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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 18 Dec 2014 11:06:21 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/18/t1418900822wahp3cgtgjoba7d.htm/, Retrieved Fri, 17 May 2024 15:25:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270799, Retrieved Fri, 17 May 2024 15:25:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-18 11:06:21] [a3de03a8fa2b95b1b988206b9ba33408] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
48	41	23	12	34	0,5
50	146	16	45	61	7,5
150	182	33	37	70	9
154	192	32	37	69	9,5
109	263	37	108	145	8,5
68	35	14	10	23	7
194	439	52	68	120	8
158	214	75	72	147	10
159	341	72	143	215	7
67	58	15	9	24	8,5
147	292	29	55	84	9
39	85	13	17	30	9,5
100	200	40	37	77	4
111	158	19	27	46	6
138	199	24	37	61	8
101	297	121	58	178	5,5
131	227	93	66	160	9,5
101	108	36	21	57	7,5
114	86	23	19	42	7
165	302	85	78	163	7,5
114	148	41	35	75	8
111	178	46	48	94	7
75	120	18	27	45	7
82	207	35	43	78	6
121	157	17	30	47	10
32	128	4	25	29	2,5
150	296	28	69	97	9
117	323	44	72	116	8
71	79	10	23	32	6
165	70	38	13	50	8,5
154	146	57	61	118	6
126	246	23	43	66	9
149	196	36	51	86	8
145	199	22	67	89	9
120	127	40	36	76	5,5
109	153	31	44	75	7
132	299	11	45	57	5,5
172	228	38	34	72	9
169	190	24	36	60	2
114	180	37	72	109	8,5
156	212	37	39	76	9
172	269	22	43	65	8,5
68	130	15	25	40	9
89	179	2	56	58	7,5
167	243	43	80	123	10
113	190	31	40	71	9
115	299	29	73	102	7,5
78	121	45	34	80	6
118	137	25	72	97	10,5
87	305	4	42	46	8,5
173	157	31	61	93	8
2	96	-4	23	19	10
162	183	66	74	140	10,5
49	52	61	16	78	6,5
122	238	32	66	98	9,5
96	40	31	9	40	8,5
100	226	39	41	80	7,5
82	190	19	57	76	5
100	214	31	48	79	8
115	145	36	51	87	10
141	119	42	53	95	7
165	222	21	29	49	7,5
165	222	21	29	49	7,5
110	159	25	55	80	9,5
118	165	32	54	86	6
158	249	26	43	69	10
146	125	28	51	79	7
49	122	32	20	52	3
90	186	41	79	120	6
121	148	29	39	69	7
155	274	33	61	94	10
104	172	17	55	72	7
147	84	13	30	43	3,5
110	168	32	55	87	8
108	102	30	22	52	10
113	106	34	37	71	5,5
115	2	59	2	61	6
61	139	13	38	51	6,5
60	95	23	27	50	6,5
109	130	10	56	67	8,5
68	72	5	25	30	4
111	141	31	39	70	9,5
77	113	19	33	52	8
73	206	32	43	75	8,5
151	268	30	57	87	5,5
89	175	25	43	69	7
78	77	48	23	72	9
110	125	35	44	79	8
220	255	67	54	121	10
65	111	15	28	43	8
141	132	22	36	58	6
117	211	18	39	57	8
122	92	33	16	50	5
63	76	46	23	69	9
44	171	24	40	64	4,5
52	83	14	24	38	8,5
131	266	12	78	90	9,5
101	186	38	57	96	8,5
42	50	12	37	49	7,5
152	117	28	27	56	7,5
107	219	41	61	102	5
77	246	12	27	40	7
154	279	31	69	100	8
103	148	33	34	67	5,5
96	137	34	44	78	8,5
175	181	21	34	55	9,5
57	98	20	39	59	7
112	226	44	51	96	8
143	234	52	34	86	8,5
49	138	7	31	38	3,5
110	85	29	13	43	6,5
131	66	11	12	23	6,5
167	236	26	51	77	10,5
56	106	24	24	48	8,5
137	135	7	19	26	8
86	122	60	30	91	10
121	218	13	81	94	10
149	199	20	42	62	9,5
168	112	52	22	74	9
140	278	28	85	114	10
88	94	25	27	52	7,5
168	113	39	25	64	4,5
94	84	9	22	31	4,5
51	86	19	19	38	0,5
48	62	13	14	27	6,5
145	222	60	45	105	4,5
66	167	19	45	64	5,5
85	82	34	28	62	5
109	207	14	51	65	6
63	184	17	41	58	4
102	83	45	31	76	8
162	183	66	74	140	10,5
86	89	48	19	68	6,5
114	225	29	51	80	8
164	237	-2	73	71	8,5
119	102	51	24	76	5,5
126	221	2	61	63	7
132	128	24	23	46	5
142	91	40	14	53	3,5
83	198	20	54	74	5
94	204	19	51	70	9
81	158	16	62	78	8,5
166	138	20	36	56	5
110	226	40	59	100	9,5
64	44	27	24	51	3
93	196	25	26	52	1,5
104	83	49	54	102	6
105	79	39	39	78	0,5
49	52	61	16	78	6,5
88	105	19	36	55	7,5
95	116	67	31	98	4,5
102	83	45	31	76	8
99	196	30	42	73	9
63	153	8	39	47	7,5
76	157	19	25	45	8,5
109	75	52	31	83	7
117	106	22	38	60	9,5
57	58	17	31	48	6,5
120	75	33	17	50	9,5
73	74	34	22	56	6
91	185	22	55	77	8
108	265	30	62	91	9,5
105	131	25	51	76	8
117	139	38	30	68	8
119	196	26	49	74	9
31	78	13	16	29	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270799&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270799&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270799&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 4.99666 + 0.0125615LFM[t] + 0.000788388B[t] -0.420637PRH[t] -0.390559CH[t] + 0.413808H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  4.99666 +  0.0125615LFM[t] +  0.000788388B[t] -0.420637PRH[t] -0.390559CH[t] +  0.413808H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270799&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  4.99666 +  0.0125615LFM[t] +  0.000788388B[t] -0.420637PRH[t] -0.390559CH[t] +  0.413808H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270799&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270799&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 4.99666 + 0.0125615LFM[t] + 0.000788388B[t] -0.420637PRH[t] -0.390559CH[t] + 0.413808H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.996660.5051249.8922.66346e-181.33173e-18
LFM0.01256150.004884952.5710.01103750.00551875
B0.0007883880.003260730.24180.8092580.404629
PRH-0.4206370.328706-1.280.2025120.101256
CH-0.3905590.327777-1.1920.2352070.117603
H0.4138080.3276461.2630.2084360.104218

\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) & 4.99666 & 0.505124 & 9.892 & 2.66346e-18 & 1.33173e-18 \tabularnewline
LFM & 0.0125615 & 0.00488495 & 2.571 & 0.0110375 & 0.00551875 \tabularnewline
B & 0.000788388 & 0.00326073 & 0.2418 & 0.809258 & 0.404629 \tabularnewline
PRH & -0.420637 & 0.328706 & -1.28 & 0.202512 & 0.101256 \tabularnewline
CH & -0.390559 & 0.327777 & -1.192 & 0.235207 & 0.117603 \tabularnewline
H & 0.413808 & 0.327646 & 1.263 & 0.208436 & 0.104218 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270799&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]4.99666[/C][C]0.505124[/C][C]9.892[/C][C]2.66346e-18[/C][C]1.33173e-18[/C][/ROW]
[ROW][C]LFM[/C][C]0.0125615[/C][C]0.00488495[/C][C]2.571[/C][C]0.0110375[/C][C]0.00551875[/C][/ROW]
[ROW][C]B[/C][C]0.000788388[/C][C]0.00326073[/C][C]0.2418[/C][C]0.809258[/C][C]0.404629[/C][/ROW]
[ROW][C]PRH[/C][C]-0.420637[/C][C]0.328706[/C][C]-1.28[/C][C]0.202512[/C][C]0.101256[/C][/ROW]
[ROW][C]CH[/C][C]-0.390559[/C][C]0.327777[/C][C]-1.192[/C][C]0.235207[/C][C]0.117603[/C][/ROW]
[ROW][C]H[/C][C]0.413808[/C][C]0.327646[/C][C]1.263[/C][C]0.208436[/C][C]0.104218[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270799&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270799&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.996660.5051249.8922.66346e-181.33173e-18
LFM0.01256150.004884952.5710.01103750.00551875
B0.0007883880.003260730.24180.8092580.404629
PRH-0.4206370.328706-1.280.2025120.101256
CH-0.3905590.327777-1.1920.2352070.117603
H0.4138080.3276461.2630.2084360.104218






Multiple Linear Regression - Regression Statistics
Multiple R0.389715
R-squared0.151878
Adjusted R-squared0.125374
F-TEST (value)5.73041
F-TEST (DF numerator)5
F-TEST (DF denominator)160
p-value6.8248e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.98218
Sum Squared Residuals628.649

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.389715 \tabularnewline
R-squared & 0.151878 \tabularnewline
Adjusted R-squared & 0.125374 \tabularnewline
F-TEST (value) & 5.73041 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 6.8248e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.98218 \tabularnewline
Sum Squared Residuals & 628.649 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270799&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.389715[/C][/ROW]
