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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 03 Nov 2012 10:15:35 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/03/t1351952197x4ks855akx0s85f.htm/, Retrieved Thu, 06 Oct 2022 14:20:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185734, Retrieved Thu, 06 Oct 2022 14:20:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsHubert Liskiewicz 3
Estimated Impact94
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [OLS Hubert Liskie...] [2012-11-03 14:15:35] [acfd67cb214b61d0a5e0fb4c8c6ef02a] [Current]
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Dataseries X:
1	41	12	12	53	13
2	39	11	11	86	16
3	30	15	14	66	19
4	31	6	12	67	15
5	34	13	21	76	14
6	35	10	12	78	13
7	39	12	22	53	19
8	34	14	11	80	15
9	36	12	10	74	14
10	37	6	13	76	15
11	38	10	10	79	16
12	36	12	8	54	16
13	38	12	15	67	16
14	39	11	14	54	16
15	33	15	10	87	17
16	32	12	14	58	15
17	36	10	14	75	15
18	38	12	11	88	20
19	39	11	10	64	18
20	32	12	13	57	16
21	32	11	7	66	16
22	31	12	14	68	16
23	39	13	12	54	19
24	37	11	14	56	16
25	39	9	11	86	17
26	41	13	9	80	17
27	36	10	11	76	16
28	33	14	15	69	15
29	33	12	14	78	16
30	34	10	13	67	14
31	31	12	9	80	15
32	27	8	15	54	12
33	37	10	10	71	14
34	34	12	11	84	16
35	34	12	13	74	14
36	32	7	8	71	7
37	29	6	20	63	10
38	36	12	12	71	14
39	29	10	10	76	16
40	35	10	10	69	16
41	37	10	9	74	16
42	34	12	14	75	14
43	38	15	8	54	20
44	35	10	14	52	14
45	38	10	11	69	14
46	37	12	13	68	11
47	38	13	9	65	14
48	33	11	11	75	15
49	36	11	15	74	16
50	38	12	11	75	14
51	32	14	10	72	16
52	32	10	14	67	14
53	32	12	18	63	12
54	34	13	14	62	16
55	32	5	11	63	9
56	37	6	12	76	14
57	39	12	13	74	16
58	29	12	9	67	16
59	37	11	10	73	15
60	35	10	15	70	16
61	30	7	20	53	12
62	38	12	12	77	16
63	34	14	12	77	16
64	31	11	14	52	14
65	34	12	13	54	16
66	35	13	11	80	17
67	36	14	17	66	18
68	30	11	12	73	18
69	39	12	13	63	12
70	35	12	14	69	16
71	38	8	13	67	10
72	31	11	15	54	14
73	34	14	13	81	18
74	38	14	10	69	18
75	34	12	11	84	16
76	39	9	19	80	17
77	37	13	13	70	16
78	34	11	17	69	16
79	28	12	13	77	13
80	37	12	9	54	16
81	33	12	11	79	16
82	37	12	10	30	20
83	35	12	9	71	16
84	37	12	12	73	15
85	32	11	12	72	15
86	33	10	13	77	16
87	38	9	13	75	14
88	33	12	12	69	16
89	29	12	15	54	16
90	33	12	22	70	15
91	31	9	13	73	12
92	36	15	15	54	17
93	35	12	13	77	16
94	32	12	15	82	15
95	29	12	10	80	13
96	39	10	11	80	16
97	37	13	16	69	16
98	35	9	11	78	16
99	37	12	11	81	16
100	32	10	10	76	14
101	38	14	10	76	16
102	37	11	16	73	16
103	36	15	12	85	20
104	32	11	11	66	15
105	33	11	16	79	16
106	40	12	19	68	13
107	38	12	11	76	17
108	41	12	16	71	16
109	36	11	15	54	16
110	43	7	24	46	12
111	30	12	14	82	16
112	31	14	15	74	16
113	32	11	11	88	17
114	32	11	15	38	13
115	37	10	12	76	12
116	37	13	10	86	18
117	33	13	14	54	14
118	34	8	13	70	14
119	33	11	9	69	13
120	38	12	15	90	16
121	33	11	15	54	13
122	31	13	14	76	16
123	38	12	11	89	13
124	37	14	8	76	16
125	33	13	11	73	15
126	31	15	11	79	16
127	39	10	8	90	15
128	44	11	10	74	17
129	33	9	11	81	15
130	35	11	13	72	12
131	32	10	11	71	16
132	28	11	20	66	10
133	40	8	10	77	16
134	27	11	15	65	12
135	37	12	12	74	14
136	32	12	14	82	15
137	28	9	23	54	13
138	34	11	14	63	15
139	30	10	16	54	11
140	35	8	11	64	12
141	31	9	12	69	8
142	32	8	10	54	16
143	30	9	14	84	15
144	30	15	12	86	17
145	31	11	12	77	16
146	40	8	11	89	10
147	32	13	12	76	18
148	36	12	13	60	13
149	32	12	11	75	16
150	35	9	19	73	13
151	38	7	12	85	10
152	42	13	17	79	15
153	34	9	9	71	16
154	35	6	12	72	16
155	35	8	19	69	14
156	33	8	18	78	10
157	36	15	15	54	17
158	32	6	14	69	13
159	33	9	11	81	15
160	34	11	9	84	16
161	32	8	18	84	12
162	34	8	16	69	13




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185734&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185734&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185734&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 6.48873817201541 -0.00436018917442241t + 0.102753545911328Connected[t] + 0.531584117706553Software[t] -0.0888415627137958Depression[t] + 0.00750114279653458Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  6.48873817201541 -0.00436018917442241t +  0.102753545911328Connected[t] +  0.531584117706553Software[t] -0.0888415627137958Depression[t] +  0.00750114279653458Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185734&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  6.48873817201541 -0.00436018917442241t +  0.102753545911328Connected[t] +  0.531584117706553Software[t] -0.0888415627137958Depression[t] +  0.00750114279653458Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185734&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185734&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 6.48873817201541 -0.00436018917442241t + 0.102753545911328Connected[t] + 0.531584117706553Software[t] -0.0888415627137958Depression[t] + 0.00750114279653458Belonging[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.488738172015412.1547973.01130.0030350.001518
t-0.004360189174422410.003212-1.35750.1765710.088286
Connected0.1027535459113280.043572.35840.0195940.009797
Software0.5315841177065530.068867.719800
Depression-0.08884156271379580.048702-1.82420.0700390.03502
Belonging0.007501142796534580.0143090.52420.6008750.300437

\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) & 6.48873817201541 & 2.154797 & 3.0113 & 0.003035 & 0.001518 \tabularnewline
t & -0.00436018917442241 & 0.003212 & -1.3575 & 0.176571 & 0.088286 \tabularnewline
Connected & 0.102753545911328 & 0.04357 & 2.3584 & 0.019594 & 0.009797 \tabularnewline
Software & 0.531584117706553 & 0.06886 & 7.7198 & 0 & 0 \tabularnewline
Depression & -0.0888415627137958 & 0.048702 & -1.8242 & 0.070039 & 0.03502 \tabularnewline
Belonging & 0.00750114279653458 & 0.014309 & 0.5242 & 0.600875 & 0.300437 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185734&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]6.48873817201541[/C][C]2.154797[/C][C]3.0113[/C][C]0.003035[/C][C]0.001518[/C][/ROW]
[ROW][C]t[/C][C]-0.00436018917442241[/C][C]0.003212[/C][C]-1.3575[/C][C]0.176571[/C][C]0.088286[/C][/ROW]
[ROW][C]Connected[/C][C]0.102753545911328[/C][C]0.04357[/C][C]2.3584[/C][C]0.019594[/C][C]0.009797[/C][/ROW]
[ROW][C]Software[/C][C]0.531584117706553[/C][C]0.06886[/C][C]7.7198[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0888415627137958[/C][C]0.048702[/C][C]-1.8242[/C][C]0.070039[/C][C]0.03502[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00750114279653458[/C][C]0.014309[/C][C]0.5242[/C][C]0.600875[/C][C]0.300437[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185734&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185734&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.488738172015412.1547973.01130.0030350.001518
t-0.004360189174422410.003212-1.35750.1765710.088286
Connected0.1027535459113280.043572.35840.0195940.009797
Software0.5315841177065530.068867.719800
Depression-0.08884156271379580.048702-1.82420.0700390.03502
Belonging0.007501142796534580.0143090.52420.6008750.300437







Multiple Linear Regression - Regression Statistics
Multiple R0.598229781698141
R-squared0.357878871710605
Adjusted R-squared0.337298066316714
F-TEST (value)17.3889633987228
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value1.15907283770866e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83674329912701
Sum Squared Residuals526.285647714523

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.598229781698141 \tabularnewline
R-squared & 0.357878871710605 \tabularnewline
Adjusted R-squared & 0.337298066316714 \tabularnewline
F-TEST (value) & 17.3889633987228 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 1.15907283770866e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.83674329912701 \tabularnewline
Sum Squared Residuals & 526.285647714523 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185734&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.598229781698141[/C][/ROW]
[ROW][C]R-squared[/C][C]0.357878871710605[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.337298066316714[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.3889633987228[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]1.15907283770866e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.83674329912701[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]526.285647714523[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185734&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185734&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.598229781698141
R-squared0.357878871710605
Adjusted R-squared0.337298066316714
F-TEST (value)17.3889633987228
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value1.15907283770866e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83674329912701
Sum Squared Residuals526.285647714523







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.4077445933349-3.40774459333487
21616.0026724696306-0.00267246963065025
31916.78331929400842.21668070599159
41512.28263985961052.71736014038953
51415.5755653528605-1.57556535286054
61314.893782706495-1.89378270649502
71915.28766073932773.71233926067231
81517.0124891013679-2.01248910136793
91416.1943024745376-2.19430247453764
101512.85166872248692.14833127751309
111615.3654266665810.634573333418978
121616.2088821765113-0.208882176511276
131615.88565299651790.114347003482112
141615.44378894190710.556211058092913
151717.5521479112317-0.552147911231733
161515.2773824310716-0.277382431071638
171514.74838761767050.251612382329491
182016.37674230022823.62325769977182
191815.85236567485552.1476343251445
201615.34128209429120.65871790570879
211615.40589744886180.594102551138179
221615.22347917807910.776520821920878
231916.6453986001782.35460139982202
241615.20968224393330.790317756066723
251714.83921988320582.16078011679417
261717.2993795253287-0.299379525328657
271614.97881155686421.02118844313579
281516.3846529503511-1.38465295035109
291615.47347637364620.526523626353834
301414.5150304869219-0.515030486921882
311515.7184590026367-0.718459002636714
321212.4486690699981-0.448669069998096
331415.1067398164601-1.10673981646012
341615.8659605186060.134039481394023
351415.6089057760386-1.60890577603862
36713.1628222916881-6.16282229168815
371011.1925094521354-1.19250945213537
381415.8676704346622-1.8676704346622
391614.29605602810561.70394397189436
401614.85570911482341.14429088517656
411615.18320329416810.816796705831856
421415.4970440319004-1.4970440319004
432017.87397575704652.12602424295351
441414.3553826797295-0.355382679729482
451415.0533272439715-1.05332724397152
461115.8241974760747-4.82419747607475
471416.7870377729838-2.78703777298378
481515.1030699213774-0.103069921377372
491615.04410297628520.955897023714785
501416.1397013902917-2.13970139029172
511616.6483262953866-0.648326295386627
521414.1247576705481-0.124757670548137
531214.7981948947455-2.7981948947455
541615.87879102315890.121208976841066
55911.6902766314474-2.69027663144736
561412.73994158317731.26005841682272
571616.026749343758-0.0267493437579635
581615.29771194674970.702288053250296
591515.5399613012248-0.539961301224763
