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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 computationMon, 05 Nov 2012 08:26:56 -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/Nov/05/t1352122046yb5zufspg60sew4.htm/, Retrieved Mon, 06 Feb 2023 00:52:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186045, Retrieved Mon, 06 Feb 2023 00:52:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Workshop 7 tegoei] [2012-11-05 13:26:56] [12383fa010e7b5252e187b5f14cfe683] [Current]
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Dataseries X:
8500	7300	8000
8350	7300	7700
8300	7200	9000
8400	8500	9200
9000	10000	9500
8300	9200	9200
7000	8200	8900
10300	8000	8700
7150	9000	10000
8100	9500	10500
7200	7400	9800
6000	10500	9900
6750	7600	10000
9200	8300	8900
7600	8400	10000
7000	4300	6500
8288	7400	9000
8400	8200	8700
14000	7500	9500
8500	8500	10000
9500	7600	8900
11811	8200	8700
10000	9200	9800
9500	7500	8750
9500	8200	9000
9452	9500	7500
9500	10000	7900
8600	11000	8700
11763	7500	8800
9766	9000	8900
11400	8000	9500
9500	8900	9600
11994	8700	8700
8400	6000	10000
7360	5000	10500
7400	7500	9250
8558	8000	8250
7000	7600	6500
7400	8900	8525
7200	9200	9255
8600	9500	8750
7800	7800	8540
7500	10000	7890
9000	8500	9000
7429	9000	10250
7206	9200	10100
7613	9450	8560
7200	8600	8000
7500	10500	7800
7500	7700	8000
9071	8500	1000
7600	7000	10250
8359	7500	9500
15000	6500	9350
6500	9000	8900
6500	8500	8750
6125	8600	8250
6000	9400	7900
7000	9200	9000
8500	8400	9250
7000	7900	10000
7000	10000	10250
6600	9000	9500
6800	8500	8900
12000	11000	9750
7200	9500	9000
7200	8900	8750
7300	9000	8000
7500	9200	8450
7000	8700	8700
7000	7000	8500
6000	7500	7500




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186045&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]7 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=186045&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186045&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 time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
A[t] = + 9529.54618451065 -0.0590986407827341B[t] + 0.00522632857877773C[t] -20.9452792402744t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
A[t] =  +  9529.54618451065 -0.0590986407827341B[t] +  0.00522632857877773C[t] -20.9452792402744t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186045&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]A[t] =  +  9529.54618451065 -0.0590986407827341B[t] +  0.00522632857877773C[t] -20.9452792402744t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186045&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186045&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
A[t] = + 9529.54618451065 -0.0590986407827341B[t] + 0.00522632857877773C[t] -20.9452792402744t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9529.546184510652049.9564744.64871.6e-058e-06
B-0.05909864078273410.1762-0.33540.7383510.369176
C0.005226328578777730.1661020.03150.9749910.487496
t-20.945279240274410.198269-2.05380.0438420.021921

\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) & 9529.54618451065 & 2049.956474 & 4.6487 & 1.6e-05 & 8e-06 \tabularnewline
B & -0.0590986407827341 & 0.1762 & -0.3354 & 0.738351 & 0.369176 \tabularnewline
C & 0.00522632857877773 & 0.166102 & 0.0315 & 0.974991 & 0.487496 \tabularnewline
t & -20.9452792402744 & 10.198269 & -2.0538 & 0.043842 & 0.021921 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186045&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]9529.54618451065[/C][C]2049.956474[/C][C]4.6487[/C][C]1.6e-05[/C][C]8e-06[/C][/ROW]
[ROW][C]B[/C][C]-0.0590986407827341[/C][C]0.1762[/C][C]-0.3354[/C][C]0.738351[/C][C]0.369176[/C][/ROW]
[ROW][C]C[/C][C]0.00522632857877773[/C][C]0.166102[/C][C]0.0315[/C][C]0.974991[/C][C]0.487496[/C][/ROW]
[ROW][C]t[/C][C]-20.9452792402744[/C][C]10.198269[/C][C]-2.0538[/C][C]0.043842[/C][C]0.021921[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186045&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186045&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)9529.546184510652049.9564744.64871.6e-058e-06
B-0.05909864078273410.1762-0.33540.7383510.369176
C0.005226328578777730.1661020.03150.9749910.487496
t-20.945279240274410.198269-2.05380.0438420.021921







Multiple Linear Regression - Regression Statistics
Multiple R0.256877786615736
R-squared0.0659861972565997
Adjusted R-squared0.0247797059590967
F-TEST (value)1.60135442690794
F-TEST (DF numerator)3
F-TEST (DF denominator)68
p-value0.197144651925275
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1755.09567185562
Sum Squared Residuals209464535.58091

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.256877786615736 \tabularnewline
R-squared & 0.0659861972565997 \tabularnewline
Adjusted R-squared & 0.0247797059590967 \tabularnewline
F-TEST (value) & 1.60135442690794 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 68 \tabularnewline
p-value & 0.197144651925275 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1755.09567185562 \tabularnewline
