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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, 16 Dec 2014 10:57:26 +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/16/t1418727507wiracro45p05e7m.htm/, Retrieved Thu, 16 May 2024 17:13:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269300, Retrieved Thu, 16 May 2024 17:13:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Fcast 2012] [2014-12-15 12:41:37] [bcf5edf18529a33bd1494456d2c6cb9a]
- R PD    [Multiple Regression] [Fcast 2012] [2014-12-16 10:57:26] [ddb851b9ced255c1d64c58a7ca49fb28] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,67	0,67	149	68	12,9
0,83	0,33	139	39	12,2
1,00	0,67	148	32	12,8
0,83	0,00	158	62	7,4
0,67	0,00	128	33	6,7
0,00	0,00	224	52	12,6
0,83	0,67	159	62	14,8
0,50	0,67	105	77	13,3
0,83	0,00	159	76	11,1
0,33	0,67	167	41	8,2
0,50	1,00	165	48	11,4
0,67	0,00	159	63	6,4
1,00	0,00	119	30	10,6
0,50	0,67	176	78	12,0
0,67	0,33	54	19	6,3
0,17	0,67	91	31	11,3
0,83	0,33	163	66	11,9
0,67	0,33	124	35	9,3
0,67	0,33	137	42	9,6
0,67	0,00	121	45	10,0
0,50	0,67	153	21	6,4
1,00	0,33	148	25	13,8
0,83	0,33	221	44	10,8
0,83	0,33	188	69	13,8
1,00	0,67	149	54	11,7
0,67	0,00	244	74	10,9
1,00	0,33	148	80	16,1
0,83	0,67	92	42	13,4
1,00	1,00	150	61	9,9
0,83	0,67	153	41	11,5
0,67	0,33	94	46	8,3
0,67	0,00	156	39	11,7
1,00	0,67	132	34	9,0
0,67	0,67	161	51	9,7
1,00	1,00	105	42	10,8
1,00	1,00	97	31	10,3
0,50	0,33	151	39	10,4
0,67	0,00	131	20	12,7
0,83	0,67	166	49	9,3
1,00	0,67	157	53	11,8
1,00	0,67	111	31	5,9
1,00	0,67	145	39	11,4
1,00	0,33	162	54	13,0
1,00	1,00	163	49	10,8
0,83	0,67	59	34	12,3
0,83	0,67	187	46	11,3
0,50	0,00	109	55	11,8
0,83	0,00	90	42	7,9
0,17	0,00	105	50	12,7
0,83	1,00	83	13	12,3
1,00	0,67	116	37	11,6
1,00	0,00	42	25	6,7
0,67	0,67	148	30	10,9
1,00	0,00	155	28	12,1
1,00	0,00	125	45	13,3
1,00	0,67	116	35	10,1
0,83	1,00	128	28	5,7
0,33	0,00	138	41	14,3
0,33	0,33	49	6	8,0
1,00	0,67	96	45	13,3
1,00	0,67	164	73	9,3
0,83	0,00	162	17	12,5
1,00	1,00	99	40	7,6
0,83	0,67	202	64	15,9
0,67	0,00	186	37	9,2
0,83	1,00	66	25	9,1
0,67	0,67	183	65	11,1
0,83	0,67	214	100	13,0
0,67	1,00	188	28	14,5
1,00	0,00	104	35	12,2
0,83	0,33	177	56	12,3
0,67	0,67	126	29	11,4
0,83	0,33	76	43	8,8
0,83	0,67	99	59	14,6
0,67	0,00	139	50	12,6
1,00	1,00	78	3	NA
0,33	0,00	162	59	13,0
0,83	0,67	108	27	12,6
1,00	0,33	159	61	13,2
0,83	0,00	74	28	9,9
0,83	0,00	110	51	7,7
0,50	0,33	96	35	10,5
0,50	0,00	116	29	13,4
0,83	0,67	87	48	10,9
1,00	0,33	97	25	4,3
0,33	0,67	127	44	10,3
1,00	0,33	106	64	11,8
0,67	0,33	80	32	11,2
0,83	1,00	74	20	11,4
1,00	0,67	91	28	8,6
0,83	0,00	133	34	13,2
0,83	0,67	74	31	12,6
1,00	0,33	114	26	5,6
0,83	0,00	140	58	9,9
1,00	0,33	95	23	8,8
0,83	0,00	98	21	7,7
0,67	0,00	121	21	9,0
0,83	0,33	126	33	7,3
0,83	0,67	98	16	11,4
0,83	0,67	95	20	13,6
0,83	0,67	110	37	7,9
0,67	0,67	70	35	10,7
0,83	1,00	102	33	10,3
0,00	0,00	86	27	8,3
0,83	0,00	130	41	9,6
1,00	0,00	96	40	14,2
0,17	0,00	102	35	8,5
0,17	0,00	100	28	13,5
0,50	1,00	94	32	4,9
0,50	0,67	52	22	6,4
1,00	0,00	98	44	9,6
0,67	0,67	118	27	11,6
0,83	0,67	99	17	11,1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269300&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]5 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=269300&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269300&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 time5 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 7.81531 + 0.0863188Graph[t] + 0.166106Prop[t] + 0.0109086LFM[t] + 0.0328344CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  7.81531 +  0.0863188Graph[t] +  0.166106Prop[t] +  0.0109086LFM[t] +  0.0328344CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269300&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  7.81531 +  0.0863188Graph[t] +  0.166106Prop[t] +  0.0109086LFM[t] +  0.0328344CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269300&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269300&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
TOT[t] = + 7.81531 + 0.0863188Graph[t] + 0.166106Prop[t] + 0.0109086LFM[t] + 0.0328344CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.815311.06037.3713.75334e-111.87667e-11
