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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 17 Dec 2014 15:56:54 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/17/t14188319319ah40onq7nid9n2.htm/, Retrieved Thu, 16 May 2024 14:37:03 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270458, Retrieved Thu, 16 May 2024 14:37:03 +0000

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

-     [Multiple Regression] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
-  MPD  [Multiple Regression] [] [2014-12-17 14:47:20] [23dea497e8c7f4d7527c256c4e83e065]
-   PD      [Multiple Regression] [] [2014-12-17 15:56:54] [d043def4c969c6fe6dac6c6c71a7875a] [Current]

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Dataseries X:

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Histogram

Boxplots

47 19 19.25
33 56 11.6
22 60 15.15
50 66 10.95
47 63 15.2
55 56 12.6
41 65 13.2
44 53 9.95
47 58 19.9
45 65 8.1
35 62 12.9
50 47 14.85
41 60 14.05
52 49 10.95
64 78 7.65
38 48 12.65
61 68 11.35
48 60 14.5
63 74 13.6
62 64 14.9
58 62 16.1
49 53 12.4
51 62 18.1
51 57 18.25
63 66 12.15
60 65 17.35
53 72 12.6
46 69 7.6
50 58 13.4
48 62 14.1
51 48 19.9
46 67 18.1
56 73 11.85
57 71 16.65
48 72 15.6
69 73 15.25
60 67 16.1
56 60 15.4
47 55 13.35
56 58 15.4
61 51 16.1
37 64 16.2
66 81 7.7
46 69 11.15
62 69 13.15
50 67 14.75
37 60 15.85
67 75 15.4
51 63 14.1
64 66 18.2
60 68 16.15
47 59 11.2
44 47 18.4
41 48 17.65
52 73 18.45
60 79 9.9
50 74 16.6
53 74 17.6
52 76 17.65
59 71 18.4
53 72 12.6
51 70 19.3
54 66 11.2
56 69 14.6
46 63 18.45
58 61 4.5
52 62 19.1
50 57 13.4
52 51 4.35
51 79 12.75
55 71 15.6
56 73 11.85
62 70 10.95
57 71 15.25
56 66 11.9
64 66 18.55
51 69 11.95
54 67 15.1
62 71 15.6
55 70 15.1
56 69 17.85
51 67 19.05
53 65 16.65
65 69 12.4
55 68 12.6
63 68 13.35
63 68 16.1
56 60 18.25
53 59 12.35
64 57 14.85
66 59 13.85
44 55 14.6
42 55 7.85
56 60 16
39 58 13.9
73 61 18.95
50 84 11.4
57 78 14.6
58 72 15.25
33 61 12.45
51 68 19.1
66 62 14.6
16 56 12.7
55 78 13.2
58 78 17.75
56 75 16.35
62 74 18.4
50 73 12.85
59 74 15.35
47 70 17.75
67 80 13.1
76 80 15.7
80 75 15.95
67 76 14.7
44 70 15.65
52 65 13.35
50 72 14.75
43 69 14.6
58 69 15.9
25 62 19.1
47 64 14.9
47 75 12.2
56 74 7.85
54 74 12.35
45 73 19.2
54 71 8.6
40 66 11.75
55 71 9.85
59 66 16.85
46 62 10.35
57 64 14.9
52 64 10.6
47 63 15.35
59 58 9.6
60 74 11.9
48 72 14.75
63 70 14.8
55 68 16.35
66 67 16.85
60 65 15.2
46 62 17.35
59 70 18.15
58 68 13.6
54 65 13.6
66 61 15
29 59 16.85
43 58 17.1
50 55 17.1
71 69 13.35
58 69 17.75
72 71 18.9
38 55 13.6
36 51 13.95
51 65 15.65
52 60 14.35
50 59 14.75
51 59 11.7
43 50 14.35
59 63 19.1
52 57 16.6
54 67 9.5
66 64 16.25
43 57 17.6
59 63 17.1
61 59 16.1
54 60 17.75
73 52 13.6
55 59 15.6
67 61 12.65
63 50 13.6
31 32 11.7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270458&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270458&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270458&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	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 15.2125 + 0.0241027AMS.I[t] -0.0306913AMS.E[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  15.2125 +  0.0241027AMS.I[t] -0.0306913AMS.E[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270458&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  15.2125 +  0.0241027AMS.I[t] -0.0306913AMS.E[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270458&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270458&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] = + 15.2125 + 0.0241027AMS.I[t] -0.0306913AMS.E[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)	15.2125	1.82565	8.333	2.69259e-14	1.34629e-14
AMS.I	0.0241027	0.0254483	0.9471	0.344935	0.172467
AMS.E	-0.0306913	0.0284954	-1.077	0.282997	0.141499

\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) & 15.2125 & 1.82565 & 8.333 & 2.69259e-14 & 1.34629e-14 \tabularnewline
AMS.I & 0.0241027 & 0.0254483 & 0.9471 & 0.344935 & 0.172467 \tabularnewline
AMS.E & -0.0306913 & 0.0284954 & -1.077 & 0.282997 & 0.141499 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270458&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]15.2125[/C][C]1.82565[/C][C]8.333[/C][C]2.69259e-14[/C][C]1.34629e-14[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0241027[/C][C]0.0254483[/C][C]0.9471[/C][C]0.344935[/C][C]0.172467[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0306913[/C][C]0.0284954[/C][C]-1.077[/C][C]0.282997[/C][C]0.141499[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270458&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270458&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)	15.2125	1.82565	8.333	2.69259e-14	1.34629e-14
AMS.I	0.0241027	0.0254483	0.9471	0.344935	0.172467
AMS.E	-0.0306913	0.0284954	-1.077	0.282997	0.141499

Multiple Linear Regression - Regression Statistics
Multiple R	0.0937053
R-squared	0.00878068
Adjusted R-squared	-0.00301955
F-TEST (value)	0.744111
F-TEST (DF numerator)	2
F-TEST (DF denominator)	168
p-value	0.476716
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.03315
Sum Squared Residuals	1545.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0937053 \tabularnewline
R-squared & 0.00878068 \tabularnewline
Adjusted R-squared & -0.00301955 \tabularnewline
F-TEST (value) & 0.744111 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 168 \tabularnewline
p-value & 0.476716 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.03315 \tabularnewline
Sum Squared Residuals & 1545.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270458&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0937053[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00878068[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00301955[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.744111[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]168[/C][/ROW]
[ROW][C]p-value[/C][C]0.476716[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.03315[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1545.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270458&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270458&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	0.0937053
R-squared	0.00878068
Adjusted R-squared	-0.00301955
F-TEST (value)	0.744111
F-TEST (DF numerator)	2
F-TEST (DF denominator)	168
p-value	0.476716
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.03315
Sum Squared Residuals	1545.6

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	19.25	15.7622	3.48781
2	11.6	14.2892	-2.68917
3	15.15	13.9013	1.24872
4	10.95	14.392	-3.44201
5	15.2	14.4118	0.788228
6	12.6	14.8194	-2.21943
7	13.2	14.2058	-1.00577
8	9.95	14.6464	-4.69638
9	19.9	14.5652	5.33477
10	8.1	14.3022	-6.20218
11	12.9	14.1532	-1.25323
12	14.85	14.9751	-0.125141
13	14.05	14.3592	-0.309229
14	10.95	14.962	-4.01196
15	7.65	14.3611	-6.71115
16	12.65	14.6552	-2.00522
17	11.35	14.5958	-3.24575
18	14.5	14.5279	-0.0279483
19	13.6	14.4598	-0.859811
20	14.9	14.7426	0.157379
21	16.1	14.7076	1.39241
22	12.4	14.7669	-2.36689
23	18.1	14.5389	3.56113
24	18.25	14.6923	3.55767
25	12.15	14.7053	-2.55534
26	17.35	14.6637	2.68628
27	12.6	14.2802	-1.68017
28	7.6	14.2035	-6.60352
29	13.4	14.6375	-1.23754
30	14.1	14.4666	-0.366566
31	19.9	14.9686	4.93145
32	18.1	14.2649	3.8351
33	11.85	14.3218	-2.47178
34	16.65	14.4073	2.24273
35	15.6	14.1597	1.44035
36	15.25	14.6351	0.614882
37	16.1	14.6023	1.49766
38	15.4	14.7208	0.67923
39	13.35	14.6573	-1.3073
40	15.4	14.7822	0.617847
41	16.1	15.1175	0.982495
42	16.2	14.1401	2.05995
43	7.7	14.3173	-6.61728
44	11.15	14.2035	-3.05352
45	13.15	14.5892	-1.43916
46	14.75	14.3613	0.388685
47	15.85	14.2628	1.58718
48	15.4	14.5255	0.87447
49	14.1	14.5082	-0.408182
50	18.2	14.7294	3.47056
51	16.15	14.5717	1.57835
52	11.2	14.5345	-3.33454
53	18.4	14.8305	3.56948
54	17.65	14.7275	2.92248
55	18.45	14.2254	4.22463
56	9.9	14.234	-4.33405
57	16.6	14.1465	2.45352
58	17.6	14.2188	3.38122
59	17.65	14.1333	3.5167
60	18.4	14.4555	3.94453
61	12.6	14.2802	-1.68017
62	19.3	14.2933	5.00666
63	11.2	14.4884	-3.28842
