Free Statistics

of Irreproducible Research!

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 17:09:01 -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/t13521533779gu7jf3itfybzg0.htm/, Retrieved Wed, 01 Feb 2023 15:21:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186328, Retrieved Wed, 01 Feb 2023 15:21:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact104
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [scat] [2011-11-24 18:31:52] [b6dc7003e7767578f97246d87c77862e]
-         [Multiple Regression] [plots] [2012-11-05 22:09:01] [69fed4bf76000787e6433dea6d892b14] [Current]
Feedback Forum

Post a new message
Dataseries X:
1	87.28	255
2	87.28	280.2
3	87.09	299.9
4	86.92	339.2
5	87.59	374.2
6	90.72	393.5
7	90.69	389.2
8	90.3	381.7
9	89.55	375.2
10	88.94	369
11	88.41	357.4
12	87.82	352.1
1	87.07	346.5
2	86.82	342.9
3	86.4	340.3
4	86.02	328.3
5	85.66	322.9
6	85.32	314.3
7	85	308.9
8	84.67	294
9	83.94	285.6
10	82.83	281.2
11	81.95	280.3
12	81.19	278.8
1	80.48	274.5
2	78.86	270.4
3	69.47	263.4
4	68.77	259.9
5	70.06	258
6	73.95	262.7
7	75.8	284.7
8	77.79	311.3
9	81.57	322.1
10	83.07	327
11	84.34	331.3
12	85.1	333.3
1	85.25	321.4
2	84.26	327
3	83.63	320
4	86.44	314.7
5	85.3	316.7
6	84.1	314.4
7	83.36	321.3
8	82.48	318.2
9	81.58	307.2
10	80.47	301.3
11	79.34	287.5
12	82.13	277.7
1	81.69	274.4
2	80.7	258.8
3	79.88	253.3
4	79.16	251
5	78.38	248.4
6	77.42	249.5
7	76.47	246.1
8	75.46	244.5
9	74.48	243.6
10	78.27	244
11	80.7	240.8
12	79.91	249.8
1	78.75	248
2	77.78	259.4
3	81.14	260.5
4	81.08	260.8
5	80.03	261.3
6	78.91	259.5
7	78.01	256.6
8	76.9	257.9
9	75.97	256.5
10	81.93	254.2
11	80.27	253.3
12	78.67	253.8
1	77.42	255.5
2	76.16	257.1
3	74.7	257.3
4	76.39	253.2
5	76.04	252.8
6	74.65	252
7	73.29	250.7
8	71.79	252.2
9	74.39	250
10	74.91	251
11	74.54	253.4
12	73.08	251.2
1	72.75	255.6
2	71.32	261.1
3	70.38	258.9
4	70.35	259.9
5	70.01	261.2
6	69.36	264.7
7	67.77	267.1
8	69.26	266.4
9	69.8	267.7
10	68.38	268.6
11	67.62	267.5
12	68.39	268.5
1	66.95	268.5
2	65.21	270.5
3	66.64	270.9
4	63.45	270.1
5	60.66	269.3
6	62.34	269.8
7	60.32	270.1
8	58.64	264.9
9	60.46	263.7
10	58.59	264.8
11	61.87	263.7
12	61.85	255.9
1	67.44	276.2
2	77.06	360.1
3	91.74	380.5
4	93.15	373.7
5	94.15	369.8
6	93.11	366.6
7	91.51	359.3
8	89.96	345.8
9	88.16	326.2
10	86.98	324.5
11	88.03	328.1
12	86.24	327.5
1	84.65	324.4
2	83.23	316.5
3	81.7	310.9
4	80.25	301.5
5	78.8	291.7
6	77.51	290.4
7	76.2	287.4
8	75.04	277.7
9	74	281.6
10	75.49	288
11	77.14	276
12	76.15	272.9
1	76.27	283
2	78.19	283.3
3	76.49	276.8
4	77.31	284.5
5	76.65	282.7
6	74.99	281.2
7	73.51	287.4
8	72.07	283.1
9	70.59	284
10	71.96	285.5
11	76.29	289.2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186328&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]9 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=186328&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186328&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 time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
month[t] = + 8.33938962516956 -0.0391199022599001col[t] + 0.00408947676083816usa[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
month[t] =  +  8.33938962516956 -0.0391199022599001col[t] +  0.00408947676083816usa[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186328&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]month[t] =  +  8.33938962516956 -0.0391199022599001col[t] +  0.00408947676083816usa[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186328&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186328&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
month[t] = + 8.33938962516956 -0.0391199022599001col[t] + 0.00408947676083816usa[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.339389625169562.9042722.87140.0047220.002361
col-0.03911990225990010.0513-0.76260.4470040.223502
usa0.004089476760838160.010640.38430.7013140.350657

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 8.33938962516956 & 2.904272 & 2.8714 & 0.004722 & 0.002361 \tabularnewline
col & -0.0391199022599001 & 0.0513 & -0.7626 & 0.447004 & 0.223502 \tabularnewline
usa & 0.00408947676083816 & 0.01064 & 0.3843 & 0.701314 & 0.350657 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186328&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]8.33938962516956[/C][C]2.904272[/C][C]2.8714[/C][C]0.004722[/C][C]0.002361[/C][/ROW]
[ROW][C]col[/C][C]-0.0391199022599001[/C][C]0.0513[/C][C]-0.7626[/C][C]0.447004[/C][C]0.223502[/C][/ROW]
[ROW][C]usa[/C][C]0.00408947676083816[/C][C]0.01064[/C][C]0.3843[/C][C]0.701314[/C][C]0.350657[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186328&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186328&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.339389625169562.9042722.87140.0047220.002361
col-0.03911990225990010.0513-0.76260.4470040.223502
usa0.004089476760838160.010640.38430.7013140.350657







Multiple Linear Regression - Regression Statistics
Multiple R0.0666125342567079
R-squared0.00443722972010109
Adjusted R-squared-0.00978509556961171
F-TEST (value)0.311990453720713
F-TEST (DF numerator)2
F-TEST (DF denominator)140
p-value0.732496115637195
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.46210025044929
Sum Squared Residuals1678.05934018255

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0666125342567079 \tabularnewline
R-squared & 0.00443722972010109 \tabularnewline
Adjusted R-squared & -0.00978509556961171 \tabularnewline
F-TEST (value) & 0.311990453720713 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 140 \tabularnewline
p-value & 0.732496115637195 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.46210025044929 \tabularnewline
Sum Squared Residuals & 1678.05934018255 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186328&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0666125342567079[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00443722972010109[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00978509556961171[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.311990453720713[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]140[/C][/ROW]
[ROW][C]p-value[/C][C]0.732496115637195[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.46210025044929[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1678.05934018255[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186328&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186328&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 R0.0666125342567079
R-squared0.00443722972010109
Adjusted R-squared-0.00978509556961171
F-TEST (value)0.311990453720713
F-TEST (DF numerator)2
F-TEST (DF denominator)140
p-value0.732496115637195
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.46210025044929
Sum Squared Residuals1678.05934018255







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
115.96782112993921-4.96782112993921
226.07087594431233-4.07087594431233
336.15887141793023-3.15887141793023
446.32623823801535-2.32623823801535
556.44315959013055-1.44315959013055
666.39964119754124-0.39964119754124
776.383230044537430.616769955462567
886.367815730712511.63218426928749
996.370574058461982.62942594153802
10106.369082442923333.63091755707667
11116.342378060695354.65762193930465
12126.343784576196255.65621542380375
1316.35022343303048-5.35022343303048
1426.34528129225644-4.34528129225644
1536.35107901162742-3.35107901162742
1646.31687085335612-2.31687085335612
1756.30887084366116-1.30887084366116
1866.28700211028632-0.287002110286319
1976.277437304500960.72256269549904
2086.229413668510241.77058633148976
2196.223619592368932.77638040763107
22106.249048986129733.75095101387027
23116.279793971033684.72020602896632
24126.303390881609955.69660911839005
2516.31358126214288-5.31358126214288
2626.36018864908448-4.36018864908448
2736.69889819397907-3.69889819397907
2846.71196895689807-2.71196895689807
2956.6537342771372-1.6537342771372
3066.52077839812213-0.520778398122133
3176.538375067679760.461624932320242
3286.569306544020851.43069345597915
3396.465599662495482.53440033750452
34106.426958245233743.57304175476626
35116.394860719435274.60513928056473
36126.373308547239425.62669145276058
3716.31877578844646-5.31877578844646
3826.38040556154446-4.38040556154446
3936.37642476264233-3.37642476264233
4046.24482361045957-2.24482361045957
4156.29759925255753-1.29759925255753
4266.33513733871948-0.33513733871948
4376.392303456041590.60769654395841
4486.41405159207171.5859484079283
4596.404275259736392.59572474026361
46106.423570438355943.57642956164406
47116.411341148610064.58865885138994
48126.262119749048725.73788025095128
4916.26583723273231-5.26583723273231
5026.24077009850054-4.24077009850054
5136.25035629616905-3.25035629616905
5246.26911682924625-2.26911682924625
5356.28899771343079-1.28899771343079
5466.33105124403722-0.331051244037216
5576.354310930197270.645689069802728
5686.387278868662431.61272113133757
5796.421935843792382.57806415620762
58106.275307204931693.72469279506831
59116.167159516805454.83284048319455
60126.234869530438325.76513046956168
6116.27288755889029-5.27288755889029
6226.35745389915595-4.35745389915595
6336.23050945199961-3.23050945199961
6446.23408348916345-2.23408348916345
6556.27720412491677-1.27720412491677
6666.31365735727835-0.313657357278347
6776.337005786705830.662994213294174
6886.38574519800341.6142548019966
6996.416401439639942.58359856036006
70106.173841025621013.82615897437899
71116.235099534287694.76490046571231
72126.299736116283945.70026388371606
7316.35558810460225-5.35558810460225
7426.41142234426706-4.41142234426706
7536.46935529691868-3.46935529691868
7646.38647580738001-2.38647580738001
7756.39853198246664-1.39853198246664
7866.44963706519923-0.449637065199235
