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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 16 Nov 2013 07:16:49 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/16/t1384604242wt1wabqj96y5aj2.htm/, Retrieved Sat, 04 May 2024 21:54:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=225552, Retrieved Sat, 04 May 2024 21:54:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [WS7] [2013-11-16 12:16:49] [d36997adcafcba0a4d18dd87f1a174ea] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14	41	38	13	12	0	12	53	32
18	39	32	16	11	0	11	83	51
11	30	35	19	15	1	14	66	42
12	31	33	15	6	0	12	67	41
16	34	37	14	13	8	21	76	46
18	35	29	13	10	0	12	78	47
14	39	31	19	12	9	22	53	37
14	34	36	15	14	0	11	80	49
15	36	35	14	12	0	10	74	45
15	37	38	15	9	0	13	76	47
17	38	31	16	10	0	10	79	49
19	36	34	16	12	0	8	54	33
10	38	35	16	12	2	15	67	42
16	39	38	16	11	1	14	54	33
18	33	37	17	15	0	10	87	53
14	32	33	15	12	1	14	58	36
14	36	32	15	10	1	14	75	45
17	38	38	20	12	0	11	88	54
14	39	38	18	11	0	10	64	41
16	32	32	16	12	0	13	57	36
18	32	33	16	11	0	9,5	66	41
11	31	31	16	12	1	14	68	44
14	39	38	19	13	0	12	54	33
12	37	39	16	11	1	14	56	37
17	39	32	17	12	0	11	86	52
9	41	32	17	13	0	9	80	47
16	36	35	16	10	0	11	76	43
14	33	37	15	14	2	15	69	44
15	33	33	16	12	1	14	78	45
11	34	33	14	10	0	13	67	44
16	31	31	15	12	0	9	80	49
13	27	32	12	8	2	15	54	33
17	37	31	14	10	0	10	71	43
15	34	37	16	12	0	11	84	54
14	34	30	14	12	0	13	74	42
16	32	33	10	7	0	8	71	44
9	29	31	10	9	7	20	63	37
15	36	33	14	12	0	12	71	43
17	29	31	16	10	0	10	76	46
13	35	33	16	10	0	10	69	42
15	37	32	16	10	0	9	74	45
16	34	33	14	12	1	14	75	44
16	38	32	20	15	0	8	54	33
12	35	33	14	10	1	14	52	31
15	38	28	14	10	0	11	69	42
11	37	35	11	12	0	13	68	40
15	38	39	14	13	0	9	65	43
15	33	34	15	11	0	11	75	46
17	36	38	16	11	2	15	74	42
13	38	32	14	12	0	11	75	45
16	32	38	16	14	0	10	72	44
14	32	30	14	10	1	14	67	40
11	32	33	12	12	5	18	63	37
12	34	38	16	13	1	14	62	46
12	32	32	9	5	0	11	63	36
15	37	35	14	6	1	14,5	76	47
16	39	34	16	12	0	13	74	45
15	29	34	16	12	0	9	67	42
12	37	36	15	11	0	10	73	43
12	35	34	16	10	2	15	70	43
8	30	28	12	7	7	20	53	32
13	38	34	16	12	0	12	77	45
11	34	35	16	14	0	12	80	48
14	31	35	14	11	1	14	52	31
15	34	31	16	12	0	13	54	33
10	35	37	17	13	0	11	80	49
11	36	35	18	14	4	17	66	42
12	30	27	18	11	0	12	73	41
15	39	40	12	12	0	13	63	38
15	35	37	16	12	1	14	69	42
14	38	36	10	8	0	13	67	44
16	31	38	14	11	2	15	54	33
15	34	39	18	14	0	13	81	48
15	38	41	18	14	0	10	69	40
13	34	27	16	12	0	11	84	50
12	39	30	17	9	6	19	80	49
17	37	37	16	13	0	13	70	43
13	34	31	16	11	4	17	69	44
15	28	31	13	12	0	13	77	47
13	37	27	16	12	0	9	54	33
15	33	36	16	12	0	11	79	46
15	35	37	16	12	0	9	71	45
16	37	33	15	12	0	12	73	43
15	32	34	15	11	0	12	72	44
14	33	31	16	10	0	13	77	47
15	38	39	14	9	0	13	75	45
14	33	34	16	12	0	12	69	42
13	29	32	16	12	2	15	54	33
7	33	33	15	12	9	22	70	43
17	31	36	12	9	0	13	73	46
13	36	32	17	15	2	15	54	33
15	35	41	16	12	0	13	77	46
14	32	28	15	12	2	15	82	48
13	29	30	13	12	0	12,5	80	47
16	39	36	16	10	0	11	80	47
12	37	35	16	13	3	16	69	43
14	35	31	16	9	0	11	78	46
17	37	34	16	12	0	11	81	48
15	32	36	14	10	0	10	76	46
17	38	36	16	14	0	10	76	45
12	37	35	16	11	3	16	73	45
16	36	37	20	15	0	12	85	52
11	32	28	15	11	0	11	66	42
15	33	39	16	11	3	16	79	47
9	40	32	13	12	6	19	68	41
16	38	35	17	12	0	11	76	47
15	41	39	16	12	3	16	71	43
10	36	35	16	11	2	15	54	33
10	43	42	12	7	11	24	46	30
15	30	34	16	12	1	14	85	52
11	31	33	16	14	2	15	74	44
13	32	41	17	11	0	11	88	55
14	32	33	13	11	2	15	38	11
18	37	34	12	10	0	12	76	47
16	37	32	18	13	0	10	86	53
14	33	40	14	13	1	14	54	33
14	34	40	14	8	0	13	67	44
14	33	35	13	11	0	9	69	42
14	38	36	16	12	2	15	90	55
12	33	37	13	11	2	15	54	33
14	31	27	16	13	1	14	76	46
15	38	39	13	12	0	11	89	54
15	37	38	16	14	0	8	76	47
15	36	31	15	13	0	11	73	45
13	31	33	16	15	0	11	79	47
17	39	32	15	10	0	8	90	55
17	44	39	17	11	0	10	74	44
19	33	36	15	9	0	11	81	53
15	35	33	12	11	0	13	72	44
13	32	33	16	10	0	11	71	42
9	28	32	10	11	7	20	66	40
15	40	37	16	8	0	10	77	46
15	27	30	12	11	2	15	65	40
15	37	38	14	12	0	12	74	46
16	32	29	15	12	1	14	85	53
11	28	22	13	9	10	23	54	33
14	34	35	15	11	1	14	63	42
11	30	35	11	10	3	16	54	35
15	35	34	12	8	0	11	64	40
13	31	35	11	9	0	12	69	41
15	32	34	16	8	0	10	54	33
16	30	37	15	9	1	14	84	51
14	30	35	17	15	0	12	86	53
15	31	23	16	11	0	12	77	46
16	40	31	10	8	0	11	89	55
16	32	27	18	13	0	12	76	47
11	36	36	13	12	0	13	60	38
12	32	31	16	12	0	11	75	46
9	35	32	13	9	6	19	73	46
16	38	39	10	7	0	12	85	53
13	42	37	15	13	4	17	79	47
16	34	38	16	9	0	9	71	41
12	35	39	16	6	0	12	72	44
9	38	34	14	8	6	19	69	43
13	33	31	10	8	5	18	78	51
13	36	32	17	15	2	15	54	33
14	32	37	13	6	1	14	69	43
19	33	36	15	9	0	11	81	53
13	34	32	16	11	0	9	84	51
12	32	38	12	8	5	18	84	50
13	34	36	13	8	3	16	69	46
10	27	26	13	10	11	24	66	43
14	31	26	12	8	1	14	81	47
16	38	33	17	14	7	20	82	50
10	34	39	15	10	5	18	72	43
11	24	30	10	8	10	23	54	33
14	30	33	14	11	0	12	78	48
12	26	25	11	12	1	14	74	44
9	34	38	13	12	3	16	82	50
9	27	37	16	12	5	18	73	41
11	37	31	12	5	7	20	55	34
16	36	37	16	12	0	12	72	44
9	41	35	12	10	0	12	78	47
13	29	25	9	7	4	17	59	35
16	36	28	12	12	0	13	72	44
13	32	35	15	11	0	9	78	44
9	37	33	12	8	3	16	68	43
12	30	30	12	9	5	18	69	41
16	31	31	14	10	0	10	67	41
11	38	37	12	9	1	14	74	42
14	36	36	16	12	0	11	54	33
13	35	30	11	6	0	9	67	41
15	31	36	19	15	0	11	70	44
14	38	32	15	12	0	10	80	48
16	22	28	8	12	0	11	89	55
13	32	36	16	12	6	19	76	44
14	36	34	17	11	1	14	74	43
15	39	31	12	7	0	12	87	52
13	28	28	11	7	1	14	54	30
11	32	36	11	5	8	21	61	39
11	32	36	14	12	0	13	38	11
14	38	40	16	12	0	10	75	44
15	32	33	12	3	2	15	69	42
11	35	37	16	11	3	16	62	41
15	32	32	13	10	1	14	72	44
12	37	38	15	12	0	12	70	44
14	34	31	16	9	6	19	79	48
14	33	37	16	12	2	15	87	53
8	33	33	14	9	6	19	62	37
13	26	32	16	12	0	13	77	44
9	30	30	16	12	4	17	69	44
15	24	30	14	10	0	12	69	40
17	34	31	11	9	0	11	75	42
13	34	32	12	12	1	14	54	35
15	33	34	15	8	0	11	72	43
15	34	36	15	11	0	13	74	45
14	35	37	16	11	0	12	85	55
16	35	36	16	12	2	15	52	31
13	36	33	11	10	1	14	70	44
16	34	33	15	10	0	12	84	50
9	34	33	12	12	4	17	64	40
16	41	44	12	12	0	11	84	53
11	32	39	15	11	5	18	87	54
10	30	32	15	8	0	13	79	49
11	35	35	16	12	4	17	67	40
15	28	25	14	10	0	13	65	41
17	33	35	17	11	0	11	85	52
14	39	34	14	10	0	12	83	52
8	36	35	13	8	9	22	61	36
15	36	39	15	12	1	14	82	52
11	35	33	13	12	0	12	76	46
16	38	36	14	10	0	12	58	31
10	33	32	15	12	4	17	72	44
15	31	32	12	9	0	9	72	44
9	34	36	13	9	8	21	38	11
16	32	36	8	6	0	10	78	46
19	31	32	14	10	0	11	54	33
12	33	34	14	9	0	12	63	34
8	34	33	11	9	10	23	66	42
11	34	35	12	9	0	13	70	43
14	34	30	13	6	0	12	71	43
9	33	38	10	10	3	16	67	44
15	32	34	16	6	0	9	58	36
13	41	33	18	14	4	17	72	46
16	34	32	13	10	0	9	72	44
11	36	31	11	10	1	14	70	43
12	37	30	4	6	4	17	76	50
13	36	27	13	12	0	13	50	33
10	29	31	16	12	0	11	72	43
11	37	30	10	7	0	12	72	44
12	27	32	12	8	0	10	88	53
8	35	35	12	11	6	19	53	34
12	28	28	10	3	3	16	58	35
12	35	33	13	6	3	16	66	40
15	37	31	15	10	1	14	82	53
11	29	35	12	8	7	20	69	42
13	32	35	14	9	2	15	68	43
14	36	32	10	9	10	23	44	29
10	19	21	12	8	7	20	56	36
12	21	20	12	9	3	16	53	30
15	31	34	11	7	1	14	70	42
13	33	32	10	7	4	17	78	47
13	36	34	12	6	0	11	71	44
13	33	32	16	9	0	13	72	45
12	37	33	12	10	4	17	68	44
12	34	33	14	11	2	15	67	43
9	35	37	16	12	8	21	75	43
9	31	32	14	8	5	18	62	40
15	37	34	13	11	2	15	67	41
10	35	30	4	3	0	8	83	52
14	27	30	15	11	0	12	64	38
15	34	38	11	12	0	12	68	41
7	40	36	11	7	9	22	62	39
14	29	32	14	9	0	12	72	43

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 14 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225552&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]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225552&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225552&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Happy[t] = + 13.2975949702707 + 0.0178243636835531Connected[t] + 0.00630781599255484Separate[t] + 0.117288227139733Open[t] -0.0180208902383338Selfassurance[t] -0.191678561824627Stress[t] -0.244289160689915Depression[t] + 0.00528832376608885Sport[t] + 0.0274134371235174SportII[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happy[t] =  +  13.2975949702707 +  0.0178243636835531Connected[t] +  0.00630781599255484Separate[t] +  0.117288227139733Open[t] -0.0180208902383338Selfassurance[t] -0.191678561824627Stress[t] -0.244289160689915Depression[t] +  0.00528832376608885Sport[t] +  0.0274134371235174SportII[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225552&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happy[t] =  +  13.2975949702707 +  0.0178243636835531Connected[t] +  0.00630781599255484Separate[t] +  0.117288227139733Open[t] -0.0180208902383338Selfassurance[t] -0.191678561824627Stress[t] -0.244289160689915Depression[t] +  0.00528832376608885Sport[t] +  0.0274134371235174SportII[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225552&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225552&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Happy[t] = + 13.2975949702707 + 0.0178243636835531Connected[t] + 0.00630781599255484Separate[t] + 0.117288227139733Open[t] -0.0180208902383338Selfassurance[t] -0.191678561824627Stress[t] -0.244289160689915Depression[t] + 0.00528832376608885Sport[t] + 0.0274134371235174SportII[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.29759497027072.1285046.247400
Connected0.01782436368355310.0375260.4750.6352030.317601
Separate0.006307815992554840.0383460.16450.8694690.434735
Open0.1172882271397330.0665721.76180.0792960.039648
Selfassurance-0.01802089023833380.069205-0.26040.7947650.397383
Stress-0.1916785618246270.136837-1.40080.1624950.081248
Depression-0.2442891606899150.103208-2.3670.0186820.009341
Sport0.005288323766088850.0405220.13050.8962710.448136
SportII0.02741343712351740.0604110.45380.6503730.325186

\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) & 13.2975949702707 & 2.128504 & 6.2474 & 0 & 0 \tabularnewline
Connected & 0.0178243636835531 & 0.037526 & 0.475 & 0.635203 & 0.317601 \tabularnewline
Separate & 0.00630781599255484 & 0.038346 & 0.1645 & 0.869469 & 0.434735 \tabularnewline
Open & 0.117288227139733 & 0.066572 & 1.7618 & 0.079296 & 0.039648 \tabularnewline
Selfassurance & -0.0180208902383338 & 0.069205 & -0.2604 & 0.794765 & 0.397383 \tabularnewline
Stress & -0.191678561824627 & 0.136837 & -1.4008 & 0.162495 & 0.081248 \tabularnewline
Depression & -0.244289160689915 & 0.103208 & -2.367 & 0.018682 & 0.009341 \tabularnewline
Sport & 0.00528832376608885 & 0.040522 & 0.1305 & 0.896271 & 0.448136 \tabularnewline
SportII & 0.0274134371235174 & 0.060411 & 0.4538 & 0.650373 & 0.325186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225552&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]13.2975949702707[/C][C]2.128504[/C][C]6.2474[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Connected[/C][C]0.0178243636835531[/C][C]0.037526[/C][C]0.475[/C][C]0.635203[/C][C]0.317601[/C][/ROW]
[ROW][C]Separate[/C][C]0.00630781599255484[/C][C]0.038346[/C][C]0.1645[/C][C]0.869469[/C][C]0.434735[/C][/ROW]
[ROW][C]Open[/C][C]0.117288227139733[/C][C]0.066572[/C][C]1.7618[/C][C]0.079296[/C][C]0.039648[/C][/ROW]
[ROW][C]Selfassurance[/C][C]-0.0180208902383338[/C][C]0.069205[/C][C]-0.2604[/C][C]0.794765[/C][C]0.397383[/C][/ROW]
[ROW][C]Stress[/C][C]-0.191678561824627[/C][C]0.136837[/C][C]-1.4008[/C][C]0.162495[/C][C]0.081248[/C][/ROW]
[ROW][C]Depression[/C][C]-0.244289160689915[/C][C]0.103208[/C][C]-2.367[/C][C]0.018682[/C][C]0.009341[/C][/ROW]
[ROW][C]Sport[/C][C]0.00528832376608885[/C][C]0.040522[/C][C]0.1305[/C][C]0.896271[/C][C]0.448136[/C][/ROW]
[ROW][C]SportII[/C][C]0.0274134371235174[/C][C]0.060411[/C][C]0.4538[/C][C]0.650373[/C][C]0.325186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225552&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225552&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.29759497027072.1285046.247400
Connected0.01782436368355310.0375260.4750.6352030.317601
Separate0.006307815992554840.0383460.16450.8694690.434735
Open0.1172882271397330.0665721.76180.0792960.039648
Selfassurance-0.01802089023833380.069205-0.26040.7947650.397383
Stress-0.1916785618246270.136837-1.40080.1624950.081248
Depression-0.2442891606899150.103208-2.3670.0186820.009341
Sport0.005288323766088850.0405220.13050.8962710.448136
SportII0.02741343712351740.0604110.45380.6503730.325186






Multiple Linear Regression - Regression Statistics
Multiple R0.606892847186546
R-squared0.368318927966193
Adjusted R-squared0.348501482569054
F-TEST (value)18.5855906543517
F-TEST (DF numerator)8
F-TEST (DF denominator)255
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.01679106960082
Sum Squared Residuals1037.19878569751

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.606892847186546 \tabularnewline
R-squared & 0.368318927966193 \tabularnewline
Adjusted R-squared & 0.348501482569054 \tabularnewline
F-TEST (value) & 18.5855906543517 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 255 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.01679106960082 \tabularnewline
Sum Squared Residuals & 1037.19878569751 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225552&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.606892847186546[/C][/ROW]
[ROW][C]R-squared[/C][C]0.368318927966193[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.348501482569054[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.5855906543517[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]255[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.01679106960082[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1037.19878569751[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225552&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225552&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.606892847186546
R-squared0.368318927966193
Adjusted R-squared0.348501482569054
F-TEST (value)18.5855906543517
F-TEST (DF numerator)8
F-TEST (DF denominator)255
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.01679106960082
Sum Squared Residuals1037.19878569751






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.80262837824630.197371621753725
21815.02281250560072.97718749439925
31113.8999293188628-2.89992931886275
41214.2563049239943-2.2563049239943
51610.54420597891325.45579402108676
61814.21836328369363.78163671630643
71410.39562282406643.60437717593356
81414.7168792077532-0.716879207753163
91514.7678791420640.232120857936039
101514.30851389128940.69148610871062
111715.18501020754151.81498979245851
121915.04999838092673.95000161907328
131013.3140428188785-3.31404281887853
141613.48931010022182.51068989977819
151815.31287939964992.6871206003501
161413.3010849635310.698915036468994
171413.73873882088450.261261179115502
181715.60264898639411.39735101360589
191415.1649124937346-1.16491249373459
201613.84274477342662.15725522657336
211814.90674764158463.09325235841538
221113.6601239296511-2.6601239296511
231414.4853898843688-0.485389884368799
241213.5801995848735-1.58019958487351
251715.16535825092391.83464174907607
26915.5027672812184-6.50276728121841
271614.74995798941531.25004201058468
281413.14961014660870.850389853391314
291513.78868496378771.21131503621227
301113.9583573776325-2.95835737763253
311615.15648713859620.843512861403774
321312.38651287970540.613487120294628
331714.72582217670872.27417782329134
341515.0347375120441-0.0347375120440604
351413.88558354129380.114416458706217
361614.75021751093541.24978248906458
37910.1406664963783-1.14066649637828
381514.19599334315370.80400665684628
391714.92648565172072.0735143482793
401314.8993754509504-1.89937545095043
411515.2816874532159-0.281687453215897
421613.528654464772.47134553522997
431615.48812171415260.511878285847358
441213.1045144797045-1.10451447970448
451514.44244384706890.557556152931061
461113.5321742140445-2.53217421404445
471514.9526056157110.0473943842889669
481514.63181995259850.368180047401495
491713.352356696093.64764330390996
501314.5456035845296-1.54560358452958
511614.87604972445451.12395027554548
521413.35816373127910.641836268720854
531111.2592044480076-0.259204448007601
541213.7628277740618-1.76282777406182
551213.6681816790932-1.66818167909321
561513.66825258402751.33174741597252
571614.31675338933181.68324661066821
581514.99640781752270.00359218247725916
591214.867205241105-2.86720524110501
601213.3335821007338-1.33358210073377
61810.2202352235618-2.22023522356183
621314.5590831576364-1.55908315763641
631114.5561570210869-3.55615702108691
