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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 17 Dec 2015 11:57:37 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/17/t1450353473oj0we1dlz0k0nwi.htm/, Retrieved Fri, 01 Nov 2024 00:29:35 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=286764, Retrieved Fri, 01 Nov 2024 00:29:35 +0000

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Estimated Impact

167

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Testing Mean with unknown Variance - Critical Value] [] [2015-10-24 09:52:55] [32b17a345b130fdf5cc88718ed94a974]
- R P   [Testing Mean with unknown Variance - Critical Value] [] [2015-11-07 09:00:23] [32b17a345b130fdf5cc88718ed94a974]
- RMPD      [Multiple Regression] [] [2015-12-17 11:57:37] [5fd2fca6b664199b2dd86155c5786748] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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=286764&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=286764&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286764&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
(1-B12)(1-B)Scheidingen[t] = -1.29684 + 0.0433647`(1-B12)(1-B)Huwelijken`[t] + 0.169126`(1-B12)(1-B)Huwelijken(t-1s)`[t] + 0.162641`(1-B12)(1-B)Huwelijken(t-2s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-B12)(1-B)Scheidingen[t] =  -1.29684 +  0.0433647`(1-B12)(1-B)Huwelijken`[t] +  0.169126`(1-B12)(1-B)Huwelijken(t-1s)`[t] +  0.162641`(1-B12)(1-B)Huwelijken(t-2s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286764&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-B12)(1-B)Scheidingen[t] =  -1.29684 +  0.0433647`(1-B12)(1-B)Huwelijken`[t] +  0.169126`(1-B12)(1-B)Huwelijken(t-1s)`[t] +  0.162641`(1-B12)(1-B)Huwelijken(t-2s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286764&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286764&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
(1-B12)(1-B)Scheidingen[t] = -1.29684 + 0.0433647`(1-B12)(1-B)Huwelijken`[t] + 0.169126`(1-B12)(1-B)Huwelijken(t-1s)`[t] + 0.162641`(1-B12)(1-B)Huwelijken(t-2s)`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-1.297	34.29	-3.7820e-02	0.9699	0.485
`(1-B12)(1-B)Huwelijken`	+0.04337	0.0643	+6.7450e-01	0.5017	0.2509
`(1-B12)(1-B)Huwelijken(t-1s)`	+0.1691	0.05739	+2.9470e+00	0.004074	0.002037
`(1-B12)(1-B)Huwelijken(t-2s)`	+0.1626	0.05461	+2.9780e+00	0.003715	0.001857

\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) & -1.297 &  34.29 & -3.7820e-02 &  0.9699 &  0.485 \tabularnewline
`(1-B12)(1-B)Huwelijken` & +0.04337 &  0.0643 & +6.7450e-01 &  0.5017 &  0.2509 \tabularnewline
`(1-B12)(1-B)Huwelijken(t-1s)` & +0.1691 &  0.05739 & +2.9470e+00 &  0.004074 &  0.002037 \tabularnewline
`(1-B12)(1-B)Huwelijken(t-2s)` & +0.1626 &  0.05461 & +2.9780e+00 &  0.003715 &  0.001857 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286764&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]-1.297[/C][C] 34.29[/C][C]-3.7820e-02[/C][C] 0.9699[/C][C] 0.485[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Huwelijken`[/C][C]+0.04337[/C][C] 0.0643[/C][C]+6.7450e-01[/C][C] 0.5017[/C][C] 0.2509[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Huwelijken(t-1s)`[/C][C]+0.1691[/C][C] 0.05739[/C][C]+2.9470e+00[/C][C] 0.004074[/C][C] 0.002037[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Huwelijken(t-2s)`[/C][C]+0.1626[/C][C] 0.05461[/C][C]+2.9780e+00[/C][C] 0.003715[/C][C] 0.001857[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286764&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286764&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-1.297	34.29	-3.7820e-02	0.9699	0.485
`(1-B12)(1-B)Huwelijken`	+0.04337	0.0643	+6.7450e-01	0.5017	0.2509
`(1-B12)(1-B)Huwelijken(t-1s)`	+0.1691	0.05739	+2.9470e+00	0.004074	0.002037
`(1-B12)(1-B)Huwelijken(t-2s)`	+0.1626	0.05461	+2.9780e+00	0.003715	0.001857

Multiple Linear Regression - Regression Statistics
Multiple R	0.4181
R-squared	0.1748
Adjusted R-squared	0.1476
F-TEST (value)	6.425
F-TEST (DF numerator)	3
F-TEST (DF denominator)	91
p-value	0.0005376
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	334.3
Sum Squared Residuals	1.017e+07

