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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 02 Nov 2012 11:32:24 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/02/t1351870573h1zpeobmj7agqk4.htm/, Retrieved Mon, 27 Jun 2022 05:45:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185640, Retrieved Mon, 27 Jun 2022 05:45:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-02 15:32:24] [acfd67cb214b61d0a5e0fb4c8c6ef02a] [Current]
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Dataseries X:
1	41	38	12	14	12	53	32	13
2	39	32	11	18	11	86	51	16
3	30	35	15	11	14	66	42	19
4	31	33	6	12	12	67	41	15
5	34	37	13	16	21	76	46	14
6	35	29	10	18	12	78	47	13
7	39	31	12	14	22	53	37	19
8	34	36	14	14	11	80	49	15
9	36	35	12	15	10	74	45	14
10	37	38	6	15	13	76	47	15
11	38	31	10	17	10	79	49	16
12	36	34	12	19	8	54	33	16
13	38	35	12	10	15	67	42	16
14	39	38	11	16	14	54	33	16
15	33	37	15	18	10	87	53	17
16	32	33	12	14	14	58	36	15
17	36	32	10	14	14	75	45	15
18	38	38	12	17	11	88	54	20
19	39	38	11	14	10	64	41	18
20	32	32	12	16	13	57	36	16
21	32	33	11	18	7	66	41	16
22	31	31	12	11	14	68	44	16
23	39	38	13	14	12	54	33	19
24	37	39	11	12	14	56	37	16
25	39	32	9	17	11	86	52	17
26	41	32	13	9	9	80	47	17
27	36	35	10	16	11	76	43	16
28	33	37	14	14	15	69	44	15
29	33	33	12	15	14	78	45	16
30	34	33	10	11	13	67	44	14
31	31	28	12	16	9	80	49	15
32	27	32	8	13	15	54	33	12
33	37	31	10	17	10	71	43	14
34	34	37	12	15	11	84	54	16
35	34	30	12	14	13	74	42	14
36	32	33	7	16	8	71	44	7
37	29	31	6	9	20	63	37	10
38	36	33	12	15	12	71	43	14
39	29	31	10	17	10	76	46	16
40	35	33	10	13	10	69	42	16
41	37	32	10	15	9	74	45	16
42	34	33	12	16	14	75	44	14
43	38	32	15	16	8	54	33	20
44	35	33	10	12	14	52	31	14
45	38	28	10	12	11	69	42	14
46	37	35	12	11	13	68	40	11
47	38	39	13	15	9	65	43	14
48	33	34	11	15	11	75	46	15
49	36	38	11	17	15	74	42	16
50	38	32	12	13	11	75	45	14
51	32	38	14	16	10	72	44	16
52	32	30	10	14	14	67	40	14
53	32	33	12	11	18	63	37	12
54	34	38	13	12	14	62	46	16
55	32	32	5	12	11	63	36	9
56	37	32	6	15	12	76	47	14
57	39	34	12	16	13	74	45	16
58	29	34	12	15	9	67	42	16
59	37	36	11	12	10	73	43	15
60	35	34	10	12	15	70	43	16
61	30	28	7	8	20	53	32	12
62	38	34	12	13	12	77	45	16
63	34	35	14	11	12	77	45	16
64	31	35	11	14	14	52	31	14
65	34	31	12	15	13	54	33	16
66	35	37	13	10	11	80	49	17
67	36	35	14	11	17	66	42	18
68	30	27	11	12	12	73	41	18
69	39	40	12	15	13	63	38	12
70	35	37	12	15	14	69	42	16
71	38	36	8	14	13	67	44	10
72	31	38	11	16	15	54	33	14
73	34	39	14	15	13	81	48	18
74	38	41	14	15	10	69	40	18
75	34	27	12	13	11	84	50	16
76	39	30	9	12	19	80	49	17
77	37	37	13	17	13	70	43	16
78	34	31	11	13	17	69	44	16
79	28	31	12	15	13	77	47	13
80	37	27	12	13	9	54	33	16
81	33	36	12	15	11	79	46	16
82	37	38	12	16	10	30	0	20
83	35	37	12	15	9	71	45	16
84	37	33	12	16	12	73	43	15
85	32	34	11	15	12	72	44	15
86	33	31	10	14	13	77	47	16
87	38	39	9	15	13	75	45	14
88	33	34	12	14	12	69	42	16
89	29	32	12	13	15	54	33	16
90	33	33	12	7	22	70	43	15
91	31	36	9	17	13	73	46	12
92	36	32	15	13	15	54	33	17
93	35	41	12	15	13	77	46	16
94	32	28	12	14	15	82	48	15
95	29	30	12	13	10	80	47	13
96	39	36	10	16	11	80	47	16
97	37	35	13	12	16	69	43	16
98	35	31	9	14	11	78	46	16
99	37	34	12	17	11	81	48	16
100	32	36	10	15	10	76	46	14
101	38	36	14	17	10	76	45	16
102	37	35	11	12	16	73	45	16
103	36	37	15	16	12	85	52	20
104	32	28	11	11	11	66	42	15
105	33	39	11	15	16	79	47	16
106	40	32	12	9	19	68	41	13
107	38	35	12	16	11	76	47	17
108	41	39	12	15	16	71	43	16
109	36	35	11	10	15	54	33	16
110	43	42	7	10	24	46	30	12
111	30	34	12	15	14	82	49	16
112	31	33	14	11	15	74	44	16
113	32	41	11	13	11	88	55	17
114	32	33	11	14	15	38	11	13
115	37	34	10	18	12	76	47	12
116	37	32	13	16	10	86	53	18
117	33	40	13	14	14	54	33	14
118	34	40	8	14	13	70	44	14
119	33	35	11	14	9	69	42	13
120	38	36	12	14	15	90	55	16
121	33	37	11	12	15	54	33	13
122	31	27	13	14	14	76	46	16
123	38	39	12	15	11	89	54	13
124	37	38	14	15	8	76	47	16
125	33	31	13	15	11	73	45	15
126	31	33	15	13	11	79	47	16
127	39	32	10	17	8	90	55	15
128	44	39	11	17	10	74	44	17
129	33	36	9	19	11	81	53	15
130	35	33	11	15	13	72	44	12
131	32	33	10	13	11	71	42	16
132	28	32	11	9	20	66	40	10
133	40	37	8	15	10	77	46	16
134	27	30	11	15	15	65	40	12
135	37	38	12	15	12	74	46	14
136	32	29	12	16	14	82	53	15
137	28	22	9	11	23	54	33	13
138	34	35	11	14	14	63	42	15
139	30	35	10	11	16	54	35	11
140	35	34	8	15	11	64	40	12
141	31	35	9	13	12	69	41	8
142	32	34	8	15	10	54	33	16
143	30	34	9	16	14	84	51	15
144	30	35	15	14	12	86	53	17
145	31	23	11	15	12	77	46	16
146	40	31	8	16	11	89	55	10
147	32	27	13	16	12	76	47	18
148	36	36	12	11	13	60	38	13
149	32	31	12	12	11	75	46	16
150	35	32	9	9	19	73	46	13
151	38	39	7	16	12	85	53	10
152	42	37	13	13	17	79	47	15
153	34	38	9	16	9	71	41	16
154	35	39	6	12	12	72	44	16
155	35	34	8	9	19	69	43	14
156	33	31	8	13	18	78	51	10
157	36	32	15	13	15	54	33	17
158	32	37	6	14	14	69	43	13
159	33	36	9	19	11	81	53	15
160	34	32	11	13	9	84	51	16
161	32	35	8	12	18	84	50	12
162	34	36	8	13	16	69	46	13




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=185640&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=185640&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185640&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.78732779537869 -0.00422414568810815t + 0.105729039901781Connected[t] -0.0145311595609417Separate[t] + 0.53026243867359Software[t] + 0.0522435975588478Happiness[t] -0.0635888549113858Depression[t] + 0.0421795958435027Belonging[t] -0.0560602068878137Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.78732779537869 -0.00422414568810815t +  0.105729039901781Connected[t] -0.0145311595609417Separate[t] +  0.53026243867359Software[t] +  0.0522435975588478Happiness[t] -0.0635888549113858Depression[t] +  0.0421795958435027Belonging[t] -0.0560602068878137Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185640&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.78732779537869 -0.00422414568810815t +  0.105729039901781Connected[t] -0.0145311595609417Separate[t] +  0.53026243867359Software[t] +  0.0522435975588478Happiness[t] -0.0635888549113858Depression[t] +  0.0421795958435027Belonging[t] -0.0560602068878137Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185640&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185640&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.78732779537869 -0.00422414568810815t + 0.105729039901781Connected[t] -0.0145311595609417Separate[t] + 0.53026243867359Software[t] + 0.0522435975588478Happiness[t] -0.0635888549113858Depression[t] + 0.0421795958435027Belonging[t] -0.0560602068878137Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.787327795378692.5999082.2260.0274780.013739
t-0.004224145688108150.003255-1.29790.1962860.098143
Connected0.1057290399017810.0472242.23890.0266050.013303
Separate-0.01453115956094170.044961-0.32320.7469890.373494
Software0.530262438673590.0694497.635300
Happiness0.05224359755884780.0764290.68360.4952870.247644
Depression-0.06358885491138580.056518-1.12510.2623010.131151
Belonging0.04217959584350270.0447060.94350.3469220.173461
Belonging_Final-0.05606020688781370.063884-0.87750.3815770.190788

\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) & 5.78732779537869 & 2.599908 & 2.226 & 0.027478 & 0.013739 \tabularnewline
t & -0.00422414568810815 & 0.003255 & -1.2979 & 0.196286 & 0.098143 \tabularnewline
Connected & 0.105729039901781 & 0.047224 & 2.2389 & 0.026605 & 0.013303 \tabularnewline
Separate & -0.0145311595609417 & 0.044961 & -0.3232 & 0.746989 & 0.373494 \tabularnewline
Software & 0.53026243867359 & 0.069449 & 7.6353 & 0 & 0 \tabularnewline
Happiness & 0.0522435975588478 & 0.076429 & 0.6836 & 0.495287 & 0.247644 \tabularnewline
Depression & -0.0635888549113858 & 0.056518 & -1.1251 & 0.262301 & 0.131151 \tabularnewline
Belonging & 0.0421795958435027 & 0.044706 & 0.9435 & 0.346922 & 0.173461 \tabularnewline
Belonging_Final & -0.0560602068878137 & 0.063884 & -0.8775 & 0.381577 & 0.190788 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185640&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]5.78732779537869[/C][C]2.599908[/C][C]2.226[/C][C]0.027478[/C][C]0.013739[/C][/ROW]
[ROW][C]t[/C][C]-0.00422414568810815[/C][C]0.003255[/C][C]-1.2979[/C][C]0.196286[/C][C]0.098143[/C][/ROW]
[ROW][C]Connected[/C][C]0.105729039901781[/C][C]0.047224[/C][C]2.2389[/C][C]0.026605[/C][C]0.013303[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0145311595609417[/C][C]0.044961[/C][C]-0.3232[/C][C]0.746989[/C][C]0.373494[/C][/ROW]
[ROW][C]Software[/C][C]0.53026243867359[/C][C]0.069449[/C][C]7.6353[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0522435975588478[/C][C]0.076429[/C][C]0.6836[/C][C]0.495287[/C][C]0.247644[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0635888549113858[/C][C]0.056518[/C][C]-1.1251[/C][C]0.262301[/C][C]0.131151[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0421795958435027[/C][C]0.044706[/C][C]0.9435[/C][C]0.346922[/C][C]0.173461[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0560602068878137[/C][C]0.063884[/C][C]-0.8775[/C][C]0.381577[/C][C]0.190788[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185640&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185640&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.787327795378692.5999082.2260.0274780.013739
t-0.004224145688108150.003255-1.29790.1962860.098143
Connected0.1057290399017810.0472242.23890.0266050.013303
Separate-0.01453115956094170.044961-0.32320.7469890.373494
Software0.530262438673590.0694497.635300
Happiness0.05224359755884780.0764290.68360.4952870.247644
Depression-0.06358885491138580.056518-1.12510.2623010.131151
Belonging0.04217959584350270.0447060.94350.3469220.173461
Belonging_Final-0.05606020688781370.063884-0.87750.3815770.190788







Multiple Linear Regression - Regression Statistics
Multiple R0.603088159481452
R-squared0.363715328106725
Adjusted R-squared0.330445541341063
F-TEST (value)10.9323011496462
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value3.96016552883793e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8462150988028
Sum Squared Residuals521.502059230256

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.603088159481452 \tabularnewline
R-squared & 0.363715328106725 \tabularnewline
Adjusted R-squared & 0.330445541341063 \tabularnewline
F-TEST (value) & 10.9323011496462 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 3.96016552883793e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.8462150988028 \tabularnewline
Sum Squared Residuals & 521.502059230256 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185640&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.603088159481452[/C][/ROW]
[ROW][C]R-squared[/C][C]0.363715328106725[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.330445541341063[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.9323011496462[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]3.96016552883793e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.8462150988028[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]521.502059230256[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185640&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185640&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.603088159481452
