Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 17 Dec 2015 12:49:58 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/17/t1450356605a6fgtwi9radeq7a.htm/, Retrieved Fri, 01 Nov 2024 00:15:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=286777, Retrieved Fri, 01 Nov 2024 00:15:41 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

153

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

-       [Multiple Regression] [] [2015-12-17 12:49:58] [5fd2fca6b664199b2dd86155c5786748] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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 Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286777&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286777&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286777&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 Maurice George Kendall' @ kendall.wessa.net

Multiple Linear Regression - Estimated Regression Equation
[t] = + 6.07441 -0.98429`(1-B12)(1-B)Scheidingen(t-1)`[t] -0.615087`(1-B12)(1-B)Scheidingen(t-2)`[t] + 0.00487098`(1-B12)(1-B)Scheidingen(t-3)`[t] -0.022921`(1-B12)(1-B)Scheidingen(t-4)`[t] -0.0237409`(t-1s)`[t] -0.0313547`(t-2s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
[t] =  +  6.07441 -0.98429`(1-B12)(1-B)Scheidingen(t-1)`[t] -0.615087`(1-B12)(1-B)Scheidingen(t-2)`[t] +  0.00487098`(1-B12)(1-B)Scheidingen(t-3)`[t] -0.022921`(1-B12)(1-B)Scheidingen(t-4)`[t] -0.0237409`(t-1s)`[t] -0.0313547`(t-2s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286777&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C][t] =  +  6.07441 -0.98429`(1-B12)(1-B)Scheidingen(t-1)`[t] -0.615087`(1-B12)(1-B)Scheidingen(t-2)`[t] +  0.00487098`(1-B12)(1-B)Scheidingen(t-3)`[t] -0.022921`(1-B12)(1-B)Scheidingen(t-4)`[t] -0.0237409`(t-1s)`[t] -0.0313547`(t-2s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286777&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286777&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
[t] = + 6.07441 -0.98429`(1-B12)(1-B)Scheidingen(t-1)`[t] -0.615087`(1-B12)(1-B)Scheidingen(t-2)`[t] + 0.00487098`(1-B12)(1-B)Scheidingen(t-3)`[t] -0.022921`(1-B12)(1-B)Scheidingen(t-4)`[t] -0.0237409`(t-1s)`[t] -0.0313547`(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)	+6.074	22.9	+2.6530e-01	0.7914	0.3957
`(1-B12)(1-B)Scheidingen(t-1)`	-0.9843	0.111	-8.8640e+00	1.115e-13	5.577e-14
`(1-B12)(1-B)Scheidingen(t-2)`	-0.6151	0.1495	-4.1150e+00	8.999e-05	4.5e-05
`(1-B12)(1-B)Scheidingen(t-3)`	+0.004871	0.1458	+3.3410e-02	0.9734	0.4867
`(1-B12)(1-B)Scheidingen(t-4)`	-0.02292	0.1096	-2.0910e-01	0.8349	0.4174
`(t-1s)`	-0.02374	0.07276	-3.2630e-01	0.745	0.3725
`(t-2s)`	-0.03136	0.06684	-4.6910e-01	0.6402	0.3201

\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) & +6.074 &  22.9 & +2.6530e-01 &  0.7914 &  0.3957 \tabularnewline
`(1-B12)(1-B)Scheidingen(t-1)` & -0.9843 &  0.111 & -8.8640e+00 &  1.115e-13 &  5.577e-14 \tabularnewline
`(1-B12)(1-B)Scheidingen(t-2)` & -0.6151 &  0.1495 & -4.1150e+00 &  8.999e-05 &  4.5e-05 \tabularnewline
`(1-B12)(1-B)Scheidingen(t-3)` & +0.004871 &  0.1458 & +3.3410e-02 &  0.9734 &  0.4867 \tabularnewline
`(1-B12)(1-B)Scheidingen(t-4)` & -0.02292 &  0.1096 & -2.0910e-01 &  0.8349 &  0.4174 \tabularnewline
`(t-1s)` & -0.02374 &  0.07276 & -3.2630e-01 &  0.745 &  0.3725 \tabularnewline
`(t-2s)` & -0.03136 &  0.06684 & -4.6910e-01 &  0.6402 &  0.3201 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286777&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]+6.074[/C][C] 22.9[/C][C]+2.6530e-01[/C][C] 0.7914[/C][C] 0.3957[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Scheidingen(t-1)`[/C][C]-0.9843[/C][C] 0.111[/C][C]-8.8640e+00[/C][C] 1.115e-13[/C][C] 5.577e-14[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Scheidingen(t-2)`[/C][C]-0.6151[/C][C] 0.1495[/C][C]-4.1150e+00[/C][C] 8.999e-05[/C][C] 4.5e-05[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Scheidingen(t-3)`[/C][C]+0.004871[/C][C] 0.1458[/C][C]+3.3410e-02[/C][C] 0.9734[/C][C] 0.4867[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Scheidingen(t-4)`[/C][C]-0.02292[/C][C] 0.1096[/C][C]-2.0910e-01[/C][C] 0.8349[/C][C] 0.4174[/C][/ROW]
