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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 computationSun, 14 Dec 2014 14:02:54 +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/14/t1418565793sxx730gng68v1eh.htm/, Retrieved Thu, 31 Oct 2024 23:54:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267586, Retrieved Thu, 31 Oct 2024 23:54:17 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-14 14:02:54] [ff8f75f765a8f6d34a5ce09978012557] [Current]
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Dataseries X:
7,5	13	13
6	13	16
6,5	11	11
1	14	10
1	15	9
5,5	14	8
8,5	11	26
6,5	13	10
4,5	16	10
2	14	8
5	14	13
0,5	15	11
5	15	8
5	13	12
2,5	14	24
5	11	21
5,5	12	5
3,5	14	14
3	13	11
4	12	9
0,5	15	8
6,5	15	17
4,5	14	18
7,5	14	16
5,5	12	23
4	12	9
7,5	12	14
7	15	13
4	14	10
5,5	16	8
2,5	12	10
5,5	12	19
3,5	14	11
2,5	16	16
4,5	15	12
4,5	12	11
4,5	14	11
6	13	10
2,5	14	13
5	16	14
0	12	8
5	14	11
6,5	15	11
5	13	13
6	16	15
4,5	16	15
5,5	12	16
1	12	12
7,5	16	12
6	12	17
5	15	14
1	12	15
5	13	12
6,5	12	13
7	14	7
4,5	14	8
0	11	16
8,5	10	20
3,5	12	14
7,5	11	10
3,5	16	16
6	14	11
1,5	14	26
9	15	9
3,5	15	15
3,5	14	12
4	13	21
6,5	11	20
7,5	16	20
6	12	10
5	15	15
5,5	14	10
3,5	15	16
7,5	14	9
6,5	13	17
6,5	12	19
6,5	12	13
7	14	8
3,5	14	11
1,5	15	9
4	11	12
7,5	13	10
4,5	14	9
0	16	14
3,5	13	14
5,5	14	10
5	16	8
4,5	11	13
2,5	13	9
7,5	13	14
7	15	8
0	12	16
4,5	13	14
3	12	14
1,5	14	8
3,5	14	11
2,5	16	11
5,5	15	13
8	14	12
1	13	13
5	14	9
4,5	15	10
3	14	12
3	12	11
8	7	13
2,5	12	17
7	15	15
0	12	15
1	13	14
3,5	11	10
5,5	14	15
5,5	13	14




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267586&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267586&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267586&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 5.40103 -0.0921829STRESS[t] + 0.0308416CESD[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  5.40103 -0.0921829STRESS[t] +  0.0308416CESD[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267586&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  5.40103 -0.0921829STRESS[t] +  0.0308416CESD[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267586&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267586&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
Ex[t] = + 5.40103 -0.0921829STRESS[t] + 0.0308416CESD[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.401032.045572.640.009497470.00474874
STRESS-0.09218290.132783-0.69420.4890110.244505
CESD0.03084160.05317930.580.5631420.281571

\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) & 5.40103 & 2.04557 & 2.64 & 0.00949747 & 0.00474874 \tabularnewline
STRESS & -0.0921829 & 0.132783 & -0.6942 & 0.489011 & 0.244505 \tabularnewline
CESD & 0.0308416 & 0.0531793 & 0.58 & 0.563142 & 0.281571 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267586&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]5.40103[/C][C]2.04557[/C][C]2.64[/C][C]0.00949747[/C][C]0.00474874[/C][/ROW]
[ROW][C]STRESS[/C][C]-0.0921829[/C][C]0.132783[/C][C]-0.6942[/C][C]0.489011[/C][C]0.244505[/C][/ROW]
[ROW][C]CESD[/C][C]0.0308416[/C][C]0.0531793[/C][C]0.58[/C][C]0.563142[/C][C]0.281571[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267586&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267586&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)5.401032.045572.640.009497470.00474874
STRESS-0.09218290.132783-0.69420.4890110.244505
CESD0.03084160.05317930.580.5631420.281571







Multiple Linear Regression - Regression Statistics
Multiple R0.095857
R-squared0.00918857
Adjusted R-squared-0.00899146
F-TEST (value)0.505421
F-TEST (DF numerator)2
F-TEST (DF denominator)109
p-value0.604658
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21835
Sum Squared Residuals536.398

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.095857 \tabularnewline
R-squared & 0.00918857 \tabularnewline
Adjusted R-squared & -0.00899146 \tabularnewline
F-TEST (value) & 0.505421 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 109 \tabularnewline
p-value & 0.604658 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.21835 \tabularnewline
Sum Squared Residuals & 536.398 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267586&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.095857[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00918857[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00899146[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.505421[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]109[/C][/ROW]
[ROW][C]p-value[/C][C]0.604658[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.21835[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]536.398[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267586&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267586&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 R0.095857
R-squared0.00918857
Adjusted R-squared-0.00899146
F-TEST (value)0.505421
F-TEST (DF numerator)2
F-TEST (DF denominator)109
p-value0.604658
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21835
