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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 10:10:58 -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/t1351951960sj0hgjbrdc1adm4.htm/, Retrieved Sun, 03 Jul 2022 15:24:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185732, Retrieved Sun, 03 Jul 2022 15:24:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsHubert Liskiewicz
Estimated Impact82
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 14:10:58] [acfd67cb214b61d0a5e0fb4c8c6ef02a] [Current]
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Dataseries X:
1	41	12	14	12	53	13
2	39	11	18	11	86	16
3	30	15	11	14	66	19
4	31	6	12	12	67	15
5	34	13	16	21	76	14
6	35	10	18	12	78	13
7	39	12	14	22	53	19
8	34	14	14	11	80	15
9	36	12	15	10	74	14
10	37	6	15	13	76	15
11	38	10	17	10	79	16
12	36	12	19	8	54	16
13	38	12	10	15	67	16
14	39	11	16	14	54	16
15	33	15	18	10	87	17
16	32	12	14	14	58	15
17	36	10	14	14	75	15
18	38	12	17	11	88	20
19	39	11	14	10	64	18
20	32	12	16	13	57	16
21	32	11	18	7	66	16
22	31	12	11	14	68	16
23	39	13	14	12	54	19
24	37	11	12	14	56	16
25	39	9	17	11	86	17
26	41	13	9	9	80	17
27	36	10	16	11	76	16
28	33	14	14	15	69	15
29	33	12	15	14	78	16
30	34	10	11	13	67	14
31	31	12	16	9	80	15
32	27	8	13	15	54	12
33	37	10	17	10	71	14
34	34	12	15	11	84	16
35	34	12	14	13	74	14
36	32	7	16	8	71	7
37	29	6	9	20	63	10
38	36	12	15	12	71	14
39	29	10	17	10	76	16
40	35	10	13	10	69	16
41	37	10	15	9	74	16
42	34	12	16	14	75	14
43	38	15	16	8	54	20
44	35	10	12	14	52	14
45	38	10	12	11	69	14
46	37	12	11	13	68	11
47	38	13	15	9	65	14
48	33	11	15	11	75	15
49	36	11	17	15	74	16
50	38	12	13	11	75	14
51	32	14	16	10	72	16
52	32	10	14	14	67	14
53	32	12	11	18	63	12
54	34	13	12	14	62	16
55	32	5	12	11	63	9
56	37	6	15	12	76	14
57	39	12	16	13	74	16
58	29	12	15	9	67	16
59	37	11	12	10	73	15
60	35	10	12	15	70	16
61	30	7	8	20	53	12
62	38	12	13	12	77	16
63	34	14	11	12	77	16
64	31	11	14	14	52	14
65	34	12	15	13	54	16
66	35	13	10	11	80	17
67	36	14	11	17	66	18
68	30	11	12	12	73	18
69	39	12	15	13	63	12
70	35	12	15	14	69	16
71	38	8	14	13	67	10
72	31	11	16	15	54	14
73	34	14	15	13	81	18
74	38	14	15	10	69	18
75	34	12	13	11	84	16
76	39	9	12	19	80	17
77	37	13	17	13	70	16
78	34	11	13	17	69	16
79	28	12	15	13	77	13
80	37	12	13	9	54	16
81	33	12	15	11	79	16
82	37	12	16	10	30	20
83	35	12	15	9	71	16
84	37	12	16	12	73	15
85	32	11	15	12	72	15
86	33	10	14	13	77	16
87	38	9	15	13	75	14
88	33	12	14	12	69	16
89	29	12	13	15	54	16
90	33	12	7	22	70	15
91	31	9	17	13	73	12
92	36	15	13	15	54	17
93	35	12	15	13	77	16
94	32	12	14	15	82	15
95	29	12	13	10	80	13
96	39	10	16	11	80	16
97	37	13	12	16	69	16
98	35	9	14	11	78	16
99	37	12	17	11	81	16
100	32	10	15	10	76	14
101	38	14	17	10	76	16
102	37	11	12	16	73	16
103	36	15	16	12	85	20
104	32	11	11	11	66	15
105	33	11	15	16	79	16
106	40	12	9	19	68	13
107	38	12	16	11	76	17
108	41	12	15	16	71	16
109	36	11	10	15	54	16
110	43	7	10	24	46	12
111	30	12	15	14	82	16
112	31	14	11	15	74	16
113	32	11	13	11	88	17
114	32	11	14	15	38	13
115	37	10	18	12	76	12
116	37	13	16	10	86	18
117	33	13	14	14	54	14
118	34	8	14	13	70	14
119	33	11	14	9	69	13
120	38	12	14	15	90	16
121	33	11	12	15	54	13
122	31	13	14	14	76	16
123	38	12	15	11	89	13
124	37	14	15	8	76	16
125	33	13	15	11	73	15
126	31	15	13	11	79	16
127	39	10	17	8	90	15
128	44	11	17	10	74	17
129	33	9	19	11	81	15
130	35	11	15	13	72	12
131	32	10	13	11	71	16
132	28	11	9	20	66	10
133	40	8	15	10	77	16
134	27	11	15	15	65	12
135	37	12	15	12	74	14
136	32	12	16	14	82	15
137	28	9	11	23	54	13
138	34	11	14	14	63	15
139	30	10	11	16	54	11
140	35	8	15	11	64	12
141	31	9	13	12	69	8
142	32	8	15	10	54	16
143	30	9	16	14	84	15
144	30	15	14	12	86	17
145	31	11	15	12	77	16
146	40	8	16	11	89	10
147	32	13	16	12	76	18
148	36	12	11	13	60	13
149	32	12	12	11	75	16
150	35	9	9	19	73	13
151	38	7	16	12	85	10
152	42	13	13	17	79	15
153	34	9	16	9	71	16
154	35	6	12	12	72	16
155	35	8	9	19	69	14
156	33	8	13	18	78	10
157	36	15	13	15	54	17
158	32	6	14	14	69	13
159	33	9	19	11	81	15
160	34	11	13	9	84	16
161	32	8	12	18	84	12
162	34	8	13	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=185732&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=185732&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185732&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] = + 5.70487680850056 -0.00422497540338048t + 0.100377769352725Connected[t] + 0.532121564262077Software[t] + 0.0520122055726539Happiness[t] -0.0700757970117463Depression[t] + 0.00575613837226509Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.70487680850056 -0.00422497540338048t +  0.100377769352725Connected[t] +  0.532121564262077Software[t] +  0.0520122055726539Happiness[t] -0.0700757970117463Depression[t] +  0.00575613837226509Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185732&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.70487680850056 -0.00422497540338048t +  0.100377769352725Connected[t] +  0.532121564262077Software[t] +  0.0520122055726539Happiness[t] -0.0700757970117463Depression[t] +  0.00575613837226509Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185732&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185732&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] = + 5.70487680850056 -0.00422497540338048t + 0.100377769352725Connected[t] + 0.532121564262077Software[t] + 0.0520122055726539Happiness[t] -0.0700757970117463Depression[t] + 0.00575613837226509Belonging[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.704876808500562.4395322.33850.0206380.010319
t-0.004224975403380480.003223-1.31080.1918670.095934
Connected0.1003777693527250.0437792.29280.0232020.011601
Software0.5321215642620770.0689817.714100
Happiness0.05201220557265390.0754360.68950.491550.245775
Depression-0.07007579701174630.055863-1.25440.2115780.105789
Belonging0.005756138372265090.0145550.39550.693040.34652

\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) & 5.70487680850056 & 2.439532 & 2.3385 & 0.020638 & 0.010319 \tabularnewline
t & -0.00422497540338048 & 0.003223 & -1.3108 & 0.191867 & 0.095934 \tabularnewline
Connected & 0.100377769352725 & 0.043779 & 2.2928 & 0.023202 & 0.011601 \tabularnewline
Software & 0.532121564262077 & 0.068981 & 7.7141 & 0 & 0 \tabularnewline
Happiness & 0.0520122055726539 & 0.075436 & 0.6895 & 0.49155 & 0.245775 \tabularnewline
Depression & -0.0700757970117463 & 0.055863 & -1.2544 & 0.211578 & 0.105789 \tabularnewline
Belonging & 0.00575613837226509 & 0.014555 & 0.3955 & 0.69304 & 0.34652 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185732&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]5.70487680850056[/C][C]2.439532[/C][C]2.3385[/C][C]0.020638[/C][C]0.010319[/C][/ROW]
[ROW][C]t[/C][C]-0.00422497540338048[/C][C]0.003223[/C][C]-1.3108[/C][C]0.191867[/C][C]0.095934[/C][/ROW]
[ROW][C]Connected[/C][C]0.100377769352725[/C][C]0.043779[/C][C]2.2928[/C][C]0.023202[/C][C]0.011601[/C][/ROW]
[ROW][C]Software[/C][C]0.532121564262077[/C][C]0.068981[/C][C]7.7141[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0520122055726539[/C][C]0.075436[/C][C]0.6895[/C][C]0.49155[/C][C]0.245775[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0700757970117463[/C][C]0.055863[/C][C]-1.2544[/C][C]0.211578[/C][C]0.105789[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00575613837226509[/C][C]0.014555[/C][C]0.3955[/C][C]0.69304[/C][C]0.34652[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185732&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185732&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)5.704876808500562.4395322.33850.0206380.010319
t-0.004224975403380480.003223-1.31080.1918670.095934
Connected0.1003777693527250.0437792.29280.0232020.011601
Software0.5321215642620770.0689817.714100
Happiness0.05201220557265390.0754360.68950.491550.245775
Depression-0.07007579701174630.055863-1.25440.2115780.105789
Belonging0.005756138372265090.0145550.39550.693040.34652







Multiple Linear Regression - Regression Statistics
Multiple R0.599868523960954
R-squared0.359842246039093
Adjusted R-squared0.335061945885768
F-TEST (value)14.5213029629427
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value4.10560474506383e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83983950285155
Sum Squared Residuals524.676456419219

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.599868523960954 \tabularnewline
R-squared & 0.359842246039093 \tabularnewline
Adjusted R-squared & 0.335061945885768 \tabularnewline
F-TEST (value) & 14.5213029629427 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 4.10560474506383e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.83983950285155 \tabularnewline
Sum Squared Residuals & 524.676456419219 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185732&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.599868523960954[/C][/ROW]
[ROW][C]R-squared[/C][C]0.359842246039093[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.335061945885768[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.5213029629427[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]4.10560474506383e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.83983950285155[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]524.676456419219[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185732&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185732&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 R0.599868523960954
R-squared0.359842246039093
Adjusted R-squared0.335061945885768
F-TEST (value)14.5213029629427
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value4.10560474506383e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83983950285155
Sum Squared Residuals524.676456419219







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3939357953101-3.39393579531011
21616.1249109025263-0.124910902526285
31916.65633666250762.34366333749243
41512.16131531606662.83868468393337
51415.8122464930912-1.81224649309125
61315.0582534552499-2.05825345524992
71915.46707243406693.53292756593311
81516.9514512436044-1.9514512436044
91416.1712898507331-2.17128985073313
101512.87599814481932.1240018551807
111615.43215741311430.567842586885705
121616.3916925733918-0.391692573391792
131615.70441250629720.295587493702802
141615.57576296759270.424237032407311
151717.6720377985983-0.672037798598309
161515.3157903379227-0.315790337922683
171514.74668766373460.253312336265446
182016.44855516215343.55144483784657
191815.78847825120012.21152174879988
201615.4672345060940.53276549390605
211615.50717240499470.492827595005336
221615.09158748315440.908412516845636
231916.63810850036462.3618914996354
241615.13622112930730.863778870692651
251714.91148113415172.08851886584827
261716.92601507371080.0739849262892251
271615.02444585025360.975554149746439
281516.4229532560422-1.42295325604217
291615.52837840004940.471621599950583
301414.3589975181008-0.358997518100822
311515.7330763779131-0.733076377913122
321212.4727030715832-0.4727030715832
331415.1927810779091-1.19278107790908
341615.85239551365410.147604486345931
351415.5984453549319-1.59844535493189
36713.1699920005999-6.16999200059992
371011.0814680427486-1.08146804274863