[ROW][C]`(t-1s)`[/C][C]-0.02374[/C][C] 0.07276[/C][C]-3.2630e-01[/C][C] 0.745[/C][C] 0.3725[/C][/ROW]
[ROW][C]`(t-2s)`[/C][C]-0.03136[/C][C] 0.06684[/C][C]-4.6910e-01[/C][C] 0.6402[/C][C] 0.3201[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286777&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286777&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)	+6.074	22.9	+2.6530e-01	0.7914	0.3957
`(1-B12)(1-B)Scheidingen(t-1)`	-0.9843	0.111	-8.8640e+00	1.115e-13	5.577e-14
`(1-B12)(1-B)Scheidingen(t-2)`	-0.6151	0.1495	-4.1150e+00	8.999e-05	4.5e-05
`(1-B12)(1-B)Scheidingen(t-3)`	+0.004871	0.1458	+3.3410e-02	0.9734	0.4867
`(1-B12)(1-B)Scheidingen(t-4)`	-0.02292	0.1096	-2.0910e-01	0.8349	0.4174
`(t-1s)`	-0.02374	0.07276	-3.2630e-01	0.745	0.3725
`(t-2s)`	-0.03136	0.06684	-4.6910e-01	0.6402	0.3201

Multiple Linear Regression - Regression Statistics
Multiple R	0.7912
R-squared	0.626
Adjusted R-squared	0.5992
F-TEST (value)	23.43
F-TEST (DF numerator)	6
F-TEST (DF denominator)	84
p-value	4.441e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	218.2
Sum Squared Residuals	3.998e+06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7912 \tabularnewline
R-squared &  0.626 \tabularnewline
Adjusted R-squared &  0.5992 \tabularnewline
F-TEST (value) &  23.43 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 84 \tabularnewline
p-value &  4.441e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  218.2 \tabularnewline
Sum Squared Residuals &  3.998e+06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286777&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7912[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.626[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.5992[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 23.43[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]84[/C][/ROW]
[ROW][C]p-value[/C][C] 4.441e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 218.2[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 3.998e+06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286777&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286777&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	0.7912
R-squared	0.626
Adjusted R-squared	0.5992
F-TEST (value)	23.43
F-TEST (DF numerator)	6
F-TEST (DF denominator)	84
p-value	4.441e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	218.2
Sum Squared Residuals	3.998e+06

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-723	-302.7	-420.3
2	657	554.3	102.7
3	58	-206.1	264.1
4	-677	-471	-206
5	620	672.3	-52.29
6	-362	-206.7	-155.3
7	-234	-27.3	-206.7
8	577	499.5	77.53
9	177	-448.8	625.8
10	-110	-513.3	403.3
11	271	35.21	235.8
12	111	-220.9	331.9
13	-504	-275.5	-228.5
14	444	450.2	-6.156
15	-183	-144.8	-38.23
16	34	-69.18	103.2
17	244	96.11	147.9
18	-227	-271.3	44.31
19	-173	93.89	-266.9
20	651	321	330
21	-378	-566.3	188.3
22	-136	12.18	-148.2
23	347	376.2	-29.15
24	-203	-281.5	78.47
25	236	35.09	200.9
26	-259	-127.7	-131.3
27	-111	109.4	-220.4
28	568	300.9	267.1
29	-626	-516.6	-109.4
30	348	295	52.99
31	-255	65.34	-320.3
32	71	-6.597	77.6
33	17	112.5	-95.51
34	83	-56.87	139.9
35	-111	-96.62	-14.38
36	113	64.07	48.93
37	-242	-26.66	-215.3
38	334	164.6	169.4
39	-492	-162.4	-329.6
40	272	266.6	5.414
41	166	55.36	110.6
42	-475	-335.8	-139.2
43	641	395.6	245.4
44	-393	-360.2	-32.79
45	-136	3.959	-140
46	176	398	-222
47	22	-108.4	130.4
48	-350	-111.8	-238.2
49	759	339.4	419.6
50	-778	-529.5	-248.5
51	758	318	440
52	-561	-274	-287
53	-39	86.53	-125.5
54	413	411.4	1.584
55	-84	-403.8	319.8
56	-246	-145.5	-100.5
57	228	305.5	-77.48
58	66	-83.69	149.7
59	-348	-195.4	-152.6
60	190	319.5	-129.5
61	-258	17.77	-275.8
62	-88	147.9	-235.9
63	246	257.7	-11.72
64	119	-182.8	301.8
65	49	-261.2	310.2
66	-201	-107	-93.95
67	-80	150.6	-230.6
68	9	224.1	-215.1
69	13	43.17	-30.17
70	200	-15.12	215.1
71	-259	-189.3	-69.67
72	204	144.3	59.69
73	-112	-52.41	-59.59
74	231	11.47	219.5
75	38	-175.1	213.1
