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Description of Statistical Computation

Author's title

multiple regression met 2 maanden minder, totale industrie zonder bouwnijve...

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 08:49:29 -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/t1258645938mpf0gppb9hwysbo.htm/, Retrieved Fri, 26 Apr 2024 17:15:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57782, Retrieved Fri, 26 Apr 2024 17:15:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168

Tree of Dependent Computations

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]
-    D      [Multiple Regression] [multiple regressi...] [2009-11-19 15:49:29] [b1ac221d009d6e5c29a4ef1869874933] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105.6	86.2	96.9	97.6
102.8	86.1	105.6	96.9
101.7	86.2	102.8	105.6
104.2	88.8	101.7	102.8
92.7	89.6	104.2	101.7
91.9	87.8	92.7	104.2
106.5	88.3	91.9	92.7
112.3	88.6	106.5	91.9
102.8	91	112.3	106.5
96.5	91.5	102.8	112.3
101	95.4	96.5	102.8
98.9	98.7	101	96.5
105.1	99.9	98.9	101
103	98.6	105.1	98.9
99	100.3	103	105.1
104.3	100.2	99	103
94.6	100.4	104.3	99
90.4	101.4	94.6	104.3
108.9	103	90.4	94.6
111.4	109.1	108.9	90.4
100.8	111.4	111.4	108.9
102.5	114.1	100.8	111.4
98.2	121.8	102.5	100.8
98.7	127.6	98.2	102.5
113.3	129.9	98.7	98.2
104.6	128	113.3	98.7
99.3	123.5	104.6	113.3
111.8	124	99.3	104.6
97.3	127.4	111.8	99.3
97.7	127.6	97.3	111.8
115.6	128.4	97.7	97.3
111.9	131.4	115.6	97.7
107	135.1	111.9	115.6
107.1	134	107	111.9
100.6	144.5	107.1	107
99.2	147.3	100.6	107.1
108.4	150.9	99.2	100.6
103	148.7	108.4	99.2
99.8	141.4	103	108.4
115	138.9	99.8	103
90.8	139.8	115	99.8
95.9	145.6	90.8	115
114.4	147.9	95.9	90.8
108.2	148.5	114.4	95.9
112.6	151.1	108.2	114.4
109.1	157.5	112.6	108.2
105	167.5	109.1	112.6
105	172.3	105	109.1
118.5	173.5	105	105
103.7	187.5	118.5	105
112.5	205.5	103.7	118.5
116.6	195.1	112.5	103.7
96.6	204.5	116.6	112.5
101.9	204.5	96.6	116.6
116.5	201.7	101.9	96.6
119.3	207	116.5	101.9
115.4	206.6	119.3	116.5
108.5	210.6	115.4	119.3
111.5	211.1	108.5	115.4
108.8	215	111.5	108.5
121.8	223.9	108.8	111.5
109.6	238.2	121.8	108.8
112.2	238.9	109.6	121.8
119.6	229.6	112.2	109.6
104.1	232.2	119.6	112.2
105.3	222.1	104.1	119.6
115	221.6	105.3	104.1
124.1	227.3	115	105.3
116.8	221	124.1	115
107.5	213.6	116.8	124.1
115.6	243.4	107.5	116.8

Tables (Output of Computation)





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=57782&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=57782&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57782&T=0

As an alternative you can also use a QR Code:  
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
tot_indus[t] = + 134.94014688261 + 0.128373522157055prijsindex[t] -0.0277011889880574`y(t-1)`[t] -0.425165410701896`y(t-2)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
tot_indus[t] =  +  134.94014688261 +  0.128373522157055prijsindex[t] -0.0277011889880574`y(t-1)`[t] -0.425165410701896`y(t-2)`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57782&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]tot_indus[t] =  +  134.94014688261 +  0.128373522157055prijsindex[t] -0.0277011889880574`y(t-1)`[t] -0.425165410701896`y(t-2)`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57782&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57782&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
tot_indus[t] = + 134.94014688261 + 0.128373522157055prijsindex[t] -0.0277011889880574`y(t-1)`[t] -0.425165410701896`y(t-2)`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)134.9401468826114.8942949.059900
prijsindex0.1283735221570550.0202266.346900
`y(t-1)`-0.02770118898805740.110892-0.24980.8035040.401752
`y(t-2)`-0.4251654107018960.111934-3.79840.0003160.000158

\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) & 134.94014688261 & 14.894294 & 9.0599 & 0 & 0 \tabularnewline
prijsindex & 0.128373522157055 & 0.020226 & 6.3469 & 0 & 0 \tabularnewline
`y(t-1)` & -0.0277011889880574 & 0.110892 & -0.2498 & 0.803504 & 0.401752 \tabularnewline
`y(t-2)` & -0.425165410701896 & 0.111934 & -3.7984 & 0.000316 & 0.000158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57782&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]134.94014688261[/C][C]14.894294[/C][C]9.0599[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]prijsindex[/C][C]0.128373522157055[/C][C]0.020226[/C][C]6.3469[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`y(t-1)`[/C][C]-0.0277011889880574[/C][C]0.110892[/C][C]-0.2498[/C][C]0.803504[/C][C]0.401752[/C][/ROW]
[ROW][C]`y(t-2)`[/C][C]-0.425165410701896[/C][C]0.111934[/C][C]-3.7984[/C][C]0.000316[/C][C]0.000158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57782&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57782&T=2

As an alternative you can also use a QR Code:  
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)134.9401468826114.8942949.059900
prijsindex0.1283735221570550.0202266.346900
`y(t-1)`-0.02770118898805740.110892-0.24980.8035040.401752
`y(t-2)`-0.4251654107018960.111934-3.79840.0003160.000158






Multiple Linear Regression - Regression Statistics
Multiple R0.657695865815495
R-squared0.432563851910793
Adjusted R-squared0.407156263190381
F-TEST (value)17.0249863798872
F-TEST (DF numerator)3
F-TEST (DF denominator)67
p-value2.52390219834808e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.01689199427035
Sum Squared Residuals2425.60028113788

