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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 13 Nov 2014 20:33:27 +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/Nov/13/t1415910858obs43uiupzq568t.htm/, Retrieved Wed, 15 May 2024 20:09:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=254637, Retrieved Wed, 15 May 2024 20:09:48 +0000

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

-       [Multiple Regression] [multiple regression] [2014-11-13 20:33:27] [8aa9b0b9e9cdf95f84c1d02ac9593640] [Current]

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41	38	13	12	14	12	53	32
39	32	16	11	18	11	86	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	6	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	7	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	9	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	28	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	7	7	16	8	71	44
29	31	10	6	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	12	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	32	14	6	15	12	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	77	45
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
37	38	20	12	16	10	30	0
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	10	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	82	49
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	70	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
33	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	82	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	8	9	13	12	69	41
32	34	16	8	15	10	54	33
30	34	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
35	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	35	12	8	12	18	84	50
34	36	13	8	13	16	69	46

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254637&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]6 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=254637&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254637&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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 18.263 + 0.336032Separate[t] + 0.324387Learning[t] -0.139121Software[t] + 0.0353031Happiness[t] -0.0228637Depression[t] + 0.034915Belonging[t] -0.0249943Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  18.263 +  0.336032Separate[t] +  0.324387Learning[t] -0.139121Software[t] +  0.0353031Happiness[t] -0.0228637Depression[t] +  0.034915Belonging[t] -0.0249943Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254637&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  18.263 +  0.336032Separate[t] +  0.324387Learning[t] -0.139121Software[t] +  0.0353031Happiness[t] -0.0228637Depression[t] +  0.034915Belonging[t] -0.0249943Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=254637&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254637&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
Connected[t] = + 18.263 + 0.336032Separate[t] + 0.324387Learning[t] -0.139121Software[t] + 0.0353031Happiness[t] -0.0228637Depression[t] + 0.034915Belonging[t] -0.0249943Belonging_Final[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)	18.263	4.19225	4.356	2.40388e-05	1.20194e-05
Separate	0.336032	0.07061	4.759	4.44651e-06	2.22326e-06
Learning	0.324387	0.133361	2.432	0.0161452	0.00807258
Software	-0.139121	0.137233	-1.014	0.312289	0.156145
Happiness	0.0353031	0.129038	0.2736	0.784768	0.392384
Depression	-0.0228637	0.0954885	-0.2394	0.811084	0.405542
Belonging	0.034915	0.0752034	0.4643	0.643107	0.321554
Belonging_Final	-0.0249943	0.108077	-0.2313	0.817417	0.408708

\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) & 18.263 & 4.19225 & 4.356 & 2.40388e-05 & 1.20194e-05 \tabularnewline
Separate & 0.336032 & 0.07061 & 4.759 & 4.44651e-06 & 2.22326e-06 \tabularnewline
Learning & 0.324387 & 0.133361 & 2.432 & 0.0161452 & 0.00807258 \tabularnewline
Software & -0.139121 & 0.137233 & -1.014 & 0.312289 & 0.156145 \tabularnewline
Happiness & 0.0353031 & 0.129038 & 0.2736 & 0.784768 & 0.392384 \tabularnewline
Depression & -0.0228637 & 0.0954885 & -0.2394 & 0.811084 & 0.405542 \tabularnewline
Belonging & 0.034915 & 0.0752034 & 0.4643 & 0.643107 & 0.321554 \tabularnewline
Belonging_Final & -0.0249943 & 0.108077 & -0.2313 & 0.817417 & 0.408708 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254637&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]18.263[/C][C]4.19225[/C][C]4.356[/C][C]2.40388e-05[/C][C]1.20194e-05[/C][/ROW]
[ROW][C]Separate[/C][C]0.336032[/C][C]0.07061[/C][C]4.759[/C][C]4.44651e-06[/C][C]2.22326e-06[/C][/ROW]
[ROW][C]Learning[/C][C]0.324387[/C][C]0.133361[/C][C]2.432[/C][C]0.0161452[/C][C]0.00807258[/C][/ROW]
[ROW][C]Software[/C][C]-0.139121[/C][C]0.137233[/C][C]-1.014[/C][C]0.312289[/C][C]0.156145[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0353031[/C][C]0.129038[/C][C]0.2736[/C][C]0.784768[/C][C]0.392384[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0228637[/C][C]0.0954885[/C][C]-0.2394[/C][C]0.811084[/C][C]0.405542[/C][/ROW]
[ROW][C]Belonging[/C][C]0.034915[/C][C]0.0752034[/C][C]0.4643[/C][C]0.643107[/C][C]0.321554[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0249943[/C][C]0.108077[/C][C]-0.2313[/C][C]0.817417[/C][C]0.408708[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=254637&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254637&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)	18.263	4.19225	4.356	2.40388e-05	1.20194e-05
Separate	0.336032	0.07061	4.759	4.44651e-06	2.22326e-06
Learning	0.324387	0.133361	2.432	0.0161452	0.00807258
Software	-0.139121	0.137233	-1.014	0.312289	0.156145
Happiness	0.0353031	0.129038	0.2736	0.784768	0.392384
Depression	-0.0228637	0.0954885	-0.2394	0.811084	0.405542
Belonging	0.034915	0.0752034	0.4643	0.643107	0.321554
Belonging_Final	-0.0249943	0.108077	-0.2313	0.817417	0.408708

Multiple Linear Regression - Regression Statistics
Multiple R	0.427052
R-squared	0.182373
Adjusted R-squared	0.145208
F-TEST (value)	4.90714
F-TEST (DF numerator)	7
F-TEST (DF denominator)	154
p-value	5.02587e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.12047
Sum Squared Residuals	1499.55

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.427052 \tabularnewline
R-squared & 0.182373 \tabularnewline
Adjusted R-squared & 0.145208 \tabularnewline
F-TEST (value) & 4.90714 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 5.02587e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.12047 \tabularnewline
Sum Squared Residuals & 1499.55 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254637&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.427052[/C][/ROW]
[ROW][C]R-squared[/C][C]0.182373[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.145208[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.90714[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]5.02587e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.12047[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1499.55[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=254637&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254637&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	0.427052
R-squared	0.182373
Adjusted R-squared	0.145208
F-TEST (value)	4.90714
F-TEST (DF numerator)	7
F-TEST (DF denominator)	154
p-value	5.02587e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.12047
Sum Squared Residuals	1499.55

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	41	34.8503	6.14966
2	39	34.7878	4.21219
3	30	35.4235	-5.42352
4	31	34.8469	-3.84693
5	34	35.0175	-1.01753
6	35	32.7435	2.25654
7	39	34.0908	4.90917
8	34	35.0895	-1.08947
9	36	34.6559	1.34405
10	37	36.7744	0.225591
11	38	34.384	3.61596
12	36	34.7573	1.24274
13	38	34.8445	3.15553
14	39	35.9974	3.00258
15	33	36.2437	-3.24366
16	32	33.8478	-1.84782
17	36	34.1586	1.84136
18	38	37.922	0.0780274
19	39	36.8162	2.18376
20	32	33.8947	-1.89473
21	32	34.7669	-2.76694
22	31	33.5434	-2.54343
23	39	36.6675	2.33254
24	37	36.1621	0.837906
25	39	35.3301	3.66986
26	41	34.4524	6.54756
27	36	35.7152	0.284776
28	33	35.075	-2.07496
29	33	34.6809	-1.68087
30	34	33.8329	0.167086
31	31	32.7958	-1.79579
32	27	32.9723	-5.97227
33	37	33.6059	3.39409
34	34	36.0781	-2.07813
35	34	32.9469	1.05312
36	32	32.4101	-0.410058
37	29	32.2244	-3.22443
38	36	33.8834	2.1166
39	29	34.3543	-5.35428
40	35	34.7407	0.259296
41	37	34.5977	2.40227
42	34	33.9876	0.012357
43	38	34.8595	3.14052
44	35	33.6466	1.35345
45	38	32.3536	5.6464
46	37	33.3885	3.61153
47	38	35.6196	2.38043
48	33	34.7705	-1.77048
49	36	36.4832	-0.483212
50	38	33.5893	4.4107
51	32	36.025	-4.02505
52	32	33.0078	-1.00784
53	32	32.8269	-0.826879
54	34	35.5324	-1.53236
55	32	32.7119	-0.711875
56	37	34.4567	2.54331
57	39	34.9354	4.0646
58	29	34.8221	-5.82213
59	37	35.3647	1.63535
60	35	34.937	0.0629648
61	30	31.4665	-1.46651
62	38	34.9571	3.0429
63	34	34.9443	-0.944287
64	31	34.2501	-3.2501
65	34	33.4936	0.506362
66	35	36.0722	-1.07219
67	36	35.1697	0.830338
68	30	33.3178	-3.31779
69	39	35.4096	3.59036
70	35	35.7857	-0.785742
71	38	33.9276	4.07239
72	31	35.3258	-4.32578
73	34	37.1202	-3.12022
74	38	37.6418	0.358154
75	34	32.7472	1.25282
76	39	34.1641	4.83586
77	37	35.75	1.24999
78	34	33.7195	0.280515
79	28	32.9736	-4.9736
80	37	32.1704	4.82964
81	33	35.7675	-2.76747
82	37	37.2342	-0.234158
83	35	35.8949	-0.894907
84	37	34.3129	2.68708
85	32	34.6929	-2.69286
86	33	34.1897	-1.1897
87	38	36.3838	1.61624
88	33	34.7881	-1.78807
89	29	33.7133	-4.71334
90	33	33.6618	-0.661813
91	31	34.7027	-3.70268
92	36	33.6204	2.37964
93	35	37.3321	-2.33208
94	32	32.6828	-0.682828
95	29	32.7403	-3.7403
96	39	36.0909	2.90906
97	37	34.7979	2.20207
98	35	34.4345	0.565542
99	37	35.1859	1.81414
100	32	35.3151	-3.31506
101	38	35.503	2.49705
102	37	35.1658	1.83416
103	36	37.0557	-1.05565
104	32	32.3988	-0.398821
105	33	36.7754	-3.77538
106	40	32.7964	7.20364
107	38	35.6614	2.33861
108	41	36.4569	4.54309
109	36	34.7546	1.24536
110	43	35.9557	7.04431
111	30	35.0566	-5.05658
112	31	34.1239	-3.12388
113	32	37.9298	-5.92982
114	32	33.2419	-1.24187
115	37	34.0294	2.97059
116	37	35.0606	1.93939
117	33	35.6719	-2.67186
118	34	36.674	-2.67403
119	33	34.3587	-1.35865
120	38	35.7998	2.20017
121	33	34.5242	-1.52415
122	31	32.3954	-1.39543
123	38	35.9516	2.0484
124	37	36.1001	0.899853
125	33	33.4393	-0.439309
126	31	34.2464	-3.24641
127	39	34.6755	4.32449
128	44	37.208	6.79204
129	33	35.8965	-2.89653
130	35	33.3608	1.6392
131	32	34.7877	-2.78767
132	28	31.8946	-3.89462
133	40	36.613	3.38698
134	27	32.1626	-5.16255
135	37	35.5933	1.40668
136	32	32.9874	-0.987358
137	28	29.5437	-1.5437
138	34	34.6836	-0.683618
139	30	33.2343	-3.23428
140	35	33.9806	1.01942
141	31	32.9361	-1.93606
142	32	35.1268	-3.12681
143	30	35.2047	-5.2047
144	30	35.3497	-5.34974
145	31	31.4455	-0.445484
146	40	32.857	7.14302
147	32	33.1355	-1.13554
148	36	34.1439	1.85606
149	32	33.8417	-1.84174
150	35	33.2633	1.73667
151	38	35.5718	2.42818
152	42	35.4072	6.59279
153	34	36.7836	-2.78358
154	35	37.2871	-2.28711
155	35	34.3342	0.665776
156	33	32.3069	0.693066
157	36	33.6204	2.37964
158	32	35.587	-3.58701
159	33	35.8965	-2.89653
160	34	34.5872	-0.58719
161	32	34.499	-2.49902
162	34	34.8167	-0.816721

