Free Statistics

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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 computationThu, 24 Nov 2011 10:50:18 -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/Nov/24/t1322150796k1tsyt9csbr095v.htm/, Retrieved Thu, 28 Mar 2024 17:29:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147025, Retrieved Thu, 28 Mar 2024 17:29:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Regressie hall of...] [2011-11-24 15:50:18] [e8e105c2e7d07131df1852088351b05f] [Current]
- RMPD    [Classical Decomposition] [classical decompo...] [2011-12-01 22:38:29] [d67ce207bd02ca41b9162077ae11c874]
- RMPD    [Decomposition by Loess] [] [2011-12-01 22:45:54] [d67ce207bd02ca41b9162077ae11c874]
- RMPD    [Structural Time Series Models] [STSM] [2011-12-01 22:50:09] [d67ce207bd02ca41b9162077ae11c874]
- RMPD    [Exponential Smoothing] [Exponential Smoot...] [2011-12-01 22:52:17] [d67ce207bd02ca41b9162077ae11c874]
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Dataseries X:
1	893	63047	52	257
2	546	66751	25	160
3	186	7176	17	70
4	1405	78306	66	360
5	2047	137944	85	721
6	3626	261308	130	938
7	845	69266	36	287
8	663	83529	33	154
9	1181	73226	33	311
10	1836	178519	65	617
11	855	66476	35	262
12	1245	98606	46	385
13	993	50001	69	369
14	1685	91093	61	558
15	742	73884	25	220
16	868	72961	41	315
17	949	69388	34	229
18	332	15629	21	88
19	1602	71693	54	494
20	525	19920	17	155
21	629	39403	38	234
22	1279	99933	51	361
23	767	56088	28	280
24	1156	62006	32	331
25	1120	81665	51	378
26	624	65638	14	227
27	1203	88794	98	396
28	745	90642	53	179
29	1568	207062	62	524
30	1235	99340	26	504
31	758	56695	29	225
32	1088	108143	24	366
33	1105	58313	60	341
34	592	29101	40	171
35	1305	113060	29	437
36	0	0	0	0
37	706	65773	34	313
38	1188	67047	34	366
39	1111	41953	22	232
40	1095	109835	36	389
41	1087	86584	35	349
42	748	59588	26	316
43	404	40064	12	140
44	1077	70227	46	419
45	673	60437	29	226
46	537	53696	38	167
47	354	40295	13	103
48	1012	103397	55	356
49	891	78982	40	293
50	1198	67317	29	460
51	518	39887	21	156
52	697	49791	36	189
53	1095	129283	44	442
54	928	104816	44	321
55	1009	101395	34	367
56	951	72824	30	309
57	779	76018	27	235
58	439	33891	12	137
59	580	63694	39	198
60	614	28266	24	220
61	500	35093	22	149
62	824	35252	35	306
63	541	36977	20	178
64	476	42406	19	145
65	434	56353	13	144
66	818	58817	23	270
67	1173	76053	43	301
68	1720	70872	49	501
69	549	42372	20	153
70	157	19144	12	40
71	1594	114177	73	500
72	668	59414	28	209
73	656	51379	33	242
74	920	40756	39	265
75	847	46956	22	298
76	497	17799	20	141
77	864	71154	30	234
78	994	58305	38	336
79	443	27454	16	124
80	615	34323	34	241
81	525	44761	33	127
82	899	113862	27	327
83	556	35027	16	175
84	896	62396	22	331
85	516	29613	26	176
86	894	65559	28	281
87	1353	114480	114	293
88	557	31095	30	152
89	472	40181	25	155
90	639	53398	22	194
91	795	56435	23	300
92	1244	77283	46	370
93	559	71738	30	187
94	584	48503	31	212
95	440	25214	23	185
96	1319	119424	65	449
97	765	79201	27	234
98	222	19349	11	67
99	965	78760	54	316
100	822	54133	36	336
101	317	21623	16	116
102	425	25497	11	141
103	711	69535	38	236
104	364	30709	22	98
105	427	37043	23	97
106	463	24716	13	152
107	546	54865	17	132
108	369	27246	19	97
109	0	0	0	0
110	596	38814	13	165
111	479	27646	32	153
112	713	65373	18	226
113	639	43021	34	182
114	477	43116	40	172
115	38	3058	4	1
116	0	0	0	0
117	593	96347	25	196
118	724	53063	35	274
119	1056	73073	50	304
120	495	45266	22	183
121	778	43410	19	292
122	875	83842	34	257
123	490	39296	21	141
124	713	38490	25	192
125	485	39841	19	129
126	285	19764	12	75
127	935	59975	21	301
128	554	64589	16	204
129	753	63339	14	257
130	256	11796	9	79
131	80	7627	8	25
132	618	68998	30	217
133	41	6836	3	11
134	550	35414	17	228
135	42	5118	3	6
136	347	20898	13	115
137	0	0	0	0
138	441	42690	18	167
139	281	14507	11	75
140	81	7131	4	27
141	61	4194	11	14
142	314	21416	10	96
143	401	37679	16	109
144	554	42419	10	228




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 8 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147025&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147025&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147025&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 time8 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
HOF[t] = + 108.897508244835 -0.0161534809472837total_pageviews[t] -6.39315458098368e-06number_logins[t] -0.371092106793346total_time[t] -0.0528702119627776total_coursecompendiumviews[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
HOF[t] =  +  108.897508244835 -0.0161534809472837total_pageviews[t] -6.39315458098368e-06number_logins[t] -0.371092106793346total_time[t] -0.0528702119627776total_coursecompendiumviews[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147025&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]HOF[t] =  +  108.897508244835 -0.0161534809472837total_pageviews[t] -6.39315458098368e-06number_logins[t] -0.371092106793346total_time[t] -0.0528702119627776total_coursecompendiumviews[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147025&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147025&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
HOF[t] = + 108.897508244835 -0.0161534809472837total_pageviews[t] -6.39315458098368e-06number_logins[t] -0.371092106793346total_time[t] -0.0528702119627776total_coursecompendiumviews[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)108.8975082448355.91644118.405900
total_pageviews-0.01615348094728370.027389-0.58980.5562950.278148
number_logins-6.39315458098368e-060.000166-0.03850.9693770.484688
total_time-0.3710921067933460.275574-1.34660.1802970.090148
total_coursecompendiumviews-0.05287021196277760.077083-0.68590.4939250.246963

\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) & 108.897508244835 & 5.916441 & 18.4059 & 0 & 0 \tabularnewline
total_pageviews & -0.0161534809472837 & 0.027389 & -0.5898 & 0.556295 & 0.278148 \tabularnewline
number_logins & -6.39315458098368e-06 & 0.000166 & -0.0385 & 0.969377 & 0.484688 \tabularnewline
total_time & -0.371092106793346 & 0.275574 & -1.3466 & 0.180297 & 0.090148 \tabularnewline
total_coursecompendiumviews & -0.0528702119627776 & 0.077083 & -0.6859 & 0.493925 & 0.246963 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147025&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]108.897508244835[/C][C]5.916441[/C][C]18.4059[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]total_pageviews[/C][C]-0.0161534809472837[/C][C]0.027389[/C][C]-0.5898[/C][C]0.556295[/C][C]0.278148[/C][/ROW]
[ROW][C]number_logins[/C][C]-6.39315458098368e-06[/C][C]0.000166[/C][C]-0.0385[/C][C]0.969377[/C][C]0.484688[/C][/ROW]
[ROW][C]total_time[/C][C]-0.371092106793346[/C][C]0.275574[/C][C]-1.3466[/C][C]0.180297[/C][C]0.090148[/C][/ROW]
[ROW][C]total_coursecompendiumviews[/C][C]-0.0528702119627776[/C][C]0.077083[/C][C]-0.6859[/C][C]0.493925[/C][C]0.246963[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147025&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147025&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)108.8975082448355.91644118.405900
total_pageviews-0.01615348094728370.027389-0.58980.5562950.278148
number_logins-6.39315458098368e-060.000166-0.03850.9693770.484688
total_time-0.3710921067933460.275574-1.34660.1802970.090148
total_coursecompendiumviews-0.05287021196277760.077083-0.68590.4939250.246963







Multiple Linear Regression - Regression Statistics
Multiple R0.523014985732201
R-squared0.273544675300454
Adjusted R-squared0.252639486100467
F-TEST (value)13.0850131363855
F-TEST (DF numerator)4
F-TEST (DF denominator)139
p-value4.52163850805931e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation36.0611604664241
Sum Squared Residuals180756.613891741

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.523014985732201 \tabularnewline
R-squared & 0.273544675300454 \tabularnewline
Adjusted R-squared & 0.252639486100467 \tabularnewline
F-TEST (value) & 13.0850131363855 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 139 \tabularnewline
p-value & 4.52163850805931e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 36.0611604664241 \tabularnewline
Sum Squared Residuals & 180756.613891741 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147025&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.523014985732201[/C][/ROW]
[ROW][C]R-squared[/C][C]0.273544675300454[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.252639486100467[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.0850131363855[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]139[/C][/ROW]
[ROW][C]p-value[/C][C]4.52163850805931e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]36.0611604664241[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]180756.613891741[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147025&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147025&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 R0.523014985732201
R-squared0.273544675300454
Adjusted R-squared0.252639486100467
F-TEST (value)13.0850131363855
F-TEST (DF numerator)4
F-TEST (DF denominator)139
p-value4.52163850805931e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation36.0611604664241
Sum Squared Residuals180756.613891741







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1161.1849465143557-60.1849465143557
2281.9144216023049-79.9144216023049
3395.8376028584858-92.8376028584858
4442.1758897963222-38.1758897963222
555.28718352762902-0.287183527629016
66-49.179828811483755.1798288114837
7766.2719219212963-59.2719219212963
8877.2656844013428-69.2656844013428
9960.6634266641416-51.6634266641416
101021.3565099403783-11.3565099403783
111167.8210714189672-56.8210714189672
121250.7307525466911-38.7307525466911
131347.4229739589727-34.4229739589727
141428.9583244287924-14.9583244287924
151575.5305242472444-60.5305242472444
161662.5409426844075-46.5409426844075
171768.3998364453477-51.3998364453477
181890.989121062006-72.989121062006
191936.4044288594593-17.4044288594593
202085.7861304385405-65.7861304385405
212172.011929601602-51.011929601602
222249.5864750314944-27.5864750314944
232370.9549707643388-47.9549707643388
242460.4526827497602-36.4526827497602
