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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 computationMon, 05 Nov 2012 11:12:48 -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/2012/Nov/05/t1352131995n7l7ogyho5ly9wp.htm/, Retrieved Fri, 03 Feb 2023 10:49:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186142, Retrieved Fri, 03 Feb 2023 10:49:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 16:12:48] [95accbcdf8b71300db10bf5b8050fd40] [Current]
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Dataseries X:
1910	61	56	51
2598	74	73	48
2144	57	62	46
1331	50	42	42
1431	48	59	38
7334	2	27	38
1133	41	59	36
1535	61	56	36
1196	31	78	35
1551	12	47	35
2108	46	51	34
1335	31	47	34
1532	60	55	32
842	49	35	31
1539	15	47	31
1065	33	48	31
1474	36	47	31
1226	55	55	30
1598	28	42	30
1546	44	54	30
914	41	60	30
1371	26	51	28
1318	28	47	27
1313	40	52	27
1743	28	38	27
1060	57	46	26
1102	67	12	26
1275	56	48	26
1253	54	48	26
1487	25	32	26
1098	19	27	26
930	28	60	25
1176	36	47	25
1290	19	47	25
903	42	58	25
1240	30	47	24
1402	28	45	24
1495	41	48	24
1493	35	42	24
826	10	41	24
1469	57	48	24
1064	48	60	24
821	32	56	24
1317	39	41	23
873	49	52	23
982	22	50	23
708	17	49	23
1174	30	42	23
853	55	39	23
872	33	39	23
1202	42	41	23
793	24	46	22
1000	13	36	22
1205	35	49	22
1671	37	48	22
1106	3	55	22
1131	15	45	22
775	19	48	21
1224	29	52	21
1375	28	39	21
804	38	45	21
923	23	32	20
1233	38	51	20
1170	35	41	20
613	27	52	20
987	23	22	20
1204	32	54	19
933	7	27	18
861	57	41	18
932	39	45	18
705	18	52	18
828	18	57	17
1083	41	47	17
779	33	41	16
792	0	46	16
587	35	43	16
918	37	31	16
649	16	40	16
843	34	24	16
1060	35	30	16
575	25	45	15
548	26	32	15
503	13	46	15
743	30	9	15
846	17	44	15
861	54	32	15
486	40	37	15
634	9	64	15
871	25	21	14
715	29	20	14
812	40	33	14
970	32	26	14
959	17	36	13
960	18	33	13
646	17	20	13
562	15	31	13
636	28	13	13
646	18	35	13
428	10	24	12
830	10	40	12
460	4	19	12
781	10	15	12
567	2	34	12
694	25	32	12
475	16	58	12
485	28	21	12
613	25	31	11
480	7	21	11
582	27	26	11
569	16	47	11
559	7	37	11
508	16	28	10
488	0	9	10
475	36	45	9
630	15	32	9
386	5	35	9
511	14	29	9
585	43	1	9
580	10	20	9
516	0	15	9
413	8	11	8
495	12	33	8
478	10	18	8
350	39	10	7
427	0	41	7
349	10	10	6
335	7	0	6
470	8	28	5
250	0	31	5
308	3	24	5
229	0	38	5
244	8	0	5
242	1	25	5
352	0	40	5
428	8	4	5
270	3	23	5
242	0	13	4
291	0	6	4
135	0	0	3
210	3	3	3
231	0	0	2
268	0	7	2
126	0	2	2
340	0	0	2
44	0	0	2
25	0	0	1
104	0	0	1
142	2	5	1
11	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186142&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186142&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186142&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 time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
LFM[t] = + 13.2170503807952 -0.00496323024730051pg[t] + 0.00661990743002763blogs[t] + 1.48120267528336hours[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
LFM[t] =  +  13.2170503807952 -0.00496323024730051pg[t] +  0.00661990743002763blogs[t] +  1.48120267528336hours[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186142&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]LFM[t] =  +  13.2170503807952 -0.00496323024730051pg[t] +  0.00661990743002763blogs[t] +  1.48120267528336hours[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186142&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186142&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
LFM[t] = + 13.2170503807952 -0.00496323024730051pg[t] + 0.00661990743002763blogs[t] + 1.48120267528336hours[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.21705038079521.9381886.819300
pg-0.004963230247300510.001994-2.48880.0139480.006974
blogs0.006619907430027630.0782690.08460.9327130.466357
hours1.481202675283360.1761578.408400

\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) & 13.2170503807952 & 1.938188 & 6.8193 & 0 & 0 \tabularnewline
pg & -0.00496323024730051 & 0.001994 & -2.4888 & 0.013948 & 0.006974 \tabularnewline
blogs & 0.00661990743002763 & 0.078269 & 0.0846 & 0.932713 & 0.466357 \tabularnewline
hours & 1.48120267528336 & 0.176157 & 8.4084 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186142&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]13.2170503807952[/C][C]1.938188[/C][C]6.8193[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]pg[/C][C]-0.00496323024730051[/C][C]0.001994[/C][C]-2.4888[/C][C]0.013948[/C][C]0.006974[/C][/ROW]
[ROW][C]blogs[/C][C]0.00661990743002763[/C][C]0.078269[/C][C]0.0846[/C][C]0.932713[/C][C]0.466357[/C][/ROW]
[ROW][C]hours[/C][C]1.48120267528336[/C][C]0.176157[/C][C]8.4084[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186142&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186142&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)13.21705038079521.9381886.819300
pg-0.004963230247300510.001994-2.48880.0139480.006974
blogs0.006619907430027630.0782690.08460.9327130.466357
hours1.481202675283360.1761578.408400







Multiple Linear Regression - Regression Statistics
Multiple R0.75062560382769
R-squared0.563438797121684
Adjusted R-squared0.554406496372478
F-TEST (value)62.3804291692989
F-TEST (DF numerator)3
F-TEST (DF denominator)145
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.8229483727053
Sum Squared Residuals20268.4056924301

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.75062560382769 \tabularnewline
R-squared & 0.563438797121684 \tabularnewline
Adjusted R-squared & 0.554406496372478 \tabularnewline
F-TEST (value) & 62.3804291692989 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 145 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.8229483727053 \tabularnewline
Sum Squared Residuals & 20268.4056924301 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186142&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.75062560382769[/C][/ROW]
[ROW][C]R-squared[/C][C]0.563438797121684[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.554406496372478[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]62.3804291692989[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]145[/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]11.8229483727053[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]20268.4056924301[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186142&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186142&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 R0.75062560382769
R-squared0.563438797121684
Adjusted R-squared0.554406496372478
F-TEST (value)62.3804291692989
F-TEST (DF numerator)3
F-TEST (DF denominator)145
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.8229483727053
Sum Squared Residuals20268.4056924301







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15679.6824314011345-23.6824314011345
27371.9101797617321.08982023826799
36271.0885425171292-9.08854251712924
44269.1524986550409-27.1524986550409
55962.7181251143173-3.71812511431735
62733.1156612227212-6.1156612227212
75961.188423025436-2.18842302543597
85659.3256026146217-3.32560261462173
97859.328337770272418.6716622297276
104757.4406127913102-10.4406127913102
115153.4199677209014-2.41996772090139
124757.1572460906143-10.1572460906143
135553.40906169680011.59093830319985
143555.2796689104238-20.2796689104238
154751.5952205754344-4.59522057543443
164854.0669500463954-6.06695004639537
174752.0568485975395-5.05684859753955
185551.93230526475723.06769473524276
194249.9072461121507-7.9072461121507
205450.27125260389083.72874739610923
216053.38815439789466.61184560210539
225148.05825421286112.94174578713887
234746.85334255554470.146657444455251
245246.95759759594165.04240240405842
253844.743969700442-6.74396970044204
264646.8446305995357-0.844630599535719
271246.7023740034494-34.7023740034494
284845.77091618893612.22908381106392
294845.86686743951662.13313256048336
303244.5134942461775-12.5134942461775
312746.4044713677972-19.4044713677972
326045.816670540930614.1833294590694
334744.64867515953492.35132484046508
344743.97032848503223.02967151496781
355846.043356461628111.9566435383719
364742.81010630384424.18989369615585
374541.99282318892143.00717681107858
384841.61730157251286.38269842748717
394241.58750858842730.412491411572734
404144.732485477626-3.73248547762601
414841.85226407782316.14773592217692
426043.802793161109516.1972068388905
435644.902939592323111.0970604076769
444141.0063140663889-0.00631406638890214
455243.27618737049068.7238126295094
465042.55645777292417.4435422270759
474943.88328332353435.1167166764657
484241.65647682488260.343523175117375
493943.4151714200168-4.41517142001678
503943.1752320818575-4.17523208185746
514141.5969452671185-0.596945267118543
524642.02654542924063.97345457075941
533640.9263377863191-4.92633778631908
544940.05451354908318.94548645091692
554837.754888068701110.2451119312989
565540.334036305804914.6659636941951
574540.28939443878284.71060556121723
584840.60158136125857.3984186387415
595238.439290054520813.5607099454792
603937.68322237974841.31677762025156
614540.58342592525734.4165740747427
623238.4123002390948-6.41230023909477
635136.97299747388214.027002526118
644137.26582125717193.73417874282812
655239.97738124547812.022618754522
662238.0946535032675-16.0946535032675
675435.596009031190218.4039909688098
682735.2943440671746-8.29434406717459
694135.98269201648165.01730798351839
704535.51114433518289.48885566481722
715236.498779545289415.5012204547106
725734.407099549588122.5929004504119
734733.293733707417113.7062662925829
744133.26839376787297.73160623212714
754632.98541482946713.014585170533
764334.23457379021468.76542620978539
773132.6049843932182-1.6049843932182
784033.80107527371156.19892472628854
792432.9573669394757-8.95736693947566
803031.8869658832415-1.88696588324148
814532.746730803598612.2532691964014
823232.8873579277057-0.887357927705722
834633.024644492243912.9753555077561
84931.9460076592022-22.9460076592022
854431.348736147139912.6512638528601
863231.51922426834140.480775731658563
