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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 computationTue, 09 Dec 2014 22:12:25 +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/09/t1418163186uipltkicxikh84i.htm/, Retrieved Tue, 28 May 2024 09:13:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=264855, Retrieved Tue, 28 May 2024 09:13:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsMLR Ruwe examenscore
Estimated Impact102

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper data] [2014-12-09 22:12:25] [99d5c1073827aabbadf7ab1e7da1d584] [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
138	145	26	22	48	8
149	196	36	51	86	8
145	199	22	67	89	9
120	127	40	36	76	5,5
138	91	18	21	39	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
62	119	23	29	53	7
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
154	130	41	21	62	7,5
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
128	85	24	24	48	8,5
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=264855&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=264855&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264855&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] = + 4.99896 + 0.0124434LFM[t] + 0.000876011B[t] -0.422733PRH[t] -0.39332CH[t] + 0.416281H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  4.99896 +  0.0124434LFM[t] +  0.000876011B[t] -0.422733PRH[t] -0.39332CH[t] +  0.416281H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264855&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  4.99896 +  0.0124434LFM[t] +  0.000876011B[t] -0.422733PRH[t] -0.39332CH[t] +  0.416281H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264855&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264855&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] = + 4.99896 + 0.0124434LFM[t] + 0.000876011B[t] -0.422733PRH[t] -0.39332CH[t] + 0.416281H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.998960.49745510.057.77254e-193.88627e-19
LFM0.01244340.004726562.6330.009276720.00463836
B0.0008760110.003211760.27280.7853860.392693
PRH-0.4227330.322182-1.3120.1913110.0956555
CH-0.393320.321243-1.2240.2225580.111279
H0.4162810.3211481.2960.1967070.0983536

\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) & 4.99896 & 0.497455 & 10.05 & 7.77254e-19 & 3.88627e-19 \tabularnewline
LFM & 0.0124434 & 0.00472656 & 2.633 & 0.00927672 & 0.00463836 \tabularnewline
B & 0.000876011 & 0.00321176 & 0.2728 & 0.785386 & 0.392693 \tabularnewline
PRH & -0.422733 & 0.322182 & -1.312 & 0.191311 & 0.0956555 \tabularnewline
CH & -0.39332 & 0.321243 & -1.224 & 0.222558 & 0.111279 \tabularnewline
H & 0.416281 & 0.321148 & 1.296 & 0.196707 & 0.0983536 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264855&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]4.99896[/C][C]0.497455[/C][C]10.05[/C][C]7.77254e-19[/C][C]3.88627e-19[/C][/ROW]
[ROW][C]LFM[/C][C]0.0124434[/C][C]0.00472656[/C][C]2.633[/C][C]0.00927672[/C][C]0.00463836[/C][/ROW]
[ROW][C]B[/C][C]0.000876011[/C][C]0.00321176[/C][C]0.2728[/C][C]0.785386[/C][C]0.392693[/C][/ROW]
[ROW][C]PRH[/C][C]-0.422733[/C][C]0.322182[/C][C]-1.312[/C][C]0.191311[/C][C]0.0956555[/C][/ROW]
[ROW][C]CH[/C][C]-0.39332[/C][C]0.321243[/C][C]-1.224[/C][C]0.222558[/C][C]0.111279[/C][/ROW]
[ROW][C]H[/C][C]0.416281[/C][C]0.321148[/C][C]1.296[/C][C]0.196707[/C][C]0.0983536[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264855&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264855&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)4.998960.49745510.057.77254e-193.88627e-19
LFM0.01244340.004726562.6330.009276720.00463836
B0.0008760110.003211760.27280.7853860.392693
PRH-0.4227330.322182-1.3120.1913110.0956555
CH-0.393320.321243-1.2240.2225580.111279
H0.4162810.3211481.2960.1967070.0983536






Multiple Linear Regression - Regression Statistics
Multiple R0.387469
R-squared0.150132
Adjusted R-squared0.124379
F-TEST (value)5.82958
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value5.50031e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.96357
Sum Squared Residuals636.173

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.387469 \tabularnewline
R-squared & 0.150132 \tabularnewline
Adjusted R-squared & 0.124379 \tabularnewline
F-TEST (value) & 5.82958 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 165 \tabularnewline
p-value & 5.50031e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.96357 \tabularnewline
Sum Squared Residuals & 636.173 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264855&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.387469[/C][/ROW]
[ROW][C]R-squared[/C][C]0.150132[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.124379[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.82958[/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]5.50031e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.96357[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]636.173[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264855&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264855&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 R0.387469
R-squared0.150132
Adjusted R-squared0.124379
F-TEST (value)5.82958
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value5.50031e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.96357
Sum Squared Residuals636.173






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.55.34302-4.84302
27.56.679060.820939
397.661561.33844
49.57.726541.77346
58.58.82679-0.326794
675.598781.40122
789.02342-1.02342
8108.32181.6782
9710.0951-3.09512
108.55.993352.50665
1198.15970.840295
129.55.865183.63482
1347.00999-3.00999
1467.01596-1.01596
1587.58520.414801
165.56.6507-1.1507
179.58.159591.34041
187.56.600270.899733
1976.780710.219287
207.58.55924-1.05924
2186.669991.33001
2277.34146-0.341461
2376.541160.458842
2466.96217-0.962173
25107.22132.7787
262.56.05751-3.55751
2798.528450.471548
2888.10712-0.107123
2965.998960.00104232
308.56.750481.74952
3168.07602-2.07602
3297.621271.37873
3387.180570.819432
3487.54720.452797
3598.374050.625949
365.57.17195-1.67195
3757.16192-2.16192
3877.29961-0.29961
395.58.28199-2.78199
4097.874461.12554
4127.9401-5.9401
428.57.989690.510312
4397.782611.21739
448.58.220270.279732
4596.436252.56375
467.57.53617-0.0361669
47108.849361.15064
4897.289951.71005
497.58.18096-0.680955
5066.98217-0.982169
5110.58.079222.42078
528.57.287291.21271
5388.90612-0.906117
54105.661874.33813
5510.58.448412.05159
566.56.044330.455674
579.58.034541.46546
588.56.23522.2648
597.57.131070.368933
6057.37198-2.37198
6187.33290.667098
62107.495732.50427
6377.80369-0.803694
647.57.36070.139298
657.57.36070.139298
669.57.60861.8914
6767.64528-1.64528
68108.002731.99727
6977.91557-0.915574
7035.96832-2.96832
7167.83123-1.83123
7277.75893-0.758928
73108.355441.64456
7477.59694-0.596943
753.57.50669-4.00669
7687.571320.428682
77106.743793.25621
785.57.12812-1.62812
7966.09695-0.0969513
806.56.66843-0.168432
816.56.300350.199653
828.58.106770.393225
8346.44996-2.44996
849.57.199182.30082
8586.691231.30877
868.56.868661.63134
875.58.22792-2.72792
8877.50205-0.502047
8996.671692.32831
9087.261720.738283
91108.767521.23248
9286.451161.54884
9367.55378-1.55378
9487.419040.580965
9557.16839-2.16839
9696.080792.91921
974.56.45988-1.95988
988.56.179472.32053
9976.808460.191538
1009.58.575630.924366
1018.57.898590.601413
1027.56.337531.16247
1037.57.84843-0.348431
