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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 19:19:37 +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/t1418930419f17qb3st4bvdz8h.htm/, Retrieved Sun, 19 May 2024 21:16:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271220, Retrieved Sun, 19 May 2024 21:16:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Regressie_1] [2014-12-15 18:51:06] [189b7d469e4e3b4e868a6af83e3b3816]
-    D    [Multiple Regression] [Regressie_2] [2014-12-18 19:19:37] [e89b1602ca7c278e2fffead05eac818b] [Current]
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Dataset

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

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'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271220&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271220&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271220&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'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
EX[t] = + 5.05474 + 0.011583LFM[t] + 0.00109176B[t] -0.00527624PRH[t] + 0.0228422CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
EX[t] =  +  5.05474 +  0.011583LFM[t] +  0.00109176B[t] -0.00527624PRH[t] +  0.0228422CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271220&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]EX[t] =  +  5.05474 +  0.011583LFM[t] +  0.00109176B[t] -0.00527624PRH[t] +  0.0228422CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271220&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271220&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] = + 5.05474 + 0.011583LFM[t] + 0.00109176B[t] -0.00527624PRH[t] + 0.0228422CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.054740.49660510.183.26276e-191.63138e-19
LFM0.0115830.00468932.470.01451920.00725958
B0.001091760.003214010.33970.734520.36726
PRH-0.005276240.00905821-0.58250.5610330.280517
CH0.02284220.01096962.0820.03884730.0194236

\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) & 5.05474 & 0.496605 & 10.18 & 3.26276e-19 & 1.63138e-19 \tabularnewline
LFM & 0.011583 & 0.0046893 & 2.47 & 0.0145192 & 0.00725958 \tabularnewline
B & 0.00109176 & 0.00321401 & 0.3397 & 0.73452 & 0.36726 \tabularnewline
PRH & -0.00527624 & 0.00905821 & -0.5825 & 0.561033 & 0.280517 \tabularnewline
CH & 0.0228422 & 0.0109696 & 2.082 & 0.0388473 & 0.0194236 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271220&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]5.05474[/C][C]0.496605[/C][C]10.18[/C][C]3.26276e-19[/C][C]1.63138e-19[/C][/ROW]
[ROW][C]LFM[/C][C]0.011583[/C][C]0.0046893[/C][C]2.47[/C][C]0.0145192[/C][C]0.00725958[/C][/ROW]
[ROW][C]B[/C][C]0.00109176[/C][C]0.00321401[/C][C]0.3397[/C][C]0.73452[/C][C]0.36726[/C][/ROW]
[ROW][C]PRH[/C][C]-0.00527624[/C][C]0.00905821[/C][C]-0.5825[/C][C]0.561033[/C][C]0.280517[/C][/ROW]
[ROW][C]CH[/C][C]0.0228422[/C][C]0.0109696[/C][C]2.082[/C][C]0.0388473[/C][C]0.0194236[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271220&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271220&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)5.054740.49660510.183.26276e-191.63138e-19
LFM0.0115830.00468932.470.01451920.00725958
B0.001091760.003214010.33970.734520.36726
PRH-0.005276240.00905821-0.58250.5610330.280517
CH0.02284220.01096962.0820.03884730.0194236






Multiple Linear Regression - Regression Statistics
Multiple R0.376136
R-squared0.141478
Adjusted R-squared0.120791
F-TEST (value)6.8389
F-TEST (DF numerator)4
F-TEST (DF denominator)166
p-value4.04225e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.96759
Sum Squared Residuals642.651

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.376136 \tabularnewline
R-squared & 0.141478 \tabularnewline
Adjusted R-squared & 0.120791 \tabularnewline
F-TEST (value) & 6.8389 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 166 \tabularnewline
p-value & 4.04225e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.96759 \tabularnewline
Sum Squared Residuals & 642.651 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271220&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.376136[/C][/ROW]
[ROW][C]R-squared[/C][C]0.141478[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.120791[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.8389[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]166[/C][/ROW]
[ROW][C]p-value[/C][C]4.04225e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.96759[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]642.651[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271220&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271220&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 R0.376136
R-squared0.141478
Adjusted R-squared0.120791
F-TEST (value)6.8389
F-TEST (DF numerator)4
F-TEST (DF denominator)166
p-value4.04225e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.96759
Sum Squared Residuals642.651






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.55.80823-5.30823
27.56.736760.76324
397.661931.33807
49.57.724451.77555
58.58.87615-0.376148
676.035140.964856
789.06002-1.06002
8108.36741.6326
9710.1553-3.15526
108.56.020552.47945
1198.179540.820465
129.55.9193.581
1347.0655-3.0655
1467.02943-1.02943
1587.588980.411023
165.57.23529-1.73529
179.57.836831.66317
187.56.632270.867733
1976.781730.218266
207.58.62885-1.12885
2187.119930.880074
2277.3885-0.388497
2376.576240.423763
2467.02808-1.02808
25107.223252.77675
262.56.11509-3.61509
2798.543720.456282
2888.17506-0.175065
2966.43598-0.435984
308.57.13881.3612
3168.09054-2.09054
3297.643621.35638
3387.176840.823164
3487.969590.0304102
3598.365880.634124
365.57.19461-1.69461
3757.13725-2.13725
3877.32581-0.325812
395.57.87998-2.37998
4097.872061.12794
4127.91538-5.91538
428.58.021130.478872
4397.788761.21124
448.58.206830.293174
4596.476222.52378
467.57.54966-0.0496553
47108.854891.14511
4897.321171.67883
497.58.22768-0.727682
5066.62951-0.629514
5110.58.083832.41617
528.57.333711.16629
5388.45981-0.459805
54105.729194.27081
5510.58.473062.02694
566.55.72270.777302
579.58.066441.43356
588.56.252392.24761
597.57.190530.309473
6057.41373-2.41373
6187.379530.620469
62107.520092.47991
6377.80689-0.806887
647.57.75992-0.259918
657.57.75992-0.259918
669.57.626871.87313
6767.6663-1.6663
68108.001721.99828
6977.89954-0.899536
7036.0435-3.0435
7167.88848-1.88848
7277.35569-0.35569
73108.36851.6315
7477.61377-0.613772
753.57.46581-3.96581
7687.599760.400241
77106.76133.2387
785.57.14511-1.64511
7966.12335-0.123347
806.56.71246-0.212464
816.56.348820.151183
828.57.685610.814392
8346.46566-2.46566
849.57.221672.27833
8586.723541.27646
868.56.938571.56143
875.58.24007-2.74007
8877.12699-0.126986
8996.314382.68562
9087.285720.71428
91108.761361.23864
9286.489251.51075
9367.53829-1.53829
9487.436180.563822
9556.75966-1.75966
9696.15012.8499
974.56.53814-2.03814
988.56.222012.27799
9976.443870.55613
1009.58.580890.919111
1018.57.529190.970809
1027.56.377661.12234
1037.57.412090.087913
10457.71026-2.71026
10576.768620.231378
10688.55566-0.555661
1075.57.01188-1.51188
1088.57.141941.35806
1097.57.24380.256198
1109.57.94521.5548
11176.607280.392722
11287.531560.468437
1138.57.468841.03116
1143.56.44414-2.94414
1156.56.5656-0.0655995
1166.56.86023-0.360228
11710.58.274522.22548
1188.56.240692.25931
11987.186060.813942
120106.552763.44724
121108.47591.5241
1229.57.851711.64829
12397.351111.64889
