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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 14 Dec 2011 07:57:16 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/14/t13238674755jpnmzame69j5om.htm/, Retrieved Wed, 01 May 2024 22:20:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154911, Retrieved Wed, 01 May 2024 22:20:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [multiple regression] [2011-12-14 12:57:16] [5363b79245edacd2d961915f77b3b63a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	78	20	17	30	28
2	46	38	17	42	39
3	18	0	0	0	0
4	84	49	22	54	54
5	125	74	30	86	80
6	215	104	31	157	144
7	50	37	19	36	36
8	48	53	25	48	48
9	37	42	30	45	42
10	86	62	26	77	71
11	69	50	20	49	49
12	59	65	25	77	74
13	85	28	15	28	27
14	84	48	22	84	83
15	44	42	12	31	31
16	67	47	19	28	28
17	49	71	28	99	98
18	47	0	12	2	2
19	77	50	28	41	43
20	20	12	13	25	24
21	49	16	14	16	16
22	81	76	27	96	95
23	58	29	25	23	22
24	45	38	30	33	33
25	73	50	18	46	45
26	22	33	17	59	59
27	138	45	22	72	66
28	74	59	28	72	70
29	102	49	25	62	56
30	35	40	16	55	55
31	39	40	23	27	27
32	38	51	20	41	37
33	88	41	11	51	48
34	102	73	20	26	26
35	42	43	21	65	64
36	1	0	0	0	0
37	54	46	27	28	21
38	46	44	14	44	44
39	41	31	29	36	36
40	49	71	31	100	89
41	56	61	19	104	101
42	47	28	30	35	31
43	25	21	23	69	65
44	62	42	20	73	71
45	41	44	22	106	102
46	72	34	19	53	53
47	26	15	32	43	41
48	77	46	18	49	46
49	75	43	26	38	37
50	51	47	25	51	51
51	28	12	22	14	14
52	54	42	19	40	40
53	64	56	24	79	77
54	67	41	26	52	51
55	48	48	27	44	43
56	44	30	10	34	33
57	55	44	26	47	47
58	17	25	21	32	31
59	55	42	21	31	31
60	72	28	34	40	40
61	47	33	29	42	42
62	62	32	18	34	35
63	45	28	16	40	40
64	29	31	23	35	30
65	25	13	22	11	11
66	37	38	29	43	41
67	60	39	31	53	53
68	57	68	21	82	82
69	32	32	21	41	41
70	15	5	21	6	6
71	102	53	15	82	81
72	52	33	9	47	47
73	53	48	21	108	100
74	58	36	18	46	46
75	51	52	31	38	38
76	31	0	24	0	0
77	50	52	24	45	45
78	78	45	22	57	56
79	23	16	21	20	18
80	66	33	26	56	54
81	56	48	22	38	37
82	51	33	26	42	40
83	24	24	20	37	37
84	32	37	25	36	36
85	36	16	19	34	34
86	42	32	22	53	49
87	180	48	25	85	82
88	83	36	19	36	36
89	46	29	21	33	33
90	40	26	20	57	55
91	33	37	23	50	50
92	66	58	22	71	71
93	52	35	21	32	31
94	51	24	12	45	42
95	30	18	9	33	31
96	89	37	32	53	51
97	49	86	24	64	64
98	12	13	1	14	14
99	83	20	24	38	37
100	51	32	20	39	37
101	24	8	4	8	8
102	19	38	15	38	38
103	44	45	21	24	23
104	52	24	23	22	22
105	35	23	12	18	18
106	22	2	16	3	1
107	32	52	24	49	48
108	22	5	9	5	5
109	0	0	0	0	0
110	26	43	22	47	46
111	48	18	17	33	33
112	35	41	18	44	41
113	47	45	21	56	57
114	55	29	17	49	49
115	5	0	0	0	0
116	0	0	0	0	0
117	37	32	20	45	45
118	65	58	26	78	78
119	81	17	26	51	46
120	32	24	20	25	25
121	19	7	1	1	1
122	58	62	24	62	59
123	33	30	14	29	29
124	42	49	26	26	26
125	37	3	12	4	4
126	12	10	2	10	10
127	41	42	16	43	43
128	23	18	22	36	36
129	35	40	28	43	41
130	9	1	2	0	0
131	9	0	0	0	0
132	49	29	17	33	32
133	3	0	1	0	0
134	41	46	17	53	53
135	3	5	0	0	0
136	16	8	4	6	6
137	0	0	0	0	0
138	41	21	25	19	18
139	31	21	26	26	26
140	4	0	0	0	0
141	11	0	0	0	0
142	20	15	15	16	16
143	40	40	18	84	84
144	16	17	19	28	22

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154911&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154911&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154911&T=0

As an alternative you can also use a QR Code:  
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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
includedhyperlinks[t] = -0.403842674781504 -0.00255571924571907Ranking[t] + 0.0173618817892399Logins[t] -0.0239197289137188BloggedComputations[t] + 0.0146256713938496ReviewedCompendiums[t] + 1.03804197658885includedblogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
includedhyperlinks[t] =  -0.403842674781504 -0.00255571924571907Ranking[t] +  0.0173618817892399Logins[t] -0.0239197289137188BloggedComputations[t] +  0.0146256713938496ReviewedCompendiums[t] +  1.03804197658885includedblogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154911&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]includedhyperlinks[t] =  -0.403842674781504 -0.00255571924571907Ranking[t] +  0.0173618817892399Logins[t] -0.0239197289137188BloggedComputations[t] +  0.0146256713938496ReviewedCompendiums[t] +  1.03804197658885includedblogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154911&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154911&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
includedhyperlinks[t] = -0.403842674781504 -0.00255571924571907Ranking[t] + 0.0173618817892399Logins[t] -0.0239197289137188BloggedComputations[t] + 0.0146256713938496ReviewedCompendiums[t] + 1.03804197658885includedblogs[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.4038426747815040.623075-0.64810.5179690.258984
Ranking-0.002555719245719070.004292-0.59540.5525530.276276
Logins0.01736188178923990.007222.40490.0175050.008753
BloggedComputations-0.02391972891371880.015407-1.55250.1228390.061419
ReviewedCompendiums0.01462567139384960.0245520.59570.5523470.276173
includedblogs1.038041976588850.01110293.49900

\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) & -0.403842674781504 & 0.623075 & -0.6481 & 0.517969 & 0.258984 \tabularnewline
Ranking & -0.00255571924571907 & 0.004292 & -0.5954 & 0.552553 & 0.276276 \tabularnewline
Logins & 0.0173618817892399 & 0.00722 & 2.4049 & 0.017505 & 0.008753 \tabularnewline
BloggedComputations & -0.0239197289137188 & 0.015407 & -1.5525 & 0.122839 & 0.061419 \tabularnewline
ReviewedCompendiums & 0.0146256713938496 & 0.024552 & 0.5957 & 0.552347 & 0.276173 \tabularnewline
includedblogs & 1.03804197658885 & 0.011102 & 93.499 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154911&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]-0.403842674781504[/C][C]0.623075[/C][C]-0.6481[/C][C]0.517969[/C][C]0.258984[/C][/ROW]
[ROW][C]Ranking[/C][C]-0.00255571924571907[/C][C]0.004292[/C][C]-0.5954[/C][C]0.552553[/C][C]0.276276[/C][/ROW]
[ROW][C]Logins[/C][C]0.0173618817892399[/C][C]0.00722[/C][C]2.4049[/C][C]0.017505[/C][C]0.008753[/C][/ROW]
[ROW][C]BloggedComputations[/C][C]-0.0239197289137188[/C][C]0.015407[/C][C]-1.5525[/C][C]0.122839[/C][C]0.061419[/C][/ROW]
[ROW][C]ReviewedCompendiums[/C][C]0.0146256713938496[/C][C]0.024552[/C][C]0.5957[/C][C]0.552347[/C][C]0.276173[/C][/ROW]
[ROW][C]includedblogs[/C][C]1.03804197658885[/C][C]0.011102[/C][C]93.499[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154911&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154911&T=2

As an alternative you can also use a QR Code:  
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)-0.4038426747815040.623075-0.64810.5179690.258984
Ranking-0.002555719245719070.004292-0.59540.5525530.276276
Logins0.01736188178923990.007222.40490.0175050.008753
BloggedComputations-0.02391972891371880.015407-1.55250.1228390.061419
ReviewedCompendiums0.01462567139384960.0245520.59570.5523470.276173
includedblogs1.038041976588850.01110293.49900






Multiple Linear Regression - Regression Statistics
Multiple R0.997700352234759
R-squared0.995405992849363
Adjusted R-squared0.99523954331492
F-TEST (value)5980.226956946
F-TEST (DF numerator)5
F-TEST (DF denominator)138
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.87435918402191
Sum Squared Residuals484.824684400362

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.997700352234759 \tabularnewline
R-squared & 0.995405992849363 \tabularnewline
Adjusted R-squared & 0.99523954331492 \tabularnewline
F-TEST (value) & 5980.226956946 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 138 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.87435918402191 \tabularnewline
Sum Squared Residuals & 484.824684400362 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154911&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.997700352234759[/C][/ROW]
[ROW][C]R-squared[/C][C]0.995405992849363[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99523954331492[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5980.226956946[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]138[/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]1.87435918402191[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]484.824684400362[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154911&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154911&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.997700352234759
R-squared0.995405992849363
Adjusted R-squared0.99523954331492
F-TEST (value)5980.226956946
F-TEST (DF numerator)5
F-TEST (DF denominator)138
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.87435918402191
Sum Squared Residuals484.824684400362






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13029.78324556544220.216754434557783
24240.21301625097121.78698374902879
30-0.09899596031231380.0989959603123138
45456.2482973082219-2.24829730822191
58683.46568228195282.53431771804717
6157150.7574162294076.2425837705929
73637.2087303238344-1.20873032383445
84849.33299292582-1.33299292582
94543.24745002237971.75254997762028
107773.66194656803353.3380534319665
114950.7265980920176-1.72659809201763
127776.21580539286410.784194607135881
132828.615458956332-0.615458956331991
148486.349877165755-2.34987716575496
153131.6719250518635-0.671925051863487
162828.9373477391921-0.937347739192098
1799100.842774057575-1.84277405757466
1822.61775483279372-0.617754832793717
194144.7338009039835-3.73380090398351
202524.70838499537660.29161500462337
211616.8239347910471-0.823934791047079
229698.1372254328728-2.13722543287275
232323.0532580576459-0.0532580576458729
243334.1013104143631-1.10131041436311
254646.5788463005914-0.578846300591438
265960.6154320024777-1.6154320024777
277269.67924001691032.32075998308966
287272.4705695930797-0.470569593079685
296258.61687916664453.38312083335552
305556.4966819086097-1.49668190860967
312727.6007780717902-0.600778071790182
324137.65428620441123.34571379558878
335150.04585256369730.95414743630266