381415.8913455548948-1.89134555489481
391614.39318976252831.60681023747168
401614.74288961234481.25711038765518
411615.14230107566530.857698924334732
421415.6085752796141-1.60857527961405
432017.90180195066072.09819804933928
441414.2958199647832-0.295819964783151
451414.9008100408017-0.900810040801691
461115.6625304866013-4.66253048660133
471416.7618884400336-2.76188844003356
481515.1089412790416-0.108941279041554
491615.22381469642240.776185303577594
501416.0304773281152-2.03047732811519
511616.6970728637325-0.697072863732525
521414.1512533402272-0.151253340227216
531214.751907135094-2.75190713509398
541615.80711851790550.192881482094495
55911.5611490191076-2.56114901910756
561412.75172507327551.24827492672446
571616.1114091539665-0.111409153966454
581615.29140449890430.708595501095701
591515.3665045305645-0.366504530564524
601614.26175505201811.73824494798191
611211.5029943773870.497005622613003
621615.92121410300740.0787858969925935
631616.475696767572-0.475696767571973
641414.445955354712-0.445955354712027
651615.40858553095780.591414469042171
661716.06661005300840.933389946991633
671816.24585589751031.75414410248975
681814.48568377244153.51431622755847
691215.9453797214583-3.94537972145832
701615.50410470186590.495895298134118
711013.6790780921669-3.67907809216693
721414.4576164423631-0.457616442363074
731816.59444459230611.4055554076939
741817.13288442488170.867115575118325
751615.57514711097020.424852889029838
761713.84080315438853.15919684561151
771616.3872633235391-0.387263323539065
781614.52355376284351.47644623715651
791314.8795604417562-1.87956044175625
801615.8226229848670.177377015133025
811615.52466320848110.475336791518864
822015.76198653283214.23801346716793
831615.81107128342520.188928716574802
841515.8608989380092-0.860898938009212
851514.76489520763520.23510479236479
861614.23561912659941.7643808734006
871414.2416613625257-0.241661362525693
881615.31543899435040.684561005649578
891614.56112126934431.43887873065573
901514.24790177278990.752098227210115
911213.6146292098439-1.61462920984387
921716.84745542138940.152544578610562
931615.5230551715780.476944828422002
941515.0543137803816-0.0543137803816209
951315.0358099996616-2.03580999966161
961615.05708040896750.942919591032457
971615.82671925820070.173280741799318
981613.9994611285982.00053887140203
991615.9656614165210.0343385834789743
1001414.332575159835-0.332575159834978
1011617.1631274687416-1.16312746874157
1021614.76437580614871.23562419385132
1032017.34568498924572.65431501075433
1041514.51211081945850.487889180541479
1051614.54076324947921.4592367505208
1061315.1856860772409-2.18568607724089
1071715.95144648521271.04855351478727
1081615.81718293537480.182817064625189
1091614.49110796576571.5088920342343
1101212.3843098386992-0.384309838699246
1111614.90382166240311.0961783375969
1121615.74004385859610.25995614140388
1131714.70474549616322.29525450383676
1141314.1844226196723-1.18442261967227
1151214.7869743982424-2.78697439824237
1161816.47280268222611.52719731777395
1171415.498542602307-1.498542602307
1181413.09626158591390.903738414086058
1191314.8625705836188-1.86257058361879
1201615.59278014298820.407219857011801
1211314.2432993640123-1.24329936401227
1221615.40329723077450.596702769225525
1231315.9066644720254-2.90666447202543
1241617.0017024479893-1.00170244798927
1251515.8363490247609-0.836349024760882
1261616.6261240582645-0.626124058264489
1271515.2459071517933-0.245907151793292
1281716.04344277943590.95655722056412
1291513.94506079536781.05493920463221
1301214.8058288255295-2.80582882552952
1311613.99872002231182.0012799776882
1321013.2575938465019-3.25759384650194
1331613.93768613619332.06231386380667
1341213.8054622064648-1.80546220646485
1351415.5991691252364-1.59916912523642
1361515.0509650215967-0.0509650215966947
1371311.99694920060381.00305079939623
1381514.4977580050150.502241994985038
1391113.2119069318468-2.21190693184677
1401213.2613168657549-1.26131686575485
141813.2423828609069-5.24238286090692
1421612.9642480201793.03575197982099
1431513.23578223902591.76421776097412
1441716.47192610881770.528073891182322
1451614.4397996059411.56020039405902
1461013.9337715249775-3.93377152497748
1471815.64222662021152.35777337978851
1481315.0851561191257-2.08515611912567
1491614.95792594149151.04207405850848
1501312.93031431180360.0696856881963832
1511013.0866691944922-3.08666919449224
1521516.1357322500619-1.13573225006194
1531613.87059274862232.12940725137774
1541611.95786077483184.04213922516822
1551412.35404331703561.64595668296442
1561012.4789926675795-2.47899266757949
1571716.57283202016970.427167979830294
1581311.58643196716511.41356803283489
1591513.81831153326641.18168846673363
1601614.82405423144421.17594576855576
1611212.3400146458708-0.340014645870803
1621312.6423669331850.357633066814957

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.3939357953101 & -3.39393579531011 \tabularnewline
2 & 16 & 16.1249109025263 & -0.124910902526285 \tabularnewline
3 & 19 & 16.6563366625076 & 2.34366333749243 \tabularnewline
4 & 15 & 12.1613153160666 & 2.83868468393337 \tabularnewline
5 & 14 & 15.8122464930912 & -1.81224649309125 \tabularnewline
6 & 13 & 15.0582534552499 & -2.05825345524992 \tabularnewline
7 & 19 & 15.4670724340669 & 3.53292756593311 \tabularnewline
8 & 15 & 16.9514512436044 & -1.9514512436044 \tabularnewline
9 & 14 & 16.1712898507331 & -2.17128985073313 \tabularnewline
10 & 15 & 12.8759981448193 & 2.1240018551807 \tabularnewline
11 & 16 & 15.4321574131143 & 0.567842586885705 \tabularnewline
12 & 16 & 16.3916925733918 & -0.391692573391792 \tabularnewline
13 & 16 & 15.7044125062972 & 0.295587493702802 \tabularnewline
14 & 16 & 15.5757629675927 & 0.424237032407311 \tabularnewline
15 & 17 & 17.6720377985983 & -0.672037798598309 \tabularnewline
16 & 15 & 15.3157903379227 & -0.315790337922683 \tabularnewline
17 & 15 & 14.7466876637346 & 0.253312336265446 \tabularnewline
18 & 20 & 16.4485551621534 & 3.55144483784657 \tabularnewline
19 & 18 & 15.7884782512001 & 2.21152174879988 \tabularnewline
20 & 16 & 15.467234506094 & 0.53276549390605 \tabularnewline
21 & 16 & 15.5071724049947 & 0.492827595005336 \tabularnewline
22 & 16 & 15.0915874831544 & 0.908412516845636 \tabularnewline
23 & 19 & 16.6381085003646 & 2.3618914996354 \tabularnewline
24 & 16 & 15.1362211293073 & 0.863778870692651 \tabularnewline
25 & 17 & 14.9114811341517 & 2.08851886584827 \tabularnewline
26 & 17 & 16.9260150737108 & 0.0739849262892251 \tabularnewline
27 & 16 & 15.0244458502536 & 0.975554149746439 \tabularnewline
28 & 15 & 16.4229532560422 & -1.42295325604217 \tabularnewline
29 & 16 & 15.5283784000494 & 0.471621599950583 \tabularnewline
30 & 14 & 14.3589975181008 & -0.358997518100822 \tabularnewline
31 & 15 & 15.7330763779131 & -0.733076377913122 \tabularnewline
32 & 12 & 12.4727030715832 & -0.4727030715832 \tabularnewline
33 & 14 & 15.1927810779091 & -1.19278107790908 \tabularnewline
34 & 16 & 15.8523955136541 & 0.147604486345931 \tabularnewline
35 & 14 & 15.5984453549319 & -1.59844535493189 \tabularnewline
36 & 7 & 13.1699920005999 & -6.16999200059992 \tabularnewline
37 & 10 & 11.0814680427486 & -1.08146804274863 \tabularnewline
38 & 14 & 15.8913455548948 & -1.89134555489481 \tabularnewline
39 & 16 & 14.3931897625283 & 1.60681023747168 \tabularnewline
40 & 16 & 14.7428896123448 & 1.25711038765518 \tabularnewline
41 & 16 & 15.1423010756653 & 0.857698924334732 \tabularnewline
42 & 14 & 15.6085752796141 & -1.60857527961405 \tabularnewline
43 & 20 & 17.9018019506607 & 2.09819804933928 \tabularnewline
44 & 14 & 14.2958199647832 & -0.295819964783151 \tabularnewline
45 & 14 & 14.9008100408017 & -0.900810040801691 \tabularnewline
46 & 11 & 15.6625304866013 & -4.66253048660133 \tabularnewline
47 & 14 & 16.7618884400336 & -2.76188844003356 \tabularnewline
48 & 15 & 15.1089412790416 & -0.108941279041554 \tabularnewline
49 & 16 & 15.2238146964224 & 0.776185303577594 \tabularnewline
50 & 14 & 16.0304773281152 & -2.03047732811519 \tabularnewline
51 & 16 & 16.6970728637325 & -0.697072863732525 \tabularnewline
52 & 14 & 14.1512533402272 & -0.151253340227216 \tabularnewline
53 & 12 & 14.751907135094 & -2.75190713509398 \tabularnewline
54 & 16 & 15.8071185179055 & 0.192881482094495 \tabularnewline
55 & 9 & 11.5611490191076 & -2.56114901910756 \tabularnewline
56 & 14 & 12.7517250732755 & 1.24827492672446 \tabularnewline
57 & 16 & 16.1114091539665 & -0.111409153966454 \tabularnewline
58 & 16 & 15.2914044989043 & 0.708595501095701 \tabularnewline
59 & 15 & 15.3665045305645 & -0.366504530564524 \tabularnewline
60 & 16 & 14.2617550520181 & 1.73824494798191 \tabularnewline
61 & 12 & 11.502994377387 & 0.497005622613003 \tabularnewline
62 & 16 & 15.9212141030074 & 0.0787858969925935 \tabularnewline
63 & 16 & 16.475696767572 & -0.475696767571973 \tabularnewline
64 & 14 & 14.445955354712 & -0.445955354712027 \tabularnewline
65 & 16 & 15.4085855309578 & 0.591414469042171 \tabularnewline
66 & 17 & 16.0666100530084 & 0.933389946991633 \tabularnewline
67 & 18 & 16.2458558975103 & 1.75414410248975 \tabularnewline
68 & 18 & 14.4856837724415 & 3.51431622755847 \tabularnewline
69 & 12 & 15.9453797214583 & -3.94537972145832 \tabularnewline
70 & 16 & 15.5041047018659 & 0.495895298134118 \tabularnewline
71 & 10 & 13.6790780921669 & -3.67907809216693 \tabularnewline
72 & 14 & 14.4576164423631 & -0.457616442363074 \tabularnewline
73 & 18 & 16.5944445923061 & 1.4055554076939 \tabularnewline
74 & 18 & 17.1328844248817 & 0.867115575118325 \tabularnewline
75 & 16 & 15.5751471109702 & 0.424852889029838 \tabularnewline
76 & 17 & 13.8408031543885 & 3.15919684561151 \tabularnewline
77 & 16 & 16.3872633235391 & -0.387263323539065 \tabularnewline
78 & 16 & 14.5235537628435 & 1.47644623715651 \tabularnewline
79 & 13 & 14.8795604417562 & -1.87956044175625 \tabularnewline
80 & 16 & 15.822622984867 & 0.177377015133025 \tabularnewline
81 & 16 & 15.5246632084811 & 0.475336791518864 \tabularnewline
82 & 20 & 15.7619865328321 & 4.23801346716793 \tabularnewline
83 & 16 & 15.8110712834252 & 0.188928716574802 \tabularnewline
84 & 15 & 15.8608989380092 & -0.860898938009212 \tabularnewline
85 & 15 & 14.7648952076352 & 0.23510479236479 \tabularnewline
86 & 16 & 14.2356191265994 & 1.7643808734006 \tabularnewline
87 & 14 & 14.2416613625257 & -0.241661362525693 \tabularnewline
88 & 16 & 15.3154389943504 & 0.684561005649578 \tabularnewline
89 & 16 & 14.5611212693443 & 1.43887873065573 \tabularnewline
90 & 15 & 14.2479017727899 & 0.752098227210115 \tabularnewline
91 & 12 & 13.6146292098439 & -1.61462920984387 \tabularnewline
92 & 17 & 16.8474554213894 & 0.152544578610562 \tabularnewline
93 & 16 & 15.523055171578 & 0.476944828422002 \tabularnewline
94 & 15 & 15.0543137803816 & -0.0543137803816209 \tabularnewline
95 & 13 & 15.0358099996616 & -2.03580999966161 \tabularnewline
96 & 16 & 15.0570804089675 & 0.942919591032457 \tabularnewline
97 & 16 & 15.8267192582007 & 0.173280741799318 \tabularnewline
98 & 16 & 13.999461128598 & 2.00053887140203 \tabularnewline
99 & 16 & 15.965661416521 & 0.0343385834789743 \tabularnewline
100 & 14 & 14.332575159835 & -0.332575159834978 \tabularnewline
