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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 13 Dec 2014 18:48:34 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/13/t1418496524x232drwzxkt9h9e.htm/, Retrieved Thu, 16 May 2024 09:28:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267264, Retrieved Thu, 16 May 2024 09:28:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact91

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [jlghlrs] [2014-12-13 18:48:34] [cf34f1111566f5ca061ad80c95189d56] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
21	26	50	4
26	51	68	9
22	57	62	4
22	37	54	5
18	67	71	4
23	43	54	4
12	52	65	9
20	52	73	8
22	43	52	11
21	84	84	4
19	67	42	4
22	49	66	6
15	70	65	4
20	52	78	8
19	58	73	4
18	68	75	4
15	62	72	11
20	43	66	4
21	56	70	4
21	56	61	6
15	74	81	6
16	65	71	4
23	63	69	8
21	58	71	5
18	57	72	4
25	63	68	9
9	53	70	4
30	57	68	7
20	51	61	10
23	64	67	4
16	53	76	4
16	29	70	7
19	54	60	12
25	51	77	4
25	58	72	7
18	43	69	5
23	51	71	8
21	53	62	5
10	54	70	4
14	56	64	9
22	61	58	7
26	47	76	4
23	39	52	4
23	48	59	4
24	50	68	4
24	35	76	4
18	30	65	7
23	68	67	4
15	49	59	7
19	61	69	4
16	67	76	4
25	47	63	4
23	56	75	4
17	50	63	8
19	43	60	4
21	67	73	4
18	62	63	4
27	57	70	4
21	41	75	7
13	54	66	12
8	45	63	4
29	48	63	4
28	61	64	4
23	56	70	5
21	41	75	15
19	43	61	5
19	53	60	10
20	44	62	9
18	66	73	8
19	58	61	4
17	46	66	5
19	37	64	4
25	51	59	9
19	51	64	4
22	56	60	10
23	66	56	4
26	45	66	7
14	37	78	4
28	59	53	6
16	42	67	7
24	38	59	5
20	66	66	4
12	34	68	4
24	53	71	4
22	49	66	4
12	55	73	4
22	49	72	4
20	59	71	6
10	40	59	10
23	58	64	7
17	60	66	4
22	63	78	4
24	56	68	7
18	54	73	4
21	52	62	8
20	34	65	11
20	69	68	6
22	32	65	14
19	48	60	5
20	67	71	4
26	58	65	8
23	57	68	9
24	42	64	4
21	64	74	4
21	58	69	5
19	66	76	4
8	26	68	5
17	61	72	4
20	52	67	4
11	51	63	7
8	55	59	10
15	50	73	4
18	60	66	5
18	56	62	4
19	63	69	4
19	61	66	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 5 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267264&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267264&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267264&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
NUMERACYTOT[t] = + 22.6015 + 0.0459639AMS.I[t] -0.0738558AMS.E[t] -0.0594782AMS.A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
NUMERACYTOT[t] =  +  22.6015 +  0.0459639AMS.I[t] -0.0738558AMS.E[t] -0.0594782AMS.A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267264&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]NUMERACYTOT[t] =  +  22.6015 +  0.0459639AMS.I[t] -0.0738558AMS.E[t] -0.0594782AMS.A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267264&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267264&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
NUMERACYTOT[t] = + 22.6015 + 0.0459639AMS.I[t] -0.0738558AMS.E[t] -0.0594782AMS.A[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)22.60154.584474.932.87235e-061.43617e-06
AMS.I0.04596390.0419261.0960.2752950.137647
AMS.E-0.07385580.0642594-1.1490.2528650.126432
AMS.A-0.05947820.171461-0.34690.7293250.364663

\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) & 22.6015 & 4.58447 & 4.93 & 2.87235e-06 & 1.43617e-06 \tabularnewline
AMS.I & 0.0459639 & 0.041926 & 1.096 & 0.275295 & 0.137647 \tabularnewline
AMS.E & -0.0738558 & 0.0642594 & -1.149 & 0.252865 & 0.126432 \tabularnewline
AMS.A & -0.0594782 & 0.171461 & -0.3469 & 0.729325 & 0.364663 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267264&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]22.6015[/C][C]4.58447[/C][C]4.93[/C][C]2.87235e-06[/C][C]1.43617e-06[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0459639[/C][C]0.041926[/C][C]1.096[/C][C]0.275295[/C][C]0.137647[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0738558[/C][C]0.0642594[/C][C]-1.149[/C][C]0.252865[/C][C]0.126432[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0594782[/C][C]0.171461[/C][C]-0.3469[/C][C]0.729325[/C][C]0.364663[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267264&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267264&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)22.60154.584474.932.87235e-061.43617e-06
AMS.I0.04596390.0419261.0960.2752950.137647
AMS.E-0.07385580.0642594-1.1490.2528650.126432
AMS.A-0.05947820.171461-0.34690.7293250.364663






Multiple Linear Regression - Regression Statistics
Multiple R0.137585
R-squared0.0189295
Adjusted R-squared-0.00734914
F-TEST (value)0.720338
F-TEST (DF numerator)3
F-TEST (DF denominator)112
p-value0.541877
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.50676
Sum Squared Residuals2274.82

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.137585 \tabularnewline
R-squared & 0.0189295 \tabularnewline
Adjusted R-squared & -0.00734914 \tabularnewline
F-TEST (value) & 0.720338 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 112 \tabularnewline
p-value & 0.541877 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.50676 \tabularnewline
Sum Squared Residuals & 2274.82 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267264&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.137585[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0189295[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00734914[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.720338[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]112[/C][/ROW]
