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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 18 Dec 2014 11:15:57 +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/18/t14189014479pc9j915zly283g.htm/, Retrieved Sun, 19 May 2024 18:45:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270808, Retrieved Sun, 19 May 2024 18:45:05 +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] [] [2014-12-18 11:15:57] [a3de03a8fa2b95b1b988206b9ba33408] [Current]

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48	41	23	12	34	1
50	146	16	45	61	1
150	182	33	37	70	4
154	192	32	37	69	4
109	263	37	108	145	3
68	35	14	10	23	2
194	439	52	68	120	4
158	214	75	72	147	4
159	341	72	143	215	4
67	58	15	9	24	2
147	292	29	55	84	4
39	85	13	17	30	1
100	200	40	37	77	3
111	158	19	27	46	3
138	199	24	37	61	4
101	297	121	58	178	3
131	227	93	66	160	4
101	108	36	21	57	3
114	86	23	19	42	3
165	302	85	78	163	4
114	148	41	35	75	3
111	178	46	48	94	3
75	120	18	27	45	2
82	207	35	43	78	2
121	157	17	30	47	3
32	128	4	25	29	1
150	296	28	69	97	4
117	323	44	72	116	3
71	79	10	23	32	2
165	70	38	13	50	4
154	146	57	61	118	4
126	246	23	43	66	4
149	196	36	51	86	4
145	199	22	67	89	4
120	127	40	36	76	3
109	153	31	44	75	3
132	299	11	45	57	4
172	228	38	34	72	4
169	190	24	36	60	4
114	180	37	72	109	3
156	212	37	39	76	4
172	269	22	43	65	4
68	130	15	25	40	2
89	179	2	56	58	2
167	243	43	80	123	4
113	190	31	40	71	3
115	299	29	73	102	3
78	121	45	34	80	2
118	137	25	72	97	3
87	305	4	42	46	2
173	157	31	61	93	4
2	96	-4	23	19	1
162	183	66	74	140	4
49	52	61	16	78	1
122	238	32	66	98	4
96	40	31	9	40	3
100	226	39	41	80	3
82	190	19	57	76	2
100	214	31	48	79	3
115	145	36	51	87	3
141	119	42	53	95	4
165	222	21	29	49	4
165	222	21	29	49	4
110	159	25	55	80	3
118	165	32	54	86	3
158	249	26	43	69	4
146	125	28	51	79	4
49	122	32	20	52	1
90	186	41	79	120	2
121	148	29	39	69	3
155	274	33	61	94	4
104	172	17	55	72	3
147	84	13	30	43	4
110	168	32	55	87	3
108	102	30	22	52	3
113	106	34	37	71	3
115	2	59	2	61	3
61	139	13	38	51	1
60	95	23	27	50	1
109	130	10	56	67	3
68	72	5	25	30	2
111	141	31	39	70	3
77	113	19	33	52	2
73	206	32	43	75	2
151	268	30	57	87	4
89	175	25	43	69	2
78	77	48	23	72	2
110	125	35	44	79	3
220	255	67	54	121	4
65	111	15	28	43	2
141	132	22	36	58	4
117	211	18	39	57	3
122	92	33	16	50	4
63	76	46	23	69	2
44	171	24	40	64	1
52	83	14	24	38	1
131	266	12	78	90	4
101	186	38	57	96	3
42	50	12	37	49	1
152	117	28	27	56	4
107	219	41	61	102	3
77	246	12	27	40	2
154	279	31	69	100	4
103	148	33	34	67	3
96	137	34	44	78	3
175	181	21	34	55	4
57	98	20	39	59	1
112	226	44	51	96	3
143	234	52	34	86	4
49	138	7	31	38	1
110	85	29	13	43	3
131	66	11	12	23	4
167	236	26	51	77	4
56	106	24	24	48	1
137	135	7	19	26	4
86	122	60	30	91	2
121	218	13	81	94	3
149	199	20	42	62	4
168	112	52	22	74	4
140	278	28	85	114	4
88	94	25	27	52	2
168	113	39	25	64	4
94	84	9	22	31	2
51	86	19	19	38	1
48	62	13	14	27	1
145	222	60	45	105	4
66	167	19	45	64	2
85	82	34	28	62	2
109	207	14	51	65	3
63	184	17	41	58	2
102	83	45	31	76	3
162	183	66	74	140	4
86	89	48	19	68	2
114	225	29	51	80	3
164	237	-2	73	71	4
119	102	51	24	76	3
126	221	2	61	63	4
132	128	24	23	46	4
142	91	40	14	53	4
83	198	20	54	74	2
94	204	19	51	70	2
81	158	16	62	78	2
166	138	20	36	56	4
110	226	40	59	100	3
64	44	27	24	51	2
93	196	25	26	52	2
104	83	49	54	102	3
105	79	39	39	78	3
49	52	61	16	78	1
88	105	19	36	55	2
95	116	67	31	98	2
102	83	45	31	76	3
99	196	30	42	73	3
63	153	8	39	47	2
76	157	19	25	45	2
109	75	52	31	83	3
117	106	22	38	60	3
57	58	17	31	48	1
120	75	33	17	50	3
73	74	34	22	56	2
91	185	22	55	77	2
108	265	30	62	91	3
105	131	25	51	76	3
117	139	38	30	68	3
119	196	26	49	74	3
31	78	13	16	29	1

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=270808&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=270808&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270808&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
PR[t] = + 0.175237 + 0.0247297LFM[t] -0.000216864B[t] + 0.00535342PRH[t] + 0.00772361CH[t] -0.00651093H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PR[t] =  +  0.175237 +  0.0247297LFM[t] -0.000216864B[t] +  0.00535342PRH[t] +  0.00772361CH[t] -0.00651093H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270808&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PR[t] =  +  0.175237 +  0.0247297LFM[t] -0.000216864B[t] +  0.00535342PRH[t] +  0.00772361CH[t] -0.00651093H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270808&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270808&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
PR[t] = + 0.175237 + 0.0247297LFM[t] -0.000216864B[t] + 0.00535342PRH[t] + 0.00772361CH[t] -0.00651093H[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)	0.175237	0.0890498	1.968	0.0508137	0.0254069
LFM	0.0247297	0.000861183	28.72	5.07887e-65	2.53944e-65
B	-0.000216864	0.000574844	-0.3773	0.706483	0.353241
PRH	0.00535342	0.0579485	0.09238	0.92651	0.463255
CH	0.00772361	0.0577848	0.1337	0.893838	0.446919
H	-0.00651093	0.0577617	-0.1127	0.910393	0.455197

\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) & 0.175237 & 0.0890498 & 1.968 & 0.0508137 & 0.0254069 \tabularnewline
LFM & 0.0247297 & 0.000861183 & 28.72 & 5.07887e-65 & 2.53944e-65 \tabularnewline
B & -0.000216864 & 0.000574844 & -0.3773 & 0.706483 & 0.353241 \tabularnewline
PRH & 0.00535342 & 0.0579485 & 0.09238 & 0.92651 & 0.463255 \tabularnewline
CH & 0.00772361 & 0.0577848 & 0.1337 & 0.893838 & 0.446919 \tabularnewline
H & -0.00651093 & 0.0577617 & -0.1127 & 0.910393 & 0.455197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270808&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]0.175237[/C][C]0.0890498[/C][C]1.968[/C][C]0.0508137[/C][C]0.0254069[/C][/ROW]
[ROW][C]LFM[/C][C]0.0247297[/C][C]0.000861183[/C][C]28.72[/C][C]5.07887e-65[/C][C]2.53944e-65[/C][/ROW]
[ROW][C]B[/C][C]-0.000216864[/C][C]0.000574844[/C][C]-0.3773[/C][C]0.706483[/C][C]0.353241[/C][/ROW]
[ROW][C]PRH[/C][C]0.00535342[/C][C]0.0579485[/C][C]0.09238[/C][C]0.92651[/C][C]0.463255[/C][/ROW]
[ROW][C]CH[/C][C]0.00772361[/C][C]0.0577848[/C][C]0.1337[/C][C]0.893838[/C][C]0.446919[/C][/ROW]
[ROW][C]H[/C][C]-0.00651093[/C][C]0.0577617[/C][C]-0.1127[/C][C]0.910393[/C][C]0.455197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270808&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270808&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)	0.175237	0.0890498	1.968	0.0508137	0.0254069
LFM	0.0247297	0.000861183	28.72	5.07887e-65	2.53944e-65
B	-0.000216864	0.000574844	-0.3773	0.706483	0.353241
PRH	0.00535342	0.0579485	0.09238	0.92651	0.463255
CH	0.00772361	0.0577848	0.1337	0.893838	0.446919
H	-0.00651093	0.0577617	-0.1127	0.910393	0.455197

Multiple Linear Regression - Regression Statistics
Multiple R	0.939986
R-squared	0.883574
Adjusted R-squared	0.879935
F-TEST (value)	242.852
F-TEST (DF numerator)	5
F-TEST (DF denominator)	160
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.349445
Sum Squared Residuals	19.5379

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.939986 \tabularnewline
R-squared & 0.883574 \tabularnewline
Adjusted R-squared & 0.879935 \tabularnewline
F-TEST (value) & 242.852 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.349445 \tabularnewline
Sum Squared Residuals & 19.5379 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270808&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.939986[/C][/ROW]
[ROW][C]R-squared[/C][C]0.883574[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.879935[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]242.852[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.349445[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]19.5379[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270808&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270808&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	0.939986
R-squared	0.883574
Adjusted R-squared	0.879935
F-TEST (value)	242.852
F-TEST (DF numerator)	5
