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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 12 Nov 2014 13:57:40 +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/12/t1415800868imk12b6kfuwtj5m.htm/, Retrieved Mon, 20 May 2024 00:28:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253846, Retrieved Mon, 20 May 2024 00:28:43 +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] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
- RMPD    [Multiple Regression] [] [2014-11-12 13:57:40] [baa7d013c3374cabca6c222951a47a9f] [Current]

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Dataseries X:

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Histogram

Boxplots

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

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	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253846&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253846&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253846&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	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 18.3731 + 0.334739Separate[t] + 0.326564Learning[t] -0.138915Software[t] + 0.0367163Happiness[t] -0.0254267Depression[t] + 0.0184529Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  18.3731 +  0.334739Separate[t] +  0.326564Learning[t] -0.138915Software[t] +  0.0367163Happiness[t] -0.0254267Depression[t] +  0.0184529Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253846&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  18.3731 +  0.334739Separate[t] +  0.326564Learning[t] -0.138915Software[t] +  0.0367163Happiness[t] -0.0254267Depression[t] +  0.0184529Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253846&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253846&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] = + 18.3731 + 0.334739Separate[t] + 0.326564Learning[t] -0.138915Software[t] + 0.0367163Happiness[t] -0.0254267Depression[t] + 0.0184529Belonging[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)	18.3731	4.15239	4.425	1.81143e-05	9.05713e-06
Separate	0.334739	0.0701731	4.77	4.2156e-06	2.1078e-06
Learning	0.326564	0.132621	2.462	0.0148969	0.00744843
Software	-0.138915	0.136811	-1.015	0.311506	0.155753
Happiness	0.0367163	0.128499	0.2857	0.775464	0.387732
Depression	-0.0254267	0.0945532	-0.2689	0.788353	0.394177
Belonging	0.0184529	0.0241858	0.763	0.446644	0.223322

\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) & 18.3731 & 4.15239 & 4.425 & 1.81143e-05 & 9.05713e-06 \tabularnewline
Separate & 0.334739 & 0.0701731 & 4.77 & 4.2156e-06 & 2.1078e-06 \tabularnewline
Learning & 0.326564 & 0.132621 & 2.462 & 0.0148969 & 0.00744843 \tabularnewline
Software & -0.138915 & 0.136811 & -1.015 & 0.311506 & 0.155753 \tabularnewline
Happiness & 0.0367163 & 0.128499 & 0.2857 & 0.775464 & 0.387732 \tabularnewline
Depression & -0.0254267 & 0.0945532 & -0.2689 & 0.788353 & 0.394177 \tabularnewline
Belonging & 0.0184529 & 0.0241858 & 0.763 & 0.446644 & 0.223322 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253846&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]18.3731[/C][C]4.15239[/C][C]4.425[/C][C]1.81143e-05[/C][C]9.05713e-06[/C][/ROW]
[ROW][C]Separate[/C][C]0.334739[/C][C]0.0701731[/C][C]4.77[/C][C]4.2156e-06[/C][C]2.1078e-06[/C][/ROW]
[ROW][C]Learning[/C][C]0.326564[/C][C]0.132621[/C][C]2.462[/C][C]0.0148969[/C][C]0.00744843[/C][/ROW]
[ROW][C]Software[/C][C]-0.138915[/C][C]0.136811[/C][C]-1.015[/C][C]0.311506[/C][C]0.155753[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0367163[/C][C]0.128499[/C][C]0.2857[/C][C]0.775464[/C][C]0.387732[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0254267[/C][C]0.0945532[/C][C]-0.2689[/C][C]0.788353[/C][C]0.394177[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0184529[/C][C]0.0241858[/C][C]0.763[/C][C]0.446644[/C][C]0.223322[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253846&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253846&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)	18.3731	4.15239	4.425	1.81143e-05	9.05713e-06
Separate	0.334739	0.0701731	4.77	4.2156e-06	2.1078e-06
Learning	0.326564	0.132621	2.462	0.0148969	0.00744843
Software	-0.138915	0.136811	-1.015	0.311506	0.155753
Happiness	0.0367163	0.128499	0.2857	0.775464	0.387732
Depression	-0.0254267	0.0945532	-0.2689	0.788353	0.394177
Belonging	0.0184529	0.0241858	0.763	0.446644	0.223322

Multiple Linear Regression - Regression Statistics
Multiple R	0.426719
R-squared	0.182089
Adjusted R-squared	0.150428
F-TEST (value)	5.75121
F-TEST (DF numerator)	6
F-TEST (DF denominator)	155
p-value	1.99091e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.11093
Sum Squared Residuals	1500.07

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.426719 \tabularnewline
R-squared & 0.182089 \tabularnewline
Adjusted R-squared & 0.150428 \tabularnewline
F-TEST (value) & 5.75121 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 1.99091e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.11093 \tabularnewline
Sum Squared Residuals & 1500.07 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253846&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.426719[/C][/ROW]
[ROW][C]R-squared[/C][C]0.182089[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.150428[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.75121[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]1.99091e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.11093[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1500.07[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253846&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253846&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	0.426719
R-squared	0.182089
Adjusted R-squared	0.150428
F-TEST (value)	5.75121
F-TEST (DF numerator)	6
F-TEST (DF denominator)	155
p-value	1.99091e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.11093
Sum Squared Residuals	1500.07

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	41	34.8584	6.14155
2	39	34.7499	4.25014
3	30	35.4758	-5.47576
4	31	34.8563	-3.85628
5	34	34.9804	-0.980366
6	35	32.7318	2.26819
7	39	34.2204	4.77961
8	34	35.0879	-1.08792
9	36	34.6559	1.34413
10	37	36.7808	0.219227
11	38	34.4136	3.58643
12	36	34.8029	1.19708
13	38	34.8691	3.13088
14	39	36.0181	2.98191
15	33	36.2383	-3.23834
16	32	33.8793	-1.87929
17	36	34.1361	1.86392
18	38	37.9258	0.0741755
19	39	36.884	2.11598
20	32	33.9515	-1.95152
21	32	34.8172	-2.81725
22	31	33.6108	-2.61076
23	39	36.6974	2.30263
24	37	36.2429	0.757134
25	39	35.3175	3.68246
26	41	34.4083	6.59172
27	36	35.635	0.364971
28	33	35.118	-2.11797
29	33	34.6116	-1.61163
30	34	33.9119	0.088089
31	31	32.8121	-1.81213
32	27	32.9846	-5.98457
33	37	33.6128	3.38718
34	34	36.1376	-2.13758
35	34	32.8692	1.13082
36	32	32.4272	-0.427234
37	29	32.1666	-3.16661
38	36	33.8802	2.11982
39	29	34.3582	-5.35822
40	35	34.7517	0.248342
41	37	34.608	2.39196
42	34	33.9399	0.0601409
43	38	34.9128	3.08719
44	35	33.6464	1.35359
45	38	32.3627	5.63731
46	37	33.3423	3.65768
47	38	35.7153	2.28473
48	33	34.7796	-1.77964
49	36	36.3984	-0.398435
50	38	33.5713	4.42875
51	32	36.0352	-4.0352
52	32	32.9924	-0.992416
53	32	32.78	-0.780007
54	34	35.741	-1.74101
55	32	32.6527	-0.652685
56	37	34.4712	2.5288
57	39	34.9347	4.0653
58	29	34.8705	-5.87052
59	37	35.3275	1.67251
60	35	34.941	0.0589997
61	30	31.4554	-1.45536
62	38	34.9053	3.09466
63	34	34.8888	-0.888813