64	14.6	14.4445	0.155452
65	18.45	14.3877	4.06233
66	4.5	14.7383	-10.2383
67	19.1	14.563	4.53702
68	13.4	14.6682	-1.26823
69	4.35	14.9006	-10.5506
70	12.75	14.0171	-1.26712
71	15.6	14.3591	1.24094
72	11.85	14.3218	-2.47178
73	10.95	14.5585	-3.60847
74	15.25	14.4073	0.842732
75	11.9	14.5366	-2.63662
76	18.55	14.7294	3.82056
77	11.95	14.324	-2.37403
78	15.1	14.4577	0.642275
79	15.6	14.5278	1.07222
80	15.1	14.3898	0.710246
81	17.85	14.4445	3.40545
82	19.05	14.3854	4.66458
83	16.65	14.495	2.15499
84	12.4	14.6615	-2.26147
85	12.6	14.4511	-1.85114
86	13.35	14.644	-1.29396
87	16.1	14.644	1.45604
88	18.25	14.7208	3.52923
89	12.35	14.6792	-2.32915
90	14.85	15.0057	-0.155666
91	13.85	14.9925	-1.14249
92	14.6	14.585	0.0150061
93	7.85	14.5368	-6.68679
94	16	14.7208	1.27923
95	13.9	14.3724	-0.472406
96	18.95	15.0998	3.85018
97	11.4	13.8396	-2.43956
98	14.6	14.1924	0.407571
99	15.25	14.4007	0.84932
100	12.45	14.1357	-1.68572
101	19.1	14.3547	4.74527
102	14.6	14.9004	-0.300415
103	12.7	13.8794	-1.17943
104	13.2	14.1442	-0.944224
105	17.75	14.2165	3.53347
106	16.35	14.2604	2.0896
107	18.4	14.4357	3.96429
108	12.85	14.1772	-1.32717
109	15.35	14.3634	0.9866
110	17.75	14.1969	3.55307
111	13.1	14.3721	-1.27207
112	15.7	14.589	1.111
113	15.95	14.8389	1.11113
114	14.7	14.4948	0.205161
115	15.65	14.1246	1.52538
116	13.35	14.4709	-1.1209
117	14.75	14.2079	0.542142
118	14.6	14.1312	0.468787
119	15.9	14.4928	1.40725
120	19.1	13.9122	5.1878
121	14.9	14.3811	0.51892
122	12.2	14.0435	-1.84348
123	7.85	14.2911	-6.44109
124	12.35	14.2429	-1.89289
125	19.2	14.0567	5.14335
126	8.6	14.335	-5.73496
127	11.75	14.151	-2.40098
128	9.85	14.3591	-4.50906
129	16.85	14.6089	2.24107
130	10.35	14.4184	-4.06836
131	14.9	14.6221	0.277893
132	10.6	14.5016	-3.90159
133	15.35	14.4118	0.938228
134	9.6	14.8545	-5.25446
135	11.9	14.3875	-2.4875
136	14.75	14.1597	0.590347
137	14.8	14.5826	0.217424
138	16.35	14.4511	1.89886
139	16.85	14.747	2.10304
140	15.2	14.6637	0.536276
141	17.35	14.4184	2.93164
142	18.15	14.4862	3.66383
143	13.6	14.5234	-0.923445
144	13.6	14.5191	-0.919108
145	15	14.9311	0.0688941
146	16.85	14.1007	2.74931
147	17.1	14.4688	2.63118
148	17.1	14.7296	2.37039
149	13.35	14.8061	-1.45609
150	17.75	14.4928	3.25725
151	18.9	14.7688	4.13119
152	13.6	14.4404	-0.840378
153	13.95	14.5149	-0.564937
154	15.65	14.4468	1.2032
155	14.35	14.6244	-0.274359
156	14.75	14.6068	0.143155
157	11.7	14.6309	-2.93095
158	14.35	14.7143	-0.364348
159	19.1	14.701	4.399
160	16.6	14.7164	1.88357
161	9.5	14.4577	-4.95773
162	16.25	14.839	1.41097
163	17.6	14.4995	3.10049
164	17.1	14.701	2.399
165	16.1	14.872	1.22803
166	17.75	14.6726	3.07744
167	13.6	15.376	-1.77605
168	15.6	14.7274	0.872641
169	12.65	14.9552	-2.30521
170	13.6	15.1964	-1.5964
171	11.7	14.9776	-3.27756

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 19.25 & 15.7622 & 3.48781 \tabularnewline
2 & 11.6 & 14.2892 & -2.68917 \tabularnewline
3 & 15.15 & 13.9013 & 1.24872 \tabularnewline
4 & 10.95 & 14.392 & -3.44201 \tabularnewline
5 & 15.2 & 14.4118 & 0.788228 \tabularnewline
6 & 12.6 & 14.8194 & -2.21943 \tabularnewline
7 & 13.2 & 14.2058 & -1.00577 \tabularnewline
8 & 9.95 & 14.6464 & -4.69638 \tabularnewline
9 & 19.9 & 14.5652 & 5.33477 \tabularnewline
10 & 8.1 & 14.3022 & -6.20218 \tabularnewline
11 & 12.9 & 14.1532 & -1.25323 \tabularnewline
12 & 14.85 & 14.9751 & -0.125141 \tabularnewline
13 & 14.05 & 14.3592 & -0.309229 \tabularnewline
14 & 10.95 & 14.962 & -4.01196 \tabularnewline
15 & 7.65 & 14.3611 & -6.71115 \tabularnewline
16 & 12.65 & 14.6552 & -2.00522 \tabularnewline
17 & 11.35 & 14.5958 & -3.24575 \tabularnewline
18 & 14.5 & 14.5279 & -0.0279483 \tabularnewline
19 & 13.6 & 14.4598 & -0.859811 \tabularnewline
20 & 14.9 & 14.7426 & 0.157379 \tabularnewline
21 & 16.1 & 14.7076 & 1.39241 \tabularnewline
22 & 12.4 & 14.7669 & -2.36689 \tabularnewline
23 & 18.1 & 14.5389 & 3.56113 \tabularnewline
24 & 18.25 & 14.6923 & 3.55767 \tabularnewline
25 & 12.15 & 14.7053 & -2.55534 \tabularnewline
26 & 17.35 & 14.6637 & 2.68628 \tabularnewline
27 & 12.6 & 14.2802 & -1.68017 \tabularnewline
28 & 7.6 & 14.2035 & -6.60352 \tabularnewline
29 & 13.4 & 14.6375 & -1.23754 \tabularnewline
30 & 14.1 & 14.4666 & -0.366566 \tabularnewline
31 & 19.9 & 14.9686 & 4.93145 \tabularnewline
32 & 18.1 & 14.2649 & 3.8351 \tabularnewline
33 & 11.85 & 14.3218 & -2.47178 \tabularnewline
34 & 16.65 & 14.4073 & 2.24273 \tabularnewline
35 & 15.6 & 14.1597 & 1.44035 \tabularnewline
36 & 15.25 & 14.6351 & 0.614882 \tabularnewline
37 & 16.1 & 14.6023 & 1.49766 \tabularnewline
38 & 15.4 & 14.7208 & 0.67923 \tabularnewline
39 & 13.35 & 14.6573 & -1.3073 \tabularnewline
40 & 15.4 & 14.7822 & 0.617847 \tabularnewline
41 & 16.1 & 15.1175 & 0.982495 \tabularnewline
42 & 16.2 & 14.1401 & 2.05995 \tabularnewline
43 & 7.7 & 14.3173 & -6.61728 \tabularnewline
44 & 11.15 & 14.2035 & -3.05352 \tabularnewline
45 & 13.15 & 14.5892 & -1.43916 \tabularnewline
46 & 14.75 & 14.3613 & 0.388685 \tabularnewline
47 & 15.85 & 14.2628 & 1.58718 \tabularnewline
48 & 15.4 & 14.5255 & 0.87447 \tabularnewline
49 & 14.1 & 14.5082 & -0.408182 \tabularnewline
50 & 18.2 & 14.7294 & 3.47056 \tabularnewline
51 & 16.15 & 14.5717 & 1.57835 \tabularnewline
52 & 11.2 & 14.5345 & -3.33454 \tabularnewline
53 & 18.4 & 14.8305 & 3.56948 \tabularnewline
54 & 17.65 & 14.7275 & 2.92248 \tabularnewline
55 & 18.45 & 14.2254 & 4.22463 \tabularnewline
56 & 9.9 & 14.234 & -4.33405 \tabularnewline
57 & 16.6 & 14.1465 & 2.45352 \tabularnewline
58 & 17.6 & 14.2188 & 3.38122 \tabularnewline
59 & 17.65 & 14.1333 & 3.5167 \tabularnewline
60 & 18.4 & 14.4555 & 3.94453 \tabularnewline
61 & 12.6 & 14.2802 & -1.68017 \tabularnewline
62 & 19.3 & 14.2933 & 5.00666 \tabularnewline
63 & 11.2 & 14.4884 & -3.28842 \tabularnewline
64 & 14.6 & 14.4445 & 0.155452 \tabularnewline
65 & 18.45 & 14.3877 & 4.06233 \tabularnewline
66 & 4.5 & 14.7383 & -10.2383 \tabularnewline
67 & 19.1 & 14.563 & 4.53702 \tabularnewline
68 & 13.4 & 14.6682 & -1.26823 \tabularnewline
69 & 4.35 & 14.9006 & -10.5506 \tabularnewline
70 & 12.75 & 14.0171 & -1.26712 \tabularnewline
71 & 15.6 & 14.3591 & 1.24094 \tabularnewline
72 & 11.85 & 14.3218 & -2.47178 \tabularnewline
73 & 10.95 & 14.5585 & -3.60847 \tabularnewline
74 & 15.25 & 14.4073 & 0.842732 \tabularnewline
75 & 11.9 & 14.5366 & -2.63662 \tabularnewline
76 & 18.55 & 14.7294 & 3.82056 \tabularnewline
77 & 11.95 & 14.324 & -2.37403 \tabularnewline
78 & 15.1 & 14.4577 & 0.642275 \tabularnewline
79 & 15.6 & 14.5278 & 1.07222 \tabularnewline
80 & 15.1 & 14.3898 & 0.710246 \tabularnewline
81 & 17.85 & 14.4445 & 3.40545 \tabularnewline
82 & 19.05 & 14.3854 & 4.66458 \tabularnewline
83 & 16.65 & 14.495 & 2.15499 \tabularnewline
84 & 12.4 & 14.6615 & -2.26147 \tabularnewline
85 & 12.6 & 14.4511 & -1.85114 \tabularnewline
86 & 13.35 & 14.644 & -1.29396 \tabularnewline
87 & 16.1 & 14.644 & 1.45604 \tabularnewline
88 & 18.25 & 14.7208 & 3.52923 \tabularnewline
89 & 12.35 & 14.6792 & -2.32915 \tabularnewline
90 & 14.85 & 15.0057 & -0.155666 \tabularnewline
91 & 13.85 & 14.9925 & -1.14249 \tabularnewline
92 & 14.6 & 14.585 & 0.0150061 \tabularnewline
93 & 7.85 & 14.5368 & -6.68679 \tabularnewline
94 & 16 & 14.7208 & 1.27923 \tabularnewline
95 & 13.9 & 14.3724 & -0.472406 \tabularnewline
96 & 18.95 & 15.0998 & 3.85018 \tabularnewline
97 & 11.4 & 13.8396 & -2.43956 \tabularnewline
98 & 14.6 & 14.1924 & 0.407571 \tabularnewline
99 & 15.25 & 14.4007 & 0.84932 \tabularnewline
100 & 12.45 & 14.1357 & -1.68572 \tabularnewline
101 & 19.1 & 14.3547 & 4.74527 \tabularnewline
102 & 14.6 & 14.9004 & -0.300415 \tabularnewline
103 & 12.7 & 13.8794 & -1.17943 \tabularnewline
104 & 13.2 & 14.1442 & -0.944224 \tabularnewline
105 & 17.75 & 14.2165 & 3.53347 \tabularnewline
106 & 16.35 & 14.2604 & 2.0896 \tabularnewline
107 & 18.4 & 14.4357 & 3.96429 \tabularnewline
108 & 12.85 & 14.1772 & -1.32717 \tabularnewline
109 & 15.35 & 14.3634 & 0.9866 \tabularnewline
110 & 17.75 & 14.1969 & 3.55307 \tabularnewline
111 & 13.1 & 14.3721 & -1.27207 \tabularnewline