641413.0278117667170.972188233282957
651513.77398040213241.22601959786763
661014.9936087319471-4.99360873194707
671112.599704996519-1.59970499651905
681214.4941238371795-2.49412383717948
691513.63538175543661.36461824456342
701513.71972972985971.2802702701403
711413.61546715226210.384532847737859
721612.67617101395943.32382898604062
731514.57696390241280.423036097587233
741515.1109770890206-0.110977089020628
751314.8620056036244-1.86200560362444
761211.98845037921970.0115496207803492
771714.20602705039262.79397294960738
781312.42900306716260.57099693283744
791513.82058910496111.17941089503886
801314.7793788719725-1.77937887197247
811514.76485621654940.235143783450631
821515.2256710540366-0.225671054036632
831614.34168258150921.65831741849082
841514.29901458267970.700985417320265
851414.2976173852748-0.297617385274772
861514.15524264581260.844757354187368
871414.3454144377194-0.345414437719386
881312.81923095467810.180769045321925
89710.0865214921559-3.08652149215591
901713.7938089873623.20619101263803
911313.0072270568877-0.00722705688767833
921514.33288905496720.667110945032759
931413.2894591769220.710540823077969
941313.9701152042954-0.970115204295363
951614.9405459400171.05945405998304
961212.8802199270978-0.880219927097795
971414.8177402109026-0.817740210902613
981714.88894156107772.11105843892232
991514.77692136845470.223078631545334
1001715.01894700675861.9810529932414
1011212.9922418768859-0.992241876885853
1021615.19164876608420.808351233915765
1031114.4189000305708-3.41890003057075
1041513.03273250296541.96726749703457
105911.2129074134332-2.21290741343318
1061614.97650691193961.02349308806044
1071513.00534618357271.99465381642726
1081012.9809458386789-2.98094583867895
109108.704545344675961.29545465532404
1101513.97043201495691.02956798504313
1111113.2324600012415-2.23246000124154
1121315.2081958982131-2.20819589821311
1131411.85745927356562.14254072643438
1141814.15768621635153.84231378364846
1151615.50067945827170.499320541728347
1161413.12436131534950.875638684650531
1171414.0385538700571-0.0385538700570794
1181414.7507459446008-0.750745944600821
1191413.79835676409690.201643235903146
1201212.5882236981942-0.588223698194197
1211413.71400523981830.285994760181704
1221514.79322753617460.206772463825371
1231515.557143470687-0.557143470687019
1241514.59233773053910.407662269460863
1251314.6836348076131-1.68363480761311
1261715.80308466562581.19691533437422
1271715.27817745003661.72182254996338
1281914.90410136752144.09589863247856
1291513.75002601562871.24997398437127
1301314.612189846742-1.61218984674204
131910.1912134508794-1.19121345087944
1321515.2017306524379-0.201730652437868
1331512.56990019829692.43009980170314
1341514.34346206946890.656537930531134
1351613.8846668723452.11533312765502
136118.952784640248442.04721535975156
1371413.55829245469310.441707545306895
1381111.9244385628498-0.924438562849789
1391514.14701448509330.852985514906668
1401313.7562816242377-0.756281624237654
1411514.5622061657660.437793834233992
1421613.89342814562022.10657185437981
1431414.7529240314682-0.752924031468215
1441514.41036096329530.589639036704709
1451614.5260460522591.47395394774105
1461614.67407637810931.32592362189071
1471113.6581006562572-2.65810065625717
1481214.6943394778387-2.69433947783869
149911.3413570701784-2.34135707017844
1501614.23861141306931.76138858693069
1511312.59123841400140.408761585998646
1521615.15856342856660.841436571433435
1531214.5914194320246-2.59141943202457
154911.4393613041574-2.43936130415737
1551311.56503326260051.43496673739951
1561313.0072270568877-0.00722705688767833
1571413.44993073594340.550069264056638
1581914.90410136752144.09589863247856
1591315.4275573323289-2.4275573323289
1601211.83025657061730.169743429382698
1611312.65353473318270.346465266817287
162108.843797184210471.15620281578953
1631413.38250402241240.617495977587559
1641611.50146737446324.49853262553675
1651011.9326820698618-1.93268206986181
166118.59810592227382.4018940777262
1671414.2811535032709-0.281153503270944
1681212.9784440221759-0.978444022175879
169912.7724687616677-3.77246876166771
170911.8270037882739-2.82700378827394
1711110.46537451958020.534625480419755
1721614.4885028222931.51149717770699
173914.2458681346105-5.24586813461049
1741311.25349615199211.74650384800788
1751613.71829040911122.28170959088883
1761315.0699198233386-2.06991982333857
177912.4832675139793-3.48326751397933
1781211.40007863446840.599921365531566
1791614.5428958252961.45710417470405
1801113.3845542018969-2.38455420189687
1811414.3297465308421-0.32974653084208
1821314.5723935042617-1.57239350426166
1831514.92458771174460.0754122882553748
1841415.0158629025602-1.01586290256022
1851613.87962404274452.12037595725547
1861311.57195535085341.42804464914657
1871413.90779481889760.0922051811023837
1881514.42371291350060.576287086499449
1891312.63356605366130.366433946338703
1901110.02333295968510.976667040314901
1911112.8475855034738-1.84758550347377
1921415.0475182903159-1.04751829031588
1931512.89809275591042.10190724408965
1941112.8013834715828-1.80138347158281
1951513.3895865034491.61041349655096
1961214.3847701272972-2.38477012729721
1971411.75564638876512.2443536112349
1981413.64485091612630.355149083873658
199811.124411890405-3.12441189040499
2001314.0608725636352-1.06087256363523
201912.3333769061975-3.33337690619746
2021513.90640235254741.09359764745264
2031714.08795599172812.91204400827194
2041312.92999446129850.0700055387015137
2051514.58777734064470.412222659355313
2061514.14097986599770.859020134002269
2071414.8589953661656-0.858995366165638
2081612.88600487897043.1139951210296
2091313.2220386723642-0.222038672364161
2101614.57431689222681.42568310777321
211911.8183495330259-2.81834953302589
2121614.70709642409441.29290357590563
2131112.2598851171064-1.25988511710639
2141014.2346091853328-4.23460918533281
2151112.3338074085518-1.33380740855175
2161513.7081317086881.29186829131196
2171715.09006808327251.90993191672748
2181414.6019968499783-0.601996849978325
21989.75062834810613-1.75062834810613
2201513.9757640785831.02423592141704
2211114.1695626825314-3.16956268253143
2221613.88889784453382.11110215546621
2231012.2980423733918-2.29804237339176
2241514.68561916813830.314380831861714
22599.33226689429981-0.332266894299805
2261614.15585221410181.84414778589822
2271914.01685877465134.9831412253487
2281213.9138632145702-1.91386321457025
22989.20472116329312-1.20472116329312
2301113.7428689797513-2.74286897975125
2311414.1322582820992-0.132258282099219
232912.1950160178092-3.19501601780919
2331514.94593071336750.0540692866325023
2341312.81759487404230.182405125957693
2351614.83835959609031.16164040390966
2361113.1820096032555-2.18200960325553
2371211.36031295683930.639687043160734
2381313.4113798890457-0.411379889045703
2391014.5427611041192-4.54276110411921
2401113.848547562382-2.84854756238195
2411214.7193875573216-2.71938755732157
242810.7722227897357-2.77222278973572
2431211.87464642312780.125353576872243
2441212.5081318353259-0.508131835325899
2451514.00658113192630.993418868073741
2461110.58729223308040.412707766919578
2471313.0592846141024-0.0592846141023526
248148.644056042068265.35594395793174
2491010.0875103406489-0.0875103406488636
2501211.66235565780390.337644342196121
2511513.13846046640691.86153953359308
2521311.91567594285191.08432405714815
2531314.3475526441102-1.34755264411024
2541314.2406775984282-1.24067759842818
2551212.0386714481116-0.0386714481116421
2561213.0409876052416-1.04098760524159
257910.727099051978-1.72709905197795
258911.6186842711688-2.61868427116876
2591512.9286534108982.07134658910196
2601014.4358887343046-4.43588873430456
2611413.97787428742190.0221257125780655
2621513.76932716878491.23067283121511
26379.6992066899284-2.6992066899284
2641414.1242659758574-0.124265975857383

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.8026283782463 & 0.197371621753725 \tabularnewline
2 & 18 & 15.0228125056007 & 2.97718749439925 \tabularnewline
3 & 11 & 13.8999293188628 & -2.89992931886275 \tabularnewline
4 & 12 & 14.2563049239943 & -2.2563049239943 \tabularnewline
5 & 16 & 10.5442059789132 & 5.45579402108676 \tabularnewline
6 & 18 & 14.2183632836936 & 3.78163671630643 \tabularnewline
7 & 14 & 10.3956228240664 & 3.60437717593356 \tabularnewline
8 & 14 & 14.7168792077532 & -0.716879207753163 \tabularnewline
9 & 15 & 14.767879142064 & 0.232120857936039 \tabularnewline
10 & 15 & 14.3085138912894 & 0.69148610871062 \tabularnewline
11 & 17 & 15.1850102075415 & 1.81498979245851 \tabularnewline
12 & 19 & 15.0499983809267 & 3.95000161907328 \tabularnewline
13 & 10 & 13.3140428188785 & -3.31404281887853 \tabularnewline
14 & 16 & 13.4893101002218 & 2.51068989977819 \tabularnewline
15 & 18 & 15.3128793996499 & 2.6871206003501 \tabularnewline
16 & 14 & 13.301084963531 & 0.698915036468994 \tabularnewline
17 & 14 & 13.7387388208845 & 0.261261179115502 \tabularnewline
18 & 17 & 15.6026489863941 & 1.39735101360589 \tabularnewline
19 & 14 & 15.1649124937346 & -1.16491249373459 \tabularnewline
20 & 16 & 13.8427447734266 & 2.15725522657336 \tabularnewline
21 & 18 & 14.9067476415846 & 3.09325235841538 \tabularnewline
22 & 11 & 13.6601239296511 & -2.6601239296511 \tabularnewline
23 & 14 & 14.4853898843688 & -0.485389884368799 \tabularnewline
24 & 12 & 13.5801995848735 & -1.58019958487351 \tabularnewline
25 & 17 & 15.1653582509239 & 1.83464174907607 \tabularnewline
26 & 9 & 15.5027672812184 & -6.50276728121841 \tabularnewline
27 & 16 & 14.7499579894153 & 1.25004201058468 \tabularnewline
28 & 14 & 13.1496101466087 & 0.850389853391314 \tabularnewline
29 & 15 & 13.7886849637877 & 1.21131503621227 \tabularnewline
30 & 11 & 13.9583573776325 & -2.95835737763253 \tabularnewline
31 & 16 & 15.1564871385962 & 0.843512861403774 \tabularnewline
32 & 13 & 12.3865128797054 & 0.613487120294628 \tabularnewline
33 & 17 & 14.7258221767087 & 2.27417782329134 \tabularnewline
34 & 15 & 15.0347375120441 & -0.0347375120440604 \tabularnewline
35 & 14 & 13.8855835412938 & 0.114416458706217 \tabularnewline
36 & 16 & 14.7502175109354 & 1.24978248906458 \tabularnewline
37 & 9 & 10.1406664963783 & -1.14066649637828 \tabularnewline
38 & 15 & 14.1959933431537 & 0.80400665684628 \tabularnewline
39 & 17 & 14.9264856517207 & 2.0735143482793 \tabularnewline
40 & 13 & 14.8993754509504 & -1.89937545095043 \tabularnewline
41 & 15 & 15.2816874532159 & -0.281687453215897 \tabularnewline
42 & 16 & 13.52865446477 & 2.47134553522997 \tabularnewline
43 & 16 & 15.4881217141526 & 0.511878285847358 \tabularnewline
44 & 12 & 13.1045144797045 & -1.10451447970448 \tabularnewline
45 & 15 & 14.4424438470689 & 0.557556152931061 \tabularnewline
46 & 11 & 13.5321742140445 & -2.53217421404445 \tabularnewline
47 & 15 & 14.952605615711 & 0.0473943842889669 \tabularnewline
48 & 15 & 14.6318199525985 & 0.368180047401495 \tabularnewline
49 & 17 & 13.35235669609 & 3.64764330390996 \tabularnewline
50 & 13 & 14.5456035845296 & -1.54560358452958 \tabularnewline
51 & 16 & 14.8760497244545 & 1.12395027554548 \tabularnewline
52 & 14 & 13.3581637312791 & 0.641836268720854 \tabularnewline
53 & 11 & 11.2592044480076 & -0.259204448007601 \tabularnewline
54 & 12 & 13.7628277740618 & -1.76282777406182 \tabularnewline
55 & 12 & 13.6681816790932 & -1.66818167909321 \tabularnewline
56 & 15 & 13.6682525840275 & 1.33174741597252 \tabularnewline
57 & 16 & 14.3167533893318 & 1.68324661066821 \tabularnewline
58 & 15 & 14.9964078175227 & 0.00359218247725916 \tabularnewline
59 & 12 & 14.867205241105 & -2.86720524110501 \tabularnewline
60 & 12 & 13.3335821007338 & -1.33358210073377 \tabularnewline
61 & 8 & 10.2202352235618 & -2.22023522356183 \tabularnewline
62 & 13 & 14.5590831576364 & -1.55908315763641 \tabularnewline
63 & 11 & 14.5561570210869 & -3.55615702108691 \tabularnewline
64 & 14 & 13.027811766717 & 0.972188233282957 \tabularnewline
65 & 15 & 13.7739804021324 & 1.22601959786763 \tabularnewline
66 & 10 & 14.9936087319471 & -4.99360873194707 \tabularnewline
67 & 11 & 12.599704996519 & -1.59970499651905 \tabularnewline
68 & 12 & 14.4941238371795 & -2.49412383717948 \tabularnewline
69 & 15 & 13.6353817554366 & 1.36461824456342 \tabularnewline
70 & 15 & 13.7197297298597 & 1.2802702701403 \tabularnewline
71 & 14 & 13.6154671522621 & 0.384532847737859 \tabularnewline
72 & 16 & 12.6761710139594 & 3.32382898604062 \tabularnewline
73 & 15 & 14.5769639024128 & 0.423036097587233 \tabularnewline
74 & 15 & 15.1109770890206 & -0.110977089020628 \tabularnewline
75 & 13 & 14.8620056036244 & -1.86200560362444 \tabularnewline
76 & 12 & 11.9884503792197 & 0.0115496207803492 \tabularnewline
77 & 17 & 14.2060270503926 & 2.79397294960738 \tabularnewline
78 & 13 & 12.4290030671626 & 0.57099693283744 \tabularnewline
79 & 15 & 13.8205891049611 & 1.17941089503886 \tabularnewline
80 & 13 & 14.7793788719725 & -1.77937887197247 \tabularnewline
81 & 15 & 14.7648562165494 & 0.235143783450631 \tabularnewline
82 & 15 & 15.2256710540366 & -0.225671054036632 \tabularnewline
83 & 16 & 14.3416825815092 & 1.65831741849082 \tabularnewline
84 & 15 & 14.2990145826797 & 0.700985417320265 \tabularnewline
85 & 14 & 14.2976173852748 & -0.297617385274772 \tabularnewline
86 & 15 & 14.1552426458126 & 0.844757354187368 \tabularnewline
87 & 14 & 14.3454144377194 & -0.345414437719386 \tabularnewline
88 & 13 & 12.8192309546781 & 0.180769045321925 \tabularnewline
89 & 7 & 10.0865214921559 & -3.08652149215591 \tabularnewline
90 & 17 & 13.793808987362 & 3.20619101263803 \tabularnewline
91 & 13 & 13.0072270568877 & -0.00722705688767833 \tabularnewline
92 & 15 & 14.3328890549672 & 0.667110945032759 \tabularnewline
93 & 14 & 13.289459176922 & 0.710540823077969 \tabularnewline
94 & 13 & 13.9701152042954 & -0.970115204295363 \tabularnewline
95 & 16 & 14.940545940017 & 1.05945405998304 \tabularnewline
96 & 12 & 12.8802199270978 & -0.880219927097795 \tabularnewline
97 & 14 & 14.8177402109026 & -0.817740210902613 \tabularnewline
98 & 17 & 14.8889415610777 & 2.11105843892232 \tabularnewline
99 & 15 & 14.7769213684547 & 0.223078631545334 \tabularnewline
100 & 17 & 15.0189470067586 & 1.9810529932414 \tabularnewline
101 & 12 & 12.9922418768859 & -0.992241876885853 \tabularnewline
102 & 16 & 15.1916487660842 & 0.808351233915765 \tabularnewline
103 & 11 & 14.4189000305708 & -3.41890003057075 \tabularnewline
104 & 15 & 13.0327325029654 & 1.96726749703457 \tabularnewline
105 & 9 & 11.2129074134332 & -2.21290741343318 \tabularnewline
106 & 16 & 14.9765069119396 & 1.02349308806044 \tabularnewline
107 & 15 & 13.0053461835727 & 1.99465381642726 \tabularnewline
108 & 10 & 12.9809458386789 & -2.98094583867895 \tabularnewline
109 & 10 & 8.70454534467596 & 1.29545465532404 \tabularnewline
110 & 15 & 13.9704320149569 & 1.02956798504313 \tabularnewline
111 & 11 & 13.2324600012415 & -2.23246000124154 \tabularnewline
112 & 13 & 15.2081958982131 & -2.20819589821311 \tabularnewline
113 & 14 & 11.8574592735656 & 2.14254072643438 \tabularnewline
114 & 18 & 14.1576862163515 & 3.84231378364846 \tabularnewline
115 & 16 & 15.5006794582717 & 0.499320541728347 \tabularnewline
116 & 14 & 13.1243613153495 & 0.875638684650531 \tabularnewline
117 & 14 & 14.0385538700571 & -0.0385538700570794 \tabularnewline
118 & 14 & 14.7507459446008 & -0.750745944600821 \tabularnewline
119 & 14 & 13.7983567640969 & 0.201643235903146 \tabularnewline
120 & 12 & 12.5882236981942 & -0.588223698194197 \tabularnewline
121 & 14 & 13.7140052398183 & 0.285994760181704 \tabularnewline
122 & 15 & 14.7932275361746 & 0.206772463825371 \tabularnewline
123 & 15 & 15.557143470687 & -0.557143470687019 \tabularnewline
124 & 15 & 14.5923377305391 & 0.407662269460863 \tabularnewline
125 & 13 & 14.6836348076131 & -1.68363480761311 \tabularnewline
126 & 17 & 15.8030846656258 & 1.19691533437422 \tabularnewline
127 & 17 & 15.2781774500366 & 1.72182254996338 \tabularnewline
128 & 19 & 14.9041013675214 & 4.09589863247856 \tabularnewline
129 & 15 & 13.7500260156287 & 1.24997398437127 \tabularnewline
130 & 13 & 14.612189846742 & -1.61218984674204 \tabularnewline
131 & 9 & 10.1912134508794 & -1.19121345087944 \tabularnewline
132 & 15 & 15.2017306524379 & -0.201730652437868 \tabularnewline
133 & 15 & 12.5699001982969 & 2.43009980170314 \tabularnewline
134 & 15 & 14.3434620694689 & 0.656537930531134 \tabularnewline