Graph0.08631880.9618080.089750.9286570.464328
Prop0.1661060.6615850.25110.8022390.40112
LFM0.01090860.006895451.5820.1166010.0583005
CH0.03283440.01619212.0280.04506590.022533

\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) & 7.81531 & 1.0603 & 7.371 & 3.75334e-11 & 1.87667e-11 \tabularnewline
Graph & 0.0863188 & 0.961808 & 0.08975 & 0.928657 & 0.464328 \tabularnewline
Prop & 0.166106 & 0.661585 & 0.2511 & 0.802239 & 0.40112 \tabularnewline
LFM & 0.0109086 & 0.00689545 & 1.582 & 0.116601 & 0.0583005 \tabularnewline
CH & 0.0328344 & 0.0161921 & 2.028 & 0.0450659 & 0.022533 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269300&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]7.81531[/C][C]1.0603[/C][C]7.371[/C][C]3.75334e-11[/C][C]1.87667e-11[/C][/ROW]
[ROW][C]Graph[/C][C]0.0863188[/C][C]0.961808[/C][C]0.08975[/C][C]0.928657[/C][C]0.464328[/C][/ROW]
[ROW][C]Prop[/C][C]0.166106[/C][C]0.661585[/C][C]0.2511[/C][C]0.802239[/C][C]0.40112[/C][/ROW]
[ROW][C]LFM[/C][C]0.0109086[/C][C]0.00689545[/C][C]1.582[/C][C]0.116601[/C][C]0.0583005[/C][/ROW]
[ROW][C]CH[/C][C]0.0328344[/C][C]0.0161921[/C][C]2.028[/C][C]0.0450659[/C][C]0.022533[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269300&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269300&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)7.815311.06037.3713.75334e-111.87667e-11
Graph0.08631880.9618080.089750.9286570.464328
Prop0.1661060.6615850.25110.8022390.40112
LFM0.01090860.006895451.5820.1166010.0583005
CH0.03283440.01619212.0280.04506590.022533






Multiple Linear Regression - Regression Statistics
Multiple R0.355412
R-squared0.126318
Adjusted R-squared0.0936567
F-TEST (value)3.86754
F-TEST (DF numerator)4
F-TEST (DF denominator)107
p-value0.00565149
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.35277
Sum Squared Residuals592.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.355412 \tabularnewline
R-squared & 0.126318 \tabularnewline
Adjusted R-squared & 0.0936567 \tabularnewline
F-TEST (value) & 3.86754 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 107 \tabularnewline
p-value & 0.00565149 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.35277 \tabularnewline
Sum Squared Residuals & 592.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269300&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.355412[/C][/ROW]
[ROW][C]R-squared[/C][C]0.126318[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0936567[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.86754[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]107[/C][/ROW]
[ROW][C]p-value[/C][C]0.00565149[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.35277[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]592.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269300&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269300&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 R0.355412
R-squared0.126318
Adjusted R-squared0.0936567
F-TEST (value)3.86754
F-TEST (DF numerator)4
F-TEST (DF denominator)107
p-value0.00565149
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.35277
Sum Squared Residuals592.3






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.911.84261.05745
212.210.73861.46139
312.810.67812.12191
47.411.6462-4.24625
56.710.353-3.65298
612.611.96620.633775
714.811.76843.03155
813.311.64341.65659
911.112.1168-1.01684
108.211.123-2.92303
1111.411.4005-0.000545206
126.411.6762-5.27618
1310.610.18480.415216
141212.4508-0.450756
156.39.14088-2.84088
1611.39.951821.34818
1711.911.88690.013059
189.310.4298-1.12983
199.610.8015-1.20148
201010.6706-0.670631
216.410.3283-3.9283
2213.810.39183.40822
2310.811.7973-0.997284
2413.812.25821.54184
2511.711.41140.288642
2610.912.9646-2.06459
2716.112.19773.90233
2813.410.38093.01912
299.911.7069-1.80692
3011.511.01350.486529
318.310.4637-2.16375
3211.710.85540.844574
33910.5692-1.56922
349.711.4153-1.71527
3510.810.59220.207818
3610.310.14370.156265
3710.410.841-0.441024
3812.79.958862.74114
399.311.418-2.11796
4011.811.46580.334207
415.910.2416-4.34164
4211.410.87520.524792
431311.49671.50331
4410.811.4547-0.654722
