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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 14 Dec 2014 15:34:25 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/14/t1418571322ss062k4tb8no4ez.htm/, Retrieved Thu, 16 May 2024 21:11:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267690, Retrieved Thu, 16 May 2024 21:11:58 +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] [paper30] [2014-12-14 15:34:25] [0015a2406d94cac8c1a56a29b9122359] [Current]

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22	20	20	91
22	18	16	137
21	16	20	148
20	18	13	92
20	19	17	131
14	9	7	59
23	20	18	90
16	22	9	83
18	22	16	116
20	16	14	42
23	24	20	155
13	20	8	128
20	14	11	49
19	19	10	96
20	14	10	66
16	14	7	104
20	20	16	76
23	21	22	99
17	13	8	108
13	13	8	74
20	15	14	96
22	18	15	116
19	21	9	87
21	17	21	97
15	18	7	127
21	20	17	106
24	18	18	80
22	25	16	74
20	20	16	91
21	19	14	133
19	18	15	74
14	12	8	114
25	22	22	140
11	16	5	95
17	18	13	98
22	23	22	121
20	20	18	126
22	20	15	98
15	16	11	95
23	22	19	110
20	19	19	70
22	23	21	102
16	6	4	86
25	19	17	130
18	24	10	96
19	19	13	102
25	15	15	100
21	18	11	94
22	18	20	52
21	22	13	98
22	23	18	118
23	18	20	99
24	16	12	109
22	16	17	68
26	25	21	131
11	12	10	71
24	20	22	68
28	19	19	89
23	22	19	115
19	12	9	78
18	17	11	118
23	18	17	87
17	24	10	162
15	18	17	49
21	18	13	122
20	23	11	96
26	21	19	100
19	21	21	82
28	28	24	100
21	17	13	115
19	21	16	141
20	18	15	110
17	17	13	146
20	18	12	90
17	14	8	121
21	20	17	104
12	14	9	147
23	17	18	110
22	21	17	108
22	23	17	113
21	24	18	115
20	21	12	61
18	14	14	60
21	24	22	109
24	16	19	68
22	21	21	111
20	8	10	77
17	17	16	73
16	17	15	89
19	16	12	78
23	22	21	110
22	21	20	65
15	20	9	117
21	8	14	63
18	11	9	52
23	15	18	62
20	13	12	131
21	18	11	101
21	19	14	42
22	22	11	77
15	11	11	96
19	14	13	57
18	21	12	112
20	21	23	49
18	18	11	56
22	21	19	86
25	23	19	88
23	20	13	48
21	21	23	85
19	18	13	63
21	19	17	102
16	18	13	162
21	18	8	86
22	19	16	114
18	18	14	94
4	11	7	81
22	20	17	110
17	20	19	64
20	21	12	104
18	12	12	105
19	15	18	49
20	18	16	88
15	14	15	95
24	18	20	102
21	16	16	99
19	19	12	63
19	7	10	76
27	21	28	109
23	24	19	117
23	21	18	57
20	20	19	120
17	22	8	73
21	17	17	91
23	19	16	108
22	20	18	105
20	20	17	119
16	16	13	31

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 Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267690&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 Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267690&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267690&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 Maurice George Kendall' @ kendall.wessa.net

Multiple Linear Regression - Estimated Regression Equation
LFM[t] = + 64.3343 -0.182315I1[t] + 2.26399I2[t] -0.457811I3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
LFM[t] =  +  64.3343 -0.182315I1[t] +  2.26399I2[t] -0.457811I3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267690&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]LFM[t] =  +  64.3343 -0.182315I1[t] +  2.26399I2[t] -0.457811I3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267690&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267690&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
LFM[t] = + 64.3343 -0.182315I1[t] + 2.26399I2[t] -0.457811I3[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)	64.3343	14.3909	4.47	1.65515e-05	8.27573e-06
I1	-0.182315	0.887899	-0.2053	0.837626	0.418813
I2	2.26399	0.684919	3.305	0.00121891	0.000609455
I3	-0.457811	0.715582	-0.6398	0.52342	0.26171

\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) & 64.3343 & 14.3909 & 4.47 & 1.65515e-05 & 8.27573e-06 \tabularnewline
I1 & -0.182315 & 0.887899 & -0.2053 & 0.837626 & 0.418813 \tabularnewline
I2 & 2.26399 & 0.684919 & 3.305 & 0.00121891 & 0.000609455 \tabularnewline
I3 & -0.457811 & 0.715582 & -0.6398 & 0.52342 & 0.26171 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267690&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]64.3343[/C][C]14.3909[/C][C]4.47[/C][C]1.65515e-05[/C][C]8.27573e-06[/C][/ROW]
[ROW][C]I1[/C][C]-0.182315[/C][C]0.887899[/C][C]-0.2053[/C][C]0.837626[/C][C]0.418813[/C][/ROW]
[ROW][C]I2[/C][C]2.26399[/C][C]0.684919[/C][C]3.305[/C][C]0.00121891[/C][C]0.000609455[/C][/ROW]
[ROW][C]I3[/C][C]-0.457811[/C][C]0.715582[/C][C]-0.6398[/C][C]0.52342[/C][C]0.26171[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267690&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267690&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)	64.3343	14.3909	4.47	1.65515e-05	8.27573e-06
I1	-0.182315	0.887899	-0.2053	0.837626	0.418813
I2	2.26399	0.684919	3.305	0.00121891	0.000609455
I3	-0.457811	0.715582	-0.6398	0.52342	0.26171

Multiple Linear Regression - Regression Statistics
Multiple R	0.287726
R-squared	0.082786
Adjusted R-squared	0.062097
F-TEST (value)	4.00144
F-TEST (DF numerator)	3
F-TEST (DF denominator)	133
p-value	0.00915453
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	25.8003
Sum Squared Residuals	88532.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.287726 \tabularnewline
R-squared & 0.082786 \tabularnewline
Adjusted R-squared & 0.062097 \tabularnewline
F-TEST (value) & 4.00144 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 133 \tabularnewline
p-value & 0.00915453 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 25.8003 \tabularnewline
Sum Squared Residuals & 88532.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267690&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.287726[/C][/ROW]
[ROW][C]R-squared[/C][C]0.082786[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.062097[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.00144[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]133[/C][/ROW]
[ROW][C]p-value[/C][C]0.00915453[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]25.8003[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]88532.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267690&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267690&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	0.287726
R-squared	0.082786
Adjusted R-squared	0.062097
F-TEST (value)	4.00144
F-TEST (DF numerator)	3
F-TEST (DF denominator)	133
p-value	0.00915453
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	25.8003
