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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 computationTue, 28 Dec 2010 11:52:19 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/28/t1293537025rushs2h0w5achcy.htm/, Retrieved Sun, 05 May 2024 02:24:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116300, Retrieved Sun, 05 May 2024 02:24:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-26 20:18:12] [dcd1a35a8985187cb1e9de87792355b2]
-   P   [Multiple Regression] [] [2010-12-26 20:25:18] [dcd1a35a8985187cb1e9de87792355b2]
-           [Multiple Regression] [] [2010-12-28 11:52:19] [82d760768aff8bf374d9817688c406af] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
27	5	26	49	35	18
36	4	25	45	34	10
25	4	17	54	13	23
27	3	37	36	35	14
25	3	35	36	28	20
44	3	15	53	32	15
50	4	27	46	35	18
41	4	36	42	36	19
48	5	25	41	27	19
43	4	30	45	29	14
47	2	27	47	27	15
41	3	33	42	28	14
44	2	29	45	29	16
47	5	30	40	28	13
40	3	25	45	30	13
46	3	23	40	25	14
28	3	26	42	15	23
56	3	24	45	33	17
49	4	35	47	31	14
25	4	39	31	37	21
41	4	23	46	37	15
26	3	32	34	34	19
50	5	29	43	32	20
47	4	26	45	21	18
52	2	21	42	25	13
37	5	35	51	32	20
41	3	23	44	28	12
45	4	21	47	22	17
26	4	28	47	25	13
	3	30	41	26	17
52	4	21	44	34	16
46	2	29	51	34	20
58	3	28	46	36	18
54	5	19	47	36	9
29	3	26	46	26	14
50	3	33	38	26	12
43	2	34	50	34	21
30	3	33	48	33	16
47	2	40	36	31	12
45	3	24	51	33	20
48	1	35	35	22	18
48	3	35	49	29	22
26	4	32	38	24	17
46	5	20	47	37	16
	3	35	36	32	14
50	3	35	47	23	19
25	4	21	46	29	21
47	2	33	43	35	18
47	2	40	53	20	23
41	3	22	55	28	20
45	2	35	39	26	10
41	4	20	55	36	16
45	5	28	41	26	18
40	3	46	33	33	12
29	4	18	52	25	15
34	5	22	42	29	19
45	5	20	56	32	11
52	3	25	46	35	16
41	4	31	33	24	12
48	3	21	51	31	18
45	3	23	46	29	14
54	2	26	46	27	20
25	3	34	50	29	15
26	4	31	46	29	17
28	4	23	51	27	20
50	4	31	48	34	14
48	4	26	44	32	16
51	3	36	38	31	15
53	3	28	42	31	17
37	3	34	39	31	20
56	2	25	45	16	14
43	3	33	31	25	20
34	3	46	29	27	20
42	3	24	48	32	15
32	3	32	38	28	21
31	5	33	55	25	22
46	3	42	32	25	11
30	5	17	51	36	20
47	4	36	53	36	17
33	4	40	47	36	19
25	4	30	45	27	17
25	5	19	33	29	15
21	4	33	49	32	20
36	5	35	46	29	12
50	3	23	42	31	13
48	3	15	56	34	18
48	2	38	35	27	19
25	3	37	40	28	13
48	4	23	44	32	12
49	5	41	46	33	16
27	5	34	46	29	21
28	3	38	39	32	19
43	2	45	35	35	19
48	3	27	48	33	12
48	4	46	42	27	22
25	1	26	39	16	9
49	4	44	39	32	9
26	3	36	41	26	18
51	3	20	52	32	14
25	4	44	45	38	14
29	3	27	42	24	23
29	4	27	44	26	19
43	2	41	33	19	24
46	3	30	42	37	12
44	3	33	46	25	20
25	3	37	45	24	21
51	2	30	40	23	18
42	5	20	48	28	20
53	5	44	32	38	18
25	4	20	53	28	18
49	2	33	39	28	17
51	3	31	45	26	18
20	3	23	36	21	14
44	3	33	38	35	23
38	4	33	49	31	19
46	5	32	46	34	14
42	4	25	43	30	17
29		22	37	30	22
46	4	16	48	24	10
49	2	36	45	27	16
51	3	35	32	26	14
38	3	25	46	30	19
41	1	27	20	15	14
47	3	32	42	28	18
44	3	36	45	34	19
47	3	51	29	29	21
46	3	30	51	26	13
44	4	20	55	31	17
28	3	29	50	28	11
47	4	26	44	33	16
28	4	20	41	32	22
41	5	40	40	33	19
45	4	29	47	31	17
46	4	32	42	37	25
46	4	33	40	27	17
22	3	32	51	19	23
33	3	34	43	27	21
41	4	24	45	31	12
47	5	25	41	38	18
25	3	41	41	22	15
42	3	39	37	35	17
47	3	21	46	35	11
50	3	38	38	30	17
55	5	28	39	41	13
21	3	37	45	25	17
	3	26	46	28	16
52	3	30	39	45	14
49	4	25	21	21	15
46	4	38	31	33	20
	4	31	35	25	14
45	3	31	49	29	16
52	3	27	40	31	14
	3	21	45	29	13
40	4	26	46	31	15
49	4	37	45	31	13
38	5	28	34	25	13
32	5	29	41	27	23
46	4	33	43	26	18
32	3	41	45	26	21
41	3	19	48	23	14
43	3	37	43	27	12
44	4	36	45	24	17
47	5	27	45	35	11
28	3	33	34	24	15
52	1	29	40	32	14
27	2	42	40	24	19
45	5	27	55	24	12
27	4	47	44	38	14
25	4	17	44	36	18
28	4	34	48	24	25
25	3	32	51	18	22
52	4	25	49	34	15
44	3	27	33	23	18
43	3	37	43	35	18
47	4	34	44	22	12
52	4	27	44	34	12
40	2	37	41	28	16
42	3	32	45	34	22
45	5	26	44	32	15
45	2	29	44	24	16
50	5	28	40	34	11
49	3	19	48	33	20
52	2	46	49	33	14
48	3	31	46	29	20
51	3	42	49	38	15
49	4	33	55	24	12
31	4	39	51	25	18
43	3	27	46	37	18
31	3	35	37	33	11
28	4	23	43	30	13
43	4	32	41	22	15
31	3	22	45	28	19
51	3	17	39	24	13
58	4	35	38	33	19
25	5	34	41	37	18

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116300&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116300&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116300&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 time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Extrinsieke_waarden[t] = + 51.6702289080872 -0.395745066818002leeftijd[t] -0.183028527512322opleiding[t] -0.0163919147761365Neuroticisme[t] + 0.0169086989274354Extraversie[t] -0.619153712609344Openheid[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Extrinsieke_waarden[t] =  +  51.6702289080872 -0.395745066818002leeftijd[t] -0.183028527512322opleiding[t] -0.0163919147761365Neuroticisme[t] +  0.0169086989274354Extraversie[t] -0.619153712609344Openheid[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116300&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Extrinsieke_waarden[t] =  +  51.6702289080872 -0.395745066818002leeftijd[t] -0.183028527512322opleiding[t] -0.0163919147761365Neuroticisme[t] +  0.0169086989274354Extraversie[t] -0.619153712609344Openheid[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116300&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116300&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
Extrinsieke_waarden[t] = + 51.6702289080872 -0.395745066818002leeftijd[t] -0.183028527512322opleiding[t] -0.0163919147761365Neuroticisme[t] + 0.0169086989274354Extraversie[t] -0.619153712609344Openheid[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)51.67022890808726.5666327.868600
leeftijd-0.3957450668180020.057026-6.939700
opleiding-0.1830285275123220.059501-3.07610.0024090.001204
Neuroticisme-0.01639191477613650.081075-0.20220.8399930.419996
Extraversie0.01690869892743540.0816750.2070.8362130.418107
Openheid-0.6191537126093440.051566-12.007100

\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) & 51.6702289080872 & 6.566632 & 7.8686 & 0 & 0 \tabularnewline
leeftijd & -0.395745066818002 & 0.057026 & -6.9397 & 0 & 0 \tabularnewline
opleiding & -0.183028527512322 & 0.059501 & -3.0761 & 0.002409 & 0.001204 \tabularnewline
Neuroticisme & -0.0163919147761365 & 0.081075 & -0.2022 & 0.839993 & 0.419996 \tabularnewline
Extraversie & 0.0169086989274354 & 0.081675 & 0.207 & 0.836213 & 0.418107 \tabularnewline
Openheid & -0.619153712609344 & 0.051566 & -12.0071 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116300&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]51.6702289080872[/C][C]6.566632[/C][C]7.8686[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]leeftijd[/C][C]-0.395745066818002[/C][C]0.057026[/C][C]-6.9397[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.183028527512322[/C][C]0.059501[/C][C]-3.0761[/C][C]0.002409[/C][C]0.001204[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]-0.0163919147761365[/C][C]0.081075[/C][C]-0.2022[/C][C]0.839993[/C][C]0.419996[/C][/ROW]
[ROW][C]Extraversie[/C][C]0.0169086989274354[/C][C]0.081675[/C][C]0.207[/C][C]0.836213[/C][C]0.418107[/C][/ROW]
[ROW][C]Openheid[/C][C]-0.619153712609344[/C][C]0.051566[/C][C]-12.0071[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116300&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116300&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)51.67022890808726.5666327.868600
leeftijd-0.3957450668180020.057026-6.939700
opleiding-0.1830285275123220.059501-3.07610.0024090.001204
Neuroticisme-0.01639191477613650.081075-0.20220.8399930.419996
Extraversie0.01690869892743540.0816750.2070.8362130.418107
Openheid-0.6191537126093440.051566-12.007100






Multiple Linear Regression - Regression Statistics
Multiple R0.772056943864005
R-squared0.596071924568628
Adjusted R-squared0.585385996647163
F-TEST (value)55.7810167679772
F-TEST (DF numerator)5
F-TEST (DF denominator)189
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.61753412815762
Sum Squared Residuals14035.4980510427

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.772056943864005 \tabularnewline
R-squared & 0.596071924568628 \tabularnewline
Adjusted R-squared & 0.585385996647163 \tabularnewline
F-TEST (value) & 55.7810167679772 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 189 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.61753412815762 \tabularnewline
Sum Squared Residuals & 14035.4980510427 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116300&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.772056943864005[/C][/ROW]
[ROW][C]R-squared[/C][C]0.596071924568628[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.585385996647163[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]55.7810167679772[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]189[/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]8.61753412815762[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]14035.4980510427[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116300&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116300&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 R0.772056943864005
R-squared0.596071924568628
Adjusted R-squared0.585385996647163
F-TEST (value)55.7810167679772
F-TEST (DF numerator)5
F-TEST (DF denominator)189
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.61753412815762
Sum Squared Residuals14035.4980510427






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11818.8019259883771-0.801925988377056
21015.9911597462033-5.9911597462033
32333.6298970545535-10.6298970545535
41418.7678588948077-4.76785889480773
52023.9262088462614-3.92620884626141
61514.54572390357110.454276096428875
7189.815699967517088.18430003248292
81912.54308982757486.45691017242522
91915.32563160943063.67436839056938
101416.2347532676433-2.23475326764332
111516.3391306227979-1.33913062279792
121417.7285238002903-3.72852380029027
131616.2214571706261-0.221457170626098
141315.0033546908312-2.00335469083116
151317.0678228568810-4.06782285688098
161417.7373613539348-3.73736135393479
172331.0369513362787-8.03695133627873
18178.894832564741068.10516743525894
191412.57383326549081.42616673450919
202118.02068575152332.97931424847673
211512.20466580276502.79533419723505
221919.8308998502609-0.830899850260884
232011.40662265149828.59337734850178
241819.6705703603506-1.67057036035061
251315.6125207079462-2.61252070794625
262016.58822662289493.4117733771051
271217.9262603459065-5.92626034590651
281719.9586837531128-2.95868375311282
291325.5056354813939-12.5056354813939
305234.234082434196417.7659175658036
314636.19084167468979.80915832531032
325832.926745334356825.0732546656432
335434.068113192005319.9318868079947
342940.4798713046882-11.4798713046882
355036.74169810819313.258301891807
364336.82994115903456.17005884096547
373031.4088408989620-1.40884089896195
384734.307768053327812.6922319466722
394536.06181385745578.93818614254427
404833.429234206172914.5707657938271
414833.521992910608514.4780070893915
422630.1427620121612-4.14276201216124
434633.487638658827312.5123613411727
44335.9796754878379-32.9796754878379
453-0.01538053930598783.01538053930599
46413.6822192009145-9.68221920091448
4725.7197628956713-3.7197628956713
4821.370830090953120.629169909046876
4930.8156738400391752.18432615996082
5024.91455172230979-2.91455172230979
5145.03863398458822-1.03863398458822
5255.50727174202696-0.507271742026957
5338.19721570131841-5.19721570131841
5449.13252796039819-5.13252796039819
55513.8219406605128-8.82194066051279
5657.26062196626641-2.26062196626641
5730.9711913907165942.02880860928341
5847.66880992076137-3.66880992076137
5933.45231065607589-0.452310656075894
6035.9594177569938-2.9594177569938
6120.4758358812778371.52416411872216
62317.3782943696117-14.3782943696117
63412.7437386882196-8.74373868821962
64413.4585979713591-9.45859797135909
6542.171544117270491.82845588272951
6640.5767809934845663.42321900651543
6731.496760527202981.50323947279702
683-2.601343185273105.6013431852731
6939.772780038826-6.77278003882601
702-3.765799222340275.76579922234027
7136.89116000594672-3.89116000594672
72311.8139072306387-8.8139072306387
7332.04926488660230.9507351133977
74313.3031483914398-10.3031483914398
75512.7536466972975-7.75364669729751
7630.02519541688570512.97480458311429
77510.3936096618548-5.39360966185483
7846.2559484230213-2.25594842302130
7946.98816097820314-2.98816097820314
80411.4903989747347-7.4903989747347
81515.9276165330697-10.9276165330697
82424.8871682212458-20.8871682212458
8357.16634290676526-2.16634290676526
843-1.830307467955674.83030746795567
8534.87317973850349-1.87317973850349
8625.51210863818375-3.51210863818375
87314.6257586715038-11.6257586715038
884-0.2520183975954834.25201839759548
8953.854668357165931.14533164283407
90510.0378246577564-5.03782465775643
91312.3389975666148-9.33899756661483
9222.66691816060563-0.666918160605627
933-2.516127504446235.51612750444623
9442.142335777677841.85766422232216
95110.2293245411558-9.22932454115584
9643.794120333909410.205879666090589
97310.6489738847821-7.64897388478212
983-1.779435671928394.77943567192839
99418.4711818380005-14.4711818380005
10037.67953358786051-4.67953358786051
101415.3379504035163-11.3379504035163
10226.20332274669885-4.20332274669885
10331.018031374124471.98196862587553
10434.4643189336306-1.4643189336306
105314.6408627314380-11.6408627314380
1062-2.82378492246114.8237849224611