10457.65836-2.65836
10577.13142-0.131416
10688.54398-0.543977
1075.56.97805-1.47805
1088.57.104471.39553
1097.57.246780.253221
1109.57.980311.51969
11176.560540.439461
11287.894020.105977
1138.57.428531.07147
1143.56.39622-2.89622
1156.56.96986-0.469864
1166.56.89143-0.391425
11710.58.287032.21297
1188.56.184872.31513
11987.213070.786931
120106.893973.10603
121108.471581.52842
1229.57.862691.63731
12397.357211.64279
124109.171910.828085
1257.56.634980.865019
1264.57.51084-3.01084
1274.56.68931-2.18931
1280.56.02259-5.52259
1296.55.888140.611861
1304.57.64387-3.14387
1315.56.87719-1.37719
13256.55203-1.55203
13367.61733-1.61733
13446.77582-2.77582
13586.762361.23764
13610.58.448412.05159
1378.57.06241.4376
1386.56.68991-0.189906
13987.598530.401466
1408.58.93638-0.436383
1415.57.20738-1.70738
14278.14817-1.14817
14356.7106-1.7106
1443.56.49273-2.99273
14557.31609-2.31609
14697.395791.60421
1478.57.465661.03434
14857.88302-2.88302
1499.58.078641.42136
15036.21075-3.21075
1511.57.17987-5.67987
15266.87327-0.873268
1530.57.01859-6.51859
1546.56.044330.455674
1557.56.889980.610017
1564.56.56222-2.06222
15786.762361.23764
15897.589651.41035
1597.56.76080.739195
1608.56.949921.55008
16176.797290.202712
1629.57.278282.22172
1636.56.361160.138837
1649.56.735292.76471
16566.25793-0.257934
16687.414310.585694
1679.57.388762.11124
16887.430010.569993
16987.020270.979733
17097.19251.8075
17155.73654-0.736541

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.5 & 5.34302 & -4.84302 \tabularnewline
2 & 7.5 & 6.67906 & 0.820939 \tabularnewline
3 & 9 & 7.66156 & 1.33844 \tabularnewline
4 & 9.5 & 7.72654 & 1.77346 \tabularnewline
5 & 8.5 & 8.82679 & -0.326794 \tabularnewline
6 & 7 & 5.59878 & 1.40122 \tabularnewline
7 & 8 & 9.02342 & -1.02342 \tabularnewline
8 & 10 & 8.3218 & 1.6782 \tabularnewline
9 & 7 & 10.0951 & -3.09512 \tabularnewline
10 & 8.5 & 5.99335 & 2.50665 \tabularnewline
11 & 9 & 8.1597 & 0.840295 \tabularnewline
12 & 9.5 & 5.86518 & 3.63482 \tabularnewline
13 & 4 & 7.00999 & -3.00999 \tabularnewline
14 & 6 & 7.01596 & -1.01596 \tabularnewline
15 & 8 & 7.5852 & 0.414801 \tabularnewline
16 & 5.5 & 6.6507 & -1.1507 \tabularnewline
17 & 9.5 & 8.15959 & 1.34041 \tabularnewline
18 & 7.5 & 6.60027 & 0.899733 \tabularnewline
19 & 7 & 6.78071 & 0.219287 \tabularnewline
20 & 7.5 & 8.55924 & -1.05924 \tabularnewline
21 & 8 & 6.66999 & 1.33001 \tabularnewline
22 & 7 & 7.34146 & -0.341461 \tabularnewline
23 & 7 & 6.54116 & 0.458842 \tabularnewline
24 & 6 & 6.96217 & -0.962173 \tabularnewline
25 & 10 & 7.2213 & 2.7787 \tabularnewline
26 & 2.5 & 6.05751 & -3.55751 \tabularnewline
27 & 9 & 8.52845 & 0.471548 \tabularnewline
28 & 8 & 8.10712 & -0.107123 \tabularnewline
29 & 6 & 5.99896 & 0.00104232 \tabularnewline
30 & 8.5 & 6.75048 & 1.74952 \tabularnewline
31 & 6 & 8.07602 & -2.07602 \tabularnewline
32 & 9 & 7.62127 & 1.37873 \tabularnewline
33 & 8 & 7.18057 & 0.819432 \tabularnewline
34 & 8 & 7.5472 & 0.452797 \tabularnewline
35 & 9 & 8.37405 & 0.625949 \tabularnewline
36 & 5.5 & 7.17195 & -1.67195 \tabularnewline
37 & 5 & 7.16192 & -2.16192 \tabularnewline
38 & 7 & 7.29961 & -0.29961 \tabularnewline
39 & 5.5 & 8.28199 & -2.78199 \tabularnewline
40 & 9 & 7.87446 & 1.12554 \tabularnewline
41 & 2 & 7.9401 & -5.9401 \tabularnewline
42 & 8.5 & 7.98969 & 0.510312 \tabularnewline
43 & 9 & 7.78261 & 1.21739 \tabularnewline
44 & 8.5 & 8.22027 & 0.279732 \tabularnewline
45 & 9 & 6.43625 & 2.56375 \tabularnewline
46 & 7.5 & 7.53617 & -0.0361669 \tabularnewline
47 & 10 & 8.84936 & 1.15064 \tabularnewline
48 & 9 & 7.28995 & 1.71005 \tabularnewline
49 & 7.5 & 8.18096 & -0.680955 \tabularnewline
50 & 6 & 6.98217 & -0.982169 \tabularnewline
51 & 10.5 & 8.07922 & 2.42078 \tabularnewline
52 & 8.5 & 7.28729 & 1.21271 \tabularnewline
53 & 8 & 8.90612 & -0.906117 \tabularnewline
54 & 10 & 5.66187 & 4.33813 \tabularnewline
55 & 10.5 & 8.44841 & 2.05159 \tabularnewline
56 & 6.5 & 6.04433 & 0.455674 \tabularnewline
57 & 9.5 & 8.03454 & 1.46546 \tabularnewline
58 & 8.5 & 6.2352 & 2.2648 \tabularnewline
59 & 7.5 & 7.13107 & 0.368933 \tabularnewline
60 & 5 & 7.37198 & -2.37198 \tabularnewline
61 & 8 & 7.3329 & 0.667098 \tabularnewline
62 & 10 & 7.49573 & 2.50427 \tabularnewline
63 & 7 & 7.80369 & -0.803694 \tabularnewline
64 & 7.5 & 7.3607 & 0.139298 \tabularnewline
65 & 7.5 & 7.3607 & 0.139298 \tabularnewline
66 & 9.5 & 7.6086 & 1.8914 \tabularnewline
67 & 6 & 7.64528 & -1.64528 \tabularnewline
68 & 10 & 8.00273 & 1.99727 \tabularnewline
69 & 7 & 7.91557 & -0.915574 \tabularnewline
70 & 3 & 5.96832 & -2.96832 \tabularnewline
71 & 6 & 7.83123 & -1.83123 \tabularnewline
72 & 7 & 7.75893 & -0.758928 \tabularnewline
73 & 10 & 8.35544 & 1.64456 \tabularnewline
74 & 7 & 7.59694 & -0.596943 \tabularnewline
75 & 3.5 & 7.50669 & -4.00669 \tabularnewline
76 & 8 & 7.57132 & 0.428682 \tabularnewline
77 & 10 & 6.74379 & 3.25621 \tabularnewline
78 & 5.5 & 7.12812 & -1.62812 \tabularnewline
79 & 6 & 6.09695 & -0.0969513 \tabularnewline
80 & 6.5 & 6.66843 & -0.168432 \tabularnewline
81 & 6.5 & 6.30035 & 0.199653 \tabularnewline
82 & 8.5 & 8.10677 & 0.393225 \tabularnewline
83 & 4 & 6.44996 & -2.44996 \tabularnewline
84 & 9.5 & 7.19918 & 2.30082 \tabularnewline
85 & 8 & 6.69123 & 1.30877 \tabularnewline
86 & 8.5 & 6.86866 & 1.63134 \tabularnewline
87 & 5.5 & 8.22792 & -2.72792 \tabularnewline
88 & 7 & 7.50205 & -0.502047 \tabularnewline
89 & 9 & 6.67169 & 2.32831 \tabularnewline
90 & 8 & 7.26172 & 0.738283 \tabularnewline
91 & 10 & 8.76752 & 1.23248 \tabularnewline
92 & 8 & 6.45116 & 1.54884 \tabularnewline
93 & 6 & 7.55378 & -1.55378 \tabularnewline
94 & 8 & 7.41904 & 0.580965 \tabularnewline
95 & 5 & 7.16839 & -2.16839 \tabularnewline
96 & 9 & 6.08079 & 2.91921 \tabularnewline
97 & 4.5 & 6.45988 & -1.95988 \tabularnewline
98 & 8.5 & 6.17947 & 2.32053 \tabularnewline
99 & 7 & 6.80846 & 0.191538 \tabularnewline
100 & 9.5 & 8.57563 & 0.924366 \tabularnewline
101 & 8.5 & 7.89859 & 0.601413 \tabularnewline
102 & 7.5 & 6.33753 & 1.16247 \tabularnewline
103 & 7.5 & 7.84843 & -0.348431 \tabularnewline
104 & 5 & 7.65836 & -2.65836 \tabularnewline
105 & 7 & 7.13142 & -0.131416 \tabularnewline
106 & 8 & 8.54398 & -0.543977 \tabularnewline
107 & 5.5 & 6.97805 & -1.47805 \tabularnewline
108 & 8.5 & 7.10447 & 1.39553 \tabularnewline
109 & 7.5 & 7.24678 & 0.253221 \tabularnewline
110 & 9.5 & 7.98031 & 1.51969 \tabularnewline
111 & 7 & 6.56054 & 0.439461 \tabularnewline
112 & 8 & 7.89402 & 0.105977 \tabularnewline
113 & 8.5 & 7.42853 & 1.07147 \tabularnewline
114 & 3.5 & 6.39622 & -2.89622 \tabularnewline
115 & 6.5 & 6.96986 & -0.469864 \tabularnewline
116 & 6.5 & 6.89143 & -0.391425 \tabularnewline
117 & 10.5 & 8.28703 & 2.21297 \tabularnewline
118 & 8.5 & 6.18487 & 2.31513 \tabularnewline
119 & 8 & 7.21307 & 0.786931 \tabularnewline
120 & 10 & 6.89397 & 3.10603 \tabularnewline
121 & 10 & 8.47158 & 1.52842 \tabularnewline
122 & 9.5 & 7.86269 & 1.63731 \tabularnewline
123 & 9 & 7.35721 & 1.64279 \tabularnewline
124 & 10 & 9.17191 & 0.828085 \tabularnewline
125 & 7.5 & 6.63498 & 0.865019 \tabularnewline
126 & 4.5 & 7.51084 & -3.01084 \tabularnewline
127 & 4.5 & 6.68931 & -2.18931 \tabularnewline
128 & 0.5 & 6.02259 & -5.52259 \tabularnewline
129 & 6.5 & 5.88814 & 0.611861 \tabularnewline
130 & 4.5 & 7.64387 & -3.14387 \tabularnewline
131 & 5.5 & 6.87719 & -1.37719 \tabularnewline
132 & 5 & 6.55203 & -1.55203 \tabularnewline