124108.773711.22629
1257.56.66150.838504
1264.57.48932-2.98932
1274.56.69028-2.19028
1280.56.07311-5.57311
1296.55.929610.570393
1304.57.68796-3.18796
1315.56.92919-1.42919
13256.589-1.589
13367.63436-1.63436
13446.83218-2.83218
13586.797491.20251
13610.58.473062.02694
1378.57.051741.44826
1386.56.328780.17122
13987.632780.367219
1408.58.89112-0.391122
1415.56.82359-1.32359
14278.13829-1.13829
14357.12217-2.12217
1443.56.90761-3.40761
14557.36024-2.36024
14697.430961.56904
1478.57.497251.00275
14857.84496-2.84496
1499.57.712241.78776
15036.24984-3.24984
1511.56.80793-5.30793
15267.32492-1.32492
1530.57.04227-6.54227
1546.55.72270.777302
1557.56.910740.589258
1564.56.63636-2.13636
15786.797491.20251
15897.216521.78348
1597.56.800140.699862
1608.56.577251.92275
16176.83290.167095
1629.57.27762.2224
1636.56.39670.103301
1649.56.740782.75922
16566.30422-0.304219
16687.4510.548995
1679.57.852941.64706
16887.447010.552986
16987.046470.953534
17097.629181.37082
17155.79585-0.79585

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.5 & 5.80823 & -5.30823 \tabularnewline
2 & 7.5 & 6.73676 & 0.76324 \tabularnewline
3 & 9 & 7.66193 & 1.33807 \tabularnewline
4 & 9.5 & 7.72445 & 1.77555 \tabularnewline
5 & 8.5 & 8.87615 & -0.376148 \tabularnewline
6 & 7 & 6.03514 & 0.964856 \tabularnewline
7 & 8 & 9.06002 & -1.06002 \tabularnewline
8 & 10 & 8.3674 & 1.6326 \tabularnewline
9 & 7 & 10.1553 & -3.15526 \tabularnewline
10 & 8.5 & 6.02055 & 2.47945 \tabularnewline
11 & 9 & 8.17954 & 0.820465 \tabularnewline
12 & 9.5 & 5.919 & 3.581 \tabularnewline
13 & 4 & 7.0655 & -3.0655 \tabularnewline
14 & 6 & 7.02943 & -1.02943 \tabularnewline
15 & 8 & 7.58898 & 0.411023 \tabularnewline
16 & 5.5 & 7.23529 & -1.73529 \tabularnewline
17 & 9.5 & 7.83683 & 1.66317 \tabularnewline
18 & 7.5 & 6.63227 & 0.867733 \tabularnewline
19 & 7 & 6.78173 & 0.218266 \tabularnewline
20 & 7.5 & 8.62885 & -1.12885 \tabularnewline
21 & 8 & 7.11993 & 0.880074 \tabularnewline
22 & 7 & 7.3885 & -0.388497 \tabularnewline
23 & 7 & 6.57624 & 0.423763 \tabularnewline
24 & 6 & 7.02808 & -1.02808 \tabularnewline
25 & 10 & 7.22325 & 2.77675 \tabularnewline
26 & 2.5 & 6.11509 & -3.61509 \tabularnewline
27 & 9 & 8.54372 & 0.456282 \tabularnewline
28 & 8 & 8.17506 & -0.175065 \tabularnewline
29 & 6 & 6.43598 & -0.435984 \tabularnewline
30 & 8.5 & 7.1388 & 1.3612 \tabularnewline
31 & 6 & 8.09054 & -2.09054 \tabularnewline
32 & 9 & 7.64362 & 1.35638 \tabularnewline
33 & 8 & 7.17684 & 0.823164 \tabularnewline
34 & 8 & 7.96959 & 0.0304102 \tabularnewline
35 & 9 & 8.36588 & 0.634124 \tabularnewline
36 & 5.5 & 7.19461 & -1.69461 \tabularnewline
37 & 5 & 7.13725 & -2.13725 \tabularnewline
38 & 7 & 7.32581 & -0.325812 \tabularnewline
39 & 5.5 & 7.87998 & -2.37998 \tabularnewline
40 & 9 & 7.87206 & 1.12794 \tabularnewline
41 & 2 & 7.91538 & -5.91538 \tabularnewline
42 & 8.5 & 8.02113 & 0.478872 \tabularnewline
43 & 9 & 7.78876 & 1.21124 \tabularnewline
44 & 8.5 & 8.20683 & 0.293174 \tabularnewline
45 & 9 & 6.47622 & 2.52378 \tabularnewline
46 & 7.5 & 7.54966 & -0.0496553 \tabularnewline
47 & 10 & 8.85489 & 1.14511 \tabularnewline
48 & 9 & 7.32117 & 1.67883 \tabularnewline
49 & 7.5 & 8.22768 & -0.727682 \tabularnewline
50 & 6 & 6.62951 & -0.629514 \tabularnewline
51 & 10.5 & 8.08383 & 2.41617 \tabularnewline
52 & 8.5 & 7.33371 & 1.16629 \tabularnewline
53 & 8 & 8.45981 & -0.459805 \tabularnewline
54 & 10 & 5.72919 & 4.27081 \tabularnewline
55 & 10.5 & 8.47306 & 2.02694 \tabularnewline
56 & 6.5 & 5.7227 & 0.777302 \tabularnewline
57 & 9.5 & 8.06644 & 1.43356 \tabularnewline
58 & 8.5 & 6.25239 & 2.24761 \tabularnewline
59 & 7.5 & 7.19053 & 0.309473 \tabularnewline
60 & 5 & 7.41373 & -2.41373 \tabularnewline
61 & 8 & 7.37953 & 0.620469 \tabularnewline
62 & 10 & 7.52009 & 2.47991 \tabularnewline
63 & 7 & 7.80689 & -0.806887 \tabularnewline
64 & 7.5 & 7.75992 & -0.259918 \tabularnewline
65 & 7.5 & 7.75992 & -0.259918 \tabularnewline
66 & 9.5 & 7.62687 & 1.87313 \tabularnewline
67 & 6 & 7.6663 & -1.6663 \tabularnewline
68 & 10 & 8.00172 & 1.99828 \tabularnewline
69 & 7 & 7.89954 & -0.899536 \tabularnewline
70 & 3 & 6.0435 & -3.0435 \tabularnewline
71 & 6 & 7.88848 & -1.88848 \tabularnewline
72 & 7 & 7.35569 & -0.35569 \tabularnewline
73 & 10 & 8.3685 & 1.6315 \tabularnewline
74 & 7 & 7.61377 & -0.613772 \tabularnewline
75 & 3.5 & 7.46581 & -3.96581 \tabularnewline
76 & 8 & 7.59976 & 0.400241 \tabularnewline
77 & 10 & 6.7613 & 3.2387 \tabularnewline
78 & 5.5 & 7.14511 & -1.64511 \tabularnewline
79 & 6 & 6.12335 & -0.123347 \tabularnewline
80 & 6.5 & 6.71246 & -0.212464 \tabularnewline
81 & 6.5 & 6.34882 & 0.151183 \tabularnewline
82 & 8.5 & 7.68561 & 0.814392 \tabularnewline
83 & 4 & 6.46566 & -2.46566 \tabularnewline
84 & 9.5 & 7.22167 & 2.27833 \tabularnewline
85 & 8 & 6.72354 & 1.27646 \tabularnewline
86 & 8.5 & 6.93857 & 1.56143 \tabularnewline
87 & 5.5 & 8.24007 & -2.74007 \tabularnewline
88 & 7 & 7.12699 & -0.126986 \tabularnewline
89 & 9 & 6.31438 & 2.68562 \tabularnewline
90 & 8 & 7.28572 & 0.71428 \tabularnewline
91 & 10 & 8.76136 & 1.23864 \tabularnewline
92 & 8 & 6.48925 & 1.51075 \tabularnewline
93 & 6 & 7.53829 & -1.53829 \tabularnewline
94 & 8 & 7.43618 & 0.563822 \tabularnewline
95 & 5 & 6.75966 & -1.75966 \tabularnewline
96 & 9 & 6.1501 & 2.8499 \tabularnewline
97 & 4.5 & 6.53814 & -2.03814 \tabularnewline
98 & 8.5 & 6.22201 & 2.27799 \tabularnewline
99 & 7 & 6.44387 & 0.55613 \tabularnewline
100 & 9.5 & 8.58089 & 0.919111 \tabularnewline
101 & 8.5 & 7.52919 & 0.970809 \tabularnewline
102 & 7.5 & 6.37766 & 1.12234 \tabularnewline
103 & 7.5 & 7.41209 & 0.087913 \tabularnewline
104 & 5 & 7.71026 & -2.71026 \tabularnewline
105 & 7 & 6.76862 & 0.231378 \tabularnewline
106 & 8 & 8.55566 & -0.555661 \tabularnewline
107 & 5.5 & 7.01188 & -1.51188 \tabularnewline
108 & 8.5 & 7.14194 & 1.35806 \tabularnewline
109 & 7.5 & 7.2438 & 0.256198 \tabularnewline
110 & 9.5 & 7.9452 & 1.5548 \tabularnewline
111 & 7 & 6.60728 & 0.392722 \tabularnewline
112 & 8 & 7.53156 & 0.468437 \tabularnewline
113 & 8.5 & 7.46884 & 1.03116 \tabularnewline
114 & 3.5 & 6.44414 & -2.94414 \tabularnewline
115 & 6.5 & 6.5656 & -0.0655995 \tabularnewline
116 & 6.5 & 6.86023 & -0.360228 \tabularnewline
117 & 10.5 & 8.27452 & 2.22548 \tabularnewline
118 & 8.5 & 6.24069 & 2.25931 \tabularnewline
119 & 8 & 7.18606 & 0.813942 \tabularnewline
120 & 10 & 6.55276 & 3.44724 \tabularnewline
121 & 10 & 8.4759 & 1.5241 \tabularnewline
122 & 9.5 & 7.85171 & 1.64829 \tabularnewline
123 & 9 & 7.35111 & 1.64889 \tabularnewline
124 & 10 & 8.77371 & 1.22629 \tabularnewline
125 & 7.5 & 6.6615 & 0.838504 \tabularnewline
126 & 4.5 & 7.48932 & -2.98932 \tabularnewline
127 & 4.5 & 6.69028 & -2.19028 \tabularnewline
128 & 0.5 & 6.07311 & -5.57311 \tabularnewline
129 & 6.5 & 5.92961 & 0.570393 \tabularnewline
130 & 4.5 & 7.68796 & -3.18796 \tabularnewline
131 & 5.5 & 6.92919 & -1.42919 \tabularnewline
132 & 5 & 6.589 & -1.589 \tabularnewline
133 & 6 & 7.63436 & -1.63436 \tabularnewline
134 & 4 & 6.83218 & -2.83218 \tabularnewline
135 & 8 & 6.79749 & 1.20251 \tabularnewline
136 & 10.5 & 8.47306 & 2.02694 \tabularnewline
137 & 8.5 & 7.05174 & 1.44826 \tabularnewline
138 & 6.5 & 6.32878 & 0.17122 \tabularnewline
139 & 8 & 7.63278 & 0.367219 \tabularnewline
140 & 8.5 & 8.89112 & -0.391122 \tabularnewline
141 & 5.5 & 6.82359 & -1.32359 \tabularnewline
142 & 7 & 8.13829 & -1.13829 \tabularnewline