10756.39962241430445-1.39962241430445
10852.034011847657292.96598815234271
109412.6031540916678-8.60315409166782
11023.56166666267819-1.56166666267819
1113-0.2758359449294423.27583594492944
112318.660940643002-15.6609406430020
11338.6287936007036-5.6287936007036
11447.94289963663601-3.94289963663601
11550.9742889965205154.02571100347948
11644.26201525734727-0.262015257347274
1172215.74660832741866.25339167258142
1181629.4773687856011-13.4773687856011
1193627.70808071499168.29191928500837
1203527.66254252949747.33745747050262
1212532.8032276691393-7.80322766913934
1222727.7377565717583-0.737756571758339
1233239.7176605639378-7.71766056393782
1243628.51560448039477.48439551960526
1255126.26453229687324.735467703127
1263033.4619034750429-3.4619034750429
1272024.7827617953287-4.78276179532871
1282922.56768576116096.43231423883908
1292624.89595973345661.10404026654335
1302025.9520626433011-5.95206264330109
1314026.824634256057913.1753657439421
1322927.77331504801111.22668495198891
1333225.41884916378946.58115083621057
1343326.16826801459646.83173198540359
1353229.13452367991562.86547632008437
1363426.33320036456127.66679963543884
1372427.583832387369-3.583832387369
1382525.6900538576242-0.690053857624205
1394126.759798992468214.2402010075318
1403930.02487905876028.97512094123978
1412129.2802481334569-8.28024813345693
1423825.867620117534012.1323798824660
1432828.6966078704013-0.696607870401336
1443727.01652832173829.98347167826177
1454612.960054731214133.0399452687859
1463918.284645747128820.7153542528712
1472115.08488967255005.91511032744997
1483116.399920229076714.6000797709233
1492513.738293219165611.2617067808344
150298.0299762391448520.9700237608551
1513111.462035030696919.5379649693031
1521328.0419159658629-15.0419159658629
1531516.2661574009106-1.26615740091061
1541312.50723203808370.492767961916336
1551320.3538530660089-7.35385306600887
1562321.59198501941411.40801498058590
1571816.82198606283411.17801393716593
1582122.4481276054442-1.44812760544419
1591421.1552313637675-7.15523136376752
1601217.5075284190865-5.5075284190865
1611718.8364252752152-1.83642527521522
1621110.80299794153130.197002058468670
1631525.2145549279422-10.2145549279422
1641411.29652053112922.7034794688708
1651925.7472534828519-6.7472534828519
1661218.5742659031445-6.57426590314448
1671416.6987196731255-2.69871967312549
1681819.2202746752643-1.22027467526427
1692525.2518562706378-0.251856270637825
1702230.4205522005948-8.4205522005948
171159.7268734728255.273126527175
1721819.5832303611929-1.58323036119289
1731812.55429871821175.44570128178825
1741218.9033726306047-6.90337263060474
175129.609546148635562.39045385136445
1761618.2248210365886-2.22482103658862
1772213.68497446937468.31502553062536
1781513.45143242884411.54856757115593
1791618.9045719679274-2.90457196792738
1801110.13398104427340.866018955726642
1812011.79773370313018.20226629686994
1821410.36785403016013.63214596983987
1832014.43957324521695.56042675478307
184157.550369665523637.44963033447637
1851217.0759626747280-5.07596267472798
1861823.4142338804761-5.4142338804761
1871811.53063653753676.46936346246327
1881118.4728785812341-7.47287858123412
1891321.6327015628821-8.63270156288208
1901520.4684106306467-5.46841063064672
1911921.9170116277901-2.9170116277901
1921316.4592325221835-3.45923252218351
193197.6216419485631911.3783580514368
1941818.088703787166-0.0887037871659828
1951818.8019259883772-0.801925988377244

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 18 & 18.8019259883771 & -0.801925988377056 \tabularnewline
2 & 10 & 15.9911597462033 & -5.9911597462033 \tabularnewline
3 & 23 & 33.6298970545535 & -10.6298970545535 \tabularnewline
4 & 14 & 18.7678588948077 & -4.76785889480773 \tabularnewline
5 & 20 & 23.9262088462614 & -3.92620884626141 \tabularnewline
6 & 15 & 14.5457239035711 & 0.454276096428875 \tabularnewline
7 & 18 & 9.81569996751708 & 8.18430003248292 \tabularnewline
8 & 19 & 12.5430898275748 & 6.45691017242522 \tabularnewline
9 & 19 & 15.3256316094306 & 3.67436839056938 \tabularnewline
10 & 14 & 16.2347532676433 & -2.23475326764332 \tabularnewline
11 & 15 & 16.3391306227979 & -1.33913062279792 \tabularnewline
12 & 14 & 17.7285238002903 & -3.72852380029027 \tabularnewline
13 & 16 & 16.2214571706261 & -0.221457170626098 \tabularnewline
14 & 13 & 15.0033546908312 & -2.00335469083116 \tabularnewline
15 & 13 & 17.0678228568810 & -4.06782285688098 \tabularnewline
16 & 14 & 17.7373613539348 & -3.73736135393479 \tabularnewline
17 & 23 & 31.0369513362787 & -8.03695133627873 \tabularnewline
18 & 17 & 8.89483256474106 & 8.10516743525894 \tabularnewline
19 & 14 & 12.5738332654908 & 1.42616673450919 \tabularnewline
20 & 21 & 18.0206857515233 & 2.97931424847673 \tabularnewline
21 & 15 & 12.2046658027650 & 2.79533419723505 \tabularnewline
22 & 19 & 19.8308998502609 & -0.830899850260884 \tabularnewline
23 & 20 & 11.4066226514982 & 8.59337734850178 \tabularnewline
24 & 18 & 19.6705703603506 & -1.67057036035061 \tabularnewline
25 & 13 & 15.6125207079462 & -2.61252070794625 \tabularnewline
26 & 20 & 16.5882266228949 & 3.4117733771051 \tabularnewline
27 & 12 & 17.9262603459065 & -5.92626034590651 \tabularnewline
28 & 17 & 19.9586837531128 & -2.95868375311282 \tabularnewline
29 & 13 & 25.5056354813939 & -12.5056354813939 \tabularnewline
30 & 52 & 34.2340824341964 & 17.7659175658036 \tabularnewline
31 & 46 & 36.1908416746897 & 9.80915832531032 \tabularnewline
32 & 58 & 32.9267453343568 & 25.0732546656432 \tabularnewline
33 & 54 & 34.0681131920053 & 19.9318868079947 \tabularnewline
34 & 29 & 40.4798713046882 & -11.4798713046882 \tabularnewline
35 & 50 & 36.741698108193 & 13.258301891807 \tabularnewline
36 & 43 & 36.8299411590345 & 6.17005884096547 \tabularnewline
37 & 30 & 31.4088408989620 & -1.40884089896195 \tabularnewline
38 & 47 & 34.3077680533278 & 12.6922319466722 \tabularnewline
39 & 45 & 36.0618138574557 & 8.93818614254427 \tabularnewline
40 & 48 & 33.4292342061729 & 14.5707657938271 \tabularnewline
41 & 48 & 33.5219929106085 & 14.4780070893915 \tabularnewline
42 & 26 & 30.1427620121612 & -4.14276201216124 \tabularnewline
43 & 46 & 33.4876386588273 & 12.5123613411727 \tabularnewline
44 & 3 & 35.9796754878379 & -32.9796754878379 \tabularnewline
45 & 3 & -0.0153805393059878 & 3.01538053930599 \tabularnewline
46 & 4 & 13.6822192009145 & -9.68221920091448 \tabularnewline
47 & 2 & 5.7197628956713 & -3.7197628956713 \tabularnewline
48 & 2 & 1.37083009095312 & 0.629169909046876 \tabularnewline
49 & 3 & 0.815673840039175 & 2.18432615996082 \tabularnewline
50 & 2 & 4.91455172230979 & -2.91455172230979 \tabularnewline
51 & 4 & 5.03863398458822 & -1.03863398458822 \tabularnewline
52 & 5 & 5.50727174202696 & -0.507271742026957 \tabularnewline
53 & 3 & 8.19721570131841 & -5.19721570131841 \tabularnewline
54 & 4 & 9.13252796039819 & -5.13252796039819 \tabularnewline
55 & 5 & 13.8219406605128 & -8.82194066051279 \tabularnewline
56 & 5 & 7.26062196626641 & -2.26062196626641 \tabularnewline
57 & 3 & 0.971191390716594 & 2.02880860928341 \tabularnewline
58 & 4 & 7.66880992076137 & -3.66880992076137 \tabularnewline
59 & 3 & 3.45231065607589 & -0.452310656075894 \tabularnewline
60 & 3 & 5.9594177569938 & -2.9594177569938 \tabularnewline
61 & 2 & 0.475835881277837 & 1.52416411872216 \tabularnewline
62 & 3 & 17.3782943696117 & -14.3782943696117 \tabularnewline
63 & 4 & 12.7437386882196 & -8.74373868821962 \tabularnewline
64 & 4 & 13.4585979713591 & -9.45859797135909 \tabularnewline
65 & 4 & 2.17154411727049 & 1.82845588272951 \tabularnewline
66 & 4 & 0.576780993484566 & 3.42321900651543 \tabularnewline
67 & 3 & 1.49676052720298 & 1.50323947279702 \tabularnewline
68 & 3 & -2.60134318527310 & 5.6013431852731 \tabularnewline
69 & 3 & 9.772780038826 & -6.77278003882601 \tabularnewline
70 & 2 & -3.76579922234027 & 5.76579922234027 \tabularnewline
71 & 3 & 6.89116000594672 & -3.89116000594672 \tabularnewline
72 & 3 & 11.8139072306387 & -8.8139072306387 \tabularnewline
73 & 3 & 2.0492648866023 & 0.9507351133977 \tabularnewline
74 & 3 & 13.3031483914398 & -10.3031483914398 \tabularnewline
75 & 5 & 12.7536466972975 & -7.75364669729751 \tabularnewline
76 & 3 & 0.0251954168857051 & 2.97480458311429 \tabularnewline
77 & 5 & 10.3936096618548 & -5.39360966185483 \tabularnewline
78 & 4 & 6.2559484230213 & -2.25594842302130 \tabularnewline
79 & 4 & 6.98816097820314 & -2.98816097820314 \tabularnewline
80 & 4 & 11.4903989747347 & -7.4903989747347 \tabularnewline
81 & 5 & 15.9276165330697 & -10.9276165330697 \tabularnewline
82 & 4 & 24.8871682212458 & -20.8871682212458 \tabularnewline
83 & 5 & 7.16634290676526 & -2.16634290676526 \tabularnewline
84 & 3 & -1.83030746795567 & 4.83030746795567 \tabularnewline
85 & 3 & 4.87317973850349 & -1.87317973850349 \tabularnewline
86 & 2 & 5.51210863818375 & -3.51210863818375 \tabularnewline
87 & 3 & 14.6257586715038 & -11.6257586715038 \tabularnewline
88 & 4 & -0.252018397595483 & 4.25201839759548 \tabularnewline
89 & 5 & 3.85466835716593 & 1.14533164283407 \tabularnewline
90 & 5 & 10.0378246577564 & -5.03782465775643 \tabularnewline
91 & 3 & 12.3389975666148 & -9.33899756661483 \tabularnewline
92 & 2 & 2.66691816060563 & -0.666918160605627 \tabularnewline
93 & 3 & -2.51612750444623 & 5.51612750444623 \tabularnewline
94 & 4 & 2.14233577767784 & 1.85766422232216 \tabularnewline
95 & 1 & 10.2293245411558 & -9.22932454115584 \tabularnewline
96 & 4 & 3.79412033390941 & 0.205879666090589 \tabularnewline
97 & 3 & 10.6489738847821 & -7.64897388478212 \tabularnewline
98 & 3 & -1.77943567192839 & 4.77943567192839 \tabularnewline
99 & 4 & 18.4711818380005 & -14.4711818380005 \tabularnewline
100 & 3 & 7.67953358786051 & -4.67953358786051 \tabularnewline
101 & 4 & 15.3379504035163 & -11.3379504035163 \tabularnewline
102 & 2 & 6.20332274669885 & -4.20332274669885 \tabularnewline
103 & 3 & 1.01803137412447 & 1.98196862587553 \tabularnewline
104 & 3 & 4.4643189336306 & -1.4643189336306 \tabularnewline
105 & 3 & 14.6408627314380 & -11.6408627314380 \tabularnewline
106 & 2 & -2.8237849224611 & 4.8237849224611 \tabularnewline
107 & 5 & 6.39962241430445 & -1.39962241430445 \tabularnewline
108 & 5 & 2.03401184765729 & 2.96598815234271 \tabularnewline
109 & 4 & 12.6031540916678 & -8.60315409166782 \tabularnewline
110 & 2 & 3.56166666267819 & -1.56166666267819 \tabularnewline
111 & 3 & -0.275835944929442 & 3.27583594492944 \tabularnewline
112 & 3 & 18.660940643002 & -15.6609406430020 \tabularnewline
113 & 3 & 8.6287936007036 & -5.6287936007036 \tabularnewline
114 & 4 & 7.94289963663601 & -3.94289963663601 \tabularnewline
115 & 5 & 0.974288996520515 & 4.02571100347948 \tabularnewline
116 & 4 & 4.26201525734727 & -0.262015257347274 \tabularnewline
117 & 22 & 15.7466083274186 & 6.25339167258142 \tabularnewline
118 & 16 & 29.4773687856011 & -13.4773687856011 \tabularnewline
119 & 36 & 27.7080807149916 & 8.29191928500837 \tabularnewline
120 & 35 & 27.6625425294974 & 7.33745747050262 \tabularnewline
121 & 25 & 32.8032276691393 & -7.80322766913934 \tabularnewline
122 & 27 & 27.7377565717583 & -0.737756571758339 \tabularnewline
123 & 32 & 39.7176605639378 & -7.71766056393782 \tabularnewline
124 & 36 & 28.5156044803947 & 7.48439551960526 \tabularnewline
125 & 51 & 26.264532296873 & 24.735467703127 \tabularnewline
126 & 30 & 33.4619034750429 & -3.4619034750429 \tabularnewline
127 & 20 & 24.7827617953287 & -4.78276179532871 \tabularnewline
128 & 29 & 22.5676857611609 & 6.43231423883908 \tabularnewline
129 & 26 & 24.8959597334566 & 1.10404026654335 \tabularnewline
130 & 20 & 25.9520626433011 & -5.95206264330109 \tabularnewline
131 & 40 & 26.8246342560579 & 13.1753657439421 \tabularnewline
132 & 29 & 27.7733150480111 & 1.22668495198891 \tabularnewline
133 & 32 & 25.4188491637894 & 6.58115083621057 \tabularnewline
134 & 33 & 26.1682680145964 & 6.83173198540359 \tabularnewline
135 & 32 & 29.1345236799156 & 2.86547632008437 \tabularnewline
136 & 34 & 26.3332003645612 & 7.66679963543884 \tabularnewline
137 & 24 & 27.583832387369 & -3.583832387369 \tabularnewline
138 & 25 & 25.6900538576242 & -0.690053857624205 \tabularnewline
139 & 41 & 26.7597989924682 & 14.2402010075318 \tabularnewline
140 & 39 & 30.0248790587602 & 8.97512094123978 \tabularnewline
141 & 21 & 29.2802481334569 & -8.28024813345693 \tabularnewline
142 & 38 & 25.8676201175340 & 12.1323798824660 \tabularnewline
143 & 28 & 28.6966078704013 & -0.696607870401336 \tabularnewline
144 & 37 & 27.0165283217382 & 9.98347167826177 \tabularnewline
145 & 46 & 12.9600547312141 & 33.0399452687859 \tabularnewline
146 & 39 & 18.2846457471288 & 20.7153542528712 \tabularnewline
147 & 21 & 15.0848896725500 & 5.91511032744997 \tabularnewline
148 & 31 & 16.3999202290767 & 14.6000797709233 \tabularnewline
149 & 25 & 13.7382932191656 & 11.2617067808344 \tabularnewline
150 & 29 & 8.02997623914485 & 20.9700237608551 \tabularnewline
151 & 31 & 11.4620350306969 & 19.5379649693031 \tabularnewline
152 & 13 & 28.0419159658629 & -15.0419159658629 \tabularnewline
153 & 15 & 16.2661574009106 & -1.26615740091061 \tabularnewline
154 & 13 & 12.5072320380837 & 0.492767961916336 \tabularnewline
155 & 13 & 20.3538530660089 & -7.35385306600887 \tabularnewline
156 & 23 & 21.5919850194141 & 1.40801498058590 \tabularnewline
157 & 18 & 16.8219860628341 & 1.17801393716593 \tabularnewline
