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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 14 Dec 2015 19:49:23 +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/2015/Dec/14/t14501227857om1776x8xw27en.htm/, Retrieved Fri, 17 May 2024 00:06:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=286394, Retrieved Fri, 17 May 2024 00:06:51 +0000

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-       [Multiple Regression] [Multiple regression] [2015-12-14 19:49:23] [d7b41ff8615e11945ad30de5daa5ba50] [Current]

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12.9	86	149	1
12.8	71	148	1
7.4	108	158	1
6.7	64	128	1
14.8	97	159	1
13.3	129	105	1
11.1	153	159	1
8.2	78	167	1
11.4	80	165	1
6.4	99	159	1
11.3	57	91	1
10	68	121	1
6.4	55	153	1
10.8	79	221	1
13.8	116	188	1
11.7	101	149	1
13.4	66	92	1
11.7	71	156	1
9	64	132	1
9.7	143	161	1
10.8	85	105	1
12.7	69	131	1
11.8	96	157	1
5.9	60	111	1
11.4	95	145	1
13	100	162	1
11.3	105	187	1
6.7	41	42	1
12.1	50	155	1
13.3	93	125	1
5.7	54	128	1
13.3	69	96	1
7.6	58	99	1
11.1	136	183	1
13	126	214	1
9.9	64	74	1
11.1	36	99	1
4.35	35	48	1
12.7	61	50	1
18.1	70	150	1
12.6	24	68	1
19.1	147	158	1
18.4	84	147	1
14.7	30	39	1
10.6	77	100	1
12.6	46	111	1
16.2	61	138	1
18.9	159	131	1
14.1	57	101	1
16.15	163	165	1
14.75	76	114	1
14.8	94	111	1
12.45	45	75	1
12.65	78	82	1
17.35	47	121	1
18.4	97	150	1
11.6	33	71	1
17.75	51	165	1
15.25	118	154	1
17.65	89	145	1
14.75	56	132	1
9.9	60	169	1
16	109	114	1
13.85	58	89	1
17.1	92	173	1
14.6	95	141	1
15.4	50	165	1
17.6	80	110	1
13.9	68	121	1
16.25	79	110	1
15.65	57	117	1
14.6	69	63	1
11.2	49	42	1
16.35	100	154	1
15.85	78	96	1
7.65	38	49	1
12.35	42	110	1
15.6	90	86	1
13.1	52	88	1
12.85	64	168	1
9.5	31	94	1
11.85	27	48	1
13.6	105	145	1
17.6	71	164	1
16.1	63	126	1
13.35	47	132	1
15.15	78	81	1
12.2	70	139	0
12.6	119	224	0
10.6	68	119	0
12	147	176	0
11.9	120	163	0
9.6	84	137	0
13.8	137	148	0
9.9	81	150	0
11.5	63	153	0
8.3	69	94	0
10.3	86	97	0
9.3	120	166	0
12.3	57	59	0
7.9	103	90	0
9.3	107	164	0
12.5	65	162	0
15.9	107	202	0
9.1	53	66	0
12.2	69	104	0
12.3	136	177	0
14.6	118	99	0
12.6	82	139	0
12.6	65	108	0
17.1	120	194	0
16.1	215	159	0
13.35	24	67	0
14.5	42	114	0
8.6	29	32	0
17.65	66	126	0
16.35	87	149	0
13.6	76	120	0
14.35	75	109	0
18.25	72	172	0
18.25	76	156	0
18.95	123	167	0
15.9	46	87	0
13.35	86	118	0
15.35	79	146	0
14.85	75	73	0
13.6	43	65	0
15.25	55	152	0
13.2	39	77	0
15.65	95	112	0
15.6	23	131	0
15.2	48	56	0
18.4	94	121	0
19.05	62	149	0
18.55	74	168	0
12.4	62	85	0
14.6	80	114	0
14.05	75	119	0
11.85	54	142	0
7.85	51	64	0
15.2	76	105	0

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=286394&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=286394&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286394&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
TOT[t] = + 11.1303 + 0.0111975H[t] + 0.0118686LFM[t] -0.638244Geslacht[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  11.1303 +  0.0111975H[t] +  0.0118686LFM[t] -0.638244Geslacht[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286394&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  11.1303 +  0.0111975H[t] +  0.0118686LFM[t] -0.638244Geslacht[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286394&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286394&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
TOT[t] = + 11.1303 + 0.0111975H[t] + 0.0118686LFM[t] -0.638244Geslacht[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)	+11.13	0.9634	+1.1550e+01	5.595e-22	2.798e-22
H	+0.0112	0.01016	+1.1020e+00	0.2725	0.1362
LFM	+0.01187	0.008046	+1.4750e+00	0.1425	0.07123
Geslacht	-0.6382	0.5497	-1.1610e+00	0.2476	0.1238

\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) & +11.13 &  0.9634 & +1.1550e+01 &  5.595e-22 &  2.798e-22 \tabularnewline
H & +0.0112 &  0.01016 & +1.1020e+00 &  0.2725 &  0.1362 \tabularnewline
LFM & +0.01187 &  0.008046 & +1.4750e+00 &  0.1425 &  0.07123 \tabularnewline
Geslacht & -0.6382 &  0.5497 & -1.1610e+00 &  0.2476 &  0.1238 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286394&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]+11.13[/C][C] 0.9634[/C][C]+1.1550e+01[/C][C] 5.595e-22[/C][C] 2.798e-22[/C][/ROW]
[ROW][C]H[/C][C]+0.0112[/C][C] 0.01016[/C][C]+1.1020e+00[/C][C] 0.2725[/C][C] 0.1362[/C][/ROW]
[ROW][C]LFM[/C][C]+0.01187[/C][C] 0.008046[/C][C]+1.4750e+00[/C][C] 0.1425[/C][C] 0.07123[/C][/ROW]
[ROW][C]Geslacht[/C][C]-0.6382[/C][C] 0.5497[/C][C]-1.1610e+00[/C][C] 0.2476[/C][C] 0.1238[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286394&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286394&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)	+11.13	0.9634	+1.1550e+01	5.595e-22	2.798e-22
H	+0.0112	0.01016	+1.1020e+00	0.2725	0.1362
LFM	+0.01187	0.008046	+1.4750e+00	0.1425	0.07123
Geslacht	-0.6382	0.5497	-1.1610e+00	0.2476	0.1238

Multiple Linear Regression - Regression Statistics
Multiple R	0.2537
R-squared	0.06435
Adjusted R-squared	0.04386
F-TEST (value)	3.141
F-TEST (DF numerator)	3
F-TEST (DF denominator)	137
p-value	0.02743
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.166
Sum Squared Residuals	1373

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2537 \tabularnewline
R-squared &  0.06435 \tabularnewline
Adjusted R-squared &  0.04386 \tabularnewline
F-TEST (value) &  3.141 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value &  0.02743 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.166 \tabularnewline
Sum Squared Residuals &  1373 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286394&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2537[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.06435[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.04386[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 3.141[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/C][/ROW]
[ROW][C]p-value[/C][C] 0.02743[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 3.166[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1373[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286394&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286394&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	0.2537
R-squared	0.06435
Adjusted R-squared	0.04386
F-TEST (value)	3.141
F-TEST (DF numerator)	3
F-TEST (DF denominator)	137
p-value	0.02743
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.166
Sum Squared Residuals	1373

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	13.22	-0.3234
2	12.8	13.04	-0.2436
3	7.4	13.58	-6.177
4	6.7	12.73	-6.028
5	14.8	13.47	1.335
6	13.3	13.18	0.1173
7	11.1	14.09	-2.992
8	8.2	13.35	-5.147
9	11.4	13.35	-1.946
10	6.4	13.49	-7.088
11	11.3	12.21	-0.9103
12	10	12.69	-2.69
13	6.4	12.92	-6.524
14	10.8	14	-3.2
15	13.8	14.02	-0.2222
16	11.7	13.39	-1.691
17	13.4	12.32	1.077
18	11.7	13.14	-1.439
19	9	12.78	-3.775
20	9.7	14	-4.304
21	10.8	12.69	-1.89
22	12.7	12.82	-0.1194
23	11.8	13.43	-1.63
24	5.9	12.48	-6.581
25	11.4	13.28	-1.877
26	13	13.53	-0.5345
27	11.3	13.89	-2.587
28	6.7	11.45	-4.75
29	12.1	12.89	-0.7915
30	13.3	13.02	0.283
31	5.7	12.62	-6.916
32	13.3	12.4	0.896
33	7.6	12.32	-4.716
34	11.1	14.19	-3.087
35	13	14.44	-1.443
36	9.9	12.09	-2.187
37	11.1	12.07	-0.9701
38	4.35	11.45	-7.104
39	12.7	11.77	0.9315
40	18.1	13.06	5.044
41	12.6	11.57	1.032
42	19.1	14.01	5.087
43	18.4	13.18	5.223
44	14.7	11.29	3.409
45	10.6	12.54	-1.941
46	12.6	12.32	0.2755
47	16.2	12.81	3.387
48	18.9	13.83	5.073
49	14.1	12.33	1.771
50	16.15	14.28	1.874
51	14.75	12.7	2.054
52	14.8	12.86	1.938
53	12.45	11.89	0.5639
54	12.65	12.34	0.3113
55	17.35	12.45	4.896
56	18.4	13.36	5.042
57	11.6	11.7	-0.1042
58	17.75	13.02	4.729
59	15.25	13.64	1.609
60	17.65	13.21	4.44
61	14.75	12.69	2.064
62	9.9	13.17	-3.27
63	16	13.07	2.934
64	13.85	12.2	1.652
65	17.1	13.58	3.525
66	14.6	13.23	1.371
67	15.4	13.01	2.39
68	17.6	12.69	4.907
69	13.9	12.69	1.21
70	16.25	12.68	3.568
71	15.65	12.52	3.131
72	14.6	12.01	2.588
73	11.2	11.54	-0.3392
74	16.35	13.44	2.91
75	15.85	12.5	3.345
76	7.65	11.5	-3.849
77	12.35	12.27	0.08213
78	15.6	12.52	3.079
79	13.1	12.12	0.9813
80	12.85	13.2	-0.3526
81	9.5	11.95	-2.455
82	11.85	11.36	0.4859
83	13.6	13.39	0.2113
84	17.6	13.23	4.367
85	16.1	12.69	3.407
86	13.35	12.59	0.765
87	15.15	12.33	2.823
88	12.2	13.56	-1.364
89	12.6	15.12	-2.521
90	10.6	13.3	-2.704
91	12	14.87	-2.865
92	11.9	14.41	-2.509
93	9.6	13.7	-4.097
94	13.8	14.42	-0.6209
95	9.9	13.82	-3.918
96	11.5	13.65	-2.152
97	8.3	13.02	-4.719
98	10.3	13.24	-2.945
99	9.3	14.44	-5.144
100	12.3	12.47	-0.1688
101	7.9	13.35	-5.452
102	9.3	14.27	-4.975
103	12.5	13.78	-1.281
104	15.9	14.73	1.174
105	9.1	12.51	-3.407
106	12.2	13.14	-0.9372
107	12.3	14.75	-2.454
108	14.6	13.63	0.9734
109	12.6	13.7	-1.098
110	12.6	13.14	-0.5399
111	17.1	14.78	2.324
112	16.1	15.42	0.6752
113	13.35	12.19	1.156
114	14.5	12.95	1.546
115	8.6	11.83	-3.235
116	17.65	13.36	4.285
117	16.35	13.87	2.477
118	13.6	13.41	0.1945
119	14.35	13.26	1.086
120	18.25	13.98	4.272
121	18.25	13.83	4.417
122	18.95	14.49	4.46
123	15.9	12.68	3.222
124	13.35	13.49	-0.1438
125	15.35	13.75	1.602
126	14.85	12.84	2.014
127	13.6	12.38	1.217
128	15.25	13.55	1.7
129	13.2	12.48	0.7191
130	15.65	13.52	2.127
131	15.6	12.94	2.657
132	15.2	12.33	2.868
133	18.4	13.62	4.781
134	19.05	13.59	5.457
135	18.55	13.95	4.597
136	12.4	12.83	-0.4333
137	14.6	13.38	1.221
138	14.05	13.38	0.6675
139	11.85	13.42	-1.57
140	7.85	12.46	-4.611
141	15.2	13.23	1.973