133 & 6 & 7.61733 & -1.61733 \tabularnewline
134 & 4 & 6.77582 & -2.77582 \tabularnewline
135 & 8 & 6.76236 & 1.23764 \tabularnewline
136 & 10.5 & 8.44841 & 2.05159 \tabularnewline
137 & 8.5 & 7.0624 & 1.4376 \tabularnewline
138 & 6.5 & 6.68991 & -0.189906 \tabularnewline
139 & 8 & 7.59853 & 0.401466 \tabularnewline
140 & 8.5 & 8.93638 & -0.436383 \tabularnewline
141 & 5.5 & 7.20738 & -1.70738 \tabularnewline
142 & 7 & 8.14817 & -1.14817 \tabularnewline
143 & 5 & 6.7106 & -1.7106 \tabularnewline
144 & 3.5 & 6.49273 & -2.99273 \tabularnewline
145 & 5 & 7.31609 & -2.31609 \tabularnewline
146 & 9 & 7.39579 & 1.60421 \tabularnewline
147 & 8.5 & 7.46566 & 1.03434 \tabularnewline
148 & 5 & 7.88302 & -2.88302 \tabularnewline
149 & 9.5 & 8.07864 & 1.42136 \tabularnewline
150 & 3 & 6.21075 & -3.21075 \tabularnewline
151 & 1.5 & 7.17987 & -5.67987 \tabularnewline
152 & 6 & 6.87327 & -0.873268 \tabularnewline
153 & 0.5 & 7.01859 & -6.51859 \tabularnewline
154 & 6.5 & 6.04433 & 0.455674 \tabularnewline
155 & 7.5 & 6.88998 & 0.610017 \tabularnewline
156 & 4.5 & 6.56222 & -2.06222 \tabularnewline
157 & 8 & 6.76236 & 1.23764 \tabularnewline
158 & 9 & 7.58965 & 1.41035 \tabularnewline
159 & 7.5 & 6.7608 & 0.739195 \tabularnewline
160 & 8.5 & 6.94992 & 1.55008 \tabularnewline
161 & 7 & 6.79729 & 0.202712 \tabularnewline
162 & 9.5 & 7.27828 & 2.22172 \tabularnewline
163 & 6.5 & 6.36116 & 0.138837 \tabularnewline
164 & 9.5 & 6.73529 & 2.76471 \tabularnewline
165 & 6 & 6.25793 & -0.257934 \tabularnewline
166 & 8 & 7.41431 & 0.585694 \tabularnewline
167 & 9.5 & 7.38876 & 2.11124 \tabularnewline
168 & 8 & 7.43001 & 0.569993 \tabularnewline
169 & 8 & 7.02027 & 0.979733 \tabularnewline
170 & 9 & 7.1925 & 1.8075 \tabularnewline
171 & 5 & 5.73654 & -0.736541 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264855&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]5.34302[/C][C]-4.84302[/C][/ROW]
[ROW][C]2[/C][C]7.5[/C][C]6.67906[/C][C]0.820939[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]7.66156[/C][C]1.33844[/C][/ROW]
[ROW][C]4[/C][C]9.5[/C][C]7.72654[/C][C]1.77346[/C][/ROW]
[ROW][C]5[/C][C]8.5[/C][C]8.82679[/C][C]-0.326794[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]5.59878[/C][C]1.40122[/C][/ROW]
[ROW][C]7[/C][C]8[/C][C]9.02342[/C][C]-1.02342[/C][/ROW]
[ROW][C]8[/C][C]10[/C][C]8.3218[/C][C]1.6782[/C][/ROW]
[ROW][C]9[/C][C]7[/C][C]10.0951[/C][C]-3.09512[/C][/ROW]
[ROW][C]10[/C][C]8.5[/C][C]5.99335[/C][C]2.50665[/C][/ROW]
[ROW][C]11[/C][C]9[/C][C]8.1597[/C][C]0.840295[/C][/ROW]
[ROW][C]12[/C][C]9.5[/C][C]5.86518[/C][C]3.63482[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]7.00999[/C][C]-3.00999[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]7.01596[/C][C]-1.01596[/C][/ROW]
[ROW][C]15[/C][C]8[/C][C]7.5852[/C][C]0.414801[/C][/ROW]
[ROW][C]16[/C][C]5.5[/C][C]6.6507[/C][C]-1.1507[/C][/ROW]
[ROW][C]17[/C][C]9.5[/C][C]8.15959[/C][C]1.34041[/C][/ROW]
[ROW][C]18[/C][C]7.5[/C][C]6.60027[/C][C]0.899733[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]6.78071[/C][C]0.219287[/C][/ROW]
[ROW][C]20[/C][C]7.5[/C][C]8.55924[/C][C]-1.05924[/C][/ROW]
[ROW][C]21[/C][C]8[/C][C]6.66999[/C][C]1.33001[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]7.34146[/C][C]-0.341461[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]6.54116[/C][C]0.458842[/C][/ROW]
[ROW][C]24[/C][C]6[/C][C]6.96217[/C][C]-0.962173[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]7.2213[/C][C]2.7787[/C][/ROW]
[ROW][C]26[/C][C]2.5[/C][C]6.05751[/C][C]-3.55751[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]8.52845[/C][C]0.471548[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]8.10712[/C][C]-0.107123[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]5.99896[/C][C]0.00104232[/C][/ROW]
[ROW][C]30[/C][C]8.5[/C][C]6.75048[/C][C]1.74952[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]8.07602[/C][C]-2.07602[/C][/ROW]
[ROW][C]32[/C][C]9[/C][C]7.62127[/C][C]1.37873[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]7.18057[/C][C]0.819432[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]7.5472[/C][C]0.452797[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]8.37405[/C][C]0.625949[/C][/ROW]
[ROW][C]36[/C][C]5.5[/C][C]7.17195[/C][C]-1.67195[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]7.16192[/C][C]-2.16192[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]7.29961[/C][C]-0.29961[/C][/ROW]
[ROW][C]39[/C][C]5.5[/C][C]8.28199[/C][C]-2.78199[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]7.87446[/C][C]1.12554[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]7.9401[/C][C]-5.9401[/C][/ROW]
[ROW][C]42[/C][C]8.5[/C][C]7.98969[/C][C]0.510312[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]7.78261[/C][C]1.21739[/C][/ROW]
[ROW][C]44[/C][C]8.5[/C][C]8.22027[/C][C]0.279732[/C][/ROW]
[ROW][C]45[/C][C]9[/C][C]6.43625[/C][C]2.56375[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]7.53617[/C][C]-0.0361669[/C][/ROW]
[ROW][C]47[/C][C]10[/C][C]8.84936[/C][C]1.15064[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]7.28995[/C][C]1.71005[/C][/ROW]
[ROW][C]49[/C][C]7.5[/C][C]8.18096[/C][C]-0.680955[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]6.98217[/C][C]-0.982169[/C][/ROW]
[ROW][C]51[/C][C]10.5[/C][C]8.07922[/C][C]2.42078[/C][/ROW]
[ROW][C]52[/C][C]8.5[/C][C]7.28729[/C][C]1.21271[/C][/ROW]
[ROW][C]53[/C][C]8[/C][C]8.90612[/C][C]-0.906117[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]5.66187[/C][C]4.33813[/C][/ROW]
[ROW][C]55[/C][C]10.5[/C][C]8.44841[/C][C]2.05159[/C][/ROW]
[ROW][C]56[/C][C]6.5[/C][C]6.04433[/C][C]0.455674[/C][/ROW]
[ROW][C]57[/C][C]9.5[/C][C]8.03454[/C][C]1.46546[/C][/ROW]
[ROW][C]58[/C][C]8.5[/C][C]6.2352[/C][C]2.2648[/C][/ROW]
[ROW][C]59[/C][C]7.5[/C][C]7.13107[/C][C]0.368933[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]7.37198[/C][C]-2.37198[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]7.3329[/C][C]0.667098[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]7.49573[/C][C]2.50427[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]7.80369[/C][C]-0.803694[/C][/ROW]
[ROW][C]64[/C][C]7.5[/C][C]7.3607[/C][C]0.139298[/C][/ROW]
[ROW][C]65[/C][C]7.5[/C][C]7.3607[/C][C]0.139298[/C][/ROW]
[ROW][C]66[/C][C]9.5[/C][C]7.6086[/C][C]1.8914[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]7.64528[/C][C]-1.64528[/C][/ROW]
[ROW][C]68[/C][C]10[/C][C]8.00273[/C][C]1.99727[/C][/ROW]
[ROW][C]69[/C][C]7[/C][C]7.91557[/C][C]-0.915574[/C][/ROW]
[ROW][C]70[/C][C]3[/C][C]5.96832[/C][C]-2.96832[/C][/ROW]
[ROW][C]71[/C][C]6[/C][C]7.83123[/C][C]-1.83123[/C][/ROW]
[ROW][C]72[/C][C]7[/C][C]7.75893[/C][C]-0.758928[/C][/ROW]
[ROW][C]73[/C][C]10[/C][C]8.35544[/C][C]1.64456[/C][/ROW]
[ROW][C]74[/C][C]7[/C][C]7.59694[/C][C]-0.596943[/C][/ROW]
[ROW][C]75[/C][C]3.5[/C][C]7.50669[/C][C]-4.00669[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]7.57132[/C][C]0.428682[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]6.74379[/C][C]3.25621[/C][/ROW]
[ROW][C]78[/C][C]5.5[/C][C]7.12812[/C][C]-1.62812[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]6.09695[/C][C]-0.0969513[/C][/ROW]
[ROW][C]80[/C][C]6.5[/C][C]6.66843[/C][C]-0.168432[/C][/ROW]
[ROW][C]81[/C][C]6.5[/C][C]6.30035[/C][C]0.199653[/C][/ROW]
[ROW][C]82[/C][C]8.5[/C][C]8.10677[/C][C]0.393225[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]6.44996[/C][C]-2.44996[/C][/ROW]
[ROW][C]84[/C][C]9.5[/C][C]7.19918[/C][C]2.30082[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]6.69123[/C][C]1.30877[/C][/ROW]