Sum Squared Residuals & 209464535.58091 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186045&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.256877786615736[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0659861972565997[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0247797059590967[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.60135442690794[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]68[/C][/ROW]
[ROW][C]p-value[/C][C]0.197144651925275[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1755.09567185562[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]209464535.58091[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186045&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186045&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 R0.256877786615736
R-squared0.0659861972565997
Adjusted R-squared0.0247797059590967
F-TEST (value)1.60135442690794
F-TEST (DF numerator)3
F-TEST (DF denominator)68
p-value0.197144651925275
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1755.09567185562
Sum Squared Residuals209464535.58091







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
185009118.99145618662-618.991456186625
283509096.47827837273-746.478278372727
383009088.23709036314-788.237090363138
484008991.50884382106-591.508843821065
590008883.48350198032116.516498019678
683008908.2492367926-608.249236792602
770008944.83469976143-1944.83469976143
8103008934.663882961941365.33611703805
971508861.41419009135-1711.41419009135
1081008813.53275474909-713.532754749095
1172008913.03619114742-1713.03619114742
1260008709.40775833854-2709.40775833855
1367508860.37117022608-2110.37117022608
1492008792.30788100123407.692118998766
1576008771.20169911934-1171.20169911934
1670008974.26869706255-1974.26869706256
1782888783.18345284275-495.183452842749
1884008713.39136240265-313.391362402654
19140008737.996194573325262.00380542668
2085008660.5654388397-160.565438839696
2195008687.05997486723812.940025132773
22118118629.610245441563181.38975455844
23100008555.31528685521444.6847131448
2495008629.35005193786870.64994806214
2595008568.34230629437931.657693705634
2694528462.72930116837989.270698831629
2795008414.325232968241085.67476703176
2886008338.46237580825261.537624191745
29117638524.884972165433238.11502783457
3097668415.814364608931350.18563539107
31114008457.103523298662942.89647670134
3295008383.49210021181116.5078997882
33119948369.662853407173624.33714659283
3484008515.07813143269-115.07813143269
3573608555.84465726454-1195.84465726454
3674008380.61986534396-980.619865343956
3785588324.89893713354233.101062866463
3870008318.4470391935-1318.4470391935
3974008231.25684230769-831.256842307691
4072008196.3971906951-996.397190695105
4186008155.08302328773444.916976712273
4278008233.50790437656-433.507904376557
4375008079.14850183806-579.148501838062
4490008152.65240849433847.347591505668
4574298108.69071958616-679.690719586163
4672068075.14176290253-869.141762902525
4776138031.37327745525-418.373277455249
4872008057.73509887618-857.735098876184
4975007923.45713643296-423.457136432959
5075008069.0333171001-569.033317100096
5190717964.224825182191106.77517481781
5276008080.27104646971-480.27104646971
5383598025.85670040399333.143299596014
54150008063.226112659636936.77388734037
5565007892.18238360207-1392.18238360207
5665007900.00247546635-1400.00247546635
5761257870.53416785841-1745.53416785841
5860007800.48076098937-1800.48076098937
5970007797.1041713423-797.104171342302
6085007824.74438687291675.25561312709
6170007837.26817445809-837.268174458086
6270007693.52233171876-693.522331718764
6366007727.75594682714-1127.75594682714
6468007733.22419083097-933.224190830966
65120007568.974688925824431.02531107418
6672007632.75762442556-432.757624425561
6772007645.96494751023-445.964947510233
6873007615.1900577576-315.190057757602
6975007584.77689822123-84.7768982212304
7070007594.68752151702-594.687521517018
7170007673.16466589164-673.164665891636
7260007617.44373768122-1617.44373768122

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8500 & 9118.99145618662 & -618.991456186625 \tabularnewline
2 & 8350 & 9096.47827837273 & -746.478278372727 \tabularnewline
3 & 8300 & 9088.23709036314 & -788.237090363138 \tabularnewline
4 & 8400 & 8991.50884382106 & -591.508843821065 \tabularnewline
5 & 9000 & 8883.48350198032 & 116.516498019678 \tabularnewline
6 & 8300 & 8908.2492367926 & -608.249236792602 \tabularnewline
7 & 7000 & 8944.83469976143 & -1944.83469976143 \tabularnewline
8 & 10300 & 8934.66388296194 & 1365.33611703805 \tabularnewline
9 & 7150 & 8861.41419009135 & -1711.41419009135 \tabularnewline
10 & 8100 & 8813.53275474909 & -713.532754749095 \tabularnewline
11 & 7200 & 8913.03619114742 & -1713.03619114742 \tabularnewline
12 & 6000 & 8709.40775833854 & -2709.40775833855 \tabularnewline
13 & 6750 & 8860.37117022608 & -2110.37117022608 \tabularnewline
14 & 9200 & 8792.30788100123 & 407.692118998766 \tabularnewline
15 & 7600 & 8771.20169911934 & -1171.20169911934 \tabularnewline
16 & 7000 & 8974.26869706255 & -1974.26869706256 \tabularnewline
