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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 23 Dec 2011 06:15:50 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/23/t1324639111szt0dp4qyumg0y3.htm/, Retrieved Thu, 31 Oct 2024 23:27:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160286, Retrieved Thu, 31 Oct 2024 23:27:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Multiple regression] [2011-12-21 15:21:41] [1ed874da5cc4aa1cd1ced057f766d90b]
-   P     [Multiple Regression] [MR pageviews] [2011-12-23 11:15:50] [452d9c400285ceb08a690c4b81b76477] [Current]
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Dataseries X:
1801	159261	91	586	111	0	74
1717	189672	59	520	76	1	80
192	7215	18	72	1	0	0
2295	129098	95	645	155	0	84
3450	230632	136	1163	125	0	124
6861	515038	263	1945	278	1	140
1795	180745	56	585	89	1	88
1681	185559	59	470	59	0	115
1897	154581	44	612	87	0	109
2974	298001	96	992	129	1	104
1946	121844	75	634	158	2	63
2148	184039	69	677	120	0	118
1832	100324	98	665	87	0	71
3183	220269	119	1079	264	4	112
1476	168265	58	413	51	4	63
1567	154647	88	469	85	3	86
1756	142018	57	431	96	0	132
1247	79030	61	361	72	5	54
2779	167047	87	877	147	0	134
726	27997	24	221	49	0	57
1048	73019	59	366	40	0	59
2805	241082	100	846	99	0	113
1760	195820	72	642	127	0	96
2266	142001	54	689	164	1	96
1848	145433	86	576	41	1	78
1665	183744	32	610	160	0	80
2084	202357	163	673	92	0	93
1440	199532	93	361	59	0	109
2741	354924	118	907	89	0	115
2112	192399	44	882	90	0	79
1684	182286	44	490	76	0	103
1616	181590	45	548	116	2	71
2227	133801	105	723	92	4	66
3088	233686	123	918	344	0	100
2389	219428	53	787	84	1	96
1	0	1	0	0	0	0
2099	223044	63	983	61	0	109
1669	100129	51	539	138	3	51
2137	145864	49	515	270	9	119
2153	249965	64	795	64	0	136
2390	242379	71	753	96	2	84
1701	145794	59	635	62	0	136
983	96404	32	361	35	2	84
2161	195891	78	804	59	1	92
1276	117156	50	394	56	2	103
1190	157787	95	320	40	2	82
745	81293	32	212	49	1	106
2330	237435	101	772	121	0	96
2289	233155	89	740	113	1	124
2639	160344	59	938	172	8	97
658	48188	28	205	37	0	82
1917	161922	69	492	51	0	79
2557	307432	74	818	89	0	97
2026	235223	79	680	73	0	107
1911	195583	59	691	49	1	126
1716	146061	56	534	74	8	40
1852	208834	67	487	58	0	96
981	93764	24	301	72	1	100
1177	151985	66	421	32	0	91
2833	193222	96	947	59	10	136
1688	148922	60	492	70	6	124
2097	132856	80	790	85	0	79
1331	129561	61	362	87	11	74
1244	112718	37	430	48	3	96
1256	160930	35	416	56	0	97
1294	99184	41	409	41	0	122
2303	192535	70	498	86	8	144
2897	138708	65	887	152	2	90
1103	114408	38	267	48	0	93
340	31970	15	101	40	0	78
2791	225558	112	1000	135	3	72
1338	139220	72	416	83	1	45
1441	113612	68	480	62	2	120
1623	108641	71	454	91	1	59
2650	162203	67	671	91	0	133
1499	100098	44	413	82	2	117
2302	174768	60	677	112	1	123
2540	158459	97	820	69	0	110
1000	80934	30	316	78	0	75
1234	84971	71	395	105	0	114
927	80545	68	217	49	0	94
2176	287191	64	818	60	0	116
957	62974	28	292	49	1	86
1551	134091	40	513	132	0	90
1014	75555	46	345	49	0	87
1771	162154	54	557	71	0	99
2613	226638	227	645	100	0	132
1205	115367	112	284	74	0	96
1337	108749	62	424	49	7	91
1524	155537	52	614	72	0	77
1829	153133	41	672	59	5	104
2229	165618	78	649	90	1	97
1233	151517	57	415	68	0	94
1365	133686	58	505	81	0	60
950	61342	40	387	33	0	46
2319	245196	117	730	166	0	135
1857	195576	70	563	94	0	90
223	19349	12	67	15	0	2
2390	225371	105	812	104	3	96
1985	153213	78	811	61	0	109
700	59117	29	281	11	0	15
1062	91762	24	338	45	0	68
1311	136769	54	413	84	0	88
1157	114798	61	298	66	1	84
823	85338	40	223	27	1	46
596	27676	22	194	59	0	59
1545	153535	48	371	127	0	116
1130	122417	37	268	48	0	29
0	0	0	0	0	0	0
1082	91529	32	332	58	0	91
1135	107205	67	371	57	0	76
1367	144664	45	465	59	0	83
1506	146445	63	447	76	1	84
870	76656	60	295	71	0	65
78	3616	5	14	5	0	0
0	0	0	0	0	0	0
1130	183088	44	388	70	0	84
1582	144677	84	564	76	0	114
2034	159104	98	562	122	2	124
919	113273	38	288	56	0	92
778	43410	19	292	63	0	3
1752	175774	73	530	92	1	109
957	95401	42	256	54	0	74
2098	134837	55	602	64	8	121
731	60493	40	174	29	3	48
285	19764	12	75	19	1	8
1834	164062	56	565	64	3	80
1148	132696	33	377	79	0	107
1646	155367	54	544	97	0	116
256	11796	9	79	22	0	8
98	10674	9	33	7	0	0
1404	142261	57	479	37	0	56
41	6836	3	11	5	0	4
1824	162563	63	626	48	6	70
42	5118	3	6	1	0	0
528	40248	16	183	34	1	14
0	0	0	0	0	0	0
1073	122641	47	334	49	0	91
1305	88837	38	269	44	0	89
81	7131	4	27	0	1	0
261	9056	14	99	18	0	12
934	76611	24	260	48	1	60
1180	132697	51	290	54	0	80
1147	100681	19	414	50	1	88




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160286&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160286&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160286&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
page_views[t] = -57.4869548351689 + 0.00130673820918927time_spent_seconds[t] + 3.68876695255697number_logins[t] + 1.94102689569532number_course_compenium_views[t] + 1.92461501617641number_compendium_views[t] + 18.6085804061399number_compediums_shared[t] + 1.0910171185301number_feedbackmessage_PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
page_views[t] =  -57.4869548351689 +  0.00130673820918927time_spent_seconds[t] +  3.68876695255697number_logins[t] +  1.94102689569532number_course_compenium_views[t] +  1.92461501617641number_compendium_views[t] +  18.6085804061399number_compediums_shared[t] +  1.0910171185301number_feedbackmessage_PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160286&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]page_views[t] =  -57.4869548351689 +  0.00130673820918927time_spent_seconds[t] +  3.68876695255697number_logins[t] +  1.94102689569532number_course_compenium_views[t] +  1.92461501617641number_compendium_views[t] +  18.6085804061399number_compediums_shared[t] +  1.0910171185301number_feedbackmessage_PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160286&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160286&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
page_views[t] = -57.4869548351689 + 0.00130673820918927time_spent_seconds[t] + 3.68876695255697number_logins[t] + 1.94102689569532number_course_compenium_views[t] + 1.92461501617641number_compendium_views[t] + 18.6085804061399number_compediums_shared[t] + 1.0910171185301number_feedbackmessage_PR[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-57.486954835168935.926502-1.60010.1118740.055937
time_spent_seconds0.001306738209189270.0004382.98370.0033720.001686
number_logins3.688766952556970.6372225.788800
number_course_compenium_views1.941026895695320.12219115.885100
number_compendium_views1.924615016176410.3926274.90193e-061e-06
number_compediums_shared18.60858040613996.6805952.78550.0061030.003051
number_feedbackmessage_PR1.09101711853010.5590241.95160.053020.02651

\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) & -57.4869548351689 & 35.926502 & -1.6001 & 0.111874 & 0.055937 \tabularnewline
time_spent_seconds & 0.00130673820918927 & 0.000438 & 2.9837 & 0.003372 & 0.001686 \tabularnewline
number_logins & 3.68876695255697 & 0.637222 & 5.7888 & 0 & 0 \tabularnewline
number_course_compenium_views & 1.94102689569532 & 0.122191 & 15.8851 & 0 & 0 \tabularnewline
number_compendium_views & 1.92461501617641 & 0.392627 & 4.9019 & 3e-06 & 1e-06 \tabularnewline
number_compediums_shared & 18.6085804061399 & 6.680595 & 2.7855 & 0.006103 & 0.003051 \tabularnewline
number_feedbackmessage_PR & 1.0910171185301 & 0.559024 & 1.9516 & 0.05302 & 0.02651 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160286&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]-57.4869548351689[/C][C]35.926502[/C][C]-1.6001[/C][C]0.111874[/C][C]0.055937[/C][/ROW]
[ROW][C]time_spent_seconds[/C][C]0.00130673820918927[/C][C]0.000438[/C][C]2.9837[/C][C]0.003372[/C][C]0.001686[/C][/ROW]
[ROW][C]number_logins[/C][C]3.68876695255697[/C][C]0.637222[/C][C]5.7888[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]number_course_compenium_views[/C][C]1.94102689569532[/C][C]0.122191[/C][C]15.8851[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]number_compendium_views[/C][C]1.92461501617641[/C][C]0.392627[/C][C]4.9019[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]number_compediums_shared[/C][C]18.6085804061399[/C][C]6.680595[/C][C]2.7855[/C][C]0.006103[/C][C]0.003051[/C][/ROW]
[ROW][C]number_feedbackmessage_PR[/C][C]1.0910171185301[/C][C]0.559024[/C][C]1.9516[/C][C]0.05302[/C][C]0.02651[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160286&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160286&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-57.486954835168935.926502-1.60010.1118740.055937
time_spent_seconds0.001306738209189270.0004382.98370.0033720.001686
number_logins3.688766952556970.6372225.788800
number_course_compenium_views1.941026895695320.12219115.885100
number_compendium_views1.924615016176410.3926274.90193e-061e-06
number_compediums_shared18.60858040613996.6805952.78550.0061030.003051
number_feedbackmessage_PR1.09101711853010.5590241.95160.053020.02651







Multiple Linear Regression - Regression Statistics
Multiple R0.98162539669506
R-squared0.963588419436734
Adjusted R-squared0.961993751674839
F-TEST (value)604.256536980318
F-TEST (DF numerator)6
F-TEST (DF denominator)137
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation171.944452783371
Sum Squared Residuals4050390.5934873

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.98162539669506 \tabularnewline
R-squared & 0.963588419436734 \tabularnewline
Adjusted R-squared & 0.961993751674839 \tabularnewline
F-TEST (value) & 604.256536980318 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 171.944452783371 \tabularnewline
Sum Squared Residuals & 4050390.5934873 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160286&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.98162539669506[/C][/ROW]