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.657695865815495 \tabularnewline
R-squared & 0.432563851910793 \tabularnewline
Adjusted R-squared & 0.407156263190381 \tabularnewline
F-TEST (value) & 17.0249863798872 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 67 \tabularnewline
p-value & 2.52390219834808e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.01689199427035 \tabularnewline
Sum Squared Residuals & 2425.60028113788 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57782&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.657695865815495[/C][/ROW]
[ROW][C]R-squared[/C][C]0.432563851910793[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.407156263190381[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.0249863798872[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]67[/C][/ROW]
[ROW][C]p-value[/C][C]2.52390219834808e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.01689199427035[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2425.60028113788[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57782&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57782&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.657695865815495
R-squared0.432563851910793
Adjusted R-squared0.407156263190381
F-TEST (value)17.0249863798872
F-TEST (DF numerator)3
F-TEST (DF denominator)67
p-value2.52390219834808e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.01689199427035
Sum Squared Residuals2425.60028113788






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1105.6101.8255551951013.77444480489936
2102.8101.8693332861800.930666713820146
3101.798.26079489445563.43920510554437
4104.299.81550050991614.38449949008386
592.7100.316628306944-7.61662830694372
691.999.341206113669-7.44120611366894
7106.5104.3169560490102.18304395099027
8112.3104.2911630749938.00883692500728
9102.898.23117763579124.56882236420876
1096.596.09256631018530.407433689814681
11101100.8068119388910.193188061109394
1298.9103.784331298985-4.88433129898457
13105.1102.0833076742893.01669232571057
14103102.6375220862330.362477913766722
1599100.277904024423-1.27790402442344
16104.3101.2687187906343.03128120936605
1794.6102.848238836236-8.24823883623624
1890.4100.991937214857-10.5919372148574
19108.9105.4377843278673.46221567213308
20111.4107.4940855416943.90591445830615
21100.899.85453157219990.945468427800138
22102.599.43185915854263.06814084145743
2398.2104.879996611312-6.6799966113123
2498.7105.020896954279-6.32089695427864
25113.3107.1305167267646.169483273236
26104.6106.269586970089-1.66958697008901
2799.399.7254914683307-0.425491468330677
28111.8103.6354336041528.1645663958476
2997.3105.979015393856-8.67901539385572
3097.7101.091789704840-3.39178970484026
31115.6107.3483065021488.25169349785182
32111.9107.0675096214524.83249037854765
33107100.0345252011256.96547479887467
34107.1101.6021621723915.49783782760893
35100.6105.030624548581-4.43062454858063
3699.2105.527611597973-6.32761159797257
37108.4108.792113111884-0.392113111883563
38103108.850071999431-5.85007199943057
3999.8104.151009929762-4.35100992976214
40115106.2146131469228.78538685307848
4190.8107.269620558490-16.4696205584905
4295.9102.222041517844-6.32204151784355
43114.4112.6650274939521.73497250604843
44108.2110.061236016387-1.86123601638707
45112.6102.7011944477369.8988055522637
46109.1106.0369253043463.06307469565425
47105105.546886880286-0.546886880286158
48105107.764733598948-2.76473359894769
49118.5109.6619600094148.83803999058607
50103.7111.085223268274-7.38522326827392
51112.5108.0661912196494.43380878035144
52116.6112.7797842045083.82021579549165
5396.6110.131464823757-13.5314648237570
54101.9108.942310419640-7.04231041964032
55116.5116.939356470002-0.43935647000178
56119.3114.9619221014884.33807789851151
57115.4108.6255943672116.77440563278858
58108.5108.0566599429280.443340057072246
59111.5109.9701300097611.52986999023873
60108.8113.321324513053-4.5213245130527
61121.8113.2631458384138.53685416158744
62109.6115.886718357309-6.28671835730882
63112.2110.7873839893481.4126160106516
64119.6114.7085051524824.89149484751802
65104.1113.731857443754-9.63185744375376
66105.3109.718429260088-4.41842926008837
67115116.211064938104-1.21106493810356
68124.1116.1638939883727.93610601162766
69116.8110.9789554951835.82104450481682
70107.5106.3622048734471.13779512655346
71115.6113.5490643894402.05093561056045