[ROW][C]R-squared[/C][C]0.151878[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.125374[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.73041[/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]6.8248e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.98218[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]628.649[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270799&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270799&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.389715
R-squared0.151878
Adjusted R-squared0.125374
F-TEST (value)5.73041
F-TEST (DF numerator)5
F-TEST (DF denominator)160
p-value6.8248e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.98218
Sum Squared Residuals628.649






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.55.34007-4.84007
27.56.676790.823207
397.659251.34075
49.57.724211.77579
58.58.83145-0.331453
675.601521.39848
789.00555-1.00555
8108.31191.6881
9710.0957-3.09575
108.55.990822.50918
1198.154080.845918
129.55.860043.63996
1346.99757-2.99757
1467.01354-1.01354
1587.583370.416632
165.56.60793-1.10793
179.58.134391.36561
187.56.592930.907072
1976.781150.218848
207.58.54042-1.04042
2186.66531.3347
2277.33317-0.333169
2376.538190.461807
2466.95062-0.950616
25107.221772.77823
262.56.05346-3.55346
2798.527230.472766
2888.09449-0.0944869
2966.00345-0.00344538
308.56.753451.74655
3168.07521-2.07521
3297.616011.38399
3387.548920.451083
3498.382420.617575
355.57.168-1.668
3677.29776-0.297765
375.58.27541-2.77541
3897.867981.13202
3927.94243-5.94243
408.57.991860.508139
4197.777451.22255
428.58.21880.281204
4396.432132.56787
447.57.54404-0.0440416
45108.852311.14769
4697.284191.71581
477.58.17612-0.676116
4866.96885-0.968854
4910.58.090152.40985
508.57.279111.22089
5188.91389-0.913893
52105.659514.34049
5310.58.445652.05435
546.56.022440.477562
559.58.032731.46727
568.56.231662.26834
577.57.117890.382109
5857.37195-2.37195
5987.32580.674201
60107.495432.50457
6177.80706-0.807056
627.57.361350.138655
637.57.361350.138655
649.57.611771.88823
6567.64594-1.64594
66107.999852.00015
6777.92369-0.923692
6835.95483-2.95483
6967.83055-1.83055
7077.75578-0.75578
71108.352561.64744
7277.60127-0.601272
733.57.51812-4.01812
7487.571060.428937
75106.740353.25965
765.57.12773-1.62773
7766.08644-0.0864379
786.56.66719-0.167192
796.56.295920.20408
808.58.115820.384178
8146.45469-2.45469
829.57.197182.30282
8386.690461.30954
848.56.857261.64274
855.58.22508-2.72508
8677.49541-0.495409
8796.657942.34206
8887.260940.739062
89108.759171.24083
9086.449221.55078
9167.55864-1.55864
9287.416510.583494
9357.16215-2.16215
9496.068582.93142
954.56.45026-1.95026
968.56.177682.32232
979.58.583410.916592
988.57.891540.608463
997.56.341941.15806
1007.57.84859-0.348586
10157.65163-2.65163
10277.11743-0.117431
10388.54359-0.543592
1045.56.97231-1.47231
1058.57.101371.39863
1069.57.984691.51531
10776.560070.439926
10887.880790.119215
1098.57.412831.08717
1103.56.39389-2.89389
1116.56.96346-0.463462
1126.56.89813-0.398125
11310.58.288652.21135
1148.56.177772.32223
11587.217950.782051
116106.874713.12529
117108.482871.51713
1189.57.86511.6349
11997.35171.6483
120109.173220.82678
1217.56.63320.866804
1224.57.511-3.011
1234.56.69369-2.19369
1240.56.01709-5.51709
1256.55.885210.614788
1264.57.62961-3.12961
1275.56.87385-1.37385
12856.54785-1.54785
12967.61916-1.61916
13046.77023-2.77023
13186.756811.24319
13210.58.445652.05435
1336.56.6749-0.174897
13487.593740.406264
1358.58.95442-0.454424
1365.57.19543-1.69543
13778.15817-1.15817
13856.71273-1.71273
1393.56.49068-2.99068
14057.31425-2.31425
14197.394231.60577
1428.57.470891.02911
14357.89106-2.89106
1449.58.068971.43103
14536.2089-3.2089
1461.57.16698-5.66698
14766.87554-0.875543
1480.57.01831-6.51831
1496.56.022440.477562
1507.56.892080.607921
1514.56.54468-2.04468
15286.756811.24319
15397.580191.41981
1547.56.760740.739257
1558.56.940411.55959
15676.790640.20936
1579.57.283162.21684
1586.56.363030.136969
1599.56.733072.76693
16066.25131-0.251307
16187.414080.58592
1629.57.3852.115
16387.433890.566113
16487.013940.986065
16597.193861.80614
16655.73078-0.730779

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.5 & 5.34007 & -4.84007 \tabularnewline
2 & 7.5 & 6.67679 & 0.823207 \tabularnewline
3 & 9 & 7.65925 & 1.34075 \tabularnewline
4 & 9.5 & 7.72421 & 1.77579 \tabularnewline
5 & 8.5 & 8.83145 & -0.331453 \tabularnewline
6 & 7 & 5.60152 & 1.39848 \tabularnewline
7 & 8 & 9.00555 & -1.00555 \tabularnewline
8 & 10 & 8.3119 & 1.6881 \tabularnewline
9 & 7 & 10.0957 & -3.09575 \tabularnewline
10 & 8.5 & 5.99082 & 2.50918 \tabularnewline
11 & 9 & 8.15408 & 0.845918 \tabularnewline
12 & 9.5 & 5.86004 & 3.63996 \tabularnewline
13 & 4 & 6.99757 & -2.99757 \tabularnewline
14 & 6 & 7.01354 & -1.01354 \tabularnewline
15 & 8 & 7.58337 & 0.416632 \tabularnewline
16 & 5.5 & 6.60793 & -1.10793 \tabularnewline
17 & 9.5 & 8.13439 & 1.36561 \tabularnewline
18 & 7.5 & 6.59293 & 0.907072 \tabularnewline
19 & 7 & 6.78115 & 0.218848 \tabularnewline
20 & 7.5 & 8.54042 & -1.04042 \tabularnewline
21 & 8 & 6.6653 & 1.3347 \tabularnewline
22 & 7 & 7.33317 & -0.333169 \tabularnewline
23 & 7 & 6.53819 & 0.461807 \tabularnewline
24 & 6 & 6.95062 & -0.950616 \tabularnewline
25 & 10 & 7.22177 & 2.77823 \tabularnewline
26 & 2.5 & 6.05346 & -3.55346 \tabularnewline
27 & 9 & 8.52723 & 0.472766 \tabularnewline
28 & 8 & 8.09449 & -0.0944869 \tabularnewline
29 & 6 & 6.00345 & -0.00344538 \tabularnewline
30 & 8.5 & 6.75345 & 1.74655 \tabularnewline
31 & 6 & 8.07521 & -2.07521 \tabularnewline
32 & 9 & 7.61601 & 1.38399 \tabularnewline
33 & 8 & 7.54892 & 0.451083 \tabularnewline
34 & 9 & 8.38242 & 0.617575 \tabularnewline
35 & 5.5 & 7.168 & -1.668 \tabularnewline
36 & 7 & 7.29776 & -0.297765 \tabularnewline
37 & 5.5 & 8.27541 & -2.77541 \tabularnewline
38 & 9 & 7.86798 & 1.13202 \tabularnewline
39 & 2 & 7.94243 & -5.94243 \tabularnewline
40 & 8.5 & 7.99186 & 0.508139 \tabularnewline
41 & 9 & 7.77745 & 1.22255 \tabularnewline
42 & 8.5 & 8.2188 & 0.281204 \tabularnewline
43 & 9 & 6.43213 & 2.56787 \tabularnewline
44 & 7.5 & 7.54404 & -0.0440416 \tabularnewline
45 & 10 & 8.85231 & 1.14769 \tabularnewline
46 & 9 & 7.28419 & 1.71581 \tabularnewline
47 & 7.5 & 8.17612 & -0.676116 \tabularnewline
48 & 6 & 6.96885 & -0.968854 \tabularnewline
49 & 10.5 & 8.09015 & 2.40985 \tabularnewline
50 & 8.5 & 7.27911 & 1.22089 \tabularnewline
51 & 8 & 8.91389 & -0.913893 \tabularnewline
52 & 10 & 5.65951 & 4.34049 \tabularnewline
53 & 10.5 & 8.44565 & 2.05435 \tabularnewline
54 & 6.5 & 6.02244 & 0.477562 \tabularnewline
55 & 9.5 & 8.03273 & 1.46727 \tabularnewline
56 & 8.5 & 6.23166 & 2.26834 \tabularnewline
57 & 7.5 & 7.11789 & 0.382109 \tabularnewline
58 & 5 & 7.37195 & -2.37195 \tabularnewline
59 & 8 & 7.3258 & 0.674201 \tabularnewline
60 & 10 & 7.49543 & 2.50457 \tabularnewline
61 & 7 & 7.80706 & -0.807056 \tabularnewline
62 & 7.5 & 7.36135 & 0.138655 \tabularnewline
63 & 7.5 & 7.36135 & 0.138655 \tabularnewline
64 & 9.5 & 7.61177 & 1.88823 \tabularnewline
65 & 6 & 7.64594 & -1.64594 \tabularnewline
66 & 10 & 7.99985 & 2.00015 \tabularnewline
67 & 7 & 7.92369 & -0.923692 \tabularnewline
68 & 3 & 5.95483 & -2.95483 \tabularnewline
69 & 6 & 7.83055 & -1.83055 \tabularnewline
70 & 7 & 7.75578 & -0.75578 \tabularnewline
71 & 10 & 8.35256 & 1.64744 \tabularnewline
72 & 7 & 7.60127 & -0.601272 \tabularnewline
73 & 3.5 & 7.51812 & -4.01812 \tabularnewline
74 & 8 & 7.57106 & 0.428937 \tabularnewline
75 & 10 & 6.74035 & 3.25965 \tabularnewline
76 & 5.5 & 7.12773 & -1.62773 \tabularnewline
77 & 6 & 6.08644 & -0.0864379 \tabularnewline
78 & 6.5 & 6.66719 & -0.167192 \tabularnewline
79 & 6.5 & 6.29592 & 0.20408 \tabularnewline
80 & 8.5 & 8.11582 & 0.384178 \tabularnewline
81 & 4 & 6.45469 & -2.45469 \tabularnewline