873733.28775690705873.71224309294126
886432.347981700127431.6520182998726
892129.7964119751143-8.79641197511427
902030.5971555234133-10.5971555234133
913330.18854117115542.81145882884459
922629.3513915326417-3.35139153264171
933627.82548577862828.17451422137176
943327.8271424558115.17285754418903
952029.3789768460333-9.3789768460333
963129.78264837194651.21735162805352
971329.5014281302366-16.5014281302366
983529.38559675346335.61440324653668
992428.9334190126512-4.93341901265125
1004026.938200453236413.0617995467636
1011928.7348762001575-9.73487620015747
1021527.1813987353542-12.1813987353542
1033428.19057074883635.80942925116374
1043227.71249837831974.28750162168027
1055828.739866635608329.2601333643917
1062128.7696732222956-7.76967322229562
1073126.63331735306774.36668264693229
1082127.1742686422182-6.17426864221818
1092626.8004173055941-0.800417305594078
1104726.792120317078720.2078796829213
1113726.782173452681410.2178265473186
1122825.61367468688062.38632531311935
113925.6070207729462-16.6070207729462
1144524.428656758358820.5713432416412
1153223.52033801399668.47966198600341
1163524.665167120037610.3348328799624
1172924.10434250599534.89565749400467
118123.9290407831659-22.9290407831659
1192023.7353999892115-3.73539998921148
1201523.9868476507384-8.98684765073844
1211123.0698169503672-12.0698169503672
1223322.689311699808710.3106883001913
1231822.7604467991528-4.76044679915277
1241022.1065149109947-12.1065149109947
1254121.466169792181519.5338302078185
1261020.4382981504878-10.4382981504878
127020.4879236516599-20.4879236516599
1282818.3433048004219.65669519957897
1293119.382256195386911.6177438046131
1302419.11424856333364.88575143666643
1313819.486484030580218.5135159694198
132019.4649948363109-19.4649948363109
1332519.42858194479545.57141805520465
1344018.876006710162321.1239932898377
135418.5517604708077-14.5517604708077
1362319.3028513127313.69714868726901
1371317.940759362082-4.94075936208196
138617.6975610799642-11.6975610799642
139016.9906223232598-16.9906223232598
140316.6382397770023-13.6382397770023
141015.0329495442355-15.0329495442355
142714.8493100250854-7.84931002508542
143215.5540887202021-13.5540887202021
144014.4919574472798-14.4919574472798
145015.9610736004807-15.9610736004807
146014.5741722998961-14.5741722998961
147014.1820771103593-14.1820771103593
148514.006714175822-9.00671417582198
149013.1624548480749-13.1624548480749

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 56 & 79.6824314011345 & -23.6824314011345 \tabularnewline
2 & 73 & 71.910179761732 & 1.08982023826799 \tabularnewline
3 & 62 & 71.0885425171292 & -9.08854251712924 \tabularnewline
4 & 42 & 69.1524986550409 & -27.1524986550409 \tabularnewline
5 & 59 & 62.7181251143173 & -3.71812511431735 \tabularnewline
6 & 27 & 33.1156612227212 & -6.1156612227212 \tabularnewline
7 & 59 & 61.188423025436 & -2.18842302543597 \tabularnewline
8 & 56 & 59.3256026146217 & -3.32560261462173 \tabularnewline
9 & 78 & 59.3283377702724 & 18.6716622297276 \tabularnewline
10 & 47 & 57.4406127913102 & -10.4406127913102 \tabularnewline
11 & 51 & 53.4199677209014 & -2.41996772090139 \tabularnewline
12 & 47 & 57.1572460906143 & -10.1572460906143 \tabularnewline
13 & 55 & 53.4090616968001 & 1.59093830319985 \tabularnewline
14 & 35 & 55.2796689104238 & -20.2796689104238 \tabularnewline
15 & 47 & 51.5952205754344 & -4.59522057543443 \tabularnewline
16 & 48 & 54.0669500463954 & -6.06695004639537 \tabularnewline
17 & 47 & 52.0568485975395 & -5.05684859753955 \tabularnewline
18 & 55 & 51.9323052647572 & 3.06769473524276 \tabularnewline
19 & 42 & 49.9072461121507 & -7.9072461121507 \tabularnewline
20 & 54 & 50.2712526038908 & 3.72874739610923 \tabularnewline
21 & 60 & 53.3881543978946 & 6.61184560210539 \tabularnewline
22 & 51 & 48.0582542128611 & 2.94174578713887 \tabularnewline
23 & 47 & 46.8533425555447 & 0.146657444455251 \tabularnewline
24 & 52 & 46.9575975959416 & 5.04240240405842 \tabularnewline
25 & 38 & 44.743969700442 & -6.74396970044204 \tabularnewline
26 & 46 & 46.8446305995357 & -0.844630599535719 \tabularnewline
27 & 12 & 46.7023740034494 & -34.7023740034494 \tabularnewline
28 & 48 & 45.7709161889361 & 2.22908381106392 \tabularnewline
29 & 48 & 45.8668674395166 & 2.13313256048336 \tabularnewline
30 & 32 & 44.5134942461775 & -12.5134942461775 \tabularnewline
31 & 27 & 46.4044713677972 & -19.4044713677972 \tabularnewline
32 & 60 & 45.8166705409306 & 14.1833294590694 \tabularnewline
33 & 47 & 44.6486751595349 & 2.35132484046508 \tabularnewline
34 & 47 & 43.9703284850322 & 3.02967151496781 \tabularnewline
35 & 58 & 46.0433564616281 & 11.9566435383719 \tabularnewline
36 & 47 & 42.8101063038442 & 4.18989369615585 \tabularnewline
37 & 45 & 41.9928231889214 & 3.00717681107858 \tabularnewline
38 & 48 & 41.6173015725128 & 6.38269842748717 \tabularnewline
39 & 42 & 41.5875085884273 & 0.412491411572734 \tabularnewline
40 & 41 & 44.732485477626 & -3.73248547762601 \tabularnewline
41 & 48 & 41.8522640778231 & 6.14773592217692 \tabularnewline
42 & 60 & 43.8027931611095 & 16.1972068388905 \tabularnewline
43 & 56 & 44.9029395923231 & 11.0970604076769 \tabularnewline
44 & 41 & 41.0063140663889 & -0.00631406638890214 \tabularnewline
45 & 52 & 43.2761873704906 & 8.7238126295094 \tabularnewline
46 & 50 & 42.5564577729241 & 7.4435422270759 \tabularnewline
47 & 49 & 43.8832833235343 & 5.1167166764657 \tabularnewline
48 & 42 & 41.6564768248826 & 0.343523175117375 \tabularnewline
49 & 39 & 43.4151714200168 & -4.41517142001678 \tabularnewline
50 & 39 & 43.1752320818575 & -4.17523208185746 \tabularnewline
51 & 41 & 41.5969452671185 & -0.596945267118543 \tabularnewline
52 & 46 & 42.0265454292406 & 3.97345457075941 \tabularnewline
53 & 36 & 40.9263377863191 & -4.92633778631908 \tabularnewline
54 & 49 & 40.0545135490831 & 8.94548645091692 \tabularnewline
55 & 48 & 37.7548880687011 & 10.2451119312989 \tabularnewline
56 & 55 & 40.3340363058049 & 14.6659636941951 \tabularnewline
57 & 45 & 40.2893944387828 & 4.71060556121723 \tabularnewline
58 & 48 & 40.6015813612585 & 7.3984186387415 \tabularnewline
59 & 52 & 38.4392900545208 & 13.5607099454792 \tabularnewline
60 & 39 & 37.6832223797484 & 1.31677762025156 \tabularnewline
61 & 45 & 40.5834259252573 & 4.4165740747427 \tabularnewline
62 & 32 & 38.4123002390948 & -6.41230023909477 \tabularnewline
63 & 51 & 36.972997473882 & 14.027002526118 \tabularnewline
64 & 41 & 37.2658212571719 & 3.73417874282812 \tabularnewline
65 & 52 & 39.977381245478 & 12.022618754522 \tabularnewline
66 & 22 & 38.0946535032675 & -16.0946535032675 \tabularnewline
67 & 54 & 35.5960090311902 & 18.4039909688098 \tabularnewline
68 & 27 & 35.2943440671746 & -8.29434406717459 \tabularnewline
69 & 41 & 35.9826920164816 & 5.01730798351839 \tabularnewline
70 & 45 & 35.5111443351828 & 9.48885566481722 \tabularnewline
71 & 52 & 36.4987795452894 & 15.5012204547106 \tabularnewline
72 & 57 & 34.4070995495881 & 22.5929004504119 \tabularnewline
73 & 47 & 33.2937337074171 & 13.7062662925829 \tabularnewline
74 & 41 & 33.2683937678729 & 7.73160623212714 \tabularnewline
75 & 46 & 32.985414829467 & 13.014585170533 \tabularnewline
76 & 43 & 34.2345737902146 & 8.76542620978539 \tabularnewline
77 & 31 & 32.6049843932182 & -1.6049843932182 \tabularnewline
78 & 40 & 33.8010752737115 & 6.19892472628854 \tabularnewline
79 & 24 & 32.9573669394757 & -8.95736693947566 \tabularnewline
80 & 30 & 31.8869658832415 & -1.88696588324148 \tabularnewline
81 & 45 & 32.7467308035986 & 12.2532691964014 \tabularnewline
82 & 32 & 32.8873579277057 & -0.887357927705722 \tabularnewline
83 & 46 & 33.0246444922439 & 12.9753555077561 \tabularnewline
84 & 9 & 31.9460076592022 & -22.9460076592022 \tabularnewline
85 & 44 & 31.3487361471399 & 12.6512638528601 \tabularnewline
86 & 32 & 31.5192242683414 & 0.480775731658563 \tabularnewline
87 & 37 & 33.2877569070587 & 3.71224309294126 \tabularnewline
88 & 64 & 32.3479817001274 & 31.6520182998726 \tabularnewline
89 & 21 & 29.7964119751143 & -8.79641197511427 \tabularnewline
90 & 20 & 30.5971555234133 & -10.5971555234133 \tabularnewline
91 & 33 & 30.1885411711554 & 2.81145882884459 \tabularnewline
92 & 26 & 29.3513915326417 & -3.35139153264171 \tabularnewline
93 & 36 & 27.8254857786282 & 8.17451422137176 \tabularnewline
94 & 33 & 27.827142455811 & 5.17285754418903 \tabularnewline
95 & 20 & 29.3789768460333 & -9.3789768460333 \tabularnewline
96 & 31 & 29.7826483719465 & 1.21735162805352 \tabularnewline
97 & 13 & 29.5014281302366 & -16.5014281302366 \tabularnewline
98 & 35 & 29.3855967534633 & 5.61440324653668 \tabularnewline
99 & 24 & 28.9334190126512 & -4.93341901265125 \tabularnewline
100 & 40 & 26.9382004532364 & 13.0617995467636 \tabularnewline
101 & 19 & 28.7348762001575 & -9.73487620015747 \tabularnewline
102 & 15 & 27.1813987353542 & -12.1813987353542 \tabularnewline
103 & 34 & 28.1905707488363 & 5.80942925116374 \tabularnewline
104 & 32 & 27.7124983783197 & 4.28750162168027 \tabularnewline
105 & 58 & 28.7398666356083 & 29.2601333643917 \tabularnewline
106 & 21 & 28.7696732222956 & -7.76967322229562 \tabularnewline
107 & 31 & 26.6333173530677 & 4.36668264693229 \tabularnewline
108 & 21 & 27.1742686422182 & -6.17426864221818 \tabularnewline
109 & 26 & 26.8004173055941 & -0.800417305594078 \tabularnewline
110 & 47 & 26.7921203170787 & 20.2078796829213 \tabularnewline
111 & 37 & 26.7821734526814 & 10.2178265473186 \tabularnewline
112 & 28 & 25.6136746868806 & 2.38632531311935 \tabularnewline
113 & 9 & 25.6070207729462 & -16.6070207729462 \tabularnewline
114 & 45 & 24.4286567583588 & 20.5713432416412 \tabularnewline
115 & 32 & 23.5203380139966 & 8.47966198600341 \tabularnewline
116 & 35 & 24.6651671200376 & 10.3348328799624 \tabularnewline
117 & 29 & 24.1043425059953 & 4.89565749400467 \tabularnewline
118 & 1 & 23.9290407831659 & -22.9290407831659 \tabularnewline
119 & 20 & 23.7353999892115 & -3.73539998921148 \tabularnewline
120 & 15 & 23.9868476507384 & -8.98684765073844 \tabularnewline