Sum Squared Residuals	88532.1

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	91	96.447	-5.44697
2	137	93.7502	43.2498
3	148	87.5733	60.4267
4	92	95.4883	-3.48829
5	131	95.921	35.079
6	59	78.9531	-19.9531
7	90	97.1803	-7.18027
8	83	107.105	-24.1048
9	116	103.535	12.4646
10	42	90.5025	-48.5025
11	155	105.321	49.6794
12	128	103.582	24.4185
13	49	87.348	-38.348
14	96	99.308	-3.30803
15	66	87.8058	-21.8058
16	104	89.9085	14.0915
17	76	98.6428	-22.6428
18	99	97.613	1.38698
19	108	87.0043	20.9957
20	74	87.7336	-13.7336
21	96	88.2385	7.76149
22	116	94.208	21.792
23	87	104.294	-17.2938
24	97	89.3795	7.6205
25	127	99.1467	27.8533
26	106	98.0027	7.99729
27	80	92.47	-12.47
28	74	109.598	-35.5982
29	91	98.6428	-7.64284
30	133	97.1122	35.8878
31	74	94.755	-20.755
32	114	85.2873	28.7127
33	140	99.5124	40.4876
34	95	96.2636	-1.26363
35	98	96.0352	1.96477
36	121	102.323	18.6767
37	126	97.7272	28.2728
38	98	98.736	-0.736021
39	95	92.7875	2.21249
40	110	101.25	8.74956
41	70	95.0054	-25.0054
42	102	102.781	-0.781126
43	86	73.17	12.83
44	130	95.0095	34.9905
45	96	110.81	-14.8103
46	102	97.9346	4.0654
47	100	86.8691	13.1309
48	94	96.2216	-2.2216
49	52	91.919	-39.919
50	98	104.362	-6.36194
51	118	104.155	13.8454
52	99	91.7367	7.26333
53	109	90.6889	18.3111
54	68	88.7644	-20.7644
55	131	106.58	24.4202
56	71	84.9186	-13.9186
57	68	95.1667	-27.1667
58	89	93.5469	-4.5469
59	115	101.25	13.7496
60	78	83.9179	-5.91791
61	118	94.5046	23.4954
62	87	93.1101	-6.1101
63	162	110.993	51.0074
64	49	94.5686	-45.5686
65	122	95.306	26.694
66	96	107.724	-11.7239
67	100	98.4395	1.56049
68	82	98.8001	-16.8001
69	100	111.634	-11.6338
70	115	93.042	21.958
71	141	101.089	39.9109
72	110	94.5727	15.4273
73	146	93.7712	52.2288
74	90	95.9461	-5.9461
75	121	89.2683	31.7317
76	104	98.0027	5.99729
77	147	89.7221	57.2779
78	110	90.3883	19.6117
79	108	100.084	7.91561
80	113	104.612	8.38763
81	115	106.601	8.39914
82	61	102.738	-41.7381
83	60	86.3391	-26.3391
84	109	104.77	4.23038
85	68	87.4842	-19.4842
86	111	98.2531	12.7469
87	77	74.2218	2.77818
88	73	92.3978	-19.3978
89	89	93.0379	-4.03794
90	78	91.6004	-13.6004
91	110	100.335	9.66518
92	65	98.711	-33.711
93	117	102.759	14.2409
94	63	72.2083	-9.20826
95	52	81.8362	-29.8362
96	62	85.8603	-23.8603
97	131	84.6262	46.3738
98	101	96.2216	4.7784
99	42	97.1122	-55.1122
100	77	105.095	-28.0952
101	96	81.4676	14.5324
102	57	86.6146	-29.6146
103	112	103.103	8.8973
104	49	97.7022	-48.7022
105	56	96.7685	-40.7685
106	86	99.1688	-13.1688
107	88	103.15	-15.1498
108	48	99.4693	-51.4693
109	85	97.5198	-12.5198
110	63	95.6706	-32.6706
111	102	95.7387	6.26128
112	162	96.2175	65.7825
113	86	97.595	-11.595
114	114	96.0142	17.9858
115	94	95.3951	-1.39511
116	81	85.3043	-4.30426
117	110	97.8204	12.1796
118	64	97.8163	-33.8163
119	104	102.738	1.26193
120	105	82.7268	22.2732
121	49	86.5896	-37.5896
122	88	94.1149	-6.11486
123	95	86.4283	8.57172
124	102	91.5544	10.4456
125	99	89.4046	9.59544
126	63	98.3924	-35.3924
127	76	72.1401	3.85986
128	109	94.1369	14.8631
129	117	105.778	11.2216
130	57	99.4443	-42.4443
131	120	97.2694	22.7306
132	73	107.38	-34.3803
133	91	91.2107	-0.210742
134	108	95.8319	12.1681
135	105	97.3626	7.63741
136	119	98.185	20.815
137	31	91.6896	-60.6896

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 91 & 96.447 & -5.44697 \tabularnewline
2 & 137 & 93.7502 & 43.2498 \tabularnewline
3 & 148 & 87.5733 & 60.4267 \tabularnewline
4 & 92 & 95.4883 & -3.48829 \tabularnewline
5 & 131 & 95.921 & 35.079 \tabularnewline
6 & 59 & 78.9531 & -19.9531 \tabularnewline
7 & 90 & 97.1803 & -7.18027 \tabularnewline
8 & 83 & 107.105 & -24.1048 \tabularnewline
9 & 116 & 103.535 & 12.4646 \tabularnewline
10 & 42 & 90.5025 & -48.5025 \tabularnewline
11 & 155 & 105.321 & 49.6794 \tabularnewline
12 & 128 & 103.582 & 24.4185 \tabularnewline
13 & 49 & 87.348 & -38.348 \tabularnewline
14 & 96 & 99.308 & -3.30803 \tabularnewline
15 & 66 & 87.8058 & -21.8058 \tabularnewline
16 & 104 & 89.9085 & 14.0915 \tabularnewline
17 & 76 & 98.6428 & -22.6428 \tabularnewline
18 & 99 & 97.613 & 1.38698 \tabularnewline
19 & 108 & 87.0043 & 20.9957 \tabularnewline
20 & 74 & 87.7336 & -13.7336 \tabularnewline
21 & 96 & 88.2385 & 7.76149 \tabularnewline
22 & 116 & 94.208 & 21.792 \tabularnewline
23 & 87 & 104.294 & -17.2938 \tabularnewline
24 & 97 & 89.3795 & 7.6205 \tabularnewline
25 & 127 & 99.1467 & 27.8533 \tabularnewline
26 & 106 & 98.0027 & 7.99729 \tabularnewline
27 & 80 & 92.47 & -12.47 \tabularnewline
28 & 74 & 109.598 & -35.5982 \tabularnewline
29 & 91 & 98.6428 & -7.64284 \tabularnewline
30 & 133 & 97.1122 & 35.8878 \tabularnewline
31 & 74 & 94.755 & -20.755 \tabularnewline
32 & 114 & 85.2873 & 28.7127 \tabularnewline
33 & 140 & 99.5124 & 40.4876 \tabularnewline
34 & 95 & 96.2636 & -1.26363 \tabularnewline
35 & 98 & 96.0352 & 1.96477 \tabularnewline
36 & 121 & 102.323 & 18.6767 \tabularnewline
37 & 126 & 97.7272 & 28.2728 \tabularnewline
38 & 98 & 98.736 & -0.736021 \tabularnewline
39 & 95 & 92.7875 & 2.21249 \tabularnewline
40 & 110 & 101.25 & 8.74956 \tabularnewline
41 & 70 & 95.0054 & -25.0054 \tabularnewline
42 & 102 & 102.781 & -0.781126 \tabularnewline
43 & 86 & 73.17 & 12.83 \tabularnewline
44 & 130 & 95.0095 & 34.9905 \tabularnewline
45 & 96 & 110.81 & -14.8103 \tabularnewline
46 & 102 & 97.9346 & 4.0654 \tabularnewline
47 & 100 & 86.8691 & 13.1309 \tabularnewline
48 & 94 & 96.2216 & -2.2216 \tabularnewline
49 & 52 & 91.919 & -39.919 \tabularnewline
50 & 98 & 104.362 & -6.36194 \tabularnewline
51 & 118 & 104.155 & 13.8454 \tabularnewline
52 & 99 & 91.7367 & 7.26333 \tabularnewline
53 & 109 & 90.6889 & 18.3111 \tabularnewline
54 & 68 & 88.7644 & -20.7644 \tabularnewline
55 & 131 & 106.58 & 24.4202 \tabularnewline
56 & 71 & 84.9186 & -13.9186 \tabularnewline
57 & 68 & 95.1667 & -27.1667 \tabularnewline
58 & 89 & 93.5469 & -4.5469 \tabularnewline
59 & 115 & 101.25 & 13.7496 \tabularnewline
60 & 78 & 83.9179 & -5.91791 \tabularnewline
61 & 118 & 94.5046 & 23.4954 \tabularnewline
62 & 87 & 93.1101 & -6.1101 \tabularnewline
63 & 162 & 110.993 & 51.0074 \tabularnewline
64 & 49 & 94.5686 & -45.5686 \tabularnewline
65 & 122 & 95.306 & 26.694 \tabularnewline
66 & 96 & 107.724 & -11.7239 \tabularnewline