601614.33179866056251.66820133943745
611211.64719114760180.352808852398237
621616.0135398430779-0.0135398430779232
631616.6613337056713-0.661333705671295
641414.3887488303023-0.388748830302275
651615.32807724487530.671922755124746
661716.33076755745620.669232442543797
671816.32267965646541.6773203435346
681814.60376165184813.39623834815193
691215.891914502903-3.89191450290301
701615.43270542414870.567294575851308
711013.6841086790028-3.68410867900277
721414.2800280397862-0.280028039786169
731816.55889482239941.44110517760059
741817.14205979145330.857940208546725
751615.68719276245470.312807237545342
761713.86111087682073.13888912317929
771616.2356180149673-0.235618014967274
781614.4969615589941.50303844100596
791314.8230396052857-1.82303960528567
801615.92630129584810.0736987041519161
811615.52077236751410.479227632485877
822015.64871192766864.35128807233139
831615.83523306404320.164766935956751
841515.7848575641432-0.784857564143164
851514.7276443849090.272355615090985
861614.24311777520821.75688222479176
871414.2059389122908-0.20593891229084
881615.3263980526140.673601947385975
891614.53198184970491.46801815029511
901514.43676318992380.563236810076243
911213.4542210486208-1.45422104862079
921716.83292845668060.167071543319429
931615.48127177822310.518728221776951
941515.0284735398697-0.0284735398697246
951315.1450582409372-2.14505824093723
961615.01622371274920.983776287250817
971615.87438840054090.125611599459096
981614.04990274745541.95009725254459
991615.86830543161290.1316945683871
1001414.3383451261999-0.338345126199856
1011617.0768426833196-1.07684268331961
1021614.81942379044181.18057620955818
1032017.28402649059592.71597350940412
1041514.68863549652960.311364503470422
1051614.44033589605251.55966410394755
1061315.3377973870606-2.33779738706061
1071715.89867175014621.10132824985382
1081615.72085867115410.279141328845915
1091614.63246876988921.36753123011082
1101212.3614637244714-0.361463724471401
1111614.83768479479571.16231520520432
1121615.85039568185960.149604318140378
1131714.81441893548352.18558106451646
1141314.0796353556272-1.0796353556272
1151214.6090268927126-2.60902689271257
1161816.45211361005071.54788638994926
1171415.4413364168867-1.44133641688672
1181413.09066903254920.909330967450792
1191314.9261727586418-1.92617275864176
1201615.5916390391750.408360960825015
1211314.2718858620621-1.27188586206213
1221615.37905352071570.620946479284289
1231315.9264235797104-2.92642357971037
1241617.0514879118242-1.05148791182416
1251515.8155013047669-0.815501304766882
1261616.7138091159621-0.713809115962118
1271515.2225939644488-0.222593964448821
1281715.96588421236541.03411578763455
1291513.73173321961531.26826678038474
1301214.7508549470802-2.75085494708019
1311614.07683198509631.92316801490371
1321013.3559619515763-3.35596195157628
1331613.9608201581182.03917984188203
1341213.6811946980885-1.68119469808855
1351415.5699890590442-1.56998905904416
1361514.93418715725780.0658128427422208
1371311.91445436859131.08554563140874
1381514.45686803989090.54313196010912
1391113.2647161387682-2.26471613876819
1401213.2301746852716-1.23017468527163
141813.2950485814273-5.29504858142732
1421612.92702380393723.07297619606275
1431513.11840867368761.88159132631242
1441716.49623860177310.503761398226868
1451614.4007852025151.59921479748498
1461013.9053098496951-3.9053098496951
1471815.55048546269412.44951453730593
1481315.2166954920001-2.21669549200006
1491615.09152138655590.908478613444067
1501313.0749346946924-0.0749346946924006
1511013.0275715603938-3.02757156039384
1521516.1345155907559-1.13451559075586
1531613.83251392280272.1674860771973
1541612.07713138107513.92286861892491
1551412.49154505992761.5084549400724
1561012.4380296268131-2.43802962681313
1571716.54951616034310.450483839656886
1581311.55124343282621.44875656717378
1591513.60092754438261.39907245561741
1601614.96267569034981.03732430965021
1611212.3584819918089-0.358481991808894
1621312.62479487793670.3752051220633

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.4077445933349 & -3.40774459333487 \tabularnewline
2 & 16 & 16.0026724696306 & -0.00267246963065025 \tabularnewline
3 & 19 & 16.7833192940084 & 2.21668070599159 \tabularnewline
4 & 15 & 12.2826398596105 & 2.71736014038953 \tabularnewline
5 & 14 & 15.5755653528605 & -1.57556535286054 \tabularnewline
6 & 13 & 14.893782706495 & -1.89378270649502 \tabularnewline
7 & 19 & 15.2876607393277 & 3.71233926067231 \tabularnewline
8 & 15 & 17.0124891013679 & -2.01248910136793 \tabularnewline
9 & 14 & 16.1943024745376 & -2.19430247453764 \tabularnewline
10 & 15 & 12.8516687224869 & 2.14833127751309 \tabularnewline
11 & 16 & 15.365426666581 & 0.634573333418978 \tabularnewline
12 & 16 & 16.2088821765113 & -0.208882176511276 \tabularnewline
13 & 16 & 15.8856529965179 & 0.114347003482112 \tabularnewline
14 & 16 & 15.4437889419071 & 0.556211058092913 \tabularnewline
15 & 17 & 17.5521479112317 & -0.552147911231733 \tabularnewline
16 & 15 & 15.2773824310716 & -0.277382431071638 \tabularnewline
17 & 15 & 14.7483876176705 & 0.251612382329491 \tabularnewline
18 & 20 & 16.3767423002282 & 3.62325769977182 \tabularnewline
19 & 18 & 15.8523656748555 & 2.1476343251445 \tabularnewline
20 & 16 & 15.3412820942912 & 0.65871790570879 \tabularnewline
21 & 16 & 15.4058974488618 & 0.594102551138179 \tabularnewline
22 & 16 & 15.2234791780791 & 0.776520821920878 \tabularnewline
23 & 19 & 16.645398600178 & 2.35460139982202 \tabularnewline
24 & 16 & 15.2096822439333 & 0.790317756066723 \tabularnewline
25 & 17 & 14.8392198832058 & 2.16078011679417 \tabularnewline
26 & 17 & 17.2993795253287 & -0.299379525328657 \tabularnewline
27 & 16 & 14.9788115568642 & 1.02118844313579 \tabularnewline
28 & 15 & 16.3846529503511 & -1.38465295035109 \tabularnewline
29 & 16 & 15.4734763736462 & 0.526523626353834 \tabularnewline
30 & 14 & 14.5150304869219 & -0.515030486921882 \tabularnewline
31 & 15 & 15.7184590026367 & -0.718459002636714 \tabularnewline
32 & 12 & 12.4486690699981 & -0.448669069998096 \tabularnewline
33 & 14 & 15.1067398164601 & -1.10673981646012 \tabularnewline
34 & 16 & 15.865960518606 & 0.134039481394023 \tabularnewline
35 & 14 & 15.6089057760386 & -1.60890577603862 \tabularnewline
36 & 7 & 13.1628222916881 & -6.16282229168815 \tabularnewline
37 & 10 & 11.1925094521354 & -1.19250945213537 \tabularnewline
38 & 14 & 15.8676704346622 & -1.8676704346622 \tabularnewline
39 & 16 & 14.2960560281056 & 1.70394397189436 \tabularnewline
40 & 16 & 14.8557091148234 & 1.14429088517656 \tabularnewline
41 & 16 & 15.1832032941681 & 0.816796705831856 \tabularnewline
42 & 14 & 15.4970440319004 & -1.4970440319004 \tabularnewline
43 & 20 & 17.8739757570465 & 2.12602424295351 \tabularnewline
44 & 14 & 14.3553826797295 & -0.355382679729482 \tabularnewline
45 & 14 & 15.0533272439715 & -1.05332724397152 \tabularnewline
46 & 11 & 15.8241974760747 & -4.82419747607475 \tabularnewline
47 & 14 & 16.7870377729838 & -2.78703777298378 \tabularnewline
48 & 15 & 15.1030699213774 & -0.103069921377372 \tabularnewline
49 & 16 & 15.0441029762852 & 0.955897023714785 \tabularnewline
50 & 14 & 16.1397013902917 & -2.13970139029172 \tabularnewline
51 & 16 & 16.6483262953866 & -0.648326295386627 \tabularnewline
52 & 14 & 14.1247576705481 & -0.124757670548137 \tabularnewline
53 & 12 & 14.7981948947455 & -2.7981948947455 \tabularnewline
54 & 16 & 15.8787910231589 & 0.121208976841066 \tabularnewline
55 & 9 & 11.6902766314474 & -2.69027663144736 \tabularnewline
56 & 14 & 12.7399415831773 & 1.26005841682272 \tabularnewline
57 & 16 & 16.026749343758 & -0.0267493437579635 \tabularnewline
58 & 16 & 15.2977119467497 & 0.702288053250296 \tabularnewline
59 & 15 & 15.5399613012248 & -0.539961301224763 \tabularnewline
60 & 16 & 14.3317986605625 & 1.66820133943745 \tabularnewline
61 & 12 & 11.6471911476018 & 0.352808852398237 \tabularnewline
62 & 16 & 16.0135398430779 & -0.0135398430779232 \tabularnewline
63 & 16 & 16.6613337056713 & -0.661333705671295 \tabularnewline
64 & 14 & 14.3887488303023 & -0.388748830302275 \tabularnewline
65 & 16 & 15.3280772448753 & 0.671922755124746 \tabularnewline
66 & 17 & 16.3307675574562 & 0.669232442543797 \tabularnewline
67 & 18 & 16.3226796564654 & 1.6773203435346 \tabularnewline
68 & 18 & 14.6037616518481 & 3.39623834815193 \tabularnewline
69 & 12 & 15.891914502903 & -3.89191450290301 \tabularnewline
70 & 16 & 15.4327054241487 & 0.567294575851308 \tabularnewline
71 & 10 & 13.6841086790028 & -3.68410867900277 \tabularnewline
72 & 14 & 14.2800280397862 & -0.280028039786169 \tabularnewline
73 & 18 & 16.5588948223994 & 1.44110517760059 \tabularnewline
74 & 18 & 17.1420597914533 & 0.857940208546725 \tabularnewline
75 & 16 & 15.6871927624547 & 0.312807237545342 \tabularnewline
76 & 17 & 13.8611108768207 & 3.13888912317929 \tabularnewline
77 & 16 & 16.2356180149673 & -0.235618014967274 \tabularnewline
78 & 16 & 14.496961558994 & 1.50303844100596 \tabularnewline
79 & 13 & 14.8230396052857 & -1.82303960528567 \tabularnewline
80 & 16 & 15.9263012958481 & 0.0736987041519161 \tabularnewline
81 & 16 & 15.5207723675141 & 0.479227632485877 \tabularnewline
82 & 20 & 15.6487119276686 & 4.35128807233139 \tabularnewline
83 & 16 & 15.8352330640432 & 0.164766935956751 \tabularnewline
84 & 15 & 15.7848575641432 & -0.784857564143164 \tabularnewline
85 & 15 & 14.727644384909 & 0.272355615090985 \tabularnewline
86 & 16 & 14.2431177752082 & 1.75688222479176 \tabularnewline
87 & 14 & 14.2059389122908 & -0.20593891229084 \tabularnewline
88 & 16 & 15.326398052614 & 0.673601947385975 \tabularnewline
89 & 16 & 14.5319818497049 & 1.46801815029511 \tabularnewline
90 & 15 & 14.4367631899238 & 0.563236810076243 \tabularnewline
91 & 12 & 13.4542210486208 & -1.45422104862079 \tabularnewline
92 & 17 & 16.8329284566806 & 0.167071543319429 \tabularnewline
93 & 16 & 15.4812717782231 & 0.518728221776951 \tabularnewline
94 & 15 & 15.0284735398697 & -0.0284735398697246 \tabularnewline
95 & 13 & 15.1450582409372 & -2.14505824093723 \tabularnewline
96 & 16 & 15.0162237127492 & 0.983776287250817 \tabularnewline
97 & 16 & 15.8743884005409 & 0.125611599459096 \tabularnewline
98 & 16 & 14.0499027474554 & 1.95009725254459 \tabularnewline
99 & 16 & 15.8683054316129 & 0.1316945683871 \tabularnewline
100 & 14 & 14.3383451261999 & -0.338345126199856 \tabularnewline
101 & 16 & 17.0768426833196 & -1.07684268331961 \tabularnewline
102 & 16 & 14.8194237904418 & 1.18057620955818 \tabularnewline
103 & 20 & 17.2840264905959 & 2.71597350940412 \tabularnewline
104 & 15 & 14.6886354965296 & 0.311364503470422 \tabularnewline
105 & 16 & 14.4403358960525 & 1.55966410394755 \tabularnewline
106 & 13 & 15.3377973870606 & -2.33779738706061 \tabularnewline
107 & 17 & 15.8986717501462 & 1.10132824985382 \tabularnewline
108 & 16 & 15.7208586711541 & 0.279141328845915 \tabularnewline
109 & 16 & 14.6324687698892 & 1.36753123011082 \tabularnewline
110 & 12 & 12.3614637244714 & -0.361463724471401 \tabularnewline
111 & 16 & 14.8376847947957 & 1.16231520520432 \tabularnewline
112 & 16 & 15.8503956818596 & 0.149604318140378 \tabularnewline