143 & 5 & 7.12217 & -2.12217 \tabularnewline
144 & 3.5 & 6.90761 & -3.40761 \tabularnewline
145 & 5 & 7.36024 & -2.36024 \tabularnewline
146 & 9 & 7.43096 & 1.56904 \tabularnewline
147 & 8.5 & 7.49725 & 1.00275 \tabularnewline
148 & 5 & 7.84496 & -2.84496 \tabularnewline
149 & 9.5 & 7.71224 & 1.78776 \tabularnewline
150 & 3 & 6.24984 & -3.24984 \tabularnewline
151 & 1.5 & 6.80793 & -5.30793 \tabularnewline
152 & 6 & 7.32492 & -1.32492 \tabularnewline
153 & 0.5 & 7.04227 & -6.54227 \tabularnewline
154 & 6.5 & 5.7227 & 0.777302 \tabularnewline
155 & 7.5 & 6.91074 & 0.589258 \tabularnewline
156 & 4.5 & 6.63636 & -2.13636 \tabularnewline
157 & 8 & 6.79749 & 1.20251 \tabularnewline
158 & 9 & 7.21652 & 1.78348 \tabularnewline
159 & 7.5 & 6.80014 & 0.699862 \tabularnewline
160 & 8.5 & 6.57725 & 1.92275 \tabularnewline
161 & 7 & 6.8329 & 0.167095 \tabularnewline
162 & 9.5 & 7.2776 & 2.2224 \tabularnewline
163 & 6.5 & 6.3967 & 0.103301 \tabularnewline
164 & 9.5 & 6.74078 & 2.75922 \tabularnewline
165 & 6 & 6.30422 & -0.304219 \tabularnewline
166 & 8 & 7.451 & 0.548995 \tabularnewline
167 & 9.5 & 7.85294 & 1.64706 \tabularnewline
168 & 8 & 7.44701 & 0.552986 \tabularnewline
169 & 8 & 7.04647 & 0.953534 \tabularnewline
170 & 9 & 7.62918 & 1.37082 \tabularnewline
171 & 5 & 5.79585 & -0.79585 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271220&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]5.80823[/C][C]-5.30823[/C][/ROW]
[ROW][C]2[/C][C]7.5[/C][C]6.73676[/C][C]0.76324[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]7.66193[/C][C]1.33807[/C][/ROW]
[ROW][C]4[/C][C]9.5[/C][C]7.72445[/C][C]1.77555[/C][/ROW]
[ROW][C]5[/C][C]8.5[/C][C]8.87615[/C][C]-0.376148[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]6.03514[/C][C]0.964856[/C][/ROW]
[ROW][C]7[/C][C]8[/C][C]9.06002[/C][C]-1.06002[/C][/ROW]
[ROW][C]8[/C][C]10[/C][C]8.3674[/C][C]1.6326[/C][/ROW]
[ROW][C]9[/C][C]7[/C][C]10.1553[/C][C]-3.15526[/C][/ROW]
[ROW][C]10[/C][C]8.5[/C][C]6.02055[/C][C]2.47945[/C][/ROW]
[ROW][C]11[/C][C]9[/C][C]8.17954[/C][C]0.820465[/C][/ROW]
[ROW][C]12[/C][C]9.5[/C][C]5.919[/C][C]3.581[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]7.0655[/C][C]-3.0655[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]7.02943[/C][C]-1.02943[/C][/ROW]
[ROW][C]15[/C][C]8[/C][C]7.58898[/C][C]0.411023[/C][/ROW]
[ROW][C]16[/C][C]5.5[/C][C]7.23529[/C][C]-1.73529[/C][/ROW]
[ROW][C]17[/C][C]9.5[/C][C]7.83683[/C][C]1.66317[/C][/ROW]
[ROW][C]18[/C][C]7.5[/C][C]6.63227[/C][C]0.867733[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]6.78173[/C][C]0.218266[/C][/ROW]
[ROW][C]20[/C][C]7.5[/C][C]8.62885[/C][C]-1.12885[/C][/ROW]
[ROW][C]21[/C][C]8[/C][C]7.11993[/C][C]0.880074[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]7.3885[/C][C]-0.388497[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]6.57624[/C][C]0.423763[/C][/ROW]
[ROW][C]24[/C][C]6[/C][C]7.02808[/C][C]-1.02808[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]7.22325[/C][C]2.77675[/C][/ROW]
[ROW][C]26[/C][C]2.5[/C][C]6.11509[/C][C]-3.61509[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]8.54372[/C][C]0.456282[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]8.17506[/C][C]-0.175065[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]6.43598[/C][C]-0.435984[/C][/ROW]
[ROW][C]30[/C][C]8.5[/C][C]7.1388[/C][C]1.3612[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]8.09054[/C][C]-2.09054[/C][/ROW]
[ROW][C]32[/C][C]9[/C][C]7.64362[/C][C]1.35638[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]7.17684[/C][C]0.823164[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]7.96959[/C][C]0.0304102[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]8.36588[/C][C]0.634124[/C][/ROW]
[ROW][C]36[/C][C]5.5[/C][C]7.19461[/C][C]-1.69461[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]7.13725[/C][C]-2.13725[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]7.32581[/C][C]-0.325812[/C][/ROW]
[ROW][C]39[/C][C]5.5[/C][C]7.87998[/C][C]-2.37998[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]7.87206[/C][C]1.12794[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]7.91538[/C][C]-5.91538[/C][/ROW]
[ROW][C]42[/C][C]8.5[/C][C]8.02113[/C][C]0.478872[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]7.78876[/C][C]1.21124[/C][/ROW]
[ROW][C]44[/C][C]8.5[/C][C]8.20683[/C][C]0.293174[/C][/ROW]
[ROW][C]45[/C][C]9[/C][C]6.47622[/C][C]2.52378[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]7.54966[/C][C]-0.0496553[/C][/ROW]
[ROW][C]47[/C][C]10[/C][C]8.85489[/C][C]1.14511[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]7.32117[/C][C]1.67883[/C][/ROW]
[ROW][C]49[/C][C]7.5[/C][C]8.22768[/C][C]-0.727682[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]6.62951[/C][C]-0.629514[/C][/ROW]
[ROW][C]51[/C][C]10.5[/C][C]8.08383[/C][C]2.41617[/C][/ROW]
[ROW][C]52[/C][C]8.5[/C][C]7.33371[/C][C]1.16629[/C][/ROW]
[ROW][C]53[/C][C]8[/C][C]8.45981[/C][C]-0.459805[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]5.72919[/C][C]4.27081[/C][/ROW]
[ROW][C]55[/C][C]10.5[/C][C]8.47306[/C][C]2.02694[/C][/ROW]
[ROW][C]56[/C][C]6.5[/C][C]5.7227[/C][C]0.777302[/C][/ROW]
[ROW][C]57[/C][C]9.5[/C][C]8.06644[/C][C]1.43356[/C][/ROW]
[ROW][C]58[/C][C]8.5[/C][C]6.25239[/C][C]2.24761[/C][/ROW]
[ROW][C]59[/C][C]7.5[/C][C]7.19053[/C][C]0.309473[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]7.41373[/C][C]-2.41373[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]7.37953[/C][C]0.620469[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]7.52009[/C][C]2.47991[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]7.80689[/C][C]-0.806887[/C][/ROW]
[ROW][C]64[/C][C]7.5[/C][C]7.75992[/C][C]-0.259918[/C][/ROW]
[ROW][C]65[/C][C]7.5[/C][C]7.75992[/C][C]-0.259918[/C][/ROW]
[ROW][C]66[/C][C]9.5[/C][C]7.62687[/C][C]1.87313[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]7.6663[/C][C]-1.6663[/C][/ROW]
[ROW][C]68[/C][C]10[/C][C]8.00172[/C][C]1.99828[/C][/ROW]
[ROW][C]69[/C][C]7[/C][C]7.89954[/C][C]-0.899536[/C][/ROW]
[ROW][C]70[/C][C]3[/C][C]6.0435[/C][C]-3.0435[/C][/ROW]
[ROW][C]71[/C][C]6[/C][C]7.88848[/C][C]-1.88848[/C][/ROW]
[ROW][C]72[/C][C]7[/C][C]7.35569[/C][C]-0.35569[/C][/ROW]
[ROW][C]73[/C][C]10[/C][C]8.3685[/C][C]1.6315[/C][/ROW]
[ROW][C]74[/C][C]7[/C][C]7.61377[/C][C]-0.613772[/C][/ROW]
[ROW][C]75[/C][C]3.5[/C][C]7.46581[/C][C]-3.96581[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]7.59976[/C][C]0.400241[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]6.7613[/C][C]3.2387[/C][/ROW]
[ROW][C]78[/C][C]5.5[/C][C]7.14511[/C][C]-1.64511[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]6.12335[/C][C]-0.123347[/C][/ROW]
[ROW][C]80[/C][C]6.5[/C][C]6.71246[/C][C]-0.212464[/C][/ROW]
[ROW][C]81[/C][C]6.5[/C][C]6.34882[/C][C]0.151183[/C][/ROW]
[ROW][C]82[/C][C]8.5[/C][C]7.68561[/C][C]0.814392[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]6.46566[/C][C]-2.46566[/C][/ROW]
[ROW][C]84[/C][C]9.5[/C][C]7.22167[/C][C]2.27833[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]6.72354[/C][C]1.27646[/C][/ROW]
[ROW][C]86[/C][C]8.5[/C][C]6.93857[/C][C]1.56143[/C][/ROW]
[ROW][C]87[/C][C]5.5[/C][C]8.24007[/C][C]-2.74007[/C][/ROW]
[ROW][C]88[/C][C]7[/C][C]7.12699[/C][C]-0.126986[/C][/ROW]
[ROW][C]89[/C][C]9[/C][C]6.31438[/C][C]2.68562[/C][/ROW]
[ROW][C]90[/C][C]8[/C][C]7.28572[/C][C]0.71428[/C][/ROW]