4512.39.758222.54178
4611.311.5485-0.248536
4711.810.85340.946602
487.910.2478-2.34777
4912.710.61712.08289
5012.39.385322.91468
5111.610.49321.10681
526.79.18065-2.48065
5310.910.58390.316061
5412.110.51181.58818
5513.310.74282.55725
5610.110.4275-0.327521
575.710.3687-4.66872
5814.310.69543.60461
5988.63014-0.630137
6013.310.53772.76231
619.312.1988-2.89884
6212.510.21232.28767
637.610.4611-2.86106
6415.912.30323.59682
659.211.117-1.91702
669.19.59389-0.493887
6711.112.1149-1.01494
681313.6161-0.616125
6914.511.00943.49057
7012.210.18532.01467
7112.311.71130.588682
7211.410.31111.08888
738.810.1827-1.3827
7414.611.01543.58457
7512.611.03121.56884
76NANA1.45178
771310.46292.5371
7812.611.09381.50619
7913.212.91360.286447
809.912.9615-3.06145
817.77.309710.390287
8210.57.176063.32394
8313.413.02330.376656
8410.916.4354-5.53544
854.34.78519-0.485191
8610.39.714160.585844
8711.810.45131.34865
8811.29.316981.88302
8911.412.725-1.32496
908.65.854172.74583
9113.210.42332.77665
9212.617.0537-4.45372
935.67.01855-1.41855
949.910.848-0.947951
958.810.7455-1.94552
967.78.58261-0.882606
97912.0998-3.09979
987.35.492641.80736
9911.47.491253.90875
10013.616.1131-2.51306
1017.97.097240.802761
10210.710.64930.0507277
10310.311.64-1.33998
1048.39.35128-1.05128
1059.65.662233.93777
10614.215.7919-1.59186
1078.54.840213.65979
10813.518.7007-5.20068
1094.97.75936-2.85936
1106.47.21538-0.815384
1119.68.158181.44182
11211.610.13641.46362
11311.1NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 11.8426 & 1.05745 \tabularnewline
2 & 12.2 & 10.7386 & 1.46139 \tabularnewline
3 & 12.8 & 10.6781 & 2.12191 \tabularnewline
4 & 7.4 & 11.6462 & -4.24625 \tabularnewline
5 & 6.7 & 10.353 & -3.65298 \tabularnewline
6 & 12.6 & 11.9662 & 0.633775 \tabularnewline
7 & 14.8 & 11.7684 & 3.03155 \tabularnewline
8 & 13.3 & 11.6434 & 1.65659 \tabularnewline
9 & 11.1 & 12.1168 & -1.01684 \tabularnewline
10 & 8.2 & 11.123 & -2.92303 \tabularnewline
11 & 11.4 & 11.4005 & -0.000545206 \tabularnewline
12 & 6.4 & 11.6762 & -5.27618 \tabularnewline
13 & 10.6 & 10.1848 & 0.415216 \tabularnewline
14 & 12 & 12.4508 & -0.450756 \tabularnewline
15 & 6.3 & 9.14088 & -2.84088 \tabularnewline
16 & 11.3 & 9.95182 & 1.34818 \tabularnewline
17 & 11.9 & 11.8869 & 0.013059 \tabularnewline
18 & 9.3 & 10.4298 & -1.12983 \tabularnewline
19 & 9.6 & 10.8015 & -1.20148 \tabularnewline
20 & 10 & 10.6706 & -0.670631 \tabularnewline
21 & 6.4 & 10.3283 & -3.9283 \tabularnewline
22 & 13.8 & 10.3918 & 3.40822 \tabularnewline
23 & 10.8 & 11.7973 & -0.997284 \tabularnewline
24 & 13.8 & 12.2582 & 1.54184 \tabularnewline
25 & 11.7 & 11.4114 & 0.288642 \tabularnewline
26 & 10.9 & 12.9646 & -2.06459 \tabularnewline
27 & 16.1 & 12.1977 & 3.90233 \tabularnewline
28 & 13.4 & 10.3809 & 3.01912 \tabularnewline
29 & 9.9 & 11.7069 & -1.80692 \tabularnewline
30 & 11.5 & 11.0135 & 0.486529 \tabularnewline
31 & 8.3 & 10.4637 & -2.16375 \tabularnewline
32 & 11.7 & 10.8554 & 0.844574 \tabularnewline
33 & 9 & 10.5692 & -1.56922 \tabularnewline
34 & 9.7 & 11.4153 & -1.71527 \tabularnewline
35 & 10.8 & 10.5922 & 0.207818 \tabularnewline
36 & 10.3 & 10.1437 & 0.156265 \tabularnewline
37 & 10.4 & 10.841 & -0.441024 \tabularnewline
38 & 12.7 & 9.95886 & 2.74114 \tabularnewline
39 & 9.3 & 11.418 & -2.11796 \tabularnewline
40 & 11.8 & 11.4658 & 0.334207 \tabularnewline
41 & 5.9 & 10.2416 & -4.34164 \tabularnewline
42 & 11.4 & 10.8752 & 0.524792 \tabularnewline
43 & 13 & 11.4967 & 1.50331 \tabularnewline
44 & 10.8 & 11.4547 & -0.654722 \tabularnewline
45 & 12.3 & 9.75822 & 2.54178 \tabularnewline
46 & 11.3 & 11.5485 & -0.248536 \tabularnewline
47 & 11.8 & 10.8534 & 0.946602 \tabularnewline
48 & 7.9 & 10.2478 & -2.34777 \tabularnewline
49 & 12.7 & 10.6171 & 2.08289 \tabularnewline
50 & 12.3 & 9.38532 & 2.91468 \tabularnewline
51 & 11.6 & 10.4932 & 1.10681 \tabularnewline
52 & 6.7 & 9.18065 & -2.48065 \tabularnewline
53 & 10.9 & 10.5839 & 0.316061 \tabularnewline
54 & 12.1 & 10.5118 & 1.58818 \tabularnewline
55 & 13.3 & 10.7428 & 2.55725 \tabularnewline
56 & 10.1 & 10.4275 & -0.327521 \tabularnewline
57 & 5.7 & 10.3687 & -4.66872 \tabularnewline
