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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 12:14:55 -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/t1322155034wq1k4m96ais3cy0.htm/, Retrieved Thu, 28 Mar 2024 15:30:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147106, Retrieved Thu, 28 Mar 2024 15:30:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Multiple Linear R...] [2011-11-23 18:09:50] [489eb911c8db04aca1fc54d886fc3144]
-   P   [Multiple Regression] [Multiple Linear R...] [2011-11-24 15:39:50] [489eb911c8db04aca1fc54d886fc3144]
-    D      [Multiple Regression] [] [2011-11-24 17:14:55] [d160b678fd2d7bb562db2147d7efddc2] [Current]
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Dataseries X:
63047	257	13	22	10345
66751	160	26	21	17607
7176	70	0	0	1423
78306	360	37	28	20050
144655	757	47	59	21212
269638	974	84	58	93979
69266	287	21	36	15524
83529	154	36	58	16182
73226	311	35	29	19238
178519	617	40	48	28909
67250	297	35	24	22357
102982	393	46	44	25560
50001	369	20	16	9954
91093	558	24	46	18490
80112	226	19	35	17777
72961	315	15	35	25268
77159	243	52	63	37525
15629	88	0	15	6023
71693	494	38	62	25042
19920	155	12	12	35713
39403	234	10	33	7039
104383	365	53	44	40841
56088	280	4	29	9214
62006	331	24	26	17446
81665	378	39	31	10295
69451	230	19	22	13206
88794	396	23	46	26093
90642	179	39	39	20744
207069	524	38	45	68013
99340	504	20	23	12840
56695	225	20	41	12672
108143	366	41	32	10872
64204	372	29	12	21325
29101	171	0	18	24542
113060	437	31	41	16401
0	0	0	0	0
65773	313	8	32	12821
67047	366	35	24	14662
41953	232	3	54	22190
113787	402	47	71	37929
86584	349	42	32	18009
59588	316	11	53	11076
40064	140	10	24	24981
74471	445	26	35	30691
60437	226	27	42	29164
55118	173	1	33	13985
40295	103	15	30	7588
103397	356	32	36	20023
78982	293	13	48	25524
67317	460	25	34	14717
39887	156	10	34	6832
59682	204	24	30	9624
132740	455	26	43	24300
104816	321	29	41	21790
101395	367	40	66	16493
72824	309	22	20	9269
76018	235	27	23	20105
33891	137	8	30	11216
63694	198	27	49	15569
33239	248	0	37	21799
35093	149	0	61	3772
35252	306	17	25	6057
36977	178	7	28	20828
42406	145	18	25	9976
56353	144	7	29	14055
58817	270	24	53	17455
81051	311	19	55	39553
70872	501	39	33	14818
42372	153	17	37	17065
19144	40	0	27	1536
114177	500	39	26	11938
59414	209	21	2	24589
51379	242	29	46	21332
40756	265	27	15	13229
53398	311	23	63	11331
17799	141	0	28	853
71154	234	31	24	19821
58305	336	19	31	34666
27454	124	12	25	15051
34323	241	23	7	27969
44761	127	33	35	17897
113862	327	21	42	6031
35027	175	17	10	7153
62396	331	27	33	13365
29613	176	14	28	11197
65559	281	12	25	25291
120064	303	22	62	28994
36149	159	15	35	10461
40181	155	14	30	16415
53398	194	22	36	8495
56435	300	25	17	18318
77283	370	36	34	25143
71738	187	10	37	20471
48503	212	16	20	14561
25214	185	12	7	16902
119424	449	20	46	12994
79201	234	38	43	29697
19349	67	13	0	3895
78760	316	12	45	9807
54133	336	11	26	10711
21623	116	8	1	2325
25497	141	22	16	19000
69535	236	14	29	22418
30709	98	7	21	7872
37043	97	14	19	5650
24716	152	2	10	3979
60734	153	35	47	14956
27246	97	5	7	3738
0	0	0	0	0
38814	165	34	11	10586
27646	153	12	28	18122
65373	226	34	27	17899
43021	182	30	46	10913
43116	172	21	9	18060
3058	1	0	0	0
0	0	0	0	0
96347	196	28	49	15452
55195	282	18	27	33996
73321	307	13	31	8877
45266	183	14	46	18708
43410	292	7	3	2781
83842	257	41	41	20854
39296	141	21	15	8179
38490	192	28	21	7139
39841	129	1	23	13798
19764	75	10	4	5619
66463	315	31	41	13050
64589	204	7	46	11297
63339	257	26	54	16170
11796	79	1	1	0
7627	25	0	0	0
68998	217	12	21	20539
6836	11	0	0	0
35414	228	18	3	10056
5118	6	5	0	0
20898	115	4	3	2418
0	0	0	0	0
42690	167	6	44	11806
14507	75	0	19	15924
7131	27	0	0	0
4194	14	0	0	0
21416	96	15	12	7084
39000	117	1	24	14831
42419	228	12	26	6585




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147106&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147106&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147106&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
TijdInRFC[t] = -3711.56060254409 + 135.919912760391CompendiumViews[t] + 484.004129696013Blogs[t] + 331.477487814427FeedbackMessages[t] + 0.656183217158333CompendiumLengte[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TijdInRFC[t] =  -3711.56060254409 +  135.919912760391CompendiumViews[t] +  484.004129696013Blogs[t] +  331.477487814427FeedbackMessages[t] +  0.656183217158333CompendiumLengte[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147106&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TijdInRFC[t] =  -3711.56060254409 +  135.919912760391CompendiumViews[t] +  484.004129696013Blogs[t] +  331.477487814427FeedbackMessages[t] +  0.656183217158333CompendiumLengte[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147106&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147106&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
TijdInRFC[t] = -3711.56060254409 + 135.919912760391CompendiumViews[t] + 484.004129696013Blogs[t] + 331.477487814427FeedbackMessages[t] + 0.656183217158333CompendiumLengte[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-3711.560602544092861.282415-1.29720.1967230.098361
CompendiumViews135.91991276039114.1338519.616600
Blogs484.004129696013142.0673653.40690.000860.00043
FeedbackMessages331.47748781442797.5999133.39630.0008910.000445
CompendiumLengte0.6561832171583330.153674.27013.6e-051.8e-05

\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) & -3711.56060254409 & 2861.282415 & -1.2972 & 0.196723 & 0.098361 \tabularnewline
CompendiumViews & 135.919912760391 & 14.133851 & 9.6166 & 0 & 0 \tabularnewline
Blogs & 484.004129696013 & 142.067365 & 3.4069 & 0.00086 & 0.00043 \tabularnewline
FeedbackMessages & 331.477487814427 & 97.599913 & 3.3963 & 0.000891 & 0.000445 \tabularnewline
CompendiumLengte & 0.656183217158333 & 0.15367 & 4.2701 & 3.6e-05 & 1.8e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147106&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]-3711.56060254409[/C][C]2861.282415[/C][C]-1.2972[/C][C]0.196723[/C][C]0.098361[/C][/ROW]
[ROW][C]CompendiumViews[/C][C]135.919912760391[/C][C]14.133851[/C][C]9.6166[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Blogs[/C][C]484.004129696013[/C][C]142.067365[/C][C]3.4069[/C][C]0.00086[/C][C]0.00043[/C][/ROW]
[ROW][C]FeedbackMessages[/C][C]331.477487814427[/C][C]97.599913[/C][C]3.3963[/C][C]0.000891[/C][C]0.000445[/C][/ROW]
[ROW][C]CompendiumLengte[/C][C]0.656183217158333[/C][C]0.15367[/C][C]4.2701[/C][C]3.6e-05[/C][C]1.8e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147106&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147106&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)-3711.560602544092861.282415-1.29720.1967230.098361
CompendiumViews135.91991276039114.1338519.616600
Blogs484.004129696013142.0673653.40690.000860.00043
FeedbackMessages331.47748781442797.5999133.39630.0008910.000445
CompendiumLengte0.6561832171583330.153674.27013.6e-051.8e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.912017526887744
R-squared0.831775969350437
Adjusted R-squared0.826934990051168
F-TEST (value)171.819774043695
F-TEST (DF numerator)4
F-TEST (DF denominator)139
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation16062.2305079843
Sum Squared Residuals35861339595.9352

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.912017526887744 \tabularnewline
R-squared & 0.831775969350437 \tabularnewline
Adjusted R-squared & 0.826934990051168 \tabularnewline
F-TEST (value) & 171.819774043695 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 139 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 16062.2305079843 \tabularnewline
Sum Squared Residuals & 35861339595.9352 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147106&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.912017526887744[/C][/ROW]
[ROW][C]R-squared[/C][C]0.831775969350437[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.826934990051168[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]171.819774043695[/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]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]16062.2305079843[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]35861339595.9352[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147106&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147106&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 R0.912017526887744
R-squared0.831775969350437
Adjusted R-squared0.826934990051168
F-TEST (value)171.819774043695
F-TEST (DF numerator)4
F-TEST (DF denominator)139
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation16062.2305079843
Sum Squared Residuals35861339595.9352







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16304751592.630776344911454.3692236551
26675149134.177959824517616.8220401754
371766736.58200869959439.417991300413
47830685565.6039527777-7259.60395277769
5144655155404.137636198-10749.1376361983
6269638250223.91817910219414.0818208984
76926667581.31890778971684.68109221026
88352964488.305744905519040.6942550945
97322677736.1766836084-4510.17668360837
10178519134391.7107983844127.2892016196
116725076222.5459202076-8972.54592020759
12102982103326.207572708-344.207572707961
135000167958.2573485853-17957.2573485853
1491093111128.64195518-20035.6419551796
158011259469.099270457220642.9007295428
167296174545.423467081-1584.42346708098
177715999991.549898599-22832.5498985989
181562917173.7455545314-1544.74555453137
1971693118808.777598111-47115.777598111
201992050576.0765198173-30656.0765198173
213940348491.3710438011-9088.37104380114
22104383112935.614664686-8552.61466468559
235608851940.95079866474147.04920133529
246200672960.216723569-10954.216723569
258166583573.5358219205-1908.53582192049
266945152704.158094280416746.8419057196
278879493614.5729583551-4820.57295835508
289064266033.751521205524608.2484787945
29207069145448.10671258861620.8932874118
309934090521.53275065818818.46724934193
315669558456.2330906861-1761.23309068606
3210814383620.600332302624522.3996676974
336420478857.6036651803-14653.6036651803
342910141601.3877756423-12500.3877756423
3511306095042.207239328518017.7927606715
360-3711.560602544083711.56060254408
376577361723.60976627514049.39023372495