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 34.8503 & 6.14966 \tabularnewline
2 & 39 & 34.7878 & 4.21219 \tabularnewline
3 & 30 & 35.4235 & -5.42352 \tabularnewline
4 & 31 & 34.8469 & -3.84693 \tabularnewline
5 & 34 & 35.0175 & -1.01753 \tabularnewline
6 & 35 & 32.7435 & 2.25654 \tabularnewline
7 & 39 & 34.0908 & 4.90917 \tabularnewline
8 & 34 & 35.0895 & -1.08947 \tabularnewline
9 & 36 & 34.6559 & 1.34405 \tabularnewline
10 & 37 & 36.7744 & 0.225591 \tabularnewline
11 & 38 & 34.384 & 3.61596 \tabularnewline
12 & 36 & 34.7573 & 1.24274 \tabularnewline
13 & 38 & 34.8445 & 3.15553 \tabularnewline
14 & 39 & 35.9974 & 3.00258 \tabularnewline
15 & 33 & 36.2437 & -3.24366 \tabularnewline
16 & 32 & 33.8478 & -1.84782 \tabularnewline
17 & 36 & 34.1586 & 1.84136 \tabularnewline
18 & 38 & 37.922 & 0.0780274 \tabularnewline
19 & 39 & 36.8162 & 2.18376 \tabularnewline
20 & 32 & 33.8947 & -1.89473 \tabularnewline
21 & 32 & 34.7669 & -2.76694 \tabularnewline
22 & 31 & 33.5434 & -2.54343 \tabularnewline
23 & 39 & 36.6675 & 2.33254 \tabularnewline
24 & 37 & 36.1621 & 0.837906 \tabularnewline
25 & 39 & 35.3301 & 3.66986 \tabularnewline
26 & 41 & 34.4524 & 6.54756 \tabularnewline
27 & 36 & 35.7152 & 0.284776 \tabularnewline
28 & 33 & 35.075 & -2.07496 \tabularnewline
29 & 33 & 34.6809 & -1.68087 \tabularnewline
30 & 34 & 33.8329 & 0.167086 \tabularnewline
31 & 31 & 32.7958 & -1.79579 \tabularnewline
32 & 27 & 32.9723 & -5.97227 \tabularnewline
33 & 37 & 33.6059 & 3.39409 \tabularnewline
34 & 34 & 36.0781 & -2.07813 \tabularnewline
35 & 34 & 32.9469 & 1.05312 \tabularnewline
36 & 32 & 32.4101 & -0.410058 \tabularnewline
37 & 29 & 32.2244 & -3.22443 \tabularnewline
38 & 36 & 33.8834 & 2.1166 \tabularnewline
39 & 29 & 34.3543 & -5.35428 \tabularnewline
40 & 35 & 34.7407 & 0.259296 \tabularnewline
41 & 37 & 34.5977 & 2.40227 \tabularnewline
42 & 34 & 33.9876 & 0.012357 \tabularnewline
43 & 38 & 34.8595 & 3.14052 \tabularnewline
44 & 35 & 33.6466 & 1.35345 \tabularnewline
45 & 38 & 32.3536 & 5.6464 \tabularnewline
46 & 37 & 33.3885 & 3.61153 \tabularnewline
47 & 38 & 35.6196 & 2.38043 \tabularnewline
48 & 33 & 34.7705 & -1.77048 \tabularnewline
49 & 36 & 36.4832 & -0.483212 \tabularnewline
50 & 38 & 33.5893 & 4.4107 \tabularnewline
51 & 32 & 36.025 & -4.02505 \tabularnewline
52 & 32 & 33.0078 & -1.00784 \tabularnewline
53 & 32 & 32.8269 & -0.826879 \tabularnewline
54 & 34 & 35.5324 & -1.53236 \tabularnewline
55 & 32 & 32.7119 & -0.711875 \tabularnewline
56 & 37 & 34.4567 & 2.54331 \tabularnewline
57 & 39 & 34.9354 & 4.0646 \tabularnewline
58 & 29 & 34.8221 & -5.82213 \tabularnewline
59 & 37 & 35.3647 & 1.63535 \tabularnewline
60 & 35 & 34.937 & 0.0629648 \tabularnewline
61 & 30 & 31.4665 & -1.46651 \tabularnewline
62 & 38 & 34.9571 & 3.0429 \tabularnewline
63 & 34 & 34.9443 & -0.944287 \tabularnewline
64 & 31 & 34.2501 & -3.2501 \tabularnewline
65 & 34 & 33.4936 & 0.506362 \tabularnewline
66 & 35 & 36.0722 & -1.07219 \tabularnewline
67 & 36 & 35.1697 & 0.830338 \tabularnewline
68 & 30 & 33.3178 & -3.31779 \tabularnewline
69 & 39 & 35.4096 & 3.59036 \tabularnewline
70 & 35 & 35.7857 & -0.785742 \tabularnewline
71 & 38 & 33.9276 & 4.07239 \tabularnewline
72 & 31 & 35.3258 & -4.32578 \tabularnewline
73 & 34 & 37.1202 & -3.12022 \tabularnewline
74 & 38 & 37.6418 & 0.358154 \tabularnewline
75 & 34 & 32.7472 & 1.25282 \tabularnewline
76 & 39 & 34.1641 & 4.83586 \tabularnewline
77 & 37 & 35.75 & 1.24999 \tabularnewline
78 & 34 & 33.7195 & 0.280515 \tabularnewline
79 & 28 & 32.9736 & -4.9736 \tabularnewline
80 & 37 & 32.1704 & 4.82964 \tabularnewline
81 & 33 & 35.7675 & -2.76747 \tabularnewline
82 & 37 & 37.2342 & -0.234158 \tabularnewline
83 & 35 & 35.8949 & -0.894907 \tabularnewline
84 & 37 & 34.3129 & 2.68708 \tabularnewline
85 & 32 & 34.6929 & -2.69286 \tabularnewline
86 & 33 & 34.1897 & -1.1897 \tabularnewline
87 & 38 & 36.3838 & 1.61624 \tabularnewline
88 & 33 & 34.7881 & -1.78807 \tabularnewline
89 & 29 & 33.7133 & -4.71334 \tabularnewline
90 & 33 & 33.6618 & -0.661813 \tabularnewline
91 & 31 & 34.7027 & -3.70268 \tabularnewline
92 & 36 & 33.6204 & 2.37964 \tabularnewline
93 & 35 & 37.3321 & -2.33208 \tabularnewline
94 & 32 & 32.6828 & -0.682828 \tabularnewline
95 & 29 & 32.7403 & -3.7403 \tabularnewline
96 & 39 & 36.0909 & 2.90906 \tabularnewline
97 & 37 & 34.7979 & 2.20207 \tabularnewline
98 & 35 & 34.4345 & 0.565542 \tabularnewline
99 & 37 & 35.1859 & 1.81414 \tabularnewline
100 & 32 & 35.3151 & -3.31506 \tabularnewline
101 & 38 & 35.503 & 2.49705 \tabularnewline
102 & 37 & 35.1658 & 1.83416 \tabularnewline
103 & 36 & 37.0557 & -1.05565 \tabularnewline
104 & 32 & 32.3988 & -0.398821 \tabularnewline
105 & 33 & 36.7754 & -3.77538 \tabularnewline
106 & 40 & 32.7964 & 7.20364 \tabularnewline
107 & 38 & 35.6614 & 2.33861 \tabularnewline
108 & 41 & 36.4569 & 4.54309 \tabularnewline
109 & 36 & 34.7546 & 1.24536 \tabularnewline
110 & 43 & 35.9557 & 7.04431 \tabularnewline
111 & 30 & 35.0566 & -5.05658 \tabularnewline
112 & 31 & 34.1239 & -3.12388 \tabularnewline
113 & 32 & 37.9298 & -5.92982 \tabularnewline
114 & 32 & 33.2419 & -1.24187 \tabularnewline
115 & 37 & 34.0294 & 2.97059 \tabularnewline
116 & 37 & 35.0606 & 1.93939 \tabularnewline
117 & 33 & 35.6719 & -2.67186 \tabularnewline
118 & 34 & 36.674 & -2.67403 \tabularnewline
119 & 33 & 34.3587 & -1.35865 \tabularnewline
120 & 38 & 35.7998 & 2.20017 \tabularnewline
121 & 33 & 34.5242 & -1.52415 \tabularnewline
122 & 31 & 32.3954 & -1.39543 \tabularnewline
123 & 38 & 35.9516 & 2.0484 \tabularnewline
124 & 37 & 36.1001 & 0.899853 \tabularnewline
125 & 33 & 33.4393 & -0.439309 \tabularnewline
126 & 31 & 34.2464 & -3.24641 \tabularnewline
127 & 39 & 34.6755 & 4.32449 \tabularnewline
128 & 44 & 37.208 & 6.79204 \tabularnewline
129 & 33 & 35.8965 & -2.89653 \tabularnewline
130 & 35 & 33.3608 & 1.6392 \tabularnewline
131 & 32 & 34.7877 & -2.78767 \tabularnewline
132 & 28 & 31.8946 & -3.89462 \tabularnewline
133 & 40 & 36.613 & 3.38698 \tabularnewline
134 & 27 & 32.1626 & -5.16255 \tabularnewline
135 & 37 & 35.5933 & 1.40668 \tabularnewline
136 & 32 & 32.9874 & -0.987358 \tabularnewline
137 & 28 & 29.5437 & -1.5437 \tabularnewline
138 & 34 & 34.6836 & -0.683618 \tabularnewline
139 & 30 & 33.2343 & -3.23428 \tabularnewline
140 & 35 & 33.9806 & 1.01942 \tabularnewline
141 & 31 & 32.9361 & -1.93606 \tabularnewline
142 & 32 & 35.1268 & -3.12681 \tabularnewline
143 & 30 & 35.2047 & -5.2047 \tabularnewline
144 & 30 & 35.3497 & -5.34974 \tabularnewline
145 & 31 & 31.4455 & -0.445484 \tabularnewline
146 & 40 & 32.857 & 7.14302 \tabularnewline
147 & 32 & 33.1355 & -1.13554 \tabularnewline
148 & 36 & 34.1439 & 1.85606 \tabularnewline
149 & 32 & 33.8417 & -1.84174 \tabularnewline
150 & 35 & 33.2633 & 1.73667 \tabularnewline
151 & 38 & 35.5718 & 2.42818 \tabularnewline
152 & 42 & 35.4072 & 6.59279 \tabularnewline
153 & 34 & 36.7836 & -2.78358 \tabularnewline
154 & 35 & 37.2871 & -2.28711 \tabularnewline
155 & 35 & 34.3342 & 0.665776 \tabularnewline
156 & 33 & 32.3069 & 0.693066 \tabularnewline
157 & 36 & 33.6204 & 2.37964 \tabularnewline
158 & 32 & 35.587 & -3.58701 \tabularnewline
159 & 33 & 35.8965 & -2.89653 \tabularnewline
160 & 34 & 34.5872 & -0.58719 \tabularnewline
161 & 32 & 34.499 & -2.49902 \tabularnewline
162 & 34 & 34.8167 & -0.816721 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254637&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]41[/C][C]34.8503[/C][C]6.14966[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.7878[/C][C]4.21219[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.4235[/C][C]-5.42352[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.8469[/C][C]-3.84693[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.0175[/C][C]-1.01753[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]32.7435[/C][C]2.25654[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.0908[/C][C]4.90917[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.0895[/C][C]-1.08947[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.6559[/C][C]1.34405[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.7744[/C][C]0.225591[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.384[/C][C]3.61596[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]34.7573[/C][C]1.24274[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.8445[/C][C]3.15553[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]35.9974[/C][C]3.00258[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.2437[/C][C]-3.24366[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]33.8478[/C][C]-1.84782[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.1586[/C][C]1.84136[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]37.922[/C][C]0.0780274[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]36.8162[/C][C]2.18376[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.8947[/C][C]-1.89473[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]34.7669[/C][C]-2.76694[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.5434[/C][C]-2.54343[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.6675[/C][C]2.33254[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.1621[/C][C]0.837906[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.3301[/C][C]3.66986[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.4524[/C][C]6.54756[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]35.7152[/C][C]0.284776[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.075[/C][C]-2.07496[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.6809[/C][C]-1.68087[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]33.8329[/C][C]0.167086[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]32.7958[/C][C]-1.79579[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]32.9723[/C][C]-5.97227[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.6059[/C][C]3.39409[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]36.0781[/C][C]-2.07813[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]32.9469[/C][C]1.05312[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]32.4101[/C][C]-0.410058[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.2244[/C][C]-3.22443[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]33.8834[/C][C]2.1166[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]34.3543[/C][C]-5.35428[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.7407[/C][C]0.259296[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.5977[/C][C]2.40227[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]33.9876[/C][C]0.012357[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]34.8595[/C][C]3.14052[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]33.6466[/C][C]1.35345[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.3536[/C][C]5.6464[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]33.3885[/C][C]3.61153[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.6196[/C][C]2.38043[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.7705[/C][C]-1.77048[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.4832[/C][C]-0.483212[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.5893[/C][C]4.4107[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.025[/C][C]-4.02505[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.0078[/C][C]-1.00784[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]32.8269[/C][C]-0.826879[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.5324[/C][C]-1.53236[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.7119[/C][C]-0.711875[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.4567[/C][C]2.54331[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.9354[/C][C]4.0646[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.8221[/C