F-TEST (DF denominator)	160
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.349445
Sum Squared Residuals	19.5379

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1	1.34781	-0.347811
2	1	1.41611	-0.416109
3	4	3.85189	0.148108
4	4	3.9498	0.0502003
5	3	2.90188	0.0981208
6	2	1.8517	0.148302
7	4	4.89986	-0.899865
8	4	4.03662	-0.0366183
9	4	4.12338	-0.123379
10	2	1.8131	0.186901
11	4	3.78031	0.219694
12	1	1.12683	-0.126829
13	3	2.6034	0.396598
14	3	2.89672	0.103283
15	4	3.56187	0.438133
16	3	2.54531	0.454686
17	4	3.33148	0.668525
18	3	2.63331	0.36669
19	3	2.97219	0.0278109
20	4	4.18634	-0.186343
21	3	2.96382	0.0361779
22	3	2.88659	0.113406
23	2	2.01585	-0.0158468
24	2	2.16981	-0.169813
25	3	3.15018	-0.150184
26	1	0.964515	0.0354849
27	4	3.87176	0.128237
28	3	3.03495	-0.0349458
29	2	1.93674	0.0632602
30	4	4.21875	-0.218745
31	4	3.95994	0.040058
32	4	3.26335	0.736649
33	4	3.84414	0.155858
34	4	3.77367	0.22633
35	3	3.11261	-0.112614
36	3	2.85507	0.144932
37	4	3.41004	0.58996
38	4	4.37654	-0.376544
39	4	4.32923	-0.329226
40	3	2.99987	0.000129189
41	4	3.99156	0.00844038
42	4	4.39709	-0.397087
43	2	1.84162	0.158382
44	2	2.40296	-0.402955
45	4	4.29964	-0.299638
46	3	2.94111	0.0588882
47	3	3.00927	-0.00926648
48	2	2.06054	-0.0605441
49	3	3.122	-0.122005
50	2	2.30688	-0.306879
51	4	4.451	-0.451005
52	1	0.236399	0.763601
53	4	4.1551	-0.155103
54	1	1.318	-0.317998
55	4	3.18364	0.816358
56	3	2.51564	0.484357
57	3	2.60377	0.396229
58	2	2.209	-0.208997
59	3	2.62412	0.375877
60	3	3.00788	-0.00788194
61	4	3.65197	0.348027
62	4	4.22486	-0.224862
63	4	4.22486	-0.224862
64	3	2.89878	0.101219
65	3	3.086	-0.0860018
66	4	4.05058	-0.0505784
67	4	3.7881	0.2119
68	1	1.34775	-0.347747
69	2	2.40892	-0.408916
70	3	3.14265	-0.142649
71	4	3.98469	0.0153066
72	3	2.75684	0.243157
73	4	3.81362	0.186383
74	3	2.88873	0.111274
75	3	2.81588	0.184123
76	3	2.95222	0.0477823
77	3	2.95285	0.0471507
78	1	1.68464	-0.684638
79	1	1.64454	-0.644536
80	3	2.8924	0.107595
81	2	1.86577	0.134229
82	3	2.90107	0.098934
83	2	2.07294	-0.0729428
84	2	1.95093	0.0490651
85	4	3.8857	0.114302
86	2	2.35492	-0.354924
87	2	2.05327	-0.0532741
88	3	2.88124	0.11876
89	4	5.5484	-1.5484
90	2	1.77519	0.224813
91	4	3.65169	0.348312
92	3	3.04931	-0.0493113
93	4	3.147	0.852999
94	2	1.69137	0.308628
95	1	1.24699	-0.246986
96	1	1.45608	-0.45608
97	4	3.43784	0.562161
98	3	2.65123	0.348775
99	1	1.23402	-0.234019
100	4	3.9026	0.0974026
101	3	2.80034	0.199664
102	2	2.03842	-0.0384155
103	4	3.9709	0.0291041
104	3	2.69333	0.306668
105	3	2.53358	0.466421
106	4	4.4806	-0.480603
107	1	1.58772	-0.587721
108	3	2.90036	0.0996441
109	4	3.64188	0.358123
110	1	1.38655	-0.386555
111	3	2.85276	0.147245
112	4	3.40233	0.597668
113	4	4.28567	-0.285666
114	1	1.53844	-0.538436
115	4	3.54887	0.451134
116	2	2.23595	-0.235951
117	3	3.20343	-0.203432
118	4	3.84459	0.155413
119	4	4.27202	-0.272024
120	4	3.64126	0.358738
121	2	2.33487	-0.334869
122	4	4.29049	-0.290493
123	2	2.49787	-0.497872
124	1	1.41885	-0.418849
125	1	1.35075	-0.350746
126	4	3.69802	0.301982
127	2	1.80376	0.196242
128	2	2.25408	-0.254077
129	3	2.87152	0.128477
130	2	1.72335	0.276654
131	3	2.66517	0.33483
132	4	4.1551	-0.155103
133	2	2.24366	-0.243658
134	3	2.97391	0.0260943
135	4	4.27035	-0.27035
136	3	3.05951	-0.0595098
137	4	3.31491	0.685091
138	4	3.41842	0.581581
139	4	3.64431	0.355694
140	2	2.2272	-0.227196
141	2	2.49544	-0.495441
142	2	2.20074	-0.200743
143	4	4.27094	-0.270944
144	3	2.86523	0.134772
145	2	1.74625	0.253754
146	2	2.42867	-0.428673
147	3	2.7444	0.255598
148	3	2.75687	0.243127
149	1	1.318	-0.317998
150	2	2.35034	-0.350342
151	2	2.45944	-0.459441
152	3	2.66517	0.33483
153	3	2.59067	0.409333
154	2	1.73806	0.261939
155	2	2.02246	-0.0224588
156	3	2.83191	0.16809
157	3	3.06624	-0.0662393
158	1	1.59017	-0.590166
159	3	3.10895	-0.108952
160	2	1.95178	0.0482204
161	2	2.42675	-0.426751
162	3	2.83555	0.164454
163	3	2.77635	0.223646
164	3	3.03086	-0.0308612
165	3	3.1114	-0.111401
166	1	0.929297	0.0707031

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 1.34781 & -0.347811 \tabularnewline
2 & 1 & 1.41611 & -0.416109 \tabularnewline
3 & 4 & 3.85189 & 0.148108 \tabularnewline
4 & 4 & 3.9498 & 0.0502003 \tabularnewline
5 & 3 & 2.90188 & 0.0981208 \tabularnewline
6 & 2 & 1.8517 & 0.148302 \tabularnewline
7 & 4 & 4.89986 & -0.899865 \tabularnewline
8 & 4 & 4.03662 & -0.0366183 \tabularnewline
9 & 4 & 4.12338 & -0.123379 \tabularnewline
10 & 2 & 1.8131 & 0.186901 \tabularnewline
11 & 4 & 3.78031 & 0.219694 \tabularnewline
12 & 1 & 1.12683 & -0.126829 \tabularnewline
13 & 3 & 2.6034 & 0.396598 \tabularnewline
14 & 3 & 2.89672 & 0.103283 \tabularnewline
15 & 4 & 3.56187 & 0.438133 \tabularnewline
16 & 3 & 2.54531 & 0.454686 \tabularnewline
17 & 4 & 3.33148 & 0.668525 \tabularnewline
18 & 3 & 2.63331 & 0.36669 \tabularnewline
19 & 3 & 2.97219 & 0.0278109 \tabularnewline
20 & 4 & 4.18634 & -0.186343 \tabularnewline
21 & 3 & 2.96382 & 0.0361779 \tabularnewline
22 & 3 & 2.88659 & 0.113406 \tabularnewline
23 & 2 & 2.01585 & -0.0158468 \tabularnewline
24 & 2 & 2.16981 & -0.169813 \tabularnewline
25 & 3 & 3.15018 & -0.150184 \tabularnewline
26 & 1 & 0.964515 & 0.0354849 \tabularnewline
27 & 4 & 3.87176 & 0.128237 \tabularnewline
28 & 3 & 3.03495 & -0.0349458 \tabularnewline
29 & 2 & 1.93674 & 0.0632602 \tabularnewline
30 & 4 & 4.21875 & -0.218745 \tabularnewline
31 & 4 & 3.95994 & 0.040058 \tabularnewline
32 & 4 & 3.26335 & 0.736649 \tabularnewline
33 & 4 & 3.84414 & 0.155858 \tabularnewline
34 & 4 & 3.77367 & 0.22633 \tabularnewline
35 & 3 & 3.11261 & -0.112614 \tabularnewline
36 & 3 & 2.85507 & 0.144932 \tabularnewline
37 & 4 & 3.41004 & 0.58996 \tabularnewline
38 & 4 & 4.37654 & -0.376544 \tabularnewline
39 & 4 & 4.32923 & -0.329226 \tabularnewline
40 & 3 & 2.99987 & 0.000129189 \tabularnewline
41 & 4 & 3.99156 & 0.00844038 \tabularnewline
42 & 4 & 4.39709 & -0.397087 \tabularnewline
43 & 2 & 1.84162 & 0.158382 \tabularnewline
44 & 2 & 2.40296 & -0.402955 \tabularnewline
45 & 4 & 4.29964 & -0.299638 \tabularnewline
46 & 3 & 2.94111 & 0.0588882 \tabularnewline
47 & 3 & 3.00927 & -0.00926648 \tabularnewline
48 & 2 & 2.06054 & -0.0605441 \tabularnewline
49 & 3 & 3.122 & -0.122005 \tabularnewline
50 & 2 & 2.30688 & -0.306879 \tabularnewline
51 & 4 & 4.451 & -0.451005 \tabularnewline
52 & 1 & 0.236399 & 0.763601 \tabularnewline
53 & 4 & 4.1551 & -0.155103 \tabularnewline
54 & 1 & 1.318 & -0.317998 \tabularnewline
55 & 4 & 3.18364 & 0.816358 \tabularnewline
56 & 3 & 2.51564 & 0.484357 \tabularnewline
57 & 3 & 2.60377 & 0.396229 \tabularnewline
58 & 2 & 2.209 & -0.208997 \tabularnewline
59 & 3 & 2.62412 & 0.375877 \tabularnewline
60 & 3 & 3.00788 & -0.00788194 \tabularnewline
61 & 4 & 3.65197 & 0.348027 \tabularnewline
62 & 4 & 4.22486 & -0.224862 \tabularnewline
63 & 4 & 4.22486 & -0.224862 \tabularnewline
64 & 3 & 2.89878 & 0.101219 \tabularnewline
65 & 3 & 3.086 & -0.0860018 \tabularnewline
66 & 4 & 4.05058 & -0.0505784 \tabularnewline
67 & 4 & 3.7881 & 0.2119 \tabularnewline
68 & 1 & 1.34775 & -0.347747 \tabularnewline
69 & 2 & 2.40892 & -0.408916 \tabularnewline
70 & 3 & 3.14265 & -0.142649 \tabularnewline
71 & 4 & 3.98469 & 0.0153066 \tabularnewline
72 & 3 & 2.75684 & 0.243157 \tabularnewline
73 & 4 & 3.81362 & 0.186383 \tabularnewline
74 & 3 & 2.88873 & 0.111274 \tabularnewline
75 & 3 & 2.81588 & 0.184123 \tabularnewline
76 & 3 & 2.95222 & 0.0477823 \tabularnewline
77 & 3 & 2.95285 & 0.0471507 \tabularnewline
78 & 1 & 1.68464 & -0.684638 \tabularnewline
79 & 1 & 1.64454 & -0.644536 \tabularnewline
80 & 3 & 2.8924 & 0.107595 \tabularnewline
81 & 2 & 1.86577 & 0.134229 \tabularnewline
82 & 3 & 2.90107 & 0.098934 \tabularnewline
83 & 2 & 2.07294 & -0.0729428 \tabularnewline
84 & 2 & 1.95093 & 0.0490651 \tabularnewline
85 & 4 & 3.8857 & 0.114302 \tabularnewline
86 & 2 & 2.35492 & -0.354924 \tabularnewline
87 & 2 & 2.05327 & -0.0532741 \tabularnewline