252551.3728750466307-26.3728750466307
262681.2012746426861-55.2012746426861
272731.593566494381-4.59356649438104
282867.1520270201946-39.1520270201946
292931.5333690559636-2.53336905596364
303052.0178816929979-22.0178816929979
313173.6332409991931-42.6332409991931
323262.3744379169222-30.3744379169222
333350.3808390880977-17.3808390880977
343475.2641098152131-41.2641098152131
353553.228451826963-18.228451826963
3636108.897508244835-72.8975082448351
373767.9071457644746-30.9071457644746
383857.3109018349204-19.3109018349204
393970.2528633734488-31.2528633734488
404056.5814261760761-16.5814261760761
414159.3452018461213-18.3452018461213
424270.0783674442305-28.0783674442305
434390.2604316406909-47.2604316406909
444451.828381472954-7.82838147295403
454574.9294934833074-29.9294934833074
464676.9489766918322-30.9489766918322
474792.6517346051763-45.6517346051763
484852.6572911895911-4.65729118959112
494963.6651562088623-14.6651562088623
505054.0333014831764-4.03330148317637
515184.2343140485168-33.2343140485168
525273.9684245593111-21.9684245593111
535350.68623401741122.31376598258884
545459.9375822962366-5.93758229623656
555559.9299126389738-4.92991263897382
565665.600314074464-9.60031407446404
575773.3839650672908-16.3839650672908
585889.8931353866527-31.8931353866527
595974.1803895739589-15.1803895739589
606078.2609048409654-18.2609048409654
616184.5547248655753-23.5547248655753
626266.1951598606074-4.19515986060739
636383.0893355101722-20.0893355101722
646486.2204124370905-22.2204124370905
656589.0891161626583-24.0891161626583
666672.4978589707704-6.49785897077036
676757.59236211541389.40763788458622
686835.988935937818132.0110640621819
696984.247371892699-15.247371892699
707099.6711074247819-29.6711074247819
717128.894078626968842.1059213730312
727276.2866867953408-4.28668679534077
737372.9317200350280.0682799649720102
747465.29254803015498.70745196984508
757570.99596340161984.00403659838024
767685.8788944330296-9.87889443302957
777770.98160938223636.01839061776371
787860.602304027750417.3976959722496
797989.0726185272441-10.0726185272441
808073.38483250356936.61516749643069
818181.1702103118585-0.170210311858532
828266.339545311078415.6604546889216
838384.5024790104576-1.50247901045759
848468.361015533700815.6389844662992
858581.41943950735413.58056049264586
868668.790058905034517.2099410949655
878728.514487907193958.4855120928061
888880.53218879335987.46781120664023
898983.54399636943455.45600363056553
909079.813204780972910.1867952190271
919171.298511167886519.7014888321135
929251.676280442210340.3237195577897
939378.389587431133114.6104125688669
949476.441447948277317.5585520517227
959583.312671959064511.6873280409355
969638.967858669833857.0321413301662
979773.642634601484223.3573653985158
989897.56341695031770.436583049682278
999956.059913528829642.9400864711704
10010064.149559205181735.8504407948183
10110191.56819730666589.43180269333423
10210290.332559518409711.6674404815903
10310370.38896520616532.611034793835
10410489.476006674190514.5239933258095
10510588.099621238565116.9003787614348
10610688.399963750963417.6000362490366
10710786.43951342695920.560486573041
10810890.583525296110917.4164747038891
109109108.8975082448350.102491755164938
11011085.474107336175924.5258926638241
11111181.019155871848229.9808441281518
11211278.333810809131233.6661891908688
11311376.06088380809336.939116191907
11411476.979289850735537.0207101492645
115115106.7268870629938.27311293700653
116116108.8975082448357.10249175516494
11711779.062668564143737.9373314358563
11811869.388486261902748.6115137380973
11911956.745115603455562.2548843965445
12012082.772867502024937.2271324979751
12112173.563721305283247.4362786947168
12212268.022421444175453.9775785558246
12312385.483443048839837.5165569511602
12412477.705620442912846.2943795570872
12512586.937352941469638.0626470585304
12612695.749040688992230.2509593110078
12712769.70370606967457.296293930326
12812882.812555389708645.1874446102914
12912977.546067103984851.4539328960152
13013097.170227764693632.8297722353064
131131103.26597702564727.734022974353
13213275.867942939911956.1320570600881
133133106.4966632693126.5033367306898
13413481.423712404497952.5762875955021
135135106.75584428774728.244155712253
13613692.254374447661343.7456255523387
137137108.89750824483528.1024917551649
13813885.991916057956752.0080839420433
13913996.218355533206942.7816444667931
140140104.6316225526235.3683774473803
141141103.06313687453237.9368631254676
14214294.901938012521547.0980619874785
14314390.478747900881152.5212520991189
14414483.911959180422460.0880408195776

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 61.1849465143557 & -60.1849465143557 \tabularnewline
2 & 2 & 81.9144216023049 & -79.9144216023049 \tabularnewline
3 & 3 & 95.8376028584858 & -92.8376028584858 \tabularnewline
4 & 4 & 42.1758897963222 & -38.1758897963222 \tabularnewline
5 & 5 & 5.28718352762902 & -0.287183527629016 \tabularnewline
6 & 6 & -49.1798288114837 & 55.1798288114837 \tabularnewline
7 & 7 & 66.2719219212963 & -59.2719219212963 \tabularnewline
8 & 8 & 77.2656844013428 & -69.2656844013428 \tabularnewline
9 & 9 & 60.6634266641416 & -51.6634266641416 \tabularnewline
10 & 10 & 21.3565099403783 & -11.3565099403783 \tabularnewline
11 & 11 & 67.8210714189672 & -56.8210714189672 \tabularnewline
12 & 12 & 50.7307525466911 & -38.7307525466911 \tabularnewline
13 & 13 & 47.4229739589727 & -34.4229739589727 \tabularnewline
14 & 14 & 28.9583244287924 & -14.9583244287924 \tabularnewline
15 & 15 & 75.5305242472444 & -60.5305242472444 \tabularnewline
16 & 16 & 62.5409426844075 & -46.5409426844075 \tabularnewline
17 & 17 & 68.3998364453477 & -51.3998364453477 \tabularnewline
18 & 18 & 90.989121062006 & -72.989121062006 \tabularnewline
19 & 19 & 36.4044288594593 & -17.4044288594593 \tabularnewline
20 & 20 & 85.7861304385405 & -65.7861304385405 \tabularnewline
21 & 21 & 72.011929601602 & -51.011929601602 \tabularnewline
22 & 22 & 49.5864750314944 & -27.5864750314944 \tabularnewline
23 & 23 & 70.9549707643388 & -47.9549707643388 \tabularnewline
24 & 24 & 60.4526827497602 & -36.4526827497602 \tabularnewline
25 & 25 & 51.3728750466307 & -26.3728750466307 \tabularnewline
26 & 26 & 81.2012746426861 & -55.2012746426861 \tabularnewline
27 & 27 & 31.593566494381 & -4.59356649438104 \tabularnewline
28 & 28 & 67.1520270201946 & -39.1520270201946 \tabularnewline
29 & 29 & 31.5333690559636 & -2.53336905596364 \tabularnewline
30 & 30 & 52.0178816929979 & -22.0178816929979 \tabularnewline
31 & 31 & 73.6332409991931 & -42.6332409991931 \tabularnewline
32 & 32 & 62.3744379169222 & -30.3744379169222 \tabularnewline
33 & 33 & 50.3808390880977 & -17.3808390880977 \tabularnewline
34 & 34 & 75.2641098152131 & -41.2641098152131 \tabularnewline
35 & 35 & 53.228451826963 & -18.228451826963 \tabularnewline
36 & 36 & 108.897508244835 & -72.8975082448351 \tabularnewline
37 & 37 & 67.9071457644746 & -30.9071457644746 \tabularnewline
38 & 38 & 57.3109018349204 & -19.3109018349204 \tabularnewline
39 & 39 & 70.2528633734488 & -31.2528633734488 \tabularnewline
40 & 40 & 56.5814261760761 & -16.5814261760761 \tabularnewline
41 & 41 & 59.3452018461213 & -18.3452018461213 \tabularnewline
42 & 42 & 70.0783674442305 & -28.0783674442305 \tabularnewline
43 & 43 & 90.2604316406909 & -47.2604316406909 \tabularnewline
44 & 44 & 51.828381472954 & -7.82838147295403 \tabularnewline
45 & 45 & 74.9294934833074 & -29.9294934833074 \tabularnewline
46 & 46 & 76.9489766918322 & -30.9489766918322 \tabularnewline
47 & 47 & 92.6517346051763 & -45.6517346051763 \tabularnewline
48 & 48 & 52.6572911895911 & -4.65729118959112 \tabularnewline
49 & 49 & 63.6651562088623 & -14.6651562088623 \tabularnewline
50 & 50 & 54.0333014831764 & -4.03330148317637 \tabularnewline
51 & 51 & 84.2343140485168 & -33.2343140485168 \tabularnewline
52 & 52 & 73.9684245593111 & -21.9684245593111 \tabularnewline
53 & 53 & 50.6862340174112 & 2.31376598258884 \tabularnewline
54 & 54 & 59.9375822962366 & -5.93758229623656 \tabularnewline
55 & 55 & 59.9299126389738 & -4.92991263897382 \tabularnewline
56 & 56 & 65.600314074464 & -9.60031407446404 \tabularnewline
57 & 57 & 73.3839650672908 & -16.3839650672908 \tabularnewline
58 & 58 & 89.8931353866527 & -31.8931353866527 \tabularnewline
59 & 59 & 74.1803895739589 & -15.1803895739589 \tabularnewline
60 & 60 & 78.2609048409654 & -18.2609048409654 \tabularnewline
61 & 61 & 84.5547248655753 & -23.5547248655753 \tabularnewline
62 & 62 & 66.1951598606074 & -4.19515986060739 \tabularnewline
63 & 63 & 83.0893355101722 & -20.0893355101722 \tabularnewline
64 & 64 & 86.2204124370905 & -22.2204124370905 \tabularnewline
65 & 65 & 89.0891161626583 & -24.0891161626583 \tabularnewline
66 & 66 & 72.4978589707704 & -6.49785897077036 \tabularnewline
67 & 67 & 57.5923621154138 & 9.40763788458622 \tabularnewline
68 & 68 & 35.9889359378181 & 32.0110640621819 \tabularnewline
69 & 69 & 84.247371892699 & -15.247371892699 \tabularnewline
70 & 70 & 99.6711074247819 & -29.6711074247819 \tabularnewline
71 & 71 & 28.8940786269688 & 42.1059213730312 \tabularnewline
72 & 72 & 76.2866867953408 & -4.28668679534077 \tabularnewline
73 & 73 & 72.931720035028 & 0.0682799649720102 \tabularnewline
74 & 74 & 65.2925480301549 & 8.70745196984508 \tabularnewline
75 & 75 & 70.9959634016198 & 4.00403659838024 \tabularnewline
76 & 76 & 85.8788944330296 & -9.87889443302957 \tabularnewline
77 & 77 & 70.9816093822363 & 6.01839061776371 \tabularnewline
78 & 78 & 60.6023040277504 & 17.3976959722496 \tabularnewline
79 & 79 & 89.0726185272441 & -10.0726185272441 \tabularnewline
80 & 80 & 73.3848325035693 & 6.61516749643069 \tabularnewline
81 & 81 & 81.1702103118585 & -0.170210311858532 \tabularnewline
82 & 82 & 66.3395453110784 & 15.6604546889216 \tabularnewline
83 & 83 & 84.5024790104576 & -1.50247901045759 \tabularnewline
84 & 84 & 68.3610155337008 & 15.6389844662992 \tabularnewline
85 & 85 & 81.4194395073541 & 3.58056049264586 \tabularnewline
86 & 86 & 68.7900589050345 & 17.2099410949655 \tabularnewline
87 & 87 & 28.5144879071939 & 58.4855120928061 \tabularnewline
88 & 88 & 80.5321887933598 & 7.46781120664023 \tabularnewline
