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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 24 Nov 2011 05:14:29 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t13221296975b21kmpzh08p3jf.htm/, Retrieved Sun, 22 Jun 2025 07:41:55 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146608, Retrieved Sun, 22 Jun 2025 07:41:55 +0000

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Estimated Impact

376

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Univariate Explorative Data Analysis] [time effect in su...] [2010-11-17 08:55:33] [b98453cac15ba1066b407e146608df68]
-    D  [Univariate Explorative Data Analysis] [Run sequency plot...] [2010-11-20 14:46:56] [95e8426e0df851c9330605aa1e892ab5]
-         [Univariate Explorative Data Analysis] [] [2010-12-01 15:00:34] [42a441ca3193af442aa2201743dfb347]
- RMPD        [Multiple Regression] [tre] [2011-11-24 10:14:29] [d519577d845e738b812f706f10c86f64] [Current]

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Dataseries X:

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1	8	1	14	4
2	8	3	82	1
3	8	2	14	3
4	8	1	16	5
5	8	5	140	7
6	8	8	173	2
7	8	3	9	8
8	8	8	13	6
1	12	12	17	4
2	12	3	16	9
3	12	8	21	7
4	12	3	14	2
5	12	3	15	12
6	12	3	10	8
7	12	3	14	1
8	12	1	16	6
9	12	2	14	10
10	12	20	17	3
11	12	2	10	5
12	12	1	23	11
1	9	1	21	2
2	9	6	14	4
3	9	8	14	7
4	9	5	14	11
5	9	1	16	5
6	9	7	14	1
7	9	7	14	9
8	9	5	7	3
9	9	8	17	10
1	14	2	14	3
2	14	5	21	4
3	14	2	24	7
4	14	5	7	6
5	14	1	30	13
6	14	2	93	16
7	14	6	14	9
8	14	3	14	1
9	14	6	107	10
10	14	6	231	5
11	14	1	385	2
12	14	2	14	11
13	14	10	29	14
14	14	1	16	15
1	13	2	7	10
2	13	1	21	3
3	13	1	14	2
4	13	1	17	13
5	13	6	14	4
6	13	4	21	1
7	13	9	15	9
8	13	10	10	5
9	13	6	15	8
10	13	1	7	7
11	13	6	12	12
12	13	18	84	6
13	13	3	17	11
1	19	4	14	4
2	19	1	10	9
3	19	3	17	15
4	19	5	91	14
5	19	4	21	17
6	19	4	21	3
7	19	1	16	7
8	19	17	35	1
9	19	2	17	16
10	19	1	15	13
11	19	6	14	5
12	19	10	28	18
13	19	9	14	6
14	19	5	14	10
15	19	1	20	12
16	19	13	35	20
17	19	11	28	8
18	19	9	17	11
19	19	4	14	19
1	13	4	10	4
2	13	5	10	1
3	13	2	14	3
4	13	1	7	9
5	13	2	14	11
6	13	4	14	12
7	13	12	10	2
8	13	14	10	7
9	13	2	21	6
10	13	7	10	5
11	13	4	17	8
12	13	1	17	10
13	13	6	24	13
1	14	2	16	2
2	14	1	63	9
3	14	4	17	4
4	14	6	21	1
5	14	7	7	14
6	14	9	49	7
7	14	1	7	10
8	14	3	14	6
9	14	6	210	11
10	14	8	35	5
11	14	8	14	3
12	14	4	28	13
13	14	8	56	12
14	14	7	31	15

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&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	6 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Postition[t] = -0.603703743713651 + 0.162678684314451starters[t] + 0.310458030411594last[t] + 0.0103730047865599since[t] + 0.352921408894487number[t] + 0.0209951325099079t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Postition[t] =  -0.603703743713651 +  0.162678684314451starters[t] +  0.310458030411594last[t] +  0.0103730047865599since[t] +  0.352921408894487number[t] +  0.0209951325099079t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Postition[t] =  -0.603703743713651 +  0.162678684314451starters[t] +  0.310458030411594last[t] +  0.0103730047865599since[t] +  0.352921408894487number[t] +  0.0209951325099079t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&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
Postition[t] = -0.603703743713651 + 0.162678684314451starters[t] + 0.310458030411594last[t] + 0.0103730047865599since[t] + 0.352921408894487number[t] + 0.0209951325099079t + 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)	-0.603703743713651	1.61545	-0.3737	0.709447	0.354723
starters	0.162678684314451	0.14057	1.1573	0.25003	0.125015
last	0.310458030411594	0.091308	3.4001	0.000983	0.000491
since	0.0103730047865599	0.006851	1.5141	0.133291	0.066646
number	0.352921408894487	0.083241	4.2398	5.1e-05	2.6e-05
t	0.0209951325099079	0.014521	1.4459	0.151469	0.075735

\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) & -0.603703743713651 & 1.61545 & -0.3737 & 0.709447 & 0.354723 \tabularnewline
starters & 0.162678684314451 & 0.14057 & 1.1573 & 0.25003 & 0.125015 \tabularnewline
last & 0.310458030411594 & 0.091308 & 3.4001 & 0.000983 & 0.000491 \tabularnewline
since & 0.0103730047865599 & 0.006851 & 1.5141 & 0.133291 & 0.066646 \tabularnewline
number & 0.352921408894487 & 0.083241 & 4.2398 & 5.1e-05 & 2.6e-05 \tabularnewline
t & 0.0209951325099079 & 0.014521 & 1.4459 & 0.151469 & 0.075735 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&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]-0.603703743713651[/C][C]1.61545[/C][C]-0.3737[/C][C]0.709447[/C][C]0.354723[/C][/ROW]
[ROW][C]starters[/C][C]0.162678684314451[/C][C]0.14057[/C][C]1.1573[/C][C]0.25003[/C][C]0.125015[/C][/ROW]
[ROW][C]last[/C][C]0.310458030411594[/C][C]0.091308[/C][C]3.4001[/C][C]0.000983[/C][C]0.000491[/C][/ROW]
[ROW][C]since[/C][C]0.0103730047865599[/C][C]0.006851[/C][C]1.5141[/C][C]0.133291[/C][C]0.066646[/C][/ROW]
[ROW][C]number[/C][C]0.352921408894487[/C][C]0.083241[/C][C]4.2398[/C][C]5.1e-05[/C][C]2.6e-05[/C][/ROW]
[ROW][C]t[/C][C]0.0209951325099079[/C][C]0.014521[/C][C]1.4459[/C][C]0.151469[/C][C]0.075735[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&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)	-0.603703743713651	1.61545	-0.3737	0.709447	0.354723
starters	0.162678684314451	0.14057	1.1573	0.25003	0.125015
last	0.310458030411594	0.091308	3.4001	0.000983	0.000491
since	0.0103730047865599	0.006851	1.5141	0.133291	0.066646
number	0.352921408894487	0.083241	4.2398	5.1e-05	2.6e-05
t	0.0209951325099079	0.014521	1.4459	0.151469	0.075735

Multiple Linear Regression - Regression Statistics
Multiple R	0.596037812000243
R-squared	0.355261073334037
Adjusted R-squared	0.321680920903518
F-TEST (value)	10.5794955537832
F-TEST (DF numerator)	5
F-TEST (DF denominator)	96
p-value	4.12188936316227e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.5663951111926
Sum Squared Residuals	1221.04071255729

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.596037812000243 \tabularnewline
R-squared & 0.355261073334037 \tabularnewline
Adjusted R-squared & 0.321680920903518 \tabularnewline
F-TEST (value) & 10.5794955537832 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 96 \tabularnewline
p-value & 4.12188936316227e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.5663951111926 \tabularnewline
Sum Squared Residuals & 1221.04071255729 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.596037812000243[/C][/ROW]
[ROW][C]R-squared[/C][C]0.355261073334037[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.321680920903518[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.5794955537832[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]96[/C][/ROW]
[ROW][C]p-value[/C][C]4.12188936316227e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.5663951111926[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1221.04071255729[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&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	0.596037812000243
R-squared	0.355261073334037
Adjusted R-squared	0.321680920903518
F-TEST (value)	10.5794955537832
F-TEST (DF numerator)	5
F-TEST (DF denominator)	96
p-value	4.12188936316227e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.5663951111926
Sum Squared Residuals	1221.04071255729