R-squared0.363715328106725
Adjusted R-squared0.330445541341063
F-TEST (value)10.9323011496462
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value3.96016552883793e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8462150988028
Sum Squared Residuals521.502059230256







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3388955526138-3.33889555261376
21616.279483822928-0.279483822928033
31916.50563279160962.49436720839039
41512.14149916699582.85850083300421
51415.8441645976698-1.84416459766976
61315.1762173264696-2.17621732646956
71915.28762113205523.71237886794481
81516.9082248755467-1.9082248755467
91416.1564607968364-2.15646079683639
101512.81426979350292.18573020649708
111615.44821469294430.551785307055723
121616.3236021851749-0.323602185174881
131615.64478348129530.355216518704726
141615.50569001444180.494309985558206
151717.6322376834409-0.632237683440852
161515.2561072478243-0.256107247824282
171514.84131681130630.158683188693718
182016.41317890678923.58682109321082
191815.60775181386162.39224818613838
201615.37963827881840.62036172118163
211615.41595618763420.584043812365804
221614.97067916357171.02932083642829
231916.55088809530112.44911190469892
241614.88860329209661.11139670790343
251715.01349979030771.98650020969229
261717.0782358678476-0.0782358678475589
271615.20503564521340.794964354786641
281516.2654518228368-1.26545182283681
291615.69821604621920.301783953780765
301414.185895180371-0.185895180371007
311515.7812717090957-0.781271709095692
321212.4369869069914-0.436986906991427
331415.248478922483-1.24847892248298
341615.66400199724160.335998002758442
351415.8330011853172-1.83300118531716
36713.1061855561432-6.1061855561432
371011.2097874108162-1.20978741081616
381415.9214268074255-1.92142680742548
391614.42001908769421.57998091230585
401614.74111612870621.25888387129381
411615.17367463096570.826325369034258
421415.7307962090918-1.73079620909176
432017.86723059111322.1327694088868
441414.3172296751758-0.317229675175774
451414.9940058653055-0.994005865305469
461115.7333789506867-4.73337895068667
471416.4756320470172-2.47563204701715
481515.0813312502265-0.0813312502264839
491615.36836259317990.631637406820142
501416.1124259279248-2.11242592792476
511616.5970065297527-0.597006529752734
521414.2424821394283-0.242482139428303
531214.8435654173719-2.84356541737189
541615.26828355172680.731716448273207
55911.6912370036679-2.69123700366785
561412.7707349041271.22926509587302
571616.1468971158982-0.146897115898174
581615.16041784303790.839582156962127
591515.4193989793538-0.419398979353813
601614.2580335722231.741966427777
611211.5942543500050.405745649995047
621616.0534441973212-0.0534441973212024
631616.5678104146945-0.567810414694512
641414.4155179164759-0.415517916475889
651615.40493919779210.595060802207908
661716.01518547706830.984814522931713
671816.14863270357361.85136729642638
681814.75700152884973.2429984711503
691215.8852227066525-3.88522270665255
701615.46692377263850.533076227361487
711013.4882338034122-3.48823380341224
721414.3512683904059-0.351268390405886
731816.61336761760421.38663238239576
741817.1360903821160.863909617883993
751615.75587725207210.2441227479279
761713.97230489785323.02769510214683
771616.3332707102837-0.333270710283678
781614.47695191229641.52304808770355
791314.8967147267191-1.89671472671906
801615.86675699494740.133243005052573
811615.61185294544460.388147054555368
822016.62928441321983.3707155867802
831615.64613272427360.3538672757264
841515.9694479349201-0.969447934920144
851514.74130159119840.258698408801565
861614.28302243150521.71697756849482
871414.2409365898123-0.240936589812325
881615.29795866139580.702041338604247
891614.52871843726721.47128156273282
901514.28856718651040.711432813489626
911213.4915980029731-1.49159800297313
921716.84693659553610.153063404463906
931615.48842857767640.511571422323589
941515.2752786446355-0.275278644635468
951315.162206752319-2.16220675231902
961615.16070310870110.839296891298947
971615.78368596727150.216314032728501
981614.13894583693191.86105416306806
991616.0645227748168-0.064522774816766
1001414.3023903275025-0.302390327502494
1011617.2141375779249-1.21413757792494
1021614.75863833108221.24136166891784
1032017.3177360928532.68226390714698
1041514.46188708388060.538112916119364
1051614.56261305012891.43738694987112
1061315.2986302763145-2.29863027631453
1071715.91484611976421.08515388023581
1081615.81283943175560.187160568244374
1091614.45375209278451.54624790721547
1101212.2253075145009-0.225307514500884
1111614.96459537635081.03540462364916
1121615.81145733001690.188542669983099
1131714.53862031252862.4613796874714
1141314.80620293213-1.80620293213
1151214.7702285367776-2.77022853677762
1161816.49397925804541.50602074195462
1171415.3632041322636-1.36320413226357
1181412.93519694575081.06480305424923
1191314.812983111564-1.81298311156403
1201615.62859113820110.371408861798866
1211314.1613001008177-1.1613001008177
1221615.51869881732720.481301182672813
1231315.8928048507027-2.89280485070269
1241616.8927609690042-0.892760969004237
1251515.831891403473-0.831891403472969
1261616.6841417023743-0.684141702374288
1271515.304203696239-0.304203696238989
1281716.07178010425390.928219895746079
1291513.71921877010241.28078122989763
1301214.8193444595885-2.81934445958847
1311614.06030208815861.93969791184137
1321013.2979037312182-3.29790373121818
1331613.97594939794462.02405060205545
1341213.8020149831286-1.80201498312858
1351415.5031160846431-1.50311608464313
1361514.9711083817640.0288916182360038
1371312.16155664951610.838443350483924
1381514.26743153377380.732568466226193
1391113.0389253729288-2.03892537292884
1401213.1857662977517-1.18576629775168
141813.2611189938697-5.26111899386971
1421612.89434523136123.10565476863882
1431513.26311777378081.73688222621917
1441716.42086639318980.579133606810225
1451614.64074413062241.35925586937759
1461013.998490492144-3.99849049214397
1471815.69442891307892.30557108692108
1481314.9569395380645-1.95693953806452
1491614.96608862050571.03391137949435
1501312.92393229528650.0760677047134599
1511012.999213144229-2.99921314422898
1521516.2371507083438-1.23715070834381
1531613.91587943573262.08412056426736
1541611.88632387457514.11367612542489
1551412.342949046341.65705095366004
1561012.3745582521669-2.3745582521669
1571716.57236712580910.427632874190936
1581311.48813339589151.51186660410855
1591513.59249439945911.40750560054087
1601614.86502413503961.13497586496043
1611212.4464780299708-0.446478029970796
1621312.41014900180560.589850998194358

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.3388955526138 & -3.33889555261376 \tabularnewline
2 & 16 & 16.279483822928 & -0.279483822928033 \tabularnewline
3 & 19 & 16.5056327916096 & 2.49436720839039 \tabularnewline
4 & 15 & 12.1414991669958 & 2.85850083300421 \tabularnewline
5 & 14 & 15.8441645976698 & -1.84416459766976 \tabularnewline
6 & 13 & 15.1762173264696 & -2.17621732646956 \tabularnewline
7 & 19 & 15.2876211320552 & 3.71237886794481 \tabularnewline
8 & 15 & 16.9082248755467 & -1.9082248755467 \tabularnewline
9 & 14 & 16.1564607968364 & -2.15646079683639 \tabularnewline
10 & 15 & 12.8142697935029 & 2.18573020649708 \tabularnewline
11 & 16 & 15.4482146929443 & 0.551785307055723 \tabularnewline
12 & 16 & 16.3236021851749 & -0.323602185174881 \tabularnewline
13 & 16 & 15.6447834812953 & 0.355216518704726 \tabularnewline
14 & 16 & 15.5056900144418 & 0.494309985558206 \tabularnewline
15 & 17 & 17.6322376834409 & -0.632237683440852 \tabularnewline
16 & 15 & 15.2561072478243 & -0.256107247824282 \tabularnewline
17 & 15 & 14.8413168113063 & 0.158683188693718 \tabularnewline
18 & 20 & 16.4131789067892 & 3.58682109321082 \tabularnewline
19 & 18 & 15.6077518138616 & 2.39224818613838 \tabularnewline
20 & 16 & 15.3796382788184 & 0.62036172118163 \tabularnewline
21 & 16 & 15.4159561876342 & 0.584043812365804 \tabularnewline
22 & 16 & 14.9706791635717 & 1.02932083642829 \tabularnewline
23 & 19 & 16.5508880953011 & 2.44911190469892 \tabularnewline
24 & 16 & 14.8886032920966 & 1.11139670790343 \tabularnewline
25 & 17 & 15.0134997903077 & 1.98650020969229 \tabularnewline
26 & 17 & 17.0782358678476 & -0.0782358678475589 \tabularnewline
27 & 16 & 15.2050356452134 & 0.794964354786641 \tabularnewline
28 & 15 & 16.2654518228368 & -1.26545182283681 \tabularnewline
29 & 16 & 15.6982160462192 & 0.301783953780765 \tabularnewline
30 & 14 & 14.185895180371 & -0.185895180371007 \tabularnewline
31 & 15 & 15.7812717090957 & -0.781271709095692 \tabularnewline
32 & 12 & 12.4369869069914 & -0.436986906991427 \tabularnewline
33 & 14 & 15.248478922483 & -1.24847892248298 \tabularnewline
34 & 16 & 15.6640019972416 & 0.335998002758442 \tabularnewline
35 & 14 & 15.8330011853172 & -1.83300118531716 \tabularnewline
36 & 7 & 13.1061855561432 & -6.1061855561432 \tabularnewline
37 & 10 & 11.2097874108162 & -1.20978741081616 \tabularnewline
38 & 14 & 15.9214268074255 & -1.92142680742548 \tabularnewline
39 & 16 & 14.4200190876942 & 1.57998091230585 \tabularnewline
40 & 16 & 14.7411161287062 & 1.25888387129381 \tabularnewline
41 & 16 & 15.1736746309657 & 0.826325369034258 \tabularnewline
42 & 14 & 15.7307962090918 & -1.73079620909176 \tabularnewline
43 & 20 & 17.8672305911132 & 2.1327694088868 \tabularnewline
44 & 14 & 14.3172296751758 & -0.317229675175774 \tabularnewline
45 & 14 & 14.9940058653055 & -0.994005865305469 \tabularnewline
46 & 11 & 15.7333789506867 & -4.73337895068667 \tabularnewline
47 & 14 & 16.4756320470172 & -2.47563204701715 \tabularnewline
48 & 15 & 15.0813312502265 & -0.0813312502264839 \tabularnewline
49 & 16 & 15.3683625931799 & 0.631637406820142 \tabularnewline
50 & 14 & 16.1124259279248 & -2.11242592792476 \tabularnewline
51 & 16 & 16.5970065297527 & -0.597006529752734 \tabularnewline
52 & 14 & 14.2424821394283 & -0.242482139428303 \tabularnewline
53 & 12 & 14.8435654173719 & -2.84356541737189 \tabularnewline
54 & 16 & 15.2682835517268 & 0.731716448273207 \tabularnewline
55 & 9 & 11.6912370036679 & -2.69123700366785 \tabularnewline
56 & 14 & 12.770734904127 & 1.22926509587302 \tabularnewline
57 & 16 & 16.1468971158982 & -0.146897115898174 \tabularnewline
58 & 16 & 15.1604178430379 & 0.839582156962127 \tabularnewline
59 & 15 & 15.4193989793538 & -0.419398979353813 \tabularnewline
60 & 16 & 14.258033572223 & 1.741966427777 \tabularnewline
61 & 12 & 11.594254350005 & 0.405745649995047 \tabularnewline
62 & 16 & 16.0534441973212 & -0.0534441973212024 \tabularnewline
63 & 16 & 16.5678104146945 & -0.567810414694512 \tabularnewline
64 & 14 & 14.4155179164759 & -0.415517916475889 \tabularnewline
65 & 16 & 15.4049391977921 & 0.595060802207908 \tabularnewline
66 & 17 & 16.0151854770683 & 0.984814522931713 \tabularnewline
67 & 18 & 16.1486327035736 & 1.85136729642638 \tabularnewline
68 & 18 & 14.7570015288497 & 3.2429984711503 \tabularnewline
69 & 12 & 15.8852227066525 & -3.88522270665255 \tabularnewline
70 & 16 & 15.4669237726385 & 0.533076227361487 \tabularnewline
71 & 10 & 13.4882338034122 & -3.48823380341224 \tabularnewline
72 & 14 & 14.3512683904059 & -0.351268390405886 \tabularnewline
73 & 18 & 16.6133676176042 & 1.38663238239576 \tabularnewline