7976.497523812483610.502476187516391
8086.562337881014721.43766211898528
8196.451629286265132.54837071373487
82106.435376413850823.56462358614918
83116.4596655219134.540334478087
84126.507783730338615.49221626966139
8516.53868699583206-5.53868699583206
8626.61712057824833-4.61712057824833
8736.64489643749879-3.64489643749879
8846.65015951132743-2.65015951132743
8956.66877659788488-1.66877659788488
9066.70851770301675-0.708517703016751
9176.7805330918360.219466908163996
9286.719381803736171.28061819626383
9396.703573376304912.29642662369509
94106.762804166598723.23719583340128
95116.788036867879324.21196313212068
96126.762004019900045.23799598009996
9716.81833667915429-5.81833667915429
9826.8945842626082-4.8945842626082
9936.84027859308088-3.84027859308088
10046.96179949988129-2.96179949988129
10157.06767244577774-2.06767244577774
10267.00399574836152-1.00399574836152
10377.08424479395477-0.0842447939547739
10487.128700950595050.871299049404953
10597.052595356369021.94740464363098
106107.130247998031962.86975200196804
107116.997436294182564.00256370581744
108126.966320773493225.03367922650678
10916.8306568981054-5.8306568981054
11026.79743053859948-4.79743053859948
11136.30657569934525-3.30657569934525
11246.22360819518509-2.22360819518509
11356.16853933355792-1.16853933355792
11466.19613770627353-0.196137706273532
11576.228876369535250.771123630464746
11686.234304281766781.76569571823322
11796.224566361322182.77543363867782
118106.263775735495433.73622426450457
119116.237421954461564.76257804553844
120126.304992893450275.69500710654973
12116.35451616008492-5.35451616008492
12226.37775955488335-4.37775955488335
12336.41471193548031-3.41471193548031
12446.43299471220528-2.43299471220528
12556.44964169822592-1.44964169822592
12666.49479005235211-0.494790052352106
12776.533768694030060.46623130596994
12886.539479856071411.46052014392859
12996.596113513788982.40388648621102
130106.563997510691093.43600248930891
131116.45037595083224.5496240491678
132126.47642727611095.5235727238891
13316.51303660312418-5.51303660312418
13426.43915323381342-4.43915323381342
13536.47907546870981-3.47907546870981
13646.47848611991514-2.47848611991514
13756.49694419723717-1.49694419723717
13866.55574901984734-0.555749019847343
13976.639001231109190.360998768890809
14086.677749140291841.32225085970816
14196.739327124721252.26067287527875
142106.691867073766443.30813292623356
143116.537608960996184.46239103900382

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 5.96782112993921 & -4.96782112993921 \tabularnewline
2 & 2 & 6.07087594431233 & -4.07087594431233 \tabularnewline
3 & 3 & 6.15887141793023 & -3.15887141793023 \tabularnewline
4 & 4 & 6.32623823801535 & -2.32623823801535 \tabularnewline
5 & 5 & 6.44315959013055 & -1.44315959013055 \tabularnewline
6 & 6 & 6.39964119754124 & -0.39964119754124 \tabularnewline
7 & 7 & 6.38323004453743 & 0.616769955462567 \tabularnewline
8 & 8 & 6.36781573071251 & 1.63218426928749 \tabularnewline
9 & 9 & 6.37057405846198 & 2.62942594153802 \tabularnewline
10 & 10 & 6.36908244292333 & 3.63091755707667 \tabularnewline
11 & 11 & 6.34237806069535 & 4.65762193930465 \tabularnewline
12 & 12 & 6.34378457619625 & 5.65621542380375 \tabularnewline
13 & 1 & 6.35022343303048 & -5.35022343303048 \tabularnewline
14 & 2 & 6.34528129225644 & -4.34528129225644 \tabularnewline
15 & 3 & 6.35107901162742 & -3.35107901162742 \tabularnewline
16 & 4 & 6.31687085335612 & -2.31687085335612 \tabularnewline
17 & 5 & 6.30887084366116 & -1.30887084366116 \tabularnewline
18 & 6 & 6.28700211028632 & -0.287002110286319 \tabularnewline
19 & 7 & 6.27743730450096 & 0.72256269549904 \tabularnewline
20 & 8 & 6.22941366851024 & 1.77058633148976 \tabularnewline
21 & 9 & 6.22361959236893 & 2.77638040763107 \tabularnewline
22 & 10 & 6.24904898612973 & 3.75095101387027 \tabularnewline
23 & 11 & 6.27979397103368 & 4.72020602896632 \tabularnewline
24 & 12 & 6.30339088160995 & 5.69660911839005 \tabularnewline
25 & 1 & 6.31358126214288 & -5.31358126214288 \tabularnewline
26 & 2 & 6.36018864908448 & -4.36018864908448 \tabularnewline
27 & 3 & 6.69889819397907 & -3.69889819397907 \tabularnewline
28 & 4 & 6.71196895689807 & -2.71196895689807 \tabularnewline
29 & 5 & 6.6537342771372 & -1.6537342771372 \tabularnewline
30 & 6 & 6.52077839812213 & -0.520778398122133 \tabularnewline
31 & 7 & 6.53837506767976 & 0.461624932320242 \tabularnewline
32 & 8 & 6.56930654402085 & 1.43069345597915 \tabularnewline
33 & 9 & 6.46559966249548 & 2.53440033750452 \tabularnewline
34 & 10 & 6.42695824523374 & 3.57304175476626 \tabularnewline
35 & 11 & 6.39486071943527 & 4.60513928056473 \tabularnewline
36 & 12 & 6.37330854723942 & 5.62669145276058 \tabularnewline
37 & 1 & 6.31877578844646 & -5.31877578844646 \tabularnewline
38 & 2 & 6.38040556154446 & -4.38040556154446 \tabularnewline
39 & 3 & 6.37642476264233 & -3.37642476264233 \tabularnewline
40 & 4 & 6.24482361045957 & -2.24482361045957 \tabularnewline
41 & 5 & 6.29759925255753 & -1.29759925255753 \tabularnewline
42 & 6 & 6.33513733871948 & -0.33513733871948 \tabularnewline
43 & 7 & 6.39230345604159 & 0.60769654395841 \tabularnewline
44 & 8 & 6.4140515920717 & 1.5859484079283 \tabularnewline
45 & 9 & 6.40427525973639 & 2.59572474026361 \tabularnewline
46 & 10 & 6.42357043835594 & 3.57642956164406 \tabularnewline
47 & 11 & 6.41134114861006 & 4.58865885138994 \tabularnewline
48 & 12 & 6.26211974904872 & 5.73788025095128 \tabularnewline
49 & 1 & 6.26583723273231 & -5.26583723273231 \tabularnewline
50 & 2 & 6.24077009850054 & -4.24077009850054 \tabularnewline
51 & 3 & 6.25035629616905 & -3.25035629616905 \tabularnewline
52 & 4 & 6.26911682924625 & -2.26911682924625 \tabularnewline
53 & 5 & 6.28899771343079 & -1.28899771343079 \tabularnewline
54 & 6 & 6.33105124403722 & -0.331051244037216 \tabularnewline
55 & 7 & 6.35431093019727 & 0.645689069802728 \tabularnewline
56 & 8 & 6.38727886866243 & 1.61272113133757 \tabularnewline
57 & 9 & 6.42193584379238 & 2.57806415620762 \tabularnewline
58 & 10 & 6.27530720493169 & 3.72469279506831 \tabularnewline
59 & 11 & 6.16715951680545 & 4.83284048319455 \tabularnewline
60 & 12 & 6.23486953043832 & 5.76513046956168 \tabularnewline
61 & 1 & 6.27288755889029 & -5.27288755889029 \tabularnewline
62 & 2 & 6.35745389915595 & -4.35745389915595 \tabularnewline
63 & 3 & 6.23050945199961 & -3.23050945199961 \tabularnewline
64 & 4 & 6.23408348916345 & -2.23408348916345 \tabularnewline
65 & 5 & 6.27720412491677 & -1.27720412491677 \tabularnewline
66 & 6 & 6.31365735727835 & -0.313657357278347 \tabularnewline
67 & 7 & 6.33700578670583 & 0.662994213294174 \tabularnewline
68 & 8 & 6.3857451980034 & 1.6142548019966 \tabularnewline
69 & 9 & 6.41640143963994 & 2.58359856036006 \tabularnewline
70 & 10 & 6.17384102562101 & 3.82615897437899 \tabularnewline
71 & 11 & 6.23509953428769 & 4.76490046571231 \tabularnewline
72 & 12 & 6.29973611628394 & 5.70026388371606 \tabularnewline
73 & 1 & 6.35558810460225 & -5.35558810460225 \tabularnewline
74 & 2 & 6.41142234426706 & -4.41142234426706 \tabularnewline
75 & 3 & 6.46935529691868 & -3.46935529691868 \tabularnewline
76 & 4 & 6.38647580738001 & -2.38647580738001 \tabularnewline
77 & 5 & 6.39853198246664 & -1.39853198246664 \tabularnewline
78 & 6 & 6.44963706519923 & -0.449637065199235 \tabularnewline
79 & 7 & 6.49752381248361 & 0.502476187516391 \tabularnewline
80 & 8 & 6.56233788101472 & 1.43766211898528 \tabularnewline
81 & 9 & 6.45162928626513 & 2.54837071373487 \tabularnewline
82 & 10 & 6.43537641385082 & 3.56462358614918 \tabularnewline
83 & 11 & 6.459665521913 & 4.540334478087 \tabularnewline
84 & 12 & 6.50778373033861 & 5.49221626966139 \tabularnewline
85 & 1 & 6.53868699583206 & -5.53868699583206 \tabularnewline
86 & 2 & 6.61712057824833 & -4.61712057824833 \tabularnewline
87 & 3 & 6.64489643749879 & -3.64489643749879 \tabularnewline
88 & 4 & 6.65015951132743 & -2.65015951132743 \tabularnewline
89 & 5 & 6.66877659788488 & -1.66877659788488 \tabularnewline
90 & 6 & 6.70851770301675 & -0.708517703016751 \tabularnewline
91 & 7 & 6.780533091836 & 0.219466908163996 \tabularnewline
92 & 8 & 6.71938180373617 & 1.28061819626383 \tabularnewline
93 & 9 & 6.70357337630491 & 2.29642662369509 \tabularnewline
94 & 10 & 6.76280416659872 & 3.23719583340128 \tabularnewline
95 & 11 & 6.78803686787932 & 4.21196313212068 \tabularnewline
96 & 12 & 6.76200401990004 & 5.23799598009996 \tabularnewline
97 & 1 & 6.81833667915429 & -5.81833667915429 \tabularnewline
98 & 2 & 6.8945842626082 & -4.8945842626082 \tabularnewline
99 & 3 & 6.84027859308088 & -3.84027859308088 \tabularnewline
100 & 4 & 6.96179949988129 & -2.96179949988129 \tabularnewline
101 & 5 & 7.06767244577774 & -2.06767244577774 \tabularnewline
102 & 6 & 7.00399574836152 & -1.00399574836152 \tabularnewline
103 & 7 & 7.08424479395477 & -0.0842447939547739 \tabularnewline
104 & 8 & 7.12870095059505 & 0.871299049404953 \tabularnewline
105 & 9 & 7.05259535636902 & 1.94740464363098 \tabularnewline
106 & 10 & 7.13024799803196 & 2.86975200196804 \tabularnewline
107 & 11 & 6.99743629418256 & 4.00256370581744 \tabularnewline
108 & 12 & 6.96632077349322 & 5.03367922650678 \tabularnewline
109 & 1 & 6.8306568981054 & -5.8306568981054 \tabularnewline
110 & 2 & 6.79743053859948 & -4.79743053859948 \tabularnewline
111 & 3 & 6.30657569934525 & -3.30657569934525 \tabularnewline
112 & 4 & 6.22360819518509 & -2.22360819518509 \tabularnewline
113 & 5 & 6.16853933355792 & -1.16853933355792 \tabularnewline
114 & 6 & 6.19613770627353 & -0.196137706273532 \tabularnewline
115 & 7 & 6.22887636953525 & 0.771123630464746 \tabularnewline
116 & 8 & 6.23430428176678 & 1.76569571823322 \tabularnewline
117 & 9 & 6.22456636132218 & 2.77543363867782 \tabularnewline