135 & 16 & 13.884666872345 & 2.11533312765502 \tabularnewline
136 & 11 & 8.95278464024844 & 2.04721535975156 \tabularnewline
137 & 14 & 13.5582924546931 & 0.441707545306895 \tabularnewline
138 & 11 & 11.9244385628498 & -0.924438562849789 \tabularnewline
139 & 15 & 14.1470144850933 & 0.852985514906668 \tabularnewline
140 & 13 & 13.7562816242377 & -0.756281624237654 \tabularnewline
141 & 15 & 14.562206165766 & 0.437793834233992 \tabularnewline
142 & 16 & 13.8934281456202 & 2.10657185437981 \tabularnewline
143 & 14 & 14.7529240314682 & -0.752924031468215 \tabularnewline
144 & 15 & 14.4103609632953 & 0.589639036704709 \tabularnewline
145 & 16 & 14.526046052259 & 1.47395394774105 \tabularnewline
146 & 16 & 14.6740763781093 & 1.32592362189071 \tabularnewline
147 & 11 & 13.6581006562572 & -2.65810065625717 \tabularnewline
148 & 12 & 14.6943394778387 & -2.69433947783869 \tabularnewline
149 & 9 & 11.3413570701784 & -2.34135707017844 \tabularnewline
150 & 16 & 14.2386114130693 & 1.76138858693069 \tabularnewline
151 & 13 & 12.5912384140014 & 0.408761585998646 \tabularnewline
152 & 16 & 15.1585634285666 & 0.841436571433435 \tabularnewline
153 & 12 & 14.5914194320246 & -2.59141943202457 \tabularnewline
154 & 9 & 11.4393613041574 & -2.43936130415737 \tabularnewline
155 & 13 & 11.5650332626005 & 1.43496673739951 \tabularnewline
156 & 13 & 13.0072270568877 & -0.00722705688767833 \tabularnewline
157 & 14 & 13.4499307359434 & 0.550069264056638 \tabularnewline
158 & 19 & 14.9041013675214 & 4.09589863247856 \tabularnewline
159 & 13 & 15.4275573323289 & -2.4275573323289 \tabularnewline
160 & 12 & 11.8302565706173 & 0.169743429382698 \tabularnewline
161 & 13 & 12.6535347331827 & 0.346465266817287 \tabularnewline
162 & 10 & 8.84379718421047 & 1.15620281578953 \tabularnewline
163 & 14 & 13.3825040224124 & 0.617495977587559 \tabularnewline
164 & 16 & 11.5014673744632 & 4.49853262553675 \tabularnewline
165 & 10 & 11.9326820698618 & -1.93268206986181 \tabularnewline
166 & 11 & 8.5981059222738 & 2.4018940777262 \tabularnewline
167 & 14 & 14.2811535032709 & -0.281153503270944 \tabularnewline
168 & 12 & 12.9784440221759 & -0.978444022175879 \tabularnewline
169 & 9 & 12.7724687616677 & -3.77246876166771 \tabularnewline
170 & 9 & 11.8270037882739 & -2.82700378827394 \tabularnewline
171 & 11 & 10.4653745195802 & 0.534625480419755 \tabularnewline
172 & 16 & 14.488502822293 & 1.51149717770699 \tabularnewline
173 & 9 & 14.2458681346105 & -5.24586813461049 \tabularnewline
174 & 13 & 11.2534961519921 & 1.74650384800788 \tabularnewline
175 & 16 & 13.7182904091112 & 2.28170959088883 \tabularnewline
176 & 13 & 15.0699198233386 & -2.06991982333857 \tabularnewline
177 & 9 & 12.4832675139793 & -3.48326751397933 \tabularnewline
178 & 12 & 11.4000786344684 & 0.599921365531566 \tabularnewline
179 & 16 & 14.542895825296 & 1.45710417470405 \tabularnewline
180 & 11 & 13.3845542018969 & -2.38455420189687 \tabularnewline
181 & 14 & 14.3297465308421 & -0.32974653084208 \tabularnewline
182 & 13 & 14.5723935042617 & -1.57239350426166 \tabularnewline
183 & 15 & 14.9245877117446 & 0.0754122882553748 \tabularnewline
184 & 14 & 15.0158629025602 & -1.01586290256022 \tabularnewline
185 & 16 & 13.8796240427445 & 2.12037595725547 \tabularnewline
186 & 13 & 11.5719553508534 & 1.42804464914657 \tabularnewline
187 & 14 & 13.9077948188976 & 0.0922051811023837 \tabularnewline
188 & 15 & 14.4237129135006 & 0.576287086499449 \tabularnewline
189 & 13 & 12.6335660536613 & 0.366433946338703 \tabularnewline
190 & 11 & 10.0233329596851 & 0.976667040314901 \tabularnewline
191 & 11 & 12.8475855034738 & -1.84758550347377 \tabularnewline
192 & 14 & 15.0475182903159 & -1.04751829031588 \tabularnewline
193 & 15 & 12.8980927559104 & 2.10190724408965 \tabularnewline
194 & 11 & 12.8013834715828 & -1.80138347158281 \tabularnewline
195 & 15 & 13.389586503449 & 1.61041349655096 \tabularnewline
196 & 12 & 14.3847701272972 & -2.38477012729721 \tabularnewline
197 & 14 & 11.7556463887651 & 2.2443536112349 \tabularnewline
198 & 14 & 13.6448509161263 & 0.355149083873658 \tabularnewline
199 & 8 & 11.124411890405 & -3.12441189040499 \tabularnewline
200 & 13 & 14.0608725636352 & -1.06087256363523 \tabularnewline
201 & 9 & 12.3333769061975 & -3.33337690619746 \tabularnewline
202 & 15 & 13.9064023525474 & 1.09359764745264 \tabularnewline
203 & 17 & 14.0879559917281 & 2.91204400827194 \tabularnewline
204 & 13 & 12.9299944612985 & 0.0700055387015137 \tabularnewline
205 & 15 & 14.5877773406447 & 0.412222659355313 \tabularnewline
206 & 15 & 14.1409798659977 & 0.859020134002269 \tabularnewline
207 & 14 & 14.8589953661656 & -0.858995366165638 \tabularnewline
208 & 16 & 12.8860048789704 & 3.1139951210296 \tabularnewline
209 & 13 & 13.2220386723642 & -0.222038672364161 \tabularnewline
210 & 16 & 14.5743168922268 & 1.42568310777321 \tabularnewline
211 & 9 & 11.8183495330259 & -2.81834953302589 \tabularnewline
212 & 16 & 14.7070964240944 & 1.29290357590563 \tabularnewline
213 & 11 & 12.2598851171064 & -1.25988511710639 \tabularnewline
214 & 10 & 14.2346091853328 & -4.23460918533281 \tabularnewline
215 & 11 & 12.3338074085518 & -1.33380740855175 \tabularnewline
216 & 15 & 13.708131708688 & 1.29186829131196 \tabularnewline
217 & 17 & 15.0900680832725 & 1.90993191672748 \tabularnewline
218 & 14 & 14.6019968499783 & -0.601996849978325 \tabularnewline
219 & 8 & 9.75062834810613 & -1.75062834810613 \tabularnewline
220 & 15 & 13.975764078583 & 1.02423592141704 \tabularnewline
221 & 11 & 14.1695626825314 & -3.16956268253143 \tabularnewline
222 & 16 & 13.8888978445338 & 2.11110215546621 \tabularnewline
223 & 10 & 12.2980423733918 & -2.29804237339176 \tabularnewline
224 & 15 & 14.6856191681383 & 0.314380831861714 \tabularnewline
225 & 9 & 9.33226689429981 & -0.332266894299805 \tabularnewline
226 & 16 & 14.1558522141018 & 1.84414778589822 \tabularnewline
227 & 19 & 14.0168587746513 & 4.9831412253487 \tabularnewline
228 & 12 & 13.9138632145702 & -1.91386321457025 \tabularnewline
229 & 8 & 9.20472116329312 & -1.20472116329312 \tabularnewline
230 & 11 & 13.7428689797513 & -2.74286897975125 \tabularnewline
231 & 14 & 14.1322582820992 & -0.132258282099219 \tabularnewline
232 & 9 & 12.1950160178092 & -3.19501601780919 \tabularnewline
233 & 15 & 14.9459307133675 & 0.0540692866325023 \tabularnewline
234 & 13 & 12.8175948740423 & 0.182405125957693 \tabularnewline
235 & 16 & 14.8383595960903 & 1.16164040390966 \tabularnewline
236 & 11 & 13.1820096032555 & -2.18200960325553 \tabularnewline
237 & 12 & 11.3603129568393 & 0.639687043160734 \tabularnewline
238 & 13 & 13.4113798890457 & -0.411379889045703 \tabularnewline
239 & 10 & 14.5427611041192 & -4.54276110411921 \tabularnewline
240 & 11 & 13.848547562382 & -2.84854756238195 \tabularnewline
241 & 12 & 14.7193875573216 & -2.71938755732157 \tabularnewline
242 & 8 & 10.7722227897357 & -2.77222278973572 \tabularnewline
243 & 12 & 11.8746464231278 & 0.125353576872243 \tabularnewline
244 & 12 & 12.5081318353259 & -0.508131835325899 \tabularnewline
245 & 15 & 14.0065811319263 & 0.993418868073741 \tabularnewline
246 & 11 & 10.5872922330804 & 0.412707766919578 \tabularnewline
247 & 13 & 13.0592846141024 & -0.0592846141023526 \tabularnewline
248 & 14 & 8.64405604206826 & 5.35594395793174 \tabularnewline
249 & 10 & 10.0875103406489 & -0.0875103406488636 \tabularnewline
250 & 12 & 11.6623556578039 & 0.337644342196121 \tabularnewline
251 & 15 & 13.1384604664069 & 1.86153953359308 \tabularnewline
252 & 13 & 11.9156759428519 & 1.08432405714815 \tabularnewline
253 & 13 & 14.3475526441102 & -1.34755264411024 \tabularnewline
254 & 13 & 14.2406775984282 & -1.24067759842818 \tabularnewline
255 & 12 & 12.0386714481116 & -0.0386714481116421 \tabularnewline
256 & 12 & 13.0409876052416 & -1.04098760524159 \tabularnewline
257 & 9 & 10.727099051978 & -1.72709905197795 \tabularnewline
258 & 9 & 11.6186842711688 & -2.61868427116876 \tabularnewline
259 & 15 & 12.928653410898 & 2.07134658910196 \tabularnewline
260 & 10 & 14.4358887343046 & -4.43588873430456 \tabularnewline
261 & 14 & 13.9778742874219 & 0.0221257125780655 \tabularnewline
262 & 15 & 13.7693271687849 & 1.23067283121511 \tabularnewline
263 & 7 & 9.6992066899284 & -2.6992066899284 \tabularnewline
264 & 14 & 14.1242659758574 & -0.124265975857383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225552&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]14[/C][C]13.8026283782463[/C][C]0.197371621753725[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.0228125056007[/C][C]2.97718749439925[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.8999293188628[/C][C]-2.89992931886275[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.2563049239943[/C][C]-2.2563049239943[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]10.5442059789132[/C][C]5.45579402108676[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.2183632836936[/C][C]3.78163671630643[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]10.3956228240664[/C][C]3.60437717593356[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.7168792077532[/C][C]-0.716879207753163[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.767879142064[/C][C]0.232120857936039[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.3085138912894[/C][C]0.69148610871062[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.1850102075415[/C][C]1.81498979245851[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.0499983809267[/C][C]3.95000161907328[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.3140428188785[/C][C]-3.31404281887853[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.4893101002218[/C][C]2.51068989977819[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.3128793996499[/C][C]2.6871206003501[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.301084963531[/C][C]0.698915036468994[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.7387388208845[/C][C]0.261261179115502[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.6026489863941[/C][C]1.39735101360589[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.1649124937346[/C][C]-1.16491249373459[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.8427447734266[/C][C]2.15725522657336[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]14.9067476415846[/C][C]3.09325235841538[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.6601239296511[/C][C]-2.6601239296511[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.4853898843688[/C][C]-0.485389884368799[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.5801995848735[/C][C]-1.58019958487351[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.1653582509239[/C][C]1.83464174907607[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.5027672812184[/C][C]-6.50276728121841[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.7499579894153[/C][C]1.25004201058468[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.1496101466087[/C][C]0.850389853391314[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.7886849637877[/C][C]1.21131503621227[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.9583573776325[/C][C]-2.95835737763253[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.1564871385962[/C][C]0.843512861403774[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.3865128797054[/C][C]0.613487120294628[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]14.7258221767087[/C][C]2.27417782329134[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.0347375120441[/C][C]-0.0347375120440604[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.8855835412938[/C][C]0.114416458706217[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.7502175109354[/C][C]1.24978248906458[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.1406664963783[/C][C]-1.14066649637828[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.1959933431537[/C][C]0.80400665684628[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]14.9264856517207[/C][C]2.0735143482793[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]14.8993754509504[/C][C]-1.89937545095043[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.2816874532159[/C][C]-0.281687453215897[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.52865446477[/C][C]2.47134553522997[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.4881217141526[/C][C]0.511878285847358[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]13.1045144797045[/C][C]-1.10451447970448[/C][/ROW]
[ROW][C]45[/C][C]15[/C][C]14.4424438470689[/C][C]0.557556152931061[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.5321742140445[/C][C]-2.53217421404445[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]14.952605615711[/C][C]0.0473943842889669[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.6318199525985[/C][C]0.368180047401495[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.35235669609[/C][C]3.64764330390996[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.5456035845296[/C][C]-1.54560358452958[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]14.8760497244545[/C][C]1.12395027554548[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.3581637312791[/C][C]0.641836268720854[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.2592044480076[/C][C]-0.259204448007601[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.7628277740618[/C][C]-1.76282777406182[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.6681816790932[/C][C]-1.66818167909321[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]13.6682525840275[/C][C]1.33174741597252[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.3167533893318[/C][C]1.68324661066821[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]14.9964078175227[/C][C]0.00359218247725916[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.867205241105[/C][C]-2.86720524110501[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.3335821007338[/C][C]-1.33358210073377[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.2202352235618[/C][C]-2.22023522356183[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.5590831576364[/C][C]-1.55908315763641[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.5561570210869[/C][C]-3.55615702108691[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.027811766717[/C][C]0.972188233282957[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.7739804021324[/C][C]1.22601959786763[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.9936087319471[/C][C]-4.99360873194707[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.599704996519[/C][C]-1.59970499651905[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.4941238371795[/C][C]-2.49412383717948[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.6353817554366[/C][C]1.36461824456342[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.7197297298597[/C][C]1.2802702701403[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.6154671522621[/C][C]0.384532847737859[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]12.6761710139594[/C][C]3.32382898604062[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.5769639024128[/C][C]0.423036097587233[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.1109770890206[/C][C]-0.110977089020628[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.8620056036244[/C][C]-1.86200560362444[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]11.9884503792197[/C][C]0.0115496207803492[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.2060270503926[/C][C]2.79397294960738[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.4290030671626[/C][C]0.57099693283744[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.8205891049611[/C][C]1.17941089503886[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.7793788719725[/C][C]-1.77937887197247[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.7648562165494[/C][C]0.235143783450631[/C][/ROW]
[ROW][C]82[/C][C]15[/C][C]15.2256710540366[/C][C]-0.225671054036632[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]14.3416825815092[/C][C]1.65831741849082[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.2990145826797[/C][C]0.700985417320265[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]14.2976173852748[/C][C]-0.297617385274772[/C][/ROW]
