380951 \tabularnewline
5 & 1671159 & 1582840.2797619 & 88318.720238095 \tabularnewline
6 & 1493308 & 1525795.44642857 & -32487.4464285713 \tabularnewline
7 & 2957796 & 2970616.2797619 & -12820.2797619045 \tabularnewline
8 & 2638691 & 2669153.61309524 & -30462.6130952377 \tabularnewline
9 & 1305669 & 1381321.11309524 & -75652.1130952381 \tabularnewline
10 & 1280496 & 1357480.44642857 & -76984.4464285713 \tabularnewline
11 & 921900 & 955149.446428571 & -33249.4464285715 \tabularnewline
12 & 867888 & 861706.946428571 & 6181.05357142854 \tabularnewline
13 & 652586 & 663970.534523809 & -11384.5345238095 \tabularnewline
14 & 913831 & 886821.367857143 & 27009.6321428571 \tabularnewline
15 & 1108544 & 1116842.86785714 & -8298.86785714284 \tabularnewline
16 & 1555827 & 1543350.53452381 & 12476.4654761905 \tabularnewline
17 & 1699283 & 1582335.03452381 & 116947.96547619 \tabularnewline
18 & 1509458 & 1525290.20119048 & -15832.2011904762 \tabularnewline
19 & 3268975 & 2970111.03452381 & 298863.96547619 \tabularnewline
20 & 2425016 & 2668648.36785714 & -243632.367857143 \tabularnewline
21 & 1312703 & 1380815.86785714 & -68112.8678571429 \tabularnewline
22 & 1365498 & 1356975.20119048 & 8522.79880952377 \tabularnewline
23 & 934453 & 954644.201190476 & -20191.2011904762 \tabularnewline
24 & 775019 & 861201.701190476 & -86182.7011904761 \tabularnewline
25 & 651142 & 663465.289285714 & -12323.2892857142 \tabularnewline
26 & 843192 & 886316.122619048 & -43124.1226190475 \tabularnewline
27 & 1146766 & 1116337.62261905 & 30428.3773809524 \tabularnewline
28 & 1652601 & 1542845.28928571 & 109755.710714286 \tabularnewline
29 & 1465906 & 1581829.78928571 & -115923.789285714 \tabularnewline
30 & 1652734 & 1524784.95595238 & 127949.044047619 \tabularnewline
31 & 2922334 & 2969605.78928571 & -47271.7892857143 \tabularnewline
32 & 2702805 & 2668143.12261905 & 34661.8773809522 \tabularnewline
33 & 1458956 & 1380310.62261905 & 78645.3773809524 \tabularnewline
34 & 1410363 & 1356469.95595238 & 53893.0440476191 \tabularnewline
35 & 1019279 & 954138.955952381 & 65140.044047619 \tabularnewline
36 & 936574 & 860696.455952381 & 75877.544047619 \tabularnewline
37 & 708917 & 662960.044047619 & 45956.955952381 \tabularnewline
38 & 885295 & 885810.877380952 & -515.877380952416 \tabularnewline
39 & 1099663 & 1115832.37738095 & -16169.3773809524 \tabularnewline
40 & 1576220 & 1542340.04404762 & 33879.955952381 \tabularnewline
41 & 1487870 & 1581324.54404762 & -93454.544047619 \tabularnewline
42 & 1488635 & 1524279.71071429 & -35644.7107142857 \tabularnewline
43 & 2882530 & 2969100.54404762 & -86570.5440476191 \tabularnewline
44 & 2677026 & 2667637.87738095 & 9388.12261904743 \tabularnewline
45 & 1404398 & 1379805.37738095 & 24592.6226190476 \tabularnewline
46 & 1344370 & 1355964.71071429 & -11594.7107142857 \tabularnewline
47 & 936865 & 953633.710714286 & -16768.7107142857 \tabularnewline
48 & 872705 & 860191.210714286 & 12513.7892857143 \tabularnewline
49 & 628151 & 662454.798809524 & -34303.7988095238 \tabularnewline
50 & 953712 & 885305.632142857 & 68406.3678571428 \tabularnewline
51 & 1160384 & 1115327.13214286 & 45056.8678571429 \tabularnewline
52 & 1400618 & 1541834.79880952 & -141216.798809524 \tabularnewline
53 & 1661511 & 1580819.29880952 & 80691.7011904762 \tabularnewline
54 & 1495347 & 1523774.46547619 & -28427.4654761905 \tabularnewline