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1	2.58608659631325	-1.58608659631325
2	2	2.87459788844895	-0.874597888448954
3	3	2.58561348285017	0.414386517149833
4	4	3.02273941231058	0.977260587689424
5	5	6.27766207778926	-1.27766207778926
6	6	5.80773341501799	0.19226658498201
7	7	4.69279406384103	2.30720593615897
8	8	5.60172854976617	2.39827145023383
9	1	6.85091974253752	-5.85091974253752
10	2	5.83202664102896	-3.83202664102896
11	3	6.75133413174066	-3.75133413174066
12	4	3.38282103421425	0.617178965785751
13	5	6.94340326045559	-1.94340326045559
14	6	5.50084773345475	0.499152266545252
15	7	3.09288502284948	3.90711497715052
16	8	4.27831714858176	3.72168285141824
17	9	6.00070993750809	2.99929006249191
18	10	9.17061876952496	0.829381230475045
19	11	4.23660113890923	6.76339886109077
20	12	6.19951575659975	5.80048424340025
21	1	2.5354361465428	-1.5354361465428
22	2	4.74195321539373	-2.74195321539373
23	3	6.44262863541029	-3.44262863541029
24	4	6.94393531226336	-2.94393531226336
25	5	3.62631587933309	1.37368412066691
26	6	4.07762754916149	1.92237245083851
27	7	6.9219939528273	0.0780060471727008
28	8	4.13193353764118	3.86806646235882
29	9	7.65848267151288	1.34151732848712
30	1	4.12855416650439	-3.12855416650439
31	2	5.50645583264948	-3.50645583264948
32	3	5.68596011496775	-2.68596011496775
33	4	6.10906684844643	-2.10906684844643
34	5	7.59725883166225	-2.59725883166225
35	6	9.6409755228205	-3.64097552282049
36	7	7.61388553657713	-0.613885536577132
37	8	3.88013530669636	4.11986469330364
38	9	8.9734866556415	0.0265133443584941
39	10	8.5161273372124	1.48387266278759
40	11	7.52351082811111	3.47648917188889
41	12	7.18287189526927	4.81712810473073
42	13	10.9018905695538	2.09810943044621
43	14	8.34683577502856	5.65316422497144
44	1	6.65764616608414	-5.65764616608414
45	2	4.04295547293288	-2.04295547293288
46	3	3.63841816304238	-0.638418163042382
47	4	7.57266780775133	-3.57266780775133
48	5	5.93854139790914	-0.93854139790914
49	6	4.35246727641832	1.64753272358168
50	7	8.68688580342273	-1.68688580342273
51	8	7.55478830683349	0.445211693166514
52	9	7.44458056831328	1.55541943168672
53	10	5.47738010157825	4.52261989842175
54	11	8.86713745455137	2.13286254544864
55	12	11.2429568432658	0.757043156734213
56	13	7.67669724337471	5.32330275662529
57	1	6.48265363556183	-5.48265363556183
58	2	7.29538970216315	-5.29538970216315
59	3	10.1274403823691	-7.12744038236909
60	4	11.1840325210131	-7.18403252101313
61	5	11.2272235147357	-6.22722351473571
62	6	6.3073189227228	-0.307318922722801
63	7	6.75676057564308	0.243239424356923
64	8	9.8246428323162	-1.8246428323162
65	9	10.2958745559114	-1.29587455591143
66	10	8.92690142175316	1.07309857824684
67	11	7.66644243037858	3.33355756962142
68	12	13.662470067175	-1.66247006717504
69	13	8.99272819552767	4.00727180447233
70	14	9.18357684196915	4.81642315803085
71	15	8.73082069934102	6.26917930065898
72	16	15.4562785397443	0.543721460255657
73	17	10.5486896711913	6.4513103288087
74	18	10.8934299169093	7.10657008309068
75	19	12.1543871541575	6.84561284584252
76	1	5.86399702821714	-4.86399702821714
77	2	5.13668596445518	-3.13668596445517
78	3	4.97364184266552	-1.97364184266552
79	4	6.72909636462483	-2.72909636462483
80	5	7.83900337884123	-2.83900337884123
81	6	8.83383598106881	-2.83383598106881
82	7	7.76778924878036	-0.767789248780358
83	8	10.1743074865859	-2.17430748658589
84	9	6.23098789791434	2.76901210208566
85	10	7.33724872093558	2.66275127906442
86	11	7.55824502240008	3.44175497759992
87	12	7.35370888146418	4.64629111853582
88	13	10.0583694262214	2.94163057377856
89	1	5.03509158526759	-4.03509158526759
90	2	7.70360977459563	-5.70360977459563
91	3	6.41421373368612	-3.41421373368612
92	4	6.038852719482	-2.038852719482
93	5	10.81306213102	-5.81306213101999
94	6	9.4201896631272	-3.42018966312719
95	7	7.5806185779923	-0.580618577992299
96	8	6.88345516925336	1.11654483074664
97	9	11.6335403756362	-2.63354037563623
98	10	8.34264727795442	1.65735272204558
99	11	7.4399664921576	3.5600335078424
100	12	9.89356565897784	2.10643434102216
101	13	11.0939156382633	1.90608436173669
102	14	11.6038918473811	2.39610815261891