76	-361	-163.9	-197.1
77	-25	341.8	-366.8
78	137	239.4	-102.4
79	-169	-111.5	-57.51
80	213	103.8	109.2
81	-41	-105.8	64.85
82	-246	-95.36	-150.6
83	296	295.4	0.6003
84	68	-149.8	217.8
85	-399	-232.4	-166.6
86	93	361.3	-268.3
87	263	144.9	118.1
88	-475	-308.7	-166.3
89	388	320.5	67.5
90	222	-81.46	303.5
91	-581	-452.9	-128.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -723 & -302.7 & -420.3 \tabularnewline
2 &  657 &  554.3 &  102.7 \tabularnewline
3 &  58 & -206.1 &  264.1 \tabularnewline
4 & -677 & -471 & -206 \tabularnewline
5 &  620 &  672.3 & -52.29 \tabularnewline
6 & -362 & -206.7 & -155.3 \tabularnewline
7 & -234 & -27.3 & -206.7 \tabularnewline
8 &  577 &  499.5 &  77.53 \tabularnewline
9 &  177 & -448.8 &  625.8 \tabularnewline
10 & -110 & -513.3 &  403.3 \tabularnewline
11 &  271 &  35.21 &  235.8 \tabularnewline
12 &  111 & -220.9 &  331.9 \tabularnewline
13 & -504 & -275.5 & -228.5 \tabularnewline
14 &  444 &  450.2 & -6.156 \tabularnewline
15 & -183 & -144.8 & -38.23 \tabularnewline
16 &  34 & -69.18 &  103.2 \tabularnewline
17 &  244 &  96.11 &  147.9 \tabularnewline
18 & -227 & -271.3 &  44.31 \tabularnewline
19 & -173 &  93.89 & -266.9 \tabularnewline
20 &  651 &  321 &  330 \tabularnewline
21 & -378 & -566.3 &  188.3 \tabularnewline
22 & -136 &  12.18 & -148.2 \tabularnewline
23 &  347 &  376.2 & -29.15 \tabularnewline
24 & -203 & -281.5 &  78.47 \tabularnewline
25 &  236 &  35.09 &  200.9 \tabularnewline
26 & -259 & -127.7 & -131.3 \tabularnewline
27 & -111 &  109.4 & -220.4 \tabularnewline
28 &  568 &  300.9 &  267.1 \tabularnewline
29 & -626 & -516.6 & -109.4 \tabularnewline
30 &  348 &  295 &  52.99 \tabularnewline
31 & -255 &  65.34 & -320.3 \tabularnewline
32 &  71 & -6.597 &  77.6 \tabularnewline
33 &  17 &  112.5 & -95.51 \tabularnewline
34 &  83 & -56.87 &  139.9 \tabularnewline
35 & -111 & -96.62 & -14.38 \tabularnewline
36 &  113 &  64.07 &  48.93 \tabularnewline
37 & -242 & -26.66 & -215.3 \tabularnewline
38 &  334 &  164.6 &  169.4 \tabularnewline
39 & -492 & -162.4 & -329.6 \tabularnewline
40 &  272 &  266.6 &  5.414 \tabularnewline
41 &  166 &  55.36 &  110.6 \tabularnewline
42 & -475 & -335.8 & -139.2 \tabularnewline
43 &  641 &  395.6 &  245.4 \tabularnewline
44 & -393 & -360.2 & -32.79 \tabularnewline
45 & -136 &  3.959 & -140 \tabularnewline
46 &  176 &  398 & -222 \tabularnewline
47 &  22 & -108.4 &  130.4 \tabularnewline
48 & -350 & -111.8 & -238.2 \tabularnewline
49 &  759 &  339.4 &  419.6 \tabularnewline
50 & -778 & -529.5 & -248.5 \tabularnewline
51 &  758 &  318 &  440 \tabularnewline
52 & -561 & -274 & -287 \tabularnewline
53 & -39 &  86.53 & -125.5 \tabularnewline
54 &  413 &  411.4 &  1.584 \tabularnewline
55 & -84 & -403.8 &  319.8 \tabularnewline
56 & -246 & -145.5 & -100.5 \tabularnewline
57 &  228 &  305.5 & -77.48 \tabularnewline
58 &  66 & -83.69 &  149.7 \tabularnewline
59 & -348 & -195.4 & -152.6 \tabularnewline
60 &  190 &  319.5 & -129.5 \tabularnewline
61 & -258 &  17.77 & -275.8 \tabularnewline
62 & -88 &  147.9 & -235.9 \tabularnewline
63 &  246 &  257.7 & -11.72 \tabularnewline
64 &  119 & -182.8 &  301.8 \tabularnewline
65 &  49 & -261.2 &  310.2 \tabularnewline
66 & -201 & -107 & -93.95 \tabularnewline
67 & -80 &  150.6 & -230.6 \tabularnewline
68 &  9 &  224.1 & -215.1 \tabularnewline
69 &  13 &  43.17 & -30.17 \tabularnewline
70 &  200 & -15.12 &  215.1 \tabularnewline
71 & -259 & -189.3 & -69.67 \tabularnewline
72 &  204 &  144.3 &  59.69 \tabularnewline
73 & -112 & -52.41 & -59.59 \tabularnewline
74 &  231 &  11.47 &  219.5 \tabularnewline
75 &  38 & -175.1 &  213.1 \tabularnewline
76 & -361 & -163.9 & -197.1 \tabularnewline
77 & -25 &  341.8 & -366.8 \tabularnewline
78 &  137 &  239.4 & -102.4 \tabularnewline
79 & -169 & -111.5 & -57.51 \tabularnewline
80 &  213 &  103.8 &  109.2 \tabularnewline
81 & -41 & -105.8 &  64.85 \tabularnewline
82 & -246 & -95.36 & -150.6 \tabularnewline
83 &  296 &  295.4 &  0.6003 \tabularnewline
84 &  68 & -149.8 &  217.8 \tabularnewline
85 & -399 & -232.4 & -166.6 \tabularnewline
