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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 15:59:32 -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/t1322168617zhkzki8bjbgbyn7.htm/, Retrieved Thu, 28 Mar 2024 21:04:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147214, Retrieved Thu, 28 Mar 2024 21:04:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact69
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Regression] [2011-11-24 20:59:32] [d9117990bcd7292ecd0ccf87cb78a2ce] [Current]
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Dataseries X:
63031	256	13	5	10345
66751	160	26	7	17607
7176	70	0	0	1423
78306	360	37	12	20050
137944	721	47	15	21212
261308	938	80	16	93979
69266	287	21	12	15524
80226	149	36	13	16182
73226	311	35	15	19238
178519	617	40	13	28909
66476	262	35	6	22357
98606	385	46	16	25560
50001	369	20	7	9954
91093	558	24	12	18490
73884	220	19	9	17777
72377	313	15	10	25268
69388	229	48	16	37525
15629	88	0	5	6023
71693	494	38	20	25042
19920	155	12	7	35713
39403	234	10	13	7039
99933	361	51	13	40841
56088	280	4	11	9214
62006	331	24	9	17446
81665	378	39	10	10295
65223	227	19	7	13206
88794	396	23	13	26093
90642	179	39	15	20744
203699	509	37	13	68013
99340	504	20	7	12840
56695	225	20	14	12672
108143	366	41	11	10872
58313	341	26	3	21325
29101	171	0	8	24542
113060	437	31	12	16401
0	0	0	0	0
65773	313	8	12	12821
67047	366	35	8	14662
41953	232	3	20	22190
109835	389	47	18	37929
82577	340	42	9	18009
59588	316	11	14	11076
40064	140	10	7	24981
70227	419	26	13	30691
60437	226	27	11	29164
47000	161	0	11	13985
40295	103	15	14	7588
103397	356	32	9	20023
78982	293	13	12	25524
60206	414	24	11	14717
39887	156	10	17	6832
49791	189	14	10	9624
129283	442	24	11	24300
104816	321	29	12	21790
101395	367	40	17	16493
72824	309	22	6	9269
76018	235	27	8	20105
33891	137	8	12	11216
62164	194	27	13	15569
28266	220	0	14	21799
35093	149	0	17	3772
35252	306	17	8	6057
36977	178	7	9	20828
42406	145	18	9	9976
56353	144	7	9	14055
58817	270	24	15	17455
76053	301	18	16	39553
70872	501	39	13	14818
42372	153	17	12	17065
19144	40	0	10	1536
114177	500	39	9	11938
53544	199	20	3	24589
51379	242	29	12	21332
40756	265	27	8	13229
46357	293	23	17	11331
17799	141	0	9	853
71154	234	31	8	19821
58305	336	19	9	34666
27454	124	12	12	15051
34323	241	23	5	27969
44761	127	33	14	17897
113862	327	21	14	6031
35027	175	17	10	7153
62396	331	27	12	13365
29613	176	14	10	11197
65559	281	12	12	25291
109788	291	21	17	28994
27883	137	14	11	10461
40181	155	14	10	16415
53398	194	22	11	8495
56435	300	25	7	18318
77283	370	36	10	25143
71738	187	10	11	20471
48096	210	16	5	14561
25214	185	12	6	16902
119332	445	20	14	12994
79201	234	38	13	29697
19349	67	13	1	3895
78760	316	12	13	9807
54133	336	11	9	10711
21623	116	8	1	2325
25497	141	22	6	19000
69535	236	14	12	22418
30709	98	7	9	7872
37043	97	14	9	5650
24716	152	2	12	3979
54865	132	35	10	14956
27246	97	5	2	3738
0	0	0	0	0
38814	165	34	8	10586
27646	153	12	7	18122
65373	226	34	11	17899
43021	182	30	14	10913
43116	172	21	4	18060
3058	1	0	0	0
0	0	0	0	0
96347	196	28	13	15452
48626	263	16	17	33996
73073	304	12	13	8877
45266	183	14	12	18708
43410	292	7	1	2781
83842	257	41	12	20854
39296	141	21	6	8179
35223	189	28	11	7139
39841	129	1	8	13798
19764	75	10	2	5619
59975	301	31	12	13050
64589	204	7	12	11297
63339	257	26	14	16170
11796	79	1	2	0
7627	25	0	0	0
68998	217	12	9	20539
6836	11	0	1	0
28834	209	17	3	10056
5118	6	5	0	0
20898	115	4	2	2418
0	0	0	0	0
42690	167	6	12	11806
14507	75	0	14	15924
7131	27	0	0	0
4194	14	0	0	0
21416	96	15	4	7084
30591	95	0	7	14831
42419	228	12	10	6585




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147214&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147214&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147214&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Yt[t] = -2688.8440155727 + 147.081501518406X_1t[t] + 513.088896305589X_2t[t] + 469.109240027626X_3t[t] + 0.674676542089624X_4t[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Yt[t] =  -2688.8440155727 +  147.081501518406X_1t[t] +  513.088896305589X_2t[t] +  469.109240027626X_3t[t] +  0.674676542089624X_4t[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147214&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Yt[t] =  -2688.8440155727 +  147.081501518406X_1t[t] +  513.088896305589X_2t[t] +  469.109240027626X_3t[t] +  0.674676542089624X_4t[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147214&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147214&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
Yt[t] = -2688.8440155727 + 147.081501518406X_1t[t] + 513.088896305589X_2t[t] + 469.109240027626X_3t[t] + 0.674676542089624X_4t[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-2688.84401557273246.199589-0.82830.4089180.204459
X_1t147.08150151840614.66357510.030400
X_2t513.088896305589145.2963.53130.0005610.000281
X_3t469.109240027626347.6422751.34940.1794020.089701
X_4t0.6746765420896240.1560094.32462.9e-051.4e-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) & -2688.8440155727 & 3246.199589 & -0.8283 & 0.408918 & 0.204459 \tabularnewline
X_1t & 147.081501518406 & 14.663575 & 10.0304 & 0 & 0 \tabularnewline
X_2t & 513.088896305589 & 145.296 & 3.5313 & 0.000561 & 0.000281 \tabularnewline
X_3t & 469.109240027626 & 347.642275 & 1.3494 & 0.179402 & 0.089701 \tabularnewline
X_4t & 0.674676542089624 & 0.156009 & 4.3246 & 2.9e-05 & 1.4e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147214&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]-2688.8440155727[/C][C]3246.199589[/C][C]-0.8283[/C][C]0.408918[/C][C]0.204459[/C][/ROW]
[ROW][C]X_1t[/C][C]147.081501518406[/C][C]14.663575[/C][C]10.0304[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_2t[/C][C]513.088896305589[/C][C]145.296[/C][C]3.5313[/C][C]0.000561[/C][C]0.000281[/C][/ROW]
[ROW][C]X_3t[/C][C]469.109240027626[/C][C]347.642275[/C][C]1.3494[/C][C]0.179402[/C][C]0.089701[/C][/ROW]
[ROW][C]X_4t[/C][C]0.674676542089624[/C][C]0.156009[/C][C]4.3246[/C][C]2.9e-05[/C][C]1.4e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147214&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147214&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)-2688.84401557273246.199589-0.82830.4089180.204459
X_1t147.08150151840614.66357510.030400
X_2t513.088896305589145.2963.53130.0005610.000281
X_3t469.109240027626347.6422751.34940.1794020.089701
X_4t0.6746765420896240.1560094.32462.9e-051.4e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.90351747176738
R-squared0.816343821788917
Adjusted R-squared0.811058751912339
F-TEST (value)154.462257047299
F-TEST (DF numerator)4
F-TEST (DF denominator)139
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation16465.8827443598
Sum Squared Residuals37686415942.5896

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.90351747176738 \tabularnewline
R-squared & 0.816343821788917 \tabularnewline
Adjusted R-squared & 0.811058751912339 \tabularnewline
F-TEST (value) & 154.462257047299 \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 & 16465.8827443598 \tabularnewline
Sum Squared Residuals & 37686415942.5896 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147214&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.90351747176738[/C][/ROW]
[ROW][C]R-squared[/C][C]0.816343821788917[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.811058751912339[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]154.462257047299[/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]16465.8827443598[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]37686415942.5896[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147214&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147214&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 R0.90351747176738
R-squared0.816343821788917
Adjusted R-squared0.811058751912339
F-TEST (value)154.462257047299
F-TEST (DF numerator)4
F-TEST (DF denominator)139
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation16465.8827443598
Sum Squared Residuals37686415942.5896







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16303150959.25105316712071.7489468329
26675149347.30208808317403.6979119171
371768566.92581010925-1390.92581010925
47830688401.3612435886-10095.3612435886
5137944148819.97411678-10875.9741167799
6261308247231.89070262214076.1092973784
76926666401.40326235792864.5967376421
88022654713.535902124425512.4640978756
97322681027.6802444816-7801.68024448162
10178519134186.64254913544332.3574508648
116647671703.0196446086-5227.01964460865
1298606102290.103555323-3684.10355532334
135000171845.5029509842-21844.5029509842
1491093109800.8474866-18707.8474866004
157388455633.483397258618250.5166027414
167237772782.818670069-405.818670068965
176938888444.0719371656-19056.0719371656
181562916663.4511311909-1034.45113119094
1971693115744.230561693-44051.2305616929
201992053644.3435032874-33724.3435032874
213940347706.5846029181-8303.5846029181
2299933110227.996519998-10294.9965199982
235608851923.00329392094164.9967060791
246200674301.6566118979-12295.6566118979
258166584555.3179153915-2890.31791539151
266522352640.888953940512582.1110460595
278879491059.2303338482-2265.23033384815
289064264681.340501661425960.6594983386
29203699143145.12519810360553.8748018967
309934093648.62215643975691.37784356035
315669555783.3022539268911.697746073187
3210814384674.915294595223468.0847054048
335831376601.064286293-18288.064286293
342910142772.8783602592-13671.8783602592
3511306094186.208780587218873.7912194128
360-2688.844015572692688.84401557269
376577361731.71595659554041.28404340446
386704782746.0782911984-15699.0782911984
394195357326.5882951354-15373.5882951354
40109835112674.811086864-2839.81108686436
418257785240.8331522606-2663.8331522606
425958863473.1350641764-3885.13506417638
434006443171.3145381943-3107.31453819425
447022799083.5342982163-28856.5342982163
456043769241.4438416435-8804.44384164354
464700035586.830810317911413.1691896821
474029531843.85904716978451.14095283026
4810339783822.046769267719574.9532307323
497898269925.94652191999056.05347808013