74 & 18 & 17.136090382116 & 0.863909617883993 \tabularnewline
75 & 16 & 15.7558772520721 & 0.2441227479279 \tabularnewline
76 & 17 & 13.9723048978532 & 3.02769510214683 \tabularnewline
77 & 16 & 16.3332707102837 & -0.333270710283678 \tabularnewline
78 & 16 & 14.4769519122964 & 1.52304808770355 \tabularnewline
79 & 13 & 14.8967147267191 & -1.89671472671906 \tabularnewline
80 & 16 & 15.8667569949474 & 0.133243005052573 \tabularnewline
81 & 16 & 15.6118529454446 & 0.388147054555368 \tabularnewline
82 & 20 & 16.6292844132198 & 3.3707155867802 \tabularnewline
83 & 16 & 15.6461327242736 & 0.3538672757264 \tabularnewline
84 & 15 & 15.9694479349201 & -0.969447934920144 \tabularnewline
85 & 15 & 14.7413015911984 & 0.258698408801565 \tabularnewline
86 & 16 & 14.2830224315052 & 1.71697756849482 \tabularnewline
87 & 14 & 14.2409365898123 & -0.240936589812325 \tabularnewline
88 & 16 & 15.2979586613958 & 0.702041338604247 \tabularnewline
89 & 16 & 14.5287184372672 & 1.47128156273282 \tabularnewline
90 & 15 & 14.2885671865104 & 0.711432813489626 \tabularnewline
91 & 12 & 13.4915980029731 & -1.49159800297313 \tabularnewline
92 & 17 & 16.8469365955361 & 0.153063404463906 \tabularnewline
93 & 16 & 15.4884285776764 & 0.511571422323589 \tabularnewline
94 & 15 & 15.2752786446355 & -0.275278644635468 \tabularnewline
95 & 13 & 15.162206752319 & -2.16220675231902 \tabularnewline
96 & 16 & 15.1607031087011 & 0.839296891298947 \tabularnewline
97 & 16 & 15.7836859672715 & 0.216314032728501 \tabularnewline
98 & 16 & 14.1389458369319 & 1.86105416306806 \tabularnewline
99 & 16 & 16.0645227748168 & -0.064522774816766 \tabularnewline
100 & 14 & 14.3023903275025 & -0.302390327502494 \tabularnewline
101 & 16 & 17.2141375779249 & -1.21413757792494 \tabularnewline
102 & 16 & 14.7586383310822 & 1.24136166891784 \tabularnewline
103 & 20 & 17.317736092853 & 2.68226390714698 \tabularnewline
104 & 15 & 14.4618870838806 & 0.538112916119364 \tabularnewline
105 & 16 & 14.5626130501289 & 1.43738694987112 \tabularnewline
106 & 13 & 15.2986302763145 & -2.29863027631453 \tabularnewline
107 & 17 & 15.9148461197642 & 1.08515388023581 \tabularnewline
108 & 16 & 15.8128394317556 & 0.187160568244374 \tabularnewline
109 & 16 & 14.4537520927845 & 1.54624790721547 \tabularnewline
110 & 12 & 12.2253075145009 & -0.225307514500884 \tabularnewline
111 & 16 & 14.9645953763508 & 1.03540462364916 \tabularnewline
112 & 16 & 15.8114573300169 & 0.188542669983099 \tabularnewline
113 & 17 & 14.5386203125286 & 2.4613796874714 \tabularnewline
114 & 13 & 14.80620293213 & -1.80620293213 \tabularnewline
115 & 12 & 14.7702285367776 & -2.77022853677762 \tabularnewline
116 & 18 & 16.4939792580454 & 1.50602074195462 \tabularnewline
117 & 14 & 15.3632041322636 & -1.36320413226357 \tabularnewline
118 & 14 & 12.9351969457508 & 1.06480305424923 \tabularnewline
119 & 13 & 14.812983111564 & -1.81298311156403 \tabularnewline
120 & 16 & 15.6285911382011 & 0.371408861798866 \tabularnewline
121 & 13 & 14.1613001008177 & -1.1613001008177 \tabularnewline
122 & 16 & 15.5186988173272 & 0.481301182672813 \tabularnewline
123 & 13 & 15.8928048507027 & -2.89280485070269 \tabularnewline
124 & 16 & 16.8927609690042 & -0.892760969004237 \tabularnewline
125 & 15 & 15.831891403473 & -0.831891403472969 \tabularnewline
126 & 16 & 16.6841417023743 & -0.684141702374288 \tabularnewline
127 & 15 & 15.304203696239 & -0.304203696238989 \tabularnewline
128 & 17 & 16.0717801042539 & 0.928219895746079 \tabularnewline
129 & 15 & 13.7192187701024 & 1.28078122989763 \tabularnewline
130 & 12 & 14.8193444595885 & -2.81934445958847 \tabularnewline
131 & 16 & 14.0603020881586 & 1.93969791184137 \tabularnewline
132 & 10 & 13.2979037312182 & -3.29790373121818 \tabularnewline
133 & 16 & 13.9759493979446 & 2.02405060205545 \tabularnewline
134 & 12 & 13.8020149831286 & -1.80201498312858 \tabularnewline
135 & 14 & 15.5031160846431 & -1.50311608464313 \tabularnewline
136 & 15 & 14.971108381764 & 0.0288916182360038 \tabularnewline
137 & 13 & 12.1615566495161 & 0.838443350483924 \tabularnewline
138 & 15 & 14.2674315337738 & 0.732568466226193 \tabularnewline
139 & 11 & 13.0389253729288 & -2.03892537292884 \tabularnewline
140 & 12 & 13.1857662977517 & -1.18576629775168 \tabularnewline
141 & 8 & 13.2611189938697 & -5.26111899386971 \tabularnewline
142 & 16 & 12.8943452313612 & 3.10565476863882 \tabularnewline
143 & 15 & 13.2631177737808 & 1.73688222621917 \tabularnewline
144 & 17 & 16.4208663931898 & 0.579133606810225 \tabularnewline
145 & 16 & 14.6407441306224 & 1.35925586937759 \tabularnewline
146 & 10 & 13.998490492144 & -3.99849049214397 \tabularnewline
147 & 18 & 15.6944289130789 & 2.30557108692108 \tabularnewline
148 & 13 & 14.9569395380645 & -1.95693953806452 \tabularnewline
149 & 16 & 14.9660886205057 & 1.03391137949435 \tabularnewline
150 & 13 & 12.9239322952865 & 0.0760677047134599 \tabularnewline
151 & 10 & 12.999213144229 & -2.99921314422898 \tabularnewline
152 & 15 & 16.2371507083438 & -1.23715070834381 \tabularnewline
153 & 16 & 13.9158794357326 & 2.08412056426736 \tabularnewline
154 & 16 & 11.8863238745751 & 4.11367612542489 \tabularnewline
155 & 14 & 12.34294904634 & 1.65705095366004 \tabularnewline
156 & 10 & 12.3745582521669 & -2.3745582521669 \tabularnewline
157 & 17 & 16.5723671258091 & 0.427632874190936 \tabularnewline
158 & 13 & 11.4881333958915 & 1.51186660410855 \tabularnewline
159 & 15 & 13.5924943994591 & 1.40750560054087 \tabularnewline
160 & 16 & 14.8650241350396 & 1.13497586496043 \tabularnewline
161 & 12 & 12.4464780299708 & -0.446478029970796 \tabularnewline
162 & 13 & 12.4101490018056 & 0.589850998194358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185640&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]13[/C][C]16.3388955526138[/C][C]-3.33889555261376[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.279483822928[/C][C]-0.279483822928033[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5056327916096[/C][C]2.49436720839039[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.1414991669958[/C][C]2.85850083300421[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.8441645976698[/C][C]-1.84416459766976[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.1762173264696[/C][C]-2.17621732646956[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.2876211320552[/C][C]3.71237886794481[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9082248755467[/C][C]-1.9082248755467[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1564607968364[/C][C]-2.15646079683639[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.8142697935029[/C][C]2.18573020649708[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4482146929443[/C][C]0.551785307055723[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.3236021851749[/C][C]-0.323602185174881[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6447834812953[/C][C]0.355216518704726[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.5056900144418[/C][C]0.494309985558206[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.6322376834409[/C][C]-0.632237683440852[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2561072478243[/C][C]-0.256107247824282[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.8413168113063[/C][C]0.158683188693718[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4131789067892[/C][C]3.58682109321082[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6077518138616[/C][C]2.39224818613838[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3796382788184[/C][C]0.62036172118163[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.4159561876342[/C][C]0.584043812365804[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.9706791635717[/C][C]1.02932083642829[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.5508880953011[/C][C]2.44911190469892[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8886032920966[/C][C]1.11139670790343[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.0134997903077[/C][C]1.98650020969229[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.0782358678476[/C][C]-0.0782358678475589[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.2050356452134[/C][C]0.794964354786641[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.2654518228368[/C][C]-1.26545182283681[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.6982160462192[/C][C]0.301783953780765[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.185895180371[/C][C]-0.185895180371007[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7812717090957[/C][C]-0.781271709095692[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4369869069914[/C][C]-0.436986906991427[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.248478922483[/C][C]-1.24847892248298[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.6640019972416[/C][C]0.335998002758442[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.8330011853172[/C][C]-1.83300118531716[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1061855561432[/C][C]-6.1061855561432[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.2097874108162[/C][C]-1.20978741081616[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.9214268074255[/C][C]-1.92142680742548[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.4200190876942[/C][C]1.57998091230585[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7411161287062[/C][C]1.25888387129381[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1736746309657[/C][C]0.826325369034258[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.7307962090918[/C][C]-1.73079620909176[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.8672305911132[/C][C]2.1327694088868[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.3172296751758[/C][C]-0.317229675175774[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9940058653055[/C][C]-0.994005865305469[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.7333789506867[/C][C]-4.73337895068667[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4756320470172[/C][C]-2.47563204701715[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0813312502265[/C][C]-0.0813312502264839[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.3683625931799[/C][C]0.631637406820142[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.1124259279248[/C][C]-2.11242592792476[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.5970065297527[/C][C]-0.597006529752734[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2424821394283[/C][C]-0.242482139428303[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.8435654173719[/C][C]-2.84356541737189[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.2682835517268[/C][C]0.731716448273207[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.6912370036679[/C][C]-2.69123700366785[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.770734904127[/C][C]1.22926509587302[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1468971158982[/C][C]-0.146897115898174[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.1604178430379[/C][C]0.839582156962127[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.4193989793538[/C][C]-0.419398979353813[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