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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 16 Dec 2012 09:24:11 -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/Dec/16/t135566786054lvtsu068pqm5r.htm/, Retrieved Thu, 18 Apr 2024 22:53:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200377, Retrieved Thu, 18 Apr 2024 22:53:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-11-04 18:57:02] [235928acca9c96310100390b3cde8f3b]
- R P     [Multiple Regression] [] [2012-12-16 14:24:11] [108821faace101f0b67e40eaa0fe63e0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	1901	61	17	56	84	4	21	51	9
2	2509	74	19	73	47	3	15	45	9
3	2114	57	18	62	63	3	17	44	9
4	1331	50	15	42	28	3	20	42	9
5	1399	48	15	59	22	2	12	38	9
6	7333	2	12	27	18	6	4	38	9
7	1170	31	20	78	27	5	11	35	9
8	1507	61	14	56	37	5	12	35	9
9	1107	36	15	59	20	5	9	34	9
10	2051	46	13	51	67	5	14	33	9
11	1290	30	17	47	28	4	11	32	9
12	820	49	10	35	45	3	14	31	9
13	1502	14	13	47	15	5	4	30	9
14	1451	12	12	47	23	6	7	30	9
15	1178	54	16	55	30	6	9	30	9
16	1514	44	15	54	27	2	14	29	9
17	883	40	15	60	43	5	13	29	9
18	1405	57	15	55	36	5	11	29	9
19	927	29	12	48	28	5	9	28	9
20	1352	32	13	47	28	9	8	27	9
21	1314	28	12	47	22	4	9	27	9
22	1307	40	15	52	27	4	11	27	9
23	1243	54	12	48	24	5	7	26	9
24	1232	56	12	48	52	3	15	26	9
25	1097	19	9	27	12	0	4	26	9
26	1100	67	12	12	24	5	10	26	9
27	1316	25	13	51	10	3	10	26	9
28	903	42	16	58	71	4	13	25	9
29	929	28	15	60	12	2	10	25	9
30	1049	57	13	46	24	5	10	25	9
31	1372	28	12	45	22	11	6	24	9
32	1470	35	13	42	21	5	8	24	9
33	821	10	12	41	13	3	7	24	9
34	1239	30	12	47	28	4	11	24	9
35	1384	23	8	32	19	5	10	24	9
36	820	32	15	56	29	5	11	24	9
37	1462	24	12	42	12	2	10	24	9
38	1202	42	12	41	32	6	8	23	9
39	1091	33	12	47	21	3	10	23	9
40	1228	19	14	47	19	4	5	23	9
41	707	17	15	49	15	8	5	23	9
42	868	49	15	52	14	14	5	23	9
43	1165	30	12	42	34	11	9	22	9
44	1106	3	13	55	8	8	2	22	9
45	1429	56	12	48	27	3	9	22	9
46	1671	37	13	48	31	3	13	22	9
47	1579	26	12	38	21	11	7	22	9
48	774	19	12	48	10	3	5	21	10
49	934	22	13	50	21	4	7	21	10
50	825	53	12	39	19	3	8	21	10
51	1375	35	12	48	27	5	8	21	10
52	968	12	9	36	17	6	5	21	10
53	1156	34	13	49	30	8	5	21	10
54	1374	28	13	39	19	3	10	21	10
55	1224	38	12	41	17	3	5	21	10
56	804	38	15	45	24	5	10	21	10
57	998	45	15	60	36	5	10	21	10
58	1112	15	13	45	16	3	7	21	10
59	1153	35	14	41	16	3	10	20	10
60	613	27	14	52	30	3	9	20	10
61	729	23	12	46	18	5	10	20	10
62	813	33	12	39	26	3	10	20	10
63	912	23	9	32	17	3	5	20	10
64	1178	26	14	52	28	6	8	20	10
65	1201	32	16	54	20	4	6	19	10
66	1165	35	15	51	27	3	7	19	10
67	705	18	13	52	13	13	6	18	10
68	814	18	16	57	10	5	3	17	10
69	1082	41	12	47	29	6	9	17	10
70	885	39	12	45	34	5	11	17	10
71	837	56	12	41	30	3	9	17	10
72	586	35	12	43	16	4	10	16	10
73	913	37	10	31	22	4	9	16	10
74	547	26	15	32	22	7	7	15	10
75	758	33	12	41	31	4	6	15	10
76	848	7	9	27	10	5	6	15	10
77	634	16	10	40	7	7	5	15	10
78	501	13	13	46	10	3	5	15	10
79	849	54	12	32	55	6	8	15	10
80	733	30	13	9	25	8	7	15	10
81	634	9	16	64	9	5	5	15	10
82	1010	35	15	30	31	5	10	15	10
83	778	0	12	46	0	0	0	15	10
84	480	40	12	37	24	3	10	15	10
85	848	22	12	22	14	5	6	15	10
86	714	29	12	20	11	3	6	14	10
87	871	25	12	21	8	8	4	14	10
88	776	17	14	44	9	9	3	14	10
89	815	32	12	24	18	9	7	14	10
90	811	40	12	33	14	4	5	14	10
91	529	24	12	45	27	2	8	13	10
92	642	18	13	35	10	0	0	13	10
93	562	15	8	31	16	3	5	13	10
94	626	17	16	20	13	7	5	13	10
95	636	28	12	13	10	5	5	13	11
96	935	18	11	33	16	3	5	13	11
97	473	16	15	58	11	3	6	12	11
98	836	28	13	26	8	3	5	12	11
99	938	17	12	36	29	7	6	12	11
100	656	25	13	32	12	4	4	12	11
101	566	2	13	34	1	0	0	12	11
102	765	10	12	15	26	5	8	12	11
103	705	9	12	40	5	5	2	11	11
104	558	7	12	37	5	5	2	11	11
105	582	27	14	26	24	6	8	11	11
106	608	25	12	31	19	6	3	11	11
107	567	16	16	47	10	5	3	11	11
108	434	28	8	21	6	6	3	11	11
109	479	7	8	21	61	0	3	11	11
110	488	0	5	9	25	25	1	10	11
111	507	16	9	28	7	2	2	10	11
112	394	10	11	24	10	5	2	10	11
113	504	0	4	15	3	3	1	9	11
114	368	2	8	19	1	1	2	9	11
115	386	5	13	35	38	5	7	9	11
116	451	36	13	45	13	4	4	9	11
117	580	10	12	20	2	0	1	9	11
118	565	43	13	1	8	4	6	9	11
119	510	14	12	29	30	10	3	9	11
120	495	12	12	33	11	6	2	8	11
121	596	15	10	32	69	23	3	8	11
122	412	8	12	11	2	0	2	8	11
123	338	39	5	10	23	6	5	7	11
124	446	10	13	18	8	4	4	7	11
125	418	0	12	41	0	0	0	7	11
126	335	7	6	0	2	0	0	6	11
127	349	10	9	10	4	2	3	6	11
128	308	3	12	24	4	4	2	5	11
129	466	8	15	28	0	0	0	5	11
130	228	0	11	38	9	9	1	5	11
131	428	8	3	4	5	5	3	5	11
132	242	1	8	25	0	0	0	5	11
133	352	0	12	40	0	0	0	5	11
134	244	8	0	0	13	4	4	5	11
135	269	3	9	23	1	0	1	5	11
136	242	0	4	13	0	0	0	4	11
137	291	0	14	6	39	0	2	4	11
138	213	0	9	31	10	0	0	4	11
139	135	0	0	0	1	0	1	3	11
140	210	3	1	3	3	3	3	3	11

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200377&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200377&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200377&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Place[t] = -79.8188579318147 -0.000111644789557989pageviews[t] -0.0782409579001187Blogs[t] + 0.264997949248526PR[t] -0.190196588730452LFM[t] + 0.107494034077762KCS[t] -0.253444101923494SPR[t] -0.0558080713377833CH[t] -2.41456704292149Hours[t] + 19.9511189918986Month[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Place[t] =  -79.8188579318147 -0.000111644789557989pageviews[t] -0.0782409579001187Blogs[t] +  0.264997949248526PR[t] -0.190196588730452LFM[t] +  0.107494034077762KCS[t] -0.253444101923494SPR[t] -0.0558080713377833CH[t] -2.41456704292149Hours[t] +  19.9511189918986Month[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200377&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Place[t] =  -79.8188579318147 -0.000111644789557989pageviews[t] -0.0782409579001187Blogs[t] +  0.264997949248526PR[t] -0.190196588730452LFM[t] +  0.107494034077762KCS[t] -0.253444101923494SPR[t] -0.0558080713377833CH[t] -2.41456704292149Hours[t] +  19.9511189918986Month[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200377&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200377&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Place[t] = -79.8188579318147 -0.000111644789557989pageviews[t] -0.0782409579001187Blogs[t] + 0.264997949248526PR[t] -0.190196588730452LFM[t] + 0.107494034077762KCS[t] -0.253444101923494SPR[t] -0.0558080713377833CH[t] -2.41456704292149Hours[t] + 19.9511189918986Month[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-79.818857931814717.628602-4.52781.3e-057e-06
pageviews-0.0001116447895579890.001239-0.09010.928320.46416
Blogs-0.07824095790011870.057297-1.36550.1744410.08722
PR0.2649979492485260.2712460.9770.3304010.1652
LFM-0.1901965887304520.063935-2.97490.0034960.001748
KCS0.1074940340777620.0573951.87290.0633310.031666
SPR-0.2534441019234940.177743-1.42590.1562930.078147
CH-0.05580807133778330.321236-0.17370.8623490.431174
Hours-2.414567042921490.188276-12.824600
Month19.95111899189861.54986412.872800

\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) & -79.8188579318147 & 17.628602 & -4.5278 & 1.3e-05 & 7e-06 \tabularnewline
pageviews & -0.000111644789557989 & 0.001239 & -0.0901 & 0.92832 & 0.46416 \tabularnewline
Blogs & -0.0782409579001187 & 0.057297 & -1.3655 & 0.174441 & 0.08722 \tabularnewline
PR & 0.264997949248526 & 0.271246 & 0.977 & 0.330401 & 0.1652 \tabularnewline
LFM & -0.190196588730452 & 0.063935 & -2.9749 & 0.003496 & 0.001748 \tabularnewline
KCS & 0.107494034077762 & 0.057395 & 1.8729 & 0.063331 & 0.031666 \tabularnewline
SPR & -0.253444101923494 & 0.177743 & -1.4259 & 0.156293 & 0.078147 \tabularnewline
CH & -0.0558080713377833 & 0.321236 & -0.1737 & 0.862349 & 0.431174 \tabularnewline
Hours & -2.41456704292149 & 0.188276 & -12.8246 & 0 & 0 \tabularnewline
Month & 19.9511189918986 & 1.549864 & 12.8728 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200377&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]-79.8188579318147[/C][C]17.628602[/C][C]-4.5278[/C][C]1.3e-05[/C][C]7e-06[/C][/ROW]
[ROW][C]pageviews[/C][C]-0.000111644789557989[/C][C]0.001239[/C][C]-0.0901[/C][C]0.92832[/C][C]0.46416[/C][/ROW]
[ROW][C]Blogs[/C][C]-0.0782409579001187[/C][C]0.057297[/C][C]-1.3655[/C][C]0.174441[/C][C]0.08722[/C][/ROW]
[ROW][C]PR[/C][C]0.264997949248526[/C][C]0.271246[/C][C]0.977[/C][C]0.330401[/C][C]0.1652[/C][/ROW]
[ROW][C]LFM[/C][C]-0.190196588730452[/C][C]0.063935[/C][C]-2.9749[/C][C]0.003496[/C][C]0.001748[/C][/ROW]
[ROW][C]KCS[/C][C]0.107494034077762[/C][C]0.057395[/C][C]1.8729[/C][C]0.063331[/C][C]0.031666[/C][/ROW]
[ROW][C]SPR[/C][C]-0.253444101923494[/C][C]0.177743[/C][C]-1.4259[/C][C]0.156293[/C][C]0.078147[/C][/ROW]
[ROW][C]CH[/C][C]-0.0558080713377833[/C][C]0.321236[/C][C]-0.1737[/C][C]0.862349[/C][C]0.431174[/C][/ROW]
[ROW][C]Hours[/C][C]-2.41456704292149[/C][C]0.188276[/C][C]-12.8246[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Month[/C][C]19.9511189918986[/C][C]1.549864[/C][C]12.8728[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200377&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200377&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-79.818857931814717.628602-4.52781.3e-057e-06
pageviews-0.0001116447895579890.001239-0.09010.928320.46416
Blogs-0.07824095790011870.057297-1.36550.1744410.08722
PR0.2649979492485260.2712460.9770.3304010.1652
LFM-0.1901965887304520.063935-2.97490.0034960.001748
KCS0.1074940340777620.0573951.87290.0633310.031666
SPR-0.2534441019234940.177743-1.42590.1562930.078147
CH-0.05580807133778330.321236-0.17370.8623490.431174
Hours-2.414567042921490.188276-12.824600
Month19.95111899189861.54986412.872800






Multiple Linear Regression - Regression Statistics
Multiple R0.986277010657811
R-squared0.972742341752108
Adjusted R-squared0.970855273104178
F-TEST (value)515.477983706984
F-TEST (DF numerator)9
F-TEST (DF denominator)130
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.9240938572226
Sum Squared Residuals6232.59984667161