121 & 11 & 23.0698169503672 & -12.0698169503672 \tabularnewline
122 & 33 & 22.6893116998087 & 10.3106883001913 \tabularnewline
123 & 18 & 22.7604467991528 & -4.76044679915277 \tabularnewline
124 & 10 & 22.1065149109947 & -12.1065149109947 \tabularnewline
125 & 41 & 21.4661697921815 & 19.5338302078185 \tabularnewline
126 & 10 & 20.4382981504878 & -10.4382981504878 \tabularnewline
127 & 0 & 20.4879236516599 & -20.4879236516599 \tabularnewline
128 & 28 & 18.343304800421 & 9.65669519957897 \tabularnewline
129 & 31 & 19.3822561953869 & 11.6177438046131 \tabularnewline
130 & 24 & 19.1142485633336 & 4.88575143666643 \tabularnewline
131 & 38 & 19.4864840305802 & 18.5135159694198 \tabularnewline
132 & 0 & 19.4649948363109 & -19.4649948363109 \tabularnewline
133 & 25 & 19.4285819447954 & 5.57141805520465 \tabularnewline
134 & 40 & 18.8760067101623 & 21.1239932898377 \tabularnewline
135 & 4 & 18.5517604708077 & -14.5517604708077 \tabularnewline
136 & 23 & 19.302851312731 & 3.69714868726901 \tabularnewline
137 & 13 & 17.940759362082 & -4.94075936208196 \tabularnewline
138 & 6 & 17.6975610799642 & -11.6975610799642 \tabularnewline
139 & 0 & 16.9906223232598 & -16.9906223232598 \tabularnewline
140 & 3 & 16.6382397770023 & -13.6382397770023 \tabularnewline
141 & 0 & 15.0329495442355 & -15.0329495442355 \tabularnewline
142 & 7 & 14.8493100250854 & -7.84931002508542 \tabularnewline
143 & 2 & 15.5540887202021 & -13.5540887202021 \tabularnewline
144 & 0 & 14.4919574472798 & -14.4919574472798 \tabularnewline
145 & 0 & 15.9610736004807 & -15.9610736004807 \tabularnewline
146 & 0 & 14.5741722998961 & -14.5741722998961 \tabularnewline
147 & 0 & 14.1820771103593 & -14.1820771103593 \tabularnewline
148 & 5 & 14.006714175822 & -9.00671417582198 \tabularnewline
149 & 0 & 13.1624548480749 & -13.1624548480749 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186142&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]56[/C][C]79.6824314011345[/C][C]-23.6824314011345[/C][/ROW]
[ROW][C]2[/C][C]73[/C][C]71.910179761732[/C][C]1.08982023826799[/C][/ROW]
[ROW][C]3[/C][C]62[/C][C]71.0885425171292[/C][C]-9.08854251712924[/C][/ROW]
[ROW][C]4[/C][C]42[/C][C]69.1524986550409[/C][C]-27.1524986550409[/C][/ROW]
[ROW][C]5[/C][C]59[/C][C]62.7181251143173[/C][C]-3.71812511431735[/C][/ROW]
[ROW][C]6[/C][C]27[/C][C]33.1156612227212[/C][C]-6.1156612227212[/C][/ROW]
[ROW][C]7[/C][C]59[/C][C]61.188423025436[/C][C]-2.18842302543597[/C][/ROW]
[ROW][C]8[/C][C]56[/C][C]59.3256026146217[/C][C]-3.32560261462173[/C][/ROW]
[ROW][C]9[/C][C]78[/C][C]59.3283377702724[/C][C]18.6716622297276[/C][/ROW]
[ROW][C]10[/C][C]47[/C][C]57.4406127913102[/C][C]-10.4406127913102[/C][/ROW]
[ROW][C]11[/C][C]51[/C][C]53.4199677209014[/C][C]-2.41996772090139[/C][/ROW]
[ROW][C]12[/C][C]47[/C][C]57.1572460906143[/C][C]-10.1572460906143[/C][/ROW]
[ROW][C]13[/C][C]55[/C][C]53.4090616968001[/C][C]1.59093830319985[/C][/ROW]
[ROW][C]14[/C][C]35[/C][C]55.2796689104238[/C][C]-20.2796689104238[/C][/ROW]
[ROW][C]15[/C][C]47[/C][C]51.5952205754344[/C][C]-4.59522057543443[/C][/ROW]
[ROW][C]16[/C][C]48[/C][C]54.0669500463954[/C][C]-6.06695004639537[/C][/ROW]
[ROW][C]17[/C][C]47[/C][C]52.0568485975395[/C][C]-5.05684859753955[/C][/ROW]
[ROW][C]18[/C][C]55[/C][C]51.9323052647572[/C][C]3.06769473524276[/C][/ROW]
[ROW][C]19[/C][C]42[/C][C]49.9072461121507[/C][C]-7.9072461121507[/C][/ROW]
[ROW][C]20[/C][C]54[/C][C]50.2712526038908[/C][C]3.72874739610923[/C][/ROW]
[ROW][C]21[/C][C]60[/C][C]53.3881543978946[/C][C]6.61184560210539[/C][/ROW]
[ROW][C]22[/C][C]51[/C][C]48.0582542128611[/C][C]2.94174578713887[/C][/ROW]
[ROW][C]23[/C][C]47[/C][C]46.8533425555447[/C][C]0.146657444455251[/C][/ROW]
[ROW][C]24[/C][C]52[/C][C]46.9575975959416[/C][C]5.04240240405842[/C][/ROW]
[ROW][C]25[/C][C]38[/C][C]44.743969700442[/C][C]-6.74396970044204[/C][/ROW]
[ROW][C]26[/C][C]46[/C][C]46.8446305995357[/C][C]-0.844630599535719[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]46.7023740034494[/C][C]-34.7023740034494[/C][/ROW]
[ROW][C]28[/C][C]48[/C][C]45.7709161889361[/C][C]2.22908381106392[/C][/ROW]
[ROW][C]29[/C][C]48[/C][C]45.8668674395166[/C][C]2.13313256048336[/C][/ROW]
[ROW][C]30[/C][C]32[/C][C]44.5134942461775[/C][C]-12.5134942461775[/C][/ROW]
[ROW][C]31[/C][C]27[/C][C]46.4044713677972[/C][C]-19.4044713677972[/C][/ROW]
[ROW][C]32[/C][C]60[/C][C]45.8166705409306[/C][C]14.1833294590694[/C][/ROW]
[ROW][C]33[/C][C]47[/C][C]44.6486751595349[/C][C]2.35132484046508[/C][/ROW]
[ROW][C]34[/C][C]47[/C][C]43.9703284850322[/C][C]3.02967151496781[/C][/ROW]
[ROW][C]35[/C][C]58[/C][C]46.0433564616281[/C][C]11.9566435383719[/C][/ROW]
[ROW][C]36[/C][C]47[/C][C]42.8101063038442[/C][C]4.18989369615585[/C][/ROW]
[ROW][C]37[/C][C]45[/C][C]41.9928231889214[/C][C]3.00717681107858[/C][/ROW]
[ROW][C]38[/C][C]48[/C][C]41.6173015725128[/C][C]6.38269842748717[/C][/ROW]
[ROW][C]39[/C][C]42[/C][C]41.5875085884273[/C][C]0.412491411572734[/C][/ROW]
[ROW][C]40[/C][C]41[/C][C]44.732485477626[/C][C]-3.73248547762601[/C][/ROW]
[ROW][C]41[/C][C]48[/C][C]41.8522640778231[/C][C]6.14773592217692[/C][/ROW]
[ROW][C]42[/C][C]60[/C][C]43.8027931611095[/C][C]16.1972068388905[/C][/ROW]
[ROW][C]43[/C][C]56[/C][C]44.9029395923231[/C][C]11.0970604076769[/C][/ROW]
[ROW][C]44[/C][C]41[/C][C]41.0063140663889[/C][C]-0.00631406638890214[/C][/ROW]
[ROW][C]45[/C][C]52[/C][C]43.2761873704906[/C][C]8.7238126295094[/C][/ROW]
[ROW][C]46[/C][C]50[/C][C]42.5564577729241[/C][C]7.4435422270759[/C][/ROW]
[ROW][C]47[/C][C]49[/C][C]43.8832833235343[/C][C]5.1167166764657[/C][/ROW]
[ROW][C]48[/C][C]42[/C][C]41.6564768248826[/C][C]0.343523175117375[/C][/ROW]
[ROW][C]49[/C][C]39[/C][C]43.4151714200168[/C][C]-4.41517142001678[/C][/ROW]
[ROW][C]50[/C][C]39[/C][C]43.1752320818575[/C][C]-4.17523208185746[/C][/ROW]
[ROW][C]51[/C][C]41[/C][C]41.5969452671185[/C][C]-0.596945267118543[/C][/ROW]
[ROW][C]52[/C][C]46[/C][C]42.0265454292406[/C][C]3.97345457075941[/C][/ROW]
[ROW][C]53[/C][C]36[/C][C]40.9263377863191[/C][C]-4.92633778631908[/C][/ROW]
[ROW][C]54[/C][C]49[/C][C]40.0545135490831[/C][C]8.94548645091692[/C][/ROW]
[ROW][C]55[/C][C]48[/C][C]37.7548880687011[/C][C]10.2451119312989[/C][/ROW]
[ROW][C]56[/C][C]55[/C][C]40.3340363058049[/C][C]14.6659636941951[/C][/ROW]
[ROW][C]57[/C][C]45[/C][C]40.2893944387828[/C][C]4.71060556121723[/C][/ROW]
[ROW][C]58[/C][C]48[/C][C]40.6015813612585[/C][C]7.3984186387415[/C][/ROW]
[ROW][C]59[/C][C]52[/C][C]38.4392900545208[/C][C]13.5607099454792[/C][/ROW]
[ROW][C]60[/C][C]39[/C][C]37.6832223797484[/C][C]1.31677762025156[/C][/ROW]
[ROW][C]61[/C][C]45[/C][C]40.5834259252573[/C][C]4.4165740747427[/C][/ROW]
[ROW][C]62[/C][C]32[/C][C]38.4123002390948[/C][C]-6.41230023909477[/C][/ROW]
[ROW][C]63[/C][C]51[/C][C]36.972997473882[/C][C]14.027002526118[/C][/ROW]
[ROW][C]64[/C][C]41[/C][C]37.2658212571719[/C][C]3.73417874282812[/C][/ROW]
[ROW][C]65[/C][C]52[/C][C]39.977381245478[/C][C]12.022618754522[/C][/ROW]
[ROW][C]66[/C][C]22[/C][C]38.0946535032675[/C][C]-16.0946535032675[/C][/ROW]
[ROW][C]67[/C][C]54[/C][C]35.5960090311902[/C][C]18.4039909688098[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]35.2943440671746[/C][C]-8.29434406717459[/C][/ROW]
[ROW][C]69[/C][C]41[/C][C]35.9826920164816[/C][C]5.01730798351839[/C][/ROW]
[ROW][C]70[/C][C]45[/C][C]35.5111443351828[/C][C]9.48885566481722[/C][/ROW]
[ROW][C]71[/C][C]52[/C][C]36.4987795452894[/C][C]15.5012204547106[/C][/ROW]
[ROW][C]72[/C][C]57[/C][C]34.4070995495881[/C][C]22.5929004504119[/C][/ROW]
[ROW][C]73[/C][C]47[/C][C]33.2937337074171[/C][C]13.7062662925829[/C][/ROW]
[ROW][C]74[/C][C]41[/C][C]33.2683937678729[/C][C]7.73160623212714[/C][/ROW]
[ROW][C]75[/C][C]46[/C][C]32.985414829467[/C][C]13.014585170533[/C][/ROW]
[ROW][C]76[/C][C]43[/C][C]34.2345737902146[/C][C]8.76542620978539[/C][/ROW]
[ROW][C]77[/C][C]31[/C][C]32.6049843932182[/C][C]-1.6049843932182[/C][/ROW]
[ROW][C]78[/C][C]40[/C][C]33.8010752737115[/C][C]6.19892472628854[/C][/ROW]
[ROW][C]79[/C][C]24[/C][C]32.9573669394757[/C][C]-8.95736693947566[/C][/ROW]
[ROW][C]80[/C][C]30[/C][C]31.8869658832415[/C][C]-1.88696588324148[/C][/ROW]
[ROW][C]81[/C][C]45[/C][C]32.7467308035986[/C][C]12.2532691964014[/C][/ROW]
[ROW][C]82[/C][C]32[/C][C]32.8873579277057[/C][C]-0.887357927705722[/C][/ROW]
[ROW][C]83[/C][C]46[/C][C]33.0246444922439[/C][C]12.9753555077561[/C][/ROW]
[ROW][C]84[/C][C]9[/C][C]31.9460076592022[/C][C]-22.9460076592022[/C][/ROW]
[ROW][C]85[/C][C]44[/C][C]31.3487361471399[/C][C]12.6512638528601[/C][/ROW]
[ROW][C]86[/C][C]32[/C][C]31.5192242683414[/C][C]0.480775731658563[/C][/ROW]
[ROW][C]87[/C][C]37[/C][C]33.2877569070587[/C][C]3.71224309294126[/C][/ROW]
[ROW][C]88[/C][C]64[/C][C]32.3479817001274[/C][C]31.6520182998726[/C][/ROW]
[ROW][C]89[/C][C]21[/C][C]29.7964119751143[/C][C]-8.79641197511427[/C][/ROW]
[ROW][C]90[/C][C]20[/C][C]30.5971555234133[/C][C]-10.5971555234133[/C][/ROW]
[ROW][C]91[/C][C]33[/C][C]30.1885411711554[/C][C]2.81145882884459[/C][/ROW]
[ROW][C]92[/C][C]26[/C][C]29.3513915326417[/C][C]-3.35139153264171[/C][/ROW]
[ROW][C]93[/C][C]36[/C][C]27.8254857786282[/C][C]8.17451422137176[/C][/ROW]
[ROW][C]94[/C][C]33[/C][C]27.827142455811[/C][C]5.17285754418903[/C][/ROW]
[ROW][C]95[/C][C]20[/C][C]29.3789768460333[/C][C]-9.3789768460333[/C][/ROW]
[ROW][C]96[/C][C]31[/C][C]29.7826483719465[/C][C]1.21735162805352[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]29.5014281302366[/C][C]-16.5014281302366[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]29.3855967534633[/C][C]5.61440324653668[/C][/ROW]
[ROW][C]99[/C][C]24[/C][C]28.9334190126512[/C][C]-4.93341901265125[/C][/ROW]
[ROW][C]100[/C][C]40[/C][C]26.9382004532364[/C][C]13.0617995467636[/C][/ROW]
[ROW][C]101[/C][C]19[/C][C]28.7348762001575[/C][C]-9.73487620015747[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]27.1813987353542[/C][C]-12.1813987353542[/C][/ROW]
[ROW][C]103[/C][C]34[/C][C]28.1905707488363[/C][C]5.80942925116374[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]27.7124983783197[/C][C]4.28750162168027[/C][/ROW]