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 2.58608659631325 & -1.58608659631325 \tabularnewline
2 & 2 & 2.87459788844895 & -0.874597888448954 \tabularnewline
3 & 3 & 2.58561348285017 & 0.414386517149833 \tabularnewline
4 & 4 & 3.02273941231058 & 0.977260587689424 \tabularnewline
5 & 5 & 6.27766207778926 & -1.27766207778926 \tabularnewline
6 & 6 & 5.80773341501799 & 0.19226658498201 \tabularnewline
7 & 7 & 4.69279406384103 & 2.30720593615897 \tabularnewline
8 & 8 & 5.60172854976617 & 2.39827145023383 \tabularnewline
9 & 1 & 6.85091974253752 & -5.85091974253752 \tabularnewline
10 & 2 & 5.83202664102896 & -3.83202664102896 \tabularnewline
11 & 3 & 6.75133413174066 & -3.75133413174066 \tabularnewline
12 & 4 & 3.38282103421425 & 0.617178965785751 \tabularnewline
13 & 5 & 6.94340326045559 & -1.94340326045559 \tabularnewline
14 & 6 & 5.50084773345475 & 0.499152266545252 \tabularnewline
15 & 7 & 3.09288502284948 & 3.90711497715052 \tabularnewline
16 & 8 & 4.27831714858176 & 3.72168285141824 \tabularnewline
17 & 9 & 6.00070993750809 & 2.99929006249191 \tabularnewline
18 & 10 & 9.17061876952496 & 0.829381230475045 \tabularnewline
19 & 11 & 4.23660113890923 & 6.76339886109077 \tabularnewline
20 & 12 & 6.19951575659975 & 5.80048424340025 \tabularnewline
21 & 1 & 2.5354361465428 & -1.5354361465428 \tabularnewline
22 & 2 & 4.74195321539373 & -2.74195321539373 \tabularnewline
23 & 3 & 6.44262863541029 & -3.44262863541029 \tabularnewline
24 & 4 & 6.94393531226336 & -2.94393531226336 \tabularnewline
25 & 5 & 3.62631587933309 & 1.37368412066691 \tabularnewline
26 & 6 & 4.07762754916149 & 1.92237245083851 \tabularnewline
27 & 7 & 6.9219939528273 & 0.0780060471727008 \tabularnewline
28 & 8 & 4.13193353764118 & 3.86806646235882 \tabularnewline
29 & 9 & 7.65848267151288 & 1.34151732848712 \tabularnewline
30 & 1 & 4.12855416650439 & -3.12855416650439 \tabularnewline
31 & 2 & 5.50645583264948 & -3.50645583264948 \tabularnewline
32 & 3 & 5.68596011496775 & -2.68596011496775 \tabularnewline
33 & 4 & 6.10906684844643 & -2.10906684844643 \tabularnewline
34 & 5 & 7.59725883166225 & -2.59725883166225 \tabularnewline
35 & 6 & 9.6409755228205 & -3.64097552282049 \tabularnewline
36 & 7 & 7.61388553657713 & -0.613885536577132 \tabularnewline
37 & 8 & 3.88013530669636 & 4.11986469330364 \tabularnewline
38 & 9 & 8.9734866556415 & 0.0265133443584941 \tabularnewline
39 & 10 & 8.5161273372124 & 1.48387266278759 \tabularnewline
40 & 11 & 7.52351082811111 & 3.47648917188889 \tabularnewline
41 & 12 & 7.18287189526927 & 4.81712810473073 \tabularnewline
42 & 13 & 10.9018905695538 & 2.09810943044621 \tabularnewline
43 & 14 & 8.34683577502856 & 5.65316422497144 \tabularnewline
44 & 1 & 6.65764616608414 & -5.65764616608414 \tabularnewline
45 & 2 & 4.04295547293288 & -2.04295547293288 \tabularnewline
46 & 3 & 3.63841816304238 & -0.638418163042382 \tabularnewline
47 & 4 & 7.57266780775133 & -3.57266780775133 \tabularnewline
48 & 5 & 5.93854139790914 & -0.93854139790914 \tabularnewline
49 & 6 & 4.35246727641832 & 1.64753272358168 \tabularnewline
50 & 7 & 8.68688580342273 & -1.68688580342273 \tabularnewline
51 & 8 & 7.55478830683349 & 0.445211693166514 \tabularnewline
52 & 9 & 7.44458056831328 & 1.55541943168672 \tabularnewline
53 & 10 & 5.47738010157825 & 4.52261989842175 \tabularnewline
54 & 11 & 8.86713745455137 & 2.13286254544864 \tabularnewline
55 & 12 & 11.2429568432658 & 0.757043156734213 \tabularnewline
56 & 13 & 7.67669724337471 & 5.32330275662529 \tabularnewline
57 & 1 & 6.48265363556183 & -5.48265363556183 \tabularnewline
58 & 2 & 7.29538970216315 & -5.29538970216315 \tabularnewline
59 & 3 & 10.1274403823691 & -7.12744038236909 \tabularnewline
60 & 4 & 11.1840325210131 & -7.18403252101313 \tabularnewline
61 & 5 & 11.2272235147357 & -6.22722351473571 \tabularnewline
62 & 6 & 6.3073189227228 & -0.307318922722801 \tabularnewline
63 & 7 & 6.75676057564308 & 0.243239424356923 \tabularnewline
64 & 8 & 9.8246428323162 & -1.8246428323162 \tabularnewline
65 & 9 & 10.2958745559114 & -1.29587455591143 \tabularnewline
66 & 10 & 8.92690142175316 & 1.07309857824684 \tabularnewline
67 & 11 & 7.66644243037858 & 3.33355756962142 \tabularnewline
68 & 12 & 13.662470067175 & -1.66247006717504 \tabularnewline
69 & 13 & 8.99272819552767 & 4.00727180447233 \tabularnewline
70 & 14 & 9.18357684196915 & 4.81642315803085 \tabularnewline
71 & 15 & 8.73082069934102 & 6.26917930065898 \tabularnewline
72 & 16 & 15.4562785397443 & 0.543721460255657 \tabularnewline
73 & 17 & 10.5486896711913 & 6.4513103288087 \tabularnewline
74 & 18 & 10.8934299169093 & 7.10657008309068 \tabularnewline
75 & 19 & 12.1543871541575 & 6.84561284584252 \tabularnewline
76 & 1 & 5.86399702821714 & -4.86399702821714 \tabularnewline
77 & 2 & 5.13668596445518 & -3.13668596445517 \tabularnewline
78 & 3 & 4.97364184266552 & -1.97364184266552 \tabularnewline
79 & 4 & 6.72909636462483 & -2.72909636462483 \tabularnewline
80 & 5 & 7.83900337884123 & -2.83900337884123 \tabularnewline
81 & 6 & 8.83383598106881 & -2.83383598106881 \tabularnewline
82 & 7 & 7.76778924878036 & -0.767789248780358 \tabularnewline
83 & 8 & 10.1743074865859 & -2.17430748658589 \tabularnewline
84 & 9 & 6.23098789791434 & 2.76901210208566 \tabularnewline
85 & 10 & 7.33724872093558 & 2.66275127906442 \tabularnewline
86 & 11 & 7.55824502240008 & 3.44175497759992 \tabularnewline
87 & 12 & 7.35370888146418 & 4.64629111853582 \tabularnewline
88 & 13 & 10.0583694262214 & 2.94163057377856 \tabularnewline
89 & 1 & 5.03509158526759 & -4.03509158526759 \tabularnewline
90 & 2 & 7.70360977459563 & -5.70360977459563 \tabularnewline
91 & 3 & 6.41421373368612 & -3.41421373368612 \tabularnewline
92 & 4 & 6.038852719482 & -2.038852719482 \tabularnewline
93 & 5 & 10.81306213102 & -5.81306213101999 \tabularnewline
94 & 6 & 9.4201896631272 & -3.42018966312719 \tabularnewline
95 & 7 & 7.5806185779923 & -0.580618577992299 \tabularnewline
96 & 8 & 6.88345516925336 & 1.11654483074664 \tabularnewline
97 & 9 & 11.6335403756362 & -2.63354037563623 \tabularnewline
98 & 10 & 8.34264727795442 & 1.65735272204558 \tabularnewline
99 & 11 & 7.4399664921576 & 3.5600335078424 \tabularnewline
100 & 12 & 9.89356565897784 & 2.10643434102216 \tabularnewline
101 & 13 & 11.0939156382633 & 1.90608436173669 \tabularnewline
102 & 14 & 11.6038918473811 & 2.39610815261891 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&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]1[/C][C]2.58608659631325[/C][C]-1.58608659631325[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]2.87459788844895[/C][C]-0.874597888448954[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]2.58561348285017[/C][C]0.414386517149833[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]3.02273941231058[/C][C]0.977260587689424[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]6.27766207778926[/C][C]-1.27766207778926[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]5.80773341501799[/C][C]0.19226658498201[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]4.69279406384103[/C][C]2.30720593615897[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]5.60172854976617[/C][C]2.39827145023383[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]6.85091974253752[/C][C]-5.85091974253752[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]5.83202664102896[/C][C]-3.83202664102896[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]6.75133413174066[/C][C]-3.75133413174066[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]3.38282103421425[/C][C]0.617178965785751[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]6.94340326045559[/C][C]-1.94340326045559[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]5.50084773345475[/C][C]0.499152266545252[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]3.09288502284948[/C][C]3.90711497715052[/C][/ROW]
[ROW][C]16[/C][C]8[/C][C]4.27831714858176[/C][C]3.72168285141824[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]6.00070993750809[/C][C]2.99929006249191[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.17061876952496[/C][C]0.829381230475045[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]4.23660113890923[/C][C]6.76339886109077[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]6.19951575659975[/C][C]5.80048424340025[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]2.5354361465428[/C][C]-1.5354361465428[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]4.74195321539373[/C][C]-2.74195321539373[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]6.44262863541029[/C][C]-3.44262863541029[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]6.94393531226336[/C][C]-2.94393531226336[/C][/ROW]
[ROW][C]25[/C][C]5[/C][C]3.62631587933309[/C][C]1.37368412066691[/C][/ROW]
[ROW][C]26[/C][C]6[/C][C]4.07762754916149[/C][C]1.92237245083851[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]6.9219939528273[/C][C]0.0780060471727008[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]4.13193353764118[/C][C]3.86806646235882[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]7.65848267151288[/C][C]1.34151732848712[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]4.12855416650439[/C][C]-3.12855416650439[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]5.50645583264948[/C][C]-3.50645583264948[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]5.68596011496775[/C][C]-2.68596011496775[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]6.10906684844643[/C][C]-2.10906684844643[/C][/ROW]
[ROW][C]34[/C][C]5[/C][C]7.59725883166225[/C][C]-2.59725883166225[/C][/ROW]
[ROW][C]35[/C][C]6[/C][C]9.6409755228205[/C][C]-3.64097552282049[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]7.61388553657713[/C][C]-0.613885536577132[/C][/ROW]
[ROW][C]37[/C][C]8[/C][C]3.88013530669636[/C][C]4.11986469330364[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]8.9734866556415[/C][C]0.0265133443584941[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]8.5161273372124[/C][C]1.48387266278759[/C][/ROW]
