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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 15 Dec 2009 12:30:45 -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/Dec/15/t1260905550gjx70eiyrgq84dj.htm/, Retrieved Wed, 08 May 2024 08:32:09 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68088, Retrieved Wed, 08 May 2024 08:32:09 +0000

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Estimated Impact

129

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] [Multiple Regressi...] [2009-12-15 19:30:45] [0f1f1142419956a95ff6f880845f2408] [Current]

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Dataseries X:

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113,08	96,90	96,33
106,46	95,10	96,33
123,38	97,00	95,05
109,87	112,70	96,84
95,74	102,90	96,92
123,06	97,40	97,44
123,39	111,40	97,78
120,28	87,40	97,69
115,33	96,80	96,67
110,40	114,10	98,29
114,49	110,30	98,20
132,03	103,90	98,71
123,16	101,60	98,54
118,82	94,60	98,20
128,32	95,90	100,80
112,24	104,70	101,33
104,53	102,80	101,88
132,57	98,10	101,85
122,52	113,90	102,04
131,80	80,90	102,22
124,55	95,70	102,63
120,96	113,20	102,65
122,60	105,90	102,54
145,52	108,80	102,37
118,57	102,30	102,68
134,25	99,00	102,76
136,70	100,70	102,82
121,37	115,50	103,31
111,63	100,70	103,23
134,42	109,90	103,60
137,65	114,60	103,95
137,86	85,40	103,93
119,77	100,50	104,25
130,69	114,80	104,38
128,28	116,50	104,36
147,45	112,90	104,32
128,42	102,00	104,58
136,90	106,00	104,68
143,95	105,30	104,92
135,64	118,80	105,46
122,48	106,10	105,23
136,83	109,30	105,58
153,04	117,20	105,34
142,71	92,50	105,28
123,46	104,20	105,70
144,37	112,50	105,67
146,15	122,40	105,71
147,61	113,30	106,19
158,51	100,00	106,93
147,40	110,70	107,44
165,05	112,80	107,85
154,64	109,80	108,71
126,20	117,30	109,32
157,36	109,10	109,49
154,15	115,90	110,20
123,21	96,00	110,62
113,07	99,80	111,22
110,45	116,80	110,88
113,57	115,70	111,15
122,44	99,40	111,29
114,93	94,30	111,09
111,85	91,00	111,24
126,04	93,20	111,45
121,34	103,10	111,75

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68088&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]5 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=68088&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68088&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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Uitvoer[t] = + 363.352795622603 + 0.921775814575904TIP[t] -3.46317522752666cons[t] -4.25013298896887M1[t] -5.0907676742516M2[t] + 5.11786220743184M3[t] -13.9124269008515M4[t] -22.1881699736302M5[t] + 3.53402423404762M6[t] -4.37096560255769M7[t] + 11.9907496437046M8[t] -10.6140628334390M9[t] -20.2941710232316M10[t] -19.5489939039977M11[t] + 1.07177313261062t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Uitvoer[t] =  +  363.352795622603 +  0.921775814575904TIP[t] -3.46317522752666cons[t] -4.25013298896887M1[t] -5.0907676742516M2[t] +  5.11786220743184M3[t] -13.9124269008515M4[t] -22.1881699736302M5[t] +  3.53402423404762M6[t] -4.37096560255769M7[t] +  11.9907496437046M8[t] -10.6140628334390M9[t] -20.2941710232316M10[t] -19.5489939039977M11[t] +  1.07177313261062t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68088&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Uitvoer[t] =  +  363.352795622603 +  0.921775814575904TIP[t] -3.46317522752666cons[t] -4.25013298896887M1[t] -5.0907676742516M2[t] +  5.11786220743184M3[t] -13.9124269008515M4[t] -22.1881699736302M5[t] +  3.53402423404762M6[t] -4.37096560255769M7[t] +  11.9907496437046M8[t] -10.6140628334390M9[t] -20.2941710232316M10[t] -19.5489939039977M11[t] +  1.07177313261062t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68088&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68088&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
Uitvoer[t] = + 363.352795622603 + 0.921775814575904TIP[t] -3.46317522752666cons[t] -4.25013298896887M1[t] -5.0907676742516M2[t] + 5.11786220743184M3[t] -13.9124269008515M4[t] -22.1881699736302M5[t] + 3.53402423404762M6[t] -4.37096560255769M7[t] + 11.9907496437046M8[t] -10.6140628334390M9[t] -20.2941710232316M10[t] -19.5489939039977M11[t] + 1.07177313261062t + 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)	363.352795622603	175.09485	2.0752	0.043242	0.021621
TIP	0.921775814575904	0.295261	3.1219	0.003012	0.001506
cons	-3.46317522752666	1.691119	-2.0479	0.045953	0.022976
M1	-4.25013298896887	6.839843	-0.6214	0.537231	0.268615
M2	-5.0907676742516	6.893627	-0.7385	0.463749	0.231874
M3	5.11786220743184	6.73564	0.7598	0.451004	0.225502
M4	-13.9124269008515	6.603993	-2.1067	0.04029	0.020145
M5	-22.1881699736302	6.781261	-3.272	0.00196	0.00098
M6	3.53402423404762	6.795058	0.5201	0.605345	0.302672
M7	-4.37096560255769	7.217354	-0.6056	0.547563	0.273781
M8	11.9907496437046	8.630667	1.3893	0.171017	0.085509
M9	-10.6140628334390	7.096831	-1.4956	0.14117	0.070585
M10	-20.2941710232316	7.083564	-2.865	0.006126	0.003063
M11	-19.5489939039977	7.012533	-2.7877	0.007534	0.003767
t	1.07177313261062	0.425616	2.5182	0.015113	0.007557