101 & 16 & 17.1631274687416 & -1.16312746874157 \tabularnewline
102 & 16 & 14.7643758061487 & 1.23562419385132 \tabularnewline
103 & 20 & 17.3456849892457 & 2.65431501075433 \tabularnewline
104 & 15 & 14.5121108194585 & 0.487889180541479 \tabularnewline
105 & 16 & 14.5407632494792 & 1.4592367505208 \tabularnewline
106 & 13 & 15.1856860772409 & -2.18568607724089 \tabularnewline
107 & 17 & 15.9514464852127 & 1.04855351478727 \tabularnewline
108 & 16 & 15.8171829353748 & 0.182817064625189 \tabularnewline
109 & 16 & 14.4911079657657 & 1.5088920342343 \tabularnewline
110 & 12 & 12.3843098386992 & -0.384309838699246 \tabularnewline
111 & 16 & 14.9038216624031 & 1.0961783375969 \tabularnewline
112 & 16 & 15.7400438585961 & 0.25995614140388 \tabularnewline
113 & 17 & 14.7047454961632 & 2.29525450383676 \tabularnewline
114 & 13 & 14.1844226196723 & -1.18442261967227 \tabularnewline
115 & 12 & 14.7869743982424 & -2.78697439824237 \tabularnewline
116 & 18 & 16.4728026822261 & 1.52719731777395 \tabularnewline
117 & 14 & 15.498542602307 & -1.498542602307 \tabularnewline
118 & 14 & 13.0962615859139 & 0.903738414086058 \tabularnewline
119 & 13 & 14.8625705836188 & -1.86257058361879 \tabularnewline
120 & 16 & 15.5927801429882 & 0.407219857011801 \tabularnewline
121 & 13 & 14.2432993640123 & -1.24329936401227 \tabularnewline
122 & 16 & 15.4032972307745 & 0.596702769225525 \tabularnewline
123 & 13 & 15.9066644720254 & -2.90666447202543 \tabularnewline
124 & 16 & 17.0017024479893 & -1.00170244798927 \tabularnewline
125 & 15 & 15.8363490247609 & -0.836349024760882 \tabularnewline
126 & 16 & 16.6261240582645 & -0.626124058264489 \tabularnewline
127 & 15 & 15.2459071517933 & -0.245907151793292 \tabularnewline
128 & 17 & 16.0434427794359 & 0.95655722056412 \tabularnewline
129 & 15 & 13.9450607953678 & 1.05493920463221 \tabularnewline
130 & 12 & 14.8058288255295 & -2.80582882552952 \tabularnewline
131 & 16 & 13.9987200223118 & 2.0012799776882 \tabularnewline
132 & 10 & 13.2575938465019 & -3.25759384650194 \tabularnewline
133 & 16 & 13.9376861361933 & 2.06231386380667 \tabularnewline
134 & 12 & 13.8054622064648 & -1.80546220646485 \tabularnewline
135 & 14 & 15.5991691252364 & -1.59916912523642 \tabularnewline
136 & 15 & 15.0509650215967 & -0.0509650215966947 \tabularnewline
137 & 13 & 11.9969492006038 & 1.00305079939623 \tabularnewline
138 & 15 & 14.497758005015 & 0.502241994985038 \tabularnewline
139 & 11 & 13.2119069318468 & -2.21190693184677 \tabularnewline
140 & 12 & 13.2613168657549 & -1.26131686575485 \tabularnewline
141 & 8 & 13.2423828609069 & -5.24238286090692 \tabularnewline
142 & 16 & 12.964248020179 & 3.03575197982099 \tabularnewline
143 & 15 & 13.2357822390259 & 1.76421776097412 \tabularnewline
144 & 17 & 16.4719261088177 & 0.528073891182322 \tabularnewline
145 & 16 & 14.439799605941 & 1.56020039405902 \tabularnewline
146 & 10 & 13.9337715249775 & -3.93377152497748 \tabularnewline
147 & 18 & 15.6422266202115 & 2.35777337978851 \tabularnewline
148 & 13 & 15.0851561191257 & -2.08515611912567 \tabularnewline
149 & 16 & 14.9579259414915 & 1.04207405850848 \tabularnewline
150 & 13 & 12.9303143118036 & 0.0696856881963832 \tabularnewline
151 & 10 & 13.0866691944922 & -3.08666919449224 \tabularnewline
152 & 15 & 16.1357322500619 & -1.13573225006194 \tabularnewline
153 & 16 & 13.8705927486223 & 2.12940725137774 \tabularnewline
154 & 16 & 11.9578607748318 & 4.04213922516822 \tabularnewline
155 & 14 & 12.3540433170356 & 1.64595668296442 \tabularnewline
156 & 10 & 12.4789926675795 & -2.47899266757949 \tabularnewline
157 & 17 & 16.5728320201697 & 0.427167979830294 \tabularnewline
158 & 13 & 11.5864319671651 & 1.41356803283489 \tabularnewline
159 & 15 & 13.8183115332664 & 1.18168846673363 \tabularnewline
160 & 16 & 14.8240542314442 & 1.17594576855576 \tabularnewline
161 & 12 & 12.3400146458708 & -0.340014645870803 \tabularnewline
162 & 13 & 12.642366933185 & 0.357633066814957 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185732&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]16.3939357953101[/C][C]-3.39393579531011[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.1249109025263[/C][C]-0.124910902526285[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.6563366625076[/C][C]2.34366333749243[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.1613153160666[/C][C]2.83868468393337[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.8122464930912[/C][C]-1.81224649309125[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.0582534552499[/C][C]-2.05825345524992[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.4670724340669[/C][C]3.53292756593311[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9514512436044[/C][C]-1.9514512436044[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1712898507331[/C][C]-2.17128985073313[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.8759981448193[/C][C]2.1240018551807[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4321574131143[/C][C]0.567842586885705[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.3916925733918[/C][C]-0.391692573391792[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.7044125062972[/C][C]0.295587493702802[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.5757629675927[/C][C]0.424237032407311[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.6720377985983[/C][C]-0.672037798598309[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.3157903379227[/C][C]-0.315790337922683[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.7466876637346[/C][C]0.253312336265446[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4485551621534[/C][C]3.55144483784657[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.7884782512001[/C][C]2.21152174879988[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.467234506094[/C][C]0.53276549390605[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.5071724049947[/C][C]0.492827595005336[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.0915874831544[/C][C]0.908412516845636[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.6381085003646[/C][C]2.3618914996354[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.1362211293073[/C][C]0.863778870692651[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.9114811341517[/C][C]2.08851886584827[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.9260150737108[/C][C]0.0739849262892251[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0244458502536[/C][C]0.975554149746439[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.4229532560422[/C][C]-1.42295325604217[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.5283784000494[/C][C]0.471621599950583[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3589975181008[/C][C]-0.358997518100822[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7330763779131[/C][C]-0.733076377913122[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4727030715832[/C][C]-0.4727030715832[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1927810779091[/C][C]-1.19278107790908[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.8523955136541[/C][C]0.147604486345931[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.5984453549319[/C][C]-1.59844535493189[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1699920005999[/C][C]-6.16999200059992[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.0814680427486[/C][C]-1.08146804274863[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8913455548948[/C][C]-1.89134555489481[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.3931897625283[/C][C]1.60681023747168[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7428896123448[/C][C]1.25711038765518[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1423010756653[/C][C]0.857698924334732[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6085752796141[/C][C]-1.60857527961405[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9018019506607[/C][C]2.09819804933928[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2958199647832[/C][C]-0.295819964783151[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9008100408017[/C][C]-0.900810040801691[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6625304866013[/C][C]-4.66253048660133[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.7618884400336[/C][C]-2.76188844003356[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1089412790416[/C][C]-0.108941279041554[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.2238146964224[/C][C]0.776185303577594[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0304773281152[/C][C]-2.03047732811519[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6970728637325[/C][C]-0.697072863732525[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1512533402272[/C][C]-0.151253340227216[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.751907135094[/C][C]-2.75190713509398[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.8071185179055[/C][C]0.192881482094495[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5611490191076[/C][C]-2.56114901910756[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.7517250732755[/C][C]1.24827492672446[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1114091539665[/C][C]-0.111409153966454[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.2914044989043[/C][C]0.708595501095701[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3665045305645[/C][C]-0.366504530564524[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2617550520181[/C][C]1.73824494798191[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.502994377387[/C][C]0.497005622613003[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9212141030074[/C][C]0.0787858969925935[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.475696767572[/C][C]-0.475696767571973[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.445955354712[/C][C]-0.445955354712027[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4085855309578[/C][C]0.591414469042171[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.0666100530084[/C][C]0.933389946991633[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.2458558975103[/C][C]1.75414410248975[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.4856837724415[/C][C]3.51431622755847[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.9453797214583[/C][C]-3.94537972145832[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.5041047018659[/C][C]0.495895298134118[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.6790780921669[/C][C]-3.67907809216693[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.4576164423631[/C][C]-0.457616442363074[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5944445923061[/C][C]1.4055554076939[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1328844248817[/C][C]0.867115575118325[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.5751471109702[/C][C]0.424852889029838[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8408031543885[/C][C]3.15919684561151[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3872633235391[/C][C]-0.387263323539