118 & 10 & 6.26377573549543 & 3.73622426450457 \tabularnewline
119 & 11 & 6.23742195446156 & 4.76257804553844 \tabularnewline
120 & 12 & 6.30499289345027 & 5.69500710654973 \tabularnewline
121 & 1 & 6.35451616008492 & -5.35451616008492 \tabularnewline
122 & 2 & 6.37775955488335 & -4.37775955488335 \tabularnewline
123 & 3 & 6.41471193548031 & -3.41471193548031 \tabularnewline
124 & 4 & 6.43299471220528 & -2.43299471220528 \tabularnewline
125 & 5 & 6.44964169822592 & -1.44964169822592 \tabularnewline
126 & 6 & 6.49479005235211 & -0.494790052352106 \tabularnewline
127 & 7 & 6.53376869403006 & 0.46623130596994 \tabularnewline
128 & 8 & 6.53947985607141 & 1.46052014392859 \tabularnewline
129 & 9 & 6.59611351378898 & 2.40388648621102 \tabularnewline
130 & 10 & 6.56399751069109 & 3.43600248930891 \tabularnewline
131 & 11 & 6.4503759508322 & 4.5496240491678 \tabularnewline
132 & 12 & 6.4764272761109 & 5.5235727238891 \tabularnewline
133 & 1 & 6.51303660312418 & -5.51303660312418 \tabularnewline
134 & 2 & 6.43915323381342 & -4.43915323381342 \tabularnewline
135 & 3 & 6.47907546870981 & -3.47907546870981 \tabularnewline
136 & 4 & 6.47848611991514 & -2.47848611991514 \tabularnewline
137 & 5 & 6.49694419723717 & -1.49694419723717 \tabularnewline
138 & 6 & 6.55574901984734 & -0.555749019847343 \tabularnewline
139 & 7 & 6.63900123110919 & 0.360998768890809 \tabularnewline
140 & 8 & 6.67774914029184 & 1.32225085970816 \tabularnewline
141 & 9 & 6.73932712472125 & 2.26067287527875 \tabularnewline
142 & 10 & 6.69186707376644 & 3.30813292623356 \tabularnewline
143 & 11 & 6.53760896099618 & 4.46239103900382 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186328&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]1[/C][C]5.96782112993921[/C][C]-4.96782112993921[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]6.07087594431233[/C][C]-4.07087594431233[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]6.15887141793023[/C][C]-3.15887141793023[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]6.32623823801535[/C][C]-2.32623823801535[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]6.44315959013055[/C][C]-1.44315959013055[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]6.39964119754124[/C][C]-0.39964119754124[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]6.38323004453743[/C][C]0.616769955462567[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]6.36781573071251[/C][C]1.63218426928749[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]6.37057405846198[/C][C]2.62942594153802[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]6.36908244292333[/C][C]3.63091755707667[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]6.34237806069535[/C][C]4.65762193930465[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]6.34378457619625[/C][C]5.65621542380375[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]6.35022343303048[/C][C]-5.35022343303048[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]6.34528129225644[/C][C]-4.34528129225644[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]6.35107901162742[/C][C]-3.35107901162742[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]6.31687085335612[/C][C]-2.31687085335612[/C][/ROW]
[ROW][C]17[/C][C]5[/C][C]6.30887084366116[/C][C]-1.30887084366116[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]6.28700211028632[/C][C]-0.287002110286319[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]6.27743730450096[/C][C]0.72256269549904[/C][/ROW]
[ROW][C]20[/C][C]8[/C][C]6.22941366851024[/C][C]1.77058633148976[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]6.22361959236893[/C][C]2.77638040763107[/C][/ROW]
[ROW][C]22[/C][C]10[/C][C]6.24904898612973[/C][C]3.75095101387027[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]6.27979397103368[/C][C]4.72020602896632[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]6.30339088160995[/C][C]5.69660911839005[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]6.31358126214288[/C][C]-5.31358126214288[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]6.36018864908448[/C][C]-4.36018864908448[/C][/ROW]
[ROW][C]27[/C][C]3[/C][C]6.69889819397907[/C][C]-3.69889819397907[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]6.71196895689807[/C][C]-2.71196895689807[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]6.6537342771372[/C][C]-1.6537342771372[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]6.52077839812213[/C][C]-0.520778398122133[/C][/ROW]
[ROW][C]31[/C][C]7[/C][C]6.53837506767976[/C][C]0.461624932320242[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]6.56930654402085[/C][C]1.43069345597915[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]6.46559966249548[/C][C]2.53440033750452[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]6.42695824523374[/C][C]3.57304175476626[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]6.39486071943527[/C][C]4.60513928056473[/C][/ROW]
[ROW][C]36[/C][C]12[/C][C]6.37330854723942[/C][C]5.62669145276058[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]6.31877578844646[/C][C]-5.31877578844646[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]6.38040556154446[/C][C]-4.38040556154446[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]6.37642476264233[/C][C]-3.37642476264233[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]6.24482361045957[/C][C]-2.24482361045957[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]6.29759925255753[/C][C]-1.29759925255753[/C][/ROW]
[ROW][C]42[/C][C]6[/C][C]6.33513733871948[/C][C]-0.33513733871948[/C][/ROW]
[ROW][C]43[/C][C]7[/C][C]6.39230345604159[/C][C]0.60769654395841[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]6.4140515920717[/C][C]1.5859484079283[/C][/ROW]
[ROW][C]45[/C][C]9[/C][C]6.40427525973639[/C][C]2.59572474026361[/C][/ROW]
[ROW][C]46[/C][C]10[/C][C]6.42357043835594[/C][C]3.57642956164406[/C][/ROW]
[ROW][C]47[/C][C]11[/C][C]6.41134114861006[/C][C]4.58865885138994[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]6.26211974904872[/C][C]5.73788025095128[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]6.26583723273231[/C][C]-5.26583723273231[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]6.24077009850054[/C][C]-4.24077009850054[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]6.25035629616905[/C][C]-3.25035629616905[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]6.26911682924625[/C][C]-2.26911682924625[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]6.28899771343079[/C][C]-1.28899771343079[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]6.33105124403722[/C][C]-0.331051244037216[/C][/ROW]
[ROW][C]55[/C][C]7[/C][C]6.35431093019727[/C][C]0.645689069802728[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]6.38727886866243[/C][C]1.61272113133757[/C][/ROW]
[ROW][C]57[/C][C]9[/C][C]6.42193584379238[/C][C]2.57806415620762[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]6.27530720493169[/C][C]3.72469279506831[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]6.16715951680545[/C][C]4.83284048319455[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]6.23486953043832[/C][C]5.76513046956168[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]6.27288755889029[/C][C]-5.27288755889029[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]6.35745389915595[/C][C]-4.35745389915595[/C][/ROW]
[ROW][C]63[/C][C]3[/C][C]6.23050945199961[/C][C]-3.23050945199961[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]6.23408348916345[/C][C]-2.23408348916345[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]6.27720412491677[/C][C]-1.27720412491677[/C][/ROW]
[ROW][C]66[/C][C]6[/C][C]6.31365735727835[/C][C]-0.313657357278347[/C][/ROW]
[ROW][C]67[/C][C]7[/C][C]6.33700578670583[/C][C]0.662994213294174[/C][/ROW]
[ROW][C]68[/C][C]8[/C][C]6.3857451980034[/C][C]1.6142548019966[/C][/ROW]
[ROW][C]69[/C][C]9[/C][C]6.41640143963994[/C][C]2.58359856036006[/C][/ROW]
[ROW][C]70[/C][C]10[/C][C]6.17384102562101[/C][C]3.82615897437899[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]6.23509953428769[/C][C]4.76490046571231[/C][/ROW]
[ROW][C]72[/C][C]12[/C][C]6.29973611628394[/C][C]5.70026388371606[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]6.35558810460225[/C][C]-5.35558810460225[/C][/ROW]
[ROW][C]74[/C][C]2[/C][C]6.41142234426706[/C][C]-4.41142234426706[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]6.46935529691868[/C][C]-3.46935529691868[/C][/ROW]
[ROW][C]76[/C][C]4[/C][C]6.38647580738001[/C][C]-2.38647580738001[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]6.39853198246664[/C][C]-1.39853198246664[/C][/ROW]
[ROW][C]78[/C][C]6[/C][C]6.44963706519923[/C][C]-0.449637065199235[/C][/ROW]
[ROW][C]79[/C][C]7[/C][C]6.49752381248361[/C][C]0.502476187516391[/C][/ROW]
[ROW][C]80[/C][C]8[/C][C]6.56233788101472[/C][C]1.43766211898528[/C][/ROW]
[ROW][C]81[/C][C]9[/C][C]6.45162928626513[/C][C]2.54837071373487[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]6.43537641385082[/C][C]3.56462358614918[/C][/ROW]
[ROW][C]83[/C][C]11[/C][C]6.459665521913[/C][C]4.540334478087[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]6.50778373033861[/C][C]5.49221626966139[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]6.53868699583206[/C][C]-5.53868699583206[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]6.61712057824833[/C][C]-4.61712057824833[/C][/ROW]
[ROW][C]87[/C][C]3[/C][C]6.64489643749879[/C][C]-3.64489643749879[/C][/ROW]
[ROW][C]88[/C][C]4[/C][C]6.65015951132743[/C][C]-2.65015951132743[/C][/ROW]
[ROW][C]89[/C][C]5[/C][C]6.66877659788488[/C][C]-1.66877659788488[/C][/ROW]
[ROW][C]90[/C][C]6[/C][C]6.70851770301675[/C][C]-0.708517703016751[/C][/ROW]