113 & 17 & 14.8144189354835 & 2.18558106451646 \tabularnewline
114 & 13 & 14.0796353556272 & -1.0796353556272 \tabularnewline
115 & 12 & 14.6090268927126 & -2.60902689271257 \tabularnewline
116 & 18 & 16.4521136100507 & 1.54788638994926 \tabularnewline
117 & 14 & 15.4413364168867 & -1.44133641688672 \tabularnewline
118 & 14 & 13.0906690325492 & 0.909330967450792 \tabularnewline
119 & 13 & 14.9261727586418 & -1.92617275864176 \tabularnewline
120 & 16 & 15.591639039175 & 0.408360960825015 \tabularnewline
121 & 13 & 14.2718858620621 & -1.27188586206213 \tabularnewline
122 & 16 & 15.3790535207157 & 0.620946479284289 \tabularnewline
123 & 13 & 15.9264235797104 & -2.92642357971037 \tabularnewline
124 & 16 & 17.0514879118242 & -1.05148791182416 \tabularnewline
125 & 15 & 15.8155013047669 & -0.815501304766882 \tabularnewline
126 & 16 & 16.7138091159621 & -0.713809115962118 \tabularnewline
127 & 15 & 15.2225939644488 & -0.222593964448821 \tabularnewline
128 & 17 & 15.9658842123654 & 1.03411578763455 \tabularnewline
129 & 15 & 13.7317332196153 & 1.26826678038474 \tabularnewline
130 & 12 & 14.7508549470802 & -2.75085494708019 \tabularnewline
131 & 16 & 14.0768319850963 & 1.92316801490371 \tabularnewline
132 & 10 & 13.3559619515763 & -3.35596195157628 \tabularnewline
133 & 16 & 13.960820158118 & 2.03917984188203 \tabularnewline
134 & 12 & 13.6811946980885 & -1.68119469808855 \tabularnewline
135 & 14 & 15.5699890590442 & -1.56998905904416 \tabularnewline
136 & 15 & 14.9341871572578 & 0.0658128427422208 \tabularnewline
137 & 13 & 11.9144543685913 & 1.08554563140874 \tabularnewline
138 & 15 & 14.4568680398909 & 0.54313196010912 \tabularnewline
139 & 11 & 13.2647161387682 & -2.26471613876819 \tabularnewline
140 & 12 & 13.2301746852716 & -1.23017468527163 \tabularnewline
141 & 8 & 13.2950485814273 & -5.29504858142732 \tabularnewline
142 & 16 & 12.9270238039372 & 3.07297619606275 \tabularnewline
143 & 15 & 13.1184086736876 & 1.88159132631242 \tabularnewline
144 & 17 & 16.4962386017731 & 0.503761398226868 \tabularnewline
145 & 16 & 14.400785202515 & 1.59921479748498 \tabularnewline
146 & 10 & 13.9053098496951 & -3.9053098496951 \tabularnewline
147 & 18 & 15.5504854626941 & 2.44951453730593 \tabularnewline
148 & 13 & 15.2166954920001 & -2.21669549200006 \tabularnewline
149 & 16 & 15.0915213865559 & 0.908478613444067 \tabularnewline
150 & 13 & 13.0749346946924 & -0.0749346946924006 \tabularnewline
151 & 10 & 13.0275715603938 & -3.02757156039384 \tabularnewline
152 & 15 & 16.1345155907559 & -1.13451559075586 \tabularnewline
153 & 16 & 13.8325139228027 & 2.1674860771973 \tabularnewline
154 & 16 & 12.0771313810751 & 3.92286861892491 \tabularnewline
155 & 14 & 12.4915450599276 & 1.5084549400724 \tabularnewline
156 & 10 & 12.4380296268131 & -2.43802962681313 \tabularnewline
157 & 17 & 16.5495161603431 & 0.450483839656886 \tabularnewline
158 & 13 & 11.5512434328262 & 1.44875656717378 \tabularnewline
159 & 15 & 13.6009275443826 & 1.39907245561741 \tabularnewline
160 & 16 & 14.9626756903498 & 1.03732430965021 \tabularnewline
161 & 12 & 12.3584819918089 & -0.358481991808894 \tabularnewline
162 & 13 & 12.6247948779367 & 0.3752051220633 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185734&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]13[/C][C]16.4077445933349[/C][C]-3.40774459333487[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.0026724696306[/C][C]-0.00267246963065025[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.7833192940084[/C][C]2.21668070599159[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.2826398596105[/C][C]2.71736014038953[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.5755653528605[/C][C]-1.57556535286054[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.893782706495[/C][C]-1.89378270649502[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.2876607393277[/C][C]3.71233926067231[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]17.0124891013679[/C][C]-2.01248910136793[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1943024745376[/C][C]-2.19430247453764[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.8516687224869[/C][C]2.14833127751309[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.365426666581[/C][C]0.634573333418978[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.2088821765113[/C][C]-0.208882176511276[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.8856529965179[/C][C]0.114347003482112[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4437889419071[/C][C]0.556211058092913[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.5521479112317[/C][C]-0.552147911231733[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2773824310716[/C][C]-0.277382431071638[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.7483876176705[/C][C]0.251612382329491[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3767423002282[/C][C]3.62325769977182[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.8523656748555[/C][C]2.1476343251445[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3412820942912[/C][C]0.65871790570879[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.4058974488618[/C][C]0.594102551138179[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.2234791780791[/C][C]0.776520821920878[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.645398600178[/C][C]2.35460139982202[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.2096822439333[/C][C]0.790317756066723[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.8392198832058[/C][C]2.16078011679417[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.2993795253287[/C][C]-0.299379525328657[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9788115568642[/C][C]1.02118844313579[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.3846529503511[/C][C]-1.38465295035109[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4734763736462[/C][C]0.526523626353834[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.5150304869219[/C][C]-0.515030486921882[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7184590026367[/C][C]-0.718459002636714[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4486690699981[/C][C]-0.448669069998096[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1067398164601[/C][C]-1.10673981646012[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.865960518606[/C][C]0.134039481394023[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6089057760386[/C][C]-1.60890577603862[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1628222916881[/C][C]-6.16282229168815[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.1925094521354[/C][C]-1.19250945213537[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8676704346622[/C][C]-1.8676704346622[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2960560281056[/C][C]1.70394397189436[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.8557091148234[/C][C]1.14429088517656[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1832032941681[/C][C]0.816796705831856[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.4970440319004[/C][C]-1.4970440319004[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.8739757570465[/C][C]2.12602424295351[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.3553826797295[/C][C]-0.355382679729482[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0533272439715[/C][C]-1.05332724397152[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.8241974760747[/C][C]-4.82419747607475[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.7870377729838[/C][C]-2.78703777298378[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1030699213774[/C][C]-0.103069921377372[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.0441029762852[/C][C]0.955897023714785[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.1397013902917[/C][C]-2.13970139029172[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6483262953866[/C][C]-0.648326295386627[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1247576705481[/C][C]-0.124757670548137[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.7981948947455[/C][C]-2.7981948947455[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.8787910231589[/C][C]0.121208976841066[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.6902766314474[/C][C]-2.69027663144736[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.7399415831773[/C][C]1.26005841682272[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.026749343758[/C][C]-0.0267493437579635[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.2977119467497[/C][C]0.702288053250296[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.5399613012248[/C][C]-0.539961301224763[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.3317986605625[/C][C]1.66820133943745[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.6471911476018[/C][C]0.352808852398237[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.0135398430779[/C][C]-0.0135398430779232[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.6613337056713[/C][C]-0.661333705671295[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.3887488303023[/C][C]-0.388748830302275[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.3280772448753[/C][C]0.671922755124746[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.3307675574562[/C][C]0.669232442543797[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.3226796564654[/C][C]1.6773203435346[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.6037616518481[/C][C]3.39623834815193[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.891914502903[/C][C]-3.89191450290301[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4327054241487[/C][C]0.567294575851308[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.6841086790028[/C][C]-3.68410867900277[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.2800280397862[/C][C]-0.280028039786169[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5588948223994[/C][C]1.44110517760059[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1420597914533[/C][C]0.857940208546725[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6871927624547[/C][C]0.312807237545342[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8611108768207[/C][C]3.13888912317929[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.2356180149673[/C][C]-0.235618014967274[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.496961558994[/C][C]1.50303844100596[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8230396052857[/C][C]-1.82303960528567[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.9263012958481[/C][C]0.0736987041519161[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5207723675141[/C][C]0.479227632485877[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.6487119276686[/C][C]4.35128807233139[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.8352330640432[/C][C]0.164766935956751[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.7848575641432[/C][C]-0.784857564143164[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