386704780551.6900446412-13504.6900446412
394195361734.3614776771-19781.3614776771
40113787122099.713301268-8312.71330126845
418658486477.145565931106.854434068998
425958869399.369423806-9811.36942380595
434006444504.8411362493-4440.84113624935
4474471101097.539139238-26626.5391392376
456043773133.4330165082-12696.4330165082
465511840402.06782453514715.932175465
474029532471.69524344667823.30475655338
4810339785236.006608908218160.9933910918
497898275064.36737214043917.63262785955
506731791838.9855022458-24521.9855022458
513988738085.26541035331801.73458964671
525968251891.63262964467790.3673703554
53132740100914.89122849831825.108771502
5410481681843.660456997422972.3395430026
5510139598231.15656470453163.84343529549
567282461647.495289858111176.5047101419
577601862114.276198640313903.7238013597
583389136085.5760812783-2194.57608127826
596369462727.2070366507966.792963349302
603323956565.3827620012-23326.3827620012
613509339235.7562505555-4142.75625055546
623525258369.4418486565-23117.4418486565
633697746818.5664824553-9841.56648245533
644240639541.9220519732864.07794802695
655635338084.438006603118268.5619933969
665881773624.8998651291-14807.8998651291
678105191940.8873482188-10889.8873482188
6870872103922.556758285-33050.5567582846
694237248774.6899045687-6402.68990456871
701914411683.02550041637460.97449958371
7111417799576.486765407214600.5132345928
725941451657.6319903297756.36800967099
735137972462.8428745401-21083.8428745401
744075659028.1378777559-18272.1378777559
755339878009.9210148758-24611.9210148758
761779925294.2410397111-7495.24103971106
777115464059.49425880547094.50574119462
785830584176.6580774295-25871.6580774296
792745437113.7089329073-9659.7089329073
803432360850.3641711208-26527.3641711208
814476152867.8277089816-8106.82770898163
8211386268777.833064607945084.1669353921
833502736310.9477658344-1283.94776583438
846239674054.6878181349-11658.6878181349
852961343615.0550003547-14002.0550003547
866555965172.45137999386.54862000998
8712006487697.244259949932366.7557400501
883614943625.8121799965-7476.81217999655
894018144847.6558351475-4666.65583514755
905339850812.45931736352585.54068263653
915643566819.5979327251-10384.5979327251
927728391771.6050025595-14488.6050025595
937173852242.898068191219495.1019318088
944850349031.760559126-528.760559126042
952521440652.8239655915-15438.8239655915
9611942490770.971984010828653.0280159892
977920180226.0608878073-1025.06088780728
981934914242.9608692825106.03913071802
997876066398.857148412612361.1428515874
1005413362928.3686337614-8795.36863376144
1012162317784.28578293693838.71421706308
1022549743872.3588810225-18375.3588810225
1036953559464.759133526310070.2408664737
1043070925123.12128541975585.87871458031
1053704326254.236196376710788.7638036233
1062471623842.0022956447873.997704355348
1076073459417.64871225431316.35128774571
1082724616665.846864132710580.1531358673
1090-3711.560602544083711.56060254408
1103881445763.9733153817-6949.97331538168
1112764644064.9575262952-16418.9575262952
1126537364157.39566587531215.60433412475
1134302157954.87929904-14933.87929904
1144311644664.7174080688-1548.71740806877
1153058-3575.640689783696633.64068978369
1160-3711.560602544083711.56060254408
1179634762862.597904418433484.4020955816
1185519574587.4259519186-19392.4259519186
1197332160408.646841905912912.3531580941
1204526655461.6813144134-10195.6813144134
1214341042184.36082172281225.63917827722
1228384278338.64810542435503.35189457568
1233929635956.31867064173339.68132935827
1243849047582.6975103357-9092.69751033565
1253984130984.11052332498856.88947667513
1261976416335.47759991583428.52240008425
1276646376261.1079218632-9798.10792186324
1286458950064.996752149114524.0032478509
1296333972314.231312402-8975.23131240205
130117967841.594123037243954.40587696276
1317627-313.5627835343137940.56278353431
1326899852029.484364130916968.5156358691
1336836-2216.441562179789052.44156217978
1343541443583.2647365408-8169.26473654077
1355118-476.0204775016785594.02047750168
1362089816436.32936621714461.67063378294
1370-3711.560602544083711.56060254408
1384269044222.9981322234-1532.99813222336
1391450723229.5666729887-8722.56667298865
1407131-41.7229580135297172.72295801353
1414194-1808.681823898616002.68182389861
1422141625222.9447320164-3806.9447320164
1433900030362.38632133928637.61367866084
1444241946025.6102313399-3606.61023133994

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 63047 & 51592.6307763449 & 11454.3692236551 \tabularnewline
2 & 66751 & 49134.1779598245 & 17616.8220401754 \tabularnewline
3 & 7176 & 6736.58200869959 & 439.417991300413 \tabularnewline
4 & 78306 & 85565.6039527777 & -7259.60395277769 \tabularnewline
5 & 144655 & 155404.137636198 & -10749.1376361983 \tabularnewline
6 & 269638 & 250223.918179102 & 19414.0818208984 \tabularnewline
7 & 69266 & 67581.3189077897 & 1684.68109221026 \tabularnewline
8 & 83529 & 64488.3057449055 & 19040.6942550945 \tabularnewline
9 & 73226 & 77736.1766836084 & -4510.17668360837 \tabularnewline
10 & 178519 & 134391.71079838 & 44127.2892016196 \tabularnewline
11 & 67250 & 76222.5459202076 & -8972.54592020759 \tabularnewline
12 & 102982 & 103326.207572708 & -344.207572707961 \tabularnewline
13 & 50001 & 67958.2573485853 & -17957.2573485853 \tabularnewline
14 & 91093 & 111128.64195518 & -20035.6419551796 \tabularnewline
15 & 80112 & 59469.0992704572 & 20642.9007295428 \tabularnewline
16 & 72961 & 74545.423467081 & -1584.42346708098 \tabularnewline
17 & 77159 & 99991.549898599 & -22832.5498985989 \tabularnewline
18 & 15629 & 17173.7455545314 & -1544.74555453137 \tabularnewline
19 & 71693 & 118808.777598111 & -47115.777598111 \tabularnewline
20 & 19920 & 50576.0765198173 & -30656.0765198173 \tabularnewline
21 & 39403 & 48491.3710438011 & -9088.37104380114 \tabularnewline
22 & 104383 & 112935.614664686 & -8552.61466468559 \tabularnewline
23 & 56088 & 51940.9507986647 & 4147.04920133529 \tabularnewline
24 & 62006 & 72960.216723569 & -10954.216723569 \tabularnewline
25 & 81665 & 83573.5358219205 & -1908.53582192049 \tabularnewline
26 & 69451 & 52704.1580942804 & 16746.8419057196 \tabularnewline
27 & 88794 & 93614.5729583551 & -4820.57295835508 \tabularnewline
28 & 90642 & 66033.7515212055 & 24608.2484787945 \tabularnewline
29 & 207069 & 145448.106712588 & 61620.8932874118 \tabularnewline
30 & 99340 & 90521.5327506581 & 8818.46724934193 \tabularnewline
31 & 56695 & 58456.2330906861 & -1761.23309068606 \tabularnewline
32 & 108143 & 83620.6003323026 & 24522.3996676974 \tabularnewline
33 & 64204 & 78857.6036651803 & -14653.6036651803 \tabularnewline
34 & 29101 & 41601.3877756423 & -12500.3877756423 \tabularnewline
35 & 113060 & 95042.2072393285 & 18017.7927606715 \tabularnewline
36 & 0 & -3711.56060254408 & 3711.56060254408 \tabularnewline
37 & 65773 & 61723.6097662751 & 4049.39023372495 \tabularnewline
38 & 67047 & 80551.6900446412 & -13504.6900446412 \tabularnewline
39 & 41953 & 61734.3614776771 & -19781.3614776771 \tabularnewline
40 & 113787 & 122099.713301268 & -8312.71330126845 \tabularnewline
41 & 86584 & 86477.145565931 & 106.854434068998 \tabularnewline
42 & 59588 & 69399.369423806 & -9811.36942380595 \tabularnewline
43 & 40064 & 44504.8411362493 & -4440.84113624935 \tabularnewline
44 & 74471 & 101097.539139238 & -26626.5391392376 \tabularnewline
45 & 60437 & 73133.4330165082 & -12696.4330165082 \tabularnewline
46 & 55118 & 40402.067824535 & 14715.932175465 \tabularnewline
47 & 40295 & 32471.6952434466 & 7823.30475655338 \tabularnewline
48 & 103397 & 85236.0066089082 & 18160.9933910918 \tabularnewline
49 & 78982 & 75064.3673721404 & 3917.63262785955 \tabularnewline
50 & 67317 & 91838.9855022458 & -24521.9855022458 \tabularnewline
51 & 39887 & 38085.2654103533 & 1801.73458964671 \tabularnewline
52 & 59682 & 51891.6326296446 & 7790.3673703554 \tabularnewline
53 & 132740 & 100914.891228498 & 31825.108771502 \tabularnewline
54 & 104816 & 81843.6604569974 & 22972.3395430026 \tabularnewline
55 & 101395 & 98231.1565647045 & 3163.84343529549 \tabularnewline
56 & 72824 & 61647.4952898581 & 11176.5047101419 \tabularnewline
57 & 76018 & 62114.2761986403 & 13903.7238013597 \tabularnewline
58 & 33891 & 36085.5760812783 & -2194.57608127826 \tabularnewline
59 & 63694 & 62727.2070366507 & 966.792963349302 \tabularnewline
60 & 33239 & 56565.3827620012 & -23326.3827620012 \tabularnewline
61 & 35093 & 39235.7562505555 & -4142.75625055546 \tabularnewline
62 & 35252 & 58369.4418486565 & -23117.4418486565 \tabularnewline
63 & 36977 & 46818.5664824553 & -9841.56648245533 \tabularnewline
64 & 42406 & 39541.922051973 & 2864.07794802695 \tabularnewline
65 & 56353 & 38084.4380066031 & 18268.5619933969 \tabularnewline
66 & 58817 & 73624.8998651291 & -14807.8998651291 \tabularnewline
67 & 81051 & 91940.8873482188 & -10889.8873482188 \tabularnewline
68 & 70872 & 103922.556758285 & -33050.5567582846 \tabularnewline
69 & 42372 & 48774.6899045687 & -6402.68990456871 \tabularnewline
70 & 19144 & 11683.0255004163 & 7460.97449958371 \tabularnewline
71 & 114177 & 99576.4867654072 & 14600.5132345928 \tabularnewline
72 & 59414 & 51657.631990329 & 7756.36800967099 \tabularnewline
73 & 51379 & 72462.8428745401 & -21083.8428745401 \tabularnewline
74 & 40756 & 59028.1378777559 & -18272.1378777559 \tabularnewline
75 & 53398 & 78009.9210148758 & -24611.9210148758 \tabularnewline
76 & 17799 & 25294.2410397111 & -7495.24103971106 \tabularnewline
77 & 71154 & 64059.4942588054 & 7094.50574119462 \tabularnewline
78 & 58305 & 84176.6580774295 & -25871.6580774296 \tabularnewline
79 & 27454 & 37113.7089329073 & -9659.7089329073 \tabularnewline
80 & 34323 & 60850.3641711208 & -26527.3641711208 \tabularnewline
81 & 44761 & 52867.8277089816 & -8106.82770898163 \tabularnewline
82 & 113862 & 68777.8330646079 & 45084.1669353921 \tabularnewline
83 & 35027 & 36310.9477658344 & -1283.94776583438 \tabularnewline
84 & 62396 & 74054.6878181349 & -11658.6878181349 \tabularnewline
85 & 29613 & 43615.0550003547 & -14002.0550003547 \tabularnewline
86 & 65559 & 65172.45137999 & 386.54862000998 \tabularnewline
87 & 120064 & 87697.2442599499 & 32366.7557400501 \tabularnewline
88 & 36149 & 43625.8121799965 & -7476.81217999655 \tabularnewline
89 & 40181 & 44847.6558351475 & -4666.65583514755 \tabularnewline
