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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 19 Dec 2010 09:44:42 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/19/t1292751800kiy2gbkjf5yv7am.htm/, Retrieved Sat, 04 May 2024 23:23:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112241, Retrieved Sat, 04 May 2024 23:23:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162

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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [ws 8 auitoregressie] [2010-11-29 18:31:52] [bd591a1ebb67d263a02e7adae3fa1a4d]
-    D        [Multiple Regression] [autoregressie] [2010-12-19 09:44:42] [09489ba95453d3f5c9e6f2eaeda915af] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
98,1	102,8	104,7	95,9	94,6	15607,4	-7,5	15172,6
113,9	98,1	102,8	104,7	95,9	17160,9	-7,8	16858,9
80,9	113,9	98,1	102,8	104,7	14915,8	-7,7	14143,5
95,7	80,9	113,9	98,1	102,8	13768	-6,6	14731,8
113,2	95,7	80,9	113,9	98,1	17487,5	-4,2	16471,6
105,9	113,2	95,7	80,9	113,9	16198,1	-2,0	15214
108,8	105,9	113,2	95,7	80,9	17535,2	-0,7	17637,4
102,3	108,8	105,9	113,2	95,7	16571,8	0,1	17972,4
99	102,3	108,8	105,9	113,2	16198,9	0,9	16896,2
100,7	99	102,3	108,8	105,9	16554,2	2,1	16698
115,5	100,7	99	102,3	108,8	19554,2	3,5	19691,6
100,7	115,5	100,7	99	102,3	15903,8	4,9	15930,7
109,9	100,7	115,5	100,7	99	18003,8	5,7	17444,6
114,6	109,9	100,7	115,5	100,7	18329,6	6,2	17699,4
85,4	114,6	109,9	100,7	115,5	16260,7	6,5	15189,8
100,5	85,4	114,6	109,9	100,7	14851,9	6,5	15672,7
114,8	100,5	85,4	114,6	109,9	18174,1	6,3	17180,8
116,5	114,8	100,5	85,4	114,6	18406,6	6,2	17664,9
112,9	116,5	114,8	100,5	85,4	18466,5	6,4	17862,9
102	112,9	116,5	114,8	100,5	16016,5	6,3	16162,3
106	102	112,9	116,5	114,8	17428,5	5,8	17463,6
105,3	106	102	112,9	116,5	17167,2	5,1	16772,1
118,8	105,3	106	102	112,9	19630	5,1	19106,9
106,1	118,8	105,3	106	102	17183,6	5,8	16721,3
109,3	106,1	118,8	105,3	106	18344,7	6,7	18161,3
117,2	109,3	106,1	118,8	105,3	19301,4	7,1	18509,9
92,5	117,2	109,3	106,1	118,8	18147,5	6,7	17802,7
104,2	92,5	117,2	109,3	106,1	16192,9	5,5	16409,9
112,5	104,2	92,5	117,2	109,3	18374,4	4,2	17967,7
122,4	112,5	104,2	92,5	117,2	20515,2	3,0	20286,6
113,3	122,4	112,5	104,2	92,5	18957,2	2,2	19537,3
100	113,3	122,4	112,5	104,2	16471,5	2,0	18021,9
110,7	100	113,3	122,4	112,5	18746,8	1,8	20194,3
112,8	110,7	100	113,3	122,4	19009,5	1,8	19049,6
109,8	112,8	110,7	100	113,3	19211,2	1,5	20244,7
117,3	109,8	112,8	110,7	100	20547,7	0,4	21473,3
109,1	117,3	109,8	112,8	110,7	19325,8	-0,9	19673,6
115,9	109,1	117,3	109,8	112,8	20605,5	-1,7	21053,2
96	115,9	109,1	117,3	109,8	20056,9	-2,6	20159,5
99,8	96	115,9	109,1	117,3	16141,4	-4,4	18203,6
116,8	99,8	96	115,9	109,1	20359,8	-8,3	21289,5
115,7	116,8	99,8	96	115,9	19711,6	-14,4	20432,3
99,4	115,7	116,8	99,8	96	15638,6	-21,3	17180,4
94,3	99,4	115,7	116,8	99,8	14384,5	-26,5	15816,8
91	94,3	99,4	115,7	116,8	13855,6	-29,2	15071,8
93,2	91	94,3	99,4	115,7	14308,3	-30,8	14521,1
103,1	93,2	91	94,3	99,4	15290,6	-30,9	15668,8
94,1	103,1	93,2	91	94,3	14423,8	-29,5	14346,9
91,8	94,1	103,1	93,2	91	13779,7	-27,1	13881
102,7	91,8	94,1	103,1	93,2	15686,3	-24,4	15465,9
82,6	102,7	91,8	94,1	103,1	14733,8	-21,9	14238,2
89,1	82,6	102,7	91,8	94,1	12522,5	-19,3	13557,7
104,5	89,1	82,6	102,7	91,8	16189,4	-17,0	16127,6
105,1	104,5	89,1	82,6	102,7	16059,1	-13,8	16793,9
95,1	105,1	104,5	89,1	82,6	16007,1	-9,9	16014
88,7	95,1	105,1	104,5	89,1	15806,8	-7,9	16867,9
86,3	88,7	95,1	105,1	104,5	15160	-7,2	16014,6
91,8	86,3	88,7	95,1	105,1	15692,1	-6,2	15878,6
111,5	91,8	86,3	88,7	95,1	18908,9	-4,5	18664,9
99,7	111,5	91,8	86,3	88,7	16969,9	-3,9	17962,5
97,5	99,7	111,5	91,8	86,3	16997,5	-5,0	17332,7
111,7	97,5	99,7	111,5	91,8	19858,9	-6,2	19542,1
86,2	111,7	97,5	99,7	111,5	17681,2	-6,1	17203,6
95,4	86,2	111,7	97,5	99,7	16006,9	-5,0	16579

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112241&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]6 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=112241&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112241&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