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.986277010657811 \tabularnewline
R-squared & 0.972742341752108 \tabularnewline
Adjusted R-squared & 0.970855273104178 \tabularnewline
F-TEST (value) & 515.477983706984 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 130 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.9240938572226 \tabularnewline
Sum Squared Residuals & 6232.59984667161 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200377&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.986277010657811[/C][/ROW]
[ROW][C]R-squared[/C][C]0.972742341752108[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.970855273104178[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]515.477983706984[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]130[/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]6.9240938572226[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6232.59984667161[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200377&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200377&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.986277010657811
R-squared0.972742341752108
Adjusted R-squared0.970855273104178
F-TEST (value)515.477983706984
F-TEST (DF numerator)9
F-TEST (DF denominator)130
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.9240938572226
Sum Squared Residuals6232.59984667161






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11-27.688932245516428.6889322455164
22-20.378875313587822.3788753135878
33-13.154659365133716.1546593651337
44-8.6111981836375812.6111981836376
55-1.98243748209986.9824374820998
665.248155418394650.751844581605346
774.161127084095632.83887291590437
885.389743579029652.61025642097035
997.839426303025841.16057369697416
101015.1309471419159-5.1309471419159
111116.931710334913-5.93171033491296
121220.2529641160823-8.2529641160823
131320.6688292092075-7.66882920920752
141421.0050910167119-7.00509101671194
151517.9287109954755-2.92871099547555
161621.4256275563604-5.42562755636045
171721.6832400286008-4.6832400286008
181820.605006011933-2.605006011933
191925.0517322288876-6.0517322288876
202026.6813535641694-6.68135356416938
212127.3000101823376-6.30001018233764
222226.6317651328697-4.63176513286968
232327.5712026200918-4.57120262009181
242430.4862053842988-6.48620538429877
252533.669787116163-8.669787116163
262633.24968835258-7.24968835257998
272728.3609960253597-1.36099602535965
282835.0914615715057-7.09146157150566
292929.8707054981417-0.870705498141737
303030.2506727911833-0.250672791183267
313133.3109445913176-2.31094459131755
323234.8894588468675-2.88945884686755
333336.5458829048383-3.54588290483826
343434.9289508163097-0.928950816309731
353536.0883237238018-1.08832372380176
363633.75652254866012.24347745133993
373735.16727444923181.83272555076816
383837.64044890050360.359551099496429
393936.68211234910242.31788765089761
404038.10279450864161.89720549135844
414136.75829558758414.24170441241588
424232.03786171185149.96213828814861
434339.699801193913.30019880609004
444437.96748031466296.03251968533712
454537.76997710852457.23002289147554
464639.70127906976216.29872093023787
474739.44152413721997.55847586278011
484861.4995396288386-13.4995396288386
494961.9489324908529-12.9489324908529
505061.3454445672277-11.3454445672277
515161.333671945374-10.333671945374
525263.5050583947589-11.5050583947589
535361.2407384831815-8.24073848318153
545463.3935573318363-9.39355733183628
555562.0465556330928-7.04655563309276
565662.0941836155393-6.09418361553932
575759.9618173990406-2.96181739904061
585863.1437032987994-5.14370329879938
595964.8472338375036-5.8472338375036
606065.0020117594573-5.00201175945735
616164.0605837452368-3.06058374523683
626265.967012601495-3.96701260149503
636366.5863456696866-3.58634566968661
646464.097661108669-0.0976611086689846
656565.9483693689665-0.948369368966529
666666.9733517937631-0.973351793763134
676766.065629811970.934370188030001
686870.184533404091-2.18453340409101
696970.6511387766244-1.65113877662443
707071.8893060230652-1.88930602306524
717171.5138832537953-0.51388325379531
727273.4049714269875-1.4049714269875
737375.6651171070745-2.66511710707453
747479.4672736742928-5.46727367429282
757578.1728534558837-3.17285345588374
767679.5540099071509-3.55400990715085
777775.89261333202531.10738666797465
787877.1322577880270.867742211972951
797980.1927554340493-1.19275543404927
808083.0471075544477-3.0471075544477
818174.1874458752896.81255412471099
828280.39871698962081.60128301037919
838377.81789900645435.18210099354572
848477.69474393502996.30525606497014
858580.5563484663584.44365153364204
868683.0029884848542.99701151514601
878781.63014102658825.36985897341178
888878.33200730598599.6679926940141
898981.17218868815217.82781131184794
909079.783798821517310.2162011784827
919182.93623238957518.06376761042486
929284.68598030753617.31401969246386
939383.97102291508759.02897708491251
949486.68328329285157.31671670714855
9595106.228425725686-11.2284257256859
9696104.060376197065-8.06037619706527
9797102.394803885569-5.39480388556901
9898106.70500624214-8.70500624213994
9999106.575095410555-7.57509541055454
100100106.050985751303-6.05098575130336
101101107.534756954795-6.53475695479513
102102111.209014986727-9.20901498672652
103103107.031080669054-4.03108066905401
104104107.774564135111-3.77456413511063
105105110.283317994218-5.28331799421817
106106108.697488489641-2.6974884896407
107107106.7090787196440.29092128035585
108108107.9267434566230.0732565433770674
109109116.997616042813-7.99761604281322
110110111.351958572738-1.35195857273815
111111111.382752265953-0.382752265953006
112112112.717745924455-0.717745924455434
113113115.569463310002-2.56946331000156
114114115.963462592007-1.96346259200718
115115116.693036935145-1.6930369351445
116116110.0918619056085.9081380943922
117117116.6004076490230.399592350977373
118118117.2510112853730.748988714627136
119119114.9472654463844.05273455361601
120120115.7864005535824.21359944641844
121121117.0708984188423.92910158115771
122122120.8461741596261.15382584037361
123123119.7480300561413.25196994385904
124124121.5536568461352.44634315386493
125125118.0767294096186.92327059038203
126126126.375936775385-0.375936775384843
127127124.5733544853672.42664551463308
128128125.2213471429712.77865285702883
129129125.5421263836033.45787361639732
130130121.863309140478.13669085952956
131131126.0339370709124.9660629290876
132132124.8304256432167.16957435678384
133133123.1034286403029.89657135969777
134134127.0778605225756.92213947742489
135135125.3680064075479.63199359245273
136136128.5456009118097.4543990881909
137137136.6021371170760.397862882923652
138138127.52523010057810.4747698994216
139139132.4363637664556.56363623354495
140140131.2307153363048.76928466369551

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & -27.6889322455164 & 28.6889322455164 \tabularnewline
2 & 2 & -20.3788753135878 & 22.3788753135878 \tabularnewline
3 & 3 & -13.1546593651337 & 16.1546593651337 \tabularnewline
4 & 4 & -8.61119818363758 & 12.6111981836376 \tabularnewline
5 & 5 & -1.9824374820998 & 6.9824374820998 \tabularnewline
6 & 6 & 5.24815541839465 & 0.751844581605346 \tabularnewline
7 & 7 & 4.16112708409563 & 2.83887291590437 \tabularnewline
8 & 8 & 5.38974357902965 & 2.61025642097035 \tabularnewline
9 & 9 & 7.83942630302584 & 1.16057369697416 \tabularnewline
10 & 10 & 15.1309471419159 & -5.1309471419159 \tabularnewline
11 & 11 & 16.931710334913 & -5.93171033491296 \tabularnewline
12 & 12 & 20.2529641160823 & -8.2529641160823 \tabularnewline
13 & 13 & 20.6688292092075 & -7.66882920920752 \tabularnewline
14 & 14 & 21.0050910167119 & -7.00509101671194 \tabularnewline
15 & 15 & 17.9287109954755 & -2.92871099547555 \tabularnewline
16 & 16 & 21.4256275563604 & -5.42562755636045 \tabularnewline
17 & 17 & 21.6832400286008 & -4.6832400286008 \tabularnewline
18 & 18 & 20.605006011933 & -2.605006011933 \tabularnewline
19 & 19 & 25.0517322288876 & -6.0517322288876 \tabularnewline
20 & 20 & 26.6813535641694 & -6.68135356416938 \tabularnewline
21 & 21 & 27.3000101823376 & -6.30001018233764 \tabularnewline
22 & 22 & 26.6317651328697 & -4.63176513286968 \tabularnewline
23 & 23 & 27.5712026200918 & -4.57120262009181 \tabularnewline
24 & 24 & 30.4862053842988 & -6.48620538429877 \tabularnewline
25 & 25 & 33.669787116163 & -8.669787116163 \tabularnewline
26 & 26 & 33.24968835258 & -7.24968835257998 \tabularnewline
27 & 27 & 28.3609960253597 & -1.36099602535965 \tabularnewline
28 & 28 & 35.0914615715057 & -7.09146157150566 \tabularnewline
29 & 29 & 29.8707054981417 & -0.870705498141737 \tabularnewline
30 & 30 & 30.2506727911833 & -0.250672791183267 \tabularnewline
31 & 31 & 33.3109445913176 & -2.31094459131755 \tabularnewline
32 & 32 & 34.8894588468675 & -2.88945884686755 \tabularnewline
33 & 33 & 36.5458829048383 & -3.54588290483826 \tabularnewline
34 & 34 & 34.9289508163097 & -0.928950816309731 \tabularnewline
35 & 35 & 36.0883237238018 & -1.08832372380176 \tabularnewline
36 & 36 & 33.7565225486601 & 2.24347745133993 \tabularnewline
37 & 37 & 35.1672744492318 & 1.83272555076816 \tabularnewline
38 & 38 & 37.6404489005036 & 0.359551099496429 \tabularnewline
39 & 39 & 36.6821123491024 & 2.31788765089761 \tabularnewline
40 & 40 & 38.1027945086416 & 1.89720549135844 \tabularnewline
41 & 41 & 36.7582955875841 & 4.24170441241588 \tabularnewline
42 & 42 & 32.0378617118514 & 9.96213828814861 \tabularnewline
43 & 43 & 39.69980119391 & 3.30019880609004 \tabularnewline
44 & 44 & 37.9674803146629 & 6.03251968533712 \tabularnewline
45 & 45 & 37.7699771085245 & 7.23002289147554 \tabularnewline
46 & 46 & 39.7012790697621 & 6.29872093023787 \tabularnewline
47 & 47 & 39.4415241372199 & 7.55847586278011 \tabularnewline
48 & 48 & 61.4995396288386 & -13.4995396288386 \tabularnewline
49 & 49 & 61.9489324908529 & -12.9489324908529 \tabularnewline