[ROW][C]86[/C][C]8.5[/C][C]6.86866[/C][C]1.63134[/C][/ROW]
[ROW][C]87[/C][C]5.5[/C][C]8.22792[/C][C]-2.72792[/C][/ROW]
[ROW][C]88[/C][C]7[/C][C]7.50205[/C][C]-0.502047[/C][/ROW]
[ROW][C]89[/C][C]9[/C][C]6.67169[/C][C]2.32831[/C][/ROW]
[ROW][C]90[/C][C]8[/C][C]7.26172[/C][C]0.738283[/C][/ROW]
[ROW][C]91[/C][C]10[/C][C]8.76752[/C][C]1.23248[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]6.45116[/C][C]1.54884[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]7.55378[/C][C]-1.55378[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]7.41904[/C][C]0.580965[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]7.16839[/C][C]-2.16839[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]6.08079[/C][C]2.91921[/C][/ROW]
[ROW][C]97[/C][C]4.5[/C][C]6.45988[/C][C]-1.95988[/C][/ROW]
[ROW][C]98[/C][C]8.5[/C][C]6.17947[/C][C]2.32053[/C][/ROW]
[ROW][C]99[/C][C]7[/C][C]6.80846[/C][C]0.191538[/C][/ROW]
[ROW][C]100[/C][C]9.5[/C][C]8.57563[/C][C]0.924366[/C][/ROW]
[ROW][C]101[/C][C]8.5[/C][C]7.89859[/C][C]0.601413[/C][/ROW]
[ROW][C]102[/C][C]7.5[/C][C]6.33753[/C][C]1.16247[/C][/ROW]
[ROW][C]103[/C][C]7.5[/C][C]7.84843[/C][C]-0.348431[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]7.65836[/C][C]-2.65836[/C][/ROW]
[ROW][C]105[/C][C]7[/C][C]7.13142[/C][C]-0.131416[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]8.54398[/C][C]-0.543977[/C][/ROW]
[ROW][C]107[/C][C]5.5[/C][C]6.97805[/C][C]-1.47805[/C][/ROW]
[ROW][C]108[/C][C]8.5[/C][C]7.10447[/C][C]1.39553[/C][/ROW]
[ROW][C]109[/C][C]7.5[/C][C]7.24678[/C][C]0.253221[/C][/ROW]
[ROW][C]110[/C][C]9.5[/C][C]7.98031[/C][C]1.51969[/C][/ROW]
[ROW][C]111[/C][C]7[/C][C]6.56054[/C][C]0.439461[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]7.89402[/C][C]0.105977[/C][/ROW]
[ROW][C]113[/C][C]8.5[/C][C]7.42853[/C][C]1.07147[/C][/ROW]
[ROW][C]114[/C][C]3.5[/C][C]6.39622[/C][C]-2.89622[/C][/ROW]
[ROW][C]115[/C][C]6.5[/C][C]6.96986[/C][C]-0.469864[/C][/ROW]
[ROW][C]116[/C][C]6.5[/C][C]6.89143[/C][C]-0.391425[/C][/ROW]
[ROW][C]117[/C][C]10.5[/C][C]8.28703[/C][C]2.21297[/C][/ROW]
[ROW][C]118[/C][C]8.5[/C][C]6.18487[/C][C]2.31513[/C][/ROW]
[ROW][C]119[/C][C]8[/C][C]7.21307[/C][C]0.786931[/C][/ROW]
[ROW][C]120[/C][C]10[/C][C]6.89397[/C][C]3.10603[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]8.47158[/C][C]1.52842[/C][/ROW]
[ROW][C]122[/C][C]9.5[/C][C]7.86269[/C][C]1.63731[/C][/ROW]
[ROW][C]123[/C][C]9[/C][C]7.35721[/C][C]1.64279[/C][/ROW]
[ROW][C]124[/C][C]10[/C][C]9.17191[/C][C]0.828085[/C][/ROW]
[ROW][C]125[/C][C]7.5[/C][C]6.63498[/C][C]0.865019[/C][/ROW]
[ROW][C]126[/C][C]4.5[/C][C]7.51084[/C][C]-3.01084[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]6.68931[/C][C]-2.18931[/C][/ROW]
[ROW][C]128[/C][C]0.5[/C][C]6.02259[/C][C]-5.52259[/C][/ROW]
[ROW][C]129[/C][C]6.5[/C][C]5.88814[/C][C]0.611861[/C][/ROW]
[ROW][C]130[/C][C]4.5[/C][C]7.64387[/C][C]-3.14387[/C][/ROW]
[ROW][C]131[/C][C]5.5[/C][C]6.87719[/C][C]-1.37719[/C][/ROW]
[ROW][C]132[/C][C]5[/C][C]6.55203[/C][C]-1.55203[/C][/ROW]
[ROW][C]133[/C][C]6[/C][C]7.61733[/C][C]-1.61733[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]6.77582[/C][C]-2.77582[/C][/ROW]
[ROW][C]135[/C][C]8[/C][C]6.76236[/C][C]1.23764[/C][/ROW]
[ROW][C]136[/C][C]10.5[/C][C]8.44841[/C][C]2.05159[/C][/ROW]
[ROW][C]137[/C][C]8.5[/C][C]7.0624[/C][C]1.4376[/C][/ROW]
[ROW][C]138[/C][C]6.5[/C][C]6.68991[/C][C]-0.189906[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]7.59853[/C][C]0.401466[/C][/ROW]
[ROW][C]140[/C][C]8.5[/C][C]8.93638[/C][C]-0.436383[/C][/ROW]
[ROW][C]141[/C][C]5.5[/C][C]7.20738[/C][C]-1.70738[/C][/ROW]
[ROW][C]142[/C][C]7[/C][C]8.14817[/C][C]-1.14817[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]6.7106[/C][C]-1.7106[/C][/ROW]
[ROW][C]144[/C][C]3.5[/C][C]6.49273[/C][C]-2.99273[/C][/ROW]
[ROW][C]145[/C][C]5[/C][C]7.31609[/C][C]-2.31609[/C][/ROW]
[ROW][C]146[/C][C]9[/C][C]7.39579[/C][C]1.60421[/C][/ROW]
[ROW][C]147[/C][C]8.5[/C][C]7.46566[/C][C]1.03434[/C][/ROW]
[ROW][C]148[/C][C]5[/C][C]7.88302[/C][C]-2.88302[/C][/ROW]
[ROW][C]149[/C][C]9.5[/C][C]8.07864[/C][C]1.42136[/C][/ROW]
[ROW][C]150[/C][C]3[/C][C]6.21075[/C][C]-3.21075[/C][/ROW]
[ROW][C]151[/C][C]1.5[/C][C]7.17987[/C][C]-5.67987[/C][/ROW]
[ROW][C]152[/C][C]6[/C][C]6.87327[/C][C]-0.873268[/C][/ROW]
[ROW][C]153[/C][C]0.5[/C][C]7.01859[/C][C]-6.51859[/C][/ROW]
[ROW][C]154[/C][C]6.5[/C][C]6.04433[/C][C]0.455674[/C][/ROW]
[ROW][C]155[/C][C]7.5[/C][C]6.88998[/C][C]0.610017[/C][/ROW]
[ROW][C]156[/C][C]4.5[/C][C]6.56222[/C][C]-2.06222[/C][/ROW]
[ROW][C]157[/C][C]8[/C][C]6.76236[/C][C]1.23764[/C][/ROW]
[ROW][C]158[/C][C]9[/C][C]7.58965[/C][C]1.41035[/C][/ROW]
[ROW][C]159[/C][C]7.5[/C][C]6.7608[/C][C]0.739195[/C][/ROW]
[ROW][C]160[/C][C]8.5[/C][C]6.94992[/C][C]1.55008[/C][/ROW]
[ROW][C]161[/C][C]7[/C][C]6.79729[/C][C]0.202712[/C][/ROW]
[ROW][C]162[/C][C]9.5[/C][C]7.27828[/C][C]2.22172[/C][/ROW]
[ROW][C]163[/C][C]6.5[/C][C]6.36116[/C][C]0.138837[/C][/ROW]
[ROW][C]164[/C][C]9.5[/C][C]6.73529[/C][C]2.76471[/C][/ROW]
[ROW][C]165[/C][C]6[/C][C]6.25793[/C][C]-0.257934[/C][/ROW]
[ROW][C]166[/C][C]8[/C][C]7.41431[/C][C]0.585694[/C][/ROW]
[ROW][C]167[/C][C]9.5[/C][C]7.38876[/C][C]2.11124[/C][/ROW]
[ROW][C]168[/C][C]8[/C][C]7.43001[/C][C]0.569993[/C][/ROW]
[ROW][C]169[/C][C]8[/C][C]7.02027[/C][C]0.979733[/C][/ROW]
[ROW][C]170[/C][C]9[/C][C]7.1925[/C][C]1.8075[/C][/ROW]
[ROW][C]171[/C][C]5[/C][C]5.73654[/C][C]-0.736541[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264855&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264855&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
10.55.34302-4.84302
27.56.679060.820939
397.661561.33844
49.57.726541.77346
58.58.82679-0.326794
675.598781.40122
789.02342-1.02342
8108.32181.6782
9710.0951-3.09512
108.55.993352.50665
1198.15970.840295
129.55.865183.63482
1347.00999-3.00999
1467.01596-1.01596
1587.58520.414801
165.56.6507-1.1507
179.58.159591.34041
187.56.600270.899733
1976.780710.219287
207.58.55924-1.05924
2186.669991.33001
2277.34146-0.341461
2376.541160.458842
2466.96217-0.962173
25107.22132.7787
262.56.05751-3.55751
2798.528450.471548
2888.10712-0.107123
2965.998960.00104232
308.56.750481.74952
3168.07602-2.07602
3297.621271.37873
3387.180570.819432
3487.54720.452797
3598.374050.625949
365.57.17195-1.67195
3757.16192-2.16192
3877.29961-0.29961
395.58.28199-2.78199
4097.874461.12554
4127.9401-5.9401
428.57.989690.510312
4397.782611.21739
448.58.220270.279732
4596.436252.56375
467.57.53617-0.0361669
47108.849361.15064
4897.289951.71005
497.58.18096-0.680955
5066.98217-0.982169
5110.58.079222.42078
528.57.287291.21271
5388.90612-0.906117
54105.661874.33813
5510.58.448412.05159
566.56.044330.455674
579.58.034541.46546
588.56.23522.2648
597.57.131070.368933
6057.37198-2.37198
6187.33290.667098
62107.495732.50427
6377.80369-0.803694
647.57.36070.139298
657.57.36070.139298
669.57.60861.8914
6767.64528-1.64528
68108.002731.99727
6977.91557-0.915574
7035.96832-2.96832
7167.83123-1.83123
7277.75893-0.758928
73108.355441.64456
7477.59694-0.596943
753.57.50669-4.00669
7687.571320.428682
77106.743793.25621
785.57.12812-1.62812
7966.09695-0.0969513
806.56.66843-0.168432
816.56.300350.199653
828.58.106770.393225
8346.44996-2.44996
849.57.199182.30082
8586.691231.30877
868.56.868661.63134
875.58.22792-2.72792
8877.50205-0.502047
8996.671692.32831
9087.261720.738283
91108.767521.23248
9286.451161.54884
9367.55378-1.55378
9487.419040.580965
9557.16839-2.16839
9696.080792.91921