88 & 3 & 2.88124 & 0.11876 \tabularnewline
89 & 4 & 5.5484 & -1.5484 \tabularnewline
90 & 2 & 1.77519 & 0.224813 \tabularnewline
91 & 4 & 3.65169 & 0.348312 \tabularnewline
92 & 3 & 3.04931 & -0.0493113 \tabularnewline
93 & 4 & 3.147 & 0.852999 \tabularnewline
94 & 2 & 1.69137 & 0.308628 \tabularnewline
95 & 1 & 1.24699 & -0.246986 \tabularnewline
96 & 1 & 1.45608 & -0.45608 \tabularnewline
97 & 4 & 3.43784 & 0.562161 \tabularnewline
98 & 3 & 2.65123 & 0.348775 \tabularnewline
99 & 1 & 1.23402 & -0.234019 \tabularnewline
100 & 4 & 3.9026 & 0.0974026 \tabularnewline
101 & 3 & 2.80034 & 0.199664 \tabularnewline
102 & 2 & 2.03842 & -0.0384155 \tabularnewline
103 & 4 & 3.9709 & 0.0291041 \tabularnewline
104 & 3 & 2.69333 & 0.306668 \tabularnewline
105 & 3 & 2.53358 & 0.466421 \tabularnewline
106 & 4 & 4.4806 & -0.480603 \tabularnewline
107 & 1 & 1.58772 & -0.587721 \tabularnewline
108 & 3 & 2.90036 & 0.0996441 \tabularnewline
109 & 4 & 3.64188 & 0.358123 \tabularnewline
110 & 1 & 1.38655 & -0.386555 \tabularnewline
111 & 3 & 2.85276 & 0.147245 \tabularnewline
112 & 4 & 3.40233 & 0.597668 \tabularnewline
113 & 4 & 4.28567 & -0.285666 \tabularnewline
114 & 1 & 1.53844 & -0.538436 \tabularnewline
115 & 4 & 3.54887 & 0.451134 \tabularnewline
116 & 2 & 2.23595 & -0.235951 \tabularnewline
117 & 3 & 3.20343 & -0.203432 \tabularnewline
118 & 4 & 3.84459 & 0.155413 \tabularnewline
119 & 4 & 4.27202 & -0.272024 \tabularnewline
120 & 4 & 3.64126 & 0.358738 \tabularnewline
121 & 2 & 2.33487 & -0.334869 \tabularnewline
122 & 4 & 4.29049 & -0.290493 \tabularnewline
123 & 2 & 2.49787 & -0.497872 \tabularnewline
124 & 1 & 1.41885 & -0.418849 \tabularnewline
125 & 1 & 1.35075 & -0.350746 \tabularnewline
126 & 4 & 3.69802 & 0.301982 \tabularnewline
127 & 2 & 1.80376 & 0.196242 \tabularnewline
128 & 2 & 2.25408 & -0.254077 \tabularnewline
129 & 3 & 2.87152 & 0.128477 \tabularnewline
130 & 2 & 1.72335 & 0.276654 \tabularnewline
131 & 3 & 2.66517 & 0.33483 \tabularnewline
132 & 4 & 4.1551 & -0.155103 \tabularnewline
133 & 2 & 2.24366 & -0.243658 \tabularnewline
134 & 3 & 2.97391 & 0.0260943 \tabularnewline
135 & 4 & 4.27035 & -0.27035 \tabularnewline
136 & 3 & 3.05951 & -0.0595098 \tabularnewline
137 & 4 & 3.31491 & 0.685091 \tabularnewline
138 & 4 & 3.41842 & 0.581581 \tabularnewline
139 & 4 & 3.64431 & 0.355694 \tabularnewline
140 & 2 & 2.2272 & -0.227196 \tabularnewline
141 & 2 & 2.49544 & -0.495441 \tabularnewline
142 & 2 & 2.20074 & -0.200743 \tabularnewline
143 & 4 & 4.27094 & -0.270944 \tabularnewline
144 & 3 & 2.86523 & 0.134772 \tabularnewline
145 & 2 & 1.74625 & 0.253754 \tabularnewline
146 & 2 & 2.42867 & -0.428673 \tabularnewline
147 & 3 & 2.7444 & 0.255598 \tabularnewline
148 & 3 & 2.75687 & 0.243127 \tabularnewline
149 & 1 & 1.318 & -0.317998 \tabularnewline
150 & 2 & 2.35034 & -0.350342 \tabularnewline
151 & 2 & 2.45944 & -0.459441 \tabularnewline
152 & 3 & 2.66517 & 0.33483 \tabularnewline
153 & 3 & 2.59067 & 0.409333 \tabularnewline
154 & 2 & 1.73806 & 0.261939 \tabularnewline
155 & 2 & 2.02246 & -0.0224588 \tabularnewline
156 & 3 & 2.83191 & 0.16809 \tabularnewline
157 & 3 & 3.06624 & -0.0662393 \tabularnewline
158 & 1 & 1.59017 & -0.590166 \tabularnewline
159 & 3 & 3.10895 & -0.108952 \tabularnewline
160 & 2 & 1.95178 & 0.0482204 \tabularnewline
161 & 2 & 2.42675 & -0.426751 \tabularnewline
162 & 3 & 2.83555 & 0.164454 \tabularnewline
163 & 3 & 2.77635 & 0.223646 \tabularnewline
164 & 3 & 3.03086 & -0.0308612 \tabularnewline
165 & 3 & 3.1114 & -0.111401 \tabularnewline
166 & 1 & 0.929297 & 0.0707031 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270808&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]1[/C][C]1.34781[/C][C]-0.347811[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.41611[/C][C]-0.416109[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]3.85189[/C][C]0.148108[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]3.9498[/C][C]0.0502003[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.90188[/C][C]0.0981208[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.8517[/C][C]0.148302[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]4.89986[/C][C]-0.899865[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.03662[/C][C]-0.0366183[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]4.12338[/C][C]-0.123379[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.8131[/C][C]0.186901[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]3.78031[/C][C]0.219694[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.12683[/C][C]-0.126829[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]2.6034[/C][C]0.396598[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]2.89672[/C][C]0.103283[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]3.56187[/C][C]0.438133[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]2.54531[/C][C]0.454686[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]3.33148[/C][C]0.668525[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]2.63331[/C][C]0.36669[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]2.97219[/C][C]0.0278109[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]4.18634[/C][C]-0.186343[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]2.96382[/C][C]0.0361779[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]2.88659[/C][C]0.113406[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]2.01585[/C][C]-0.0158468[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]2.16981[/C][C]-0.169813[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]3.15018[/C][C]-0.150184[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.964515[/C][C]0.0354849[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]3.87176[/C][C]0.128237[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]3.03495[/C][C]-0.0349458[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]1.93674[/C][C]0.0632602[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]4.21875[/C][C]-0.218745[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]3.95994[/C][C]0.040058[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]3.26335[/C][C]0.736649[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]3.84414[/C][C]0.155858[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]3.77367[/C][C]0.22633[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]3.11261[/C][C]-0.112614[/C][/ROW]
[ROW][C]36[/C][C]3[/C][C]2.85507[/C][C]0.144932[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]3.41004[/C][C]0.58996[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]4.37654[/C][C]-0.376544[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]4.32923[/C][C]-0.329226[/C][/ROW]
[ROW][C]40[/C][C]3[/C][C]2.99987[/C][C]0.000129189[/C][/ROW]
[ROW][C]41[/C][C]4[/C][C]3.99156[/C][C]0.00844038[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]4.39709[/C][C]-0.397087[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]1.84162[/C][C]0.158382[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]2.40296[/C][C]-0.402955[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]4.29964[/C][C]-0.299638[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]2.94111[/C][C]0.0588882[/C][/ROW]
[ROW][C]47[/C][C]3[/C][C]3.00927[/C][C]-0.00926648[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]2.06054[/C][C]-0.0605441[/C][/ROW]
[ROW][C]49[/C][C]3[/C][C]3.122[/C][C]-0.122005[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]2.30688[/C][C]-0.306879[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]4.451[/C][C]-0.451005[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.236399[/C][C]0.763601[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]4.1551[/C][C]-0.155103[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.318[/C][C]-0.317998[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]3.18364[/C][C]0.816358[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]2.51564[/C][C]0.484357[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]2.60377[/C][C]0.396229[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]2.209[/C][C]-0.208997[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]2.62412[/C][C]0.375877[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]3.00788[/C][C]-0.00788194[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]3.65197[/C][C]0.348027[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]4.22486[/C][C]-0.224862[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]4.22486[/C][C]-0.224862[/C][/ROW]
