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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 09:05:58 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352124402og3hyb0jzyzy7fg.htm/, Retrieved Mon, 06 Feb 2023 00:23:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186071, Retrieved Mon, 06 Feb 2023 00:23:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Concern, tut] [2010-12-02 14:07:40] [e73e9643c012a54583c6a406017b2645]
-    D  [Multiple Regression] [WS7 - Eerste Regr...] [2011-11-21 15:46:36] [b8fde34a99ee6a7d49500940cae4da2a]
-    D    [Multiple Regression] [multiple regression] [2012-11-04 15:20:41] [456f9f31a5baae2eb9a0b13ee35c0d42]
-    D      [Multiple Regression] [multiple regression] [2012-11-04 17:07:32] [456f9f31a5baae2eb9a0b13ee35c0d42]
-               [Multiple Regression] [] [2012-11-05 14:05:58] [c138fbd6e7c7784b8fd4dab04951100b] [Current]
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Dataseries X:
1	1901	61	17	56	84	4	21	51
2	2509	74	19	73	47	3	15	45
3	2114	57	18	62	63	3	17	44
4	1331	50	15	42	28	3	20	42
5	1399	48	15	59	22	2	12	38
6	7333	2	12	27	18	6	4	38
7	1170	31	20	78	27	5	11	35
8	1507	61	14	56	37	5	12	35
9	1107	36	15	59	20	5	9	34
10	2051	46	13	51	67	5	14	33
11	1290	30	17	47	28	4	11	32
12	820	49	10	35	45	3	14	31
13	1502	14	13	47	15	5	4	30
14	1451	12	12	47	23	6	7	30
15	1178	54	16	55	30	6	9	30
16	1514	44	15	54	27	2	14	29
17	883	40	15	60	43	5	13	29
18	1405	57	15	55	36	5	11	29
19	927	29	12	48	28	5	9	28
20	1352	32	13	47	28	9	8	27
21	1314	28	12	47	22	4	9	27
22	1307	40	15	52	27	4	11	27
23	1243	54	12	48	24	5	7	26
24	1232	56	12	48	52	3	15	26
25	1097	19	9	27	12	0	4	26
26	1100	67	12	12	24	5	10	26
27	1316	25	13	51	10	3	10	26
28	903	42	16	58	71	4	13	25
29	929	28	15	60	12	2	10	25
30	1049	57	13	46	24	5	10	25
31	1372	28	12	45	22	11	6	24
32	1470	35	13	42	21	5	8	24
33	821	10	12	41	13	3	7	24
34	1239	30	12	47	28	4	11	24
35	1384	23	8	32	19	5	10	24
36	820	32	15	56	29	5	11	24
37	1462	24	12	42	12	2	10	24
38	1202	42	12	41	32	6	8	23
39	1091	33	12	47	21	3	10	23
40	1228	19	14	47	19	4	5	23
41	707	17	15	49	15	8	5	23
42	868	49	15	52	14	14	5	23
43	1165	30	12	42	34	11	9	22
44	1106	3	13	55	8	8	2	22
45	1429	56	12	48	27	3	9	22
46	1671	37	13	48	31	3	13	22
47	1579	26	12	38	21	11	7	22
48	774	19	12	48	10	3	5	21
49	934	22	13	50	21	4	7	21
50	825	53	12	39	19	3	8	21
51	1375	35	12	48	27	5	8	21
52	968	12	9	36	17	6	5	21
53	1156	34	13	49	30	8	5	21
54	1374	28	13	39	19	3	10	21
55	1224	38	12	41	17	3	5	21
56	804	38	15	45	24	5	10	21
57	998	45	15	60	36	5	10	21
58	1112	15	13	45	16	3	7	21
59	1153	35	14	41	16	3	10	20
60	613	27	14	52	30	3	9	20
61	729	23	12	46	18	5	10	20
62	813	33	12	39	26	3	10	20
63	912	23	9	32	17	3	5	20
64	1178	26	14	52	28	6	8	20
65	1201	32	16	54	20	4	6	19
66	1165	35	15	51	27	3	7	19
67	705	18	13	52	13	13	6	18
68	814	18	16	57	10	5	3	17
69	1082	41	12	47	29	6	9	17
70	885	39	12	45	34	5	11	17
71	837	56	12	41	30	3	9	17
72	586	35	12	43	16	4	10	16
73	913	37	10	31	22	4	9	16
74	547	26	15	32	22	7	7	15
75	758	33	12	41	31	4	6	15
76	848	7	9	27	10	5	6	15
77	634	16	10	40	7	7	5	15
78	501	13	13	46	10	3	5	15
79	849	54	12	32	55	6	8	15
80	733	30	13	9	25	8	7	15
81	634	9	16	64	9	5	5	15
82	1010	35	15	30	31	5	10	15
83	778	0	12	46	0	0	0	15
84	480	40	12	37	24	3	10	15
85	848	22	12	22	14	5	6	15
86	714	29	12	20	11	3	6	14
87	871	25	12	21	8	8	4	14
88	776	17	14	44	9	9	3	14
89	815	32	12	24	18	9	7	14
90	811	40	12	33	14	4	5	14
91	529	24	12	45	27	2	8	13
92	642	18	13	35	10	0	0	13
93	562	15	8	31	16	3	5	13
94	626	17	16	20	13	7	5	13
95	636	28	12	13	10	5	5	13
96	935	18	11	33	16	3	5	13
97	473	16	15	58	11	3	6	12
98	836	28	13	26	8	3	5	12
99	938	17	12	36	29	7	6	12
100	656	25	13	32	12	4	4	12
101	566	2	13	34	1	0	0	12
102	765	10	12	15	26	5	8	12
103	705	9	12	40	5	5	2	11
104	558	7	12	37	5	5	2	11
105	582	27	14	26	24	6	8	11
106	608	25	12	31	19	6	3	11
107	567	16	16	47	10	5	3	11
108	434	28	8	21	6	6	3	11
109	479	7	8	21	61	0	3	11
110	488	0	5	9	25	25	1	10
111	507	16	9	28	7	2	2	10
112	394	10	11	24	10	5	2	10
113	504	0	4	15	3	3	1	9
114	368	2	8	19	1	1	2	9
115	386	5	13	35	38	5	7	9
116	451	36	13	45	13	4	4	9
117	580	10	12	20	2	0	1	9
118	565	43	13	1	8	4	6	9
119	510	14	12	29	30	10	3	9
120	495	12	12	33	11	6	2	8
121	596	15	10	32	69	23	3	8
122	412	8	12	11	2	0	2	8
123	338	39	5	10	23	6	5	7
124	446	10	13	18	8	4	4	7
125	418	0	12	41	0	0	0	7
126	335	7	6	0	2	0	0	6
127	349	10	9	10	4	2	3	6
128	308	3	12	24	4	4	2	5
129	466	8	15	28	0	0	0	5
130	228	0	11	38	9	9	1	5
131	428	8	3	4	5	5	3	5
132	242	1	8	25	0	0	0	5
133	352	0	12	40	0	0	0	5
134	244	8	0	0	13	4	4	5
135	269	3	9	23	1	0	1	5
136	242	0	4	13	0	0	0	4
137	291	0	14	6	39	0	2	4
138	213	0	9	31	10	0	0	4
139	135	0	0	0	1	0	1	3
140	210	3	1	3	3	3	3	3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186071&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186071&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186071&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
A[t] = + 145.016754183974 -0.000129868926349765B[t] -0.155238040790202C[t] + 0.90237237994061D[t] -0.352726507365147E[t] + 0.258857530986131F[t] -0.587179897836293G[t] -0.354649644645532H[t] -3.78028069397647I[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
A[t] =  +  145.016754183974 -0.000129868926349765B[t] -0.155238040790202C[t] +  0.90237237994061D[t] -0.352726507365147E[t] +  0.258857530986131F[t] -0.587179897836293G[t] -0.354649644645532H[t] -3.78028069397647I[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186071&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]A[t] =  +  145.016754183974 -0.000129868926349765B[t] -0.155238040790202C[t] +  0.90237237994061D[t] -0.352726507365147E[t] +  0.258857530986131F[t] -0.587179897836293G[t] -0.354649644645532H[t] -3.78028069397647I[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186071&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186071&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
A[t] = + 145.016754183974 -0.000129868926349765B[t] -0.155238040790202C[t] + 0.90237237994061D[t] -0.352726507365147E[t] + 0.258857530986131F[t] -0.587179897836293G[t] -0.354649644645532H[t] -3.78028069397647I[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)145.0167541839743.58999440.394700
B-0.0001298689263497650.001861-0.06980.9444720.472236
C-0.1552380407902020.085614-1.81320.0720870.036043
D0.902372379940610.4006842.25210.0259810.012991
E-0.3527265073651470.094166-3.74580.0002680.000134
F0.2588575309861310.0844043.06690.0026280.001314
G-0.5871798978362930.264192-2.22260.0279610.013981
H-0.3546496446455320.481376-0.73670.4625970.231299
I-3.780280693976470.233686-16.176700

\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) & 145.016754183974 & 3.589994 & 40.3947 & 0 & 0 \tabularnewline
B & -0.000129868926349765 & 0.001861 & -0.0698 & 0.944472 & 0.472236 \tabularnewline
C & -0.155238040790202 & 0.085614 & -1.8132 & 0.072087 & 0.036043 \tabularnewline
D & 0.90237237994061 & 0.400684 & 2.2521 & 0.025981 & 0.012991 \tabularnewline
E & -0.352726507365147 & 0.094166 & -3.7458 & 0.000268 & 0.000134 \tabularnewline
F & 0.258857530986131 & 0.084404 & 3.0669 & 0.002628 & 0.001314 \tabularnewline
G & -0.587179897836293 & 0.264192 & -2.2226 & 0.027961 & 0.013981 \tabularnewline
H & -0.354649644645532 & 0.481376 & -0.7367 & 0.462597 & 0.231299 \tabularnewline
I & -3.78028069397647 & 0.233686 & -16.1767 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186071&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]145.016754183974[/C][C]3.589994[/C][C]40.3947[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]B[/C][C]-0.000129868926349765[/C][C]0.001861[/C][C]-0.0698[/C][C]0.944472[/C][C]0.472236[/C][/ROW]
[ROW][C]C[/C][C]-0.155238040790202[/C][C]0.085614[/C][C]-1.8132[/C][C]0.072087[/C][C]0.036043[/C][/ROW]
[ROW][C]D[/C][C]0.90237237994061[/C][C]0.400684[/C][C]2.2521[/C][C]0.025981[/C][C]0.012991[/C][/ROW]
[ROW][C]E[/C][C]-0.352726507365147[/C][C]0.094166[/C][C]-3.7458[/C][C]0.000268[/C][C]0.000134[/C][/ROW]
[ROW][C]F[/C][C]0.258857530986131[/C][C]0.084404[/C][C]3.0669[/C][C]0.002628[/C][C]0.001314[/C][/ROW]
[ROW][C]G[/C][C]-0.587179897836293[/C][C]0.264192[/C][C]-2.2226[/C][C]0.027961[/C][C]0.013981[/C][/ROW]
[ROW][C]H[/C][C]-0.354649644645532[/C][C]0.481376[/C][C]-0.7367[/C][C]0.462597[/C][C]0.231299[/C][/ROW]
[ROW][C]I[/C][C]-3.78028069397647[/C][C]0.233686[/C][C]-16.1767[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186071&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186071&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)145.0167541839743.58999440.394700
B-0.0001298689263497650.001861-0.06980.9444720.472236
C-0.1552380407902020.085614-1.81320.0720870.036043
D0.902372379940610.4006842.25210.0259810.012991
E-0.3527265073651470.094166-3.74580.0002680.000134
F0.2588575309861310.0844043.06690.0026280.001314
G-0.5871798978362930.264192-2.22260.0279610.013981
H-0.3546496446455320.481376-0.73670.4625970.231299
I-3.780280693976470.233686-16.176700







Multiple Linear Regression - Regression Statistics
Multiple R0.968502611781777
R-squared0.937997309028124
Adjusted R-squared0.934210885151979
F-TEST (value)247.72644049115
F-TEST (DF numerator)8
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.4030329195381
Sum Squared Residuals14177.2253041742

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.968502611781777 \tabularnewline
R-squared & 0.937997309028124 \tabularnewline
Adjusted R-squared & 0.934210885151979 \tabularnewline
F-TEST (value) & 247.72644049115 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 131 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.4030329195381 \tabularnewline
Sum Squared Residuals & 14177.2253041742 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186071&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.968502611781777[/C][/ROW]