158 & 21 & 22.4481276054442 & -1.44812760544419 \tabularnewline
159 & 14 & 21.1552313637675 & -7.15523136376752 \tabularnewline
160 & 12 & 17.5075284190865 & -5.5075284190865 \tabularnewline
161 & 17 & 18.8364252752152 & -1.83642527521522 \tabularnewline
162 & 11 & 10.8029979415313 & 0.197002058468670 \tabularnewline
163 & 15 & 25.2145549279422 & -10.2145549279422 \tabularnewline
164 & 14 & 11.2965205311292 & 2.7034794688708 \tabularnewline
165 & 19 & 25.7472534828519 & -6.7472534828519 \tabularnewline
166 & 12 & 18.5742659031445 & -6.57426590314448 \tabularnewline
167 & 14 & 16.6987196731255 & -2.69871967312549 \tabularnewline
168 & 18 & 19.2202746752643 & -1.22027467526427 \tabularnewline
169 & 25 & 25.2518562706378 & -0.251856270637825 \tabularnewline
170 & 22 & 30.4205522005948 & -8.4205522005948 \tabularnewline
171 & 15 & 9.726873472825 & 5.273126527175 \tabularnewline
172 & 18 & 19.5832303611929 & -1.58323036119289 \tabularnewline
173 & 18 & 12.5542987182117 & 5.44570128178825 \tabularnewline
174 & 12 & 18.9033726306047 & -6.90337263060474 \tabularnewline
175 & 12 & 9.60954614863556 & 2.39045385136445 \tabularnewline
176 & 16 & 18.2248210365886 & -2.22482103658862 \tabularnewline
177 & 22 & 13.6849744693746 & 8.31502553062536 \tabularnewline
178 & 15 & 13.4514324288441 & 1.54856757115593 \tabularnewline
179 & 16 & 18.9045719679274 & -2.90457196792738 \tabularnewline
180 & 11 & 10.1339810442734 & 0.866018955726642 \tabularnewline
181 & 20 & 11.7977337031301 & 8.20226629686994 \tabularnewline
182 & 14 & 10.3678540301601 & 3.63214596983987 \tabularnewline
183 & 20 & 14.4395732452169 & 5.56042675478307 \tabularnewline
184 & 15 & 7.55036966552363 & 7.44963033447637 \tabularnewline
185 & 12 & 17.0759626747280 & -5.07596267472798 \tabularnewline
186 & 18 & 23.4142338804761 & -5.4142338804761 \tabularnewline
187 & 18 & 11.5306365375367 & 6.46936346246327 \tabularnewline
188 & 11 & 18.4728785812341 & -7.47287858123412 \tabularnewline
189 & 13 & 21.6327015628821 & -8.63270156288208 \tabularnewline
190 & 15 & 20.4684106306467 & -5.46841063064672 \tabularnewline
191 & 19 & 21.9170116277901 & -2.9170116277901 \tabularnewline
192 & 13 & 16.4592325221835 & -3.45923252218351 \tabularnewline
193 & 19 & 7.62164194856319 & 11.3783580514368 \tabularnewline
194 & 18 & 18.088703787166 & -0.0887037871659828 \tabularnewline
195 & 18 & 18.8019259883772 & -0.801925988377244 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116300&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]18[/C][C]18.8019259883771[/C][C]-0.801925988377056[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]15.9911597462033[/C][C]-5.9911597462033[/C][/ROW]
[ROW][C]3[/C][C]23[/C][C]33.6298970545535[/C][C]-10.6298970545535[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]18.7678588948077[/C][C]-4.76785889480773[/C][/ROW]
[ROW][C]5[/C][C]20[/C][C]23.9262088462614[/C][C]-3.92620884626141[/C][/ROW]
[ROW][C]6[/C][C]15[/C][C]14.5457239035711[/C][C]0.454276096428875[/C][/ROW]
[ROW][C]7[/C][C]18[/C][C]9.81569996751708[/C][C]8.18430003248292[/C][/ROW]
[ROW][C]8[/C][C]19[/C][C]12.5430898275748[/C][C]6.45691017242522[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]15.3256316094306[/C][C]3.67436839056938[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]16.2347532676433[/C][C]-2.23475326764332[/C][/ROW]
[ROW][C]11[/C][C]15[/C][C]16.3391306227979[/C][C]-1.33913062279792[/C][/ROW]
[ROW][C]12[/C][C]14[/C][C]17.7285238002903[/C][C]-3.72852380029027[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]16.2214571706261[/C][C]-0.221457170626098[/C][/ROW]
[ROW][C]14[/C][C]13[/C][C]15.0033546908312[/C][C]-2.00335469083116[/C][/ROW]
[ROW][C]15[/C][C]13[/C][C]17.0678228568810[/C][C]-4.06782285688098[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]17.7373613539348[/C][C]-3.73736135393479[/C][/ROW]
[ROW][C]17[/C][C]23[/C][C]31.0369513362787[/C][C]-8.03695133627873[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]8.89483256474106[/C][C]8.10516743525894[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]12.5738332654908[/C][C]1.42616673450919[/C][/ROW]
[ROW][C]20[/C][C]21[/C][C]18.0206857515233[/C][C]2.97931424847673[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]12.2046658027650[/C][C]2.79533419723505[/C][/ROW]
[ROW][C]22[/C][C]19[/C][C]19.8308998502609[/C][C]-0.830899850260884[/C][/ROW]
[ROW][C]23[/C][C]20[/C][C]11.4066226514982[/C][C]8.59337734850178[/C][/ROW]
[ROW][C]24[/C][C]18[/C][C]19.6705703603506[/C][C]-1.67057036035061[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]15.6125207079462[/C][C]-2.61252070794625[/C][/ROW]
[ROW][C]26[/C][C]20[/C][C]16.5882266228949[/C][C]3.4117733771051[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]17.9262603459065[/C][C]-5.92626034590651[/C][/ROW]
[ROW][C]28[/C][C]17[/C][C]19.9586837531128[/C][C]-2.95868375311282[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]25.5056354813939[/C][C]-12.5056354813939[/C][/ROW]
[ROW][C]30[/C][C]52[/C][C]34.2340824341964[/C][C]17.7659175658036[/C][/ROW]
[ROW][C]31[/C][C]46[/C][C]36.1908416746897[/C][C]9.80915832531032[/C][/ROW]
[ROW][C]32[/C][C]58[/C][C]32.9267453343568[/C][C]25.0732546656432[/C][/ROW]
[ROW][C]33[/C][C]54[/C][C]34.0681131920053[/C][C]19.9318868079947[/C][/ROW]
[ROW][C]34[/C][C]29[/C][C]40.4798713046882[/C][C]-11.4798713046882[/C][/ROW]
[ROW][C]35[/C][C]50[/C][C]36.741698108193[/C][C]13.258301891807[/C][/ROW]
[ROW][C]36[/C][C]43[/C][C]36.8299411590345[/C][C]6.17005884096547[/C][/ROW]
[ROW][C]37[/C][C]30[/C][C]31.4088408989620[/C][C]-1.40884089896195[/C][/ROW]
[ROW][C]38[/C][C]47[/C][C]34.3077680533278[/C][C]12.6922319466722[/C][/ROW]
[ROW][C]39[/C][C]45[/C][C]36.0618138574557[/C][C]8.93818614254427[/C][/ROW]
[ROW][C]40[/C][C]48[/C][C]33.4292342061729[/C][C]14.5707657938271[/C][/ROW]
[ROW][C]41[/C][C]48[/C][C]33.5219929106085[/C][C]14.4780070893915[/C][/ROW]
[ROW][C]42[/C][C]26[/C][C]30.1427620121612[/C][C]-4.14276201216124[/C][/ROW]
[ROW][C]43[/C][C]46[/C][C]33.4876386588273[/C][C]12.5123613411727[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]35.9796754878379[/C][C]-32.9796754878379[/C][/ROW]
[ROW][C]45[/C][C]3[/C][C]-0.0153805393059878[/C][C]3.01538053930599[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]13.6822192009145[/C][C]-9.68221920091448[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]5.7197628956713[/C][C]-3.7197628956713[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.37083009095312[/C][C]0.629169909046876[/C][/ROW]
[ROW][C]49[/C][C]3[/C][C]0.815673840039175[/C][C]2.18432615996082[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]4.91455172230979[/C][C]-2.91455172230979[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]5.03863398458822[/C][C]-1.03863398458822[/C][/ROW]
[ROW][C]52[/C][C]5[/C][C]5.50727174202696[/C][C]-0.507271742026957[/C][/ROW]
[ROW][C]53[/C][C]3[/C][C]8.19721570131841[/C][C]-5.19721570131841[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]9.13252796039819[/C][C]-5.13252796039819[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]13.8219406605128[/C][C]-8.82194066051279[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]7.26062196626641[/C][C]-2.26062196626641[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]0.971191390716594[/C][C]2.02880860928341[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]7.66880992076137[/C][C]-3.66880992076137[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]3.45231065607589[/C][C]-0.452310656075894[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]5.9594177569938[/C][C]-2.9594177569938[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]0.475835881277837[/C][C]1.52416411872216[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]17.3782943696117[/C][C]-14.3782943696117[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]12.7437386882196[/C][C]-8.74373868821962[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]13.4585979713591[/C][C]-9.45859797135909[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]2.17154411727049[/C][C]1.82845588272951[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]0.576780993484566[/C][C]3.42321900651543[/C][/ROW]
[ROW][C]67[/C][C]3[/C][C]1.49676052720298[/C][C]1.50323947279702[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]-2.60134318527310[/C][C]5.6013431852731[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]9.772780038826[/C][C]-6.77278003882601[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]-3.76579922234027[/C][C]5.76579922234027[/C][/ROW]
[ROW][C]71[/C][C]3[/C][C]6.89116000594672[/C][C]-3.89116000594672[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]11.8139072306387[/C][C]-8.8139072306387[/C][/ROW]
[ROW][C]73[/C][C]3[/C][C]2.0492648866023[/C][C]0.9507351133977[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]13.3031483914398[/C][C]-10.3031483914398[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]12.7536466972975[/C][C]-7.75364669729751[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]0.0251954168857051[/C][C]2.97480458311429[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]10.3936096618548[/C][C]-5.39360966185483[/C][/ROW]
[ROW][C]78[/C][C]4[/C][C]6.2559484230213[/C][C]-2.25594842302130[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]6.98816097820314[/C][C]-2.98816097820314[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]11.4903989747347[/C][C]-7.4903989747347[/C][/ROW]
[ROW][C]81[/C][C]5[/C][C]15.9276165330697[/C][C]-10.9276165330697[/C][/ROW]
[ROW][C]82[/C][C]4[/C][C]24.8871682212458[/C][C]-20.8871682212458[/C][/ROW]
[ROW][C]83[/C][C]5[/C][C]7.16634290676526[/C][C]-2.16634290676526[/C][/ROW]
[ROW][C]84[/C][C]3[/C][C]-1.83030746795567[/C][C]4.83030746795567[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]4.87317973850349[/C][C]-1.87317973850349[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]5.51210863818375[/C][C]-3.51210863818375[/C][/ROW]
[ROW][C]87[/C][C]3[/C][C]14.6257586715038[/C][C]-11.6257586715038[/C][/ROW]
[ROW][C]88[/C][C]4[/C][C]-0.252018397595483[/C][C]4.25201839759548[/C][/ROW]
[ROW][C]89[/C][C]5[/C][C]3.85466835716593[/C][C]1.14533164283407[/C][/ROW]
[ROW][C]90[/C][C]5[/C][C]10.0378246577564[/C][C]-5.03782465775643[/C][/ROW]
[ROW][C]91[/C][C]3[/C][C]12.3389975666148[/C][C]-9.33899756661483[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]2.66691816060563[/C][C]-0.666918160605627[/C][/ROW]
[ROW][C]93[/C][C]3[/C][C]-2.51612750444623[/C][C]5.51612750444623[/C][/ROW]
[ROW][C]94[/C][C]4[/C][C]2.14233577767784[/C][C]1.85766422232216[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]10.2293245411558[/C][C]-9.22932454115584[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]3.79412033390941[/C][C]0.205879666090589[/C][/ROW]
[ROW][C]97[/C][C]3[/C][C]10.6489738847821[/C][C]-7.64897388478212[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]-1.77943567192839[/C][C]4.77943567192839[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]18.4711818380005[/C][C]-14.4711818380005[/C][/ROW]
[ROW][C]100[/C][C]3[/C][C]7.67953358786051[/C][C]-4.67953358786051[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]15.3379504035163[/C][C]-11.3379504035163[/C][/ROW]
[ROW][C]102[/C][C]2[/C][C]6.20332274669885[/C][C]-4.20332274669885[/C][/ROW]
[ROW][C]103[/C][C]3[/C][C]1.01803137412447[/C][C]1.98196862587553[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]4.4643189336306[/C][C]-1.4643189336306[/C][/ROW]
[ROW][C]105[/C][C]3[/C][C]14.6408627314380[/C][C]-11.6408627314380[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]-2.8237849224611[/C][C]4.8237849224611[/C][/ROW]
[ROW][C]107[/C][C]5[/C][C]6.39962241430445[/C][C]-1.39962241430445[/C][/ROW]
[ROW][C]108[/C][C]5[/C][C]2.03401184765729[/C][C]2.96598815234271[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]12.6031540916678[/C][C]-8.60315409166782[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]3.56166666267819[/C][C]-1.56166666267819[/C][/ROW]
[ROW][C]111[/C][C]3[/C][C]-0.275835944929442[/C][C]3.27583594492944[/C][/ROW]
[ROW][C]112[/C][C]3[/C][C]18.660940643002[/C][C]-15.6609406430020[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]8.6287936007036[/C][C]-5.6287936007036[/C][/ROW]
[ROW][C]114[/C][C]4[/C][C]7.94289963663601[/C][C]-3.94289963663601[/C][/ROW]
[ROW][C]115[/C][C]5[/C][C]0.974288996520515[/C][C]4.02571100347948[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]4.26201525734727[/C][C]-0.262015257347274[/C][/ROW]
[ROW][C]117[/C][C]22[/C][C]15.7466083274186[/C][C]6.25339167258142[/C][/ROW]
[ROW][C]118[/C][C]16[/C][C]29.4773687856011[/C][C]-13.4773687856011[/C][/ROW]
[ROW][C]119[/C][C]36[/C][C]27.7080807149916[/C][C]8.29191928500837[/C][/ROW]
[ROW][C]120[/C][C]35[/C][C]27.6625425294974[/C][C]7.33745747050262[/C][/ROW]
[ROW][C]121[/C][C]25[/C][C]32.8032276691393[/C][C]-7.80322766913934[/C][/ROW]
[ROW][C]122[/C][C]27[/C][C]27.7377565717583[/C][C]-0.737756571758339[/C][/ROW]
[ROW][C]123[/C][C]32[/C][C]39.7176605639378[/C][C]-7.71766056393782[/C][/ROW]
[ROW][C]124[/C][C]36[/C][C]28.5156044803947[/C][C]7.48439551960526[/C][/ROW]
[ROW][C]125[/C][C]51[/C][C]26.264532296873[/C][C]24.735467703127[/C][/ROW]
[ROW][C]126[/C][C]30[/C][C]33.4619034750429[/C][C]-3.4619034750429[/C][/ROW]
[ROW][C]127[/C][C]20[/C][C]24.7827617953287[/C][C]-4.78276179532871[/C][/ROW]