67 & 100 & 98.4395 & 1.56049 \tabularnewline
68 & 82 & 98.8001 & -16.8001 \tabularnewline
69 & 100 & 111.634 & -11.6338 \tabularnewline
70 & 115 & 93.042 & 21.958 \tabularnewline
71 & 141 & 101.089 & 39.9109 \tabularnewline
72 & 110 & 94.5727 & 15.4273 \tabularnewline
73 & 146 & 93.7712 & 52.2288 \tabularnewline
74 & 90 & 95.9461 & -5.9461 \tabularnewline
75 & 121 & 89.2683 & 31.7317 \tabularnewline
76 & 104 & 98.0027 & 5.99729 \tabularnewline
77 & 147 & 89.7221 & 57.2779 \tabularnewline
78 & 110 & 90.3883 & 19.6117 \tabularnewline
79 & 108 & 100.084 & 7.91561 \tabularnewline
80 & 113 & 104.612 & 8.38763 \tabularnewline
81 & 115 & 106.601 & 8.39914 \tabularnewline
82 & 61 & 102.738 & -41.7381 \tabularnewline
83 & 60 & 86.3391 & -26.3391 \tabularnewline
84 & 109 & 104.77 & 4.23038 \tabularnewline
85 & 68 & 87.4842 & -19.4842 \tabularnewline
86 & 111 & 98.2531 & 12.7469 \tabularnewline
87 & 77 & 74.2218 & 2.77818 \tabularnewline
88 & 73 & 92.3978 & -19.3978 \tabularnewline
89 & 89 & 93.0379 & -4.03794 \tabularnewline
90 & 78 & 91.6004 & -13.6004 \tabularnewline
91 & 110 & 100.335 & 9.66518 \tabularnewline
92 & 65 & 98.711 & -33.711 \tabularnewline
93 & 117 & 102.759 & 14.2409 \tabularnewline
94 & 63 & 72.2083 & -9.20826 \tabularnewline
95 & 52 & 81.8362 & -29.8362 \tabularnewline
96 & 62 & 85.8603 & -23.8603 \tabularnewline
97 & 131 & 84.6262 & 46.3738 \tabularnewline
98 & 101 & 96.2216 & 4.7784 \tabularnewline
99 & 42 & 97.1122 & -55.1122 \tabularnewline
100 & 77 & 105.095 & -28.0952 \tabularnewline
101 & 96 & 81.4676 & 14.5324 \tabularnewline
102 & 57 & 86.6146 & -29.6146 \tabularnewline
103 & 112 & 103.103 & 8.8973 \tabularnewline
104 & 49 & 97.7022 & -48.7022 \tabularnewline
105 & 56 & 96.7685 & -40.7685 \tabularnewline
106 & 86 & 99.1688 & -13.1688 \tabularnewline
107 & 88 & 103.15 & -15.1498 \tabularnewline
108 & 48 & 99.4693 & -51.4693 \tabularnewline
109 & 85 & 97.5198 & -12.5198 \tabularnewline
110 & 63 & 95.6706 & -32.6706 \tabularnewline
111 & 102 & 95.7387 & 6.26128 \tabularnewline
112 & 162 & 96.2175 & 65.7825 \tabularnewline
113 & 86 & 97.595 & -11.595 \tabularnewline
114 & 114 & 96.0142 & 17.9858 \tabularnewline
115 & 94 & 95.3951 & -1.39511 \tabularnewline
116 & 81 & 85.3043 & -4.30426 \tabularnewline
117 & 110 & 97.8204 & 12.1796 \tabularnewline
118 & 64 & 97.8163 & -33.8163 \tabularnewline
119 & 104 & 102.738 & 1.26193 \tabularnewline
120 & 105 & 82.7268 & 22.2732 \tabularnewline
121 & 49 & 86.5896 & -37.5896 \tabularnewline
122 & 88 & 94.1149 & -6.11486 \tabularnewline
123 & 95 & 86.4283 & 8.57172 \tabularnewline
124 & 102 & 91.5544 & 10.4456 \tabularnewline
125 & 99 & 89.4046 & 9.59544 \tabularnewline
126 & 63 & 98.3924 & -35.3924 \tabularnewline
127 & 76 & 72.1401 & 3.85986 \tabularnewline
128 & 109 & 94.1369 & 14.8631 \tabularnewline
129 & 117 & 105.778 & 11.2216 \tabularnewline
130 & 57 & 99.4443 & -42.4443 \tabularnewline
131 & 120 & 97.2694 & 22.7306 \tabularnewline
132 & 73 & 107.38 & -34.3803 \tabularnewline
133 & 91 & 91.2107 & -0.210742 \tabularnewline
134 & 108 & 95.8319 & 12.1681 \tabularnewline
135 & 105 & 97.3626 & 7.63741 \tabularnewline
136 & 119 & 98.185 & 20.815 \tabularnewline
137 & 31 & 91.6896 & -60.6896 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267690&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]91[/C][C]96.447[/C][C]-5.44697[/C][/ROW]
[ROW][C]2[/C][C]137[/C][C]93.7502[/C][C]43.2498[/C][/ROW]
[ROW][C]3[/C][C]148[/C][C]87.5733[/C][C]60.4267[/C][/ROW]
[ROW][C]4[/C][C]92[/C][C]95.4883[/C][C]-3.48829[/C][/ROW]
[ROW][C]5[/C][C]131[/C][C]95.921[/C][C]35.079[/C][/ROW]
[ROW][C]6[/C][C]59[/C][C]78.9531[/C][C]-19.9531[/C][/ROW]
[ROW][C]7[/C][C]90[/C][C]97.1803[/C][C]-7.18027[/C][/ROW]
[ROW][C]8[/C][C]83[/C][C]107.105[/C][C]-24.1048[/C][/ROW]
[ROW][C]9[/C][C]116[/C][C]103.535[/C][C]12.4646[/C][/ROW]
[ROW][C]10[/C][C]42[/C][C]90.5025[/C][C]-48.5025[/C][/ROW]
[ROW][C]11[/C][C]155[/C][C]105.321[/C][C]49.6794[/C][/ROW]
[ROW][C]12[/C][C]128[/C][C]103.582[/C][C]24.4185[/C][/ROW]
[ROW][C]13[/C][C]49[/C][C]87.348[/C][C]-38.348[/C][/ROW]
[ROW][C]14[/C][C]96[/C][C]99.308[/C][C]-3.30803[/C][/ROW]
[ROW][C]15[/C][C]66[/C][C]87.8058[/C][C]-21.8058[/C][/ROW]
[ROW][C]16[/C][C]104[/C][C]89.9085[/C][C]14.0915[/C][/ROW]
[ROW][C]17[/C][C]76[/C][C]98.6428[/C][C]-22.6428[/C][/ROW]
[ROW][C]18[/C][C]99[/C][C]97.613[/C][C]1.38698[/C][/ROW]
[ROW][C]19[/C][C]108[/C][C]87.0043[/C][C]20.9957[/C][/ROW]
[ROW][C]20[/C][C]74[/C][C]87.7336[/C][C]-13.7336[/C][/ROW]
[ROW][C]21[/C][C]96[/C][C]88.2385[/C][C]7.76149[/C][/ROW]
[ROW][C]22[/C][C]116[/C][C]94.208[/C][C]21.792[/C][/ROW]
[ROW][C]23[/C][C]87[/C][C]104.294[/C][C]-17.2938[/C][/ROW]
[ROW][C]24[/C][C]97[/C][C]89.3795[/C][C]7.6205[/C][/ROW]
[ROW][C]25[/C][C]127[/C][C]99.1467[/C][C]27.8533[/C][/ROW]
[ROW][C]26[/C][C]106[/C][C]98.0027[/C][C]7.99729[/C][/ROW]
[ROW][C]27[/C][C]80[/C][C]92.47[/C][C]-12.47[/C][/ROW]
[ROW][C]28[/C][C]74[/C][C]109.598[/C][C]-35.5982[/C][/ROW]
[ROW][C]29[/C][C]91[/C][C]98.6428[/C][C]-7.64284[/C][/ROW]
[ROW][C]30[/C][C]133[/C][C]97.1122[/C][C]35.8878[/C][/ROW]
[ROW][C]31[/C][C]74[/C][C]94.755[/C][C]-20.755[/C][/ROW]
[ROW][C]32[/C][C]114[/C][C]85.2873[/C][C]28.7127[/C][/ROW]
[ROW][C]33[/C][C]140[/C][C]99.5124[/C][C]40.4876[/C][/ROW]
[ROW][C]34[/C][C]95[/C][C]96.2636[/C][C]-1.26363[/C][/ROW]
[ROW][C]35[/C][C]98[/C][C]96.0352[/C][C]1.96477[/C][/ROW]
[ROW][C]36[/C][C]121[/C][C]102.323[/C][C]18.6767[/C][/ROW]
[ROW][C]37[/C][C]126[/C][C]97.7272[/C][C]28.2728[/C][/ROW]
[ROW][C]38[/C][C]98[/C][C]98.736[/C][C]-0.736021[/C][/ROW]
[ROW][C]39[/C][C]95[/C][C]92.7875[/C][C]2.21249[/C][/ROW]
[ROW][C]40[/C][C]110[/C][C]101.25[/C][C]8.74956[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]95.0054[/C][C]-25.0054[/C][/ROW]
[ROW][C]42[/C][C]102[/C][C]102.781[/C][C]-0.781126[/C][/ROW]
[ROW][C]43[/C][C]86[/C][C]73.17[/C][C]12.83[/C][/ROW]
[ROW][C]44[/C][C]130[/C][C]95.0095[/C][C]34.9905[/C][/ROW]
[ROW][C]45[/C][C]96[/C][C]110.81[/C][C]-14.8103[/C][/ROW]
[ROW][C]46[/C][C]102[/C][C]97.9346[/C][C]4.0654[/C][/ROW]
[ROW][C]47[/C][C]100[/C][C]86.8691[/C][C]13.1309[/C][/ROW]
[ROW][C]48[/C][C]94[/C][C]96.2216[/C][C]-2.2216[/C][/ROW]
[ROW][C]49[/C][C]52[/C][C]91.919[/C][C]-39.919[/C][/ROW]
[ROW][C]50[/C][C]98[/C][C]104.362[/C][C]-6.36194[/C][/ROW]