065[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5235537628435[/C][C]1.47644623715651[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8795604417562[/C][C]-1.87956044175625[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.822622984867[/C][C]0.177377015133025[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5246632084811[/C][C]0.475336791518864[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.7619865328321[/C][C]4.23801346716793[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.8110712834252[/C][C]0.188928716574802[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.8608989380092[/C][C]-0.860898938009212[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7648952076352[/C][C]0.23510479236479[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2356191265994[/C][C]1.7643808734006[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.2416613625257[/C][C]-0.241661362525693[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3154389943504[/C][C]0.684561005649578[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5611212693443[/C][C]1.43887873065573[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2479017727899[/C][C]0.752098227210115[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.6146292098439[/C][C]-1.61462920984387[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.8474554213894[/C][C]0.152544578610562[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.523055171578[/C][C]0.476944828422002[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.0543137803816[/C][C]-0.0543137803816209[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.0358099996616[/C][C]-2.03580999966161[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0570804089675[/C][C]0.942919591032457[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8267192582007[/C][C]0.173280741799318[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]13.999461128598[/C][C]2.00053887140203[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.965661416521[/C][C]0.0343385834789743[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.332575159835[/C][C]-0.332575159834978[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.1631274687416[/C][C]-1.16312746874157[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7643758061487[/C][C]1.23562419385132[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3456849892457[/C][C]2.65431501075433[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5121108194585[/C][C]0.487889180541479[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5407632494792[/C][C]1.4592367505208[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.1856860772409[/C][C]-2.18568607724089[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9514464852127[/C][C]1.04855351478727[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8171829353748[/C][C]0.182817064625189[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.4911079657657[/C][C]1.5088920342343[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3843098386992[/C][C]-0.384309838699246[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9038216624031[/C][C]1.0961783375969[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.7400438585961[/C][C]0.25995614140388[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.7047454961632[/C][C]2.29525450383676[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.1844226196723[/C][C]-1.18442261967227[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.7869743982424[/C][C]-2.78697439824237[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.4728026822261[/C][C]1.52719731777395[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.498542602307[/C][C]-1.498542602307[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0962615859139[/C][C]0.903738414086058[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.8625705836188[/C][C]-1.86257058361879[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.5927801429882[/C][C]0.407219857011801[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.2432993640123[/C][C]-1.24329936401227[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.4032972307745[/C][C]0.596702769225525[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.9066644720254[/C][C]-2.90666447202543[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.0017024479893[/C][C]-1.00170244798927[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8363490247609[/C][C]-0.836349024760882[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6261240582645[/C][C]-0.626124058264489[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.2459071517933[/C][C]-0.245907151793292[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0434427794359[/C][C]0.95655722056412[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.9450607953678[/C][C]1.05493920463221[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.8058288255295[/C][C]-2.80582882552952[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]13.9987200223118[/C][C]2.0012799776882[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2575938465019[/C][C]-3.25759384650194[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9376861361933[/C][C]2.06231386380667[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8054622064648[/C][C]-1.80546220646485[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.5991691252364[/C][C]-1.59916912523642[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.0509650215967[/C][C]-0.0509650215966947[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]11.9969492006038[/C][C]1.00305079939623[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.497758005015[/C][C]0.502241994985038[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.2119069318468[/C][C]-2.21190693184677[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.2613168657549[/C][C]-1.26131686575485[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2423828609069[/C][C]-5.24238286090692[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.964248020179[/C][C]3.03575197982099[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2357822390259[/C][C]1.76421776097412[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.4719261088177[/C][C]0.528073891182322[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.439799605941[/C][C]1.56020039405902[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.9337715249775[/C][C]-3.93377152497748[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.6422266202115[/C][C]2.35777337978851[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.0851561191257[/C][C]-2.08515611912567[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.9579259414915[/C][C]1.04207405850848[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9303143118036[/C][C]0.0696856881963832[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.0866691944922[/C][C]-3.08666919449224[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.1357322500619[/C][C]-1.13573225006194[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8705927486223[/C][C]2.12940725137774[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.9578607748318[/C][C]4.04213922516822[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.3540433170356[/C][C]1.64595668296442[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.4789926675795[/C][C]-2.47899266757949[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.5728320201697[/C][C]0.427167979830294[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.5864319671651[/C][C]1.41356803283489[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.8183115332664[/C][C]1.18168846673363[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.8240542314442[/C][C]1.17594576855576[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.3400146458708[/C][C]-0.340014645870803[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.642366933185[/C][C]0.357633066814957[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185732&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185732&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
11316.3939357953101-3.39393579531011
21616.1249109025263-0.124910902526285
31916.65633666250762.34366333749243
41512.16131531606662.83868468393337
51415.8122464930912-1.81224649309125
61315.0582534552499-2.05825345524992
71915.46707243406693.53292756593311
81516.9514512436044-1.9514512436044
91416.1712898507331-2.17128985073313
101512.87599814481932.1240018551807
111615.43215741311430.567842586885705
121616.3916925733918-0.391692573391792
131615.70441250629720.295587493702802
141615.57576296759270.424237032407311
151717.6720377985983-0.672037798598309
161515.3157903379227-0.315790337922683
171514.74668766373460.253312336265446
182016.44855516215343.55144483784657
191815.78847825120012.21152174879988
201615.4672345060940.53276549390605
211615.50717240499470.492827595005336
221615.09158748315440.908412516845636
231916.63810850036462.3618914996354
241615.13622112930730.863778870692651
251714.91148113415172.08851886584827
261716.92601507371080.0739849262892251
271615.02444585025360.975554149746439
281516.4229532560422-1.42295325604217
291615.52837840004940.471621599950583
301414.3589975181008-0.358997518100822
311515.7330763779131-0.733076377913122
321212.4727030715832-0.4727030715832
331415.1927810779091-1.19278107790908
341615.85239551365410.147604486345931
351415.5984453549319-1.59844535493189
36713.1699920005999-6.16999200059992
371011.0814680427486-1.08146804274863
381415.8913455548948-1.89134555489481
391614.39318976252831.60681023747168
401614.74288961234481.25711038765518
411615.14230107566530.857698924334732
421415.6085752796141-1.60857527961405
432017.90180195066072.09819804933928
441414.2958199647832-0.295819964783151
451414.9008100408017-0.900810040801691
461115.6625304866013-4.66253048660133
471416.7618884400336-2.76188844003356
481515.1089412790416-0.108941279041554
491615.22381469642240.776185303577594
501416.0304773281152-2.03047732811519
511616.6970728637325-0.697072863732525
521414.1512533402272-0.151253340227216
531214.751907135094-2.75190713509398
541615.80711851790550.192881482094495
55911.5611490191076-2.56114901910756
561412.75172507327551.24827492672446
571616.1114091539665-0.111409153966454
581615.29140449890430.708595501095701
591515.3665045305645-0.366504530564524
601614.26175505201811.73824494798191
611211.5029943773870.497005622613003
621615.92121410300740.0787858969925935
631616.475696767572-0.475696767571973
641414.445955354712-0.445955354712027
651615.40858553095780.591414469042171
661716.06661005300840.933389946991633
671816.24585589751031.75414410248975
681814.48568377244153.51431622755847
691215.9453797214583-3.94537972145832
701615.50410470186590.495895298134118
711013.6790780921669-3.67907809216693
721414.4576164423631-0.457616442363074
731816.59444459230611.4055554076939
741817.13288442488170.867115575118325
751615.57514711097020.424852889029838
761713.84080315438853.15919684561151
771616.3872633235391-0.387263323539065
781614.52355376284351.47644623715651
791314.8795604417562-1.87956044175625
801615.8226229848670.177377015133025
811615.52466320848110.475336791518864
822015.76198653283214.23801346716793