[ROW][C]R-squared[/C][C]0.937997309028124[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.934210885151979[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]247.72644049115[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]131[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]10.4030329195381[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]14177.2253041742[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186071&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186071&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 R0.968502611781777
R-squared0.937997309028124
Adjusted R-squared0.934210885151979
F-TEST (value)247.72644049115
F-TEST (DF numerator)8
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.4030329195381
Sum Squared Residuals14177.2253041742







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11-49.958646005542750.9586460055427
22-40.428273425281242.4282734252812
33-27.547607404400130.5476074044001
44-24.57524187255428.575241872554
55-13.277592858183218.2775928581832
661.125893346943954.87410665305605
77-1.570508232853728.57050823285372
88-1.691740737134299.69174073713429
992.529002312154626.47099768784538
101016.0444293639154-6.0444293639154
111118.9834296360241-7.98342963602406
121221.7151463698325-9.71514636983251
131323.9209976563343-10.9209976563343
141423.7554560893341-9.75545608933414
151519.1612934814923-4.16129348149229
161623.6995715267009-7.69957152670091
171725.0209423851123-8.02094238511229
181822.9750332213382-4.97503322133816
191929.5644638573717-10.5644638573717
202032.084865075885-12.084865075885
212132.8364845369256-11.8364845369256
222232.5030100985634-10.5030100985634
232332.8769048101896-9.87690481018959
242438.153030792919-14.153030792919
252543.9228946672244-18.9228946672244
262642.5115868675937-16.5115868675937
272733.6999258472572-6.69992584725723
282849.2720078610296-21.2720078610296
292936.7998528467639-7.7998528467639
303036.7605424142416-6.76054241424161
313141.8289368849396-10.8289368849396
323245.2450179134025-13.2450179134025
333348.1187571862122-15.1187571862122
343444.7204366033766-10.7204366033766
353544.9074319532361-9.90743195323611
363643.6686318086221-7.66863180862209
373744.7738255589076-7.77382555890756
383849.684044264545-11.684044264545
393947.1840506016611-8.18405060166113
404051.8126891531144-11.8126891531144
414149.0035965952436-8.00359659524361
424239.17495395271552.82504604728448
434352.206426019059-9.20642601905896
444450.2362345716309-6.23623457163092
454548.9050289835539-3.90502898355392
464652.3423274036941-6.34232740369412
474751.5286698626429-4.52866986264291
484855.5322018853448-7.53220188534485
494956.7935817536887-7.79358175368868
505054.6877925944591-4.68779259445906
515155.1326113051205-4.13261130512046
525258.1697375711184-6.16973757111838
535355.9449183467428-2.94491834674284
545458.6905186642976-4.69051866429762
555556.8053263619326-1.80532636193257
565657.0204771193634-1.02047711936339
575753.72400902347653.27599097652351
585858.9136561500612-0.913656150061204
595960.8331808777179-1.8331808777179
606062.2438779217031-2.24387792170314
616158.52607976156532.47392023843466
626262.6770959589676-0.677095958967611
636363.4221181992477-0.422118199247679
646460.40113490827013.59886509172988
656564.15909095442490.840909045575145
666665.89839222565130.101607774348649
676761.07883328441255.92116671558746
686870.7732583920815-2.77325839208153
696969.2889694585612-0.288969458561185
707071.5026509968387-1.50265099683868
717171.1289730023498-0.12897300234975
727272.9305616624163-0.930561662416291
737376.9133866009824-3.91338660098238
747485.5557127588152-11.5557127588152
757583.0058955407454-8.00589554074539
767683.2382623126643-7.23826231266433
777776.58955693599960.410443064000378
787880.7885939055093-2.78859390550926
797987.2375388368432-8.23753883684316
808091.4079625799926-11.4079625799926
818176.3170961820564.68290381794398
828287.2440227341449-5.24402273414487
838382.81455497011760.185445029882385
848480.72281744855113.27718255144895
858586.4158715014034-1.4158715014034
868690.2301285634238-4.23012856342381
878787.4747920049338-0.474792004933763
888882.44739624753685.55260375246318
898986.27466533527092.72533466472907
909084.46851057289645.53148942710361
919190.01206363292070.987936367079345
929294.969433068649-2.96943306864903
939390.36293810406542.6370618959346
949498.0178288474365-4.01782884743654
959595.5662949439886-0.566294943988613
969691.85044699725794.14955300274213
979789.14359275284597.85640724715409
989896.29417437058921.70582562941084
999996.29154764991972.70845235008033
10010095.46980572626564.53019427373441
10110199.26640118216131.73359881783873
102102104.496425827801-2.4964258278008
10310396.31346373118426.68653626881578
10410497.70121006703356.29878993296652
105105102.4812840609222.51871593907811
10610699.69896682200736.30103317799275
10710797.32476133598069.67523866401939
10810895.808477543890812.1915224561092
109109116.822875890054-7.82287589005431
11011099.92518572566810.074814274332
111111102.8376478910888.16235210891239
112112106.0144350132985.98556498670234
113113107.9077491633955.09225083660508
114114110.1155138351553.88448616484527
115115113.9714606858851.02853931411545
116116100.80306542464415.1969345753563
117117113.3035273823123.69647261768779
118118113.2179734613534.78202653864684
119119110.1840400776678.81595992233275
120120110.6509150049139.349084995087
121121113.3970947577987.6029052422019
122122120.2359910591371.76400894086336
123123114.0986024667028.90139753329798
124124120.6297932617023.37020673829805
125125114.86718587224110.132814127759
126126127.136846985873-1.13684698587273
127127124.1285730970672.87142690293333
128128125.9500805882382.04991941176239
129129128.4721709609760.5278290390237
130130119.29867855205810.7013214479417
131131123.4085128294667.59148717053362
132132124.3295007485217.67049925147878
133133122.78904511669810.2109548833018
134134124.4395881026339.56041189736669
135135125.5275514869419.47244851305912
136136128.8882480519077.11175194809279
137137149.760838244646-12.7608382446463
138138129.6433743277638.35662567223693
139139133.5625876833285.43741231667183
140140130.9782023284999.02179767150117

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & -49.9586460055427 & 50.9586460055427 \tabularnewline
2 & 2 & -40.4282734252812 & 42.4282734252812 \tabularnewline
3 & 3 & -27.5476074044001 & 30.5476074044001 \tabularnewline
4 & 4 & -24.575241872554 & 28.575241872554 \tabularnewline
5 & 5 & -13.2775928581832 & 18.2775928581832 \tabularnewline
6 & 6 & 1.12589334694395 & 4.87410665305605 \tabularnewline
7 & 7 & -1.57050823285372 & 8.57050823285372 \tabularnewline
8 & 8 & -1.69174073713429 & 9.69174073713429 \tabularnewline
9 & 9 & 2.52900231215462 & 6.47099768784538 \tabularnewline
10 & 10 & 16.0444293639154 & -6.0444293639154 \tabularnewline
11 & 11 & 18.9834296360241 & -7.98342963602406 \tabularnewline
12 & 12 & 21.7151463698325 & -9.71514636983251 \tabularnewline
13 & 13 & 23.9209976563343 & -10.9209976563343 \tabularnewline
14 & 14 & 23.7554560893341 & -9.75545608933414 \tabularnewline
15 & 15 & 19.1612934814923 & -4.16129348149229 \tabularnewline
16 & 16 & 23.6995715267009 & -7.69957152670091 \tabularnewline
17 & 17 & 25.0209423851123 & -8.02094238511229 \tabularnewline
18 & 18 & 22.9750332213382 & -4.97503322133816 \tabularnewline
19 & 19 & 29.5644638573717 & -10.5644638573717 \tabularnewline
20 & 20 & 32.084865075885 & -12.084865075885 \tabularnewline
21 & 21 & 32.8364845369256 & -11.8364845369256 \tabularnewline
22 & 22 & 32.5030100985634 & -10.5030100985634 \tabularnewline
23 & 23 & 32.8769048101896 & -9.87690481018959 \tabularnewline
24 & 24 & 38.153030792919 & -14.153030792919 \tabularnewline
25 & 25 & 43.9228946672244 & -18.9228946672244 \tabularnewline
26 & 26 & 42.5115868675937 & -16.5115868675937 \tabularnewline
27 & 27 & 33.6999258472572 & -6.69992584725723 \tabularnewline
28 & 28 & 49.2720078610296 & -21.2720078610296 \tabularnewline
29 & 29 & 36.7998528467639 & -7.7998528467639 \tabularnewline
30 & 30 & 36.7605424142416 & -6.76054241424161 \tabularnewline
31 & 31 & 41.8289368849396 & -10.8289368849396 \tabularnewline
32 & 32 & 45.2450179134025 & -13.2450179134025 \tabularnewline
33 & 33 & 48.1187571862122 & -15.1187571862122 \tabularnewline
34 & 34 & 44.7204366033766 & -10.7204366033766 \tabularnewline
35 & 35 & 44.9074319532361 & -9.90743195323611 \tabularnewline
36 & 36 & 43.6686318086221 & -7.66863180862209 \tabularnewline
37 & 37 & 44.7738255589076 & -7.77382555890756 \tabularnewline
38 & 38 & 49.684044264545 & -11.684044264545 \tabularnewline
39 & 39 & 47.1840506016611 & -8.18405060166113 \tabularnewline
40 & 40 & 51.8126891531144 & -11.8126891531144 \tabularnewline
41 & 41 & 49.0035965952436 & -8.00359659524361 \tabularnewline
42 & 42 & 39.1749539527155 & 2.82504604728448 \tabularnewline
43 & 43 & 52.206426019059 & -9.20642601905896 \tabularnewline
44 & 44 & 50.2362345716309 & -6.23623457163092 \tabularnewline
45 & 45 & 48.9050289835539 & -3.90502898355392 \tabularnewline
46 & 46 & 52.3423274036941 & -6.34232740369412 \tabularnewline
47 & 47 & 51.5286698626429 & -4.52866986264291 \tabularnewline
48 & 48 & 55.5322018853448 & -7.53220188534485 \tabularnewline
49 & 49 & 56.7935817536887 & -7.79358175368868 \tabularnewline
50 & 50 & 54.6877925944591 & -4.68779259445906 \tabularnewline
51 & 51 & 55.1326113051205 & -4.13261130512046 \tabularnewline
52 & 52 & 58.1697375711184 & -6.16973757111838 \tabularnewline
53 & 53 & 55.9449183467428 & -2.94491834674284 \tabularnewline