506020685606.4474346182-25400.4474346182
513988737971.00640038041915.99359961959
524979143476.9837610316314.016238969
5312928396190.154779978433092.8452200215
5410481679734.40869716225081.591302838
5510139590915.920183059510479.0798169405
567282463115.5279811329708.47201886794
577601863045.954840436412972.0451595636
583389134762.5158393023-871.515839302316
596216456300.82668340145863.17331659863
602826650943.889619875-22677.889619875
613509329746.03670790145346.96329209856
623525258879.9964219123-23627.9964219123
633697745357.4317077339-8380.43170773395
644240638826.13018223143579.86981776857
655635335787.076436535120565.9235634649
665881768150.4125483197-9333.41254831972
677605385009.5171846809-8956.5171846809
6870872107105.23232211-36233.2323221096
694237245679.8030250293-3307.80302502931
70191448921.8116140894610222.1883859105
71114177103138.64541926311038.3545807374
725354454839.1019262264-1295.10192622643
735137967805.9682209309-16426.9682209309
744075662819.3239825803-22063.3239825803
754635767826.6975232358-21469.6975232358
761779922847.1299491736-5048.12994917358
777115464759.62078618696394.3792138131
785830584089.5496927453-25784.5496927453
792745437490.1964436991-10036.1964436991
803432365774.4168712344-31451.4168712344
814476151564.655689514-6803.655689514
8211386266818.177389092647043.8226109074
833502741289.9836931866-6262.98369318663
846239674494.8960526298-12098.8960526298
852961342626.1904419987-13013.1904419987
866555967490.6799730865-1931.67997308653
8710978878423.168490516931364.8315094831
882788336862.5591878306-8979.55918783055
894018143057.9411067359-2876.94110673585
905339848024.50186307625373.49813692382
915643569905.3184257798-13470.3184257798
927728391856.9965112742-14573.9965112742
937173848917.790864845622820.2091351544
944809648577.204973687-481.204973687041
952521444896.338875564-19682.338875564
9611933288358.47843452930973.5215654711
977920177359.89479014131841.10520985872
981934916932.74660959982416.25339040015
997876062660.950188542616099.0498114574
1005413363822.9619565437-9689.96195654366
1012162320515.05353139311107.94646860693
1022549744971.1131571141-19474.1131571141
1036953559959.8444919469575.15550805403
1043070924849.80230694835859.19769305167
1053704326795.211803045910247.7881969541
1062471629007.5708491423-4291.57084914226
1075486549465.58031932115399.41968067888
1082724617603.66550762699642.33449237314
1090-2688.844015572692688.84401557269
1103881449919.626004136-11105.626004136
1112764641481.945448352-13835.945448352
1126537365232.834869143140.165130856957
1134302153402.9306141556-10381.9306141556
1144311647445.1363782595-4329.13637825954
1153058-2541.762514054295599.76251405429
1160-2688.844015572692688.84401557269
1179634757029.141427319339317.8585726807
1184862675114.1740300059-26488.1740300059
1197307360268.522986178412804.4770138216
1204526649661.474940318-4395.47494031797
1214341046195.9614055197-2785.96140551973
1228384275846.76211225527995.2378877448
1233929637157.34939885662138.65060114336
1243522349452.7663422442-14229.7663422442
1253984129859.81942458099981.18057541914
1261976418202.38353142051561.61646857954
1275997571922.2834815417-11947.2834815417
1286458944158.536344639220430.4636553608
1296333965928.4622245788-2589.46222457883
1301179610381.90198074221414.09801925781
1317627988.193522387456638.80647761255
1326899853464.073227815815533.9267721842
1336836-601.8382588426027437.8382588426
1342883444965.5760663052-16131.5760663052
1355118759.0894750656854358.91052493431
1362089818847.47060309432050.52939690573
1370-2688.844015572692688.84401557269
1384269038546.84225207624143.15774792382
1391450725653.3472149297-11146.3472149297
14071311282.356525424265848.64347457574
1414194-629.7029943150114823.70299431501
1422141625783.1591590515-4367.15915905147
1433059124573.79110460046017.20889539957
1444241946136.6425162273-3717.64251622728

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 63031 & 50959.251053167 & 12071.7489468329 \tabularnewline
2 & 66751 & 49347.302088083 & 17403.6979119171 \tabularnewline
3 & 7176 & 8566.92581010925 & -1390.92581010925 \tabularnewline
4 & 78306 & 88401.3612435886 & -10095.3612435886 \tabularnewline
5 & 137944 & 148819.97411678 & -10875.9741167799 \tabularnewline
6 & 261308 & 247231.890702622 & 14076.1092973784 \tabularnewline
7 & 69266 & 66401.4032623579 & 2864.5967376421 \tabularnewline
8 & 80226 & 54713.5359021244 & 25512.4640978756 \tabularnewline
9 & 73226 & 81027.6802444816 & -7801.68024448162 \tabularnewline
10 & 178519 & 134186.642549135 & 44332.3574508648 \tabularnewline
11 & 66476 & 71703.0196446086 & -5227.01964460865 \tabularnewline
12 & 98606 & 102290.103555323 & -3684.10355532334 \tabularnewline
13 & 50001 & 71845.5029509842 & -21844.5029509842 \tabularnewline
14 & 91093 & 109800.8474866 & -18707.8474866004 \tabularnewline
15 & 73884 & 55633.4833972586 & 18250.5166027414 \tabularnewline
16 & 72377 & 72782.818670069 & -405.818670068965 \tabularnewline
17 & 69388 & 88444.0719371656 & -19056.0719371656 \tabularnewline
18 & 15629 & 16663.4511311909 & -1034.45113119094 \tabularnewline
19 & 71693 & 115744.230561693 & -44051.2305616929 \tabularnewline
20 & 19920 & 53644.3435032874 & -33724.3435032874 \tabularnewline
21 & 39403 & 47706.5846029181 & -8303.5846029181 \tabularnewline
22 & 99933 & 110227.996519998 & -10294.9965199982 \tabularnewline
23 & 56088 & 51923.0032939209 & 4164.9967060791 \tabularnewline
24 & 62006 & 74301.6566118979 & -12295.6566118979 \tabularnewline
25 & 81665 & 84555.3179153915 & -2890.31791539151 \tabularnewline
26 & 65223 & 52640.8889539405 & 12582.1110460595 \tabularnewline
27 & 88794 & 91059.2303338482 & -2265.23033384815 \tabularnewline
28 & 90642 & 64681.3405016614 & 25960.6594983386 \tabularnewline
29 & 203699 & 143145.125198103 & 60553.8748018967 \tabularnewline
30 & 99340 & 93648.6221564397 & 5691.37784356035 \tabularnewline
31 & 56695 & 55783.3022539268 & 911.697746073187 \tabularnewline
32 & 108143 & 84674.9152945952 & 23468.0847054048 \tabularnewline
33 & 58313 & 76601.064286293 & -18288.064286293 \tabularnewline
34 & 29101 & 42772.8783602592 & -13671.8783602592 \tabularnewline
35 & 113060 & 94186.2087805872 & 18873.7912194128 \tabularnewline
36 & 0 & -2688.84401557269 & 2688.84401557269 \tabularnewline
37 & 65773 & 61731.7159565955 & 4041.28404340446 \tabularnewline
38 & 67047 & 82746.0782911984 & -15699.0782911984 \tabularnewline
39 & 41953 & 57326.5882951354 & -15373.5882951354 \tabularnewline
40 & 109835 & 112674.811086864 & -2839.81108686436 \tabularnewline
41 & 82577 & 85240.8331522606 & -2663.8331522606 \tabularnewline
42 & 59588 & 63473.1350641764 & -3885.13506417638 \tabularnewline
43 & 40064 & 43171.3145381943 & -3107.31453819425 \tabularnewline
44 & 70227 & 99083.5342982163 & -28856.5342982163 \tabularnewline
45 & 60437 & 69241.4438416435 & -8804.44384164354 \tabularnewline
46 & 47000 & 35586.8308103179 & 11413.1691896821 \tabularnewline
47 & 40295 & 31843.8590471697 & 8451.14095283026 \tabularnewline
48 & 103397 & 83822.0467692677 & 19574.9532307323 \tabularnewline
49 & 78982 & 69925.9465219199 & 9056.05347808013 \tabularnewline
50 & 60206 & 85606.4474346182 & -25400.4474346182 \tabularnewline
51 & 39887 & 37971.0064003804 & 1915.99359961959 \tabularnewline
52 & 49791 & 43476.983761031 & 6314.016238969 \tabularnewline
53 & 129283 & 96190.1547799784 & 33092.8452200215 \tabularnewline
54 & 104816 & 79734.408697162 & 25081.591302838 \tabularnewline
55 & 101395 & 90915.9201830595 & 10479.0798169405 \tabularnewline
56 & 72824 & 63115.527981132 & 9708.47201886794 \tabularnewline
57 & 76018 & 63045.9548404364 & 12972.0451595636 \tabularnewline
58 & 33891 & 34762.5158393023 & -871.515839302316 \tabularnewline
59 & 62164 & 56300.8266834014 & 5863.17331659863 \tabularnewline
60 & 28266 & 50943.889619875 & -22677.889619875 \tabularnewline
61 & 35093 & 29746.0367079014 & 5346.96329209856 \tabularnewline
62 & 35252 & 58879.9964219123 & -23627.9964219123 \tabularnewline
63 & 36977 & 45357.4317077339 & -8380.43170773395 \tabularnewline
64 & 42406 & 38826.1301822314 & 3579.86981776857 \tabularnewline
65 & 56353 & 35787.0764365351 & 20565.9235634649 \tabularnewline
66 & 58817 & 68150.4125483197 & -9333.41254831972 \tabularnewline
67 & 76053 & 85009.5171846809 & -8956.5171846809 \tabularnewline
68 & 70872 & 107105.23232211 & -36233.2323221096 \tabularnewline
69 & 42372 & 45679.8030250293 & -3307.80302502931 \tabularnewline
70 & 19144 & 8921.81161408946 & 10222.1883859105 \tabularnewline
71 & 114177 & 103138.645419263 & 11038.3545807374 \tabularnewline
72 & 53544 & 54839.1019262264 & -1295.10192622643 \tabularnewline
73 & 51379 & 67805.9682209309 & -16426.9682209309 \tabularnewline
74 & 40756 & 62819.3239825803 & -22063.3239825803 \tabularnewline
75 & 46357 & 67826.6975232358 & -21469.6975232358 \tabularnewline
76 & 17799 & 22847.1299491736 & -5048.12994917358 \tabularnewline
77 & 71154 & 64759.6207861869 & 6394.3792138131 \tabularnewline
78 & 58305 & 84089.5496927453 & -25784.5496927453 \tabularnewline
79 & 27454 & 37490.1964436991 & -10036.1964436991 \tabularnewline
80 & 34323 & 65774.4168712344 & -31451.4168712344 \tabularnewline
81 & 44761 & 51564.655689514 & -6803.655689514 \tabularnewline
82 & 113862 & 66818.1773890926 & 47043.8226109074 \tabularnewline
83 & 35027 & 41289.9836931866 & -6262.98369318663 \tabularnewline
84 & 62396 & 74494.8960526298 & -12098.8960526298 \tabularnewline
85 & 29613 & 42626.1904419987 & -13013.1904419987 \tabularnewline
86 & 65559 & 67490.6799730865 & -1931.67997308653 \tabularnewline
87 & 109788 & 78423.1684905169 & 31364.8315094831 \tabularnewline
88 & 27883 & 36862.5591878306 & -8979.55918783055 \tabularnewline
89 & 40181 & 43057.9411067359 & -2876.94110673585 \tabularnewline
90 & 53398 & 48024.5018630762 & 5373.49813692382 \tabularnewline
91 & 56435 & 69905.3184257798 & -13470.3184257798 \tabularnewline
92 & 77283 & 91856.9965112742 & -14573.9965112742 \tabularnewline
93 & 71738 & 48917.7908648456 & 22820.2091351544 \tabularnewline