89 & 89 & 83.5439963694345 & 5.45600363056553 \tabularnewline
90 & 90 & 79.8132047809729 & 10.1867952190271 \tabularnewline
91 & 91 & 71.2985111678865 & 19.7014888321135 \tabularnewline
92 & 92 & 51.6762804422103 & 40.3237195577897 \tabularnewline
93 & 93 & 78.3895874311331 & 14.6104125688669 \tabularnewline
94 & 94 & 76.4414479482773 & 17.5585520517227 \tabularnewline
95 & 95 & 83.3126719590645 & 11.6873280409355 \tabularnewline
96 & 96 & 38.9678586698338 & 57.0321413301662 \tabularnewline
97 & 97 & 73.6426346014842 & 23.3573653985158 \tabularnewline
98 & 98 & 97.5634169503177 & 0.436583049682278 \tabularnewline
99 & 99 & 56.0599135288296 & 42.9400864711704 \tabularnewline
100 & 100 & 64.1495592051817 & 35.8504407948183 \tabularnewline
101 & 101 & 91.5681973066658 & 9.43180269333423 \tabularnewline
102 & 102 & 90.3325595184097 & 11.6674404815903 \tabularnewline
103 & 103 & 70.388965206165 & 32.611034793835 \tabularnewline
104 & 104 & 89.4760066741905 & 14.5239933258095 \tabularnewline
105 & 105 & 88.0996212385651 & 16.9003787614348 \tabularnewline
106 & 106 & 88.3999637509634 & 17.6000362490366 \tabularnewline
107 & 107 & 86.439513426959 & 20.560486573041 \tabularnewline
108 & 108 & 90.5835252961109 & 17.4164747038891 \tabularnewline
109 & 109 & 108.897508244835 & 0.102491755164938 \tabularnewline
110 & 110 & 85.4741073361759 & 24.5258926638241 \tabularnewline
111 & 111 & 81.0191558718482 & 29.9808441281518 \tabularnewline
112 & 112 & 78.3338108091312 & 33.6661891908688 \tabularnewline
113 & 113 & 76.060883808093 & 36.939116191907 \tabularnewline
114 & 114 & 76.9792898507355 & 37.0207101492645 \tabularnewline
115 & 115 & 106.726887062993 & 8.27311293700653 \tabularnewline
116 & 116 & 108.897508244835 & 7.10249175516494 \tabularnewline
117 & 117 & 79.0626685641437 & 37.9373314358563 \tabularnewline
118 & 118 & 69.3884862619027 & 48.6115137380973 \tabularnewline
119 & 119 & 56.7451156034555 & 62.2548843965445 \tabularnewline
120 & 120 & 82.7728675020249 & 37.2271324979751 \tabularnewline
121 & 121 & 73.5637213052832 & 47.4362786947168 \tabularnewline
122 & 122 & 68.0224214441754 & 53.9775785558246 \tabularnewline
123 & 123 & 85.4834430488398 & 37.5165569511602 \tabularnewline
124 & 124 & 77.7056204429128 & 46.2943795570872 \tabularnewline
125 & 125 & 86.9373529414696 & 38.0626470585304 \tabularnewline
126 & 126 & 95.7490406889922 & 30.2509593110078 \tabularnewline
127 & 127 & 69.703706069674 & 57.296293930326 \tabularnewline
128 & 128 & 82.8125553897086 & 45.1874446102914 \tabularnewline
129 & 129 & 77.5460671039848 & 51.4539328960152 \tabularnewline
130 & 130 & 97.1702277646936 & 32.8297722353064 \tabularnewline
131 & 131 & 103.265977025647 & 27.734022974353 \tabularnewline
132 & 132 & 75.8679429399119 & 56.1320570600881 \tabularnewline
133 & 133 & 106.49666326931 & 26.5033367306898 \tabularnewline
134 & 134 & 81.4237124044979 & 52.5762875955021 \tabularnewline
135 & 135 & 106.755844287747 & 28.244155712253 \tabularnewline
136 & 136 & 92.2543744476613 & 43.7456255523387 \tabularnewline
137 & 137 & 108.897508244835 & 28.1024917551649 \tabularnewline
138 & 138 & 85.9919160579567 & 52.0080839420433 \tabularnewline
139 & 139 & 96.2183555332069 & 42.7816444667931 \tabularnewline
140 & 140 & 104.63162255262 & 35.3683774473803 \tabularnewline
141 & 141 & 103.063136874532 & 37.9368631254676 \tabularnewline
142 & 142 & 94.9019380125215 & 47.0980619874785 \tabularnewline
143 & 143 & 90.4787479008811 & 52.5212520991189 \tabularnewline
144 & 144 & 83.9119591804224 & 60.0880408195776 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147025&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1[/C][C]61.1849465143557[/C][C]-60.1849465143557[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]81.9144216023049[/C][C]-79.9144216023049[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]95.8376028584858[/C][C]-92.8376028584858[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]42.1758897963222[/C][C]-38.1758897963222[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]5.28718352762902[/C][C]-0.287183527629016[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]-49.1798288114837[/C][C]55.1798288114837[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]66.2719219212963[/C][C]-59.2719219212963[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]77.2656844013428[/C][C]-69.2656844013428[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]60.6634266641416[/C][C]-51.6634266641416[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]21.3565099403783[/C][C]-11.3565099403783[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]67.8210714189672[/C][C]-56.8210714189672[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]50.7307525466911[/C][C]-38.7307525466911[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]47.4229739589727[/C][C]-34.4229739589727[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]28.9583244287924[/C][C]-14.9583244287924[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]75.5305242472444[/C][C]-60.5305242472444[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]62.5409426844075[/C][C]-46.5409426844075[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]68.3998364453477[/C][C]-51.3998364453477[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]90.989121062006[/C][C]-72.989121062006[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]36.4044288594593[/C][C]-17.4044288594593[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]85.7861304385405[/C][C]-65.7861304385405[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]72.011929601602[/C][C]-51.011929601602[/C][/ROW]
[ROW][C]22[/C][C]22[/C][C]49.5864750314944[/C][C]-27.5864750314944[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]70.9549707643388[/C][C]-47.9549707643388[/C][/ROW]
[ROW][C]24[/C][C]24[/C][C]60.4526827497602[/C][C]-36.4526827497602[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]51.3728750466307[/C][C]-26.3728750466307[/C][/ROW]
[ROW][C]26[/C][C]26[/C][C]81.2012746426861[/C][C]-55.2012746426861[/C][/ROW]
[ROW][C]27[/C][C]27[/C][C]31.593566494381[/C][C]-4.59356649438104[/C][/ROW]
[ROW][C]28[/C][C]28[/C][C]67.1520270201946[/C][C]-39.1520270201946[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]31.5333690559636[/C][C]-2.53336905596364[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]52.0178816929979[/C][C]-22.0178816929979[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]73.6332409991931[/C][C]-42.6332409991931[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]62.3744379169222[/C][C]-30.3744379169222[/C][/ROW]
[ROW][C]33[/C][C]33[/C][C]50.3808390880977[/C][C]-17.3808390880977[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]75.2641098152131[/C][C]-41.2641098152131[/C][/ROW]
[ROW][C]35[/C][C]35[/C][C]53.228451826963[/C][C]-18.228451826963[/C][/ROW]
[ROW][C]36[/C][C]36[/C][C]108.897508244835[/C][C]-72.8975082448351[/C][/ROW]
[ROW][C]37[/C][C]37[/C][C]67.9071457644746[/C][C]-30.9071457644746[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]57.3109018349204[/C][C]-19.3109018349204[/C][/ROW]
[ROW][C]39[/C][C]39[/C][C]70.2528633734488[/C][C]-31.2528633734488[/C][/ROW]
[ROW][C]40[/C][C]40[/C][C]56.5814261760761[/C][C]-16.5814261760761[/C][/ROW]
[ROW][C]41[/C][C]41[/C][C]59.3452018461213[/C][C]-18.3452018461213[/C][/ROW]
[ROW][C]42[/C][C]42[/C][C]70.0783674442305[/C][C]-28.0783674442305[/C][/ROW]
[ROW][C]43[/C][C]43[/C][C]90.2604316406909[/C][C]-47.2604316406909[/C][/ROW]
[ROW][C]44[/C][C]44[/C][C]51.828381472954[/C][C]-7.82838147295403[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]74.9294934833074[/C][C]-29.9294934833074[/C][/ROW]
[ROW][C]46[/C][C]46[/C][C]76.9489766918322[/C][C]-30.9489766918322[/C][/ROW]
[ROW][C]47[/C][C]47[/C][C]92.6517346051763[/C][C]-45.6517346051763[/C][/ROW]
[ROW][C]48[/C][C]48[/C][C]52.6572911895911[/C][C]-4.65729118959112[/C][/ROW]
[ROW][C]49[/C][C]49[/C][C]63.6651562088623[/C][C]-14.6651562088623[/C][/ROW]
[ROW][C]50[/C][C]50[/C][C]54.0333014831764[/C][C]-4.03330148317637[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]84.2343140485168[/C][C]-33.2343140485168[/C][/ROW]
[ROW][C]52[/C][C]52[/C][C]73.9684245593111[/C][C]-21.9684245593111[/C][/ROW]
[ROW][C]53[/C][C]53[/C][C]50.6862340174112[/C][C]2.31376598258884[/C][/ROW]
[ROW][C]54[/C][C]54[/C][C]59.9375822962366[/C][C]-5.93758229623656[/C][/ROW]
[ROW][C]55[/C][C]55[/C][C]59.9299126389738[/C][C]-4.92991263897382[/C][/ROW]
[ROW][C]56[/C][C]56[/C][C]65.600314074464[/C][C]-9.60031407446404[/C][/ROW]
[ROW][C]57[/C][C]57[/C][C]73.3839650672908[/C][C]-16.3839650672908[/C][/ROW]
[ROW][C]58[/C][C]58[/C][C]89.8931353866527[/C][C]-31.8931353866527[/C][/ROW]
[ROW][C]59[/C][C]59[/C][C]74.1803895739589[/C][C]-15.1803895739589[/C][/ROW]
[ROW][C]60[/C][C]60[/C][C]78.2609048409654[/C][C]-18.2609048409654[/C][/ROW]
[ROW][C]61[/C][C]61[/C][C]84.5547248655753[/C][C]-23.5547248655753[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]66.1951598606074[/C][C]-4.19515986060739[/C][/ROW]
[ROW][C]63[/C][C]63[/C][C]83.0893355101722[/C][C]-20.0893355101722[/C][/ROW]
[ROW][C]64[/C][C]64[/C][C]86.2204124370905[/C][C]-22.2204124370905[/C][/ROW]
[ROW][C]65[/C][C]65[/C][C]89.0891161626583[/C][C]-24.0891161626583[/C][/ROW]
[ROW][C]66[/C][C]66[/C][C]72.4978589707704[/C][C]-6.49785897077036[/C][/ROW]
[ROW][C]67[/C][C]67[/C][C]57.5923621154138[/C][C]9.40763788458622[/C][/ROW]
[ROW][C]68[/C][C]68[/C][C]35.9889359378181[/C][C]32.0110640621819[/C][/ROW]
[ROW][C]69[/C][C]69[/C][C]84.247371892699[/C][C]-15.247371892699[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]99.6711074247819[/C][C]-29.6711074247819[/C][/ROW]
[ROW][C]71[/C][C]71[/C][C]28.8940786269688[/C][C]42.1059213730312[/C][/ROW]
[ROW][C]72[/C][C]72[/C][C]76.2866867953408[/C][C]-4.28668679534077[/C][/ROW]
[ROW][C]73[/C][C]73[/C][C]72.931720035028[/C][C]0.0682799649720102[/C][/ROW]
[ROW][C]74[/C][C]74[/C][C]65.2925480301549[/C][C]8.70745196984508[/C][/ROW]
[ROW][C]75[/C][C]75[/C][C]70.9959634016198[/C][C]4.00403659838024[/C][/ROW]
[ROW][C]76[/C][C]76[/C][C]85.8788944330296[/C][C]-9.87889443302957[/C][/ROW]
[ROW][C]77[/C][C]77[/C][C]70.9816093822363[/C][C]6.01839061776371[/C][/ROW]
[ROW][C]78[/C][C]78[/C][C]60.6023040277504[/C][C]17.3976959722496[/C][/ROW]