831615.81107128342520.188928716574802
841515.8608989380092-0.860898938009212
851514.76489520763520.23510479236479
861614.23561912659941.7643808734006
871414.2416613625257-0.241661362525693
881615.31543899435040.684561005649578
891614.56112126934431.43887873065573
901514.24790177278990.752098227210115
911213.6146292098439-1.61462920984387
921716.84745542138940.152544578610562
931615.5230551715780.476944828422002
941515.0543137803816-0.0543137803816209
951315.0358099996616-2.03580999966161
961615.05708040896750.942919591032457
971615.82671925820070.173280741799318
981613.9994611285982.00053887140203
991615.9656614165210.0343385834789743
1001414.332575159835-0.332575159834978
1011617.1631274687416-1.16312746874157
1021614.76437580614871.23562419385132
1032017.34568498924572.65431501075433
1041514.51211081945850.487889180541479
1051614.54076324947921.4592367505208
1061315.1856860772409-2.18568607724089
1071715.95144648521271.04855351478727
1081615.81718293537480.182817064625189
1091614.49110796576571.5088920342343
1101212.3843098386992-0.384309838699246
1111614.90382166240311.0961783375969
1121615.74004385859610.25995614140388
1131714.70474549616322.29525450383676
1141314.1844226196723-1.18442261967227
1151214.7869743982424-2.78697439824237
1161816.47280268222611.52719731777395
1171415.498542602307-1.498542602307
1181413.09626158591390.903738414086058
1191314.8625705836188-1.86257058361879
1201615.59278014298820.407219857011801
1211314.2432993640123-1.24329936401227
1221615.40329723077450.596702769225525
1231315.9066644720254-2.90666447202543
1241617.0017024479893-1.00170244798927
1251515.8363490247609-0.836349024760882
1261616.6261240582645-0.626124058264489
1271515.2459071517933-0.245907151793292
1281716.04344277943590.95655722056412
1291513.94506079536781.05493920463221
1301214.8058288255295-2.80582882552952
1311613.99872002231182.0012799776882
1321013.2575938465019-3.25759384650194
1331613.93768613619332.06231386380667
1341213.8054622064648-1.80546220646485
1351415.5991691252364-1.59916912523642
1361515.0509650215967-0.0509650215966947
1371311.99694920060381.00305079939623
1381514.4977580050150.502241994985038
1391113.2119069318468-2.21190693184677
1401213.2613168657549-1.26131686575485
141813.2423828609069-5.24238286090692
1421612.9642480201793.03575197982099
1431513.23578223902591.76421776097412
1441716.47192610881770.528073891182322
1451614.4397996059411.56020039405902
1461013.9337715249775-3.93377152497748
1471815.64222662021152.35777337978851
1481315.0851561191257-2.08515611912567
1491614.95792594149151.04207405850848
1501312.93031431180360.0696856881963832
1511013.0866691944922-3.08666919449224
1521516.1357322500619-1.13573225006194
1531613.87059274862232.12940725137774
1541611.95786077483184.04213922516822
1551412.35404331703561.64595668296442
1561012.4789926675795-2.47899266757949
1571716.57283202016970.427167979830294
1581311.58643196716511.41356803283489
1591513.81831153326641.18168846673363
1601614.82405423144421.17594576855576
1611212.3400146458708-0.340014645870803
1621312.6423669331850.357633066814957







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.917966559278250.16406688144350.0820334407217502
110.9088175964991490.1823648070017030.0911824035008514
120.8955000111184060.2089999777631880.104499988881594
130.8524384789312970.2951230421374070.147561521068703
140.7799154458955820.4401691082088370.220084554104418
150.7106859816154160.5786280367691690.289314018384584
160.7061131446603080.5877737106793850.293886855339693
170.6315179394853680.7369641210292650.368482060514632
180.8269919499562120.3460161000875770.173008050043788
190.796571305309680.406857389380640.20342869469032
200.7399545669398070.5200908661203870.260045433060193
210.671421228452270.6571575430954610.32857877154773
220.6345184927998590.7309630144002830.365481507200141
230.6040105781398240.7919788437203530.395989421860176
240.5746938975028320.8506122049943360.425306102497168
250.5187528808456550.962494238308690.481247119154345
260.4799614023109650.959922804621930.520038597689035
270.4292670543071820.8585341086143650.570732945692818
280.4756339494036150.951267898807230.524366050596385
290.4192760500841420.8385521001682850.580723949915858
300.4362580240605890.8725160481211780.563741975939411
310.389921834091170.7798436681823410.610078165908829
320.3879150162634320.7758300325268650.612084983736568
330.3828603914596160.7657207829192330.617139608540384
340.3257027205061190.6514054410122380.674297279493881
350.3235567046070770.6471134092141540.676443295392923
360.8180653366794430.3638693266411140.181934663320557
370.8008160343406430.3983679313187140.199183965659357
380.7844926622010710.4310146755978580.215507337798929
390.8191993771267040.3616012457465910.180800622873296
400.805548775787480.388902448425040.19445122421252
410.7778534576786140.4442930846427720.222146542321386
420.7551752665519660.4896494668960680.244824733448034
430.7750806530313110.4498386939373780.224919346968689
440.7355479678776440.5289040642447120.264452032122356
450.7034976637258070.5930046725483860.296502336274193
460.8694006069615760.2611987860768490.130599393038424
470.8814916688902670.2370166622194670.118508331109733
480.8588051508022430.2823896983955130.141194849197757
490.842249298935590.315501402128820.15775070106441
500.8339504849028430.3320990301943150.166049515097158
510.8052173764998340.3895652470003330.194782623500166
520.7719593514643960.4560812970712090.228040648535604
530.7889554493780050.422089101243990.211044550621995
540.7623647804132810.4752704391734380.237635219586719
550.7829447654452690.4341104691094620.217055234554731
560.7739967233245150.4520065533509710.226003276675485
570.7372690226870660.5254619546258690.262730977312934
580.7262543546329980.5474912907340040.273745645367002
590.691418384150920.6171632316981590.30858161584908
600.6999927936760460.6000144126479080.300007206323954
610.6638813798913140.6722372402173720.336118620108686
620.6224369384825640.7551261230348720.377563061517436
630.5833868929588310.8332262140823380.416613107041169
640.540442667981450.91911466403710.45955733201855
650.5024471572055260.9951056855889490.497552842794474
660.4785046010502380.9570092021004760.521495398949762
670.4730645460102580.9461290920205170.526935453989742
680.599541297031890.800917405936220.40045870296811
690.7397956622108320.5204086755783350.260204337789168
700.704795091663860.590409816672280.29520490833614
710.8126108811684640.3747782376630720.187389118831536
720.7837097098539870.4325805802920260.216290290146013
730.7703903861315930.4592192277368150.229609613868407
740.7446748443933480.5106503112133040.255325155606652
750.7080045312079070.5839909375841870.291995468792093
760.766139682509810.4677206349803810.23386031749019
770.7306226764913510.5387546470172990.269377323508649
780.7125159240759350.5749681518481290.287484075924065
790.7170959520448280.5658080959103440.282904047955172
800.6832000551747060.6335998896505890.316799944825294
810.6434690454925940.7130619090148120.356530954507406
820.7982252486047870.4035495027904250.201774751395213
830.7642644293423060.4714711413153880.235735570657694
840.7366297791034640.5267404417930730.263370220896536
850.6978271243215660.6043457513568680.302172875678434
860.686444496118710.627111007762580.31355550388129
870.6458353912521190.7083292174957610.354164608747881
880.6051846434202830.7896307131594350.394815356579717
890.5838399453027520.8323201093944970.416160054697248
900.5466459804485230.9067080391029550.453354019551477
910.5406240633218080.9187518733563840.459375936678192
920.4985858062879010.9971716125758010.501414193712099
930.453800698627150.9076013972543010.54619930137285
940.4074960047694990.8149920095389980.592503995230501
950.4323476212039360.8646952424078720.567652378796064
960.3938852982441740.7877705964883470.606114701755826
970.3513487859190120.7026975718380250.648651214080988
980.3459663620931950.6919327241863890.654033637906805
990.3029245562229570.6058491124459150.697075443777043
1000.2677243243494210.5354486486988420.732275675650579
1010.2441429036673990.4882858073347980.755857096332601
1020.2231359442250860.4462718884501730.776864055774914
1030.2673918566341970.5347837132683940.732608143365803
1040.2299529216614150.459905843322830.770047078338585
1050.2202501766762660.4405003533525320.779749823323734
1060.2281574690698720.4563149381397440.771842530930128
1070.2070828136324290.4141656272648570.792917186367571
1080.1846511133781570.3693022267563140.815348886621843
1090.1816864009007650.3633728018015310.818313599099235
1100.1786961087189790.3573922174379590.821303891281021
1110.163624333115630.3272486662312610.83637566688437
1120.1418213393925290.2836426787850590.858178660607471
1130.1701937095781970.3403874191563930.829806290421803
1140.1459598488818390.2919196977636790.854040151118161
1150.1626633041495310.3253266082990610.837336695850469
1160.1734536634562280.3469073269124570.826546336543772
1170.151830575304220.3036611506084410.84816942469578
1180.1414668771229090.2829337542458180.858533122877091
1190.1322168851312420.2644337702624840.867783114868758
1200.1414462848861890.2828925697723780.858553715113811
1210.1174375233261820.2348750466523640.882562476673818
1220.1106805997883070.2213611995766140.889319400211693
1230.111721787709170.223443575418340.88827821229083
1240.08985425122703690.1797085024540740.910145748772963
1250.0703531386438340.1407062772876680.929646861356166
1260.05380099528966990.107601990579340.94619900471033
1270.04027974521821140.08055949043642280.959720254781789
1280.04034447238975740.08068894477951480.959655527610243
1290.03653478447998820.07306956895997630.963465215520012
1300.03644855408120480.07289710816240950.963551445918795
1310.0424404436783220.0848808873566440.957559556321678
1320.04739080958538210.09478161917076420.952609190414618
1330.1053073367902270.2106146735804530.894692663209773
1340.1103898268553420.2207796537106840.889610173144658
1350.08643000729101860.1728600145820370.913569992708981
1360.06736913244628290.1347382648925660.932630867553717
1370.05756150970803170.1151230194160630.942438490291968
1380.05173065374544770.1034613074908950.948269346254552
1390.05377685954897650.1075537190979530.946223140451024
1400.03844962597447110.07689925194894220.961550374025529
1410.5069374504156090.9861250991687830.493062549584391
1420.4555061173441090.9110122346882180.544493882655891
1430.4185229533248380.8370459066496750.581477046675162
1440.3375356393952350.6750712787904690.662464360604765
1450.2749128261351680.5498256522703370.725087173864831
1460.2879916213866780.5759832427733550.712008378613322
1470.4124022174345340.8248044348690670.587597782565466
1480.6459050399702120.7081899200595750.354094960029788
1490.5304177412673320.9391645174653360.469582258732668
1500.4039790770244340.8079581540488680.596020922975566
1510.7365982044840330.5268035910319340.263401795515967
1520.6233834807327020.7532330385345960.376616519267298

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.91796655927825 & 0.1640668814435 & 0.0820334407217502 \tabularnewline
11 & 0.908817596499149 & 0.182364807001703 & 0.0911824035008514 \tabularnewline
