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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 19 Nov 2009 12:58:46 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258660805ydsoorzq3q97gpa.htm/, Retrieved Thu, 25 Apr 2024 10:42:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57921, Retrieved Thu, 25 Apr 2024 10:42:00 +0000

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User-defined keywords

Estimated Impact

145

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]
-   PD      [Multiple Regression] [] [2009-11-19 19:58:46] [24029b2c7217429de6ff94b5379eb52c] [Current]
-    D        [Multiple Regression] [] [2009-11-19 20:05:09] [5edbdb7a459c4059b6c3b063ba86821c] 

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Dataseries X:

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101.9	122.2	19	73	77.8	74.8	80.2
102	123.7	22	72	73	77.8	74.8
100.7	122.6	23	75.8	72	73	77.8
99	115.7	20	72.6	75.8	72	73
96.5	116.1	14	71.9	72.6	75.8	72
101.8	120.5	14	74.8	71.9	72.6	75.8
100.5	122.6	14	72.9	74.8	71.9	72.6
103.3	119.9	15	72.9	72.9	74.8	71.9
102.3	120.7	11	79.9	72.9	72.9	74.8
100.4	120.2	17	74	79.9	72.9	72.9
103	122.1	16	76	74	79.9	72.9
99	119.3	20	69.6	76	74	79.9
104.8	121.7	24	77.3	69.6	76	74
104.5	113.5	23	75.2	77.3	69.6	76
104.8	123.7	20	75.8	75.2	77.3	69.6
103.8	123.4	21	77.6	75.8	75.2	77.3
106.3	126.4	19	76.7	77.6	75.8	75.2
105.2	124.1	23	77	76.7	77.6	75.8
108.2	125.6	23	77.9	77	76.7	77.6
106.2	124.8	23	76.7	77.9	77	76.7
103.9	123	23	71.9	76.7	77.9	77
104.9	126.9	27	73.4	71.9	76.7	77.9
106.2	127.3	26	72.5	73.4	71.9	76.7
107.9	129	17	73.7	72.5	73.4	71.9
106.9	126.2	24	69.5	73.7	72.5	73.4
110.3	125.4	26	74.7	69.5	73.7	72.5
109.8	126.3	24	72.5	74.7	69.5	73.7
108.3	126.3	27	72.1	72.5	74.7	69.5
110.9	128.4	27	70.7	72.1	72.5	74.7
109.8	127.2	26	71.4	70.7	72.1	72.5
109.3	128.5	24	69.5	71.4	70.7	72.1
109	129	23	73.5	69.5	71.4	70.7
107.9	128.9	23	72.4	73.5	69.5	71.4
108.4	128.3	24	74.5	72.4	73.5	69.5
107.2	124.6	17	72.2	74.5	72.4	73.5
109.5	126.2	21	73	72.2	74.5	72.4
109.9	129.1	19	73.3	73	72.2	74.5
108	127.3	22	71.3	73.3	73	72.2
114.7	129.2	22	73.6	71.3	73.3	73
115.6	130.4	18	71.3	73.6	71.3	73.3
107.6	125.9	16	71.2	71.3	73.6	71.3
115.9	135.8	14	81.4	71.2	71.3	73.6
111.8	126.4	12	76.1	81.4	71.2	71.3
110	129.5	14	71.1	76.1	81.4	71.2
109.2	128.4	16	75.7	71.1	76.1	81.4
108	125.6	8	70	75.7	71.1	76.1
105.6	127.7	3	68.5	70	75.7	71.1
103	126.4	0	56.7	68.5	70	75.7
99.6	124.2	5	57.9	56.7	68.5	70
97.9	126.4	1	58.8	57.9	56.7	68.5
97.6	123.7	1	59.3	58.8	57.9	56.7
96.2	121.8	3	61.3	59.3	58.8	57.9
97.9	124	6	62.9	61.3	59.3	58.8
94.5	122.7	7	61.4	62.9	61.3	59.3
95.4	122.9	8	64.5	61.4	62.9	61.3
94.4	121	14	63.8	64.5	61.4	62.9
96.3	122.8	14	61.6	63.8	64.5	61.4
95.1	122.9	13	64.7	61.6	63.8	64.5