[ROW][C]128[/C][C]29[/C][C]22.5676857611609[/C][C]6.43231423883908[/C][/ROW]
[ROW][C]129[/C][C]26[/C][C]24.8959597334566[/C][C]1.10404026654335[/C][/ROW]
[ROW][C]130[/C][C]20[/C][C]25.9520626433011[/C][C]-5.95206264330109[/C][/ROW]
[ROW][C]131[/C][C]40[/C][C]26.8246342560579[/C][C]13.1753657439421[/C][/ROW]
[ROW][C]132[/C][C]29[/C][C]27.7733150480111[/C][C]1.22668495198891[/C][/ROW]
[ROW][C]133[/C][C]32[/C][C]25.4188491637894[/C][C]6.58115083621057[/C][/ROW]
[ROW][C]134[/C][C]33[/C][C]26.1682680145964[/C][C]6.83173198540359[/C][/ROW]
[ROW][C]135[/C][C]32[/C][C]29.1345236799156[/C][C]2.86547632008437[/C][/ROW]
[ROW][C]136[/C][C]34[/C][C]26.3332003645612[/C][C]7.66679963543884[/C][/ROW]
[ROW][C]137[/C][C]24[/C][C]27.583832387369[/C][C]-3.583832387369[/C][/ROW]
[ROW][C]138[/C][C]25[/C][C]25.6900538576242[/C][C]-0.690053857624205[/C][/ROW]
[ROW][C]139[/C][C]41[/C][C]26.7597989924682[/C][C]14.2402010075318[/C][/ROW]
[ROW][C]140[/C][C]39[/C][C]30.0248790587602[/C][C]8.97512094123978[/C][/ROW]
[ROW][C]141[/C][C]21[/C][C]29.2802481334569[/C][C]-8.28024813345693[/C][/ROW]
[ROW][C]142[/C][C]38[/C][C]25.8676201175340[/C][C]12.1323798824660[/C][/ROW]
[ROW][C]143[/C][C]28[/C][C]28.6966078704013[/C][C]-0.696607870401336[/C][/ROW]
[ROW][C]144[/C][C]37[/C][C]27.0165283217382[/C][C]9.98347167826177[/C][/ROW]
[ROW][C]145[/C][C]46[/C][C]12.9600547312141[/C][C]33.0399452687859[/C][/ROW]
[ROW][C]146[/C][C]39[/C][C]18.2846457471288[/C][C]20.7153542528712[/C][/ROW]
[ROW][C]147[/C][C]21[/C][C]15.0848896725500[/C][C]5.91511032744997[/C][/ROW]
[ROW][C]148[/C][C]31[/C][C]16.3999202290767[/C][C]14.6000797709233[/C][/ROW]
[ROW][C]149[/C][C]25[/C][C]13.7382932191656[/C][C]11.2617067808344[/C][/ROW]
[ROW][C]150[/C][C]29[/C][C]8.02997623914485[/C][C]20.9700237608551[/C][/ROW]
[ROW][C]151[/C][C]31[/C][C]11.4620350306969[/C][C]19.5379649693031[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]28.0419159658629[/C][C]-15.0419159658629[/C][/ROW]
[ROW][C]153[/C][C]15[/C][C]16.2661574009106[/C][C]-1.26615740091061[/C][/ROW]
[ROW][C]154[/C][C]13[/C][C]12.5072320380837[/C][C]0.492767961916336[/C][/ROW]
[ROW][C]155[/C][C]13[/C][C]20.3538530660089[/C][C]-7.35385306600887[/C][/ROW]
[ROW][C]156[/C][C]23[/C][C]21.5919850194141[/C][C]1.40801498058590[/C][/ROW]
[ROW][C]157[/C][C]18[/C][C]16.8219860628341[/C][C]1.17801393716593[/C][/ROW]
[ROW][C]158[/C][C]21[/C][C]22.4481276054442[/C][C]-1.44812760544419[/C][/ROW]
[ROW][C]159[/C][C]14[/C][C]21.1552313637675[/C][C]-7.15523136376752[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]17.5075284190865[/C][C]-5.5075284190865[/C][/ROW]
[ROW][C]161[/C][C]17[/C][C]18.8364252752152[/C][C]-1.83642527521522[/C][/ROW]
[ROW][C]162[/C][C]11[/C][C]10.8029979415313[/C][C]0.197002058468670[/C][/ROW]
[ROW][C]163[/C][C]15[/C][C]25.2145549279422[/C][C]-10.2145549279422[/C][/ROW]
[ROW][C]164[/C][C]14[/C][C]11.2965205311292[/C][C]2.7034794688708[/C][/ROW]
[ROW][C]165[/C][C]19[/C][C]25.7472534828519[/C][C]-6.7472534828519[/C][/ROW]
[ROW][C]166[/C][C]12[/C][C]18.5742659031445[/C][C]-6.57426590314448[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]16.6987196731255[/C][C]-2.69871967312549[/C][/ROW]
[ROW][C]168[/C][C]18[/C][C]19.2202746752643[/C][C]-1.22027467526427[/C][/ROW]
[ROW][C]169[/C][C]25[/C][C]25.2518562706378[/C][C]-0.251856270637825[/C][/ROW]
[ROW][C]170[/C][C]22[/C][C]30.4205522005948[/C][C]-8.4205522005948[/C][/ROW]
[ROW][C]171[/C][C]15[/C][C]9.726873472825[/C][C]5.273126527175[/C][/ROW]
[ROW][C]172[/C][C]18[/C][C]19.5832303611929[/C][C]-1.58323036119289[/C][/ROW]
[ROW][C]173[/C][C]18[/C][C]12.5542987182117[/C][C]5.44570128178825[/C][/ROW]
[ROW][C]174[/C][C]12[/C][C]18.9033726306047[/C][C]-6.90337263060474[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]9.60954614863556[/C][C]2.39045385136445[/C][/ROW]
[ROW][C]176[/C][C]16[/C][C]18.2248210365886[/C][C]-2.22482103658862[/C][/ROW]
[ROW][C]177[/C][C]22[/C][C]13.6849744693746[/C][C]8.31502553062536[/C][/ROW]
[ROW][C]178[/C][C]15[/C][C]13.4514324288441[/C][C]1.54856757115593[/C][/ROW]
[ROW][C]179[/C][C]16[/C][C]18.9045719679274[/C][C]-2.90457196792738[/C][/ROW]
[ROW][C]180[/C][C]11[/C][C]10.1339810442734[/C][C]0.866018955726642[/C][/ROW]
[ROW][C]181[/C][C]20[/C][C]11.7977337031301[/C][C]8.20226629686994[/C][/ROW]
[ROW][C]182[/C][C]14[/C][C]10.3678540301601[/C][C]3.63214596983987[/C][/ROW]
[ROW][C]183[/C][C]20[/C][C]14.4395732452169[/C][C]5.56042675478307[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]7.55036966552363[/C][C]7.44963033447637[/C][/ROW]
[ROW][C]185[/C][C]12[/C][C]17.0759626747280[/C][C]-5.07596267472798[/C][/ROW]
[ROW][C]186[/C][C]18[/C][C]23.4142338804761[/C][C]-5.4142338804761[/C][/ROW]
[ROW][C]187[/C][C]18[/C][C]11.5306365375367[/C][C]6.46936346246327[/C][/ROW]
[ROW][C]188[/C][C]11[/C][C]18.4728785812341[/C][C]-7.47287858123412[/C][/ROW]
[ROW][C]189[/C][C]13[/C][C]21.6327015628821[/C][C]-8.63270156288208[/C][/ROW]
[ROW][C]190[/C][C]15[/C][C]20.4684106306467[/C][C]-5.46841063064672[/C][/ROW]
[ROW][C]191[/C][C]19[/C][C]21.9170116277901[/C][C]-2.9170116277901[/C][/ROW]
[ROW][C]192[/C][C]13[/C][C]16.4592325221835[/C][C]-3.45923252218351[/C][/ROW]
[ROW][C]193[/C][C]19[/C][C]7.62164194856319[/C][C]11.3783580514368[/C][/ROW]
[ROW][C]194[/C][C]18[/C][C]18.088703787166[/C][C]-0.0887037871659828[/C][/ROW]
[ROW][C]195[/C][C]18[/C][C]18.8019259883772[/C][C]-0.801925988377244[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116300&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116300&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
11818.8019259883771-0.801925988377056
21015.9911597462033-5.9911597462033
32333.6298970545535-10.6298970545535
41418.7678588948077-4.76785889480773
52023.9262088462614-3.92620884626141
61514.54572390357110.454276096428875
7189.815699967517088.18430003248292
81912.54308982757486.45691017242522
91915.32563160943063.67436839056938
101416.2347532676433-2.23475326764332
111516.3391306227979-1.33913062279792
121417.7285238002903-3.72852380029027
131616.2214571706261-0.221457170626098
141315.0033546908312-2.00335469083116
151317.0678228568810-4.06782285688098
161417.7373613539348-3.73736135393479
172331.0369513362787-8.03695133627873
18178.894832564741068.10516743525894
191412.57383326549081.42616673450919
202118.02068575152332.97931424847673
211512.20466580276502.79533419723505
221919.8308998502609-0.830899850260884
232011.40662265149828.59337734850178
241819.6705703603506-1.67057036035061
251315.6125207079462-2.61252070794625
262016.58822662289493.4117733771051
271217.9262603459065-5.92626034590651
281719.9586837531128-2.95868375311282
291325.5056354813939-12.5056354813939
305234.234082434196417.7659175658036
314636.19084167468979.80915832531032
325832.926745334356825.0732546656432
335434.068113192005319.9318868079947
342940.4798713046882-11.4798713046882
355036.74169810819313.258301891807
364336.82994115903456.17005884096547
373031.4088408989620-1.40884089896195
384734.307768053327812.6922319466722
394536.06181385745578.93818614254427
404833.429234206172914.5707657938271
414833.521992910608514.4780070893915
422630.1427620121612-4.14276201216124
434633.487638658827312.5123613411727
44335.9796754878379-32.9796754878379
453-0.01538053930598783.01538053930599
46413.6822192009145-9.68221920091448
4725.7197628956713-3.7197628956713
4821.370830090953120.629169909046876
4930.8156738400391752.18432615996082
5024.91455172230979-2.91455172230979
5145.03863398458822-1.03863398458822
5255.50727174202696-0.507271742026957
5338.19721570131841-5.19721570131841
5449.13252796039819-5.13252796039819
55513.8219406605128-8.82194066051279
5657.26062196626641-2.26062196626641
5730.9711913907165942.02880860928341
5847.66880992076137-3.66880992076137
5933.45231065607589-0.452310656075894
6035.9594177569938-2.9594177569938
6120.4758358812778371.52416411872216
62317.3782943696117-14.3782943696117
63412.7437386882196-8.74373868821962
64413.4585979713591-9.45859797135909
6542.171544117270491.82845588272951
6640.5767809934845663.42321900651543
6731.496760527202981.50323947279702
683-2.601343185273105.6013431852731
6939.772780038826-6.77278003882601
702-3.765799222340275.76579922234027
7136.89116000594672-3.89116000594672
72311.8139072306387-8.8139072306387
7332.04926488660230.9507351133977
74313.3031483914398-10.3031483914398
75512.7536466972975-7.75364669729751
7630.02519541688570512.97480458311429
77510.3936096618548-5.39360966185483
7846.2559484230213-2.25594842302130
7946.98816097820314-2.98816097820314
80411.4903989747347-7.4903989747347
81515.9276165330697-10.9276165330697
82424.8871682212458-20.8871682212458
8357.16634290676526-2.16634290676526
843-1.830307467955674.83030746795567
8534.87317973850349-1.87317973850349
8625.51210863818375-3.51210863818375
87314.6257586715038-11.6257586715038
884-0.2520183975954834.25201839759548
8953.854668357165931.14533164283407
90510.0378246577564-5.03782465775643
91312.3389975666148-9.33899756661483
9222.66691816060563-0.666918160605627
933-2.516127504446235.51612750444623
9442.142335777677841.85766422232216
95110.2293245411558-9.22932454115584
9643.794120333909410.205879666090589
97310.6489738847821-7.64897388478212
983-1.779435671928394.77943567192839
99418.4711818380005-14.4711818380005
10037.67953358786051-4.67953358786051
101415.3379504035163-11.3379504035163
10226.20332274669885-4.20332274669885
10331.018031374124471.98196862587553
10434.4643189336306-1.4643189336306
105314.6408627314380-11.6408627314380
1062-2.82378492246114.8237849224611
10756.39962241430445-1.39962241430445
10852.034011847657292.96598815234271
109412.6031540916678-8.60315409166782
11023.56166666267819-1.56166666267819
1113-0.2758359449294423.27583594492944
112318.660940643002-15.6609406430020
11338.6287936007036-5.6287936007036
11447.94289963663601-3.94289963663601
11550.9742889965205154.02571100347948
11644.26201525734727-0.262015257347274
1172215.74660832741866.25339167258142
1181629.4773687856011-13.4773687856011
1193627.70808071499168.29191928500837
1203527.66254252949747.33745747050262
1212532.8032276691393-7.80322766913934
1222727.7377565717583-0.737756571758339
1233239.7176605639378-7.71766056393782
1243628.51560448039477.48439551960526
1255126.26453229687324.735467703127
1263033.4619034750429-3.4619034750429
1272024.7827617953287-4.78276179532871
1282922.56768576116096.43231423883908
1292624.89595973345661.10404026654335
1302025.9520626433011-5.95206264330109
1314026.824634256057913.1753657439421
1322927.77331504801111.22668495198891
1333225.41884916378946.58115083621057
1343326.16826801459646.83173198540359
1353229.13452367991562.86547632008437
1363426.33320036456127.66679963543884
1372427.583832387369-3.583832387369
1382525.6900538576242-0.690053857624205
1394126.759798992468214.2402010075318
1403930.02487905876028.97512094123978
1412129.2802481334569-8.28024813345693
1423825.867620117534012.1323798824660
1432828.6966078704013-0.696607870401336
1443727.01652832173829.98347167826177
1454612.960054731214133.0399452687859
1463918.284645747128820.7153542528712
1472115.08488967255005.91511032744997
1483116.399920229076714.6000797709233
1492513.738293219165611.2617067808344
150298.0299762391448520.9700237608551
1513111.462035030696919.5379649693031
1521328.0419159658629-15.0419159658629
1531516.2661574009106-1.26615740091061
1541312.50723203808370.492767961916336
1551320.3538530660089-7.35385306600887
1562321.59198501941411.40801498058590
1571816.82198606283411.17801393716593
1582122.4481276054442-1.44812760544419
1591421.1552313637675-7.15523136376752
1601217.5075284190865-5.5075284190865
1611718.8364252752152-1.83642527521522
1621110.80299794153130.197002058468670
1631525.2145549279422-10.2145549279422
1641411.29652053112922.7034794688708
1651925.7472534828519-6.7472534828519
1661218.5742659031445-6.57426590314448
1671416.6987196731255-2.69871967312549
1681819.2202746752643-1.22027467526427
1692525.2518562706378-0.251856270637825
1702230.4205522005948-8.4205522005948
171159.7268734728255.273126527175
1721819.5832303611929-1.58323036119289
1731812.55429871821175.44570128178825
1741218.9033726306047-6.90337263060474
175129.609546148635562.39045385136445
1761618.2248210365886-2.22482103658862
1772213.68497446937468.31502553062536
1781513.45143242884411.54856757115593
1791618.9045719679274-2.90457196792738
1801110.13398104427340.866018955726642
1812011.79773370313018.20226629686994
1821410.36785403016013.63214596983987
1832014.43957324521695.56042675478307
184157.550369665523637.44963033447637
1851217.0759626747280-5.07596267472798
1861823.4142338804761-5.4142338804761
1871811.53063653753676.46936346246327
1881118.4728785812341-7.47287858123412
1891321.6327015628821-8.63270156288208
1901520.4684106306467-5.46841063064672
1911921.9170116277901-2.9170116277901
1921316.4592325221835-3.45923252218351
193197.6216419485631911.3783580514368
1941818.088703787166-0.0887037871659828
1951818.8019259883772-0.801925988377244






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.07239473086286680.1447894617257340.927605269137133
100.07628945112818780.1525789022563760.923710548871812
110.03189224811061780.06378449622123560.968107751889382
120.01642364993085310.03284729986170620.983576350069147
130.006389040910829750.01277808182165950.99361095908917
140.004560058230166830.009120116460333670.995439941769833
150.001966874874958360.003933749749916720.998033125125042
