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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sun, 22 Jan 2017 19:29:35 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/22/t1485110716inhxhs2xog7klbg.htm/, Retrieved Tue, 14 May 2024 16:51:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=303504, Retrieved Tue, 14 May 2024 16:51:58 +0000

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-       [Multiple Regression] [regressie] [2017-01-22 18:29:35] [fd005a509166a1985dac46f39e8d81c5] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303504&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=303504&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303504&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	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
LFM[t] = + 128.603 -8.7735gender[t] + 41.3298group[t] -0.140681`LFM(t-1)`[t] + 0.0161649`LFM(t-1s)`[t] -0.00939194`LFM(t-2s)`[t] -0.0197202`LFM(t-3s)`[t] -0.114259`LFM(t-4s)`[t] -0.0958412`LFM(t-5s)`[t] + 0.0949937`LFM(t-6s)`[t] -0.121777`LFM(t-7s)`[t] -0.0677883`LFM(t-8s)`[t] + 0.0566012`LFM(t-9s)`[t] -0.0330812`LFM(t-10s)`[t] + 0.104582`LFM(t-11s)`[t] + 0.0288914`LFM(t-12s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
LFM[t] =  +  128.603 -8.7735gender[t] +  41.3298group[t] -0.140681`LFM(t-1)`[t] +  0.0161649`LFM(t-1s)`[t] -0.00939194`LFM(t-2s)`[t] -0.0197202`LFM(t-3s)`[t] -0.114259`LFM(t-4s)`[t] -0.0958412`LFM(t-5s)`[t] +  0.0949937`LFM(t-6s)`[t] -0.121777`LFM(t-7s)`[t] -0.0677883`LFM(t-8s)`[t] +  0.0566012`LFM(t-9s)`[t] -0.0330812`LFM(t-10s)`[t] +  0.104582`LFM(t-11s)`[t] +  0.0288914`LFM(t-12s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303504&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]LFM[t] =  +  128.603 -8.7735gender[t] +  41.3298group[t] -0.140681`LFM(t-1)`[t] +  0.0161649`LFM(t-1s)`[t] -0.00939194`LFM(t-2s)`[t] -0.0197202`LFM(t-3s)`[t] -0.114259`LFM(t-4s)`[t] -0.0958412`LFM(t-5s)`[t] +  0.0949937`LFM(t-6s)`[t] -0.121777`LFM(t-7s)`[t] -0.0677883`LFM(t-8s)`[t] +  0.0566012`LFM(t-9s)`[t] -0.0330812`LFM(t-10s)`[t] +  0.104582`LFM(t-11s)`[t] +  0.0288914`LFM(t-12s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303504&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303504&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] = + 128.603 -8.7735gender[t] + 41.3298group[t] -0.140681`LFM(t-1)`[t] + 0.0161649`LFM(t-1s)`[t] -0.00939194`LFM(t-2s)`[t] -0.0197202`LFM(t-3s)`[t] -0.114259`LFM(t-4s)`[t] -0.0958412`LFM(t-5s)`[t] + 0.0949937`LFM(t-6s)`[t] -0.121777`LFM(t-7s)`[t] -0.0677883`LFM(t-8s)`[t] + 0.0566012`LFM(t-9s)`[t] -0.0330812`LFM(t-10s)`[t] + 0.104582`LFM(t-11s)`[t] + 0.0288914`LFM(t-12s)`[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)	+128.6	33.75	+3.8100e+00	0.000223	0.0001115
gender	-8.774	5.979	-1.4670e+00	0.1449	0.07247
group	+41.33	6.207	+6.6590e+00	9.438e-10	4.719e-10
`LFM(t-1)`	-0.1407	0.0789	-1.7830e+00	0.07716	0.03858
`LFM(t-1s)`	+0.01616	0.07729	+2.0910e-01	0.8347	0.4173
`LFM(t-2s)`	-0.009392	0.07778	-1.2080e-01	0.9041	0.452
`LFM(t-3s)`	-0.01972	0.07407	-2.6630e-01	0.7905	0.3953
`LFM(t-4s)`	-0.1143	0.07769	-1.4710e+00	0.144	0.07202
`LFM(t-5s)`	-0.09584	0.08335	-1.1500e+00	0.2526	0.1263
`LFM(t-6s)`	+0.09499	0.08433	+1.1260e+00	0.2623	0.1311
`LFM(t-7s)`	-0.1218	0.08008	-1.5210e+00	0.131	0.06552
`LFM(t-8s)`	-0.06779	0.076	-8.9200e-01	0.3742	0.1871
`LFM(t-9s)`	+0.0566	0.07583	+7.4640e-01	0.4569	0.2285
`LFM(t-10s)`	-0.03308	0.07617	-4.3430e-01	0.6649	0.3324
`LFM(t-11s)`	+0.1046	0.07249	+1.4430e+00	0.1518	0.07588
`LFM(t-12s)`	+0.02889	0.0719	+4.0180e-01	0.6886	0.3443

\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) & +128.6 &  33.75 & +3.8100e+00 &  0.000223 &  0.0001115 \tabularnewline
gender & -8.774 &  5.979 & -1.4670e+00 &  0.1449 &  0.07247 \tabularnewline
group & +41.33 &  6.207 & +6.6590e+00 &  9.438e-10 &  4.719e-10 \tabularnewline
`LFM(t-1)` & -0.1407 &  0.0789 & -1.7830e+00 &  0.07716 &  0.03858 \tabularnewline
`LFM(t-1s)` & +0.01616 &  0.07729 & +2.0910e-01 &  0.8347 &  0.4173 \tabularnewline
`LFM(t-2s)` & -0.009392 &  0.07778 & -1.2080e-01 &  0.9041 &  0.452 \tabularnewline
`LFM(t-3s)` & -0.01972 &  0.07407 & -2.6630e-01 &  0.7905 &  0.3953 \tabularnewline
`LFM(t-4s)` & -0.1143 &  0.07769 & -1.4710e+00 &  0.144 &  0.07202 \tabularnewline
`LFM(t-5s)` & -0.09584 &  0.08335 & -1.1500e+00 &  0.2526 &  0.1263 \tabularnewline
`LFM(t-6s)` & +0.09499 &  0.08433 & +1.1260e+00 &  0.2623 &  0.1311 \tabularnewline
`LFM(t-7s)` & -0.1218 &  0.08008 & -1.5210e+00 &  0.131 &  0.06552 \tabularnewline
`LFM(t-8s)` & -0.06779 &  0.076 & -8.9200e-01 &  0.3742 &  0.1871 \tabularnewline
`LFM(t-9s)` & +0.0566 &  0.07583 & +7.4640e-01 &  0.4569 &  0.2285 \tabularnewline
`LFM(t-10s)` & -0.03308 &  0.07617 & -4.3430e-01 &  0.6649 &  0.3324 \tabularnewline
`LFM(t-11s)` & +0.1046 &  0.07249 & +1.4430e+00 &  0.1518 &  0.07588 \tabularnewline
`LFM(t-12s)` & +0.02889 &  0.0719 & +4.0180e-01 &  0.6886 &  0.3443 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303504&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]+128.6[/C][C] 33.75[/C][C]+3.8100e+00[/C][C] 0.000223[/C][C] 0.0001115[/C][/ROW]
[ROW][C]gender[/C][C]-8.774[/C][C] 5.979[/C][C]-1.4670e+00[/C][C] 0.1449[/C][C] 0.07247[/C][/ROW]
[ROW][C]group[/C][C]+41.33[/C][C] 6.207[/C][C]+6.6590e+00[/C][C] 9.438e-10[/C][C] 4.719e-10[/C][/ROW]
[ROW][C]`LFM(t-1)`[/C][C]-0.1407[/C][C] 0.0789[/C][C]-1.7830e+00[/C][C] 0.07716[/C][C] 0.03858[/C][/ROW]
[ROW][C]`LFM(t-1s)`[/C][C]+0.01616[/C][C] 0.07729[/C][C]+2.0910e-01[/C][C] 0.8347[/C][C] 0.4173[/C][/ROW]
[ROW][C]`LFM(t-2s)`[/C][C]-0.009392[/C][C] 0.07778[/C][C]-1.2080e-01[/C][C] 0.9041[/C][C] 0.452[/C][/ROW]
[ROW][C]`LFM(t-3s)`[/C][C]-0.01972[/C][C] 0.07407[/C][C]-2.6630e-01[/C][C] 0.7905[/C][C] 0.3953[/C][/ROW]
[ROW][C]`LFM(t-4s)`[/C][C]-0.1143[/C][C] 0.07769[/C][C]-1.4710e+00[/C][C] 0.144[/C][C] 0.07202[/C][/ROW]
[ROW][C]`LFM(t-5s)`[/C][C]-0.09584[/C][C] 0.08335[/C][C]-1.1500e+00[/C][C] 0.2526[/C][C] 0.1263[/C][/ROW]
[ROW][C]`LFM(t-6s)`[/C][C]+0.09499[/C][C] 0.08433[/C][C]+1.1260e+00[/C][C] 0.2623[/C][C] 0.1311[/C][/ROW]
[ROW][C]`LFM(t-7s)`[/C][C]-0.1218[/C][C] 0.08008[/C][C]-1.5210e+00[/C][C] 0.131[/C][C] 0.06552[/C][/ROW]
[ROW][C]`LFM(t-8s)`[/C][C]-0.06779[/C][C] 0.076[/C][C]-8.9200e-01[/C][C] 0.3742[/C][C] 0.1871[/C][/ROW]
[ROW][C]`LFM(t-9s)`[/C][C]+0.0566[/C][C] 0.07583[/C][C]+7.4640e-01[/C][C] 0.4569[/C][C] 0.2285[/C][/ROW]
[ROW][C]`LFM(t-10s)`[/C][C]-0.03308[/C][C] 0.07617[/C][C]-4.3430e-01[/C][C] 0.6649[/C][C] 0.3324[/C][/ROW]
[ROW][C]`LFM(t-11s)`[/C][C]+0.1046[/C][C] 0.07249[/C][C]+1.4430e+00[/C][C] 0.1518[/C][C] 0.07588[/C][/ROW]
[ROW][C]`LFM(t-12s)`[/C][C]+0.02889[/C][C] 0.0719[/C][C]+4.0180e-01[/C][C] 0.6886[/C][C] 0.3443[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303504&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303504&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)	+128.6	33.75	+3.8100e+00	0.000223	0.0001115
gender	-8.774	5.979	-1.4670e+00	0.1449	0.07247
group	+41.33	6.207	+6.6590e+00	9.438e-10	4.719e-10
`LFM(t-1)`	-0.1407	0.0789	-1.7830e+00	0.07716	0.03858
`LFM(t-1s)`	+0.01616	0.07729	+2.0910e-01	0.8347	0.4173
`LFM(t-2s)`	-0.009392	0.07778	-1.2080e-01	0.9041	0.452
`LFM(t-3s)`	-0.01972	0.07407	-2.6630e-01	0.7905	0.3953
`LFM(t-4s)`	-0.1143	0.07769	-1.4710e+00	0.144	0.07202
`LFM(t-5s)`	-0.09584	0.08335	-1.1500e+00	0.2526	0.1263
`LFM(t-6s)`	+0.09499	0.08433	+1.1260e+00	0.2623	0.1311
`LFM(t-7s)`	-0.1218	0.08008	-1.5210e+00	0.131	0.06552
`LFM(t-8s)`	-0.06779	0.076	-8.9200e-01	0.3742	0.1871
`LFM(t-9s)`	+0.0566	0.07583	+7.4640e-01	0.4569	0.2285
`LFM(t-10s)`	-0.03308	0.07617	-4.3430e-01	0.6649	0.3324
`LFM(t-11s)`	+0.1046	0.07249	+1.4430e+00	0.1518	0.07588
`LFM(t-12s)`	+0.02889	0.0719	+4.0180e-01	0.6886	0.3443