[ROW][C]79[/C][C]79[/C][C]89.0726185272441[/C][C]-10.0726185272441[/C][/ROW]
[ROW][C]80[/C][C]80[/C][C]73.3848325035693[/C][C]6.61516749643069[/C][/ROW]
[ROW][C]81[/C][C]81[/C][C]81.1702103118585[/C][C]-0.170210311858532[/C][/ROW]
[ROW][C]82[/C][C]82[/C][C]66.3395453110784[/C][C]15.6604546889216[/C][/ROW]
[ROW][C]83[/C][C]83[/C][C]84.5024790104576[/C][C]-1.50247901045759[/C][/ROW]
[ROW][C]84[/C][C]84[/C][C]68.3610155337008[/C][C]15.6389844662992[/C][/ROW]
[ROW][C]85[/C][C]85[/C][C]81.4194395073541[/C][C]3.58056049264586[/C][/ROW]
[ROW][C]86[/C][C]86[/C][C]68.7900589050345[/C][C]17.2099410949655[/C][/ROW]
[ROW][C]87[/C][C]87[/C][C]28.5144879071939[/C][C]58.4855120928061[/C][/ROW]
[ROW][C]88[/C][C]88[/C][C]80.5321887933598[/C][C]7.46781120664023[/C][/ROW]
[ROW][C]89[/C][C]89[/C][C]83.5439963694345[/C][C]5.45600363056553[/C][/ROW]
[ROW][C]90[/C][C]90[/C][C]79.8132047809729[/C][C]10.1867952190271[/C][/ROW]
[ROW][C]91[/C][C]91[/C][C]71.2985111678865[/C][C]19.7014888321135[/C][/ROW]
[ROW][C]92[/C][C]92[/C][C]51.6762804422103[/C][C]40.3237195577897[/C][/ROW]
[ROW][C]93[/C][C]93[/C][C]78.3895874311331[/C][C]14.6104125688669[/C][/ROW]
[ROW][C]94[/C][C]94[/C][C]76.4414479482773[/C][C]17.5585520517227[/C][/ROW]
[ROW][C]95[/C][C]95[/C][C]83.3126719590645[/C][C]11.6873280409355[/C][/ROW]
[ROW][C]96[/C][C]96[/C][C]38.9678586698338[/C][C]57.0321413301662[/C][/ROW]
[ROW][C]97[/C][C]97[/C][C]73.6426346014842[/C][C]23.3573653985158[/C][/ROW]
[ROW][C]98[/C][C]98[/C][C]97.5634169503177[/C][C]0.436583049682278[/C][/ROW]
[ROW][C]99[/C][C]99[/C][C]56.0599135288296[/C][C]42.9400864711704[/C][/ROW]
[ROW][C]100[/C][C]100[/C][C]64.1495592051817[/C][C]35.8504407948183[/C][/ROW]
[ROW][C]101[/C][C]101[/C][C]91.5681973066658[/C][C]9.43180269333423[/C][/ROW]
[ROW][C]102[/C][C]102[/C][C]90.3325595184097[/C][C]11.6674404815903[/C][/ROW]
[ROW][C]103[/C][C]103[/C][C]70.388965206165[/C][C]32.611034793835[/C][/ROW]
[ROW][C]104[/C][C]104[/C][C]89.4760066741905[/C][C]14.5239933258095[/C][/ROW]
[ROW][C]105[/C][C]105[/C][C]88.0996212385651[/C][C]16.9003787614348[/C][/ROW]
[ROW][C]106[/C][C]106[/C][C]88.3999637509634[/C][C]17.6000362490366[/C][/ROW]
[ROW][C]107[/C][C]107[/C][C]86.439513426959[/C][C]20.560486573041[/C][/ROW]
[ROW][C]108[/C][C]108[/C][C]90.5835252961109[/C][C]17.4164747038891[/C][/ROW]
[ROW][C]109[/C][C]109[/C][C]108.897508244835[/C][C]0.102491755164938[/C][/ROW]
[ROW][C]110[/C][C]110[/C][C]85.4741073361759[/C][C]24.5258926638241[/C][/ROW]
[ROW][C]111[/C][C]111[/C][C]81.0191558718482[/C][C]29.9808441281518[/C][/ROW]
[ROW][C]112[/C][C]112[/C][C]78.3338108091312[/C][C]33.6661891908688[/C][/ROW]
[ROW][C]113[/C][C]113[/C][C]76.060883808093[/C][C]36.939116191907[/C][/ROW]
[ROW][C]114[/C][C]114[/C][C]76.9792898507355[/C][C]37.0207101492645[/C][/ROW]
[ROW][C]115[/C][C]115[/C][C]106.726887062993[/C][C]8.27311293700653[/C][/ROW]
[ROW][C]116[/C][C]116[/C][C]108.897508244835[/C][C]7.10249175516494[/C][/ROW]
[ROW][C]117[/C][C]117[/C][C]79.0626685641437[/C][C]37.9373314358563[/C][/ROW]
[ROW][C]118[/C][C]118[/C][C]69.3884862619027[/C][C]48.6115137380973[/C][/ROW]
[ROW][C]119[/C][C]119[/C][C]56.7451156034555[/C][C]62.2548843965445[/C][/ROW]
[ROW][C]120[/C][C]120[/C][C]82.7728675020249[/C][C]37.2271324979751[/C][/ROW]
[ROW][C]121[/C][C]121[/C][C]73.5637213052832[/C][C]47.4362786947168[/C][/ROW]
[ROW][C]122[/C][C]122[/C][C]68.0224214441754[/C][C]53.9775785558246[/C][/ROW]
[ROW][C]123[/C][C]123[/C][C]85.4834430488398[/C][C]37.5165569511602[/C][/ROW]
[ROW][C]124[/C][C]124[/C][C]77.7056204429128[/C][C]46.2943795570872[/C][/ROW]
[ROW][C]125[/C][C]125[/C][C]86.9373529414696[/C][C]38.0626470585304[/C][/ROW]
[ROW][C]126[/C][C]126[/C][C]95.7490406889922[/C][C]30.2509593110078[/C][/ROW]
[ROW][C]127[/C][C]127[/C][C]69.703706069674[/C][C]57.296293930326[/C][/ROW]
[ROW][C]128[/C][C]128[/C][C]82.8125553897086[/C][C]45.1874446102914[/C][/ROW]
[ROW][C]129[/C][C]129[/C][C]77.5460671039848[/C][C]51.4539328960152[/C][/ROW]
[ROW][C]130[/C][C]130[/C][C]97.1702277646936[/C][C]32.8297722353064[/C][/ROW]
[ROW][C]131[/C][C]131[/C][C]103.265977025647[/C][C]27.734022974353[/C][/ROW]
[ROW][C]132[/C][C]132[/C][C]75.8679429399119[/C][C]56.1320570600881[/C][/ROW]
[ROW][C]133[/C][C]133[/C][C]106.49666326931[/C][C]26.5033367306898[/C][/ROW]
[ROW][C]134[/C][C]134[/C][C]81.4237124044979[/C][C]52.5762875955021[/C][/ROW]
[ROW][C]135[/C][C]135[/C][C]106.755844287747[/C][C]28.244155712253[/C][/ROW]
[ROW][C]136[/C][C]136[/C][C]92.2543744476613[/C][C]43.7456255523387[/C][/ROW]
[ROW][C]137[/C][C]137[/C][C]108.897508244835[/C][C]28.1024917551649[/C][/ROW]
[ROW][C]138[/C][C]138[/C][C]85.9919160579567[/C][C]52.0080839420433[/C][/ROW]
[ROW][C]139[/C][C]139[/C][C]96.2183555332069[/C][C]42.7816444667931[/C][/ROW]
[ROW][C]140[/C][C]140[/C][C]104.63162255262[/C][C]35.3683774473803[/C][/ROW]
[ROW][C]141[/C][C]141[/C][C]103.063136874532[/C][C]37.9368631254676[/C][/ROW]
[ROW][C]142[/C][C]142[/C][C]94.9019380125215[/C][C]47.0980619874785[/C][/ROW]
[ROW][C]143[/C][C]143[/C][C]90.4787479008811[/C][C]52.5212520991189[/C][/ROW]
[ROW][C]144[/C][C]144[/C][C]83.9119591804224[/C][C]60.0880408195776[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147025&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147025&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
1161.1849465143557-60.1849465143557
2281.9144216023049-79.9144216023049
3395.8376028584858-92.8376028584858
4442.1758897963222-38.1758897963222
555.28718352762902-0.287183527629016
66-49.179828811483755.1798288114837
7766.2719219212963-59.2719219212963
8877.2656844013428-69.2656844013428
9960.6634266641416-51.6634266641416
101021.3565099403783-11.3565099403783
111167.8210714189672-56.8210714189672
121250.7307525466911-38.7307525466911
131347.4229739589727-34.4229739589727
141428.9583244287924-14.9583244287924
151575.5305242472444-60.5305242472444
161662.5409426844075-46.5409426844075
171768.3998364453477-51.3998364453477
181890.989121062006-72.989121062006
191936.4044288594593-17.4044288594593
202085.7861304385405-65.7861304385405
212172.011929601602-51.011929601602
222249.5864750314944-27.5864750314944
232370.9549707643388-47.9549707643388
242460.4526827497602-36.4526827497602
252551.3728750466307-26.3728750466307
262681.2012746426861-55.2012746426861
272731.593566494381-4.59356649438104
282867.1520270201946-39.1520270201946
292931.5333690559636-2.53336905596364
303052.0178816929979-22.0178816929979
313173.6332409991931-42.6332409991931
323262.3744379169222-30.3744379169222
333350.3808390880977-17.3808390880977
343475.2641098152131-41.2641098152131
353553.228451826963-18.228451826963
3636108.897508244835-72.8975082448351
373767.9071457644746-30.9071457644746
383857.3109018349204-19.3109018349204
393970.2528633734488-31.2528633734488
404056.5814261760761-16.5814261760761
414159.3452018461213-18.3452018461213
424270.0783674442305-28.0783674442305
434390.2604316406909-47.2604316406909
444451.828381472954-7.82838147295403
454574.9294934833074-29.9294934833074
464676.9489766918322-30.9489766918322
474792.6517346051763-45.6517346051763
484852.6572911895911-4.65729118959112
494963.6651562088623-14.6651562088623
505054.0333014831764-4.03330148317637
515184.2343140485168-33.2343140485168
525273.9684245593111-21.9684245593111
535350.68623401741122.31376598258884
545459.9375822962366-5.93758229623656
555559.9299126389738-4.92991263897382
565665.600314074464-9.60031407446404
575773.3839650672908-16.3839650672908
585889.8931353866527-31.8931353866527
595974.1803895739589-15.1803895739589
606078.2609048409654-18.2609048409654
616184.5547248655753-23.5547248655753
626266.1951598606074-4.19515986060739
636383.0893355101722-20.0893355101722
646486.2204124370905-22.2204124370905
656589.0891161626583-24.0891161626583
666672.4978589707704-6.49785897077036
676757.59236211541389.40763788458622
686835.988935937818132.0110640621819
696984.247371892699-15.247371892699
707099.6711074247819-29.6711074247819
717128.894078626968842.1059213730312
727276.2866867953408-4.28668679534077
737372.9317200350280.0682799649720102
747465.29254803015498.70745196984508
757570.99596340161984.00403659838024
767685.8788944330296-9.87889443302957
777770.98160938223636.01839061776371
787860.602304027750417.3976959722496
797989.0726185272441-10.0726185272441
808073.38483250356936.61516749643069
818181.1702103118585-0.170210311858532
828266.339545311078415.6604546889216
838384.5024790104576-1.50247901045759
848468.361015533700815.6389844662992
858581.41943950735413.58056049264586
868668.790058905034517.2099410949655
878728.514487907193958.4855120928061
888880.53218879335987.46781120664023
898983.54399636943455.45600363056553
909079.813204780972910.1867952190271
919171.298511167886519.7014888321135
929251.676280442210340.3237195577897
939378.389587431133114.6104125688669
949476.441447948277317.5585520517227
959583.312671959064511.6873280409355
969638.967858669833857.0321413301662
979773.642634601484223.3573653985158
989897.56341695031770.436583049682278
999956.059913528829642.9400864711704
10010064.149559205181735.8504407948183
10110191.56819730666589.43180269333423
10210290.332559518409711.6674404815903
10310370.38896520616532.611034793835
10410489.476006674190514.5239933258095
10510588.099621238565116.9003787614348
10610688.399963750963417.6000362490366
10710786.43951342695920.560486573041
10810890.583525296110917.4164747038891
109109108.8975082448350.102491755164938
11011085.474107336175924.5258926638241
11111181.019155871848229.9808441281518
11211278.333810809131233.6661891908688
11311376.06088380809336.939116191907
11411476.979289850735537.0207101492645
115115106.7268870629938.27311293700653
116116108.8975082448357.10249175516494
11711779.062668564143737.9373314358563
11811869.388486261902748.6115137380973
11911956.745115603455562.2548843965445
12012082.772867502024937.2271324979751
12112173.563721305283247.4362786947168
12212268.022421444175453.9775785558246
12312385.483443048839837.5165569511602
12412477.705620442912846.2943795570872
12512586.937352941469638.0626470585304
12612695.749040688992230.2509593110078
12712769.70370606967457.296293930326
12812882.812555389708645.1874446102914
12912977.546067103984851.4539328960152