[ROW][C]p-value[/C][C]0.541877[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.50676[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2274.82[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267264&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267264&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.137585
R-squared0.0189295
Adjusted R-squared-0.00734914
F-TEST (value)0.720338
F-TEST (DF numerator)3
F-TEST (DF denominator)112
p-value0.541877
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.50676
Sum Squared Residuals2274.82






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12119.86581.13417
22619.38816.61187
32220.40441.59556
42220.01651.98347
51820.1994-2.19938
62320.35182.64821
71219.6557-7.65566
82019.12430.875706
92220.08321.91684
102120.02060.979363
111922.3412-3.3412
122219.62232.37765
131520.7804-5.7804
142018.7551.24498
151919.638-0.63799
161819.9499-1.94992
171519.4794-4.47935
182019.46550.534477
192119.76761.23237
202120.31340.686624
211519.6636-4.66361
221620.1074-4.10745
232319.92533.07468
242119.72621.27378
251819.6659-1.66588
262519.93975.0603
27919.6297-10.6297
283019.782910.2171
292019.84560.154356
302320.35692.64309
311619.1866-3.1866
321618.3482-2.34817
331919.9384-0.938435
342519.02085.97918
352519.53345.46659
361819.1845-1.18448
372319.2263.77396
382120.16110.838893
391019.6757-9.6757
401419.9134-5.91337
412220.70531.29472
422618.91087.08918
432320.31562.68435
442320.21232.78767
452419.63964.36044
462418.35935.64075
471818.7634-0.763414
482320.54082.45924
491520.0799-5.07986
501920.0713-1.07131
511619.8301-3.8301
522519.87095.12905
532319.39843.60165
541719.7709-2.77092
551919.9087-0.908658
562120.05170.948335
571820.5604-2.5604
582719.81367.18641
592118.53052.46954
601319.4953-6.4953
61819.779-11.779
622919.91699.08309
632820.44067.55942
642319.70823.29185
652118.05462.94537
661919.7753-0.775324
671920.0114-1.01143
682019.50950.490481
691819.7678-1.76779
701920.5243-1.52426
711719.5439-2.54394
721919.3375-0.337451
732520.05284.94717
741919.9809-0.980945
752220.14931.85068
762321.26131.73875
772619.3796.62098
781418.3035-4.30347
792821.04216.95789
801619.1673-3.16727
812419.69324.30678
822020.5227-0.522692
831218.9041-6.90414
842419.55594.44412
852219.74132.25869
861219.5001-7.5001
872219.29822.70183
882019.71270.287291
891019.4878-9.48775
902320.12432.87574
911720.2469-3.24691
922219.49852.50147
932419.73694.26309
941819.4541-1.45413
952119.93671.06329
962018.70941.29064
972020.3939-0.393916
982218.4393.56101
991920.079-1.079
1002020.1994-0.199377
1012619.99096.00908
1022319.66393.33609
1032419.56734.43273
1042119.83991.16008
1052119.87391.12606
1061919.7841-0.784134
107818.4769-10.4769
1081719.8497-2.84974
1092019.80530.194658
1101119.8764-8.87637
111820.1772-12.1772
1121519.2703-4.27028
1131820.1874-2.18743
1141820.3585-2.35848
1151920.1632-1.16323
1161920.2929-1.29287

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 21 & 19.8658 & 1.13417 \tabularnewline
2 & 26 & 19.3881 & 6.61187 \tabularnewline
3 & 22 & 20.4044 & 1.59556 \tabularnewline
4 & 22 & 20.0165 & 1.98347 \tabularnewline
5 & 18 & 20.1994 & -2.19938 \tabularnewline
6 & 23 & 20.3518 & 2.64821 \tabularnewline
7 & 12 & 19.6557 & -7.65566 \tabularnewline
8 & 20 & 19.1243 & 0.875706 \tabularnewline
9 & 22 & 20.0832 & 1.91684 \tabularnewline
10 & 21 & 20.0206 & 0.979363 \tabularnewline
11 & 19 & 22.3412 & -3.3412 \tabularnewline
12 & 22 & 19.6223 & 2.37765 \tabularnewline
13 & 15 & 20.7804 & -5.7804 \tabularnewline
14 & 20 & 18.755 & 1.24498 \tabularnewline
15 & 19 & 19.638 & -0.63799 \tabularnewline
16 & 18 & 19.9499 & -1.94992 \tabularnewline
17 & 15 & 19.4794 & -4.47935 \tabularnewline
18 & 20 & 19.4655 & 0.534477 \tabularnewline
19 & 21 & 19.7676 & 1.23237 \tabularnewline
20 & 21 & 20.3134 & 0.686624 \tabularnewline
21 & 15 & 19.6636 & -4.66361 \tabularnewline
22 & 16 & 20.1074 & -4.10745 \tabularnewline
23 & 23 & 19.9253 & 3.07468 \tabularnewline
24 & 21 & 19.7262 & 1.27378 \tabularnewline
25 & 18 & 19.6659 & -1.66588 \tabularnewline
26 & 25 & 19.9397 & 5.0603 \tabularnewline
27 & 9 & 19.6297 & -10.6297 \tabularnewline
28 & 30 & 19.7829 & 10.2171 \tabularnewline
29 & 20 & 19.8456 & 0.154356 \tabularnewline
30 & 23 & 20.3569 & 2.64309 \tabularnewline
31 & 16 & 19.1866 & -3.1866 \tabularnewline
32 & 16 & 18.3482 & -2.34817 \tabularnewline
33 & 19 & 19.9384 & -0.938435 \tabularnewline
34 & 25 & 19.0208 & 5.97918 \tabularnewline
35 & 25 & 19.5334 & 5.46659 \tabularnewline
36 & 18 & 19.1845 & -1.18448 \tabularnewline
37 & 23 & 19.226 & 3.77396 \tabularnewline
38 & 21 & 20.1611 & 0.838893 \tabularnewline
39 & 10 & 19.6757 & -9.6757 \tabularnewline
40 & 14 & 19.9134 & -5.91337 \tabularnewline
41 & 22 & 20.7053 & 1.29472 \tabularnewline
42 & 26 & 18.9108 & 7.08918 \tabularnewline
43 & 23 & 20.3156 & 2.68435 \tabularnewline
44 & 23 & 20.2123 & 2.78767 \tabularnewline
45 & 24 & 19.6396 & 4.36044 \tabularnewline
46 & 24 & 18.3593 & 5.64075 \tabularnewline
47 & 18 & 18.7634 & -0.763414 \tabularnewline
48 & 23 & 20.5408 & 2.45924 \tabularnewline
49 & 15 & 20.0799 & -5.07986 \tabularnewline
50 & 19 & 20.0713 & -1.07131 \tabularnewline
51 & 16 & 19.8301 & -3.8301 \tabularnewline
52 & 25 & 19.8709 & 5.12905 \tabularnewline
53 & 23 & 19.3984 & 3.60165 \tabularnewline
