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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 08:48:21 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258645769wrz17j6s0llelaf.htm/, Retrieved Fri, 29 Mar 2024 08:15:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57780, Retrieved Fri, 29 Mar 2024 08:15:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsBouwvergunningen volgens effectieve datum van toekenning - woongebouwen - koninkrijk, Ruimte
Estimated Impact116
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [Bouwvergunningen ...] [2009-11-19 15:48:21] [a4292616308a56e4faddaa97386e0403] [Current]
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Dataseries X:
100	0
108.1560276	0
114.0150276	0
102.1880309	0
110.3672031	0
96.8602511	0
94.1944583	0
99.51621961	0
94.06333487	0
97.5541476	0
78.15062422	0
81.2434643	0
92.36262465	0
96.06324371	0
114.0523777	0
110.6616666	0
104.9171949	0
90.00187193	0
95.7008067	0
86.02741157	0
84.85287668	0
100.04328	0
80.91713823	0
74.06539709	0
77.30281369	0
97.23043249	0
90.75515676	0
100.5614455	0
92.01293267	0
99.24012138	0
105.8672755	0
90.9920463	0
93.30624423	0
91.17419413	0
77.33295039	0
91.1277721	0
85.01249943	0
83.90390242	0
104.8626302	0
110.9039108	0
95.43714373	0
111.6238727	0
108.8925403	0
96.17511682	0
101.9740205	0
99.11953031	0
86.78158147	0
118.4195003	0
118.7441447	0
106.5296192	0
134.7772694	0
104.6778714	0
105.2954304	0
139.4139849	0
103.6060491	0
99.78182974	0
103.4610301	0
120.0594945	0
96.71377168	0
107.1308929	0
105.3608372	0
111.6942359	0
132.0519998	0
126.8037879	0
154.4824253	0
141.5570984	0
109.9506882	0
127.904198	0
133.0888617	0
120.0796299	0
117.5557142	0
143.0362309	0
159.982927	1
128.5991124	1
149.7373327	1
126.8169313	1
140.9639674	1
137.6691981	1
117.9402337	1
122.3095247	1
127.7804207	1
136.1677176	1
116.2405856	1
123.1576893	1
116.3400234	1
108.6119282	1
125.8982264	1
112.8003105	1
107.5182447	1
135.0955413	1
115.5096488	1
115.8640759	1
104.5883906	1
163.7213386	1
113.4482275	1
98.0428844	1
116.7868521	1
126.5330444	1
113.0336597	1
124.3392163	1
109.8298759	1
124.4434777	1
111.5039454	1
102.0350019	1
116.8726598	1
112.2073122	1
101.1513902	1
124.4255108	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57780&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57780&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57780&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
BouwV[t] = + 100.737929464167 + 18.0026589708333X[t] + 1.24926445333335M1[t] + 0.741356025555553M2[t] + 13.1704820188889M3[t] + 6.56709212333335M4[t] + 6.68611955666668M5[t] + 12.8062306022222M6[t] + 0.279589323333329M7[t] -2.22710195000001M8[t] -0.0735003233333385M9[t] + 8.83081141666668M10[t] -10.2619287333333M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BouwV[t] =  +  100.737929464167 +  18.0026589708333X[t] +  1.24926445333335M1[t] +  0.741356025555553M2[t] +  13.1704820188889M3[t] +  6.56709212333335M4[t] +  6.68611955666668M5[t] +  12.8062306022222M6[t] +  0.279589323333329M7[t] -2.22710195000001M8[t] -0.0735003233333385M9[t] +  8.83081141666668M10[t] -10.2619287333333M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57780&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BouwV[t] =  +  100.737929464167 +  18.0026589708333X[t] +  1.24926445333335M1[t] +  0.741356025555553M2[t] +  13.1704820188889M3[t] +  6.56709212333335M4[t] +  6.68611955666668M5[t] +  12.8062306022222M6[t] +  0.279589323333329M7[t] -2.22710195000001M8[t] -0.0735003233333385M9[t] +  8.83081141666668M10[t] -10.2619287333333M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57780&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57780&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
BouwV[t] = + 100.737929464167 + 18.0026589708333X[t] + 1.24926445333335M1[t] + 0.741356025555553M2[t] + 13.1704820188889M3[t] + 6.56709212333335M4[t] + 6.68611955666668M5[t] + 12.8062306022222M6[t] + 0.279589323333329M7[t] -2.22710195000001M8[t] -0.0735003233333385M9[t] + 8.83081141666668M10[t] -10.2619287333333M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)100.7379294641675.34478618.847900
X18.00265897083333.2068715.613800
M11.249264453333357.4059520.16870.8664040.433202
M20.7413560255555537.4059520.10010.9204740.460237
M313.17048201888897.4059521.77840.0785430.039272
M46.567092123333357.4059520.88670.3774630.188731
M56.686119556666687.4059520.90280.3689140.184457
M612.80623060222227.4059521.72920.0870250.043512
M70.2795893233333297.4059520.03780.9699650.484982
M8-2.227101950000017.405952-0.30070.7642870.382143
M9-0.07350032333333857.405952-0.00990.9921020.496051
M108.830811416666687.4059521.19240.2360770.118038
M11-10.26192873333337.405952-1.38560.1691040.084552

\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) & 100.737929464167 & 5.344786 & 18.8479 & 0 & 0 \tabularnewline
X & 18.0026589708333 & 3.206871 & 5.6138 & 0 & 0 \tabularnewline
M1 & 1.24926445333335 & 7.405952 & 0.1687 & 0.866404 & 0.433202 \tabularnewline
M2 & 0.741356025555553 & 7.405952 & 0.1001 & 0.920474 & 0.460237 \tabularnewline
M3 & 13.1704820188889 & 7.405952 & 1.7784 & 0.078543 & 0.039272 \tabularnewline
M4 & 6.56709212333335 & 7.405952 & 0.8867 & 0.377463 & 0.188731 \tabularnewline
M5 & 6.68611955666668 & 7.405952 & 0.9028 & 0.368914 & 0.184457 \tabularnewline
M6 & 12.8062306022222 & 7.405952 & 1.7292 & 0.087025 & 0.043512 \tabularnewline
M7 & 0.279589323333329 & 7.405952 & 0.0378 & 0.969965 & 0.484982 \tabularnewline
M8 & -2.22710195000001 & 7.405952 & -0.3007 & 0.764287 & 0.382143 \tabularnewline
M9 & -0.0735003233333385 & 7.405952 & -0.0099 & 0.992102 & 0.496051 \tabularnewline
M10 & 8.83081141666668 & 7.405952 & 1.1924 & 0.236077 & 0.118038 \tabularnewline
M11 & -10.2619287333333 & 7.405952 & -1.3856 & 0.169104 & 0.084552 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57780&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]100.737929464167[/C][C]5.344786[/C][C]18.8479[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]18.0026589708333[/C][C]3.206871[/C][C]5.6138[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]1.24926445333335[/C][C]7.405952[/C][C]0.1687[/C][C]0.866404[/C][C]0.433202[/C][/ROW]
[ROW][C]M2[/C][C]0.741356025555553[/C][C]7.405952[/C][C]0.1001[/C][C]0.920474[/C][C]0.460237[/C][/ROW]
[ROW][C]M3[/C][C]13.1704820188889[/C][C]7.405952[/C][C]1.7784[/C][C]0.078543[/C][C]0.039272[/C][/ROW]
[ROW][C]M4[/C][C]6.56709212333335[/C][C]7.405952[/C][C]0.8867[/C][C]0.377463[/C][C]0.188731[/C][/ROW]
[ROW][C]M5[/C][C]6.68611955666668[/C][C]7.405952[/C][C]0.9028[/C][C]0.368914[/C][C]0.184457[/C][/ROW]
[ROW][C]M6[/C][C]12.8062306022222[/C][C]7.405952[/C][C]1.7292[/C][C]0.087025[/C][C]0.043512[/C][/ROW]
[ROW][C]M7[/C][C]0.279589323333329[/C][C]7.405952[/C][C]0.0378[/C][C]0.969965[/C][C]0.484982[/C][/ROW]
[ROW][C]M8[/C][C]-2.22710195000001[/C][C]7.405952[/C][C]-0.3007[/C][C]0.764287[/C][C]0.382143[/C][/ROW]
[ROW][C]M9[/C][C]-0.0735003233333385[/C][C]7.405952[/C][C]-0.0099[/C][C]0.992102[/C][C]0.496051[/C][/ROW]
[ROW][C]M10[/C][C]8.83081141666668[/C][C]7.405952[/C][C]1.1924[/C][C]0.236077[/C][C]0.118038[/C][/ROW]
[ROW][C]M11[/C][C]-10.2619287333333[/C][C]7.405952[/C][C]-1.3856[/C][C]0.169104[/C][C]0.084552[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57780&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57780&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)100.7379294641675.34478618.847900
X18.00265897083333.2068715.613800
M11.249264453333357.4059520.16870.8664040.433202
M20.7413560255555537.4059520.10010.9204740.460237