[ROW][C]40[/C][C]11[/C][C]7.52351082811111[/C][C]3.47648917188889[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]7.18287189526927[/C][C]4.81712810473073[/C][/ROW]
[ROW][C]42[/C][C]13[/C][C]10.9018905695538[/C][C]2.09810943044621[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]8.34683577502856[/C][C]5.65316422497144[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]6.65764616608414[/C][C]-5.65764616608414[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]4.04295547293288[/C][C]-2.04295547293288[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]3.63841816304238[/C][C]-0.638418163042382[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]7.57266780775133[/C][C]-3.57266780775133[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]5.93854139790914[/C][C]-0.93854139790914[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]4.35246727641832[/C][C]1.64753272358168[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]8.68688580342273[/C][C]-1.68688580342273[/C][/ROW]
[ROW][C]51[/C][C]8[/C][C]7.55478830683349[/C][C]0.445211693166514[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]7.44458056831328[/C][C]1.55541943168672[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]5.47738010157825[/C][C]4.52261989842175[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]8.86713745455137[/C][C]2.13286254544864[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]11.2429568432658[/C][C]0.757043156734213[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]7.67669724337471[/C][C]5.32330275662529[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]6.48265363556183[/C][C]-5.48265363556183[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]7.29538970216315[/C][C]-5.29538970216315[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]10.1274403823691[/C][C]-7.12744038236909[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]11.1840325210131[/C][C]-7.18403252101313[/C][/ROW]
[ROW][C]61[/C][C]5[/C][C]11.2272235147357[/C][C]-6.22722351473571[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]6.3073189227228[/C][C]-0.307318922722801[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]6.75676057564308[/C][C]0.243239424356923[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]9.8246428323162[/C][C]-1.8246428323162[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.2958745559114[/C][C]-1.29587455591143[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]8.92690142175316[/C][C]1.07309857824684[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]7.66644243037858[/C][C]3.33355756962142[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.662470067175[/C][C]-1.66247006717504[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]8.99272819552767[/C][C]4.00727180447233[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]9.18357684196915[/C][C]4.81642315803085[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]8.73082069934102[/C][C]6.26917930065898[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]15.4562785397443[/C][C]0.543721460255657[/C][/ROW]
[ROW][C]73[/C][C]17[/C][C]10.5486896711913[/C][C]6.4513103288087[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]10.8934299169093[/C][C]7.10657008309068[/C][/ROW]
[ROW][C]75[/C][C]19[/C][C]12.1543871541575[/C][C]6.84561284584252[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]5.86399702821714[/C][C]-4.86399702821714[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]5.13668596445518[/C][C]-3.13668596445517[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]4.97364184266552[/C][C]-1.97364184266552[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]6.72909636462483[/C][C]-2.72909636462483[/C][/ROW]
[ROW][C]80[/C][C]5[/C][C]7.83900337884123[/C][C]-2.83900337884123[/C][/ROW]
[ROW][C]81[/C][C]6[/C][C]8.83383598106881[/C][C]-2.83383598106881[/C][/ROW]
[ROW][C]82[/C][C]7[/C][C]7.76778924878036[/C][C]-0.767789248780358[/C][/ROW]
[ROW][C]83[/C][C]8[/C][C]10.1743074865859[/C][C]-2.17430748658589[/C][/ROW]
[ROW][C]84[/C][C]9[/C][C]6.23098789791434[/C][C]2.76901210208566[/C][/ROW]
[ROW][C]85[/C][C]10[/C][C]7.33724872093558[/C][C]2.66275127906442[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]7.55824502240008[/C][C]3.44175497759992[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]7.35370888146418[/C][C]4.64629111853582[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]10.0583694262214[/C][C]2.94163057377856[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]5.03509158526759[/C][C]-4.03509158526759[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]7.70360977459563[/C][C]-5.70360977459563[/C][/ROW]
[ROW][C]91[/C][C]3[/C][C]6.41421373368612[/C][C]-3.41421373368612[/C][/ROW]
[ROW][C]92[/C][C]4[/C][C]6.038852719482[/C][C]-2.038852719482[/C][/ROW]
[ROW][C]93[/C][C]5[/C][C]10.81306213102[/C][C]-5.81306213101999[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]9.4201896631272[/C][C]-3.42018966312719[/C][/ROW]
[ROW][C]95[/C][C]7[/C][C]7.5806185779923[/C][C]-0.580618577992299[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]6.88345516925336[/C][C]1.11654483074664[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]11.6335403756362[/C][C]-2.63354037563623[/C][/ROW]
[ROW][C]98[/C][C]10[/C][C]8.34264727795442[/C][C]1.65735272204558[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]7.4399664921576[/C][C]3.5600335078424[/C][/ROW]
[ROW][C]100[/C][C]12[/C][C]9.89356565897784[/C][C]2.10643434102216[/C][/ROW]
[ROW][C]101[/C][C]13[/C][C]11.0939156382633[/C][C]1.90608436173669[/C][/ROW]
[ROW][C]102[/C][C]14[/C][C]11.6038918473811[/C][C]2.39610815261891[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&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	1	2.58608659631325	-1.58608659631325
2	2	2.87459788844895	-0.874597888448954
3	3	2.58561348285017	0.414386517149833
4	4	3.02273941231058	0.977260587689424
5	5	6.27766207778926	-1.27766207778926
6	6	5.80773341501799	0.19226658498201
7	7	4.69279406384103	2.30720593615897
8	8	5.60172854976617	2.39827145023383
9	1	6.85091974253752	-5.85091974253752
10	2	5.83202664102896	-3.83202664102896
11	3	6.75133413174066	-3.75133413174066
12	4	3.38282103421425	0.617178965785751
13	5	6.94340326045559	-1.94340326045559
14	6	5.50084773345475	0.499152266545252
15	7	3.09288502284948	3.90711497715052
16	8	4.27831714858176	3.72168285141824
17	9	6.00070993750809	2.99929006249191
18	10	9.17061876952496	0.829381230475045
19	11	4.23660113890923	6.76339886109077
20	12	6.19951575659975	5.80048424340025
21	1	2.5354361465428	-1.5354361465428
22	2	4.74195321539373	-2.74195321539373
23	3	6.44262863541029	-3.44262863541029
24	4	6.94393531226336	-2.94393531226336
25	5	3.62631587933309	1.37368412066691
26	6	4.07762754916149	1.92237245083851
27	7	6.9219939528273	0.0780060471727008
28	8	4.13193353764118	3.86806646235882
29	9	7.65848267151288	1.34151732848712
30	1	4.12855416650439	-3.12855416650439
31	2	5.50645583264948	-3.50645583264948
32	3	5.68596011496775	-2.68596011496775
33	4	6.10906684844643	-2.10906684844643
34	5	7.59725883166225	-2.59725883166225
35	6	9.6409755228205	-3.64097552282049
36	7	7.61388553657713	-0.613885536577132
37	8	3.88013530669636	4.11986469330364
38	9	8.9734866556415	0.0265133443584941
39	10	8.5161273372124	1.48387266278759
40	11	7.52351082811111	3.47648917188889
41	12	7.18287189526927	4.81712810473073
42	13	10.9018905695538	2.09810943044621
43	14	8.34683577502856	5.65316422497144
44	1	6.65764616608414	-5.65764616608414
45	2	4.04295547293288	-2.04295547293288
46	3	3.63841816304238	-0.638418163042382
47	4	7.57266780775133	-3.57266780775133
48	5	5.93854139790914	-0.93854139790914
49	6	4.35246727641832	1.64753272358168
50	7	8.68688580342273	-1.68688580342273
51	8	7.55478830683349	0.445211693166514
52	9	7.44458056831328	1.55541943168672
53	10	5.47738010157825	4.52261989842175
54	11	8.86713745455137	2.13286254544864
55	12	11.2429568432658	0.757043156734213
56	13	7.67669724337471	5.32330275662529
57	1	6.48265363556183	-5.48265363556183
58	2	7.29538970216315	-5.29538970216315
59	3	10.1274403823691	-7.12744038236909
60	4	11.1840325210131	-7.18403252101313
61	5	11.2272235147357	-6.22722351473571
62	6	6.3073189227228	-0.307318922722801
63	7	6.75676057564308	0.243239424356923
64	8	9.8246428323162	-1.8246428323162
65	9	10.2958745559114	-1.29587455591143
66	10	8.92690142175316	1.07309857824684
67	11	7.66644243037858	3.33355756962142
68	12	13.662470067175	-1.66247006717504
69	13	8.99272819552767	4.00727180447233
70	14	9.18357684196915	4.81642315803085
71	15	8.73082069934102	6.26917930065898
72	16	15.4562785397443	0.543721460255657
73	17	10.5486896711913	6.4513103288087
74	18	10.8934299169093	7.10657008309068
75	19	12.1543871541575	6.84561284584252
76	1	5.86399702821714	-4.86399702821714
77	2	5.13668596445518	-3.13668596445517
78	3	4.97364184266552	-1.97364184266552
79	4	6.72909636462483	-2.72909636462483
80	5	7.83900337884123	-2.83900337884123
81	6	8.83383598106881	-2.83383598106881
82	7	7.76778924878036	-0.767789248780358
83	8	10.1743074865859	-2.17430748658589
84	9	6.23098789791434	2.76901210208566
85	10	7.33724872093558	2.66275127906442
86	11	7.55824502240008	3.44175497759992
87	12	7.35370888146418	4.64629111853582
88	13	10.0583694262214	2.94163057377856
89	1	5.03509158526759	-4.03509158526759
90	2	7.70360977459563	-5.70360977459563
91	3	6.41421373368612	-3.41421373368612
92	4	6.038852719482	-2.038852719482
93	5	10.81306213102	-5.81306213101999
94	6	9.4201896631272	-3.42018966312719
95	7	7.5806185779923	-0.580618577992299
96	8	6.88345516925336	1.11654483074664
97	9	11.6335403756362	-2.63354037563623
98	10	8.34264727795442	1.65735272204558
99	11	7.4399664921576	3.5600335078424
100	12	9.89356565897784	2.10643434102216
101	13	11.0939156382633	1.90608436173669
102	14	11.6038918473811	2.39610815261891