55 & 2918786 & 2968595.29880952 & -49809.2988095238 \tabularnewline
56 & 2775677 & 2667132.63214286 & 108544.367857143 \tabularnewline
57 & 1407026 & 1379300.13214286 & 27725.8678571429 \tabularnewline
58 & 1370199 & 1355459.46547619 & 14739.5345238095 \tabularnewline
59 & 964526 & 953128.46547619 & 11397.5345238095 \tabularnewline
60 & 850851 & 859685.96547619 & -8834.96547619048 \tabularnewline
61 & 683118 & 661949.553571429 & 21168.4464285715 \tabularnewline
62 & 847224 & 884800.386904762 & -37576.3869047618 \tabularnewline
63 & 1073256 & 1114821.88690476 & -41565.8869047619 \tabularnewline
64 & 1514326 & 1541329.55357143 & -27003.5535714285 \tabularnewline
65 & 1503734 & 1580314.05357143 & -76580.0535714285 \tabularnewline
66 & 1507712 & 1523269.2202381 & -15557.2202380953 \tabularnewline
67 & 2865698 & 2968090.05357143 & -102392.053571429 \tabularnewline
68 & 2788128 & 2666627.38690476 & 121500.613095238 \tabularnewline
69 & 1391596 & 1378794.88690476 & 12801.1130952381 \tabularnewline
70 & 1366378 & 1354954.2202381 & 11423.7797619047 \tabularnewline
71 & 946295 & 952623.220238095 & -6328.22023809521 \tabularnewline
72 & 859626 & 859180.720238095 & 445.279761904761 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201549&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]655362[/C][C]664475.779761905[/C][C]-9113.77976190497[/C][/ROW]
[ROW][C]2[/C][C]873127[/C][C]887326.613095238[/C][C]-14199.6130952381[/C][/ROW]
[ROW][C]3[/C][C]1107897[/C][C]1117348.11309524[/C][C]-9451.11309523815[/C][/ROW]
[ROW][C]4[/C][C]1555964[/C][C]1543855.7797619[/C][C]12108.2202380951[/C][/ROW]
[ROW][C]5[/C][C]1671159[/C][C]1582840.2797619[/C][C]88318.720238095[/C][/ROW]
[ROW][C]6[/C][C]1493308[/C][C]1525795.44642857[/C][C]-32487.4464285713[/C][/ROW]
[ROW][C]7[/C][C]2957796[/C][C]2970616.2797619[/C][C]-12820.2797619045[/C][/ROW]
[ROW][C]8[/C][C]2638691[/C][C]2669153.61309524[/C][C]-30462.6130952377[/C][/ROW]
[ROW][C]9[/C][C]1305669[/C][C]1381321.11309524[/C][C]-75652.1130952381[/C][/ROW]
[ROW][C]10[/C][C]1280496[/C][C]1357480.44642857[/C][C]-76984.4464285713[/C][/ROW]
[ROW][C]11[/C][C]921900[/C][C]955149.446428571[/C][C]-33249.4464285715[/C][/ROW]
[ROW][C]12[/C][C]867888[/C][C]861706.946428571[/C][C]6181.05357142854[/C][/ROW]
[ROW][C]13[/C][C]652586[/C][C]663970.534523809[/C][C]-11384.5345238095[/C][/ROW]
[ROW][C]14[/C][C]913831[/C][C]886821.367857143[/C][C]27009.6321428571[/C][/ROW]
[ROW][C]15[/C][C]1108544[/C][C]1116842.86785714[/C][C]-8298.86785714284[/C][/ROW]
[ROW][C]16[/C][C]1555827[/C][C]1543350.53452381[/C][C]12476.4654761905[/C][/ROW]
[ROW][C]17[/C][C]1699283[/C][C]1582335.03452381[/C][C]116947.96547619[/C][/ROW]
[ROW][C]18[/C][C]1509458[/C][C]1525290.20119048[/C][C]-15832.2011904762[/C][/ROW]
[ROW][C]19[/C][C]3268975[/C][C]2970111.03452381[/C][C]298863.96547619[/C][/ROW]
[ROW][C]20[/C][C]2425016[/C][C]2668648.36785714[/C][C]-243632.367857143[/C][/ROW]
[ROW][C]21[/C][C]1312703[/C][C]1380815.86785714[/C][C]-68112.8678571429[/C][/ROW]
[ROW][C]22[/C][C]1365498[/C][C]1356975.20119048[/C][C]8522.79880952377[/C][/ROW]
[ROW][C]23[/C][C]934453[/C][C]954644.201190476[/C][C]-20191.2011904762[/C][/ROW]
[ROW][C]24[/C][C]775019[/C][C]861201.701190476[/C][C]-86182.7011904761[/C][/ROW]
[ROW][C]25[/C][C]651142[/C][C]663465.289285714[/C][C]-12323.2892857142[/C][/ROW]
[ROW][C]26[/C][C]843192[/C][C]886316.122619048[/C][C]-43124.1226190475[/C][/ROW]
