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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 computationThu, 20 Dec 2012 16:55:25 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/20/t1356040664ozt4aodyslcghgu.htm/, Retrieved Fri, 29 Mar 2024 13:59:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203160, Retrieved Fri, 29 Mar 2024 13:59:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact70
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [paper stat 15] [2012-12-20 21:55:25] [df532ced13173cb9c37ecd9ae05d6384] [Current]
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Dataseries X:
111	45	27	52
102	50	18	55
108	49	19	80
109	55	20	45
118	39	29	60
79	68	46	34
88	69	27	45
102	56	23	68
105	58	29	26
92	48	38	70
131	34	20	85
104	50	37	54
83	76	32	55
84	49	26	40
85	51	40	55
110	53	30	50
121	36	26	71
120	62	23	55
100	46	27	70
94	50	38	55
89	47	25	60
93	50	33	65
128	44	45	66
84	50	34	55
127	29	20	90
106	49	24	55
129	26	26	60
82	79	26	35
106	53	39	55
109	53	27	26
91	72	18	14
111	35	34	45
105	42	25	35
118	37	26	65
103	46	28	35
101	48	21	60
101	46	39	60
95	49	25	60
108	65	29	65
95	52	37	45
98	75	34	20
82	58	30	50
100	43	28	60
100	60	25	48
107	43	27	40
95	51	33	55
97	70	30	54
93	69	26	40
81	65	18	40
89	63	21	34
111	44	39	60
95	61	36	30
106	40	32	75
83	62	23	24
81	59	27	30
115	47	45	80
112	50	24	60
92	50	29	46
85	65	21	35
95	54	28	60
115	44	37	75
91	66	22	54
107	34	31	78
102	74	32	20
86	57	20	45
96	60	33	60
114	36	32	70
105	50	18	35
82	60	44	20
120	45	24	60
88	55	21	20
90	44	29	50
85	57	30	50
106	33	37	75
109	30	33	70
75	64	25	20
91	49	19	45
96	76	16	20
108	40	31	50
86	48	29	55
98	65	37	15
99	50	41	26
95	70	28	25
88	78	19	30
111	44	28	60
103	48	33	40
107	52	32	40
118	40	28	50




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 8 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203160&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203160&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203160&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 time8 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
IQ[t] = + 124.806271001475 -0.539555259424816Add[t] -0.12564858068688MumAge[t] + 0.146475237115816Grade[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
IQ[t] =  +  124.806271001475 -0.539555259424816Add[t] -0.12564858068688MumAge[t] +  0.146475237115816Grade[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203160&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]IQ[t] =  +  124.806271001475 -0.539555259424816Add[t] -0.12564858068688MumAge[t] +  0.146475237115816Grade[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203160&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203160&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
IQ[t] = + 124.806271001475 -0.539555259424816Add[t] -0.12564858068688MumAge[t] + 0.146475237115816Grade[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)124.80627100147510.32308612.0900
Add-0.5395552594248160.115387-4.67611.1e-056e-06
MumAge-0.125648580686880.152349-0.82470.4118550.205927
Grade0.1464752371158160.0788361.8580.0666760.033338

\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) & 124.806271001475 & 10.323086 & 12.09 & 0 & 0 \tabularnewline
Add & -0.539555259424816 & 0.115387 & -4.6761 & 1.1e-05 & 6e-06 \tabularnewline
MumAge & -0.12564858068688 & 0.152349 & -0.8247 & 0.411855 & 0.205927 \tabularnewline
Grade & 0.146475237115816 & 0.078836 & 1.858 & 0.066676 & 0.033338 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203160&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]124.806271001475[/C][C]10.323086[/C][C]12.09[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Add[/C][C]-0.539555259424816[/C][C]0.115387[/C][C]-4.6761[/C][C]1.1e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]MumAge[/C][C]-0.12564858068688[/C][C]0.152349[/C][C]-0.8247[/C][C]0.411855[/C][C]0.205927[/C][/ROW]
[ROW][C]Grade[/C][C]0.146475237115816[/C][C]0.078836[/C][C]1.858[/C][C]0.066676[/C][C]0.033338[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203160&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203160&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)124.80627100147510.32308612.0900
Add-0.5395552594248160.115387-4.67611.1e-056e-06
MumAge-0.125648580686880.152349-0.82470.4118550.205927
Grade0.1464752371158160.0788361.8580.0666760.033338







Multiple Linear Regression - Regression Statistics
Multiple R0.650848372128899
R-squared0.423603603502838
Adjusted R-squared0.403018017913654
F-TEST (value)20.5776805166718
F-TEST (DF numerator)3
F-TEST (DF denominator)84
p-value4.34927538428553e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.8517620375016
Sum Squared Residuals8152.80608045885

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.650848372128899 \tabularnewline
R-squared & 0.423603603502838 \tabularnewline
Adjusted R-squared & 0.403018017913654 \tabularnewline
F-TEST (value) & 20.5776805166718 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 84 \tabularnewline
p-value & 4.34927538428553e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.8517620375016 \tabularnewline
Sum Squared Residuals & 8152.80608045885 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203160&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.650848372128899[/C][/ROW]
[ROW][C]R-squared[/C][C]0.423603603502838[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.403018017913654[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20.5776805166718[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]84[/C][/ROW]
[ROW][C]p-value[/C][C]4.34927538428553e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.8517620375016[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8152.80608045885[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203160&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203160&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 R0.650848372128899
R-squared0.423603603502838
Adjusted R-squared0.403018017913654