17 & 8288 & 8783.18345284275 & -495.183452842749 \tabularnewline
18 & 8400 & 8713.39136240265 & -313.391362402654 \tabularnewline
19 & 14000 & 8737.99619457332 & 5262.00380542668 \tabularnewline
20 & 8500 & 8660.5654388397 & -160.565438839696 \tabularnewline
21 & 9500 & 8687.05997486723 & 812.940025132773 \tabularnewline
22 & 11811 & 8629.61024544156 & 3181.38975455844 \tabularnewline
23 & 10000 & 8555.3152868552 & 1444.6847131448 \tabularnewline
24 & 9500 & 8629.35005193786 & 870.64994806214 \tabularnewline
25 & 9500 & 8568.34230629437 & 931.657693705634 \tabularnewline
26 & 9452 & 8462.72930116837 & 989.270698831629 \tabularnewline
27 & 9500 & 8414.32523296824 & 1085.67476703176 \tabularnewline
28 & 8600 & 8338.46237580825 & 261.537624191745 \tabularnewline
29 & 11763 & 8524.88497216543 & 3238.11502783457 \tabularnewline
30 & 9766 & 8415.81436460893 & 1350.18563539107 \tabularnewline
31 & 11400 & 8457.10352329866 & 2942.89647670134 \tabularnewline
32 & 9500 & 8383.4921002118 & 1116.5078997882 \tabularnewline
33 & 11994 & 8369.66285340717 & 3624.33714659283 \tabularnewline
34 & 8400 & 8515.07813143269 & -115.07813143269 \tabularnewline
35 & 7360 & 8555.84465726454 & -1195.84465726454 \tabularnewline
36 & 7400 & 8380.61986534396 & -980.619865343956 \tabularnewline
37 & 8558 & 8324.89893713354 & 233.101062866463 \tabularnewline
38 & 7000 & 8318.4470391935 & -1318.4470391935 \tabularnewline
39 & 7400 & 8231.25684230769 & -831.256842307691 \tabularnewline
40 & 7200 & 8196.3971906951 & -996.397190695105 \tabularnewline
41 & 8600 & 8155.08302328773 & 444.916976712273 \tabularnewline
42 & 7800 & 8233.50790437656 & -433.507904376557 \tabularnewline
43 & 7500 & 8079.14850183806 & -579.148501838062 \tabularnewline
44 & 9000 & 8152.65240849433 & 847.347591505668 \tabularnewline
45 & 7429 & 8108.69071958616 & -679.690719586163 \tabularnewline
46 & 7206 & 8075.14176290253 & -869.141762902525 \tabularnewline
47 & 7613 & 8031.37327745525 & -418.373277455249 \tabularnewline
48 & 7200 & 8057.73509887618 & -857.735098876184 \tabularnewline
49 & 7500 & 7923.45713643296 & -423.457136432959 \tabularnewline
50 & 7500 & 8069.0333171001 & -569.033317100096 \tabularnewline
51 & 9071 & 7964.22482518219 & 1106.77517481781 \tabularnewline
52 & 7600 & 8080.27104646971 & -480.27104646971 \tabularnewline
53 & 8359 & 8025.85670040399 & 333.143299596014 \tabularnewline
54 & 15000 & 8063.22611265963 & 6936.77388734037 \tabularnewline
55 & 6500 & 7892.18238360207 & -1392.18238360207 \tabularnewline
56 & 6500 & 7900.00247546635 & -1400.00247546635 \tabularnewline
57 & 6125 & 7870.53416785841 & -1745.53416785841 \tabularnewline
58 & 6000 & 7800.48076098937 & -1800.48076098937 \tabularnewline
59 & 7000 & 7797.1041713423 & -797.104171342302 \tabularnewline
60 & 8500 & 7824.74438687291 & 675.25561312709 \tabularnewline
61 & 7000 & 7837.26817445809 & -837.268174458086 \tabularnewline
62 & 7000 & 7693.52233171876 & -693.522331718764 \tabularnewline
63 & 6600 & 7727.75594682714 & -1127.75594682714 \tabularnewline
64 & 6800 & 7733.22419083097 & -933.224190830966 \tabularnewline
65 & 12000 & 7568.97468892582 & 4431.02531107418 \tabularnewline
66 & 7200 & 7632.75762442556 & -432.757624425561 \tabularnewline
67 & 7200 & 7645.96494751023 & -445.964947510233 \tabularnewline
68 & 7300 & 7615.1900577576 & -315.190057757602 \tabularnewline
69 & 7500 & 7584.77689822123 & -84.7768982212304 \tabularnewline
70 & 7000 & 7594.68752151702 & -594.687521517018 \tabularnewline
71 & 7000 & 7673.16466589164 & -673.164665891636 \tabularnewline
72 & 6000 & 7617.44373768122 & -1617.44373768122 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186045&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]8500[/C][C]9118.99145618662[/C][C]-618.991456186625[/C][/ROW]
[ROW][C]2[/C][C]8350[/C][C]9096.47827837273[/C][C]-746.478278372727[/C][/ROW]
[ROW][C]3[/C][C]8300[/C][C]9088.23709036314[/C][C]-788.237090363138[/C][/ROW]
[ROW][C]4[/C][C]8400[/C][C]8991.50884382106[/C][C]-591.508843821065[/C][/ROW]
[ROW][C]5[/C][C]9000[/C][C]8883.48350198032[/C][C]116.516498019678[/C][/ROW]
[ROW][C]6[/C][C]8300[/C][C]8908.2492367926[/C][C]-608.249236792602[/C][/ROW]
[ROW][C]7[/C][C]7000[/C][C]8944.83469976143[/C][C]-1944.83469976143[/C][/ROW]
[ROW][C]8[/C][C]10300[/C][C]8934.66388296194[/C][C]1365.33611703805[/C][/ROW]
[ROW][C]9[/C][C]7150[/C][C]8861.41419009135[/C][C]-1711.41419009135[/C][/ROW]
[ROW][C]10[/C][C]8100[/C][C]8813.53275474909[/C][C]-713.532754749095[/C][/ROW]
[ROW][C]11[/C][C]7200[/C][C]8913.03619114742[/C][C]-1713.03619114742[/C][/ROW]
[ROW][C]12[/C][C]6000[/C][C]8709.40775833854[/C][C]-2709.40775833855[/C][/ROW]
[ROW][C]13[/C][C]6750[/C][C]8860.37117022608[/C][C]-2110.37117022608[/C][/ROW]
[ROW][C]14[/C][C]9200[/C][C]8792.30788100123[/C][C]407.692118998766[/C][/ROW]
[ROW][C]15[/C][C]7600[/C][C]8771.20169911934[/C][C]-1171.20169911934[/C][/ROW]
[ROW][C]16[/C][C]7000[/C][C]8974.26869706255[/C][C]-1974.26869706256[/C][/ROW]
[ROW][C]17[/C][C]8288[/C][C]8783.18345284275[/C][C]-495.183452842749[/C][/ROW]