y[t] = + 29.3309960681547 + 0.0190513769829433y1[t] + 0.0543223455157287y2[t] + 0.178983788445719y3[t] -0.0339591841183791y4[t] + 0.00494920051812905uitvoer[t] -0.109468457659687ondernemersvertrouwen[t] -0.00165756844252513invoer[t] -2.68869089991944M1[t] + 0.792315193615192M2[t] -18.3121838438258M3[t] + 1.01821395587077M4[t] + 2.14089750440338M5[t] + 7.12195831693119M6[t] -0.0412927194513832M7[t] -4.42967383373736M8[t] -4.17140693402091M9[t] -2.3862059845378M10[t] + 3.62119270753337M11[t] -0.102508756541094t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  29.3309960681547 +  0.0190513769829433y1[t] +  0.0543223455157287y2[t] +  0.178983788445719y3[t] -0.0339591841183791y4[t] +  0.00494920051812905uitvoer[t] -0.109468457659687ondernemersvertrouwen[t] -0.00165756844252513invoer[t] -2.68869089991944M1[t] +  0.792315193615192M2[t] -18.3121838438258M3[t] +  1.01821395587077M4[t] +  2.14089750440338M5[t] +  7.12195831693119M6[t] -0.0412927194513832M7[t] -4.42967383373736M8[t] -4.17140693402091M9[t] -2.3862059845378M10[t] +  3.62119270753337M11[t] -0.102508756541094t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112241&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  29.3309960681547 +  0.0190513769829433y1[t] +  0.0543223455157287y2[t] +  0.178983788445719y3[t] -0.0339591841183791y4[t] +  0.00494920051812905uitvoer[t] -0.109468457659687ondernemersvertrouwen[t] -0.00165756844252513invoer[t] -2.68869089991944M1[t] +  0.792315193615192M2[t] -18.3121838438258M3[t] +  1.01821395587077M4[t] +  2.14089750440338M5[t] +  7.12195831693119M6[t] -0.0412927194513832M7[t] -4.42967383373736M8[t] -4.17140693402091M9[t] -2.3862059845378M10[t] +  3.62119270753337M11[t] -0.102508756541094t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112241&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112241&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
y[t] = + 29.3309960681547 + 0.0190513769829433y1[t] + 0.0543223455157287y2[t] + 0.178983788445719y3[t] -0.0339591841183791y4[t] + 0.00494920051812905uitvoer[t] -0.109468457659687ondernemersvertrouwen[t] -0.00165756844252513invoer[t] -2.68869089991944M1[t] + 0.792315193615192M2[t] -18.3121838438258M3[t] + 1.01821395587077M4[t] + 2.14089750440338M5[t] + 7.12195831693119M6[t] -0.0412927194513832M7[t] -4.42967383373736M8[t] -4.17140693402091M9[t] -2.3862059845378M10[t] + 3.62119270753337M11[t] -0.102508756541094t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)29.33099606815479.5987493.05570.0038070.001904
y10.01905137698294330.1009620.18870.8511960.425598
y20.05432234551572870.0837930.64830.5201670.260084
y30.1789837884457190.0861662.07720.0436540.021827
y4-0.03395918411837910.090674-0.37450.7098160.354908
uitvoer0.004949200518129050.001024.85381.6e-058e-06
ondernemersvertrouwen-0.1094684576596870.067112-1.63110.1100020.055001
invoer-0.001657568442525130.000801-2.06870.0444840.022242
M1-2.688690899919441.873314-1.43530.1582840.079142
M20.7923151936151922.1083890.37580.7088780.354439
M3-18.31218384382581.910392-9.585600
M41.018213955870773.717130.27390.7854240.392712
M52.140897504403383.1157040.68710.4956060.247803
M67.121958316931192.6469132.69070.0100410.005021
M7-0.04129271945138322.252253-0.01830.9854550.492728
M8-4.429673833737362.612258-1.69570.0970040.048502
M9-4.171406934020913.36313-1.24030.2214250.110713
M10-2.38620598453783.067236-0.7780.4407520.220376
M113.621192707533372.403651.50650.1390750.069538
t-0.1025087565410940.025845-3.96630.0002650.000133

\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) & 29.3309960681547 & 9.598749 & 3.0557 & 0.003807 & 0.001904 \tabularnewline
y1 & 0.0190513769829433 & 0.100962 & 0.1887 & 0.851196 & 0.425598 \tabularnewline
y2 & 0.0543223455157287 & 0.083793 & 0.6483 & 0.520167 & 0.260084 \tabularnewline
y3 & 0.178983788445719 & 0.086166 & 2.0772 & 0.043654 & 0.021827 \tabularnewline
y4 & -0.0339591841183791 & 0.090674 & -0.3745 & 0.709816 & 0.354908 \tabularnewline
uitvoer & 0.00494920051812905 & 0.00102 & 4.8538 & 1.6e-05 & 8e-06 \tabularnewline
ondernemersvertrouwen & -0.109468457659687 & 0.067112 & -1.6311 & 0.110002 & 0.055001 \tabularnewline
invoer & -0.00165756844252513 & 0.000801 & -2.0687 & 0.044484 & 0.022242 \tabularnewline
M1 & -2.68869089991944 & 1.873314 & -1.4353 & 0.158284 & 0.079142 \tabularnewline
M2 & 0.792315193615192 & 2.108389 & 0.3758 & 0.708878 & 0.354439 \tabularnewline
M3 & -18.3121838438258 & 1.910392 & -9.5856 & 0 & 0 \tabularnewline
M4 & 1.01821395587077 & 3.71713 & 0.2739 & 0.785424 & 0.392712 \tabularnewline
M5 & 2.14089750440338 & 3.115704 & 0.6871 & 0.495606 & 0.247803 \tabularnewline
M6 & 7.12195831693119 & 2.646913 & 2.6907 & 0.010041 & 0.005021 \tabularnewline
M7 & -0.0412927194513832 & 2.252253 & -0.0183 & 0.985455 & 0.492728 \tabularnewline
M8 & -4.42967383373736 & 2.612258 & -1.6957 & 0.097004 & 0.048502 \tabularnewline
