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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 14 Dec 2014 16:29:04 +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/2014/Dec/14/t1418574578fdyu8ysaofrua0u.htm/, Retrieved Thu, 16 May 2024 18:44:26 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267735, Retrieved Thu, 16 May 2024 18:44:26 +0000

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Estimated Impact

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

-       [Multiple Regression] [] [2014-12-14 16:29:04] [04df4205f362f56e0d1a9032a00a5d3d] [Current]

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137	99	42	42	3,0
148	137	109	80	7,5
131	108	49	20	6,0
59	62	23	34	6,0
90	72	61	42	1,0
83	58	38	13	6,0
116	97	32	37	5,0
42	88	16	25	1,0
155	104	22	28	6,5
96	99	24	45	7,5
66	60	28	25	3,5
99	75	59	59	7,5
108	69	38	27	6,5
97	34	12	25	0,0
106	95	37	64	5,5
80	57	47	32	5,0
74	54	13	31	7,0
114	64	14	26	0,0
140	76	-2	58	4,5
98	88	24	21	1,5
126	102	23	33	2,5
98	61	24	16	5,5
95	80	14	20	8,0
110	49	52	37	1,0
70	78	15	35	5,0
86	45	19	27	3,0
130	55	35	41	3,0
96	96	24	40	8,0
99	38	19	17	5,5
68	35	14	10	7
131	227	93	66	9,5
71	79	10	23	6
68	130	15	25	9
89	179	2	56	7,5
87	305	4	42	8,5
49	52	61	16	6,5
96	40	31	9	8,5
100	214	31	48	8
141	119	42	53	7
110	159	25	55	9,5
146	125	28	51	7
147	84	13	30	3,5
61	139	13	38	6,5
60	95	23	27	6,5
109	130	10	56	8,5
68	72	5	25	4
73	206	32	43	8,5
65	111	15	28	8
52	83	14	24	8,5
62	119	23	29	7
101	186	38	57	8,5
42	50	12	37	7,5
96	137	34	44	8,5
57	98	20	39	7
86	122	60	30	10
88	94	25	27	7,5
85	82	34	28	5
102	83	45	31	8
86	89	48	19	6,5
114	225	29	51	8
94	204	19	51	9
64	44	27	24	3
105	79	39	39	0,5
49	52	61	16	6,5
95	116	67	31	4,5
102	83	45	31	8
63	153	8	39	7,5
117	106	22	38	9,5
57	58	17	31	6,5
73	74	34	22	6
105	131	25	51	8
31	78	13	16	5

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=267735&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=267735&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267735&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
Ex[t] = + 4.6833 -0.0172386LFM[t] + 0.0250107B[t] + 0.0103742PRH[t] + 0.00434793CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  4.6833 -0.0172386LFM[t] +  0.0250107B[t] +  0.0103742PRH[t] +  0.00434793CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267735&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  4.6833 -0.0172386LFM[t] +  0.0250107B[t] +  0.0103742PRH[t] +  0.00434793CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267735&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267735&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 4.6833 -0.0172386LFM[t] + 0.0250107B[t] + 0.0103742PRH[t] + 0.00434793CH[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)	4.6833	0.947163	4.945	5.39485e-06	2.69742e-06
LFM	-0.0172386	0.0104877	-1.644	0.104923	0.0524617
B	0.0250107	0.00611017	4.093	0.000116682	5.83411e-05
PRH	0.0103742	0.0137691	0.7534	0.453822	0.226911
CH	0.00434793	0.0247446	0.1757	0.86105	0.430525

\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) & 4.6833 & 0.947163 & 4.945 & 5.39485e-06 & 2.69742e-06 \tabularnewline
LFM & -0.0172386 & 0.0104877 & -1.644 & 0.104923 & 0.0524617 \tabularnewline
B & 0.0250107 & 0.00611017 & 4.093 & 0.000116682 & 5.83411e-05 \tabularnewline
PRH & 0.0103742 & 0.0137691 & 0.7534 & 0.453822 & 0.226911 \tabularnewline
CH & 0.00434793 & 0.0247446 & 0.1757 & 0.86105 & 0.430525 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267735&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]4.6833[/C][C]0.947163[/C][C]4.945[/C][C]5.39485e-06[/C][C]2.69742e-06[/C][/ROW]