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  12.9 &  13.22 & -0.3234 \tabularnewline
2 &  12.8 &  13.04 & -0.2436 \tabularnewline
3 &  7.4 &  13.58 & -6.177 \tabularnewline
4 &  6.7 &  12.73 & -6.028 \tabularnewline
5 &  14.8 &  13.47 &  1.335 \tabularnewline
6 &  13.3 &  13.18 &  0.1173 \tabularnewline
7 &  11.1 &  14.09 & -2.992 \tabularnewline
8 &  8.2 &  13.35 & -5.147 \tabularnewline
9 &  11.4 &  13.35 & -1.946 \tabularnewline
10 &  6.4 &  13.49 & -7.088 \tabularnewline
11 &  11.3 &  12.21 & -0.9103 \tabularnewline
12 &  10 &  12.69 & -2.69 \tabularnewline
13 &  6.4 &  12.92 & -6.524 \tabularnewline
14 &  10.8 &  14 & -3.2 \tabularnewline
15 &  13.8 &  14.02 & -0.2222 \tabularnewline
16 &  11.7 &  13.39 & -1.691 \tabularnewline
17 &  13.4 &  12.32 &  1.077 \tabularnewline
18 &  11.7 &  13.14 & -1.439 \tabularnewline
19 &  9 &  12.78 & -3.775 \tabularnewline
20 &  9.7 &  14 & -4.304 \tabularnewline
21 &  10.8 &  12.69 & -1.89 \tabularnewline
22 &  12.7 &  12.82 & -0.1194 \tabularnewline
23 &  11.8 &  13.43 & -1.63 \tabularnewline
24 &  5.9 &  12.48 & -6.581 \tabularnewline
25 &  11.4 &  13.28 & -1.877 \tabularnewline
26 &  13 &  13.53 & -0.5345 \tabularnewline
27 &  11.3 &  13.89 & -2.587 \tabularnewline
28 &  6.7 &  11.45 & -4.75 \tabularnewline
29 &  12.1 &  12.89 & -0.7915 \tabularnewline
30 &  13.3 &  13.02 &  0.283 \tabularnewline
31 &  5.7 &  12.62 & -6.916 \tabularnewline
32 &  13.3 &  12.4 &  0.896 \tabularnewline
33 &  7.6 &  12.32 & -4.716 \tabularnewline
34 &  11.1 &  14.19 & -3.087 \tabularnewline
35 &  13 &  14.44 & -1.443 \tabularnewline
36 &  9.9 &  12.09 & -2.187 \tabularnewline
37 &  11.1 &  12.07 & -0.9701 \tabularnewline
38 &  4.35 &  11.45 & -7.104 \tabularnewline
39 &  12.7 &  11.77 &  0.9315 \tabularnewline
40 &  18.1 &  13.06 &  5.044 \tabularnewline
41 &  12.6 &  11.57 &  1.032 \tabularnewline
42 &  19.1 &  14.01 &  5.087 \tabularnewline
43 &  18.4 &  13.18 &  5.223 \tabularnewline
44 &  14.7 &  11.29 &  3.409 \tabularnewline
45 &  10.6 &  12.54 & -1.941 \tabularnewline
46 &  12.6 &  12.32 &  0.2755 \tabularnewline
47 &  16.2 &  12.81 &  3.387 \tabularnewline
48 &  18.9 &  13.83 &  5.073 \tabularnewline
49 &  14.1 &  12.33 &  1.771 \tabularnewline
50 &  16.15 &  14.28 &  1.874 \tabularnewline
51 &  14.75 &  12.7 &  2.054 \tabularnewline
52 &  14.8 &  12.86 &  1.938 \tabularnewline
53 &  12.45 &  11.89 &  0.5639 \tabularnewline
54 &  12.65 &  12.34 &  0.3113 \tabularnewline
55 &  17.35 &  12.45 &  4.896 \tabularnewline
56 &  18.4 &  13.36 &  5.042 \tabularnewline
57 &  11.6 &  11.7 & -0.1042 \tabularnewline
58 &  17.75 &  13.02 &  4.729 \tabularnewline
59 &  15.25 &  13.64 &  1.609 \tabularnewline
60 &  17.65 &  13.21 &  4.44 \tabularnewline
61 &  14.75 &  12.69 &  2.064 \tabularnewline
62 &  9.9 &  13.17 & -3.27 \tabularnewline
63 &  16 &  13.07 &  2.934 \tabularnewline
64 &  13.85 &  12.2 &  1.652 \tabularnewline
65 &  17.1 &  13.58 &  3.525 \tabularnewline
66 &  14.6 &  13.23 &  1.371 \tabularnewline
67 &  15.4 &  13.01 &  2.39 \tabularnewline
68 &  17.6 &  12.69 &  4.907 \tabularnewline
69 &  13.9 &  12.69 &  1.21 \tabularnewline
70 &  16.25 &  12.68 &  3.568 \tabularnewline
71 &  15.65 &  12.52 &  3.131 \tabularnewline
72 &  14.6 &  12.01 &  2.588 \tabularnewline
73 &  11.2 &  11.54 & -0.3392 \tabularnewline
74 &  16.35 &  13.44 &  2.91 \tabularnewline
75 &  15.85 &  12.5 &  3.345 \tabularnewline
76 &  7.65 &  11.5 & -3.849 \tabularnewline
77 &  12.35 &  12.27 &  0.08213 \tabularnewline
78 &  15.6 &  12.52 &  3.079 \tabularnewline
79 &  13.1 &  12.12 &  0.9813 \tabularnewline
80 &  12.85 &  13.2 & -0.3526 \tabularnewline
81 &  9.5 &  11.95 & -2.455 \tabularnewline
82 &  11.85 &  11.36 &  0.4859 \tabularnewline
83 &  13.6 &  13.39 &  0.2113 \tabularnewline
84 &  17.6 &  13.23 &  4.367 \tabularnewline
85 &  16.1 &  12.69 &  3.407 \tabularnewline
86 &  13.35 &  12.59 &  0.765 \tabularnewline
87 &  15.15 &  12.33 &  2.823 \tabularnewline
88 &  12.2 &  13.56 & -1.364 \tabularnewline
89 &  12.6 &  15.12 & -2.521 \tabularnewline
90 &  10.6 &  13.3 & -2.704 \tabularnewline
91 &  12 &  14.87 & -2.865 \tabularnewline
92 &  11.9 &  14.41 & -2.509 \tabularnewline
93 &  9.6 &  13.7 & -4.097 \tabularnewline
94 &  13.8 &  14.42 & -0.6209 \tabularnewline
95 &  9.9 &  13.82 & -3.918 \tabularnewline
96 &  11.5 &  13.65 & -2.152 \tabularnewline
97 &  8.3 &  13.02 & -4.719 \tabularnewline
98 &  10.3 &  13.24 & -2.945 \tabularnewline
99 &  9.3 &  14.44 & -5.144 \tabularnewline
100 &  12.3 &  12.47 & -0.1688 \tabularnewline
101 &  7.9 &  13.35 & -5.452 \tabularnewline
102 &  9.3 &  14.27 & -4.975 \tabularnewline
103 &  12.5 &  13.78 & -1.281 \tabularnewline
104 &  15.9 &  14.73 &  1.174 \tabularnewline
105 &  9.1 &  12.51 & -3.407 \tabularnewline
106 &  12.2 &  13.14 & -0.9372 \tabularnewline
107 &  12.3 &  14.75 & -2.454 \tabularnewline
108 &  14.6 &  13.63 &  0.9734 \tabularnewline
109 &  12.6 &  13.7 & -1.098 \tabularnewline
110 &  12.6 &  13.14 & -0.5399 \tabularnewline
111 &  17.1 &  14.78 &  2.324 \tabularnewline
112 &  16.1 &  15.42 &  0.6752 \tabularnewline
113 &  13.35 &  12.19 &  1.156 \tabularnewline
114 &  14.5 &  12.95 &  1.546 \tabularnewline
115 &  8.6 &  11.83 & -3.235 \tabularnewline
116 &  17.65 &  13.36 &  4.285 \tabularnewline
117 &  16.35 &  13.87 &  2.477 \tabularnewline
118 &  13.6 &  13.41 &  0.1945 \tabularnewline
119 &  14.35 &  13.26 &  1.086 \tabularnewline
120 &  18.25 &  13.98 &  4.272 \tabularnewline
121 &  18.25 &  13.83 &  4.417 \tabularnewline
122 &  18.95 &  14.49 &  4.46 \tabularnewline
123 &  15.9 &  12.68 &  3.222 \tabularnewline
124 &  13.35 &  13.49 & -0.1438 \tabularnewline
125 &  15.35 &  13.75 &  1.602 \tabularnewline
126 &  14.85 &  12.84 &  2.014 \tabularnewline
127 &  13.6 &  12.38 &  1.217 \tabularnewline
128 &  15.25 &  13.55 &  1.7 \tabularnewline
129 &  13.2 &  12.48 &  0.7191 \tabularnewline
130 &  15.65 &  13.52 &  2.127 \tabularnewline
131 &  15.6 &  12.94 &  2.657 \tabularnewline
132 &  15.2 &  12.33 &  2.868 \tabularnewline