90 & 53398 & 50812.4593173635 & 2585.54068263653 \tabularnewline
91 & 56435 & 66819.5979327251 & -10384.5979327251 \tabularnewline
92 & 77283 & 91771.6050025595 & -14488.6050025595 \tabularnewline
93 & 71738 & 52242.8980681912 & 19495.1019318088 \tabularnewline
94 & 48503 & 49031.760559126 & -528.760559126042 \tabularnewline
95 & 25214 & 40652.8239655915 & -15438.8239655915 \tabularnewline
96 & 119424 & 90770.9719840108 & 28653.0280159892 \tabularnewline
97 & 79201 & 80226.0608878073 & -1025.06088780728 \tabularnewline
98 & 19349 & 14242.960869282 & 5106.03913071802 \tabularnewline
99 & 78760 & 66398.8571484126 & 12361.1428515874 \tabularnewline
100 & 54133 & 62928.3686337614 & -8795.36863376144 \tabularnewline
101 & 21623 & 17784.2857829369 & 3838.71421706308 \tabularnewline
102 & 25497 & 43872.3588810225 & -18375.3588810225 \tabularnewline
103 & 69535 & 59464.7591335263 & 10070.2408664737 \tabularnewline
104 & 30709 & 25123.1212854197 & 5585.87871458031 \tabularnewline
105 & 37043 & 26254.2361963767 & 10788.7638036233 \tabularnewline
106 & 24716 & 23842.0022956447 & 873.997704355348 \tabularnewline
107 & 60734 & 59417.6487122543 & 1316.35128774571 \tabularnewline
108 & 27246 & 16665.8468641327 & 10580.1531358673 \tabularnewline
109 & 0 & -3711.56060254408 & 3711.56060254408 \tabularnewline
110 & 38814 & 45763.9733153817 & -6949.97331538168 \tabularnewline
111 & 27646 & 44064.9575262952 & -16418.9575262952 \tabularnewline
112 & 65373 & 64157.3956658753 & 1215.60433412475 \tabularnewline
113 & 43021 & 57954.87929904 & -14933.87929904 \tabularnewline
114 & 43116 & 44664.7174080688 & -1548.71740806877 \tabularnewline
115 & 3058 & -3575.64068978369 & 6633.64068978369 \tabularnewline
116 & 0 & -3711.56060254408 & 3711.56060254408 \tabularnewline
117 & 96347 & 62862.5979044184 & 33484.4020955816 \tabularnewline
118 & 55195 & 74587.4259519186 & -19392.4259519186 \tabularnewline
119 & 73321 & 60408.6468419059 & 12912.3531580941 \tabularnewline
120 & 45266 & 55461.6813144134 & -10195.6813144134 \tabularnewline
121 & 43410 & 42184.3608217228 & 1225.63917827722 \tabularnewline
122 & 83842 & 78338.6481054243 & 5503.35189457568 \tabularnewline
123 & 39296 & 35956.3186706417 & 3339.68132935827 \tabularnewline
124 & 38490 & 47582.6975103357 & -9092.69751033565 \tabularnewline
125 & 39841 & 30984.1105233249 & 8856.88947667513 \tabularnewline
126 & 19764 & 16335.4775999158 & 3428.52240008425 \tabularnewline
127 & 66463 & 76261.1079218632 & -9798.10792186324 \tabularnewline
128 & 64589 & 50064.9967521491 & 14524.0032478509 \tabularnewline
129 & 63339 & 72314.231312402 & -8975.23131240205 \tabularnewline
130 & 11796 & 7841.59412303724 & 3954.40587696276 \tabularnewline
131 & 7627 & -313.562783534313 & 7940.56278353431 \tabularnewline
132 & 68998 & 52029.4843641309 & 16968.5156358691 \tabularnewline
133 & 6836 & -2216.44156217978 & 9052.44156217978 \tabularnewline
134 & 35414 & 43583.2647365408 & -8169.26473654077 \tabularnewline
135 & 5118 & -476.020477501678 & 5594.02047750168 \tabularnewline
136 & 20898 & 16436.3293662171 & 4461.67063378294 \tabularnewline
137 & 0 & -3711.56060254408 & 3711.56060254408 \tabularnewline
138 & 42690 & 44222.9981322234 & -1532.99813222336 \tabularnewline
139 & 14507 & 23229.5666729887 & -8722.56667298865 \tabularnewline
140 & 7131 & -41.722958013529 & 7172.72295801353 \tabularnewline
141 & 4194 & -1808.68182389861 & 6002.68182389861 \tabularnewline
142 & 21416 & 25222.9447320164 & -3806.9447320164 \tabularnewline
143 & 39000 & 30362.3863213392 & 8637.61367866084 \tabularnewline
144 & 42419 & 46025.6102313399 & -3606.61023133994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147106&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]63047[/C][C]51592.6307763449[/C][C]11454.3692236551[/C][/ROW]
[ROW][C]2[/C][C]66751[/C][C]49134.1779598245[/C][C]17616.8220401754[/C][/ROW]
[ROW][C]3[/C][C]7176[/C][C]6736.58200869959[/C][C]439.417991300413[/C][/ROW]
[ROW][C]4[/C][C]78306[/C][C]85565.6039527777[/C][C]-7259.60395277769[/C][/ROW]
[ROW][C]5[/C][C]144655[/C][C]155404.137636198[/C][C]-10749.1376361983[/C][/ROW]
[ROW][C]6[/C][C]269638[/C][C]250223.918179102[/C][C]19414.0818208984[/C][/ROW]
[ROW][C]7[/C][C]69266[/C][C]67581.3189077897[/C][C]1684.68109221026[/C][/ROW]
[ROW][C]8[/C][C]83529[/C][C]64488.3057449055[/C][C]19040.6942550945[/C][/ROW]
[ROW][C]9[/C][C]73226[/C][C]77736.1766836084[/C][C]-4510.17668360837[/C][/ROW]
[ROW][C]10[/C][C]178519[/C][C]134391.71079838[/C][C]44127.2892016196[/C][/ROW]
[ROW][C]11[/C][C]67250[/C][C]76222.5459202076[/C][C]-8972.54592020759[/C][/ROW]
[ROW][C]12[/C][C]102982[/C][C]103326.207572708[/C][C]-344.207572707961[/C][/ROW]
[ROW][C]13[/C][C]50001[/C][C]67958.2573485853[/C][C]-17957.2573485853[/C][/ROW]
[ROW][C]14[/C][C]91093[/C][C]111128.64195518[/C][C]-20035.6419551796[/C][/ROW]
[ROW][C]15[/C][C]80112[/C][C]59469.0992704572[/C][C]20642.9007295428[/C][/ROW]
[ROW][C]16[/C][C]72961[/C][C]74545.423467081[/C][C]-1584.42346708098[/C][/ROW]
[ROW][C]17[/C][C]77159[/C][C]99991.549898599[/C][C]-22832.5498985989[/C][/ROW]
[ROW][C]18[/C][C]15629[/C][C]17173.7455545314[/C][C]-1544.74555453137[/C][/ROW]
[ROW][C]19[/C][C]71693[/C][C]118808.777598111[/C][C]-47115.777598111[/C][/ROW]
[ROW][C]20[/C][C]19920[/C][C]50576.0765198173[/C][C]-30656.0765198173[/C][/ROW]
[ROW][C]21[/C][C]39403[/C][C]48491.3710438011[/C][C]-9088.37104380114[/C][/ROW]
[ROW][C]22[/C][C]104383[/C][C]112935.614664686[/C][C]-8552.61466468559[/C][/ROW]
[ROW][C]23[/C][C]56088[/C][C]51940.9507986647[/C][C]4147.04920133529[/C][/ROW]
[ROW][C]24[/C][C]62006[/C][C]72960.216723569[/C][C]-10954.216723569[/C][/ROW]
[ROW][C]25[/C][C]81665[/C][C]83573.5358219205[/C][C]-1908.53582192049[/C][/ROW]
[ROW][C]26[/C][C]69451[/C][C]52704.1580942804[/C][C]16746.8419057196[/C][/ROW]
[ROW][C]27[/C][C]88794[/C][C]93614.5729583551[/C][C]-4820.57295835508[/C][/ROW]
[ROW][C]28[/C][C]90642[/C][C]66033.7515212055[/C][C]24608.2484787945[/C][/ROW]
[ROW][C]29[/C][C]207069[/C][C]145448.106712588[/C][C]61620.8932874118[/C][/ROW]
[ROW][C]30[/C][C]99340[/C][C]90521.5327506581[/C][C]8818.46724934193[/C][/ROW]
[ROW][C]31[/C][C]56695[/C][C]58456.2330906861[/C][C]-1761.23309068606[/C][/ROW]
[ROW][C]32[/C][C]108143[/C][C]83620.6003323026[/C][C]24522.3996676974[/C][/ROW]
[ROW][C]33[/C][C]64204[/C][C]78857.6036651803[/C][C]-14653.6036651803[/C][/ROW]
[ROW][C]34[/C][C]29101[/C][C]41601.3877756423[/C][C]-12500.3877756423[/C][/ROW]
[ROW][C]35[/C][C]113060[/C][C]95042.2072393285[/C][C]18017.7927606715[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-3711.56060254408[/C][C]3711.56060254408[/C][/ROW]
[ROW][C]37[/C][C]65773[/C][C]61723.6097662751[/C][C]4049.39023372495[/C][/ROW]
[ROW][C]38[/C][C]67047[/C][C]80551.6900446412[/C][C]-13504.6900446412[/C][/ROW]
[ROW][C]39[/C][C]41953[/C][C]61734.3614776771[/C][C]-19781.3614776771[/C][/ROW]
[ROW][C]40[/C][C]113787[/C][C]122099.713301268[/C][C]-8312.71330126845[/C][/ROW]
[ROW][C]41[/C][C]86584[/C][C]86477.145565931[/C][C]106.854434068998[/C][/ROW]
[ROW][C]42[/C][C]59588[/C][C]69399.369423806[/C][C]-9811.36942380595[/C][/ROW]
[ROW][C]43[/C][C]40064[/C][C]44504.8411362493[/C][C]-4440.84113624935[/C][/ROW]
[ROW][C]44[/C][C]74471[/C][C]101097.539139238[/C][C]-26626.5391392376[/C][/ROW]
[ROW][C]45[/C][C]60437[/C][C]73133.4330165082[/C][C]-12696.4330165082[/C][/ROW]
[ROW][C]46[/C][C]55118[/C][C]40402.067824535[/C][C]14715.932175465[/C][/ROW]
[ROW][C]47[/C][C]40295[/C][C]32471.6952434466[/C][C]7823.30475655338[/C][/ROW]
[ROW][C]48[/C][C]103397[/C][C]85236.0066089082[/C][C]18160.9933910918[/C][/ROW]
[ROW][C]49[/C][C]78982[/C][C]75064.3673721404[/C][C]3917.63262785955[/C][/ROW]
[ROW][C]50[/C][C]67317[/C][C]91838.9855022458[/C][C]-24521.9855022458[/C][/ROW]
[ROW][C]51[/C][C]39887[/C][C]38085.2654103533[/C][C]1801.73458964671[/C][/ROW]
[ROW][C]52[/C][C]59682[/C][C]51891.6326296446[/C][C]7790.3673703554[/C][/ROW]
[ROW][C]53[/C][C]132740[/C][C]100914.891228498[/C][C]31825.108771502[/C][/ROW]
[ROW][C]54[/C][C]104816[/C][C]81843.6604569974[/C][C]22972.3395430026[/C][/ROW]
[ROW][C]55[/C][C]101395[/C][C]98231.1565647045[/C][C]3163.84343529549[/C][/ROW]
[ROW][C]56[/C][C]72824[/C][C]61647.4952898581[/C][C]11176.5047101419[/C][/ROW]
[ROW][C]57[/C][C]76018[/C][C]62114.2761986403[/C][C]13903.7238013597[/C][/ROW]
[ROW][C]58[/C][C]33891[/C][C]36085.5760812783[/C][C]-2194.57608127826[/C][/ROW]
[ROW][C]59[/C][C]63694[/C][C]62727.2070366507[/C][C]966.792963349302[/C][/ROW]
[ROW][C]60[/C][C]33239[/C][C]56565.3827620012[/C][C]-23326.3827620012[/C][/ROW]
[ROW][C]61[/C][C]35093[/C][C]39235.7562505555[/C][C]-4142.75625055546[/C][/ROW]
[ROW][C]62[/C][C]35252[/C][C]58369.4418486565[/C][C]-23117.4418486565[/C][/ROW]
[ROW][C]63[/C][C]36977[/C][C]46818.5664824553[/C][C]-9841.56648245533[/C][/ROW]
[ROW][C]64[/C][C]42406[/C][C]39541.922051973[/C][C]2864.07794802695[/C][/ROW]
[ROW][C]65[/C][C]56353[/C][C]38084.4380066031[/C][C]18268.5619933969[/C][/ROW]
[ROW][C]66[/C][C]58817[/C][C]73624.8998651291[/C][C]-14807.8998651291[/C][/ROW]
[ROW][C]67[/C][C]81051[/C][C]91940.8873482188[/C][C]-10889.8873482188[/C][/ROW]
[ROW][C]68[/C][C]70872[/C][C]103922.556758285[/C][C]-33050.5567582846[/C][/ROW]
[ROW][C]69[/C][C]42372[/C][C]48774.6899045687[/C][C]-6402.68990456871[/C][/ROW]
[ROW][C]70[/C][C]19144[/C][C]11683.0255004163[/C][C]7460.97449958371[/C][/ROW]
[ROW][C]71[/C][C]114177[/C][C]99576.4867654072[/C][C]14600.5132345928[/C][/ROW]
[ROW][C]72[/C][C]59414[/C][C]51657.631990329[/C][C]7756.36800967099[/C][/ROW]
[ROW][C]73[/C][C]51379[/C][C]72462.8428745401[/C][C]-21083.8428745401[/C][/ROW]
[ROW][C]74[/C][C]40756[/C][C]59028.1378777559[/C][C]-18272.1378777559[/C][/ROW]
[ROW][C]75[/C][C]53398[/C][C]78009.9210148758[/C][C]-24611.9210148758[/C][/ROW]
[ROW][C]76[/C][C]17799[/C][C]25294.2410397111[/C][C]-7495.24103971106[/C][/ROW]
[ROW][C]77[/C][C]71154[/C][C]64059.4942588054[/C][C]7094.50574119462[/C][/ROW]
[ROW][C]78[/C][C]58305[/C][C]84176.6580774295[/C][C]-25871.6580774296[/C][/ROW]
[ROW][C]79[/C][C]27454[/C][C]37113.7089329073[/C][C]-9659.7089329073[/C][/ROW]
[ROW][C]80[/C][C]34323[/C][C]60850.3641711208[/C][C]-26527.3641711208[/C][/ROW]
[ROW][C]81[/C][C]44761[/C][C]52867.8277089816[/C][C]-8106.82770898163[/C][/ROW]