[ROW][C]LFM[/C][C]-0.0172386[/C][C]0.0104877[/C][C]-1.644[/C][C]0.104923[/C][C]0.0524617[/C][/ROW]
[ROW][C]B[/C][C]0.0250107[/C][C]0.00611017[/C][C]4.093[/C][C]0.000116682[/C][C]5.83411e-05[/C][/ROW]
[ROW][C]PRH[/C][C]0.0103742[/C][C]0.0137691[/C][C]0.7534[/C][C]0.453822[/C][C]0.226911[/C][/ROW]
[ROW][C]CH[/C][C]0.00434793[/C][C]0.0247446[/C][C]0.1757[/C][C]0.86105[/C][C]0.430525[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267735&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267735&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)	4.6833	0.947163	4.945	5.39485e-06	2.69742e-06
LFM	-0.0172386	0.0104877	-1.644	0.104923	0.0524617
B	0.0250107	0.00611017	4.093	0.000116682	5.83411e-05
PRH	0.0103742	0.0137691	0.7534	0.453822	0.226911
CH	0.00434793	0.0247446	0.1757	0.86105	0.430525

Multiple Linear Regression - Regression Statistics
Multiple R	0.533807
R-squared	0.28495
Adjusted R-squared	0.24226
F-TEST (value)	6.67493
F-TEST (DF numerator)	4
F-TEST (DF denominator)	67
p-value	0.000139109
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.17272
Sum Squared Residuals	316.288

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.533807 \tabularnewline
R-squared & 0.28495 \tabularnewline
Adjusted R-squared & 0.24226 \tabularnewline
F-TEST (value) & 6.67493 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 67 \tabularnewline
p-value & 0.000139109 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.17272 \tabularnewline
Sum Squared Residuals & 316.288 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267735&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.533807[/C][/ROW]
[ROW][C]R-squared[/C][C]0.28495[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.24226[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.67493[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]67[/C][/ROW]
[ROW][C]p-value[/C][C]0.000139109[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.17272[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]316.288[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267735&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267735&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	0.533807
R-squared	0.28495
Adjusted R-squared	0.24226
F-TEST (value)	6.67493
F-TEST (DF numerator)	4
F-TEST (DF denominator)	67
p-value	0.000139109
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.17272
Sum Squared Residuals	316.288

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	3	5.41601	-2.41601
2	7.5	7.03709	0.462915
3	6	5.7215	0.278501
4	6	5.60333	0.396674
5	1	5.74804	-4.74804
6	6	5.15386	0.846137
7	5	5.60251	-0.602513
8	1	6.43491	-5.43491
9	6.5	4.96241	1.53759
10	7.5	5.9491	1.5509
11	3.5	5.44537	-1.94537
12	7.5	5.72109	1.77891
13	6.5	5.05889	1.44111
14	0	4.09471	-4.09471
15	5.5	5.89414	-0.394143
16	5	5.35655	-0.356547
17	7	5.02787	1.97213
18	0	4.57707	-4.57707
19	4.5	4.40214	0.0978567
20	1.5	5.53515	-4.03515
21	2.5	5.44442	-2.94442
22	5.5	4.83812	0.661879
23	8	5.27869	2.72131
24	1	4.71291	-3.71291
25	5	5.73523	-0.735227
26	3	4.64077	-1.64077
27	3	4.35924	-1.35924
28	8	5.85232	2.14768
29	5.5	4.19811	1.30189
30	7	4.57517	2.42483
31	9.5	9.35425	0.14575
32	6	5.63895	0.361047
33	9	7.02678	1.97322
34	7.5	7.89022	-0.39022
35	8.5	11.0359	-2.53593
36	6.5	5.84156	0.658437
37	8.5	4.38956	4.11044
38	8	8.84204	-0.842041
39	7	5.89509	1.10491
40	9.5	7.26225	2.23775
41	7	5.80503	1.19497
42	3.5	4.51543	-1.01543
43	6.5	7.40833	-0.908325
44	6.5	6.38101	0.118994
45	8.5	6.40292	2.09708
46	4	5.47242	-1.47242
47	8.5	9.09603	-0.596032
48	8	6.61634	1.38366
49	8.5	6.11237	2.38763