342626.815639421852-0.815639421852013
356565.9491834444335-0.949183444433467
360-0.4784866858381470.478486685838147
372821.53260443571456.46739556428552
384445.1238248534057-1.12382485340568
393637.2604654592891-1.26046545928909
4010091.48549173980518.51450826019494
41104104.124662144561-0.124662144561147
423532.25314456747812.74685543252185
436967.2271130555291.77288694447102
447373.5490075006486-0.549007500648577
45106105.3425654230430.657434576956686
465355.2094914613662-2.20949146136621
474342.596394038230.403605961769988
484947.72323317734061.27676682265942
493838.5323404631087-0.532340463108717
505152.5353826661164-1.53538266611636
511414.5192640297294-0.519264029729444
524041.1957397467208-1.19573974672084
537979.512608131332-0.512608131331986
545252.9610939426375-0.961093942637483
554444.1715142256833-0.171514225683259
563433.90100992014360.0989900798563789
574748.5211571103329-1.52115711033291
583231.6315247500660.368475249934044
593131.8820851472781-0.882085147278126
604042.0420691406612-2.04206914066121
614243.4888233283243-1.48882332832434
623436.3434393433767-2.34343934337667
634041.3023690895253-1.30236908952528
643530.67222400877914.32777599122094
651111.2933526562414-0.293352656241404
664342.14478529304590.855214706954092
675355.0033881878928-2.00338818789283
688284.2120352919196-2.21203529191957
694142.0768217286941-1.07682172869407
7066.09347751909208-0.0934775190920829
718284.2186527434521-2.21865274345205
724748.4452162806349-1.44521628063487
73108103.2929593254084.70704067459235
744647.5661060120935-1.56610601209351
753838.9450993731129-0.945099373112892
7600.291157111462675-0.291157111462675
774546.0865401891972-1.08654018919718
785758.1267656621358-1.12676566213582
792018.40275780120981.59724219879023
805656.1827671215358-0.182767121535835
813837.94258036310620.0574196368938408
824241.3846397839620.61536021603804
833737.9267108585006-0.9267108585006
843636.7871800980709-0.787180098070855
853435.1925482316294-1.1925482316294
865350.52595480351392.47404519648614
878586.8358853501772-1.83588535017719
883637.5985788928898-1.59857889288983
893334.0361970628594-1.03619706285943
905756.82352705318020.176472946819807
915051.2899882745962-1.28998827459621
927173.1423161841792-2.14231618417921
933231.9105431499520.0894568500480084
944543.44057326690061.5594267330994
953331.75459764690431.2454023530957
965353.4191480776985-0.419148077698527
976464.9275906946152-0.927590694615192
981413.79029628836820.209703711631754
993839.0643519773645-1.06435197736451
1003938.16067660832310.839323391676902
10187.926195511319040.0738044886809614
1023838.4213801987133-0.421380198713273
1032423.20255780133290.797442198667074
1042222.8324208097881-0.832420809788075
1051818.2455825373513-0.24558253735127
10630.931425745598552.06857425440145
1074948.81148066938580.188519330614166
10854.904343326964390.0956566730356068
1090-0.6824160725648860.682416072564886
1104746.80958448517130.190415514828684
1113334.2193093355076-1.2193093355076
1124441.75985687209082.24014312790915
1135658.5225134582642-2.52251345826421
1144950.6787299576657-1.67872995766574
1150-0.6109409790929980.610940979092998
1160-0.7003061072849190.700306107284919
1174546.1784988488073-1.17849884880728
1187880.3833021236986-2.38330212369859
1195148.42190214770012.57809785229988
1202525.5145405816568-0.514540581656762
12110.5020205960687040.497979403931296
1226260.40381826056041.5961817394396
1232929.4451308102188-0.445130810218816
1242626.2057393046753-0.205739304675256
12544.1749988220459-0.174998822045901
126109.652953101267730.347046898732266
1274343.8486052556168-0.848605255616773
1283636.9290693503352-0.929069350335167
1294341.88658608776581.11341391223416
1300-0.5744976267478470.574497626747847
1310-0.5823849598675490.582384959867549
1323332.8818421184970.118157881503015
1330-0.6770420177005710.677042017700571
1345354.1300817425242-1.13008174252417
1350-0.8163777721544550.816377772154455
13665.621766330227260.378233669772744
1370-0.7539762114450210.753976211445021
1381918.50338827892550.496611721074538
1392626.646175225892-0.646175225891957
1400-0.6921958420252160.692195842025216
1410-0.5732183887462590.573218388746259
1421616.0497435907347-0.0497435907346726
1438486.4271637066538-2.42716370665382
1442822.21409971236735.78590028763269

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 30 & 29.7832455654422 & 0.216754434557783 \tabularnewline
2 & 42 & 40.2130162509712 & 1.78698374902879 \tabularnewline
3 & 0 & -0.0989959603123138 & 0.0989959603123138 \tabularnewline
4 & 54 & 56.2482973082219 & -2.24829730822191 \tabularnewline
5 & 86 & 83.4656822819528 & 2.53431771804717 \tabularnewline
6 & 157 & 150.757416229407 & 6.2425837705929 \tabularnewline
7 & 36 & 37.2087303238344 & -1.20873032383445 \tabularnewline
8 & 48 & 49.33299292582 & -1.33299292582 \tabularnewline
9 & 45 & 43.2474500223797 & 1.75254997762028 \tabularnewline
10 & 77 & 73.6619465680335 & 3.3380534319665 \tabularnewline
11 & 49 & 50.7265980920176 & -1.72659809201763 \tabularnewline
12 & 77 & 76.2158053928641 & 0.784194607135881 \tabularnewline
13 & 28 & 28.615458956332 & -0.615458956331991 \tabularnewline
14 & 84 & 86.349877165755 & -2.34987716575496 \tabularnewline
15 & 31 & 31.6719250518635 & -0.671925051863487 \tabularnewline
16 & 28 & 28.9373477391921 & -0.937347739192098 \tabularnewline
17 & 99 & 100.842774057575 & -1.84277405757466 \tabularnewline
18 & 2 & 2.61775483279372 & -0.617754832793717 \tabularnewline
19 & 41 & 44.7338009039835 & -3.73380090398351 \tabularnewline
20 & 25 & 24.7083849953766 & 0.29161500462337 \tabularnewline
21 & 16 & 16.8239347910471 & -0.823934791047079 \tabularnewline
22 & 96 & 98.1372254328728 & -2.13722543287275 \tabularnewline
23 & 23 & 23.0532580576459 & -0.0532580576458729 \tabularnewline
24 & 33 & 34.1013104143631 & -1.10131041436311 \tabularnewline
25 & 46 & 46.5788463005914 & -0.578846300591438 \tabularnewline
26 & 59 & 60.6154320024777 & -1.6154320024777 \tabularnewline
27 & 72 & 69.6792400169103 & 2.32075998308966 \tabularnewline
28 & 72 & 72.4705695930797 & -0.470569593079685 \tabularnewline
29 & 62 & 58.6168791666445 & 3.38312083335552 \tabularnewline
30 & 55 & 56.4966819086097 & -1.49668190860967 \tabularnewline
31 & 27 & 27.6007780717902 & -0.600778071790182 \tabularnewline
32 & 41 & 37.6542862044112 & 3.34571379558878 \tabularnewline
33 & 51 & 50.0458525636973 & 0.95414743630266 \tabularnewline
34 & 26 & 26.815639421852 & -0.815639421852013 \tabularnewline
35 & 65 & 65.9491834444335 & -0.949183444433467 \tabularnewline
36 & 0 & -0.478486685838147 & 0.478486685838147 \tabularnewline
37 & 28 & 21.5326044357145 & 6.46739556428552 \tabularnewline
38 & 44 & 45.1238248534057 & -1.12382485340568 \tabularnewline
39 & 36 & 37.2604654592891 & -1.26046545928909 \tabularnewline
40 & 100 & 91.4854917398051 & 8.51450826019494 \tabularnewline
41 & 104 & 104.124662144561 & -0.124662144561147 \tabularnewline
42 & 35 & 32.2531445674781 & 2.74685543252185 \tabularnewline
43 & 69 & 67.227113055529 & 1.77288694447102 \tabularnewline
44 & 73 & 73.5490075006486 & -0.549007500648577 \tabularnewline
45 & 106 & 105.342565423043 & 0.657434576956686 \tabularnewline
46 & 53 & 55.2094914613662 & -2.20949146136621 \tabularnewline
47 & 43 & 42.59639403823 & 0.403605961769988 \tabularnewline
48 & 49 & 47.7232331773406 & 1.27676682265942 \tabularnewline
49 & 38 & 38.5323404631087 & -0.532340463108717 \tabularnewline
50 & 51 & 52.5353826661164 & -1.53538266611636 \tabularnewline
51 & 14 & 14.5192640297294 & -0.519264029729444 \tabularnewline
52 & 40 & 41.1957397467208 & -1.19573974672084 \tabularnewline
53 & 79 & 79.512608131332 & -0.512608131331986 \tabularnewline
54 & 52 & 52.9610939426375 & -0.961093942637483 \tabularnewline
55 & 44 & 44.1715142256833 & -0.171514225683259 \tabularnewline
56 & 34 & 33.9010099201436 & 0.0989900798563789 \tabularnewline
57 & 47 & 48.5211571103329 & -1.52115711033291 \tabularnewline
58 & 32 & 31.631524750066 & 0.368475249934044 \tabularnewline
59 & 31 & 31.8820851472781 & -0.882085147278126 \tabularnewline
60 & 40 & 42.0420691406612 & -2.04206914066121 \tabularnewline
61 & 42 & 43.4888233283243 & -1.48882332832434 \tabularnewline
62 & 34 & 36.3434393433767 & -2.34343934337667 \tabularnewline
63 & 40 & 41.3023690895253 & -1.30236908952528 \tabularnewline
64 & 35 & 30.6722240087791 & 4.32777599122094 \tabularnewline
65 & 11 & 11.2933526562414 & -0.293352656241404 \tabularnewline
66 & 43 & 42.1447852930459 & 0.855214706954092 \tabularnewline
67 & 53 & 55.0033881878928 & -2.00338818789283 \tabularnewline
68 & 82 & 84.2120352919196 & -2.21203529191957 \tabularnewline
69 & 41 & 42.0768217286941 & -1.07682172869407 \tabularnewline
70 & 6 & 6.09347751909208 & -0.0934775190920829 \tabularnewline
71 & 82 & 84.2186527434521 & -2.21865274345205 \tabularnewline
72 & 47 & 48.4452162806349 & -1.44521628063487 \tabularnewline
73 & 108 & 103.292959325408 & 4.70704067459235 \tabularnewline
74 & 46 & 47.5661060120935 & -1.56610601209351 \tabularnewline
75 & 38 & 38.9450993731129 & -0.945099373112892 \tabularnewline
76 & 0 & 0.291157111462675 & -0.291157111462675 \tabularnewline
77 & 45 & 46.0865401891972 & -1.08654018919718 \tabularnewline
78 & 57 & 58.1267656621358 & -1.12676566213582 \tabularnewline
79 & 20 & 18.4027578012098 & 1.59724219879023 \tabularnewline
80 & 56 & 56.1827671215358 & -0.182767121535835 \tabularnewline
81 & 38 & 37.9425803631062 & 0.0574196368938408 \tabularnewline
82 & 42 & 41.384639783962 & 0.61536021603804 \tabularnewline
83 & 37 & 37.9267108585006 & -0.9267108585006 \tabularnewline
84 & 36 & 36.7871800980709 & -0.787180098070855 \tabularnewline
85 & 34 & 35.1925482316294 & -1.1925482316294 \tabularnewline
86 & 53 & 50.5259548035139 & 2.47404519648614 \tabularnewline
87 & 85 & 86.8358853501772 & -1.83588535017719 \tabularnewline
88 & 36 & 37.5985788928898 & -1.59857889288983 \tabularnewline
89 & 33 & 34.0361970628594 & -1.03619706285943 \tabularnewline
