Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 15 Dec 2014 09:15:00 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/15/t1418634999gswof2i8cfjv5lk.htm/, Retrieved Thu, 16 May 2024 22:07:24 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267969, Retrieved Thu, 16 May 2024 22:07:24 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Multiple Regression Peer Review score

Estimated Impact

103

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

-       [Multiple Regression] [Paper data] [2014-12-15 09:15:00] [99d5c1073827aabbadf7ab1e7da1d584] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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
138	145	26	22	48	4
149	196	36	51	86	4
145	199	22	67	89	4
120	127	40	36	76	3
138	91	18	21	39	4
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
62	119	23	29	53	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
154	130	41	21	62	4
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
128	85	24	24	48	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	6 seconds
R Server	'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267969&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267969&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267969&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
PR[t] = + 0.167543 + 0.0252074LFM[t] -0.0003436B[t] + 0.0198816PRH[t] + 0.0222509CH[t] -0.0212918H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PR[t] =  +  0.167543 +  0.0252074LFM[t] -0.0003436B[t] +  0.0198816PRH[t] +  0.0222509CH[t] -0.0212918H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267969&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PR[t] =  +  0.167543 +  0.0252074LFM[t] -0.0003436B[t] +  0.0198816PRH[t] +  0.0222509CH[t] -0.0212918H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267969&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267969&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] = + 0.167543 + 0.0252074LFM[t] -0.0003436B[t] + 0.0198816PRH[t] + 0.0222509CH[t] -0.0212918H[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)	0.167543	0.089918	1.863	0.0641999	0.0321
LFM	0.0252074	0.000854355	29.5	1.05075e-67	5.25374e-68
B	-0.0003436	0.000580545	-0.5919	0.554755	0.277378
PRH	0.0198816	0.0582364	0.3414	0.733241	0.366621
CH	0.0222509	0.0580666	0.3832	0.702067	0.351034
H	-0.0212918	0.0580495	-0.3668	0.714247	0.357124

\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) & 0.167543 & 0.089918 & 1.863 & 0.0641999 & 0.0321 \tabularnewline
LFM & 0.0252074 & 0.000854355 & 29.5 & 1.05075e-67 & 5.25374e-68 \tabularnewline
B & -0.0003436 & 0.000580545 & -0.5919 & 0.554755 & 0.277378 \tabularnewline
PRH & 0.0198816 & 0.0582364 & 0.3414 & 0.733241 & 0.366621 \tabularnewline
CH & 0.0222509 & 0.0580666 & 0.3832 & 0.702067 & 0.351034 \tabularnewline
H & -0.0212918 & 0.0580495 & -0.3668 & 0.714247 & 0.357124 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267969&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]0.167543[/C][C]0.089918[/C][C]1.863[/C][C]0.0641999[/C][C]0.0321[/C][/ROW]
[ROW][C]LFM[/C][C]0.0252074[/C][C]0.000854355[/C][C]29.5[/C][C]1.05075e-67[/C][C]5.25374e-68[/C][/ROW]
[ROW][C]B[/C][C]-0.0003436[/C][C]0.000580545[/C][C]-0.5919[/C][C]0.554755[/C][C]0.277378[/C][/ROW]
[ROW][C]PRH[/C][C]0.0198816[/C][C]0.0582364[/C][C]0.3414[/C][C]0.733241[/C][C]0.366621[/C][/ROW]
[ROW][C]CH[/C][C]0.0222509[/C][C]0.0580666[/C][C]0.3832[/C][C]0.702067[/C][C]0.351034[/C][/ROW]
[ROW][C]H[/C][C]-0.0212918[/C][C]0.0580495[/C][C]-0.3668[/C][C]0.714247[/C][C]0.357124[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267969&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267969&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)	0.167543	0.089918	1.863	0.0641999	0.0321
LFM	0.0252074	0.000854355	29.5	1.05075e-67	5.25374e-68
B	-0.0003436	0.000580545	-0.5919	0.554755	0.277378
PRH	0.0198816	0.0582364	0.3414	0.733241	0.366621
CH	0.0222509	0.0580666	0.3832	0.702067	0.351034
H	-0.0212918	0.0580495	-0.3668	0.714247	0.357124

Multiple Linear Regression - Regression Statistics
Multiple R	0.939246
R-squared	0.882182
Adjusted R-squared	0.878612
F-TEST (value)	247.094
F-TEST (DF numerator)	5
F-TEST (DF denominator)	165
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.354927
Sum Squared Residuals	20.7855

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.939246 \tabularnewline
R-squared & 0.882182 \tabularnewline
Adjusted R-squared & 0.878612 \tabularnewline
F-TEST (value) & 247.094 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 165 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.354927 \tabularnewline
Sum Squared Residuals & 20.7855 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267969&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.939246[/C][/ROW]
[ROW][C]R-squared[/C][C]0.882182[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.878612[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]247.094[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]165[/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]0.354927[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]20.7855[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267969&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267969&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	0.939246
R-squared	0.882182
Adjusted R-squared	0.878612
F-TEST (value)	247.094
F-TEST (DF numerator)	5
F-TEST (DF denominator)	165
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.354927
Sum Squared Residuals	20.7855

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1	1.36377	-0.363775
2	1	1.39834	-0.398342
3	4	3.87507	0.124934
4	4	3.97387	0.0261302
5	3	2.87618	0.123816
6	2	1.88076	0.119241
7	4	4.89882	-0.898823
8	4	4.04007	-0.0400656
9	4	4.09396	-0.0939613
10	2	1.82399	0.176012
11	4	3.78455	0.215448
12	1	1.1194	-0.119396
13	3	2.59864	0.401362
14	3	2.91038	0.0896237
15	4	3.57943	0.420572
16	3	2.51772	0.482284
17	4	3.30257	0.697433
18	3	2.64575	0.354247
19	3	2.99742	0.00257601
20	4	4.17793	-0.177932
21	3	2.98737	0.0126273
22	3	2.88557	0.114433
23	2	2.01738	-0.0173765
24	2	2.15531	-0.155306
25	3	3.16849	-0.168492
26	1	0.948535	0.0514652
27	4	3.87364	0.126363
28	3	3.01283	-0.0128281
29	2	1.95937	0.0406277
30	4	4.28288	-0.282883
31	4	3.97744	0.0225639
32	4	3.26795	0.732045
33	4	3.58078	0.419224
34	4	3.87554	0.124464
35	4	3.78747	0.212527
36	3	3.12691	-0.12691
37	4	3.60965	0.390346
38	3	2.86106	0.13894
39	4	3.39854	0.601462
40	4	4.40389	-0.403895
41	4	4.36299	-0.362991
42	3	2.99621	0.00378725
43	4	4.01228	-0.0122793
44	4	4.421	-0.421003
45	2	1.8398	0.160199
46	2	2.40039	-0.400386
47	4	4.30977	-0.30977
48	3	2.94534	0.0546595
49	3	2.99277	0.00722687
50	2	2.04	-0.0399991
51	3	3.12874	-0.128741
52	2	2.29043	-0.29043
53	4	4.46797	-0.467973
54	1	0.212672	0.787328
55	4	4.16616	-0.166158
56	1	1.29286	-0.292864
57	4	3.17924	0.82076
58	3	2.53862	0.461377
59	3	2.59495	0.405049
60	2	2.19714	-0.197139
61	3	2.61707	0.38293
62	3	3.01472	-0.0147158
63	4	3.6725	0.327501
64	4	4.26998	-0.269976
65	4	4.26998	-0.269976
66	3	2.90322	0.0967822
67	3	3.09198	-0.0919844
68	4	4.06933	-0.0693304
69	4	3.8143	0.1857
70	1	1.33484	-0.334839
71	2	2.39025	-0.390246
72	3	3.142	-0.142001
73	4	3.99251	0.00749042
74	3	2.75879	0.241211
75	4	3.85461	0.145391
76	3	2.89025	0.109747
77	3	2.83369	0.166313
78	3	2.96709	0.0329052
79	3	2.98442	0.0155809
80	1	1.67555	-0.675546
81	1	1.6408	-0.640804
82	3	2.8888	0.111204
83	2	1.87383	0.126167
84	3	2.9108	0.089197
85	2	2.07454	-0.0745407
86	2	1.93301	0.0669867
87	4	3.89414	0.105864
88	2	2.33556	-0.335564
89	2	2.04034	-0.0403367
90	3	2.89025	0.109753
91	4	5.58286	-1.58286
92	2	1.77358	0.226415
93	4	3.67993	0.320067
94	3	3.05633	-0.0563292
95	4	3.15875	0.84125
96	2	1.68668	0.313318
97	1	1.22243	-0.222429
98	1	1.45308	-0.453083
99	1	1.6636	-0.663599