54 & 54 & 58.6905186642976 & -4.69051866429762 \tabularnewline
55 & 55 & 56.8053263619326 & -1.80532636193257 \tabularnewline
56 & 56 & 57.0204771193634 & -1.02047711936339 \tabularnewline
57 & 57 & 53.7240090234765 & 3.27599097652351 \tabularnewline
58 & 58 & 58.9136561500612 & -0.913656150061204 \tabularnewline
59 & 59 & 60.8331808777179 & -1.8331808777179 \tabularnewline
60 & 60 & 62.2438779217031 & -2.24387792170314 \tabularnewline
61 & 61 & 58.5260797615653 & 2.47392023843466 \tabularnewline
62 & 62 & 62.6770959589676 & -0.677095958967611 \tabularnewline
63 & 63 & 63.4221181992477 & -0.422118199247679 \tabularnewline
64 & 64 & 60.4011349082701 & 3.59886509172988 \tabularnewline
65 & 65 & 64.1590909544249 & 0.840909045575145 \tabularnewline
66 & 66 & 65.8983922256513 & 0.101607774348649 \tabularnewline
67 & 67 & 61.0788332844125 & 5.92116671558746 \tabularnewline
68 & 68 & 70.7732583920815 & -2.77325839208153 \tabularnewline
69 & 69 & 69.2889694585612 & -0.288969458561185 \tabularnewline
70 & 70 & 71.5026509968387 & -1.50265099683868 \tabularnewline
71 & 71 & 71.1289730023498 & -0.12897300234975 \tabularnewline
72 & 72 & 72.9305616624163 & -0.930561662416291 \tabularnewline
73 & 73 & 76.9133866009824 & -3.91338660098238 \tabularnewline
74 & 74 & 85.5557127588152 & -11.5557127588152 \tabularnewline
75 & 75 & 83.0058955407454 & -8.00589554074539 \tabularnewline
76 & 76 & 83.2382623126643 & -7.23826231266433 \tabularnewline
77 & 77 & 76.5895569359996 & 0.410443064000378 \tabularnewline
78 & 78 & 80.7885939055093 & -2.78859390550926 \tabularnewline
79 & 79 & 87.2375388368432 & -8.23753883684316 \tabularnewline
80 & 80 & 91.4079625799926 & -11.4079625799926 \tabularnewline
81 & 81 & 76.317096182056 & 4.68290381794398 \tabularnewline
82 & 82 & 87.2440227341449 & -5.24402273414487 \tabularnewline
83 & 83 & 82.8145549701176 & 0.185445029882385 \tabularnewline
84 & 84 & 80.7228174485511 & 3.27718255144895 \tabularnewline
85 & 85 & 86.4158715014034 & -1.4158715014034 \tabularnewline
86 & 86 & 90.2301285634238 & -4.23012856342381 \tabularnewline
87 & 87 & 87.4747920049338 & -0.474792004933763 \tabularnewline
88 & 88 & 82.4473962475368 & 5.55260375246318 \tabularnewline
89 & 89 & 86.2746653352709 & 2.72533466472907 \tabularnewline
90 & 90 & 84.4685105728964 & 5.53148942710361 \tabularnewline
91 & 91 & 90.0120636329207 & 0.987936367079345 \tabularnewline
92 & 92 & 94.969433068649 & -2.96943306864903 \tabularnewline
93 & 93 & 90.3629381040654 & 2.6370618959346 \tabularnewline
94 & 94 & 98.0178288474365 & -4.01782884743654 \tabularnewline
95 & 95 & 95.5662949439886 & -0.566294943988613 \tabularnewline
96 & 96 & 91.8504469972579 & 4.14955300274213 \tabularnewline
97 & 97 & 89.1435927528459 & 7.85640724715409 \tabularnewline
98 & 98 & 96.2941743705892 & 1.70582562941084 \tabularnewline
99 & 99 & 96.2915476499197 & 2.70845235008033 \tabularnewline
100 & 100 & 95.4698057262656 & 4.53019427373441 \tabularnewline
101 & 101 & 99.2664011821613 & 1.73359881783873 \tabularnewline
102 & 102 & 104.496425827801 & -2.4964258278008 \tabularnewline
103 & 103 & 96.3134637311842 & 6.68653626881578 \tabularnewline
104 & 104 & 97.7012100670335 & 6.29878993296652 \tabularnewline
105 & 105 & 102.481284060922 & 2.51871593907811 \tabularnewline
106 & 106 & 99.6989668220073 & 6.30103317799275 \tabularnewline
107 & 107 & 97.3247613359806 & 9.67523866401939 \tabularnewline
108 & 108 & 95.8084775438908 & 12.1915224561092 \tabularnewline
109 & 109 & 116.822875890054 & -7.82287589005431 \tabularnewline
110 & 110 & 99.925185725668 & 10.074814274332 \tabularnewline
111 & 111 & 102.837647891088 & 8.16235210891239 \tabularnewline
112 & 112 & 106.014435013298 & 5.98556498670234 \tabularnewline
113 & 113 & 107.907749163395 & 5.09225083660508 \tabularnewline
114 & 114 & 110.115513835155 & 3.88448616484527 \tabularnewline
115 & 115 & 113.971460685885 & 1.02853931411545 \tabularnewline
116 & 116 & 100.803065424644 & 15.1969345753563 \tabularnewline
117 & 117 & 113.303527382312 & 3.69647261768779 \tabularnewline
118 & 118 & 113.217973461353 & 4.78202653864684 \tabularnewline
119 & 119 & 110.184040077667 & 8.81595992233275 \tabularnewline
120 & 120 & 110.650915004913 & 9.349084995087 \tabularnewline
121 & 121 & 113.397094757798 & 7.6029052422019 \tabularnewline
122 & 122 & 120.235991059137 & 1.76400894086336 \tabularnewline
123 & 123 & 114.098602466702 & 8.90139753329798 \tabularnewline
124 & 124 & 120.629793261702 & 3.37020673829805 \tabularnewline
125 & 125 & 114.867185872241 & 10.132814127759 \tabularnewline
126 & 126 & 127.136846985873 & -1.13684698587273 \tabularnewline
127 & 127 & 124.128573097067 & 2.87142690293333 \tabularnewline
128 & 128 & 125.950080588238 & 2.04991941176239 \tabularnewline
129 & 129 & 128.472170960976 & 0.5278290390237 \tabularnewline
130 & 130 & 119.298678552058 & 10.7013214479417 \tabularnewline
131 & 131 & 123.408512829466 & 7.59148717053362 \tabularnewline
132 & 132 & 124.329500748521 & 7.67049925147878 \tabularnewline
133 & 133 & 122.789045116698 & 10.2109548833018 \tabularnewline
134 & 134 & 124.439588102633 & 9.56041189736669 \tabularnewline
135 & 135 & 125.527551486941 & 9.47244851305912 \tabularnewline
136 & 136 & 128.888248051907 & 7.11175194809279 \tabularnewline
137 & 137 & 149.760838244646 & -12.7608382446463 \tabularnewline
138 & 138 & 129.643374327763 & 8.35662567223693 \tabularnewline
139 & 139 & 133.562587683328 & 5.43741231667183 \tabularnewline
140 & 140 & 130.978202328499 & 9.02179767150117 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186071&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]-49.9586460055427[/C][C]50.9586460055427[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]-40.4282734252812[/C][C]42.4282734252812[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]-27.5476074044001[/C][C]30.5476074044001[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]-24.575241872554[/C][C]28.575241872554[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]-13.2775928581832[/C][C]18.2775928581832[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]1.12589334694395[/C][C]4.87410665305605[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]-1.57050823285372[/C][C]8.57050823285372[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]-1.69174073713429[/C][C]9.69174073713429[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]2.52900231215462[/C][C]6.47099768784538[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]16.0444293639154[/C][C]-6.0444293639154[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]18.9834296360241[/C][C]-7.98342963602406[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]21.7151463698325[/C][C]-9.71514636983251[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]23.9209976563343[/C][C]-10.9209976563343[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]23.7554560893341[/C][C]-9.75545608933414[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]19.1612934814923[/C][C]-4.16129348149229[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]23.6995715267009[/C][C]-7.69957152670091[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]25.0209423851123[/C][C]-8.02094238511229[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]22.9750332213382[/C][C]-4.97503322133816[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]29.5644638573717[/C][C]-10.5644638573717[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]32.084865075885[/C][C]-12.084865075885[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]32.8364845369256[/C][C]-11.8364845369256[/C][/ROW]
[ROW][C]22[/C][C]22[/C][C]32.5030100985634[/C][C]-10.5030100985634[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]32.8769048101896[/C][C]-9.87690481018959[/C][/ROW]
[ROW][C]24[/C][C]24[/C][C]38.153030792919[/C][C]-14.153030792919[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]43.9228946672244[/C][C]-18.9228946672244[/C][/ROW]
[ROW][C]26[/C][C]26[/C][C]42.5115868675937[/C][C]-16.5115868675937[/C][/ROW]
[ROW][C]27[/C][C]27[/C][C]33.6999258472572[/C][C]-6.69992584725723[/C][/ROW]
[ROW][C]28[/C][C]28[/C][C]49.2720078610296[/C][C]-21.2720078610296[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]36.7998528467639[/C][C]-7.7998528467639[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]36.7605424142416[/C][C]-6.76054241424161[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]41.8289368849396[/C][C]-10.8289368849396[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]45.2450179134025[/C][C]-13.2450179134025[/C][/ROW]
[ROW][C]33[/C][C]33[/C][C]48.1187571862122[/C][C]-15.1187571862122[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]44.7204366033766[/C][C]-10.7204366033766[/C][/ROW]
[ROW][C]35[/C][C]35[/C][C]44.9074319532361[/C][C]-9.90743195323611[/C][/ROW]
[ROW][C]36[/C][C]36[/C][C]43.6686318086221[/C][C]-7.66863180862209[/C][/ROW]
[ROW][C]37[/C][C]37[/C][C]44.7738255589076[/C][C]-7.77382555890756[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]49.684044264545[/C][C]-11.684044264545[/C][/ROW]
[ROW][C]39[/C][C]39[/C][C]47.1840506016611[/C][C]-8.18405060166113[/C][/ROW]
[ROW][C]40[/C][C]40[/C][C]51.8126891531144[/C][C]-11.8126891531144[/C][/ROW]
[ROW][C]41[/C][C]41[/C][C]49.0035965952436[/C][C]-8.00359659524361[/C][/ROW]
[ROW][C]42[/C][C]42[/C][C]39.1749539527155[/C][C]2.82504604728448[/C][/ROW]
[ROW][C]43[/C][C]43[/C][C]52.206426019059[/C][C]-9.20642601905896[/C][/ROW]
[ROW][C]44[/C][C]44[/C][C]50.2362345716309[/C][C]-6.23623457163092[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]48.9050289835539[/C][C]-3.90502898355392[/C][/ROW]
[ROW][C]46[/C][C]46[/C][C]52.3423274036941[/C][C]-6.34232740369412[/C][/ROW]