\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) & 363.352795622603 & 175.09485 & 2.0752 & 0.043242 & 0.021621 \tabularnewline
TIP & 0.921775814575904 & 0.295261 & 3.1219 & 0.003012 & 0.001506 \tabularnewline
cons & -3.46317522752666 & 1.691119 & -2.0479 & 0.045953 & 0.022976 \tabularnewline
M1 & -4.25013298896887 & 6.839843 & -0.6214 & 0.537231 & 0.268615 \tabularnewline
M2 & -5.0907676742516 & 6.893627 & -0.7385 & 0.463749 & 0.231874 \tabularnewline
M3 & 5.11786220743184 & 6.73564 & 0.7598 & 0.451004 & 0.225502 \tabularnewline
M4 & -13.9124269008515 & 6.603993 & -2.1067 & 0.04029 & 0.020145 \tabularnewline
M5 & -22.1881699736302 & 6.781261 & -3.272 & 0.00196 & 0.00098 \tabularnewline
M6 & 3.53402423404762 & 6.795058 & 0.5201 & 0.605345 & 0.302672 \tabularnewline
M7 & -4.37096560255769 & 7.217354 & -0.6056 & 0.547563 & 0.273781 \tabularnewline
M8 & 11.9907496437046 & 8.630667 & 1.3893 & 0.171017 & 0.085509 \tabularnewline
M9 & -10.6140628334390 & 7.096831 & -1.4956 & 0.14117 & 0.070585 \tabularnewline
M10 & -20.2941710232316 & 7.083564 & -2.865 & 0.006126 & 0.003063 \tabularnewline
M11 & -19.5489939039977 & 7.012533 & -2.7877 & 0.007534 & 0.003767 \tabularnewline
t & 1.07177313261062 & 0.425616 & 2.5182 & 0.015113 & 0.007557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68088&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]363.352795622603[/C][C]175.09485[/C][C]2.0752[/C][C]0.043242[/C][C]0.021621[/C][/ROW]
[ROW][C]TIP[/C][C]0.921775814575904[/C][C]0.295261[/C][C]3.1219[/C][C]0.003012[/C][C]0.001506[/C][/ROW]
[ROW][C]cons[/C][C]-3.46317522752666[/C][C]1.691119[/C][C]-2.0479[/C][C]0.045953[/C][C]0.022976[/C][/ROW]
[ROW][C]M1[/C][C]-4.25013298896887[/C][C]6.839843[/C][C]-0.6214[/C][C]0.537231[/C][C]0.268615[/C][/ROW]
[ROW][C]M2[/C][C]-5.0907676742516[/C][C]6.893627[/C][C]-0.7385[/C][C]0.463749[/C][C]0.231874[/C][/ROW]
[ROW][C]M3[/C][C]5.11786220743184[/C][C]6.73564[/C][C]0.7598[/C][C]0.451004[/C][C]0.225502[/C][/ROW]
[ROW][C]M4[/C][C]-13.9124269008515[/C][C]6.603993[/C][C]-2.1067[/C][C]0.04029[/C][C]0.020145[/C][/ROW]
[ROW][C]M5[/C][C]-22.1881699736302[/C][C]6.781261[/C][C]-3.272[/C][C]0.00196[/C][C]0.00098[/C][/ROW]
[ROW][C]M6[/C][C]3.53402423404762[/C][C]6.795058[/C][C]0.5201[/C][C]0.605345[/C][C]0.302672[/C][/ROW]
[ROW][C]M7[/C][C]-4.37096560255769[/C][C]7.217354[/C][C]-0.6056[/C][C]0.547563[/C][C]0.273781[/C][/ROW]
[ROW][C]M8[/C][C]11.9907496437046[/C][C]8.630667[/C][C]1.3893[/C][C]0.171017[/C][C]0.085509[/C][/ROW]
[ROW][C]M9[/C][C]-10.6140628334390[/C][C]7.096831[/C][C]-1.4956[/C][C]0.14117[/C][C]0.070585[/C][/ROW]
[ROW][C]M10[/C][C]-20.2941710232316[/C][C]7.083564[/C][C]-2.865[/C][C]0.006126[/C][C]0.003063[/C][/ROW]
[ROW][C]M11[/C][C]-19.5489939039977[/C][C]7.012533[/C][C]-2.7877[/C][C]0.007534[/C][C]0.003767[/C][/ROW]
[ROW][C]t[/C][C]1.07177313261062[/C][C]0.425616[/C][C]2.5182[/C][C]0.015113[/C][C]0.007557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68088&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68088&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)	363.352795622603	175.09485	2.0752	0.043242	0.021621
TIP	0.921775814575904	0.295261	3.1219	0.003012	0.001506
cons	-3.46317522752666	1.691119	-2.0479	0.045953	0.022976
M1	-4.25013298896887	6.839843	-0.6214	0.537231	0.268615
M2	-5.0907676742516	6.893627	-0.7385	0.463749	0.231874
M3	5.11786220743184	6.73564	0.7598	0.451004	0.225502
M4	-13.9124269008515	6.603993	-2.1067	0.04029	0.020145
M5	-22.1881699736302	6.781261	-3.272	0.00196	0.00098
M6	3.53402423404762	6.795058	0.5201	0.605345	0.302672
M7	-4.37096560255769	7.217354	-0.6056	0.547563	0.273781
M8	11.9907496437046	8.630667	1.3893	0.171017	0.085509
M9	-10.6140628334390	7.096831	-1.4956	0.14117	0.070585
M10	-20.2941710232316	7.083564	-2.865	0.006126	0.003063
M11	-19.5489939039977	7.012533	-2.7877	0.007534	0.003767
t	1.07177313261062	0.425616	2.5182	0.015113	0.007557