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 105.6 & 101.825555195101 & 3.77444480489936 \tabularnewline
2 & 102.8 & 101.869333286180 & 0.930666713820146 \tabularnewline
3 & 101.7 & 98.2607948944556 & 3.43920510554437 \tabularnewline
4 & 104.2 & 99.8155005099161 & 4.38449949008386 \tabularnewline
5 & 92.7 & 100.316628306944 & -7.61662830694372 \tabularnewline
6 & 91.9 & 99.341206113669 & -7.44120611366894 \tabularnewline
7 & 106.5 & 104.316956049010 & 2.18304395099027 \tabularnewline
8 & 112.3 & 104.291163074993 & 8.00883692500728 \tabularnewline
9 & 102.8 & 98.2311776357912 & 4.56882236420876 \tabularnewline
10 & 96.5 & 96.0925663101853 & 0.407433689814681 \tabularnewline
11 & 101 & 100.806811938891 & 0.193188061109394 \tabularnewline
12 & 98.9 & 103.784331298985 & -4.88433129898457 \tabularnewline
13 & 105.1 & 102.083307674289 & 3.01669232571057 \tabularnewline
14 & 103 & 102.637522086233 & 0.362477913766722 \tabularnewline
15 & 99 & 100.277904024423 & -1.27790402442344 \tabularnewline
16 & 104.3 & 101.268718790634 & 3.03128120936605 \tabularnewline
17 & 94.6 & 102.848238836236 & -8.24823883623624 \tabularnewline
18 & 90.4 & 100.991937214857 & -10.5919372148574 \tabularnewline
19 & 108.9 & 105.437784327867 & 3.46221567213308 \tabularnewline
20 & 111.4 & 107.494085541694 & 3.90591445830615 \tabularnewline
21 & 100.8 & 99.8545315721999 & 0.945468427800138 \tabularnewline
22 & 102.5 & 99.4318591585426 & 3.06814084145743 \tabularnewline
23 & 98.2 & 104.879996611312 & -6.6799966113123 \tabularnewline
24 & 98.7 & 105.020896954279 & -6.32089695427864 \tabularnewline
25 & 113.3 & 107.130516726764 & 6.169483273236 \tabularnewline
26 & 104.6 & 106.269586970089 & -1.66958697008901 \tabularnewline
27 & 99.3 & 99.7254914683307 & -0.425491468330677 \tabularnewline
28 & 111.8 & 103.635433604152 & 8.1645663958476 \tabularnewline
29 & 97.3 & 105.979015393856 & -8.67901539385572 \tabularnewline
30 & 97.7 & 101.091789704840 & -3.39178970484026 \tabularnewline
31 & 115.6 & 107.348306502148 & 8.25169349785182 \tabularnewline
32 & 111.9 & 107.067509621452 & 4.83249037854765 \tabularnewline
33 & 107 & 100.034525201125 & 6.96547479887467 \tabularnewline
34 & 107.1 & 101.602162172391 & 5.49783782760893 \tabularnewline
35 & 100.6 & 105.030624548581 & -4.43062454858063 \tabularnewline
36 & 99.2 & 105.527611597973 & -6.32761159797257 \tabularnewline
37 & 108.4 & 108.792113111884 & -0.392113111883563 \tabularnewline
38 & 103 & 108.850071999431 & -5.85007199943057 \tabularnewline
39 & 99.8 & 104.151009929762 & -4.35100992976214 \tabularnewline
40 & 115 & 106.214613146922 & 8.78538685307848 \tabularnewline
41 & 90.8 & 107.269620558490 & -16.4696205584905 \tabularnewline
42 & 95.9 & 102.222041517844 & -6.32204151784355 \tabularnewline
43 & 114.4 & 112.665027493952 & 1.73497250604843 \tabularnewline
44 & 108.2 & 110.061236016387 & -1.86123601638707 \tabularnewline
45 & 112.6 & 102.701194447736 & 9.8988055522637 \tabularnewline
46 & 109.1 & 106.036925304346 & 3.06307469565425 \tabularnewline
47 & 105 & 105.546886880286 & -0.546886880286158 \tabularnewline
48 & 105 & 107.764733598948 & -2.76473359894769 \tabularnewline
49 & 118.5 & 109.661960009414 & 8.83803999058607 \tabularnewline
50 & 103.7 & 111.085223268274 & -7.38522326827392 \tabularnewline
51 & 112.5 & 108.066191219649 & 4.43380878035144 \tabularnewline
52 & 116.6 & 112.779784204508 & 3.82021579549165 \tabularnewline
53 & 96.6 & 110.131464823757 & -13.5314648237570 \tabularnewline
54 & 101.9 & 108.942310419640 & -7.04231041964032 \tabularnewline
55 & 116.5 & 116.939356470002 & -0.43935647000178 \tabularnewline
56 & 119.3 & 114.961922101488 & 4.33807789851151 \tabularnewline
57 & 115.4 & 108.625594367211 & 6.77440563278858 \tabularnewline
58 & 108.5 & 108.056659942928 & 0.443340057072246 \tabularnewline
59 & 111.5 & 109.970130009761 & 1.52986999023873 \tabularnewline
60 & 108.8 & 113.321324513053 & -4.5213245130527 \tabularnewline
61 & 121.8 & 113.263145838413 & 8.53685416158744 \tabularnewline
62 & 109.6 & 115.886718357309 & -6.28671835730882 \tabularnewline
63 & 112.2 & 110.787383989348 & 1.4126160106516 \tabularnewline
64 & 119.6 & 114.708505152482 & 4.89149484751802 \tabularnewline
65 & 104.1 & 113.731857443754 & -9.63185744375376 \tabularnewline
66 & 105.3 & 109.718429260088 & -4.41842926008837 \tabularnewline
67 & 115 & 116.211064938104 & -1.21106493810356 \tabularnewline
68 & 124.1 & 116.163893988372 & 7.93610601162766 \tabularnewline