][C]-5.82213[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.3647[/C][C]1.63535[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.937[/C][C]0.0629648[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.4665[/C][C]-1.46651[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.9571[/C][C]3.0429[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.9443[/C][C]-0.944287[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.2501[/C][C]-3.2501[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.4936[/C][C]0.506362[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]36.0722[/C][C]-1.07219[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]35.1697[/C][C]0.830338[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]33.3178[/C][C]-3.31779[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]35.4096[/C][C]3.59036[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.7857[/C][C]-0.785742[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]33.9276[/C][C]4.07239[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.3258[/C][C]-4.32578[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]37.1202[/C][C]-3.12022[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.6418[/C][C]0.358154[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.7472[/C][C]1.25282[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]34.1641[/C][C]4.83586[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.75[/C][C]1.24999[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.7195[/C][C]0.280515[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]32.9736[/C][C]-4.9736[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]32.1704[/C][C]4.82964[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.7675[/C][C]-2.76747[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]37.2342[/C][C]-0.234158[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.8949[/C][C]-0.894907[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.3129[/C][C]2.68708[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.6929[/C][C]-2.69286[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]34.1897[/C][C]-1.1897[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.3838[/C][C]1.61624[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.7881[/C][C]-1.78807[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]33.7133[/C][C]-4.71334[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.6618[/C][C]-0.661813[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.7027[/C][C]-3.70268[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.6204[/C][C]2.37964[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.3321[/C][C]-2.33208[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.6828[/C][C]-0.682828[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]32.7403[/C][C]-3.7403[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]36.0909[/C][C]2.90906[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.7979[/C][C]2.20207[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.4345[/C][C]0.565542[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.1859[/C][C]1.81414[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.3151[/C][C]-3.31506[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.503[/C][C]2.49705[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]35.1658[/C][C]1.83416[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]37.0557[/C][C]-1.05565[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.3988[/C][C]-0.398821[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.7754[/C][C]-3.77538[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]32.7964[/C][C]7.20364[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.6614[/C][C]2.33861[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.4569[/C][C]4.54309[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.7546[/C][C]1.24536[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]35.9557[/C][C]7.04431[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]35.0566[/C][C]-5.05658[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]34.1239[/C][C]-3.12388[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.9298[/C][C]-5.92982[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]33.2419[/C][C]-1.24187[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.0294[/C][C]2.97059[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]35.0606[/C][C]1.93939[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]35.6719[/C][C]-2.67186[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.674[/C][C]-2.67403[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.3587[/C][C]-1.35865[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.7998[/C][C]2.20017[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]34.5242[/C][C]-1.52415[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.3954[/C][C]-1.39543[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]35.9516[/C][C]2.0484[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]36.1001[/C][C]0.899853[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.4393[/C][C]-0.439309[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.2464[/C][C]-3.24641[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.6755[/C][C]4.32449[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]37.208[/C][C]6.79204[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.8965[/C][C]-2.89653[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.3608[/C][C]1.6392[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.7877[/C][C]-2.78767[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]31.8946[/C][C]-3.89462[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]36.613[/C][C]3.38698[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]32.1626[/C][C]-5.16255[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]35.5933[/C][C]1.40668[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]32.9874[/C][C]-0.987358[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]29.5437[/C][C]-1.5437[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.6836[/C][C]-0.683618[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.2343[/C][C]-3.23428[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]33.9806[/C][C]1.01942[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]32.9361[/C][C]-1.93606[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]35.1268[/C][C]-3.12681[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]35.2047[/C][C]-5.2047[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]35.3497[/C][C]-5.34974[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.4455[/C][C]-0.445484[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]32.857[/C][C]7.14302[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]33.1355[/C][C]-1.13554[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.1439[/C][C]1.85606[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.8417[/C][C]-1.84174[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.2633[/C][C]1.73667[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]35.5718[/C][C]2.42818[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]35.4072[/C][C]6.59279[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.7836[/C][C]-2.78358[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]37.2871[/C][C]-2.28711[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]34.3342[/C][C]0.665776[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]32.3069[/C][C]0.693066[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.6204[/C][C]2.37964[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.587[/C][C]-3.58701[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.8965[/C][C]-2.89653[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.5872[/C][C]-0.58719[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]34.499[/C][C]-2.49902[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.8167[/C][C]-0.816721[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=254637&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254637&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	41	34.8503	6.14966
2	39	34.7878	4.21219
3	30	35.4235	-5.42352
4	31	34.8469	-3.84693
5	34	35.0175	-1.01753
6	35	32.7435	2.25654
7	39	34.0908	4.90917
8	34	35.0895	-1.08947
9	36	34.6559	1.34405
10	37	36.7744	0.225591
11	38	34.384	3.61596
12	36	34.7573	1.24274
13	38	34.8445	3.15553
14	39	35.9974	3.00258
15	33	36.2437	-3.24366
16	32	33.8478	-1.84782
17	36	34.1586	1.84136
18	38	37.922	0.0780274
19	39	36.8162	2.18376
20	32	33.8947	-1.89473
21	32	34.7669	-2.76694
22	31	33.5434	-2.54343
23	39	36.6675	2.33254
24	37	36.1621	0.837906
25	39	35.3301	3.66986
26	41	34.4524	6.54756
27	36	35.7152	0.284776
28	33	35.075	-2.07496
29	33	34.6809	-1.68087
30	34	33.8329	0.167086
31	31	32.7958	-1.79579
32	27	32.9723	-5.97227
33	37	33.6059	3.39409
34	34	36.0781	-2.07813
35	34	32.9469	1.05312
36	32	32.4101	-0.410058
37	29	32.2244	-3.22443
38	36	33.8834	2.1166
39	29	34.3543	-5.35428
40	35	34.7407	0.259296
41	37	34.5977	2.40227
42	34	33.9876	0.012357
43	38	34.8595	3.14052
44	35	33.6466	1.35345
45	38	32.3536	5.6464
46	37	33.3885	3.61153
47	38	35.6196	2.38043
48	33	34.7705	-1.77048
49	36	36.4832	-0.483212
50	38	33.5893	4.4107
51	32	36.025	-4.02505
52	32	33.0078	-1.00784
53	32	32.8269	-0.826879
54	34	35.5324	-1.53236
55	32	32.7119	-0.711875
56	37	34.4567	2.54331
57	39	34.9354	4.0646
58	29	34.8221	-5.82213
59	37	35.3647	1.63535
60	35	34.937	0.0629648
61	30	31.4665	-1.46651
62	38	34.9571	3.0429
63	34	34.9443	-0.944287
64	31	34.2501	-3.2501
65	34	33.4936	0.506362
66	35	36.0722	-1.07219
67	36	35.1697	0.830338
68	30	33.3178	-3.31779
69	39	35.4096	3.59036
70	35	35.7857	-0.785742
71	38	33.9276	4.07239
72	31	35.3258	-4.32578
73	34	37.1202	-3.12022
74	38	37.6418	0.358154
75	34	32.7472	1.25282
76	39	34.1641	4.83586
77	37	35.75	1.24999
78	34	33.7195	0.280515
79	28	32.9736	-4.9736
80	37	32.1704	4.82964
81	33	35.7675	-2.76747
82	37	37.2342	-0.234158
83	35	35.8949	-0.894907
84	37	34.3129	2.68708
85	32	34.6929	-2.69286
86	33	34.1897	-1.1897
87	38	36.3838	1.61624
88	33	34.7881	-1.78807
89	29	33.7133	-4.71334
90	33	33.6618	-0.661813
91	31	34.7027	-3.70268
92	36	33.6204	2.37964
93	35	37.3321	-2.33208
94	32	32.6828	-0.682828
95	29	32.7403	-3.7403
96	39	36.0909	2.90906
97	37	34.7979	2.20207
98	35	34.4345	0.565542
99	37	35.1859	1.81414
100	32	35.3151	-3.31506
101	38	35.503	2.49705
102	37	35.1658	1.83416
103	36	37.0557	-1.05565
104	32	32.3988	-0.398821
105	33	36.7754	-3.77538
106	40	32.7964	7.20364
107	38	35.6614	2.33861
108	41	36.4569	4.54309
109	36	34.7546	1.24536
110	43	35.9557	7.04431
111	30	35.0566	-5.05658
112	31	34.1239	-3.12388
113	32	37.9298	-5.92982
114	32	33.2419	-1.24187
115	37	34.0294	2.97059
116	37	35.0606	1.93939
117	33	35.6719	-2.67186
118	34	36.674	-2.67403
119	33	34.3587	-1.35865
120	38	35.7998	2.20017
121	33	34.5242	-1.52415
122	31	32.3954	-1.39543
123	38	35.9516	2.0484
124	37	36.1001	0.899853
125	33	33.4393	-0.439309
126	31	34.2464	-3.24641
127	39	34.6755	4.32449
128	44	37.208	6.79204
129	33	35.8965	-2.89653
130	35	33.3608	1.6392
131	32	34.7877	-2.78767
132	28	31.8946	-3.89462
133	40	36.613	3.38698
134	27	32.1626	-5.16255
135	37	35.5933	1.40668
136	32	32.9874	-0.987358
137	28	29.5437	-1.5437
138	34	34.6836	-0.683618
139	30	33.2343	-3.23428
140	35	33.9806	1.01942
141	31	32.9361	-1.93606
142	32	35.1268	-3.12681
143	30	35.2047	-5.2047
144	30	35.3497	-5.34974
145	31	31.4455	-0.445484
146	40	32.857	7.14302
147	32	33.1355	-1.13554
148	36	34.1439	1.85606
149	32	33.8417	-1.84174
150	35	33.2633	1.73667
151	38	35.5718	2.42818
152	42	35.4072	6.59279
153	34	36.7836	-2.78358
154	35	37.2871	-2.28711
155	35	34.3342	0.665776
156	33	32.3069	0.693066
157	36	33.6204	2.37964
158	32	35.587	-3.58701
159	33	35.8965	-2.89653
160	34	34.5872	-0.58719
161	32	34.499	-2.49902
162	34	34.8167	-0.816721