[ROW][C]105[/C][C]58[/C][C]28.7398666356083[/C][C]29.2601333643917[/C][/ROW]
[ROW][C]106[/C][C]21[/C][C]28.7696732222956[/C][C]-7.76967322229562[/C][/ROW]
[ROW][C]107[/C][C]31[/C][C]26.6333173530677[/C][C]4.36668264693229[/C][/ROW]
[ROW][C]108[/C][C]21[/C][C]27.1742686422182[/C][C]-6.17426864221818[/C][/ROW]
[ROW][C]109[/C][C]26[/C][C]26.8004173055941[/C][C]-0.800417305594078[/C][/ROW]
[ROW][C]110[/C][C]47[/C][C]26.7921203170787[/C][C]20.2078796829213[/C][/ROW]
[ROW][C]111[/C][C]37[/C][C]26.7821734526814[/C][C]10.2178265473186[/C][/ROW]
[ROW][C]112[/C][C]28[/C][C]25.6136746868806[/C][C]2.38632531311935[/C][/ROW]
[ROW][C]113[/C][C]9[/C][C]25.6070207729462[/C][C]-16.6070207729462[/C][/ROW]
[ROW][C]114[/C][C]45[/C][C]24.4286567583588[/C][C]20.5713432416412[/C][/ROW]
[ROW][C]115[/C][C]32[/C][C]23.5203380139966[/C][C]8.47966198600341[/C][/ROW]
[ROW][C]116[/C][C]35[/C][C]24.6651671200376[/C][C]10.3348328799624[/C][/ROW]
[ROW][C]117[/C][C]29[/C][C]24.1043425059953[/C][C]4.89565749400467[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]23.9290407831659[/C][C]-22.9290407831659[/C][/ROW]
[ROW][C]119[/C][C]20[/C][C]23.7353999892115[/C][C]-3.73539998921148[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]23.9868476507384[/C][C]-8.98684765073844[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]23.0698169503672[/C][C]-12.0698169503672[/C][/ROW]
[ROW][C]122[/C][C]33[/C][C]22.6893116998087[/C][C]10.3106883001913[/C][/ROW]
[ROW][C]123[/C][C]18[/C][C]22.7604467991528[/C][C]-4.76044679915277[/C][/ROW]
[ROW][C]124[/C][C]10[/C][C]22.1065149109947[/C][C]-12.1065149109947[/C][/ROW]
[ROW][C]125[/C][C]41[/C][C]21.4661697921815[/C][C]19.5338302078185[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]20.4382981504878[/C][C]-10.4382981504878[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]20.4879236516599[/C][C]-20.4879236516599[/C][/ROW]
[ROW][C]128[/C][C]28[/C][C]18.343304800421[/C][C]9.65669519957897[/C][/ROW]
[ROW][C]129[/C][C]31[/C][C]19.3822561953869[/C][C]11.6177438046131[/C][/ROW]
[ROW][C]130[/C][C]24[/C][C]19.1142485633336[/C][C]4.88575143666643[/C][/ROW]
[ROW][C]131[/C][C]38[/C][C]19.4864840305802[/C][C]18.5135159694198[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]19.4649948363109[/C][C]-19.4649948363109[/C][/ROW]
[ROW][C]133[/C][C]25[/C][C]19.4285819447954[/C][C]5.57141805520465[/C][/ROW]
[ROW][C]134[/C][C]40[/C][C]18.8760067101623[/C][C]21.1239932898377[/C][/ROW]
[ROW][C]135[/C][C]4[/C][C]18.5517604708077[/C][C]-14.5517604708077[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]19.302851312731[/C][C]3.69714868726901[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]17.940759362082[/C][C]-4.94075936208196[/C][/ROW]
[ROW][C]138[/C][C]6[/C][C]17.6975610799642[/C][C]-11.6975610799642[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]16.9906223232598[/C][C]-16.9906223232598[/C][/ROW]
[ROW][C]140[/C][C]3[/C][C]16.6382397770023[/C][C]-13.6382397770023[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]15.0329495442355[/C][C]-15.0329495442355[/C][/ROW]
[ROW][C]142[/C][C]7[/C][C]14.8493100250854[/C][C]-7.84931002508542[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]15.5540887202021[/C][C]-13.5540887202021[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]14.4919574472798[/C][C]-14.4919574472798[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]15.9610736004807[/C][C]-15.9610736004807[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]14.5741722998961[/C][C]-14.5741722998961[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]14.1820771103593[/C][C]-14.1820771103593[/C][/ROW]
[ROW][C]148[/C][C]5[/C][C]14.006714175822[/C][C]-9.00671417582198[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]13.1624548480749[/C][C]-13.1624548480749[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186142&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186142&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
15679.6824314011345-23.6824314011345
27371.9101797617321.08982023826799
36271.0885425171292-9.08854251712924
44269.1524986550409-27.1524986550409
55962.7181251143173-3.71812511431735
62733.1156612227212-6.1156612227212
75961.188423025436-2.18842302543597
85659.3256026146217-3.32560261462173
97859.328337770272418.6716622297276
104757.4406127913102-10.4406127913102
115153.4199677209014-2.41996772090139
124757.1572460906143-10.1572460906143
135553.40906169680011.59093830319985
143555.2796689104238-20.2796689104238
154751.5952205754344-4.59522057543443
164854.0669500463954-6.06695004639537
174752.0568485975395-5.05684859753955
185551.93230526475723.06769473524276
194249.9072461121507-7.9072461121507
205450.27125260389083.72874739610923
216053.38815439789466.61184560210539
225148.05825421286112.94174578713887
234746.85334255554470.146657444455251
245246.95759759594165.04240240405842
253844.743969700442-6.74396970044204
264646.8446305995357-0.844630599535719
271246.7023740034494-34.7023740034494
284845.77091618893612.22908381106392
294845.86686743951662.13313256048336
303244.5134942461775-12.5134942461775
312746.4044713677972-19.4044713677972
326045.816670540930614.1833294590694
334744.64867515953492.35132484046508
344743.97032848503223.02967151496781
355846.043356461628111.9566435383719
364742.81010630384424.18989369615585
374541.99282318892143.00717681107858
384841.61730157251286.38269842748717
394241.58750858842730.412491411572734
404144.732485477626-3.73248547762601
414841.85226407782316.14773592217692
426043.802793161109516.1972068388905
435644.902939592323111.0970604076769
444141.0063140663889-0.00631406638890214
455243.27618737049068.7238126295094
465042.55645777292417.4435422270759
474943.88328332353435.1167166764657
484241.65647682488260.343523175117375
493943.4151714200168-4.41517142001678
503943.1752320818575-4.17523208185746
514141.5969452671185-0.596945267118543
524642.02654542924063.97345457075941
533640.9263377863191-4.92633778631908
544940.05451354908318.94548645091692
554837.754888068701110.2451119312989
565540.334036305804914.6659636941951
574540.28939443878284.71060556121723
584840.60158136125857.3984186387415
595238.439290054520813.5607099454792
603937.68322237974841.31677762025156
614540.58342592525734.4165740747427
623238.4123002390948-6.41230023909477
635136.97299747388214.027002526118
644137.26582125717193.73417874282812
655239.97738124547812.022618754522
662238.0946535032675-16.0946535032675
675435.596009031190218.4039909688098
682735.2943440671746-8.29434406717459
694135.98269201648165.01730798351839
704535.51114433518289.48885566481722
715236.498779545289415.5012204547106
725734.407099549588122.5929004504119
734733.293733707417113.7062662925829
744133.26839376787297.73160623212714
754632.98541482946713.014585170533
764334.23457379021468.76542620978539
773132.6049843932182-1.6049843932182
784033.80107527371156.19892472628854
792432.9573669394757-8.95736693947566
803031.8869658832415-1.88696588324148
814532.746730803598612.2532691964014
823232.8873579277057-0.887357927705722
834633.024644492243912.9753555077561
84931.9460076592022-22.9460076592022
854431.348736147139912.6512638528601
863231.51922426834140.480775731658563
873733.28775690705873.71224309294126
886432.347981700127431.6520182998726
892129.7964119751143-8.79641197511427
902030.5971555234133-10.5971555234133
913330.18854117115542.81145882884459
922629.3513915326417-3.35139153264171
933627.82548577862828.17451422137176
943327.8271424558115.17285754418903
952029.3789768460333-9.3789768460333
963129.78264837194651.21735162805352
971329.5014281302366-16.5014281302366
983529.38559675346335.61440324653668
992428.9334190126512-4.93341901265125
1004026.938200453236413.0617995467636
1011928.7348762001575-9.73487620015747
1021527.1813987353542-12.1813987353542
1033428.19057074883635.80942925116374
1043227.71249837831974.28750162168027
1055828.739866635608329.2601333643917
1062128.7696732222956-7.76967322229562
1073126.63331735306774.36668264693229
1082127.1742686422182-6.17426864221818
1092626.8004173055941-0.800417305594078
1104726.792120317078720.2078796829213
1113726.782173452681410.2178265473186
1122825.61367468688062.38632531311935
113925.6070207729462-16.6070207729462
1144524.428656758358820.5713432416412
1153223.52033801399668.47966198600341
1163524.665167120037610.3348328799624
1172924.10434250599534.89565749400467
118123.9290407831659-22.9290407831659
1192023.7353999892115-3.73539998921148
1201523.9868476507384-8.98684765073844
1211123.0698169503672-12.0698169503672
1223322.689311699808710.3106883001913
1231822.7604467991528-4.76044679915277
1241022.1065149109947-12.1065149109947
1254121.466169792181519.5338302078185
1261020.4382981504878-10.4382981504878
127020.4879236516599-20.4879236516599
1282818.3433048004219.65669519957897
1293119.382256195386911.6177438046131
1302419.11424856333364.88575143666643
1313819.486484030580218.5135159694198
132019.4649948363109-19.4649948363109
1332519.42858194479545.57141805520465
1344018.876006710162321.1239932898377
135418.5517604708077-14.5517604708077
1362319.3028513127313.69714868726901
1371317.940759362082-4.94075936208196
138617.6975610799642-11.6975610799642
139016.9906223232598-16.9906223232598
140316.6382397770023-13.6382397770023
141015.0329495442355-15.0329495442355
142714.8493100250854-7.84931002508542
143215.5540887202021-13.5540887202021
144014.4919574472798-14.4919574472798
145015.9610736004807-15.9610736004807
146014.5741722998961-14.5741722998961
147014.1820771103593-14.1820771103593
148514.006714175822-9.00671417582198
149013.1624548480749-13.1624548480749







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.3250746357817990.6501492715635970.674925364218201
80.3716077824389070.7432155648778140.628392217561093
90.8001764321363070.3996471357273860.199823567863693
100.7254024371469150.5491951257061710.274597562853086
110.6521432041124550.6957135917750890.347856795887545