64	31	34.2504	-3.2504
65	34	33.5247	0.475292
66	35	36.0678	-1.06784
67	36	35.2118	0.788174
68	30	33.2437	-3.24368
69	39	35.3972	3.60282
70	35	35.7845	-0.78451
71	38	33.9979	4.00215
72	31	35.3395	-4.33953
73	34	37.0761	-3.07615
74	38	37.6005	0.399528
75	34	32.7168	1.28324
76	39	34.1503	4.84965
77	37	35.7629	1.23709
78	34	33.7653	0.234722
79	28	32.9694	-4.96943
80	37	32.214	4.78597
81	33	35.7106	-2.71058
82	37	36.8443	0.155734
83	35	35.9485	-0.94855
84	37	34.2804	2.71963
85	32	34.6989	-2.69886
86	33	34.1902	-1.19024
87	38	36.3537	1.64625
88	33	34.7944	-1.79443
89	29	33.7352	-4.73516
90	33	33.6403	-0.640298
91	31	34.7329	-3.73293
92	36	33.645	2.35502
93	35	37.2965	-2.29652
94	32	32.623	-0.623039
95	29	32.6929	-3.6929
96	39	36.0436	2.95642
97	37	34.8151	2.18489
98	35	34.3985	0.601539
99	37	35.1514	1.84856
100	32	35.3054	-3.30535
101	38	35.4763	2.52375
102	37	35.1668	1.83324
103	36	37.0568	-1.05684
104	32	32.4583	-0.458265
105	33	36.7266	-3.72658
106	40	32.7652	7.23476
107	38	35.6838	2.31624
108	41	36.44	4.55996
109	36	34.7681	1.23186
110	43	35.9843	7.01574
111	30	35.0202	-5.02018
112	31	34.0877	-3.0877
113	32	37.9424	-5.9424
114	32	32.9706	-0.970593
115	37	34.042	2.95796
116	37	35.0772	1.92285
117	33	35.6832	-2.68317
118	34	36.6984	-2.69842
119	33	34.3647	-1.36467
120	38	35.7751	2.22486
121	33	34.5314	-1.53136
122	31	32.3907	-1.39066
123	38	35.9196	2.08037
124	37	36.1231	0.87685
125	33	33.4607	-0.460688
126	31	34.2162	-3.21618
127	39	34.6756	4.32442
128	44	37.1869	6.81313
129	33	35.9845	-2.98453
130	35	33.359	1.641
131	32	34.7631	-2.76314
132	28	31.8621	-3.86213
133	40	36.5895	3.4105
134	27	32.1748	-5.17476
135	37	35.6092	1.39076
136	32	33.0566	-1.05664
137	28	29.548	-1.54798
138	34	34.7799	-0.779949
139	30	33.2855	-3.28553
140	35	34.0137	0.986288
141	31	32.8967	-1.89668
142	32	35.1609	-3.16087
143	30	35.184	-5.18398
144	30	35.3527	-5.35269
145	31	31.4356	-0.435555
146	40	32.8544	7.14559
147	32	33.1681	-1.16807
148	36	34.1826	1.81744
149	32	33.8529	-1.85292
150	35	33.2742	1.72575
151	38	35.572	2.428
152	42	35.3538	6.64615
153	34	36.7368	-2.73675
154	35	37.2835	-2.28354
155	35	34.3354	0.664607
156	33	32.3633	0.636713
157	36	33.645	2.35502
158	32	35.6016	-3.60159
159	33	35.9845	-2.98453
160	34	34.5802	-0.580224
161	32	34.4294	-2.42937
162	34	34.9015	-0.901453

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 34.8584 & 6.14155 \tabularnewline
2 & 39 & 34.7499 & 4.25014 \tabularnewline
3 & 30 & 35.4758 & -5.47576 \tabularnewline
4 & 31 & 34.8563 & -3.85628 \tabularnewline
5 & 34 & 34.9804 & -0.980366 \tabularnewline
6 & 35 & 32.7318 & 2.26819 \tabularnewline
7 & 39 & 34.2204 & 4.77961 \tabularnewline
8 & 34 & 35.0879 & -1.08792 \tabularnewline
9 & 36 & 34.6559 & 1.34413 \tabularnewline
10 & 37 & 36.7808 & 0.219227 \tabularnewline
11 & 38 & 34.4136 & 3.58643 \tabularnewline
12 & 36 & 34.8029 & 1.19708 \tabularnewline
13 & 38 & 34.8691 & 3.13088 \tabularnewline
14 & 39 & 36.0181 & 2.98191 \tabularnewline
15 & 33 & 36.2383 & -3.23834 \tabularnewline
16 & 32 & 33.8793 & -1.87929 \tabularnewline
17 & 36 & 34.1361 & 1.86392 \tabularnewline
18 & 38 & 37.9258 & 0.0741755 \tabularnewline
19 & 39 & 36.884 & 2.11598 \tabularnewline
20 & 32 & 33.9515 & -1.95152 \tabularnewline
21 & 32 & 34.8172 & -2.81725 \tabularnewline
22 & 31 & 33.6108 & -2.61076 \tabularnewline
23 & 39 & 36.6974 & 2.30263 \tabularnewline
24 & 37 & 36.2429 & 0.757134 \tabularnewline
25 & 39 & 35.3175 & 3.68246 \tabularnewline
26 & 41 & 34.4083 & 6.59172 \tabularnewline
27 & 36 & 35.635 & 0.364971 \tabularnewline
28 & 33 & 35.118 & -2.11797 \tabularnewline
29 & 33 & 34.6116 & -1.61163 \tabularnewline
30 & 34 & 33.9119 & 0.088089 \tabularnewline
31 & 31 & 32.8121 & -1.81213 \tabularnewline
32 & 27 & 32.9846 & -5.98457 \tabularnewline
33 & 37 & 33.6128 & 3.38718 \tabularnewline
34 & 34 & 36.1376 & -2.13758 \tabularnewline
35 & 34 & 32.8692 & 1.13082 \tabularnewline
36 & 32 & 32.4272 & -0.427234 \tabularnewline
37 & 29 & 32.1666 & -3.16661 \tabularnewline
38 & 36 & 33.8802 & 2.11982 \tabularnewline
39 & 29 & 34.3582 & -5.35822 \tabularnewline
40 & 35 & 34.7517 & 0.248342 \tabularnewline
41 & 37 & 34.608 & 2.39196 \tabularnewline
42 & 34 & 33.9399 & 0.0601409 \tabularnewline
43 & 38 & 34.9128 & 3.08719 \tabularnewline
44 & 35 & 33.6464 & 1.35359 \tabularnewline
45 & 38 & 32.3627 & 5.63731 \tabularnewline
46 & 37 & 33.3423 & 3.65768 \tabularnewline
47 & 38 & 35.7153 & 2.28473 \tabularnewline
48 & 33 & 34.7796 & -1.77964 \tabularnewline
49 & 36 & 36.3984 & -0.398435 \tabularnewline
50 & 38 & 33.5713 & 4.42875 \tabularnewline
51 & 32 & 36.0352 & -4.0352 \tabularnewline
52 & 32 & 32.9924 & -0.992416 \tabularnewline
53 & 32 & 32.78 & -0.780007 \tabularnewline
54 & 34 & 35.741 & -1.74101 \tabularnewline
55 & 32 & 32.6527 & -0.652685 \tabularnewline
56 & 37 & 34.4712 & 2.5288 \tabularnewline
57 & 39 & 34.9347 & 4.0653 \tabularnewline
58 & 29 & 34.8705 & -5.87052 \tabularnewline
59 & 37 & 35.3275 & 1.67251 \tabularnewline
60 & 35 & 34.941 & 0.0589997 \tabularnewline
61 & 30 & 31.4554 & -1.45536 \tabularnewline
62 & 38 & 34.9053 & 3.09466 \tabularnewline
63 & 34 & 34.8888 & -0.888813 \tabularnewline
64 & 31 & 34.2504 & -3.2504 \tabularnewline
65 & 34 & 33.5247 & 0.475292 \tabularnewline
66 & 35 & 36.0678 & -1.06784 \tabularnewline
67 & 36 & 35.2118 & 0.788174 \tabularnewline
68 & 30 & 33.2437 & -3.24368 \tabularnewline
69 & 39 & 35.3972 & 3.60282 \tabularnewline
70 & 35 & 35.7845 & -0.78451 \tabularnewline
71 & 38 & 33.9979 & 4.00215 \tabularnewline
72 & 31 & 35.3395 & -4.33953 \tabularnewline
73 & 34 & 37.0761 & -3.07615 \tabularnewline
74 & 38 & 37.6005 & 0.399528 \tabularnewline
75 & 34 & 32.7168 & 1.28324 \tabularnewline
76 & 39 & 34.1503 & 4.84965 \tabularnewline
77 & 37 & 35.7629 & 1.23709 \tabularnewline
78 & 34 & 33.7653 & 0.234722 \tabularnewline
79 & 28 & 32.9694 & -4.96943 \tabularnewline
80 & 37 & 32.214 & 4.78597 \tabularnewline
81 & 33 & 35.7106 & -2.71058 \tabularnewline
82 & 37 & 36.8443 & 0.155734 \tabularnewline
83 & 35 & 35.9485 & -0.94855 \tabularnewline
84 & 37 & 34.2804 & 2.71963 \tabularnewline
85 & 32 & 34.6989 & -2.69886 \tabularnewline
86 & 33 & 34.1902 & -1.19024 \tabularnewline
87 & 38 & 36.3537 & 1.64625 \tabularnewline
88 & 33 & 34.7944 & -1.79443 \tabularnewline
89 & 29 & 33.7352 & -4.73516 \tabularnewline
90 & 33 & 33.6403 & -0.640298 \tabularnewline
91 & 31 & 34.7329 & -3.73293 \tabularnewline
92 & 36 & 33.645 & 2.35502 \tabularnewline
93 & 35 & 37.2965 & -2.29652 \tabularnewline
94 & 32 & 32.623 & -0.623039 \tabularnewline
95 & 29 & 32.6929 & -3.6929 \tabularnewline
96 & 39 & 36.0436 & 2.95642 \tabularnewline
97 & 37 & 34.8151 & 2.18489 \tabularnewline
98 & 35 & 34.3985 & 0.601539 \tabularnewline
99 & 37 & 35.1514 & 1.84856 \tabularnewline
100 & 32 & 35.3054 & -3.30535 \tabularnewline
101 & 38 & 35.4763 & 2.52375 \tabularnewline
102 & 37 & 35.1668 & 1.83324 \tabularnewline
103 & 36 & 37.0568 & -1.05684 \tabularnewline
104 & 32 & 32.4583 & -0.458265 \tabularnewline
105 & 33 & 36.7266 & -3.72658 \tabularnewline
106 & 40 & 32.7652 & 7.23476 \tabularnewline
107 & 38 & 35.6838 & 2.31624 \tabularnewline
108 & 41 & 36.44 & 4.55996 \tabularnewline
109 & 36 & 34.7681 & 1.23186 \tabularnewline
110 & 43 & 35.9843 & 7.01574 \tabularnewline
111 & 30 & 35.0202 & -5.02018 \tabularnewline
112 & 31 & 34.0877 & -3.0877 \tabularnewline
113 & 32 & 37.9424 & -5.9424 \tabularnewline
114 & 32 & 32.9706 & -0.970593 \tabularnewline