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.185869	0.371738	0.814131
12	0.871127	0.257747	0.128873
13	0.917786	0.164429	0.0822144
14	0.878255	0.24349	0.121745
15	0.840975	0.31805	0.159025
16	0.865021	0.269957	0.134979
17	0.821694	0.356612	0.178306
18	0.789006	0.421987	0.210994
19	0.742707	0.514585	0.257293
20	0.770547	0.458905	0.229453
21	0.790255	0.41949	0.209745
22	0.749218	0.501564	0.250782
23	0.705947	0.588105	0.294053
24	0.638801	0.722398	0.361199
25	0.633268	0.733463	0.366732
26	0.779503	0.440993	0.220497
27	0.773103	0.453795	0.226897
28	0.729322	0.541355	0.270678
29	0.73839	0.52322	0.26161
30	0.68261	0.634779	0.31739
31	0.645134	0.709732	0.354866
32	0.803653	0.392694	0.196347
33	0.801254	0.397493	0.198746
34	0.765075	0.469851	0.234925
35	0.717559	0.564882	0.282441
36	0.67111	0.657779	0.32889
37	0.659073	0.681854	0.340927
38	0.626295	0.74741	0.373705
39	0.744233	0.511534	0.255767
40	0.697111	0.605777	0.302889
41	0.667213	0.665574	0.332787
42	0.615449	0.769103	0.384551
43	0.586125	0.82775	0.413875
44	0.537555	0.924889	0.462445
45	0.63473	0.73054	0.36527
46	0.641041	0.717919	0.358959
47	0.623761	0.752479	0.376239
48	0.59305	0.8139	0.40695
49	0.545345	0.909309	0.454655
50	0.572387	0.855226	0.427613
51	0.618863	0.762274	0.381137
52	0.579867	0.840265	0.420133
53	0.534978	0.930044	0.465022
54	0.486234	0.972468	0.513766
55	0.441307	0.882614	0.558693
56	0.430487	0.860973	0.569513
57	0.457975	0.915951	0.542025
58	0.593084	0.813833	0.406916
59	0.553953	0.892094	0.446047
60	0.505559	0.988881	0.494441
61	0.469026	0.938053	0.530974
62	0.453523	0.907047	0.546477
63	0.41981	0.83962	0.58019
64	0.426247	0.852494	0.573753
65	0.380744	0.761488	0.619256
66	0.344715	0.68943	0.655285
67	0.304224	0.608448	0.695776
68	0.334893	0.669785	0.665107
69	0.345486	0.690973	0.654514
70	0.306021	0.612042	0.693979
71	0.339072	0.678145	0.660928
72	0.377915	0.755829	0.622085
73	0.378925	0.75785	0.621075
74	0.335407	0.670814	0.664593
75	0.300772	0.601544	0.699228
76	0.356309	0.712617	0.643691
77	0.318767	0.637535	0.681233
78	0.278254	0.556507	0.721746
79	0.342166	0.684332	0.657834
80	0.417807	0.835615	0.582193
81	0.407568	0.815136	0.592432
82	0.362945	0.72589	0.637055
83	0.323405	0.64681	0.676595
84	0.311795	0.623589	0.688205
85	0.298877	0.597753	0.701123
86	0.265083	0.530166	0.734917
87	0.238293	0.476587	0.761707
88	0.213008	0.426016	0.786992
89	0.250384	0.500768	0.749616
90	0.216435	0.432869	0.783565
91	0.230629	0.461259	0.769371
92	0.216068	0.432136	0.783932
93	0.202442	0.404885	0.797558
94	0.172236	0.344473	0.827764
95	0.179582	0.359164	0.820418
96	0.175492	0.350985	0.824508
97	0.160289	0.320578	0.839711
98	0.137049	0.274097	0.862951
99	0.12027	0.24054	0.87973
100	0.119164	0.238328	0.880836
101	0.109987	0.219974	0.890013
102	0.0965295	0.193059	0.903471
103	0.0790856	0.158171	0.920914
104	0.0649092	0.129818	0.935091
105	0.076272	0.152544	0.923728
106	0.182707	0.365413	0.817293
107	0.169859	0.339718	0.830141
108	0.193795	0.38759	0.806205
109	0.180041	0.360081	0.819959
110	0.380724	0.761449	0.619276
111	0.456715	0.91343	0.543285
112	0.440209	0.880419	0.559791
113	0.569096	0.861809	0.430904
114	0.522293	0.955415	0.477707
115	0.50772	0.984561	0.49228
116	0.472082	0.944163	0.527918
117	0.442332	0.884665	0.557668
118	0.418308	0.836617	0.581692
119	0.372599	0.745197	0.627401
120	0.336403	0.672806	0.663597
121	0.291973	0.583946	0.708027
122	0.250944	0.501888	0.749056
123	0.215646	0.431291	0.784354
124	0.178445	0.356889	0.821555
125	0.143759	0.287517	0.856241
126	0.153441	0.306883	0.846559
127	0.170796	0.341591	0.829204
128	0.349629	0.699257	0.650371
129	0.320751	0.641502	0.679249
130	0.284001	0.568002	0.715999
131	0.252359	0.504719	0.747641
132	0.294714	0.589429	0.705286
133	0.365842	0.731683	0.634158
134	0.476186	0.952373	0.523814
135	0.420225	0.840449	0.579775
136	0.358131	0.716263	0.641869
137	0.314392	0.628784	0.685608
138	0.254387	0.508774	0.745613
139	0.303572	0.607144	0.696428
140	0.248589	0.497177	0.751411
141	0.476523	0.953046	0.523477
142	0.414987	0.829974	0.585013
143	0.441212	0.882424	0.558788
144	0.702761	0.594477	0.297239
145	0.613255	0.773489	0.386745
146	0.944289	0.111422	0.0557111
147	0.924297	0.151407	0.0757034
148	0.946481	0.107038	0.0535192
149	0.944753	0.110494	0.055247
150	0.882424	0.235151	0.117576
151	0.769368	0.461264	0.230632