50 & 50 & 61.3454445672277 & -11.3454445672277 \tabularnewline
51 & 51 & 61.333671945374 & -10.333671945374 \tabularnewline
52 & 52 & 63.5050583947589 & -11.5050583947589 \tabularnewline
53 & 53 & 61.2407384831815 & -8.24073848318153 \tabularnewline
54 & 54 & 63.3935573318363 & -9.39355733183628 \tabularnewline
55 & 55 & 62.0465556330928 & -7.04655563309276 \tabularnewline
56 & 56 & 62.0941836155393 & -6.09418361553932 \tabularnewline
57 & 57 & 59.9618173990406 & -2.96181739904061 \tabularnewline
58 & 58 & 63.1437032987994 & -5.14370329879938 \tabularnewline
59 & 59 & 64.8472338375036 & -5.8472338375036 \tabularnewline
60 & 60 & 65.0020117594573 & -5.00201175945735 \tabularnewline
61 & 61 & 64.0605837452368 & -3.06058374523683 \tabularnewline
62 & 62 & 65.967012601495 & -3.96701260149503 \tabularnewline
63 & 63 & 66.5863456696866 & -3.58634566968661 \tabularnewline
64 & 64 & 64.097661108669 & -0.0976611086689846 \tabularnewline
65 & 65 & 65.9483693689665 & -0.948369368966529 \tabularnewline
66 & 66 & 66.9733517937631 & -0.973351793763134 \tabularnewline
67 & 67 & 66.06562981197 & 0.934370188030001 \tabularnewline
68 & 68 & 70.184533404091 & -2.18453340409101 \tabularnewline
69 & 69 & 70.6511387766244 & -1.65113877662443 \tabularnewline
70 & 70 & 71.8893060230652 & -1.88930602306524 \tabularnewline
71 & 71 & 71.5138832537953 & -0.51388325379531 \tabularnewline
72 & 72 & 73.4049714269875 & -1.4049714269875 \tabularnewline
73 & 73 & 75.6651171070745 & -2.66511710707453 \tabularnewline
74 & 74 & 79.4672736742928 & -5.46727367429282 \tabularnewline
75 & 75 & 78.1728534558837 & -3.17285345588374 \tabularnewline
76 & 76 & 79.5540099071509 & -3.55400990715085 \tabularnewline
77 & 77 & 75.8926133320253 & 1.10738666797465 \tabularnewline
78 & 78 & 77.132257788027 & 0.867742211972951 \tabularnewline
79 & 79 & 80.1927554340493 & -1.19275543404927 \tabularnewline
80 & 80 & 83.0471075544477 & -3.0471075544477 \tabularnewline
81 & 81 & 74.187445875289 & 6.81255412471099 \tabularnewline
82 & 82 & 80.3987169896208 & 1.60128301037919 \tabularnewline
83 & 83 & 77.8178990064543 & 5.18210099354572 \tabularnewline
84 & 84 & 77.6947439350299 & 6.30525606497014 \tabularnewline
85 & 85 & 80.556348466358 & 4.44365153364204 \tabularnewline
86 & 86 & 83.002988484854 & 2.99701151514601 \tabularnewline
87 & 87 & 81.6301410265882 & 5.36985897341178 \tabularnewline
88 & 88 & 78.3320073059859 & 9.6679926940141 \tabularnewline
89 & 89 & 81.1721886881521 & 7.82781131184794 \tabularnewline
90 & 90 & 79.7837988215173 & 10.2162011784827 \tabularnewline
91 & 91 & 82.9362323895751 & 8.06376761042486 \tabularnewline
92 & 92 & 84.6859803075361 & 7.31401969246386 \tabularnewline
93 & 93 & 83.9710229150875 & 9.02897708491251 \tabularnewline
94 & 94 & 86.6832832928515 & 7.31671670714855 \tabularnewline
95 & 95 & 106.228425725686 & -11.2284257256859 \tabularnewline
96 & 96 & 104.060376197065 & -8.06037619706527 \tabularnewline
97 & 97 & 102.394803885569 & -5.39480388556901 \tabularnewline
98 & 98 & 106.70500624214 & -8.70500624213994 \tabularnewline
99 & 99 & 106.575095410555 & -7.57509541055454 \tabularnewline
100 & 100 & 106.050985751303 & -6.05098575130336 \tabularnewline
101 & 101 & 107.534756954795 & -6.53475695479513 \tabularnewline
102 & 102 & 111.209014986727 & -9.20901498672652 \tabularnewline
103 & 103 & 107.031080669054 & -4.03108066905401 \tabularnewline
104 & 104 & 107.774564135111 & -3.77456413511063 \tabularnewline
105 & 105 & 110.283317994218 & -5.28331799421817 \tabularnewline
106 & 106 & 108.697488489641 & -2.6974884896407 \tabularnewline
107 & 107 & 106.709078719644 & 0.29092128035585 \tabularnewline
108 & 108 & 107.926743456623 & 0.0732565433770674 \tabularnewline
109 & 109 & 116.997616042813 & -7.99761604281322 \tabularnewline
110 & 110 & 111.351958572738 & -1.35195857273815 \tabularnewline
111 & 111 & 111.382752265953 & -0.382752265953006 \tabularnewline
112 & 112 & 112.717745924455 & -0.717745924455434 \tabularnewline
113 & 113 & 115.569463310002 & -2.56946331000156 \tabularnewline
114 & 114 & 115.963462592007 & -1.96346259200718 \tabularnewline
115 & 115 & 116.693036935145 & -1.6930369351445 \tabularnewline
116 & 116 & 110.091861905608 & 5.9081380943922 \tabularnewline
117 & 117 & 116.600407649023 & 0.399592350977373 \tabularnewline
118 & 118 & 117.251011285373 & 0.748988714627136 \tabularnewline
119 & 119 & 114.947265446384 & 4.05273455361601 \tabularnewline
120 & 120 & 115.786400553582 & 4.21359944641844 \tabularnewline
121 & 121 & 117.070898418842 & 3.92910158115771 \tabularnewline
122 & 122 & 120.846174159626 & 1.15382584037361 \tabularnewline
123 & 123 & 119.748030056141 & 3.25196994385904 \tabularnewline
124 & 124 & 121.553656846135 & 2.44634315386493 \tabularnewline
125 & 125 & 118.076729409618 & 6.92327059038203 \tabularnewline
126 & 126 & 126.375936775385 & -0.375936775384843 \tabularnewline
127 & 127 & 124.573354485367 & 2.42664551463308 \tabularnewline
128 & 128 & 125.221347142971 & 2.77865285702883 \tabularnewline
129 & 129 & 125.542126383603 & 3.45787361639732 \tabularnewline
130 & 130 & 121.86330914047 & 8.13669085952956 \tabularnewline
131 & 131 & 126.033937070912 & 4.9660629290876 \tabularnewline
132 & 132 & 124.830425643216 & 7.16957435678384 \tabularnewline
133 & 133 & 123.103428640302 & 9.89657135969777 \tabularnewline
134 & 134 & 127.077860522575 & 6.92213947742489 \tabularnewline
135 & 135 & 125.368006407547 & 9.63199359245273 \tabularnewline
136 & 136 & 128.545600911809 & 7.4543990881909 \tabularnewline
137 & 137 & 136.602137117076 & 0.397862882923652 \tabularnewline
138 & 138 & 127.525230100578 & 10.4747698994216 \tabularnewline
139 & 139 & 132.436363766455 & 6.56363623354495 \tabularnewline
140 & 140 & 131.230715336304 & 8.76928466369551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200377&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]-27.6889322455164[/C][C]28.6889322455164[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]-20.3788753135878[/C][C]22.3788753135878[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]-13.1546593651337[/C][C]16.1546593651337[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]-8.61119818363758[/C][C]12.6111981836376[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]-1.9824374820998[/C][C]6.9824374820998[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]5.24815541839465[/C][C]0.751844581605346[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]4.16112708409563[/C][C]2.83887291590437[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]5.38974357902965[/C][C]2.61025642097035[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]7.83942630302584[/C][C]1.16057369697416[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]15.1309471419159[/C][C]-5.1309471419159[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]16.931710334913[/C][C]-5.93171033491296[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]20.2529641160823[/C][C]-8.2529641160823[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]20.6688292092075[/C][C]-7.66882920920752[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]21.0050910167119[/C][C]-7.00509101671194[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]17.9287109954755[/C][C]-2.92871099547555[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]21.4256275563604[/C][C]-5.42562755636045[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]21.6832400286008[/C][C]-4.6832400286008[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]20.605006011933[/C][C]-2.605006011933[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]25.0517322288876[/C][C]-6.0517322288876[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]26.6813535641694[/C][C]-6.68135356416938[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]27.3000101823376[/C][C]-6.30001018233764[/C][/ROW]
[ROW][C]22[/C][C]22[/C][C]26.6317651328697[/C][C]-4.63176513286968[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]27.5712026200918[/C][C]-4.57120262009181[/C][/ROW]
[ROW][C]24[/C][C]24[/C][C]30.4862053842988[/C][C]-6.48620538429877[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]33.669787116163[/C][C]-8.669787116163[/C][/ROW]
[ROW][C]26[/C][C]26[/C][C]33.24968835258[/C][C]-7.24968835257998[/C][/ROW]
[ROW][C]27[/C][C]27[/C][C]28.3609960253597[/C][C]-1.36099602535965[/C][/ROW]
[ROW][C]28[/C][C]28[/C][C]35.0914615715057[/C][C]-7.09146157150566[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]29.8707054981417[/C][C]-0.870705498141737[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]30.2506727911833[/C][C]-0.250672791183267[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]33.3109445913176[/C][C]-2.31094459131755[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]34.8894588468675[/C][C]-2.88945884686755[/C][/ROW]
[ROW][C]33[/C][C]33[/C][C]36.5458829048383[/C][C]-3.54588290483826[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]34.9289508163097[/C][C]-0.928950816309731[/C][/ROW]
[ROW][C]35[/C][C]35[/C][C]36.0883237238018[/C][C]-1.08832372380176[/C][/ROW]
[ROW][C]36[/C][C]36[/C][C]33.7565225486601[/C][C]2.24347745133993[/C][/ROW]
[ROW][C]37[/C][C]37[/C][C]35.1672744492318[/C][C]1.83272555076816[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]37.6404489005036[/C][C]0.359551099496429[/C][/ROW]
[ROW][C]39[/C][C]39[/C][C]36.6821123491024[/C][C]2.31788765089761[/C][/ROW]
[ROW][C]40[/C][C]40[/C][C]38.1027945086416[/C][C]1.89720549135844[/C][/ROW]
[ROW][C]41[/C][C]41[/C][C]36.7582955875841[/C][C]4.24170441241588[/C][/ROW]
[ROW][C]42[/C][C]42[/C][C]32.0378617118514[/C][C]9.96213828814861[/C][/ROW]
[ROW][C]43[/C][C]43[/C][C]39.69980119391[/C][C]3.30019880609004[/C][/ROW]
[ROW][C]44[/C][C]44[/C][C]37.9674803146629[/C][C]6.03251968533712[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]37.7699771085245[/C][C]7.23002289147554[/C][/ROW]