M313.17048201888897.4059521.77840.0785430.039272
M46.567092123333357.4059520.88670.3774630.188731
M56.686119556666687.4059520.90280.3689140.184457
M612.80623060222227.4059521.72920.0870250.043512
M70.2795893233333297.4059520.03780.9699650.484982
M8-2.227101950000017.405952-0.30070.7642870.382143
M9-0.07350032333333857.405952-0.00990.9921020.496051
M108.830811416666687.4059521.19240.2360770.118038
M11-10.26192873333337.405952-1.38560.1691040.084552







Multiple Linear Regression - Regression Statistics
Multiple R0.585800390694066
R-squared0.343162097737320
Adjusted R-squared0.260193099556772
F-TEST (value)4.13602797746894
F-TEST (DF numerator)12
F-TEST (DF denominator)95
p-value3.41703775448288e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.7103976677556
Sum Squared Residuals23447.5765135068

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.585800390694066 \tabularnewline
R-squared & 0.343162097737320 \tabularnewline
Adjusted R-squared & 0.260193099556772 \tabularnewline
F-TEST (value) & 4.13602797746894 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 95 \tabularnewline
p-value & 3.41703775448288e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 15.7103976677556 \tabularnewline
Sum Squared Residuals & 23447.5765135068 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57780&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.585800390694066[/C][/ROW]
[ROW][C]R-squared[/C][C]0.343162097737320[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.260193099556772[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.13602797746894[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]95[/C][/ROW]
[ROW][C]p-value[/C][C]3.41703775448288e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]15.7103976677556[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]23447.5765135068[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57780&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57780&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 R0.585800390694066
R-squared0.343162097737320
Adjusted R-squared0.260193099556772
F-TEST (value)4.13602797746894
F-TEST (DF numerator)12
F-TEST (DF denominator)95
p-value3.41703775448288e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.7103976677556
Sum Squared Residuals23447.5765135068







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1100101.9871939175-1.98719391749993
2108.1560276101.4792854897226.67674211027781
3114.0150276113.9084114830560.106616116944433
4102.1880309107.3050215875-5.11699068749999
5110.3672031107.4240490208332.94315407916666
696.8602511113.544160066389-16.6839089663889
794.1944583101.0175187875-6.82306048750004
899.5162196198.51082751416661.00539209583334
994.06333487100.664429140833-6.60109427083333
1097.5541476109.568740880833-12.0145932808333
1178.1506242290.4760007308333-12.3253765108333
1281.2434643100.737929464167-19.4944651641667
1392.36262465101.9871939175-9.6245692675
1496.06324371101.479285489722-5.41604177972223
15114.0523777113.9084114830560.143966216944441
16110.6616666107.30502158753.35664501250000
17104.9171949107.424049020833-2.50685412083333
1890.00187193113.544160066389-23.5422881363889
1995.7008067101.0175187875-5.31671208749999
2086.0274115798.5108275141667-12.4834159441667
2184.85287668100.664429140833-15.8115524608333
22100.04328109.568740880833-9.52546088083333
2380.9171382390.4760007308333-9.55886250083333
2474.06539709100.737929464167-26.6725323741667
2577.30281369101.9871939175-24.6843802275
2697.23043249101.479285489722-4.24885299972222
2790.75515676113.908411483056-23.1532547230555
28100.5614455107.3050215875-6.7435760875
2992.01293267107.424049020833-15.4111163508333
3099.24012138113.544160066389-14.3040386863889
31105.8672755101.01751878754.84975671250001
3290.992046398.5108275141667-7.51878121416667
3393.30624423100.664429140833-7.35818491083332
3491.17419413109.568740880833-18.3945467508333
3577.3329503990.4760007308333-13.1430503408333
3691.1277721100.737929464167-9.61015736416667
3785.01249943101.9871939175-16.9746944875
3883.90390242101.479285489722-17.5753830697222
39104.8626302113.908411483056-9.04578128305556
40110.9039108107.30502158753.59888921250001
4195.43714373107.424049020833-11.9869052908333
42111.6238727113.544160066389-1.92028736638888
43108.8925403101.01751878757.8750215125
4496.1751168298.5108275141667-2.33571069416667
45101.9740205100.6644291408331.30959135916666
4699.11953031109.568740880833-10.4492105708333
4786.7815814790.4760007308333-3.69441926083333
48118.4195003100.73792946416717.6815708358333
49118.7441447101.987193917516.7569507825
50106.5296192101.4792854897225.05033371027778
51134.7772694113.90841148305620.8688579169444
52104.6778714107.3050215875-2.6271501875
53105.2954304107.424049020833-2.12861862083333
54139.4139849113.54416006638925.8698248336111
55103.6060491101.01751878752.58853031250001
5699.7818297498.51082751416671.27100222583334
57103.4610301100.6644291408332.79660095916667
58120.0594945109.56874088083310.4907536191667
5996.7137716890.47600073083336.23777094916666
60107.1308929100.7379294641676.39296343583333
61105.3608372101.98719391753.37364328249999
62111.6942359101.47928548972210.2149504102778
63132.0519998113.90841148305618.1435883169445
64126.8037879107.305021587519.4987663125
65154.4824253107.42404902083347.0583762791667
66141.5570984113.54416006638928.0129383336111
67109.9506882101.01751878758.93316941250001
68127.90419898.510827514166729.3933704858333
69133.0888617100.66442914083332.4244325591667
70120.0796299109.56874088083310.5108890191667
71117.555714290.476000730833327.0797134691667
72143.0362309100.73792946416742.2983014358333
73159.982927119.98985288833339.9930741116666
74128.5991124119.4819444605569.11716793944444
75149.7373327131.91107045388917.8262622461111
76126.8169313125.3076805583331.50925074166666
77140.9639674125.42670799166715.5372594083333
78137.6691981131.5468190372226.12237906277777
79117.9402337119.020177758333-1.07994405833333
80122.3095247116.5134864855.796038215
81127.7804207118.6670881116679.11333258833333
82136.1677176127.5713998516678.59631774833334
83116.2405856108.4786597016677.76192589833334
84123.1576893118.7405884354.41710086499999
85116.3400234119.989852888333-3.64982948833334
86108.6119282119.481944460556-10.8700162605556
87125.8982264131.911070453889-6.01284405388889
88112.8003105125.307680558333-12.5073700583333
89107.5182447125.426707991667-17.9084632916667
90135.0955413131.5468190372223.54872226277779
91115.5096488119.020177758333-3.51052895833333
92115.8640759116.513486485-0.649410584999995
93104.5883906118.667088111667-14.0786975116667
94163.7213386127.57139985166736.1499387483333
95113.4482275108.4786597016674.96956779833334
9698.0428844118.740588435-20.697704035
97116.7868521119.989852888333-3.20300078833334
98126.5330444119.4819444605567.05109993944444
99113.0336597131.911070453889-18.8774107538889
100124.3392163125.307680558333-0.96846425833333
101109.8298759125.426707991667-15.5968320916667
102124.4434777131.546819037222-7.10334133722222
103111.5039454119.020177758333-7.51623235833332
104102.0350019116.513486485-14.478484585
105116.8726598118.667088111667-1.79442831166667
106112.2073122127.571399851667-15.3640876516667
107101.1513902108.478659701667-7.32726950166667
108124.4255108118.7405884355.68492236499999

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100 & 101.9871939175 & -1.98719391749993 \tabularnewline
2 & 108.1560276 & 101.479285489722 & 6.67674211027781 \tabularnewline
3 & 114.0150276 & 113.908411483056 & 0.106616116944433 \tabularnewline