120.5988033032948970.8023933934102060.401196696705103
130.5087370379754660.9825259240490690.491262962024534
140.6940787934491630.6118424131016750.305921206550837
150.616519759537460.7669604809250810.38348024046254
160.5407320687200330.9185358625599340.459267931279967
170.4643142292869680.9286284585739370.535685770713032
180.3896283872330740.7792567744661480.610371612766926
190.3384105044491020.6768210088982040.661589495550898
200.2806829431946490.5613658863892980.719317056805351
210.2525160049248740.5050320098497480.747483995075126
220.2025690064739990.4051380129479990.797430993526001
230.155485327623610.3109706552472210.84451467237639
240.117554225307360.2351084506147190.88244577469264
250.1070445293560520.2140890587121040.892955470643948
260.08419373361860850.1683874672372170.915806266381392
270.5710598031430810.8578803937138380.428940196856919
280.5242731946437530.9514536107124950.475726805356247
290.4732286737833960.9464573475667910.526771326216604
300.500604314038130.998791371923740.49939568596187
310.6283642320981170.7432715358037660.371635767901883
320.6753957456830160.6492085086339680.324604254316984
330.6296485179966050.7407029640067890.370351482003395
340.5839985443558020.8320029112883970.416001455644198
350.5915497664964280.8169004670071440.408450233503572
360.5425483745155570.9149032509688860.457451625484443
370.4918090202909020.9836180405818040.508190979709098
380.4480200310904930.8960400621809850.551979968909507
390.4007680717045590.8015361434091180.599231928295441
400.3746433952029590.7492867904059180.625356604797041
410.331825755797130.6636515115942610.66817424420287
420.361951845290770.7239036905815410.63804815470923
430.343480784555380.6869615691107590.656519215444621
440.3032911341276530.6065822682553070.696708865872346
450.2690234461099850.538046892219970.730976553890015
460.2342524278206480.4685048556412960.765747572179352
470.1988139033564320.3976278067128650.801186096643568
480.1706485189980450.341297037996090.829351481001955
490.1557098196578380.3114196393156760.844290180342162
500.1448123101068240.2896246202136470.855187689893176
510.1235986148796910.2471972297593820.876401385120309
520.1012275374358490.2024550748716990.898772462564151
530.1024798332060980.2049596664121960.897520166793902
540.08614699682153270.1722939936430650.913853003178467
550.07291964009629140.1458392801925830.927080359903709
560.07147152483357090.1429430496671420.928528475166429
570.05827033077839650.1165406615567930.941729669221604
580.04640187231398360.09280374462796720.953598127686016
590.0412873191563690.0825746383127380.958712680843631
600.03374449641699570.06748899283399140.966255503583004
610.02570340688434650.0514068137686930.974296593115654
620.03056922224584920.06113844449169840.969430777754151
630.02739266567426850.0547853313485370.972607334325732
640.0208883990308860.0417767980617720.979111600969114
650.01706369156584390.03412738313168780.982936308434156
660.05152910458853160.1030582091770630.948470895411468
670.05473023025007330.1094604605001470.945269769749927
680.08230462664073410.1646092532814680.917695373359266
690.06695070339776120.1339014067955220.933049296602239
700.05401634388991270.1080326877798250.945983656110087
710.04893951197304690.09787902394609390.951060488026953
720.0609179372431450.121835874486290.939082062756855
730.05564089225013580.1112817845002720.944359107749864
740.04430463900286510.08860927800573030.955695360997135
750.03550657187041070.07101314374082130.964493428129589
760.02816940224935220.05633880449870450.971830597750648
770.02530021068169250.0506004213633850.974699789318307
780.01918397633074410.03836795266148810.980816023669256
790.02562255464409320.05124510928818630.974377445355907
800.02256471985625960.04512943971251930.97743528014374
810.01861098779856590.03722197559713190.981389012201434
820.01585248922938850.0317049784587770.984147510770611
830.01258589928563070.02517179857126150.987414100714369
840.06430838623916340.1286167724783270.935691613760837
850.05343398062593230.1068679612518650.946566019374068
860.04375331350600150.0875066270120030.956246686493998
870.03442804211305550.0688560842261110.965571957886944
880.08501489120289850.1700297824057970.914985108797101
890.1002885952618040.2005771905236080.899711404738196
900.1205966476078420.2411932952156850.879403352392158
910.09910344733246460.1982068946649290.900896552667535
920.09094486922180730.1818897384436150.909055130778193
930.07260226843575530.1452045368715110.927397731564245
940.05761343268103740.1152268653620750.942386567318963
950.06999587797784160.1399917559556830.930004122022158
960.05706385174903350.1141277034980670.942936148250966
970.100240314524870.200480629049740.89975968547513
980.08002754109174990.16005508218350.91997245890825
990.07761067536736090.1552213507347220.922389324632639
1000.06587299660350250.1317459932070050.934127003396497
1010.09106006904461310.1821201380892260.908939930955387
1020.1383602852425720.2767205704851430.861639714757428
1030.1163168079498440.2326336158996870.883683192050156
1040.09325995424959680.1865199084991940.906740045750403
1050.187385704079370.3747714081587410.81261429592063
1060.1830398876484880.3660797752969750.816960112351512
1070.1504828816393990.3009657632787970.849517118360601
1080.1610343945997730.3220687891995460.838965605400227
1090.1353316003314060.2706632006628110.864668399668594
1100.1539509840274650.3079019680549310.846049015972535
1110.1261075443754870.2522150887509740.873892455624513
1120.1006782316969340.2013564633938680.899321768303066
1130.232033183350780.4640663667015590.767966816649221
1140.4913344888494740.9826689776989490.508665511150526
1150.454956441106870.9099128822137410.545043558893129
1160.406347910665550.8126958213310990.59365208933445
1170.3590624581286590.7181249162573180.640937541871341
1180.3910795130309830.7821590260619650.608920486969017
1190.3641330064426910.7282660128853810.635866993557309
1200.5110388667115480.9779222665769040.488961133288452
1210.6328477451419440.7343045097161110.367152254858056
1220.5837611077031720.8324777845936560.416238892296828
1230.5992353121951020.8015293756097960.400764687804898
1240.8293340952638950.3413318094722090.170665904736105
1250.7908869096074510.4182261807850980.209113090392549
1260.7505426895366070.4989146209267870.249457310463394
1270.8616713695844620.2766572608310760.138328630415538
1280.9403631509440440.1192736981119120.0596368490559562
1290.9175234185551690.1649531628896610.0824765814448306
1300.8939043457260430.2121913085479150.106095654273957
1310.9266425042348840.1467149915302320.0733574957651158
1320.9173913004418790.1652173991162420.0826086995581209
1330.8856414049805560.2287171900388880.114358595019444
1340.9957215313539870.008556937292026050.00427846864601303
1350.9983549799208650.003290040158270010.001645020079135
1360.9995057675311260.0009884649377480620.000494232468874031
1370.9999262004870720.0001475990258553317.37995129276657e-05
1380.9998685458660040.0002629082679928650.000131454133996432
1390.9994083289007930.001183342198414130.000591671099207067
1400.9981903743114020.00361925137719510.00180962568859755
1410.9938054233162350.01238915336753080.00619457668376541
1420.9992595328681850.001480934263629370.000740467131814686

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.325074635781799 & 0.650149271563597 & 0.674925364218201 \tabularnewline
8 & 0.371607782438907 & 0.743215564877814 & 0.628392217561093 \tabularnewline
9 & 0.800176432136307 & 0.399647135727386 & 0.199823567863693 \tabularnewline
10 & 0.725402437146915 & 0.549195125706171 & 0.274597562853086 \tabularnewline
11 & 0.652143204112455 & 0.695713591775089 & 0.347856795887545 \tabularnewline
12 & 0.598803303294897 & 0.802393393410206 & 0.401196696705103 \tabularnewline
13 & 0.508737037975466 & 0.982525924049069 & 0.491262962024534 \tabularnewline
14 & 0.694078793449163 & 0.611842413101675 & 0.305921206550837 \tabularnewline
15 & 0.61651975953746 & 0.766960480925081 & 0.38348024046254 \tabularnewline
16 & 0.540732068720033 & 0.918535862559934 & 0.459267931279967 \tabularnewline
17 & 0.464314229286968 & 0.928628458573937 & 0.535685770713032 \tabularnewline
18 & 0.389628387233074 & 0.779256774466148 & 0.610371612766926 \tabularnewline
19 & 0.338410504449102 & 0.676821008898204 & 0.661589495550898 \tabularnewline
20 & 0.280682943194649 & 0.561365886389298 & 0.719317056805351 \tabularnewline
21 & 0.252516004924874 & 0.505032009849748 & 0.747483995075126 \tabularnewline
22 & 0.202569006473999 & 0.405138012947999 & 0.797430993526001 \tabularnewline
23 & 0.15548532762361 & 0.310970655247221 & 0.84451467237639 \tabularnewline
24 & 0.11755422530736 & 0.235108450614719 & 0.88244577469264 \tabularnewline
25 & 0.107044529356052 & 0.214089058712104 & 0.892955470643948 \tabularnewline
26 & 0.0841937336186085 & 0.168387467237217 & 0.915806266381392 \tabularnewline
27 & 0.571059803143081 & 0.857880393713838 & 0.428940196856919 \tabularnewline
28 & 0.524273194643753 & 0.951453610712495 & 0.475726805356247 \tabularnewline
29 & 0.473228673783396 & 0.946457347566791 & 0.526771326216604 \tabularnewline
30 & 0.50060431403813 & 0.99879137192374 & 0.49939568596187 \tabularnewline
31 & 0.628364232098117 & 0.743271535803766 & 0.371635767901883 \tabularnewline
32 & 0.675395745683016 & 0.649208508633968 & 0.324604254316984 \tabularnewline
33 & 0.629648517996605 & 0.740702964006789 & 0.370351482003395 \tabularnewline
34 & 0.583998544355802 & 0.832002911288397 & 0.416001455644198 \tabularnewline
35 & 0.591549766496428 & 0.816900467007144 & 0.408450233503572 \tabularnewline
36 & 0.542548374515557 & 0.914903250968886 & 0.457451625484443 \tabularnewline
37 & 0.491809020290902 & 0.983618040581804 & 0.508190979709098 \tabularnewline
38 & 0.448020031090493 & 0.896040062180985 & 0.551979968909507 \tabularnewline
39 & 0.400768071704559 & 0.801536143409118 & 0.599231928295441 \tabularnewline
40 & 0.374643395202959 & 0.749286790405918 & 0.625356604797041 \tabularnewline