[ROW][C]86[/C][C]15[/C][C]14.1552426458126[/C][C]0.844757354187368[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.3454144377194[/C][C]-0.345414437719386[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]12.8192309546781[/C][C]0.180769045321925[/C][/ROW]
[ROW][C]89[/C][C]7[/C][C]10.0865214921559[/C][C]-3.08652149215591[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]13.793808987362[/C][C]3.20619101263803[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]13.0072270568877[/C][C]-0.00722705688767833[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]14.3328890549672[/C][C]0.667110945032759[/C][/ROW]
[ROW][C]93[/C][C]14[/C][C]13.289459176922[/C][C]0.710540823077969[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]13.9701152042954[/C][C]-0.970115204295363[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.940545940017[/C][C]1.05945405998304[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]12.8802199270978[/C][C]-0.880219927097795[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]14.8177402109026[/C][C]-0.817740210902613[/C][/ROW]
[ROW][C]98[/C][C]17[/C][C]14.8889415610777[/C][C]2.11105843892232[/C][/ROW]
[ROW][C]99[/C][C]15[/C][C]14.7769213684547[/C][C]0.223078631545334[/C][/ROW]
[ROW][C]100[/C][C]17[/C][C]15.0189470067586[/C][C]1.9810529932414[/C][/ROW]
[ROW][C]101[/C][C]12[/C][C]12.9922418768859[/C][C]-0.992241876885853[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]15.1916487660842[/C][C]0.808351233915765[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]14.4189000305708[/C][C]-3.41890003057075[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]13.0327325029654[/C][C]1.96726749703457[/C][/ROW]
[ROW][C]105[/C][C]9[/C][C]11.2129074134332[/C][C]-2.21290741343318[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]14.9765069119396[/C][C]1.02349308806044[/C][/ROW]
[ROW][C]107[/C][C]15[/C][C]13.0053461835727[/C][C]1.99465381642726[/C][/ROW]
[ROW][C]108[/C][C]10[/C][C]12.9809458386789[/C][C]-2.98094583867895[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]8.70454534467596[/C][C]1.29545465532404[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]13.9704320149569[/C][C]1.02956798504313[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]13.2324600012415[/C][C]-2.23246000124154[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]15.2081958982131[/C][C]-2.20819589821311[/C][/ROW]
[ROW][C]113[/C][C]14[/C][C]11.8574592735656[/C][C]2.14254072643438[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]14.1576862163515[/C][C]3.84231378364846[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]15.5006794582717[/C][C]0.499320541728347[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]13.1243613153495[/C][C]0.875638684650531[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.0385538700571[/C][C]-0.0385538700570794[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.7507459446008[/C][C]-0.750745944600821[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.7983567640969[/C][C]0.201643235903146[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]12.5882236981942[/C][C]-0.588223698194197[/C][/ROW]
[ROW][C]121[/C][C]14[/C][C]13.7140052398183[/C][C]0.285994760181704[/C][/ROW]
[ROW][C]122[/C][C]15[/C][C]14.7932275361746[/C][C]0.206772463825371[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.557143470687[/C][C]-0.557143470687019[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.5923377305391[/C][C]0.407662269460863[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]14.6836348076131[/C][C]-1.68363480761311[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]15.8030846656258[/C][C]1.19691533437422[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.2781774500366[/C][C]1.72182254996338[/C][/ROW]
[ROW][C]128[/C][C]19[/C][C]14.9041013675214[/C][C]4.09589863247856[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.7500260156287[/C][C]1.24997398437127[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]14.612189846742[/C][C]-1.61218984674204[/C][/ROW]
[ROW][C]131[/C][C]9[/C][C]10.1912134508794[/C][C]-1.19121345087944[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]15.2017306524379[/C][C]-0.201730652437868[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]12.5699001982969[/C][C]2.43009980170314[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]14.3434620694689[/C][C]0.656537930531134[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]13.884666872345[/C][C]2.11533312765502[/C][/ROW]
[ROW][C]136[/C][C]11[/C][C]8.95278464024844[/C][C]2.04721535975156[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.5582924546931[/C][C]0.441707545306895[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.9244385628498[/C][C]-0.924438562849789[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]14.1470144850933[/C][C]0.852985514906668[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]13.7562816242377[/C][C]-0.756281624237654[/C][/ROW]
[ROW][C]141[/C][C]15[/C][C]14.562206165766[/C][C]0.437793834233992[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.8934281456202[/C][C]2.10657185437981[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]14.7529240314682[/C][C]-0.752924031468215[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]14.4103609632953[/C][C]0.589639036704709[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.526046052259[/C][C]1.47395394774105[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.6740763781093[/C][C]1.32592362189071[/C][/ROW]
[ROW][C]147[/C][C]11[/C][C]13.6581006562572[/C][C]-2.65810065625717[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]14.6943394778387[/C][C]-2.69433947783869[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]11.3413570701784[/C][C]-2.34135707017844[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.2386114130693[/C][C]1.76138858693069[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]12.5912384140014[/C][C]0.408761585998646[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.1585634285666[/C][C]0.841436571433435[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]14.5914194320246[/C][C]-2.59141943202457[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]11.4393613041574[/C][C]-2.43936130415737[/C][/ROW]
[ROW][C]155[/C][C]13[/C][C]11.5650332626005[/C][C]1.43496673739951[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]13.0072270568877[/C][C]-0.00722705688767833[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]13.4499307359434[/C][C]0.550069264056638[/C][/ROW]
[ROW][C]158[/C][C]19[/C][C]14.9041013675214[/C][C]4.09589863247856[/C][/ROW]
[ROW][C]159[/C][C]13[/C][C]15.4275573323289[/C][C]-2.4275573323289[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]11.8302565706173[/C][C]0.169743429382698[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.6535347331827[/C][C]0.346465266817287[/C][/ROW]
[ROW][C]162[/C][C]10[/C][C]8.84379718421047[/C][C]1.15620281578953[/C][/ROW]
[ROW][C]163[/C][C]14[/C][C]13.3825040224124[/C][C]0.617495977587559[/C][/ROW]
[ROW][C]164[/C][C]16[/C][C]11.5014673744632[/C][C]4.49853262553675[/C][/ROW]
[ROW][C]165[/C][C]10[/C][C]11.9326820698618[/C][C]-1.93268206986181[/C][/ROW]
[ROW][C]166[/C][C]11[/C][C]8.5981059222738[/C][C]2.4018940777262[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.2811535032709[/C][C]-0.281153503270944[/C][/ROW]
[ROW][C]168[/C][C]12[/C][C]12.9784440221759[/C][C]-0.978444022175879[/C][/ROW]
[ROW][C]169[/C][C]9[/C][C]12.7724687616677[/C][C]-3.77246876166771[/C][/ROW]
[ROW][C]170[/C][C]9[/C][C]11.8270037882739[/C][C]-2.82700378827394[/C][/ROW]
[ROW][C]171[/C][C]11[/C][C]10.4653745195802[/C][C]0.534625480419755[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]14.488502822293[/C][C]1.51149717770699[/C][/ROW]
[ROW][C]173[/C][C]9[/C][C]14.2458681346105[/C][C]-5.24586813461049[/C][/ROW]
[ROW][C]174[/C][C]13[/C][C]11.2534961519921[/C][C]1.74650384800788[/C][/ROW]
[ROW][C]175[/C][C]16[/C][C]13.7182904091112[/C][C]2.28170959088883[/C][/ROW]
[ROW][C]176[/C][C]13[/C][C]15.0699198233386[/C][C]-2.06991982333857[/C][/ROW]
[ROW][C]177[/C][C]9[/C][C]12.4832675139793[/C][C]-3.48326751397933[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]11.4000786344684[/C][C]0.599921365531566[/C][/ROW]
[ROW][C]179[/C][C]16[/C][C]14.542895825296[/C][C]1.45710417470405[/C][/ROW]
[ROW][C]180[/C][C]11[/C][C]13.3845542018969[/C][C]-2.38455420189687[/C][/ROW]
[ROW][C]181[/C][C]14[/C][C]14.3297465308421[/C][C]-0.32974653084208[/C][/ROW]
[ROW][C]182[/C][C]13[/C][C]14.5723935042617[/C][C]-1.57239350426166[/C][/ROW]
[ROW][C]183[/C][C]15[/C][C]14.9245877117446[/C][C]0.0754122882553748[/C][/ROW]
[ROW][C]184[/C][C]14[/C][C]15.0158629025602[/C][C]-1.01586290256022[/C][/ROW]
[ROW][C]185[/C][C]16[/C][C]13.8796240427445[/C][C]2.12037595725547[/C][/ROW]
[ROW][C]186[/C][C]13[/C][C]11.5719553508534[/C][C]1.42804464914657[/C][/ROW]
[ROW][C]187[/C][C]14[/C][C]13.9077948188976[/C][C]0.0922051811023837[/C][/ROW]
[ROW][C]188[/C][C]15[/C][C]14.4237129135006[/C][C]0.576287086499449[/C][/ROW]
[ROW][C]189[/C][C]13[/C][C]12.6335660536613[/C][C]0.366433946338703[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.0233329596851[/C][C]0.976667040314901[/C][/ROW]
[ROW][C]191[/C][C]11[/C][C]12.8475855034738[/C][C]-1.84758550347377[/C][/ROW]
[ROW][C]192[/C][C]14[/C][C]15.0475182903159[/C][C]-1.04751829031588[/C][/ROW]
[ROW][C]193[/C][C]15[/C][C]12.8980927559104[/C][C]2.10190724408965[/C][/ROW]
[ROW][C]194[/C][C]11[/C][C]12.8013834715828[/C][C]-1.80138347158281[/C][/ROW]
[ROW][C]195[/C][C]15[/C][C]13.389586503449[/C][C]1.61041349655096[/C][/ROW]
[ROW][C]196[/C][C]12[/C][C]14.3847701272972[/C][C]-2.38477012729721[/C][/ROW]
[ROW][C]197[/C][C]14[/C][C]11.7556463887651[/C][C]2.2443536112349[/C][/ROW]
[ROW][C]198[/C][C]14[/C][C]13.6448509161263[/C][C]0.355149083873658[/C][/ROW]
[ROW][C]199[/C][C]8[/C][C]11.124411890405[/C][C]-3.12441189040499[/C][/ROW]
[ROW][C]200[/C][C]13[/C][C]14.0608725636352[/C][C]-1.06087256363523[/C][/ROW]
[ROW][C]201[/C][C]9[/C][C]12.3333769061975[/C][C]-3.33337690619746[/C][/ROW]
[ROW][C]202[/C][C]15[/C][C]13.9064023525474[/C][C]1.09359764745264[/C][/ROW]
[ROW][C]203[/C][C]17[/C][C]14.0879559917281[/C][C]2.91204400827194[/C][/ROW]
[ROW][C]204[/C][C]13[/C][C]12.9299944612985[/C][C]0.0700055387015137[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]14.5877773406447[/C][C]0.412222659355313[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.1409798659977[/C][C]0.859020134002269[/C][/ROW]
[ROW][C]207[/C][C]14[/C][C]14.8589953661656[/C][C]-0.858995366165638[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]12.8860048789704[/C][C]3.1139951210296[/C][/ROW]
[ROW][C]209[/C][C]13[/C][C]13.2220386723642[/C][C]-0.222038672364161[/C][/ROW]
[ROW][C]210[/C][C]16[/C][C]14.5743168922268[/C][C]1.42568310777321[/C][/ROW]
[ROW][C]211[/C][C]9[/C][C]11.8183495330259[/C][C]-2.81834953302589[/C][/ROW]
[ROW][C]212[/C][C]16[/C][C]14.7070964240944[/C][C]1.29290357590563[/C][/ROW]
[ROW][C]213[/C][C]11[/C][C]12.2598851171064[/C][C]-1.25988511710639[/C][/ROW]
[ROW][C]214[/C][C]10[/C][C]14.2346091853328[/C][C]-4.23460918533281[/C][/ROW]
[ROW][C]215[/C][C]11[/C][C]12.3338074085518[/C][C]-1.33380740855175[/C][/ROW]
[ROW][C]216[/C][C]15[/C][C]13.708131708688[/C][C]1.29186829131196[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]15.0900680832725[/C][C]1.90993191672748[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]14.6019968499783[/C][C]-0.601996849978325[/C][/ROW]
[ROW][C]219[/C][C]8[/C][C]9.75062834810613[/C][C]-1.75062834810613[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]13.975764078583[/C][C]1.02423592141704[/C][/ROW]
[ROW][C]221[/C][C]11[/C][C]14.1695626825314[/C][C]-3.16956268253143[/C][/ROW]
[ROW][C]222[/C][C]16[/C][C]13.8888978445338[/C][C]2.11110215546621[/C][/ROW]
[ROW][C]223[/C][C]10[/C][C]12.2980423733918[/C][C]-2.29804237339176[/C][/ROW]
[ROW][C]224[/C][C]15[/C][C]14.6856191681383[/C][C]0.314380831861714[/C][/ROW]
[ROW][C]225[/C][C]9[/C][C]9.33226689429981[/C][C]-0.332266894299805[/C][/ROW]
[ROW][C]226[/C][C]16[/C][C]14.1558522141018[/C][C]1.84414778589822[/C][/ROW]
[ROW][C]227[/C][C]19[/C][C]14.0168587746513[/C][C]4.9831412253487[/C][/ROW]
[ROW][C]228[/C][C]12[/C][C]13.9138632145702[/C][C]-1.91386321457025[/C][/ROW]
[ROW][C]229[/C][C]8[/C][C]9.20472116329312[/C][C]-1.20472116329312[/C][/ROW]
[ROW][C]230[/C][C]11[/C][C]13.7428689797513[/C][C]-2.74286897975125[/C][/ROW]
[ROW][C]231[/C][C]14[/C][C]14.1322582820992[/C][C]-0.132258282099219[/C][/ROW]
[ROW][C]232[/C][C]9[/C][C]12.1950160178092[/C][C]-3.19501601780919[/C][/ROW]
[ROW][C]233[/C][C]15[/C][C]14.9459307133675[/C][C]0.0540692866325023[/C][/ROW]
[ROW][C]234[/C][C]13[/C][C]12.8175948740423[/C][C]0.182405125957693[/C][/ROW]
[ROW][C]235[/C][C]16[/C][C]14.8383595960903[/C][C]1.16164040390966[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]13.1820096032555[/C][C]-2.18200960325553[/C][/ROW]
[ROW][C]237[/C][C]12[/C][C]11.3603129568393[/C][C]0.639687043160734[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]13.4113798890457[/C][C]-0.411379889045703[/C][/ROW]
[ROW][C]239[/C][C]10[/C][C]14.5427611041192[/C][C]-4.54276110411921[/C][/ROW]
[ROW][C]240[/C][C]11[/C][C]13.848547562382[/C][C]-2.84854756238195[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]14.7193875573216[/C][C]-2.71938755732157[/C][/ROW]
[ROW][C]242[/C][C]8[/C][C]10.7722227897357[/C][C]-2.77222278973572[/C][/ROW]
[ROW][C]243[/C][C]12[/C][C]11.8746464231278[/C][C]0.125353576872243[/C][/ROW]
[ROW][C]244[/C][C]12[/C][C]12.5081318353259[/C][C]-0.508131835325899[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]14.0065811319263[/C][C]0.993418868073741[/C][/ROW]
[ROW][C]246[/C][C]11[/C][C]10.5872922330804[/C][C]0.412707766919578[/C][/ROW]
[ROW][C]247[/C][C]13[/C][C]13.0592846141024[/C][C]-0.0592846141023526[/C][/ROW]
[ROW][C]248[/C][C]14[/C][C]8.64405604206826[/C][C]5.35594395793174[/C][/ROW]
[ROW][C]249[/C][C]10[/C][C]10.0875103406489[/C][C]-0.0875103406488636[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.6623556578039[/C][C]0.337644342196121[/C][/ROW]
[ROW][C]251[/C][C]15[/C][C]13.1384604664069[/C][C]1.86153953359308[/C][/ROW]
[ROW][C]252[/C][C]13[/C][C]11.9156759428519[/C][C]1.08432405714815[/C][/ROW]
[ROW][C]253[/C][C]13[/C][C]14.3475526441102[/C][C]-1.34755264411024[/C][/ROW]
[ROW][C]254[/C][C]13[/C][C]14.2406775984282[/C][C]-1.24067759842818[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]12.0386714481116[/C][C]-0.0386714481116421[/C][/ROW]
[ROW][C]256[/C][C]12[/C][C]13.0409876052416[/C][C]-1.04098760524159[/C][/ROW]
[ROW][C]257[/C][C]9[/C][C]10.727099051978[/C][C]-1.72709905197795[/C][/ROW]
[ROW][C]258[/C][C]9[/C][C]11.6186842711688[/C][C]-2.61868427116876[/C][/ROW]
[ROW][C]259[/C][C]15[/C][C]12.928653410898[/C][C]2.07134658910196[/C][/ROW]
[ROW][C]260[/C][C]10[/C][C]14.4358887343046[/C][C]-4.43588873430456[/C][/ROW]
[ROW][C]261[/C][C]14[/C][C]13.9778742874219[/C][C]0.0221257125780655[/C][/ROW]
[ROW][C]262[/C][C]15[/C][C]13.7693271687849[/C][C]1.23067283121511[/C][/ROW]
[ROW][C]263[/C][C]7[/C][C]9.6992066899284[/C][C]-2.6992066899284[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]14.1242659758574[/C][C]-0.124265975857383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225552&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225552&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.80262837824630.197371621753725
21815.02281250560072.97718749439925
31113.8999293188628-2.89992931886275
41214.2563049239943-2.2563049239943
51610.54420597891325.45579402108676
61814.21836328369363.78163671630643
71410.39562282406643.60437717593356
81414.7168792077532-0.716879207753163
91514.7678791420640.232120857936039
101514.30851389128940.69148610871062
111715.18501020754151.81498979245851
121915.04999838092673.95000161907328
131013.3140428188785-3.31404281887853
141613.48931010022182.51068989977819
151815.31287939964992.6871206003501
161413.3010849635310.698915036468994
171413.73873882088450.261261179115502
181715.60264898639411.39735101360589
191415.1649124937346-1.16491249373459
201613.84274477342662.15725522657336
211814.90674764158463.09325235841538
221113.6601239296511-2.6601239296511
231414.4853898843688-0.485389884368799
241213.5801995848735-1.58019958487351
251715.16535825092391.83464174907607
26915.5027672812184-6.50276728121841
271614.74995798941531.25004201058468
281413.14961014660870.850389853391314
291513.78868496378771.21131503621227
301113.9583573776325-2.95835737763253
311615.15648713859620.843512861403774