Sum Squared Residuals536.398







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.54.603592.89641
264.696111.30389
36.54.726271.77373
414.41888-3.41888
514.29586-3.29586
65.54.35721.1428
78.55.188893.31111
86.54.511061.98894
94.54.234520.265485
1024.3572-2.3572
1154.511410.488594
120.54.35754-3.85754
1354.265020.734985
1454.572750.427253
152.54.85066-2.35066
1655.03469-0.0346869
175.54.449041.05096
183.54.54225-1.04225
1934.54191-1.54191
2044.57241-0.572405
210.54.26502-3.76502
226.54.542591.95741
234.54.66561-0.165614
247.54.603932.89607
255.55.004190.495813
2644.57241-0.572405
277.54.726612.77339
2874.419222.58078
2944.41888-0.418881
305.54.172831.32717
312.54.60325-2.10325
325.54.880820.619179
333.54.44972-0.949723
342.54.41956-1.91956
354.54.388380.111619
364.54.63409-0.134088
374.54.449720.0502774
3864.511061.48894
392.54.51141-2.01141
4054.357880.642118
4104.54156-4.54156
4254.449720.550277
436.54.357542.14246
4454.603590.396411
4564.388721.61128
464.54.388720.111277
475.54.78830.711704
4814.66493-3.66493
497.54.29623.2038
5064.819141.18086
5154.450060.549936
5214.75745-3.75745
5354.572750.427253
546.54.695771.80423
5574.326362.67364
564.54.35720.142802
5704.88048-4.88048
588.55.096033.40397
593.54.72661-1.22661
607.54.695432.80457
613.54.41956-0.919565
6264.449721.55028
631.54.91235-3.41235
6494.295864.70414
653.54.48091-0.980906
663.54.48056-0.980564
6744.85032-0.850321
686.55.003851.49615
697.54.542932.95707
7064.603251.39675
7154.480910.519094
725.54.418881.08112
733.54.51175-1.01175
747.54.388043.11196
756.54.726951.77305
766.54.880821.61918
776.54.695771.80423
7874.35722.6428
793.54.44972-0.949723
801.54.29586-2.79586
8144.75711-0.757113
827.54.511062.98894
834.54.388040.111961
8404.35788-4.35788
853.54.63443-1.13443
865.54.418881.08112
8754.172830.827168
884.54.78795-0.287954
892.54.48022-1.98022
907.54.634432.86557
9174.265022.73498
9204.7883-4.7883
934.54.63443-0.13443
9434.72661-1.72661
951.54.3572-2.8572
963.54.44972-0.949723
972.54.26536-1.76536
985.54.419221.08078
9984.480563.51944
10014.60359-3.60359
10154.388040.611961
1024.54.32670.173302
10334.48056-1.48056
10434.63409-1.63409
10585.156692.84331
1062.54.81914-2.31914
10774.480912.51909
10804.75745-4.75745
10914.63443-3.63443
1103.54.69543-1.19543
1115.54.573090.926911
1125.54.634430.86557

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.5 & 4.60359 & 2.89641 \tabularnewline
2 & 6 & 4.69611 & 1.30389 \tabularnewline
3 & 6.5 & 4.72627 & 1.77373 \tabularnewline
4 & 1 & 4.41888 & -3.41888 \tabularnewline
5 & 1 & 4.29586 & -3.29586 \tabularnewline
6 & 5.5 & 4.3572 & 1.1428 \tabularnewline
7 & 8.5 & 5.18889 & 3.31111 \tabularnewline
8 & 6.5 & 4.51106 & 1.98894 \tabularnewline
9 & 4.5 & 4.23452 & 0.265485 \tabularnewline
10 & 2 & 4.3572 & -2.3572 \tabularnewline
11 & 5 & 4.51141 & 0.488594 \tabularnewline
12 & 0.5 & 4.35754 & -3.85754 \tabularnewline
13 & 5 & 4.26502 & 0.734985 \tabularnewline
14 & 5 & 4.57275 & 0.427253 \tabularnewline
15 & 2.5 & 4.85066 & -2.35066 \tabularnewline
16 & 5 & 5.03469 & -0.0346869 \tabularnewline
17 & 5.5 & 4.44904 & 1.05096 \tabularnewline
18 & 3.5 & 4.54225 & -1.04225 \tabularnewline
19 & 3 & 4.54191 & -1.54191 \tabularnewline
20 & 4 & 4.57241 & -0.572405 \tabularnewline
21 & 0.5 & 4.26502 & -3.76502 \tabularnewline
22 & 6.5 & 4.54259 & 1.95741 \tabularnewline
23 & 4.5 & 4.66561 & -0.165614 \tabularnewline
24 & 7.5 & 4.60393 & 2.89607 \tabularnewline
25 & 5.5 & 5.00419 & 0.495813 \tabularnewline
26 & 4 & 4.57241 & -0.572405 \tabularnewline
27 & 7.5 & 4.72661 & 2.77339 \tabularnewline
28 & 7 & 4.41922 & 2.58078 \tabularnewline
29 & 4 & 4.41888 & -0.418881 \tabularnewline
30 & 5.5 & 4.17283 & 1.32717 \tabularnewline
31 & 2.5 & 4.60325 & -2.10325 \tabularnewline
32 & 5.5 & 4.88082 & 0.619179 \tabularnewline
33 & 3.5 & 4.44972 & -0.949723 \tabularnewline
34 & 2.5 & 4.41956 & -1.91956 \tabularnewline
35 & 4.5 & 4.38838 & 0.111619 \tabularnewline
36 & 4.5 & 4.63409 & -0.134088 \tabularnewline
37 & 4.5 & 4.44972 & 0.0502774 \tabularnewline
38 & 6 & 4.51106 & 1.48894 \tabularnewline
39 & 2.5 & 4.51141 & -2.01141 \tabularnewline
40 & 5 & 4.35788 & 0.642118 \tabularnewline
41 & 0 & 4.54156 & -4.54156 \tabularnewline
42 & 5 & 4.44972 & 0.550277 \tabularnewline
43 & 6.5 & 4.35754 & 2.14246 \tabularnewline
44 & 5 & 4.60359 & 0.396411 \tabularnewline
45 & 6 & 4.38872 & 1.61128 \tabularnewline
46 & 4.5 & 4.38872 & 0.111277 \tabularnewline
47 & 5.5 & 4.7883 & 0.711704 \tabularnewline
48 & 1 & 4.66493 & -3.66493 \tabularnewline
49 & 7.5 & 4.2962 & 3.2038 \tabularnewline
50 & 6 & 4.81914 & 1.18086 \tabularnewline
51 & 5 & 4.45006 & 0.549936 \tabularnewline
52 & 1 & 4.75745 & -3.75745 \tabularnewline
53 & 5 & 4.57275 & 0.427253 \tabularnewline
54 & 6.5 & 4.69577 & 1.80423 \tabularnewline
55 & 7 & 4.32636 & 2.67364 \tabularnewline
56 & 4.5 & 4.3572 & 0.142802 \tabularnewline
57 & 0 & 4.88048 & -4.88048 \tabularnewline
58 & 8.5 & 5.09603 & 3.40397 \tabularnewline
59 & 3.5 & 4.72661 & -1.22661 \tabularnewline
60 & 7.5 & 4.69543 & 2.80457 \tabularnewline