.258033572223[/C][C]1.741966427777[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.594254350005[/C][C]0.405745649995047[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.0534441973212[/C][C]-0.0534441973212024[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5678104146945[/C][C]-0.567810414694512[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4155179164759[/C][C]-0.415517916475889[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4049391977921[/C][C]0.595060802207908[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.0151854770683[/C][C]0.984814522931713[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1486327035736[/C][C]1.85136729642638[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.7570015288497[/C][C]3.2429984711503[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8852227066525[/C][C]-3.88522270665255[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4669237726385[/C][C]0.533076227361487[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4882338034122[/C][C]-3.48823380341224[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3512683904059[/C][C]-0.351268390405886[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.6133676176042[/C][C]1.38663238239576[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.136090382116[/C][C]0.863909617883993[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.7558772520721[/C][C]0.2441227479279[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.9723048978532[/C][C]3.02769510214683[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3332707102837[/C][C]-0.333270710283678[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4769519122964[/C][C]1.52304808770355[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8967147267191[/C][C]-1.89671472671906[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.8667569949474[/C][C]0.133243005052573[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.6118529454446[/C][C]0.388147054555368[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.6292844132198[/C][C]3.3707155867802[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.6461327242736[/C][C]0.3538672757264[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9694479349201[/C][C]-0.969447934920144[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7413015911984[/C][C]0.258698408801565[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2830224315052[/C][C]1.71697756849482[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.2409365898123[/C][C]-0.240936589812325[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2979586613958[/C][C]0.702041338604247[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5287184372672[/C][C]1.47128156273282[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2885671865104[/C][C]0.711432813489626[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4915980029731[/C][C]-1.49159800297313[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.8469365955361[/C][C]0.153063404463906[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4884285776764[/C][C]0.511571422323589[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2752786446355[/C][C]-0.275278644635468[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.162206752319[/C][C]-2.16220675231902[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1607031087011[/C][C]0.839296891298947[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.7836859672715[/C][C]0.216314032728501[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1389458369319[/C][C]1.86105416306806[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.0645227748168[/C][C]-0.064522774816766[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.3023903275025[/C][C]-0.302390327502494[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2141375779249[/C][C]-1.21413757792494[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7586383310822[/C][C]1.24136166891784[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.317736092853[/C][C]2.68226390714698[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.4618870838806[/C][C]0.538112916119364[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5626130501289[/C][C]1.43738694987112[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.2986302763145[/C][C]-2.29863027631453[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9148461197642[/C][C]1.08515388023581[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8128394317556[/C][C]0.187160568244374[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.4537520927845[/C][C]1.54624790721547[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.2253075145009[/C][C]-0.225307514500884[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9645953763508[/C][C]1.03540462364916[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8114573300169[/C][C]0.188542669983099[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5386203125286[/C][C]2.4613796874714[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.80620293213[/C][C]-1.80620293213[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.7702285367776[/C][C]-2.77022853677762[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.4939792580454[/C][C]1.50602074195462[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.3632041322636[/C][C]-1.36320413226357[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.9351969457508[/C][C]1.06480305424923[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.812983111564[/C][C]-1.81298311156403[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6285911382011[/C][C]0.371408861798866[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.1613001008177[/C][C]-1.1613001008177[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.5186988173272[/C][C]0.481301182672813[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8928048507027[/C][C]-2.89280485070269[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.8927609690042[/C][C]-0.892760969004237[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.831891403473[/C][C]-0.831891403472969[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6841417023743[/C][C]-0.684141702374288[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.304203696239[/C][C]-0.304203696238989[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0717801042539[/C][C]0.928219895746079[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.7192187701024[/C][C]1.28078122989763[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.8193444595885[/C][C]-2.81934445958847[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0603020881586[/C][C]1.93969791184137[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2979037312182[/C][C]-3.29790373121818[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9759493979446[/C][C]2.02405060205545[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8020149831286[/C][C]-1.80201498312858[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.5031160846431[/C][C]-1.50311608464313[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.971108381764[/C][C]0.0288916182360038[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1615566495161[/C][C]0.838443350483924[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.2674315337738[/C][C]0.732568466226193[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.0389253729288[/C][C]-2.03892537292884[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.1857662977517[/C][C]-1.18576629775168[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2611189938697[/C][C]-5.26111899386971[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.8943452313612[/C][C]3.10565476863882[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2631177737808[/C][C]1.73688222621917[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.4208663931898[/C][C]0.579133606810225[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.6407441306224[/C][C]1.35925586937759[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.998490492144[/C][C]-3.99849049214397[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.6944289130789[/C][C]2.30557108692108[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.9569395380645[/C][C]-1.95693953806452[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.9660886205057[/C][C]1.03391137949435[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9239322952865[/C][C]0.0760677047134599[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.999213144229[/C][C]-2.99921314422898[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.2371507083438[/C][C]-1.23715070834381[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.9158794357326[/C][C]2.08412056426736[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8863238745751[/C][C]4.11367612542489[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.34294904634[/C][C]1.65705095366004[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.3745582521669[/C][C]-2.3745582521669[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.5723671258091[/C][C]0.427632874190936[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.4881333958915[/C][C]1.51186660410855[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.5924943994591[/C][C]1.40750560054087[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.8650241350396[/C][C]1.13497586496043[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.4464780299708[/C][C]-0.446478029970796[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.4101490018056[/C][C]0.589850998194358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185640&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185640&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3388955526138-3.33889555261376
21616.279483822928-0.279483822928033
31916.50563279160962.49436720839039
41512.14149916699582.85850083300421
51415.8441645976698-1.84416459766976
61315.1762173264696-2.17621732646956
71915.28762113205523.71237886794481
81516.9082248755467-1.9082248755467
91416.1564607968364-2.15646079683639
101512.81426979350292.18573020649708
111615.44821469294430.551785307055723
121616.3236021851749-0.323602185174881
131615.64478348129530.355216518704726
141615.50569001444180.494309985558206
151717.6322376834409-0.632237683440852
161515.2561072478243-0.256107247824282
171514.84131681130630.158683188693718
182016.41317890678923.58682109321082
191815.60775181386162.39224818613838
201615.37963827881840.62036172118163
211615.41595618763420.584043812365804
221614.97067916357171.02932083642829
231916.55088809530112.44911190469892
241614.88860329209661.11139670790343
251715.01349979030771.98650020969229
261717.0782358678476-0.0782358678475589
271615.20503564521340.794964354786641
281516.2654518228368-1.26545182283681
291615.69821604621920.301783953780765
301414.185895180371-0.185895180371007
311515.7812717090957-0.781271709095692
321212.4369869069914-0.436986906991427
331415.248478922483-1.24847892248298
341615.66400199724160.335998002758442
351415.8330011853172-1.83300118531716
36713.1061855561432-6.1061855561432
371011.2097874108162-1.20978741081616
381415.9214268074255-1.92142680742548
391614.42001908769421.57998091230585
401614.74111612870621.25888387129381
411615.17367463096570.826325369034258
421415.7307962090918-1.73079620909176
432017.86723059111322.1327694088868