[ROW][C]27[/C][C]1146766[/C][C]1116337.62261905[/C][C]30428.3773809524[/C][/ROW]
[ROW][C]28[/C][C]1652601[/C][C]1542845.28928571[/C][C]109755.710714286[/C][/ROW]
[ROW][C]29[/C][C]1465906[/C][C]1581829.78928571[/C][C]-115923.789285714[/C][/ROW]
[ROW][C]30[/C][C]1652734[/C][C]1524784.95595238[/C][C]127949.044047619[/C][/ROW]
[ROW][C]31[/C][C]2922334[/C][C]2969605.78928571[/C][C]-47271.7892857143[/C][/ROW]
[ROW][C]32[/C][C]2702805[/C][C]2668143.12261905[/C][C]34661.8773809522[/C][/ROW]
[ROW][C]33[/C][C]1458956[/C][C]1380310.62261905[/C][C]78645.3773809524[/C][/ROW]
[ROW][C]34[/C][C]1410363[/C][C]1356469.95595238[/C][C]53893.0440476191[/C][/ROW]
[ROW][C]35[/C][C]1019279[/C][C]954138.955952381[/C][C]65140.044047619[/C][/ROW]
[ROW][C]36[/C][C]936574[/C][C]860696.455952381[/C][C]75877.544047619[/C][/ROW]
[ROW][C]37[/C][C]708917[/C][C]662960.044047619[/C][C]45956.955952381[/C][/ROW]
[ROW][C]38[/C][C]885295[/C][C]885810.877380952[/C][C]-515.877380952416[/C][/ROW]
[ROW][C]39[/C][C]1099663[/C][C]1115832.37738095[/C][C]-16169.3773809524[/C][/ROW]
[ROW][C]40[/C][C]1576220[/C][C]1542340.04404762[/C][C]33879.955952381[/C][/ROW]
[ROW][C]41[/C][C]1487870[/C][C]1581324.54404762[/C][C]-93454.544047619[/C][/ROW]
[ROW][C]42[/C][C]1488635[/C][C]1524279.71071429[/C][C]-35644.7107142857[/C][/ROW]
[ROW][C]43[/C][C]2882530[/C][C]2969100.54404762[/C][C]-86570.5440476191[/C][/ROW]
[ROW][C]44[/C][C]2677026[/C][C]2667637.87738095[/C][C]9388.12261904743[/C][/ROW]
[ROW][C]45[/C][C]1404398[/C][C]1379805.37738095[/C][C]24592.6226190476[/C][/ROW]
[ROW][C]46[/C][C]1344370[/C][C]1355964.71071429[/C][C]-11594.7107142857[/C][/ROW]
[ROW][C]47[/C][C]936865[/C][C]953633.710714286[/C][C]-16768.7107142857[/C][/ROW]
[ROW][C]48[/C][C]872705[/C][C]860191.210714286[/C][C]12513.7892857143[/C][/ROW]
[ROW][C]49[/C][C]628151[/C][C]662454.798809524[/C][C]-34303.7988095238[/C][/ROW]
[ROW][C]50[/C][C]953712[/C][C]885305.632142857[/C][C]68406.3678571428[/C][/ROW]
[ROW][C]51[/C][C]1160384[/C][C]1115327.13214286[/C][C]45056.8678571429[/C][/ROW]
[ROW][C]52[/C][C]1400618[/C][C]1541834.79880952[/C][C]-141216.798809524[/C][/ROW]
[ROW][C]53[/C][C]1661511[/C][C]1580819.29880952[/C][C]80691.7011904762[/C][/ROW]
[ROW][C]54[/C][C]1495347[/C][C]1523774.46547619[/C][C]-28427.4654761905[/C][/ROW]
[ROW][C]55[/C][C]2918786[/C][C]2968595.29880952[/C][C]-49809.2988095238[/C][/ROW]
[ROW][C]56[/C][C]2775677[/C][C]2667132.63214286[/C][C]108544.367857143[/C][/ROW]
[ROW][C]57[/C][C]1407026[/C][C]1379300.13214286[/C][C]27725.8678571429[/C][/ROW]
[ROW][C]58[/C][C]1370199[/C][C]1355459.46547619[/C][C]14739.5345238095[/C][/ROW]
[ROW][C]59[/C][C]964526[/C][C]953128.46547619[/C][C]11397.5345238095[/C][/ROW]
[ROW][C]60[/C][C]850851[/C][C]859685.96547619[/C][C]-8834.96547619048[/C][/ROW]
[ROW][C]61[/C][C]683118[/C][C]661949.553571429[/C][C]21168.4464285715[/C][/ROW]
[ROW][C]62[/C][C]847224[/C][C]884800.386904762[/C][C]-37576.3869047618[/C][/ROW]
[ROW][C]63[/C][C]1073256[/C][C]1114821.88690476[/C][C]-41565.8869047619[/C][/ROW]
[ROW][C]64[/C][C]1514326[/C][C]1541329.55357143[/C][C]-27003.5535714285[/C][/ROW]
[ROW][C]65[/C][C]1503734[/C][C]1580314.05357143[/C][C]-76580.0535714285[/C][/ROW]
[ROW][C]66[/C][C]1507712[/C][C]1523269.2202381[/C][C]-15557.2202380953[/C][/ROW]
[ROW][C]67[/C][C]2865698[/C][C]2968090.05357143[/C][C]-102392.053571429[/C][/ROW]