M9 & -4.17140693402091 & 3.36313 & -1.2403 & 0.221425 & 0.110713 \tabularnewline
M10 & -2.3862059845378 & 3.067236 & -0.778 & 0.440752 & 0.220376 \tabularnewline
M11 & 3.62119270753337 & 2.40365 & 1.5065 & 0.139075 & 0.069538 \tabularnewline
t & -0.102508756541094 & 0.025845 & -3.9663 & 0.000265 & 0.000133 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112241&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]29.3309960681547[/C][C]9.598749[/C][C]3.0557[/C][C]0.003807[/C][C]0.001904[/C][/ROW]
[ROW][C]y1[/C][C]0.0190513769829433[/C][C]0.100962[/C][C]0.1887[/C][C]0.851196[/C][C]0.425598[/C][/ROW]
[ROW][C]y2[/C][C]0.0543223455157287[/C][C]0.083793[/C][C]0.6483[/C][C]0.520167[/C][C]0.260084[/C][/ROW]
[ROW][C]y3[/C][C]0.178983788445719[/C][C]0.086166[/C][C]2.0772[/C][C]0.043654[/C][C]0.021827[/C][/ROW]
[ROW][C]y4[/C][C]-0.0339591841183791[/C][C]0.090674[/C][C]-0.3745[/C][C]0.709816[/C][C]0.354908[/C][/ROW]
[ROW][C]uitvoer[/C][C]0.00494920051812905[/C][C]0.00102[/C][C]4.8538[/C][C]1.6e-05[/C][C]8e-06[/C][/ROW]
[ROW][C]ondernemersvertrouwen[/C][C]-0.109468457659687[/C][C]0.067112[/C][C]-1.6311[/C][C]0.110002[/C][C]0.055001[/C][/ROW]
[ROW][C]invoer[/C][C]-0.00165756844252513[/C][C]0.000801[/C][C]-2.0687[/C][C]0.044484[/C][C]0.022242[/C][/ROW]
[ROW][C]M1[/C][C]-2.68869089991944[/C][C]1.873314[/C][C]-1.4353[/C][C]0.158284[/C][C]0.079142[/C][/ROW]
[ROW][C]M2[/C][C]0.792315193615192[/C][C]2.108389[/C][C]0.3758[/C][C]0.708878[/C][C]0.354439[/C][/ROW]
[ROW][C]M3[/C][C]-18.3121838438258[/C][C]1.910392[/C][C]-9.5856[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]1.01821395587077[/C][C]3.71713[/C][C]0.2739[/C][C]0.785424[/C][C]0.392712[/C][/ROW]
[ROW][C]M5[/C][C]2.14089750440338[/C][C]3.115704[/C][C]0.6871[/C][C]0.495606[/C][C]0.247803[/C][/ROW]
[ROW][C]M6[/C][C]7.12195831693119[/C][C]2.646913[/C][C]2.6907[/C][C]0.010041[/C][C]0.005021[/C][/ROW]
[ROW][C]M7[/C][C]-0.0412927194513832[/C][C]2.252253[/C][C]-0.0183[/C][C]0.985455[/C][C]0.492728[/C][/ROW]
[ROW][C]M8[/C][C]-4.42967383373736[/C][C]2.612258[/C][C]-1.6957[/C][C]0.097004[/C][C]0.048502[/C][/ROW]
[ROW][C]M9[/C][C]-4.17140693402091[/C][C]3.36313[/C][C]-1.2403[/C][C]0.221425[/C][C]0.110713[/C][/ROW]
[ROW][C]M10[/C][C]-2.3862059845378[/C][C]3.067236[/C][C]-0.778[/C][C]0.440752[/C][C]0.220376[/C][/ROW]
[ROW][C]M11[/C][C]3.62119270753337[/C][C]2.40365[/C][C]1.5065[/C][C]0.139075[/C][C]0.069538[/C][/ROW]
[ROW][C]t[/C][C]-0.102508756541094[/C][C]0.025845[/C][C]-3.9663[/C][C]0.000265[/C][C]0.000133[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112241&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112241&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)29.33099606815479.5987493.05570.0038070.001904
y10.01905137698294330.1009620.18870.8511960.425598
y20.05432234551572870.0837930.64830.5201670.260084
y30.1789837884457190.0861662.07720.0436540.021827
y4-0.03395918411837910.090674-0.37450.7098160.354908
uitvoer0.004949200518129050.001024.85381.6e-058e-06
ondernemersvertrouwen-0.1094684576596870.067112-1.63110.1100020.055001
invoer-0.001657568442525130.000801-2.06870.0444840.022242
M1-2.688690899919441.873314-1.43530.1582840.079142
M20.7923151936151922.1083890.37580.7088780.354439
M3-18.31218384382581.910392-9.585600
M41.018213955870773.717130.27390.7854240.392712
M52.140897504403383.1157040.68710.4956060.247803
M67.121958316931192.6469132.69070.0100410.005021
M7-0.04129271945138322.252253-0.01830.9854550.492728
M8-4.429673833737362.612258-1.69570.0970040.048502
M9-4.171406934020913.36313-1.24030.2214250.110713
M10-2.38620598453783.067236-0.7780.4407520.220376
M113.621192707533372.403651.50650.1390750.069538
t-0.1025087565410940.025845-3.96630.0002650.000133






Multiple Linear Regression - Regression Statistics
Multiple R0.979407569564116
R-squared0.959239187319488
Adjusted R-squared0.941637927298357
F-TEST (value)54.49832490219
F-TEST (DF numerator)19
F-TEST (DF denominator)44
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.45407124111589
Sum Squared Residuals264.988488884771

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.979407569564116 \tabularnewline
R-squared & 0.959239187319488 \tabularnewline
Adjusted R-squared & 0.941637927298357 \tabularnewline
F-TEST (value) & 54.49832490219 \tabularnewline
F-TEST (DF numerator) & 19 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.45407124111589 \tabularnewline