82 & 9.5 & 7.19718 & 2.30282 \tabularnewline
83 & 8 & 6.69046 & 1.30954 \tabularnewline
84 & 8.5 & 6.85726 & 1.64274 \tabularnewline
85 & 5.5 & 8.22508 & -2.72508 \tabularnewline
86 & 7 & 7.49541 & -0.495409 \tabularnewline
87 & 9 & 6.65794 & 2.34206 \tabularnewline
88 & 8 & 7.26094 & 0.739062 \tabularnewline
89 & 10 & 8.75917 & 1.24083 \tabularnewline
90 & 8 & 6.44922 & 1.55078 \tabularnewline
91 & 6 & 7.55864 & -1.55864 \tabularnewline
92 & 8 & 7.41651 & 0.583494 \tabularnewline
93 & 5 & 7.16215 & -2.16215 \tabularnewline
94 & 9 & 6.06858 & 2.93142 \tabularnewline
95 & 4.5 & 6.45026 & -1.95026 \tabularnewline
96 & 8.5 & 6.17768 & 2.32232 \tabularnewline
97 & 9.5 & 8.58341 & 0.916592 \tabularnewline
98 & 8.5 & 7.89154 & 0.608463 \tabularnewline
99 & 7.5 & 6.34194 & 1.15806 \tabularnewline
100 & 7.5 & 7.84859 & -0.348586 \tabularnewline
101 & 5 & 7.65163 & -2.65163 \tabularnewline
102 & 7 & 7.11743 & -0.117431 \tabularnewline
103 & 8 & 8.54359 & -0.543592 \tabularnewline
104 & 5.5 & 6.97231 & -1.47231 \tabularnewline
105 & 8.5 & 7.10137 & 1.39863 \tabularnewline
106 & 9.5 & 7.98469 & 1.51531 \tabularnewline
107 & 7 & 6.56007 & 0.439926 \tabularnewline
108 & 8 & 7.88079 & 0.119215 \tabularnewline
109 & 8.5 & 7.41283 & 1.08717 \tabularnewline
110 & 3.5 & 6.39389 & -2.89389 \tabularnewline
111 & 6.5 & 6.96346 & -0.463462 \tabularnewline
112 & 6.5 & 6.89813 & -0.398125 \tabularnewline
113 & 10.5 & 8.28865 & 2.21135 \tabularnewline
114 & 8.5 & 6.17777 & 2.32223 \tabularnewline
115 & 8 & 7.21795 & 0.782051 \tabularnewline
116 & 10 & 6.87471 & 3.12529 \tabularnewline
117 & 10 & 8.48287 & 1.51713 \tabularnewline
118 & 9.5 & 7.8651 & 1.6349 \tabularnewline
119 & 9 & 7.3517 & 1.6483 \tabularnewline
120 & 10 & 9.17322 & 0.82678 \tabularnewline
121 & 7.5 & 6.6332 & 0.866804 \tabularnewline
122 & 4.5 & 7.511 & -3.011 \tabularnewline
123 & 4.5 & 6.69369 & -2.19369 \tabularnewline
124 & 0.5 & 6.01709 & -5.51709 \tabularnewline
125 & 6.5 & 5.88521 & 0.614788 \tabularnewline
126 & 4.5 & 7.62961 & -3.12961 \tabularnewline
127 & 5.5 & 6.87385 & -1.37385 \tabularnewline
128 & 5 & 6.54785 & -1.54785 \tabularnewline
129 & 6 & 7.61916 & -1.61916 \tabularnewline
130 & 4 & 6.77023 & -2.77023 \tabularnewline
131 & 8 & 6.75681 & 1.24319 \tabularnewline
132 & 10.5 & 8.44565 & 2.05435 \tabularnewline
133 & 6.5 & 6.6749 & -0.174897 \tabularnewline
134 & 8 & 7.59374 & 0.406264 \tabularnewline
135 & 8.5 & 8.95442 & -0.454424 \tabularnewline
136 & 5.5 & 7.19543 & -1.69543 \tabularnewline
137 & 7 & 8.15817 & -1.15817 \tabularnewline
138 & 5 & 6.71273 & -1.71273 \tabularnewline
139 & 3.5 & 6.49068 & -2.99068 \tabularnewline
140 & 5 & 7.31425 & -2.31425 \tabularnewline
141 & 9 & 7.39423 & 1.60577 \tabularnewline
142 & 8.5 & 7.47089 & 1.02911 \tabularnewline
143 & 5 & 7.89106 & -2.89106 \tabularnewline
144 & 9.5 & 8.06897 & 1.43103 \tabularnewline
145 & 3 & 6.2089 & -3.2089 \tabularnewline
146 & 1.5 & 7.16698 & -5.66698 \tabularnewline
147 & 6 & 6.87554 & -0.875543 \tabularnewline
148 & 0.5 & 7.01831 & -6.51831 \tabularnewline
149 & 6.5 & 6.02244 & 0.477562 \tabularnewline
150 & 7.5 & 6.89208 & 0.607921 \tabularnewline
151 & 4.5 & 6.54468 & -2.04468 \tabularnewline
152 & 8 & 6.75681 & 1.24319 \tabularnewline
153 & 9 & 7.58019 & 1.41981 \tabularnewline
154 & 7.5 & 6.76074 & 0.739257 \tabularnewline
155 & 8.5 & 6.94041 & 1.55959 \tabularnewline
156 & 7 & 6.79064 & 0.20936 \tabularnewline
157 & 9.5 & 7.28316 & 2.21684 \tabularnewline
158 & 6.5 & 6.36303 & 0.136969 \tabularnewline
159 & 9.5 & 6.73307 & 2.76693 \tabularnewline
160 & 6 & 6.25131 & -0.251307 \tabularnewline
161 & 8 & 7.41408 & 0.58592 \tabularnewline
162 & 9.5 & 7.385 & 2.115 \tabularnewline
163 & 8 & 7.43389 & 0.566113 \tabularnewline
164 & 8 & 7.01394 & 0.986065 \tabularnewline
165 & 9 & 7.19386 & 1.80614 \tabularnewline
166 & 5 & 5.73078 & -0.730779 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270799&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]0.5[/C][C]5.34007[/C][C]-4.84007[/C][/ROW]
[ROW][C]2[/C][C]7.5[/C][C]6.67679[/C][C]0.823207[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]7.65925[/C][C]1.34075[/C][/ROW]
[ROW][C]4[/C][C]9.5[/C][C]7.72421[/C][C]1.77579[/C][/ROW]
[ROW][C]5[/C][C]8.5[/C][C]8.83145[/C][C]-0.331453[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]5.60152[/C][C]1.39848[/C][/ROW]
[ROW][C]7[/C][C]8[/C][C]9.00555[/C][C]-1.00555[/C][/ROW]
[ROW][C]8[/C][C]10[/C][C]8.3119[/C][C]1.6881[/C][/ROW]
[ROW][C]9[/C][C]7[/C][C]10.0957[/C][C]-3.09575[/C][/ROW]
[ROW][C]10[/C][C]8.5[/C][C]5.99082[/C][C]2.50918[/C][/ROW]
[ROW][C]11[/C][C]9[/C][C]8.15408[/C][C]0.845918[/C][/ROW]
[ROW][C]12[/C][C]9.5[/C][C]5.86004[/C][C]3.63996[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]6.99757[/C][C]-2.99757[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]7.01354[/C][C]-1.01354[/C][/ROW]
[ROW][C]15[/C][C]8[/C][C]7.58337[/C][C]0.416632[/C][/ROW]
[ROW][C]16[/C][C]5.5[/C][C]6.60793[/C][C]-1.10793[/C][/ROW]
[ROW][C]17[/C][C]9.5[/C][C]8.13439[/C][C]1.36561[/C][/ROW]
[ROW][C]18[/C][C]7.5[/C][C]6.59293[/C][C]0.907072[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]6.78115[/C][C]0.218848[/C][/ROW]
[ROW][C]20[/C][C]7.5[/C][C]8.54042[/C][C]-1.04042[/C][/ROW]
[ROW][C]21[/C][C]8[/C][C]6.6653[/C][C]1.3347[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]7.33317[/C][C]-0.333169[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]6.53819[/C][C]0.461807[/C][/ROW]
[ROW][C]24[/C][C]6[/C][C]6.95062[/C][C]-0.950616[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]7.22177[/C][C]2.77823[/C][/ROW]
[ROW][C]26[/C][C]2.5[/C][C]6.05346[/C][C]-3.55346[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]8.52723[/C][C]0.472766[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]8.09449[/C][C]-0.0944869[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]6.00345[/C][C]-0.00344538[/C][/ROW]
[ROW][C]30[/C][C]8.5[/C][C]6.75345[/C][C]1.74655[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]8.07521[/C][C]-2.07521[/C][/ROW]
[ROW][C]32[/C][C]9[/C][C]7.61601[/C][C]1.38399[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]7.54892[/C][C]0.451083[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]8.38242[/C][C]0.617575[/C][/ROW]
[ROW][C]35[/C][C]5.5[/C][C]7.168[/C][C]-1.668[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]7.29776[/C][C]-0.297765[/C][/ROW]
[ROW][C]37[/C][C]5.5[/C][C]8.27541[/C][C]-2.77541[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]7.86798[/C][C]1.13202[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]7.94243[/C][C]-5.94243[/C][/ROW]
[ROW][C]40[/C][C]8.5[/C][C]7.99186[/C][C]0.508139[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]7.77745[/C][C]1.22255[/C][/ROW]
[ROW][C]42[/C][C]8.5[/C][C]8.2188[/C][C]0.281204[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]6.43213[/C][C]2.56787[/C][/ROW]
[ROW][C]44[/C][C]7.5[/C][C]7.54404[/C][C]-0.0440416[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]8.85231[/C][C]1.14769[/C][/ROW]
[ROW][C]46[/C][C]9[/C][C]7.28419[/C][C]1.71581[/C][/ROW]
[ROW][C]47[/C][C]7.5[/C][C]8.17612[/C][C]-0.676116[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]6.96885[/C][C]-0.968854[/C][/ROW]
[ROW][C]49[/C][C]10.5[/C][C]8.09015[/C][C]2.40985[/C][/ROW]
[ROW][C]50[/C][C]8.5[/C][C]7.27911[/C][C]1.22089[/C][/ROW]
[ROW][C]51[/C][C]8[/C][C]8.91389[/C][C]-0.913893[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]5.65951[/C][C]4.34049[/C][/ROW]
[ROW][C]53[/C][C]10.5[/C][C]8.44565[/C][C]2.05435[/C][/ROW]
[ROW][C]54[/C][C]6.5[/C][C]6.02244[/C][C]0.477562[/C][/ROW]
[ROW][C]55[/C][C]9.5[/C][C]8.03273[/C][C]1.46727[/C][/ROW]