90 & 57 & 56.8235270531802 & 0.176472946819807 \tabularnewline
91 & 50 & 51.2899882745962 & -1.28998827459621 \tabularnewline
92 & 71 & 73.1423161841792 & -2.14231618417921 \tabularnewline
93 & 32 & 31.910543149952 & 0.0894568500480084 \tabularnewline
94 & 45 & 43.4405732669006 & 1.5594267330994 \tabularnewline
95 & 33 & 31.7545976469043 & 1.2454023530957 \tabularnewline
96 & 53 & 53.4191480776985 & -0.419148077698527 \tabularnewline
97 & 64 & 64.9275906946152 & -0.927590694615192 \tabularnewline
98 & 14 & 13.7902962883682 & 0.209703711631754 \tabularnewline
99 & 38 & 39.0643519773645 & -1.06435197736451 \tabularnewline
100 & 39 & 38.1606766083231 & 0.839323391676902 \tabularnewline
101 & 8 & 7.92619551131904 & 0.0738044886809614 \tabularnewline
102 & 38 & 38.4213801987133 & -0.421380198713273 \tabularnewline
103 & 24 & 23.2025578013329 & 0.797442198667074 \tabularnewline
104 & 22 & 22.8324208097881 & -0.832420809788075 \tabularnewline
105 & 18 & 18.2455825373513 & -0.24558253735127 \tabularnewline
106 & 3 & 0.93142574559855 & 2.06857425440145 \tabularnewline
107 & 49 & 48.8114806693858 & 0.188519330614166 \tabularnewline
108 & 5 & 4.90434332696439 & 0.0956566730356068 \tabularnewline
109 & 0 & -0.682416072564886 & 0.682416072564886 \tabularnewline
110 & 47 & 46.8095844851713 & 0.190415514828684 \tabularnewline
111 & 33 & 34.2193093355076 & -1.2193093355076 \tabularnewline
112 & 44 & 41.7598568720908 & 2.24014312790915 \tabularnewline
113 & 56 & 58.5225134582642 & -2.52251345826421 \tabularnewline
114 & 49 & 50.6787299576657 & -1.67872995766574 \tabularnewline
115 & 0 & -0.610940979092998 & 0.610940979092998 \tabularnewline
116 & 0 & -0.700306107284919 & 0.700306107284919 \tabularnewline
117 & 45 & 46.1784988488073 & -1.17849884880728 \tabularnewline
118 & 78 & 80.3833021236986 & -2.38330212369859 \tabularnewline
119 & 51 & 48.4219021477001 & 2.57809785229988 \tabularnewline
120 & 25 & 25.5145405816568 & -0.514540581656762 \tabularnewline
121 & 1 & 0.502020596068704 & 0.497979403931296 \tabularnewline
122 & 62 & 60.4038182605604 & 1.5961817394396 \tabularnewline
123 & 29 & 29.4451308102188 & -0.445130810218816 \tabularnewline
124 & 26 & 26.2057393046753 & -0.205739304675256 \tabularnewline
125 & 4 & 4.1749988220459 & -0.174998822045901 \tabularnewline
126 & 10 & 9.65295310126773 & 0.347046898732266 \tabularnewline
127 & 43 & 43.8486052556168 & -0.848605255616773 \tabularnewline
128 & 36 & 36.9290693503352 & -0.929069350335167 \tabularnewline
129 & 43 & 41.8865860877658 & 1.11341391223416 \tabularnewline
130 & 0 & -0.574497626747847 & 0.574497626747847 \tabularnewline
131 & 0 & -0.582384959867549 & 0.582384959867549 \tabularnewline
132 & 33 & 32.881842118497 & 0.118157881503015 \tabularnewline
133 & 0 & -0.677042017700571 & 0.677042017700571 \tabularnewline
134 & 53 & 54.1300817425242 & -1.13008174252417 \tabularnewline
135 & 0 & -0.816377772154455 & 0.816377772154455 \tabularnewline
136 & 6 & 5.62176633022726 & 0.378233669772744 \tabularnewline
137 & 0 & -0.753976211445021 & 0.753976211445021 \tabularnewline
138 & 19 & 18.5033882789255 & 0.496611721074538 \tabularnewline
139 & 26 & 26.646175225892 & -0.646175225891957 \tabularnewline
140 & 0 & -0.692195842025216 & 0.692195842025216 \tabularnewline
141 & 0 & -0.573218388746259 & 0.573218388746259 \tabularnewline
142 & 16 & 16.0497435907347 & -0.0497435907346726 \tabularnewline
143 & 84 & 86.4271637066538 & -2.42716370665382 \tabularnewline
144 & 28 & 22.2140997123673 & 5.78590028763269 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154911&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]30[/C][C]29.7832455654422[/C][C]0.216754434557783[/C][/ROW]
[ROW][C]2[/C][C]42[/C][C]40.2130162509712[/C][C]1.78698374902879[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.0989959603123138[/C][C]0.0989959603123138[/C][/ROW]
[ROW][C]4[/C][C]54[/C][C]56.2482973082219[/C][C]-2.24829730822191[/C][/ROW]
[ROW][C]5[/C][C]86[/C][C]83.4656822819528[/C][C]2.53431771804717[/C][/ROW]
[ROW][C]6[/C][C]157[/C][C]150.757416229407[/C][C]6.2425837705929[/C][/ROW]
[ROW][C]7[/C][C]36[/C][C]37.2087303238344[/C][C]-1.20873032383445[/C][/ROW]
[ROW][C]8[/C][C]48[/C][C]49.33299292582[/C][C]-1.33299292582[/C][/ROW]
[ROW][C]9[/C][C]45[/C][C]43.2474500223797[/C][C]1.75254997762028[/C][/ROW]
[ROW][C]10[/C][C]77[/C][C]73.6619465680335[/C][C]3.3380534319665[/C][/ROW]
[ROW][C]11[/C][C]49[/C][C]50.7265980920176[/C][C]-1.72659809201763[/C][/ROW]
[ROW][C]12[/C][C]77[/C][C]76.2158053928641[/C][C]0.784194607135881[/C][/ROW]
[ROW][C]13[/C][C]28[/C][C]28.615458956332[/C][C]-0.615458956331991[/C][/ROW]
[ROW][C]14[/C][C]84[/C][C]86.349877165755[/C][C]-2.34987716575496[/C][/ROW]
[ROW][C]15[/C][C]31[/C][C]31.6719250518635[/C][C]-0.671925051863487[/C][/ROW]
[ROW][C]16[/C][C]28[/C][C]28.9373477391921[/C][C]-0.937347739192098[/C][/ROW]
[ROW][C]17[/C][C]99[/C][C]100.842774057575[/C][C]-1.84277405757466[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]2.61775483279372[/C][C]-0.617754832793717[/C][/ROW]
[ROW][C]19[/C][C]41[/C][C]44.7338009039835[/C][C]-3.73380090398351[/C][/ROW]
[ROW][C]20[/C][C]25[/C][C]24.7083849953766[/C][C]0.29161500462337[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]16.8239347910471[/C][C]-0.823934791047079[/C][/ROW]
[ROW][C]22[/C][C]96[/C][C]98.1372254328728[/C][C]-2.13722543287275[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]23.0532580576459[/C][C]-0.0532580576458729[/C][/ROW]
[ROW][C]24[/C][C]33[/C][C]34.1013104143631[/C][C]-1.10131041436311[/C][/ROW]
[ROW][C]25[/C][C]46[/C][C]46.5788463005914[/C][C]-0.578846300591438[/C][/ROW]
[ROW][C]26[/C][C]59[/C][C]60.6154320024777[/C][C]-1.6154320024777[/C][/ROW]
[ROW][C]27[/C][C]72[/C][C]69.6792400169103[/C][C]2.32075998308966[/C][/ROW]
[ROW][C]28[/C][C]72[/C][C]72.4705695930797[/C][C]-0.470569593079685[/C][/ROW]
[ROW][C]29[/C][C]62[/C][C]58.6168791666445[/C][C]3.38312083335552[/C][/ROW]
[ROW][C]30[/C][C]55[/C][C]56.4966819086097[/C][C]-1.49668190860967[/C][/ROW]
[ROW][C]31[/C][C]27[/C][C]27.6007780717902[/C][C]-0.600778071790182[/C][/ROW]
[ROW][C]32[/C][C]41[/C][C]37.6542862044112[/C][C]3.34571379558878[/C][/ROW]
[ROW][C]33[/C][C]51[/C][C]50.0458525636973[/C][C]0.95414743630266[/C][/ROW]
[ROW][C]34[/C][C]26[/C][C]26.815639421852[/C][C]-0.815639421852013[/C][/ROW]
[ROW][C]35[/C][C]65[/C][C]65.9491834444335[/C][C]-0.949183444433467[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.478486685838147[/C][C]0.478486685838147[/C][/ROW]
[ROW][C]37[/C][C]28[/C][C]21.5326044357145[/C][C]6.46739556428552[/C][/ROW]
[ROW][C]38[/C][C]44[/C][C]45.1238248534057[/C][C]-1.12382485340568[/C][/ROW]
[ROW][C]39[/C][C]36[/C][C]37.2604654592891[/C][C]-1.26046545928909[/C][/ROW]
[ROW][C]40[/C][C]100[/C][C]91.4854917398051[/C][C]8.51450826019494[/C][/ROW]
[ROW][C]41[/C][C]104[/C][C]104.124662144561[/C][C]-0.124662144561147[/C][/ROW]
[ROW][C]42[/C][C]35[/C][C]32.2531445674781[/C][C]2.74685543252185[/C][/ROW]
[ROW][C]43[/C][C]69[/C][C]67.227113055529[/C][C]1.77288694447102[/C][/ROW]
[ROW][C]44[/C][C]73[/C][C]73.5490075006486[/C][C]-0.549007500648577[/C][/ROW]
[ROW][C]45[/C][C]106[/C][C]105.342565423043[/C][C]0.657434576956686[/C][/ROW]
[ROW][C]46[/C][C]53[/C][C]55.2094914613662[/C][C]-2.20949146136621[/C][/ROW]
[ROW][C]47[/C][C]43[/C][C]42.59639403823[/C][C]0.403605961769988[/C][/ROW]
[ROW][C]48[/C][C]49[/C][C]47.7232331773406[/C][C]1.27676682265942[/C][/ROW]
[ROW][C]49[/C][C]38[/C][C]38.5323404631087[/C][C]-0.532340463108717[/C][/ROW]
[ROW][C]50[/C][C]51[/C][C]52.5353826661164[/C][C]-1.53538266611636[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]14.5192640297294[/C][C]-0.519264029729444[/C][/ROW]
[ROW][C]52[/C][C]40[/C][C]41.1957397467208[/C][C]-1.19573974672084[/C][/ROW]
[ROW][C]53[/C][C]79[/C][C]79.512608131332[/C][C]-0.512608131331986[/C][/ROW]
[ROW][C]54[/C][C]52[/C][C]52.9610939426375[/C][C]-0.961093942637483[/C][/ROW]
[ROW][C]55[/C][C]44[/C][C]44.1715142256833[/C][C]-0.171514225683259[/C][/ROW]
[ROW][C]56[/C][C]34[/C][C]33.9010099201436[/C][C]0.0989900798563789[/C][/ROW]
[ROW][C]57[/C][C]47[/C][C]48.5211571103329[/C][C]-1.52115711033291[/C][/ROW]
[ROW][C]58[/C][C]32[/C][C]31.631524750066[/C][C]0.368475249934044[/C][/ROW]
[ROW][C]59[/C][C]31[/C][C]31.8820851472781[/C][C]-0.882085147278126[/C][/ROW]
[ROW][C]60[/C][C]40[/C][C]42.0420691406612[/C][C]-2.04206914066121[/C][/ROW]
[ROW][C]61[/C][C]42[/C][C]43.4888233283243[/C][C]-1.48882332832434[/C][/ROW]
[ROW][C]62[/C][C]34[/C][C]36.3434393433767[/C][C]-2.34343934337667[/C][/ROW]
[ROW][C]63[/C][C]40[/C][C]41.3023690895253[/C][C]-1.30236908952528[/C][/ROW]
[ROW][C]64[/C][C]35[/C][C]30.6722240087791[/C][C]4.32777599122094[/C][/ROW]
[ROW][C]65[/C][C]11[/C][C]11.2933526562414[/C][C]-0.293352656241404[/C][/ROW]
[ROW][C]66[/C][C]43[/C][C]42.1447852930459[/C][C]0.855214706954092[/C][/ROW]
[ROW][C]67[/C][C]53[/C][C]55.0033881878928[/C][C]-2.00338818789283[/C][/ROW]
[ROW][C]68[/C][C]82[/C][C]84.2120352919196[/C][C]-2.21203529191957[/C][/ROW]
[ROW][C]69[/C][C]41[/C][C]42.0768217286941[/C][C]-1.07682172869407[/C][/ROW]
[ROW][C]70[/C][C]6[/C][C]6.09347751909208[/C][C]-0.0934775190920829[/C][/ROW]
[ROW][C]71[/C][C]82[/C][C]84.2186527434521[/C][C]-2.21865274345205[/C][/ROW]
[ROW][C]72[/C][C]47[/C][C]48.4452162806349[/C][C]-1.44521628063487[/C][/ROW]
[ROW][C]73[/C][C]108[/C][C]103.292959325408[/C][C]4.70704067459235[/C][/ROW]
[ROW][C]74[/C][C]46[/C][C]47.5661060120935[/C][C]-1.56610601209351[/C][/ROW]
[ROW][C]75[/C][C]38[/C][C]38.9450993731129[/C][C]-0.945099373112892[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.291157111462675[/C][C]-0.291157111462675[/C][/ROW]
[ROW][C]77[/C][C]45[/C][C]46.0865401891972[/C][C]-1.08654018919718[/C][/ROW]
[ROW][C]78[/C][C]57[/C][C]58.1267656621358[/C][C]-1.12676566213582[/C][/ROW]
[ROW][C]79[/C][C]20[/C][C]18.4027578012098[/C][C]1.59724219879023[/C][/ROW]
[ROW][C]80[/C][C]56[/C][C]56.1827671215358[/C][C]-0.182767121535835[/C][/ROW]
[ROW][C]81[/C][C]38[/C][C]37.9425803631062[/C][C]0.0574196368938408[/C][/ROW]