974.56.45988-1.95988
988.56.179472.32053
9976.808460.191538
1009.58.575630.924366
1018.57.898590.601413
1027.56.337531.16247
1037.57.84843-0.348431
10457.65836-2.65836
10577.13142-0.131416
10688.54398-0.543977
1075.56.97805-1.47805
1088.57.104471.39553
1097.57.246780.253221
1109.57.980311.51969
11176.560540.439461
11287.894020.105977
1138.57.428531.07147
1143.56.39622-2.89622
1156.56.96986-0.469864
1166.56.89143-0.391425
11710.58.287032.21297
1188.56.184872.31513
11987.213070.786931
120106.893973.10603
121108.471581.52842
1229.57.862691.63731
12397.357211.64279
124109.171910.828085
1257.56.634980.865019
1264.57.51084-3.01084
1274.56.68931-2.18931
1280.56.02259-5.52259
1296.55.888140.611861
1304.57.64387-3.14387
1315.56.87719-1.37719
13256.55203-1.55203
13367.61733-1.61733
13446.77582-2.77582
13586.762361.23764
13610.58.448412.05159
1378.57.06241.4376
1386.56.68991-0.189906
13987.598530.401466
1408.58.93638-0.436383
1415.57.20738-1.70738
14278.14817-1.14817
14356.7106-1.7106
1443.56.49273-2.99273
14557.31609-2.31609
14697.395791.60421
1478.57.465661.03434
14857.88302-2.88302
1499.58.078641.42136
15036.21075-3.21075
1511.57.17987-5.67987
15266.87327-0.873268
1530.57.01859-6.51859
1546.56.044330.455674
1557.56.889980.610017
1564.56.56222-2.06222
15786.762361.23764
15897.589651.41035
1597.56.76080.739195
1608.56.949921.55008
16176.797290.202712
1629.57.278282.22172
1636.56.361160.138837
1649.56.735292.76471
16566.25793-0.257934
16687.414310.585694
1679.57.388762.11124
16887.430010.569993
16987.020270.979733
17097.19251.8075
17155.73654-0.736541






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8101390.3797220.189861
100.6927770.6144470.307223
110.561920.8761610.43808
120.54810.90380.4519
130.7231010.5537970.276899
140.8067640.3864720.193236
150.7532730.4934550.246727
160.7991680.4016630.200832
170.775420.449160.22458
180.7097390.5805220.290261
190.6563850.687230.343615
200.5909840.8180320.409016
210.6155430.7689140.384457
220.5497380.9005240.450262
230.4768440.9536880.523156
240.4207090.8414190.579291
250.4288580.8577150.571142
260.5474170.9051670.452583
270.4911270.9822550.508873
280.4475610.8951230.552439
290.3871530.7743060.612847
300.3361770.6723530.663823
310.4203060.8406110.579694
320.3804140.7608280.619586
330.3308430.6616850.669157
340.2838170.5676350.716183
350.2363850.472770.763615
360.2564410.5128830.743559
370.3453710.6907420.654629
380.2957980.5915970.704202
390.3773930.7547850.622607
400.3316640.6633290.668336
410.7781190.4437610.221881
420.7439190.5121620.256081
430.7146090.5707830.285391
440.6697360.6605280.330264
450.7000810.5998380.299919
460.6544410.6911170.345559
470.6274420.7451160.372558
480.6130460.7739080.386954
490.5685440.8629110.431456
500.5360490.9279020.463951
510.5563450.8873110.443655
520.5334370.9331250.466563
530.497770.9955390.50223
540.658330.683340.34167
550.6601520.6796960.339848
560.6151670.7696660.384833
570.5909750.818050.409025
580.5937690.8124620.406231
590.5476290.9047420.452371
600.5768410.8463180.423159
610.5338190.9323630.466181
620.55030.89940.4497
630.5168020.9663950.483198
640.4708620.9417250.529138
650.42570.8513990.5743
660.4148240.8296470.585176
670.4083470.8166940.591653
680.4094140.8188280.590586
690.3792480.7584960.620752
700.4471230.8942460.552877
710.4516570.9033150.548343
720.4162250.8324510.583775
730.400930.8018610.59907
740.3628620.7257240.637138
750.5072070.9855860.492793
760.4632680.9265360.536732
770.5395360.9209280.460464
780.5289810.9420370.471019
790.4852520.9705050.514748
800.4413840.8827680.558616
810.3985230.7970460.601477
820.3566880.7133760.643312
830.3784080.7568150.621592
840.3915820.7831640.608418
850.3679850.7359710.632015
860.3558560.7117110.644144
870.3934240.7868480.606576
880.3546450.7092890.645355
890.3634950.726990.636505
900.326220.6524410.67378
910.2987830.5975660.701217
920.2873470.5746930.712653
930.2730840.5461670.726916
940.2416760.4833530.758324
950.2511140.5022290.748886
960.3000250.6000490.699975
970.2954370.5908740.704563
980.322110.6442210.67789
990.2835210.5670420.716479
1000.2530160.5060330.746984
1010.219520.4390390.78048
1020.2014450.402890.798555
1030.1726510.3453030.827349
1040.20360.40720.7964
1050.1736380.3472760.826362
1060.1520170.3040350.847983
1070.1393570.2787150.860643
1080.1261070.2522150.873893
1090.1043090.2086180.895691
1100.09602860.1920570.903971
1110.07968690.1593740.920313
1120.06411380.1282280.935886
1130.05435970.1087190.94564
1140.06364840.1272970.936352
1150.05086030.1017210.94914
1160.04053830.08107650.959462
1170.04260530.08521060.957395
1180.0523140.1046280.947686
1190.04763970.09527930.95236
1200.06682350.1336470.933176
1210.05696140.1139230.943039
1220.05660550.1132110.943395
1230.06201510.124030.937985
1240.04960750.0992150.950393
1250.04343610.08687220.956564
1260.04887780.09775570.951122
1270.04516940.09033880.954831
1280.1485350.297070.851465
1290.1297640.2595290.870236
1300.1629630.3259270.837037
1310.1435250.287050.856475
1320.1252390.2504780.874761
1330.1134060.2268120.886594
1340.1360110.2720210.863989
1350.1224490.2448980.877551
1360.1136470.2272930.886353
1370.1220980.2441970.877902
1380.09766360.1953270.902336
1390.07508890.1501780.924911
1400.05693660.1138730.943063
1410.045160.090320.95484
1420.03822520.07645040.961775
1430.02990890.05981780.970091
1440.03216470.06432940.967835
1450.04239960.08479930.9576
1460.0321170.06423410.967883
1470.02269680.04539360.977303
1480.03490340.06980690.965097
1490.02874750.0574950.971253
1500.03878470.07756940.961215
1510.5039060.9921880.496094
1520.4671470.9342950.532853
1530.9982890.003422030.00171102
1540.999940.0001195585.9779e-05
1550.9998570.0002863450.000143173
1560.9998460.0003082260.000154113
1570.9998230.0003544440.000177222
1580.9993140.001371420.000685711
1590.9974170.005166010.002583
1600.9937960.01240770.00620383
1610.9782240.04355110.0217756
1620.9327470.1345060.0672532

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.810139 & 0.379722 & 0.189861 \tabularnewline
10 & 0.692777 & 0.614447 & 0.307223 \tabularnewline
11 & 0.56192 & 0.876161 & 0.43808 \tabularnewline
12 & 0.5481 & 0.9038 & 0.4519 \tabularnewline
13 & 0.723101 & 0.553797 & 0.276899 \tabularnewline
14 & 0.806764 & 0.386472 & 0.193236 \tabularnewline
15 & 0.753273 & 0.493455 & 0.246727 \tabularnewline
16 & 0.799168 & 0.401663 & 0.200832 \tabularnewline
17 & 0.77542 & 0.44916 & 0.22458 \tabularnewline
18 & 0.709739 & 0.580522 & 0.290261 \tabularnewline
19 & 0.656385 & 0.68723 & 0.343615 \tabularnewline
20 & 0.590984 & 0.818032 & 0.409016 \tabularnewline
21 & 0.615543 & 0.768914 & 0.384457 \tabularnewline
22 & 0.549738 & 0.900524 & 0.450262 \tabularnewline
23 & 0.476844 & 0.953688 & 0.523156 \tabularnewline
24 & 0.420709 & 0.841419 & 0.579291 \tabularnewline
25 & 0.428858 & 0.857715 & 0.571142 \tabularnewline
26 & 0.547417 & 0.905167 & 0.452583 \tabularnewline
27 & 0.491127 & 0.982255 & 0.508873 \tabularnewline
28 & 0.447561 & 0.895123 & 0.552439 \tabularnewline
29 & 0.387153 & 0.774306 & 0.612847 \tabularnewline
30 & 0.336177 & 0.672353 & 0.663823 \tabularnewline
31 & 0.420306 & 0.840611 & 0.579694 \tabularnewline
32 & 0.380414 & 0.760828 & 0.619586 \tabularnewline
33 & 0.330843 & 0.661685 & 0.669157 \tabularnewline
34 & 0.283817 & 0.567635 & 0.716183 \tabularnewline
35 & 0.236385 & 0.47277 & 0.763615 \tabularnewline
36 & 0.256441 & 0.512883 & 0.743559 \tabularnewline
37 & 0.345371 & 0.690742 & 0.654629 \tabularnewline