[ROW][C]R-squared[/C][C]0.963588419436734[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.961993751674839[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]604.256536980318[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/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]171.944452783371[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4050390.5934873[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160286&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160286&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.98162539669506
R-squared0.963588419436734
Adjusted R-squared0.961993751674839
F-TEST (value)604.256536980318
F-TEST (DF numerator)6
F-TEST (DF denominator)137
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation171.944452783371
Sum Squared Residuals4050390.5934873







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
118011918.11256622547-117.112566225473
217171669.4966218585647.503378141438
3192160.01751799639731.9824820036035
422952103.56630817501191.433691824988
534503378.8382767877671.1617232122406
668616067.36985109653793.630148903469
717951806.67994938619-11.6799493861871
816811553.92922628681127.070773713186
918971781.08652268469115.913477315312
1029742991.8923440367-17.8923440366964
1119462019.04024067344-73.0402406734428
1221482111.297788485736.7022115143015
1318321970.79601807449-138.796018074491
1431833468.40485474478-285.404854744775
1514761419.3077070214956.6922929785124
1615671692.79478469445-125.794784694446
1717561503.71300169681252.286998303189
1812471262.04016688507-15.0401668850709
1927792613.11775645349165.882243546511
20726653.08925716639672.9107428336044
2110481107.3374671273-59.3374671273029
2228052582.3513761219222.648623878098
2317602059.29275934206-299.292759342065
2422662103.61521061803162.384789381971
2518481750.4384842969897.5615157030217
2616651879.90507160071-214.905071600706
2720842393.04995654301-309.04995654301
2814401479.48832132878-39.4883213287823
2927412898.84939719116-157.849397191163
3021122327.62534161017-215.625341610171
3116841552.76755560633131.232444393673
3216161747.41560638199-131.41560638199
3322272231.54493283454-4.54493283453851
3430883254.62977314794-166.629773147941
3523892237.3546974724151.645302527602
361-53.798187882612154.7981878826121
3720992610.71730068137-511.717300681373
3816691684.76053316873-15.7605331687256
3921372130.451854396376.54814560363285
4021532319.90301782164-166.903017821636
4123902296.3602919817893.6397080182186
4217011850.92142372579-149.921423725792
439831083.46321164633-100.463211646329
4421612279.33518740492-118.335187404924
4512761302.17857625917-26.1785762591725
4611901323.9259792724-133.925979272397
47745806.84248953666-61.8424895366595
4823302461.43271788496-131.432717884962
4922892383.22195385227-94.221953852269
5026392776.09224147103-137.092241471028
51658667.352293596374-9.35229359637444
5219171547.95858007058369.041419929421
5325572482.0943383974174.9056616025855
5420262118.43453313338-92.4345331333755
5519112007.3585325926-96.3585325925997
5617161711.386685569094.61331443090655
5718521624.19721068472227.802789315278
589811004.10012250076-23.1001225007634
5911771362.61883214884-185.618832148836
6028332835.29413122564-2.29413122563743
6116881695.08701885615-7.08701885615113
6220972194.41628922761-97.4162892276137
6313311492.3530322794-161.353032279397
6412441313.8768103956-69.8768103955993
6512561302.98755852171-46.9875585217058
6612941229.2533172231964.7466827768141
6723031890.44296671497412.557033285031
6828972513.17898102181383.821018978189
691103944.287788349339158.712211650661
70340397.748622358598-57.7486223585977
7127912985.62909747125-194.629097471247
7213381424.94294492415-86.9429449241527
7314411560.9685953337-119.968595333699
7416231485.72561209805137.274387901948
7526502024.29157897939625.708421020609
7614991359.9493752697139.050624730301
7723022074.65086184467227.349138155329
7825402351.84024307743188.159756922575
7910001004.2463581553-4.24635815530291
8012341408.61650318-174.616503180003
81927916.66500829821310.3349917017874
8221762383.6724705626-207.672470562601
83957901.61109375731755.3889062426831
8415511613.2730767702-62.2730767702024
8510141069.80583449739-55.8058344973949
8617711679.4096299610891.5903700389176
8726132664.4577866366-51.457786636597
8812051304.82020378417-99.8202037841651
8913371460.16722993116-123.167229931165
9015241751.94618078891-227.946180788911
9118291918.28827462694-89.2882746269399
9222292004.03528385948224.964716140518
9312331389.72140665769-156.721406657692
9413651532.72755839504-167.727558395044
959501035.09815011549-85.0981501154928
9623192578.2287980988-259.2287980988
9718571828.1968583089828.8031416910237
98223173.16238769641149.8376123035895
9923902561.17165771076-171.171657710764
10019852240.94134302424-255.941343024237
101700709.702309147902-9.70230914790206
1021062967.81629411082794.1837058891727
10313111379.74901444715-68.7490144471489
10411571133.2433865568323.7566134431674
105823755.18711959824867.8128804017524
106596614.312718511183-18.3127185111825
10715451411.3089809423133.691019057701
1081130883.180619023948246.819380976052
1090-57.486954835169157.4869548351691
11010821035.4891872908646.5108127091444
11111351242.49063593559-107.49063593559
11213671404.22974761478-37.2297476147814
11315061490.4344221885315.5655778114738
1148701044.17509956496-174.175099564963
115782.4794969126606675.5205030873393
1160-57.486954835169157.4869548351691
11711301323.55380094004-193.553800940044
11815821806.81029498449-224.810294984491
11920342010.0829134145723.9170865854324
120919997.872108302392-78.8721083023922
121778760.62877384205717.3712261579428
12217521784.82191721619-32.8219172161945
123957903.67275200984553.3272479901553
12420981894.14715430305203.852845696948
125731670.85931598356260.1406840164378
126285222.08604240081362.9139575991873
12718341726.432746388107.567253611995
12811481238.19184564358-90.1918456435829
12916461713.89472952687-67.8947295268739
130256195.53702371749260.4629762825076
1319867.186264053910330.8137359460897
13214041400.730243112323.26975688767595
13341-2.1493521718293843.1493521718294
13418241882.81968489204-58.8196848920395
13542-26.161991432519268.1619914325192
136528508.65456837699519.3454316230051
1370-57.486954835169157.4869548351691
13810731118.03644938931-45.0364493893098
1391305902.692710854725402.307289145275
1408137.602769734700943.3972302652991
141261245.8865421104215.1134578895802
142934812.272094145594121.727905854406
14311801058.1487799976121.851220002398
14411471158.59729936627-11.5972993662697

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1801 & 1918.11256622547 & -117.112566225473 \tabularnewline
2 & 1717 & 1669.49662185856 & 47.503378141438 \tabularnewline
3 & 192 & 160.017517996397 & 31.9824820036035 \tabularnewline
4 & 2295 & 2103.56630817501 & 191.433691824988 \tabularnewline
5 & 3450 & 3378.83827678776 & 71.1617232122406 \tabularnewline
6 & 6861 & 6067.36985109653 & 793.630148903469 \tabularnewline
7 & 1795 & 1806.67994938619 & -11.6799493861871 \tabularnewline
8 & 1681 & 1553.92922628681 & 127.070773713186 \tabularnewline
9 & 1897 & 1781.08652268469 & 115.913477315312 \tabularnewline
10 & 2974 & 2991.8923440367 & -17.8923440366964 \tabularnewline
11 & 1946 & 2019.04024067344 & -73.0402406734428 \tabularnewline
12 & 2148 & 2111.2977884857 & 36.7022115143015 \tabularnewline
13 & 1832 & 1970.79601807449 & -138.796018074491 \tabularnewline
14 & 3183 & 3468.40485474478 & -285.404854744775 \tabularnewline
15 & 1476 & 1419.30770702149 & 56.6922929785124 \tabularnewline
16 & 1567 & 1692.79478469445 & -125.794784694446 \tabularnewline
17 & 1756 & 1503.71300169681 & 252.286998303189 \tabularnewline
18 & 1247 & 1262.04016688507 & -15.0401668850709 \tabularnewline
19 & 2779 & 2613.11775645349 & 165.882243546511 \tabularnewline
20 & 726 & 653.089257166396 & 72.9107428336044 \tabularnewline
21 & 1048 & 1107.3374671273 & -59.3374671273029 \tabularnewline
22 & 2805 & 2582.3513761219 & 222.648623878098 \tabularnewline
23 & 1760 & 2059.29275934206 & -299.292759342065 \tabularnewline
24 & 2266 & 2103.61521061803 & 162.384789381971 \tabularnewline
25 & 1848 & 1750.43848429698 & 97.5615157030217 \tabularnewline
26 & 1665 & 1879.90507160071 & -214.905071600706 \tabularnewline
27 & 2084 & 2393.04995654301 & -309.04995654301 \tabularnewline
28 & 1440 & 1479.48832132878 & -39.4883213287823 \tabularnewline
29 & 2741 & 2898.84939719116 & -157.849397191163 \tabularnewline
30 & 2112 & 2327.62534161017 & -215.625341610171 \tabularnewline
31 & 1684 & 1552.76755560633 & 131.232444393673 \tabularnewline
32 & 1616 & 1747.41560638199 & -131.41560638199 \tabularnewline
33 & 2227 & 2231.54493283454 & -4.54493283453851 \tabularnewline
34 & 3088 & 3254.62977314794 & -166.629773147941 \tabularnewline
35 & 2389 & 2237.3546974724 & 151.645302527602 \tabularnewline
36 & 1 & -53.7981878826121 & 54.7981878826121 \tabularnewline
37 & 2099 & 2610.71730068137 & -511.717300681373 \tabularnewline
38 & 1669 & 1684.76053316873 & -15.7605331687256 \tabularnewline
39 & 2137 & 2130.45185439637 & 6.54814560363285 \tabularnewline
40 & 2153 & 2319.90301782164 & -166.903017821636 \tabularnewline
41 & 2390 & 2296.36029198178 & 93.6397080182186 \tabularnewline
42 & 1701 & 1850.92142372579 & -149.921423725792 \tabularnewline
43 & 983 & 1083.46321164633 & -100.463211646329 \tabularnewline
44 & 2161 & 2279.33518740492 & -118.335187404924 \tabularnewline
45 & 1276 & 1302.17857625917 & -26.1785762591725 \tabularnewline
46 & 1190 & 1323.9259792724 & -133.925979272397 \tabularnewline
47 & 745 & 806.84248953666 & -61.8424895366595 \tabularnewline
48 & 2330 & 2461.43271788496 & -131.432717884962 \tabularnewline
49 & 2289 & 2383.22195385227 & -94.221953852269 \tabularnewline