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.4181 \tabularnewline
R-squared &  0.1748 \tabularnewline
Adjusted R-squared &  0.1476 \tabularnewline
F-TEST (value) &  6.425 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 91 \tabularnewline
p-value &  0.0005376 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  334.3 \tabularnewline
Sum Squared Residuals &  1.017e+07 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286764&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.4181[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1748[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1476[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 6.425[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]91[/C][/ROW]
[ROW][C]p-value[/C][C] 0.0005376[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 334.3[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.017e+07[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286764&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286764&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 R	0.4181
R-squared	0.1748
Adjusted R-squared	0.1476
F-TEST (value)	6.425
F-TEST (DF numerator)	3
F-TEST (DF denominator)	91
p-value	0.0005376
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	334.3
Sum Squared Residuals	1.017e+07

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	864	40.31	823.7
2	-877	-71.95	-805.1
3	-96	-28.42	-67.58
4	327	113.7	213.3
5	-723	-120.2	-602.8
6	657	-54.39	711.4
7	58	213.3	-155.3
8	-677	-350.9	-326.1
9	620	262.7	357.3
10	-362	-8.224	-353.8
11	-234	-68.44	-165.6
12	577	78.89	498.1
13	177	-11.3	188.3
14	-110	-127.5	17.49
15	271	139.9	131.1
16	111	-123	234
17	-504	-159.4	-344.6
18	444	259.8	184.2
19	-183	-195.3	12.31
20	34	163	-129
21	244	37.43	206.6
22	-227	-39.5	-187.5
23	-173	8.762	-181.8
24	651	35.49	615.5
25	-378	-18.59	-359.4
26	-136	-2.484	-133.5
27	347	-11.59	358.6
28	-203	-116.2	-86.84
29	236	12.98	223
30	-259	195.9	-454.9
31	-111	-256	145
32	568	234.4	333.6
33	-626	-89.37	-536.6
34	348	20.13	327.9
35	-255	45	-300
36	71	-18.8	89.8
37	17	-25.24	42.24
38	83	31.01	51.99
39	-111	-17.24	-93.76
40	113	-67.62	180.6
41	-242	105.7	-347.7
42	334	-24.1	358.1
43	-492	-61.8	-430.2
44	272	191.3	80.74
45	166	-224.6	390.6
46	-475	51.49	-526.5
47	641	67.23	573.8
48	-393	-55.76	-337.2
49	-136	13.95	-150
50	176	14.99	161
51	22	-125.6	147.6
52	-350	37.77	-387.8
53	759	193.1	565.9
54	-778	-415.4	-362.6
55	758	326.1	431.9
56	-561	-76	-485
57	-39	-54.27	15.27
58	413	60.92	352.1
59	-84	11.54	-95.54
60	-246	-51.58	-194.4
61	228	64.4	163.6
62	66	-33.94	99.94
63	-348	-123.7	-224.3
64	190	90.4	99.6
65	-258	77.82	-335.8
66	-88	-306.9	218.9
67	246	385.4	-139.4
68	119	-369.1	488.1
69	49	289.3	-240.3
70	-201	-0.06766	-200.9
71	-80	-69.33	-10.67
72	9	26.88	-17.88
73	13	50.08	-37.08
74	200	-75.66	275.7
75	-259	89.5	-348.5
76	204	5.628	198.4
77	-112	-186.1	74.1
78	231	191.5	39.46
79	38	-22.76	60.76
80	-361	-171.7	-189.3
81	-25	176.6	-201.6
82	137	-28	165
83	-169	-58.18	-110.8
84	213	98.21	114.8
85	-41	-34.91	-6.088
86	-246	-45.41	-200.6
87	296	158.7	137.3
88	68	-102.3	170.3
89	-399	-124.7	-274.3
90	93	293.5	-200.5
91	263	-373.4	636.4
92	-475	229.5	-704.5
93	388	-55.68	443.7
94	222	-10.09	232.1
95	-581	16.6	-597.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  864 &  40.31 &  823.7 \tabularnewline
2 & -877 & -71.95 & -805.1 \tabularnewline
3 & -96 & -28.42 & -67.58 \tabularnewline
4 &  327 &  113.7 &  213.3 \tabularnewline
5 & -723 & -120.2 & -602.8 \tabularnewline
6 &  657 & -54.39 &  711.4 \tabularnewline
7 &  58 &  213.3 & -155.3 \tabularnewline
8 & -677 & -350.9 & -326.1 \tabularnewline
9 &  620 &  262.7 &  357.3 \tabularnewline
10 & -362 & -8.224 & -353.8 \tabularnewline
11 & -234 & -68.44 & -165.6 \tabularnewline
12 &  577 &  78.89 &  498.1 \tabularnewline
13 &  177 & -11.3 &  188.3 \tabularnewline
14 & -110 & -127.5 &  17.49 \tabularnewline
15 &  271 &  139.9 &  131.1 \tabularnewline
16 &  111 & -123 &  234 \tabularnewline
17 & -504 & -159.4 & -344.6 \tabularnewline
18 &  444 &  259.8 &  184.2 \tabularnewline
19 & -183 & -195.3 &  12.31 \tabularnewline
20 &  34 &  163 & -129 \tabularnewline
21 &  244 &  37.43 &  206.6 \tabularnewline
22 & -227 & -39.5 & -187.5 \tabularnewline