115 & 37 & 34.042 & 2.95796 \tabularnewline
116 & 37 & 35.0772 & 1.92285 \tabularnewline
117 & 33 & 35.6832 & -2.68317 \tabularnewline
118 & 34 & 36.6984 & -2.69842 \tabularnewline
119 & 33 & 34.3647 & -1.36467 \tabularnewline
120 & 38 & 35.7751 & 2.22486 \tabularnewline
121 & 33 & 34.5314 & -1.53136 \tabularnewline
122 & 31 & 32.3907 & -1.39066 \tabularnewline
123 & 38 & 35.9196 & 2.08037 \tabularnewline
124 & 37 & 36.1231 & 0.87685 \tabularnewline
125 & 33 & 33.4607 & -0.460688 \tabularnewline
126 & 31 & 34.2162 & -3.21618 \tabularnewline
127 & 39 & 34.6756 & 4.32442 \tabularnewline
128 & 44 & 37.1869 & 6.81313 \tabularnewline
129 & 33 & 35.9845 & -2.98453 \tabularnewline
130 & 35 & 33.359 & 1.641 \tabularnewline
131 & 32 & 34.7631 & -2.76314 \tabularnewline
132 & 28 & 31.8621 & -3.86213 \tabularnewline
133 & 40 & 36.5895 & 3.4105 \tabularnewline
134 & 27 & 32.1748 & -5.17476 \tabularnewline
135 & 37 & 35.6092 & 1.39076 \tabularnewline
136 & 32 & 33.0566 & -1.05664 \tabularnewline
137 & 28 & 29.548 & -1.54798 \tabularnewline
138 & 34 & 34.7799 & -0.779949 \tabularnewline
139 & 30 & 33.2855 & -3.28553 \tabularnewline
140 & 35 & 34.0137 & 0.986288 \tabularnewline
141 & 31 & 32.8967 & -1.89668 \tabularnewline
142 & 32 & 35.1609 & -3.16087 \tabularnewline
143 & 30 & 35.184 & -5.18398 \tabularnewline
144 & 30 & 35.3527 & -5.35269 \tabularnewline
145 & 31 & 31.4356 & -0.435555 \tabularnewline
146 & 40 & 32.8544 & 7.14559 \tabularnewline
147 & 32 & 33.1681 & -1.16807 \tabularnewline
148 & 36 & 34.1826 & 1.81744 \tabularnewline
149 & 32 & 33.8529 & -1.85292 \tabularnewline
150 & 35 & 33.2742 & 1.72575 \tabularnewline
151 & 38 & 35.572 & 2.428 \tabularnewline
152 & 42 & 35.3538 & 6.64615 \tabularnewline
153 & 34 & 36.7368 & -2.73675 \tabularnewline
154 & 35 & 37.2835 & -2.28354 \tabularnewline
155 & 35 & 34.3354 & 0.664607 \tabularnewline
156 & 33 & 32.3633 & 0.636713 \tabularnewline
157 & 36 & 33.645 & 2.35502 \tabularnewline
158 & 32 & 35.6016 & -3.60159 \tabularnewline
159 & 33 & 35.9845 & -2.98453 \tabularnewline
160 & 34 & 34.5802 & -0.580224 \tabularnewline
161 & 32 & 34.4294 & -2.42937 \tabularnewline
162 & 34 & 34.9015 & -0.901453 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253846&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]34.8584[/C][C]6.14155[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.7499[/C][C]4.25014[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.4758[/C][C]-5.47576[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.8563[/C][C]-3.85628[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]34.9804[/C][C]-0.980366[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]32.7318[/C][C]2.26819[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.2204[/C][C]4.77961[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.0879[/C][C]-1.08792[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.6559[/C][C]1.34413[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.7808[/C][C]0.219227[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.4136[/C][C]3.58643[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]34.8029[/C][C]1.19708[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.8691[/C][C]3.13088[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]36.0181[/C][C]2.98191[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.2383[/C][C]-3.23834[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]33.8793[/C][C]-1.87929[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.1361[/C][C]1.86392[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]37.9258[/C][C]0.0741755[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]36.884[/C][C]2.11598[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.9515[/C][C]-1.95152[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]34.8172[/C][C]-2.81725[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.6108[/C][C]-2.61076[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.6974[/C][C]2.30263[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.2429[/C][C]0.757134[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.3175[/C][C]3.68246[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.4083[/C][C]6.59172[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]35.635[/C][C]0.364971[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.118[/C][C]-2.11797[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.6116[/C][C]-1.61163[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]33.9119[/C][C]0.088089[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]32.8121[/C][C]-1.81213[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]32.9846[/C][C]-5.98457[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.6128[/C][C]3.38718[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]36.1376[/C][C]-2.13758[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]32.8692[/C][C]1.13082[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]32.4272[/C][C]-0.427234[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.1666[/C][C]-3.16661[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]33.8802[/C][C]2.11982[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]34.3582[/C][C]-5.35822[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.7517[/C][C]0.248342[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.608[/C][C]2.39196[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]33.9399[/C][C]0.0601409[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]34.9128[/C][C]3.08719[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]33.6464[/C][C]1.35359[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.3627[/C][C]5.63731[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]33.3423[/C][C]3.65768[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.7153[/C][C]2.28473[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.7796[/C][C]-1.77964[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.3984[/C][C]-0.398435[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.5713[/C][C]4.42875[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.0352[/C][C]-4.0352[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]32.9924[/C][C]-0.992416[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]32.78[/C][C]-0.780007[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.741[/C][C]-1.74101[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.6527[/C][C]-0.652685[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.4712[/C][C]2.5288[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.9347[/C][C]4.0653[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.8705[/C][C]-5.87052[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.3275[/C][C]1.67251[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.941[/C][C]0.0589997[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.4554[/C][C]-1.45536[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.9053[/C][C]3.09466[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.8888[/C][C]-0.888813[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.2504[/C][C]-3.2504[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.5247[/C][C]0.475292[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]36.0678[/C][C]-1.06784[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]35.2118[/C][C]0.788174[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]33.2437[/C][C]-3.24368[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]35.3972[/C][C]3.60282[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