41 & 0.33182575579713 & 0.663651511594261 & 0.66817424420287 \tabularnewline
42 & 0.36195184529077 & 0.723903690581541 & 0.63804815470923 \tabularnewline
43 & 0.34348078455538 & 0.686961569110759 & 0.656519215444621 \tabularnewline
44 & 0.303291134127653 & 0.606582268255307 & 0.696708865872346 \tabularnewline
45 & 0.269023446109985 & 0.53804689221997 & 0.730976553890015 \tabularnewline
46 & 0.234252427820648 & 0.468504855641296 & 0.765747572179352 \tabularnewline
47 & 0.198813903356432 & 0.397627806712865 & 0.801186096643568 \tabularnewline
48 & 0.170648518998045 & 0.34129703799609 & 0.829351481001955 \tabularnewline
49 & 0.155709819657838 & 0.311419639315676 & 0.844290180342162 \tabularnewline
50 & 0.144812310106824 & 0.289624620213647 & 0.855187689893176 \tabularnewline
51 & 0.123598614879691 & 0.247197229759382 & 0.876401385120309 \tabularnewline
52 & 0.101227537435849 & 0.202455074871699 & 0.898772462564151 \tabularnewline
53 & 0.102479833206098 & 0.204959666412196 & 0.897520166793902 \tabularnewline
54 & 0.0861469968215327 & 0.172293993643065 & 0.913853003178467 \tabularnewline
55 & 0.0729196400962914 & 0.145839280192583 & 0.927080359903709 \tabularnewline
56 & 0.0714715248335709 & 0.142943049667142 & 0.928528475166429 \tabularnewline
57 & 0.0582703307783965 & 0.116540661556793 & 0.941729669221604 \tabularnewline
58 & 0.0464018723139836 & 0.0928037446279672 & 0.953598127686016 \tabularnewline
59 & 0.041287319156369 & 0.082574638312738 & 0.958712680843631 \tabularnewline
60 & 0.0337444964169957 & 0.0674889928339914 & 0.966255503583004 \tabularnewline
61 & 0.0257034068843465 & 0.051406813768693 & 0.974296593115654 \tabularnewline
62 & 0.0305692222458492 & 0.0611384444916984 & 0.969430777754151 \tabularnewline
63 & 0.0273926656742685 & 0.054785331348537 & 0.972607334325732 \tabularnewline
64 & 0.020888399030886 & 0.041776798061772 & 0.979111600969114 \tabularnewline
65 & 0.0170636915658439 & 0.0341273831316878 & 0.982936308434156 \tabularnewline
66 & 0.0515291045885316 & 0.103058209177063 & 0.948470895411468 \tabularnewline
67 & 0.0547302302500733 & 0.109460460500147 & 0.945269769749927 \tabularnewline
68 & 0.0823046266407341 & 0.164609253281468 & 0.917695373359266 \tabularnewline
69 & 0.0669507033977612 & 0.133901406795522 & 0.933049296602239 \tabularnewline
70 & 0.0540163438899127 & 0.108032687779825 & 0.945983656110087 \tabularnewline
71 & 0.0489395119730469 & 0.0978790239460939 & 0.951060488026953 \tabularnewline
72 & 0.060917937243145 & 0.12183587448629 & 0.939082062756855 \tabularnewline
73 & 0.0556408922501358 & 0.111281784500272 & 0.944359107749864 \tabularnewline
74 & 0.0443046390028651 & 0.0886092780057303 & 0.955695360997135 \tabularnewline
75 & 0.0355065718704107 & 0.0710131437408213 & 0.964493428129589 \tabularnewline
76 & 0.0281694022493522 & 0.0563388044987045 & 0.971830597750648 \tabularnewline
77 & 0.0253002106816925 & 0.050600421363385 & 0.974699789318307 \tabularnewline
78 & 0.0191839763307441 & 0.0383679526614881 & 0.980816023669256 \tabularnewline
79 & 0.0256225546440932 & 0.0512451092881863 & 0.974377445355907 \tabularnewline
80 & 0.0225647198562596 & 0.0451294397125193 & 0.97743528014374 \tabularnewline
81 & 0.0186109877985659 & 0.0372219755971319 & 0.981389012201434 \tabularnewline
82 & 0.0158524892293885 & 0.031704978458777 & 0.984147510770611 \tabularnewline
83 & 0.0125858992856307 & 0.0251717985712615 & 0.987414100714369 \tabularnewline
84 & 0.0643083862391634 & 0.128616772478327 & 0.935691613760837 \tabularnewline
85 & 0.0534339806259323 & 0.106867961251865 & 0.946566019374068 \tabularnewline
86 & 0.0437533135060015 & 0.087506627012003 & 0.956246686493998 \tabularnewline
87 & 0.0344280421130555 & 0.068856084226111 & 0.965571957886944 \tabularnewline
88 & 0.0850148912028985 & 0.170029782405797 & 0.914985108797101 \tabularnewline
89 & 0.100288595261804 & 0.200577190523608 & 0.899711404738196 \tabularnewline
90 & 0.120596647607842 & 0.241193295215685 & 0.879403352392158 \tabularnewline
91 & 0.0991034473324646 & 0.198206894664929 & 0.900896552667535 \tabularnewline
92 & 0.0909448692218073 & 0.181889738443615 & 0.909055130778193 \tabularnewline
93 & 0.0726022684357553 & 0.145204536871511 & 0.927397731564245 \tabularnewline
94 & 0.0576134326810374 & 0.115226865362075 & 0.942386567318963 \tabularnewline
95 & 0.0699958779778416 & 0.139991755955683 & 0.930004122022158 \tabularnewline
96 & 0.0570638517490335 & 0.114127703498067 & 0.942936148250966 \tabularnewline
97 & 0.10024031452487 & 0.20048062904974 & 0.89975968547513 \tabularnewline
98 & 0.0800275410917499 & 0.1600550821835 & 0.91997245890825 \tabularnewline
99 & 0.0776106753673609 & 0.155221350734722 & 0.922389324632639 \tabularnewline
100 & 0.0658729966035025 & 0.131745993207005 & 0.934127003396497 \tabularnewline
101 & 0.0910600690446131 & 0.182120138089226 & 0.908939930955387 \tabularnewline
102 & 0.138360285242572 & 0.276720570485143 & 0.861639714757428 \tabularnewline
103 & 0.116316807949844 & 0.232633615899687 & 0.883683192050156 \tabularnewline
104 & 0.0932599542495968 & 0.186519908499194 & 0.906740045750403 \tabularnewline
105 & 0.18738570407937 & 0.374771408158741 & 0.81261429592063 \tabularnewline
106 & 0.183039887648488 & 0.366079775296975 & 0.816960112351512 \tabularnewline
107 & 0.150482881639399 & 0.300965763278797 & 0.849517118360601 \tabularnewline
108 & 0.161034394599773 & 0.322068789199546 & 0.838965605400227 \tabularnewline
109 & 0.135331600331406 & 0.270663200662811 & 0.864668399668594 \tabularnewline
110 & 0.153950984027465 & 0.307901968054931 & 0.846049015972535 \tabularnewline
111 & 0.126107544375487 & 0.252215088750974 & 0.873892455624513 \tabularnewline
112 & 0.100678231696934 & 0.201356463393868 & 0.899321768303066 \tabularnewline
113 & 0.23203318335078 & 0.464066366701559 & 0.767966816649221 \tabularnewline
114 & 0.491334488849474 & 0.982668977698949 & 0.508665511150526 \tabularnewline
115 & 0.45495644110687 & 0.909912882213741 & 0.545043558893129 \tabularnewline
116 & 0.40634791066555 & 0.812695821331099 & 0.59365208933445 \tabularnewline
117 & 0.359062458128659 & 0.718124916257318 & 0.640937541871341 \tabularnewline
118 & 0.391079513030983 & 0.782159026061965 & 0.608920486969017 \tabularnewline
119 & 0.364133006442691 & 0.728266012885381 & 0.635866993557309 \tabularnewline
120 & 0.511038866711548 & 0.977922266576904 & 0.488961133288452 \tabularnewline
121 & 0.632847745141944 & 0.734304509716111 & 0.367152254858056 \tabularnewline
122 & 0.583761107703172 & 0.832477784593656 & 0.416238892296828 \tabularnewline
123 & 0.599235312195102 & 0.801529375609796 & 0.400764687804898 \tabularnewline
124 & 0.829334095263895 & 0.341331809472209 & 0.170665904736105 \tabularnewline
125 & 0.790886909607451 & 0.418226180785098 & 0.209113090392549 \tabularnewline
126 & 0.750542689536607 & 0.498914620926787 & 0.249457310463394 \tabularnewline
127 & 0.861671369584462 & 0.276657260831076 & 0.138328630415538 \tabularnewline
128 & 0.940363150944044 & 0.119273698111912 & 0.0596368490559562 \tabularnewline
129 & 0.917523418555169 & 0.164953162889661 & 0.0824765814448306 \tabularnewline
130 & 0.893904345726043 & 0.212191308547915 & 0.106095654273957 \tabularnewline
131 & 0.926642504234884 & 0.146714991530232 & 0.0733574957651158 \tabularnewline
132 & 0.917391300441879 & 0.165217399116242 & 0.0826086995581209 \tabularnewline
133 & 0.885641404980556 & 0.228717190038888 & 0.114358595019444 \tabularnewline
134 & 0.995721531353987 & 0.00855693729202605 & 0.00427846864601303 \tabularnewline
135 & 0.998354979920865 & 0.00329004015827001 & 0.001645020079135 \tabularnewline
136 & 0.999505767531126 & 0.000988464937748062 & 0.000494232468874031 \tabularnewline
137 & 0.999926200487072 & 0.000147599025855331 & 7.37995129276657e-05 \tabularnewline
138 & 0.999868545866004 & 0.000262908267992865 & 0.000131454133996432 \tabularnewline
139 & 0.999408328900793 & 0.00118334219841413 & 0.000591671099207067 \tabularnewline
140 & 0.998190374311402 & 0.0036192513771951 & 0.00180962568859755 \tabularnewline
141 & 0.993805423316235 & 0.0123891533675308 & 0.00619457668376541 \tabularnewline
142 & 0.999259532868185 & 0.00148093426362937 & 0.000740467131814686 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186142&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]7[/C][C]0.325074635781799[/C][C]0.650149271563597[/C][C]0.674925364218201[/C][/ROW]
[ROW][C]8[/C][C]0.371607782438907[/C][C]0.743215564877814[/C][C]0.628392217561093[/C][/ROW]
[ROW][C]9[/C][C]0.800176432136307[/C][C]0.399647135727386[/C][C]0.199823567863693[/C][/ROW]
[ROW][C]10[/C][C]0.725402437146915[/C][C]0.549195125706171[/C][C]0.274597562853086[/C][/ROW]
[ROW][C]11[/C][C]0.652143204112455[/C][C]0.695713591775089[/C][C]0.347856795887545[/C][/ROW]
[ROW][C]12[/C][C]0.598803303294897[/C][C]0.802393393410206[/C][C]0.401196696705103[/C][/ROW]
[ROW][C]13[/C][C]0.508737037975466[/C][C]0.982525924049069[/C][C]0.491262962024534[/C][/ROW]
[ROW][C]14[/C][C]0.694078793449163[/C][C]0.611842413101675[/C][C]0.305921206550837[/C][/ROW]
[ROW][C]15[/C][C]0.61651975953746[/C][C]0.766960480925081[/C][C]0.38348024046254[/C][/ROW]
[ROW][C]16[/C][C]0.540732068720033[/C][C]0.918535862559934[/C][C]0.459267931279967[/C][/ROW]
[ROW][C]17[/C][C]0.464314229286968[/C][C]0.928628458573937[/C][C]0.535685770713032[/C][/ROW]
[ROW][C]18[/C][C]0.389628387233074[/C][C]0.779256774466148[/C][C]0.610371612766926[/C][/ROW]
[ROW][C]19[/C][C]0.338410504449102[/C][C]0.676821008898204[/C][C]0.661589495550898[/C][/ROW]
[ROW][C]20[/C][C]0.280682943194649[/C][C]0.561365886389298[/C][C]0.719317056805351[/C][/ROW]
[ROW][C]21[/C][C]0.252516004924874[/C][C]0.505032009849748[/C][C]0.747483995075126[/C][/ROW]
[ROW][C]22[/C][C]0.202569006473999[/C][C]0.405138012947999[/C][C]0.797430993526001[/C][/ROW]
[ROW][C]23[/C][C]0.15548532762361[/C][C]0.310970655247221[/C][C]0.84451467237639[/C][/ROW]