[ROW][C]82[/C][C]113862[/C][C]68777.8330646079[/C][C]45084.1669353921[/C][/ROW]
[ROW][C]83[/C][C]35027[/C][C]36310.9477658344[/C][C]-1283.94776583438[/C][/ROW]
[ROW][C]84[/C][C]62396[/C][C]74054.6878181349[/C][C]-11658.6878181349[/C][/ROW]
[ROW][C]85[/C][C]29613[/C][C]43615.0550003547[/C][C]-14002.0550003547[/C][/ROW]
[ROW][C]86[/C][C]65559[/C][C]65172.45137999[/C][C]386.54862000998[/C][/ROW]
[ROW][C]87[/C][C]120064[/C][C]87697.2442599499[/C][C]32366.7557400501[/C][/ROW]
[ROW][C]88[/C][C]36149[/C][C]43625.8121799965[/C][C]-7476.81217999655[/C][/ROW]
[ROW][C]89[/C][C]40181[/C][C]44847.6558351475[/C][C]-4666.65583514755[/C][/ROW]
[ROW][C]90[/C][C]53398[/C][C]50812.4593173635[/C][C]2585.54068263653[/C][/ROW]
[ROW][C]91[/C][C]56435[/C][C]66819.5979327251[/C][C]-10384.5979327251[/C][/ROW]
[ROW][C]92[/C][C]77283[/C][C]91771.6050025595[/C][C]-14488.6050025595[/C][/ROW]
[ROW][C]93[/C][C]71738[/C][C]52242.8980681912[/C][C]19495.1019318088[/C][/ROW]
[ROW][C]94[/C][C]48503[/C][C]49031.760559126[/C][C]-528.760559126042[/C][/ROW]
[ROW][C]95[/C][C]25214[/C][C]40652.8239655915[/C][C]-15438.8239655915[/C][/ROW]
[ROW][C]96[/C][C]119424[/C][C]90770.9719840108[/C][C]28653.0280159892[/C][/ROW]
[ROW][C]97[/C][C]79201[/C][C]80226.0608878073[/C][C]-1025.06088780728[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]14242.960869282[/C][C]5106.03913071802[/C][/ROW]
[ROW][C]99[/C][C]78760[/C][C]66398.8571484126[/C][C]12361.1428515874[/C][/ROW]
[ROW][C]100[/C][C]54133[/C][C]62928.3686337614[/C][C]-8795.36863376144[/C][/ROW]
[ROW][C]101[/C][C]21623[/C][C]17784.2857829369[/C][C]3838.71421706308[/C][/ROW]
[ROW][C]102[/C][C]25497[/C][C]43872.3588810225[/C][C]-18375.3588810225[/C][/ROW]
[ROW][C]103[/C][C]69535[/C][C]59464.7591335263[/C][C]10070.2408664737[/C][/ROW]
[ROW][C]104[/C][C]30709[/C][C]25123.1212854197[/C][C]5585.87871458031[/C][/ROW]
[ROW][C]105[/C][C]37043[/C][C]26254.2361963767[/C][C]10788.7638036233[/C][/ROW]
[ROW][C]106[/C][C]24716[/C][C]23842.0022956447[/C][C]873.997704355348[/C][/ROW]
[ROW][C]107[/C][C]60734[/C][C]59417.6487122543[/C][C]1316.35128774571[/C][/ROW]
[ROW][C]108[/C][C]27246[/C][C]16665.8468641327[/C][C]10580.1531358673[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]-3711.56060254408[/C][C]3711.56060254408[/C][/ROW]
[ROW][C]110[/C][C]38814[/C][C]45763.9733153817[/C][C]-6949.97331538168[/C][/ROW]
[ROW][C]111[/C][C]27646[/C][C]44064.9575262952[/C][C]-16418.9575262952[/C][/ROW]
[ROW][C]112[/C][C]65373[/C][C]64157.3956658753[/C][C]1215.60433412475[/C][/ROW]
[ROW][C]113[/C][C]43021[/C][C]57954.87929904[/C][C]-14933.87929904[/C][/ROW]
[ROW][C]114[/C][C]43116[/C][C]44664.7174080688[/C][C]-1548.71740806877[/C][/ROW]
[ROW][C]115[/C][C]3058[/C][C]-3575.64068978369[/C][C]6633.64068978369[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]-3711.56060254408[/C][C]3711.56060254408[/C][/ROW]
[ROW][C]117[/C][C]96347[/C][C]62862.5979044184[/C][C]33484.4020955816[/C][/ROW]
[ROW][C]118[/C][C]55195[/C][C]74587.4259519186[/C][C]-19392.4259519186[/C][/ROW]
[ROW][C]119[/C][C]73321[/C][C]60408.6468419059[/C][C]12912.3531580941[/C][/ROW]
[ROW][C]120[/C][C]45266[/C][C]55461.6813144134[/C][C]-10195.6813144134[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]42184.3608217228[/C][C]1225.63917827722[/C][/ROW]
[ROW][C]122[/C][C]83842[/C][C]78338.6481054243[/C][C]5503.35189457568[/C][/ROW]
[ROW][C]123[/C][C]39296[/C][C]35956.3186706417[/C][C]3339.68132935827[/C][/ROW]
[ROW][C]124[/C][C]38490[/C][C]47582.6975103357[/C][C]-9092.69751033565[/C][/ROW]
[ROW][C]125[/C][C]39841[/C][C]30984.1105233249[/C][C]8856.88947667513[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]16335.4775999158[/C][C]3428.52240008425[/C][/ROW]
[ROW][C]127[/C][C]66463[/C][C]76261.1079218632[/C][C]-9798.10792186324[/C][/ROW]
[ROW][C]128[/C][C]64589[/C][C]50064.9967521491[/C][C]14524.0032478509[/C][/ROW]
[ROW][C]129[/C][C]63339[/C][C]72314.231312402[/C][C]-8975.23131240205[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]7841.59412303724[/C][C]3954.40587696276[/C][/ROW]
[ROW][C]131[/C][C]7627[/C][C]-313.562783534313[/C][C]7940.56278353431[/C][/ROW]
[ROW][C]132[/C][C]68998[/C][C]52029.4843641309[/C][C]16968.5156358691[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]-2216.44156217978[/C][C]9052.44156217978[/C][/ROW]
[ROW][C]134[/C][C]35414[/C][C]43583.2647365408[/C][C]-8169.26473654077[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]-476.020477501678[/C][C]5594.02047750168[/C][/ROW]
[ROW][C]136[/C][C]20898[/C][C]16436.3293662171[/C][C]4461.67063378294[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]-3711.56060254408[/C][C]3711.56060254408[/C][/ROW]
[ROW][C]138[/C][C]42690[/C][C]44222.9981322234[/C][C]-1532.99813222336[/C][/ROW]
[ROW][C]139[/C][C]14507[/C][C]23229.5666729887[/C][C]-8722.56667298865[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]-41.722958013529[/C][C]7172.72295801353[/C][/ROW]
[ROW][C]141[/C][C]4194[/C][C]-1808.68182389861[/C][C]6002.68182389861[/C][/ROW]
[ROW][C]142[/C][C]21416[/C][C]25222.9447320164[/C][C]-3806.9447320164[/C][/ROW]
[ROW][C]143[/C][C]39000[/C][C]30362.3863213392[/C][C]8637.61367866084[/C][/ROW]
[ROW][C]144[/C][C]42419[/C][C]46025.6102313399[/C][C]-3606.61023133994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147106&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147106&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
16304751592.630776344911454.3692236551
26675149134.177959824517616.8220401754
371766736.58200869959439.417991300413
47830685565.6039527777-7259.60395277769
5144655155404.137636198-10749.1376361983
6269638250223.91817910219414.0818208984
76926667581.31890778971684.68109221026
88352964488.305744905519040.6942550945
97322677736.1766836084-4510.17668360837
10178519134391.7107983844127.2892016196
116725076222.5459202076-8972.54592020759
12102982103326.207572708-344.207572707961
135000167958.2573485853-17957.2573485853
1491093111128.64195518-20035.6419551796
158011259469.099270457220642.9007295428
167296174545.423467081-1584.42346708098
177715999991.549898599-22832.5498985989
181562917173.7455545314-1544.74555453137
1971693118808.777598111-47115.777598111
201992050576.0765198173-30656.0765198173
213940348491.3710438011-9088.37104380114
22104383112935.614664686-8552.61466468559
235608851940.95079866474147.04920133529
246200672960.216723569-10954.216723569
258166583573.5358219205-1908.53582192049
266945152704.158094280416746.8419057196
278879493614.5729583551-4820.57295835508
289064266033.751521205524608.2484787945
29207069145448.10671258861620.8932874118
309934090521.53275065818818.46724934193
315669558456.2330906861-1761.23309068606
3210814383620.600332302624522.3996676974
336420478857.6036651803-14653.6036651803
342910141601.3877756423-12500.3877756423
3511306095042.207239328518017.7927606715
360-3711.560602544083711.56060254408
376577361723.60976627514049.39023372495
386704780551.6900446412-13504.6900446412
394195361734.3614776771-19781.3614776771
40113787122099.713301268-8312.71330126845
418658486477.145565931106.854434068998
425958869399.369423806-9811.36942380595
434006444504.8411362493-4440.84113624935
4474471101097.539139238-26626.5391392376
456043773133.4330165082-12696.4330165082
465511840402.06782453514715.932175465
474029532471.69524344667823.30475655338
4810339785236.006608908218160.9933910918
497898275064.36737214043917.63262785955
506731791838.9855022458-24521.9855022458
513988738085.26541035331801.73458964671
525968251891.63262964467790.3673703554
53132740100914.89122849831825.108771502
5410481681843.660456997422972.3395430026
5510139598231.15656470453163.84343529549
567282461647.495289858111176.5047101419
577601862114.276198640313903.7238013597
583389136085.5760812783-2194.57608127826
596369462727.2070366507966.792963349302
603323956565.3827620012-23326.3827620012
613509339235.7562505555-4142.75625055546
623525258369.4418486565-23117.4418486565
633697746818.5664824553-9841.56648245533
644240639541.9220519732864.07794802695
655635338084.438006603118268.5619933969
665881773624.8998651291-14807.8998651291
678105191940.8873482188-10889.8873482188
6870872103922.556758285-33050.5567582846
694237248774.6899045687-6402.68990456871
701914411683.02550041637460.97449958371
7111417799576.486765407214600.5132345928
725941451657.6319903297756.36800967099
735137972462.8428745401-21083.8428745401
744075659028.1378777559-18272.1378777559
755339878009.9210148758-24611.9210148758
761779925294.2410397111-7495.24103971106
777115464059.49425880547094.50574119462
785830584176.6580774295-25871.6580774296
792745437113.7089329073-9659.7089329073
803432360850.3641711208-26527.3641711208
814476152867.8277089816-8106.82770898163
8211386268777.833064607945084.1669353921
833502736310.9477658344-1283.94776583438
846239674054.6878181349-11658.6878181349
852961343615.0550003547-14002.0550003547
866555965172.45137999386.54862000998
8712006487697.244259949932366.7557400501
883614943625.8121799965-7476.81217999655
894018144847.6558351475-4666.65583514755
905339850812.45931736352585.54068263653
915643566819.5979327251-10384.5979327251
927728391771.6050025595-14488.6050025595
937173852242.898068191219495.1019318088
944850349031.760559126-528.760559126042
952521440652.8239655915-15438.8239655915
9611942490770.971984010828653.0280159892
977920180226.0608878073-1025.06088780728
981934914242.9608692825106.03913071802
997876066398.857148412612361.1428515874
1005413362928.3686337614-8795.36863376144
1012162317784.28578293693838.71421706308
1022549743872.3588810225-18375.3588810225
1036953559464.759133526310070.2408664737
1043070925123.12128541975585.87871458031
1053704326254.236196376710788.7638036233
1062471623842.0022956447873.997704355348
1076073459417.64871225431316.35128774571
1082724616665.846864132710580.1531358673
1090-3711.560602544083711.56060254408
1103881445763.9733153817-6949.97331538168
1112764644064.9575262952-16418.9575262952
1126537364157.39566587531215.60433412475
1134302157954.87929904-14933.87929904
1144311644664.7174080688-1548.71740806877
1153058-3575.640689783696633.64068978369
1160-3711.560602544083711.56060254408
1179634762862.597904418433484.4020955816
1185519574587.4259519186-19392.4259519186
1197332160408.646841905912912.3531580941