[ROW][C]82[/C][C]42[/C][C]41.384639783962[/C][C]0.61536021603804[/C][/ROW]
[ROW][C]83[/C][C]37[/C][C]37.9267108585006[/C][C]-0.9267108585006[/C][/ROW]
[ROW][C]84[/C][C]36[/C][C]36.7871800980709[/C][C]-0.787180098070855[/C][/ROW]
[ROW][C]85[/C][C]34[/C][C]35.1925482316294[/C][C]-1.1925482316294[/C][/ROW]
[ROW][C]86[/C][C]53[/C][C]50.5259548035139[/C][C]2.47404519648614[/C][/ROW]
[ROW][C]87[/C][C]85[/C][C]86.8358853501772[/C][C]-1.83588535017719[/C][/ROW]
[ROW][C]88[/C][C]36[/C][C]37.5985788928898[/C][C]-1.59857889288983[/C][/ROW]
[ROW][C]89[/C][C]33[/C][C]34.0361970628594[/C][C]-1.03619706285943[/C][/ROW]
[ROW][C]90[/C][C]57[/C][C]56.8235270531802[/C][C]0.176472946819807[/C][/ROW]
[ROW][C]91[/C][C]50[/C][C]51.2899882745962[/C][C]-1.28998827459621[/C][/ROW]
[ROW][C]92[/C][C]71[/C][C]73.1423161841792[/C][C]-2.14231618417921[/C][/ROW]
[ROW][C]93[/C][C]32[/C][C]31.910543149952[/C][C]0.0894568500480084[/C][/ROW]
[ROW][C]94[/C][C]45[/C][C]43.4405732669006[/C][C]1.5594267330994[/C][/ROW]
[ROW][C]95[/C][C]33[/C][C]31.7545976469043[/C][C]1.2454023530957[/C][/ROW]
[ROW][C]96[/C][C]53[/C][C]53.4191480776985[/C][C]-0.419148077698527[/C][/ROW]
[ROW][C]97[/C][C]64[/C][C]64.9275906946152[/C][C]-0.927590694615192[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]13.7902962883682[/C][C]0.209703711631754[/C][/ROW]
[ROW][C]99[/C][C]38[/C][C]39.0643519773645[/C][C]-1.06435197736451[/C][/ROW]
[ROW][C]100[/C][C]39[/C][C]38.1606766083231[/C][C]0.839323391676902[/C][/ROW]
[ROW][C]101[/C][C]8[/C][C]7.92619551131904[/C][C]0.0738044886809614[/C][/ROW]
[ROW][C]102[/C][C]38[/C][C]38.4213801987133[/C][C]-0.421380198713273[/C][/ROW]
[ROW][C]103[/C][C]24[/C][C]23.2025578013329[/C][C]0.797442198667074[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]22.8324208097881[/C][C]-0.832420809788075[/C][/ROW]
[ROW][C]105[/C][C]18[/C][C]18.2455825373513[/C][C]-0.24558253735127[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]0.93142574559855[/C][C]2.06857425440145[/C][/ROW]
[ROW][C]107[/C][C]49[/C][C]48.8114806693858[/C][C]0.188519330614166[/C][/ROW]
[ROW][C]108[/C][C]5[/C][C]4.90434332696439[/C][C]0.0956566730356068[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]-0.682416072564886[/C][C]0.682416072564886[/C][/ROW]
[ROW][C]110[/C][C]47[/C][C]46.8095844851713[/C][C]0.190415514828684[/C][/ROW]
[ROW][C]111[/C][C]33[/C][C]34.2193093355076[/C][C]-1.2193093355076[/C][/ROW]
[ROW][C]112[/C][C]44[/C][C]41.7598568720908[/C][C]2.24014312790915[/C][/ROW]
[ROW][C]113[/C][C]56[/C][C]58.5225134582642[/C][C]-2.52251345826421[/C][/ROW]
[ROW][C]114[/C][C]49[/C][C]50.6787299576657[/C][C]-1.67872995766574[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]-0.610940979092998[/C][C]0.610940979092998[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]-0.700306107284919[/C][C]0.700306107284919[/C][/ROW]
[ROW][C]117[/C][C]45[/C][C]46.1784988488073[/C][C]-1.17849884880728[/C][/ROW]
[ROW][C]118[/C][C]78[/C][C]80.3833021236986[/C][C]-2.38330212369859[/C][/ROW]
[ROW][C]119[/C][C]51[/C][C]48.4219021477001[/C][C]2.57809785229988[/C][/ROW]
[ROW][C]120[/C][C]25[/C][C]25.5145405816568[/C][C]-0.514540581656762[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0.502020596068704[/C][C]0.497979403931296[/C][/ROW]
[ROW][C]122[/C][C]62[/C][C]60.4038182605604[/C][C]1.5961817394396[/C][/ROW]
[ROW][C]123[/C][C]29[/C][C]29.4451308102188[/C][C]-0.445130810218816[/C][/ROW]
[ROW][C]124[/C][C]26[/C][C]26.2057393046753[/C][C]-0.205739304675256[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]4.1749988220459[/C][C]-0.174998822045901[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]9.65295310126773[/C][C]0.347046898732266[/C][/ROW]
[ROW][C]127[/C][C]43[/C][C]43.8486052556168[/C][C]-0.848605255616773[/C][/ROW]
[ROW][C]128[/C][C]36[/C][C]36.9290693503352[/C][C]-0.929069350335167[/C][/ROW]
[ROW][C]129[/C][C]43[/C][C]41.8865860877658[/C][C]1.11341391223416[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]-0.574497626747847[/C][C]0.574497626747847[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-0.582384959867549[/C][C]0.582384959867549[/C][/ROW]
[ROW][C]132[/C][C]33[/C][C]32.881842118497[/C][C]0.118157881503015[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]-0.677042017700571[/C][C]0.677042017700571[/C][/ROW]
[ROW][C]134[/C][C]53[/C][C]54.1300817425242[/C][C]-1.13008174252417[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]-0.816377772154455[/C][C]0.816377772154455[/C][/ROW]
[ROW][C]136[/C][C]6[/C][C]5.62176633022726[/C][C]0.378233669772744[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]-0.753976211445021[/C][C]0.753976211445021[/C][/ROW]
[ROW][C]138[/C][C]19[/C][C]18.5033882789255[/C][C]0.496611721074538[/C][/ROW]
[ROW][C]139[/C][C]26[/C][C]26.646175225892[/C][C]-0.646175225891957[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]-0.692195842025216[/C][C]0.692195842025216[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]-0.573218388746259[/C][C]0.573218388746259[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]16.0497435907347[/C][C]-0.0497435907346726[/C][/ROW]
[ROW][C]143[/C][C]84[/C][C]86.4271637066538[/C][C]-2.42716370665382[/C][/ROW]
[ROW][C]144[/C][C]28[/C][C]22.2140997123673[/C][C]5.78590028763269[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154911&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154911&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13029.78324556544220.216754434557783
24240.21301625097121.78698374902879
30-0.09899596031231380.0989959603123138
45456.2482973082219-2.24829730822191
58683.46568228195282.53431771804717
6157150.7574162294076.2425837705929
73637.2087303238344-1.20873032383445
84849.33299292582-1.33299292582
94543.24745002237971.75254997762028
107773.66194656803353.3380534319665
114950.7265980920176-1.72659809201763
127776.21580539286410.784194607135881
132828.615458956332-0.615458956331991
148486.349877165755-2.34987716575496
153131.6719250518635-0.671925051863487
162828.9373477391921-0.937347739192098
1799100.842774057575-1.84277405757466
1822.61775483279372-0.617754832793717
194144.7338009039835-3.73380090398351
202524.70838499537660.29161500462337
211616.8239347910471-0.823934791047079
229698.1372254328728-2.13722543287275
232323.0532580576459-0.0532580576458729
243334.1013104143631-1.10131041436311
254646.5788463005914-0.578846300591438
265960.6154320024777-1.6154320024777
277269.67924001691032.32075998308966
287272.4705695930797-0.470569593079685
296258.61687916664453.38312083335552
305556.4966819086097-1.49668190860967
312727.6007780717902-0.600778071790182
324137.65428620441123.34571379558878
335150.04585256369730.95414743630266
342626.815639421852-0.815639421852013
356565.9491834444335-0.949183444433467
360-0.4784866858381470.478486685838147
372821.53260443571456.46739556428552
384445.1238248534057-1.12382485340568
393637.2604654592891-1.26046545928909
4010091.48549173980518.51450826019494
41104104.124662144561-0.124662144561147
423532.25314456747812.74685543252185
436967.2271130555291.77288694447102
447373.5490075006486-0.549007500648577
45106105.3425654230430.657434576956686
465355.2094914613662-2.20949146136621
474342.596394038230.403605961769988
484947.72323317734061.27676682265942
493838.5323404631087-0.532340463108717
505152.5353826661164-1.53538266611636
511414.5192640297294-0.519264029729444
524041.1957397467208-1.19573974672084
537979.512608131332-0.512608131331986
545252.9610939426375-0.961093942637483
554444.1715142256833-0.171514225683259
563433.90100992014360.0989900798563789
574748.5211571103329-1.52115711033291
583231.6315247500660.368475249934044
593131.8820851472781-0.882085147278126
604042.0420691406612-2.04206914066121
614243.4888233283243-1.48882332832434
623436.3434393433767-2.34343934337667
634041.3023690895253-1.30236908952528
643530.67222400877914.32777599122094
651111.2933526562414-0.293352656241404
664342.14478529304590.855214706954092
675355.0033881878928-2.00338818789283
688284.2120352919196-2.21203529191957
694142.0768217286941-1.07682172869407
7066.09347751909208-0.0934775190920829
718284.2186527434521-2.21865274345205
724748.4452162806349-1.44521628063487
73108103.2929593254084.70704067459235
744647.5661060120935-1.56610601209351
753838.9450993731129-0.945099373112892
7600.291157111462675-0.291157111462675
774546.0865401891972-1.08654018919718
785758.1267656621358-1.12676566213582
792018.40275780120981.59724219879023
805656.1827671215358-0.182767121535835
813837.94258036310620.0574196368938408
824241.3846397839620.61536021603804
833737.9267108585006-0.9267108585006
843636.7871800980709-0.787180098070855
853435.1925482316294-1.1925482316294
865350.52595480351392.47404519648614
878586.8358853501772-1.83588535017719
883637.5985788928898-1.59857889288983
893334.0361970628594-1.03619706285943
905756.82352705318020.176472946819807
915051.2899882745962-1.28998827459621
927173.1423161841792-2.14231618417921
933231.9105431499520.0894568500480084
944543.44057326690061.5594267330994
953331.75459764690431.2454023530957
965353.4191480776985-0.419148077698527
976464.9275906946152-0.927590694615192
981413.79029628836820.209703711631754
993839.0643519773645-1.06435197736451
1003938.16067660832310.839323391676902
10187.926195511319040.0738044886809614
1023838.4213801987133-0.421380198713273
1032423.20255780133290.797442198667074
1042222.8324208097881-0.832420809788075
1051818.2455825373513-0.24558253735127
10630.931425745598552.06857425440145
1074948.81148066938580.188519330614166
10854.904343326964390.0956566730356068
1090-0.6824160725648860.682416072564886
1104746.80958448517130.190415514828684
1113334.2193093355076-1.2193093355076
1124441.75985687209082.24014312790915
1135658.5225134582642-2.52251345826421
1144950.6787299576657-1.67872995766574
1150-0.6109409790929980.610940979092998
1160-0.7003061072849190.700306107284919
1174546.1784988488073-1.17849884880728
1187880.3833021236986-2.38330212369859
1195148.42190214770012.57809785229988