[ROW][C]46[/C][C]46[/C][C]39.7012790697621[/C][C]6.29872093023787[/C][/ROW]
[ROW][C]47[/C][C]47[/C][C]39.4415241372199[/C][C]7.55847586278011[/C][/ROW]
[ROW][C]48[/C][C]48[/C][C]61.4995396288386[/C][C]-13.4995396288386[/C][/ROW]
[ROW][C]49[/C][C]49[/C][C]61.9489324908529[/C][C]-12.9489324908529[/C][/ROW]
[ROW][C]50[/C][C]50[/C][C]61.3454445672277[/C][C]-11.3454445672277[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]61.333671945374[/C][C]-10.333671945374[/C][/ROW]
[ROW][C]52[/C][C]52[/C][C]63.5050583947589[/C][C]-11.5050583947589[/C][/ROW]
[ROW][C]53[/C][C]53[/C][C]61.2407384831815[/C][C]-8.24073848318153[/C][/ROW]
[ROW][C]54[/C][C]54[/C][C]63.3935573318363[/C][C]-9.39355733183628[/C][/ROW]
[ROW][C]55[/C][C]55[/C][C]62.0465556330928[/C][C]-7.04655563309276[/C][/ROW]
[ROW][C]56[/C][C]56[/C][C]62.0941836155393[/C][C]-6.09418361553932[/C][/ROW]
[ROW][C]57[/C][C]57[/C][C]59.9618173990406[/C][C]-2.96181739904061[/C][/ROW]
[ROW][C]58[/C][C]58[/C][C]63.1437032987994[/C][C]-5.14370329879938[/C][/ROW]
[ROW][C]59[/C][C]59[/C][C]64.8472338375036[/C][C]-5.8472338375036[/C][/ROW]
[ROW][C]60[/C][C]60[/C][C]65.0020117594573[/C][C]-5.00201175945735[/C][/ROW]
[ROW][C]61[/C][C]61[/C][C]64.0605837452368[/C][C]-3.06058374523683[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]65.967012601495[/C][C]-3.96701260149503[/C][/ROW]
[ROW][C]63[/C][C]63[/C][C]66.5863456696866[/C][C]-3.58634566968661[/C][/ROW]
[ROW][C]64[/C][C]64[/C][C]64.097661108669[/C][C]-0.0976611086689846[/C][/ROW]
[ROW][C]65[/C][C]65[/C][C]65.9483693689665[/C][C]-0.948369368966529[/C][/ROW]
[ROW][C]66[/C][C]66[/C][C]66.9733517937631[/C][C]-0.973351793763134[/C][/ROW]
[ROW][C]67[/C][C]67[/C][C]66.06562981197[/C][C]0.934370188030001[/C][/ROW]
[ROW][C]68[/C][C]68[/C][C]70.184533404091[/C][C]-2.18453340409101[/C][/ROW]
[ROW][C]69[/C][C]69[/C][C]70.6511387766244[/C][C]-1.65113877662443[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]71.8893060230652[/C][C]-1.88930602306524[/C][/ROW]
[ROW][C]71[/C][C]71[/C][C]71.5138832537953[/C][C]-0.51388325379531[/C][/ROW]
[ROW][C]72[/C][C]72[/C][C]73.4049714269875[/C][C]-1.4049714269875[/C][/ROW]
[ROW][C]73[/C][C]73[/C][C]75.6651171070745[/C][C]-2.66511710707453[/C][/ROW]
[ROW][C]74[/C][C]74[/C][C]79.4672736742928[/C][C]-5.46727367429282[/C][/ROW]
[ROW][C]75[/C][C]75[/C][C]78.1728534558837[/C][C]-3.17285345588374[/C][/ROW]
[ROW][C]76[/C][C]76[/C][C]79.5540099071509[/C][C]-3.55400990715085[/C][/ROW]
[ROW][C]77[/C][C]77[/C][C]75.8926133320253[/C][C]1.10738666797465[/C][/ROW]
[ROW][C]78[/C][C]78[/C][C]77.132257788027[/C][C]0.867742211972951[/C][/ROW]
[ROW][C]79[/C][C]79[/C][C]80.1927554340493[/C][C]-1.19275543404927[/C][/ROW]
[ROW][C]80[/C][C]80[/C][C]83.0471075544477[/C][C]-3.0471075544477[/C][/ROW]
[ROW][C]81[/C][C]81[/C][C]74.187445875289[/C][C]6.81255412471099[/C][/ROW]
[ROW][C]82[/C][C]82[/C][C]80.3987169896208[/C][C]1.60128301037919[/C][/ROW]
[ROW][C]83[/C][C]83[/C][C]77.8178990064543[/C][C]5.18210099354572[/C][/ROW]
[ROW][C]84[/C][C]84[/C][C]77.6947439350299[/C][C]6.30525606497014[/C][/ROW]
[ROW][C]85[/C][C]85[/C][C]80.556348466358[/C][C]4.44365153364204[/C][/ROW]
[ROW][C]86[/C][C]86[/C][C]83.002988484854[/C][C]2.99701151514601[/C][/ROW]
[ROW][C]87[/C][C]87[/C][C]81.6301410265882[/C][C]5.36985897341178[/C][/ROW]
[ROW][C]88[/C][C]88[/C][C]78.3320073059859[/C][C]9.6679926940141[/C][/ROW]
[ROW][C]89[/C][C]89[/C][C]81.1721886881521[/C][C]7.82781131184794[/C][/ROW]
[ROW][C]90[/C][C]90[/C][C]79.7837988215173[/C][C]10.2162011784827[/C][/ROW]
[ROW][C]91[/C][C]91[/C][C]82.9362323895751[/C][C]8.06376761042486[/C][/ROW]
[ROW][C]92[/C][C]92[/C][C]84.6859803075361[/C][C]7.31401969246386[/C][/ROW]
[ROW][C]93[/C][C]93[/C][C]83.9710229150875[/C][C]9.02897708491251[/C][/ROW]
[ROW][C]94[/C][C]94[/C][C]86.6832832928515[/C][C]7.31671670714855[/C][/ROW]
[ROW][C]95[/C][C]95[/C][C]106.228425725686[/C][C]-11.2284257256859[/C][/ROW]
[ROW][C]96[/C][C]96[/C][C]104.060376197065[/C][C]-8.06037619706527[/C][/ROW]
[ROW][C]97[/C][C]97[/C][C]102.394803885569[/C][C]-5.39480388556901[/C][/ROW]
[ROW][C]98[/C][C]98[/C][C]106.70500624214[/C][C]-8.70500624213994[/C][/ROW]
[ROW][C]99[/C][C]99[/C][C]106.575095410555[/C][C]-7.57509541055454[/C][/ROW]
[ROW][C]100[/C][C]100[/C][C]106.050985751303[/C][C]-6.05098575130336[/C][/ROW]
[ROW][C]101[/C][C]101[/C][C]107.534756954795[/C][C]-6.53475695479513[/C][/ROW]
[ROW][C]102[/C][C]102[/C][C]111.209014986727[/C][C]-9.20901498672652[/C][/ROW]
[ROW][C]103[/C][C]103[/C][C]107.031080669054[/C][C]-4.03108066905401[/C][/ROW]
[ROW][C]104[/C][C]104[/C][C]107.774564135111[/C][C]-3.77456413511063[/C][/ROW]
[ROW][C]105[/C][C]105[/C][C]110.283317994218[/C][C]-5.28331799421817[/C][/ROW]
[ROW][C]106[/C][C]106[/C][C]108.697488489641[/C][C]-2.6974884896407[/C][/ROW]
[ROW][C]107[/C][C]107[/C][C]106.709078719644[/C][C]0.29092128035585[/C][/ROW]
[ROW][C]108[/C][C]108[/C][C]107.926743456623[/C][C]0.0732565433770674[/C][/ROW]
[ROW][C]109[/C][C]109[/C][C]116.997616042813[/C][C]-7.99761604281322[/C][/ROW]
[ROW][C]110[/C][C]110[/C][C]111.351958572738[/C][C]-1.35195857273815[/C][/ROW]
[ROW][C]111[/C][C]111[/C][C]111.382752265953[/C][C]-0.382752265953006[/C][/ROW]
[ROW][C]112[/C][C]112[/C][C]112.717745924455[/C][C]-0.717745924455434[/C][/ROW]
[ROW][C]113[/C][C]113[/C][C]115.569463310002[/C][C]-2.56946331000156[/C][/ROW]
[ROW][C]114[/C][C]114[/C][C]115.963462592007[/C][C]-1.96346259200718[/C][/ROW]
[ROW][C]115[/C][C]115[/C][C]116.693036935145[/C][C]-1.6930369351445[/C][/ROW]
[ROW][C]116[/C][C]116[/C][C]110.091861905608[/C][C]5.9081380943922[/C][/ROW]
[ROW][C]117[/C][C]117[/C][C]116.600407649023[/C][C]0.399592350977373[/C][/ROW]
[ROW][C]118[/C][C]118[/C][C]117.251011285373[/C][C]0.748988714627136[/C][/ROW]
[ROW][C]119[/C][C]119[/C][C]114.947265446384[/C][C]4.05273455361601[/C][/ROW]
[ROW][C]120[/C][C]120[/C][C]115.786400553582[/C][C]4.21359944641844[/C][/ROW]
[ROW][C]121[/C][C]121[/C][C]117.070898418842[/C][C]3.92910158115771[/C][/ROW]
[ROW][C]122[/C][C]122[/C][C]120.846174159626[/C][C]1.15382584037361[/C][/ROW]
[ROW][C]123[/C][C]123[/C][C]119.748030056141[/C][C]3.25196994385904[/C][/ROW]
[ROW][C]124[/C][C]124[/C][C]121.553656846135[/C][C]2.44634315386493[/C][/ROW]
[ROW][C]125[/C][C]125[/C][C]118.076729409618[/C][C]6.92327059038203[/C][/ROW]
[ROW][C]126[/C][C]126[/C][C]126.375936775385[/C][C]-0.375936775384843[/C][/ROW]
[ROW][C]127[/C][C]127[/C][C]124.573354485367[/C][C]2.42664551463308[/C][/ROW]
[ROW][C]128[/C][C]128[/C][C]125.221347142971[/C][C]2.77865285702883[/C][/ROW]
[ROW][C]129[/C][C]129[/C][C]125.542126383603[/C][C]3.45787361639732[/C][/ROW]
[ROW][C]130[/C][C]130[/C][C]121.86330914047[/C][C]8.13669085952956[/C][/ROW]
[ROW][C]131[/C][C]131[/C][C]126.033937070912[/C][C]4.9660629290876[/C][/ROW]
[ROW][C]132[/C][C]132[/C][C]124.830425643216[/C][C]7.16957435678384[/C][/ROW]
[ROW][C]133[/C][C]133[/C][C]123.103428640302[/C][C]9.89657135969777[/C][/ROW]
[ROW][C]134[/C][C]134[/C][C]127.077860522575[/C][C]6.92213947742489[/C][/ROW]
[ROW][C]135[/C][C]135[/C][C]125.368006407547[/C][C]9.63199359245273[/C][/ROW]
[ROW][C]136[/C][C]136[/C][C]128.545600911809[/C][C]7.4543990881909[/C][/ROW]
[ROW][C]137[/C][C]137[/C][C]136.602137117076[/C][C]0.397862882923652[/C][/ROW]
[ROW][C]138[/C][C]138[/C][C]127.525230100578[/C][C]10.4747698994216[/C][/ROW]
[ROW][C]139[/C][C]139[/C][C]132.436363766455[/C][C]6.56363623354495[/C][/ROW]
[ROW][C]140[/C][C]140[/C][C]131.230715336304[/C][C]8.76928466369551[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200377&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200377&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11-27.688932245516428.6889322455164
22-20.378875313587822.3788753135878
33-13.154659365133716.1546593651337
44-8.6111981836375812.6111981836376
55-1.98243748209986.9824374820998
665.248155418394650.751844581605346
774.161127084095632.83887291590437
885.389743579029652.61025642097035
997.839426303025841.16057369697416
101015.1309471419159-5.1309471419159
111116.931710334913-5.93171033491296
121220.2529641160823-8.2529641160823
131320.6688292092075-7.66882920920752
141421.0050910167119-7.00509101671194
151517.9287109954755-2.92871099547555
161621.4256275563604-5.42562755636045
171721.6832400286008-4.6832400286008
181820.605006011933-2.605006011933
191925.0517322288876-6.0517322288876
202026.6813535641694-6.68135356416938
212127.3000101823376-6.30001018233764
222226.6317651328697-4.63176513286968
232327.5712026200918-4.57120262009181
242430.4862053842988-6.48620538429877
252533.669787116163-8.669787116163
262633.24968835258-7.24968835257998
272728.3609960253597-1.36099602535965
282835.0914615715057-7.09146157150566
292929.8707054981417-0.870705498141737
303030.2506727911833-0.250672791183267
313133.3109445913176-2.31094459131755
323234.8894588468675-2.88945884686755
333336.5458829048383-3.54588290483826
343434.9289508163097-0.928950816309731
353536.0883237238018-1.08832372380176
363633.75652254866012.24347745133993
373735.16727444923181.83272555076816
383837.64044890050360.359551099496429
393936.68211234910242.31788765089761
404038.10279450864161.89720549135844
414136.75829558758414.24170441241588
424232.03786171185149.96213828814861
434339.699801193913.30019880609004
444437.96748031466296.03251968533712
454537.76997710852457.23002289147554
464639.70127906976216.29872093023787
474739.44152413721997.55847586278011
484861.4995396288386-13.4995396288386
494961.9489324908529-12.9489324908529
505061.3454445672277-11.3454445672277
515161.333671945374-10.333671945374
525263.5050583947589-11.5050583947589
535361.2407384831815-8.24073848318153
545463.3935573318363-9.39355733183628
555562.0465556330928-7.04655563309276
565662.0941836155393-6.09418361553932
575759.9618173990406-2.96181739904061
585863.1437032987994-5.14370329879938
595964.8472338375036-5.8472338375036