112 & 15.7 & 14.589 & 1.111 \tabularnewline
113 & 15.95 & 14.8389 & 1.11113 \tabularnewline
114 & 14.7 & 14.4948 & 0.205161 \tabularnewline
115 & 15.65 & 14.1246 & 1.52538 \tabularnewline
116 & 13.35 & 14.4709 & -1.1209 \tabularnewline
117 & 14.75 & 14.2079 & 0.542142 \tabularnewline
118 & 14.6 & 14.1312 & 0.468787 \tabularnewline
119 & 15.9 & 14.4928 & 1.40725 \tabularnewline
120 & 19.1 & 13.9122 & 5.1878 \tabularnewline
121 & 14.9 & 14.3811 & 0.51892 \tabularnewline
122 & 12.2 & 14.0435 & -1.84348 \tabularnewline
123 & 7.85 & 14.2911 & -6.44109 \tabularnewline
124 & 12.35 & 14.2429 & -1.89289 \tabularnewline
125 & 19.2 & 14.0567 & 5.14335 \tabularnewline
126 & 8.6 & 14.335 & -5.73496 \tabularnewline
127 & 11.75 & 14.151 & -2.40098 \tabularnewline
128 & 9.85 & 14.3591 & -4.50906 \tabularnewline
129 & 16.85 & 14.6089 & 2.24107 \tabularnewline
130 & 10.35 & 14.4184 & -4.06836 \tabularnewline
131 & 14.9 & 14.6221 & 0.277893 \tabularnewline
132 & 10.6 & 14.5016 & -3.90159 \tabularnewline
133 & 15.35 & 14.4118 & 0.938228 \tabularnewline
134 & 9.6 & 14.8545 & -5.25446 \tabularnewline
135 & 11.9 & 14.3875 & -2.4875 \tabularnewline
136 & 14.75 & 14.1597 & 0.590347 \tabularnewline
137 & 14.8 & 14.5826 & 0.217424 \tabularnewline
138 & 16.35 & 14.4511 & 1.89886 \tabularnewline
139 & 16.85 & 14.747 & 2.10304 \tabularnewline
140 & 15.2 & 14.6637 & 0.536276 \tabularnewline
141 & 17.35 & 14.4184 & 2.93164 \tabularnewline
142 & 18.15 & 14.4862 & 3.66383 \tabularnewline
143 & 13.6 & 14.5234 & -0.923445 \tabularnewline
144 & 13.6 & 14.5191 & -0.919108 \tabularnewline
145 & 15 & 14.9311 & 0.0688941 \tabularnewline
146 & 16.85 & 14.1007 & 2.74931 \tabularnewline
147 & 17.1 & 14.4688 & 2.63118 \tabularnewline
148 & 17.1 & 14.7296 & 2.37039 \tabularnewline
149 & 13.35 & 14.8061 & -1.45609 \tabularnewline
150 & 17.75 & 14.4928 & 3.25725 \tabularnewline
151 & 18.9 & 14.7688 & 4.13119 \tabularnewline
152 & 13.6 & 14.4404 & -0.840378 \tabularnewline
153 & 13.95 & 14.5149 & -0.564937 \tabularnewline
154 & 15.65 & 14.4468 & 1.2032 \tabularnewline
155 & 14.35 & 14.6244 & -0.274359 \tabularnewline
156 & 14.75 & 14.6068 & 0.143155 \tabularnewline
157 & 11.7 & 14.6309 & -2.93095 \tabularnewline
158 & 14.35 & 14.7143 & -0.364348 \tabularnewline
159 & 19.1 & 14.701 & 4.399 \tabularnewline
160 & 16.6 & 14.7164 & 1.88357 \tabularnewline
161 & 9.5 & 14.4577 & -4.95773 \tabularnewline
162 & 16.25 & 14.839 & 1.41097 \tabularnewline
163 & 17.6 & 14.4995 & 3.10049 \tabularnewline
164 & 17.1 & 14.701 & 2.399 \tabularnewline
165 & 16.1 & 14.872 & 1.22803 \tabularnewline
166 & 17.75 & 14.6726 & 3.07744 \tabularnewline
167 & 13.6 & 15.376 & -1.77605 \tabularnewline
168 & 15.6 & 14.7274 & 0.872641 \tabularnewline
169 & 12.65 & 14.9552 & -2.30521 \tabularnewline
170 & 13.6 & 15.1964 & -1.5964 \tabularnewline
171 & 11.7 & 14.9776 & -3.27756 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270458&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]19.25[/C][C]15.7622[/C][C]3.48781[/C][/ROW]
[ROW][C]2[/C][C]11.6[/C][C]14.2892[/C][C]-2.68917[/C][/ROW]
[ROW][C]3[/C][C]15.15[/C][C]13.9013[/C][C]1.24872[/C][/ROW]
[ROW][C]4[/C][C]10.95[/C][C]14.392[/C][C]-3.44201[/C][/ROW]
[ROW][C]5[/C][C]15.2[/C][C]14.4118[/C][C]0.788228[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]14.8194[/C][C]-2.21943[/C][/ROW]
[ROW][C]7[/C][C]13.2[/C][C]14.2058[/C][C]-1.00577[/C][/ROW]
[ROW][C]8[/C][C]9.95[/C][C]14.6464[/C][C]-4.69638[/C][/ROW]
[ROW][C]9[/C][C]19.9[/C][C]14.5652[/C][C]5.33477[/C][/ROW]
[ROW][C]10[/C][C]8.1[/C][C]14.3022[/C][C]-6.20218[/C][/ROW]
[ROW][C]11[/C][C]12.9[/C][C]14.1532[/C][C]-1.25323[/C][/ROW]
[ROW][C]12[/C][C]14.85[/C][C]14.9751[/C][C]-0.125141[/C][/ROW]
[ROW][C]13[/C][C]14.05[/C][C]14.3592[/C][C]-0.309229[/C][/ROW]
[ROW][C]14[/C][C]10.95[/C][C]14.962[/C][C]-4.01196[/C][/ROW]
[ROW][C]15[/C][C]7.65[/C][C]14.3611[/C][C]-6.71115[/C][/ROW]
[ROW][C]16[/C][C]12.65[/C][C]14.6552[/C][C]-2.00522[/C][/ROW]
[ROW][C]17[/C][C]11.35[/C][C]14.5958[/C][C]-3.24575[/C][/ROW]
[ROW][C]18[/C][C]14.5[/C][C]14.5279[/C][C]-0.0279483[/C][/ROW]
[ROW][C]19[/C][C]13.6[/C][C]14.4598[/C][C]-0.859811[/C][/ROW]
[ROW][C]20[/C][C]14.9[/C][C]14.7426[/C][C]0.157379[/C][/ROW]
[ROW][C]21[/C][C]16.1[/C][C]14.7076[/C][C]1.39241[/C][/ROW]
[ROW][C]22[/C][C]12.4[/C][C]14.7669[/C][C]-2.36689[/C][/ROW]
[ROW][C]23[/C][C]18.1[/C][C]14.5389[/C][C]3.56113[/C][/ROW]
[ROW][C]24[/C][C]18.25[/C][C]14.6923[/C][C]3.55767[/C][/ROW]
[ROW][C]25[/C][C]12.15[/C][C]14.7053[/C][C]-2.55534[/C][/ROW]
[ROW][C]26[/C][C]17.35[/C][C]14.6637[/C][C]2.68628[/C][/ROW]
[ROW][C]27[/C][C]12.6[/C][C]14.2802[/C][C]-1.68017[/C][/ROW]
[ROW][C]28[/C][C]7.6[/C][C]14.2035[/C][C]-6.60352[/C][/ROW]
[ROW][C]29[/C][C]13.4[/C][C]14.6375[/C][C]-1.23754[/C][/ROW]
[ROW][C]30[/C][C]14.1[/C][C]14.4666[/C][C]-0.366566[/C][/ROW]
[ROW][C]31[/C][C]19.9[/C][C]14.9686[/C][C]4.93145[/C][/ROW]
[ROW][C]32[/C][C]18.1[/C][C]14.2649[/C][C]3.8351[/C][/ROW]
[ROW][C]33[/C][C]11.85[/C][C]14.3218[/C][C]-2.47178[/C][/ROW]
[ROW][C]34[/C][C]16.65[/C][C]14.4073[/C][C]2.24273[/C][/ROW]
[ROW][C]35[/C][C]15.6[/C][C]14.1597[/C][C]1.44035[/C][/ROW]
[ROW][C]36[/C][C]15.25[/C][C]14.6351[/C][C]0.614882[/C][/ROW]
[ROW][C]37[/C][C]16.1[/C][C]14.6023[/C][C]1.49766[/C][/ROW]
[ROW][C]38[/C][C]15.4[/C][C]14.7208[/C][C]0.67923[/C][/ROW]
[ROW][C]39[/C][C]13.35[/C][C]14.6573[/C][C]-1.3073[/C][/ROW]
[ROW][C]40[/C][C]15.4[/C][C]14.7822[/C][C]0.617847[/C][/ROW]
[ROW][C]41[/C][C]16.1[/C][C]15.1175[/C][C]0.982495[/C][/ROW]
[ROW][C]42[/C][C]16.2[/C][C]14.1401[/C][C]2.05995[/C][/ROW]
[ROW][C]43[/C][C]7.7[/C][C]14.3173[/C][C]-6.61728[/C][/ROW]
[ROW][C]44[/C][C]11.15[/C][C]14.2035[/C][C]-3.05352[/C][/ROW]
[ROW][C]45[/C][C]13.15[/C][C]14.5892[/C][C]-1.43916[/C][/ROW]
[ROW][C]46[/C][C]14.75[/C][C]14.3613[/C][C]0.388685[/C][/ROW]
[ROW][C]47[/C][C]15.85[/C][C]14.2628[/C][C]1.58718[/C][/ROW]
[ROW][C]48[/C][C]15.4[/C][C]14.5255[/C][C]0.87447[/C][/ROW]
[ROW][C]49[/C][C]14.1[/C][C]14.5082[/C][C]-0.408182[/C][/ROW]
[ROW][C]50[/C][C]18.2[/C][C]14.7294[/C][C]3.47056[/C][/ROW]
[ROW][C]51[/C][C]16.15[/C][C]14.5717[/C][C]1.57835[/C][/ROW]
[ROW][C]52[/C][C]11.2[/C][C]14.5345[/C][C]-3.33454[/C][/ROW]
[ROW][C]53[/C][C]18.4[/C][C]14.8305[/C][C]3.56948[/C][/ROW]
[ROW][C]54[/C][C]17.65[/C][C]14.7275[/C][C]2.92248[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]14.2254[/C][C]4.22463[/C][/ROW]
[ROW][C]56[/C][C]9.9[/C][C]14.234[/C][C]-4.33405[/C][/ROW]
[ROW][C]57[/C][C]16.6[/C][C]14.1465[/C][C]2.45352[/C][/ROW]
[ROW][C]58[/C][C]17.6[/C][C]14.2188[/C][C]3.38122[/C][/ROW]
[ROW][C]59[/C][C]17.65[/C][C]14.1333[/C][C]3.5167[/C][/ROW]
[ROW][C]60[/C][C]18.4[/C][C]14.4555[/C][C]3.94453[/C][/ROW]
[ROW][C]61[/C][C]12.6[/C][C]14.2802[/C][C]-1.68017[/C][/ROW]
[ROW][C]62[/C][C]19.3[/C][C]14.2933[/C][C]5.00666[/C][/ROW]
[ROW][C]63[/C][C]11.2[/C][C]14.4884[/C][C]-3.28842[/C][/ROW]
[ROW][C]64[/C][C]14.6[/C][C]14.4445[/C][C]0.155452[/C][/ROW]
[ROW][C]65[/C][C]18.45[/C][C]14.3877[/C][C]4.06233[/C][/ROW]
[ROW][C]66[/C][C]4.5[/C][C]14.7383[/C][C]-10.2383[/C][/ROW]
[ROW][C]67[/C][C]19.1[/C][C]14.563[/C][C]4.53702[/C][/ROW]
[ROW][C]68[/C][C]13.4[/C][C]14.6682[/C][C]-1.26823[/C][/ROW]
[ROW][C]69[/C][C]4.35[/C][C]14.9006[/C][C]-10.5506[/C][/ROW]
[ROW][C]70[/C][C]12.75[/C][C]14.0171[/C][C]-1.26712[/C][/ROW]
[ROW][C]71[/C][C]15.6[/C][C]14.3591[/C][C]1.24094[/C][/ROW]
[ROW][C]72[/C][C]11.85[/C][C]14.3218[/C][C]-2.47178[/C][/ROW]
[ROW][C]73[/C][C]10.95[/C][C]14.5585[/C][C]-3.60847[/C][/ROW]
[ROW][C]74[/C][C]15.25[/C][C]14.4073[/C][C]0.842732[/C][/ROW]
[ROW][C]75[/C][C]11.9[/C][C]14.5366[/C][C]-2.63662[/C][/ROW]
[ROW][C]76[/C][C]18.55[/C][C]14.7294[/C][C]3.82056[/C][/ROW]
[ROW][C]77[/C][C]11.95[/C][C]14.324[/C][C]-2.37403[/C][/ROW]
[ROW][C]78[/C][C]15.1[/C][C]14.4577[/C][C]0.642275[/C][/ROW]
[ROW][C]79[/C][C]15.6[/C][C]14.5278[/C][C]1.07222[/C][/ROW]
[ROW][C]80[/C][C]15.1[/C][C]14.3898[/C][C]0.710246[/C][/ROW]
[ROW][C]81[/C][C]17.85[/C][C]14.4445[/C][C]3.40545[/C][/ROW]
[ROW][C]82[/C][C]19.05[/C][C]14.3854[/C][C]4.66458[/C][/ROW]
[ROW][C]83[/C][C]16.65[/C][C]14.495[/C][C]2.15499[/C][/ROW]
[ROW][C]84[/C][C]12.4[/C][C]14.6615[/C][C]-2.26147[/C][/ROW]