[ROW][C]64[/C][C]3[/C][C]2.89878[/C][C]0.101219[/C][/ROW]
[ROW][C]65[/C][C]3[/C][C]3.086[/C][C]-0.0860018[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]4.05058[/C][C]-0.0505784[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]3.7881[/C][C]0.2119[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.34775[/C][C]-0.347747[/C][/ROW]
[ROW][C]69[/C][C]2[/C][C]2.40892[/C][C]-0.408916[/C][/ROW]
[ROW][C]70[/C][C]3[/C][C]3.14265[/C][C]-0.142649[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]3.98469[/C][C]0.0153066[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]2.75684[/C][C]0.243157[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]3.81362[/C][C]0.186383[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]2.88873[/C][C]0.111274[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]2.81588[/C][C]0.184123[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]2.95222[/C][C]0.0477823[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]2.95285[/C][C]0.0471507[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.68464[/C][C]-0.684638[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.64454[/C][C]-0.644536[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]2.8924[/C][C]0.107595[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]1.86577[/C][C]0.134229[/C][/ROW]
[ROW][C]82[/C][C]3[/C][C]2.90107[/C][C]0.098934[/C][/ROW]
[ROW][C]83[/C][C]2[/C][C]2.07294[/C][C]-0.0729428[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]1.95093[/C][C]0.0490651[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]3.8857[/C][C]0.114302[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]2.35492[/C][C]-0.354924[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]2.05327[/C][C]-0.0532741[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]2.88124[/C][C]0.11876[/C][/ROW]
[ROW][C]89[/C][C]4[/C][C]5.5484[/C][C]-1.5484[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]1.77519[/C][C]0.224813[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]3.65169[/C][C]0.348312[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]3.04931[/C][C]-0.0493113[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]3.147[/C][C]0.852999[/C][/ROW]
[ROW][C]94[/C][C]2[/C][C]1.69137[/C][C]0.308628[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.24699[/C][C]-0.246986[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.45608[/C][C]-0.45608[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]3.43784[/C][C]0.562161[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]2.65123[/C][C]0.348775[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.23402[/C][C]-0.234019[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]3.9026[/C][C]0.0974026[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]2.80034[/C][C]0.199664[/C][/ROW]
[ROW][C]102[/C][C]2[/C][C]2.03842[/C][C]-0.0384155[/C][/ROW]
[ROW][C]103[/C][C]4[/C][C]3.9709[/C][C]0.0291041[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]2.69333[/C][C]0.306668[/C][/ROW]
[ROW][C]105[/C][C]3[/C][C]2.53358[/C][C]0.466421[/C][/ROW]
[ROW][C]106[/C][C]4[/C][C]4.4806[/C][C]-0.480603[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]1.58772[/C][C]-0.587721[/C][/ROW]
[ROW][C]108[/C][C]3[/C][C]2.90036[/C][C]0.0996441[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]3.64188[/C][C]0.358123[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]1.38655[/C][C]-0.386555[/C][/ROW]
[ROW][C]111[/C][C]3[/C][C]2.85276[/C][C]0.147245[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]3.40233[/C][C]0.597668[/C][/ROW]
[ROW][C]113[/C][C]4[/C][C]4.28567[/C][C]-0.285666[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.53844[/C][C]-0.538436[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]3.54887[/C][C]0.451134[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]2.23595[/C][C]-0.235951[/C][/ROW]
[ROW][C]117[/C][C]3[/C][C]3.20343[/C][C]-0.203432[/C][/ROW]
[ROW][C]118[/C][C]4[/C][C]3.84459[/C][C]0.155413[/C][/ROW]
[ROW][C]119[/C][C]4[/C][C]4.27202[/C][C]-0.272024[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]3.64126[/C][C]0.358738[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]2.33487[/C][C]-0.334869[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]4.29049[/C][C]-0.290493[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]2.49787[/C][C]-0.497872[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]1.41885[/C][C]-0.418849[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]1.35075[/C][C]-0.350746[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]3.69802[/C][C]0.301982[/C][/ROW]
[ROW][C]127[/C][C]2[/C][C]1.80376[/C][C]0.196242[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]2.25408[/C][C]-0.254077[/C][/ROW]
[ROW][C]129[/C][C]3[/C][C]2.87152[/C][C]0.128477[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.72335[/C][C]0.276654[/C][/ROW]
[ROW][C]131[/C][C]3[/C][C]2.66517[/C][C]0.33483[/C][/ROW]
[ROW][C]132[/C][C]4[/C][C]4.1551[/C][C]-0.155103[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]2.24366[/C][C]-0.243658[/C][/ROW]
[ROW][C]134[/C][C]3[/C][C]2.97391[/C][C]0.0260943[/C][/ROW]
[ROW][C]135[/C][C]4[/C][C]4.27035[/C][C]-0.27035[/C][/ROW]
[ROW][C]136[/C][C]3[/C][C]3.05951[/C][C]-0.0595098[/C][/ROW]
[ROW][C]137[/C][C]4[/C][C]3.31491[/C][C]0.685091[/C][/ROW]
[ROW][C]138[/C][C]4[/C][C]3.41842[/C][C]0.581581[/C][/ROW]
[ROW][C]139[/C][C]4[/C][C]3.64431[/C][C]0.355694[/C][/ROW]
[ROW][C]140[/C][C]2[/C][C]2.2272[/C][C]-0.227196[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]2.49544[/C][C]-0.495441[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]2.20074[/C][C]-0.200743[/C][/ROW]
[ROW][C]143[/C][C]4[/C][C]4.27094[/C][C]-0.270944[/C][/ROW]
[ROW][C]144[/C][C]3[/C][C]2.86523[/C][C]0.134772[/C][/ROW]
[ROW][C]145[/C][C]2[/C][C]1.74625[/C][C]0.253754[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]2.42867[/C][C]-0.428673[/C][/ROW]
[ROW][C]147[/C][C]3[/C][C]2.7444[/C][C]0.255598[/C][/ROW]
[ROW][C]148[/C][C]3[/C][C]2.75687[/C][C]0.243127[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]1.318[/C][C]-0.317998[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]2.35034[/C][C]-0.350342[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]2.45944[/C][C]-0.459441[/C][/ROW]
[ROW][C]152[/C][C]3[/C][C]2.66517[/C][C]0.33483[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]2.59067[/C][C]0.409333[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]1.73806[/C][C]0.261939[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]2.02246[/C][C]-0.0224588[/C][/ROW]
[ROW][C]156[/C][C]3[/C][C]2.83191[/C][C]0.16809[/C][/ROW]
[ROW][C]157[/C][C]3[/C][C]3.06624[/C][C]-0.0662393[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]1.59017[/C][C]-0.590166[/C][/ROW]
[ROW][C]159[/C][C]3[/C][C]3.10895[/C][C]-0.108952[/C][/ROW]
[ROW][C]160[/C][C]2[/C][C]1.95178[/C][C]0.0482204[/C][/ROW]
[ROW][C]161[/C][C]2[/C][C]2.42675[/C][C]-0.426751[/C][/ROW]
[ROW][C]162[/C][C]3[/C][C]2.83555[/C][C]0.164454[/C][/ROW]
[ROW][C]163[/C][C]3[/C][C]2.77635[/C][C]0.223646[/C][/ROW]
[ROW][C]164[/C][C]3[/C][C]3.03086[/C][C]-0.0308612[/C][/ROW]
[ROW][C]165[/C][C]3[/C][C]3.1114[/C][C]-0.111401[/C][/ROW]
[ROW][C]166[/C][C]1[/C][C]0.929297[/C][C]0.0707031[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270808&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270808&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	1	1.34781	-0.347811