133 &  18.4 &  13.62 &  4.781 \tabularnewline
134 &  19.05 &  13.59 &  5.457 \tabularnewline
135 &  18.55 &  13.95 &  4.597 \tabularnewline
136 &  12.4 &  12.83 & -0.4333 \tabularnewline
137 &  14.6 &  13.38 &  1.221 \tabularnewline
138 &  14.05 &  13.38 &  0.6675 \tabularnewline
139 &  11.85 &  13.42 & -1.57 \tabularnewline
140 &  7.85 &  12.46 & -4.611 \tabularnewline
141 &  15.2 &  13.23 &  1.973 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286394&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] 12.9[/C][C] 13.22[/C][C]-0.3234[/C][/ROW]
[ROW][C]2[/C][C] 12.8[/C][C] 13.04[/C][C]-0.2436[/C][/ROW]
[ROW][C]3[/C][C] 7.4[/C][C] 13.58[/C][C]-6.177[/C][/ROW]
[ROW][C]4[/C][C] 6.7[/C][C] 12.73[/C][C]-6.028[/C][/ROW]
[ROW][C]5[/C][C] 14.8[/C][C] 13.47[/C][C] 1.335[/C][/ROW]
[ROW][C]6[/C][C] 13.3[/C][C] 13.18[/C][C] 0.1173[/C][/ROW]
[ROW][C]7[/C][C] 11.1[/C][C] 14.09[/C][C]-2.992[/C][/ROW]
[ROW][C]8[/C][C] 8.2[/C][C] 13.35[/C][C]-5.147[/C][/ROW]
[ROW][C]9[/C][C] 11.4[/C][C] 13.35[/C][C]-1.946[/C][/ROW]
[ROW][C]10[/C][C] 6.4[/C][C] 13.49[/C][C]-7.088[/C][/ROW]
[ROW][C]11[/C][C] 11.3[/C][C] 12.21[/C][C]-0.9103[/C][/ROW]
[ROW][C]12[/C][C] 10[/C][C] 12.69[/C][C]-2.69[/C][/ROW]
[ROW][C]13[/C][C] 6.4[/C][C] 12.92[/C][C]-6.524[/C][/ROW]
[ROW][C]14[/C][C] 10.8[/C][C] 14[/C][C]-3.2[/C][/ROW]
[ROW][C]15[/C][C] 13.8[/C][C] 14.02[/C][C]-0.2222[/C][/ROW]
[ROW][C]16[/C][C] 11.7[/C][C] 13.39[/C][C]-1.691[/C][/ROW]
[ROW][C]17[/C][C] 13.4[/C][C] 12.32[/C][C] 1.077[/C][/ROW]
[ROW][C]18[/C][C] 11.7[/C][C] 13.14[/C][C]-1.439[/C][/ROW]
[ROW][C]19[/C][C] 9[/C][C] 12.78[/C][C]-3.775[/C][/ROW]
[ROW][C]20[/C][C] 9.7[/C][C] 14[/C][C]-4.304[/C][/ROW]
[ROW][C]21[/C][C] 10.8[/C][C] 12.69[/C][C]-1.89[/C][/ROW]
[ROW][C]22[/C][C] 12.7[/C][C] 12.82[/C][C]-0.1194[/C][/ROW]
[ROW][C]23[/C][C] 11.8[/C][C] 13.43[/C][C]-1.63[/C][/ROW]
[ROW][C]24[/C][C] 5.9[/C][C] 12.48[/C][C]-6.581[/C][/ROW]
[ROW][C]25[/C][C] 11.4[/C][C] 13.28[/C][C]-1.877[/C][/ROW]
[ROW][C]26[/C][C] 13[/C][C] 13.53[/C][C]-0.5345[/C][/ROW]
[ROW][C]27[/C][C] 11.3[/C][C] 13.89[/C][C]-2.587[/C][/ROW]
[ROW][C]28[/C][C] 6.7[/C][C] 11.45[/C][C]-4.75[/C][/ROW]
[ROW][C]29[/C][C] 12.1[/C][C] 12.89[/C][C]-0.7915[/C][/ROW]
[ROW][C]30[/C][C] 13.3[/C][C] 13.02[/C][C] 0.283[/C][/ROW]
[ROW][C]31[/C][C] 5.7[/C][C] 12.62[/C][C]-6.916[/C][/ROW]
[ROW][C]32[/C][C] 13.3[/C][C] 12.4[/C][C] 0.896[/C][/ROW]
[ROW][C]33[/C][C] 7.6[/C][C] 12.32[/C][C]-4.716[/C][/ROW]
[ROW][C]34[/C][C] 11.1[/C][C] 14.19[/C][C]-3.087[/C][/ROW]
[ROW][C]35[/C][C] 13[/C][C] 14.44[/C][C]-1.443[/C][/ROW]
[ROW][C]36[/C][C] 9.9[/C][C] 12.09[/C][C]-2.187[/C][/ROW]
[ROW][C]37[/C][C] 11.1[/C][C] 12.07[/C][C]-0.9701[/C][/ROW]
[ROW][C]38[/C][C] 4.35[/C][C] 11.45[/C][C]-7.104[/C][/ROW]
[ROW][C]39[/C][C] 12.7[/C][C] 11.77[/C][C] 0.9315[/C][/ROW]
[ROW][C]40[/C][C] 18.1[/C][C] 13.06[/C][C] 5.044[/C][/ROW]
[ROW][C]41[/C][C] 12.6[/C][C] 11.57[/C][C] 1.032[/C][/ROW]
[ROW][C]42[/C][C] 19.1[/C][C] 14.01[/C][C] 5.087[/C][/ROW]
[ROW][C]43[/C][C] 18.4[/C][C] 13.18[/C][C] 5.223[/C][/ROW]
[ROW][C]44[/C][C] 14.7[/C][C] 11.29[/C][C] 3.409[/C][/ROW]
[ROW][C]45[/C][C] 10.6[/C][C] 12.54[/C][C]-1.941[/C][/ROW]
[ROW][C]46[/C][C] 12.6[/C][C] 12.32[/C][C] 0.2755[/C][/ROW]
[ROW][C]47[/C][C] 16.2[/C][C] 12.81[/C][C] 3.387[/C][/ROW]
[ROW][C]48[/C][C] 18.9[/C][C] 13.83[/C][C] 5.073[/C][/ROW]
[ROW][C]49[/C][C] 14.1[/C][C] 12.33[/C][C] 1.771[/C][/ROW]
[ROW][C]50[/C][C] 16.15[/C][C] 14.28[/C][C] 1.874[/C][/ROW]
[ROW][C]51[/C][C] 14.75[/C][C] 12.7[/C][C] 2.054[/C][/ROW]
[ROW][C]52[/C][C] 14.8[/C][C] 12.86[/C][C] 1.938[/C][/ROW]
[ROW][C]53[/C][C] 12.45[/C][C] 11.89[/C][C] 0.5639[/C][/ROW]
[ROW][C]54[/C][C] 12.65[/C][C] 12.34[/C][C] 0.3113[/C][/ROW]
[ROW][C]55[/C][C] 17.35[/C][C] 12.45[/C][C] 4.896[/C][/ROW]
[ROW][C]56[/C][C] 18.4[/C][C] 13.36[/C][C] 5.042[/C][/ROW]
[ROW][C]57[/C][C] 11.6[/C][C] 11.7[/C][C]-0.1042[/C][/ROW]
[ROW][C]58[/C][C] 17.75[/C][C] 13.02[/C][C] 4.729[/C][/ROW]
[ROW][C]59[/C][C] 15.25[/C][C] 13.64[/C][C] 1.609[/C][/ROW]
[ROW][C]60[/C][C] 17.65[/C][C] 13.21[/C][C] 4.44[/C][/ROW]
[ROW][C]61[/C][C] 14.75[/C][C] 12.69[/C][C] 2.064[/C][/ROW]
[ROW][C]62[/C][C] 9.9[/C][C] 13.17[/C][C]-3.27[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 13.07[/C][C] 2.934[/C][/ROW]
[ROW][C]64[/C][C] 13.85[/C][C] 12.2[/C][C] 1.652[/C][/ROW]
[ROW][C]65[/C][C] 17.1[/C][C] 13.58[/C][C] 3.525[/C][/ROW]
[ROW][C]66[/C][C] 14.6[/C][C] 13.23[/C][C] 1.371[/C][/ROW]
[ROW][C]67[/C][C] 15.4[/C][C] 13.01[/C][C] 2.39[/C][/ROW]
[ROW][C]68[/C][C] 17.6[/C][C] 12.69[/C][C] 4.907[/C][/ROW]
[ROW][C]69[/C][C] 13.9[/C][C] 12.69[/C][C] 1.21[/C][/ROW]
[ROW][C]70[/C][C] 16.25[/C][C] 12.68[/C][C] 3.568[/C][/ROW]
[ROW][C]71[/C][C] 15.65[/C][C] 12.52[/C][C] 3.131[/C][/ROW]
[ROW][C]72[/C][C] 14.6[/C][C] 12.01[/C][C] 2.588[/C][/ROW]
[ROW][C]73[/C][C] 11.2[/C][C] 11.54[/C][C]-0.3392[/C][/ROW]
[ROW][C]74[/C][C] 16.35[/C][C] 13.44[/C][C] 2.91[/C][/ROW]
[ROW][C]75[/C][C] 15.85[/C][C] 12.5[/C][C] 3.345[/C][/ROW]
[ROW][C]76[/C][C] 7.65[/C][C] 11.5[/C][C]-3.849[/C][/ROW]
[ROW][C]77[/C][C] 12.35[/C][C] 12.27[/C][C] 0.08213[/C][/ROW]
[ROW][C]78[/C][C] 15.6[/C][C] 12.52[/C][C] 3.079[/C][/ROW]
[ROW][C]79[/C][C] 13.1[/C][C] 12.12[/C][C] 0.9813[/C][/ROW]
[ROW][C]80[/C][C] 12.85[/C][C] 13.2[/C][C]-0.3526[/C][/ROW]
[ROW][C]81[/C][C] 9.5[/C][C] 11.95[/C][C]-2.455[/C][/ROW]
[ROW][C]82[/C][C] 11.85[/C][C] 11.36[/C][C] 0.4859[/C][/ROW]
[ROW][C]83[/C][C] 13.6[/C][C] 13.39[/C][C] 0.2113[/C][/ROW]
[ROW][C]84[/C][C] 17.6[/C][C] 13.23[/C][C] 4.367[/C][/ROW]
[ROW][C]85[/C][C] 16.1[/C][C] 12.69[/C][C] 3.407[/C][/ROW]
[ROW][C]86[/C][C] 13.35[/C][C] 12.59[/C][C] 0.765[/C][/ROW]
[ROW][C]87[/C][C] 15.15[/C][C] 12.33[/C][C] 2.823[/C][/ROW]
[ROW][C]88[/C][C] 12.2[/C][C] 13.56[/C][C]-1.364[/C][/ROW]
[ROW][C]89[/C][C] 12.6[/C][C] 15.12[/C][C]-2.521[/C][/ROW]
[ROW][C]90[/C][C] 10.6[/C][C] 13.3[/C][C]-2.704[/C][/ROW]
[ROW][C]91[/C][C] 12[/C][C] 14.87[/C][C]-2.865[/C][/ROW]
[ROW][C]92[/C][C] 11.9[/C][C] 14.41[/C][C]-2.509[/C][/ROW]
[ROW][C]93[/C][C] 9.6[/C][C] 13.7[/C][C]-4.097[/C][/ROW]