441414.3172296751758-0.317229675175774
451414.9940058653055-0.994005865305469
461115.7333789506867-4.73337895068667
471416.4756320470172-2.47563204701715
481515.0813312502265-0.0813312502264839
491615.36836259317990.631637406820142
501416.1124259279248-2.11242592792476
511616.5970065297527-0.597006529752734
521414.2424821394283-0.242482139428303
531214.8435654173719-2.84356541737189
541615.26828355172680.731716448273207
55911.6912370036679-2.69123700366785
561412.7707349041271.22926509587302
571616.1468971158982-0.146897115898174
581615.16041784303790.839582156962127
591515.4193989793538-0.419398979353813
601614.2580335722231.741966427777
611211.5942543500050.405745649995047
621616.0534441973212-0.0534441973212024
631616.5678104146945-0.567810414694512
641414.4155179164759-0.415517916475889
651615.40493919779210.595060802207908
661716.01518547706830.984814522931713
671816.14863270357361.85136729642638
681814.75700152884973.2429984711503
691215.8852227066525-3.88522270665255
701615.46692377263850.533076227361487
711013.4882338034122-3.48823380341224
721414.3512683904059-0.351268390405886
731816.61336761760421.38663238239576
741817.1360903821160.863909617883993
751615.75587725207210.2441227479279
761713.97230489785323.02769510214683
771616.3332707102837-0.333270710283678
781614.47695191229641.52304808770355
791314.8967147267191-1.89671472671906
801615.86675699494740.133243005052573
811615.61185294544460.388147054555368
822016.62928441321983.3707155867802
831615.64613272427360.3538672757264
841515.9694479349201-0.969447934920144
851514.74130159119840.258698408801565
861614.28302243150521.71697756849482
871414.2409365898123-0.240936589812325
881615.29795866139580.702041338604247
891614.52871843726721.47128156273282
901514.28856718651040.711432813489626
911213.4915980029731-1.49159800297313
921716.84693659553610.153063404463906
931615.48842857767640.511571422323589
941515.2752786446355-0.275278644635468
951315.162206752319-2.16220675231902
961615.16070310870110.839296891298947
971615.78368596727150.216314032728501
981614.13894583693191.86105416306806
991616.0645227748168-0.064522774816766
1001414.3023903275025-0.302390327502494
1011617.2141375779249-1.21413757792494
1021614.75863833108221.24136166891784
1032017.3177360928532.68226390714698
1041514.46188708388060.538112916119364
1051614.56261305012891.43738694987112
1061315.2986302763145-2.29863027631453
1071715.91484611976421.08515388023581
1081615.81283943175560.187160568244374
1091614.45375209278451.54624790721547
1101212.2253075145009-0.225307514500884
1111614.96459537635081.03540462364916
1121615.81145733001690.188542669983099
1131714.53862031252862.4613796874714
1141314.80620293213-1.80620293213
1151214.7702285367776-2.77022853677762
1161816.49397925804541.50602074195462
1171415.3632041322636-1.36320413226357
1181412.93519694575081.06480305424923
1191314.812983111564-1.81298311156403
1201615.62859113820110.371408861798866
1211314.1613001008177-1.1613001008177
1221615.51869881732720.481301182672813
1231315.8928048507027-2.89280485070269
1241616.8927609690042-0.892760969004237
1251515.831891403473-0.831891403472969
1261616.6841417023743-0.684141702374288
1271515.304203696239-0.304203696238989
1281716.07178010425390.928219895746079
1291513.71921877010241.28078122989763
1301214.8193444595885-2.81934445958847
1311614.06030208815861.93969791184137
1321013.2979037312182-3.29790373121818
1331613.97594939794462.02405060205545
1341213.8020149831286-1.80201498312858
1351415.5031160846431-1.50311608464313
1361514.9711083817640.0288916182360038
1371312.16155664951610.838443350483924
1381514.26743153377380.732568466226193
1391113.0389253729288-2.03892537292884
1401213.1857662977517-1.18576629775168
141813.2611189938697-5.26111899386971
1421612.89434523136123.10565476863882
1431513.26311777378081.73688222621917
1441716.42086639318980.579133606810225
1451614.64074413062241.35925586937759
1461013.998490492144-3.99849049214397
1471815.69442891307892.30557108692108
1481314.9569395380645-1.95693953806452
1491614.96608862050571.03391137949435
1501312.92393229528650.0760677047134599
1511012.999213144229-2.99921314422898
1521516.2371507083438-1.23715070834381
1531613.91587943573262.08412056426736
1541611.88632387457514.11367612542489
1551412.342949046341.65705095366004
1561012.3745582521669-2.3745582521669
1571716.57236712580910.427632874190936
1581311.48813339589151.51186660410855
1591513.59249439945911.40750560054087
1601614.86502413503961.13497586496043
1611212.4464780299708-0.446478029970796
1621312.41014900180560.589850998194358







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3725268919792380.7450537839584770.627473108020762
130.5945533437517690.8108933124964610.405446656248231
140.585790251762550.8284194964748990.41420974823745
150.4656201247738460.9312402495476930.534379875226154
160.3631647041506040.7263294083012070.636835295849396
170.2979510144841720.5959020289683430.702048985515828
180.4568090305842450.913618061168490.543190969415755
190.3773103478829610.7546206957659210.622689652117039
200.2979206647397890.5958413294795770.702079335260211
210.2375944908690430.4751889817380860.762405509130957
220.2339670513869610.4679341027739230.766032948613039
230.3548520345649250.7097040691298510.645147965435075
240.4291823023196620.8583646046393240.570817697680338
250.3782680193283860.7565360386567730.621731980671614
260.3150690423688790.6301380847377590.684930957631121
270.2898219212652020.5796438425304040.710178078734798
280.3916726245731510.7833452491463020.608327375426849
290.3374230968702260.6748461937404510.662576903129774
300.447047056778220.8940941135564410.55295294322178
310.3983022931751820.7966045863503640.601697706824818
320.363294439171460.7265888783429190.63670556082854
330.3532465094398620.7064930188797240.646753490560138
340.323694144358770.6473882887175390.67630585564123
350.2794000999248740.5588001998497480.720599900075126
360.8406038467250590.3187923065498810.159396153274941
370.81159446423170.3768110715365990.1884055357683
380.7929466213675040.4141067572649910.207053378632496
390.8265976689575840.3468046620848310.173402331042416
400.8136337880713160.3727324238573670.186366211928684
410.7844553760849660.4310892478300670.215544623915034
420.7563010616413630.4873978767172750.243698938358637
430.7870346227499230.4259307545001550.212965377250077
440.7453674772330580.5092650455338850.254632522766942
450.7154366869947450.569126626010510.284563313005255
460.8631276610625230.2737446778749540.136872338937477
470.8889001075780810.2221997848438390.111099892421919
480.8658254342937830.2683491314124340.134174565706217
490.8626766149979360.2746467700041270.137323385002064
500.855921572937390.288156854125220.14407842706261
510.8276412585936750.3447174828126510.172358741406325
520.7967508969158250.4064982061683510.203249103084175
530.8093652320477670.3812695359044670.190634767952233
540.7792474523583110.4415050952833780.220752547641689
550.7988814683760380.4022370632479240.201118531623962
560.7781005388594610.4437989222810790.221899461140539
570.7405264909366750.5189470181266510.259473509063325
580.7259547314245080.5480905371509850.274045268575492
590.6953655637228630.6092688725542730.304634436277137
600.7017310300098340.5965379399803330.298268969990166
610.667246730218060.665506539563880.33275326978194
620.6312801266532510.7374397466934980.368719873346749
630.5985645438099110.8028709123801770.401435456190089
640.5548143723981430.8903712552037150.445185627601857
650.5179749959103290.9640500081793430.482025004089671
660.4915466207268430.9830932414536850.508453379273157
670.4865238838489580.9730477676979150.513476116151042
680.629305405047870.7413891899042610.37069459495213
690.7401553970492380.5196892059015230.259844602950762
700.7071007909062590.5857984181874830.292899209093741
710.8130665987781370.3738668024437270.186933401221863
720.7827413830238550.434517233952290.217258616976145
730.7767347695781760.4465304608436480.223265230421824
740.7603347771962140.4793304456075730.239665222803786
750.7227245556759450.554550888648110.277275444324055
760.7629216293250350.474156741349930.237078370674965
770.72573387966020.54853224067960.2742661203398
780.706207565600860.587584868798280.29379243439914
790.7139696330710540.5720607338578920.286030366928946
800.6730130810923260.6539738378153480.326986918907674
810.6360228211844650.727954357631070.363977178815535
820.7616116271093060.4767767457813890.238388372890694
830.7267262674186690.5465474651626620.273273732581331
840.6976386248937190.6047227502125620.302361375106281
850.656726164745940.686547670508120.34327383525406
860.6437722524391450.7124554951217110.356227747560855
870.5997968132680510.8004063734638980.400203186731949
880.5578766896592840.8842466206814320.442123310340716
890.535434807975490.9291303840490210.46456519202451
900.4969917356147440.9939834712294870.503008264385256
910.4841191764218920.9682383528437830.515880823578108
920.4420932736221820.8841865472443630.557906726377818
930.3991230350130270.7982460700260540.600876964986973
940.3555230370612370.7110460741224740.644476962938763
950.3819030892278180.7638061784556350.618096910772182
960.3443674173184810.6887348346369630.655632582681519
970.3029521280858090.6059042561716190.697047871914191
980.2954724196324810.5909448392649620.704527580367519
990.2550271606570970.5100543213141940.744972839342903
1000.222277068802560.4445541376051190.77772293119744
1010.2004182766743160.4008365533486330.799581723325684
1020.1820399229814990.3640798459629970.817960077018501
1030.2226381960993140.4452763921986290.777361803900686
1040.1887281107028070.3774562214056130.811271889297193
1050.181503864450450.36300772890090.81849613554955
1060.1923929007521850.384785801504370.807607099247815
1070.1726878868769670.3453757737539340.827312113123033
1080.1526296448675570.3052592897351140.847370355132443
1090.148622836263080.297245672526160.85137716373692
1100.1456226838786670.2912453677573350.854377316121333
1110.1320146013725010.2640292027450010.867985398627499
1120.1127740941163480.2255481882326950.887225905883652
1130.1517801634573730.3035603269147450.848219836542627
1140.1405544662233730.2811089324467450.859445533776627
1150.1582098831110110.3164197662220220.841790116888989
1160.1664033503304190.3328067006608380.833596649669581
1170.1418044827522090.2836089655044180.858195517247791
1180.1434436785503050.286887357100610.856556321449695
1190.1308367070214870.2616734140429740.869163292978513
1200.1465908024125690.2931816048251390.853409197587431
1210.1201335984043910.2402671968087810.879866401595609
1220.1058892252803410.2117784505606830.894110774719659
1230.1022341722474930.2044683444949860.897765827752507
1240.07995871142935910.1599174228587180.920041288570641
1250.06159196803549340.1231839360709870.938408031964507
1260.04657756090725690.09315512181451390.953422439092743
1270.03406131068378660.06812262136757320.965938689316213
1280.03239001192099940.06478002384199880.967609988079001
1290.03555068084381750.07110136168763490.964449319156182
1300.03527709718346420.07055419436692850.964722902816536
1310.0383926186527260.0767852373054520.961607381347274
1320.04082878099366850.08165756198733710.959171219006332