Multiple Linear Regression - Regression Statistics
Multiple R	0.5988
R-squared	0.3585
Adjusted R-squared	0.2763
F-TEST (value)	4.36
F-TEST (DF numerator)	15
F-TEST (DF denominator)	117
p-value	2.011e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	32.19
Sum Squared Residuals	1.212e+05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5988 \tabularnewline
R-squared &  0.3585 \tabularnewline
Adjusted R-squared &  0.2763 \tabularnewline
F-TEST (value) &  4.36 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 117 \tabularnewline
p-value &  2.011e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  32.19 \tabularnewline
Sum Squared Residuals &  1.212e+05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303504&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5988[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3585[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2763[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 4.36[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]117[/C][/ROW]
[ROW][C]p-value[/C][C] 2.011e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 32.19[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.212e+05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303504&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303504&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	0.5988
R-squared	0.3585
Adjusted R-squared	0.2763
F-TEST (value)	4.36
F-TEST (DF numerator)	15
F-TEST (DF denominator)	117
p-value	2.011e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	32.19
Sum Squared Residuals	1.212e+05

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	145	132.7	12.34
2	120	127.5	-7.452
3	109	140.3	-31.27
4	132	137.6	-5.591
5	172	135.8	36.18
6	169	128.2	40.76
7	114	105.8	8.21
8	156	115.6	40.38
9	172	142.2	29.83
10	68	86.37	-18.37
11	89	98.8	-9.801
12	167	140.5	26.52
13	113	143.8	-30.83
14	115	90.53	24.47
15	78	73.84	4.164
16	118	112.4	5.552
17	87	100.4	-13.39
18	173	132	40.97
19	2	109.4	-107.4
20	162	118.3	43.68
21	49	89.32	-40.32
22	122	90.61	31.39
23	96	81.78	14.22
24	100	108.3	-8.256
25	82	112.6	-30.63
26	100	92.7	7.296
27	115	118.8	-3.788
28	141	95.73	45.27
29	165	123.9	41.08
30	165	126.1	38.92
31	110	87.81	22.19
32	118	122	-3.977
33	158	147	11.01
34	146	78.78	67.22
35	49	117.4	-68.42
36	90	85.76	4.238
37	121	97.14	23.86
38	155	135.4	19.61
39	104	95.08	8.924
40	147	88.87	58.13
41	110	102.6	7.385
42	108	95.12	12.88
43	113	96.93	16.07
44	115	92.83	22.17
45	61	84.38	-23.38
46	60	89.55	-29.55
47	109	88.64	20.36
48	68	98.86	-30.86
49	111	100.6	10.36
50	77	98.18	-21.18
51	73	93.11	-20.11
52	151	155.9	-4.925
53	89	94.34	-5.344
54	78	92.68	-14.68
55	110	104.7	5.281
56	220	139.4	80.62
57	65	62.75	2.248
58	141	138.5	2.526
59	117	95.58	21.42
60	122	113.2	8.821
61	63	96.49	-33.49
62	44	126.8	-82.83
63	52	115.1	-63.1
64	131	107.1	23.88
65	101	78.17	22.83
66	42	69.37	-27.37
67	152	152.3	-0.2934
68	107	114.9	-7.914
69	77	105.4	-28.36
70	154	141.7	12.29
71	103	116.2	-13.17
72	96	81.21	14.79
73	175	137	37.99
74	57	77.91	-20.91
75	112	110.2	1.818
76	143	128.6	14.38
77	49	102.8	-53.8
78	110	120.6	-10.6
79	131	141.4	-10.37
80	167	137.6	29.41
81	56	84.95	-28.95
82	137	134.1	2.924
83	86	87.39	-1.389
84	121	142.4	-21.38
85	149	130.7	18.31
86	168	128.6	39.44
87	140	124.4	15.57
88	88	68.88	19.12
89	168	122.8	45.23
90	94	108.9	-14.88
91	51	118.9	-67.89
92	48	103.8	-55.81
93	145	120.6	24.4
94	66	124.1	-58.09
95	85	98.69	-13.69
96	109	129.6	-20.56
97	63	89.86	-26.86
98	102	90.45	11.55
99	162	107.1	54.95
100	86	63.85	22.15
101	114	87.02	26.98
102	164	139.5	24.46
103	119	136.9	-17.91
104	126	108.9	17.1
105	132	143.9	-11.9
106	142	140	2.002
107	83	132.5	-49.52
108	94	94.27	-0.2733
109	81	101.9	-20.93
110	166	154.1	11.85
111	110	92.39	17.61
112	64	76.77	-12.77
113	93	146.3	-53.35
114	104	104.7	-0.6527
115	105	90.95	14.05
116	49	78.94	-29.94
117	88	106.4	-18.39
118	95	71.25	23.75
119	102	102.9	-0.9002
120	99	99.46	-0.4622
121	63	83.04	-20.04
122	76	103.7	-27.71
123	109	94.61	14.39
124	117	78.83	38.17
125	57	74.87	-17.87
126	120	109.2	10.77
127	73	73.71	-0.7142
128	91	101.8	-10.77
129	108	104.6	3.36
130	105	93.37	11.63
131	117	149.6	-32.61
132	119	103.9	15.1
133	31	71.85	-40.85