F-TEST (value)20.5776805166718
F-TEST (DF numerator)3
F-TEST (DF denominator)84
p-value4.34927538428553e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.8517620375016
Sum Squared Residuals8152.80608045885







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1111104.7504849788356.24951502116548
2102103.62297161924-1.62297161923987
3108107.6987592258730.301240774126788
410999.20914578958399.79085421041612
5118108.9083212709369.09167872906374
67987.3168367109284-8.31683671092842
78890.7758320928283-2.77583209282829
8102101.6615752417620.338424758237811
910593.67661327992711.323386720073
1092104.386239081089-12.3862390810891
11131116.39881572213814.6011842778623
12104101.0891733490732.91082665092666
138387.8354547445783-4.83545474457834
1484100.960209676432-16.9602096764324
1585100.319147584704-15.3191475847037
1611099.764146687143810.2358533128562
17121112.5151603995458.48483960045468
1812096.520065602707723.4799343972923
19100106.847483987494-6.84748398749446
2094101.110000005502-7.11000000550227
2189105.094473518285-16.0944735182852
2293103.202995280095-10.2029952800948
23128105.07901910551722.920980894483
2484101.61259432825-17.6125943282498
25127119.8289682048417.17103179515919
26106103.4086353945432.59136460545659
27129116.2994853855212.7005146144805
288284.0411757081089-2.04117570810886
2910699.3656856465416.63431435345905
3010996.625686738424812.3743132615751
319185.74727119014555.25272880985452
32111108.2411708484642.75882915153612
33105104.1303688875140.86963111248607
34118111.0967537174266.90324628257439
35103101.5952021077541.40479789224598
36101105.057512581608-4.05751258160795
37101103.874948648094-2.87494864809374
3895104.015362999436-9.01536299943561
3910895.612260711470112.3877392885299
409598.6917856961814-3.69178569618137
419882.997079543575815.0029204564242
428297.0663703900197-15.0663703900197
43100106.875748813924-6.87574881392387
4410096.32255230037283.67744769962715
45107104.0718926522942.92810734770557
4695101.198687649512-6.19868764951186
479791.17760822538525.82239177461482
489390.16910448793612.8308955120639
498193.3325141711304-12.3325141711304
508993.1558275252245-4.1558275252245
51111104.9540591669436.04594083305663
529591.76430838530773.23569161469234
53106110.188948826188-4.18894882618803
548391.9793332521174-8.9793332521174
558193.9742561303392-12.9742561303392
56115105.5110066468649.48899335313605
57112103.6014563206988.39854367930232
5892100.922560097642-8.92256009764186
598592.2231922434907-7.22319224349068
6095100.940640960251-5.94064096025089
61115107.4024848850547.59751511494563
629194.3410179085795-3.34101790857949
63107113.991354674771-6.99135467477126
6410283.787931964374418.2120680356256
658698.1300352707343-12.1300352707342
669697.0750665002676-1.0750665002676
67114111.6147936783082.38520632169178
68105100.6934668769244.30653312307644
698289.8339226280793-7.83392262807928
70120106.29923261782213.7007673821782
718895.4216162810016-7.4216162810016
7290104.745792602654-14.745792602654
738597.6059256494445-12.6059256494445
74106113.337592738727-7.33759273872735
75109114.72647665417-5.72647665417024
767590.0630246234307-15.0630246234307
7791102.57212592682-11.5721259268197
789684.719198736514911.2808012634851
79108106.652716478981.34728352102048
8086103.319947750534-17.3199477505338
819887.283310210184310.7166897898157
829996.4852723870832.51472761291702
839587.18112351040037.81887648959972
848884.72789484676283.27210515323724
85111106.3361935544994.66380644550095
86103100.6202248710492.37977512895093
8710798.58765241403678.41234758596331
88118107.0296622210410.9703377789598

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 111 & 104.750484978835 & 6.24951502116548 \tabularnewline
2 & 102 & 103.62297161924 & -1.62297161923987 \tabularnewline
3 & 108 & 107.698759225873 & 0.301240774126788 \tabularnewline
4 & 109 & 99.2091457895839 & 9.79085421041612 \tabularnewline
5 & 118 & 108.908321270936 & 9.09167872906374 \tabularnewline
6 & 79 & 87.3168367109284 & -8.31683671092842 \tabularnewline
7 & 88 & 90.7758320928283 & -2.77583209282829 \tabularnewline
8 & 102 & 101.661575241762 & 0.338424758237811 \tabularnewline
9 & 105 & 93.676613279927 & 11.323386720073 \tabularnewline
10 & 92 & 104.386239081089 & -12.3862390810891 \tabularnewline
11 & 131 & 116.398815722138 & 14.6011842778623 \tabularnewline
12 & 104 & 101.089173349073 & 2.91082665092666 \tabularnewline
13 & 83 & 87.8354547445783 & -4.83545474457834 \tabularnewline
14 & 84 & 100.960209676432 & -16.9602096764324 \tabularnewline
15 & 85 & 100.319147584704 & -15.3191475847037 \tabularnewline
16 & 110 & 99.7641466871438 & 10.2358533128562 \tabularnewline
17 & 121 & 112.515160399545 & 8.48483960045468 \tabularnewline
18 & 120 & 96.5200656027077 & 23.4799343972923 \tabularnewline
19 & 100 & 106.847483987494 & -6.84748398749446 \tabularnewline
20 & 94 & 101.110000005502 & -7.11000000550227 \tabularnewline
21 & 89 & 105.094473518285 & -16.0944735182852 \tabularnewline
22 & 93 & 103.202995280095 & -10.2029952800948 \tabularnewline
23 & 128 & 105.079019105517 & 22.920980894483 \tabularnewline
24 & 84 & 101.61259432825 & -17.6125943282498 \tabularnewline
25 & 127 & 119.828968204841 & 7.17103179515919 \tabularnewline
26 & 106 & 103.408635394543 & 2.59136460545659 \tabularnewline
27 & 129 & 116.29948538552 & 12.7005146144805 \tabularnewline
28 & 82 & 84.0411757081089 & -2.04117570810886 \tabularnewline
29 & 106 & 99.365685646541 & 6.63431435345905 \tabularnewline
30 & 109 & 96.6256867384248 & 12.3743132615751 \tabularnewline
31 & 91 & 85.7472711901455 & 5.25272880985452 \tabularnewline
32 & 111 & 108.241170848464 & 2.75882915153612 \tabularnewline
33 & 105 & 104.130368887514 & 0.86963111248607 \tabularnewline
34 & 118 & 111.096753717426 & 6.90324628257439 \tabularnewline
35 & 103 & 101.595202107754 & 1.40479789224598 \tabularnewline
36 & 101 & 105.057512581608 & -4.05751258160795 \tabularnewline
37 & 101 & 103.874948648094 & -2.87494864809374 \tabularnewline