[ROW][C]94[/C][C] 13.8[/C][C] 14.42[/C][C]-0.6209[/C][/ROW]
[ROW][C]95[/C][C] 9.9[/C][C] 13.82[/C][C]-3.918[/C][/ROW]
[ROW][C]96[/C][C] 11.5[/C][C] 13.65[/C][C]-2.152[/C][/ROW]
[ROW][C]97[/C][C] 8.3[/C][C] 13.02[/C][C]-4.719[/C][/ROW]
[ROW][C]98[/C][C] 10.3[/C][C] 13.24[/C][C]-2.945[/C][/ROW]
[ROW][C]99[/C][C] 9.3[/C][C] 14.44[/C][C]-5.144[/C][/ROW]
[ROW][C]100[/C][C] 12.3[/C][C] 12.47[/C][C]-0.1688[/C][/ROW]
[ROW][C]101[/C][C] 7.9[/C][C] 13.35[/C][C]-5.452[/C][/ROW]
[ROW][C]102[/C][C] 9.3[/C][C] 14.27[/C][C]-4.975[/C][/ROW]
[ROW][C]103[/C][C] 12.5[/C][C] 13.78[/C][C]-1.281[/C][/ROW]
[ROW][C]104[/C][C] 15.9[/C][C] 14.73[/C][C] 1.174[/C][/ROW]
[ROW][C]105[/C][C] 9.1[/C][C] 12.51[/C][C]-3.407[/C][/ROW]
[ROW][C]106[/C][C] 12.2[/C][C] 13.14[/C][C]-0.9372[/C][/ROW]
[ROW][C]107[/C][C] 12.3[/C][C] 14.75[/C][C]-2.454[/C][/ROW]
[ROW][C]108[/C][C] 14.6[/C][C] 13.63[/C][C] 0.9734[/C][/ROW]
[ROW][C]109[/C][C] 12.6[/C][C] 13.7[/C][C]-1.098[/C][/ROW]
[ROW][C]110[/C][C] 12.6[/C][C] 13.14[/C][C]-0.5399[/C][/ROW]
[ROW][C]111[/C][C] 17.1[/C][C] 14.78[/C][C] 2.324[/C][/ROW]
[ROW][C]112[/C][C] 16.1[/C][C] 15.42[/C][C] 0.6752[/C][/ROW]
[ROW][C]113[/C][C] 13.35[/C][C] 12.19[/C][C] 1.156[/C][/ROW]
[ROW][C]114[/C][C] 14.5[/C][C] 12.95[/C][C] 1.546[/C][/ROW]
[ROW][C]115[/C][C] 8.6[/C][C] 11.83[/C][C]-3.235[/C][/ROW]
[ROW][C]116[/C][C] 17.65[/C][C] 13.36[/C][C] 4.285[/C][/ROW]
[ROW][C]117[/C][C] 16.35[/C][C] 13.87[/C][C] 2.477[/C][/ROW]
[ROW][C]118[/C][C] 13.6[/C][C] 13.41[/C][C] 0.1945[/C][/ROW]
[ROW][C]119[/C][C] 14.35[/C][C] 13.26[/C][C] 1.086[/C][/ROW]
[ROW][C]120[/C][C] 18.25[/C][C] 13.98[/C][C] 4.272[/C][/ROW]
[ROW][C]121[/C][C] 18.25[/C][C] 13.83[/C][C] 4.417[/C][/ROW]
[ROW][C]122[/C][C] 18.95[/C][C] 14.49[/C][C] 4.46[/C][/ROW]
[ROW][C]123[/C][C] 15.9[/C][C] 12.68[/C][C] 3.222[/C][/ROW]
[ROW][C]124[/C][C] 13.35[/C][C] 13.49[/C][C]-0.1438[/C][/ROW]
[ROW][C]125[/C][C] 15.35[/C][C] 13.75[/C][C] 1.602[/C][/ROW]
[ROW][C]126[/C][C] 14.85[/C][C] 12.84[/C][C] 2.014[/C][/ROW]
[ROW][C]127[/C][C] 13.6[/C][C] 12.38[/C][C] 1.217[/C][/ROW]
[ROW][C]128[/C][C] 15.25[/C][C] 13.55[/C][C] 1.7[/C][/ROW]
[ROW][C]129[/C][C] 13.2[/C][C] 12.48[/C][C] 0.7191[/C][/ROW]
[ROW][C]130[/C][C] 15.65[/C][C] 13.52[/C][C] 2.127[/C][/ROW]
[ROW][C]131[/C][C] 15.6[/C][C] 12.94[/C][C] 2.657[/C][/ROW]
[ROW][C]132[/C][C] 15.2[/C][C] 12.33[/C][C] 2.868[/C][/ROW]
[ROW][C]133[/C][C] 18.4[/C][C] 13.62[/C][C] 4.781[/C][/ROW]
[ROW][C]134[/C][C] 19.05[/C][C] 13.59[/C][C] 5.457[/C][/ROW]
[ROW][C]135[/C][C] 18.55[/C][C] 13.95[/C][C] 4.597[/C][/ROW]
[ROW][C]136[/C][C] 12.4[/C][C] 12.83[/C][C]-0.4333[/C][/ROW]
[ROW][C]137[/C][C] 14.6[/C][C] 13.38[/C][C] 1.221[/C][/ROW]
[ROW][C]138[/C][C] 14.05[/C][C] 13.38[/C][C] 0.6675[/C][/ROW]
[ROW][C]139[/C][C] 11.85[/C][C] 13.42[/C][C]-1.57[/C][/ROW]
[ROW][C]140[/C][C] 7.85[/C][C] 12.46[/C][C]-4.611[/C][/ROW]
[ROW][C]141[/C][C] 15.2[/C][C] 13.23[/C][C] 1.973[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286394&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286394&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	12.9	13.22	-0.3234
2	12.8	13.04	-0.2436
3	7.4	13.58	-6.177
4	6.7	12.73	-6.028
5	14.8	13.47	1.335
6	13.3	13.18	0.1173
7	11.1	14.09	-2.992
8	8.2	13.35	-5.147
9	11.4	13.35	-1.946
10	6.4	13.49	-7.088
11	11.3	12.21	-0.9103
12	10	12.69	-2.69
13	6.4	12.92	-6.524
14	10.8	14	-3.2
15	13.8	14.02	-0.2222
16	11.7	13.39	-1.691
17	13.4	12.32	1.077
18	11.7	13.14	-1.439
19	9	12.78	-3.775
20	9.7	14	-4.304
21	10.8	12.69	-1.89
22	12.7	12.82	-0.1194
23	11.8	13.43	-1.63
24	5.9	12.48	-6.581
25	11.4	13.28	-1.877
26	13	13.53	-0.5345
27	11.3	13.89	-2.587
28	6.7	11.45	-4.75
29	12.1	12.89	-0.7915
30	13.3	13.02	0.283
31	5.7	12.62	-6.916
32	13.3	12.4	0.896
33	7.6	12.32	-4.716
34	11.1	14.19	-3.087
35	13	14.44	-1.443
36	9.9	12.09	-2.187
37	11.1	12.07	-0.9701
38	4.35	11.45	-7.104
39	12.7	11.77	0.9315
40	18.1	13.06	5.044
41	12.6	11.57	1.032
42	19.1	14.01	5.087
43	18.4	13.18	5.223
44	14.7	11.29	3.409
45	10.6	12.54	-1.941
46	12.6	12.32	0.2755
47	16.2	12.81	3.387
48	18.9	13.83	5.073
49	14.1	12.33	1.771
50	16.15	14.28	1.874
51	14.75	12.7	2.054
52	14.8	12.86	1.938
53	12.45	11.89	0.5639
54	12.65	12.34	0.3113
55	17.35	12.45	4.896
56	18.4	13.36	5.042
57	11.6	11.7	-0.1042
58	17.75	13.02	4.729
59	15.25	13.64	1.609
60	17.65	13.21	4.44
61	14.75	12.69	2.064
62	9.9	13.17	-3.27
63	16	13.07	2.934
64	13.85	12.2	1.652
65	17.1	13.58	3.525
66	14.6	13.23	1.371
67	15.4	13.01	2.39
68	17.6	12.69	4.907
69	13.9	12.69	1.21
70	16.25	12.68	3.568
71	15.65	12.52	3.131
72	14.6	12.01	2.588
73	11.2	11.54	-0.3392
74	16.35	13.44	2.91
75	15.85	12.5	3.345
76	7.65	11.5	-3.849
77	12.35	12.27	0.08213
78	15.6	12.52	3.079
79	13.1	12.12	0.9813
80	12.85	13.2	-0.3526
81	9.5	11.95	-2.455
82	11.85	11.36	0.4859
83	13.6	13.39	0.2113
84	17.6	13.23	4.367
85	16.1	12.69	3.407
86	13.35	12.59	0.765
87	15.15	12.33	2.823
88	12.2	13.56	-1.364
89	12.6	15.12	-2.521
90	10.6	13.3	-2.704
91	12	14.87	-2.865
92	11.9	14.41	-2.509
93	9.6	13.7	-4.097
94	13.8	14.42	-0.6209
95	9.9	13.82	-3.918
96	11.5	13.65	-2.152
97	8.3	13.02	-4.719
98	10.3	13.24	-2.945
99	9.3	14.44	-5.144
100	12.3	12.47	-0.1688
101	7.9	13.35	-5.452
102	9.3	14.27	-4.975
103	12.5	13.78	-1.281
104	15.9	14.73	1.174
105	9.1	12.51	-3.407
106	12.2	13.14	-0.9372
107	12.3	14.75	-2.454
108	14.6	13.63	0.9734
109	12.6	13.7	-1.098
110	12.6	13.14	-0.5399
111	17.1	14.78	2.324
112	16.1	15.42	0.6752
113	13.35	12.19	1.156
114	14.5	12.95	1.546
115	8.6	11.83	-3.235
116	17.65	13.36	4.285
117	16.35	13.87	2.477
118	13.6	13.41	0.1945
119	14.35	13.26	1.086
120	18.25	13.98	4.272
121	18.25	13.83	4.417
122	18.95	14.49	4.46
123	15.9	12.68	3.222
124	13.35	13.49	-0.1438
125	15.35	13.75	1.602
126	14.85	12.84	2.014
127	13.6	12.38	1.217
128	15.25	13.55	1.7
129	13.2	12.48	0.7191
130	15.65	13.52	2.127
131	15.6	12.94	2.657
132	15.2	12.33	2.868
133	18.4	13.62	4.781
134	19.05	13.59	5.457
135	18.55	13.95	4.597
136	12.4	12.83	-0.4333
137	14.6	13.38	1.221
138	14.05	13.38	0.6675
139	11.85	13.42	-1.57
140	7.85	12.46	-4.611
141	15.2	13.23	1.973