50 & 2639 & 2776.09224147103 & -137.092241471028 \tabularnewline
51 & 658 & 667.352293596374 & -9.35229359637444 \tabularnewline
52 & 1917 & 1547.95858007058 & 369.041419929421 \tabularnewline
53 & 2557 & 2482.09433839741 & 74.9056616025855 \tabularnewline
54 & 2026 & 2118.43453313338 & -92.4345331333755 \tabularnewline
55 & 1911 & 2007.3585325926 & -96.3585325925997 \tabularnewline
56 & 1716 & 1711.38668556909 & 4.61331443090655 \tabularnewline
57 & 1852 & 1624.19721068472 & 227.802789315278 \tabularnewline
58 & 981 & 1004.10012250076 & -23.1001225007634 \tabularnewline
59 & 1177 & 1362.61883214884 & -185.618832148836 \tabularnewline
60 & 2833 & 2835.29413122564 & -2.29413122563743 \tabularnewline
61 & 1688 & 1695.08701885615 & -7.08701885615113 \tabularnewline
62 & 2097 & 2194.41628922761 & -97.4162892276137 \tabularnewline
63 & 1331 & 1492.3530322794 & -161.353032279397 \tabularnewline
64 & 1244 & 1313.8768103956 & -69.8768103955993 \tabularnewline
65 & 1256 & 1302.98755852171 & -46.9875585217058 \tabularnewline
66 & 1294 & 1229.25331722319 & 64.7466827768141 \tabularnewline
67 & 2303 & 1890.44296671497 & 412.557033285031 \tabularnewline
68 & 2897 & 2513.17898102181 & 383.821018978189 \tabularnewline
69 & 1103 & 944.287788349339 & 158.712211650661 \tabularnewline
70 & 340 & 397.748622358598 & -57.7486223585977 \tabularnewline
71 & 2791 & 2985.62909747125 & -194.629097471247 \tabularnewline
72 & 1338 & 1424.94294492415 & -86.9429449241527 \tabularnewline
73 & 1441 & 1560.9685953337 & -119.968595333699 \tabularnewline
74 & 1623 & 1485.72561209805 & 137.274387901948 \tabularnewline
75 & 2650 & 2024.29157897939 & 625.708421020609 \tabularnewline
76 & 1499 & 1359.9493752697 & 139.050624730301 \tabularnewline
77 & 2302 & 2074.65086184467 & 227.349138155329 \tabularnewline
78 & 2540 & 2351.84024307743 & 188.159756922575 \tabularnewline
79 & 1000 & 1004.2463581553 & -4.24635815530291 \tabularnewline
80 & 1234 & 1408.61650318 & -174.616503180003 \tabularnewline
81 & 927 & 916.665008298213 & 10.3349917017874 \tabularnewline
82 & 2176 & 2383.6724705626 & -207.672470562601 \tabularnewline
83 & 957 & 901.611093757317 & 55.3889062426831 \tabularnewline
84 & 1551 & 1613.2730767702 & -62.2730767702024 \tabularnewline
85 & 1014 & 1069.80583449739 & -55.8058344973949 \tabularnewline
86 & 1771 & 1679.40962996108 & 91.5903700389176 \tabularnewline
87 & 2613 & 2664.4577866366 & -51.457786636597 \tabularnewline
88 & 1205 & 1304.82020378417 & -99.8202037841651 \tabularnewline
89 & 1337 & 1460.16722993116 & -123.167229931165 \tabularnewline
90 & 1524 & 1751.94618078891 & -227.946180788911 \tabularnewline
91 & 1829 & 1918.28827462694 & -89.2882746269399 \tabularnewline
92 & 2229 & 2004.03528385948 & 224.964716140518 \tabularnewline
93 & 1233 & 1389.72140665769 & -156.721406657692 \tabularnewline
94 & 1365 & 1532.72755839504 & -167.727558395044 \tabularnewline
95 & 950 & 1035.09815011549 & -85.0981501154928 \tabularnewline
96 & 2319 & 2578.2287980988 & -259.2287980988 \tabularnewline
97 & 1857 & 1828.19685830898 & 28.8031416910237 \tabularnewline
98 & 223 & 173.162387696411 & 49.8376123035895 \tabularnewline
99 & 2390 & 2561.17165771076 & -171.171657710764 \tabularnewline
100 & 1985 & 2240.94134302424 & -255.941343024237 \tabularnewline
101 & 700 & 709.702309147902 & -9.70230914790206 \tabularnewline
102 & 1062 & 967.816294110827 & 94.1837058891727 \tabularnewline
103 & 1311 & 1379.74901444715 & -68.7490144471489 \tabularnewline
104 & 1157 & 1133.24338655683 & 23.7566134431674 \tabularnewline
105 & 823 & 755.187119598248 & 67.8128804017524 \tabularnewline
106 & 596 & 614.312718511183 & -18.3127185111825 \tabularnewline
107 & 1545 & 1411.3089809423 & 133.691019057701 \tabularnewline
108 & 1130 & 883.180619023948 & 246.819380976052 \tabularnewline
109 & 0 & -57.4869548351691 & 57.4869548351691 \tabularnewline
110 & 1082 & 1035.48918729086 & 46.5108127091444 \tabularnewline
111 & 1135 & 1242.49063593559 & -107.49063593559 \tabularnewline
112 & 1367 & 1404.22974761478 & -37.2297476147814 \tabularnewline
113 & 1506 & 1490.43442218853 & 15.5655778114738 \tabularnewline
114 & 870 & 1044.17509956496 & -174.175099564963 \tabularnewline
115 & 78 & 2.47949691266066 & 75.5205030873393 \tabularnewline
116 & 0 & -57.4869548351691 & 57.4869548351691 \tabularnewline
117 & 1130 & 1323.55380094004 & -193.553800940044 \tabularnewline
118 & 1582 & 1806.81029498449 & -224.810294984491 \tabularnewline
119 & 2034 & 2010.08291341457 & 23.9170865854324 \tabularnewline
120 & 919 & 997.872108302392 & -78.8721083023922 \tabularnewline
121 & 778 & 760.628773842057 & 17.3712261579428 \tabularnewline
122 & 1752 & 1784.82191721619 & -32.8219172161945 \tabularnewline
123 & 957 & 903.672752009845 & 53.3272479901553 \tabularnewline
124 & 2098 & 1894.14715430305 & 203.852845696948 \tabularnewline
125 & 731 & 670.859315983562 & 60.1406840164378 \tabularnewline
126 & 285 & 222.086042400813 & 62.9139575991873 \tabularnewline
127 & 1834 & 1726.432746388 & 107.567253611995 \tabularnewline
128 & 1148 & 1238.19184564358 & -90.1918456435829 \tabularnewline
129 & 1646 & 1713.89472952687 & -67.8947295268739 \tabularnewline
130 & 256 & 195.537023717492 & 60.4629762825076 \tabularnewline
131 & 98 & 67.1862640539103 & 30.8137359460897 \tabularnewline
132 & 1404 & 1400.73024311232 & 3.26975688767595 \tabularnewline
133 & 41 & -2.14935217182938 & 43.1493521718294 \tabularnewline
134 & 1824 & 1882.81968489204 & -58.8196848920395 \tabularnewline
135 & 42 & -26.1619914325192 & 68.1619914325192 \tabularnewline
136 & 528 & 508.654568376995 & 19.3454316230051 \tabularnewline
137 & 0 & -57.4869548351691 & 57.4869548351691 \tabularnewline
138 & 1073 & 1118.03644938931 & -45.0364493893098 \tabularnewline
139 & 1305 & 902.692710854725 & 402.307289145275 \tabularnewline
140 & 81 & 37.6027697347009 & 43.3972302652991 \tabularnewline
141 & 261 & 245.88654211042 & 15.1134578895802 \tabularnewline
142 & 934 & 812.272094145594 & 121.727905854406 \tabularnewline
143 & 1180 & 1058.1487799976 & 121.851220002398 \tabularnewline
144 & 1147 & 1158.59729936627 & -11.5972993662697 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160286&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]1801[/C][C]1918.11256622547[/C][C]-117.112566225473[/C][/ROW]
[ROW][C]2[/C][C]1717[/C][C]1669.49662185856[/C][C]47.503378141438[/C][/ROW]
[ROW][C]3[/C][C]192[/C][C]160.017517996397[/C][C]31.9824820036035[/C][/ROW]
[ROW][C]4[/C][C]2295[/C][C]2103.56630817501[/C][C]191.433691824988[/C][/ROW]
[ROW][C]5[/C][C]3450[/C][C]3378.83827678776[/C][C]71.1617232122406[/C][/ROW]
[ROW][C]6[/C][C]6861[/C][C]6067.36985109653[/C][C]793.630148903469[/C][/ROW]
[ROW][C]7[/C][C]1795[/C][C]1806.67994938619[/C][C]-11.6799493861871[/C][/ROW]
[ROW][C]8[/C][C]1681[/C][C]1553.92922628681[/C][C]127.070773713186[/C][/ROW]
[ROW][C]9[/C][C]1897[/C][C]1781.08652268469[/C][C]115.913477315312[/C][/ROW]
[ROW][C]10[/C][C]2974[/C][C]2991.8923440367[/C][C]-17.8923440366964[/C][/ROW]
[ROW][C]11[/C][C]1946[/C][C]2019.04024067344[/C][C]-73.0402406734428[/C][/ROW]
[ROW][C]12[/C][C]2148[/C][C]2111.2977884857[/C][C]36.7022115143015[/C][/ROW]
[ROW][C]13[/C][C]1832[/C][C]1970.79601807449[/C][C]-138.796018074491[/C][/ROW]
[ROW][C]14[/C][C]3183[/C][C]3468.40485474478[/C][C]-285.404854744775[/C][/ROW]
[ROW][C]15[/C][C]1476[/C][C]1419.30770702149[/C][C]56.6922929785124[/C][/ROW]
[ROW][C]16[/C][C]1567[/C][C]1692.79478469445[/C][C]-125.794784694446[/C][/ROW]
[ROW][C]17[/C][C]1756[/C][C]1503.71300169681[/C][C]252.286998303189[/C][/ROW]
[ROW][C]18[/C][C]1247[/C][C]1262.04016688507[/C][C]-15.0401668850709[/C][/ROW]
[ROW][C]19[/C][C]2779[/C][C]2613.11775645349[/C][C]165.882243546511[/C][/ROW]
[ROW][C]20[/C][C]726[/C][C]653.089257166396[/C][C]72.9107428336044[/C][/ROW]
[ROW][C]21[/C][C]1048[/C][C]1107.3374671273[/C][C]-59.3374671273029[/C][/ROW]
[ROW][C]22[/C][C]2805[/C][C]2582.3513761219[/C][C]222.648623878098[/C][/ROW]
[ROW][C]23[/C][C]1760[/C][C]2059.29275934206[/C][C]-299.292759342065[/C][/ROW]
[ROW][C]24[/C][C]2266[/C][C]2103.61521061803[/C][C]162.384789381971[/C][/ROW]
[ROW][C]25[/C][C]1848[/C][C]1750.43848429698[/C][C]97.5615157030217[/C][/ROW]
[ROW][C]26[/C][C]1665[/C][C]1879.90507160071[/C][C]-214.905071600706[/C][/ROW]
[ROW][C]27[/C][C]2084[/C][C]2393.04995654301[/C][C]-309.04995654301[/C][/ROW]
[ROW][C]28[/C][C]1440[/C][C]1479.48832132878[/C][C]-39.4883213287823[/C][/ROW]
[ROW][C]29[/C][C]2741[/C][C]2898.84939719116[/C][C]-157.849397191163[/C][/ROW]
[ROW][C]30[/C][C]2112[/C][C]2327.62534161017[/C][C]-215.625341610171[/C][/ROW]
[ROW][C]31[/C][C]1684[/C][C]1552.76755560633[/C][C]131.232444393673[/C][/ROW]
[ROW][C]32[/C][C]1616[/C][C]1747.41560638199[/C][C]-131.41560638199[/C][/ROW]
[ROW][C]33[/C][C]2227[/C][C]2231.54493283454[/C][C]-4.54493283453851[/C][/ROW]
[ROW][C]34[/C][C]3088[/C][C]3254.62977314794[/C][C]-166.629773147941[/C][/ROW]
[ROW][C]35[/C][C]2389[/C][C]2237.3546974724[/C][C]151.645302527602[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]-53.7981878826121[/C][C]54.7981878826121[/C][/ROW]
[ROW][C]37[/C][C]2099[/C][C]2610.71730068137[/C][C]-511.717300681373[/C][/ROW]
[ROW][C]38[/C][C]1669[/C][C]1684.76053316873[/C][C]-15.7605331687256[/C][/ROW]
[ROW][C]39[/C][C]2137[/C][C]2130.45185439637[/C][C]6.54814560363285[/C][/ROW]
[ROW][C]40[/C][C]2153[/C][C]2319.90301782164[/C][C]-166.903017821636[/C][/ROW]
[ROW][C]41[/C][C]2390[/C][C]2296.36029198178[/C][C]93.6397080182186[/C][/ROW]
[ROW][C]42[/C][C]1701[/C][C]1850.92142372579[/C][C]-149.921423725792[/C][/ROW]
[ROW][C]43[/C][C]983[/C][C]1083.46321164633[/C][C]-100.463211646329[/C][/ROW]
[ROW][C]44[/C][C]2161[/C][C]2279.33518740492[/C][C]-118.335187404924[/C][/ROW]
[ROW][C]45[/C][C]1276[/C][C]1302.17857625917[/C][C]-26.1785762591725[/C][/ROW]