54 & 17 & 19.7709 & -2.77092 \tabularnewline
55 & 19 & 19.9087 & -0.908658 \tabularnewline
56 & 21 & 20.0517 & 0.948335 \tabularnewline
57 & 18 & 20.5604 & -2.5604 \tabularnewline
58 & 27 & 19.8136 & 7.18641 \tabularnewline
59 & 21 & 18.5305 & 2.46954 \tabularnewline
60 & 13 & 19.4953 & -6.4953 \tabularnewline
61 & 8 & 19.779 & -11.779 \tabularnewline
62 & 29 & 19.9169 & 9.08309 \tabularnewline
63 & 28 & 20.4406 & 7.55942 \tabularnewline
64 & 23 & 19.7082 & 3.29185 \tabularnewline
65 & 21 & 18.0546 & 2.94537 \tabularnewline
66 & 19 & 19.7753 & -0.775324 \tabularnewline
67 & 19 & 20.0114 & -1.01143 \tabularnewline
68 & 20 & 19.5095 & 0.490481 \tabularnewline
69 & 18 & 19.7678 & -1.76779 \tabularnewline
70 & 19 & 20.5243 & -1.52426 \tabularnewline
71 & 17 & 19.5439 & -2.54394 \tabularnewline
72 & 19 & 19.3375 & -0.337451 \tabularnewline
73 & 25 & 20.0528 & 4.94717 \tabularnewline
74 & 19 & 19.9809 & -0.980945 \tabularnewline
75 & 22 & 20.1493 & 1.85068 \tabularnewline
76 & 23 & 21.2613 & 1.73875 \tabularnewline
77 & 26 & 19.379 & 6.62098 \tabularnewline
78 & 14 & 18.3035 & -4.30347 \tabularnewline
79 & 28 & 21.0421 & 6.95789 \tabularnewline
80 & 16 & 19.1673 & -3.16727 \tabularnewline
81 & 24 & 19.6932 & 4.30678 \tabularnewline
82 & 20 & 20.5227 & -0.522692 \tabularnewline
83 & 12 & 18.9041 & -6.90414 \tabularnewline
84 & 24 & 19.5559 & 4.44412 \tabularnewline
85 & 22 & 19.7413 & 2.25869 \tabularnewline
86 & 12 & 19.5001 & -7.5001 \tabularnewline
87 & 22 & 19.2982 & 2.70183 \tabularnewline
88 & 20 & 19.7127 & 0.287291 \tabularnewline
89 & 10 & 19.4878 & -9.48775 \tabularnewline
90 & 23 & 20.1243 & 2.87574 \tabularnewline
91 & 17 & 20.2469 & -3.24691 \tabularnewline
92 & 22 & 19.4985 & 2.50147 \tabularnewline
93 & 24 & 19.7369 & 4.26309 \tabularnewline
94 & 18 & 19.4541 & -1.45413 \tabularnewline
95 & 21 & 19.9367 & 1.06329 \tabularnewline
96 & 20 & 18.7094 & 1.29064 \tabularnewline
97 & 20 & 20.3939 & -0.393916 \tabularnewline
98 & 22 & 18.439 & 3.56101 \tabularnewline
99 & 19 & 20.079 & -1.079 \tabularnewline
100 & 20 & 20.1994 & -0.199377 \tabularnewline
101 & 26 & 19.9909 & 6.00908 \tabularnewline
102 & 23 & 19.6639 & 3.33609 \tabularnewline
103 & 24 & 19.5673 & 4.43273 \tabularnewline
104 & 21 & 19.8399 & 1.16008 \tabularnewline
105 & 21 & 19.8739 & 1.12606 \tabularnewline
106 & 19 & 19.7841 & -0.784134 \tabularnewline
107 & 8 & 18.4769 & -10.4769 \tabularnewline
108 & 17 & 19.8497 & -2.84974 \tabularnewline
109 & 20 & 19.8053 & 0.194658 \tabularnewline
110 & 11 & 19.8764 & -8.87637 \tabularnewline
111 & 8 & 20.1772 & -12.1772 \tabularnewline
112 & 15 & 19.2703 & -4.27028 \tabularnewline
113 & 18 & 20.1874 & -2.18743 \tabularnewline
114 & 18 & 20.3585 & -2.35848 \tabularnewline
115 & 19 & 20.1632 & -1.16323 \tabularnewline
116 & 19 & 20.2929 & -1.29287 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267264&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]21[/C][C]19.8658[/C][C]1.13417[/C][/ROW]
[ROW][C]2[/C][C]26[/C][C]19.3881[/C][C]6.61187[/C][/ROW]
[ROW][C]3[/C][C]22[/C][C]20.4044[/C][C]1.59556[/C][/ROW]
[ROW][C]4[/C][C]22[/C][C]20.0165[/C][C]1.98347[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]20.1994[/C][C]-2.19938[/C][/ROW]
[ROW][C]6[/C][C]23[/C][C]20.3518[/C][C]2.64821[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]19.6557[/C][C]-7.65566[/C][/ROW]
[ROW][C]8[/C][C]20[/C][C]19.1243[/C][C]0.875706[/C][/ROW]
[ROW][C]9[/C][C]22[/C][C]20.0832[/C][C]1.91684[/C][/ROW]
[ROW][C]10[/C][C]21[/C][C]20.0206[/C][C]0.979363[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]22.3412[/C][C]-3.3412[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]19.6223[/C][C]2.37765[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]20.7804[/C][C]-5.7804[/C][/ROW]
[ROW][C]14[/C][C]20[/C][C]18.755[/C][C]1.24498[/C][/ROW]
[ROW][C]15[/C][C]19[/C][C]19.638[/C][C]-0.63799[/C][/ROW]
[ROW][C]16[/C][C]18[/C][C]19.9499[/C][C]-1.94992[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]19.4794[/C][C]-4.47935[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]19.4655[/C][C]0.534477[/C][/ROW]
[ROW][C]19[/C][C]21[/C][C]19.7676[/C][C]1.23237[/C][/ROW]
[ROW][C]20[/C][C]21[/C][C]20.3134[/C][C]0.686624[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]19.6636[/C][C]-4.66361[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]20.1074[/C][C]-4.10745[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]19.9253[/C][C]3.07468[/C][/ROW]
[ROW][C]24[/C][C]21[/C][C]19.7262[/C][C]1.27378[/C][/ROW]
[ROW][C]25[/C][C]18[/C][C]19.6659[/C][C]-1.66588[/C][/ROW]
[ROW][C]26[/C][C]25[/C][C]19.9397[/C][C]5.0603[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]19.6297[/C][C]-10.6297[/C][/ROW]
[ROW][C]28[/C][C]30[/C][C]19.7829[/C][C]10.2171[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]19.8456[/C][C]0.154356[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]20.3569[/C][C]2.64309[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]19.1866[/C][C]-3.1866[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]18.3482[/C][C]-2.34817[/C][/ROW]
[ROW][C]33[/C][C]19[/C][C]19.9384[/C][C]-0.938435[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]19.0208[/C][C]5.97918[/C][/ROW]
[ROW][C]35[/C][C]25[/C][C]19.5334[/C][C]5.46659[/C][/ROW]
[ROW][C]36[/C][C]18[/C][C]19.1845[/C][C]-1.18448[/C][/ROW]
[ROW][C]37[/C][C]23[/C][C]19.226[/C][C]3.77396[/C][/ROW]