.727644384909[/C][C]0.272355615090985[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2431177752082[/C][C]1.75688222479176[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.2059389122908[/C][C]-0.20593891229084[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.326398052614[/C][C]0.673601947385975[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5319818497049[/C][C]1.46801815029511[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.4367631899238[/C][C]0.563236810076243[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4542210486208[/C][C]-1.45422104862079[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.8329284566806[/C][C]0.167071543319429[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4812717782231[/C][C]0.518728221776951[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.0284735398697[/C][C]-0.0284735398697246[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1450582409372[/C][C]-2.14505824093723[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0162237127492[/C][C]0.983776287250817[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8743884005409[/C][C]0.125611599459096[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.0499027474554[/C][C]1.95009725254459[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.8683054316129[/C][C]0.1316945683871[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.3383451261999[/C][C]-0.338345126199856[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.0768426833196[/C][C]-1.07684268331961[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8194237904418[/C][C]1.18057620955818[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.2840264905959[/C][C]2.71597350940412[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.6886354965296[/C][C]0.311364503470422[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.4403358960525[/C][C]1.55966410394755[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3377973870606[/C][C]-2.33779738706061[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.8986717501462[/C][C]1.10132824985382[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.7208586711541[/C][C]0.279141328845915[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.6324687698892[/C][C]1.36753123011082[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3614637244714[/C][C]-0.361463724471401[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.8376847947957[/C][C]1.16231520520432[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8503956818596[/C][C]0.149604318140378[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.8144189354835[/C][C]2.18558106451646[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.0796353556272[/C][C]-1.0796353556272[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.6090268927126[/C][C]-2.60902689271257[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.4521136100507[/C][C]1.54788638994926[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.4413364168867[/C][C]-1.44133641688672[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0906690325492[/C][C]0.909330967450792[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9261727586418[/C][C]-1.92617275864176[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.591639039175[/C][C]0.408360960825015[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.2718858620621[/C][C]-1.27188586206213[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.3790535207157[/C][C]0.620946479284289[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.9264235797104[/C][C]-2.92642357971037[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.0514879118242[/C][C]-1.05148791182416[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8155013047669[/C][C]-0.815501304766882[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.7138091159621[/C][C]-0.713809115962118[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.2225939644488[/C][C]-0.222593964448821[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.9658842123654[/C][C]1.03411578763455[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.7317332196153[/C][C]1.26826678038474[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.7508549470802[/C][C]-2.75085494708019[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0768319850963[/C][C]1.92316801490371[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3559619515763[/C][C]-3.35596195157628[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.960820158118[/C][C]2.03917984188203[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.6811946980885[/C][C]-1.68119469808855[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.5699890590442[/C][C]-1.56998905904416[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.9341871572578[/C][C]0.0658128427422208[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]11.9144543685913[/C][C]1.08554563140874[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.4568680398909[/C][C]0.54313196010912[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.2647161387682[/C][C]-2.26471613876819[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.2301746852716[/C][C]-1.23017468527163[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2950485814273[/C][C]-5.29504858142732[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.9270238039372[/C][C]3.07297619606275[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.1184086736876[/C][C]1.88159132631242[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.4962386017731[/C][C]0.503761398226868[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.400785202515[/C][C]1.59921479748498[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.9053098496951[/C][C]-3.9053098496951[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.5504854626941[/C][C]2.44951453730593[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.2166954920001[/C][C]-2.21669549200006[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.0915213865559[/C][C]0.908478613444067[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.0749346946924[/C][C]-0.0749346946924006[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.0275715603938[/C][C]-3.02757156039384[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.1345155907559[/C][C]-1.13451559075586[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8325139228027[/C][C]2.1674860771973[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.0771313810751[/C][C]3.92286861892491[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.4915450599276[/C][C]1.5084549400724[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.4380296268131[/C][C]-2.43802962681313[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.5495161603431[/C][C]0.450483839656886[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.5512434328262[/C][C]1.44875656717378[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.6009275443826[/C][C]1.39907245561741[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.9626756903498[/C][C]1.03732430965021[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.3584819918089[/C][C]-0.358481991808894[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6247948779367[/C][C]0.3752051220633[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185734&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185734&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.4077445933349-3.40774459333487
21616.0026724696306-0.00267246963065025
31916.78331929400842.21668070599159
41512.28263985961052.71736014038953
51415.5755653528605-1.57556535286054
61314.893782706495-1.89378270649502
71915.28766073932773.71233926067231
81517.0124891013679-2.01248910136793
91416.1943024745376-2.19430247453764
101512.85166872248692.14833127751309
111615.3654266665810.634573333418978
121616.2088821765113-0.208882176511276
131615.88565299651790.114347003482112
141615.44378894190710.556211058092913
151717.5521479112317-0.552147911231733
161515.2773824310716-0.277382431071638
171514.74838761767050.251612382329491
182016.37674230022823.62325769977182
191815.85236567485552.1476343251445
201615.34128209429120.65871790570879
211615.40589744886180.594102551138179
221615.22347917807910.776520821920878
231916.6453986001782.35460139982202
241615.20968224393330.790317756066723
251714.83921988320582.16078011679417
261717.2993795253287-0.299379525328657
271614.97881155686421.02118844313579
281516.3846529503511-1.38465295035109
291615.47347637364620.526523626353834
301414.5150304869219-0.515030486921882
311515.7184590026367-0.718459002636714
321212.4486690699981-0.448669069998096
331415.1067398164601-1.10673981646012
341615.8659605186060.134039481394023
351415.6089057760386-1.60890577603862
36713.1628222916881-6.16282229168815
371011.1925094521354-1.19250945213537
381415.8676704346622-1.8676704346622
391614.29605602810561.70394397189436
401614.85570911482341.14429088517656
411615.18320329416810.816796705831856
421415.4970440319004-1.4970440319004
432017.87397575704652.12602424295351
441414.3553826797295-0.355382679729482
451415.0533272439715-1.05332724397152
461115.8241974760747-4.82419747607475
471416.7870377729838-2.78703777298378
481515.1030699213774-0.103069921377372
491615.04410297628520.955897023714785
501416.1397013902917-2.13970139029172
511616.6483262953866-0.648326295386627
521414.1247576705481-0.124757670548137
531214.7981948947455-2.7981948947455
541615.87879102315890.121208976841066
55911.6902766314474-2.69027663144736
561412.73994158317731.26005841682272
571616.026749343758-0.0267493437579635
581615.29771194674970.702288053250296
591515.5399613012248-0.539961301224763
601614.33179866056251.66820133943745
611211.64719114760180.352808852398237
621616.0135398430779-0.0135398430779232
631616.6613337056713-0.661333705671295
641414.3887488303023-0.388748830302275
651615.32807724487530.671922755124746
661716.33076755745620.669232442543797
671816.32267965646541.6773203435346
681814.60376165184813.39623834815193
691215.891914502903-3.89191450290301
701615.43270542414870.567294575851308
711013.6841086790028-3.68410867900277
721414.2800280397862-0.280028039786169
731816.55889482239941.44110517760059
741817.14205979145330.857940208546725
751615.68719276245470.312807237545342
761713.86111087682073.13888912317929
771616.2356180149673-0.235618014967274
781614.4969615589941.50303844100596
791314.8230396052857-1.82303960528567
801615.92630129584810.0736987041519161
811615.52077236751410.479227632485877
822015.64871192766864.35128807233139
831615.83523306404320.164766935956751
841515.7848575641432-0.784857564143164
851514.7276443849090.272355615090985
861614.24311777520821.75688222479176
871414.2059389122908-0.20593891229084
881615.3263980526140.673601947385975
891614.53198184970491.46801815029511
901514.43676318992380.563236810076243
911213.4542210486208-1.45422104862079
921716.83292845668060.167071543319429
931615.48127177822310.518728221776951
941515.0284735398697-0.0284735398697246
951315.1450582409372-2.14505824093723
961615.01622371274920.983776287250817
971615.87438840054090.125611599459096
981614.04990274745541.95009725254459
991615.86830543161290.1316945683871