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57921&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57921&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57921&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 19.5447097642314 + 0.474153907672798totid[t] -0.087206661204238ndzcg[t] + 0.0461297131404523indc[t] + 0.163565208603853y1[t] + 0.115836079954216y2[t] -0.0837072924607436`y3 `[t] + 1.37117081963841M1[t] + 1.77995473905426M2[t] + 2.41301826429556M3[t] + 2.28548228718490M4[t] + 2.58505687122945M5[t] + 4.83378346325529M6[t] + 3.70223862569186M7[t] + 3.24366895236396M8[t] + 4.85075294798024M9[t] + 4.14558329093348M10[t] + 3.09304594699270M11[t] -0.158704064647780t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  19.5447097642314 +  0.474153907672798totid[t] -0.087206661204238ndzcg[t] +  0.0461297131404523indc[t] +  0.163565208603853y1[t] +  0.115836079954216y2[t] -0.0837072924607436`y3
`[t] +  1.37117081963841M1[t] +  1.77995473905426M2[t] +  2.41301826429556M3[t] +  2.28548228718490M4[t] +  2.58505687122945M5[t] +  4.83378346325529M6[t] +  3.70223862569186M7[t] +  3.24366895236396M8[t] +  4.85075294798024M9[t] +  4.14558329093348M10[t] +  3.09304594699270M11[t] -0.158704064647780t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57921&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  19.5447097642314 +  0.474153907672798totid[t] -0.087206661204238ndzcg[t] +  0.0461297131404523indc[t] +  0.163565208603853y1[t] +  0.115836079954216y2[t] -0.0837072924607436`y3
`[t] +  1.37117081963841M1[t] +  1.77995473905426M2[t] +  2.41301826429556M3[t] +  2.28548228718490M4[t] +  2.58505687122945M5[t] +  4.83378346325529M6[t] +  3.70223862569186M7[t] +  3.24366895236396M8[t] +  4.85075294798024M9[t] +  4.14558329093348M10[t] +  3.09304594699270M11[t] -0.158704064647780t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57921&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57921&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 19.5447097642314 + 0.474153907672798totid[t] -0.087206661204238ndzcg[t] + 0.0461297131404523indc[t] + 0.163565208603853y1[t] + 0.115836079954216y2[t] -0.0837072924607436`y3 `[t] + 1.37117081963841M1[t] + 1.77995473905426M2[t] + 2.41301826429556M3[t] + 2.28548228718490M4[t] + 2.58505687122945M5[t] + 4.83378346325529M6[t] + 3.70223862569186M7[t] + 3.24366895236396M8[t] + 4.85075294798024M9[t] + 4.14558329093348M10[t] + 3.09304594699270M11[t] -0.158704064647780t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	19.5447097642314	18.446578	1.0595	0.295876	0.147938
totid	0.474153907672798	0.184449	2.5707	0.014082	0.007041
ndzcg	-0.087206661204238	0.2114	-0.4125	0.682218	0.341109
indc	0.0461297131404523	0.079521	0.5801	0.565188	0.282594
y1	0.163565208603853	0.158225	1.0338	0.307619	0.15381
y2	0.115836079954216	0.148703	0.779	0.440696	0.220348
`y3 `	-0.0837072924607436	0.144047	-0.5811	0.564509	0.282254
M1	1.37117081963841	2.058699	0.666	0.509308	0.254654
M2	1.77995473905426	2.13776	0.8326	0.410126	0.205063
M3	2.41301826429556	2.175475	1.1092	0.274141	0.13707
M4	2.28548228718490	2.145102	1.0654	0.293228	0.146614
M5	2.58505687122945	2.081456	1.2419	0.221673	0.110837
M6	4.83378346325529	2.072542	2.3323	0.02494	0.01247
M7	3.70223862569186	2.094088	1.7679	0.084892	0.042446
M8	3.24366895236396	2.091477	1.5509	0.129004	0.064502
M9	4.85075294798024	2.0226	2.3983	0.021355	0.010677
M10	4.14558329093348	2.060549	2.0119	0.051177	0.025589
M11	3.09304594699270	2.13337	1.4498	0.155098	0.077549
t	-0.158704064647780	0.052286	-3.0353	0.004265	0.002133