4 & 102.1880309 & 107.3050215875 & -5.11699068749999 \tabularnewline
5 & 110.3672031 & 107.424049020833 & 2.94315407916666 \tabularnewline
6 & 96.8602511 & 113.544160066389 & -16.6839089663889 \tabularnewline
7 & 94.1944583 & 101.0175187875 & -6.82306048750004 \tabularnewline
8 & 99.51621961 & 98.5108275141666 & 1.00539209583334 \tabularnewline
9 & 94.06333487 & 100.664429140833 & -6.60109427083333 \tabularnewline
10 & 97.5541476 & 109.568740880833 & -12.0145932808333 \tabularnewline
11 & 78.15062422 & 90.4760007308333 & -12.3253765108333 \tabularnewline
12 & 81.2434643 & 100.737929464167 & -19.4944651641667 \tabularnewline
13 & 92.36262465 & 101.9871939175 & -9.6245692675 \tabularnewline
14 & 96.06324371 & 101.479285489722 & -5.41604177972223 \tabularnewline
15 & 114.0523777 & 113.908411483056 & 0.143966216944441 \tabularnewline
16 & 110.6616666 & 107.3050215875 & 3.35664501250000 \tabularnewline
17 & 104.9171949 & 107.424049020833 & -2.50685412083333 \tabularnewline
18 & 90.00187193 & 113.544160066389 & -23.5422881363889 \tabularnewline
19 & 95.7008067 & 101.0175187875 & -5.31671208749999 \tabularnewline
20 & 86.02741157 & 98.5108275141667 & -12.4834159441667 \tabularnewline
21 & 84.85287668 & 100.664429140833 & -15.8115524608333 \tabularnewline
22 & 100.04328 & 109.568740880833 & -9.52546088083333 \tabularnewline
23 & 80.91713823 & 90.4760007308333 & -9.55886250083333 \tabularnewline
24 & 74.06539709 & 100.737929464167 & -26.6725323741667 \tabularnewline
25 & 77.30281369 & 101.9871939175 & -24.6843802275 \tabularnewline
26 & 97.23043249 & 101.479285489722 & -4.24885299972222 \tabularnewline
27 & 90.75515676 & 113.908411483056 & -23.1532547230555 \tabularnewline
28 & 100.5614455 & 107.3050215875 & -6.7435760875 \tabularnewline
29 & 92.01293267 & 107.424049020833 & -15.4111163508333 \tabularnewline
30 & 99.24012138 & 113.544160066389 & -14.3040386863889 \tabularnewline
31 & 105.8672755 & 101.0175187875 & 4.84975671250001 \tabularnewline
32 & 90.9920463 & 98.5108275141667 & -7.51878121416667 \tabularnewline
33 & 93.30624423 & 100.664429140833 & -7.35818491083332 \tabularnewline
34 & 91.17419413 & 109.568740880833 & -18.3945467508333 \tabularnewline
35 & 77.33295039 & 90.4760007308333 & -13.1430503408333 \tabularnewline
36 & 91.1277721 & 100.737929464167 & -9.61015736416667 \tabularnewline
37 & 85.01249943 & 101.9871939175 & -16.9746944875 \tabularnewline
38 & 83.90390242 & 101.479285489722 & -17.5753830697222 \tabularnewline
39 & 104.8626302 & 113.908411483056 & -9.04578128305556 \tabularnewline
40 & 110.9039108 & 107.3050215875 & 3.59888921250001 \tabularnewline
41 & 95.43714373 & 107.424049020833 & -11.9869052908333 \tabularnewline
42 & 111.6238727 & 113.544160066389 & -1.92028736638888 \tabularnewline
43 & 108.8925403 & 101.0175187875 & 7.8750215125 \tabularnewline
44 & 96.17511682 & 98.5108275141667 & -2.33571069416667 \tabularnewline
45 & 101.9740205 & 100.664429140833 & 1.30959135916666 \tabularnewline
46 & 99.11953031 & 109.568740880833 & -10.4492105708333 \tabularnewline
47 & 86.78158147 & 90.4760007308333 & -3.69441926083333 \tabularnewline
48 & 118.4195003 & 100.737929464167 & 17.6815708358333 \tabularnewline
49 & 118.7441447 & 101.9871939175 & 16.7569507825 \tabularnewline
50 & 106.5296192 & 101.479285489722 & 5.05033371027778 \tabularnewline
51 & 134.7772694 & 113.908411483056 & 20.8688579169444 \tabularnewline
52 & 104.6778714 & 107.3050215875 & -2.6271501875 \tabularnewline
53 & 105.2954304 & 107.424049020833 & -2.12861862083333 \tabularnewline
54 & 139.4139849 & 113.544160066389 & 25.8698248336111 \tabularnewline
55 & 103.6060491 & 101.0175187875 & 2.58853031250001 \tabularnewline
56 & 99.78182974 & 98.5108275141667 & 1.27100222583334 \tabularnewline
57 & 103.4610301 & 100.664429140833 & 2.79660095916667 \tabularnewline
58 & 120.0594945 & 109.568740880833 & 10.4907536191667 \tabularnewline
59 & 96.71377168 & 90.4760007308333 & 6.23777094916666 \tabularnewline
60 & 107.1308929 & 100.737929464167 & 6.39296343583333 \tabularnewline
61 & 105.3608372 & 101.9871939175 & 3.37364328249999 \tabularnewline
62 & 111.6942359 & 101.479285489722 & 10.2149504102778 \tabularnewline
63 & 132.0519998 & 113.908411483056 & 18.1435883169445 \tabularnewline
64 & 126.8037879 & 107.3050215875 & 19.4987663125 \tabularnewline
65 & 154.4824253 & 107.424049020833 & 47.0583762791667 \tabularnewline
66 & 141.5570984 & 113.544160066389 & 28.0129383336111 \tabularnewline
67 & 109.9506882 & 101.0175187875 & 8.93316941250001 \tabularnewline
68 & 127.904198 & 98.5108275141667 & 29.3933704858333 \tabularnewline
69 & 133.0888617 & 100.664429140833 & 32.4244325591667 \tabularnewline
70 & 120.0796299 & 109.568740880833 & 10.5108890191667 \tabularnewline
71 & 117.5557142 & 90.4760007308333 & 27.0797134691667 \tabularnewline
72 & 143.0362309 & 100.737929464167 & 42.2983014358333 \tabularnewline
73 & 159.982927 & 119.989852888333 & 39.9930741116666 \tabularnewline
74 & 128.5991124 & 119.481944460556 & 9.11716793944444 \tabularnewline
75 & 149.7373327 & 131.911070453889 & 17.8262622461111 \tabularnewline
76 & 126.8169313 & 125.307680558333 & 1.50925074166666 \tabularnewline
77 & 140.9639674 & 125.426707991667 & 15.5372594083333 \tabularnewline
78 & 137.6691981 & 131.546819037222 & 6.12237906277777 \tabularnewline
79 & 117.9402337 & 119.020177758333 & -1.07994405833333 \tabularnewline
80 & 122.3095247 & 116.513486485 & 5.796038215 \tabularnewline
81 & 127.7804207 & 118.667088111667 & 9.11333258833333 \tabularnewline
82 & 136.1677176 & 127.571399851667 & 8.59631774833334 \tabularnewline
83 & 116.2405856 & 108.478659701667 & 7.76192589833334 \tabularnewline
84 & 123.1576893 & 118.740588435 & 4.41710086499999 \tabularnewline
85 & 116.3400234 & 119.989852888333 & -3.64982948833334 \tabularnewline
86 & 108.6119282 & 119.481944460556 & -10.8700162605556 \tabularnewline
87 & 125.8982264 & 131.911070453889 & -6.01284405388889 \tabularnewline
88 & 112.8003105 & 125.307680558333 & -12.5073700583333 \tabularnewline
89 & 107.5182447 & 125.426707991667 & -17.9084632916667 \tabularnewline
90 & 135.0955413 & 131.546819037222 & 3.54872226277779 \tabularnewline
91 & 115.5096488 & 119.020177758333 & -3.51052895833333 \tabularnewline
92 & 115.8640759 & 116.513486485 & -0.649410584999995 \tabularnewline
93 & 104.5883906 & 118.667088111667 & -14.0786975116667 \tabularnewline
94 & 163.7213386 & 127.571399851667 & 36.1499387483333 \tabularnewline
95 & 113.4482275 & 108.478659701667 & 4.96956779833334 \tabularnewline
96 & 98.0428844 & 118.740588435 & -20.697704035 \tabularnewline
97 & 116.7868521 & 119.989852888333 & -3.20300078833334 \tabularnewline
98 & 126.5330444 & 119.481944460556 & 7.05109993944444 \tabularnewline
99 & 113.0336597 & 131.911070453889 & -18.8774107538889 \tabularnewline
100 & 124.3392163 & 125.307680558333 & -0.96846425833333 \tabularnewline
101 & 109.8298759 & 125.426707991667 & -15.5968320916667 \tabularnewline
102 & 124.4434777 & 131.546819037222 & -7.10334133722222 \tabularnewline
103 & 111.5039454 & 119.020177758333 & -7.51623235833332 \tabularnewline
104 & 102.0350019 & 116.513486485 & -14.478484585 \tabularnewline
105 & 116.8726598 & 118.667088111667 & -1.79442831166667 \tabularnewline
106 & 112.2073122 & 127.571399851667 & -15.3640876516667 \tabularnewline
107 & 101.1513902 & 108.478659701667 & -7.32726950166667 \tabularnewline
108 & 124.4255108 & 118.740588435 & 5.68492236499999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57780&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]100[/C][C]101.9871939175[/C][C]-1.98719391749993[/C][/ROW]