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	1.57180728852578e-47	3.14361457705156e-47	1
10	3.93870366397687e-63	7.87740732795375e-63	1
11	1.14380453574025e-78	2.28760907148051e-78	1
12	3.98484071634166e-95	7.96968143268332e-95	1
13	1.8843187198294e-107	3.7686374396588e-107	1
14	6.89424775519183e-121	1.37884955103837e-120	1
15	1.49761744202907e-138	2.99523488405813e-138	1
16	2.43609504946008e-155	4.87219009892016e-155	1
17	2.76359697851623e-164	5.52719395703245e-164	1
18	6.088111807342e-183	1.2176223614684e-182	1
19	4.38423432809635e-201	8.7684686561927e-201	1
20	9.87002194058952e-217	1.9740043881179e-216	1
21	0.174869082892652	0.349738165785305	0.825130917107348
22	0.287995783319953	0.575991566639906	0.712004216680047
23	0.306539079743496	0.613078159486993	0.693460920256504
24	0.280667716849643	0.561335433699286	0.719332283150357
25	0.221728197130022	0.443456394260044	0.778271802869978
26	0.177994335680961	0.355988671361922	0.822005664319039
27	0.134585097119284	0.269170194238567	0.865414902880716
28	0.1227032595994	0.245406519198801	0.8772967404006
29	0.0965180457240818	0.193036091448164	0.903481954275918
30	0.157261806648217	0.314523613296434	0.842738193351783
31	0.168187813599071	0.336375627198142	0.83181218640093
32	0.15215845648183	0.30431691296366	0.84784154351817
33	0.123247746936477	0.246495493872954	0.876752253063523
34	0.101121761879926	0.202243523759853	0.898878238120074
35	0.084077694987567	0.168155389975134	0.915922305012433
36	0.0629606913343909	0.125921382668782	0.93703930866561
37	0.0694982791684538	0.138996558336908	0.930501720831546
38	0.0555294566226544	0.111058913245309	0.944470543377346
39	0.0457017638231732	0.0914035276463463	0.954298236176827
40	0.04935932830757	0.09871865661514	0.95064067169243
41	0.0691793725837296	0.138358745167459	0.93082062741627
42	0.0647012006154345	0.129402401230869	0.935298799384566
43	0.098956864387295	0.19791372877459	0.901043135612705
44	0.177330161510336	0.354660323020671	0.822669838489664
45	0.163829837987142	0.327659675974284	0.836170162012858
46	0.134510844219937	0.269021688439874	0.865489155780063
47	0.133269396413441	0.266538792826881	0.86673060358656
48	0.104368819367202	0.208737638734405	0.895631180632798
49	0.0876209380622089	0.175241876124418	0.912379061937791
50	0.0687367323983312	0.137473464796662	0.931263267601669
51	0.0517346996575493	0.103469399315099	0.94826530034245
52	0.0409652907306016	0.0819305814612032	0.959034709269398
53	0.0554724952938851	0.11094499058777	0.944527504706115
54	0.0507458504585762	0.101491700917152	0.949254149541424
55	0.0473095941703488	0.0946191883406975	0.95269040582965
56	0.191461968015855	0.38292393603171	0.808538031984145
57	0.229052036578788	0.458104073157575	0.770947963421212
58	0.2575641756602	0.515128351320399	0.7424358243398
59	0.363945352378296	0.727890704756592	0.636054647621704
60	0.421059068662965	0.84211813732593	0.578940931337035
61	0.550599445503528	0.898801108992944	0.449400554496472
62	0.506410894048308	0.987178211903385	0.493589105951692
63	0.466710901680713	0.933421803361427	0.533289098319287
64	0.451102420148215	0.90220484029643	0.548897579851785
65	0.455568005432382	0.911136010864763	0.544431994567618
66	0.436955124424503	0.873910248849007	0.563044875575497
67	0.419635633851368	0.839271267702735	0.580364366148632
68	0.482714742396444	0.965429484792889	0.517285257603556
69	0.469948525571184	0.939897051142368	0.530051474428816
70	0.462443215153112	0.924886430306224	0.537556784846888
71	0.480854474260006	0.961708948520012	0.519145525739994
72	0.537501847349335	0.92499630530133	0.462498152650665
73	0.541775510286285	0.916448979427431	0.458224489713715
74	0.570888163148955	0.85822367370209	0.429111836851045
75	1	1.22027020086253e-301	6.10135100431266e-302
76	1	3.21008248997008e-280	1.60504124498504e-280
77	1	8.01781461923734e-268	4.00890730961867e-268
78	1	5.71301031733854e-273	2.85650515866927e-273
79	1	2.83413109987659e-259	1.41706554993829e-259
80	1	2.98652411702093e-232	1.49326205851047e-232
81	1	3.22908405731299e-217	1.6145420286565e-217
82	1	8.06074562615027e-207	4.03037281307513e-207
83	1	4.40941413512038e-186	2.20470706756019e-186
84	1	1.87549703198455e-177	9.37748515992274e-178
85	1	3.0669965342982e-155	1.5334982671491e-155
86	1	1.62951554010327e-145	8.14757770051634e-146
87	1	4.4441042194752e-139	2.2220521097376e-139
88	1	1.25965073606402e-117	6.29825368032009e-118
89	1	3.94837735300578e-105	1.97418867650289e-105
90	1	1.29366600790676e-87	6.4683300395338e-88
91	1	5.33125328368102e-73	2.66562664184051e-73
92	1	3.07142248092887e-61	1.53571124046443e-61
93	1	1.79752503440215e-46	8.98762517201076e-47