[ROW][C]47[/C][C]47[/C][C]51.5286698626429[/C][C]-4.52866986264291[/C][/ROW]
[ROW][C]48[/C][C]48[/C][C]55.5322018853448[/C][C]-7.53220188534485[/C][/ROW]
[ROW][C]49[/C][C]49[/C][C]56.7935817536887[/C][C]-7.79358175368868[/C][/ROW]
[ROW][C]50[/C][C]50[/C][C]54.6877925944591[/C][C]-4.68779259445906[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]55.1326113051205[/C][C]-4.13261130512046[/C][/ROW]
[ROW][C]52[/C][C]52[/C][C]58.1697375711184[/C][C]-6.16973757111838[/C][/ROW]
[ROW][C]53[/C][C]53[/C][C]55.9449183467428[/C][C]-2.94491834674284[/C][/ROW]
[ROW][C]54[/C][C]54[/C][C]58.6905186642976[/C][C]-4.69051866429762[/C][/ROW]
[ROW][C]55[/C][C]55[/C][C]56.8053263619326[/C][C]-1.80532636193257[/C][/ROW]
[ROW][C]56[/C][C]56[/C][C]57.0204771193634[/C][C]-1.02047711936339[/C][/ROW]
[ROW][C]57[/C][C]57[/C][C]53.7240090234765[/C][C]3.27599097652351[/C][/ROW]
[ROW][C]58[/C][C]58[/C][C]58.9136561500612[/C][C]-0.913656150061204[/C][/ROW]
[ROW][C]59[/C][C]59[/C][C]60.8331808777179[/C][C]-1.8331808777179[/C][/ROW]
[ROW][C]60[/C][C]60[/C][C]62.2438779217031[/C][C]-2.24387792170314[/C][/ROW]
[ROW][C]61[/C][C]61[/C][C]58.5260797615653[/C][C]2.47392023843466[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]62.6770959589676[/C][C]-0.677095958967611[/C][/ROW]
[ROW][C]63[/C][C]63[/C][C]63.4221181992477[/C][C]-0.422118199247679[/C][/ROW]
[ROW][C]64[/C][C]64[/C][C]60.4011349082701[/C][C]3.59886509172988[/C][/ROW]
[ROW][C]65[/C][C]65[/C][C]64.1590909544249[/C][C]0.840909045575145[/C][/ROW]
[ROW][C]66[/C][C]66[/C][C]65.8983922256513[/C][C]0.101607774348649[/C][/ROW]
[ROW][C]67[/C][C]67[/C][C]61.0788332844125[/C][C]5.92116671558746[/C][/ROW]
[ROW][C]68[/C][C]68[/C][C]70.7732583920815[/C][C]-2.77325839208153[/C][/ROW]
[ROW][C]69[/C][C]69[/C][C]69.2889694585612[/C][C]-0.288969458561185[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]71.5026509968387[/C][C]-1.50265099683868[/C][/ROW]
[ROW][C]71[/C][C]71[/C][C]71.1289730023498[/C][C]-0.12897300234975[/C][/ROW]
[ROW][C]72[/C][C]72[/C][C]72.9305616624163[/C][C]-0.930561662416291[/C][/ROW]
[ROW][C]73[/C][C]73[/C][C]76.9133866009824[/C][C]-3.91338660098238[/C][/ROW]
[ROW][C]74[/C][C]74[/C][C]85.5557127588152[/C][C]-11.5557127588152[/C][/ROW]
[ROW][C]75[/C][C]75[/C][C]83.0058955407454[/C][C]-8.00589554074539[/C][/ROW]
[ROW][C]76[/C][C]76[/C][C]83.2382623126643[/C][C]-7.23826231266433[/C][/ROW]
[ROW][C]77[/C][C]77[/C][C]76.5895569359996[/C][C]0.410443064000378[/C][/ROW]
[ROW][C]78[/C][C]78[/C][C]80.7885939055093[/C][C]-2.78859390550926[/C][/ROW]
[ROW][C]79[/C][C]79[/C][C]87.2375388368432[/C][C]-8.23753883684316[/C][/ROW]
[ROW][C]80[/C][C]80[/C][C]91.4079625799926[/C][C]-11.4079625799926[/C][/ROW]
[ROW][C]81[/C][C]81[/C][C]76.317096182056[/C][C]4.68290381794398[/C][/ROW]
[ROW][C]82[/C][C]82[/C][C]87.2440227341449[/C][C]-5.24402273414487[/C][/ROW]
[ROW][C]83[/C][C]83[/C][C]82.8145549701176[/C][C]0.185445029882385[/C][/ROW]
[ROW][C]84[/C][C]84[/C][C]80.7228174485511[/C][C]3.27718255144895[/C][/ROW]
[ROW][C]85[/C][C]85[/C][C]86.4158715014034[/C][C]-1.4158715014034[/C][/ROW]
[ROW][C]86[/C][C]86[/C][C]90.2301285634238[/C][C]-4.23012856342381[/C][/ROW]
[ROW][C]87[/C][C]87[/C][C]87.4747920049338[/C][C]-0.474792004933763[/C][/ROW]
[ROW][C]88[/C][C]88[/C][C]82.4473962475368[/C][C]5.55260375246318[/C][/ROW]
[ROW][C]89[/C][C]89[/C][C]86.2746653352709[/C][C]2.72533466472907[/C][/ROW]
[ROW][C]90[/C][C]90[/C][C]84.4685105728964[/C][C]5.53148942710361[/C][/ROW]
[ROW][C]91[/C][C]91[/C][C]90.0120636329207[/C][C]0.987936367079345[/C][/ROW]
[ROW][C]92[/C][C]92[/C][C]94.969433068649[/C][C]-2.96943306864903[/C][/ROW]
[ROW][C]93[/C][C]93[/C][C]90.3629381040654[/C][C]2.6370618959346[/C][/ROW]
[ROW][C]94[/C][C]94[/C][C]98.0178288474365[/C][C]-4.01782884743654[/C][/ROW]
[ROW][C]95[/C][C]95[/C][C]95.5662949439886[/C][C]-0.566294943988613[/C][/ROW]
[ROW][C]96[/C][C]96[/C][C]91.8504469972579[/C][C]4.14955300274213[/C][/ROW]
[ROW][C]97[/C][C]97[/C][C]89.1435927528459[/C][C]7.85640724715409[/C][/ROW]
[ROW][C]98[/C][C]98[/C][C]96.2941743705892[/C][C]1.70582562941084[/C][/ROW]
[ROW][C]99[/C][C]99[/C][C]96.2915476499197[/C][C]2.70845235008033[/C][/ROW]
[ROW][C]100[/C][C]100[/C][C]95.4698057262656[/C][C]4.53019427373441[/C][/ROW]
[ROW][C]101[/C][C]101[/C][C]99.2664011821613[/C][C]1.73359881783873[/C][/ROW]
[ROW][C]102[/C][C]102[/C][C]104.496425827801[/C][C]-2.4964258278008[/C][/ROW]
[ROW][C]103[/C][C]103[/C][C]96.3134637311842[/C][C]6.68653626881578[/C][/ROW]
[ROW][C]104[/C][C]104[/C][C]97.7012100670335[/C][C]6.29878993296652[/C][/ROW]
[ROW][C]105[/C][C]105[/C][C]102.481284060922[/C][C]2.51871593907811[/C][/ROW]
[ROW][C]106[/C][C]106[/C][C]99.6989668220073[/C][C]6.30103317799275[/C][/ROW]
[ROW][C]107[/C][C]107[/C][C]97.3247613359806[/C][C]9.67523866401939[/C][/ROW]
[ROW][C]108[/C][C]108[/C][C]95.8084775438908[/C][C]12.1915224561092[/C][/ROW]
[ROW][C]109[/C][C]109[/C][C]116.822875890054[/C][C]-7.82287589005431[/C][/ROW]
[ROW][C]110[/C][C]110[/C][C]99.925185725668[/C][C]10.074814274332[/C][/ROW]
[ROW][C]111[/C][C]111[/C][C]102.837647891088[/C][C]8.16235210891239[/C][/ROW]
[ROW][C]112[/C][C]112[/C][C]106.014435013298[/C][C]5.98556498670234[/C][/ROW]
[ROW][C]113[/C][C]113[/C][C]107.907749163395[/C][C]5.09225083660508[/C][/ROW]
[ROW][C]114[/C][C]114[/C][C]110.115513835155[/C][C]3.88448616484527[/C][/ROW]
[ROW][C]115[/C][C]115[/C][C]113.971460685885[/C][C]1.02853931411545[/C][/ROW]
[ROW][C]116[/C][C]116[/C][C]100.803065424644[/C][C]15.1969345753563[/C][/ROW]
[ROW][C]117[/C][C]117[/C][C]113.303527382312[/C][C]3.69647261768779[/C][/ROW]
[ROW][C]118[/C][C]118[/C][C]113.217973461353[/C][C]4.78202653864684[/C][/ROW]
[ROW][C]119[/C][C]119[/C][C]110.184040077667[/C][C]8.81595992233275[/C][/ROW]
[ROW][C]120[/C][C]120[/C][C]110.650915004913[/C][C]9.349084995087[/C][/ROW]
[ROW][C]121[/C][C]121[/C][C]113.397094757798[/C][C]7.6029052422019[/C][/ROW]
[ROW][C]122[/C][C]122[/C][C]120.235991059137[/C][C]1.76400894086336[/C][/ROW]
[ROW][C]123[/C][C]123[/C][C]114.098602466702[/C][C]8.90139753329798[/C][/ROW]
[ROW][C]124[/C][C]124[/C][C]120.629793261702[/C][C]3.37020673829805[/C][/ROW]
[ROW][C]125[/C][C]125[/C][C]114.867185872241[/C][C]10.132814127759[/C][/ROW]
[ROW][C]126[/C][C]126[/C][C]127.136846985873[/C][C]-1.13684698587273[/C][/ROW]
[ROW][C]127[/C][C]127[/C][C]124.128573097067[/C][C]2.87142690293333[/C][/ROW]
[ROW][C]128[/C][C]128[/C][C]125.950080588238[/C][C]2.04991941176239[/C][/ROW]
[ROW][C]129[/C][C]129[/C][C]128.472170960976[/C][C]0.5278290390237[/C][/ROW]
[ROW][C]130[/C][C]130[/C][C]119.298678552058[/C][C]10.7013214479417[/C][/ROW]
[ROW][C]131[/C][C]131[/C][C]123.408512829466[/C][C]7.59148717053362[/C][/ROW]
[ROW][C]132[/C][C]132[/C][C]124.329500748521[/C][C]7.67049925147878[/C][/ROW]
[ROW][C]133[/C][C]133[/C][C]122.789045116698[/C][C]10.2109548833018[/C][/ROW]
[ROW][C]134[/C][C]134[/C][C]124.439588102633[/C][C]9.56041189736669[/C][/ROW]
[ROW][C]135[/C][C]135[/C][C]125.527551486941[/C][C]9.47244851305912[/C][/ROW]
[ROW][C]136[/C][C]136[/C][C]128.888248051907[/C][C]7.11175194809279[/C][/ROW]
[ROW][C]137[/C][C]137[/C][C]149.760838244646[/C][C]-12.7608382446463[/C][/ROW]
[ROW][C]138[/C][C]138[/C][C]129.643374327763[/C][C]8.35662567223693[/C][/ROW]
[ROW][C]139[/C][C]139[/C][C]133.562587683328[/C][C]5.43741231667183[/C][/ROW]
[ROW][C]140[/C][C]140[/C][C]130.978202328499[/C][C]9.02179767150117[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186071&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186071&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 IndexActualsInterpolationForecastResidualsPrediction Error
11-49.958646005542750.9586460055427
22-40.428273425281242.4282734252812
33-27.547607404400130.5476074044001
44-24.57524187255428.575241872554
55-13.277592858183218.2775928581832
661.125893346943954.87410665305605
77-1.570508232853728.57050823285372
88-1.691740737134299.69174073713429
992.529002312154626.47099768784538
101016.0444293639154-6.0444293639154
111118.9834296360241-7.98342963602406
121221.7151463698325-9.71514636983251
131323.9209976563343-10.9209976563343
141423.7554560893341-9.75545608933414
151519.1612934814923-4.16129348149229
161623.6995715267009-7.69957152670091
171725.0209423851123-8.02094238511229
181822.9750332213382-4.97503322133816
191929.5644638573717-10.5644638573717
202032.084865075885-12.084865075885
212132.8364845369256-11.8364845369256
222232.5030100985634-10.5030100985634
232332.8769048101896-9.87690481018959
242438.153030792919-14.153030792919
252543.9228946672244-18.9228946672244
262642.5115868675937-16.5115868675937
272733.6999258472572-6.69992584725723
282849.2720078610296-21.2720078610296
292936.7998528467639-7.7998528467639
303036.7605424142416-6.76054241424161
313141.8289368849396-10.8289368849396
323245.2450179134025-13.2450179134025
333348.1187571862122-15.1187571862122
343444.7204366033766-10.7204366033766
353544.9074319532361-9.90743195323611
363643.6686318086221-7.66863180862209
373744.7738255589076-7.77382555890756
383849.684044264545-11.684044264545
393947.1840506016611-8.18405060166113
404051.8126891531144-11.8126891531144
414149.0035965952436-8.00359659524361
424239.17495395271552.82504604728448
434352.206426019059-9.20642601905896
444450.2362345716309-6.23623457163092
454548.9050289835539-3.90502898355392
464652.3423274036941-6.34232740369412
474751.5286698626429-4.52866986264291
484855.5322018853448-7.53220188534485
494956.7935817536887-7.79358175368868
505054.6877925944591-4.68779259445906
515155.1326113051205-4.13261130512046
525258.1697375711184-6.16973757111838
535355.9449183467428-2.94491834674284
545458.6905186642976-4.69051866429762
555556.8053263619326-1.80532636193257
565657.0204771193634-1.02047711936339