100	4	3.4362	0.563799
101	3	2.62937	0.370634
102	1	1.22764	-0.227636
103	4	3.92398	0.0760165
104	3	2.79017	0.209831
105	2	2.01167	-0.0116676
106	4	3.97608	0.0239226
107	3	2.69912	0.300877
108	3	2.53463	0.465369
109	4	3.96714	0.0328649
110	4	4.51964	-0.519641
111	1	1.57989	-0.579891
112	3	2.87869	0.121312
113	4	3.65107	0.348926
114	1	1.37515	-0.375148
115	3	2.86143	0.13857
116	4	3.44303	0.556968
117	4	4.30834	-0.308337
118	1	1.53191	-0.531907
119	4	3.58292	0.417077
120	2	2.21632	-0.216324
121	3	3.20209	-0.202086
122	4	3.86715	0.132854
123	4	4.31167	-0.31167
124	4	3.6218	0.378199
125	2	2.34413	-0.344134
126	4	4.33254	-0.332537
127	2	2.51658	-0.516584
128	1	1.415	-0.414998
129	1	1.35129	-0.351289
130	4	3.70488	0.29512
131	2	1.79021	0.209786
132	2	2.2609	-0.260902
133	3	2.87319	0.126806
134	2	1.70773	0.292265
135	3	2.67645	0.323552
136	4	4.16616	-0.166158
137	4	3.35406	0.645944
138	2	2.23404	-0.234037
139	3	2.97189	0.0281076
140	4	4.29296	-0.292959
141	3	3.06198	-0.0619789
142	4	3.32342	0.676576
143	4	3.46044	0.539556
144	4	3.69404	0.305965
145	2	2.21531	-0.21531
146	2	2.48906	-0.489063
147	2	2.19195	-0.191953
148	4	4.31088	-0.310878
149	3	2.84159	0.158413
150	2	1.75064	0.249361
151	2	2.41287	-0.412873
152	3	2.76457	0.235427
153	3	2.76958	0.230421
154	1	1.29286	-0.292864
155	2	2.35745	-0.357448
156	2	2.45763	-0.457631
157	3	2.67645	0.323552
158	3	2.57241	0.427588
159	2	1.72916	0.270839
160	2	2.00525	-0.00525043
161	3	2.84578	0.154223
162	3	3.08581	-0.085807
163	1	1.59019	-0.590193
164	3	3.13643	-0.136427
165	2	1.95541	0.0445927
166	2	2.41957	-0.419574
167	3	2.83734	0.162665
168	3	2.78297	0.217035
169	3	3.04423	-0.044231
170	3	3.1315	-0.131498
171	1	0.919183	0.0808169

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 1.36377 & -0.363775 \tabularnewline
2 & 1 & 1.39834 & -0.398342 \tabularnewline
3 & 4 & 3.87507 & 0.124934 \tabularnewline
4 & 4 & 3.97387 & 0.0261302 \tabularnewline
5 & 3 & 2.87618 & 0.123816 \tabularnewline
6 & 2 & 1.88076 & 0.119241 \tabularnewline
7 & 4 & 4.89882 & -0.898823 \tabularnewline
8 & 4 & 4.04007 & -0.0400656 \tabularnewline
9 & 4 & 4.09396 & -0.0939613 \tabularnewline
10 & 2 & 1.82399 & 0.176012 \tabularnewline
11 & 4 & 3.78455 & 0.215448 \tabularnewline
12 & 1 & 1.1194 & -0.119396 \tabularnewline
13 & 3 & 2.59864 & 0.401362 \tabularnewline
14 & 3 & 2.91038 & 0.0896237 \tabularnewline
15 & 4 & 3.57943 & 0.420572 \tabularnewline
16 & 3 & 2.51772 & 0.482284 \tabularnewline
17 & 4 & 3.30257 & 0.697433 \tabularnewline
18 & 3 & 2.64575 & 0.354247 \tabularnewline
19 & 3 & 2.99742 & 0.00257601 \tabularnewline
20 & 4 & 4.17793 & -0.177932 \tabularnewline
21 & 3 & 2.98737 & 0.0126273 \tabularnewline
22 & 3 & 2.88557 & 0.114433 \tabularnewline
23 & 2 & 2.01738 & -0.0173765 \tabularnewline
24 & 2 & 2.15531 & -0.155306 \tabularnewline
25 & 3 & 3.16849 & -0.168492 \tabularnewline
26 & 1 & 0.948535 & 0.0514652 \tabularnewline
27 & 4 & 3.87364 & 0.126363 \tabularnewline
28 & 3 & 3.01283 & -0.0128281 \tabularnewline
29 & 2 & 1.95937 & 0.0406277 \tabularnewline
30 & 4 & 4.28288 & -0.282883 \tabularnewline
31 & 4 & 3.97744 & 0.0225639 \tabularnewline
32 & 4 & 3.26795 & 0.732045 \tabularnewline
33 & 4 & 3.58078 & 0.419224 \tabularnewline
34 & 4 & 3.87554 & 0.124464 \tabularnewline
35 & 4 & 3.78747 & 0.212527 \tabularnewline
36 & 3 & 3.12691 & -0.12691 \tabularnewline
37 & 4 & 3.60965 & 0.390346 \tabularnewline
38 & 3 & 2.86106 & 0.13894 \tabularnewline
39 & 4 & 3.39854 & 0.601462 \tabularnewline
40 & 4 & 4.40389 & -0.403895 \tabularnewline
41 & 4 & 4.36299 & -0.362991 \tabularnewline
42 & 3 & 2.99621 & 0.00378725 \tabularnewline
43 & 4 & 4.01228 & -0.0122793 \tabularnewline
44 & 4 & 4.421 & -0.421003 \tabularnewline
45 & 2 & 1.8398 & 0.160199 \tabularnewline
46 & 2 & 2.40039 & -0.400386 \tabularnewline
47 & 4 & 4.30977 & -0.30977 \tabularnewline
48 & 3 & 2.94534 & 0.0546595 \tabularnewline
49 & 3 & 2.99277 & 0.00722687 \tabularnewline
50 & 2 & 2.04 & -0.0399991 \tabularnewline
51 & 3 & 3.12874 & -0.128741 \tabularnewline
52 & 2 & 2.29043 & -0.29043 \tabularnewline
53 & 4 & 4.46797 & -0.467973 \tabularnewline
54 & 1 & 0.212672 & 0.787328 \tabularnewline
55 & 4 & 4.16616 & -0.166158 \tabularnewline
56 & 1 & 1.29286 & -0.292864 \tabularnewline
57 & 4 & 3.17924 & 0.82076 \tabularnewline
58 & 3 & 2.53862 & 0.461377 \tabularnewline
59 & 3 & 2.59495 & 0.405049 \tabularnewline
60 & 2 & 2.19714 & -0.197139 \tabularnewline
61 & 3 & 2.61707 & 0.38293 \tabularnewline
62 & 3 & 3.01472 & -0.0147158 \tabularnewline
63 & 4 & 3.6725 & 0.327501 \tabularnewline
64 & 4 & 4.26998 & -0.269976 \tabularnewline
65 & 4 & 4.26998 & -0.269976 \tabularnewline
66 & 3 & 2.90322 & 0.0967822 \tabularnewline
67 & 3 & 3.09198 & -0.0919844 \tabularnewline
68 & 4 & 4.06933 & -0.0693304 \tabularnewline
69 & 4 & 3.8143 & 0.1857 \tabularnewline
70 & 1 & 1.33484 & -0.334839 \tabularnewline
71 & 2 & 2.39025 & -0.390246 \tabularnewline
72 & 3 & 3.142 & -0.142001 \tabularnewline
73 & 4 & 3.99251 & 0.00749042 \tabularnewline
74 & 3 & 2.75879 & 0.241211 \tabularnewline
75 & 4 & 3.85461 & 0.145391 \tabularnewline
76 & 3 & 2.89025 & 0.109747 \tabularnewline
77 & 3 & 2.83369 & 0.166313 \tabularnewline
78 & 3 & 2.96709 & 0.0329052 \tabularnewline
79 & 3 & 2.98442 & 0.0155809 \tabularnewline
80 & 1 & 1.67555 & -0.675546 \tabularnewline
81 & 1 & 1.6408 & -0.640804 \tabularnewline
82 & 3 & 2.8888 & 0.111204 \tabularnewline
83 & 2 & 1.87383 & 0.126167 \tabularnewline
84 & 3 & 2.9108 & 0.089197 \tabularnewline
85 & 2 & 2.07454 & -0.0745407 \tabularnewline
86 & 2 & 1.93301 & 0.0669867 \tabularnewline
87 & 4 & 3.89414 & 0.105864 \tabularnewline
88 & 2 & 2.33556 & -0.335564 \tabularnewline
89 & 2 & 2.04034 & -0.0403367 \tabularnewline
90 & 3 & 2.89025 & 0.109753 \tabularnewline
91 & 4 & 5.58286 & -1.58286 \tabularnewline
92 & 2 & 1.77358 & 0.226415 \tabularnewline
93 & 4 & 3.67993 & 0.320067 \tabularnewline
94 & 3 & 3.05633 & -0.0563292 \tabularnewline
95 & 4 & 3.15875 & 0.84125 \tabularnewline
96 & 2 & 1.68668 & 0.313318 \tabularnewline
97 & 1 & 1.22243 & -0.222429 \tabularnewline
98 & 1 & 1.45308 & -0.453083 \tabularnewline
99 & 1 & 1.6636 & -0.663599 \tabularnewline
100 & 4 & 3.4362 & 0.563799 \tabularnewline
101 & 3 & 2.62937 & 0.370634 \tabularnewline
102 & 1 & 1.22764 & -0.227636 \tabularnewline
103 & 4 & 3.92398 & 0.0760165 \tabularnewline
104 & 3 & 2.79017 & 0.209831 \tabularnewline
105 & 2 & 2.01167 & -0.0116676 \tabularnewline
106 & 4 & 3.97608 & 0.0239226 \tabularnewline
107 & 3 & 2.69912 & 0.300877 \tabularnewline
108 & 3 & 2.53463 & 0.465369 \tabularnewline
109 & 4 & 3.96714 & 0.0328649 \tabularnewline
110 & 4 & 4.51964 & -0.519641 \tabularnewline
111 & 1 & 1.57989 & -0.579891 \tabularnewline
112 & 3 & 2.87869 & 0.121312 \tabularnewline
113 & 4 & 3.65107 & 0.348926 \tabularnewline
114 & 1 & 1.37515 & -0.375148 \tabularnewline
115 & 3 & 2.86143 & 0.13857 \tabularnewline
116 & 4 & 3.44303 & 0.556968 \tabularnewline
117 & 4 & 4.30834 & -0.308337 \tabularnewline
118 & 1 & 1.53191 & -0.531907 \tabularnewline
119 & 4 & 3.58292 & 0.417077 \tabularnewline
120 & 2 & 2.21632 & -0.216324 \tabularnewline
121 & 3 & 3.20209 & -0.202086 \tabularnewline
122 & 4 & 3.86715 & 0.132854 \tabularnewline
123 & 4 & 4.31167 & -0.31167 \tabularnewline
124 & 4 & 3.6218 & 0.378199 \tabularnewline
125 & 2 & 2.34413 & -0.344134 \tabularnewline
126 & 4 & 4.33254 & -0.332537 \tabularnewline
127 & 2 & 2.51658 & -0.516584 \tabularnewline
128 & 1 & 1.415 & -0.414998 \tabularnewline
129 & 1 & 1.35129 & -0.351289 \tabularnewline
130 & 4 & 3.70488 & 0.29512 \tabularnewline
131 & 2 & 1.79021 & 0.209786 \tabularnewline
132 & 2 & 2.2609 & -0.260902 \tabularnewline
133 & 3 & 2.87319 & 0.126806 \tabularnewline
134 & 2 & 1.70773 & 0.292265 \tabularnewline
135 & 3 & 2.67645 & 0.323552 \tabularnewline