[ROW][C]38[/C][C]21[/C][C]20.1611[/C][C]0.838893[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]19.6757[/C][C]-9.6757[/C][/ROW]
[ROW][C]40[/C][C]14[/C][C]19.9134[/C][C]-5.91337[/C][/ROW]
[ROW][C]41[/C][C]22[/C][C]20.7053[/C][C]1.29472[/C][/ROW]
[ROW][C]42[/C][C]26[/C][C]18.9108[/C][C]7.08918[/C][/ROW]
[ROW][C]43[/C][C]23[/C][C]20.3156[/C][C]2.68435[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]20.2123[/C][C]2.78767[/C][/ROW]
[ROW][C]45[/C][C]24[/C][C]19.6396[/C][C]4.36044[/C][/ROW]
[ROW][C]46[/C][C]24[/C][C]18.3593[/C][C]5.64075[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]18.7634[/C][C]-0.763414[/C][/ROW]
[ROW][C]48[/C][C]23[/C][C]20.5408[/C][C]2.45924[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]20.0799[/C][C]-5.07986[/C][/ROW]
[ROW][C]50[/C][C]19[/C][C]20.0713[/C][C]-1.07131[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]19.8301[/C][C]-3.8301[/C][/ROW]
[ROW][C]52[/C][C]25[/C][C]19.8709[/C][C]5.12905[/C][/ROW]
[ROW][C]53[/C][C]23[/C][C]19.3984[/C][C]3.60165[/C][/ROW]
[ROW][C]54[/C][C]17[/C][C]19.7709[/C][C]-2.77092[/C][/ROW]
[ROW][C]55[/C][C]19[/C][C]19.9087[/C][C]-0.908658[/C][/ROW]
[ROW][C]56[/C][C]21[/C][C]20.0517[/C][C]0.948335[/C][/ROW]
[ROW][C]57[/C][C]18[/C][C]20.5604[/C][C]-2.5604[/C][/ROW]
[ROW][C]58[/C][C]27[/C][C]19.8136[/C][C]7.18641[/C][/ROW]
[ROW][C]59[/C][C]21[/C][C]18.5305[/C][C]2.46954[/C][/ROW]
[ROW][C]60[/C][C]13[/C][C]19.4953[/C][C]-6.4953[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]19.779[/C][C]-11.779[/C][/ROW]
[ROW][C]62[/C][C]29[/C][C]19.9169[/C][C]9.08309[/C][/ROW]
[ROW][C]63[/C][C]28[/C][C]20.4406[/C][C]7.55942[/C][/ROW]
[ROW][C]64[/C][C]23[/C][C]19.7082[/C][C]3.29185[/C][/ROW]
[ROW][C]65[/C][C]21[/C][C]18.0546[/C][C]2.94537[/C][/ROW]
[ROW][C]66[/C][C]19[/C][C]19.7753[/C][C]-0.775324[/C][/ROW]
[ROW][C]67[/C][C]19[/C][C]20.0114[/C][C]-1.01143[/C][/ROW]
[ROW][C]68[/C][C]20[/C][C]19.5095[/C][C]0.490481[/C][/ROW]
[ROW][C]69[/C][C]18[/C][C]19.7678[/C][C]-1.76779[/C][/ROW]
[ROW][C]70[/C][C]19[/C][C]20.5243[/C][C]-1.52426[/C][/ROW]
[ROW][C]71[/C][C]17[/C][C]19.5439[/C][C]-2.54394[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]19.3375[/C][C]-0.337451[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]20.0528[/C][C]4.94717[/C][/ROW]
[ROW][C]74[/C][C]19[/C][C]19.9809[/C][C]-0.980945[/C][/ROW]
[ROW][C]75[/C][C]22[/C][C]20.1493[/C][C]1.85068[/C][/ROW]
[ROW][C]76[/C][C]23[/C][C]21.2613[/C][C]1.73875[/C][/ROW]
[ROW][C]77[/C][C]26[/C][C]19.379[/C][C]6.62098[/C][/ROW]
[ROW][C]78[/C][C]14[/C][C]18.3035[/C][C]-4.30347[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]21.0421[/C][C]6.95789[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]19.1673[/C][C]-3.16727[/C][/ROW]
[ROW][C]81[/C][C]24[/C][C]19.6932[/C][C]4.30678[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]20.5227[/C][C]-0.522692[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]18.9041[/C][C]-6.90414[/C][/ROW]
[ROW][C]84[/C][C]24[/C][C]19.5559[/C][C]4.44412[/C][/ROW]
[ROW][C]85[/C][C]22[/C][C]19.7413[/C][C]2.25869[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]19.5001[/C][C]-7.5001[/C][/ROW]
[ROW][C]87[/C][C]22[/C][C]19.2982[/C][C]2.70183[/C][/ROW]
[ROW][C]88[/C][C]20[/C][C]19.7127[/C][C]0.287291[/C][/ROW]
[ROW][C]89[/C][C]10[/C][C]19.4878[/C][C]-9.48775[/C][/ROW]
[ROW][C]90[/C][C]23[/C][C]20.1243[/C][C]2.87574[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]20.2469[/C][C]-3.24691[/C][/ROW]
[ROW][C]92[/C][C]22[/C][C]19.4985[/C][C]2.50147[/C][/ROW]
[ROW][C]93[/C][C]24[/C][C]19.7369[/C][C]4.26309[/C][/ROW]
[ROW][C]94[/C][C]18[/C][C]19.4541[/C][C]-1.45413[/C][/ROW]
[ROW][C]95[/C][C]21[/C][C]19.9367[/C][C]1.06329[/C][/ROW]
[ROW][C]96[/C][C]20[/C][C]18.7094[/C][C]1.29064[/C][/ROW]
[ROW][C]97[/C][C]20[/C][C]20.3939[/C][C]-0.393916[/C][/ROW]
[ROW][C]98[/C][C]22[/C][C]18.439[/C][C]3.56101[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]20.079[/C][C]-1.079[/C][/ROW]
[ROW][C]100[/C][C]20[/C][C]20.1994[/C][C]-0.199377[/C][/ROW]
[ROW][C]101[/C][C]26[/C][C]19.9909[/C][C]6.00908[/C][/ROW]
[ROW][C]102[/C][C]23[/C][C]19.6639[/C][C]3.33609[/C][/ROW]
[ROW][C]103[/C][C]24[/C][C]19.5673[/C][C]4.43273[/C][/ROW]
[ROW][C]104[/C][C]21[/C][C]19.8399[/C][C]1.16008[/C][/ROW]
[ROW][C]105[/C][C]21[/C][C]19.8739[/C][C]1.12606[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]19.7841[/C][C]-0.784134[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]18.4769[/C][C]-10.4769[/C][/ROW]
[ROW][C]108[/C][C]17[/C][C]19.8497[/C][C]-2.84974[/C][/ROW]
[ROW][C]109[/C][C]20[/C][C]19.8053[/C][C]0.194658[/C][/ROW]
[ROW][C]110[/C][C]11[/C][C]19.8764[/C][C]-8.87637[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]20.1772[/C][C]-12.1772[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]19.2703[/C][C]-4.27028[/C][/ROW]
[ROW][C]113[/C][C]18[/C][C]20.1874[/C][C]-2.18743[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]20.3585[/C][C]-2.35848[/C][/ROW]
[ROW][C]115[/C][C]19[/C][C]20.1632[/C][C]-1.16323[/C][/ROW]
[ROW][C]116[/C][C]19[/C][C]20.2929[/C][C]-1.29287[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267264&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267264&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12119.86581.13417
22619.38816.61187
32220.40441.59556
42220.01651.98347
51820.1994-2.19938
62320.35182.64821
71219.6557-7.65566
82019.12430.875706