38 & 95 & 104.015362999436 & -9.01536299943561 \tabularnewline
39 & 108 & 95.6122607114701 & 12.3877392885299 \tabularnewline
40 & 95 & 98.6917856961814 & -3.69178569618137 \tabularnewline
41 & 98 & 82.9970795435758 & 15.0029204564242 \tabularnewline
42 & 82 & 97.0663703900197 & -15.0663703900197 \tabularnewline
43 & 100 & 106.875748813924 & -6.87574881392387 \tabularnewline
44 & 100 & 96.3225523003728 & 3.67744769962715 \tabularnewline
45 & 107 & 104.071892652294 & 2.92810734770557 \tabularnewline
46 & 95 & 101.198687649512 & -6.19868764951186 \tabularnewline
47 & 97 & 91.1776082253852 & 5.82239177461482 \tabularnewline
48 & 93 & 90.1691044879361 & 2.8308955120639 \tabularnewline
49 & 81 & 93.3325141711304 & -12.3325141711304 \tabularnewline
50 & 89 & 93.1558275252245 & -4.1558275252245 \tabularnewline
51 & 111 & 104.954059166943 & 6.04594083305663 \tabularnewline
52 & 95 & 91.7643083853077 & 3.23569161469234 \tabularnewline
53 & 106 & 110.188948826188 & -4.18894882618803 \tabularnewline
54 & 83 & 91.9793332521174 & -8.9793332521174 \tabularnewline
55 & 81 & 93.9742561303392 & -12.9742561303392 \tabularnewline
56 & 115 & 105.511006646864 & 9.48899335313605 \tabularnewline
57 & 112 & 103.601456320698 & 8.39854367930232 \tabularnewline
58 & 92 & 100.922560097642 & -8.92256009764186 \tabularnewline
59 & 85 & 92.2231922434907 & -7.22319224349068 \tabularnewline
60 & 95 & 100.940640960251 & -5.94064096025089 \tabularnewline
61 & 115 & 107.402484885054 & 7.59751511494563 \tabularnewline
62 & 91 & 94.3410179085795 & -3.34101790857949 \tabularnewline
63 & 107 & 113.991354674771 & -6.99135467477126 \tabularnewline
64 & 102 & 83.7879319643744 & 18.2120680356256 \tabularnewline
65 & 86 & 98.1300352707343 & -12.1300352707342 \tabularnewline
66 & 96 & 97.0750665002676 & -1.0750665002676 \tabularnewline
67 & 114 & 111.614793678308 & 2.38520632169178 \tabularnewline
68 & 105 & 100.693466876924 & 4.30653312307644 \tabularnewline
69 & 82 & 89.8339226280793 & -7.83392262807928 \tabularnewline
70 & 120 & 106.299232617822 & 13.7007673821782 \tabularnewline
71 & 88 & 95.4216162810016 & -7.4216162810016 \tabularnewline
72 & 90 & 104.745792602654 & -14.745792602654 \tabularnewline
73 & 85 & 97.6059256494445 & -12.6059256494445 \tabularnewline
74 & 106 & 113.337592738727 & -7.33759273872735 \tabularnewline
75 & 109 & 114.72647665417 & -5.72647665417024 \tabularnewline
76 & 75 & 90.0630246234307 & -15.0630246234307 \tabularnewline
77 & 91 & 102.57212592682 & -11.5721259268197 \tabularnewline
78 & 96 & 84.7191987365149 & 11.2808012634851 \tabularnewline
79 & 108 & 106.65271647898 & 1.34728352102048 \tabularnewline
80 & 86 & 103.319947750534 & -17.3199477505338 \tabularnewline
81 & 98 & 87.2833102101843 & 10.7166897898157 \tabularnewline
82 & 99 & 96.485272387083 & 2.51472761291702 \tabularnewline
83 & 95 & 87.1811235104003 & 7.81887648959972 \tabularnewline
84 & 88 & 84.7278948467628 & 3.27210515323724 \tabularnewline
85 & 111 & 106.336193554499 & 4.66380644550095 \tabularnewline
86 & 103 & 100.620224871049 & 2.37977512895093 \tabularnewline
87 & 107 & 98.5876524140367 & 8.41234758596331 \tabularnewline
88 & 118 & 107.02966222104 & 10.9703377789598 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203160&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]111[/C][C]104.750484978835[/C][C]6.24951502116548[/C][/ROW]
[ROW][C]2[/C][C]102[/C][C]103.62297161924[/C][C]-1.62297161923987[/C][/ROW]
[ROW][C]3[/C][C]108[/C][C]107.698759225873[/C][C]0.301240774126788[/C][/ROW]
[ROW][C]4[/C][C]109[/C][C]99.2091457895839[/C][C]9.79085421041612[/C][/ROW]
[ROW][C]5[/C][C]118[/C][C]108.908321270936[/C][C]9.09167872906374[/C][/ROW]
[ROW][C]6[/C][C]79[/C][C]87.3168367109284[/C][C]-8.31683671092842[/C][/ROW]
[ROW][C]7[/C][C]88[/C][C]90.7758320928283[/C][C]-2.77583209282829[/C][/ROW]
[ROW][C]8[/C][C]102[/C][C]101.661575241762[/C][C]0.338424758237811[/C][/ROW]
[ROW][C]9[/C][C]105[/C][C]93.676613279927[/C][C]11.323386720073[/C][/ROW]
[ROW][C]10[/C][C]92[/C][C]104.386239081089[/C][C]-12.3862390810891[/C][/ROW]
[ROW][C]11[/C][C]131[/C][C]116.398815722138[/C][C]14.6011842778623[/C][/ROW]
[ROW][C]12[/C][C]104[/C][C]101.089173349073[/C][C]2.91082665092666[/C][/ROW]
[ROW][C]13[/C][C]83[/C][C]87.8354547445783[/C][C]-4.83545474457834[/C][/ROW]
[ROW][C]14[/C][C]84[/C][C]100.960209676432[/C][C]-16.9602096764324[/C][/ROW]
[ROW][C]15[/C][C]85[/C][C]100.319147584704[/C][C]-15.3191475847037[/C][/ROW]
[ROW][C]16[/C][C]110[/C][C]99.7641466871438[/C][C]10.2358533128562[/C][/ROW]
[ROW][C]17[/C][C]121[/C][C]112.515160399545[/C][C]8.48483960045468[/C][/ROW]
[ROW][C]18[/C][C]120[/C][C]96.5200656027077[/C][C]23.4799343972923[/C][/ROW]
[ROW][C]19[/C][C]100[/C][C]106.847483987494[/C][C]-6.84748398749446[/C][/ROW]
[ROW][C]20[/C][C]94[/C][C]101.110000005502[/C][C]-7.11000000550227[/C][/ROW]
[ROW][C]21[/C][C]89[/C][C]105.094473518285[/C][C]-16.0944735182852[/C][/ROW]
[ROW][C]22[/C][C]93[/C][C]103.202995280095[/C][C]-10.2029952800948[/C][/ROW]
[ROW][C]23[/C][C]128[/C][C]105.079019105517[/C][C]22.920980894483[/C][/ROW]
[ROW][C]24[/C][C]84[/C][C]101.61259432825[/C][C]-17.6125943282498[/C][/ROW]
[ROW][C]25[/C][C]127[/C][C]119.828968204841[/C][C]7.17103179515919[/C][/ROW]
[ROW][C]26[/C][C]106[/C][C]103.408635394543[/C][C]2.59136460545659[/C][/ROW]
[ROW][C]27[/C][C]129[/C][C]116.29948538552[/C][C]12.7005146144805[/C][/ROW]
[ROW][C]28[/C][C]82[/C][C]84.0411757081089[/C][C]-2.04117570810886[/C][/ROW]
[ROW][C]29[/C][C]106[/C][C]99.365685646541[/C][C]6.63431435345905[/C][/ROW]
[ROW][C]30[/C][C]109[/C][C]96.6256867384248[/C][C]12.3743132615751[/C][/ROW]
[ROW][C]31[/C][C]91[/C][C]85.7472711901455[/C][C]5.25272880985452[/C][/ROW]
[ROW][C]32[/C][C]111[/C][C]108.241170848464[/C][C]2.75882915153612[/C][/ROW]
[ROW][C]33[/C][C]105[/C][C]104.130368887514[/C][C]0.86963111248607[/C][/ROW]
[ROW][C]34[/C][C]118[/C][C]111.096753717426[/C][C]6.90324628257439[/C][/ROW]