.7845[/C][C]-0.78451[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]33.9979[/C][C]4.00215[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.3395[/C][C]-4.33953[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]37.0761[/C][C]-3.07615[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.6005[/C][C]0.399528[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.7168[/C][C]1.28324[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]34.1503[/C][C]4.84965[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.7629[/C][C]1.23709[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.7653[/C][C]0.234722[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]32.9694[/C][C]-4.96943[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]32.214[/C][C]4.78597[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.7106[/C][C]-2.71058[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]36.8443[/C][C]0.155734[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.9485[/C][C]-0.94855[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.2804[/C][C]2.71963[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.6989[/C][C]-2.69886[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]34.1902[/C][C]-1.19024[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.3537[/C][C]1.64625[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.7944[/C][C]-1.79443[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]33.7352[/C][C]-4.73516[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.6403[/C][C]-0.640298[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.7329[/C][C]-3.73293[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.645[/C][C]2.35502[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.2965[/C][C]-2.29652[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.623[/C][C]-0.623039[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]32.6929[/C][C]-3.6929[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]36.0436[/C][C]2.95642[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.8151[/C][C]2.18489[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.3985[/C][C]0.601539[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.1514[/C][C]1.84856[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.3054[/C][C]-3.30535[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.4763[/C][C]2.52375[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]35.1668[/C][C]1.83324[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]37.0568[/C][C]-1.05684[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.4583[/C][C]-0.458265[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.7266[/C][C]-3.72658[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]32.7652[/C][C]7.23476[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.6838[/C][C]2.31624[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.44[/C][C]4.55996[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.7681[/C][C]1.23186[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]35.9843[/C][C]7.01574[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]35.0202[/C][C]-5.02018[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]34.0877[/C][C]-3.0877[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.9424[/C][C]-5.9424[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]32.9706[/C][C]-0.970593[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.042[/C][C]2.95796[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]35.0772[/C][C]1.92285[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]35.6832[/C][C]-2.68317[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.6984[/C][C]-2.69842[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.3647[/C][C]-1.36467[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.7751[/C][C]2.22486[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]34.5314[/C][C]-1.53136[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.3907[/C][C]-1.39066[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]35.9196[/C][C]2.08037[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]36.1231[/C][C]0.87685[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.4607[/C][C]-0.460688[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.2162[/C][C]-3.21618[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.6756[/C][C]4.32442[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]37.1869[/C][C]6.81313[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.9845[/C][C]-2.98453[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.359[/C][C]1.641[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.7631[/C][C]-2.76314[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]31.8621[/C][C]-3.86213[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]36.5895[/C][C]3.4105[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]32.1748[/C][C]-5.17476[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]35.6092[/C][C]1.39076[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]33.0566[/C][C]-1.05664[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]29.548[/C][C]-1.54798[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.7799[/C][C]-0.779949[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.2855[/C][C]-3.28553[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]34.0137[/C][C]0.986288[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]32.8967[/C][C]-1.89668[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]35.1609[/C][C]-3.16087[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]35.184[/C][C]-5.18398[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]35.3527[/C][C]-5.35269[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.4356[/C][C]-0.435555[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]32.8544[/C][C]7.14559[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]33.1681[/C][C]-1.16807[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.1826[/C][C]1.81744[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.8529[/C][C]-1.85292[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.2742[/C][C]1.72575[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]35.572[/C][C]2.428[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]35.3538[/C][C]6.64615[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.7368[/C][C]-2.73675[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]37.2835[/C][C]-2.28354[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]34.3354[/C][C]0.664607[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]32.3633[/C][C]0.636713[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.645[/C][C]2.35502[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.6016[/C][C]-3.60159[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.9845[/C][C]-2.98453[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.5802[/C][C]-0.580224[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]34.4294[/C][C]-2.42937[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.9015[/C][C]-0.901453[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253846&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253846&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	34.8584	6.14155
2	39	34.7499	4.25014
3	30	35.4758	-5.47576
4	31	34.8563	-3.85628
5	34	34.9804	-0.980366
6	35	32.7318	2.26819
7	39	34.2204	4.77961
8	34	35.0879	-1.08792
9	36	34.6559	1.34413
10	37	36.7808	0.219227
11	38	34.4136	3.58643
12	36	34.8029	1.19708
13	38	34.8691	3.13088
14	39	36.0181	2.98191
15	33	36.2383	-3.23834