94 & 48096 & 48577.204973687 & -481.204973687041 \tabularnewline
95 & 25214 & 44896.338875564 & -19682.338875564 \tabularnewline
96 & 119332 & 88358.478434529 & 30973.5215654711 \tabularnewline
97 & 79201 & 77359.8947901413 & 1841.10520985872 \tabularnewline
98 & 19349 & 16932.7466095998 & 2416.25339040015 \tabularnewline
99 & 78760 & 62660.9501885426 & 16099.0498114574 \tabularnewline
100 & 54133 & 63822.9619565437 & -9689.96195654366 \tabularnewline
101 & 21623 & 20515.0535313931 & 1107.94646860693 \tabularnewline
102 & 25497 & 44971.1131571141 & -19474.1131571141 \tabularnewline
103 & 69535 & 59959.844491946 & 9575.15550805403 \tabularnewline
104 & 30709 & 24849.8023069483 & 5859.19769305167 \tabularnewline
105 & 37043 & 26795.2118030459 & 10247.7881969541 \tabularnewline
106 & 24716 & 29007.5708491423 & -4291.57084914226 \tabularnewline
107 & 54865 & 49465.5803193211 & 5399.41968067888 \tabularnewline
108 & 27246 & 17603.6655076269 & 9642.33449237314 \tabularnewline
109 & 0 & -2688.84401557269 & 2688.84401557269 \tabularnewline
110 & 38814 & 49919.626004136 & -11105.626004136 \tabularnewline
111 & 27646 & 41481.945448352 & -13835.945448352 \tabularnewline
112 & 65373 & 65232.834869143 & 140.165130856957 \tabularnewline
113 & 43021 & 53402.9306141556 & -10381.9306141556 \tabularnewline
114 & 43116 & 47445.1363782595 & -4329.13637825954 \tabularnewline
115 & 3058 & -2541.76251405429 & 5599.76251405429 \tabularnewline
116 & 0 & -2688.84401557269 & 2688.84401557269 \tabularnewline
117 & 96347 & 57029.1414273193 & 39317.8585726807 \tabularnewline
118 & 48626 & 75114.1740300059 & -26488.1740300059 \tabularnewline
119 & 73073 & 60268.5229861784 & 12804.4770138216 \tabularnewline
120 & 45266 & 49661.474940318 & -4395.47494031797 \tabularnewline
121 & 43410 & 46195.9614055197 & -2785.96140551973 \tabularnewline
122 & 83842 & 75846.7621122552 & 7995.2378877448 \tabularnewline
123 & 39296 & 37157.3493988566 & 2138.65060114336 \tabularnewline
124 & 35223 & 49452.7663422442 & -14229.7663422442 \tabularnewline
125 & 39841 & 29859.8194245809 & 9981.18057541914 \tabularnewline
126 & 19764 & 18202.3835314205 & 1561.61646857954 \tabularnewline
127 & 59975 & 71922.2834815417 & -11947.2834815417 \tabularnewline
128 & 64589 & 44158.5363446392 & 20430.4636553608 \tabularnewline
129 & 63339 & 65928.4622245788 & -2589.46222457883 \tabularnewline
130 & 11796 & 10381.9019807422 & 1414.09801925781 \tabularnewline
131 & 7627 & 988.19352238745 & 6638.80647761255 \tabularnewline
132 & 68998 & 53464.0732278158 & 15533.9267721842 \tabularnewline
133 & 6836 & -601.838258842602 & 7437.8382588426 \tabularnewline
134 & 28834 & 44965.5760663052 & -16131.5760663052 \tabularnewline
135 & 5118 & 759.089475065685 & 4358.91052493431 \tabularnewline
136 & 20898 & 18847.4706030943 & 2050.52939690573 \tabularnewline
137 & 0 & -2688.84401557269 & 2688.84401557269 \tabularnewline
138 & 42690 & 38546.8422520762 & 4143.15774792382 \tabularnewline
139 & 14507 & 25653.3472149297 & -11146.3472149297 \tabularnewline
140 & 7131 & 1282.35652542426 & 5848.64347457574 \tabularnewline
141 & 4194 & -629.702994315011 & 4823.70299431501 \tabularnewline
142 & 21416 & 25783.1591590515 & -4367.15915905147 \tabularnewline
143 & 30591 & 24573.7911046004 & 6017.20889539957 \tabularnewline
144 & 42419 & 46136.6425162273 & -3717.64251622728 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147214&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]63031[/C][C]50959.251053167[/C][C]12071.7489468329[/C][/ROW]
[ROW][C]2[/C][C]66751[/C][C]49347.302088083[/C][C]17403.6979119171[/C][/ROW]
[ROW][C]3[/C][C]7176[/C][C]8566.92581010925[/C][C]-1390.92581010925[/C][/ROW]
[ROW][C]4[/C][C]78306[/C][C]88401.3612435886[/C][C]-10095.3612435886[/C][/ROW]
[ROW][C]5[/C][C]137944[/C][C]148819.97411678[/C][C]-10875.9741167799[/C][/ROW]
[ROW][C]6[/C][C]261308[/C][C]247231.890702622[/C][C]14076.1092973784[/C][/ROW]
[ROW][C]7[/C][C]69266[/C][C]66401.4032623579[/C][C]2864.5967376421[/C][/ROW]
[ROW][C]8[/C][C]80226[/C][C]54713.5359021244[/C][C]25512.4640978756[/C][/ROW]
[ROW][C]9[/C][C]73226[/C][C]81027.6802444816[/C][C]-7801.68024448162[/C][/ROW]
[ROW][C]10[/C][C]178519[/C][C]134186.642549135[/C][C]44332.3574508648[/C][/ROW]
[ROW][C]11[/C][C]66476[/C][C]71703.0196446086[/C][C]-5227.01964460865[/C][/ROW]
[ROW][C]12[/C][C]98606[/C][C]102290.103555323[/C][C]-3684.10355532334[/C][/ROW]
[ROW][C]13[/C][C]50001[/C][C]71845.5029509842[/C][C]-21844.5029509842[/C][/ROW]
[ROW][C]14[/C][C]91093[/C][C]109800.8474866[/C][C]-18707.8474866004[/C][/ROW]
[ROW][C]15[/C][C]73884[/C][C]55633.4833972586[/C][C]18250.5166027414[/C][/ROW]
[ROW][C]16[/C][C]72377[/C][C]72782.818670069[/C][C]-405.818670068965[/C][/ROW]
[ROW][C]17[/C][C]69388[/C][C]88444.0719371656[/C][C]-19056.0719371656[/C][/ROW]
[ROW][C]18[/C][C]15629[/C][C]16663.4511311909[/C][C]-1034.45113119094[/C][/ROW]
[ROW][C]19[/C][C]71693[/C][C]115744.230561693[/C][C]-44051.2305616929[/C][/ROW]
[ROW][C]20[/C][C]19920[/C][C]53644.3435032874[/C][C]-33724.3435032874[/C][/ROW]
[ROW][C]21[/C][C]39403[/C][C]47706.5846029181[/C][C]-8303.5846029181[/C][/ROW]
[ROW][C]22[/C][C]99933[/C][C]110227.996519998[/C][C]-10294.9965199982[/C][/ROW]
[ROW][C]23[/C][C]56088[/C][C]51923.0032939209[/C][C]4164.9967060791[/C][/ROW]
[ROW][C]24[/C][C]62006[/C][C]74301.6566118979[/C][C]-12295.6566118979[/C][/ROW]
[ROW][C]25[/C][C]81665[/C][C]84555.3179153915[/C][C]-2890.31791539151[/C][/ROW]
[ROW][C]26[/C][C]65223[/C][C]52640.8889539405[/C][C]12582.1110460595[/C][/ROW]
[ROW][C]27[/C][C]88794[/C][C]91059.2303338482[/C][C]-2265.23033384815[/C][/ROW]
[ROW][C]28[/C][C]90642[/C][C]64681.3405016614[/C][C]25960.6594983386[/C][/ROW]
[ROW][C]29[/C][C]203699[/C][C]143145.125198103[/C][C]60553.8748018967[/C][/ROW]
[ROW][C]30[/C][C]99340[/C][C]93648.6221564397[/C][C]5691.37784356035[/C][/ROW]
[ROW][C]31[/C][C]56695[/C][C]55783.3022539268[/C][C]911.697746073187[/C][/ROW]
[ROW][C]32[/C][C]108143[/C][C]84674.9152945952[/C][C]23468.0847054048[/C][/ROW]
[ROW][C]33[/C][C]58313[/C][C]76601.064286293[/C][C]-18288.064286293[/C][/ROW]
[ROW][C]34[/C][C]29101[/C][C]42772.8783602592[/C][C]-13671.8783602592[/C][/ROW]
[ROW][C]35[/C][C]113060[/C][C]94186.2087805872[/C][C]18873.7912194128[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-2688.84401557269[/C][C]2688.84401557269[/C][/ROW]
[ROW][C]37[/C][C]65773[/C][C]61731.7159565955[/C][C]4041.28404340446[/C][/ROW]
[ROW][C]38[/C][C]67047[/C][C]82746.0782911984[/C][C]-15699.0782911984[/C][/ROW]
[ROW][C]39[/C][C]41953[/C][C]57326.5882951354[/C][C]-15373.5882951354[/C][/ROW]
[ROW][C]40[/C][C]109835[/C][C]112674.811086864[/C][C]-2839.81108686436[/C][/ROW]
[ROW][C]41[/C][C]82577[/C][C]85240.8331522606[/C][C]-2663.8331522606[/C][/ROW]
[ROW][C]42[/C][C]59588[/C][C]63473.1350641764[/C][C]-3885.13506417638[/C][/ROW]
[ROW][C]43[/C][C]40064[/C][C]43171.3145381943[/C][C]-3107.31453819425[/C][/ROW]
[ROW][C]44[/C][C]70227[/C][C]99083.5342982163[/C][C]-28856.5342982163[/C][/ROW]
[ROW][C]45[/C][C]60437[/C][C]69241.4438416435[/C][C]-8804.44384164354[/C][/ROW]
[ROW][C]46[/C][C]47000[/C][C]35586.8308103179[/C][C]11413.1691896821[/C][/ROW]
[ROW][C]47[/C][C]40295[/C][C]31843.8590471697[/C][C]8451.14095283026[/C][/ROW]
[ROW][C]48[/C][C]103397[/C][C]83822.0467692677[/C][C]19574.9532307323[/C][/ROW]
[ROW][C]49[/C][C]78982[/C][C]69925.9465219199[/C][C]9056.05347808013[/C][/ROW]
[ROW][C]50[/C][C]60206[/C][C]85606.4474346182[/C][C]-25400.4474346182[/C][/ROW]
[ROW][C]51[/C][C]39887[/C][C]37971.0064003804[/C][C]1915.99359961959[/C][/ROW]
[ROW][C]52[/C][C]49791[/C][C]43476.983761031[/C][C]6314.016238969[/C][/ROW]
[ROW][C]53[/C][C]129283[/C][C]96190.1547799784[/C][C]33092.8452200215[/C][/ROW]
[ROW][C]54[/C][C]104816[/C][C]79734.408697162[/C][C]25081.591302838[/C][/ROW]
[ROW][C]55[/C][C]101395[/C][C]90915.9201830595[/C][C]10479.0798169405[/C][/ROW]
[ROW][C]56[/C][C]72824[/C][C]63115.527981132[/C][C]9708.47201886794[/C][/ROW]
[ROW][C]57[/C][C]76018[/C][C]63045.9548404364[/C][C]12972.0451595636[/C][/ROW]
[ROW][C]58[/C][C]33891[/C][C]34762.5158393023[/C][C]-871.515839302316[/C][/ROW]
[ROW][C]59[/C][C]62164[/C][C]56300.8266834014[/C][C]5863.17331659863[/C][/ROW]
[ROW][C]60[/C][C]28266[/C][C]50943.889619875[/C][C]-22677.889619875[/C][/ROW]
[ROW][C]61[/C][C]35093[/C][C]29746.0367079014[/C][C]5346.96329209856[/C][/ROW]
[ROW][C]62[/C][C]35252[/C][C]58879.9964219123[/C][C]-23627.9964219123[/C][/ROW]
[ROW][C]63[/C][C]36977[/C][C]45357.4317077339[/C][C]-8380.43170773395[/C][/ROW]
[ROW][C]64[/C][C]42406[/C][C]38826.1301822314[/C][C]3579.86981776857[/C][/ROW]
[ROW][C]65[/C][C]56353[/C][C]35787.0764365351[/C][C]20565.9235634649[/C][/ROW]
[ROW][C]66[/C][C]58817[/C][C]68150.4125483197[/C][C]-9333.41254831972[/C][/ROW]
[ROW][C]67[/C][C]76053[/C][C]85009.5171846809[/C][C]-8956.5171846809[/C][/ROW]
[ROW][C]68[/C][C]70872[/C][C]107105.23232211[/C][C]-36233.2323221096[/C][/ROW]
[ROW][C]69[/C][C]42372[/C][C]45679.8030250293[/C][C]-3307.80302502931[/C][/ROW]
[ROW][C]70[/C][C]19144[/C][C]8921.81161408946[/C][C]10222.1883859105[/C][/ROW]
[ROW][C]71[/C][C]114177[/C][C]103138.645419263[/C][C]11038.3545807374[/C][/ROW]
[ROW][C]72[/C][C]53544[/C][C]54839.1019262264[/C][C]-1295.10192622643[/C][/ROW]
[ROW][C]73[/C][C]51379[/C][C]67805.9682209309[/C][C]-16426.9682209309[/C][/ROW]
[ROW][C]74[/C][C]40756[/C][C]62819.3239825803[/C][C]-22063.3239825803[/C][/ROW]
[ROW][C]75[/C][C]46357[/C][C]67826.6975232358[/C][C]-21469.6975232358[/C][/ROW]
[ROW][C]76[/C][C]17799[/C][C]22847.1299491736[/C][C]-5048.12994917358[/C][/ROW]
[ROW][C]77[/C][C]71154[/C][C]64759.6207861869[/C][C]6394.3792138131[/C][/ROW]
[ROW][C]78[/C][C]58305[/C][C]84089.5496927453[/C][C]-25784.5496927453[/C][/ROW]
[ROW][C]79[/C][C]27454[/C][C]37490.1964436991[/C][C]-10036.1964436991[/C][/ROW]
[ROW][C]80[/C][C]34323[/C][C]65774.4168712344[/C][C]-31451.4168712344[/C][/ROW]
[ROW][C]81[/C][C]44761[/C][C]51564.655689514[/C][C]-6803.655689514[/C][/ROW]
[ROW][C]82[/C][C]113862[/C][C]66818.1773890926[/C][C]47043.8226109074[/C][/ROW]
[ROW][C]83[/C][C]35027[/C][C]41289.9836931866[/C][C]-6262.98369318663[/C][/ROW]