[ROW][C]91[/C][C]10[/C][C]8.76136[/C][C]1.23864[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]6.48925[/C][C]1.51075[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]7.53829[/C][C]-1.53829[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]7.43618[/C][C]0.563822[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]6.75966[/C][C]-1.75966[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]6.1501[/C][C]2.8499[/C][/ROW]
[ROW][C]97[/C][C]4.5[/C][C]6.53814[/C][C]-2.03814[/C][/ROW]
[ROW][C]98[/C][C]8.5[/C][C]6.22201[/C][C]2.27799[/C][/ROW]
[ROW][C]99[/C][C]7[/C][C]6.44387[/C][C]0.55613[/C][/ROW]
[ROW][C]100[/C][C]9.5[/C][C]8.58089[/C][C]0.919111[/C][/ROW]
[ROW][C]101[/C][C]8.5[/C][C]7.52919[/C][C]0.970809[/C][/ROW]
[ROW][C]102[/C][C]7.5[/C][C]6.37766[/C][C]1.12234[/C][/ROW]
[ROW][C]103[/C][C]7.5[/C][C]7.41209[/C][C]0.087913[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]7.71026[/C][C]-2.71026[/C][/ROW]
[ROW][C]105[/C][C]7[/C][C]6.76862[/C][C]0.231378[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]8.55566[/C][C]-0.555661[/C][/ROW]
[ROW][C]107[/C][C]5.5[/C][C]7.01188[/C][C]-1.51188[/C][/ROW]
[ROW][C]108[/C][C]8.5[/C][C]7.14194[/C][C]1.35806[/C][/ROW]
[ROW][C]109[/C][C]7.5[/C][C]7.2438[/C][C]0.256198[/C][/ROW]
[ROW][C]110[/C][C]9.5[/C][C]7.9452[/C][C]1.5548[/C][/ROW]
[ROW][C]111[/C][C]7[/C][C]6.60728[/C][C]0.392722[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]7.53156[/C][C]0.468437[/C][/ROW]
[ROW][C]113[/C][C]8.5[/C][C]7.46884[/C][C]1.03116[/C][/ROW]
[ROW][C]114[/C][C]3.5[/C][C]6.44414[/C][C]-2.94414[/C][/ROW]
[ROW][C]115[/C][C]6.5[/C][C]6.5656[/C][C]-0.0655995[/C][/ROW]
[ROW][C]116[/C][C]6.5[/C][C]6.86023[/C][C]-0.360228[/C][/ROW]
[ROW][C]117[/C][C]10.5[/C][C]8.27452[/C][C]2.22548[/C][/ROW]
[ROW][C]118[/C][C]8.5[/C][C]6.24069[/C][C]2.25931[/C][/ROW]
[ROW][C]119[/C][C]8[/C][C]7.18606[/C][C]0.813942[/C][/ROW]
[ROW][C]120[/C][C]10[/C][C]6.55276[/C][C]3.44724[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]8.4759[/C][C]1.5241[/C][/ROW]
[ROW][C]122[/C][C]9.5[/C][C]7.85171[/C][C]1.64829[/C][/ROW]
[ROW][C]123[/C][C]9[/C][C]7.35111[/C][C]1.64889[/C][/ROW]
[ROW][C]124[/C][C]10[/C][C]8.77371[/C][C]1.22629[/C][/ROW]
[ROW][C]125[/C][C]7.5[/C][C]6.6615[/C][C]0.838504[/C][/ROW]
[ROW][C]126[/C][C]4.5[/C][C]7.48932[/C][C]-2.98932[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]6.69028[/C][C]-2.19028[/C][/ROW]
[ROW][C]128[/C][C]0.5[/C][C]6.07311[/C][C]-5.57311[/C][/ROW]
[ROW][C]129[/C][C]6.5[/C][C]5.92961[/C][C]0.570393[/C][/ROW]
[ROW][C]130[/C][C]4.5[/C][C]7.68796[/C][C]-3.18796[/C][/ROW]
[ROW][C]131[/C][C]5.5[/C][C]6.92919[/C][C]-1.42919[/C][/ROW]
[ROW][C]132[/C][C]5[/C][C]6.589[/C][C]-1.589[/C][/ROW]
[ROW][C]133[/C][C]6[/C][C]7.63436[/C][C]-1.63436[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]6.83218[/C][C]-2.83218[/C][/ROW]
[ROW][C]135[/C][C]8[/C][C]6.79749[/C][C]1.20251[/C][/ROW]
[ROW][C]136[/C][C]10.5[/C][C]8.47306[/C][C]2.02694[/C][/ROW]
[ROW][C]137[/C][C]8.5[/C][C]7.05174[/C][C]1.44826[/C][/ROW]
[ROW][C]138[/C][C]6.5[/C][C]6.32878[/C][C]0.17122[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]7.63278[/C][C]0.367219[/C][/ROW]
[ROW][C]140[/C][C]8.5[/C][C]8.89112[/C][C]-0.391122[/C][/ROW]
[ROW][C]141[/C][C]5.5[/C][C]6.82359[/C][C]-1.32359[/C][/ROW]
[ROW][C]142[/C][C]7[/C][C]8.13829[/C][C]-1.13829[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]7.12217[/C][C]-2.12217[/C][/ROW]
[ROW][C]144[/C][C]3.5[/C][C]6.90761[/C][C]-3.40761[/C][/ROW]
[ROW][C]145[/C][C]5[/C][C]7.36024[/C][C]-2.36024[/C][/ROW]
[ROW][C]146[/C][C]9[/C][C]7.43096[/C][C]1.56904[/C][/ROW]
[ROW][C]147[/C][C]8.5[/C][C]7.49725[/C][C]1.00275[/C][/ROW]
[ROW][C]148[/C][C]5[/C][C]7.84496[/C][C]-2.84496[/C][/ROW]
[ROW][C]149[/C][C]9.5[/C][C]7.71224[/C][C]1.78776[/C][/ROW]
[ROW][C]150[/C][C]3[/C][C]6.24984[/C][C]-3.24984[/C][/ROW]
[ROW][C]151[/C][C]1.5[/C][C]6.80793[/C][C]-5.30793[/C][/ROW]
[ROW][C]152[/C][C]6[/C][C]7.32492[/C][C]-1.32492[/C][/ROW]
[ROW][C]153[/C][C]0.5[/C][C]7.04227[/C][C]-6.54227[/C][/ROW]
[ROW][C]154[/C][C]6.5[/C][C]5.7227[/C][C]0.777302[/C][/ROW]
[ROW][C]155[/C][C]7.5[/C][C]6.91074[/C][C]0.589258[/C][/ROW]
[ROW][C]156[/C][C]4.5[/C][C]6.63636[/C][C]-2.13636[/C][/ROW]
[ROW][C]157[/C][C]8[/C][C]6.79749[/C][C]1.20251[/C][/ROW]
[ROW][C]158[/C][C]9[/C][C]7.21652[/C][C]1.78348[/C][/ROW]
[ROW][C]159[/C][C]7.5[/C][C]6.80014[/C][C]0.699862[/C][/ROW]
[ROW][C]160[/C][C]8.5[/C][C]6.57725[/C][C]1.92275[/C][/ROW]
[ROW][C]161[/C][C]7[/C][C]6.8329[/C][C]0.167095[/C][/ROW]
[ROW][C]162[/C][C]9.5[/C][C]7.2776[/C][C]2.2224[/C][/ROW]
[ROW][C]163[/C][C]6.5[/C][C]6.3967[/C][C]0.103301[/C][/ROW]
[ROW][C]164[/C][C]9.5[/C][C]6.74078[/C][C]2.75922[/C][/ROW]
[ROW][C]165[/C][C]6[/C][C]6.30422[/C][C]-0.304219[/C][/ROW]
[ROW][C]166[/C][C]8[/C][C]7.451[/C][C]0.548995[/C][/ROW]
[ROW][C]167[/C][C]9.5[/C][C]7.85294[/C][C]1.64706[/C][/ROW]
[ROW][C]168[/C][C]8[/C][C]7.44701[/C][C]0.552986[/C][/ROW]
[ROW][C]169[/C][C]8[/C][C]7.04647[/C][C]0.953534[/C][/ROW]
[ROW][C]170[/C][C]9[/C][C]7.62918[/C][C]1.37082[/C][/ROW]
[ROW][C]171[/C][C]5[/C][C]5.79585[/C][C]-0.79585[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271220&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271220&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
10.55.80823-5.30823
27.56.736760.76324
397.661931.33807
49.57.724451.77555
58.58.87615-0.376148
676.035140.964856
789.06002-1.06002
8108.36741.6326
9710.1553-3.15526
108.56.020552.47945
1198.179540.820465
129.55.9193.581
1347.0655-3.0655
1467.02943-1.02943
1587.588980.411023
165.57.23529-1.73529
179.57.836831.66317
187.56.632270.867733
1976.781730.218266
207.58.62885-1.12885
2187.119930.880074
2277.3885-0.388497
2376.576240.423763
2467.02808-1.02808
25107.223252.77675
262.56.11509-3.61509
2798.543720.456282
2888.17506-0.175065
2966.43598-0.435984
308.57.13881.3612
3168.09054-2.09054
3297.643621.35638
3387.176840.823164
3487.969590.0304102
3598.365880.634124
365.57.19461-1.69461
3757.13725-2.13725
3877.32581-0.325812
395.57.87998-2.37998
4097.872061.12794
4127.91538-5.91538
428.58.021130.478872
4397.788761.21124
448.58.206830.293174
4596.476222.52378
467.57.54966-0.0496553
47108.854891.14511
4897.321171.67883
497.58.22768-0.727682
5066.62951-0.629514
5110.58.083832.41617
528.57.333711.16629
5388.45981-0.459805
54105.729194.27081
5510.58.473062.02694
566.55.72270.777302
579.58.066441.43356
588.56.252392.24761
597.57.190530.309473
6057.41373-2.41373
6187.379530.620469
62107.520092.47991
6377.80689-0.806887
647.57.75992-0.259918
657.57.75992-0.259918
669.57.626871.87313
6767.6663-1.6663
68108.001721.99828
6977.89954-0.899536
7036.0435-3.0435
7167.88848-1.88848
7277.35569-0.35569
73108.36851.6315
7477.61377-0.613772
753.57.46581-3.96581
7687.599760.400241
77106.76133.2387
785.57.14511-1.64511
7966.12335-0.123347
806.56.71246-0.212464
816.56.348820.151183
828.57.685610.814392
8346.46566-2.46566
849.57.221672.27833
8586.723541.27646
868.56.938571.56143
875.58.24007-2.74007
8877.12699-0.126986
8996.314382.68562
9087.285720.71428
91108.761361.23864
9286.489251.51075
9367.53829-1.53829
9487.436180.563822
9556.75966-1.75966
9696.15012.8499
974.56.53814-2.03814
988.56.222012.27799
9976.443870.55613
1009.58.580890.919111
1018.57.529190.970809
1027.56.377661.12234
1037.57.412090.087913
10457.71026-2.71026
10576.768620.231378
10688.55566-0.555661
1075.57.01188-1.51188
1088.57.141941.35806
1097.57.24380.256198
1109.57.94521.5548
11176.607280.392722
11287.531560.468437
1138.57.468841.03116
1143.56.44414-2.94414
1156.56.5656-0.0655995
1166.56.86023-0.360228
11710.58.274522.22548
1188.56.240692.25931