16	32	33.8793	-1.87929
17	36	34.1361	1.86392
18	38	37.9258	0.0741755
19	39	36.884	2.11598
20	32	33.9515	-1.95152
21	32	34.8172	-2.81725
22	31	33.6108	-2.61076
23	39	36.6974	2.30263
24	37	36.2429	0.757134
25	39	35.3175	3.68246
26	41	34.4083	6.59172
27	36	35.635	0.364971
28	33	35.118	-2.11797
29	33	34.6116	-1.61163
30	34	33.9119	0.088089
31	31	32.8121	-1.81213
32	27	32.9846	-5.98457
33	37	33.6128	3.38718
34	34	36.1376	-2.13758
35	34	32.8692	1.13082
36	32	32.4272	-0.427234
37	29	32.1666	-3.16661
38	36	33.8802	2.11982
39	29	34.3582	-5.35822
40	35	34.7517	0.248342
41	37	34.608	2.39196
42	34	33.9399	0.0601409
43	38	34.9128	3.08719
44	35	33.6464	1.35359
45	38	32.3627	5.63731
46	37	33.3423	3.65768
47	38	35.7153	2.28473
48	33	34.7796	-1.77964
49	36	36.3984	-0.398435
50	38	33.5713	4.42875
51	32	36.0352	-4.0352
52	32	32.9924	-0.992416
53	32	32.78	-0.780007
54	34	35.741	-1.74101
55	32	32.6527	-0.652685
56	37	34.4712	2.5288
57	39	34.9347	4.0653
58	29	34.8705	-5.87052
59	37	35.3275	1.67251
60	35	34.941	0.0589997
61	30	31.4554	-1.45536
62	38	34.9053	3.09466
63	34	34.8888	-0.888813
64	31	34.2504	-3.2504
65	34	33.5247	0.475292
66	35	36.0678	-1.06784
67	36	35.2118	0.788174
68	30	33.2437	-3.24368
69	39	35.3972	3.60282
70	35	35.7845	-0.78451
71	38	33.9979	4.00215
72	31	35.3395	-4.33953
73	34	37.0761	-3.07615
74	38	37.6005	0.399528
75	34	32.7168	1.28324
76	39	34.1503	4.84965
77	37	35.7629	1.23709
78	34	33.7653	0.234722
79	28	32.9694	-4.96943
80	37	32.214	4.78597
81	33	35.7106	-2.71058
82	37	36.8443	0.155734
83	35	35.9485	-0.94855
84	37	34.2804	2.71963
85	32	34.6989	-2.69886
86	33	34.1902	-1.19024
87	38	36.3537	1.64625
88	33	34.7944	-1.79443
89	29	33.7352	-4.73516
90	33	33.6403	-0.640298
91	31	34.7329	-3.73293
92	36	33.645	2.35502
93	35	37.2965	-2.29652
94	32	32.623	-0.623039
95	29	32.6929	-3.6929
96	39	36.0436	2.95642
97	37	34.8151	2.18489
98	35	34.3985	0.601539
99	37	35.1514	1.84856
100	32	35.3054	-3.30535
101	38	35.4763	2.52375
102	37	35.1668	1.83324
103	36	37.0568	-1.05684
104	32	32.4583	-0.458265
105	33	36.7266	-3.72658
106	40	32.7652	7.23476
107	38	35.6838	2.31624
108	41	36.44	4.55996
109	36	34.7681	1.23186
110	43	35.9843	7.01574
111	30	35.0202	-5.02018
112	31	34.0877	-3.0877
113	32	37.9424	-5.9424
114	32	32.9706	-0.970593
115	37	34.042	2.95796
116	37	35.0772	1.92285
117	33	35.6832	-2.68317
118	34	36.6984	-2.69842
119	33	34.3647	-1.36467
120	38	35.7751	2.22486
121	33	34.5314	-1.53136
122	31	32.3907	-1.39066
123	38	35.9196	2.08037
124	37	36.1231	0.87685
125	33	33.4607	-0.460688
126	31	34.2162	-3.21618
127	39	34.6756	4.32442
128	44	37.1869	6.81313
129	33	35.9845	-2.98453
130	35	33.359	1.641
131	32	34.7631	-2.76314
132	28	31.8621	-3.86213
133	40	36.5895	3.4105
134	27	32.1748	-5.17476
135	37	35.6092	1.39076
136	32	33.0566	-1.05664
137	28	29.548	-1.54798
138	34	34.7799	-0.779949
139	30	33.2855	-3.28553
140	35	34.0137	0.986288
141	31	32.8967	-1.89668
142	32	35.1609	-3.16087
143	30	35.184	-5.18398
144	30	35.3527	-5.35269
145	31	31.4356	-0.435555
146	40	32.8544	7.14559
147	32	33.1681	-1.16807
148	36	34.1826	1.81744
149	32	33.8529	-1.85292
150	35	33.2742	1.72575
151	38	35.572	2.428
152	42	35.3538	6.64615
153	34	36.7368	-2.73675
154	35	37.2835	-2.28354
155	35	34.3354	0.664607
156	33	32.3633	0.636713
157	36	33.645	2.35502
158	32	35.6016	-3.60159
159	33	35.9845	-2.98453
160	34	34.5802	-0.580224
161	32	34.4294	-2.42937
162	34	34.9015	-0.901453

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.194535	0.38907	0.805465
11	0.0905833	0.181167	0.909417
12	0.847559	0.304882	0.152441
13	0.892355	0.215291	0.107645
14	0.840634	0.318731	0.159366
15	0.792325	0.41535	0.207675
16	0.832182	0.335636	0.167818
17	0.774697	0.450606	0.225303
18	0.73414	0.53172	0.26586
19	0.695199	0.609601	0.304801
20	0.725906	0.548187	0.274094
21	0.74182	0.516359	0.25818
22	0.697039	0.605922	0.302961
23	0.658099	0.683802	0.341901
24	0.588547	0.822906	0.411453
25	0.59214	0.815721	0.40786
26	0.836854	0.326292	0.163146
27	0.793576	0.412848	0.206424
28	0.767847	0.464306	0.232153
29	0.737208	0.525585	0.262792
30	0.686368	0.627264	0.313632
31	0.657672	0.684655	0.342328
32	0.794963	0.410075	0.205037
33	0.792142	0.415716	0.207858
34	0.77139	0.457221	0.22861
35	0.730481	0.539037	0.269519
36	0.68058	0.638839	0.31942
37	0.656096	0.687809	0.343904
38	0.625282	0.749435	0.374718
39	0.742208	0.515584	0.257792
40	0.695349	0.609302	0.304651
41	0.666083	0.667834	0.333917
42	0.614809	0.770382	0.385191
43	0.587619	0.824762	0.412381
44	0.543421	0.913157	0.456579
45	0.636803	0.726395	0.363197
46	0.654356	0.691288	0.345644
47	0.623496	0.753008	0.376504
48	0.594739	0.810523	0.405261
49	0.544126	0.911749	0.455874
50	0.575479	0.849042	0.424521
51	0.620209	0.759582	0.379791
52	0.579783	0.840435	0.420217
53	0.532845	0.93431	0.467155
54	0.500342	0.999316	0.499658
55	0.452746	0.905493	0.547254
56	0.436486	0.872972	0.563514
57	0.465089	0.930177	0.534911
58	0.607432	0.785137	0.392568
59	0.572217	0.855565	0.427783
60	0.524329	0.951341	0.475671
61	0.486999	0.973999	0.513001
62	0.479584	0.959168	0.520416
63	0.439439	0.878879	0.560561
64	0.438581	0.877162	0.561419
65	0.393028	0.786056	0.606972
66	0.356312	0.712624	0.643688
67	0.315047	0.630093	0.684953
68	0.333469	0.666938	0.666531
69	0.347699	0.695397	0.652301
70	0.307919	0.615837	0.692081
71	0.333038	0.666076	0.666962
72	0.371908	0.743817	0.628092
73	0.37205	0.7441	0.62795
74	0.329194	0.658387	0.670806
75	0.295612	0.591223	0.704388
76	0.35206	0.70412	0.64794
77	0.314905	0.62981	0.685095
78	0.274825	0.549649	0.725175
79	0.339518	0.679036	0.660482
80	0.411775	0.82355	0.588225
81	0.399937	0.799874	0.600063
82	0.359737	0.719475	0.640263
83	0.320635	0.641271	0.679365
84	0.310442	0.620883	0.689558
85	0.298111	0.596221	0.701889
86	0.264643	0.529285	0.735357
87	0.238592	0.477184	0.761408
88	0.213659	0.427319	0.786341
89	0.252554	0.505107	0.747446
90	0.218647	0.437294	0.781353
91	0.234144	0.468287	0.765856
92	0.21943	0.43886	0.78057
93	0.205447	0.410894	0.794553
94	0.174949	0.349897	0.825051
95	0.181805	0.36361	0.818195
96	0.179394	0.358787	0.820606
97	0.163935	0.327869	0.836065
98	0.141068	0.282135	0.858932
99	0.12445	0.2489	0.87555
100	0.123512	0.247024	0.876488
101	0.114549	0.229099	0.885451
102	0.100897	0.201794	0.899103
103	0.0830131	0.166026	0.916987
104	0.0682831	0.136566	0.931717
105	0.0789954	0.157991	0.921005
106	0.190817	0.381635	0.809183
107	0.177476	0.354951	0.822524
108	0.203909	0.407817	0.796091
109	0.189239	0.378478	0.810761
110	0.388451	0.776902	0.611549
111	0.455929	0.911858	0.544071
112	0.437246	0.874491	0.562754
113	0.566997	0.866006	0.433003
114	0.533461	0.933077	0.466539
115	0.523673	0.952654	0.476327
116	0.487364	0.974727	0.512636
117	0.45519	0.910379	0.54481
118	0.433068	0.866137	0.566932
119	0.387568	0.775136	0.612432
120	0.352258	0.704517	0.647742
121	0.306915	0.61383	0.693085
122	0.264954	0.529908	0.735046
123	0.229412	0.458825	0.770588
124	0.190835	0.381671	0.809165
125	0.154983	0.309967	0.845017
126	0.163183	0.326366	0.836817
127	0.181676	0.363353	0.818324
128	0.385884	0.771769	0.614116
129	0.356661	0.713323	0.643339
130	0.318583	0.637167	0.681417
131	0.286723	0.573446	0.713277
132	0.332352	0.664705	0.667648
133	0.41025	0.820499	0.58975
134	0.524251	0.951498	0.475749
135	0.469468	0.938936	0.530532
136	0.408071	0.816141	0.591929
137	0.360971	0.721942	0.639029
138	0.297053	0.594107	0.702947
139	0.356757	0.713514	0.643243
140	0.298945	0.59789	0.701055
141	0.48358	0.967159	0.51642
142	0.41925	0.838501	0.58075