[ROW][C]35[/C][C]103[/C][C]101.595202107754[/C][C]1.40479789224598[/C][/ROW]
[ROW][C]36[/C][C]101[/C][C]105.057512581608[/C][C]-4.05751258160795[/C][/ROW]
[ROW][C]37[/C][C]101[/C][C]103.874948648094[/C][C]-2.87494864809374[/C][/ROW]
[ROW][C]38[/C][C]95[/C][C]104.015362999436[/C][C]-9.01536299943561[/C][/ROW]
[ROW][C]39[/C][C]108[/C][C]95.6122607114701[/C][C]12.3877392885299[/C][/ROW]
[ROW][C]40[/C][C]95[/C][C]98.6917856961814[/C][C]-3.69178569618137[/C][/ROW]
[ROW][C]41[/C][C]98[/C][C]82.9970795435758[/C][C]15.0029204564242[/C][/ROW]
[ROW][C]42[/C][C]82[/C][C]97.0663703900197[/C][C]-15.0663703900197[/C][/ROW]
[ROW][C]43[/C][C]100[/C][C]106.875748813924[/C][C]-6.87574881392387[/C][/ROW]
[ROW][C]44[/C][C]100[/C][C]96.3225523003728[/C][C]3.67744769962715[/C][/ROW]
[ROW][C]45[/C][C]107[/C][C]104.071892652294[/C][C]2.92810734770557[/C][/ROW]
[ROW][C]46[/C][C]95[/C][C]101.198687649512[/C][C]-6.19868764951186[/C][/ROW]
[ROW][C]47[/C][C]97[/C][C]91.1776082253852[/C][C]5.82239177461482[/C][/ROW]
[ROW][C]48[/C][C]93[/C][C]90.1691044879361[/C][C]2.8308955120639[/C][/ROW]
[ROW][C]49[/C][C]81[/C][C]93.3325141711304[/C][C]-12.3325141711304[/C][/ROW]
[ROW][C]50[/C][C]89[/C][C]93.1558275252245[/C][C]-4.1558275252245[/C][/ROW]
[ROW][C]51[/C][C]111[/C][C]104.954059166943[/C][C]6.04594083305663[/C][/ROW]
[ROW][C]52[/C][C]95[/C][C]91.7643083853077[/C][C]3.23569161469234[/C][/ROW]
[ROW][C]53[/C][C]106[/C][C]110.188948826188[/C][C]-4.18894882618803[/C][/ROW]
[ROW][C]54[/C][C]83[/C][C]91.9793332521174[/C][C]-8.9793332521174[/C][/ROW]
[ROW][C]55[/C][C]81[/C][C]93.9742561303392[/C][C]-12.9742561303392[/C][/ROW]
[ROW][C]56[/C][C]115[/C][C]105.511006646864[/C][C]9.48899335313605[/C][/ROW]
[ROW][C]57[/C][C]112[/C][C]103.601456320698[/C][C]8.39854367930232[/C][/ROW]
[ROW][C]58[/C][C]92[/C][C]100.922560097642[/C][C]-8.92256009764186[/C][/ROW]
[ROW][C]59[/C][C]85[/C][C]92.2231922434907[/C][C]-7.22319224349068[/C][/ROW]
[ROW][C]60[/C][C]95[/C][C]100.940640960251[/C][C]-5.94064096025089[/C][/ROW]
[ROW][C]61[/C][C]115[/C][C]107.402484885054[/C][C]7.59751511494563[/C][/ROW]
[ROW][C]62[/C][C]91[/C][C]94.3410179085795[/C][C]-3.34101790857949[/C][/ROW]
[ROW][C]63[/C][C]107[/C][C]113.991354674771[/C][C]-6.99135467477126[/C][/ROW]
[ROW][C]64[/C][C]102[/C][C]83.7879319643744[/C][C]18.2120680356256[/C][/ROW]
[ROW][C]65[/C][C]86[/C][C]98.1300352707343[/C][C]-12.1300352707342[/C][/ROW]
[ROW][C]66[/C][C]96[/C][C]97.0750665002676[/C][C]-1.0750665002676[/C][/ROW]
[ROW][C]67[/C][C]114[/C][C]111.614793678308[/C][C]2.38520632169178[/C][/ROW]
[ROW][C]68[/C][C]105[/C][C]100.693466876924[/C][C]4.30653312307644[/C][/ROW]
[ROW][C]69[/C][C]82[/C][C]89.8339226280793[/C][C]-7.83392262807928[/C][/ROW]
[ROW][C]70[/C][C]120[/C][C]106.299232617822[/C][C]13.7007673821782[/C][/ROW]
[ROW][C]71[/C][C]88[/C][C]95.4216162810016[/C][C]-7.4216162810016[/C][/ROW]
[ROW][C]72[/C][C]90[/C][C]104.745792602654[/C][C]-14.745792602654[/C][/ROW]
[ROW][C]73[/C][C]85[/C][C]97.6059256494445[/C][C]-12.6059256494445[/C][/ROW]
[ROW][C]74[/C][C]106[/C][C]113.337592738727[/C][C]-7.33759273872735[/C][/ROW]
[ROW][C]75[/C][C]109[/C][C]114.72647665417[/C][C]-5.72647665417024[/C][/ROW]
[ROW][C]76[/C][C]75[/C][C]90.0630246234307[/C][C]-15.0630246234307[/C][/ROW]
[ROW][C]77[/C][C]91[/C][C]102.57212592682[/C][C]-11.5721259268197[/C][/ROW]
[ROW][C]78[/C][C]96[/C][C]84.7191987365149[/C][C]11.2808012634851[/C][/ROW]
[ROW][C]79[/C][C]108[/C][C]106.65271647898[/C][C]1.34728352102048[/C][/ROW]
[ROW][C]80[/C][C]86[/C][C]103.319947750534[/C][C]-17.3199477505338[/C][/ROW]
[ROW][C]81[/C][C]98[/C][C]87.2833102101843[/C][C]10.7166897898157[/C][/ROW]
[ROW][C]82[/C][C]99[/C][C]96.485272387083[/C][C]2.51472761291702[/C][/ROW]
[ROW][C]83[/C][C]95[/C][C]87.1811235104003[/C][C]7.81887648959972[/C][/ROW]
[ROW][C]84[/C][C]88[/C][C]84.7278948467628[/C][C]3.27210515323724[/C][/ROW]
[ROW][C]85[/C][C]111[/C][C]106.336193554499[/C][C]4.66380644550095[/C][/ROW]
[ROW][C]86[/C][C]103[/C][C]100.620224871049[/C][C]2.37977512895093[/C][/ROW]
[ROW][C]87[/C][C]107[/C][C]98.5876524140367[/C][C]8.41234758596331[/C][/ROW]
[ROW][C]88[/C][C]118[/C][C]107.02966222104[/C][C]10.9703377789598[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203160&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203160&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
1111104.7504849788356.24951502116548
2102103.62297161924-1.62297161923987
3108107.6987592258730.301240774126788
410999.20914578958399.79085421041612
5118108.9083212709369.09167872906374
67987.3168367109284-8.31683671092842
78890.7758320928283-2.77583209282829
8102101.6615752417620.338424758237811
910593.67661327992711.323386720073
1092104.386239081089-12.3862390810891
11131116.39881572213814.6011842778623
12104101.0891733490732.91082665092666
138387.8354547445783-4.83545474457834
1484100.960209676432-16.9602096764324
1585100.319147584704-15.3191475847037
1611099.764146687143810.2358533128562
17121112.5151603995458.48483960045468
1812096.520065602707723.4799343972923
19100106.847483987494-6.84748398749446
2094101.110000005502-7.11000000550227
2189105.094473518285-16.0944735182852
2293103.202995280095-10.2029952800948
23128105.07901910551722.920980894483
2484101.61259432825-17.6125943282498
25127119.8289682048417.17103179515919
26106103.4086353945432.59136460545659
27129116.2994853855212.7005146144805
288284.0411757081089-2.04117570810886
2910699.3656856465416.63431435345905
3010996.625686738424812.3743132615751
319185.74727119014555.25272880985452
32111108.2411708484642.75882915153612
33105104.1303688875140.86963111248607
34118111.0967537174266.90324628257439
35103101.5952021077541.40479789224598
36101105.057512581608-4.05751258160795
37101103.874948648094-2.87494864809374
3895104.015362999436-9.01536299943561
3910895.612260711470112.3877392885299
409598.6917856961814-3.69178569618137
419882.997079543575815.0029204564242
428297.0663703900197-15.0663703900197
43100106.875748813924-6.87574881392387
4410096.32255230037283.67744769962715