[ROW][C]85[/C][C]12.6[/C][C]14.4511[/C][C]-1.85114[/C][/ROW]
[ROW][C]86[/C][C]13.35[/C][C]14.644[/C][C]-1.29396[/C][/ROW]
[ROW][C]87[/C][C]16.1[/C][C]14.644[/C][C]1.45604[/C][/ROW]
[ROW][C]88[/C][C]18.25[/C][C]14.7208[/C][C]3.52923[/C][/ROW]
[ROW][C]89[/C][C]12.35[/C][C]14.6792[/C][C]-2.32915[/C][/ROW]
[ROW][C]90[/C][C]14.85[/C][C]15.0057[/C][C]-0.155666[/C][/ROW]
[ROW][C]91[/C][C]13.85[/C][C]14.9925[/C][C]-1.14249[/C][/ROW]
[ROW][C]92[/C][C]14.6[/C][C]14.585[/C][C]0.0150061[/C][/ROW]
[ROW][C]93[/C][C]7.85[/C][C]14.5368[/C][C]-6.68679[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]14.7208[/C][C]1.27923[/C][/ROW]
[ROW][C]95[/C][C]13.9[/C][C]14.3724[/C][C]-0.472406[/C][/ROW]
[ROW][C]96[/C][C]18.95[/C][C]15.0998[/C][C]3.85018[/C][/ROW]
[ROW][C]97[/C][C]11.4[/C][C]13.8396[/C][C]-2.43956[/C][/ROW]
[ROW][C]98[/C][C]14.6[/C][C]14.1924[/C][C]0.407571[/C][/ROW]
[ROW][C]99[/C][C]15.25[/C][C]14.4007[/C][C]0.84932[/C][/ROW]
[ROW][C]100[/C][C]12.45[/C][C]14.1357[/C][C]-1.68572[/C][/ROW]
[ROW][C]101[/C][C]19.1[/C][C]14.3547[/C][C]4.74527[/C][/ROW]
[ROW][C]102[/C][C]14.6[/C][C]14.9004[/C][C]-0.300415[/C][/ROW]
[ROW][C]103[/C][C]12.7[/C][C]13.8794[/C][C]-1.17943[/C][/ROW]
[ROW][C]104[/C][C]13.2[/C][C]14.1442[/C][C]-0.944224[/C][/ROW]
[ROW][C]105[/C][C]17.75[/C][C]14.2165[/C][C]3.53347[/C][/ROW]
[ROW][C]106[/C][C]16.35[/C][C]14.2604[/C][C]2.0896[/C][/ROW]
[ROW][C]107[/C][C]18.4[/C][C]14.4357[/C][C]3.96429[/C][/ROW]
[ROW][C]108[/C][C]12.85[/C][C]14.1772[/C][C]-1.32717[/C][/ROW]
[ROW][C]109[/C][C]15.35[/C][C]14.3634[/C][C]0.9866[/C][/ROW]
[ROW][C]110[/C][C]17.75[/C][C]14.1969[/C][C]3.55307[/C][/ROW]
[ROW][C]111[/C][C]13.1[/C][C]14.3721[/C][C]-1.27207[/C][/ROW]
[ROW][C]112[/C][C]15.7[/C][C]14.589[/C][C]1.111[/C][/ROW]
[ROW][C]113[/C][C]15.95[/C][C]14.8389[/C][C]1.11113[/C][/ROW]
[ROW][C]114[/C][C]14.7[/C][C]14.4948[/C][C]0.205161[/C][/ROW]
[ROW][C]115[/C][C]15.65[/C][C]14.1246[/C][C]1.52538[/C][/ROW]
[ROW][C]116[/C][C]13.35[/C][C]14.4709[/C][C]-1.1209[/C][/ROW]
[ROW][C]117[/C][C]14.75[/C][C]14.2079[/C][C]0.542142[/C][/ROW]
[ROW][C]118[/C][C]14.6[/C][C]14.1312[/C][C]0.468787[/C][/ROW]
[ROW][C]119[/C][C]15.9[/C][C]14.4928[/C][C]1.40725[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]13.9122[/C][C]5.1878[/C][/ROW]
[ROW][C]121[/C][C]14.9[/C][C]14.3811[/C][C]0.51892[/C][/ROW]
[ROW][C]122[/C][C]12.2[/C][C]14.0435[/C][C]-1.84348[/C][/ROW]
[ROW][C]123[/C][C]7.85[/C][C]14.2911[/C][C]-6.44109[/C][/ROW]
[ROW][C]124[/C][C]12.35[/C][C]14.2429[/C][C]-1.89289[/C][/ROW]
[ROW][C]125[/C][C]19.2[/C][C]14.0567[/C][C]5.14335[/C][/ROW]
[ROW][C]126[/C][C]8.6[/C][C]14.335[/C][C]-5.73496[/C][/ROW]
[ROW][C]127[/C][C]11.75[/C][C]14.151[/C][C]-2.40098[/C][/ROW]
[ROW][C]128[/C][C]9.85[/C][C]14.3591[/C][C]-4.50906[/C][/ROW]
[ROW][C]129[/C][C]16.85[/C][C]14.6089[/C][C]2.24107[/C][/ROW]
[ROW][C]130[/C][C]10.35[/C][C]14.4184[/C][C]-4.06836[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]14.6221[/C][C]0.277893[/C][/ROW]
[ROW][C]132[/C][C]10.6[/C][C]14.5016[/C][C]-3.90159[/C][/ROW]
[ROW][C]133[/C][C]15.35[/C][C]14.4118[/C][C]0.938228[/C][/ROW]
[ROW][C]134[/C][C]9.6[/C][C]14.8545[/C][C]-5.25446[/C][/ROW]
[ROW][C]135[/C][C]11.9[/C][C]14.3875[/C][C]-2.4875[/C][/ROW]
[ROW][C]136[/C][C]14.75[/C][C]14.1597[/C][C]0.590347[/C][/ROW]
[ROW][C]137[/C][C]14.8[/C][C]14.5826[/C][C]0.217424[/C][/ROW]
[ROW][C]138[/C][C]16.35[/C][C]14.4511[/C][C]1.89886[/C][/ROW]
[ROW][C]139[/C][C]16.85[/C][C]14.747[/C][C]2.10304[/C][/ROW]
[ROW][C]140[/C][C]15.2[/C][C]14.6637[/C][C]0.536276[/C][/ROW]
[ROW][C]141[/C][C]17.35[/C][C]14.4184[/C][C]2.93164[/C][/ROW]
[ROW][C]142[/C][C]18.15[/C][C]14.4862[/C][C]3.66383[/C][/ROW]
[ROW][C]143[/C][C]13.6[/C][C]14.5234[/C][C]-0.923445[/C][/ROW]
[ROW][C]144[/C][C]13.6[/C][C]14.5191[/C][C]-0.919108[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]14.9311[/C][C]0.0688941[/C][/ROW]
[ROW][C]146[/C][C]16.85[/C][C]14.1007[/C][C]2.74931[/C][/ROW]
[ROW][C]147[/C][C]17.1[/C][C]14.4688[/C][C]2.63118[/C][/ROW]
[ROW][C]148[/C][C]17.1[/C][C]14.7296[/C][C]2.37039[/C][/ROW]
[ROW][C]149[/C][C]13.35[/C][C]14.8061[/C][C]-1.45609[/C][/ROW]
[ROW][C]150[/C][C]17.75[/C][C]14.4928[/C][C]3.25725[/C][/ROW]
[ROW][C]151[/C][C]18.9[/C][C]14.7688[/C][C]4.13119[/C][/ROW]
[ROW][C]152[/C][C]13.6[/C][C]14.4404[/C][C]-0.840378[/C][/ROW]
[ROW][C]153[/C][C]13.95[/C][C]14.5149[/C][C]-0.564937[/C][/ROW]
[ROW][C]154[/C][C]15.65[/C][C]14.4468[/C][C]1.2032[/C][/ROW]
[ROW][C]155[/C][C]14.35[/C][C]14.6244[/C][C]-0.274359[/C][/ROW]
[ROW][C]156[/C][C]14.75[/C][C]14.6068[/C][C]0.143155[/C][/ROW]
[ROW][C]157[/C][C]11.7[/C][C]14.6309[/C][C]-2.93095[/C][/ROW]
[ROW][C]158[/C][C]14.35[/C][C]14.7143[/C][C]-0.364348[/C][/ROW]
[ROW][C]159[/C][C]19.1[/C][C]14.701[/C][C]4.399[/C][/ROW]
[ROW][C]160[/C][C]16.6[/C][C]14.7164[/C][C]1.88357[/C][/ROW]
[ROW][C]161[/C][C]9.5[/C][C]14.4577[/C][C]-4.95773[/C][/ROW]
[ROW][C]162[/C][C]16.25[/C][C]14.839[/C][C]1.41097[/C][/ROW]
[ROW][C]163[/C][C]17.6[/C][C]14.4995[/C][C]3.10049[/C][/ROW]
[ROW][C]164[/C][C]17.1[/C][C]14.701[/C][C]2.399[/C][/ROW]
[ROW][C]165[/C][C]16.1[/C][C]14.872[/C][C]1.22803[/C][/ROW]
[ROW][C]166[/C][C]17.75[/C][C]14.6726[/C][C]3.07744[/C][/ROW]
[ROW][C]167[/C][C]13.6[/C][C]15.376[/C][C]-1.77605[/C][/ROW]
[ROW][C]168[/C][C]15.6[/C][C]14.7274[/C][C]0.872641[/C][/ROW]
[ROW][C]169[/C][C]12.65[/C][C]14.9552[/C][C]-2.30521[/C][/ROW]
[ROW][C]170[/C][C]13.6[/C][C]15.1964[/C][C]-1.5964[/C][/ROW]
[ROW][C]171[/C][C]11.7[/C][C]14.9776[/C][C]-3.27756[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270458&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270458&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	19.25	15.7622	3.48781
2	11.6	14.2892	-2.68917
3	15.15	13.9013	1.24872
4	10.95	14.392	-3.44201
5	15.2	14.4118	0.788228
6	12.6	14.8194	-2.21943
7	13.2	14.2058	-1.00577
8	9.95	14.6464	-4.69638
9	19.9	14.5652	5.33477
10	8.1	14.3022	-6.20218
11	12.9	14.1532	-1.25323
12	14.85	14.9751	-0.125141
13	14.05	14.3592	-0.309229
14	10.95	14.962	-4.01196
15	7.65	14.3611	-6.71115
16	12.65	14.6552	-2.00522
17	11.35	14.5958	-3.24575
18	14.5	14.5279	-0.0279483
19	13.6	14.4598	-0.859811
20	14.9	14.7426	0.157379
21	16.1	14.7076	1.39241
22	12.4	14.7669	-2.36689
23	18.1	14.5389	3.56113
24	18.25	14.6923	3.55767
25	12.15	14.7053	-2.55534
26	17.35	14.6637	2.68628
27	12.6	14.2802	-1.68017
28	7.6	14.2035	-6.60352
29	13.4	14.6375	-1.23754
30	14.1	14.4666	-0.366566
31	19.9	14.9686	4.93145
32	18.1	14.2649	3.8351
33	11.85	14.3218	-2.47178
34	16.65	14.4073	2.24273
35	15.6	14.1597	1.44035
36	15.25	14.6351	0.614882
37	16.1	14.6023	1.49766
38	15.4	14.7208	0.67923
39	13.35	14.6573	-1.3073
40	15.4	14.7822	0.617847
41	16.1	15.1175	0.982495
42	16.2	14.1401	2.05995
43	7.7	14.3173	-6.61728
44	11.15	14.2035	-3.05352
45	13.15	14.5892	-1.43916
46	14.75	14.3613	0.388685
47	15.85	14.2628	1.58718
48	15.4	14.5255	0.87447
49	14.1	14.5082	-0.408182
50	18.2	14.7294	3.47056
51	16.15	14.5717	1.57835
52	11.2	14.5345	-3.33454
53	18.4	14.8305	3.56948
54	17.65	14.7275	2.92248
55	18.45	14.2254	4.22463
56	9.9	14.234	-4.33405
57	16.6	14.1465	2.45352
58	17.6	14.2188	3.38122
59	17.65	14.1333	3.5167
60	18.4	14.4555	3.94453
61	12.6	14.2802	-1.68017
62	19.3	14.2933	5.00666
63	11.2	14.4884	-3.28842
64	14.6	14.4445	0.155452
65	18.45	14.3877	4.06233
66	4.5	14.7383	-10.2383
67	19.1	14.563	4.53702
68	13.4	14.6682	-1.26823
69	4.35	14.9006	-10.5506
70	12.75	14.0171	-1.26712
71	15.6	14.3591	1.24094
72	11.85	14.3218	-2.47178
73	10.95	14.5585	-3.60847
74	15.25	14.4073	0.842732
75	11.9	14.5366	-2.63662
76	18.55	14.7294	3.82056
77	11.95	14.324	-2.37403
78	15.1	14.4577	0.642275
79	15.6	14.5278	1.07222
80	15.1	14.3898	0.710246
81	17.85	14.4445	3.40545
82	19.05	14.3854	4.66458
83	16.65	14.495	2.15499
84	12.4	14.6615	-2.26147
85	12.6	14.4511	-1.85114
86	13.35	14.644	-1.29396
87	16.1	14.644	1.45604
88	18.25	14.7208	3.52923
89	12.35	14.6792	-2.32915
90	14.85	15.0057	-0.155666
91	13.85	14.9925	-1.14249
92	14.6	14.585	0.0150061
93	7.85	14.5368	-6.68679
94	16	14.7208	1.27923
95	13.9	14.3724	-0.472406
96	18.95	15.0998	3.85018
97	11.4	13.8396	-2.43956
98	14.6	14.1924	0.407571