[ROW][C]68[/C][C]2788128[/C][C]2666627.38690476[/C][C]121500.613095238[/C][/ROW]
[ROW][C]69[/C][C]1391596[/C][C]1378794.88690476[/C][C]12801.1130952381[/C][/ROW]
[ROW][C]70[/C][C]1366378[/C][C]1354954.2202381[/C][C]11423.7797619047[/C][/ROW]
[ROW][C]71[/C][C]946295[/C][C]952623.220238095[/C][C]-6328.22023809521[/C][/ROW]
[ROW][C]72[/C][C]859626[/C][C]859180.720238095[/C][C]445.279761904761[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201549&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201549&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
1655362664475.779761905-9113.77976190497
2873127887326.613095238-14199.6130952381
311078971117348.11309524-9451.11309523815
415559641543855.779761912108.2202380951
516711591582840.279761988318.720238095
614933081525795.44642857-32487.4464285713
729577962970616.2797619-12820.2797619045
826386912669153.61309524-30462.6130952377
913056691381321.11309524-75652.1130952381
1012804961357480.44642857-76984.4464285713
11921900955149.446428571-33249.4464285715
12867888861706.9464285716181.05357142854
13652586663970.534523809-11384.5345238095
14913831886821.36785714327009.6321428571
1511085441116842.86785714-8298.86785714284
1615558271543350.5345238112476.4654761905
1716992831582335.03452381116947.96547619
1815094581525290.20119048-15832.2011904762
1932689752970111.03452381298863.96547619
2024250162668648.36785714-243632.367857143
2113127031380815.86785714-68112.8678571429
2213654981356975.201190488522.79880952377
23934453954644.201190476-20191.2011904762
24775019861201.701190476-86182.7011904761
25651142663465.289285714-12323.2892857142
26843192886316.122619048-43124.1226190475
2711467661116337.6226190530428.3773809524
2816526011542845.28928571109755.710714286
2914659061581829.78928571-115923.789285714
3016527341524784.95595238127949.044047619
3129223342969605.78928571-47271.7892857143
3227028052668143.1226190534661.8773809522
3314589561380310.6226190578645.3773809524
3414103631356469.9559523853893.0440476191
351019279954138.95595238165140.044047619
36936574860696.45595238175877.544047619
37708917662960.04404761945956.955952381
38885295885810.877380952-515.877380952416
3910996631115832.37738095-16169.3773809524
4015762201542340.0440476233879.955952381
4114878701581324.54404762-93454.544047619
4214886351524279.71071429-35644.7107142857
4328825302969100.54404762-86570.5440476191
4426770262667637.877380959388.12261904743
4514043981379805.3773809524592.6226190476
4613443701355964.71071429-11594.7107142857
47936865953633.710714286-16768.7107142857
48872705860191.21071428612513.7892857143
49628151662454.798809524-34303.7988095238
50953712885305.63214285768406.3678571428
5111603841115327.1321428645056.8678571429
5214006181541834.79880952-141216.798809524
5316615111580819.2988095280691.7011904762
5414953471523774.46547619-28427.4654761905
5529187862968595.29880952-49809.2988095238
5627756772667132.63214286108544.367857143
5714070261379300.1321428627725.8678571429
5813701991355459.4654761914739.5345238095
59964526953128.4654761911397.5345238095
60850851859685.96547619-8834.96547619048
61683118661949.55357142921168.4464285715
62847224884800.386904762-37576.3869047618
6310732561114821.88690476-41565.8869047619
6415143261541329.55357143-27003.5535714285
6515037341580314.05357143-76580.0535714285
6615077121523269.2202381-15557.2202380953
6728656982968090.05357143-102392.053571429
6827881282666627.38690476121500.613095238