[ROW][C]84[/C][C]62396[/C][C]74494.8960526298[/C][C]-12098.8960526298[/C][/ROW]
[ROW][C]85[/C][C]29613[/C][C]42626.1904419987[/C][C]-13013.1904419987[/C][/ROW]
[ROW][C]86[/C][C]65559[/C][C]67490.6799730865[/C][C]-1931.67997308653[/C][/ROW]
[ROW][C]87[/C][C]109788[/C][C]78423.1684905169[/C][C]31364.8315094831[/C][/ROW]
[ROW][C]88[/C][C]27883[/C][C]36862.5591878306[/C][C]-8979.55918783055[/C][/ROW]
[ROW][C]89[/C][C]40181[/C][C]43057.9411067359[/C][C]-2876.94110673585[/C][/ROW]
[ROW][C]90[/C][C]53398[/C][C]48024.5018630762[/C][C]5373.49813692382[/C][/ROW]
[ROW][C]91[/C][C]56435[/C][C]69905.3184257798[/C][C]-13470.3184257798[/C][/ROW]
[ROW][C]92[/C][C]77283[/C][C]91856.9965112742[/C][C]-14573.9965112742[/C][/ROW]
[ROW][C]93[/C][C]71738[/C][C]48917.7908648456[/C][C]22820.2091351544[/C][/ROW]
[ROW][C]94[/C][C]48096[/C][C]48577.204973687[/C][C]-481.204973687041[/C][/ROW]
[ROW][C]95[/C][C]25214[/C][C]44896.338875564[/C][C]-19682.338875564[/C][/ROW]
[ROW][C]96[/C][C]119332[/C][C]88358.478434529[/C][C]30973.5215654711[/C][/ROW]
[ROW][C]97[/C][C]79201[/C][C]77359.8947901413[/C][C]1841.10520985872[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]16932.7466095998[/C][C]2416.25339040015[/C][/ROW]
[ROW][C]99[/C][C]78760[/C][C]62660.9501885426[/C][C]16099.0498114574[/C][/ROW]
[ROW][C]100[/C][C]54133[/C][C]63822.9619565437[/C][C]-9689.96195654366[/C][/ROW]
[ROW][C]101[/C][C]21623[/C][C]20515.0535313931[/C][C]1107.94646860693[/C][/ROW]
[ROW][C]102[/C][C]25497[/C][C]44971.1131571141[/C][C]-19474.1131571141[/C][/ROW]
[ROW][C]103[/C][C]69535[/C][C]59959.844491946[/C][C]9575.15550805403[/C][/ROW]
[ROW][C]104[/C][C]30709[/C][C]24849.8023069483[/C][C]5859.19769305167[/C][/ROW]
[ROW][C]105[/C][C]37043[/C][C]26795.2118030459[/C][C]10247.7881969541[/C][/ROW]
[ROW][C]106[/C][C]24716[/C][C]29007.5708491423[/C][C]-4291.57084914226[/C][/ROW]
[ROW][C]107[/C][C]54865[/C][C]49465.5803193211[/C][C]5399.41968067888[/C][/ROW]
[ROW][C]108[/C][C]27246[/C][C]17603.6655076269[/C][C]9642.33449237314[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]-2688.84401557269[/C][C]2688.84401557269[/C][/ROW]
[ROW][C]110[/C][C]38814[/C][C]49919.626004136[/C][C]-11105.626004136[/C][/ROW]
[ROW][C]111[/C][C]27646[/C][C]41481.945448352[/C][C]-13835.945448352[/C][/ROW]
[ROW][C]112[/C][C]65373[/C][C]65232.834869143[/C][C]140.165130856957[/C][/ROW]
[ROW][C]113[/C][C]43021[/C][C]53402.9306141556[/C][C]-10381.9306141556[/C][/ROW]
[ROW][C]114[/C][C]43116[/C][C]47445.1363782595[/C][C]-4329.13637825954[/C][/ROW]
[ROW][C]115[/C][C]3058[/C][C]-2541.76251405429[/C][C]5599.76251405429[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]-2688.84401557269[/C][C]2688.84401557269[/C][/ROW]
[ROW][C]117[/C][C]96347[/C][C]57029.1414273193[/C][C]39317.8585726807[/C][/ROW]
[ROW][C]118[/C][C]48626[/C][C]75114.1740300059[/C][C]-26488.1740300059[/C][/ROW]
[ROW][C]119[/C][C]73073[/C][C]60268.5229861784[/C][C]12804.4770138216[/C][/ROW]
[ROW][C]120[/C][C]45266[/C][C]49661.474940318[/C][C]-4395.47494031797[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]46195.9614055197[/C][C]-2785.96140551973[/C][/ROW]
[ROW][C]122[/C][C]83842[/C][C]75846.7621122552[/C][C]7995.2378877448[/C][/ROW]
[ROW][C]123[/C][C]39296[/C][C]37157.3493988566[/C][C]2138.65060114336[/C][/ROW]
[ROW][C]124[/C][C]35223[/C][C]49452.7663422442[/C][C]-14229.7663422442[/C][/ROW]
[ROW][C]125[/C][C]39841[/C][C]29859.8194245809[/C][C]9981.18057541914[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]18202.3835314205[/C][C]1561.61646857954[/C][/ROW]
[ROW][C]127[/C][C]59975[/C][C]71922.2834815417[/C][C]-11947.2834815417[/C][/ROW]
[ROW][C]128[/C][C]64589[/C][C]44158.5363446392[/C][C]20430.4636553608[/C][/ROW]
[ROW][C]129[/C][C]63339[/C][C]65928.4622245788[/C][C]-2589.46222457883[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]10381.9019807422[/C][C]1414.09801925781[/C][/ROW]
[ROW][C]131[/C][C]7627[/C][C]988.19352238745[/C][C]6638.80647761255[/C][/ROW]
[ROW][C]132[/C][C]68998[/C][C]53464.0732278158[/C][C]15533.9267721842[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]-601.838258842602[/C][C]7437.8382588426[/C][/ROW]
[ROW][C]134[/C][C]28834[/C][C]44965.5760663052[/C][C]-16131.5760663052[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]759.089475065685[/C][C]4358.91052493431[/C][/ROW]
[ROW][C]136[/C][C]20898[/C][C]18847.4706030943[/C][C]2050.52939690573[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]-2688.84401557269[/C][C]2688.84401557269[/C][/ROW]
[ROW][C]138[/C][C]42690[/C][C]38546.8422520762[/C][C]4143.15774792382[/C][/ROW]
[ROW][C]139[/C][C]14507[/C][C]25653.3472149297[/C][C]-11146.3472149297[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]1282.35652542426[/C][C]5848.64347457574[/C][/ROW]
[ROW][C]141[/C][C]4194[/C][C]-629.702994315011[/C][C]4823.70299431501[/C][/ROW]
[ROW][C]142[/C][C]21416[/C][C]25783.1591590515[/C][C]-4367.15915905147[/C][/ROW]
[ROW][C]143[/C][C]30591[/C][C]24573.7911046004[/C][C]6017.20889539957[/C][/ROW]
[ROW][C]144[/C][C]42419[/C][C]46136.6425162273[/C][C]-3717.64251622728[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147214&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147214&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
16303150959.25105316712071.7489468329
26675149347.30208808317403.6979119171
371768566.92581010925-1390.92581010925
47830688401.3612435886-10095.3612435886
5137944148819.97411678-10875.9741167799
6261308247231.89070262214076.1092973784
76926666401.40326235792864.5967376421
88022654713.535902124425512.4640978756
97322681027.6802444816-7801.68024448162
10178519134186.64254913544332.3574508648
116647671703.0196446086-5227.01964460865
1298606102290.103555323-3684.10355532334
135000171845.5029509842-21844.5029509842
1491093109800.8474866-18707.8474866004
157388455633.483397258618250.5166027414
167237772782.818670069-405.818670068965
176938888444.0719371656-19056.0719371656
181562916663.4511311909-1034.45113119094
1971693115744.230561693-44051.2305616929
201992053644.3435032874-33724.3435032874
213940347706.5846029181-8303.5846029181
2299933110227.996519998-10294.9965199982
235608851923.00329392094164.9967060791
246200674301.6566118979-12295.6566118979
258166584555.3179153915-2890.31791539151
266522352640.888953940512582.1110460595
278879491059.2303338482-2265.23033384815
289064264681.340501661425960.6594983386
29203699143145.12519810360553.8748018967
309934093648.62215643975691.37784356035
315669555783.3022539268911.697746073187
3210814384674.915294595223468.0847054048
335831376601.064286293-18288.064286293
342910142772.8783602592-13671.8783602592
3511306094186.208780587218873.7912194128
360-2688.844015572692688.84401557269
376577361731.71595659554041.28404340446
386704782746.0782911984-15699.0782911984
394195357326.5882951354-15373.5882951354
40109835112674.811086864-2839.81108686436
418257785240.8331522606-2663.8331522606
425958863473.1350641764-3885.13506417638
434006443171.3145381943-3107.31453819425
447022799083.5342982163-28856.5342982163
456043769241.4438416435-8804.44384164354
464700035586.830810317911413.1691896821
474029531843.85904716978451.14095283026
4810339783822.046769267719574.9532307323
497898269925.94652191999056.05347808013
506020685606.4474346182-25400.4474346182
513988737971.00640038041915.99359961959
524979143476.9837610316314.016238969
5312928396190.154779978433092.8452200215
5410481679734.40869716225081.591302838
5510139590915.920183059510479.0798169405
567282463115.5279811329708.47201886794
577601863045.954840436412972.0451595636
583389134762.5158393023-871.515839302316
596216456300.82668340145863.17331659863
602826650943.889619875-22677.889619875
613509329746.03670790145346.96329209856
623525258879.9964219123-23627.9964219123
633697745357.4317077339-8380.43170773395
644240638826.13018223143579.86981776857
655635335787.076436535120565.9235634649
665881768150.4125483197-9333.41254831972
677605385009.5171846809-8956.5171846809
6870872107105.23232211-36233.2323221096
694237245679.8030250293-3307.80302502931
70191448921.8116140894610222.1883859105
71114177103138.64541926311038.3545807374
725354454839.1019262264-1295.10192622643
735137967805.9682209309-16426.9682209309
744075662819.3239825803-22063.3239825803
754635767826.6975232358-21469.6975232358
761779922847.1299491736-5048.12994917358
777115464759.62078618696394.3792138131
785830584089.5496927453-25784.5496927453
792745437490.1964436991-10036.1964436991
803432365774.4168712344-31451.4168712344
814476151564.655689514-6803.655689514
8211386266818.177389092647043.8226109074
833502741289.9836931866-6262.98369318663
846239674494.8960526298-12098.8960526298
852961342626.1904419987-13013.1904419987
866555967490.6799730865-1931.67997308653
8710978878423.168490516931364.8315094831
882788336862.5591878306-8979.55918783055
894018143057.9411067359-2876.94110673585
905339848024.50186307625373.49813692382
915643569905.3184257798-13470.3184257798
927728391856.9965112742-14573.9965112742
937173848917.790864845622820.2091351544
944809648577.204973687-481.204973687041
952521444896.338875564-19682.338875564
9611933288358.47843452930973.5215654711
977920177359.89479014131841.10520985872
981934916932.74660959982416.25339040015
997876062660.950188542616099.0498114574
1005413363822.9619565437-9689.96195654366
1012162320515.05353139311107.94646860693
1022549744971.1131571141-19474.1131571141
1036953559959.8444919469575.15550805403
1043070924849.80230694835859.19769305167
1053704326795.211803045910247.7881969541
1062471629007.5708491423-4291.57084914226
1075486549465.58031932115399.41968067888
1082724617603.66550762699642.33449237314
1090-2688.844015572692688.84401557269
1103881449919.626004136-11105.626004136
1112764641481.945448352-13835.945448352
1126537365232.834869143140.165130856957
1134302153402.9306141556-10381.9306141556
1144311647445.1363782595-4329.13637825954
1153058-2541.762514054295599.76251405429
1160-2688.844015572692688.84401557269
1179634757029.141427319339317.8585726807
1184862675114.1740300059-26488.1740300059