321312.38651287970540.613487120294628
331714.72582217670872.27417782329134
341515.0347375120441-0.0347375120440604
351413.88558354129380.114416458706217
361614.75021751093541.24978248906458
37910.1406664963783-1.14066649637828
381514.19599334315370.80400665684628
391714.92648565172072.0735143482793
401314.8993754509504-1.89937545095043
411515.2816874532159-0.281687453215897
421613.528654464772.47134553522997
431615.48812171415260.511878285847358
441213.1045144797045-1.10451447970448
451514.44244384706890.557556152931061
461113.5321742140445-2.53217421404445
471514.9526056157110.0473943842889669
481514.63181995259850.368180047401495
491713.352356696093.64764330390996
501314.5456035845296-1.54560358452958
511614.87604972445451.12395027554548
521413.35816373127910.641836268720854
531111.2592044480076-0.259204448007601
541213.7628277740618-1.76282777406182
551213.6681816790932-1.66818167909321
561513.66825258402751.33174741597252
571614.31675338933181.68324661066821
581514.99640781752270.00359218247725916
591214.867205241105-2.86720524110501
601213.3335821007338-1.33358210073377
61810.2202352235618-2.22023522356183
621314.5590831576364-1.55908315763641
631114.5561570210869-3.55615702108691
641413.0278117667170.972188233282957
651513.77398040213241.22601959786763
661014.9936087319471-4.99360873194707
671112.599704996519-1.59970499651905
681214.4941238371795-2.49412383717948
691513.63538175543661.36461824456342
701513.71972972985971.2802702701403
711413.61546715226210.384532847737859
721612.67617101395943.32382898604062
731514.57696390241280.423036097587233
741515.1109770890206-0.110977089020628
751314.8620056036244-1.86200560362444
761211.98845037921970.0115496207803492
771714.20602705039262.79397294960738
781312.42900306716260.57099693283744
791513.82058910496111.17941089503886
801314.7793788719725-1.77937887197247
811514.76485621654940.235143783450631
821515.2256710540366-0.225671054036632
831614.34168258150921.65831741849082
841514.29901458267970.700985417320265
851414.2976173852748-0.297617385274772
861514.15524264581260.844757354187368
871414.3454144377194-0.345414437719386
881312.81923095467810.180769045321925
89710.0865214921559-3.08652149215591
901713.7938089873623.20619101263803
911313.0072270568877-0.00722705688767833
921514.33288905496720.667110945032759
931413.2894591769220.710540823077969
941313.9701152042954-0.970115204295363
951614.9405459400171.05945405998304
961212.8802199270978-0.880219927097795
971414.8177402109026-0.817740210902613
981714.88894156107772.11105843892232
991514.77692136845470.223078631545334
1001715.01894700675861.9810529932414
1011212.9922418768859-0.992241876885853
1021615.19164876608420.808351233915765
1031114.4189000305708-3.41890003057075
1041513.03273250296541.96726749703457
105911.2129074134332-2.21290741343318
1061614.97650691193961.02349308806044
1071513.00534618357271.99465381642726
1081012.9809458386789-2.98094583867895
109108.704545344675961.29545465532404
1101513.97043201495691.02956798504313
1111113.2324600012415-2.23246000124154
1121315.2081958982131-2.20819589821311
1131411.85745927356562.14254072643438
1141814.15768621635153.84231378364846
1151615.50067945827170.499320541728347
1161413.12436131534950.875638684650531
1171414.0385538700571-0.0385538700570794
1181414.7507459446008-0.750745944600821
1191413.79835676409690.201643235903146
1201212.5882236981942-0.588223698194197
1211413.71400523981830.285994760181704
1221514.79322753617460.206772463825371
1231515.557143470687-0.557143470687019
1241514.59233773053910.407662269460863
1251314.6836348076131-1.68363480761311
1261715.80308466562581.19691533437422
1271715.27817745003661.72182254996338
1281914.90410136752144.09589863247856
1291513.75002601562871.24997398437127
1301314.612189846742-1.61218984674204
131910.1912134508794-1.19121345087944
1321515.2017306524379-0.201730652437868
1331512.56990019829692.43009980170314
1341514.34346206946890.656537930531134
1351613.8846668723452.11533312765502
136118.952784640248442.04721535975156
1371413.55829245469310.441707545306895
1381111.9244385628498-0.924438562849789
1391514.14701448509330.852985514906668
1401313.7562816242377-0.756281624237654
1411514.5622061657660.437793834233992
1421613.89342814562022.10657185437981
1431414.7529240314682-0.752924031468215
1441514.41036096329530.589639036704709
1451614.5260460522591.47395394774105
1461614.67407637810931.32592362189071
1471113.6581006562572-2.65810065625717
1481214.6943394778387-2.69433947783869
149911.3413570701784-2.34135707017844
1501614.23861141306931.76138858693069
1511312.59123841400140.408761585998646
1521615.15856342856660.841436571433435
1531214.5914194320246-2.59141943202457
154911.4393613041574-2.43936130415737
1551311.56503326260051.43496673739951
1561313.0072270568877-0.00722705688767833
1571413.44993073594340.550069264056638
1581914.90410136752144.09589863247856
1591315.4275573323289-2.4275573323289
1601211.83025657061730.169743429382698
1611312.65353473318270.346465266817287
162108.843797184210471.15620281578953
1631413.38250402241240.617495977587559
1641611.50146737446324.49853262553675
1651011.9326820698618-1.93268206986181
166118.59810592227382.4018940777262
1671414.2811535032709-0.281153503270944
1681212.9784440221759-0.978444022175879
169912.7724687616677-3.77246876166771
170911.8270037882739-2.82700378827394
1711110.46537451958020.534625480419755
1721614.4885028222931.51149717770699
173914.2458681346105-5.24586813461049
1741311.25349615199211.74650384800788
1751613.71829040911122.28170959088883
1761315.0699198233386-2.06991982333857
177912.4832675139793-3.48326751397933
1781211.40007863446840.599921365531566
1791614.5428958252961.45710417470405
1801113.3845542018969-2.38455420189687
1811414.3297465308421-0.32974653084208
1821314.5723935042617-1.57239350426166
1831514.92458771174460.0754122882553748
1841415.0158629025602-1.01586290256022
1851613.87962404274452.12037595725547
1861311.57195535085341.42804464914657
1871413.90779481889760.0922051811023837
1881514.42371291350060.576287086499449
1891312.63356605366130.366433946338703
1901110.02333295968510.976667040314901
1911112.8475855034738-1.84758550347377
1921415.0475182903159-1.04751829031588
1931512.89809275591042.10190724408965
1941112.8013834715828-1.80138347158281
1951513.3895865034491.61041349655096
1961214.3847701272972-2.38477012729721
1971411.75564638876512.2443536112349
1981413.64485091612630.355149083873658
199811.124411890405-3.12441189040499
2001314.0608725636352-1.06087256363523
201912.3333769061975-3.33337690619746
2021513.90640235254741.09359764745264
2031714.08795599172812.91204400827194
2041312.92999446129850.0700055387015137
2051514.58777734064470.412222659355313
2061514.14097986599770.859020134002269
2071414.8589953661656-0.858995366165638
2081612.88600487897043.1139951210296
2091313.2220386723642-0.222038672364161
2101614.57431689222681.42568310777321
211911.8183495330259-2.81834953302589
2121614.70709642409441.29290357590563
2131112.2598851171064-1.25988511710639
2141014.2346091853328-4.23460918533281
2151112.3338074085518-1.33380740855175
2161513.7081317086881.29186829131196
2171715.09006808327251.90993191672748
2181414.6019968499783-0.601996849978325
21989.75062834810613-1.75062834810613
2201513.9757640785831.02423592141704
2211114.1695626825314-3.16956268253143
2221613.88889784453382.11110215546621
2231012.2980423733918-2.29804237339176
2241514.68561916813830.314380831861714
22599.33226689429981-0.332266894299805
2261614.15585221410181.84414778589822
2271914.01685877465134.9831412253487
2281213.9138632145702-1.91386321457025
22989.20472116329312-1.20472116329312
2301113.7428689797513-2.74286897975125
2311414.1322582820992-0.132258282099219
232912.1950160178092-3.19501601780919
2331514.94593071336750.0540692866325023
2341312.81759487404230.182405125957693
2351614.83835959609031.16164040390966
2361113.1820096032555-2.18200960325553
2371211.36031295683930.639687043160734
2381313.4113798890457-0.411379889045703
2391014.5427611041192-4.54276110411921
2401113.848547562382-2.84854756238195
2411214.7193875573216-2.71938755732157
242810.7722227897357-2.77222278973572
2431211.87464642312780.125353576872243
2441212.5081318353259-0.508131835325899
2451514.00658113192630.993418868073741
2461110.58729223308040.412707766919578
2471313.0592846141024-0.0592846141023526
248148.644056042068265.35594395793174
2491010.0875103406489-0.0875103406488636
2501211.66235565780390.337644342196121
2511513.13846046640691.86153953359308
2521311.91567594285191.08432405714815
2531314.3475526441102-1.34755264411024
2541314.2406775984282-1.24067759842818
2551212.0386714481116-0.0386714481116421
2561213.0409876052416-1.04098760524159
257910.727099051978-1.72709905197795
258911.6186842711688-2.61868427116876
2591512.9286534108982.07134658910196
2601014.4358887343046-4.43588873430456
2611413.97787428742190.0221257125780655
2621513.76932716878491.23067283121511
26379.6992066899284-2.6992066899284
2641414.1242659758574-0.124265975857383






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.6315596246967050.7368807506065910.368440375303295
130.875156208642690.2496875827146210.12484379135731
140.8457589538417270.3084820923165460.154241046158273
150.8029678101812710.3940643796374590.197032189818729
160.7959080886109260.4081838227781480.204091911389074
170.8066628226195280.3866743547609440.193337177380472
180.7357642493402120.5284715013195760.264235750659788
190.6694385558444980.6611228883110030.330561444155502
200.7901610743007860.4196778513984290.209838925699214
210.7760770307984260.4478459384031490.223922969201574
220.7295475669644270.5409048660711460.270452433035573
230.6952955477619480.6094089044761050.304704452238052
240.6599463674887440.6801072650225120.340053632511256
250.6012502695225470.7974994609549050.398749730477453
260.997934359212690.004131281574620880.00206564078731044
270.9967272701185810.006545459762838770.00327272988141938
280.9948854589988140.01022908200237110.00511454100118554
290.9924792951285110.0150414097429780.00752070487148901
300.993280795746530.01343840850693940.00671920425346972
310.9905719198425080.01885616031498490.00942808015749245
320.987952066612810.02409586677437950.0120479333871897
330.9849231660113150.03015366797737010.0150768339886851
340.9786795901645380.04264081967092330.0213204098354617
350.9706906702497630.05861865950047360.0293093297502368
360.9654816813955770.06903663720884560.0345183186044228
370.9863848189468440.0272303621063120.013615181053156
380.9816240241475130.03675195170497350.0183759758524867
390.9781035043444230.04379299131115390.0218964956555769
400.9802317684791920.03953646304161660.0197682315208083
410.9753671488986120.04926570220277590.024632851101388
420.9743368928143150.05132621437137070.0256631071856854
430.966467160208230.06706567958354070.0335328397917704
440.9591434743611930.0817130512776150.0408565256388075
450.9476227721057180.1047544557885630.0523772278942817
460.9526637069860640.09467258602787140.0473362930139357
470.939651214434350.12069757113130.0603487855656502
480.9241064011160180.1517871977679640.0758935988839819
490.933301673905030.1333966521899390.0666983260949697
500.9277562513510550.144487497297890.0722437486489449
510.9124766389396120.1750467221207760.0875233610603879
520.8936872318478020.2126255363043950.106312768152198
530.8860860886382740.2278278227234520.113913911361726
540.8674624257584550.265075148483090.132537574241545
550.8626393967619990.2747212064760030.137360603238001
560.844613233140830.310773533718340.15538676685917
570.8370661800538260.3258676398923490.162933819946174
580.809732852307710.3805342953845790.19026714769229
590.8575881657384170.2848236685231670.142411834261583
600.8531365784493090.2937268431013810.146863421550691
610.8823628752697490.2352742494605020.117637124730251
620.8796261855096870.2407476289806260.120373814490313
630.9219484820809790.1561030358380420.0780515179190212
640.9117484540702640.1765030918594730.0882515459297364
650.9053289612728550.189342077454290.0946710387271448
660.9673971203759430.06520575924811430.0326028796240572
670.9666167427019520.06676651459609540.0333832572980477
680.9702584523219490.05948309535610170.0297415476780508
690.9662448270913870.06751034581722640.0337551729086132
700.9609082917862120.07818341642757680.0390917082137884
710.9521165948033050.09576681039338940.0478834051966947
720.9628824845675390.07423503086492280.0371175154324614
730.9541785656206740.09164286875865140.0458214343793257
740.9451795955363690.1096408089272620.054820404463631
750.9402332537571170.1195334924857660.0597667462428828
760.9289579579249230.1420840841501550.0710420420750773
770.9394020164825940.1211959670348110.0605979835174057
780.9272734315007760.1454531369984470.0727265684992235
790.9198581476211730.1602837047576550.0801418523788273
800.9119267870445290.1761464259109410.0880732129554706
810.8954908249182070.2090183501635860.104509175081793
820.8776536990886990.2446926018226020.122346300911301
830.8704739807041270.2590520385917470.129526019295873
840.8514577182217270.2970845635565450.148542281778273
850.8281146212491130.3437707575017740.171885378750887
860.8045974282974560.3908051434050870.195402571702544
870.777447446013920.4451051079721590.22255255398608
880.7480018270341080.5039963459317850.251998172965892
890.8129831639711010.3740336720577980.187016836028899
900.840601724826990.3187965503460190.15939827517301
910.8167175671165140.3665648657669720.183282432883486
920.7929482451510470.4141035096979050.207051754848953
930.7690784533457250.4618430933085510.230921546654275
940.7456924995487570.5086150009024860.254307500451243
950.7198978241646180.5602043516707630.280102175835382
960.6947188165319170.6105623669361670.305281183468083
970.6671286137859570.6657427724280860.332871386214043
980.6663603882916760.6672792234166470.333639611708324
990.6326804335133410.7346391329733180.367319566486659
1000.6269300824858580.7461398350282830.373069917514142
1010.60295450917510.79409098164980.3970454908249
1020.572816707260660.854366585478680.42718329273934
1030.6196365740730080.7607268518539830.380363425926992
1040.6077735003880070.7844529992239870.392226499611993
1050.6191040134723350.7617919730553290.380895986527664
1060.5921455719061870.8157088561876270.407854428093813
1070.5834104330424120.8331791339151750.416589566957588
1080.6265079421144990.7469841157710030.373492057885501
1090.6014030244391250.797193951121750.398596975560875
1100.5740349283940370.8519301432119270.425965071605963
1110.5803248677580560.8393502644838880.419675132241944
1120.6059042068928420.7881915862143160.394095793107158
1130.6013544173287910.7972911653424170.398645582671209
1140.6847245474564950.6305509050870090.315275452543505
1150.6546931106125340.6906137787749330.345306889387466
1160.6287785095261280.7424429809477450.371221490473872
1170.5957424424988710.8085151150022580.404257557501129
1180.5677951174907680.8644097650184640.432204882509232
1190.5333117232311510.9333765535376990.466688276768849
1200.5024180075910690.9951639848178620.497581992408931
1210.4703584510167450.940716902033490.529641548983255
1220.4394816935408170.8789633870816330.560518306459183
1230.4080952834836010.8161905669672020.591904716516399
1240.3757209257238690.7514418514477380.624279074276131
1250.3624321709553150.724864341910630.637567829044685
1260.3381237737953290.6762475475906570.661876226204671
1270.329040088782910.6580801775658190.67095991121709
1280.4350949310885450.8701898621770910.564905068911455
1290.4138094757301840.8276189514603680.586190524269816
1300.4011731763729260.8023463527458520.598826823627074
1310.3798600297377210.7597200594754430.620139970262279
1320.3519561678073520.7039123356147050.648043832192648
1330.3700826581972130.7401653163944260.629917341802787
1340.3428917447652410.6857834895304830.657108255234759
1350.3491834248987610.6983668497975230.650816575101239
1360.3492060932337430.6984121864674850.650793906766257
1370.3188250927885620.6376501855771240.681174907211438
1380.2951055155973260.5902110311946520.704894484402674
1390.2700659501617420.5401319003234850.729934049838258
1400.2475714933275430.4951429866550870.752428506672457
1410.2216848883425770.4433697766851540.778315111657423
1420.2237627554598220.4475255109196440.776237244540178
1430.1996855812491850.3993711624983690.800314418750815
1440.1784270438138750.3568540876277510.821572956186125