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  145 &  132.7 &  12.34 \tabularnewline
2 &  120 &  127.5 & -7.452 \tabularnewline
3 &  109 &  140.3 & -31.27 \tabularnewline
4 &  132 &  137.6 & -5.591 \tabularnewline
5 &  172 &  135.8 &  36.18 \tabularnewline
6 &  169 &  128.2 &  40.76 \tabularnewline
7 &  114 &  105.8 &  8.21 \tabularnewline
8 &  156 &  115.6 &  40.38 \tabularnewline
9 &  172 &  142.2 &  29.83 \tabularnewline
10 &  68 &  86.37 & -18.37 \tabularnewline
11 &  89 &  98.8 & -9.801 \tabularnewline
12 &  167 &  140.5 &  26.52 \tabularnewline
13 &  113 &  143.8 & -30.83 \tabularnewline
14 &  115 &  90.53 &  24.47 \tabularnewline
15 &  78 &  73.84 &  4.164 \tabularnewline
16 &  118 &  112.4 &  5.552 \tabularnewline
17 &  87 &  100.4 & -13.39 \tabularnewline
18 &  173 &  132 &  40.97 \tabularnewline
19 &  2 &  109.4 & -107.4 \tabularnewline
20 &  162 &  118.3 &  43.68 \tabularnewline
21 &  49 &  89.32 & -40.32 \tabularnewline
22 &  122 &  90.61 &  31.39 \tabularnewline
23 &  96 &  81.78 &  14.22 \tabularnewline
24 &  100 &  108.3 & -8.256 \tabularnewline
25 &  82 &  112.6 & -30.63 \tabularnewline
26 &  100 &  92.7 &  7.296 \tabularnewline
27 &  115 &  118.8 & -3.788 \tabularnewline
28 &  141 &  95.73 &  45.27 \tabularnewline
29 &  165 &  123.9 &  41.08 \tabularnewline
30 &  165 &  126.1 &  38.92 \tabularnewline
31 &  110 &  87.81 &  22.19 \tabularnewline
32 &  118 &  122 & -3.977 \tabularnewline
33 &  158 &  147 &  11.01 \tabularnewline
34 &  146 &  78.78 &  67.22 \tabularnewline
35 &  49 &  117.4 & -68.42 \tabularnewline
36 &  90 &  85.76 &  4.238 \tabularnewline
37 &  121 &  97.14 &  23.86 \tabularnewline
38 &  155 &  135.4 &  19.61 \tabularnewline
39 &  104 &  95.08 &  8.924 \tabularnewline
40 &  147 &  88.87 &  58.13 \tabularnewline
41 &  110 &  102.6 &  7.385 \tabularnewline
42 &  108 &  95.12 &  12.88 \tabularnewline
43 &  113 &  96.93 &  16.07 \tabularnewline
44 &  115 &  92.83 &  22.17 \tabularnewline
45 &  61 &  84.38 & -23.38 \tabularnewline
46 &  60 &  89.55 & -29.55 \tabularnewline
47 &  109 &  88.64 &  20.36 \tabularnewline
48 &  68 &  98.86 & -30.86 \tabularnewline
49 &  111 &  100.6 &  10.36 \tabularnewline
50 &  77 &  98.18 & -21.18 \tabularnewline
51 &  73 &  93.11 & -20.11 \tabularnewline
52 &  151 &  155.9 & -4.925 \tabularnewline
53 &  89 &  94.34 & -5.344 \tabularnewline
54 &  78 &  92.68 & -14.68 \tabularnewline
55 &  110 &  104.7 &  5.281 \tabularnewline
56 &  220 &  139.4 &  80.62 \tabularnewline
57 &  65 &  62.75 &  2.248 \tabularnewline
58 &  141 &  138.5 &  2.526 \tabularnewline
59 &  117 &  95.58 &  21.42 \tabularnewline
60 &  122 &  113.2 &  8.821 \tabularnewline
61 &  63 &  96.49 & -33.49 \tabularnewline
62 &  44 &  126.8 & -82.83 \tabularnewline
63 &  52 &  115.1 & -63.1 \tabularnewline
64 &  131 &  107.1 &  23.88 \tabularnewline
65 &  101 &  78.17 &  22.83 \tabularnewline
66 &  42 &  69.37 & -27.37 \tabularnewline
67 &  152 &  152.3 & -0.2934 \tabularnewline
68 &  107 &  114.9 & -7.914 \tabularnewline
69 &  77 &  105.4 & -28.36 \tabularnewline
70 &  154 &  141.7 &  12.29 \tabularnewline
71 &  103 &  116.2 & -13.17 \tabularnewline
72 &  96 &  81.21 &  14.79 \tabularnewline
73 &  175 &  137 &  37.99 \tabularnewline
74 &  57 &  77.91 & -20.91 \tabularnewline
75 &  112 &  110.2 &  1.818 \tabularnewline
76 &  143 &  128.6 &  14.38 \tabularnewline
77 &  49 &  102.8 & -53.8 \tabularnewline
78 &  110 &  120.6 & -10.6 \tabularnewline
79 &  131 &  141.4 & -10.37 \tabularnewline
80 &  167 &  137.6 &  29.41 \tabularnewline
81 &  56 &  84.95 & -28.95 \tabularnewline
82 &  137 &  134.1 &  2.924 \tabularnewline
83 &  86 &  87.39 & -1.389 \tabularnewline
84 &  121 &  142.4 & -21.38 \tabularnewline
85 &  149 &  130.7 &  18.31 \tabularnewline
86 &  168 &  128.6 &  39.44 \tabularnewline
87 &  140 &  124.4 &  15.57 \tabularnewline
88 &  88 &  68.88 &  19.12 \tabularnewline
89 &  168 &  122.8 &  45.23 \tabularnewline
90 &  94 &  108.9 & -14.88 \tabularnewline
91 &  51 &  118.9 & -67.89 \tabularnewline
92 &  48 &  103.8 & -55.81 \tabularnewline
93 &  145 &  120.6 &  24.4 \tabularnewline
94 &  66 &  124.1 & -58.09 \tabularnewline
95 &  85 &  98.69 & -13.69 \tabularnewline
96 &  109 &  129.6 & -20.56 \tabularnewline
97 &  63 &  89.86 & -26.86 \tabularnewline
98 &  102 &  90.45 &  11.55 \tabularnewline
99 &  162 &  107.1 &  54.95 \tabularnewline
100 &  86 &  63.85 &  22.15 \tabularnewline
101 &  114 &  87.02 &  26.98 \tabularnewline
102 &  164 &  139.5 &  24.46 \tabularnewline
103 &  119 &  136.9 & -17.91 \tabularnewline
104 &  126 &  108.9 &  17.1 \tabularnewline
105 &  132 &  143.9 & -11.9 \tabularnewline
106 &  142 &  140 &  2.002 \tabularnewline
107 &  83 &  132.5 & -49.52 \tabularnewline
108 &  94 &  94.27 & -0.2733 \tabularnewline
109 &  81 &  101.9 & -20.93 \tabularnewline
110 &  166 &  154.1 &  11.85 \tabularnewline
111 &  110 &  92.39 &  17.61 \tabularnewline
112 &  64 &  76.77 & -12.77 \tabularnewline
113 &  93 &  146.3 & -53.35 \tabularnewline
114 &  104 &  104.7 & -0.6527 \tabularnewline
115 &  105 &  90.95 &  14.05 \tabularnewline
116 &  49 &  78.94 & -29.94 \tabularnewline
117 &  88 &  106.4 & -18.39 \tabularnewline
118 &  95 &  71.25 &  23.75 \tabularnewline
119 &  102 &  102.9 & -0.9002 \tabularnewline
120 &  99 &  99.46 & -0.4622 \tabularnewline
121 &  63 &  83.04 & -20.04 \tabularnewline
122 &  76 &  103.7 & -27.71 \tabularnewline
123 &  109 &  94.61 &  14.39 \tabularnewline
124 &  117 &  78.83 &  38.17 \tabularnewline
125 &  57 &  74.87 & -17.87 \tabularnewline
126 &  120 &  109.2 &  10.77 \tabularnewline
127 &  73 &  73.71 & -0.7142 \tabularnewline
128 &  91 &  101.8 & -10.77 \tabularnewline
129 &  108 &  104.6 &  3.36 \tabularnewline
130 &  105 &  93.37 &  11.63 \tabularnewline
131 &  117 &  149.6 & -32.61 \tabularnewline
132 &  119 &  103.9 &  15.1 \tabularnewline
133 &  31 &  71.85 & -40.85 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303504&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] 145[/C][C] 132.7[/C][C] 12.34[/C][/ROW]
[ROW][C]2[/C][C] 120[/C][C] 127.5[/C][C]-7.452[/C][/ROW]
[ROW][C]3[/C][C] 109[/C][C] 140.3[/C][C]-31.27[/C][/ROW]
[ROW][C]4[/C][C] 132[/C][C] 137.6[/C][C]-5.591[/C][/ROW]
[ROW][C]5[/C][C] 172[/C][C] 135.8[/C][C] 36.18[/C][/ROW]
[ROW][C]6[/C][C] 169[/C][C] 128.2[/C][C] 40.76[/C][/ROW]
[ROW][C]7[/C][C] 114[/C][C] 105.8[/C][C] 8.21[/C][/ROW]
[ROW][C]8[/C][C] 156[/C][C] 115.6[/C][C] 40.38[/C][/ROW]
[ROW][C]9[/C][C] 172[/C][C] 142.2[/C][C] 29.83[/C][/ROW]
[ROW][C]10[/C][C] 68[/C][C] 86.37[/C][C]-18.37[/C][/ROW]
[ROW][C]11[/C][C] 89[/C][C] 98.8[/C][C]-9.801[/C][/ROW]
[ROW][C]12[/C][C] 167[/C][C] 140.5[/C][C] 26.52[/C][/ROW]
[ROW][C]13[/C][C] 113[/C][C] 143.8[/C][C]-30.83[/C][/ROW]
[ROW][C]14[/C][C] 115[/C][C] 90.53[/C][C] 24.47[/C][/ROW]
[ROW][C]15[/C][C] 78[/C][C] 73.84[/C][C] 4.164[/C][/ROW]
[ROW][C]16[/C][C] 118[/C][C] 112.4[/C][C] 5.552[/C][/ROW]
[ROW][C]17[/C][C] 87[/C][C] 100.4[/C][C]-13.39[/C][/ROW]
[ROW][C]18[/C][C] 173[/C][C] 132[/C][C] 40.97[/C][/ROW]
[ROW][C]19[/C][C] 2[/C][C] 109.4[/C][C]-107.4[/C][/ROW]
[ROW][C]20[/C][C] 162[/C][C] 118.3[/C][C] 43.68[/C][/ROW]
[ROW][C]21[/C][C] 49[/C][C] 89.32[/C][C]-40.32[/C][/ROW]
[ROW][C]22[/C][C] 122[/C][C] 90.61[/C][C] 31.39[/C][/ROW]
[ROW][C]23[/C][C] 96[/C][C] 81.78[/C][C] 14.22[/C][/ROW]
[ROW][C]24[/C][C] 100[/C][C] 108.3[/C][C]-8.256[/C][/ROW]
[ROW][C]25[/C][C] 82[/C][C] 112.6[/C][C]-30.63[/C][/ROW]
[ROW][C]26[/C][C] 100[/C][C] 92.7[/C][C] 7.296[/C][/ROW]
[ROW][C]27[/C][C] 115[/C][C] 118.8[/C][C]-3.788[/C][/ROW]
[ROW][C]28[/C][C] 141[/C][C] 95.73[/C][C] 45.27[/C][/ROW]
[ROW][C]29[/C][C] 165[/C][C] 123.9[/C][C] 41.08[/C][/ROW]
[ROW][C]30[/C][C] 165[/C][C] 126.1[/C][C] 38.92[/C][/ROW]
[ROW][C]31[/C][C] 110[/C][C] 87.81[/C][C] 22.19[/C][/ROW]
[ROW][C]32[/C][C] 118[/C][C] 122[/C][C]-3.977[/C][/ROW]
[ROW][C]33[/C][C] 158[/C][C] 147[/C][C] 11.01[/C][/ROW]
[ROW][C]34[/C][C] 146[/C][C] 78.78[/C][C] 67.22[/C][/ROW]
[ROW][C]35[/C][C] 49[/C][C] 117.4[/C][C]-68.42[/C][/ROW]
[ROW][C]36[/C][C] 90[/C][C] 85.76[/C][C] 4.238[/C][/ROW]
[ROW][C]37[/C][C] 121[/C][C] 97.14[/C][C] 23.86[/C][/ROW]
[ROW][C]38[/C][C] 155[/C][C] 135.4[/C][C] 19.61[/C][/ROW]
[ROW][C]39[/C][C] 104[/C][C] 95.08[/C][C] 8.924[/C][/ROW]