61 & 3.5 & 4.41956 & -0.919565 \tabularnewline
62 & 6 & 4.44972 & 1.55028 \tabularnewline
63 & 1.5 & 4.91235 & -3.41235 \tabularnewline
64 & 9 & 4.29586 & 4.70414 \tabularnewline
65 & 3.5 & 4.48091 & -0.980906 \tabularnewline
66 & 3.5 & 4.48056 & -0.980564 \tabularnewline
67 & 4 & 4.85032 & -0.850321 \tabularnewline
68 & 6.5 & 5.00385 & 1.49615 \tabularnewline
69 & 7.5 & 4.54293 & 2.95707 \tabularnewline
70 & 6 & 4.60325 & 1.39675 \tabularnewline
71 & 5 & 4.48091 & 0.519094 \tabularnewline
72 & 5.5 & 4.41888 & 1.08112 \tabularnewline
73 & 3.5 & 4.51175 & -1.01175 \tabularnewline
74 & 7.5 & 4.38804 & 3.11196 \tabularnewline
75 & 6.5 & 4.72695 & 1.77305 \tabularnewline
76 & 6.5 & 4.88082 & 1.61918 \tabularnewline
77 & 6.5 & 4.69577 & 1.80423 \tabularnewline
78 & 7 & 4.3572 & 2.6428 \tabularnewline
79 & 3.5 & 4.44972 & -0.949723 \tabularnewline
80 & 1.5 & 4.29586 & -2.79586 \tabularnewline
81 & 4 & 4.75711 & -0.757113 \tabularnewline
82 & 7.5 & 4.51106 & 2.98894 \tabularnewline
83 & 4.5 & 4.38804 & 0.111961 \tabularnewline
84 & 0 & 4.35788 & -4.35788 \tabularnewline
85 & 3.5 & 4.63443 & -1.13443 \tabularnewline
86 & 5.5 & 4.41888 & 1.08112 \tabularnewline
87 & 5 & 4.17283 & 0.827168 \tabularnewline
88 & 4.5 & 4.78795 & -0.287954 \tabularnewline
89 & 2.5 & 4.48022 & -1.98022 \tabularnewline
90 & 7.5 & 4.63443 & 2.86557 \tabularnewline
91 & 7 & 4.26502 & 2.73498 \tabularnewline
92 & 0 & 4.7883 & -4.7883 \tabularnewline
93 & 4.5 & 4.63443 & -0.13443 \tabularnewline
94 & 3 & 4.72661 & -1.72661 \tabularnewline
95 & 1.5 & 4.3572 & -2.8572 \tabularnewline
96 & 3.5 & 4.44972 & -0.949723 \tabularnewline
97 & 2.5 & 4.26536 & -1.76536 \tabularnewline
98 & 5.5 & 4.41922 & 1.08078 \tabularnewline
99 & 8 & 4.48056 & 3.51944 \tabularnewline
100 & 1 & 4.60359 & -3.60359 \tabularnewline
101 & 5 & 4.38804 & 0.611961 \tabularnewline
102 & 4.5 & 4.3267 & 0.173302 \tabularnewline
103 & 3 & 4.48056 & -1.48056 \tabularnewline
104 & 3 & 4.63409 & -1.63409 \tabularnewline
105 & 8 & 5.15669 & 2.84331 \tabularnewline
106 & 2.5 & 4.81914 & -2.31914 \tabularnewline
107 & 7 & 4.48091 & 2.51909 \tabularnewline
108 & 0 & 4.75745 & -4.75745 \tabularnewline
109 & 1 & 4.63443 & -3.63443 \tabularnewline
110 & 3.5 & 4.69543 & -1.19543 \tabularnewline
111 & 5.5 & 4.57309 & 0.926911 \tabularnewline
112 & 5.5 & 4.63443 & 0.86557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267586&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]7.5[/C][C]4.60359[/C][C]2.89641[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]4.69611[/C][C]1.30389[/C][/ROW]
[ROW][C]3[/C][C]6.5[/C][C]4.72627[/C][C]1.77373[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]4.41888[/C][C]-3.41888[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]4.29586[/C][C]-3.29586[/C][/ROW]
[ROW][C]6[/C][C]5.5[/C][C]4.3572[/C][C]1.1428[/C][/ROW]
[ROW][C]7[/C][C]8.5[/C][C]5.18889[/C][C]3.31111[/C][/ROW]
[ROW][C]8[/C][C]6.5[/C][C]4.51106[/C][C]1.98894[/C][/ROW]
[ROW][C]9[/C][C]4.5[/C][C]4.23452[/C][C]0.265485[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]4.3572[/C][C]-2.3572[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]4.51141[/C][C]0.488594[/C][/ROW]
[ROW][C]12[/C][C]0.5[/C][C]4.35754[/C][C]-3.85754[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]4.26502[/C][C]0.734985[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]4.57275[/C][C]0.427253[/C][/ROW]
[ROW][C]15[/C][C]2.5[/C][C]4.85066[/C][C]-2.35066[/C][/ROW]
[ROW][C]16[/C][C]5[/C][C]5.03469[/C][C]-0.0346869[/C][/ROW]
[ROW][C]17[/C][C]5.5[/C][C]4.44904[/C][C]1.05096[/C][/ROW]
[ROW][C]18[/C][C]3.5[/C][C]4.54225[/C][C]-1.04225[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]4.54191[/C][C]-1.54191[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]4.57241[/C][C]-0.572405[/C][/ROW]
[ROW][C]21[/C][C]0.5[/C][C]4.26502[/C][C]-3.76502[/C][/ROW]
[ROW][C]22[/C][C]6.5[/C][C]4.54259[/C][C]1.95741[/C][/ROW]
[ROW][C]23[/C][C]4.5[/C][C]4.66561[/C][C]-0.165614[/C][/ROW]
[ROW][C]24[/C][C]7.5[/C][C]4.60393[/C][C]2.89607[/C][/ROW]
[ROW][C]25[/C][C]5.5[/C][C]5.00419[/C][C]0.495813[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]4.57241[/C][C]-0.572405[/C][/ROW]
[ROW][C]27[/C][C]7.5[/C][C]4.72661[/C][C]2.77339[/C][/ROW]
[ROW][C]28[/C][C]7[/C][C]4.41922[/C][C]2.58078[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]4.41888[/C][C]-0.418881[/C][/ROW]
[ROW][C]30[/C][C]5.5[/C][C]4.17283[/C][C]1.32717[/C][/ROW]
[ROW][C]31[/C][C]2.5[/C][C]4.60325[/C][C]-2.10325[/C][/ROW]
[ROW][C]32[/C][C]5.5[/C][C]4.88082[/C][C]0.619179[/C][/ROW]
[ROW][C]33[/C][C]3.5[/C][C]4.44972[/C][C]-0.949723[/C][/ROW]
[ROW][C]34[/C][C]2.5[/C][C]4.41956[/C][C]-1.91956[/C][/ROW]
[ROW][C]35[/C][C]4.5[/C][C]4.38838[/C][C]0.111619[/C][/ROW]
[ROW][C]36[/C][C]4.5[/C][C]4.63409[/C][C]-0.134088[/C][/ROW]
[ROW][C]37[/C][C]4.5[/C][C]4.44972[/C][C]0.0502774[/C][/ROW]
[ROW][C]38[/C][C]6[/C][C]4.51106[/C][C]1.48894[/C][/ROW]
[ROW][C]39[/C][C]2.5[/C][C]4.51141[/C][C]-2.01141[/C][/ROW]