[ROW][C]56[/C][C]8.5[/C][C]6.23166[/C][C]2.26834[/C][/ROW]
[ROW][C]57[/C][C]7.5[/C][C]7.11789[/C][C]0.382109[/C][/ROW]
[ROW][C]58[/C][C]5[/C][C]7.37195[/C][C]-2.37195[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]7.3258[/C][C]0.674201[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]7.49543[/C][C]2.50457[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]7.80706[/C][C]-0.807056[/C][/ROW]
[ROW][C]62[/C][C]7.5[/C][C]7.36135[/C][C]0.138655[/C][/ROW]
[ROW][C]63[/C][C]7.5[/C][C]7.36135[/C][C]0.138655[/C][/ROW]
[ROW][C]64[/C][C]9.5[/C][C]7.61177[/C][C]1.88823[/C][/ROW]
[ROW][C]65[/C][C]6[/C][C]7.64594[/C][C]-1.64594[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]7.99985[/C][C]2.00015[/C][/ROW]
[ROW][C]67[/C][C]7[/C][C]7.92369[/C][C]-0.923692[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]5.95483[/C][C]-2.95483[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]7.83055[/C][C]-1.83055[/C][/ROW]
[ROW][C]70[/C][C]7[/C][C]7.75578[/C][C]-0.75578[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]8.35256[/C][C]1.64744[/C][/ROW]
[ROW][C]72[/C][C]7[/C][C]7.60127[/C][C]-0.601272[/C][/ROW]
[ROW][C]73[/C][C]3.5[/C][C]7.51812[/C][C]-4.01812[/C][/ROW]
[ROW][C]74[/C][C]8[/C][C]7.57106[/C][C]0.428937[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]6.74035[/C][C]3.25965[/C][/ROW]
[ROW][C]76[/C][C]5.5[/C][C]7.12773[/C][C]-1.62773[/C][/ROW]
[ROW][C]77[/C][C]6[/C][C]6.08644[/C][C]-0.0864379[/C][/ROW]
[ROW][C]78[/C][C]6.5[/C][C]6.66719[/C][C]-0.167192[/C][/ROW]
[ROW][C]79[/C][C]6.5[/C][C]6.29592[/C][C]0.20408[/C][/ROW]
[ROW][C]80[/C][C]8.5[/C][C]8.11582[/C][C]0.384178[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]6.45469[/C][C]-2.45469[/C][/ROW]
[ROW][C]82[/C][C]9.5[/C][C]7.19718[/C][C]2.30282[/C][/ROW]
[ROW][C]83[/C][C]8[/C][C]6.69046[/C][C]1.30954[/C][/ROW]
[ROW][C]84[/C][C]8.5[/C][C]6.85726[/C][C]1.64274[/C][/ROW]
[ROW][C]85[/C][C]5.5[/C][C]8.22508[/C][C]-2.72508[/C][/ROW]
[ROW][C]86[/C][C]7[/C][C]7.49541[/C][C]-0.495409[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]6.65794[/C][C]2.34206[/C][/ROW]
[ROW][C]88[/C][C]8[/C][C]7.26094[/C][C]0.739062[/C][/ROW]
[ROW][C]89[/C][C]10[/C][C]8.75917[/C][C]1.24083[/C][/ROW]
[ROW][C]90[/C][C]8[/C][C]6.44922[/C][C]1.55078[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]7.55864[/C][C]-1.55864[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]7.41651[/C][C]0.583494[/C][/ROW]
[ROW][C]93[/C][C]5[/C][C]7.16215[/C][C]-2.16215[/C][/ROW]
[ROW][C]94[/C][C]9[/C][C]6.06858[/C][C]2.93142[/C][/ROW]
[ROW][C]95[/C][C]4.5[/C][C]6.45026[/C][C]-1.95026[/C][/ROW]
[ROW][C]96[/C][C]8.5[/C][C]6.17768[/C][C]2.32232[/C][/ROW]
[ROW][C]97[/C][C]9.5[/C][C]8.58341[/C][C]0.916592[/C][/ROW]
[ROW][C]98[/C][C]8.5[/C][C]7.89154[/C][C]0.608463[/C][/ROW]
[ROW][C]99[/C][C]7.5[/C][C]6.34194[/C][C]1.15806[/C][/ROW]
[ROW][C]100[/C][C]7.5[/C][C]7.84859[/C][C]-0.348586[/C][/ROW]
[ROW][C]101[/C][C]5[/C][C]7.65163[/C][C]-2.65163[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]7.11743[/C][C]-0.117431[/C][/ROW]
[ROW][C]103[/C][C]8[/C][C]8.54359[/C][C]-0.543592[/C][/ROW]
[ROW][C]104[/C][C]5.5[/C][C]6.97231[/C][C]-1.47231[/C][/ROW]
[ROW][C]105[/C][C]8.5[/C][C]7.10137[/C][C]1.39863[/C][/ROW]
[ROW][C]106[/C][C]9.5[/C][C]7.98469[/C][C]1.51531[/C][/ROW]
[ROW][C]107[/C][C]7[/C][C]6.56007[/C][C]0.439926[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]7.88079[/C][C]0.119215[/C][/ROW]
[ROW][C]109[/C][C]8.5[/C][C]7.41283[/C][C]1.08717[/C][/ROW]
[ROW][C]110[/C][C]3.5[/C][C]6.39389[/C][C]-2.89389[/C][/ROW]
[ROW][C]111[/C][C]6.5[/C][C]6.96346[/C][C]-0.463462[/C][/ROW]
[ROW][C]112[/C][C]6.5[/C][C]6.89813[/C][C]-0.398125[/C][/ROW]
[ROW][C]113[/C][C]10.5[/C][C]8.28865[/C][C]2.21135[/C][/ROW]
[ROW][C]114[/C][C]8.5[/C][C]6.17777[/C][C]2.32223[/C][/ROW]
[ROW][C]115[/C][C]8[/C][C]7.21795[/C][C]0.782051[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]6.87471[/C][C]3.12529[/C][/ROW]
[ROW][C]117[/C][C]10[/C][C]8.48287[/C][C]1.51713[/C][/ROW]
[ROW][C]118[/C][C]9.5[/C][C]7.8651[/C][C]1.6349[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]7.3517[/C][C]1.6483[/C][/ROW]
[ROW][C]120[/C][C]10[/C][C]9.17322[/C][C]0.82678[/C][/ROW]
[ROW][C]121[/C][C]7.5[/C][C]6.6332[/C][C]0.866804[/C][/ROW]
[ROW][C]122[/C][C]4.5[/C][C]7.511[/C][C]-3.011[/C][/ROW]
[ROW][C]123[/C][C]4.5[/C][C]6.69369[/C][C]-2.19369[/C][/ROW]
[ROW][C]124[/C][C]0.5[/C][C]6.01709[/C][C]-5.51709[/C][/ROW]
[ROW][C]125[/C][C]6.5[/C][C]5.88521[/C][C]0.614788[/C][/ROW]
[ROW][C]126[/C][C]4.5[/C][C]7.62961[/C][C]-3.12961[/C][/ROW]
[ROW][C]127[/C][C]5.5[/C][C]6.87385[/C][C]-1.37385[/C][/ROW]
[ROW][C]128[/C][C]5[/C][C]6.54785[/C][C]-1.54785[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]7.61916[/C][C]-1.61916[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]6.77023[/C][C]-2.77023[/C][/ROW]
[ROW][C]131[/C][C]8[/C][C]6.75681[/C][C]1.24319[/C][/ROW]
[ROW][C]132[/C][C]10.5[/C][C]8.44565[/C][C]2.05435[/C][/ROW]
[ROW][C]133[/C][C]6.5[/C][C]6.6749[/C][C]-0.174897[/C][/ROW]
[ROW][C]134[/C][C]8[/C][C]7.59374[/C][C]0.406264[/C][/ROW]
[ROW][C]135[/C][C]8.5[/C][C]8.95442[/C][C]-0.454424[/C][/ROW]
[ROW][C]136[/C][C]5.5[/C][C]7.19543[/C][C]-1.69543[/C][/ROW]
[ROW][C]137[/C][C]7[/C][C]8.15817[/C][C]-1.15817[/C][/ROW]
[ROW][C]138[/C][C]5[/C][C]6.71273[/C][C]-1.71273[/C][/ROW]
[ROW][C]139[/C][C]3.5[/C][C]6.49068[/C][C]-2.99068[/C][/ROW]
[ROW][C]140[/C][C]5[/C][C]7.31425[/C][C]-2.31425[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.39423[/C][C]1.60577[/C][/ROW]
[ROW][C]142[/C][C]8.5[/C][C]7.47089[/C][C]1.02911[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]7.89106[/C][C]-2.89106[/C][/ROW]
[ROW][C]144[/C][C]9.5[/C][C]8.06897[/C][C]1.43103[/C][/ROW]
[ROW][C]145[/C][C]3[/C][C]6.2089[/C][C]-3.2089[/C][/ROW]
[ROW][C]146[/C][C]1.5[/C][C]7.16698[/C][C]-5.66698[/C][/ROW]
[ROW][C]147[/C][C]6[/C][C]6.87554[/C][C]-0.875543[/C][/ROW]
[ROW][C]148[/C][C]0.5[/C][C]7.01831[/C][C]-6.51831[/C][/ROW]
[ROW][C]149[/C][C]6.5[/C][C]6.02244[/C][C]0.477562[/C][/ROW]
[ROW][C]150[/C][C]7.5[/C][C]6.89208[/C][C]0.607921[/C][/ROW]
[ROW][C]151[/C][C]4.5[/C][C]6.54468[/C][C]-2.04468[/C][/ROW]
[ROW][C]152[/C][C]8[/C][C]6.75681[/C][C]1.24319[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]7.58019[/C][C]1.41981[/C][/ROW]
[ROW][C]154[/C][C]7.5[/C][C]6.76074[/C][C]0.739257[/C][/ROW]
[ROW][C]155[/C][C]8.5[/C][C]6.94041[/C][C]1.55959[/C][/ROW]
[ROW][C]156[/C][C]7[/C][C]6.79064[/C][C]0.20936[/C][/ROW]
[ROW][C]157[/C][C]9.5[/C][C]7.28316[/C][C]2.21684[/C][/ROW]
[ROW][C]158[/C][C]6.5[/C][C]6.36303[/C][C]0.136969[/C][/ROW]
[ROW][C]159[/C][C]9.5[/C][C]6.73307[/C][C]2.76693[/C][/ROW]
[ROW][C]160[/C][C]6[/C][C]6.25131[/C][C]-0.251307[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]7.41408[/C][C]0.58592[/C][/ROW]
[ROW][C]162[/C][C]9.5[/C][C]7.385[/C][C]2.115[/C][/ROW]
[ROW][C]163[/C][C]8[/C][C]7.43389[/C][C]0.566113[/C][/ROW]
[ROW][C]164[/C][C]8[/C][C]7.01394[/C][C]0.986065[/C][/ROW]
[ROW][C]165[/C][C]9[/C][C]7.19386[/C][C]1.80614[/C][/ROW]
[ROW][C]166[/C][C]5[/C][C]5.73078[/C][C]-0.730779[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270799&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270799&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.55.34007-4.84007
27.56.676790.823207
397.659251.34075
49.57.724211.77579
58.58.83145-0.331453
675.601521.39848
789.00555-1.00555
8108.31191.6881
9710.0957-3.09575
108.55.990822.50918
1198.154080.845918
129.55.860043.63996
1346.99757-2.99757
1467.01354-1.01354
1587.583370.416632
165.56.60793-1.10793
179.58.134391.36561
187.56.592930.907072
1976.781150.218848
207.58.54042-1.04042
2186.66531.3347