6913915961378794.8869047612801.1130952381
7013663781354954.220238111423.7797619047
71946295952623.220238095-6328.22023809521
72859626859180.720238095445.279761904761







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.006083052827022960.01216610565404590.993916947172977
170.001311021101911570.002622042203823130.998688978898088
180.0001775137198562990.0003550274397125980.999822486280144
190.686164190035740.627671619928520.31383580996426
200.9848276689190350.03034466216192960.0151723310809648
210.9806457139395130.03870857212097340.0193542860604867
220.9695836422304860.06083271553902790.030416357769514
230.9518245257317310.09635094853653880.0481754742682694
240.968616990504320.06276601899135940.0313830094956797
250.9525732745148690.09485345097026180.0474267254851309
260.9469393431284580.1061213137430830.0530606568715417
270.9202702902509480.1594594194981050.0797297097490523
280.9462596734840520.1074806530318970.0537403265159484
290.9922792825220620.01544143495587650.00772071747793824
300.9980272662928210.003945467414358170.00197273370717908
310.9988947602278460.002210479544307260.00110523977215363
320.9993332083558460.001333583288307590.000666791644153795
330.9992632605344170.00147347893116580.000736739465582898
340.9987591013777060.002481797244587290.00124089862229365
350.9982933039590380.003413392081924070.00170669604096203
360.9981090579659440.003781884068112350.00189094203405618
370.9970278556679090.005944288664181550.00297214433209077
380.9945212056242010.01095758875159880.00547879437579939
390.9907750519985230.01844989600295390.00922494800147694
400.9949156578211160.01016868435776720.00508434217888361
410.9969153315296310.006169336940738220.00308466847036911
420.9945827195617970.01083456087640610.00541728043820307
430.9936489081736020.01270218365279620.0063510918263981
440.9965318829107340.006936234178531590.0034681170892658
450.9929557958258650.01408840834827030.00704420417413517
460.9880149965233330.02397000695333350.0119850034766668
470.9804010523449490.03919789531010230.0195989476550512
480.9635693567675720.07286128646485690.0364306432324285
490.9553755833128960.08924883337420710.0446244166871035
500.9498357083237440.1003285833525110.0501642916762556
510.9347898227098960.1304203545802070.0652101772901037
520.9796129438965290.0407741122069420.020387056103471
530.9997634552025510.000473089594896970.000236544797448485
540.9991284522120880.001743095575823340.000871547787911668
550.9996527052705820.0006945894588365230.000347294729418262
560.9986267815401650.002746436919669250.00137321845983462

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.00608305282702296 & 0.0121661056540459 & 0.993916947172977 \tabularnewline
17 & 0.00131102110191157 & 0.00262204220382313 & 0.998688978898088 \tabularnewline
18 & 0.000177513719856299 & 0.000355027439712598 & 0.999822486280144 \tabularnewline
19 & 0.68616419003574 & 0.62767161992852 & 0.31383580996426 \tabularnewline
20 & 0.984827668919035 & 0.0303446621619296 & 0.0151723310809648 \tabularnewline
21 & 0.980645713939513 & 0.0387085721209734 & 0.0193542860604867 \tabularnewline
22 & 0.969583642230486 & 0.0608327155390279 & 0.030416357769514 \tabularnewline
23 & 0.951824525731731 & 0.0963509485365388 & 0.0481754742682694 \tabularnewline
24 & 0.96861699050432 & 0.0627660189913594 & 0.0313830094956797 \tabularnewline