99	15.25	14.4007	0.84932
100	12.45	14.1357	-1.68572
101	19.1	14.3547	4.74527
102	14.6	14.9004	-0.300415
103	12.7	13.8794	-1.17943
104	13.2	14.1442	-0.944224
105	17.75	14.2165	3.53347
106	16.35	14.2604	2.0896
107	18.4	14.4357	3.96429
108	12.85	14.1772	-1.32717
109	15.35	14.3634	0.9866
110	17.75	14.1969	3.55307
111	13.1	14.3721	-1.27207
112	15.7	14.589	1.111
113	15.95	14.8389	1.11113
114	14.7	14.4948	0.205161
115	15.65	14.1246	1.52538
116	13.35	14.4709	-1.1209
117	14.75	14.2079	0.542142
118	14.6	14.1312	0.468787
119	15.9	14.4928	1.40725
120	19.1	13.9122	5.1878
121	14.9	14.3811	0.51892
122	12.2	14.0435	-1.84348
123	7.85	14.2911	-6.44109
124	12.35	14.2429	-1.89289
125	19.2	14.0567	5.14335
126	8.6	14.335	-5.73496
127	11.75	14.151	-2.40098
128	9.85	14.3591	-4.50906
129	16.85	14.6089	2.24107
130	10.35	14.4184	-4.06836
131	14.9	14.6221	0.277893
132	10.6	14.5016	-3.90159
133	15.35	14.4118	0.938228
134	9.6	14.8545	-5.25446
135	11.9	14.3875	-2.4875
136	14.75	14.1597	0.590347
137	14.8	14.5826	0.217424
138	16.35	14.4511	1.89886
139	16.85	14.747	2.10304
140	15.2	14.6637	0.536276
141	17.35	14.4184	2.93164
142	18.15	14.4862	3.66383
143	13.6	14.5234	-0.923445
144	13.6	14.5191	-0.919108
145	15	14.9311	0.0688941
146	16.85	14.1007	2.74931
147	17.1	14.4688	2.63118
148	17.1	14.7296	2.37039
149	13.35	14.8061	-1.45609
150	17.75	14.4928	3.25725
151	18.9	14.7688	4.13119
152	13.6	14.4404	-0.840378
153	13.95	14.5149	-0.564937
154	15.65	14.4468	1.2032
155	14.35	14.6244	-0.274359
156	14.75	14.6068	0.143155
157	11.7	14.6309	-2.93095
158	14.35	14.7143	-0.364348
159	19.1	14.701	4.399
160	16.6	14.7164	1.88357
161	9.5	14.4577	-4.95773
162	16.25	14.839	1.41097
163	17.6	14.4995	3.10049
164	17.1	14.701	2.399
165	16.1	14.872	1.22803
166	17.75	14.6726	3.07744
167	13.6	15.376	-1.77605
168	15.6	14.7274	0.872641
169	12.65	14.9552	-2.30521
170	13.6	15.1964	-1.5964
171	11.7	14.9776	-3.27756

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.375443	0.750886	0.624557
7	0.226472	0.452944	0.773528
8	0.375648	0.751296	0.624352
9	0.773776	0.452449	0.226224
10	0.839079	0.321843	0.160921
11	0.768557	0.462887	0.231443
12	0.686313	0.627375	0.313687
13	0.60479	0.790421	0.39521
14	0.614171	0.771659	0.385829
15	0.570915	0.85817	0.429085
16	0.531122	0.937757	0.468878
17	0.464352	0.928704	0.535648
18	0.419374	0.838748	0.580626
19	0.434806	0.869612	0.565194
20	0.41897	0.837941	0.58103
21	0.428641	0.857282	0.571359
22	0.381398	0.762795	0.618602
23	0.502666	0.994667	0.497334
24	0.572646	0.854709	0.427354
25	0.517964	0.964072	0.482036
26	0.56323	0.87354	0.43677
27	0.503912	0.992177	0.496088
28	0.618021	0.763958	0.381979
29	0.561152	0.877695	0.438848
30	0.506139	0.987722	0.493861
31	0.588098	0.823804	0.411902
32	0.690862	0.618276	0.309138
33	0.648594	0.702812	0.351406
34	0.668763	0.662474	0.331237
35	0.66123	0.677539	0.33877
36	0.624414	0.751173	0.375586
37	0.596341	0.807319	0.403659
38	0.547252	0.905496	0.452748
39	0.502002	0.995996	0.497998
40	0.451361	0.902722	0.548639
41	0.401982	0.803963	0.598018
42	0.398037	0.796075	0.601963
43	0.511553	0.976894	0.488447
44	0.489762	0.979524	0.510238
45	0.443168	0.886336	0.556832
46	0.403008	0.806015	0.596992
47	0.375323	0.750646	0.624677
48	0.353116	0.706232	0.646884
49	0.308288	0.616575	0.691712
50	0.341205	0.68241	0.658795
51	0.318719	0.637438	0.681281
52	0.323963	0.647927	0.676037
53	0.332151	0.664302	0.667849
54	0.323929	0.647858	0.676071
55	0.409155	0.81831	0.590845
56	0.432644	0.865289	0.567356
57	0.443193	0.886387	0.556807
58	0.480531	0.961061	0.519469
59	0.519487	0.961026	0.480513
60	0.561777	0.876447	0.438223
61	0.527225	0.94555	0.472775
62	0.614017	0.771967	0.385983
63	0.61859	0.762819	0.38141
64	0.574976	0.850048	0.425024
65	0.611242	0.777516	0.388758
66	0.921665	0.15667	0.0783352
67	0.940332	0.119336	0.0596681
68	0.928382	0.143237	0.0716184
69	0.996019	0.00796181	0.00398091
70	0.994828	0.0103449	0.00517245
71	0.993334	0.0133329	0.00666646
72	0.992588	0.0148237	0.00741183
73	0.993372	0.0132561	0.00662805
74	0.99131	0.0173796	0.00868978
75	0.990606	0.0187877	0.00939385
76	0.992441	0.0151174	0.00755869
77	0.991586	0.0168274	0.00841371
78	0.988878	0.0222439	0.0111219
79	0.985852	0.0282954	0.0141477
80	0.981695	0.0366098	0.0183049
81	0.982998	0.0340037	0.0170018
82	0.988434	0.0231311	0.0115655
83	0.986691	0.0266189	0.0133094
84	0.985014	0.0299725	0.0149862
85	0.982323	0.0353538	0.0176769
86	0.978115	0.0437705	0.0218853
87	0.973339	0.0533218	0.0266609
88	0.976051	0.0478979	0.0239489
89	0.973317	0.0533666	0.0266833
90	0.965662	0.0686767	0.0343384
91	0.957647	0.0847062	0.0423531
92	0.946591	0.106817	0.0534086
93	0.980353	0.0392948	0.0196474
94	0.975482	0.0490351	0.0245175
95	0.968497	0.0630061	0.0315031
96	0.973466	0.0530683	0.0265341
97	0.973205	0.0535909	0.0267954
98	0.965735	0.0685297	0.0342648
99	0.956836	0.0863285	0.0431642
100	0.950198	0.0996035	0.0498017
101	0.964458	0.0710837	0.0355419
102	0.954595	0.0908099	0.045405
103	0.945955	0.10809	0.0540449
104	0.935654	0.128693	0.0643464
105	0.938186	0.123628	0.0618142
106	0.929056	0.141887	0.0709436
107	0.939008	0.121984	0.0609918
108	0.928496	0.143009	0.0715043
109	0.912533	0.174933	0.0874665
110	0.917223	0.165555	0.0827773
111	0.902323	0.195354	0.0976772
112	0.883352	0.233296	0.116648
113	0.863688	0.272624	0.136312
114	0.836069	0.327862	0.163931
115	0.810964	0.378071	0.189036
116	0.7822	0.435601	0.2178
117	0.745685	0.508631	0.254315
118	0.705811	0.588377	0.294189
119	0.672117	0.655767	0.327883
120	0.744392	0.511216	0.255608
121	0.70469	0.59062	0.29531
122	0.677122	0.645756	0.322878
123	0.829328	0.341343	0.170672
124	0.817675	0.36465	0.182325
125	0.866321	0.267358	0.133679
126	0.938534	0.122932	0.0614662
127	0.93842	0.123159	0.0615795
128	0.96983	0.0603404	0.0301702
129	0.96388	0.0722404	0.0361202
130	0.97916	0.041679	0.0208395
131	0.971165	0.0576695	0.0288347
132	0.984247	0.0315056	0.0157528
133	0.977784	0.0444314	0.0222157
134	0.992129	0.0157417	0.00787085
135	0.995924	0.008152	0.004076
136	0.994911	0.0101785	0.00508927
137	0.993048	0.0139039	0.00695195
138	0.989666	0.0206687	0.0103344
139	0.98572	0.0285609	0.0142805
140	0.979202	0.041596	0.020798
141	0.974403	0.0511945	0.0255972
142	0.971058	0.057885	0.0289425
143	0.968126	0.0637484	0.0318742
144	0.964468	0.0710646	0.0355323
145	0.948919	0.102162	0.051081
146	0.933291	0.133419	0.0667093
147	0.921852	0.156297	0.0781484
148	0.915962	0.168077	0.0840385
149	0.92047	0.15906	0.07953
150	0.900697	0.198606	0.099303
151	0.898345	0.20331	0.101655
152	0.863965	0.272071	0.136035
153	0.815781	0.368439	0.184219
154	0.755928	0.488144	0.244072
155	0.68982	0.620361	0.31018
156	0.610086	0.779827	0.389914
157	0.65216	0.69568	0.34784
158	0.56259	0.87482	0.43741
159	0.605126	0.789748	0.394874
160	0.534697	0.930606	0.465303
161	0.989819	0.0203617	0.0101809
162	0.974408	0.0511848	0.0255924
163	0.945009	0.109982	0.0549912
164	0.883992	0.232016	0.116008
165	0.777622	0.444757	0.222378

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.375443 & 0.750886 & 0.624557 \tabularnewline
7 & 0.226472 & 0.452944 & 0.773528 \tabularnewline
8 & 0.375648 & 0.751296 & 0.624352 \tabularnewline
9 & 0.773776 & 0.452449 & 0.226224 \tabularnewline
10 & 0.839079 & 0.321843 & 0.160921 \tabularnewline
11 & 0.768557 & 0.462887 & 0.231443 \tabularnewline
12 & 0.686313 & 0.627375 & 0.313687 \tabularnewline
13 & 0.60479 & 0.790421 & 0.39521 \tabularnewline
14 & 0.614171 & 0.771659 & 0.385829 \tabularnewline
15 & 0.570915 & 0.85817 & 0.429085 \tabularnewline
16 & 0.531122 & 0.937757 & 0.468878 \tabularnewline
17 & 0.464352 & 0.928704 & 0.535648 \tabularnewline
18 & 0.419374 & 0.838748 & 0.580626 \tabularnewline
19 & 0.434806 & 0.869612 & 0.565194 \tabularnewline
20 & 0.41897 & 0.837941 & 0.58103 \tabularnewline
21 & 0.428641 & 0.857282 & 0.571359 \tabularnewline
22 & 0.381398 & 0.762795 & 0.618602 \tabularnewline
23 & 0.502666 & 0.994667 & 0.497334 \tabularnewline
24 & 0.572646 & 0.854709 & 0.427354 \tabularnewline
25 & 0.517964 & 0.964072 & 0.482036 \tabularnewline
26 & 0.56323 & 0.87354 & 0.43677 \tabularnewline
27 & 0.503912 & 0.992177 & 0.496088 \tabularnewline