Multiple Linear Regression - Regression Statistics
Multiple R	0.776726195507704
R-squared	0.603303582787871
Adjusted R-squared	0.489961749298692
F-TEST (value)	5.32286768455591
F-TEST (DF numerator)	14
F-TEST (DF denominator)	49
p-value	5.45203608637301e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.6353811503737
Sum Squared Residuals	5542.45527847249

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.776726195507704 \tabularnewline
R-squared & 0.603303582787871 \tabularnewline
Adjusted R-squared & 0.489961749298692 \tabularnewline
F-TEST (value) & 5.32286768455591 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 49 \tabularnewline
p-value & 5.45203608637301e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.6353811503737 \tabularnewline
Sum Squared Residuals & 5542.45527847249 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68088&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.776726195507704[/C][/ROW]
[ROW][C]R-squared[/C][C]0.603303582787871[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.489961749298692[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.32286768455591[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]49[/C][/ROW]
[ROW][C]p-value[/C][C]5.45203608637301e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]10.6353811503737[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5542.45527847249[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68088&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68088&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	0.776726195507704
R-squared	0.603303582787871
Adjusted R-squared	0.489961749298692
F-TEST (value)	5.32286768455591
F-TEST (DF numerator)	14
F-TEST (DF denominator)	49
p-value	5.45203608637301e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.6353811503737
Sum Squared Residuals	5542.45527847249

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	113.08	115.886842531008	-2.8068425310083
2	106.46	114.458784512099	-7.99878451209865
3	123.38	131.923425865321	-8.5434258653211
4	109.87	122.237706521217	-12.3677065212173
5	95.74	105.723279580003	-9.98327958000328
6	123.06	125.646628821810	-2.58662882181037
7	123.39	130.540793944519	-7.15079394451928
8	120.28	126.163348544048	-5.88334854404793
9	115.33	116.827440588606	-1.49744058860558
10	110.4	118.555483254994	-8.15548325499351
11	114.49	117.181371181927	-2.69137118192711
12	132.03	130.136553639211	1.89344636078896
13	123.16	125.426849198008	-2.26684919800769
14	118.82	120.383036520663	-1.56303652066334
15	128.32	123.857492502337	4.46250749766319
16	112.24	112.175120824343	0.0648791756570806
17	104.53	101.315030461341	3.21496953865905
18	132.57	123.880546729948	8.68945327005155
19	122.52	130.953384603023	-8.43338460302296
20	131.8	117.344899559936	14.4551004400637
21	124.55	108.034240427841	16.5157595721593
22	120.96	115.487718621186	5.4722813788135
23	122.6	110.956654701655	11.6433452983451
24	145.52	134.839311389213	10.6806886107872
25	118.57	124.595824417578	-6.02582441757795
26	134.25	121.508048658603	12.7419513413968
27	136.7	134.147680044025	2.55231995597521
28	121.37	128.134490262587	-6.76449026258735
29	111.63	107.565292284898	4.06470771510198
30	134.42	141.5582222851	-7.13822228509996
31	137.65	137.845240579978	-0.195240579977643
32	137.86	128.432138677785	9.42786132221529
33	119.77	119.709698060539	0.0603019394606686
34	130.69	123.832544372214	6.85745562778567
35	128.28	127.285777013388	0.994222986611525
36	147.45	144.726678126625	2.7233218733754
37	128.42	130.600536332232	-2.18053633223205
38	136.9	134.172460515111	2.72753948488916
39	143.95	143.976458404595	-0.0264584045954077
40	135.64	136.591801302833	-0.951801302833051
41	122.48	118.477808819882	4.00219118011794
42	136.83	147.009347437179	-10.1793474371791
43	153.04	148.289321722940	4.75067827705957
44	142.71	143.16273799544	-0.452737995440148
45	123.46	130.959942085884	-7.499942085884
46	144.37	130.106241546508	14.2637584534922
47	146.15	140.910245353553	5.23975464644719
48	147.61	151.480528368308	-3.87052836830754
49	158.51	133.47980050972	25.0301994902800
50	147.4	141.807720806972	5.59227919302847
51	165.05	153.603951188589	11.4460488114109
52	154.64	129.901777073516	24.7382229264842
53	126.2	127.498588853876	-1.29858885387568
54	157.36	146.145254725962	11.2147452740378
55	154.15	143.121259149540	11.0287408504603
56	123.21	140.756875222791	-17.5468752227909
57	113.07	120.648678837130	-7.57867883713037
58	110.45	128.888012205098	-18.4380122050978
59	113.57	128.755951749477	-15.1859517494767
60	122.44	133.866928476644	-11.426928476644
61	114.93	126.680147011454	-11.7501470114540
62	111.85	123.349948986552	-11.4999489865524
63	126.04	135.930991995133	-9.89099199513284
64	121.34	126.059104015504	-4.7191040155036