136 & 4 & 4.16616 & -0.166158 \tabularnewline
137 & 4 & 3.35406 & 0.645944 \tabularnewline
138 & 2 & 2.23404 & -0.234037 \tabularnewline
139 & 3 & 2.97189 & 0.0281076 \tabularnewline
140 & 4 & 4.29296 & -0.292959 \tabularnewline
141 & 3 & 3.06198 & -0.0619789 \tabularnewline
142 & 4 & 3.32342 & 0.676576 \tabularnewline
143 & 4 & 3.46044 & 0.539556 \tabularnewline
144 & 4 & 3.69404 & 0.305965 \tabularnewline
145 & 2 & 2.21531 & -0.21531 \tabularnewline
146 & 2 & 2.48906 & -0.489063 \tabularnewline
147 & 2 & 2.19195 & -0.191953 \tabularnewline
148 & 4 & 4.31088 & -0.310878 \tabularnewline
149 & 3 & 2.84159 & 0.158413 \tabularnewline
150 & 2 & 1.75064 & 0.249361 \tabularnewline
151 & 2 & 2.41287 & -0.412873 \tabularnewline
152 & 3 & 2.76457 & 0.235427 \tabularnewline
153 & 3 & 2.76958 & 0.230421 \tabularnewline
154 & 1 & 1.29286 & -0.292864 \tabularnewline
155 & 2 & 2.35745 & -0.357448 \tabularnewline
156 & 2 & 2.45763 & -0.457631 \tabularnewline
157 & 3 & 2.67645 & 0.323552 \tabularnewline
158 & 3 & 2.57241 & 0.427588 \tabularnewline
159 & 2 & 1.72916 & 0.270839 \tabularnewline
160 & 2 & 2.00525 & -0.00525043 \tabularnewline
161 & 3 & 2.84578 & 0.154223 \tabularnewline
162 & 3 & 3.08581 & -0.085807 \tabularnewline
163 & 1 & 1.59019 & -0.590193 \tabularnewline
164 & 3 & 3.13643 & -0.136427 \tabularnewline
165 & 2 & 1.95541 & 0.0445927 \tabularnewline
166 & 2 & 2.41957 & -0.419574 \tabularnewline
167 & 3 & 2.83734 & 0.162665 \tabularnewline
168 & 3 & 2.78297 & 0.217035 \tabularnewline
169 & 3 & 3.04423 & -0.044231 \tabularnewline
170 & 3 & 3.1315 & -0.131498 \tabularnewline
171 & 1 & 0.919183 & 0.0808169 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267969&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]1.36377[/C][C]-0.363775[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.39834[/C][C]-0.398342[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]3.87507[/C][C]0.124934[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]3.97387[/C][C]0.0261302[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.87618[/C][C]0.123816[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.88076[/C][C]0.119241[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]4.89882[/C][C]-0.898823[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.04007[/C][C]-0.0400656[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]4.09396[/C][C]-0.0939613[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.82399[/C][C]0.176012[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]3.78455[/C][C]0.215448[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.1194[/C][C]-0.119396[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]2.59864[/C][C]0.401362[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]2.91038[/C][C]0.0896237[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]3.57943[/C][C]0.420572[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]2.51772[/C][C]0.482284[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]3.30257[/C][C]0.697433[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]2.64575[/C][C]0.354247[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]2.99742[/C][C]0.00257601[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]4.17793[/C][C]-0.177932[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]2.98737[/C][C]0.0126273[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]2.88557[/C][C]0.114433[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]2.01738[/C][C]-0.0173765[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]2.15531[/C][C]-0.155306[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]3.16849[/C][C]-0.168492[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.948535[/C][C]0.0514652[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]3.87364[/C][C]0.126363[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]3.01283[/C][C]-0.0128281[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]1.95937[/C][C]0.0406277[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]4.28288[/C][C]-0.282883[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]3.97744[/C][C]0.0225639[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]3.26795[/C][C]0.732045[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]3.58078[/C][C]0.419224[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]3.87554[/C][C]0.124464[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]3.78747[/C][C]0.212527[/C][/ROW]
[ROW][C]36[/C][C]3[/C][C]3.12691[/C][C]-0.12691[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]3.60965[/C][C]0.390346[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]2.86106[/C][C]0.13894[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]3.39854[/C][C]0.601462[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.40389[/C][C]-0.403895[/C][/ROW]
[ROW][C]41[/C][C]4[/C][C]4.36299[/C][C]-0.362991[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]2.99621[/C][C]0.00378725[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]4.01228[/C][C]-0.0122793[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]4.421[/C][C]-0.421003[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.8398[/C][C]0.160199[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]2.40039[/C][C]-0.400386[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]4.30977[/C][C]-0.30977[/C][/ROW]
[ROW][C]48[/C][C]3[/C][C]2.94534[/C][C]0.0546595[/C][/ROW]
[ROW][C]49[/C][C]3[/C][C]2.99277[/C][C]0.00722687[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]2.04[/C][C]-0.0399991[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]3.12874[/C][C]-0.128741[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]2.29043[/C][C]-0.29043[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]4.46797[/C][C]-0.467973[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.212672[/C][C]0.787328[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]4.16616[/C][C]-0.166158[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.29286[/C][C]-0.292864[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]3.17924[/C][C]0.82076[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]2.53862[/C][C]0.461377[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]2.59495[/C][C]0.405049[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]2.19714[/C][C]-0.197139[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]2.61707[/C][C]0.38293[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]3.01472[/C][C]-0.0147158[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]3.6725[/C][C]0.327501[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]4.26998[/C][C]-0.269976[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]4.26998[/C][C]-0.269976[/C][/ROW]
[ROW][C]66[/C][C]3[/C][C]2.90322[/C][C]0.0967822[/C][/ROW]
[ROW][C]67[/C][C]3[/C][C]3.09198[/C][C]-0.0919844[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]4.06933[/C][C]-0.0693304[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]3.8143[/C][C]0.1857[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.33484[/C][C]-0.334839[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]2.39025[/C][C]-0.390246[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]3.142[/C][C]-0.142001[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]3.99251[/C][C]0.00749042[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]2.75879[/C][C]0.241211[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]3.85461[/C][C]0.145391[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]2.89025[/C][C]0.109747[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]2.83369[/C][C]0.166313[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]2.96709[/C][C]0.0329052[/C][/ROW]
[ROW][C]79[/C][C]3[/C][C]2.98442[/C][C]0.0155809[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.67555[/C][C]-0.675546[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.6408[/C][C]-0.640804[/C][/ROW]