28 & 0.618021 & 0.763958 & 0.381979 \tabularnewline
29 & 0.561152 & 0.877695 & 0.438848 \tabularnewline
30 & 0.506139 & 0.987722 & 0.493861 \tabularnewline
31 & 0.588098 & 0.823804 & 0.411902 \tabularnewline
32 & 0.690862 & 0.618276 & 0.309138 \tabularnewline
33 & 0.648594 & 0.702812 & 0.351406 \tabularnewline
34 & 0.668763 & 0.662474 & 0.331237 \tabularnewline
35 & 0.66123 & 0.677539 & 0.33877 \tabularnewline
36 & 0.624414 & 0.751173 & 0.375586 \tabularnewline
37 & 0.596341 & 0.807319 & 0.403659 \tabularnewline
38 & 0.547252 & 0.905496 & 0.452748 \tabularnewline
39 & 0.502002 & 0.995996 & 0.497998 \tabularnewline
40 & 0.451361 & 0.902722 & 0.548639 \tabularnewline
41 & 0.401982 & 0.803963 & 0.598018 \tabularnewline
42 & 0.398037 & 0.796075 & 0.601963 \tabularnewline
43 & 0.511553 & 0.976894 & 0.488447 \tabularnewline
44 & 0.489762 & 0.979524 & 0.510238 \tabularnewline
45 & 0.443168 & 0.886336 & 0.556832 \tabularnewline
46 & 0.403008 & 0.806015 & 0.596992 \tabularnewline
47 & 0.375323 & 0.750646 & 0.624677 \tabularnewline
48 & 0.353116 & 0.706232 & 0.646884 \tabularnewline
49 & 0.308288 & 0.616575 & 0.691712 \tabularnewline
50 & 0.341205 & 0.68241 & 0.658795 \tabularnewline
51 & 0.318719 & 0.637438 & 0.681281 \tabularnewline
52 & 0.323963 & 0.647927 & 0.676037 \tabularnewline
53 & 0.332151 & 0.664302 & 0.667849 \tabularnewline
54 & 0.323929 & 0.647858 & 0.676071 \tabularnewline
55 & 0.409155 & 0.81831 & 0.590845 \tabularnewline
56 & 0.432644 & 0.865289 & 0.567356 \tabularnewline
57 & 0.443193 & 0.886387 & 0.556807 \tabularnewline
58 & 0.480531 & 0.961061 & 0.519469 \tabularnewline
59 & 0.519487 & 0.961026 & 0.480513 \tabularnewline
60 & 0.561777 & 0.876447 & 0.438223 \tabularnewline
61 & 0.527225 & 0.94555 & 0.472775 \tabularnewline
62 & 0.614017 & 0.771967 & 0.385983 \tabularnewline
63 & 0.61859 & 0.762819 & 0.38141 \tabularnewline
64 & 0.574976 & 0.850048 & 0.425024 \tabularnewline
65 & 0.611242 & 0.777516 & 0.388758 \tabularnewline
66 & 0.921665 & 0.15667 & 0.0783352 \tabularnewline
67 & 0.940332 & 0.119336 & 0.0596681 \tabularnewline
68 & 0.928382 & 0.143237 & 0.0716184 \tabularnewline
69 & 0.996019 & 0.00796181 & 0.00398091 \tabularnewline
70 & 0.994828 & 0.0103449 & 0.00517245 \tabularnewline
71 & 0.993334 & 0.0133329 & 0.00666646 \tabularnewline
72 & 0.992588 & 0.0148237 & 0.00741183 \tabularnewline
73 & 0.993372 & 0.0132561 & 0.00662805 \tabularnewline
74 & 0.99131 & 0.0173796 & 0.00868978 \tabularnewline
75 & 0.990606 & 0.0187877 & 0.00939385 \tabularnewline
76 & 0.992441 & 0.0151174 & 0.00755869 \tabularnewline
77 & 0.991586 & 0.0168274 & 0.00841371 \tabularnewline
78 & 0.988878 & 0.0222439 & 0.0111219 \tabularnewline
79 & 0.985852 & 0.0282954 & 0.0141477 \tabularnewline
80 & 0.981695 & 0.0366098 & 0.0183049 \tabularnewline
81 & 0.982998 & 0.0340037 & 0.0170018 \tabularnewline
82 & 0.988434 & 0.0231311 & 0.0115655 \tabularnewline
83 & 0.986691 & 0.0266189 & 0.0133094 \tabularnewline
84 & 0.985014 & 0.0299725 & 0.0149862 \tabularnewline
85 & 0.982323 & 0.0353538 & 0.0176769 \tabularnewline
86 & 0.978115 & 0.0437705 & 0.0218853 \tabularnewline
87 & 0.973339 & 0.0533218 & 0.0266609 \tabularnewline
88 & 0.976051 & 0.0478979 & 0.0239489 \tabularnewline
89 & 0.973317 & 0.0533666 & 0.0266833 \tabularnewline
90 & 0.965662 & 0.0686767 & 0.0343384 \tabularnewline
91 & 0.957647 & 0.0847062 & 0.0423531 \tabularnewline
92 & 0.946591 & 0.106817 & 0.0534086 \tabularnewline
93 & 0.980353 & 0.0392948 & 0.0196474 \tabularnewline
94 & 0.975482 & 0.0490351 & 0.0245175 \tabularnewline
95 & 0.968497 & 0.0630061 & 0.0315031 \tabularnewline
96 & 0.973466 & 0.0530683 & 0.0265341 \tabularnewline
97 & 0.973205 & 0.0535909 & 0.0267954 \tabularnewline
98 & 0.965735 & 0.0685297 & 0.0342648 \tabularnewline
99 & 0.956836 & 0.0863285 & 0.0431642 \tabularnewline
100 & 0.950198 & 0.0996035 & 0.0498017 \tabularnewline
101 & 0.964458 & 0.0710837 & 0.0355419 \tabularnewline
102 & 0.954595 & 0.0908099 & 0.045405 \tabularnewline
103 & 0.945955 & 0.10809 & 0.0540449 \tabularnewline
104 & 0.935654 & 0.128693 & 0.0643464 \tabularnewline
105 & 0.938186 & 0.123628 & 0.0618142 \tabularnewline
106 & 0.929056 & 0.141887 & 0.0709436 \tabularnewline
107 & 0.939008 & 0.121984 & 0.0609918 \tabularnewline
108 & 0.928496 & 0.143009 & 0.0715043 \tabularnewline
109 & 0.912533 & 0.174933 & 0.0874665 \tabularnewline
110 & 0.917223 & 0.165555 & 0.0827773 \tabularnewline
111 & 0.902323 & 0.195354 & 0.0976772 \tabularnewline
112 & 0.883352 & 0.233296 & 0.116648 \tabularnewline
113 & 0.863688 & 0.272624 & 0.136312 \tabularnewline
114 & 0.836069 & 0.327862 & 0.163931 \tabularnewline
115 & 0.810964 & 0.378071 & 0.189036 \tabularnewline
116 & 0.7822 & 0.435601 & 0.2178 \tabularnewline
117 & 0.745685 & 0.508631 & 0.254315 \tabularnewline
118 & 0.705811 & 0.588377 & 0.294189 \tabularnewline
119 & 0.672117 & 0.655767 & 0.327883 \tabularnewline
120 & 0.744392 & 0.511216 & 0.255608 \tabularnewline
121 & 0.70469 & 0.59062 & 0.29531 \tabularnewline
122 & 0.677122 & 0.645756 & 0.322878 \tabularnewline
123 & 0.829328 & 0.341343 & 0.170672 \tabularnewline
124 & 0.817675 & 0.36465 & 0.182325 \tabularnewline
125 & 0.866321 & 0.267358 & 0.133679 \tabularnewline
126 & 0.938534 & 0.122932 & 0.0614662 \tabularnewline
127 & 0.93842 & 0.123159 & 0.0615795 \tabularnewline
128 & 0.96983 & 0.0603404 & 0.0301702 \tabularnewline
129 & 0.96388 & 0.0722404 & 0.0361202 \tabularnewline
130 & 0.97916 & 0.041679 & 0.0208395 \tabularnewline
131 & 0.971165 & 0.0576695 & 0.0288347 \tabularnewline
132 & 0.984247 & 0.0315056 & 0.0157528 \tabularnewline
133 & 0.977784 & 0.0444314 & 0.0222157 \tabularnewline
134 & 0.992129 & 0.0157417 & 0.00787085 \tabularnewline
135 & 0.995924 & 0.008152 & 0.004076 \tabularnewline
136 & 0.994911 & 0.0101785 & 0.00508927 \tabularnewline
137 & 0.993048 & 0.0139039 & 0.00695195 \tabularnewline
138 & 0.989666 & 0.0206687 & 0.0103344 \tabularnewline
139 & 0.98572 & 0.0285609 & 0.0142805 \tabularnewline
140 & 0.979202 & 0.041596 & 0.020798 \tabularnewline
141 & 0.974403 & 0.0511945 & 0.0255972 \tabularnewline
142 & 0.971058 & 0.057885 & 0.0289425 \tabularnewline
143 & 0.968126 & 0.0637484 & 0.0318742 \tabularnewline
144 & 0.964468 & 0.0710646 & 0.0355323 \tabularnewline
145 & 0.948919 & 0.102162 & 0.051081 \tabularnewline
146 & 0.933291 & 0.133419 & 0.0667093 \tabularnewline
147 & 0.921852 & 0.156297 & 0.0781484 \tabularnewline
148 & 0.915962 & 0.168077 & 0.0840385 \tabularnewline
149 & 0.92047 & 0.15906 & 0.07953 \tabularnewline
150 & 0.900697 & 0.198606 & 0.099303 \tabularnewline
151 & 0.898345 & 0.20331 & 0.101655 \tabularnewline
152 & 0.863965 & 0.272071 & 0.136035 \tabularnewline
153 & 0.815781 & 0.368439 & 0.184219 \tabularnewline
154 & 0.755928 & 0.488144 & 0.244072 \tabularnewline
155 & 0.68982 & 0.620361 & 0.31018 \tabularnewline
156 & 0.610086 & 0.779827 & 0.389914 \tabularnewline
157 & 0.65216 & 0.69568 & 0.34784 \tabularnewline
158 & 0.56259 & 0.87482 & 0.43741 \tabularnewline
159 & 0.605126 & 0.789748 & 0.394874 \tabularnewline
160 & 0.534697 & 0.930606 & 0.465303 \tabularnewline
161 & 0.989819 & 0.0203617 & 0.0101809 \tabularnewline
162 & 0.974408 & 0.0511848 & 0.0255924 \tabularnewline
163 & 0.945009 & 0.109982 & 0.0549912 \tabularnewline
164 & 0.883992 & 0.232016 & 0.116008 \tabularnewline
165 & 0.777622 & 0.444757 & 0.222378 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270458&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]6[/C][C]0.375443[/C][C]0.750886[/C][C]0.624557[/C][/ROW]
[ROW][C]7[/C][C]0.226472[/C][C]0.452944[/C][C]0.773528[/C][/ROW]
[ROW][C]8[/C][C]0.375648[/C][C]0.751296[/C][C]0.624352[/C][/ROW]
[ROW][C]9[/C][C]0.773776[/C][C]0.452449[/C][C]0.226224[/C][/ROW]
[ROW][C]10[/C][C]0.839079[/C][C]0.321843[/C][C]0.160921[/C][/ROW]
[ROW][C]11[/C][C]0.768557[/C][C]0.462887[/C][C]0.231443[/C][/ROW]
[ROW][C]12[/C][C]0.686313[/C][C]0.627375[/C][C]0.313687[/C][/ROW]
[ROW][C]13[/C][C]0.60479[/C][C]0.790421[/C][C]0.39521[/C][/ROW]
[ROW][C]14[/C][C]0.614171[/C][C]0.771659[/C][C]0.385829[/C][/ROW]