25 & 0.952573274514869 & 0.0948534509702618 & 0.0474267254851309 \tabularnewline
26 & 0.946939343128458 & 0.106121313743083 & 0.0530606568715417 \tabularnewline
27 & 0.920270290250948 & 0.159459419498105 & 0.0797297097490523 \tabularnewline
28 & 0.946259673484052 & 0.107480653031897 & 0.0537403265159484 \tabularnewline
29 & 0.992279282522062 & 0.0154414349558765 & 0.00772071747793824 \tabularnewline
30 & 0.998027266292821 & 0.00394546741435817 & 0.00197273370717908 \tabularnewline
31 & 0.998894760227846 & 0.00221047954430726 & 0.00110523977215363 \tabularnewline
32 & 0.999333208355846 & 0.00133358328830759 & 0.000666791644153795 \tabularnewline
33 & 0.999263260534417 & 0.0014734789311658 & 0.000736739465582898 \tabularnewline
34 & 0.998759101377706 & 0.00248179724458729 & 0.00124089862229365 \tabularnewline
35 & 0.998293303959038 & 0.00341339208192407 & 0.00170669604096203 \tabularnewline
36 & 0.998109057965944 & 0.00378188406811235 & 0.00189094203405618 \tabularnewline
37 & 0.997027855667909 & 0.00594428866418155 & 0.00297214433209077 \tabularnewline
38 & 0.994521205624201 & 0.0109575887515988 & 0.00547879437579939 \tabularnewline
39 & 0.990775051998523 & 0.0184498960029539 & 0.00922494800147694 \tabularnewline
40 & 0.994915657821116 & 0.0101686843577672 & 0.00508434217888361 \tabularnewline
41 & 0.996915331529631 & 0.00616933694073822 & 0.00308466847036911 \tabularnewline
42 & 0.994582719561797 & 0.0108345608764061 & 0.00541728043820307 \tabularnewline
43 & 0.993648908173602 & 0.0127021836527962 & 0.0063510918263981 \tabularnewline
44 & 0.996531882910734 & 0.00693623417853159 & 0.0034681170892658 \tabularnewline
45 & 0.992955795825865 & 0.0140884083482703 & 0.00704420417413517 \tabularnewline
46 & 0.988014996523333 & 0.0239700069533335 & 0.0119850034766668 \tabularnewline
47 & 0.980401052344949 & 0.0391978953101023 & 0.0195989476550512 \tabularnewline
48 & 0.963569356767572 & 0.0728612864648569 & 0.0364306432324285 \tabularnewline
49 & 0.955375583312896 & 0.0892488333742071 & 0.0446244166871035 \tabularnewline
50 & 0.949835708323744 & 0.100328583352511 & 0.0501642916762556 \tabularnewline
51 & 0.934789822709896 & 0.130420354580207 & 0.0652101772901037 \tabularnewline
52 & 0.979612943896529 & 0.040774112206942 & 0.020387056103471 \tabularnewline
53 & 0.999763455202551 & 0.00047308959489697 & 0.000236544797448485 \tabularnewline
54 & 0.999128452212088 & 0.00174309557582334 & 0.000871547787911668 \tabularnewline
55 & 0.999652705270582 & 0.000694589458836523 & 0.000347294729418262 \tabularnewline
56 & 0.998626781540165 & 0.00274643691966925 & 0.00137321845983462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201549&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]16[/C][C]0.00608305282702296[/C][C]0.0121661056540459[/C][C]0.993916947172977[/C][/ROW]
[ROW][C]17[/C][C]0.00131102110191157[/C][C]0.00262204220382313[/C][C]0.998688978898088[/C][/ROW]
[ROW][C]18[/C][C]0.000177513719856299[/C][C]0.000355027439712598[/C][C]0.999822486280144[/C][/ROW]
[ROW][C]19[/C][C]0.68616419003574[/C][C]0.62767161992852[/C][C]0.31383580996426[/C][/ROW]
[ROW][C]20[/C][C]0.984827668919035[/C][C]0.0303446621619296[/C][C]0.0151723310809648[/C][/ROW]
[ROW][C]21[/C][C]0.980645713939513[/C][C]0.0387085721209734[/C][C]0.0193542860604867[/C][/ROW]
[ROW][C]22[/C][C]0.969583642230486[/C][C]0.0608327155390279[/C][C]0.030416357769514[/C][/ROW]