92220.08321.91684
102120.02060.979363
111922.3412-3.3412
122219.62232.37765
131520.7804-5.7804
142018.7551.24498
151919.638-0.63799
161819.9499-1.94992
171519.4794-4.47935
182019.46550.534477
192119.76761.23237
202120.31340.686624
211519.6636-4.66361
221620.1074-4.10745
232319.92533.07468
242119.72621.27378
251819.6659-1.66588
262519.93975.0603
27919.6297-10.6297
283019.782910.2171
292019.84560.154356
302320.35692.64309
311619.1866-3.1866
321618.3482-2.34817
331919.9384-0.938435
342519.02085.97918
352519.53345.46659
361819.1845-1.18448
372319.2263.77396
382120.16110.838893
391019.6757-9.6757
401419.9134-5.91337
412220.70531.29472
422618.91087.08918
432320.31562.68435
442320.21232.78767
452419.63964.36044
462418.35935.64075
471818.7634-0.763414
482320.54082.45924
491520.0799-5.07986
501920.0713-1.07131
511619.8301-3.8301
522519.87095.12905
532319.39843.60165
541719.7709-2.77092
551919.9087-0.908658
562120.05170.948335
571820.5604-2.5604
582719.81367.18641
592118.53052.46954
601319.4953-6.4953
61819.779-11.779
622919.91699.08309
632820.44067.55942
642319.70823.29185
652118.05462.94537
661919.7753-0.775324
671920.0114-1.01143
682019.50950.490481
691819.7678-1.76779
701920.5243-1.52426
711719.5439-2.54394
721919.3375-0.337451
732520.05284.94717
741919.9809-0.980945
752220.14931.85068
762321.26131.73875
772619.3796.62098
781418.3035-4.30347
792821.04216.95789
801619.1673-3.16727
812419.69324.30678
822020.5227-0.522692
831218.9041-6.90414
842419.55594.44412
852219.74132.25869
861219.5001-7.5001
872219.29822.70183
882019.71270.287291
891019.4878-9.48775
902320.12432.87574
911720.2469-3.24691
922219.49852.50147
932419.73694.26309
941819.4541-1.45413
952119.93671.06329
962018.70941.29064
972020.3939-0.393916
982218.4393.56101
991920.079-1.079
1002020.1994-0.199377
1012619.99096.00908
1022319.66393.33609
1032419.56734.43273
1042119.83991.16008
1052119.87391.12606
1061919.7841-0.784134
107818.4769-10.4769
1081719.8497-2.84974
1092019.80530.194658
1101119.8764-8.87637
111820.1772-12.1772
1121519.2703-4.27028
1131820.1874-2.18743
1141820.3585-2.35848
1151920.1632-1.16323
1161920.2929-1.29287






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.8258830.3482340.174117
80.7148590.5702820.285141
90.6210580.7578830.378942
100.5177540.9644920.482246
110.4050070.8100150.594993
120.3047170.6094340.695283
130.315980.631960.68402
140.2330170.4660350.766983
150.1694120.3388240.830588
160.1201190.2402380.879881
170.1091120.2182250.890888
180.07658650.1531730.923413
190.05054230.1010850.949458
200.03370760.06741530.966292
210.02888110.05776210.971119
220.02458440.04916880.975416
230.0284060.0568120.971594
240.01899490.03798970.981005
250.01297870.02595750.987021
260.02301620.04603230.976984
270.1558410.3116820.844159
280.3943380.7886760.605662
290.3374610.6749220.662539
300.3176590.6353180.682341
310.2873650.5747290.712635
320.266830.5336590.73317
330.2302010.4604020.769799
340.2768210.5536410.723179
350.298480.5969590.70152
360.2540610.5081210.745939
370.2315430.4630860.768457
380.1897120.3794240.810288
390.3585970.7171950.641403
400.4097070.8194150.590293
410.3604480.7208960.639552
420.4347220.8694440.565278
430.3971060.7942130.602894
440.3635730.7271470.636427
450.3561250.7122510.643875
460.3670250.7340510.632975
470.3336940.6673870.666306
480.3039670.6079340.696033
490.3211070.6422140.678893
500.2753530.5507070.724647
510.2622820.5245630.737718
520.2750410.5500830.724959
530.2560860.5121730.743914
540.2313070.4626150.768693
550.1950630.3901260.804937
560.1614840.3229670.838516
570.1393680.2787370.860632
580.1906520.3813050.809348
590.1690630.3381260.830937
600.214410.428820.78559
610.5027810.9944390.497219
620.6726290.6547420.327371
630.7575370.4849270.242463
640.7393980.5212040.260602
650.7127240.5745520.287276
660.6665430.6669140.333457
670.6200770.7598460.379923
680.567070.865860.43293
690.5245550.9508890.475445
700.4753090.9506190.524691
710.4352490.8704970.564751
720.3897940.7795880.610206
730.3960030.7920070.603997
740.3441940.6883880.655806
750.2992250.5984490.700775
760.2573220.5146450.742678
770.3324220.6648430.667578
780.3135420.6270840.686458
790.4087040.8174080.591296
800.3703740.7407470.629626
810.466630.9332590.53337
820.4071850.814370.592815
830.4250950.850190.574905
840.4383160.8766310.561684
850.4360920.8721830.563908
860.5443710.9112580.455629
870.5180530.9638940.481947
880.4527410.9054820.547259
890.5862930.8274140.413707
900.5599550.8800910.440045
910.5044360.9911280.495564
920.4419670.8839340.558033
930.4371440.8742880.562856
940.3695780.7391560.630422
950.3202940.6405870.679706
960.2716670.5433330.728333
970.2123920.4247840.787608
980.2859510.5719010.714049
990.2356070.4712140.764393
1000.1791010.3582020.820899
1010.3806320.7612640.619368
1020.8879510.2240980.112049
1030.9915670.0168670.00843348
1040.9844570.03108610.015543
1050.9980520.003896220.00194811
1060.9939920.01201640.00600821
1070.9873040.02539140.0126957
1080.9668450.06630970.0331549
1090.9966940.006612110.00330606

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.825883 & 0.348234 & 0.174117 \tabularnewline
8 & 0.714859 & 0.570282 & 0.285141 \tabularnewline
9 & 0.621058 & 0.757883 & 0.378942 \tabularnewline
10 & 0.517754 & 0.964492 & 0.482246 \tabularnewline
11 & 0.405007 & 0.810015 & 0.594993 \tabularnewline
12 & 0.304717 & 0.609434 & 0.695283 \tabularnewline