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 1.57180728852578e-47 & 3.14361457705156e-47 & 1 \tabularnewline
10 & 3.93870366397687e-63 & 7.87740732795375e-63 & 1 \tabularnewline
11 & 1.14380453574025e-78 & 2.28760907148051e-78 & 1 \tabularnewline
12 & 3.98484071634166e-95 & 7.96968143268332e-95 & 1 \tabularnewline
13 & 1.8843187198294e-107 & 3.7686374396588e-107 & 1 \tabularnewline
14 & 6.89424775519183e-121 & 1.37884955103837e-120 & 1 \tabularnewline
15 & 1.49761744202907e-138 & 2.99523488405813e-138 & 1 \tabularnewline
16 & 2.43609504946008e-155 & 4.87219009892016e-155 & 1 \tabularnewline
17 & 2.76359697851623e-164 & 5.52719395703245e-164 & 1 \tabularnewline
18 & 6.088111807342e-183 & 1.2176223614684e-182 & 1 \tabularnewline
19 & 4.38423432809635e-201 & 8.7684686561927e-201 & 1 \tabularnewline
20 & 9.87002194058952e-217 & 1.9740043881179e-216 & 1 \tabularnewline
21 & 0.174869082892652 & 0.349738165785305 & 0.825130917107348 \tabularnewline
22 & 0.287995783319953 & 0.575991566639906 & 0.712004216680047 \tabularnewline
23 & 0.306539079743496 & 0.613078159486993 & 0.693460920256504 \tabularnewline
24 & 0.280667716849643 & 0.561335433699286 & 0.719332283150357 \tabularnewline
25 & 0.221728197130022 & 0.443456394260044 & 0.778271802869978 \tabularnewline
26 & 0.177994335680961 & 0.355988671361922 & 0.822005664319039 \tabularnewline
27 & 0.134585097119284 & 0.269170194238567 & 0.865414902880716 \tabularnewline
28 & 0.1227032595994 & 0.245406519198801 & 0.8772967404006 \tabularnewline
29 & 0.0965180457240818 & 0.193036091448164 & 0.903481954275918 \tabularnewline
30 & 0.157261806648217 & 0.314523613296434 & 0.842738193351783 \tabularnewline
31 & 0.168187813599071 & 0.336375627198142 & 0.83181218640093 \tabularnewline
32 & 0.15215845648183 & 0.30431691296366 & 0.84784154351817 \tabularnewline
33 & 0.123247746936477 & 0.246495493872954 & 0.876752253063523 \tabularnewline
34 & 0.101121761879926 & 0.202243523759853 & 0.898878238120074 \tabularnewline
35 & 0.084077694987567 & 0.168155389975134 & 0.915922305012433 \tabularnewline
36 & 0.0629606913343909 & 0.125921382668782 & 0.93703930866561 \tabularnewline
37 & 0.0694982791684538 & 0.138996558336908 & 0.930501720831546 \tabularnewline
38 & 0.0555294566226544 & 0.111058913245309 & 0.944470543377346 \tabularnewline
39 & 0.0457017638231732 & 0.0914035276463463 & 0.954298236176827 \tabularnewline
40 & 0.04935932830757 & 0.09871865661514 & 0.95064067169243 \tabularnewline
41 & 0.0691793725837296 & 0.138358745167459 & 0.93082062741627 \tabularnewline
42 & 0.0647012006154345 & 0.129402401230869 & 0.935298799384566 \tabularnewline
43 & 0.098956864387295 & 0.19791372877459 & 0.901043135612705 \tabularnewline
44 & 0.177330161510336 & 0.354660323020671 & 0.822669838489664 \tabularnewline
45 & 0.163829837987142 & 0.327659675974284 & 0.836170162012858 \tabularnewline
46 & 0.134510844219937 & 0.269021688439874 & 0.865489155780063 \tabularnewline
47 & 0.133269396413441 & 0.266538792826881 & 0.86673060358656 \tabularnewline
48 & 0.104368819367202 & 0.208737638734405 & 0.895631180632798 \tabularnewline
49 & 0.0876209380622089 & 0.175241876124418 & 0.912379061937791 \tabularnewline
50 & 0.0687367323983312 & 0.137473464796662 & 0.931263267601669 \tabularnewline
51 & 0.0517346996575493 & 0.103469399315099 & 0.94826530034245 \tabularnewline
52 & 0.0409652907306016 & 0.0819305814612032 & 0.959034709269398 \tabularnewline
53 & 0.0554724952938851 & 0.11094499058777 & 0.944527504706115 \tabularnewline
54 & 0.0507458504585762 & 0.101491700917152 & 0.949254149541424 \tabularnewline
55 & 0.0473095941703488 & 0.0946191883406975 & 0.95269040582965 \tabularnewline
56 & 0.191461968015855 & 0.38292393603171 & 0.808538031984145 \tabularnewline
57 & 0.229052036578788 & 0.458104073157575 & 0.770947963421212 \tabularnewline
58 & 0.2575641756602 & 0.515128351320399 & 0.7424358243398 \tabularnewline
59 & 0.363945352378296 & 0.727890704756592 & 0.636054647621704 \tabularnewline
60 & 0.421059068662965 & 0.84211813732593 & 0.578940931337035 \tabularnewline
61 & 0.550599445503528 & 0.898801108992944 & 0.449400554496472 \tabularnewline
62 & 0.506410894048308 & 0.987178211903385 & 0.493589105951692 \tabularnewline
63 & 0.466710901680713 & 0.933421803361427 & 0.533289098319287 \tabularnewline
64 & 0.451102420148215 & 0.90220484029643 & 0.548897579851785 \tabularnewline
65 & 0.455568005432382 & 0.911136010864763 & 0.544431994567618 \tabularnewline
66 & 0.436955124424503 & 0.873910248849007 & 0.563044875575497 \tabularnewline
67 & 0.419635633851368 & 0.839271267702735 & 0.580364366148632 \tabularnewline
68 & 0.482714742396444 & 0.965429484792889 & 0.517285257603556 \tabularnewline
69 & 0.469948525571184 & 0.939897051142368 & 0.530051474428816 \tabularnewline
70 & 0.462443215153112 & 0.924886430306224 & 0.537556784846888 \tabularnewline
71 & 0.480854474260006 & 0.961708948520012 & 0.519145525739994 \tabularnewline
72 & 0.537501847349335 & 0.92499630530133 & 0.462498152650665 \tabularnewline
73 & 0.541775510286285 & 0.916448979427431 & 0.458224489713715 \tabularnewline
74 & 0.570888163148955 & 0.85822367370209 & 0.429111836851045 \tabularnewline
75 & 1 & 1.22027020086253e-301 & 6.10135100431266e-302 \tabularnewline
76 & 1 & 3.21008248997008e-280 & 1.60504124498504e-280 \tabularnewline
77 & 1 & 8.01781461923734e-268 & 4.00890730961867e-268 \tabularnewline
78 & 1 & 5.71301031733854e-273 & 2.85650515866927e-273 \tabularnewline
79 & 1 & 2.83413109987659e-259 & 1.41706554993829e-259 \tabularnewline
80 & 1 & 2.98652411702093e-232 & 1.49326205851047e-232 \tabularnewline
81 & 1 & 3.22908405731299e-217 & 1.6145420286565e-217 \tabularnewline
82 & 1 & 8.06074562615027e-207 & 4.03037281307513e-207 \tabularnewline
83 & 1 & 4.40941413512038e-186 & 2.20470706756019e-186 \tabularnewline
84 & 1 & 1.87549703198455e-177 & 9.37748515992274e-178 \tabularnewline
85 & 1 & 3.0669965342982e-155 & 1.5334982671491e-155 \tabularnewline
86 & 1 & 1.62951554010327e-145 & 8.14757770051634e-146 \tabularnewline
87 & 1 & 4.4441042194752e-139 & 2.2220521097376e-139 \tabularnewline
88 & 1 & 1.25965073606402e-117 & 6.29825368032009e-118 \tabularnewline
89 & 1 & 3.94837735300578e-105 & 1.97418867650289e-105 \tabularnewline
90 & 1 & 1.29366600790676e-87 & 6.4683300395338e-88 \tabularnewline
91 & 1 & 5.33125328368102e-73 & 2.66562664184051e-73 \tabularnewline
92 & 1 & 3.07142248092887e-61 & 1.53571124046443e-61 \tabularnewline