[ROW][C]82[/C][C]3[/C][C]2.8888[/C][C]0.111204[/C][/ROW]
[ROW][C]83[/C][C]2[/C][C]1.87383[/C][C]0.126167[/C][/ROW]
[ROW][C]84[/C][C]3[/C][C]2.9108[/C][C]0.089197[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]2.07454[/C][C]-0.0745407[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]1.93301[/C][C]0.0669867[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]3.89414[/C][C]0.105864[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]2.33556[/C][C]-0.335564[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]2.04034[/C][C]-0.0403367[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]2.89025[/C][C]0.109753[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]5.58286[/C][C]-1.58286[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.77358[/C][C]0.226415[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]3.67993[/C][C]0.320067[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]3.05633[/C][C]-0.0563292[/C][/ROW]
[ROW][C]95[/C][C]4[/C][C]3.15875[/C][C]0.84125[/C][/ROW]
[ROW][C]96[/C][C]2[/C][C]1.68668[/C][C]0.313318[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]1.22243[/C][C]-0.222429[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.45308[/C][C]-0.453083[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.6636[/C][C]-0.663599[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]3.4362[/C][C]0.563799[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]2.62937[/C][C]0.370634[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.22764[/C][C]-0.227636[/C][/ROW]
[ROW][C]103[/C][C]4[/C][C]3.92398[/C][C]0.0760165[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]2.79017[/C][C]0.209831[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]2.01167[/C][C]-0.0116676[/C][/ROW]
[ROW][C]106[/C][C]4[/C][C]3.97608[/C][C]0.0239226[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]2.69912[/C][C]0.300877[/C][/ROW]
[ROW][C]108[/C][C]3[/C][C]2.53463[/C][C]0.465369[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]3.96714[/C][C]0.0328649[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]4.51964[/C][C]-0.519641[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]1.57989[/C][C]-0.579891[/C][/ROW]
[ROW][C]112[/C][C]3[/C][C]2.87869[/C][C]0.121312[/C][/ROW]
[ROW][C]113[/C][C]4[/C][C]3.65107[/C][C]0.348926[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.37515[/C][C]-0.375148[/C][/ROW]
[ROW][C]115[/C][C]3[/C][C]2.86143[/C][C]0.13857[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]3.44303[/C][C]0.556968[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]4.30834[/C][C]-0.308337[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]1.53191[/C][C]-0.531907[/C][/ROW]
[ROW][C]119[/C][C]4[/C][C]3.58292[/C][C]0.417077[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]2.21632[/C][C]-0.216324[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]3.20209[/C][C]-0.202086[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]3.86715[/C][C]0.132854[/C][/ROW]
[ROW][C]123[/C][C]4[/C][C]4.31167[/C][C]-0.31167[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]3.6218[/C][C]0.378199[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]2.34413[/C][C]-0.344134[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]4.33254[/C][C]-0.332537[/C][/ROW]
[ROW][C]127[/C][C]2[/C][C]2.51658[/C][C]-0.516584[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]1.415[/C][C]-0.414998[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]1.35129[/C][C]-0.351289[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]3.70488[/C][C]0.29512[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]1.79021[/C][C]0.209786[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]2.2609[/C][C]-0.260902[/C][/ROW]
[ROW][C]133[/C][C]3[/C][C]2.87319[/C][C]0.126806[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]1.70773[/C][C]0.292265[/C][/ROW]
[ROW][C]135[/C][C]3[/C][C]2.67645[/C][C]0.323552[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]4.16616[/C][C]-0.166158[/C][/ROW]
[ROW][C]137[/C][C]4[/C][C]3.35406[/C][C]0.645944[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]2.23404[/C][C]-0.234037[/C][/ROW]
[ROW][C]139[/C][C]3[/C][C]2.97189[/C][C]0.0281076[/C][/ROW]
[ROW][C]140[/C][C]4[/C][C]4.29296[/C][C]-0.292959[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]3.06198[/C][C]-0.0619789[/C][/ROW]
[ROW][C]142[/C][C]4[/C][C]3.32342[/C][C]0.676576[/C][/ROW]
[ROW][C]143[/C][C]4[/C][C]3.46044[/C][C]0.539556[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]3.69404[/C][C]0.305965[/C][/ROW]
[ROW][C]145[/C][C]2[/C][C]2.21531[/C][C]-0.21531[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]2.48906[/C][C]-0.489063[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]2.19195[/C][C]-0.191953[/C][/ROW]
[ROW][C]148[/C][C]4[/C][C]4.31088[/C][C]-0.310878[/C][/ROW]
[ROW][C]149[/C][C]3[/C][C]2.84159[/C][C]0.158413[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]1.75064[/C][C]0.249361[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]2.41287[/C][C]-0.412873[/C][/ROW]
[ROW][C]152[/C][C]3[/C][C]2.76457[/C][C]0.235427[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]2.76958[/C][C]0.230421[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]1.29286[/C][C]-0.292864[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]2.35745[/C][C]-0.357448[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]2.45763[/C][C]-0.457631[/C][/ROW]
[ROW][C]157[/C][C]3[/C][C]2.67645[/C][C]0.323552[/C][/ROW]
[ROW][C]158[/C][C]3[/C][C]2.57241[/C][C]0.427588[/C][/ROW]
[ROW][C]159[/C][C]2[/C][C]1.72916[/C][C]0.270839[/C][/ROW]
[ROW][C]160[/C][C]2[/C][C]2.00525[/C][C]-0.00525043[/C][/ROW]
[ROW][C]161[/C][C]3[/C][C]2.84578[/C][C]0.154223[/C][/ROW]
[ROW][C]162[/C][C]3[/C][C]3.08581[/C][C]-0.085807[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]1.59019[/C][C]-0.590193[/C][/ROW]
[ROW][C]164[/C][C]3[/C][C]3.13643[/C][C]-0.136427[/C][/ROW]
[ROW][C]165[/C][C]2[/C][C]1.95541[/C][C]0.0445927[/C][/ROW]
[ROW][C]166[/C][C]2[/C][C]2.41957[/C][C]-0.419574[/C][/ROW]
[ROW][C]167[/C][C]3[/C][C]2.83734[/C][C]0.162665[/C][/ROW]
[ROW][C]168[/C][C]3[/C][C]2.78297[/C][C]0.217035[/C][/ROW]
[ROW][C]169[/C][C]3[/C][C]3.04423[/C][C]-0.044231[/C][/ROW]
[ROW][C]170[/C][C]3[/C][C]3.1315[/C][C]-0.131498[/C][/ROW]
[ROW][C]171[/C][C]1[/C][C]0.919183[/C][C]0.0808169[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267969&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267969&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	1.36377	-0.363775
2	1	1.39834	-0.398342
3	4	3.87507	0.124934
4	4	3.97387	0.0261302
5	3	2.87618	0.123816
6	2	1.88076	0.119241
7	4	4.89882	-0.898823
8	4	4.04007	-0.0400656
9	4	4.09396	-0.0939613
10	2	1.82399	0.176012
11	4	3.78455	0.215448
12	1	1.1194	-0.119396
13	3	2.59864	0.401362
14	3	2.91038	0.0896237
15	4	3.57943	0.420572
16	3	2.51772	0.482284
17	4	3.30257	0.697433
18	3	2.64575	0.354247
19	3	2.99742	0.00257601
20	4	4.17793	-0.177932
21	3	2.98737	0.0126273
22	3	2.88557	0.114433
23	2	2.01738	-0.0173765
24	2	2.15531	-0.155306
25	3	3.16849	-0.168492
26	1	0.948535	0.0514652
27	4	3.87364	0.126363
28	3	3.01283	-0.0128281
29	2	1.95937	0.0406277
30	4	4.28288	-0.282883
31	4	3.97744	0.0225639
32	4	3.26795	0.732045
33	4	3.58078	0.419224
34	4	3.87554	0.124464
35	4	3.78747	0.212527
36	3	3.12691	-0.12691
37	4	3.60965	0.390346
38	3	2.86106	0.13894
39	4	3.39854	0.601462
40	4	4.40389	-0.403895
41	4	4.36299	-0.362991
42	3	2.99621	0.00378725
43	4	4.01228	-0.0122793
44	4	4.421	-0.421003
45	2	1.8398	0.160199
46	2	2.40039	-0.400386
47	4	4.30977	-0.30977
48	3	2.94534	0.0546595
49	3	2.99277	0.00722687
50	2	2.04	-0.0399991
51	3	3.12874	-0.128741
52	2	2.29043	-0.29043
53	4	4.46797	-0.467973
54	1	0.212672	0.787328
55	4	4.16616	-0.166158
56	1	1.29286	-0.292864
57	4	3.17924	0.82076
58	3	2.53862	0.461377
59	3	2.59495	0.405049