[ROW][C]40[/C][C] 147[/C][C] 88.87[/C][C] 58.13[/C][/ROW]
[ROW][C]41[/C][C] 110[/C][C] 102.6[/C][C] 7.385[/C][/ROW]
[ROW][C]42[/C][C] 108[/C][C] 95.12[/C][C] 12.88[/C][/ROW]
[ROW][C]43[/C][C] 113[/C][C] 96.93[/C][C] 16.07[/C][/ROW]
[ROW][C]44[/C][C] 115[/C][C] 92.83[/C][C] 22.17[/C][/ROW]
[ROW][C]45[/C][C] 61[/C][C] 84.38[/C][C]-23.38[/C][/ROW]
[ROW][C]46[/C][C] 60[/C][C] 89.55[/C][C]-29.55[/C][/ROW]
[ROW][C]47[/C][C] 109[/C][C] 88.64[/C][C] 20.36[/C][/ROW]
[ROW][C]48[/C][C] 68[/C][C] 98.86[/C][C]-30.86[/C][/ROW]
[ROW][C]49[/C][C] 111[/C][C] 100.6[/C][C] 10.36[/C][/ROW]
[ROW][C]50[/C][C] 77[/C][C] 98.18[/C][C]-21.18[/C][/ROW]
[ROW][C]51[/C][C] 73[/C][C] 93.11[/C][C]-20.11[/C][/ROW]
[ROW][C]52[/C][C] 151[/C][C] 155.9[/C][C]-4.925[/C][/ROW]
[ROW][C]53[/C][C] 89[/C][C] 94.34[/C][C]-5.344[/C][/ROW]
[ROW][C]54[/C][C] 78[/C][C] 92.68[/C][C]-14.68[/C][/ROW]
[ROW][C]55[/C][C] 110[/C][C] 104.7[/C][C] 5.281[/C][/ROW]
[ROW][C]56[/C][C] 220[/C][C] 139.4[/C][C] 80.62[/C][/ROW]
[ROW][C]57[/C][C] 65[/C][C] 62.75[/C][C] 2.248[/C][/ROW]
[ROW][C]58[/C][C] 141[/C][C] 138.5[/C][C] 2.526[/C][/ROW]
[ROW][C]59[/C][C] 117[/C][C] 95.58[/C][C] 21.42[/C][/ROW]
[ROW][C]60[/C][C] 122[/C][C] 113.2[/C][C] 8.821[/C][/ROW]
[ROW][C]61[/C][C] 63[/C][C] 96.49[/C][C]-33.49[/C][/ROW]
[ROW][C]62[/C][C] 44[/C][C] 126.8[/C][C]-82.83[/C][/ROW]
[ROW][C]63[/C][C] 52[/C][C] 115.1[/C][C]-63.1[/C][/ROW]
[ROW][C]64[/C][C] 131[/C][C] 107.1[/C][C] 23.88[/C][/ROW]
[ROW][C]65[/C][C] 101[/C][C] 78.17[/C][C] 22.83[/C][/ROW]
[ROW][C]66[/C][C] 42[/C][C] 69.37[/C][C]-27.37[/C][/ROW]
[ROW][C]67[/C][C] 152[/C][C] 152.3[/C][C]-0.2934[/C][/ROW]
[ROW][C]68[/C][C] 107[/C][C] 114.9[/C][C]-7.914[/C][/ROW]
[ROW][C]69[/C][C] 77[/C][C] 105.4[/C][C]-28.36[/C][/ROW]
[ROW][C]70[/C][C] 154[/C][C] 141.7[/C][C] 12.29[/C][/ROW]
[ROW][C]71[/C][C] 103[/C][C] 116.2[/C][C]-13.17[/C][/ROW]
[ROW][C]72[/C][C] 96[/C][C] 81.21[/C][C] 14.79[/C][/ROW]
[ROW][C]73[/C][C] 175[/C][C] 137[/C][C] 37.99[/C][/ROW]
[ROW][C]74[/C][C] 57[/C][C] 77.91[/C][C]-20.91[/C][/ROW]
[ROW][C]75[/C][C] 112[/C][C] 110.2[/C][C] 1.818[/C][/ROW]
[ROW][C]76[/C][C] 143[/C][C] 128.6[/C][C] 14.38[/C][/ROW]
[ROW][C]77[/C][C] 49[/C][C] 102.8[/C][C]-53.8[/C][/ROW]
[ROW][C]78[/C][C] 110[/C][C] 120.6[/C][C]-10.6[/C][/ROW]
[ROW][C]79[/C][C] 131[/C][C] 141.4[/C][C]-10.37[/C][/ROW]
[ROW][C]80[/C][C] 167[/C][C] 137.6[/C][C] 29.41[/C][/ROW]
[ROW][C]81[/C][C] 56[/C][C] 84.95[/C][C]-28.95[/C][/ROW]
[ROW][C]82[/C][C] 137[/C][C] 134.1[/C][C] 2.924[/C][/ROW]
[ROW][C]83[/C][C] 86[/C][C] 87.39[/C][C]-1.389[/C][/ROW]
[ROW][C]84[/C][C] 121[/C][C] 142.4[/C][C]-21.38[/C][/ROW]
[ROW][C]85[/C][C] 149[/C][C] 130.7[/C][C] 18.31[/C][/ROW]
[ROW][C]86[/C][C] 168[/C][C] 128.6[/C][C] 39.44[/C][/ROW]
[ROW][C]87[/C][C] 140[/C][C] 124.4[/C][C] 15.57[/C][/ROW]
[ROW][C]88[/C][C] 88[/C][C] 68.88[/C][C] 19.12[/C][/ROW]
[ROW][C]89[/C][C] 168[/C][C] 122.8[/C][C] 45.23[/C][/ROW]
[ROW][C]90[/C][C] 94[/C][C] 108.9[/C][C]-14.88[/C][/ROW]
[ROW][C]91[/C][C] 51[/C][C] 118.9[/C][C]-67.89[/C][/ROW]
[ROW][C]92[/C][C] 48[/C][C] 103.8[/C][C]-55.81[/C][/ROW]
[ROW][C]93[/C][C] 145[/C][C] 120.6[/C][C] 24.4[/C][/ROW]
[ROW][C]94[/C][C] 66[/C][C] 124.1[/C][C]-58.09[/C][/ROW]
[ROW][C]95[/C][C] 85[/C][C] 98.69[/C][C]-13.69[/C][/ROW]
[ROW][C]96[/C][C] 109[/C][C] 129.6[/C][C]-20.56[/C][/ROW]
[ROW][C]97[/C][C] 63[/C][C] 89.86[/C][C]-26.86[/C][/ROW]
[ROW][C]98[/C][C] 102[/C][C] 90.45[/C][C] 11.55[/C][/ROW]
[ROW][C]99[/C][C] 162[/C][C] 107.1[/C][C] 54.95[/C][/ROW]
[ROW][C]100[/C][C] 86[/C][C] 63.85[/C][C] 22.15[/C][/ROW]
[ROW][C]101[/C][C] 114[/C][C] 87.02[/C][C] 26.98[/C][/ROW]
[ROW][C]102[/C][C] 164[/C][C] 139.5[/C][C] 24.46[/C][/ROW]
[ROW][C]103[/C][C] 119[/C][C] 136.9[/C][C]-17.91[/C][/ROW]
[ROW][C]104[/C][C] 126[/C][C] 108.9[/C][C] 17.1[/C][/ROW]
[ROW][C]105[/C][C] 132[/C][C] 143.9[/C][C]-11.9[/C][/ROW]
[ROW][C]106[/C][C] 142[/C][C] 140[/C][C] 2.002[/C][/ROW]
[ROW][C]107[/C][C] 83[/C][C] 132.5[/C][C]-49.52[/C][/ROW]
[ROW][C]108[/C][C] 94[/C][C] 94.27[/C][C]-0.2733[/C][/ROW]
[ROW][C]109[/C][C] 81[/C][C] 101.9[/C][C]-20.93[/C][/ROW]
[ROW][C]110[/C][C] 166[/C][C] 154.1[/C][C] 11.85[/C][/ROW]
[ROW][C]111[/C][C] 110[/C][C] 92.39[/C][C] 17.61[/C][/ROW]
[ROW][C]112[/C][C] 64[/C][C] 76.77[/C][C]-12.77[/C][/ROW]
[ROW][C]113[/C][C] 93[/C][C] 146.3[/C][C]-53.35[/C][/ROW]
[ROW][C]114[/C][C] 104[/C][C] 104.7[/C][C]-0.6527[/C][/ROW]
[ROW][C]115[/C][C] 105[/C][C] 90.95[/C][C] 14.05[/C][/ROW]
[ROW][C]116[/C][C] 49[/C][C] 78.94[/C][C]-29.94[/C][/ROW]
[ROW][C]117[/C][C] 88[/C][C] 106.4[/C][C]-18.39[/C][/ROW]
[ROW][C]118[/C][C] 95[/C][C] 71.25[/C][C] 23.75[/C][/ROW]
[ROW][C]119[/C][C] 102[/C][C] 102.9[/C][C]-0.9002[/C][/ROW]
[ROW][C]120[/C][C] 99[/C][C] 99.46[/C][C]-0.4622[/C][/ROW]
[ROW][C]121[/C][C] 63[/C][C] 83.04[/C][C]-20.04[/C][/ROW]
[ROW][C]122[/C][C] 76[/C][C] 103.7[/C][C]-27.71[/C][/ROW]
[ROW][C]123[/C][C] 109[/C][C] 94.61[/C][C] 14.39[/C][/ROW]
[ROW][C]124[/C][C] 117[/C][C] 78.83[/C][C] 38.17[/C][/ROW]
[ROW][C]125[/C][C] 57[/C][C] 74.87[/C][C]-17.87[/C][/ROW]
[ROW][C]126[/C][C] 120[/C][C] 109.2[/C][C] 10.77[/C][/ROW]
[ROW][C]127[/C][C] 73[/C][C] 73.71[/C][C]-0.7142[/C][/ROW]
[ROW][C]128[/C][C] 91[/C][C] 101.8[/C][C]-10.77[/C][/ROW]
[ROW][C]129[/C][C] 108[/C][C] 104.6[/C][C] 3.36[/C][/ROW]
[ROW][C]130[/C][C] 105[/C][C] 93.37[/C][C] 11.63[/C][/ROW]
[ROW][C]131[/C][C] 117[/C][C] 149.6[/C][C]-32.61[/C][/ROW]
[ROW][C]132[/C][C] 119[/C][C] 103.9[/C][C] 15.1[/C][/ROW]
[ROW][C]133[/C][C] 31[/C][C] 71.85[/C][C]-40.85[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303504&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303504&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	145	132.7	12.34
2	120	127.5	-7.452
3	109	140.3	-31.27
4	132	137.6	-5.591
5	172	135.8	36.18
6	169	128.2	40.76
7	114	105.8	8.21
8	156	115.6	40.38
9	172	142.2	29.83
10	68	86.37	-18.37
11	89	98.8	-9.801
12	167	140.5	26.52
13	113	143.8	-30.83
14	115	90.53	24.47
15	78	73.84	4.164
16	118	112.4	5.552
17	87	100.4	-13.39
18	173	132	40.97
19	2	109.4	-107.4
20	162	118.3	43.68
21	49	89.32	-40.32
22	122	90.61	31.39
23	96	81.78	14.22
24	100	108.3	-8.256
25	82	112.6	-30.63
26	100	92.7	7.296
27	115	118.8	-3.788
28	141	95.73	45.27
29	165	123.9	41.08
30	165	126.1	38.92
31	110	87.81	22.19
32	118	122	-3.977
33	158	147	11.01
34	146	78.78	67.22
35	49	117.4	-68.42
36	90	85.76	4.238
37	121	97.14	23.86
38	155	135.4	19.61
39	104	95.08	8.924
40	147	88.87	58.13
41	110	102.6	7.385
42	108	95.12	12.88
43	113	96.93	16.07
44	115	92.83	22.17
45	61	84.38	-23.38
46	60	89.55	-29.55
47	109	88.64	20.36
48	68	98.86	-30.86
49	111	100.6	10.36
50	77	98.18	-21.18
51	73	93.11	-20.11
52	151	155.9	-4.925
53	89	94.34	-5.344
54	78	92.68	-14.68
55	110	104.7	5.281
56	220	139.4	80.62
57	65	62.75	2.248
58	141	138.5	2.526
59	117	95.58	21.42
60	122	113.2	8.821
61	63	96.49	-33.49
62	44	126.8	-82.83
63	52	115.1	-63.1
64	131	107.1	23.88
65	101	78.17	22.83
66	42	69.37	-27.37
67	152	152.3	-0.2934
68	107	114.9	-7.914
69	77	105.4	-28.36
70	154	141.7	12.29
71	103	116.2	-13.17
72	96	81.21	14.79
73	175	137	37.99
74	57	77.91	-20.91
75	112	110.2	1.818
76	143	128.6	14.38
77	49	102.8	-53.8
78	110	120.6	-10.6
79	131	141.4	-10.37
80	167	137.6	29.41
81	56	84.95	-28.95
82	137	134.1	2.924
83	86	87.39	-1.389
84	121	142.4	-21.38
85	149	130.7	18.31
86	168	128.6	39.44
87	140	124.4	15.57
88	88	68.88	19.12
89	168	122.8	45.23
90	94	108.9	-14.88
91	51	118.9	-67.89
92	48	103.8	-55.81
93	145	120.6	24.4
94	66	124.1	-58.09
95	85	98.69	-13.69
96	109	129.6	-20.56
97	63	89.86	-26.86
98	102	90.45	11.55
99	162	107.1	54.95
100	86	63.85	22.15
101	114	87.02	26.98
102	164	139.5	24.46
103	119	136.9	-17.91
104	126	108.9	17.1
105	132	143.9	-11.9
106	142	140	2.002
107	83	132.5	-49.52
108	94	94.27	-0.2733
109	81	101.9	-20.93
110	166	154.1	11.85
111	110	92.39	17.61
112	64	76.77	-12.77
113	93	146.3	-53.35
114	104	104.7	-0.6527
115	105	90.95	14.05
116	49	78.94	-29.94
117	88	106.4	-18.39
118	95	71.25	23.75
119	102	102.9	-0.9002
120	99	99.46	-0.4622
121	63	83.04	-20.04
122	76	103.7	-27.71
123	109	94.61	14.39
124	117	78.83	38.17
125	57	74.87	-17.87
126	120	109.2	10.77
127	73	73.71	-0.7142
128	91	101.8	-10.77
129	108	104.6	3.36
130	105	93.37	11.63
131	117	149.6	-32.61
132	119	103.9	15.1
133	31	71.85	-40.85