[ROW][C]46[/C][C]1190[/C][C]1323.9259792724[/C][C]-133.925979272397[/C][/ROW]
[ROW][C]47[/C][C]745[/C][C]806.84248953666[/C][C]-61.8424895366595[/C][/ROW]
[ROW][C]48[/C][C]2330[/C][C]2461.43271788496[/C][C]-131.432717884962[/C][/ROW]
[ROW][C]49[/C][C]2289[/C][C]2383.22195385227[/C][C]-94.221953852269[/C][/ROW]
[ROW][C]50[/C][C]2639[/C][C]2776.09224147103[/C][C]-137.092241471028[/C][/ROW]
[ROW][C]51[/C][C]658[/C][C]667.352293596374[/C][C]-9.35229359637444[/C][/ROW]
[ROW][C]52[/C][C]1917[/C][C]1547.95858007058[/C][C]369.041419929421[/C][/ROW]
[ROW][C]53[/C][C]2557[/C][C]2482.09433839741[/C][C]74.9056616025855[/C][/ROW]
[ROW][C]54[/C][C]2026[/C][C]2118.43453313338[/C][C]-92.4345331333755[/C][/ROW]
[ROW][C]55[/C][C]1911[/C][C]2007.3585325926[/C][C]-96.3585325925997[/C][/ROW]
[ROW][C]56[/C][C]1716[/C][C]1711.38668556909[/C][C]4.61331443090655[/C][/ROW]
[ROW][C]57[/C][C]1852[/C][C]1624.19721068472[/C][C]227.802789315278[/C][/ROW]
[ROW][C]58[/C][C]981[/C][C]1004.10012250076[/C][C]-23.1001225007634[/C][/ROW]
[ROW][C]59[/C][C]1177[/C][C]1362.61883214884[/C][C]-185.618832148836[/C][/ROW]
[ROW][C]60[/C][C]2833[/C][C]2835.29413122564[/C][C]-2.29413122563743[/C][/ROW]
[ROW][C]61[/C][C]1688[/C][C]1695.08701885615[/C][C]-7.08701885615113[/C][/ROW]
[ROW][C]62[/C][C]2097[/C][C]2194.41628922761[/C][C]-97.4162892276137[/C][/ROW]
[ROW][C]63[/C][C]1331[/C][C]1492.3530322794[/C][C]-161.353032279397[/C][/ROW]
[ROW][C]64[/C][C]1244[/C][C]1313.8768103956[/C][C]-69.8768103955993[/C][/ROW]
[ROW][C]65[/C][C]1256[/C][C]1302.98755852171[/C][C]-46.9875585217058[/C][/ROW]
[ROW][C]66[/C][C]1294[/C][C]1229.25331722319[/C][C]64.7466827768141[/C][/ROW]
[ROW][C]67[/C][C]2303[/C][C]1890.44296671497[/C][C]412.557033285031[/C][/ROW]
[ROW][C]68[/C][C]2897[/C][C]2513.17898102181[/C][C]383.821018978189[/C][/ROW]
[ROW][C]69[/C][C]1103[/C][C]944.287788349339[/C][C]158.712211650661[/C][/ROW]
[ROW][C]70[/C][C]340[/C][C]397.748622358598[/C][C]-57.7486223585977[/C][/ROW]
[ROW][C]71[/C][C]2791[/C][C]2985.62909747125[/C][C]-194.629097471247[/C][/ROW]
[ROW][C]72[/C][C]1338[/C][C]1424.94294492415[/C][C]-86.9429449241527[/C][/ROW]
[ROW][C]73[/C][C]1441[/C][C]1560.9685953337[/C][C]-119.968595333699[/C][/ROW]
[ROW][C]74[/C][C]1623[/C][C]1485.72561209805[/C][C]137.274387901948[/C][/ROW]
[ROW][C]75[/C][C]2650[/C][C]2024.29157897939[/C][C]625.708421020609[/C][/ROW]
[ROW][C]76[/C][C]1499[/C][C]1359.9493752697[/C][C]139.050624730301[/C][/ROW]
[ROW][C]77[/C][C]2302[/C][C]2074.65086184467[/C][C]227.349138155329[/C][/ROW]
[ROW][C]78[/C][C]2540[/C][C]2351.84024307743[/C][C]188.159756922575[/C][/ROW]
[ROW][C]79[/C][C]1000[/C][C]1004.2463581553[/C][C]-4.24635815530291[/C][/ROW]
[ROW][C]80[/C][C]1234[/C][C]1408.61650318[/C][C]-174.616503180003[/C][/ROW]
[ROW][C]81[/C][C]927[/C][C]916.665008298213[/C][C]10.3349917017874[/C][/ROW]
[ROW][C]82[/C][C]2176[/C][C]2383.6724705626[/C][C]-207.672470562601[/C][/ROW]
[ROW][C]83[/C][C]957[/C][C]901.611093757317[/C][C]55.3889062426831[/C][/ROW]
[ROW][C]84[/C][C]1551[/C][C]1613.2730767702[/C][C]-62.2730767702024[/C][/ROW]
[ROW][C]85[/C][C]1014[/C][C]1069.80583449739[/C][C]-55.8058344973949[/C][/ROW]
[ROW][C]86[/C][C]1771[/C][C]1679.40962996108[/C][C]91.5903700389176[/C][/ROW]
[ROW][C]87[/C][C]2613[/C][C]2664.4577866366[/C][C]-51.457786636597[/C][/ROW]
[ROW][C]88[/C][C]1205[/C][C]1304.82020378417[/C][C]-99.8202037841651[/C][/ROW]
[ROW][C]89[/C][C]1337[/C][C]1460.16722993116[/C][C]-123.167229931165[/C][/ROW]
[ROW][C]90[/C][C]1524[/C][C]1751.94618078891[/C][C]-227.946180788911[/C][/ROW]
[ROW][C]91[/C][C]1829[/C][C]1918.28827462694[/C][C]-89.2882746269399[/C][/ROW]
[ROW][C]92[/C][C]2229[/C][C]2004.03528385948[/C][C]224.964716140518[/C][/ROW]
[ROW][C]93[/C][C]1233[/C][C]1389.72140665769[/C][C]-156.721406657692[/C][/ROW]
[ROW][C]94[/C][C]1365[/C][C]1532.72755839504[/C][C]-167.727558395044[/C][/ROW]
[ROW][C]95[/C][C]950[/C][C]1035.09815011549[/C][C]-85.0981501154928[/C][/ROW]
[ROW][C]96[/C][C]2319[/C][C]2578.2287980988[/C][C]-259.2287980988[/C][/ROW]
[ROW][C]97[/C][C]1857[/C][C]1828.19685830898[/C][C]28.8031416910237[/C][/ROW]
[ROW][C]98[/C][C]223[/C][C]173.162387696411[/C][C]49.8376123035895[/C][/ROW]
[ROW][C]99[/C][C]2390[/C][C]2561.17165771076[/C][C]-171.171657710764[/C][/ROW]
[ROW][C]100[/C][C]1985[/C][C]2240.94134302424[/C][C]-255.941343024237[/C][/ROW]
[ROW][C]101[/C][C]700[/C][C]709.702309147902[/C][C]-9.70230914790206[/C][/ROW]
[ROW][C]102[/C][C]1062[/C][C]967.816294110827[/C][C]94.1837058891727[/C][/ROW]
[ROW][C]103[/C][C]1311[/C][C]1379.74901444715[/C][C]-68.7490144471489[/C][/ROW]
[ROW][C]104[/C][C]1157[/C][C]1133.24338655683[/C][C]23.7566134431674[/C][/ROW]
[ROW][C]105[/C][C]823[/C][C]755.187119598248[/C][C]67.8128804017524[/C][/ROW]
[ROW][C]106[/C][C]596[/C][C]614.312718511183[/C][C]-18.3127185111825[/C][/ROW]
[ROW][C]107[/C][C]1545[/C][C]1411.3089809423[/C][C]133.691019057701[/C][/ROW]
[ROW][C]108[/C][C]1130[/C][C]883.180619023948[/C][C]246.819380976052[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]-57.4869548351691[/C][C]57.4869548351691[/C][/ROW]
[ROW][C]110[/C][C]1082[/C][C]1035.48918729086[/C][C]46.5108127091444[/C][/ROW]
[ROW][C]111[/C][C]1135[/C][C]1242.49063593559[/C][C]-107.49063593559[/C][/ROW]
[ROW][C]112[/C][C]1367[/C][C]1404.22974761478[/C][C]-37.2297476147814[/C][/ROW]
[ROW][C]113[/C][C]1506[/C][C]1490.43442218853[/C][C]15.5655778114738[/C][/ROW]
[ROW][C]114[/C][C]870[/C][C]1044.17509956496[/C][C]-174.175099564963[/C][/ROW]
[ROW][C]115[/C][C]78[/C][C]2.47949691266066[/C][C]75.5205030873393[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]-57.4869548351691[/C][C]57.4869548351691[/C][/ROW]
[ROW][C]117[/C][C]1130[/C][C]1323.55380094004[/C][C]-193.553800940044[/C][/ROW]
[ROW][C]118[/C][C]1582[/C][C]1806.81029498449[/C][C]-224.810294984491[/C][/ROW]
[ROW][C]119[/C][C]2034[/C][C]2010.08291341457[/C][C]23.9170865854324[/C][/ROW]
[ROW][C]120[/C][C]919[/C][C]997.872108302392[/C][C]-78.8721083023922[/C][/ROW]
[ROW][C]121[/C][C]778[/C][C]760.628773842057[/C][C]17.3712261579428[/C][/ROW]
[ROW][C]122[/C][C]1752[/C][C]1784.82191721619[/C][C]-32.8219172161945[/C][/ROW]
[ROW][C]123[/C][C]957[/C][C]903.672752009845[/C][C]53.3272479901553[/C][/ROW]
[ROW][C]124[/C][C]2098[/C][C]1894.14715430305[/C][C]203.852845696948[/C][/ROW]
[ROW][C]125[/C][C]731[/C][C]670.859315983562[/C][C]60.1406840164378[/C][/ROW]
[ROW][C]126[/C][C]285[/C][C]222.086042400813[/C][C]62.9139575991873[/C][/ROW]
[ROW][C]127[/C][C]1834[/C][C]1726.432746388[/C][C]107.567253611995[/C][/ROW]
[ROW][C]128[/C][C]1148[/C][C]1238.19184564358[/C][C]-90.1918456435829[/C][/ROW]
[ROW][C]129[/C][C]1646[/C][C]1713.89472952687[/C][C]-67.8947295268739[/C][/ROW]
[ROW][C]130[/C][C]256[/C][C]195.537023717492[/C][C]60.4629762825076[/C][/ROW]
[ROW][C]131[/C][C]98[/C][C]67.1862640539103[/C][C]30.8137359460897[/C][/ROW]
[ROW][C]132[/C][C]1404[/C][C]1400.73024311232[/C][C]3.26975688767595[/C][/ROW]
[ROW][C]133[/C][C]41[/C][C]-2.14935217182938[/C][C]43.1493521718294[/C][/ROW]
[ROW][C]134[/C][C]1824[/C][C]1882.81968489204[/C][C]-58.8196848920395[/C][/ROW]
[ROW][C]135[/C][C]42[/C][C]-26.1619914325192[/C][C]68.1619914325192[/C][/ROW]
[ROW][C]136[/C][C]528[/C][C]508.654568376995[/C][C]19.3454316230051[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]-57.4869548351691[/C][C]57.4869548351691[/C][/ROW]
[ROW][C]138[/C][C]1073[/C][C]1118.03644938931[/C][C]-45.0364493893098[/C][/ROW]
[ROW][C]139[/C][C]1305[/C][C]902.692710854725[/C][C]402.307289145275[/C][/ROW]
[ROW][C]140[/C][C]81[/C][C]37.6027697347009[/C][C]43.3972302652991[/C][/ROW]
[ROW][C]141[/C][C]261[/C][C]245.88654211042[/C][C]15.1134578895802[/C][/ROW]
[ROW][C]142[/C][C]934[/C][C]812.272094145594[/C][C]121.727905854406[/C][/ROW]
[ROW][C]143[/C][C]1180[/C][C]1058.1487799976[/C][C]121.851220002398[/C][/ROW]
[ROW][C]144[/C][C]1147[/C][C]1158.59729936627[/C][C]-11.5972993662697[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160286&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160286&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
118011918.11256622547-117.112566225473
217171669.4966218585647.503378141438
3192160.01751799639731.9824820036035
422952103.56630817501191.433691824988
534503378.8382767877671.1617232122406
668616067.36985109653793.630148903469
717951806.67994938619-11.6799493861871
816811553.92922628681127.070773713186
918971781.08652268469115.913477315312
1029742991.8923440367-17.8923440366964
1119462019.04024067344-73.0402406734428
1221482111.297788485736.7022115143015
1318321970.79601807449-138.796018074491
1431833468.40485474478-285.404854744775
1514761419.3077070214956.6922929785124
1615671692.79478469445-125.794784694446
1717561503.71300169681252.286998303189
1812471262.04016688507-15.0401668850709
1927792613.11775645349165.882243546511
20726653.08925716639672.9107428336044
2110481107.3374671273-59.3374671273029
2228052582.3513761219222.648623878098
2317602059.29275934206-299.292759342065
2422662103.61521061803162.384789381971
2518481750.4384842969897.5615157030217
2616651879.90507160071-214.905071600706
2720842393.04995654301-309.04995654301
2814401479.48832132878-39.4883213287823
2927412898.84939719116-157.849397191163
3021122327.62534161017-215.625341610171
3116841552.76755560633131.232444393673
3216161747.41560638199-131.41560638199
3322272231.54493283454-4.54493283453851
3430883254.62977314794-166.629773147941
3523892237.3546974724151.645302527602
361-53.798187882612154.7981878826121
3720992610.71730068137-511.717300681373
3816691684.76053316873-15.7605331687256
3921372130.451854396376.54814560363285
4021532319.90301782164-166.903017821636
4123902296.3602919817893.6397080182186
4217011850.92142372579-149.921423725792
439831083.46321164633-100.463211646329
4421612279.33518740492-118.335187404924
4512761302.17857625917-26.1785762591725
4611901323.9259792724-133.925979272397