38 & 0.295798 & 0.591597 & 0.704202 \tabularnewline
39 & 0.377393 & 0.754785 & 0.622607 \tabularnewline
40 & 0.331664 & 0.663329 & 0.668336 \tabularnewline
41 & 0.778119 & 0.443761 & 0.221881 \tabularnewline
42 & 0.743919 & 0.512162 & 0.256081 \tabularnewline
43 & 0.714609 & 0.570783 & 0.285391 \tabularnewline
44 & 0.669736 & 0.660528 & 0.330264 \tabularnewline
45 & 0.700081 & 0.599838 & 0.299919 \tabularnewline
46 & 0.654441 & 0.691117 & 0.345559 \tabularnewline
47 & 0.627442 & 0.745116 & 0.372558 \tabularnewline
48 & 0.613046 & 0.773908 & 0.386954 \tabularnewline
49 & 0.568544 & 0.862911 & 0.431456 \tabularnewline
50 & 0.536049 & 0.927902 & 0.463951 \tabularnewline
51 & 0.556345 & 0.887311 & 0.443655 \tabularnewline
52 & 0.533437 & 0.933125 & 0.466563 \tabularnewline
53 & 0.49777 & 0.995539 & 0.50223 \tabularnewline
54 & 0.65833 & 0.68334 & 0.34167 \tabularnewline
55 & 0.660152 & 0.679696 & 0.339848 \tabularnewline
56 & 0.615167 & 0.769666 & 0.384833 \tabularnewline
57 & 0.590975 & 0.81805 & 0.409025 \tabularnewline
58 & 0.593769 & 0.812462 & 0.406231 \tabularnewline
59 & 0.547629 & 0.904742 & 0.452371 \tabularnewline
60 & 0.576841 & 0.846318 & 0.423159 \tabularnewline
61 & 0.533819 & 0.932363 & 0.466181 \tabularnewline
62 & 0.5503 & 0.8994 & 0.4497 \tabularnewline
63 & 0.516802 & 0.966395 & 0.483198 \tabularnewline
64 & 0.470862 & 0.941725 & 0.529138 \tabularnewline
65 & 0.4257 & 0.851399 & 0.5743 \tabularnewline
66 & 0.414824 & 0.829647 & 0.585176 \tabularnewline
67 & 0.408347 & 0.816694 & 0.591653 \tabularnewline
68 & 0.409414 & 0.818828 & 0.590586 \tabularnewline
69 & 0.379248 & 0.758496 & 0.620752 \tabularnewline
70 & 0.447123 & 0.894246 & 0.552877 \tabularnewline
71 & 0.451657 & 0.903315 & 0.548343 \tabularnewline
72 & 0.416225 & 0.832451 & 0.583775 \tabularnewline
73 & 0.40093 & 0.801861 & 0.59907 \tabularnewline
74 & 0.362862 & 0.725724 & 0.637138 \tabularnewline
75 & 0.507207 & 0.985586 & 0.492793 \tabularnewline
76 & 0.463268 & 0.926536 & 0.536732 \tabularnewline
77 & 0.539536 & 0.920928 & 0.460464 \tabularnewline
78 & 0.528981 & 0.942037 & 0.471019 \tabularnewline
79 & 0.485252 & 0.970505 & 0.514748 \tabularnewline
80 & 0.441384 & 0.882768 & 0.558616 \tabularnewline
81 & 0.398523 & 0.797046 & 0.601477 \tabularnewline
82 & 0.356688 & 0.713376 & 0.643312 \tabularnewline
83 & 0.378408 & 0.756815 & 0.621592 \tabularnewline
84 & 0.391582 & 0.783164 & 0.608418 \tabularnewline
85 & 0.367985 & 0.735971 & 0.632015 \tabularnewline
86 & 0.355856 & 0.711711 & 0.644144 \tabularnewline
87 & 0.393424 & 0.786848 & 0.606576 \tabularnewline
88 & 0.354645 & 0.709289 & 0.645355 \tabularnewline
89 & 0.363495 & 0.72699 & 0.636505 \tabularnewline
90 & 0.32622 & 0.652441 & 0.67378 \tabularnewline
91 & 0.298783 & 0.597566 & 0.701217 \tabularnewline
92 & 0.287347 & 0.574693 & 0.712653 \tabularnewline
93 & 0.273084 & 0.546167 & 0.726916 \tabularnewline
94 & 0.241676 & 0.483353 & 0.758324 \tabularnewline
95 & 0.251114 & 0.502229 & 0.748886 \tabularnewline
96 & 0.300025 & 0.600049 & 0.699975 \tabularnewline
97 & 0.295437 & 0.590874 & 0.704563 \tabularnewline
98 & 0.32211 & 0.644221 & 0.67789 \tabularnewline
99 & 0.283521 & 0.567042 & 0.716479 \tabularnewline
100 & 0.253016 & 0.506033 & 0.746984 \tabularnewline
101 & 0.21952 & 0.439039 & 0.78048 \tabularnewline
102 & 0.201445 & 0.40289 & 0.798555 \tabularnewline
103 & 0.172651 & 0.345303 & 0.827349 \tabularnewline
104 & 0.2036 & 0.4072 & 0.7964 \tabularnewline
105 & 0.173638 & 0.347276 & 0.826362 \tabularnewline
106 & 0.152017 & 0.304035 & 0.847983 \tabularnewline
107 & 0.139357 & 0.278715 & 0.860643 \tabularnewline
108 & 0.126107 & 0.252215 & 0.873893 \tabularnewline
109 & 0.104309 & 0.208618 & 0.895691 \tabularnewline
110 & 0.0960286 & 0.192057 & 0.903971 \tabularnewline
111 & 0.0796869 & 0.159374 & 0.920313 \tabularnewline
112 & 0.0641138 & 0.128228 & 0.935886 \tabularnewline
113 & 0.0543597 & 0.108719 & 0.94564 \tabularnewline
114 & 0.0636484 & 0.127297 & 0.936352 \tabularnewline
115 & 0.0508603 & 0.101721 & 0.94914 \tabularnewline
116 & 0.0405383 & 0.0810765 & 0.959462 \tabularnewline
117 & 0.0426053 & 0.0852106 & 0.957395 \tabularnewline
118 & 0.052314 & 0.104628 & 0.947686 \tabularnewline
119 & 0.0476397 & 0.0952793 & 0.95236 \tabularnewline
120 & 0.0668235 & 0.133647 & 0.933176 \tabularnewline
121 & 0.0569614 & 0.113923 & 0.943039 \tabularnewline
122 & 0.0566055 & 0.113211 & 0.943395 \tabularnewline
123 & 0.0620151 & 0.12403 & 0.937985 \tabularnewline
124 & 0.0496075 & 0.099215 & 0.950393 \tabularnewline
125 & 0.0434361 & 0.0868722 & 0.956564 \tabularnewline
126 & 0.0488778 & 0.0977557 & 0.951122 \tabularnewline
127 & 0.0451694 & 0.0903388 & 0.954831 \tabularnewline
128 & 0.148535 & 0.29707 & 0.851465 \tabularnewline
129 & 0.129764 & 0.259529 & 0.870236 \tabularnewline
130 & 0.162963 & 0.325927 & 0.837037 \tabularnewline
131 & 0.143525 & 0.28705 & 0.856475 \tabularnewline
132 & 0.125239 & 0.250478 & 0.874761 \tabularnewline
133 & 0.113406 & 0.226812 & 0.886594 \tabularnewline
134 & 0.136011 & 0.272021 & 0.863989 \tabularnewline
135 & 0.122449 & 0.244898 & 0.877551 \tabularnewline
136 & 0.113647 & 0.227293 & 0.886353 \tabularnewline
137 & 0.122098 & 0.244197 & 0.877902 \tabularnewline
138 & 0.0976636 & 0.195327 & 0.902336 \tabularnewline
139 & 0.0750889 & 0.150178 & 0.924911 \tabularnewline
140 & 0.0569366 & 0.113873 & 0.943063 \tabularnewline
141 & 0.04516 & 0.09032 & 0.95484 \tabularnewline
142 & 0.0382252 & 0.0764504 & 0.961775 \tabularnewline
143 & 0.0299089 & 0.0598178 & 0.970091 \tabularnewline
144 & 0.0321647 & 0.0643294 & 0.967835 \tabularnewline
145 & 0.0423996 & 0.0847993 & 0.9576 \tabularnewline
146 & 0.032117 & 0.0642341 & 0.967883 \tabularnewline
147 & 0.0226968 & 0.0453936 & 0.977303 \tabularnewline
148 & 0.0349034 & 0.0698069 & 0.965097 \tabularnewline
149 & 0.0287475 & 0.057495 & 0.971253 \tabularnewline
150 & 0.0387847 & 0.0775694 & 0.961215 \tabularnewline
151 & 0.503906 & 0.992188 & 0.496094 \tabularnewline
152 & 0.467147 & 0.934295 & 0.532853 \tabularnewline
153 & 0.998289 & 0.00342203 & 0.00171102 \tabularnewline
154 & 0.99994 & 0.000119558 & 5.9779e-05 \tabularnewline
155 & 0.999857 & 0.000286345 & 0.000143173 \tabularnewline
156 & 0.999846 & 0.000308226 & 0.000154113 \tabularnewline
157 & 0.999823 & 0.000354444 & 0.000177222 \tabularnewline
158 & 0.999314 & 0.00137142 & 0.000685711 \tabularnewline
159 & 0.997417 & 0.00516601 & 0.002583 \tabularnewline
160 & 0.993796 & 0.0124077 & 0.00620383 \tabularnewline
161 & 0.978224 & 0.0435511 & 0.0217756 \tabularnewline
162 & 0.932747 & 0.134506 & 0.0672532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264855&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]0.810139[/C][C]0.379722[/C][C]0.189861[/C][/ROW]
[ROW][C]10[/C][C]0.692777[/C][C]0.614447[/C][C]0.307223[/C][/ROW]
[ROW][C]11[/C][C]0.56192[/C][C]0.876161[/C][C]0.43808[/C][/ROW]
[ROW][C]12[/C][C]0.5481[/C][C]0.9038[/C][C]0.4519[/C][/ROW]
[ROW][C]13[/C][C]0.723101[/C][C]0.553797[/C][C]0.276899[/C][/ROW]
[ROW][C]14[/C][C]0.806764[/C][C]0.386472[/C][C]0.193236[/C][/ROW]
[ROW][C]15[/C][C]0.753273[/C][C]0.493455[/C][C]0.246727[/C][/ROW]
[ROW][C]16[/C][C]0.799168[/C][C]0.401663[/C][C]0.200832[/C][/ROW]
[ROW][C]17[/C][C]0.77542[/C][C]0.44916[/C][C]0.22458[/C][/ROW]
[ROW][C]18[/C][C]0.709739[/C][C]0.580522[/C][C]0.290261[/C][/ROW]
[ROW][C]19[/C][C]0.656385[/C][C]0.68723[/C][C]0.343615[/C][/ROW]