2	1	1.41611	-0.416109
3	4	3.85189	0.148108
4	4	3.9498	0.0502003
5	3	2.90188	0.0981208
6	2	1.8517	0.148302
7	4	4.89986	-0.899865
8	4	4.03662	-0.0366183
9	4	4.12338	-0.123379
10	2	1.8131	0.186901
11	4	3.78031	0.219694
12	1	1.12683	-0.126829
13	3	2.6034	0.396598
14	3	2.89672	0.103283
15	4	3.56187	0.438133
16	3	2.54531	0.454686
17	4	3.33148	0.668525
18	3	2.63331	0.36669
19	3	2.97219	0.0278109
20	4	4.18634	-0.186343
21	3	2.96382	0.0361779
22	3	2.88659	0.113406
23	2	2.01585	-0.0158468
24	2	2.16981	-0.169813
25	3	3.15018	-0.150184
26	1	0.964515	0.0354849
27	4	3.87176	0.128237
28	3	3.03495	-0.0349458
29	2	1.93674	0.0632602
30	4	4.21875	-0.218745
31	4	3.95994	0.040058
32	4	3.26335	0.736649
33	4	3.84414	0.155858
34	4	3.77367	0.22633
35	3	3.11261	-0.112614
36	3	2.85507	0.144932
37	4	3.41004	0.58996
38	4	4.37654	-0.376544
39	4	4.32923	-0.329226
40	3	2.99987	0.000129189
41	4	3.99156	0.00844038
42	4	4.39709	-0.397087
43	2	1.84162	0.158382
44	2	2.40296	-0.402955
45	4	4.29964	-0.299638
46	3	2.94111	0.0588882
47	3	3.00927	-0.00926648
48	2	2.06054	-0.0605441
49	3	3.122	-0.122005
50	2	2.30688	-0.306879
51	4	4.451	-0.451005
52	1	0.236399	0.763601
53	4	4.1551	-0.155103
54	1	1.318	-0.317998
55	4	3.18364	0.816358
56	3	2.51564	0.484357
57	3	2.60377	0.396229
58	2	2.209	-0.208997
59	3	2.62412	0.375877
60	3	3.00788	-0.00788194
61	4	3.65197	0.348027
62	4	4.22486	-0.224862
63	4	4.22486	-0.224862
64	3	2.89878	0.101219
65	3	3.086	-0.0860018
66	4	4.05058	-0.0505784
67	4	3.7881	0.2119
68	1	1.34775	-0.347747
69	2	2.40892	-0.408916
70	3	3.14265	-0.142649
71	4	3.98469	0.0153066
72	3	2.75684	0.243157
73	4	3.81362	0.186383
74	3	2.88873	0.111274
75	3	2.81588	0.184123
76	3	2.95222	0.0477823
77	3	2.95285	0.0471507
78	1	1.68464	-0.684638
79	1	1.64454	-0.644536
80	3	2.8924	0.107595
81	2	1.86577	0.134229
82	3	2.90107	0.098934
83	2	2.07294	-0.0729428
84	2	1.95093	0.0490651
85	4	3.8857	0.114302
86	2	2.35492	-0.354924
87	2	2.05327	-0.0532741
88	3	2.88124	0.11876
89	4	5.5484	-1.5484
90	2	1.77519	0.224813
91	4	3.65169	0.348312
92	3	3.04931	-0.0493113
93	4	3.147	0.852999
94	2	1.69137	0.308628
95	1	1.24699	-0.246986
96	1	1.45608	-0.45608
97	4	3.43784	0.562161
98	3	2.65123	0.348775
99	1	1.23402	-0.234019
100	4	3.9026	0.0974026
101	3	2.80034	0.199664
102	2	2.03842	-0.0384155
103	4	3.9709	0.0291041
104	3	2.69333	0.306668
105	3	2.53358	0.466421
106	4	4.4806	-0.480603
107	1	1.58772	-0.587721
108	3	2.90036	0.0996441
109	4	3.64188	0.358123
110	1	1.38655	-0.386555
111	3	2.85276	0.147245
112	4	3.40233	0.597668
113	4	4.28567	-0.285666
114	1	1.53844	-0.538436
115	4	3.54887	0.451134
116	2	2.23595	-0.235951
117	3	3.20343	-0.203432
118	4	3.84459	0.155413
119	4	4.27202	-0.272024
120	4	3.64126	0.358738
121	2	2.33487	-0.334869
122	4	4.29049	-0.290493
123	2	2.49787	-0.497872
124	1	1.41885	-0.418849
125	1	1.35075	-0.350746
126	4	3.69802	0.301982
127	2	1.80376	0.196242
128	2	2.25408	-0.254077
129	3	2.87152	0.128477
130	2	1.72335	0.276654
131	3	2.66517	0.33483
132	4	4.1551	-0.155103
133	2	2.24366	-0.243658
134	3	2.97391	0.0260943
135	4	4.27035	-0.27035
136	3	3.05951	-0.0595098
137	4	3.31491	0.685091
138	4	3.41842	0.581581
139	4	3.64431	0.355694
140	2	2.2272	-0.227196
141	2	2.49544	-0.495441
142	2	2.20074	-0.200743
143	4	4.27094	-0.270944
144	3	2.86523	0.134772
145	2	1.74625	0.253754
146	2	2.42867	-0.428673
147	3	2.7444	0.255598
148	3	2.75687	0.243127
149	1	1.318	-0.317998
150	2	2.35034	-0.350342
151	2	2.45944	-0.459441
152	3	2.66517	0.33483
153	3	2.59067	0.409333
154	2	1.73806	0.261939
155	2	2.02246	-0.0224588
156	3	2.83191	0.16809
157	3	3.06624	-0.0662393
158	1	1.59017	-0.590166
159	3	3.10895	-0.108952
160	2	1.95178	0.0482204
161	2	2.42675	-0.426751
162	3	2.83555	0.164454
163	3	2.77635	0.223646
164	3	3.03086	-0.0308612
165	3	3.1114	-0.111401
166	1	0.929297	0.0707031

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0601335	0.120267	0.939866
10	0.0434965	0.0869929	0.956504
11	0.202174	0.404348	0.797826
12	0.125511	0.251022	0.874489
13	0.541111	0.917778	0.458889
14	0.430599	0.861198	0.569401
15	0.402635	0.80527	0.597365
16	0.683506	0.632989	0.316494
17	0.625968	0.748063	0.374032
18	0.553224	0.893553	0.446776
19	0.497428	0.994856	0.502572
20	0.499293	0.998586	0.500707
21	0.423499	0.846998	0.576501
22	0.349952	0.699904	0.650048
23	0.284146	0.568292	0.715854
24	0.232118	0.464236	0.767882
25	0.189147	0.378295	0.810853
26	0.152017	0.304034	0.847983
27	0.15538	0.31076	0.84462
28	0.122369	0.244738	0.877631
29	0.0972661	0.194532	0.902734
30	0.0921885	0.184377	0.907812
31	0.0719502	0.1439	0.92805
32	0.245019	0.490038	0.754981
33	0.230871	0.461743	0.769129
34	0.207689	0.415378	0.792311
35	0.187942	0.375883	0.812058
36	0.152116	0.304233	0.847884
37	0.188474	0.376948	0.811526
38	0.216259	0.432518	0.783741
39	0.214988	0.429977	0.785012
40	0.17663	0.353259	0.82337
41	0.142456	0.284913	0.857544
42	0.142396	0.284791	0.857604
43	0.114881	0.229761	0.885119
44	0.123583	0.247166	0.876417
45	0.111434	0.222868	0.888566
46	0.0880031	0.176006	0.911997
47	0.0686817	0.137363	0.931318
48	0.0738435	0.147687	0.926156
49	0.0583518	0.116704	0.941648
50	0.0526915	0.105383	0.947309
51	0.0694057	0.138811	0.930594
52	0.131726	0.263452	0.868274
53	0.110825	0.221651	0.889175
54	0.162282	0.324565	0.837718
55	0.340674	0.681347	0.659326
56	0.373394	0.746788	0.626606
57	0.378341	0.756682	0.621659
58	0.355755	0.71151	0.644245
59	0.357921	0.715843	0.642079
60	0.313984	0.627968	0.686016
61	0.315831	0.631662	0.684169
62	0.283201	0.566403	0.716799
63	0.253229	0.506458	0.746771
64	0.218897	0.437793	0.781103
65	0.188	0.376001	0.812
66	0.158261	0.316522	0.841739
67	0.142992	0.285984	0.857008
68	0.161812	0.323625	0.838188
69	0.1785	0.357001	0.8215
70	0.157464	0.314927	0.842536
71	0.131368	0.262735	0.868632
72	0.118552	0.237103	0.881448
73	0.103318	0.206635	0.896682
74	0.0853868	0.170774	0.914613
75	0.071931	0.143862	0.928069
76	0.0574737	0.114947	0.942526
77	0.0459274	0.0918548	0.954073
78	0.0945348	0.18907	0.905465
79	0.156337	0.312674	0.843663
80	0.131839	0.263678	0.868161
81	0.111304	0.222608	0.888696
82	0.0923097	0.184619	0.90769
83	0.0757653	0.151531	0.924235
84	0.0611927	0.122385	0.938807
85	0.0499509	0.0999018	0.950049
86	0.0519629	0.103926	0.948037
87	0.0417981	0.0835961	0.958202
88	0.0334329	0.0668659	0.966567
89	0.643966	0.712068	0.356034
90	0.625734	0.748533	0.374266
91	0.623221	0.753558	0.376779
92	0.58093	0.838139	0.41907
93	0.786044	0.427912	0.213956
94	0.786068	0.427863	0.213932
95	0.769115	0.461771	0.230885
96	0.784492	0.431015	0.215508
97	0.827258	0.345483	0.172742
98	0.831171	0.337658	0.168829
99	0.810394	0.379213	0.189606
100	0.780751	0.438497	0.219249
101	0.753651	0.492698	0.246349
102	0.716622	0.566756	0.283378
103	0.677477	0.645046	0.322523
104	0.666862	0.666276	0.333138
105	0.708516	0.582968	0.291484
106	0.776055	0.44789	0.223945
107	0.817432	0.365136	0.182568
108	0.787375	0.425249	0.212625
109	0.775548	0.448904	0.224452
110	0.773678	0.452645	0.226322
111	0.749003	0.501995	0.250997
112	0.829484	0.341032	0.170516
113	0.835858	0.328283	0.164142
114	0.866877	0.266246	0.133123
115	0.891121	0.217759	0.108879
116	0.874594	0.250812	0.125406
117	0.860159	0.279681	0.139841
118	0.834248	0.331504	0.165752
119	0.823613	0.352774	0.176387
120	0.8274	0.345199	0.1726
121	0.817282	0.365435	0.182718
122	0.814237	0.371526	0.185763
123	0.842169	0.315661	0.157831
124	0.85049	0.29902	0.14951
125	0.849354	0.301292	0.150646
126	0.833067	0.333865	0.166933
127	0.810709	0.378581	0.189291
128	0.793473	0.413054	0.206527
129	0.755127	0.489746	0.244873
130	0.7457	0.5086	0.2543
131	0.743059	0.513881	0.256941
132	0.69989	0.600219	0.30011
133	0.657774	0.684453	0.342226
134	0.599871	0.800257	0.400129
135	0.606371	0.787258	0.393629
136	0.544988	0.910024	0.455012
137	0.711596	0.576807	0.288404
138	0.765	0.470001	0.235
139	0.760063	0.479873	0.239937
140	0.716914	0.566172	0.283086
141	0.761546	0.476908	0.238454
142	0.746169	0.507662	0.253831