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.7626	0.4748	0.2374
20	0.8403	0.3194	0.1597
21	0.7594	0.4812	0.2406
22	0.6907	0.6187	0.3093
23	0.6636	0.6728	0.3364
24	0.747	0.506	0.253
25	0.9404	0.1191	0.05956
26	0.9086	0.1828	0.09138
27	0.869	0.262	0.131
28	0.8319	0.3361	0.1681
29	0.8245	0.3511	0.1755
30	0.8141	0.3718	0.1859
31	0.8448	0.3104	0.1552
32	0.8318	0.3364	0.1682
33	0.8156	0.3688	0.1844
34	0.8659	0.2682	0.1341
35	0.9757	0.04866	0.02433
36	0.9676	0.06472	0.03236
37	0.9581	0.08372	0.04186
38	0.9445	0.1109	0.05547
39	0.9249	0.1502	0.07508
40	0.9311	0.1379	0.06894
41	0.9155	0.1691	0.08453
42	0.9317	0.1366	0.0683
43	0.9136	0.1729	0.08644
44	0.9033	0.1933	0.09666
45	0.9063	0.1875	0.09372
46	0.8971	0.2058	0.1029
47	0.8739	0.2521	0.1261
48	0.8649	0.2702	0.1351
49	0.833	0.3341	0.167
50	0.802	0.3961	0.198
51	0.8026	0.3948	0.1974
52	0.7627	0.4746	0.2373
53	0.716	0.5679	0.2839
54	0.6722	0.6555	0.3278
55	0.6317	0.7366	0.3683
56	0.8607	0.2785	0.1393
57	0.8453	0.3094	0.1547
58	0.8098	0.3804	0.1902
59	0.8088	0.3823	0.1912
60	0.7732	0.4536	0.2268
61	0.7642	0.4716	0.2358
62	0.9432	0.1135	0.05677
63	0.9754	0.04921	0.02461
64	0.9734	0.05328	0.02664
65	0.9717	0.05668	0.02834
66	0.9689	0.0621	0.03105
67	0.9582	0.08363	0.04181
68	0.9451	0.1097	0.05486
69	0.9398	0.1204	0.06021
70	0.9261	0.1477	0.07385
71	0.9082	0.1836	0.09178
72	0.8903	0.2194	0.1097
73	0.9007	0.1986	0.09931
74	0.8843	0.2314	0.1157
75	0.8535	0.293	0.1465
76	0.8303	0.3395	0.1697
77	0.8973	0.2053	0.1027
78	0.8755	0.2491	0.1245
79	0.8488	0.3023	0.1512
80	0.8687	0.2626	0.1313
81	0.9574	0.08522	0.04261
82	0.9687	0.06268	0.03134
83	0.9598	0.08049	0.04024
84	0.9592	0.08152	0.04076
85	0.9515	0.09705	0.04852
86	0.962	0.07607	0.03804
87	0.9503	0.09949	0.04974
88	0.9395	0.121	0.06052
89	0.9698	0.06031	0.03016
90	0.9581	0.08387	0.04193
91	0.9673	0.06539	0.0327
92	0.9718	0.05647	0.02823
93	0.987	0.02608	0.01304
94	0.9915	0.01691	0.008457
95	0.9866	0.02689	0.01344
96	0.9798	0.04046	0.02023
97	0.9886	0.02273	0.01137
98	0.9842	0.03164	0.01582
99	0.9965	0.006988	0.003494
100	0.9938	0.01233	0.006163
101	0.99	0.02005	0.01003
102	0.9934	0.01322	0.006612
103	0.99	0.02003	0.01001
104	0.9862	0.02768	0.01384
105	0.9775	0.04493	0.02247
106	0.9827	0.03462	0.01731
107	0.976	0.04791	0.02396
108	0.9575	0.08492	0.04246
109	0.9412	0.1176	0.05878
110	0.8986	0.2028	0.1014
111	0.8275	0.3451	0.1725
112	0.8133	0.3733	0.1867
113	0.7344	0.5312	0.2656
114	0.6083	0.7835	0.3917