45107104.0718926522942.92810734770557
4695101.198687649512-6.19868764951186
479791.17760822538525.82239177461482
489390.16910448793612.8308955120639
498193.3325141711304-12.3325141711304
508993.1558275252245-4.1558275252245
51111104.9540591669436.04594083305663
529591.76430838530773.23569161469234
53106110.188948826188-4.18894882618803
548391.9793332521174-8.9793332521174
558193.9742561303392-12.9742561303392
56115105.5110066468649.48899335313605
57112103.6014563206988.39854367930232
5892100.922560097642-8.92256009764186
598592.2231922434907-7.22319224349068
6095100.940640960251-5.94064096025089
61115107.4024848850547.59751511494563
629194.3410179085795-3.34101790857949
63107113.991354674771-6.99135467477126
6410283.787931964374418.2120680356256
658698.1300352707343-12.1300352707342
669697.0750665002676-1.0750665002676
67114111.6147936783082.38520632169178
68105100.6934668769244.30653312307644
698289.8339226280793-7.83392262807928
70120106.29923261782213.7007673821782
718895.4216162810016-7.4216162810016
7290104.745792602654-14.745792602654
738597.6059256494445-12.6059256494445
74106113.337592738727-7.33759273872735
75109114.72647665417-5.72647665417024
767590.0630246234307-15.0630246234307
7791102.57212592682-11.5721259268197
789684.719198736514911.2808012634851
79108106.652716478981.34728352102048
8086103.319947750534-17.3199477505338
819887.283310210184310.7166897898157
829996.4852723870832.51472761291702
839587.18112351040037.81887648959972
848884.72789484676283.27210515323724
85111106.3361935544994.66380644550095
86103100.6202248710492.37977512895093
8710798.58765241403678.41234758596331
88118107.0296622210410.9703377789598







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1635038695875910.3270077391751830.836496130412409
80.07381399668964690.1476279933792940.926186003310353
90.04602219551379020.09204439102758040.95397780448621
100.03808567592764780.07617135185529550.961914324072352
110.06618619145945570.1323723829189110.933813808540544
120.0394000465245230.07880009304904590.960599953475477
130.0361063881494460.0722127762988920.963893611850554
140.4127913910356160.8255827820712320.587208608964384
150.4496885661285750.8993771322571510.550311433871425
160.464128232568570.9282564651371390.53587176743143
170.4037897030962230.8075794061924460.596210296903777
180.7134595773621320.5730808452757350.286540422637868
190.6907236498135740.6185527003728520.309276350186426
200.6325207160902290.7349585678195420.367479283909771
210.7866612710837720.4266774578324560.213338728916228
220.7651490972475790.4697018055048420.234850902752421
230.9635675443664750.0728649112670510.0364324556335255
240.9822659161570450.03546816768591050.0177340838429552
250.9780446789844690.04391064203106260.0219553210155313
260.9682454659480560.06350906810388690.0317545340519435
270.9729358390757620.05412832184847550.0270641609242378
280.9621281262346920.07574374753061630.0378718737653082
290.9553213513548270.08935729729034510.0446786486451725
300.9588720316942370.08225593661152650.0411279683057632
310.9458283443934030.1083433112131950.0541716556065974
320.9291479699965120.1417040600069760.0708520300034878
330.9136138966374670.1727722067250660.0863861033625329
340.9075013720988110.1849972558023790.0924986279011893
350.8841908865089190.2316182269821630.115809113491082
360.862089019089070.2758219618218610.13791098091093
370.8279054756192970.3441890487614060.172094524380703
380.821629602427640.3567407951447190.17837039757236
390.8466335377488940.3067329245022120.153366462251106
400.814006068314080.3719878633718390.18599393168592
410.850048797792310.299902404415380.14995120220769
420.900218825727080.199562348545840.0997811742729199
430.8833404163017450.2333191673965110.116659583698256
440.8544558284162140.2910883431675730.145544171583786
450.8290934949415810.3418130101168380.170906505058419
460.8048240168011190.3903519663977610.195175983198881
470.7688984040025550.462203191994890.231101595997445
480.7187894828184220.5624210343631560.281210517181578
490.7502812379451470.4994375241097060.249718762054853
500.7064106360647140.5871787278705730.293589363935287
510.6683459483569140.6633081032861720.331654051643086
520.6105376185909650.778924762818070.389462381409035
530.5550894257449540.8898211485100910.444910574255046
540.5386242336467740.9227515327064510.461375766353226
550.5812702770593550.837459445881290.418729722940645
560.5577521383026430.8844957233947140.442247861697357
570.5567875378969790.8864249242060430.443212462103021
580.5349780022248540.9300439955502920.465021997775146
590.5002414972964280.9995170054071440.499758502703572
600.4528808631936360.9057617263872730.547119136806363
610.4337784561406020.8675569122812030.566221543859398
620.3724997924445190.7449995848890380.627500207555481
630.3249836861672550.649967372334510.675016313832745
640.4400744029238150.8801488058476310.559925597076185
650.465342729231330.930685458462660.53465727076867
660.3913419176940.7826838353880010.608658082306
670.3357944788591050.671588957718210.664205521140895
680.2821100257625890.5642200515251780.717889974237411
690.2726441685923940.5452883371847890.727355831407606
700.4241582828160360.8483165656320710.575841717183964
710.3834070863414710.7668141726829410.61659291365853
720.4315829170492430.8631658340984870.568417082950757
730.4640380523204280.9280761046408560.535961947679572
740.3987288075359570.7974576150719130.601271192464043
750.3174569432428380.6349138864856750.682543056757162
760.5825163648123690.8349672703752610.417483635187631
770.8050348060574230.3899303878851540.194965193942577
780.7727966728713060.4544066542573870.227203327128694
790.6818989518953020.6362020962093960.318101048104698
800.9471122548729180.1057754902541650.0528877451270825
810.9302810697088920.1394378605822160.0697189302911081

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.163503869587591 & 0.327007739175183 & 0.836496130412409 \tabularnewline
8 & 0.0738139966896469 & 0.147627993379294 & 0.926186003310353 \tabularnewline