50	7	6.95548	0.0445173
51	8.5	8.23625	0.263747
52	7.5	5.49518	2.00482
53	8.5	6.9989	1.5011
54	7	6.52881	0.471193
55	10	7.00498	2.99502
56	7.5	5.89406	1.60594
57	5	5.74337	-0.743366
58	8	5.60248	2.39752
59	6.5	6.00731	0.49269
60	8	8.86811	-0.868114
61	9	8.58392	0.416082
62	3	5.06496	-2.06496
63	0.5	5.42326	-4.92326
64	6.5	5.84156	0.658437
65	4.5	6.77674	-2.27674
66	8	5.60248	2.39752
67	7.5	7.67648	-0.176475
68	9.5	5.71098	3.78902
69	6.5	5.46247	1.03753
70	6	5.72406	0.275944
71	8	6.63075	1.36925
72	5	6.30417	-1.30417

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3 & 5.41601 & -2.41601 \tabularnewline
2 & 7.5 & 7.03709 & 0.462915 \tabularnewline
3 & 6 & 5.7215 & 0.278501 \tabularnewline
4 & 6 & 5.60333 & 0.396674 \tabularnewline
5 & 1 & 5.74804 & -4.74804 \tabularnewline
6 & 6 & 5.15386 & 0.846137 \tabularnewline
7 & 5 & 5.60251 & -0.602513 \tabularnewline
8 & 1 & 6.43491 & -5.43491 \tabularnewline
9 & 6.5 & 4.96241 & 1.53759 \tabularnewline
10 & 7.5 & 5.9491 & 1.5509 \tabularnewline
11 & 3.5 & 5.44537 & -1.94537 \tabularnewline
12 & 7.5 & 5.72109 & 1.77891 \tabularnewline
13 & 6.5 & 5.05889 & 1.44111 \tabularnewline
14 & 0 & 4.09471 & -4.09471 \tabularnewline
15 & 5.5 & 5.89414 & -0.394143 \tabularnewline
16 & 5 & 5.35655 & -0.356547 \tabularnewline
17 & 7 & 5.02787 & 1.97213 \tabularnewline
18 & 0 & 4.57707 & -4.57707 \tabularnewline
19 & 4.5 & 4.40214 & 0.0978567 \tabularnewline
20 & 1.5 & 5.53515 & -4.03515 \tabularnewline
21 & 2.5 & 5.44442 & -2.94442 \tabularnewline
22 & 5.5 & 4.83812 & 0.661879 \tabularnewline
23 & 8 & 5.27869 & 2.72131 \tabularnewline
24 & 1 & 4.71291 & -3.71291 \tabularnewline
25 & 5 & 5.73523 & -0.735227 \tabularnewline
26 & 3 & 4.64077 & -1.64077 \tabularnewline
27 & 3 & 4.35924 & -1.35924 \tabularnewline
28 & 8 & 5.85232 & 2.14768 \tabularnewline
29 & 5.5 & 4.19811 & 1.30189 \tabularnewline
30 & 7 & 4.57517 & 2.42483 \tabularnewline
31 & 9.5 & 9.35425 & 0.14575 \tabularnewline
32 & 6 & 5.63895 & 0.361047 \tabularnewline
33 & 9 & 7.02678 & 1.97322 \tabularnewline
34 & 7.5 & 7.89022 & -0.39022 \tabularnewline
35 & 8.5 & 11.0359 & -2.53593 \tabularnewline
36 & 6.5 & 5.84156 & 0.658437 \tabularnewline
37 & 8.5 & 4.38956 & 4.11044 \tabularnewline
38 & 8 & 8.84204 & -0.842041 \tabularnewline
39 & 7 & 5.89509 & 1.10491 \tabularnewline
40 & 9.5 & 7.26225 & 2.23775 \tabularnewline
41 & 7 & 5.80503 & 1.19497 \tabularnewline
42 & 3.5 & 4.51543 & -1.01543 \tabularnewline
43 & 6.5 & 7.40833 & -0.908325 \tabularnewline
44 & 6.5 & 6.38101 & 0.118994 \tabularnewline
45 & 8.5 & 6.40292 & 2.09708 \tabularnewline
46 & 4 & 5.47242 & -1.47242 \tabularnewline
47 & 8.5 & 9.09603 & -0.596032 \tabularnewline
48 & 8 & 6.61634 & 1.38366 \tabularnewline
49 & 8.5 & 6.11237 & 2.38763 \tabularnewline
50 & 7 & 6.95548 & 0.0445173 \tabularnewline
51 & 8.5 & 8.23625 & 0.263747 \tabularnewline
52 & 7.5 & 5.49518 & 2.00482 \tabularnewline
53 & 8.5 & 6.9989 & 1.5011 \tabularnewline
54 & 7 & 6.52881 & 0.471193 \tabularnewline
55 & 10 & 7.00498 & 2.99502 \tabularnewline
56 & 7.5 & 5.89406 & 1.60594 \tabularnewline
57 & 5 & 5.74337 & -0.743366 \tabularnewline
58 & 8 & 5.60248 & 2.39752 \tabularnewline
59 & 6.5 & 6.00731 & 0.49269 \tabularnewline
60 & 8 & 8.86811 & -0.868114 \tabularnewline
61 & 9 & 8.58392 & 0.416082 \tabularnewline
62 & 3 & 5.06496 & -2.06496 \tabularnewline
63 & 0.5 & 5.42326 & -4.92326 \tabularnewline
64 & 6.5 & 5.84156 & 0.658437 \tabularnewline
65 & 4.5 & 6.77674 & -2.27674 \tabularnewline
66 & 8 & 5.60248 & 2.39752 \tabularnewline
67 & 7.5 & 7.67648 & -0.176475 \tabularnewline
68 & 9.5 & 5.71098 & 3.78902 \tabularnewline