[ROW][C]91[/C][C]7[/C][C]6.780533091836[/C][C]0.219466908163996[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]6.71938180373617[/C][C]1.28061819626383[/C][/ROW]
[ROW][C]93[/C][C]9[/C][C]6.70357337630491[/C][C]2.29642662369509[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]6.76280416659872[/C][C]3.23719583340128[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]6.78803686787932[/C][C]4.21196313212068[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]6.76200401990004[/C][C]5.23799598009996[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]6.81833667915429[/C][C]-5.81833667915429[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]6.8945842626082[/C][C]-4.8945842626082[/C][/ROW]
[ROW][C]99[/C][C]3[/C][C]6.84027859308088[/C][C]-3.84027859308088[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]6.96179949988129[/C][C]-2.96179949988129[/C][/ROW]
[ROW][C]101[/C][C]5[/C][C]7.06767244577774[/C][C]-2.06767244577774[/C][/ROW]
[ROW][C]102[/C][C]6[/C][C]7.00399574836152[/C][C]-1.00399574836152[/C][/ROW]
[ROW][C]103[/C][C]7[/C][C]7.08424479395477[/C][C]-0.0842447939547739[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]7.12870095059505[/C][C]0.871299049404953[/C][/ROW]
[ROW][C]105[/C][C]9[/C][C]7.05259535636902[/C][C]1.94740464363098[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]7.13024799803196[/C][C]2.86975200196804[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]6.99743629418256[/C][C]4.00256370581744[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]6.96632077349322[/C][C]5.03367922650678[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]6.8306568981054[/C][C]-5.8306568981054[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]6.79743053859948[/C][C]-4.79743053859948[/C][/ROW]
[ROW][C]111[/C][C]3[/C][C]6.30657569934525[/C][C]-3.30657569934525[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]6.22360819518509[/C][C]-2.22360819518509[/C][/ROW]
[ROW][C]113[/C][C]5[/C][C]6.16853933355792[/C][C]-1.16853933355792[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]6.19613770627353[/C][C]-0.196137706273532[/C][/ROW]
[ROW][C]115[/C][C]7[/C][C]6.22887636953525[/C][C]0.771123630464746[/C][/ROW]
[ROW][C]116[/C][C]8[/C][C]6.23430428176678[/C][C]1.76569571823322[/C][/ROW]
[ROW][C]117[/C][C]9[/C][C]6.22456636132218[/C][C]2.77543363867782[/C][/ROW]
[ROW][C]118[/C][C]10[/C][C]6.26377573549543[/C][C]3.73622426450457[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]6.23742195446156[/C][C]4.76257804553844[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]6.30499289345027[/C][C]5.69500710654973[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]6.35451616008492[/C][C]-5.35451616008492[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]6.37775955488335[/C][C]-4.37775955488335[/C][/ROW]
[ROW][C]123[/C][C]3[/C][C]6.41471193548031[/C][C]-3.41471193548031[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]6.43299471220528[/C][C]-2.43299471220528[/C][/ROW]
[ROW][C]125[/C][C]5[/C][C]6.44964169822592[/C][C]-1.44964169822592[/C][/ROW]
[ROW][C]126[/C][C]6[/C][C]6.49479005235211[/C][C]-0.494790052352106[/C][/ROW]
[ROW][C]127[/C][C]7[/C][C]6.53376869403006[/C][C]0.46623130596994[/C][/ROW]
[ROW][C]128[/C][C]8[/C][C]6.53947985607141[/C][C]1.46052014392859[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]6.59611351378898[/C][C]2.40388648621102[/C][/ROW]
[ROW][C]130[/C][C]10[/C][C]6.56399751069109[/C][C]3.43600248930891[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]6.4503759508322[/C][C]4.5496240491678[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]6.4764272761109[/C][C]5.5235727238891[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]6.51303660312418[/C][C]-5.51303660312418[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]6.43915323381342[/C][C]-4.43915323381342[/C][/ROW]
[ROW][C]135[/C][C]3[/C][C]6.47907546870981[/C][C]-3.47907546870981[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]6.47848611991514[/C][C]-2.47848611991514[/C][/ROW]
[ROW][C]137[/C][C]5[/C][C]6.49694419723717[/C][C]-1.49694419723717[/C][/ROW]
[ROW][C]138[/C][C]6[/C][C]6.55574901984734[/C][C]-0.555749019847343[/C][/ROW]
[ROW][C]139[/C][C]7[/C][C]6.63900123110919[/C][C]0.360998768890809[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]6.67774914029184[/C][C]1.32225085970816[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]6.73932712472125[/C][C]2.26067287527875[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]6.69186707376644[/C][C]3.30813292623356[/C][/ROW]
[ROW][C]143[/C][C]11[/C][C]6.53760896099618[/C][C]4.46239103900382[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186328&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186328&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
115.96782112993921-4.96782112993921
226.07087594431233-4.07087594431233
336.15887141793023-3.15887141793023
446.32623823801535-2.32623823801535
556.44315959013055-1.44315959013055
666.39964119754124-0.39964119754124
776.383230044537430.616769955462567
886.367815730712511.63218426928749
996.370574058461982.62942594153802
10106.369082442923333.63091755707667
11116.342378060695354.65762193930465
12126.343784576196255.65621542380375
1316.35022343303048-5.35022343303048
1426.34528129225644-4.34528129225644
1536.35107901162742-3.35107901162742
1646.31687085335612-2.31687085335612
1756.30887084366116-1.30887084366116
1866.28700211028632-0.287002110286319
1976.277437304500960.72256269549904
2086.229413668510241.77058633148976
2196.223619592368932.77638040763107
22106.249048986129733.75095101387027
23116.279793971033684.72020602896632
24126.303390881609955.69660911839005
2516.31358126214288-5.31358126214288
2626.36018864908448-4.36018864908448
2736.69889819397907-3.69889819397907
2846.71196895689807-2.71196895689807
2956.6537342771372-1.6537342771372
3066.52077839812213-0.520778398122133
3176.538375067679760.461624932320242
3286.569306544020851.43069345597915
3396.465599662495482.53440033750452
34106.426958245233743.57304175476626
35116.394860719435274.60513928056473
36126.373308547239425.62669145276058
3716.31877578844646-5.31877578844646
3826.38040556154446-4.38040556154446
3936.37642476264233-3.37642476264233
4046.24482361045957-2.24482361045957
4156.29759925255753-1.29759925255753
4266.33513733871948-0.33513733871948
4376.392303456041590.60769654395841
4486.41405159207171.5859484079283
4596.404275259736392.59572474026361
46106.423570438355943.57642956164406
47116.411341148610064.58865885138994
48126.262119749048725.73788025095128
4916.26583723273231-5.26583723273231
5026.24077009850054-4.24077009850054
5136.25035629616905-3.25035629616905
5246.26911682924625-2.26911682924625
5356.28899771343079-1.28899771343079
5466.33105124403722-0.331051244037216
5576.354310930197270.645689069802728
5686.387278868662431.61272113133757
5796.421935843792382.57806415620762
58106.275307204931693.72469279506831
59116.167159516805454.83284048319455
60126.234869530438325.76513046956168
6116.27288755889029-5.27288755889029
6226.35745389915595-4.35745389915595
6336.23050945199961-3.23050945199961
6446.23408348916345-2.23408348916345
6556.27720412491677-1.27720412491677
6666.31365735727835-0.313657357278347
6776.337005786705830.662994213294174
6886.38574519800341.6142548019966
6996.416401439639942.58359856036006
70106.173841025621013.82615897437899
71116.235099534287694.76490046571231
72126.299736116283945.70026388371606
7316.35558810460225-5.35558810460225
7426.41142234426706-4.41142234426706
7536.46935529691868-3.46935529691868
7646.38647580738001-2.38647580738001
7756.39853198246664-1.39853198246664
7866.44963706519923-0.449637065199235
7976.497523812483610.502476187516391
8086.562337881014721.43766211898528
8196.451629286265132.54837071373487
82106.435376413850823.56462358614918
83116.4596655219134.540334478087
84126.507783730338615.49221626966139
8516.53868699583206-5.53868699583206
8626.61712057824833-4.61712057824833
8736.64489643749879-3.64489643749879
8846.65015951132743-2.65015951132743
8956.66877659788488-1.66877659788488
9066.70851770301675-0.708517703016751
9176.7805330918360.219466908163996
9286.719381803736171.28061819626383
9396.703573376304912.29642662369509
94106.762804166598723.23719583340128
95116.788036867879324.21196313212068
96126.762004019900045.23799598009996
9716.81833667915429-5.81833667915429
9826.8945842626082-4.8945842626082
9936.84027859308088-3.84027859308088
10046.96179949988129-2.96179949988129
10157.06767244577774-2.06767244577774
10267.00399574836152-1.00399574836152
10377.08424479395477-0.0842447939547739
10487.128700950595050.871299049404953
10597.052595356369021.94740464363098
106107.130247998031962.86975200196804
107116.997436294182564.00256370581744
108126.966320773493225.03367922650678
10916.8306568981054-5.8306568981054
11026.79743053859948-4.79743053859948
11136.30657569934525-3.30657569934525
11246.22360819518509-2.22360819518509
11356.16853933355792-1.16853933355792
11466.19613770627353-0.196137706273532
11576.228876369535250.771123630464746
11686.234304281766781.76569571823322
11796.224566361322182.77543363867782
118106.263775735495433.73622426450457
119116.237421954461564.76257804553844
120126.304992893450275.69500710654973
12116.35451616008492-5.35451616008492
12226.37775955488335-4.37775955488335
12336.41471193548031-3.41471193548031
12446.43299471220528-2.43299471220528
12556.44964169822592-1.44964169822592
12666.49479005235211-0.494790052352106
12776.533768694030060.46623130596994
12886.539479856071411.46052014392859
12996.596113513788982.40388648621102
130106.563997510691093.43600248930891
131116.45037595083224.5496240491678
132126.47642727611095.5235727238891
13316.51303660312418-5.51303660312418
13426.43915323381342-4.43915323381342
13536.47907546870981-3.47907546870981