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.185869 & 0.371738 & 0.814131 \tabularnewline
12 & 0.871127 & 0.257747 & 0.128873 \tabularnewline
13 & 0.917786 & 0.164429 & 0.0822144 \tabularnewline
14 & 0.878255 & 0.24349 & 0.121745 \tabularnewline
15 & 0.840975 & 0.31805 & 0.159025 \tabularnewline
16 & 0.865021 & 0.269957 & 0.134979 \tabularnewline
17 & 0.821694 & 0.356612 & 0.178306 \tabularnewline
18 & 0.789006 & 0.421987 & 0.210994 \tabularnewline
19 & 0.742707 & 0.514585 & 0.257293 \tabularnewline
20 & 0.770547 & 0.458905 & 0.229453 \tabularnewline
21 & 0.790255 & 0.41949 & 0.209745 \tabularnewline
22 & 0.749218 & 0.501564 & 0.250782 \tabularnewline
23 & 0.705947 & 0.588105 & 0.294053 \tabularnewline
24 & 0.638801 & 0.722398 & 0.361199 \tabularnewline
25 & 0.633268 & 0.733463 & 0.366732 \tabularnewline
26 & 0.779503 & 0.440993 & 0.220497 \tabularnewline
27 & 0.773103 & 0.453795 & 0.226897 \tabularnewline
28 & 0.729322 & 0.541355 & 0.270678 \tabularnewline
29 & 0.73839 & 0.52322 & 0.26161 \tabularnewline
30 & 0.68261 & 0.634779 & 0.31739 \tabularnewline
31 & 0.645134 & 0.709732 & 0.354866 \tabularnewline
32 & 0.803653 & 0.392694 & 0.196347 \tabularnewline
33 & 0.801254 & 0.397493 & 0.198746 \tabularnewline
34 & 0.765075 & 0.469851 & 0.234925 \tabularnewline
35 & 0.717559 & 0.564882 & 0.282441 \tabularnewline
36 & 0.67111 & 0.657779 & 0.32889 \tabularnewline
37 & 0.659073 & 0.681854 & 0.340927 \tabularnewline
38 & 0.626295 & 0.74741 & 0.373705 \tabularnewline
39 & 0.744233 & 0.511534 & 0.255767 \tabularnewline
40 & 0.697111 & 0.605777 & 0.302889 \tabularnewline
41 & 0.667213 & 0.665574 & 0.332787 \tabularnewline
42 & 0.615449 & 0.769103 & 0.384551 \tabularnewline
43 & 0.586125 & 0.82775 & 0.413875 \tabularnewline
44 & 0.537555 & 0.924889 & 0.462445 \tabularnewline
45 & 0.63473 & 0.73054 & 0.36527 \tabularnewline
46 & 0.641041 & 0.717919 & 0.358959 \tabularnewline
47 & 0.623761 & 0.752479 & 0.376239 \tabularnewline
48 & 0.59305 & 0.8139 & 0.40695 \tabularnewline
49 & 0.545345 & 0.909309 & 0.454655 \tabularnewline
50 & 0.572387 & 0.855226 & 0.427613 \tabularnewline
51 & 0.618863 & 0.762274 & 0.381137 \tabularnewline
52 & 0.579867 & 0.840265 & 0.420133 \tabularnewline
53 & 0.534978 & 0.930044 & 0.465022 \tabularnewline
54 & 0.486234 & 0.972468 & 0.513766 \tabularnewline
55 & 0.441307 & 0.882614 & 0.558693 \tabularnewline
56 & 0.430487 & 0.860973 & 0.569513 \tabularnewline
57 & 0.457975 & 0.915951 & 0.542025 \tabularnewline
58 & 0.593084 & 0.813833 & 0.406916 \tabularnewline
59 & 0.553953 & 0.892094 & 0.446047 \tabularnewline
60 & 0.505559 & 0.988881 & 0.494441 \tabularnewline
61 & 0.469026 & 0.938053 & 0.530974 \tabularnewline
62 & 0.453523 & 0.907047 & 0.546477 \tabularnewline
63 & 0.41981 & 0.83962 & 0.58019 \tabularnewline
64 & 0.426247 & 0.852494 & 0.573753 \tabularnewline
65 & 0.380744 & 0.761488 & 0.619256 \tabularnewline
66 & 0.344715 & 0.68943 & 0.655285 \tabularnewline
67 & 0.304224 & 0.608448 & 0.695776 \tabularnewline
68 & 0.334893 & 0.669785 & 0.665107 \tabularnewline
69 & 0.345486 & 0.690973 & 0.654514 \tabularnewline
70 & 0.306021 & 0.612042 & 0.693979 \tabularnewline
71 & 0.339072 & 0.678145 & 0.660928 \tabularnewline
72 & 0.377915 & 0.755829 & 0.622085 \tabularnewline
73 & 0.378925 & 0.75785 & 0.621075 \tabularnewline
74 & 0.335407 & 0.670814 & 0.664593 \tabularnewline
75 & 0.300772 & 0.601544 & 0.699228 \tabularnewline
76 & 0.356309 & 0.712617 & 0.643691 \tabularnewline
77 & 0.318767 & 0.637535 & 0.681233 \tabularnewline
78 & 0.278254 & 0.556507 & 0.721746 \tabularnewline
79 & 0.342166 & 0.684332 & 0.657834 \tabularnewline
80 & 0.417807 & 0.835615 & 0.582193 \tabularnewline
81 & 0.407568 & 0.815136 & 0.592432 \tabularnewline
82 & 0.362945 & 0.72589 & 0.637055 \tabularnewline
83 & 0.323405 & 0.64681 & 0.676595 \tabularnewline
84 & 0.311795 & 0.623589 & 0.688205 \tabularnewline
85 & 0.298877 & 0.597753 & 0.701123 \tabularnewline
86 & 0.265083 & 0.530166 & 0.734917 \tabularnewline
87 & 0.238293 & 0.476587 & 0.761707 \tabularnewline
88 & 0.213008 & 0.426016 & 0.786992 \tabularnewline
89 & 0.250384 & 0.500768 & 0.749616 \tabularnewline
90 & 0.216435 & 0.432869 & 0.783565 \tabularnewline
91 & 0.230629 & 0.461259 & 0.769371 \tabularnewline
92 & 0.216068 & 0.432136 & 0.783932 \tabularnewline
93 & 0.202442 & 0.404885 & 0.797558 \tabularnewline
94 & 0.172236 & 0.344473 & 0.827764 \tabularnewline
95 & 0.179582 & 0.359164 & 0.820418 \tabularnewline
96 & 0.175492 & 0.350985 & 0.824508 \tabularnewline
97 & 0.160289 & 0.320578 & 0.839711 \tabularnewline
98 & 0.137049 & 0.274097 & 0.862951 \tabularnewline
99 & 0.12027 & 0.24054 & 0.87973 \tabularnewline
100 & 0.119164 & 0.238328 & 0.880836 \tabularnewline
101 & 0.109987 & 0.219974 & 0.890013 \tabularnewline
102 & 0.0965295 & 0.193059 & 0.903471 \tabularnewline
103 & 0.0790856 & 0.158171 & 0.920914 \tabularnewline
104 & 0.0649092 & 0.129818 & 0.935091 \tabularnewline
105 & 0.076272 & 0.152544 & 0.923728 \tabularnewline
106 & 0.182707 & 0.365413 & 0.817293 \tabularnewline
107 & 0.169859 & 0.339718 & 0.830141 \tabularnewline
108 & 0.193795 & 0.38759 & 0.806205 \tabularnewline
109 & 0.180041 & 0.360081 & 0.819959 \tabularnewline
110 & 0.380724 & 0.761449 & 0.619276 \tabularnewline
111 & 0.456715 & 0.91343 & 0.543285 \tabularnewline
112 & 0.440209 & 0.880419 & 0.559791 \tabularnewline
113 & 0.569096 & 0.861809 & 0.430904 \tabularnewline
114 & 0.522293 & 0.955415 & 0.477707 \tabularnewline