143	0.423327	0.846653	0.576673
144	0.753684	0.492631	0.246316
145	0.683919	0.632161	0.316081
146	0.96499	0.07002	0.03501
147	0.948796	0.102408	0.0512039
148	0.9445	0.111	0.0555
149	0.929949	0.140101	0.0700507
150	0.873908	0.252185	0.126092
151	0.802971	0.394059	0.197029
152	0.9585	0.0830001	0.0415

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.194535 & 0.38907 & 0.805465 \tabularnewline
11 & 0.0905833 & 0.181167 & 0.909417 \tabularnewline
12 & 0.847559 & 0.304882 & 0.152441 \tabularnewline
13 & 0.892355 & 0.215291 & 0.107645 \tabularnewline
14 & 0.840634 & 0.318731 & 0.159366 \tabularnewline
15 & 0.792325 & 0.41535 & 0.207675 \tabularnewline
16 & 0.832182 & 0.335636 & 0.167818 \tabularnewline
17 & 0.774697 & 0.450606 & 0.225303 \tabularnewline
18 & 0.73414 & 0.53172 & 0.26586 \tabularnewline
19 & 0.695199 & 0.609601 & 0.304801 \tabularnewline
20 & 0.725906 & 0.548187 & 0.274094 \tabularnewline
21 & 0.74182 & 0.516359 & 0.25818 \tabularnewline
22 & 0.697039 & 0.605922 & 0.302961 \tabularnewline
23 & 0.658099 & 0.683802 & 0.341901 \tabularnewline
24 & 0.588547 & 0.822906 & 0.411453 \tabularnewline
25 & 0.59214 & 0.815721 & 0.40786 \tabularnewline
26 & 0.836854 & 0.326292 & 0.163146 \tabularnewline
27 & 0.793576 & 0.412848 & 0.206424 \tabularnewline
28 & 0.767847 & 0.464306 & 0.232153 \tabularnewline
29 & 0.737208 & 0.525585 & 0.262792 \tabularnewline
30 & 0.686368 & 0.627264 & 0.313632 \tabularnewline
31 & 0.657672 & 0.684655 & 0.342328 \tabularnewline
32 & 0.794963 & 0.410075 & 0.205037 \tabularnewline
33 & 0.792142 & 0.415716 & 0.207858 \tabularnewline
34 & 0.77139 & 0.457221 & 0.22861 \tabularnewline
35 & 0.730481 & 0.539037 & 0.269519 \tabularnewline
36 & 0.68058 & 0.638839 & 0.31942 \tabularnewline
37 & 0.656096 & 0.687809 & 0.343904 \tabularnewline
38 & 0.625282 & 0.749435 & 0.374718 \tabularnewline
39 & 0.742208 & 0.515584 & 0.257792 \tabularnewline
40 & 0.695349 & 0.609302 & 0.304651 \tabularnewline
41 & 0.666083 & 0.667834 & 0.333917 \tabularnewline
42 & 0.614809 & 0.770382 & 0.385191 \tabularnewline
43 & 0.587619 & 0.824762 & 0.412381 \tabularnewline
44 & 0.543421 & 0.913157 & 0.456579 \tabularnewline
45 & 0.636803 & 0.726395 & 0.363197 \tabularnewline
46 & 0.654356 & 0.691288 & 0.345644 \tabularnewline
47 & 0.623496 & 0.753008 & 0.376504 \tabularnewline
48 & 0.594739 & 0.810523 & 0.405261 \tabularnewline
49 & 0.544126 & 0.911749 & 0.455874 \tabularnewline
50 & 0.575479 & 0.849042 & 0.424521 \tabularnewline
51 & 0.620209 & 0.759582 & 0.379791 \tabularnewline
52 & 0.579783 & 0.840435 & 0.420217 \tabularnewline
53 & 0.532845 & 0.93431 & 0.467155 \tabularnewline
54 & 0.500342 & 0.999316 & 0.499658 \tabularnewline
55 & 0.452746 & 0.905493 & 0.547254 \tabularnewline
56 & 0.436486 & 0.872972 & 0.563514 \tabularnewline
57 & 0.465089 & 0.930177 & 0.534911 \tabularnewline
58 & 0.607432 & 0.785137 & 0.392568 \tabularnewline
59 & 0.572217 & 0.855565 & 0.427783 \tabularnewline
60 & 0.524329 & 0.951341 & 0.475671 \tabularnewline
61 & 0.486999 & 0.973999 & 0.513001 \tabularnewline
62 & 0.479584 & 0.959168 & 0.520416 \tabularnewline
63 & 0.439439 & 0.878879 & 0.560561 \tabularnewline
64 & 0.438581 & 0.877162 & 0.561419 \tabularnewline
65 & 0.393028 & 0.786056 & 0.606972 \tabularnewline
66 & 0.356312 & 0.712624 & 0.643688 \tabularnewline
67 & 0.315047 & 0.630093 & 0.684953 \tabularnewline
68 & 0.333469 & 0.666938 & 0.666531 \tabularnewline
69 & 0.347699 & 0.695397 & 0.652301 \tabularnewline
70 & 0.307919 & 0.615837 & 0.692081 \tabularnewline
71 & 0.333038 & 0.666076 & 0.666962 \tabularnewline
72 & 0.371908 & 0.743817 & 0.628092 \tabularnewline
73 & 0.37205 & 0.7441 & 0.62795 \tabularnewline
74 & 0.329194 & 0.658387 & 0.670806 \tabularnewline
75 & 0.295612 & 0.591223 & 0.704388 \tabularnewline
76 & 0.35206 & 0.70412 & 0.64794 \tabularnewline
77 & 0.314905 & 0.62981 & 0.685095 \tabularnewline
78 & 0.274825 & 0.549649 & 0.725175 \tabularnewline
79 & 0.339518 & 0.679036 & 0.660482 \tabularnewline
80 & 0.411775 & 0.82355 & 0.588225 \tabularnewline
81 & 0.399937 & 0.799874 & 0.600063 \tabularnewline
82 & 0.359737 & 0.719475 & 0.640263 \tabularnewline
83 & 0.320635 & 0.641271 & 0.679365 \tabularnewline
84 & 0.310442 & 0.620883 & 0.689558 \tabularnewline
85 & 0.298111 & 0.596221 & 0.701889 \tabularnewline
86 & 0.264643 & 0.529285 & 0.735357 \tabularnewline
87 & 0.238592 & 0.477184 & 0.761408 \tabularnewline
88 & 0.213659 & 0.427319 & 0.786341 \tabularnewline
89 & 0.252554 & 0.505107 & 0.747446 \tabularnewline
90 & 0.218647 & 0.437294 & 0.781353 \tabularnewline
91 & 0.234144 & 0.468287 & 0.765856 \tabularnewline
92 & 0.21943 & 0.43886 & 0.78057 \tabularnewline
93 & 0.205447 & 0.410894 & 0.794553 \tabularnewline
94 & 0.174949 & 0.349897 & 0.825051 \tabularnewline
95 & 0.181805 & 0.36361 & 0.818195 \tabularnewline
96 & 0.179394 & 0.358787 & 0.820606 \tabularnewline
97 & 0.163935 & 0.327869 & 0.836065 \tabularnewline
98 & 0.141068 & 0.282135 & 0.858932 \tabularnewline
99 & 0.12445 & 0.2489 & 0.87555 \tabularnewline
100 & 0.123512 & 0.247024 & 0.876488 \tabularnewline
101 & 0.114549 & 0.229099 & 0.885451 \tabularnewline
102 & 0.100897 & 0.201794 & 0.899103 \tabularnewline
103 & 0.0830131 & 0.166026 & 0.916987 \tabularnewline
104 & 0.0682831 & 0.136566 & 0.931717 \tabularnewline
105 & 0.0789954 & 0.157991 & 0.921005 \tabularnewline
106 & 0.190817 & 0.381635 & 0.809183 \tabularnewline
107 & 0.177476 & 0.354951 & 0.822524 \tabularnewline
108 & 0.203909 & 0.407817 & 0.796091 \tabularnewline
109 & 0.189239 & 0.378478 & 0.810761 \tabularnewline
110 & 0.388451 & 0.776902 & 0.611549 \tabularnewline
111 & 0.455929 & 0.911858 & 0.544071 \tabularnewline
112 & 0.437246 & 0.874491 & 0.562754 \tabularnewline
113 & 0.566997 & 0.866006 & 0.433003 \tabularnewline
114 & 0.533461 & 0.933077 & 0.466539 \tabularnewline
115 & 0.523673 & 0.952654 & 0.476327 \tabularnewline
116 & 0.487364 & 0.974727 & 0.512636 \tabularnewline
117 & 0.45519 & 0.910379 & 0.54481 \tabularnewline
118 & 0.433068 & 0.866137 & 0.566932 \tabularnewline
119 & 0.387568 & 0.775136 & 0.612432 \tabularnewline
120 & 0.352258 & 0.704517 & 0.647742 \tabularnewline
121 & 0.306915 & 0.61383 & 0.693085 \tabularnewline
122 & 0.264954 & 0.529908 & 0.735046 \tabularnewline
123 & 0.229412 & 0.458825 & 0.770588 \tabularnewline
124 & 0.190835 & 0.381671 & 0.809165 \tabularnewline
125 & 0.154983 & 0.309967 & 0.845017 \tabularnewline
126 & 0.163183 & 0.326366 & 0.836817 \tabularnewline
127 & 0.181676 & 0.363353 & 0.818324 \tabularnewline
128 & 0.385884 & 0.771769 & 0.614116 \tabularnewline
129 & 0.356661 & 0.713323 & 0.643339 \tabularnewline
130 & 0.318583 & 0.637167 & 0.681417 \tabularnewline
131 & 0.286723 & 0.573446 & 0.713277 \tabularnewline
132 & 0.332352 & 0.664705 & 0.667648 \tabularnewline
133 & 0.41025 & 0.820499 & 0.58975 \tabularnewline
134 & 0.524251 & 0.951498 & 0.475749 \tabularnewline
135 & 0.469468 & 0.938936 & 0.530532 \tabularnewline
136 & 0.408071 & 0.816141 & 0.591929 \tabularnewline
137 & 0.360971 & 0.721942 & 0.639029 \tabularnewline
138 & 0.297053 & 0.594107 & 0.702947 \tabularnewline
139 & 0.356757 & 0.713514 & 0.643243 \tabularnewline
140 & 0.298945 & 0.59789 & 0.701055 \tabularnewline
141 & 0.48358 & 0.967159 & 0.51642 \tabularnewline
142 & 0.41925 & 0.838501 & 0.58075 \tabularnewline
143 & 0.423327 & 0.846653 & 0.576673 \tabularnewline
144 & 0.753684 & 0.492631 & 0.246316 \tabularnewline
145 & 0.683919 & 0.632161 & 0.316081 \tabularnewline