\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) & 19.5447097642314 & 18.446578 & 1.0595 & 0.295876 & 0.147938 \tabularnewline
totid & 0.474153907672798 & 0.184449 & 2.5707 & 0.014082 & 0.007041 \tabularnewline
ndzcg & -0.087206661204238 & 0.2114 & -0.4125 & 0.682218 & 0.341109 \tabularnewline
indc & 0.0461297131404523 & 0.079521 & 0.5801 & 0.565188 & 0.282594 \tabularnewline
y1 & 0.163565208603853 & 0.158225 & 1.0338 & 0.307619 & 0.15381 \tabularnewline
y2 & 0.115836079954216 & 0.148703 & 0.779 & 0.440696 & 0.220348 \tabularnewline
`y3
` & -0.0837072924607436 & 0.144047 & -0.5811 & 0.564509 & 0.282254 \tabularnewline
M1 & 1.37117081963841 & 2.058699 & 0.666 & 0.509308 & 0.254654 \tabularnewline
M2 & 1.77995473905426 & 2.13776 & 0.8326 & 0.410126 & 0.205063 \tabularnewline
M3 & 2.41301826429556 & 2.175475 & 1.1092 & 0.274141 & 0.13707 \tabularnewline
M4 & 2.28548228718490 & 2.145102 & 1.0654 & 0.293228 & 0.146614 \tabularnewline
M5 & 2.58505687122945 & 2.081456 & 1.2419 & 0.221673 & 0.110837 \tabularnewline
M6 & 4.83378346325529 & 2.072542 & 2.3323 & 0.02494 & 0.01247 \tabularnewline
M7 & 3.70223862569186 & 2.094088 & 1.7679 & 0.084892 & 0.042446 \tabularnewline
M8 & 3.24366895236396 & 2.091477 & 1.5509 & 0.129004 & 0.064502 \tabularnewline
M9 & 4.85075294798024 & 2.0226 & 2.3983 & 0.021355 & 0.010677 \tabularnewline
M10 & 4.14558329093348 & 2.060549 & 2.0119 & 0.051177 & 0.025589 \tabularnewline
M11 & 3.09304594699270 & 2.13337 & 1.4498 & 0.155098 & 0.077549 \tabularnewline
t & -0.158704064647780 & 0.052286 & -3.0353 & 0.004265 & 0.002133 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57921&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]19.5447097642314[/C][C]18.446578[/C][C]1.0595[/C][C]0.295876[/C][C]0.147938[/C][/ROW]
[ROW][C]totid[/C][C]0.474153907672798[/C][C]0.184449[/C][C]2.5707[/C][C]0.014082[/C][C]0.007041[/C][/ROW]
[ROW][C]ndzcg[/C][C]-0.087206661204238[/C][C]0.2114[/C][C]-0.4125[/C][C]0.682218[/C][C]0.341109[/C][/ROW]
[ROW][C]indc[/C][C]0.0461297131404523[/C][C]0.079521[/C][C]0.5801[/C][C]0.565188[/C][C]0.282594[/C][/ROW]
[ROW][C]y1[/C][C]0.163565208603853[/C][C]0.158225[/C][C]1.0338[/C][C]0.307619[/C][C]0.15381[/C][/ROW]
[ROW][C]y2[/C][C]0.115836079954216[/C][C]0.148703[/C][C]0.779[/C][C]0.440696[/C][C]0.220348[/C][/ROW]
[ROW][C]`y3
`[/C][C]-0.0837072924607436[/C][C]0.144047[/C][C]-0.5811[/C][C]0.564509[/C][C]0.282254[/C][/ROW]
[ROW][C]M1[/C][C]1.37117081963841[/C][C]2.058699[/C][C]0.666[/C][C]0.509308[/C][C]0.254654[/C][/ROW]
[ROW][C]M2[/C][C]1.77995473905426[/C][C]2.13776[/C][C]0.8326[/C][C]0.410126[/C][C]0.205063[/C][/ROW]
[ROW][C]M3[/C][C]2.41301826429556[/C][C]2.175475[/C][C]1.1092[/C][C]0.274141[/C][C]0.13707[/C][/ROW]
[ROW][C]M4[/C][C]2.28548228718490[/C][C]2.145102[/C][C]1.0654[/C][C]0.293228[/C][C]0.146614[/C][/ROW]
[ROW][C]M5[/C][C]2.58505687122945[/C][C]2.081456[/C][C]1.2419[/C][C]0.221673[/C][C]0.110837[/C][/ROW]
[ROW][C]M6[/C][C]4.83378346325529[/C][C]2.072542[/C][C]2.3323[/C][C]0.02494[/C][C]0.01247[/C][/ROW]
[ROW][C]M7[/C][C]3.70223862569186[/C][C]2.094088[/C][C]1.7679[/C][C]0.084892[/C][C]0.042446[/C][/ROW]
[ROW][C]M8[/C][C]3.24366895236396[/C][C]2.091477[/C][C]1.5509[/C][C]0.129004[/C][C]0.064502[/C][/ROW]
[ROW][C]M9[/C][C]4.85075294798024[/C][C]2.0226[/C][C]2.3983[/C][C]0.021355[/C][C]0.010677[/C][/ROW]
[ROW][C]M10[/C][C]4.14558329093348[/C][C]2.060549[/C][C]2.0119[/C][C]0.051177[/C][C]0.025589[/C][/ROW]
[ROW][C]M11[/C][C]3.09304594699270[/C][C]2.13337[/C][C]1.4498[/C][C]0.155098[/C][C]0.077549[/C][/ROW]
[ROW][C]t[/C][C]-0.158704064647780[/C][C]0.052286[/C][C]-3.0353[/C][C]0.004265[/C][C]0.002133[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57921&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57921&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	19.5447097642314	18.446578	1.0595	0.295876	0.147938
totid	0.474153907672798	0.184449	2.5707	0.014082	0.007041
ndzcg	-0.087206661204238	0.2114	-0.4125	0.682218	0.341109
indc	0.0461297131404523	0.079521	0.5801	0.565188	0.282594
y1	0.163565208603853	0.158225	1.0338	0.307619	0.15381
y2	0.115836079954216	0.148703	0.779	0.440696	0.220348
`y3 `	-0.0837072924607436	0.144047	-0.5811	0.564509	0.282254
M1	1.37117081963841	2.058699	0.666	0.509308	0.254654
M2	1.77995473905426	2.13776	0.8326	0.410126	0.205063
M3	2.41301826429556	2.175475	1.1092	0.274141	0.13707
M4	2.28548228718490	2.145102	1.0654	0.293228	0.146614
M5	2.58505687122945	2.081456	1.2419	0.221673	0.110837
M6	4.83378346325529	2.072542	2.3323	0.02494	0.01247
M7	3.70223862569186	2.094088	1.7679	0.084892	0.042446
M8	3.24366895236396	2.091477	1.5509	0.129004	0.064502
M9	4.85075294798024	2.0226	2.3983	0.021355	0.010677
M10	4.14558329093348	2.060549	2.0119	0.051177	0.025589
M11	3.09304594699270	2.13337	1.4498	0.155098	0.077549
t	-0.158704064647780	0.052286	-3.0353	0.004265	0.002133