69 & 116.8 & 110.978955495183 & 5.82104450481682 \tabularnewline
70 & 107.5 & 106.362204873447 & 1.13779512655346 \tabularnewline
71 & 115.6 & 113.549064389440 & 2.05093561056045 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57782&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]105.6[/C][C]101.825555195101[/C][C]3.77444480489936[/C][/ROW]
[ROW][C]2[/C][C]102.8[/C][C]101.869333286180[/C][C]0.930666713820146[/C][/ROW]
[ROW][C]3[/C][C]101.7[/C][C]98.2607948944556[/C][C]3.43920510554437[/C][/ROW]
[ROW][C]4[/C][C]104.2[/C][C]99.8155005099161[/C][C]4.38449949008386[/C][/ROW]
[ROW][C]5[/C][C]92.7[/C][C]100.316628306944[/C][C]-7.61662830694372[/C][/ROW]
[ROW][C]6[/C][C]91.9[/C][C]99.341206113669[/C][C]-7.44120611366894[/C][/ROW]
[ROW][C]7[/C][C]106.5[/C][C]104.316956049010[/C][C]2.18304395099027[/C][/ROW]
[ROW][C]8[/C][C]112.3[/C][C]104.291163074993[/C][C]8.00883692500728[/C][/ROW]
[ROW][C]9[/C][C]102.8[/C][C]98.2311776357912[/C][C]4.56882236420876[/C][/ROW]
[ROW][C]10[/C][C]96.5[/C][C]96.0925663101853[/C][C]0.407433689814681[/C][/ROW]
[ROW][C]11[/C][C]101[/C][C]100.806811938891[/C][C]0.193188061109394[/C][/ROW]
[ROW][C]12[/C][C]98.9[/C][C]103.784331298985[/C][C]-4.88433129898457[/C][/ROW]
[ROW][C]13[/C][C]105.1[/C][C]102.083307674289[/C][C]3.01669232571057[/C][/ROW]
[ROW][C]14[/C][C]103[/C][C]102.637522086233[/C][C]0.362477913766722[/C][/ROW]
[ROW][C]15[/C][C]99[/C][C]100.277904024423[/C][C]-1.27790402442344[/C][/ROW]
[ROW][C]16[/C][C]104.3[/C][C]101.268718790634[/C][C]3.03128120936605[/C][/ROW]
[ROW][C]17[/C][C]94.6[/C][C]102.848238836236[/C][C]-8.24823883623624[/C][/ROW]
[ROW][C]18[/C][C]90.4[/C][C]100.991937214857[/C][C]-10.5919372148574[/C][/ROW]
[ROW][C]19[/C][C]108.9[/C][C]105.437784327867[/C][C]3.46221567213308[/C][/ROW]
[ROW][C]20[/C][C]111.4[/C][C]107.494085541694[/C][C]3.90591445830615[/C][/ROW]
[ROW][C]21[/C][C]100.8[/C][C]99.8545315721999[/C][C]0.945468427800138[/C][/ROW]
[ROW][C]22[/C][C]102.5[/C][C]99.4318591585426[/C][C]3.06814084145743[/C][/ROW]
[ROW][C]23[/C][C]98.2[/C][C]104.879996611312[/C][C]-6.6799966113123[/C][/ROW]
[ROW][C]24[/C][C]98.7[/C][C]105.020896954279[/C][C]-6.32089695427864[/C][/ROW]
[ROW][C]25[/C][C]113.3[/C][C]107.130516726764[/C][C]6.169483273236[/C][/ROW]
[ROW][C]26[/C][C]104.6[/C][C]106.269586970089[/C][C]-1.66958697008901[/C][/ROW]
[ROW][C]27[/C][C]99.3[/C][C]99.7254914683307[/C][C]-0.425491468330677[/C][/ROW]
[ROW][C]28[/C][C]111.8[/C][C]103.635433604152[/C][C]8.1645663958476[/C][/ROW]
[ROW][C]29[/C][C]97.3[/C][C]105.979015393856[/C][C]-8.67901539385572[/C][/ROW]
[ROW][C]30[/C][C]97.7[/C][C]101.091789704840[/C][C]-3.39178970484026[/C][/ROW]
[ROW][C]31[/C][C]115.6[/C][C]107.348306502148[/C][C]8.25169349785182[/C][/ROW]
[ROW][C]32[/C][C]111.9[/C][C]107.067509621452[/C][C]4.83249037854765[/C][/ROW]
[ROW][C]33[/C][C]107[/C][C]100.034525201125[/C][C]6.96547479887467[/C][/ROW]
[ROW][C]34[/C][C]107.1[/C][C]101.602162172391[/C][C]5.49783782760893[/C][/ROW]
[ROW][C]35[/C][C]100.6[/C][C]105.030624548581[/C][C]-4.43062454858063[/C][/ROW]
[ROW][C]36[/C][C]99.2[/C][C]105.527611597973[/C][C]-6.32761159797257[/C][/ROW]
[ROW][C]37[/C][C]108.4[/C][C]108.792113111884[/C][C]-0.392113111883563[/C][/ROW]
[ROW][C]38[/C][C]103[/C][C]108.850071999431[/C][C]-5.85007199943057[/C][/ROW]
[ROW][C]39[/C][C]99.8[/C][C]104.151009929762[/C][C]-4.35100992976214[/C][/ROW]
[ROW][C]40[/C][C]115[/C][C]106.214613146922[/C][C]8.78538685307848[/C][/ROW]
[ROW][C]41[/C][C]90.8[/C][C]107.269620558490[/C][C]-16.4696205584905[/C][/ROW]
[ROW][C]42[/C][C]95.9[/C][C]102.222041517844[/C][C]-6.32204151784355[/C][/ROW]
[ROW][C]43[/C][C]114.4[/C][C]112.665027493952[/C][C]1.73497250604843[/C][/ROW]
[ROW][C]44[/C][C]108.2[/C][C]110.061236016387[/C][C]-1.86123601638707[/C][/ROW]
[ROW][C]45[/C][C]112.6[/C][C]102.701194447736[/C][C]9.8988055522637[/C][/ROW]
[ROW][C]46[/C][C]109.1[/C][C]106.036925304346[/C][C]3.06307469565425[/C][/ROW]
[ROW][C]47[/C][C]105[/C][C]105.546886880286[/C][C]-0.546886880286158[/C][/ROW]
[ROW][C]48[/C][C]105[/C][C]107.764733598948[/C][C]-2.76473359894769[/C][/ROW]
[ROW][C]49[/C][C]118.5[/C][C]109.661960009414[/C][C]8.83803999058607[/C][/ROW]
[ROW][C]50[/C][C]103.7[/C][C]111.085223268274[/C][C]-7.38522326827392[/C][/ROW]
[ROW][C]51[/C][C]112.5[/C][C]108.066191219649[/C][C]4.43380878035144[/C][/ROW]
[ROW][C]52[/C][C]116.6[/C][C]112.779784204508[/C][C]3.82021579549165[/C][/ROW]
[ROW][C]53[/C][C]96.6[/C][C]110.131464823757[/C][C]-13.5314648237570[/C][/ROW]