9 & 0.0460221955137902 & 0.0920443910275804 & 0.95397780448621 \tabularnewline
10 & 0.0380856759276478 & 0.0761713518552955 & 0.961914324072352 \tabularnewline
11 & 0.0661861914594557 & 0.132372382918911 & 0.933813808540544 \tabularnewline
12 & 0.039400046524523 & 0.0788000930490459 & 0.960599953475477 \tabularnewline
13 & 0.036106388149446 & 0.072212776298892 & 0.963893611850554 \tabularnewline
14 & 0.412791391035616 & 0.825582782071232 & 0.587208608964384 \tabularnewline
15 & 0.449688566128575 & 0.899377132257151 & 0.550311433871425 \tabularnewline
16 & 0.46412823256857 & 0.928256465137139 & 0.53587176743143 \tabularnewline
17 & 0.403789703096223 & 0.807579406192446 & 0.596210296903777 \tabularnewline
18 & 0.713459577362132 & 0.573080845275735 & 0.286540422637868 \tabularnewline
19 & 0.690723649813574 & 0.618552700372852 & 0.309276350186426 \tabularnewline
20 & 0.632520716090229 & 0.734958567819542 & 0.367479283909771 \tabularnewline
21 & 0.786661271083772 & 0.426677457832456 & 0.213338728916228 \tabularnewline
22 & 0.765149097247579 & 0.469701805504842 & 0.234850902752421 \tabularnewline
23 & 0.963567544366475 & 0.072864911267051 & 0.0364324556335255 \tabularnewline
24 & 0.982265916157045 & 0.0354681676859105 & 0.0177340838429552 \tabularnewline
25 & 0.978044678984469 & 0.0439106420310626 & 0.0219553210155313 \tabularnewline
26 & 0.968245465948056 & 0.0635090681038869 & 0.0317545340519435 \tabularnewline
27 & 0.972935839075762 & 0.0541283218484755 & 0.0270641609242378 \tabularnewline
28 & 0.962128126234692 & 0.0757437475306163 & 0.0378718737653082 \tabularnewline
29 & 0.955321351354827 & 0.0893572972903451 & 0.0446786486451725 \tabularnewline
30 & 0.958872031694237 & 0.0822559366115265 & 0.0411279683057632 \tabularnewline
31 & 0.945828344393403 & 0.108343311213195 & 0.0541716556065974 \tabularnewline
32 & 0.929147969996512 & 0.141704060006976 & 0.0708520300034878 \tabularnewline
33 & 0.913613896637467 & 0.172772206725066 & 0.0863861033625329 \tabularnewline
34 & 0.907501372098811 & 0.184997255802379 & 0.0924986279011893 \tabularnewline
35 & 0.884190886508919 & 0.231618226982163 & 0.115809113491082 \tabularnewline
36 & 0.86208901908907 & 0.275821961821861 & 0.13791098091093 \tabularnewline
37 & 0.827905475619297 & 0.344189048761406 & 0.172094524380703 \tabularnewline
38 & 0.82162960242764 & 0.356740795144719 & 0.17837039757236 \tabularnewline
39 & 0.846633537748894 & 0.306732924502212 & 0.153366462251106 \tabularnewline
40 & 0.81400606831408 & 0.371987863371839 & 0.18599393168592 \tabularnewline
41 & 0.85004879779231 & 0.29990240441538 & 0.14995120220769 \tabularnewline
42 & 0.90021882572708 & 0.19956234854584 & 0.0997811742729199 \tabularnewline
43 & 0.883340416301745 & 0.233319167396511 & 0.116659583698256 \tabularnewline
44 & 0.854455828416214 & 0.291088343167573 & 0.145544171583786 \tabularnewline
45 & 0.829093494941581 & 0.341813010116838 & 0.170906505058419 \tabularnewline
46 & 0.804824016801119 & 0.390351966397761 & 0.195175983198881 \tabularnewline
47 & 0.768898404002555 & 0.46220319199489 & 0.231101595997445 \tabularnewline
48 & 0.718789482818422 & 0.562421034363156 & 0.281210517181578 \tabularnewline
49 & 0.750281237945147 & 0.499437524109706 & 0.249718762054853 \tabularnewline
50 & 0.706410636064714 & 0.587178727870573 & 0.293589363935287 \tabularnewline
51 & 0.668345948356914 & 0.663308103286172 & 0.331654051643086 \tabularnewline
52 & 0.610537618590965 & 0.77892476281807 & 0.389462381409035 \tabularnewline
53 & 0.555089425744954 & 0.889821148510091 & 0.444910574255046 \tabularnewline
54 & 0.538624233646774 & 0.922751532706451 & 0.461375766353226 \tabularnewline
55 & 0.581270277059355 & 0.83745944588129 & 0.418729722940645 \tabularnewline
56 & 0.557752138302643 & 0.884495723394714 & 0.442247861697357 \tabularnewline
57 & 0.556787537896979 & 0.886424924206043 & 0.443212462103021 \tabularnewline
58 & 0.534978002224854 & 0.930043995550292 & 0.465021997775146 \tabularnewline
59 & 0.500241497296428 & 0.999517005407144 & 0.499758502703572 \tabularnewline
60 & 0.452880863193636 & 0.905761726387273 & 0.547119136806363 \tabularnewline
61 & 0.433778456140602 & 0.867556912281203 & 0.566221543859398 \tabularnewline
62 & 0.372499792444519 & 0.744999584889038 & 0.627500207555481 \tabularnewline
63 & 0.324983686167255 & 0.64996737233451 & 0.675016313832745 \tabularnewline
64 & 0.440074402923815 & 0.880148805847631 & 0.559925597076185 \tabularnewline
65 & 0.46534272923133 & 0.93068545846266 & 0.53465727076867 \tabularnewline
66 & 0.391341917694 & 0.782683835388001 & 0.608658082306 \tabularnewline
67 & 0.335794478859105 & 0.67158895771821 & 0.664205521140895 \tabularnewline
68 & 0.282110025762589 & 0.564220051525178 & 0.717889974237411 \tabularnewline
69 & 0.272644168592394 & 0.545288337184789 & 0.727355831407606 \tabularnewline
70 & 0.424158282816036 & 0.848316565632071 & 0.575841717183964 \tabularnewline
71 & 0.383407086341471 & 0.766814172682941 & 0.61659291365853 \tabularnewline
72 & 0.431582917049243 & 0.863165834098487 & 0.568417082950757 \tabularnewline
73 & 0.464038052320428 & 0.928076104640856 & 0.535961947679572 \tabularnewline
74 & 0.398728807535957 & 0.797457615071913 & 0.601271192464043 \tabularnewline
75 & 0.317456943242838 & 0.634913886485675 & 0.682543056757162 \tabularnewline
76 & 0.582516364812369 & 0.834967270375261 & 0.417483635187631 \tabularnewline
77 & 0.805034806057423 & 0.389930387885154 & 0.194965193942577 \tabularnewline
78 & 0.772796672871306 & 0.454406654257387 & 0.227203327128694 \tabularnewline
79 & 0.681898951895302 & 0.636202096209396 & 0.318101048104698 \tabularnewline
80 & 0.947112254872918 & 0.105775490254165 & 0.0528877451270825 \tabularnewline
81 & 0.930281069708892 & 0.139437860582216 & 0.0697189302911081 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203160&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]0.163503869587591[/C][C]0.327007739175183[/C][C]0.836496130412409[/C][/ROW]