1204526655461.6813144134-10195.6813144134
1214341042184.36082172281225.63917827722
1228384278338.64810542435503.35189457568
1233929635956.31867064173339.68132935827
1243849047582.6975103357-9092.69751033565
1253984130984.11052332498856.88947667513
1261976416335.47759991583428.52240008425
1276646376261.1079218632-9798.10792186324
1286458950064.996752149114524.0032478509
1296333972314.231312402-8975.23131240205
130117967841.594123037243954.40587696276
1317627-313.5627835343137940.56278353431
1326899852029.484364130916968.5156358691
1336836-2216.441562179789052.44156217978
1343541443583.2647365408-8169.26473654077
1355118-476.0204775016785594.02047750168
1362089816436.32936621714461.67063378294
1370-3711.560602544083711.56060254408
1384269044222.9981322234-1532.99813222336
1391450723229.5666729887-8722.56667298865
1407131-41.7229580135297172.72295801353
1414194-1808.681823898616002.68182389861
1422141625222.9447320164-3806.9447320164
1433900030362.38632133928637.61367866084
1444241946025.6102313399-3606.61023133994







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1668904901627490.3337809803254990.833109509837251
90.08807562484066750.1761512496813350.911924375159332
100.7606497249416860.4787005501166280.239350275058314
110.6702134150081330.6595731699837350.329786584991867
120.5586675057426110.8826649885147780.441332494257389
130.5103352139307030.9793295721385940.489664786069297
140.7273628520141690.5452742959716610.272637147985831
150.6628999775013690.6742000449972620.337100022498631
160.6676628119729510.6646743760540980.332337188027049
170.8758104913867240.2483790172265520.124189508613276
180.8341281644449150.331743671110170.165871835555085
190.9756763141952930.04864737160941330.0243236858047067
200.9927032788191670.01459344236166510.00729672118083255
210.9886818764778850.02263624704423080.0113181235221154
220.9843298880048360.03134022399032710.0156701119951635
230.9785738155996870.04285236880062690.0214261844003134
240.9718174531461470.05636509370770650.0281825468538533
250.9595178695573780.0809642608852440.040482130442622
260.9602314641082160.07953707178356790.039768535891784
270.9449218293110910.1101563413778180.055078170688909
280.9603778592862090.07924428142758220.0396221407137911
290.9995713136934850.0008573726130302580.000428686306515129
300.9994070043407890.001185991318422790.000592995659211397
310.9990332740442230.001933451911554760.000966725955777382
320.9995174128579550.0009651742840890240.000482587142044512
330.999480215772580.001039568454840480.00051978422742024
340.999367709849520.001264580300959470.000632290150479736
350.9994457671947210.001108465610558370.000554232805279183
360.9991433168419270.001713366316145190.000856683158072593
370.9987004194859730.002599161028054560.00129958051402728
380.9984222926681050.003155414663790790.00157770733189539
390.9985586969665370.002882606066926550.00144130303346327
400.9979381910697240.004123617860552560.00206180893027628
410.9969228904484260.006154219103148080.00307710955157404
420.9959870154956420.008025969008716130.00401298450435807
430.9942555655587460.01148886888250780.00574443444125391
440.9964910731563390.007017853687322740.00350892684366137
450.9955851036905260.008829792618947160.00441489630947358
460.9956710598694350.008657880261128980.00432894013056449
470.9943688012623320.01126239747533550.00563119873766777
480.9951291324336220.009741735132755450.00487086756637772
490.9933366750256410.01332664994871710.00666332497435853
500.9955114378407830.008977124318433230.00448856215921662
510.9937176838148670.01256463237026630.00628231618513317
520.9918045934792470.0163908130415070.00819540652075348
530.9975865423483750.004826915303249930.00241345765162496
540.9986255373010470.002748925397906040.00137446269895302
550.9980043697251660.003991260549668650.00199563027483432
560.9976103010698910.004779397860217990.00238969893010899
570.9976666935605590.004666612878881030.00233330643944051
580.996631493052510.006737013894979590.0033685069474898
590.9951560536025340.009687892794931230.00484394639746562
600.9965817204296670.00683655914066690.00341827957033345
610.9961918882444670.007616223511066070.00380811175553304
620.9977186175389020.004562764922196010.00228138246109801
630.9970563236999640.005887352600072070.00294367630003603
640.9957853251336680.008429349732664480.00421467486633224
650.9962313036876060.007537392624787850.00376869631239392
660.9961311289252670.007737742149466320.00386887107473316
670.9948949280727720.01021014385445680.00510507192722842
680.9985718660041190.002856267991761320.00142813399588066
690.9979923096909260.004015380618147010.00200769030907351
700.9972448666296160.005510266740767670.00275513337038383
710.9972130723669890.005573855266022530.00278692763301127
720.9974167900565020.005166419886996890.00258320994349845
730.9980359243134520.003928151373096140.00196407568654807
740.9981357646494660.003728470701067990.00186423535053399
750.9996513934324210.0006972131351582820.000348606567579141
760.9997517116610130.0004965766779746260.000248288338987313
770.9997404427617410.000519114476517510.000259557238258755
780.9998205261816090.0003589476367813860.000179473818390693
790.9997703689969890.0004592620060219290.000229631003010965
800.9998190776250930.0003618447498138140.000180922374906907
810.9997270203856580.0005459592286837250.000272979614341863
820.9999911999908751.76000182493079e-058.80000912465397e-06
830.999984271757263.14564854806187e-051.57282427403093e-05
840.999981226051373.75478972604318e-051.87739486302159e-05
850.9999858332947472.83334105065767e-051.41667052532884e-05
860.9999750976235494.98047529015498e-052.49023764507749e-05
870.999997630815634.73836873919056e-062.36918436959528e-06
880.9999972840702845.43185943224249e-062.71592971612125e-06
890.999995377210859.24557829914052e-064.62278914957026e-06
900.9999915980032771.68039934464315e-058.40199672321573e-06
910.999986037276262.79254474794478e-051.39627237397239e-05
920.9999808179282053.83641435892144e-051.91820717946072e-05
930.9999890408389882.19183220244569e-051.09591610122284e-05
940.9999796590917654.06818164696906e-052.03409082348453e-05
950.9999764882357554.70235284891504e-052.35117642445752e-05
960.9999954361029959.12779401080973e-064.56389700540487e-06
970.9999924093388831.51813222347419e-057.59066111737097e-06
980.9999866284720722.67430558553712e-051.33715279276856e-05
990.999980557981083.88840378396182e-051.94420189198091e-05
1000.9999743387172875.13225654251608e-052.56612827125804e-05
1010.9999529225433689.41549132645972e-054.70774566322986e-05
1020.9999595574707478.08850585054172e-054.04425292527086e-05
1030.9999581460643498.37078713025948e-054.18539356512974e-05
1040.9999236425225660.0001527149548680117.63574774340055e-05
1050.9998883905945410.0002232188109175750.000111609405458788
1060.9998023354245950.0003953291508097980.000197664575404899
1070.9996458734577580.0007082530844834610.000354126542241731
1080.9994815474001820.001036905199636940.000518452599818472
1090.9991058021701220.001788395659756310.000894197829878157
1100.9985210185588050.002957962882390240.00147898144119512
1110.9988615676286680.002276864742664610.00113843237133231
1120.9981811039724150.00363779205516970.00181889602758485
1130.998930553933950.002138892132100010.00106944606605
1140.9981628033730450.003674393253909640.00183719662695482
1150.9968633107771820.006273378445635030.00313668922281752
1160.9947821535291880.01043569294162310.00521784647081156
1170.9998740727570120.0002518544859752370.000125927242987619
1180.9999391898247170.0001216203505654786.08101752827389e-05
1190.9999515556151749.68887696510776e-054.84443848255388e-05
1200.9999612375912857.75248174302045e-053.87624087151023e-05
1210.9999076883366090.0001846233267813279.23116633906637e-05
1220.9999477639279530.0001044721440938975.22360720469486e-05
1230.9999266870946720.000146625810655487.33129053277401e-05
1240.9998138743407360.0003722513185286850.000186125659264342
1250.9995497338311180.0009005323377632170.000450266168881608
1260.9990072185959850.001985562808029920.000992781404014961
1270.9977022108313290.004595578337341180.00229778916867059
1280.9982821697475290.003435660504941410.0017178302524707
1290.9959448866189880.008110226762023990.00405511338101199
1300.990733573136730.01853285372654010.00926642686327005
1310.9801685927919890.03966281441602290.0198314072080114
1320.9989140653120030.002171869375993810.0010859346879969
1330.996506883867880.006986232264239880.00349311613211994
1340.9883779551237840.02324408975243140.0116220448762157
1350.9665627606550520.06687447868989570.0334372393449478
1360.9034929219207080.1930141561585850.0965070780792923

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.166890490162749 & 0.333780980325499 & 0.833109509837251 \tabularnewline
9 & 0.0880756248406675 & 0.176151249681335 & 0.911924375159332 \tabularnewline
10 & 0.760649724941686 & 0.478700550116628 & 0.239350275058314 \tabularnewline
11 & 0.670213415008133 & 0.659573169983735 & 0.329786584991867 \tabularnewline
12 & 0.558667505742611 & 0.882664988514778 & 0.441332494257389 \tabularnewline
13 & 0.510335213930703 & 0.979329572138594 & 0.489664786069297 \tabularnewline
14 & 0.727362852014169 & 0.545274295971661 & 0.272637147985831 \tabularnewline
15 & 0.662899977501369 & 0.674200044997262 & 0.337100022498631 \tabularnewline
16 & 0.667662811972951 & 0.664674376054098 & 0.332337188027049 \tabularnewline
17 & 0.875810491386724 & 0.248379017226552 & 0.124189508613276 \tabularnewline
18 & 0.834128164444915 & 0.33174367111017 & 0.165871835555085 \tabularnewline
19 & 0.975676314195293 & 0.0486473716094133 & 0.0243236858047067 \tabularnewline
20 & 0.992703278819167 & 0.0145934423616651 & 0.00729672118083255 \tabularnewline
21 & 0.988681876477885 & 0.0226362470442308 & 0.0113181235221154 \tabularnewline
22 & 0.984329888004836 & 0.0313402239903271 & 0.0156701119951635 \tabularnewline
23 & 0.978573815599687 & 0.0428523688006269 & 0.0214261844003134 \tabularnewline
24 & 0.971817453146147 & 0.0563650937077065 & 0.0281825468538533 \tabularnewline
25 & 0.959517869557378 & 0.080964260885244 & 0.040482130442622 \tabularnewline
26 & 0.960231464108216 & 0.0795370717835679 & 0.039768535891784 \tabularnewline