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 &  0.7626 &  0.4748 &  0.2374 \tabularnewline
20 &  0.8403 &  0.3194 &  0.1597 \tabularnewline
21 &  0.7594 &  0.4812 &  0.2406 \tabularnewline
22 &  0.6907 &  0.6187 &  0.3093 \tabularnewline
23 &  0.6636 &  0.6728 &  0.3364 \tabularnewline
24 &  0.747 &  0.506 &  0.253 \tabularnewline
25 &  0.9404 &  0.1191 &  0.05956 \tabularnewline
26 &  0.9086 &  0.1828 &  0.09138 \tabularnewline
27 &  0.869 &  0.262 &  0.131 \tabularnewline
28 &  0.8319 &  0.3361 &  0.1681 \tabularnewline
29 &  0.8245 &  0.3511 &  0.1755 \tabularnewline
30 &  0.8141 &  0.3718 &  0.1859 \tabularnewline
31 &  0.8448 &  0.3104 &  0.1552 \tabularnewline
32 &  0.8318 &  0.3364 &  0.1682 \tabularnewline
33 &  0.8156 &  0.3688 &  0.1844 \tabularnewline
34 &  0.8659 &  0.2682 &  0.1341 \tabularnewline
35 &  0.9757 &  0.04866 &  0.02433 \tabularnewline
36 &  0.9676 &  0.06472 &  0.03236 \tabularnewline
37 &  0.9581 &  0.08372 &  0.04186 \tabularnewline
38 &  0.9445 &  0.1109 &  0.05547 \tabularnewline
39 &  0.9249 &  0.1502 &  0.07508 \tabularnewline
40 &  0.9311 &  0.1379 &  0.06894 \tabularnewline
41 &  0.9155 &  0.1691 &  0.08453 \tabularnewline
42 &  0.9317 &  0.1366 &  0.0683 \tabularnewline
43 &  0.9136 &  0.1729 &  0.08644 \tabularnewline
44 &  0.9033 &  0.1933 &  0.09666 \tabularnewline
45 &  0.9063 &  0.1875 &  0.09372 \tabularnewline
46 &  0.8971 &  0.2058 &  0.1029 \tabularnewline
47 &  0.8739 &  0.2521 &  0.1261 \tabularnewline
48 &  0.8649 &  0.2702 &  0.1351 \tabularnewline
49 &  0.833 &  0.3341 &  0.167 \tabularnewline
50 &  0.802 &  0.3961 &  0.198 \tabularnewline
51 &  0.8026 &  0.3948 &  0.1974 \tabularnewline
52 &  0.7627 &  0.4746 &  0.2373 \tabularnewline
53 &  0.716 &  0.5679 &  0.2839 \tabularnewline
54 &  0.6722 &  0.6555 &  0.3278 \tabularnewline
55 &  0.6317 &  0.7366 &  0.3683 \tabularnewline
56 &  0.8607 &  0.2785 &  0.1393 \tabularnewline
57 &  0.8453 &  0.3094 &  0.1547 \tabularnewline
58 &  0.8098 &  0.3804 &  0.1902 \tabularnewline
59 &  0.8088 &  0.3823 &  0.1912 \tabularnewline
60 &  0.7732 &  0.4536 &  0.2268 \tabularnewline
61 &  0.7642 &  0.4716 &  0.2358 \tabularnewline
62 &  0.9432 &  0.1135 &  0.05677 \tabularnewline
63 &  0.9754 &  0.04921 &  0.02461 \tabularnewline
64 &  0.9734 &  0.05328 &  0.02664 \tabularnewline
65 &  0.9717 &  0.05668 &  0.02834 \tabularnewline
66 &  0.9689 &  0.0621 &  0.03105 \tabularnewline
67 &  0.9582 &  0.08363 &  0.04181 \tabularnewline
68 &  0.9451 &  0.1097 &  0.05486 \tabularnewline
69 &  0.9398 &  0.1204 &  0.06021 \tabularnewline
70 &  0.9261 &  0.1477 &  0.07385 \tabularnewline
71 &  0.9082 &  0.1836 &  0.09178 \tabularnewline
72 &  0.8903 &  0.2194 &  0.1097 \tabularnewline
73 &  0.9007 &  0.1986 &  0.09931 \tabularnewline
74 &  0.8843 &  0.2314 &  0.1157 \tabularnewline
75 &  0.8535 &  0.293 &  0.1465 \tabularnewline
76 &  0.8303 &  0.3395 &  0.1697 \tabularnewline
77 &  0.8973 &  0.2053 &  0.1027 \tabularnewline
78 &  0.8755 &  0.2491 &  0.1245 \tabularnewline
79 &  0.8488 &  0.3023 &  0.1512 \tabularnewline
80 &  0.8687 &  0.2626 &  0.1313 \tabularnewline
81 &  0.9574 &  0.08522 &  0.04261 \tabularnewline
82 &  0.9687 &  0.06268 &  0.03134 \tabularnewline
83 &  0.9598 &  0.08049 &  0.04024 \tabularnewline
84 &  0.9592 &  0.08152 &  0.04076 \tabularnewline
85 &  0.9515 &  0.09705 &  0.04852 \tabularnewline
86 &  0.962 &  0.07607 &  0.03804 \tabularnewline
87 &  0.9503 &  0.09949 &  0.04974 \tabularnewline
88 &  0.9395 &  0.121 &  0.06052 \tabularnewline
89 &  0.9698 &  0.06031 &  0.03016 \tabularnewline
90 &  0.9581 &  0.08387 &  0.04193 \tabularnewline
91 &  0.9673 &  0.06539 &  0.0327 \tabularnewline
92 &  0.9718 &  0.05647 &  0.02823 \tabularnewline
93 &  0.987 &  0.02608 &  0.01304 \tabularnewline
94 &  0.9915 &  0.01691 &  0.008457 \tabularnewline
95 &  0.9866 &  0.02689 &  0.01344 \tabularnewline
96 &  0.9798 &  0.04046 &  0.02023 \tabularnewline
97 &  0.9886 &  0.02273 &  0.01137 \tabularnewline
98 &  0.9842 &  0.03164 &  0.01582 \tabularnewline
99 &  0.9965 &  0.006988 &  0.003494 \tabularnewline
100 &  0.9938 &  0.01233 &  0.006163 \tabularnewline
101 &  0.99 &  0.02005 &  0.01003 \tabularnewline
102 &  0.9934 &  0.01322 &  0.006612 \tabularnewline
103 &  0.99 &  0.02003 &  0.01001 \tabularnewline
104 &  0.9862 &  0.02768 &  0.01384 \tabularnewline
105 &  0.9775 &  0.04493 &  0.02247 \tabularnewline
106 &  0.9827 &  0.03462 &  0.01731 \tabularnewline
107 &  0.976 &  0.04791 &  0.02396 \tabularnewline
108 &  0.9575 &  0.08492 &  0.04246 \tabularnewline
109 &  0.9412 &  0.1176 &  0.05878 \tabularnewline
110 &  0.8986 &  0.2028 &  0.1014 \tabularnewline
111 &  0.8275 &  0.3451 &  0.1725 \tabularnewline
112 &  0.8133 &  0.3733 &  0.1867 \tabularnewline
113 &  0.7344 &  0.5312 &  0.2656 \tabularnewline
114 &  0.6083 &  0.7835 &  0.3917 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303504&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]19[/C][C] 0.7626[/C][C] 0.4748[/C][C] 0.2374[/C][/ROW]
[ROW][C]20[/C][C] 0.8403[/C][C] 0.3194[/C][C] 0.1597[/C][/ROW]
[ROW][C]21[/C][C] 0.7594[/C][C] 0.4812[/C][C] 0.2406[/C][/ROW]
[ROW][C]22[/C][C] 0.6907[/C][C] 0.6187[/C][C] 0.3093[/C][/ROW]
[ROW][C]23[/C][C] 0.6636[/C][C] 0.6728[/C][C] 0.3364[/C][/ROW]
[ROW][C]24[/C][C] 0.747[/C][C] 0.506[/C][C] 0.253[/C][/ROW]
[ROW][C]25[/C][C] 0.9404[/C][C] 0.1191[/C][C] 0.05956[/C][/ROW]
[ROW][C]26[/C][C] 0.9086[/C][C] 0.1828[/C][C] 0.09138[/C][/ROW]
[ROW][C]27[/C][C] 0.869[/C][C] 0.262[/C][C] 0.131[/C][/ROW]
[ROW][C]28[/C][C] 0.8319[/C][C] 0.3361[/C][C] 0.1681[/C][/ROW]
[ROW][C]29[/C][C] 0.8245[/C][C] 0.3511[/C][C] 0.1755[/C][/ROW]
[ROW][C]30[/C][C] 0.8141[/C][C] 0.3718[/C][C] 0.1859[/C][/ROW]
[ROW][C]31[/C][C] 0.8448[/C][C] 0.3104[/C][C] 0.1552[/C][/ROW]
[ROW][C]32[/C][C] 0.8318[/C][C] 0.3364[/C][C] 0.1682[/C][/ROW]
[ROW][C]33[/C][C] 0.8156[/C][C] 0.3688[/C][C] 0.1844[/C][/ROW]
[ROW][C]34[/C][C] 0.8659[/C][C] 0.2682[/C][C] 0.1341[/C][/ROW]
[ROW][C]35[/C][C] 0.9757[/C][C] 0.04866[/C][C] 0.02433[/C][/ROW]
[ROW][C]36[/C][C] 0.9676[/C][C] 0.06472[/C][C] 0.03236[/C][/ROW]
[ROW][C]37[/C][C] 0.9581[/C][C] 0.08372[/C][C] 0.04186[/C][/ROW]
[ROW][C]38[/C][C] 0.9445[/C][C] 0.1109[/C][C] 0.05547[/C][/ROW]
[ROW][C]39[/C][C] 0.9249[/C][C] 0.1502[/C][C] 0.07508[/C][/ROW]
[ROW][C]40[/C][C] 0.9311[/C][C] 0.1379[/C][C] 0.06894[/C][/ROW]
[ROW][C]41[/C][C] 0.9155[/C][C] 0.1691[/C][C] 0.08453[/C][/ROW]
[ROW][C]42[/C][C] 0.9317[/C][C] 0.1366[/C][C] 0.0683[/C][/ROW]
[ROW][C]43[/C][C] 0.9136[/C][C] 0.1729[/C][C] 0.08644[/C][/ROW]
[ROW][C]44[/C][C] 0.9033[/C][C] 0.1933[/C][C] 0.09666[/C][/ROW]
[ROW][C]45[/C][C] 0.9063[/C][C] 0.1875[/C][C] 0.09372[/C][/ROW]
[ROW][C]46[/C][C] 0.8971[/C][C] 0.2058[/C][C] 0.1029[/C][/ROW]
[ROW][C]47[/C][C] 0.8739[/C][C] 0.2521[/C][C] 0.1261[/C][/ROW]
[ROW][C]48[/C][C] 0.8649[/C][C] 0.2702[/C][C] 0.1351[/C][/ROW]
[ROW][C]49[/C][C] 0.833[/C][C] 0.3341[/C][C] 0.167[/C][/ROW]
[ROW][C]50[/C][C] 0.802[/C][C] 0.3961[/C][C] 0.198[/C][/ROW]
[ROW][C]51[/C][C] 0.8026[/C][C] 0.3948[/C][C] 0.1974[/C][/ROW]
[ROW][C]52[/C][C] 0.7627[/C][C] 0.4746[/C][C] 0.2373[/C][/ROW]
[ROW][C]53[/C][C] 0.716[/C][C] 0.5679[/C][C] 0.2839[/C][/ROW]
[ROW][C]54[/C][C] 0.6722[/C][C] 0.6555[/C][C] 0.3278[/C][/ROW]
[ROW][C]55[/C][C] 0.6317[/C][C] 0.7366[/C][C] 0.3683[/C][/ROW]
[ROW][C]56[/C][C] 0.8607[/C][C] 0.2785[/C][C] 0.1393[/C][/ROW]
[ROW][C]57[/C][C] 0.8453[/C][C] 0.3094[/C][C] 0.1547[/C][/ROW]
[ROW][C]58[/C][C] 0.8098[/C][C] 0.3804[/C][C] 0.1902[/C][/ROW]
[ROW][C]59[/C][C] 0.8088[/C][C] 0.3823[/C][C] 0.1912[/C][/ROW]
[ROW][C]60[/C][C] 0.7732[/C][C] 0.4536[/C][C] 0.2268[/C][/ROW]
[ROW][C]61[/C][C] 0.7642[/C][C] 0.4716[/C][C] 0.2358[/C][/ROW]
[ROW][C]62[/C][C] 0.9432[/C][C] 0.1135[/C][C] 0.05677[/C][/ROW]
[ROW][C]63[/C][C] 0.9754[/C][C] 0.04921[/C][C] 0.02461[/C][/ROW]
[ROW][C]64[/C][C] 0.9734[/C][C] 0.05328[/C][C] 0.02664[/C][/ROW]
[ROW][C]65[/C][C] 0.9717[/C][C] 0.05668[/C][C] 0.02834[/C][/ROW]
[ROW][C]66[/C][C] 0.9689[/C][C] 0.0621[/C][C] 0.03105[/C][/ROW]
[ROW][C]67[/C][C] 0.9582[/C][C] 0.08363[/C][C] 0.04181[/C][/ROW]
[ROW][C]68[/C][C] 0.9451[/C][C] 0.1097[/C][C] 0.05486[/C][/ROW]
[ROW][C]69[/C][C] 0.9398[/C][C] 0.1204[/C][C] 0.06021[/C][/ROW]
[ROW][C]70[/C][C] 0.9261[/C][C] 0.1477[/C][C] 0.07385[/C][/ROW]
[ROW][C]71[/C][C] 0.9082[/C][C] 0.1836[/C][C] 0.09178[/C][/ROW]
[ROW][C]72[/C][C] 0.8903[/C][C] 0.2194[/C][C] 0.1097[/C][/ROW]
[ROW][C]73[/C][C] 0.9007[/C][C] 0.1986[/C][C] 0.09931[/C][/ROW]
[ROW][C]74[/C][C] 0.8843[/C][C] 0.2314[/C][C] 0.1157[/C][/ROW]
[ROW][C]75[/C][C] 0.8535[/C][C] 0.293[/C][C] 0.1465[/C][/ROW]
[ROW][C]76[/C][C] 0.8303[/C][C] 0.3395[/C][C] 0.1697[/C][/ROW]
[ROW][C]77[/C][C] 0.8973[/C][C] 0.2053[/C][C] 0.1027[/C][/ROW]
[ROW][C]78[/C][C] 0.8755[/C][C] 0.2491[/C][C] 0.1245[/C][/ROW]
[ROW][C]79[/C][C] 0.8488[/C][C] 0.3023[/C][C] 0.1512[/C][/ROW]
[ROW][C]80[/C][C] 0.8687[/C][C] 0.2626[/C][C] 0.1313[/C][/ROW]
[ROW][C]81[/C][C] 0.9574[/C][C] 0.08522[/C][C] 0.04261[/C][/ROW]
[ROW][C]82[/C][C] 0.9687[/C][C] 0.06268[/C][C] 0.03134[/C][/ROW]
[ROW][C]83[/C][C] 0.9598[/C][C] 0.08049[/C][C] 0.04024[/C][/ROW]
[ROW][C]84[/C][C] 0.9592[/C][C] 0.08152[/C][C] 0.04076[/C][/ROW]
[ROW][C]85[/C][C] 0.9515[/C][C] 0.09705[/C][C] 0.04852[/C][/ROW]
[ROW][C]86[/C][C] 0.962[/C][C] 0.07607[/C][C] 0.03804[/C][/ROW]
[ROW][C]87[/C][C] 0.9503[/C][C] 0.09949[/C][C] 0.04974[/C][/ROW]
[ROW][C]88[/C][C] 0.9395[/C][C] 0.121[/C][C] 0.06052[/C][/ROW]
[ROW][C]89[/C][C] 0.9698[/C][C] 0.06031[/C][C] 0.03016[/C][/ROW]
[ROW][C]90[/C][C] 0.9581[/C][C] 0.08387[/C][C] 0.04193[/C][/ROW]
[ROW][C]91[/C][C] 0.9673[/C][C] 0.06539[/C][C] 0.0327[/C][/ROW]
[ROW][C]92[/C][C] 0.9718[/C][C] 0.05647[/C][C] 0.02823[/C][/ROW]
[ROW][C]93[/C][C] 0.987[/C][C] 0.02608[/C][C] 0.01304[/C][/ROW]
[ROW][C]94[/C][C] 0.9915[/C][C] 0.01691[/C][C] 0.008457[/C][/ROW]
[ROW][C]95[/C][C] 0.9866[/C][C] 0.02689[/C][C] 0.01344[/C][/ROW]
[ROW][C]96[/C][C] 0.9798[/C][C] 0.04046[/C][C] 0.02023[/C][/ROW]
[ROW][C]97[/C][C] 0.9886[/C][C] 0.02273[/C][C] 0.01137[/C][/ROW]
[ROW][C]98[/C][C] 0.9842[/C][C] 0.03164[/C][C] 0.01582[/C][/ROW]
[ROW][C]99[/C][C] 0.9965[/C][C] 0.006988[/C][C] 0.003494[/C][/ROW]
[ROW][C]100[/C][C] 0.9938[/C][C] 0.01233[/C][C] 0.006163[/C][/ROW]
[ROW][C]101[/C][C] 0.99[/C][C] 0.02005[/C][C] 0.01003[/C][/ROW]
[ROW][C]102[/C][C] 0.9934[/C][C] 0.01322[/C][C] 0.006612[/C][/ROW]
[ROW][C]103[/C][C] 0.99[/C][C] 0.02003[/C][C] 0.01001[/C][/ROW]
[ROW][C]104[/C][C] 0.9862[/C][C] 0.02768[/C][C] 0.01384[/C][/ROW]
[ROW][C]105[/C][C] 0.9775[/C][C] 0.04493[/C][C] 0.02247[/C][/ROW]
[ROW][C]106[/C][C] 0.9827[/C][C] 0.03462[/C][C] 0.01731[/C][/ROW]
[ROW][C]107[/C][C] 0.976[/C][C] 0.04791[/C][C] 0.02396[/C][/ROW]
[ROW][C]108[/C][C] 0.9575[/C][C] 0.08492[/C][C] 0.04246[/C][/ROW]
[ROW][C]109[/C][C] 0.9412[/C][C] 0.1176[/C][C] 0.05878[/C][/ROW]
[ROW][C]110[/C][C] 0.8986[/C][C] 0.2028[/C][C] 0.1014[/C][/ROW]
[ROW][C]111[/C][C] 0.8275[/C][C] 0.3451[/C][C] 0.1725[/C][/ROW]
[ROW][C]112[/C][C] 0.8133[/C][C] 0.3733[/C][C] 0.1867[/C][/ROW]
[ROW][C]113[/C][C] 0.7344[/C][C] 0.5312[/C][C] 0.2656[/C][/ROW]
[ROW][C]114[/C][C] 0.6083[/C][C] 0.7835[/C][C] 0.3917[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303504&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303504&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
19	0.7626	0.4748	0.2374
20	0.8403	0.3194	0.1597
21	0.7594	0.4812	0.2406
22	0.6907	0.6187	0.3093
23	0.6636	0.6728	0.3364
24	0.747	0.506	0.253
25	0.9404	0.1191	0.05956
26	0.9086	0.1828	0.09138
27	0.869	0.262	0.131
28	0.8319	0.3361	0.1681
29	0.8245	0.3511	0.1755
30	0.8141	0.3718	0.1859
31	0.8448	0.3104	0.1552
32	0.8318	0.3364	0.1682
33	0.8156	0.3688	0.1844
34	0.8659	0.2682	0.1341
35	0.9757	0.04866	0.02433
36	0.9676	0.06472	0.03236
37	0.9581	0.08372	0.04186
38	0.9445	0.1109	0.05547
39	0.9249	0.1502	0.07508
40	0.9311	0.1379	0.06894
41	0.9155	0.1691	0.08453
42	0.9317	0.1366	0.0683
43	0.9136	0.1729	0.08644
44	0.9033	0.1933	0.09666
45	0.9063	0.1875	0.09372
46	0.8971	0.2058	0.1029
47	0.8739	0.2521	0.1261
48	0.8649	0.2702	0.1351
49	0.833	0.3341	0.167
50	0.802	0.3961	0.198
51	0.8026	0.3948	0.1974
52	0.7627	0.4746	0.2373
53	0.716	0.5679	0.2839
54	0.6722	0.6555	0.3278
55	0.6317	0.7366	0.3683
56	0.8607	0.2785	0.1393
57	0.8453	0.3094	0.1547
58	0.8098	0.3804	0.1902
59	0.8088	0.3823	0.1912
60	0.7732	0.4536	0.2268
61	0.7642	0.4716	0.2358
62	0.9432	0.1135	0.05677
63	0.9754	0.04921	0.02461
64	0.9734	0.05328	0.02664
65	0.9717	0.05668	0.02834
66	0.9689	0.0621	0.03105
67	0.9582	0.08363	0.04181
68	0.9451	0.1097	0.05486
69	0.9398	0.1204	0.06021
70	0.9261	0.1477	0.07385
71	0.9082	0.1836	0.09178
72	0.8903	0.2194	0.1097
73	0.9007	0.1986	0.09931
74	0.8843	0.2314	0.1157
75	0.8535	0.293	0.1465
76	0.8303	0.3395	0.1697
77	0.8973	0.2053	0.1027
78	0.8755	0.2491	0.1245
79	0.8488	0.3023	0.1512
80	0.8687	0.2626	0.1313
81	0.9574	0.08522	0.04261
82	0.9687	0.06268	0.03134
83	0.9598	0.08049	0.04024
84	0.9592	0.08152	0.04076
85	0.9515	0.09705	0.04852
86	0.962	0.07607	0.03804
87	0.9503	0.09949	0.04974
88	0.9395	0.121	0.06052
89	0.9698	0.06031	0.03016
90	0.9581	0.08387	0.04193
91	0.9673	0.06539	0.0327
92	0.9718	0.05647	0.02823
93	0.987	0.02608	0.01304
94	0.9915	0.01691	0.008457
95	0.9866	0.02689	0.01344
96	0.9798	0.04046	0.02023
97	0.9886	0.02273	0.01137
98	0.9842	0.03164	0.01582
99	0.9965	0.006988	0.003494
100	0.9938	0.01233	0.006163
101	0.99	0.02005	0.01003
102	0.9934	0.01322	0.006612
103	0.99	0.02003	0.01001
104	0.9862	0.02768	0.01384
105	0.9775	0.04493	0.02247
106	0.9827	0.03462	0.01731
107	0.976	0.04791	0.02396
108	0.9575	0.08492	0.04246
109	0.9412	0.1176	0.05878
110	0.8986	0.2028	0.1014
111	0.8275	0.3451	0.1725
112	0.8133	0.3733	0.1867
113	0.7344	0.5312	0.2656
114	0.6083	0.7835	0.3917