2277.33317-0.333169
2376.538190.461807
2466.95062-0.950616
25107.221772.77823
262.56.05346-3.55346
2798.527230.472766
2888.09449-0.0944869
2966.00345-0.00344538
308.56.753451.74655
3168.07521-2.07521
3297.616011.38399
3387.548920.451083
3498.382420.617575
355.57.168-1.668
3677.29776-0.297765
375.58.27541-2.77541
3897.867981.13202
3927.94243-5.94243
408.57.991860.508139
4197.777451.22255
428.58.21880.281204
4396.432132.56787
447.57.54404-0.0440416
45108.852311.14769
4697.284191.71581
477.58.17612-0.676116
4866.96885-0.968854
4910.58.090152.40985
508.57.279111.22089
5188.91389-0.913893
52105.659514.34049
5310.58.445652.05435
546.56.022440.477562
559.58.032731.46727
568.56.231662.26834
577.57.117890.382109
5857.37195-2.37195
5987.32580.674201
60107.495432.50457
6177.80706-0.807056
627.57.361350.138655
637.57.361350.138655
649.57.611771.88823
6567.64594-1.64594
66107.999852.00015
6777.92369-0.923692
6835.95483-2.95483
6967.83055-1.83055
7077.75578-0.75578
71108.352561.64744
7277.60127-0.601272
733.57.51812-4.01812
7487.571060.428937
75106.740353.25965
765.57.12773-1.62773
7766.08644-0.0864379
786.56.66719-0.167192
796.56.295920.20408
808.58.115820.384178
8146.45469-2.45469
829.57.197182.30282
8386.690461.30954
848.56.857261.64274
855.58.22508-2.72508
8677.49541-0.495409
8796.657942.34206
8887.260940.739062
89108.759171.24083
9086.449221.55078
9167.55864-1.55864
9287.416510.583494
9357.16215-2.16215
9496.068582.93142
954.56.45026-1.95026
968.56.177682.32232
979.58.583410.916592
988.57.891540.608463
997.56.341941.15806
1007.57.84859-0.348586
10157.65163-2.65163
10277.11743-0.117431
10388.54359-0.543592
1045.56.97231-1.47231
1058.57.101371.39863
1069.57.984691.51531
10776.560070.439926
10887.880790.119215
1098.57.412831.08717
1103.56.39389-2.89389
1116.56.96346-0.463462
1126.56.89813-0.398125
11310.58.288652.21135
1148.56.177772.32223
11587.217950.782051
116106.874713.12529
117108.482871.51713
1189.57.86511.6349
11997.35171.6483
120109.173220.82678
1217.56.63320.866804
1224.57.511-3.011
1234.56.69369-2.19369
1240.56.01709-5.51709
1256.55.885210.614788
1264.57.62961-3.12961
1275.56.87385-1.37385
12856.54785-1.54785
12967.61916-1.61916
13046.77023-2.77023
13186.756811.24319
13210.58.445652.05435
1336.56.6749-0.174897
13487.593740.406264
1358.58.95442-0.454424
1365.57.19543-1.69543
13778.15817-1.15817
13856.71273-1.71273
1393.56.49068-2.99068
14057.31425-2.31425
14197.394231.60577
1428.57.470891.02911
14357.89106-2.89106
1449.58.068971.43103
14536.2089-3.2089
1461.57.16698-5.66698
14766.87554-0.875543
1480.57.01831-6.51831
1496.56.022440.477562
1507.56.892080.607921
1514.56.54468-2.04468
15286.756811.24319
15397.580191.41981
1547.56.760740.739257
1558.56.940411.55959
15676.790640.20936
1579.57.283162.21684
1586.56.363030.136969
1599.56.733072.76693
16066.25131-0.251307
16187.414080.58592
1629.57.3852.115
16387.433890.566113
16487.013940.986065
16597.193861.80614
16655.73078-0.730779






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8025380.3949230.197462
100.6825550.634890.317445
110.5500110.8999780.449989
120.5352120.9295750.464788
130.7099990.5800030.290001
140.7945590.4108820.205441
150.7388160.5223680.261184
160.78510.4298010.2149
170.7598610.4802790.240139
180.6917770.6164460.308223
190.6366930.7266140.363307
200.5698620.8602770.430138
210.5936970.8126050.406303
220.5267740.9464530.473226
230.4535450.9070890.546455
240.3974620.7949230.602538
250.4051510.8103010.594849
260.5217510.9564980.478249
270.4649730.9299460.535027
280.4212570.8425130.578743
290.3614690.7229380.638531
300.312130.624260.68787
310.3929450.7858910.607055
320.3535820.7071650.646418
330.3039620.6079250.696038
340.253840.507680.74616
350.2747190.5494380.725281
360.2305460.4610920.769454
370.3209670.6419340.679033
380.2751410.5502820.724859
390.7666520.4666960.233348
400.7293710.5412580.270629
410.6961120.6077760.303888
420.6483870.7032260.351613
430.6782370.6435260.321763
440.6300260.7399480.369974
450.5988040.8023920.401196
460.5821360.8357270.417864
470.5360510.9278980.463949
480.5055590.9888830.494441
490.519670.960660.48033
500.5005410.9989190.499459
510.4695460.9390910.530454
520.6346640.7306710.365336
530.6331210.7337580.366879
540.5861970.8276070.413803
550.5608160.8783680.439184
560.5603520.8792950.439648
570.5128980.9742050.487102
580.5420660.9158680.457934
590.4978760.9957520.502124
600.5118370.9763260.488163
610.4798310.9596620.520169
620.4335830.8671660.566417
630.3886650.7773290.611335
640.3763480.7526950.623652
650.3710780.7421560.628922
660.3703880.7407770.629612
670.3428340.6856680.657166
680.4089460.8178930.591054
690.413190.826380.58681
700.3792370.7584730.620763
710.3635220.7270450.636478
720.3264090.6528180.673591
730.4745160.9490320.525484
740.4300060.8600120.569994
750.504720.990560.49528
760.4943620.9887240.505638
770.4510270.9020550.548973
780.4069830.8139660.593017
790.3644640.7289280.635536
800.3229360.6458730.677064
810.3439840.6879680.656016
820.3560130.7120250.643987
830.3326750.6653510.667325
840.3201670.6403340.679833
850.3561220.7122430.643878
860.3181030.6362070.681897
870.326190.652380.67381
880.2899560.5799130.710044
890.2631410.5262810.736859
900.2521250.504250.747875
910.2383330.4766650.761667
920.2088510.4177010.791149
930.2166330.4332670.783367
940.2616750.5233510.738325
950.2567760.5135520.743224
960.2818140.5636280.718186
970.2507230.5014460.749277
980.2168330.4336660.783167
990.1984760.3969530.801524
1000.169180.3383610.83082
1010.2002720.4005430.799728
1020.169970.339940.83003
1030.1483460.2966920.851654
1040.1354690.2709380.864531
1050.1221270.2442540.877873
1060.1131930.2263860.886807
1070.09448870.1889770.905511
1080.07657790.1531560.923422
1090.06491350.1298270.935086
1100.07545260.1509050.924547
1110.06080030.1216010.9392
1120.04936870.09873740.950631
1130.05192040.1038410.94808
1140.06307530.1261510.936925
1150.05932910.1186580.940671
1160.08117720.1623540.918823
1170.06944220.1388840.930558
1180.07025020.14050.92975
1190.08023340.1604670.919767
1200.06482530.1296510.935175
1210.05798820.1159760.942012
1220.06248970.1249790.93751
1230.05683520.113670.943165
1240.1748630.3497260.825137
1250.1553540.3107080.844646
1260.1948660.3897320.805134
1270.173750.3475010.82625
1280.1514610.3029230.848539
1290.1370080.2740150.862992
1300.1663470.3326930.833653
1310.1525450.3050910.847455
1320.1405740.2811490.859426
1330.1132870.2265740.886713
1340.08785970.1757190.91214
1350.06719830.1343970.932802
1360.05365510.107310.946345
1370.04555180.09110360.954448
1380.03589590.07179170.964104
1390.03842010.07684020.96158
1400.04987920.09975850.950121
1410.03803120.07606240.961969
1420.02708560.05417130.972914
1430.04089190.08178380.959108
1440.03375550.0675110.966245
1450.04490.08979990.9551
1460.5251650.9496710.474835
1470.4877240.9754490.512276
1480.9984580.003084880.00154244
1490.9999460.0001076435.38214e-05
1500.999870.0002604130.000130207
1510.9998590.0002828510.000141425
1520.9998360.0003280950.000164048
1530.9993590.001282320.000641158
1540.997560.004880350.00244017
1550.9940840.01183230.00591617
1560.9790240.04195220.0209761
1570.9345840.1308320.0654161

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.802538 & 0.394923 & 0.197462 \tabularnewline
10 & 0.682555 & 0.63489 & 0.317445 \tabularnewline
11 & 0.550011 & 0.899978 & 0.449989 \tabularnewline
12 & 0.535212 & 0.929575 & 0.464788 \tabularnewline
13 & 0.709999 & 0.580003 & 0.290001 \tabularnewline
14 & 0.794559 & 0.410882 & 0.205441 \tabularnewline
15 & 0.738816 & 0.522368 & 0.261184 \tabularnewline
16 & 0.7851 & 0.429801 & 0.2149 \tabularnewline