[ROW][C]24[/C][C]0.11755422530736[/C][C]0.235108450614719[/C][C]0.88244577469264[/C][/ROW]
[ROW][C]25[/C][C]0.107044529356052[/C][C]0.214089058712104[/C][C]0.892955470643948[/C][/ROW]
[ROW][C]26[/C][C]0.0841937336186085[/C][C]0.168387467237217[/C][C]0.915806266381392[/C][/ROW]
[ROW][C]27[/C][C]0.571059803143081[/C][C]0.857880393713838[/C][C]0.428940196856919[/C][/ROW]
[ROW][C]28[/C][C]0.524273194643753[/C][C]0.951453610712495[/C][C]0.475726805356247[/C][/ROW]
[ROW][C]29[/C][C]0.473228673783396[/C][C]0.946457347566791[/C][C]0.526771326216604[/C][/ROW]
[ROW][C]30[/C][C]0.50060431403813[/C][C]0.99879137192374[/C][C]0.49939568596187[/C][/ROW]
[ROW][C]31[/C][C]0.628364232098117[/C][C]0.743271535803766[/C][C]0.371635767901883[/C][/ROW]
[ROW][C]32[/C][C]0.675395745683016[/C][C]0.649208508633968[/C][C]0.324604254316984[/C][/ROW]
[ROW][C]33[/C][C]0.629648517996605[/C][C]0.740702964006789[/C][C]0.370351482003395[/C][/ROW]
[ROW][C]34[/C][C]0.583998544355802[/C][C]0.832002911288397[/C][C]0.416001455644198[/C][/ROW]
[ROW][C]35[/C][C]0.591549766496428[/C][C]0.816900467007144[/C][C]0.408450233503572[/C][/ROW]
[ROW][C]36[/C][C]0.542548374515557[/C][C]0.914903250968886[/C][C]0.457451625484443[/C][/ROW]
[ROW][C]37[/C][C]0.491809020290902[/C][C]0.983618040581804[/C][C]0.508190979709098[/C][/ROW]
[ROW][C]38[/C][C]0.448020031090493[/C][C]0.896040062180985[/C][C]0.551979968909507[/C][/ROW]
[ROW][C]39[/C][C]0.400768071704559[/C][C]0.801536143409118[/C][C]0.599231928295441[/C][/ROW]
[ROW][C]40[/C][C]0.374643395202959[/C][C]0.749286790405918[/C][C]0.625356604797041[/C][/ROW]
[ROW][C]41[/C][C]0.33182575579713[/C][C]0.663651511594261[/C][C]0.66817424420287[/C][/ROW]
[ROW][C]42[/C][C]0.36195184529077[/C][C]0.723903690581541[/C][C]0.63804815470923[/C][/ROW]
[ROW][C]43[/C][C]0.34348078455538[/C][C]0.686961569110759[/C][C]0.656519215444621[/C][/ROW]
[ROW][C]44[/C][C]0.303291134127653[/C][C]0.606582268255307[/C][C]0.696708865872346[/C][/ROW]
[ROW][C]45[/C][C]0.269023446109985[/C][C]0.53804689221997[/C][C]0.730976553890015[/C][/ROW]
[ROW][C]46[/C][C]0.234252427820648[/C][C]0.468504855641296[/C][C]0.765747572179352[/C][/ROW]
[ROW][C]47[/C][C]0.198813903356432[/C][C]0.397627806712865[/C][C]0.801186096643568[/C][/ROW]
[ROW][C]48[/C][C]0.170648518998045[/C][C]0.34129703799609[/C][C]0.829351481001955[/C][/ROW]
[ROW][C]49[/C][C]0.155709819657838[/C][C]0.311419639315676[/C][C]0.844290180342162[/C][/ROW]
[ROW][C]50[/C][C]0.144812310106824[/C][C]0.289624620213647[/C][C]0.855187689893176[/C][/ROW]
[ROW][C]51[/C][C]0.123598614879691[/C][C]0.247197229759382[/C][C]0.876401385120309[/C][/ROW]
[ROW][C]52[/C][C]0.101227537435849[/C][C]0.202455074871699[/C][C]0.898772462564151[/C][/ROW]
[ROW][C]53[/C][C]0.102479833206098[/C][C]0.204959666412196[/C][C]0.897520166793902[/C][/ROW]
[ROW][C]54[/C][C]0.0861469968215327[/C][C]0.172293993643065[/C][C]0.913853003178467[/C][/ROW]
[ROW][C]55[/C][C]0.0729196400962914[/C][C]0.145839280192583[/C][C]0.927080359903709[/C][/ROW]
[ROW][C]56[/C][C]0.0714715248335709[/C][C]0.142943049667142[/C][C]0.928528475166429[/C][/ROW]
[ROW][C]57[/C][C]0.0582703307783965[/C][C]0.116540661556793[/C][C]0.941729669221604[/C][/ROW]
[ROW][C]58[/C][C]0.0464018723139836[/C][C]0.0928037446279672[/C][C]0.953598127686016[/C][/ROW]
[ROW][C]59[/C][C]0.041287319156369[/C][C]0.082574638312738[/C][C]0.958712680843631[/C][/ROW]
[ROW][C]60[/C][C]0.0337444964169957[/C][C]0.0674889928339914[/C][C]0.966255503583004[/C][/ROW]
[ROW][C]61[/C][C]0.0257034068843465[/C][C]0.051406813768693[/C][C]0.974296593115654[/C][/ROW]
[ROW][C]62[/C][C]0.0305692222458492[/C][C]0.0611384444916984[/C][C]0.969430777754151[/C][/ROW]
[ROW][C]63[/C][C]0.0273926656742685[/C][C]0.054785331348537[/C][C]0.972607334325732[/C][/ROW]
[ROW][C]64[/C][C]0.020888399030886[/C][C]0.041776798061772[/C][C]0.979111600969114[/C][/ROW]
[ROW][C]65[/C][C]0.0170636915658439[/C][C]0.0341273831316878[/C][C]0.982936308434156[/C][/ROW]
[ROW][C]66[/C][C]0.0515291045885316[/C][C]0.103058209177063[/C][C]0.948470895411468[/C][/ROW]
[ROW][C]67[/C][C]0.0547302302500733[/C][C]0.109460460500147[/C][C]0.945269769749927[/C][/ROW]
[ROW][C]68[/C][C]0.0823046266407341[/C][C]0.164609253281468[/C][C]0.917695373359266[/C][/ROW]
[ROW][C]69[/C][C]0.0669507033977612[/C][C]0.133901406795522[/C][C]0.933049296602239[/C][/ROW]
[ROW][C]70[/C][C]0.0540163438899127[/C][C]0.108032687779825[/C][C]0.945983656110087[/C][/ROW]
[ROW][C]71[/C][C]0.0489395119730469[/C][C]0.0978790239460939[/C][C]0.951060488026953[/C][/ROW]
[ROW][C]72[/C][C]0.060917937243145[/C][C]0.12183587448629[/C][C]0.939082062756855[/C][/ROW]
[ROW][C]73[/C][C]0.0556408922501358[/C][C]0.111281784500272[/C][C]0.944359107749864[/C][/ROW]
[ROW][C]74[/C][C]0.0443046390028651[/C][C]0.0886092780057303[/C][C]0.955695360997135[/C][/ROW]
[ROW][C]75[/C][C]0.0355065718704107[/C][C]0.0710131437408213[/C][C]0.964493428129589[/C][/ROW]
[ROW][C]76[/C][C]0.0281694022493522[/C][C]0.0563388044987045[/C][C]0.971830597750648[/C][/ROW]
[ROW][C]77[/C][C]0.0253002106816925[/C][C]0.050600421363385[/C][C]0.974699789318307[/C][/ROW]
[ROW][C]78[/C][C]0.0191839763307441[/C][C]0.0383679526614881[/C][C]0.980816023669256[/C][/ROW]
[ROW][C]79[/C][C]0.0256225546440932[/C][C]0.0512451092881863[/C][C]0.974377445355907[/C][/ROW]
[ROW][C]80[/C][C]0.0225647198562596[/C][C]0.0451294397125193[/C][C]0.97743528014374[/C][/ROW]
[ROW][C]81[/C][C]0.0186109877985659[/C][C]0.0372219755971319[/C][C]0.981389012201434[/C][/ROW]
[ROW][C]82[/C][C]0.0158524892293885[/C][C]0.031704978458777[/C][C]0.984147510770611[/C][/ROW]
[ROW][C]83[/C][C]0.0125858992856307[/C][C]0.0251717985712615[/C][C]0.987414100714369[/C][/ROW]
[ROW][C]84[/C][C]0.0643083862391634[/C][C]0.128616772478327[/C][C]0.935691613760837[/C][/ROW]
[ROW][C]85[/C][C]0.0534339806259323[/C][C]0.106867961251865[/C][C]0.946566019374068[/C][/ROW]
[ROW][C]86[/C][C]0.0437533135060015[/C][C]0.087506627012003[/C][C]0.956246686493998[/C][/ROW]
[ROW][C]87[/C][C]0.0344280421130555[/C][C]0.068856084226111[/C][C]0.965571957886944[/C][/ROW]
[ROW][C]88[/C][C]0.0850148912028985[/C][C]0.170029782405797[/C][C]0.914985108797101[/C][/ROW]
[ROW][C]89[/C][C]0.100288595261804[/C][C]0.200577190523608[/C][C]0.899711404738196[/C][/ROW]
[ROW][C]90[/C][C]0.120596647607842[/C][C]0.241193295215685[/C][C]0.879403352392158[/C][/ROW]
[ROW][C]91[/C][C]0.0991034473324646[/C][C]0.198206894664929[/C][C]0.900896552667535[/C][/ROW]
[ROW][C]92[/C][C]0.0909448692218073[/C][C]0.181889738443615[/C][C]0.909055130778193[/C][/ROW]
[ROW][C]93[/C][C]0.0726022684357553[/C][C]0.145204536871511[/C][C]0.927397731564245[/C][/ROW]
[ROW][C]94[/C][C]0.0576134326810374[/C][C]0.115226865362075[/C][C]0.942386567318963[/C][/ROW]
[ROW][C]95[/C][C]0.0699958779778416[/C][C]0.139991755955683[/C][C]0.930004122022158[/C][/ROW]
[ROW][C]96[/C][C]0.0570638517490335[/C][C]0.114127703498067[/C][C]0.942936148250966[/C][/ROW]
[ROW][C]97[/C][C]0.10024031452487[/C][C]0.20048062904974[/C][C]0.89975968547513[/C][/ROW]
[ROW][C]98[/C][C]0.0800275410917499[/C][C]0.1600550821835[/C][C]0.91997245890825[/C][/ROW]
[ROW][C]99[/C][C]0.0776106753673609[/C][C]0.155221350734722[/C][C]0.922389324632639[/C][/ROW]
[ROW][C]100[/C][C]0.0658729966035025[/C][C]0.131745993207005[/C][C]0.934127003396497[/C][/ROW]
[ROW][C]101[/C][C]0.0910600690446131[/C][C]0.182120138089226[/C][C]0.908939930955387[/C][/ROW]
[ROW][C]102[/C][C]0.138360285242572[/C][C]0.276720570485143[/C][C]0.861639714757428[/C][/ROW]
[ROW][C]103[/C][C]0.116316807949844[/C][C]0.232633615899687[/C][C]0.883683192050156[/C][/ROW]
[ROW][C]104[/C][C]0.0932599542495968[/C][C]0.186519908499194[/C][C]0.906740045750403[/C][/ROW]
[ROW][C]105[/C][C]0.18738570407937[/C][C]0.374771408158741[/C][C]0.81261429592063[/C][/ROW]
[ROW][C]106[/C][C]0.183039887648488[/C][C]0.366079775296975[/C][C]0.816960112351512[/C][/ROW]
[ROW][C]107[/C][C]0.150482881639399[/C][C]0.300965763278797[/C][C]0.849517118360601[/C][/ROW]
[ROW][C]108[/C][C]0.161034394599773[/C][C]0.322068789199546[/C][C]0.838965605400227[/C][/ROW]
[ROW][C]109[/C][C]0.135331600331406[/C][C]0.270663200662811[/C][C]0.864668399668594[/C][/ROW]
[ROW][C]110[/C][C]0.153950984027465[/C][C]0.307901968054931[/C][C]0.846049015972535[/C][/ROW]
[ROW][C]111[/C][C]0.126107544375487[/C][C]0.252215088750974[/C][C]0.873892455624513[/C][/ROW]
[ROW][C]112[/C][C]0.100678231696934[/C][C]0.201356463393868[/C][C]0.899321768303066[/C][/ROW]
[ROW][C]113[/C][C]0.23203318335078[/C][C]0.464066366701559[/C][C]0.767966816649221[/C][/ROW]
[ROW][C]114[/C][C]0.491334488849474[/C][C]0.982668977698949[/C][C]0.508665511150526[/C][/ROW]
[ROW][C]115[/C][C]0.45495644110687[/C][C]0.909912882213741[/C][C]0.545043558893129[/C][/ROW]
[ROW][C]116[/C][C]0.40634791066555[/C][C]0.812695821331099[/C][C]0.59365208933445[/C][/ROW]
[ROW][C]117[/C][C]0.359062458128659[/C][C]0.718124916257318[/C][C]0.640937541871341[/C][/ROW]
[ROW][C]118[/C][C]0.391079513030983[/C][C]0.782159026061965[/C][C]0.608920486969017[/C][/ROW]
[ROW][C]119[/C][C]0.364133006442691[/C][C]0.728266012885381[/C][C]0.635866993557309[/C][/ROW]
[ROW][C]120[/C][C]0.511038866711548[/C][C]0.977922266576904[/C][C]0.488961133288452[/C][/ROW]
[ROW][C]121[/C][C]0.632847745141944[/C][C]0.734304509716111[/C][C]0.367152254858056[/C][/ROW]
[ROW][C]122[/C][C]0.583761107703172[/C][C]0.832477784593656[/C][C]0.416238892296828[/C][/ROW]
[ROW][C]123[/C][C]0.599235312195102[/C][C]0.801529375609796[/C][C]0.400764687804898[/C][/ROW]
[ROW][C]124[/C][C]0.829334095263895[/C][C]0.341331809472209[/C][C]0.170665904736105[/C][/ROW]
[ROW][C]125[/C][C]0.790886909607451[/C][C]0.418226180785098[/C][C]0.209113090392549[/C][/ROW]
[ROW][C]126[/C][C]0.750542689536607[/C][C]0.498914620926787[/C][C]0.249457310463394[/C][/ROW]
[ROW][C]127[/C][C]0.861671369584462[/C][C]0.276657260831076[/C][C]0.138328630415538[/C][/ROW]
[ROW][C]128[/C][C]0.940363150944044[/C][C]0.119273698111912[/C][C]0.0596368490559562[/C][/ROW]
[ROW][C]129[/C][C]0.917523418555169[/C][C]0.164953162889661[/C][C]0.0824765814448306[/C][/ROW]