60	2	2.19714	-0.197139
61	3	2.61707	0.38293
62	3	3.01472	-0.0147158
63	4	3.6725	0.327501
64	4	4.26998	-0.269976
65	4	4.26998	-0.269976
66	3	2.90322	0.0967822
67	3	3.09198	-0.0919844
68	4	4.06933	-0.0693304
69	4	3.8143	0.1857
70	1	1.33484	-0.334839
71	2	2.39025	-0.390246
72	3	3.142	-0.142001
73	4	3.99251	0.00749042
74	3	2.75879	0.241211
75	4	3.85461	0.145391
76	3	2.89025	0.109747
77	3	2.83369	0.166313
78	3	2.96709	0.0329052
79	3	2.98442	0.0155809
80	1	1.67555	-0.675546
81	1	1.6408	-0.640804
82	3	2.8888	0.111204
83	2	1.87383	0.126167
84	3	2.9108	0.089197
85	2	2.07454	-0.0745407
86	2	1.93301	0.0669867
87	4	3.89414	0.105864
88	2	2.33556	-0.335564
89	2	2.04034	-0.0403367
90	3	2.89025	0.109753
91	4	5.58286	-1.58286
92	2	1.77358	0.226415
93	4	3.67993	0.320067
94	3	3.05633	-0.0563292
95	4	3.15875	0.84125
96	2	1.68668	0.313318
97	1	1.22243	-0.222429
98	1	1.45308	-0.453083
99	1	1.6636	-0.663599
100	4	3.4362	0.563799
101	3	2.62937	0.370634
102	1	1.22764	-0.227636
103	4	3.92398	0.0760165
104	3	2.79017	0.209831
105	2	2.01167	-0.0116676
106	4	3.97608	0.0239226
107	3	2.69912	0.300877
108	3	2.53463	0.465369
109	4	3.96714	0.0328649
110	4	4.51964	-0.519641
111	1	1.57989	-0.579891
112	3	2.87869	0.121312
113	4	3.65107	0.348926
114	1	1.37515	-0.375148
115	3	2.86143	0.13857
116	4	3.44303	0.556968
117	4	4.30834	-0.308337
118	1	1.53191	-0.531907
119	4	3.58292	0.417077
120	2	2.21632	-0.216324
121	3	3.20209	-0.202086
122	4	3.86715	0.132854
123	4	4.31167	-0.31167
124	4	3.6218	0.378199
125	2	2.34413	-0.344134
126	4	4.33254	-0.332537
127	2	2.51658	-0.516584
128	1	1.415	-0.414998
129	1	1.35129	-0.351289
130	4	3.70488	0.29512
131	2	1.79021	0.209786
132	2	2.2609	-0.260902
133	3	2.87319	0.126806
134	2	1.70773	0.292265
135	3	2.67645	0.323552
136	4	4.16616	-0.166158
137	4	3.35406	0.645944
138	2	2.23404	-0.234037
139	3	2.97189	0.0281076
140	4	4.29296	-0.292959
141	3	3.06198	-0.0619789
142	4	3.32342	0.676576
143	4	3.46044	0.539556
144	4	3.69404	0.305965
145	2	2.21531	-0.21531
146	2	2.48906	-0.489063
147	2	2.19195	-0.191953
148	4	4.31088	-0.310878
149	3	2.84159	0.158413
150	2	1.75064	0.249361
151	2	2.41287	-0.412873
152	3	2.76457	0.235427
153	3	2.76958	0.230421
154	1	1.29286	-0.292864
155	2	2.35745	-0.357448
156	2	2.45763	-0.457631
157	3	2.67645	0.323552
158	3	2.57241	0.427588
159	2	1.72916	0.270839
160	2	2.00525	-0.00525043
161	3	2.84578	0.154223
162	3	3.08581	-0.085807
163	1	1.59019	-0.590193
164	3	3.13643	-0.136427
165	2	1.95541	0.0445927
166	2	2.41957	-0.419574
167	3	2.83734	0.162665
168	3	2.78297	0.217035
169	3	3.04423	-0.044231
170	3	3.1315	-0.131498
171	1	0.919183	0.0808169

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0573049	0.11461	0.942695
10	0.0408457	0.0816915	0.959154
11	0.190303	0.380606	0.809697
12	0.116436	0.232872	0.883564
13	0.516429	0.967141	0.483571
14	0.405746	0.811491	0.594254
15	0.37594	0.751879	0.62406
16	0.655454	0.689093	0.344546
17	0.596871	0.806258	0.403129
18	0.521539	0.956921	0.478461
19	0.465058	0.930115	0.534942
20	0.465725	0.931449	0.534275
21	0.390387	0.780773	0.609613
22	0.318679	0.637359	0.681321
23	0.255564	0.511128	0.744436
24	0.2061	0.4122	0.7939
25	0.166291	0.332581	0.833709
26	0.132147	0.264294	0.867853
27	0.134837	0.269673	0.865163
28	0.104952	0.209905	0.895048
29	0.0824428	0.164886	0.917557
30	0.0786064	0.157213	0.921394
31	0.0606555	0.121311	0.939345
32	0.21454	0.429079	0.78546
33	0.216255	0.432511	0.783745
34	0.20092	0.40184	0.79908
35	0.177516	0.355031	0.822484
36	0.162726	0.325452	0.837274
37	0.152477	0.304954	0.847523
38	0.121678	0.243356	0.878322
39	0.145903	0.291805	0.854097
40	0.181602	0.363205	0.818398
41	0.191826	0.383651	0.808174
42	0.157224	0.314447	0.842776
43	0.127037	0.254073	0.872963
44	0.131314	0.262627	0.868686
45	0.105826	0.211652	0.894174
46	0.113091	0.226183	0.886909
47	0.103488	0.206977	0.896512
48	0.0817934	0.163587	0.918207
49	0.0642722	0.128544	0.935728
50	0.0721953	0.144391	0.927805
51	0.0577093	0.115419	0.942291
52	0.0505766	0.101153	0.949423
53	0.0733806	0.146761	0.926619
54	0.138646	0.277291	0.861354
55	0.118492	0.236984	0.881508
56	0.172973	0.345947	0.827027
57	0.345469	0.690938	0.654531
58	0.363011	0.726021	0.636989
59	0.368043	0.736085	0.631957
60	0.343968	0.687936	0.656032
61	0.345877	0.691755	0.654123
62	0.303577	0.607155	0.696423
63	0.297631	0.595262	0.702369
64	0.270364	0.540727	0.729636
65	0.245253	0.490507	0.754747
66	0.211624	0.423247	0.788376
67	0.182363	0.364727	0.817637
68	0.153875	0.30775	0.846125
69	0.135954	0.271907	0.864046
70	0.152614	0.305228	0.847386
71	0.16391	0.32782	0.83609
72	0.145992	0.291985	0.854008
73	0.121475	0.242951	0.878525
74	0.108924	0.217847	0.891076
75	0.0923405	0.184681	0.90766
76	0.075991	0.151982	0.924009
77	0.0631008	0.126202	0.936899
78	0.0502379	0.100476	0.949762
79	0.0402364	0.0804728	0.959764
80	0.0812039	0.162408	0.918796
81	0.134626	0.269253	0.865374
82	0.112636	0.225272	0.887364
83	0.0941804	0.188361	0.90582
84	0.0774042	0.154808	0.922596
85	0.063285	0.12657	0.936715
86	0.0510366	0.102073	0.948963
87	0.0412853	0.0825706	0.958715
88	0.0423198	0.0846396	0.95768
89	0.0340699	0.0681397	0.96593
90	0.0269821	0.0539643	0.973018
91	0.604738	0.790524	0.395262
92	0.585577	0.828845	0.414423
93	0.575621	0.848757	0.424379
94	0.533168	0.933665	0.466832
95	0.731173	0.537654	0.268827
96	0.730349	0.539302	0.269651
97	0.708715	0.58257	0.291285
98	0.725181	0.549638	0.274819
99	0.803918	0.392164	0.196082
100	0.841193	0.317614	0.158807
101	0.846227	0.307546	0.153773
102	0.825744	0.348512	0.174256
103	0.796128	0.407743	0.203872
104	0.771761	0.456477	0.228239
105	0.735683	0.528633	0.264317
106	0.697804	0.604393	0.302196
107	0.685823	0.628354	0.314177
108	0.721948	0.556105	0.278052
109	0.681361	0.637278	0.318639
110	0.760542	0.478915	0.239458
111	0.798814	0.402373	0.201186
112	0.768484	0.463032	0.231516
113	0.755968	0.488064	0.244032
114	0.750725	0.498551	0.249275
115	0.720641	0.558719	0.279359
116	0.77968	0.44064	0.22032
117	0.789127	0.421746	0.210873
118	0.820675	0.35865	0.179325
119	0.833748	0.332504	0.166252
120	0.809578	0.380845	0.190422
121	0.790442	0.419115	0.209558
122	0.755073	0.489854	0.244927
123	0.752213	0.495575	0.247787
124	0.757275	0.48545	0.242725
125	0.74812	0.50376	0.25188
126	0.761636	0.476728	0.238364
127	0.805862	0.388275	0.194138
128	0.81441	0.37118	0.18559
129	0.816076	0.367848	0.183924
130	0.798761	0.402477	0.201239
131	0.776416	0.447168	0.223584
132	0.760309	0.479382	0.239691
133	0.718803	0.562394	0.281197
134	0.712919	0.574162	0.287081
135	0.702885	0.594231	0.297115
136	0.656438	0.687124	0.343562
137	0.74779	0.50442	0.25221
138	0.707344	0.585311	0.292656
139	0.652467	0.695067	0.347533
140	0.658709	0.682581	0.341291
141	0.599255	0.80149	0.400745
142	0.752526	0.494948	0.247474
143	0.796274	0.407451	0.203726
144	0.788965	0.42207	0.211035
145	0.74787	0.50426	0.25213
146	0.787894	0.424212	0.212106
147	0.772827	0.454345	0.227173
148	0.754882	0.490236	0.245118
149	0.694968	0.610064	0.305032
150	0.680955	0.638089	0.319045
151	0.692297	0.615407	0.307703
152	0.687105	0.625791	0.312895
153	0.681488	0.637024	0.318512
154	0.616162	0.767676	0.383838
155	0.592463	0.815073	0.407537
156	0.756062	0.487876	0.243938
157	0.722815	0.554369	0.277185
158	0.672507	0.654987	0.327493
159	0.726392	0.547216	0.273608
160	0.603738	0.792523	0.396262
161	0.460563	0.921126	0.539437
162	0.329167	0.658334	0.670833

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0573049 & 0.11461 & 0.942695 \tabularnewline
10 & 0.0408457 & 0.0816915 & 0.959154 \tabularnewline
11 & 0.190303 & 0.380606 & 0.809697 \tabularnewline
12 & 0.116436 & 0.232872 & 0.883564 \tabularnewline
13 & 0.516429 & 0.967141 & 0.483571 \tabularnewline
14 & 0.405746 & 0.811491 & 0.594254 \tabularnewline
15 & 0.37594 & 0.751879 & 0.62406 \tabularnewline
16 & 0.655454 & 0.689093 & 0.344546 \tabularnewline