27 & 0.944921829311091 & 0.110156341377818 & 0.055078170688909 \tabularnewline
28 & 0.960377859286209 & 0.0792442814275822 & 0.0396221407137911 \tabularnewline
29 & 0.999571313693485 & 0.000857372613030258 & 0.000428686306515129 \tabularnewline
30 & 0.999407004340789 & 0.00118599131842279 & 0.000592995659211397 \tabularnewline
31 & 0.999033274044223 & 0.00193345191155476 & 0.000966725955777382 \tabularnewline
32 & 0.999517412857955 & 0.000965174284089024 & 0.000482587142044512 \tabularnewline
33 & 0.99948021577258 & 0.00103956845484048 & 0.00051978422742024 \tabularnewline
34 & 0.99936770984952 & 0.00126458030095947 & 0.000632290150479736 \tabularnewline
35 & 0.999445767194721 & 0.00110846561055837 & 0.000554232805279183 \tabularnewline
36 & 0.999143316841927 & 0.00171336631614519 & 0.000856683158072593 \tabularnewline
37 & 0.998700419485973 & 0.00259916102805456 & 0.00129958051402728 \tabularnewline
38 & 0.998422292668105 & 0.00315541466379079 & 0.00157770733189539 \tabularnewline
39 & 0.998558696966537 & 0.00288260606692655 & 0.00144130303346327 \tabularnewline
40 & 0.997938191069724 & 0.00412361786055256 & 0.00206180893027628 \tabularnewline
41 & 0.996922890448426 & 0.00615421910314808 & 0.00307710955157404 \tabularnewline
42 & 0.995987015495642 & 0.00802596900871613 & 0.00401298450435807 \tabularnewline
43 & 0.994255565558746 & 0.0114888688825078 & 0.00574443444125391 \tabularnewline
44 & 0.996491073156339 & 0.00701785368732274 & 0.00350892684366137 \tabularnewline
45 & 0.995585103690526 & 0.00882979261894716 & 0.00441489630947358 \tabularnewline
46 & 0.995671059869435 & 0.00865788026112898 & 0.00432894013056449 \tabularnewline
47 & 0.994368801262332 & 0.0112623974753355 & 0.00563119873766777 \tabularnewline
48 & 0.995129132433622 & 0.00974173513275545 & 0.00487086756637772 \tabularnewline
49 & 0.993336675025641 & 0.0133266499487171 & 0.00666332497435853 \tabularnewline
50 & 0.995511437840783 & 0.00897712431843323 & 0.00448856215921662 \tabularnewline
51 & 0.993717683814867 & 0.0125646323702663 & 0.00628231618513317 \tabularnewline
52 & 0.991804593479247 & 0.016390813041507 & 0.00819540652075348 \tabularnewline
53 & 0.997586542348375 & 0.00482691530324993 & 0.00241345765162496 \tabularnewline
54 & 0.998625537301047 & 0.00274892539790604 & 0.00137446269895302 \tabularnewline
55 & 0.998004369725166 & 0.00399126054966865 & 0.00199563027483432 \tabularnewline
56 & 0.997610301069891 & 0.00477939786021799 & 0.00238969893010899 \tabularnewline
57 & 0.997666693560559 & 0.00466661287888103 & 0.00233330643944051 \tabularnewline
58 & 0.99663149305251 & 0.00673701389497959 & 0.0033685069474898 \tabularnewline
59 & 0.995156053602534 & 0.00968789279493123 & 0.00484394639746562 \tabularnewline
60 & 0.996581720429667 & 0.0068365591406669 & 0.00341827957033345 \tabularnewline
61 & 0.996191888244467 & 0.00761622351106607 & 0.00380811175553304 \tabularnewline
62 & 0.997718617538902 & 0.00456276492219601 & 0.00228138246109801 \tabularnewline
63 & 0.997056323699964 & 0.00588735260007207 & 0.00294367630003603 \tabularnewline
64 & 0.995785325133668 & 0.00842934973266448 & 0.00421467486633224 \tabularnewline
65 & 0.996231303687606 & 0.00753739262478785 & 0.00376869631239392 \tabularnewline
66 & 0.996131128925267 & 0.00773774214946632 & 0.00386887107473316 \tabularnewline
67 & 0.994894928072772 & 0.0102101438544568 & 0.00510507192722842 \tabularnewline
68 & 0.998571866004119 & 0.00285626799176132 & 0.00142813399588066 \tabularnewline
69 & 0.997992309690926 & 0.00401538061814701 & 0.00200769030907351 \tabularnewline
70 & 0.997244866629616 & 0.00551026674076767 & 0.00275513337038383 \tabularnewline
71 & 0.997213072366989 & 0.00557385526602253 & 0.00278692763301127 \tabularnewline
72 & 0.997416790056502 & 0.00516641988699689 & 0.00258320994349845 \tabularnewline
73 & 0.998035924313452 & 0.00392815137309614 & 0.00196407568654807 \tabularnewline
74 & 0.998135764649466 & 0.00372847070106799 & 0.00186423535053399 \tabularnewline
75 & 0.999651393432421 & 0.000697213135158282 & 0.000348606567579141 \tabularnewline
76 & 0.999751711661013 & 0.000496576677974626 & 0.000248288338987313 \tabularnewline
77 & 0.999740442761741 & 0.00051911447651751 & 0.000259557238258755 \tabularnewline
78 & 0.999820526181609 & 0.000358947636781386 & 0.000179473818390693 \tabularnewline
79 & 0.999770368996989 & 0.000459262006021929 & 0.000229631003010965 \tabularnewline
80 & 0.999819077625093 & 0.000361844749813814 & 0.000180922374906907 \tabularnewline
81 & 0.999727020385658 & 0.000545959228683725 & 0.000272979614341863 \tabularnewline
82 & 0.999991199990875 & 1.76000182493079e-05 & 8.80000912465397e-06 \tabularnewline
83 & 0.99998427175726 & 3.14564854806187e-05 & 1.57282427403093e-05 \tabularnewline
84 & 0.99998122605137 & 3.75478972604318e-05 & 1.87739486302159e-05 \tabularnewline
85 & 0.999985833294747 & 2.83334105065767e-05 & 1.41667052532884e-05 \tabularnewline
86 & 0.999975097623549 & 4.98047529015498e-05 & 2.49023764507749e-05 \tabularnewline
87 & 0.99999763081563 & 4.73836873919056e-06 & 2.36918436959528e-06 \tabularnewline
88 & 0.999997284070284 & 5.43185943224249e-06 & 2.71592971612125e-06 \tabularnewline
89 & 0.99999537721085 & 9.24557829914052e-06 & 4.62278914957026e-06 \tabularnewline
90 & 0.999991598003277 & 1.68039934464315e-05 & 8.40199672321573e-06 \tabularnewline
91 & 0.99998603727626 & 2.79254474794478e-05 & 1.39627237397239e-05 \tabularnewline
92 & 0.999980817928205 & 3.83641435892144e-05 & 1.91820717946072e-05 \tabularnewline
93 & 0.999989040838988 & 2.19183220244569e-05 & 1.09591610122284e-05 \tabularnewline
94 & 0.999979659091765 & 4.06818164696906e-05 & 2.03409082348453e-05 \tabularnewline
95 & 0.999976488235755 & 4.70235284891504e-05 & 2.35117642445752e-05 \tabularnewline
96 & 0.999995436102995 & 9.12779401080973e-06 & 4.56389700540487e-06 \tabularnewline
97 & 0.999992409338883 & 1.51813222347419e-05 & 7.59066111737097e-06 \tabularnewline
98 & 0.999986628472072 & 2.67430558553712e-05 & 1.33715279276856e-05 \tabularnewline
99 & 0.99998055798108 & 3.88840378396182e-05 & 1.94420189198091e-05 \tabularnewline
100 & 0.999974338717287 & 5.13225654251608e-05 & 2.56612827125804e-05 \tabularnewline
101 & 0.999952922543368 & 9.41549132645972e-05 & 4.70774566322986e-05 \tabularnewline
102 & 0.999959557470747 & 8.08850585054172e-05 & 4.04425292527086e-05 \tabularnewline
103 & 0.999958146064349 & 8.37078713025948e-05 & 4.18539356512974e-05 \tabularnewline
104 & 0.999923642522566 & 0.000152714954868011 & 7.63574774340055e-05 \tabularnewline
105 & 0.999888390594541 & 0.000223218810917575 & 0.000111609405458788 \tabularnewline
106 & 0.999802335424595 & 0.000395329150809798 & 0.000197664575404899 \tabularnewline
107 & 0.999645873457758 & 0.000708253084483461 & 0.000354126542241731 \tabularnewline
108 & 0.999481547400182 & 0.00103690519963694 & 0.000518452599818472 \tabularnewline
109 & 0.999105802170122 & 0.00178839565975631 & 0.000894197829878157 \tabularnewline
110 & 0.998521018558805 & 0.00295796288239024 & 0.00147898144119512 \tabularnewline
111 & 0.998861567628668 & 0.00227686474266461 & 0.00113843237133231 \tabularnewline
112 & 0.998181103972415 & 0.0036377920551697 & 0.00181889602758485 \tabularnewline
113 & 0.99893055393395 & 0.00213889213210001 & 0.00106944606605 \tabularnewline
114 & 0.998162803373045 & 0.00367439325390964 & 0.00183719662695482 \tabularnewline
115 & 0.996863310777182 & 0.00627337844563503 & 0.00313668922281752 \tabularnewline
116 & 0.994782153529188 & 0.0104356929416231 & 0.00521784647081156 \tabularnewline
117 & 0.999874072757012 & 0.000251854485975237 & 0.000125927242987619 \tabularnewline
118 & 0.999939189824717 & 0.000121620350565478 & 6.08101752827389e-05 \tabularnewline
119 & 0.999951555615174 & 9.68887696510776e-05 & 4.84443848255388e-05 \tabularnewline
120 & 0.999961237591285 & 7.75248174302045e-05 & 3.87624087151023e-05 \tabularnewline
121 & 0.999907688336609 & 0.000184623326781327 & 9.23116633906637e-05 \tabularnewline
122 & 0.999947763927953 & 0.000104472144093897 & 5.22360720469486e-05 \tabularnewline
123 & 0.999926687094672 & 0.00014662581065548 & 7.33129053277401e-05 \tabularnewline
124 & 0.999813874340736 & 0.000372251318528685 & 0.000186125659264342 \tabularnewline
125 & 0.999549733831118 & 0.000900532337763217 & 0.000450266168881608 \tabularnewline
126 & 0.999007218595985 & 0.00198556280802992 & 0.000992781404014961 \tabularnewline
127 & 0.997702210831329 & 0.00459557833734118 & 0.00229778916867059 \tabularnewline
128 & 0.998282169747529 & 0.00343566050494141 & 0.0017178302524707 \tabularnewline
129 & 0.995944886618988 & 0.00811022676202399 & 0.00405511338101199 \tabularnewline
130 & 0.99073357313673 & 0.0185328537265401 & 0.00926642686327005 \tabularnewline
131 & 0.980168592791989 & 0.0396628144160229 & 0.0198314072080114 \tabularnewline
132 & 0.998914065312003 & 0.00217186937599381 & 0.0010859346879969 \tabularnewline
133 & 0.99650688386788 & 0.00698623226423988 & 0.00349311613211994 \tabularnewline
134 & 0.988377955123784 & 0.0232440897524314 & 0.0116220448762157 \tabularnewline
135 & 0.966562760655052 & 0.0668744786898957 & 0.0334372393449478 \tabularnewline
136 & 0.903492921920708 & 0.193014156158585 & 0.0965070780792923 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147106&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]0.166890490162749[/C][C]0.333780980325499[/C][C]0.833109509837251[/C][/ROW]
[ROW][C]9[/C][C]0.0880756248406675[/C][C]0.176151249681335[/C][C]0.911924375159332[/C][/ROW]
[ROW][C]10[/C][C]0.760649724941686[/C][C]0.478700550116628[/C][C]0.239350275058314[/C][/ROW]
[ROW][C]11[/C][C]0.670213415008133[/C][C]0.659573169983735[/C][C]0.329786584991867[/C][/ROW]
[ROW][C]12[/C][C]0.558667505742611[/C][C]0.882664988514778[/C][C]0.441332494257389[/C][/ROW]
[ROW][C]13[/C][C]0.510335213930703[/C][C]0.979329572138594[/C][C]0.489664786069297[/C][/ROW]
[ROW][C]14[/C][C]0.727362852014169[/C][C]0.545274295971661[/C][C]0.272637147985831[/C][/ROW]
[ROW][C]15[/C][C]0.662899977501369[/C][C]0.674200044997262[/C][C]0.337100022498631[/C][/ROW]
[ROW][C]16[/C][C]0.667662811972951[/C][C]0.664674376054098[/C][C]0.332337188027049[/C][/ROW]
[ROW][C]17[/C][C]0.875810491386724[/C][C]0.248379017226552[/C][C]0.124189508613276[/C][/ROW]