Multiple Linear Regression - Regression Statistics
Multiple R	0.900418032880322
R-squared	0.810752633936068
Adjusted R-squared	0.723407695752715
F-TEST (value)	9.28219368859303
F-TEST (DF numerator)	18
F-TEST (DF denominator)	39
p-value	4.30708124721235e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.9653736316565
Sum Squared Residuals	342.944190237623

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.900418032880322 \tabularnewline
R-squared & 0.810752633936068 \tabularnewline
Adjusted R-squared & 0.723407695752715 \tabularnewline
F-TEST (value) & 9.28219368859303 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 39 \tabularnewline
p-value & 4.30708124721235e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.9653736316565 \tabularnewline
Sum Squared Residuals & 342.944190237623 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57921&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.900418032880322[/C][/ROW]
[ROW][C]R-squared[/C][C]0.810752633936068[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.723407695752715[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.28219368859303[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]39[/C][/ROW]
[ROW][C]p-value[/C][C]4.30708124721235e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.9653736316565[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]342.944190237623[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57921&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57921&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.900418032880322
R-squared	0.810752633936068
Adjusted R-squared	0.723407695752715
F-TEST (value)	9.28219368859303
F-TEST (DF numerator)	18
F-TEST (DF denominator)	39
p-value	4.30708124721235e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.9653736316565
Sum Squared Residuals	342.944190237623

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	73	73.9698574161942	-0.969857416194245
2	72	74.2893464271969	-2.28934642719692
3	75.8	73.3186625785146	2.48133742148541
4	72.6	73.5972044331523	-0.99720443315227
5	71.9	72.241504968797	-0.341504968797058
6	74.8	75.6575750843153	-0.857575084315297
7	72.9	74.2289092984581	-1.32890929845814
8	72.9	75.3046000406006	-2.40460004060062
9	79.9	75.561702182322	4.33829781767805
10	74	75.4213179613963	-1.42131796139633
11	76	75.0768721722453	0.923127827754687
12	69.6	69.4149505321	0.185049467900048
13	77.3	73.0314606676266	4.26853933237341
14	75.2	74.1589458684483	1.04105413155168
15	75.8	74.8318319669672	0.968168033032772
16	77.6	73.3540669343484	4.24593306565155
17	76.7	74.8661471506606	1.83385284933938
18	77	76.8307664336278	0.169233566372159
19	77.9	76.6263132268217	1.27368677317832
20	76.7	75.3877930774082	1.31220692259180
21	71.9	75.7854530447928	-3.88545304479284
22	73.4	74.2406932441782	-0.840693244178151
23	72.5	73.3546229180205	-0.854622918020528
24	73.7	70.773856243112	2.92614375688805
25	69.5	72.0457205734595	-2.54572057345952
26	74.7	73.597314452583	1.10268554741705
27	72.5	73.9274303359548	-1.42743033595479
28	72.1	73.6624233572771	-1.56242335727708
29	70.7	74.0974166679575	-3.39741666795754
30	71.4	75.6332184965867	-4.2332184965867
31	69.5	73.8860726057638	-4.38607260576378
32	73.5	72.9243212208094	0.575678779190643
33	72.4	74.2354296972381	-1.83542969723809
34	74.5	74.149553085271	0.350446914729022
35	72.2	72.250321722223	-0.0503217222230579
36	73	70.0932477026869	2.90675229731315
37	73.3	70.8388611457942	2.46113885420577
38	71.3	70.8176749047772	0.482325095222791
39	73.6	73.9438274633004	-0.343827463300385
40	71.3	73.9145747245828	-2.61457472458277
41	71.2	70.6200221207626	0.579977879237361
42	81.4	75.2146104322073	6.18538956779272
43	76.1	74.5571219900001	1.54287800999987
44	71.1	73.2312931339398	-2.13129313393983
45	75.7	72.2029650424995	3.49703495750055
46	70	71.2621157876944	-1.26211578769439
47	68.5	68.5181831875111	-0.0181831875111015
48	56.7	62.7179455221013	-6.01794552210127
49	57.9	61.1141001969254	-3.21410019692541
50	58.8	59.1367183469946	-0.336718346994596
51	59.3	60.978247655263	-1.67824765526301
52	61.3	60.3717305506394	0.92826944936056
53	62.9	61.5749090918221	1.32509090817785
54	61.4	62.6638295532629	-1.26382955326288
55	64.5	61.6015828789563	2.89841712104373
56	63.8	61.151992527242	2.64800747275801
57	61.6	63.7144500331477	-2.11445003314766
58	64.7	61.5263199214601	3.17368007853985