13646.47848611991514-2.47848611991514
13756.49694419723717-1.49694419723717
13866.55574901984734-0.555749019847343
13976.639001231109190.360998768890809
14086.677749140291841.32225085970816
14196.739327124721252.26067287527875
142106.691867073766443.30813292623356
143116.537608960996184.46239103900382







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.0004621183254264550.0009242366508529090.999537881674574
70.0006569192094812220.001313838418962440.999343080790519
80.002286938304982030.004573876609964070.997713061695018
90.01019971158648270.02039942317296550.989800288413517
100.03933891115333160.07867782230666330.960661088846668
110.1322100745601240.2644201491202490.867789925439876
120.2903256329291420.5806512658582850.709674367070858
130.4405074288967330.8810148577934660.559492571103267
140.4275762396787840.8551524793575680.572423760321216
150.3558636915485610.7117273830971220.644136308451439
160.2821166612503010.5642333225006020.717883338749699
170.2332885867175570.4665771734351140.766711413282443
180.2155225856298390.4310451712596770.784477414370161
190.2210183162179070.4420366324358140.778981683782093
200.2634388391895880.5268776783791760.736561160810412
210.3216073871821540.6432147743643080.678392612817846
220.3642596392660170.7285192785320340.635740360733983
230.3821608179624710.7643216359249430.617839182037529
240.3876235120488170.7752470240976350.612376487951183
250.6197504712283890.7604990575432220.380249528771611
260.6916379482127830.6167241035744340.308362051787217
270.6942263569904340.6115472860191330.305773643009566
280.6462973932821410.7074052134357180.353702606717859
290.5906291072164420.8187417855671170.409370892783558
300.535581786508710.928836426982580.46441821349129
310.4847656620642740.9695313241285490.515234337935725
320.441667810682570.883335621365140.55833218931743
330.4184695329676740.8369390659353490.581530467032326
340.4232338106180940.8464676212361870.576766189381906
350.4636544203618090.9273088407236190.536345579638191
360.5479594273338860.9040811453322270.452040572666114
370.6192038220889170.7615923558221660.380796177911083
380.6487239434218620.7025521131562770.351276056578138
390.6408719786762610.7182560426474770.359128021323739
400.604422574723570.7911548505528610.39557742527643
410.5557692354563560.8884615290872890.444230764543644
420.5020321239157560.9959357521684870.497967876084244
430.4509583427795240.9019166855590480.549041657220476
440.4115982739173930.8231965478347870.588401726082607
450.3953623038604850.790724607720970.604637696139515
460.4071016518645760.8142033037291520.592898348135424
470.4588865832716140.9177731665432270.541113416728386
480.5691675619065030.8616648761869940.430832438093497
490.6161052954953860.7677894090092290.383894704504614
500.6217152947251030.7565694105497940.378284705274897
510.6026991166316510.7946017667366970.397300883368349
520.5681180500208590.8637638999582810.431881949979141
530.5252459694627410.9495080610745180.474754030537259
540.4801134838330960.9602269676661910.519886516166905
550.4404299851718080.8808599703436170.559570014828192
560.4112838858382460.8225677716764930.588716114161754
570.396947673415560.793895346831120.60305232658444
580.4171985620007280.8343971240014550.582801437999272
590.4759465445993510.9518930891987020.524053455400649
600.5630198630162590.8739602739674830.436980136983741
610.6236072554476380.7527854891047240.376392744552362
620.6503934532788350.6992130934423290.349606546721165
630.6454921046206270.7090157907587460.354507895379373
640.6213917898513780.7572164202972430.378608210148622
650.5839175539441060.8321648921117870.416082446055894
660.5389235698203360.9221528603593280.461076430179664
670.4935007908115850.987001581623170.506499209188415
680.4543533684554770.9087067369109540.545646631544523
690.4288769512098530.8577539024197060.571123048790147
700.4357993857736960.8715987715473910.564200614226304
710.4712178049917270.9424356099834540.528782195008273
720.5460841542804350.907831691439130.453915845719565
730.6148076447226260.7703847105547490.385192355277374
740.6497150691598080.7005698616803840.350284930840192
750.6566574739470420.6866850521059170.343342526052958
760.6429014109370950.7141971781258090.357098589062905
770.6135475003079290.7729049993841420.386452499692071
780.5730521369489310.8538957261021380.426947863051069
790.5276572696967280.9446854606065440.472342730303272
800.4845500874899690.9691001749799390.515449912510031
810.4525749312642460.9051498625284920.547425068735754
820.4397920644614620.8795841289229240.560207935538538
830.4562598695240720.9125197390481450.543740130475928
840.5121636048335880.9756727903328230.487836395166412
850.6018682079023440.7962635841953130.398131792097656
860.6512738658505250.697452268298950.348726134149475
870.6686139671409470.6627720657181060.331386032859053
880.6608529706892920.6782940586214150.339147029310708
890.6334749455972390.7330501088055220.366525054402761
900.5912897340360540.8174205319278920.408710265963946
910.5428539276614780.9142921446770440.457146072338522
920.4974546839768110.9949093679536230.502545316023189
930.4632280680845430.9264561361690860.536771931915457
940.449694699431460.8993893988629210.55030530056854
950.4640338691288210.9280677382576420.535966130871179
960.5170651078351480.9658697843297040.482934892164852
970.6191440662176680.7617118675646640.380855933782332
980.6696615948047470.6606768103905060.330338405195253
990.6867134455102510.6265731089794980.313286554489749
1000.6762519430991510.6474961138016990.323748056900849
1010.6466829884983890.7066340230032230.353317011501611
1020.603946599902540.7921068001949190.39605340009746
1030.5534198313017710.8931603373964590.446580168698229
1040.5023497988611960.9953004022776070.497650201138804
1050.4587922489714550.9175844979429110.541207751028545
1060.4375936016291180.8751872032582370.562406398370882
1070.4610354540159360.9220709080318730.538964545984064
1080.5627701529150560.8744596941698880.437229847084944
1090.62398490894260.75203018211480.3760150910574
1100.6105031363608030.7789937272783930.389496863639197
1110.5933800778854030.8132398442291930.406619922114597
1120.5629826799060650.8740346401878690.437017320093935
1130.5171889724260910.9656220551478170.482811027573909
1140.4630600466469460.9261200932938930.536939953353054
1150.4042075913677550.808415182735510.595792408632245
1160.3492799580069880.6985599160139760.650720041993012
1170.3225226137259190.6450452274518380.677477386274081
1180.3387374194243910.6774748388487830.661262580575608
1190.489530541518680.979061083037360.51046945848132
1200.8999944720821150.2000110558357690.100005527917885
1210.8749023176788020.2501953646423970.125097682321198
1220.840440067049050.31911986590190.15955993295095
1230.7963033481768930.4073933036462140.203696651823107
1240.7422234581794190.5155530836411620.257776541820581
1250.6764420959923440.6471158080153130.323557904007657
1260.6051847896893210.7896304206213580.394815210310679
1270.5293545502644960.9412908994710080.470645449735504
1280.4451630145652260.8903260291304530.554836985434774
1290.3687622562312270.7375245124624540.631237743768773
1300.3946888103853090.7893776207706180.605311189614691
1310.478413803041360.956827606082720.52158619695864
1320.9361224672558750.127755065488250.0638775327441248
1330.9743773633269380.05124527334612380.0256226366730619
1340.9743557916896490.05128841662070290.0256442083103514
1350.9459877409327040.1080245181345920.0540122590672962
1360.9304987245783450.139002550843310.0695012754216552
1370.859328371700930.281343256598140.14067162829907

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.000462118325426455 & 0.000924236650852909 & 0.999537881674574 \tabularnewline
7 & 0.000656919209481222 & 0.00131383841896244 & 0.999343080790519 \tabularnewline
8 & 0.00228693830498203 & 0.00457387660996407 & 0.997713061695018 \tabularnewline
9 & 0.0101997115864827 & 0.0203994231729655 & 0.989800288413517 \tabularnewline
10 & 0.0393389111533316 & 0.0786778223066633 & 0.960661088846668 \tabularnewline
11 & 0.132210074560124 & 0.264420149120249 & 0.867789925439876 \tabularnewline
12 & 0.290325632929142 & 0.580651265858285 & 0.709674367070858 \tabularnewline
13 & 0.440507428896733 & 0.881014857793466 & 0.559492571103267 \tabularnewline
14 & 0.427576239678784 & 0.855152479357568 & 0.572423760321216 \tabularnewline
15 & 0.355863691548561 & 0.711727383097122 & 0.644136308451439 \tabularnewline
16 & 0.282116661250301 & 0.564233322500602 & 0.717883338749699 \tabularnewline
17 & 0.233288586717557 & 0.466577173435114 & 0.766711413282443 \tabularnewline
18 & 0.215522585629839 & 0.431045171259677 & 0.784477414370161 \tabularnewline
19 & 0.221018316217907 & 0.442036632435814 & 0.778981683782093 \tabularnewline
20 & 0.263438839189588 & 0.526877678379176 & 0.736561160810412 \tabularnewline
21 & 0.321607387182154 & 0.643214774364308 & 0.678392612817846 \tabularnewline
22 & 0.364259639266017 & 0.728519278532034 & 0.635740360733983 \tabularnewline
23 & 0.382160817962471 & 0.764321635924943 & 0.617839182037529 \tabularnewline
24 & 0.387623512048817 & 0.775247024097635 & 0.612376487951183 \tabularnewline
25 & 0.619750471228389 & 0.760499057543222 & 0.380249528771611 \tabularnewline
26 & 0.691637948212783 & 0.616724103574434 & 0.308362051787217 \tabularnewline
27 & 0.694226356990434 & 0.611547286019133 & 0.305773643009566 \tabularnewline
28 & 0.646297393282141 & 0.707405213435718 & 0.353702606717859 \tabularnewline
29 & 0.590629107216442 & 0.818741785567117 & 0.409370892783558 \tabularnewline
30 & 0.53558178650871 & 0.92883642698258 & 0.46441821349129 \tabularnewline
31 & 0.484765662064274 & 0.969531324128549 & 0.515234337935725 \tabularnewline
32 & 0.44166781068257 & 0.88333562136514 & 0.55833218931743 \tabularnewline