58 & 14.3 & 10.6954 & 3.60461 \tabularnewline
59 & 8 & 8.63014 & -0.630137 \tabularnewline
60 & 13.3 & 10.5377 & 2.76231 \tabularnewline
61 & 9.3 & 12.1988 & -2.89884 \tabularnewline
62 & 12.5 & 10.2123 & 2.28767 \tabularnewline
63 & 7.6 & 10.4611 & -2.86106 \tabularnewline
64 & 15.9 & 12.3032 & 3.59682 \tabularnewline
65 & 9.2 & 11.117 & -1.91702 \tabularnewline
66 & 9.1 & 9.59389 & -0.493887 \tabularnewline
67 & 11.1 & 12.1149 & -1.01494 \tabularnewline
68 & 13 & 13.6161 & -0.616125 \tabularnewline
69 & 14.5 & 11.0094 & 3.49057 \tabularnewline
70 & 12.2 & 10.1853 & 2.01467 \tabularnewline
71 & 12.3 & 11.7113 & 0.588682 \tabularnewline
72 & 11.4 & 10.3111 & 1.08888 \tabularnewline
73 & 8.8 & 10.1827 & -1.3827 \tabularnewline
74 & 14.6 & 11.0154 & 3.58457 \tabularnewline
75 & 12.6 & 11.0312 & 1.56884 \tabularnewline
76 & NA & NA & 1.45178 \tabularnewline
77 & 13 & 10.4629 & 2.5371 \tabularnewline
78 & 12.6 & 11.0938 & 1.50619 \tabularnewline
79 & 13.2 & 12.9136 & 0.286447 \tabularnewline
80 & 9.9 & 12.9615 & -3.06145 \tabularnewline
81 & 7.7 & 7.30971 & 0.390287 \tabularnewline
82 & 10.5 & 7.17606 & 3.32394 \tabularnewline
83 & 13.4 & 13.0233 & 0.376656 \tabularnewline
84 & 10.9 & 16.4354 & -5.53544 \tabularnewline
85 & 4.3 & 4.78519 & -0.485191 \tabularnewline
86 & 10.3 & 9.71416 & 0.585844 \tabularnewline
87 & 11.8 & 10.4513 & 1.34865 \tabularnewline
88 & 11.2 & 9.31698 & 1.88302 \tabularnewline
89 & 11.4 & 12.725 & -1.32496 \tabularnewline
90 & 8.6 & 5.85417 & 2.74583 \tabularnewline
91 & 13.2 & 10.4233 & 2.77665 \tabularnewline
92 & 12.6 & 17.0537 & -4.45372 \tabularnewline
93 & 5.6 & 7.01855 & -1.41855 \tabularnewline
94 & 9.9 & 10.848 & -0.947951 \tabularnewline
95 & 8.8 & 10.7455 & -1.94552 \tabularnewline
96 & 7.7 & 8.58261 & -0.882606 \tabularnewline
97 & 9 & 12.0998 & -3.09979 \tabularnewline
98 & 7.3 & 5.49264 & 1.80736 \tabularnewline
99 & 11.4 & 7.49125 & 3.90875 \tabularnewline
100 & 13.6 & 16.1131 & -2.51306 \tabularnewline
101 & 7.9 & 7.09724 & 0.802761 \tabularnewline
102 & 10.7 & 10.6493 & 0.0507277 \tabularnewline
103 & 10.3 & 11.64 & -1.33998 \tabularnewline
104 & 8.3 & 9.35128 & -1.05128 \tabularnewline
105 & 9.6 & 5.66223 & 3.93777 \tabularnewline
106 & 14.2 & 15.7919 & -1.59186 \tabularnewline
107 & 8.5 & 4.84021 & 3.65979 \tabularnewline
108 & 13.5 & 18.7007 & -5.20068 \tabularnewline
109 & 4.9 & 7.75936 & -2.85936 \tabularnewline
110 & 6.4 & 7.21538 & -0.815384 \tabularnewline
111 & 9.6 & 8.15818 & 1.44182 \tabularnewline
112 & 11.6 & 10.1364 & 1.46362 \tabularnewline
113 & 11.1 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269300&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]12.9[/C][C]11.8426[/C][C]1.05745[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.7386[/C][C]1.46139[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]10.6781[/C][C]2.12191[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.6462[/C][C]-4.24625[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.353[/C][C]-3.65298[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.9662[/C][C]0.633775[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.7684[/C][C]3.03155[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]11.6434[/C][C]1.65659[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.1168[/C][C]-1.01684[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]11.123[/C][C]-2.92303[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.4005[/C][C]-0.000545206[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.6762[/C][C]-5.27618[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.1848[/C][C]0.415216[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]12.4508[/C][C]-0.450756[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]9.14088[/C][C]-2.84088[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]9.95182[/C][C]1.34818[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]11.8869[/C][C]0.013059[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]10.4298[/C][C]-1.12983[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]10.8015[/C][C]-1.20148[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]10.6706[/C][C]-0.670631[