[ROW][C]51[/C][C]118[/C][C]104.155[/C][C]13.8454[/C][/ROW]
[ROW][C]52[/C][C]99[/C][C]91.7367[/C][C]7.26333[/C][/ROW]
[ROW][C]53[/C][C]109[/C][C]90.6889[/C][C]18.3111[/C][/ROW]
[ROW][C]54[/C][C]68[/C][C]88.7644[/C][C]-20.7644[/C][/ROW]
[ROW][C]55[/C][C]131[/C][C]106.58[/C][C]24.4202[/C][/ROW]
[ROW][C]56[/C][C]71[/C][C]84.9186[/C][C]-13.9186[/C][/ROW]
[ROW][C]57[/C][C]68[/C][C]95.1667[/C][C]-27.1667[/C][/ROW]
[ROW][C]58[/C][C]89[/C][C]93.5469[/C][C]-4.5469[/C][/ROW]
[ROW][C]59[/C][C]115[/C][C]101.25[/C][C]13.7496[/C][/ROW]
[ROW][C]60[/C][C]78[/C][C]83.9179[/C][C]-5.91791[/C][/ROW]
[ROW][C]61[/C][C]118[/C][C]94.5046[/C][C]23.4954[/C][/ROW]
[ROW][C]62[/C][C]87[/C][C]93.1101[/C][C]-6.1101[/C][/ROW]
[ROW][C]63[/C][C]162[/C][C]110.993[/C][C]51.0074[/C][/ROW]
[ROW][C]64[/C][C]49[/C][C]94.5686[/C][C]-45.5686[/C][/ROW]
[ROW][C]65[/C][C]122[/C][C]95.306[/C][C]26.694[/C][/ROW]
[ROW][C]66[/C][C]96[/C][C]107.724[/C][C]-11.7239[/C][/ROW]
[ROW][C]67[/C][C]100[/C][C]98.4395[/C][C]1.56049[/C][/ROW]
[ROW][C]68[/C][C]82[/C][C]98.8001[/C][C]-16.8001[/C][/ROW]
[ROW][C]69[/C][C]100[/C][C]111.634[/C][C]-11.6338[/C][/ROW]
[ROW][C]70[/C][C]115[/C][C]93.042[/C][C]21.958[/C][/ROW]
[ROW][C]71[/C][C]141[/C][C]101.089[/C][C]39.9109[/C][/ROW]
[ROW][C]72[/C][C]110[/C][C]94.5727[/C][C]15.4273[/C][/ROW]
[ROW][C]73[/C][C]146[/C][C]93.7712[/C][C]52.2288[/C][/ROW]
[ROW][C]74[/C][C]90[/C][C]95.9461[/C][C]-5.9461[/C][/ROW]
[ROW][C]75[/C][C]121[/C][C]89.2683[/C][C]31.7317[/C][/ROW]
[ROW][C]76[/C][C]104[/C][C]98.0027[/C][C]5.99729[/C][/ROW]
[ROW][C]77[/C][C]147[/C][C]89.7221[/C][C]57.2779[/C][/ROW]
[ROW][C]78[/C][C]110[/C][C]90.3883[/C][C]19.6117[/C][/ROW]
[ROW][C]79[/C][C]108[/C][C]100.084[/C][C]7.91561[/C][/ROW]
[ROW][C]80[/C][C]113[/C][C]104.612[/C][C]8.38763[/C][/ROW]
[ROW][C]81[/C][C]115[/C][C]106.601[/C][C]8.39914[/C][/ROW]
[ROW][C]82[/C][C]61[/C][C]102.738[/C][C]-41.7381[/C][/ROW]
[ROW][C]83[/C][C]60[/C][C]86.3391[/C][C]-26.3391[/C][/ROW]
[ROW][C]84[/C][C]109[/C][C]104.77[/C][C]4.23038[/C][/ROW]
[ROW][C]85[/C][C]68[/C][C]87.4842[/C][C]-19.4842[/C][/ROW]
[ROW][C]86[/C][C]111[/C][C]98.2531[/C][C]12.7469[/C][/ROW]
[ROW][C]87[/C][C]77[/C][C]74.2218[/C][C]2.77818[/C][/ROW]
[ROW][C]88[/C][C]73[/C][C]92.3978[/C][C]-19.3978[/C][/ROW]
[ROW][C]89[/C][C]89[/C][C]93.0379[/C][C]-4.03794[/C][/ROW]
[ROW][C]90[/C][C]78[/C][C]91.6004[/C][C]-13.6004[/C][/ROW]
[ROW][C]91[/C][C]110[/C][C]100.335[/C][C]9.66518[/C][/ROW]
[ROW][C]92[/C][C]65[/C][C]98.711[/C][C]-33.711[/C][/ROW]
[ROW][C]93[/C][C]117[/C][C]102.759[/C][C]14.2409[/C][/ROW]
[ROW][C]94[/C][C]63[/C][C]72.2083[/C][C]-9.20826[/C][/ROW]
[ROW][C]95[/C][C]52[/C][C]81.8362[/C][C]-29.8362[/C][/ROW]
[ROW][C]96[/C][C]62[/C][C]85.8603[/C][C]-23.8603[/C][/ROW]
[ROW][C]97[/C][C]131[/C][C]84.6262[/C][C]46.3738[/C][/ROW]
[ROW][C]98[/C][C]101[/C][C]96.2216[/C][C]4.7784[/C][/ROW]
[ROW][C]99[/C][C]42[/C][C]97.1122[/C][C]-55.1122[/C][/ROW]
[ROW][C]100[/C][C]77[/C][C]105.095[/C][C]-28.0952[/C][/ROW]
[ROW][C]101[/C][C]96[/C][C]81.4676[/C][C]14.5324[/C][/ROW]
[ROW][C]102[/C][C]57[/C][C]86.6146[/C][C]-29.6146[/C][/ROW]
[ROW][C]103[/C][C]112[/C][C]103.103[/C][C]8.8973[/C][/ROW]
[ROW][C]104[/C][C]49[/C][C]97.7022[/C][C]-48.7022[/C][/ROW]
[ROW][C]105[/C][C]56[/C][C]96.7685[/C][C]-40.7685[/C][/ROW]
[ROW][C]106[/C][C]86[/C][C]99.1688[/C][C]-13.1688[/C][/ROW]
[ROW][C]107[/C][C]88[/C][C]103.15[/C][C]-15.1498[/C][/ROW]
[ROW][C]108[/C][C]48[/C][C]99.4693[/C][C]-51.4693[/C][/ROW]
[ROW][C]109[/C][C]85[/C][C]97.5198[/C][C]-12.5198[/C][/ROW]
[ROW][C]110[/C][C]63[/C][C]95.6706[/C][C]-32.6706[/C][/ROW]
[ROW][C]111[/C][C]102[/C][C]95.7387[/C][C]6.26128[/C][/ROW]
[ROW][C]112[/C][C]162[/C][C]96.2175[/C][C]65.7825[/C][/ROW]
[ROW][C]113[/C][C]86[/C][C]97.595[/C][C]-11.595[/C][/ROW]
[ROW][C]114[/C][C]114[/C][C]96.0142[/C][C]17.9858[/C][/ROW]
[ROW][C]115[/C][C]94[/C][C]95.3951[/C][C]-1.39511[/C][/ROW]
[ROW][C]116[/C][C]81[/C][C]85.3043[/C][C]-4.30426[/C][/ROW]
[ROW][C]117[/C][C]110[/C][C]97.8204[/C][C]12.1796[/C][/ROW]
[ROW][C]118[/C][C]64[/C][C]97.8163[/C][C]-33.8163[/C][/ROW]
[ROW][C]119[/C][C]104[/C][C]102.738[/C][C]1.26193[/C][/ROW]
[ROW][C]120[/C][C]105[/C][C]82.7268[/C][C]22.2732[/C][/ROW]
[ROW][C]121[/C][C]49[/C][C]86.5896[/C][C]-37.5896[/C][/ROW]
[ROW][C]122[/C][C]88[/C][C]94.1149[/C][C]-6.11486[/C][/ROW]
[ROW][C]123[/C][C]95[/C][C]86.4283[/C][C]8.57172[/C][/ROW]
[ROW][C]124[/C][C]102[/C][C]91.5544[/C][C]10.4456[/C][/ROW]
[ROW][C]125[/C][C]99[/C][C]89.4046[/C][C]9.59544[/C][/ROW]
[ROW][C]126[/C][C]63[/C][C]98.3924[/C][C]-35.3924[/C][/ROW]
[ROW][C]127[/C][C]76[/C][C]72.1401[/C][C]3.85986[/C][/ROW]
[ROW][C]128[/C][C]109[/C][C]94.1369[/C][C]14.8631[/C][/ROW]
[ROW][C]129[/C][C]117[/C][C]105.778[/C][C]11.2216[/C][/ROW]
[ROW][C]130[/C][C]57[/C][C]99.4443[/C][C]-42.4443[/C][/ROW]
[ROW][C]131[/C][C]120[/C][C]97.2694[/C][C]22.7306[/C][/ROW]
[ROW][C]132[/C][C]73[/C][C]107.38[/C][C]-34.3803[/C][/ROW]
[ROW][C]133[/C][C]91[/C][C]91.2107[/C][C]-0.210742[/C][/ROW]
[ROW][C]134[/C][C]108[/C][C]95.8319[/C][C]12.1681[/C][/ROW]
[ROW][C]135[/C][C]105[/C][C]97.3626[/C][C]7.63741[/C][/ROW]
[ROW][C]136[/C][C]119[/C][C]98.185[/C][C]20.815[/C][/ROW]
[ROW][C]137[/C][C]31[/C][C]91.6896[/C][C]-60.6896[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267690&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267690&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	91	96.447	-5.44697
2	137	93.7502	43.2498
3	148	87.5733	60.4267
4	92	95.4883	-3.48829
5	131	95.921	35.079
6	59	78.9531	-19.9531
7	90	97.1803	-7.18027
8	83	107.105	-24.1048
9	116	103.535	12.4646
10	42	90.5025	-48.5025
11	155	105.321	49.6794
12	128	103.582	24.4185
13	49	87.348	-38.348
14	96	99.308	-3.30803
15	66	87.8058	-21.8058
16	104	89.9085	14.0915
17	76	98.6428	-22.6428
18	99	97.613	1.38698
19	108	87.0043	20.9957
20	74	87.7336	-13.7336
21	96	88.2385	7.76149
22	116	94.208	21.792
23	87	104.294	-17.2938
24	97	89.3795	7.6205
25	127	99.1467	27.8533
26	106	98.0027	7.99729
27	80	92.47	-12.47
28	74	109.598	-35.5982
29	91	98.6428	-7.64284
30	133	97.1122	35.8878
31	74	94.755	-20.755
32	114	85.2873	28.7127
33	140	99.5124	40.4876
34	95	96.2636	-1.26363
35	98	96.0352	1.96477
36	121	102.323	18.6767
37	126	97.7272	28.2728
38	98	98.736	-0.736021
39	95	92.7875	2.21249
40	110	101.25	8.74956
41	70	95.0054	-25.0054
42	102	102.781	-0.781126