160.000695822144429560.001391644288859120.99930417785557
170.0003428454874276210.0006856909748552430.999657154512572
180.0002439546345230160.0004879092690460310.999756045365477
190.0001111606432808300.0002223212865616600.99988883935672
209.03019693515803e-050.0001806039387031610.999909698030648
213.19821097182198e-056.39642194364396e-050.999968017890282
221.19745945306387e-052.39491890612774e-050.99998802540547
238.83423140540362e-061.76684628108072e-050.999991165768595
243.00479589071574e-066.00959178143148e-060.99999699520411
251.14563864693173e-062.29127729386345e-060.999998854361353
264.39428532750162e-078.78857065500325e-070.999999560571467
272.88079486265597e-075.76158972531194e-070.999999711920514
289.4298437677921e-081.88596875355842e-070.999999905701562
292.08316000049346e-074.16632000098692e-070.999999791684
308.92803331469963e-081.78560666293993e-070.999999910719667
314.64100000938749e-089.28200001877498e-080.99999995359
325.7954760689335e-081.1590952137867e-070.99999994204524
333.40784191186697e-086.81568382373394e-080.99999996592158
344.81310594233458e-069.62621188466916e-060.999995186894058
352.81668785757114e-065.63337571514228e-060.999997183312142
365.07145750806358e-050.0001014291501612720.99994928542492
370.01118613238311790.02237226476623580.988813867616882
380.01025261826503600.02050523653007210.989747381734964
390.01247377053195620.02494754106391240.987526229468044
400.01831751560772700.03663503121545390.981682484392273
410.02146478434138820.04292956868277650.978535215658612
420.1470270828403820.2940541656807630.852972917159618
430.1894821981847150.378964396369430.810517801815285
440.860942017292960.2781159654140820.139057982707041
450.9573652613797840.08526947724043250.0426347386202163
460.985966487942970.028067024114060.01403351205703
470.9869740108347230.02605197833055450.0130259891652773
480.9841706898862830.03165862022743440.0158293101137172
490.978854480559030.04229103888193830.0211455194409691
500.9748207122554440.05035857548911130.0251792877445556
510.9679079035878720.06418419282425550.0320920964121277
520.9620974233487590.0758051533024820.037902576651241
530.9555717016951470.08885659660970640.0444282983048532
540.95247688122310.09504623755379840.0475231187768992
550.9488644182875660.1022711634248680.051135581712434
560.936008506455270.1279829870894600.0639914935447301
570.922362010807850.1552759783843000.0776379891921502
580.9098932272089280.1802135455821450.0901067727910724
590.8929417950201750.2141164099596500.107058204979825
600.8733872562938870.2532254874122270.126612743706113
610.8502279476082580.2995441047834850.149772052391742
620.8782492156172460.2435015687655080.121750784382754
630.8705680163718850.2588639672562290.129431983628115
640.8641905420794080.2716189158411850.135809457920592
650.8412507853693170.3174984292613660.158749214630683
660.8172548465974960.3654903068050080.182745153402504
670.7877227569144570.4245544861710870.212277243085543
680.7645378165413980.4709243669172030.235462183458602
690.7454269241294880.5091461517410230.254573075870512
700.718195893063430.563608213873140.28180410693657
710.6899689172709110.6200621654581780.310031082729089
720.6829949337479150.6340101325041690.317005066252085
730.6514088611391170.6971822777217670.348591138860883
740.6501991647307130.6996016705385740.349800835269287
750.6300772396046010.7398455207907970.369922760395399
760.5976414464322310.8047171071355380.402358553567769
770.5803303239113160.8393393521773670.419669676088684
780.5531009209604660.8937981580790690.446899079039534
790.5128091963230870.9743816073538270.487190803676913
800.4891096486177850.978219297235570.510890351382215
810.484120871155780.968241742311560.51587912884422
820.6151971393035280.7696057213929450.384802860696472
830.5746361444058760.8507277111882490.425363855594124
840.5473395846561730.9053208306876530.452660415343827
850.5074009205228620.9851981589542760.492599079477138
860.479780349365520.959560698731040.52021965063448
870.5006193083567430.9987613832865150.499380691643257
880.473245271352910.946490542705820.52675472864709
890.4321207730001340.8642415460002680.567879226999866
900.4024735442187760.8049470884375520.597526455781224
910.3952549368708700.7905098737417410.60474506312913
920.3580874785008090.7161749570016180.641912521499191
930.3253313135015450.650662627003090.674668686498455
940.2892572184455730.5785144368911450.710742781554427
950.3001537159579420.6003074319158830.699846284042058
960.2858677119019710.5717354238039420.714132288098029
970.2968548324181040.5937096648362070.703145167581896
980.270409092061730.540818184123460.72959090793827
990.3066942945723670.6133885891447350.693305705427633
1000.2944673322617940.5889346645235890.705532667738206
1010.3175279748281470.6350559496562940.682472025171853
1020.3007697022996920.6015394045993840.699230297700308
1030.2873746513883200.5747493027766390.71262534861168
1040.2640489719563850.528097943912770.735951028043615
1050.3167673770986380.6335347541972760.683232622901362
1060.2963571143794260.5927142287588530.703642885620574
1070.2861840404890850.572368080978170.713815959510915
1080.2529859137519470.5059718275038930.747014086248053
1090.2977862329007320.5955724658014640.702213767099268
1100.2891185120265950.578237024053190.710881487973405
1110.2769589656416010.5539179312832020.723041034358399
1120.4745970354610390.9491940709220780.525402964538961
1130.5725409776987740.8549180446024520.427459022301226
1140.6453847486804980.7092305026390040.354615251319502
1150.7290392711460880.5419214577078240.270960728853912
1160.9142570162877260.1714859674245480.085742983712274
1170.9499261234662180.1001477530675650.0500738765337825
1180.9732917053206360.05341658935872880.0267082946793644
1190.9905098324632550.01898033507349020.0094901675367451
1200.9935447913117420.01291041737651670.00645520868825833
1210.9927932673126710.01441346537465800.00720673268732901
1220.9921111487465440.01577770250691230.00788885125345614
1230.9911212582968090.01775748340638200.00887874170319099
1240.9929027707260530.01419445854789360.0070972292739468
1250.9998791506428470.0002416987143056610.000120849357152830
1260.9998132141206720.0003735717586558410.000186785879327920
1270.999813832721450.0003723345571002110.000186167278550105
1280.9998410496266830.0003179007466342940.000158950373317147
1290.9997633177850160.0004733644299672460.000236682214983623
1300.9999587937463418.24125073173378e-054.12062536586689e-05
1310.9999801657046823.96685906359102e-051.98342953179551e-05
1320.99996787649836.42470033992808e-053.21235016996404e-05
1330.9999540559998069.18880003874685e-054.59440001937342e-05
1340.9999277177688470.0001445644623059167.22822311529578e-05
1350.999919948287150.0001601034257014628.00517128507312e-05
1360.9999226529513510.0001546940972979197.73470486489596e-05
1370.9999112961398340.0001774077203322468.87038601661228e-05
1380.9998837296280870.0002325407438267680.000116270371913384
1390.999903087271160.0001938254576806169.69127288403079e-05
1400.9999723315941125.53368117752725e-052.76684058876362e-05
1410.999990245425611.95091487794429e-059.75457438972146e-06
1420.9999948075925651.03848148699478e-055.19240743497392e-06
1430.999992064386511.58712269793950e-057.93561348969751e-06
1440.9999899057696482.01884607037678e-051.00942303518839e-05
1450.9999999836050123.27899768223162e-081.63949884111581e-08
1460.9999999992489461.50210778805624e-097.51053894028118e-10
1470.9999999984775093.04498298034612e-091.52249149017306e-09
1480.99999999923821.52360107378778e-097.6180053689389e-10
1490.9999999992838771.43224624477926e-097.16123122389631e-10
1500.999999999121551.75689946393858e-098.78449731969291e-10
1510.999999999305071.38985904424466e-096.94929522122331e-10
1520.9999999997417575.16485347143555e-102.58242673571777e-10
1530.9999999993170021.36599522993272e-096.8299761496636e-10
1540.9999999982980273.40394630649229e-091.70197315324615e-09
1550.9999999962949287.41014420560039e-093.70507210280020e-09
1560.9999999980714263.85714812974830e-091.92857406487415e-09
1570.9999999963684077.26318532274455e-093.63159266137227e-09
1580.9999999934536171.30927665955757e-086.54638329778787e-09
1590.9999999887293252.25413489443101e-081.12706744721550e-08
1600.9999999827933023.44133956909037e-081.72066978454518e-08
1610.9999999582626148.34747718957871e-084.17373859478935e-08
1620.9999999183487731.63302454774757e-078.16512273873784e-08
1630.9999998296999233.40600154753477e-071.70300077376738e-07
1640.9999996784499576.43100086565324e-073.21550043282662e-07
1650.999999169563231.66087353855275e-068.30436769276376e-07
1660.9999986690224222.66195515550043e-061.33097757775022e-06
1670.9999974223877435.15522451391295e-062.57761225695647e-06
1680.999993400895021.31982099609260e-056.59910498046298e-06
1690.99999739928235.20143540045805e-062.60071770022902e-06
1700.9999969072961786.18540764446703e-063.09270382223351e-06
1710.9999917745523171.64508953661313e-058.22544768306563e-06
1720.9999871171655622.57656688756474e-051.28828344378237e-05
1730.999967890384316.42192313814328e-053.21096156907164e-05
1740.9999196310683250.0001607378633503498.03689316751746e-05
1750.9998768012659580.0002463974680838480.000123198734041924
1760.9996719398382450.0006561203235106550.000328060161755328
1770.9996803317793310.0006393364413375920.000319668220668796
1780.9991454286534330.001709142693134760.000854571346567379
1790.9978032142383830.004393571523233110.00219678576161656
1800.997964025266120.004071949467758970.00203597473387949
1810.9955035559168770.00899288816624520.0044964440831226
1820.9886489320215450.02270213595691030.0113510679784551
1830.985588468810450.02882306237910040.0144115311895502
1840.966018923067120.067962153865760.03398107693288
1850.9805321038857660.03893579222846730.0194678961142336
1860.939365304787580.121269390424840.06063469521242

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0723947308628668 & 0.144789461725734 & 0.927605269137133 \tabularnewline
10 & 0.0762894511281878 & 0.152578902256376 & 0.923710548871812 \tabularnewline
11 & 0.0318922481106178 & 0.0637844962212356 & 0.968107751889382 \tabularnewline
12 & 0.0164236499308531 & 0.0328472998617062 & 0.983576350069147 \tabularnewline
13 & 0.00638904091082975 & 0.0127780818216595 & 0.99361095908917 \tabularnewline
14 & 0.00456005823016683 & 0.00912011646033367 & 0.995439941769833 \tabularnewline
15 & 0.00196687487495836 & 0.00393374974991672 & 0.998033125125042 \tabularnewline
16 & 0.00069582214442956 & 0.00139164428885912 & 0.99930417785557 \tabularnewline
17 & 0.000342845487427621 & 0.000685690974855243 & 0.999657154512572 \tabularnewline
18 & 0.000243954634523016 & 0.000487909269046031 & 0.999756045365477 \tabularnewline
19 & 0.000111160643280830 & 0.000222321286561660 & 0.99988883935672 \tabularnewline
20 & 9.03019693515803e-05 & 0.000180603938703161 & 0.999909698030648 \tabularnewline
21 & 3.19821097182198e-05 & 6.39642194364396e-05 & 0.999968017890282 \tabularnewline
22 & 1.19745945306387e-05 & 2.39491890612774e-05 & 0.99998802540547 \tabularnewline
23 & 8.83423140540362e-06 & 1.76684628108072e-05 & 0.999991165768595 \tabularnewline
24 & 3.00479589071574e-06 & 6.00959178143148e-06 & 0.99999699520411 \tabularnewline
25 & 1.14563864693173e-06 & 2.29127729386345e-06 & 0.999998854361353 \tabularnewline
26 & 4.39428532750162e-07 & 8.78857065500325e-07 & 0.999999560571467 \tabularnewline
27 & 2.88079486265597e-07 & 5.76158972531194e-07 & 0.999999711920514 \tabularnewline
28 & 9.4298437677921e-08 & 1.88596875355842e-07 & 0.999999905701562 \tabularnewline
29 & 2.08316000049346e-07 & 4.16632000098692e-07 & 0.999999791684 \tabularnewline
30 & 8.92803331469963e-08 & 1.78560666293993e-07 & 0.999999910719667 \tabularnewline
31 & 4.64100000938749e-08 & 9.28200001877498e-08 & 0.99999995359 \tabularnewline
32 & 5.7954760689335e-08 & 1.1590952137867e-07 & 0.99999994204524 \tabularnewline
33 & 3.40784191186697e-08 & 6.81568382373394e-08 & 0.99999996592158 \tabularnewline
34 & 4.81310594233458e-06 & 9.62621188466916e-06 & 0.999995186894058 \tabularnewline
35 & 2.81668785757114e-06 & 5.63337571514228e-06 & 0.999997183312142 \tabularnewline
36 & 5.07145750806358e-05 & 0.000101429150161272 & 0.99994928542492 \tabularnewline
37 & 0.0111861323831179 & 0.0223722647662358 & 0.988813867616882 \tabularnewline
38 & 0.0102526182650360 & 0.0205052365300721 & 0.989747381734964 \tabularnewline
39 & 0.0124737705319562 & 0.0249475410639124 & 0.987526229468044 \tabularnewline
40 & 0.0183175156077270 & 0.0366350312154539 & 0.981682484392273 \tabularnewline
41 & 0.0214647843413882 & 0.0429295686827765 & 0.978535215658612 \tabularnewline
42 & 0.147027082840382 & 0.294054165680763 & 0.852972917159618 \tabularnewline
43 & 0.189482198184715 & 0.37896439636943 & 0.810517801815285 \tabularnewline
44 & 0.86094201729296 & 0.278115965414082 & 0.139057982707041 \tabularnewline
45 & 0.957365261379784 & 0.0852694772404325 & 0.0426347386202163 \tabularnewline
46 & 0.98596648794297 & 0.02806702411406 & 0.01403351205703 \tabularnewline
47 & 0.986974010834723 & 0.0260519783305545 & 0.0130259891652773 \tabularnewline
48 & 0.984170689886283 & 0.0316586202274344 & 0.0158293101137172 \tabularnewline
49 & 0.97885448055903 & 0.0422910388819383 & 0.0211455194409691 \tabularnewline