606065.0020117594573-5.00201175945735
616164.0605837452368-3.06058374523683
626265.967012601495-3.96701260149503
636366.5863456696866-3.58634566968661
646464.097661108669-0.0976611086689846
656565.9483693689665-0.948369368966529
666666.9733517937631-0.973351793763134
676766.065629811970.934370188030001
686870.184533404091-2.18453340409101
696970.6511387766244-1.65113877662443
707071.8893060230652-1.88930602306524
717171.5138832537953-0.51388325379531
727273.4049714269875-1.4049714269875
737375.6651171070745-2.66511710707453
747479.4672736742928-5.46727367429282
757578.1728534558837-3.17285345588374
767679.5540099071509-3.55400990715085
777775.89261333202531.10738666797465
787877.1322577880270.867742211972951
797980.1927554340493-1.19275543404927
808083.0471075544477-3.0471075544477
818174.1874458752896.81255412471099
828280.39871698962081.60128301037919
838377.81789900645435.18210099354572
848477.69474393502996.30525606497014
858580.5563484663584.44365153364204
868683.0029884848542.99701151514601
878781.63014102658825.36985897341178
888878.33200730598599.6679926940141
898981.17218868815217.82781131184794
909079.783798821517310.2162011784827
919182.93623238957518.06376761042486
929284.68598030753617.31401969246386
939383.97102291508759.02897708491251
949486.68328329285157.31671670714855
9595106.228425725686-11.2284257256859
9696104.060376197065-8.06037619706527
9797102.394803885569-5.39480388556901
9898106.70500624214-8.70500624213994
9999106.575095410555-7.57509541055454
100100106.050985751303-6.05098575130336
101101107.534756954795-6.53475695479513
102102111.209014986727-9.20901498672652
103103107.031080669054-4.03108066905401
104104107.774564135111-3.77456413511063
105105110.283317994218-5.28331799421817
106106108.697488489641-2.6974884896407
107107106.7090787196440.29092128035585
108108107.9267434566230.0732565433770674
109109116.997616042813-7.99761604281322
110110111.351958572738-1.35195857273815
111111111.382752265953-0.382752265953006
112112112.717745924455-0.717745924455434
113113115.569463310002-2.56946331000156
114114115.963462592007-1.96346259200718
115115116.693036935145-1.6930369351445
116116110.0918619056085.9081380943922
117117116.6004076490230.399592350977373
118118117.2510112853730.748988714627136
119119114.9472654463844.05273455361601
120120115.7864005535824.21359944641844
121121117.0708984188423.92910158115771
122122120.8461741596261.15382584037361
123123119.7480300561413.25196994385904
124124121.5536568461352.44634315386493
125125118.0767294096186.92327059038203
126126126.375936775385-0.375936775384843
127127124.5733544853672.42664551463308
128128125.2213471429712.77865285702883
129129125.5421263836033.45787361639732
130130121.863309140478.13669085952956
131131126.0339370709124.9660629290876
132132124.8304256432167.16957435678384
133133123.1034286403029.89657135969777
134134127.0778605225756.92213947742489
135135125.3680064075479.63199359245273
136136128.5456009118097.4543990881909
137137136.6021371170760.397862882923652
138138127.52523010057810.4747698994216
139139132.4363637664556.56363623354495
140140131.2307153363048.76928466369551






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.01666899361055560.03333798722111120.983331006389444
140.02057435890400360.04114871780800730.979425641095996
150.02625564334287810.05251128668575630.973744356657122
160.2771331477190080.5542662954380170.722866852280992
170.3053602322199030.6107204644398050.694639767780097
180.3650919486351610.7301838972703230.634908051364839
190.4868914665223820.9737829330447650.513108533477618
200.4106794976872380.8213589953744770.589320502312762
210.5914708455823120.8170583088353770.408529154417688
220.69086541032720.6182691793455990.3091345896728
230.742845747075740.5143085058485190.25715425292426
240.7190526157068940.5618947685862120.280947384293106
250.7966820058405710.4066359883188570.203317994159428
260.7402158993582730.5195682012834540.259784100641727
270.9335184194865940.1329631610268120.066481580513406
280.9344570262814460.1310859474371080.0655429737185538
290.969417670808180.06116465838363930.0305823291918197
300.9867651968412720.02646960631745560.0132348031587278
310.9983087595174050.003382480965190780.00169124048259539
320.99930992670620.001380146587600640.000690073293800322
330.9998182979061680.0003634041876629380.000181702093831469
340.999922495743510.0001550085129799287.75042564899642e-05
350.9999582567634358.3486473129337e-054.17432365646685e-05
360.9999873615081212.52769837583658e-051.26384918791829e-05
370.9999947605608671.04788782653225e-055.23943913266123e-06
380.999998722296672.55540665958293e-061.27770332979147e-06
390.9999993856023731.22879525465792e-066.14397627328961e-07
400.9999999098529921.80294016147139e-079.01470080735693e-08
410.9999999839347913.21304181507866e-081.60652090753933e-08
420.9999999962968967.40620709577983e-093.70310354788991e-09
430.9999999953627699.2744627562855e-094.63723137814275e-09
440.9999999985044232.99115316043581e-091.4955765802179e-09
450.9999999998160463.67907203491048e-101.83953601745524e-10
460.9999999998981282.03743186035836e-101.01871593017918e-10
470.9999999999297531.40493676994152e-107.02468384970758e-11
480.9999999999560718.78572403761949e-114.39286201880974e-11
490.9999999999680916.38170783331766e-113.19085391665883e-11
500.9999999999725225.49559805142957e-112.74779902571478e-11
510.9999999999597338.05348320047736e-114.02674160023868e-11
520.9999999999475251.04950181457704e-105.24750907288521e-11
530.9999999999177361.64527475106627e-108.22637375533133e-11
540.9999999998120173.75966220414389e-101.87983110207195e-10
550.9999999997499945.00011503692273e-102.50005751846136e-10
560.9999999994545851.09083031467719e-095.45415157338597e-10
570.9999999991208031.75839333678688e-098.79196668393442e-10
580.9999999990458841.90823115172802e-099.54115575864009e-10
590.99999999795494.09020041572858e-092.04510020786429e-09
600.9999999965838696.83226298561286e-093.41613149280643e-09
610.9999999957319288.53614478397799e-094.26807239198899e-09
620.999999996233167.53367979290743e-093.76683989645371e-09
630.9999999984471593.10568156173195e-091.55284078086597e-09
640.9999999995155669.68868349014072e-104.84434174507036e-10
650.9999999993704461.25910829740666e-096.29554148703329e-10
660.9999999994927521.01449650582899e-095.07248252914497e-10
670.999999998913372.17326017089911e-091.08663008544956e-09
680.9999999989964742.0070529990578e-091.0035264995289e-09
690.9999999982532833.49343353635687e-091.74671676817844e-09
700.9999999965312756.93744930886618e-093.46872465443309e-09
710.9999999953129499.37410181434828e-094.68705090717414e-09
720.9999999938172891.23654218131384e-086.1827109065692e-09
730.9999999928137911.43724169854561e-087.18620849272806e-09
740.9999999994630111.07397816745238e-095.3698908372619e-10
750.9999999999694186.11640165129768e-113.05820082564884e-11
760.9999999999886892.26216403780012e-111.13108201890006e-11
770.9999999999970575.88621019576525e-122.94310509788263e-12
780.9999999999994871.02614988609785e-125.13074943048925e-13
790.9999999999999627.5363524520474e-143.7681762260237e-14
800.999999999999992.0201940303192e-141.0100970151596e-14
810.9999999999999882.37862915002193e-141.18931457501096e-14
820.9999999999999696.1929814113376e-143.0964907056688e-14
830.9999999999999872.55893282660563e-141.27946641330281e-14
840.9999999999999823.50858813756965e-141.75429406878482e-14
850.9999999999999784.30574116384973e-142.15287058192486e-14
860.9999999999999754.993942719055e-142.4969713595275e-14
870.9999999999999519.80620378512684e-144.90310189256342e-14
880.9999999999999171.66154094476454e-138.30770472382272e-14
890.9999999999998273.46647304020747e-131.73323652010374e-13
900.9999999999998552.90678782864258e-131.45339391432129e-13
910.9999999999998223.55899543829041e-131.77949771914521e-13
920.999999999999823.60170274851362e-131.80085137425681e-13
930.9999999999998033.93165372128739e-131.9658268606437e-13
940.9999999999993241.35270003744104e-126.76350018720521e-13
950.9999999999993151.3693525775713e-126.84676288785652e-13
960.9999999999978144.37303338534777e-122.18651669267389e-12
970.9999999999987632.47469409305435e-121.23734704652718e-12
980.9999999999982143.57203480896194e-121.78601740448097e-12
990.9999999999947621.04754777585247e-115.23773887926237e-12
1000.999999999990271.94591135238007e-119.72955676190037e-12
1010.99999999996936.1399505056252e-113.0699752528126e-11
1020.9999999999079471.84106092898119e-109.20530464490593e-11
1030.9999999998495123.00975942598041e-101.50487971299021e-10
1040.9999999997441625.11676874876396e-102.55838437438198e-10
1050.9999999995594658.81070038644375e-104.40535019322187e-10
1060.9999999990507411.89851820785031e-099.49259103925154e-10
1070.999999996742446.51512038817123e-093.25756019408562e-09
1080.9999999899519222.00961558916633e-081.00480779458317e-08
1090.9999999689064926.21870155228317e-083.10935077614158e-08
1100.9999998960879952.07824009981648e-071.03912004990824e-07
1110.9999997272930015.45413997073271e-072.72706998536636e-07
1120.9999991393110991.72137780184221e-068.60688900921106e-07
1130.9999990674311251.86513774961353e-069.32568874806766e-07
1140.9999986852412752.62951744900942e-061.31475872450471e-06
1150.9999996856327886.28734425066662e-073.14367212533331e-07
1160.9999994950563141.00988737262834e-065.04943686314172e-07
1170.9999982466488043.5067023923222e-061.7533511961611e-06
1180.9999996652087086.6958258331182e-073.3479129165591e-07
1190.9999986734181352.6531637292252e-061.3265818646126e-06
1200.9999943158533211.13682933588986e-055.6841466794493e-06
1210.9999830501954423.38996091169799e-051.69498045584899e-05
1220.9999596281161828.07437676359872e-054.03718838179936e-05
1230.9999043171136040.0001913657727914619.56828863957303e-05
1240.9996235730067530.0007528539864938580.000376426993246929
1250.9986230260579980.002753947884004490.00137697394200224