146 & 0.96499 & 0.07002 & 0.03501 \tabularnewline
147 & 0.948796 & 0.102408 & 0.0512039 \tabularnewline
148 & 0.9445 & 0.111 & 0.0555 \tabularnewline
149 & 0.929949 & 0.140101 & 0.0700507 \tabularnewline
150 & 0.873908 & 0.252185 & 0.126092 \tabularnewline
151 & 0.802971 & 0.394059 & 0.197029 \tabularnewline
152 & 0.9585 & 0.0830001 & 0.0415 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253846&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.194535[/C][C]0.38907[/C][C]0.805465[/C][/ROW]
[ROW][C]11[/C][C]0.0905833[/C][C]0.181167[/C][C]0.909417[/C][/ROW]
[ROW][C]12[/C][C]0.847559[/C][C]0.304882[/C][C]0.152441[/C][/ROW]
[ROW][C]13[/C][C]0.892355[/C][C]0.215291[/C][C]0.107645[/C][/ROW]
[ROW][C]14[/C][C]0.840634[/C][C]0.318731[/C][C]0.159366[/C][/ROW]
[ROW][C]15[/C][C]0.792325[/C][C]0.41535[/C][C]0.207675[/C][/ROW]
[ROW][C]16[/C][C]0.832182[/C][C]0.335636[/C][C]0.167818[/C][/ROW]
[ROW][C]17[/C][C]0.774697[/C][C]0.450606[/C][C]0.225303[/C][/ROW]
[ROW][C]18[/C][C]0.73414[/C][C]0.53172[/C][C]0.26586[/C][/ROW]
[ROW][C]19[/C][C]0.695199[/C][C]0.609601[/C][C]0.304801[/C][/ROW]
[ROW][C]20[/C][C]0.725906[/C][C]0.548187[/C][C]0.274094[/C][/ROW]
[ROW][C]21[/C][C]0.74182[/C][C]0.516359[/C][C]0.25818[/C][/ROW]
[ROW][C]22[/C][C]0.697039[/C][C]0.605922[/C][C]0.302961[/C][/ROW]
[ROW][C]23[/C][C]0.658099[/C][C]0.683802[/C][C]0.341901[/C][/ROW]
[ROW][C]24[/C][C]0.588547[/C][C]0.822906[/C][C]0.411453[/C][/ROW]
[ROW][C]25[/C][C]0.59214[/C][C]0.815721[/C][C]0.40786[/C][/ROW]
[ROW][C]26[/C][C]0.836854[/C][C]0.326292[/C][C]0.163146[/C][/ROW]
[ROW][C]27[/C][C]0.793576[/C][C]0.412848[/C][C]0.206424[/C][/ROW]
[ROW][C]28[/C][C]0.767847[/C][C]0.464306[/C][C]0.232153[/C][/ROW]
[ROW][C]29[/C][C]0.737208[/C][C]0.525585[/C][C]0.262792[/C][/ROW]
[ROW][C]30[/C][C]0.686368[/C][C]0.627264[/C][C]0.313632[/C][/ROW]
[ROW][C]31[/C][C]0.657672[/C][C]0.684655[/C][C]0.342328[/C][/ROW]
[ROW][C]32[/C][C]0.794963[/C][C]0.410075[/C][C]0.205037[/C][/ROW]
[ROW][C]33[/C][C]0.792142[/C][C]0.415716[/C][C]0.207858[/C][/ROW]
[ROW][C]34[/C][C]0.77139[/C][C]0.457221[/C][C]0.22861[/C][/ROW]
[ROW][C]35[/C][C]0.730481[/C][C]0.539037[/C][C]0.269519[/C][/ROW]
[ROW][C]36[/C][C]0.68058[/C][C]0.638839[/C][C]0.31942[/C][/ROW]
[ROW][C]37[/C][C]0.656096[/C][C]0.687809[/C][C]0.343904[/C][/ROW]
[ROW][C]38[/C][C]0.625282[/C][C]0.749435[/C][C]0.374718[/C][/ROW]
[ROW][C]39[/C][C]0.742208[/C][C]0.515584[/C][C]0.257792[/C][/ROW]
[ROW][C]40[/C][C]0.695349[/C][C]0.609302[/C][C]0.304651[/C][/ROW]
[ROW][C]41[/C][C]0.666083[/C][C]0.667834[/C][C]0.333917[/C][/ROW]
[ROW][C]42[/C][C]0.614809[/C][C]0.770382[/C][C]0.385191[/C][/ROW]
[ROW][C]43[/C][C]0.587619[/C][C]0.824762[/C][C]0.412381[/C][/ROW]
[ROW][C]44[/C][C]0.543421[/C][C]0.913157[/C][C]0.456579[/C][/ROW]
[ROW][C]45[/C][C]0.636803[/C][C]0.726395[/C][C]0.363197[/C][/ROW]
[ROW][C]46[/C][C]0.654356[/C][C]0.691288[/C][C]0.345644[/C][/ROW]
[ROW][C]47[/C][C]0.623496[/C][C]0.753008[/C][C]0.376504[/C][/ROW]
[ROW][C]48[/C][C]0.594739[/C][C]0.810523[/C][C]0.405261[/C][/ROW]
[ROW][C]49[/C][C]0.544126[/C][C]0.911749[/C][C]0.455874[/C][/ROW]
[ROW][C]50[/C][C]0.575479[/C][C]0.849042[/C][C]0.424521[/C][/ROW]
[ROW][C]51[/C][C]0.620209[/C][C]0.759582[/C][C]0.379791[/C][/ROW]
[ROW][C]52[/C][C]0.579783[/C][C]0.840435[/C][C]0.420217[/C][/ROW]
[ROW][C]53[/C][C]0.532845[/C][C]0.93431[/C][C]0.467155[/C][/ROW]
[ROW][C]54[/C][C]0.500342[/C][C]0.999316[/C][C]0.499658[/C][/ROW]
[ROW][C]55[/C][C]0.452746[/C][C]0.905493[/C][C]0.547254[/C][/ROW]
[ROW][C]56[/C][C]0.436486[/C][C]0.872972[/C][C]0.563514[/C][/ROW]
[ROW][C]57[/C][C]0.465089[/C][C]0.930177[/C][C]0.534911[/C][/ROW]
[ROW][C]58[/C][C]0.607432[/C][C]0.785137[/C][C]0.392568[/C][/ROW]
[ROW][C]59[/C][C]0.572217[/C][C]0.855565[/C][C]0.427783[/C][/ROW]
[ROW][C]60[/C][C]0.524329[/C][C]0.951341[/C][C]0.475671[/C][/ROW]
[ROW][C]61[/C][C]0.486999[/C][C]0.973999[/C][C]0.513001[/C][/ROW]
[ROW][C]62[/C][C]0.479584[/C][C]0.959168[/C][C]0.520416[/C][/ROW]
[ROW][C]63[/C][C]0.439439[/C][C]0.878879[/C][C]0.560561[/C][/ROW]
[ROW][C]64[/C][C]0.438581[/C][C]0.877162[/C][C]0.561419[/C][/ROW]
[ROW][C]65[/C][C]0.393028[/C][C]0.786056[/C][C]0.606972[/C][/ROW]
[ROW][C]66[/C][C]0.356312[/C][C]0.712624[/C][C]0.643688[/C][/ROW]
[ROW][C]67[/C][C]0.315047[/C][C]0.630093[/C][C]0.684953[/C][/ROW]
[ROW][C]68[/C][C]0.333469[/C][C]0.666938[/C][C]0.666531[/C][/ROW]
[ROW][C]69[/C][C]0.347699[/C][C]0.695397[/C][C]0.652301[/C][/ROW]
[ROW][C]70[/C][C]0.307919[/C][C]0.615837[/C][C]0.692081[/C][/ROW]
[ROW][C]71[/C][C]0.333038[/C][C]0.666076[/C][C]0.666962[/C][/ROW]
[ROW][C]72[/C][C]0.371908[/C][C]0.743817[/C][C]0.628092[/C][/ROW]
[ROW][C]73[/C][C]0.37205[/C][C]0.7441[/C][C]0.62795[/C][/ROW]
[ROW][C]74[/C][C]0.329194[/C][C]0.658387[/C][C]0.670806[/C][/ROW]
[ROW][C]75[/C][C]0.295612[/C][C]0.591223[/C][C]0.704388[/C][/ROW]
[ROW][C]76[/C][C]0.35206[/C][C]0.70412[/C][C]0.64794[/C][/ROW]
[ROW][C]77[/C][C]0.314905[/C][C]0.62981[/C][C]0.685095[/C][/ROW]
[ROW][C]78[/C][C]0.274825[/C][C]0.549649[/C][C]0.725175[/C][/ROW]
[ROW][C]79[/C][C]0.339518[/C][C]0.679036[/C][C]0.660482[/C][/ROW]
[ROW][C]80[/C][C]0.411775[/C][C]0.82355[/C][C]0.588225[/C][/ROW]
[ROW][C]81[/C][C]0.399937[/C][C]0.799874[/C][C]0.600063[/C][/ROW]
[ROW][C]82[/C][C]0.359737[/C][C]0.719475[/C][C]0.640263[/C][/ROW]
[ROW][C]83[/C][C]0.320635[/C][C]0.641271[/C][C]0.679365[/C][/ROW]
[ROW][C]84[/C][C]0.310442[/C][C]0.620883[/C][C]0.689558[/C][/ROW]
[ROW][C]85[/C][C]0.298111[/C][C]0.596221[/C][C]0.701889[/C][/ROW]
[ROW][C]86[/C][C]0.264643[/C][C]0.529285[/C][C]0.735357[/C][/ROW]
[ROW][C]87[/C][C]0.238592[/C][C]0.477184[/C][C]0.761408[/C][/ROW]
[ROW][C]88[/C][C]0.213659[/C][C]0.427319[/C][C]0.786341[/C][/ROW]
[ROW][C]89[/C][C]0.252554[/C][C]0.505107[/C][C]0.747446[/C][/ROW]
[ROW][C]90[/C][C]0.218647[/C][C]0.437294[/C][C]0.781353[/C][/ROW]
[ROW][C]91[/C][C]0.234144[/C][C]0.468287[/C][C]0.765856[/C][/ROW]
[ROW][C]92[/C][C]0.21943[/C][C]0.43886[/C][C]0.78057[/C][/ROW]
[ROW][C]93[/C][C]0.205447[/C][C]0.410894[/C][C]0.794553[/C][/ROW]
[ROW][C]94[/C][C]0.174949[/C][C]0.349897[/C][C]0.825051[/C][/ROW]
[ROW][C]95[/C][C]0.181805[/C][C]0.36361[/C][C]0.818195[/C][/ROW]
[ROW][C]96[/C][C]0.179394[/C][C]0.358787[/C][C]0.820606[/C][/ROW]
[ROW][C]97[/C][C]0.163935[/C][C]0.327869[/C][C]0.836065[/C][/ROW]
[ROW][C]98[/C][C]0.141068[/C][C]0.282135[/C][C]0.858932[/C][/ROW]
[ROW][C]99[/C][C]0.12445[/C][C]0.2489[/C][C]0.87555[/C][/ROW]
[ROW][C]100[/C][C]0.123512[/C][C]0.247024[/C][C]0.876488[/C][/ROW]
[ROW][C]101[/C][C]0.114549[/C][C]0.229099[/C][C]0.885451[/C][/ROW]
[ROW][C]102[/C][C]0.100897[/C][C]0.201794[/C][C]0.899103[/C][/ROW]
[ROW][C]103[/C][C]0.0830131[/C][C]0.166026[/C][C]0.916987[/C][/ROW]
[ROW][C]104[/C][C]0.0682831[/C][C]0.136566[/C][C]0.931717[/C][/ROW]
[ROW][C]105[/C][C]0.0789954[/C][C]0.157991[/C][C]0.921005[/C][/ROW]
[ROW][C]106[/C][C]0.190817[/C][C]0.381635[/C][C]0.809183[/C][/ROW]
[ROW][C]107[/C][C]0.177476[/C][C]0.354951[/C][C]0.822524[/C][/ROW]
[ROW][C]108[/C][C]0.203909[/C][C]0.407817[/C][C]0.796091[/C][/ROW]
[ROW][C]109[/C][C]0.189239[/C][C]0.378478[/C][C]0.810761[/C][/ROW]
[ROW][C]110[/C][C]0.388451[/C][C]0.776902[/C][C]0.611549[/C][/ROW]
[ROW][C]111[/C][C]0.455929[/C][C]0.911858[/C][C]0.544071[/C][/ROW]
[ROW][C]112[/C][C]0.437246[/C][C]0.874491[/C][C]0.562754[/C][/ROW]
[ROW][C]113[/C][C]0.566997[/C][C]0.866006[/C][C]0.433003[/C][/ROW]
[ROW][C]114[/C][C]0.533461[/C][C]0.933077[/C][C]0.466539[/C][/ROW]
[ROW][C]115[/C][C]0.523673[/C][C]0.952654[/C][C]0.476327[/C][/ROW]