69 & 6.5 & 5.46247 & 1.03753 \tabularnewline
70 & 6 & 5.72406 & 0.275944 \tabularnewline
71 & 8 & 6.63075 & 1.36925 \tabularnewline
72 & 5 & 6.30417 & -1.30417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267735&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]3[/C][C]5.41601[/C][C]-2.41601[/C][/ROW]
[ROW][C]2[/C][C]7.5[/C][C]7.03709[/C][C]0.462915[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]5.7215[/C][C]0.278501[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]5.60333[/C][C]0.396674[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]5.74804[/C][C]-4.74804[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]5.15386[/C][C]0.846137[/C][/ROW]
[ROW][C]7[/C][C]5[/C][C]5.60251[/C][C]-0.602513[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]6.43491[/C][C]-5.43491[/C][/ROW]
[ROW][C]9[/C][C]6.5[/C][C]4.96241[/C][C]1.53759[/C][/ROW]
[ROW][C]10[/C][C]7.5[/C][C]5.9491[/C][C]1.5509[/C][/ROW]
[ROW][C]11[/C][C]3.5[/C][C]5.44537[/C][C]-1.94537[/C][/ROW]
[ROW][C]12[/C][C]7.5[/C][C]5.72109[/C][C]1.77891[/C][/ROW]
[ROW][C]13[/C][C]6.5[/C][C]5.05889[/C][C]1.44111[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]4.09471[/C][C]-4.09471[/C][/ROW]
[ROW][C]15[/C][C]5.5[/C][C]5.89414[/C][C]-0.394143[/C][/ROW]
[ROW][C]16[/C][C]5[/C][C]5.35655[/C][C]-0.356547[/C][/ROW]
[ROW][C]17[/C][C]7[/C][C]5.02787[/C][C]1.97213[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]4.57707[/C][C]-4.57707[/C][/ROW]
[ROW][C]19[/C][C]4.5[/C][C]4.40214[/C][C]0.0978567[/C][/ROW]
[ROW][C]20[/C][C]1.5[/C][C]5.53515[/C][C]-4.03515[/C][/ROW]
[ROW][C]21[/C][C]2.5[/C][C]5.44442[/C][C]-2.94442[/C][/ROW]
[ROW][C]22[/C][C]5.5[/C][C]4.83812[/C][C]0.661879[/C][/ROW]
[ROW][C]23[/C][C]8[/C][C]5.27869[/C][C]2.72131[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]4.71291[/C][C]-3.71291[/C][/ROW]
[ROW][C]25[/C][C]5[/C][C]5.73523[/C][C]-0.735227[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]4.64077[/C][C]-1.64077[/C][/ROW]
[ROW][C]27[/C][C]3[/C][C]4.35924[/C][C]-1.35924[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]5.85232[/C][C]2.14768[/C][/ROW]
[ROW][C]29[/C][C]5.5[/C][C]4.19811[/C][C]1.30189[/C][/ROW]
[ROW][C]30[/C][C]7[/C][C]4.57517[/C][C]2.42483[/C][/ROW]
[ROW][C]31[/C][C]9.5[/C][C]9.35425[/C][C]0.14575[/C][/ROW]
[ROW][C]32[/C][C]6[/C][C]5.63895[/C][C]0.361047[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]7.02678[/C][C]1.97322[/C][/ROW]
[ROW][C]34[/C][C]7.5[/C][C]7.89022[/C][C]-0.39022[/C][/ROW]
[ROW][C]35[/C][C]8.5[/C][C]11.0359[/C][C]-2.53593[/C][/ROW]
[ROW][C]36[/C][C]6.5[/C][C]5.84156[/C][C]0.658437[/C][/ROW]
[ROW][C]37[/C][C]8.5[/C][C]4.38956[/C][C]4.11044[/C][/ROW]
[ROW][C]38[/C][C]8[/C][C]8.84204[/C][C]-0.842041[/C][/ROW]
[ROW][C]39[/C][C]7[/C][C]5.89509[/C][C]1.10491[/C][/ROW]
[ROW][C]40[/C][C]9.5[/C][C]7.26225[/C][C]2.23775[/C][/ROW]
[ROW][C]41[/C][C]7[/C][C]5.80503[/C][C]1.19497[/C][/ROW]
[ROW][C]42[/C][C]3.5[/C][C]4.51543[/C][C]-1.01543[/C][/ROW]
[ROW][C]43[/C][C]6.5[/C][C]7.40833[/C][C]-0.908325[/C][/ROW]
[ROW][C]44[/C][C]6.5[/C][C]6.38101[/C][C]0.118994[/C][/ROW]
[ROW][C]45[/C][C]8.5[/C][C]6.40292[/C][C]2.09708[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]5.47242[/C][C]-1.47242[/C][/ROW]
[ROW][C]47[/C][C]8.5[/C][C]9.09603[/C][C]-0.596032[/C][/ROW]
[ROW][C]48[/C][C]8[/C][C]6.61634[/C][C]1.38366[/C][/ROW]
[ROW][C]49[/C][C]8.5[/C][C]6.11237[/C][C]2.38763[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]6.95548[/C][C]0.0445173[/C][/ROW]
[ROW][C]51[/C][C]8.5[/C][C]8.23625[/C][C]0.263747[/C][/ROW]
[ROW][C]52[/C][C]7.5[/C][C]5.49518[/C][C]2.00482[/C][/ROW]
[ROW][C]53[/C][C]8.5[/C][C]6.9989[/C][C]1.5011[/C][/ROW]