143	0.726745	0.546511	0.273255
144	0.663788	0.672424	0.336212
145	0.650243	0.699513	0.349757
146	0.664502	0.670996	0.335498
147	0.660639	0.678722	0.339361
148	0.656098	0.687804	0.343902
149	0.590725	0.81855	0.409275
150	0.567655	0.864691	0.432345
151	0.738358	0.523284	0.261642
152	0.705259	0.589482	0.294741
153	0.654326	0.691348	0.345674
154	0.71133	0.577339	0.28867
155	0.587013	0.825975	0.412987
156	0.444302	0.888605	0.555698
157	0.315287	0.630574	0.684713

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0601335 & 0.120267 & 0.939866 \tabularnewline
10 & 0.0434965 & 0.0869929 & 0.956504 \tabularnewline
11 & 0.202174 & 0.404348 & 0.797826 \tabularnewline
12 & 0.125511 & 0.251022 & 0.874489 \tabularnewline
13 & 0.541111 & 0.917778 & 0.458889 \tabularnewline
14 & 0.430599 & 0.861198 & 0.569401 \tabularnewline
15 & 0.402635 & 0.80527 & 0.597365 \tabularnewline
16 & 0.683506 & 0.632989 & 0.316494 \tabularnewline
17 & 0.625968 & 0.748063 & 0.374032 \tabularnewline
18 & 0.553224 & 0.893553 & 0.446776 \tabularnewline
19 & 0.497428 & 0.994856 & 0.502572 \tabularnewline
20 & 0.499293 & 0.998586 & 0.500707 \tabularnewline
21 & 0.423499 & 0.846998 & 0.576501 \tabularnewline
22 & 0.349952 & 0.699904 & 0.650048 \tabularnewline
23 & 0.284146 & 0.568292 & 0.715854 \tabularnewline
24 & 0.232118 & 0.464236 & 0.767882 \tabularnewline
25 & 0.189147 & 0.378295 & 0.810853 \tabularnewline
26 & 0.152017 & 0.304034 & 0.847983 \tabularnewline
27 & 0.15538 & 0.31076 & 0.84462 \tabularnewline
28 & 0.122369 & 0.244738 & 0.877631 \tabularnewline
29 & 0.0972661 & 0.194532 & 0.902734 \tabularnewline
30 & 0.0921885 & 0.184377 & 0.907812 \tabularnewline
31 & 0.0719502 & 0.1439 & 0.92805 \tabularnewline
32 & 0.245019 & 0.490038 & 0.754981 \tabularnewline
33 & 0.230871 & 0.461743 & 0.769129 \tabularnewline
34 & 0.207689 & 0.415378 & 0.792311 \tabularnewline
35 & 0.187942 & 0.375883 & 0.812058 \tabularnewline
36 & 0.152116 & 0.304233 & 0.847884 \tabularnewline
37 & 0.188474 & 0.376948 & 0.811526 \tabularnewline
38 & 0.216259 & 0.432518 & 0.783741 \tabularnewline
39 & 0.214988 & 0.429977 & 0.785012 \tabularnewline
40 & 0.17663 & 0.353259 & 0.82337 \tabularnewline
41 & 0.142456 & 0.284913 & 0.857544 \tabularnewline
42 & 0.142396 & 0.284791 & 0.857604 \tabularnewline
43 & 0.114881 & 0.229761 & 0.885119 \tabularnewline
44 & 0.123583 & 0.247166 & 0.876417 \tabularnewline
45 & 0.111434 & 0.222868 & 0.888566 \tabularnewline
46 & 0.0880031 & 0.176006 & 0.911997 \tabularnewline
47 & 0.0686817 & 0.137363 & 0.931318 \tabularnewline
48 & 0.0738435 & 0.147687 & 0.926156 \tabularnewline
49 & 0.0583518 & 0.116704 & 0.941648 \tabularnewline
50 & 0.0526915 & 0.105383 & 0.947309 \tabularnewline
51 & 0.0694057 & 0.138811 & 0.930594 \tabularnewline
52 & 0.131726 & 0.263452 & 0.868274 \tabularnewline
53 & 0.110825 & 0.221651 & 0.889175 \tabularnewline
54 & 0.162282 & 0.324565 & 0.837718 \tabularnewline
55 & 0.340674 & 0.681347 & 0.659326 \tabularnewline
56 & 0.373394 & 0.746788 & 0.626606 \tabularnewline
57 & 0.378341 & 0.756682 & 0.621659 \tabularnewline
58 & 0.355755 & 0.71151 & 0.644245 \tabularnewline
59 & 0.357921 & 0.715843 & 0.642079 \tabularnewline
60 & 0.313984 & 0.627968 & 0.686016 \tabularnewline
61 & 0.315831 & 0.631662 & 0.684169 \tabularnewline
62 & 0.283201 & 0.566403 & 0.716799 \tabularnewline
63 & 0.253229 & 0.506458 & 0.746771 \tabularnewline
64 & 0.218897 & 0.437793 & 0.781103 \tabularnewline
65 & 0.188 & 0.376001 & 0.812 \tabularnewline
66 & 0.158261 & 0.316522 & 0.841739 \tabularnewline
67 & 0.142992 & 0.285984 & 0.857008 \tabularnewline
68 & 0.161812 & 0.323625 & 0.838188 \tabularnewline
69 & 0.1785 & 0.357001 & 0.8215 \tabularnewline
70 & 0.157464 & 0.314927 & 0.842536 \tabularnewline
71 & 0.131368 & 0.262735 & 0.868632 \tabularnewline
72 & 0.118552 & 0.237103 & 0.881448 \tabularnewline
73 & 0.103318 & 0.206635 & 0.896682 \tabularnewline
74 & 0.0853868 & 0.170774 & 0.914613 \tabularnewline
75 & 0.071931 & 0.143862 & 0.928069 \tabularnewline
76 & 0.0574737 & 0.114947 & 0.942526 \tabularnewline
77 & 0.0459274 & 0.0918548 & 0.954073 \tabularnewline
78 & 0.0945348 & 0.18907 & 0.905465 \tabularnewline
79 & 0.156337 & 0.312674 & 0.843663 \tabularnewline
80 & 0.131839 & 0.263678 & 0.868161 \tabularnewline
81 & 0.111304 & 0.222608 & 0.888696 \tabularnewline
82 & 0.0923097 & 0.184619 & 0.90769 \tabularnewline
83 & 0.0757653 & 0.151531 & 0.924235 \tabularnewline
84 & 0.0611927 & 0.122385 & 0.938807 \tabularnewline
85 & 0.0499509 & 0.0999018 & 0.950049 \tabularnewline
86 & 0.0519629 & 0.103926 & 0.948037 \tabularnewline
87 & 0.0417981 & 0.0835961 & 0.958202 \tabularnewline
88 & 0.0334329 & 0.0668659 & 0.966567 \tabularnewline
89 & 0.643966 & 0.712068 & 0.356034 \tabularnewline
90 & 0.625734 & 0.748533 & 0.374266 \tabularnewline
91 & 0.623221 & 0.753558 & 0.376779 \tabularnewline
92 & 0.58093 & 0.838139 & 0.41907 \tabularnewline
93 & 0.786044 & 0.427912 & 0.213956 \tabularnewline
94 & 0.786068 & 0.427863 & 0.213932 \tabularnewline
95 & 0.769115 & 0.461771 & 0.230885 \tabularnewline
96 & 0.784492 & 0.431015 & 0.215508 \tabularnewline
97 & 0.827258 & 0.345483 & 0.172742 \tabularnewline
98 & 0.831171 & 0.337658 & 0.168829 \tabularnewline
99 & 0.810394 & 0.379213 & 0.189606 \tabularnewline
100 & 0.780751 & 0.438497 & 0.219249 \tabularnewline
101 & 0.753651 & 0.492698 & 0.246349 \tabularnewline
102 & 0.716622 & 0.566756 & 0.283378 \tabularnewline
103 & 0.677477 & 0.645046 & 0.322523 \tabularnewline
104 & 0.666862 & 0.666276 & 0.333138 \tabularnewline
105 & 0.708516 & 0.582968 & 0.291484 \tabularnewline
106 & 0.776055 & 0.44789 & 0.223945 \tabularnewline
107 & 0.817432 & 0.365136 & 0.182568 \tabularnewline
108 & 0.787375 & 0.425249 & 0.212625 \tabularnewline
109 & 0.775548 & 0.448904 & 0.224452 \tabularnewline
110 & 0.773678 & 0.452645 & 0.226322 \tabularnewline
111 & 0.749003 & 0.501995 & 0.250997 \tabularnewline
112 & 0.829484 & 0.341032 & 0.170516 \tabularnewline
113 & 0.835858 & 0.328283 & 0.164142 \tabularnewline
114 & 0.866877 & 0.266246 & 0.133123 \tabularnewline
115 & 0.891121 & 0.217759 & 0.108879 \tabularnewline
116 & 0.874594 & 0.250812 & 0.125406 \tabularnewline
117 & 0.860159 & 0.279681 & 0.139841 \tabularnewline
118 & 0.834248 & 0.331504 & 0.165752 \tabularnewline
119 & 0.823613 & 0.352774 & 0.176387 \tabularnewline
120 & 0.8274 & 0.345199 & 0.1726 \tabularnewline
121 & 0.817282 & 0.365435 & 0.182718 \tabularnewline
122 & 0.814237 & 0.371526 & 0.185763 \tabularnewline
123 & 0.842169 & 0.315661 & 0.157831 \tabularnewline
124 & 0.85049 & 0.29902 & 0.14951 \tabularnewline
125 & 0.849354 & 0.301292 & 0.150646 \tabularnewline
126 & 0.833067 & 0.333865 & 0.166933 \tabularnewline
127 & 0.810709 & 0.378581 & 0.189291 \tabularnewline
128 & 0.793473 & 0.413054 & 0.206527 \tabularnewline
129 & 0.755127 & 0.489746 & 0.244873 \tabularnewline
130 & 0.7457 & 0.5086 & 0.2543 \tabularnewline
131 & 0.743059 & 0.513881 & 0.256941 \tabularnewline
132 & 0.69989 & 0.600219 & 0.30011 \tabularnewline
133 & 0.657774 & 0.684453 & 0.342226 \tabularnewline
134 & 0.599871 & 0.800257 & 0.400129 \tabularnewline
135 & 0.606371 & 0.787258 & 0.393629 \tabularnewline
136 & 0.544988 & 0.910024 & 0.455012 \tabularnewline
137 & 0.711596 & 0.576807 & 0.288404 \tabularnewline
138 & 0.765 & 0.470001 & 0.235 \tabularnewline
139 & 0.760063 & 0.479873 & 0.239937 \tabularnewline
140 & 0.716914 & 0.566172 & 0.283086 \tabularnewline
141 & 0.761546 & 0.476908 & 0.238454 \tabularnewline
142 & 0.746169 & 0.507662 & 0.253831 \tabularnewline
143 & 0.726745 & 0.546511 & 0.273255 \tabularnewline
144 & 0.663788 & 0.672424 & 0.336212 \tabularnewline
145 & 0.650243 & 0.699513 & 0.349757 \tabularnewline
146 & 0.664502 & 0.670996 & 0.335498 \tabularnewline
147 & 0.660639 & 0.678722 & 0.339361 \tabularnewline
148 & 0.656098 & 0.687804 & 0.343902 \tabularnewline
149 & 0.590725 & 0.81855 & 0.409275 \tabularnewline