23 & -173 &  8.762 & -181.8 \tabularnewline
24 &  651 &  35.49 &  615.5 \tabularnewline
25 & -378 & -18.59 & -359.4 \tabularnewline
26 & -136 & -2.484 & -133.5 \tabularnewline
27 &  347 & -11.59 &  358.6 \tabularnewline
28 & -203 & -116.2 & -86.84 \tabularnewline
29 &  236 &  12.98 &  223 \tabularnewline
30 & -259 &  195.9 & -454.9 \tabularnewline
31 & -111 & -256 &  145 \tabularnewline
32 &  568 &  234.4 &  333.6 \tabularnewline
33 & -626 & -89.37 & -536.6 \tabularnewline
34 &  348 &  20.13 &  327.9 \tabularnewline
35 & -255 &  45 & -300 \tabularnewline
36 &  71 & -18.8 &  89.8 \tabularnewline
37 &  17 & -25.24 &  42.24 \tabularnewline
38 &  83 &  31.01 &  51.99 \tabularnewline
39 & -111 & -17.24 & -93.76 \tabularnewline
40 &  113 & -67.62 &  180.6 \tabularnewline
41 & -242 &  105.7 & -347.7 \tabularnewline
42 &  334 & -24.1 &  358.1 \tabularnewline
43 & -492 & -61.8 & -430.2 \tabularnewline
44 &  272 &  191.3 &  80.74 \tabularnewline
45 &  166 & -224.6 &  390.6 \tabularnewline
46 & -475 &  51.49 & -526.5 \tabularnewline
47 &  641 &  67.23 &  573.8 \tabularnewline
48 & -393 & -55.76 & -337.2 \tabularnewline
49 & -136 &  13.95 & -150 \tabularnewline
50 &  176 &  14.99 &  161 \tabularnewline
51 &  22 & -125.6 &  147.6 \tabularnewline
52 & -350 &  37.77 & -387.8 \tabularnewline
53 &  759 &  193.1 &  565.9 \tabularnewline
54 & -778 & -415.4 & -362.6 \tabularnewline
55 &  758 &  326.1 &  431.9 \tabularnewline
56 & -561 & -76 & -485 \tabularnewline
57 & -39 & -54.27 &  15.27 \tabularnewline
58 &  413 &  60.92 &  352.1 \tabularnewline
59 & -84 &  11.54 & -95.54 \tabularnewline
60 & -246 & -51.58 & -194.4 \tabularnewline
61 &  228 &  64.4 &  163.6 \tabularnewline
62 &  66 & -33.94 &  99.94 \tabularnewline
63 & -348 & -123.7 & -224.3 \tabularnewline
64 &  190 &  90.4 &  99.6 \tabularnewline
65 & -258 &  77.82 & -335.8 \tabularnewline
66 & -88 & -306.9 &  218.9 \tabularnewline
67 &  246 &  385.4 & -139.4 \tabularnewline
68 &  119 & -369.1 &  488.1 \tabularnewline
69 &  49 &  289.3 & -240.3 \tabularnewline
70 & -201 & -0.06766 & -200.9 \tabularnewline
71 & -80 & -69.33 & -10.67 \tabularnewline
72 &  9 &  26.88 & -17.88 \tabularnewline
73 &  13 &  50.08 & -37.08 \tabularnewline
74 &  200 & -75.66 &  275.7 \tabularnewline
75 & -259 &  89.5 & -348.5 \tabularnewline
76 &  204 &  5.628 &  198.4 \tabularnewline
77 & -112 & -186.1 &  74.1 \tabularnewline
78 &  231 &  191.5 &  39.46 \tabularnewline
79 &  38 & -22.76 &  60.76 \tabularnewline
80 & -361 & -171.7 & -189.3 \tabularnewline
81 & -25 &  176.6 & -201.6 \tabularnewline
82 &  137 & -28 &  165 \tabularnewline
83 & -169 & -58.18 & -110.8 \tabularnewline
84 &  213 &  98.21 &  114.8 \tabularnewline
85 & -41 & -34.91 & -6.088 \tabularnewline
86 & -246 & -45.41 & -200.6 \tabularnewline
87 &  296 &  158.7 &  137.3 \tabularnewline
88 &  68 & -102.3 &  170.3 \tabularnewline
89 & -399 & -124.7 & -274.3 \tabularnewline
90 &  93 &  293.5 & -200.5 \tabularnewline
91 &  263 & -373.4 &  636.4 \tabularnewline
92 & -475 &  229.5 & -704.5 \tabularnewline
93 &  388 & -55.68 &  443.7 \tabularnewline
94 &  222 & -10.09 &  232.1 \tabularnewline
95 & -581 &  16.6 & -597.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286764&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] 864[/C][C] 40.31[/C][C] 823.7[/C][/ROW]
[ROW][C]2[/C][C]-877[/C][C]-71.95[/C][C]-805.1[/C][/ROW]
[ROW][C]3[/C][C]-96[/C][C]-28.42[/C][C]-67.58[/C][/ROW]
[ROW][C]4[/C][C] 327[/C][C] 113.7[/C][C] 213.3[/C][/ROW]
[ROW][C]5[/C][C]-723[/C][C]-120.2[/C][C]-602.8[/C][/ROW]
[ROW][C]6[/C][C] 657[/C][C]-54.39[/C][C] 711.4[/C][/ROW]
[ROW][C]7[/C][C] 58[/C][C] 213.3[/C][C]-155.3[/C][/ROW]
[ROW][C]8[/C][C]-677[/C][C]-350.9[/C][C]-326.1[/C][/ROW]
[ROW][C]9[/C][C] 620[/C][C] 262.7[/C][C] 357.3[/C][/ROW]
[ROW][C]10[/C][C]-362[/C][C]-8.224[/C][C]-353.8[/C][/ROW]
[ROW][C]11[/C][C]-234[/C][C]-68.44[/C][C]-165.6[/C][/ROW]
[ROW][C]12[/C][C] 577[/C][C] 78.89[/C][C] 498.1[/C][/ROW]
[ROW][C]13[/C][C] 177[/C][C]-11.3[/C][C] 188.3[/C][/ROW]
[ROW][C]14[/C][C]-110[/C][C]-127.5[/C][C] 17.49[/C][/ROW]
[ROW][C]15[/C][C] 271[/C][C] 139.9[/C][C] 131.1[/C][/ROW]
[ROW][C]16[/C][C] 111[/C][C]-123[/C][C] 234[/C][/ROW]