[ROW][C]54[/C][C]7[/C][C]6.52881[/C][C]0.471193[/C][/ROW]
[ROW][C]55[/C][C]10[/C][C]7.00498[/C][C]2.99502[/C][/ROW]
[ROW][C]56[/C][C]7.5[/C][C]5.89406[/C][C]1.60594[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]5.74337[/C][C]-0.743366[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]5.60248[/C][C]2.39752[/C][/ROW]
[ROW][C]59[/C][C]6.5[/C][C]6.00731[/C][C]0.49269[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]8.86811[/C][C]-0.868114[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]8.58392[/C][C]0.416082[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]5.06496[/C][C]-2.06496[/C][/ROW]
[ROW][C]63[/C][C]0.5[/C][C]5.42326[/C][C]-4.92326[/C][/ROW]
[ROW][C]64[/C][C]6.5[/C][C]5.84156[/C][C]0.658437[/C][/ROW]
[ROW][C]65[/C][C]4.5[/C][C]6.77674[/C][C]-2.27674[/C][/ROW]
[ROW][C]66[/C][C]8[/C][C]5.60248[/C][C]2.39752[/C][/ROW]
[ROW][C]67[/C][C]7.5[/C][C]7.67648[/C][C]-0.176475[/C][/ROW]
[ROW][C]68[/C][C]9.5[/C][C]5.71098[/C][C]3.78902[/C][/ROW]
[ROW][C]69[/C][C]6.5[/C][C]5.46247[/C][C]1.03753[/C][/ROW]
[ROW][C]70[/C][C]6[/C][C]5.72406[/C][C]0.275944[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]6.63075[/C][C]1.36925[/C][/ROW]
[ROW][C]72[/C][C]5[/C][C]6.30417[/C][C]-1.30417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267735&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267735&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	3	5.41601	-2.41601
2	7.5	7.03709	0.462915
3	6	5.7215	0.278501
4	6	5.60333	0.396674
5	1	5.74804	-4.74804
6	6	5.15386	0.846137
7	5	5.60251	-0.602513
8	1	6.43491	-5.43491
9	6.5	4.96241	1.53759
10	7.5	5.9491	1.5509
11	3.5	5.44537	-1.94537
12	7.5	5.72109	1.77891
13	6.5	5.05889	1.44111
14	0	4.09471	-4.09471
15	5.5	5.89414	-0.394143
16	5	5.35655	-0.356547
17	7	5.02787	1.97213
18	0	4.57707	-4.57707
19	4.5	4.40214	0.0978567
20	1.5	5.53515	-4.03515
21	2.5	5.44442	-2.94442
22	5.5	4.83812	0.661879
23	8	5.27869	2.72131
24	1	4.71291	-3.71291
25	5	5.73523	-0.735227
26	3	4.64077	-1.64077
27	3	4.35924	-1.35924
28	8	5.85232	2.14768
29	5.5	4.19811	1.30189
30	7	4.57517	2.42483
31	9.5	9.35425	0.14575
32	6	5.63895	0.361047
33	9	7.02678	1.97322
34	7.5	7.89022	-0.39022
35	8.5	11.0359	-2.53593
36	6.5	5.84156	0.658437
37	8.5	4.38956	4.11044
38	8	8.84204	-0.842041
39	7	5.89509	1.10491
40	9.5	7.26225	2.23775
41	7	5.80503	1.19497
42	3.5	4.51543	-1.01543
43	6.5	7.40833	-0.908325
44	6.5	6.38101	0.118994
45	8.5	6.40292	2.09708
46	4	5.47242	-1.47242
47	8.5	9.09603	-0.596032
48	8	6.61634	1.38366
49	8.5	6.11237	2.38763
50	7	6.95548	0.0445173
51	8.5	8.23625	0.263747
52	7.5	5.49518	2.00482
53	8.5	6.9989	1.5011
54	7	6.52881	0.471193
55	10	7.00498	2.99502
56	7.5	5.89406	1.60594
57	5	5.74337	-0.743366
58	8	5.60248	2.39752
59	6.5	6.00731	0.49269
60	8	8.86811	-0.868114
61	9	8.58392	0.416082
62	3	5.06496	-2.06496
63	0.5	5.42326	-4.92326
64	6.5	5.84156	0.658437
65	4.5	6.77674	-2.27674
66	8	5.60248	2.39752
67	7.5	7.67648	-0.176475
68	9.5	5.71098	3.78902
69	6.5	5.46247	1.03753
70	6	5.72406	0.275944
71	8	6.63075	1.36925
72	5	6.30417	-1.30417

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.935754	0.128493	0.0642465
9	0.887599	0.224803	0.112401
10	0.90236	0.195279	0.0976395
11	0.848397	0.303205	0.151603
12	0.821111	0.357779	0.178889
13	0.764365	0.471269	0.235635
14	0.908651	0.182699	0.0913493
15	0.863583	0.272834	0.136417
16	0.817785	0.364429	0.182215
17	0.847207	0.305586	0.152793
18	0.939206	0.121588	0.060794
19	0.909593	0.180813	0.0904067
20	0.945076	0.109848	0.054924
21	0.954537	0.0909258	0.0454629
22	0.944795	0.11041	0.0552051
23	0.967155	0.0656906	0.0328453
24	0.985798	0.0284031	0.0142016