33 & 0.418469532967674 & 0.836939065935349 & 0.581530467032326 \tabularnewline
34 & 0.423233810618094 & 0.846467621236187 & 0.576766189381906 \tabularnewline
35 & 0.463654420361809 & 0.927308840723619 & 0.536345579638191 \tabularnewline
36 & 0.547959427333886 & 0.904081145332227 & 0.452040572666114 \tabularnewline
37 & 0.619203822088917 & 0.761592355822166 & 0.380796177911083 \tabularnewline
38 & 0.648723943421862 & 0.702552113156277 & 0.351276056578138 \tabularnewline
39 & 0.640871978676261 & 0.718256042647477 & 0.359128021323739 \tabularnewline
40 & 0.60442257472357 & 0.791154850552861 & 0.39557742527643 \tabularnewline
41 & 0.555769235456356 & 0.888461529087289 & 0.444230764543644 \tabularnewline
42 & 0.502032123915756 & 0.995935752168487 & 0.497967876084244 \tabularnewline
43 & 0.450958342779524 & 0.901916685559048 & 0.549041657220476 \tabularnewline
44 & 0.411598273917393 & 0.823196547834787 & 0.588401726082607 \tabularnewline
45 & 0.395362303860485 & 0.79072460772097 & 0.604637696139515 \tabularnewline
46 & 0.407101651864576 & 0.814203303729152 & 0.592898348135424 \tabularnewline
47 & 0.458886583271614 & 0.917773166543227 & 0.541113416728386 \tabularnewline
48 & 0.569167561906503 & 0.861664876186994 & 0.430832438093497 \tabularnewline
49 & 0.616105295495386 & 0.767789409009229 & 0.383894704504614 \tabularnewline
50 & 0.621715294725103 & 0.756569410549794 & 0.378284705274897 \tabularnewline
51 & 0.602699116631651 & 0.794601766736697 & 0.397300883368349 \tabularnewline
52 & 0.568118050020859 & 0.863763899958281 & 0.431881949979141 \tabularnewline
53 & 0.525245969462741 & 0.949508061074518 & 0.474754030537259 \tabularnewline
54 & 0.480113483833096 & 0.960226967666191 & 0.519886516166905 \tabularnewline
55 & 0.440429985171808 & 0.880859970343617 & 0.559570014828192 \tabularnewline
56 & 0.411283885838246 & 0.822567771676493 & 0.588716114161754 \tabularnewline
57 & 0.39694767341556 & 0.79389534683112 & 0.60305232658444 \tabularnewline
58 & 0.417198562000728 & 0.834397124001455 & 0.582801437999272 \tabularnewline
59 & 0.475946544599351 & 0.951893089198702 & 0.524053455400649 \tabularnewline
60 & 0.563019863016259 & 0.873960273967483 & 0.436980136983741 \tabularnewline
61 & 0.623607255447638 & 0.752785489104724 & 0.376392744552362 \tabularnewline
62 & 0.650393453278835 & 0.699213093442329 & 0.349606546721165 \tabularnewline
63 & 0.645492104620627 & 0.709015790758746 & 0.354507895379373 \tabularnewline
64 & 0.621391789851378 & 0.757216420297243 & 0.378608210148622 \tabularnewline
65 & 0.583917553944106 & 0.832164892111787 & 0.416082446055894 \tabularnewline
66 & 0.538923569820336 & 0.922152860359328 & 0.461076430179664 \tabularnewline
67 & 0.493500790811585 & 0.98700158162317 & 0.506499209188415 \tabularnewline
68 & 0.454353368455477 & 0.908706736910954 & 0.545646631544523 \tabularnewline
69 & 0.428876951209853 & 0.857753902419706 & 0.571123048790147 \tabularnewline
70 & 0.435799385773696 & 0.871598771547391 & 0.564200614226304 \tabularnewline
71 & 0.471217804991727 & 0.942435609983454 & 0.528782195008273 \tabularnewline
72 & 0.546084154280435 & 0.90783169143913 & 0.453915845719565 \tabularnewline
73 & 0.614807644722626 & 0.770384710554749 & 0.385192355277374 \tabularnewline
74 & 0.649715069159808 & 0.700569861680384 & 0.350284930840192 \tabularnewline
75 & 0.656657473947042 & 0.686685052105917 & 0.343342526052958 \tabularnewline
76 & 0.642901410937095 & 0.714197178125809 & 0.357098589062905 \tabularnewline
77 & 0.613547500307929 & 0.772904999384142 & 0.386452499692071 \tabularnewline
78 & 0.573052136948931 & 0.853895726102138 & 0.426947863051069 \tabularnewline
79 & 0.527657269696728 & 0.944685460606544 & 0.472342730303272 \tabularnewline
80 & 0.484550087489969 & 0.969100174979939 & 0.515449912510031 \tabularnewline
81 & 0.452574931264246 & 0.905149862528492 & 0.547425068735754 \tabularnewline
82 & 0.439792064461462 & 0.879584128922924 & 0.560207935538538 \tabularnewline
83 & 0.456259869524072 & 0.912519739048145 & 0.543740130475928 \tabularnewline
84 & 0.512163604833588 & 0.975672790332823 & 0.487836395166412 \tabularnewline
85 & 0.601868207902344 & 0.796263584195313 & 0.398131792097656 \tabularnewline
86 & 0.651273865850525 & 0.69745226829895 & 0.348726134149475 \tabularnewline
87 & 0.668613967140947 & 0.662772065718106 & 0.331386032859053 \tabularnewline
88 & 0.660852970689292 & 0.678294058621415 & 0.339147029310708 \tabularnewline
89 & 0.633474945597239 & 0.733050108805522 & 0.366525054402761 \tabularnewline
90 & 0.591289734036054 & 0.817420531927892 & 0.408710265963946 \tabularnewline
91 & 0.542853927661478 & 0.914292144677044 & 0.457146072338522 \tabularnewline
92 & 0.497454683976811 & 0.994909367953623 & 0.502545316023189 \tabularnewline
93 & 0.463228068084543 & 0.926456136169086 & 0.536771931915457 \tabularnewline
94 & 0.44969469943146 & 0.899389398862921 & 0.55030530056854 \tabularnewline
95 & 0.464033869128821 & 0.928067738257642 & 0.535966130871179 \tabularnewline
96 & 0.517065107835148 & 0.965869784329704 & 0.482934892164852 \tabularnewline
97 & 0.619144066217668 & 0.761711867564664 & 0.380855933782332 \tabularnewline
98 & 0.669661594804747 & 0.660676810390506 & 0.330338405195253 \tabularnewline
99 & 0.686713445510251 & 0.626573108979498 & 0.313286554489749 \tabularnewline
100 & 0.676251943099151 & 0.647496113801699 & 0.323748056900849 \tabularnewline
101 & 0.646682988498389 & 0.706634023003223 & 0.353317011501611 \tabularnewline
102 & 0.60394659990254 & 0.792106800194919 & 0.39605340009746 \tabularnewline
103 & 0.553419831301771 & 0.893160337396459 & 0.446580168698229 \tabularnewline
104 & 0.502349798861196 & 0.995300402277607 & 0.497650201138804 \tabularnewline
105 & 0.458792248971455 & 0.917584497942911 & 0.541207751028545 \tabularnewline
106 & 0.437593601629118 & 0.875187203258237 & 0.562406398370882 \tabularnewline
107 & 0.461035454015936 & 0.922070908031873 & 0.538964545984064 \tabularnewline
108 & 0.562770152915056 & 0.874459694169888 & 0.437229847084944 \tabularnewline
109 & 0.6239849089426 & 0.7520301821148 & 0.3760150910574 \tabularnewline
110 & 0.610503136360803 & 0.778993727278393 & 0.389496863639197 \tabularnewline
111 & 0.593380077885403 & 0.813239844229193 & 0.406619922114597 \tabularnewline
112 & 0.562982679906065 & 0.874034640187869 & 0.437017320093935 \tabularnewline
113 & 0.517188972426091 & 0.965622055147817 & 0.482811027573909 \tabularnewline
114 & 0.463060046646946 & 0.926120093293893 & 0.536939953353054 \tabularnewline
115 & 0.404207591367755 & 0.80841518273551 & 0.595792408632245 \tabularnewline
116 & 0.349279958006988 & 0.698559916013976 & 0.650720041993012 \tabularnewline
117 & 0.322522613725919 & 0.645045227451838 & 0.677477386274081 \tabularnewline
118 & 0.338737419424391 & 0.677474838848783 & 0.661262580575608 \tabularnewline
119 & 0.48953054151868 & 0.97906108303736 & 0.51046945848132 \tabularnewline
120 & 0.899994472082115 & 0.200011055835769 & 0.100005527917885 \tabularnewline
121 & 0.874902317678802 & 0.250195364642397 & 0.125097682321198 \tabularnewline
122 & 0.84044006704905 & 0.3191198659019 & 0.15955993295095 \tabularnewline
123 & 0.796303348176893 & 0.407393303646214 & 0.203696651823107 \tabularnewline
124 & 0.742223458179419 & 0.515553083641162 & 0.257776541820581 \tabularnewline
125 & 0.676442095992344 & 0.647115808015313 & 0.323557904007657 \tabularnewline
126 & 0.605184789689321 & 0.789630420621358 & 0.394815210310679 \tabularnewline
127 & 0.529354550264496 & 0.941290899471008 & 0.470645449735504 \tabularnewline
128 & 0.445163014565226 & 0.890326029130453 & 0.554836985434774 \tabularnewline
129 & 0.368762256231227 & 0.737524512462454 & 0.631237743768773 \tabularnewline
130 & 0.394688810385309 & 0.789377620770618 & 0.605311189614691 \tabularnewline
131 & 0.47841380304136 & 0.95682760608272 & 0.52158619695864 \tabularnewline
132 & 0.936122467255875 & 0.12775506548825 & 0.0638775327441248 \tabularnewline
133 & 0.974377363326938 & 0.0512452733461238 & 0.0256226366730619 \tabularnewline
134 & 0.974355791689649 & 0.0512884166207029 & 0.0256442083103514 \tabularnewline
135 & 0.945987740932704 & 0.108024518134592 & 0.0540122590672962 \tabularnewline
136 & 0.930498724578345 & 0.13900255084331 & 0.0695012754216552 \tabularnewline
137 & 0.85932837170093 & 0.28134325659814 & 0.14067162829907 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186328&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]6[/C][C]0.000462118325426455[/C][C]0.000924236650852909[/C][C]0.999537881674574[/C][/ROW]
[ROW][C]7[/C][C]0.000656919209481222[/C][C]0.00131383841896244[/C][C]0.999343080790519[/C][/ROW]
[ROW][C]8[/C][C]0.00228693830498203[/C][C]0.00457387660996407[/C][C]0.997713061695018[/C][/ROW]
[ROW][C]9[/C][C]0.0101997115864827[/C][C]0.0203994231729655[/C][C]0.989800288413517[/C][/ROW]
[ROW][C]10[/C][C]0.0393389111533316[/C][C]0.0786778223066633[/C][C]0.960661088846668[/C][/ROW]
[ROW][C]11[/C][C]0.132210074560124[/C][C]0.264420149120249[/C][C]0.867789925439876[/C][/ROW]
[ROW][C]12[/C][C]0.290325632929142[/C][C]0.580651265858285[/C][C]0.709674367070858[/C][/ROW]
[ROW][C]13[/C][C]0.440507428896733[/C][C]0.881014857793466[/C][C]0.559492571103267[/C][/ROW]
[ROW][C]14[/C][C]0.427576239678784[/C][C]0.855152479357568[/C][C]0.572423760321216[/C][/ROW]
[ROW][C]15[/C][C]0.355863691548561[/C][C]0.711727383097122[/C][C]0.644136308451439[/C][/ROW]
[ROW][C]16[/C][C]0.282116661250301[/C][C]0.564233322500602[/C][C]0.717883338749699[/C][/ROW]
[ROW][C]17[/C][C]0.233288586717557[/C][C]0.466577173435114[/C][C]0.766711413282443[/C][/ROW]
[ROW][C]18[/C][C]0.215522585629839[/C][C]0.431045171259677[/C][C]0.784477414370161[/C][/ROW]