[ROW][C]40[/C][C]5[/C][C]4.35788[/C][C]0.642118[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]4.54156[/C][C]-4.54156[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]4.44972[/C][C]0.550277[/C][/ROW]
[ROW][C]43[/C][C]6.5[/C][C]4.35754[/C][C]2.14246[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]4.60359[/C][C]0.396411[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]4.38872[/C][C]1.61128[/C][/ROW]
[ROW][C]46[/C][C]4.5[/C][C]4.38872[/C][C]0.111277[/C][/ROW]
[ROW][C]47[/C][C]5.5[/C][C]4.7883[/C][C]0.711704[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]4.66493[/C][C]-3.66493[/C][/ROW]
[ROW][C]49[/C][C]7.5[/C][C]4.2962[/C][C]3.2038[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]4.81914[/C][C]1.18086[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]4.45006[/C][C]0.549936[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]4.75745[/C][C]-3.75745[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]4.57275[/C][C]0.427253[/C][/ROW]
[ROW][C]54[/C][C]6.5[/C][C]4.69577[/C][C]1.80423[/C][/ROW]
[ROW][C]55[/C][C]7[/C][C]4.32636[/C][C]2.67364[/C][/ROW]
[ROW][C]56[/C][C]4.5[/C][C]4.3572[/C][C]0.142802[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]4.88048[/C][C]-4.88048[/C][/ROW]
[ROW][C]58[/C][C]8.5[/C][C]5.09603[/C][C]3.40397[/C][/ROW]
[ROW][C]59[/C][C]3.5[/C][C]4.72661[/C][C]-1.22661[/C][/ROW]
[ROW][C]60[/C][C]7.5[/C][C]4.69543[/C][C]2.80457[/C][/ROW]
[ROW][C]61[/C][C]3.5[/C][C]4.41956[/C][C]-0.919565[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]4.44972[/C][C]1.55028[/C][/ROW]
[ROW][C]63[/C][C]1.5[/C][C]4.91235[/C][C]-3.41235[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]4.29586[/C][C]4.70414[/C][/ROW]
[ROW][C]65[/C][C]3.5[/C][C]4.48091[/C][C]-0.980906[/C][/ROW]
[ROW][C]66[/C][C]3.5[/C][C]4.48056[/C][C]-0.980564[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]4.85032[/C][C]-0.850321[/C][/ROW]
[ROW][C]68[/C][C]6.5[/C][C]5.00385[/C][C]1.49615[/C][/ROW]
[ROW][C]69[/C][C]7.5[/C][C]4.54293[/C][C]2.95707[/C][/ROW]
[ROW][C]70[/C][C]6[/C][C]4.60325[/C][C]1.39675[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]4.48091[/C][C]0.519094[/C][/ROW]
[ROW][C]72[/C][C]5.5[/C][C]4.41888[/C][C]1.08112[/C][/ROW]
[ROW][C]73[/C][C]3.5[/C][C]4.51175[/C][C]-1.01175[/C][/ROW]
[ROW][C]74[/C][C]7.5[/C][C]4.38804[/C][C]3.11196[/C][/ROW]
[ROW][C]75[/C][C]6.5[/C][C]4.72695[/C][C]1.77305[/C][/ROW]
[ROW][C]76[/C][C]6.5[/C][C]4.88082[/C][C]1.61918[/C][/ROW]
[ROW][C]77[/C][C]6.5[/C][C]4.69577[/C][C]1.80423[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]4.3572[/C][C]2.6428[/C][/ROW]
[ROW][C]79[/C][C]3.5[/C][C]4.44972[/C][C]-0.949723[/C][/ROW]
[ROW][C]80[/C][C]1.5[/C][C]4.29586[/C][C]-2.79586[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]4.75711[/C][C]-0.757113[/C][/ROW]
[ROW][C]82[/C][C]7.5[/C][C]4.51106[/C][C]2.98894[/C][/ROW]
[ROW][C]83[/C][C]4.5[/C][C]4.38804[/C][C]0.111961[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]4.35788[/C][C]-4.35788[/C][/ROW]
[ROW][C]85[/C][C]3.5[/C][C]4.63443[/C][C]-1.13443[/C][/ROW]
[ROW][C]86[/C][C]5.5[/C][C]4.41888[/C][C]1.08112[/C][/ROW]
[ROW][C]87[/C][C]5[/C][C]4.17283[/C][C]0.827168[/C][/ROW]
[ROW][C]88[/C][C]4.5[/C][C]4.78795[/C][C]-0.287954[/C][/ROW]
[ROW][C]89[/C][C]2.5[/C][C]4.48022[/C][C]-1.98022[/C][/ROW]
[ROW][C]90[/C][C]7.5[/C][C]4.63443[/C][C]2.86557[/C][/ROW]
[ROW][C]91[/C][C]7[/C][C]4.26502[/C][C]2.73498[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]4.7883[/C][C]-4.7883[/C][/ROW]
[ROW][C]93[/C][C]4.5[/C][C]4.63443[/C][C]-0.13443[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]4.72661[/C][C]-1.72661[/C][/ROW]
[ROW][C]95[/C][C]1.5[/C][C]4.3572[/C][C]-2.8572[/C][/ROW]
[ROW][C]96[/C][C]3.5[/C][C]4.44972[/C][C]-0.949723[/C][/ROW]
[ROW][C]97[/C][C]2.5[/C][C]4.26536[/C][C]-1.76536[/C][/ROW]
[ROW][C]98[/C][C]5.5[/C][C]4.41922[/C][C]1.08078[/C][/ROW]
[ROW][C]99[/C][C]8[/C][C]4.48056[/C][C]3.51944[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]4.60359[/C][C]-3.60359[/C][/ROW]
[ROW][C]101[/C][C]5[/C][C]4.38804[/C][C]0.611961[/C][/ROW]
[ROW][C]102[/C][C]4.5[/C][C]4.3267[/C][C]0.173302[/C][/ROW]
[ROW][C]103[/C][C]3[/C][C]4.48056[/C][C]-1.48056[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]4.63409[/C][C]-1.63409[/C][/ROW]
[ROW][C]105[/C][C]8[/C][C]5.15669[/C][C]2.84331[/C][/ROW]
[ROW][C]106[/C][C]2.5[/C][C]4.81914[/C][C]-2.31914[/C][/ROW]
[ROW][C]107[/C][C]7[/C][C]4.48091[/C][C]2.51909[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]4.75745[/C][C]-4.75745[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]4.63443[/C][C]-3.63443[/C][/ROW]
[ROW][C]110[/C][C]3.5[/C][C]4.69543[/C][C]-1.19543[/C][/ROW]
[ROW][C]111[/C][C]5.5[/C][C]4.57309[/C][C]0.926911[/C][/ROW]
[ROW][C]112[/C][C]5.5[/C][C]4.63443[/C][C]0.86557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267586&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267586&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
17.54.603592.89641
264.696111.30389
36.54.726271.77373
414.41888-3.41888
514.29586-3.29586
65.54.35721.1428
78.55.188893.31111
86.54.511061.98894
94.54.234520.265485
1024.3572-2.3572
1154.511410.488594
120.54.35754-3.85754