25	0.979278	0.0414442	0.0207221
26	0.977095	0.04581	0.022905
27	0.976998	0.0460049	0.0230024
28	0.97618	0.0476403	0.0238202
29	0.973597	0.0528058	0.0264029
30	0.978839	0.0423217	0.0211608
31	0.967687	0.0646265	0.0323133
32	0.953657	0.0926858	0.0463429
33	0.950514	0.098972	0.049486
34	0.932367	0.135266	0.0676331
35	0.928547	0.142906	0.071453
36	0.904342	0.191316	0.0956581
37	0.958255	0.0834903	0.0417451
38	0.942424	0.115152	0.0575758
39	0.922808	0.154384	0.0771921
40	0.918814	0.162372	0.081186
41	0.891929	0.216141	0.108071
42	0.878163	0.243675	0.121837
43	0.844944	0.310113	0.155056
44	0.796511	0.406977	0.203489
45	0.770731	0.458538	0.229269
46	0.767218	0.465565	0.232782
47	0.705699	0.588603	0.294301
48	0.65268	0.694639	0.34732
49	0.64948	0.70104	0.35052
50	0.571485	0.85703	0.428515
51	0.489981	0.979961	0.510019
52	0.48543	0.97086	0.51457
53	0.431501	0.863002	0.568499
54	0.371932	0.743863	0.628068
55	0.474061	0.948123	0.525939
56	0.392877	0.785753	0.607123
57	0.324672	0.649343	0.675328
58	0.304736	0.609472	0.695264
59	0.220126	0.440252	0.779874
60	0.171877	0.343755	0.828123
61	0.109365	0.218729	0.890635
62	0.0896165	0.179233	0.910384
63	0.819157	0.361686	0.180843
64	0.963103	0.0737934	0.0368967

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.935754 & 0.128493 & 0.0642465 \tabularnewline
9 & 0.887599 & 0.224803 & 0.112401 \tabularnewline
10 & 0.90236 & 0.195279 & 0.0976395 \tabularnewline
11 & 0.848397 & 0.303205 & 0.151603 \tabularnewline
12 & 0.821111 & 0.357779 & 0.178889 \tabularnewline
13 & 0.764365 & 0.471269 & 0.235635 \tabularnewline
14 & 0.908651 & 0.182699 & 0.0913493 \tabularnewline
15 & 0.863583 & 0.272834 & 0.136417 \tabularnewline
16 & 0.817785 & 0.364429 & 0.182215 \tabularnewline
17 & 0.847207 & 0.305586 & 0.152793 \tabularnewline
18 & 0.939206 & 0.121588 & 0.060794 \tabularnewline
19 & 0.909593 & 0.180813 & 0.0904067 \tabularnewline
20 & 0.945076 & 0.109848 & 0.054924 \tabularnewline
21 & 0.954537 & 0.0909258 & 0.0454629 \tabularnewline
22 & 0.944795 & 0.11041 & 0.0552051 \tabularnewline
23 & 0.967155 & 0.0656906 & 0.0328453 \tabularnewline
24 & 0.985798 & 0.0284031 & 0.0142016 \tabularnewline
25 & 0.979278 & 0.0414442 & 0.0207221 \tabularnewline
26 & 0.977095 & 0.04581 & 0.022905 \tabularnewline
27 & 0.976998 & 0.0460049 & 0.0230024 \tabularnewline
28 & 0.97618 & 0.0476403 & 0.0238202 \tabularnewline
29 & 0.973597 & 0.0528058 & 0.0264029 \tabularnewline
30 & 0.978839 & 0.0423217 & 0.0211608 \tabularnewline
31 & 0.967687 & 0.0646265 & 0.0323133 \tabularnewline
32 & 0.953657 & 0.0926858 & 0.0463429 \tabularnewline
33 & 0.950514 & 0.098972 & 0.049486 \tabularnewline
34 & 0.932367 & 0.135266 & 0.0676331 \tabularnewline
35 & 0.928547 & 0.142906 & 0.071453 \tabularnewline
36 & 0.904342 & 0.191316 & 0.0956581 \tabularnewline
37 & 0.958255 & 0.0834903 & 0.0417451 \tabularnewline
38 & 0.942424 & 0.115152 & 0.0575758 \tabularnewline
39 & 0.922808 & 0.154384 & 0.0771921 \tabularnewline
40 & 0.918814 & 0.162372 & 0.081186 \tabularnewline
41 & 0.891929 & 0.216141 & 0.108071 \tabularnewline
42 & 0.878163 & 0.243675 & 0.121837 \tabularnewline
43 & 0.844944 & 0.310113 & 0.155056 \tabularnewline
44 & 0.796511 & 0.406977 & 0.203489 \tabularnewline
45 & 0.770731 & 0.458538 & 0.229269 \tabularnewline
46 & 0.767218 & 0.465565 & 0.232782 \tabularnewline
47 & 0.705699 & 0.588603 & 0.294301 \tabularnewline
48 & 0.65268 & 0.694639 & 0.34732 \tabularnewline
49 & 0.64948 & 0.70104 & 0.35052 \tabularnewline
50 & 0.571485 & 0.85703 & 0.428515 \tabularnewline
51 & 0.489981 & 0.979961 & 0.510019 \tabularnewline