115 & 0.50772 & 0.984561 & 0.49228 \tabularnewline
116 & 0.472082 & 0.944163 & 0.527918 \tabularnewline
117 & 0.442332 & 0.884665 & 0.557668 \tabularnewline
118 & 0.418308 & 0.836617 & 0.581692 \tabularnewline
119 & 0.372599 & 0.745197 & 0.627401 \tabularnewline
120 & 0.336403 & 0.672806 & 0.663597 \tabularnewline
121 & 0.291973 & 0.583946 & 0.708027 \tabularnewline
122 & 0.250944 & 0.501888 & 0.749056 \tabularnewline
123 & 0.215646 & 0.431291 & 0.784354 \tabularnewline
124 & 0.178445 & 0.356889 & 0.821555 \tabularnewline
125 & 0.143759 & 0.287517 & 0.856241 \tabularnewline
126 & 0.153441 & 0.306883 & 0.846559 \tabularnewline
127 & 0.170796 & 0.341591 & 0.829204 \tabularnewline
128 & 0.349629 & 0.699257 & 0.650371 \tabularnewline
129 & 0.320751 & 0.641502 & 0.679249 \tabularnewline
130 & 0.284001 & 0.568002 & 0.715999 \tabularnewline
131 & 0.252359 & 0.504719 & 0.747641 \tabularnewline
132 & 0.294714 & 0.589429 & 0.705286 \tabularnewline
133 & 0.365842 & 0.731683 & 0.634158 \tabularnewline
134 & 0.476186 & 0.952373 & 0.523814 \tabularnewline
135 & 0.420225 & 0.840449 & 0.579775 \tabularnewline
136 & 0.358131 & 0.716263 & 0.641869 \tabularnewline
137 & 0.314392 & 0.628784 & 0.685608 \tabularnewline
138 & 0.254387 & 0.508774 & 0.745613 \tabularnewline
139 & 0.303572 & 0.607144 & 0.696428 \tabularnewline
140 & 0.248589 & 0.497177 & 0.751411 \tabularnewline
141 & 0.476523 & 0.953046 & 0.523477 \tabularnewline
142 & 0.414987 & 0.829974 & 0.585013 \tabularnewline
143 & 0.441212 & 0.882424 & 0.558788 \tabularnewline
144 & 0.702761 & 0.594477 & 0.297239 \tabularnewline
145 & 0.613255 & 0.773489 & 0.386745 \tabularnewline
146 & 0.944289 & 0.111422 & 0.0557111 \tabularnewline
147 & 0.924297 & 0.151407 & 0.0757034 \tabularnewline
148 & 0.946481 & 0.107038 & 0.0535192 \tabularnewline
149 & 0.944753 & 0.110494 & 0.055247 \tabularnewline
150 & 0.882424 & 0.235151 & 0.117576 \tabularnewline
151 & 0.769368 & 0.461264 & 0.230632 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254637&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]11[/C][C]0.185869[/C][C]0.371738[/C][C]0.814131[/C][/ROW]
[ROW][C]12[/C][C]0.871127[/C][C]0.257747[/C][C]0.128873[/C][/ROW]
[ROW][C]13[/C][C]0.917786[/C][C]0.164429[/C][C]0.0822144[/C][/ROW]
[ROW][C]14[/C][C]0.878255[/C][C]0.24349[/C][C]0.121745[/C][/ROW]
[ROW][C]15[/C][C]0.840975[/C][C]0.31805[/C][C]0.159025[/C][/ROW]
[ROW][C]16[/C][C]0.865021[/C][C]0.269957[/C][C]0.134979[/C][/ROW]
[ROW][C]17[/C][C]0.821694[/C][C]0.356612[/C][C]0.178306[/C][/ROW]
[ROW][C]18[/C][C]0.789006[/C][C]0.421987[/C][C]0.210994[/C][/ROW]
[ROW][C]19[/C][C]0.742707[/C][C]0.514585[/C][C]0.257293[/C][/ROW]
[ROW][C]20[/C][C]0.770547[/C][C]0.458905[/C][C]0.229453[/C][/ROW]
[ROW][C]21[/C][C]0.790255[/C][C]0.41949[/C][C]0.209745[/C][/ROW]
[ROW][C]22[/C][C]0.749218[/C][C]0.501564[/C][C]0.250782[/C][/ROW]
[ROW][C]23[/C][C]0.705947[/C][C]0.588105[/C][C]0.294053[/C][/ROW]
[ROW][C]24[/C][C]0.638801[/C][C]0.722398[/C][C]0.361199[/C][/ROW]
[ROW][C]25[/C][C]0.633268[/C][C]0.733463[/C][C]0.366732[/C][/ROW]
[ROW][C]26[/C][C]0.779503[/C][C]0.440993[/C][C]0.220497[/C][/ROW]
[ROW][C]27[/C][C]0.773103[/C][C]0.453795[/C][C]0.226897[/C][/ROW]
[ROW][C]28[/C][C]0.729322[/C][C]0.541355[/C][C]0.270678[/C][/ROW]
[ROW][C]29[/C][C]0.73839[/C][C]0.52322[/C][C]0.26161[/C][/ROW]
[ROW][C]30[/C][C]0.68261[/C][C]0.634779[/C][C]0.31739[/C][/ROW]
[ROW][C]31[/C][C]0.645134[/C][C]0.709732[/C][C]0.354866[/C][/ROW]
[ROW][C]32[/C][C]0.803653[/C][C]0.392694[/C][C]0.196347[/C][/ROW]
[ROW][C]33[/C][C]0.801254[/C][C]0.397493[/C][C]0.198746[/C][/ROW]
[ROW][C]34[/C][C]0.765075[/C][C]0.469851[/C][C]0.234925[/C][/ROW]
[ROW][C]35[/C][C]0.717559[/C][C]0.564882[/C][C]0.282441[/C][/ROW]
[ROW][C]36[/C][C]0.67111[/C][C]0.657779[/C][C]0.32889[/C][/ROW]
[ROW][C]37[/C][C]0.659073[/C][C]0.681854[/C][C]0.340927[/C][/ROW]
[ROW][C]38[/C][C]0.626295[/C][C]0.74741[/C][C]0.373705[/C][/ROW]
[ROW][C]39[/C][C]0.744233[/C][C]0.511534[/C][C]0.255767[/C][/ROW]
[ROW][C]40[/C][C]0.697111[/C][C]0.605777[/C][C]0.302889[/C][/ROW]
[ROW][C]41[/C][C]0.667213[/C][C]0.665574[/C][C]0.332787[/C][/ROW]
[ROW][C]42[/C][C]0.615449[/C][C]0.769103[/C][C]0.384551[/C][/ROW]
[ROW][C]43[/C][C]0.586125[/C][C]0.82775[/C][C]0.413875[/C][/ROW]
[ROW][C]44[/C][C]0.537555[/C][C]0.924889[/C][C]0.462445[/C][/ROW]
[ROW][C]45[/C][C]0.63473[/C][C]0.73054[/C][C]0.36527[/C][/ROW]
[ROW][C]46[/C][C]0.641041[/C][C]0.717919[/C][C]0.358959[/C][/ROW]
[ROW][C]47[/C][C]0.623761[/C][C]0.752479[/C][C]0.376239[/C][/ROW]
[ROW][C]48[/C][C]0.59305[/C][C]0.8139[/C][C]0.40695[/C][/ROW]
[ROW][C]49[/C][C]0.545345[/C][C]0.909309[/C][C]0.454655[/C][/ROW]
[ROW][C]50[/C][C]0.572387[/C][C]0.855226[/C][C]0.427613[/C][/ROW]
[ROW][C]51[/C][C]0.618863[/C][C]0.762274[/C][C]0.381137[/C][/ROW]
[ROW][C]52[/C][C]0.579867[/C][C]0.840265[/C][C]0.420133[/C][/ROW]
[ROW][C]53[/C][C]0.534978[/C][C]0.930044[/C][C]0.465022[/C][/ROW]
[ROW][C]54[/C][C]0.486234[/C][C]0.972468[/C][C]0.513766[/C][/ROW]
[ROW][C]55[/C][C]0.441307[/C][C]0.882614[/C][C]0.558693[/C][/ROW]
[ROW][C]56[/C][C]0.430487[/C][C]0.860973[/C][C]0.569513[/C][/ROW]
[ROW][C]57[/C][C]0.457975[/C][C]0.915951[/C][C]0.542025[/C][/ROW]
[ROW][C]58[/C][C]0.593084[/C][C]0.813833[/C][C]0.406916[/C][/ROW]
[ROW][C]59[/C][C]0.553953[/C][C]0.892094[/C][C]0.446047[/C][/ROW]
[ROW][C]60[/C][C]0.505559[/C][C]0.988881[/C][C]0.494441[/C][/ROW]
[ROW][C]61[/C][C]0.469026[/C][C]0.938053[/C][C]0.530974[/C][/ROW]
[ROW][C]62[/C][C]0.453523[/C][C]0.907047[/C][C]0.546477[/C][/ROW]