[ROW][C]23[/C][C]0.951824525731731[/C][C]0.0963509485365388[/C][C]0.0481754742682694[/C][/ROW]
[ROW][C]24[/C][C]0.96861699050432[/C][C]0.0627660189913594[/C][C]0.0313830094956797[/C][/ROW]
[ROW][C]25[/C][C]0.952573274514869[/C][C]0.0948534509702618[/C][C]0.0474267254851309[/C][/ROW]
[ROW][C]26[/C][C]0.946939343128458[/C][C]0.106121313743083[/C][C]0.0530606568715417[/C][/ROW]
[ROW][C]27[/C][C]0.920270290250948[/C][C]0.159459419498105[/C][C]0.0797297097490523[/C][/ROW]
[ROW][C]28[/C][C]0.946259673484052[/C][C]0.107480653031897[/C][C]0.0537403265159484[/C][/ROW]
[ROW][C]29[/C][C]0.992279282522062[/C][C]0.0154414349558765[/C][C]0.00772071747793824[/C][/ROW]
[ROW][C]30[/C][C]0.998027266292821[/C][C]0.00394546741435817[/C][C]0.00197273370717908[/C][/ROW]
[ROW][C]31[/C][C]0.998894760227846[/C][C]0.00221047954430726[/C][C]0.00110523977215363[/C][/ROW]
[ROW][C]32[/C][C]0.999333208355846[/C][C]0.00133358328830759[/C][C]0.000666791644153795[/C][/ROW]
[ROW][C]33[/C][C]0.999263260534417[/C][C]0.0014734789311658[/C][C]0.000736739465582898[/C][/ROW]
[ROW][C]34[/C][C]0.998759101377706[/C][C]0.00248179724458729[/C][C]0.00124089862229365[/C][/ROW]
[ROW][C]35[/C][C]0.998293303959038[/C][C]0.00341339208192407[/C][C]0.00170669604096203[/C][/ROW]
[ROW][C]36[/C][C]0.998109057965944[/C][C]0.00378188406811235[/C][C]0.00189094203405618[/C][/ROW]
[ROW][C]37[/C][C]0.997027855667909[/C][C]0.00594428866418155[/C][C]0.00297214433209077[/C][/ROW]
[ROW][C]38[/C][C]0.994521205624201[/C][C]0.0109575887515988[/C][C]0.00547879437579939[/C][/ROW]
[ROW][C]39[/C][C]0.990775051998523[/C][C]0.0184498960029539[/C][C]0.00922494800147694[/C][/ROW]
[ROW][C]40[/C][C]0.994915657821116[/C][C]0.0101686843577672[/C][C]0.00508434217888361[/C][/ROW]
[ROW][C]41[/C][C]0.996915331529631[/C][C]0.00616933694073822[/C][C]0.00308466847036911[/C][/ROW]
[ROW][C]42[/C][C]0.994582719561797[/C][C]0.0108345608764061[/C][C]0.00541728043820307[/C][/ROW]
[ROW][C]43[/C][C]0.993648908173602[/C][C]0.0127021836527962[/C][C]0.0063510918263981[/C][/ROW]
[ROW][C]44[/C][C]0.996531882910734[/C][C]0.00693623417853159[/C][C]0.0034681170892658[/C][/ROW]
[ROW][C]45[/C][C]0.992955795825865[/C][C]0.0140884083482703[/C][C]0.00704420417413517[/C][/ROW]
[ROW][C]46[/C][C]0.988014996523333[/C][C]0.0239700069533335[/C][C]0.0119850034766668[/C][/ROW]
[ROW][C]47[/C][C]0.980401052344949[/C][C]0.0391978953101023[/C][C]0.0195989476550512[/C][/ROW]
[ROW][C]48[/C][C]0.963569356767572[/C][C]0.0728612864648569[/C][C]0.0364306432324285[/C][/ROW]
[ROW][C]49[/C][C]0.955375583312896[/C][C]0.0892488333742071[/C][C]0.0446244166871035[/C][/ROW]
[ROW][C]50[/C][C]0.949835708323744[/C][C]0.100328583352511[/C][C]0.0501642916762556[/C][/ROW]
[ROW][C]51[/C][C]0.934789822709896[/C][C]0.130420354580207[/C][C]0.0652101772901037[/C][/ROW]
[ROW][C]52[/C][C]0.979612943896529[/C][C]0.040774112206942[/C][C]0.020387056103471[/C][/ROW]
[ROW][C]53[/C][C]0.999763455202551[/C][C]0.00047308959489697[/C][C]0.000236544797448485[/C][/ROW]
[ROW][C]54[/C][C]0.999128452212088[/C][C]0.00174309557582334[/C][C]0.000871547787911668[/C][/ROW]
[ROW][C]55[/C][C]0.999652705270582[/C][C]0.000694589458836523[/C][C]0.000347294729418262[/C][/ROW]
[ROW][C]56[/C][C]0.998626781540165[/C][C]0.00274643691966925[/C][C]0.00137321845983462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201549&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201549&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