150 & 0.567655 & 0.864691 & 0.432345 \tabularnewline
151 & 0.738358 & 0.523284 & 0.261642 \tabularnewline
152 & 0.705259 & 0.589482 & 0.294741 \tabularnewline
153 & 0.654326 & 0.691348 & 0.345674 \tabularnewline
154 & 0.71133 & 0.577339 & 0.28867 \tabularnewline
155 & 0.587013 & 0.825975 & 0.412987 \tabularnewline
156 & 0.444302 & 0.888605 & 0.555698 \tabularnewline
157 & 0.315287 & 0.630574 & 0.684713 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270808&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]9[/C][C]0.0601335[/C][C]0.120267[/C][C]0.939866[/C][/ROW]
[ROW][C]10[/C][C]0.0434965[/C][C]0.0869929[/C][C]0.956504[/C][/ROW]
[ROW][C]11[/C][C]0.202174[/C][C]0.404348[/C][C]0.797826[/C][/ROW]
[ROW][C]12[/C][C]0.125511[/C][C]0.251022[/C][C]0.874489[/C][/ROW]
[ROW][C]13[/C][C]0.541111[/C][C]0.917778[/C][C]0.458889[/C][/ROW]
[ROW][C]14[/C][C]0.430599[/C][C]0.861198[/C][C]0.569401[/C][/ROW]
[ROW][C]15[/C][C]0.402635[/C][C]0.80527[/C][C]0.597365[/C][/ROW]
[ROW][C]16[/C][C]0.683506[/C][C]0.632989[/C][C]0.316494[/C][/ROW]
[ROW][C]17[/C][C]0.625968[/C][C]0.748063[/C][C]0.374032[/C][/ROW]
[ROW][C]18[/C][C]0.553224[/C][C]0.893553[/C][C]0.446776[/C][/ROW]
[ROW][C]19[/C][C]0.497428[/C][C]0.994856[/C][C]0.502572[/C][/ROW]
[ROW][C]20[/C][C]0.499293[/C][C]0.998586[/C][C]0.500707[/C][/ROW]
[ROW][C]21[/C][C]0.423499[/C][C]0.846998[/C][C]0.576501[/C][/ROW]
[ROW][C]22[/C][C]0.349952[/C][C]0.699904[/C][C]0.650048[/C][/ROW]
[ROW][C]23[/C][C]0.284146[/C][C]0.568292[/C][C]0.715854[/C][/ROW]
[ROW][C]24[/C][C]0.232118[/C][C]0.464236[/C][C]0.767882[/C][/ROW]
[ROW][C]25[/C][C]0.189147[/C][C]0.378295[/C][C]0.810853[/C][/ROW]
[ROW][C]26[/C][C]0.152017[/C][C]0.304034[/C][C]0.847983[/C][/ROW]
[ROW][C]27[/C][C]0.15538[/C][C]0.31076[/C][C]0.84462[/C][/ROW]
[ROW][C]28[/C][C]0.122369[/C][C]0.244738[/C][C]0.877631[/C][/ROW]
[ROW][C]29[/C][C]0.0972661[/C][C]0.194532[/C][C]0.902734[/C][/ROW]
[ROW][C]30[/C][C]0.0921885[/C][C]0.184377[/C][C]0.907812[/C][/ROW]
[ROW][C]31[/C][C]0.0719502[/C][C]0.1439[/C][C]0.92805[/C][/ROW]
[ROW][C]32[/C][C]0.245019[/C][C]0.490038[/C][C]0.754981[/C][/ROW]
[ROW][C]33[/C][C]0.230871[/C][C]0.461743[/C][C]0.769129[/C][/ROW]
[ROW][C]34[/C][C]0.207689[/C][C]0.415378[/C][C]0.792311[/C][/ROW]
[ROW][C]35[/C][C]0.187942[/C][C]0.375883[/C][C]0.812058[/C][/ROW]
[ROW][C]36[/C][C]0.152116[/C][C]0.304233[/C][C]0.847884[/C][/ROW]
[ROW][C]37[/C][C]0.188474[/C][C]0.376948[/C][C]0.811526[/C][/ROW]
[ROW][C]38[/C][C]0.216259[/C][C]0.432518[/C][C]0.783741[/C][/ROW]
[ROW][C]39[/C][C]0.214988[/C][C]0.429977[/C][C]0.785012[/C][/ROW]
[ROW][C]40[/C][C]0.17663[/C][C]0.353259[/C][C]0.82337[/C][/ROW]
[ROW][C]41[/C][C]0.142456[/C][C]0.284913[/C][C]0.857544[/C][/ROW]
[ROW][C]42[/C][C]0.142396[/C][C]0.284791[/C][C]0.857604[/C][/ROW]
[ROW][C]43[/C][C]0.114881[/C][C]0.229761[/C][C]0.885119[/C][/ROW]
[ROW][C]44[/C][C]0.123583[/C][C]0.247166[/C][C]0.876417[/C][/ROW]
[ROW][C]45[/C][C]0.111434[/C][C]0.222868[/C][C]0.888566[/C][/ROW]
[ROW][C]46[/C][C]0.0880031[/C][C]0.176006[/C][C]0.911997[/C][/ROW]
[ROW][C]47[/C][C]0.0686817[/C][C]0.137363[/C][C]0.931318[/C][/ROW]
[ROW][C]48[/C][C]0.0738435[/C][C]0.147687[/C][C]0.926156[/C][/ROW]
[ROW][C]49[/C][C]0.0583518[/C][C]0.116704[/C][C]0.941648[/C][/ROW]
[ROW][C]50[/C][C]0.0526915[/C][C]0.105383[/C][C]0.947309[/C][/ROW]
[ROW][C]51[/C][C]0.0694057[/C][C]0.138811[/C][C]0.930594[/C][/ROW]
[ROW][C]52[/C][C]0.131726[/C][C]0.263452[/C][C]0.868274[/C][/ROW]
[ROW][C]53[/C][C]0.110825[/C][C]0.221651[/C][C]0.889175[/C][/ROW]
[ROW][C]54[/C][C]0.162282[/C][C]0.324565[/C][C]0.837718[/C][/ROW]
[ROW][C]55[/C][C]0.340674[/C][C]0.681347[/C][C]0.659326[/C][/ROW]
[ROW][C]56[/C][C]0.373394[/C][C]0.746788[/C][C]0.626606[/C][/ROW]
[ROW][C]57[/C][C]0.378341[/C][C]0.756682[/C][C]0.621659[/C][/ROW]
[ROW][C]58[/C][C]0.355755[/C][C]0.71151[/C][C]0.644245[/C][/ROW]
[ROW][C]59[/C][C]0.357921[/C][C]0.715843[/C][C]0.642079[/C][/ROW]
[ROW][C]60[/C][C]0.313984[/C][C]0.627968[/C][C]0.686016[/C][/ROW]
[ROW][C]61[/C][C]0.315831[/C][C]0.631662[/C][C]0.684169[/C][/ROW]
[ROW][C]62[/C][C]0.283201[/C][C]0.566403[/C][C]0.716799[/C][/ROW]
[ROW][C]63[/C][C]0.253229[/C][C]0.506458[/C][C]0.746771[/C][/ROW]
[ROW][C]64[/C][C]0.218897[/C][C]0.437793[/C][C]0.781103[/C][/ROW]
[ROW][C]65[/C][C]0.188[/C][C]0.376001[/C][C]0.812[/C][/ROW]
[ROW][C]66[/C][C]0.158261[/C][C]0.316522[/C][C]0.841739[/C][/ROW]
[ROW][C]67[/C][C]0.142992[/C][C]0.285984[/C][C]0.857008[/C][/ROW]
[ROW][C]68[/C][C]0.161812[/C][C]0.323625[/C][C]0.838188[/C][/ROW]
[ROW][C]69[/C][C]0.1785[/C][C]0.357001[/C][C]0.8215[/C][/ROW]
[ROW][C]70[/C][C]0.157464[/C][C]0.314927[/C][C]0.842536[/C][/ROW]
[ROW][C]71[/C][C]0.131368[/C][C]0.262735[/C][C]0.868632[/C][/ROW]
[ROW][C]72[/C][C]0.118552[/C][C]0.237103[/C][C]0.881448[/C][/ROW]
[ROW][C]73[/C][C]0.103318[/C][C]0.206635[/C][C]0.896682[/C][/ROW]
[ROW][C]74[/C][C]0.0853868[/C][C]0.170774[/C][C]0.914613[/C][/ROW]
[ROW][C]75[/C][C]0.071931[/C][C]0.143862[/C][C]0.928069[/C][/ROW]
[ROW][C]76[/C][C]0.0574737[/C][C]0.114947[/C][C]0.942526[/C][/ROW]
[ROW][C]77[/C][C]0.0459274[/C][C]0.0918548[/C][C]0.954073[/C][/ROW]
[ROW][C]78[/C][C]0.0945348[/C][C]0.18907[/C][C]0.905465[/C][/ROW]
[ROW][C]79[/C][C]0.156337[/C][C]0.312674[/C][C]0.843663[/C][/ROW]
[ROW][C]80[/C][C]0.131839[/C][C]0.263678[/C][C]0.868161[/C][/ROW]
[ROW][C]81[/C][C]0.111304[/C][C]0.222608[/C][C]0.888696[/C][/ROW]
[ROW][C]82[/C][C]0.0923097[/C][C]0.184619[/C][C]0.90769[/C][/ROW]
[ROW][C]83[/C][C]0.0757653[/C][C]0.151531[/C][C]0.924235[/C][/ROW]
[ROW][C]84[/C][C]0.0611927[/C][C]0.122385[/C][C]0.938807[/C][/ROW]
[ROW][C]85[/C][C]0.0499509[/C][C]0.0999018[/C][C]0.950049[/C][/ROW]
[ROW][C]86[/C][C]0.0519629[/C][C]0.103926[/C][C]0.948037[/C][/ROW]
[ROW][C]87[/C][C]0.0417981[/C][C]0.0835961[/C][C]0.958202[/C][/ROW]
[ROW][C]88[/C][C]0.0334329[/C][C]0.0668659[/C][C]0.966567[/C][/ROW]
[ROW][C]89[/C][C]0.643966[/C][C]0.712068[/C][C]0.356034[/C][/ROW]
[ROW][C]90[/C][C]0.625734[/C][C]0.748533[/C][C]0.374266[/C][/ROW]
[ROW][C]91[/C][C]0.623221[/C][C]0.753558[/C][C]0.376779[/C][/ROW]
[ROW][C]92[/C][C]0.58093[/C][C]0.838139[/C][C]0.41907[/C][/ROW]
[ROW][C]93[/C][C]0.786044[/C][C]0.427912[/C][C]0.213956[/C][/ROW]
[ROW][C]94[/C][C]0.786068[/C][C]0.427863[/C][C]0.213932[/C][/ROW]
[ROW][C]95[/C][C]0.769115[/C][C]0.461771[/C][C]0.230885[/C][/ROW]
[ROW][C]96[/C][C]0.784492[/C][C]0.431015[/C][C]0.215508[/C][/ROW]
[ROW][C]97[/C][C]0.827258[/C][C]0.345483[/C][C]0.172742[/C][/ROW]
[ROW][C]98[/C][C]0.831171[/C][C]0.337658[/C][C]0.168829[/C][/ROW]
[ROW][C]99[/C][C]0.810394[/C][C]0.379213[/C][C]0.189606[/C][/ROW]
[ROW][C]100[/C][C]0.780751[/C][C]0.438497[/C][C]0.219249[/C][/ROW]
[ROW][C]101[/C][C]0.753651[/C][C]0.492698[/C][C]0.246349[/C][/ROW]
[ROW][C]102[/C][C]0.716622[/C][C]0.566756[/C][C]0.283378[/C][/ROW]
[ROW][C]103[/C][C]0.677477[/C][C]0.645046[/C][C]0.322523[/C][/ROW]
[ROW][C]104[/C][C]0.666862[/C][C]0.666276[/C][C]0.333138[/C][/ROW]
[ROW][C]105[/C][C]0.708516[/C][C]0.582968[/C][C]0.291484[/C][/ROW]
[ROW][C]106[/C][C]0.776055[/C][C]0.44789[/C][C]0.223945[/C][/ROW]
[ROW][C]107[/C][C]0.817432[/C][C]0.365136[/C][C]0.182568[/C][/ROW]
[ROW][C]108[/C][C]0.787375[/C][C]0.425249[/C][C]0.212625[/C][/ROW]
[ROW][C]109[/C][C]0.775548[/C][C]0.448904[/C][C]0.224452[/C][/ROW]
[ROW][C]110[/C][C]0.773678[/C][C]0.452645[/C][C]0.226322[/C][/ROW]
[ROW][C]111[/C][C]0.749003[/C][C]0.501995[/C][C]0.250997[/C][/ROW]
[ROW][C]112[/C][C]0.829484[/C][C]0.341032[/C][C]0.170516[/C][/ROW]
[ROW][C]113[/C][C]0.835858[/C][C]0.328283[/C][C]0.164142[/C][/ROW]
[ROW][C]114[/C][C]0.866877[/C][C]0.266246[/C][C]0.133123[/C][/ROW]
[ROW][C]115[/C][C]0.891121[/C][C]0.217759[/C][C]0.108879[/C][/ROW]
[ROW][C]116[/C][C]0.874594[/C][C]0.250812[/C][C]0.125406[/C][/ROW]
[ROW][C]117[/C][C]0.860159[/C][C]0.279681[/C][C]0.139841[/C][/ROW]
[ROW][C]118[/C][C]0.834248[/C][C]0.331504[/C][C]0.165752[/C][/ROW]
[ROW][C]119[/C][C]0.823613[/C][C]0.352774[/C][C]0.176387[/C][/ROW]