47745806.84248953666-61.8424895366595
4823302461.43271788496-131.432717884962
4922892383.22195385227-94.221953852269
5026392776.09224147103-137.092241471028
51658667.352293596374-9.35229359637444
5219171547.95858007058369.041419929421
5325572482.0943383974174.9056616025855
5420262118.43453313338-92.4345331333755
5519112007.3585325926-96.3585325925997
5617161711.386685569094.61331443090655
5718521624.19721068472227.802789315278
589811004.10012250076-23.1001225007634
5911771362.61883214884-185.618832148836
6028332835.29413122564-2.29413122563743
6116881695.08701885615-7.08701885615113
6220972194.41628922761-97.4162892276137
6313311492.3530322794-161.353032279397
6412441313.8768103956-69.8768103955993
6512561302.98755852171-46.9875585217058
6612941229.2533172231964.7466827768141
6723031890.44296671497412.557033285031
6828972513.17898102181383.821018978189
691103944.287788349339158.712211650661
70340397.748622358598-57.7486223585977
7127912985.62909747125-194.629097471247
7213381424.94294492415-86.9429449241527
7314411560.9685953337-119.968595333699
7416231485.72561209805137.274387901948
7526502024.29157897939625.708421020609
7614991359.9493752697139.050624730301
7723022074.65086184467227.349138155329
7825402351.84024307743188.159756922575
7910001004.2463581553-4.24635815530291
8012341408.61650318-174.616503180003
81927916.66500829821310.3349917017874
8221762383.6724705626-207.672470562601
83957901.61109375731755.3889062426831
8415511613.2730767702-62.2730767702024
8510141069.80583449739-55.8058344973949
8617711679.4096299610891.5903700389176
8726132664.4577866366-51.457786636597
8812051304.82020378417-99.8202037841651
8913371460.16722993116-123.167229931165
9015241751.94618078891-227.946180788911
9118291918.28827462694-89.2882746269399
9222292004.03528385948224.964716140518
9312331389.72140665769-156.721406657692
9413651532.72755839504-167.727558395044
959501035.09815011549-85.0981501154928
9623192578.2287980988-259.2287980988
9718571828.1968583089828.8031416910237
98223173.16238769641149.8376123035895
9923902561.17165771076-171.171657710764
10019852240.94134302424-255.941343024237
101700709.702309147902-9.70230914790206
1021062967.81629411082794.1837058891727
10313111379.74901444715-68.7490144471489
10411571133.2433865568323.7566134431674
105823755.18711959824867.8128804017524
106596614.312718511183-18.3127185111825
10715451411.3089809423133.691019057701
1081130883.180619023948246.819380976052
1090-57.486954835169157.4869548351691
11010821035.4891872908646.5108127091444
11111351242.49063593559-107.49063593559
11213671404.22974761478-37.2297476147814
11315061490.4344221885315.5655778114738
1148701044.17509956496-174.175099564963
115782.4794969126606675.5205030873393
1160-57.486954835169157.4869548351691
11711301323.55380094004-193.553800940044
11815821806.81029498449-224.810294984491
11920342010.0829134145723.9170865854324
120919997.872108302392-78.8721083023922
121778760.62877384205717.3712261579428
12217521784.82191721619-32.8219172161945
123957903.67275200984553.3272479901553
12420981894.14715430305203.852845696948
125731670.85931598356260.1406840164378
126285222.08604240081362.9139575991873
12718341726.432746388107.567253611995
12811481238.19184564358-90.1918456435829
12916461713.89472952687-67.8947295268739
130256195.53702371749260.4629762825076
1319867.186264053910330.8137359460897
13214041400.730243112323.26975688767595
13341-2.1493521718293843.1493521718294
13418241882.81968489204-58.8196848920395
13542-26.161991432519268.1619914325192
136528508.65456837699519.3454316230051
1370-57.486954835169157.4869548351691
13810731118.03644938931-45.0364493893098
1391305902.692710854725402.307289145275
1408137.602769734700943.3972302652991
141261245.8865421104215.1134578895802
142934812.272094145594121.727905854406
14311801058.1487799976121.851220002398
14411471158.59729936627-11.5972993662697







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9277846439011380.1444307121977240.0722153560988618
110.8627197367063420.2745605265873170.137280263293658
120.7788006817001320.4423986365997350.221199318299868
130.7434471463413250.513105707317350.256552853658675
140.6611102654057690.6777794691884620.338889734594231
150.7132957987896450.573408402420710.286704201210355
160.6578036903323480.6843926193353050.342196309667652
170.723060826984890.5538783460302210.27693917301511
180.7810692695800650.437861460839870.218930730419935
190.7893557850235710.4212884299528590.210644214976429
200.7828544629233010.4342910741533980.217145537076699
210.7387271679307480.5225456641385050.261272832069252
220.7044521426626150.5910957146747690.295547857337385
230.8875073725164260.2249852549671490.112492627483574
240.9254893804358570.1490212391282870.0745106195641434
250.9021182397135050.1957635205729910.0978817602864954
260.891519911935560.216960176128880.10848008806444
270.9763820345739090.0472359308521820.023617965426091
280.9658879800994640.06822403980107140.0341120199005357
290.9800580664737190.03988386705256250.0199419335262813
300.9889360698793360.02212786024132820.0110639301206641
310.9870420745051960.02591585098960760.0129579254948038
320.9824199900006770.03516001999864570.0175800099993228
330.9752585906114020.04948281877719610.024741409388598
340.9670271770199920.06594564596001640.0329728229800082
350.9619150808417930.07616983831641460.0380849191582073
360.9630877139544890.07382457209102290.0369122860455115
370.9985539268075720.002892146384855970.00144607319242799
380.99783026734730.00433946530539930.00216973265269965
390.9968790610767280.00624187784654430.00312093892327215
400.9967003827248460.006599234550307190.00329961727515359
410.9957579084801380.008484183039723110.00424209151986155
420.9950808384146770.009838323170646670.00491916158532333
430.9934711371155740.01305772576885230.00652886288442614
440.991618017267760.01676396546447930.00838198273223964
450.9882219524237660.02355609515246790.011778047576234
460.9863415035274630.02731699294507380.0136584964725369
470.9822137915125440.03557241697491250.0177862084874562
480.9789685754783270.04206284904334540.0210314245216727
490.9733889913499610.05322201730007750.0266110086500388
500.969008291746580.06198341650683930.0309917082534196
510.9603098037245010.07938039255099830.0396901962754992
520.9882602964923930.02347940701521380.0117397035076069
530.9858516755639550.02829664887208950.0141483244360447
540.9816541982514220.03669160349715650.0183458017485782
550.976534727705750.04693054458850.02346527229425
560.9689697590569770.06206048188604550.0310302409430227
570.9782432285113890.04351354297722130.0217567714886106
580.9724479957740730.05510400845185450.0275520042259273
590.9715809957402480.0568380085195030.0284190042597515
600.9627021389755310.07459572204893810.0372978610244691
610.9523901450618290.09521970987634290.0476098549381714
620.9416077393672250.1167845212655510.0583922606327754
630.9476918998623950.1046162002752110.0523081001376053
640.9387879375081990.1224241249836020.0612120624918008
650.9228761558053690.1542476883892630.0771238441946315
660.9083033681488010.1833932637023980.0916966318511991
670.9640665897588420.07186682048231590.0359334102411579
680.9909577941775780.01808441164484420.00904220582242212
690.9903745900732530.01925081985349480.0096254099267474
700.9884975810674060.02300483786518730.0115024189325937
710.9869812220284030.0260375559431950.0130187779715975
720.9824857576674260.03502848466514710.0175142423325736
730.9813820687324380.03723586253512330.0186179312675617
740.9816856158486350.03662876830273020.0183143841513651
750.9999456498513120.0001087002973766765.4350148688338e-05
760.9999305871967890.0001388256064219666.94128032109832e-05
770.9999773384331494.53231337020555e-052.26615668510278e-05
780.9999970327102325.93457953641647e-062.96728976820823e-06
790.9999945481338671.09037322668884e-055.4518661334442e-06
800.9999948341765441.03316469127386e-055.1658234563693e-06
810.9999906593625041.86812749914953e-059.34063749574766e-06
820.9999900155603471.99688793052624e-059.98443965263122e-06
830.9999830986944263.38026111478891e-051.69013055739446e-05
840.9999702990144685.94019710632313e-052.97009855316156e-05
850.9999496787893340.0001006424213314845.03212106657422e-05
860.9999476201514850.000104759697029345.23798485146699e-05
870.9999551862661128.96274677756105e-054.48137338878053e-05
880.9999264032186290.0001471935627419467.3596781370973e-05
890.9999591684696638.16630606734736e-054.08315303367368e-05
900.9999568243210918.63513578187509e-054.31756789093755e-05
910.9999537458500229.25082999556601e-054.625414997783e-05
920.9999972527345565.49453088829393e-062.74726544414696e-06
930.9999974376478875.12470422605112e-062.56235211302556e-06
940.9999958788007728.24239845524576e-064.12119922762288e-06
950.9999919387303211.61225393579491e-058.06126967897455e-06
960.9999931200425751.37599148509451e-056.87995742547256e-06
970.9999895927149552.08145700896183e-051.04072850448091e-05
980.9999818083253183.63833493633457e-051.81916746816728e-05
990.9999715404681195.69190637625876e-052.84595318812938e-05
1000.9999601379618397.9724076321086e-053.9862038160543e-05
1010.9999280050504270.0001439898991460567.19949495730278e-05
1020.9999008580873720.0001982838252555659.91419126277824e-05
1030.9998331590957020.0003336818085955980.000166840904297799
1040.9997008952136530.0005982095726945440.000299104786347272
1050.9994974686530610.00100506269387720.000502531346938602
1060.9991625656682750.001674868663449390.000837434331724695
1070.9988176908416520.002364618316696980.00118230915834849
1080.9997757404060530.0004485191878945750.000224259593947287
1090.9995971464332060.000805707133587440.00040285356679372
1100.9992678921731240.001464215653751630.000732107826875814
1110.9988795493276630.002240901344673980.00112045067233699
1120.9980151412324510.003969717535097920.00198485876754896