[ROW][C]18[/C][C]8400[/C][C]8713.39136240265[/C][C]-313.391362402654[/C][/ROW]
[ROW][C]19[/C][C]14000[/C][C]8737.99619457332[/C][C]5262.00380542668[/C][/ROW]
[ROW][C]20[/C][C]8500[/C][C]8660.5654388397[/C][C]-160.565438839696[/C][/ROW]
[ROW][C]21[/C][C]9500[/C][C]8687.05997486723[/C][C]812.940025132773[/C][/ROW]
[ROW][C]22[/C][C]11811[/C][C]8629.61024544156[/C][C]3181.38975455844[/C][/ROW]
[ROW][C]23[/C][C]10000[/C][C]8555.3152868552[/C][C]1444.6847131448[/C][/ROW]
[ROW][C]24[/C][C]9500[/C][C]8629.35005193786[/C][C]870.64994806214[/C][/ROW]
[ROW][C]25[/C][C]9500[/C][C]8568.34230629437[/C][C]931.657693705634[/C][/ROW]
[ROW][C]26[/C][C]9452[/C][C]8462.72930116837[/C][C]989.270698831629[/C][/ROW]
[ROW][C]27[/C][C]9500[/C][C]8414.32523296824[/C][C]1085.67476703176[/C][/ROW]
[ROW][C]28[/C][C]8600[/C][C]8338.46237580825[/C][C]261.537624191745[/C][/ROW]
[ROW][C]29[/C][C]11763[/C][C]8524.88497216543[/C][C]3238.11502783457[/C][/ROW]
[ROW][C]30[/C][C]9766[/C][C]8415.81436460893[/C][C]1350.18563539107[/C][/ROW]
[ROW][C]31[/C][C]11400[/C][C]8457.10352329866[/C][C]2942.89647670134[/C][/ROW]
[ROW][C]32[/C][C]9500[/C][C]8383.4921002118[/C][C]1116.5078997882[/C][/ROW]
[ROW][C]33[/C][C]11994[/C][C]8369.66285340717[/C][C]3624.33714659283[/C][/ROW]
[ROW][C]34[/C][C]8400[/C][C]8515.07813143269[/C][C]-115.07813143269[/C][/ROW]
[ROW][C]35[/C][C]7360[/C][C]8555.84465726454[/C][C]-1195.84465726454[/C][/ROW]
[ROW][C]36[/C][C]7400[/C][C]8380.61986534396[/C][C]-980.619865343956[/C][/ROW]
[ROW][C]37[/C][C]8558[/C][C]8324.89893713354[/C][C]233.101062866463[/C][/ROW]
[ROW][C]38[/C][C]7000[/C][C]8318.4470391935[/C][C]-1318.4470391935[/C][/ROW]
[ROW][C]39[/C][C]7400[/C][C]8231.25684230769[/C][C]-831.256842307691[/C][/ROW]
[ROW][C]40[/C][C]7200[/C][C]8196.3971906951[/C][C]-996.397190695105[/C][/ROW]
[ROW][C]41[/C][C]8600[/C][C]8155.08302328773[/C][C]444.916976712273[/C][/ROW]
[ROW][C]42[/C][C]7800[/C][C]8233.50790437656[/C][C]-433.507904376557[/C][/ROW]
[ROW][C]43[/C][C]7500[/C][C]8079.14850183806[/C][C]-579.148501838062[/C][/ROW]
[ROW][C]44[/C][C]9000[/C][C]8152.65240849433[/C][C]847.347591505668[/C][/ROW]
[ROW][C]45[/C][C]7429[/C][C]8108.69071958616[/C][C]-679.690719586163[/C][/ROW]
[ROW][C]46[/C][C]7206[/C][C]8075.14176290253[/C][C]-869.141762902525[/C][/ROW]
[ROW][C]47[/C][C]7613[/C][C]8031.37327745525[/C][C]-418.373277455249[/C][/ROW]
[ROW][C]48[/C][C]7200[/C][C]8057.73509887618[/C][C]-857.735098876184[/C][/ROW]
[ROW][C]49[/C][C]7500[/C][C]7923.45713643296[/C][C]-423.457136432959[/C][/ROW]
[ROW][C]50[/C][C]7500[/C][C]8069.0333171001[/C][C]-569.033317100096[/C][/ROW]
[ROW][C]51[/C][C]9071[/C][C]7964.22482518219[/C][C]1106.77517481781[/C][/ROW]
[ROW][C]52[/C][C]7600[/C][C]8080.27104646971[/C][C]-480.27104646971[/C][/ROW]
[ROW][C]53[/C][C]8359[/C][C]8025.85670040399[/C][C]333.143299596014[/C][/ROW]
[ROW][C]54[/C][C]15000[/C][C]8063.22611265963[/C][C]6936.77388734037[/C][/ROW]
[ROW][C]55[/C][C]6500[/C][C]7892.18238360207[/C][C]-1392.18238360207[/C][/ROW]
[ROW][C]56[/C][C]6500[/C][C]7900.00247546635[/C][C]-1400.00247546635[/C][/ROW]
[ROW][C]57[/C][C]6125[/C][C]7870.53416785841[/C][C]-1745.53416785841[/C][/ROW]
[ROW][C]58[/C][C]6000[/C][C]7800.48076098937[/C][C]-1800.48076098937[/C][/ROW]
[ROW][C]59[/C][C]7000[/C][C]7797.1041713423[/C][C]-797.104171342302[/C][/ROW]
[ROW][C]60[/C][C]8500[/C][C]7824.74438687291[/C][C]675.25561312709[/C][/ROW]
[ROW][C]61[/C][C]7000[/C][C]7837.26817445809[/C][C]-837.268174458086[/C][/ROW]
[ROW][C]62[/C][C]7000[/C][C]7693.52233171876[/C][C]-693.522331718764[/C][/ROW]
[ROW][C]63[/C][C]6600[/C][C]7727.75594682714[/C][C]-1127.75594682714[/C][/ROW]
[ROW][C]64[/C][C]6800[/C][C]7733.22419083097[/C][C]-933.224190830966[/C][/ROW]
[ROW][C]65[/C][C]12000[/C][C]7568.97468892582[/C][C]4431.02531107418[/C][/ROW]
[ROW][C]66[/C][C]7200[/C][C]7632.75762442556[/C][C]-432.757624425561[/C][/ROW]
[ROW][C]67[/C][C]7200[/C][C]7645.96494751023[/C][C]-445.964947510233[/C][/ROW]
[ROW][C]68[/C][C]7300[/C][C]7615.1900577576[/C][C]-315.190057757602[/C][/ROW]
[ROW][C]69[/C][C]7500[/C][C]7584.77689822123[/C][C]-84.7768982212304[/C][/ROW]
[ROW][C]70[/C][C]7000[/C][C]7594.68752151702[/C][C]-594.687521517018[/C][/ROW]
[ROW][C]71[/C][C]7000[/C][C]7673.16466589164[/C][C]-673.164665891636[/C][/ROW]
[ROW][C]72[/C][C]6000[/C][C]7617.44373768122[/C][C]-1617.44373768122[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186045&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186045&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
185009118.99145618662-618.991456186625
283509096.47827837273-746.478278372727
383009088.23709036314-788.237090363138
484008991.50884382106-591.508843821065
590008883.48350198032116.516498019678
683008908.2492367926-608.249236792602
770008944.83469976143-1944.83469976143
8103008934.663882961941365.33611703805
971508861.41419009135-1711.41419009135
1081008813.53275474909-713.532754749095
1172008913.03619114742-1713.03619114742
1260008709.40775833854-2709.40775833855
1367508860.37117022608-2110.37117022608
1492008792.30788100123407.692118998766
1576008771.20169911934-1171.20169911934