13 & 0.31598 & 0.63196 & 0.68402 \tabularnewline
14 & 0.233017 & 0.466035 & 0.766983 \tabularnewline
15 & 0.169412 & 0.338824 & 0.830588 \tabularnewline
16 & 0.120119 & 0.240238 & 0.879881 \tabularnewline
17 & 0.109112 & 0.218225 & 0.890888 \tabularnewline
18 & 0.0765865 & 0.153173 & 0.923413 \tabularnewline
19 & 0.0505423 & 0.101085 & 0.949458 \tabularnewline
20 & 0.0337076 & 0.0674153 & 0.966292 \tabularnewline
21 & 0.0288811 & 0.0577621 & 0.971119 \tabularnewline
22 & 0.0245844 & 0.0491688 & 0.975416 \tabularnewline
23 & 0.028406 & 0.056812 & 0.971594 \tabularnewline
24 & 0.0189949 & 0.0379897 & 0.981005 \tabularnewline
25 & 0.0129787 & 0.0259575 & 0.987021 \tabularnewline
26 & 0.0230162 & 0.0460323 & 0.976984 \tabularnewline
27 & 0.155841 & 0.311682 & 0.844159 \tabularnewline
28 & 0.394338 & 0.788676 & 0.605662 \tabularnewline
29 & 0.337461 & 0.674922 & 0.662539 \tabularnewline
30 & 0.317659 & 0.635318 & 0.682341 \tabularnewline
31 & 0.287365 & 0.574729 & 0.712635 \tabularnewline
32 & 0.26683 & 0.533659 & 0.73317 \tabularnewline
33 & 0.230201 & 0.460402 & 0.769799 \tabularnewline
34 & 0.276821 & 0.553641 & 0.723179 \tabularnewline
35 & 0.29848 & 0.596959 & 0.70152 \tabularnewline
36 & 0.254061 & 0.508121 & 0.745939 \tabularnewline
37 & 0.231543 & 0.463086 & 0.768457 \tabularnewline
38 & 0.189712 & 0.379424 & 0.810288 \tabularnewline
39 & 0.358597 & 0.717195 & 0.641403 \tabularnewline
40 & 0.409707 & 0.819415 & 0.590293 \tabularnewline
41 & 0.360448 & 0.720896 & 0.639552 \tabularnewline
42 & 0.434722 & 0.869444 & 0.565278 \tabularnewline
43 & 0.397106 & 0.794213 & 0.602894 \tabularnewline
44 & 0.363573 & 0.727147 & 0.636427 \tabularnewline
45 & 0.356125 & 0.712251 & 0.643875 \tabularnewline
46 & 0.367025 & 0.734051 & 0.632975 \tabularnewline
47 & 0.333694 & 0.667387 & 0.666306 \tabularnewline
48 & 0.303967 & 0.607934 & 0.696033 \tabularnewline
49 & 0.321107 & 0.642214 & 0.678893 \tabularnewline
50 & 0.275353 & 0.550707 & 0.724647 \tabularnewline
51 & 0.262282 & 0.524563 & 0.737718 \tabularnewline
52 & 0.275041 & 0.550083 & 0.724959 \tabularnewline
53 & 0.256086 & 0.512173 & 0.743914 \tabularnewline
54 & 0.231307 & 0.462615 & 0.768693 \tabularnewline
55 & 0.195063 & 0.390126 & 0.804937 \tabularnewline
56 & 0.161484 & 0.322967 & 0.838516 \tabularnewline
57 & 0.139368 & 0.278737 & 0.860632 \tabularnewline
58 & 0.190652 & 0.381305 & 0.809348 \tabularnewline
59 & 0.169063 & 0.338126 & 0.830937 \tabularnewline
60 & 0.21441 & 0.42882 & 0.78559 \tabularnewline
61 & 0.502781 & 0.994439 & 0.497219 \tabularnewline
62 & 0.672629 & 0.654742 & 0.327371 \tabularnewline
63 & 0.757537 & 0.484927 & 0.242463 \tabularnewline
64 & 0.739398 & 0.521204 & 0.260602 \tabularnewline
65 & 0.712724 & 0.574552 & 0.287276 \tabularnewline
66 & 0.666543 & 0.666914 & 0.333457 \tabularnewline
67 & 0.620077 & 0.759846 & 0.379923 \tabularnewline
68 & 0.56707 & 0.86586 & 0.43293 \tabularnewline
69 & 0.524555 & 0.950889 & 0.475445 \tabularnewline
70 & 0.475309 & 0.950619 & 0.524691 \tabularnewline
71 & 0.435249 & 0.870497 & 0.564751 \tabularnewline
72 & 0.389794 & 0.779588 & 0.610206 \tabularnewline
73 & 0.396003 & 0.792007 & 0.603997 \tabularnewline
74 & 0.344194 & 0.688388 & 0.655806 \tabularnewline
75 & 0.299225 & 0.598449 & 0.700775 \tabularnewline
76 & 0.257322 & 0.514645 & 0.742678 \tabularnewline
77 & 0.332422 & 0.664843 & 0.667578 \tabularnewline
78 & 0.313542 & 0.627084 & 0.686458 \tabularnewline
79 & 0.408704 & 0.817408 & 0.591296 \tabularnewline
80 & 0.370374 & 0.740747 & 0.629626 \tabularnewline
81 & 0.46663 & 0.933259 & 0.53337 \tabularnewline
82 & 0.407185 & 0.81437 & 0.592815 \tabularnewline
83 & 0.425095 & 0.85019 & 0.574905 \tabularnewline
84 & 0.438316 & 0.876631 & 0.561684 \tabularnewline
85 & 0.436092 & 0.872183 & 0.563908 \tabularnewline
86 & 0.544371 & 0.911258 & 0.455629 \tabularnewline
87 & 0.518053 & 0.963894 & 0.481947 \tabularnewline
88 & 0.452741 & 0.905482 & 0.547259 \tabularnewline
89 & 0.586293 & 0.827414 & 0.413707 \tabularnewline
90 & 0.559955 & 0.880091 & 0.440045 \tabularnewline
91 & 0.504436 & 0.991128 & 0.495564 \tabularnewline
92 & 0.441967 & 0.883934 & 0.558033 \tabularnewline
93 & 0.437144 & 0.874288 & 0.562856 \tabularnewline
94 & 0.369578 & 0.739156 & 0.630422 \tabularnewline
95 & 0.320294 & 0.640587 & 0.679706 \tabularnewline
96 & 0.271667 & 0.543333 & 0.728333 \tabularnewline
97 & 0.212392 & 0.424784 & 0.787608 \tabularnewline
98 & 0.285951 & 0.571901 & 0.714049 \tabularnewline
99 & 0.235607 & 0.471214 & 0.764393 \tabularnewline
100 & 0.179101 & 0.358202 & 0.820899 \tabularnewline
101 & 0.380632 & 0.761264 & 0.619368 \tabularnewline
102 & 0.887951 & 0.224098 & 0.112049 \tabularnewline
103 & 0.991567 & 0.016867 & 0.00843348 \tabularnewline
104 & 0.984457 & 0.0310861 & 0.015543 \tabularnewline
105 & 0.998052 & 0.00389622 & 0.00194811 \tabularnewline
106 & 0.993992 & 0.0120164 & 0.00600821 \tabularnewline
107 & 0.987304 & 0.0253914 & 0.0126957 \tabularnewline
108 & 0.966845 & 0.0663097 & 0.0331549 \tabularnewline
109 & 0.996694 & 0.00661211 & 0.00330606 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267264&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.825883[/C][C]0.348234[/C][C]0.174117[/C][/ROW]
[ROW][C]8[/C][C]0.714859[/C][C]0.570282[/C][C]0.285141[/C][/ROW]