1197307360268.522986178412804.4770138216
1204526649661.474940318-4395.47494031797
1214341046195.9614055197-2785.96140551973
1228384275846.76211225527995.2378877448
1233929637157.34939885662138.65060114336
1243522349452.7663422442-14229.7663422442
1253984129859.81942458099981.18057541914
1261976418202.38353142051561.61646857954
1275997571922.2834815417-11947.2834815417
1286458944158.536344639220430.4636553608
1296333965928.4622245788-2589.46222457883
1301179610381.90198074221414.09801925781
1317627988.193522387456638.80647761255
1326899853464.073227815815533.9267721842
1336836-601.8382588426027437.8382588426
1342883444965.5760663052-16131.5760663052
1355118759.0894750656854358.91052493431
1362089818847.47060309432050.52939690573
1370-2688.844015572692688.84401557269
1384269038546.84225207624143.15774792382
1391450725653.3472149297-11146.3472149297
14071311282.356525424265848.64347457574
1414194-629.7029943150114823.70299431501
1422141625783.1591590515-4367.15915905147
1433059124573.79110460046017.20889539957
1444241946136.6425162273-3717.64251622728







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.3882693500268120.7765387000536230.611730649973188
90.3507498529100540.7014997058201080.649250147089946
100.9199376514947010.1601246970105980.080062348505299
110.8853124595800280.2293750808399430.114687540419972
120.8373584014670180.3252831970659630.162641598532982
130.8731008343423410.2537983313153180.126899165657659
140.8700682882333090.2598634235333820.129931711766691
150.8408107458883540.3183785082232930.159189254111646
160.7946566343096450.4106867313807110.205343365690355
170.8670661584749480.2658676830501040.132933841525052
180.8198198458537240.3603603082925520.180180154146276
190.9224385203368120.1551229593263770.0775614796631883
200.9614924359593080.07701512808138480.0385075640406924
210.9501664834686920.09966703306261570.0498335165313078
220.9406665271604220.1186669456791560.059333472839578
230.936214835230910.1275703295381790.0637851647690897
240.9240516265455390.1518967469089230.0759483734544613
250.9017529168341380.1964941663317240.0982470831658621
260.885584042090570.2288319158188610.114415957909431
270.8547248396846380.2905503206307240.145275160315362
280.9085081557898640.1829836884202720.0914918442101359
290.998198280769940.003603438460119920.00180171923005996
300.9973091445059470.005381710988105070.00269085549405254
310.9960099369953130.007980126009374440.00399006300468722
320.9972925199031780.005414960193644570.00270748009682229
330.9978922201366680.004215559726664460.00210777986333223
340.9973801112574840.005239777485031510.00261988874251575
350.9977231024989710.004553795002057280.00227689750102864
360.9965386508789640.006922698242072250.00346134912103612
370.995161989235090.009676021529817880.00483801076490894
380.9948542531347030.01029149373059390.00514574686529694
390.9938334123962420.01233317520751620.0061665876037581
400.9913256967904670.01734860641906570.00867430320953287
410.9878959877207350.02420802455852950.0121040122792647
420.9835493514714850.03290129705703020.0164506485285151
430.9777057926158250.04458841476834960.0222942073841748
440.9862422803311370.02751543933772510.0137577196688626
450.982187246787120.03562550642575880.0178127532128794
460.9803500096985770.03929998060284590.0196499903014229
470.9762871788980920.0474256422038150.0237128211019075
480.9794305784414670.04113884311706520.0205694215585326
490.975486427873150.04902714425369890.0245135721268494
500.9825552294855020.03488954102899510.0174447705144975
510.9775701147060160.04485977058796770.0224298852939838
520.9712468953832030.05750620923359490.0287531046167974
530.9902116844251330.01957663114973360.00978831557486679
540.9945003660548540.01099926789029160.00549963394514578
550.9933659096092340.01326818078153270.00663409039076633
560.9919127499827230.01617450003455440.0080872500172772
570.9916889127960760.0166221744078490.0083110872039245
580.9885617017407020.02287659651859690.0114382982592984
590.9849356554367290.03012868912654270.0150643445632714
600.9884171620634410.02316567587311810.011582837936559
610.9860335997653460.02793280046930870.0139664002346544
620.9905093639359720.01898127212805540.0094906360640277
630.9877100239824540.02457995203509140.0122899760175457
640.9833678848845240.0332642302309530.0166321151154765
650.9860349429860950.02793011402780940.0139650570139047
660.9828172289952440.03436554200951110.0171827710047555
670.9778467653760660.04430646924786830.0221532346239342
680.993707652781780.0125846944364390.00629234721821952
690.9912597282089640.01748054358207220.0087402717910361
700.9887777274255750.02244454514884930.0112222725744246
710.9868437024349850.02631259513003020.0131562975650151
720.984292818680570.031414362638860.01570718131943
730.983448918363210.03310216327357970.0165510816367899
740.9870047363623670.02599052727526620.0129952636376331
750.9933103298356110.01337934032877750.00668967016438876
760.9929187941774860.01416241164502710.00708120582251357
770.9919242497215880.01615150055682380.00807575027841188
780.993024178566090.0139516428678190.0069758214339095
790.9919778904910130.01604421901797360.00802210950898679
800.9951161298639930.009767740272014380.00488387013600719
810.9933807063884050.01323858722319050.00661929361159525
820.9994771818170420.001045636365916390.000522818182958194
830.999295258013790.001409483972420510.000704741986210257
840.9992168783105750.001566243378850560.00078312168942528
850.9992326725932020.001534654813596570.000767327406798285
860.998821600165850.002356799668301310.00117839983415066
870.9997599901648380.0004800196703231540.000240009835161577
880.9997202606042320.000559478791536690.000279739395768345
890.999553814627870.0008923707442604050.000446185372130202
900.9992979560808740.001404087838251890.000702043919125945
910.9990988708209820.001802258358035550.000901129179017775
920.998866206408010.00226758718398240.0011337935919912
930.9994576631955820.001084673608836160.000542336804418081
940.999131424704780.001737150590439330.000868575295219663
950.9992841743229650.001431651354070320.000715825677035158
960.999816173700550.0003676525989006760.000183826299450338
970.9997401538040380.0005196923919240470.000259846195962023
980.9995690818086250.0008618363827506290.000430918191375314
990.9995656132120950.0008687735758093980.000434386787904699
1000.999405453364250.001189093271497980.000594546635748992
1010.9990222364525770.001955527094845050.000977763547422527
1020.9991587578722870.00168248425542690.00084124212771345
1030.999009878741790.001980242516422290.000990121258211143
1040.9984190269521340.003161946095732220.00158097304786611
1050.9977548358807920.004490328238416090.00224516411920804
1060.9970718578647210.00585628427055760.0029281421352788
1070.9958804044656530.008239191068694270.00411959553434713
1080.9943646915419150.01127061691616920.00563530845808462
1090.9913222578188920.0173554843622160.00867774218110799
1100.9891122779867280.02177544402654430.0108877220132721
1110.987317529517440.02536494096512180.0126824704825609
1120.9810168561988790.03796628760224230.0189831438011211
1130.9800755023630870.03984899527382660.0199244976369133
1140.9703970332692310.05920593346153720.0296029667307686
1150.9574776570668550.08504468586629040.0425223429331452
1160.9398413719654050.120317256069190.0601586280345952
1170.9976029147372380.00479417052552440.0023970852627622
1180.9996820971918950.000635805616208960.00031790280810448
1190.999742255884760.000515488230477940.00025774411523897
1200.9996121545711360.0007756908577276840.000387845428863842
1210.9992440346839970.001511930632006920.000755965316003462
1220.9996220339252220.0007559321495562220.000377966074778111
1230.9994930399315650.001013920136870040.000506960068435019
1240.9989042281649370.002191543670126710.00109577183506336
1250.9977005932473230.004598813505354930.00229940675267746
1260.9953475052839690.009304989432062770.00465249471603138
1270.991040680052310.01791863989537970.00895931994768983
1280.9966701740751790.00665965184964180.0033298259248209
1290.995320406919180.009359186161639230.00467959308081961
1300.9897504057107280.02049918857854320.0102495942892716
1310.9777435384923330.04451292301533350.0222564615076668
1320.996253305496170.007493389007657920.00374669450382896
1330.989268690953530.02146261809293950.0107313090464697
1340.9956726448005870.008654710398825260.00432735519941263
1350.9880980595392580.02380388092148380.0119019404607419
1360.973749459752170.05250108049566050.0262505402478303

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.388269350026812 & 0.776538700053623 & 0.611730649973188 \tabularnewline
9 & 0.350749852910054 & 0.701499705820108 & 0.649250147089946 \tabularnewline
10 & 0.919937651494701 & 0.160124697010598 & 0.080062348505299 \tabularnewline
11 & 0.885312459580028 & 0.229375080839943 & 0.114687540419972 \tabularnewline
12 & 0.837358401467018 & 0.325283197065963 & 0.162641598532982 \tabularnewline
13 & 0.873100834342341 & 0.253798331315318 & 0.126899165657659 \tabularnewline
14 & 0.870068288233309 & 0.259863423533382 & 0.129931711766691 \tabularnewline
15 & 0.840810745888354 & 0.318378508223293 & 0.159189254111646 \tabularnewline
16 & 0.794656634309645 & 0.410686731380711 & 0.205343365690355 \tabularnewline
17 & 0.867066158474948 & 0.265867683050104 & 0.132933841525052 \tabularnewline
18 & 0.819819845853724 & 0.360360308292552 & 0.180180154146276 \tabularnewline
19 & 0.922438520336812 & 0.155122959326377 & 0.0775614796631883 \tabularnewline
20 & 0.961492435959308 & 0.0770151280813848 & 0.0385075640406924 \tabularnewline
21 & 0.950166483468692 & 0.0996670330626157 & 0.0498335165313078 \tabularnewline
22 & 0.940666527160422 & 0.118666945679156 & 0.059333472839578 \tabularnewline
23 & 0.93621483523091 & 0.127570329538179 & 0.0637851647690897 \tabularnewline
24 & 0.924051626545539 & 0.151896746908923 & 0.0759483734544613 \tabularnewline
25 & 0.901752916834138 & 0.196494166331724 & 0.0982470831658621 \tabularnewline
26 & 0.88558404209057 & 0.228831915818861 & 0.114415957909431 \tabularnewline