1670008974.26869706255-1974.26869706256
1782888783.18345284275-495.183452842749
1884008713.39136240265-313.391362402654
19140008737.996194573325262.00380542668
2085008660.5654388397-160.565438839696
2195008687.05997486723812.940025132773
22118118629.610245441563181.38975455844
23100008555.31528685521444.6847131448
2495008629.35005193786870.64994806214
2595008568.34230629437931.657693705634
2694528462.72930116837989.270698831629
2795008414.325232968241085.67476703176
2886008338.46237580825261.537624191745
29117638524.884972165433238.11502783457
3097668415.814364608931350.18563539107
31114008457.103523298662942.89647670134
3295008383.49210021181116.5078997882
33119948369.662853407173624.33714659283
3484008515.07813143269-115.07813143269
3573608555.84465726454-1195.84465726454
3674008380.61986534396-980.619865343956
3785588324.89893713354233.101062866463
3870008318.4470391935-1318.4470391935
3974008231.25684230769-831.256842307691
4072008196.3971906951-996.397190695105
4186008155.08302328773444.916976712273
4278008233.50790437656-433.507904376557
4375008079.14850183806-579.148501838062
4490008152.65240849433847.347591505668
4574298108.69071958616-679.690719586163
4672068075.14176290253-869.141762902525
4776138031.37327745525-418.373277455249
4872008057.73509887618-857.735098876184
4975007923.45713643296-423.457136432959
5075008069.0333171001-569.033317100096
5190717964.224825182191106.77517481781
5276008080.27104646971-480.27104646971
5383598025.85670040399333.143299596014
54150008063.226112659636936.77388734037
5565007892.18238360207-1392.18238360207
5665007900.00247546635-1400.00247546635
5761257870.53416785841-1745.53416785841
5860007800.48076098937-1800.48076098937
5970007797.1041713423-797.104171342302
6085007824.74438687291675.25561312709
6170007837.26817445809-837.268174458086
6270007693.52233171876-693.522331718764
6366007727.75594682714-1127.75594682714
6468007733.22419083097-933.224190830966
65120007568.974688925824431.02531107418
6672007632.75762442556-432.757624425561
6772007645.96494751023-445.964947510233
6873007615.1900577576-315.190057757602
6975007584.77689822123-84.7768982212304
7070007594.68752151702-594.687521517018
7170007673.16466589164-673.164665891636
7260007617.44373768122-1617.44373768122







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.00216174445968060.004323488919361190.997838255540319
80.1968054656846350.393610931369270.803194534315365
90.1461167273464310.2922334546928610.853883272653569
100.0796080086997720.1592160173995440.920391991300228
110.04379276464549790.08758552929099590.956207235354502
120.08369276385454330.1673855277090870.916307236145457
130.05584386514977070.1116877302995410.944156134850229
140.05909634296600850.1181926859320170.940903657033991
150.0379660976698570.0759321953397140.962033902330143
160.03947660942028840.07895321884057690.960523390579712
170.03169230745981090.06338461491962180.968307692540189
180.02192367201236110.04384734402472230.978076327987639
190.727259304509920.5454813909801590.27274069549008
200.6680625147835470.6638749704329060.331937485216453
210.5984280469738350.8031439060523290.401571953026165
220.65281558770770.6943688245846010.347184412292301
230.5811080911840020.8377838176319960.418891908815998
240.5063189050076230.9873621899847540.493681094992377
250.4332244813446210.8664489626892410.566775518655379
260.3772432265401280.7544864530802550.622756773459872
270.3133499870241270.6266999740482550.686650012975873
280.2703225693989270.5406451387978540.729677430601073
290.3026447255988170.6052894511976340.697355274401183
300.2481397230674350.496279446134870.751860276932565
310.259265245403360.5185304908067210.74073475459664
320.2201177773900870.4402355547801730.779882222609913
330.3231351459742920.6462702919485850.676864854025708
340.3295846150824510.6591692301649020.670415384917549
350.3484973199903760.6969946399807510.651502680009624
360.3773441598898820.7546883197797650.622655840110118
370.3475622245454750.695124449090950.652437775454525
380.4153499497471020.8306998994942050.584650050252898
390.4083558637785870.8167117275571740.591644136221413
400.4006011646695630.8012023293391260.599398835330437
410.3419477996200150.683895599240030.658052200379985
420.2975659263433750.5951318526867490.702434073656625
430.2603832761655690.5207665523311370.739616723834431
440.2106066426694940.4212132853389870.789393357330506
450.1778368431030050.3556736862060110.822163156896994
460.1516507165948910.3033014331897820.848349283405109
470.11880897912090.23761795824180.8811910208791
480.09970387585107150.1994077517021430.900296124148928
490.07452364303294580.1490472860658920.925476356967054
500.05761020022644340.1152204004528870.942389799773557
510.06987106512630670.1397421302526130.930128934873693
520.0696275881012290.1392551762024580.930372411898771
530.04983465873945460.09966931747890920.950165341260545
540.983331427168340.03333714566331910.0166685728316596
550.9733625137749980.0532749724500030.0266374862250015
560.9589733384134390.08205332317312290.0410266615865614