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.8632	0.2735	0.1368
8	0.8105	0.379	0.1895
9	0.718	0.564	0.282
10	0.7849	0.4302	0.2151
11	0.6935	0.613	0.3065
12	0.6023	0.7954	0.3977
13	0.6086	0.7828	0.3914
14	0.5768	0.8464	0.4232
15	0.56	0.8801	0.44
16	0.4809	0.9619	0.5191
17	0.4704	0.9409	0.5296
18	0.4132	0.8264	0.5868
19	0.3661	0.7322	0.6339
20	0.3554	0.7108	0.6446
21	0.2918	0.5837	0.7082
22	0.2639	0.5278	0.7361
23	0.2193	0.4385	0.7807
24	0.3431	0.6862	0.6569
25	0.2932	0.5865	0.7068
26	0.2681	0.5363	0.7319
27	0.2343	0.4687	0.7657
28	0.2477	0.4953	0.7523
29	0.2353	0.4707	0.7647
30	0.2234	0.4467	0.7766
31	0.3508	0.7017	0.6492
32	0.3614	0.7227	0.6386
33	0.3863	0.7726	0.6137
34	0.3842	0.7684	0.6158
35	0.3684	0.7368	0.6316
36	0.3321	0.6642	0.6679
37	0.3154	0.6308	0.6846
38	0.499	0.9981	0.501
39	0.5134	0.9732	0.4866
40	0.7749	0.4501	0.2251
41	0.783	0.4339	0.217
42	0.8755	0.249	0.1245
43	0.9467	0.1066	0.05328
44	0.9623	0.07539	0.03769
45	0.958	0.08392	0.04196
46	0.9525	0.0951	0.04755
47	0.9646	0.07078	0.03539
48	0.9755	0.04905	0.02453
49	0.9732	0.05365	0.02683
50	0.9658	0.06831	0.03415
51	0.9623	0.07534	0.03767
52	0.9554	0.08929	0.04464
53	0.9454	0.1092	0.05462
54	0.9312	0.1376	0.06882
55	0.9602	0.07959	0.0398
56	0.9741	0.05173	0.02586
57	0.9674	0.06519	0.03259
58	0.9801	0.03989	0.01995
59	0.9748	0.0504	0.0252
60	0.9797	0.04051	0.02026
61	0.9764	0.04713	0.02357
62	0.9826	0.03476	0.01738
63	0.9801	0.03979	0.01989
64	0.975	0.05009	0.02505
65	0.975	0.04999	0.025
66	0.9681	0.06378	0.03189
67	0.9647	0.07055	0.03527
68	0.9727	0.05451	0.02726
69	0.9652	0.06957	0.03479
70	0.9645	0.07102	0.03551
71	0.9616	0.07672	0.03836
72	0.9562	0.08766	0.04383
73	0.944	0.112	0.05598
74	0.9377	0.1245	0.06225
75	0.9365	0.1269	0.06346
76	0.9483	0.1033	0.05167
77	0.9363	0.1273	0.06365
78	0.9324	0.1353	0.06763
79	0.9153	0.1693	0.08466
80	0.9027	0.1946	0.09732
81	0.9131	0.1737	0.08687
82	0.8951	0.2099	0.1049
83	0.8762	0.2476	0.1238
84	0.8763	0.2475	0.1237
85	0.8643	0.2714	0.1357
86	0.8499	0.3003	0.1501
87	0.8242	0.3517	0.1758
88	0.7977	0.4046	0.2023
89	0.7942	0.4116	0.2058
90	0.782	0.4361	0.218
91	0.7629	0.4743	0.2371
92	0.7439	0.5121	0.2561
93	0.7717	0.4567	0.2283
94	0.7319	0.5362	0.2681
95	0.7724	0.4551	0.2276
96	0.7828	0.4344	0.2172
97	0.8219	0.3562	0.1781
98	0.8096	0.3808	0.1904
99	0.8945	0.2109	0.1055
100	0.8716	0.2568	0.1284
101	0.9187	0.1626	0.0813
102	0.9764	0.04713	0.02356
103	0.9828	0.03439	0.01719
104	0.9831	0.0337	0.01685
105	0.9856	0.02882	0.01441
106	0.9829	0.03413	0.01707
107	0.994	0.01202	0.006008
108	0.9914	0.01725	0.008625
109	0.9936	0.01276	0.006379
110	0.9925	0.0151	0.00755
111	0.9917	0.01659	0.008294
112	0.9906	0.01883	0.009417
113	0.988	0.02394	0.01197
114	0.9829	0.0342	0.0171
115	0.9794	0.04111	0.02055
116	0.9809	0.0382	0.0191
117	0.9731	0.05371	0.02686
118	0.9669	0.06624	0.03312
119	0.9523	0.0955	0.04775
120	0.9395	0.121	0.06048
121	0.9278	0.1444	0.07221
122	0.9062	0.1876	0.0938
123	0.9025	0.1951	0.09755
124	0.8874	0.2251	0.1126
125	0.8509	0.2982	0.1491
126	0.8037	0.3927	0.1963
127	0.7547	0.4905	0.2453
128	0.6835	0.6329	0.3165
129	0.598	0.8039	0.402
130	0.4906	0.9813	0.5094
131	0.4421	0.8842	0.5579
132	0.8252	0.3496	0.1748
133	0.7136	0.5728	0.2864
134	0.8905	0.219	0.1095