1260.9934606536055980.01307869278880480.00653934639440239
1270.9714371398136460.05712572037270870.0285628601863543

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.0166689936105556 & 0.0333379872211112 & 0.983331006389444 \tabularnewline
14 & 0.0205743589040036 & 0.0411487178080073 & 0.979425641095996 \tabularnewline
15 & 0.0262556433428781 & 0.0525112866857563 & 0.973744356657122 \tabularnewline
16 & 0.277133147719008 & 0.554266295438017 & 0.722866852280992 \tabularnewline
17 & 0.305360232219903 & 0.610720464439805 & 0.694639767780097 \tabularnewline
18 & 0.365091948635161 & 0.730183897270323 & 0.634908051364839 \tabularnewline
19 & 0.486891466522382 & 0.973782933044765 & 0.513108533477618 \tabularnewline
20 & 0.410679497687238 & 0.821358995374477 & 0.589320502312762 \tabularnewline
21 & 0.591470845582312 & 0.817058308835377 & 0.408529154417688 \tabularnewline
22 & 0.6908654103272 & 0.618269179345599 & 0.3091345896728 \tabularnewline
23 & 0.74284574707574 & 0.514308505848519 & 0.25715425292426 \tabularnewline
24 & 0.719052615706894 & 0.561894768586212 & 0.280947384293106 \tabularnewline
25 & 0.796682005840571 & 0.406635988318857 & 0.203317994159428 \tabularnewline
26 & 0.740215899358273 & 0.519568201283454 & 0.259784100641727 \tabularnewline
27 & 0.933518419486594 & 0.132963161026812 & 0.066481580513406 \tabularnewline
28 & 0.934457026281446 & 0.131085947437108 & 0.0655429737185538 \tabularnewline
29 & 0.96941767080818 & 0.0611646583836393 & 0.0305823291918197 \tabularnewline
30 & 0.986765196841272 & 0.0264696063174556 & 0.0132348031587278 \tabularnewline
31 & 0.998308759517405 & 0.00338248096519078 & 0.00169124048259539 \tabularnewline
32 & 0.9993099267062 & 0.00138014658760064 & 0.000690073293800322 \tabularnewline
33 & 0.999818297906168 & 0.000363404187662938 & 0.000181702093831469 \tabularnewline
34 & 0.99992249574351 & 0.000155008512979928 & 7.75042564899642e-05 \tabularnewline
35 & 0.999958256763435 & 8.3486473129337e-05 & 4.17432365646685e-05 \tabularnewline
36 & 0.999987361508121 & 2.52769837583658e-05 & 1.26384918791829e-05 \tabularnewline
37 & 0.999994760560867 & 1.04788782653225e-05 & 5.23943913266123e-06 \tabularnewline
38 & 0.99999872229667 & 2.55540665958293e-06 & 1.27770332979147e-06 \tabularnewline
39 & 0.999999385602373 & 1.22879525465792e-06 & 6.14397627328961e-07 \tabularnewline
40 & 0.999999909852992 & 1.80294016147139e-07 & 9.01470080735693e-08 \tabularnewline
41 & 0.999999983934791 & 3.21304181507866e-08 & 1.60652090753933e-08 \tabularnewline
42 & 0.999999996296896 & 7.40620709577983e-09 & 3.70310354788991e-09 \tabularnewline
43 & 0.999999995362769 & 9.2744627562855e-09 & 4.63723137814275e-09 \tabularnewline
44 & 0.999999998504423 & 2.99115316043581e-09 & 1.4955765802179e-09 \tabularnewline
45 & 0.999999999816046 & 3.67907203491048e-10 & 1.83953601745524e-10 \tabularnewline
46 & 0.999999999898128 & 2.03743186035836e-10 & 1.01871593017918e-10 \tabularnewline
47 & 0.999999999929753 & 1.40493676994152e-10 & 7.02468384970758e-11 \tabularnewline
48 & 0.999999999956071 & 8.78572403761949e-11 & 4.39286201880974e-11 \tabularnewline
49 & 0.999999999968091 & 6.38170783331766e-11 & 3.19085391665883e-11 \tabularnewline
50 & 0.999999999972522 & 5.49559805142957e-11 & 2.74779902571478e-11 \tabularnewline
51 & 0.999999999959733 & 8.05348320047736e-11 & 4.02674160023868e-11 \tabularnewline
52 & 0.999999999947525 & 1.04950181457704e-10 & 5.24750907288521e-11 \tabularnewline
53 & 0.999999999917736 & 1.64527475106627e-10 & 8.22637375533133e-11 \tabularnewline
54 & 0.999999999812017 & 3.75966220414389e-10 & 1.87983110207195e-10 \tabularnewline
55 & 0.999999999749994 & 5.00011503692273e-10 & 2.50005751846136e-10 \tabularnewline
56 & 0.999999999454585 & 1.09083031467719e-09 & 5.45415157338597e-10 \tabularnewline
57 & 0.999999999120803 & 1.75839333678688e-09 & 8.79196668393442e-10 \tabularnewline
58 & 0.999999999045884 & 1.90823115172802e-09 & 9.54115575864009e-10 \tabularnewline
59 & 0.9999999979549 & 4.09020041572858e-09 & 2.04510020786429e-09 \tabularnewline
60 & 0.999999996583869 & 6.83226298561286e-09 & 3.41613149280643e-09 \tabularnewline
61 & 0.999999995731928 & 8.53614478397799e-09 & 4.26807239198899e-09 \tabularnewline
62 & 0.99999999623316 & 7.53367979290743e-09 & 3.76683989645371e-09 \tabularnewline
63 & 0.999999998447159 & 3.10568156173195e-09 & 1.55284078086597e-09 \tabularnewline
64 & 0.999999999515566 & 9.68868349014072e-10 & 4.84434174507036e-10 \tabularnewline
65 & 0.999999999370446 & 1.25910829740666e-09 & 6.29554148703329e-10 \tabularnewline
66 & 0.999999999492752 & 1.01449650582899e-09 & 5.07248252914497e-10 \tabularnewline
67 & 0.99999999891337 & 2.17326017089911e-09 & 1.08663008544956e-09 \tabularnewline
68 & 0.999999998996474 & 2.0070529990578e-09 & 1.0035264995289e-09 \tabularnewline
69 & 0.999999998253283 & 3.49343353635687e-09 & 1.74671676817844e-09 \tabularnewline
70 & 0.999999996531275 & 6.93744930886618e-09 & 3.46872465443309e-09 \tabularnewline
71 & 0.999999995312949 & 9.37410181434828e-09 & 4.68705090717414e-09 \tabularnewline
72 & 0.999999993817289 & 1.23654218131384e-08 & 6.1827109065692e-09 \tabularnewline
73 & 0.999999992813791 & 1.43724169854561e-08 & 7.18620849272806e-09 \tabularnewline
74 & 0.999999999463011 & 1.07397816745238e-09 & 5.3698908372619e-10 \tabularnewline
75 & 0.999999999969418 & 6.11640165129768e-11 & 3.05820082564884e-11 \tabularnewline
76 & 0.999999999988689 & 2.26216403780012e-11 & 1.13108201890006e-11 \tabularnewline
77 & 0.999999999997057 & 5.88621019576525e-12 & 2.94310509788263e-12 \tabularnewline
78 & 0.999999999999487 & 1.02614988609785e-12 & 5.13074943048925e-13 \tabularnewline
79 & 0.999999999999962 & 7.5363524520474e-14 & 3.7681762260237e-14 \tabularnewline
80 & 0.99999999999999 & 2.0201940303192e-14 & 1.0100970151596e-14 \tabularnewline
81 & 0.999999999999988 & 2.37862915002193e-14 & 1.18931457501096e-14 \tabularnewline
82 & 0.999999999999969 & 6.1929814113376e-14 & 3.0964907056688e-14 \tabularnewline
83 & 0.999999999999987 & 2.55893282660563e-14 & 1.27946641330281e-14 \tabularnewline
84 & 0.999999999999982 & 3.50858813756965e-14 & 1.75429406878482e-14 \tabularnewline
85 & 0.999999999999978 & 4.30574116384973e-14 & 2.15287058192486e-14 \tabularnewline
86 & 0.999999999999975 & 4.993942719055e-14 & 2.4969713595275e-14 \tabularnewline
87 & 0.999999999999951 & 9.80620378512684e-14 & 4.90310189256342e-14 \tabularnewline
88 & 0.999999999999917 & 1.66154094476454e-13 & 8.30770472382272e-14 \tabularnewline
89 & 0.999999999999827 & 3.46647304020747e-13 & 1.73323652010374e-13 \tabularnewline
90 & 0.999999999999855 & 2.90678782864258e-13 & 1.45339391432129e-13 \tabularnewline
91 & 0.999999999999822 & 3.55899543829041e-13 & 1.77949771914521e-13 \tabularnewline
92 & 0.99999999999982 & 3.60170274851362e-13 & 1.80085137425681e-13 \tabularnewline
93 & 0.999999999999803 & 3.93165372128739e-13 & 1.9658268606437e-13 \tabularnewline
94 & 0.999999999999324 & 1.35270003744104e-12 & 6.76350018720521e-13 \tabularnewline
95 & 0.999999999999315 & 1.3693525775713e-12 & 6.84676288785652e-13 \tabularnewline
96 & 0.999999999997814 & 4.37303338534777e-12 & 2.18651669267389e-12 \tabularnewline
97 & 0.999999999998763 & 2.47469409305435e-12 & 1.23734704652718e-12 \tabularnewline
98 & 0.999999999998214 & 3.57203480896194e-12 & 1.78601740448097e-12 \tabularnewline
99 & 0.999999999994762 & 1.04754777585247e-11 & 5.23773887926237e-12 \tabularnewline
100 & 0.99999999999027 & 1.94591135238007e-11 & 9.72955676190037e-12 \tabularnewline
101 & 0.9999999999693 & 6.1399505056252e-11 & 3.0699752528126e-11 \tabularnewline
102 & 0.999999999907947 & 1.84106092898119e-10 & 9.20530464490593e-11 \tabularnewline
103 & 0.999999999849512 & 3.00975942598041e-10 & 1.50487971299021e-10 \tabularnewline
104 & 0.999999999744162 & 5.11676874876396e-10 & 2.55838437438198e-10 \tabularnewline
105 & 0.999999999559465 & 8.81070038644375e-10 & 4.40535019322187e-10 \tabularnewline
106 & 0.999999999050741 & 1.89851820785031e-09 & 9.49259103925154e-10 \tabularnewline
107 & 0.99999999674244 & 6.51512038817123e-09 & 3.25756019408562e-09 \tabularnewline
108 & 0.999999989951922 & 2.00961558916633e-08 & 1.00480779458317e-08 \tabularnewline
109 & 0.999999968906492 & 6.21870155228317e-08 & 3.10935077614158e-08 \tabularnewline
110 & 0.999999896087995 & 2.07824009981648e-07 & 1.03912004990824e-07 \tabularnewline
111 & 0.999999727293001 & 5.45413997073271e-07 & 2.72706998536636e-07 \tabularnewline
112 & 0.999999139311099 & 1.72137780184221e-06 & 8.60688900921106e-07 \tabularnewline
113 & 0.999999067431125 & 1.86513774961353e-06 & 9.32568874806766e-07 \tabularnewline
114 & 0.999998685241275 & 2.62951744900942e-06 & 1.31475872450471e-06 \tabularnewline
115 & 0.999999685632788 & 6.28734425066662e-07 & 3.14367212533331e-07 \tabularnewline
116 & 0.999999495056314 & 1.00988737262834e-06 & 5.04943686314172e-07 \tabularnewline
117 & 0.999998246648804 & 3.5067023923222e-06 & 1.7533511961611e-06 \tabularnewline
118 & 0.999999665208708 & 6.6958258331182e-07 & 3.3479129165591e-07 \tabularnewline
119 & 0.999998673418135 & 2.6531637292252e-06 & 1.3265818646126e-06 \tabularnewline
120 & 0.999994315853321 & 1.13682933588986e-05 & 5.6841466794493e-06 \tabularnewline
121 & 0.999983050195442 & 3.38996091169799e-05 & 1.69498045584899e-05 \tabularnewline
122 & 0.999959628116182 & 8.07437676359872e-05 & 4.03718838179936e-05 \tabularnewline
123 & 0.999904317113604 & 0.000191365772791461 & 9.56828863957303e-05 \tabularnewline
124 & 0.999623573006753 & 0.000752853986493858 & 0.000376426993246929 \tabularnewline
125 & 0.998623026057998 & 0.00275394788400449 & 0.00137697394200224 \tabularnewline
126 & 0.993460653605598 & 0.0130786927888048 & 0.00653934639440239 \tabularnewline