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]10.3283[/C][C]-3.9283[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]10.3918[/C][C]3.40822[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]11.7973[/C][C]-0.997284[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]12.2582[/C][C]1.54184[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]11.4114[/C][C]0.288642[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]12.9646[/C][C]-2.06459[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]12.1977[/C][C]3.90233[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]10.3809[/C][C]3.01912[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]11.7069[/C][C]-1.80692[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]11.0135[/C][C]0.486529[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]10.4637[/C][C]-2.16375[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]10.8554[/C][C]0.844574[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]10.5692[/C][C]-1.56922[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]11.4153[/C][C]-1.71527[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]10.5922[/C][C]0.207818[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]10.1437[/C][C]0.156265[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]10.841[/C][C]-0.441024[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]9.95886[/C][C]2.74114[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]11.418[/C][C]-2.11796[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]11.4658[/C][C]0.334207[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]10.2416[/C][C]-4.34164[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]10.8752[/C][C]0.524792[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]11.4967[/C][C]1.50331[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]11.4547[/C][C]-0.654722[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]9.75822[/C][C]2.54178[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]11.5485[/C][C]-0.248536[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]10.8534[/C][C]0.946602[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]10.2478[/C][C]-2.34777[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]10.6171[/C][C]2.08289[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]9.38532[/C][C]2.91468[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.4932[/C][C]1.10681[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]9.18065[/C][C]-2.48065[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]10.5839[/C][C]0.316061[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]10.5118[/C][C]1.58818[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]10.7428[/C][C]2.55725[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.4275[/C][C]-0.327521[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]10.3687[/C][C]-4.66872[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]10.6954[/C][C]3.60461[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]8.63014[/C][C]-0.630137[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]10.5377[/C][C]2.76231[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]12.1988[/C][C]-2.89884[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]10.2123[/C][C]2.28767[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]10.4611[/C][C]-2.86106[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]12.3032[/C][C]3.59682[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]11.117[/C][C]-1.91702[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]9.59389[/C][C]-0.493887[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]12.1149[/C][C]-1.01494[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]13.6161[/C][C]-0.616125[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]11.0094[/C][C]3.49057[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]10.1853[/C][C]2.01467[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]11.7113[/C][C]0.588682[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]10.3111[/C][C]1.08888[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]10.1827[/C][C]-1.3827[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]11.0154[/C][C]3.58457[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]11.0312[/C][C]1.56884[/C][/ROW]