1001414.3383451261999-0.338345126199856
1011617.0768426833196-1.07684268331961
1021614.81942379044181.18057620955818
1032017.28402649059592.71597350940412
1041514.68863549652960.311364503470422
1051614.44033589605251.55966410394755
1061315.3377973870606-2.33779738706061
1071715.89867175014621.10132824985382
1081615.72085867115410.279141328845915
1091614.63246876988921.36753123011082
1101212.3614637244714-0.361463724471401
1111614.83768479479571.16231520520432
1121615.85039568185960.149604318140378
1131714.81441893548352.18558106451646
1141314.0796353556272-1.0796353556272
1151214.6090268927126-2.60902689271257
1161816.45211361005071.54788638994926
1171415.4413364168867-1.44133641688672
1181413.09066903254920.909330967450792
1191314.9261727586418-1.92617275864176
1201615.5916390391750.408360960825015
1211314.2718858620621-1.27188586206213
1221615.37905352071570.620946479284289
1231315.9264235797104-2.92642357971037
1241617.0514879118242-1.05148791182416
1251515.8155013047669-0.815501304766882
1261616.7138091159621-0.713809115962118
1271515.2225939644488-0.222593964448821
1281715.96588421236541.03411578763455
1291513.73173321961531.26826678038474
1301214.7508549470802-2.75085494708019
1311614.07683198509631.92316801490371
1321013.3559619515763-3.35596195157628
1331613.9608201581182.03917984188203
1341213.6811946980885-1.68119469808855
1351415.5699890590442-1.56998905904416
1361514.93418715725780.0658128427422208
1371311.91445436859131.08554563140874
1381514.45686803989090.54313196010912
1391113.2647161387682-2.26471613876819
1401213.2301746852716-1.23017468527163
141813.2950485814273-5.29504858142732
1421612.92702380393723.07297619606275
1431513.11840867368761.88159132631242
1441716.49623860177310.503761398226868
1451614.4007852025151.59921479748498
1461013.9053098496951-3.9053098496951
1471815.55048546269412.44951453730593
1481315.2166954920001-2.21669549200006
1491615.09152138655590.908478613444067
1501313.0749346946924-0.0749346946924006
1511013.0275715603938-3.02757156039384
1521516.1345155907559-1.13451559075586
1531613.83251392280272.1674860771973
1541612.07713138107513.92286861892491
1551412.49154505992761.5084549400724
1561012.4380296268131-2.43802962681313
1571716.54951616034310.450483839656886
1581311.55124343282621.44875656717378
1591513.60092754438261.39907245561741
1601614.96267569034981.03732430965021
1611212.3584819918089-0.358481991808894
1621312.62479487793670.3752051220633







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9623459951222670.0753080097554650.0376540048777325
100.9396614385389730.1206771229220540.0603385614610268
110.9174720191537190.1650559616925620.0825279808462808
120.8633474365752680.2733051268494640.136652563424732
130.7947164568782680.4105670862434640.205283543121732
140.7149831630093120.5700336739813760.285016836990688
150.6361741067564620.7276517864870760.363825893243538
160.6383395576190420.7233208847619160.361660442380958
170.5581850845892660.8836298308214670.441814915410734
180.7848852487578280.4302295024843440.215114751242172
190.761404376350140.4771912472997190.23859562364986
200.7102580299997250.579483940000550.289741970000275
210.641983258836190.7160334823276190.358016741163809
220.5882095966175930.8235808067648140.411790403382407
230.565109678540330.8697806429193390.43489032145967
240.5273542018645450.9452915962709090.472645798135455
250.4744005340711150.948801068142230.525599465928885
260.424056111159010.848112222318020.57594388884099
270.377211748805380.754423497610760.62278825119462
280.4262432189348590.8524864378697170.573756781065141
290.3736082832858370.7472165665716730.626391716714163
300.3882278230756680.7764556461513350.611772176924332
310.3445402132713440.6890804265426880.655459786728656
320.3441905849265230.6883811698530470.655809415073477
330.3435747094369170.6871494188738330.656425290563083
340.2896616867080960.5793233734161920.710338313291904
350.2886455485675290.5772910971350580.711354451432471
360.796353256967170.407293486065660.20364674303283
370.7755858055746950.448828388850610.224414194425305
380.759383047894510.481233904210980.24061695210549
390.7953276023910010.4093447952179980.204672397608999
400.7811183032389830.4377633935220340.218881696761017
410.7518120458295550.4963759083408890.248187954170445
420.7281528529729580.5436942940540840.271847147027042
430.7491673228508240.5016653542983510.250832677149176
440.7077454736986770.5845090526026460.292254526301323
450.6750381462019990.6499237075960020.324961853798001
460.8579341043655730.2841317912688540.142065895634427
470.8709676418164120.2580647163671770.129032358183588
480.8475526143415930.3048947713168130.152447385658407
490.8347642456566340.3304715086867310.165235754343366
500.8285533708668270.3428932582663470.171446629133173
510.8001116577624790.3997766844750430.199888342237521
520.7673020149584870.4653959700830260.232697985041513
530.7869127785132740.4261744429734520.213087221486726
540.7593150372663260.4813699254673470.240684962733674
550.7829895676215210.4340208647569570.217010432378479
560.7780794203529410.4438411592941180.221920579647059
570.7439952491852320.5120095016295350.256004750814768
580.7349400741765730.5301198516468550.265059925823427
590.6990651318338450.601869736332310.300934868166155
600.7056929977762760.5886140044474480.294307002223724
610.6678805308887070.6642389382225870.332119469111293
620.6262127494736660.7475745010526690.373787250526334
630.5858172818895540.8283654362208930.414182718110446
640.5444103425605740.9111793148788520.455589657439426
650.5114538809159940.9770922381680120.488546119084006
660.4798849265692630.9597698531385250.520115073430737
670.4745628125877210.9491256251754430.525437187412279
680.5980354889646760.8039290220706480.401964511035324
690.7348225724727350.5303548550545290.265177427527265
700.70060665094380.5987866981124010.2993933490562
710.8100378500702040.3799242998595930.189962149929796
720.7807873661987440.4384252676025130.219212633801256
730.7684969713195930.4630060573608140.231503028680407
740.743290369140570.513419261718860.25670963085943
750.7066747355571660.5866505288856680.293325264442834
760.7656946844255920.4686106311488160.234305315574408
770.7297181887622730.5405636224754550.270281811237727
780.7126814957728310.5746370084543380.287318504227169
790.7160079856207710.5679840287584590.283992014379229
800.6829716237181340.6340567525637330.317028376281866
810.6437093304507950.7125813390984090.356290669549205
820.8051679529853330.3896640940293350.194832047014667
830.7721859337689930.4556281324620140.227814066231007
840.7442240950204260.5115518099591480.255775904979574
850.7065128500059160.5869742999881680.293487149994084
860.6961619082782590.6076761834434810.303838091721741
870.6560099354117830.6879801291764350.343990064588217
880.6160871471570880.7678257056858240.383912852842912
890.5958474412644290.8083051174711420.404152558735571
900.5584992131325810.8830015737348380.441500786867419
910.5470884905327850.905823018934430.452911509467215
920.5057309005684670.9885381988630660.494269099431533
930.4617212268456110.9234424536912210.538278773154389
940.4155006792109350.831001358421870.584499320789065
950.4457328341466180.8914656682932360.554267165853382
960.408234953070890.816469906141780.59176504692911
970.3655720569920350.731144113984070.634427943007965
980.3595031910147080.7190063820294160.640496808985292
990.3160526971028660.6321053942057320.683947302897134
1000.2806391708924920.5612783417849830.719360829107508
1010.2551303998781560.5102607997563120.744869600121844
1020.2328621196390980.4657242392781970.767137880360902
1030.2820531723331890.5641063446663790.717946827666811
1040.2431090403789250.486218080757850.756890959621075
1050.2353929482513550.470785896502710.764607051748645
1060.2485302338404790.4970604676809570.751469766159521
1070.2273019080900660.4546038161801320.772698091909934
1080.2028095137903820.4056190275807640.797190486209618
1090.199643350571180.3992867011423610.80035664942882
1100.1964050740395950.3928101480791910.803594925960404
1110.1815186734819430.3630373469638850.818481326518057
1120.1581882951246780.3163765902493560.841811704875322
1130.1817130937557290.3634261875114570.818286906244271
1140.1554203491044820.3108406982089640.844579650895518
1150.16331014783640.3266202956727990.8366898521636
1160.1743907916313580.3487815832627160.825609208368642
1170.1515338864853990.3030677729707980.848466113514601
1180.1401412732890220.2802825465780450.859858726710978
1190.1357018310883820.2714036621767640.864298168911618
1200.1435185367665080.2870370735330170.856481463233492
1210.1199588207465990.2399176414931980.880041179253401
1220.1133222844982590.2266445689965170.886677715501741
1230.116839439803640.233678879607280.88316056019636
1240.09543279333856020.190865586677120.90456720666144
1250.07524603863148580.1504920772629720.924753961368514
1260.05806026589670570.1161205317934110.941939734103294
1270.0436158655609230.08723173112184590.956384134439077
1280.04567020525133570.09134041050267130.954329794748664
1290.04433210457427030.08866420914854050.95566789542573
1300.04382000307945530.08764000615891060.956179996920545
1310.04805266150746550.0961053230149310.951947338492535
1320.05779491699530480.115589833990610.942205083004695
1330.1216630307028380.2433260614056750.878336969297162
1340.1225847474698860.2451694949397710.877415252530114
1350.09723413956982920.1944682791396580.902765860430171
1360.07888350105177140.1577670021035430.921116498948229
1370.07140684818032030.1428136963606410.92859315181968
1380.06642619417055520.132852388341110.933573805829445
1390.06998666730431670.1399733346086330.930013332695683
1400.051175679845670.102351359691340.94882432015433
1410.5708933499718010.8582133000563980.429106650028199
1420.52226442960130.95547114079740.4777355703987
1430.4776570911977740.9553141823955490.522342908802225
1440.3955624533552530.7911249067105050.604437546644747
1450.3275169466536020.6550338933072040.672483053346398
1460.3532455911669550.706491182333910.646754408833045
1470.4001049263573150.800209852714630.599895073642685
1480.5695905465864040.8608189068271920.430409453413596
1490.4619532457898220.9239064915796430.538046754210178
1500.3757562734500350.751512546900070.624243726549965
1510.7877292928025540.4245414143948930.212270707197446
1520.7684007103720010.4631985792559980.231599289627999
1530.6199340085379880.7601319829240250.380065991462012

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.962345995122267 & 0.075308009755465 & 0.0376540048777325 \tabularnewline
10 & 0.939661438538973 & 0.120677122922054 & 0.0603385614610268 \tabularnewline
11 & 0.917472019153719 & 0.165055961692562 & 0.0825279808462808 \tabularnewline
12 & 0.863347436575268 & 0.273305126849464 & 0.136652563424732 \tabularnewline
13 & 0.794716456878268 & 0.410567086243464 & 0.205283543121732 \tabularnewline
14 & 0.714983163009312 & 0.570033673981376 & 0.285016836990688 \tabularnewline
15 & 0.636174106756462 & 0.727651786487076 & 0.363825893243538 \tabularnewline
16 & 0.638339557619042 & 0.723320884761916 & 0.361660442380958 \tabularnewline