[ROW][C]17[/C][C]-504[/C][C]-159.4[/C][C]-344.6[/C][/ROW]
[ROW][C]18[/C][C] 444[/C][C] 259.8[/C][C] 184.2[/C][/ROW]
[ROW][C]19[/C][C]-183[/C][C]-195.3[/C][C] 12.31[/C][/ROW]
[ROW][C]20[/C][C] 34[/C][C] 163[/C][C]-129[/C][/ROW]
[ROW][C]21[/C][C] 244[/C][C] 37.43[/C][C] 206.6[/C][/ROW]
[ROW][C]22[/C][C]-227[/C][C]-39.5[/C][C]-187.5[/C][/ROW]
[ROW][C]23[/C][C]-173[/C][C] 8.762[/C][C]-181.8[/C][/ROW]
[ROW][C]24[/C][C] 651[/C][C] 35.49[/C][C] 615.5[/C][/ROW]
[ROW][C]25[/C][C]-378[/C][C]-18.59[/C][C]-359.4[/C][/ROW]
[ROW][C]26[/C][C]-136[/C][C]-2.484[/C][C]-133.5[/C][/ROW]
[ROW][C]27[/C][C] 347[/C][C]-11.59[/C][C] 358.6[/C][/ROW]
[ROW][C]28[/C][C]-203[/C][C]-116.2[/C][C]-86.84[/C][/ROW]
[ROW][C]29[/C][C] 236[/C][C] 12.98[/C][C] 223[/C][/ROW]
[ROW][C]30[/C][C]-259[/C][C] 195.9[/C][C]-454.9[/C][/ROW]
[ROW][C]31[/C][C]-111[/C][C]-256[/C][C] 145[/C][/ROW]
[ROW][C]32[/C][C] 568[/C][C] 234.4[/C][C] 333.6[/C][/ROW]
[ROW][C]33[/C][C]-626[/C][C]-89.37[/C][C]-536.6[/C][/ROW]
[ROW][C]34[/C][C] 348[/C][C] 20.13[/C][C] 327.9[/C][/ROW]
[ROW][C]35[/C][C]-255[/C][C] 45[/C][C]-300[/C][/ROW]
[ROW][C]36[/C][C] 71[/C][C]-18.8[/C][C] 89.8[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C]-25.24[/C][C] 42.24[/C][/ROW]
[ROW][C]38[/C][C] 83[/C][C] 31.01[/C][C] 51.99[/C][/ROW]
[ROW][C]39[/C][C]-111[/C][C]-17.24[/C][C]-93.76[/C][/ROW]
[ROW][C]40[/C][C] 113[/C][C]-67.62[/C][C] 180.6[/C][/ROW]
[ROW][C]41[/C][C]-242[/C][C] 105.7[/C][C]-347.7[/C][/ROW]
[ROW][C]42[/C][C] 334[/C][C]-24.1[/C][C] 358.1[/C][/ROW]
[ROW][C]43[/C][C]-492[/C][C]-61.8[/C][C]-430.2[/C][/ROW]
[ROW][C]44[/C][C] 272[/C][C] 191.3[/C][C] 80.74[/C][/ROW]
[ROW][C]45[/C][C] 166[/C][C]-224.6[/C][C] 390.6[/C][/ROW]
[ROW][C]46[/C][C]-475[/C][C] 51.49[/C][C]-526.5[/C][/ROW]
[ROW][C]47[/C][C] 641[/C][C] 67.23[/C][C] 573.8[/C][/ROW]
[ROW][C]48[/C][C]-393[/C][C]-55.76[/C][C]-337.2[/C][/ROW]
[ROW][C]49[/C][C]-136[/C][C] 13.95[/C][C]-150[/C][/ROW]
[ROW][C]50[/C][C] 176[/C][C] 14.99[/C][C] 161[/C][/ROW]
[ROW][C]51[/C][C] 22[/C][C]-125.6[/C][C] 147.6[/C][/ROW]
[ROW][C]52[/C][C]-350[/C][C] 37.77[/C][C]-387.8[/C][/ROW]
[ROW][C]53[/C][C] 759[/C][C] 193.1[/C][C] 565.9[/C][/ROW]
[ROW][C]54[/C][C]-778[/C][C]-415.4[/C][C]-362.6[/C][/ROW]
[ROW][C]55[/C][C] 758[/C][C] 326.1[/C][C] 431.9[/C][/ROW]
[ROW][C]56[/C][C]-561[/C][C]-76[/C][C]-485[/C][/ROW]
[ROW][C]57[/C][C]-39[/C][C]-54.27[/C][C] 15.27[/C][/ROW]
[ROW][C]58[/C][C] 413[/C][C] 60.92[/C][C] 352.1[/C][/ROW]
[ROW][C]59[/C][C]-84[/C][C] 11.54[/C][C]-95.54[/C][/ROW]
[ROW][C]60[/C][C]-246[/C][C]-51.58[/C][C]-194.4[/C][/ROW]
[ROW][C]61[/C][C] 228[/C][C] 64.4[/C][C] 163.6[/C][/ROW]
[ROW][C]62[/C][C] 66[/C][C]-33.94[/C][C] 99.94[/C][/ROW]
[ROW][C]63[/C][C]-348[/C][C]-123.7[/C][C]-224.3[/C][/ROW]
[ROW][C]64[/C][C] 190[/C][C] 90.4[/C][C] 99.6[/C][/ROW]
[ROW][C]65[/C][C]-258[/C][C] 77.82[/C][C]-335.8[/C][/ROW]
[ROW][C]66[/C][C]-88[/C][C]-306.9[/C][C] 218.9[/C][/ROW]
[ROW][C]67[/C][C] 246[/C][C] 385.4[/C][C]-139.4[/C][/ROW]
[ROW][C]68[/C][C] 119[/C][C]-369.1[/C][C] 488.1[/C][/ROW]
[ROW][C]69[/C][C] 49[/C][C] 289.3[/C][C]-240.3[/C][/ROW]
[ROW][C]70[/C][C]-201[/C][C]-0.06766[/C][C]-200.9[/C][/ROW]
[ROW][C]71[/C][C]-80[/C][C]-69.33[/C][C]-10.67[/C][/ROW]
[ROW][C]72[/C][C] 9[/C][C] 26.88[/C][C]-17.88[/C][/ROW]
[ROW][C]73[/C][C] 13[/C][C] 50.08[/C][C]-37.08[/C][/ROW]
[ROW][C]74[/C][C] 200[/C][C]-75.66[/C][C] 275.7[/C][/ROW]
[ROW][C]75[/C][C]-259[/C][C] 89.5[/C][C]-348.5[/C][/ROW]
[ROW][C]76[/C][C] 204[/C][C] 5.628[/C][C] 198.4[/C][/ROW]
[ROW][C]77[/C][C]-112[/C][C]-186.1[/C][C] 74.1[/C][/ROW]
[ROW][C]78[/C][C] 231[/C][C] 191.5[/C][C] 39.46[/C][/ROW]
[ROW][C]79[/C][C] 38[/C][C]-22.76[/C][C] 60.76[/C][/ROW]
[ROW][C]80[/C][C]-361[/C][C]-171.7[/C][C]-189.3[/C][/ROW]
[ROW][C]81[/C][C]-25[/C][C] 176.6[/C][C]-201.6[/C][/ROW]
[ROW][C]82[/C][C] 137[/C][C]-28[/C][C] 165[/C][/ROW]
[ROW][C]83[/C][C]-169[/C][C]-58.18[/C][C]-110.8[/C][/ROW]
[ROW][C]84[/C][C] 213[/C][C] 98.21[/C][C] 114.8[/C][/ROW]
[ROW][C]85[/C][C]-41[/C][C]-34.91[/C][C]-6.088[/C][/ROW]
[ROW][C]86[/C][C]-246[/C][C]-45.41[/C][C]-200.6[/C][/ROW]
[ROW][C]87[/C][C] 296[/C][C] 158.7[/C][C] 137.3[/C][/ROW]
[ROW][C]88[/C][C] 68[/C][C]-102.3[/C][C] 170.3[/C][/ROW]
[ROW][C]89[/C][C]-399[/C][C]-124.7[/C][C]-274.3[/C][/ROW]