17 & 0.596871 & 0.806258 & 0.403129 \tabularnewline
18 & 0.521539 & 0.956921 & 0.478461 \tabularnewline
19 & 0.465058 & 0.930115 & 0.534942 \tabularnewline
20 & 0.465725 & 0.931449 & 0.534275 \tabularnewline
21 & 0.390387 & 0.780773 & 0.609613 \tabularnewline
22 & 0.318679 & 0.637359 & 0.681321 \tabularnewline
23 & 0.255564 & 0.511128 & 0.744436 \tabularnewline
24 & 0.2061 & 0.4122 & 0.7939 \tabularnewline
25 & 0.166291 & 0.332581 & 0.833709 \tabularnewline
26 & 0.132147 & 0.264294 & 0.867853 \tabularnewline
27 & 0.134837 & 0.269673 & 0.865163 \tabularnewline
28 & 0.104952 & 0.209905 & 0.895048 \tabularnewline
29 & 0.0824428 & 0.164886 & 0.917557 \tabularnewline
30 & 0.0786064 & 0.157213 & 0.921394 \tabularnewline
31 & 0.0606555 & 0.121311 & 0.939345 \tabularnewline
32 & 0.21454 & 0.429079 & 0.78546 \tabularnewline
33 & 0.216255 & 0.432511 & 0.783745 \tabularnewline
34 & 0.20092 & 0.40184 & 0.79908 \tabularnewline
35 & 0.177516 & 0.355031 & 0.822484 \tabularnewline
36 & 0.162726 & 0.325452 & 0.837274 \tabularnewline
37 & 0.152477 & 0.304954 & 0.847523 \tabularnewline
38 & 0.121678 & 0.243356 & 0.878322 \tabularnewline
39 & 0.145903 & 0.291805 & 0.854097 \tabularnewline
40 & 0.181602 & 0.363205 & 0.818398 \tabularnewline
41 & 0.191826 & 0.383651 & 0.808174 \tabularnewline
42 & 0.157224 & 0.314447 & 0.842776 \tabularnewline
43 & 0.127037 & 0.254073 & 0.872963 \tabularnewline
44 & 0.131314 & 0.262627 & 0.868686 \tabularnewline
45 & 0.105826 & 0.211652 & 0.894174 \tabularnewline
46 & 0.113091 & 0.226183 & 0.886909 \tabularnewline
47 & 0.103488 & 0.206977 & 0.896512 \tabularnewline
48 & 0.0817934 & 0.163587 & 0.918207 \tabularnewline
49 & 0.0642722 & 0.128544 & 0.935728 \tabularnewline
50 & 0.0721953 & 0.144391 & 0.927805 \tabularnewline
51 & 0.0577093 & 0.115419 & 0.942291 \tabularnewline
52 & 0.0505766 & 0.101153 & 0.949423 \tabularnewline
53 & 0.0733806 & 0.146761 & 0.926619 \tabularnewline
54 & 0.138646 & 0.277291 & 0.861354 \tabularnewline
55 & 0.118492 & 0.236984 & 0.881508 \tabularnewline
56 & 0.172973 & 0.345947 & 0.827027 \tabularnewline
57 & 0.345469 & 0.690938 & 0.654531 \tabularnewline
58 & 0.363011 & 0.726021 & 0.636989 \tabularnewline
59 & 0.368043 & 0.736085 & 0.631957 \tabularnewline
60 & 0.343968 & 0.687936 & 0.656032 \tabularnewline
61 & 0.345877 & 0.691755 & 0.654123 \tabularnewline
62 & 0.303577 & 0.607155 & 0.696423 \tabularnewline
63 & 0.297631 & 0.595262 & 0.702369 \tabularnewline
64 & 0.270364 & 0.540727 & 0.729636 \tabularnewline
65 & 0.245253 & 0.490507 & 0.754747 \tabularnewline
66 & 0.211624 & 0.423247 & 0.788376 \tabularnewline
67 & 0.182363 & 0.364727 & 0.817637 \tabularnewline
68 & 0.153875 & 0.30775 & 0.846125 \tabularnewline
69 & 0.135954 & 0.271907 & 0.864046 \tabularnewline
70 & 0.152614 & 0.305228 & 0.847386 \tabularnewline
71 & 0.16391 & 0.32782 & 0.83609 \tabularnewline
72 & 0.145992 & 0.291985 & 0.854008 \tabularnewline
73 & 0.121475 & 0.242951 & 0.878525 \tabularnewline
74 & 0.108924 & 0.217847 & 0.891076 \tabularnewline
75 & 0.0923405 & 0.184681 & 0.90766 \tabularnewline
76 & 0.075991 & 0.151982 & 0.924009 \tabularnewline
77 & 0.0631008 & 0.126202 & 0.936899 \tabularnewline
78 & 0.0502379 & 0.100476 & 0.949762 \tabularnewline
79 & 0.0402364 & 0.0804728 & 0.959764 \tabularnewline
80 & 0.0812039 & 0.162408 & 0.918796 \tabularnewline
81 & 0.134626 & 0.269253 & 0.865374 \tabularnewline
82 & 0.112636 & 0.225272 & 0.887364 \tabularnewline
83 & 0.0941804 & 0.188361 & 0.90582 \tabularnewline
84 & 0.0774042 & 0.154808 & 0.922596 \tabularnewline
85 & 0.063285 & 0.12657 & 0.936715 \tabularnewline
86 & 0.0510366 & 0.102073 & 0.948963 \tabularnewline
87 & 0.0412853 & 0.0825706 & 0.958715 \tabularnewline
88 & 0.0423198 & 0.0846396 & 0.95768 \tabularnewline
89 & 0.0340699 & 0.0681397 & 0.96593 \tabularnewline
90 & 0.0269821 & 0.0539643 & 0.973018 \tabularnewline
91 & 0.604738 & 0.790524 & 0.395262 \tabularnewline
92 & 0.585577 & 0.828845 & 0.414423 \tabularnewline
93 & 0.575621 & 0.848757 & 0.424379 \tabularnewline
94 & 0.533168 & 0.933665 & 0.466832 \tabularnewline
95 & 0.731173 & 0.537654 & 0.268827 \tabularnewline
96 & 0.730349 & 0.539302 & 0.269651 \tabularnewline
97 & 0.708715 & 0.58257 & 0.291285 \tabularnewline
98 & 0.725181 & 0.549638 & 0.274819 \tabularnewline
99 & 0.803918 & 0.392164 & 0.196082 \tabularnewline
100 & 0.841193 & 0.317614 & 0.158807 \tabularnewline
101 & 0.846227 & 0.307546 & 0.153773 \tabularnewline
102 & 0.825744 & 0.348512 & 0.174256 \tabularnewline
103 & 0.796128 & 0.407743 & 0.203872 \tabularnewline
104 & 0.771761 & 0.456477 & 0.228239 \tabularnewline
105 & 0.735683 & 0.528633 & 0.264317 \tabularnewline
106 & 0.697804 & 0.604393 & 0.302196 \tabularnewline
107 & 0.685823 & 0.628354 & 0.314177 \tabularnewline
108 & 0.721948 & 0.556105 & 0.278052 \tabularnewline
109 & 0.681361 & 0.637278 & 0.318639 \tabularnewline
110 & 0.760542 & 0.478915 & 0.239458 \tabularnewline
111 & 0.798814 & 0.402373 & 0.201186 \tabularnewline
112 & 0.768484 & 0.463032 & 0.231516 \tabularnewline
113 & 0.755968 & 0.488064 & 0.244032 \tabularnewline
114 & 0.750725 & 0.498551 & 0.249275 \tabularnewline
115 & 0.720641 & 0.558719 & 0.279359 \tabularnewline
116 & 0.77968 & 0.44064 & 0.22032 \tabularnewline
117 & 0.789127 & 0.421746 & 0.210873 \tabularnewline
118 & 0.820675 & 0.35865 & 0.179325 \tabularnewline
119 & 0.833748 & 0.332504 & 0.166252 \tabularnewline
120 & 0.809578 & 0.380845 & 0.190422 \tabularnewline
121 & 0.790442 & 0.419115 & 0.209558 \tabularnewline
122 & 0.755073 & 0.489854 & 0.244927 \tabularnewline
123 & 0.752213 & 0.495575 & 0.247787 \tabularnewline
124 & 0.757275 & 0.48545 & 0.242725 \tabularnewline
125 & 0.74812 & 0.50376 & 0.25188 \tabularnewline
126 & 0.761636 & 0.476728 & 0.238364 \tabularnewline
127 & 0.805862 & 0.388275 & 0.194138 \tabularnewline
128 & 0.81441 & 0.37118 & 0.18559 \tabularnewline
129 & 0.816076 & 0.367848 & 0.183924 \tabularnewline
130 & 0.798761 & 0.402477 & 0.201239 \tabularnewline
131 & 0.776416 & 0.447168 & 0.223584 \tabularnewline
132 & 0.760309 & 0.479382 & 0.239691 \tabularnewline
133 & 0.718803 & 0.562394 & 0.281197 \tabularnewline
134 & 0.712919 & 0.574162 & 0.287081 \tabularnewline
135 & 0.702885 & 0.594231 & 0.297115 \tabularnewline
136 & 0.656438 & 0.687124 & 0.343562 \tabularnewline
137 & 0.74779 & 0.50442 & 0.25221 \tabularnewline
138 & 0.707344 & 0.585311 & 0.292656 \tabularnewline
139 & 0.652467 & 0.695067 & 0.347533 \tabularnewline
140 & 0.658709 & 0.682581 & 0.341291 \tabularnewline
141 & 0.599255 & 0.80149 & 0.400745 \tabularnewline
142 & 0.752526 & 0.494948 & 0.247474 \tabularnewline
143 & 0.796274 & 0.407451 & 0.203726 \tabularnewline
144 & 0.788965 & 0.42207 & 0.211035 \tabularnewline
145 & 0.74787 & 0.50426 & 0.25213 \tabularnewline
146 & 0.787894 & 0.424212 & 0.212106 \tabularnewline
147 & 0.772827 & 0.454345 & 0.227173 \tabularnewline
148 & 0.754882 & 0.490236 & 0.245118 \tabularnewline
149 & 0.694968 & 0.610064 & 0.305032 \tabularnewline
150 & 0.680955 & 0.638089 & 0.319045 \tabularnewline
151 & 0.692297 & 0.615407 & 0.307703 \tabularnewline
152 & 0.687105 & 0.625791 & 0.312895 \tabularnewline
153 & 0.681488 & 0.637024 & 0.318512 \tabularnewline
154 & 0.616162 & 0.767676 & 0.383838 \tabularnewline
155 & 0.592463 & 0.815073 & 0.407537 \tabularnewline
156 & 0.756062 & 0.487876 & 0.243938 \tabularnewline
157 & 0.722815 & 0.554369 & 0.277185 \tabularnewline
158 & 0.672507 & 0.654987 & 0.327493 \tabularnewline
159 & 0.726392 & 0.547216 & 0.273608 \tabularnewline
160 & 0.603738 & 0.792523 & 0.396262 \tabularnewline
161 & 0.460563 & 0.921126 & 0.539437 \tabularnewline
162 & 0.329167 & 0.658334 & 0.670833 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267969&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]0.0573049[/C][C]0.11461[/C][C]0.942695[/C][/ROW]