[ROW][C]15[/C][C]0.570915[/C][C]0.85817[/C][C]0.429085[/C][/ROW]
[ROW][C]16[/C][C]0.531122[/C][C]0.937757[/C][C]0.468878[/C][/ROW]
[ROW][C]17[/C][C]0.464352[/C][C]0.928704[/C][C]0.535648[/C][/ROW]
[ROW][C]18[/C][C]0.419374[/C][C]0.838748[/C][C]0.580626[/C][/ROW]
[ROW][C]19[/C][C]0.434806[/C][C]0.869612[/C][C]0.565194[/C][/ROW]
[ROW][C]20[/C][C]0.41897[/C][C]0.837941[/C][C]0.58103[/C][/ROW]
[ROW][C]21[/C][C]0.428641[/C][C]0.857282[/C][C]0.571359[/C][/ROW]
[ROW][C]22[/C][C]0.381398[/C][C]0.762795[/C][C]0.618602[/C][/ROW]
[ROW][C]23[/C][C]0.502666[/C][C]0.994667[/C][C]0.497334[/C][/ROW]
[ROW][C]24[/C][C]0.572646[/C][C]0.854709[/C][C]0.427354[/C][/ROW]
[ROW][C]25[/C][C]0.517964[/C][C]0.964072[/C][C]0.482036[/C][/ROW]
[ROW][C]26[/C][C]0.56323[/C][C]0.87354[/C][C]0.43677[/C][/ROW]
[ROW][C]27[/C][C]0.503912[/C][C]0.992177[/C][C]0.496088[/C][/ROW]
[ROW][C]28[/C][C]0.618021[/C][C]0.763958[/C][C]0.381979[/C][/ROW]
[ROW][C]29[/C][C]0.561152[/C][C]0.877695[/C][C]0.438848[/C][/ROW]
[ROW][C]30[/C][C]0.506139[/C][C]0.987722[/C][C]0.493861[/C][/ROW]
[ROW][C]31[/C][C]0.588098[/C][C]0.823804[/C][C]0.411902[/C][/ROW]
[ROW][C]32[/C][C]0.690862[/C][C]0.618276[/C][C]0.309138[/C][/ROW]
[ROW][C]33[/C][C]0.648594[/C][C]0.702812[/C][C]0.351406[/C][/ROW]
[ROW][C]34[/C][C]0.668763[/C][C]0.662474[/C][C]0.331237[/C][/ROW]
[ROW][C]35[/C][C]0.66123[/C][C]0.677539[/C][C]0.33877[/C][/ROW]
[ROW][C]36[/C][C]0.624414[/C][C]0.751173[/C][C]0.375586[/C][/ROW]
[ROW][C]37[/C][C]0.596341[/C][C]0.807319[/C][C]0.403659[/C][/ROW]
[ROW][C]38[/C][C]0.547252[/C][C]0.905496[/C][C]0.452748[/C][/ROW]
[ROW][C]39[/C][C]0.502002[/C][C]0.995996[/C][C]0.497998[/C][/ROW]
[ROW][C]40[/C][C]0.451361[/C][C]0.902722[/C][C]0.548639[/C][/ROW]
[ROW][C]41[/C][C]0.401982[/C][C]0.803963[/C][C]0.598018[/C][/ROW]
[ROW][C]42[/C][C]0.398037[/C][C]0.796075[/C][C]0.601963[/C][/ROW]
[ROW][C]43[/C][C]0.511553[/C][C]0.976894[/C][C]0.488447[/C][/ROW]
[ROW][C]44[/C][C]0.489762[/C][C]0.979524[/C][C]0.510238[/C][/ROW]
[ROW][C]45[/C][C]0.443168[/C][C]0.886336[/C][C]0.556832[/C][/ROW]
[ROW][C]46[/C][C]0.403008[/C][C]0.806015[/C][C]0.596992[/C][/ROW]
[ROW][C]47[/C][C]0.375323[/C][C]0.750646[/C][C]0.624677[/C][/ROW]
[ROW][C]48[/C][C]0.353116[/C][C]0.706232[/C][C]0.646884[/C][/ROW]
[ROW][C]49[/C][C]0.308288[/C][C]0.616575[/C][C]0.691712[/C][/ROW]
[ROW][C]50[/C][C]0.341205[/C][C]0.68241[/C][C]0.658795[/C][/ROW]
[ROW][C]51[/C][C]0.318719[/C][C]0.637438[/C][C]0.681281[/C][/ROW]
[ROW][C]52[/C][C]0.323963[/C][C]0.647927[/C][C]0.676037[/C][/ROW]
[ROW][C]53[/C][C]0.332151[/C][C]0.664302[/C][C]0.667849[/C][/ROW]
[ROW][C]54[/C][C]0.323929[/C][C]0.647858[/C][C]0.676071[/C][/ROW]
[ROW][C]55[/C][C]0.409155[/C][C]0.81831[/C][C]0.590845[/C][/ROW]
[ROW][C]56[/C][C]0.432644[/C][C]0.865289[/C][C]0.567356[/C][/ROW]
[ROW][C]57[/C][C]0.443193[/C][C]0.886387[/C][C]0.556807[/C][/ROW]
[ROW][C]58[/C][C]0.480531[/C][C]0.961061[/C][C]0.519469[/C][/ROW]
[ROW][C]59[/C][C]0.519487[/C][C]0.961026[/C][C]0.480513[/C][/ROW]
[ROW][C]60[/C][C]0.561777[/C][C]0.876447[/C][C]0.438223[/C][/ROW]
[ROW][C]61[/C][C]0.527225[/C][C]0.94555[/C][C]0.472775[/C][/ROW]
[ROW][C]62[/C][C]0.614017[/C][C]0.771967[/C][C]0.385983[/C][/ROW]
[ROW][C]63[/C][C]0.61859[/C][C]0.762819[/C][C]0.38141[/C][/ROW]
[ROW][C]64[/C][C]0.574976[/C][C]0.850048[/C][C]0.425024[/C][/ROW]
[ROW][C]65[/C][C]0.611242[/C][C]0.777516[/C][C]0.388758[/C][/ROW]
[ROW][C]66[/C][C]0.921665[/C][C]0.15667[/C][C]0.0783352[/C][/ROW]
[ROW][C]67[/C][C]0.940332[/C][C]0.119336[/C][C]0.0596681[/C][/ROW]
[ROW][C]68[/C][C]0.928382[/C][C]0.143237[/C][C]0.0716184[/C][/ROW]
[ROW][C]69[/C][C]0.996019[/C][C]0.00796181[/C][C]0.00398091[/C][/ROW]
[ROW][C]70[/C][C]0.994828[/C][C]0.0103449[/C][C]0.00517245[/C][/ROW]
[ROW][C]71[/C][C]0.993334[/C][C]0.0133329[/C][C]0.00666646[/C][/ROW]
[ROW][C]72[/C][C]0.992588[/C][C]0.0148237[/C][C]0.00741183[/C][/ROW]
[ROW][C]73[/C][C]0.993372[/C][C]0.0132561[/C][C]0.00662805[/C][/ROW]
[ROW][C]74[/C][C]0.99131[/C][C]0.0173796[/C][C]0.00868978[/C][/ROW]
[ROW][C]75[/C][C]0.990606[/C][C]0.0187877[/C][C]0.00939385[/C][/ROW]
[ROW][C]76[/C][C]0.992441[/C][C]0.0151174[/C][C]0.00755869[/C][/ROW]
[ROW][C]77[/C][C]0.991586[/C][C]0.0168274[/C][C]0.00841371[/C][/ROW]
[ROW][C]78[/C][C]0.988878[/C][C]0.0222439[/C][C]0.0111219[/C][/ROW]
[ROW][C]79[/C][C]0.985852[/C][C]0.0282954[/C][C]0.0141477[/C][/ROW]
[ROW][C]80[/C][C]0.981695[/C][C]0.0366098[/C][C]0.0183049[/C][/ROW]
[ROW][C]81[/C][C]0.982998[/C][C]0.0340037[/C][C]0.0170018[/C][/ROW]
[ROW][C]82[/C][C]0.988434[/C][C]0.0231311[/C][C]0.0115655[/C][/ROW]
[ROW][C]83[/C][C]0.986691[/C][C]0.0266189[/C][C]0.0133094[/C][/ROW]
[ROW][C]84[/C][C]0.985014[/C][C]0.0299725[/C][C]0.0149862[/C][/ROW]
[ROW][C]85[/C][C]0.982323[/C][C]0.0353538[/C][C]0.0176769[/C][/ROW]
[ROW][C]86[/C][C]0.978115[/C][C]0.0437705[/C][C]0.0218853[/C][/ROW]
[ROW][C]87[/C][C]0.973339[/C][C]0.0533218[/C][C]0.0266609[/C][/ROW]
[ROW][C]88[/C][C]0.976051[/C][C]0.0478979[/C][C]0.0239489[/C][/ROW]
[ROW][C]89[/C][C]0.973317[/C][C]0.0533666[/C][C]0.0266833[/C][/ROW]
[ROW][C]90[/C][C]0.965662[/C][C]0.0686767[/C][C]0.0343384[/C][/ROW]
[ROW][C]91[/C][C]0.957647[/C][C]0.0847062[/C][C]0.0423531[/C][/ROW]
[ROW][C]92[/C][C]0.946591[/C][C]0.106817[/C][C]0.0534086[/C][/ROW]
[ROW][C]93[/C][C]0.980353[/C][C]0.0392948[/C][C]0.0196474[/C][/ROW]
[ROW][C]94[/C][C]0.975482[/C][C]0.0490351[/C][C]0.0245175[/C][/ROW]
[ROW][C]95[/C][C]0.968497[/C][C]0.0630061[/C][C]0.0315031[/C][/ROW]
[ROW][C]96[/C][C]0.973466[/C][C]0.0530683[/C][C]0.0265341[/C][/ROW]
[ROW][C]97[/C][C]0.973205[/C][C]0.0535909[/C][C]0.0267954[/C][/ROW]
[ROW][C]98[/C][C]0.965735[/C][C]0.0685297[/C][C]0.0342648[/C][/ROW]
[ROW][C]99[/C][C]0.956836[/C][C]0.0863285[/C][C]0.0431642[/C][/ROW]
[ROW][C]100[/C][C]0.950198[/C][C]0.0996035[/C][C]0.0498017[/C][/ROW]
[ROW][C]101[/C][C]0.964458[/C][C]0.0710837[/C][C]0.0355419[/C][/ROW]
[ROW][C]102[/C][C]0.954595[/C][C]0.0908099[/C][C]0.045405[/C][/ROW]
[ROW][C]103[/C][C]0.945955[/C][C]0.10809[/C][C]0.0540449[/C][/ROW]
[ROW][C]104[/C][C]0.935654[/C][C]0.128693[/C][C]0.0643464[/C][/ROW]
[ROW][C]105[/C][C]0.938186[/C][C]0.123628[/C][C]0.0618142[/C][/ROW]
[ROW][C]106[/C][C]0.929056[/C][C]0.141887[/C][C]0.0709436[/C][/ROW]
[ROW][C]107[/C][C]0.939008[/C][C]0.121984[/C][C]0.0609918[/C][/ROW]
[ROW][C]108[/C][C]0.928496[/C][C]0.143009[/C][C]0.0715043[/C][/ROW]
[ROW][C]109[/C][C]0.912533[/C][C]0.174933[/C][C]0.0874665[/C][/ROW]
[ROW][C]110[/C][C]0.917223[/C][C]0.165555[/C][C]0.0827773[/C][/ROW]
[ROW][C]111[/C][C]0.902323[/C][C]0.195354[/C][C]0.0976772[/C][/ROW]
[ROW][C]112[/C][C]0.883352[/C][C]0.233296[/C][C]0.116648[/C][/ROW]
[ROW][C]113[/C][C]0.863688[/C][C]0.272624[/C][C]0.136312[/C][/ROW]
[ROW][C]114[/C][C]0.836069[/C][C]0.327862[/C][C]0.163931[/C][/ROW]
[ROW][C]115[/C][C]0.810964[/C][C]0.378071[/C][C]0.189036[/C][/ROW]
[ROW][C]116[/C][C]0.7822[/C][C]0.435601[/C][C]0.2178[/C][/ROW]
[ROW][C]117[/C][C]0.745685[/C][C]0.508631[/C][C]0.254315[/C][/ROW]
[ROW][C]118[/C][C]0.705811[/C][C]0.588377[/C][C]0.294189[/C][/ROW]
[ROW][C]119[/C][C]0.672117[/C][C]0.655767[/C][C]0.327883[/C][/ROW]
[ROW][C]120[/C][C]0.744392[/C][C]0.511216[/C][C]0.255608[/C][/ROW]
[ROW][C]121[/C][C]0.70469[/C][C]0.59062[/C][C]0.29531[/C][/ROW]
[ROW][C]122[/C][C]0.677122[/C][C]0.645756[/C][C]0.322878[/C][/ROW]
[ROW][C]123[/C][C]0.829328[/C][C]0.341343[/C][C]0.170672[/C][/ROW]
[ROW][C]124[/C][C]0.817675[/C][C]0.36465[/C][C]0.182325[/C][/ROW]
[ROW][C]125[/C][C]0.866321[/C][C]0.267358[/C][C]0.133679[/C][/ROW]
[ROW][C]126[/C][C]0.938534[/C][C]0.122932[/C][C]0.0614662[/C][/ROW]
[ROW][C]127[/C][C]0.93842[/C][C]0.123159[/C][C]0.0615795[/C][/ROW]
[ROW][C]128[/C][C]0.96983[/C][C]0.0603404[/C][C]0.0301702[/C][/ROW]
[ROW][C]129[/C][C]0.96388[/C][C]0.0722404[/C][C]0.0361202[/C][/ROW]
[ROW][C]130[/C][C]0.97916[/C][C]0.041679[/C][C]0.0208395[/C][/ROW]
[ROW][C]131[/C][C]0.971165[/C][C]0.0576695[/C][C]0.0288347[/C][/ROW]
[ROW][C]132[/C][C]0.984247[/C][C]0.0315056[/C][C]0.0157528[/C][/ROW]
[ROW][C]133[/C][C]0.977784[/C][C]0.0444314[/C][C]0.0222157[/C][/ROW]
[ROW][C]134[/C][C]0.992129[/C][C]0.0157417[/C][C]0.00787085[/C][/ROW]