1130.9968650943492310.006269811301538850.00313490565076942
1140.99757700012720.004845999745600680.00242299987280034
1150.9958801408500490.008239718299902440.00411985914995122
1160.9931019954725650.01379600905487070.00689800452743533
1170.9936246264903060.01275074701938760.00637537350969381
1180.9980554522878550.003889095424290920.00194454771214546
1190.9970075910056460.005984817988707920.00299240899435396
1200.9967079573265710.006584085346857550.00329204267342877
1210.9961826283589760.007634743282048320.00381737164102416
1220.9930477989216760.01390440215664690.00695220107832347
1230.9876501291846630.02469974163067470.0123498708153373
1240.9800458219931090.0399083560137830.0199541780068915
1250.9787677652498360.04246446950032810.021232234750164
1260.9622311729565570.07553765408688580.0377688270434429
1270.9758719826858740.04825603462825090.0241280173141255
1280.961219925866020.07756014826796040.0387800741339802
1290.9486622476671150.1026755046657690.0513377523328847
1300.9060233354515070.1879533290969850.0939766645484926
1310.8383872789438880.3232254421122250.161612721056112
1320.9217432475982030.1565135048035950.0782567524017973
1330.8410673035768050.317865392846390.158932696423195
1340.7680219793870810.4639560412258370.231978020612918

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.927784643901138 & 0.144430712197724 & 0.0722153560988618 \tabularnewline
11 & 0.862719736706342 & 0.274560526587317 & 0.137280263293658 \tabularnewline
12 & 0.778800681700132 & 0.442398636599735 & 0.221199318299868 \tabularnewline
13 & 0.743447146341325 & 0.51310570731735 & 0.256552853658675 \tabularnewline
14 & 0.661110265405769 & 0.677779469188462 & 0.338889734594231 \tabularnewline
15 & 0.713295798789645 & 0.57340840242071 & 0.286704201210355 \tabularnewline
16 & 0.657803690332348 & 0.684392619335305 & 0.342196309667652 \tabularnewline
17 & 0.72306082698489 & 0.553878346030221 & 0.27693917301511 \tabularnewline
18 & 0.781069269580065 & 0.43786146083987 & 0.218930730419935 \tabularnewline
19 & 0.789355785023571 & 0.421288429952859 & 0.210644214976429 \tabularnewline
20 & 0.782854462923301 & 0.434291074153398 & 0.217145537076699 \tabularnewline
21 & 0.738727167930748 & 0.522545664138505 & 0.261272832069252 \tabularnewline
22 & 0.704452142662615 & 0.591095714674769 & 0.295547857337385 \tabularnewline
23 & 0.887507372516426 & 0.224985254967149 & 0.112492627483574 \tabularnewline
24 & 0.925489380435857 & 0.149021239128287 & 0.0745106195641434 \tabularnewline
25 & 0.902118239713505 & 0.195763520572991 & 0.0978817602864954 \tabularnewline
26 & 0.89151991193556 & 0.21696017612888 & 0.10848008806444 \tabularnewline
27 & 0.976382034573909 & 0.047235930852182 & 0.023617965426091 \tabularnewline
28 & 0.965887980099464 & 0.0682240398010714 & 0.0341120199005357 \tabularnewline
29 & 0.980058066473719 & 0.0398838670525625 & 0.0199419335262813 \tabularnewline
30 & 0.988936069879336 & 0.0221278602413282 & 0.0110639301206641 \tabularnewline
31 & 0.987042074505196 & 0.0259158509896076 & 0.0129579254948038 \tabularnewline
32 & 0.982419990000677 & 0.0351600199986457 & 0.0175800099993228 \tabularnewline
33 & 0.975258590611402 & 0.0494828187771961 & 0.024741409388598 \tabularnewline
34 & 0.967027177019992 & 0.0659456459600164 & 0.0329728229800082 \tabularnewline
35 & 0.961915080841793 & 0.0761698383164146 & 0.0380849191582073 \tabularnewline
36 & 0.963087713954489 & 0.0738245720910229 & 0.0369122860455115 \tabularnewline
37 & 0.998553926807572 & 0.00289214638485597 & 0.00144607319242799 \tabularnewline
38 & 0.9978302673473 & 0.0043394653053993 & 0.00216973265269965 \tabularnewline
39 & 0.996879061076728 & 0.0062418778465443 & 0.00312093892327215 \tabularnewline
40 & 0.996700382724846 & 0.00659923455030719 & 0.00329961727515359 \tabularnewline
41 & 0.995757908480138 & 0.00848418303972311 & 0.00424209151986155 \tabularnewline
42 & 0.995080838414677 & 0.00983832317064667 & 0.00491916158532333 \tabularnewline
43 & 0.993471137115574 & 0.0130577257688523 & 0.00652886288442614 \tabularnewline
44 & 0.99161801726776 & 0.0167639654644793 & 0.00838198273223964 \tabularnewline
45 & 0.988221952423766 & 0.0235560951524679 & 0.011778047576234 \tabularnewline
46 & 0.986341503527463 & 0.0273169929450738 & 0.0136584964725369 \tabularnewline
47 & 0.982213791512544 & 0.0355724169749125 & 0.0177862084874562 \tabularnewline
48 & 0.978968575478327 & 0.0420628490433454 & 0.0210314245216727 \tabularnewline
49 & 0.973388991349961 & 0.0532220173000775 & 0.0266110086500388 \tabularnewline
50 & 0.96900829174658 & 0.0619834165068393 & 0.0309917082534196 \tabularnewline
51 & 0.960309803724501 & 0.0793803925509983 & 0.0396901962754992 \tabularnewline
52 & 0.988260296492393 & 0.0234794070152138 & 0.0117397035076069 \tabularnewline
53 & 0.985851675563955 & 0.0282966488720895 & 0.0141483244360447 \tabularnewline
54 & 0.981654198251422 & 0.0366916034971565 & 0.0183458017485782 \tabularnewline
55 & 0.97653472770575 & 0.0469305445885 & 0.02346527229425 \tabularnewline
56 & 0.968969759056977 & 0.0620604818860455 & 0.0310302409430227 \tabularnewline
57 & 0.978243228511389 & 0.0435135429772213 & 0.0217567714886106 \tabularnewline
58 & 0.972447995774073 & 0.0551040084518545 & 0.0275520042259273 \tabularnewline
59 & 0.971580995740248 & 0.056838008519503 & 0.0284190042597515 \tabularnewline
60 & 0.962702138975531 & 0.0745957220489381 & 0.0372978610244691 \tabularnewline
61 & 0.952390145061829 & 0.0952197098763429 & 0.0476098549381714 \tabularnewline
62 & 0.941607739367225 & 0.116784521265551 & 0.0583922606327754 \tabularnewline
63 & 0.947691899862395 & 0.104616200275211 & 0.0523081001376053 \tabularnewline
64 & 0.938787937508199 & 0.122424124983602 & 0.0612120624918008 \tabularnewline
65 & 0.922876155805369 & 0.154247688389263 & 0.0771238441946315 \tabularnewline
66 & 0.908303368148801 & 0.183393263702398 & 0.0916966318511991 \tabularnewline
67 & 0.964066589758842 & 0.0718668204823159 & 0.0359334102411579 \tabularnewline
68 & 0.990957794177578 & 0.0180844116448442 & 0.00904220582242212 \tabularnewline
69 & 0.990374590073253 & 0.0192508198534948 & 0.0096254099267474 \tabularnewline
70 & 0.988497581067406 & 0.0230048378651873 & 0.0115024189325937 \tabularnewline
71 & 0.986981222028403 & 0.026037555943195 & 0.0130187779715975 \tabularnewline
72 & 0.982485757667426 & 0.0350284846651471 & 0.0175142423325736 \tabularnewline
73 & 0.981382068732438 & 0.0372358625351233 & 0.0186179312675617 \tabularnewline
74 & 0.981685615848635 & 0.0366287683027302 & 0.0183143841513651 \tabularnewline
75 & 0.999945649851312 & 0.000108700297376676 & 5.4350148688338e-05 \tabularnewline
76 & 0.999930587196789 & 0.000138825606421966 & 6.94128032109832e-05 \tabularnewline
77 & 0.999977338433149 & 4.53231337020555e-05 & 2.26615668510278e-05 \tabularnewline
78 & 0.999997032710232 & 5.93457953641647e-06 & 2.96728976820823e-06 \tabularnewline
79 & 0.999994548133867 & 1.09037322668884e-05 & 5.4518661334442e-06 \tabularnewline
80 & 0.999994834176544 & 1.03316469127386e-05 & 5.1658234563693e-06 \tabularnewline
81 & 0.999990659362504 & 1.86812749914953e-05 & 9.34063749574766e-06 \tabularnewline
82 & 0.999990015560347 & 1.99688793052624e-05 & 9.98443965263122e-06 \tabularnewline
83 & 0.999983098694426 & 3.38026111478891e-05 & 1.69013055739446e-05 \tabularnewline
84 & 0.999970299014468 & 5.94019710632313e-05 & 2.97009855316156e-05 \tabularnewline
85 & 0.999949678789334 & 0.000100642421331484 & 5.03212106657422e-05 \tabularnewline
86 & 0.999947620151485 & 0.00010475969702934 & 5.23798485146699e-05 \tabularnewline
87 & 0.999955186266112 & 8.96274677756105e-05 & 4.48137338878053e-05 \tabularnewline
88 & 0.999926403218629 & 0.000147193562741946 & 7.3596781370973e-05 \tabularnewline
89 & 0.999959168469663 & 8.16630606734736e-05 & 4.08315303367368e-05 \tabularnewline
90 & 0.999956824321091 & 8.63513578187509e-05 & 4.31756789093755e-05 \tabularnewline
91 & 0.999953745850022 & 9.25082999556601e-05 & 4.625414997783e-05 \tabularnewline
92 & 0.999997252734556 & 5.49453088829393e-06 & 2.74726544414696e-06 \tabularnewline
93 & 0.999997437647887 & 5.12470422605112e-06 & 2.56235211302556e-06 \tabularnewline
94 & 0.999995878800772 & 8.24239845524576e-06 & 4.12119922762288e-06 \tabularnewline
95 & 0.999991938730321 & 1.61225393579491e-05 & 8.06126967897455e-06 \tabularnewline
96 & 0.999993120042575 & 1.37599148509451e-05 & 6.87995742547256e-06 \tabularnewline
97 & 0.999989592714955 & 2.08145700896183e-05 & 1.04072850448091e-05 \tabularnewline
98 & 0.999981808325318 & 3.63833493633457e-05 & 1.81916746816728e-05 \tabularnewline
99 & 0.999971540468119 & 5.69190637625876e-05 & 2.84595318812938e-05 \tabularnewline
100 & 0.999960137961839 & 7.9724076321086e-05 & 3.9862038160543e-05 \tabularnewline
101 & 0.999928005050427 & 0.000143989899146056 & 7.19949495730278e-05 \tabularnewline
102 & 0.999900858087372 & 0.000198283825255565 & 9.91419126277824e-05 \tabularnewline
103 & 0.999833159095702 & 0.000333681808595598 & 0.000166840904297799 \tabularnewline
104 & 0.999700895213653 & 0.000598209572694544 & 0.000299104786347272 \tabularnewline
105 & 0.999497468653061 & 0.0010050626938772 & 0.000502531346938602 \tabularnewline
106 & 0.999162565668275 & 0.00167486866344939 & 0.000837434331724695 \tabularnewline
107 & 0.998817690841652 & 0.00236461831669698 & 0.00118230915834849 \tabularnewline
108 & 0.999775740406053 & 0.000448519187894575 & 0.000224259593947287 \tabularnewline
109 & 0.999597146433206 & 0.00080570713358744 & 0.00040285356679372 \tabularnewline
110 & 0.999267892173124 & 0.00146421565375163 & 0.000732107826875814 \tabularnewline
111 & 0.998879549327663 & 0.00224090134467398 & 0.00112045067233699 \tabularnewline
112 & 0.998015141232451 & 0.00396971753509792 & 0.00198485876754896 \tabularnewline