575753.72400902347653.27599097652351
585858.9136561500612-0.913656150061204
595960.8331808777179-1.8331808777179
606062.2438779217031-2.24387792170314
616158.52607976156532.47392023843466
626262.6770959589676-0.677095958967611
636363.4221181992477-0.422118199247679
646460.40113490827013.59886509172988
656564.15909095442490.840909045575145
666665.89839222565130.101607774348649
676761.07883328441255.92116671558746
686870.7732583920815-2.77325839208153
696969.2889694585612-0.288969458561185
707071.5026509968387-1.50265099683868
717171.1289730023498-0.12897300234975
727272.9305616624163-0.930561662416291
737376.9133866009824-3.91338660098238
747485.5557127588152-11.5557127588152
757583.0058955407454-8.00589554074539
767683.2382623126643-7.23826231266433
777776.58955693599960.410443064000378
787880.7885939055093-2.78859390550926
797987.2375388368432-8.23753883684316
808091.4079625799926-11.4079625799926
818176.3170961820564.68290381794398
828287.2440227341449-5.24402273414487
838382.81455497011760.185445029882385
848480.72281744855113.27718255144895
858586.4158715014034-1.4158715014034
868690.2301285634238-4.23012856342381
878787.4747920049338-0.474792004933763
888882.44739624753685.55260375246318
898986.27466533527092.72533466472907
909084.46851057289645.53148942710361
919190.01206363292070.987936367079345
929294.969433068649-2.96943306864903
939390.36293810406542.6370618959346
949498.0178288474365-4.01782884743654
959595.5662949439886-0.566294943988613
969691.85044699725794.14955300274213
979789.14359275284597.85640724715409
989896.29417437058921.70582562941084
999996.29154764991972.70845235008033
10010095.46980572626564.53019427373441
10110199.26640118216131.73359881783873
102102104.496425827801-2.4964258278008
10310396.31346373118426.68653626881578
10410497.70121006703356.29878993296652
105105102.4812840609222.51871593907811
10610699.69896682200736.30103317799275
10710797.32476133598069.67523866401939
10810895.808477543890812.1915224561092
109109116.822875890054-7.82287589005431
11011099.92518572566810.074814274332
111111102.8376478910888.16235210891239
112112106.0144350132985.98556498670234
113113107.9077491633955.09225083660508
114114110.1155138351553.88448616484527
115115113.9714606858851.02853931411545
116116100.80306542464415.1969345753563
117117113.3035273823123.69647261768779
118118113.2179734613534.78202653864684
119119110.1840400776678.81595992233275
120120110.6509150049139.349084995087
121121113.3970947577987.6029052422019
122122120.2359910591371.76400894086336
123123114.0986024667028.90139753329798
124124120.6297932617023.37020673829805
125125114.86718587224110.132814127759
126126127.136846985873-1.13684698587273
127127124.1285730970672.87142690293333
128128125.9500805882382.04991941176239
129129128.4721709609760.5278290390237
130130119.29867855205810.7013214479417
131131123.4085128294667.59148717053362
132132124.3295007485217.67049925147878
133133122.78904511669810.2109548833018
134134124.4395881026339.56041189736669
135135125.5275514869419.47244851305912
136136128.8882480519077.11175194809279
137137149.760838244646-12.7608382446463
138138129.6433743277638.35662567223693
139139133.5625876833285.43741231667183
140140130.9782023284999.02179767150117







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.005682772932640320.01136554586528060.99431722706736
130.001372556333774620.002745112667549250.998627443666225
140.002147752262702290.004295504525404570.997852247737298
150.00302741343313290.006054826866265790.996972586566867
160.06918358634039280.1383671726807860.930816413659607
170.07630582211723340.1526116442344670.923694177882767
180.09564101832019010.191282036640380.90435898167981
190.1480906963366660.2961813926733330.851909303663334
200.1056292589167920.2112585178335850.894370741083208
210.1953251131641160.3906502263282330.804674886835884
220.26106539460760.5221307892152010.7389346053924
230.2990655796685170.5981311593370340.700934420331483
240.2687796727184180.5375593454368360.731220327281582
250.3400201014693380.6800402029386760.659979898530662
260.2728346815155630.5456693630311250.727165318484437
270.5637634516580430.8724730966839150.436236548341958
280.5676182206711410.8647635586577190.432381779328859
290.6891052941488810.6217894117022370.310894705851119
300.7893088847746950.421382230450610.210691115225305
310.9295411579994470.1409176840011060.070458842000553
320.9586639226278440.08267215474431130.0413360773721556
330.9819482338277080.03610353234458320.0180517661722916
340.9896133793797590.02077324124048220.0103866206202411
350.9929602336441660.01407953271166850.00703976635583423
360.9965796379256560.006840724148688870.00342036207434444
370.9979247253116050.004150549376790520.00207527468839526
380.9993330168384240.001333966323152340.000666983161576168
390.9996304899236970.0007390201526062920.000369510076303146
400.9999210843053880.0001578313892232147.89156946116069e-05
410.9999782171662274.35656675463065e-052.17828337731532e-05
420.9999910301340941.79397318120342e-058.96986590601711e-06
430.9999927446455391.45107089211208e-057.25535446056039e-06
440.9999979398069424.12038611519914e-062.06019305759957e-06
450.9999996679140916.64171817175457e-073.32085908587728e-07
460.9999997867110444.2657791228762e-072.1328895614381e-07
470.9999998179438283.64112344043032e-071.82056172021516e-07
480.999999974361815.12763790920695e-082.56381895460348e-08
490.9999999922936311.54127376398761e-087.70636881993804e-09
500.999999997728274.54346079722624e-092.27173039861312e-09
510.9999999989968012.00639721208881e-091.00319860604441e-09
520.9999999997033065.93387685255278e-102.96693842627639e-10
530.9999999998534822.93036554167122e-101.46518277083561e-10
540.999999999918411.63179661161187e-108.15898305805934e-11
550.9999999999695336.09335007391686e-113.04667503695843e-11
560.9999999999819173.61655626367602e-111.80827813183801e-11
570.9999999999920521.5896061318983e-117.94803065949148e-12
580.9999999999976214.75741959537606e-122.37870979768803e-12
590.9999999999972665.46773556597028e-122.73386778298514e-12
600.9999999999977794.44214899927688e-122.22107449963844e-12
610.999999999999371.25942009235662e-126.29710046178309e-13
620.9999999999998143.72918805053534e-131.86459402526767e-13
630.9999999999999764.71922803874804e-142.35961401937402e-14
640.9999999999999967.29306887531553e-153.64653443765777e-15
650.9999999999999951.02381713995228e-145.11908569976141e-15
660.9999999999999951.00784698991714e-145.0392349495857e-15
670.9999999999999967.92195228598328e-153.96097614299164e-15
680.9999999999999968.70559171225156e-154.35279585612578e-15
690.9999999999999941.25876383142954e-146.29381915714771e-15
700.9999999999999872.51297171058686e-141.25648585529343e-14
710.9999999999999794.25053325582036e-142.12526662791018e-14
720.9999999999999666.80247516555149e-143.40123758277575e-14
730.9999999999999588.36129674877817e-144.18064837438909e-14
740.9999999999999968.36774642576975e-154.18387321288487e-15
7516.85409215982486e-163.42704607991243e-16
7612.14594335139559e-161.0729716756978e-16
7714.69410453413289e-172.34705226706644e-17
7811.47145310289212e-177.35726551446062e-18
7911.21262270495144e-186.0631135247572e-19
8012.83331598692707e-191.41665799346354e-19
8114.4908750586645e-192.24543752933225e-19
8219.74421951474871e-194.87210975737436e-19
8311.36536487386336e-186.82682436931681e-19
8412.94696464127018e-181.47348232063509e-18
8518.41361074921124e-184.20680537460562e-18
8615.83935767029717e-182.91967883514858e-18
8715.91075209949756e-182.95537604974878e-18
8819.31808895421638e-184.65904447710819e-18
8912.11852699188789e-171.05926349594395e-17
9014.46012588904787e-172.23006294452393e-17
9112.2409895493625e-171.12049477468125e-17
9214.58992565064664e-182.29496282532332e-18
9313.07371740824819e-181.53685870412409e-18
9415.82835701109395e-182.91417850554698e-18
9511.03205547129351e-175.16027735646753e-18
9613.18430817222848e-171.59215408611424e-17
9711.75156592462775e-178.75782962313873e-18
9813.48914276519211e-171.74457138259606e-17
9911.17582284688626e-165.87911423443128e-17
10012.47834076904873e-161.23917038452437e-16
10119.87102500311394e-164.93551250155697e-16
1020.9999999999999984.5291811303737e-152.26459056518685e-15
1030.9999999999999967.18659802768236e-153.59329901384118e-15
1040.9999999999999941.26940376539756e-146.34701882698779e-15
1050.9999999999999853.00280234578758e-141.50140117289379e-14
1060.9999999999999647.26356354965487e-143.63178177482744e-14
1070.9999999999998323.35174679688071e-131.67587339844036e-13
1080.9999999999995499.02167931180117e-134.51083965590058e-13
1090.9999999999979144.17108141317254e-122.08554070658627e-12
1100.9999999999936151.27692803046152e-116.3846401523076e-12
1110.9999999999781784.36440904728317e-112.18220452364159e-11
1120.9999999998985652.02869850166807e-101.01434925083403e-10
1130.9999999998654032.69194129618786e-101.34597064809393e-10
1140.9999999997374735.25053103918915e-102.62526551959458e-10
1150.9999999999269891.46021717742457e-107.30108588712287e-11
1160.9999999998451413.09717423512096e-101.54858711756048e-10
1170.9999999991324511.73509893962209e-098.67549469811044e-10
1180.9999999998130323.73936992366756e-101.86968496183378e-10
1190.9999999988274932.34501299534003e-091.17250649767001e-09
1200.9999999919002191.61995613515735e-088.09978067578675e-09
1210.9999999629172747.41654524807731e-083.70827262403866e-08
1220.9999998654062642.69187472772377e-071.34593736386188e-07
1230.9999995348715919.30256817948001e-074.65128408974e-07
1240.9999970344258825.93114823608881e-062.96557411804441e-06
1250.9999822290570483.55418859048649e-051.77709429524324e-05
1260.9998545229976330.0002909540047342370.000145477002367118