Sum Squared Residuals & 264.988488884771 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112241&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.979407569564116[/C][/ROW]
[ROW][C]R-squared[/C][C]0.959239187319488[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.941637927298357[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]54.49832490219[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]19[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.45407124111589[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]264.988488884771[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112241&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112241&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 R0.979407569564116
R-squared0.959239187319488
Adjusted R-squared0.941637927298357
F-TEST (value)54.49832490219
F-TEST (DF numerator)19
F-TEST (DF denominator)44
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.45407124111589
Sum Squared Residuals264.988488884771






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.1101.053376683421-2.95337668342122
2113.9110.6962963686653.20370363133496
380.984.274639708616-3.37463970861609
495.796.1791698417413-0.479169841741336
5113.2113.938209086353-0.738209086352653
6105.9108.973339143787-3.07333914378726
7108.8108.7470741445340.0529258554661365
8102.3101.1335805440011.16641945599863
99999.2729156795653-0.272915679565312
10100.7103.262216973376-2.56221697337623
11115.5117.490603074855-1.99060307485530
12100.7101.785429915795-1.08542991579462
13109.9107.7289317983722.17106820162784
14114.6114.2053274067930.394672593207415
1585.486.3236629435323-0.923662943532304
16100.599.62694009052680.873059909473157
17114.8113.8417261823950.958273817604575
18116.5114.7862526417591.71374735824108
19112.9111.9703179802520.929682019747777
20102100.2541421039581.74585789604242
21106104.7113474477971.28865255220274
22105.3105.1056697341250.194330265875441
23118.8117.7046430930381.09535690696214
24106.1107.056143045621-0.95614304562059
25109.3107.7563137300231.54368626997676
26117.2117.0592186517780.140781348221566
2792.590.95014245050171.54985754949833
28104.2103.9069549162880.293045083711955
29112.5113.470401229774-0.97040122977434
30122.4121.3363491851401.06365081486016
31113.3111.2617120575342.03828794246650
3210098.4550340481031.54496595189709
33110.7107.0350617565313.66493824346855
34112.8109.4317416624753.36825833752521
35109.8112.936567011979-3.13656701197929
36117.3116.3351052351010.964894764898578
37109.1110.614333363003-1.51433336300331
38115.9117.770046593989-1.87004659398874
399698.5561600206925-2.55616002069252
4099.8100.112444377040-0.312444377039719
41116.8117.809098790765-1.00909879076508
42115.7118.305802811152-2.60580281115219
4399.499.28597800312380.114021996876163
4494.393.96117954960690.338820450393129
459191.272954312284-0.272954312284033
4693.293.06412789802820.135872101971792
47103.1101.4425397160131.65746028398668
4894.195.3574182556736-1.25741825567361
4991.890.76013385440581.03986614559418
50102.7101.8166418628750.883358137125183
5182.677.79251480375814.80748519624186
5289.187.02274490880262.07725509119740
53104.5102.7405647107131.7594352892875
54105.1102.1982562181622.90174378183820
5595.198.2349178145566-3.13491781455658
5688.793.4960637543313-4.79606375433127
5786.390.707720803822-4.40772080382195
5891.892.9362437319962-1.13624373199622
59111.5109.1256471041142.37435289588578
6099.797.36590354780982.33409645219025
6197.597.7869105707743-0.286910570774256
62111.7114.452469115900-2.75246911590038
6386.285.70288007289930.497119927100712
6495.497.8517458656015-2.45174586560146

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 98.1 & 101.053376683421 & -2.95337668342122 \tabularnewline
2 & 113.9 & 110.696296368665 & 3.20370363133496 \tabularnewline
3 & 80.9 & 84.274639708616 & -3.37463970861609 \tabularnewline
4 & 95.7 & 96.1791698417413 & -0.479169841741336 \tabularnewline
5 & 113.2 & 113.938209086353 & -0.738209086352653 \tabularnewline
6 & 105.9 & 108.973339143787 & -3.07333914378726 \tabularnewline
7 & 108.8 & 108.747074144534 & 0.0529258554661365 \tabularnewline
8 & 102.3 & 101.133580544001 & 1.16641945599863 \tabularnewline
9 & 99 & 99.2729156795653 & -0.272915679565312 \tabularnewline
10 & 100.7 & 103.262216973376 & -2.56221697337623 \tabularnewline
11 & 115.5 & 117.490603074855 & -1.99060307485530 \tabularnewline
12 & 100.7 & 101.785429915795 & -1.08542991579462 \tabularnewline
13 & 109.9 & 107.728931798372 & 2.17106820162784 \tabularnewline