50 & 0.974820712255444 & 0.0503585754891113 & 0.0251792877445556 \tabularnewline
51 & 0.967907903587872 & 0.0641841928242555 & 0.0320920964121277 \tabularnewline
52 & 0.962097423348759 & 0.075805153302482 & 0.037902576651241 \tabularnewline
53 & 0.955571701695147 & 0.0888565966097064 & 0.0444282983048532 \tabularnewline
54 & 0.9524768812231 & 0.0950462375537984 & 0.0475231187768992 \tabularnewline
55 & 0.948864418287566 & 0.102271163424868 & 0.051135581712434 \tabularnewline
56 & 0.93600850645527 & 0.127982987089460 & 0.0639914935447301 \tabularnewline
57 & 0.92236201080785 & 0.155275978384300 & 0.0776379891921502 \tabularnewline
58 & 0.909893227208928 & 0.180213545582145 & 0.0901067727910724 \tabularnewline
59 & 0.892941795020175 & 0.214116409959650 & 0.107058204979825 \tabularnewline
60 & 0.873387256293887 & 0.253225487412227 & 0.126612743706113 \tabularnewline
61 & 0.850227947608258 & 0.299544104783485 & 0.149772052391742 \tabularnewline
62 & 0.878249215617246 & 0.243501568765508 & 0.121750784382754 \tabularnewline
63 & 0.870568016371885 & 0.258863967256229 & 0.129431983628115 \tabularnewline
64 & 0.864190542079408 & 0.271618915841185 & 0.135809457920592 \tabularnewline
65 & 0.841250785369317 & 0.317498429261366 & 0.158749214630683 \tabularnewline
66 & 0.817254846597496 & 0.365490306805008 & 0.182745153402504 \tabularnewline
67 & 0.787722756914457 & 0.424554486171087 & 0.212277243085543 \tabularnewline
68 & 0.764537816541398 & 0.470924366917203 & 0.235462183458602 \tabularnewline
69 & 0.745426924129488 & 0.509146151741023 & 0.254573075870512 \tabularnewline
70 & 0.71819589306343 & 0.56360821387314 & 0.28180410693657 \tabularnewline
71 & 0.689968917270911 & 0.620062165458178 & 0.310031082729089 \tabularnewline
72 & 0.682994933747915 & 0.634010132504169 & 0.317005066252085 \tabularnewline
73 & 0.651408861139117 & 0.697182277721767 & 0.348591138860883 \tabularnewline
74 & 0.650199164730713 & 0.699601670538574 & 0.349800835269287 \tabularnewline
75 & 0.630077239604601 & 0.739845520790797 & 0.369922760395399 \tabularnewline
76 & 0.597641446432231 & 0.804717107135538 & 0.402358553567769 \tabularnewline
77 & 0.580330323911316 & 0.839339352177367 & 0.419669676088684 \tabularnewline
78 & 0.553100920960466 & 0.893798158079069 & 0.446899079039534 \tabularnewline
79 & 0.512809196323087 & 0.974381607353827 & 0.487190803676913 \tabularnewline
80 & 0.489109648617785 & 0.97821929723557 & 0.510890351382215 \tabularnewline
81 & 0.48412087115578 & 0.96824174231156 & 0.51587912884422 \tabularnewline
82 & 0.615197139303528 & 0.769605721392945 & 0.384802860696472 \tabularnewline
83 & 0.574636144405876 & 0.850727711188249 & 0.425363855594124 \tabularnewline
84 & 0.547339584656173 & 0.905320830687653 & 0.452660415343827 \tabularnewline
85 & 0.507400920522862 & 0.985198158954276 & 0.492599079477138 \tabularnewline
86 & 0.47978034936552 & 0.95956069873104 & 0.52021965063448 \tabularnewline
87 & 0.500619308356743 & 0.998761383286515 & 0.499380691643257 \tabularnewline
88 & 0.47324527135291 & 0.94649054270582 & 0.52675472864709 \tabularnewline
89 & 0.432120773000134 & 0.864241546000268 & 0.567879226999866 \tabularnewline
90 & 0.402473544218776 & 0.804947088437552 & 0.597526455781224 \tabularnewline
91 & 0.395254936870870 & 0.790509873741741 & 0.60474506312913 \tabularnewline
92 & 0.358087478500809 & 0.716174957001618 & 0.641912521499191 \tabularnewline
93 & 0.325331313501545 & 0.65066262700309 & 0.674668686498455 \tabularnewline
94 & 0.289257218445573 & 0.578514436891145 & 0.710742781554427 \tabularnewline
95 & 0.300153715957942 & 0.600307431915883 & 0.699846284042058 \tabularnewline
96 & 0.285867711901971 & 0.571735423803942 & 0.714132288098029 \tabularnewline
97 & 0.296854832418104 & 0.593709664836207 & 0.703145167581896 \tabularnewline
98 & 0.27040909206173 & 0.54081818412346 & 0.72959090793827 \tabularnewline
99 & 0.306694294572367 & 0.613388589144735 & 0.693305705427633 \tabularnewline
100 & 0.294467332261794 & 0.588934664523589 & 0.705532667738206 \tabularnewline
101 & 0.317527974828147 & 0.635055949656294 & 0.682472025171853 \tabularnewline
102 & 0.300769702299692 & 0.601539404599384 & 0.699230297700308 \tabularnewline
103 & 0.287374651388320 & 0.574749302776639 & 0.71262534861168 \tabularnewline
104 & 0.264048971956385 & 0.52809794391277 & 0.735951028043615 \tabularnewline
105 & 0.316767377098638 & 0.633534754197276 & 0.683232622901362 \tabularnewline
106 & 0.296357114379426 & 0.592714228758853 & 0.703642885620574 \tabularnewline
107 & 0.286184040489085 & 0.57236808097817 & 0.713815959510915 \tabularnewline
108 & 0.252985913751947 & 0.505971827503893 & 0.747014086248053 \tabularnewline
109 & 0.297786232900732 & 0.595572465801464 & 0.702213767099268 \tabularnewline
110 & 0.289118512026595 & 0.57823702405319 & 0.710881487973405 \tabularnewline
111 & 0.276958965641601 & 0.553917931283202 & 0.723041034358399 \tabularnewline
112 & 0.474597035461039 & 0.949194070922078 & 0.525402964538961 \tabularnewline
113 & 0.572540977698774 & 0.854918044602452 & 0.427459022301226 \tabularnewline
114 & 0.645384748680498 & 0.709230502639004 & 0.354615251319502 \tabularnewline
115 & 0.729039271146088 & 0.541921457707824 & 0.270960728853912 \tabularnewline
116 & 0.914257016287726 & 0.171485967424548 & 0.085742983712274 \tabularnewline
117 & 0.949926123466218 & 0.100147753067565 & 0.0500738765337825 \tabularnewline
118 & 0.973291705320636 & 0.0534165893587288 & 0.0267082946793644 \tabularnewline
119 & 0.990509832463255 & 0.0189803350734902 & 0.0094901675367451 \tabularnewline
120 & 0.993544791311742 & 0.0129104173765167 & 0.00645520868825833 \tabularnewline
121 & 0.992793267312671 & 0.0144134653746580 & 0.00720673268732901 \tabularnewline
122 & 0.992111148746544 & 0.0157777025069123 & 0.00788885125345614 \tabularnewline
123 & 0.991121258296809 & 0.0177574834063820 & 0.00887874170319099 \tabularnewline
124 & 0.992902770726053 & 0.0141944585478936 & 0.0070972292739468 \tabularnewline
125 & 0.999879150642847 & 0.000241698714305661 & 0.000120849357152830 \tabularnewline
126 & 0.999813214120672 & 0.000373571758655841 & 0.000186785879327920 \tabularnewline
127 & 0.99981383272145 & 0.000372334557100211 & 0.000186167278550105 \tabularnewline
128 & 0.999841049626683 & 0.000317900746634294 & 0.000158950373317147 \tabularnewline
129 & 0.999763317785016 & 0.000473364429967246 & 0.000236682214983623 \tabularnewline
130 & 0.999958793746341 & 8.24125073173378e-05 & 4.12062536586689e-05 \tabularnewline
131 & 0.999980165704682 & 3.96685906359102e-05 & 1.98342953179551e-05 \tabularnewline
132 & 0.9999678764983 & 6.42470033992808e-05 & 3.21235016996404e-05 \tabularnewline
133 & 0.999954055999806 & 9.18880003874685e-05 & 4.59440001937342e-05 \tabularnewline
134 & 0.999927717768847 & 0.000144564462305916 & 7.22822311529578e-05 \tabularnewline
135 & 0.99991994828715 & 0.000160103425701462 & 8.00517128507312e-05 \tabularnewline
136 & 0.999922652951351 & 0.000154694097297919 & 7.73470486489596e-05 \tabularnewline
137 & 0.999911296139834 & 0.000177407720332246 & 8.87038601661228e-05 \tabularnewline
138 & 0.999883729628087 & 0.000232540743826768 & 0.000116270371913384 \tabularnewline
139 & 0.99990308727116 & 0.000193825457680616 & 9.69127288403079e-05 \tabularnewline
140 & 0.999972331594112 & 5.53368117752725e-05 & 2.76684058876362e-05 \tabularnewline
141 & 0.99999024542561 & 1.95091487794429e-05 & 9.75457438972146e-06 \tabularnewline
142 & 0.999994807592565 & 1.03848148699478e-05 & 5.19240743497392e-06 \tabularnewline
143 & 0.99999206438651 & 1.58712269793950e-05 & 7.93561348969751e-06 \tabularnewline
144 & 0.999989905769648 & 2.01884607037678e-05 & 1.00942303518839e-05 \tabularnewline
145 & 0.999999983605012 & 3.27899768223162e-08 & 1.63949884111581e-08 \tabularnewline
146 & 0.999999999248946 & 1.50210778805624e-09 & 7.51053894028118e-10 \tabularnewline
147 & 0.999999998477509 & 3.04498298034612e-09 & 1.52249149017306e-09 \tabularnewline
148 & 0.9999999992382 & 1.52360107378778e-09 & 7.6180053689389e-10 \tabularnewline
149 & 0.999999999283877 & 1.43224624477926e-09 & 7.16123122389631e-10 \tabularnewline
150 & 0.99999999912155 & 1.75689946393858e-09 & 8.78449731969291e-10 \tabularnewline
151 & 0.99999999930507 & 1.38985904424466e-09 & 6.94929522122331e-10 \tabularnewline
152 & 0.999999999741757 & 5.16485347143555e-10 & 2.58242673571777e-10 \tabularnewline
153 & 0.999999999317002 & 1.36599522993272e-09 & 6.8299761496636e-10 \tabularnewline
154 & 0.999999998298027 & 3.40394630649229e-09 & 1.70197315324615e-09 \tabularnewline
155 & 0.999999996294928 & 7.41014420560039e-09 & 3.70507210280020e-09 \tabularnewline
156 & 0.999999998071426 & 3.85714812974830e-09 & 1.92857406487415e-09 \tabularnewline
157 & 0.999999996368407 & 7.26318532274455e-09 & 3.63159266137227e-09 \tabularnewline
158 & 0.999999993453617 & 1.30927665955757e-08 & 6.54638329778787e-09 \tabularnewline
159 & 0.999999988729325 & 2.25413489443101e-08 & 1.12706744721550e-08 \tabularnewline
160 & 0.999999982793302 & 3.44133956909037e-08 & 1.72066978454518e-08 \tabularnewline
161 & 0.999999958262614 & 8.34747718957871e-08 & 4.17373859478935e-08 \tabularnewline
162 & 0.999999918348773 & 1.63302454774757e-07 & 8.16512273873784e-08 \tabularnewline
163 & 0.999999829699923 & 3.40600154753477e-07 & 1.70300077376738e-07 \tabularnewline
164 & 0.999999678449957 & 6.43100086565324e-07 & 3.21550043282662e-07 \tabularnewline
165 & 0.99999916956323 & 1.66087353855275e-06 & 8.30436769276376e-07 \tabularnewline
166 & 0.999998669022422 & 2.66195515550043e-06 & 1.33097757775022e-06 \tabularnewline
167 & 0.999997422387743 & 5.15522451391295e-06 & 2.57761225695647e-06 \tabularnewline
168 & 0.99999340089502 & 1.31982099609260e-05 & 6.59910498046298e-06 \tabularnewline
169 & 0.9999973992823 & 5.20143540045805e-06 & 2.60071770022902e-06 \tabularnewline
170 & 0.999996907296178 & 6.18540764446703e-06 & 3.09270382223351e-06 \tabularnewline
171 & 0.999991774552317 & 1.64508953661313e-05 & 8.22544768306563e-06 \tabularnewline
172 & 0.999987117165562 & 2.57656688756474e-05 & 1.28828344378237e-05 \tabularnewline
173 & 0.99996789038431 & 6.42192313814328e-05 & 3.21096156907164e-05 \tabularnewline
174 & 0.999919631068325 & 0.000160737863350349 & 8.03689316751746e-05 \tabularnewline
175 & 0.999876801265958 & 0.000246397468083848 & 0.000123198734041924 \tabularnewline
176 & 0.999671939838245 & 0.000656120323510655 & 0.000328060161755328 \tabularnewline
177 & 0.999680331779331 & 0.000639336441337592 & 0.000319668220668796 \tabularnewline
178 & 0.999145428653433 & 0.00170914269313476 & 0.000854571346567379 \tabularnewline
179 & 0.997803214238383 & 0.00439357152323311 & 0.00219678576161656 \tabularnewline
180 & 0.99796402526612 & 0.00407194946775897 & 0.00203597473387949 \tabularnewline
181 & 0.995503555916877 & 0.0089928881662452 & 0.0044964440831226 \tabularnewline
182 & 0.988648932021545 & 0.0227021359569103 & 0.0113510679784551 \tabularnewline
183 & 0.98558846881045 & 0.0288230623791004 & 0.0144115311895502 \tabularnewline
184 & 0.96601892306712 & 0.06796215386576 & 0.03398107693288 \tabularnewline
185 & 0.980532103885766 & 0.0389357922284673 & 0.0194678961142336 \tabularnewline
186 & 0.93936530478758 & 0.12126939042484 & 0.06063469521242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116300&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.0723947308628668[/C][C]0.144789461725734[/C][C]0.927605269137133[/C][/ROW]
[ROW][C]10[/C][C]0.0762894511281878[/C][C]0.152578902256376[/C][C]0.923710548871812[/C][/ROW]
[ROW][C]11[/C][C]0.0318922481106178[/C][C]0.0637844962212356[/C][C]0.968107751889382[/C][/ROW]
[ROW][C]12[/C][C]0.0164236499308531[/C][C]0.0328472998617062[/C][C]0.983576350069147[/C][/ROW]
[ROW][C]13[/C][C]0.00638904091082975[/C][C]0.0127780818216595[/C][C]0.99361095908917[/C][/ROW]
[ROW][C]14[/C][C]0.00456005823016683[/C][C]0.00912011646033367[/C][C]0.995439941769833[/C][/ROW]
[ROW][C]15[/C][C]0.00196687487495836[/C][C]0.00393374974991672[/C][C]0.998033125125042[/C][/ROW]
[ROW][C]16[/C][C]0.00069582214442956[/C][C]0.00139164428885912[/C][C]0.99930417785557[/C][/ROW]
[ROW][C]17[/C][C]0.000342845487427621[/C][C]0.000685690974855243[/C][C]0.999657154512572[/C][/ROW]
[ROW][C]18[/C][C]0.000243954634523016[/C][C]0.000487909269046031[/C][C]0.999756045365477[/C][/ROW]
[ROW][C]19[/C][C]0.000111160643280830[/C][C]0.000222321286561660[/C][C]0.99988883935672[/C][/ROW]
[ROW][C]20[/C][C]9.03019693515803e-05[/C][C]0.000180603938703161[/C][C]0.999909698030648[/C][/ROW]
[ROW][C]21[/C][C]3.19821097182198e-05[/C][C]6.39642194364396e-05[/C][C]0.999968017890282[/C][/ROW]
[ROW][C]22[/C][C]1.19745945306387e-05[/C][C]2.39491890612774e-05[/C][C]0.99998802540547[/C][/ROW]
[ROW][C]23[/C][C]8.83423140540362e-06[/C][C]1.76684628108072e-05[/C][C]0.999991165768595[/C][/ROW]
[ROW][C]24[/C][C]3.00479589071574e-06[/C][C]6.00959178143148e-06[/C][C]0.99999699520411[/C][/ROW]
[ROW][C]25[/C][C]1.14563864693173e-06[/C][C]2.29127729386345e-06[/C][C]0.999998854361353[/C][/ROW]
[ROW][C]26[/C][C]4.39428532750162e-07[/C][C]8.78857065500325e-07[/C][C]0.999999560571467[/C][/ROW]
[ROW][C]27[/C][C]2.88079486265597e-07[/C][C]5.76158972531194e-07[/C][C]0.999999711920514[/C][/ROW]
[ROW][C]28[/C][C]9.4298437677921e-08[/C][C]1.88596875355842e-07[/C][C]0.999999905701562[/C][/ROW]