[ROW][C]10[/C][C]0.0408457[/C][C]0.0816915[/C][C]0.959154[/C][/ROW]
[ROW][C]11[/C][C]0.190303[/C][C]0.380606[/C][C]0.809697[/C][/ROW]
[ROW][C]12[/C][C]0.116436[/C][C]0.232872[/C][C]0.883564[/C][/ROW]
[ROW][C]13[/C][C]0.516429[/C][C]0.967141[/C][C]0.483571[/C][/ROW]
[ROW][C]14[/C][C]0.405746[/C][C]0.811491[/C][C]0.594254[/C][/ROW]
[ROW][C]15[/C][C]0.37594[/C][C]0.751879[/C][C]0.62406[/C][/ROW]
[ROW][C]16[/C][C]0.655454[/C][C]0.689093[/C][C]0.344546[/C][/ROW]
[ROW][C]17[/C][C]0.596871[/C][C]0.806258[/C][C]0.403129[/C][/ROW]
[ROW][C]18[/C][C]0.521539[/C][C]0.956921[/C][C]0.478461[/C][/ROW]
[ROW][C]19[/C][C]0.465058[/C][C]0.930115[/C][C]0.534942[/C][/ROW]
[ROW][C]20[/C][C]0.465725[/C][C]0.931449[/C][C]0.534275[/C][/ROW]
[ROW][C]21[/C][C]0.390387[/C][C]0.780773[/C][C]0.609613[/C][/ROW]
[ROW][C]22[/C][C]0.318679[/C][C]0.637359[/C][C]0.681321[/C][/ROW]
[ROW][C]23[/C][C]0.255564[/C][C]0.511128[/C][C]0.744436[/C][/ROW]
[ROW][C]24[/C][C]0.2061[/C][C]0.4122[/C][C]0.7939[/C][/ROW]
[ROW][C]25[/C][C]0.166291[/C][C]0.332581[/C][C]0.833709[/C][/ROW]
[ROW][C]26[/C][C]0.132147[/C][C]0.264294[/C][C]0.867853[/C][/ROW]
[ROW][C]27[/C][C]0.134837[/C][C]0.269673[/C][C]0.865163[/C][/ROW]
[ROW][C]28[/C][C]0.104952[/C][C]0.209905[/C][C]0.895048[/C][/ROW]
[ROW][C]29[/C][C]0.0824428[/C][C]0.164886[/C][C]0.917557[/C][/ROW]
[ROW][C]30[/C][C]0.0786064[/C][C]0.157213[/C][C]0.921394[/C][/ROW]
[ROW][C]31[/C][C]0.0606555[/C][C]0.121311[/C][C]0.939345[/C][/ROW]
[ROW][C]32[/C][C]0.21454[/C][C]0.429079[/C][C]0.78546[/C][/ROW]
[ROW][C]33[/C][C]0.216255[/C][C]0.432511[/C][C]0.783745[/C][/ROW]
[ROW][C]34[/C][C]0.20092[/C][C]0.40184[/C][C]0.79908[/C][/ROW]
[ROW][C]35[/C][C]0.177516[/C][C]0.355031[/C][C]0.822484[/C][/ROW]
[ROW][C]36[/C][C]0.162726[/C][C]0.325452[/C][C]0.837274[/C][/ROW]
[ROW][C]37[/C][C]0.152477[/C][C]0.304954[/C][C]0.847523[/C][/ROW]
[ROW][C]38[/C][C]0.121678[/C][C]0.243356[/C][C]0.878322[/C][/ROW]
[ROW][C]39[/C][C]0.145903[/C][C]0.291805[/C][C]0.854097[/C][/ROW]
[ROW][C]40[/C][C]0.181602[/C][C]0.363205[/C][C]0.818398[/C][/ROW]
[ROW][C]41[/C][C]0.191826[/C][C]0.383651[/C][C]0.808174[/C][/ROW]
[ROW][C]42[/C][C]0.157224[/C][C]0.314447[/C][C]0.842776[/C][/ROW]
[ROW][C]43[/C][C]0.127037[/C][C]0.254073[/C][C]0.872963[/C][/ROW]
[ROW][C]44[/C][C]0.131314[/C][C]0.262627[/C][C]0.868686[/C][/ROW]
[ROW][C]45[/C][C]0.105826[/C][C]0.211652[/C][C]0.894174[/C][/ROW]
[ROW][C]46[/C][C]0.113091[/C][C]0.226183[/C][C]0.886909[/C][/ROW]
[ROW][C]47[/C][C]0.103488[/C][C]0.206977[/C][C]0.896512[/C][/ROW]
[ROW][C]48[/C][C]0.0817934[/C][C]0.163587[/C][C]0.918207[/C][/ROW]
[ROW][C]49[/C][C]0.0642722[/C][C]0.128544[/C][C]0.935728[/C][/ROW]
[ROW][C]50[/C][C]0.0721953[/C][C]0.144391[/C][C]0.927805[/C][/ROW]
[ROW][C]51[/C][C]0.0577093[/C][C]0.115419[/C][C]0.942291[/C][/ROW]
[ROW][C]52[/C][C]0.0505766[/C][C]0.101153[/C][C]0.949423[/C][/ROW]
[ROW][C]53[/C][C]0.0733806[/C][C]0.146761[/C][C]0.926619[/C][/ROW]
[ROW][C]54[/C][C]0.138646[/C][C]0.277291[/C][C]0.861354[/C][/ROW]
[ROW][C]55[/C][C]0.118492[/C][C]0.236984[/C][C]0.881508[/C][/ROW]
[ROW][C]56[/C][C]0.172973[/C][C]0.345947[/C][C]0.827027[/C][/ROW]
[ROW][C]57[/C][C]0.345469[/C][C]0.690938[/C][C]0.654531[/C][/ROW]
[ROW][C]58[/C][C]0.363011[/C][C]0.726021[/C][C]0.636989[/C][/ROW]
[ROW][C]59[/C][C]0.368043[/C][C]0.736085[/C][C]0.631957[/C][/ROW]
[ROW][C]60[/C][C]0.343968[/C][C]0.687936[/C][C]0.656032[/C][/ROW]
[ROW][C]61[/C][C]0.345877[/C][C]0.691755[/C][C]0.654123[/C][/ROW]
[ROW][C]62[/C][C]0.303577[/C][C]0.607155[/C][C]0.696423[/C][/ROW]
[ROW][C]63[/C][C]0.297631[/C][C]0.595262[/C][C]0.702369[/C][/ROW]
[ROW][C]64[/C][C]0.270364[/C][C]0.540727[/C][C]0.729636[/C][/ROW]
[ROW][C]65[/C][C]0.245253[/C][C]0.490507[/C][C]0.754747[/C][/ROW]
[ROW][C]66[/C][C]0.211624[/C][C]0.423247[/C][C]0.788376[/C][/ROW]
[ROW][C]67[/C][C]0.182363[/C][C]0.364727[/C][C]0.817637[/C][/ROW]
[ROW][C]68[/C][C]0.153875[/C][C]0.30775[/C][C]0.846125[/C][/ROW]
[ROW][C]69[/C][C]0.135954[/C][C]0.271907[/C][C]0.864046[/C][/ROW]
[ROW][C]70[/C][C]0.152614[/C][C]0.305228[/C][C]0.847386[/C][/ROW]
[ROW][C]71[/C][C]0.16391[/C][C]0.32782[/C][C]0.83609[/C][/ROW]
[ROW][C]72[/C][C]0.145992[/C][C]0.291985[/C][C]0.854008[/C][/ROW]
[ROW][C]73[/C][C]0.121475[/C][C]0.242951[/C][C]0.878525[/C][/ROW]
[ROW][C]74[/C][C]0.108924[/C][C]0.217847[/C][C]0.891076[/C][/ROW]
[ROW][C]75[/C][C]0.0923405[/C][C]0.184681[/C][C]0.90766[/C][/ROW]
[ROW][C]76[/C][C]0.075991[/C][C]0.151982[/C][C]0.924009[/C][/ROW]
[ROW][C]77[/C][C]0.0631008[/C][C]0.126202[/C][C]0.936899[/C][/ROW]
[ROW][C]78[/C][C]0.0502379[/C][C]0.100476[/C][C]0.949762[/C][/ROW]
[ROW][C]79[/C][C]0.0402364[/C][C]0.0804728[/C][C]0.959764[/C][/ROW]
[ROW][C]80[/C][C]0.0812039[/C][C]0.162408[/C][C]0.918796[/C][/ROW]
[ROW][C]81[/C][C]0.134626[/C][C]0.269253[/C][C]0.865374[/C][/ROW]
[ROW][C]82[/C][C]0.112636[/C][C]0.225272[/C][C]0.887364[/C][/ROW]
[ROW][C]83[/C][C]0.0941804[/C][C]0.188361[/C][C]0.90582[/C][/ROW]
[ROW][C]84[/C][C]0.0774042[/C][C]0.154808[/C][C]0.922596[/C][/ROW]
[ROW][C]85[/C][C]0.063285[/C][C]0.12657[/C][C]0.936715[/C][/ROW]
[ROW][C]86[/C][C]0.0510366[/C][C]0.102073[/C][C]0.948963[/C][/ROW]
[ROW][C]87[/C][C]0.0412853[/C][C]0.0825706[/C][C]0.958715[/C][/ROW]
[ROW][C]88[/C][C]0.0423198[/C][C]0.0846396[/C][C]0.95768[/C][/ROW]
[ROW][C]89[/C][C]0.0340699[/C][C]0.0681397[/C][C]0.96593[/C][/ROW]
[ROW][C]90[/C][C]0.0269821[/C][C]0.0539643[/C][C]0.973018[/C][/ROW]
[ROW][C]91[/C][C]0.604738[/C][C]0.790524[/C][C]0.395262[/C][/ROW]
[ROW][C]92[/C][C]0.585577[/C][C]0.828845[/C][C]0.414423[/C][/ROW]
[ROW][C]93[/C][C]0.575621[/C][C]0.848757[/C][C]0.424379[/C][/ROW]
[ROW][C]94[/C][C]0.533168[/C][C]0.933665[/C][C]0.466832[/C][/ROW]
[ROW][C]95[/C][C]0.731173[/C][C]0.537654[/C][C]0.268827[/C][/ROW]
[ROW][C]96[/C][C]0.730349[/C][C]0.539302[/C][C]0.269651[/C][/ROW]
[ROW][C]97[/C][C]0.708715[/C][C]0.58257[/C][C]0.291285[/C][/ROW]
[ROW][C]98[/C][C]0.725181[/C][C]0.549638[/C][C]0.274819[/C][/ROW]
[ROW][C]99[/C][C]0.803918[/C][C]0.392164[/C][C]0.196082[/C][/ROW]
[ROW][C]100[/C][C]0.841193[/C][C]0.317614[/C][C]0.158807[/C][/ROW]
[ROW][C]101[/C][C]0.846227[/C][C]0.307546[/C][C]0.153773[/C][/ROW]
[ROW][C]102[/C][C]0.825744[/C][C]0.348512[/C][C]0.174256[/C][/ROW]
[ROW][C]103[/C][C]0.796128[/C][C]0.407743[/C][C]0.203872[/C][/ROW]
[ROW][C]104[/C][C]0.771761[/C][C]0.456477[/C][C]0.228239[/C][/ROW]
[ROW][C]105[/C][C]0.735683[/C][C]0.528633[/C][C]0.264317[/C][/ROW]
[ROW][C]106[/C][C]0.697804[/C][C]0.604393[/C][C]0.302196[/C][/ROW]
[ROW][C]107[/C][C]0.685823[/C][C]0.628354[/C][C]0.314177[/C][/ROW]
[ROW][C]108[/C][C]0.721948[/C][C]0.556105[/C][C]0.278052[/C][/ROW]
[ROW][C]109[/C][C]0.681361[/C][C]0.637278[/C][C]0.318639[/C][/ROW]
[ROW][C]110[/C][C]0.760542[/C][C]0.478915[/C][C]0.239458[/C][/ROW]
[ROW][C]111[/C][C]0.798814[/C][C]0.402373[/C][C]0.201186[/C][/ROW]
[ROW][C]112[/C][C]0.768484[/C][C]0.463032[/C][C]0.231516[/C][/ROW]
[ROW][C]113[/C][C]0.755968[/C][C]0.488064[/C][C]0.244032[/C][/ROW]
[ROW][C]114[/C][C]0.750725[/C][C]0.498551[/C][C]0.249275[/C][/ROW]
[ROW][C]115[/C][C]0.720641[/C][C]0.558719[/C][C]0.279359[/C][/ROW]
[ROW][C]116[/C][C]0.77968[/C][C]0.44064[/C][C]0.22032[/C][/ROW]
[ROW][C]117[/C][C]0.789127[/C][C]0.421746[/C][C]0.210873[/C][/ROW]
[ROW][C]118[/C][C]0.820675[/C][C]0.35865[/C][C]0.179325[/C][/ROW]
[ROW][C]119[/C][C]0.833748[/C][C]0.332504[/C][C]0.166252[/C][/ROW]
[ROW][C]120[/C][C]0.809578[/C][C]0.380845[/C][C]0.190422[/C][/ROW]
[ROW][C]121[/C][C]0.790442[/C][C]0.419115[/C][C]0.209558[/C][/ROW]
[ROW][C]122[/C][C]0.755073[/C][C]0.489854[/C][C]0.244927[/C][/ROW]
[ROW][C]123[/C][C]0.752213[/C][C]0.495575[/C][C]0.247787[/C][/ROW]
[ROW][C]124[/C][C]0.757275[/C][C]0.48545[/C][C]0.242725[/C][/ROW]
[ROW][C]125[/C][C]0.74812[/C][C]0.50376[/C][C]0.25188[/C][/ROW]
[ROW][C]126[/C][C]0.761636[/C][C]0.476728[/C][C]0.238364[/C][/ROW]