17 & 0.558185084589266 & 0.883629830821467 & 0.441814915410734 \tabularnewline
18 & 0.784885248757828 & 0.430229502484344 & 0.215114751242172 \tabularnewline
19 & 0.76140437635014 & 0.477191247299719 & 0.23859562364986 \tabularnewline
20 & 0.710258029999725 & 0.57948394000055 & 0.289741970000275 \tabularnewline
21 & 0.64198325883619 & 0.716033482327619 & 0.358016741163809 \tabularnewline
22 & 0.588209596617593 & 0.823580806764814 & 0.411790403382407 \tabularnewline
23 & 0.56510967854033 & 0.869780642919339 & 0.43489032145967 \tabularnewline
24 & 0.527354201864545 & 0.945291596270909 & 0.472645798135455 \tabularnewline
25 & 0.474400534071115 & 0.94880106814223 & 0.525599465928885 \tabularnewline
26 & 0.42405611115901 & 0.84811222231802 & 0.57594388884099 \tabularnewline
27 & 0.37721174880538 & 0.75442349761076 & 0.62278825119462 \tabularnewline
28 & 0.426243218934859 & 0.852486437869717 & 0.573756781065141 \tabularnewline
29 & 0.373608283285837 & 0.747216566571673 & 0.626391716714163 \tabularnewline
30 & 0.388227823075668 & 0.776455646151335 & 0.611772176924332 \tabularnewline
31 & 0.344540213271344 & 0.689080426542688 & 0.655459786728656 \tabularnewline
32 & 0.344190584926523 & 0.688381169853047 & 0.655809415073477 \tabularnewline
33 & 0.343574709436917 & 0.687149418873833 & 0.656425290563083 \tabularnewline
34 & 0.289661686708096 & 0.579323373416192 & 0.710338313291904 \tabularnewline
35 & 0.288645548567529 & 0.577291097135058 & 0.711354451432471 \tabularnewline
36 & 0.79635325696717 & 0.40729348606566 & 0.20364674303283 \tabularnewline
37 & 0.775585805574695 & 0.44882838885061 & 0.224414194425305 \tabularnewline
38 & 0.75938304789451 & 0.48123390421098 & 0.24061695210549 \tabularnewline
39 & 0.795327602391001 & 0.409344795217998 & 0.204672397608999 \tabularnewline
40 & 0.781118303238983 & 0.437763393522034 & 0.218881696761017 \tabularnewline
41 & 0.751812045829555 & 0.496375908340889 & 0.248187954170445 \tabularnewline
42 & 0.728152852972958 & 0.543694294054084 & 0.271847147027042 \tabularnewline
43 & 0.749167322850824 & 0.501665354298351 & 0.250832677149176 \tabularnewline
44 & 0.707745473698677 & 0.584509052602646 & 0.292254526301323 \tabularnewline
45 & 0.675038146201999 & 0.649923707596002 & 0.324961853798001 \tabularnewline
46 & 0.857934104365573 & 0.284131791268854 & 0.142065895634427 \tabularnewline
47 & 0.870967641816412 & 0.258064716367177 & 0.129032358183588 \tabularnewline
48 & 0.847552614341593 & 0.304894771316813 & 0.152447385658407 \tabularnewline
49 & 0.834764245656634 & 0.330471508686731 & 0.165235754343366 \tabularnewline
50 & 0.828553370866827 & 0.342893258266347 & 0.171446629133173 \tabularnewline
51 & 0.800111657762479 & 0.399776684475043 & 0.199888342237521 \tabularnewline
52 & 0.767302014958487 & 0.465395970083026 & 0.232697985041513 \tabularnewline
53 & 0.786912778513274 & 0.426174442973452 & 0.213087221486726 \tabularnewline
54 & 0.759315037266326 & 0.481369925467347 & 0.240684962733674 \tabularnewline
55 & 0.782989567621521 & 0.434020864756957 & 0.217010432378479 \tabularnewline
56 & 0.778079420352941 & 0.443841159294118 & 0.221920579647059 \tabularnewline
57 & 0.743995249185232 & 0.512009501629535 & 0.256004750814768 \tabularnewline
58 & 0.734940074176573 & 0.530119851646855 & 0.265059925823427 \tabularnewline
59 & 0.699065131833845 & 0.60186973633231 & 0.300934868166155 \tabularnewline
60 & 0.705692997776276 & 0.588614004447448 & 0.294307002223724 \tabularnewline
61 & 0.667880530888707 & 0.664238938222587 & 0.332119469111293 \tabularnewline
62 & 0.626212749473666 & 0.747574501052669 & 0.373787250526334 \tabularnewline
63 & 0.585817281889554 & 0.828365436220893 & 0.414182718110446 \tabularnewline
64 & 0.544410342560574 & 0.911179314878852 & 0.455589657439426 \tabularnewline
65 & 0.511453880915994 & 0.977092238168012 & 0.488546119084006 \tabularnewline
66 & 0.479884926569263 & 0.959769853138525 & 0.520115073430737 \tabularnewline
67 & 0.474562812587721 & 0.949125625175443 & 0.525437187412279 \tabularnewline
68 & 0.598035488964676 & 0.803929022070648 & 0.401964511035324 \tabularnewline
69 & 0.734822572472735 & 0.530354855054529 & 0.265177427527265 \tabularnewline
70 & 0.7006066509438 & 0.598786698112401 & 0.2993933490562 \tabularnewline
71 & 0.810037850070204 & 0.379924299859593 & 0.189962149929796 \tabularnewline
72 & 0.780787366198744 & 0.438425267602513 & 0.219212633801256 \tabularnewline
73 & 0.768496971319593 & 0.463006057360814 & 0.231503028680407 \tabularnewline
74 & 0.74329036914057 & 0.51341926171886 & 0.25670963085943 \tabularnewline
75 & 0.706674735557166 & 0.586650528885668 & 0.293325264442834 \tabularnewline
76 & 0.765694684425592 & 0.468610631148816 & 0.234305315574408 \tabularnewline
77 & 0.729718188762273 & 0.540563622475455 & 0.270281811237727 \tabularnewline
78 & 0.712681495772831 & 0.574637008454338 & 0.287318504227169 \tabularnewline
79 & 0.716007985620771 & 0.567984028758459 & 0.283992014379229 \tabularnewline
80 & 0.682971623718134 & 0.634056752563733 & 0.317028376281866 \tabularnewline
81 & 0.643709330450795 & 0.712581339098409 & 0.356290669549205 \tabularnewline
82 & 0.805167952985333 & 0.389664094029335 & 0.194832047014667 \tabularnewline
83 & 0.772185933768993 & 0.455628132462014 & 0.227814066231007 \tabularnewline
84 & 0.744224095020426 & 0.511551809959148 & 0.255775904979574 \tabularnewline
85 & 0.706512850005916 & 0.586974299988168 & 0.293487149994084 \tabularnewline
86 & 0.696161908278259 & 0.607676183443481 & 0.303838091721741 \tabularnewline
87 & 0.656009935411783 & 0.687980129176435 & 0.343990064588217 \tabularnewline
88 & 0.616087147157088 & 0.767825705685824 & 0.383912852842912 \tabularnewline
89 & 0.595847441264429 & 0.808305117471142 & 0.404152558735571 \tabularnewline
90 & 0.558499213132581 & 0.883001573734838 & 0.441500786867419 \tabularnewline
91 & 0.547088490532785 & 0.90582301893443 & 0.452911509467215 \tabularnewline
92 & 0.505730900568467 & 0.988538198863066 & 0.494269099431533 \tabularnewline
93 & 0.461721226845611 & 0.923442453691221 & 0.538278773154389 \tabularnewline
94 & 0.415500679210935 & 0.83100135842187 & 0.584499320789065 \tabularnewline
95 & 0.445732834146618 & 0.891465668293236 & 0.554267165853382 \tabularnewline
96 & 0.40823495307089 & 0.81646990614178 & 0.59176504692911 \tabularnewline
97 & 0.365572056992035 & 0.73114411398407 & 0.634427943007965 \tabularnewline
98 & 0.359503191014708 & 0.719006382029416 & 0.640496808985292 \tabularnewline
99 & 0.316052697102866 & 0.632105394205732 & 0.683947302897134 \tabularnewline
100 & 0.280639170892492 & 0.561278341784983 & 0.719360829107508 \tabularnewline
101 & 0.255130399878156 & 0.510260799756312 & 0.744869600121844 \tabularnewline
102 & 0.232862119639098 & 0.465724239278197 & 0.767137880360902 \tabularnewline
103 & 0.282053172333189 & 0.564106344666379 & 0.717946827666811 \tabularnewline
104 & 0.243109040378925 & 0.48621808075785 & 0.756890959621075 \tabularnewline
105 & 0.235392948251355 & 0.47078589650271 & 0.764607051748645 \tabularnewline
106 & 0.248530233840479 & 0.497060467680957 & 0.751469766159521 \tabularnewline
107 & 0.227301908090066 & 0.454603816180132 & 0.772698091909934 \tabularnewline
108 & 0.202809513790382 & 0.405619027580764 & 0.797190486209618 \tabularnewline
109 & 0.19964335057118 & 0.399286701142361 & 0.80035664942882 \tabularnewline
110 & 0.196405074039595 & 0.392810148079191 & 0.803594925960404 \tabularnewline
111 & 0.181518673481943 & 0.363037346963885 & 0.818481326518057 \tabularnewline
112 & 0.158188295124678 & 0.316376590249356 & 0.841811704875322 \tabularnewline
113 & 0.181713093755729 & 0.363426187511457 & 0.818286906244271 \tabularnewline
114 & 0.155420349104482 & 0.310840698208964 & 0.844579650895518 \tabularnewline
115 & 0.1633101478364 & 0.326620295672799 & 0.8366898521636 \tabularnewline
116 & 0.174390791631358 & 0.348781583262716 & 0.825609208368642 \tabularnewline
117 & 0.151533886485399 & 0.303067772970798 & 0.848466113514601 \tabularnewline
118 & 0.140141273289022 & 0.280282546578045 & 0.859858726710978 \tabularnewline
119 & 0.135701831088382 & 0.271403662176764 & 0.864298168911618 \tabularnewline
120 & 0.143518536766508 & 0.287037073533017 & 0.856481463233492 \tabularnewline
121 & 0.119958820746599 & 0.239917641493198 & 0.880041179253401 \tabularnewline
122 & 0.113322284498259 & 0.226644568996517 & 0.886677715501741 \tabularnewline
123 & 0.11683943980364 & 0.23367887960728 & 0.88316056019636 \tabularnewline
124 & 0.0954327933385602 & 0.19086558667712 & 0.90456720666144 \tabularnewline
125 & 0.0752460386314858 & 0.150492077262972 & 0.924753961368514 \tabularnewline
126 & 0.0580602658967057 & 0.116120531793411 & 0.941939734103294 \tabularnewline
127 & 0.043615865560923 & 0.0872317311218459 & 0.956384134439077 \tabularnewline
128 & 0.0456702052513357 & 0.0913404105026713 & 0.954329794748664 \tabularnewline
129 & 0.0443321045742703 & 0.0886642091485405 & 0.95566789542573 \tabularnewline
130 & 0.0438200030794553 & 0.0876400061589106 & 0.956179996920545 \tabularnewline
131 & 0.0480526615074655 & 0.096105323014931 & 0.951947338492535 \tabularnewline
132 & 0.0577949169953048 & 0.11558983399061 & 0.942205083004695 \tabularnewline
133 & 0.121663030702838 & 0.243326061405675 & 0.878336969297162 \tabularnewline
134 & 0.122584747469886 & 0.245169494939771 & 0.877415252530114 \tabularnewline
135 & 0.0972341395698292 & 0.194468279139658 & 0.902765860430171 \tabularnewline
136 & 0.0788835010517714 & 0.157767002103543 & 0.921116498948229 \tabularnewline
137 & 0.0714068481803203 & 0.142813696360641 & 0.92859315181968 \tabularnewline
138 & 0.0664261941705552 & 0.13285238834111 & 0.933573805829445 \tabularnewline
139 & 0.0699866673043167 & 0.139973334608633 & 0.930013332695683 \tabularnewline
140 & 0.05117567984567 & 0.10235135969134 & 0.94882432015433 \tabularnewline
141 & 0.570893349971801 & 0.858213300056398 & 0.429106650028199 \tabularnewline
142 & 0.5222644296013 & 0.9554711407974 & 0.4777355703987 \tabularnewline
143 & 0.477657091197774 & 0.955314182395549 & 0.522342908802225 \tabularnewline
144 & 0.395562453355253 & 0.791124906710505 & 0.604437546644747 \tabularnewline
145 & 0.327516946653602 & 0.655033893307204 & 0.672483053346398 \tabularnewline
146 & 0.353245591166955 & 0.70649118233391 & 0.646754408833045 \tabularnewline
147 & 0.400104926357315 & 0.80020985271463 & 0.599895073642685 \tabularnewline
148 & 0.569590546586404 & 0.860818906827192 & 0.430409453413596 \tabularnewline
149 & 0.461953245789822 & 0.923906491579643 & 0.538046754210178 \tabularnewline
150 & 0.375756273450035 & 0.75151254690007 & 0.624243726549965 \tabularnewline
151 & 0.787729292802554 & 0.424541414394893 & 0.212270707197446 \tabularnewline
152 & 0.768400710372001 & 0.463198579255998 & 0.231599289627999 \tabularnewline
153 & 0.619934008537988 & 0.760131982924025 & 0.380065991462012 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185734&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]0.962345995122267[/C][C]0.075308009755465[/C][C]0.0376540048777325[/C][/ROW]