14 & 114.6 & 114.205327406793 & 0.394672593207415 \tabularnewline
15 & 85.4 & 86.3236629435323 & -0.923662943532304 \tabularnewline
16 & 100.5 & 99.6269400905268 & 0.873059909473157 \tabularnewline
17 & 114.8 & 113.841726182395 & 0.958273817604575 \tabularnewline
18 & 116.5 & 114.786252641759 & 1.71374735824108 \tabularnewline
19 & 112.9 & 111.970317980252 & 0.929682019747777 \tabularnewline
20 & 102 & 100.254142103958 & 1.74585789604242 \tabularnewline
21 & 106 & 104.711347447797 & 1.28865255220274 \tabularnewline
22 & 105.3 & 105.105669734125 & 0.194330265875441 \tabularnewline
23 & 118.8 & 117.704643093038 & 1.09535690696214 \tabularnewline
24 & 106.1 & 107.056143045621 & -0.95614304562059 \tabularnewline
25 & 109.3 & 107.756313730023 & 1.54368626997676 \tabularnewline
26 & 117.2 & 117.059218651778 & 0.140781348221566 \tabularnewline
27 & 92.5 & 90.9501424505017 & 1.54985754949833 \tabularnewline
28 & 104.2 & 103.906954916288 & 0.293045083711955 \tabularnewline
29 & 112.5 & 113.470401229774 & -0.97040122977434 \tabularnewline
30 & 122.4 & 121.336349185140 & 1.06365081486016 \tabularnewline
31 & 113.3 & 111.261712057534 & 2.03828794246650 \tabularnewline
32 & 100 & 98.455034048103 & 1.54496595189709 \tabularnewline
33 & 110.7 & 107.035061756531 & 3.66493824346855 \tabularnewline
34 & 112.8 & 109.431741662475 & 3.36825833752521 \tabularnewline
35 & 109.8 & 112.936567011979 & -3.13656701197929 \tabularnewline
36 & 117.3 & 116.335105235101 & 0.964894764898578 \tabularnewline
37 & 109.1 & 110.614333363003 & -1.51433336300331 \tabularnewline
38 & 115.9 & 117.770046593989 & -1.87004659398874 \tabularnewline
39 & 96 & 98.5561600206925 & -2.55616002069252 \tabularnewline
40 & 99.8 & 100.112444377040 & -0.312444377039719 \tabularnewline
41 & 116.8 & 117.809098790765 & -1.00909879076508 \tabularnewline
42 & 115.7 & 118.305802811152 & -2.60580281115219 \tabularnewline
43 & 99.4 & 99.2859780031238 & 0.114021996876163 \tabularnewline
44 & 94.3 & 93.9611795496069 & 0.338820450393129 \tabularnewline
45 & 91 & 91.272954312284 & -0.272954312284033 \tabularnewline
46 & 93.2 & 93.0641278980282 & 0.135872101971792 \tabularnewline
47 & 103.1 & 101.442539716013 & 1.65746028398668 \tabularnewline
48 & 94.1 & 95.3574182556736 & -1.25741825567361 \tabularnewline
49 & 91.8 & 90.7601338544058 & 1.03986614559418 \tabularnewline
50 & 102.7 & 101.816641862875 & 0.883358137125183 \tabularnewline
51 & 82.6 & 77.7925148037581 & 4.80748519624186 \tabularnewline
52 & 89.1 & 87.0227449088026 & 2.07725509119740 \tabularnewline
53 & 104.5 & 102.740564710713 & 1.7594352892875 \tabularnewline
54 & 105.1 & 102.198256218162 & 2.90174378183820 \tabularnewline
55 & 95.1 & 98.2349178145566 & -3.13491781455658 \tabularnewline
56 & 88.7 & 93.4960637543313 & -4.79606375433127 \tabularnewline
57 & 86.3 & 90.707720803822 & -4.40772080382195 \tabularnewline
58 & 91.8 & 92.9362437319962 & -1.13624373199622 \tabularnewline
59 & 111.5 & 109.125647104114 & 2.37435289588578 \tabularnewline
60 & 99.7 & 97.3659035478098 & 2.33409645219025 \tabularnewline
61 & 97.5 & 97.7869105707743 & -0.286910570774256 \tabularnewline
62 & 111.7 & 114.452469115900 & -2.75246911590038 \tabularnewline
63 & 86.2 & 85.7028800728993 & 0.497119927100712 \tabularnewline
64 & 95.4 & 97.8517458656015 & -2.45174586560146 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112241&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]98.1[/C][C]101.053376683421[/C][C]-2.95337668342122[/C][/ROW]
[ROW][C]2[/C][C]113.9[/C][C]110.696296368665[/C][C]3.20370363133496[/C][/ROW]
[ROW][C]3[/C][C]80.9[/C][C]84.274639708616[/C][C]-3.37463970861609[/C][/ROW]
[ROW][C]4[/C][C]95.7[/C][C]96.1791698417413[/C][C]-0.479169841741336[/C][/ROW]
[ROW][C]5[/C][C]113.2[/C][C]113.938209086353[/C][C]-0.738209086352653[/C][/ROW]
[ROW][C]6[/C][C]105.9[/C][C]108.973339143787[/C][C]-3.07333914378726[/C][/ROW]
[ROW][C]7[/C][C]108.8[/C][C]108.747074144534[/C][C]0.0529258554661365[/C][/ROW]
[ROW][C]8[/C][C]102.3[/C][C]101.133580544001[/C][C]1.16641945599863[/C][/ROW]
[ROW][C]9[/C][C]99[/C][C]99.2729156795653[/C][C]-0.272915679565312[/C][/ROW]
[ROW][C]10[/C][C]100.7[/C][C]103.262216973376[/C][C]-2.56221697337623[/C][/ROW]
[ROW][C]11[/C][C]115.5[/C][C]117.490603074855[/C][C]-1.99060307485530[/C][/ROW]
[ROW][C]12[/C][C]100.7[/C][C]101.785429915795[/C][C]-1.08542991579462[/C][/ROW]
[ROW][C]13[/C][C]109.9[/C][C]107.728931798372[/C][C]2.17106820162784[/C][/ROW]
[ROW][C]14[/C][C]114.6[/C][C]114.205327406793[/C][C]0.394672593207415[/C][/ROW]