[ROW][C]135[/C][C]0.995924[/C][C]0.008152[/C][C]0.004076[/C][/ROW]
[ROW][C]136[/C][C]0.994911[/C][C]0.0101785[/C][C]0.00508927[/C][/ROW]
[ROW][C]137[/C][C]0.993048[/C][C]0.0139039[/C][C]0.00695195[/C][/ROW]
[ROW][C]138[/C][C]0.989666[/C][C]0.0206687[/C][C]0.0103344[/C][/ROW]
[ROW][C]139[/C][C]0.98572[/C][C]0.0285609[/C][C]0.0142805[/C][/ROW]
[ROW][C]140[/C][C]0.979202[/C][C]0.041596[/C][C]0.020798[/C][/ROW]
[ROW][C]141[/C][C]0.974403[/C][C]0.0511945[/C][C]0.0255972[/C][/ROW]
[ROW][C]142[/C][C]0.971058[/C][C]0.057885[/C][C]0.0289425[/C][/ROW]
[ROW][C]143[/C][C]0.968126[/C][C]0.0637484[/C][C]0.0318742[/C][/ROW]
[ROW][C]144[/C][C]0.964468[/C][C]0.0710646[/C][C]0.0355323[/C][/ROW]
[ROW][C]145[/C][C]0.948919[/C][C]0.102162[/C][C]0.051081[/C][/ROW]
[ROW][C]146[/C][C]0.933291[/C][C]0.133419[/C][C]0.0667093[/C][/ROW]
[ROW][C]147[/C][C]0.921852[/C][C]0.156297[/C][C]0.0781484[/C][/ROW]
[ROW][C]148[/C][C]0.915962[/C][C]0.168077[/C][C]0.0840385[/C][/ROW]
[ROW][C]149[/C][C]0.92047[/C][C]0.15906[/C][C]0.07953[/C][/ROW]
[ROW][C]150[/C][C]0.900697[/C][C]0.198606[/C][C]0.099303[/C][/ROW]
[ROW][C]151[/C][C]0.898345[/C][C]0.20331[/C][C]0.101655[/C][/ROW]
[ROW][C]152[/C][C]0.863965[/C][C]0.272071[/C][C]0.136035[/C][/ROW]
[ROW][C]153[/C][C]0.815781[/C][C]0.368439[/C][C]0.184219[/C][/ROW]
[ROW][C]154[/C][C]0.755928[/C][C]0.488144[/C][C]0.244072[/C][/ROW]
[ROW][C]155[/C][C]0.68982[/C][C]0.620361[/C][C]0.31018[/C][/ROW]
[ROW][C]156[/C][C]0.610086[/C][C]0.779827[/C][C]0.389914[/C][/ROW]
[ROW][C]157[/C][C]0.65216[/C][C]0.69568[/C][C]0.34784[/C][/ROW]
[ROW][C]158[/C][C]0.56259[/C][C]0.87482[/C][C]0.43741[/C][/ROW]
[ROW][C]159[/C][C]0.605126[/C][C]0.789748[/C][C]0.394874[/C][/ROW]
[ROW][C]160[/C][C]0.534697[/C][C]0.930606[/C][C]0.465303[/C][/ROW]
[ROW][C]161[/C][C]0.989819[/C][C]0.0203617[/C][C]0.0101809[/C][/ROW]
[ROW][C]162[/C][C]0.974408[/C][C]0.0511848[/C][C]0.0255924[/C][/ROW]
[ROW][C]163[/C][C]0.945009[/C][C]0.109982[/C][C]0.0549912[/C][/ROW]
[ROW][C]164[/C][C]0.883992[/C][C]0.232016[/C][C]0.116008[/C][/ROW]
[ROW][C]165[/C][C]0.777622[/C][C]0.444757[/C][C]0.222378[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270458&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270458&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
6	0.375443	0.750886	0.624557
7	0.226472	0.452944	0.773528
8	0.375648	0.751296	0.624352
9	0.773776	0.452449	0.226224
10	0.839079	0.321843	0.160921
11	0.768557	0.462887	0.231443
12	0.686313	0.627375	0.313687
13	0.60479	0.790421	0.39521
14	0.614171	0.771659	0.385829
15	0.570915	0.85817	0.429085
16	0.531122	0.937757	0.468878
17	0.464352	0.928704	0.535648
18	0.419374	0.838748	0.580626
19	0.434806	0.869612	0.565194
20	0.41897	0.837941	0.58103
21	0.428641	0.857282	0.571359
22	0.381398	0.762795	0.618602
23	0.502666	0.994667	0.497334
24	0.572646	0.854709	0.427354
25	0.517964	0.964072	0.482036
26	0.56323	0.87354	0.43677
27	0.503912	0.992177	0.496088
28	0.618021	0.763958	0.381979
29	0.561152	0.877695	0.438848
30	0.506139	0.987722	0.493861
31	0.588098	0.823804	0.411902
32	0.690862	0.618276	0.309138
33	0.648594	0.702812	0.351406
34	0.668763	0.662474	0.331237
35	0.66123	0.677539	0.33877
36	0.624414	0.751173	0.375586
37	0.596341	0.807319	0.403659
38	0.547252	0.905496	0.452748
39	0.502002	0.995996	0.497998
40	0.451361	0.902722	0.548639
41	0.401982	0.803963	0.598018
42	0.398037	0.796075	0.601963
43	0.511553	0.976894	0.488447
44	0.489762	0.979524	0.510238
45	0.443168	0.886336	0.556832
46	0.403008	0.806015	0.596992
47	0.375323	0.750646	0.624677
48	0.353116	0.706232	0.646884
49	0.308288	0.616575	0.691712
50	0.341205	0.68241	0.658795
51	0.318719	0.637438	0.681281
52	0.323963	0.647927	0.676037
53	0.332151	0.664302	0.667849
54	0.323929	0.647858	0.676071
55	0.409155	0.81831	0.590845
56	0.432644	0.865289	0.567356
57	0.443193	0.886387	0.556807
58	0.480531	0.961061	0.519469
59	0.519487	0.961026	0.480513
60	0.561777	0.876447	0.438223
61	0.527225	0.94555	0.472775
62	0.614017	0.771967	0.385983
63	0.61859	0.762819	0.38141
64	0.574976	0.850048	0.425024
65	0.611242	0.777516	0.388758
66	0.921665	0.15667	0.0783352
67	0.940332	0.119336	0.0596681
68	0.928382	0.143237	0.0716184
69	0.996019	0.00796181	0.00398091
70	0.994828	0.0103449	0.00517245
71	0.993334	0.0133329	0.00666646
72	0.992588	0.0148237	0.00741183
73	0.993372	0.0132561	0.00662805
74	0.99131	0.0173796	0.00868978
75	0.990606	0.0187877	0.00939385
76	0.992441	0.0151174	0.00755869
77	0.991586	0.0168274	0.00841371
78	0.988878	0.0222439	0.0111219
79	0.985852	0.0282954	0.0141477
80	0.981695	0.0366098	0.0183049
81	0.982998	0.0340037	0.0170018
82	0.988434	0.0231311	0.0115655
83	0.986691	0.0266189	0.0133094
84	0.985014	0.0299725	0.0149862
85	0.982323	0.0353538	0.0176769
86	0.978115	0.0437705	0.0218853
87	0.973339	0.0533218	0.0266609
88	0.976051	0.0478979	0.0239489
89	0.973317	0.0533666	0.0266833
90	0.965662	0.0686767	0.0343384
91	0.957647	0.0847062	0.0423531
92	0.946591	0.106817	0.0534086
93	0.980353	0.0392948	0.0196474
94	0.975482	0.0490351	0.0245175
95	0.968497	0.0630061	0.0315031
96	0.973466	0.0530683	0.0265341
97	0.973205	0.0535909	0.0267954
98	0.965735	0.0685297	0.0342648
99	0.956836	0.0863285	0.0431642
100	0.950198	0.0996035	0.0498017
101	0.964458	0.0710837	0.0355419
102	0.954595	0.0908099	0.045405
103	0.945955	0.10809	0.0540449
104	0.935654	0.128693	0.0643464
105	0.938186	0.123628	0.0618142
106	0.929056	0.141887	0.0709436
107	0.939008	0.121984	0.0609918
108	0.928496	0.143009	0.0715043
109	0.912533	0.174933	0.0874665
110	0.917223	0.165555	0.0827773
111	0.902323	0.195354	0.0976772
112	0.883352	0.233296	0.116648
113	0.863688	0.272624	0.136312
114	0.836069	0.327862	0.163931
115	0.810964	0.378071	0.189036
116	0.7822	0.435601	0.2178
117	0.745685	0.508631	0.254315
118	0.705811	0.588377	0.294189
119	0.672117	0.655767	0.327883
120	0.744392	0.511216	0.255608
121	0.70469	0.59062	0.29531
122	0.677122	0.645756	0.322878
123	0.829328	0.341343	0.170672
124	0.817675	0.36465	0.182325
125	0.866321	0.267358	0.133679
126	0.938534	0.122932	0.0614662
127	0.93842	0.123159	0.0615795
128	0.96983	0.0603404	0.0301702
129	0.96388	0.0722404	0.0361202
130	0.97916	0.041679	0.0208395
131	0.971165	0.0576695	0.0288347
132	0.984247	0.0315056	0.0157528
133	0.977784	0.0444314	0.0222157
134	0.992129	0.0157417	0.00787085
135	0.995924	0.008152	0.004076
136	0.994911	0.0101785	0.00508927
137	0.993048	0.0139039	0.00695195
138	0.989666	0.0206687	0.0103344
139	0.98572	0.0285609	0.0142805
140	0.979202	0.041596	0.020798
141	0.974403	0.0511945	0.0255972
142	0.971058	0.057885	0.0289425
143	0.968126	0.0637484	0.0318742
144	0.964468	0.0710646	0.0355323
145	0.948919	0.102162	0.051081
146	0.933291	0.133419	0.0667093
147	0.921852	0.156297	0.0781484
148	0.915962	0.168077	0.0840385
149	0.92047	0.15906	0.07953
150	0.900697	0.198606	0.099303
151	0.898345	0.20331	0.101655
152	0.863965	0.272071	0.136035
153	0.815781	0.368439	0.184219
154	0.755928	0.488144	0.244072
155	0.68982	0.620361	0.31018
156	0.610086	0.779827	0.389914
157	0.65216	0.69568	0.34784
158	0.56259	0.87482	0.43741
159	0.605126	0.789748	0.394874
160	0.534697	0.930606	0.465303
161	0.989819	0.0203617	0.0101809
162	0.974408	0.0511848	0.0255924
163	0.945009	0.109982	0.0549912
164	0.883992	0.232016	0.116008
165	0.777622	0.444757	0.222378

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0125	NOK
5% type I error level	32	0.2	NOK
10% type I error level	52	0.325	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 & 2 & 0.0125 & NOK \tabularnewline
5% type I error level & 32 & 0.2 & NOK \tabularnewline
10% type I error level & 52 & 0.325 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270458&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]2[/C][C]0.0125[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.2[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]52[/C][C]0.325[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270458&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270458&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	2	0.0125	NOK
5% type I error level	32	0.2	NOK
10% type I error level	52	0.325	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code