[ROW][C]18[/C][C]0.834128164444915[/C][C]0.33174367111017[/C][C]0.165871835555085[/C][/ROW]
[ROW][C]19[/C][C]0.975676314195293[/C][C]0.0486473716094133[/C][C]0.0243236858047067[/C][/ROW]
[ROW][C]20[/C][C]0.992703278819167[/C][C]0.0145934423616651[/C][C]0.00729672118083255[/C][/ROW]
[ROW][C]21[/C][C]0.988681876477885[/C][C]0.0226362470442308[/C][C]0.0113181235221154[/C][/ROW]
[ROW][C]22[/C][C]0.984329888004836[/C][C]0.0313402239903271[/C][C]0.0156701119951635[/C][/ROW]
[ROW][C]23[/C][C]0.978573815599687[/C][C]0.0428523688006269[/C][C]0.0214261844003134[/C][/ROW]
[ROW][C]24[/C][C]0.971817453146147[/C][C]0.0563650937077065[/C][C]0.0281825468538533[/C][/ROW]
[ROW][C]25[/C][C]0.959517869557378[/C][C]0.080964260885244[/C][C]0.040482130442622[/C][/ROW]
[ROW][C]26[/C][C]0.960231464108216[/C][C]0.0795370717835679[/C][C]0.039768535891784[/C][/ROW]
[ROW][C]27[/C][C]0.944921829311091[/C][C]0.110156341377818[/C][C]0.055078170688909[/C][/ROW]
[ROW][C]28[/C][C]0.960377859286209[/C][C]0.0792442814275822[/C][C]0.0396221407137911[/C][/ROW]
[ROW][C]29[/C][C]0.999571313693485[/C][C]0.000857372613030258[/C][C]0.000428686306515129[/C][/ROW]
[ROW][C]30[/C][C]0.999407004340789[/C][C]0.00118599131842279[/C][C]0.000592995659211397[/C][/ROW]
[ROW][C]31[/C][C]0.999033274044223[/C][C]0.00193345191155476[/C][C]0.000966725955777382[/C][/ROW]
[ROW][C]32[/C][C]0.999517412857955[/C][C]0.000965174284089024[/C][C]0.000482587142044512[/C][/ROW]
[ROW][C]33[/C][C]0.99948021577258[/C][C]0.00103956845484048[/C][C]0.00051978422742024[/C][/ROW]
[ROW][C]34[/C][C]0.99936770984952[/C][C]0.00126458030095947[/C][C]0.000632290150479736[/C][/ROW]
[ROW][C]35[/C][C]0.999445767194721[/C][C]0.00110846561055837[/C][C]0.000554232805279183[/C][/ROW]
[ROW][C]36[/C][C]0.999143316841927[/C][C]0.00171336631614519[/C][C]0.000856683158072593[/C][/ROW]
[ROW][C]37[/C][C]0.998700419485973[/C][C]0.00259916102805456[/C][C]0.00129958051402728[/C][/ROW]
[ROW][C]38[/C][C]0.998422292668105[/C][C]0.00315541466379079[/C][C]0.00157770733189539[/C][/ROW]
[ROW][C]39[/C][C]0.998558696966537[/C][C]0.00288260606692655[/C][C]0.00144130303346327[/C][/ROW]
[ROW][C]40[/C][C]0.997938191069724[/C][C]0.00412361786055256[/C][C]0.00206180893027628[/C][/ROW]
[ROW][C]41[/C][C]0.996922890448426[/C][C]0.00615421910314808[/C][C]0.00307710955157404[/C][/ROW]
[ROW][C]42[/C][C]0.995987015495642[/C][C]0.00802596900871613[/C][C]0.00401298450435807[/C][/ROW]
[ROW][C]43[/C][C]0.994255565558746[/C][C]0.0114888688825078[/C][C]0.00574443444125391[/C][/ROW]
[ROW][C]44[/C][C]0.996491073156339[/C][C]0.00701785368732274[/C][C]0.00350892684366137[/C][/ROW]
[ROW][C]45[/C][C]0.995585103690526[/C][C]0.00882979261894716[/C][C]0.00441489630947358[/C][/ROW]
[ROW][C]46[/C][C]0.995671059869435[/C][C]0.00865788026112898[/C][C]0.00432894013056449[/C][/ROW]
[ROW][C]47[/C][C]0.994368801262332[/C][C]0.0112623974753355[/C][C]0.00563119873766777[/C][/ROW]
[ROW][C]48[/C][C]0.995129132433622[/C][C]0.00974173513275545[/C][C]0.00487086756637772[/C][/ROW]
[ROW][C]49[/C][C]0.993336675025641[/C][C]0.0133266499487171[/C][C]0.00666332497435853[/C][/ROW]
[ROW][C]50[/C][C]0.995511437840783[/C][C]0.00897712431843323[/C][C]0.00448856215921662[/C][/ROW]
[ROW][C]51[/C][C]0.993717683814867[/C][C]0.0125646323702663[/C][C]0.00628231618513317[/C][/ROW]
[ROW][C]52[/C][C]0.991804593479247[/C][C]0.016390813041507[/C][C]0.00819540652075348[/C][/ROW]
[ROW][C]53[/C][C]0.997586542348375[/C][C]0.00482691530324993[/C][C]0.00241345765162496[/C][/ROW]
[ROW][C]54[/C][C]0.998625537301047[/C][C]0.00274892539790604[/C][C]0.00137446269895302[/C][/ROW]
[ROW][C]55[/C][C]0.998004369725166[/C][C]0.00399126054966865[/C][C]0.00199563027483432[/C][/ROW]
[ROW][C]56[/C][C]0.997610301069891[/C][C]0.00477939786021799[/C][C]0.00238969893010899[/C][/ROW]
[ROW][C]57[/C][C]0.997666693560559[/C][C]0.00466661287888103[/C][C]0.00233330643944051[/C][/ROW]
[ROW][C]58[/C][C]0.99663149305251[/C][C]0.00673701389497959[/C][C]0.0033685069474898[/C][/ROW]
[ROW][C]59[/C][C]0.995156053602534[/C][C]0.00968789279493123[/C][C]0.00484394639746562[/C][/ROW]
[ROW][C]60[/C][C]0.996581720429667[/C][C]0.0068365591406669[/C][C]0.00341827957033345[/C][/ROW]
[ROW][C]61[/C][C]0.996191888244467[/C][C]0.00761622351106607[/C][C]0.00380811175553304[/C][/ROW]
[ROW][C]62[/C][C]0.997718617538902[/C][C]0.00456276492219601[/C][C]0.00228138246109801[/C][/ROW]
[ROW][C]63[/C][C]0.997056323699964[/C][C]0.00588735260007207[/C][C]0.00294367630003603[/C][/ROW]
[ROW][C]64[/C][C]0.995785325133668[/C][C]0.00842934973266448[/C][C]0.00421467486633224[/C][/ROW]
[ROW][C]65[/C][C]0.996231303687606[/C][C]0.00753739262478785[/C][C]0.00376869631239392[/C][/ROW]
[ROW][C]66[/C][C]0.996131128925267[/C][C]0.00773774214946632[/C][C]0.00386887107473316[/C][/ROW]
[ROW][C]67[/C][C]0.994894928072772[/C][C]0.0102101438544568[/C][C]0.00510507192722842[/C][/ROW]
[ROW][C]68[/C][C]0.998571866004119[/C][C]0.00285626799176132[/C][C]0.00142813399588066[/C][/ROW]
[ROW][C]69[/C][C]0.997992309690926[/C][C]0.00401538061814701[/C][C]0.00200769030907351[/C][/ROW]
[ROW][C]70[/C][C]0.997244866629616[/C][C]0.00551026674076767[/C][C]0.00275513337038383[/C][/ROW]
[ROW][C]71[/C][C]0.997213072366989[/C][C]0.00557385526602253[/C][C]0.00278692763301127[/C][/ROW]
[ROW][C]72[/C][C]0.997416790056502[/C][C]0.00516641988699689[/C][C]0.00258320994349845[/C][/ROW]
[ROW][C]73[/C][C]0.998035924313452[/C][C]0.00392815137309614[/C][C]0.00196407568654807[/C][/ROW]
[ROW][C]74[/C][C]0.998135764649466[/C][C]0.00372847070106799[/C][C]0.00186423535053399[/C][/ROW]
[ROW][C]75[/C][C]0.999651393432421[/C][C]0.000697213135158282[/C][C]0.000348606567579141[/C][/ROW]
[ROW][C]76[/C][C]0.999751711661013[/C][C]0.000496576677974626[/C][C]0.000248288338987313[/C][/ROW]
[ROW][C]77[/C][C]0.999740442761741[/C][C]0.00051911447651751[/C][C]0.000259557238258755[/C][/ROW]
[ROW][C]78[/C][C]0.999820526181609[/C][C]0.000358947636781386[/C][C]0.000179473818390693[/C][/ROW]
[ROW][C]79[/C][C]0.999770368996989[/C][C]0.000459262006021929[/C][C]0.000229631003010965[/C][/ROW]
[ROW][C]80[/C][C]0.999819077625093[/C][C]0.000361844749813814[/C][C]0.000180922374906907[/C][/ROW]
[ROW][C]81[/C][C]0.999727020385658[/C][C]0.000545959228683725[/C][C]0.000272979614341863[/C][/ROW]
[ROW][C]82[/C][C]0.999991199990875[/C][C]1.76000182493079e-05[/C][C]8.80000912465397e-06[/C][/ROW]
[ROW][C]83[/C][C]0.99998427175726[/C][C]3.14564854806187e-05[/C][C]1.57282427403093e-05[/C][/ROW]
[ROW][C]84[/C][C]0.99998122605137[/C][C]3.75478972604318e-05[/C][C]1.87739486302159e-05[/C][/ROW]
[ROW][C]85[/C][C]0.999985833294747[/C][C]2.83334105065767e-05[/C][C]1.41667052532884e-05[/C][/ROW]
[ROW][C]86[/C][C]0.999975097623549[/C][C]4.98047529015498e-05[/C][C]2.49023764507749e-05[/C][/ROW]
[ROW][C]87[/C][C]0.99999763081563[/C][C]4.73836873919056e-06[/C][C]2.36918436959528e-06[/C][/ROW]
[ROW][C]88[/C][C]0.999997284070284[/C][C]5.43185943224249e-06[/C][C]2.71592971612125e-06[/C][/ROW]
[ROW][C]89[/C][C]0.99999537721085[/C][C]9.24557829914052e-06[/C][C]4.62278914957026e-06[/C][/ROW]
[ROW][C]90[/C][C]0.999991598003277[/C][C]1.68039934464315e-05[/C][C]8.40199672321573e-06[/C][/ROW]
[ROW][C]91[/C][C]0.99998603727626[/C][C]2.79254474794478e-05[/C][C]1.39627237397239e-05[/C][/ROW]
[ROW][C]92[/C][C]0.999980817928205[/C][C]3.83641435892144e-05[/C][C]1.91820717946072e-05[/C][/ROW]
[ROW][C]93[/C][C]0.999989040838988[/C][C]2.19183220244569e-05[/C][C]1.09591610122284e-05[/C][/ROW]
[ROW][C]94[/C][C]0.999979659091765[/C][C]4.06818164696906e-05[/C][C]2.03409082348453e-05[/C][/ROW]
[ROW][C]95[/C][C]0.999976488235755[/C][C]4.70235284891504e-05[/C][C]2.35117642445752e-05[/C][/ROW]
[ROW][C]96[/C][C]0.999995436102995[/C][C]9.12779401080973e-06[/C][C]4.56389700540487e-06[/C][/ROW]
[ROW][C]97[/C][C]0.999992409338883[/C][C]1.51813222347419e-05[/C][C]7.59066111737097e-06[/C][/ROW]
[ROW][C]98[/C][C]0.999986628472072[/C][C]2.67430558553712e-05[/C][C]1.33715279276856e-05[/C][/ROW]
[ROW][C]99[/C][C]0.99998055798108[/C][C]3.88840378396182e-05[/C][C]1.94420189198091e-05[/C][/ROW]
[ROW][C]100[/C][C]0.999974338717287[/C][C]5.13225654251608e-05[/C][C]2.56612827125804e-05[/C][/ROW]
[ROW][C]101[/C][C]0.999952922543368[/C][C]9.41549132645972e-05[/C][C]4.70774566322986e-05[/C][/ROW]
[ROW][C]102[/C][C]0.999959557470747[/C][C]8.08850585054172e-05[/C][C]4.04425292527086e-05[/C][/ROW]
[ROW][C]103[/C][C]0.999958146064349[/C][C]8.37078713025948e-05[/C][C]4.18539356512974e-05[/C][/ROW]
[ROW][C]104[/C][C]0.999923642522566[/C][C]0.000152714954868011[/C][C]7.63574774340055e-05[/C][/ROW]
[ROW][C]105[/C][C]0.999888390594541[/C][C]0.000223218810917575[/C][C]0.000111609405458788[/C][/ROW]
[ROW][C]106[/C][C]0.999802335424595[/C][C]0.000395329150809798[/C][C]0.000197664575404899[/C][/ROW]
[ROW][C]107[/C][C]0.999645873457758[/C][C]0.000708253084483461[/C][C]0.000354126542241731[/C][/ROW]
[ROW][C]108[/C][C]0.999481547400182[/C][C]0.00103690519963694[/C][C]0.000518452599818472[/C][/ROW]
[ROW][C]109[/C][C]0.999105802170122[/C][C]0.00178839565975631[/C][C]0.000894197829878157[/C][/ROW]
[ROW][C]110[/C][C]0.998521018558805[/C][C]0.00295796288239024[/C][C]0.00147898144119512[/C][/ROW]
[ROW][C]111[/C][C]0.998861567628668[/C][C]0.00227686474266461[/C][C]0.00113843237133231[/C][/ROW]
[ROW][C]112[/C][C]0.998181103972415[/C][C]0.0036377920551697[/C][C]0.00181889602758485[/C][/ROW]
[ROW][C]113[/C][C]0.99893055393395[/C][C]0.00213889213210001[/C][C]0.00106944606605[/C][/ROW]
[ROW][C]114[/C][C]0.998162803373045[/C][C]0.00367439325390964[/C][C]0.00183719662695482[/C][/ROW]
[ROW][C]115[/C][C]0.996863310777182[/C][C]0.00627337844563503[/C][C]0.00313668922281752[/C][/ROW]
[ROW][C]116[/C][C]0.994782153529188[/C][C]0.0104356929416231[/C][C]0.00521784647081156[/C][/ROW]
[ROW][C]117[/C][C]0.999874072757012[/C][C]0.000251854485975237[/C][C]0.000125927242987619[/C][/ROW]
[ROW][C]118[/C][C]0.999939189824717[/C][C]0.000121620350565478[/C][C]6.08101752827389e-05[/C][/ROW]
[ROW][C]119[/C][C]0.999951555615174[/C][C]9.68887696510776e-05[/C][C]4.84443848255388e-05[/C][/ROW]
[ROW][C]120[/C][C]0.999961237591285[/C][C]7.75248174302045e-05[/C][C]3.87624087151023e-05[/C][/ROW]
[ROW][C]121[/C][C]0.999907688336609[/C][C]0.000184623326781327[/C][C]9.23116633906637e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999947763927953[/C][C]0.000104472144093897[/C][C]5.22360720469486e-05[/C][/ROW]