570.9376333565525990.1247332868948010.0623666434474006
580.9112303022607780.1775393954784440.0887696977392219
590.8649248707583960.2701502584832080.135075129241604
600.908020127645960.1839597447080810.0919798723540403
610.8996157563824990.2007684872350030.100384243617501
620.9184588676088680.1630822647822640.081541132391132
630.883427433599320.233145132801360.11657256640068
640.7849682745311450.430063450937710.215031725468855
650.9817284740028260.0365430519943480.018271525997174

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0021617444596806 & 0.00432348891936119 & 0.997838255540319 \tabularnewline
8 & 0.196805465684635 & 0.39361093136927 & 0.803194534315365 \tabularnewline
9 & 0.146116727346431 & 0.292233454692861 & 0.853883272653569 \tabularnewline
10 & 0.079608008699772 & 0.159216017399544 & 0.920391991300228 \tabularnewline
11 & 0.0437927646454979 & 0.0875855292909959 & 0.956207235354502 \tabularnewline
12 & 0.0836927638545433 & 0.167385527709087 & 0.916307236145457 \tabularnewline
13 & 0.0558438651497707 & 0.111687730299541 & 0.944156134850229 \tabularnewline
14 & 0.0590963429660085 & 0.118192685932017 & 0.940903657033991 \tabularnewline
15 & 0.037966097669857 & 0.075932195339714 & 0.962033902330143 \tabularnewline
16 & 0.0394766094202884 & 0.0789532188405769 & 0.960523390579712 \tabularnewline
17 & 0.0316923074598109 & 0.0633846149196218 & 0.968307692540189 \tabularnewline
18 & 0.0219236720123611 & 0.0438473440247223 & 0.978076327987639 \tabularnewline
19 & 0.72725930450992 & 0.545481390980159 & 0.27274069549008 \tabularnewline
20 & 0.668062514783547 & 0.663874970432906 & 0.331937485216453 \tabularnewline
21 & 0.598428046973835 & 0.803143906052329 & 0.401571953026165 \tabularnewline
22 & 0.6528155877077 & 0.694368824584601 & 0.347184412292301 \tabularnewline
23 & 0.581108091184002 & 0.837783817631996 & 0.418891908815998 \tabularnewline
24 & 0.506318905007623 & 0.987362189984754 & 0.493681094992377 \tabularnewline
25 & 0.433224481344621 & 0.866448962689241 & 0.566775518655379 \tabularnewline
26 & 0.377243226540128 & 0.754486453080255 & 0.622756773459872 \tabularnewline
27 & 0.313349987024127 & 0.626699974048255 & 0.686650012975873 \tabularnewline
28 & 0.270322569398927 & 0.540645138797854 & 0.729677430601073 \tabularnewline
29 & 0.302644725598817 & 0.605289451197634 & 0.697355274401183 \tabularnewline
30 & 0.248139723067435 & 0.49627944613487 & 0.751860276932565 \tabularnewline
31 & 0.25926524540336 & 0.518530490806721 & 0.74073475459664 \tabularnewline
32 & 0.220117777390087 & 0.440235554780173 & 0.779882222609913 \tabularnewline
33 & 0.323135145974292 & 0.646270291948585 & 0.676864854025708 \tabularnewline
34 & 0.329584615082451 & 0.659169230164902 & 0.670415384917549 \tabularnewline
35 & 0.348497319990376 & 0.696994639980751 & 0.651502680009624 \tabularnewline
36 & 0.377344159889882 & 0.754688319779765 & 0.622655840110118 \tabularnewline
37 & 0.347562224545475 & 0.69512444909095 & 0.652437775454525 \tabularnewline
38 & 0.415349949747102 & 0.830699899494205 & 0.584650050252898 \tabularnewline
39 & 0.408355863778587 & 0.816711727557174 & 0.591644136221413 \tabularnewline
40 & 0.400601164669563 & 0.801202329339126 & 0.599398835330437 \tabularnewline
41 & 0.341947799620015 & 0.68389559924003 & 0.658052200379985 \tabularnewline
42 & 0.297565926343375 & 0.595131852686749 & 0.702434073656625 \tabularnewline
43 & 0.260383276165569 & 0.520766552331137 & 0.739616723834431 \tabularnewline
44 & 0.210606642669494 & 0.421213285338987 & 0.789393357330506 \tabularnewline
45 & 0.177836843103005 & 0.355673686206011 & 0.822163156896994 \tabularnewline
46 & 0.151650716594891 & 0.303301433189782 & 0.848349283405109 \tabularnewline
47 & 0.1188089791209 & 0.2376179582418 & 0.8811910208791 \tabularnewline
48 & 0.0997038758510715 & 0.199407751702143 & 0.900296124148928 \tabularnewline
49 & 0.0745236430329458 & 0.149047286065892 & 0.925476356967054 \tabularnewline
50 & 0.0576102002264434 & 0.115220400452887 & 0.942389799773557 \tabularnewline
51 & 0.0698710651263067 & 0.139742130252613 & 0.930128934873693 \tabularnewline
52 & 0.069627588101229 & 0.139255176202458 & 0.930372411898771 \tabularnewline
53 & 0.0498346587394546 & 0.0996693174789092 & 0.950165341260545 \tabularnewline
54 & 0.98333142716834 & 0.0333371456633191 & 0.0166685728316596 \tabularnewline
55 & 0.973362513774998 & 0.053274972450003 & 0.0266374862250015 \tabularnewline
56 & 0.958973338413439 & 0.0820533231731229 & 0.0410266615865614 \tabularnewline
57 & 0.937633356552599 & 0.124733286894801 & 0.0623666434474006 \tabularnewline
58 & 0.911230302260778 & 0.177539395478444 & 0.0887696977392219 \tabularnewline
59 & 0.864924870758396 & 0.270150258483208 & 0.135075129241604 \tabularnewline
60 & 0.90802012764596 & 0.183959744708081 & 0.0919798723540403 \tabularnewline
61 & 0.899615756382499 & 0.200768487235003 & 0.100384243617501 \tabularnewline
62 & 0.918458867608868 & 0.163082264782264 & 0.081541132391132 \tabularnewline
63 & 0.88342743359932 & 0.23314513280136 & 0.11657256640068 \tabularnewline