[ROW][C]9[/C][C]0.621058[/C][C]0.757883[/C][C]0.378942[/C][/ROW]
[ROW][C]10[/C][C]0.517754[/C][C]0.964492[/C][C]0.482246[/C][/ROW]
[ROW][C]11[/C][C]0.405007[/C][C]0.810015[/C][C]0.594993[/C][/ROW]
[ROW][C]12[/C][C]0.304717[/C][C]0.609434[/C][C]0.695283[/C][/ROW]
[ROW][C]13[/C][C]0.31598[/C][C]0.63196[/C][C]0.68402[/C][/ROW]
[ROW][C]14[/C][C]0.233017[/C][C]0.466035[/C][C]0.766983[/C][/ROW]
[ROW][C]15[/C][C]0.169412[/C][C]0.338824[/C][C]0.830588[/C][/ROW]
[ROW][C]16[/C][C]0.120119[/C][C]0.240238[/C][C]0.879881[/C][/ROW]
[ROW][C]17[/C][C]0.109112[/C][C]0.218225[/C][C]0.890888[/C][/ROW]
[ROW][C]18[/C][C]0.0765865[/C][C]0.153173[/C][C]0.923413[/C][/ROW]
[ROW][C]19[/C][C]0.0505423[/C][C]0.101085[/C][C]0.949458[/C][/ROW]
[ROW][C]20[/C][C]0.0337076[/C][C]0.0674153[/C][C]0.966292[/C][/ROW]
[ROW][C]21[/C][C]0.0288811[/C][C]0.0577621[/C][C]0.971119[/C][/ROW]
[ROW][C]22[/C][C]0.0245844[/C][C]0.0491688[/C][C]0.975416[/C][/ROW]
[ROW][C]23[/C][C]0.028406[/C][C]0.056812[/C][C]0.971594[/C][/ROW]
[ROW][C]24[/C][C]0.0189949[/C][C]0.0379897[/C][C]0.981005[/C][/ROW]
[ROW][C]25[/C][C]0.0129787[/C][C]0.0259575[/C][C]0.987021[/C][/ROW]
[ROW][C]26[/C][C]0.0230162[/C][C]0.0460323[/C][C]0.976984[/C][/ROW]
[ROW][C]27[/C][C]0.155841[/C][C]0.311682[/C][C]0.844159[/C][/ROW]
[ROW][C]28[/C][C]0.394338[/C][C]0.788676[/C][C]0.605662[/C][/ROW]
[ROW][C]29[/C][C]0.337461[/C][C]0.674922[/C][C]0.662539[/C][/ROW]
[ROW][C]30[/C][C]0.317659[/C][C]0.635318[/C][C]0.682341[/C][/ROW]
[ROW][C]31[/C][C]0.287365[/C][C]0.574729[/C][C]0.712635[/C][/ROW]
[ROW][C]32[/C][C]0.26683[/C][C]0.533659[/C][C]0.73317[/C][/ROW]
[ROW][C]33[/C][C]0.230201[/C][C]0.460402[/C][C]0.769799[/C][/ROW]
[ROW][C]34[/C][C]0.276821[/C][C]0.553641[/C][C]0.723179[/C][/ROW]
[ROW][C]35[/C][C]0.29848[/C][C]0.596959[/C][C]0.70152[/C][/ROW]
[ROW][C]36[/C][C]0.254061[/C][C]0.508121[/C][C]0.745939[/C][/ROW]
[ROW][C]37[/C][C]0.231543[/C][C]0.463086[/C][C]0.768457[/C][/ROW]
[ROW][C]38[/C][C]0.189712[/C][C]0.379424[/C][C]0.810288[/C][/ROW]
[ROW][C]39[/C][C]0.358597[/C][C]0.717195[/C][C]0.641403[/C][/ROW]
[ROW][C]40[/C][C]0.409707[/C][C]0.819415[/C][C]0.590293[/C][/ROW]
[ROW][C]41[/C][C]0.360448[/C][C]0.720896[/C][C]0.639552[/C][/ROW]
[ROW][C]42[/C][C]0.434722[/C][C]0.869444[/C][C]0.565278[/C][/ROW]
[ROW][C]43[/C][C]0.397106[/C][C]0.794213[/C][C]0.602894[/C][/ROW]
[ROW][C]44[/C][C]0.363573[/C][C]0.727147[/C][C]0.636427[/C][/ROW]
[ROW][C]45[/C][C]0.356125[/C][C]0.712251[/C][C]0.643875[/C][/ROW]
[ROW][C]46[/C][C]0.367025[/C][C]0.734051[/C][C]0.632975[/C][/ROW]
[ROW][C]47[/C][C]0.333694[/C][C]0.667387[/C][C]0.666306[/C][/ROW]
[ROW][C]48[/C][C]0.303967[/C][C]0.607934[/C][C]0.696033[/C][/ROW]
[ROW][C]49[/C][C]0.321107[/C][C]0.642214[/C][C]0.678893[/C][/ROW]
[ROW][C]50[/C][C]0.275353[/C][C]0.550707[/C][C]0.724647[/C][/ROW]
[ROW][C]51[/C][C]0.262282[/C][C]0.524563[/C][C]0.737718[/C][/ROW]
[ROW][C]52[/C][C]0.275041[/C][C]0.550083[/C][C]0.724959[/C][/ROW]
[ROW][C]53[/C][C]0.256086[/C][C]0.512173[/C][C]0.743914[/C][/ROW]
[ROW][C]54[/C][C]0.231307[/C][C]0.462615[/C][C]0.768693[/C][/ROW]
[ROW][C]55[/C][C]0.195063[/C][C]0.390126[/C][C]0.804937[/C][/ROW]
[ROW][C]56[/C][C]0.161484[/C][C]0.322967[/C][C]0.838516[/C][/ROW]
[ROW][C]57[/C][C]0.139368[/C][C]0.278737[/C][C]0.860632[/C][/ROW]
[ROW][C]58[/C][C]0.190652[/C][C]0.381305[/C][C]0.809348[/C][/ROW]
[ROW][C]59[/C][C]0.169063[/C][C]0.338126[/C][C]0.830937[/C][/ROW]
[ROW][C]60[/C][C]0.21441[/C][C]0.42882[/C][C]0.78559[/C][/ROW]
[ROW][C]61[/C][C]0.502781[/C][C]0.994439[/C][C]0.497219[/C][/ROW]
[ROW][C]62[/C][C]0.672629[/C][C]0.654742[/C][C]0.327371[/C][/ROW]
[ROW][C]63[/C][C]0.757537[/C][C]0.484927[/C][C]0.242463[/C][/ROW]
[ROW][C]64[/C][C]0.739398[/C][C]0.521204[/C][C]0.260602[/C][/ROW]
[ROW][C]65[/C][C]0.712724[/C][C]0.574552[/C][C]0.287276[/C][/ROW]
[ROW][C]66[/C][C]0.666543[/C][C]0.666914[/C][C]0.333457[/C][/ROW]
[ROW][C]67[/C][C]0.620077[/C][C]0.759846[/C][C]0.379923[/C][/ROW]
[ROW][C]68[/C][C]0.56707[/C][C]0.86586[/C][C]0.43293[/C][/ROW]
[ROW][C]69[/C][C]0.524555[/C][C]0.950889[/C][C]0.475445[/C][/ROW]
[ROW][C]70[/C][C]0.475309[/C][C]0.950619[/C][C]0.524691[/C][/ROW]
[ROW][C]71[/C][C]0.435249[/C][C]0.870497[/C][C]0.564751[/C][/ROW]
[ROW][C]72[/C][C]0.389794[/C][C]0.779588[/C][C]0.610206[/C][/ROW]
[ROW][C]73[/C][C]0.396003[/C][C]0.792007[/C][C]0.603997[/C][/ROW]
[ROW][C]74[/C][C]0.344194[/C][C]0.688388[/C][C]0.655806[/C][/ROW]
[ROW][C]75[/C][C]0.299225[/C][C]0.598449[/C][C]0.700775[/C][/ROW]
[ROW][C]76[/C][C]0.257322[/C][C]0.514645[/C][C]0.742678[/C][/ROW]
[ROW][C]77[/C][C]0.332422[/C][C]0.664843[/C][C]0.667578[/C][/ROW]
[ROW][C]78[/C][C]0.313542[/C][C]0.627084[/C][C]0.686458[/C][/ROW]
[ROW][C]79[/C][C]0.408704[/C][C]0.817408[/C][C]0.591296[/C][/ROW]
[ROW][C]80[/C][C]0.370374[/C][C]0.740747[/C][C]0.629626[/C][/ROW]
[ROW][C]81[/C][C]0.46663[/C][C]0.933259[/C][C]0.53337[/C][/ROW]
[ROW][C]82[/C][C]0.407185[/C][C]0.81437[/C][C]0.592815[/C][/ROW]
[ROW][C]83[/C][C]0.425095[/C][C]0.85019[/C][C]0.574905[/C][/ROW]
[ROW][C]84[/C][C]0.438316[/C][C]0.876631[/C][C]0.561684[/C][/ROW]
[ROW][C]85[/C][C]0.436092[/C][C]0.872183[/C][C]0.563908[/C][/ROW]
[ROW][C]86[/C][C]0.544371[/C][C]0.911258[/C][C]0.455629[/C][/ROW]
[ROW][C]87[/C][C]0.518053[/C][C]0.963894[/C][C]0.481947[/C][/ROW]
[ROW][C]88[/C][C]0.452741[/C][C]0.905482[/C][C]0.547259[/C][/ROW]
[ROW][C]89[/C][C]0.586293[/C][C]0.827414[/C][C]0.413707[/C][/ROW]