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 73 & 73.9698574161942 & -0.969857416194245 \tabularnewline
2 & 72 & 74.2893464271969 & -2.28934642719692 \tabularnewline
3 & 75.8 & 73.3186625785146 & 2.48133742148541 \tabularnewline
4 & 72.6 & 73.5972044331523 & -0.99720443315227 \tabularnewline
5 & 71.9 & 72.241504968797 & -0.341504968797058 \tabularnewline
6 & 74.8 & 75.6575750843153 & -0.857575084315297 \tabularnewline
7 & 72.9 & 74.2289092984581 & -1.32890929845814 \tabularnewline
8 & 72.9 & 75.3046000406006 & -2.40460004060062 \tabularnewline
9 & 79.9 & 75.561702182322 & 4.33829781767805 \tabularnewline
10 & 74 & 75.4213179613963 & -1.42131796139633 \tabularnewline
11 & 76 & 75.0768721722453 & 0.923127827754687 \tabularnewline
12 & 69.6 & 69.4149505321 & 0.185049467900048 \tabularnewline
13 & 77.3 & 73.0314606676266 & 4.26853933237341 \tabularnewline
14 & 75.2 & 74.1589458684483 & 1.04105413155168 \tabularnewline
15 & 75.8 & 74.8318319669672 & 0.968168033032772 \tabularnewline
16 & 77.6 & 73.3540669343484 & 4.24593306565155 \tabularnewline
17 & 76.7 & 74.8661471506606 & 1.83385284933938 \tabularnewline
18 & 77 & 76.8307664336278 & 0.169233566372159 \tabularnewline
19 & 77.9 & 76.6263132268217 & 1.27368677317832 \tabularnewline
20 & 76.7 & 75.3877930774082 & 1.31220692259180 \tabularnewline
21 & 71.9 & 75.7854530447928 & -3.88545304479284 \tabularnewline
22 & 73.4 & 74.2406932441782 & -0.840693244178151 \tabularnewline
23 & 72.5 & 73.3546229180205 & -0.854622918020528 \tabularnewline
24 & 73.7 & 70.773856243112 & 2.92614375688805 \tabularnewline
25 & 69.5 & 72.0457205734595 & -2.54572057345952 \tabularnewline
26 & 74.7 & 73.597314452583 & 1.10268554741705 \tabularnewline
27 & 72.5 & 73.9274303359548 & -1.42743033595479 \tabularnewline
28 & 72.1 & 73.6624233572771 & -1.56242335727708 \tabularnewline
29 & 70.7 & 74.0974166679575 & -3.39741666795754 \tabularnewline
30 & 71.4 & 75.6332184965867 & -4.2332184965867 \tabularnewline
31 & 69.5 & 73.8860726057638 & -4.38607260576378 \tabularnewline
32 & 73.5 & 72.9243212208094 & 0.575678779190643 \tabularnewline
33 & 72.4 & 74.2354296972381 & -1.83542969723809 \tabularnewline
34 & 74.5 & 74.149553085271 & 0.350446914729022 \tabularnewline
35 & 72.2 & 72.250321722223 & -0.0503217222230579 \tabularnewline
36 & 73 & 70.0932477026869 & 2.90675229731315 \tabularnewline
37 & 73.3 & 70.8388611457942 & 2.46113885420577 \tabularnewline
38 & 71.3 & 70.8176749047772 & 0.482325095222791 \tabularnewline
39 & 73.6 & 73.9438274633004 & -0.343827463300385 \tabularnewline
40 & 71.3 & 73.9145747245828 & -2.61457472458277 \tabularnewline
41 & 71.2 & 70.6200221207626 & 0.579977879237361 \tabularnewline
42 & 81.4 & 75.2146104322073 & 6.18538956779272 \tabularnewline
43 & 76.1 & 74.5571219900001 & 1.54287800999987 \tabularnewline
44 & 71.1 & 73.2312931339398 & -2.13129313393983 \tabularnewline
45 & 75.7 & 72.2029650424995 & 3.49703495750055 \tabularnewline
46 & 70 & 71.2621157876944 & -1.26211578769439 \tabularnewline
47 & 68.5 & 68.5181831875111 & -0.0181831875111015 \tabularnewline
48 & 56.7 & 62.7179455221013 & -6.01794552210127 \tabularnewline
49 & 57.9 & 61.1141001969254 & -3.21410019692541 \tabularnewline
50 & 58.8 & 59.1367183469946 & -0.336718346994596 \tabularnewline
51 & 59.3 & 60.978247655263 & -1.67824765526301 \tabularnewline
52 & 61.3 & 60.3717305506394 & 0.92826944936056 \tabularnewline
53 & 62.9 & 61.5749090918221 & 1.32509090817785 \tabularnewline
54 & 61.4 & 62.6638295532629 & -1.26382955326288 \tabularnewline
55 & 64.5 & 61.6015828789563 & 2.89841712104373 \tabularnewline
56 & 63.8 & 61.151992527242 & 2.64800747275801 \tabularnewline