17 & 0.759861 & 0.480279 & 0.240139 \tabularnewline
18 & 0.691777 & 0.616446 & 0.308223 \tabularnewline
19 & 0.636693 & 0.726614 & 0.363307 \tabularnewline
20 & 0.569862 & 0.860277 & 0.430138 \tabularnewline
21 & 0.593697 & 0.812605 & 0.406303 \tabularnewline
22 & 0.526774 & 0.946453 & 0.473226 \tabularnewline
23 & 0.453545 & 0.907089 & 0.546455 \tabularnewline
24 & 0.397462 & 0.794923 & 0.602538 \tabularnewline
25 & 0.405151 & 0.810301 & 0.594849 \tabularnewline
26 & 0.521751 & 0.956498 & 0.478249 \tabularnewline
27 & 0.464973 & 0.929946 & 0.535027 \tabularnewline
28 & 0.421257 & 0.842513 & 0.578743 \tabularnewline
29 & 0.361469 & 0.722938 & 0.638531 \tabularnewline
30 & 0.31213 & 0.62426 & 0.68787 \tabularnewline
31 & 0.392945 & 0.785891 & 0.607055 \tabularnewline
32 & 0.353582 & 0.707165 & 0.646418 \tabularnewline
33 & 0.303962 & 0.607925 & 0.696038 \tabularnewline
34 & 0.25384 & 0.50768 & 0.74616 \tabularnewline
35 & 0.274719 & 0.549438 & 0.725281 \tabularnewline
36 & 0.230546 & 0.461092 & 0.769454 \tabularnewline
37 & 0.320967 & 0.641934 & 0.679033 \tabularnewline
38 & 0.275141 & 0.550282 & 0.724859 \tabularnewline
39 & 0.766652 & 0.466696 & 0.233348 \tabularnewline
40 & 0.729371 & 0.541258 & 0.270629 \tabularnewline
41 & 0.696112 & 0.607776 & 0.303888 \tabularnewline
42 & 0.648387 & 0.703226 & 0.351613 \tabularnewline
43 & 0.678237 & 0.643526 & 0.321763 \tabularnewline
44 & 0.630026 & 0.739948 & 0.369974 \tabularnewline
45 & 0.598804 & 0.802392 & 0.401196 \tabularnewline
46 & 0.582136 & 0.835727 & 0.417864 \tabularnewline
47 & 0.536051 & 0.927898 & 0.463949 \tabularnewline
48 & 0.505559 & 0.988883 & 0.494441 \tabularnewline
49 & 0.51967 & 0.96066 & 0.48033 \tabularnewline
50 & 0.500541 & 0.998919 & 0.499459 \tabularnewline
51 & 0.469546 & 0.939091 & 0.530454 \tabularnewline
52 & 0.634664 & 0.730671 & 0.365336 \tabularnewline
53 & 0.633121 & 0.733758 & 0.366879 \tabularnewline
54 & 0.586197 & 0.827607 & 0.413803 \tabularnewline
55 & 0.560816 & 0.878368 & 0.439184 \tabularnewline
56 & 0.560352 & 0.879295 & 0.439648 \tabularnewline
57 & 0.512898 & 0.974205 & 0.487102 \tabularnewline
58 & 0.542066 & 0.915868 & 0.457934 \tabularnewline
59 & 0.497876 & 0.995752 & 0.502124 \tabularnewline
60 & 0.511837 & 0.976326 & 0.488163 \tabularnewline
61 & 0.479831 & 0.959662 & 0.520169 \tabularnewline
62 & 0.433583 & 0.867166 & 0.566417 \tabularnewline
63 & 0.388665 & 0.777329 & 0.611335 \tabularnewline
64 & 0.376348 & 0.752695 & 0.623652 \tabularnewline
65 & 0.371078 & 0.742156 & 0.628922 \tabularnewline
66 & 0.370388 & 0.740777 & 0.629612 \tabularnewline
67 & 0.342834 & 0.685668 & 0.657166 \tabularnewline
68 & 0.408946 & 0.817893 & 0.591054 \tabularnewline
69 & 0.41319 & 0.82638 & 0.58681 \tabularnewline
70 & 0.379237 & 0.758473 & 0.620763 \tabularnewline
71 & 0.363522 & 0.727045 & 0.636478 \tabularnewline
72 & 0.326409 & 0.652818 & 0.673591 \tabularnewline
73 & 0.474516 & 0.949032 & 0.525484 \tabularnewline
74 & 0.430006 & 0.860012 & 0.569994 \tabularnewline
75 & 0.50472 & 0.99056 & 0.49528 \tabularnewline
76 & 0.494362 & 0.988724 & 0.505638 \tabularnewline
77 & 0.451027 & 0.902055 & 0.548973 \tabularnewline
78 & 0.406983 & 0.813966 & 0.593017 \tabularnewline
79 & 0.364464 & 0.728928 & 0.635536 \tabularnewline
80 & 0.322936 & 0.645873 & 0.677064 \tabularnewline
81 & 0.343984 & 0.687968 & 0.656016 \tabularnewline
82 & 0.356013 & 0.712025 & 0.643987 \tabularnewline
83 & 0.332675 & 0.665351 & 0.667325 \tabularnewline
84 & 0.320167 & 0.640334 & 0.679833 \tabularnewline
85 & 0.356122 & 0.712243 & 0.643878 \tabularnewline
86 & 0.318103 & 0.636207 & 0.681897 \tabularnewline
87 & 0.32619 & 0.65238 & 0.67381 \tabularnewline
88 & 0.289956 & 0.579913 & 0.710044 \tabularnewline
89 & 0.263141 & 0.526281 & 0.736859 \tabularnewline
90 & 0.252125 & 0.50425 & 0.747875 \tabularnewline
91 & 0.238333 & 0.476665 & 0.761667 \tabularnewline
92 & 0.208851 & 0.417701 & 0.791149 \tabularnewline
93 & 0.216633 & 0.433267 & 0.783367 \tabularnewline
94 & 0.261675 & 0.523351 & 0.738325 \tabularnewline
95 & 0.256776 & 0.513552 & 0.743224 \tabularnewline
96 & 0.281814 & 0.563628 & 0.718186 \tabularnewline
97 & 0.250723 & 0.501446 & 0.749277 \tabularnewline
98 & 0.216833 & 0.433666 & 0.783167 \tabularnewline
99 & 0.198476 & 0.396953 & 0.801524 \tabularnewline
100 & 0.16918 & 0.338361 & 0.83082 \tabularnewline
101 & 0.200272 & 0.400543 & 0.799728 \tabularnewline
102 & 0.16997 & 0.33994 & 0.83003 \tabularnewline
103 & 0.148346 & 0.296692 & 0.851654 \tabularnewline
104 & 0.135469 & 0.270938 & 0.864531 \tabularnewline
105 & 0.122127 & 0.244254 & 0.877873 \tabularnewline
106 & 0.113193 & 0.226386 & 0.886807 \tabularnewline
107 & 0.0944887 & 0.188977 & 0.905511 \tabularnewline
108 & 0.0765779 & 0.153156 & 0.923422 \tabularnewline
109 & 0.0649135 & 0.129827 & 0.935086 \tabularnewline
110 & 0.0754526 & 0.150905 & 0.924547 \tabularnewline
111 & 0.0608003 & 0.121601 & 0.9392 \tabularnewline
112 & 0.0493687 & 0.0987374 & 0.950631 \tabularnewline
113 & 0.0519204 & 0.103841 & 0.94808 \tabularnewline
114 & 0.0630753 & 0.126151 & 0.936925 \tabularnewline
115 & 0.0593291 & 0.118658 & 0.940671 \tabularnewline
116 & 0.0811772 & 0.162354 & 0.918823 \tabularnewline
117 & 0.0694422 & 0.138884 & 0.930558 \tabularnewline
118 & 0.0702502 & 0.1405 & 0.92975 \tabularnewline
119 & 0.0802334 & 0.160467 & 0.919767 \tabularnewline
120 & 0.0648253 & 0.129651 & 0.935175 \tabularnewline
121 & 0.0579882 & 0.115976 & 0.942012 \tabularnewline
122 & 0.0624897 & 0.124979 & 0.93751 \tabularnewline
123 & 0.0568352 & 0.11367 & 0.943165 \tabularnewline
124 & 0.174863 & 0.349726 & 0.825137 \tabularnewline
125 & 0.155354 & 0.310708 & 0.844646 \tabularnewline
126 & 0.194866 & 0.389732 & 0.805134 \tabularnewline
127 & 0.17375 & 0.347501 & 0.82625 \tabularnewline
128 & 0.151461 & 0.302923 & 0.848539 \tabularnewline
129 & 0.137008 & 0.274015 & 0.862992 \tabularnewline
130 & 0.166347 & 0.332693 & 0.833653 \tabularnewline
131 & 0.152545 & 0.305091 & 0.847455 \tabularnewline
132 & 0.140574 & 0.281149 & 0.859426 \tabularnewline
133 & 0.113287 & 0.226574 & 0.886713 \tabularnewline
134 & 0.0878597 & 0.175719 & 0.91214 \tabularnewline
135 & 0.0671983 & 0.134397 & 0.932802 \tabularnewline
136 & 0.0536551 & 0.10731 & 0.946345 \tabularnewline
137 & 0.0455518 & 0.0911036 & 0.954448 \tabularnewline
138 & 0.0358959 & 0.0717917 & 0.964104 \tabularnewline
139 & 0.0384201 & 0.0768402 & 0.96158 \tabularnewline
140 & 0.0498792 & 0.0997585 & 0.950121 \tabularnewline
141 & 0.0380312 & 0.0760624 & 0.961969 \tabularnewline
142 & 0.0270856 & 0.0541713 & 0.972914 \tabularnewline
143 & 0.0408919 & 0.0817838 & 0.959108 \tabularnewline
144 & 0.0337555 & 0.067511 & 0.966245 \tabularnewline
145 & 0.0449 & 0.0897999 & 0.9551 \tabularnewline
146 & 0.525165 & 0.949671 & 0.474835 \tabularnewline
147 & 0.487724 & 0.975449 & 0.512276 \tabularnewline
148 & 0.998458 & 0.00308488 & 0.00154244 \tabularnewline
149 & 0.999946 & 0.000107643 & 5.38214e-05 \tabularnewline
150 & 0.99987 & 0.000260413 & 0.000130207 \tabularnewline
151 & 0.999859 & 0.000282851 & 0.000141425 \tabularnewline
152 & 0.999836 & 0.000328095 & 0.000164048 \tabularnewline
153 & 0.999359 & 0.00128232 & 0.000641158 \tabularnewline
154 & 0.99756 & 0.00488035 & 0.00244017 \tabularnewline
155 & 0.994084 & 0.0118323 & 0.00591617 \tabularnewline
156 & 0.979024 & 0.0419522 & 0.0209761 \tabularnewline
157 & 0.934584 & 0.130832 & 0.0654161 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270799&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]0.802538[/C][C]0.394923[/C][C]0.197462[/C][/ROW]