113 & 0.996865094349231 & 0.00626981130153885 & 0.00313490565076942 \tabularnewline
114 & 0.9975770001272 & 0.00484599974560068 & 0.00242299987280034 \tabularnewline
115 & 0.995880140850049 & 0.00823971829990244 & 0.00411985914995122 \tabularnewline
116 & 0.993101995472565 & 0.0137960090548707 & 0.00689800452743533 \tabularnewline
117 & 0.993624626490306 & 0.0127507470193876 & 0.00637537350969381 \tabularnewline
118 & 0.998055452287855 & 0.00388909542429092 & 0.00194454771214546 \tabularnewline
119 & 0.997007591005646 & 0.00598481798870792 & 0.00299240899435396 \tabularnewline
120 & 0.996707957326571 & 0.00658408534685755 & 0.00329204267342877 \tabularnewline
121 & 0.996182628358976 & 0.00763474328204832 & 0.00381737164102416 \tabularnewline
122 & 0.993047798921676 & 0.0139044021566469 & 0.00695220107832347 \tabularnewline
123 & 0.987650129184663 & 0.0246997416306747 & 0.0123498708153373 \tabularnewline
124 & 0.980045821993109 & 0.039908356013783 & 0.0199541780068915 \tabularnewline
125 & 0.978767765249836 & 0.0424644695003281 & 0.021232234750164 \tabularnewline
126 & 0.962231172956557 & 0.0755376540868858 & 0.0377688270434429 \tabularnewline
127 & 0.975871982685874 & 0.0482560346282509 & 0.0241280173141255 \tabularnewline
128 & 0.96121992586602 & 0.0775601482679604 & 0.0387800741339802 \tabularnewline
129 & 0.948662247667115 & 0.102675504665769 & 0.0513377523328847 \tabularnewline
130 & 0.906023335451507 & 0.187953329096985 & 0.0939766645484926 \tabularnewline
131 & 0.838387278943888 & 0.323225442112225 & 0.161612721056112 \tabularnewline
132 & 0.921743247598203 & 0.156513504803595 & 0.0782567524017973 \tabularnewline
133 & 0.841067303576805 & 0.31786539284639 & 0.158932696423195 \tabularnewline
134 & 0.768021979387081 & 0.463956041225837 & 0.231978020612918 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160286&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]10[/C][C]0.927784643901138[/C][C]0.144430712197724[/C][C]0.0722153560988618[/C][/ROW]
[ROW][C]11[/C][C]0.862719736706342[/C][C]0.274560526587317[/C][C]0.137280263293658[/C][/ROW]
[ROW][C]12[/C][C]0.778800681700132[/C][C]0.442398636599735[/C][C]0.221199318299868[/C][/ROW]
[ROW][C]13[/C][C]0.743447146341325[/C][C]0.51310570731735[/C][C]0.256552853658675[/C][/ROW]
[ROW][C]14[/C][C]0.661110265405769[/C][C]0.677779469188462[/C][C]0.338889734594231[/C][/ROW]
[ROW][C]15[/C][C]0.713295798789645[/C][C]0.57340840242071[/C][C]0.286704201210355[/C][/ROW]
[ROW][C]16[/C][C]0.657803690332348[/C][C]0.684392619335305[/C][C]0.342196309667652[/C][/ROW]
[ROW][C]17[/C][C]0.72306082698489[/C][C]0.553878346030221[/C][C]0.27693917301511[/C][/ROW]
[ROW][C]18[/C][C]0.781069269580065[/C][C]0.43786146083987[/C][C]0.218930730419935[/C][/ROW]
[ROW][C]19[/C][C]0.789355785023571[/C][C]0.421288429952859[/C][C]0.210644214976429[/C][/ROW]
[ROW][C]20[/C][C]0.782854462923301[/C][C]0.434291074153398[/C][C]0.217145537076699[/C][/ROW]
[ROW][C]21[/C][C]0.738727167930748[/C][C]0.522545664138505[/C][C]0.261272832069252[/C][/ROW]
[ROW][C]22[/C][C]0.704452142662615[/C][C]0.591095714674769[/C][C]0.295547857337385[/C][/ROW]
[ROW][C]23[/C][C]0.887507372516426[/C][C]0.224985254967149[/C][C]0.112492627483574[/C][/ROW]
[ROW][C]24[/C][C]0.925489380435857[/C][C]0.149021239128287[/C][C]0.0745106195641434[/C][/ROW]
[ROW][C]25[/C][C]0.902118239713505[/C][C]0.195763520572991[/C][C]0.0978817602864954[/C][/ROW]
[ROW][C]26[/C][C]0.89151991193556[/C][C]0.21696017612888[/C][C]0.10848008806444[/C][/ROW]
[ROW][C]27[/C][C]0.976382034573909[/C][C]0.047235930852182[/C][C]0.023617965426091[/C][/ROW]
[ROW][C]28[/C][C]0.965887980099464[/C][C]0.0682240398010714[/C][C]0.0341120199005357[/C][/ROW]
[ROW][C]29[/C][C]0.980058066473719[/C][C]0.0398838670525625[/C][C]0.0199419335262813[/C][/ROW]
[ROW][C]30[/C][C]0.988936069879336[/C][C]0.0221278602413282[/C][C]0.0110639301206641[/C][/ROW]
[ROW][C]31[/C][C]0.987042074505196[/C][C]0.0259158509896076[/C][C]0.0129579254948038[/C][/ROW]
[ROW][C]32[/C][C]0.982419990000677[/C][C]0.0351600199986457[/C][C]0.0175800099993228[/C][/ROW]
[ROW][C]33[/C][C]0.975258590611402[/C][C]0.0494828187771961[/C][C]0.024741409388598[/C][/ROW]
[ROW][C]34[/C][C]0.967027177019992[/C][C]0.0659456459600164[/C][C]0.0329728229800082[/C][/ROW]
[ROW][C]35[/C][C]0.961915080841793[/C][C]0.0761698383164146[/C][C]0.0380849191582073[/C][/ROW]
[ROW][C]36[/C][C]0.963087713954489[/C][C]0.0738245720910229[/C][C]0.0369122860455115[/C][/ROW]
[ROW][C]37[/C][C]0.998553926807572[/C][C]0.00289214638485597[/C][C]0.00144607319242799[/C][/ROW]
[ROW][C]38[/C][C]0.9978302673473[/C][C]0.0043394653053993[/C][C]0.00216973265269965[/C][/ROW]
[ROW][C]39[/C][C]0.996879061076728[/C][C]0.0062418778465443[/C][C]0.00312093892327215[/C][/ROW]
[ROW][C]40[/C][C]0.996700382724846[/C][C]0.00659923455030719[/C][C]0.00329961727515359[/C][/ROW]
[ROW][C]41[/C][C]0.995757908480138[/C][C]0.00848418303972311[/C][C]0.00424209151986155[/C][/ROW]
[ROW][C]42[/C][C]0.995080838414677[/C][C]0.00983832317064667[/C][C]0.00491916158532333[/C][/ROW]
[ROW][C]43[/C][C]0.993471137115574[/C][C]0.0130577257688523[/C][C]0.00652886288442614[/C][/ROW]
[ROW][C]44[/C][C]0.99161801726776[/C][C]0.0167639654644793[/C][C]0.00838198273223964[/C][/ROW]
[ROW][C]45[/C][C]0.988221952423766[/C][C]0.0235560951524679[/C][C]0.011778047576234[/C][/ROW]
[ROW][C]46[/C][C]0.986341503527463[/C][C]0.0273169929450738[/C][C]0.0136584964725369[/C][/ROW]
[ROW][C]47[/C][C]0.982213791512544[/C][C]0.0355724169749125[/C][C]0.0177862084874562[/C][/ROW]
[ROW][C]48[/C][C]0.978968575478327[/C][C]0.0420628490433454[/C][C]0.0210314245216727[/C][/ROW]
[ROW][C]49[/C][C]0.973388991349961[/C][C]0.0532220173000775[/C][C]0.0266110086500388[/C][/ROW]
[ROW][C]50[/C][C]0.96900829174658[/C][C]0.0619834165068393[/C][C]0.0309917082534196[/C][/ROW]
[ROW][C]51[/C][C]0.960309803724501[/C][C]0.0793803925509983[/C][C]0.0396901962754992[/C][/ROW]
[ROW][C]52[/C][C]0.988260296492393[/C][C]0.0234794070152138[/C][C]0.0117397035076069[/C][/ROW]
[ROW][C]53[/C][C]0.985851675563955[/C][C]0.0282966488720895[/C][C]0.0141483244360447[/C][/ROW]
[ROW][C]54[/C][C]0.981654198251422[/C][C]0.0366916034971565[/C][C]0.0183458017485782[/C][/ROW]
[ROW][C]55[/C][C]0.97653472770575[/C][C]0.0469305445885[/C][C]0.02346527229425[/C][/ROW]
[ROW][C]56[/C][C]0.968969759056977[/C][C]0.0620604818860455[/C][C]0.0310302409430227[/C][/ROW]
[ROW][C]57[/C][C]0.978243228511389[/C][C]0.0435135429772213[/C][C]0.0217567714886106[/C][/ROW]
[ROW][C]58[/C][C]0.972447995774073[/C][C]0.0551040084518545[/C][C]0.0275520042259273[/C][/ROW]
[ROW][C]59[/C][C]0.971580995740248[/C][C]0.056838008519503[/C][C]0.0284190042597515[/C][/ROW]
[ROW][C]60[/C][C]0.962702138975531[/C][C]0.0745957220489381[/C][C]0.0372978610244691[/C][/ROW]
[ROW][C]61[/C][C]0.952390145061829[/C][C]0.0952197098763429[/C][C]0.0476098549381714[/C][/ROW]
[ROW][C]62[/C][C]0.941607739367225[/C][C]0.116784521265551[/C][C]0.0583922606327754[/C][/ROW]
[ROW][C]63[/C][C]0.947691899862395[/C][C]0.104616200275211[/C][C]0.0523081001376053[/C][/ROW]
[ROW][C]64[/C][C]0.938787937508199[/C][C]0.122424124983602[/C][C]0.0612120624918008[/C][/ROW]
[ROW][C]65[/C][C]0.922876155805369[/C][C]0.154247688389263[/C][C]0.0771238441946315[/C][/ROW]
[ROW][C]66[/C][C]0.908303368148801[/C][C]0.183393263702398[/C][C]0.0916966318511991[/C][/ROW]
[ROW][C]67[/C][C]0.964066589758842[/C][C]0.0718668204823159[/C][C]0.0359334102411579[/C][/ROW]
[ROW][C]68[/C][C]0.990957794177578[/C][C]0.0180844116448442[/C][C]0.00904220582242212[/C][/ROW]
[ROW][C]69[/C][C]0.990374590073253[/C][C]0.0192508198534948[/C][C]0.0096254099267474[/C][/ROW]
[ROW][C]70[/C][C]0.988497581067406[/C][C]0.0230048378651873[/C][C]0.0115024189325937[/C][/ROW]
[ROW][C]71[/C][C]0.986981222028403[/C][C]0.026037555943195[/C][C]0.0130187779715975[/C][/ROW]
[ROW][C]72[/C][C]0.982485757667426[/C][C]0.0350284846651471[/C][C]0.0175142423325736[/C][/ROW]
[ROW][C]73[/C][C]0.981382068732438[/C][C]0.0372358625351233[/C][C]0.0186179312675617[/C][/ROW]
[ROW][C]74[/C][C]0.981685615848635[/C][C]0.0366287683027302[/C][C]0.0183143841513651[/C][/ROW]
[ROW][C]75[/C][C]0.999945649851312[/C][C]0.000108700297376676[/C][C]5.4350148688338e-05[/C][/ROW]
[ROW][C]76[/C][C]0.999930587196789[/C][C]0.000138825606421966[/C][C]6.94128032109832e-05[/C][/ROW]
[ROW][C]77[/C][C]0.999977338433149[/C][C]4.53231337020555e-05[/C][C]2.26615668510278e-05[/C][/ROW]
[ROW][C]78[/C][C]0.999997032710232[/C][C]5.93457953641647e-06[/C][C]2.96728976820823e-06[/C][/ROW]
[ROW][C]79[/C][C]0.999994548133867[/C][C]1.09037322668884e-05[/C][C]5.4518661334442e-06[/C][/ROW]
[ROW][C]80[/C][C]0.999994834176544[/C][C]1.03316469127386e-05[/C][C]5.1658234563693e-06[/C][/ROW]
[ROW][C]81[/C][C]0.999990659362504[/C][C]1.86812749914953e-05[/C][C]9.34063749574766e-06[/C][/ROW]
[ROW][C]82[/C][C]0.999990015560347[/C][C]1.99688793052624e-05[/C][C]9.98443965263122e-06[/C][/ROW]
[ROW][C]83[/C][C]0.999983098694426[/C][C]3.38026111478891e-05[/C][C]1.69013055739446e-05[/C][/ROW]
[ROW][C]84[/C][C]0.999970299014468[/C][C]5.94019710632313e-05[/C][C]2.97009855316156e-05[/C][/ROW]
[ROW][C]85[/C][C]0.999949678789334[/C][C]0.000100642421331484[/C][C]5.03212106657422e-05[/C][/ROW]
[ROW][C]86[/C][C]0.999947620151485[/C][C]0.00010475969702934[/C][C]5.23798485146699e-05[/C][/ROW]
[ROW][C]87[/C][C]0.999955186266112[/C][C]8.96274677756105e-05[/C][C]4.48137338878053e-05[/C][/ROW]
[ROW][C]88[/C][C]0.999926403218629[/C][C]0.000147193562741946[/C][C]7.3596781370973e-05[/C][/ROW]
[ROW][C]89[/C][C]0.999959168469663[/C][C]8.16630606734736e-05[/C][C]4.08315303367368e-05[/C][/ROW]
[ROW][C]90[/C][C]0.999956824321091[/C][C]8.63513578187509e-05[/C][C]4.31756789093755e-05[/C][/ROW]
[ROW][C]91[/C][C]0.999953745850022[/C][C]9.25082999556601e-05[/C][C]4.625414997783e-05[/C][/ROW]
[ROW][C]92[/C][C]0.999997252734556[/C][C]5.49453088829393e-06[/C][C]2.74726544414696e-06[/C][/ROW]