1330.0830130325454490.1660260650908980.916986967454551
1340.08634322242197640.1726864448439530.913656777578024
1350.06667565781353030.1333513156270610.93332434218647
1360.05456748350182160.1091349670036430.945432516498178
1370.03958577240960750.0791715448192150.960414227590393
1380.04726413473757710.09452826947515420.952735865262423
1390.0397856340712280.0795712681424560.960214365928772
1400.02662964102019810.05325928204039610.973370358979802
1410.5421231839356860.9157536321286280.457876816064314
1420.4819037858908010.9638075717816030.518096214109198
1430.4089564228565460.8179128457130930.591043577143453
1440.3198860337329430.6397720674658870.680113966267057
1450.2398151145191340.4796302290382680.760184885480866
1460.2848157411406480.5696314822812970.715184258859352
1470.3159954290958420.6319908581916840.684004570904158
1480.603462698428780.7930746031424390.39653730157122
1490.6107946987679740.7784106024640510.389205301232026
1500.6089627421375270.7820745157249450.391037257862473

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.372526891979238 & 0.745053783958477 & 0.627473108020762 \tabularnewline
13 & 0.594553343751769 & 0.810893312496461 & 0.405446656248231 \tabularnewline
14 & 0.58579025176255 & 0.828419496474899 & 0.41420974823745 \tabularnewline
15 & 0.465620124773846 & 0.931240249547693 & 0.534379875226154 \tabularnewline
16 & 0.363164704150604 & 0.726329408301207 & 0.636835295849396 \tabularnewline
17 & 0.297951014484172 & 0.595902028968343 & 0.702048985515828 \tabularnewline
18 & 0.456809030584245 & 0.91361806116849 & 0.543190969415755 \tabularnewline
19 & 0.377310347882961 & 0.754620695765921 & 0.622689652117039 \tabularnewline
20 & 0.297920664739789 & 0.595841329479577 & 0.702079335260211 \tabularnewline
21 & 0.237594490869043 & 0.475188981738086 & 0.762405509130957 \tabularnewline
22 & 0.233967051386961 & 0.467934102773923 & 0.766032948613039 \tabularnewline
23 & 0.354852034564925 & 0.709704069129851 & 0.645147965435075 \tabularnewline
24 & 0.429182302319662 & 0.858364604639324 & 0.570817697680338 \tabularnewline
25 & 0.378268019328386 & 0.756536038656773 & 0.621731980671614 \tabularnewline
26 & 0.315069042368879 & 0.630138084737759 & 0.684930957631121 \tabularnewline
27 & 0.289821921265202 & 0.579643842530404 & 0.710178078734798 \tabularnewline
28 & 0.391672624573151 & 0.783345249146302 & 0.608327375426849 \tabularnewline
29 & 0.337423096870226 & 0.674846193740451 & 0.662576903129774 \tabularnewline
30 & 0.44704705677822 & 0.894094113556441 & 0.55295294322178 \tabularnewline
31 & 0.398302293175182 & 0.796604586350364 & 0.601697706824818 \tabularnewline
32 & 0.36329443917146 & 0.726588878342919 & 0.63670556082854 \tabularnewline
33 & 0.353246509439862 & 0.706493018879724 & 0.646753490560138 \tabularnewline
34 & 0.32369414435877 & 0.647388288717539 & 0.67630585564123 \tabularnewline
35 & 0.279400099924874 & 0.558800199849748 & 0.720599900075126 \tabularnewline
36 & 0.840603846725059 & 0.318792306549881 & 0.159396153274941 \tabularnewline
37 & 0.8115944642317 & 0.376811071536599 & 0.1884055357683 \tabularnewline
38 & 0.792946621367504 & 0.414106757264991 & 0.207053378632496 \tabularnewline
39 & 0.826597668957584 & 0.346804662084831 & 0.173402331042416 \tabularnewline
40 & 0.813633788071316 & 0.372732423857367 & 0.186366211928684 \tabularnewline
41 & 0.784455376084966 & 0.431089247830067 & 0.215544623915034 \tabularnewline
42 & 0.756301061641363 & 0.487397876717275 & 0.243698938358637 \tabularnewline
43 & 0.787034622749923 & 0.425930754500155 & 0.212965377250077 \tabularnewline
44 & 0.745367477233058 & 0.509265045533885 & 0.254632522766942 \tabularnewline
45 & 0.715436686994745 & 0.56912662601051 & 0.284563313005255 \tabularnewline
46 & 0.863127661062523 & 0.273744677874954 & 0.136872338937477 \tabularnewline
47 & 0.888900107578081 & 0.222199784843839 & 0.111099892421919 \tabularnewline
48 & 0.865825434293783 & 0.268349131412434 & 0.134174565706217 \tabularnewline
49 & 0.862676614997936 & 0.274646770004127 & 0.137323385002064 \tabularnewline
50 & 0.85592157293739 & 0.28815685412522 & 0.14407842706261 \tabularnewline
51 & 0.827641258593675 & 0.344717482812651 & 0.172358741406325 \tabularnewline
52 & 0.796750896915825 & 0.406498206168351 & 0.203249103084175 \tabularnewline
53 & 0.809365232047767 & 0.381269535904467 & 0.190634767952233 \tabularnewline
54 & 0.779247452358311 & 0.441505095283378 & 0.220752547641689 \tabularnewline
55 & 0.798881468376038 & 0.402237063247924 & 0.201118531623962 \tabularnewline
56 & 0.778100538859461 & 0.443798922281079 & 0.221899461140539 \tabularnewline
57 & 0.740526490936675 & 0.518947018126651 & 0.259473509063325 \tabularnewline
58 & 0.725954731424508 & 0.548090537150985 & 0.274045268575492 \tabularnewline
59 & 0.695365563722863 & 0.609268872554273 & 0.304634436277137 \tabularnewline
60 & 0.701731030009834 & 0.596537939980333 & 0.298268969990166 \tabularnewline
61 & 0.66724673021806 & 0.66550653956388 & 0.33275326978194 \tabularnewline
62 & 0.631280126653251 & 0.737439746693498 & 0.368719873346749 \tabularnewline
63 & 0.598564543809911 & 0.802870912380177 & 0.401435456190089 \tabularnewline
64 & 0.554814372398143 & 0.890371255203715 & 0.445185627601857 \tabularnewline
65 & 0.517974995910329 & 0.964050008179343 & 0.482025004089671 \tabularnewline
66 & 0.491546620726843 & 0.983093241453685 & 0.508453379273157 \tabularnewline
67 & 0.486523883848958 & 0.973047767697915 & 0.513476116151042 \tabularnewline
68 & 0.62930540504787 & 0.741389189904261 & 0.37069459495213 \tabularnewline
69 & 0.740155397049238 & 0.519689205901523 & 0.259844602950762 \tabularnewline
70 & 0.707100790906259 & 0.585798418187483 & 0.292899209093741 \tabularnewline
71 & 0.813066598778137 & 0.373866802443727 & 0.186933401221863 \tabularnewline
72 & 0.782741383023855 & 0.43451723395229 & 0.217258616976145 \tabularnewline
73 & 0.776734769578176 & 0.446530460843648 & 0.223265230421824 \tabularnewline
74 & 0.760334777196214 & 0.479330445607573 & 0.239665222803786 \tabularnewline
75 & 0.722724555675945 & 0.55455088864811 & 0.277275444324055 \tabularnewline
76 & 0.762921629325035 & 0.47415674134993 & 0.237078370674965 \tabularnewline
77 & 0.7257338796602 & 0.5485322406796 & 0.2742661203398 \tabularnewline
78 & 0.70620756560086 & 0.58758486879828 & 0.29379243439914 \tabularnewline
79 & 0.713969633071054 & 0.572060733857892 & 0.286030366928946 \tabularnewline
80 & 0.673013081092326 & 0.653973837815348 & 0.326986918907674 \tabularnewline
81 & 0.636022821184465 & 0.72795435763107 & 0.363977178815535 \tabularnewline
82 & 0.761611627109306 & 0.476776745781389 & 0.238388372890694 \tabularnewline
83 & 0.726726267418669 & 0.546547465162662 & 0.273273732581331 \tabularnewline
84 & 0.697638624893719 & 0.604722750212562 & 0.302361375106281 \tabularnewline
85 & 0.65672616474594 & 0.68654767050812 & 0.34327383525406 \tabularnewline
86 & 0.643772252439145 & 0.712455495121711 & 0.356227747560855 \tabularnewline
87 & 0.599796813268051 & 0.800406373463898 & 0.400203186731949 \tabularnewline
88 & 0.557876689659284 & 0.884246620681432 & 0.442123310340716 \tabularnewline
89 & 0.53543480797549 & 0.929130384049021 & 0.46456519202451 \tabularnewline
90 & 0.496991735614744 & 0.993983471229487 & 0.503008264385256 \tabularnewline
91 & 0.484119176421892 & 0.968238352843783 & 0.515880823578108 \tabularnewline
92 & 0.442093273622182 & 0.884186547244363 & 0.557906726377818 \tabularnewline
93 & 0.399123035013027 & 0.798246070026054 & 0.600876964986973 \tabularnewline
94 & 0.355523037061237 & 0.711046074122474 & 0.644476962938763 \tabularnewline
95 & 0.381903089227818 & 0.763806178455635 & 0.618096910772182 \tabularnewline
96 & 0.344367417318481 & 0.688734834636963 & 0.655632582681519 \tabularnewline
97 & 0.302952128085809 & 0.605904256171619 & 0.697047871914191 \tabularnewline
98 & 0.295472419632481 & 0.590944839264962 & 0.704527580367519 \tabularnewline
99 & 0.255027160657097 & 0.510054321314194 & 0.744972839342903 \tabularnewline
100 & 0.22227706880256 & 0.444554137605119 & 0.77772293119744 \tabularnewline
101 & 0.200418276674316 & 0.400836553348633 & 0.799581723325684 \tabularnewline
102 & 0.182039922981499 & 0.364079845962997 & 0.817960077018501 \tabularnewline
103 & 0.222638196099314 & 0.445276392198629 & 0.777361803900686 \tabularnewline
104 & 0.188728110702807 & 0.377456221405613 & 0.811271889297193 \tabularnewline
105 & 0.18150386445045 & 0.3630077289009 & 0.81849613554955 \tabularnewline
106 & 0.192392900752185 & 0.38478580150437 & 0.807607099247815 \tabularnewline
107 & 0.172687886876967 & 0.345375773753934 & 0.827312113123033 \tabularnewline
108 & 0.152629644867557 & 0.305259289735114 & 0.847370355132443 \tabularnewline
109 & 0.14862283626308 & 0.29724567252616 & 0.85137716373692 \tabularnewline
110 & 0.145622683878667 & 0.291245367757335 & 0.854377316121333 \tabularnewline
111 & 0.132014601372501 & 0.264029202745001 & 0.867985398627499 \tabularnewline
112 & 0.112774094116348 & 0.225548188232695 & 0.887225905883652 \tabularnewline
113 & 0.151780163457373 & 0.303560326914745 & 0.848219836542627 \tabularnewline
114 & 0.140554466223373 & 0.281108932446745 & 0.859445533776627 \tabularnewline
115 & 0.158209883111011 & 0.316419766222022 & 0.841790116888989 \tabularnewline
116 & 0.166403350330419 & 0.332806700660838 & 0.833596649669581 \tabularnewline
117 & 0.141804482752209 & 0.283608965504418 & 0.858195517247791 \tabularnewline
118 & 0.143443678550305 & 0.28688735710061 & 0.856556321449695 \tabularnewline
119 & 0.130836707021487 & 0.261673414042974 & 0.869163292978513 \tabularnewline
120 & 0.146590802412569 & 0.293181604825139 & 0.853409197587431 \tabularnewline
121 & 0.120133598404391 & 0.240267196808781 & 0.879866401595609 \tabularnewline
122 & 0.105889225280341 & 0.211778450560683 & 0.894110774719659 \tabularnewline
123 & 0.102234172247493 & 0.204468344494986 & 0.897765827752507 \tabularnewline
124 & 0.0799587114293591 & 0.159917422858718 & 0.920041288570641 \tabularnewline
125 & 0.0615919680354934 & 0.123183936070987 & 0.938408031964507 \tabularnewline
126 & 0.0465775609072569 & 0.0931551218145139 & 0.953422439092743 \tabularnewline
127 & 0.0340613106837866 & 0.0681226213675732 & 0.965938689316213 \tabularnewline
128 & 0.0323900119209994 & 0.0647800238419988 & 0.967609988079001 \tabularnewline
129 & 0.0355506808438175 & 0.0711013616876349 & 0.964449319156182 \tabularnewline
130 & 0.0352770971834642 & 0.0705541943669285 & 0.964722902816536 \tabularnewline
131 & 0.038392618652726 & 0.076785237305452 & 0.961607381347274 \tabularnewline
132 & 0.0408287809936685 & 0.0816575619873371 & 0.959171219006332 \tabularnewline
133 & 0.083013032545449 & 0.166026065090898 & 0.916986967454551 \tabularnewline
134 & 0.0863432224219764 & 0.172686444843953 & 0.913656777578024 \tabularnewline
135 & 0.0666756578135303 & 0.133351315627061 & 0.93332434218647 \tabularnewline
136 & 0.0545674835018216 & 0.109134967003643 & 0.945432516498178 \tabularnewline
137 & 0.0395857724096075 & 0.079171544819215 & 0.960414227590393 \tabularnewline
138 & 0.0472641347375771 & 0.0945282694751542 & 0.952735865262423 \tabularnewline