[ROW][C]10[/C][C]0.682555[/C][C]0.63489[/C][C]0.317445[/C][/ROW]
[ROW][C]11[/C][C]0.550011[/C][C]0.899978[/C][C]0.449989[/C][/ROW]
[ROW][C]12[/C][C]0.535212[/C][C]0.929575[/C][C]0.464788[/C][/ROW]
[ROW][C]13[/C][C]0.709999[/C][C]0.580003[/C][C]0.290001[/C][/ROW]
[ROW][C]14[/C][C]0.794559[/C][C]0.410882[/C][C]0.205441[/C][/ROW]
[ROW][C]15[/C][C]0.738816[/C][C]0.522368[/C][C]0.261184[/C][/ROW]
[ROW][C]16[/C][C]0.7851[/C][C]0.429801[/C][C]0.2149[/C][/ROW]
[ROW][C]17[/C][C]0.759861[/C][C]0.480279[/C][C]0.240139[/C][/ROW]
[ROW][C]18[/C][C]0.691777[/C][C]0.616446[/C][C]0.308223[/C][/ROW]
[ROW][C]19[/C][C]0.636693[/C][C]0.726614[/C][C]0.363307[/C][/ROW]
[ROW][C]20[/C][C]0.569862[/C][C]0.860277[/C][C]0.430138[/C][/ROW]
[ROW][C]21[/C][C]0.593697[/C][C]0.812605[/C][C]0.406303[/C][/ROW]
[ROW][C]22[/C][C]0.526774[/C][C]0.946453[/C][C]0.473226[/C][/ROW]
[ROW][C]23[/C][C]0.453545[/C][C]0.907089[/C][C]0.546455[/C][/ROW]
[ROW][C]24[/C][C]0.397462[/C][C]0.794923[/C][C]0.602538[/C][/ROW]
[ROW][C]25[/C][C]0.405151[/C][C]0.810301[/C][C]0.594849[/C][/ROW]
[ROW][C]26[/C][C]0.521751[/C][C]0.956498[/C][C]0.478249[/C][/ROW]
[ROW][C]27[/C][C]0.464973[/C][C]0.929946[/C][C]0.535027[/C][/ROW]
[ROW][C]28[/C][C]0.421257[/C][C]0.842513[/C][C]0.578743[/C][/ROW]
[ROW][C]29[/C][C]0.361469[/C][C]0.722938[/C][C]0.638531[/C][/ROW]
[ROW][C]30[/C][C]0.31213[/C][C]0.62426[/C][C]0.68787[/C][/ROW]
[ROW][C]31[/C][C]0.392945[/C][C]0.785891[/C][C]0.607055[/C][/ROW]
[ROW][C]32[/C][C]0.353582[/C][C]0.707165[/C][C]0.646418[/C][/ROW]
[ROW][C]33[/C][C]0.303962[/C][C]0.607925[/C][C]0.696038[/C][/ROW]
[ROW][C]34[/C][C]0.25384[/C][C]0.50768[/C][C]0.74616[/C][/ROW]
[ROW][C]35[/C][C]0.274719[/C][C]0.549438[/C][C]0.725281[/C][/ROW]
[ROW][C]36[/C][C]0.230546[/C][C]0.461092[/C][C]0.769454[/C][/ROW]
[ROW][C]37[/C][C]0.320967[/C][C]0.641934[/C][C]0.679033[/C][/ROW]
[ROW][C]38[/C][C]0.275141[/C][C]0.550282[/C][C]0.724859[/C][/ROW]
[ROW][C]39[/C][C]0.766652[/C][C]0.466696[/C][C]0.233348[/C][/ROW]
[ROW][C]40[/C][C]0.729371[/C][C]0.541258[/C][C]0.270629[/C][/ROW]
[ROW][C]41[/C][C]0.696112[/C][C]0.607776[/C][C]0.303888[/C][/ROW]
[ROW][C]42[/C][C]0.648387[/C][C]0.703226[/C][C]0.351613[/C][/ROW]
[ROW][C]43[/C][C]0.678237[/C][C]0.643526[/C][C]0.321763[/C][/ROW]
[ROW][C]44[/C][C]0.630026[/C][C]0.739948[/C][C]0.369974[/C][/ROW]
[ROW][C]45[/C][C]0.598804[/C][C]0.802392[/C][C]0.401196[/C][/ROW]
[ROW][C]46[/C][C]0.582136[/C][C]0.835727[/C][C]0.417864[/C][/ROW]
[ROW][C]47[/C][C]0.536051[/C][C]0.927898[/C][C]0.463949[/C][/ROW]
[ROW][C]48[/C][C]0.505559[/C][C]0.988883[/C][C]0.494441[/C][/ROW]
[ROW][C]49[/C][C]0.51967[/C][C]0.96066[/C][C]0.48033[/C][/ROW]
[ROW][C]50[/C][C]0.500541[/C][C]0.998919[/C][C]0.499459[/C][/ROW]
[ROW][C]51[/C][C]0.469546[/C][C]0.939091[/C][C]0.530454[/C][/ROW]
[ROW][C]52[/C][C]0.634664[/C][C]0.730671[/C][C]0.365336[/C][/ROW]
[ROW][C]53[/C][C]0.633121[/C][C]0.733758[/C][C]0.366879[/C][/ROW]
[ROW][C]54[/C][C]0.586197[/C][C]0.827607[/C][C]0.413803[/C][/ROW]
[ROW][C]55[/C][C]0.560816[/C][C]0.878368[/C][C]0.439184[/C][/ROW]
[ROW][C]56[/C][C]0.560352[/C][C]0.879295[/C][C]0.439648[/C][/ROW]
[ROW][C]57[/C][C]0.512898[/C][C]0.974205[/C][C]0.487102[/C][/ROW]
[ROW][C]58[/C][C]0.542066[/C][C]0.915868[/C][C]0.457934[/C][/ROW]
[ROW][C]59[/C][C]0.497876[/C][C]0.995752[/C][C]0.502124[/C][/ROW]
[ROW][C]60[/C][C]0.511837[/C][C]0.976326[/C][C]0.488163[/C][/ROW]
[ROW][C]61[/C][C]0.479831[/C][C]0.959662[/C][C]0.520169[/C][/ROW]
[ROW][C]62[/C][C]0.433583[/C][C]0.867166[/C][C]0.566417[/C][/ROW]
[ROW][C]63[/C][C]0.388665[/C][C]0.777329[/C][C]0.611335[/C][/ROW]
[ROW][C]64[/C][C]0.376348[/C][C]0.752695[/C][C]0.623652[/C][/ROW]
[ROW][C]65[/C][C]0.371078[/C][C]0.742156[/C][C]0.628922[/C][/ROW]
[ROW][C]66[/C][C]0.370388[/C][C]0.740777[/C][C]0.629612[/C][/ROW]
[ROW][C]67[/C][C]0.342834[/C][C]0.685668[/C][C]0.657166[/C][/ROW]
[ROW][C]68[/C][C]0.408946[/C][C]0.817893[/C][C]0.591054[/C][/ROW]
[ROW][C]69[/C][C]0.41319[/C][C]0.82638[/C][C]0.58681[/C][/ROW]
[ROW][C]70[/C][C]0.379237[/C][C]0.758473[/C][C]0.620763[/C][/ROW]
[ROW][C]71[/C][C]0.363522[/C][C]0.727045[/C][C]0.636478[/C][/ROW]
[ROW][C]72[/C][C]0.326409[/C][C]0.652818[/C][C]0.673591[/C][/ROW]
[ROW][C]73[/C][C]0.474516[/C][C]0.949032[/C][C]0.525484[/C][/ROW]
[ROW][C]74[/C][C]0.430006[/C][C]0.860012[/C][C]0.569994[/C][/ROW]
[ROW][C]75[/C][C]0.50472[/C][C]0.99056[/C][C]0.49528[/C][/ROW]
[ROW][C]76[/C][C]0.494362[/C][C]0.988724[/C][C]0.505638[/C][/ROW]
[ROW][C]77[/C][C]0.451027[/C][C]0.902055[/C][C]0.548973[/C][/ROW]
[ROW][C]78[/C][C]0.406983[/C][C]0.813966[/C][C]0.593017[/C][/ROW]
[ROW][C]79[/C][C]0.364464[/C][C]0.728928[/C][C]0.635536[/C][/ROW]
[ROW][C]80[/C][C]0.322936[/C][C]0.645873[/C][C]0.677064[/C][/ROW]
[ROW][C]81[/C][C]0.343984[/C][C]0.687968[/C][C]0.656016[/C][/ROW]
[ROW][C]82[/C][C]0.356013[/C][C]0.712025[/C][C]0.643987[/C][/ROW]
[ROW][C]83[/C][C]0.332675[/C][C]0.665351[/C][C]0.667325[/C][/ROW]
[ROW][C]84[/C][C]0.320167[/C][C]0.640334[/C][C]0.679833[/C][/ROW]
[ROW][C]85[/C][C]0.356122[/C][C]0.712243[/C][C]0.643878[/C][/ROW]
[ROW][C]86[/C][C]0.318103[/C][C]0.636207[/C][C]0.681897[/C][/ROW]
[ROW][C]87[/C][C]0.32619[/C][C]0.65238[/C][C]0.67381[/C][/ROW]
[ROW][C]88[/C][C]0.289956[/C][C]0.579913[/C][C]0.710044[/C][/ROW]
[ROW][C]89[/C][C]0.263141[/C][C]0.526281[/C][C]0.736859[/C][/ROW]
[ROW][C]90[/C][C]0.252125[/C][C]0.50425[/C][C]0.747875[/C][/ROW]
[ROW][C]91[/C][C]0.238333[/C][C]0.476665[/C][C]0.761667[/C][/ROW]
[ROW][C]92[/C][C]0.208851[/C][C]0.417701[/C][C]0.791149[/C][/ROW]
[ROW][C]93[/C][C]0.216633[/C][C]0.433267[/C][C]0.783367[/C][/ROW]
[ROW][C]94[/C][C]0.261675[/C][C]0.523351[/C][C]0.738325[/C][/ROW]
[ROW][C]95[/C][C]0.256776[/C][C]0.513552[/C][C]0.743224[/C][/ROW]
[ROW][C]96[/C][C]0.281814[/C][C]0.563628[/C][C]0.718186[/C][/ROW]
[ROW][C]97[/C][C]0.250723[/C][C]0.501446[/C][C]0.749277[/C][/ROW]
[ROW][C]98[/C][C]0.216833[/C][C]0.433666[/C][C]0.783167[/C][/ROW]
[ROW][C]99[/C][C]0.198476[/C][C]0.396953[/C][C]0.801524[/C][/ROW]
[ROW][C]100[/C][C]0.16918[/C][C]0.338361[/C][C]0.83082[/C][/ROW]
[ROW][C]101[/C][C]0.200272[/C][C]0.400543[/C][C]0.799728[/C][/ROW]
[ROW][C]102[/C][C]0.16997[/C][C]0.33994[/C][C]0.83003[/C][/ROW]
[ROW][C]103[/C][C]0.148346[/C][C]0.296692[/C][C]0.851654[/C][/ROW]
[ROW][C]104[/C][C]0.135469[/C][C]0.270938[/C][C]0.864531[/C][/ROW]
[ROW][C]105[/C][C]0.122127[/C][C]0.244254[/C][C]0.877873[/C][/ROW]
[ROW][C]106[/C][C]0.113193[/C][C]0.226386[/C][C]0.886807[/C][/ROW]
[ROW][C]107[/C][C]0.0944887[/C][C]0.188977[/C][C]0.905511[/C][/ROW]
[ROW][C]108[/C][C]0.0765779[/C][C]0.153156[/C][C]0.923422[/C][/ROW]
[ROW][C]109[/C][C]0.0649135[/C][C]0.129827[/C][C]0.935086[/C][/ROW]
[ROW][C]110[/C][C]0.0754526[/C][C]0.150905[/C][C]0.924547[/C][/ROW]
[ROW][C]111[/C][C]0.0608003[/C][C]0.121601[/C][C]0.9392[/C][/ROW]
[ROW][C]112[/C][C]0.0493687[/C][C]0.0987374[/C][C]0.950631[/C][/ROW]
[ROW][C]113[/C][C]0.0519204[/C][C]0.103841[/C][C]0.94808[/C][/ROW]
[ROW][C]114[/C][C]0.0630753[/C][C]0.126151[/C][C]0.936925[/C][/ROW]
[ROW][C]115[/C][C]0.0593291[/C][C]0.118658[/C][C]0.940671[/C][/ROW]
[ROW][C]116[/C][C]0.0811772[/C][C]0.162354[/C][C]0.918823[/C][/ROW]
[ROW][C]117[/C][C]0.0694422[/C][C]0.138884[/C][C]0.930558[/C][/ROW]
[ROW][C]118[/C][C]0.0702502[/C][C]0.1405[/C][C]0.92975[/C][/ROW]
[ROW][C]119[/C][C]0.0802334[/C][C]0.160467[/C][C]0.919767[/C][/ROW]
[ROW][C]120[/C][C]0.0648253[/C][C]0.129651[/C][C]0.935175[/C][/ROW]
[ROW][C]121[/C][C]0.0579882[/C][C]0.115976[/C][C]0.942012[/C][/ROW]
[ROW][C]122[/C][C]0.0624897[/C][C]0.124979[/C][C]0.93751[/C][/ROW]
[ROW][C]123[/C][C]0.0568352[/C][C]0.11367[/C][C]0.943165[/C][/ROW]