13013097.170227764693632.8297722353064
131131103.26597702564727.734022974353
13213275.867942939911956.1320570600881
133133106.4966632693126.5033367306898
13413481.423712404497952.5762875955021
135135106.75584428774728.244155712253
13613692.254374447661343.7456255523387
137137108.89750824483528.1024917551649
13813885.991916057956752.0080839420433
13913996.218355533206942.7816444667931
140140104.6316225526235.3683774473803
141141103.06313687453237.9368631254676
14214294.901938012521547.0980619874785
14314390.478747900881152.5212520991189
14414483.911959180422460.0880408195776







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.001115247575313060.002230495150626110.998884752424687
99.69124996634808e-050.0001938249993269620.999903087500337
107.90663165079272e-061.58132633015854e-050.999992093368349
112.75619097124033e-065.51238194248066e-060.999997243809029
125.94355623412773e-071.18871124682555e-060.999999405644377
131.31857370139661e-062.63714740279322e-060.999998681426299
142.4322377500371e-074.8644755000742e-070.999999756776225
151.14851648913566e-072.29703297827131e-070.999999885148351
164.90475000907427e-089.80950001814855e-080.9999999509525
173.25981748503224e-086.51963497006449e-080.999999967401825
182.2375419969195e-084.47508399383899e-080.99999997762458
195.17046934395444e-091.03409386879089e-080.999999994829531
201.84803306327929e-093.69606612655858e-090.999999998151967
211.98469871600648e-093.96939743201297e-090.999999998015301
222.57610418748778e-095.15220837497556e-090.999999997423896
231.39006013094224e-092.78012026188449e-090.99999999860994
244.91890715867502e-109.83781431735004e-100.999999999508109
256.96557256346625e-101.39311451269325e-090.999999999303443
263.976000294208e-107.952000588416e-100.9999999996024
273.55964902040279e-097.11929804080559e-090.999999996440351
286.42212611469939e-091.28442522293988e-080.999999993577874
294.11470158109174e-098.22940316218349e-090.999999995885298
302.28851316037694e-094.57702632075388e-090.999999997711487
313.54095808786317e-097.08191617572634e-090.999999996459042
322.62776399421916e-095.25552798843831e-090.999999997372236
335.4866120697733e-091.09732241395466e-080.999999994513388
341.18248736559438e-082.36497473118877e-080.999999988175126
351.08357811859646e-082.16715623719292e-080.999999989164219
362.68760660250172e-085.37521320500344e-080.999999973123934
372.62090954701577e-085.24181909403154e-080.999999973790905
384.10064164997298e-088.20128329994597e-080.999999958993584
399.70739094710611e-081.94147818942122e-070.999999902926091
401.07607633758448e-072.15215267516895e-070.999999892392366
411.46352004638416e-072.92704009276831e-070.999999853647995
421.60562489702126e-073.21124979404251e-070.99999983943751
433.02470015756603e-076.04940031513205e-070.999999697529984
443.79702265270413e-077.59404530540826e-070.999999620297735
457.47463754857271e-071.49492750971454e-060.999999252536245
462.17282236683723e-064.34564473367445e-060.999997827177633
475.24947698424185e-061.04989539684837e-050.999994750523016
489.56254526144444e-061.91250905228889e-050.999990437454739
491.76883283298945e-053.53766566597889e-050.99998231167167
501.94397851045389e-053.88795702090777e-050.999980560214895
514.62670559311357e-059.25341118622715e-050.999953732944069
520.0001240242373530020.0002480484747060040.999875975762647
530.0001392306582642440.0002784613165284890.999860769341736
540.0002289850805126470.0004579701610252950.999771014919487
550.0003198671859456160.0006397343718912320.999680132814054
560.0005581018575891470.001116203715178290.999441898142411
570.001088755117905010.002177510235810020.998911244882095
580.002312996847059340.004625993694118680.997687003152941
590.004897944318580450.009795888637160910.99510205568142
600.008715429315110750.01743085863022150.991284570684889
610.01696932196765550.0339386439353110.983030678032344
620.02638524921500340.05277049843000670.973614750784997
630.04464956060410330.08929912120820670.955350439395897
640.07650694573905980.153013891478120.92349305426094
650.1312933623944130.2625867247888260.868706637605587
660.1808660714600540.3617321429201070.819133928539946
670.2562052315396850.5124104630793690.743794768460315
680.3204101284987620.6408202569975240.679589871501238
690.4184927932042820.8369855864085630.581507206795718
700.5811260789267790.8377478421464420.418873921073221
710.6750603810236970.6498792379526050.324939618976303
720.7602592240927660.4794815518144690.239740775907234
730.8224550890406860.3550898219186290.177544910959314
740.8530979735107020.2938040529785960.146902026489298
750.8791491702816970.2417016594366050.120850829718303
760.9171863388853310.1656273222293390.0828136611146694
770.9449745515730850.1100508968538310.0550254484269153
780.9570190703558050.08596185928839020.0429809296441951
790.9769612001505630.04607759969887340.0230387998494367
800.9843896434303660.03122071313926780.0156103565696339
810.9919599812936580.01608003741268460.0080400187063423
820.9956222460925390.00875550781492140.0043777539074607
830.9978360343651470.004327931269706660.00216396563485333
840.9986807773632210.002638445273557370.00131922263677869
850.9993163586129310.001367282774137170.000683641387068584
860.9996387576826790.000722484634641170.000361242317320585
870.9999019977266070.0001960045467864199.80022733932097e-05
880.9999363459790.000127308041999196.36540209995948e-05
890.9999674923411576.50153176858216e-053.25076588429108e-05
900.9999861528926992.76942146022253e-051.38471073011126e-05
910.9999949148962811.01702074383272e-055.08510371916358e-06
920.9999963745764687.25084706326515e-063.62542353163257e-06
930.999997802500664.39499867985984e-062.19749933992992e-06
940.9999986028178672.79436426683863e-061.39718213341931e-06
950.9999993864808531.22703829491067e-066.13519147455335e-07
960.9999994267319121.14653617599662e-065.73268087998311e-07
970.9999996876206846.24758631369395e-073.12379315684697e-07
980.9999998877896322.24420736666603e-071.12210368333302e-07
990.9999998686188582.62762284887442e-071.31381142443721e-07
1000.9999999329545281.34090943004024e-076.70454715020122e-08
1010.9999999748522315.02955372842251e-082.51477686421126e-08
1020.9999999927955191.4408962220319e-087.20448111015951e-09
1030.9999999937153981.25692038738182e-086.28460193690911e-09
1040.9999999948356221.03287562879336e-085.1643781439668e-09
1050.9999999942061.15880003258281e-085.79400016291407e-09
1060.9999999976768964.6462070879399e-092.32310354396995e-09
1070.9999999976886654.62266951921827e-092.31133475960914e-09
1080.9999999980676153.86477073819723e-091.93238536909861e-09
1090.9999999996433257.13350111713757e-103.56675055856878e-10
1100.9999999998421473.15706116467085e-101.57853058233543e-10
1110.9999999997915954.16809710867681e-102.0840485543384e-10
1120.9999999998868722.26256252013776e-101.13128126006888e-10
1130.9999999998042373.91525720829037e-101.95762860414518e-10
1140.9999999995826988.34603011467955e-104.17301505733978e-10
1150.9999999998801892.39622950389643e-101.19811475194821e-10
1160.9999999999940761.18481246821226e-115.9240623410613e-12
1170.9999999999926271.47468958364318e-117.37344791821588e-12
1180.99999999998363.28009319479075e-111.64004659739538e-11
1190.9999999999517459.6510151083399e-114.82550755416995e-11
1200.9999999999580348.39315283467823e-114.19657641733912e-11
1210.9999999999669056.6189486706858e-113.3094743353429e-11
1220.9999999998513212.97358420822486e-101.48679210411243e-10
1230.9999999996243717.51257848644822e-103.75628924322411e-10
1240.9999999984003433.19931481327922e-091.59965740663961e-09
1250.9999999948638581.02722846559426e-085.13614232797131e-09
1260.9999999915081341.6983731385226e-088.491865692613e-09
1270.9999999784278334.31443346625241e-082.1572167331262e-08
1280.9999999154452761.69109448101361e-078.45547240506805e-08
1290.9999999361560061.27687988267098e-076.38439941335489e-08
1300.9999999222307971.55538405739006e-077.77692028695029e-08
1310.9999996242909497.51418100965494e-073.75709050482747e-07
1320.9999985568845692.88623086297748e-061.44311543148874e-06
1330.9999947308692181.0538261563071e-055.2691307815355e-06
1340.99997100591985.79881604005324e-052.89940802002662e-05
1350.9998319284895860.0003361430208286210.00016807151041431
1360.9991224287201660.001755142559667390.000877571279833697

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.00111524757531306 & 0.00223049515062611 & 0.998884752424687 \tabularnewline
9 & 9.69124996634808e-05 & 0.000193824999326962 & 0.999903087500337 \tabularnewline
10 & 7.90663165079272e-06 & 1.58132633015854e-05 & 0.999992093368349 \tabularnewline
11 & 2.75619097124033e-06 & 5.51238194248066e-06 & 0.999997243809029 \tabularnewline
12 & 5.94355623412773e-07 & 1.18871124682555e-06 & 0.999999405644377 \tabularnewline
13 & 1.31857370139661e-06 & 2.63714740279322e-06 & 0.999998681426299 \tabularnewline
14 & 2.4322377500371e-07 & 4.8644755000742e-07 & 0.999999756776225 \tabularnewline
15 & 1.14851648913566e-07 & 2.29703297827131e-07 & 0.999999885148351 \tabularnewline
16 & 4.90475000907427e-08 & 9.80950001814855e-08 & 0.9999999509525 \tabularnewline
17 & 3.25981748503224e-08 & 6.51963497006449e-08 & 0.999999967401825 \tabularnewline
18 & 2.2375419969195e-08 & 4.47508399383899e-08 & 0.99999997762458 \tabularnewline
19 & 5.17046934395444e-09 & 1.03409386879089e-08 & 0.999999994829531 \tabularnewline
20 & 1.84803306327929e-09 & 3.69606612655858e-09 & 0.999999998151967 \tabularnewline
21 & 1.98469871600648e-09 & 3.96939743201297e-09 & 0.999999998015301 \tabularnewline
22 & 2.57610418748778e-09 & 5.15220837497556e-09 & 0.999999997423896 \tabularnewline
23 & 1.39006013094224e-09 & 2.78012026188449e-09 & 0.99999999860994 \tabularnewline
24 & 4.91890715867502e-10 & 9.83781431735004e-10 & 0.999999999508109 \tabularnewline
25 & 6.96557256346625e-10 & 1.39311451269325e-09 & 0.999999999303443 \tabularnewline
26 & 3.976000294208e-10 & 7.952000588416e-10 & 0.9999999996024 \tabularnewline
27 & 3.55964902040279e-09 & 7.11929804080559e-09 & 0.999999996440351 \tabularnewline
28 & 6.42212611469939e-09 & 1.28442522293988e-08 & 0.999999993577874 \tabularnewline
29 & 4.11470158109174e-09 & 8.22940316218349e-09 & 0.999999995885298 \tabularnewline