[ROW][C]2[/C][C]108.1560276[/C][C]101.479285489722[/C][C]6.67674211027781[/C][/ROW]
[ROW][C]3[/C][C]114.0150276[/C][C]113.908411483056[/C][C]0.106616116944433[/C][/ROW]
[ROW][C]4[/C][C]102.1880309[/C][C]107.3050215875[/C][C]-5.11699068749999[/C][/ROW]
[ROW][C]5[/C][C]110.3672031[/C][C]107.424049020833[/C][C]2.94315407916666[/C][/ROW]
[ROW][C]6[/C][C]96.8602511[/C][C]113.544160066389[/C][C]-16.6839089663889[/C][/ROW]
[ROW][C]7[/C][C]94.1944583[/C][C]101.0175187875[/C][C]-6.82306048750004[/C][/ROW]
[ROW][C]8[/C][C]99.51621961[/C][C]98.5108275141666[/C][C]1.00539209583334[/C][/ROW]
[ROW][C]9[/C][C]94.06333487[/C][C]100.664429140833[/C][C]-6.60109427083333[/C][/ROW]
[ROW][C]10[/C][C]97.5541476[/C][C]109.568740880833[/C][C]-12.0145932808333[/C][/ROW]
[ROW][C]11[/C][C]78.15062422[/C][C]90.4760007308333[/C][C]-12.3253765108333[/C][/ROW]
[ROW][C]12[/C][C]81.2434643[/C][C]100.737929464167[/C][C]-19.4944651641667[/C][/ROW]
[ROW][C]13[/C][C]92.36262465[/C][C]101.9871939175[/C][C]-9.6245692675[/C][/ROW]
[ROW][C]14[/C][C]96.06324371[/C][C]101.479285489722[/C][C]-5.41604177972223[/C][/ROW]
[ROW][C]15[/C][C]114.0523777[/C][C]113.908411483056[/C][C]0.143966216944441[/C][/ROW]
[ROW][C]16[/C][C]110.6616666[/C][C]107.3050215875[/C][C]3.35664501250000[/C][/ROW]
[ROW][C]17[/C][C]104.9171949[/C][C]107.424049020833[/C][C]-2.50685412083333[/C][/ROW]
[ROW][C]18[/C][C]90.00187193[/C][C]113.544160066389[/C][C]-23.5422881363889[/C][/ROW]
[ROW][C]19[/C][C]95.7008067[/C][C]101.0175187875[/C][C]-5.31671208749999[/C][/ROW]
[ROW][C]20[/C][C]86.02741157[/C][C]98.5108275141667[/C][C]-12.4834159441667[/C][/ROW]
[ROW][C]21[/C][C]84.85287668[/C][C]100.664429140833[/C][C]-15.8115524608333[/C][/ROW]
[ROW][C]22[/C][C]100.04328[/C][C]109.568740880833[/C][C]-9.52546088083333[/C][/ROW]
[ROW][C]23[/C][C]80.91713823[/C][C]90.4760007308333[/C][C]-9.55886250083333[/C][/ROW]
[ROW][C]24[/C][C]74.06539709[/C][C]100.737929464167[/C][C]-26.6725323741667[/C][/ROW]
[ROW][C]25[/C][C]77.30281369[/C][C]101.9871939175[/C][C]-24.6843802275[/C][/ROW]
[ROW][C]26[/C][C]97.23043249[/C][C]101.479285489722[/C][C]-4.24885299972222[/C][/ROW]
[ROW][C]27[/C][C]90.75515676[/C][C]113.908411483056[/C][C]-23.1532547230555[/C][/ROW]
[ROW][C]28[/C][C]100.5614455[/C][C]107.3050215875[/C][C]-6.7435760875[/C][/ROW]
[ROW][C]29[/C][C]92.01293267[/C][C]107.424049020833[/C][C]-15.4111163508333[/C][/ROW]
[ROW][C]30[/C][C]99.24012138[/C][C]113.544160066389[/C][C]-14.3040386863889[/C][/ROW]
[ROW][C]31[/C][C]105.8672755[/C][C]101.0175187875[/C][C]4.84975671250001[/C][/ROW]
[ROW][C]32[/C][C]90.9920463[/C][C]98.5108275141667[/C][C]-7.51878121416667[/C][/ROW]
[ROW][C]33[/C][C]93.30624423[/C][C]100.664429140833[/C][C]-7.35818491083332[/C][/ROW]
[ROW][C]34[/C][C]91.17419413[/C][C]109.568740880833[/C][C]-18.3945467508333[/C][/ROW]
[ROW][C]35[/C][C]77.33295039[/C][C]90.4760007308333[/C][C]-13.1430503408333[/C][/ROW]
[ROW][C]36[/C][C]91.1277721[/C][C]100.737929464167[/C][C]-9.61015736416667[/C][/ROW]
[ROW][C]37[/C][C]85.01249943[/C][C]101.9871939175[/C][C]-16.9746944875[/C][/ROW]
[ROW][C]38[/C][C]83.90390242[/C][C]101.479285489722[/C][C]-17.5753830697222[/C][/ROW]
[ROW][C]39[/C][C]104.8626302[/C][C]113.908411483056[/C][C]-9.04578128305556[/C][/ROW]
[ROW][C]40[/C][C]110.9039108[/C][C]107.3050215875[/C][C]3.59888921250001[/C][/ROW]
[ROW][C]41[/C][C]95.43714373[/C][C]107.424049020833[/C][C]-11.9869052908333[/C][/ROW]
[ROW][C]42[/C][C]111.6238727[/C][C]113.544160066389[/C][C]-1.92028736638888[/C][/ROW]
[ROW][C]43[/C][C]108.8925403[/C][C]101.0175187875[/C][C]7.8750215125[/C][/ROW]
[ROW][C]44[/C][C]96.17511682[/C][C]98.5108275141667[/C][C]-2.33571069416667[/C][/ROW]
[ROW][C]45[/C][C]101.9740205[/C][C]100.664429140833[/C][C]1.30959135916666[/C][/ROW]
[ROW][C]46[/C][C]99.11953031[/C][C]109.568740880833[/C][C]-10.4492105708333[/C][/ROW]
[ROW][C]47[/C][C]86.78158147[/C][C]90.4760007308333[/C][C]-3.69441926083333[/C][/ROW]
[ROW][C]48[/C][C]118.4195003[/C][C]100.737929464167[/C][C]17.6815708358333[/C][/ROW]
[ROW][C]49[/C][C]118.7441447[/C][C]101.9871939175[/C][C]16.7569507825[/C][/ROW]
[ROW][C]50[/C][C]106.5296192[/C][C]101.479285489722[/C][C]5.05033371027778[/C][/ROW]
[ROW][C]51[/C][C]134.7772694[/C][C]113.908411483056[/C][C]20.8688579169444[/C][/ROW]
[ROW][C]52[/C][C]104.6778714[/C][C]107.3050215875[/C][C]-2.6271501875[/C][/ROW]
[ROW][C]53[/C][C]105.2954304[/C][C]107.424049020833[/C][C]-2.12861862083333[/C][/ROW]
[ROW][C]54[/C][C]139.4139849[/C][C]113.544160066389[/C][C]25.8698248336111[/C][/ROW]
[ROW][C]55[/C][C]103.6060491[/C][C]101.0175187875[/C][C]2.58853031250001[/C][/ROW]
[ROW][C]56[/C][C]99.78182974[/C][C]98.5108275141667[/C][C]1.27100222583334[/C][/ROW]
[ROW][C]57[/C][C]103.4610301[/C][C]100.664429140833[/C][C]2.79660095916667[/C][/ROW]
[ROW][C]58[/C][C]120.0594945[/C][C]109.568740880833[/C][C]10.4907536191667[/C][/ROW]
[ROW][C]59[/C][C]96.71377168[/C][C]90.4760007308333[/C][C]6.23777094916666[/C][/ROW]
[ROW][C]60[/C][C]107.1308929[/C][C]100.737929464167[/C][C]6.39296343583333[/C][/ROW]
[ROW][C]61[/C][C]105.3608372[/C][C]101.9871939175[/C][C]3.37364328249999[/C][/ROW]
[ROW][C]62[/C][C]111.6942359[/C][C]101.479285489722[/C][C]10.2149504102778[/C][/ROW]
[ROW][C]63[/C][C]132.0519998[/C][C]113.908411483056[/C][C]18.1435883169445[/C][/ROW]
[ROW][C]64[/C][C]126.8037879[/C][C]107.3050215875[/C][C]19.4987663125[/C][/ROW]
[ROW][C]65[/C][C]154.4824253[/C][C]107.424049020833[/C][C]47.0583762791667[/C][/ROW]
[ROW][C]66[/C][C]141.5570984[/C][C]113.544160066389[/C][C]28.0129383336111[/C][/ROW]
[ROW][C]67[/C][C]109.9506882[/C][C]101.0175187875[/C][C]8.93316941250001[/C][/ROW]
[ROW][C]68[/C][C]127.904198[/C][C]98.5108275141667[/C][C]29.3933704858333[/C][/ROW]
[ROW][C]69[/C][C]133.0888617[/C][C]100.664429140833[/C][C]32.4244325591667[/C][/ROW]
[ROW][C]70[/C][C]120.0796299[/C][C]109.568740880833[/C][C]10.5108890191667[/C][/ROW]
[ROW][C]71[/C][C]117.5557142[/C][C]90.4760007308333[/C][C]27.0797134691667[/C][/ROW]
[ROW][C]72[/C][C]143.0362309[/C][C]100.737929464167[/C][C]42.2983014358333[/C][/ROW]
[ROW][C]73[/C][C]159.982927[/C][C]119.989852888333[/C][C]39.9930741116666[/C][/ROW]
[ROW][C]74[/C][C]128.5991124[/C][C]119.481944460556[/C][C]9.11716793944444[/C][/ROW]
[ROW][C]75[/C][C]149.7373327[/C][C]131.911070453889[/C][C]17.8262622461111[/C][/ROW]
[ROW][C]76[/C][C]126.8169313[/C][C]125.307680558333[/C][C]1.50925074166666[/C][/ROW]
[ROW][C]77[/C][C]140.9639674[/C][C]125.426707991667[/C][C]15.5372594083333[/C][/ROW]
[ROW][C]78[/C][C]137.6691981[/C][C]131.546819037222[/C][C]6.12237906277777[/C][/ROW]
[ROW][C]79[/C][C]117.9402337[/C][C]119.020177758333[/C][C]-1.07994405833333[/C][/ROW]
[ROW][C]80[/C][C]122.3095247[/C][C]116.513486485[/C][C]5.796038215[/C][/ROW]
[ROW][C]81[/C][C]127.7804207[/C][C]118.667088111667[/C][C]9.11333258833333[/C][/ROW]
[ROW][C]82[/C][C]136.1677176[/C][C]127.571399851667[/C][C]8.59631774833334[/C][/ROW]
[ROW][C]83[/C][C]116.2405856[/C][C]108.478659701667[/C][C]7.76192589833334[/C][/ROW]
[ROW][C]84[/C][C]123.1576893[/C][C]118.740588435[/C][C]4.41710086499999[/C][/ROW]
[ROW][C]85[/C][C]116.3400234[/C][C]119.989852888333[/C][C]-3.64982948833334[/C][/ROW]
[ROW][C]86[/C][C]108.6119282[/C][C]119.481944460556[/C][C]-10.8700162605556[/C][/ROW]
[ROW][C]87[/C][C]125.8982264[/C][C]131.911070453889[/C][C]-6.01284405388889[/C][/ROW]
[ROW][C]88[/C][C]112.8003105[/C][C]125.307680558333[/C][C]-12.5073700583333[/C][/ROW]
[ROW][C]89[/C][C]107.5182447[/C][C]125.426707991667[/C][C]-17.9084632916667[/C][/ROW]
[ROW][C]90[/C][C]135.0955413[/C][C]131.546819037222[/C][C]3.54872226277779[/C][/ROW]