64 & 0.784968274531145 & 0.43006345093771 & 0.215031725468855 \tabularnewline
65 & 0.981728474002826 & 0.036543051994348 & 0.018271525997174 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186045&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]7[/C][C]0.0021617444596806[/C][C]0.00432348891936119[/C][C]0.997838255540319[/C][/ROW]
[ROW][C]8[/C][C]0.196805465684635[/C][C]0.39361093136927[/C][C]0.803194534315365[/C][/ROW]
[ROW][C]9[/C][C]0.146116727346431[/C][C]0.292233454692861[/C][C]0.853883272653569[/C][/ROW]
[ROW][C]10[/C][C]0.079608008699772[/C][C]0.159216017399544[/C][C]0.920391991300228[/C][/ROW]
[ROW][C]11[/C][C]0.0437927646454979[/C][C]0.0875855292909959[/C][C]0.956207235354502[/C][/ROW]
[ROW][C]12[/C][C]0.0836927638545433[/C][C]0.167385527709087[/C][C]0.916307236145457[/C][/ROW]
[ROW][C]13[/C][C]0.0558438651497707[/C][C]0.111687730299541[/C][C]0.944156134850229[/C][/ROW]
[ROW][C]14[/C][C]0.0590963429660085[/C][C]0.118192685932017[/C][C]0.940903657033991[/C][/ROW]
[ROW][C]15[/C][C]0.037966097669857[/C][C]0.075932195339714[/C][C]0.962033902330143[/C][/ROW]
[ROW][C]16[/C][C]0.0394766094202884[/C][C]0.0789532188405769[/C][C]0.960523390579712[/C][/ROW]
[ROW][C]17[/C][C]0.0316923074598109[/C][C]0.0633846149196218[/C][C]0.968307692540189[/C][/ROW]
[ROW][C]18[/C][C]0.0219236720123611[/C][C]0.0438473440247223[/C][C]0.978076327987639[/C][/ROW]
[ROW][C]19[/C][C]0.72725930450992[/C][C]0.545481390980159[/C][C]0.27274069549008[/C][/ROW]
[ROW][C]20[/C][C]0.668062514783547[/C][C]0.663874970432906[/C][C]0.331937485216453[/C][/ROW]
[ROW][C]21[/C][C]0.598428046973835[/C][C]0.803143906052329[/C][C]0.401571953026165[/C][/ROW]
[ROW][C]22[/C][C]0.6528155877077[/C][C]0.694368824584601[/C][C]0.347184412292301[/C][/ROW]
[ROW][C]23[/C][C]0.581108091184002[/C][C]0.837783817631996[/C][C]0.418891908815998[/C][/ROW]
[ROW][C]24[/C][C]0.506318905007623[/C][C]0.987362189984754[/C][C]0.493681094992377[/C][/ROW]
[ROW][C]25[/C][C]0.433224481344621[/C][C]0.866448962689241[/C][C]0.566775518655379[/C][/ROW]
[ROW][C]26[/C][C]0.377243226540128[/C][C]0.754486453080255[/C][C]0.622756773459872[/C][/ROW]
[ROW][C]27[/C][C]0.313349987024127[/C][C]0.626699974048255[/C][C]0.686650012975873[/C][/ROW]
[ROW][C]28[/C][C]0.270322569398927[/C][C]0.540645138797854[/C][C]0.729677430601073[/C][/ROW]
[ROW][C]29[/C][C]0.302644725598817[/C][C]0.605289451197634[/C][C]0.697355274401183[/C][/ROW]
[ROW][C]30[/C][C]0.248139723067435[/C][C]0.49627944613487[/C][C]0.751860276932565[/C][/ROW]
[ROW][C]31[/C][C]0.25926524540336[/C][C]0.518530490806721[/C][C]0.74073475459664[/C][/ROW]
[ROW][C]32[/C][C]0.220117777390087[/C][C]0.440235554780173[/C][C]0.779882222609913[/C][/ROW]
[ROW][C]33[/C][C]0.323135145974292[/C][C]0.646270291948585[/C][C]0.676864854025708[/C][/ROW]
[ROW][C]34[/C][C]0.329584615082451[/C][C]0.659169230164902[/C][C]0.670415384917549[/C][/ROW]
[ROW][C]35[/C][C]0.348497319990376[/C][C]0.696994639980751[/C][C]0.651502680009624[/C][/ROW]
[ROW][C]36[/C][C]0.377344159889882[/C][C]0.754688319779765[/C][C]0.622655840110118[/C][/ROW]
[ROW][C]37[/C][C]0.347562224545475[/C][C]0.69512444909095[/C][C]0.652437775454525[/C][/ROW]
[ROW][C]38[/C][C]0.415349949747102[/C][C]0.830699899494205[/C][C]0.584650050252898[/C][/ROW]
[ROW][C]39[/C][C]0.408355863778587[/C][C]0.816711727557174[/C][C]0.591644136221413[/C][/ROW]
[ROW][C]40[/C][C]0.400601164669563[/C][C]0.801202329339126[/C][C]0.599398835330437[/C][/ROW]
[ROW][C]41[/C][C]0.341947799620015[/C][C]0.68389559924003[/C][C]0.658052200379985[/C][/ROW]
[ROW][C]42[/C][C]0.297565926343375[/C][C]0.595131852686749[/C][C]0.702434073656625[/C][/ROW]
[ROW][C]43[/C][C]0.260383276165569[/C][C]0.520766552331137[/C][C]0.739616723834431[/C][/ROW]
[ROW][C]44[/C][C]0.210606642669494[/C][C]0.421213285338987[/C][C]0.789393357330506[/C][/ROW]
[ROW][C]45[/C][C]0.177836843103005[/C][C]0.355673686206011[/C][C]0.822163156896994[/C][/ROW]
[ROW][C]46[/C][C]0.151650716594891[/C][C]0.303301433189782[/C][C]0.848349283405109[/C][/ROW]
[ROW][C]47[/C][C]0.1188089791209[/C][C]0.2376179582418[/C][C]0.8811910208791[/C][/ROW]
[ROW][C]48[/C][C]0.0997038758510715[/C][C]0.199407751702143[/C][C]0.900296124148928[/C][/ROW]
[ROW][C]49[/C][C]0.0745236430329458[/C][C]0.149047286065892[/C][C]0.925476356967054[/C][/ROW]
[ROW][C]50[/C][C]0.0576102002264434[/C][C]0.115220400452887[/C][C]0.942389799773557[/C][/ROW]
[ROW][C]51[/C][C]0.0698710651263067[/C][C]0.139742130252613[/C][C]0.930128934873693[/C][/ROW]
[ROW][C]52[/C][C]0.069627588101229[/C][C]0.139255176202458[/C][C]0.930372411898771[/C][/ROW]
[ROW][C]53[/C][C]0.0498346587394546[/C][C]0.0996693174789092[/C][C]0.950165341260545[/C][/ROW]
[ROW][C]54[/C][C]0.98333142716834[/C][C]0.0333371456633191[/C][C]0.0166685728316596[/C][/ROW]
[ROW][C]55[/C][C]0.973362513774998[/C][C]0.053274972450003[/C][C]0.0266374862250015[/C][/ROW]
[ROW][C]56[/C][C]0.958973338413439[/C][C]0.0820533231731229[/C][C]0.0410266615865614[/C][/ROW]
[ROW][C]57[/C][C]0.937633356552599[/C][C]0.124733286894801[/C][C]0.0623666434474006[/C][/ROW]
[ROW][C]58[/C][C]0.911230302260778[/C][C]0.177539395478444[/C][C]0.0887696977392219[/C][/ROW]