52 & 0.48543 & 0.97086 & 0.51457 \tabularnewline
53 & 0.431501 & 0.863002 & 0.568499 \tabularnewline
54 & 0.371932 & 0.743863 & 0.628068 \tabularnewline
55 & 0.474061 & 0.948123 & 0.525939 \tabularnewline
56 & 0.392877 & 0.785753 & 0.607123 \tabularnewline
57 & 0.324672 & 0.649343 & 0.675328 \tabularnewline
58 & 0.304736 & 0.609472 & 0.695264 \tabularnewline
59 & 0.220126 & 0.440252 & 0.779874 \tabularnewline
60 & 0.171877 & 0.343755 & 0.828123 \tabularnewline
61 & 0.109365 & 0.218729 & 0.890635 \tabularnewline
62 & 0.0896165 & 0.179233 & 0.910384 \tabularnewline
63 & 0.819157 & 0.361686 & 0.180843 \tabularnewline
64 & 0.963103 & 0.0737934 & 0.0368967 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267735&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]8[/C][C]0.935754[/C][C]0.128493[/C][C]0.0642465[/C][/ROW]
[ROW][C]9[/C][C]0.887599[/C][C]0.224803[/C][C]0.112401[/C][/ROW]
[ROW][C]10[/C][C]0.90236[/C][C]0.195279[/C][C]0.0976395[/C][/ROW]
[ROW][C]11[/C][C]0.848397[/C][C]0.303205[/C][C]0.151603[/C][/ROW]
[ROW][C]12[/C][C]0.821111[/C][C]0.357779[/C][C]0.178889[/C][/ROW]
[ROW][C]13[/C][C]0.764365[/C][C]0.471269[/C][C]0.235635[/C][/ROW]
[ROW][C]14[/C][C]0.908651[/C][C]0.182699[/C][C]0.0913493[/C][/ROW]
[ROW][C]15[/C][C]0.863583[/C][C]0.272834[/C][C]0.136417[/C][/ROW]
[ROW][C]16[/C][C]0.817785[/C][C]0.364429[/C][C]0.182215[/C][/ROW]
[ROW][C]17[/C][C]0.847207[/C][C]0.305586[/C][C]0.152793[/C][/ROW]
[ROW][C]18[/C][C]0.939206[/C][C]0.121588[/C][C]0.060794[/C][/ROW]
[ROW][C]19[/C][C]0.909593[/C][C]0.180813[/C][C]0.0904067[/C][/ROW]
[ROW][C]20[/C][C]0.945076[/C][C]0.109848[/C][C]0.054924[/C][/ROW]
[ROW][C]21[/C][C]0.954537[/C][C]0.0909258[/C][C]0.0454629[/C][/ROW]
[ROW][C]22[/C][C]0.944795[/C][C]0.11041[/C][C]0.0552051[/C][/ROW]
[ROW][C]23[/C][C]0.967155[/C][C]0.0656906[/C][C]0.0328453[/C][/ROW]
[ROW][C]24[/C][C]0.985798[/C][C]0.0284031[/C][C]0.0142016[/C][/ROW]
[ROW][C]25[/C][C]0.979278[/C][C]0.0414442[/C][C]0.0207221[/C][/ROW]
[ROW][C]26[/C][C]0.977095[/C][C]0.04581[/C][C]0.022905[/C][/ROW]
[ROW][C]27[/C][C]0.976998[/C][C]0.0460049[/C][C]0.0230024[/C][/ROW]
[ROW][C]28[/C][C]0.97618[/C][C]0.0476403[/C][C]0.0238202[/C][/ROW]
[ROW][C]29[/C][C]0.973597[/C][C]0.0528058[/C][C]0.0264029[/C][/ROW]
[ROW][C]30[/C][C]0.978839[/C][C]0.0423217[/C][C]0.0211608[/C][/ROW]
[ROW][C]31[/C][C]0.967687[/C][C]0.0646265[/C][C]0.0323133[/C][/ROW]
[ROW][C]32[/C][C]0.953657[/C][C]0.0926858[/C][C]0.0463429[/C][/ROW]
[ROW][C]33[/C][C]0.950514[/C][C]0.098972[/C][C]0.049486[/C][/ROW]
[ROW][C]34[/C][C]0.932367[/C][C]0.135266[/C][C]0.0676331[/C][/ROW]
[ROW][C]35[/C][C]0.928547[/C][C]0.142906[/C][C]0.071453[/C][/ROW]
[ROW][C]36[/C][C]0.904342[/C][C]0.191316[/C][C]0.0956581[/C][/ROW]
[ROW][C]37[/C][C]0.958255[/C][C]0.0834903[/C][C]0.0417451[/C][/ROW]
[ROW][C]38[/C][C]0.942424[/C][C]0.115152[/C][C]0.0575758[/C][/ROW]
[ROW][C]39[/C][C]0.922808[/C][C]0.154384[/C][C]0.0771921[/C][/ROW]
[ROW][C]40[/C][C]0.918814[/C][C]0.162372[/C][C]0.081186[/C][/ROW]
[ROW][C]41[/C][C]0.891929[/C][C]0.216141[/C][C]0.108071[/C][/ROW]
[ROW][C]42[/C][C]0.878163[/C][C]0.243675[/C][C]0.121837[/C][/ROW]
[ROW][C]43[/C][C]0.844944[/C][C]0.310113[/C][C]0.155056[/C][/ROW]
[ROW][C]44[/C][C]0.796511[/C][C]0.406977[/C][C]0.203489[/C][/ROW]
[ROW][C]45[/C][C]0.770731[/C][C]0.458538[/C][C]0.229269[/C][/ROW]
[ROW][C]46[/C][C]0.767218[/C][C]0.465565[/C][C]0.232782[/C][/ROW]
[ROW][C]47[/C][C]0.705699[/C][C]0.588603[/C][C]0.294301[/C][/ROW]
[ROW][C]48[/C][C]0.65268[/C][C]0.694639[/C][C]0.34732[/C][/ROW]
[ROW][C]49[/C][C]0.64948[/C][C]0.70104[/C][C]0.35052[/C][/ROW]
[ROW][C]50[/C][C]0.571485[/C][C]0.85703[/C][C]0.428515[/C][/ROW]