[ROW][C]91[/C][C]115.5096488[/C][C]119.020177758333[/C][C]-3.51052895833333[/C][/ROW]
[ROW][C]92[/C][C]115.8640759[/C][C]116.513486485[/C][C]-0.649410584999995[/C][/ROW]
[ROW][C]93[/C][C]104.5883906[/C][C]118.667088111667[/C][C]-14.0786975116667[/C][/ROW]
[ROW][C]94[/C][C]163.7213386[/C][C]127.571399851667[/C][C]36.1499387483333[/C][/ROW]
[ROW][C]95[/C][C]113.4482275[/C][C]108.478659701667[/C][C]4.96956779833334[/C][/ROW]
[ROW][C]96[/C][C]98.0428844[/C][C]118.740588435[/C][C]-20.697704035[/C][/ROW]
[ROW][C]97[/C][C]116.7868521[/C][C]119.989852888333[/C][C]-3.20300078833334[/C][/ROW]
[ROW][C]98[/C][C]126.5330444[/C][C]119.481944460556[/C][C]7.05109993944444[/C][/ROW]
[ROW][C]99[/C][C]113.0336597[/C][C]131.911070453889[/C][C]-18.8774107538889[/C][/ROW]
[ROW][C]100[/C][C]124.3392163[/C][C]125.307680558333[/C][C]-0.96846425833333[/C][/ROW]
[ROW][C]101[/C][C]109.8298759[/C][C]125.426707991667[/C][C]-15.5968320916667[/C][/ROW]
[ROW][C]102[/C][C]124.4434777[/C][C]131.546819037222[/C][C]-7.10334133722222[/C][/ROW]
[ROW][C]103[/C][C]111.5039454[/C][C]119.020177758333[/C][C]-7.51623235833332[/C][/ROW]
[ROW][C]104[/C][C]102.0350019[/C][C]116.513486485[/C][C]-14.478484585[/C][/ROW]
[ROW][C]105[/C][C]116.8726598[/C][C]118.667088111667[/C][C]-1.79442831166667[/C][/ROW]
[ROW][C]106[/C][C]112.2073122[/C][C]127.571399851667[/C][C]-15.3640876516667[/C][/ROW]
[ROW][C]107[/C][C]101.1513902[/C][C]108.478659701667[/C][C]-7.32726950166667[/C][/ROW]
[ROW][C]108[/C][C]124.4255108[/C][C]118.740588435[/C][C]5.68492236499999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57780&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57780&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
1100101.9871939175-1.98719391749993
2108.1560276101.4792854897226.67674211027781
3114.0150276113.9084114830560.106616116944433
4102.1880309107.3050215875-5.11699068749999
5110.3672031107.4240490208332.94315407916666
696.8602511113.544160066389-16.6839089663889
794.1944583101.0175187875-6.82306048750004
899.5162196198.51082751416661.00539209583334
994.06333487100.664429140833-6.60109427083333
1097.5541476109.568740880833-12.0145932808333
1178.1506242290.4760007308333-12.3253765108333
1281.2434643100.737929464167-19.4944651641667
1392.36262465101.9871939175-9.6245692675
1496.06324371101.479285489722-5.41604177972223
15114.0523777113.9084114830560.143966216944441
16110.6616666107.30502158753.35664501250000
17104.9171949107.424049020833-2.50685412083333
1890.00187193113.544160066389-23.5422881363889
1995.7008067101.0175187875-5.31671208749999
2086.0274115798.5108275141667-12.4834159441667
2184.85287668100.664429140833-15.8115524608333
22100.04328109.568740880833-9.52546088083333
2380.9171382390.4760007308333-9.55886250083333
2474.06539709100.737929464167-26.6725323741667
2577.30281369101.9871939175-24.6843802275
2697.23043249101.479285489722-4.24885299972222
2790.75515676113.908411483056-23.1532547230555
28100.5614455107.3050215875-6.7435760875
2992.01293267107.424049020833-15.4111163508333
3099.24012138113.544160066389-14.3040386863889
31105.8672755101.01751878754.84975671250001
3290.992046398.5108275141667-7.51878121416667
3393.30624423100.664429140833-7.35818491083332
3491.17419413109.568740880833-18.3945467508333
3577.3329503990.4760007308333-13.1430503408333
3691.1277721100.737929464167-9.61015736416667
3785.01249943101.9871939175-16.9746944875
3883.90390242101.479285489722-17.5753830697222
39104.8626302113.908411483056-9.04578128305556
40110.9039108107.30502158753.59888921250001
4195.43714373107.424049020833-11.9869052908333
42111.6238727113.544160066389-1.92028736638888
43108.8925403101.01751878757.8750215125
4496.1751168298.5108275141667-2.33571069416667
45101.9740205100.6644291408331.30959135916666
4699.11953031109.568740880833-10.4492105708333
4786.7815814790.4760007308333-3.69441926083333
48118.4195003100.73792946416717.6815708358333
49118.7441447101.987193917516.7569507825
50106.5296192101.4792854897225.05033371027778
51134.7772694113.90841148305620.8688579169444
52104.6778714107.3050215875-2.6271501875
53105.2954304107.424049020833-2.12861862083333
54139.4139849113.54416006638925.8698248336111
55103.6060491101.01751878752.58853031250001
5699.7818297498.51082751416671.27100222583334
57103.4610301100.6644291408332.79660095916667
58120.0594945109.56874088083310.4907536191667
5996.7137716890.47600073083336.23777094916666
60107.1308929100.7379294641676.39296343583333
61105.3608372101.98719391753.37364328249999
62111.6942359101.47928548972210.2149504102778
63132.0519998113.90841148305618.1435883169445
64126.8037879107.305021587519.4987663125
65154.4824253107.42404902083347.0583762791667
66141.5570984113.54416006638928.0129383336111
67109.9506882101.01751878758.93316941250001
68127.90419898.510827514166729.3933704858333
69133.0888617100.66442914083332.4244325591667
70120.0796299109.56874088083310.5108890191667
71117.555714290.476000730833327.0797134691667
72143.0362309100.73792946416742.2983014358333
73159.982927119.98985288833339.9930741116666
74128.5991124119.4819444605569.11716793944444
75149.7373327131.91107045388917.8262622461111
76126.8169313125.3076805583331.50925074166666
77140.9639674125.42670799166715.5372594083333
78137.6691981131.5468190372226.12237906277777
79117.9402337119.020177758333-1.07994405833333
80122.3095247116.5134864855.796038215
81127.7804207118.6670881116679.11333258833333
82136.1677176127.5713998516678.59631774833334
83116.2405856108.4786597016677.76192589833334
84123.1576893118.7405884354.41710086499999
85116.3400234119.989852888333-3.64982948833334
86108.6119282119.481944460556-10.8700162605556
87125.8982264131.911070453889-6.01284405388889
88112.8003105125.307680558333-12.5073700583333
89107.5182447125.426707991667-17.9084632916667
90135.0955413131.5468190372223.54872226277779
91115.5096488119.020177758333-3.51052895833333
92115.8640759116.513486485-0.649410584999995
93104.5883906118.667088111667-14.0786975116667
94163.7213386127.57139985166736.1499387483333
95113.4482275108.4786597016674.96956779833334
9698.0428844118.740588435-20.697704035
97116.7868521119.989852888333-3.20300078833334
98126.5330444119.4819444605567.05109993944444
99113.0336597131.911070453889-18.8774107538889
100124.3392163125.307680558333-0.96846425833333
101109.8298759125.426707991667-15.5968320916667
102124.4434777131.546819037222-7.10334133722222
103111.5039454119.020177758333-7.51623235833332
104102.0350019116.513486485-14.478484585
105116.8726598118.667088111667-1.79442831166667
106112.2073122127.571399851667-15.3640876516667
107101.1513902108.478659701667-7.32726950166667
108124.4255108118.7405884355.68492236499999







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.0806756218981150.161351243796230.919324378101885
170.0313450063796530.0626900127593060.968654993620347
180.01446203324241140.02892406648482270.98553796675759
190.004547493385962860.009094986771925720.995452506614037
200.004977853862255390.009955707724510770.995022146137745
210.002929203407488930.005858406814977870.997070796592511
220.001056207428204570.002112414856409130.998943792571795
230.0003710188415281660.0007420376830563310.999628981158472
240.0002184950460686370.0004369900921372730.999781504953931
250.001345031071877150.002690062143754300.998654968928123
260.0006246607036036940.001249321407207390.999375339296396
270.004216690433071130.008433380866142260.995783309566929
280.002369639621216520.004739279242433050.997630360378783
290.00311747658782330.00623495317564660.996882523412177
300.002150303031653350.00430060606330670.997849696968347