[ROW][C]90[/C][C] 93[/C][C] 293.5[/C][C]-200.5[/C][/ROW]
[ROW][C]91[/C][C] 263[/C][C]-373.4[/C][C] 636.4[/C][/ROW]
[ROW][C]92[/C][C]-475[/C][C] 229.5[/C][C]-704.5[/C][/ROW]
[ROW][C]93[/C][C] 388[/C][C]-55.68[/C][C] 443.7[/C][/ROW]
[ROW][C]94[/C][C] 222[/C][C]-10.09[/C][C] 232.1[/C][/ROW]
[ROW][C]95[/C][C]-581[/C][C] 16.6[/C][C]-597.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286764&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286764&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	864	40.31	823.7
2	-877	-71.95	-805.1
3	-96	-28.42	-67.58
4	327	113.7	213.3
5	-723	-120.2	-602.8
6	657	-54.39	711.4
7	58	213.3	-155.3
8	-677	-350.9	-326.1
9	620	262.7	357.3
10	-362	-8.224	-353.8
11	-234	-68.44	-165.6
12	577	78.89	498.1
13	177	-11.3	188.3
14	-110	-127.5	17.49
15	271	139.9	131.1
16	111	-123	234
17	-504	-159.4	-344.6
18	444	259.8	184.2
19	-183	-195.3	12.31
20	34	163	-129
21	244	37.43	206.6
22	-227	-39.5	-187.5
23	-173	8.762	-181.8
24	651	35.49	615.5
25	-378	-18.59	-359.4
26	-136	-2.484	-133.5
27	347	-11.59	358.6
28	-203	-116.2	-86.84
29	236	12.98	223
30	-259	195.9	-454.9
31	-111	-256	145
32	568	234.4	333.6
33	-626	-89.37	-536.6
34	348	20.13	327.9
35	-255	45	-300
36	71	-18.8	89.8
37	17	-25.24	42.24
38	83	31.01	51.99
39	-111	-17.24	-93.76
40	113	-67.62	180.6
41	-242	105.7	-347.7
42	334	-24.1	358.1
43	-492	-61.8	-430.2
44	272	191.3	80.74
45	166	-224.6	390.6
46	-475	51.49	-526.5
47	641	67.23	573.8
48	-393	-55.76	-337.2
49	-136	13.95	-150
50	176	14.99	161
51	22	-125.6	147.6
52	-350	37.77	-387.8
53	759	193.1	565.9
54	-778	-415.4	-362.6
55	758	326.1	431.9
56	-561	-76	-485
57	-39	-54.27	15.27
58	413	60.92	352.1
59	-84	11.54	-95.54
60	-246	-51.58	-194.4
61	228	64.4	163.6
62	66	-33.94	99.94
63	-348	-123.7	-224.3
64	190	90.4	99.6
65	-258	77.82	-335.8
66	-88	-306.9	218.9
67	246	385.4	-139.4
68	119	-369.1	488.1
69	49	289.3	-240.3
70	-201	-0.06766	-200.9
71	-80	-69.33	-10.67
72	9	26.88	-17.88
73	13	50.08	-37.08
74	200	-75.66	275.7
75	-259	89.5	-348.5
76	204	5.628	198.4
77	-112	-186.1	74.1
78	231	191.5	39.46
79	38	-22.76	60.76
80	-361	-171.7	-189.3
81	-25	176.6	-201.6
82	137	-28	165
83	-169	-58.18	-110.8
84	213	98.21	114.8
85	-41	-34.91	-6.088
86	-246	-45.41	-200.6
87	296	158.7	137.3
88	68	-102.3	170.3
89	-399	-124.7	-274.3
90	93	293.5	-200.5
91	263	-373.4	636.4
92	-475	229.5	-704.5
93	388	-55.68	443.7
94	222	-10.09	232.1
95	-581	16.6	-597.6

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.6158	0.7684	0.3842
8	0.5318	0.9365	0.4682
9	0.8973	0.2055	0.1027
10	0.8948	0.2104	0.1052
11	0.8367	0.3267	0.1633
12	0.7992	0.4017	0.2008
13	0.7833	0.4334	0.2167
14	0.725	0.5501	0.275
15	0.6636	0.6728	0.3364
16	0.8474	0.3053	0.1526
17	0.8196	0.3608	0.1804
18	0.8656	0.2689	0.1344
19	0.8587	0.2825	0.1413
20	0.8566	0.2868	0.1434
21	0.8159	0.3681	0.1841
22	0.7857	0.4285	0.2142
23	0.7397	0.5205	0.2603
24	0.81	0.38	0.19
25	0.8071	0.3859	0.1929
26	0.7806	0.4388	0.2194
27	0.8089	0.3821	0.1911
28	0.7616	0.4767	0.2384
29	0.7258	0.5485	0.2742
30	0.8884	0.2233	0.1116
31	0.8873	0.2255	0.1127
32	0.8761	0.2478	0.1239
33	0.9073	0.1854	0.09268
34	0.9029	0.1941	0.09705
35	0.9041	0.1919	0.09593
36	0.8776	0.2448	0.1224
37	0.8435	0.313	0.1565
38	0.8048	0.3905	0.1952
39	0.7647	0.4705	0.2353
40	0.7286	0.5428	0.2714
41	0.7637	0.4727	0.2363
42	0.7692	0.4615	0.2308
43	0.7827	0.4347	0.2173
44	0.746	0.5081	0.254
45	0.8017	0.3966	0.1983
46	0.8604	0.2791	0.1396
47	0.9164	0.1672	0.08358
48	0.9182	0.1637	0.08184
49	0.8973	0.2054	0.1027
50	0.8734	0.2531	0.1266
51	0.8501	0.2999	0.1499
52	0.8648	0.2705	0.1352
53	0.9203	0.1595	0.07973
54	0.9406	0.1187	0.05935
55	0.9751	0.04988	0.02494
56	0.9907	0.01863	0.009314
57	0.9865	0.02696	0.01348
58	0.9901	0.01974	0.00987
59	0.9856	0.02874	0.01437
60	0.9823	0.03543	0.01771
61	0.9795	0.0411	0.02055
62	0.9703	0.05932	0.02966
63	0.9664	0.0672	0.0336
64	0.9571	0.08585	0.04292
65	0.9659	0.06823	0.03411
66	0.9586	0.0827	0.04135
67	0.9473	0.1054	0.0527
68	0.9496	0.1008	0.05041
69	0.9344	0.1312	0.0656
70	0.92	0.1601	0.08004
71	0.89	0.2199	0.11
72	0.8534	0.2933	0.1466
73	0.8041	0.3918	0.1959
74	0.7874	0.4252	0.2126
75	0.769	0.4621	0.231
76	0.7326	0.5347	0.2674
77	0.6694	0.6611	0.3306
78	0.6507	0.6987	0.3493
79	0.6327	0.7347	0.3673
80	0.5719	0.8563	0.4281