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.8632 &  0.2735 &  0.1368 \tabularnewline
8 &  0.8105 &  0.379 &  0.1895 \tabularnewline
9 &  0.718 &  0.564 &  0.282 \tabularnewline
10 &  0.7849 &  0.4302 &  0.2151 \tabularnewline
11 &  0.6935 &  0.613 &  0.3065 \tabularnewline
12 &  0.6023 &  0.7954 &  0.3977 \tabularnewline
13 &  0.6086 &  0.7828 &  0.3914 \tabularnewline
14 &  0.5768 &  0.8464 &  0.4232 \tabularnewline
15 &  0.56 &  0.8801 &  0.44 \tabularnewline
16 &  0.4809 &  0.9619 &  0.5191 \tabularnewline
17 &  0.4704 &  0.9409 &  0.5296 \tabularnewline
18 &  0.4132 &  0.8264 &  0.5868 \tabularnewline
19 &  0.3661 &  0.7322 &  0.6339 \tabularnewline
20 &  0.3554 &  0.7108 &  0.6446 \tabularnewline
21 &  0.2918 &  0.5837 &  0.7082 \tabularnewline
22 &  0.2639 &  0.5278 &  0.7361 \tabularnewline
23 &  0.2193 &  0.4385 &  0.7807 \tabularnewline
24 &  0.3431 &  0.6862 &  0.6569 \tabularnewline
25 &  0.2932 &  0.5865 &  0.7068 \tabularnewline
26 &  0.2681 &  0.5363 &  0.7319 \tabularnewline
27 &  0.2343 &  0.4687 &  0.7657 \tabularnewline
28 &  0.2477 &  0.4953 &  0.7523 \tabularnewline
29 &  0.2353 &  0.4707 &  0.7647 \tabularnewline
30 &  0.2234 &  0.4467 &  0.7766 \tabularnewline
31 &  0.3508 &  0.7017 &  0.6492 \tabularnewline
32 &  0.3614 &  0.7227 &  0.6386 \tabularnewline
33 &  0.3863 &  0.7726 &  0.6137 \tabularnewline
34 &  0.3842 &  0.7684 &  0.6158 \tabularnewline
35 &  0.3684 &  0.7368 &  0.6316 \tabularnewline
36 &  0.3321 &  0.6642 &  0.6679 \tabularnewline
37 &  0.3154 &  0.6308 &  0.6846 \tabularnewline
38 &  0.499 &  0.9981 &  0.501 \tabularnewline
39 &  0.5134 &  0.9732 &  0.4866 \tabularnewline
40 &  0.7749 &  0.4501 &  0.2251 \tabularnewline
41 &  0.783 &  0.4339 &  0.217 \tabularnewline
42 &  0.8755 &  0.249 &  0.1245 \tabularnewline
43 &  0.9467 &  0.1066 &  0.05328 \tabularnewline
44 &  0.9623 &  0.07539 &  0.03769 \tabularnewline
45 &  0.958 &  0.08392 &  0.04196 \tabularnewline
46 &  0.9525 &  0.0951 &  0.04755 \tabularnewline
47 &  0.9646 &  0.07078 &  0.03539 \tabularnewline
48 &  0.9755 &  0.04905 &  0.02453 \tabularnewline
49 &  0.9732 &  0.05365 &  0.02683 \tabularnewline
50 &  0.9658 &  0.06831 &  0.03415 \tabularnewline
51 &  0.9623 &  0.07534 &  0.03767 \tabularnewline
52 &  0.9554 &  0.08929 &  0.04464 \tabularnewline
53 &  0.9454 &  0.1092 &  0.05462 \tabularnewline
54 &  0.9312 &  0.1376 &  0.06882 \tabularnewline
55 &  0.9602 &  0.07959 &  0.0398 \tabularnewline
56 &  0.9741 &  0.05173 &  0.02586 \tabularnewline
57 &  0.9674 &  0.06519 &  0.03259 \tabularnewline
58 &  0.9801 &  0.03989 &  0.01995 \tabularnewline
59 &  0.9748 &  0.0504 &  0.0252 \tabularnewline
60 &  0.9797 &  0.04051 &  0.02026 \tabularnewline
61 &  0.9764 &  0.04713 &  0.02357 \tabularnewline
62 &  0.9826 &  0.03476 &  0.01738 \tabularnewline
63 &  0.9801 &  0.03979 &  0.01989 \tabularnewline
64 &  0.975 &  0.05009 &  0.02505 \tabularnewline
65 &  0.975 &  0.04999 &  0.025 \tabularnewline
66 &  0.9681 &  0.06378 &  0.03189 \tabularnewline
67 &  0.9647 &  0.07055 &  0.03527 \tabularnewline
68 &  0.9727 &  0.05451 &  0.02726 \tabularnewline
69 &  0.9652 &  0.06957 &  0.03479 \tabularnewline
70 &  0.9645 &  0.07102 &  0.03551 \tabularnewline
71 &  0.9616 &  0.07672 &  0.03836 \tabularnewline
72 &  0.9562 &  0.08766 &  0.04383 \tabularnewline
73 &  0.944 &  0.112 &  0.05598 \tabularnewline
74 &  0.9377 &  0.1245 &  0.06225 \tabularnewline
75 &  0.9365 &  0.1269 &  0.06346 \tabularnewline
76 &  0.9483 &  0.1033 &  0.05167 \tabularnewline
77 &  0.9363 &  0.1273 &  0.06365 \tabularnewline
78 &  0.9324 &  0.1353 &  0.06763 \tabularnewline
79 &  0.9153 &  0.1693 &  0.08466 \tabularnewline
80 &  0.9027 &  0.1946 &  0.09732 \tabularnewline
81 &  0.9131 &  0.1737 &  0.08687 \tabularnewline
82 &  0.8951 &  0.2099 &  0.1049 \tabularnewline
83 &  0.8762 &  0.2476 &  0.1238 \tabularnewline
84 &  0.8763 &  0.2475 &  0.1237 \tabularnewline
85 &  0.8643 &  0.2714 &  0.1357 \tabularnewline
86 &  0.8499 &  0.3003 &  0.1501 \tabularnewline
87 &  0.8242 &  0.3517 &  0.1758 \tabularnewline
88 &  0.7977 &  0.4046 &  0.2023 \tabularnewline
89 &  0.7942 &  0.4116 &  0.2058 \tabularnewline
90 &  0.782 &  0.4361 &  0.218 \tabularnewline
91 &  0.7629 &  0.4743 &  0.2371 \tabularnewline
92 &  0.7439 &  0.5121 &  0.2561 \tabularnewline
93 &  0.7717 &  0.4567 &  0.2283 \tabularnewline
94 &  0.7319 &  0.5362 &  0.2681 \tabularnewline
95 &  0.7724 &  0.4551 &  0.2276 \tabularnewline
96 &  0.7828 &  0.4344 &  0.2172 \tabularnewline
97 &  0.8219 &  0.3562 &  0.1781 \tabularnewline
98 &  0.8096 &  0.3808 &  0.1904 \tabularnewline
99 &  0.8945 &  0.2109 &  0.1055 \tabularnewline
100 &  0.8716 &  0.2568 &  0.1284 \tabularnewline
101 &  0.9187 &  0.1626 &  0.0813 \tabularnewline
102 &  0.9764 &  0.04713 &  0.02356 \tabularnewline
103 &  0.9828 &  0.03439 &  0.01719 \tabularnewline
104 &  0.9831 &  0.0337 &  0.01685 \tabularnewline
105 &  0.9856 &  0.02882 &  0.01441 \tabularnewline
106 &  0.9829 &  0.03413 &  0.01707 \tabularnewline
107 &  0.994 &  0.01202 &  0.006008 \tabularnewline
108 &  0.9914 &  0.01725 &  0.008625 \tabularnewline
109 &  0.9936 &  0.01276 &  0.006379 \tabularnewline
110 &  0.9925 &  0.0151 &  0.00755 \tabularnewline
111 &  0.9917 &  0.01659 &  0.008294 \tabularnewline
112 &  0.9906 &  0.01883 &  0.009417 \tabularnewline
113 &  0.988 &  0.02394 &  0.01197 \tabularnewline
114 &  0.9829 &  0.0342 &  0.0171 \tabularnewline
115 &  0.9794 &  0.04111 &  0.02055 \tabularnewline
116 &  0.9809 &  0.0382 &  0.0191 \tabularnewline
117 &  0.9731 &  0.05371 &  0.02686 \tabularnewline
118 &  0.9669 &  0.06624 &  0.03312 \tabularnewline
119 &  0.9523 &  0.0955 &  0.04775 \tabularnewline
120 &  0.9395 &  0.121 &  0.06048 \tabularnewline
121 &  0.9278 &  0.1444 &  0.07221 \tabularnewline
122 &  0.9062 &  0.1876 &  0.0938 \tabularnewline
123 &  0.9025 &  0.1951 &  0.09755 \tabularnewline
124 &  0.8874 &  0.2251 &  0.1126 \tabularnewline
125 &  0.8509 &  0.2982 &  0.1491 \tabularnewline
126 &  0.8037 &  0.3927 &  0.1963 \tabularnewline
127 &  0.7547 &  0.4905 &  0.2453 \tabularnewline
128 &  0.6835 &  0.6329 &  0.3165 \tabularnewline
129 &  0.598 &  0.8039 &  0.402 \tabularnewline
130 &  0.4906 &  0.9813 &  0.5094 \tabularnewline
131 &  0.4421 &  0.8842 &  0.5579 \tabularnewline
132 &  0.8252 &  0.3496 &  0.1748 \tabularnewline
133 &  0.7136 &  0.5728 &  0.2864 \tabularnewline