27 & 0.854724839684638 & 0.290550320630724 & 0.145275160315362 \tabularnewline
28 & 0.908508155789864 & 0.182983688420272 & 0.0914918442101359 \tabularnewline
29 & 0.99819828076994 & 0.00360343846011992 & 0.00180171923005996 \tabularnewline
30 & 0.997309144505947 & 0.00538171098810507 & 0.00269085549405254 \tabularnewline
31 & 0.996009936995313 & 0.00798012600937444 & 0.00399006300468722 \tabularnewline
32 & 0.997292519903178 & 0.00541496019364457 & 0.00270748009682229 \tabularnewline
33 & 0.997892220136668 & 0.00421555972666446 & 0.00210777986333223 \tabularnewline
34 & 0.997380111257484 & 0.00523977748503151 & 0.00261988874251575 \tabularnewline
35 & 0.997723102498971 & 0.00455379500205728 & 0.00227689750102864 \tabularnewline
36 & 0.996538650878964 & 0.00692269824207225 & 0.00346134912103612 \tabularnewline
37 & 0.99516198923509 & 0.00967602152981788 & 0.00483801076490894 \tabularnewline
38 & 0.994854253134703 & 0.0102914937305939 & 0.00514574686529694 \tabularnewline
39 & 0.993833412396242 & 0.0123331752075162 & 0.0061665876037581 \tabularnewline
40 & 0.991325696790467 & 0.0173486064190657 & 0.00867430320953287 \tabularnewline
41 & 0.987895987720735 & 0.0242080245585295 & 0.0121040122792647 \tabularnewline
42 & 0.983549351471485 & 0.0329012970570302 & 0.0164506485285151 \tabularnewline
43 & 0.977705792615825 & 0.0445884147683496 & 0.0222942073841748 \tabularnewline
44 & 0.986242280331137 & 0.0275154393377251 & 0.0137577196688626 \tabularnewline
45 & 0.98218724678712 & 0.0356255064257588 & 0.0178127532128794 \tabularnewline
46 & 0.980350009698577 & 0.0392999806028459 & 0.0196499903014229 \tabularnewline
47 & 0.976287178898092 & 0.047425642203815 & 0.0237128211019075 \tabularnewline
48 & 0.979430578441467 & 0.0411388431170652 & 0.0205694215585326 \tabularnewline
49 & 0.97548642787315 & 0.0490271442536989 & 0.0245135721268494 \tabularnewline
50 & 0.982555229485502 & 0.0348895410289951 & 0.0174447705144975 \tabularnewline
51 & 0.977570114706016 & 0.0448597705879677 & 0.0224298852939838 \tabularnewline
52 & 0.971246895383203 & 0.0575062092335949 & 0.0287531046167974 \tabularnewline
53 & 0.990211684425133 & 0.0195766311497336 & 0.00978831557486679 \tabularnewline
54 & 0.994500366054854 & 0.0109992678902916 & 0.00549963394514578 \tabularnewline
55 & 0.993365909609234 & 0.0132681807815327 & 0.00663409039076633 \tabularnewline
56 & 0.991912749982723 & 0.0161745000345544 & 0.0080872500172772 \tabularnewline
57 & 0.991688912796076 & 0.016622174407849 & 0.0083110872039245 \tabularnewline
58 & 0.988561701740702 & 0.0228765965185969 & 0.0114382982592984 \tabularnewline
59 & 0.984935655436729 & 0.0301286891265427 & 0.0150643445632714 \tabularnewline
60 & 0.988417162063441 & 0.0231656758731181 & 0.011582837936559 \tabularnewline
61 & 0.986033599765346 & 0.0279328004693087 & 0.0139664002346544 \tabularnewline
62 & 0.990509363935972 & 0.0189812721280554 & 0.0094906360640277 \tabularnewline
63 & 0.987710023982454 & 0.0245799520350914 & 0.0122899760175457 \tabularnewline
64 & 0.983367884884524 & 0.033264230230953 & 0.0166321151154765 \tabularnewline
65 & 0.986034942986095 & 0.0279301140278094 & 0.0139650570139047 \tabularnewline
66 & 0.982817228995244 & 0.0343655420095111 & 0.0171827710047555 \tabularnewline
67 & 0.977846765376066 & 0.0443064692478683 & 0.0221532346239342 \tabularnewline
68 & 0.99370765278178 & 0.012584694436439 & 0.00629234721821952 \tabularnewline
69 & 0.991259728208964 & 0.0174805435820722 & 0.0087402717910361 \tabularnewline
70 & 0.988777727425575 & 0.0224445451488493 & 0.0112222725744246 \tabularnewline
71 & 0.986843702434985 & 0.0263125951300302 & 0.0131562975650151 \tabularnewline
72 & 0.98429281868057 & 0.03141436263886 & 0.01570718131943 \tabularnewline
73 & 0.98344891836321 & 0.0331021632735797 & 0.0165510816367899 \tabularnewline
74 & 0.987004736362367 & 0.0259905272752662 & 0.0129952636376331 \tabularnewline
75 & 0.993310329835611 & 0.0133793403287775 & 0.00668967016438876 \tabularnewline
76 & 0.992918794177486 & 0.0141624116450271 & 0.00708120582251357 \tabularnewline
77 & 0.991924249721588 & 0.0161515005568238 & 0.00807575027841188 \tabularnewline
78 & 0.99302417856609 & 0.013951642867819 & 0.0069758214339095 \tabularnewline
79 & 0.991977890491013 & 0.0160442190179736 & 0.00802210950898679 \tabularnewline
80 & 0.995116129863993 & 0.00976774027201438 & 0.00488387013600719 \tabularnewline
81 & 0.993380706388405 & 0.0132385872231905 & 0.00661929361159525 \tabularnewline
82 & 0.999477181817042 & 0.00104563636591639 & 0.000522818182958194 \tabularnewline
83 & 0.99929525801379 & 0.00140948397242051 & 0.000704741986210257 \tabularnewline
84 & 0.999216878310575 & 0.00156624337885056 & 0.00078312168942528 \tabularnewline
85 & 0.999232672593202 & 0.00153465481359657 & 0.000767327406798285 \tabularnewline
86 & 0.99882160016585 & 0.00235679966830131 & 0.00117839983415066 \tabularnewline
87 & 0.999759990164838 & 0.000480019670323154 & 0.000240009835161577 \tabularnewline
88 & 0.999720260604232 & 0.00055947879153669 & 0.000279739395768345 \tabularnewline
89 & 0.99955381462787 & 0.000892370744260405 & 0.000446185372130202 \tabularnewline
90 & 0.999297956080874 & 0.00140408783825189 & 0.000702043919125945 \tabularnewline
91 & 0.999098870820982 & 0.00180225835803555 & 0.000901129179017775 \tabularnewline
92 & 0.99886620640801 & 0.0022675871839824 & 0.0011337935919912 \tabularnewline
93 & 0.999457663195582 & 0.00108467360883616 & 0.000542336804418081 \tabularnewline
94 & 0.99913142470478 & 0.00173715059043933 & 0.000868575295219663 \tabularnewline
95 & 0.999284174322965 & 0.00143165135407032 & 0.000715825677035158 \tabularnewline
96 & 0.99981617370055 & 0.000367652598900676 & 0.000183826299450338 \tabularnewline
97 & 0.999740153804038 & 0.000519692391924047 & 0.000259846195962023 \tabularnewline
98 & 0.999569081808625 & 0.000861836382750629 & 0.000430918191375314 \tabularnewline
99 & 0.999565613212095 & 0.000868773575809398 & 0.000434386787904699 \tabularnewline
100 & 0.99940545336425 & 0.00118909327149798 & 0.000594546635748992 \tabularnewline
101 & 0.999022236452577 & 0.00195552709484505 & 0.000977763547422527 \tabularnewline
102 & 0.999158757872287 & 0.0016824842554269 & 0.00084124212771345 \tabularnewline
103 & 0.99900987874179 & 0.00198024251642229 & 0.000990121258211143 \tabularnewline
104 & 0.998419026952134 & 0.00316194609573222 & 0.00158097304786611 \tabularnewline
105 & 0.997754835880792 & 0.00449032823841609 & 0.00224516411920804 \tabularnewline
106 & 0.997071857864721 & 0.0058562842705576 & 0.0029281421352788 \tabularnewline
107 & 0.995880404465653 & 0.00823919106869427 & 0.00411959553434713 \tabularnewline
108 & 0.994364691541915 & 0.0112706169161692 & 0.00563530845808462 \tabularnewline
109 & 0.991322257818892 & 0.017355484362216 & 0.00867774218110799 \tabularnewline
110 & 0.989112277986728 & 0.0217754440265443 & 0.0108877220132721 \tabularnewline
111 & 0.98731752951744 & 0.0253649409651218 & 0.0126824704825609 \tabularnewline
112 & 0.981016856198879 & 0.0379662876022423 & 0.0189831438011211 \tabularnewline
113 & 0.980075502363087 & 0.0398489952738266 & 0.0199244976369133 \tabularnewline
114 & 0.970397033269231 & 0.0592059334615372 & 0.0296029667307686 \tabularnewline
115 & 0.957477657066855 & 0.0850446858662904 & 0.0425223429331452 \tabularnewline
116 & 0.939841371965405 & 0.12031725606919 & 0.0601586280345952 \tabularnewline
117 & 0.997602914737238 & 0.0047941705255244 & 0.0023970852627622 \tabularnewline
118 & 0.999682097191895 & 0.00063580561620896 & 0.00031790280810448 \tabularnewline
119 & 0.99974225588476 & 0.00051548823047794 & 0.00025774411523897 \tabularnewline
120 & 0.999612154571136 & 0.000775690857727684 & 0.000387845428863842 \tabularnewline
121 & 0.999244034683997 & 0.00151193063200692 & 0.000755965316003462 \tabularnewline
122 & 0.999622033925222 & 0.000755932149556222 & 0.000377966074778111 \tabularnewline
123 & 0.999493039931565 & 0.00101392013687004 & 0.000506960068435019 \tabularnewline
124 & 0.998904228164937 & 0.00219154367012671 & 0.00109577183506336 \tabularnewline
125 & 0.997700593247323 & 0.00459881350535493 & 0.00229940675267746 \tabularnewline
126 & 0.995347505283969 & 0.00930498943206277 & 0.00465249471603138 \tabularnewline
127 & 0.99104068005231 & 0.0179186398953797 & 0.00895931994768983 \tabularnewline
128 & 0.996670174075179 & 0.0066596518496418 & 0.0033298259248209 \tabularnewline
129 & 0.99532040691918 & 0.00935918616163923 & 0.00467959308081961 \tabularnewline
130 & 0.989750405710728 & 0.0204991885785432 & 0.0102495942892716 \tabularnewline
131 & 0.977743538492333 & 0.0445129230153335 & 0.0222564615076668 \tabularnewline
132 & 0.99625330549617 & 0.00749338900765792 & 0.00374669450382896 \tabularnewline
133 & 0.98926869095353 & 0.0214626180929395 & 0.0107313090464697 \tabularnewline
134 & 0.995672644800587 & 0.00865471039882526 & 0.00432735519941263 \tabularnewline
135 & 0.988098059539258 & 0.0238038809214838 & 0.0119019404607419 \tabularnewline
136 & 0.97374945975217 & 0.0525010804956605 & 0.0262505402478303 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147214&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]0.388269350026812[/C][C]0.776538700053623[/C][C]0.611730649973188[/C][/ROW]
[ROW][C]9[/C][C]0.350749852910054[/C][C]0.701499705820108[/C][C]0.649250147089946[/C][/ROW]
[ROW][C]10[/C][C]0.919937651494701[/C][C]0.160124697010598[/C][C]0.080062348505299[/C][/ROW]
[ROW][C]11[/C][C]0.885312459580028[/C][C]0.229375080839943[/C][C]0.114687540419972[/C][/ROW]
[ROW][C]12[/C][C]0.837358401467018[/C][C]0.325283197065963[/C][C]0.162641598532982[/C][/ROW]
[ROW][C]13[/C][C]0.873100834342341[/C][C]0.253798331315318[/C][C]0.126899165657659[/C][/ROW]
[ROW][C]14[/C][C]0.870068288233309[/C][C]0.259863423533382[/C][C]0.129931711766691[/C][/ROW]
[ROW][C]15[/C][C]0.840810745888354[/C][C]0.318378508223293[/C][C]0.159189254111646[/C][/ROW]
[ROW][C]16[/C][C]0.794656634309645[/C][C]0.410686731380711[/C][C]0.205343365690355[/C][/ROW]
[ROW][C]17[/C][C]0.867066158474948[/C][C]0.265867683050104[/C][C]0.132933841525052[/C][/ROW]