81	0.5557	0.8885	0.4443
82	0.4827	0.9655	0.5173
83	0.381	0.7621	0.619
84	0.2851	0.5702	0.7149
85	0.2222	0.4443	0.7778
86	0.1493	0.2986	0.8507
87	0.1671	0.3342	0.8329
88	0.09164	0.1833	0.9084

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.6158 &  0.7684 &  0.3842 \tabularnewline
8 &  0.5318 &  0.9365 &  0.4682 \tabularnewline
9 &  0.8973 &  0.2055 &  0.1027 \tabularnewline
10 &  0.8948 &  0.2104 &  0.1052 \tabularnewline
11 &  0.8367 &  0.3267 &  0.1633 \tabularnewline
12 &  0.7992 &  0.4017 &  0.2008 \tabularnewline
13 &  0.7833 &  0.4334 &  0.2167 \tabularnewline
14 &  0.725 &  0.5501 &  0.275 \tabularnewline
15 &  0.6636 &  0.6728 &  0.3364 \tabularnewline
16 &  0.8474 &  0.3053 &  0.1526 \tabularnewline
17 &  0.8196 &  0.3608 &  0.1804 \tabularnewline
18 &  0.8656 &  0.2689 &  0.1344 \tabularnewline
19 &  0.8587 &  0.2825 &  0.1413 \tabularnewline
20 &  0.8566 &  0.2868 &  0.1434 \tabularnewline
21 &  0.8159 &  0.3681 &  0.1841 \tabularnewline
22 &  0.7857 &  0.4285 &  0.2142 \tabularnewline
23 &  0.7397 &  0.5205 &  0.2603 \tabularnewline
24 &  0.81 &  0.38 &  0.19 \tabularnewline
25 &  0.8071 &  0.3859 &  0.1929 \tabularnewline
26 &  0.7806 &  0.4388 &  0.2194 \tabularnewline
27 &  0.8089 &  0.3821 &  0.1911 \tabularnewline
28 &  0.7616 &  0.4767 &  0.2384 \tabularnewline
29 &  0.7258 &  0.5485 &  0.2742 \tabularnewline
30 &  0.8884 &  0.2233 &  0.1116 \tabularnewline
31 &  0.8873 &  0.2255 &  0.1127 \tabularnewline
32 &  0.8761 &  0.2478 &  0.1239 \tabularnewline
33 &  0.9073 &  0.1854 &  0.09268 \tabularnewline
34 &  0.9029 &  0.1941 &  0.09705 \tabularnewline
35 &  0.9041 &  0.1919 &  0.09593 \tabularnewline
36 &  0.8776 &  0.2448 &  0.1224 \tabularnewline
37 &  0.8435 &  0.313 &  0.1565 \tabularnewline
38 &  0.8048 &  0.3905 &  0.1952 \tabularnewline
39 &  0.7647 &  0.4705 &  0.2353 \tabularnewline
40 &  0.7286 &  0.5428 &  0.2714 \tabularnewline
41 &  0.7637 &  0.4727 &  0.2363 \tabularnewline
42 &  0.7692 &  0.4615 &  0.2308 \tabularnewline
43 &  0.7827 &  0.4347 &  0.2173 \tabularnewline
44 &  0.746 &  0.5081 &  0.254 \tabularnewline
45 &  0.8017 &  0.3966 &  0.1983 \tabularnewline
46 &  0.8604 &  0.2791 &  0.1396 \tabularnewline
47 &  0.9164 &  0.1672 &  0.08358 \tabularnewline
48 &  0.9182 &  0.1637 &  0.08184 \tabularnewline
49 &  0.8973 &  0.2054 &  0.1027 \tabularnewline
50 &  0.8734 &  0.2531 &  0.1266 \tabularnewline
51 &  0.8501 &  0.2999 &  0.1499 \tabularnewline
52 &  0.8648 &  0.2705 &  0.1352 \tabularnewline
53 &  0.9203 &  0.1595 &  0.07973 \tabularnewline
54 &  0.9406 &  0.1187 &  0.05935 \tabularnewline
55 &  0.9751 &  0.04988 &  0.02494 \tabularnewline
56 &  0.9907 &  0.01863 &  0.009314 \tabularnewline
57 &  0.9865 &  0.02696 &  0.01348 \tabularnewline
58 &  0.9901 &  0.01974 &  0.00987 \tabularnewline
59 &  0.9856 &  0.02874 &  0.01437 \tabularnewline
60 &  0.9823 &  0.03543 &  0.01771 \tabularnewline
61 &  0.9795 &  0.0411 &  0.02055 \tabularnewline
62 &  0.9703 &  0.05932 &  0.02966 \tabularnewline
63 &  0.9664 &  0.0672 &  0.0336 \tabularnewline
64 &  0.9571 &  0.08585 &  0.04292 \tabularnewline
65 &  0.9659 &  0.06823 &  0.03411 \tabularnewline
66 &  0.9586 &  0.0827 &  0.04135 \tabularnewline
67 &  0.9473 &  0.1054 &  0.0527 \tabularnewline
68 &  0.9496 &  0.1008 &  0.05041 \tabularnewline
69 &  0.9344 &  0.1312 &  0.0656 \tabularnewline
70 &  0.92 &  0.1601 &  0.08004 \tabularnewline
71 &  0.89 &  0.2199 &  0.11 \tabularnewline
72 &  0.8534 &  0.2933 &  0.1466 \tabularnewline
73 &  0.8041 &  0.3918 &  0.1959 \tabularnewline
74 &  0.7874 &  0.4252 &  0.2126 \tabularnewline
75 &  0.769 &  0.4621 &  0.231 \tabularnewline
76 &  0.7326 &  0.5347 &  0.2674 \tabularnewline
77 &  0.6694 &  0.6611 &  0.3306 \tabularnewline
78 &  0.6507 &  0.6987 &  0.3493 \tabularnewline
79 &  0.6327 &  0.7347 &  0.3673 \tabularnewline
80 &  0.5719 &  0.8563 &  0.4281 \tabularnewline
81 &  0.5557 &  0.8885 &  0.4443 \tabularnewline
82 &  0.4827 &  0.9655 &  0.5173 \tabularnewline
83 &  0.381 &  0.7621 &  0.619 \tabularnewline
84 &  0.2851 &  0.5702 &  0.7149 \tabularnewline
85 &  0.2222 &  0.4443 &  0.7778 \tabularnewline
86 &  0.1493 &  0.2986 &  0.8507 \tabularnewline
87 &  0.1671 &  0.3342 &  0.8329 \tabularnewline