[ROW][C]54[/C][C]101.9[/C][C]108.942310419640[/C][C]-7.04231041964032[/C][/ROW]
[ROW][C]55[/C][C]116.5[/C][C]116.939356470002[/C][C]-0.43935647000178[/C][/ROW]
[ROW][C]56[/C][C]119.3[/C][C]114.961922101488[/C][C]4.33807789851151[/C][/ROW]
[ROW][C]57[/C][C]115.4[/C][C]108.625594367211[/C][C]6.77440563278858[/C][/ROW]
[ROW][C]58[/C][C]108.5[/C][C]108.056659942928[/C][C]0.443340057072246[/C][/ROW]
[ROW][C]59[/C][C]111.5[/C][C]109.970130009761[/C][C]1.52986999023873[/C][/ROW]
[ROW][C]60[/C][C]108.8[/C][C]113.321324513053[/C][C]-4.5213245130527[/C][/ROW]
[ROW][C]61[/C][C]121.8[/C][C]113.263145838413[/C][C]8.53685416158744[/C][/ROW]
[ROW][C]62[/C][C]109.6[/C][C]115.886718357309[/C][C]-6.28671835730882[/C][/ROW]
[ROW][C]63[/C][C]112.2[/C][C]110.787383989348[/C][C]1.4126160106516[/C][/ROW]
[ROW][C]64[/C][C]119.6[/C][C]114.708505152482[/C][C]4.89149484751802[/C][/ROW]
[ROW][C]65[/C][C]104.1[/C][C]113.731857443754[/C][C]-9.63185744375376[/C][/ROW]
[ROW][C]66[/C][C]105.3[/C][C]109.718429260088[/C][C]-4.41842926008837[/C][/ROW]
[ROW][C]67[/C][C]115[/C][C]116.211064938104[/C][C]-1.21106493810356[/C][/ROW]
[ROW][C]68[/C][C]124.1[/C][C]116.163893988372[/C][C]7.93610601162766[/C][/ROW]
[ROW][C]69[/C][C]116.8[/C][C]110.978955495183[/C][C]5.82104450481682[/C][/ROW]
[ROW][C]70[/C][C]107.5[/C][C]106.362204873447[/C][C]1.13779512655346[/C][/ROW]
[ROW][C]71[/C][C]115.6[/C][C]113.549064389440[/C][C]2.05093561056045[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57782&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57782&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1105.6101.8255551951013.77444480489936
2102.8101.8693332861800.930666713820146
3101.798.26079489445563.43920510554437
4104.299.81550050991614.38449949008386
592.7100.316628306944-7.61662830694372
691.999.341206113669-7.44120611366894
7106.5104.3169560490102.18304395099027
8112.3104.2911630749938.00883692500728
9102.898.23117763579124.56882236420876
1096.596.09256631018530.407433689814681
11101100.8068119388910.193188061109394
1298.9103.784331298985-4.88433129898457
13105.1102.0833076742893.01669232571057
14103102.6375220862330.362477913766722
1599100.277904024423-1.27790402442344
16104.3101.2687187906343.03128120936605
1794.6102.848238836236-8.24823883623624
1890.4100.991937214857-10.5919372148574
19108.9105.4377843278673.46221567213308
20111.4107.4940855416943.90591445830615
21100.899.85453157219990.945468427800138
22102.599.43185915854263.06814084145743
2398.2104.879996611312-6.6799966113123
2498.7105.020896954279-6.32089695427864
25113.3107.1305167267646.169483273236
26104.6106.269586970089-1.66958697008901
2799.399.7254914683307-0.425491468330677
28111.8103.6354336041528.1645663958476
2997.3105.979015393856-8.67901539385572
3097.7101.091789704840-3.39178970484026
31115.6107.3483065021488.25169349785182
32111.9107.0675096214524.83249037854765
33107100.0345252011256.96547479887467
34107.1101.6021621723915.49783782760893
35100.6105.030624548581-4.43062454858063
3699.2105.527611597973-6.32761159797257
37108.4108.792113111884-0.392113111883563
38103108.850071999431-5.85007199943057
3999.8104.151009929762-4.35100992976214
40115106.2146131469228.78538685307848
4190.8107.269620558490-16.4696205584905
4295.9102.222041517844-6.32204151784355
43114.4112.6650274939521.73497250604843
44108.2110.061236016387-1.86123601638707
45112.6102.7011944477369.8988055522637
46109.1106.0369253043463.06307469565425
47105105.546886880286-0.546886880286158
48105107.764733598948-2.76473359894769
49118.5109.6619600094148.83803999058607
50103.7111.085223268274-7.38522326827392
51112.5108.0661912196494.43380878035144
52116.6112.7797842045083.82021579549165
5396.6110.131464823757-13.5314648237570
54101.9108.942310419640-7.04231041964032
55116.5116.939356470002-0.43935647000178
56119.3114.9619221014884.33807789851151
57115.4108.6255943672116.77440563278858
58108.5108.0566599429280.443340057072246
59111.5109.9701300097611.52986999023873
60108.8113.321324513053-4.5213245130527
61121.8113.2631458384138.53685416158744
62109.6115.886718357309-6.28671835730882
63112.2110.7873839893481.4126160106516
64119.6114.7085051524824.89149484751802
65104.1113.731857443754-9.63185744375376
66105.3109.718429260088-4.41842926008837
67115116.211064938104-1.21106493810356
68124.1116.1638939883727.93610601162766
69116.8110.9789554951835.82104450481682
70107.5106.3622048734471.13779512655346
71115.6113.5490643894402.05093561056045