[ROW][C]63[/C][C]0.41981[/C][C]0.83962[/C][C]0.58019[/C][/ROW]
[ROW][C]64[/C][C]0.426247[/C][C]0.852494[/C][C]0.573753[/C][/ROW]
[ROW][C]65[/C][C]0.380744[/C][C]0.761488[/C][C]0.619256[/C][/ROW]
[ROW][C]66[/C][C]0.344715[/C][C]0.68943[/C][C]0.655285[/C][/ROW]
[ROW][C]67[/C][C]0.304224[/C][C]0.608448[/C][C]0.695776[/C][/ROW]
[ROW][C]68[/C][C]0.334893[/C][C]0.669785[/C][C]0.665107[/C][/ROW]
[ROW][C]69[/C][C]0.345486[/C][C]0.690973[/C][C]0.654514[/C][/ROW]
[ROW][C]70[/C][C]0.306021[/C][C]0.612042[/C][C]0.693979[/C][/ROW]
[ROW][C]71[/C][C]0.339072[/C][C]0.678145[/C][C]0.660928[/C][/ROW]
[ROW][C]72[/C][C]0.377915[/C][C]0.755829[/C][C]0.622085[/C][/ROW]
[ROW][C]73[/C][C]0.378925[/C][C]0.75785[/C][C]0.621075[/C][/ROW]
[ROW][C]74[/C][C]0.335407[/C][C]0.670814[/C][C]0.664593[/C][/ROW]
[ROW][C]75[/C][C]0.300772[/C][C]0.601544[/C][C]0.699228[/C][/ROW]
[ROW][C]76[/C][C]0.356309[/C][C]0.712617[/C][C]0.643691[/C][/ROW]
[ROW][C]77[/C][C]0.318767[/C][C]0.637535[/C][C]0.681233[/C][/ROW]
[ROW][C]78[/C][C]0.278254[/C][C]0.556507[/C][C]0.721746[/C][/ROW]
[ROW][C]79[/C][C]0.342166[/C][C]0.684332[/C][C]0.657834[/C][/ROW]
[ROW][C]80[/C][C]0.417807[/C][C]0.835615[/C][C]0.582193[/C][/ROW]
[ROW][C]81[/C][C]0.407568[/C][C]0.815136[/C][C]0.592432[/C][/ROW]
[ROW][C]82[/C][C]0.362945[/C][C]0.72589[/C][C]0.637055[/C][/ROW]
[ROW][C]83[/C][C]0.323405[/C][C]0.64681[/C][C]0.676595[/C][/ROW]
[ROW][C]84[/C][C]0.311795[/C][C]0.623589[/C][C]0.688205[/C][/ROW]
[ROW][C]85[/C][C]0.298877[/C][C]0.597753[/C][C]0.701123[/C][/ROW]
[ROW][C]86[/C][C]0.265083[/C][C]0.530166[/C][C]0.734917[/C][/ROW]
[ROW][C]87[/C][C]0.238293[/C][C]0.476587[/C][C]0.761707[/C][/ROW]
[ROW][C]88[/C][C]0.213008[/C][C]0.426016[/C][C]0.786992[/C][/ROW]
[ROW][C]89[/C][C]0.250384[/C][C]0.500768[/C][C]0.749616[/C][/ROW]
[ROW][C]90[/C][C]0.216435[/C][C]0.432869[/C][C]0.783565[/C][/ROW]
[ROW][C]91[/C][C]0.230629[/C][C]0.461259[/C][C]0.769371[/C][/ROW]
[ROW][C]92[/C][C]0.216068[/C][C]0.432136[/C][C]0.783932[/C][/ROW]
[ROW][C]93[/C][C]0.202442[/C][C]0.404885[/C][C]0.797558[/C][/ROW]
[ROW][C]94[/C][C]0.172236[/C][C]0.344473[/C][C]0.827764[/C][/ROW]
[ROW][C]95[/C][C]0.179582[/C][C]0.359164[/C][C]0.820418[/C][/ROW]
[ROW][C]96[/C][C]0.175492[/C][C]0.350985[/C][C]0.824508[/C][/ROW]
[ROW][C]97[/C][C]0.160289[/C][C]0.320578[/C][C]0.839711[/C][/ROW]
[ROW][C]98[/C][C]0.137049[/C][C]0.274097[/C][C]0.862951[/C][/ROW]
[ROW][C]99[/C][C]0.12027[/C][C]0.24054[/C][C]0.87973[/C][/ROW]
[ROW][C]100[/C][C]0.119164[/C][C]0.238328[/C][C]0.880836[/C][/ROW]
[ROW][C]101[/C][C]0.109987[/C][C]0.219974[/C][C]0.890013[/C][/ROW]
[ROW][C]102[/C][C]0.0965295[/C][C]0.193059[/C][C]0.903471[/C][/ROW]
[ROW][C]103[/C][C]0.0790856[/C][C]0.158171[/C][C]0.920914[/C][/ROW]
[ROW][C]104[/C][C]0.0649092[/C][C]0.129818[/C][C]0.935091[/C][/ROW]
[ROW][C]105[/C][C]0.076272[/C][C]0.152544[/C][C]0.923728[/C][/ROW]
[ROW][C]106[/C][C]0.182707[/C][C]0.365413[/C][C]0.817293[/C][/ROW]
[ROW][C]107[/C][C]0.169859[/C][C]0.339718[/C][C]0.830141[/C][/ROW]
[ROW][C]108[/C][C]0.193795[/C][C]0.38759[/C][C]0.806205[/C][/ROW]
[ROW][C]109[/C][C]0.180041[/C][C]0.360081[/C][C]0.819959[/C][/ROW]
[ROW][C]110[/C][C]0.380724[/C][C]0.761449[/C][C]0.619276[/C][/ROW]
[ROW][C]111[/C][C]0.456715[/C][C]0.91343[/C][C]0.543285[/C][/ROW]
[ROW][C]112[/C][C]0.440209[/C][C]0.880419[/C][C]0.559791[/C][/ROW]
[ROW][C]113[/C][C]0.569096[/C][C]0.861809[/C][C]0.430904[/C][/ROW]
[ROW][C]114[/C][C]0.522293[/C][C]0.955415[/C][C]0.477707[/C][/ROW]
[ROW][C]115[/C][C]0.50772[/C][C]0.984561[/C][C]0.49228[/C][/ROW]
[ROW][C]116[/C][C]0.472082[/C][C]0.944163[/C][C]0.527918[/C][/ROW]
[ROW][C]117[/C][C]0.442332[/C][C]0.884665[/C][C]0.557668[/C][/ROW]
[ROW][C]118[/C][C]0.418308[/C][C]0.836617[/C][C]0.581692[/C][/ROW]
[ROW][C]119[/C][C]0.372599[/C][C]0.745197[/C][C]0.627401[/C][/ROW]
[ROW][C]120[/C][C]0.336403[/C][C]0.672806[/C][C]0.663597[/C][/ROW]
[ROW][C]121[/C][C]0.291973[/C][C]0.583946[/C][C]0.708027[/C][/ROW]
[ROW][C]122[/C][C]0.250944[/C][C]0.501888[/C][C]0.749056[/C][/ROW]
[ROW][C]123[/C][C]0.215646[/C][C]0.431291[/C][C]0.784354[/C][/ROW]
[ROW][C]124[/C][C]0.178445[/C][C]0.356889[/C][C]0.821555[/C][/ROW]
[ROW][C]125[/C][C]0.143759[/C][C]0.287517[/C][C]0.856241[/C][/ROW]
[ROW][C]126[/C][C]0.153441[/C][C]0.306883[/C][C]0.846559[/C][/ROW]
[ROW][C]127[/C][C]0.170796[/C][C]0.341591[/C][C]0.829204[/C][/ROW]
[ROW][C]128[/C][C]0.349629[/C][C]0.699257[/C][C]0.650371[/C][/ROW]
[ROW][C]129[/C][C]0.320751[/C][C]0.641502[/C][C]0.679249[/C][/ROW]
[ROW][C]130[/C][C]0.284001[/C][C]0.568002[/C][C]0.715999[/C][/ROW]
[ROW][C]131[/C][C]0.252359[/C][C]0.504719[/C][C]0.747641[/C][/ROW]
[ROW][C]132[/C][C]0.294714[/C][C]0.589429[/C][C]0.705286[/C][/ROW]
[ROW][C]133[/C][C]0.365842[/C][C]0.731683[/C][C]0.634158[/C][/ROW]
[ROW][C]134[/C][C]0.476186[/C][C]0.952373[/C][C]0.523814[/C][/ROW]
[ROW][C]135[/C][C]0.420225[/C][C]0.840449[/C][C]0.579775[/C][/ROW]
[ROW][C]136[/C][C]0.358131[/C][C]0.716263[/C][C]0.641869[/C][/ROW]
[ROW][C]137[/C][C]0.314392[/C][C]0.628784[/C][C]0.685608[/C][/ROW]
[ROW][C]138[/C][C]0.254387[/C][C]0.508774[/C][C]0.745613[/C][/ROW]
[ROW][C]139[/C][C]0.303572[/C][C]0.607144[/C][C]0.696428[/C][/ROW]
[ROW][C]140[/C][C]0.248589[/C][C]0.497177[/C][C]0.751411[/C][/ROW]
[ROW][C]141[/C][C]0.476523[/C][C]0.953046[/C][C]0.523477[/C][/ROW]
[ROW][C]142[/C][C]0.414987[/C][C]0.829974[/C][C]0.585013[/C][/ROW]
[ROW][C]143[/C][C]0.441212[/C][C]0.882424[/C][C]0.558788[/C][/ROW]
[ROW][C]144[/C][C]0.702761[/C][C]0.594477[/C][C]0.297239[/C][/ROW]