[ROW][C]123[/C][C]0.999926687094672[/C][C]0.00014662581065548[/C][C]7.33129053277401e-05[/C][/ROW]
[ROW][C]124[/C][C]0.999813874340736[/C][C]0.000372251318528685[/C][C]0.000186125659264342[/C][/ROW]
[ROW][C]125[/C][C]0.999549733831118[/C][C]0.000900532337763217[/C][C]0.000450266168881608[/C][/ROW]
[ROW][C]126[/C][C]0.999007218595985[/C][C]0.00198556280802992[/C][C]0.000992781404014961[/C][/ROW]
[ROW][C]127[/C][C]0.997702210831329[/C][C]0.00459557833734118[/C][C]0.00229778916867059[/C][/ROW]
[ROW][C]128[/C][C]0.998282169747529[/C][C]0.00343566050494141[/C][C]0.0017178302524707[/C][/ROW]
[ROW][C]129[/C][C]0.995944886618988[/C][C]0.00811022676202399[/C][C]0.00405511338101199[/C][/ROW]
[ROW][C]130[/C][C]0.99073357313673[/C][C]0.0185328537265401[/C][C]0.00926642686327005[/C][/ROW]
[ROW][C]131[/C][C]0.980168592791989[/C][C]0.0396628144160229[/C][C]0.0198314072080114[/C][/ROW]
[ROW][C]132[/C][C]0.998914065312003[/C][C]0.00217186937599381[/C][C]0.0010859346879969[/C][/ROW]
[ROW][C]133[/C][C]0.99650688386788[/C][C]0.00698623226423988[/C][C]0.00349311613211994[/C][/ROW]
[ROW][C]134[/C][C]0.988377955123784[/C][C]0.0232440897524314[/C][C]0.0116220448762157[/C][/ROW]
[ROW][C]135[/C][C]0.966562760655052[/C][C]0.0668744786898957[/C][C]0.0334372393449478[/C][/ROW]
[ROW][C]136[/C][C]0.903492921920708[/C][C]0.193014156158585[/C][C]0.0965070780792923[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147106&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147106&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
80.1668904901627490.3337809803254990.833109509837251
90.08807562484066750.1761512496813350.911924375159332
100.7606497249416860.4787005501166280.239350275058314
110.6702134150081330.6595731699837350.329786584991867
120.5586675057426110.8826649885147780.441332494257389
130.5103352139307030.9793295721385940.489664786069297
140.7273628520141690.5452742959716610.272637147985831
150.6628999775013690.6742000449972620.337100022498631
160.6676628119729510.6646743760540980.332337188027049
170.8758104913867240.2483790172265520.124189508613276
180.8341281644449150.331743671110170.165871835555085
190.9756763141952930.04864737160941330.0243236858047067
200.9927032788191670.01459344236166510.00729672118083255
210.9886818764778850.02263624704423080.0113181235221154
220.9843298880048360.03134022399032710.0156701119951635
230.9785738155996870.04285236880062690.0214261844003134
240.9718174531461470.05636509370770650.0281825468538533
250.9595178695573780.0809642608852440.040482130442622
260.9602314641082160.07953707178356790.039768535891784
270.9449218293110910.1101563413778180.055078170688909
280.9603778592862090.07924428142758220.0396221407137911
290.9995713136934850.0008573726130302580.000428686306515129
300.9994070043407890.001185991318422790.000592995659211397
310.9990332740442230.001933451911554760.000966725955777382
320.9995174128579550.0009651742840890240.000482587142044512
330.999480215772580.001039568454840480.00051978422742024
340.999367709849520.001264580300959470.000632290150479736
350.9994457671947210.001108465610558370.000554232805279183
360.9991433168419270.001713366316145190.000856683158072593
370.9987004194859730.002599161028054560.00129958051402728
380.9984222926681050.003155414663790790.00157770733189539
390.9985586969665370.002882606066926550.00144130303346327
400.9979381910697240.004123617860552560.00206180893027628
410.9969228904484260.006154219103148080.00307710955157404
420.9959870154956420.008025969008716130.00401298450435807
430.9942555655587460.01148886888250780.00574443444125391
440.9964910731563390.007017853687322740.00350892684366137
450.9955851036905260.008829792618947160.00441489630947358
460.9956710598694350.008657880261128980.00432894013056449
470.9943688012623320.01126239747533550.00563119873766777
480.9951291324336220.009741735132755450.00487086756637772
490.9933366750256410.01332664994871710.00666332497435853
500.9955114378407830.008977124318433230.00448856215921662
510.9937176838148670.01256463237026630.00628231618513317
520.9918045934792470.0163908130415070.00819540652075348
530.9975865423483750.004826915303249930.00241345765162496
540.9986255373010470.002748925397906040.00137446269895302
550.9980043697251660.003991260549668650.00199563027483432
560.9976103010698910.004779397860217990.00238969893010899
570.9976666935605590.004666612878881030.00233330643944051
580.996631493052510.006737013894979590.0033685069474898
590.9951560536025340.009687892794931230.00484394639746562
600.9965817204296670.00683655914066690.00341827957033345
610.9961918882444670.007616223511066070.00380811175553304
620.9977186175389020.004562764922196010.00228138246109801
630.9970563236999640.005887352600072070.00294367630003603
640.9957853251336680.008429349732664480.00421467486633224
650.9962313036876060.007537392624787850.00376869631239392
660.9961311289252670.007737742149466320.00386887107473316
670.9948949280727720.01021014385445680.00510507192722842
680.9985718660041190.002856267991761320.00142813399588066
690.9979923096909260.004015380618147010.00200769030907351
700.9972448666296160.005510266740767670.00275513337038383
710.9972130723669890.005573855266022530.00278692763301127
720.9974167900565020.005166419886996890.00258320994349845
730.9980359243134520.003928151373096140.00196407568654807
740.9981357646494660.003728470701067990.00186423535053399
750.9996513934324210.0006972131351582820.000348606567579141
760.9997517116610130.0004965766779746260.000248288338987313
770.9997404427617410.000519114476517510.000259557238258755
780.9998205261816090.0003589476367813860.000179473818390693
790.9997703689969890.0004592620060219290.000229631003010965
800.9998190776250930.0003618447498138140.000180922374906907
810.9997270203856580.0005459592286837250.000272979614341863
820.9999911999908751.76000182493079e-058.80000912465397e-06
830.999984271757263.14564854806187e-051.57282427403093e-05
840.999981226051373.75478972604318e-051.87739486302159e-05
850.9999858332947472.83334105065767e-051.41667052532884e-05
860.9999750976235494.98047529015498e-052.49023764507749e-05
870.999997630815634.73836873919056e-062.36918436959528e-06
880.9999972840702845.43185943224249e-062.71592971612125e-06
890.999995377210859.24557829914052e-064.62278914957026e-06
900.9999915980032771.68039934464315e-058.40199672321573e-06
910.999986037276262.79254474794478e-051.39627237397239e-05
920.9999808179282053.83641435892144e-051.91820717946072e-05
930.9999890408389882.19183220244569e-051.09591610122284e-05
940.9999796590917654.06818164696906e-052.03409082348453e-05
950.9999764882357554.70235284891504e-052.35117642445752e-05
960.9999954361029959.12779401080973e-064.56389700540487e-06
970.9999924093388831.51813222347419e-057.59066111737097e-06
980.9999866284720722.67430558553712e-051.33715279276856e-05
990.999980557981083.88840378396182e-051.94420189198091e-05
1000.9999743387172875.13225654251608e-052.56612827125804e-05
1010.9999529225433689.41549132645972e-054.70774566322986e-05
1020.9999595574707478.08850585054172e-054.04425292527086e-05
1030.9999581460643498.37078713025948e-054.18539356512974e-05
1040.9999236425225660.0001527149548680117.63574774340055e-05
1050.9998883905945410.0002232188109175750.000111609405458788
1060.9998023354245950.0003953291508097980.000197664575404899
1070.9996458734577580.0007082530844834610.000354126542241731
1080.9994815474001820.001036905199636940.000518452599818472
1090.9991058021701220.001788395659756310.000894197829878157
1100.9985210185588050.002957962882390240.00147898144119512
1110.9988615676286680.002276864742664610.00113843237133231
1120.9981811039724150.00363779205516970.00181889602758485
1130.998930553933950.002138892132100010.00106944606605
1140.9981628033730450.003674393253909640.00183719662695482
1150.9968633107771820.006273378445635030.00313668922281752
1160.9947821535291880.01043569294162310.00521784647081156
1170.9998740727570120.0002518544859752370.000125927242987619
1180.9999391898247170.0001216203505654786.08101752827389e-05
1190.9999515556151749.68887696510776e-054.84443848255388e-05
1200.9999612375912857.75248174302045e-053.87624087151023e-05
1210.9999076883366090.0001846233267813279.23116633906637e-05
1220.9999477639279530.0001044721440938975.22360720469486e-05
1230.9999266870946720.000146625810655487.33129053277401e-05
1240.9998138743407360.0003722513185286850.000186125659264342
1250.9995497338311180.0009005323377632170.000450266168881608
1260.9990072185959850.001985562808029920.000992781404014961
1270.9977022108313290.004595578337341180.00229778916867059
1280.9982821697475290.003435660504941410.0017178302524707
1290.9959448866189880.008110226762023990.00405511338101199
1300.990733573136730.01853285372654010.00926642686327005
1310.9801685927919890.03966281441602290.0198314072080114
1320.9989140653120030.002171869375993810.0010859346879969
1330.996506883867880.006986232264239880.00349311613211994
1340.9883779551237840.02324408975243140.0116220448762157
1350.9665627606550520.06687447868989570.0334372393449478
1360.9034929219207080.1930141561585850.0965070780792923







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level960.744186046511628NOK
5% type I error level1110.86046511627907NOK
10% type I error level1160.89922480620155NOK

\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 & 96 & 0.744186046511628 & NOK \tabularnewline
5% type I error level & 111 & 0.86046511627907 & NOK \tabularnewline
10% type I error level & 116 & 0.89922480620155 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147106&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]96[/C][C]0.744186046511628[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]111[/C][C]0.86046511627907[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]116[/C][C]0.89922480620155[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147106&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147106&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 level960.744186046511628NOK
5% type I error level1110.86046511627907NOK
10% type I error level1160.89922480620155NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}