[ROW][C]8[/C][C]0.0738139966896469[/C][C]0.147627993379294[/C][C]0.926186003310353[/C][/ROW]
[ROW][C]9[/C][C]0.0460221955137902[/C][C]0.0920443910275804[/C][C]0.95397780448621[/C][/ROW]
[ROW][C]10[/C][C]0.0380856759276478[/C][C]0.0761713518552955[/C][C]0.961914324072352[/C][/ROW]
[ROW][C]11[/C][C]0.0661861914594557[/C][C]0.132372382918911[/C][C]0.933813808540544[/C][/ROW]
[ROW][C]12[/C][C]0.039400046524523[/C][C]0.0788000930490459[/C][C]0.960599953475477[/C][/ROW]
[ROW][C]13[/C][C]0.036106388149446[/C][C]0.072212776298892[/C][C]0.963893611850554[/C][/ROW]
[ROW][C]14[/C][C]0.412791391035616[/C][C]0.825582782071232[/C][C]0.587208608964384[/C][/ROW]
[ROW][C]15[/C][C]0.449688566128575[/C][C]0.899377132257151[/C][C]0.550311433871425[/C][/ROW]
[ROW][C]16[/C][C]0.46412823256857[/C][C]0.928256465137139[/C][C]0.53587176743143[/C][/ROW]
[ROW][C]17[/C][C]0.403789703096223[/C][C]0.807579406192446[/C][C]0.596210296903777[/C][/ROW]
[ROW][C]18[/C][C]0.713459577362132[/C][C]0.573080845275735[/C][C]0.286540422637868[/C][/ROW]
[ROW][C]19[/C][C]0.690723649813574[/C][C]0.618552700372852[/C][C]0.309276350186426[/C][/ROW]
[ROW][C]20[/C][C]0.632520716090229[/C][C]0.734958567819542[/C][C]0.367479283909771[/C][/ROW]
[ROW][C]21[/C][C]0.786661271083772[/C][C]0.426677457832456[/C][C]0.213338728916228[/C][/ROW]
[ROW][C]22[/C][C]0.765149097247579[/C][C]0.469701805504842[/C][C]0.234850902752421[/C][/ROW]
[ROW][C]23[/C][C]0.963567544366475[/C][C]0.072864911267051[/C][C]0.0364324556335255[/C][/ROW]
[ROW][C]24[/C][C]0.982265916157045[/C][C]0.0354681676859105[/C][C]0.0177340838429552[/C][/ROW]
[ROW][C]25[/C][C]0.978044678984469[/C][C]0.0439106420310626[/C][C]0.0219553210155313[/C][/ROW]
[ROW][C]26[/C][C]0.968245465948056[/C][C]0.0635090681038869[/C][C]0.0317545340519435[/C][/ROW]
[ROW][C]27[/C][C]0.972935839075762[/C][C]0.0541283218484755[/C][C]0.0270641609242378[/C][/ROW]
[ROW][C]28[/C][C]0.962128126234692[/C][C]0.0757437475306163[/C][C]0.0378718737653082[/C][/ROW]
[ROW][C]29[/C][C]0.955321351354827[/C][C]0.0893572972903451[/C][C]0.0446786486451725[/C][/ROW]
[ROW][C]30[/C][C]0.958872031694237[/C][C]0.0822559366115265[/C][C]0.0411279683057632[/C][/ROW]
[ROW][C]31[/C][C]0.945828344393403[/C][C]0.108343311213195[/C][C]0.0541716556065974[/C][/ROW]
[ROW][C]32[/C][C]0.929147969996512[/C][C]0.141704060006976[/C][C]0.0708520300034878[/C][/ROW]
[ROW][C]33[/C][C]0.913613896637467[/C][C]0.172772206725066[/C][C]0.0863861033625329[/C][/ROW]
[ROW][C]34[/C][C]0.907501372098811[/C][C]0.184997255802379[/C][C]0.0924986279011893[/C][/ROW]
[ROW][C]35[/C][C]0.884190886508919[/C][C]0.231618226982163[/C][C]0.115809113491082[/C][/ROW]
[ROW][C]36[/C][C]0.86208901908907[/C][C]0.275821961821861[/C][C]0.13791098091093[/C][/ROW]
[ROW][C]37[/C][C]0.827905475619297[/C][C]0.344189048761406[/C][C]0.172094524380703[/C][/ROW]
[ROW][C]38[/C][C]0.82162960242764[/C][C]0.356740795144719[/C][C]0.17837039757236[/C][/ROW]
[ROW][C]39[/C][C]0.846633537748894[/C][C]0.306732924502212[/C][C]0.153366462251106[/C][/ROW]
[ROW][C]40[/C][C]0.81400606831408[/C][C]0.371987863371839[/C][C]0.18599393168592[/C][/ROW]
[ROW][C]41[/C][C]0.85004879779231[/C][C]0.29990240441538[/C][C]0.14995120220769[/C][/ROW]
[ROW][C]42[/C][C]0.90021882572708[/C][C]0.19956234854584[/C][C]0.0997811742729199[/C][/ROW]
[ROW][C]43[/C][C]0.883340416301745[/C][C]0.233319167396511[/C][C]0.116659583698256[/C][/ROW]
[ROW][C]44[/C][C]0.854455828416214[/C][C]0.291088343167573[/C][C]0.145544171583786[/C][/ROW]
[ROW][C]45[/C][C]0.829093494941581[/C][C]0.341813010116838[/C][C]0.170906505058419[/C][/ROW]
[ROW][C]46[/C][C]0.804824016801119[/C][C]0.390351966397761[/C][C]0.195175983198881[/C][/ROW]
[ROW][C]47[/C][C]0.768898404002555[/C][C]0.46220319199489[/C][C]0.231101595997445[/C][/ROW]
[ROW][C]48[/C][C]0.718789482818422[/C][C]0.562421034363156[/C][C]0.281210517181578[/C][/ROW]
[ROW][C]49[/C][C]0.750281237945147[/C][C]0.499437524109706[/C][C]0.249718762054853[/C][/ROW]
[ROW][C]50[/C][C]0.706410636064714[/C][C]0.587178727870573[/C][C]0.293589363935287[/C][/ROW]
[ROW][C]51[/C][C]0.668345948356914[/C][C]0.663308103286172[/C][C]0.331654051643086[/C][/ROW]
[ROW][C]52[/C][C]0.610537618590965[/C][C]0.77892476281807[/C][C]0.389462381409035[/C][/ROW]
[ROW][C]53[/C][C]0.555089425744954[/C][C]0.889821148510091[/C][C]0.444910574255046[/C][/ROW]
[ROW][C]54[/C][C]0.538624233646774[/C][C]0.922751532706451[/C][C]0.461375766353226[/C][/ROW]
[ROW][C]55[/C][C]0.581270277059355[/C][C]0.83745944588129[/C][C]0.418729722940645[/C][/ROW]
[ROW][C]56[/C][C]0.557752138302643[/C][C]0.884495723394714[/C][C]0.442247861697357[/C][/ROW]
[ROW][C]57[/C][C]0.556787537896979[/C][C]0.886424924206043[/C][C]0.443212462103021[/C][/ROW]
[ROW][C]58[/C][C]0.534978002224854[/C][C]0.930043995550292[/C][C]0.465021997775146[/C][/ROW]
[ROW][C]59[/C][C]0.500241497296428[/C][C]0.999517005407144[/C][C]0.499758502703572[/C][/ROW]
[ROW][C]60[/C][C]0.452880863193636[/C][C]0.905761726387273[/C][C]0.547119136806363[/C][/ROW]
[ROW][C]61[/C][C]0.433778456140602[/C][C]0.867556912281203[/C][C]0.566221543859398[/C][/ROW]
[ROW][C]62[/C][C]0.372499792444519[/C][C]0.744999584889038[/C][C]0.627500207555481[/C][/ROW]
[ROW][C]63[/C][C]0.324983686167255[/C][C]0.64996737233451[/C][C]0.675016313832745[/C][/ROW]
[ROW][C]64[/C][C]0.440074402923815[/C][C]0.880148805847631[/C][C]0.559925597076185[/C][/ROW]
[ROW][C]65[/C][C]0.46534272923133[/C][C]0.93068545846266[/C][C]0.53465727076867[/C][/ROW]
[ROW][C]66[/C][C]0.391341917694[/C][C]0.782683835388001[/C][C]0.608658082306[/C][/ROW]
[ROW][C]67[/C][C]0.335794478859105[/C][C]0.67158895771821[/C][C]0.664205521140895[/C][/ROW]
[ROW][C]68[/C][C]0.282110025762589[/C][C]0.564220051525178[/C][C]0.717889974237411[/C][/ROW]
[ROW][C]69[/C][C]0.272644168592394[/C][C]0.545288337184789[/C][C]0.727355831407606[/C][/ROW]
[ROW][C]70[/C][C]0.424158282816036[/C][C]0.848316565632071[/C][C]0.575841717183964[/C][/ROW]
[ROW][C]71[/C][C]0.383407086341471[/C][C]0.766814172682941[/C][C]0.61659291365853[/C][/ROW]