134 &  0.8905 &  0.219 &  0.1095 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286394&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] 0.8632[/C][C] 0.2735[/C][C] 0.1368[/C][/ROW]
[ROW][C]8[/C][C] 0.8105[/C][C] 0.379[/C][C] 0.1895[/C][/ROW]
[ROW][C]9[/C][C] 0.718[/C][C] 0.564[/C][C] 0.282[/C][/ROW]
[ROW][C]10[/C][C] 0.7849[/C][C] 0.4302[/C][C] 0.2151[/C][/ROW]
[ROW][C]11[/C][C] 0.6935[/C][C] 0.613[/C][C] 0.3065[/C][/ROW]
[ROW][C]12[/C][C] 0.6023[/C][C] 0.7954[/C][C] 0.3977[/C][/ROW]
[ROW][C]13[/C][C] 0.6086[/C][C] 0.7828[/C][C] 0.3914[/C][/ROW]
[ROW][C]14[/C][C] 0.5768[/C][C] 0.8464[/C][C] 0.4232[/C][/ROW]
[ROW][C]15[/C][C] 0.56[/C][C] 0.8801[/C][C] 0.44[/C][/ROW]
[ROW][C]16[/C][C] 0.4809[/C][C] 0.9619[/C][C] 0.5191[/C][/ROW]
[ROW][C]17[/C][C] 0.4704[/C][C] 0.9409[/C][C] 0.5296[/C][/ROW]
[ROW][C]18[/C][C] 0.4132[/C][C] 0.8264[/C][C] 0.5868[/C][/ROW]
[ROW][C]19[/C][C] 0.3661[/C][C] 0.7322[/C][C] 0.6339[/C][/ROW]
[ROW][C]20[/C][C] 0.3554[/C][C] 0.7108[/C][C] 0.6446[/C][/ROW]
[ROW][C]21[/C][C] 0.2918[/C][C] 0.5837[/C][C] 0.7082[/C][/ROW]
[ROW][C]22[/C][C] 0.2639[/C][C] 0.5278[/C][C] 0.7361[/C][/ROW]
[ROW][C]23[/C][C] 0.2193[/C][C] 0.4385[/C][C] 0.7807[/C][/ROW]
[ROW][C]24[/C][C] 0.3431[/C][C] 0.6862[/C][C] 0.6569[/C][/ROW]
[ROW][C]25[/C][C] 0.2932[/C][C] 0.5865[/C][C] 0.7068[/C][/ROW]
[ROW][C]26[/C][C] 0.2681[/C][C] 0.5363[/C][C] 0.7319[/C][/ROW]
[ROW][C]27[/C][C] 0.2343[/C][C] 0.4687[/C][C] 0.7657[/C][/ROW]
[ROW][C]28[/C][C] 0.2477[/C][C] 0.4953[/C][C] 0.7523[/C][/ROW]
[ROW][C]29[/C][C] 0.2353[/C][C] 0.4707[/C][C] 0.7647[/C][/ROW]
[ROW][C]30[/C][C] 0.2234[/C][C] 0.4467[/C][C] 0.7766[/C][/ROW]
[ROW][C]31[/C][C] 0.3508[/C][C] 0.7017[/C][C] 0.6492[/C][/ROW]
[ROW][C]32[/C][C] 0.3614[/C][C] 0.7227[/C][C] 0.6386[/C][/ROW]
[ROW][C]33[/C][C] 0.3863[/C][C] 0.7726[/C][C] 0.6137[/C][/ROW]
[ROW][C]34[/C][C] 0.3842[/C][C] 0.7684[/C][C] 0.6158[/C][/ROW]
[ROW][C]35[/C][C] 0.3684[/C][C] 0.7368[/C][C] 0.6316[/C][/ROW]
[ROW][C]36[/C][C] 0.3321[/C][C] 0.6642[/C][C] 0.6679[/C][/ROW]
[ROW][C]37[/C][C] 0.3154[/C][C] 0.6308[/C][C] 0.6846[/C][/ROW]
[ROW][C]38[/C][C] 0.499[/C][C] 0.9981[/C][C] 0.501[/C][/ROW]
[ROW][C]39[/C][C] 0.5134[/C][C] 0.9732[/C][C] 0.4866[/C][/ROW]
[ROW][C]40[/C][C] 0.7749[/C][C] 0.4501[/C][C] 0.2251[/C][/ROW]
[ROW][C]41[/C][C] 0.783[/C][C] 0.4339[/C][C] 0.217[/C][/ROW]
[ROW][C]42[/C][C] 0.8755[/C][C] 0.249[/C][C] 0.1245[/C][/ROW]
[ROW][C]43[/C][C] 0.9467[/C][C] 0.1066[/C][C] 0.05328[/C][/ROW]
[ROW][C]44[/C][C] 0.9623[/C][C] 0.07539[/C][C] 0.03769[/C][/ROW]
[ROW][C]45[/C][C] 0.958[/C][C] 0.08392[/C][C] 0.04196[/C][/ROW]
[ROW][C]46[/C][C] 0.9525[/C][C] 0.0951[/C][C] 0.04755[/C][/ROW]
[ROW][C]47[/C][C] 0.9646[/C][C] 0.07078[/C][C] 0.03539[/C][/ROW]
[ROW][C]48[/C][C] 0.9755[/C][C] 0.04905[/C][C] 0.02453[/C][/ROW]
[ROW][C]49[/C][C] 0.9732[/C][C] 0.05365[/C][C] 0.02683[/C][/ROW]
[ROW][C]50[/C][C] 0.9658[/C][C] 0.06831[/C][C] 0.03415[/C][/ROW]
[ROW][C]51[/C][C] 0.9623[/C][C] 0.07534[/C][C] 0.03767[/C][/ROW]
[ROW][C]52[/C][C] 0.9554[/C][C] 0.08929[/C][C] 0.04464[/C][/ROW]
[ROW][C]53[/C][C] 0.9454[/C][C] 0.1092[/C][C] 0.05462[/C][/ROW]
[ROW][C]54[/C][C] 0.9312[/C][C] 0.1376[/C][C] 0.06882[/C][/ROW]
[ROW][C]55[/C][C] 0.9602[/C][C] 0.07959[/C][C] 0.0398[/C][/ROW]
[ROW][C]56[/C][C] 0.9741[/C][C] 0.05173[/C][C] 0.02586[/C][/ROW]
[ROW][C]57[/C][C] 0.9674[/C][C] 0.06519[/C][C] 0.03259[/C][/ROW]
[ROW][C]58[/C][C] 0.9801[/C][C] 0.03989[/C][C] 0.01995[/C][/ROW]
[ROW][C]59[/C][C] 0.9748[/C][C] 0.0504[/C][C] 0.0252[/C][/ROW]
[ROW][C]60[/C][C] 0.9797[/C][C] 0.04051[/C][C] 0.02026[/C][/ROW]
[ROW][C]61[/C][C] 0.9764[/C][C] 0.04713[/C][C] 0.02357[/C][/ROW]
[ROW][C]62[/C][C] 0.9826[/C][C] 0.03476[/C][C] 0.01738[/C][/ROW]
[ROW][C]63[/C][C] 0.9801[/C][C] 0.03979[/C][C] 0.01989[/C][/ROW]
[ROW][C]64[/C][C] 0.975[/C][C] 0.05009[/C][C] 0.02505[/C][/ROW]
[ROW][C]65[/C][C] 0.975[/C][C] 0.04999[/C][C] 0.025[/C][/ROW]
[ROW][C]66[/C][C] 0.9681[/C][C] 0.06378[/C][C] 0.03189[/C][/ROW]
[ROW][C]67[/C][C] 0.9647[/C][C] 0.07055[/C][C] 0.03527[/C][/ROW]
[ROW][C]68[/C][C] 0.9727[/C][C] 0.05451[/C][C] 0.02726[/C][/ROW]
[ROW][C]69[/C][C] 0.9652[/C][C] 0.06957[/C][C] 0.03479[/C][/ROW]
[ROW][C]70[/C][C] 0.9645[/C][C] 0.07102[/C][C] 0.03551[/C][/ROW]
[ROW][C]71[/C][C] 0.9616[/C][C] 0.07672[/C][C] 0.03836[/C][/ROW]
[ROW][C]72[/C][C] 0.9562[/C][C] 0.08766[/C][C] 0.04383[/C][/ROW]
[ROW][C]73[/C][C] 0.944[/C][C] 0.112[/C][C] 0.05598[/C][/ROW]
[ROW][C]74[/C][C] 0.9377[/C][C] 0.1245[/C][C] 0.06225[/C][/ROW]
[ROW][C]75[/C][C] 0.9365[/C][C] 0.1269[/C][C] 0.06346[/C][/ROW]
[ROW][C]76[/C][C] 0.9483[/C][C] 0.1033[/C][C] 0.05167[/C][/ROW]
[ROW][C]77[/C][C] 0.9363[/C][C] 0.1273[/C][C] 0.06365[/C][/ROW]
[ROW][C]78[/C][C] 0.9324[/C][C] 0.1353[/C][C] 0.06763[/C][/ROW]
[ROW][C]79[/C][C] 0.9153[/C][C] 0.1693[/C][C] 0.08466[/C][/ROW]
[ROW][C]80[/C][C] 0.9027[/C][C] 0.1946[/C][C] 0.09732[/C][/ROW]
[ROW][C]81[/C][C] 0.9131[/C][C] 0.1737[/C][C] 0.08687[/C][/ROW]
[ROW][C]82[/C][C] 0.8951[/C][C] 0.2099[/C][C] 0.1049[/C][/ROW]
[ROW][C]83[/C][C] 0.8762[/C][C] 0.2476[/C][C] 0.1238[/C][/ROW]
[ROW][C]84[/C][C] 0.8763[/C][C] 0.2475[/C][C] 0.1237[/C][/ROW]
[ROW][C]85[/C][C] 0.8643[/C][C] 0.2714[/C][C] 0.1357[/C][/ROW]
[ROW][C]86[/C][C] 0.8499[/C][C] 0.3003[/C][C] 0.1501[/C][/ROW]
[ROW][C]87[/C][C] 0.8242[/C][C] 0.3517[/C][C] 0.1758[/C][/ROW]
[ROW][C]88[/C][C] 0.7977[/C][C] 0.4046[/C][C] 0.2023[/C][/ROW]
[ROW][C]89[/C][C] 0.7942[/C][C] 0.4116[/C][C] 0.2058[/C][/ROW]
[ROW][C]90[/C][C] 0.782[/C][C] 0.4361[/C][C] 0.218[/C][/ROW]
[ROW][C]91[/C][C] 0.7629[/C][C] 0.4743[/C][C] 0.2371[/C][/ROW]
[ROW][C]92[/C][C] 0.7439[/C][C] 0.5121[/C][C] 0.2561[/C][/ROW]
[ROW][C]93[/C][C] 0.7717[/C][C] 0.4567[/C][C] 0.2283[/C][/ROW]
[ROW][C]94[/C][C] 0.7319[/C][C] 0.5362[/C][C] 0.2681[/C][/ROW]
[ROW][C]95[/C][C] 0.7724[/C][C] 0.4551[/C][C] 0.2276[/C][/ROW]
[ROW][C]96[/C][C] 0.7828[/C][C] 0.4344[/C][C] 0.2172[/C][/ROW]
[ROW][C]97[/C][C] 0.8219[/C][C] 0.3562[/C][C] 0.1781[/C][/ROW]
[ROW][C]98[/C][C] 0.8096[/C][C] 0.3808[/C][C] 0.1904[/C][/ROW]
[ROW][C]99[/C][C] 0.8945[/C][C] 0.2109[/C][C] 0.1055[/C][/ROW]
[ROW][C]100[/C][C] 0.8716[/C][C] 0.2568[/C][C] 0.1284[/C][/ROW]
[ROW][C]101[/C][C] 0.9187[/C][C] 0.1626[/C][C] 0.0813[/C][/ROW]
[ROW][C]102[/C][C] 0.9764[/C][C] 0.04713[/C][C] 0.02356[/C][/ROW]
[ROW][C]103[/C][C] 0.9828[/C][C] 0.03439[/C][C] 0.01719[/C][/ROW]
[ROW][C]104[/C][C] 0.9831[/C][C] 0.0337[/C][C] 0.01685[/C][/ROW]