310.001605250329495220.003210500658990450.998394749670505
320.0008467389248140780.001693477849628160.999153261075186
330.0004683795194060680.0009367590388121350.999531620480594
340.0003905551566221820.0007811103132443640.999609444843378
350.0002376593687078380.0004753187374156770.999762340631292
360.0003001784537590930.0006003569075181860.99969982154624
370.0002801133333715050.000560226666743010.999719886666628
380.0006496401829795370.001299280365959070.99935035981702
390.000442642617038620.000885285234077240.999557357382961
400.000267784727613260.000535569455226520.999732215272387
410.0002405516591725220.0004811033183450440.999759448340827
420.0004516968019348460.0009033936038696910.999548303198065
430.0003470684567075380.0006941369134150750.999652931543292
440.0002220429427900040.0004440858855800080.99977795705721
450.0002040217484335080.0004080434968670160.999795978251567
460.0002111883197902180.0004223766395804370.99978881168021
470.000197365201425840.000394730402851680.999802634798574
480.00402197290887630.00804394581775260.995978027091124
490.01385607780511920.02771215561023840.98614392219488
500.01151085716247110.02302171432494230.98848914283753
510.02539220245387450.05078440490774910.974607797546125
520.02047313651573380.04094627303146750.979526863484266
530.01942687134749650.03885374269499310.980573128652503
540.07234860626992530.1446972125398510.927651393730075
550.05612589689951030.1122517937990210.94387410310049
560.05015766304345410.1003153260869080.949842336956546
570.04779773375807210.09559546751614430.952202266241928
580.06116892612408490.1223378522481700.938831073875915
590.06560686137112890.1312137227422580.934393138628871
600.07108623646818180.1421724729363640.928913763531818
610.1033284854308800.2066569708617610.89667151456912
620.1066653897773560.2133307795547120.893334610222644
630.1084710491590530.2169420983181050.891528950840947
640.1086808306185680.2173616612371360.891319169381432
650.3631852562347090.7263705124694180.636814743765291
660.4083505271825670.8167010543651340.591649472817433
670.3760095474710990.7520190949421970.623990452528902
680.4087907848590730.8175815697181450.591209215140927
690.4635528035586050.927105607117210.536447196441395
700.5394035615578060.9211928768843880.460596438442194
710.5793758659946280.8412482680107450.420624134005372
720.6494458263501580.7011083472996840.350554173649842
730.8182686298037780.3634627403924450.181731370196222
740.8049100466528030.3901799066943940.195089953347197
750.8615635281055130.2768729437889750.138436471894487
760.836911887099490.3261762258010220.163088112900511
770.9063850367959030.1872299264081930.0936149632040967
780.8768273245330080.2463453509339850.123172675466992
790.8398241839020060.3203516321959880.160175816097994
800.8125359209370260.3749281581259470.187464079062974
810.7990781345506730.4018437308986550.200921865449327
820.7312389705890760.5375220588218470.268761029410924
830.6679948194182750.664010361163450.332005180581725
840.6135450105431160.7729099789137680.386454989456884
850.524483204113320.951033591773360.47551679588668
860.494412731136620.988825462273240.50558726886338
870.4284906763406610.8569813526813220.571509323659339
880.3582511334038490.7165022668076980.641748866596151
890.275859759215160.551719518430320.72414024078484
900.1953857384412530.3907714768825060.804614261558747
910.1184017621110110.2368035242220210.88159823788899
920.07075324220750340.1415064844150070.929246757792497

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.080675621898115 & 0.16135124379623 & 0.919324378101885 \tabularnewline
17 & 0.031345006379653 & 0.062690012759306 & 0.968654993620347 \tabularnewline
18 & 0.0144620332424114 & 0.0289240664848227 & 0.98553796675759 \tabularnewline
19 & 0.00454749338596286 & 0.00909498677192572 & 0.995452506614037 \tabularnewline
20 & 0.00497785386225539 & 0.00995570772451077 & 0.995022146137745 \tabularnewline
21 & 0.00292920340748893 & 0.00585840681497787 & 0.997070796592511 \tabularnewline
22 & 0.00105620742820457 & 0.00211241485640913 & 0.998943792571795 \tabularnewline
23 & 0.000371018841528166 & 0.000742037683056331 & 0.999628981158472 \tabularnewline
24 & 0.000218495046068637 & 0.000436990092137273 & 0.999781504953931 \tabularnewline
25 & 0.00134503107187715 & 0.00269006214375430 & 0.998654968928123 \tabularnewline
26 & 0.000624660703603694 & 0.00124932140720739 & 0.999375339296396 \tabularnewline
27 & 0.00421669043307113 & 0.00843338086614226 & 0.995783309566929 \tabularnewline
28 & 0.00236963962121652 & 0.00473927924243305 & 0.997630360378783 \tabularnewline
29 & 0.0031174765878233 & 0.0062349531756466 & 0.996882523412177 \tabularnewline
30 & 0.00215030303165335 & 0.0043006060633067 & 0.997849696968347 \tabularnewline
31 & 0.00160525032949522 & 0.00321050065899045 & 0.998394749670505 \tabularnewline
32 & 0.000846738924814078 & 0.00169347784962816 & 0.999153261075186 \tabularnewline
33 & 0.000468379519406068 & 0.000936759038812135 & 0.999531620480594 \tabularnewline
34 & 0.000390555156622182 & 0.000781110313244364 & 0.999609444843378 \tabularnewline
35 & 0.000237659368707838 & 0.000475318737415677 & 0.999762340631292 \tabularnewline
36 & 0.000300178453759093 & 0.000600356907518186 & 0.99969982154624 \tabularnewline
37 & 0.000280113333371505 & 0.00056022666674301 & 0.999719886666628 \tabularnewline
38 & 0.000649640182979537 & 0.00129928036595907 & 0.99935035981702 \tabularnewline
39 & 0.00044264261703862 & 0.00088528523407724 & 0.999557357382961 \tabularnewline
40 & 0.00026778472761326 & 0.00053556945522652 & 0.999732215272387 \tabularnewline
41 & 0.000240551659172522 & 0.000481103318345044 & 0.999759448340827 \tabularnewline
42 & 0.000451696801934846 & 0.000903393603869691 & 0.999548303198065 \tabularnewline
43 & 0.000347068456707538 & 0.000694136913415075 & 0.999652931543292 \tabularnewline
44 & 0.000222042942790004 & 0.000444085885580008 & 0.99977795705721 \tabularnewline
45 & 0.000204021748433508 & 0.000408043496867016 & 0.999795978251567 \tabularnewline
46 & 0.000211188319790218 & 0.000422376639580437 & 0.99978881168021 \tabularnewline
47 & 0.00019736520142584 & 0.00039473040285168 & 0.999802634798574 \tabularnewline
48 & 0.0040219729088763 & 0.0080439458177526 & 0.995978027091124 \tabularnewline
49 & 0.0138560778051192 & 0.0277121556102384 & 0.98614392219488 \tabularnewline
50 & 0.0115108571624711 & 0.0230217143249423 & 0.98848914283753 \tabularnewline
51 & 0.0253922024538745 & 0.0507844049077491 & 0.974607797546125 \tabularnewline
52 & 0.0204731365157338 & 0.0409462730314675 & 0.979526863484266 \tabularnewline
53 & 0.0194268713474965 & 0.0388537426949931 & 0.980573128652503 \tabularnewline
54 & 0.0723486062699253 & 0.144697212539851 & 0.927651393730075 \tabularnewline
55 & 0.0561258968995103 & 0.112251793799021 & 0.94387410310049 \tabularnewline
56 & 0.0501576630434541 & 0.100315326086908 & 0.949842336956546 \tabularnewline
57 & 0.0477977337580721 & 0.0955954675161443 & 0.952202266241928 \tabularnewline
58 & 0.0611689261240849 & 0.122337852248170 & 0.938831073875915 \tabularnewline
59 & 0.0656068613711289 & 0.131213722742258 & 0.934393138628871 \tabularnewline
60 & 0.0710862364681818 & 0.142172472936364 & 0.928913763531818 \tabularnewline
61 & 0.103328485430880 & 0.206656970861761 & 0.89667151456912 \tabularnewline
62 & 0.106665389777356 & 0.213330779554712 & 0.893334610222644 \tabularnewline
63 & 0.108471049159053 & 0.216942098318105 & 0.891528950840947 \tabularnewline
64 & 0.108680830618568 & 0.217361661237136 & 0.891319169381432 \tabularnewline