[ROW][C]18[/C][C]0.819819845853724[/C][C]0.360360308292552[/C][C]0.180180154146276[/C][/ROW]
[ROW][C]19[/C][C]0.922438520336812[/C][C]0.155122959326377[/C][C]0.0775614796631883[/C][/ROW]
[ROW][C]20[/C][C]0.961492435959308[/C][C]0.0770151280813848[/C][C]0.0385075640406924[/C][/ROW]
[ROW][C]21[/C][C]0.950166483468692[/C][C]0.0996670330626157[/C][C]0.0498335165313078[/C][/ROW]
[ROW][C]22[/C][C]0.940666527160422[/C][C]0.118666945679156[/C][C]0.059333472839578[/C][/ROW]
[ROW][C]23[/C][C]0.93621483523091[/C][C]0.127570329538179[/C][C]0.0637851647690897[/C][/ROW]
[ROW][C]24[/C][C]0.924051626545539[/C][C]0.151896746908923[/C][C]0.0759483734544613[/C][/ROW]
[ROW][C]25[/C][C]0.901752916834138[/C][C]0.196494166331724[/C][C]0.0982470831658621[/C][/ROW]
[ROW][C]26[/C][C]0.88558404209057[/C][C]0.228831915818861[/C][C]0.114415957909431[/C][/ROW]
[ROW][C]27[/C][C]0.854724839684638[/C][C]0.290550320630724[/C][C]0.145275160315362[/C][/ROW]
[ROW][C]28[/C][C]0.908508155789864[/C][C]0.182983688420272[/C][C]0.0914918442101359[/C][/ROW]
[ROW][C]29[/C][C]0.99819828076994[/C][C]0.00360343846011992[/C][C]0.00180171923005996[/C][/ROW]
[ROW][C]30[/C][C]0.997309144505947[/C][C]0.00538171098810507[/C][C]0.00269085549405254[/C][/ROW]
[ROW][C]31[/C][C]0.996009936995313[/C][C]0.00798012600937444[/C][C]0.00399006300468722[/C][/ROW]
[ROW][C]32[/C][C]0.997292519903178[/C][C]0.00541496019364457[/C][C]0.00270748009682229[/C][/ROW]
[ROW][C]33[/C][C]0.997892220136668[/C][C]0.00421555972666446[/C][C]0.00210777986333223[/C][/ROW]
[ROW][C]34[/C][C]0.997380111257484[/C][C]0.00523977748503151[/C][C]0.00261988874251575[/C][/ROW]
[ROW][C]35[/C][C]0.997723102498971[/C][C]0.00455379500205728[/C][C]0.00227689750102864[/C][/ROW]
[ROW][C]36[/C][C]0.996538650878964[/C][C]0.00692269824207225[/C][C]0.00346134912103612[/C][/ROW]
[ROW][C]37[/C][C]0.99516198923509[/C][C]0.00967602152981788[/C][C]0.00483801076490894[/C][/ROW]
[ROW][C]38[/C][C]0.994854253134703[/C][C]0.0102914937305939[/C][C]0.00514574686529694[/C][/ROW]
[ROW][C]39[/C][C]0.993833412396242[/C][C]0.0123331752075162[/C][C]0.0061665876037581[/C][/ROW]
[ROW][C]40[/C][C]0.991325696790467[/C][C]0.0173486064190657[/C][C]0.00867430320953287[/C][/ROW]
[ROW][C]41[/C][C]0.987895987720735[/C][C]0.0242080245585295[/C][C]0.0121040122792647[/C][/ROW]
[ROW][C]42[/C][C]0.983549351471485[/C][C]0.0329012970570302[/C][C]0.0164506485285151[/C][/ROW]
[ROW][C]43[/C][C]0.977705792615825[/C][C]0.0445884147683496[/C][C]0.0222942073841748[/C][/ROW]
[ROW][C]44[/C][C]0.986242280331137[/C][C]0.0275154393377251[/C][C]0.0137577196688626[/C][/ROW]
[ROW][C]45[/C][C]0.98218724678712[/C][C]0.0356255064257588[/C][C]0.0178127532128794[/C][/ROW]
[ROW][C]46[/C][C]0.980350009698577[/C][C]0.0392999806028459[/C][C]0.0196499903014229[/C][/ROW]
[ROW][C]47[/C][C]0.976287178898092[/C][C]0.047425642203815[/C][C]0.0237128211019075[/C][/ROW]
[ROW][C]48[/C][C]0.979430578441467[/C][C]0.0411388431170652[/C][C]0.0205694215585326[/C][/ROW]
[ROW][C]49[/C][C]0.97548642787315[/C][C]0.0490271442536989[/C][C]0.0245135721268494[/C][/ROW]
[ROW][C]50[/C][C]0.982555229485502[/C][C]0.0348895410289951[/C][C]0.0174447705144975[/C][/ROW]
[ROW][C]51[/C][C]0.977570114706016[/C][C]0.0448597705879677[/C][C]0.0224298852939838[/C][/ROW]
[ROW][C]52[/C][C]0.971246895383203[/C][C]0.0575062092335949[/C][C]0.0287531046167974[/C][/ROW]
[ROW][C]53[/C][C]0.990211684425133[/C][C]0.0195766311497336[/C][C]0.00978831557486679[/C][/ROW]
[ROW][C]54[/C][C]0.994500366054854[/C][C]0.0109992678902916[/C][C]0.00549963394514578[/C][/ROW]
[ROW][C]55[/C][C]0.993365909609234[/C][C]0.0132681807815327[/C][C]0.00663409039076633[/C][/ROW]
[ROW][C]56[/C][C]0.991912749982723[/C][C]0.0161745000345544[/C][C]0.0080872500172772[/C][/ROW]
[ROW][C]57[/C][C]0.991688912796076[/C][C]0.016622174407849[/C][C]0.0083110872039245[/C][/ROW]
[ROW][C]58[/C][C]0.988561701740702[/C][C]0.0228765965185969[/C][C]0.0114382982592984[/C][/ROW]
[ROW][C]59[/C][C]0.984935655436729[/C][C]0.0301286891265427[/C][C]0.0150643445632714[/C][/ROW]
[ROW][C]60[/C][C]0.988417162063441[/C][C]0.0231656758731181[/C][C]0.011582837936559[/C][/ROW]
[ROW][C]61[/C][C]0.986033599765346[/C][C]0.0279328004693087[/C][C]0.0139664002346544[/C][/ROW]
[ROW][C]62[/C][C]0.990509363935972[/C][C]0.0189812721280554[/C][C]0.0094906360640277[/C][/ROW]
[ROW][C]63[/C][C]0.987710023982454[/C][C]0.0245799520350914[/C][C]0.0122899760175457[/C][/ROW]
[ROW][C]64[/C][C]0.983367884884524[/C][C]0.033264230230953[/C][C]0.0166321151154765[/C][/ROW]
[ROW][C]65[/C][C]0.986034942986095[/C][C]0.0279301140278094[/C][C]0.0139650570139047[/C][/ROW]
[ROW][C]66[/C][C]0.982817228995244[/C][C]0.0343655420095111[/C][C]0.0171827710047555[/C][/ROW]
[ROW][C]67[/C][C]0.977846765376066[/C][C]0.0443064692478683[/C][C]0.0221532346239342[/C][/ROW]
[ROW][C]68[/C][C]0.99370765278178[/C][C]0.012584694436439[/C][C]0.00629234721821952[/C][/ROW]
[ROW][C]69[/C][C]0.991259728208964[/C][C]0.0174805435820722[/C][C]0.0087402717910361[/C][/ROW]
[ROW][C]70[/C][C]0.988777727425575[/C][C]0.0224445451488493[/C][C]0.0112222725744246[/C][/ROW]
[ROW][C]71[/C][C]0.986843702434985[/C][C]0.0263125951300302[/C][C]0.0131562975650151[/C][/ROW]
[ROW][C]72[/C][C]0.98429281868057[/C][C]0.03141436263886[/C][C]0.01570718131943[/C][/ROW]
[ROW][C]73[/C][C]0.98344891836321[/C][C]0.0331021632735797[/C][C]0.0165510816367899[/C][/ROW]
[ROW][C]74[/C][C]0.987004736362367[/C][C]0.0259905272752662[/C][C]0.0129952636376331[/C][/ROW]
[ROW][C]75[/C][C]0.993310329835611[/C][C]0.0133793403287775[/C][C]0.00668967016438876[/C][/ROW]
[ROW][C]76[/C][C]0.992918794177486[/C][C]0.0141624116450271[/C][C]0.00708120582251357[/C][/ROW]
[ROW][C]77[/C][C]0.991924249721588[/C][C]0.0161515005568238[/C][C]0.00807575027841188[/C][/ROW]
[ROW][C]78[/C][C]0.99302417856609[/C][C]0.013951642867819[/C][C]0.0069758214339095[/C][/ROW]
[ROW][C]79[/C][C]0.991977890491013[/C][C]0.0160442190179736[/C][C]0.00802210950898679[/C][/ROW]
[ROW][C]80[/C][C]0.995116129863993[/C][C]0.00976774027201438[/C][C]0.00488387013600719[/C][/ROW]
[ROW][C]81[/C][C]0.993380706388405[/C][C]0.0132385872231905[/C][C]0.00661929361159525[/C][/ROW]
[ROW][C]82[/C][C]0.999477181817042[/C][C]0.00104563636591639[/C][C]0.000522818182958194[/C][/ROW]
[ROW][C]83[/C][C]0.99929525801379[/C][C]0.00140948397242051[/C][C]0.000704741986210257[/C][/ROW]
[ROW][C]84[/C][C]0.999216878310575[/C][C]0.00156624337885056[/C][C]0.00078312168942528[/C][/ROW]
[ROW][C]85[/C][C]0.999232672593202[/C][C]0.00153465481359657[/C][C]0.000767327406798285[/C][/ROW]
[ROW][C]86[/C][C]0.99882160016585[/C][C]0.00235679966830131[/C][C]0.00117839983415066[/C][/ROW]
[ROW][C]87[/C][C]0.999759990164838[/C][C]0.000480019670323154[/C][C]0.000240009835161577[/C][/ROW]
[ROW][C]88[/C][C]0.999720260604232[/C][C]0.00055947879153669[/C][C]0.000279739395768345[/C][/ROW]
[ROW][C]89[/C][C]0.99955381462787[/C][C]0.000892370744260405[/C][C]0.000446185372130202[/C][/ROW]
[ROW][C]90[/C][C]0.999297956080874[/C][C]0.00140408783825189[/C][C]0.000702043919125945[/C][/ROW]
[ROW][C]91[/C][C]0.999098870820982[/C][C]0.00180225835803555[/C][C]0.000901129179017775[/C][/ROW]
[ROW][C]92[/C][C]0.99886620640801[/C][C]0.0022675871839824[/C][C]0.0011337935919912[/C][/ROW]
[ROW][C]93[/C][C]0.999457663195582[/C][C]0.00108467360883616[/C][C]0.000542336804418081[/C][/ROW]
[ROW][C]94[/C][C]0.99913142470478[/C][C]0.00173715059043933[/C][C]0.000868575295219663[/C][/ROW]
[ROW][C]95[/C][C]0.999284174322965[/C][C]0.00143165135407032[/C][C]0.000715825677035158[/C][/ROW]
[ROW][C]96[/C][C]0.99981617370055[/C][C]0.000367652598900676[/C][C]0.000183826299450338[/C][/ROW]
[ROW][C]97[/C][C]0.999740153804038[/C][C]0.000519692391924047[/C][C]0.000259846195962023[/C][/ROW]
[ROW][C]98[/C][C]0.999569081808625[/C][C]0.000861836382750629[/C][C]0.000430918191375314[/C][/ROW]
[ROW][C]99[/C][C]0.999565613212095[/C][C]0.000868773575809398[/C][C]0.000434386787904699[/C][/ROW]
[ROW][C]100[/C][C]0.99940545336425[/C][C]0.00118909327149798[/C][C]0.000594546635748992[/C][/ROW]
[ROW][C]101[/C][C]0.999022236452577[/C][C]0.00195552709484505[/C][C]0.000977763547422527[/C][/ROW]
[ROW][C]102[/C][C]0.999158757872287[/C][C]0.0016824842554269[/C][C]0.00084124212771345[/C][/ROW]
[ROW][C]103[/C][C]0.99900987874179[/C][C]0.00198024251642229[/C][C]0.000990121258211143[/C][/ROW]
[ROW][C]104[/C][C]0.998419026952134[/C][C]0.00316194609573222[/C][C]0.00158097304786611[/C][/ROW]
[ROW][C]105[/C][C]0.997754835880792[/C][C]0.00449032823841609[/C][C]0.00224516411920804[/C][/ROW]
[ROW][C]106[/C][C]0.997071857864721[/C][C]0.0058562842705576[/C][C]0.0029281421352788[/C][/ROW]
[ROW][C]107[/C][C]0.995880404465653[/C][C]0.00823919106869427[/C][C]0.00411959553434713[/C][/ROW]
[ROW][C]108[/C][C]0.994364691541915[/C][C]0.0112706169161692[/C][C]0.00563530845808462[/C][/ROW]
[ROW][C]109[/C][C]0.991322257818892[/C][C]0.017355484362216[/C][C]0.00867774218110799[/C][/ROW]
[ROW][C]110[/C][C]0.989112277986728[/C][C]0.0217754440265443[/C][C]0.0108877220132721[/C][/ROW]
[ROW][C]111[/C][C]0.98731752951744[/C][C]0.0253649409651218[/C][C]0.0126824704825609[/C][/ROW]
[ROW][C]112[/C][C]0.981016856198879[/C][C]0.0379662876022423[/C][C]0.0189831438011211[/C][/ROW]
[ROW][C]113[/C][C]0.980075502363087[/C][C]0.0398489952738266[/C][C]0.0199244976369133[/C][/ROW]
[ROW][C]114[/C][C]0.970397033269231[/C][C]0.0592059334615372[/C][C]0.0296029667307686[/C][/ROW]
[ROW][C]115[/C][C]0.957477657066855[/C][C]0.0850446858662904[/C][C]0.0425223429331452[/C][/ROW]
[ROW][C]116[/C][C]0.939841371965405[/C][C]0.12031725606919[/C][C]0.0601586280345952[/C][/ROW]
[ROW][C]117[/C][C]0.997602914737238[/C][C]0.0047941705255244[/C][C]0.0023970852627622[/C][/ROW]
[ROW][C]118[/C][C]0.999682097191895[/C][C]0.00063580561620896[/C][C]0.00031790280810448[/C][/ROW]
[ROW][C]119[/C][C]0.99974225588476[/C][C]0.00051548823047794[/C][C]0.00025774411523897[/C][/ROW]
[ROW][C]120[/C][C]0.999612154571136[/C][C]0.000775690857727684[/C][C]0.000387845428863842[/C][/ROW]
[ROW][C]121[/C][C]0.999244034683997[/C][C]0.00151193063200692[/C][C]0.000755965316003462[/C][/ROW]