[ROW][C]124[/C][C]0.174863[/C][C]0.349726[/C][C]0.825137[/C][/ROW]
[ROW][C]125[/C][C]0.155354[/C][C]0.310708[/C][C]0.844646[/C][/ROW]
[ROW][C]126[/C][C]0.194866[/C][C]0.389732[/C][C]0.805134[/C][/ROW]
[ROW][C]127[/C][C]0.17375[/C][C]0.347501[/C][C]0.82625[/C][/ROW]
[ROW][C]128[/C][C]0.151461[/C][C]0.302923[/C][C]0.848539[/C][/ROW]
[ROW][C]129[/C][C]0.137008[/C][C]0.274015[/C][C]0.862992[/C][/ROW]
[ROW][C]130[/C][C]0.166347[/C][C]0.332693[/C][C]0.833653[/C][/ROW]
[ROW][C]131[/C][C]0.152545[/C][C]0.305091[/C][C]0.847455[/C][/ROW]
[ROW][C]132[/C][C]0.140574[/C][C]0.281149[/C][C]0.859426[/C][/ROW]
[ROW][C]133[/C][C]0.113287[/C][C]0.226574[/C][C]0.886713[/C][/ROW]
[ROW][C]134[/C][C]0.0878597[/C][C]0.175719[/C][C]0.91214[/C][/ROW]
[ROW][C]135[/C][C]0.0671983[/C][C]0.134397[/C][C]0.932802[/C][/ROW]
[ROW][C]136[/C][C]0.0536551[/C][C]0.10731[/C][C]0.946345[/C][/ROW]
[ROW][C]137[/C][C]0.0455518[/C][C]0.0911036[/C][C]0.954448[/C][/ROW]
[ROW][C]138[/C][C]0.0358959[/C][C]0.0717917[/C][C]0.964104[/C][/ROW]
[ROW][C]139[/C][C]0.0384201[/C][C]0.0768402[/C][C]0.96158[/C][/ROW]
[ROW][C]140[/C][C]0.0498792[/C][C]0.0997585[/C][C]0.950121[/C][/ROW]
[ROW][C]141[/C][C]0.0380312[/C][C]0.0760624[/C][C]0.961969[/C][/ROW]
[ROW][C]142[/C][C]0.0270856[/C][C]0.0541713[/C][C]0.972914[/C][/ROW]
[ROW][C]143[/C][C]0.0408919[/C][C]0.0817838[/C][C]0.959108[/C][/ROW]
[ROW][C]144[/C][C]0.0337555[/C][C]0.067511[/C][C]0.966245[/C][/ROW]
[ROW][C]145[/C][C]0.0449[/C][C]0.0897999[/C][C]0.9551[/C][/ROW]
[ROW][C]146[/C][C]0.525165[/C][C]0.949671[/C][C]0.474835[/C][/ROW]
[ROW][C]147[/C][C]0.487724[/C][C]0.975449[/C][C]0.512276[/C][/ROW]
[ROW][C]148[/C][C]0.998458[/C][C]0.00308488[/C][C]0.00154244[/C][/ROW]
[ROW][C]149[/C][C]0.999946[/C][C]0.000107643[/C][C]5.38214e-05[/C][/ROW]
[ROW][C]150[/C][C]0.99987[/C][C]0.000260413[/C][C]0.000130207[/C][/ROW]
[ROW][C]151[/C][C]0.999859[/C][C]0.000282851[/C][C]0.000141425[/C][/ROW]
[ROW][C]152[/C][C]0.999836[/C][C]0.000328095[/C][C]0.000164048[/C][/ROW]
[ROW][C]153[/C][C]0.999359[/C][C]0.00128232[/C][C]0.000641158[/C][/ROW]
[ROW][C]154[/C][C]0.99756[/C][C]0.00488035[/C][C]0.00244017[/C][/ROW]
[ROW][C]155[/C][C]0.994084[/C][C]0.0118323[/C][C]0.00591617[/C][/ROW]
[ROW][C]156[/C][C]0.979024[/C][C]0.0419522[/C][C]0.0209761[/C][/ROW]
[ROW][C]157[/C][C]0.934584[/C][C]0.130832[/C][C]0.0654161[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270799&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270799&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8025380.3949230.197462
100.6825550.634890.317445
110.5500110.8999780.449989
120.5352120.9295750.464788
130.7099990.5800030.290001
140.7945590.4108820.205441
150.7388160.5223680.261184
160.78510.4298010.2149
170.7598610.4802790.240139
180.6917770.6164460.308223
190.6366930.7266140.363307
200.5698620.8602770.430138
210.5936970.8126050.406303
220.5267740.9464530.473226
230.4535450.9070890.546455
240.3974620.7949230.602538
250.4051510.8103010.594849
260.5217510.9564980.478249
270.4649730.9299460.535027
280.4212570.8425130.578743
290.3614690.7229380.638531
300.312130.624260.68787
310.3929450.7858910.607055
320.3535820.7071650.646418
330.3039620.6079250.696038
340.253840.507680.74616
350.2747190.5494380.725281
360.2305460.4610920.769454
370.3209670.6419340.679033
380.2751410.5502820.724859
390.7666520.4666960.233348
400.7293710.5412580.270629
410.6961120.6077760.303888
420.6483870.7032260.351613
430.6782370.6435260.321763
440.6300260.7399480.369974
450.5988040.8023920.401196
460.5821360.8357270.417864
470.5360510.9278980.463949
480.5055590.9888830.494441
490.519670.960660.48033
500.5005410.9989190.499459
510.4695460.9390910.530454
520.6346640.7306710.365336
530.6331210.7337580.366879
540.5861970.8276070.413803
550.5608160.8783680.439184
560.5603520.8792950.439648
570.5128980.9742050.487102
580.5420660.9158680.457934
590.4978760.9957520.502124
600.5118370.9763260.488163
610.4798310.9596620.520169
620.4335830.8671660.566417
630.3886650.7773290.611335
640.3763480.7526950.623652
650.3710780.7421560.628922
660.3703880.7407770.629612
670.3428340.6856680.657166
680.4089460.8178930.591054
690.413190.826380.58681
700.3792370.7584730.620763
710.3635220.7270450.636478
720.3264090.6528180.673591
730.4745160.9490320.525484
740.4300060.8600120.569994
750.504720.990560.49528
760.4943620.9887240.505638
770.4510270.9020550.548973
780.4069830.8139660.593017
790.3644640.7289280.635536
800.3229360.6458730.677064
810.3439840.6879680.656016
820.3560130.7120250.643987
830.3326750.6653510.667325
840.3201670.6403340.679833
850.3561220.7122430.643878
860.3181030.6362070.681897
870.326190.652380.67381
880.2899560.5799130.710044
890.2631410.5262810.736859
900.2521250.504250.747875
910.2383330.4766650.761667
920.2088510.4177010.791149
930.2166330.4332670.783367
940.2616750.5233510.738325
950.2567760.5135520.743224
960.2818140.5636280.718186
970.2507230.5014460.749277
980.2168330.4336660.783167
990.1984760.3969530.801524
1000.169180.3383610.83082
1010.2002720.4005430.799728
1020.169970.339940.83003
1030.1483460.2966920.851654
1040.1354690.2709380.864531
1050.1221270.2442540.877873
1060.1131930.2263860.886807
1070.09448870.1889770.905511
1080.07657790.1531560.923422
1090.06491350.1298270.935086
1100.07545260.1509050.924547
1110.06080030.1216010.9392
1120.04936870.09873740.950631
1130.05192040.1038410.94808
1140.06307530.1261510.936925
1150.05932910.1186580.940671
1160.08117720.1623540.918823
1170.06944220.1388840.930558
1180.07025020.14050.92975
1190.08023340.1604670.919767
1200.06482530.1296510.935175
1210.05798820.1159760.942012
1220.06248970.1249790.93751
1230.05683520.113670.943165
1240.1748630.3497260.825137
1250.1553540.3107080.844646
1260.1948660.3897320.805134
1270.173750.3475010.82625
1280.1514610.3029230.848539
1290.1370080.2740150.862992
1300.1663470.3326930.833653
1310.1525450.3050910.847455
1320.1405740.2811490.859426
1330.1132870.2265740.886713
1340.08785970.1757190.91214
1350.06719830.1343970.932802
1360.05365510.107310.946345
1370.04555180.09110360.954448
1380.03589590.07179170.964104
1390.03842010.07684020.96158
1400.04987920.09975850.950121
1410.03803120.07606240.961969
1420.02708560.05417130.972914
1430.04089190.08178380.959108
1440.03375550.0675110.966245
1450.04490.08979990.9551
1460.5251650.9496710.474835
1470.4877240.9754490.512276
1480.9984580.003084880.00154244
1490.9999460.0001076435.38214e-05
1500.999870.0002604130.000130207
1510.9998590.0002828510.000141425
1520.9998360.0003280950.000164048
1530.9993590.001282320.000641158
1540.997560.004880350.00244017
1550.9940840.01183230.00591617
1560.9790240.04195220.0209761
1570.9345840.1308320.0654161






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.0469799NOK
5% type I error level90.0604027NOK
10% type I error level190.127517NOK

\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 & 7 & 0.0469799 & NOK \tabularnewline
5% type I error level & 9 & 0.0604027 & NOK \tabularnewline
10% type I error level & 19 & 0.127517 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270799&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]7[/C][C]0.0469799[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.0604027[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]19[/C][C]0.127517[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270799&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270799&T=6

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level70.0469799NOK
5% type I error level90.0604027NOK
10% type I error level190.127517NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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')
}