160.006083052827022960.01216610565404590.993916947172977
170.001311021101911570.002622042203823130.998688978898088
180.0001775137198562990.0003550274397125980.999822486280144
190.686164190035740.627671619928520.31383580996426
200.9848276689190350.03034466216192960.0151723310809648
210.9806457139395130.03870857212097340.0193542860604867
220.9695836422304860.06083271553902790.030416357769514
230.9518245257317310.09635094853653880.0481754742682694
240.968616990504320.06276601899135940.0313830094956797
250.9525732745148690.09485345097026180.0474267254851309
260.9469393431284580.1061213137430830.0530606568715417
270.9202702902509480.1594594194981050.0797297097490523
280.9462596734840520.1074806530318970.0537403265159484
290.9922792825220620.01544143495587650.00772071747793824
300.9980272662928210.003945467414358170.00197273370717908
310.9988947602278460.002210479544307260.00110523977215363
320.9993332083558460.001333583288307590.000666791644153795
330.9992632605344170.00147347893116580.000736739465582898
340.9987591013777060.002481797244587290.00124089862229365
350.9982933039590380.003413392081924070.00170669604096203
360.9981090579659440.003781884068112350.00189094203405618
370.9970278556679090.005944288664181550.00297214433209077
380.9945212056242010.01095758875159880.00547879437579939
390.9907750519985230.01844989600295390.00922494800147694
400.9949156578211160.01016868435776720.00508434217888361
410.9969153315296310.006169336940738220.00308466847036911
420.9945827195617970.01083456087640610.00541728043820307
430.9936489081736020.01270218365279620.0063510918263981
440.9965318829107340.006936234178531590.0034681170892658
450.9929557958258650.01408840834827030.00704420417413517
460.9880149965233330.02397000695333350.0119850034766668
470.9804010523449490.03919789531010230.0195989476550512
480.9635693567675720.07286128646485690.0364306432324285
490.9553755833128960.08924883337420710.0446244166871035
500.9498357083237440.1003285833525110.0501642916762556
510.9347898227098960.1304203545802070.0652101772901037
520.9796129438965290.0407741122069420.020387056103471
530.9997634552025510.000473089594896970.000236544797448485
540.9991284522120880.001743095575823340.000871547787911668
550.9996527052705820.0006945894588365230.000347294729418262
560.9986267815401650.002746436919669250.00137321845983462







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.390243902439024NOK
5% type I error level290.707317073170732NOK
10% type I error level350.853658536585366NOK

\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 & 16 & 0.390243902439024 & NOK \tabularnewline
5% type I error level & 29 & 0.707317073170732 & NOK \tabularnewline
10% type I error level & 35 & 0.853658536585366 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201549&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]16[/C][C]0.390243902439024[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.707317073170732[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.853658536585366[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201549&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201549&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 level160.390243902439024NOK
5% type I error level290.707317073170732NOK
10% type I error level350.853658536585366NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}