[ROW][C]76[/C][C]NA[/C][C]NA[/C][C]1.45178[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.4629[/C][C]2.5371[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]11.0938[/C][C]1.50619[/C][/ROW]
[ROW][C]79[/C][C]13.2[/C][C]12.9136[/C][C]0.286447[/C][/ROW]
[ROW][C]80[/C][C]9.9[/C][C]12.9615[/C][C]-3.06145[/C][/ROW]
[ROW][C]81[/C][C]7.7[/C][C]7.30971[/C][C]0.390287[/C][/ROW]
[ROW][C]82[/C][C]10.5[/C][C]7.17606[/C][C]3.32394[/C][/ROW]
[ROW][C]83[/C][C]13.4[/C][C]13.0233[/C][C]0.376656[/C][/ROW]
[ROW][C]84[/C][C]10.9[/C][C]16.4354[/C][C]-5.53544[/C][/ROW]
[ROW][C]85[/C][C]4.3[/C][C]4.78519[/C][C]-0.485191[/C][/ROW]
[ROW][C]86[/C][C]10.3[/C][C]9.71416[/C][C]0.585844[/C][/ROW]
[ROW][C]87[/C][C]11.8[/C][C]10.4513[/C][C]1.34865[/C][/ROW]
[ROW][C]88[/C][C]11.2[/C][C]9.31698[/C][C]1.88302[/C][/ROW]
[ROW][C]89[/C][C]11.4[/C][C]12.725[/C][C]-1.32496[/C][/ROW]
[ROW][C]90[/C][C]8.6[/C][C]5.85417[/C][C]2.74583[/C][/ROW]
[ROW][C]91[/C][C]13.2[/C][C]10.4233[/C][C]2.77665[/C][/ROW]
[ROW][C]92[/C][C]12.6[/C][C]17.0537[/C][C]-4.45372[/C][/ROW]
[ROW][C]93[/C][C]5.6[/C][C]7.01855[/C][C]-1.41855[/C][/ROW]
[ROW][C]94[/C][C]9.9[/C][C]10.848[/C][C]-0.947951[/C][/ROW]
[ROW][C]95[/C][C]8.8[/C][C]10.7455[/C][C]-1.94552[/C][/ROW]
[ROW][C]96[/C][C]7.7[/C][C]8.58261[/C][C]-0.882606[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]12.0998[/C][C]-3.09979[/C][/ROW]
[ROW][C]98[/C][C]7.3[/C][C]5.49264[/C][C]1.80736[/C][/ROW]
[ROW][C]99[/C][C]11.4[/C][C]7.49125[/C][C]3.90875[/C][/ROW]
[ROW][C]100[/C][C]13.6[/C][C]16.1131[/C][C]-2.51306[/C][/ROW]
[ROW][C]101[/C][C]7.9[/C][C]7.09724[/C][C]0.802761[/C][/ROW]
[ROW][C]102[/C][C]10.7[/C][C]10.6493[/C][C]0.0507277[/C][/ROW]
[ROW][C]103[/C][C]10.3[/C][C]11.64[/C][C]-1.33998[/C][/ROW]
[ROW][C]104[/C][C]8.3[/C][C]9.35128[/C][C]-1.05128[/C][/ROW]
[ROW][C]105[/C][C]9.6[/C][C]5.66223[/C][C]3.93777[/C][/ROW]
[ROW][C]106[/C][C]14.2[/C][C]15.7919[/C][C]-1.59186[/C][/ROW]
[ROW][C]107[/C][C]8.5[/C][C]4.84021[/C][C]3.65979[/C][/ROW]
[ROW][C]108[/C][C]13.5[/C][C]18.7007[/C][C]-5.20068[/C][/ROW]
[ROW][C]109[/C][C]4.9[/C][C]7.75936[/C][C]-2.85936[/C][/ROW]
[ROW][C]110[/C][C]6.4[/C][C]7.21538[/C][C]-0.815384[/C][/ROW]
[ROW][C]111[/C][C]9.6[/C][C]8.15818[/C][C]1.44182[/C][/ROW]
[ROW][C]112[/C][C]11.6[/C][C]10.1364[/C][C]1.46362[/C][/ROW]
[ROW][C]113[/C][C]11.1[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269300&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269300&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
112.911.84261.05745
212.210.73861.46139
312.810.67812.12191
47.411.6462-4.24625
56.710.353-3.65298
612.611.96620.633775
714.811.76843.03155
813.311.64341.65659
911.112.1168-1.01684
108.211.123-2.92303
1111.411.4005-0.000545206
126.411.6762-5.27618
1310.610.18480.415216
141212.4508-0.450756
156.39.14088-2.84088
1611.39.951821.34818
1711.911.88690.013059
189.310.4298-1.12983
199.610.8015-1.20148
201010.6706-0.670631
216.410.3283-3.9283
2213.810.39183.40822
2310.811.7973-0.997284
2413.812.25821.54184
2511.711.41140.288642
2610.912.9646-2.06459
2716.112.19773.90233
2813.410.38093.01912
299.911.7069-1.80692
3011.511.01350.486529
318.310.4637-2.16375
3211.710.85540.844574
33910.5692-1.56922
349.711.4153-1.71527
3510.810.59220.207818
3610.310.14370.156265
3710.410.841-0.441024
3812.79.958862.74114
399.311.418-2.11796
4011.811.46580.334207
415.910.2416-4.34164
4211.410.87520.524792
431311.49671.50331
4410.811.4547-0.654722
4512.39.758222.54178
4611.311.5485-0.248536
4711.810.85340.946602
487.910.2478-2.34777
4912.710.61712.08289
5012.39.385322.91468
5111.610.49321.10681
526.79.18065-2.48065
5310.910.58390.316061
5412.110.51181.58818
5513.310.74282.55725
5610.110.4275-0.327521
575.710.3687-4.66872
5814.310.69543.60461
5988.63014-0.630137
6013.310.53772.76231
619.312.1988-2.89884
6212.510.21232.28767
637.610.4611-2.86106
6415.912.30323.59682
659.211.117-1.91702
669.19.59389-0.493887
6711.112.1149-1.01494
681313.6161-0.616125
6914.511.00943.49057
7012.210.18532.01467
7112.311.71130.588682
7211.410.31111.08888
738.810.1827-1.3827
7414.611.01543.58457
7512.611.03121.56884