[ROW][C]15[/C][C]85.4[/C][C]86.3236629435323[/C][C]-0.923662943532304[/C][/ROW]
[ROW][C]16[/C][C]100.5[/C][C]99.6269400905268[/C][C]0.873059909473157[/C][/ROW]
[ROW][C]17[/C][C]114.8[/C][C]113.841726182395[/C][C]0.958273817604575[/C][/ROW]
[ROW][C]18[/C][C]116.5[/C][C]114.786252641759[/C][C]1.71374735824108[/C][/ROW]
[ROW][C]19[/C][C]112.9[/C][C]111.970317980252[/C][C]0.929682019747777[/C][/ROW]
[ROW][C]20[/C][C]102[/C][C]100.254142103958[/C][C]1.74585789604242[/C][/ROW]
[ROW][C]21[/C][C]106[/C][C]104.711347447797[/C][C]1.28865255220274[/C][/ROW]
[ROW][C]22[/C][C]105.3[/C][C]105.105669734125[/C][C]0.194330265875441[/C][/ROW]
[ROW][C]23[/C][C]118.8[/C][C]117.704643093038[/C][C]1.09535690696214[/C][/ROW]
[ROW][C]24[/C][C]106.1[/C][C]107.056143045621[/C][C]-0.95614304562059[/C][/ROW]
[ROW][C]25[/C][C]109.3[/C][C]107.756313730023[/C][C]1.54368626997676[/C][/ROW]
[ROW][C]26[/C][C]117.2[/C][C]117.059218651778[/C][C]0.140781348221566[/C][/ROW]
[ROW][C]27[/C][C]92.5[/C][C]90.9501424505017[/C][C]1.54985754949833[/C][/ROW]
[ROW][C]28[/C][C]104.2[/C][C]103.906954916288[/C][C]0.293045083711955[/C][/ROW]
[ROW][C]29[/C][C]112.5[/C][C]113.470401229774[/C][C]-0.97040122977434[/C][/ROW]
[ROW][C]30[/C][C]122.4[/C][C]121.336349185140[/C][C]1.06365081486016[/C][/ROW]
[ROW][C]31[/C][C]113.3[/C][C]111.261712057534[/C][C]2.03828794246650[/C][/ROW]
[ROW][C]32[/C][C]100[/C][C]98.455034048103[/C][C]1.54496595189709[/C][/ROW]
[ROW][C]33[/C][C]110.7[/C][C]107.035061756531[/C][C]3.66493824346855[/C][/ROW]
[ROW][C]34[/C][C]112.8[/C][C]109.431741662475[/C][C]3.36825833752521[/C][/ROW]
[ROW][C]35[/C][C]109.8[/C][C]112.936567011979[/C][C]-3.13656701197929[/C][/ROW]
[ROW][C]36[/C][C]117.3[/C][C]116.335105235101[/C][C]0.964894764898578[/C][/ROW]
[ROW][C]37[/C][C]109.1[/C][C]110.614333363003[/C][C]-1.51433336300331[/C][/ROW]
[ROW][C]38[/C][C]115.9[/C][C]117.770046593989[/C][C]-1.87004659398874[/C][/ROW]
[ROW][C]39[/C][C]96[/C][C]98.5561600206925[/C][C]-2.55616002069252[/C][/ROW]
[ROW][C]40[/C][C]99.8[/C][C]100.112444377040[/C][C]-0.312444377039719[/C][/ROW]
[ROW][C]41[/C][C]116.8[/C][C]117.809098790765[/C][C]-1.00909879076508[/C][/ROW]
[ROW][C]42[/C][C]115.7[/C][C]118.305802811152[/C][C]-2.60580281115219[/C][/ROW]
[ROW][C]43[/C][C]99.4[/C][C]99.2859780031238[/C][C]0.114021996876163[/C][/ROW]
[ROW][C]44[/C][C]94.3[/C][C]93.9611795496069[/C][C]0.338820450393129[/C][/ROW]
[ROW][C]45[/C][C]91[/C][C]91.272954312284[/C][C]-0.272954312284033[/C][/ROW]
[ROW][C]46[/C][C]93.2[/C][C]93.0641278980282[/C][C]0.135872101971792[/C][/ROW]
[ROW][C]47[/C][C]103.1[/C][C]101.442539716013[/C][C]1.65746028398668[/C][/ROW]
[ROW][C]48[/C][C]94.1[/C][C]95.3574182556736[/C][C]-1.25741825567361[/C][/ROW]
[ROW][C]49[/C][C]91.8[/C][C]90.7601338544058[/C][C]1.03986614559418[/C][/ROW]
[ROW][C]50[/C][C]102.7[/C][C]101.816641862875[/C][C]0.883358137125183[/C][/ROW]
[ROW][C]51[/C][C]82.6[/C][C]77.7925148037581[/C][C]4.80748519624186[/C][/ROW]
[ROW][C]52[/C][C]89.1[/C][C]87.0227449088026[/C][C]2.07725509119740[/C][/ROW]
[ROW][C]53[/C][C]104.5[/C][C]102.740564710713[/C][C]1.7594352892875[/C][/ROW]
[ROW][C]54[/C][C]105.1[/C][C]102.198256218162[/C][C]2.90174378183820[/C][/ROW]
[ROW][C]55[/C][C]95.1[/C][C]98.2349178145566[/C][C]-3.13491781455658[/C][/ROW]
[ROW][C]56[/C][C]88.7[/C][C]93.4960637543313[/C][C]-4.79606375433127[/C][/ROW]
[ROW][C]57[/C][C]86.3[/C][C]90.707720803822[/C][C]-4.40772080382195[/C][/ROW]
[ROW][C]58[/C][C]91.8[/C][C]92.9362437319962[/C][C]-1.13624373199622[/C][/ROW]
[ROW][C]59[/C][C]111.5[/C][C]109.125647104114[/C][C]2.37435289588578[/C][/ROW]
[ROW][C]60[/C][C]99.7[/C][C]97.3659035478098[/C][C]2.33409645219025[/C][/ROW]
[ROW][C]61[/C][C]97.5[/C][C]97.7869105707743[/C][C]-0.286910570774256[/C][/ROW]
[ROW][C]62[/C][C]111.7[/C][C]114.452469115900[/C][C]-2.75246911590038[/C][/ROW]
[ROW][C]63[/C][C]86.2[/C][C]85.7028800728993[/C][C]0.497119927100712[/C][/ROW]
[ROW][C]64[/C][C]95.4[/C][C]97.8517458656015[/C][C]-2.45174586560146[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112241&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112241&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
198.1101.053376683421-2.95337668342122
2113.9110.6962963686653.20370363133496
380.984.274639708616-3.37463970861609
495.796.1791698417413-0.479169841741336
5113.2113.938209086353-0.738209086352653
6105.9108.973339143787-3.07333914378726
7108.8108.7470741445340.0529258554661365
8102.3101.1335805440011.16641945599863