12 & 0.895500011118406 & 0.208999977763188 & 0.104499988881594 \tabularnewline
13 & 0.852438478931297 & 0.295123042137407 & 0.147561521068703 \tabularnewline
14 & 0.779915445895582 & 0.440169108208837 & 0.220084554104418 \tabularnewline
15 & 0.710685981615416 & 0.578628036769169 & 0.289314018384584 \tabularnewline
16 & 0.706113144660308 & 0.587773710679385 & 0.293886855339693 \tabularnewline
17 & 0.631517939485368 & 0.736964121029265 & 0.368482060514632 \tabularnewline
18 & 0.826991949956212 & 0.346016100087577 & 0.173008050043788 \tabularnewline
19 & 0.79657130530968 & 0.40685738938064 & 0.20342869469032 \tabularnewline
20 & 0.739954566939807 & 0.520090866120387 & 0.260045433060193 \tabularnewline
21 & 0.67142122845227 & 0.657157543095461 & 0.32857877154773 \tabularnewline
22 & 0.634518492799859 & 0.730963014400283 & 0.365481507200141 \tabularnewline
23 & 0.604010578139824 & 0.791978843720353 & 0.395989421860176 \tabularnewline
24 & 0.574693897502832 & 0.850612204994336 & 0.425306102497168 \tabularnewline
25 & 0.518752880845655 & 0.96249423830869 & 0.481247119154345 \tabularnewline
26 & 0.479961402310965 & 0.95992280462193 & 0.520038597689035 \tabularnewline
27 & 0.429267054307182 & 0.858534108614365 & 0.570732945692818 \tabularnewline
28 & 0.475633949403615 & 0.95126789880723 & 0.524366050596385 \tabularnewline
29 & 0.419276050084142 & 0.838552100168285 & 0.580723949915858 \tabularnewline
30 & 0.436258024060589 & 0.872516048121178 & 0.563741975939411 \tabularnewline
31 & 0.38992183409117 & 0.779843668182341 & 0.610078165908829 \tabularnewline
32 & 0.387915016263432 & 0.775830032526865 & 0.612084983736568 \tabularnewline
33 & 0.382860391459616 & 0.765720782919233 & 0.617139608540384 \tabularnewline
34 & 0.325702720506119 & 0.651405441012238 & 0.674297279493881 \tabularnewline
35 & 0.323556704607077 & 0.647113409214154 & 0.676443295392923 \tabularnewline
36 & 0.818065336679443 & 0.363869326641114 & 0.181934663320557 \tabularnewline
37 & 0.800816034340643 & 0.398367931318714 & 0.199183965659357 \tabularnewline
38 & 0.784492662201071 & 0.431014675597858 & 0.215507337798929 \tabularnewline
39 & 0.819199377126704 & 0.361601245746591 & 0.180800622873296 \tabularnewline
40 & 0.80554877578748 & 0.38890244842504 & 0.19445122421252 \tabularnewline
41 & 0.777853457678614 & 0.444293084642772 & 0.222146542321386 \tabularnewline
42 & 0.755175266551966 & 0.489649466896068 & 0.244824733448034 \tabularnewline
43 & 0.775080653031311 & 0.449838693937378 & 0.224919346968689 \tabularnewline
44 & 0.735547967877644 & 0.528904064244712 & 0.264452032122356 \tabularnewline
45 & 0.703497663725807 & 0.593004672548386 & 0.296502336274193 \tabularnewline
46 & 0.869400606961576 & 0.261198786076849 & 0.130599393038424 \tabularnewline
47 & 0.881491668890267 & 0.237016662219467 & 0.118508331109733 \tabularnewline
48 & 0.858805150802243 & 0.282389698395513 & 0.141194849197757 \tabularnewline
49 & 0.84224929893559 & 0.31550140212882 & 0.15775070106441 \tabularnewline
50 & 0.833950484902843 & 0.332099030194315 & 0.166049515097158 \tabularnewline
51 & 0.805217376499834 & 0.389565247000333 & 0.194782623500166 \tabularnewline
52 & 0.771959351464396 & 0.456081297071209 & 0.228040648535604 \tabularnewline
53 & 0.788955449378005 & 0.42208910124399 & 0.211044550621995 \tabularnewline
54 & 0.762364780413281 & 0.475270439173438 & 0.237635219586719 \tabularnewline
55 & 0.782944765445269 & 0.434110469109462 & 0.217055234554731 \tabularnewline
56 & 0.773996723324515 & 0.452006553350971 & 0.226003276675485 \tabularnewline
57 & 0.737269022687066 & 0.525461954625869 & 0.262730977312934 \tabularnewline
58 & 0.726254354632998 & 0.547491290734004 & 0.273745645367002 \tabularnewline
59 & 0.69141838415092 & 0.617163231698159 & 0.30858161584908 \tabularnewline
60 & 0.699992793676046 & 0.600014412647908 & 0.300007206323954 \tabularnewline
61 & 0.663881379891314 & 0.672237240217372 & 0.336118620108686 \tabularnewline
62 & 0.622436938482564 & 0.755126123034872 & 0.377563061517436 \tabularnewline
63 & 0.583386892958831 & 0.833226214082338 & 0.416613107041169 \tabularnewline
64 & 0.54044266798145 & 0.9191146640371 & 0.45955733201855 \tabularnewline
65 & 0.502447157205526 & 0.995105685588949 & 0.497552842794474 \tabularnewline
66 & 0.478504601050238 & 0.957009202100476 & 0.521495398949762 \tabularnewline
67 & 0.473064546010258 & 0.946129092020517 & 0.526935453989742 \tabularnewline
68 & 0.59954129703189 & 0.80091740593622 & 0.40045870296811 \tabularnewline
69 & 0.739795662210832 & 0.520408675578335 & 0.260204337789168 \tabularnewline
70 & 0.70479509166386 & 0.59040981667228 & 0.29520490833614 \tabularnewline
71 & 0.812610881168464 & 0.374778237663072 & 0.187389118831536 \tabularnewline
72 & 0.783709709853987 & 0.432580580292026 & 0.216290290146013 \tabularnewline
73 & 0.770390386131593 & 0.459219227736815 & 0.229609613868407 \tabularnewline
74 & 0.744674844393348 & 0.510650311213304 & 0.255325155606652 \tabularnewline
75 & 0.708004531207907 & 0.583990937584187 & 0.291995468792093 \tabularnewline
76 & 0.76613968250981 & 0.467720634980381 & 0.23386031749019 \tabularnewline
77 & 0.730622676491351 & 0.538754647017299 & 0.269377323508649 \tabularnewline
78 & 0.712515924075935 & 0.574968151848129 & 0.287484075924065 \tabularnewline
79 & 0.717095952044828 & 0.565808095910344 & 0.282904047955172 \tabularnewline
80 & 0.683200055174706 & 0.633599889650589 & 0.316799944825294 \tabularnewline
81 & 0.643469045492594 & 0.713061909014812 & 0.356530954507406 \tabularnewline
82 & 0.798225248604787 & 0.403549502790425 & 0.201774751395213 \tabularnewline
83 & 0.764264429342306 & 0.471471141315388 & 0.235735570657694 \tabularnewline
84 & 0.736629779103464 & 0.526740441793073 & 0.263370220896536 \tabularnewline
85 & 0.697827124321566 & 0.604345751356868 & 0.302172875678434 \tabularnewline
86 & 0.68644449611871 & 0.62711100776258 & 0.31355550388129 \tabularnewline
87 & 0.645835391252119 & 0.708329217495761 & 0.354164608747881 \tabularnewline
88 & 0.605184643420283 & 0.789630713159435 & 0.394815356579717 \tabularnewline
89 & 0.583839945302752 & 0.832320109394497 & 0.416160054697248 \tabularnewline
90 & 0.546645980448523 & 0.906708039102955 & 0.453354019551477 \tabularnewline
91 & 0.540624063321808 & 0.918751873356384 & 0.459375936678192 \tabularnewline
92 & 0.498585806287901 & 0.997171612575801 & 0.501414193712099 \tabularnewline
93 & 0.45380069862715 & 0.907601397254301 & 0.54619930137285 \tabularnewline
94 & 0.407496004769499 & 0.814992009538998 & 0.592503995230501 \tabularnewline
95 & 0.432347621203936 & 0.864695242407872 & 0.567652378796064 \tabularnewline
96 & 0.393885298244174 & 0.787770596488347 & 0.606114701755826 \tabularnewline
97 & 0.351348785919012 & 0.702697571838025 & 0.648651214080988 \tabularnewline
98 & 0.345966362093195 & 0.691932724186389 & 0.654033637906805 \tabularnewline
99 & 0.302924556222957 & 0.605849112445915 & 0.697075443777043 \tabularnewline
100 & 0.267724324349421 & 0.535448648698842 & 0.732275675650579 \tabularnewline
101 & 0.244142903667399 & 0.488285807334798 & 0.755857096332601 \tabularnewline
102 & 0.223135944225086 & 0.446271888450173 & 0.776864055774914 \tabularnewline
103 & 0.267391856634197 & 0.534783713268394 & 0.732608143365803 \tabularnewline
104 & 0.229952921661415 & 0.45990584332283 & 0.770047078338585 \tabularnewline
105 & 0.220250176676266 & 0.440500353352532 & 0.779749823323734 \tabularnewline
106 & 0.228157469069872 & 0.456314938139744 & 0.771842530930128 \tabularnewline
107 & 0.207082813632429 & 0.414165627264857 & 0.792917186367571 \tabularnewline
108 & 0.184651113378157 & 0.369302226756314 & 0.815348886621843 \tabularnewline
109 & 0.181686400900765 & 0.363372801801531 & 0.818313599099235 \tabularnewline
110 & 0.178696108718979 & 0.357392217437959 & 0.821303891281021 \tabularnewline
111 & 0.16362433311563 & 0.327248666231261 & 0.83637566688437 \tabularnewline
112 & 0.141821339392529 & 0.283642678785059 & 0.858178660607471 \tabularnewline
113 & 0.170193709578197 & 0.340387419156393 & 0.829806290421803 \tabularnewline
114 & 0.145959848881839 & 0.291919697763679 & 0.854040151118161 \tabularnewline
115 & 0.162663304149531 & 0.325326608299061 & 0.837336695850469 \tabularnewline
116 & 0.173453663456228 & 0.346907326912457 & 0.826546336543772 \tabularnewline
117 & 0.15183057530422 & 0.303661150608441 & 0.84816942469578 \tabularnewline
118 & 0.141466877122909 & 0.282933754245818 & 0.858533122877091 \tabularnewline
119 & 0.132216885131242 & 0.264433770262484 & 0.867783114868758 \tabularnewline
120 & 0.141446284886189 & 0.282892569772378 & 0.858553715113811 \tabularnewline
121 & 0.117437523326182 & 0.234875046652364 & 0.882562476673818 \tabularnewline
122 & 0.110680599788307 & 0.221361199576614 & 0.889319400211693 \tabularnewline
123 & 0.11172178770917 & 0.22344357541834 & 0.88827821229083 \tabularnewline
124 & 0.0898542512270369 & 0.179708502454074 & 0.910145748772963 \tabularnewline
125 & 0.070353138643834 & 0.140706277287668 & 0.929646861356166 \tabularnewline
126 & 0.0538009952896699 & 0.10760199057934 & 0.94619900471033 \tabularnewline
127 & 0.0402797452182114 & 0.0805594904364228 & 0.959720254781789 \tabularnewline
128 & 0.0403444723897574 & 0.0806889447795148 & 0.959655527610243 \tabularnewline
129 & 0.0365347844799882 & 0.0730695689599763 & 0.963465215520012 \tabularnewline
130 & 0.0364485540812048 & 0.0728971081624095 & 0.963551445918795 \tabularnewline
131 & 0.042440443678322 & 0.084880887356644 & 0.957559556321678 \tabularnewline
132 & 0.0473908095853821 & 0.0947816191707642 & 0.952609190414618 \tabularnewline
133 & 0.105307336790227 & 0.210614673580453 & 0.894692663209773 \tabularnewline
134 & 0.110389826855342 & 0.220779653710684 & 0.889610173144658 \tabularnewline
135 & 0.0864300072910186 & 0.172860014582037 & 0.913569992708981 \tabularnewline
136 & 0.0673691324462829 & 0.134738264892566 & 0.932630867553717 \tabularnewline
137 & 0.0575615097080317 & 0.115123019416063 & 0.942438490291968 \tabularnewline
138 & 0.0517306537454477 & 0.103461307490895 & 0.948269346254552 \tabularnewline
139 & 0.0537768595489765 & 0.107553719097953 & 0.946223140451024 \tabularnewline
140 & 0.0384496259744711 & 0.0768992519489422 & 0.961550374025529 \tabularnewline
141 & 0.506937450415609 & 0.986125099168783 & 0.493062549584391 \tabularnewline
142 & 0.455506117344109 & 0.911012234688218 & 0.544493882655891 \tabularnewline
143 & 0.418522953324838 & 0.837045906649675 & 0.581477046675162 \tabularnewline
144 & 0.337535639395235 & 0.675071278790469 & 0.662464360604765 \tabularnewline
145 & 0.274912826135168 & 0.549825652270337 & 0.725087173864831 \tabularnewline
146 & 0.287991621386678 & 0.575983242773355 & 0.712008378613322 \tabularnewline
147 & 0.412402217434534 & 0.824804434869067 & 0.587597782565466 \tabularnewline
148 & 0.645905039970212 & 0.708189920059575 & 0.354094960029788 \tabularnewline
149 & 0.530417741267332 & 0.939164517465336 & 0.469582258732668 \tabularnewline
150 & 0.403979077024434 & 0.807958154048868 & 0.596020922975566 \tabularnewline