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.01042	NOK
5% type I error level	17	0.177083	NOK
10% type I error level	35	0.364583	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 1 &  0.01042 & NOK \tabularnewline
5% type I error level & 17 & 0.177083 & NOK \tabularnewline
10% type I error level & 35 & 0.364583 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303504&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]1[/C][C] 0.01042[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]17[/C][C]0.177083[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.364583[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303504&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303504&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	1	0.01042	NOK
5% type I error level	17	0.177083	NOK
10% type I error level	35	0.364583	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.49789, df1 = 2, df2 = 115, p-value = 0.6091
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.77401, df1 = 30, df2 = 87, p-value = 0.7836
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.016654, df1 = 2, df2 = 115, p-value = 0.9835

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.49789, df1 = 2, df2 = 115, p-value = 0.6091
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.77401, df1 = 30, df2 = 87, p-value = 0.7836
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.016654, df1 = 2, df2 = 115, p-value = 0.9835
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303504&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.49789, df1 = 2, df2 = 115, p-value = 0.6091
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.77401, df1 = 30, df2 = 87, p-value = 0.7836
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.016654, df1 = 2, df2 = 115, p-value = 0.9835
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303504&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303504&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.49789, df1 = 2, df2 = 115, p-value = 0.6091
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.77401, df1 = 30, df2 = 87, p-value = 0.7836
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.016654, df1 = 2, df2 = 115, p-value = 0.9835