30 & 2.28851316037694e-09 & 4.57702632075388e-09 & 0.999999997711487 \tabularnewline
31 & 3.54095808786317e-09 & 7.08191617572634e-09 & 0.999999996459042 \tabularnewline
32 & 2.62776399421916e-09 & 5.25552798843831e-09 & 0.999999997372236 \tabularnewline
33 & 5.4866120697733e-09 & 1.09732241395466e-08 & 0.999999994513388 \tabularnewline
34 & 1.18248736559438e-08 & 2.36497473118877e-08 & 0.999999988175126 \tabularnewline
35 & 1.08357811859646e-08 & 2.16715623719292e-08 & 0.999999989164219 \tabularnewline
36 & 2.68760660250172e-08 & 5.37521320500344e-08 & 0.999999973123934 \tabularnewline
37 & 2.62090954701577e-08 & 5.24181909403154e-08 & 0.999999973790905 \tabularnewline
38 & 4.10064164997298e-08 & 8.20128329994597e-08 & 0.999999958993584 \tabularnewline
39 & 9.70739094710611e-08 & 1.94147818942122e-07 & 0.999999902926091 \tabularnewline
40 & 1.07607633758448e-07 & 2.15215267516895e-07 & 0.999999892392366 \tabularnewline
41 & 1.46352004638416e-07 & 2.92704009276831e-07 & 0.999999853647995 \tabularnewline
42 & 1.60562489702126e-07 & 3.21124979404251e-07 & 0.99999983943751 \tabularnewline
43 & 3.02470015756603e-07 & 6.04940031513205e-07 & 0.999999697529984 \tabularnewline
44 & 3.79702265270413e-07 & 7.59404530540826e-07 & 0.999999620297735 \tabularnewline
45 & 7.47463754857271e-07 & 1.49492750971454e-06 & 0.999999252536245 \tabularnewline
46 & 2.17282236683723e-06 & 4.34564473367445e-06 & 0.999997827177633 \tabularnewline
47 & 5.24947698424185e-06 & 1.04989539684837e-05 & 0.999994750523016 \tabularnewline
48 & 9.56254526144444e-06 & 1.91250905228889e-05 & 0.999990437454739 \tabularnewline
49 & 1.76883283298945e-05 & 3.53766566597889e-05 & 0.99998231167167 \tabularnewline
50 & 1.94397851045389e-05 & 3.88795702090777e-05 & 0.999980560214895 \tabularnewline
51 & 4.62670559311357e-05 & 9.25341118622715e-05 & 0.999953732944069 \tabularnewline
52 & 0.000124024237353002 & 0.000248048474706004 & 0.999875975762647 \tabularnewline
53 & 0.000139230658264244 & 0.000278461316528489 & 0.999860769341736 \tabularnewline
54 & 0.000228985080512647 & 0.000457970161025295 & 0.999771014919487 \tabularnewline
55 & 0.000319867185945616 & 0.000639734371891232 & 0.999680132814054 \tabularnewline
56 & 0.000558101857589147 & 0.00111620371517829 & 0.999441898142411 \tabularnewline
57 & 0.00108875511790501 & 0.00217751023581002 & 0.998911244882095 \tabularnewline
58 & 0.00231299684705934 & 0.00462599369411868 & 0.997687003152941 \tabularnewline
59 & 0.00489794431858045 & 0.00979588863716091 & 0.99510205568142 \tabularnewline
60 & 0.00871542931511075 & 0.0174308586302215 & 0.991284570684889 \tabularnewline
61 & 0.0169693219676555 & 0.033938643935311 & 0.983030678032344 \tabularnewline
62 & 0.0263852492150034 & 0.0527704984300067 & 0.973614750784997 \tabularnewline
63 & 0.0446495606041033 & 0.0892991212082067 & 0.955350439395897 \tabularnewline
64 & 0.0765069457390598 & 0.15301389147812 & 0.92349305426094 \tabularnewline
65 & 0.131293362394413 & 0.262586724788826 & 0.868706637605587 \tabularnewline
66 & 0.180866071460054 & 0.361732142920107 & 0.819133928539946 \tabularnewline
67 & 0.256205231539685 & 0.512410463079369 & 0.743794768460315 \tabularnewline
68 & 0.320410128498762 & 0.640820256997524 & 0.679589871501238 \tabularnewline
69 & 0.418492793204282 & 0.836985586408563 & 0.581507206795718 \tabularnewline
70 & 0.581126078926779 & 0.837747842146442 & 0.418873921073221 \tabularnewline
71 & 0.675060381023697 & 0.649879237952605 & 0.324939618976303 \tabularnewline
72 & 0.760259224092766 & 0.479481551814469 & 0.239740775907234 \tabularnewline
73 & 0.822455089040686 & 0.355089821918629 & 0.177544910959314 \tabularnewline
74 & 0.853097973510702 & 0.293804052978596 & 0.146902026489298 \tabularnewline
75 & 0.879149170281697 & 0.241701659436605 & 0.120850829718303 \tabularnewline
76 & 0.917186338885331 & 0.165627322229339 & 0.0828136611146694 \tabularnewline
77 & 0.944974551573085 & 0.110050896853831 & 0.0550254484269153 \tabularnewline
78 & 0.957019070355805 & 0.0859618592883902 & 0.0429809296441951 \tabularnewline
79 & 0.976961200150563 & 0.0460775996988734 & 0.0230387998494367 \tabularnewline
80 & 0.984389643430366 & 0.0312207131392678 & 0.0156103565696339 \tabularnewline
81 & 0.991959981293658 & 0.0160800374126846 & 0.0080400187063423 \tabularnewline
82 & 0.995622246092539 & 0.0087555078149214 & 0.0043777539074607 \tabularnewline
83 & 0.997836034365147 & 0.00432793126970666 & 0.00216396563485333 \tabularnewline
84 & 0.998680777363221 & 0.00263844527355737 & 0.00131922263677869 \tabularnewline
85 & 0.999316358612931 & 0.00136728277413717 & 0.000683641387068584 \tabularnewline
86 & 0.999638757682679 & 0.00072248463464117 & 0.000361242317320585 \tabularnewline
87 & 0.999901997726607 & 0.000196004546786419 & 9.80022733932097e-05 \tabularnewline
88 & 0.999936345979 & 0.00012730804199919 & 6.36540209995948e-05 \tabularnewline
89 & 0.999967492341157 & 6.50153176858216e-05 & 3.25076588429108e-05 \tabularnewline
90 & 0.999986152892699 & 2.76942146022253e-05 & 1.38471073011126e-05 \tabularnewline
91 & 0.999994914896281 & 1.01702074383272e-05 & 5.08510371916358e-06 \tabularnewline
92 & 0.999996374576468 & 7.25084706326515e-06 & 3.62542353163257e-06 \tabularnewline
93 & 0.99999780250066 & 4.39499867985984e-06 & 2.19749933992992e-06 \tabularnewline
94 & 0.999998602817867 & 2.79436426683863e-06 & 1.39718213341931e-06 \tabularnewline
95 & 0.999999386480853 & 1.22703829491067e-06 & 6.13519147455335e-07 \tabularnewline
96 & 0.999999426731912 & 1.14653617599662e-06 & 5.73268087998311e-07 \tabularnewline
97 & 0.999999687620684 & 6.24758631369395e-07 & 3.12379315684697e-07 \tabularnewline
98 & 0.999999887789632 & 2.24420736666603e-07 & 1.12210368333302e-07 \tabularnewline
99 & 0.999999868618858 & 2.62762284887442e-07 & 1.31381142443721e-07 \tabularnewline
100 & 0.999999932954528 & 1.34090943004024e-07 & 6.70454715020122e-08 \tabularnewline
101 & 0.999999974852231 & 5.02955372842251e-08 & 2.51477686421126e-08 \tabularnewline
102 & 0.999999992795519 & 1.4408962220319e-08 & 7.20448111015951e-09 \tabularnewline
103 & 0.999999993715398 & 1.25692038738182e-08 & 6.28460193690911e-09 \tabularnewline
104 & 0.999999994835622 & 1.03287562879336e-08 & 5.1643781439668e-09 \tabularnewline
105 & 0.999999994206 & 1.15880003258281e-08 & 5.79400016291407e-09 \tabularnewline
106 & 0.999999997676896 & 4.6462070879399e-09 & 2.32310354396995e-09 \tabularnewline
107 & 0.999999997688665 & 4.62266951921827e-09 & 2.31133475960914e-09 \tabularnewline
108 & 0.999999998067615 & 3.86477073819723e-09 & 1.93238536909861e-09 \tabularnewline
109 & 0.999999999643325 & 7.13350111713757e-10 & 3.56675055856878e-10 \tabularnewline
110 & 0.999999999842147 & 3.15706116467085e-10 & 1.57853058233543e-10 \tabularnewline
111 & 0.999999999791595 & 4.16809710867681e-10 & 2.0840485543384e-10 \tabularnewline
112 & 0.999999999886872 & 2.26256252013776e-10 & 1.13128126006888e-10 \tabularnewline
113 & 0.999999999804237 & 3.91525720829037e-10 & 1.95762860414518e-10 \tabularnewline
114 & 0.999999999582698 & 8.34603011467955e-10 & 4.17301505733978e-10 \tabularnewline
115 & 0.999999999880189 & 2.39622950389643e-10 & 1.19811475194821e-10 \tabularnewline
116 & 0.999999999994076 & 1.18481246821226e-11 & 5.9240623410613e-12 \tabularnewline
117 & 0.999999999992627 & 1.47468958364318e-11 & 7.37344791821588e-12 \tabularnewline
118 & 0.9999999999836 & 3.28009319479075e-11 & 1.64004659739538e-11 \tabularnewline
119 & 0.999999999951745 & 9.6510151083399e-11 & 4.82550755416995e-11 \tabularnewline
120 & 0.999999999958034 & 8.39315283467823e-11 & 4.19657641733912e-11 \tabularnewline
121 & 0.999999999966905 & 6.6189486706858e-11 & 3.3094743353429e-11 \tabularnewline
122 & 0.999999999851321 & 2.97358420822486e-10 & 1.48679210411243e-10 \tabularnewline
123 & 0.999999999624371 & 7.51257848644822e-10 & 3.75628924322411e-10 \tabularnewline
124 & 0.999999998400343 & 3.19931481327922e-09 & 1.59965740663961e-09 \tabularnewline
125 & 0.999999994863858 & 1.02722846559426e-08 & 5.13614232797131e-09 \tabularnewline
126 & 0.999999991508134 & 1.6983731385226e-08 & 8.491865692613e-09 \tabularnewline
127 & 0.999999978427833 & 4.31443346625241e-08 & 2.1572167331262e-08 \tabularnewline
128 & 0.999999915445276 & 1.69109448101361e-07 & 8.45547240506805e-08 \tabularnewline
129 & 0.999999936156006 & 1.27687988267098e-07 & 6.38439941335489e-08 \tabularnewline
130 & 0.999999922230797 & 1.55538405739006e-07 & 7.77692028695029e-08 \tabularnewline
131 & 0.999999624290949 & 7.51418100965494e-07 & 3.75709050482747e-07 \tabularnewline
132 & 0.999998556884569 & 2.88623086297748e-06 & 1.44311543148874e-06 \tabularnewline
133 & 0.999994730869218 & 1.0538261563071e-05 & 5.2691307815355e-06 \tabularnewline
134 & 0.9999710059198 & 5.79881604005324e-05 & 2.89940802002662e-05 \tabularnewline
135 & 0.999831928489586 & 0.000336143020828621 & 0.00016807151041431 \tabularnewline
136 & 0.999122428720166 & 0.00175514255966739 & 0.000877571279833697 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147025&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]8[/C][C]0.00111524757531306[/C][C]0.00223049515062611[/C][C]0.998884752424687[/C][/ROW]
[ROW][C]9[/C][C]9.69124996634808e-05[/C][C]0.000193824999326962[/C][C]0.999903087500337[/C][/ROW]
[ROW][C]10[/C][C]7.90663165079272e-06[/C][C]1.58132633015854e-05[/C][C]0.999992093368349[/C][/ROW]
[ROW][C]11[/C][C]2.75619097124033e-06[/C][C]5.51238194248066e-06[/C][C]0.999997243809029[/C][/ROW]
[ROW][C]12[/C][C]5.94355623412773e-07[/C][C]1.18871124682555e-06[/C][C]0.999999405644377[/C][/ROW]
[ROW][C]13[/C][C]1.31857370139661e-06[/C][C]2.63714740279322e-06[/C][C]0.999998681426299[/C][/ROW]
[ROW][C]14[/C][C]2.4322377500371e-07[/C][C]4.8644755000742e-07[/C][C]0.999999756776225[/C][/ROW]
[ROW][C]15[/C][C]1.14851648913566e-07[/C][C]2.29703297827131e-07[/C][C]0.999999885148351[/C][/ROW]
[ROW][C]16[/C][C]4.90475000907427e-08[/C][C]9.80950001814855e-08[/C][C]0.9999999509525[/C][/ROW]
[ROW][C]17[/C][C]3.25981748503224e-08[/C][C]6.51963497006449e-08[/C][C]0.999999967401825[/C][/ROW]
[ROW][C]18[/C][C]2.2375419969195e-08[/C][C]4.47508399383899e-08[/C][C]0.99999997762458[/C][/ROW]
[ROW][C]19[/C][C]5.17046934395444e-09[/C][C]1.03409386879089e-08[/C][C]0.999999994829531[/C][/ROW]