1354.265020.734985
1454.572750.427253
152.54.85066-2.35066
1655.03469-0.0346869
175.54.449041.05096
183.54.54225-1.04225
1934.54191-1.54191
2044.57241-0.572405
210.54.26502-3.76502
226.54.542591.95741
234.54.66561-0.165614
247.54.603932.89607
255.55.004190.495813
2644.57241-0.572405
277.54.726612.77339
2874.419222.58078
2944.41888-0.418881
305.54.172831.32717
312.54.60325-2.10325
325.54.880820.619179
333.54.44972-0.949723
342.54.41956-1.91956
354.54.388380.111619
364.54.63409-0.134088
374.54.449720.0502774
3864.511061.48894
392.54.51141-2.01141
4054.357880.642118
4104.54156-4.54156
4254.449720.550277
436.54.357542.14246
4454.603590.396411
4564.388721.61128
464.54.388720.111277
475.54.78830.711704
4814.66493-3.66493
497.54.29623.2038
5064.819141.18086
5154.450060.549936
5214.75745-3.75745
5354.572750.427253
546.54.695771.80423
5574.326362.67364
564.54.35720.142802
5704.88048-4.88048
588.55.096033.40397
593.54.72661-1.22661
607.54.695432.80457
613.54.41956-0.919565
6264.449721.55028
631.54.91235-3.41235
6494.295864.70414
653.54.48091-0.980906
663.54.48056-0.980564
6744.85032-0.850321
686.55.003851.49615
697.54.542932.95707
7064.603251.39675
7154.480910.519094
725.54.418881.08112
733.54.51175-1.01175
747.54.388043.11196
756.54.726951.77305
766.54.880821.61918
776.54.695771.80423
7874.35722.6428
793.54.44972-0.949723
801.54.29586-2.79586
8144.75711-0.757113
827.54.511062.98894
834.54.388040.111961
8404.35788-4.35788
853.54.63443-1.13443
865.54.418881.08112
8754.172830.827168
884.54.78795-0.287954
892.54.48022-1.98022
907.54.634432.86557
9174.265022.73498
9204.7883-4.7883
934.54.63443-0.13443
9434.72661-1.72661
951.54.3572-2.8572
963.54.44972-0.949723
972.54.26536-1.76536
985.54.419221.08078
9984.480563.51944
10014.60359-3.60359
10154.388040.611961
1024.54.32670.173302
10334.48056-1.48056
10434.63409-1.63409
10585.156692.84331
1062.54.81914-2.31914
10774.480912.51909
10804.75745-4.75745
10914.63443-3.63443
1103.54.69543-1.19543
1115.54.573090.926911
1125.54.634430.86557







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.7138250.5723490.286175
70.6063650.7872710.393635
80.5295610.9408790.470439
90.5519410.8961190.448059
100.5246550.950690.475345
110.4161390.8322780.583861
120.4942210.9884410.505779
130.4924430.9848850.507557
140.3990320.7980650.600968
150.4120780.8241560.587922
160.4099670.8199350.590033
170.3351870.6703730.664813
180.2674070.5348140.732593
190.2523840.5047670.747616
200.2229660.4459320.777034
210.2538760.5077510.746124
220.3291960.6583930.670804
230.265720.531440.73428
240.3384070.6768130.661593
250.2893640.5787270.710636
260.2451160.4902320.754884
270.248560.4971190.75144
280.3220510.6441030.677949
290.2662340.5324680.733766
300.2773230.5546450.722677
310.292010.584020.70799
320.2449390.4898780.755061
330.2046490.4092980.795351
340.1828030.3656060.817197
350.1474790.2949590.852521
360.1161380.2322760.883862
370.08942120.1788420.910579
380.07718250.1543650.922817
390.07366850.1473370.926332
400.06070680.1214140.939293
410.1582010.3164030.841799
420.1294120.2588240.870588
430.1372250.2744510.862775
440.1085640.2171280.891436
450.09875820.1975160.901242
460.07615120.1523020.923849
470.05914710.1182940.940853
480.1004540.2009080.899546
490.1352930.2705860.864707
500.1145010.2290020.885499
510.09099130.1819830.909009
520.1468010.2936030.853199
530.1185290.2370580.881471
540.1106810.2213630.889319
550.1278830.2557650.872117
560.1020360.2040730.897964
570.2276460.4552920.772354
580.2979210.5958420.702079
590.2644520.5289040.735548
600.2928740.5857480.707126
610.2551290.5102580.744871
620.2317140.4634280.768286
630.2799970.5599950.720003
640.4476530.8953070.552347
650.4020.8039990.598
660.3582480.7164960.641752
670.3128030.6256070.687197
680.2900720.5801430.709928
690.357530.7150610.64247
700.3215280.6430560.678472
710.2812940.5625880.718706
720.2442690.4885380.755731
730.2058150.4116290.794185
740.2397460.4794920.760254
750.243930.487860.75607
760.2682710.5365420.731729
770.2644660.5289320.735534
780.2694230.5388460.730577
790.227580.4551590.77242
800.2577230.5154470.742277
810.21450.4290.7855
820.247140.4942810.75286
830.2001650.400330.799835
840.2882280.5764570.711772
850.2410250.4820510.758975
860.2043190.4086380.795681
870.1640730.3281450.835927
880.1282350.256470.871765
890.117940.235880.88206
900.1688870.3377750.831113
910.1825970.3651930.817403
920.297850.5956990.70215
930.2391550.478310.760845
940.1983060.3966120.801694
950.2118360.4236720.788164
960.1629460.3258920.837054
970.1388420.2776840.861158
980.109820.2196410.89018
990.196610.3932210.80339
1000.2325160.4650310.767484
1010.1710390.3420780.828961
1020.1212970.2425940.878703
1030.07738240.1547650.922618
1040.05001510.100030.949985
1050.436630.873260.56337
1060.5186870.9626260.481313

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.713825 & 0.572349 & 0.286175 \tabularnewline
7 & 0.606365 & 0.787271 & 0.393635 \tabularnewline
8 & 0.529561 & 0.940879 & 0.470439 \tabularnewline
9 & 0.551941 & 0.896119 & 0.448059 \tabularnewline
10 & 0.524655 & 0.95069 & 0.475345 \tabularnewline