57 & 61.6 & 63.7144500331477 & -2.11445003314766 \tabularnewline
58 & 64.7 & 61.5263199214601 & 3.17368007853985 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57921&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]73[/C][C]73.9698574161942[/C][C]-0.969857416194245[/C][/ROW]
[ROW][C]2[/C][C]72[/C][C]74.2893464271969[/C][C]-2.28934642719692[/C][/ROW]
[ROW][C]3[/C][C]75.8[/C][C]73.3186625785146[/C][C]2.48133742148541[/C][/ROW]
[ROW][C]4[/C][C]72.6[/C][C]73.5972044331523[/C][C]-0.99720443315227[/C][/ROW]
[ROW][C]5[/C][C]71.9[/C][C]72.241504968797[/C][C]-0.341504968797058[/C][/ROW]
[ROW][C]6[/C][C]74.8[/C][C]75.6575750843153[/C][C]-0.857575084315297[/C][/ROW]
[ROW][C]7[/C][C]72.9[/C][C]74.2289092984581[/C][C]-1.32890929845814[/C][/ROW]
[ROW][C]8[/C][C]72.9[/C][C]75.3046000406006[/C][C]-2.40460004060062[/C][/ROW]
[ROW][C]9[/C][C]79.9[/C][C]75.561702182322[/C][C]4.33829781767805[/C][/ROW]
[ROW][C]10[/C][C]74[/C][C]75.4213179613963[/C][C]-1.42131796139633[/C][/ROW]
[ROW][C]11[/C][C]76[/C][C]75.0768721722453[/C][C]0.923127827754687[/C][/ROW]
[ROW][C]12[/C][C]69.6[/C][C]69.4149505321[/C][C]0.185049467900048[/C][/ROW]
[ROW][C]13[/C][C]77.3[/C][C]73.0314606676266[/C][C]4.26853933237341[/C][/ROW]
[ROW][C]14[/C][C]75.2[/C][C]74.1589458684483[/C][C]1.04105413155168[/C][/ROW]
[ROW][C]15[/C][C]75.8[/C][C]74.8318319669672[/C][C]0.968168033032772[/C][/ROW]
[ROW][C]16[/C][C]77.6[/C][C]73.3540669343484[/C][C]4.24593306565155[/C][/ROW]
[ROW][C]17[/C][C]76.7[/C][C]74.8661471506606[/C][C]1.83385284933938[/C][/ROW]
[ROW][C]18[/C][C]77[/C][C]76.8307664336278[/C][C]0.169233566372159[/C][/ROW]
[ROW][C]19[/C][C]77.9[/C][C]76.6263132268217[/C][C]1.27368677317832[/C][/ROW]
[ROW][C]20[/C][C]76.7[/C][C]75.3877930774082[/C][C]1.31220692259180[/C][/ROW]
[ROW][C]21[/C][C]71.9[/C][C]75.7854530447928[/C][C]-3.88545304479284[/C][/ROW]
[ROW][C]22[/C][C]73.4[/C][C]74.2406932441782[/C][C]-0.840693244178151[/C][/ROW]
[ROW][C]23[/C][C]72.5[/C][C]73.3546229180205[/C][C]-0.854622918020528[/C][/ROW]
[ROW][C]24[/C][C]73.7[/C][C]70.773856243112[/C][C]2.92614375688805[/C][/ROW]
[ROW][C]25[/C][C]69.5[/C][C]72.0457205734595[/C][C]-2.54572057345952[/C][/ROW]
[ROW][C]26[/C][C]74.7[/C][C]73.597314452583[/C][C]1.10268554741705[/C][/ROW]
[ROW][C]27[/C][C]72.5[/C][C]73.9274303359548[/C][C]-1.42743033595479[/C][/ROW]
[ROW][C]28[/C][C]72.1[/C][C]73.6624233572771[/C][C]-1.56242335727708[/C][/ROW]
[ROW][C]29[/C][C]70.7[/C][C]74.0974166679575[/C][C]-3.39741666795754[/C][/ROW]
[ROW][C]30[/C][C]71.4[/C][C]75.6332184965867[/C][C]-4.2332184965867[/C][/ROW]
[ROW][C]31[/C][C]69.5[/C][C]73.8860726057638[/C][C]-4.38607260576378[/C][/ROW]
[ROW][C]32[/C][C]73.5[/C][C]72.9243212208094[/C][C]0.575678779190643[/C][/ROW]
[ROW][C]33[/C][C]72.4[/C][C]74.2354296972381[/C][C]-1.83542969723809[/C][/ROW]
[ROW][C]34[/C][C]74.5[/C][C]74.149553085271[/C][C]0.350446914729022[/C][/ROW]
[ROW][C]35[/C][C]72.2[/C][C]72.250321722223[/C][C]-0.0503217222230579[/C][/ROW]
[ROW][C]36[/C][C]73[/C][C]70.0932477026869[/C][C]2.90675229731315[/C][/ROW]
[ROW][C]37[/C][C]73.3[/C][C]70.8388611457942[/C][C]2.46113885420577[/C][/ROW]
[ROW][C]38[/C][C]71.3[/C][C]70.8176749047772[/C][C]0.482325095222791[/C][/ROW]
[ROW][C]39[/C][C]73.6[/C][C]73.9438274633004[/C][C]-0.343827463300385[/C][/ROW]
[ROW][C]40[/C][C]71.3[/C][C]73.9145747245828[/C][C]-2.61457472458277[/C][/ROW]
[ROW][C]41[/C][C]71.2[/C][C]70.6200221207626[/C][C]0.579977879237361[/C][/ROW]
[ROW][C]42[/C][C]81.4[/C][C]75.2146104322073[/C][C]6.18538956779272[/C][/ROW]
[ROW][C]43[/C][C]76.1[/C][C]74.5571219900001[/C][C]1.54287800999987[/C][/ROW]
[ROW][C]44[/C][C]71.1[/C][C]73.2312931339398[/C][C]-2.13129313393983[/C][/ROW]
[ROW][C]45[/C][C]75.7[/C][C]72.2029650424995[/C][C]3.49703495750055[/C][/ROW]