139 & 0.039785634071228 & 0.079571268142456 & 0.960214365928772 \tabularnewline
140 & 0.0266296410201981 & 0.0532592820403961 & 0.973370358979802 \tabularnewline
141 & 0.542123183935686 & 0.915753632128628 & 0.457876816064314 \tabularnewline
142 & 0.481903785890801 & 0.963807571781603 & 0.518096214109198 \tabularnewline
143 & 0.408956422856546 & 0.817912845713093 & 0.591043577143453 \tabularnewline
144 & 0.319886033732943 & 0.639772067465887 & 0.680113966267057 \tabularnewline
145 & 0.239815114519134 & 0.479630229038268 & 0.760184885480866 \tabularnewline
146 & 0.284815741140648 & 0.569631482281297 & 0.715184258859352 \tabularnewline
147 & 0.315995429095842 & 0.631990858191684 & 0.684004570904158 \tabularnewline
148 & 0.60346269842878 & 0.793074603142439 & 0.39653730157122 \tabularnewline
149 & 0.610794698767974 & 0.778410602464051 & 0.389205301232026 \tabularnewline
150 & 0.608962742137527 & 0.782074515724945 & 0.391037257862473 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185640&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.372526891979238[/C][C]0.745053783958477[/C][C]0.627473108020762[/C][/ROW]
[ROW][C]13[/C][C]0.594553343751769[/C][C]0.810893312496461[/C][C]0.405446656248231[/C][/ROW]
[ROW][C]14[/C][C]0.58579025176255[/C][C]0.828419496474899[/C][C]0.41420974823745[/C][/ROW]
[ROW][C]15[/C][C]0.465620124773846[/C][C]0.931240249547693[/C][C]0.534379875226154[/C][/ROW]
[ROW][C]16[/C][C]0.363164704150604[/C][C]0.726329408301207[/C][C]0.636835295849396[/C][/ROW]
[ROW][C]17[/C][C]0.297951014484172[/C][C]0.595902028968343[/C][C]0.702048985515828[/C][/ROW]
[ROW][C]18[/C][C]0.456809030584245[/C][C]0.91361806116849[/C][C]0.543190969415755[/C][/ROW]
[ROW][C]19[/C][C]0.377310347882961[/C][C]0.754620695765921[/C][C]0.622689652117039[/C][/ROW]
[ROW][C]20[/C][C]0.297920664739789[/C][C]0.595841329479577[/C][C]0.702079335260211[/C][/ROW]
[ROW][C]21[/C][C]0.237594490869043[/C][C]0.475188981738086[/C][C]0.762405509130957[/C][/ROW]
[ROW][C]22[/C][C]0.233967051386961[/C][C]0.467934102773923[/C][C]0.766032948613039[/C][/ROW]
[ROW][C]23[/C][C]0.354852034564925[/C][C]0.709704069129851[/C][C]0.645147965435075[/C][/ROW]
[ROW][C]24[/C][C]0.429182302319662[/C][C]0.858364604639324[/C][C]0.570817697680338[/C][/ROW]
[ROW][C]25[/C][C]0.378268019328386[/C][C]0.756536038656773[/C][C]0.621731980671614[/C][/ROW]
[ROW][C]26[/C][C]0.315069042368879[/C][C]0.630138084737759[/C][C]0.684930957631121[/C][/ROW]
[ROW][C]27[/C][C]0.289821921265202[/C][C]0.579643842530404[/C][C]0.710178078734798[/C][/ROW]
[ROW][C]28[/C][C]0.391672624573151[/C][C]0.783345249146302[/C][C]0.608327375426849[/C][/ROW]
[ROW][C]29[/C][C]0.337423096870226[/C][C]0.674846193740451[/C][C]0.662576903129774[/C][/ROW]
[ROW][C]30[/C][C]0.44704705677822[/C][C]0.894094113556441[/C][C]0.55295294322178[/C][/ROW]
[ROW][C]31[/C][C]0.398302293175182[/C][C]0.796604586350364[/C][C]0.601697706824818[/C][/ROW]
[ROW][C]32[/C][C]0.36329443917146[/C][C]0.726588878342919[/C][C]0.63670556082854[/C][/ROW]
[ROW][C]33[/C][C]0.353246509439862[/C][C]0.706493018879724[/C][C]0.646753490560138[/C][/ROW]
[ROW][C]34[/C][C]0.32369414435877[/C][C]0.647388288717539[/C][C]0.67630585564123[/C][/ROW]
[ROW][C]35[/C][C]0.279400099924874[/C][C]0.558800199849748[/C][C]0.720599900075126[/C][/ROW]
[ROW][C]36[/C][C]0.840603846725059[/C][C]0.318792306549881[/C][C]0.159396153274941[/C][/ROW]
[ROW][C]37[/C][C]0.8115944642317[/C][C]0.376811071536599[/C][C]0.1884055357683[/C][/ROW]
[ROW][C]38[/C][C]0.792946621367504[/C][C]0.414106757264991[/C][C]0.207053378632496[/C][/ROW]
[ROW][C]39[/C][C]0.826597668957584[/C][C]0.346804662084831[/C][C]0.173402331042416[/C][/ROW]
[ROW][C]40[/C][C]0.813633788071316[/C][C]0.372732423857367[/C][C]0.186366211928684[/C][/ROW]
[ROW][C]41[/C][C]0.784455376084966[/C][C]0.431089247830067[/C][C]0.215544623915034[/C][/ROW]
[ROW][C]42[/C][C]0.756301061641363[/C][C]0.487397876717275[/C][C]0.243698938358637[/C][/ROW]
[ROW][C]43[/C][C]0.787034622749923[/C][C]0.425930754500155[/C][C]0.212965377250077[/C][/ROW]
[ROW][C]44[/C][C]0.745367477233058[/C][C]0.509265045533885[/C][C]0.254632522766942[/C][/ROW]
[ROW][C]45[/C][C]0.715436686994745[/C][C]0.56912662601051[/C][C]0.284563313005255[/C][/ROW]
[ROW][C]46[/C][C]0.863127661062523[/C][C]0.273744677874954[/C][C]0.136872338937477[/C][/ROW]
[ROW][C]47[/C][C]0.888900107578081[/C][C]0.222199784843839[/C][C]0.111099892421919[/C][/ROW]
[ROW][C]48[/C][C]0.865825434293783[/C][C]0.268349131412434[/C][C]0.134174565706217[/C][/ROW]
[ROW][C]49[/C][C]0.862676614997936[/C][C]0.274646770004127[/C][C]0.137323385002064[/C][/ROW]
[ROW][C]50[/C][C]0.85592157293739[/C][C]0.28815685412522[/C][C]0.14407842706261[/C][/ROW]
[ROW][C]51[/C][C]0.827641258593675[/C][C]0.344717482812651[/C][C]0.172358741406325[/C][/ROW]
[ROW][C]52[/C][C]0.796750896915825[/C][C]0.406498206168351[/C][C]0.203249103084175[/C][/ROW]
[ROW][C]53[/C][C]0.809365232047767[/C][C]0.381269535904467[/C][C]0.190634767952233[/C][/ROW]
[ROW][C]54[/C][C]0.779247452358311[/C][C]0.441505095283378[/C][C]0.220752547641689[/C][/ROW]
[ROW][C]55[/C][C]0.798881468376038[/C][C]0.402237063247924[/C][C]0.201118531623962[/C][/ROW]
[ROW][C]56[/C][C]0.778100538859461[/C][C]0.443798922281079[/C][C]0.221899461140539[/C][/ROW]
[ROW][C]57[/C][C]0.740526490936675[/C][C]0.518947018126651[/C][C]0.259473509063325[/C][/ROW]
[ROW][C]58[/C][C]0.725954731424508[/C][C]0.548090537150985[/C][C]0.274045268575492[/C][/ROW]
[ROW][C]59[/C][C]0.695365563722863[/C][C]0.609268872554273[/C][C]0.304634436277137[/C][/ROW]
[ROW][C]60[/C][C]0.701731030009834[/C][C]0.596537939980333[/C][C]0.298268969990166[/C][/ROW]
[ROW][C]61[/C][C]0.66724673021806[/C][C]0.66550653956388[/C][C]0.33275326978194[/C][/ROW]
[ROW][C]62[/C][C]0.631280126653251[/C][C]0.737439746693498[/C][C]0.368719873346749[/C][/ROW]
[ROW][C]63[/C][C]0.598564543809911[/C][C]0.802870912380177[/C][C]0.401435456190089[/C][/ROW]
[ROW][C]64[/C][C]0.554814372398143[/C][C]0.890371255203715[/C][C]0.445185627601857[/C][/ROW]
[ROW][C]65[/C][C]0.517974995910329[/C][C]0.964050008179343[/C][C]0.482025004089671[/C][/ROW]
[ROW][C]66[/C][C]0.491546620726843[/C][C]0.983093241453685[/C][C]0.508453379273157[/C][/ROW]
[ROW][C]67[/C][C]0.486523883848958[/C][C]0.973047767697915[/C][C]0.513476116151042[/C][/ROW]
[ROW][C]68[/C][C]0.62930540504787[/C][C]0.741389189904261[/C][C]0.37069459495213[/C][/ROW]
[ROW][C]69[/C][C]0.740155397049238[/C][C]0.519689205901523[/C][C]0.259844602950762[/C][/ROW]
[ROW][C]70[/C][C]0.707100790906259[/C][C]0.585798418187483[/C][C]0.292899209093741[/C][/ROW]
[ROW][C]71[/C][C]0.813066598778137[/C][C]0.373866802443727[/C][C]0.186933401221863[/C][/ROW]
[ROW][C]72[/C][C]0.782741383023855[/C][C]0.43451723395229[/C][C]0.217258616976145[/C][/ROW]
[ROW][C]73[/C][C]0.776734769578176[/C][C]0.446530460843648[/C][C]0.223265230421824[/C][/ROW]
[ROW][C]74[/C][C]0.760334777196214[/C][C]0.479330445607573[/C][C]0.239665222803786[/C][/ROW]
[ROW][C]75[/C][C]0.722724555675945[/C][C]0.55455088864811[/C][C]0.277275444324055[/C][/ROW]
[ROW][C]76[/C][C]0.762921629325035[/C][C]0.47415674134993[/C][C]0.237078370674965[/C][/ROW]
[ROW][C]77[/C][C]0.7257338796602[/C][C]0.5485322406796[/C][C]0.2742661203398[/C][/ROW]
[ROW][C]78[/C][C]0.70620756560086[/C][C]0.58758486879828[/C][C]0.29379243439914[/C][/ROW]
[ROW][C]79[/C][C]0.713969633071054[/C][C]0.572060733857892[/C][C]0.286030366928946[/C][/ROW]
[ROW][C]80[/C][C]0.673013081092326[/C][C]0.653973837815348[/C][C]0.326986918907674[/C][/ROW]
[ROW][C]81[/C][C]0.636022821184465[/C][C]0.72795435763107[/C][C]0.363977178815535[/C][/ROW]
[ROW][C]82[/C][C]0.761611627109306[/C][C]0.476776745781389[/C][C]0.238388372890694[/C][/ROW]
[ROW][C]83[/C][C]0.726726267418669[/C][C]0.546547465162662[/C][C]0.273273732581331[/C][/ROW]
[ROW][C]84[/C][C]0.697638624893719[/C][C]0.604722750212562[/C][C]0.302361375106281[/C][/ROW]
[ROW][C]85[/C][C]0.65672616474594[/C][C]0.68654767050812[/C][C]0.34327383525406[/C][/ROW]
[ROW][C]86[/C][C]0.643772252439145[/C][C]0.712455495121711[/C][C]0.356227747560855[/C][/ROW]
[ROW][C]87[/C][C]0.599796813268051[/C][C]0.800406373463898[/C][C]0.400203186731949[/C][/ROW]
[ROW][C]88[/C][C]0.557876689659284[/C][C]0.884246620681432[/C][C]0.442123310340716[/C][/ROW]
[ROW][C]89[/C][C]0.53543480797549[/C][C]0.929130384049021[/C][C]0.46456519202451[/C][/ROW]
[ROW][C]90[/C][C]0.496991735614744[/C][C]0.993983471229487[/C][C]0.503008264385256[/C][/ROW]
[ROW][C]91[/C][C]0.484119176421892[/C][C]0.968238352843783[/C][C]0.515880823578108[/C][/ROW]
[ROW][C]92[/C][C]0.442093273622182[/C][C]0.884186547244363[/C][C]0.557906726377818[/C][/ROW]
[ROW][C]93[/C][C]0.399123035013027[/C][C]0.798246070026054[/C][C]0.600876964986973[/C][/ROW]
[ROW][C]94[/C][C]0.355523037061237[/C][C]0.711046074122474[/C][C]0.644476962938763[/C][/ROW]
[ROW][C]95[/C][C]0.381903089227818[/C][C]0.763806178455635[/C][C]0.618096910772182[/C][/ROW]
[ROW][C]96[/C][C]0.344367417318481[/C][C]0.688734834636963[/C][C]0.655632582681519[/C][/ROW]
[ROW][C]97[/C][C]0.302952128085809[/C][C]0.605904256171619[/C][C]0.697047871914191[/C][/ROW]
[ROW][C]98[/C][C]0.295472419632481[/C][C]0.590944839264962[/C][C]0.704527580367519[/C][/ROW]
[ROW][C]99[/C][C]0.255027160657097[/C][C]0.510054321314194[/C][C]0.744972839342903[/C][/ROW]
[ROW][C]100[/C][C]0.22227706880256[/C][C]0.444554137605119[/C][C]0.77772293119744[/C][/ROW]
[ROW][C]101[/C][C]0.200418276674316[/C][C]0.400836553348633[/C][C]0.799581723325684[/C][/ROW]
[ROW][C]102[/C][C]0.182039922981499[/C][C]0.364079845962997[/C][C]0.817960077018501[/C][/ROW]
[ROW][C]103[/C][C]0.222638196099314[/C][C]0.445276392198629[/C][C]0.777361803900686[/C][/ROW]
[ROW][C]104[/C][C]0.188728110702807[/C][C]0.377456221405613[/C][C]0.811271889297193[/C][/ROW]
[ROW][C]105[/C][C]0.18150386445045[/C][C]0.3630077289009[/C][C]0.81849613554955[/C][/ROW]
[ROW][C]106[/C][C]0.192392900752185[/C][C]0.38478580150437[/C][C]0.807607099247815[/C][/ROW]
[ROW][C]107[/C][C]0.172687886876967[/C][C]0.345375773753934[/C][C]0.827312113123033[/C][/ROW]
[ROW][C]108[/C][C]0.152629644867557[/C][C]0.305259289735114[/C][C]0.847370355132443[/C][/ROW]
[ROW][C]109[/C][C]0.14862283626308[/C][C]0.29724567252616[/C][C]0.85137716373692[/C][/ROW]
[ROW][C]110[/C][C]0.145622683878667[/C][C]0.291245367757335[/C][C]0.854377316121333[/C][/ROW]
[ROW][C]111[/C][C]0.132014601372501[/C][C]0.264029202745001[/C][C]0.867985398627499[/C][/ROW]
[ROW][C]112[/C][C]0.112774094116348[/C][C]0.225548188232695[/C][C]0.887225905883652[/C][/ROW]
[ROW][C]113[/C][C]0.151780163457373[/C][C]0.303560326914745[/C][C]0.848219836542627[/C][/ROW]
[ROW][C]114[/C][C]0.140554466223373[/C][C]0.281108932446745[/C][C]0.859445533776627[/C][/ROW]
[ROW][C]115[/C][C]0.158209883111011[/C][C]0.316419766222022[/C][C]0.841790116888989[/C][/ROW]
[ROW][C]116[/C][C]0.166403350330419[/C][C]0.332806700660838[/C][C]0.833596649669581[/C][/ROW]
[ROW][C]117[/C][C]0.141804482752209[/C][C]0.283608965504418[/C][C]0.858195517247791[/C][/ROW]
[ROW][C]118[/C][C]0.143443678550305[/C][C]0.28688735710061[/C][C]0.856556321449695[/C][/ROW]
[ROW][C]119[/C][C]0.130836707021487[/C][C]0.261673414042974[/C][C]0.869163292978513[/C][/ROW]
[ROW][C]120[/C][C]0.146590802412569[/C][C]0.293181604825139[/C][C]0.853409197587431[/C][/ROW]