43	86	73.17	12.83
44	130	95.0095	34.9905
45	96	110.81	-14.8103
46	102	97.9346	4.0654
47	100	86.8691	13.1309
48	94	96.2216	-2.2216
49	52	91.919	-39.919
50	98	104.362	-6.36194
51	118	104.155	13.8454
52	99	91.7367	7.26333
53	109	90.6889	18.3111
54	68	88.7644	-20.7644
55	131	106.58	24.4202
56	71	84.9186	-13.9186
57	68	95.1667	-27.1667
58	89	93.5469	-4.5469
59	115	101.25	13.7496
60	78	83.9179	-5.91791
61	118	94.5046	23.4954
62	87	93.1101	-6.1101
63	162	110.993	51.0074
64	49	94.5686	-45.5686
65	122	95.306	26.694
66	96	107.724	-11.7239
67	100	98.4395	1.56049
68	82	98.8001	-16.8001
69	100	111.634	-11.6338
70	115	93.042	21.958
71	141	101.089	39.9109
72	110	94.5727	15.4273
73	146	93.7712	52.2288
74	90	95.9461	-5.9461
75	121	89.2683	31.7317
76	104	98.0027	5.99729
77	147	89.7221	57.2779
78	110	90.3883	19.6117
79	108	100.084	7.91561
80	113	104.612	8.38763
81	115	106.601	8.39914
82	61	102.738	-41.7381
83	60	86.3391	-26.3391
84	109	104.77	4.23038
85	68	87.4842	-19.4842
86	111	98.2531	12.7469
87	77	74.2218	2.77818
88	73	92.3978	-19.3978
89	89	93.0379	-4.03794
90	78	91.6004	-13.6004
91	110	100.335	9.66518
92	65	98.711	-33.711
93	117	102.759	14.2409
94	63	72.2083	-9.20826
95	52	81.8362	-29.8362
96	62	85.8603	-23.8603
97	131	84.6262	46.3738
98	101	96.2216	4.7784
99	42	97.1122	-55.1122
100	77	105.095	-28.0952
101	96	81.4676	14.5324
102	57	86.6146	-29.6146
103	112	103.103	8.8973
104	49	97.7022	-48.7022
105	56	96.7685	-40.7685
106	86	99.1688	-13.1688
107	88	103.15	-15.1498
108	48	99.4693	-51.4693
109	85	97.5198	-12.5198
110	63	95.6706	-32.6706
111	102	95.7387	6.26128
112	162	96.2175	65.7825
113	86	97.595	-11.595
114	114	96.0142	17.9858
115	94	95.3951	-1.39511
116	81	85.3043	-4.30426
117	110	97.8204	12.1796
118	64	97.8163	-33.8163
119	104	102.738	1.26193
120	105	82.7268	22.2732
121	49	86.5896	-37.5896
122	88	94.1149	-6.11486
123	95	86.4283	8.57172
124	102	91.5544	10.4456
125	99	89.4046	9.59544
126	63	98.3924	-35.3924
127	76	72.1401	3.85986
128	109	94.1369	14.8631
129	117	105.778	11.2216
130	57	99.4443	-42.4443
131	120	97.2694	22.7306
132	73	107.38	-34.3803
133	91	91.2107	-0.210742
134	108	95.8319	12.1681
135	105	97.3626	7.63741
136	119	98.185	20.815
137	31	91.6896	-60.6896

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.848058	0.303885	0.151942
8	0.774685	0.45063	0.225315
9	0.661108	0.677784	0.338892
10	0.861308	0.277383	0.138692
11	0.8637	0.2726	0.1363
12	0.869055	0.26189	0.130945
13	0.825888	0.348223	0.174112
14	0.801156	0.397688	0.198844
15	0.749537	0.500926	0.250463
16	0.793306	0.413387	0.206694
17	0.83138	0.337241	0.16862
18	0.819799	0.360401	0.180201
19	0.843167	0.313666	0.156833
20	0.816389	0.367223	0.183611
21	0.768116	0.463767	0.231884
22	0.750403	0.499193	0.249597
23	0.698856	0.602289	0.301144
24	0.657555	0.684889	0.342445
25	0.682181	0.635639	0.317819
26	0.622436	0.755129	0.377564
27	0.57547	0.84906	0.42453
28	0.644027	0.711946	0.355973
29	0.599562	0.800876	0.400438
30	0.664857	0.670286	0.335143
31	0.665064	0.669872	0.334936
32	0.66153	0.67694	0.33847
33	0.701025	0.59795	0.298975
34	0.648908	0.702184	0.351092
35	0.595824	0.808353	0.404176
36	0.550576	0.898848	0.449424
37	0.52766	0.94468	0.47234
38	0.470487	0.940974	0.529513
39	0.417243	0.834486	0.582757
40	0.366172	0.732344	0.633828
41	0.425632	0.851263	0.574368
42	0.384993	0.769985	0.615007
43	0.357587	0.715174	0.642413
44	0.397138	0.794276	0.602862
45	0.356124	0.712249	0.643876
46	0.307722	0.615445	0.692278
47	0.270296	0.540591	0.729704
48	0.227969	0.455938	0.772031
49	0.336044	0.672088	0.663956
50	0.291636	0.583272	0.708364
51	0.257428	0.514857	0.742572
52	0.220149	0.440297	0.779851
53	0.200643	0.401285	0.799357
54	0.199113	0.398226	0.800887
55	0.19028	0.38056	0.80972
56	0.167157	0.334313	0.832843
57	0.185001	0.370001	0.814999
58	0.157219	0.314438	0.842781
59	0.135255	0.270511	0.864745
60	0.110739	0.221478	0.889261
61	0.106219	0.212437	0.893781
62	0.0870531	0.174106	0.912947
63	0.156905	0.313811	0.843095
64	0.230423	0.460846	0.769577
65	0.231717	0.463435	0.768283
66	0.208019	0.416039	0.791981
67	0.177138	0.354276	0.822862
68	0.158735	0.317469	0.841265
69	0.141257	0.282514	0.858743
70	0.134416	0.268832	0.865584
71	0.178038	0.356076	0.821962
72	0.159	0.318	0.841
73	0.274466	0.548932	0.725534
74	0.237876	0.475751	0.762124
75	0.264649	0.529299	0.735351
76	0.229804	0.459607	0.770196
77	0.419819	0.839638	0.580181
78	0.403138	0.806276	0.596862
79	0.366099	0.732197	0.633901
80	0.333811	0.667622	0.666189
81	0.303551	0.607102	0.696449
82	0.358983	0.717966	0.641017
83	0.359449	0.718897	0.640551
84	0.318765	0.63753	0.681235
85	0.298012	0.596024	0.701988
86	0.271303	0.542606	0.728697
87	0.230577	0.461154	0.769423
88	0.210639	0.421278	0.789361
89	0.17606	0.352119	0.82394
90	0.150499	0.300998	0.849501
91	0.130561	0.261122	0.869439
92	0.141141	0.282282	0.858859
93	0.135099	0.270199	0.864901
94	0.114426	0.228852	0.885574
95	0.122796	0.245593	0.877204
96	0.123402	0.246804	0.876598
97	0.189782	0.379565	0.810218
98	0.164265	0.328529	0.835735
99	0.269765	0.53953	0.730235
100	0.251564	0.503128	0.748436
101	0.223123	0.446246	0.776877
102	0.228398	0.456796	0.771602
103	0.213079	0.426157	0.786921
104	0.323923	0.647846	0.676077
105	0.354329	0.708658	0.645671
106	0.308432	0.616864	0.691568
107	0.265918	0.531836	0.734082
108	0.378398	0.756797	0.621602
109	0.337405	0.674809	0.662595
110	0.35049	0.70098	0.64951
111	0.294631	0.589262	0.705369
112	0.736559	0.526881	0.263441
113	0.678273	0.643455	0.321727
114	0.652227	0.695546	0.347773
115	0.587728	0.824544	0.412272
116	0.635728	0.728545	0.364272
117	0.580924	0.838152	0.419076
118	0.546519	0.906962	0.453481
119	0.496499	0.992997	0.503501
120	0.525423	0.949155	0.474577
121	0.638589	0.722822	0.361411
122	0.550645	0.898709	0.449355
123	0.512047	0.975905	0.487953
124	0.416595	0.83319	0.583405
125	0.337332	0.674663	0.662668
126	0.288981	0.577963	0.711019
127	0.254107	0.508214	0.745893
128	0.230371	0.460743	0.769629
129	0.149301	0.298603	0.850699
130	0.875923	0.248154	0.124077

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.848058 & 0.303885 & 0.151942 \tabularnewline
8 & 0.774685 & 0.45063 & 0.225315 \tabularnewline
9 & 0.661108 & 0.677784 & 0.338892 \tabularnewline
10 & 0.861308 & 0.277383 & 0.138692 \tabularnewline