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 113.08 & 115.886842531008 & -2.8068425310083 \tabularnewline
2 & 106.46 & 114.458784512099 & -7.99878451209865 \tabularnewline
3 & 123.38 & 131.923425865321 & -8.5434258653211 \tabularnewline
4 & 109.87 & 122.237706521217 & -12.3677065212173 \tabularnewline
5 & 95.74 & 105.723279580003 & -9.98327958000328 \tabularnewline
6 & 123.06 & 125.646628821810 & -2.58662882181037 \tabularnewline
7 & 123.39 & 130.540793944519 & -7.15079394451928 \tabularnewline
8 & 120.28 & 126.163348544048 & -5.88334854404793 \tabularnewline
9 & 115.33 & 116.827440588606 & -1.49744058860558 \tabularnewline
10 & 110.4 & 118.555483254994 & -8.15548325499351 \tabularnewline
11 & 114.49 & 117.181371181927 & -2.69137118192711 \tabularnewline
12 & 132.03 & 130.136553639211 & 1.89344636078896 \tabularnewline
13 & 123.16 & 125.426849198008 & -2.26684919800769 \tabularnewline
14 & 118.82 & 120.383036520663 & -1.56303652066334 \tabularnewline
15 & 128.32 & 123.857492502337 & 4.46250749766319 \tabularnewline
16 & 112.24 & 112.175120824343 & 0.0648791756570806 \tabularnewline
17 & 104.53 & 101.315030461341 & 3.21496953865905 \tabularnewline
18 & 132.57 & 123.880546729948 & 8.68945327005155 \tabularnewline
19 & 122.52 & 130.953384603023 & -8.43338460302296 \tabularnewline
20 & 131.8 & 117.344899559936 & 14.4551004400637 \tabularnewline
21 & 124.55 & 108.034240427841 & 16.5157595721593 \tabularnewline
22 & 120.96 & 115.487718621186 & 5.4722813788135 \tabularnewline
23 & 122.6 & 110.956654701655 & 11.6433452983451 \tabularnewline
24 & 145.52 & 134.839311389213 & 10.6806886107872 \tabularnewline
25 & 118.57 & 124.595824417578 & -6.02582441757795 \tabularnewline
26 & 134.25 & 121.508048658603 & 12.7419513413968 \tabularnewline
27 & 136.7 & 134.147680044025 & 2.55231995597521 \tabularnewline
28 & 121.37 & 128.134490262587 & -6.76449026258735 \tabularnewline
29 & 111.63 & 107.565292284898 & 4.06470771510198 \tabularnewline
30 & 134.42 & 141.5582222851 & -7.13822228509996 \tabularnewline
31 & 137.65 & 137.845240579978 & -0.195240579977643 \tabularnewline
32 & 137.86 & 128.432138677785 & 9.42786132221529 \tabularnewline
33 & 119.77 & 119.709698060539 & 0.0603019394606686 \tabularnewline
34 & 130.69 & 123.832544372214 & 6.85745562778567 \tabularnewline
35 & 128.28 & 127.285777013388 & 0.994222986611525 \tabularnewline
36 & 147.45 & 144.726678126625 & 2.7233218733754 \tabularnewline
37 & 128.42 & 130.600536332232 & -2.18053633223205 \tabularnewline
38 & 136.9 & 134.172460515111 & 2.72753948488916 \tabularnewline
39 & 143.95 & 143.976458404595 & -0.0264584045954077 \tabularnewline
40 & 135.64 & 136.591801302833 & -0.951801302833051 \tabularnewline
41 & 122.48 & 118.477808819882 & 4.00219118011794 \tabularnewline
42 & 136.83 & 147.009347437179 & -10.1793474371791 \tabularnewline
43 & 153.04 & 148.289321722940 & 4.75067827705957 \tabularnewline
44 & 142.71 & 143.16273799544 & -0.452737995440148 \tabularnewline
45 & 123.46 & 130.959942085884 & -7.499942085884 \tabularnewline
46 & 144.37 & 130.106241546508 & 14.2637584534922 \tabularnewline
47 & 146.15 & 140.910245353553 & 5.23975464644719 \tabularnewline
48 & 147.61 & 151.480528368308 & -3.87052836830754 \tabularnewline
49 & 158.51 & 133.47980050972 & 25.0301994902800 \tabularnewline
50 & 147.4 & 141.807720806972 & 5.59227919302847 \tabularnewline
51 & 165.05 & 153.603951188589 & 11.4460488114109 \tabularnewline
52 & 154.64 & 129.901777073516 & 24.7382229264842 \tabularnewline
53 & 126.2 & 127.498588853876 & -1.29858885387568 \tabularnewline
54 & 157.36 & 146.145254725962 & 11.2147452740378 \tabularnewline
55 & 154.15 & 143.121259149540 & 11.0287408504603 \tabularnewline
56 & 123.21 & 140.756875222791 & -17.5468752227909 \tabularnewline
57 & 113.07 & 120.648678837130 & -7.57867883713037 \tabularnewline
58 & 110.45 & 128.888012205098 & -18.4380122050978 \tabularnewline
59 & 113.57 & 128.755951749477 & -15.1859517494767 \tabularnewline
60 & 122.44 & 133.866928476644 & -11.426928476644 \tabularnewline
61 & 114.93 & 126.680147011454 & -11.7501470114540 \tabularnewline
62 & 111.85 & 123.349948986552 & -11.4999489865524 \tabularnewline