93 & 1 & 1.79752503440215e-46 & 8.98762517201076e-47 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&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]9[/C][C]1.57180728852578e-47[/C][C]3.14361457705156e-47[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]3.93870366397687e-63[/C][C]7.87740732795375e-63[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]1.14380453574025e-78[/C][C]2.28760907148051e-78[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]3.98484071634166e-95[/C][C]7.96968143268332e-95[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]1.8843187198294e-107[/C][C]3.7686374396588e-107[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]6.89424775519183e-121[/C][C]1.37884955103837e-120[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]1.49761744202907e-138[/C][C]2.99523488405813e-138[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]2.43609504946008e-155[/C][C]4.87219009892016e-155[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]2.76359697851623e-164[/C][C]5.52719395703245e-164[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]6.088111807342e-183[/C][C]1.2176223614684e-182[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]4.38423432809635e-201[/C][C]8.7684686561927e-201[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]9.87002194058952e-217[/C][C]1.9740043881179e-216[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]0.174869082892652[/C][C]0.349738165785305[/C][C]0.825130917107348[/C][/ROW]
[ROW][C]22[/C][C]0.287995783319953[/C][C]0.575991566639906[/C][C]0.712004216680047[/C][/ROW]
[ROW][C]23[/C][C]0.306539079743496[/C][C]0.613078159486993[/C][C]0.693460920256504[/C][/ROW]
[ROW][C]24[/C][C]0.280667716849643[/C][C]0.561335433699286[/C][C]0.719332283150357[/C][/ROW]
[ROW][C]25[/C][C]0.221728197130022[/C][C]0.443456394260044[/C][C]0.778271802869978[/C][/ROW]
[ROW][C]26[/C][C]0.177994335680961[/C][C]0.355988671361922[/C][C]0.822005664319039[/C][/ROW]
[ROW][C]27[/C][C]0.134585097119284[/C][C]0.269170194238567[/C][C]0.865414902880716[/C][/ROW]
[ROW][C]28[/C][C]0.1227032595994[/C][C]0.245406519198801[/C][C]0.8772967404006[/C][/ROW]
[ROW][C]29[/C][C]0.0965180457240818[/C][C]0.193036091448164[/C][C]0.903481954275918[/C][/ROW]
[ROW][C]30[/C][C]0.157261806648217[/C][C]0.314523613296434[/C][C]0.842738193351783[/C][/ROW]
[ROW][C]31[/C][C]0.168187813599071[/C][C]0.336375627198142[/C][C]0.83181218640093[/C][/ROW]
[ROW][C]32[/C][C]0.15215845648183[/C][C]0.30431691296366[/C][C]0.84784154351817[/C][/ROW]
[ROW][C]33[/C][C]0.123247746936477[/C][C]0.246495493872954[/C][C]0.876752253063523[/C][/ROW]
[ROW][C]34[/C][C]0.101121761879926[/C][C]0.202243523759853[/C][C]0.898878238120074[/C][/ROW]
[ROW][C]35[/C][C]0.084077694987567[/C][C]0.168155389975134[/C][C]0.915922305012433[/C][/ROW]
[ROW][C]36[/C][C]0.0629606913343909[/C][C]0.125921382668782[/C][C]0.93703930866561[/C][/ROW]
[ROW][C]37[/C][C]0.0694982791684538[/C][C]0.138996558336908[/C][C]0.930501720831546[/C][/ROW]
[ROW][C]38[/C][C]0.0555294566226544[/C][C]0.111058913245309[/C][C]0.944470543377346[/C][/ROW]
[ROW][C]39[/C][C]0.0457017638231732[/C][C]0.0914035276463463[/C][C]0.954298236176827[/C][/ROW]
[ROW][C]40[/C][C]0.04935932830757[/C][C]0.09871865661514[/C][C]0.95064067169243[/C][/ROW]
[ROW][C]41[/C][C]0.0691793725837296[/C][C]0.138358745167459[/C][C]0.93082062741627[/C][/ROW]
[ROW][C]42[/C][C]0.0647012006154345[/C][C]0.129402401230869[/C][C]0.935298799384566[/C][/ROW]
[ROW][C]43[/C][C]0.098956864387295[/C][C]0.19791372877459[/C][C]0.901043135612705[/C][/ROW]
[ROW][C]44[/C][C]0.177330161510336[/C][C]0.354660323020671[/C][C]0.822669838489664[/C][/ROW]
[ROW][C]45[/C][C]0.163829837987142[/C][C]0.327659675974284[/C][C]0.836170162012858[/C][/ROW]
[ROW][C]46[/C][C]0.134510844219937[/C][C]0.269021688439874[/C][C]0.865489155780063[/C][/ROW]
[ROW][C]47[/C][C]0.133269396413441[/C][C]0.266538792826881[/C][C]0.86673060358656[/C][/ROW]
[ROW][C]48[/C][C]0.104368819367202[/C][C]0.208737638734405[/C][C]0.895631180632798[/C][/ROW]
[ROW][C]49[/C][C]0.0876209380622089[/C][C]0.175241876124418[/C][C]0.912379061937791[/C][/ROW]
[ROW][C]50[/C][C]0.0687367323983312[/C][C]0.137473464796662[/C][C]0.931263267601669[/C][/ROW]
[ROW][C]51[/C][C]0.0517346996575493[/C][C]0.103469399315099[/C][C]0.94826530034245[/C][/ROW]
[ROW][C]52[/C][C]0.0409652907306016[/C][C]0.0819305814612032[/C][C]0.959034709269398[/C][/ROW]
[ROW][C]53[/C][C]0.0554724952938851[/C][C]0.11094499058777[/C][C]0.944527504706115[/C][/ROW]
[ROW][C]54[/C][C]0.0507458504585762[/C][C]0.101491700917152[/C][C]0.949254149541424[/C][/ROW]
[ROW][C]55[/C][C]0.0473095941703488[/C][C]0.0946191883406975[/C][C]0.95269040582965[/C][/ROW]
[ROW][C]56[/C][C]0.191461968015855[/C][C]0.38292393603171[/C][C]0.808538031984145[/C][/ROW]
[ROW][C]57[/C][C]0.229052036578788[/C][C]0.458104073157575[/C][C]0.770947963421212[/C][/ROW]
[ROW][C]58[/C][C]0.2575641756602[/C][C]0.515128351320399[/C][C]0.7424358243398[/C][/ROW]
[ROW][C]59[/C][C]0.363945352378296[/C][C]0.727890704756592[/C][C]0.636054647621704[/C][/ROW]
[ROW][C]60[/C][C]0.421059068662965[/C][C]0.84211813732593[/C][C]0.578940931337035[/C][/ROW]
[ROW][C]61[/C][C]0.550599445503528[/C][C]0.898801108992944[/C][C]0.449400554496472[/C][/ROW]
[ROW][C]62[/C][C]0.506410894048308[/C][C]0.987178211903385[/C][C]0.493589105951692[/C][/ROW]
[ROW][C]63[/C][C]0.466710901680713[/C][C]0.933421803361427[/C][C]0.533289098319287[/C][/ROW]
[ROW][C]64[/C][C]0.451102420148215[/C][C]0.90220484029643[/C][C]0.548897579851785[/C][/ROW]
[ROW][C]65[/C][C]0.455568005432382[/C][C]0.911136010864763[/C][C]0.544431994567618[/C][/ROW]
[ROW][C]66[/C][C]0.436955124424503[/C][C]0.873910248849007[/C][C]0.563044875575497[/C][/ROW]
[ROW][C]67[/C][C]0.419635633851368[/C][C]0.839271267702735[/C][C]0.580364366148632[/C][/ROW]
[ROW][C]68[/C][C]0.482714742396444[/C][C]0.965429484792889[/C][C]0.517285257603556[/C][/ROW]
[ROW][C]69[/C][C]0.469948525571184[/C][C]0.939897051142368[/C][C]0.530051474428816[/C][/ROW]
[ROW][C]70[/C][C]0.462443215153112[/C][C]0.924886430306224[/C][C]0.537556784846888[/C][/ROW]
[ROW][C]71[/C][C]0.480854474260006[/C][C]0.961708948520012[/C][C]0.519145525739994[/C][/ROW]
[ROW][C]72[/C][C]0.537501847349335[/C][C]0.92499630530133[/C][C]0.462498152650665[/C][/ROW]
[ROW][C]73[/C][C]0.541775510286285[/C][C]0.916448979427431[/C][C]0.458224489713715[/C][/ROW]