[ROW][C]130[/C][C]0.893904345726043[/C][C]0.212191308547915[/C][C]0.106095654273957[/C][/ROW]
[ROW][C]131[/C][C]0.926642504234884[/C][C]0.146714991530232[/C][C]0.0733574957651158[/C][/ROW]
[ROW][C]132[/C][C]0.917391300441879[/C][C]0.165217399116242[/C][C]0.0826086995581209[/C][/ROW]
[ROW][C]133[/C][C]0.885641404980556[/C][C]0.228717190038888[/C][C]0.114358595019444[/C][/ROW]
[ROW][C]134[/C][C]0.995721531353987[/C][C]0.00855693729202605[/C][C]0.00427846864601303[/C][/ROW]
[ROW][C]135[/C][C]0.998354979920865[/C][C]0.00329004015827001[/C][C]0.001645020079135[/C][/ROW]
[ROW][C]136[/C][C]0.999505767531126[/C][C]0.000988464937748062[/C][C]0.000494232468874031[/C][/ROW]
[ROW][C]137[/C][C]0.999926200487072[/C][C]0.000147599025855331[/C][C]7.37995129276657e-05[/C][/ROW]
[ROW][C]138[/C][C]0.999868545866004[/C][C]0.000262908267992865[/C][C]0.000131454133996432[/C][/ROW]
[ROW][C]139[/C][C]0.999408328900793[/C][C]0.00118334219841413[/C][C]0.000591671099207067[/C][/ROW]
[ROW][C]140[/C][C]0.998190374311402[/C][C]0.0036192513771951[/C][C]0.00180962568859755[/C][/ROW]
[ROW][C]141[/C][C]0.993805423316235[/C][C]0.0123891533675308[/C][C]0.00619457668376541[/C][/ROW]
[ROW][C]142[/C][C]0.999259532868185[/C][C]0.00148093426362937[/C][C]0.000740467131814686[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186142&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186142&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
70.3250746357817990.6501492715635970.674925364218201
80.3716077824389070.7432155648778140.628392217561093
90.8001764321363070.3996471357273860.199823567863693
100.7254024371469150.5491951257061710.274597562853086
110.6521432041124550.6957135917750890.347856795887545
120.5988033032948970.8023933934102060.401196696705103
130.5087370379754660.9825259240490690.491262962024534
140.6940787934491630.6118424131016750.305921206550837
150.616519759537460.7669604809250810.38348024046254
160.5407320687200330.9185358625599340.459267931279967
170.4643142292869680.9286284585739370.535685770713032
180.3896283872330740.7792567744661480.610371612766926
190.3384105044491020.6768210088982040.661589495550898
200.2806829431946490.5613658863892980.719317056805351
210.2525160049248740.5050320098497480.747483995075126
220.2025690064739990.4051380129479990.797430993526001
230.155485327623610.3109706552472210.84451467237639
240.117554225307360.2351084506147190.88244577469264
250.1070445293560520.2140890587121040.892955470643948
260.08419373361860850.1683874672372170.915806266381392
270.5710598031430810.8578803937138380.428940196856919
280.5242731946437530.9514536107124950.475726805356247
290.4732286737833960.9464573475667910.526771326216604
300.500604314038130.998791371923740.49939568596187
310.6283642320981170.7432715358037660.371635767901883
320.6753957456830160.6492085086339680.324604254316984
330.6296485179966050.7407029640067890.370351482003395
340.5839985443558020.8320029112883970.416001455644198
350.5915497664964280.8169004670071440.408450233503572
360.5425483745155570.9149032509688860.457451625484443
370.4918090202909020.9836180405818040.508190979709098
380.4480200310904930.8960400621809850.551979968909507
390.4007680717045590.8015361434091180.599231928295441
400.3746433952029590.7492867904059180.625356604797041
410.331825755797130.6636515115942610.66817424420287
420.361951845290770.7239036905815410.63804815470923
430.343480784555380.6869615691107590.656519215444621
440.3032911341276530.6065822682553070.696708865872346
450.2690234461099850.538046892219970.730976553890015
460.2342524278206480.4685048556412960.765747572179352
470.1988139033564320.3976278067128650.801186096643568
480.1706485189980450.341297037996090.829351481001955
490.1557098196578380.3114196393156760.844290180342162
500.1448123101068240.2896246202136470.855187689893176
510.1235986148796910.2471972297593820.876401385120309
520.1012275374358490.2024550748716990.898772462564151
530.1024798332060980.2049596664121960.897520166793902
540.08614699682153270.1722939936430650.913853003178467
550.07291964009629140.1458392801925830.927080359903709
560.07147152483357090.1429430496671420.928528475166429
570.05827033077839650.1165406615567930.941729669221604
580.04640187231398360.09280374462796720.953598127686016
590.0412873191563690.0825746383127380.958712680843631
600.03374449641699570.06748899283399140.966255503583004
610.02570340688434650.0514068137686930.974296593115654
620.03056922224584920.06113844449169840.969430777754151
630.02739266567426850.0547853313485370.972607334325732
640.0208883990308860.0417767980617720.979111600969114
650.01706369156584390.03412738313168780.982936308434156
660.05152910458853160.1030582091770630.948470895411468
670.05473023025007330.1094604605001470.945269769749927
680.08230462664073410.1646092532814680.917695373359266
690.06695070339776120.1339014067955220.933049296602239
700.05401634388991270.1080326877798250.945983656110087
710.04893951197304690.09787902394609390.951060488026953
720.0609179372431450.121835874486290.939082062756855
730.05564089225013580.1112817845002720.944359107749864
740.04430463900286510.08860927800573030.955695360997135
750.03550657187041070.07101314374082130.964493428129589
760.02816940224935220.05633880449870450.971830597750648
770.02530021068169250.0506004213633850.974699789318307
780.01918397633074410.03836795266148810.980816023669256
790.02562255464409320.05124510928818630.974377445355907
800.02256471985625960.04512943971251930.97743528014374
810.01861098779856590.03722197559713190.981389012201434
820.01585248922938850.0317049784587770.984147510770611
830.01258589928563070.02517179857126150.987414100714369
840.06430838623916340.1286167724783270.935691613760837
850.05343398062593230.1068679612518650.946566019374068
860.04375331350600150.0875066270120030.956246686493998
870.03442804211305550.0688560842261110.965571957886944
880.08501489120289850.1700297824057970.914985108797101
890.1002885952618040.2005771905236080.899711404738196
900.1205966476078420.2411932952156850.879403352392158
910.09910344733246460.1982068946649290.900896552667535
920.09094486922180730.1818897384436150.909055130778193
930.07260226843575530.1452045368715110.927397731564245
940.05761343268103740.1152268653620750.942386567318963
950.06999587797784160.1399917559556830.930004122022158
960.05706385174903350.1141277034980670.942936148250966
970.100240314524870.200480629049740.89975968547513
980.08002754109174990.16005508218350.91997245890825
990.07761067536736090.1552213507347220.922389324632639
1000.06587299660350250.1317459932070050.934127003396497
1010.09106006904461310.1821201380892260.908939930955387
1020.1383602852425720.2767205704851430.861639714757428
1030.1163168079498440.2326336158996870.883683192050156
1040.09325995424959680.1865199084991940.906740045750403
1050.187385704079370.3747714081587410.81261429592063
1060.1830398876484880.3660797752969750.816960112351512
1070.1504828816393990.3009657632787970.849517118360601
1080.1610343945997730.3220687891995460.838965605400227
1090.1353316003314060.2706632006628110.864668399668594
1100.1539509840274650.3079019680549310.846049015972535
1110.1261075443754870.2522150887509740.873892455624513
1120.1006782316969340.2013564633938680.899321768303066
1130.232033183350780.4640663667015590.767966816649221
1140.4913344888494740.9826689776989490.508665511150526
1150.454956441106870.9099128822137410.545043558893129
1160.406347910665550.8126958213310990.59365208933445
1170.3590624581286590.7181249162573180.640937541871341
1180.3910795130309830.7821590260619650.608920486969017
1190.3641330064426910.7282660128853810.635866993557309
1200.5110388667115480.9779222665769040.488961133288452
1210.6328477451419440.7343045097161110.367152254858056
1220.5837611077031720.8324777845936560.416238892296828
1230.5992353121951020.8015293756097960.400764687804898
1240.8293340952638950.3413318094722090.170665904736105
1250.7908869096074510.4182261807850980.209113090392549
1260.7505426895366070.4989146209267870.249457310463394
1270.8616713695844620.2766572608310760.138328630415538
1280.9403631509440440.1192736981119120.0596368490559562
1290.9175234185551690.1649531628896610.0824765814448306
1300.8939043457260430.2121913085479150.106095654273957
1310.9266425042348840.1467149915302320.0733574957651158
1320.9173913004418790.1652173991162420.0826086995581209
1330.8856414049805560.2287171900388880.114358595019444
1340.9957215313539870.008556937292026050.00427846864601303
1350.9983549799208650.003290040158270010.001645020079135
1360.9995057675311260.0009884649377480620.000494232468874031
1370.9999262004870720.0001475990258553317.37995129276657e-05
1380.9998685458660040.0002629082679928650.000131454133996432
1390.9994083289007930.001183342198414130.000591671099207067
1400.9981903743114020.00361925137719510.00180962568859755
1410.9938054233162350.01238915336753080.00619457668376541
1420.9992595328681850.001480934263629370.000740467131814686







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0588235294117647NOK
5% type I error level160.117647058823529NOK
10% type I error level300.220588235294118NOK

\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 & 8 & 0.0588235294117647 & NOK \tabularnewline
5% type I error level & 16 & 0.117647058823529 & NOK \tabularnewline
10% type I error level & 30 & 0.220588235294118 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186142&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]8[/C][C]0.0588235294117647[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.117647058823529[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.220588235294118[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186142&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186142&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 level80.0588235294117647NOK
5% type I error level160.117647058823529NOK
10% type I error level300.220588235294118NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}