[ROW][C]46[/C][C]70[/C][C]71.2621157876944[/C][C]-1.26211578769439[/C][/ROW]
[ROW][C]47[/C][C]68.5[/C][C]68.5181831875111[/C][C]-0.0181831875111015[/C][/ROW]
[ROW][C]48[/C][C]56.7[/C][C]62.7179455221013[/C][C]-6.01794552210127[/C][/ROW]
[ROW][C]49[/C][C]57.9[/C][C]61.1141001969254[/C][C]-3.21410019692541[/C][/ROW]
[ROW][C]50[/C][C]58.8[/C][C]59.1367183469946[/C][C]-0.336718346994596[/C][/ROW]
[ROW][C]51[/C][C]59.3[/C][C]60.978247655263[/C][C]-1.67824765526301[/C][/ROW]
[ROW][C]52[/C][C]61.3[/C][C]60.3717305506394[/C][C]0.92826944936056[/C][/ROW]
[ROW][C]53[/C][C]62.9[/C][C]61.5749090918221[/C][C]1.32509090817785[/C][/ROW]
[ROW][C]54[/C][C]61.4[/C][C]62.6638295532629[/C][C]-1.26382955326288[/C][/ROW]
[ROW][C]55[/C][C]64.5[/C][C]61.6015828789563[/C][C]2.89841712104373[/C][/ROW]
[ROW][C]56[/C][C]63.8[/C][C]61.151992527242[/C][C]2.64800747275801[/C][/ROW]
[ROW][C]57[/C][C]61.6[/C][C]63.7144500331477[/C][C]-2.11445003314766[/C][/ROW]
[ROW][C]58[/C][C]64.7[/C][C]61.5263199214601[/C][C]3.17368007853985[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57921&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57921&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	73	73.9698574161942	-0.969857416194245
2	72	74.2893464271969	-2.28934642719692
3	75.8	73.3186625785146	2.48133742148541
4	72.6	73.5972044331523	-0.99720443315227
5	71.9	72.241504968797	-0.341504968797058
6	74.8	75.6575750843153	-0.857575084315297
7	72.9	74.2289092984581	-1.32890929845814
8	72.9	75.3046000406006	-2.40460004060062
9	79.9	75.561702182322	4.33829781767805
10	74	75.4213179613963	-1.42131796139633
11	76	75.0768721722453	0.923127827754687
12	69.6	69.4149505321	0.185049467900048
13	77.3	73.0314606676266	4.26853933237341
14	75.2	74.1589458684483	1.04105413155168
15	75.8	74.8318319669672	0.968168033032772
16	77.6	73.3540669343484	4.24593306565155
17	76.7	74.8661471506606	1.83385284933938
18	77	76.8307664336278	0.169233566372159
19	77.9	76.6263132268217	1.27368677317832
20	76.7	75.3877930774082	1.31220692259180
21	71.9	75.7854530447928	-3.88545304479284
22	73.4	74.2406932441782	-0.840693244178151
23	72.5	73.3546229180205	-0.854622918020528
24	73.7	70.773856243112	2.92614375688805
25	69.5	72.0457205734595	-2.54572057345952
26	74.7	73.597314452583	1.10268554741705
27	72.5	73.9274303359548	-1.42743033595479
28	72.1	73.6624233572771	-1.56242335727708
29	70.7	74.0974166679575	-3.39741666795754
30	71.4	75.6332184965867	-4.2332184965867
31	69.5	73.8860726057638	-4.38607260576378
32	73.5	72.9243212208094	0.575678779190643
33	72.4	74.2354296972381	-1.83542969723809
34	74.5	74.149553085271	0.350446914729022
35	72.2	72.250321722223	-0.0503217222230579
36	73	70.0932477026869	2.90675229731315
37	73.3	70.8388611457942	2.46113885420577
38	71.3	70.8176749047772	0.482325095222791
39	73.6	73.9438274633004	-0.343827463300385
40	71.3	73.9145747245828	-2.61457472458277
41	71.2	70.6200221207626	0.579977879237361
42	81.4	75.2146104322073	6.18538956779272
43	76.1	74.5571219900001	1.54287800999987
44	71.1	73.2312931339398	-2.13129313393983
45	75.7	72.2029650424995	3.49703495750055
46	70	71.2621157876944	-1.26211578769439
47	68.5	68.5181831875111	-0.0181831875111015
48	56.7	62.7179455221013	-6.01794552210127
49	57.9	61.1141001969254	-3.21410019692541
50	58.8	59.1367183469946	-0.336718346994596
51	59.3	60.978247655263	-1.67824765526301
52	61.3	60.3717305506394	0.92826944936056
53	62.9	61.5749090918221	1.32509090817785
54	61.4	62.6638295532629	-1.26382955326288
55	64.5	61.6015828789563	2.89841712104373
56	63.8	61.151992527242	2.64800747275801
57	61.6	63.7144500331477	-2.11445003314766
58	64.7	61.5263199214601	3.17368007853985