1202525.5145405816568-0.514540581656762
12110.5020205960687040.497979403931296
1226260.40381826056041.5961817394396
1232929.4451308102188-0.445130810218816
1242626.2057393046753-0.205739304675256
12544.1749988220459-0.174998822045901
126109.652953101267730.347046898732266
1274343.8486052556168-0.848605255616773
1283636.9290693503352-0.929069350335167
1294341.88658608776581.11341391223416
1300-0.5744976267478470.574497626747847
1310-0.5823849598675490.582384959867549
1323332.8818421184970.118157881503015
1330-0.6770420177005710.677042017700571
1345354.1300817425242-1.13008174252417
1350-0.8163777721544550.816377772154455
13665.621766330227260.378233669772744
1370-0.7539762114450210.753976211445021
1381918.50338827892550.496611721074538
1392626.646175225892-0.646175225891957
1400-0.6921958420252160.692195842025216
1410-0.5732183887462590.573218388746259
1421616.0497435907347-0.0497435907346726
1438486.4271637066538-2.42716370665382
1442822.21409971236735.78590028763269






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8360464549611040.3279070900777920.163953545038896
100.8012946966823910.3974106066352180.198705303317609
110.7121265985823850.5757468028352290.287873401417615
120.6936661227035670.6126677545928660.306333877296433
130.629266406901270.7414671861974610.37073359309873
140.8631916190944730.2736167618110550.136808380905527
150.8089688124278740.3820623751442530.191031187572126
160.7394185921040550.521162815791890.260581407895945
170.6851230523767680.6297538952464650.314876947623233
180.6926811358422080.6146377283155850.307318864157792
190.770597350906120.4588052981877610.22940264909388
200.8250855473800270.3498289052399460.174914452619973
210.7846809065799480.4306381868401050.215319093420052
220.7591478556718710.4817042886562590.240852144328129
230.7429868388830650.514026322233870.257013161116935
240.696442596827270.607114806345460.30355740317273
250.6427611516698910.7144776966602180.357238848330109
260.5954163220592010.8091673558815980.404583677940799
270.5983959448821470.8032081102357060.401604055117853
280.53696107787530.92607784424940.4630389221247
290.6924140536753330.6151718926493350.307585946324667
300.6476398787910560.7047202424178880.352360121208944
310.6099358208753130.7801283582493750.390064179124687
320.8265880978370470.3468238043259060.173411902162953
330.7974570377586950.405085924482610.202542962241305
340.7909160167740910.4181679664518180.209083983225909
350.7512312860044630.4975374279910740.248768713995537
360.7393030704583790.5213938590832410.260696929541621
370.98772183477380.02455633045240090.0122781652262004
380.9840450610868410.0319098778263190.0159549389131595
390.9814299697682940.03714006046341290.0185700302317065
400.9999979898714794.02025704126672e-062.01012852063336e-06
410.9999967635339376.4729321258134e-063.2364660629067e-06
420.9999979164736464.16705270715391e-062.08352635357695e-06
430.9999970669090635.8661818744992e-062.9330909372496e-06
440.9999960128792657.974241469579e-063.9871207347895e-06
450.9999934520844061.30958311881264e-056.54791559406321e-06
460.9999961422104357.71557913045719e-063.8577895652286e-06
470.9999932062256451.35875487109213e-056.79377435546064e-06
480.9999940130690321.19738619351711e-055.98693096758554e-06
490.9999925133188521.49733622967412e-057.48668114837062e-06
500.9999919061182511.6187763498906e-058.09388174945299e-06
510.9999867358769972.65282460068346e-051.32641230034173e-05
520.9999809547148283.8090570344439e-051.90452851722195e-05
530.99997149992115.70001577998169e-052.85000788999085e-05
540.9999601305910817.97388178382857e-053.98694089191428e-05
550.9999361614932990.0001276770134024486.38385067012242e-05
560.9999032292200370.0001935415599254019.67707799627006e-05
570.9998816699598620.0002366600802766720.000118330040138336
580.9998160976578690.0003678046842619290.000183902342130964
590.9997213384552260.0005573230895473670.000278661544773683
600.9997635596483890.0004728807032218230.000236440351610911
610.9997185882761450.0005628234477099960.000281411723854998
620.9997225811718830.0005548376562345770.000277418828117288
630.9996141175504830.0007717648990349210.000385882449517461
640.9999756039954844.87920090310475e-052.43960045155238e-05
650.9999604444669317.91110661373665e-053.95555330686833e-05
660.9999435031883980.0001129936232038455.64968116019225e-05
670.9999472234910220.0001055530179563355.27765089781673e-05
680.9999435394587870.0001129210824268235.64605412134116e-05
690.9999198022346510.00016039553069728.01977653485999e-05
700.9998841846050690.0002316307898627890.000115815394931395
710.9998523602009850.0002952795980307620.000147639799015381
720.9997900259767080.0004199480465850460.000209974023292523
730.9999989403465542.11930689282875e-061.05965344641437e-06
740.999998353510543.29297892035093e-061.64648946017546e-06
750.9999974164257995.16714840120595e-062.58357420060298e-06
760.9999969025535186.19489296316515e-063.09744648158258e-06
770.9999949573441221.00853117561885e-055.04265587809425e-06
780.9999912824048361.74351903269991e-058.71759516349956e-06
790.999989824456222.03510875598459e-051.01755437799229e-05
800.9999823979032763.52041934469939e-051.76020967234969e-05
810.9999702275988925.95448022166856e-052.97724011083428e-05
820.999954504729849.09905403200517e-054.54952701600258e-05
830.9999318473915280.0001363052169440946.8152608472047e-05
840.9999006341179580.0001987317640839239.93658820419617e-05
850.9998792478334290.0002415043331428960.000120752166571448
860.9999595817336698.08365326619532e-054.04182663309766e-05
870.9999480287974050.0001039424051893055.19712025946523e-05
880.9999249034546420.0001501930907150887.50965453575438e-05
890.9998962284225440.0002075431549115750.000103771577455787
900.9998458954282680.0003082091434643550.000154104571732177
910.9997879441039690.0004241117920613130.000212055896030657
920.9997150415672830.0005699168654350290.000284958432717514
930.9995419720696950.0009160558606102470.000458027930305123
940.9997503646136750.0004992707726508860.000249635386325443
950.9997986623216810.0004026753566373360.000201337678318668
960.9996617358339630.000676528332073910.000338264166036955
970.9994394621529320.001121075694136130.000560537847068067
980.9991703315114730.001659336977053750.000829668488526874
990.9987256594139760.002548681172048920.00127434058602446
1000.9984580737417170.003083852516566170.00154192625828309
1010.9976283048569380.004743390286124310.00237169514306216
1020.996268373708140.00746325258371940.0037316262918597
1030.9946074698400640.01078506031987170.00539253015993586
1040.99345552961140.01308894077719950.00654447038859973
1050.9903315759958020.01933684800839550.00966842400419773
1060.9889077707645460.02218445847090870.0110922292354543
1070.9840590521270930.03188189574581310.0159409478729066
1080.9772893946421450.04542121071571060.0227106053578553
1090.9691710700511390.0616578598977210.0308289299488605
1100.958764191255630.08247161748874020.0412358087443701
1110.9477740659258380.1044518681483240.0522259340741622
1120.9759767002344340.04804659953113110.0240232997655656
1130.9727395360789630.05452092784207360.0272604639210368
1140.9648613206184240.07027735876315210.035138679381576
1150.9519735272785780.09605294544284340.0480264727214217
1160.9392391592886980.1215216814226050.0607608407113023
1170.916341981467830.167316037064340.0836580185321699
1180.9026831530908420.1946336938183160.0973168469091582
1190.9684907596792780.06301848064144460.0315092403207223
1200.9527344126503030.09453117469939390.047265587349697
1210.9345275119559030.1309449760881940.065472488044097
1220.968686474903580.06262705019283920.0313135250964196
1230.9512378075573860.09752438488522890.0487621924426144
1240.9383835383757330.1232329232485340.0616164616242672
1250.9198924545870270.1602150908259470.0801075454129733
1260.8866367729092110.2267264541815780.113363227090789
1270.8337158074905010.3325683850189990.166284192509499
1280.7728549908616520.4542900182766950.227145009138348
1290.6909530173732010.6180939652535980.309046982626799
1300.5976896233128350.804620753374330.402310376687165
1310.5138467568518140.9723064862963720.486153243148186
1320.6409826134478790.7180347731042420.359017386552121
1330.5646834612623520.8706330774752950.435316538737648
1340.432345997710120.8646919954202410.56765400228988
1350.2868784565155170.5737569130310330.713121543484483

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.836046454961104 & 0.327907090077792 & 0.163953545038896 \tabularnewline
10 & 0.801294696682391 & 0.397410606635218 & 0.198705303317609 \tabularnewline
11 & 0.712126598582385 & 0.575746802835229 & 0.287873401417615 \tabularnewline
12 & 0.693666122703567 & 0.612667754592866 & 0.306333877296433 \tabularnewline
13 & 0.62926640690127 & 0.741467186197461 & 0.37073359309873 \tabularnewline
14 & 0.863191619094473 & 0.273616761811055 & 0.136808380905527 \tabularnewline
15 & 0.808968812427874 & 0.382062375144253 & 0.191031187572126 \tabularnewline
16 & 0.739418592104055 & 0.52116281579189 & 0.260581407895945 \tabularnewline
17 & 0.685123052376768 & 0.629753895246465 & 0.314876947623233 \tabularnewline
18 & 0.692681135842208 & 0.614637728315585 & 0.307318864157792 \tabularnewline
19 & 0.77059735090612 & 0.458805298187761 & 0.22940264909388 \tabularnewline
20 & 0.825085547380027 & 0.349828905239946 & 0.174914452619973 \tabularnewline
21 & 0.784680906579948 & 0.430638186840105 & 0.215319093420052 \tabularnewline
22 & 0.759147855671871 & 0.481704288656259 & 0.240852144328129 \tabularnewline
23 & 0.742986838883065 & 0.51402632223387 & 0.257013161116935 \tabularnewline
24 & 0.69644259682727 & 0.60711480634546 & 0.30355740317273 \tabularnewline
25 & 0.642761151669891 & 0.714477696660218 & 0.357238848330109 \tabularnewline
26 & 0.595416322059201 & 0.809167355881598 & 0.404583677940799 \tabularnewline
27 & 0.598395944882147 & 0.803208110235706 & 0.401604055117853 \tabularnewline
28 & 0.5369610778753 & 0.9260778442494 & 0.4630389221247 \tabularnewline