[ROW][C]29[/C][C]2.08316000049346e-07[/C][C]4.16632000098692e-07[/C][C]0.999999791684[/C][/ROW]
[ROW][C]30[/C][C]8.92803331469963e-08[/C][C]1.78560666293993e-07[/C][C]0.999999910719667[/C][/ROW]
[ROW][C]31[/C][C]4.64100000938749e-08[/C][C]9.28200001877498e-08[/C][C]0.99999995359[/C][/ROW]
[ROW][C]32[/C][C]5.7954760689335e-08[/C][C]1.1590952137867e-07[/C][C]0.99999994204524[/C][/ROW]
[ROW][C]33[/C][C]3.40784191186697e-08[/C][C]6.81568382373394e-08[/C][C]0.99999996592158[/C][/ROW]
[ROW][C]34[/C][C]4.81310594233458e-06[/C][C]9.62621188466916e-06[/C][C]0.999995186894058[/C][/ROW]
[ROW][C]35[/C][C]2.81668785757114e-06[/C][C]5.63337571514228e-06[/C][C]0.999997183312142[/C][/ROW]
[ROW][C]36[/C][C]5.07145750806358e-05[/C][C]0.000101429150161272[/C][C]0.99994928542492[/C][/ROW]
[ROW][C]37[/C][C]0.0111861323831179[/C][C]0.0223722647662358[/C][C]0.988813867616882[/C][/ROW]
[ROW][C]38[/C][C]0.0102526182650360[/C][C]0.0205052365300721[/C][C]0.989747381734964[/C][/ROW]
[ROW][C]39[/C][C]0.0124737705319562[/C][C]0.0249475410639124[/C][C]0.987526229468044[/C][/ROW]
[ROW][C]40[/C][C]0.0183175156077270[/C][C]0.0366350312154539[/C][C]0.981682484392273[/C][/ROW]
[ROW][C]41[/C][C]0.0214647843413882[/C][C]0.0429295686827765[/C][C]0.978535215658612[/C][/ROW]
[ROW][C]42[/C][C]0.147027082840382[/C][C]0.294054165680763[/C][C]0.852972917159618[/C][/ROW]
[ROW][C]43[/C][C]0.189482198184715[/C][C]0.37896439636943[/C][C]0.810517801815285[/C][/ROW]
[ROW][C]44[/C][C]0.86094201729296[/C][C]0.278115965414082[/C][C]0.139057982707041[/C][/ROW]
[ROW][C]45[/C][C]0.957365261379784[/C][C]0.0852694772404325[/C][C]0.0426347386202163[/C][/ROW]
[ROW][C]46[/C][C]0.98596648794297[/C][C]0.02806702411406[/C][C]0.01403351205703[/C][/ROW]
[ROW][C]47[/C][C]0.986974010834723[/C][C]0.0260519783305545[/C][C]0.0130259891652773[/C][/ROW]
[ROW][C]48[/C][C]0.984170689886283[/C][C]0.0316586202274344[/C][C]0.0158293101137172[/C][/ROW]
[ROW][C]49[/C][C]0.97885448055903[/C][C]0.0422910388819383[/C][C]0.0211455194409691[/C][/ROW]
[ROW][C]50[/C][C]0.974820712255444[/C][C]0.0503585754891113[/C][C]0.0251792877445556[/C][/ROW]
[ROW][C]51[/C][C]0.967907903587872[/C][C]0.0641841928242555[/C][C]0.0320920964121277[/C][/ROW]
[ROW][C]52[/C][C]0.962097423348759[/C][C]0.075805153302482[/C][C]0.037902576651241[/C][/ROW]
[ROW][C]53[/C][C]0.955571701695147[/C][C]0.0888565966097064[/C][C]0.0444282983048532[/C][/ROW]
[ROW][C]54[/C][C]0.9524768812231[/C][C]0.0950462375537984[/C][C]0.0475231187768992[/C][/ROW]
[ROW][C]55[/C][C]0.948864418287566[/C][C]0.102271163424868[/C][C]0.051135581712434[/C][/ROW]
[ROW][C]56[/C][C]0.93600850645527[/C][C]0.127982987089460[/C][C]0.0639914935447301[/C][/ROW]
[ROW][C]57[/C][C]0.92236201080785[/C][C]0.155275978384300[/C][C]0.0776379891921502[/C][/ROW]
[ROW][C]58[/C][C]0.909893227208928[/C][C]0.180213545582145[/C][C]0.0901067727910724[/C][/ROW]
[ROW][C]59[/C][C]0.892941795020175[/C][C]0.214116409959650[/C][C]0.107058204979825[/C][/ROW]
[ROW][C]60[/C][C]0.873387256293887[/C][C]0.253225487412227[/C][C]0.126612743706113[/C][/ROW]
[ROW][C]61[/C][C]0.850227947608258[/C][C]0.299544104783485[/C][C]0.149772052391742[/C][/ROW]
[ROW][C]62[/C][C]0.878249215617246[/C][C]0.243501568765508[/C][C]0.121750784382754[/C][/ROW]
[ROW][C]63[/C][C]0.870568016371885[/C][C]0.258863967256229[/C][C]0.129431983628115[/C][/ROW]
[ROW][C]64[/C][C]0.864190542079408[/C][C]0.271618915841185[/C][C]0.135809457920592[/C][/ROW]
[ROW][C]65[/C][C]0.841250785369317[/C][C]0.317498429261366[/C][C]0.158749214630683[/C][/ROW]
[ROW][C]66[/C][C]0.817254846597496[/C][C]0.365490306805008[/C][C]0.182745153402504[/C][/ROW]
[ROW][C]67[/C][C]0.787722756914457[/C][C]0.424554486171087[/C][C]0.212277243085543[/C][/ROW]
[ROW][C]68[/C][C]0.764537816541398[/C][C]0.470924366917203[/C][C]0.235462183458602[/C][/ROW]
[ROW][C]69[/C][C]0.745426924129488[/C][C]0.509146151741023[/C][C]0.254573075870512[/C][/ROW]
[ROW][C]70[/C][C]0.71819589306343[/C][C]0.56360821387314[/C][C]0.28180410693657[/C][/ROW]
[ROW][C]71[/C][C]0.689968917270911[/C][C]0.620062165458178[/C][C]0.310031082729089[/C][/ROW]
[ROW][C]72[/C][C]0.682994933747915[/C][C]0.634010132504169[/C][C]0.317005066252085[/C][/ROW]
[ROW][C]73[/C][C]0.651408861139117[/C][C]0.697182277721767[/C][C]0.348591138860883[/C][/ROW]
[ROW][C]74[/C][C]0.650199164730713[/C][C]0.699601670538574[/C][C]0.349800835269287[/C][/ROW]
[ROW][C]75[/C][C]0.630077239604601[/C][C]0.739845520790797[/C][C]0.369922760395399[/C][/ROW]
[ROW][C]76[/C][C]0.597641446432231[/C][C]0.804717107135538[/C][C]0.402358553567769[/C][/ROW]
[ROW][C]77[/C][C]0.580330323911316[/C][C]0.839339352177367[/C][C]0.419669676088684[/C][/ROW]
[ROW][C]78[/C][C]0.553100920960466[/C][C]0.893798158079069[/C][C]0.446899079039534[/C][/ROW]
[ROW][C]79[/C][C]0.512809196323087[/C][C]0.974381607353827[/C][C]0.487190803676913[/C][/ROW]
[ROW][C]80[/C][C]0.489109648617785[/C][C]0.97821929723557[/C][C]0.510890351382215[/C][/ROW]
[ROW][C]81[/C][C]0.48412087115578[/C][C]0.96824174231156[/C][C]0.51587912884422[/C][/ROW]
[ROW][C]82[/C][C]0.615197139303528[/C][C]0.769605721392945[/C][C]0.384802860696472[/C][/ROW]
[ROW][C]83[/C][C]0.574636144405876[/C][C]0.850727711188249[/C][C]0.425363855594124[/C][/ROW]
[ROW][C]84[/C][C]0.547339584656173[/C][C]0.905320830687653[/C][C]0.452660415343827[/C][/ROW]
[ROW][C]85[/C][C]0.507400920522862[/C][C]0.985198158954276[/C][C]0.492599079477138[/C][/ROW]
[ROW][C]86[/C][C]0.47978034936552[/C][C]0.95956069873104[/C][C]0.52021965063448[/C][/ROW]
[ROW][C]87[/C][C]0.500619308356743[/C][C]0.998761383286515[/C][C]0.499380691643257[/C][/ROW]
[ROW][C]88[/C][C]0.47324527135291[/C][C]0.94649054270582[/C][C]0.52675472864709[/C][/ROW]
[ROW][C]89[/C][C]0.432120773000134[/C][C]0.864241546000268[/C][C]0.567879226999866[/C][/ROW]
[ROW][C]90[/C][C]0.402473544218776[/C][C]0.804947088437552[/C][C]0.597526455781224[/C][/ROW]
[ROW][C]91[/C][C]0.395254936870870[/C][C]0.790509873741741[/C][C]0.60474506312913[/C][/ROW]
[ROW][C]92[/C][C]0.358087478500809[/C][C]0.716174957001618[/C][C]0.641912521499191[/C][/ROW]
[ROW][C]93[/C][C]0.325331313501545[/C][C]0.65066262700309[/C][C]0.674668686498455[/C][/ROW]
[ROW][C]94[/C][C]0.289257218445573[/C][C]0.578514436891145[/C][C]0.710742781554427[/C][/ROW]
[ROW][C]95[/C][C]0.300153715957942[/C][C]0.600307431915883[/C][C]0.699846284042058[/C][/ROW]
[ROW][C]96[/C][C]0.285867711901971[/C][C]0.571735423803942[/C][C]0.714132288098029[/C][/ROW]
[ROW][C]97[/C][C]0.296854832418104[/C][C]0.593709664836207[/C][C]0.703145167581896[/C][/ROW]
[ROW][C]98[/C][C]0.27040909206173[/C][C]0.54081818412346[/C][C]0.72959090793827[/C][/ROW]
[ROW][C]99[/C][C]0.306694294572367[/C][C]0.613388589144735[/C][C]0.693305705427633[/C][/ROW]
[ROW][C]100[/C][C]0.294467332261794[/C][C]0.588934664523589[/C][C]0.705532667738206[/C][/ROW]
[ROW][C]101[/C][C]0.317527974828147[/C][C]0.635055949656294[/C][C]0.682472025171853[/C][/ROW]
[ROW][C]102[/C][C]0.300769702299692[/C][C]0.601539404599384[/C][C]0.699230297700308[/C][/ROW]
[ROW][C]103[/C][C]0.287374651388320[/C][C]0.574749302776639[/C][C]0.71262534861168[/C][/ROW]
[ROW][C]104[/C][C]0.264048971956385[/C][C]0.52809794391277[/C][C]0.735951028043615[/C][/ROW]
[ROW][C]105[/C][C]0.316767377098638[/C][C]0.633534754197276[/C][C]0.683232622901362[/C][/ROW]
[ROW][C]106[/C][C]0.296357114379426[/C][C]0.592714228758853[/C][C]0.703642885620574[/C][/ROW]
[ROW][C]107[/C][C]0.286184040489085[/C][C]0.57236808097817[/C][C]0.713815959510915[/C][/ROW]
[ROW][C]108[/C][C]0.252985913751947[/C][C]0.505971827503893[/C][C]0.747014086248053[/C][/ROW]
[ROW][C]109[/C][C]0.297786232900732[/C][C]0.595572465801464[/C][C]0.702213767099268[/C][/ROW]
[ROW][C]110[/C][C]0.289118512026595[/C][C]0.57823702405319[/C][C]0.710881487973405[/C][/ROW]
[ROW][C]111[/C][C]0.276958965641601[/C][C]0.553917931283202[/C][C]0.723041034358399[/C][/ROW]
[ROW][C]112[/C][C]0.474597035461039[/C][C]0.949194070922078[/C][C]0.525402964538961[/C][/ROW]
[ROW][C]113[/C][C]0.572540977698774[/C][C]0.854918044602452[/C][C]0.427459022301226[/C][/ROW]
[ROW][C]114[/C][C]0.645384748680498[/C][C]0.709230502639004[/C][C]0.354615251319502[/C][/ROW]
[ROW][C]115[/C][C]0.729039271146088[/C][C]0.541921457707824[/C][C]0.270960728853912[/C][/ROW]
[ROW][C]116[/C][C]0.914257016287726[/C][C]0.171485967424548[/C][C]0.085742983712274[/C][/ROW]
[ROW][C]117[/C][C]0.949926123466218[/C][C]0.100147753067565[/C][C]0.0500738765337825[/C][/ROW]
[ROW][C]118[/C][C]0.973291705320636[/C][C]0.0534165893587288[/C][C]0.0267082946793644[/C][/ROW]
[ROW][C]119[/C][C]0.990509832463255[/C][C]0.0189803350734902[/C][C]0.0094901675367451[/C][/ROW]
[ROW][C]120[/C][C]0.993544791311742[/C][C]0.0129104173765167[/C][C]0.00645520868825833[/C][/ROW]
[ROW][C]121[/C][C]0.992793267312671[/C][C]0.0144134653746580[/C][C]0.00720673268732901[/C][/ROW]
[ROW][C]122[/C][C]0.992111148746544[/C][C]0.0157777025069123[/C][C]0.00788885125345614[/C][/ROW]
[ROW][C]123[/C][C]0.991121258296809[/C][C]0.0177574834063820[/C][C]0.00887874170319099[/C][/ROW]
[ROW][C]124[/C][C]0.992902770726053[/C][C]0.0141944585478936[/C][C]0.0070972292739468[/C][/ROW]
[ROW][C]125[/C][C]0.999879150642847[/C][C]0.000241698714305661[/C][C]0.000120849357152830[/C][/ROW]
[ROW][C]126[/C][C]0.999813214120672[/C][C]0.000373571758655841[/C][C]0.000186785879327920[/C][/ROW]
[ROW][C]127[/C][C]0.99981383272145[/C][C]0.000372334557100211[/C][C]0.000186167278550105[/C][/ROW]
[ROW][C]128[/C][C]0.999841049626683[/C][C]0.000317900746634294[/C][C]0.000158950373317147[/C][/ROW]
[ROW][C]129[/C][C]0.999763317785016[/C][C]0.000473364429967246[/C][C]0.000236682214983623[/C][/ROW]
[ROW][C]130[/C][C]0.999958793746341[/C][C]8.24125073173378e-05[/C][C]4.12062536586689e-05[/C][/ROW]
[ROW][C]131[/C][C]0.999980165704682[/C][C]3.96685906359102e-05[/C][C]1.98342953179551e-05[/C][/ROW]
[ROW][C]132[/C][C]0.9999678764983[/C][C]6.42470033992808e-05[/C][C]3.21235016996404e-05[/C][/ROW]
[ROW][C]133[/C][C]0.999954055999806[/C][C]9.18880003874685e-05[/C][C]4.59440001937342e-05[/C][/ROW]
[ROW][C]134[/C][C]0.999927717768847[/C][C]0.000144564462305916[/C][C]7.22822311529578e-05[/C][/ROW]
[ROW][C]135[/C][C]0.99991994828715[/C][C]0.000160103425701462[/C][C]8.00517128507312e-05[/C][/ROW]
[ROW][C]136[/C][C]0.999922652951351[/C][C]0.000154694097297919[/C][C]7.73470486489596e-05[/C][/ROW]
[ROW][C]137[/C][C]0.999911296139834[/C][C]0.000177407720332246[/C][C]8.87038601661228e-05[/C][/ROW]
[ROW][C]138[/C][C]0.999883729628087[/C][C]0.000232540743826768[/C][C]0.000116270371913384[/C][/ROW]
[ROW][C]139[/C][C]0.99990308727116[/C][C]0.000193825457680616[/C][C]9.69127288403079e-05[/C][/ROW]
[ROW][C]140[/C][C]0.999972331594112[/C][C]5.53368117752725e-05[/C][C]2.76684058876362e-05[/C][/ROW]
[ROW][C]141[/C][C]0.99999024542561[/C][C]1.95091487794429e-05[/C][C]9.75457438972146e-06[/C][/ROW]
[ROW][C]142[/C][C]0.999994807592565[/C][C]1.03848148699478e-05[/C][C]5.19240743497392e-06[/C][/ROW]
[ROW][C]143[/C][C]0.99999206438651[/C][C]1.58712269793950e-05[/C][C]7.93561348969751e-06[/C][/ROW]
[ROW][C]144[/C][C]0.999989905769648[/C][C]2.01884607037678e-05[/C][C]1.00942303518839e-05[/C][/ROW]
[ROW][C]145[/C][C]0.999999983605012[/C][C]3.27899768223162e-08[/C][C]1.63949884111581e-08[/C][/ROW]
[ROW][C]146[/C][C]0.999999999248946[/C][C]1.50210778805624e-09[/C][C]7.51053894028118e-10[/C][/ROW]
[ROW][C]147[/C][C]0.999999998477509[/C][C]3.04498298034612e-09[/C][C]1.52249149017306e-09[/C][/ROW]
[ROW][C]148[/C][C]0.9999999992382[/C][C]1.52360107378778e-09[/C][C]7.6180053689389e-10[/C][/ROW]
[ROW][C]149[/C][C]0.999999999283877[/C][C]1.43224624477926e-09[/C][C]7.16123122389631e-10[/C][/ROW]
[ROW][C]150[/C][C]0.99999999912155[/C][C]1.75689946393858e-09[/C][C]8.78449731969291e-10[/C][/ROW]
[ROW][C]151[/C][C]0.99999999930507[/C][C]1.38985904424466e-09[/C][C]6.94929522122331e-10[/C][/ROW]
[ROW][C]152[/C][C]0.999999999741757[/C][C]5.16485347143555e-10[/C][C]2.58242673571777e-10[/C][/ROW]
[ROW][C]153[/C][C]0.999999999317002[/C][C]1.36599522993272e-09[/C][C]6.8299761496636e-10[/C][/ROW]
[ROW][C]154[/C][C]0.999999998298027[/C][C]3.40394630649229e-09[/C][C]1.70197315324615e-09[/C][/ROW]
[ROW][C]155[/C][C]0.999999996294928[/C][C]7.41014420560039e-09[/C][C]3.70507210280020e-09[/C][/ROW]
[ROW][C]156[/C][C]0.999999998071426[/C][C]3.85714812974830e-09[/C][C]1.92857406487415e-09[/C][/ROW]
[ROW][C]157[/C][C]0.999999996368407[/C][C]7.26318532274455e-09[/C][C]3.63159266137227e-09[/C][/ROW]
[ROW][C]158[/C][C]0.999999993453617[/C][C]1.30927665955757e-08[/C][C]6.54638329778787e-09[/C][/ROW]
[ROW][C]159[/C][C]0.999999988729325[/C][C]2.25413489443101e-08[/C][C]1.12706744721550e-08[/C][/ROW]
[ROW][C]160[/C][C]0.999999982793302[/C][C]3.44133956909037e-08[/C][C]1.72066978454518e-08[/C][/ROW]
[ROW][C]161[/C][C]0.999999958262614[/C][C]8.34747718957871e-08[/C][C]4.17373859478935e-08[/C][/ROW]
[ROW][C]162[/C][C]0.999999918348773[/C][C]1.63302454774757e-07[/C][C]8.16512273873784e-08[/C][/ROW]
[ROW][C]163[/C][C]0.999999829699923[/C][C]3.40600154753477e-07[/C][C]1.70300077376738e-07[/C][/ROW]
[ROW][C]164[/C][C]0.999999678449957[/C][C]6.43100086565324e-07[/C][C]3.21550043282662e-07[/C][/ROW]
[ROW][C]165[/C][C]0.99999916956323[/C][C]1.66087353855275e-06[/C][C]8.30436769276376e-07[/C][/ROW]
[ROW][C]166[/C][C]0.999998669022422[/C][C]2.66195515550043e-06[/C][C]1.33097757775022e-06[/C][/ROW]
[ROW][C]167[/C][C]0.999997422387743[/C][C]5.15522451391295e-06[/C][C]2.57761225695647e-06[/C][/ROW]