99999.2729156795653-0.272915679565312
10100.7103.262216973376-2.56221697337623
11115.5117.490603074855-1.99060307485530
12100.7101.785429915795-1.08542991579462
13109.9107.7289317983722.17106820162784
14114.6114.2053274067930.394672593207415
1585.486.3236629435323-0.923662943532304
16100.599.62694009052680.873059909473157
17114.8113.8417261823950.958273817604575
18116.5114.7862526417591.71374735824108
19112.9111.9703179802520.929682019747777
20102100.2541421039581.74585789604242
21106104.7113474477971.28865255220274
22105.3105.1056697341250.194330265875441
23118.8117.7046430930381.09535690696214
24106.1107.056143045621-0.95614304562059
25109.3107.7563137300231.54368626997676
26117.2117.0592186517780.140781348221566
2792.590.95014245050171.54985754949833
28104.2103.9069549162880.293045083711955
29112.5113.470401229774-0.97040122977434
30122.4121.3363491851401.06365081486016
31113.3111.2617120575342.03828794246650
3210098.4550340481031.54496595189709
33110.7107.0350617565313.66493824346855
34112.8109.4317416624753.36825833752521
35109.8112.936567011979-3.13656701197929
36117.3116.3351052351010.964894764898578
37109.1110.614333363003-1.51433336300331
38115.9117.770046593989-1.87004659398874
399698.5561600206925-2.55616002069252
4099.8100.112444377040-0.312444377039719
41116.8117.809098790765-1.00909879076508
42115.7118.305802811152-2.60580281115219
4399.499.28597800312380.114021996876163
4494.393.96117954960690.338820450393129
459191.272954312284-0.272954312284033
4693.293.06412789802820.135872101971792
47103.1101.4425397160131.65746028398668
4894.195.3574182556736-1.25741825567361
4991.890.76013385440581.03986614559418
50102.7101.8166418628750.883358137125183
5182.677.79251480375814.80748519624186
5289.187.02274490880262.07725509119740
53104.5102.7405647107131.7594352892875
54105.1102.1982562181622.90174378183820
5595.198.2349178145566-3.13491781455658
5688.793.4960637543313-4.79606375433127
5786.390.707720803822-4.40772080382195
5891.892.9362437319962-1.13624373199622
59111.5109.1256471041142.37435289588578
6099.797.36590354780982.33409645219025
6197.597.7869105707743-0.286910570774256
62111.7114.452469115900-2.75246911590038
6386.285.70288007289930.497119927100712
6495.497.8517458656015-2.45174586560146






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
230.06818188239119090.1363637647823820.93181811760881
240.05286865976486730.1057373195297350.947131340235133
250.02733187935396380.05466375870792750.972668120646036
260.1368906470370260.2737812940740520.863109352962974
270.0735057310764840.1470114621529680.926494268923516
280.05421738963025750.1084347792605150.945782610369743
290.0341553020903720.0683106041807440.965844697909628
300.01871487076485660.03742974152971320.981285129235143
310.01658847054899990.03317694109799990.983411529451
320.00954327589838760.01908655179677520.990456724101612
330.01556326044686500.03112652089373000.984436739553135
340.06323004249422350.1264600849884470.936769957505777
350.0520258634202190.1040517268404380.94797413657978
360.1153050525024180.2306101050048350.884694947497582
370.0763643773437170.1527287546874340.923635622656283
380.1269872144683630.2539744289367270.873012785531637
390.1057904003838840.2115808007677670.894209599616116
400.05552150503752010.1110430100750400.94447849496248
410.02787743877518580.05575487755037150.972122561224814

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
23 & 0.0681818823911909 & 0.136363764782382 & 0.93181811760881 \tabularnewline
24 & 0.0528686597648673 & 0.105737319529735 & 0.947131340235133 \tabularnewline
25 & 0.0273318793539638 & 0.0546637587079275 & 0.972668120646036 \tabularnewline
26 & 0.136890647037026 & 0.273781294074052 & 0.863109352962974 \tabularnewline
27 & 0.073505731076484 & 0.147011462152968 & 0.926494268923516 \tabularnewline
28 & 0.0542173896302575 & 0.108434779260515 & 0.945782610369743 \tabularnewline
29 & 0.034155302090372 & 0.068310604180744 & 0.965844697909628 \tabularnewline
30 & 0.0187148707648566 & 0.0374297415297132 & 0.981285129235143 \tabularnewline
31 & 0.0165884705489999 & 0.0331769410979999 & 0.983411529451 \tabularnewline
32 & 0.0095432758983876 & 0.0190865517967752 & 0.990456724101612 \tabularnewline
33 & 0.0155632604468650 & 0.0311265208937300 & 0.984436739553135 \tabularnewline
34 & 0.0632300424942235 & 0.126460084988447 & 0.936769957505777 \tabularnewline
35 & 0.052025863420219 & 0.104051726840438 & 0.94797413657978 \tabularnewline