[ROW][C]90[/C][C]0.559955[/C][C]0.880091[/C][C]0.440045[/C][/ROW]
[ROW][C]91[/C][C]0.504436[/C][C]0.991128[/C][C]0.495564[/C][/ROW]
[ROW][C]92[/C][C]0.441967[/C][C]0.883934[/C][C]0.558033[/C][/ROW]
[ROW][C]93[/C][C]0.437144[/C][C]0.874288[/C][C]0.562856[/C][/ROW]
[ROW][C]94[/C][C]0.369578[/C][C]0.739156[/C][C]0.630422[/C][/ROW]
[ROW][C]95[/C][C]0.320294[/C][C]0.640587[/C][C]0.679706[/C][/ROW]
[ROW][C]96[/C][C]0.271667[/C][C]0.543333[/C][C]0.728333[/C][/ROW]
[ROW][C]97[/C][C]0.212392[/C][C]0.424784[/C][C]0.787608[/C][/ROW]
[ROW][C]98[/C][C]0.285951[/C][C]0.571901[/C][C]0.714049[/C][/ROW]
[ROW][C]99[/C][C]0.235607[/C][C]0.471214[/C][C]0.764393[/C][/ROW]
[ROW][C]100[/C][C]0.179101[/C][C]0.358202[/C][C]0.820899[/C][/ROW]
[ROW][C]101[/C][C]0.380632[/C][C]0.761264[/C][C]0.619368[/C][/ROW]
[ROW][C]102[/C][C]0.887951[/C][C]0.224098[/C][C]0.112049[/C][/ROW]
[ROW][C]103[/C][C]0.991567[/C][C]0.016867[/C][C]0.00843348[/C][/ROW]
[ROW][C]104[/C][C]0.984457[/C][C]0.0310861[/C][C]0.015543[/C][/ROW]
[ROW][C]105[/C][C]0.998052[/C][C]0.00389622[/C][C]0.00194811[/C][/ROW]
[ROW][C]106[/C][C]0.993992[/C][C]0.0120164[/C][C]0.00600821[/C][/ROW]
[ROW][C]107[/C][C]0.987304[/C][C]0.0253914[/C][C]0.0126957[/C][/ROW]
[ROW][C]108[/C][C]0.966845[/C][C]0.0663097[/C][C]0.0331549[/C][/ROW]
[ROW][C]109[/C][C]0.996694[/C][C]0.00661211[/C][C]0.00330606[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267264&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267264&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.8258830.3482340.174117
80.7148590.5702820.285141
90.6210580.7578830.378942
100.5177540.9644920.482246
110.4050070.8100150.594993
120.3047170.6094340.695283
130.315980.631960.68402
140.2330170.4660350.766983
150.1694120.3388240.830588
160.1201190.2402380.879881
170.1091120.2182250.890888
180.07658650.1531730.923413
190.05054230.1010850.949458
200.03370760.06741530.966292
210.02888110.05776210.971119
220.02458440.04916880.975416
230.0284060.0568120.971594
240.01899490.03798970.981005
250.01297870.02595750.987021
260.02301620.04603230.976984
270.1558410.3116820.844159
280.3943380.7886760.605662
290.3374610.6749220.662539
300.3176590.6353180.682341
310.2873650.5747290.712635
320.266830.5336590.73317
330.2302010.4604020.769799
340.2768210.5536410.723179
350.298480.5969590.70152
360.2540610.5081210.745939
370.2315430.4630860.768457
380.1897120.3794240.810288
390.3585970.7171950.641403
400.4097070.8194150.590293
410.3604480.7208960.639552
420.4347220.8694440.565278
430.3971060.7942130.602894
440.3635730.7271470.636427
450.3561250.7122510.643875
460.3670250.7340510.632975
470.3336940.6673870.666306
480.3039670.6079340.696033
490.3211070.6422140.678893
500.2753530.5507070.724647
510.2622820.5245630.737718
520.2750410.5500830.724959
530.2560860.5121730.743914
540.2313070.4626150.768693
550.1950630.3901260.804937
560.1614840.3229670.838516
570.1393680.2787370.860632
580.1906520.3813050.809348
590.1690630.3381260.830937
600.214410.428820.78559
610.5027810.9944390.497219
620.6726290.6547420.327371
630.7575370.4849270.242463
640.7393980.5212040.260602
650.7127240.5745520.287276
660.6665430.6669140.333457
670.6200770.7598460.379923
680.567070.865860.43293
690.5245550.9508890.475445
700.4753090.9506190.524691
710.4352490.8704970.564751
720.3897940.7795880.610206
730.3960030.7920070.603997
740.3441940.6883880.655806
750.2992250.5984490.700775
760.2573220.5146450.742678
770.3324220.6648430.667578
780.3135420.6270840.686458
790.4087040.8174080.591296
800.3703740.7407470.629626
810.466630.9332590.53337
820.4071850.814370.592815
830.4250950.850190.574905
840.4383160.8766310.561684
850.4360920.8721830.563908
860.5443710.9112580.455629
870.5180530.9638940.481947
880.4527410.9054820.547259
890.5862930.8274140.413707
900.5599550.8800910.440045
910.5044360.9911280.495564
920.4419670.8839340.558033
930.4371440.8742880.562856
940.3695780.7391560.630422
950.3202940.6405870.679706
960.2716670.5433330.728333
970.2123920.4247840.787608
980.2859510.5719010.714049
990.2356070.4712140.764393
1000.1791010.3582020.820899
1010.3806320.7612640.619368
1020.8879510.2240980.112049
1030.9915670.0168670.00843348
1040.9844570.03108610.015543
1050.9980520.003896220.00194811
1060.9939920.01201640.00600821
1070.9873040.02539140.0126957
1080.9668450.06630970.0331549
1090.9966940.006612110.00330606






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0194175NOK
5% type I error level100.0970874NOK
10% type I error level140.135922NOK

\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 & 2 & 0.0194175 & NOK \tabularnewline
5% type I error level & 10 & 0.0970874 & NOK \tabularnewline
10% type I error level & 14 & 0.135922 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267264&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]2[/C][C]0.0194175[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.0970874[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.135922[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267264&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267264&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0194175NOK
5% type I error level100.0970874NOK
10% type I error level140.135922NOK


Figures (Output of Computation)

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Input Parameters & R Code

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