[ROW][C]20[/C][C]1.84803306327929e-09[/C][C]3.69606612655858e-09[/C][C]0.999999998151967[/C][/ROW]
[ROW][C]21[/C][C]1.98469871600648e-09[/C][C]3.96939743201297e-09[/C][C]0.999999998015301[/C][/ROW]
[ROW][C]22[/C][C]2.57610418748778e-09[/C][C]5.15220837497556e-09[/C][C]0.999999997423896[/C][/ROW]
[ROW][C]23[/C][C]1.39006013094224e-09[/C][C]2.78012026188449e-09[/C][C]0.99999999860994[/C][/ROW]
[ROW][C]24[/C][C]4.91890715867502e-10[/C][C]9.83781431735004e-10[/C][C]0.999999999508109[/C][/ROW]
[ROW][C]25[/C][C]6.96557256346625e-10[/C][C]1.39311451269325e-09[/C][C]0.999999999303443[/C][/ROW]
[ROW][C]26[/C][C]3.976000294208e-10[/C][C]7.952000588416e-10[/C][C]0.9999999996024[/C][/ROW]
[ROW][C]27[/C][C]3.55964902040279e-09[/C][C]7.11929804080559e-09[/C][C]0.999999996440351[/C][/ROW]
[ROW][C]28[/C][C]6.42212611469939e-09[/C][C]1.28442522293988e-08[/C][C]0.999999993577874[/C][/ROW]
[ROW][C]29[/C][C]4.11470158109174e-09[/C][C]8.22940316218349e-09[/C][C]0.999999995885298[/C][/ROW]
[ROW][C]30[/C][C]2.28851316037694e-09[/C][C]4.57702632075388e-09[/C][C]0.999999997711487[/C][/ROW]
[ROW][C]31[/C][C]3.54095808786317e-09[/C][C]7.08191617572634e-09[/C][C]0.999999996459042[/C][/ROW]
[ROW][C]32[/C][C]2.62776399421916e-09[/C][C]5.25552798843831e-09[/C][C]0.999999997372236[/C][/ROW]
[ROW][C]33[/C][C]5.4866120697733e-09[/C][C]1.09732241395466e-08[/C][C]0.999999994513388[/C][/ROW]
[ROW][C]34[/C][C]1.18248736559438e-08[/C][C]2.36497473118877e-08[/C][C]0.999999988175126[/C][/ROW]
[ROW][C]35[/C][C]1.08357811859646e-08[/C][C]2.16715623719292e-08[/C][C]0.999999989164219[/C][/ROW]
[ROW][C]36[/C][C]2.68760660250172e-08[/C][C]5.37521320500344e-08[/C][C]0.999999973123934[/C][/ROW]
[ROW][C]37[/C][C]2.62090954701577e-08[/C][C]5.24181909403154e-08[/C][C]0.999999973790905[/C][/ROW]
[ROW][C]38[/C][C]4.10064164997298e-08[/C][C]8.20128329994597e-08[/C][C]0.999999958993584[/C][/ROW]
[ROW][C]39[/C][C]9.70739094710611e-08[/C][C]1.94147818942122e-07[/C][C]0.999999902926091[/C][/ROW]
[ROW][C]40[/C][C]1.07607633758448e-07[/C][C]2.15215267516895e-07[/C][C]0.999999892392366[/C][/ROW]
[ROW][C]41[/C][C]1.46352004638416e-07[/C][C]2.92704009276831e-07[/C][C]0.999999853647995[/C][/ROW]
[ROW][C]42[/C][C]1.60562489702126e-07[/C][C]3.21124979404251e-07[/C][C]0.99999983943751[/C][/ROW]
[ROW][C]43[/C][C]3.02470015756603e-07[/C][C]6.04940031513205e-07[/C][C]0.999999697529984[/C][/ROW]
[ROW][C]44[/C][C]3.79702265270413e-07[/C][C]7.59404530540826e-07[/C][C]0.999999620297735[/C][/ROW]
[ROW][C]45[/C][C]7.47463754857271e-07[/C][C]1.49492750971454e-06[/C][C]0.999999252536245[/C][/ROW]
[ROW][C]46[/C][C]2.17282236683723e-06[/C][C]4.34564473367445e-06[/C][C]0.999997827177633[/C][/ROW]
[ROW][C]47[/C][C]5.24947698424185e-06[/C][C]1.04989539684837e-05[/C][C]0.999994750523016[/C][/ROW]
[ROW][C]48[/C][C]9.56254526144444e-06[/C][C]1.91250905228889e-05[/C][C]0.999990437454739[/C][/ROW]
[ROW][C]49[/C][C]1.76883283298945e-05[/C][C]3.53766566597889e-05[/C][C]0.99998231167167[/C][/ROW]
[ROW][C]50[/C][C]1.94397851045389e-05[/C][C]3.88795702090777e-05[/C][C]0.999980560214895[/C][/ROW]
[ROW][C]51[/C][C]4.62670559311357e-05[/C][C]9.25341118622715e-05[/C][C]0.999953732944069[/C][/ROW]
[ROW][C]52[/C][C]0.000124024237353002[/C][C]0.000248048474706004[/C][C]0.999875975762647[/C][/ROW]
[ROW][C]53[/C][C]0.000139230658264244[/C][C]0.000278461316528489[/C][C]0.999860769341736[/C][/ROW]
[ROW][C]54[/C][C]0.000228985080512647[/C][C]0.000457970161025295[/C][C]0.999771014919487[/C][/ROW]
[ROW][C]55[/C][C]0.000319867185945616[/C][C]0.000639734371891232[/C][C]0.999680132814054[/C][/ROW]
[ROW][C]56[/C][C]0.000558101857589147[/C][C]0.00111620371517829[/C][C]0.999441898142411[/C][/ROW]
[ROW][C]57[/C][C]0.00108875511790501[/C][C]0.00217751023581002[/C][C]0.998911244882095[/C][/ROW]
[ROW][C]58[/C][C]0.00231299684705934[/C][C]0.00462599369411868[/C][C]0.997687003152941[/C][/ROW]
[ROW][C]59[/C][C]0.00489794431858045[/C][C]0.00979588863716091[/C][C]0.99510205568142[/C][/ROW]
[ROW][C]60[/C][C]0.00871542931511075[/C][C]0.0174308586302215[/C][C]0.991284570684889[/C][/ROW]
[ROW][C]61[/C][C]0.0169693219676555[/C][C]0.033938643935311[/C][C]0.983030678032344[/C][/ROW]
[ROW][C]62[/C][C]0.0263852492150034[/C][C]0.0527704984300067[/C][C]0.973614750784997[/C][/ROW]
[ROW][C]63[/C][C]0.0446495606041033[/C][C]0.0892991212082067[/C][C]0.955350439395897[/C][/ROW]
[ROW][C]64[/C][C]0.0765069457390598[/C][C]0.15301389147812[/C][C]0.92349305426094[/C][/ROW]
[ROW][C]65[/C][C]0.131293362394413[/C][C]0.262586724788826[/C][C]0.868706637605587[/C][/ROW]
[ROW][C]66[/C][C]0.180866071460054[/C][C]0.361732142920107[/C][C]0.819133928539946[/C][/ROW]
[ROW][C]67[/C][C]0.256205231539685[/C][C]0.512410463079369[/C][C]0.743794768460315[/C][/ROW]
[ROW][C]68[/C][C]0.320410128498762[/C][C]0.640820256997524[/C][C]0.679589871501238[/C][/ROW]
[ROW][C]69[/C][C]0.418492793204282[/C][C]0.836985586408563[/C][C]0.581507206795718[/C][/ROW]
[ROW][C]70[/C][C]0.581126078926779[/C][C]0.837747842146442[/C][C]0.418873921073221[/C][/ROW]
[ROW][C]71[/C][C]0.675060381023697[/C][C]0.649879237952605[/C][C]0.324939618976303[/C][/ROW]
[ROW][C]72[/C][C]0.760259224092766[/C][C]0.479481551814469[/C][C]0.239740775907234[/C][/ROW]
[ROW][C]73[/C][C]0.822455089040686[/C][C]0.355089821918629[/C][C]0.177544910959314[/C][/ROW]
[ROW][C]74[/C][C]0.853097973510702[/C][C]0.293804052978596[/C][C]0.146902026489298[/C][/ROW]
[ROW][C]75[/C][C]0.879149170281697[/C][C]0.241701659436605[/C][C]0.120850829718303[/C][/ROW]
[ROW][C]76[/C][C]0.917186338885331[/C][C]0.165627322229339[/C][C]0.0828136611146694[/C][/ROW]
[ROW][C]77[/C][C]0.944974551573085[/C][C]0.110050896853831[/C][C]0.0550254484269153[/C][/ROW]
[ROW][C]78[/C][C]0.957019070355805[/C][C]0.0859618592883902[/C][C]0.0429809296441951[/C][/ROW]
[ROW][C]79[/C][C]0.976961200150563[/C][C]0.0460775996988734[/C][C]0.0230387998494367[/C][/ROW]
[ROW][C]80[/C][C]0.984389643430366[/C][C]0.0312207131392678[/C][C]0.0156103565696339[/C][/ROW]
[ROW][C]81[/C][C]0.991959981293658[/C][C]0.0160800374126846[/C][C]0.0080400187063423[/C][/ROW]
[ROW][C]82[/C][C]0.995622246092539[/C][C]0.0087555078149214[/C][C]0.0043777539074607[/C][/ROW]
[ROW][C]83[/C][C]0.997836034365147[/C][C]0.00432793126970666[/C][C]0.00216396563485333[/C][/ROW]
[ROW][C]84[/C][C]0.998680777363221[/C][C]0.00263844527355737[/C][C]0.00131922263677869[/C][/ROW]
[ROW][C]85[/C][C]0.999316358612931[/C][C]0.00136728277413717[/C][C]0.000683641387068584[/C][/ROW]
[ROW][C]86[/C][C]0.999638757682679[/C][C]0.00072248463464117[/C][C]0.000361242317320585[/C][/ROW]
[ROW][C]87[/C][C]0.999901997726607[/C][C]0.000196004546786419[/C][C]9.80022733932097e-05[/C][/ROW]
[ROW][C]88[/C][C]0.999936345979[/C][C]0.00012730804199919[/C][C]6.36540209995948e-05[/C][/ROW]
[ROW][C]89[/C][C]0.999967492341157[/C][C]6.50153176858216e-05[/C][C]3.25076588429108e-05[/C][/ROW]
[ROW][C]90[/C][C]0.999986152892699[/C][C]2.76942146022253e-05[/C][C]1.38471073011126e-05[/C][/ROW]
[ROW][C]91[/C][C]0.999994914896281[/C][C]1.01702074383272e-05[/C][C]5.08510371916358e-06[/C][/ROW]
[ROW][C]92[/C][C]0.999996374576468[/C][C]7.25084706326515e-06[/C][C]3.62542353163257e-06[/C][/ROW]
[ROW][C]93[/C][C]0.99999780250066[/C][C]4.39499867985984e-06[/C][C]2.19749933992992e-06[/C][/ROW]
[ROW][C]94[/C][C]0.999998602817867[/C][C]2.79436426683863e-06[/C][C]1.39718213341931e-06[/C][/ROW]
[ROW][C]95[/C][C]0.999999386480853[/C][C]1.22703829491067e-06[/C][C]6.13519147455335e-07[/C][/ROW]
[ROW][C]96[/C][C]0.999999426731912[/C][C]1.14653617599662e-06[/C][C]5.73268087998311e-07[/C][/ROW]
[ROW][C]97[/C][C]0.999999687620684[/C][C]6.24758631369395e-07[/C][C]3.12379315684697e-07[/C][/ROW]
[ROW][C]98[/C][C]0.999999887789632[/C][C]2.24420736666603e-07[/C][C]1.12210368333302e-07[/C][/ROW]
[ROW][C]99[/C][C]0.999999868618858[/C][C]2.62762284887442e-07[/C][C]1.31381142443721e-07[/C][/ROW]
[ROW][C]100[/C][C]0.999999932954528[/C][C]1.34090943004024e-07[/C][C]6.70454715020122e-08[/C][/ROW]
[ROW][C]101[/C][C]0.999999974852231[/C][C]5.02955372842251e-08[/C][C]2.51477686421126e-08[/C][/ROW]
[ROW][C]102[/C][C]0.999999992795519[/C][C]1.4408962220319e-08[/C][C]7.20448111015951e-09[/C][/ROW]
[ROW][C]103[/C][C]0.999999993715398[/C][C]1.25692038738182e-08[/C][C]6.28460193690911e-09[/C][/ROW]
[ROW][C]104[/C][C]0.999999994835622[/C][C]1.03287562879336e-08[/C][C]5.1643781439668e-09[/C][/ROW]
[ROW][C]105[/C][C]0.999999994206[/C][C]1.15880003258281e-08[/C][C]5.79400016291407e-09[/C][/ROW]
[ROW][C]106[/C][C]0.999999997676896[/C][C]4.6462070879399e-09[/C][C]2.32310354396995e-09[/C][/ROW]
[ROW][C]107[/C][C]0.999999997688665[/C][C]4.62266951921827e-09[/C][C]2.31133475960914e-09[/C][/ROW]
[ROW][C]108[/C][C]0.999999998067615[/C][C]3.86477073819723e-09[/C][C]1.93238536909861e-09[/C][/ROW]
[ROW][C]109[/C][C]0.999999999643325[/C][C]7.13350111713757e-10[/C][C]3.56675055856878e-10[/C][/ROW]
[ROW][C]110[/C][C]0.999999999842147[/C][C]3.15706116467085e-10[/C][C]1.57853058233543e-10[/C][/ROW]
[ROW][C]111[/C][C]0.999999999791595[/C][C]4.16809710867681e-10[/C][C]2.0840485543384e-10[/C][/ROW]
[ROW][C]112[/C][C]0.999999999886872[/C][C]2.26256252013776e-10[/C][C]1.13128126006888e-10[/C][/ROW]
[ROW][C]113[/C][C]0.999999999804237[/C][C]3.91525720829037e-10[/C][C]1.95762860414518e-10[/C][/ROW]
[ROW][C]114[/C][C]0.999999999582698[/C][C]8.34603011467955e-10[/C][C]4.17301505733978e-10[/C][/ROW]
[ROW][C]115[/C][C]0.999999999880189[/C][C]2.39622950389643e-10[/C][C]1.19811475194821e-10[/C][/ROW]
[ROW][C]116[/C][C]0.999999999994076[/C][C]1.18481246821226e-11[/C][C]5.9240623410613e-12[/C][/ROW]
[ROW][C]117[/C][C]0.999999999992627[/C][C]1.47468958364318e-11[/C][C]7.37344791821588e-12[/C][/ROW]
[ROW][C]118[/C][C]0.9999999999836[/C][C]3.28009319479075e-11[/C][C]1.64004659739538e-11[/C][/ROW]
[ROW][C]119[/C][C]0.999999999951745[/C][C]9.6510151083399e-11[/C][C]4.82550755416995e-11[/C][/ROW]
[ROW][C]120[/C][C]0.999999999958034[/C][C]8.39315283467823e-11[/C][C]4.19657641733912e-11[/C][/ROW]
[ROW][C]121[/C][C]0.999999999966905[/C][C]6.6189486706858e-11[/C][C]3.3094743353429e-11[/C][/ROW]
[ROW][C]122[/C][C]0.999999999851321[/C][C]2.97358420822486e-10[/C][C]1.48679210411243e-10[/C][/ROW]
[ROW][C]123[/C][C]0.999999999624371[/C][C]7.51257848644822e-10[/C][C]3.75628924322411e-10[/C][/ROW]