[ROW][C]105[/C][C] 0.9856[/C][C] 0.02882[/C][C] 0.01441[/C][/ROW]
[ROW][C]106[/C][C] 0.9829[/C][C] 0.03413[/C][C] 0.01707[/C][/ROW]
[ROW][C]107[/C][C] 0.994[/C][C] 0.01202[/C][C] 0.006008[/C][/ROW]
[ROW][C]108[/C][C] 0.9914[/C][C] 0.01725[/C][C] 0.008625[/C][/ROW]
[ROW][C]109[/C][C] 0.9936[/C][C] 0.01276[/C][C] 0.006379[/C][/ROW]
[ROW][C]110[/C][C] 0.9925[/C][C] 0.0151[/C][C] 0.00755[/C][/ROW]
[ROW][C]111[/C][C] 0.9917[/C][C] 0.01659[/C][C] 0.008294[/C][/ROW]
[ROW][C]112[/C][C] 0.9906[/C][C] 0.01883[/C][C] 0.009417[/C][/ROW]
[ROW][C]113[/C][C] 0.988[/C][C] 0.02394[/C][C] 0.01197[/C][/ROW]
[ROW][C]114[/C][C] 0.9829[/C][C] 0.0342[/C][C] 0.0171[/C][/ROW]
[ROW][C]115[/C][C] 0.9794[/C][C] 0.04111[/C][C] 0.02055[/C][/ROW]
[ROW][C]116[/C][C] 0.9809[/C][C] 0.0382[/C][C] 0.0191[/C][/ROW]
[ROW][C]117[/C][C] 0.9731[/C][C] 0.05371[/C][C] 0.02686[/C][/ROW]
[ROW][C]118[/C][C] 0.9669[/C][C] 0.06624[/C][C] 0.03312[/C][/ROW]
[ROW][C]119[/C][C] 0.9523[/C][C] 0.0955[/C][C] 0.04775[/C][/ROW]
[ROW][C]120[/C][C] 0.9395[/C][C] 0.121[/C][C] 0.06048[/C][/ROW]
[ROW][C]121[/C][C] 0.9278[/C][C] 0.1444[/C][C] 0.07221[/C][/ROW]
[ROW][C]122[/C][C] 0.9062[/C][C] 0.1876[/C][C] 0.0938[/C][/ROW]
[ROW][C]123[/C][C] 0.9025[/C][C] 0.1951[/C][C] 0.09755[/C][/ROW]
[ROW][C]124[/C][C] 0.8874[/C][C] 0.2251[/C][C] 0.1126[/C][/ROW]
[ROW][C]125[/C][C] 0.8509[/C][C] 0.2982[/C][C] 0.1491[/C][/ROW]
[ROW][C]126[/C][C] 0.8037[/C][C] 0.3927[/C][C] 0.1963[/C][/ROW]
[ROW][C]127[/C][C] 0.7547[/C][C] 0.4905[/C][C] 0.2453[/C][/ROW]
[ROW][C]128[/C][C] 0.6835[/C][C] 0.6329[/C][C] 0.3165[/C][/ROW]
[ROW][C]129[/C][C] 0.598[/C][C] 0.8039[/C][C] 0.402[/C][/ROW]
[ROW][C]130[/C][C] 0.4906[/C][C] 0.9813[/C][C] 0.5094[/C][/ROW]
[ROW][C]131[/C][C] 0.4421[/C][C] 0.8842[/C][C] 0.5579[/C][/ROW]
[ROW][C]132[/C][C] 0.8252[/C][C] 0.3496[/C][C] 0.1748[/C][/ROW]
[ROW][C]133[/C][C] 0.7136[/C][C] 0.5728[/C][C] 0.2864[/C][/ROW]
[ROW][C]134[/C][C] 0.8905[/C][C] 0.219[/C][C] 0.1095[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286394&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286394&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	0.8632	0.2735	0.1368
8	0.8105	0.379	0.1895
9	0.718	0.564	0.282
10	0.7849	0.4302	0.2151
11	0.6935	0.613	0.3065
12	0.6023	0.7954	0.3977
13	0.6086	0.7828	0.3914
14	0.5768	0.8464	0.4232
15	0.56	0.8801	0.44
16	0.4809	0.9619	0.5191
17	0.4704	0.9409	0.5296
18	0.4132	0.8264	0.5868
19	0.3661	0.7322	0.6339
20	0.3554	0.7108	0.6446
21	0.2918	0.5837	0.7082
22	0.2639	0.5278	0.7361
23	0.2193	0.4385	0.7807
24	0.3431	0.6862	0.6569
25	0.2932	0.5865	0.7068
26	0.2681	0.5363	0.7319
27	0.2343	0.4687	0.7657
28	0.2477	0.4953	0.7523
29	0.2353	0.4707	0.7647
30	0.2234	0.4467	0.7766
31	0.3508	0.7017	0.6492
32	0.3614	0.7227	0.6386
33	0.3863	0.7726	0.6137
34	0.3842	0.7684	0.6158
35	0.3684	0.7368	0.6316
36	0.3321	0.6642	0.6679
37	0.3154	0.6308	0.6846
38	0.499	0.9981	0.501
39	0.5134	0.9732	0.4866
40	0.7749	0.4501	0.2251
41	0.783	0.4339	0.217
42	0.8755	0.249	0.1245
43	0.9467	0.1066	0.05328
44	0.9623	0.07539	0.03769
45	0.958	0.08392	0.04196
46	0.9525	0.0951	0.04755
47	0.9646	0.07078	0.03539
48	0.9755	0.04905	0.02453
49	0.9732	0.05365	0.02683
50	0.9658	0.06831	0.03415
51	0.9623	0.07534	0.03767
52	0.9554	0.08929	0.04464
53	0.9454	0.1092	0.05462
54	0.9312	0.1376	0.06882
55	0.9602	0.07959	0.0398
56	0.9741	0.05173	0.02586
57	0.9674	0.06519	0.03259
58	0.9801	0.03989	0.01995
59	0.9748	0.0504	0.0252
60	0.9797	0.04051	0.02026
61	0.9764	0.04713	0.02357
62	0.9826	0.03476	0.01738
63	0.9801	0.03979	0.01989
64	0.975	0.05009	0.02505
65	0.975	0.04999	0.025
66	0.9681	0.06378	0.03189
67	0.9647	0.07055	0.03527
68	0.9727	0.05451	0.02726
69	0.9652	0.06957	0.03479
70	0.9645	0.07102	0.03551
71	0.9616	0.07672	0.03836
72	0.9562	0.08766	0.04383
73	0.944	0.112	0.05598
74	0.9377	0.1245	0.06225
75	0.9365	0.1269	0.06346
76	0.9483	0.1033	0.05167
77	0.9363	0.1273	0.06365
78	0.9324	0.1353	0.06763
79	0.9153	0.1693	0.08466
80	0.9027	0.1946	0.09732
81	0.9131	0.1737	0.08687
82	0.8951	0.2099	0.1049
83	0.8762	0.2476	0.1238
84	0.8763	0.2475	0.1237
85	0.8643	0.2714	0.1357
86	0.8499	0.3003	0.1501
87	0.8242	0.3517	0.1758
88	0.7977	0.4046	0.2023
89	0.7942	0.4116	0.2058
90	0.782	0.4361	0.218
91	0.7629	0.4743	0.2371
92	0.7439	0.5121	0.2561
93	0.7717	0.4567	0.2283
94	0.7319	0.5362	0.2681
95	0.7724	0.4551	0.2276
96	0.7828	0.4344	0.2172
97	0.8219	0.3562	0.1781
98	0.8096	0.3808	0.1904
99	0.8945	0.2109	0.1055
100	0.8716	0.2568	0.1284
101	0.9187	0.1626	0.0813
102	0.9764	0.04713	0.02356
103	0.9828	0.03439	0.01719
104	0.9831	0.0337	0.01685
105	0.9856	0.02882	0.01441
106	0.9829	0.03413	0.01707
107	0.994	0.01202	0.006008
108	0.9914	0.01725	0.008625
109	0.9936	0.01276	0.006379
110	0.9925	0.0151	0.00755
111	0.9917	0.01659	0.008294
112	0.9906	0.01883	0.009417
113	0.988	0.02394	0.01197
114	0.9829	0.0342	0.0171
115	0.9794	0.04111	0.02055
116	0.9809	0.0382	0.0191
117	0.9731	0.05371	0.02686
118	0.9669	0.06624	0.03312
119	0.9523	0.0955	0.04775
120	0.9395	0.121	0.06048
121	0.9278	0.1444	0.07221
122	0.9062	0.1876	0.0938
123	0.9025	0.1951	0.09755
124	0.8874	0.2251	0.1126
125	0.8509	0.2982	0.1491
126	0.8037	0.3927	0.1963
127	0.7547	0.4905	0.2453
128	0.6835	0.6329	0.3165
129	0.598	0.8039	0.402
130	0.4906	0.9813	0.5094
131	0.4421	0.8842	0.5579
132	0.8252	0.3496	0.1748
133	0.7136	0.5728	0.2864
134	0.8905	0.219	0.1095

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	22	0.171875	NOK
10% type I error level	45	0.351562	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 22 & 0.171875 & NOK \tabularnewline
10% type I error level & 45 & 0.351562 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286394&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]22[/C][C]0.171875[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]45[/C][C]0.351562[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286394&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286394&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	22	0.171875	NOK
10% type I error level	45	0.351562	NOK

Figure 1

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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

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;

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

par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
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'
}
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code