11 & 0.416139 & 0.832278 & 0.583861 \tabularnewline
12 & 0.494221 & 0.988441 & 0.505779 \tabularnewline
13 & 0.492443 & 0.984885 & 0.507557 \tabularnewline
14 & 0.399032 & 0.798065 & 0.600968 \tabularnewline
15 & 0.412078 & 0.824156 & 0.587922 \tabularnewline
16 & 0.409967 & 0.819935 & 0.590033 \tabularnewline
17 & 0.335187 & 0.670373 & 0.664813 \tabularnewline
18 & 0.267407 & 0.534814 & 0.732593 \tabularnewline
19 & 0.252384 & 0.504767 & 0.747616 \tabularnewline
20 & 0.222966 & 0.445932 & 0.777034 \tabularnewline
21 & 0.253876 & 0.507751 & 0.746124 \tabularnewline
22 & 0.329196 & 0.658393 & 0.670804 \tabularnewline
23 & 0.26572 & 0.53144 & 0.73428 \tabularnewline
24 & 0.338407 & 0.676813 & 0.661593 \tabularnewline
25 & 0.289364 & 0.578727 & 0.710636 \tabularnewline
26 & 0.245116 & 0.490232 & 0.754884 \tabularnewline
27 & 0.24856 & 0.497119 & 0.75144 \tabularnewline
28 & 0.322051 & 0.644103 & 0.677949 \tabularnewline
29 & 0.266234 & 0.532468 & 0.733766 \tabularnewline
30 & 0.277323 & 0.554645 & 0.722677 \tabularnewline
31 & 0.29201 & 0.58402 & 0.70799 \tabularnewline
32 & 0.244939 & 0.489878 & 0.755061 \tabularnewline
33 & 0.204649 & 0.409298 & 0.795351 \tabularnewline
34 & 0.182803 & 0.365606 & 0.817197 \tabularnewline
35 & 0.147479 & 0.294959 & 0.852521 \tabularnewline
36 & 0.116138 & 0.232276 & 0.883862 \tabularnewline
37 & 0.0894212 & 0.178842 & 0.910579 \tabularnewline
38 & 0.0771825 & 0.154365 & 0.922817 \tabularnewline
39 & 0.0736685 & 0.147337 & 0.926332 \tabularnewline
40 & 0.0607068 & 0.121414 & 0.939293 \tabularnewline
41 & 0.158201 & 0.316403 & 0.841799 \tabularnewline
42 & 0.129412 & 0.258824 & 0.870588 \tabularnewline
43 & 0.137225 & 0.274451 & 0.862775 \tabularnewline
44 & 0.108564 & 0.217128 & 0.891436 \tabularnewline
45 & 0.0987582 & 0.197516 & 0.901242 \tabularnewline
46 & 0.0761512 & 0.152302 & 0.923849 \tabularnewline
47 & 0.0591471 & 0.118294 & 0.940853 \tabularnewline
48 & 0.100454 & 0.200908 & 0.899546 \tabularnewline
49 & 0.135293 & 0.270586 & 0.864707 \tabularnewline
50 & 0.114501 & 0.229002 & 0.885499 \tabularnewline
51 & 0.0909913 & 0.181983 & 0.909009 \tabularnewline
52 & 0.146801 & 0.293603 & 0.853199 \tabularnewline
53 & 0.118529 & 0.237058 & 0.881471 \tabularnewline
54 & 0.110681 & 0.221363 & 0.889319 \tabularnewline
55 & 0.127883 & 0.255765 & 0.872117 \tabularnewline
56 & 0.102036 & 0.204073 & 0.897964 \tabularnewline
57 & 0.227646 & 0.455292 & 0.772354 \tabularnewline
58 & 0.297921 & 0.595842 & 0.702079 \tabularnewline
59 & 0.264452 & 0.528904 & 0.735548 \tabularnewline
60 & 0.292874 & 0.585748 & 0.707126 \tabularnewline
61 & 0.255129 & 0.510258 & 0.744871 \tabularnewline
62 & 0.231714 & 0.463428 & 0.768286 \tabularnewline
63 & 0.279997 & 0.559995 & 0.720003 \tabularnewline
64 & 0.447653 & 0.895307 & 0.552347 \tabularnewline
65 & 0.402 & 0.803999 & 0.598 \tabularnewline
66 & 0.358248 & 0.716496 & 0.641752 \tabularnewline
67 & 0.312803 & 0.625607 & 0.687197 \tabularnewline
68 & 0.290072 & 0.580143 & 0.709928 \tabularnewline
69 & 0.35753 & 0.715061 & 0.64247 \tabularnewline
70 & 0.321528 & 0.643056 & 0.678472 \tabularnewline
71 & 0.281294 & 0.562588 & 0.718706 \tabularnewline
72 & 0.244269 & 0.488538 & 0.755731 \tabularnewline
73 & 0.205815 & 0.411629 & 0.794185 \tabularnewline
74 & 0.239746 & 0.479492 & 0.760254 \tabularnewline
75 & 0.24393 & 0.48786 & 0.75607 \tabularnewline
76 & 0.268271 & 0.536542 & 0.731729 \tabularnewline
77 & 0.264466 & 0.528932 & 0.735534 \tabularnewline
78 & 0.269423 & 0.538846 & 0.730577 \tabularnewline
79 & 0.22758 & 0.455159 & 0.77242 \tabularnewline
80 & 0.257723 & 0.515447 & 0.742277 \tabularnewline
81 & 0.2145 & 0.429 & 0.7855 \tabularnewline
82 & 0.24714 & 0.494281 & 0.75286 \tabularnewline
83 & 0.200165 & 0.40033 & 0.799835 \tabularnewline
84 & 0.288228 & 0.576457 & 0.711772 \tabularnewline
85 & 0.241025 & 0.482051 & 0.758975 \tabularnewline
86 & 0.204319 & 0.408638 & 0.795681 \tabularnewline
87 & 0.164073 & 0.328145 & 0.835927 \tabularnewline
88 & 0.128235 & 0.25647 & 0.871765 \tabularnewline
89 & 0.11794 & 0.23588 & 0.88206 \tabularnewline
90 & 0.168887 & 0.337775 & 0.831113 \tabularnewline
91 & 0.182597 & 0.365193 & 0.817403 \tabularnewline
92 & 0.29785 & 0.595699 & 0.70215 \tabularnewline
93 & 0.239155 & 0.47831 & 0.760845 \tabularnewline
94 & 0.198306 & 0.396612 & 0.801694 \tabularnewline
95 & 0.211836 & 0.423672 & 0.788164 \tabularnewline
96 & 0.162946 & 0.325892 & 0.837054 \tabularnewline
97 & 0.138842 & 0.277684 & 0.861158 \tabularnewline
98 & 0.10982 & 0.219641 & 0.89018 \tabularnewline
99 & 0.19661 & 0.393221 & 0.80339 \tabularnewline
100 & 0.232516 & 0.465031 & 0.767484 \tabularnewline
101 & 0.171039 & 0.342078 & 0.828961 \tabularnewline
102 & 0.121297 & 0.242594 & 0.878703 \tabularnewline
103 & 0.0773824 & 0.154765 & 0.922618 \tabularnewline
104 & 0.0500151 & 0.10003 & 0.949985 \tabularnewline
105 & 0.43663 & 0.87326 & 0.56337 \tabularnewline