11 & 0.8637 & 0.2726 & 0.1363 \tabularnewline
12 & 0.869055 & 0.26189 & 0.130945 \tabularnewline
13 & 0.825888 & 0.348223 & 0.174112 \tabularnewline
14 & 0.801156 & 0.397688 & 0.198844 \tabularnewline
15 & 0.749537 & 0.500926 & 0.250463 \tabularnewline
16 & 0.793306 & 0.413387 & 0.206694 \tabularnewline
17 & 0.83138 & 0.337241 & 0.16862 \tabularnewline
18 & 0.819799 & 0.360401 & 0.180201 \tabularnewline
19 & 0.843167 & 0.313666 & 0.156833 \tabularnewline
20 & 0.816389 & 0.367223 & 0.183611 \tabularnewline
21 & 0.768116 & 0.463767 & 0.231884 \tabularnewline
22 & 0.750403 & 0.499193 & 0.249597 \tabularnewline
23 & 0.698856 & 0.602289 & 0.301144 \tabularnewline
24 & 0.657555 & 0.684889 & 0.342445 \tabularnewline
25 & 0.682181 & 0.635639 & 0.317819 \tabularnewline
26 & 0.622436 & 0.755129 & 0.377564 \tabularnewline
27 & 0.57547 & 0.84906 & 0.42453 \tabularnewline
28 & 0.644027 & 0.711946 & 0.355973 \tabularnewline
29 & 0.599562 & 0.800876 & 0.400438 \tabularnewline
30 & 0.664857 & 0.670286 & 0.335143 \tabularnewline
31 & 0.665064 & 0.669872 & 0.334936 \tabularnewline
32 & 0.66153 & 0.67694 & 0.33847 \tabularnewline
33 & 0.701025 & 0.59795 & 0.298975 \tabularnewline
34 & 0.648908 & 0.702184 & 0.351092 \tabularnewline
35 & 0.595824 & 0.808353 & 0.404176 \tabularnewline
36 & 0.550576 & 0.898848 & 0.449424 \tabularnewline
37 & 0.52766 & 0.94468 & 0.47234 \tabularnewline
38 & 0.470487 & 0.940974 & 0.529513 \tabularnewline
39 & 0.417243 & 0.834486 & 0.582757 \tabularnewline
40 & 0.366172 & 0.732344 & 0.633828 \tabularnewline
41 & 0.425632 & 0.851263 & 0.574368 \tabularnewline
42 & 0.384993 & 0.769985 & 0.615007 \tabularnewline
43 & 0.357587 & 0.715174 & 0.642413 \tabularnewline
44 & 0.397138 & 0.794276 & 0.602862 \tabularnewline
45 & 0.356124 & 0.712249 & 0.643876 \tabularnewline
46 & 0.307722 & 0.615445 & 0.692278 \tabularnewline
47 & 0.270296 & 0.540591 & 0.729704 \tabularnewline
48 & 0.227969 & 0.455938 & 0.772031 \tabularnewline
49 & 0.336044 & 0.672088 & 0.663956 \tabularnewline
50 & 0.291636 & 0.583272 & 0.708364 \tabularnewline
51 & 0.257428 & 0.514857 & 0.742572 \tabularnewline
52 & 0.220149 & 0.440297 & 0.779851 \tabularnewline
53 & 0.200643 & 0.401285 & 0.799357 \tabularnewline
54 & 0.199113 & 0.398226 & 0.800887 \tabularnewline
55 & 0.19028 & 0.38056 & 0.80972 \tabularnewline
56 & 0.167157 & 0.334313 & 0.832843 \tabularnewline
57 & 0.185001 & 0.370001 & 0.814999 \tabularnewline
58 & 0.157219 & 0.314438 & 0.842781 \tabularnewline
59 & 0.135255 & 0.270511 & 0.864745 \tabularnewline
60 & 0.110739 & 0.221478 & 0.889261 \tabularnewline
61 & 0.106219 & 0.212437 & 0.893781 \tabularnewline
62 & 0.0870531 & 0.174106 & 0.912947 \tabularnewline
63 & 0.156905 & 0.313811 & 0.843095 \tabularnewline
64 & 0.230423 & 0.460846 & 0.769577 \tabularnewline
65 & 0.231717 & 0.463435 & 0.768283 \tabularnewline
66 & 0.208019 & 0.416039 & 0.791981 \tabularnewline
67 & 0.177138 & 0.354276 & 0.822862 \tabularnewline
68 & 0.158735 & 0.317469 & 0.841265 \tabularnewline
69 & 0.141257 & 0.282514 & 0.858743 \tabularnewline
70 & 0.134416 & 0.268832 & 0.865584 \tabularnewline
71 & 0.178038 & 0.356076 & 0.821962 \tabularnewline
72 & 0.159 & 0.318 & 0.841 \tabularnewline
73 & 0.274466 & 0.548932 & 0.725534 \tabularnewline
74 & 0.237876 & 0.475751 & 0.762124 \tabularnewline
75 & 0.264649 & 0.529299 & 0.735351 \tabularnewline
76 & 0.229804 & 0.459607 & 0.770196 \tabularnewline
77 & 0.419819 & 0.839638 & 0.580181 \tabularnewline
78 & 0.403138 & 0.806276 & 0.596862 \tabularnewline
79 & 0.366099 & 0.732197 & 0.633901 \tabularnewline
80 & 0.333811 & 0.667622 & 0.666189 \tabularnewline
81 & 0.303551 & 0.607102 & 0.696449 \tabularnewline
82 & 0.358983 & 0.717966 & 0.641017 \tabularnewline
83 & 0.359449 & 0.718897 & 0.640551 \tabularnewline
84 & 0.318765 & 0.63753 & 0.681235 \tabularnewline
85 & 0.298012 & 0.596024 & 0.701988 \tabularnewline
86 & 0.271303 & 0.542606 & 0.728697 \tabularnewline
87 & 0.230577 & 0.461154 & 0.769423 \tabularnewline
88 & 0.210639 & 0.421278 & 0.789361 \tabularnewline
89 & 0.17606 & 0.352119 & 0.82394 \tabularnewline
90 & 0.150499 & 0.300998 & 0.849501 \tabularnewline
91 & 0.130561 & 0.261122 & 0.869439 \tabularnewline
92 & 0.141141 & 0.282282 & 0.858859 \tabularnewline
93 & 0.135099 & 0.270199 & 0.864901 \tabularnewline
94 & 0.114426 & 0.228852 & 0.885574 \tabularnewline
95 & 0.122796 & 0.245593 & 0.877204 \tabularnewline
96 & 0.123402 & 0.246804 & 0.876598 \tabularnewline
97 & 0.189782 & 0.379565 & 0.810218 \tabularnewline
98 & 0.164265 & 0.328529 & 0.835735 \tabularnewline
99 & 0.269765 & 0.53953 & 0.730235 \tabularnewline
100 & 0.251564 & 0.503128 & 0.748436 \tabularnewline
101 & 0.223123 & 0.446246 & 0.776877 \tabularnewline
102 & 0.228398 & 0.456796 & 0.771602 \tabularnewline
103 & 0.213079 & 0.426157 & 0.786921 \tabularnewline
104 & 0.323923 & 0.647846 & 0.676077 \tabularnewline
105 & 0.354329 & 0.708658 & 0.645671 \tabularnewline
106 & 0.308432 & 0.616864 & 0.691568 \tabularnewline
107 & 0.265918 & 0.531836 & 0.734082 \tabularnewline
108 & 0.378398 & 0.756797 & 0.621602 \tabularnewline
109 & 0.337405 & 0.674809 & 0.662595 \tabularnewline
110 & 0.35049 & 0.70098 & 0.64951 \tabularnewline
111 & 0.294631 & 0.589262 & 0.705369 \tabularnewline
112 & 0.736559 & 0.526881 & 0.263441 \tabularnewline
113 & 0.678273 & 0.643455 & 0.321727 \tabularnewline
114 & 0.652227 & 0.695546 & 0.347773 \tabularnewline
115 & 0.587728 & 0.824544 & 0.412272 \tabularnewline
116 & 0.635728 & 0.728545 & 0.364272 \tabularnewline
117 & 0.580924 & 0.838152 & 0.419076 \tabularnewline
118 & 0.546519 & 0.906962 & 0.453481 \tabularnewline
119 & 0.496499 & 0.992997 & 0.503501 \tabularnewline
120 & 0.525423 & 0.949155 & 0.474577 \tabularnewline
121 & 0.638589 & 0.722822 & 0.361411 \tabularnewline
122 & 0.550645 & 0.898709 & 0.449355 \tabularnewline
123 & 0.512047 & 0.975905 & 0.487953 \tabularnewline
124 & 0.416595 & 0.83319 & 0.583405 \tabularnewline
125 & 0.337332 & 0.674663 & 0.662668 \tabularnewline
126 & 0.288981 & 0.577963 & 0.711019 \tabularnewline
127 & 0.254107 & 0.508214 & 0.745893 \tabularnewline
128 & 0.230371 & 0.460743 & 0.769629 \tabularnewline
129 & 0.149301 & 0.298603 & 0.850699 \tabularnewline