[ROW][C]19[/C][C]0.221018316217907[/C][C]0.442036632435814[/C][C]0.778981683782093[/C][/ROW]
[ROW][C]20[/C][C]0.263438839189588[/C][C]0.526877678379176[/C][C]0.736561160810412[/C][/ROW]
[ROW][C]21[/C][C]0.321607387182154[/C][C]0.643214774364308[/C][C]0.678392612817846[/C][/ROW]
[ROW][C]22[/C][C]0.364259639266017[/C][C]0.728519278532034[/C][C]0.635740360733983[/C][/ROW]
[ROW][C]23[/C][C]0.382160817962471[/C][C]0.764321635924943[/C][C]0.617839182037529[/C][/ROW]
[ROW][C]24[/C][C]0.387623512048817[/C][C]0.775247024097635[/C][C]0.612376487951183[/C][/ROW]
[ROW][C]25[/C][C]0.619750471228389[/C][C]0.760499057543222[/C][C]0.380249528771611[/C][/ROW]
[ROW][C]26[/C][C]0.691637948212783[/C][C]0.616724103574434[/C][C]0.308362051787217[/C][/ROW]
[ROW][C]27[/C][C]0.694226356990434[/C][C]0.611547286019133[/C][C]0.305773643009566[/C][/ROW]
[ROW][C]28[/C][C]0.646297393282141[/C][C]0.707405213435718[/C][C]0.353702606717859[/C][/ROW]
[ROW][C]29[/C][C]0.590629107216442[/C][C]0.818741785567117[/C][C]0.409370892783558[/C][/ROW]
[ROW][C]30[/C][C]0.53558178650871[/C][C]0.92883642698258[/C][C]0.46441821349129[/C][/ROW]
[ROW][C]31[/C][C]0.484765662064274[/C][C]0.969531324128549[/C][C]0.515234337935725[/C][/ROW]
[ROW][C]32[/C][C]0.44166781068257[/C][C]0.88333562136514[/C][C]0.55833218931743[/C][/ROW]
[ROW][C]33[/C][C]0.418469532967674[/C][C]0.836939065935349[/C][C]0.581530467032326[/C][/ROW]
[ROW][C]34[/C][C]0.423233810618094[/C][C]0.846467621236187[/C][C]0.576766189381906[/C][/ROW]
[ROW][C]35[/C][C]0.463654420361809[/C][C]0.927308840723619[/C][C]0.536345579638191[/C][/ROW]
[ROW][C]36[/C][C]0.547959427333886[/C][C]0.904081145332227[/C][C]0.452040572666114[/C][/ROW]
[ROW][C]37[/C][C]0.619203822088917[/C][C]0.761592355822166[/C][C]0.380796177911083[/C][/ROW]
[ROW][C]38[/C][C]0.648723943421862[/C][C]0.702552113156277[/C][C]0.351276056578138[/C][/ROW]
[ROW][C]39[/C][C]0.640871978676261[/C][C]0.718256042647477[/C][C]0.359128021323739[/C][/ROW]
[ROW][C]40[/C][C]0.60442257472357[/C][C]0.791154850552861[/C][C]0.39557742527643[/C][/ROW]
[ROW][C]41[/C][C]0.555769235456356[/C][C]0.888461529087289[/C][C]0.444230764543644[/C][/ROW]
[ROW][C]42[/C][C]0.502032123915756[/C][C]0.995935752168487[/C][C]0.497967876084244[/C][/ROW]
[ROW][C]43[/C][C]0.450958342779524[/C][C]0.901916685559048[/C][C]0.549041657220476[/C][/ROW]
[ROW][C]44[/C][C]0.411598273917393[/C][C]0.823196547834787[/C][C]0.588401726082607[/C][/ROW]
[ROW][C]45[/C][C]0.395362303860485[/C][C]0.79072460772097[/C][C]0.604637696139515[/C][/ROW]
[ROW][C]46[/C][C]0.407101651864576[/C][C]0.814203303729152[/C][C]0.592898348135424[/C][/ROW]
[ROW][C]47[/C][C]0.458886583271614[/C][C]0.917773166543227[/C][C]0.541113416728386[/C][/ROW]
[ROW][C]48[/C][C]0.569167561906503[/C][C]0.861664876186994[/C][C]0.430832438093497[/C][/ROW]
[ROW][C]49[/C][C]0.616105295495386[/C][C]0.767789409009229[/C][C]0.383894704504614[/C][/ROW]
[ROW][C]50[/C][C]0.621715294725103[/C][C]0.756569410549794[/C][C]0.378284705274897[/C][/ROW]
[ROW][C]51[/C][C]0.602699116631651[/C][C]0.794601766736697[/C][C]0.397300883368349[/C][/ROW]
[ROW][C]52[/C][C]0.568118050020859[/C][C]0.863763899958281[/C][C]0.431881949979141[/C][/ROW]
[ROW][C]53[/C][C]0.525245969462741[/C][C]0.949508061074518[/C][C]0.474754030537259[/C][/ROW]
[ROW][C]54[/C][C]0.480113483833096[/C][C]0.960226967666191[/C][C]0.519886516166905[/C][/ROW]
[ROW][C]55[/C][C]0.440429985171808[/C][C]0.880859970343617[/C][C]0.559570014828192[/C][/ROW]
[ROW][C]56[/C][C]0.411283885838246[/C][C]0.822567771676493[/C][C]0.588716114161754[/C][/ROW]
[ROW][C]57[/C][C]0.39694767341556[/C][C]0.79389534683112[/C][C]0.60305232658444[/C][/ROW]
[ROW][C]58[/C][C]0.417198562000728[/C][C]0.834397124001455[/C][C]0.582801437999272[/C][/ROW]
[ROW][C]59[/C][C]0.475946544599351[/C][C]0.951893089198702[/C][C]0.524053455400649[/C][/ROW]
[ROW][C]60[/C][C]0.563019863016259[/C][C]0.873960273967483[/C][C]0.436980136983741[/C][/ROW]
[ROW][C]61[/C][C]0.623607255447638[/C][C]0.752785489104724[/C][C]0.376392744552362[/C][/ROW]
[ROW][C]62[/C][C]0.650393453278835[/C][C]0.699213093442329[/C][C]0.349606546721165[/C][/ROW]
[ROW][C]63[/C][C]0.645492104620627[/C][C]0.709015790758746[/C][C]0.354507895379373[/C][/ROW]
[ROW][C]64[/C][C]0.621391789851378[/C][C]0.757216420297243[/C][C]0.378608210148622[/C][/ROW]
[ROW][C]65[/C][C]0.583917553944106[/C][C]0.832164892111787[/C][C]0.416082446055894[/C][/ROW]
[ROW][C]66[/C][C]0.538923569820336[/C][C]0.922152860359328[/C][C]0.461076430179664[/C][/ROW]
[ROW][C]67[/C][C]0.493500790811585[/C][C]0.98700158162317[/C][C]0.506499209188415[/C][/ROW]
[ROW][C]68[/C][C]0.454353368455477[/C][C]0.908706736910954[/C][C]0.545646631544523[/C][/ROW]
[ROW][C]69[/C][C]0.428876951209853[/C][C]0.857753902419706[/C][C]0.571123048790147[/C][/ROW]
[ROW][C]70[/C][C]0.435799385773696[/C][C]0.871598771547391[/C][C]0.564200614226304[/C][/ROW]
[ROW][C]71[/C][C]0.471217804991727[/C][C]0.942435609983454[/C][C]0.528782195008273[/C][/ROW]
[ROW][C]72[/C][C]0.546084154280435[/C][C]0.90783169143913[/C][C]0.453915845719565[/C][/ROW]
[ROW][C]73[/C][C]0.614807644722626[/C][C]0.770384710554749[/C][C]0.385192355277374[/C][/ROW]
[ROW][C]74[/C][C]0.649715069159808[/C][C]0.700569861680384[/C][C]0.350284930840192[/C][/ROW]
[ROW][C]75[/C][C]0.656657473947042[/C][C]0.686685052105917[/C][C]0.343342526052958[/C][/ROW]
[ROW][C]76[/C][C]0.642901410937095[/C][C]0.714197178125809[/C][C]0.357098589062905[/C][/ROW]
[ROW][C]77[/C][C]0.613547500307929[/C][C]0.772904999384142[/C][C]0.386452499692071[/C][/ROW]
[ROW][C]78[/C][C]0.573052136948931[/C][C]0.853895726102138[/C][C]0.426947863051069[/C][/ROW]
[ROW][C]79[/C][C]0.527657269696728[/C][C]0.944685460606544[/C][C]0.472342730303272[/C][/ROW]
[ROW][C]80[/C][C]0.484550087489969[/C][C]0.969100174979939[/C][C]0.515449912510031[/C][/ROW]
[ROW][C]81[/C][C]0.452574931264246[/C][C]0.905149862528492[/C][C]0.547425068735754[/C][/ROW]
[ROW][C]82[/C][C]0.439792064461462[/C][C]0.879584128922924[/C][C]0.560207935538538[/C][/ROW]
[ROW][C]83[/C][C]0.456259869524072[/C][C]0.912519739048145[/C][C]0.543740130475928[/C][/ROW]
[ROW][C]84[/C][C]0.512163604833588[/C][C]0.975672790332823[/C][C]0.487836395166412[/C][/ROW]
[ROW][C]85[/C][C]0.601868207902344[/C][C]0.796263584195313[/C][C]0.398131792097656[/C][/ROW]
[ROW][C]86[/C][C]0.651273865850525[/C][C]0.69745226829895[/C][C]0.348726134149475[/C][/ROW]
[ROW][C]87[/C][C]0.668613967140947[/C][C]0.662772065718106[/C][C]0.331386032859053[/C][/ROW]
[ROW][C]88[/C][C]0.660852970689292[/C][C]0.678294058621415[/C][C]0.339147029310708[/C][/ROW]
[ROW][C]89[/C][C]0.633474945597239[/C][C]0.733050108805522[/C][C]0.366525054402761[/C][/ROW]
[ROW][C]90[/C][C]0.591289734036054[/C][C]0.817420531927892[/C][C]0.408710265963946[/C][/ROW]
[ROW][C]91[/C][C]0.542853927661478[/C][C]0.914292144677044[/C][C]0.457146072338522[/C][/ROW]
[ROW][C]92[/C][C]0.497454683976811[/C][C]0.994909367953623[/C][C]0.502545316023189[/C][/ROW]
[ROW][C]93[/C][C]0.463228068084543[/C][C]0.926456136169086[/C][C]0.536771931915457[/C][/ROW]
[ROW][C]94[/C][C]0.44969469943146[/C][C]0.899389398862921[/C][C]0.55030530056854[/C][/ROW]
[ROW][C]95[/C][C]0.464033869128821[/C][C]0.928067738257642[/C][C]0.535966130871179[/C][/ROW]
[ROW][C]96[/C][C]0.517065107835148[/C][C]0.965869784329704[/C][C]0.482934892164852[/C][/ROW]
[ROW][C]97[/C][C]0.619144066217668[/C][C]0.761711867564664[/C][C]0.380855933782332[/C][/ROW]
[ROW][C]98[/C][C]0.669661594804747[/C][C]0.660676810390506[/C][C]0.330338405195253[/C][/ROW]
[ROW][C]99[/C][C]0.686713445510251[/C][C]0.626573108979498[/C][C]0.313286554489749[/C][/ROW]
[ROW][C]100[/C][C]0.676251943099151[/C][C]0.647496113801699[/C][C]0.323748056900849[/C][/ROW]
[ROW][C]101[/C][C]0.646682988498389[/C][C]0.706634023003223[/C][C]0.353317011501611[/C][/ROW]
[ROW][C]102[/C][C]0.60394659990254[/C][C]0.792106800194919[/C][C]0.39605340009746[/C][/ROW]
[ROW][C]103[/C][C]0.553419831301771[/C][C]0.893160337396459[/C][C]0.446580168698229[/C][/ROW]
[ROW][C]104[/C][C]0.502349798861196[/C][C]0.995300402277607[/C][C]0.497650201138804[/C][/ROW]
[ROW][C]105[/C][C]0.458792248971455[/C][C]0.917584497942911[/C][C]0.541207751028545[/C][/ROW]
[ROW][C]106[/C][C]0.437593601629118[/C][C]0.875187203258237[/C][C]0.562406398370882[/C][/ROW]
[ROW][C]107[/C][C]0.461035454015936[/C][C]0.922070908031873[/C][C]0.538964545984064[/C][/ROW]
[ROW][C]108[/C][C]0.562770152915056[/C][C]0.874459694169888[/C][C]0.437229847084944[/C][/ROW]
[ROW][C]109[/C][C]0.6239849089426[/C][C]0.7520301821148[/C][C]0.3760150910574[/C][/ROW]
[ROW][C]110[/C][C]0.610503136360803[/C][C]0.778993727278393[/C][C]0.389496863639197[/C][/ROW]
[ROW][C]111[/C][C]0.593380077885403[/C][C]0.813239844229193[/C][C]0.406619922114597[/C][/ROW]
[ROW][C]112[/C][C]0.562982679906065[/C][C]0.874034640187869[/C][C]0.437017320093935[/C][/ROW]
[ROW][C]113[/C][C]0.517188972426091[/C][C]0.965622055147817[/C][C]0.482811027573909[/C][/ROW]
[ROW][C]114[/C][C]0.463060046646946[/C][C]0.926120093293893[/C][C]0.536939953353054[/C][/ROW]
[ROW][C]115[/C][C]0.404207591367755[/C][C]0.80841518273551[/C][C]0.595792408632245[/C][/ROW]
[ROW][C]116[/C][C]0.349279958006988[/C][C]0.698559916013976[/C][C]0.650720041993012[/C][/ROW]
[ROW][C]117[/C][C]0.322522613725919[/C][C]0.645045227451838[/C][C]0.677477386274081[/C][/ROW]
[ROW][C]118[/C][C]0.338737419424391[/C][C]0.677474838848783[/C][C]0.661262580575608[/C][/ROW]
[ROW][C]119[/C][C]0.48953054151868[/C][C]0.97906108303736[/C][C]0.51046945848132[/C][/ROW]
[ROW][C]120[/C][C]0.899994472082115[/C][C]0.200011055835769[/C][C]0.100005527917885[/C][/ROW]
[ROW][C]121[/C][C]0.874902317678802[/C][C]0.250195364642397[/C][C]0.125097682321198[/C][/ROW]
[ROW][C]122[/C][C]0.84044006704905[/C][C]0.3191198659019[/C][C]0.15955993295095[/C][/ROW]