65 & 0.363185256234709 & 0.726370512469418 & 0.636814743765291 \tabularnewline
66 & 0.408350527182567 & 0.816701054365134 & 0.591649472817433 \tabularnewline
67 & 0.376009547471099 & 0.752019094942197 & 0.623990452528902 \tabularnewline
68 & 0.408790784859073 & 0.817581569718145 & 0.591209215140927 \tabularnewline
69 & 0.463552803558605 & 0.92710560711721 & 0.536447196441395 \tabularnewline
70 & 0.539403561557806 & 0.921192876884388 & 0.460596438442194 \tabularnewline
71 & 0.579375865994628 & 0.841248268010745 & 0.420624134005372 \tabularnewline
72 & 0.649445826350158 & 0.701108347299684 & 0.350554173649842 \tabularnewline
73 & 0.818268629803778 & 0.363462740392445 & 0.181731370196222 \tabularnewline
74 & 0.804910046652803 & 0.390179906694394 & 0.195089953347197 \tabularnewline
75 & 0.861563528105513 & 0.276872943788975 & 0.138436471894487 \tabularnewline
76 & 0.83691188709949 & 0.326176225801022 & 0.163088112900511 \tabularnewline
77 & 0.906385036795903 & 0.187229926408193 & 0.0936149632040967 \tabularnewline
78 & 0.876827324533008 & 0.246345350933985 & 0.123172675466992 \tabularnewline
79 & 0.839824183902006 & 0.320351632195988 & 0.160175816097994 \tabularnewline
80 & 0.812535920937026 & 0.374928158125947 & 0.187464079062974 \tabularnewline
81 & 0.799078134550673 & 0.401843730898655 & 0.200921865449327 \tabularnewline
82 & 0.731238970589076 & 0.537522058821847 & 0.268761029410924 \tabularnewline
83 & 0.667994819418275 & 0.66401036116345 & 0.332005180581725 \tabularnewline
84 & 0.613545010543116 & 0.772909978913768 & 0.386454989456884 \tabularnewline
85 & 0.52448320411332 & 0.95103359177336 & 0.47551679588668 \tabularnewline
86 & 0.49441273113662 & 0.98882546227324 & 0.50558726886338 \tabularnewline
87 & 0.428490676340661 & 0.856981352681322 & 0.571509323659339 \tabularnewline
88 & 0.358251133403849 & 0.716502266807698 & 0.641748866596151 \tabularnewline
89 & 0.27585975921516 & 0.55171951843032 & 0.72414024078484 \tabularnewline
90 & 0.195385738441253 & 0.390771476882506 & 0.804614261558747 \tabularnewline
91 & 0.118401762111011 & 0.236803524222021 & 0.88159823788899 \tabularnewline
92 & 0.0707532422075034 & 0.141506484415007 & 0.929246757792497 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57780&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.080675621898115[/C][C]0.16135124379623[/C][C]0.919324378101885[/C][/ROW]
[ROW][C]17[/C][C]0.031345006379653[/C][C]0.062690012759306[/C][C]0.968654993620347[/C][/ROW]
[ROW][C]18[/C][C]0.0144620332424114[/C][C]0.0289240664848227[/C][C]0.98553796675759[/C][/ROW]
[ROW][C]19[/C][C]0.00454749338596286[/C][C]0.00909498677192572[/C][C]0.995452506614037[/C][/ROW]
[ROW][C]20[/C][C]0.00497785386225539[/C][C]0.00995570772451077[/C][C]0.995022146137745[/C][/ROW]
[ROW][C]21[/C][C]0.00292920340748893[/C][C]0.00585840681497787[/C][C]0.997070796592511[/C][/ROW]
[ROW][C]22[/C][C]0.00105620742820457[/C][C]0.00211241485640913[/C][C]0.998943792571795[/C][/ROW]
[ROW][C]23[/C][C]0.000371018841528166[/C][C]0.000742037683056331[/C][C]0.999628981158472[/C][/ROW]
[ROW][C]24[/C][C]0.000218495046068637[/C][C]0.000436990092137273[/C][C]0.999781504953931[/C][/ROW]
[ROW][C]25[/C][C]0.00134503107187715[/C][C]0.00269006214375430[/C][C]0.998654968928123[/C][/ROW]
[ROW][C]26[/C][C]0.000624660703603694[/C][C]0.00124932140720739[/C][C]0.999375339296396[/C][/ROW]
[ROW][C]27[/C][C]0.00421669043307113[/C][C]0.00843338086614226[/C][C]0.995783309566929[/C][/ROW]
[ROW][C]28[/C][C]0.00236963962121652[/C][C]0.00473927924243305[/C][C]0.997630360378783[/C][/ROW]
[ROW][C]29[/C][C]0.0031174765878233[/C][C]0.0062349531756466[/C][C]0.996882523412177[/C][/ROW]
[ROW][C]30[/C][C]0.00215030303165335[/C][C]0.0043006060633067[/C][C]0.997849696968347[/C][/ROW]
[ROW][C]31[/C][C]0.00160525032949522[/C][C]0.00321050065899045[/C][C]0.998394749670505[/C][/ROW]
[ROW][C]32[/C][C]0.000846738924814078[/C][C]0.00169347784962816[/C][C]0.999153261075186[/C][/ROW]
[ROW][C]33[/C][C]0.000468379519406068[/C][C]0.000936759038812135[/C][C]0.999531620480594[/C][/ROW]
[ROW][C]34[/C][C]0.000390555156622182[/C][C]0.000781110313244364[/C][C]0.999609444843378[/C][/ROW]
[ROW][C]35[/C][C]0.000237659368707838[/C][C]0.000475318737415677[/C][C]0.999762340631292[/C][/ROW]
[ROW][C]36[/C][C]0.000300178453759093[/C][C]0.000600356907518186[/C][C]0.99969982154624[/C][/ROW]
[ROW][C]37[/C][C]0.000280113333371505[/C][C]0.00056022666674301[/C][C]0.999719886666628[/C][/ROW]
[ROW][C]38[/C][C]0.000649640182979537[/C][C]0.00129928036595907[/C][C]0.99935035981702[/C][/ROW]
[ROW][C]39[/C][C]0.00044264261703862[/C][C]0.00088528523407724[/C][C]0.999557357382961[/C][/ROW]
[ROW][C]40[/C][C]0.00026778472761326[/C][C]0.00053556945522652[/C][C]0.999732215272387[/C][/ROW]
[ROW][C]41[/C][C]0.000240551659172522[/C][C]0.000481103318345044[/C][C]0.999759448340827[/C][/ROW]
[ROW][C]42[/C][C]0.000451696801934846[/C][C]0.000903393603869691[/C][C]0.999548303198065[/C][/ROW]
[ROW][C]43[/C][C]0.000347068456707538[/C][C]0.000694136913415075[/C][C]0.999652931543292[/C][/ROW]
[ROW][C]44[/C][C]0.000222042942790004[/C][C]0.000444085885580008[/C][C]0.99977795705721[/C][/ROW]
[ROW][C]45[/C][C]0.000204021748433508[/C][C]0.000408043496867016[/C][C]0.999795978251567[/C][/ROW]
[ROW][C]46[/C][C]0.000211188319790218[/C][C]0.000422376639580437[/C][C]0.99978881168021[/C][/ROW]
[ROW][C]47[/C][C]0.00019736520142584[/C][C]0.00039473040285168[/C][C]0.999802634798574[/C][/ROW]
[ROW][C]48[/C][C]0.0040219729088763[/C][C]0.0080439458177526[/C][C]0.995978027091124[/C][/ROW]
[ROW][C]49[/C][C]0.0138560778051192[/C][C]0.0277121556102384[/C][C]0.98614392219488[/C][/ROW]
[ROW][C]50[/C][C]0.0115108571624711[/C][C]0.0230217143249423[/C][C]0.98848914283753[/C][/ROW]
[ROW][C]51[/C][C]0.0253922024538745[/C][C]0.0507844049077491[/C][C]0.974607797546125[/C][/ROW]
[ROW][C]52[/C][C]0.0204731365157338[/C][C]0.0409462730314675[/C][C]0.979526863484266[/C][/ROW]
[ROW][C]53[/C][C]0.0194268713474965[/C][C]0.0388537426949931[/C][C]0.980573128652503[/C][/ROW]
[ROW][C]54[/C][C]0.0723486062699253[/C][C]0.144697212539851[/C][C]0.927651393730075[/C][/ROW]
[ROW][C]55[/C][C]0.0561258968995103[/C][C]0.112251793799021[/C][C]0.94387410310049[/C][/ROW]
[ROW][C]56[/C][C]0.0501576630434541[/C][C]0.100315326086908[/C][C]0.949842336956546[/C][/ROW]
[ROW][C]57[/C][C]0.0477977337580721[/C][C]0.0955954675161443[/C][C]0.952202266241928[/C][/ROW]
[ROW][C]58[/C][C]0.0611689261240849[/C][C]0.122337852248170[/C][C]0.938831073875915[/C][/ROW]
[ROW][C]59[/C][C]0.0656068613711289[/C][C]0.131213722742258[/C][C]0.934393138628871[/C][/ROW]
[ROW][C]60[/C][C]0.0710862364681818[/C][C]0.142172472936364[/C][C]0.928913763531818[/C][/ROW]
[ROW][C]61[/C][C]0.103328485430880[/C][C]0.206656970861761[/C][C]0.89667151456912[/C][/ROW]
[ROW][C]62[/C][C]0.106665389777356[/C][C]0.213330779554712[/C][C]0.893334610222644[/C][/ROW]
[ROW][C]63[/C][C]0.108471049159053[/C][C]0.216942098318105[/C][C]0.891528950840947[/C][/ROW]
[ROW][C]64[/C][C]0.108680830618568[/C][C]0.217361661237136[/C][C]0.891319169381432[/C][/ROW]
[ROW][C]65[/C][C]0.363185256234709[/C][C]0.726370512469418[/C][C]0.636814743765291[/C][/ROW]
[ROW][C]66[/C][C]0.408350527182567[/C][C]0.816701054365134[/C][C]0.591649472817433[/C][/ROW]
[ROW][C]67[/C][C]0.376009547471099[/C][C]0.752019094942197[/C][C]0.623990452528902[/C][/ROW]
[ROW][C]68[/C][C]0.408790784859073[/C][C]0.817581569718145[/C][C]0.591209215140927[/C][/ROW]
[ROW][C]69[/C][C]0.463552803558605[/C][C]0.92710560711721[/C][C]0.536447196441395[/C][/ROW]
[ROW][C]70[/C][C]0.539403561557806[/C][C]0.921192876884388[/C][C]0.460596438442194[/C][/ROW]