1270.9988923456266710.002215308746658650.00110765437332932
1280.997282273347050.005435453305900310.00271772665295016

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.00568277293264032 & 0.0113655458652806 & 0.99431722706736 \tabularnewline
13 & 0.00137255633377462 & 0.00274511266754925 & 0.998627443666225 \tabularnewline
14 & 0.00214775226270229 & 0.00429550452540457 & 0.997852247737298 \tabularnewline
15 & 0.0030274134331329 & 0.00605482686626579 & 0.996972586566867 \tabularnewline
16 & 0.0691835863403928 & 0.138367172680786 & 0.930816413659607 \tabularnewline
17 & 0.0763058221172334 & 0.152611644234467 & 0.923694177882767 \tabularnewline
18 & 0.0956410183201901 & 0.19128203664038 & 0.90435898167981 \tabularnewline
19 & 0.148090696336666 & 0.296181392673333 & 0.851909303663334 \tabularnewline
20 & 0.105629258916792 & 0.211258517833585 & 0.894370741083208 \tabularnewline
21 & 0.195325113164116 & 0.390650226328233 & 0.804674886835884 \tabularnewline
22 & 0.2610653946076 & 0.522130789215201 & 0.7389346053924 \tabularnewline
23 & 0.299065579668517 & 0.598131159337034 & 0.700934420331483 \tabularnewline
24 & 0.268779672718418 & 0.537559345436836 & 0.731220327281582 \tabularnewline
25 & 0.340020101469338 & 0.680040202938676 & 0.659979898530662 \tabularnewline
26 & 0.272834681515563 & 0.545669363031125 & 0.727165318484437 \tabularnewline
27 & 0.563763451658043 & 0.872473096683915 & 0.436236548341958 \tabularnewline
28 & 0.567618220671141 & 0.864763558657719 & 0.432381779328859 \tabularnewline
29 & 0.689105294148881 & 0.621789411702237 & 0.310894705851119 \tabularnewline
30 & 0.789308884774695 & 0.42138223045061 & 0.210691115225305 \tabularnewline
31 & 0.929541157999447 & 0.140917684001106 & 0.070458842000553 \tabularnewline
32 & 0.958663922627844 & 0.0826721547443113 & 0.0413360773721556 \tabularnewline
33 & 0.981948233827708 & 0.0361035323445832 & 0.0180517661722916 \tabularnewline
34 & 0.989613379379759 & 0.0207732412404822 & 0.0103866206202411 \tabularnewline
35 & 0.992960233644166 & 0.0140795327116685 & 0.00703976635583423 \tabularnewline
36 & 0.996579637925656 & 0.00684072414868887 & 0.00342036207434444 \tabularnewline
37 & 0.997924725311605 & 0.00415054937679052 & 0.00207527468839526 \tabularnewline
38 & 0.999333016838424 & 0.00133396632315234 & 0.000666983161576168 \tabularnewline
39 & 0.999630489923697 & 0.000739020152606292 & 0.000369510076303146 \tabularnewline
40 & 0.999921084305388 & 0.000157831389223214 & 7.89156946116069e-05 \tabularnewline
41 & 0.999978217166227 & 4.35656675463065e-05 & 2.17828337731532e-05 \tabularnewline
42 & 0.999991030134094 & 1.79397318120342e-05 & 8.96986590601711e-06 \tabularnewline
43 & 0.999992744645539 & 1.45107089211208e-05 & 7.25535446056039e-06 \tabularnewline
44 & 0.999997939806942 & 4.12038611519914e-06 & 2.06019305759957e-06 \tabularnewline
45 & 0.999999667914091 & 6.64171817175457e-07 & 3.32085908587728e-07 \tabularnewline
46 & 0.999999786711044 & 4.2657791228762e-07 & 2.1328895614381e-07 \tabularnewline
47 & 0.999999817943828 & 3.64112344043032e-07 & 1.82056172021516e-07 \tabularnewline
48 & 0.99999997436181 & 5.12763790920695e-08 & 2.56381895460348e-08 \tabularnewline
49 & 0.999999992293631 & 1.54127376398761e-08 & 7.70636881993804e-09 \tabularnewline
50 & 0.99999999772827 & 4.54346079722624e-09 & 2.27173039861312e-09 \tabularnewline
51 & 0.999999998996801 & 2.00639721208881e-09 & 1.00319860604441e-09 \tabularnewline
52 & 0.999999999703306 & 5.93387685255278e-10 & 2.96693842627639e-10 \tabularnewline
53 & 0.999999999853482 & 2.93036554167122e-10 & 1.46518277083561e-10 \tabularnewline
54 & 0.99999999991841 & 1.63179661161187e-10 & 8.15898305805934e-11 \tabularnewline
55 & 0.999999999969533 & 6.09335007391686e-11 & 3.04667503695843e-11 \tabularnewline
56 & 0.999999999981917 & 3.61655626367602e-11 & 1.80827813183801e-11 \tabularnewline
57 & 0.999999999992052 & 1.5896061318983e-11 & 7.94803065949148e-12 \tabularnewline
58 & 0.999999999997621 & 4.75741959537606e-12 & 2.37870979768803e-12 \tabularnewline
59 & 0.999999999997266 & 5.46773556597028e-12 & 2.73386778298514e-12 \tabularnewline
60 & 0.999999999997779 & 4.44214899927688e-12 & 2.22107449963844e-12 \tabularnewline
61 & 0.99999999999937 & 1.25942009235662e-12 & 6.29710046178309e-13 \tabularnewline
62 & 0.999999999999814 & 3.72918805053534e-13 & 1.86459402526767e-13 \tabularnewline
63 & 0.999999999999976 & 4.71922803874804e-14 & 2.35961401937402e-14 \tabularnewline
64 & 0.999999999999996 & 7.29306887531553e-15 & 3.64653443765777e-15 \tabularnewline
65 & 0.999999999999995 & 1.02381713995228e-14 & 5.11908569976141e-15 \tabularnewline
66 & 0.999999999999995 & 1.00784698991714e-14 & 5.0392349495857e-15 \tabularnewline
67 & 0.999999999999996 & 7.92195228598328e-15 & 3.96097614299164e-15 \tabularnewline
68 & 0.999999999999996 & 8.70559171225156e-15 & 4.35279585612578e-15 \tabularnewline
69 & 0.999999999999994 & 1.25876383142954e-14 & 6.29381915714771e-15 \tabularnewline
70 & 0.999999999999987 & 2.51297171058686e-14 & 1.25648585529343e-14 \tabularnewline
71 & 0.999999999999979 & 4.25053325582036e-14 & 2.12526662791018e-14 \tabularnewline
72 & 0.999999999999966 & 6.80247516555149e-14 & 3.40123758277575e-14 \tabularnewline
73 & 0.999999999999958 & 8.36129674877817e-14 & 4.18064837438909e-14 \tabularnewline
74 & 0.999999999999996 & 8.36774642576975e-15 & 4.18387321288487e-15 \tabularnewline
75 & 1 & 6.85409215982486e-16 & 3.42704607991243e-16 \tabularnewline
76 & 1 & 2.14594335139559e-16 & 1.0729716756978e-16 \tabularnewline
77 & 1 & 4.69410453413289e-17 & 2.34705226706644e-17 \tabularnewline
78 & 1 & 1.47145310289212e-17 & 7.35726551446062e-18 \tabularnewline
79 & 1 & 1.21262270495144e-18 & 6.0631135247572e-19 \tabularnewline
80 & 1 & 2.83331598692707e-19 & 1.41665799346354e-19 \tabularnewline
81 & 1 & 4.4908750586645e-19 & 2.24543752933225e-19 \tabularnewline
82 & 1 & 9.74421951474871e-19 & 4.87210975737436e-19 \tabularnewline
83 & 1 & 1.36536487386336e-18 & 6.82682436931681e-19 \tabularnewline
84 & 1 & 2.94696464127018e-18 & 1.47348232063509e-18 \tabularnewline
85 & 1 & 8.41361074921124e-18 & 4.20680537460562e-18 \tabularnewline
86 & 1 & 5.83935767029717e-18 & 2.91967883514858e-18 \tabularnewline
87 & 1 & 5.91075209949756e-18 & 2.95537604974878e-18 \tabularnewline
88 & 1 & 9.31808895421638e-18 & 4.65904447710819e-18 \tabularnewline
89 & 1 & 2.11852699188789e-17 & 1.05926349594395e-17 \tabularnewline
90 & 1 & 4.46012588904787e-17 & 2.23006294452393e-17 \tabularnewline
91 & 1 & 2.2409895493625e-17 & 1.12049477468125e-17 \tabularnewline
92 & 1 & 4.58992565064664e-18 & 2.29496282532332e-18 \tabularnewline
93 & 1 & 3.07371740824819e-18 & 1.53685870412409e-18 \tabularnewline
94 & 1 & 5.82835701109395e-18 & 2.91417850554698e-18 \tabularnewline
95 & 1 & 1.03205547129351e-17 & 5.16027735646753e-18 \tabularnewline
96 & 1 & 3.18430817222848e-17 & 1.59215408611424e-17 \tabularnewline
97 & 1 & 1.75156592462775e-17 & 8.75782962313873e-18 \tabularnewline
98 & 1 & 3.48914276519211e-17 & 1.74457138259606e-17 \tabularnewline
99 & 1 & 1.17582284688626e-16 & 5.87911423443128e-17 \tabularnewline
100 & 1 & 2.47834076904873e-16 & 1.23917038452437e-16 \tabularnewline
101 & 1 & 9.87102500311394e-16 & 4.93551250155697e-16 \tabularnewline
102 & 0.999999999999998 & 4.5291811303737e-15 & 2.26459056518685e-15 \tabularnewline
103 & 0.999999999999996 & 7.18659802768236e-15 & 3.59329901384118e-15 \tabularnewline
104 & 0.999999999999994 & 1.26940376539756e-14 & 6.34701882698779e-15 \tabularnewline
105 & 0.999999999999985 & 3.00280234578758e-14 & 1.50140117289379e-14 \tabularnewline
106 & 0.999999999999964 & 7.26356354965487e-14 & 3.63178177482744e-14 \tabularnewline
107 & 0.999999999999832 & 3.35174679688071e-13 & 1.67587339844036e-13 \tabularnewline
108 & 0.999999999999549 & 9.02167931180117e-13 & 4.51083965590058e-13 \tabularnewline
109 & 0.999999999997914 & 4.17108141317254e-12 & 2.08554070658627e-12 \tabularnewline
110 & 0.999999999993615 & 1.27692803046152e-11 & 6.3846401523076e-12 \tabularnewline
111 & 0.999999999978178 & 4.36440904728317e-11 & 2.18220452364159e-11 \tabularnewline
112 & 0.999999999898565 & 2.02869850166807e-10 & 1.01434925083403e-10 \tabularnewline
113 & 0.999999999865403 & 2.69194129618786e-10 & 1.34597064809393e-10 \tabularnewline
114 & 0.999999999737473 & 5.25053103918915e-10 & 2.62526551959458e-10 \tabularnewline
115 & 0.999999999926989 & 1.46021717742457e-10 & 7.30108588712287e-11 \tabularnewline
116 & 0.999999999845141 & 3.09717423512096e-10 & 1.54858711756048e-10 \tabularnewline
117 & 0.999999999132451 & 1.73509893962209e-09 & 8.67549469811044e-10 \tabularnewline
118 & 0.999999999813032 & 3.73936992366756e-10 & 1.86968496183378e-10 \tabularnewline
119 & 0.999999998827493 & 2.34501299534003e-09 & 1.17250649767001e-09 \tabularnewline
120 & 0.999999991900219 & 1.61995613515735e-08 & 8.09978067578675e-09 \tabularnewline
121 & 0.999999962917274 & 7.41654524807731e-08 & 3.70827262403866e-08 \tabularnewline
122 & 0.999999865406264 & 2.69187472772377e-07 & 1.34593736386188e-07 \tabularnewline
123 & 0.999999534871591 & 9.30256817948001e-07 & 4.65128408974e-07 \tabularnewline
124 & 0.999997034425882 & 5.93114823608881e-06 & 2.96557411804441e-06 \tabularnewline
125 & 0.999982229057048 & 3.55418859048649e-05 & 1.77709429524324e-05 \tabularnewline
126 & 0.999854522997633 & 0.000290954004734237 & 0.000145477002367118 \tabularnewline
127 & 0.998892345626671 & 0.00221530874665865 & 0.00110765437332932 \tabularnewline
128 & 0.99728227334705 & 0.00543545330590031 & 0.00271772665295016 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186071&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]12[/C][C]0.00568277293264032[/C][C]0.0113655458652806[/C][C]0.99431722706736[/C][/ROW]