106 & 0.518687 & 0.962626 & 0.481313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267586&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]6[/C][C]0.713825[/C][C]0.572349[/C][C]0.286175[/C][/ROW]
[ROW][C]7[/C][C]0.606365[/C][C]0.787271[/C][C]0.393635[/C][/ROW]
[ROW][C]8[/C][C]0.529561[/C][C]0.940879[/C][C]0.470439[/C][/ROW]
[ROW][C]9[/C][C]0.551941[/C][C]0.896119[/C][C]0.448059[/C][/ROW]
[ROW][C]10[/C][C]0.524655[/C][C]0.95069[/C][C]0.475345[/C][/ROW]
[ROW][C]11[/C][C]0.416139[/C][C]0.832278[/C][C]0.583861[/C][/ROW]
[ROW][C]12[/C][C]0.494221[/C][C]0.988441[/C][C]0.505779[/C][/ROW]
[ROW][C]13[/C][C]0.492443[/C][C]0.984885[/C][C]0.507557[/C][/ROW]
[ROW][C]14[/C][C]0.399032[/C][C]0.798065[/C][C]0.600968[/C][/ROW]
[ROW][C]15[/C][C]0.412078[/C][C]0.824156[/C][C]0.587922[/C][/ROW]
[ROW][C]16[/C][C]0.409967[/C][C]0.819935[/C][C]0.590033[/C][/ROW]
[ROW][C]17[/C][C]0.335187[/C][C]0.670373[/C][C]0.664813[/C][/ROW]
[ROW][C]18[/C][C]0.267407[/C][C]0.534814[/C][C]0.732593[/C][/ROW]
[ROW][C]19[/C][C]0.252384[/C][C]0.504767[/C][C]0.747616[/C][/ROW]
[ROW][C]20[/C][C]0.222966[/C][C]0.445932[/C][C]0.777034[/C][/ROW]
[ROW][C]21[/C][C]0.253876[/C][C]0.507751[/C][C]0.746124[/C][/ROW]
[ROW][C]22[/C][C]0.329196[/C][C]0.658393[/C][C]0.670804[/C][/ROW]
[ROW][C]23[/C][C]0.26572[/C][C]0.53144[/C][C]0.73428[/C][/ROW]
[ROW][C]24[/C][C]0.338407[/C][C]0.676813[/C][C]0.661593[/C][/ROW]
[ROW][C]25[/C][C]0.289364[/C][C]0.578727[/C][C]0.710636[/C][/ROW]
[ROW][C]26[/C][C]0.245116[/C][C]0.490232[/C][C]0.754884[/C][/ROW]
[ROW][C]27[/C][C]0.24856[/C][C]0.497119[/C][C]0.75144[/C][/ROW]
[ROW][C]28[/C][C]0.322051[/C][C]0.644103[/C][C]0.677949[/C][/ROW]
[ROW][C]29[/C][C]0.266234[/C][C]0.532468[/C][C]0.733766[/C][/ROW]
[ROW][C]30[/C][C]0.277323[/C][C]0.554645[/C][C]0.722677[/C][/ROW]
[ROW][C]31[/C][C]0.29201[/C][C]0.58402[/C][C]0.70799[/C][/ROW]
[ROW][C]32[/C][C]0.244939[/C][C]0.489878[/C][C]0.755061[/C][/ROW]
[ROW][C]33[/C][C]0.204649[/C][C]0.409298[/C][C]0.795351[/C][/ROW]
[ROW][C]34[/C][C]0.182803[/C][C]0.365606[/C][C]0.817197[/C][/ROW]
[ROW][C]35[/C][C]0.147479[/C][C]0.294959[/C][C]0.852521[/C][/ROW]
[ROW][C]36[/C][C]0.116138[/C][C]0.232276[/C][C]0.883862[/C][/ROW]
[ROW][C]37[/C][C]0.0894212[/C][C]0.178842[/C][C]0.910579[/C][/ROW]
[ROW][C]38[/C][C]0.0771825[/C][C]0.154365[/C][C]0.922817[/C][/ROW]
[ROW][C]39[/C][C]0.0736685[/C][C]0.147337[/C][C]0.926332[/C][/ROW]
[ROW][C]40[/C][C]0.0607068[/C][C]0.121414[/C][C]0.939293[/C][/ROW]
[ROW][C]41[/C][C]0.158201[/C][C]0.316403[/C][C]0.841799[/C][/ROW]
[ROW][C]42[/C][C]0.129412[/C][C]0.258824[/C][C]0.870588[/C][/ROW]
[ROW][C]43[/C][C]0.137225[/C][C]0.274451[/C][C]0.862775[/C][/ROW]
[ROW][C]44[/C][C]0.108564[/C][C]0.217128[/C][C]0.891436[/C][/ROW]
[ROW][C]45[/C][C]0.0987582[/C][C]0.197516[/C][C]0.901242[/C][/ROW]
[ROW][C]46[/C][C]0.0761512[/C][C]0.152302[/C][C]0.923849[/C][/ROW]
[ROW][C]47[/C][C]0.0591471[/C][C]0.118294[/C][C]0.940853[/C][/ROW]
[ROW][C]48[/C][C]0.100454[/C][C]0.200908[/C][C]0.899546[/C][/ROW]
[ROW][C]49[/C][C]0.135293[/C][C]0.270586[/C][C]0.864707[/C][/ROW]
[ROW][C]50[/C][C]0.114501[/C][C]0.229002[/C][C]0.885499[/C][/ROW]
[ROW][C]51[/C][C]0.0909913[/C][C]0.181983[/C][C]0.909009[/C][/ROW]
[ROW][C]52[/C][C]0.146801[/C][C]0.293603[/C][C]0.853199[/C][/ROW]
[ROW][C]53[/C][C]0.118529[/C][C]0.237058[/C][C]0.881471[/C][/ROW]
[ROW][C]54[/C][C]0.110681[/C][C]0.221363[/C][C]0.889319[/C][/ROW]
[ROW][C]55[/C][C]0.127883[/C][C]0.255765[/C][C]0.872117[/C][/ROW]
[ROW][C]56[/C][C]0.102036[/C][C]0.204073[/C][C]0.897964[/C][/ROW]
[ROW][C]57[/C][C]0.227646[/C][C]0.455292[/C][C]0.772354[/C][/ROW]
[ROW][C]58[/C][C]0.297921[/C][C]0.595842[/C][C]0.702079[/C][/ROW]
[ROW][C]59[/C][C]0.264452[/C][C]0.528904[/C][C]0.735548[/C][/ROW]
[ROW][C]60[/C][C]0.292874[/C][C]0.585748[/C][C]0.707126[/C][/ROW]
[ROW][C]61[/C][C]0.255129[/C][C]0.510258[/C][C]0.744871[/C][/ROW]
[ROW][C]62[/C][C]0.231714[/C][C]0.463428[/C][C]0.768286[/C][/ROW]
[ROW][C]63[/C][C]0.279997[/C][C]0.559995[/C][C]0.720003[/C][/ROW]
[ROW][C]64[/C][C]0.447653[/C][C]0.895307[/C][C]0.552347[/C][/ROW]
[ROW][C]65[/C][C]0.402[/C][C]0.803999[/C][C]0.598[/C][/ROW]
[ROW][C]66[/C][C]0.358248[/C][C]0.716496[/C][C]0.641752[/C][/ROW]
[ROW][C]67[/C][C]0.312803[/C][C]0.625607[/C][C]0.687197[/C][/ROW]
[ROW][C]68[/C][C]0.290072[/C][C]0.580143[/C][C]0.709928[/C][/ROW]
[ROW][C]69[/C][C]0.35753[/C][C]0.715061[/C][C]0.64247[/C][/ROW]
[ROW][C]70[/C][C]0.321528[/C][C]0.643056[/C][C]0.678472[/C][/ROW]
[ROW][C]71[/C][C]0.281294[/C][C]0.562588[/C][C]0.718706[/C][/ROW]
[ROW][C]72[/C][C]0.244269[/C][C]0.488538[/C][C]0.755731[/C][/ROW]
[ROW][C]73[/C][C]0.205815[/C][C]0.411629[/C][C]0.794185[/C][/ROW]
[ROW][C]74[/C][C]0.239746[/C][C]0.479492[/C][C]0.760254[/C][/ROW]
[ROW][C]75[/C][C]0.24393[/C][C]0.48786[/C][C]0.75607[/C][/ROW]
[ROW][C]76[/C][C]0.268271[/C][C]0.536542[/C][C]0.731729[/C][/ROW]
[ROW][C]77[/C][C]0.264466[/C][C]0.528932[/C][C]0.735534[/C][/ROW]
[ROW][C]78[/C][C]0.269423[/C][C]0.538846[/C][C]0.730577[/C][/ROW]
[ROW][C]79[/C][C]0.22758[/C][C]0.455159[/C][C]0.77242[/C][/ROW]
[ROW][C]80[/C][C]0.257723[/C][C]0.515447[/C][C]0.742277[/C][/ROW]
[ROW][C]81[/C][C]0.2145[/C][C]0.429[/C][C]0.7855[/C][/ROW]