1450.1671306258484330.3342612516968670.832869374151567
1460.1582221771112790.3164443542225590.841777822888721
1470.1715640127241740.3431280254483490.828435987275825
1480.185801550219040.371603100438080.81419844978096
1490.1985085947404870.3970171894809750.801491405259513
1500.1972220554375480.3944441108750950.802777944562452
1510.1774443314758920.3548886629517840.822555668524108
1520.16108366987570.32216733975140.8389163301243
1530.1772757261641030.3545514523282070.822724273835897
1540.189453837408590.378907674817180.81054616259141
1550.1760677858545390.3521355717090780.823932214145461
1560.1536008899023730.3072017798047450.846399110097627
1570.1352833057760180.2705666115520350.864716694223982
1580.2161250806956560.4322501613913110.783874919304344
1590.2246865063494850.449373012698970.775313493650515
1600.2037398108196890.4074796216393780.796260189180311
1610.1815149323213820.3630298646427650.818485067678618
1620.1625337860009950.3250675720019910.837466213999005
1630.1425204292630680.2850408585261350.857479570736932
1640.255245653284360.510491306568720.74475434671564
1650.2498834225602860.4997668451205710.750116577439714
1660.2543659699534330.5087319399068670.745634030046567
1670.2260624358043490.4521248716086970.773937564195651
1680.2053149319986810.4106298639973610.794685068001319
1690.2642309936945720.5284619873891450.735769006305428
1700.2857832052875540.5715664105751070.714216794712446
1710.2590350493519650.518070098703930.740964950648035
1720.2522509343316080.5045018686632160.747749065668392
1730.4261887383661150.8523774767322310.573811261633885
1740.4111934517985040.8223869035970090.588806548201496
1750.4249670617551750.849934123510350.575032938244825
1760.4201273398754320.8402546797508630.579872660124569
1770.4834068018260420.9668136036520840.516593198173958
1780.4501711676847580.9003423353695160.549828832315242
1790.4337291995936730.8674583991873460.566270800406327
1800.4421091670499860.8842183340999720.557890832950014
1810.4039745685596970.8079491371193940.596025431440303
1820.3858892858548330.7717785717096660.614110714145167
1830.3499124679392320.6998249358784650.650087532060768
1840.3189499344844780.6378998689689550.681050065515522
1850.3386648085116150.677329617023230.661335191488385
1860.3359688583284210.6719377166568420.664031141671579
1870.3009441005401780.6018882010803570.699055899459821
1880.2722394494184260.5444788988368520.727760550581574
1890.2402833822928490.4805667645856990.75971661770715
1900.2187088503119910.4374177006239810.781291149688009
1910.2341457212835660.4682914425671320.765854278716434
1920.2099380912924780.4198761825849560.790061908707522
1930.2161713213979440.4323426427958870.783828678602056
1940.2054354351666780.4108708703333570.794564564833322
1950.2002454283531490.4004908567062980.799754571646851
1960.2121362764093210.4242725528186420.787863723590679
1970.2639134529401610.5278269058803230.736086547059839
1980.2442989671935370.4885979343870750.755701032806463
1990.2733194836395550.5466389672791090.726680516360445
2000.2426555191693310.4853110383386630.757344480830669
2010.275795617863780.551591235727560.72420438213622
2020.2531685373311060.5063370746622110.746831462668894
2030.3162001694749220.6324003389498430.683799830525078
2040.2820316991562070.5640633983124140.717968300843793
2050.249195451457010.4983909029140190.75080454854299
2060.2240450504869730.4480901009739460.775954949513027
2070.1941871120414410.3883742240828820.805812887958559
2080.2090529775015140.4181059550030290.790947022498486
2090.1784745535744140.3569491071488280.821525446425586
2100.1926754243544650.385350848708930.807324575645535
2110.2185996456466340.4371992912932690.781400354353366
2120.2043607741748380.4087215483496760.795639225825162
2130.1778894127004370.3557788254008740.822110587299563
2140.2386208738184580.4772417476369160.761379126181542
2150.2111783944388370.4223567888776740.788821605561163
2160.1929721210169470.3859442420338950.807027878983053
2170.2328866665485620.4657733330971240.767113333451438
2180.2023708906445220.4047417812890440.797629109355478
2190.1828297559455550.3656595118911090.817170244054445
2200.1837836587272570.3675673174545150.816216341272743
2210.1929967759679220.3859935519358450.807003224032078
2220.1900653754724190.3801307509448390.809934624527581
2230.1740499571403830.3480999142807660.825950042859617
2240.1477723067814240.2955446135628470.852227693218576
2250.1314473525473450.262894705094690.868552647452655
2260.1636285371197560.3272570742395130.836371462880244
2270.3681780103653250.736356020730650.631821989634675
2280.3503746049987580.7007492099975170.649625395001242
2290.3095314004326310.6190628008652620.690468599567369
2300.3174713474985590.6349426949971180.682528652501441
2310.2691396508480560.5382793016961120.730860349151944
2320.3355373062780480.6710746125560960.664462693721952
2330.3233356253066510.6466712506133020.676664374693349
2340.2884957590908620.5769915181817230.711504240909138
2350.5195209365716990.9609581268566020.480479063428301
2360.5584358767035670.8831282465928670.441564123296433
2370.5312225938004450.937554812399110.468777406199555
2380.4751605728278210.9503211456556430.524839427172179
2390.4841122992081530.9682245984163070.515887700791847
2400.5652373894302640.8695252211394720.434762610569736
2410.5078990752163410.9842018495673180.492100924783659
2420.7690070149186380.4619859701627230.230992985081362
2430.6978360952675570.6043278094648860.302163904732443
2440.615025265003560.7699494699928790.38497473499644
2450.726511184957550.5469776300849010.27348881504245
2460.6461868256623940.7076263486752110.353813174337606
2470.5455435822166370.9089128355667260.454456417783363
2480.9615414991383870.0769170017232260.038458500861613
2490.9900530757079830.01989384858403310.00994692429201656
2500.9897965412276330.02040691754473360.0102034587723668
2510.9675747876852160.06485042462956850.0324252123147842
2520.9087326042842280.1825347914315440.0912673957157718

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.631559624696705 & 0.736880750606591 & 0.368440375303295 \tabularnewline
13 & 0.87515620864269 & 0.249687582714621 & 0.12484379135731 \tabularnewline
14 & 0.845758953841727 & 0.308482092316546 & 0.154241046158273 \tabularnewline
15 & 0.802967810181271 & 0.394064379637459 & 0.197032189818729 \tabularnewline
16 & 0.795908088610926 & 0.408183822778148 & 0.204091911389074 \tabularnewline
17 & 0.806662822619528 & 0.386674354760944 & 0.193337177380472 \tabularnewline
18 & 0.735764249340212 & 0.528471501319576 & 0.264235750659788 \tabularnewline
19 & 0.669438555844498 & 0.661122888311003 & 0.330561444155502 \tabularnewline
20 & 0.790161074300786 & 0.419677851398429 & 0.209838925699214 \tabularnewline
21 & 0.776077030798426 & 0.447845938403149 & 0.223922969201574 \tabularnewline
22 & 0.729547566964427 & 0.540904866071146 & 0.270452433035573 \tabularnewline
23 & 0.695295547761948 & 0.609408904476105 & 0.304704452238052 \tabularnewline
24 & 0.659946367488744 & 0.680107265022512 & 0.340053632511256 \tabularnewline
25 & 0.601250269522547 & 0.797499460954905 & 0.398749730477453 \tabularnewline
26 & 0.99793435921269 & 0.00413128157462088 & 0.00206564078731044 \tabularnewline
27 & 0.996727270118581 & 0.00654545976283877 & 0.00327272988141938 \tabularnewline
28 & 0.994885458998814 & 0.0102290820023711 & 0.00511454100118554 \tabularnewline
29 & 0.992479295128511 & 0.015041409742978 & 0.00752070487148901 \tabularnewline
30 & 0.99328079574653 & 0.0134384085069394 & 0.00671920425346972 \tabularnewline
31 & 0.990571919842508 & 0.0188561603149849 & 0.00942808015749245 \tabularnewline
32 & 0.98795206661281 & 0.0240958667743795 & 0.0120479333871897 \tabularnewline
33 & 0.984923166011315 & 0.0301536679773701 & 0.0150768339886851 \tabularnewline
34 & 0.978679590164538 & 0.0426408196709233 & 0.0213204098354617 \tabularnewline
35 & 0.970690670249763 & 0.0586186595004736 & 0.0293093297502368 \tabularnewline
36 & 0.965481681395577 & 0.0690366372088456 & 0.0345183186044228 \tabularnewline
37 & 0.986384818946844 & 0.027230362106312 & 0.013615181053156 \tabularnewline
38 & 0.981624024147513 & 0.0367519517049735 & 0.0183759758524867 \tabularnewline
39 & 0.978103504344423 & 0.0437929913111539 & 0.0218964956555769 \tabularnewline
40 & 0.980231768479192 & 0.0395364630416166 & 0.0197682315208083 \tabularnewline
41 & 0.975367148898612 & 0.0492657022027759 & 0.024632851101388 \tabularnewline
42 & 0.974336892814315 & 0.0513262143713707 & 0.0256631071856854 \tabularnewline
43 & 0.96646716020823 & 0.0670656795835407 & 0.0335328397917704 \tabularnewline
44 & 0.959143474361193 & 0.081713051277615 & 0.0408565256388075 \tabularnewline
45 & 0.947622772105718 & 0.104754455788563 & 0.0523772278942817 \tabularnewline
46 & 0.952663706986064 & 0.0946725860278714 & 0.0473362930139357 \tabularnewline
47 & 0.93965121443435 & 0.1206975711313 & 0.0603487855656502 \tabularnewline
48 & 0.924106401116018 & 0.151787197767964 & 0.0758935988839819 \tabularnewline
49 & 0.93330167390503 & 0.133396652189939 & 0.0666983260949697 \tabularnewline
50 & 0.927756251351055 & 0.14448749729789 & 0.0722437486489449 \tabularnewline
51 & 0.912476638939612 & 0.175046722120776 & 0.0875233610603879 \tabularnewline
52 & 0.893687231847802 & 0.212625536304395 & 0.106312768152198 \tabularnewline
53 & 0.886086088638274 & 0.227827822723452 & 0.113913911361726 \tabularnewline
54 & 0.867462425758455 & 0.26507514848309 & 0.132537574241545 \tabularnewline
55 & 0.862639396761999 & 0.274721206476003 & 0.137360603238001 \tabularnewline
56 & 0.84461323314083 & 0.31077353371834 & 0.15538676685917 \tabularnewline
57 & 0.837066180053826 & 0.325867639892349 & 0.162933819946174 \tabularnewline
58 & 0.80973285230771 & 0.380534295384579 & 0.19026714769229 \tabularnewline
59 & 0.857588165738417 & 0.284823668523167 & 0.142411834261583 \tabularnewline
60 & 0.853136578449309 & 0.293726843101381 & 0.146863421550691 \tabularnewline
61 & 0.882362875269749 & 0.235274249460502 & 0.117637124730251 \tabularnewline
62 & 0.879626185509687 & 0.240747628980626 & 0.120373814490313 \tabularnewline
63 & 0.921948482080979 & 0.156103035838042 & 0.0780515179190212 \tabularnewline
64 & 0.911748454070264 & 0.176503091859473 & 0.0882515459297364 \tabularnewline
65 & 0.905328961272855 & 0.18934207745429 & 0.0946710387271448 \tabularnewline
66 & 0.967397120375943 & 0.0652057592481143 & 0.0326028796240572 \tabularnewline
67 & 0.966616742701952 & 0.0667665145960954 & 0.0333832572980477 \tabularnewline
68 & 0.970258452321949 & 0.0594830953561017 & 0.0297415476780508 \tabularnewline
69 & 0.966244827091387 & 0.0675103458172264 & 0.0337551729086132 \tabularnewline
70 & 0.960908291786212 & 0.0781834164275768 & 0.0390917082137884 \tabularnewline
71 & 0.952116594803305 & 0.0957668103933894 & 0.0478834051966947 \tabularnewline
72 & 0.962882484567539 & 0.0742350308649228 & 0.0371175154324614 \tabularnewline
73 & 0.954178565620674 & 0.0916428687586514 & 0.0458214343793257 \tabularnewline
74 & 0.945179595536369 & 0.109640808927262 & 0.054820404463631 \tabularnewline
75 & 0.940233253757117 & 0.119533492485766 & 0.0597667462428828 \tabularnewline
76 & 0.928957957924923 & 0.142084084150155 & 0.0710420420750773 \tabularnewline
77 & 0.939402016482594 & 0.121195967034811 & 0.0605979835174057 \tabularnewline
78 & 0.927273431500776 & 0.145453136998447 & 0.0727265684992235 \tabularnewline
79 & 0.919858147621173 & 0.160283704757655 & 0.0801418523788273 \tabularnewline
80 & 0.911926787044529 & 0.176146425910941 & 0.0880732129554706 \tabularnewline
81 & 0.895490824918207 & 0.209018350163586 & 0.104509175081793 \tabularnewline
82 & 0.877653699088699 & 0.244692601822602 & 0.122346300911301 \tabularnewline
83 & 0.870473980704127 & 0.259052038591747 & 0.129526019295873 \tabularnewline
84 & 0.851457718221727 & 0.297084563556545 & 0.148542281778273 \tabularnewline
85 & 0.828114621249113 & 0.343770757501774 & 0.171885378750887 \tabularnewline
86 & 0.804597428297456 & 0.390805143405087 & 0.195402571702544 \tabularnewline
87 & 0.77744744601392 & 0.445105107972159 & 0.22255255398608 \tabularnewline
88 & 0.748001827034108 & 0.503996345931785 & 0.251998172965892 \tabularnewline
89 & 0.812983163971101 & 0.374033672057798 & 0.187016836028899 \tabularnewline
90 & 0.84060172482699 & 0.318796550346019 & 0.15939827517301 \tabularnewline
91 & 0.816717567116514 & 0.366564865766972 & 0.183282432883486 \tabularnewline
92 & 0.792948245151047 & 0.414103509697905 & 0.207051754848953 \tabularnewline
93 & 0.769078453345725 & 0.461843093308551 & 0.230921546654275 \tabularnewline
94 & 0.745692499548757 & 0.508615000902486 & 0.254307500451243 \tabularnewline
95 & 0.719897824164618 & 0.560204351670763 & 0.280102175835382 \tabularnewline
96 & 0.694718816531917 & 0.610562366936167 & 0.305281183468083 \tabularnewline
97 & 0.667128613785957 & 0.665742772428086 & 0.332871386214043 \tabularnewline
98 & 0.666360388291676 & 0.667279223416647 & 0.333639611708324 \tabularnewline
99 & 0.632680433513341 & 0.734639132973318 & 0.367319566486659 \tabularnewline
100 & 0.626930082485858 & 0.746139835028283 & 0.373069917514142 \tabularnewline
101 & 0.6029545091751 & 0.7940909816498 & 0.3970454908249 \tabularnewline
102 & 0.57281670726066 & 0.85436658547868 & 0.42718329273934 \tabularnewline
103 & 0.619636574073008 & 0.760726851853983 & 0.380363425926992 \tabularnewline
104 & 0.607773500388007 & 0.784452999223987 & 0.392226499611993 \tabularnewline
105 & 0.619104013472335 & 0.761791973055329 & 0.380895986527664 \tabularnewline
106 & 0.592145571906187 & 0.815708856187627 & 0.407854428093813 \tabularnewline
107 & 0.583410433042412 & 0.833179133915175 & 0.416589566957588 \tabularnewline
108 & 0.626507942114499 & 0.746984115771003 & 0.373492057885501 \tabularnewline
109 & 0.601403024439125 & 0.79719395112175 & 0.398596975560875 \tabularnewline
110 & 0.574034928394037 & 0.851930143211927 & 0.425965071605963 \tabularnewline
111 & 0.580324867758056 & 0.839350264483888 & 0.419675132241944 \tabularnewline
112 & 0.605904206892842 & 0.788191586214316 & 0.394095793107158 \tabularnewline
113 & 0.601354417328791 & 0.797291165342417 & 0.398645582671209 \tabularnewline
114 & 0.684724547456495 & 0.630550905087009 & 0.315275452543505 \tabularnewline
115 & 0.654693110612534 & 0.690613778774933 & 0.345306889387466 \tabularnewline
116 & 0.628778509526128 & 0.742442980947745 & 0.371221490473872 \tabularnewline
117 & 0.595742442498871 & 0.808515115002258 & 0.404257557501129 \tabularnewline
118 & 0.567795117490768 & 0.864409765018464 & 0.432204882509232 \tabularnewline
119 & 0.533311723231151 & 0.933376553537699 & 0.466688276768849 \tabularnewline
120 & 0.502418007591069 & 0.995163984817862 & 0.497581992408931 \tabularnewline
121 & 0.470358451016745 & 0.94071690203349 & 0.529641548983255 \tabularnewline
122 & 0.439481693540817 & 0.878963387081633 & 0.560518306459183 \tabularnewline
123 & 0.408095283483601 & 0.816190566967202 & 0.591904716516399 \tabularnewline
124 & 0.375720925723869 & 0.751441851447738 & 0.624279074276131 \tabularnewline
125 & 0.362432170955315 & 0.72486434191063 & 0.637567829044685 \tabularnewline
126 & 0.338123773795329 & 0.676247547590657 & 0.661876226204671 \tabularnewline
127 & 0.32904008878291 & 0.658080177565819 & 0.67095991121709 \tabularnewline
128 & 0.435094931088545 & 0.870189862177091 & 0.564905068911455 \tabularnewline
129 & 0.413809475730184 & 0.827618951460368 & 0.586190524269816 \tabularnewline
130 & 0.401173176372926 & 0.802346352745852 & 0.598826823627074 \tabularnewline
131 & 0.379860029737721 & 0.759720059475443 & 0.620139970262279 \tabularnewline
132 & 0.351956167807352 & 0.703912335614705 & 0.648043832192648 \tabularnewline
133 & 0.370082658197213 & 0.740165316394426 & 0.629917341802787 \tabularnewline
134 & 0.342891744765241 & 0.685783489530483 & 0.657108255234759 \tabularnewline
135 & 0.349183424898761 & 0.698366849797523 & 0.650816575101239 \tabularnewline
136 & 0.349206093233743 & 0.698412186467485 & 0.650793906766257 \tabularnewline
137 & 0.318825092788562 & 0.637650185577124 & 0.681174907211438 \tabularnewline
138 & 0.295105515597326 & 0.590211031194652 & 0.704894484402674 \tabularnewline
139 & 0.270065950161742 & 0.540131900323485 & 0.729934049838258 \tabularnewline
140 & 0.247571493327543 & 0.495142986655087 & 0.752428506672457 \tabularnewline