[ROW][C]123[/C][C]0.796303348176893[/C][C]0.407393303646214[/C][C]0.203696651823107[/C][/ROW]
[ROW][C]124[/C][C]0.742223458179419[/C][C]0.515553083641162[/C][C]0.257776541820581[/C][/ROW]
[ROW][C]125[/C][C]0.676442095992344[/C][C]0.647115808015313[/C][C]0.323557904007657[/C][/ROW]
[ROW][C]126[/C][C]0.605184789689321[/C][C]0.789630420621358[/C][C]0.394815210310679[/C][/ROW]
[ROW][C]127[/C][C]0.529354550264496[/C][C]0.941290899471008[/C][C]0.470645449735504[/C][/ROW]
[ROW][C]128[/C][C]0.445163014565226[/C][C]0.890326029130453[/C][C]0.554836985434774[/C][/ROW]
[ROW][C]129[/C][C]0.368762256231227[/C][C]0.737524512462454[/C][C]0.631237743768773[/C][/ROW]
[ROW][C]130[/C][C]0.394688810385309[/C][C]0.789377620770618[/C][C]0.605311189614691[/C][/ROW]
[ROW][C]131[/C][C]0.47841380304136[/C][C]0.95682760608272[/C][C]0.52158619695864[/C][/ROW]
[ROW][C]132[/C][C]0.936122467255875[/C][C]0.12775506548825[/C][C]0.0638775327441248[/C][/ROW]
[ROW][C]133[/C][C]0.974377363326938[/C][C]0.0512452733461238[/C][C]0.0256226366730619[/C][/ROW]
[ROW][C]134[/C][C]0.974355791689649[/C][C]0.0512884166207029[/C][C]0.0256442083103514[/C][/ROW]
[ROW][C]135[/C][C]0.945987740932704[/C][C]0.108024518134592[/C][C]0.0540122590672962[/C][/ROW]
[ROW][C]136[/C][C]0.930498724578345[/C][C]0.13900255084331[/C][C]0.0695012754216552[/C][/ROW]
[ROW][C]137[/C][C]0.85932837170093[/C][C]0.28134325659814[/C][C]0.14067162829907[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186328&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186328&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
60.0004621183254264550.0009242366508529090.999537881674574
70.0006569192094812220.001313838418962440.999343080790519
80.002286938304982030.004573876609964070.997713061695018
90.01019971158648270.02039942317296550.989800288413517
100.03933891115333160.07867782230666330.960661088846668
110.1322100745601240.2644201491202490.867789925439876
120.2903256329291420.5806512658582850.709674367070858
130.4405074288967330.8810148577934660.559492571103267
140.4275762396787840.8551524793575680.572423760321216
150.3558636915485610.7117273830971220.644136308451439
160.2821166612503010.5642333225006020.717883338749699
170.2332885867175570.4665771734351140.766711413282443
180.2155225856298390.4310451712596770.784477414370161
190.2210183162179070.4420366324358140.778981683782093
200.2634388391895880.5268776783791760.736561160810412
210.3216073871821540.6432147743643080.678392612817846
220.3642596392660170.7285192785320340.635740360733983
230.3821608179624710.7643216359249430.617839182037529
240.3876235120488170.7752470240976350.612376487951183
250.6197504712283890.7604990575432220.380249528771611
260.6916379482127830.6167241035744340.308362051787217
270.6942263569904340.6115472860191330.305773643009566
280.6462973932821410.7074052134357180.353702606717859
290.5906291072164420.8187417855671170.409370892783558
300.535581786508710.928836426982580.46441821349129
310.4847656620642740.9695313241285490.515234337935725
320.441667810682570.883335621365140.55833218931743
330.4184695329676740.8369390659353490.581530467032326
340.4232338106180940.8464676212361870.576766189381906
350.4636544203618090.9273088407236190.536345579638191
360.5479594273338860.9040811453322270.452040572666114
370.6192038220889170.7615923558221660.380796177911083
380.6487239434218620.7025521131562770.351276056578138
390.6408719786762610.7182560426474770.359128021323739
400.604422574723570.7911548505528610.39557742527643
410.5557692354563560.8884615290872890.444230764543644
420.5020321239157560.9959357521684870.497967876084244
430.4509583427795240.9019166855590480.549041657220476
440.4115982739173930.8231965478347870.588401726082607
450.3953623038604850.790724607720970.604637696139515
460.4071016518645760.8142033037291520.592898348135424
470.4588865832716140.9177731665432270.541113416728386
480.5691675619065030.8616648761869940.430832438093497
490.6161052954953860.7677894090092290.383894704504614
500.6217152947251030.7565694105497940.378284705274897
510.6026991166316510.7946017667366970.397300883368349
520.5681180500208590.8637638999582810.431881949979141
530.5252459694627410.9495080610745180.474754030537259
540.4801134838330960.9602269676661910.519886516166905
550.4404299851718080.8808599703436170.559570014828192
560.4112838858382460.8225677716764930.588716114161754
570.396947673415560.793895346831120.60305232658444
580.4171985620007280.8343971240014550.582801437999272
590.4759465445993510.9518930891987020.524053455400649
600.5630198630162590.8739602739674830.436980136983741
610.6236072554476380.7527854891047240.376392744552362
620.6503934532788350.6992130934423290.349606546721165
630.6454921046206270.7090157907587460.354507895379373
640.6213917898513780.7572164202972430.378608210148622
650.5839175539441060.8321648921117870.416082446055894
660.5389235698203360.9221528603593280.461076430179664
670.4935007908115850.987001581623170.506499209188415
680.4543533684554770.9087067369109540.545646631544523
690.4288769512098530.8577539024197060.571123048790147
700.4357993857736960.8715987715473910.564200614226304
710.4712178049917270.9424356099834540.528782195008273
720.5460841542804350.907831691439130.453915845719565
730.6148076447226260.7703847105547490.385192355277374
740.6497150691598080.7005698616803840.350284930840192
750.6566574739470420.6866850521059170.343342526052958
760.6429014109370950.7141971781258090.357098589062905
770.6135475003079290.7729049993841420.386452499692071
780.5730521369489310.8538957261021380.426947863051069
790.5276572696967280.9446854606065440.472342730303272
800.4845500874899690.9691001749799390.515449912510031
810.4525749312642460.9051498625284920.547425068735754
820.4397920644614620.8795841289229240.560207935538538
830.4562598695240720.9125197390481450.543740130475928
840.5121636048335880.9756727903328230.487836395166412
850.6018682079023440.7962635841953130.398131792097656
860.6512738658505250.697452268298950.348726134149475
870.6686139671409470.6627720657181060.331386032859053
880.6608529706892920.6782940586214150.339147029310708
890.6334749455972390.7330501088055220.366525054402761
900.5912897340360540.8174205319278920.408710265963946
910.5428539276614780.9142921446770440.457146072338522
920.4974546839768110.9949093679536230.502545316023189
930.4632280680845430.9264561361690860.536771931915457
940.449694699431460.8993893988629210.55030530056854
950.4640338691288210.9280677382576420.535966130871179
960.5170651078351480.9658697843297040.482934892164852
970.6191440662176680.7617118675646640.380855933782332
980.6696615948047470.6606768103905060.330338405195253
990.6867134455102510.6265731089794980.313286554489749
1000.6762519430991510.6474961138016990.323748056900849
1010.6466829884983890.7066340230032230.353317011501611
1020.603946599902540.7921068001949190.39605340009746
1030.5534198313017710.8931603373964590.446580168698229
1040.5023497988611960.9953004022776070.497650201138804
1050.4587922489714550.9175844979429110.541207751028545
1060.4375936016291180.8751872032582370.562406398370882
1070.4610354540159360.9220709080318730.538964545984064
1080.5627701529150560.8744596941698880.437229847084944
1090.62398490894260.75203018211480.3760150910574
1100.6105031363608030.7789937272783930.389496863639197
1110.5933800778854030.8132398442291930.406619922114597
1120.5629826799060650.8740346401878690.437017320093935
1130.5171889724260910.9656220551478170.482811027573909
1140.4630600466469460.9261200932938930.536939953353054
1150.4042075913677550.808415182735510.595792408632245
1160.3492799580069880.6985599160139760.650720041993012
1170.3225226137259190.6450452274518380.677477386274081
1180.3387374194243910.6774748388487830.661262580575608
1190.489530541518680.979061083037360.51046945848132
1200.8999944720821150.2000110558357690.100005527917885
1210.8749023176788020.2501953646423970.125097682321198
1220.840440067049050.31911986590190.15955993295095
1230.7963033481768930.4073933036462140.203696651823107
1240.7422234581794190.5155530836411620.257776541820581
1250.6764420959923440.6471158080153130.323557904007657
1260.6051847896893210.7896304206213580.394815210310679
1270.5293545502644960.9412908994710080.470645449735504
1280.4451630145652260.8903260291304530.554836985434774
1290.3687622562312270.7375245124624540.631237743768773
1300.3946888103853090.7893776207706180.605311189614691
1310.478413803041360.956827606082720.52158619695864
1320.9361224672558750.127755065488250.0638775327441248
1330.9743773633269380.05124527334612380.0256226366730619
1340.9743557916896490.05128841662070290.0256442083103514
1350.9459877409327040.1080245181345920.0540122590672962
1360.9304987245783450.139002550843310.0695012754216552
1370.859328371700930.281343256598140.14067162829907







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.0227272727272727NOK
5% type I error level40.0303030303030303OK
10% type I error level70.053030303030303OK

\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 & 3 & 0.0227272727272727 & NOK \tabularnewline
5% type I error level & 4 & 0.0303030303030303 & OK \tabularnewline
10% type I error level & 7 & 0.053030303030303 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186328&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]3[/C][C]0.0227272727272727[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0303030303030303[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.053030303030303[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186328&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186328&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 level30.0227272727272727NOK
5% type I error level40.0303030303030303OK
10% type I error level70.053030303030303OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}