[ROW][C]59[/C][C]0.864924870758396[/C][C]0.270150258483208[/C][C]0.135075129241604[/C][/ROW]
[ROW][C]60[/C][C]0.90802012764596[/C][C]0.183959744708081[/C][C]0.0919798723540403[/C][/ROW]
[ROW][C]61[/C][C]0.899615756382499[/C][C]0.200768487235003[/C][C]0.100384243617501[/C][/ROW]
[ROW][C]62[/C][C]0.918458867608868[/C][C]0.163082264782264[/C][C]0.081541132391132[/C][/ROW]
[ROW][C]63[/C][C]0.88342743359932[/C][C]0.23314513280136[/C][C]0.11657256640068[/C][/ROW]
[ROW][C]64[/C][C]0.784968274531145[/C][C]0.43006345093771[/C][C]0.215031725468855[/C][/ROW]
[ROW][C]65[/C][C]0.981728474002826[/C][C]0.036543051994348[/C][C]0.018271525997174[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186045&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186045&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
70.00216174445968060.004323488919361190.997838255540319
80.1968054656846350.393610931369270.803194534315365
90.1461167273464310.2922334546928610.853883272653569
100.0796080086997720.1592160173995440.920391991300228
110.04379276464549790.08758552929099590.956207235354502
120.08369276385454330.1673855277090870.916307236145457
130.05584386514977070.1116877302995410.944156134850229
140.05909634296600850.1181926859320170.940903657033991
150.0379660976698570.0759321953397140.962033902330143
160.03947660942028840.07895321884057690.960523390579712
170.03169230745981090.06338461491962180.968307692540189
180.02192367201236110.04384734402472230.978076327987639
190.727259304509920.5454813909801590.27274069549008
200.6680625147835470.6638749704329060.331937485216453
210.5984280469738350.8031439060523290.401571953026165
220.65281558770770.6943688245846010.347184412292301
230.5811080911840020.8377838176319960.418891908815998
240.5063189050076230.9873621899847540.493681094992377
250.4332244813446210.8664489626892410.566775518655379
260.3772432265401280.7544864530802550.622756773459872
270.3133499870241270.6266999740482550.686650012975873
280.2703225693989270.5406451387978540.729677430601073
290.3026447255988170.6052894511976340.697355274401183
300.2481397230674350.496279446134870.751860276932565
310.259265245403360.5185304908067210.74073475459664
320.2201177773900870.4402355547801730.779882222609913
330.3231351459742920.6462702919485850.676864854025708
340.3295846150824510.6591692301649020.670415384917549
350.3484973199903760.6969946399807510.651502680009624
360.3773441598898820.7546883197797650.622655840110118
370.3475622245454750.695124449090950.652437775454525
380.4153499497471020.8306998994942050.584650050252898
390.4083558637785870.8167117275571740.591644136221413
400.4006011646695630.8012023293391260.599398835330437
410.3419477996200150.683895599240030.658052200379985
420.2975659263433750.5951318526867490.702434073656625
430.2603832761655690.5207665523311370.739616723834431
440.2106066426694940.4212132853389870.789393357330506
450.1778368431030050.3556736862060110.822163156896994
460.1516507165948910.3033014331897820.848349283405109
470.11880897912090.23761795824180.8811910208791
480.09970387585107150.1994077517021430.900296124148928
490.07452364303294580.1490472860658920.925476356967054
500.05761020022644340.1152204004528870.942389799773557
510.06987106512630670.1397421302526130.930128934873693
520.0696275881012290.1392551762024580.930372411898771
530.04983465873945460.09966931747890920.950165341260545
540.983331427168340.03333714566331910.0166685728316596
550.9733625137749980.0532749724500030.0266374862250015
560.9589733384134390.08205332317312290.0410266615865614
570.9376333565525990.1247332868948010.0623666434474006
580.9112303022607780.1775393954784440.0887696977392219
590.8649248707583960.2701502584832080.135075129241604
600.908020127645960.1839597447080810.0919798723540403
610.8996157563824990.2007684872350030.100384243617501
620.9184588676088680.1630822647822640.081541132391132
630.883427433599320.233145132801360.11657256640068
640.7849682745311450.430063450937710.215031725468855
650.9817284740028260.0365430519943480.018271525997174







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0169491525423729NOK
5% type I error level40.0677966101694915NOK
10% type I error level110.186440677966102NOK

\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 & 1 & 0.0169491525423729 & NOK \tabularnewline
5% type I error level & 4 & 0.0677966101694915 & NOK \tabularnewline
10% type I error level & 11 & 0.186440677966102 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186045&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]1[/C][C]0.0169491525423729[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0677966101694915[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.186440677966102[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186045&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186045&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 level10.0169491525423729NOK
5% type I error level40.0677966101694915NOK
10% type I error level110.186440677966102NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}