[ROW][C]74[/C][C]0.570888163148955[/C][C]0.85822367370209[/C][C]0.429111836851045[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.22027020086253e-301[/C][C]6.10135100431266e-302[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]3.21008248997008e-280[/C][C]1.60504124498504e-280[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]8.01781461923734e-268[/C][C]4.00890730961867e-268[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]5.71301031733854e-273[/C][C]2.85650515866927e-273[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]2.83413109987659e-259[/C][C]1.41706554993829e-259[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]2.98652411702093e-232[/C][C]1.49326205851047e-232[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]3.22908405731299e-217[/C][C]1.6145420286565e-217[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]8.06074562615027e-207[/C][C]4.03037281307513e-207[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]4.40941413512038e-186[/C][C]2.20470706756019e-186[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]1.87549703198455e-177[/C][C]9.37748515992274e-178[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]3.0669965342982e-155[/C][C]1.5334982671491e-155[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.62951554010327e-145[/C][C]8.14757770051634e-146[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]4.4441042194752e-139[/C][C]2.2220521097376e-139[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]1.25965073606402e-117[/C][C]6.29825368032009e-118[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]3.94837735300578e-105[/C][C]1.97418867650289e-105[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.29366600790676e-87[/C][C]6.4683300395338e-88[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]5.33125328368102e-73[/C][C]2.66562664184051e-73[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]3.07142248092887e-61[/C][C]1.53571124046443e-61[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.79752503440215e-46[/C][C]8.98762517201076e-47[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&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
9	1.57180728852578e-47	3.14361457705156e-47	1
10	3.93870366397687e-63	7.87740732795375e-63	1
11	1.14380453574025e-78	2.28760907148051e-78	1
12	3.98484071634166e-95	7.96968143268332e-95	1
13	1.8843187198294e-107	3.7686374396588e-107	1
14	6.89424775519183e-121	1.37884955103837e-120	1
15	1.49761744202907e-138	2.99523488405813e-138	1
16	2.43609504946008e-155	4.87219009892016e-155	1
17	2.76359697851623e-164	5.52719395703245e-164	1
18	6.088111807342e-183	1.2176223614684e-182	1
19	4.38423432809635e-201	8.7684686561927e-201	1
20	9.87002194058952e-217	1.9740043881179e-216	1
21	0.174869082892652	0.349738165785305	0.825130917107348
22	0.287995783319953	0.575991566639906	0.712004216680047
23	0.306539079743496	0.613078159486993	0.693460920256504
24	0.280667716849643	0.561335433699286	0.719332283150357
25	0.221728197130022	0.443456394260044	0.778271802869978
26	0.177994335680961	0.355988671361922	0.822005664319039
27	0.134585097119284	0.269170194238567	0.865414902880716
28	0.1227032595994	0.245406519198801	0.8772967404006
29	0.0965180457240818	0.193036091448164	0.903481954275918
30	0.157261806648217	0.314523613296434	0.842738193351783
31	0.168187813599071	0.336375627198142	0.83181218640093
32	0.15215845648183	0.30431691296366	0.84784154351817
33	0.123247746936477	0.246495493872954	0.876752253063523
34	0.101121761879926	0.202243523759853	0.898878238120074
35	0.084077694987567	0.168155389975134	0.915922305012433
36	0.0629606913343909	0.125921382668782	0.93703930866561
37	0.0694982791684538	0.138996558336908	0.930501720831546
38	0.0555294566226544	0.111058913245309	0.944470543377346
39	0.0457017638231732	0.0914035276463463	0.954298236176827
40	0.04935932830757	0.09871865661514	0.95064067169243
41	0.0691793725837296	0.138358745167459	0.93082062741627
42	0.0647012006154345	0.129402401230869	0.935298799384566
43	0.098956864387295	0.19791372877459	0.901043135612705
44	0.177330161510336	0.354660323020671	0.822669838489664
45	0.163829837987142	0.327659675974284	0.836170162012858
46	0.134510844219937	0.269021688439874	0.865489155780063
47	0.133269396413441	0.266538792826881	0.86673060358656
48	0.104368819367202	0.208737638734405	0.895631180632798
49	0.0876209380622089	0.175241876124418	0.912379061937791
50	0.0687367323983312	0.137473464796662	0.931263267601669
51	0.0517346996575493	0.103469399315099	0.94826530034245
52	0.0409652907306016	0.0819305814612032	0.959034709269398
53	0.0554724952938851	0.11094499058777	0.944527504706115
54	0.0507458504585762	0.101491700917152	0.949254149541424
55	0.0473095941703488	0.0946191883406975	0.95269040582965
56	0.191461968015855	0.38292393603171	0.808538031984145
57	0.229052036578788	0.458104073157575	0.770947963421212
58	0.2575641756602	0.515128351320399	0.7424358243398
59	0.363945352378296	0.727890704756592	0.636054647621704
60	0.421059068662965	0.84211813732593	0.578940931337035
61	0.550599445503528	0.898801108992944	0.449400554496472
62	0.506410894048308	0.987178211903385	0.493589105951692
63	0.466710901680713	0.933421803361427	0.533289098319287
64	0.451102420148215	0.90220484029643	0.548897579851785
65	0.455568005432382	0.911136010864763	0.544431994567618
66	0.436955124424503	0.873910248849007	0.563044875575497
67	0.419635633851368	0.839271267702735	0.580364366148632
68	0.482714742396444	0.965429484792889	0.517285257603556
69	0.469948525571184	0.939897051142368	0.530051474428816
70	0.462443215153112	0.924886430306224	0.537556784846888
71	0.480854474260006	0.961708948520012	0.519145525739994
72	0.537501847349335	0.92499630530133	0.462498152650665
73	0.541775510286285	0.916448979427431	0.458224489713715
74	0.570888163148955	0.85822367370209	0.429111836851045
75	1	1.22027020086253e-301	6.10135100431266e-302
76	1	3.21008248997008e-280	1.60504124498504e-280
77	1	8.01781461923734e-268	4.00890730961867e-268
78	1	5.71301031733854e-273	2.85650515866927e-273
79	1	2.83413109987659e-259	1.41706554993829e-259
80	1	2.98652411702093e-232	1.49326205851047e-232
81	1	3.22908405731299e-217	1.6145420286565e-217
82	1	8.06074562615027e-207	4.03037281307513e-207
83	1	4.40941413512038e-186	2.20470706756019e-186
84	1	1.87549703198455e-177	9.37748515992274e-178
85	1	3.0669965342982e-155	1.5334982671491e-155
86	1	1.62951554010327e-145	8.14757770051634e-146
87	1	4.4441042194752e-139	2.2220521097376e-139
88	1	1.25965073606402e-117	6.29825368032009e-118
89	1	3.94837735300578e-105	1.97418867650289e-105
90	1	1.29366600790676e-87	6.4683300395338e-88
91	1	5.33125328368102e-73	2.66562664184051e-73
92	1	3.07142248092887e-61	1.53571124046443e-61
93	1	1.79752503440215e-46	8.98762517201076e-47

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	31	0.364705882352941	NOK
5% type I error level	31	0.364705882352941	NOK
10% type I error level	35	0.411764705882353	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 & 31 & 0.364705882352941 & NOK \tabularnewline
5% type I error level & 31 & 0.364705882352941 & NOK \tabularnewline
10% type I error level & 35 & 0.411764705882353 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&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]31[/C][C]0.364705882352941[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.364705882352941[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.411764705882353[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&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	31	0.364705882352941	NOK
5% type I error level	31	0.364705882352941	NOK
10% type I error level	35	0.411764705882353	NOK

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

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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