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.6047096652132330.7905806695735340.395290334786767
80.5136155815814380.9727688368371240.486384418418562
90.4664479192969470.9328958385938930.533552080703053
100.3816457984110210.7632915968220420.618354201588979
110.2722978110397610.5445956220795230.727702188960239
120.244256819943790.488513639887580.75574318005621
130.2320047080201280.4640094160402560.767995291979872
140.1591182985930710.3182365971861410.84088170140693
150.1035045792836920.2070091585673850.896495420716308
160.08698754598674570.1739750919734910.913012454013254
170.1486160709860270.2972321419720540.851383929013973
180.1795072518876570.3590145037753140.820492748112343
190.2009311516127920.4018623032255830.799068848387208
200.1578036967581370.3156073935162740.842196303241863
210.1216242885171980.2432485770343960.878375711482802
220.1251588649845550.2503177299691090.874841135015445
230.1210856911680290.2421713823360590.87891430883197
240.09712675009605170.1942535001921030.902873249903948
250.1398693294717960.2797386589435910.860130670528204
260.1066773604060600.2133547208121200.89332263959394
270.07878996137022350.1575799227404470.921210038629777
280.1277259595188980.2554519190377960.872274040481102
290.1764276842575170.3528553685150340.823572315742483
300.1389387506356440.2778775012712870.861061249364356
310.1848327371089530.3696654742179060.815167262891047
320.1669442268850470.3338884537700940.833055773114953
330.1921386343269070.3842772686538150.807861365673093
340.1886960166276160.3773920332552330.811303983372384
350.1683563480301130.3367126960602250.831643651969887
360.1624219256549950.324843851309990.837578074345005
370.1220080333754210.2440160667508420.877991966624579
380.1134133126022520.2268266252045040.886586687397748
390.09162482596988150.1832496519397630.908375174030118
400.1412101550117870.2824203100235730.858789844988213
410.469099994220720.938199988441440.53090000577928
420.4828670359999180.9657340719998360.517132964000082
430.4165504447941560.8331008895883130.583449555205844
440.3624234412342500.7248468824684990.63757655876575
450.4585627304195590.9171254608391180.541437269580441
460.4038496373939570.8076992747879140.596150362606043
470.3318523053113960.6637046106227920.668147694688604
480.2788982949595150.557796589919030.721101705040485
490.3560546144355770.7121092288711540.643945385564423
500.3693814907173680.7387629814347370.630618509282632
510.3462679027714990.6925358055429970.653732097228501
520.3015789339319520.6031578678639040.698421066068048
530.6315794018244090.7368411963511820.368420598175591
540.6515454851840740.6969090296318520.348454514815926
550.5864807660281270.8270384679437460.413519233971873
560.5115940505000180.9768118989999640.488405949499982
570.4944129379868240.9888258759736480.505587062013176
580.3944041313198490.7888082626396980.605595868680151
590.2985535838893290.5971071677786580.701446416110671
600.2979150366894720.5958300733789440.702084963310528
610.317848501692450.63569700338490.68215149830755
620.3345988708926720.6691977417853450.665401129107328
630.2316103299056220.4632206598112440.768389670094378
640.1588596155930220.3177192311860450.841140384406978