[ROW][C]82[/C][C]0.24714[/C][C]0.494281[/C][C]0.75286[/C][/ROW]
[ROW][C]83[/C][C]0.200165[/C][C]0.40033[/C][C]0.799835[/C][/ROW]
[ROW][C]84[/C][C]0.288228[/C][C]0.576457[/C][C]0.711772[/C][/ROW]
[ROW][C]85[/C][C]0.241025[/C][C]0.482051[/C][C]0.758975[/C][/ROW]
[ROW][C]86[/C][C]0.204319[/C][C]0.408638[/C][C]0.795681[/C][/ROW]
[ROW][C]87[/C][C]0.164073[/C][C]0.328145[/C][C]0.835927[/C][/ROW]
[ROW][C]88[/C][C]0.128235[/C][C]0.25647[/C][C]0.871765[/C][/ROW]
[ROW][C]89[/C][C]0.11794[/C][C]0.23588[/C][C]0.88206[/C][/ROW]
[ROW][C]90[/C][C]0.168887[/C][C]0.337775[/C][C]0.831113[/C][/ROW]
[ROW][C]91[/C][C]0.182597[/C][C]0.365193[/C][C]0.817403[/C][/ROW]
[ROW][C]92[/C][C]0.29785[/C][C]0.595699[/C][C]0.70215[/C][/ROW]
[ROW][C]93[/C][C]0.239155[/C][C]0.47831[/C][C]0.760845[/C][/ROW]
[ROW][C]94[/C][C]0.198306[/C][C]0.396612[/C][C]0.801694[/C][/ROW]
[ROW][C]95[/C][C]0.211836[/C][C]0.423672[/C][C]0.788164[/C][/ROW]
[ROW][C]96[/C][C]0.162946[/C][C]0.325892[/C][C]0.837054[/C][/ROW]
[ROW][C]97[/C][C]0.138842[/C][C]0.277684[/C][C]0.861158[/C][/ROW]
[ROW][C]98[/C][C]0.10982[/C][C]0.219641[/C][C]0.89018[/C][/ROW]
[ROW][C]99[/C][C]0.19661[/C][C]0.393221[/C][C]0.80339[/C][/ROW]
[ROW][C]100[/C][C]0.232516[/C][C]0.465031[/C][C]0.767484[/C][/ROW]
[ROW][C]101[/C][C]0.171039[/C][C]0.342078[/C][C]0.828961[/C][/ROW]
[ROW][C]102[/C][C]0.121297[/C][C]0.242594[/C][C]0.878703[/C][/ROW]
[ROW][C]103[/C][C]0.0773824[/C][C]0.154765[/C][C]0.922618[/C][/ROW]
[ROW][C]104[/C][C]0.0500151[/C][C]0.10003[/C][C]0.949985[/C][/ROW]
[ROW][C]105[/C][C]0.43663[/C][C]0.87326[/C][C]0.56337[/C][/ROW]
[ROW][C]106[/C][C]0.518687[/C][C]0.962626[/C][C]0.481313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267586&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267586&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
60.7138250.5723490.286175
70.6063650.7872710.393635
80.5295610.9408790.470439
90.5519410.8961190.448059
100.5246550.950690.475345
110.4161390.8322780.583861
120.4942210.9884410.505779
130.4924430.9848850.507557
140.3990320.7980650.600968
150.4120780.8241560.587922
160.4099670.8199350.590033
170.3351870.6703730.664813
180.2674070.5348140.732593
190.2523840.5047670.747616
200.2229660.4459320.777034
210.2538760.5077510.746124
220.3291960.6583930.670804
230.265720.531440.73428
240.3384070.6768130.661593
250.2893640.5787270.710636
260.2451160.4902320.754884
270.248560.4971190.75144
280.3220510.6441030.677949
290.2662340.5324680.733766
300.2773230.5546450.722677
310.292010.584020.70799
320.2449390.4898780.755061
330.2046490.4092980.795351
340.1828030.3656060.817197
350.1474790.2949590.852521
360.1161380.2322760.883862
370.08942120.1788420.910579
380.07718250.1543650.922817
390.07366850.1473370.926332
400.06070680.1214140.939293
410.1582010.3164030.841799
420.1294120.2588240.870588
430.1372250.2744510.862775
440.1085640.2171280.891436
450.09875820.1975160.901242
460.07615120.1523020.923849
470.05914710.1182940.940853
480.1004540.2009080.899546
490.1352930.2705860.864707
500.1145010.2290020.885499
510.09099130.1819830.909009
520.1468010.2936030.853199
530.1185290.2370580.881471
540.1106810.2213630.889319
550.1278830.2557650.872117
560.1020360.2040730.897964
570.2276460.4552920.772354
580.2979210.5958420.702079
590.2644520.5289040.735548
600.2928740.5857480.707126
610.2551290.5102580.744871
620.2317140.4634280.768286
630.2799970.5599950.720003
640.4476530.8953070.552347
650.4020.8039990.598
660.3582480.7164960.641752
670.3128030.6256070.687197
680.2900720.5801430.709928
690.357530.7150610.64247
700.3215280.6430560.678472
710.2812940.5625880.718706
720.2442690.4885380.755731
730.2058150.4116290.794185
740.2397460.4794920.760254
750.243930.487860.75607
760.2682710.5365420.731729
770.2644660.5289320.735534
780.2694230.5388460.730577
790.227580.4551590.77242
800.2577230.5154470.742277
810.21450.4290.7855
820.247140.4942810.75286
830.2001650.400330.799835
840.2882280.5764570.711772
850.2410250.4820510.758975
860.2043190.4086380.795681
870.1640730.3281450.835927
880.1282350.256470.871765
890.117940.235880.88206
900.1688870.3377750.831113
910.1825970.3651930.817403
920.297850.5956990.70215
930.2391550.478310.760845
940.1983060.3966120.801694
950.2118360.4236720.788164
960.1629460.3258920.837054
970.1388420.2776840.861158
980.109820.2196410.89018
990.196610.3932210.80339
1000.2325160.4650310.767484
1010.1710390.3420780.828961
1020.1212970.2425940.878703
1030.07738240.1547650.922618
1040.05001510.100030.949985
1050.436630.873260.56337
1060.5186870.9626260.481313







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 level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267586&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267586&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267586&T=6

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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 level00OK



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):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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')
}