76NANA1.45178
771310.46292.5371
7812.611.09381.50619
7913.212.91360.286447
809.912.9615-3.06145
817.77.309710.390287
8210.57.176063.32394
8313.413.02330.376656
8410.916.4354-5.53544
854.34.78519-0.485191
8610.39.714160.585844
8711.810.45131.34865
8811.29.316981.88302
8911.412.725-1.32496
908.65.854172.74583
9113.210.42332.77665
9212.617.0537-4.45372
935.67.01855-1.41855
949.910.848-0.947951
958.810.7455-1.94552
967.78.58261-0.882606
97912.0998-3.09979
987.35.492641.80736
9911.47.491253.90875
10013.616.1131-2.51306
1017.97.097240.802761
10210.710.64930.0507277
10310.311.64-1.33998
1048.39.35128-1.05128
1059.65.662233.93777
10614.215.7919-1.59186
1078.54.840213.65979
10813.518.7007-5.20068
1094.97.75936-2.85936
1106.47.21538-0.815384
1119.68.158181.44182
11211.610.13641.46362
11311.1NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.2445790.4891590.755421
90.2862380.5724760.713762
100.7245940.5508120.275406
110.6949340.6101320.305066
120.7999880.4000240.200012
130.7961670.4076670.203833
140.727980.5440410.27202
150.6665450.6669110.333455
160.6503070.6993860.349693
170.5669420.8661160.433058
180.4841760.9683530.515824
190.4059440.8118890.594056
200.3579720.7159440.642028
210.4947060.9894120.505294
220.6110140.7779710.388986
230.5511080.8977850.448892
240.5060110.9879790.493989
250.4447580.8895160.555242
260.4023040.8046090.597696
270.477410.9548190.52259
280.4788990.9577980.521101
290.5873940.8252110.412606
300.5224680.9550630.477532
310.4980270.9960540.501973
320.4817630.9635260.518237
330.4647560.9295110.535244
340.4401560.8803120.559844
350.3868490.7736980.613151
360.3326160.6652320.667384
370.2849890.5699770.715011
380.3588950.7177890.641105
390.354170.708340.64583
400.3000720.6001430.699928
410.437860.8757210.56214
420.3831550.766310.616845
430.3494350.698870.650565
440.3043470.6086940.695653
450.3170460.6340910.682954
460.2705080.5410160.729492
470.2355280.4710560.764472
480.2306330.4612660.769367
490.2310280.4620560.768972
500.2538590.5077190.746141
510.2184980.4369960.781502
520.2165640.4331280.783436
530.1784740.3569480.821526
540.1666530.3333060.833347
550.174660.3493210.82534
560.141710.2834210.85829
570.2573510.5147010.742649
580.3196990.6393980.680301
590.2735210.5470420.726479
600.2906980.5813950.709302
610.3184330.6368670.681567
620.3077280.6154550.692272
630.3303710.6607420.669629
640.3795580.7591150.620442
650.3706530.7413060.629347
660.3194280.6388560.680572
670.2862690.5725380.713731
680.2559310.5118620.744069
690.2784510.5569020.721549
700.2657040.5314080.734296
710.2212390.4424790.778761
720.1861010.3722030.813899
730.1602280.3204560.839772
740.1999930.3999860.800007
750.1747340.3494680.825266
760.1504190.3008380.849581
770.1541110.3082210.845889
780.1444410.2888820.855559
790.1122480.2244950.887752
800.1234840.2469670.876516
810.09492430.1898490.905076
820.1210330.2420660.878967
830.09252950.1850590.907471
840.2550390.5100790.744961
850.213530.4270590.78647
860.177310.3546210.82269
870.1451020.2902030.854898
880.1279570.2559140.872043
890.102630.205260.89737
900.1201820.2403640.879818
910.1282890.2565780.871711
920.240540.481080.75946
930.1867090.3734170.813291
940.1616720.3233450.838328
950.2402990.4805970.759701
960.287290.574580.71271
970.4091410.8182830.590859
980.3433410.6866820.656659
990.3203460.6406910.679654
1000.2649140.5298280.735086
1010.3213980.6427960.678602
1020.3745380.7490770.625462
1030.308190.6163810.69181
1040.3952780.7905560.604722
1050.4993180.9986360.500682

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.244579 & 0.489159 & 0.755421 \tabularnewline
9 & 0.286238 & 0.572476 & 0.713762 \tabularnewline
10 & 0.724594 & 0.550812 & 0.275406 \tabularnewline
11 & 0.694934 & 0.610132 & 0.305066 \tabularnewline
12 & 0.799988 & 0.400024 & 0.200012 \tabularnewline
13 & 0.796167 & 0.407667 & 0.203833 \tabularnewline
14 & 0.72798 & 0.544041 & 0.27202 \tabularnewline
15 & 0.666545 & 0.666911 & 0.333455 \tabularnewline
16 & 0.650307 & 0.699386 & 0.349693 \tabularnewline
17 & 0.566942 & 0.866116 & 0.433058 \tabularnewline
18 & 0.484176 & 0.968353 & 0.515824 \tabularnewline
19 & 0.405944 & 0.811889 & 0.594056 \tabularnewline