29 & 0.692414053675333 & 0.615171892649335 & 0.307585946324667 \tabularnewline
30 & 0.647639878791056 & 0.704720242417888 & 0.352360121208944 \tabularnewline
31 & 0.609935820875313 & 0.780128358249375 & 0.390064179124687 \tabularnewline
32 & 0.826588097837047 & 0.346823804325906 & 0.173411902162953 \tabularnewline
33 & 0.797457037758695 & 0.40508592448261 & 0.202542962241305 \tabularnewline
34 & 0.790916016774091 & 0.418167966451818 & 0.209083983225909 \tabularnewline
35 & 0.751231286004463 & 0.497537427991074 & 0.248768713995537 \tabularnewline
36 & 0.739303070458379 & 0.521393859083241 & 0.260696929541621 \tabularnewline
37 & 0.9877218347738 & 0.0245563304524009 & 0.0122781652262004 \tabularnewline
38 & 0.984045061086841 & 0.031909877826319 & 0.0159549389131595 \tabularnewline
39 & 0.981429969768294 & 0.0371400604634129 & 0.0185700302317065 \tabularnewline
40 & 0.999997989871479 & 4.02025704126672e-06 & 2.01012852063336e-06 \tabularnewline
41 & 0.999996763533937 & 6.4729321258134e-06 & 3.2364660629067e-06 \tabularnewline
42 & 0.999997916473646 & 4.16705270715391e-06 & 2.08352635357695e-06 \tabularnewline
43 & 0.999997066909063 & 5.8661818744992e-06 & 2.9330909372496e-06 \tabularnewline
44 & 0.999996012879265 & 7.974241469579e-06 & 3.9871207347895e-06 \tabularnewline
45 & 0.999993452084406 & 1.30958311881264e-05 & 6.54791559406321e-06 \tabularnewline
46 & 0.999996142210435 & 7.71557913045719e-06 & 3.8577895652286e-06 \tabularnewline
47 & 0.999993206225645 & 1.35875487109213e-05 & 6.79377435546064e-06 \tabularnewline
48 & 0.999994013069032 & 1.19738619351711e-05 & 5.98693096758554e-06 \tabularnewline
49 & 0.999992513318852 & 1.49733622967412e-05 & 7.48668114837062e-06 \tabularnewline
50 & 0.999991906118251 & 1.6187763498906e-05 & 8.09388174945299e-06 \tabularnewline
51 & 0.999986735876997 & 2.65282460068346e-05 & 1.32641230034173e-05 \tabularnewline
52 & 0.999980954714828 & 3.8090570344439e-05 & 1.90452851722195e-05 \tabularnewline
53 & 0.9999714999211 & 5.70001577998169e-05 & 2.85000788999085e-05 \tabularnewline
54 & 0.999960130591081 & 7.97388178382857e-05 & 3.98694089191428e-05 \tabularnewline
55 & 0.999936161493299 & 0.000127677013402448 & 6.38385067012242e-05 \tabularnewline
56 & 0.999903229220037 & 0.000193541559925401 & 9.67707799627006e-05 \tabularnewline
57 & 0.999881669959862 & 0.000236660080276672 & 0.000118330040138336 \tabularnewline
58 & 0.999816097657869 & 0.000367804684261929 & 0.000183902342130964 \tabularnewline
59 & 0.999721338455226 & 0.000557323089547367 & 0.000278661544773683 \tabularnewline
60 & 0.999763559648389 & 0.000472880703221823 & 0.000236440351610911 \tabularnewline
61 & 0.999718588276145 & 0.000562823447709996 & 0.000281411723854998 \tabularnewline
62 & 0.999722581171883 & 0.000554837656234577 & 0.000277418828117288 \tabularnewline
63 & 0.999614117550483 & 0.000771764899034921 & 0.000385882449517461 \tabularnewline
64 & 0.999975603995484 & 4.87920090310475e-05 & 2.43960045155238e-05 \tabularnewline
65 & 0.999960444466931 & 7.91110661373665e-05 & 3.95555330686833e-05 \tabularnewline
66 & 0.999943503188398 & 0.000112993623203845 & 5.64968116019225e-05 \tabularnewline
67 & 0.999947223491022 & 0.000105553017956335 & 5.27765089781673e-05 \tabularnewline
68 & 0.999943539458787 & 0.000112921082426823 & 5.64605412134116e-05 \tabularnewline
69 & 0.999919802234651 & 0.0001603955306972 & 8.01977653485999e-05 \tabularnewline
70 & 0.999884184605069 & 0.000231630789862789 & 0.000115815394931395 \tabularnewline
71 & 0.999852360200985 & 0.000295279598030762 & 0.000147639799015381 \tabularnewline
72 & 0.999790025976708 & 0.000419948046585046 & 0.000209974023292523 \tabularnewline
73 & 0.999998940346554 & 2.11930689282875e-06 & 1.05965344641437e-06 \tabularnewline
74 & 0.99999835351054 & 3.29297892035093e-06 & 1.64648946017546e-06 \tabularnewline
75 & 0.999997416425799 & 5.16714840120595e-06 & 2.58357420060298e-06 \tabularnewline
76 & 0.999996902553518 & 6.19489296316515e-06 & 3.09744648158258e-06 \tabularnewline
77 & 0.999994957344122 & 1.00853117561885e-05 & 5.04265587809425e-06 \tabularnewline
78 & 0.999991282404836 & 1.74351903269991e-05 & 8.71759516349956e-06 \tabularnewline
79 & 0.99998982445622 & 2.03510875598459e-05 & 1.01755437799229e-05 \tabularnewline
80 & 0.999982397903276 & 3.52041934469939e-05 & 1.76020967234969e-05 \tabularnewline
81 & 0.999970227598892 & 5.95448022166856e-05 & 2.97724011083428e-05 \tabularnewline
82 & 0.99995450472984 & 9.09905403200517e-05 & 4.54952701600258e-05 \tabularnewline
83 & 0.999931847391528 & 0.000136305216944094 & 6.8152608472047e-05 \tabularnewline
84 & 0.999900634117958 & 0.000198731764083923 & 9.93658820419617e-05 \tabularnewline
85 & 0.999879247833429 & 0.000241504333142896 & 0.000120752166571448 \tabularnewline
86 & 0.999959581733669 & 8.08365326619532e-05 & 4.04182663309766e-05 \tabularnewline
87 & 0.999948028797405 & 0.000103942405189305 & 5.19712025946523e-05 \tabularnewline
88 & 0.999924903454642 & 0.000150193090715088 & 7.50965453575438e-05 \tabularnewline
89 & 0.999896228422544 & 0.000207543154911575 & 0.000103771577455787 \tabularnewline
90 & 0.999845895428268 & 0.000308209143464355 & 0.000154104571732177 \tabularnewline
91 & 0.999787944103969 & 0.000424111792061313 & 0.000212055896030657 \tabularnewline
92 & 0.999715041567283 & 0.000569916865435029 & 0.000284958432717514 \tabularnewline
93 & 0.999541972069695 & 0.000916055860610247 & 0.000458027930305123 \tabularnewline
94 & 0.999750364613675 & 0.000499270772650886 & 0.000249635386325443 \tabularnewline
95 & 0.999798662321681 & 0.000402675356637336 & 0.000201337678318668 \tabularnewline
96 & 0.999661735833963 & 0.00067652833207391 & 0.000338264166036955 \tabularnewline
97 & 0.999439462152932 & 0.00112107569413613 & 0.000560537847068067 \tabularnewline
98 & 0.999170331511473 & 0.00165933697705375 & 0.000829668488526874 \tabularnewline
99 & 0.998725659413976 & 0.00254868117204892 & 0.00127434058602446 \tabularnewline
100 & 0.998458073741717 & 0.00308385251656617 & 0.00154192625828309 \tabularnewline
101 & 0.997628304856938 & 0.00474339028612431 & 0.00237169514306216 \tabularnewline
102 & 0.99626837370814 & 0.0074632525837194 & 0.0037316262918597 \tabularnewline
103 & 0.994607469840064 & 0.0107850603198717 & 0.00539253015993586 \tabularnewline
104 & 0.9934555296114 & 0.0130889407771995 & 0.00654447038859973 \tabularnewline
105 & 0.990331575995802 & 0.0193368480083955 & 0.00966842400419773 \tabularnewline
106 & 0.988907770764546 & 0.0221844584709087 & 0.0110922292354543 \tabularnewline
107 & 0.984059052127093 & 0.0318818957458131 & 0.0159409478729066 \tabularnewline
108 & 0.977289394642145 & 0.0454212107157106 & 0.0227106053578553 \tabularnewline
109 & 0.969171070051139 & 0.061657859897721 & 0.0308289299488605 \tabularnewline
110 & 0.95876419125563 & 0.0824716174887402 & 0.0412358087443701 \tabularnewline
111 & 0.947774065925838 & 0.104451868148324 & 0.0522259340741622 \tabularnewline
112 & 0.975976700234434 & 0.0480465995311311 & 0.0240232997655656 \tabularnewline
113 & 0.972739536078963 & 0.0545209278420736 & 0.0272604639210368 \tabularnewline
114 & 0.964861320618424 & 0.0702773587631521 & 0.035138679381576 \tabularnewline
115 & 0.951973527278578 & 0.0960529454428434 & 0.0480264727214217 \tabularnewline
116 & 0.939239159288698 & 0.121521681422605 & 0.0607608407113023 \tabularnewline
117 & 0.91634198146783 & 0.16731603706434 & 0.0836580185321699 \tabularnewline
118 & 0.902683153090842 & 0.194633693818316 & 0.0973168469091582 \tabularnewline
119 & 0.968490759679278 & 0.0630184806414446 & 0.0315092403207223 \tabularnewline
120 & 0.952734412650303 & 0.0945311746993939 & 0.047265587349697 \tabularnewline
121 & 0.934527511955903 & 0.130944976088194 & 0.065472488044097 \tabularnewline
122 & 0.96868647490358 & 0.0626270501928392 & 0.0313135250964196 \tabularnewline
123 & 0.951237807557386 & 0.0975243848852289 & 0.0487621924426144 \tabularnewline
124 & 0.938383538375733 & 0.123232923248534 & 0.0616164616242672 \tabularnewline
125 & 0.919892454587027 & 0.160215090825947 & 0.0801075454129733 \tabularnewline
126 & 0.886636772909211 & 0.226726454181578 & 0.113363227090789 \tabularnewline
127 & 0.833715807490501 & 0.332568385018999 & 0.166284192509499 \tabularnewline
128 & 0.772854990861652 & 0.454290018276695 & 0.227145009138348 \tabularnewline
129 & 0.690953017373201 & 0.618093965253598 & 0.309046982626799 \tabularnewline
130 & 0.597689623312835 & 0.80462075337433 & 0.402310376687165 \tabularnewline
131 & 0.513846756851814 & 0.972306486296372 & 0.486153243148186 \tabularnewline
132 & 0.640982613447879 & 0.718034773104242 & 0.359017386552121 \tabularnewline
133 & 0.564683461262352 & 0.870633077475295 & 0.435316538737648 \tabularnewline
134 & 0.43234599771012 & 0.864691995420241 & 0.56765400228988 \tabularnewline
135 & 0.286878456515517 & 0.573756913031033 & 0.713121543484483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154911&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]9[/C][C]0.836046454961104[/C][C]0.327907090077792[/C][C]0.163953545038896[/C][/ROW]
[ROW][C]10[/C][C]0.801294696682391[/C][C]0.397410606635218[/C][C]0.198705303317609[/C][/ROW]
[ROW][C]11[/C][C]0.712126598582385[/C][C]0.575746802835229[/C][C]0.287873401417615[/C][/ROW]
[ROW][C]12[/C][C]0.693666122703567[/C][C]0.612667754592866[/C][C]0.306333877296433[/C][/ROW]
[ROW][C]13[/C][C]0.62926640690127[/C][C]0.741467186197461[/C][C]0.37073359309873[/C][/ROW]
[ROW][C]14[/C][C]0.863191619094473[/C][C]0.273616761811055[/C][C]0.136808380905527[/C][/ROW]
[ROW][C]15[/C][C]0.808968812427874[/C][C]0.382062375144253[/C][C]0.191031187572126[/C][/ROW]
[ROW][C]16[/C][C]0.739418592104055[/C][C]0.52116281579189[/C][C]0.260581407895945[/C][/ROW]
[ROW][C]17[/C][C]0.685123052376768[/C][C]0.629753895246465[/C][C]0.314876947623233[/C][/ROW]
[ROW][C]18[/C][C]0.692681135842208[/C][C]0.614637728315585[/C][C]0.307318864157792[/C][/ROW]
[ROW][C]19[/C][C]0.77059735090612[/C][C]0.458805298187761[/C][C]0.22940264909388[/C][/ROW]