141 & 0.221684888342577 & 0.443369776685154 & 0.778315111657423 \tabularnewline
142 & 0.223762755459822 & 0.447525510919644 & 0.776237244540178 \tabularnewline
143 & 0.199685581249185 & 0.399371162498369 & 0.800314418750815 \tabularnewline
144 & 0.178427043813875 & 0.356854087627751 & 0.821572956186125 \tabularnewline
145 & 0.167130625848433 & 0.334261251696867 & 0.832869374151567 \tabularnewline
146 & 0.158222177111279 & 0.316444354222559 & 0.841777822888721 \tabularnewline
147 & 0.171564012724174 & 0.343128025448349 & 0.828435987275825 \tabularnewline
148 & 0.18580155021904 & 0.37160310043808 & 0.81419844978096 \tabularnewline
149 & 0.198508594740487 & 0.397017189480975 & 0.801491405259513 \tabularnewline
150 & 0.197222055437548 & 0.394444110875095 & 0.802777944562452 \tabularnewline
151 & 0.177444331475892 & 0.354888662951784 & 0.822555668524108 \tabularnewline
152 & 0.1610836698757 & 0.3221673397514 & 0.8389163301243 \tabularnewline
153 & 0.177275726164103 & 0.354551452328207 & 0.822724273835897 \tabularnewline
154 & 0.18945383740859 & 0.37890767481718 & 0.81054616259141 \tabularnewline
155 & 0.176067785854539 & 0.352135571709078 & 0.823932214145461 \tabularnewline
156 & 0.153600889902373 & 0.307201779804745 & 0.846399110097627 \tabularnewline
157 & 0.135283305776018 & 0.270566611552035 & 0.864716694223982 \tabularnewline
158 & 0.216125080695656 & 0.432250161391311 & 0.783874919304344 \tabularnewline
159 & 0.224686506349485 & 0.44937301269897 & 0.775313493650515 \tabularnewline
160 & 0.203739810819689 & 0.407479621639378 & 0.796260189180311 \tabularnewline
161 & 0.181514932321382 & 0.363029864642765 & 0.818485067678618 \tabularnewline
162 & 0.162533786000995 & 0.325067572001991 & 0.837466213999005 \tabularnewline
163 & 0.142520429263068 & 0.285040858526135 & 0.857479570736932 \tabularnewline
164 & 0.25524565328436 & 0.51049130656872 & 0.74475434671564 \tabularnewline
165 & 0.249883422560286 & 0.499766845120571 & 0.750116577439714 \tabularnewline
166 & 0.254365969953433 & 0.508731939906867 & 0.745634030046567 \tabularnewline
167 & 0.226062435804349 & 0.452124871608697 & 0.773937564195651 \tabularnewline
168 & 0.205314931998681 & 0.410629863997361 & 0.794685068001319 \tabularnewline
169 & 0.264230993694572 & 0.528461987389145 & 0.735769006305428 \tabularnewline
170 & 0.285783205287554 & 0.571566410575107 & 0.714216794712446 \tabularnewline
171 & 0.259035049351965 & 0.51807009870393 & 0.740964950648035 \tabularnewline
172 & 0.252250934331608 & 0.504501868663216 & 0.747749065668392 \tabularnewline
173 & 0.426188738366115 & 0.852377476732231 & 0.573811261633885 \tabularnewline
174 & 0.411193451798504 & 0.822386903597009 & 0.588806548201496 \tabularnewline
175 & 0.424967061755175 & 0.84993412351035 & 0.575032938244825 \tabularnewline
176 & 0.420127339875432 & 0.840254679750863 & 0.579872660124569 \tabularnewline
177 & 0.483406801826042 & 0.966813603652084 & 0.516593198173958 \tabularnewline
178 & 0.450171167684758 & 0.900342335369516 & 0.549828832315242 \tabularnewline
179 & 0.433729199593673 & 0.867458399187346 & 0.566270800406327 \tabularnewline
180 & 0.442109167049986 & 0.884218334099972 & 0.557890832950014 \tabularnewline
181 & 0.403974568559697 & 0.807949137119394 & 0.596025431440303 \tabularnewline
182 & 0.385889285854833 & 0.771778571709666 & 0.614110714145167 \tabularnewline
183 & 0.349912467939232 & 0.699824935878465 & 0.650087532060768 \tabularnewline
184 & 0.318949934484478 & 0.637899868968955 & 0.681050065515522 \tabularnewline
185 & 0.338664808511615 & 0.67732961702323 & 0.661335191488385 \tabularnewline
186 & 0.335968858328421 & 0.671937716656842 & 0.664031141671579 \tabularnewline
187 & 0.300944100540178 & 0.601888201080357 & 0.699055899459821 \tabularnewline
188 & 0.272239449418426 & 0.544478898836852 & 0.727760550581574 \tabularnewline
189 & 0.240283382292849 & 0.480566764585699 & 0.75971661770715 \tabularnewline
190 & 0.218708850311991 & 0.437417700623981 & 0.781291149688009 \tabularnewline
191 & 0.234145721283566 & 0.468291442567132 & 0.765854278716434 \tabularnewline
192 & 0.209938091292478 & 0.419876182584956 & 0.790061908707522 \tabularnewline
193 & 0.216171321397944 & 0.432342642795887 & 0.783828678602056 \tabularnewline
194 & 0.205435435166678 & 0.410870870333357 & 0.794564564833322 \tabularnewline
195 & 0.200245428353149 & 0.400490856706298 & 0.799754571646851 \tabularnewline
196 & 0.212136276409321 & 0.424272552818642 & 0.787863723590679 \tabularnewline
197 & 0.263913452940161 & 0.527826905880323 & 0.736086547059839 \tabularnewline
198 & 0.244298967193537 & 0.488597934387075 & 0.755701032806463 \tabularnewline
199 & 0.273319483639555 & 0.546638967279109 & 0.726680516360445 \tabularnewline
200 & 0.242655519169331 & 0.485311038338663 & 0.757344480830669 \tabularnewline
201 & 0.27579561786378 & 0.55159123572756 & 0.72420438213622 \tabularnewline
202 & 0.253168537331106 & 0.506337074662211 & 0.746831462668894 \tabularnewline
203 & 0.316200169474922 & 0.632400338949843 & 0.683799830525078 \tabularnewline
204 & 0.282031699156207 & 0.564063398312414 & 0.717968300843793 \tabularnewline
205 & 0.24919545145701 & 0.498390902914019 & 0.75080454854299 \tabularnewline
206 & 0.224045050486973 & 0.448090100973946 & 0.775954949513027 \tabularnewline
207 & 0.194187112041441 & 0.388374224082882 & 0.805812887958559 \tabularnewline
208 & 0.209052977501514 & 0.418105955003029 & 0.790947022498486 \tabularnewline
209 & 0.178474553574414 & 0.356949107148828 & 0.821525446425586 \tabularnewline
210 & 0.192675424354465 & 0.38535084870893 & 0.807324575645535 \tabularnewline
211 & 0.218599645646634 & 0.437199291293269 & 0.781400354353366 \tabularnewline
212 & 0.204360774174838 & 0.408721548349676 & 0.795639225825162 \tabularnewline
213 & 0.177889412700437 & 0.355778825400874 & 0.822110587299563 \tabularnewline
214 & 0.238620873818458 & 0.477241747636916 & 0.761379126181542 \tabularnewline
215 & 0.211178394438837 & 0.422356788877674 & 0.788821605561163 \tabularnewline
216 & 0.192972121016947 & 0.385944242033895 & 0.807027878983053 \tabularnewline
217 & 0.232886666548562 & 0.465773333097124 & 0.767113333451438 \tabularnewline
218 & 0.202370890644522 & 0.404741781289044 & 0.797629109355478 \tabularnewline
219 & 0.182829755945555 & 0.365659511891109 & 0.817170244054445 \tabularnewline
220 & 0.183783658727257 & 0.367567317454515 & 0.816216341272743 \tabularnewline
221 & 0.192996775967922 & 0.385993551935845 & 0.807003224032078 \tabularnewline
222 & 0.190065375472419 & 0.380130750944839 & 0.809934624527581 \tabularnewline
223 & 0.174049957140383 & 0.348099914280766 & 0.825950042859617 \tabularnewline
224 & 0.147772306781424 & 0.295544613562847 & 0.852227693218576 \tabularnewline
225 & 0.131447352547345 & 0.26289470509469 & 0.868552647452655 \tabularnewline
226 & 0.163628537119756 & 0.327257074239513 & 0.836371462880244 \tabularnewline
227 & 0.368178010365325 & 0.73635602073065 & 0.631821989634675 \tabularnewline
228 & 0.350374604998758 & 0.700749209997517 & 0.649625395001242 \tabularnewline
229 & 0.309531400432631 & 0.619062800865262 & 0.690468599567369 \tabularnewline
230 & 0.317471347498559 & 0.634942694997118 & 0.682528652501441 \tabularnewline
231 & 0.269139650848056 & 0.538279301696112 & 0.730860349151944 \tabularnewline
232 & 0.335537306278048 & 0.671074612556096 & 0.664462693721952 \tabularnewline
233 & 0.323335625306651 & 0.646671250613302 & 0.676664374693349 \tabularnewline
234 & 0.288495759090862 & 0.576991518181723 & 0.711504240909138 \tabularnewline
235 & 0.519520936571699 & 0.960958126856602 & 0.480479063428301 \tabularnewline
236 & 0.558435876703567 & 0.883128246592867 & 0.441564123296433 \tabularnewline
237 & 0.531222593800445 & 0.93755481239911 & 0.468777406199555 \tabularnewline
238 & 0.475160572827821 & 0.950321145655643 & 0.524839427172179 \tabularnewline
239 & 0.484112299208153 & 0.968224598416307 & 0.515887700791847 \tabularnewline
240 & 0.565237389430264 & 0.869525221139472 & 0.434762610569736 \tabularnewline
241 & 0.507899075216341 & 0.984201849567318 & 0.492100924783659 \tabularnewline
242 & 0.769007014918638 & 0.461985970162723 & 0.230992985081362 \tabularnewline
243 & 0.697836095267557 & 0.604327809464886 & 0.302163904732443 \tabularnewline
244 & 0.61502526500356 & 0.769949469992879 & 0.38497473499644 \tabularnewline
245 & 0.72651118495755 & 0.546977630084901 & 0.27348881504245 \tabularnewline
246 & 0.646186825662394 & 0.707626348675211 & 0.353813174337606 \tabularnewline
247 & 0.545543582216637 & 0.908912835566726 & 0.454456417783363 \tabularnewline
248 & 0.961541499138387 & 0.076917001723226 & 0.038458500861613 \tabularnewline
249 & 0.990053075707983 & 0.0198938485840331 & 0.00994692429201656 \tabularnewline
250 & 0.989796541227633 & 0.0204069175447336 & 0.0102034587723668 \tabularnewline
251 & 0.967574787685216 & 0.0648504246295685 & 0.0324252123147842 \tabularnewline
252 & 0.908732604284228 & 0.182534791431544 & 0.0912673957157718 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225552&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]0.631559624696705[/C][C]0.736880750606591[/C][C]0.368440375303295[/C][/ROW]
[ROW][C]13[/C][C]0.87515620864269[/C][C]0.249687582714621[/C][C]0.12484379135731[/C][/ROW]
[ROW][C]14[/C][C]0.845758953841727[/C][C]0.308482092316546[/C][C]0.154241046158273[/C][/ROW]
[ROW][C]15[/C][C]0.802967810181271[/C][C]0.394064379637459[/C][C]0.197032189818729[/C][/ROW]
[ROW][C]16[/C][C]0.795908088610926[/C][C]0.408183822778148[/C][C]0.204091911389074[/C][/ROW]
[ROW][C]17[/C][C]0.806662822619528[/C][C]0.386674354760944[/C][C]0.193337177380472[/C][/ROW]
[ROW][C]18[/C][C]0.735764249340212[/C][C]0.528471501319576[/C][C]0.264235750659788[/C][/ROW]
[ROW][C]19[/C][C]0.669438555844498[/C][C]0.661122888311003[/C][C]0.330561444155502[/C][/ROW]
[ROW][C]20[/C][C]0.790161074300786[/C][C]0.419677851398429[/C][C]0.209838925699214[/C][/ROW]
[ROW][C]21[/C][C]0.776077030798426[/C][C]0.447845938403149[/C][C]0.223922969201574[/C][/ROW]
[ROW][C]22[/C][C]0.729547566964427[/C][C]0.540904866071146[/C][C]0.270452433035573[/C][/ROW]
[ROW][C]23[/C][C]0.695295547761948[/C][C]0.609408904476105[/C][C]0.304704452238052[/C][/ROW]
[ROW][C]24[/C][C]0.659946367488744[/C][C]0.680107265022512[/C][C]0.340053632511256[/C][/ROW]
[ROW][C]25[/C][C]0.601250269522547[/C][C]0.797499460954905[/C][C]0.398749730477453[/C][/ROW]
[ROW][C]26[/C][C]0.99793435921269[/C][C]0.00413128157462088[/C][C]0.00206564078731044[/C][/ROW]
[ROW][C]27[/C][C]0.996727270118581[/C][C]0.00654545976283877[/C][C]0.00327272988141938[/C][/ROW]
[ROW][C]28[/C][C]0.994885458998814[/C][C]0.0102290820023711[/C][C]0.00511454100118554[/C][/ROW]
[ROW][C]29[/C][C]0.992479295128511[/C][C]0.015041409742978[/C][C]0.00752070487148901[/C][/ROW]
[ROW][C]30[/C][C]0.99328079574653[/C][C]0.0134384085069394[/C][C]0.00671920425346972[/C][/ROW]
[ROW][C]31[/C][C]0.990571919842508[/C][C]0.0188561603149849[/C][C]0.00942808015749245[/C][/ROW]
[ROW][C]32[/C][C]0.98795206661281[/C][C]0.0240958667743795[/C][C]0.0120479333871897[/C][/ROW]
[ROW][C]33[/C][C]0.984923166011315[/C][C]0.0301536679773701[/C][C]0.0150768339886851[/C][/ROW]
[ROW][C]34[/C][C]0.978679590164538[/C][C]0.0426408196709233[/C][C]0.0213204098354617[/C][/ROW]
[ROW][C]35[/C][C]0.970690670249763[/C][C]0.0586186595004736[/C][C]0.0293093297502368[/C][/ROW]
[ROW][C]36[/C][C]0.965481681395577[/C][C]0.0690366372088456[/C][C]0.0345183186044228[/C][/ROW]
[ROW][C]37[/C][C]0.986384818946844[/C][C]0.027230362106312[/C][C]0.013615181053156[/C][/ROW]
[ROW][C]38[/C][C]0.981624024147513[/C][C]0.0367519517049735[/C][C]0.0183759758524867[/C][/ROW]
[ROW][C]39[/C][C]0.978103504344423[/C][C]0.0437929913111539[/C][C]0.0218964956555769[/C][/ROW]
[ROW][C]40[/C][C]0.980231768479192[/C][C]0.0395364630416166[/C][C]0.0197682315208083[/C][/ROW]
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[ROW][C]186[/C][C]0.335968858328421[/C][C]0.671937716656842[/C][C]0.664031141671579[/C][/ROW]
[ROW][C]187[/C][C]0.300944100540178[/C][C]0.601888201080357[/C][C]0.699055899459821[/C][/ROW]
[ROW][C]188[/C][C]0.272239449418426[/C][C]0.544478898836852[/C][C]0.727760550581574[/C][/ROW]
[ROW][C]189[/C][C]0.240283382292849[/C][C]0.480566764585699[/C][C]0.75971661770715[/C][/ROW]
[ROW][C]190[/C][C]0.218708850311991[/C][C]0.437417700623981[/C][C]0.781291149688009[/C][/ROW]
[ROW][C]191[/C][C]0.234145721283566[/C][C]0.468291442567132[/C][C]0.765854278716434[/C][/ROW]
[ROW][C]192[/C][C]0.209938091292478[/C][C]0.419876182584956[/C][C]0.790061908707522[/C][/ROW]
[ROW][C]193[/C][C]0.216171321397944[/C][C]0.432342642795887[/C][C]0.783828678602056[/C][/ROW]
[ROW][C]194[/C][C]0.205435435166678[/C][C]0.410870870333357[/C][C]0.794564564833322[/C][/ROW]
[ROW][C]195[/C][C]0.200245428353149[/C][C]0.400490856706298[/C][C]0.799754571646851[/C][/ROW]
[ROW][C]196[/C][C]0.212136276409321[/C][C]0.424272552818642[/C][C]0.787863723590679[/C][/ROW]
[ROW][C]197[/C][C]0.263913452940161[/C][C]0.527826905880323[/C][C]0.736086547059839[/C][/ROW]
[ROW][C]198[/C][C]0.244298967193537[/C][C]0.488597934387075[/C][C]0.755701032806463[/C][/ROW]
[ROW][C]199[/C][C]0.273319483639555[/C][C]0.546638967279109[/C][C]0.726680516360445[/C][/ROW]
[ROW][C]200[/C][C]0.242655519169331[/C][C]0.485311038338663[/C][C]0.757344480830669[/C][/ROW]
[ROW][C]201[/C][C]0.27579561786378[/C][C]0.55159123572756[/C][C]0.72420438213622[/C][/ROW]
[ROW][C]202[/C][C]0.253168537331106[/C][C]0.506337074662211[/C][C]0.746831462668894[/C][/ROW]
[ROW][C]203[/C][C]0.316200169474922[/C][C]0.632400338949843[/C][C]0.683799830525078[/C][/ROW]
[ROW][C]204[/C][C]0.282031699156207[/C][C]0.564063398312414[/C][C]0.717968300843793[/C][/ROW]
[ROW][C]205[/C][C]0.24919545145701[/C][C]0.498390902914019[/C][C]0.75080454854299[/C][/ROW]
[ROW][C]206[/C][C]0.224045050486973[/C][C]0.448090100973946[/C][C]0.775954949513027[/C][/ROW]
[ROW][C]207[/C][C]0.194187112041441[/C][C]0.388374224082882[/C][C]0.805812887958559[/C][/ROW]
[ROW][C]208[/C][C]0.209052977501514[/C][C]0.418105955003029[/C][C]0.790947022498486[/C][/ROW]
[ROW][C]209[/C][C]0.178474553574414[/C][C]0.356949107148828[/C][C]0.821525446425586[/C][/ROW]
[ROW][C]210[/C][C]0.192675424354465[/C][C]0.38535084870893[/C][C]0.807324575645535[/C][/ROW]
[ROW][C]211[/C][C]0.218599645646634[/C][C]0.437199291293269[/C][C]0.781400354353366[/C][/ROW]
[ROW][C]212[/C][C]0.204360774174838[/C][C]0.408721548349676[/C][C]0.795639225825162[/C][/ROW]
[ROW][C]213[/C][C]0.177889412700437[/C][C]0.355778825400874[/C][C]0.822110587299563[/C][/ROW]
[ROW][C]214[/C][C]0.238620873818458[/C][C]0.477241747636916[/C][C]0.761379126181542[/C][/ROW]
[ROW][C]215[/C][C]0.211178394438837[/C][C]0.422356788877674[/C][C]0.788821605561163[/C][/ROW]
[ROW][C]216[/C][C]0.192972121016947[/C][C]0.385944242033895[/C][C]0.807027878983053[/C][/ROW]
[ROW][C]217[/C][C]0.232886666548562[/C][C]0.465773333097124[/C][C]0.767113333451438[/C][/ROW]
[ROW][C]218[/C][C]0.202370890644522[/C][C]0.404741781289044[/C][C]0.797629109355478[/C][/ROW]
[ROW][C]219[/C][C]0.182829755945555[/C][C]0.365659511891109[/C][C]0.817170244054445[/C][/ROW]
[ROW][C]220[/C][C]0.183783658727257[/C][C]0.367567317454515[/C][C]0.816216341272743[/C][/ROW]
[ROW][C]221[/C][C]0.192996775967922[/C][C]0.385993551935845[/C][C]0.807003224032078[/C][/ROW]
[ROW][C]222[/C][C]0.190065375472419[/C][C]0.380130750944839[/C][C]0.809934624527581[/C][/ROW]
[ROW][C]223[/C][C]0.174049957140383[/C][C]0.348099914280766[/C][C]0.825950042859617[/C][/ROW]
[ROW][C]224[/C][C]0.147772306781424[/C][C]0.295544613562847[/C][C]0.852227693218576[/C][/ROW]
[ROW][C]225[/C][C]0.131447352547345[/C][C]0.26289470509469[/C][C]0.868552647452655[/C][/ROW]
[ROW][C]226[/C][C]0.163628537119756[/C][C]0.327257074239513[/C][C]0.836371462880244[/C][/ROW]
[ROW][C]227[/C][C]0.368178010365325[/C][C]0.73635602073065[/C][C]0.631821989634675[/C][/ROW]
[ROW][C]228[/C][C]0.350374604998758[/C][C]0.700749209997517[/C][C]0.649625395001242[/C][/ROW]
[ROW][C]229[/C][C]0.309531400432631[/C][C]0.619062800865262[/C][C]0.690468599567369[/C][/ROW]
[ROW][C]230[/C][C]0.317471347498559[/C][C]0.634942694997118[/C][C]0.682528652501441[/C][/ROW]
[ROW][C]231[/C][C]0.269139650848056[/C][C]0.538279301696112[/C][C]0.730860349151944[/C][/ROW]
[ROW][C]232[/C][C]0.335537306278048[/C][C]0.671074612556096[/C][C]0.664462693721952[/C][/ROW]