[ROW][C]93[/C][C]0.999997437647887[/C][C]5.12470422605112e-06[/C][C]2.56235211302556e-06[/C][/ROW]
[ROW][C]94[/C][C]0.999995878800772[/C][C]8.24239845524576e-06[/C][C]4.12119922762288e-06[/C][/ROW]
[ROW][C]95[/C][C]0.999991938730321[/C][C]1.61225393579491e-05[/C][C]8.06126967897455e-06[/C][/ROW]
[ROW][C]96[/C][C]0.999993120042575[/C][C]1.37599148509451e-05[/C][C]6.87995742547256e-06[/C][/ROW]
[ROW][C]97[/C][C]0.999989592714955[/C][C]2.08145700896183e-05[/C][C]1.04072850448091e-05[/C][/ROW]
[ROW][C]98[/C][C]0.999981808325318[/C][C]3.63833493633457e-05[/C][C]1.81916746816728e-05[/C][/ROW]
[ROW][C]99[/C][C]0.999971540468119[/C][C]5.69190637625876e-05[/C][C]2.84595318812938e-05[/C][/ROW]
[ROW][C]100[/C][C]0.999960137961839[/C][C]7.9724076321086e-05[/C][C]3.9862038160543e-05[/C][/ROW]
[ROW][C]101[/C][C]0.999928005050427[/C][C]0.000143989899146056[/C][C]7.19949495730278e-05[/C][/ROW]
[ROW][C]102[/C][C]0.999900858087372[/C][C]0.000198283825255565[/C][C]9.91419126277824e-05[/C][/ROW]
[ROW][C]103[/C][C]0.999833159095702[/C][C]0.000333681808595598[/C][C]0.000166840904297799[/C][/ROW]
[ROW][C]104[/C][C]0.999700895213653[/C][C]0.000598209572694544[/C][C]0.000299104786347272[/C][/ROW]
[ROW][C]105[/C][C]0.999497468653061[/C][C]0.0010050626938772[/C][C]0.000502531346938602[/C][/ROW]
[ROW][C]106[/C][C]0.999162565668275[/C][C]0.00167486866344939[/C][C]0.000837434331724695[/C][/ROW]
[ROW][C]107[/C][C]0.998817690841652[/C][C]0.00236461831669698[/C][C]0.00118230915834849[/C][/ROW]
[ROW][C]108[/C][C]0.999775740406053[/C][C]0.000448519187894575[/C][C]0.000224259593947287[/C][/ROW]
[ROW][C]109[/C][C]0.999597146433206[/C][C]0.00080570713358744[/C][C]0.00040285356679372[/C][/ROW]
[ROW][C]110[/C][C]0.999267892173124[/C][C]0.00146421565375163[/C][C]0.000732107826875814[/C][/ROW]
[ROW][C]111[/C][C]0.998879549327663[/C][C]0.00224090134467398[/C][C]0.00112045067233699[/C][/ROW]
[ROW][C]112[/C][C]0.998015141232451[/C][C]0.00396971753509792[/C][C]0.00198485876754896[/C][/ROW]
[ROW][C]113[/C][C]0.996865094349231[/C][C]0.00626981130153885[/C][C]0.00313490565076942[/C][/ROW]
[ROW][C]114[/C][C]0.9975770001272[/C][C]0.00484599974560068[/C][C]0.00242299987280034[/C][/ROW]
[ROW][C]115[/C][C]0.995880140850049[/C][C]0.00823971829990244[/C][C]0.00411985914995122[/C][/ROW]
[ROW][C]116[/C][C]0.993101995472565[/C][C]0.0137960090548707[/C][C]0.00689800452743533[/C][/ROW]
[ROW][C]117[/C][C]0.993624626490306[/C][C]0.0127507470193876[/C][C]0.00637537350969381[/C][/ROW]
[ROW][C]118[/C][C]0.998055452287855[/C][C]0.00388909542429092[/C][C]0.00194454771214546[/C][/ROW]
[ROW][C]119[/C][C]0.997007591005646[/C][C]0.00598481798870792[/C][C]0.00299240899435396[/C][/ROW]
[ROW][C]120[/C][C]0.996707957326571[/C][C]0.00658408534685755[/C][C]0.00329204267342877[/C][/ROW]
[ROW][C]121[/C][C]0.996182628358976[/C][C]0.00763474328204832[/C][C]0.00381737164102416[/C][/ROW]
[ROW][C]122[/C][C]0.993047798921676[/C][C]0.0139044021566469[/C][C]0.00695220107832347[/C][/ROW]
[ROW][C]123[/C][C]0.987650129184663[/C][C]0.0246997416306747[/C][C]0.0123498708153373[/C][/ROW]
[ROW][C]124[/C][C]0.980045821993109[/C][C]0.039908356013783[/C][C]0.0199541780068915[/C][/ROW]
[ROW][C]125[/C][C]0.978767765249836[/C][C]0.0424644695003281[/C][C]0.021232234750164[/C][/ROW]
[ROW][C]126[/C][C]0.962231172956557[/C][C]0.0755376540868858[/C][C]0.0377688270434429[/C][/ROW]
[ROW][C]127[/C][C]0.975871982685874[/C][C]0.0482560346282509[/C][C]0.0241280173141255[/C][/ROW]
[ROW][C]128[/C][C]0.96121992586602[/C][C]0.0775601482679604[/C][C]0.0387800741339802[/C][/ROW]
[ROW][C]129[/C][C]0.948662247667115[/C][C]0.102675504665769[/C][C]0.0513377523328847[/C][/ROW]
[ROW][C]130[/C][C]0.906023335451507[/C][C]0.187953329096985[/C][C]0.0939766645484926[/C][/ROW]
[ROW][C]131[/C][C]0.838387278943888[/C][C]0.323225442112225[/C][C]0.161612721056112[/C][/ROW]
[ROW][C]132[/C][C]0.921743247598203[/C][C]0.156513504803595[/C][C]0.0782567524017973[/C][/ROW]
[ROW][C]133[/C][C]0.841067303576805[/C][C]0.31786539284639[/C][C]0.158932696423195[/C][/ROW]
[ROW][C]134[/C][C]0.768021979387081[/C][C]0.463956041225837[/C][C]0.231978020612918[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160286&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160286&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9277846439011380.1444307121977240.0722153560988618
110.8627197367063420.2745605265873170.137280263293658
120.7788006817001320.4423986365997350.221199318299868
130.7434471463413250.513105707317350.256552853658675
140.6611102654057690.6777794691884620.338889734594231
150.7132957987896450.573408402420710.286704201210355
160.6578036903323480.6843926193353050.342196309667652
170.723060826984890.5538783460302210.27693917301511
180.7810692695800650.437861460839870.218930730419935
190.7893557850235710.4212884299528590.210644214976429
200.7828544629233010.4342910741533980.217145537076699
210.7387271679307480.5225456641385050.261272832069252
220.7044521426626150.5910957146747690.295547857337385
230.8875073725164260.2249852549671490.112492627483574
240.9254893804358570.1490212391282870.0745106195641434
250.9021182397135050.1957635205729910.0978817602864954
260.891519911935560.216960176128880.10848008806444
270.9763820345739090.0472359308521820.023617965426091
280.9658879800994640.06822403980107140.0341120199005357
290.9800580664737190.03988386705256250.0199419335262813
300.9889360698793360.02212786024132820.0110639301206641
310.9870420745051960.02591585098960760.0129579254948038
320.9824199900006770.03516001999864570.0175800099993228
330.9752585906114020.04948281877719610.024741409388598
340.9670271770199920.06594564596001640.0329728229800082
350.9619150808417930.07616983831641460.0380849191582073
360.9630877139544890.07382457209102290.0369122860455115
370.9985539268075720.002892146384855970.00144607319242799
380.99783026734730.00433946530539930.00216973265269965
390.9968790610767280.00624187784654430.00312093892327215
400.9967003827248460.006599234550307190.00329961727515359
410.9957579084801380.008484183039723110.00424209151986155
420.9950808384146770.009838323170646670.00491916158532333
430.9934711371155740.01305772576885230.00652886288442614
440.991618017267760.01676396546447930.00838198273223964
450.9882219524237660.02355609515246790.011778047576234
460.9863415035274630.02731699294507380.0136584964725369
470.9822137915125440.03557241697491250.0177862084874562
480.9789685754783270.04206284904334540.0210314245216727
490.9733889913499610.05322201730007750.0266110086500388
500.969008291746580.06198341650683930.0309917082534196
510.9603098037245010.07938039255099830.0396901962754992
520.9882602964923930.02347940701521380.0117397035076069
530.9858516755639550.02829664887208950.0141483244360447
540.9816541982514220.03669160349715650.0183458017485782
550.976534727705750.04693054458850.02346527229425
560.9689697590569770.06206048188604550.0310302409430227
570.9782432285113890.04351354297722130.0217567714886106
580.9724479957740730.05510400845185450.0275520042259273
590.9715809957402480.0568380085195030.0284190042597515
600.9627021389755310.07459572204893810.0372978610244691
610.9523901450618290.09521970987634290.0476098549381714
620.9416077393672250.1167845212655510.0583922606327754
630.9476918998623950.1046162002752110.0523081001376053
640.9387879375081990.1224241249836020.0612120624918008
650.9228761558053690.1542476883892630.0771238441946315
660.9083033681488010.1833932637023980.0916966318511991
670.9640665897588420.07186682048231590.0359334102411579
680.9909577941775780.01808441164484420.00904220582242212
690.9903745900732530.01925081985349480.0096254099267474
700.9884975810674060.02300483786518730.0115024189325937
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740.9816856158486350.03662876830273020.0183143841513651
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860.9999476201514850.000104759697029345.23798485146699e-05
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880.9999264032186290.0001471935627419467.3596781370973e-05
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900.9999568243210918.63513578187509e-054.31756789093755e-05
910.9999537458500229.25082999556601e-054.625414997783e-05
920.9999972527345565.49453088829393e-062.74726544414696e-06
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940.9999958788007728.24239845524576e-064.12119922762288e-06
950.9999919387303211.61225393579491e-058.06126967897455e-06
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980.9999818083253183.63833493633457e-051.81916746816728e-05
990.9999715404681195.69190637625876e-052.84595318812938e-05
1000.9999601379618397.9724076321086e-053.9862038160543e-05
1010.9999280050504270.0001439898991460567.19949495730278e-05
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1330.8410673035768050.317865392846390.158932696423195
1340.7680219793870810.4639560412258370.231978020612918







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level510.408NOK
5% type I error level820.656NOK
10% type I error level970.776NOK

\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 & 51 & 0.408 & NOK \tabularnewline
5% type I error level & 82 & 0.656 & NOK \tabularnewline
10% type I error level & 97 & 0.776 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160286&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]51[/C][C]0.408[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]82[/C][C]0.656[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]97[/C][C]0.776[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160286&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160286&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level510.408NOK
5% type I error level820.656NOK
10% type I error level970.776NOK



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