88 &  0.09164 &  0.1833 &  0.9084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286764&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] 0.6158[/C][C] 0.7684[/C][C] 0.3842[/C][/ROW]
[ROW][C]8[/C][C] 0.5318[/C][C] 0.9365[/C][C] 0.4682[/C][/ROW]
[ROW][C]9[/C][C] 0.8973[/C][C] 0.2055[/C][C] 0.1027[/C][/ROW]
[ROW][C]10[/C][C] 0.8948[/C][C] 0.2104[/C][C] 0.1052[/C][/ROW]
[ROW][C]11[/C][C] 0.8367[/C][C] 0.3267[/C][C] 0.1633[/C][/ROW]
[ROW][C]12[/C][C] 0.7992[/C][C] 0.4017[/C][C] 0.2008[/C][/ROW]
[ROW][C]13[/C][C] 0.7833[/C][C] 0.4334[/C][C] 0.2167[/C][/ROW]
[ROW][C]14[/C][C] 0.725[/C][C] 0.5501[/C][C] 0.275[/C][/ROW]
[ROW][C]15[/C][C] 0.6636[/C][C] 0.6728[/C][C] 0.3364[/C][/ROW]
[ROW][C]16[/C][C] 0.8474[/C][C] 0.3053[/C][C] 0.1526[/C][/ROW]
[ROW][C]17[/C][C] 0.8196[/C][C] 0.3608[/C][C] 0.1804[/C][/ROW]
[ROW][C]18[/C][C] 0.8656[/C][C] 0.2689[/C][C] 0.1344[/C][/ROW]
[ROW][C]19[/C][C] 0.8587[/C][C] 0.2825[/C][C] 0.1413[/C][/ROW]
[ROW][C]20[/C][C] 0.8566[/C][C] 0.2868[/C][C] 0.1434[/C][/ROW]
[ROW][C]21[/C][C] 0.8159[/C][C] 0.3681[/C][C] 0.1841[/C][/ROW]
[ROW][C]22[/C][C] 0.7857[/C][C] 0.4285[/C][C] 0.2142[/C][/ROW]
[ROW][C]23[/C][C] 0.7397[/C][C] 0.5205[/C][C] 0.2603[/C][/ROW]
[ROW][C]24[/C][C] 0.81[/C][C] 0.38[/C][C] 0.19[/C][/ROW]
[ROW][C]25[/C][C] 0.8071[/C][C] 0.3859[/C][C] 0.1929[/C][/ROW]
[ROW][C]26[/C][C] 0.7806[/C][C] 0.4388[/C][C] 0.2194[/C][/ROW]
[ROW][C]27[/C][C] 0.8089[/C][C] 0.3821[/C][C] 0.1911[/C][/ROW]
[ROW][C]28[/C][C] 0.7616[/C][C] 0.4767[/C][C] 0.2384[/C][/ROW]
[ROW][C]29[/C][C] 0.7258[/C][C] 0.5485[/C][C] 0.2742[/C][/ROW]
[ROW][C]30[/C][C] 0.8884[/C][C] 0.2233[/C][C] 0.1116[/C][/ROW]
[ROW][C]31[/C][C] 0.8873[/C][C] 0.2255[/C][C] 0.1127[/C][/ROW]
[ROW][C]32[/C][C] 0.8761[/C][C] 0.2478[/C][C] 0.1239[/C][/ROW]
[ROW][C]33[/C][C] 0.9073[/C][C] 0.1854[/C][C] 0.09268[/C][/ROW]
[ROW][C]34[/C][C] 0.9029[/C][C] 0.1941[/C][C] 0.09705[/C][/ROW]
[ROW][C]35[/C][C] 0.9041[/C][C] 0.1919[/C][C] 0.09593[/C][/ROW]
[ROW][C]36[/C][C] 0.8776[/C][C] 0.2448[/C][C] 0.1224[/C][/ROW]
[ROW][C]37[/C][C] 0.8435[/C][C] 0.313[/C][C] 0.1565[/C][/ROW]
[ROW][C]38[/C][C] 0.8048[/C][C] 0.3905[/C][C] 0.1952[/C][/ROW]
[ROW][C]39[/C][C] 0.7647[/C][C] 0.4705[/C][C] 0.2353[/C][/ROW]
[ROW][C]40[/C][C] 0.7286[/C][C] 0.5428[/C][C] 0.2714[/C][/ROW]
[ROW][C]41[/C][C] 0.7637[/C][C] 0.4727[/C][C] 0.2363[/C][/ROW]
[ROW][C]42[/C][C] 0.7692[/C][C] 0.4615[/C][C] 0.2308[/C][/ROW]
[ROW][C]43[/C][C] 0.7827[/C][C] 0.4347[/C][C] 0.2173[/C][/ROW]
[ROW][C]44[/C][C] 0.746[/C][C] 0.5081[/C][C] 0.254[/C][/ROW]
[ROW][C]45[/C][C] 0.8017[/C][C] 0.3966[/C][C] 0.1983[/C][/ROW]
[ROW][C]46[/C][C] 0.8604[/C][C] 0.2791[/C][C] 0.1396[/C][/ROW]
[ROW][C]47[/C][C] 0.9164[/C][C] 0.1672[/C][C] 0.08358[/C][/ROW]
[ROW][C]48[/C][C] 0.9182[/C][C] 0.1637[/C][C] 0.08184[/C][/ROW]
[ROW][C]49[/C][C] 0.8973[/C][C] 0.2054[/C][C] 0.1027[/C][/ROW]
[ROW][C]50[/C][C] 0.8734[/C][C] 0.2531[/C][C] 0.1266[/C][/ROW]
[ROW][C]51[/C][C] 0.8501[/C][C] 0.2999[/C][C] 0.1499[/C][/ROW]
[ROW][C]52[/C][C] 0.8648[/C][C] 0.2705[/C][C] 0.1352[/C][/ROW]
[ROW][C]53[/C][C] 0.9203[/C][C] 0.1595[/C][C] 0.07973[/C][/ROW]
[ROW][C]54[/C][C] 0.9406[/C][C] 0.1187[/C][C] 0.05935[/C][/ROW]
[ROW][C]55[/C][C] 0.9751[/C][C] 0.04988[/C][C] 0.02494[/C][/ROW]
[ROW][C]56[/C][C] 0.9907[/C][C] 0.01863[/C][C] 0.009314[/C][/ROW]
[ROW][C]57[/C][C] 0.9865[/C][C] 0.02696[/C][C] 0.01348[/C][/ROW]
[ROW][C]58[/C][C] 0.9901[/C][C] 0.01974[/C][C] 0.00987[/C][/ROW]
[ROW][C]59[/C][C] 0.9856[/C][C] 0.02874[/C][C] 0.01437[/C][/ROW]
[ROW][C]60[/C][C] 0.9823[/C][C] 0.03543[/C][C] 0.01771[/C][/ROW]
[ROW][C]61[/C][C] 0.9795[/C][C] 0.0411[/C][C] 0.02055[/C][/ROW]
[ROW][C]62[/C][C] 0.9703[/C][C] 0.05932[/C][C] 0.02966[/C][/ROW]
[ROW][C]63[/C][C] 0.9664[/C][C] 0.0672[/C][C] 0.0336[/C][/ROW]
[ROW][C]64[/C][C] 0.9571[/C][C] 0.08585[/C][C] 0.04292[/C][/ROW]
[ROW][C]65[/C][C] 0.9659[/C][C] 0.06823[/C][C] 0.03411[/C][/ROW]
[ROW][C]66[/C][C] 0.9586[/C][C] 0.0827[/C][C] 0.04135[/C][/ROW]
[ROW][C]67[/C][C] 0.9473[/C][C] 0.1054[/C][C] 0.0527[/C][/ROW]
[ROW][C]68[/C][C] 0.9496[/C][C] 0.1008[/C][C] 0.05041[/C][/ROW]
[ROW][C]69[/C][C] 0.9344[/C][C] 0.1312[/C][C] 0.0656[/C][/ROW]
[ROW][C]70[/C][C] 0.92[/C][C] 0.1601[/C][C] 0.08004[/C][/ROW]