127 & 0.971437139813646 & 0.0571257203727087 & 0.0285628601863543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200377&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]13[/C][C]0.0166689936105556[/C][C]0.0333379872211112[/C][C]0.983331006389444[/C][/ROW]
[ROW][C]14[/C][C]0.0205743589040036[/C][C]0.0411487178080073[/C][C]0.979425641095996[/C][/ROW]
[ROW][C]15[/C][C]0.0262556433428781[/C][C]0.0525112866857563[/C][C]0.973744356657122[/C][/ROW]
[ROW][C]16[/C][C]0.277133147719008[/C][C]0.554266295438017[/C][C]0.722866852280992[/C][/ROW]
[ROW][C]17[/C][C]0.305360232219903[/C][C]0.610720464439805[/C][C]0.694639767780097[/C][/ROW]
[ROW][C]18[/C][C]0.365091948635161[/C][C]0.730183897270323[/C][C]0.634908051364839[/C][/ROW]
[ROW][C]19[/C][C]0.486891466522382[/C][C]0.973782933044765[/C][C]0.513108533477618[/C][/ROW]
[ROW][C]20[/C][C]0.410679497687238[/C][C]0.821358995374477[/C][C]0.589320502312762[/C][/ROW]
[ROW][C]21[/C][C]0.591470845582312[/C][C]0.817058308835377[/C][C]0.408529154417688[/C][/ROW]
[ROW][C]22[/C][C]0.6908654103272[/C][C]0.618269179345599[/C][C]0.3091345896728[/C][/ROW]
[ROW][C]23[/C][C]0.74284574707574[/C][C]0.514308505848519[/C][C]0.25715425292426[/C][/ROW]
[ROW][C]24[/C][C]0.719052615706894[/C][C]0.561894768586212[/C][C]0.280947384293106[/C][/ROW]
[ROW][C]25[/C][C]0.796682005840571[/C][C]0.406635988318857[/C][C]0.203317994159428[/C][/ROW]
[ROW][C]26[/C][C]0.740215899358273[/C][C]0.519568201283454[/C][C]0.259784100641727[/C][/ROW]
[ROW][C]27[/C][C]0.933518419486594[/C][C]0.132963161026812[/C][C]0.066481580513406[/C][/ROW]
[ROW][C]28[/C][C]0.934457026281446[/C][C]0.131085947437108[/C][C]0.0655429737185538[/C][/ROW]
[ROW][C]29[/C][C]0.96941767080818[/C][C]0.0611646583836393[/C][C]0.0305823291918197[/C][/ROW]
[ROW][C]30[/C][C]0.986765196841272[/C][C]0.0264696063174556[/C][C]0.0132348031587278[/C][/ROW]
[ROW][C]31[/C][C]0.998308759517405[/C][C]0.00338248096519078[/C][C]0.00169124048259539[/C][/ROW]
[ROW][C]32[/C][C]0.9993099267062[/C][C]0.00138014658760064[/C][C]0.000690073293800322[/C][/ROW]
[ROW][C]33[/C][C]0.999818297906168[/C][C]0.000363404187662938[/C][C]0.000181702093831469[/C][/ROW]
[ROW][C]34[/C][C]0.99992249574351[/C][C]0.000155008512979928[/C][C]7.75042564899642e-05[/C][/ROW]
[ROW][C]35[/C][C]0.999958256763435[/C][C]8.3486473129337e-05[/C][C]4.17432365646685e-05[/C][/ROW]
[ROW][C]36[/C][C]0.999987361508121[/C][C]2.52769837583658e-05[/C][C]1.26384918791829e-05[/C][/ROW]
[ROW][C]37[/C][C]0.999994760560867[/C][C]1.04788782653225e-05[/C][C]5.23943913266123e-06[/C][/ROW]
[ROW][C]38[/C][C]0.99999872229667[/C][C]2.55540665958293e-06[/C][C]1.27770332979147e-06[/C][/ROW]
[ROW][C]39[/C][C]0.999999385602373[/C][C]1.22879525465792e-06[/C][C]6.14397627328961e-07[/C][/ROW]
[ROW][C]40[/C][C]0.999999909852992[/C][C]1.80294016147139e-07[/C][C]9.01470080735693e-08[/C][/ROW]
[ROW][C]41[/C][C]0.999999983934791[/C][C]3.21304181507866e-08[/C][C]1.60652090753933e-08[/C][/ROW]
[ROW][C]42[/C][C]0.999999996296896[/C][C]7.40620709577983e-09[/C][C]3.70310354788991e-09[/C][/ROW]
[ROW][C]43[/C][C]0.999999995362769[/C][C]9.2744627562855e-09[/C][C]4.63723137814275e-09[/C][/ROW]
[ROW][C]44[/C][C]0.999999998504423[/C][C]2.99115316043581e-09[/C][C]1.4955765802179e-09[/C][/ROW]
[ROW][C]45[/C][C]0.999999999816046[/C][C]3.67907203491048e-10[/C][C]1.83953601745524e-10[/C][/ROW]
[ROW][C]46[/C][C]0.999999999898128[/C][C]2.03743186035836e-10[/C][C]1.01871593017918e-10[/C][/ROW]
[ROW][C]47[/C][C]0.999999999929753[/C][C]1.40493676994152e-10[/C][C]7.02468384970758e-11[/C][/ROW]
[ROW][C]48[/C][C]0.999999999956071[/C][C]8.78572403761949e-11[/C][C]4.39286201880974e-11[/C][/ROW]
[ROW][C]49[/C][C]0.999999999968091[/C][C]6.38170783331766e-11[/C][C]3.19085391665883e-11[/C][/ROW]
[ROW][C]50[/C][C]0.999999999972522[/C][C]5.49559805142957e-11[/C][C]2.74779902571478e-11[/C][/ROW]
[ROW][C]51[/C][C]0.999999999959733[/C][C]8.05348320047736e-11[/C][C]4.02674160023868e-11[/C][/ROW]
[ROW][C]52[/C][C]0.999999999947525[/C][C]1.04950181457704e-10[/C][C]5.24750907288521e-11[/C][/ROW]
[ROW][C]53[/C][C]0.999999999917736[/C][C]1.64527475106627e-10[/C][C]8.22637375533133e-11[/C][/ROW]
[ROW][C]54[/C][C]0.999999999812017[/C][C]3.75966220414389e-10[/C][C]1.87983110207195e-10[/C][/ROW]
[ROW][C]55[/C][C]0.999999999749994[/C][C]5.00011503692273e-10[/C][C]2.50005751846136e-10[/C][/ROW]
[ROW][C]56[/C][C]0.999999999454585[/C][C]1.09083031467719e-09[/C][C]5.45415157338597e-10[/C][/ROW]
[ROW][C]57[/C][C]0.999999999120803[/C][C]1.75839333678688e-09[/C][C]8.79196668393442e-10[/C][/ROW]
[ROW][C]58[/C][C]0.999999999045884[/C][C]1.90823115172802e-09[/C][C]9.54115575864009e-10[/C][/ROW]
[ROW][C]59[/C][C]0.9999999979549[/C][C]4.09020041572858e-09[/C][C]2.04510020786429e-09[/C][/ROW]
[ROW][C]60[/C][C]0.999999996583869[/C][C]6.83226298561286e-09[/C][C]3.41613149280643e-09[/C][/ROW]
[ROW][C]61[/C][C]0.999999995731928[/C][C]8.53614478397799e-09[/C][C]4.26807239198899e-09[/C][/ROW]
[ROW][C]62[/C][C]0.99999999623316[/C][C]7.53367979290743e-09[/C][C]3.76683989645371e-09[/C][/ROW]
[ROW][C]63[/C][C]0.999999998447159[/C][C]3.10568156173195e-09[/C][C]1.55284078086597e-09[/C][/ROW]
[ROW][C]64[/C][C]0.999999999515566[/C][C]9.68868349014072e-10[/C][C]4.84434174507036e-10[/C][/ROW]
[ROW][C]65[/C][C]0.999999999370446[/C][C]1.25910829740666e-09[/C][C]6.29554148703329e-10[/C][/ROW]
[ROW][C]66[/C][C]0.999999999492752[/C][C]1.01449650582899e-09[/C][C]5.07248252914497e-10[/C][/ROW]
[ROW][C]67[/C][C]0.99999999891337[/C][C]2.17326017089911e-09[/C][C]1.08663008544956e-09[/C][/ROW]
[ROW][C]68[/C][C]0.999999998996474[/C][C]2.0070529990578e-09[/C][C]1.0035264995289e-09[/C][/ROW]
[ROW][C]69[/C][C]0.999999998253283[/C][C]3.49343353635687e-09[/C][C]1.74671676817844e-09[/C][/ROW]
[ROW][C]70[/C][C]0.999999996531275[/C][C]6.93744930886618e-09[/C][C]3.46872465443309e-09[/C][/ROW]
[ROW][C]71[/C][C]0.999999995312949[/C][C]9.37410181434828e-09[/C][C]4.68705090717414e-09[/C][/ROW]
[ROW][C]72[/C][C]0.999999993817289[/C][C]1.23654218131384e-08[/C][C]6.1827109065692e-09[/C][/ROW]
[ROW][C]73[/C][C]0.999999992813791[/C][C]1.43724169854561e-08[/C][C]7.18620849272806e-09[/C][/ROW]
[ROW][C]74[/C][C]0.999999999463011[/C][C]1.07397816745238e-09[/C][C]5.3698908372619e-10[/C][/ROW]
[ROW][C]75[/C][C]0.999999999969418[/C][C]6.11640165129768e-11[/C][C]3.05820082564884e-11[/C][/ROW]
[ROW][C]76[/C][C]0.999999999988689[/C][C]2.26216403780012e-11[/C][C]1.13108201890006e-11[/C][/ROW]
[ROW][C]77[/C][C]0.999999999997057[/C][C]5.88621019576525e-12[/C][C]2.94310509788263e-12[/C][/ROW]
[ROW][C]78[/C][C]0.999999999999487[/C][C]1.02614988609785e-12[/C][C]5.13074943048925e-13[/C][/ROW]
[ROW][C]79[/C][C]0.999999999999962[/C][C]7.5363524520474e-14[/C][C]3.7681762260237e-14[/C][/ROW]
[ROW][C]80[/C][C]0.99999999999999[/C][C]2.0201940303192e-14[/C][C]1.0100970151596e-14[/C][/ROW]
[ROW][C]81[/C][C]0.999999999999988[/C][C]2.37862915002193e-14[/C][C]1.18931457501096e-14[/C][/ROW]
[ROW][C]82[/C][C]0.999999999999969[/C][C]6.1929814113376e-14[/C][C]3.0964907056688e-14[/C][/ROW]
[ROW][C]83[/C][C]0.999999999999987[/C][C]2.55893282660563e-14[/C][C]1.27946641330281e-14[/C][/ROW]
[ROW][C]84[/C][C]0.999999999999982[/C][C]3.50858813756965e-14[/C][C]1.75429406878482e-14[/C][/ROW]
[ROW][C]85[/C][C]0.999999999999978[/C][C]4.30574116384973e-14[/C][C]2.15287058192486e-14[/C][/ROW]
[ROW][C]86[/C][C]0.999999999999975[/C][C]4.993942719055e-14[/C][C]2.4969713595275e-14[/C][/ROW]
[ROW][C]87[/C][C]0.999999999999951[/C][C]9.80620378512684e-14[/C][C]4.90310189256342e-14[/C][/ROW]
[ROW][C]88[/C][C]0.999999999999917[/C][C]1.66154094476454e-13[/C][C]8.30770472382272e-14[/C][/ROW]
[ROW][C]89[/C][C]0.999999999999827[/C][C]3.46647304020747e-13[/C][C]1.73323652010374e-13[/C][/ROW]
[ROW][C]90[/C][C]0.999999999999855[/C][C]2.90678782864258e-13[/C][C]1.45339391432129e-13[/C][/ROW]
[ROW][C]91[/C][C]0.999999999999822[/C][C]3.55899543829041e-13[/C][C]1.77949771914521e-13[/C][/ROW]
[ROW][C]92[/C][C]0.99999999999982[/C][C]3.60170274851362e-13[/C][C]1.80085137425681e-13[/C][/ROW]
[ROW][C]93[/C][C]0.999999999999803[/C][C]3.93165372128739e-13[/C][C]1.9658268606437e-13[/C][/ROW]
[ROW][C]94[/C][C]0.999999999999324[/C][C]1.35270003744104e-12[/C][C]6.76350018720521e-13[/C][/ROW]
[ROW][C]95[/C][C]0.999999999999315[/C][C]1.3693525775713e-12[/C][C]6.84676288785652e-13[/C][/ROW]
[ROW][C]96[/C][C]0.999999999997814[/C][C]4.37303338534777e-12[/C][C]2.18651669267389e-12[/C][/ROW]
[ROW][C]97[/C][C]0.999999999998763[/C][C]2.47469409305435e-12[/C][C]1.23734704652718e-12[/C][/ROW]
[ROW][C]98[/C][C]0.999999999998214[/C][C]3.57203480896194e-12[/C][C]1.78601740448097e-12[/C][/ROW]
[ROW][C]99[/C][C]0.999999999994762[/C][C]1.04754777585247e-11[/C][C]5.23773887926237e-12[/C][/ROW]
[ROW][C]100[/C][C]0.99999999999027[/C][C]1.94591135238007e-11[/C][C]9.72955676190037e-12[/C][/ROW]
[ROW][C]101[/C][C]0.9999999999693[/C][C]6.1399505056252e-11[/C][C]3.0699752528126e-11[/C][/ROW]
[ROW][C]102[/C][C]0.999999999907947[/C][C]1.84106092898119e-10[/C][C]9.20530464490593e-11[/C][/ROW]
[ROW][C]103[/C][C]0.999999999849512[/C][C]3.00975942598041e-10[/C][C]1.50487971299021e-10[/C][/ROW]
[ROW][C]104[/C][C]0.999999999744162[/C][C]5.11676874876396e-10[/C][C]2.55838437438198e-10[/C][/ROW]
[ROW][C]105[/C][C]0.999999999559465[/C][C]8.81070038644375e-10[/C][C]4.40535019322187e-10[/C][/ROW]
[ROW][C]106[/C][C]0.999999999050741[/C][C]1.89851820785031e-09[/C][C]9.49259103925154e-10[/C][/ROW]
[ROW][C]107[/C][C]0.99999999674244[/C][C]6.51512038817123e-09[/C][C]3.25756019408562e-09[/C][/ROW]
[ROW][C]108[/C][C]0.999999989951922[/C][C]2.00961558916633e-08[/C][C]1.00480779458317e-08[/C][/ROW]
[ROW][C]109[/C][C]0.999999968906492[/C][C]6.21870155228317e-08[/C][C]3.10935077614158e-08[/C][/ROW]
[ROW][C]110[/C][C]0.999999896087995[/C][C]2.07824009981648e-07[/C][C]1.03912004990824e-07[/C][/ROW]
[ROW][C]111[/C][C]0.999999727293001[/C][C]5.45413997073271e-07[/C][C]2.72706998536636e-07[/C][/ROW]