11987.186060.813942
120106.552763.44724
121108.47591.5241
1229.57.851711.64829
12397.351111.64889
124108.773711.22629
1257.56.66150.838504
1264.57.48932-2.98932
1274.56.69028-2.19028
1280.56.07311-5.57311
1296.55.929610.570393
1304.57.68796-3.18796
1315.56.92919-1.42919
13256.589-1.589
13367.63436-1.63436
13446.83218-2.83218
13586.797491.20251
13610.58.473062.02694
1378.57.051741.44826
1386.56.328780.17122
13987.632780.367219
1408.58.89112-0.391122
1415.56.82359-1.32359
14278.13829-1.13829
14357.12217-2.12217
1443.56.90761-3.40761
14557.36024-2.36024
14697.430961.56904
1478.57.497251.00275
14857.84496-2.84496
1499.57.712241.78776
15036.24984-3.24984
1511.56.80793-5.30793
15267.32492-1.32492
1530.57.04227-6.54227
1546.55.72270.777302
1557.56.910740.589258
1564.56.63636-2.13636
15786.797491.20251
15897.216521.78348
1597.56.800140.699862
1608.56.577251.92275
16176.83290.167095
1629.57.27762.2224
1636.56.39670.103301
1649.56.740782.75922
16566.30422-0.304219
16687.4510.548995
1679.57.852941.64706
16887.447010.552986
16987.046470.953534
17097.629181.37082
17155.79585-0.79585






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8812640.2374720.118736
90.9207730.1584540.0792271
100.9216690.1566610.0783305
110.8700390.2599220.129961
120.9516450.09671020.0483551
130.9523660.09526890.0476344
140.9502950.09940940.0497047
150.9266760.1466480.0733239
160.9181830.1636350.0818175
170.9187350.162530.0812648
180.8853290.2293410.114671
190.8515370.2969260.148463
200.8132130.3735740.186787
210.7628910.4742180.237109
220.7058750.5882490.294125
230.6429810.7140380.357019
240.5819780.8360440.418022
250.5933670.8132670.406633
260.6545820.6908350.345418
270.5981840.8036330.401816
280.556460.887080.44354
290.500280.9994410.49972
300.464640.9292790.53536
310.5178990.9642020.482101
320.4784570.9569150.521543
330.4235760.8471530.576424
340.3690760.7381520.630924
350.3170570.6341130.682943
360.3188340.6376680.681166
370.3840210.7680410.615979
380.3320170.6640340.667983
390.3658340.7316680.634166
400.3214270.6428530.678573
410.7645030.4709930.235497
420.7304760.5390490.269524
430.7014460.5971090.298554
440.6557750.6884490.344225
450.6904430.6191130.309557
460.644530.7109410.35547
470.6170190.7659630.382981
480.6039790.7920410.396021
490.5605260.8789480.439474
500.5155520.9688960.484448
510.5380480.9239050.461952
520.5123560.9752870.487644
530.4683110.9366220.531689
540.6301260.7397480.369874
550.6325560.7348870.367444
560.5900140.8199720.409986
570.5647640.8704720.435236
580.5683660.8632670.431634
590.5219960.9560080.478004
600.5530980.8938040.446902
610.5096720.9806560.490328
620.5257540.9484920.474246
630.4919710.9839420.508029
640.4468550.893710.553145
650.4023590.8047180.597641
660.3914810.7829620.608519
670.3854080.7708160.614592
680.3858530.7717070.614147
690.3554210.7108430.644579
700.4256150.851230.574385
710.4320780.8641560.567922
720.3903370.7806740.609663
730.3742860.7485730.625714
740.3374630.6749260.662537
750.4729410.9458820.527059
760.4294850.8589710.570515
770.502990.994020.49701
780.492520.985040.50748
790.448720.8974410.55128
800.4056310.8112620.594369
810.3633940.7267880.636606
820.3279220.6558440.672078
830.3485290.6970590.651471
840.3597630.7195260.640237
850.3358330.6716650.664167
860.3190190.6380380.680981
870.3586570.7173130.641343
880.318030.6360590.68197
890.3479360.6958720.652064
900.3113480.6226970.688652
910.2841350.568270.715865
920.2710540.5421080.728946
930.25640.5128010.7436
940.2250550.4501110.774945
950.2173350.434670.782665
960.2538920.5077840.746108
970.2551370.5102740.744863
980.2753060.5506130.724694
990.2445280.4890560.755472
1000.2165240.4330480.783476
1010.1902170.3804340.809783
1020.1747640.3495270.825236
1030.1473320.2946640.852668
1040.1802020.3604050.819798
1050.1539730.3079460.846027
1060.1349820.2699650.865018
1070.1246740.2493490.875326
1080.1117070.2234140.888293
1090.09174970.1834990.90825
1100.0848760.1697520.915124
1110.0699410.1398820.930059
1120.05590570.1118110.944094
1130.04602040.09204080.95398
1140.0555880.1111760.944412
1150.04469890.08939770.955301
1160.03584880.07169750.964151
1170.03762150.07524290.962379
1180.04423610.08847220.955764
1190.04095980.08191960.95904
1200.06672220.1334440.933278
1210.05716170.1143230.942838
1220.05702120.1140420.942979
1230.06274880.1254980.937251
1240.0514350.102870.948565
1250.04496040.08992080.95504
1260.04975460.09950920.950245
1270.04583750.09167490.954163
1280.1571090.3142190.842891
1290.13620.2723990.8638
1300.175870.351740.82413
1310.1573060.3146110.842694
1320.1387520.2775030.861248
1330.126470.2529390.87353
1340.1560950.312190.843905
1350.1410890.2821770.858911
1360.1335020.2670030.866498
1370.1451230.2902470.854877
1380.1187850.2375710.881215
1390.09279960.1855990.9072
1400.07120540.1424110.928795
1410.05558740.1111750.944413
1420.04660050.0932010.9534
1430.03921330.07842660.960787
1440.04564590.09129190.954354
1450.05971590.1194320.940284
1460.04622480.09244960.953775
1470.03341640.06683290.966584
1480.05062080.1012420.949379
1490.04165660.08331310.958343
1500.05431390.1086280.945686
1510.5746290.8507420.425371
1520.5338790.9322430.466121
1530.9981470.003705980.00185299
1540.9999696.10386e-053.05193e-05
1550.9999260.0001483197.41596e-05
1560.999920.000160728.03602e-05
1570.9999080.0001847989.23991e-05
1580.9997740.0004512680.000225634
1590.9991290.001742080.00087104
1600.9982680.003463770.00173188
1610.9936370.01272510.00636256
1620.9785560.04288840.0214442
1630.9492090.1015820.0507911

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.881264 & 0.237472 & 0.118736 \tabularnewline
9 & 0.920773 & 0.158454 & 0.0792271 \tabularnewline
10 & 0.921669 & 0.156661 & 0.0783305 \tabularnewline
11 & 0.870039 & 0.259922 & 0.129961 \tabularnewline
12 & 0.951645 & 0.0967102 & 0.0483551 \tabularnewline
13 & 0.952366 & 0.0952689 & 0.0476344 \tabularnewline
14 & 0.950295 & 0.0994094 & 0.0497047 \tabularnewline
15 & 0.926676 & 0.146648 & 0.0733239 \tabularnewline
16 & 0.918183 & 0.163635 & 0.0818175 \tabularnewline
17 & 0.918735 & 0.16253 & 0.0812648 \tabularnewline
18 & 0.885329 & 0.229341 & 0.114671 \tabularnewline
19 & 0.851537 & 0.296926 & 0.148463 \tabularnewline
20 & 0.813213 & 0.373574 & 0.186787 \tabularnewline
21 & 0.762891 & 0.474218 & 0.237109 \tabularnewline
22 & 0.705875 & 0.588249 & 0.294125 \tabularnewline
23 & 0.642981 & 0.714038 & 0.357019 \tabularnewline
24 & 0.581978 & 0.836044 & 0.418022 \tabularnewline
25 & 0.593367 & 0.813267 & 0.406633 \tabularnewline
26 & 0.654582 & 0.690835 & 0.345418 \tabularnewline
27 & 0.598184 & 0.803633 & 0.401816 \tabularnewline
28 & 0.55646 & 0.88708 & 0.44354 \tabularnewline
29 & 0.50028 & 0.999441 & 0.49972 \tabularnewline
30 & 0.46464 & 0.929279 & 0.53536 \tabularnewline
31 & 0.517899 & 0.964202 & 0.482101 \tabularnewline
32 & 0.478457 & 0.956915 & 0.521543 \tabularnewline
33 & 0.423576 & 0.847153 & 0.576424 \tabularnewline
34 & 0.369076 & 0.738152 & 0.630924 \tabularnewline
35 & 0.317057 & 0.634113 & 0.682943 \tabularnewline
36 & 0.318834 & 0.637668 & 0.681166 \tabularnewline
37 & 0.384021 & 0.768041 & 0.615979 \tabularnewline
38 & 0.332017 & 0.664034 & 0.667983 \tabularnewline
39 & 0.365834 & 0.731668 & 0.634166 \tabularnewline
40 & 0.321427 & 0.642853 & 0.678573 \tabularnewline
41 & 0.764503 & 0.470993 & 0.235497 \tabularnewline
42 & 0.730476 & 0.539049 & 0.269524 \tabularnewline
43 & 0.701446 & 0.597109 & 0.298554 \tabularnewline
44 & 0.655775 & 0.688449 & 0.344225 \tabularnewline