130 & 0.875923 & 0.248154 & 0.124077 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267690&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.848058[/C][C]0.303885[/C][C]0.151942[/C][/ROW]
[ROW][C]8[/C][C]0.774685[/C][C]0.45063[/C][C]0.225315[/C][/ROW]
[ROW][C]9[/C][C]0.661108[/C][C]0.677784[/C][C]0.338892[/C][/ROW]
[ROW][C]10[/C][C]0.861308[/C][C]0.277383[/C][C]0.138692[/C][/ROW]
[ROW][C]11[/C][C]0.8637[/C][C]0.2726[/C][C]0.1363[/C][/ROW]
[ROW][C]12[/C][C]0.869055[/C][C]0.26189[/C][C]0.130945[/C][/ROW]
[ROW][C]13[/C][C]0.825888[/C][C]0.348223[/C][C]0.174112[/C][/ROW]
[ROW][C]14[/C][C]0.801156[/C][C]0.397688[/C][C]0.198844[/C][/ROW]
[ROW][C]15[/C][C]0.749537[/C][C]0.500926[/C][C]0.250463[/C][/ROW]
[ROW][C]16[/C][C]0.793306[/C][C]0.413387[/C][C]0.206694[/C][/ROW]
[ROW][C]17[/C][C]0.83138[/C][C]0.337241[/C][C]0.16862[/C][/ROW]
[ROW][C]18[/C][C]0.819799[/C][C]0.360401[/C][C]0.180201[/C][/ROW]
[ROW][C]19[/C][C]0.843167[/C][C]0.313666[/C][C]0.156833[/C][/ROW]
[ROW][C]20[/C][C]0.816389[/C][C]0.367223[/C][C]0.183611[/C][/ROW]
[ROW][C]21[/C][C]0.768116[/C][C]0.463767[/C][C]0.231884[/C][/ROW]
[ROW][C]22[/C][C]0.750403[/C][C]0.499193[/C][C]0.249597[/C][/ROW]
[ROW][C]23[/C][C]0.698856[/C][C]0.602289[/C][C]0.301144[/C][/ROW]
[ROW][C]24[/C][C]0.657555[/C][C]0.684889[/C][C]0.342445[/C][/ROW]
[ROW][C]25[/C][C]0.682181[/C][C]0.635639[/C][C]0.317819[/C][/ROW]
[ROW][C]26[/C][C]0.622436[/C][C]0.755129[/C][C]0.377564[/C][/ROW]
[ROW][C]27[/C][C]0.57547[/C][C]0.84906[/C][C]0.42453[/C][/ROW]
[ROW][C]28[/C][C]0.644027[/C][C]0.711946[/C][C]0.355973[/C][/ROW]
[ROW][C]29[/C][C]0.599562[/C][C]0.800876[/C][C]0.400438[/C][/ROW]
[ROW][C]30[/C][C]0.664857[/C][C]0.670286[/C][C]0.335143[/C][/ROW]
[ROW][C]31[/C][C]0.665064[/C][C]0.669872[/C][C]0.334936[/C][/ROW]
[ROW][C]32[/C][C]0.66153[/C][C]0.67694[/C][C]0.33847[/C][/ROW]
[ROW][C]33[/C][C]0.701025[/C][C]0.59795[/C][C]0.298975[/C][/ROW]
[ROW][C]34[/C][C]0.648908[/C][C]0.702184[/C][C]0.351092[/C][/ROW]
[ROW][C]35[/C][C]0.595824[/C][C]0.808353[/C][C]0.404176[/C][/ROW]
[ROW][C]36[/C][C]0.550576[/C][C]0.898848[/C][C]0.449424[/C][/ROW]
[ROW][C]37[/C][C]0.52766[/C][C]0.94468[/C][C]0.47234[/C][/ROW]
[ROW][C]38[/C][C]0.470487[/C][C]0.940974[/C][C]0.529513[/C][/ROW]
[ROW][C]39[/C][C]0.417243[/C][C]0.834486[/C][C]0.582757[/C][/ROW]
[ROW][C]40[/C][C]0.366172[/C][C]0.732344[/C][C]0.633828[/C][/ROW]
[ROW][C]41[/C][C]0.425632[/C][C]0.851263[/C][C]0.574368[/C][/ROW]
[ROW][C]42[/C][C]0.384993[/C][C]0.769985[/C][C]0.615007[/C][/ROW]
[ROW][C]43[/C][C]0.357587[/C][C]0.715174[/C][C]0.642413[/C][/ROW]
[ROW][C]44[/C][C]0.397138[/C][C]0.794276[/C][C]0.602862[/C][/ROW]
[ROW][C]45[/C][C]0.356124[/C][C]0.712249[/C][C]0.643876[/C][/ROW]
[ROW][C]46[/C][C]0.307722[/C][C]0.615445[/C][C]0.692278[/C][/ROW]
[ROW][C]47[/C][C]0.270296[/C][C]0.540591[/C][C]0.729704[/C][/ROW]
[ROW][C]48[/C][C]0.227969[/C][C]0.455938[/C][C]0.772031[/C][/ROW]
[ROW][C]49[/C][C]0.336044[/C][C]0.672088[/C][C]0.663956[/C][/ROW]
[ROW][C]50[/C][C]0.291636[/C][C]0.583272[/C][C]0.708364[/C][/ROW]
[ROW][C]51[/C][C]0.257428[/C][C]0.514857[/C][C]0.742572[/C][/ROW]
[ROW][C]52[/C][C]0.220149[/C][C]0.440297[/C][C]0.779851[/C][/ROW]
[ROW][C]53[/C][C]0.200643[/C][C]0.401285[/C][C]0.799357[/C][/ROW]
[ROW][C]54[/C][C]0.199113[/C][C]0.398226[/C][C]0.800887[/C][/ROW]
[ROW][C]55[/C][C]0.19028[/C][C]0.38056[/C][C]0.80972[/C][/ROW]
[ROW][C]56[/C][C]0.167157[/C][C]0.334313[/C][C]0.832843[/C][/ROW]
[ROW][C]57[/C][C]0.185001[/C][C]0.370001[/C][C]0.814999[/C][/ROW]
[ROW][C]58[/C][C]0.157219[/C][C]0.314438[/C][C]0.842781[/C][/ROW]
[ROW][C]59[/C][C]0.135255[/C][C]0.270511[/C][C]0.864745[/C][/ROW]
[ROW][C]60[/C][C]0.110739[/C][C]0.221478[/C][C]0.889261[/C][/ROW]
[ROW][C]61[/C][C]0.106219[/C][C]0.212437[/C][C]0.893781[/C][/ROW]
[ROW][C]62[/C][C]0.0870531[/C][C]0.174106[/C][C]0.912947[/C][/ROW]
[ROW][C]63[/C][C]0.156905[/C][C]0.313811[/C][C]0.843095[/C][/ROW]
[ROW][C]64[/C][C]0.230423[/C][C]0.460846[/C][C]0.769577[/C][/ROW]
[ROW][C]65[/C][C]0.231717[/C][C]0.463435[/C][C]0.768283[/C][/ROW]
[ROW][C]66[/C][C]0.208019[/C][C]0.416039[/C][C]0.791981[/C][/ROW]
[ROW][C]67[/C][C]0.177138[/C][C]0.354276[/C][C]0.822862[/C][/ROW]
[ROW][C]68[/C][C]0.158735[/C][C]0.317469[/C][C]0.841265[/C][/ROW]
[ROW][C]69[/C][C]0.141257[/C][C]0.282514[/C][C]0.858743[/C][/ROW]
[ROW][C]70[/C][C]0.134416[/C][C]0.268832[/C][C]0.865584[/C][/ROW]
[ROW][C]71[/C][C]0.178038[/C][C]0.356076[/C][C]0.821962[/C][/ROW]
[ROW][C]72[/C][C]0.159[/C][C]0.318[/C][C]0.841[/C][/ROW]
[ROW][C]73[/C][C]0.274466[/C][C]0.548932[/C][C]0.725534[/C][/ROW]
[ROW][C]74[/C][C]0.237876[/C][C]0.475751[/C][C]0.762124[/C][/ROW]
[ROW][C]75[/C][C]0.264649[/C][C]0.529299[/C][C]0.735351[/C][/ROW]
[ROW][C]76[/C][C]0.229804[/C][C]0.459607[/C][C]0.770196[/C][/ROW]
[ROW][C]77[/C][C]0.419819[/C][C]0.839638[/C][C]0.580181[/C][/ROW]
[ROW][C]78[/C][C]0.403138[/C][C]0.806276[/C][C]0.596862[/C][/ROW]
[ROW][C]79[/C][C]0.366099[/C][C]0.732197[/C][C]0.633901[/C][/ROW]
[ROW][C]80[/C][C]0.333811[/C][C]0.667622[/C][C]0.666189[/C][/ROW]
[ROW][C]81[/C][C]0.303551[/C][C]0.607102[/C][C]0.696449[/C][/ROW]
[ROW][C]82[/C][C]0.358983[/C][C]0.717966[/C][C]0.641017[/C][/ROW]
[ROW][C]83[/C][C]0.359449[/C][C]0.718897[/C][C]0.640551[/C][/ROW]
[ROW][C]84[/C][C]0.318765[/C][C]0.63753[/C][C]0.681235[/C][/ROW]
[ROW][C]85[/C][C]0.298012[/C][C]0.596024[/C][C]0.701988[/C][/ROW]
[ROW][C]86[/C][C]0.271303[/C][C]0.542606[/C][C]0.728697[/C][/ROW]
[ROW][C]87[/C][C]0.230577[/C][C]0.461154[/C][C]0.769423[/C][/ROW]
[ROW][C]88[/C][C]0.210639[/C][C]0.421278[/C][C]0.789361[/C][/ROW]
[ROW][C]89[/C][C]0.17606[/C][C]0.352119[/C][C]0.82394[/C][/ROW]
[ROW][C]90[/C][C]0.150499[/C][C]0.300998[/C][C]0.849501[/C][/ROW]
[ROW][C]91[/C][C]0.130561[/C][C]0.261122[/C][C]0.869439[/C][/ROW]
[ROW][C]92[/C][C]0.141141[/C][C]0.282282[/C][C]0.858859[/C][/ROW]
[ROW][C]93[/C][C]0.135099[/C][C]0.270199[/C][C]0.864901[/C][/ROW]
[ROW][C]94[/C][C]0.114426[/C][C]0.228852[/C][C]0.885574[/C][/ROW]
[ROW][C]95[/C][C]0.122796[/C][C]0.245593[/C][C]0.877204[/C][/ROW]
[ROW][C]96[/C][C]0.123402[/C][C]0.246804[/C][C]0.876598[/C][/ROW]
[ROW][C]97[/C][C]0.189782[/C][C]0.379565[/C][C]0.810218[/C][/ROW]
[ROW][C]98[/C][C]0.164265[/C][C]0.328529[/C][C]0.835735[/C][/ROW]
[ROW][C]99[/C][C]0.269765[/C][C]0.53953[/C][C]0.730235[/C][/ROW]
[ROW][C]100[/C][C]0.251564[/C][C]0.503128[/C][C]0.748436[/C][/ROW]