[ROW][C]51[/C][C]0.489981[/C][C]0.979961[/C][C]0.510019[/C][/ROW]
[ROW][C]52[/C][C]0.48543[/C][C]0.97086[/C][C]0.51457[/C][/ROW]
[ROW][C]53[/C][C]0.431501[/C][C]0.863002[/C][C]0.568499[/C][/ROW]
[ROW][C]54[/C][C]0.371932[/C][C]0.743863[/C][C]0.628068[/C][/ROW]
[ROW][C]55[/C][C]0.474061[/C][C]0.948123[/C][C]0.525939[/C][/ROW]
[ROW][C]56[/C][C]0.392877[/C][C]0.785753[/C][C]0.607123[/C][/ROW]
[ROW][C]57[/C][C]0.324672[/C][C]0.649343[/C][C]0.675328[/C][/ROW]
[ROW][C]58[/C][C]0.304736[/C][C]0.609472[/C][C]0.695264[/C][/ROW]
[ROW][C]59[/C][C]0.220126[/C][C]0.440252[/C][C]0.779874[/C][/ROW]
[ROW][C]60[/C][C]0.171877[/C][C]0.343755[/C][C]0.828123[/C][/ROW]
[ROW][C]61[/C][C]0.109365[/C][C]0.218729[/C][C]0.890635[/C][/ROW]
[ROW][C]62[/C][C]0.0896165[/C][C]0.179233[/C][C]0.910384[/C][/ROW]
[ROW][C]63[/C][C]0.819157[/C][C]0.361686[/C][C]0.180843[/C][/ROW]
[ROW][C]64[/C][C]0.963103[/C][C]0.0737934[/C][C]0.0368967[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267735&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267735&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
8	0.935754	0.128493	0.0642465
9	0.887599	0.224803	0.112401
10	0.90236	0.195279	0.0976395
11	0.848397	0.303205	0.151603
12	0.821111	0.357779	0.178889
13	0.764365	0.471269	0.235635
14	0.908651	0.182699	0.0913493
15	0.863583	0.272834	0.136417
16	0.817785	0.364429	0.182215
17	0.847207	0.305586	0.152793
18	0.939206	0.121588	0.060794
19	0.909593	0.180813	0.0904067
20	0.945076	0.109848	0.054924
21	0.954537	0.0909258	0.0454629
22	0.944795	0.11041	0.0552051
23	0.967155	0.0656906	0.0328453
24	0.985798	0.0284031	0.0142016
25	0.979278	0.0414442	0.0207221
26	0.977095	0.04581	0.022905
27	0.976998	0.0460049	0.0230024
28	0.97618	0.0476403	0.0238202
29	0.973597	0.0528058	0.0264029
30	0.978839	0.0423217	0.0211608
31	0.967687	0.0646265	0.0323133
32	0.953657	0.0926858	0.0463429
33	0.950514	0.098972	0.049486
34	0.932367	0.135266	0.0676331
35	0.928547	0.142906	0.071453
36	0.904342	0.191316	0.0956581
37	0.958255	0.0834903	0.0417451
38	0.942424	0.115152	0.0575758
39	0.922808	0.154384	0.0771921
40	0.918814	0.162372	0.081186
41	0.891929	0.216141	0.108071
42	0.878163	0.243675	0.121837
43	0.844944	0.310113	0.155056
44	0.796511	0.406977	0.203489
45	0.770731	0.458538	0.229269
46	0.767218	0.465565	0.232782
47	0.705699	0.588603	0.294301
48	0.65268	0.694639	0.34732
49	0.64948	0.70104	0.35052
50	0.571485	0.85703	0.428515
51	0.489981	0.979961	0.510019
52	0.48543	0.97086	0.51457
53	0.431501	0.863002	0.568499
54	0.371932	0.743863	0.628068
55	0.474061	0.948123	0.525939
56	0.392877	0.785753	0.607123
57	0.324672	0.649343	0.675328
58	0.304736	0.609472	0.695264
59	0.220126	0.440252	0.779874
60	0.171877	0.343755	0.828123
61	0.109365	0.218729	0.890635
62	0.0896165	0.179233	0.910384
63	0.819157	0.361686	0.180843
64	0.963103	0.0737934	0.0368967

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	6	0.105263	NOK
10% type I error level	14	0.245614	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 & 6 & 0.105263 & NOK \tabularnewline
10% type I error level & 14 & 0.245614 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267735&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]6[/C][C]0.105263[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.245614[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267735&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267735&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	6	0.105263	NOK
10% type I error level	14	0.245614	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 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code