[ROW][C]112[/C][C]0.999999139311099[/C][C]1.72137780184221e-06[/C][C]8.60688900921106e-07[/C][/ROW]
[ROW][C]113[/C][C]0.999999067431125[/C][C]1.86513774961353e-06[/C][C]9.32568874806766e-07[/C][/ROW]
[ROW][C]114[/C][C]0.999998685241275[/C][C]2.62951744900942e-06[/C][C]1.31475872450471e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999999685632788[/C][C]6.28734425066662e-07[/C][C]3.14367212533331e-07[/C][/ROW]
[ROW][C]116[/C][C]0.999999495056314[/C][C]1.00988737262834e-06[/C][C]5.04943686314172e-07[/C][/ROW]
[ROW][C]117[/C][C]0.999998246648804[/C][C]3.5067023923222e-06[/C][C]1.7533511961611e-06[/C][/ROW]
[ROW][C]118[/C][C]0.999999665208708[/C][C]6.6958258331182e-07[/C][C]3.3479129165591e-07[/C][/ROW]
[ROW][C]119[/C][C]0.999998673418135[/C][C]2.6531637292252e-06[/C][C]1.3265818646126e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999994315853321[/C][C]1.13682933588986e-05[/C][C]5.6841466794493e-06[/C][/ROW]
[ROW][C]121[/C][C]0.999983050195442[/C][C]3.38996091169799e-05[/C][C]1.69498045584899e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999959628116182[/C][C]8.07437676359872e-05[/C][C]4.03718838179936e-05[/C][/ROW]
[ROW][C]123[/C][C]0.999904317113604[/C][C]0.000191365772791461[/C][C]9.56828863957303e-05[/C][/ROW]
[ROW][C]124[/C][C]0.999623573006753[/C][C]0.000752853986493858[/C][C]0.000376426993246929[/C][/ROW]
[ROW][C]125[/C][C]0.998623026057998[/C][C]0.00275394788400449[/C][C]0.00137697394200224[/C][/ROW]
[ROW][C]126[/C][C]0.993460653605598[/C][C]0.0130786927888048[/C][C]0.00653934639440239[/C][/ROW]
[ROW][C]127[/C][C]0.971437139813646[/C][C]0.0571257203727087[/C][C]0.0285628601863543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200377&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200377&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.01666899361055560.03333798722111120.983331006389444
140.02057435890400360.04114871780800730.979425641095996
150.02625564334287810.05251128668575630.973744356657122
160.2771331477190080.5542662954380170.722866852280992
170.3053602322199030.6107204644398050.694639767780097
180.3650919486351610.7301838972703230.634908051364839
190.4868914665223820.9737829330447650.513108533477618
200.4106794976872380.8213589953744770.589320502312762
210.5914708455823120.8170583088353770.408529154417688
220.69086541032720.6182691793455990.3091345896728
230.742845747075740.5143085058485190.25715425292426
240.7190526157068940.5618947685862120.280947384293106
250.7966820058405710.4066359883188570.203317994159428
260.7402158993582730.5195682012834540.259784100641727
270.9335184194865940.1329631610268120.066481580513406
280.9344570262814460.1310859474371080.0655429737185538
290.969417670808180.06116465838363930.0305823291918197
300.9867651968412720.02646960631745560.0132348031587278
310.9983087595174050.003382480965190780.00169124048259539
320.99930992670620.001380146587600640.000690073293800322
330.9998182979061680.0003634041876629380.000181702093831469
340.999922495743510.0001550085129799287.75042564899642e-05
350.9999582567634358.3486473129337e-054.17432365646685e-05
360.9999873615081212.52769837583658e-051.26384918791829e-05
370.9999947605608671.04788782653225e-055.23943913266123e-06
380.999998722296672.55540665958293e-061.27770332979147e-06
390.9999993856023731.22879525465792e-066.14397627328961e-07
400.9999999098529921.80294016147139e-079.01470080735693e-08
410.9999999839347913.21304181507866e-081.60652090753933e-08
420.9999999962968967.40620709577983e-093.70310354788991e-09
430.9999999953627699.2744627562855e-094.63723137814275e-09
440.9999999985044232.99115316043581e-091.4955765802179e-09
450.9999999998160463.67907203491048e-101.83953601745524e-10
460.9999999998981282.03743186035836e-101.01871593017918e-10
470.9999999999297531.40493676994152e-107.02468384970758e-11
480.9999999999560718.78572403761949e-114.39286201880974e-11
490.9999999999680916.38170783331766e-113.19085391665883e-11
500.9999999999725225.49559805142957e-112.74779902571478e-11
510.9999999999597338.05348320047736e-114.02674160023868e-11
520.9999999999475251.04950181457704e-105.24750907288521e-11
530.9999999999177361.64527475106627e-108.22637375533133e-11
540.9999999998120173.75966220414389e-101.87983110207195e-10
550.9999999997499945.00011503692273e-102.50005751846136e-10
560.9999999994545851.09083031467719e-095.45415157338597e-10
570.9999999991208031.75839333678688e-098.79196668393442e-10
580.9999999990458841.90823115172802e-099.54115575864009e-10
590.99999999795494.09020041572858e-092.04510020786429e-09
600.9999999965838696.83226298561286e-093.41613149280643e-09
610.9999999957319288.53614478397799e-094.26807239198899e-09
620.999999996233167.53367979290743e-093.76683989645371e-09
630.9999999984471593.10568156173195e-091.55284078086597e-09
640.9999999995155669.68868349014072e-104.84434174507036e-10
650.9999999993704461.25910829740666e-096.29554148703329e-10
660.9999999994927521.01449650582899e-095.07248252914497e-10
670.999999998913372.17326017089911e-091.08663008544956e-09
680.9999999989964742.0070529990578e-091.0035264995289e-09
690.9999999982532833.49343353635687e-091.74671676817844e-09
700.9999999965312756.93744930886618e-093.46872465443309e-09
710.9999999953129499.37410181434828e-094.68705090717414e-09
720.9999999938172891.23654218131384e-086.1827109065692e-09
730.9999999928137911.43724169854561e-087.18620849272806e-09
740.9999999994630111.07397816745238e-095.3698908372619e-10
750.9999999999694186.11640165129768e-113.05820082564884e-11
760.9999999999886892.26216403780012e-111.13108201890006e-11
770.9999999999970575.88621019576525e-122.94310509788263e-12
780.9999999999994871.02614988609785e-125.13074943048925e-13
790.9999999999999627.5363524520474e-143.7681762260237e-14
800.999999999999992.0201940303192e-141.0100970151596e-14
810.9999999999999882.37862915002193e-141.18931457501096e-14
820.9999999999999696.1929814113376e-143.0964907056688e-14
830.9999999999999872.55893282660563e-141.27946641330281e-14
840.9999999999999823.50858813756965e-141.75429406878482e-14
850.9999999999999784.30574116384973e-142.15287058192486e-14
860.9999999999999754.993942719055e-142.4969713595275e-14
870.9999999999999519.80620378512684e-144.90310189256342e-14
880.9999999999999171.66154094476454e-138.30770472382272e-14
890.9999999999998273.46647304020747e-131.73323652010374e-13
900.9999999999998552.90678782864258e-131.45339391432129e-13
910.9999999999998223.55899543829041e-131.77949771914521e-13
920.999999999999823.60170274851362e-131.80085137425681e-13
930.9999999999998033.93165372128739e-131.9658268606437e-13
940.9999999999993241.35270003744104e-126.76350018720521e-13
950.9999999999993151.3693525775713e-126.84676288785652e-13
960.9999999999978144.37303338534777e-122.18651669267389e-12
970.9999999999987632.47469409305435e-121.23734704652718e-12
980.9999999999982143.57203480896194e-121.78601740448097e-12
990.9999999999947621.04754777585247e-115.23773887926237e-12
1000.999999999990271.94591135238007e-119.72955676190037e-12
1010.99999999996936.1399505056252e-113.0699752528126e-11
1020.9999999999079471.84106092898119e-109.20530464490593e-11
1030.9999999998495123.00975942598041e-101.50487971299021e-10
1040.9999999997441625.11676874876396e-102.55838437438198e-10
1050.9999999995594658.81070038644375e-104.40535019322187e-10
1060.9999999990507411.89851820785031e-099.49259103925154e-10
1070.999999996742446.51512038817123e-093.25756019408562e-09
1080.9999999899519222.00961558916633e-081.00480779458317e-08
1090.9999999689064926.21870155228317e-083.10935077614158e-08
1100.9999998960879952.07824009981648e-071.03912004990824e-07
1110.9999997272930015.45413997073271e-072.72706998536636e-07
1120.9999991393110991.72137780184221e-068.60688900921106e-07
1130.9999990674311251.86513774961353e-069.32568874806766e-07
1140.9999986852412752.62951744900942e-061.31475872450471e-06
1150.9999996856327886.28734425066662e-073.14367212533331e-07
1160.9999994950563141.00988737262834e-065.04943686314172e-07
1170.9999982466488043.5067023923222e-061.7533511961611e-06
1180.9999996652087086.6958258331182e-073.3479129165591e-07
1190.9999986734181352.6531637292252e-061.3265818646126e-06
1200.9999943158533211.13682933588986e-055.6841466794493e-06
1210.9999830501954423.38996091169799e-051.69498045584899e-05
1220.9999596281161828.07437676359872e-054.03718838179936e-05
1230.9999043171136040.0001913657727914619.56828863957303e-05
1240.9996235730067530.0007528539864938580.000376426993246929
1250.9986230260579980.002753947884004490.00137697394200224
1260.9934606536055980.01307869278880480.00653934639440239
1270.9714371398136460.05712572037270870.0285628601863543






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level950.826086956521739NOK
5% type I error level990.860869565217391NOK
10% type I error level1020.88695652173913NOK

\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 & 95 & 0.826086956521739 & NOK \tabularnewline
5% type I error level & 99 & 0.860869565217391 & NOK \tabularnewline
10% type I error level & 102 & 0.88695652173913 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200377&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]95[/C][C]0.826086956521739[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]99[/C][C]0.860869565217391[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]102[/C][C]0.88695652173913[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200377&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200377&T=6

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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 level950.826086956521739NOK
5% type I error level990.860869565217391NOK
10% type I error level1020.88695652173913NOK


Figures (Output of Computation)

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Input Parameters & R Code

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')
}