[ROW][C]101[/C][C]0.223123[/C][C]0.446246[/C][C]0.776877[/C][/ROW]
[ROW][C]102[/C][C]0.228398[/C][C]0.456796[/C][C]0.771602[/C][/ROW]
[ROW][C]103[/C][C]0.213079[/C][C]0.426157[/C][C]0.786921[/C][/ROW]
[ROW][C]104[/C][C]0.323923[/C][C]0.647846[/C][C]0.676077[/C][/ROW]
[ROW][C]105[/C][C]0.354329[/C][C]0.708658[/C][C]0.645671[/C][/ROW]
[ROW][C]106[/C][C]0.308432[/C][C]0.616864[/C][C]0.691568[/C][/ROW]
[ROW][C]107[/C][C]0.265918[/C][C]0.531836[/C][C]0.734082[/C][/ROW]
[ROW][C]108[/C][C]0.378398[/C][C]0.756797[/C][C]0.621602[/C][/ROW]
[ROW][C]109[/C][C]0.337405[/C][C]0.674809[/C][C]0.662595[/C][/ROW]
[ROW][C]110[/C][C]0.35049[/C][C]0.70098[/C][C]0.64951[/C][/ROW]
[ROW][C]111[/C][C]0.294631[/C][C]0.589262[/C][C]0.705369[/C][/ROW]
[ROW][C]112[/C][C]0.736559[/C][C]0.526881[/C][C]0.263441[/C][/ROW]
[ROW][C]113[/C][C]0.678273[/C][C]0.643455[/C][C]0.321727[/C][/ROW]
[ROW][C]114[/C][C]0.652227[/C][C]0.695546[/C][C]0.347773[/C][/ROW]
[ROW][C]115[/C][C]0.587728[/C][C]0.824544[/C][C]0.412272[/C][/ROW]
[ROW][C]116[/C][C]0.635728[/C][C]0.728545[/C][C]0.364272[/C][/ROW]
[ROW][C]117[/C][C]0.580924[/C][C]0.838152[/C][C]0.419076[/C][/ROW]
[ROW][C]118[/C][C]0.546519[/C][C]0.906962[/C][C]0.453481[/C][/ROW]
[ROW][C]119[/C][C]0.496499[/C][C]0.992997[/C][C]0.503501[/C][/ROW]
[ROW][C]120[/C][C]0.525423[/C][C]0.949155[/C][C]0.474577[/C][/ROW]
[ROW][C]121[/C][C]0.638589[/C][C]0.722822[/C][C]0.361411[/C][/ROW]
[ROW][C]122[/C][C]0.550645[/C][C]0.898709[/C][C]0.449355[/C][/ROW]
[ROW][C]123[/C][C]0.512047[/C][C]0.975905[/C][C]0.487953[/C][/ROW]
[ROW][C]124[/C][C]0.416595[/C][C]0.83319[/C][C]0.583405[/C][/ROW]
[ROW][C]125[/C][C]0.337332[/C][C]0.674663[/C][C]0.662668[/C][/ROW]
[ROW][C]126[/C][C]0.288981[/C][C]0.577963[/C][C]0.711019[/C][/ROW]
[ROW][C]127[/C][C]0.254107[/C][C]0.508214[/C][C]0.745893[/C][/ROW]
[ROW][C]128[/C][C]0.230371[/C][C]0.460743[/C][C]0.769629[/C][/ROW]
[ROW][C]129[/C][C]0.149301[/C][C]0.298603[/C][C]0.850699[/C][/ROW]
[ROW][C]130[/C][C]0.875923[/C][C]0.248154[/C][C]0.124077[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267690&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267690&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.848058	0.303885	0.151942
8	0.774685	0.45063	0.225315
9	0.661108	0.677784	0.338892
10	0.861308	0.277383	0.138692
11	0.8637	0.2726	0.1363
12	0.869055	0.26189	0.130945
13	0.825888	0.348223	0.174112
14	0.801156	0.397688	0.198844
15	0.749537	0.500926	0.250463
16	0.793306	0.413387	0.206694
17	0.83138	0.337241	0.16862
18	0.819799	0.360401	0.180201
19	0.843167	0.313666	0.156833
20	0.816389	0.367223	0.183611
21	0.768116	0.463767	0.231884
22	0.750403	0.499193	0.249597
23	0.698856	0.602289	0.301144
24	0.657555	0.684889	0.342445
25	0.682181	0.635639	0.317819
26	0.622436	0.755129	0.377564
27	0.57547	0.84906	0.42453
28	0.644027	0.711946	0.355973
29	0.599562	0.800876	0.400438
30	0.664857	0.670286	0.335143
31	0.665064	0.669872	0.334936
32	0.66153	0.67694	0.33847
33	0.701025	0.59795	0.298975
34	0.648908	0.702184	0.351092
35	0.595824	0.808353	0.404176
36	0.550576	0.898848	0.449424
37	0.52766	0.94468	0.47234
38	0.470487	0.940974	0.529513
39	0.417243	0.834486	0.582757
40	0.366172	0.732344	0.633828
41	0.425632	0.851263	0.574368
42	0.384993	0.769985	0.615007
43	0.357587	0.715174	0.642413
44	0.397138	0.794276	0.602862
45	0.356124	0.712249	0.643876
46	0.307722	0.615445	0.692278
47	0.270296	0.540591	0.729704
48	0.227969	0.455938	0.772031
49	0.336044	0.672088	0.663956
50	0.291636	0.583272	0.708364
51	0.257428	0.514857	0.742572
52	0.220149	0.440297	0.779851
53	0.200643	0.401285	0.799357
54	0.199113	0.398226	0.800887
55	0.19028	0.38056	0.80972
56	0.167157	0.334313	0.832843
57	0.185001	0.370001	0.814999
58	0.157219	0.314438	0.842781
59	0.135255	0.270511	0.864745
60	0.110739	0.221478	0.889261
61	0.106219	0.212437	0.893781
62	0.0870531	0.174106	0.912947
63	0.156905	0.313811	0.843095
64	0.230423	0.460846	0.769577
65	0.231717	0.463435	0.768283
66	0.208019	0.416039	0.791981
67	0.177138	0.354276	0.822862
68	0.158735	0.317469	0.841265
69	0.141257	0.282514	0.858743
70	0.134416	0.268832	0.865584
71	0.178038	0.356076	0.821962
72	0.159	0.318	0.841
73	0.274466	0.548932	0.725534
74	0.237876	0.475751	0.762124
75	0.264649	0.529299	0.735351
76	0.229804	0.459607	0.770196
77	0.419819	0.839638	0.580181
78	0.403138	0.806276	0.596862
79	0.366099	0.732197	0.633901
80	0.333811	0.667622	0.666189
81	0.303551	0.607102	0.696449
82	0.358983	0.717966	0.641017
83	0.359449	0.718897	0.640551
84	0.318765	0.63753	0.681235
85	0.298012	0.596024	0.701988
86	0.271303	0.542606	0.728697
87	0.230577	0.461154	0.769423
88	0.210639	0.421278	0.789361
89	0.17606	0.352119	0.82394
90	0.150499	0.300998	0.849501
91	0.130561	0.261122	0.869439
92	0.141141	0.282282	0.858859
93	0.135099	0.270199	0.864901
94	0.114426	0.228852	0.885574
95	0.122796	0.245593	0.877204
96	0.123402	0.246804	0.876598
97	0.189782	0.379565	0.810218
98	0.164265	0.328529	0.835735
99	0.269765	0.53953	0.730235
100	0.251564	0.503128	0.748436
101	0.223123	0.446246	0.776877
102	0.228398	0.456796	0.771602
103	0.213079	0.426157	0.786921
104	0.323923	0.647846	0.676077
105	0.354329	0.708658	0.645671
106	0.308432	0.616864	0.691568
107	0.265918	0.531836	0.734082
108	0.378398	0.756797	0.621602
109	0.337405	0.674809	0.662595
110	0.35049	0.70098	0.64951
111	0.294631	0.589262	0.705369
112	0.736559	0.526881	0.263441
113	0.678273	0.643455	0.321727
114	0.652227	0.695546	0.347773
115	0.587728	0.824544	0.412272
116	0.635728	0.728545	0.364272
117	0.580924	0.838152	0.419076
118	0.546519	0.906962	0.453481
119	0.496499	0.992997	0.503501
120	0.525423	0.949155	0.474577
121	0.638589	0.722822	0.361411
122	0.550645	0.898709	0.449355
123	0.512047	0.975905	0.487953
124	0.416595	0.83319	0.583405
125	0.337332	0.674663	0.662668
126	0.288981	0.577963	0.711019
127	0.254107	0.508214	0.745893
128	0.230371	0.460743	0.769629
129	0.149301	0.298603	0.850699
130	0.875923	0.248154	0.124077

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=267690&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=267690&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267690&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

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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