151 & 0.736598204484033 & 0.526803591031934 & 0.263401795515967 \tabularnewline
152 & 0.623383480732702 & 0.753233038534596 & 0.376616519267298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185732&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]10[/C][C]0.91796655927825[/C][C]0.1640668814435[/C][C]0.0820334407217502[/C][/ROW]
[ROW][C]11[/C][C]0.908817596499149[/C][C]0.182364807001703[/C][C]0.0911824035008514[/C][/ROW]
[ROW][C]12[/C][C]0.895500011118406[/C][C]0.208999977763188[/C][C]0.104499988881594[/C][/ROW]
[ROW][C]13[/C][C]0.852438478931297[/C][C]0.295123042137407[/C][C]0.147561521068703[/C][/ROW]
[ROW][C]14[/C][C]0.779915445895582[/C][C]0.440169108208837[/C][C]0.220084554104418[/C][/ROW]
[ROW][C]15[/C][C]0.710685981615416[/C][C]0.578628036769169[/C][C]0.289314018384584[/C][/ROW]
[ROW][C]16[/C][C]0.706113144660308[/C][C]0.587773710679385[/C][C]0.293886855339693[/C][/ROW]
[ROW][C]17[/C][C]0.631517939485368[/C][C]0.736964121029265[/C][C]0.368482060514632[/C][/ROW]
[ROW][C]18[/C][C]0.826991949956212[/C][C]0.346016100087577[/C][C]0.173008050043788[/C][/ROW]
[ROW][C]19[/C][C]0.79657130530968[/C][C]0.40685738938064[/C][C]0.20342869469032[/C][/ROW]
[ROW][C]20[/C][C]0.739954566939807[/C][C]0.520090866120387[/C][C]0.260045433060193[/C][/ROW]
[ROW][C]21[/C][C]0.67142122845227[/C][C]0.657157543095461[/C][C]0.32857877154773[/C][/ROW]
[ROW][C]22[/C][C]0.634518492799859[/C][C]0.730963014400283[/C][C]0.365481507200141[/C][/ROW]
[ROW][C]23[/C][C]0.604010578139824[/C][C]0.791978843720353[/C][C]0.395989421860176[/C][/ROW]
[ROW][C]24[/C][C]0.574693897502832[/C][C]0.850612204994336[/C][C]0.425306102497168[/C][/ROW]
[ROW][C]25[/C][C]0.518752880845655[/C][C]0.96249423830869[/C][C]0.481247119154345[/C][/ROW]
[ROW][C]26[/C][C]0.479961402310965[/C][C]0.95992280462193[/C][C]0.520038597689035[/C][/ROW]
[ROW][C]27[/C][C]0.429267054307182[/C][C]0.858534108614365[/C][C]0.570732945692818[/C][/ROW]
[ROW][C]28[/C][C]0.475633949403615[/C][C]0.95126789880723[/C][C]0.524366050596385[/C][/ROW]
[ROW][C]29[/C][C]0.419276050084142[/C][C]0.838552100168285[/C][C]0.580723949915858[/C][/ROW]
[ROW][C]30[/C][C]0.436258024060589[/C][C]0.872516048121178[/C][C]0.563741975939411[/C][/ROW]
[ROW][C]31[/C][C]0.38992183409117[/C][C]0.779843668182341[/C][C]0.610078165908829[/C][/ROW]
[ROW][C]32[/C][C]0.387915016263432[/C][C]0.775830032526865[/C][C]0.612084983736568[/C][/ROW]
[ROW][C]33[/C][C]0.382860391459616[/C][C]0.765720782919233[/C][C]0.617139608540384[/C][/ROW]
[ROW][C]34[/C][C]0.325702720506119[/C][C]0.651405441012238[/C][C]0.674297279493881[/C][/ROW]
[ROW][C]35[/C][C]0.323556704607077[/C][C]0.647113409214154[/C][C]0.676443295392923[/C][/ROW]
[ROW][C]36[/C][C]0.818065336679443[/C][C]0.363869326641114[/C][C]0.181934663320557[/C][/ROW]
[ROW][C]37[/C][C]0.800816034340643[/C][C]0.398367931318714[/C][C]0.199183965659357[/C][/ROW]
[ROW][C]38[/C][C]0.784492662201071[/C][C]0.431014675597858[/C][C]0.215507337798929[/C][/ROW]
[ROW][C]39[/C][C]0.819199377126704[/C][C]0.361601245746591[/C][C]0.180800622873296[/C][/ROW]
[ROW][C]40[/C][C]0.80554877578748[/C][C]0.38890244842504[/C][C]0.19445122421252[/C][/ROW]
[ROW][C]41[/C][C]0.777853457678614[/C][C]0.444293084642772[/C][C]0.222146542321386[/C][/ROW]
[ROW][C]42[/C][C]0.755175266551966[/C][C]0.489649466896068[/C][C]0.244824733448034[/C][/ROW]
[ROW][C]43[/C][C]0.775080653031311[/C][C]0.449838693937378[/C][C]0.224919346968689[/C][/ROW]
[ROW][C]44[/C][C]0.735547967877644[/C][C]0.528904064244712[/C][C]0.264452032122356[/C][/ROW]
[ROW][C]45[/C][C]0.703497663725807[/C][C]0.593004672548386[/C][C]0.296502336274193[/C][/ROW]
[ROW][C]46[/C][C]0.869400606961576[/C][C]0.261198786076849[/C][C]0.130599393038424[/C][/ROW]
[ROW][C]47[/C][C]0.881491668890267[/C][C]0.237016662219467[/C][C]0.118508331109733[/C][/ROW]
[ROW][C]48[/C][C]0.858805150802243[/C][C]0.282389698395513[/C][C]0.141194849197757[/C][/ROW]
[ROW][C]49[/C][C]0.84224929893559[/C][C]0.31550140212882[/C][C]0.15775070106441[/C][/ROW]
[ROW][C]50[/C][C]0.833950484902843[/C][C]0.332099030194315[/C][C]0.166049515097158[/C][/ROW]
[ROW][C]51[/C][C]0.805217376499834[/C][C]0.389565247000333[/C][C]0.194782623500166[/C][/ROW]
[ROW][C]52[/C][C]0.771959351464396[/C][C]0.456081297071209[/C][C]0.228040648535604[/C][/ROW]
[ROW][C]53[/C][C]0.788955449378005[/C][C]0.42208910124399[/C][C]0.211044550621995[/C][/ROW]
[ROW][C]54[/C][C]0.762364780413281[/C][C]0.475270439173438[/C][C]0.237635219586719[/C][/ROW]
[ROW][C]55[/C][C]0.782944765445269[/C][C]0.434110469109462[/C][C]0.217055234554731[/C][/ROW]
[ROW][C]56[/C][C]0.773996723324515[/C][C]0.452006553350971[/C][C]0.226003276675485[/C][/ROW]
[ROW][C]57[/C][C]0.737269022687066[/C][C]0.525461954625869[/C][C]0.262730977312934[/C][/ROW]
[ROW][C]58[/C][C]0.726254354632998[/C][C]0.547491290734004[/C][C]0.273745645367002[/C][/ROW]
[ROW][C]59[/C][C]0.69141838415092[/C][C]0.617163231698159[/C][C]0.30858161584908[/C][/ROW]
[ROW][C]60[/C][C]0.699992793676046[/C][C]0.600014412647908[/C][C]0.300007206323954[/C][/ROW]
[ROW][C]61[/C][C]0.663881379891314[/C][C]0.672237240217372[/C][C]0.336118620108686[/C][/ROW]
[ROW][C]62[/C][C]0.622436938482564[/C][C]0.755126123034872[/C][C]0.377563061517436[/C][/ROW]
[ROW][C]63[/C][C]0.583386892958831[/C][C]0.833226214082338[/C][C]0.416613107041169[/C][/ROW]
[ROW][C]64[/C][C]0.54044266798145[/C][C]0.9191146640371[/C][C]0.45955733201855[/C][/ROW]
[ROW][C]65[/C][C]0.502447157205526[/C][C]0.995105685588949[/C][C]0.497552842794474[/C][/ROW]
[ROW][C]66[/C][C]0.478504601050238[/C][C]0.957009202100476[/C][C]0.521495398949762[/C][/ROW]
[ROW][C]67[/C][C]0.473064546010258[/C][C]0.946129092020517[/C][C]0.526935453989742[/C][/ROW]
[ROW][C]68[/C][C]0.59954129703189[/C][C]0.80091740593622[/C][C]0.40045870296811[/C][/ROW]
[ROW][C]69[/C][C]0.739795662210832[/C][C]0.520408675578335[/C][C]0.260204337789168[/C][/ROW]
[ROW][C]70[/C][C]0.70479509166386[/C][C]0.59040981667228[/C][C]0.29520490833614[/C][/ROW]
[ROW][C]71[/C][C]0.812610881168464[/C][C]0.374778237663072[/C][C]0.187389118831536[/C][/ROW]
[ROW][C]72[/C][C]0.783709709853987[/C][C]0.432580580292026[/C][C]0.216290290146013[/C][/ROW]
[ROW][C]73[/C][C]0.770390386131593[/C][C]0.459219227736815[/C][C]0.229609613868407[/C][/ROW]
[ROW][C]74[/C][C]0.744674844393348[/C][C]0.510650311213304[/C][C]0.255325155606652[/C][/ROW]
[ROW][C]75[/C][C]0.708004531207907[/C][C]0.583990937584187[/C][C]0.291995468792093[/C][/ROW]
[ROW][C]76[/C][C]0.76613968250981[/C][C]0.467720634980381[/C][C]0.23386031749019[/C][/ROW]
[ROW][C]77[/C][C]0.730622676491351[/C][C]0.538754647017299[/C][C]0.269377323508649[/C][/ROW]
[ROW][C]78[/C][C]0.712515924075935[/C][C]0.574968151848129[/C][C]0.287484075924065[/C][/ROW]
[ROW][C]79[/C][C]0.717095952044828[/C][C]0.565808095910344[/C][C]0.282904047955172[/C][/ROW]
[ROW][C]80[/C][C]0.683200055174706[/C][C]0.633599889650589[/C][C]0.316799944825294[/C][/ROW]
[ROW][C]81[/C][C]0.643469045492594[/C][C]0.713061909014812[/C][C]0.356530954507406[/C][/ROW]
[ROW][C]82[/C][C]0.798225248604787[/C][C]0.403549502790425[/C][C]0.201774751395213[/C][/ROW]
[ROW][C]83[/C][C]0.764264429342306[/C][C]0.471471141315388[/C][C]0.235735570657694[/C][/ROW]
[ROW][C]84[/C][C]0.736629779103464[/C][C]0.526740441793073[/C][C]0.263370220896536[/C][/ROW]
[ROW][C]85[/C][C]0.697827124321566[/C][C]0.604345751356868[/C][C]0.302172875678434[/C][/ROW]
[ROW][C]86[/C][C]0.68644449611871[/C][C]0.62711100776258[/C][C]0.31355550388129[/C][/ROW]
[ROW][C]87[/C][C]0.645835391252119[/C][C]0.708329217495761[/C][C]0.354164608747881[/C][/ROW]
[ROW][C]88[/C][C]0.605184643420283[/C][C]0.789630713159435[/C][C]0.394815356579717[/C][/ROW]
[ROW][C]89[/C][C]0.583839945302752[/C][C]0.832320109394497[/C][C]0.416160054697248[/C][/ROW]
[ROW][C]90[/C][C]0.546645980448523[/C][C]0.906708039102955[/C][C]0.453354019551477[/C][/ROW]
[ROW][C]91[/C][C]0.540624063321808[/C][C]0.918751873356384[/C][C]0.459375936678192[/C][/ROW]
[ROW][C]92[/C][C]0.498585806287901[/C][C]0.997171612575801[/C][C]0.501414193712099[/C][/ROW]
[ROW][C]93[/C][C]0.45380069862715[/C][C]0.907601397254301[/C][C]0.54619930137285[/C][/ROW]
[ROW][C]94[/C][C]0.407496004769499[/C][C]0.814992009538998[/C][C]0.592503995230501[/C][/ROW]
[ROW][C]95[/C][C]0.432347621203936[/C][C]0.864695242407872[/C][C]0.567652378796064[/C][/ROW]
[ROW][C]96[/C][C]0.393885298244174[/C][C]0.787770596488347[/C][C]0.606114701755826[/C][/ROW]
[ROW][C]97[/C][C]0.351348785919012[/C][C]0.702697571838025[/C][C]0.648651214080988[/C][/ROW]
[ROW][C]98[/C][C]0.345966362093195[/C][C]0.691932724186389[/C][C]0.654033637906805[/C][/ROW]
[ROW][C]99[/C][C]0.302924556222957[/C][C]0.605849112445915[/C][C]0.697075443777043[/C][/ROW]
[ROW][C]100[/C][C]0.267724324349421[/C][C]0.535448648698842[/C][C]0.732275675650579[/C][/ROW]
[ROW][C]101[/C][C]0.244142903667399[/C][C]0.488285807334798[/C][C]0.755857096332601[/C][/ROW]
[ROW][C]102[/C][C]0.223135944225086[/C][C]0.446271888450173[/C][C]0.776864055774914[/C][/ROW]
[ROW][C]103[/C][C]0.267391856634197[/C][C]0.534783713268394[/C][C]0.732608143365803[/C][/ROW]
[ROW][C]104[/C][C]0.229952921661415[/C][C]0.45990584332283[/C][C]0.770047078338585[/C][/ROW]
[ROW][C]105[/C][C]0.220250176676266[/C][C]0.440500353352532[/C][C]0.779749823323734[/C][/ROW]
[ROW][C]106[/C][C]0.228157469069872[/C][C]0.456314938139744[/C][C]0.771842530930128[/C][/ROW]
[ROW][C]107[/C][C]0.207082813632429[/C][C]0.414165627264857[/C][C]0.792917186367571[/C][/ROW]
[ROW][C]108[/C][C]0.184651113378157[/C][C]0.369302226756314[/C][C]0.815348886621843[/C][/ROW]
[ROW][C]109[/C][C]0.181686400900765[/C][C]0.363372801801531[/C][C]0.818313599099235[/C][/ROW]
[ROW][C]110[/C][C]0.178696108718979[/C][C]0.357392217437959[/C][C]0.821303891281021[/C][/ROW]
[ROW][C]111[/C][C]0.16362433311563[/C][C]0.327248666231261[/C][C]0.83637566688437[/C][/ROW]
[ROW][C]112[/C][C]0.141821339392529[/C][C]0.283642678785059[/C][C]0.858178660607471[/C][/ROW]
[ROW][C]113[/C][C]0.170193709578197[/C][C]0.340387419156393[/C][C]0.829806290421803[/C][/ROW]
[ROW][C]114[/C][C]0.145959848881839[/C][C]0.291919697763679[/C][C]0.854040151118161[/C][/ROW]
[ROW][C]115[/C][C]0.162663304149531[/C][C]0.325326608299061[/C][C]0.837336695850469[/C][/ROW]
[ROW][C]116[/C][C]0.173453663456228[/C][C]0.346907326912457[/C][C]0.826546336543772[/C][/ROW]
[ROW][C]117[/C][C]0.15183057530422[/C][C]0.303661150608441[/C][C]0.84816942469578[/C][/ROW]
[ROW][C]118[/C][C]0.141466877122909[/C][C]0.282933754245818[/C][C]0.858533122877091[/C][/ROW]
[ROW][C]119[/C][C]0.132216885131242[/C][C]0.264433770262484[/C][C]0.867783114868758[/C][/ROW]
[ROW][C]120[/C][C]0.141446284886189[/C][C]0.282892569772378[/C][C]0.858553715113811[/C][/ROW]
[ROW][C]121[/C][C]0.117437523326182[/C][C]0.234875046652364[/C][C]0.882562476673818[/C][/ROW]
[ROW][C]122[/C][C]0.110680599788307[/C][C]0.221361199576614[/C][C]0.889319400211693[/C][/ROW]
[ROW][C]123[/C][C]0.11172178770917[/C][C]0.22344357541834[/C][C]0.88827821229083[/C][/ROW]
[ROW][C]124[/C][C]0.0898542512270369[/C][C]0.179708502454074[/C][C]0.910145748772963[/C][/ROW]
[ROW][C]125[/C][C]0.070353138643834[/C][C]0.140706277287668[/C][C]0.929646861356166[/C][/ROW]
[ROW][C]126[/C][C]0.0538009952896699[/C][C]0.10760199057934[/C][C]0.94619900471033[/C][/ROW]