86 &  93 &  361.3 & -268.3 \tabularnewline
87 &  263 &  144.9 &  118.1 \tabularnewline
88 & -475 & -308.7 & -166.3 \tabularnewline
89 &  388 &  320.5 &  67.5 \tabularnewline
90 &  222 & -81.46 &  303.5 \tabularnewline
91 & -581 & -452.9 & -128.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286777&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]-723[/C][C]-302.7[/C][C]-420.3[/C][/ROW]
[ROW][C]2[/C][C] 657[/C][C] 554.3[/C][C] 102.7[/C][/ROW]
[ROW][C]3[/C][C] 58[/C][C]-206.1[/C][C] 264.1[/C][/ROW]
[ROW][C]4[/C][C]-677[/C][C]-471[/C][C]-206[/C][/ROW]
[ROW][C]5[/C][C] 620[/C][C] 672.3[/C][C]-52.29[/C][/ROW]
[ROW][C]6[/C][C]-362[/C][C]-206.7[/C][C]-155.3[/C][/ROW]
[ROW][C]7[/C][C]-234[/C][C]-27.3[/C][C]-206.7[/C][/ROW]
[ROW][C]8[/C][C] 577[/C][C] 499.5[/C][C] 77.53[/C][/ROW]
[ROW][C]9[/C][C] 177[/C][C]-448.8[/C][C] 625.8[/C][/ROW]
[ROW][C]10[/C][C]-110[/C][C]-513.3[/C][C] 403.3[/C][/ROW]
[ROW][C]11[/C][C] 271[/C][C] 35.21[/C][C] 235.8[/C][/ROW]
[ROW][C]12[/C][C] 111[/C][C]-220.9[/C][C] 331.9[/C][/ROW]
[ROW][C]13[/C][C]-504[/C][C]-275.5[/C][C]-228.5[/C][/ROW]
[ROW][C]14[/C][C] 444[/C][C] 450.2[/C][C]-6.156[/C][/ROW]
[ROW][C]15[/C][C]-183[/C][C]-144.8[/C][C]-38.23[/C][/ROW]
[ROW][C]16[/C][C] 34[/C][C]-69.18[/C][C] 103.2[/C][/ROW]
[ROW][C]17[/C][C] 244[/C][C] 96.11[/C][C] 147.9[/C][/ROW]
[ROW][C]18[/C][C]-227[/C][C]-271.3[/C][C] 44.31[/C][/ROW]
[ROW][C]19[/C][C]-173[/C][C] 93.89[/C][C]-266.9[/C][/ROW]
[ROW][C]20[/C][C] 651[/C][C] 321[/C][C] 330[/C][/ROW]
[ROW][C]21[/C][C]-378[/C][C]-566.3[/C][C] 188.3[/C][/ROW]
[ROW][C]22[/C][C]-136[/C][C] 12.18[/C][C]-148.2[/C][/ROW]
[ROW][C]23[/C][C] 347[/C][C] 376.2[/C][C]-29.15[/C][/ROW]
[ROW][C]24[/C][C]-203[/C][C]-281.5[/C][C] 78.47[/C][/ROW]
[ROW][C]25[/C][C] 236[/C][C] 35.09[/C][C] 200.9[/C][/ROW]
[ROW][C]26[/C][C]-259[/C][C]-127.7[/C][C]-131.3[/C][/ROW]
[ROW][C]27[/C][C]-111[/C][C] 109.4[/C][C]-220.4[/C][/ROW]
[ROW][C]28[/C][C] 568[/C][C] 300.9[/C][C] 267.1[/C][/ROW]
[ROW][C]29[/C][C]-626[/C][C]-516.6[/C][C]-109.4[/C][/ROW]
[ROW][C]30[/C][C] 348[/C][C] 295[/C][C] 52.99[/C][/ROW]
[ROW][C]31[/C][C]-255[/C][C] 65.34[/C][C]-320.3[/C][/ROW]
[ROW][C]32[/C][C] 71[/C][C]-6.597[/C][C] 77.6[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 112.5[/C][C]-95.51[/C][/ROW]
[ROW][C]34[/C][C] 83[/C][C]-56.87[/C][C] 139.9[/C][/ROW]
[ROW][C]35[/C][C]-111[/C][C]-96.62[/C][C]-14.38[/C][/ROW]
[ROW][C]36[/C][C] 113[/C][C] 64.07[/C][C] 48.93[/C][/ROW]
[ROW][C]37[/C][C]-242[/C][C]-26.66[/C][C]-215.3[/C][/ROW]
[ROW][C]38[/C][C] 334[/C][C] 164.6[/C][C] 169.4[/C][/ROW]
[ROW][C]39[/C][C]-492[/C][C]-162.4[/C][C]-329.6[/C][/ROW]
[ROW][C]40[/C][C] 272[/C][C] 266.6[/C][C] 5.414[/C][/ROW]
[ROW][C]41[/C][C] 166[/C][C] 55.36[/C][C] 110.6[/C][/ROW]
[ROW][C]42[/C][C]-475[/C][C]-335.8[/C][C]-139.2[/C][/ROW]
[ROW][C]43[/C][C] 641[/C][C] 395.6[/C][C] 245.4[/C][/ROW]
[ROW][C]44[/C][C]-393[/C][C]-360.2[/C][C]-32.79[/C][/ROW]
[ROW][C]45[/C][C]-136[/C][C] 3.959[/C][C]-140[/C][/ROW]
[ROW][C]46[/C][C] 176[/C][C] 398[/C][C]-222[/C][/ROW]
[ROW][C]47[/C][C] 22[/C][C]-108.4[/C][C] 130.4[/C][/ROW]
[ROW][C]48[/C][C]-350[/C][C]-111.8[/C][C]-238.2[/C][/ROW]
[ROW][C]49[/C][C] 759[/C][C] 339.4[/C][C] 419.6[/C][/ROW]
[ROW][C]50[/C][C]-778[/C][C]-529.5[/C][C]-248.5[/C][/ROW]
[ROW][C]51[/C][C] 758[/C][C] 318[/C][C] 440[/C][/ROW]
[ROW][C]52[/C][C]-561[/C][C]-274[/C][C]-287[/C][/ROW]
[ROW][C]53[/C][C]-39[/C][C] 86.53[/C][C]-125.5[/C][/ROW]
[ROW][C]54[/C][C] 413[/C][C] 411.4[/C][C] 1.584[/C][/ROW]
[ROW][C]55[/C][C]-84[/C][C]-403.8[/C][C] 319.8[/C][/ROW]
[ROW][C]56[/C][C]-246[/C][C]-145.5[/C][C]-100.5[/C][/ROW]
[ROW][C]57[/C][C] 228[/C][C] 305.5[/C][C]-77.48[/C][/ROW]
[ROW][C]58[/C][C] 66[/C][C]-83.69[/C][C] 149.7[/C][/ROW]
[ROW][C]59[/C][C]-348[/C][C]-195.4[/C][C]-152.6[/C][/ROW]
[ROW][C]60[/C][C] 190[/C][C] 319.5[/C][C]-129.5[/C][/ROW]
[ROW][C]61[/C][C]-258[/C][C] 17.77[/C][C]-275.8[/C][/ROW]
[ROW][C]62[/C][C]-88[/C][C] 147.9[/C][C]-235.9[/C][/ROW]
[ROW][C]63[/C][C] 246[/C][C] 257.7[/C][C]-11.72[/C][/ROW]
[ROW][C]64[/C][C] 119[/C][C]-182.8[/C][C] 301.8[/C][/ROW]