[ROW][C]72[/C][C]0.431582917049243[/C][C]0.863165834098487[/C][C]0.568417082950757[/C][/ROW]
[ROW][C]73[/C][C]0.464038052320428[/C][C]0.928076104640856[/C][C]0.535961947679572[/C][/ROW]
[ROW][C]74[/C][C]0.398728807535957[/C][C]0.797457615071913[/C][C]0.601271192464043[/C][/ROW]
[ROW][C]75[/C][C]0.317456943242838[/C][C]0.634913886485675[/C][C]0.682543056757162[/C][/ROW]
[ROW][C]76[/C][C]0.582516364812369[/C][C]0.834967270375261[/C][C]0.417483635187631[/C][/ROW]
[ROW][C]77[/C][C]0.805034806057423[/C][C]0.389930387885154[/C][C]0.194965193942577[/C][/ROW]
[ROW][C]78[/C][C]0.772796672871306[/C][C]0.454406654257387[/C][C]0.227203327128694[/C][/ROW]
[ROW][C]79[/C][C]0.681898951895302[/C][C]0.636202096209396[/C][C]0.318101048104698[/C][/ROW]
[ROW][C]80[/C][C]0.947112254872918[/C][C]0.105775490254165[/C][C]0.0528877451270825[/C][/ROW]
[ROW][C]81[/C][C]0.930281069708892[/C][C]0.139437860582216[/C][C]0.0697189302911081[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203160&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203160&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
70.1635038695875910.3270077391751830.836496130412409
80.07381399668964690.1476279933792940.926186003310353
90.04602219551379020.09204439102758040.95397780448621
100.03808567592764780.07617135185529550.961914324072352
110.06618619145945570.1323723829189110.933813808540544
120.0394000465245230.07880009304904590.960599953475477
130.0361063881494460.0722127762988920.963893611850554
140.4127913910356160.8255827820712320.587208608964384
150.4496885661285750.8993771322571510.550311433871425
160.464128232568570.9282564651371390.53587176743143
170.4037897030962230.8075794061924460.596210296903777
180.7134595773621320.5730808452757350.286540422637868
190.6907236498135740.6185527003728520.309276350186426
200.6325207160902290.7349585678195420.367479283909771
210.7866612710837720.4266774578324560.213338728916228
220.7651490972475790.4697018055048420.234850902752421
230.9635675443664750.0728649112670510.0364324556335255
240.9822659161570450.03546816768591050.0177340838429552
250.9780446789844690.04391064203106260.0219553210155313
260.9682454659480560.06350906810388690.0317545340519435
270.9729358390757620.05412832184847550.0270641609242378
280.9621281262346920.07574374753061630.0378718737653082
290.9553213513548270.08935729729034510.0446786486451725
300.9588720316942370.08225593661152650.0411279683057632
310.9458283443934030.1083433112131950.0541716556065974
320.9291479699965120.1417040600069760.0708520300034878
330.9136138966374670.1727722067250660.0863861033625329
340.9075013720988110.1849972558023790.0924986279011893
350.8841908865089190.2316182269821630.115809113491082
360.862089019089070.2758219618218610.13791098091093
370.8279054756192970.3441890487614060.172094524380703
380.821629602427640.3567407951447190.17837039757236
390.8466335377488940.3067329245022120.153366462251106
400.814006068314080.3719878633718390.18599393168592
410.850048797792310.299902404415380.14995120220769
420.900218825727080.199562348545840.0997811742729199
430.8833404163017450.2333191673965110.116659583698256
440.8544558284162140.2910883431675730.145544171583786
450.8290934949415810.3418130101168380.170906505058419
460.8048240168011190.3903519663977610.195175983198881
470.7688984040025550.462203191994890.231101595997445
480.7187894828184220.5624210343631560.281210517181578
490.7502812379451470.4994375241097060.249718762054853
500.7064106360647140.5871787278705730.293589363935287
510.6683459483569140.6633081032861720.331654051643086
520.6105376185909650.778924762818070.389462381409035
530.5550894257449540.8898211485100910.444910574255046
540.5386242336467740.9227515327064510.461375766353226
550.5812702770593550.837459445881290.418729722940645
560.5577521383026430.8844957233947140.442247861697357
570.5567875378969790.8864249242060430.443212462103021
580.5349780022248540.9300439955502920.465021997775146
590.5002414972964280.9995170054071440.499758502703572
600.4528808631936360.9057617263872730.547119136806363
610.4337784561406020.8675569122812030.566221543859398
620.3724997924445190.7449995848890380.627500207555481
630.3249836861672550.649967372334510.675016313832745
640.4400744029238150.8801488058476310.559925597076185
650.465342729231330.930685458462660.53465727076867
660.3913419176940.7826838353880010.608658082306
670.3357944788591050.671588957718210.664205521140895
680.2821100257625890.5642200515251780.717889974237411
690.2726441685923940.5452883371847890.727355831407606
700.4241582828160360.8483165656320710.575841717183964
710.3834070863414710.7668141726829410.61659291365853
720.4315829170492430.8631658340984870.568417082950757
730.4640380523204280.9280761046408560.535961947679572
740.3987288075359570.7974576150719130.601271192464043
750.3174569432428380.6349138864856750.682543056757162
760.5825163648123690.8349672703752610.417483635187631
770.8050348060574230.3899303878851540.194965193942577
780.7727966728713060.4544066542573870.227203327128694
790.6818989518953020.6362020962093960.318101048104698
800.9471122548729180.1057754902541650.0528877451270825
810.9302810697088920.1394378605822160.0697189302911081







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0266666666666667OK
10% type I error level120.16NOK

\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 & 2 & 0.0266666666666667 & OK \tabularnewline
10% type I error level & 12 & 0.16 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203160&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]2[/C][C]0.0266666666666667[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.16[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203160&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203160&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0266666666666667OK
10% type I error level120.16NOK



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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}