[ROW][C]127[/C][C]0.0402797452182114[/C][C]0.0805594904364228[/C][C]0.959720254781789[/C][/ROW]
[ROW][C]128[/C][C]0.0403444723897574[/C][C]0.0806889447795148[/C][C]0.959655527610243[/C][/ROW]
[ROW][C]129[/C][C]0.0365347844799882[/C][C]0.0730695689599763[/C][C]0.963465215520012[/C][/ROW]
[ROW][C]130[/C][C]0.0364485540812048[/C][C]0.0728971081624095[/C][C]0.963551445918795[/C][/ROW]
[ROW][C]131[/C][C]0.042440443678322[/C][C]0.084880887356644[/C][C]0.957559556321678[/C][/ROW]
[ROW][C]132[/C][C]0.0473908095853821[/C][C]0.0947816191707642[/C][C]0.952609190414618[/C][/ROW]
[ROW][C]133[/C][C]0.105307336790227[/C][C]0.210614673580453[/C][C]0.894692663209773[/C][/ROW]
[ROW][C]134[/C][C]0.110389826855342[/C][C]0.220779653710684[/C][C]0.889610173144658[/C][/ROW]
[ROW][C]135[/C][C]0.0864300072910186[/C][C]0.172860014582037[/C][C]0.913569992708981[/C][/ROW]
[ROW][C]136[/C][C]0.0673691324462829[/C][C]0.134738264892566[/C][C]0.932630867553717[/C][/ROW]
[ROW][C]137[/C][C]0.0575615097080317[/C][C]0.115123019416063[/C][C]0.942438490291968[/C][/ROW]
[ROW][C]138[/C][C]0.0517306537454477[/C][C]0.103461307490895[/C][C]0.948269346254552[/C][/ROW]
[ROW][C]139[/C][C]0.0537768595489765[/C][C]0.107553719097953[/C][C]0.946223140451024[/C][/ROW]
[ROW][C]140[/C][C]0.0384496259744711[/C][C]0.0768992519489422[/C][C]0.961550374025529[/C][/ROW]
[ROW][C]141[/C][C]0.506937450415609[/C][C]0.986125099168783[/C][C]0.493062549584391[/C][/ROW]
[ROW][C]142[/C][C]0.455506117344109[/C][C]0.911012234688218[/C][C]0.544493882655891[/C][/ROW]
[ROW][C]143[/C][C]0.418522953324838[/C][C]0.837045906649675[/C][C]0.581477046675162[/C][/ROW]
[ROW][C]144[/C][C]0.337535639395235[/C][C]0.675071278790469[/C][C]0.662464360604765[/C][/ROW]
[ROW][C]145[/C][C]0.274912826135168[/C][C]0.549825652270337[/C][C]0.725087173864831[/C][/ROW]
[ROW][C]146[/C][C]0.287991621386678[/C][C]0.575983242773355[/C][C]0.712008378613322[/C][/ROW]
[ROW][C]147[/C][C]0.412402217434534[/C][C]0.824804434869067[/C][C]0.587597782565466[/C][/ROW]
[ROW][C]148[/C][C]0.645905039970212[/C][C]0.708189920059575[/C][C]0.354094960029788[/C][/ROW]
[ROW][C]149[/C][C]0.530417741267332[/C][C]0.939164517465336[/C][C]0.469582258732668[/C][/ROW]
[ROW][C]150[/C][C]0.403979077024434[/C][C]0.807958154048868[/C][C]0.596020922975566[/C][/ROW]
[ROW][C]151[/C][C]0.736598204484033[/C][C]0.526803591031934[/C][C]0.263401795515967[/C][/ROW]
[ROW][C]152[/C][C]0.623383480732702[/C][C]0.753233038534596[/C][C]0.376616519267298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185732&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185732&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
100.917966559278250.16406688144350.0820334407217502
110.9088175964991490.1823648070017030.0911824035008514
120.8955000111184060.2089999777631880.104499988881594
130.8524384789312970.2951230421374070.147561521068703
140.7799154458955820.4401691082088370.220084554104418
150.7106859816154160.5786280367691690.289314018384584
160.7061131446603080.5877737106793850.293886855339693
170.6315179394853680.7369641210292650.368482060514632
180.8269919499562120.3460161000875770.173008050043788
190.796571305309680.406857389380640.20342869469032
200.7399545669398070.5200908661203870.260045433060193
210.671421228452270.6571575430954610.32857877154773
220.6345184927998590.7309630144002830.365481507200141
230.6040105781398240.7919788437203530.395989421860176
240.5746938975028320.8506122049943360.425306102497168
250.5187528808456550.962494238308690.481247119154345
260.4799614023109650.959922804621930.520038597689035
270.4292670543071820.8585341086143650.570732945692818
280.4756339494036150.951267898807230.524366050596385
290.4192760500841420.8385521001682850.580723949915858
300.4362580240605890.8725160481211780.563741975939411
310.389921834091170.7798436681823410.610078165908829
320.3879150162634320.7758300325268650.612084983736568
330.3828603914596160.7657207829192330.617139608540384
340.3257027205061190.6514054410122380.674297279493881
350.3235567046070770.6471134092141540.676443295392923
360.8180653366794430.3638693266411140.181934663320557
370.8008160343406430.3983679313187140.199183965659357
380.7844926622010710.4310146755978580.215507337798929
390.8191993771267040.3616012457465910.180800622873296
400.805548775787480.388902448425040.19445122421252
410.7778534576786140.4442930846427720.222146542321386
420.7551752665519660.4896494668960680.244824733448034
430.7750806530313110.4498386939373780.224919346968689
440.7355479678776440.5289040642447120.264452032122356
450.7034976637258070.5930046725483860.296502336274193
460.8694006069615760.2611987860768490.130599393038424
470.8814916688902670.2370166622194670.118508331109733
480.8588051508022430.2823896983955130.141194849197757
490.842249298935590.315501402128820.15775070106441
500.8339504849028430.3320990301943150.166049515097158
510.8052173764998340.3895652470003330.194782623500166
520.7719593514643960.4560812970712090.228040648535604
530.7889554493780050.422089101243990.211044550621995
540.7623647804132810.4752704391734380.237635219586719
550.7829447654452690.4341104691094620.217055234554731
560.7739967233245150.4520065533509710.226003276675485
570.7372690226870660.5254619546258690.262730977312934
580.7262543546329980.5474912907340040.273745645367002
590.691418384150920.6171632316981590.30858161584908
600.6999927936760460.6000144126479080.300007206323954
610.6638813798913140.6722372402173720.336118620108686
620.6224369384825640.7551261230348720.377563061517436
630.5833868929588310.8332262140823380.416613107041169
640.540442667981450.91911466403710.45955733201855
650.5024471572055260.9951056855889490.497552842794474
660.4785046010502380.9570092021004760.521495398949762
670.4730645460102580.9461290920205170.526935453989742
680.599541297031890.800917405936220.40045870296811
690.7397956622108320.5204086755783350.260204337789168
700.704795091663860.590409816672280.29520490833614
710.8126108811684640.3747782376630720.187389118831536
720.7837097098539870.4325805802920260.216290290146013
730.7703903861315930.4592192277368150.229609613868407
740.7446748443933480.5106503112133040.255325155606652
750.7080045312079070.5839909375841870.291995468792093
760.766139682509810.4677206349803810.23386031749019
770.7306226764913510.5387546470172990.269377323508649
780.7125159240759350.5749681518481290.287484075924065
790.7170959520448280.5658080959103440.282904047955172
800.6832000551747060.6335998896505890.316799944825294
810.6434690454925940.7130619090148120.356530954507406
820.7982252486047870.4035495027904250.201774751395213
830.7642644293423060.4714711413153880.235735570657694
840.7366297791034640.5267404417930730.263370220896536
850.6978271243215660.6043457513568680.302172875678434
860.686444496118710.627111007762580.31355550388129
870.6458353912521190.7083292174957610.354164608747881
880.6051846434202830.7896307131594350.394815356579717
890.5838399453027520.8323201093944970.416160054697248
900.5466459804485230.9067080391029550.453354019551477
910.5406240633218080.9187518733563840.459375936678192
920.4985858062879010.9971716125758010.501414193712099
930.453800698627150.9076013972543010.54619930137285
940.4074960047694990.8149920095389980.592503995230501
950.4323476212039360.8646952424078720.567652378796064
960.3938852982441740.7877705964883470.606114701755826
970.3513487859190120.7026975718380250.648651214080988
980.3459663620931950.6919327241863890.654033637906805
990.3029245562229570.6058491124459150.697075443777043
1000.2677243243494210.5354486486988420.732275675650579
1010.2441429036673990.4882858073347980.755857096332601
1020.2231359442250860.4462718884501730.776864055774914
1030.2673918566341970.5347837132683940.732608143365803
1040.2299529216614150.459905843322830.770047078338585
1050.2202501766762660.4405003533525320.779749823323734
1060.2281574690698720.4563149381397440.771842530930128
1070.2070828136324290.4141656272648570.792917186367571
1080.1846511133781570.3693022267563140.815348886621843
1090.1816864009007650.3633728018015310.818313599099235
1100.1786961087189790.3573922174379590.821303891281021
1110.163624333115630.3272486662312610.83637566688437
1120.1418213393925290.2836426787850590.858178660607471
1130.1701937095781970.3403874191563930.829806290421803
1140.1459598488818390.2919196977636790.854040151118161
1150.1626633041495310.3253266082990610.837336695850469
1160.1734536634562280.3469073269124570.826546336543772
1170.151830575304220.3036611506084410.84816942469578
1180.1414668771229090.2829337542458180.858533122877091
1190.1322168851312420.2644337702624840.867783114868758
1200.1414462848861890.2828925697723780.858553715113811
1210.1174375233261820.2348750466523640.882562476673818
1220.1106805997883070.2213611995766140.889319400211693
1230.111721787709170.223443575418340.88827821229083
1240.08985425122703690.1797085024540740.910145748772963
1250.0703531386438340.1407062772876680.929646861356166
1260.05380099528966990.107601990579340.94619900471033
1270.04027974521821140.08055949043642280.959720254781789
1280.04034447238975740.08068894477951480.959655527610243
1290.03653478447998820.07306956895997630.963465215520012
1300.03644855408120480.07289710816240950.963551445918795
1310.0424404436783220.0848808873566440.957559556321678
1320.04739080958538210.09478161917076420.952609190414618
1330.1053073367902270.2106146735804530.894692663209773
1340.1103898268553420.2207796537106840.889610173144658
1350.08643000729101860.1728600145820370.913569992708981
1360.06736913244628290.1347382648925660.932630867553717
1370.05756150970803170.1151230194160630.942438490291968
1380.05173065374544770.1034613074908950.948269346254552
1390.05377685954897650.1075537190979530.946223140451024
1400.03844962597447110.07689925194894220.961550374025529
1410.5069374504156090.9861250991687830.493062549584391
1420.4555061173441090.9110122346882180.544493882655891
1430.4185229533248380.8370459066496750.581477046675162
1440.3375356393952350.6750712787904690.662464360604765
1450.2749128261351680.5498256522703370.725087173864831
1460.2879916213866780.5759832427733550.712008378613322
1470.4124022174345340.8248044348690670.587597782565466
1480.6459050399702120.7081899200595750.354094960029788
1490.5304177412673320.9391645174653360.469582258732668
1500.4039790770244340.8079581540488680.596020922975566
1510.7365982044840330.5268035910319340.263401795515967
1520.6233834807327020.7532330385345960.376616519267298







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 level70.048951048951049OK

\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 & 7 & 0.048951048951049 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185732&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]7[/C][C]0.048951048951049[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185732&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185732&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 level70.048951048951049OK



Parameters (Session):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; 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')
}