[ROW][C]20[/C][C]0.590984[/C][C]0.818032[/C][C]0.409016[/C][/ROW]
[ROW][C]21[/C][C]0.615543[/C][C]0.768914[/C][C]0.384457[/C][/ROW]
[ROW][C]22[/C][C]0.549738[/C][C]0.900524[/C][C]0.450262[/C][/ROW]
[ROW][C]23[/C][C]0.476844[/C][C]0.953688[/C][C]0.523156[/C][/ROW]
[ROW][C]24[/C][C]0.420709[/C][C]0.841419[/C][C]0.579291[/C][/ROW]
[ROW][C]25[/C][C]0.428858[/C][C]0.857715[/C][C]0.571142[/C][/ROW]
[ROW][C]26[/C][C]0.547417[/C][C]0.905167[/C][C]0.452583[/C][/ROW]
[ROW][C]27[/C][C]0.491127[/C][C]0.982255[/C][C]0.508873[/C][/ROW]
[ROW][C]28[/C][C]0.447561[/C][C]0.895123[/C][C]0.552439[/C][/ROW]
[ROW][C]29[/C][C]0.387153[/C][C]0.774306[/C][C]0.612847[/C][/ROW]
[ROW][C]30[/C][C]0.336177[/C][C]0.672353[/C][C]0.663823[/C][/ROW]
[ROW][C]31[/C][C]0.420306[/C][C]0.840611[/C][C]0.579694[/C][/ROW]
[ROW][C]32[/C][C]0.380414[/C][C]0.760828[/C][C]0.619586[/C][/ROW]
[ROW][C]33[/C][C]0.330843[/C][C]0.661685[/C][C]0.669157[/C][/ROW]
[ROW][C]34[/C][C]0.283817[/C][C]0.567635[/C][C]0.716183[/C][/ROW]
[ROW][C]35[/C][C]0.236385[/C][C]0.47277[/C][C]0.763615[/C][/ROW]
[ROW][C]36[/C][C]0.256441[/C][C]0.512883[/C][C]0.743559[/C][/ROW]
[ROW][C]37[/C][C]0.345371[/C][C]0.690742[/C][C]0.654629[/C][/ROW]
[ROW][C]38[/C][C]0.295798[/C][C]0.591597[/C][C]0.704202[/C][/ROW]
[ROW][C]39[/C][C]0.377393[/C][C]0.754785[/C][C]0.622607[/C][/ROW]
[ROW][C]40[/C][C]0.331664[/C][C]0.663329[/C][C]0.668336[/C][/ROW]
[ROW][C]41[/C][C]0.778119[/C][C]0.443761[/C][C]0.221881[/C][/ROW]
[ROW][C]42[/C][C]0.743919[/C][C]0.512162[/C][C]0.256081[/C][/ROW]
[ROW][C]43[/C][C]0.714609[/C][C]0.570783[/C][C]0.285391[/C][/ROW]
[ROW][C]44[/C][C]0.669736[/C][C]0.660528[/C][C]0.330264[/C][/ROW]
[ROW][C]45[/C][C]0.700081[/C][C]0.599838[/C][C]0.299919[/C][/ROW]
[ROW][C]46[/C][C]0.654441[/C][C]0.691117[/C][C]0.345559[/C][/ROW]
[ROW][C]47[/C][C]0.627442[/C][C]0.745116[/C][C]0.372558[/C][/ROW]
[ROW][C]48[/C][C]0.613046[/C][C]0.773908[/C][C]0.386954[/C][/ROW]
[ROW][C]49[/C][C]0.568544[/C][C]0.862911[/C][C]0.431456[/C][/ROW]
[ROW][C]50[/C][C]0.536049[/C][C]0.927902[/C][C]0.463951[/C][/ROW]
[ROW][C]51[/C][C]0.556345[/C][C]0.887311[/C][C]0.443655[/C][/ROW]
[ROW][C]52[/C][C]0.533437[/C][C]0.933125[/C][C]0.466563[/C][/ROW]
[ROW][C]53[/C][C]0.49777[/C][C]0.995539[/C][C]0.50223[/C][/ROW]
[ROW][C]54[/C][C]0.65833[/C][C]0.68334[/C][C]0.34167[/C][/ROW]
[ROW][C]55[/C][C]0.660152[/C][C]0.679696[/C][C]0.339848[/C][/ROW]
[ROW][C]56[/C][C]0.615167[/C][C]0.769666[/C][C]0.384833[/C][/ROW]
[ROW][C]57[/C][C]0.590975[/C][C]0.81805[/C][C]0.409025[/C][/ROW]
[ROW][C]58[/C][C]0.593769[/C][C]0.812462[/C][C]0.406231[/C][/ROW]
[ROW][C]59[/C][C]0.547629[/C][C]0.904742[/C][C]0.452371[/C][/ROW]
[ROW][C]60[/C][C]0.576841[/C][C]0.846318[/C][C]0.423159[/C][/ROW]
[ROW][C]61[/C][C]0.533819[/C][C]0.932363[/C][C]0.466181[/C][/ROW]
[ROW][C]62[/C][C]0.5503[/C][C]0.8994[/C][C]0.4497[/C][/ROW]
[ROW][C]63[/C][C]0.516802[/C][C]0.966395[/C][C]0.483198[/C][/ROW]
[ROW][C]64[/C][C]0.470862[/C][C]0.941725[/C][C]0.529138[/C][/ROW]
[ROW][C]65[/C][C]0.4257[/C][C]0.851399[/C][C]0.5743[/C][/ROW]
[ROW][C]66[/C][C]0.414824[/C][C]0.829647[/C][C]0.585176[/C][/ROW]
[ROW][C]67[/C][C]0.408347[/C][C]0.816694[/C][C]0.591653[/C][/ROW]
[ROW][C]68[/C][C]0.409414[/C][C]0.818828[/C][C]0.590586[/C][/ROW]
[ROW][C]69[/C][C]0.379248[/C][C]0.758496[/C][C]0.620752[/C][/ROW]
[ROW][C]70[/C][C]0.447123[/C][C]0.894246[/C][C]0.552877[/C][/ROW]
[ROW][C]71[/C][C]0.451657[/C][C]0.903315[/C][C]0.548343[/C][/ROW]
[ROW][C]72[/C][C]0.416225[/C][C]0.832451[/C][C]0.583775[/C][/ROW]
[ROW][C]73[/C][C]0.40093[/C][C]0.801861[/C][C]0.59907[/C][/ROW]
[ROW][C]74[/C][C]0.362862[/C][C]0.725724[/C][C]0.637138[/C][/ROW]
[ROW][C]75[/C][C]0.507207[/C][C]0.985586[/C][C]0.492793[/C][/ROW]
[ROW][C]76[/C][C]0.463268[/C][C]0.926536[/C][C]0.536732[/C][/ROW]
[ROW][C]77[/C][C]0.539536[/C][C]0.920928[/C][C]0.460464[/C][/ROW]
[ROW][C]78[/C][C]0.528981[/C][C]0.942037[/C][C]0.471019[/C][/ROW]
[ROW][C]79[/C][C]0.485252[/C][C]0.970505[/C][C]0.514748[/C][/ROW]
[ROW][C]80[/C][C]0.441384[/C][C]0.882768[/C][C]0.558616[/C][/ROW]
[ROW][C]81[/C][C]0.398523[/C][C]0.797046[/C][C]0.601477[/C][/ROW]
[ROW][C]82[/C][C]0.356688[/C][C]0.713376[/C][C]0.643312[/C][/ROW]
[ROW][C]83[/C][C]0.378408[/C][C]0.756815[/C][C]0.621592[/C][/ROW]
[ROW][C]84[/C][C]0.391582[/C][C]0.783164[/C][C]0.608418[/C][/ROW]
[ROW][C]85[/C][C]0.367985[/C][C]0.735971[/C][C]0.632015[/C][/ROW]
[ROW][C]86[/C][C]0.355856[/C][C]0.711711[/C][C]0.644144[/C][/ROW]
[ROW][C]87[/C][C]0.393424[/C][C]0.786848[/C][C]0.606576[/C][/ROW]
[ROW][C]88[/C][C]0.354645[/C][C]0.709289[/C][C]0.645355[/C][/ROW]
[ROW][C]89[/C][C]0.363495[/C][C]0.72699[/C][C]0.636505[/C][/ROW]
[ROW][C]90[/C][C]0.32622[/C][C]0.652441[/C][C]0.67378[/C][/ROW]
[ROW][C]91[/C][C]0.298783[/C][C]0.597566[/C][C]0.701217[/C][/ROW]
[ROW][C]92[/C][C]0.287347[/C][C]0.574693[/C][C]0.712653[/C][/ROW]
[ROW][C]93[/C][C]0.273084[/C][C]0.546167[/C][C]0.726916[/C][/ROW]
[ROW][C]94[/C][C]0.241676[/C][C]0.483353[/C][C]0.758324[/C][/ROW]
[ROW][C]95[/C][C]0.251114[/C][C]0.502229[/C][C]0.748886[/C][/ROW]
[ROW][C]96[/C][C]0.300025[/C][C]0.600049[/C][C]0.699975[/C][/ROW]
[ROW][C]97[/C][C]0.295437[/C][C]0.590874[/C][C]0.704563[/C][/ROW]
[ROW][C]98[/C][C]0.32211[/C][C]0.644221[/C][C]0.67789[/C][/ROW]
[ROW][C]99[/C][C]0.283521[/C][C]0.567042[/C][C]0.716479[/C][/ROW]
[ROW][C]100[/C][C]0.253016[/C][C]0.506033[/C][C]0.746984[/C][/ROW]
[ROW][C]101[/C][C]0.21952[/C][C]0.439039[/C][C]0.78048[/C][/ROW]
[ROW][C]102[/C][C]0.201445[/C][C]0.40289[/C][C]0.798555[/C][/ROW]
[ROW][C]103[/C][C]0.172651[/C][C]0.345303[/C][C]0.827349[/C][/ROW]
[ROW][C]104[/C][C]0.2036[/C][C]0.4072[/C][C]0.7964[/C][/ROW]
[ROW][C]105[/C][C]0.173638[/C][C]0.347276[/C][C]0.826362[/C][/ROW]
[ROW][C]106[/C][C]0.152017[/C][C]0.304035[/C][C]0.847983[/C][/ROW]
[ROW][C]107[/C][C]0.139357[/C][C]0.278715[/C][C]0.860643[/C][/ROW]
[ROW][C]108[/C][C]0.126107[/C][C]0.252215[/C][C]0.873893[/C][/ROW]
[ROW][C]109[/C][C]0.104309[/C][C]0.208618[/C][C]0.895691[/C][/ROW]
[ROW][C]110[/C][C]0.0960286[/C][C]0.192057[/C][C]0.903971[/C][/ROW]
[ROW][C]111[/C][C]0.0796869[/C][C]0.159374[/C][C]0.920313[/C][/ROW]
[ROW][C]112[/C][C]0.0641138[/C][C]0.128228[/C][C]0.935886[/C][/ROW]
[ROW][C]113[/C][C]0.0543597[/C][C]0.108719[/C][C]0.94564[/C][/ROW]
[ROW][C]114[/C][C]0.0636484[/C][C]0.127297[/C][C]0.936352[/C][/ROW]
[ROW][C]115[/C][C]0.0508603[/C][C]0.101721[/C][C]0.94914[/C][/ROW]
[ROW][C]116[/C][C]0.0405383[/C][C]0.0810765[/C][C]0.959462[/C][/ROW]
[ROW][C]117[/C][C]0.0426053[/C][C]0.0852106[/C][C]0.957395[/C][/ROW]
[ROW][C]118[/C][C]0.052314[/C][C]0.104628[/C][C]0.947686[/C][/ROW]
[ROW][C]119[/C][C]0.0476397[/C][C]0.0952793[/C][C]0.95236[/C][/ROW]
[ROW][C]120[/C][C]0.0668235[/C][C]0.133647[/C][C]0.933176[/C][/ROW]
[ROW][C]121[/C][C]0.0569614[/C][C]0.113923[/C][C]0.943039[/C][/ROW]
[ROW][C]122[/C][C]0.0566055[/C][C]0.113211[/C][C]0.943395[/C][/ROW]
[ROW][C]123[/C][C]0.0620151[/C][C]0.12403[/C][C]0.937985[/C][/ROW]
[ROW][C]124[/C][C]0.0496075[/C][C]0.099215[/C][C]0.950393[/C][/ROW]
[ROW][C]125[/C][C]0.0434361[/C][C]0.0868722[/C][C]0.956564[/C][/ROW]
[ROW][C]126[/C][C]0.0488778[/C][C]0.0977557[/C][C]0.951122[/C][/ROW]
[ROW][C]127[/C][C]0.0451694[/C][C]0.0903388[/C][C]0.954831[/C][/ROW]
[ROW][C]128[/C][C]0.148535[/C][C]0.29707[/C][C]0.851465[/C][/ROW]
[ROW][C]129[/C][C]0.129764[/C][C]0.259529[/C][C]0.870236[/C][/ROW]
[ROW][C]130[/C][C]0.162963[/C][C]0.325927[/C][C]0.837037[/C][/ROW]
[ROW][C]131[/C][C]0.143525[/C][C]0.28705[/C][C]0.856475[/C][/ROW]