[ROW][C]116[/C][C]0.487364[/C][C]0.974727[/C][C]0.512636[/C][/ROW]
[ROW][C]117[/C][C]0.45519[/C][C]0.910379[/C][C]0.54481[/C][/ROW]
[ROW][C]118[/C][C]0.433068[/C][C]0.866137[/C][C]0.566932[/C][/ROW]
[ROW][C]119[/C][C]0.387568[/C][C]0.775136[/C][C]0.612432[/C][/ROW]
[ROW][C]120[/C][C]0.352258[/C][C]0.704517[/C][C]0.647742[/C][/ROW]
[ROW][C]121[/C][C]0.306915[/C][C]0.61383[/C][C]0.693085[/C][/ROW]
[ROW][C]122[/C][C]0.264954[/C][C]0.529908[/C][C]0.735046[/C][/ROW]
[ROW][C]123[/C][C]0.229412[/C][C]0.458825[/C][C]0.770588[/C][/ROW]
[ROW][C]124[/C][C]0.190835[/C][C]0.381671[/C][C]0.809165[/C][/ROW]
[ROW][C]125[/C][C]0.154983[/C][C]0.309967[/C][C]0.845017[/C][/ROW]
[ROW][C]126[/C][C]0.163183[/C][C]0.326366[/C][C]0.836817[/C][/ROW]
[ROW][C]127[/C][C]0.181676[/C][C]0.363353[/C][C]0.818324[/C][/ROW]
[ROW][C]128[/C][C]0.385884[/C][C]0.771769[/C][C]0.614116[/C][/ROW]
[ROW][C]129[/C][C]0.356661[/C][C]0.713323[/C][C]0.643339[/C][/ROW]
[ROW][C]130[/C][C]0.318583[/C][C]0.637167[/C][C]0.681417[/C][/ROW]
[ROW][C]131[/C][C]0.286723[/C][C]0.573446[/C][C]0.713277[/C][/ROW]
[ROW][C]132[/C][C]0.332352[/C][C]0.664705[/C][C]0.667648[/C][/ROW]
[ROW][C]133[/C][C]0.41025[/C][C]0.820499[/C][C]0.58975[/C][/ROW]
[ROW][C]134[/C][C]0.524251[/C][C]0.951498[/C][C]0.475749[/C][/ROW]
[ROW][C]135[/C][C]0.469468[/C][C]0.938936[/C][C]0.530532[/C][/ROW]
[ROW][C]136[/C][C]0.408071[/C][C]0.816141[/C][C]0.591929[/C][/ROW]
[ROW][C]137[/C][C]0.360971[/C][C]0.721942[/C][C]0.639029[/C][/ROW]
[ROW][C]138[/C][C]0.297053[/C][C]0.594107[/C][C]0.702947[/C][/ROW]
[ROW][C]139[/C][C]0.356757[/C][C]0.713514[/C][C]0.643243[/C][/ROW]
[ROW][C]140[/C][C]0.298945[/C][C]0.59789[/C][C]0.701055[/C][/ROW]
[ROW][C]141[/C][C]0.48358[/C][C]0.967159[/C][C]0.51642[/C][/ROW]
[ROW][C]142[/C][C]0.41925[/C][C]0.838501[/C][C]0.58075[/C][/ROW]
[ROW][C]143[/C][C]0.423327[/C][C]0.846653[/C][C]0.576673[/C][/ROW]
[ROW][C]144[/C][C]0.753684[/C][C]0.492631[/C][C]0.246316[/C][/ROW]
[ROW][C]145[/C][C]0.683919[/C][C]0.632161[/C][C]0.316081[/C][/ROW]
[ROW][C]146[/C][C]0.96499[/C][C]0.07002[/C][C]0.03501[/C][/ROW]
[ROW][C]147[/C][C]0.948796[/C][C]0.102408[/C][C]0.0512039[/C][/ROW]
[ROW][C]148[/C][C]0.9445[/C][C]0.111[/C][C]0.0555[/C][/ROW]
[ROW][C]149[/C][C]0.929949[/C][C]0.140101[/C][C]0.0700507[/C][/ROW]
[ROW][C]150[/C][C]0.873908[/C][C]0.252185[/C][C]0.126092[/C][/ROW]
[ROW][C]151[/C][C]0.802971[/C][C]0.394059[/C][C]0.197029[/C][/ROW]
[ROW][C]152[/C][C]0.9585[/C][C]0.0830001[/C][C]0.0415[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253846&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253846&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.194535	0.38907	0.805465
11	0.0905833	0.181167	0.909417
12	0.847559	0.304882	0.152441
13	0.892355	0.215291	0.107645
14	0.840634	0.318731	0.159366
15	0.792325	0.41535	0.207675
16	0.832182	0.335636	0.167818
17	0.774697	0.450606	0.225303
18	0.73414	0.53172	0.26586
19	0.695199	0.609601	0.304801
20	0.725906	0.548187	0.274094
21	0.74182	0.516359	0.25818
22	0.697039	0.605922	0.302961
23	0.658099	0.683802	0.341901
24	0.588547	0.822906	0.411453
25	0.59214	0.815721	0.40786
26	0.836854	0.326292	0.163146
27	0.793576	0.412848	0.206424
28	0.767847	0.464306	0.232153
29	0.737208	0.525585	0.262792
30	0.686368	0.627264	0.313632
31	0.657672	0.684655	0.342328
32	0.794963	0.410075	0.205037
33	0.792142	0.415716	0.207858
34	0.77139	0.457221	0.22861
35	0.730481	0.539037	0.269519
36	0.68058	0.638839	0.31942
37	0.656096	0.687809	0.343904
38	0.625282	0.749435	0.374718
39	0.742208	0.515584	0.257792
40	0.695349	0.609302	0.304651
41	0.666083	0.667834	0.333917
42	0.614809	0.770382	0.385191
43	0.587619	0.824762	0.412381
44	0.543421	0.913157	0.456579
45	0.636803	0.726395	0.363197
46	0.654356	0.691288	0.345644
47	0.623496	0.753008	0.376504
48	0.594739	0.810523	0.405261
49	0.544126	0.911749	0.455874
50	0.575479	0.849042	0.424521
51	0.620209	0.759582	0.379791
52	0.579783	0.840435	0.420217
53	0.532845	0.93431	0.467155
54	0.500342	0.999316	0.499658
55	0.452746	0.905493	0.547254
56	0.436486	0.872972	0.563514
57	0.465089	0.930177	0.534911
58	0.607432	0.785137	0.392568
59	0.572217	0.855565	0.427783
60	0.524329	0.951341	0.475671
61	0.486999	0.973999	0.513001
62	0.479584	0.959168	0.520416
63	0.439439	0.878879	0.560561
64	0.438581	0.877162	0.561419
65	0.393028	0.786056	0.606972
66	0.356312	0.712624	0.643688
67	0.315047	0.630093	0.684953
68	0.333469	0.666938	0.666531
69	0.347699	0.695397	0.652301
70	0.307919	0.615837	0.692081
71	0.333038	0.666076	0.666962
72	0.371908	0.743817	0.628092
73	0.37205	0.7441	0.62795
74	0.329194	0.658387	0.670806
75	0.295612	0.591223	0.704388
76	0.35206	0.70412	0.64794
77	0.314905	0.62981	0.685095
78	0.274825	0.549649	0.725175
79	0.339518	0.679036	0.660482
80	0.411775	0.82355	0.588225
81	0.399937	0.799874	0.600063
82	0.359737	0.719475	0.640263
83	0.320635	0.641271	0.679365
84	0.310442	0.620883	0.689558
85	0.298111	0.596221	0.701889
86	0.264643	0.529285	0.735357
87	0.238592	0.477184	0.761408
88	0.213659	0.427319	0.786341
89	0.252554	0.505107	0.747446
90	0.218647	0.437294	0.781353
91	0.234144	0.468287	0.765856
92	0.21943	0.43886	0.78057
93	0.205447	0.410894	0.794553
94	0.174949	0.349897	0.825051
95	0.181805	0.36361	0.818195
96	0.179394	0.358787	0.820606
97	0.163935	0.327869	0.836065
98	0.141068	0.282135	0.858932
99	0.12445	0.2489	0.87555
100	0.123512	0.247024	0.876488
101	0.114549	0.229099	0.885451
102	0.100897	0.201794	0.899103
103	0.0830131	0.166026	0.916987
104	0.0682831	0.136566	0.931717
105	0.0789954	0.157991	0.921005
106	0.190817	0.381635	0.809183
107	0.177476	0.354951	0.822524
108	0.203909	0.407817	0.796091
109	0.189239	0.378478	0.810761
110	0.388451	0.776902	0.611549
111	0.455929	0.911858	0.544071
112	0.437246	0.874491	0.562754
113	0.566997	0.866006	0.433003
114	0.533461	0.933077	0.466539
115	0.523673	0.952654	0.476327
116	0.487364	0.974727	0.512636
117	0.45519	0.910379	0.54481
118	0.433068	0.866137	0.566932
119	0.387568	0.775136	0.612432
120	0.352258	0.704517	0.647742
121	0.306915	0.61383	0.693085
122	0.264954	0.529908	0.735046
123	0.229412	0.458825	0.770588
124	0.190835	0.381671	0.809165
125	0.154983	0.309967	0.845017
126	0.163183	0.326366	0.836817
127	0.181676	0.363353	0.818324
128	0.385884	0.771769	0.614116
129	0.356661	0.713323	0.643339
130	0.318583	0.637167	0.681417
131	0.286723	0.573446	0.713277
132	0.332352	0.664705	0.667648
133	0.41025	0.820499	0.58975
134	0.524251	0.951498	0.475749
135	0.469468	0.938936	0.530532
136	0.408071	0.816141	0.591929
137	0.360971	0.721942	0.639029
138	0.297053	0.594107	0.702947
139	0.356757	0.713514	0.643243
140	0.298945	0.59789	0.701055
141	0.48358	0.967159	0.51642
142	0.41925	0.838501	0.58075
143	0.423327	0.846653	0.576673
144	0.753684	0.492631	0.246316
145	0.683919	0.632161	0.316081
146	0.96499	0.07002	0.03501
147	0.948796	0.102408	0.0512039
148	0.9445	0.111	0.0555
149	0.929949	0.140101	0.0700507
150	0.873908	0.252185	0.126092
151	0.802971	0.394059	0.197029
152	0.9585	0.0830001	0.0415

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	2	0.013986	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 & 2 & 0.013986 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253846&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]2[/C][C]0.013986[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253846&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253846&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	2	0.013986	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