[ROW][C]20[/C][C]0.825085547380027[/C][C]0.349828905239946[/C][C]0.174914452619973[/C][/ROW]
[ROW][C]21[/C][C]0.784680906579948[/C][C]0.430638186840105[/C][C]0.215319093420052[/C][/ROW]
[ROW][C]22[/C][C]0.759147855671871[/C][C]0.481704288656259[/C][C]0.240852144328129[/C][/ROW]
[ROW][C]23[/C][C]0.742986838883065[/C][C]0.51402632223387[/C][C]0.257013161116935[/C][/ROW]
[ROW][C]24[/C][C]0.69644259682727[/C][C]0.60711480634546[/C][C]0.30355740317273[/C][/ROW]
[ROW][C]25[/C][C]0.642761151669891[/C][C]0.714477696660218[/C][C]0.357238848330109[/C][/ROW]
[ROW][C]26[/C][C]0.595416322059201[/C][C]0.809167355881598[/C][C]0.404583677940799[/C][/ROW]
[ROW][C]27[/C][C]0.598395944882147[/C][C]0.803208110235706[/C][C]0.401604055117853[/C][/ROW]
[ROW][C]28[/C][C]0.5369610778753[/C][C]0.9260778442494[/C][C]0.4630389221247[/C][/ROW]
[ROW][C]29[/C][C]0.692414053675333[/C][C]0.615171892649335[/C][C]0.307585946324667[/C][/ROW]
[ROW][C]30[/C][C]0.647639878791056[/C][C]0.704720242417888[/C][C]0.352360121208944[/C][/ROW]
[ROW][C]31[/C][C]0.609935820875313[/C][C]0.780128358249375[/C][C]0.390064179124687[/C][/ROW]
[ROW][C]32[/C][C]0.826588097837047[/C][C]0.346823804325906[/C][C]0.173411902162953[/C][/ROW]
[ROW][C]33[/C][C]0.797457037758695[/C][C]0.40508592448261[/C][C]0.202542962241305[/C][/ROW]
[ROW][C]34[/C][C]0.790916016774091[/C][C]0.418167966451818[/C][C]0.209083983225909[/C][/ROW]
[ROW][C]35[/C][C]0.751231286004463[/C][C]0.497537427991074[/C][C]0.248768713995537[/C][/ROW]
[ROW][C]36[/C][C]0.739303070458379[/C][C]0.521393859083241[/C][C]0.260696929541621[/C][/ROW]
[ROW][C]37[/C][C]0.9877218347738[/C][C]0.0245563304524009[/C][C]0.0122781652262004[/C][/ROW]
[ROW][C]38[/C][C]0.984045061086841[/C][C]0.031909877826319[/C][C]0.0159549389131595[/C][/ROW]
[ROW][C]39[/C][C]0.981429969768294[/C][C]0.0371400604634129[/C][C]0.0185700302317065[/C][/ROW]
[ROW][C]40[/C][C]0.999997989871479[/C][C]4.02025704126672e-06[/C][C]2.01012852063336e-06[/C][/ROW]
[ROW][C]41[/C][C]0.999996763533937[/C][C]6.4729321258134e-06[/C][C]3.2364660629067e-06[/C][/ROW]
[ROW][C]42[/C][C]0.999997916473646[/C][C]4.16705270715391e-06[/C][C]2.08352635357695e-06[/C][/ROW]
[ROW][C]43[/C][C]0.999997066909063[/C][C]5.8661818744992e-06[/C][C]2.9330909372496e-06[/C][/ROW]
[ROW][C]44[/C][C]0.999996012879265[/C][C]7.974241469579e-06[/C][C]3.9871207347895e-06[/C][/ROW]
[ROW][C]45[/C][C]0.999993452084406[/C][C]1.30958311881264e-05[/C][C]6.54791559406321e-06[/C][/ROW]
[ROW][C]46[/C][C]0.999996142210435[/C][C]7.71557913045719e-06[/C][C]3.8577895652286e-06[/C][/ROW]
[ROW][C]47[/C][C]0.999993206225645[/C][C]1.35875487109213e-05[/C][C]6.79377435546064e-06[/C][/ROW]
[ROW][C]48[/C][C]0.999994013069032[/C][C]1.19738619351711e-05[/C][C]5.98693096758554e-06[/C][/ROW]
[ROW][C]49[/C][C]0.999992513318852[/C][C]1.49733622967412e-05[/C][C]7.48668114837062e-06[/C][/ROW]
[ROW][C]50[/C][C]0.999991906118251[/C][C]1.6187763498906e-05[/C][C]8.09388174945299e-06[/C][/ROW]
[ROW][C]51[/C][C]0.999986735876997[/C][C]2.65282460068346e-05[/C][C]1.32641230034173e-05[/C][/ROW]
[ROW][C]52[/C][C]0.999980954714828[/C][C]3.8090570344439e-05[/C][C]1.90452851722195e-05[/C][/ROW]
[ROW][C]53[/C][C]0.9999714999211[/C][C]5.70001577998169e-05[/C][C]2.85000788999085e-05[/C][/ROW]
[ROW][C]54[/C][C]0.999960130591081[/C][C]7.97388178382857e-05[/C][C]3.98694089191428e-05[/C][/ROW]
[ROW][C]55[/C][C]0.999936161493299[/C][C]0.000127677013402448[/C][C]6.38385067012242e-05[/C][/ROW]
[ROW][C]56[/C][C]0.999903229220037[/C][C]0.000193541559925401[/C][C]9.67707799627006e-05[/C][/ROW]
[ROW][C]57[/C][C]0.999881669959862[/C][C]0.000236660080276672[/C][C]0.000118330040138336[/C][/ROW]
[ROW][C]58[/C][C]0.999816097657869[/C][C]0.000367804684261929[/C][C]0.000183902342130964[/C][/ROW]
[ROW][C]59[/C][C]0.999721338455226[/C][C]0.000557323089547367[/C][C]0.000278661544773683[/C][/ROW]
[ROW][C]60[/C][C]0.999763559648389[/C][C]0.000472880703221823[/C][C]0.000236440351610911[/C][/ROW]
[ROW][C]61[/C][C]0.999718588276145[/C][C]0.000562823447709996[/C][C]0.000281411723854998[/C][/ROW]
[ROW][C]62[/C][C]0.999722581171883[/C][C]0.000554837656234577[/C][C]0.000277418828117288[/C][/ROW]
[ROW][C]63[/C][C]0.999614117550483[/C][C]0.000771764899034921[/C][C]0.000385882449517461[/C][/ROW]
[ROW][C]64[/C][C]0.999975603995484[/C][C]4.87920090310475e-05[/C][C]2.43960045155238e-05[/C][/ROW]
[ROW][C]65[/C][C]0.999960444466931[/C][C]7.91110661373665e-05[/C][C]3.95555330686833e-05[/C][/ROW]
[ROW][C]66[/C][C]0.999943503188398[/C][C]0.000112993623203845[/C][C]5.64968116019225e-05[/C][/ROW]
[ROW][C]67[/C][C]0.999947223491022[/C][C]0.000105553017956335[/C][C]5.27765089781673e-05[/C][/ROW]
[ROW][C]68[/C][C]0.999943539458787[/C][C]0.000112921082426823[/C][C]5.64605412134116e-05[/C][/ROW]
[ROW][C]69[/C][C]0.999919802234651[/C][C]0.0001603955306972[/C][C]8.01977653485999e-05[/C][/ROW]
[ROW][C]70[/C][C]0.999884184605069[/C][C]0.000231630789862789[/C][C]0.000115815394931395[/C][/ROW]
[ROW][C]71[/C][C]0.999852360200985[/C][C]0.000295279598030762[/C][C]0.000147639799015381[/C][/ROW]
[ROW][C]72[/C][C]0.999790025976708[/C][C]0.000419948046585046[/C][C]0.000209974023292523[/C][/ROW]
[ROW][C]73[/C][C]0.999998940346554[/C][C]2.11930689282875e-06[/C][C]1.05965344641437e-06[/C][/ROW]
[ROW][C]74[/C][C]0.99999835351054[/C][C]3.29297892035093e-06[/C][C]1.64648946017546e-06[/C][/ROW]
[ROW][C]75[/C][C]0.999997416425799[/C][C]5.16714840120595e-06[/C][C]2.58357420060298e-06[/C][/ROW]
[ROW][C]76[/C][C]0.999996902553518[/C][C]6.19489296316515e-06[/C][C]3.09744648158258e-06[/C][/ROW]
[ROW][C]77[/C][C]0.999994957344122[/C][C]1.00853117561885e-05[/C][C]5.04265587809425e-06[/C][/ROW]
[ROW][C]78[/C][C]0.999991282404836[/C][C]1.74351903269991e-05[/C][C]8.71759516349956e-06[/C][/ROW]
[ROW][C]79[/C][C]0.99998982445622[/C][C]2.03510875598459e-05[/C][C]1.01755437799229e-05[/C][/ROW]
[ROW][C]80[/C][C]0.999982397903276[/C][C]3.52041934469939e-05[/C][C]1.76020967234969e-05[/C][/ROW]
[ROW][C]81[/C][C]0.999970227598892[/C][C]5.95448022166856e-05[/C][C]2.97724011083428e-05[/C][/ROW]
[ROW][C]82[/C][C]0.99995450472984[/C][C]9.09905403200517e-05[/C][C]4.54952701600258e-05[/C][/ROW]
[ROW][C]83[/C][C]0.999931847391528[/C][C]0.000136305216944094[/C][C]6.8152608472047e-05[/C][/ROW]
[ROW][C]84[/C][C]0.999900634117958[/C][C]0.000198731764083923[/C][C]9.93658820419617e-05[/C][/ROW]
[ROW][C]85[/C][C]0.999879247833429[/C][C]0.000241504333142896[/C][C]0.000120752166571448[/C][/ROW]
[ROW][C]86[/C][C]0.999959581733669[/C][C]8.08365326619532e-05[/C][C]4.04182663309766e-05[/C][/ROW]
[ROW][C]87[/C][C]0.999948028797405[/C][C]0.000103942405189305[/C][C]5.19712025946523e-05[/C][/ROW]
[ROW][C]88[/C][C]0.999924903454642[/C][C]0.000150193090715088[/C][C]7.50965453575438e-05[/C][/ROW]
[ROW][C]89[/C][C]0.999896228422544[/C][C]0.000207543154911575[/C][C]0.000103771577455787[/C][/ROW]
[ROW][C]90[/C][C]0.999845895428268[/C][C]0.000308209143464355[/C][C]0.000154104571732177[/C][/ROW]
[ROW][C]91[/C][C]0.999787944103969[/C][C]0.000424111792061313[/C][C]0.000212055896030657[/C][/ROW]
[ROW][C]92[/C][C]0.999715041567283[/C][C]0.000569916865435029[/C][C]0.000284958432717514[/C][/ROW]
[ROW][C]93[/C][C]0.999541972069695[/C][C]0.000916055860610247[/C][C]0.000458027930305123[/C][/ROW]
[ROW][C]94[/C][C]0.999750364613675[/C][C]0.000499270772650886[/C][C]0.000249635386325443[/C][/ROW]
[ROW][C]95[/C][C]0.999798662321681[/C][C]0.000402675356637336[/C][C]0.000201337678318668[/C][/ROW]
[ROW][C]96[/C][C]0.999661735833963[/C][C]0.00067652833207391[/C][C]0.000338264166036955[/C][/ROW]
[ROW][C]97[/C][C]0.999439462152932[/C][C]0.00112107569413613[/C][C]0.000560537847068067[/C][/ROW]
[ROW][C]98[/C][C]0.999170331511473[/C][C]0.00165933697705375[/C][C]0.000829668488526874[/C][/ROW]
[ROW][C]99[/C][C]0.998725659413976[/C][C]0.00254868117204892[/C][C]0.00127434058602446[/C][/ROW]
[ROW][C]100[/C][C]0.998458073741717[/C][C]0.00308385251656617[/C][C]0.00154192625828309[/C][/ROW]
[ROW][C]101[/C][C]0.997628304856938[/C][C]0.00474339028612431[/C][C]0.00237169514306216[/C][/ROW]
[ROW][C]102[/C][C]0.99626837370814[/C][C]0.0074632525837194[/C][C]0.0037316262918597[/C][/ROW]
[ROW][C]103[/C][C]0.994607469840064[/C][C]0.0107850603198717[/C][C]0.00539253015993586[/C][/ROW]
[ROW][C]104[/C][C]0.9934555296114[/C][C]0.0130889407771995[/C][C]0.00654447038859973[/C][/ROW]
[ROW][C]105[/C][C]0.990331575995802[/C][C]0.0193368480083955[/C][C]0.00966842400419773[/C][/ROW]
[ROW][C]106[/C][C]0.988907770764546[/C][C]0.0221844584709087[/C][C]0.0110922292354543[/C][/ROW]
[ROW][C]107[/C][C]0.984059052127093[/C][C]0.0318818957458131[/C][C]0.0159409478729066[/C][/ROW]
[ROW][C]108[/C][C]0.977289394642145[/C][C]0.0454212107157106[/C][C]0.0227106053578553[/C][/ROW]
[ROW][C]109[/C][C]0.969171070051139[/C][C]0.061657859897721[/C][C]0.0308289299488605[/C][/ROW]
[ROW][C]110[/C][C]0.95876419125563[/C][C]0.0824716174887402[/C][C]0.0412358087443701[/C][/ROW]
[ROW][C]111[/C][C]0.947774065925838[/C][C]0.104451868148324[/C][C]0.0522259340741622[/C][/ROW]
[ROW][C]112[/C][C]0.975976700234434[/C][C]0.0480465995311311[/C][C]0.0240232997655656[/C][/ROW]
[ROW][C]113[/C][C]0.972739536078963[/C][C]0.0545209278420736[/C][C]0.0272604639210368[/C][/ROW]
[ROW][C]114[/C][C]0.964861320618424[/C][C]0.0702773587631521[/C][C]0.035138679381576[/C][/ROW]
[ROW][C]115[/C][C]0.951973527278578[/C][C]0.0960529454428434[/C][C]0.0480264727214217[/C][/ROW]
[ROW][C]116[/C][C]0.939239159288698[/C][C]0.121521681422605[/C][C]0.0607608407113023[/C][/ROW]
[ROW][C]117[/C][C]0.91634198146783[/C][C]0.16731603706434[/C][C]0.0836580185321699[/C][/ROW]
[ROW][C]118[/C][C]0.902683153090842[/C][C]0.194633693818316[/C][C]0.0973168469091582[/C][/ROW]
[ROW][C]119[/C][C]0.968490759679278[/C][C]0.0630184806414446[/C][C]0.0315092403207223[/C][/ROW]
[ROW][C]120[/C][C]0.952734412650303[/C][C]0.0945311746993939[/C][C]0.047265587349697[/C][/ROW]
[ROW][C]121[/C][C]0.934527511955903[/C][C]0.130944976088194[/C][C]0.065472488044097[/C][/ROW]