[ROW][C]10[/C][C]0.939661438538973[/C][C]0.120677122922054[/C][C]0.0603385614610268[/C][/ROW]
[ROW][C]11[/C][C]0.917472019153719[/C][C]0.165055961692562[/C][C]0.0825279808462808[/C][/ROW]
[ROW][C]12[/C][C]0.863347436575268[/C][C]0.273305126849464[/C][C]0.136652563424732[/C][/ROW]
[ROW][C]13[/C][C]0.794716456878268[/C][C]0.410567086243464[/C][C]0.205283543121732[/C][/ROW]
[ROW][C]14[/C][C]0.714983163009312[/C][C]0.570033673981376[/C][C]0.285016836990688[/C][/ROW]
[ROW][C]15[/C][C]0.636174106756462[/C][C]0.727651786487076[/C][C]0.363825893243538[/C][/ROW]
[ROW][C]16[/C][C]0.638339557619042[/C][C]0.723320884761916[/C][C]0.361660442380958[/C][/ROW]
[ROW][C]17[/C][C]0.558185084589266[/C][C]0.883629830821467[/C][C]0.441814915410734[/C][/ROW]
[ROW][C]18[/C][C]0.784885248757828[/C][C]0.430229502484344[/C][C]0.215114751242172[/C][/ROW]
[ROW][C]19[/C][C]0.76140437635014[/C][C]0.477191247299719[/C][C]0.23859562364986[/C][/ROW]
[ROW][C]20[/C][C]0.710258029999725[/C][C]0.57948394000055[/C][C]0.289741970000275[/C][/ROW]
[ROW][C]21[/C][C]0.64198325883619[/C][C]0.716033482327619[/C][C]0.358016741163809[/C][/ROW]
[ROW][C]22[/C][C]0.588209596617593[/C][C]0.823580806764814[/C][C]0.411790403382407[/C][/ROW]
[ROW][C]23[/C][C]0.56510967854033[/C][C]0.869780642919339[/C][C]0.43489032145967[/C][/ROW]
[ROW][C]24[/C][C]0.527354201864545[/C][C]0.945291596270909[/C][C]0.472645798135455[/C][/ROW]
[ROW][C]25[/C][C]0.474400534071115[/C][C]0.94880106814223[/C][C]0.525599465928885[/C][/ROW]
[ROW][C]26[/C][C]0.42405611115901[/C][C]0.84811222231802[/C][C]0.57594388884099[/C][/ROW]
[ROW][C]27[/C][C]0.37721174880538[/C][C]0.75442349761076[/C][C]0.62278825119462[/C][/ROW]
[ROW][C]28[/C][C]0.426243218934859[/C][C]0.852486437869717[/C][C]0.573756781065141[/C][/ROW]
[ROW][C]29[/C][C]0.373608283285837[/C][C]0.747216566571673[/C][C]0.626391716714163[/C][/ROW]
[ROW][C]30[/C][C]0.388227823075668[/C][C]0.776455646151335[/C][C]0.611772176924332[/C][/ROW]
[ROW][C]31[/C][C]0.344540213271344[/C][C]0.689080426542688[/C][C]0.655459786728656[/C][/ROW]
[ROW][C]32[/C][C]0.344190584926523[/C][C]0.688381169853047[/C][C]0.655809415073477[/C][/ROW]
[ROW][C]33[/C][C]0.343574709436917[/C][C]0.687149418873833[/C][C]0.656425290563083[/C][/ROW]
[ROW][C]34[/C][C]0.289661686708096[/C][C]0.579323373416192[/C][C]0.710338313291904[/C][/ROW]
[ROW][C]35[/C][C]0.288645548567529[/C][C]0.577291097135058[/C][C]0.711354451432471[/C][/ROW]
[ROW][C]36[/C][C]0.79635325696717[/C][C]0.40729348606566[/C][C]0.20364674303283[/C][/ROW]
[ROW][C]37[/C][C]0.775585805574695[/C][C]0.44882838885061[/C][C]0.224414194425305[/C][/ROW]
[ROW][C]38[/C][C]0.75938304789451[/C][C]0.48123390421098[/C][C]0.24061695210549[/C][/ROW]
[ROW][C]39[/C][C]0.795327602391001[/C][C]0.409344795217998[/C][C]0.204672397608999[/C][/ROW]
[ROW][C]40[/C][C]0.781118303238983[/C][C]0.437763393522034[/C][C]0.218881696761017[/C][/ROW]
[ROW][C]41[/C][C]0.751812045829555[/C][C]0.496375908340889[/C][C]0.248187954170445[/C][/ROW]
[ROW][C]42[/C][C]0.728152852972958[/C][C]0.543694294054084[/C][C]0.271847147027042[/C][/ROW]
[ROW][C]43[/C][C]0.749167322850824[/C][C]0.501665354298351[/C][C]0.250832677149176[/C][/ROW]
[ROW][C]44[/C][C]0.707745473698677[/C][C]0.584509052602646[/C][C]0.292254526301323[/C][/ROW]
[ROW][C]45[/C][C]0.675038146201999[/C][C]0.649923707596002[/C][C]0.324961853798001[/C][/ROW]
[ROW][C]46[/C][C]0.857934104365573[/C][C]0.284131791268854[/C][C]0.142065895634427[/C][/ROW]
[ROW][C]47[/C][C]0.870967641816412[/C][C]0.258064716367177[/C][C]0.129032358183588[/C][/ROW]
[ROW][C]48[/C][C]0.847552614341593[/C][C]0.304894771316813[/C][C]0.152447385658407[/C][/ROW]
[ROW][C]49[/C][C]0.834764245656634[/C][C]0.330471508686731[/C][C]0.165235754343366[/C][/ROW]
[ROW][C]50[/C][C]0.828553370866827[/C][C]0.342893258266347[/C][C]0.171446629133173[/C][/ROW]
[ROW][C]51[/C][C]0.800111657762479[/C][C]0.399776684475043[/C][C]0.199888342237521[/C][/ROW]
[ROW][C]52[/C][C]0.767302014958487[/C][C]0.465395970083026[/C][C]0.232697985041513[/C][/ROW]
[ROW][C]53[/C][C]0.786912778513274[/C][C]0.426174442973452[/C][C]0.213087221486726[/C][/ROW]
[ROW][C]54[/C][C]0.759315037266326[/C][C]0.481369925467347[/C][C]0.240684962733674[/C][/ROW]
[ROW][C]55[/C][C]0.782989567621521[/C][C]0.434020864756957[/C][C]0.217010432378479[/C][/ROW]
[ROW][C]56[/C][C]0.778079420352941[/C][C]0.443841159294118[/C][C]0.221920579647059[/C][/ROW]
[ROW][C]57[/C][C]0.743995249185232[/C][C]0.512009501629535[/C][C]0.256004750814768[/C][/ROW]
[ROW][C]58[/C][C]0.734940074176573[/C][C]0.530119851646855[/C][C]0.265059925823427[/C][/ROW]
[ROW][C]59[/C][C]0.699065131833845[/C][C]0.60186973633231[/C][C]0.300934868166155[/C][/ROW]
[ROW][C]60[/C][C]0.705692997776276[/C][C]0.588614004447448[/C][C]0.294307002223724[/C][/ROW]
[ROW][C]61[/C][C]0.667880530888707[/C][C]0.664238938222587[/C][C]0.332119469111293[/C][/ROW]
[ROW][C]62[/C][C]0.626212749473666[/C][C]0.747574501052669[/C][C]0.373787250526334[/C][/ROW]
[ROW][C]63[/C][C]0.585817281889554[/C][C]0.828365436220893[/C][C]0.414182718110446[/C][/ROW]
[ROW][C]64[/C][C]0.544410342560574[/C][C]0.911179314878852[/C][C]0.455589657439426[/C][/ROW]
[ROW][C]65[/C][C]0.511453880915994[/C][C]0.977092238168012[/C][C]0.488546119084006[/C][/ROW]
[ROW][C]66[/C][C]0.479884926569263[/C][C]0.959769853138525[/C][C]0.520115073430737[/C][/ROW]
[ROW][C]67[/C][C]0.474562812587721[/C][C]0.949125625175443[/C][C]0.525437187412279[/C][/ROW]
[ROW][C]68[/C][C]0.598035488964676[/C][C]0.803929022070648[/C][C]0.401964511035324[/C][/ROW]
[ROW][C]69[/C][C]0.734822572472735[/C][C]0.530354855054529[/C][C]0.265177427527265[/C][/ROW]
[ROW][C]70[/C][C]0.7006066509438[/C][C]0.598786698112401[/C][C]0.2993933490562[/C][/ROW]
[ROW][C]71[/C][C]0.810037850070204[/C][C]0.379924299859593[/C][C]0.189962149929796[/C][/ROW]
[ROW][C]72[/C][C]0.780787366198744[/C][C]0.438425267602513[/C][C]0.219212633801256[/C][/ROW]
[ROW][C]73[/C][C]0.768496971319593[/C][C]0.463006057360814[/C][C]0.231503028680407[/C][/ROW]
[ROW][C]74[/C][C]0.74329036914057[/C][C]0.51341926171886[/C][C]0.25670963085943[/C][/ROW]
[ROW][C]75[/C][C]0.706674735557166[/C][C]0.586650528885668[/C][C]0.293325264442834[/C][/ROW]
[ROW][C]76[/C][C]0.765694684425592[/C][C]0.468610631148816[/C][C]0.234305315574408[/C][/ROW]
[ROW][C]77[/C][C]0.729718188762273[/C][C]0.540563622475455[/C][C]0.270281811237727[/C][/ROW]
[ROW][C]78[/C][C]0.712681495772831[/C][C]0.574637008454338[/C][C]0.287318504227169[/C][/ROW]
[ROW][C]79[/C][C]0.716007985620771[/C][C]0.567984028758459[/C][C]0.283992014379229[/C][/ROW]
[ROW][C]80[/C][C]0.682971623718134[/C][C]0.634056752563733[/C][C]0.317028376281866[/C][/ROW]
[ROW][C]81[/C][C]0.643709330450795[/C][C]0.712581339098409[/C][C]0.356290669549205[/C][/ROW]
[ROW][C]82[/C][C]0.805167952985333[/C][C]0.389664094029335[/C][C]0.194832047014667[/C][/ROW]
[ROW][C]83[/C][C]0.772185933768993[/C][C]0.455628132462014[/C][C]0.227814066231007[/C][/ROW]
[ROW][C]84[/C][C]0.744224095020426[/C][C]0.511551809959148[/C][C]0.255775904979574[/C][/ROW]
[ROW][C]85[/C][C]0.706512850005916[/C][C]0.586974299988168[/C][C]0.293487149994084[/C][/ROW]
[ROW][C]86[/C][C]0.696161908278259[/C][C]0.607676183443481[/C][C]0.303838091721741[/C][/ROW]
[ROW][C]87[/C][C]0.656009935411783[/C][C]0.687980129176435[/C][C]0.343990064588217[/C][/ROW]
[ROW][C]88[/C][C]0.616087147157088[/C][C]0.767825705685824[/C][C]0.383912852842912[/C][/ROW]
[ROW][C]89[/C][C]0.595847441264429[/C][C]0.808305117471142[/C][C]0.404152558735571[/C][/ROW]
[ROW][C]90[/C][C]0.558499213132581[/C][C]0.883001573734838[/C][C]0.441500786867419[/C][/ROW]
[ROW][C]91[/C][C]0.547088490532785[/C][C]0.90582301893443[/C][C]0.452911509467215[/C][/ROW]
[ROW][C]92[/C][C]0.505730900568467[/C][C]0.988538198863066[/C][C]0.494269099431533[/C][/ROW]
[ROW][C]93[/C][C]0.461721226845611[/C][C]0.923442453691221[/C][C]0.538278773154389[/C][/ROW]
[ROW][C]94[/C][C]0.415500679210935[/C][C]0.83100135842187[/C][C]0.584499320789065[/C][/ROW]
[ROW][C]95[/C][C]0.445732834146618[/C][C]0.891465668293236[/C][C]0.554267165853382[/C][/ROW]
[ROW][C]96[/C][C]0.40823495307089[/C][C]0.81646990614178[/C][C]0.59176504692911[/C][/ROW]
[ROW][C]97[/C][C]0.365572056992035[/C][C]0.73114411398407[/C][C]0.634427943007965[/C][/ROW]
[ROW][C]98[/C][C]0.359503191014708[/C][C]0.719006382029416[/C][C]0.640496808985292[/C][/ROW]
[ROW][C]99[/C][C]0.316052697102866[/C][C]0.632105394205732[/C][C]0.683947302897134[/C][/ROW]
[ROW][C]100[/C][C]0.280639170892492[/C][C]0.561278341784983[/C][C]0.719360829107508[/C][/ROW]
[ROW][C]101[/C][C]0.255130399878156[/C][C]0.510260799756312[/C][C]0.744869600121844[/C][/ROW]
[ROW][C]102[/C][C]0.232862119639098[/C][C]0.465724239278197[/C][C]0.767137880360902[/C][/ROW]
[ROW][C]103[/C][C]0.282053172333189[/C][C]0.564106344666379[/C][C]0.717946827666811[/C][/ROW]
[ROW][C]104[/C][C]0.243109040378925[/C][C]0.48621808075785[/C][C]0.756890959621075[/C][/ROW]
[ROW][C]105[/C][C]0.235392948251355[/C][C]0.47078589650271[/C][C]0.764607051748645[/C][/ROW]
[ROW][C]106[/C][C]0.248530233840479[/C][C]0.497060467680957[/C][C]0.751469766159521[/C][/ROW]
[ROW][C]107[/C][C]0.227301908090066[/C][C]0.454603816180132[/C][C]0.772698091909934[/C][/ROW]
[ROW][C]108[/C][C]0.202809513790382[/C][C]0.405619027580764[/C][C]0.797190486209618[/C][/ROW]
[ROW][C]109[/C][C]0.19964335057118[/C][C]0.399286701142361[/C][C]0.80035664942882[/C][/ROW]
[ROW][C]110[/C][C]0.196405074039595[/C][C]0.392810148079191[/C][C]0.803594925960404[/C][/ROW]
[ROW][C]111[/C][C]0.181518673481943[/C][C]0.363037346963885[/C][C]0.818481326518057[/C][/ROW]
[ROW][C]112[/C][C]0.158188295124678[/C][C]0.316376590249356[/C][C]0.841811704875322[/C][/ROW]
[ROW][C]113[/C][C]0.181713093755729[/C][C]0.363426187511457[/C][C]0.818286906244271[/C][/ROW]
[ROW][C]114[/C][C]0.155420349104482[/C][C]0.310840698208964[/C][C]0.844579650895518[/C][/ROW]
[ROW][C]115[/C][C]0.1633101478364[/C][C]0.326620295672799[/C][C]0.8366898521636[/C][/ROW]
[ROW][C]116[/C][C]0.174390791631358[/C][C]0.348781583262716[/C][C]0.825609208368642[/C][/ROW]
[ROW][C]117[/C][C]0.151533886485399[/C][C]0.303067772970798[/C][C]0.848466113514601[/C][/ROW]
[ROW][C]118[/C][C]0.140141273289022[/C][C]0.280282546578045[/C][C]0.859858726710978[/C][/ROW]
[ROW][C]119[/C][C]0.135701831088382[/C][C]0.271403662176764[/C][C]0.864298168911618[/C][/ROW]
[ROW][C]120[/C][C]0.143518536766508[/C][C]0.287037073533017[/C][C]0.856481463233492[/C][/ROW]
[ROW][C]121[/C][C]0.119958820746599[/C][C]0.239917641493198[/C][C]0.880041179253401[/C][/ROW]
[ROW][C]122[/C][C]0.113322284498259[/C][C]0.226644568996517[/C][C]0.886677715501741[/C][/ROW]
[ROW][C]123[/C][C]0.11683943980364[/C][C]0.23367887960728[/C][C]0.88316056019636[/C][/ROW]
[ROW][C]124[/C][C]0.0954327933385602[/C][C]0.19086558667712[/C][C]0.90456720666144[/C][/ROW]
[ROW][C]125[/C][C]0.0752460386314858[/C][C]0.150492077262972[/C][C]0.924753961368514[/C][/ROW]
[ROW][C]126[/C][C]0.0580602658967057[/C][C]0.116120531793411[/C][C]0.941939734103294[/C][/ROW]
[ROW][C]127[/C][C]0.043615865560923[/C][C]0.0872317311218459[/C][C]0.956384134439077[/C][/ROW]