63 & 126.04 & 135.930991995133 & -9.89099199513284 \tabularnewline
64 & 121.34 & 126.059104015504 & -4.7191040155036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68088&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]113.08[/C][C]115.886842531008[/C][C]-2.8068425310083[/C][/ROW]
[ROW][C]2[/C][C]106.46[/C][C]114.458784512099[/C][C]-7.99878451209865[/C][/ROW]
[ROW][C]3[/C][C]123.38[/C][C]131.923425865321[/C][C]-8.5434258653211[/C][/ROW]
[ROW][C]4[/C][C]109.87[/C][C]122.237706521217[/C][C]-12.3677065212173[/C][/ROW]
[ROW][C]5[/C][C]95.74[/C][C]105.723279580003[/C][C]-9.98327958000328[/C][/ROW]
[ROW][C]6[/C][C]123.06[/C][C]125.646628821810[/C][C]-2.58662882181037[/C][/ROW]
[ROW][C]7[/C][C]123.39[/C][C]130.540793944519[/C][C]-7.15079394451928[/C][/ROW]
[ROW][C]8[/C][C]120.28[/C][C]126.163348544048[/C][C]-5.88334854404793[/C][/ROW]
[ROW][C]9[/C][C]115.33[/C][C]116.827440588606[/C][C]-1.49744058860558[/C][/ROW]
[ROW][C]10[/C][C]110.4[/C][C]118.555483254994[/C][C]-8.15548325499351[/C][/ROW]
[ROW][C]11[/C][C]114.49[/C][C]117.181371181927[/C][C]-2.69137118192711[/C][/ROW]
[ROW][C]12[/C][C]132.03[/C][C]130.136553639211[/C][C]1.89344636078896[/C][/ROW]
[ROW][C]13[/C][C]123.16[/C][C]125.426849198008[/C][C]-2.26684919800769[/C][/ROW]
[ROW][C]14[/C][C]118.82[/C][C]120.383036520663[/C][C]-1.56303652066334[/C][/ROW]
[ROW][C]15[/C][C]128.32[/C][C]123.857492502337[/C][C]4.46250749766319[/C][/ROW]
[ROW][C]16[/C][C]112.24[/C][C]112.175120824343[/C][C]0.0648791756570806[/C][/ROW]
[ROW][C]17[/C][C]104.53[/C][C]101.315030461341[/C][C]3.21496953865905[/C][/ROW]
[ROW][C]18[/C][C]132.57[/C][C]123.880546729948[/C][C]8.68945327005155[/C][/ROW]
[ROW][C]19[/C][C]122.52[/C][C]130.953384603023[/C][C]-8.43338460302296[/C][/ROW]
[ROW][C]20[/C][C]131.8[/C][C]117.344899559936[/C][C]14.4551004400637[/C][/ROW]
[ROW][C]21[/C][C]124.55[/C][C]108.034240427841[/C][C]16.5157595721593[/C][/ROW]
[ROW][C]22[/C][C]120.96[/C][C]115.487718621186[/C][C]5.4722813788135[/C][/ROW]
[ROW][C]23[/C][C]122.6[/C][C]110.956654701655[/C][C]11.6433452983451[/C][/ROW]
[ROW][C]24[/C][C]145.52[/C][C]134.839311389213[/C][C]10.6806886107872[/C][/ROW]
[ROW][C]25[/C][C]118.57[/C][C]124.595824417578[/C][C]-6.02582441757795[/C][/ROW]
[ROW][C]26[/C][C]134.25[/C][C]121.508048658603[/C][C]12.7419513413968[/C][/ROW]
[ROW][C]27[/C][C]136.7[/C][C]134.147680044025[/C][C]2.55231995597521[/C][/ROW]
[ROW][C]28[/C][C]121.37[/C][C]128.134490262587[/C][C]-6.76449026258735[/C][/ROW]
[ROW][C]29[/C][C]111.63[/C][C]107.565292284898[/C][C]4.06470771510198[/C][/ROW]
[ROW][C]30[/C][C]134.42[/C][C]141.5582222851[/C][C]-7.13822228509996[/C][/ROW]
[ROW][C]31[/C][C]137.65[/C][C]137.845240579978[/C][C]-0.195240579977643[/C][/ROW]
[ROW][C]32[/C][C]137.86[/C][C]128.432138677785[/C][C]9.42786132221529[/C][/ROW]
[ROW][C]33[/C][C]119.77[/C][C]119.709698060539[/C][C]0.0603019394606686[/C][/ROW]
[ROW][C]34[/C][C]130.69[/C][C]123.832544372214[/C][C]6.85745562778567[/C][/ROW]
[ROW][C]35[/C][C]128.28[/C][C]127.285777013388[/C][C]0.994222986611525[/C][/ROW]
[ROW][C]36[/C][C]147.45[/C][C]144.726678126625[/C][C]2.7233218733754[/C][/ROW]
[ROW][C]37[/C][C]128.42[/C][C]130.600536332232[/C][C]-2.18053633223205[/C][/ROW]
[ROW][C]38[/C][C]136.9[/C][C]134.172460515111[/C][C]2.72753948488916[/C][/ROW]
[ROW][C]39[/C][C]143.95[/C][C]143.976458404595[/C][C]-0.0264584045954077[/C][/ROW]
[ROW][C]40[/C][C]135.64[/C][C]136.591801302833[/C][C]-0.951801302833051[/C][/ROW]
[ROW][C]41[/C][C]122.48[/C][C]118.477808819882[/C][C]4.00219118011794[/C][/ROW]
[ROW][C]42[/C][C]136.83[/C][C]147.009347437179[/C][C]-10.1793474371791[/C][/ROW]
[ROW][C]43[/C][C]153.04[/C][C]148.289321722940[/C][C]4.75067827705957[/C][/ROW]
[ROW][C]44[/C][C]142.71[/C][C]143.16273799544[/C][C]-0.452737995440148[/C][/ROW]
[ROW][C]45[/C][C]123.46[/C][C]130.959942085884[/C][C]-7.499942085884[/C][/ROW]
[ROW][C]46[/C][C]144.37[/C][C]130.106241546508[/C][C]14.2637584534922[/C][/ROW]
[ROW][C]47[/C][C]146.15[/C][C]140.910245353553[/C][C]5.23975464644719[/C][/ROW]
[ROW][C]48[/C][C]147.61[/C][C]151.480528368308[/C][C]-3.87052836830754[/C][/ROW]
[ROW][C]49[/C][C]158.51[/C][C]133.47980050972[/C][C]25.0301994902800[/C][/ROW]