[ROW][C]13[/C][C]0.00137255633377462[/C][C]0.00274511266754925[/C][C]0.998627443666225[/C][/ROW]
[ROW][C]14[/C][C]0.00214775226270229[/C][C]0.00429550452540457[/C][C]0.997852247737298[/C][/ROW]
[ROW][C]15[/C][C]0.0030274134331329[/C][C]0.00605482686626579[/C][C]0.996972586566867[/C][/ROW]
[ROW][C]16[/C][C]0.0691835863403928[/C][C]0.138367172680786[/C][C]0.930816413659607[/C][/ROW]
[ROW][C]17[/C][C]0.0763058221172334[/C][C]0.152611644234467[/C][C]0.923694177882767[/C][/ROW]
[ROW][C]18[/C][C]0.0956410183201901[/C][C]0.19128203664038[/C][C]0.90435898167981[/C][/ROW]
[ROW][C]19[/C][C]0.148090696336666[/C][C]0.296181392673333[/C][C]0.851909303663334[/C][/ROW]
[ROW][C]20[/C][C]0.105629258916792[/C][C]0.211258517833585[/C][C]0.894370741083208[/C][/ROW]
[ROW][C]21[/C][C]0.195325113164116[/C][C]0.390650226328233[/C][C]0.804674886835884[/C][/ROW]
[ROW][C]22[/C][C]0.2610653946076[/C][C]0.522130789215201[/C][C]0.7389346053924[/C][/ROW]
[ROW][C]23[/C][C]0.299065579668517[/C][C]0.598131159337034[/C][C]0.700934420331483[/C][/ROW]
[ROW][C]24[/C][C]0.268779672718418[/C][C]0.537559345436836[/C][C]0.731220327281582[/C][/ROW]
[ROW][C]25[/C][C]0.340020101469338[/C][C]0.680040202938676[/C][C]0.659979898530662[/C][/ROW]
[ROW][C]26[/C][C]0.272834681515563[/C][C]0.545669363031125[/C][C]0.727165318484437[/C][/ROW]
[ROW][C]27[/C][C]0.563763451658043[/C][C]0.872473096683915[/C][C]0.436236548341958[/C][/ROW]
[ROW][C]28[/C][C]0.567618220671141[/C][C]0.864763558657719[/C][C]0.432381779328859[/C][/ROW]
[ROW][C]29[/C][C]0.689105294148881[/C][C]0.621789411702237[/C][C]0.310894705851119[/C][/ROW]
[ROW][C]30[/C][C]0.789308884774695[/C][C]0.42138223045061[/C][C]0.210691115225305[/C][/ROW]
[ROW][C]31[/C][C]0.929541157999447[/C][C]0.140917684001106[/C][C]0.070458842000553[/C][/ROW]
[ROW][C]32[/C][C]0.958663922627844[/C][C]0.0826721547443113[/C][C]0.0413360773721556[/C][/ROW]
[ROW][C]33[/C][C]0.981948233827708[/C][C]0.0361035323445832[/C][C]0.0180517661722916[/C][/ROW]
[ROW][C]34[/C][C]0.989613379379759[/C][C]0.0207732412404822[/C][C]0.0103866206202411[/C][/ROW]
[ROW][C]35[/C][C]0.992960233644166[/C][C]0.0140795327116685[/C][C]0.00703976635583423[/C][/ROW]
[ROW][C]36[/C][C]0.996579637925656[/C][C]0.00684072414868887[/C][C]0.00342036207434444[/C][/ROW]
[ROW][C]37[/C][C]0.997924725311605[/C][C]0.00415054937679052[/C][C]0.00207527468839526[/C][/ROW]
[ROW][C]38[/C][C]0.999333016838424[/C][C]0.00133396632315234[/C][C]0.000666983161576168[/C][/ROW]
[ROW][C]39[/C][C]0.999630489923697[/C][C]0.000739020152606292[/C][C]0.000369510076303146[/C][/ROW]
[ROW][C]40[/C][C]0.999921084305388[/C][C]0.000157831389223214[/C][C]7.89156946116069e-05[/C][/ROW]
[ROW][C]41[/C][C]0.999978217166227[/C][C]4.35656675463065e-05[/C][C]2.17828337731532e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999991030134094[/C][C]1.79397318120342e-05[/C][C]8.96986590601711e-06[/C][/ROW]
[ROW][C]43[/C][C]0.999992744645539[/C][C]1.45107089211208e-05[/C][C]7.25535446056039e-06[/C][/ROW]
[ROW][C]44[/C][C]0.999997939806942[/C][C]4.12038611519914e-06[/C][C]2.06019305759957e-06[/C][/ROW]
[ROW][C]45[/C][C]0.999999667914091[/C][C]6.64171817175457e-07[/C][C]3.32085908587728e-07[/C][/ROW]
[ROW][C]46[/C][C]0.999999786711044[/C][C]4.2657791228762e-07[/C][C]2.1328895614381e-07[/C][/ROW]
[ROW][C]47[/C][C]0.999999817943828[/C][C]3.64112344043032e-07[/C][C]1.82056172021516e-07[/C][/ROW]
[ROW][C]48[/C][C]0.99999997436181[/C][C]5.12763790920695e-08[/C][C]2.56381895460348e-08[/C][/ROW]
[ROW][C]49[/C][C]0.999999992293631[/C][C]1.54127376398761e-08[/C][C]7.70636881993804e-09[/C][/ROW]
[ROW][C]50[/C][C]0.99999999772827[/C][C]4.54346079722624e-09[/C][C]2.27173039861312e-09[/C][/ROW]
[ROW][C]51[/C][C]0.999999998996801[/C][C]2.00639721208881e-09[/C][C]1.00319860604441e-09[/C][/ROW]
[ROW][C]52[/C][C]0.999999999703306[/C][C]5.93387685255278e-10[/C][C]2.96693842627639e-10[/C][/ROW]
[ROW][C]53[/C][C]0.999999999853482[/C][C]2.93036554167122e-10[/C][C]1.46518277083561e-10[/C][/ROW]
[ROW][C]54[/C][C]0.99999999991841[/C][C]1.63179661161187e-10[/C][C]8.15898305805934e-11[/C][/ROW]
[ROW][C]55[/C][C]0.999999999969533[/C][C]6.09335007391686e-11[/C][C]3.04667503695843e-11[/C][/ROW]
[ROW][C]56[/C][C]0.999999999981917[/C][C]3.61655626367602e-11[/C][C]1.80827813183801e-11[/C][/ROW]
[ROW][C]57[/C][C]0.999999999992052[/C][C]1.5896061318983e-11[/C][C]7.94803065949148e-12[/C][/ROW]
[ROW][C]58[/C][C]0.999999999997621[/C][C]4.75741959537606e-12[/C][C]2.37870979768803e-12[/C][/ROW]
[ROW][C]59[/C][C]0.999999999997266[/C][C]5.46773556597028e-12[/C][C]2.73386778298514e-12[/C][/ROW]
[ROW][C]60[/C][C]0.999999999997779[/C][C]4.44214899927688e-12[/C][C]2.22107449963844e-12[/C][/ROW]
[ROW][C]61[/C][C]0.99999999999937[/C][C]1.25942009235662e-12[/C][C]6.29710046178309e-13[/C][/ROW]
[ROW][C]62[/C][C]0.999999999999814[/C][C]3.72918805053534e-13[/C][C]1.86459402526767e-13[/C][/ROW]
[ROW][C]63[/C][C]0.999999999999976[/C][C]4.71922803874804e-14[/C][C]2.35961401937402e-14[/C][/ROW]
[ROW][C]64[/C][C]0.999999999999996[/C][C]7.29306887531553e-15[/C][C]3.64653443765777e-15[/C][/ROW]
[ROW][C]65[/C][C]0.999999999999995[/C][C]1.02381713995228e-14[/C][C]5.11908569976141e-15[/C][/ROW]
[ROW][C]66[/C][C]0.999999999999995[/C][C]1.00784698991714e-14[/C][C]5.0392349495857e-15[/C][/ROW]
[ROW][C]67[/C][C]0.999999999999996[/C][C]7.92195228598328e-15[/C][C]3.96097614299164e-15[/C][/ROW]
[ROW][C]68[/C][C]0.999999999999996[/C][C]8.70559171225156e-15[/C][C]4.35279585612578e-15[/C][/ROW]
[ROW][C]69[/C][C]0.999999999999994[/C][C]1.25876383142954e-14[/C][C]6.29381915714771e-15[/C][/ROW]
[ROW][C]70[/C][C]0.999999999999987[/C][C]2.51297171058686e-14[/C][C]1.25648585529343e-14[/C][/ROW]
[ROW][C]71[/C][C]0.999999999999979[/C][C]4.25053325582036e-14[/C][C]2.12526662791018e-14[/C][/ROW]
[ROW][C]72[/C][C]0.999999999999966[/C][C]6.80247516555149e-14[/C][C]3.40123758277575e-14[/C][/ROW]
[ROW][C]73[/C][C]0.999999999999958[/C][C]8.36129674877817e-14[/C][C]4.18064837438909e-14[/C][/ROW]
[ROW][C]74[/C][C]0.999999999999996[/C][C]8.36774642576975e-15[/C][C]4.18387321288487e-15[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]6.85409215982486e-16[/C][C]3.42704607991243e-16[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]2.14594335139559e-16[/C][C]1.0729716756978e-16[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]4.69410453413289e-17[/C][C]2.34705226706644e-17[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.47145310289212e-17[/C][C]7.35726551446062e-18[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.21262270495144e-18[/C][C]6.0631135247572e-19[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]2.83331598692707e-19[/C][C]1.41665799346354e-19[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]4.4908750586645e-19[/C][C]2.24543752933225e-19[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]9.74421951474871e-19[/C][C]4.87210975737436e-19[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]1.36536487386336e-18[/C][C]6.82682436931681e-19[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]2.94696464127018e-18[/C][C]1.47348232063509e-18[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]8.41361074921124e-18[/C][C]4.20680537460562e-18[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]5.83935767029717e-18[/C][C]2.91967883514858e-18[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]5.91075209949756e-18[/C][C]2.95537604974878e-18[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]9.31808895421638e-18[/C][C]4.65904447710819e-18[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]2.11852699188789e-17[/C][C]1.05926349594395e-17[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]4.46012588904787e-17[/C][C]2.23006294452393e-17[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]2.2409895493625e-17[/C][C]1.12049477468125e-17[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]4.58992565064664e-18[/C][C]2.29496282532332e-18[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]3.07371740824819e-18[/C][C]1.53685870412409e-18[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]5.82835701109395e-18[/C][C]2.91417850554698e-18[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.03205547129351e-17[/C][C]5.16027735646753e-18[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]3.18430817222848e-17[/C][C]1.59215408611424e-17[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]1.75156592462775e-17[/C][C]8.75782962313873e-18[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]3.48914276519211e-17[/C][C]1.74457138259606e-17[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.17582284688626e-16[/C][C]5.87911423443128e-17[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]2.47834076904873e-16[/C][C]1.23917038452437e-16[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]9.87102500311394e-16[/C][C]4.93551250155697e-16[/C][/ROW]
[ROW][C]102[/C][C]0.999999999999998[/C][C]4.5291811303737e-15[/C][C]2.26459056518685e-15[/C][/ROW]
[ROW][C]103[/C][C]0.999999999999996[/C][C]7.18659802768236e-15[/C][C]3.59329901384118e-15[/C][/ROW]
[ROW][C]104[/C][C]0.999999999999994[/C][C]1.26940376539756e-14[/C][C]6.34701882698779e-15[/C][/ROW]
[ROW][C]105[/C][C]0.999999999999985[/C][C]3.00280234578758e-14[/C][C]1.50140117289379e-14[/C][/ROW]
[ROW][C]106[/C][C]0.999999999999964[/C][C]7.26356354965487e-14[/C][C]3.63178177482744e-14[/C][/ROW]
[ROW][C]107[/C][C]0.999999999999832[/C][C]3.35174679688071e-13[/C][C]1.67587339844036e-13[/C][/ROW]
[ROW][C]108[/C][C]0.999999999999549[/C][C]9.02167931180117e-13[/C][C]4.51083965590058e-13[/C][/ROW]
[ROW][C]109[/C][C]0.999999999997914[/C][C]4.17108141317254e-12[/C][C]2.08554070658627e-12[/C][/ROW]