[ROW][C]71[/C][C]0.579375865994628[/C][C]0.841248268010745[/C][C]0.420624134005372[/C][/ROW]
[ROW][C]72[/C][C]0.649445826350158[/C][C]0.701108347299684[/C][C]0.350554173649842[/C][/ROW]
[ROW][C]73[/C][C]0.818268629803778[/C][C]0.363462740392445[/C][C]0.181731370196222[/C][/ROW]
[ROW][C]74[/C][C]0.804910046652803[/C][C]0.390179906694394[/C][C]0.195089953347197[/C][/ROW]
[ROW][C]75[/C][C]0.861563528105513[/C][C]0.276872943788975[/C][C]0.138436471894487[/C][/ROW]
[ROW][C]76[/C][C]0.83691188709949[/C][C]0.326176225801022[/C][C]0.163088112900511[/C][/ROW]
[ROW][C]77[/C][C]0.906385036795903[/C][C]0.187229926408193[/C][C]0.0936149632040967[/C][/ROW]
[ROW][C]78[/C][C]0.876827324533008[/C][C]0.246345350933985[/C][C]0.123172675466992[/C][/ROW]
[ROW][C]79[/C][C]0.839824183902006[/C][C]0.320351632195988[/C][C]0.160175816097994[/C][/ROW]
[ROW][C]80[/C][C]0.812535920937026[/C][C]0.374928158125947[/C][C]0.187464079062974[/C][/ROW]
[ROW][C]81[/C][C]0.799078134550673[/C][C]0.401843730898655[/C][C]0.200921865449327[/C][/ROW]
[ROW][C]82[/C][C]0.731238970589076[/C][C]0.537522058821847[/C][C]0.268761029410924[/C][/ROW]
[ROW][C]83[/C][C]0.667994819418275[/C][C]0.66401036116345[/C][C]0.332005180581725[/C][/ROW]
[ROW][C]84[/C][C]0.613545010543116[/C][C]0.772909978913768[/C][C]0.386454989456884[/C][/ROW]
[ROW][C]85[/C][C]0.52448320411332[/C][C]0.95103359177336[/C][C]0.47551679588668[/C][/ROW]
[ROW][C]86[/C][C]0.49441273113662[/C][C]0.98882546227324[/C][C]0.50558726886338[/C][/ROW]
[ROW][C]87[/C][C]0.428490676340661[/C][C]0.856981352681322[/C][C]0.571509323659339[/C][/ROW]
[ROW][C]88[/C][C]0.358251133403849[/C][C]0.716502266807698[/C][C]0.641748866596151[/C][/ROW]
[ROW][C]89[/C][C]0.27585975921516[/C][C]0.55171951843032[/C][C]0.72414024078484[/C][/ROW]
[ROW][C]90[/C][C]0.195385738441253[/C][C]0.390771476882506[/C][C]0.804614261558747[/C][/ROW]
[ROW][C]91[/C][C]0.118401762111011[/C][C]0.236803524222021[/C][C]0.88159823788899[/C][/ROW]
[ROW][C]92[/C][C]0.0707532422075034[/C][C]0.141506484415007[/C][C]0.929246757792497[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57780&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57780&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.0806756218981150.161351243796230.919324378101885
170.0313450063796530.0626900127593060.968654993620347
180.01446203324241140.02892406648482270.98553796675759
190.004547493385962860.009094986771925720.995452506614037
200.004977853862255390.009955707724510770.995022146137745
210.002929203407488930.005858406814977870.997070796592511
220.001056207428204570.002112414856409130.998943792571795
230.0003710188415281660.0007420376830563310.999628981158472
240.0002184950460686370.0004369900921372730.999781504953931
250.001345031071877150.002690062143754300.998654968928123
260.0006246607036036940.001249321407207390.999375339296396
270.004216690433071130.008433380866142260.995783309566929
280.002369639621216520.004739279242433050.997630360378783
290.00311747658782330.00623495317564660.996882523412177
300.002150303031653350.00430060606330670.997849696968347
310.001605250329495220.003210500658990450.998394749670505
320.0008467389248140780.001693477849628160.999153261075186
330.0004683795194060680.0009367590388121350.999531620480594
340.0003905551566221820.0007811103132443640.999609444843378
350.0002376593687078380.0004753187374156770.999762340631292
360.0003001784537590930.0006003569075181860.99969982154624
370.0002801133333715050.000560226666743010.999719886666628
380.0006496401829795370.001299280365959070.99935035981702
390.000442642617038620.000885285234077240.999557357382961
400.000267784727613260.000535569455226520.999732215272387
410.0002405516591725220.0004811033183450440.999759448340827
420.0004516968019348460.0009033936038696910.999548303198065
430.0003470684567075380.0006941369134150750.999652931543292
440.0002220429427900040.0004440858855800080.99977795705721
450.0002040217484335080.0004080434968670160.999795978251567
460.0002111883197902180.0004223766395804370.99978881168021
470.000197365201425840.000394730402851680.999802634798574
480.00402197290887630.00804394581775260.995978027091124
490.01385607780511920.02771215561023840.98614392219488
500.01151085716247110.02302171432494230.98848914283753
510.02539220245387450.05078440490774910.974607797546125
520.02047313651573380.04094627303146750.979526863484266
530.01942687134749650.03885374269499310.980573128652503
540.07234860626992530.1446972125398510.927651393730075
550.05612589689951030.1122517937990210.94387410310049
560.05015766304345410.1003153260869080.949842336956546
570.04779773375807210.09559546751614430.952202266241928
580.06116892612408490.1223378522481700.938831073875915
590.06560686137112890.1312137227422580.934393138628871
600.07108623646818180.1421724729363640.928913763531818
610.1033284854308800.2066569708617610.89667151456912
620.1066653897773560.2133307795547120.893334610222644
630.1084710491590530.2169420983181050.891528950840947
640.1086808306185680.2173616612371360.891319169381432
650.3631852562347090.7263705124694180.636814743765291
660.4083505271825670.8167010543651340.591649472817433
670.3760095474710990.7520190949421970.623990452528902
680.4087907848590730.8175815697181450.591209215140927
690.4635528035586050.927105607117210.536447196441395
700.5394035615578060.9211928768843880.460596438442194
710.5793758659946280.8412482680107450.420624134005372
720.6494458263501580.7011083472996840.350554173649842
730.8182686298037780.3634627403924450.181731370196222
740.8049100466528030.3901799066943940.195089953347197
750.8615635281055130.2768729437889750.138436471894487
760.836911887099490.3261762258010220.163088112900511
770.9063850367959030.1872299264081930.0936149632040967
780.8768273245330080.2463453509339850.123172675466992
790.8398241839020060.3203516321959880.160175816097994
800.8125359209370260.3749281581259470.187464079062974
810.7990781345506730.4018437308986550.200921865449327
820.7312389705890760.5375220588218470.268761029410924
830.6679948194182750.664010361163450.332005180581725
840.6135450105431160.7729099789137680.386454989456884
850.524483204113320.951033591773360.47551679588668
860.494412731136620.988825462273240.50558726886338
870.4284906763406610.8569813526813220.571509323659339
880.3582511334038490.7165022668076980.641748866596151
890.275859759215160.551719518430320.72414024078484
900.1953857384412530.3907714768825060.804614261558747
910.1184017621110110.2368035242220210.88159823788899
920.07075324220750340.1415064844150070.929246757792497







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level300.389610389610390NOK
5% type I error level350.454545454545455NOK
10% type I error level380.493506493506494NOK

\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 & 30 & 0.389610389610390 & NOK \tabularnewline
5% type I error level & 35 & 0.454545454545455 & NOK \tabularnewline
10% type I error level & 38 & 0.493506493506494 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57780&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]30[/C][C]0.389610389610390[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.454545454545455[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]38[/C][C]0.493506493506494[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57780&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57780&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 level300.389610389610390NOK
5% type I error level350.454545454545455NOK
10% type I error level380.493506493506494NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}