[ROW][C]50[/C][C]147.4[/C][C]141.807720806972[/C][C]5.59227919302847[/C][/ROW]
[ROW][C]51[/C][C]165.05[/C][C]153.603951188589[/C][C]11.4460488114109[/C][/ROW]
[ROW][C]52[/C][C]154.64[/C][C]129.901777073516[/C][C]24.7382229264842[/C][/ROW]
[ROW][C]53[/C][C]126.2[/C][C]127.498588853876[/C][C]-1.29858885387568[/C][/ROW]
[ROW][C]54[/C][C]157.36[/C][C]146.145254725962[/C][C]11.2147452740378[/C][/ROW]
[ROW][C]55[/C][C]154.15[/C][C]143.121259149540[/C][C]11.0287408504603[/C][/ROW]
[ROW][C]56[/C][C]123.21[/C][C]140.756875222791[/C][C]-17.5468752227909[/C][/ROW]
[ROW][C]57[/C][C]113.07[/C][C]120.648678837130[/C][C]-7.57867883713037[/C][/ROW]
[ROW][C]58[/C][C]110.45[/C][C]128.888012205098[/C][C]-18.4380122050978[/C][/ROW]
[ROW][C]59[/C][C]113.57[/C][C]128.755951749477[/C][C]-15.1859517494767[/C][/ROW]
[ROW][C]60[/C][C]122.44[/C][C]133.866928476644[/C][C]-11.426928476644[/C][/ROW]
[ROW][C]61[/C][C]114.93[/C][C]126.680147011454[/C][C]-11.7501470114540[/C][/ROW]
[ROW][C]62[/C][C]111.85[/C][C]123.349948986552[/C][C]-11.4999489865524[/C][/ROW]
[ROW][C]63[/C][C]126.04[/C][C]135.930991995133[/C][C]-9.89099199513284[/C][/ROW]
[ROW][C]64[/C][C]121.34[/C][C]126.059104015504[/C][C]-4.7191040155036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68088&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68088&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	113.08	115.886842531008	-2.8068425310083
2	106.46	114.458784512099	-7.99878451209865
3	123.38	131.923425865321	-8.5434258653211
4	109.87	122.237706521217	-12.3677065212173
5	95.74	105.723279580003	-9.98327958000328
6	123.06	125.646628821810	-2.58662882181037
7	123.39	130.540793944519	-7.15079394451928
8	120.28	126.163348544048	-5.88334854404793
9	115.33	116.827440588606	-1.49744058860558
10	110.4	118.555483254994	-8.15548325499351
11	114.49	117.181371181927	-2.69137118192711
12	132.03	130.136553639211	1.89344636078896
13	123.16	125.426849198008	-2.26684919800769
14	118.82	120.383036520663	-1.56303652066334
15	128.32	123.857492502337	4.46250749766319
16	112.24	112.175120824343	0.0648791756570806
17	104.53	101.315030461341	3.21496953865905
18	132.57	123.880546729948	8.68945327005155
19	122.52	130.953384603023	-8.43338460302296
20	131.8	117.344899559936	14.4551004400637
21	124.55	108.034240427841	16.5157595721593
22	120.96	115.487718621186	5.4722813788135
23	122.6	110.956654701655	11.6433452983451
24	145.52	134.839311389213	10.6806886107872
25	118.57	124.595824417578	-6.02582441757795
26	134.25	121.508048658603	12.7419513413968
27	136.7	134.147680044025	2.55231995597521
28	121.37	128.134490262587	-6.76449026258735
29	111.63	107.565292284898	4.06470771510198
30	134.42	141.5582222851	-7.13822228509996
31	137.65	137.845240579978	-0.195240579977643
32	137.86	128.432138677785	9.42786132221529
33	119.77	119.709698060539	0.0603019394606686
34	130.69	123.832544372214	6.85745562778567
35	128.28	127.285777013388	0.994222986611525
36	147.45	144.726678126625	2.7233218733754
37	128.42	130.600536332232	-2.18053633223205
38	136.9	134.172460515111	2.72753948488916
39	143.95	143.976458404595	-0.0264584045954077
40	135.64	136.591801302833	-0.951801302833051
41	122.48	118.477808819882	4.00219118011794
42	136.83	147.009347437179	-10.1793474371791
43	153.04	148.289321722940	4.75067827705957
44	142.71	143.16273799544	-0.452737995440148
45	123.46	130.959942085884	-7.499942085884
46	144.37	130.106241546508	14.2637584534922
47	146.15	140.910245353553	5.23975464644719
48	147.61	151.480528368308	-3.87052836830754
49	158.51	133.47980050972	25.0301994902800
50	147.4	141.807720806972	5.59227919302847
51	165.05	153.603951188589	11.4460488114109
52	154.64	129.901777073516	24.7382229264842
53	126.2	127.498588853876	-1.29858885387568
54	157.36	146.145254725962	11.2147452740378
55	154.15	143.121259149540	11.0287408504603
56	123.21	140.756875222791	-17.5468752227909
57	113.07	120.648678837130	-7.57867883713037
58	110.45	128.888012205098	-18.4380122050978
59	113.57	128.755951749477	-15.1859517494767
60	122.44	133.866928476644	-11.426928476644
61	114.93	126.680147011454	-11.7501470114540
62	111.85	123.349948986552	-11.4999489865524
63	126.04	135.930991995133	-9.89099199513284
64	121.34	126.059104015504	-4.7191040155036