36 & 0.115305052502418 & 0.230610105004835 & 0.884694947497582 \tabularnewline
37 & 0.076364377343717 & 0.152728754687434 & 0.923635622656283 \tabularnewline
38 & 0.126987214468363 & 0.253974428936727 & 0.873012785531637 \tabularnewline
39 & 0.105790400383884 & 0.211580800767767 & 0.894209599616116 \tabularnewline
40 & 0.0555215050375201 & 0.111043010075040 & 0.94447849496248 \tabularnewline
41 & 0.0278774387751858 & 0.0557548775503715 & 0.972122561224814 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112241&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]23[/C][C]0.0681818823911909[/C][C]0.136363764782382[/C][C]0.93181811760881[/C][/ROW]
[ROW][C]24[/C][C]0.0528686597648673[/C][C]0.105737319529735[/C][C]0.947131340235133[/C][/ROW]
[ROW][C]25[/C][C]0.0273318793539638[/C][C]0.0546637587079275[/C][C]0.972668120646036[/C][/ROW]
[ROW][C]26[/C][C]0.136890647037026[/C][C]0.273781294074052[/C][C]0.863109352962974[/C][/ROW]
[ROW][C]27[/C][C]0.073505731076484[/C][C]0.147011462152968[/C][C]0.926494268923516[/C][/ROW]
[ROW][C]28[/C][C]0.0542173896302575[/C][C]0.108434779260515[/C][C]0.945782610369743[/C][/ROW]
[ROW][C]29[/C][C]0.034155302090372[/C][C]0.068310604180744[/C][C]0.965844697909628[/C][/ROW]
[ROW][C]30[/C][C]0.0187148707648566[/C][C]0.0374297415297132[/C][C]0.981285129235143[/C][/ROW]
[ROW][C]31[/C][C]0.0165884705489999[/C][C]0.0331769410979999[/C][C]0.983411529451[/C][/ROW]
[ROW][C]32[/C][C]0.0095432758983876[/C][C]0.0190865517967752[/C][C]0.990456724101612[/C][/ROW]
[ROW][C]33[/C][C]0.0155632604468650[/C][C]0.0311265208937300[/C][C]0.984436739553135[/C][/ROW]
[ROW][C]34[/C][C]0.0632300424942235[/C][C]0.126460084988447[/C][C]0.936769957505777[/C][/ROW]
[ROW][C]35[/C][C]0.052025863420219[/C][C]0.104051726840438[/C][C]0.94797413657978[/C][/ROW]
[ROW][C]36[/C][C]0.115305052502418[/C][C]0.230610105004835[/C][C]0.884694947497582[/C][/ROW]
[ROW][C]37[/C][C]0.076364377343717[/C][C]0.152728754687434[/C][C]0.923635622656283[/C][/ROW]
[ROW][C]38[/C][C]0.126987214468363[/C][C]0.253974428936727[/C][C]0.873012785531637[/C][/ROW]
[ROW][C]39[/C][C]0.105790400383884[/C][C]0.211580800767767[/C][C]0.894209599616116[/C][/ROW]
[ROW][C]40[/C][C]0.0555215050375201[/C][C]0.111043010075040[/C][C]0.94447849496248[/C][/ROW]
[ROW][C]41[/C][C]0.0278774387751858[/C][C]0.0557548775503715[/C][C]0.972122561224814[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112241&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112241&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
230.06818188239119090.1363637647823820.93181811760881
240.05286865976486730.1057373195297350.947131340235133
250.02733187935396380.05466375870792750.972668120646036
260.1368906470370260.2737812940740520.863109352962974
270.0735057310764840.1470114621529680.926494268923516
280.05421738963025750.1084347792605150.945782610369743
290.0341553020903720.0683106041807440.965844697909628
300.01871487076485660.03742974152971320.981285129235143
310.01658847054899990.03317694109799990.983411529451
320.00954327589838760.01908655179677520.990456724101612
330.01556326044686500.03112652089373000.984436739553135
340.06323004249422350.1264600849884470.936769957505777
350.0520258634202190.1040517268404380.94797413657978
360.1153050525024180.2306101050048350.884694947497582
370.0763643773437170.1527287546874340.923635622656283
380.1269872144683630.2539744289367270.873012785531637
390.1057904003838840.2115808007677670.894209599616116
400.05552150503752010.1110430100750400.94447849496248
410.02787743877518580.05575487755037150.972122561224814






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.210526315789474NOK
10% type I error level70.368421052631579NOK

\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 & 4 & 0.210526315789474 & NOK \tabularnewline
10% type I error level & 7 & 0.368421052631579 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112241&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]4[/C][C]0.210526315789474[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.368421052631579[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112241&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112241&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 level40.210526315789474NOK
10% type I error level70.368421052631579NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 7
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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 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}