[ROW][C]65[/C][C] 49[/C][C]-261.2[/C][C] 310.2[/C][/ROW]
[ROW][C]66[/C][C]-201[/C][C]-107[/C][C]-93.95[/C][/ROW]
[ROW][C]67[/C][C]-80[/C][C] 150.6[/C][C]-230.6[/C][/ROW]
[ROW][C]68[/C][C] 9[/C][C] 224.1[/C][C]-215.1[/C][/ROW]
[ROW][C]69[/C][C] 13[/C][C] 43.17[/C][C]-30.17[/C][/ROW]
[ROW][C]70[/C][C] 200[/C][C]-15.12[/C][C] 215.1[/C][/ROW]
[ROW][C]71[/C][C]-259[/C][C]-189.3[/C][C]-69.67[/C][/ROW]
[ROW][C]72[/C][C] 204[/C][C] 144.3[/C][C] 59.69[/C][/ROW]
[ROW][C]73[/C][C]-112[/C][C]-52.41[/C][C]-59.59[/C][/ROW]
[ROW][C]74[/C][C] 231[/C][C] 11.47[/C][C] 219.5[/C][/ROW]
[ROW][C]75[/C][C] 38[/C][C]-175.1[/C][C] 213.1[/C][/ROW]
[ROW][C]76[/C][C]-361[/C][C]-163.9[/C][C]-197.1[/C][/ROW]
[ROW][C]77[/C][C]-25[/C][C] 341.8[/C][C]-366.8[/C][/ROW]
[ROW][C]78[/C][C] 137[/C][C] 239.4[/C][C]-102.4[/C][/ROW]
[ROW][C]79[/C][C]-169[/C][C]-111.5[/C][C]-57.51[/C][/ROW]
[ROW][C]80[/C][C] 213[/C][C] 103.8[/C][C] 109.2[/C][/ROW]
[ROW][C]81[/C][C]-41[/C][C]-105.8[/C][C] 64.85[/C][/ROW]
[ROW][C]82[/C][C]-246[/C][C]-95.36[/C][C]-150.6[/C][/ROW]
[ROW][C]83[/C][C] 296[/C][C] 295.4[/C][C] 0.6003[/C][/ROW]
[ROW][C]84[/C][C] 68[/C][C]-149.8[/C][C] 217.8[/C][/ROW]
[ROW][C]85[/C][C]-399[/C][C]-232.4[/C][C]-166.6[/C][/ROW]
[ROW][C]86[/C][C] 93[/C][C] 361.3[/C][C]-268.3[/C][/ROW]
[ROW][C]87[/C][C] 263[/C][C] 144.9[/C][C] 118.1[/C][/ROW]
[ROW][C]88[/C][C]-475[/C][C]-308.7[/C][C]-166.3[/C][/ROW]
[ROW][C]89[/C][C] 388[/C][C] 320.5[/C][C] 67.5[/C][/ROW]
[ROW][C]90[/C][C] 222[/C][C]-81.46[/C][C] 303.5[/C][/ROW]
[ROW][C]91[/C][C]-581[/C][C]-452.9[/C][C]-128.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286777&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286777&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	-723	-302.7	-420.3
2	657	554.3	102.7
3	58	-206.1	264.1
4	-677	-471	-206
5	620	672.3	-52.29
6	-362	-206.7	-155.3
7	-234	-27.3	-206.7
8	577	499.5	77.53
9	177	-448.8	625.8
10	-110	-513.3	403.3
11	271	35.21	235.8
12	111	-220.9	331.9
13	-504	-275.5	-228.5
14	444	450.2	-6.156
15	-183	-144.8	-38.23
16	34	-69.18	103.2
17	244	96.11	147.9
18	-227	-271.3	44.31
19	-173	93.89	-266.9
20	651	321	330
21	-378	-566.3	188.3
22	-136	12.18	-148.2
23	347	376.2	-29.15
24	-203	-281.5	78.47
25	236	35.09	200.9
26	-259	-127.7	-131.3
27	-111	109.4	-220.4
28	568	300.9	267.1
29	-626	-516.6	-109.4
30	348	295	52.99
31	-255	65.34	-320.3
32	71	-6.597	77.6
33	17	112.5	-95.51
34	83	-56.87	139.9
35	-111	-96.62	-14.38
36	113	64.07	48.93
37	-242	-26.66	-215.3
38	334	164.6	169.4
39	-492	-162.4	-329.6
40	272	266.6	5.414
41	166	55.36	110.6
42	-475	-335.8	-139.2
43	641	395.6	245.4
44	-393	-360.2	-32.79
45	-136	3.959	-140
46	176	398	-222
47	22	-108.4	130.4
48	-350	-111.8	-238.2
49	759	339.4	419.6
50	-778	-529.5	-248.5
51	758	318	440
52	-561	-274	-287
53	-39	86.53	-125.5
54	413	411.4	1.584
55	-84	-403.8	319.8
56	-246	-145.5	-100.5
57	228	305.5	-77.48
58	66	-83.69	149.7
59	-348	-195.4	-152.6
60	190	319.5	-129.5
61	-258	17.77	-275.8
62	-88	147.9	-235.9
63	246	257.7	-11.72
64	119	-182.8	301.8
65	49	-261.2	310.2
66	-201	-107	-93.95
67	-80	150.6	-230.6
68	9	224.1	-215.1
69	13	43.17	-30.17
70	200	-15.12	215.1
71	-259	-189.3	-69.67
72	204	144.3	59.69
73	-112	-52.41	-59.59
74	231	11.47	219.5
75	38	-175.1	213.1
76	-361	-163.9	-197.1
77	-25	341.8	-366.8
78	137	239.4	-102.4
79	-169	-111.5	-57.51
80	213	103.8	109.2
81	-41	-105.8	64.85
82	-246	-95.36	-150.6
83	296	295.4	0.6003
84	68	-149.8	217.8
85	-399	-232.4	-166.6
86	93	361.3	-268.3
87	263	144.9	118.1
88	-475	-308.7	-166.3
89	388	320.5	67.5
90	222	-81.46	303.5
91	-581	-452.9	-128.1

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.6962	0.6076	0.3038
11	0.7571	0.4858	0.2429
12	0.9614	0.07718	0.03859
13	0.9385	0.123	0.06148
14	0.9122	0.1757	0.08783
15	0.8673	0.2653	0.1327
16	0.8878	0.2244	0.1122
17	0.8982	0.2035	0.1018
18	0.862	0.276	0.138
19	0.8962	0.2076	0.1038
20	0.9335	0.133	0.06649
21	0.9115	0.177	0.08849
22	0.9174	0.1652	0.08262
23	0.8902	0.2196	0.1098
24	0.8637	0.2727	0.1363
25	0.8364	0.3271	0.1636
26	0.8323	0.3355	0.1677
27	0.8037	0.3926	0.1963
28	0.8067	0.3866	0.1933
29	0.7967	0.4067	0.2033