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.00503860649539688	0.0100772129907938	0.994961393504603
19	0.0180586013285341	0.0361172026570683	0.981941398671466
20	0.00889422292250569	0.0177884458450114	0.991105777077494
21	0.00440926768896815	0.0088185353779363	0.995590732311032
22	0.00148766000787155	0.0029753200157431	0.998512339992129
23	0.000450160917219224	0.000900321834438447	0.99954983908278
24	0.000243328712372217	0.000486657424744435	0.999756671287628
25	0.00157924174012796	0.00315848348025592	0.998420758259872
26	0.00311670811365039	0.00623341622730079	0.99688329188635
27	0.00127069775086635	0.00254139550173269	0.998729302249134
28	0.00071908489700439	0.00143816979400878	0.999280915102996
29	0.000259223289937400	0.000518446579874799	0.999740776710063
30	0.000148984106938715	0.000297968213877431	0.999851015893061
31	7.3035433993487e-05	0.000146070867986974	0.999926964566006
32	3.89677351472816e-05	7.79354702945631e-05	0.999961032264853
33	8.52978689950896e-05	0.000170595737990179	0.999914702131005
34	4.14686043859618e-05	8.29372087719237e-05	0.999958531395614
35	1.61430712382156e-05	3.22861424764311e-05	0.999983856928762
36	6.65845344149485e-06	1.33169068829897e-05	0.999993341546559
37	3.70513943824805e-06	7.41027887649609e-06	0.999996294860562
38	1.42819484418818e-06	2.85638968837636e-06	0.999998571805156
39	4.06984092859831e-07	8.13968185719662e-07	0.999999593015907
40	2.34487063389262e-06	4.68974126778523e-06	0.999997655129366
41	7.90672203590424e-07	1.58134440718085e-06	0.999999209327796
42	0.000929344413405446	0.00185868882681089	0.999070655586595
43	0.0705576115275448	0.141115223055090	0.929442388472455
44	0.0543804183153715	0.108760836630743	0.945619581684628
45	0.49096916736119	0.98193833472238	0.50903083263881
46	0.341818377918636	0.683636755837272	0.658181622081364