[ROW][C]145[/C][C]0.613255[/C][C]0.773489[/C][C]0.386745[/C][/ROW]
[ROW][C]146[/C][C]0.944289[/C][C]0.111422[/C][C]0.0557111[/C][/ROW]
[ROW][C]147[/C][C]0.924297[/C][C]0.151407[/C][C]0.0757034[/C][/ROW]
[ROW][C]148[/C][C]0.946481[/C][C]0.107038[/C][C]0.0535192[/C][/ROW]
[ROW][C]149[/C][C]0.944753[/C][C]0.110494[/C][C]0.055247[/C][/ROW]
[ROW][C]150[/C][C]0.882424[/C][C]0.235151[/C][C]0.117576[/C][/ROW]
[ROW][C]151[/C][C]0.769368[/C][C]0.461264[/C][C]0.230632[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=254637&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254637&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
11	0.185869	0.371738	0.814131
12	0.871127	0.257747	0.128873
13	0.917786	0.164429	0.0822144
14	0.878255	0.24349	0.121745
15	0.840975	0.31805	0.159025
16	0.865021	0.269957	0.134979
17	0.821694	0.356612	0.178306
18	0.789006	0.421987	0.210994
19	0.742707	0.514585	0.257293
20	0.770547	0.458905	0.229453
21	0.790255	0.41949	0.209745
22	0.749218	0.501564	0.250782
23	0.705947	0.588105	0.294053
24	0.638801	0.722398	0.361199
25	0.633268	0.733463	0.366732
26	0.779503	0.440993	0.220497
27	0.773103	0.453795	0.226897
28	0.729322	0.541355	0.270678
29	0.73839	0.52322	0.26161
30	0.68261	0.634779	0.31739
31	0.645134	0.709732	0.354866
32	0.803653	0.392694	0.196347
33	0.801254	0.397493	0.198746
34	0.765075	0.469851	0.234925
35	0.717559	0.564882	0.282441
36	0.67111	0.657779	0.32889
37	0.659073	0.681854	0.340927
38	0.626295	0.74741	0.373705
39	0.744233	0.511534	0.255767
40	0.697111	0.605777	0.302889
41	0.667213	0.665574	0.332787
42	0.615449	0.769103	0.384551
43	0.586125	0.82775	0.413875
44	0.537555	0.924889	0.462445
45	0.63473	0.73054	0.36527
46	0.641041	0.717919	0.358959
47	0.623761	0.752479	0.376239
48	0.59305	0.8139	0.40695
49	0.545345	0.909309	0.454655
50	0.572387	0.855226	0.427613
51	0.618863	0.762274	0.381137
52	0.579867	0.840265	0.420133
53	0.534978	0.930044	0.465022
54	0.486234	0.972468	0.513766
55	0.441307	0.882614	0.558693
56	0.430487	0.860973	0.569513
57	0.457975	0.915951	0.542025
58	0.593084	0.813833	0.406916
59	0.553953	0.892094	0.446047
60	0.505559	0.988881	0.494441
61	0.469026	0.938053	0.530974
62	0.453523	0.907047	0.546477
63	0.41981	0.83962	0.58019
64	0.426247	0.852494	0.573753
65	0.380744	0.761488	0.619256
66	0.344715	0.68943	0.655285
67	0.304224	0.608448	0.695776
68	0.334893	0.669785	0.665107
69	0.345486	0.690973	0.654514
70	0.306021	0.612042	0.693979
71	0.339072	0.678145	0.660928
72	0.377915	0.755829	0.622085
73	0.378925	0.75785	0.621075
74	0.335407	0.670814	0.664593
75	0.300772	0.601544	0.699228
76	0.356309	0.712617	0.643691
77	0.318767	0.637535	0.681233
78	0.278254	0.556507	0.721746
79	0.342166	0.684332	0.657834
80	0.417807	0.835615	0.582193
81	0.407568	0.815136	0.592432
82	0.362945	0.72589	0.637055
83	0.323405	0.64681	0.676595
84	0.311795	0.623589	0.688205
85	0.298877	0.597753	0.701123
86	0.265083	0.530166	0.734917
87	0.238293	0.476587	0.761707
88	0.213008	0.426016	0.786992
89	0.250384	0.500768	0.749616
90	0.216435	0.432869	0.783565
91	0.230629	0.461259	0.769371
92	0.216068	0.432136	0.783932
93	0.202442	0.404885	0.797558
94	0.172236	0.344473	0.827764
95	0.179582	0.359164	0.820418
96	0.175492	0.350985	0.824508
97	0.160289	0.320578	0.839711
98	0.137049	0.274097	0.862951
99	0.12027	0.24054	0.87973
100	0.119164	0.238328	0.880836
101	0.109987	0.219974	0.890013
102	0.0965295	0.193059	0.903471
103	0.0790856	0.158171	0.920914
104	0.0649092	0.129818	0.935091
105	0.076272	0.152544	0.923728
106	0.182707	0.365413	0.817293
107	0.169859	0.339718	0.830141
108	0.193795	0.38759	0.806205
109	0.180041	0.360081	0.819959
110	0.380724	0.761449	0.619276
111	0.456715	0.91343	0.543285
112	0.440209	0.880419	0.559791
113	0.569096	0.861809	0.430904
114	0.522293	0.955415	0.477707
115	0.50772	0.984561	0.49228
116	0.472082	0.944163	0.527918
117	0.442332	0.884665	0.557668
118	0.418308	0.836617	0.581692
119	0.372599	0.745197	0.627401
120	0.336403	0.672806	0.663597
121	0.291973	0.583946	0.708027
122	0.250944	0.501888	0.749056
123	0.215646	0.431291	0.784354
124	0.178445	0.356889	0.821555
125	0.143759	0.287517	0.856241
126	0.153441	0.306883	0.846559
127	0.170796	0.341591	0.829204
128	0.349629	0.699257	0.650371
129	0.320751	0.641502	0.679249
130	0.284001	0.568002	0.715999
131	0.252359	0.504719	0.747641
132	0.294714	0.589429	0.705286
133	0.365842	0.731683	0.634158
134	0.476186	0.952373	0.523814
135	0.420225	0.840449	0.579775
136	0.358131	0.716263	0.641869
137	0.314392	0.628784	0.685608
138	0.254387	0.508774	0.745613
139	0.303572	0.607144	0.696428
140	0.248589	0.497177	0.751411
141	0.476523	0.953046	0.523477
142	0.414987	0.829974	0.585013
143	0.441212	0.882424	0.558788
144	0.702761	0.594477	0.297239
145	0.613255	0.773489	0.386745
146	0.944289	0.111422	0.0557111
147	0.924297	0.151407	0.0757034
148	0.946481	0.107038	0.0535192
149	0.944753	0.110494	0.055247
150	0.882424	0.235151	0.117576
151	0.769368	0.461264	0.230632

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	0	0	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254637&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=254637&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254637&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	0	0	OK

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

PNG link

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PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$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