30	0.761	0.4781	0.239
31	0.8795	0.241	0.1205
32	0.8707	0.2586	0.1293
33	0.8463	0.3074	0.1537
34	0.8257	0.3485	0.1743
35	0.7864	0.4272	0.2136
36	0.7405	0.5191	0.2595
37	0.7592	0.4815	0.2408
38	0.7509	0.4983	0.2491
39	0.8142	0.3717	0.1858
40	0.7671	0.4657	0.2329
41	0.7315	0.537	0.2685
42	0.7033	0.5935	0.2967
43	0.723	0.554	0.277
44	0.6683	0.6633	0.3317
45	0.6325	0.735	0.3675
46	0.6246	0.7509	0.3754
47	0.585	0.8301	0.415
48	0.6014	0.7972	0.3986
49	0.753	0.4941	0.247
50	0.7581	0.4839	0.2419
51	0.9188	0.1624	0.08118
52	0.9613	0.0775	0.03875
53	0.956	0.088	0.044
54	0.9391	0.1218	0.06088
55	0.9619	0.07612	0.03806
56	0.9532	0.0936	0.0468
57	0.9365	0.1269	0.06345
58	0.9331	0.1337	0.06686
59	0.9236	0.1527	0.07637
60	0.9009	0.1982	0.09909
61	0.9267	0.1466	0.07331
62	0.9113	0.1774	0.08868
63	0.9016	0.1967	0.09836
64	0.9464	0.1071	0.05357
65	0.9675	0.06497	0.03248
66	0.9625	0.07506	0.03753
67	0.9514	0.09724	0.04862
68	0.9499	0.1002	0.05008
69	0.945	0.1099	0.05496
70	0.9311	0.1378	0.06889
71	0.8957	0.2087	0.1043
72	0.8755	0.2491	0.1245
73	0.8188	0.3624	0.1812
74	0.938	0.124	0.06202
75	0.9005	0.1989	0.09946
76	0.8447	0.3106	0.1553
77	0.8863	0.2274	0.1137
78	0.8278	0.3443	0.1722
79	0.7661	0.4677	0.2339
80	0.6447	0.7106	0.3553
81	0.5188	0.9624	0.4812

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.6962 &  0.6076 &  0.3038 \tabularnewline
11 &  0.7571 &  0.4858 &  0.2429 \tabularnewline
12 &  0.9614 &  0.07718 &  0.03859 \tabularnewline
13 &  0.9385 &  0.123 &  0.06148 \tabularnewline
14 &  0.9122 &  0.1757 &  0.08783 \tabularnewline
15 &  0.8673 &  0.2653 &  0.1327 \tabularnewline
16 &  0.8878 &  0.2244 &  0.1122 \tabularnewline
17 &  0.8982 &  0.2035 &  0.1018 \tabularnewline
18 &  0.862 &  0.276 &  0.138 \tabularnewline
19 &  0.8962 &  0.2076 &  0.1038 \tabularnewline
20 &  0.9335 &  0.133 &  0.06649 \tabularnewline
21 &  0.9115 &  0.177 &  0.08849 \tabularnewline
22 &  0.9174 &  0.1652 &  0.08262 \tabularnewline
23 &  0.8902 &  0.2196 &  0.1098 \tabularnewline
24 &  0.8637 &  0.2727 &  0.1363 \tabularnewline
25 &  0.8364 &  0.3271 &  0.1636 \tabularnewline
26 &  0.8323 &  0.3355 &  0.1677 \tabularnewline
27 &  0.8037 &  0.3926 &  0.1963 \tabularnewline
28 &  0.8067 &  0.3866 &  0.1933 \tabularnewline
29 &  0.7967 &  0.4067 &  0.2033 \tabularnewline
30 &  0.761 &  0.4781 &  0.239 \tabularnewline
31 &  0.8795 &  0.241 &  0.1205 \tabularnewline
32 &  0.8707 &  0.2586 &  0.1293 \tabularnewline
33 &  0.8463 &  0.3074 &  0.1537 \tabularnewline
34 &  0.8257 &  0.3485 &  0.1743 \tabularnewline
35 &  0.7864 &  0.4272 &  0.2136 \tabularnewline
36 &  0.7405 &  0.5191 &  0.2595 \tabularnewline
37 &  0.7592 &  0.4815 &  0.2408 \tabularnewline
38 &  0.7509 &  0.4983 &  0.2491 \tabularnewline
39 &  0.8142 &  0.3717 &  0.1858 \tabularnewline
40 &  0.7671 &  0.4657 &  0.2329 \tabularnewline
41 &  0.7315 &  0.537 &  0.2685 \tabularnewline
42 &  0.7033 &  0.5935 &  0.2967 \tabularnewline
43 &  0.723 &  0.554 &  0.277 \tabularnewline
44 &  0.6683 &  0.6633 &  0.3317 \tabularnewline
45 &  0.6325 &  0.735 &  0.3675 \tabularnewline
46 &  0.6246 &  0.7509 &  0.3754 \tabularnewline
47 &  0.585 &  0.8301 &  0.415 \tabularnewline
48 &  0.6014 &  0.7972 &  0.3986 \tabularnewline
49 &  0.753 &  0.4941 &  0.247 \tabularnewline
50 &  0.7581 &  0.4839 &  0.2419 \tabularnewline
51 &  0.9188 &  0.1624 &  0.08118 \tabularnewline
52 &  0.9613 &  0.0775 &  0.03875 \tabularnewline
53 &  0.956 &  0.088 &  0.044 \tabularnewline
54 &  0.9391 &  0.1218 &  0.06088 \tabularnewline
55 &  0.9619 &  0.07612 &  0.03806 \tabularnewline
56 &  0.9532 &  0.0936 &  0.0468 \tabularnewline
57 &  0.9365 &  0.1269 &  0.06345 \tabularnewline
58 &  0.9331 &  0.1337 &  0.06686 \tabularnewline
59 &  0.9236 &  0.1527 &  0.07637 \tabularnewline
60 &  0.9009 &  0.1982 &  0.09909 \tabularnewline
61 &  0.9267 &  0.1466 &  0.07331 \tabularnewline
62 &  0.9113 &  0.1774 &  0.08868 \tabularnewline
63 &  0.9016 &  0.1967 &  0.09836 \tabularnewline
64 &  0.9464 &  0.1071 &  0.05357 \tabularnewline
65 &  0.9675 &  0.06497 &  0.03248 \tabularnewline