Variance Inflation Factors (Multicollinearity)

> vif
      gender        group   `LFM(t-1)`  `LFM(t-1s)`  `LFM(t-2s)`  `LFM(t-3s)` 
    1.147121     1.185449     1.108834     1.125810     1.140731     1.113224 
 `LFM(t-4s)`  `LFM(t-5s)`  `LFM(t-6s)`  `LFM(t-7s)`  `LFM(t-8s)`  `LFM(t-9s)` 
    1.140268     1.238493     1.173360     1.188791     1.103449     1.127262 
`LFM(t-10s)` `LFM(t-11s)` `LFM(t-12s)` 
    1.122167     1.079630     1.065691

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      gender        group   `LFM(t-1)`  `LFM(t-1s)`  `LFM(t-2s)`  `LFM(t-3s)` 
    1.147121     1.185449     1.108834     1.125810     1.140731     1.113224 
 `LFM(t-4s)`  `LFM(t-5s)`  `LFM(t-6s)`  `LFM(t-7s)`  `LFM(t-8s)`  `LFM(t-9s)` 
    1.140268     1.238493     1.173360     1.188791     1.103449     1.127262 
`LFM(t-10s)` `LFM(t-11s)` `LFM(t-12s)` 
    1.122167     1.079630     1.065691 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303504&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      gender        group   `LFM(t-1)`  `LFM(t-1s)`  `LFM(t-2s)`  `LFM(t-3s)` 
    1.147121     1.185449     1.108834     1.125810     1.140731     1.113224 
 `LFM(t-4s)`  `LFM(t-5s)`  `LFM(t-6s)`  `LFM(t-7s)`  `LFM(t-8s)`  `LFM(t-9s)` 
    1.140268     1.238493     1.173360     1.188791     1.103449     1.127262 
`LFM(t-10s)` `LFM(t-11s)` `LFM(t-12s)` 
    1.122167     1.079630     1.065691 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303504&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303504&T=8

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)

> vif
      gender        group   `LFM(t-1)`  `LFM(t-1s)`  `LFM(t-2s)`  `LFM(t-3s)` 
    1.147121     1.185449     1.108834     1.125810     1.140731     1.113224 
 `LFM(t-4s)`  `LFM(t-5s)`  `LFM(t-6s)`  `LFM(t-7s)`  `LFM(t-8s)`  `LFM(t-9s)` 
    1.140268     1.238493     1.173360     1.188791     1.103449     1.127262 
`LFM(t-10s)` `LFM(t-11s)` `LFM(t-12s)` 
    1.122167     1.079630     1.065691

Figure 1

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Figure 7

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Figure 10

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Parameters (Session):

par1 = 12 ; par2 = -0.3 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 2 ; par9 = 0 ; par10 = FALSE ;

Parameters (R input):

par1 = 1 ; par2 = -0.3 ; par3 = 0 ; par4 = 1 ; par5 = 12 ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code