[ROW][C]233[/C][C]0.323335625306651[/C][C]0.646671250613302[/C][C]0.676664374693349[/C][/ROW]
[ROW][C]234[/C][C]0.288495759090862[/C][C]0.576991518181723[/C][C]0.711504240909138[/C][/ROW]
[ROW][C]235[/C][C]0.519520936571699[/C][C]0.960958126856602[/C][C]0.480479063428301[/C][/ROW]
[ROW][C]236[/C][C]0.558435876703567[/C][C]0.883128246592867[/C][C]0.441564123296433[/C][/ROW]
[ROW][C]237[/C][C]0.531222593800445[/C][C]0.93755481239911[/C][C]0.468777406199555[/C][/ROW]
[ROW][C]238[/C][C]0.475160572827821[/C][C]0.950321145655643[/C][C]0.524839427172179[/C][/ROW]
[ROW][C]239[/C][C]0.484112299208153[/C][C]0.968224598416307[/C][C]0.515887700791847[/C][/ROW]
[ROW][C]240[/C][C]0.565237389430264[/C][C]0.869525221139472[/C][C]0.434762610569736[/C][/ROW]
[ROW][C]241[/C][C]0.507899075216341[/C][C]0.984201849567318[/C][C]0.492100924783659[/C][/ROW]
[ROW][C]242[/C][C]0.769007014918638[/C][C]0.461985970162723[/C][C]0.230992985081362[/C][/ROW]
[ROW][C]243[/C][C]0.697836095267557[/C][C]0.604327809464886[/C][C]0.302163904732443[/C][/ROW]
[ROW][C]244[/C][C]0.61502526500356[/C][C]0.769949469992879[/C][C]0.38497473499644[/C][/ROW]
[ROW][C]245[/C][C]0.72651118495755[/C][C]0.546977630084901[/C][C]0.27348881504245[/C][/ROW]
[ROW][C]246[/C][C]0.646186825662394[/C][C]0.707626348675211[/C][C]0.353813174337606[/C][/ROW]
[ROW][C]247[/C][C]0.545543582216637[/C][C]0.908912835566726[/C][C]0.454456417783363[/C][/ROW]
[ROW][C]248[/C][C]0.961541499138387[/C][C]0.076917001723226[/C][C]0.038458500861613[/C][/ROW]
[ROW][C]249[/C][C]0.990053075707983[/C][C]0.0198938485840331[/C][C]0.00994692429201656[/C][/ROW]
[ROW][C]250[/C][C]0.989796541227633[/C][C]0.0204069175447336[/C][C]0.0102034587723668[/C][/ROW]
[ROW][C]251[/C][C]0.967574787685216[/C][C]0.0648504246295685[/C][C]0.0324252123147842[/C][/ROW]
[ROW][C]252[/C][C]0.908732604284228[/C][C]0.182534791431544[/C][C]0.0912673957157718[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225552&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225552&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.6315596246967050.7368807506065910.368440375303295
130.875156208642690.2496875827146210.12484379135731
140.8457589538417270.3084820923165460.154241046158273
150.8029678101812710.3940643796374590.197032189818729
160.7959080886109260.4081838227781480.204091911389074
170.8066628226195280.3866743547609440.193337177380472
180.7357642493402120.5284715013195760.264235750659788
190.6694385558444980.6611228883110030.330561444155502
200.7901610743007860.4196778513984290.209838925699214
210.7760770307984260.4478459384031490.223922969201574
220.7295475669644270.5409048660711460.270452433035573
230.6952955477619480.6094089044761050.304704452238052
240.6599463674887440.6801072650225120.340053632511256
250.6012502695225470.7974994609549050.398749730477453
260.997934359212690.004131281574620880.00206564078731044
270.9967272701185810.006545459762838770.00327272988141938
280.9948854589988140.01022908200237110.00511454100118554
290.9924792951285110.0150414097429780.00752070487148901
300.993280795746530.01343840850693940.00671920425346972
310.9905719198425080.01885616031498490.00942808015749245
320.987952066612810.02409586677437950.0120479333871897
330.9849231660113150.03015366797737010.0150768339886851
340.9786795901645380.04264081967092330.0213204098354617
350.9706906702497630.05861865950047360.0293093297502368
360.9654816813955770.06903663720884560.0345183186044228
370.9863848189468440.0272303621063120.013615181053156
380.9816240241475130.03675195170497350.0183759758524867
390.9781035043444230.04379299131115390.0218964956555769
400.9802317684791920.03953646304161660.0197682315208083
410.9753671488986120.04926570220277590.024632851101388
420.9743368928143150.05132621437137070.0256631071856854
430.966467160208230.06706567958354070.0335328397917704
440.9591434743611930.0817130512776150.0408565256388075
450.9476227721057180.1047544557885630.0523772278942817
460.9526637069860640.09467258602787140.0473362930139357
470.939651214434350.12069757113130.0603487855656502
480.9241064011160180.1517871977679640.0758935988839819
490.933301673905030.1333966521899390.0666983260949697
500.9277562513510550.144487497297890.0722437486489449
510.9124766389396120.1750467221207760.0875233610603879
520.8936872318478020.2126255363043950.106312768152198
530.8860860886382740.2278278227234520.113913911361726
540.8674624257584550.265075148483090.132537574241545
550.8626393967619990.2747212064760030.137360603238001
560.844613233140830.310773533718340.15538676685917
570.8370661800538260.3258676398923490.162933819946174
580.809732852307710.3805342953845790.19026714769229
590.8575881657384170.2848236685231670.142411834261583
600.8531365784493090.2937268431013810.146863421550691
610.8823628752697490.2352742494605020.117637124730251
620.8796261855096870.2407476289806260.120373814490313
630.9219484820809790.1561030358380420.0780515179190212
640.9117484540702640.1765030918594730.0882515459297364
650.9053289612728550.189342077454290.0946710387271448
660.9673971203759430.06520575924811430.0326028796240572
670.9666167427019520.06676651459609540.0333832572980477
680.9702584523219490.05948309535610170.0297415476780508
690.9662448270913870.06751034581722640.0337551729086132
700.9609082917862120.07818341642757680.0390917082137884
710.9521165948033050.09576681039338940.0478834051966947
720.9628824845675390.07423503086492280.0371175154324614
730.9541785656206740.09164286875865140.0458214343793257
740.9451795955363690.1096408089272620.054820404463631
750.9402332537571170.1195334924857660.0597667462428828
760.9289579579249230.1420840841501550.0710420420750773
770.9394020164825940.1211959670348110.0605979835174057
780.9272734315007760.1454531369984470.0727265684992235
790.9198581476211730.1602837047576550.0801418523788273
800.9119267870445290.1761464259109410.0880732129554706
810.8954908249182070.2090183501635860.104509175081793
820.8776536990886990.2446926018226020.122346300911301
830.8704739807041270.2590520385917470.129526019295873
840.8514577182217270.2970845635565450.148542281778273
850.8281146212491130.3437707575017740.171885378750887
860.8045974282974560.3908051434050870.195402571702544
870.777447446013920.4451051079721590.22255255398608
880.7480018270341080.5039963459317850.251998172965892
890.8129831639711010.3740336720577980.187016836028899
900.840601724826990.3187965503460190.15939827517301
910.8167175671165140.3665648657669720.183282432883486
920.7929482451510470.4141035096979050.207051754848953
930.7690784533457250.4618430933085510.230921546654275
940.7456924995487570.5086150009024860.254307500451243
950.7198978241646180.5602043516707630.280102175835382
960.6947188165319170.6105623669361670.305281183468083
970.6671286137859570.6657427724280860.332871386214043
980.6663603882916760.6672792234166470.333639611708324
990.6326804335133410.7346391329733180.367319566486659
1000.6269300824858580.7461398350282830.373069917514142
1010.60295450917510.79409098164980.3970454908249
1020.572816707260660.854366585478680.42718329273934
1030.6196365740730080.7607268518539830.380363425926992
1040.6077735003880070.7844529992239870.392226499611993
1050.6191040134723350.7617919730553290.380895986527664
1060.5921455719061870.8157088561876270.407854428093813
1070.5834104330424120.8331791339151750.416589566957588
1080.6265079421144990.7469841157710030.373492057885501
1090.6014030244391250.797193951121750.398596975560875
1100.5740349283940370.8519301432119270.425965071605963
1110.5803248677580560.8393502644838880.419675132241944
1120.6059042068928420.7881915862143160.394095793107158
1130.6013544173287910.7972911653424170.398645582671209
1140.6847245474564950.6305509050870090.315275452543505
1150.6546931106125340.6906137787749330.345306889387466
1160.6287785095261280.7424429809477450.371221490473872
1170.5957424424988710.8085151150022580.404257557501129
1180.5677951174907680.8644097650184640.432204882509232
1190.5333117232311510.9333765535376990.466688276768849
1200.5024180075910690.9951639848178620.497581992408931
1210.4703584510167450.940716902033490.529641548983255
1220.4394816935408170.8789633870816330.560518306459183
1230.4080952834836010.8161905669672020.591904716516399
1240.3757209257238690.7514418514477380.624279074276131
1250.3624321709553150.724864341910630.637567829044685
1260.3381237737953290.6762475475906570.661876226204671
1270.329040088782910.6580801775658190.67095991121709
1280.4350949310885450.8701898621770910.564905068911455
1290.4138094757301840.8276189514603680.586190524269816
1300.4011731763729260.8023463527458520.598826823627074
1310.3798600297377210.7597200594754430.620139970262279
1320.3519561678073520.7039123356147050.648043832192648
1330.3700826581972130.7401653163944260.629917341802787
1340.3428917447652410.6857834895304830.657108255234759
1350.3491834248987610.6983668497975230.650816575101239
1360.3492060932337430.6984121864674850.650793906766257
1370.3188250927885620.6376501855771240.681174907211438
1380.2951055155973260.5902110311946520.704894484402674
1390.2700659501617420.5401319003234850.729934049838258
1400.2475714933275430.4951429866550870.752428506672457
1410.2216848883425770.4433697766851540.778315111657423
1420.2237627554598220.4475255109196440.776237244540178
1430.1996855812491850.3993711624983690.800314418750815
1440.1784270438138750.3568540876277510.821572956186125
1450.1671306258484330.3342612516968670.832869374151567
1460.1582221771112790.3164443542225590.841777822888721
1470.1715640127241740.3431280254483490.828435987275825
1480.185801550219040.371603100438080.81419844978096
1490.1985085947404870.3970171894809750.801491405259513
1500.1972220554375480.3944441108750950.802777944562452
1510.1774443314758920.3548886629517840.822555668524108
1520.16108366987570.32216733975140.8389163301243
1530.1772757261641030.3545514523282070.822724273835897
1540.189453837408590.378907674817180.81054616259141
1550.1760677858545390.3521355717090780.823932214145461
1560.1536008899023730.3072017798047450.846399110097627
1570.1352833057760180.2705666115520350.864716694223982
1580.2161250806956560.4322501613913110.783874919304344
1590.2246865063494850.449373012698970.775313493650515
1600.2037398108196890.4074796216393780.796260189180311
1610.1815149323213820.3630298646427650.818485067678618
1620.1625337860009950.3250675720019910.837466213999005
1630.1425204292630680.2850408585261350.857479570736932
1640.255245653284360.510491306568720.74475434671564
1650.2498834225602860.4997668451205710.750116577439714
1660.2543659699534330.5087319399068670.745634030046567
1670.2260624358043490.4521248716086970.773937564195651
1680.2053149319986810.4106298639973610.794685068001319
1690.2642309936945720.5284619873891450.735769006305428
1700.2857832052875540.5715664105751070.714216794712446
1710.2590350493519650.518070098703930.740964950648035
1720.2522509343316080.5045018686632160.747749065668392
1730.4261887383661150.8523774767322310.573811261633885
1740.4111934517985040.8223869035970090.588806548201496
1750.4249670617551750.849934123510350.575032938244825
1760.4201273398754320.8402546797508630.579872660124569
1770.4834068018260420.9668136036520840.516593198173958
1780.4501711676847580.9003423353695160.549828832315242
1790.4337291995936730.8674583991873460.566270800406327
1800.4421091670499860.8842183340999720.557890832950014
1810.4039745685596970.8079491371193940.596025431440303
1820.3858892858548330.7717785717096660.614110714145167
1830.3499124679392320.6998249358784650.650087532060768
1840.3189499344844780.6378998689689550.681050065515522
1850.3386648085116150.677329617023230.661335191488385
1860.3359688583284210.6719377166568420.664031141671579
1870.3009441005401780.6018882010803570.699055899459821
1880.2722394494184260.5444788988368520.727760550581574
1890.2402833822928490.4805667645856990.75971661770715
1900.2187088503119910.4374177006239810.781291149688009
1910.2341457212835660.4682914425671320.765854278716434
1920.2099380912924780.4198761825849560.790061908707522
1930.2161713213979440.4323426427958870.783828678602056
1940.2054354351666780.4108708703333570.794564564833322
1950.2002454283531490.4004908567062980.799754571646851
1960.2121362764093210.4242725528186420.787863723590679
1970.2639134529401610.5278269058803230.736086547059839
1980.2442989671935370.4885979343870750.755701032806463
1990.2733194836395550.5466389672791090.726680516360445
2000.2426555191693310.4853110383386630.757344480830669
2010.275795617863780.551591235727560.72420438213622
2020.2531685373311060.5063370746622110.746831462668894
2030.3162001694749220.6324003389498430.683799830525078
2040.2820316991562070.5640633983124140.717968300843793
2050.249195451457010.4983909029140190.75080454854299
2060.2240450504869730.4480901009739460.775954949513027
2070.1941871120414410.3883742240828820.805812887958559
2080.2090529775015140.4181059550030290.790947022498486
2090.1784745535744140.3569491071488280.821525446425586
2100.1926754243544650.385350848708930.807324575645535
2110.2185996456466340.4371992912932690.781400354353366
2120.2043607741748380.4087215483496760.795639225825162
2130.1778894127004370.3557788254008740.822110587299563
2140.2386208738184580.4772417476369160.761379126181542
2150.2111783944388370.4223567888776740.788821605561163
2160.1929721210169470.3859442420338950.807027878983053
2170.2328866665485620.4657733330971240.767113333451438
2180.2023708906445220.4047417812890440.797629109355478
2190.1828297559455550.3656595118911090.817170244054445
2200.1837836587272570.3675673174545150.816216341272743
2210.1929967759679220.3859935519358450.807003224032078
2220.1900653754724190.3801307509448390.809934624527581
2230.1740499571403830.3480999142807660.825950042859617
2240.1477723067814240.2955446135628470.852227693218576
2250.1314473525473450.262894705094690.868552647452655
2260.1636285371197560.3272570742395130.836371462880244
2270.3681780103653250.736356020730650.631821989634675
2280.3503746049987580.7007492099975170.649625395001242
2290.3095314004326310.6190628008652620.690468599567369
2300.3174713474985590.6349426949971180.682528652501441
2310.2691396508480560.5382793016961120.730860349151944
2320.3355373062780480.6710746125560960.664462693721952
2330.3233356253066510.6466712506133020.676664374693349
2340.2884957590908620.5769915181817230.711504240909138
2350.5195209365716990.9609581268566020.480479063428301
2360.5584358767035670.8831282465928670.441564123296433
2370.5312225938004450.937554812399110.468777406199555
2380.4751605728278210.9503211456556430.524839427172179
2390.4841122992081530.9682245984163070.515887700791847
2400.5652373894302640.8695252211394720.434762610569736
2410.5078990752163410.9842018495673180.492100924783659
2420.7690070149186380.4619859701627230.230992985081362
2430.6978360952675570.6043278094648860.302163904732443
2440.615025265003560.7699494699928790.38497473499644
2450.726511184957550.5469776300849010.27348881504245
2460.6461868256623940.7076263486752110.353813174337606
2470.5455435822166370.9089128355667260.454456417783363
2480.9615414991383870.0769170017232260.038458500861613
2490.9900530757079830.01989384858403310.00994692429201656
2500.9897965412276330.02040691754473360.0102034587723668
2510.9675747876852160.06485042462956850.0324252123147842
2520.9087326042842280.1825347914315440.0912673957157718






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.00829875518672199OK
5% type I error level160.0663900414937759NOK
10% type I error level320.132780082987552NOK

\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 & 2 & 0.00829875518672199 & OK \tabularnewline
5% type I error level & 16 & 0.0663900414937759 & NOK \tabularnewline
10% type I error level & 32 & 0.132780082987552 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225552&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]2[/C][C]0.00829875518672199[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.0663900414937759[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.132780082987552[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225552&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225552&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.00829875518672199OK
5% type I error level160.0663900414937759NOK
10% type I error level320.132780082987552NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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