66 &  0.9625 &  0.07506 &  0.03753 \tabularnewline
67 &  0.9514 &  0.09724 &  0.04862 \tabularnewline
68 &  0.9499 &  0.1002 &  0.05008 \tabularnewline
69 &  0.945 &  0.1099 &  0.05496 \tabularnewline
70 &  0.9311 &  0.1378 &  0.06889 \tabularnewline
71 &  0.8957 &  0.2087 &  0.1043 \tabularnewline
72 &  0.8755 &  0.2491 &  0.1245 \tabularnewline
73 &  0.8188 &  0.3624 &  0.1812 \tabularnewline
74 &  0.938 &  0.124 &  0.06202 \tabularnewline
75 &  0.9005 &  0.1989 &  0.09946 \tabularnewline
76 &  0.8447 &  0.3106 &  0.1553 \tabularnewline
77 &  0.8863 &  0.2274 &  0.1137 \tabularnewline
78 &  0.8278 &  0.3443 &  0.1722 \tabularnewline
79 &  0.7661 &  0.4677 &  0.2339 \tabularnewline
80 &  0.6447 &  0.7106 &  0.3553 \tabularnewline
81 &  0.5188 &  0.9624 &  0.4812 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286777&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C] 0.6962[/C][C] 0.6076[/C][C] 0.3038[/C][/ROW]
[ROW][C]11[/C][C] 0.7571[/C][C] 0.4858[/C][C] 0.2429[/C][/ROW]
[ROW][C]12[/C][C] 0.9614[/C][C] 0.07718[/C][C] 0.03859[/C][/ROW]
[ROW][C]13[/C][C] 0.9385[/C][C] 0.123[/C][C] 0.06148[/C][/ROW]
[ROW][C]14[/C][C] 0.9122[/C][C] 0.1757[/C][C] 0.08783[/C][/ROW]
[ROW][C]15[/C][C] 0.8673[/C][C] 0.2653[/C][C] 0.1327[/C][/ROW]
[ROW][C]16[/C][C] 0.8878[/C][C] 0.2244[/C][C] 0.1122[/C][/ROW]
[ROW][C]17[/C][C] 0.8982[/C][C] 0.2035[/C][C] 0.1018[/C][/ROW]
[ROW][C]18[/C][C] 0.862[/C][C] 0.276[/C][C] 0.138[/C][/ROW]
[ROW][C]19[/C][C] 0.8962[/C][C] 0.2076[/C][C] 0.1038[/C][/ROW]
[ROW][C]20[/C][C] 0.9335[/C][C] 0.133[/C][C] 0.06649[/C][/ROW]
[ROW][C]21[/C][C] 0.9115[/C][C] 0.177[/C][C] 0.08849[/C][/ROW]
[ROW][C]22[/C][C] 0.9174[/C][C] 0.1652[/C][C] 0.08262[/C][/ROW]
[ROW][C]23[/C][C] 0.8902[/C][C] 0.2196[/C][C] 0.1098[/C][/ROW]
[ROW][C]24[/C][C] 0.8637[/C][C] 0.2727[/C][C] 0.1363[/C][/ROW]
[ROW][C]25[/C][C] 0.8364[/C][C] 0.3271[/C][C] 0.1636[/C][/ROW]
[ROW][C]26[/C][C] 0.8323[/C][C] 0.3355[/C][C] 0.1677[/C][/ROW]
[ROW][C]27[/C][C] 0.8037[/C][C] 0.3926[/C][C] 0.1963[/C][/ROW]
[ROW][C]28[/C][C] 0.8067[/C][C] 0.3866[/C][C] 0.1933[/C][/ROW]
[ROW][C]29[/C][C] 0.7967[/C][C] 0.4067[/C][C] 0.2033[/C][/ROW]
[ROW][C]30[/C][C] 0.761[/C][C] 0.4781[/C][C] 0.239[/C][/ROW]
[ROW][C]31[/C][C] 0.8795[/C][C] 0.241[/C][C] 0.1205[/C][/ROW]
[ROW][C]32[/C][C] 0.8707[/C][C] 0.2586[/C][C] 0.1293[/C][/ROW]
[ROW][C]33[/C][C] 0.8463[/C][C] 0.3074[/C][C] 0.1537[/C][/ROW]
[ROW][C]34[/C][C] 0.8257[/C][C] 0.3485[/C][C] 0.1743[/C][/ROW]
[ROW][C]35[/C][C] 0.7864[/C][C] 0.4272[/C][C] 0.2136[/C][/ROW]
[ROW][C]36[/C][C] 0.7405[/C][C] 0.5191[/C][C] 0.2595[/C][/ROW]
[ROW][C]37[/C][C] 0.7592[/C][C] 0.4815[/C][C] 0.2408[/C][/ROW]
[ROW][C]38[/C][C] 0.7509[/C][C] 0.4983[/C][C] 0.2491[/C][/ROW]
[ROW][C]39[/C][C] 0.8142[/C][C] 0.3717[/C][C] 0.1858[/C][/ROW]
[ROW][C]40[/C][C] 0.7671[/C][C] 0.4657[/C][C] 0.2329[/C][/ROW]
[ROW][C]41[/C][C] 0.7315[/C][C] 0.537[/C][C] 0.2685[/C][/ROW]
[ROW][C]42[/C][C] 0.7033[/C][C] 0.5935[/C][C] 0.2967[/C][/ROW]
[ROW][C]43[/C][C] 0.723[/C][C] 0.554[/C][C] 0.277[/C][/ROW]
[ROW][C]44[/C][C] 0.6683[/C][C] 0.6633[/C][C] 0.3317[/C][/ROW]
[ROW][C]45[/C][C] 0.6325[/C][C] 0.735[/C][C] 0.3675[/C][/ROW]
[ROW][C]46[/C][C] 0.6246[/C][C] 0.7509[/C][C] 0.3754[/C][/ROW]
[ROW][C]47[/C][C] 0.585[/C][C] 0.8301[/C][C] 0.415[/C][/ROW]
[ROW][C]48[/C][C] 0.6014[/C][C] 0.7972[/C][C] 0.3986[/C][/ROW]
[ROW][C]49[/C][C] 0.753[/C][C] 0.4941[/C][C] 0.247[/C][/ROW]
[ROW][C]50[/C][C] 0.7581[/C][C] 0.4839[/C][C] 0.2419[/C][/ROW]
[ROW][C]51[/C][C] 0.9188[/C][C] 0.1624[/C][C] 0.08118[/C][/ROW]
[ROW][C]52[/C][C] 0.9613[/C][C] 0.0775[/C][C] 0.03875[/C][/ROW]
[ROW][C]53[/C][C] 0.956[/C][C] 0.088[/C][C] 0.044[/C][/ROW]
[ROW][C]54[/C][C] 0.9391[/C][C] 0.1218[/C][C] 0.06088[/C][/ROW]
[ROW][C]55[/C][C] 0.9619[/C][C] 0.07612[/C][C] 0.03806[/C][/ROW]
[ROW][C]56[/C][C] 0.9532[/C][C] 0.0936[/C][C] 0.0468[/C][/ROW]
[ROW][C]57[/C][C] 0.9365[/C][C] 0.1269[/C][C] 0.06345[/C][/ROW]
[ROW][C]58[/C][C] 0.9331[/C][C] 0.1337[/C][C] 0.06686[/C][/ROW]
[ROW][C]59[/C][C] 0.9236[/C][C] 0.1527[/C][C] 0.07637[/C][/ROW]