[ROW][C]110[/C][C]0.999999999993615[/C][C]1.27692803046152e-11[/C][C]6.3846401523076e-12[/C][/ROW]
[ROW][C]111[/C][C]0.999999999978178[/C][C]4.36440904728317e-11[/C][C]2.18220452364159e-11[/C][/ROW]
[ROW][C]112[/C][C]0.999999999898565[/C][C]2.02869850166807e-10[/C][C]1.01434925083403e-10[/C][/ROW]
[ROW][C]113[/C][C]0.999999999865403[/C][C]2.69194129618786e-10[/C][C]1.34597064809393e-10[/C][/ROW]
[ROW][C]114[/C][C]0.999999999737473[/C][C]5.25053103918915e-10[/C][C]2.62526551959458e-10[/C][/ROW]
[ROW][C]115[/C][C]0.999999999926989[/C][C]1.46021717742457e-10[/C][C]7.30108588712287e-11[/C][/ROW]
[ROW][C]116[/C][C]0.999999999845141[/C][C]3.09717423512096e-10[/C][C]1.54858711756048e-10[/C][/ROW]
[ROW][C]117[/C][C]0.999999999132451[/C][C]1.73509893962209e-09[/C][C]8.67549469811044e-10[/C][/ROW]
[ROW][C]118[/C][C]0.999999999813032[/C][C]3.73936992366756e-10[/C][C]1.86968496183378e-10[/C][/ROW]
[ROW][C]119[/C][C]0.999999998827493[/C][C]2.34501299534003e-09[/C][C]1.17250649767001e-09[/C][/ROW]
[ROW][C]120[/C][C]0.999999991900219[/C][C]1.61995613515735e-08[/C][C]8.09978067578675e-09[/C][/ROW]
[ROW][C]121[/C][C]0.999999962917274[/C][C]7.41654524807731e-08[/C][C]3.70827262403866e-08[/C][/ROW]
[ROW][C]122[/C][C]0.999999865406264[/C][C]2.69187472772377e-07[/C][C]1.34593736386188e-07[/C][/ROW]
[ROW][C]123[/C][C]0.999999534871591[/C][C]9.30256817948001e-07[/C][C]4.65128408974e-07[/C][/ROW]
[ROW][C]124[/C][C]0.999997034425882[/C][C]5.93114823608881e-06[/C][C]2.96557411804441e-06[/C][/ROW]
[ROW][C]125[/C][C]0.999982229057048[/C][C]3.55418859048649e-05[/C][C]1.77709429524324e-05[/C][/ROW]
[ROW][C]126[/C][C]0.999854522997633[/C][C]0.000290954004734237[/C][C]0.000145477002367118[/C][/ROW]
[ROW][C]127[/C][C]0.998892345626671[/C][C]0.00221530874665865[/C][C]0.00110765437332932[/C][/ROW]
[ROW][C]128[/C][C]0.99728227334705[/C][C]0.00543545330590031[/C][C]0.00271772665295016[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186071&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186071&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.005682772932640320.01136554586528060.99431722706736
130.001372556333774620.002745112667549250.998627443666225
140.002147752262702290.004295504525404570.997852247737298
150.00302741343313290.006054826866265790.996972586566867
160.06918358634039280.1383671726807860.930816413659607
170.07630582211723340.1526116442344670.923694177882767
180.09564101832019010.191282036640380.90435898167981
190.1480906963366660.2961813926733330.851909303663334
200.1056292589167920.2112585178335850.894370741083208
210.1953251131641160.3906502263282330.804674886835884
220.26106539460760.5221307892152010.7389346053924
230.2990655796685170.5981311593370340.700934420331483
240.2687796727184180.5375593454368360.731220327281582
250.3400201014693380.6800402029386760.659979898530662
260.2728346815155630.5456693630311250.727165318484437
270.5637634516580430.8724730966839150.436236548341958
280.5676182206711410.8647635586577190.432381779328859
290.6891052941488810.6217894117022370.310894705851119
300.7893088847746950.421382230450610.210691115225305
310.9295411579994470.1409176840011060.070458842000553
320.9586639226278440.08267215474431130.0413360773721556
330.9819482338277080.03610353234458320.0180517661722916
340.9896133793797590.02077324124048220.0103866206202411
350.9929602336441660.01407953271166850.00703976635583423
360.9965796379256560.006840724148688870.00342036207434444
370.9979247253116050.004150549376790520.00207527468839526
380.9993330168384240.001333966323152340.000666983161576168
390.9996304899236970.0007390201526062920.000369510076303146
400.9999210843053880.0001578313892232147.89156946116069e-05
410.9999782171662274.35656675463065e-052.17828337731532e-05
420.9999910301340941.79397318120342e-058.96986590601711e-06
430.9999927446455391.45107089211208e-057.25535446056039e-06
440.9999979398069424.12038611519914e-062.06019305759957e-06
450.9999996679140916.64171817175457e-073.32085908587728e-07
460.9999997867110444.2657791228762e-072.1328895614381e-07
470.9999998179438283.64112344043032e-071.82056172021516e-07
480.999999974361815.12763790920695e-082.56381895460348e-08
490.9999999922936311.54127376398761e-087.70636881993804e-09
500.999999997728274.54346079722624e-092.27173039861312e-09
510.9999999989968012.00639721208881e-091.00319860604441e-09
520.9999999997033065.93387685255278e-102.96693842627639e-10
530.9999999998534822.93036554167122e-101.46518277083561e-10
540.999999999918411.63179661161187e-108.15898305805934e-11
550.9999999999695336.09335007391686e-113.04667503695843e-11
560.9999999999819173.61655626367602e-111.80827813183801e-11
570.9999999999920521.5896061318983e-117.94803065949148e-12
580.9999999999976214.75741959537606e-122.37870979768803e-12
590.9999999999972665.46773556597028e-122.73386778298514e-12
600.9999999999977794.44214899927688e-122.22107449963844e-12
610.999999999999371.25942009235662e-126.29710046178309e-13
620.9999999999998143.72918805053534e-131.86459402526767e-13
630.9999999999999764.71922803874804e-142.35961401937402e-14
640.9999999999999967.29306887531553e-153.64653443765777e-15
650.9999999999999951.02381713995228e-145.11908569976141e-15
660.9999999999999951.00784698991714e-145.0392349495857e-15
670.9999999999999967.92195228598328e-153.96097614299164e-15
680.9999999999999968.70559171225156e-154.35279585612578e-15
690.9999999999999941.25876383142954e-146.29381915714771e-15
700.9999999999999872.51297171058686e-141.25648585529343e-14
710.9999999999999794.25053325582036e-142.12526662791018e-14
720.9999999999999666.80247516555149e-143.40123758277575e-14
730.9999999999999588.36129674877817e-144.18064837438909e-14
740.9999999999999968.36774642576975e-154.18387321288487e-15
7516.85409215982486e-163.42704607991243e-16
7612.14594335139559e-161.0729716756978e-16
7714.69410453413289e-172.34705226706644e-17
7811.47145310289212e-177.35726551446062e-18
7911.21262270495144e-186.0631135247572e-19
8012.83331598692707e-191.41665799346354e-19
8114.4908750586645e-192.24543752933225e-19
8219.74421951474871e-194.87210975737436e-19
8311.36536487386336e-186.82682436931681e-19
8412.94696464127018e-181.47348232063509e-18
8518.41361074921124e-184.20680537460562e-18
8615.83935767029717e-182.91967883514858e-18
8715.91075209949756e-182.95537604974878e-18
8819.31808895421638e-184.65904447710819e-18
8912.11852699188789e-171.05926349594395e-17
9014.46012588904787e-172.23006294452393e-17
9112.2409895493625e-171.12049477468125e-17
9214.58992565064664e-182.29496282532332e-18
9313.07371740824819e-181.53685870412409e-18
9415.82835701109395e-182.91417850554698e-18
9511.03205547129351e-175.16027735646753e-18
9613.18430817222848e-171.59215408611424e-17
9711.75156592462775e-178.75782962313873e-18
9813.48914276519211e-171.74457138259606e-17
9911.17582284688626e-165.87911423443128e-17
10012.47834076904873e-161.23917038452437e-16
10119.87102500311394e-164.93551250155697e-16
1020.9999999999999984.5291811303737e-152.26459056518685e-15
1030.9999999999999967.18659802768236e-153.59329901384118e-15
1040.9999999999999941.26940376539756e-146.34701882698779e-15
1050.9999999999999853.00280234578758e-141.50140117289379e-14
1060.9999999999999647.26356354965487e-143.63178177482744e-14
1070.9999999999998323.35174679688071e-131.67587339844036e-13
1080.9999999999995499.02167931180117e-134.51083965590058e-13
1090.9999999999979144.17108141317254e-122.08554070658627e-12
1100.9999999999936151.27692803046152e-116.3846401523076e-12
1110.9999999999781784.36440904728317e-112.18220452364159e-11
1120.9999999998985652.02869850166807e-101.01434925083403e-10
1130.9999999998654032.69194129618786e-101.34597064809393e-10
1140.9999999997374735.25053103918915e-102.62526551959458e-10
1150.9999999999269891.46021717742457e-107.30108588712287e-11
1160.9999999998451413.09717423512096e-101.54858711756048e-10
1170.9999999991324511.73509893962209e-098.67549469811044e-10
1180.9999999998130323.73936992366756e-101.86968496183378e-10
1190.9999999988274932.34501299534003e-091.17250649767001e-09
1200.9999999919002191.61995613515735e-088.09978067578675e-09
1210.9999999629172747.41654524807731e-083.70827262403866e-08
1220.9999998654062642.69187472772377e-071.34593736386188e-07
1230.9999995348715919.30256817948001e-074.65128408974e-07
1240.9999970344258825.93114823608881e-062.96557411804441e-06
1250.9999822290570483.55418859048649e-051.77709429524324e-05
1260.9998545229976330.0002909540047342370.000145477002367118
1270.9988923456266710.002215308746658650.00110765437332932
1280.997282273347050.005435453305900310.00271772665295016







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level960.82051282051282NOK
5% type I error level1000.854700854700855NOK
10% type I error level1010.863247863247863NOK

\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 & 96 & 0.82051282051282 & NOK \tabularnewline
5% type I error level & 100 & 0.854700854700855 & NOK \tabularnewline
10% type I error level & 101 & 0.863247863247863 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186071&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]96[/C][C]0.82051282051282[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]100[/C][C]0.854700854700855[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]101[/C][C]0.863247863247863[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186071&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186071&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 testsOK/NOK
1% type I error level960.82051282051282NOK
5% type I error level1000.854700854700855NOK
10% type I error level1010.863247863247863NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}