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
22	0.665854023285714	0.668291953428572	0.334145976714286
23	0.534210096213425	0.93157980757315	0.465789903786575
24	0.463126610737488	0.926253221474975	0.536873389262512
25	0.422005942714747	0.844011885429494	0.577994057285253
26	0.314905449707632	0.629810899415263	0.685094550292368
27	0.334823666270497	0.669647332540993	0.665176333729503
28	0.283442596682463	0.566885193364925	0.716557403317537
29	0.251386965553277	0.502773931106555	0.748613034446723
30	0.195758530524326	0.391517061048653	0.804241469475674
31	0.248388918640582	0.496777837281165	0.751611081359418
32	0.201234190110752	0.402468380221503	0.798765809889249
33	0.159425690536585	0.318851381073171	0.840574309463414
34	0.174751971165059	0.349503942330118	0.825248028834941
35	0.385718076499658	0.771436152999317	0.614281923500342
36	0.274070415002665	0.548140830005331	0.725929584997335

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 & 0.665854023285714 & 0.668291953428572 & 0.334145976714286 \tabularnewline
23 & 0.534210096213425 & 0.93157980757315 & 0.465789903786575 \tabularnewline
24 & 0.463126610737488 & 0.926253221474975 & 0.536873389262512 \tabularnewline
25 & 0.422005942714747 & 0.844011885429494 & 0.577994057285253 \tabularnewline
26 & 0.314905449707632 & 0.629810899415263 & 0.685094550292368 \tabularnewline
27 & 0.334823666270497 & 0.669647332540993 & 0.665176333729503 \tabularnewline
28 & 0.283442596682463 & 0.566885193364925 & 0.716557403317537 \tabularnewline
29 & 0.251386965553277 & 0.502773931106555 & 0.748613034446723 \tabularnewline
30 & 0.195758530524326 & 0.391517061048653 & 0.804241469475674 \tabularnewline
31 & 0.248388918640582 & 0.496777837281165 & 0.751611081359418 \tabularnewline
32 & 0.201234190110752 & 0.402468380221503 & 0.798765809889249 \tabularnewline
33 & 0.159425690536585 & 0.318851381073171 & 0.840574309463414 \tabularnewline
34 & 0.174751971165059 & 0.349503942330118 & 0.825248028834941 \tabularnewline
35 & 0.385718076499658 & 0.771436152999317 & 0.614281923500342 \tabularnewline
36 & 0.274070415002665 & 0.548140830005331 & 0.725929584997335 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57921&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]22[/C][C]0.665854023285714[/C][C]0.668291953428572[/C][C]0.334145976714286[/C][/ROW]
[ROW][C]23[/C][C]0.534210096213425[/C][C]0.93157980757315[/C][C]0.465789903786575[/C][/ROW]
[ROW][C]24[/C][C]0.463126610737488[/C][C]0.926253221474975[/C][C]0.536873389262512[/C][/ROW]
[ROW][C]25[/C][C]0.422005942714747[/C][C]0.844011885429494[/C][C]0.577994057285253[/C][/ROW]
[ROW][C]26[/C][C]0.314905449707632[/C][C]0.629810899415263[/C][C]0.685094550292368[/C][/ROW]
[ROW][C]27[/C][C]0.334823666270497[/C][C]0.669647332540993[/C][C]0.665176333729503[/C][/ROW]
[ROW][C]28[/C][C]0.283442596682463[/C][C]0.566885193364925[/C][C]0.716557403317537[/C][/ROW]
[ROW][C]29[/C][C]0.251386965553277[/C][C]0.502773931106555[/C][C]0.748613034446723[/C][/ROW]
[ROW][C]30[/C][C]0.195758530524326[/C][C]0.391517061048653[/C][C]0.804241469475674[/C][/ROW]
[ROW][C]31[/C][C]0.248388918640582[/C][C]0.496777837281165[/C][C]0.751611081359418[/C][/ROW]
[ROW][C]32[/C][C]0.201234190110752[/C][C]0.402468380221503[/C][C]0.798765809889249[/C][/ROW]
[ROW][C]33[/C][C]0.159425690536585[/C][C]0.318851381073171[/C][C]0.840574309463414[/C][/ROW]
[ROW][C]34[/C][C]0.174751971165059[/C][C]0.349503942330118[/C][C]0.825248028834941[/C][/ROW]
[ROW][C]35[/C][C]0.385718076499658[/C][C]0.771436152999317[/C][C]0.614281923500342[/C][/ROW]
[ROW][C]36[/C][C]0.274070415002665[/C][C]0.548140830005331[/C][C]0.725929584997335[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57921&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57921&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
22	0.665854023285714	0.668291953428572	0.334145976714286
23	0.534210096213425	0.93157980757315	0.465789903786575
24	0.463126610737488	0.926253221474975	0.536873389262512
25	0.422005942714747	0.844011885429494	0.577994057285253
26	0.314905449707632	0.629810899415263	0.685094550292368
27	0.334823666270497	0.669647332540993	0.665176333729503
28	0.283442596682463	0.566885193364925	0.716557403317537
29	0.251386965553277	0.502773931106555	0.748613034446723
30	0.195758530524326	0.391517061048653	0.804241469475674
31	0.248388918640582	0.496777837281165	0.751611081359418
32	0.201234190110752	0.402468380221503	0.798765809889249
33	0.159425690536585	0.318851381073171	0.840574309463414
34	0.174751971165059	0.349503942330118	0.825248028834941
35	0.385718076499658	0.771436152999317	0.614281923500342
36	0.274070415002665	0.548140830005331	0.725929584997335

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\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=57921&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=57921&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57921&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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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

PNG link

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PDF link

Parameters (Session):

par1 = 4 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 4 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code