[ROW][C]71[/C][C] 0.89[/C][C] 0.2199[/C][C] 0.11[/C][/ROW]
[ROW][C]72[/C][C] 0.8534[/C][C] 0.2933[/C][C] 0.1466[/C][/ROW]
[ROW][C]73[/C][C] 0.8041[/C][C] 0.3918[/C][C] 0.1959[/C][/ROW]
[ROW][C]74[/C][C] 0.7874[/C][C] 0.4252[/C][C] 0.2126[/C][/ROW]
[ROW][C]75[/C][C] 0.769[/C][C] 0.4621[/C][C] 0.231[/C][/ROW]
[ROW][C]76[/C][C] 0.7326[/C][C] 0.5347[/C][C] 0.2674[/C][/ROW]
[ROW][C]77[/C][C] 0.6694[/C][C] 0.6611[/C][C] 0.3306[/C][/ROW]
[ROW][C]78[/C][C] 0.6507[/C][C] 0.6987[/C][C] 0.3493[/C][/ROW]
[ROW][C]79[/C][C] 0.6327[/C][C] 0.7347[/C][C] 0.3673[/C][/ROW]
[ROW][C]80[/C][C] 0.5719[/C][C] 0.8563[/C][C] 0.4281[/C][/ROW]
[ROW][C]81[/C][C] 0.5557[/C][C] 0.8885[/C][C] 0.4443[/C][/ROW]
[ROW][C]82[/C][C] 0.4827[/C][C] 0.9655[/C][C] 0.5173[/C][/ROW]
[ROW][C]83[/C][C] 0.381[/C][C] 0.7621[/C][C] 0.619[/C][/ROW]
[ROW][C]84[/C][C] 0.2851[/C][C] 0.5702[/C][C] 0.7149[/C][/ROW]
[ROW][C]85[/C][C] 0.2222[/C][C] 0.4443[/C][C] 0.7778[/C][/ROW]
[ROW][C]86[/C][C] 0.1493[/C][C] 0.2986[/C][C] 0.8507[/C][/ROW]
[ROW][C]87[/C][C] 0.1671[/C][C] 0.3342[/C][C] 0.8329[/C][/ROW]
[ROW][C]88[/C][C] 0.09164[/C][C] 0.1833[/C][C] 0.9084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286764&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286764&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.6158	0.7684	0.3842
8	0.5318	0.9365	0.4682
9	0.8973	0.2055	0.1027
10	0.8948	0.2104	0.1052
11	0.8367	0.3267	0.1633
12	0.7992	0.4017	0.2008
13	0.7833	0.4334	0.2167
14	0.725	0.5501	0.275
15	0.6636	0.6728	0.3364
16	0.8474	0.3053	0.1526
17	0.8196	0.3608	0.1804
18	0.8656	0.2689	0.1344
19	0.8587	0.2825	0.1413
20	0.8566	0.2868	0.1434
21	0.8159	0.3681	0.1841
22	0.7857	0.4285	0.2142
23	0.7397	0.5205	0.2603
24	0.81	0.38	0.19
25	0.8071	0.3859	0.1929
26	0.7806	0.4388	0.2194
27	0.8089	0.3821	0.1911
28	0.7616	0.4767	0.2384
29	0.7258	0.5485	0.2742
30	0.8884	0.2233	0.1116
31	0.8873	0.2255	0.1127
32	0.8761	0.2478	0.1239
33	0.9073	0.1854	0.09268
34	0.9029	0.1941	0.09705
35	0.9041	0.1919	0.09593
36	0.8776	0.2448	0.1224
37	0.8435	0.313	0.1565
38	0.8048	0.3905	0.1952
39	0.7647	0.4705	0.2353
40	0.7286	0.5428	0.2714
41	0.7637	0.4727	0.2363
42	0.7692	0.4615	0.2308
43	0.7827	0.4347	0.2173
44	0.746	0.5081	0.254
45	0.8017	0.3966	0.1983
46	0.8604	0.2791	0.1396
47	0.9164	0.1672	0.08358
48	0.9182	0.1637	0.08184
49	0.8973	0.2054	0.1027
50	0.8734	0.2531	0.1266
51	0.8501	0.2999	0.1499
52	0.8648	0.2705	0.1352
53	0.9203	0.1595	0.07973
54	0.9406	0.1187	0.05935
55	0.9751	0.04988	0.02494
56	0.9907	0.01863	0.009314
57	0.9865	0.02696	0.01348
58	0.9901	0.01974	0.00987
59	0.9856	0.02874	0.01437
60	0.9823	0.03543	0.01771
61	0.9795	0.0411	0.02055
62	0.9703	0.05932	0.02966
63	0.9664	0.0672	0.0336
64	0.9571	0.08585	0.04292
65	0.9659	0.06823	0.03411
66	0.9586	0.0827	0.04135
67	0.9473	0.1054	0.0527
68	0.9496	0.1008	0.05041
69	0.9344	0.1312	0.0656
70	0.92	0.1601	0.08004
71	0.89	0.2199	0.11
72	0.8534	0.2933	0.1466
73	0.8041	0.3918	0.1959
74	0.7874	0.4252	0.2126
75	0.769	0.4621	0.231
76	0.7326	0.5347	0.2674
77	0.6694	0.6611	0.3306
78	0.6507	0.6987	0.3493
79	0.6327	0.7347	0.3673
80	0.5719	0.8563	0.4281
81	0.5557	0.8885	0.4443
82	0.4827	0.9655	0.5173
83	0.381	0.7621	0.619
84	0.2851	0.5702	0.7149
85	0.2222	0.4443	0.7778
86	0.1493	0.2986	0.8507
87	0.1671	0.3342	0.8329
88	0.09164	0.1833	0.9084

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	7	0.0853659	NOK
10% type I error level	12	0.146341	NOK

\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 & 7 & 0.0853659 & NOK \tabularnewline
10% type I error level & 12 & 0.146341 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286764&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]7[/C][C]0.0853659[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.146341[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286764&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286764&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 tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	7	0.0853659	NOK
10% type I error level	12	0.146341	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s=12) ; par5 = 2 ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s=12) ; par4 = ; par5 = 2 ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code