45 & 0.690443 & 0.619113 & 0.309557 \tabularnewline
46 & 0.64453 & 0.710941 & 0.35547 \tabularnewline
47 & 0.617019 & 0.765963 & 0.382981 \tabularnewline
48 & 0.603979 & 0.792041 & 0.396021 \tabularnewline
49 & 0.560526 & 0.878948 & 0.439474 \tabularnewline
50 & 0.515552 & 0.968896 & 0.484448 \tabularnewline
51 & 0.538048 & 0.923905 & 0.461952 \tabularnewline
52 & 0.512356 & 0.975287 & 0.487644 \tabularnewline
53 & 0.468311 & 0.936622 & 0.531689 \tabularnewline
54 & 0.630126 & 0.739748 & 0.369874 \tabularnewline
55 & 0.632556 & 0.734887 & 0.367444 \tabularnewline
56 & 0.590014 & 0.819972 & 0.409986 \tabularnewline
57 & 0.564764 & 0.870472 & 0.435236 \tabularnewline
58 & 0.568366 & 0.863267 & 0.431634 \tabularnewline
59 & 0.521996 & 0.956008 & 0.478004 \tabularnewline
60 & 0.553098 & 0.893804 & 0.446902 \tabularnewline
61 & 0.509672 & 0.980656 & 0.490328 \tabularnewline
62 & 0.525754 & 0.948492 & 0.474246 \tabularnewline
63 & 0.491971 & 0.983942 & 0.508029 \tabularnewline
64 & 0.446855 & 0.89371 & 0.553145 \tabularnewline
65 & 0.402359 & 0.804718 & 0.597641 \tabularnewline
66 & 0.391481 & 0.782962 & 0.608519 \tabularnewline
67 & 0.385408 & 0.770816 & 0.614592 \tabularnewline
68 & 0.385853 & 0.771707 & 0.614147 \tabularnewline
69 & 0.355421 & 0.710843 & 0.644579 \tabularnewline
70 & 0.425615 & 0.85123 & 0.574385 \tabularnewline
71 & 0.432078 & 0.864156 & 0.567922 \tabularnewline
72 & 0.390337 & 0.780674 & 0.609663 \tabularnewline
73 & 0.374286 & 0.748573 & 0.625714 \tabularnewline
74 & 0.337463 & 0.674926 & 0.662537 \tabularnewline
75 & 0.472941 & 0.945882 & 0.527059 \tabularnewline
76 & 0.429485 & 0.858971 & 0.570515 \tabularnewline
77 & 0.50299 & 0.99402 & 0.49701 \tabularnewline
78 & 0.49252 & 0.98504 & 0.50748 \tabularnewline
79 & 0.44872 & 0.897441 & 0.55128 \tabularnewline
80 & 0.405631 & 0.811262 & 0.594369 \tabularnewline
81 & 0.363394 & 0.726788 & 0.636606 \tabularnewline
82 & 0.327922 & 0.655844 & 0.672078 \tabularnewline
83 & 0.348529 & 0.697059 & 0.651471 \tabularnewline
84 & 0.359763 & 0.719526 & 0.640237 \tabularnewline
85 & 0.335833 & 0.671665 & 0.664167 \tabularnewline
86 & 0.319019 & 0.638038 & 0.680981 \tabularnewline
87 & 0.358657 & 0.717313 & 0.641343 \tabularnewline
88 & 0.31803 & 0.636059 & 0.68197 \tabularnewline
89 & 0.347936 & 0.695872 & 0.652064 \tabularnewline
90 & 0.311348 & 0.622697 & 0.688652 \tabularnewline
91 & 0.284135 & 0.56827 & 0.715865 \tabularnewline
92 & 0.271054 & 0.542108 & 0.728946 \tabularnewline
93 & 0.2564 & 0.512801 & 0.7436 \tabularnewline
94 & 0.225055 & 0.450111 & 0.774945 \tabularnewline
95 & 0.217335 & 0.43467 & 0.782665 \tabularnewline
96 & 0.253892 & 0.507784 & 0.746108 \tabularnewline
97 & 0.255137 & 0.510274 & 0.744863 \tabularnewline
98 & 0.275306 & 0.550613 & 0.724694 \tabularnewline
99 & 0.244528 & 0.489056 & 0.755472 \tabularnewline
100 & 0.216524 & 0.433048 & 0.783476 \tabularnewline
101 & 0.190217 & 0.380434 & 0.809783 \tabularnewline
102 & 0.174764 & 0.349527 & 0.825236 \tabularnewline
103 & 0.147332 & 0.294664 & 0.852668 \tabularnewline
104 & 0.180202 & 0.360405 & 0.819798 \tabularnewline
105 & 0.153973 & 0.307946 & 0.846027 \tabularnewline
106 & 0.134982 & 0.269965 & 0.865018 \tabularnewline
107 & 0.124674 & 0.249349 & 0.875326 \tabularnewline
108 & 0.111707 & 0.223414 & 0.888293 \tabularnewline
109 & 0.0917497 & 0.183499 & 0.90825 \tabularnewline
110 & 0.084876 & 0.169752 & 0.915124 \tabularnewline
111 & 0.069941 & 0.139882 & 0.930059 \tabularnewline
112 & 0.0559057 & 0.111811 & 0.944094 \tabularnewline
113 & 0.0460204 & 0.0920408 & 0.95398 \tabularnewline
114 & 0.055588 & 0.111176 & 0.944412 \tabularnewline
115 & 0.0446989 & 0.0893977 & 0.955301 \tabularnewline
116 & 0.0358488 & 0.0716975 & 0.964151 \tabularnewline
117 & 0.0376215 & 0.0752429 & 0.962379 \tabularnewline
118 & 0.0442361 & 0.0884722 & 0.955764 \tabularnewline
119 & 0.0409598 & 0.0819196 & 0.95904 \tabularnewline
120 & 0.0667222 & 0.133444 & 0.933278 \tabularnewline
121 & 0.0571617 & 0.114323 & 0.942838 \tabularnewline
122 & 0.0570212 & 0.114042 & 0.942979 \tabularnewline
123 & 0.0627488 & 0.125498 & 0.937251 \tabularnewline
124 & 0.051435 & 0.10287 & 0.948565 \tabularnewline
125 & 0.0449604 & 0.0899208 & 0.95504 \tabularnewline
126 & 0.0497546 & 0.0995092 & 0.950245 \tabularnewline
127 & 0.0458375 & 0.0916749 & 0.954163 \tabularnewline
128 & 0.157109 & 0.314219 & 0.842891 \tabularnewline
129 & 0.1362 & 0.272399 & 0.8638 \tabularnewline
130 & 0.17587 & 0.35174 & 0.82413 \tabularnewline
131 & 0.157306 & 0.314611 & 0.842694 \tabularnewline
132 & 0.138752 & 0.277503 & 0.861248 \tabularnewline
133 & 0.12647 & 0.252939 & 0.87353 \tabularnewline
134 & 0.156095 & 0.31219 & 0.843905 \tabularnewline
135 & 0.141089 & 0.282177 & 0.858911 \tabularnewline
136 & 0.133502 & 0.267003 & 0.866498 \tabularnewline
137 & 0.145123 & 0.290247 & 0.854877 \tabularnewline
138 & 0.118785 & 0.237571 & 0.881215 \tabularnewline
139 & 0.0927996 & 0.185599 & 0.9072 \tabularnewline
140 & 0.0712054 & 0.142411 & 0.928795 \tabularnewline
141 & 0.0555874 & 0.111175 & 0.944413 \tabularnewline
142 & 0.0466005 & 0.093201 & 0.9534 \tabularnewline
143 & 0.0392133 & 0.0784266 & 0.960787 \tabularnewline
144 & 0.0456459 & 0.0912919 & 0.954354 \tabularnewline
145 & 0.0597159 & 0.119432 & 0.940284 \tabularnewline
146 & 0.0462248 & 0.0924496 & 0.953775 \tabularnewline
147 & 0.0334164 & 0.0668329 & 0.966584 \tabularnewline
148 & 0.0506208 & 0.101242 & 0.949379 \tabularnewline
149 & 0.0416566 & 0.0833131 & 0.958343 \tabularnewline
150 & 0.0543139 & 0.108628 & 0.945686 \tabularnewline
151 & 0.574629 & 0.850742 & 0.425371 \tabularnewline
152 & 0.533879 & 0.932243 & 0.466121 \tabularnewline
153 & 0.998147 & 0.00370598 & 0.00185299 \tabularnewline
154 & 0.999969 & 6.10386e-05 & 3.05193e-05 \tabularnewline
155 & 0.999926 & 0.000148319 & 7.41596e-05 \tabularnewline
156 & 0.99992 & 0.00016072 & 8.03602e-05 \tabularnewline
157 & 0.999908 & 0.000184798 & 9.23991e-05 \tabularnewline
158 & 0.999774 & 0.000451268 & 0.000225634 \tabularnewline
159 & 0.999129 & 0.00174208 & 0.00087104 \tabularnewline
160 & 0.998268 & 0.00346377 & 0.00173188 \tabularnewline
161 & 0.993637 & 0.0127251 & 0.00636256 \tabularnewline
162 & 0.978556 & 0.0428884 & 0.0214442 \tabularnewline
163 & 0.949209 & 0.101582 & 0.0507911 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271220&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]8[/C][C]0.881264[/C][C]0.237472[/C][C]0.118736[/C][/ROW]
[ROW][C]9[/C][C]0.920773[/C][C]0.158454[/C][C]0.0792271[/C][/ROW]
[ROW][C]10[/C][C]0.921669[/C][C]0.156661[/C][C]0.0783305[/C][/ROW]
[ROW][C]11[/C][C]0.870039[/C][C]0.259922[/C][C]0.129961[/C][/ROW]
[ROW][C]12[/C][C]0.951645[/C][C]0.0967102[/C][C]0.0483551[/C][/ROW]
[ROW][C]13[/C][C]0.952366[/C][C]0.0952689[/C][C]0.0476344[/C][/ROW]
[ROW][C]14[/C][C]0.950295[/C][C]0.0994094[/C][C]0.0497047[/C][/ROW]
[ROW][C]15[/C][C]0.926676[/C][C]0.146648[/C][C]0.0733239[/C][/ROW]
[ROW][C]16[/C][C]0.918183[/C][C]0.163635[/C][C]0.0818175[/C][/ROW]
[ROW][C]17[/C][C]0.918735[/C][C]0.16253[/C][C]0.0812648[/C][/ROW]
[ROW][C]18[/C][C]0.885329[/C][C]0.229341[/C][C]0.114671[/C][/ROW]
[ROW][C]19[/C][C]0.851537[/C][C]0.296926[/C][C]0.148463[/C][/ROW]
[ROW][C]20[/C][C]0.813213[/C][C]0.373574[/C][C]0.186787[/C][/ROW]
[ROW][C]21[/C][C]0.762891[/C][C]0.474218[/C][C]0.237109[/C][/ROW]
[ROW][C]22[/C][C]0.705875[/C][C]0.588249[/C][C]0.294125[/C][/ROW]