[ROW][C]122[/C][C]0.96868647490358[/C][C]0.0626270501928392[/C][C]0.0313135250964196[/C][/ROW]
[ROW][C]123[/C][C]0.951237807557386[/C][C]0.0975243848852289[/C][C]0.0487621924426144[/C][/ROW]
[ROW][C]124[/C][C]0.938383538375733[/C][C]0.123232923248534[/C][C]0.0616164616242672[/C][/ROW]
[ROW][C]125[/C][C]0.919892454587027[/C][C]0.160215090825947[/C][C]0.0801075454129733[/C][/ROW]
[ROW][C]126[/C][C]0.886636772909211[/C][C]0.226726454181578[/C][C]0.113363227090789[/C][/ROW]
[ROW][C]127[/C][C]0.833715807490501[/C][C]0.332568385018999[/C][C]0.166284192509499[/C][/ROW]
[ROW][C]128[/C][C]0.772854990861652[/C][C]0.454290018276695[/C][C]0.227145009138348[/C][/ROW]
[ROW][C]129[/C][C]0.690953017373201[/C][C]0.618093965253598[/C][C]0.309046982626799[/C][/ROW]
[ROW][C]130[/C][C]0.597689623312835[/C][C]0.80462075337433[/C][C]0.402310376687165[/C][/ROW]
[ROW][C]131[/C][C]0.513846756851814[/C][C]0.972306486296372[/C][C]0.486153243148186[/C][/ROW]
[ROW][C]132[/C][C]0.640982613447879[/C][C]0.718034773104242[/C][C]0.359017386552121[/C][/ROW]
[ROW][C]133[/C][C]0.564683461262352[/C][C]0.870633077475295[/C][C]0.435316538737648[/C][/ROW]
[ROW][C]134[/C][C]0.43234599771012[/C][C]0.864691995420241[/C][C]0.56765400228988[/C][/ROW]
[ROW][C]135[/C][C]0.286878456515517[/C][C]0.573756913031033[/C][C]0.713121543484483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154911&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154911&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8360464549611040.3279070900777920.163953545038896
100.8012946966823910.3974106066352180.198705303317609
110.7121265985823850.5757468028352290.287873401417615
120.6936661227035670.6126677545928660.306333877296433
130.629266406901270.7414671861974610.37073359309873
140.8631916190944730.2736167618110550.136808380905527
150.8089688124278740.3820623751442530.191031187572126
160.7394185921040550.521162815791890.260581407895945
170.6851230523767680.6297538952464650.314876947623233
180.6926811358422080.6146377283155850.307318864157792
190.770597350906120.4588052981877610.22940264909388
200.8250855473800270.3498289052399460.174914452619973
210.7846809065799480.4306381868401050.215319093420052
220.7591478556718710.4817042886562590.240852144328129
230.7429868388830650.514026322233870.257013161116935
240.696442596827270.607114806345460.30355740317273
250.6427611516698910.7144776966602180.357238848330109
260.5954163220592010.8091673558815980.404583677940799
270.5983959448821470.8032081102357060.401604055117853
280.53696107787530.92607784424940.4630389221247
290.6924140536753330.6151718926493350.307585946324667
300.6476398787910560.7047202424178880.352360121208944
310.6099358208753130.7801283582493750.390064179124687
320.8265880978370470.3468238043259060.173411902162953
330.7974570377586950.405085924482610.202542962241305
340.7909160167740910.4181679664518180.209083983225909
350.7512312860044630.4975374279910740.248768713995537
360.7393030704583790.5213938590832410.260696929541621
370.98772183477380.02455633045240090.0122781652262004
380.9840450610868410.0319098778263190.0159549389131595
390.9814299697682940.03714006046341290.0185700302317065
400.9999979898714794.02025704126672e-062.01012852063336e-06
410.9999967635339376.4729321258134e-063.2364660629067e-06
420.9999979164736464.16705270715391e-062.08352635357695e-06
430.9999970669090635.8661818744992e-062.9330909372496e-06
440.9999960128792657.974241469579e-063.9871207347895e-06
450.9999934520844061.30958311881264e-056.54791559406321e-06
460.9999961422104357.71557913045719e-063.8577895652286e-06
470.9999932062256451.35875487109213e-056.79377435546064e-06
480.9999940130690321.19738619351711e-055.98693096758554e-06
490.9999925133188521.49733622967412e-057.48668114837062e-06
500.9999919061182511.6187763498906e-058.09388174945299e-06
510.9999867358769972.65282460068346e-051.32641230034173e-05
520.9999809547148283.8090570344439e-051.90452851722195e-05
530.99997149992115.70001577998169e-052.85000788999085e-05
540.9999601305910817.97388178382857e-053.98694089191428e-05
550.9999361614932990.0001276770134024486.38385067012242e-05
560.9999032292200370.0001935415599254019.67707799627006e-05
570.9998816699598620.0002366600802766720.000118330040138336
580.9998160976578690.0003678046842619290.000183902342130964
590.9997213384552260.0005573230895473670.000278661544773683
600.9997635596483890.0004728807032218230.000236440351610911
610.9997185882761450.0005628234477099960.000281411723854998
620.9997225811718830.0005548376562345770.000277418828117288
630.9996141175504830.0007717648990349210.000385882449517461
640.9999756039954844.87920090310475e-052.43960045155238e-05
650.9999604444669317.91110661373665e-053.95555330686833e-05
660.9999435031883980.0001129936232038455.64968116019225e-05
670.9999472234910220.0001055530179563355.27765089781673e-05
680.9999435394587870.0001129210824268235.64605412134116e-05
690.9999198022346510.00016039553069728.01977653485999e-05
700.9998841846050690.0002316307898627890.000115815394931395
710.9998523602009850.0002952795980307620.000147639799015381
720.9997900259767080.0004199480465850460.000209974023292523
730.9999989403465542.11930689282875e-061.05965344641437e-06
740.999998353510543.29297892035093e-061.64648946017546e-06
750.9999974164257995.16714840120595e-062.58357420060298e-06
760.9999969025535186.19489296316515e-063.09744648158258e-06
770.9999949573441221.00853117561885e-055.04265587809425e-06
780.9999912824048361.74351903269991e-058.71759516349956e-06
790.999989824456222.03510875598459e-051.01755437799229e-05
800.9999823979032763.52041934469939e-051.76020967234969e-05
810.9999702275988925.95448022166856e-052.97724011083428e-05
820.999954504729849.09905403200517e-054.54952701600258e-05
830.9999318473915280.0001363052169440946.8152608472047e-05
840.9999006341179580.0001987317640839239.93658820419617e-05
850.9998792478334290.0002415043331428960.000120752166571448
860.9999595817336698.08365326619532e-054.04182663309766e-05
870.9999480287974050.0001039424051893055.19712025946523e-05
880.9999249034546420.0001501930907150887.50965453575438e-05
890.9998962284225440.0002075431549115750.000103771577455787
900.9998458954282680.0003082091434643550.000154104571732177
910.9997879441039690.0004241117920613130.000212055896030657
920.9997150415672830.0005699168654350290.000284958432717514
930.9995419720696950.0009160558606102470.000458027930305123
940.9997503646136750.0004992707726508860.000249635386325443
950.9997986623216810.0004026753566373360.000201337678318668
960.9996617358339630.000676528332073910.000338264166036955
970.9994394621529320.001121075694136130.000560537847068067
980.9991703315114730.001659336977053750.000829668488526874
990.9987256594139760.002548681172048920.00127434058602446
1000.9984580737417170.003083852516566170.00154192625828309
1010.9976283048569380.004743390286124310.00237169514306216
1020.996268373708140.00746325258371940.0037316262918597
1030.9946074698400640.01078506031987170.00539253015993586
1040.99345552961140.01308894077719950.00654447038859973
1050.9903315759958020.01933684800839550.00966842400419773
1060.9889077707645460.02218445847090870.0110922292354543
1070.9840590521270930.03188189574581310.0159409478729066
1080.9772893946421450.04542121071571060.0227106053578553
1090.9691710700511390.0616578598977210.0308289299488605
1100.958764191255630.08247161748874020.0412358087443701
1110.9477740659258380.1044518681483240.0522259340741622
1120.9759767002344340.04804659953113110.0240232997655656
1130.9727395360789630.05452092784207360.0272604639210368
1140.9648613206184240.07027735876315210.035138679381576
1150.9519735272785780.09605294544284340.0480264727214217
1160.9392391592886980.1215216814226050.0607608407113023
1170.916341981467830.167316037064340.0836580185321699
1180.9026831530908420.1946336938183160.0973168469091582
1190.9684907596792780.06301848064144460.0315092403207223
1200.9527344126503030.09453117469939390.047265587349697
1210.9345275119559030.1309449760881940.065472488044097
1220.968686474903580.06262705019283920.0313135250964196
1230.9512378075573860.09752438488522890.0487621924426144
1240.9383835383757330.1232329232485340.0616164616242672
1250.9198924545870270.1602150908259470.0801075454129733
1260.8866367729092110.2267264541815780.113363227090789
1270.8337158074905010.3325683850189990.166284192509499
1280.7728549908616520.4542900182766950.227145009138348
1290.6909530173732010.6180939652535980.309046982626799
1300.5976896233128350.804620753374330.402310376687165
1310.5138467568518140.9723064862963720.486153243148186
1320.6409826134478790.7180347731042420.359017386552121
1330.5646834612623520.8706330774752950.435316538737648
1340.432345997710120.8646919954202410.56765400228988
1350.2868784565155170.5737569130310330.713121543484483






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level630.496062992125984NOK
5% type I error level730.574803149606299NOK
10% type I error level820.645669291338583NOK

\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 & 63 & 0.496062992125984 & NOK \tabularnewline
5% type I error level & 73 & 0.574803149606299 & NOK \tabularnewline
10% type I error level & 82 & 0.645669291338583 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154911&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]63[/C][C]0.496062992125984[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]73[/C][C]0.574803149606299[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]82[/C][C]0.645669291338583[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154911&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154911&T=6

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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 level630.496062992125984NOK
5% type I error level730.574803149606299NOK
10% type I error level820.645669291338583NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = kendall ;
Parameters (R input):
par1 = 5 ; 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')
}