[ROW][C]122[/C][C]0.999622033925222[/C][C]0.000755932149556222[/C][C]0.000377966074778111[/C][/ROW]
[ROW][C]123[/C][C]0.999493039931565[/C][C]0.00101392013687004[/C][C]0.000506960068435019[/C][/ROW]
[ROW][C]124[/C][C]0.998904228164937[/C][C]0.00219154367012671[/C][C]0.00109577183506336[/C][/ROW]
[ROW][C]125[/C][C]0.997700593247323[/C][C]0.00459881350535493[/C][C]0.00229940675267746[/C][/ROW]
[ROW][C]126[/C][C]0.995347505283969[/C][C]0.00930498943206277[/C][C]0.00465249471603138[/C][/ROW]
[ROW][C]127[/C][C]0.99104068005231[/C][C]0.0179186398953797[/C][C]0.00895931994768983[/C][/ROW]
[ROW][C]128[/C][C]0.996670174075179[/C][C]0.0066596518496418[/C][C]0.0033298259248209[/C][/ROW]
[ROW][C]129[/C][C]0.99532040691918[/C][C]0.00935918616163923[/C][C]0.00467959308081961[/C][/ROW]
[ROW][C]130[/C][C]0.989750405710728[/C][C]0.0204991885785432[/C][C]0.0102495942892716[/C][/ROW]
[ROW][C]131[/C][C]0.977743538492333[/C][C]0.0445129230153335[/C][C]0.0222564615076668[/C][/ROW]
[ROW][C]132[/C][C]0.99625330549617[/C][C]0.00749338900765792[/C][C]0.00374669450382896[/C][/ROW]
[ROW][C]133[/C][C]0.98926869095353[/C][C]0.0214626180929395[/C][C]0.0107313090464697[/C][/ROW]
[ROW][C]134[/C][C]0.995672644800587[/C][C]0.00865471039882526[/C][C]0.00432735519941263[/C][/ROW]
[ROW][C]135[/C][C]0.988098059539258[/C][C]0.0238038809214838[/C][C]0.0119019404607419[/C][/ROW]
[ROW][C]136[/C][C]0.97374945975217[/C][C]0.0525010804956605[/C][C]0.0262505402478303[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147214&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147214&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
80.3882693500268120.7765387000536230.611730649973188
90.3507498529100540.7014997058201080.649250147089946
100.9199376514947010.1601246970105980.080062348505299
110.8853124595800280.2293750808399430.114687540419972
120.8373584014670180.3252831970659630.162641598532982
130.8731008343423410.2537983313153180.126899165657659
140.8700682882333090.2598634235333820.129931711766691
150.8408107458883540.3183785082232930.159189254111646
160.7946566343096450.4106867313807110.205343365690355
170.8670661584749480.2658676830501040.132933841525052
180.8198198458537240.3603603082925520.180180154146276
190.9224385203368120.1551229593263770.0775614796631883
200.9614924359593080.07701512808138480.0385075640406924
210.9501664834686920.09966703306261570.0498335165313078
220.9406665271604220.1186669456791560.059333472839578
230.936214835230910.1275703295381790.0637851647690897
240.9240516265455390.1518967469089230.0759483734544613
250.9017529168341380.1964941663317240.0982470831658621
260.885584042090570.2288319158188610.114415957909431
270.8547248396846380.2905503206307240.145275160315362
280.9085081557898640.1829836884202720.0914918442101359
290.998198280769940.003603438460119920.00180171923005996
300.9973091445059470.005381710988105070.00269085549405254
310.9960099369953130.007980126009374440.00399006300468722
320.9972925199031780.005414960193644570.00270748009682229
330.9978922201366680.004215559726664460.00210777986333223
340.9973801112574840.005239777485031510.00261988874251575
350.9977231024989710.004553795002057280.00227689750102864
360.9965386508789640.006922698242072250.00346134912103612
370.995161989235090.009676021529817880.00483801076490894
380.9948542531347030.01029149373059390.00514574686529694
390.9938334123962420.01233317520751620.0061665876037581
400.9913256967904670.01734860641906570.00867430320953287
410.9878959877207350.02420802455852950.0121040122792647
420.9835493514714850.03290129705703020.0164506485285151
430.9777057926158250.04458841476834960.0222942073841748
440.9862422803311370.02751543933772510.0137577196688626
450.982187246787120.03562550642575880.0178127532128794
460.9803500096985770.03929998060284590.0196499903014229
470.9762871788980920.0474256422038150.0237128211019075
480.9794305784414670.04113884311706520.0205694215585326
490.975486427873150.04902714425369890.0245135721268494
500.9825552294855020.03488954102899510.0174447705144975
510.9775701147060160.04485977058796770.0224298852939838
520.9712468953832030.05750620923359490.0287531046167974
530.9902116844251330.01957663114973360.00978831557486679
540.9945003660548540.01099926789029160.00549963394514578
550.9933659096092340.01326818078153270.00663409039076633
560.9919127499827230.01617450003455440.0080872500172772
570.9916889127960760.0166221744078490.0083110872039245
580.9885617017407020.02287659651859690.0114382982592984
590.9849356554367290.03012868912654270.0150643445632714
600.9884171620634410.02316567587311810.011582837936559
610.9860335997653460.02793280046930870.0139664002346544
620.9905093639359720.01898127212805540.0094906360640277
630.9877100239824540.02457995203509140.0122899760175457
640.9833678848845240.0332642302309530.0166321151154765
650.9860349429860950.02793011402780940.0139650570139047
660.9828172289952440.03436554200951110.0171827710047555
670.9778467653760660.04430646924786830.0221532346239342
680.993707652781780.0125846944364390.00629234721821952
690.9912597282089640.01748054358207220.0087402717910361
700.9887777274255750.02244454514884930.0112222725744246
710.9868437024349850.02631259513003020.0131562975650151
720.984292818680570.031414362638860.01570718131943
730.983448918363210.03310216327357970.0165510816367899
740.9870047363623670.02599052727526620.0129952636376331
750.9933103298356110.01337934032877750.00668967016438876
760.9929187941774860.01416241164502710.00708120582251357
770.9919242497215880.01615150055682380.00807575027841188
780.993024178566090.0139516428678190.0069758214339095
790.9919778904910130.01604421901797360.00802210950898679
800.9951161298639930.009767740272014380.00488387013600719
810.9933807063884050.01323858722319050.00661929361159525
820.9994771818170420.001045636365916390.000522818182958194
830.999295258013790.001409483972420510.000704741986210257
840.9992168783105750.001566243378850560.00078312168942528
850.9992326725932020.001534654813596570.000767327406798285
860.998821600165850.002356799668301310.00117839983415066
870.9997599901648380.0004800196703231540.000240009835161577
880.9997202606042320.000559478791536690.000279739395768345
890.999553814627870.0008923707442604050.000446185372130202
900.9992979560808740.001404087838251890.000702043919125945
910.9990988708209820.001802258358035550.000901129179017775
920.998866206408010.00226758718398240.0011337935919912
930.9994576631955820.001084673608836160.000542336804418081
940.999131424704780.001737150590439330.000868575295219663
950.9992841743229650.001431651354070320.000715825677035158
960.999816173700550.0003676525989006760.000183826299450338
970.9997401538040380.0005196923919240470.000259846195962023
980.9995690818086250.0008618363827506290.000430918191375314
990.9995656132120950.0008687735758093980.000434386787904699
1000.999405453364250.001189093271497980.000594546635748992
1010.9990222364525770.001955527094845050.000977763547422527
1020.9991587578722870.00168248425542690.00084124212771345
1030.999009878741790.001980242516422290.000990121258211143
1040.9984190269521340.003161946095732220.00158097304786611
1050.9977548358807920.004490328238416090.00224516411920804
1060.9970718578647210.00585628427055760.0029281421352788
1070.9958804044656530.008239191068694270.00411959553434713
1080.9943646915419150.01127061691616920.00563530845808462
1090.9913222578188920.0173554843622160.00867774218110799
1100.9891122779867280.02177544402654430.0108877220132721
1110.987317529517440.02536494096512180.0126824704825609
1120.9810168561988790.03796628760224230.0189831438011211
1130.9800755023630870.03984899527382660.0199244976369133
1140.9703970332692310.05920593346153720.0296029667307686
1150.9574776570668550.08504468586629040.0425223429331452
1160.9398413719654050.120317256069190.0601586280345952
1170.9976029147372380.00479417052552440.0023970852627622
1180.9996820971918950.000635805616208960.00031790280810448
1190.999742255884760.000515488230477940.00025774411523897
1200.9996121545711360.0007756908577276840.000387845428863842
1210.9992440346839970.001511930632006920.000755965316003462
1220.9996220339252220.0007559321495562220.000377966074778111
1230.9994930399315650.001013920136870040.000506960068435019
1240.9989042281649370.002191543670126710.00109577183506336
1250.9977005932473230.004598813505354930.00229940675267746
1260.9953475052839690.009304989432062770.00465249471603138
1270.991040680052310.01791863989537970.00895931994768983
1280.9966701740751790.00665965184964180.0033298259248209
1290.995320406919180.009359186161639230.00467959308081961
1300.9897504057107280.02049918857854320.0102495942892716
1310.9777435384923330.04451292301533350.0222564615076668
1320.996253305496170.007493389007657920.00374669450382896
1330.989268690953530.02146261809293950.0107313090464697
1340.9956726448005870.008654710398825260.00432735519941263
1350.9880980595392580.02380388092148380.0119019404607419
1360.973749459752170.05250108049566050.0262505402478303







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level500.387596899224806NOK
5% type I error level1030.7984496124031NOK
10% type I error level1090.844961240310077NOK

\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 & 50 & 0.387596899224806 & NOK \tabularnewline
5% type I error level & 103 & 0.7984496124031 & NOK \tabularnewline
10% type I error level & 109 & 0.844961240310077 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147214&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]50[/C][C]0.387596899224806[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]103[/C][C]0.7984496124031[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]109[/C][C]0.844961240310077[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147214&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147214&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 level500.387596899224806NOK
5% type I error level1030.7984496124031NOK
10% type I error level1090.844961240310077NOK



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