[ROW][C]121[/C][C]0.120133598404391[/C][C]0.240267196808781[/C][C]0.879866401595609[/C][/ROW]
[ROW][C]122[/C][C]0.105889225280341[/C][C]0.211778450560683[/C][C]0.894110774719659[/C][/ROW]
[ROW][C]123[/C][C]0.102234172247493[/C][C]0.204468344494986[/C][C]0.897765827752507[/C][/ROW]
[ROW][C]124[/C][C]0.0799587114293591[/C][C]0.159917422858718[/C][C]0.920041288570641[/C][/ROW]
[ROW][C]125[/C][C]0.0615919680354934[/C][C]0.123183936070987[/C][C]0.938408031964507[/C][/ROW]
[ROW][C]126[/C][C]0.0465775609072569[/C][C]0.0931551218145139[/C][C]0.953422439092743[/C][/ROW]
[ROW][C]127[/C][C]0.0340613106837866[/C][C]0.0681226213675732[/C][C]0.965938689316213[/C][/ROW]
[ROW][C]128[/C][C]0.0323900119209994[/C][C]0.0647800238419988[/C][C]0.967609988079001[/C][/ROW]
[ROW][C]129[/C][C]0.0355506808438175[/C][C]0.0711013616876349[/C][C]0.964449319156182[/C][/ROW]
[ROW][C]130[/C][C]0.0352770971834642[/C][C]0.0705541943669285[/C][C]0.964722902816536[/C][/ROW]
[ROW][C]131[/C][C]0.038392618652726[/C][C]0.076785237305452[/C][C]0.961607381347274[/C][/ROW]
[ROW][C]132[/C][C]0.0408287809936685[/C][C]0.0816575619873371[/C][C]0.959171219006332[/C][/ROW]
[ROW][C]133[/C][C]0.083013032545449[/C][C]0.166026065090898[/C][C]0.916986967454551[/C][/ROW]
[ROW][C]134[/C][C]0.0863432224219764[/C][C]0.172686444843953[/C][C]0.913656777578024[/C][/ROW]
[ROW][C]135[/C][C]0.0666756578135303[/C][C]0.133351315627061[/C][C]0.93332434218647[/C][/ROW]
[ROW][C]136[/C][C]0.0545674835018216[/C][C]0.109134967003643[/C][C]0.945432516498178[/C][/ROW]
[ROW][C]137[/C][C]0.0395857724096075[/C][C]0.079171544819215[/C][C]0.960414227590393[/C][/ROW]
[ROW][C]138[/C][C]0.0472641347375771[/C][C]0.0945282694751542[/C][C]0.952735865262423[/C][/ROW]
[ROW][C]139[/C][C]0.039785634071228[/C][C]0.079571268142456[/C][C]0.960214365928772[/C][/ROW]
[ROW][C]140[/C][C]0.0266296410201981[/C][C]0.0532592820403961[/C][C]0.973370358979802[/C][/ROW]
[ROW][C]141[/C][C]0.542123183935686[/C][C]0.915753632128628[/C][C]0.457876816064314[/C][/ROW]
[ROW][C]142[/C][C]0.481903785890801[/C][C]0.963807571781603[/C][C]0.518096214109198[/C][/ROW]
[ROW][C]143[/C][C]0.408956422856546[/C][C]0.817912845713093[/C][C]0.591043577143453[/C][/ROW]
[ROW][C]144[/C][C]0.319886033732943[/C][C]0.639772067465887[/C][C]0.680113966267057[/C][/ROW]
[ROW][C]145[/C][C]0.239815114519134[/C][C]0.479630229038268[/C][C]0.760184885480866[/C][/ROW]
[ROW][C]146[/C][C]0.284815741140648[/C][C]0.569631482281297[/C][C]0.715184258859352[/C][/ROW]
[ROW][C]147[/C][C]0.315995429095842[/C][C]0.631990858191684[/C][C]0.684004570904158[/C][/ROW]
[ROW][C]148[/C][C]0.60346269842878[/C][C]0.793074603142439[/C][C]0.39653730157122[/C][/ROW]
[ROW][C]149[/C][C]0.610794698767974[/C][C]0.778410602464051[/C][C]0.389205301232026[/C][/ROW]
[ROW][C]150[/C][C]0.608962742137527[/C][C]0.782074515724945[/C][C]0.391037257862473[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185640&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185640&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3725268919792380.7450537839584770.627473108020762
130.5945533437517690.8108933124964610.405446656248231
140.585790251762550.8284194964748990.41420974823745
150.4656201247738460.9312402495476930.534379875226154
160.3631647041506040.7263294083012070.636835295849396
170.2979510144841720.5959020289683430.702048985515828
180.4568090305842450.913618061168490.543190969415755
190.3773103478829610.7546206957659210.622689652117039
200.2979206647397890.5958413294795770.702079335260211
210.2375944908690430.4751889817380860.762405509130957
220.2339670513869610.4679341027739230.766032948613039
230.3548520345649250.7097040691298510.645147965435075
240.4291823023196620.8583646046393240.570817697680338
250.3782680193283860.7565360386567730.621731980671614
260.3150690423688790.6301380847377590.684930957631121
270.2898219212652020.5796438425304040.710178078734798
280.3916726245731510.7833452491463020.608327375426849
290.3374230968702260.6748461937404510.662576903129774
300.447047056778220.8940941135564410.55295294322178
310.3983022931751820.7966045863503640.601697706824818
320.363294439171460.7265888783429190.63670556082854
330.3532465094398620.7064930188797240.646753490560138
340.323694144358770.6473882887175390.67630585564123
350.2794000999248740.5588001998497480.720599900075126
360.8406038467250590.3187923065498810.159396153274941
370.81159446423170.3768110715365990.1884055357683
380.7929466213675040.4141067572649910.207053378632496
390.8265976689575840.3468046620848310.173402331042416
400.8136337880713160.3727324238573670.186366211928684
410.7844553760849660.4310892478300670.215544623915034
420.7563010616413630.4873978767172750.243698938358637
430.7870346227499230.4259307545001550.212965377250077
440.7453674772330580.5092650455338850.254632522766942
450.7154366869947450.569126626010510.284563313005255
460.8631276610625230.2737446778749540.136872338937477
470.8889001075780810.2221997848438390.111099892421919
480.8658254342937830.2683491314124340.134174565706217
490.8626766149979360.2746467700041270.137323385002064
500.855921572937390.288156854125220.14407842706261
510.8276412585936750.3447174828126510.172358741406325
520.7967508969158250.4064982061683510.203249103084175
530.8093652320477670.3812695359044670.190634767952233
540.7792474523583110.4415050952833780.220752547641689
550.7988814683760380.4022370632479240.201118531623962
560.7781005388594610.4437989222810790.221899461140539
570.7405264909366750.5189470181266510.259473509063325
580.7259547314245080.5480905371509850.274045268575492
590.6953655637228630.6092688725542730.304634436277137
600.7017310300098340.5965379399803330.298268969990166
610.667246730218060.665506539563880.33275326978194
620.6312801266532510.7374397466934980.368719873346749
630.5985645438099110.8028709123801770.401435456190089
640.5548143723981430.8903712552037150.445185627601857
650.5179749959103290.9640500081793430.482025004089671
660.4915466207268430.9830932414536850.508453379273157
670.4865238838489580.9730477676979150.513476116151042
680.629305405047870.7413891899042610.37069459495213
690.7401553970492380.5196892059015230.259844602950762
700.7071007909062590.5857984181874830.292899209093741
710.8130665987781370.3738668024437270.186933401221863
720.7827413830238550.434517233952290.217258616976145
730.7767347695781760.4465304608436480.223265230421824
740.7603347771962140.4793304456075730.239665222803786
750.7227245556759450.554550888648110.277275444324055
760.7629216293250350.474156741349930.237078370674965
770.72573387966020.54853224067960.2742661203398
780.706207565600860.587584868798280.29379243439914
790.7139696330710540.5720607338578920.286030366928946
800.6730130810923260.6539738378153480.326986918907674
810.6360228211844650.727954357631070.363977178815535
820.7616116271093060.4767767457813890.238388372890694
830.7267262674186690.5465474651626620.273273732581331
840.6976386248937190.6047227502125620.302361375106281
850.656726164745940.686547670508120.34327383525406
860.6437722524391450.7124554951217110.356227747560855
870.5997968132680510.8004063734638980.400203186731949
880.5578766896592840.8842466206814320.442123310340716
890.535434807975490.9291303840490210.46456519202451
900.4969917356147440.9939834712294870.503008264385256
910.4841191764218920.9682383528437830.515880823578108
920.4420932736221820.8841865472443630.557906726377818
930.3991230350130270.7982460700260540.600876964986973
940.3555230370612370.7110460741224740.644476962938763
950.3819030892278180.7638061784556350.618096910772182
960.3443674173184810.6887348346369630.655632582681519
970.3029521280858090.6059042561716190.697047871914191
980.2954724196324810.5909448392649620.704527580367519
990.2550271606570970.5100543213141940.744972839342903
1000.222277068802560.4445541376051190.77772293119744
1010.2004182766743160.4008365533486330.799581723325684
1020.1820399229814990.3640798459629970.817960077018501
1030.2226381960993140.4452763921986290.777361803900686
1040.1887281107028070.3774562214056130.811271889297193
1050.181503864450450.36300772890090.81849613554955
1060.1923929007521850.384785801504370.807607099247815
1070.1726878868769670.3453757737539340.827312113123033
1080.1526296448675570.3052592897351140.847370355132443
1090.148622836263080.297245672526160.85137716373692
1100.1456226838786670.2912453677573350.854377316121333
1110.1320146013725010.2640292027450010.867985398627499
1120.1127740941163480.2255481882326950.887225905883652
1130.1517801634573730.3035603269147450.848219836542627
1140.1405544662233730.2811089324467450.859445533776627
1150.1582098831110110.3164197662220220.841790116888989
1160.1664033503304190.3328067006608380.833596649669581
1170.1418044827522090.2836089655044180.858195517247791
1180.1434436785503050.286887357100610.856556321449695
1190.1308367070214870.2616734140429740.869163292978513
1200.1465908024125690.2931816048251390.853409197587431
1210.1201335984043910.2402671968087810.879866401595609
1220.1058892252803410.2117784505606830.894110774719659
1230.1022341722474930.2044683444949860.897765827752507
1240.07995871142935910.1599174228587180.920041288570641
1250.06159196803549340.1231839360709870.938408031964507
1260.04657756090725690.09315512181451390.953422439092743
1270.03406131068378660.06812262136757320.965938689316213
1280.03239001192099940.06478002384199880.967609988079001
1290.03555068084381750.07110136168763490.964449319156182
1300.03527709718346420.07055419436692850.964722902816536
1310.0383926186527260.0767852373054520.961607381347274
1320.04082878099366850.08165756198733710.959171219006332
1330.0830130325454490.1660260650908980.916986967454551
1340.08634322242197640.1726864448439530.913656777578024
1350.06667565781353030.1333513156270610.93332434218647
1360.05456748350182160.1091349670036430.945432516498178
1370.03958577240960750.0791715448192150.960414227590393
1380.04726413473757710.09452826947515420.952735865262423
1390.0397856340712280.0795712681424560.960214365928772
1400.02662964102019810.05325928204039610.973370358979802
1410.5421231839356860.9157536321286280.457876816064314
1420.4819037858908010.9638075717816030.518096214109198
1430.4089564228565460.8179128457130930.591043577143453
1440.3198860337329430.6397720674658870.680113966267057
1450.2398151145191340.4796302290382680.760184885480866
1460.2848157411406480.5696314822812970.715184258859352
1470.3159954290958420.6319908581916840.684004570904158
1480.603462698428780.7930746031424390.39653730157122
1490.6107946987679740.7784106024640510.389205301232026
1500.6089627421375270.7820745157249450.391037257862473







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level110.079136690647482OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 11 & 0.079136690647482 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185640&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.079136690647482[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185640&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185640&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level110.079136690647482OK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 9 ; 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')
}