[ROW][C]128[/C][C]0.0456702052513357[/C][C]0.0913404105026713[/C][C]0.954329794748664[/C][/ROW]
[ROW][C]129[/C][C]0.0443321045742703[/C][C]0.0886642091485405[/C][C]0.95566789542573[/C][/ROW]
[ROW][C]130[/C][C]0.0438200030794553[/C][C]0.0876400061589106[/C][C]0.956179996920545[/C][/ROW]
[ROW][C]131[/C][C]0.0480526615074655[/C][C]0.096105323014931[/C][C]0.951947338492535[/C][/ROW]
[ROW][C]132[/C][C]0.0577949169953048[/C][C]0.11558983399061[/C][C]0.942205083004695[/C][/ROW]
[ROW][C]133[/C][C]0.121663030702838[/C][C]0.243326061405675[/C][C]0.878336969297162[/C][/ROW]
[ROW][C]134[/C][C]0.122584747469886[/C][C]0.245169494939771[/C][C]0.877415252530114[/C][/ROW]
[ROW][C]135[/C][C]0.0972341395698292[/C][C]0.194468279139658[/C][C]0.902765860430171[/C][/ROW]
[ROW][C]136[/C][C]0.0788835010517714[/C][C]0.157767002103543[/C][C]0.921116498948229[/C][/ROW]
[ROW][C]137[/C][C]0.0714068481803203[/C][C]0.142813696360641[/C][C]0.92859315181968[/C][/ROW]
[ROW][C]138[/C][C]0.0664261941705552[/C][C]0.13285238834111[/C][C]0.933573805829445[/C][/ROW]
[ROW][C]139[/C][C]0.0699866673043167[/C][C]0.139973334608633[/C][C]0.930013332695683[/C][/ROW]
[ROW][C]140[/C][C]0.05117567984567[/C][C]0.10235135969134[/C][C]0.94882432015433[/C][/ROW]
[ROW][C]141[/C][C]0.570893349971801[/C][C]0.858213300056398[/C][C]0.429106650028199[/C][/ROW]
[ROW][C]142[/C][C]0.5222644296013[/C][C]0.9554711407974[/C][C]0.4777355703987[/C][/ROW]
[ROW][C]143[/C][C]0.477657091197774[/C][C]0.955314182395549[/C][C]0.522342908802225[/C][/ROW]
[ROW][C]144[/C][C]0.395562453355253[/C][C]0.791124906710505[/C][C]0.604437546644747[/C][/ROW]
[ROW][C]145[/C][C]0.327516946653602[/C][C]0.655033893307204[/C][C]0.672483053346398[/C][/ROW]
[ROW][C]146[/C][C]0.353245591166955[/C][C]0.70649118233391[/C][C]0.646754408833045[/C][/ROW]
[ROW][C]147[/C][C]0.400104926357315[/C][C]0.80020985271463[/C][C]0.599895073642685[/C][/ROW]
[ROW][C]148[/C][C]0.569590546586404[/C][C]0.860818906827192[/C][C]0.430409453413596[/C][/ROW]
[ROW][C]149[/C][C]0.461953245789822[/C][C]0.923906491579643[/C][C]0.538046754210178[/C][/ROW]
[ROW][C]150[/C][C]0.375756273450035[/C][C]0.75151254690007[/C][C]0.624243726549965[/C][/ROW]
[ROW][C]151[/C][C]0.787729292802554[/C][C]0.424541414394893[/C][C]0.212270707197446[/C][/ROW]
[ROW][C]152[/C][C]0.768400710372001[/C][C]0.463198579255998[/C][C]0.231599289627999[/C][/ROW]
[ROW][C]153[/C][C]0.619934008537988[/C][C]0.760131982924025[/C][C]0.380065991462012[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185734&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185734&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9623459951222670.0753080097554650.0376540048777325
100.9396614385389730.1206771229220540.0603385614610268
110.9174720191537190.1650559616925620.0825279808462808
120.8633474365752680.2733051268494640.136652563424732
130.7947164568782680.4105670862434640.205283543121732
140.7149831630093120.5700336739813760.285016836990688
150.6361741067564620.7276517864870760.363825893243538
160.6383395576190420.7233208847619160.361660442380958
170.5581850845892660.8836298308214670.441814915410734
180.7848852487578280.4302295024843440.215114751242172
190.761404376350140.4771912472997190.23859562364986
200.7102580299997250.579483940000550.289741970000275
210.641983258836190.7160334823276190.358016741163809
220.5882095966175930.8235808067648140.411790403382407
230.565109678540330.8697806429193390.43489032145967
240.5273542018645450.9452915962709090.472645798135455
250.4744005340711150.948801068142230.525599465928885
260.424056111159010.848112222318020.57594388884099
270.377211748805380.754423497610760.62278825119462
280.4262432189348590.8524864378697170.573756781065141
290.3736082832858370.7472165665716730.626391716714163
300.3882278230756680.7764556461513350.611772176924332
310.3445402132713440.6890804265426880.655459786728656
320.3441905849265230.6883811698530470.655809415073477
330.3435747094369170.6871494188738330.656425290563083
340.2896616867080960.5793233734161920.710338313291904
350.2886455485675290.5772910971350580.711354451432471
360.796353256967170.407293486065660.20364674303283
370.7755858055746950.448828388850610.224414194425305
380.759383047894510.481233904210980.24061695210549
390.7953276023910010.4093447952179980.204672397608999
400.7811183032389830.4377633935220340.218881696761017
410.7518120458295550.4963759083408890.248187954170445
420.7281528529729580.5436942940540840.271847147027042
430.7491673228508240.5016653542983510.250832677149176
440.7077454736986770.5845090526026460.292254526301323
450.6750381462019990.6499237075960020.324961853798001
460.8579341043655730.2841317912688540.142065895634427
470.8709676418164120.2580647163671770.129032358183588
480.8475526143415930.3048947713168130.152447385658407
490.8347642456566340.3304715086867310.165235754343366
500.8285533708668270.3428932582663470.171446629133173
510.8001116577624790.3997766844750430.199888342237521
520.7673020149584870.4653959700830260.232697985041513
530.7869127785132740.4261744429734520.213087221486726
540.7593150372663260.4813699254673470.240684962733674
550.7829895676215210.4340208647569570.217010432378479
560.7780794203529410.4438411592941180.221920579647059
570.7439952491852320.5120095016295350.256004750814768
580.7349400741765730.5301198516468550.265059925823427
590.6990651318338450.601869736332310.300934868166155
600.7056929977762760.5886140044474480.294307002223724
610.6678805308887070.6642389382225870.332119469111293
620.6262127494736660.7475745010526690.373787250526334
630.5858172818895540.8283654362208930.414182718110446
640.5444103425605740.9111793148788520.455589657439426
650.5114538809159940.9770922381680120.488546119084006
660.4798849265692630.9597698531385250.520115073430737
670.4745628125877210.9491256251754430.525437187412279
680.5980354889646760.8039290220706480.401964511035324
690.7348225724727350.5303548550545290.265177427527265
700.70060665094380.5987866981124010.2993933490562
710.8100378500702040.3799242998595930.189962149929796
720.7807873661987440.4384252676025130.219212633801256
730.7684969713195930.4630060573608140.231503028680407
740.743290369140570.513419261718860.25670963085943
750.7066747355571660.5866505288856680.293325264442834
760.7656946844255920.4686106311488160.234305315574408
770.7297181887622730.5405636224754550.270281811237727
780.7126814957728310.5746370084543380.287318504227169
790.7160079856207710.5679840287584590.283992014379229
800.6829716237181340.6340567525637330.317028376281866
810.6437093304507950.7125813390984090.356290669549205
820.8051679529853330.3896640940293350.194832047014667
830.7721859337689930.4556281324620140.227814066231007
840.7442240950204260.5115518099591480.255775904979574
850.7065128500059160.5869742999881680.293487149994084
860.6961619082782590.6076761834434810.303838091721741
870.6560099354117830.6879801291764350.343990064588217
880.6160871471570880.7678257056858240.383912852842912
890.5958474412644290.8083051174711420.404152558735571
900.5584992131325810.8830015737348380.441500786867419
910.5470884905327850.905823018934430.452911509467215
920.5057309005684670.9885381988630660.494269099431533
930.4617212268456110.9234424536912210.538278773154389
940.4155006792109350.831001358421870.584499320789065
950.4457328341466180.8914656682932360.554267165853382
960.408234953070890.816469906141780.59176504692911
970.3655720569920350.731144113984070.634427943007965
980.3595031910147080.7190063820294160.640496808985292
990.3160526971028660.6321053942057320.683947302897134
1000.2806391708924920.5612783417849830.719360829107508
1010.2551303998781560.5102607997563120.744869600121844
1020.2328621196390980.4657242392781970.767137880360902
1030.2820531723331890.5641063446663790.717946827666811
1040.2431090403789250.486218080757850.756890959621075
1050.2353929482513550.470785896502710.764607051748645
1060.2485302338404790.4970604676809570.751469766159521
1070.2273019080900660.4546038161801320.772698091909934
1080.2028095137903820.4056190275807640.797190486209618
1090.199643350571180.3992867011423610.80035664942882
1100.1964050740395950.3928101480791910.803594925960404
1110.1815186734819430.3630373469638850.818481326518057
1120.1581882951246780.3163765902493560.841811704875322
1130.1817130937557290.3634261875114570.818286906244271
1140.1554203491044820.3108406982089640.844579650895518
1150.16331014783640.3266202956727990.8366898521636
1160.1743907916313580.3487815832627160.825609208368642
1170.1515338864853990.3030677729707980.848466113514601
1180.1401412732890220.2802825465780450.859858726710978
1190.1357018310883820.2714036621767640.864298168911618
1200.1435185367665080.2870370735330170.856481463233492
1210.1199588207465990.2399176414931980.880041179253401
1220.1133222844982590.2266445689965170.886677715501741
1230.116839439803640.233678879607280.88316056019636
1240.09543279333856020.190865586677120.90456720666144
1250.07524603863148580.1504920772629720.924753961368514
1260.05806026589670570.1161205317934110.941939734103294
1270.0436158655609230.08723173112184590.956384134439077
1280.04567020525133570.09134041050267130.954329794748664
1290.04433210457427030.08866420914854050.95566789542573
1300.04382000307945530.08764000615891060.956179996920545
1310.04805266150746550.0961053230149310.951947338492535
1320.05779491699530480.115589833990610.942205083004695
1330.1216630307028380.2433260614056750.878336969297162
1340.1225847474698860.2451694949397710.877415252530114
1350.09723413956982920.1944682791396580.902765860430171
1360.07888350105177140.1577670021035430.921116498948229
1370.07140684818032030.1428136963606410.92859315181968
1380.06642619417055520.132852388341110.933573805829445
1390.06998666730431670.1399733346086330.930013332695683
1400.051175679845670.102351359691340.94882432015433
1410.5708933499718010.8582133000563980.429106650028199
1420.52226442960130.95547114079740.4777355703987
1430.4776570911977740.9553141823955490.522342908802225
1440.3955624533552530.7911249067105050.604437546644747
1450.3275169466536020.6550338933072040.672483053346398
1460.3532455911669550.706491182333910.646754408833045
1470.4001049263573150.800209852714630.599895073642685
1480.5695905465864040.8608189068271920.430409453413596
1490.4619532457898220.9239064915796430.538046754210178
1500.3757562734500350.751512546900070.624243726549965
1510.7877292928025540.4245414143948930.212270707197446
1520.7684007103720010.4631985792559980.231599289627999
1530.6199340085379880.7601319829240250.380065991462012







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.0413793103448276OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 6 & 0.0413793103448276 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185734&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.0413793103448276[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185734&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185734&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.0413793103448276OK



Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}