[ROW][C]23[/C][C]0.642981[/C][C]0.714038[/C][C]0.357019[/C][/ROW]
[ROW][C]24[/C][C]0.581978[/C][C]0.836044[/C][C]0.418022[/C][/ROW]
[ROW][C]25[/C][C]0.593367[/C][C]0.813267[/C][C]0.406633[/C][/ROW]
[ROW][C]26[/C][C]0.654582[/C][C]0.690835[/C][C]0.345418[/C][/ROW]
[ROW][C]27[/C][C]0.598184[/C][C]0.803633[/C][C]0.401816[/C][/ROW]
[ROW][C]28[/C][C]0.55646[/C][C]0.88708[/C][C]0.44354[/C][/ROW]
[ROW][C]29[/C][C]0.50028[/C][C]0.999441[/C][C]0.49972[/C][/ROW]
[ROW][C]30[/C][C]0.46464[/C][C]0.929279[/C][C]0.53536[/C][/ROW]
[ROW][C]31[/C][C]0.517899[/C][C]0.964202[/C][C]0.482101[/C][/ROW]
[ROW][C]32[/C][C]0.478457[/C][C]0.956915[/C][C]0.521543[/C][/ROW]
[ROW][C]33[/C][C]0.423576[/C][C]0.847153[/C][C]0.576424[/C][/ROW]
[ROW][C]34[/C][C]0.369076[/C][C]0.738152[/C][C]0.630924[/C][/ROW]
[ROW][C]35[/C][C]0.317057[/C][C]0.634113[/C][C]0.682943[/C][/ROW]
[ROW][C]36[/C][C]0.318834[/C][C]0.637668[/C][C]0.681166[/C][/ROW]
[ROW][C]37[/C][C]0.384021[/C][C]0.768041[/C][C]0.615979[/C][/ROW]
[ROW][C]38[/C][C]0.332017[/C][C]0.664034[/C][C]0.667983[/C][/ROW]
[ROW][C]39[/C][C]0.365834[/C][C]0.731668[/C][C]0.634166[/C][/ROW]
[ROW][C]40[/C][C]0.321427[/C][C]0.642853[/C][C]0.678573[/C][/ROW]
[ROW][C]41[/C][C]0.764503[/C][C]0.470993[/C][C]0.235497[/C][/ROW]
[ROW][C]42[/C][C]0.730476[/C][C]0.539049[/C][C]0.269524[/C][/ROW]
[ROW][C]43[/C][C]0.701446[/C][C]0.597109[/C][C]0.298554[/C][/ROW]
[ROW][C]44[/C][C]0.655775[/C][C]0.688449[/C][C]0.344225[/C][/ROW]
[ROW][C]45[/C][C]0.690443[/C][C]0.619113[/C][C]0.309557[/C][/ROW]
[ROW][C]46[/C][C]0.64453[/C][C]0.710941[/C][C]0.35547[/C][/ROW]
[ROW][C]47[/C][C]0.617019[/C][C]0.765963[/C][C]0.382981[/C][/ROW]
[ROW][C]48[/C][C]0.603979[/C][C]0.792041[/C][C]0.396021[/C][/ROW]
[ROW][C]49[/C][C]0.560526[/C][C]0.878948[/C][C]0.439474[/C][/ROW]
[ROW][C]50[/C][C]0.515552[/C][C]0.968896[/C][C]0.484448[/C][/ROW]
[ROW][C]51[/C][C]0.538048[/C][C]0.923905[/C][C]0.461952[/C][/ROW]
[ROW][C]52[/C][C]0.512356[/C][C]0.975287[/C][C]0.487644[/C][/ROW]
[ROW][C]53[/C][C]0.468311[/C][C]0.936622[/C][C]0.531689[/C][/ROW]
[ROW][C]54[/C][C]0.630126[/C][C]0.739748[/C][C]0.369874[/C][/ROW]
[ROW][C]55[/C][C]0.632556[/C][C]0.734887[/C][C]0.367444[/C][/ROW]
[ROW][C]56[/C][C]0.590014[/C][C]0.819972[/C][C]0.409986[/C][/ROW]
[ROW][C]57[/C][C]0.564764[/C][C]0.870472[/C][C]0.435236[/C][/ROW]
[ROW][C]58[/C][C]0.568366[/C][C]0.863267[/C][C]0.431634[/C][/ROW]
[ROW][C]59[/C][C]0.521996[/C][C]0.956008[/C][C]0.478004[/C][/ROW]
[ROW][C]60[/C][C]0.553098[/C][C]0.893804[/C][C]0.446902[/C][/ROW]
[ROW][C]61[/C][C]0.509672[/C][C]0.980656[/C][C]0.490328[/C][/ROW]
[ROW][C]62[/C][C]0.525754[/C][C]0.948492[/C][C]0.474246[/C][/ROW]
[ROW][C]63[/C][C]0.491971[/C][C]0.983942[/C][C]0.508029[/C][/ROW]
[ROW][C]64[/C][C]0.446855[/C][C]0.89371[/C][C]0.553145[/C][/ROW]
[ROW][C]65[/C][C]0.402359[/C][C]0.804718[/C][C]0.597641[/C][/ROW]
[ROW][C]66[/C][C]0.391481[/C][C]0.782962[/C][C]0.608519[/C][/ROW]
[ROW][C]67[/C][C]0.385408[/C][C]0.770816[/C][C]0.614592[/C][/ROW]
[ROW][C]68[/C][C]0.385853[/C][C]0.771707[/C][C]0.614147[/C][/ROW]
[ROW][C]69[/C][C]0.355421[/C][C]0.710843[/C][C]0.644579[/C][/ROW]
[ROW][C]70[/C][C]0.425615[/C][C]0.85123[/C][C]0.574385[/C][/ROW]
[ROW][C]71[/C][C]0.432078[/C][C]0.864156[/C][C]0.567922[/C][/ROW]
[ROW][C]72[/C][C]0.390337[/C][C]0.780674[/C][C]0.609663[/C][/ROW]
[ROW][C]73[/C][C]0.374286[/C][C]0.748573[/C][C]0.625714[/C][/ROW]
[ROW][C]74[/C][C]0.337463[/C][C]0.674926[/C][C]0.662537[/C][/ROW]
[ROW][C]75[/C][C]0.472941[/C][C]0.945882[/C][C]0.527059[/C][/ROW]
[ROW][C]76[/C][C]0.429485[/C][C]0.858971[/C][C]0.570515[/C][/ROW]
[ROW][C]77[/C][C]0.50299[/C][C]0.99402[/C][C]0.49701[/C][/ROW]
[ROW][C]78[/C][C]0.49252[/C][C]0.98504[/C][C]0.50748[/C][/ROW]
[ROW][C]79[/C][C]0.44872[/C][C]0.897441[/C][C]0.55128[/C][/ROW]
[ROW][C]80[/C][C]0.405631[/C][C]0.811262[/C][C]0.594369[/C][/ROW]
[ROW][C]81[/C][C]0.363394[/C][C]0.726788[/C][C]0.636606[/C][/ROW]
[ROW][C]82[/C][C]0.327922[/C][C]0.655844[/C][C]0.672078[/C][/ROW]
[ROW][C]83[/C][C]0.348529[/C][C]0.697059[/C][C]0.651471[/C][/ROW]
[ROW][C]84[/C][C]0.359763[/C][C]0.719526[/C][C]0.640237[/C][/ROW]
[ROW][C]85[/C][C]0.335833[/C][C]0.671665[/C][C]0.664167[/C][/ROW]
[ROW][C]86[/C][C]0.319019[/C][C]0.638038[/C][C]0.680981[/C][/ROW]
[ROW][C]87[/C][C]0.358657[/C][C]0.717313[/C][C]0.641343[/C][/ROW]
[ROW][C]88[/C][C]0.31803[/C][C]0.636059[/C][C]0.68197[/C][/ROW]
[ROW][C]89[/C][C]0.347936[/C][C]0.695872[/C][C]0.652064[/C][/ROW]
[ROW][C]90[/C][C]0.311348[/C][C]0.622697[/C][C]0.688652[/C][/ROW]
[ROW][C]91[/C][C]0.284135[/C][C]0.56827[/C][C]0.715865[/C][/ROW]
[ROW][C]92[/C][C]0.271054[/C][C]0.542108[/C][C]0.728946[/C][/ROW]
[ROW][C]93[/C][C]0.2564[/C][C]0.512801[/C][C]0.7436[/C][/ROW]
[ROW][C]94[/C][C]0.225055[/C][C]0.450111[/C][C]0.774945[/C][/ROW]
[ROW][C]95[/C][C]0.217335[/C][C]0.43467[/C][C]0.782665[/C][/ROW]
[ROW][C]96[/C][C]0.253892[/C][C]0.507784[/C][C]0.746108[/C][/ROW]
[ROW][C]97[/C][C]0.255137[/C][C]0.510274[/C][C]0.744863[/C][/ROW]
[ROW][C]98[/C][C]0.275306[/C][C]0.550613[/C][C]0.724694[/C][/ROW]
[ROW][C]99[/C][C]0.244528[/C][C]0.489056[/C][C]0.755472[/C][/ROW]
[ROW][C]100[/C][C]0.216524[/C][C]0.433048[/C][C]0.783476[/C][/ROW]
[ROW][C]101[/C][C]0.190217[/C][C]0.380434[/C][C]0.809783[/C][/ROW]
[ROW][C]102[/C][C]0.174764[/C][C]0.349527[/C][C]0.825236[/C][/ROW]
[ROW][C]103[/C][C]0.147332[/C][C]0.294664[/C][C]0.852668[/C][/ROW]
[ROW][C]104[/C][C]0.180202[/C][C]0.360405[/C][C]0.819798[/C][/ROW]
[ROW][C]105[/C][C]0.153973[/C][C]0.307946[/C][C]0.846027[/C][/ROW]
[ROW][C]106[/C][C]0.134982[/C][C]0.269965[/C][C]0.865018[/C][/ROW]
[ROW][C]107[/C][C]0.124674[/C][C]0.249349[/C][C]0.875326[/C][/ROW]
[ROW][C]108[/C][C]0.111707[/C][C]0.223414[/C][C]0.888293[/C][/ROW]
[ROW][C]109[/C][C]0.0917497[/C][C]0.183499[/C][C]0.90825[/C][/ROW]
[ROW][C]110[/C][C]0.084876[/C][C]0.169752[/C][C]0.915124[/C][/ROW]
[ROW][C]111[/C][C]0.069941[/C][C]0.139882[/C][C]0.930059[/C][/ROW]
[ROW][C]112[/C][C]0.0559057[/C][C]0.111811[/C][C]0.944094[/C][/ROW]
[ROW][C]113[/C][C]0.0460204[/C][C]0.0920408[/C][C]0.95398[/C][/ROW]
[ROW][C]114[/C][C]0.055588[/C][C]0.111176[/C][C]0.944412[/C][/ROW]
[ROW][C]115[/C][C]0.0446989[/C][C]0.0893977[/C][C]0.955301[/C][/ROW]
[ROW][C]116[/C][C]0.0358488[/C][C]0.0716975[/C][C]0.964151[/C][/ROW]
[ROW][C]117[/C][C]0.0376215[/C][C]0.0752429[/C][C]0.962379[/C][/ROW]
[ROW][C]118[/C][C]0.0442361[/C][C]0.0884722[/C][C]0.955764[/C][/ROW]
[ROW][C]119[/C][C]0.0409598[/C][C]0.0819196[/C][C]0.95904[/C][/ROW]
[ROW][C]120[/C][C]0.0667222[/C][C]0.133444[/C][C]0.933278[/C][/ROW]
[ROW][C]121[/C][C]0.0571617[/C][C]0.114323[/C][C]0.942838[/C][/ROW]
[ROW][C]122[/C][C]0.0570212[/C][C]0.114042[/C][C]0.942979[/C][/ROW]
[ROW][C]123[/C][C]0.0627488[/C][C]0.125498[/C][C]0.937251[/C][/ROW]
[ROW][C]124[/C][C]0.051435[/C][C]0.10287[/C][C]0.948565[/C][/ROW]
[ROW][C]125[/C][C]0.0449604[/C][C]0.0899208[/C][C]0.95504[/C][/ROW]
[ROW][C]126[/C][C]0.0497546[/C][C]0.0995092[/C][C]0.950245[/C][/ROW]
[ROW][C]127[/C][C]0.0458375[/C][C]0.0916749[/C][C]0.954163[/C][/ROW]
[ROW][C]128[/C][C]0.157109[/C][C]0.314219[/C][C]0.842891[/C][/ROW]
[ROW][C]129[/C][C]0.1362[/C][C]0.272399[/C][C]0.8638[/C][/ROW]
[ROW][C]130[/C][C]0.17587[/C][C]0.35174[/C][C]0.82413[/C][/ROW]
[ROW][C]131[/C][C]0.157306[/C][C]0.314611[/C][C]0.842694[/C][/ROW]
[ROW][C]132[/C][C]0.138752[/C][C]0.277503[/C][C]0.861248[/C][/ROW]
[ROW][C]133[/C][C]0.12647[/C][C]0.252939[/C][C]0.87353[/C][/ROW]
[ROW][C]134[/C][C]0.156095[/C][C]0.31219[/C][C]0.843905[/C][/ROW]