[ROW][C]132[/C][C]0.125239[/C][C]0.250478[/C][C]0.874761[/C][/ROW]
[ROW][C]133[/C][C]0.113406[/C][C]0.226812[/C][C]0.886594[/C][/ROW]
[ROW][C]134[/C][C]0.136011[/C][C]0.272021[/C][C]0.863989[/C][/ROW]
[ROW][C]135[/C][C]0.122449[/C][C]0.244898[/C][C]0.877551[/C][/ROW]
[ROW][C]136[/C][C]0.113647[/C][C]0.227293[/C][C]0.886353[/C][/ROW]
[ROW][C]137[/C][C]0.122098[/C][C]0.244197[/C][C]0.877902[/C][/ROW]
[ROW][C]138[/C][C]0.0976636[/C][C]0.195327[/C][C]0.902336[/C][/ROW]
[ROW][C]139[/C][C]0.0750889[/C][C]0.150178[/C][C]0.924911[/C][/ROW]
[ROW][C]140[/C][C]0.0569366[/C][C]0.113873[/C][C]0.943063[/C][/ROW]
[ROW][C]141[/C][C]0.04516[/C][C]0.09032[/C][C]0.95484[/C][/ROW]
[ROW][C]142[/C][C]0.0382252[/C][C]0.0764504[/C][C]0.961775[/C][/ROW]
[ROW][C]143[/C][C]0.0299089[/C][C]0.0598178[/C][C]0.970091[/C][/ROW]
[ROW][C]144[/C][C]0.0321647[/C][C]0.0643294[/C][C]0.967835[/C][/ROW]
[ROW][C]145[/C][C]0.0423996[/C][C]0.0847993[/C][C]0.9576[/C][/ROW]
[ROW][C]146[/C][C]0.032117[/C][C]0.0642341[/C][C]0.967883[/C][/ROW]
[ROW][C]147[/C][C]0.0226968[/C][C]0.0453936[/C][C]0.977303[/C][/ROW]
[ROW][C]148[/C][C]0.0349034[/C][C]0.0698069[/C][C]0.965097[/C][/ROW]
[ROW][C]149[/C][C]0.0287475[/C][C]0.057495[/C][C]0.971253[/C][/ROW]
[ROW][C]150[/C][C]0.0387847[/C][C]0.0775694[/C][C]0.961215[/C][/ROW]
[ROW][C]151[/C][C]0.503906[/C][C]0.992188[/C][C]0.496094[/C][/ROW]
[ROW][C]152[/C][C]0.467147[/C][C]0.934295[/C][C]0.532853[/C][/ROW]
[ROW][C]153[/C][C]0.998289[/C][C]0.00342203[/C][C]0.00171102[/C][/ROW]
[ROW][C]154[/C][C]0.99994[/C][C]0.000119558[/C][C]5.9779e-05[/C][/ROW]
[ROW][C]155[/C][C]0.999857[/C][C]0.000286345[/C][C]0.000143173[/C][/ROW]
[ROW][C]156[/C][C]0.999846[/C][C]0.000308226[/C][C]0.000154113[/C][/ROW]
[ROW][C]157[/C][C]0.999823[/C][C]0.000354444[/C][C]0.000177222[/C][/ROW]
[ROW][C]158[/C][C]0.999314[/C][C]0.00137142[/C][C]0.000685711[/C][/ROW]
[ROW][C]159[/C][C]0.997417[/C][C]0.00516601[/C][C]0.002583[/C][/ROW]
[ROW][C]160[/C][C]0.993796[/C][C]0.0124077[/C][C]0.00620383[/C][/ROW]
[ROW][C]161[/C][C]0.978224[/C][C]0.0435511[/C][C]0.0217756[/C][/ROW]
[ROW][C]162[/C][C]0.932747[/C][C]0.134506[/C][C]0.0672532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264855&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264855&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
90.8101390.3797220.189861
100.6927770.6144470.307223
110.561920.8761610.43808
120.54810.90380.4519
130.7231010.5537970.276899
140.8067640.3864720.193236
150.7532730.4934550.246727
160.7991680.4016630.200832
170.775420.449160.22458
180.7097390.5805220.290261
190.6563850.687230.343615
200.5909840.8180320.409016
210.6155430.7689140.384457
220.5497380.9005240.450262
230.4768440.9536880.523156
240.4207090.8414190.579291
250.4288580.8577150.571142
260.5474170.9051670.452583
270.4911270.9822550.508873
280.4475610.8951230.552439
290.3871530.7743060.612847
300.3361770.6723530.663823
310.4203060.8406110.579694
320.3804140.7608280.619586
330.3308430.6616850.669157
340.2838170.5676350.716183
350.2363850.472770.763615
360.2564410.5128830.743559
370.3453710.6907420.654629
380.2957980.5915970.704202
390.3773930.7547850.622607
400.3316640.6633290.668336
410.7781190.4437610.221881
420.7439190.5121620.256081
430.7146090.5707830.285391
440.6697360.6605280.330264
450.7000810.5998380.299919
460.6544410.6911170.345559
470.6274420.7451160.372558
480.6130460.7739080.386954
490.5685440.8629110.431456
500.5360490.9279020.463951
510.5563450.8873110.443655
520.5334370.9331250.466563
530.497770.9955390.50223
540.658330.683340.34167
550.6601520.6796960.339848
560.6151670.7696660.384833
570.5909750.818050.409025
580.5937690.8124620.406231
590.5476290.9047420.452371
600.5768410.8463180.423159
610.5338190.9323630.466181
620.55030.89940.4497
630.5168020.9663950.483198
640.4708620.9417250.529138
650.42570.8513990.5743
660.4148240.8296470.585176
670.4083470.8166940.591653
680.4094140.8188280.590586
690.3792480.7584960.620752
700.4471230.8942460.552877
710.4516570.9033150.548343
720.4162250.8324510.583775
730.400930.8018610.59907
740.3628620.7257240.637138
750.5072070.9855860.492793
760.4632680.9265360.536732
770.5395360.9209280.460464
780.5289810.9420370.471019
790.4852520.9705050.514748
800.4413840.8827680.558616
810.3985230.7970460.601477
820.3566880.7133760.643312
830.3784080.7568150.621592
840.3915820.7831640.608418
850.3679850.7359710.632015
860.3558560.7117110.644144
870.3934240.7868480.606576
880.3546450.7092890.645355
890.3634950.726990.636505
900.326220.6524410.67378
910.2987830.5975660.701217
920.2873470.5746930.712653
930.2730840.5461670.726916
940.2416760.4833530.758324
950.2511140.5022290.748886
960.3000250.6000490.699975
970.2954370.5908740.704563
980.322110.6442210.67789
990.2835210.5670420.716479
1000.2530160.5060330.746984
1010.219520.4390390.78048
1020.2014450.402890.798555
1030.1726510.3453030.827349
1040.20360.40720.7964
1050.1736380.3472760.826362
1060.1520170.3040350.847983
1070.1393570.2787150.860643
1080.1261070.2522150.873893
1090.1043090.2086180.895691
1100.09602860.1920570.903971
1110.07968690.1593740.920313
1120.06411380.1282280.935886
1130.05435970.1087190.94564
1140.06364840.1272970.936352
1150.05086030.1017210.94914
1160.04053830.08107650.959462
1170.04260530.08521060.957395
1180.0523140.1046280.947686
1190.04763970.09527930.95236
1200.06682350.1336470.933176
1210.05696140.1139230.943039
1220.05660550.1132110.943395
1230.06201510.124030.937985
1240.04960750.0992150.950393
1250.04343610.08687220.956564
1260.04887780.09775570.951122
1270.04516940.09033880.954831
1280.1485350.297070.851465
1290.1297640.2595290.870236
1300.1629630.3259270.837037
1310.1435250.287050.856475
1320.1252390.2504780.874761
1330.1134060.2268120.886594
1340.1360110.2720210.863989
1350.1224490.2448980.877551
1360.1136470.2272930.886353
1370.1220980.2441970.877902
1380.09766360.1953270.902336
1390.07508890.1501780.924911
1400.05693660.1138730.943063
1410.045160.090320.95484
1420.03822520.07645040.961775
1430.02990890.05981780.970091
1440.03216470.06432940.967835
1450.04239960.08479930.9576
1460.0321170.06423410.967883
1470.02269680.04539360.977303
1480.03490340.06980690.965097
1490.02874750.0574950.971253
1500.03878470.07756940.961215
1510.5039060.9921880.496094
1520.4671470.9342950.532853
1530.9982890.003422030.00171102
1540.999940.0001195585.9779e-05
1550.9998570.0002863450.000143173
1560.9998460.0003082260.000154113
1570.9998230.0003544440.000177222
1580.9993140.001371420.000685711
1590.9974170.005166010.002583
1600.9937960.01240770.00620383
1610.9782240.04355110.0217756
1620.9327470.1345060.0672532






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.0454545NOK
5% type I error level100.0649351NOK
10% type I error level260.168831NOK

\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 & 0.0454545 & NOK \tabularnewline
5% type I error level & 10 & 0.0649351 & NOK \tabularnewline
10% type I error level & 26 & 0.168831 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264855&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]0.0454545[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.0649351[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.168831[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264855&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264855&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 level70.0454545NOK
5% type I error level100.0649351NOK
10% type I error level260.168831NOK


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')
}