[ROW][C]120[/C][C]0.8274[/C][C]0.345199[/C][C]0.1726[/C][/ROW]
[ROW][C]121[/C][C]0.817282[/C][C]0.365435[/C][C]0.182718[/C][/ROW]
[ROW][C]122[/C][C]0.814237[/C][C]0.371526[/C][C]0.185763[/C][/ROW]
[ROW][C]123[/C][C]0.842169[/C][C]0.315661[/C][C]0.157831[/C][/ROW]
[ROW][C]124[/C][C]0.85049[/C][C]0.29902[/C][C]0.14951[/C][/ROW]
[ROW][C]125[/C][C]0.849354[/C][C]0.301292[/C][C]0.150646[/C][/ROW]
[ROW][C]126[/C][C]0.833067[/C][C]0.333865[/C][C]0.166933[/C][/ROW]
[ROW][C]127[/C][C]0.810709[/C][C]0.378581[/C][C]0.189291[/C][/ROW]
[ROW][C]128[/C][C]0.793473[/C][C]0.413054[/C][C]0.206527[/C][/ROW]
[ROW][C]129[/C][C]0.755127[/C][C]0.489746[/C][C]0.244873[/C][/ROW]
[ROW][C]130[/C][C]0.7457[/C][C]0.5086[/C][C]0.2543[/C][/ROW]
[ROW][C]131[/C][C]0.743059[/C][C]0.513881[/C][C]0.256941[/C][/ROW]
[ROW][C]132[/C][C]0.69989[/C][C]0.600219[/C][C]0.30011[/C][/ROW]
[ROW][C]133[/C][C]0.657774[/C][C]0.684453[/C][C]0.342226[/C][/ROW]
[ROW][C]134[/C][C]0.599871[/C][C]0.800257[/C][C]0.400129[/C][/ROW]
[ROW][C]135[/C][C]0.606371[/C][C]0.787258[/C][C]0.393629[/C][/ROW]
[ROW][C]136[/C][C]0.544988[/C][C]0.910024[/C][C]0.455012[/C][/ROW]
[ROW][C]137[/C][C]0.711596[/C][C]0.576807[/C][C]0.288404[/C][/ROW]
[ROW][C]138[/C][C]0.765[/C][C]0.470001[/C][C]0.235[/C][/ROW]
[ROW][C]139[/C][C]0.760063[/C][C]0.479873[/C][C]0.239937[/C][/ROW]
[ROW][C]140[/C][C]0.716914[/C][C]0.566172[/C][C]0.283086[/C][/ROW]
[ROW][C]141[/C][C]0.761546[/C][C]0.476908[/C][C]0.238454[/C][/ROW]
[ROW][C]142[/C][C]0.746169[/C][C]0.507662[/C][C]0.253831[/C][/ROW]
[ROW][C]143[/C][C]0.726745[/C][C]0.546511[/C][C]0.273255[/C][/ROW]
[ROW][C]144[/C][C]0.663788[/C][C]0.672424[/C][C]0.336212[/C][/ROW]
[ROW][C]145[/C][C]0.650243[/C][C]0.699513[/C][C]0.349757[/C][/ROW]
[ROW][C]146[/C][C]0.664502[/C][C]0.670996[/C][C]0.335498[/C][/ROW]
[ROW][C]147[/C][C]0.660639[/C][C]0.678722[/C][C]0.339361[/C][/ROW]
[ROW][C]148[/C][C]0.656098[/C][C]0.687804[/C][C]0.343902[/C][/ROW]
[ROW][C]149[/C][C]0.590725[/C][C]0.81855[/C][C]0.409275[/C][/ROW]
[ROW][C]150[/C][C]0.567655[/C][C]0.864691[/C][C]0.432345[/C][/ROW]
[ROW][C]151[/C][C]0.738358[/C][C]0.523284[/C][C]0.261642[/C][/ROW]
[ROW][C]152[/C][C]0.705259[/C][C]0.589482[/C][C]0.294741[/C][/ROW]
[ROW][C]153[/C][C]0.654326[/C][C]0.691348[/C][C]0.345674[/C][/ROW]
[ROW][C]154[/C][C]0.71133[/C][C]0.577339[/C][C]0.28867[/C][/ROW]
[ROW][C]155[/C][C]0.587013[/C][C]0.825975[/C][C]0.412987[/C][/ROW]
[ROW][C]156[/C][C]0.444302[/C][C]0.888605[/C][C]0.555698[/C][/ROW]
[ROW][C]157[/C][C]0.315287[/C][C]0.630574[/C][C]0.684713[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270808&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270808&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
9	0.0601335	0.120267	0.939866
10	0.0434965	0.0869929	0.956504
11	0.202174	0.404348	0.797826
12	0.125511	0.251022	0.874489
13	0.541111	0.917778	0.458889
14	0.430599	0.861198	0.569401
15	0.402635	0.80527	0.597365
16	0.683506	0.632989	0.316494
17	0.625968	0.748063	0.374032
18	0.553224	0.893553	0.446776
19	0.497428	0.994856	0.502572
20	0.499293	0.998586	0.500707
21	0.423499	0.846998	0.576501
22	0.349952	0.699904	0.650048
23	0.284146	0.568292	0.715854
24	0.232118	0.464236	0.767882
25	0.189147	0.378295	0.810853
26	0.152017	0.304034	0.847983
27	0.15538	0.31076	0.84462
28	0.122369	0.244738	0.877631
29	0.0972661	0.194532	0.902734
30	0.0921885	0.184377	0.907812
31	0.0719502	0.1439	0.92805
32	0.245019	0.490038	0.754981
33	0.230871	0.461743	0.769129
34	0.207689	0.415378	0.792311
35	0.187942	0.375883	0.812058
36	0.152116	0.304233	0.847884
37	0.188474	0.376948	0.811526
38	0.216259	0.432518	0.783741
39	0.214988	0.429977	0.785012
40	0.17663	0.353259	0.82337
41	0.142456	0.284913	0.857544
42	0.142396	0.284791	0.857604
43	0.114881	0.229761	0.885119
44	0.123583	0.247166	0.876417
45	0.111434	0.222868	0.888566
46	0.0880031	0.176006	0.911997
47	0.0686817	0.137363	0.931318
48	0.0738435	0.147687	0.926156
49	0.0583518	0.116704	0.941648
50	0.0526915	0.105383	0.947309
51	0.0694057	0.138811	0.930594
52	0.131726	0.263452	0.868274
53	0.110825	0.221651	0.889175
54	0.162282	0.324565	0.837718
55	0.340674	0.681347	0.659326
56	0.373394	0.746788	0.626606
57	0.378341	0.756682	0.621659
58	0.355755	0.71151	0.644245
59	0.357921	0.715843	0.642079
60	0.313984	0.627968	0.686016
61	0.315831	0.631662	0.684169
62	0.283201	0.566403	0.716799
63	0.253229	0.506458	0.746771
64	0.218897	0.437793	0.781103
65	0.188	0.376001	0.812
66	0.158261	0.316522	0.841739
67	0.142992	0.285984	0.857008
68	0.161812	0.323625	0.838188
69	0.1785	0.357001	0.8215
70	0.157464	0.314927	0.842536
71	0.131368	0.262735	0.868632
72	0.118552	0.237103	0.881448
73	0.103318	0.206635	0.896682
74	0.0853868	0.170774	0.914613
75	0.071931	0.143862	0.928069
76	0.0574737	0.114947	0.942526
77	0.0459274	0.0918548	0.954073
78	0.0945348	0.18907	0.905465
79	0.156337	0.312674	0.843663
80	0.131839	0.263678	0.868161
81	0.111304	0.222608	0.888696
82	0.0923097	0.184619	0.90769
83	0.0757653	0.151531	0.924235
84	0.0611927	0.122385	0.938807
85	0.0499509	0.0999018	0.950049
86	0.0519629	0.103926	0.948037
87	0.0417981	0.0835961	0.958202
88	0.0334329	0.0668659	0.966567
89	0.643966	0.712068	0.356034
90	0.625734	0.748533	0.374266
91	0.623221	0.753558	0.376779
92	0.58093	0.838139	0.41907
93	0.786044	0.427912	0.213956
94	0.786068	0.427863	0.213932
95	0.769115	0.461771	0.230885
96	0.784492	0.431015	0.215508
97	0.827258	0.345483	0.172742
98	0.831171	0.337658	0.168829
99	0.810394	0.379213	0.189606
100	0.780751	0.438497	0.219249
101	0.753651	0.492698	0.246349
102	0.716622	0.566756	0.283378
103	0.677477	0.645046	0.322523
104	0.666862	0.666276	0.333138
105	0.708516	0.582968	0.291484
106	0.776055	0.44789	0.223945
107	0.817432	0.365136	0.182568
108	0.787375	0.425249	0.212625
109	0.775548	0.448904	0.224452
110	0.773678	0.452645	0.226322
111	0.749003	0.501995	0.250997
112	0.829484	0.341032	0.170516
113	0.835858	0.328283	0.164142
114	0.866877	0.266246	0.133123
115	0.891121	0.217759	0.108879
116	0.874594	0.250812	0.125406
117	0.860159	0.279681	0.139841
118	0.834248	0.331504	0.165752
119	0.823613	0.352774	0.176387
120	0.8274	0.345199	0.1726
121	0.817282	0.365435	0.182718
122	0.814237	0.371526	0.185763
123	0.842169	0.315661	0.157831
124	0.85049	0.29902	0.14951
125	0.849354	0.301292	0.150646
126	0.833067	0.333865	0.166933
127	0.810709	0.378581	0.189291
128	0.793473	0.413054	0.206527
129	0.755127	0.489746	0.244873
130	0.7457	0.5086	0.2543
131	0.743059	0.513881	0.256941
132	0.69989	0.600219	0.30011
133	0.657774	0.684453	0.342226
134	0.599871	0.800257	0.400129
135	0.606371	0.787258	0.393629
136	0.544988	0.910024	0.455012
137	0.711596	0.576807	0.288404
138	0.765	0.470001	0.235
139	0.760063	0.479873	0.239937
140	0.716914	0.566172	0.283086
141	0.761546	0.476908	0.238454
142	0.746169	0.507662	0.253831
143	0.726745	0.546511	0.273255
144	0.663788	0.672424	0.336212
145	0.650243	0.699513	0.349757
146	0.664502	0.670996	0.335498
147	0.660639	0.678722	0.339361
148	0.656098	0.687804	0.343902
149	0.590725	0.81855	0.409275
150	0.567655	0.864691	0.432345
151	0.738358	0.523284	0.261642
152	0.705259	0.589482	0.294741
153	0.654326	0.691348	0.345674
154	0.71133	0.577339	0.28867
155	0.587013	0.825975	0.412987
156	0.444302	0.888605	0.555698
157	0.315287	0.630574	0.684713

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	5	0.033557	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 5 & 0.033557 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270808&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.033557[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270808&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270808&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	5	0.033557	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

PNG link

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Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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