[ROW][C]135[/C][C]0.141089[/C][C]0.282177[/C][C]0.858911[/C][/ROW]
[ROW][C]136[/C][C]0.133502[/C][C]0.267003[/C][C]0.866498[/C][/ROW]
[ROW][C]137[/C][C]0.145123[/C][C]0.290247[/C][C]0.854877[/C][/ROW]
[ROW][C]138[/C][C]0.118785[/C][C]0.237571[/C][C]0.881215[/C][/ROW]
[ROW][C]139[/C][C]0.0927996[/C][C]0.185599[/C][C]0.9072[/C][/ROW]
[ROW][C]140[/C][C]0.0712054[/C][C]0.142411[/C][C]0.928795[/C][/ROW]
[ROW][C]141[/C][C]0.0555874[/C][C]0.111175[/C][C]0.944413[/C][/ROW]
[ROW][C]142[/C][C]0.0466005[/C][C]0.093201[/C][C]0.9534[/C][/ROW]
[ROW][C]143[/C][C]0.0392133[/C][C]0.0784266[/C][C]0.960787[/C][/ROW]
[ROW][C]144[/C][C]0.0456459[/C][C]0.0912919[/C][C]0.954354[/C][/ROW]
[ROW][C]145[/C][C]0.0597159[/C][C]0.119432[/C][C]0.940284[/C][/ROW]
[ROW][C]146[/C][C]0.0462248[/C][C]0.0924496[/C][C]0.953775[/C][/ROW]
[ROW][C]147[/C][C]0.0334164[/C][C]0.0668329[/C][C]0.966584[/C][/ROW]
[ROW][C]148[/C][C]0.0506208[/C][C]0.101242[/C][C]0.949379[/C][/ROW]
[ROW][C]149[/C][C]0.0416566[/C][C]0.0833131[/C][C]0.958343[/C][/ROW]
[ROW][C]150[/C][C]0.0543139[/C][C]0.108628[/C][C]0.945686[/C][/ROW]
[ROW][C]151[/C][C]0.574629[/C][C]0.850742[/C][C]0.425371[/C][/ROW]
[ROW][C]152[/C][C]0.533879[/C][C]0.932243[/C][C]0.466121[/C][/ROW]
[ROW][C]153[/C][C]0.998147[/C][C]0.00370598[/C][C]0.00185299[/C][/ROW]
[ROW][C]154[/C][C]0.999969[/C][C]6.10386e-05[/C][C]3.05193e-05[/C][/ROW]
[ROW][C]155[/C][C]0.999926[/C][C]0.000148319[/C][C]7.41596e-05[/C][/ROW]
[ROW][C]156[/C][C]0.99992[/C][C]0.00016072[/C][C]8.03602e-05[/C][/ROW]
[ROW][C]157[/C][C]0.999908[/C][C]0.000184798[/C][C]9.23991e-05[/C][/ROW]
[ROW][C]158[/C][C]0.999774[/C][C]0.000451268[/C][C]0.000225634[/C][/ROW]
[ROW][C]159[/C][C]0.999129[/C][C]0.00174208[/C][C]0.00087104[/C][/ROW]
[ROW][C]160[/C][C]0.998268[/C][C]0.00346377[/C][C]0.00173188[/C][/ROW]
[ROW][C]161[/C][C]0.993637[/C][C]0.0127251[/C][C]0.00636256[/C][/ROW]
[ROW][C]162[/C][C]0.978556[/C][C]0.0428884[/C][C]0.0214442[/C][/ROW]
[ROW][C]163[/C][C]0.949209[/C][C]0.101582[/C][C]0.0507911[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271220&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271220&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
80.8812640.2374720.118736
90.9207730.1584540.0792271
100.9216690.1566610.0783305
110.8700390.2599220.129961
120.9516450.09671020.0483551
130.9523660.09526890.0476344
140.9502950.09940940.0497047
150.9266760.1466480.0733239
160.9181830.1636350.0818175
170.9187350.162530.0812648
180.8853290.2293410.114671
190.8515370.2969260.148463
200.8132130.3735740.186787
210.7628910.4742180.237109
220.7058750.5882490.294125
230.6429810.7140380.357019
240.5819780.8360440.418022
250.5933670.8132670.406633
260.6545820.6908350.345418
270.5981840.8036330.401816
280.556460.887080.44354
290.500280.9994410.49972
300.464640.9292790.53536
310.5178990.9642020.482101
320.4784570.9569150.521543
330.4235760.8471530.576424
340.3690760.7381520.630924
350.3170570.6341130.682943
360.3188340.6376680.681166
370.3840210.7680410.615979
380.3320170.6640340.667983
390.3658340.7316680.634166
400.3214270.6428530.678573
410.7645030.4709930.235497
420.7304760.5390490.269524
430.7014460.5971090.298554
440.6557750.6884490.344225
450.6904430.6191130.309557
460.644530.7109410.35547
470.6170190.7659630.382981
480.6039790.7920410.396021
490.5605260.8789480.439474
500.5155520.9688960.484448
510.5380480.9239050.461952
520.5123560.9752870.487644
530.4683110.9366220.531689
540.6301260.7397480.369874
550.6325560.7348870.367444
560.5900140.8199720.409986
570.5647640.8704720.435236
580.5683660.8632670.431634
590.5219960.9560080.478004
600.5530980.8938040.446902
610.5096720.9806560.490328
620.5257540.9484920.474246
630.4919710.9839420.508029
640.4468550.893710.553145
650.4023590.8047180.597641
660.3914810.7829620.608519
670.3854080.7708160.614592
680.3858530.7717070.614147
690.3554210.7108430.644579
700.4256150.851230.574385
710.4320780.8641560.567922
720.3903370.7806740.609663
730.3742860.7485730.625714
740.3374630.6749260.662537
750.4729410.9458820.527059
760.4294850.8589710.570515
770.502990.994020.49701
780.492520.985040.50748
790.448720.8974410.55128
800.4056310.8112620.594369
810.3633940.7267880.636606
820.3279220.6558440.672078
830.3485290.6970590.651471
840.3597630.7195260.640237
850.3358330.6716650.664167
860.3190190.6380380.680981
870.3586570.7173130.641343
880.318030.6360590.68197
890.3479360.6958720.652064
900.3113480.6226970.688652
910.2841350.568270.715865
920.2710540.5421080.728946
930.25640.5128010.7436
940.2250550.4501110.774945
950.2173350.434670.782665
960.2538920.5077840.746108
970.2551370.5102740.744863
980.2753060.5506130.724694
990.2445280.4890560.755472
1000.2165240.4330480.783476
1010.1902170.3804340.809783
1020.1747640.3495270.825236
1030.1473320.2946640.852668
1040.1802020.3604050.819798
1050.1539730.3079460.846027
1060.1349820.2699650.865018
1070.1246740.2493490.875326
1080.1117070.2234140.888293
1090.09174970.1834990.90825
1100.0848760.1697520.915124
1110.0699410.1398820.930059
1120.05590570.1118110.944094
1130.04602040.09204080.95398
1140.0555880.1111760.944412
1150.04469890.08939770.955301
1160.03584880.07169750.964151
1170.03762150.07524290.962379
1180.04423610.08847220.955764
1190.04095980.08191960.95904
1200.06672220.1334440.933278
1210.05716170.1143230.942838
1220.05702120.1140420.942979
1230.06274880.1254980.937251
1240.0514350.102870.948565
1250.04496040.08992080.95504
1260.04975460.09950920.950245
1270.04583750.09167490.954163
1280.1571090.3142190.842891
1290.13620.2723990.8638
1300.175870.351740.82413
1310.1573060.3146110.842694
1320.1387520.2775030.861248
1330.126470.2529390.87353
1340.1560950.312190.843905
1350.1410890.2821770.858911
1360.1335020.2670030.866498
1370.1451230.2902470.854877
1380.1187850.2375710.881215
1390.09279960.1855990.9072
1400.07120540.1424110.928795
1410.05558740.1111750.944413
1420.04660050.0932010.9534
1430.03921330.07842660.960787
1440.04564590.09129190.954354
1450.05971590.1194320.940284
1460.04622480.09244960.953775
1470.03341640.06683290.966584
1480.05062080.1012420.949379
1490.04165660.08331310.958343
1500.05431390.1086280.945686
1510.5746290.8507420.425371
1520.5338790.9322430.466121
1530.9981470.003705980.00185299
1540.9999696.10386e-053.05193e-05
1550.9999260.0001483197.41596e-05
1560.999920.000160728.03602e-05
1570.9999080.0001847989.23991e-05
1580.9997740.0004512680.000225634
1590.9991290.001742080.00087104
1600.9982680.003463770.00173188
1610.9936370.01272510.00636256
1620.9785560.04288840.0214442
1630.9492090.1015820.0507911






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0512821NOK
5% type I error level100.0641026NOK
10% type I error level280.179487NOK

\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 & 8 & 0.0512821 & NOK \tabularnewline
5% type I error level & 10 & 0.0641026 & NOK \tabularnewline
10% type I error level & 28 & 0.179487 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271220&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]8[/C][C]0.0512821[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.0641026[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.179487[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271220&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271220&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 level80.0512821NOK
5% type I error level100.0641026NOK
10% type I error level280.179487NOK


Figures (Output of Computation)

Figure 1
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}