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.604709665213233 & 0.790580669573534 & 0.395290334786767 \tabularnewline
8 & 0.513615581581438 & 0.972768836837124 & 0.486384418418562 \tabularnewline
9 & 0.466447919296947 & 0.932895838593893 & 0.533552080703053 \tabularnewline
10 & 0.381645798411021 & 0.763291596822042 & 0.618354201588979 \tabularnewline
11 & 0.272297811039761 & 0.544595622079523 & 0.727702188960239 \tabularnewline
12 & 0.24425681994379 & 0.48851363988758 & 0.75574318005621 \tabularnewline
13 & 0.232004708020128 & 0.464009416040256 & 0.767995291979872 \tabularnewline
14 & 0.159118298593071 & 0.318236597186141 & 0.84088170140693 \tabularnewline
15 & 0.103504579283692 & 0.207009158567385 & 0.896495420716308 \tabularnewline
16 & 0.0869875459867457 & 0.173975091973491 & 0.913012454013254 \tabularnewline
17 & 0.148616070986027 & 0.297232141972054 & 0.851383929013973 \tabularnewline
18 & 0.179507251887657 & 0.359014503775314 & 0.820492748112343 \tabularnewline
19 & 0.200931151612792 & 0.401862303225583 & 0.799068848387208 \tabularnewline
20 & 0.157803696758137 & 0.315607393516274 & 0.842196303241863 \tabularnewline
21 & 0.121624288517198 & 0.243248577034396 & 0.878375711482802 \tabularnewline
22 & 0.125158864984555 & 0.250317729969109 & 0.874841135015445 \tabularnewline
23 & 0.121085691168029 & 0.242171382336059 & 0.87891430883197 \tabularnewline
24 & 0.0971267500960517 & 0.194253500192103 & 0.902873249903948 \tabularnewline
25 & 0.139869329471796 & 0.279738658943591 & 0.860130670528204 \tabularnewline
26 & 0.106677360406060 & 0.213354720812120 & 0.89332263959394 \tabularnewline
27 & 0.0787899613702235 & 0.157579922740447 & 0.921210038629777 \tabularnewline
28 & 0.127725959518898 & 0.255451919037796 & 0.872274040481102 \tabularnewline
29 & 0.176427684257517 & 0.352855368515034 & 0.823572315742483 \tabularnewline
30 & 0.138938750635644 & 0.277877501271287 & 0.861061249364356 \tabularnewline
31 & 0.184832737108953 & 0.369665474217906 & 0.815167262891047 \tabularnewline
32 & 0.166944226885047 & 0.333888453770094 & 0.833055773114953 \tabularnewline
33 & 0.192138634326907 & 0.384277268653815 & 0.807861365673093 \tabularnewline
34 & 0.188696016627616 & 0.377392033255233 & 0.811303983372384 \tabularnewline
35 & 0.168356348030113 & 0.336712696060225 & 0.831643651969887 \tabularnewline
36 & 0.162421925654995 & 0.32484385130999 & 0.837578074345005 \tabularnewline
37 & 0.122008033375421 & 0.244016066750842 & 0.877991966624579 \tabularnewline
38 & 0.113413312602252 & 0.226826625204504 & 0.886586687397748 \tabularnewline
39 & 0.0916248259698815 & 0.183249651939763 & 0.908375174030118 \tabularnewline
40 & 0.141210155011787 & 0.282420310023573 & 0.858789844988213 \tabularnewline
41 & 0.46909999422072 & 0.93819998844144 & 0.53090000577928 \tabularnewline
42 & 0.482867035999918 & 0.965734071999836 & 0.517132964000082 \tabularnewline
43 & 0.416550444794156 & 0.833100889588313 & 0.583449555205844 \tabularnewline
44 & 0.362423441234250 & 0.724846882468499 & 0.63757655876575 \tabularnewline
45 & 0.458562730419559 & 0.917125460839118 & 0.541437269580441 \tabularnewline
46 & 0.403849637393957 & 0.807699274787914 & 0.596150362606043 \tabularnewline
47 & 0.331852305311396 & 0.663704610622792 & 0.668147694688604 \tabularnewline
48 & 0.278898294959515 & 0.55779658991903 & 0.721101705040485 \tabularnewline
49 & 0.356054614435577 & 0.712109228871154 & 0.643945385564423 \tabularnewline
50 & 0.369381490717368 & 0.738762981434737 & 0.630618509282632 \tabularnewline
51 & 0.346267902771499 & 0.692535805542997 & 0.653732097228501 \tabularnewline
52 & 0.301578933931952 & 0.603157867863904 & 0.698421066068048 \tabularnewline
53 & 0.631579401824409 & 0.736841196351182 & 0.368420598175591 \tabularnewline
54 & 0.651545485184074 & 0.696909029631852 & 0.348454514815926 \tabularnewline
55 & 0.586480766028127 & 0.827038467943746 & 0.413519233971873 \tabularnewline
56 & 0.511594050500018 & 0.976811898999964 & 0.488405949499982 \tabularnewline
57 & 0.494412937986824 & 0.988825875973648 & 0.505587062013176 \tabularnewline
58 & 0.394404131319849 & 0.788808262639698 & 0.605595868680151 \tabularnewline
59 & 0.298553583889329 & 0.597107167778658 & 0.701446416110671 \tabularnewline
60 & 0.297915036689472 & 0.595830073378944 & 0.702084963310528 \tabularnewline
61 & 0.31784850169245 & 0.6356970033849 & 0.68215149830755 \tabularnewline
62 & 0.334598870892672 & 0.669197741785345 & 0.665401129107328 \tabularnewline
63 & 0.231610329905622 & 0.463220659811244 & 0.768389670094378 \tabularnewline
64 & 0.158859615593022 & 0.317719231186045 & 0.841140384406978 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57782&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.604709665213233[/C][C]0.790580669573534[/C][C]0.395290334786767[/C][/ROW]
[ROW][C]8[/C][C]0.513615581581438[/C][C]0.972768836837124[/C][C]0.486384418418562[/C][/ROW]
[ROW][C]9[/C][C]0.466447919296947[/C][C]0.932895838593893[/C][C]0.533552080703053[/C][/ROW]
[ROW][C]10[/C][C]0.381645798411021[/C][C]0.763291596822042[/C][C]0.618354201588979[/C][/ROW]
[ROW][C]11[/C][C]0.272297811039761[/C][C]0.544595622079523[/C][C]0.727702188960239[/C][/ROW]
[ROW][C]12[/C][C]0.24425681994379[/C][C]0.48851363988758[/C][C]0.75574318005621[/C][/ROW]
[ROW][C]13[/C][C]0.232004708020128[/C][C]0.464009416040256[/C][C]0.767995291979872[/C][/ROW]
[ROW][C]14[/C][C]0.159118298593071[/C][C]0.318236597186141[/C][C]0.84088170140693[/C][/ROW]
[ROW][C]15[/C][C]0.103504579283692[/C][C]0.207009158567385[/C][C]0.896495420716308[/C][/ROW]
[ROW][C]16[/C][C]0.0869875459867457[/C][C]0.173975091973491[/C][C]0.913012454013254[/C][/ROW]
[ROW][C]17[/C][C]0.148616070986027[/C][C]0.297232141972054[/C][C]0.851383929013973[/C][/ROW]
[ROW][C]18[/C][C]0.179507251887657[/C][C]0.359014503775314[/C][C]0.820492748112343[/C][/ROW]
[ROW][C]19[/C][C]0.200931151612792[/C][C]0.401862303225583[/C][C]0.799068848387208[/C][/ROW]
[ROW][C]20[/C][C]0.157803696758137[/C][C]0.315607393516274[/C][C]0.842196303241863[/C][/ROW]
[ROW][C]21[/C][C]0.121624288517198[/C][C]0.243248577034396[/C][C]0.878375711482802[/C][/ROW]
[ROW][C]22[/C][C]0.125158864984555[/C][C]0.250317729969109[/C][C]0.874841135015445[/C][/ROW]
[ROW][C]23[/C][C]0.121085691168029[/C][C]0.242171382336059[/C][C]0.87891430883197[/C][/ROW]
[ROW][C]24[/C][C]0.0971267500960517[/C][C]0.194253500192103[/C][C]0.902873249903948[/C][/ROW]
[ROW][C]25[/C][C]0.139869329471796[/C][C]0.279738658943591[/C][C]0.860130670528204[/C][/ROW]
[ROW][C]26[/C][C]0.106677360406060[/C][C]0.213354720812120[/C][C]0.89332263959394[/C][/ROW]
[ROW][C]27[/C][C]0.0787899613702235[/C][C]0.157579922740447[/C][C]0.921210038629777[/C][/ROW]
[ROW][C]28[/C][C]0.127725959518898[/C][C]0.255451919037796[/C][C]0.872274040481102[/C][/ROW]
[ROW][C]29[/C][C]0.176427684257517[/C][C]0.352855368515034[/C][C]0.823572315742483[/C][/ROW]
[ROW][C]30[/C][C]0.138938750635644[/C][C]0.277877501271287[/C][C]0.861061249364356[/C][/ROW]