[ROW][C]60[/C][C] 0.9009[/C][C] 0.1982[/C][C] 0.09909[/C][/ROW]
[ROW][C]61[/C][C] 0.9267[/C][C] 0.1466[/C][C] 0.07331[/C][/ROW]
[ROW][C]62[/C][C] 0.9113[/C][C] 0.1774[/C][C] 0.08868[/C][/ROW]
[ROW][C]63[/C][C] 0.9016[/C][C] 0.1967[/C][C] 0.09836[/C][/ROW]
[ROW][C]64[/C][C] 0.9464[/C][C] 0.1071[/C][C] 0.05357[/C][/ROW]
[ROW][C]65[/C][C] 0.9675[/C][C] 0.06497[/C][C] 0.03248[/C][/ROW]
[ROW][C]66[/C][C] 0.9625[/C][C] 0.07506[/C][C] 0.03753[/C][/ROW]
[ROW][C]67[/C][C] 0.9514[/C][C] 0.09724[/C][C] 0.04862[/C][/ROW]
[ROW][C]68[/C][C] 0.9499[/C][C] 0.1002[/C][C] 0.05008[/C][/ROW]
[ROW][C]69[/C][C] 0.945[/C][C] 0.1099[/C][C] 0.05496[/C][/ROW]
[ROW][C]70[/C][C] 0.9311[/C][C] 0.1378[/C][C] 0.06889[/C][/ROW]
[ROW][C]71[/C][C] 0.8957[/C][C] 0.2087[/C][C] 0.1043[/C][/ROW]
[ROW][C]72[/C][C] 0.8755[/C][C] 0.2491[/C][C] 0.1245[/C][/ROW]
[ROW][C]73[/C][C] 0.8188[/C][C] 0.3624[/C][C] 0.1812[/C][/ROW]
[ROW][C]74[/C][C] 0.938[/C][C] 0.124[/C][C] 0.06202[/C][/ROW]
[ROW][C]75[/C][C] 0.9005[/C][C] 0.1989[/C][C] 0.09946[/C][/ROW]
[ROW][C]76[/C][C] 0.8447[/C][C] 0.3106[/C][C] 0.1553[/C][/ROW]
[ROW][C]77[/C][C] 0.8863[/C][C] 0.2274[/C][C] 0.1137[/C][/ROW]
[ROW][C]78[/C][C] 0.8278[/C][C] 0.3443[/C][C] 0.1722[/C][/ROW]
[ROW][C]79[/C][C] 0.7661[/C][C] 0.4677[/C][C] 0.2339[/C][/ROW]
[ROW][C]80[/C][C] 0.6447[/C][C] 0.7106[/C][C] 0.3553[/C][/ROW]
[ROW][C]81[/C][C] 0.5188[/C][C] 0.9624[/C][C] 0.4812[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286777&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286777&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
10	0.6962	0.6076	0.3038
11	0.7571	0.4858	0.2429
12	0.9614	0.07718	0.03859
13	0.9385	0.123	0.06148
14	0.9122	0.1757	0.08783
15	0.8673	0.2653	0.1327
16	0.8878	0.2244	0.1122
17	0.8982	0.2035	0.1018
18	0.862	0.276	0.138
19	0.8962	0.2076	0.1038
20	0.9335	0.133	0.06649
21	0.9115	0.177	0.08849
22	0.9174	0.1652	0.08262
23	0.8902	0.2196	0.1098
24	0.8637	0.2727	0.1363
25	0.8364	0.3271	0.1636
26	0.8323	0.3355	0.1677
27	0.8037	0.3926	0.1963
28	0.8067	0.3866	0.1933
29	0.7967	0.4067	0.2033
30	0.761	0.4781	0.239
31	0.8795	0.241	0.1205
32	0.8707	0.2586	0.1293
33	0.8463	0.3074	0.1537
34	0.8257	0.3485	0.1743
35	0.7864	0.4272	0.2136
36	0.7405	0.5191	0.2595
37	0.7592	0.4815	0.2408
38	0.7509	0.4983	0.2491
39	0.8142	0.3717	0.1858
40	0.7671	0.4657	0.2329
41	0.7315	0.537	0.2685
42	0.7033	0.5935	0.2967
43	0.723	0.554	0.277
44	0.6683	0.6633	0.3317
45	0.6325	0.735	0.3675
46	0.6246	0.7509	0.3754
47	0.585	0.8301	0.415
48	0.6014	0.7972	0.3986
49	0.753	0.4941	0.247
50	0.7581	0.4839	0.2419
51	0.9188	0.1624	0.08118
52	0.9613	0.0775	0.03875
53	0.956	0.088	0.044
54	0.9391	0.1218	0.06088
55	0.9619	0.07612	0.03806
56	0.9532	0.0936	0.0468
57	0.9365	0.1269	0.06345
58	0.9331	0.1337	0.06686
59	0.9236	0.1527	0.07637
60	0.9009	0.1982	0.09909
61	0.9267	0.1466	0.07331
62	0.9113	0.1774	0.08868
63	0.9016	0.1967	0.09836
64	0.9464	0.1071	0.05357
65	0.9675	0.06497	0.03248
66	0.9625	0.07506	0.03753
67	0.9514	0.09724	0.04862
68	0.9499	0.1002	0.05008
69	0.945	0.1099	0.05496
70	0.9311	0.1378	0.06889
71	0.8957	0.2087	0.1043
72	0.8755	0.2491	0.1245
73	0.8188	0.3624	0.1812
74	0.938	0.124	0.06202
75	0.9005	0.1989	0.09946
76	0.8447	0.3106	0.1553
77	0.8863	0.2274	0.1137
78	0.8278	0.3443	0.1722
79	0.7661	0.4677	0.2339
80	0.6447	0.7106	0.3553
81	0.5188	0.9624	0.4812

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	0	0	OK
10% type I error level	8	0.111111	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 & 0 & 0 & OK \tabularnewline
10% type I error level & 8 & 0.111111 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286777&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.111111[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286777&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286777&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	0	0	OK
10% type I error level	8	0.111111	NOK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s=12) ; par4 = 4 ; par5 = 2 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s=12) ; par4 = 4 ; 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