[ROW][C]124[/C][C]0.999999998400343[/C][C]3.19931481327922e-09[/C][C]1.59965740663961e-09[/C][/ROW]
[ROW][C]125[/C][C]0.999999994863858[/C][C]1.02722846559426e-08[/C][C]5.13614232797131e-09[/C][/ROW]
[ROW][C]126[/C][C]0.999999991508134[/C][C]1.6983731385226e-08[/C][C]8.491865692613e-09[/C][/ROW]
[ROW][C]127[/C][C]0.999999978427833[/C][C]4.31443346625241e-08[/C][C]2.1572167331262e-08[/C][/ROW]
[ROW][C]128[/C][C]0.999999915445276[/C][C]1.69109448101361e-07[/C][C]8.45547240506805e-08[/C][/ROW]
[ROW][C]129[/C][C]0.999999936156006[/C][C]1.27687988267098e-07[/C][C]6.38439941335489e-08[/C][/ROW]
[ROW][C]130[/C][C]0.999999922230797[/C][C]1.55538405739006e-07[/C][C]7.77692028695029e-08[/C][/ROW]
[ROW][C]131[/C][C]0.999999624290949[/C][C]7.51418100965494e-07[/C][C]3.75709050482747e-07[/C][/ROW]
[ROW][C]132[/C][C]0.999998556884569[/C][C]2.88623086297748e-06[/C][C]1.44311543148874e-06[/C][/ROW]
[ROW][C]133[/C][C]0.999994730869218[/C][C]1.0538261563071e-05[/C][C]5.2691307815355e-06[/C][/ROW]
[ROW][C]134[/C][C]0.9999710059198[/C][C]5.79881604005324e-05[/C][C]2.89940802002662e-05[/C][/ROW]
[ROW][C]135[/C][C]0.999831928489586[/C][C]0.000336143020828621[/C][C]0.00016807151041431[/C][/ROW]
[ROW][C]136[/C][C]0.999122428720166[/C][C]0.00175514255966739[/C][C]0.000877571279833697[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147025&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147025&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
80.001115247575313060.002230495150626110.998884752424687
99.69124996634808e-050.0001938249993269620.999903087500337
107.90663165079272e-061.58132633015854e-050.999992093368349
112.75619097124033e-065.51238194248066e-060.999997243809029
125.94355623412773e-071.18871124682555e-060.999999405644377
131.31857370139661e-062.63714740279322e-060.999998681426299
142.4322377500371e-074.8644755000742e-070.999999756776225
151.14851648913566e-072.29703297827131e-070.999999885148351
164.90475000907427e-089.80950001814855e-080.9999999509525
173.25981748503224e-086.51963497006449e-080.999999967401825
182.2375419969195e-084.47508399383899e-080.99999997762458
195.17046934395444e-091.03409386879089e-080.999999994829531
201.84803306327929e-093.69606612655858e-090.999999998151967
211.98469871600648e-093.96939743201297e-090.999999998015301
222.57610418748778e-095.15220837497556e-090.999999997423896
231.39006013094224e-092.78012026188449e-090.99999999860994
244.91890715867502e-109.83781431735004e-100.999999999508109
256.96557256346625e-101.39311451269325e-090.999999999303443
263.976000294208e-107.952000588416e-100.9999999996024
273.55964902040279e-097.11929804080559e-090.999999996440351
286.42212611469939e-091.28442522293988e-080.999999993577874
294.11470158109174e-098.22940316218349e-090.999999995885298
302.28851316037694e-094.57702632075388e-090.999999997711487
313.54095808786317e-097.08191617572634e-090.999999996459042
322.62776399421916e-095.25552798843831e-090.999999997372236
335.4866120697733e-091.09732241395466e-080.999999994513388
341.18248736559438e-082.36497473118877e-080.999999988175126
351.08357811859646e-082.16715623719292e-080.999999989164219
362.68760660250172e-085.37521320500344e-080.999999973123934
372.62090954701577e-085.24181909403154e-080.999999973790905
384.10064164997298e-088.20128329994597e-080.999999958993584
399.70739094710611e-081.94147818942122e-070.999999902926091
401.07607633758448e-072.15215267516895e-070.999999892392366
411.46352004638416e-072.92704009276831e-070.999999853647995
421.60562489702126e-073.21124979404251e-070.99999983943751
433.02470015756603e-076.04940031513205e-070.999999697529984
443.79702265270413e-077.59404530540826e-070.999999620297735
457.47463754857271e-071.49492750971454e-060.999999252536245
462.17282236683723e-064.34564473367445e-060.999997827177633
475.24947698424185e-061.04989539684837e-050.999994750523016
489.56254526144444e-061.91250905228889e-050.999990437454739
491.76883283298945e-053.53766566597889e-050.99998231167167
501.94397851045389e-053.88795702090777e-050.999980560214895
514.62670559311357e-059.25341118622715e-050.999953732944069
520.0001240242373530020.0002480484747060040.999875975762647
530.0001392306582642440.0002784613165284890.999860769341736
540.0002289850805126470.0004579701610252950.999771014919487
550.0003198671859456160.0006397343718912320.999680132814054
560.0005581018575891470.001116203715178290.999441898142411
570.001088755117905010.002177510235810020.998911244882095
580.002312996847059340.004625993694118680.997687003152941
590.004897944318580450.009795888637160910.99510205568142
600.008715429315110750.01743085863022150.991284570684889
610.01696932196765550.0339386439353110.983030678032344
620.02638524921500340.05277049843000670.973614750784997
630.04464956060410330.08929912120820670.955350439395897
640.07650694573905980.153013891478120.92349305426094
650.1312933623944130.2625867247888260.868706637605587
660.1808660714600540.3617321429201070.819133928539946
670.2562052315396850.5124104630793690.743794768460315
680.3204101284987620.6408202569975240.679589871501238
690.4184927932042820.8369855864085630.581507206795718
700.5811260789267790.8377478421464420.418873921073221
710.6750603810236970.6498792379526050.324939618976303
720.7602592240927660.4794815518144690.239740775907234
730.8224550890406860.3550898219186290.177544910959314
740.8530979735107020.2938040529785960.146902026489298
750.8791491702816970.2417016594366050.120850829718303
760.9171863388853310.1656273222293390.0828136611146694
770.9449745515730850.1100508968538310.0550254484269153
780.9570190703558050.08596185928839020.0429809296441951
790.9769612001505630.04607759969887340.0230387998494367
800.9843896434303660.03122071313926780.0156103565696339
810.9919599812936580.01608003741268460.0080400187063423
820.9956222460925390.00875550781492140.0043777539074607
830.9978360343651470.004327931269706660.00216396563485333
840.9986807773632210.002638445273557370.00131922263677869
850.9993163586129310.001367282774137170.000683641387068584
860.9996387576826790.000722484634641170.000361242317320585
870.9999019977266070.0001960045467864199.80022733932097e-05
880.9999363459790.000127308041999196.36540209995948e-05
890.9999674923411576.50153176858216e-053.25076588429108e-05
900.9999861528926992.76942146022253e-051.38471073011126e-05
910.9999949148962811.01702074383272e-055.08510371916358e-06
920.9999963745764687.25084706326515e-063.62542353163257e-06
930.999997802500664.39499867985984e-062.19749933992992e-06
940.9999986028178672.79436426683863e-061.39718213341931e-06
950.9999993864808531.22703829491067e-066.13519147455335e-07
960.9999994267319121.14653617599662e-065.73268087998311e-07
970.9999996876206846.24758631369395e-073.12379315684697e-07
980.9999998877896322.24420736666603e-071.12210368333302e-07
990.9999998686188582.62762284887442e-071.31381142443721e-07
1000.9999999329545281.34090943004024e-076.70454715020122e-08
1010.9999999748522315.02955372842251e-082.51477686421126e-08
1020.9999999927955191.4408962220319e-087.20448111015951e-09
1030.9999999937153981.25692038738182e-086.28460193690911e-09
1040.9999999948356221.03287562879336e-085.1643781439668e-09
1050.9999999942061.15880003258281e-085.79400016291407e-09
1060.9999999976768964.6462070879399e-092.32310354396995e-09
1070.9999999976886654.62266951921827e-092.31133475960914e-09
1080.9999999980676153.86477073819723e-091.93238536909861e-09
1090.9999999996433257.13350111713757e-103.56675055856878e-10
1100.9999999998421473.15706116467085e-101.57853058233543e-10
1110.9999999997915954.16809710867681e-102.0840485543384e-10
1120.9999999998868722.26256252013776e-101.13128126006888e-10
1130.9999999998042373.91525720829037e-101.95762860414518e-10
1140.9999999995826988.34603011467955e-104.17301505733978e-10
1150.9999999998801892.39622950389643e-101.19811475194821e-10
1160.9999999999940761.18481246821226e-115.9240623410613e-12
1170.9999999999926271.47468958364318e-117.37344791821588e-12
1180.99999999998363.28009319479075e-111.64004659739538e-11
1190.9999999999517459.6510151083399e-114.82550755416995e-11
1200.9999999999580348.39315283467823e-114.19657641733912e-11
1210.9999999999669056.6189486706858e-113.3094743353429e-11
1220.9999999998513212.97358420822486e-101.48679210411243e-10
1230.9999999996243717.51257848644822e-103.75628924322411e-10
1240.9999999984003433.19931481327922e-091.59965740663961e-09
1250.9999999948638581.02722846559426e-085.13614232797131e-09
1260.9999999915081341.6983731385226e-088.491865692613e-09
1270.9999999784278334.31443346625241e-082.1572167331262e-08
1280.9999999154452761.69109448101361e-078.45547240506805e-08
1290.9999999361560061.27687988267098e-076.38439941335489e-08
1300.9999999222307971.55538405739006e-077.77692028695029e-08
1310.9999996242909497.51418100965494e-073.75709050482747e-07
1320.9999985568845692.88623086297748e-061.44311543148874e-06
1330.9999947308692181.0538261563071e-055.2691307815355e-06
1340.99997100591985.79881604005324e-052.89940802002662e-05
1350.9998319284895860.0003361430208286210.00016807151041431
1360.9991224287201660.001755142559667390.000877571279833697







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1070.829457364341085NOK
5% type I error level1120.868217054263566NOK
10% type I error level1150.891472868217054NOK

\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 & 107 & 0.829457364341085 & NOK \tabularnewline
5% type I error level & 112 & 0.868217054263566 & NOK \tabularnewline
10% type I error level & 115 & 0.891472868217054 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147025&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]107[/C][C]0.829457364341085[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]112[/C][C]0.868217054263566[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]115[/C][C]0.891472868217054[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147025&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147025&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 level1070.829457364341085NOK
5% type I error level1120.868217054263566NOK
10% type I error level1150.891472868217054NOK



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')
}