[ROW][C]168[/C][C]0.99999340089502[/C][C]1.31982099609260e-05[/C][C]6.59910498046298e-06[/C][/ROW]
[ROW][C]169[/C][C]0.9999973992823[/C][C]5.20143540045805e-06[/C][C]2.60071770022902e-06[/C][/ROW]
[ROW][C]170[/C][C]0.999996907296178[/C][C]6.18540764446703e-06[/C][C]3.09270382223351e-06[/C][/ROW]
[ROW][C]171[/C][C]0.999991774552317[/C][C]1.64508953661313e-05[/C][C]8.22544768306563e-06[/C][/ROW]
[ROW][C]172[/C][C]0.999987117165562[/C][C]2.57656688756474e-05[/C][C]1.28828344378237e-05[/C][/ROW]
[ROW][C]173[/C][C]0.99996789038431[/C][C]6.42192313814328e-05[/C][C]3.21096156907164e-05[/C][/ROW]
[ROW][C]174[/C][C]0.999919631068325[/C][C]0.000160737863350349[/C][C]8.03689316751746e-05[/C][/ROW]
[ROW][C]175[/C][C]0.999876801265958[/C][C]0.000246397468083848[/C][C]0.000123198734041924[/C][/ROW]
[ROW][C]176[/C][C]0.999671939838245[/C][C]0.000656120323510655[/C][C]0.000328060161755328[/C][/ROW]
[ROW][C]177[/C][C]0.999680331779331[/C][C]0.000639336441337592[/C][C]0.000319668220668796[/C][/ROW]
[ROW][C]178[/C][C]0.999145428653433[/C][C]0.00170914269313476[/C][C]0.000854571346567379[/C][/ROW]
[ROW][C]179[/C][C]0.997803214238383[/C][C]0.00439357152323311[/C][C]0.00219678576161656[/C][/ROW]
[ROW][C]180[/C][C]0.99796402526612[/C][C]0.00407194946775897[/C][C]0.00203597473387949[/C][/ROW]
[ROW][C]181[/C][C]0.995503555916877[/C][C]0.0089928881662452[/C][C]0.0044964440831226[/C][/ROW]
[ROW][C]182[/C][C]0.988648932021545[/C][C]0.0227021359569103[/C][C]0.0113510679784551[/C][/ROW]
[ROW][C]183[/C][C]0.98558846881045[/C][C]0.0288230623791004[/C][C]0.0144115311895502[/C][/ROW]
[ROW][C]184[/C][C]0.96601892306712[/C][C]0.06796215386576[/C][C]0.03398107693288[/C][/ROW]
[ROW][C]185[/C][C]0.980532103885766[/C][C]0.0389357922284673[/C][C]0.0194678961142336[/C][/ROW]
[ROW][C]186[/C][C]0.93936530478758[/C][C]0.12126939042484[/C][C]0.06063469521242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116300&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116300&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
90.07239473086286680.1447894617257340.927605269137133
100.07628945112818780.1525789022563760.923710548871812
110.03189224811061780.06378449622123560.968107751889382
120.01642364993085310.03284729986170620.983576350069147
130.006389040910829750.01277808182165950.99361095908917
140.004560058230166830.009120116460333670.995439941769833
150.001966874874958360.003933749749916720.998033125125042
160.000695822144429560.001391644288859120.99930417785557
170.0003428454874276210.0006856909748552430.999657154512572
180.0002439546345230160.0004879092690460310.999756045365477
190.0001111606432808300.0002223212865616600.99988883935672
209.03019693515803e-050.0001806039387031610.999909698030648
213.19821097182198e-056.39642194364396e-050.999968017890282
221.19745945306387e-052.39491890612774e-050.99998802540547
238.83423140540362e-061.76684628108072e-050.999991165768595
243.00479589071574e-066.00959178143148e-060.99999699520411
251.14563864693173e-062.29127729386345e-060.999998854361353
264.39428532750162e-078.78857065500325e-070.999999560571467
272.88079486265597e-075.76158972531194e-070.999999711920514
289.4298437677921e-081.88596875355842e-070.999999905701562
292.08316000049346e-074.16632000098692e-070.999999791684
308.92803331469963e-081.78560666293993e-070.999999910719667
314.64100000938749e-089.28200001877498e-080.99999995359
325.7954760689335e-081.1590952137867e-070.99999994204524
333.40784191186697e-086.81568382373394e-080.99999996592158
344.81310594233458e-069.62621188466916e-060.999995186894058
352.81668785757114e-065.63337571514228e-060.999997183312142
365.07145750806358e-050.0001014291501612720.99994928542492
370.01118613238311790.02237226476623580.988813867616882
380.01025261826503600.02050523653007210.989747381734964
390.01247377053195620.02494754106391240.987526229468044
400.01831751560772700.03663503121545390.981682484392273
410.02146478434138820.04292956868277650.978535215658612
420.1470270828403820.2940541656807630.852972917159618
430.1894821981847150.378964396369430.810517801815285
440.860942017292960.2781159654140820.139057982707041
450.9573652613797840.08526947724043250.0426347386202163
460.985966487942970.028067024114060.01403351205703
470.9869740108347230.02605197833055450.0130259891652773
480.9841706898862830.03165862022743440.0158293101137172
490.978854480559030.04229103888193830.0211455194409691
500.9748207122554440.05035857548911130.0251792877445556
510.9679079035878720.06418419282425550.0320920964121277
520.9620974233487590.0758051533024820.037902576651241
530.9555717016951470.08885659660970640.0444282983048532
540.95247688122310.09504623755379840.0475231187768992
550.9488644182875660.1022711634248680.051135581712434
560.936008506455270.1279829870894600.0639914935447301
570.922362010807850.1552759783843000.0776379891921502
580.9098932272089280.1802135455821450.0901067727910724
590.8929417950201750.2141164099596500.107058204979825
600.8733872562938870.2532254874122270.126612743706113
610.8502279476082580.2995441047834850.149772052391742
620.8782492156172460.2435015687655080.121750784382754
630.8705680163718850.2588639672562290.129431983628115
640.8641905420794080.2716189158411850.135809457920592
650.8412507853693170.3174984292613660.158749214630683
660.8172548465974960.3654903068050080.182745153402504
670.7877227569144570.4245544861710870.212277243085543
680.7645378165413980.4709243669172030.235462183458602
690.7454269241294880.5091461517410230.254573075870512
700.718195893063430.563608213873140.28180410693657
710.6899689172709110.6200621654581780.310031082729089
720.6829949337479150.6340101325041690.317005066252085
730.6514088611391170.6971822777217670.348591138860883
740.6501991647307130.6996016705385740.349800835269287
750.6300772396046010.7398455207907970.369922760395399
760.5976414464322310.8047171071355380.402358553567769
770.5803303239113160.8393393521773670.419669676088684
780.5531009209604660.8937981580790690.446899079039534
790.5128091963230870.9743816073538270.487190803676913
800.4891096486177850.978219297235570.510890351382215
810.484120871155780.968241742311560.51587912884422
820.6151971393035280.7696057213929450.384802860696472
830.5746361444058760.8507277111882490.425363855594124
840.5473395846561730.9053208306876530.452660415343827
850.5074009205228620.9851981589542760.492599079477138
860.479780349365520.959560698731040.52021965063448
870.5006193083567430.9987613832865150.499380691643257
880.473245271352910.946490542705820.52675472864709
890.4321207730001340.8642415460002680.567879226999866
900.4024735442187760.8049470884375520.597526455781224
910.3952549368708700.7905098737417410.60474506312913
920.3580874785008090.7161749570016180.641912521499191
930.3253313135015450.650662627003090.674668686498455
940.2892572184455730.5785144368911450.710742781554427
950.3001537159579420.6003074319158830.699846284042058
960.2858677119019710.5717354238039420.714132288098029
970.2968548324181040.5937096648362070.703145167581896
980.270409092061730.540818184123460.72959090793827
990.3066942945723670.6133885891447350.693305705427633
1000.2944673322617940.5889346645235890.705532667738206
1010.3175279748281470.6350559496562940.682472025171853
1020.3007697022996920.6015394045993840.699230297700308
1030.2873746513883200.5747493027766390.71262534861168
1040.2640489719563850.528097943912770.735951028043615
1050.3167673770986380.6335347541972760.683232622901362
1060.2963571143794260.5927142287588530.703642885620574
1070.2861840404890850.572368080978170.713815959510915
1080.2529859137519470.5059718275038930.747014086248053
1090.2977862329007320.5955724658014640.702213767099268
1100.2891185120265950.578237024053190.710881487973405
1110.2769589656416010.5539179312832020.723041034358399
1120.4745970354610390.9491940709220780.525402964538961
1130.5725409776987740.8549180446024520.427459022301226
1140.6453847486804980.7092305026390040.354615251319502
1150.7290392711460880.5419214577078240.270960728853912
1160.9142570162877260.1714859674245480.085742983712274
1170.9499261234662180.1001477530675650.0500738765337825
1180.9732917053206360.05341658935872880.0267082946793644
1190.9905098324632550.01898033507349020.0094901675367451
1200.9935447913117420.01291041737651670.00645520868825833
1210.9927932673126710.01441346537465800.00720673268732901
1220.9921111487465440.01577770250691230.00788885125345614
1230.9911212582968090.01775748340638200.00887874170319099
1240.9929027707260530.01419445854789360.0070972292739468
1250.9998791506428470.0002416987143056610.000120849357152830
1260.9998132141206720.0003735717586558410.000186785879327920
1270.999813832721450.0003723345571002110.000186167278550105
1280.9998410496266830.0003179007466342940.000158950373317147
1290.9997633177850160.0004733644299672460.000236682214983623
1300.9999587937463418.24125073173378e-054.12062536586689e-05
1310.9999801657046823.96685906359102e-051.98342953179551e-05
1320.99996787649836.42470033992808e-053.21235016996404e-05
1330.9999540559998069.18880003874685e-054.59440001937342e-05
1340.9999277177688470.0001445644623059167.22822311529578e-05
1350.999919948287150.0001601034257014628.00517128507312e-05
1360.9999226529513510.0001546940972979197.73470486489596e-05
1370.9999112961398340.0001774077203322468.87038601661228e-05
1380.9998837296280870.0002325407438267680.000116270371913384
1390.999903087271160.0001938254576806169.69127288403079e-05
1400.9999723315941125.53368117752725e-052.76684058876362e-05
1410.999990245425611.95091487794429e-059.75457438972146e-06
1420.9999948075925651.03848148699478e-055.19240743497392e-06
1430.999992064386511.58712269793950e-057.93561348969751e-06
1440.9999899057696482.01884607037678e-051.00942303518839e-05
1450.9999999836050123.27899768223162e-081.63949884111581e-08
1460.9999999992489461.50210778805624e-097.51053894028118e-10
1470.9999999984775093.04498298034612e-091.52249149017306e-09
1480.99999999923821.52360107378778e-097.6180053689389e-10
1490.9999999992838771.43224624477926e-097.16123122389631e-10
1500.999999999121551.75689946393858e-098.78449731969291e-10
1510.999999999305071.38985904424466e-096.94929522122331e-10
1520.9999999997417575.16485347143555e-102.58242673571777e-10
1530.9999999993170021.36599522993272e-096.8299761496636e-10
1540.9999999982980273.40394630649229e-091.70197315324615e-09
1550.9999999962949287.41014420560039e-093.70507210280020e-09
1560.9999999980714263.85714812974830e-091.92857406487415e-09
1570.9999999963684077.26318532274455e-093.63159266137227e-09
1580.9999999934536171.30927665955757e-086.54638329778787e-09
1590.9999999887293252.25413489443101e-081.12706744721550e-08
1600.9999999827933023.44133956909037e-081.72066978454518e-08
1610.9999999582626148.34747718957871e-084.17373859478935e-08
1620.9999999183487731.63302454774757e-078.16512273873784e-08
1630.9999998296999233.40600154753477e-071.70300077376738e-07
1640.9999996784499576.43100086565324e-073.21550043282662e-07
1650.999999169563231.66087353855275e-068.30436769276376e-07
1660.9999986690224222.66195515550043e-061.33097757775022e-06
1670.9999974223877435.15522451391295e-062.57761225695647e-06
1680.999993400895021.31982099609260e-056.59910498046298e-06
1690.99999739928235.20143540045805e-062.60071770022902e-06
1700.9999969072961786.18540764446703e-063.09270382223351e-06
1710.9999917745523171.64508953661313e-058.22544768306563e-06
1720.9999871171655622.57656688756474e-051.28828344378237e-05
1730.999967890384316.42192313814328e-053.21096156907164e-05
1740.9999196310683250.0001607378633503498.03689316751746e-05
1750.9998768012659580.0002463974680838480.000123198734041924
1760.9996719398382450.0006561203235106550.000328060161755328
1770.9996803317793310.0006393364413375920.000319668220668796
1780.9991454286534330.001709142693134760.000854571346567379
1790.9978032142383830.004393571523233110.00219678576161656
1800.997964025266120.004071949467758970.00203597473387949
1810.9955035559168770.00899288816624520.0044964440831226
1820.9886489320215450.02270213595691030.0113510679784551
1830.985588468810450.02882306237910040.0144115311895502
1840.966018923067120.067962153865760.03398107693288
1850.9805321038857660.03893579222846730.0194678961142336
1860.939365304787580.121269390424840.06063469521242






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level800.449438202247191NOK
5% type I error level1000.561797752808989NOK
10% type I error level1090.612359550561798NOK

\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 & 80 & 0.449438202247191 & NOK \tabularnewline
5% type I error level & 100 & 0.561797752808989 & NOK \tabularnewline
10% type I error level & 109 & 0.612359550561798 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116300&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]80[/C][C]0.449438202247191[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]100[/C][C]0.561797752808989[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]109[/C][C]0.612359550561798[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116300&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116300&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level800.449438202247191NOK
5% type I error level1000.561797752808989NOK
10% type I error level1090.612359550561798NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}