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.00503860649539688 & 0.0100772129907938 & 0.994961393504603 \tabularnewline
19 & 0.0180586013285341 & 0.0361172026570683 & 0.981941398671466 \tabularnewline
20 & 0.00889422292250569 & 0.0177884458450114 & 0.991105777077494 \tabularnewline
21 & 0.00440926768896815 & 0.0088185353779363 & 0.995590732311032 \tabularnewline
22 & 0.00148766000787155 & 0.0029753200157431 & 0.998512339992129 \tabularnewline
23 & 0.000450160917219224 & 0.000900321834438447 & 0.99954983908278 \tabularnewline
24 & 0.000243328712372217 & 0.000486657424744435 & 0.999756671287628 \tabularnewline
25 & 0.00157924174012796 & 0.00315848348025592 & 0.998420758259872 \tabularnewline
26 & 0.00311670811365039 & 0.00623341622730079 & 0.99688329188635 \tabularnewline
27 & 0.00127069775086635 & 0.00254139550173269 & 0.998729302249134 \tabularnewline
28 & 0.00071908489700439 & 0.00143816979400878 & 0.999280915102996 \tabularnewline
29 & 0.000259223289937400 & 0.000518446579874799 & 0.999740776710063 \tabularnewline
30 & 0.000148984106938715 & 0.000297968213877431 & 0.999851015893061 \tabularnewline
31 & 7.3035433993487e-05 & 0.000146070867986974 & 0.999926964566006 \tabularnewline
32 & 3.89677351472816e-05 & 7.79354702945631e-05 & 0.999961032264853 \tabularnewline
33 & 8.52978689950896e-05 & 0.000170595737990179 & 0.999914702131005 \tabularnewline
34 & 4.14686043859618e-05 & 8.29372087719237e-05 & 0.999958531395614 \tabularnewline
35 & 1.61430712382156e-05 & 3.22861424764311e-05 & 0.999983856928762 \tabularnewline
36 & 6.65845344149485e-06 & 1.33169068829897e-05 & 0.999993341546559 \tabularnewline
37 & 3.70513943824805e-06 & 7.41027887649609e-06 & 0.999996294860562 \tabularnewline
38 & 1.42819484418818e-06 & 2.85638968837636e-06 & 0.999998571805156 \tabularnewline
39 & 4.06984092859831e-07 & 8.13968185719662e-07 & 0.999999593015907 \tabularnewline
40 & 2.34487063389262e-06 & 4.68974126778523e-06 & 0.999997655129366 \tabularnewline
41 & 7.90672203590424e-07 & 1.58134440718085e-06 & 0.999999209327796 \tabularnewline
42 & 0.000929344413405446 & 0.00185868882681089 & 0.999070655586595 \tabularnewline
43 & 0.0705576115275448 & 0.141115223055090 & 0.929442388472455 \tabularnewline
44 & 0.0543804183153715 & 0.108760836630743 & 0.945619581684628 \tabularnewline
45 & 0.49096916736119 & 0.98193833472238 & 0.50903083263881 \tabularnewline
46 & 0.341818377918636 & 0.683636755837272 & 0.658181622081364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68088&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]18[/C][C]0.00503860649539688[/C][C]0.0100772129907938[/C][C]0.994961393504603[/C][/ROW]
[ROW][C]19[/C][C]0.0180586013285341[/C][C]0.0361172026570683[/C][C]0.981941398671466[/C][/ROW]
[ROW][C]20[/C][C]0.00889422292250569[/C][C]0.0177884458450114[/C][C]0.991105777077494[/C][/ROW]
[ROW][C]21[/C][C]0.00440926768896815[/C][C]0.0088185353779363[/C][C]0.995590732311032[/C][/ROW]
[ROW][C]22[/C][C]0.00148766000787155[/C][C]0.0029753200157431[/C][C]0.998512339992129[/C][/ROW]
[ROW][C]23[/C][C]0.000450160917219224[/C][C]0.000900321834438447[/C][C]0.99954983908278[/C][/ROW]
[ROW][C]24[/C][C]0.000243328712372217[/C][C]0.000486657424744435[/C][C]0.999756671287628[/C][/ROW]
[ROW][C]25[/C][C]0.00157924174012796[/C][C]0.00315848348025592[/C][C]0.998420758259872[/C][/ROW]
[ROW][C]26[/C][C]0.00311670811365039[/C][C]0.00623341622730079[/C][C]0.99688329188635[/C][/ROW]
[ROW][C]27[/C][C]0.00127069775086635[/C][C]0.00254139550173269[/C][C]0.998729302249134[/C][/ROW]
[ROW][C]28[/C][C]0.00071908489700439[/C][C]0.00143816979400878[/C][C]0.999280915102996[/C][/ROW]
[ROW][C]29[/C][C]0.000259223289937400[/C][C]0.000518446579874799[/C][C]0.999740776710063[/C][/ROW]
[ROW][C]30[/C][C]0.000148984106938715[/C][C]0.000297968213877431[/C][C]0.999851015893061[/C][/ROW]
[ROW][C]31[/C][C]7.3035433993487e-05[/C][C]0.000146070867986974[/C][C]0.999926964566006[/C][/ROW]
[ROW][C]32[/C][C]3.89677351472816e-05[/C][C]7.79354702945631e-05[/C][C]0.999961032264853[/C][/ROW]
[ROW][C]33[/C][C]8.52978689950896e-05[/C][C]0.000170595737990179[/C][C]0.999914702131005[/C][/ROW]
[ROW][C]34[/C][C]4.14686043859618e-05[/C][C]8.29372087719237e-05[/C][C]0.999958531395614[/C][/ROW]
[ROW][C]35[/C][C]1.61430712382156e-05[/C][C]3.22861424764311e-05[/C][C]0.999983856928762[/C][/ROW]
[ROW][C]36[/C][C]6.65845344149485e-06[/C][C]1.33169068829897e-05[/C][C]0.999993341546559[/C][/ROW]
[ROW][C]37[/C][C]3.70513943824805e-06[/C][C]7.41027887649609e-06[/C][C]0.999996294860562[/C][/ROW]
[ROW][C]38[/C][C]1.42819484418818e-06[/C][C]2.85638968837636e-06[/C][C]0.999998571805156[/C][/ROW]
[ROW][C]39[/C][C]4.06984092859831e-07[/C][C]8.13968185719662e-07[/C][C]0.999999593015907[/C][/ROW]
[ROW][C]40[/C][C]2.34487063389262e-06[/C][C]4.68974126778523e-06[/C][C]0.999997655129366[/C][/ROW]
[ROW][C]41[/C][C]7.90672203590424e-07[/C][C]1.58134440718085e-06[/C][C]0.999999209327796[/C][/ROW]
[ROW][C]42[/C][C]0.000929344413405446[/C][C]0.00185868882681089[/C][C]0.999070655586595[/C][/ROW]
[ROW][C]43[/C][C]0.0705576115275448[/C][C]0.141115223055090[/C][C]0.929442388472455[/C][/ROW]
[ROW][C]44[/C][C]0.0543804183153715[/C][C]0.108760836630743[/C][C]0.945619581684628[/C][/ROW]
[ROW][C]45[/C][C]0.49096916736119[/C][C]0.98193833472238[/C][C]0.50903083263881[/C][/ROW]
[ROW][C]46[/C][C]0.341818377918636[/C][C]0.683636755837272[/C][C]0.658181622081364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68088&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68088&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
18	0.00503860649539688	0.0100772129907938	0.994961393504603
19	0.0180586013285341	0.0361172026570683	0.981941398671466
20	0.00889422292250569	0.0177884458450114	0.991105777077494
21	0.00440926768896815	0.0088185353779363	0.995590732311032
22	0.00148766000787155	0.0029753200157431	0.998512339992129
23	0.000450160917219224	0.000900321834438447	0.99954983908278
24	0.000243328712372217	0.000486657424744435	0.999756671287628
25	0.00157924174012796	0.00315848348025592	0.998420758259872
26	0.00311670811365039	0.00623341622730079	0.99688329188635
27	0.00127069775086635	0.00254139550173269	0.998729302249134
28	0.00071908489700439	0.00143816979400878	0.999280915102996
29	0.000259223289937400	0.000518446579874799	0.999740776710063
30	0.000148984106938715	0.000297968213877431	0.999851015893061
31	7.3035433993487e-05	0.000146070867986974	0.999926964566006
32	3.89677351472816e-05	7.79354702945631e-05	0.999961032264853
33	8.52978689950896e-05	0.000170595737990179	0.999914702131005
34	4.14686043859618e-05	8.29372087719237e-05	0.999958531395614
35	1.61430712382156e-05	3.22861424764311e-05	0.999983856928762
36	6.65845344149485e-06	1.33169068829897e-05	0.999993341546559
37	3.70513943824805e-06	7.41027887649609e-06	0.999996294860562
38	1.42819484418818e-06	2.85638968837636e-06	0.999998571805156
39	4.06984092859831e-07	8.13968185719662e-07	0.999999593015907
40	2.34487063389262e-06	4.68974126778523e-06	0.999997655129366
41	7.90672203590424e-07	1.58134440718085e-06	0.999999209327796
42	0.000929344413405446	0.00185868882681089	0.999070655586595
43	0.0705576115275448	0.141115223055090	0.929442388472455
44	0.0543804183153715	0.108760836630743	0.945619581684628
45	0.49096916736119	0.98193833472238	0.50903083263881
46	0.341818377918636	0.683636755837272	0.658181622081364

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	22	0.758620689655172	NOK
5% type I error level	25	0.862068965517241	NOK
10% type I error level	25	0.862068965517241	NOK

\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 & 22 & 0.758620689655172 & NOK \tabularnewline
5% type I error level & 25 & 0.862068965517241 & NOK \tabularnewline
10% type I error level & 25 & 0.862068965517241 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68088&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]22[/C][C]0.758620689655172[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.862068965517241[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.862068965517241[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68088&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68088&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	22	0.758620689655172	NOK
5% type I error level	25	0.862068965517241	NOK
10% type I error level	25	0.862068965517241	NOK

Figure 1

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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

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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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code