[ROW][C]127[/C][C]0.805862[/C][C]0.388275[/C][C]0.194138[/C][/ROW]
[ROW][C]128[/C][C]0.81441[/C][C]0.37118[/C][C]0.18559[/C][/ROW]
[ROW][C]129[/C][C]0.816076[/C][C]0.367848[/C][C]0.183924[/C][/ROW]
[ROW][C]130[/C][C]0.798761[/C][C]0.402477[/C][C]0.201239[/C][/ROW]
[ROW][C]131[/C][C]0.776416[/C][C]0.447168[/C][C]0.223584[/C][/ROW]
[ROW][C]132[/C][C]0.760309[/C][C]0.479382[/C][C]0.239691[/C][/ROW]
[ROW][C]133[/C][C]0.718803[/C][C]0.562394[/C][C]0.281197[/C][/ROW]
[ROW][C]134[/C][C]0.712919[/C][C]0.574162[/C][C]0.287081[/C][/ROW]
[ROW][C]135[/C][C]0.702885[/C][C]0.594231[/C][C]0.297115[/C][/ROW]
[ROW][C]136[/C][C]0.656438[/C][C]0.687124[/C][C]0.343562[/C][/ROW]
[ROW][C]137[/C][C]0.74779[/C][C]0.50442[/C][C]0.25221[/C][/ROW]
[ROW][C]138[/C][C]0.707344[/C][C]0.585311[/C][C]0.292656[/C][/ROW]
[ROW][C]139[/C][C]0.652467[/C][C]0.695067[/C][C]0.347533[/C][/ROW]
[ROW][C]140[/C][C]0.658709[/C][C]0.682581[/C][C]0.341291[/C][/ROW]
[ROW][C]141[/C][C]0.599255[/C][C]0.80149[/C][C]0.400745[/C][/ROW]
[ROW][C]142[/C][C]0.752526[/C][C]0.494948[/C][C]0.247474[/C][/ROW]
[ROW][C]143[/C][C]0.796274[/C][C]0.407451[/C][C]0.203726[/C][/ROW]
[ROW][C]144[/C][C]0.788965[/C][C]0.42207[/C][C]0.211035[/C][/ROW]
[ROW][C]145[/C][C]0.74787[/C][C]0.50426[/C][C]0.25213[/C][/ROW]
[ROW][C]146[/C][C]0.787894[/C][C]0.424212[/C][C]0.212106[/C][/ROW]
[ROW][C]147[/C][C]0.772827[/C][C]0.454345[/C][C]0.227173[/C][/ROW]
[ROW][C]148[/C][C]0.754882[/C][C]0.490236[/C][C]0.245118[/C][/ROW]
[ROW][C]149[/C][C]0.694968[/C][C]0.610064[/C][C]0.305032[/C][/ROW]
[ROW][C]150[/C][C]0.680955[/C][C]0.638089[/C][C]0.319045[/C][/ROW]
[ROW][C]151[/C][C]0.692297[/C][C]0.615407[/C][C]0.307703[/C][/ROW]
[ROW][C]152[/C][C]0.687105[/C][C]0.625791[/C][C]0.312895[/C][/ROW]
[ROW][C]153[/C][C]0.681488[/C][C]0.637024[/C][C]0.318512[/C][/ROW]
[ROW][C]154[/C][C]0.616162[/C][C]0.767676[/C][C]0.383838[/C][/ROW]
[ROW][C]155[/C][C]0.592463[/C][C]0.815073[/C][C]0.407537[/C][/ROW]
[ROW][C]156[/C][C]0.756062[/C][C]0.487876[/C][C]0.243938[/C][/ROW]
[ROW][C]157[/C][C]0.722815[/C][C]0.554369[/C][C]0.277185[/C][/ROW]
[ROW][C]158[/C][C]0.672507[/C][C]0.654987[/C][C]0.327493[/C][/ROW]
[ROW][C]159[/C][C]0.726392[/C][C]0.547216[/C][C]0.273608[/C][/ROW]
[ROW][C]160[/C][C]0.603738[/C][C]0.792523[/C][C]0.396262[/C][/ROW]
[ROW][C]161[/C][C]0.460563[/C][C]0.921126[/C][C]0.539437[/C][/ROW]
[ROW][C]162[/C][C]0.329167[/C][C]0.658334[/C][C]0.670833[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267969&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267969&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	0.0573049	0.11461	0.942695
10	0.0408457	0.0816915	0.959154
11	0.190303	0.380606	0.809697
12	0.116436	0.232872	0.883564
13	0.516429	0.967141	0.483571
14	0.405746	0.811491	0.594254
15	0.37594	0.751879	0.62406
16	0.655454	0.689093	0.344546
17	0.596871	0.806258	0.403129
18	0.521539	0.956921	0.478461
19	0.465058	0.930115	0.534942
20	0.465725	0.931449	0.534275
21	0.390387	0.780773	0.609613
22	0.318679	0.637359	0.681321
23	0.255564	0.511128	0.744436
24	0.2061	0.4122	0.7939
25	0.166291	0.332581	0.833709
26	0.132147	0.264294	0.867853
27	0.134837	0.269673	0.865163
28	0.104952	0.209905	0.895048
29	0.0824428	0.164886	0.917557
30	0.0786064	0.157213	0.921394
31	0.0606555	0.121311	0.939345
32	0.21454	0.429079	0.78546
33	0.216255	0.432511	0.783745
34	0.20092	0.40184	0.79908
35	0.177516	0.355031	0.822484
36	0.162726	0.325452	0.837274
37	0.152477	0.304954	0.847523
38	0.121678	0.243356	0.878322
39	0.145903	0.291805	0.854097
40	0.181602	0.363205	0.818398
41	0.191826	0.383651	0.808174
42	0.157224	0.314447	0.842776
43	0.127037	0.254073	0.872963
44	0.131314	0.262627	0.868686
45	0.105826	0.211652	0.894174
46	0.113091	0.226183	0.886909
47	0.103488	0.206977	0.896512
48	0.0817934	0.163587	0.918207
49	0.0642722	0.128544	0.935728
50	0.0721953	0.144391	0.927805
51	0.0577093	0.115419	0.942291
52	0.0505766	0.101153	0.949423
53	0.0733806	0.146761	0.926619
54	0.138646	0.277291	0.861354
55	0.118492	0.236984	0.881508
56	0.172973	0.345947	0.827027
57	0.345469	0.690938	0.654531
58	0.363011	0.726021	0.636989
59	0.368043	0.736085	0.631957
60	0.343968	0.687936	0.656032
61	0.345877	0.691755	0.654123
62	0.303577	0.607155	0.696423
63	0.297631	0.595262	0.702369
64	0.270364	0.540727	0.729636
65	0.245253	0.490507	0.754747
66	0.211624	0.423247	0.788376
67	0.182363	0.364727	0.817637
68	0.153875	0.30775	0.846125
69	0.135954	0.271907	0.864046
70	0.152614	0.305228	0.847386
71	0.16391	0.32782	0.83609
72	0.145992	0.291985	0.854008
73	0.121475	0.242951	0.878525
74	0.108924	0.217847	0.891076
75	0.0923405	0.184681	0.90766
76	0.075991	0.151982	0.924009
77	0.0631008	0.126202	0.936899
78	0.0502379	0.100476	0.949762
79	0.0402364	0.0804728	0.959764
80	0.0812039	0.162408	0.918796
81	0.134626	0.269253	0.865374
82	0.112636	0.225272	0.887364
83	0.0941804	0.188361	0.90582
84	0.0774042	0.154808	0.922596
85	0.063285	0.12657	0.936715
86	0.0510366	0.102073	0.948963
87	0.0412853	0.0825706	0.958715
88	0.0423198	0.0846396	0.95768
89	0.0340699	0.0681397	0.96593
90	0.0269821	0.0539643	0.973018
91	0.604738	0.790524	0.395262
92	0.585577	0.828845	0.414423
93	0.575621	0.848757	0.424379
94	0.533168	0.933665	0.466832
95	0.731173	0.537654	0.268827
96	0.730349	0.539302	0.269651
97	0.708715	0.58257	0.291285
98	0.725181	0.549638	0.274819
99	0.803918	0.392164	0.196082
100	0.841193	0.317614	0.158807
101	0.846227	0.307546	0.153773
102	0.825744	0.348512	0.174256
103	0.796128	0.407743	0.203872
104	0.771761	0.456477	0.228239
105	0.735683	0.528633	0.264317
106	0.697804	0.604393	0.302196
107	0.685823	0.628354	0.314177
108	0.721948	0.556105	0.278052
109	0.681361	0.637278	0.318639
110	0.760542	0.478915	0.239458
111	0.798814	0.402373	0.201186
112	0.768484	0.463032	0.231516
113	0.755968	0.488064	0.244032
114	0.750725	0.498551	0.249275
115	0.720641	0.558719	0.279359
116	0.77968	0.44064	0.22032
117	0.789127	0.421746	0.210873
118	0.820675	0.35865	0.179325
119	0.833748	0.332504	0.166252
120	0.809578	0.380845	0.190422
121	0.790442	0.419115	0.209558
122	0.755073	0.489854	0.244927
123	0.752213	0.495575	0.247787
124	0.757275	0.48545	0.242725
125	0.74812	0.50376	0.25188
126	0.761636	0.476728	0.238364
127	0.805862	0.388275	0.194138
128	0.81441	0.37118	0.18559
129	0.816076	0.367848	0.183924
130	0.798761	0.402477	0.201239
131	0.776416	0.447168	0.223584
132	0.760309	0.479382	0.239691
133	0.718803	0.562394	0.281197
134	0.712919	0.574162	0.287081
135	0.702885	0.594231	0.297115
136	0.656438	0.687124	0.343562
137	0.74779	0.50442	0.25221
138	0.707344	0.585311	0.292656
139	0.652467	0.695067	0.347533
140	0.658709	0.682581	0.341291
141	0.599255	0.80149	0.400745
142	0.752526	0.494948	0.247474
143	0.796274	0.407451	0.203726
144	0.788965	0.42207	0.211035
145	0.74787	0.50426	0.25213
146	0.787894	0.424212	0.212106
147	0.772827	0.454345	0.227173
148	0.754882	0.490236	0.245118
149	0.694968	0.610064	0.305032
150	0.680955	0.638089	0.319045
151	0.692297	0.615407	0.307703
152	0.687105	0.625791	0.312895
153	0.681488	0.637024	0.318512
154	0.616162	0.767676	0.383838
155	0.592463	0.815073	0.407537
156	0.756062	0.487876	0.243938
157	0.722815	0.554369	0.277185
158	0.672507	0.654987	0.327493
159	0.726392	0.547216	0.273608
160	0.603738	0.792523	0.396262
161	0.460563	0.921126	0.539437
162	0.329167	0.658334	0.670833

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	6	0.038961	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 & 6 & 0.038961 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267969&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]6[/C][C]0.038961[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267969&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267969&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	6	0.038961	OK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

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