[ROW][C]31[/C][C]0.184832737108953[/C][C]0.369665474217906[/C][C]0.815167262891047[/C][/ROW]
[ROW][C]32[/C][C]0.166944226885047[/C][C]0.333888453770094[/C][C]0.833055773114953[/C][/ROW]
[ROW][C]33[/C][C]0.192138634326907[/C][C]0.384277268653815[/C][C]0.807861365673093[/C][/ROW]
[ROW][C]34[/C][C]0.188696016627616[/C][C]0.377392033255233[/C][C]0.811303983372384[/C][/ROW]
[ROW][C]35[/C][C]0.168356348030113[/C][C]0.336712696060225[/C][C]0.831643651969887[/C][/ROW]
[ROW][C]36[/C][C]0.162421925654995[/C][C]0.32484385130999[/C][C]0.837578074345005[/C][/ROW]
[ROW][C]37[/C][C]0.122008033375421[/C][C]0.244016066750842[/C][C]0.877991966624579[/C][/ROW]
[ROW][C]38[/C][C]0.113413312602252[/C][C]0.226826625204504[/C][C]0.886586687397748[/C][/ROW]
[ROW][C]39[/C][C]0.0916248259698815[/C][C]0.183249651939763[/C][C]0.908375174030118[/C][/ROW]
[ROW][C]40[/C][C]0.141210155011787[/C][C]0.282420310023573[/C][C]0.858789844988213[/C][/ROW]
[ROW][C]41[/C][C]0.46909999422072[/C][C]0.93819998844144[/C][C]0.53090000577928[/C][/ROW]
[ROW][C]42[/C][C]0.482867035999918[/C][C]0.965734071999836[/C][C]0.517132964000082[/C][/ROW]
[ROW][C]43[/C][C]0.416550444794156[/C][C]0.833100889588313[/C][C]0.583449555205844[/C][/ROW]
[ROW][C]44[/C][C]0.362423441234250[/C][C]0.724846882468499[/C][C]0.63757655876575[/C][/ROW]
[ROW][C]45[/C][C]0.458562730419559[/C][C]0.917125460839118[/C][C]0.541437269580441[/C][/ROW]
[ROW][C]46[/C][C]0.403849637393957[/C][C]0.807699274787914[/C][C]0.596150362606043[/C][/ROW]
[ROW][C]47[/C][C]0.331852305311396[/C][C]0.663704610622792[/C][C]0.668147694688604[/C][/ROW]
[ROW][C]48[/C][C]0.278898294959515[/C][C]0.55779658991903[/C][C]0.721101705040485[/C][/ROW]
[ROW][C]49[/C][C]0.356054614435577[/C][C]0.712109228871154[/C][C]0.643945385564423[/C][/ROW]
[ROW][C]50[/C][C]0.369381490717368[/C][C]0.738762981434737[/C][C]0.630618509282632[/C][/ROW]
[ROW][C]51[/C][C]0.346267902771499[/C][C]0.692535805542997[/C][C]0.653732097228501[/C][/ROW]
[ROW][C]52[/C][C]0.301578933931952[/C][C]0.603157867863904[/C][C]0.698421066068048[/C][/ROW]
[ROW][C]53[/C][C]0.631579401824409[/C][C]0.736841196351182[/C][C]0.368420598175591[/C][/ROW]
[ROW][C]54[/C][C]0.651545485184074[/C][C]0.696909029631852[/C][C]0.348454514815926[/C][/ROW]
[ROW][C]55[/C][C]0.586480766028127[/C][C]0.827038467943746[/C][C]0.413519233971873[/C][/ROW]
[ROW][C]56[/C][C]0.511594050500018[/C][C]0.976811898999964[/C][C]0.488405949499982[/C][/ROW]
[ROW][C]57[/C][C]0.494412937986824[/C][C]0.988825875973648[/C][C]0.505587062013176[/C][/ROW]
[ROW][C]58[/C][C]0.394404131319849[/C][C]0.788808262639698[/C][C]0.605595868680151[/C][/ROW]
[ROW][C]59[/C][C]0.298553583889329[/C][C]0.597107167778658[/C][C]0.701446416110671[/C][/ROW]
[ROW][C]60[/C][C]0.297915036689472[/C][C]0.595830073378944[/C][C]0.702084963310528[/C][/ROW]
[ROW][C]61[/C][C]0.31784850169245[/C][C]0.6356970033849[/C][C]0.68215149830755[/C][/ROW]
[ROW][C]62[/C][C]0.334598870892672[/C][C]0.669197741785345[/C][C]0.665401129107328[/C][/ROW]
[ROW][C]63[/C][C]0.231610329905622[/C][C]0.463220659811244[/C][C]0.768389670094378[/C][/ROW]
[ROW][C]64[/C][C]0.158859615593022[/C][C]0.317719231186045[/C][C]0.841140384406978[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57782&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57782&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.6047096652132330.7905806695735340.395290334786767
80.5136155815814380.9727688368371240.486384418418562
90.4664479192969470.9328958385938930.533552080703053
100.3816457984110210.7632915968220420.618354201588979
110.2722978110397610.5445956220795230.727702188960239
120.244256819943790.488513639887580.75574318005621
130.2320047080201280.4640094160402560.767995291979872
140.1591182985930710.3182365971861410.84088170140693
150.1035045792836920.2070091585673850.896495420716308
160.08698754598674570.1739750919734910.913012454013254
170.1486160709860270.2972321419720540.851383929013973
180.1795072518876570.3590145037753140.820492748112343
190.2009311516127920.4018623032255830.799068848387208
200.1578036967581370.3156073935162740.842196303241863
210.1216242885171980.2432485770343960.878375711482802
220.1251588649845550.2503177299691090.874841135015445
230.1210856911680290.2421713823360590.87891430883197
240.09712675009605170.1942535001921030.902873249903948
250.1398693294717960.2797386589435910.860130670528204
260.1066773604060600.2133547208121200.89332263959394
270.07878996137022350.1575799227404470.921210038629777
280.1277259595188980.2554519190377960.872274040481102
290.1764276842575170.3528553685150340.823572315742483
300.1389387506356440.2778775012712870.861061249364356
310.1848327371089530.3696654742179060.815167262891047
320.1669442268850470.3338884537700940.833055773114953
330.1921386343269070.3842772686538150.807861365673093
340.1886960166276160.3773920332552330.811303983372384
350.1683563480301130.3367126960602250.831643651969887
360.1624219256549950.324843851309990.837578074345005
370.1220080333754210.2440160667508420.877991966624579
380.1134133126022520.2268266252045040.886586687397748
390.09162482596988150.1832496519397630.908375174030118
400.1412101550117870.2824203100235730.858789844988213
410.469099994220720.938199988441440.53090000577928
420.4828670359999180.9657340719998360.517132964000082
430.4165504447941560.8331008895883130.583449555205844
440.3624234412342500.7248468824684990.63757655876575
450.4585627304195590.9171254608391180.541437269580441
460.4038496373939570.8076992747879140.596150362606043
470.3318523053113960.6637046106227920.668147694688604
480.2788982949595150.557796589919030.721101705040485
490.3560546144355770.7121092288711540.643945385564423
500.3693814907173680.7387629814347370.630618509282632
510.3462679027714990.6925358055429970.653732097228501
520.3015789339319520.6031578678639040.698421066068048
530.6315794018244090.7368411963511820.368420598175591
540.6515454851840740.6969090296318520.348454514815926
550.5864807660281270.8270384679437460.413519233971873
560.5115940505000180.9768118989999640.488405949499982
570.4944129379868240.9888258759736480.505587062013176
580.3944041313198490.7888082626396980.605595868680151
590.2985535838893290.5971071677786580.701446416110671
600.2979150366894720.5958300733789440.702084963310528
610.317848501692450.63569700338490.68215149830755
620.3345988708926720.6691977417853450.665401129107328
630.2316103299056220.4632206598112440.768389670094378
640.1588596155930220.3177192311860450.841140384406978






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57782&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57782&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57782&T=6

As an alternative you can also use a QR Code:  
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 level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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')
}