20 & 0.357972 & 0.715944 & 0.642028 \tabularnewline
21 & 0.494706 & 0.989412 & 0.505294 \tabularnewline
22 & 0.611014 & 0.777971 & 0.388986 \tabularnewline
23 & 0.551108 & 0.897785 & 0.448892 \tabularnewline
24 & 0.506011 & 0.987979 & 0.493989 \tabularnewline
25 & 0.444758 & 0.889516 & 0.555242 \tabularnewline
26 & 0.402304 & 0.804609 & 0.597696 \tabularnewline
27 & 0.47741 & 0.954819 & 0.52259 \tabularnewline
28 & 0.478899 & 0.957798 & 0.521101 \tabularnewline
29 & 0.587394 & 0.825211 & 0.412606 \tabularnewline
30 & 0.522468 & 0.955063 & 0.477532 \tabularnewline
31 & 0.498027 & 0.996054 & 0.501973 \tabularnewline
32 & 0.481763 & 0.963526 & 0.518237 \tabularnewline
33 & 0.464756 & 0.929511 & 0.535244 \tabularnewline
34 & 0.440156 & 0.880312 & 0.559844 \tabularnewline
35 & 0.386849 & 0.773698 & 0.613151 \tabularnewline
36 & 0.332616 & 0.665232 & 0.667384 \tabularnewline
37 & 0.284989 & 0.569977 & 0.715011 \tabularnewline
38 & 0.358895 & 0.717789 & 0.641105 \tabularnewline
39 & 0.35417 & 0.70834 & 0.64583 \tabularnewline
40 & 0.300072 & 0.600143 & 0.699928 \tabularnewline
41 & 0.43786 & 0.875721 & 0.56214 \tabularnewline
42 & 0.383155 & 0.76631 & 0.616845 \tabularnewline
43 & 0.349435 & 0.69887 & 0.650565 \tabularnewline
44 & 0.304347 & 0.608694 & 0.695653 \tabularnewline
45 & 0.317046 & 0.634091 & 0.682954 \tabularnewline
46 & 0.270508 & 0.541016 & 0.729492 \tabularnewline
47 & 0.235528 & 0.471056 & 0.764472 \tabularnewline
48 & 0.230633 & 0.461266 & 0.769367 \tabularnewline
49 & 0.231028 & 0.462056 & 0.768972 \tabularnewline
50 & 0.253859 & 0.507719 & 0.746141 \tabularnewline
51 & 0.218498 & 0.436996 & 0.781502 \tabularnewline
52 & 0.216564 & 0.433128 & 0.783436 \tabularnewline
53 & 0.178474 & 0.356948 & 0.821526 \tabularnewline
54 & 0.166653 & 0.333306 & 0.833347 \tabularnewline
55 & 0.17466 & 0.349321 & 0.82534 \tabularnewline
56 & 0.14171 & 0.283421 & 0.85829 \tabularnewline
57 & 0.257351 & 0.514701 & 0.742649 \tabularnewline
58 & 0.319699 & 0.639398 & 0.680301 \tabularnewline
59 & 0.273521 & 0.547042 & 0.726479 \tabularnewline
60 & 0.290698 & 0.581395 & 0.709302 \tabularnewline
61 & 0.318433 & 0.636867 & 0.681567 \tabularnewline
62 & 0.307728 & 0.615455 & 0.692272 \tabularnewline
63 & 0.330371 & 0.660742 & 0.669629 \tabularnewline
64 & 0.379558 & 0.759115 & 0.620442 \tabularnewline
65 & 0.370653 & 0.741306 & 0.629347 \tabularnewline
66 & 0.319428 & 0.638856 & 0.680572 \tabularnewline
67 & 0.286269 & 0.572538 & 0.713731 \tabularnewline
68 & 0.255931 & 0.511862 & 0.744069 \tabularnewline
69 & 0.278451 & 0.556902 & 0.721549 \tabularnewline
70 & 0.265704 & 0.531408 & 0.734296 \tabularnewline
71 & 0.221239 & 0.442479 & 0.778761 \tabularnewline
72 & 0.186101 & 0.372203 & 0.813899 \tabularnewline
73 & 0.160228 & 0.320456 & 0.839772 \tabularnewline
74 & 0.199993 & 0.399986 & 0.800007 \tabularnewline
75 & 0.174734 & 0.349468 & 0.825266 \tabularnewline
76 & 0.150419 & 0.300838 & 0.849581 \tabularnewline
77 & 0.154111 & 0.308221 & 0.845889 \tabularnewline
78 & 0.144441 & 0.288882 & 0.855559 \tabularnewline
79 & 0.112248 & 0.224495 & 0.887752 \tabularnewline
80 & 0.123484 & 0.246967 & 0.876516 \tabularnewline
81 & 0.0949243 & 0.189849 & 0.905076 \tabularnewline
82 & 0.121033 & 0.242066 & 0.878967 \tabularnewline
83 & 0.0925295 & 0.185059 & 0.907471 \tabularnewline
84 & 0.255039 & 0.510079 & 0.744961 \tabularnewline
85 & 0.21353 & 0.427059 & 0.78647 \tabularnewline
86 & 0.17731 & 0.354621 & 0.82269 \tabularnewline
87 & 0.145102 & 0.290203 & 0.854898 \tabularnewline
88 & 0.127957 & 0.255914 & 0.872043 \tabularnewline
89 & 0.10263 & 0.20526 & 0.89737 \tabularnewline
90 & 0.120182 & 0.240364 & 0.879818 \tabularnewline
91 & 0.128289 & 0.256578 & 0.871711 \tabularnewline
92 & 0.24054 & 0.48108 & 0.75946 \tabularnewline
93 & 0.186709 & 0.373417 & 0.813291 \tabularnewline
94 & 0.161672 & 0.323345 & 0.838328 \tabularnewline
95 & 0.240299 & 0.480597 & 0.759701 \tabularnewline
96 & 0.28729 & 0.57458 & 0.71271 \tabularnewline
97 & 0.409141 & 0.818283 & 0.590859 \tabularnewline
98 & 0.343341 & 0.686682 & 0.656659 \tabularnewline
99 & 0.320346 & 0.640691 & 0.679654 \tabularnewline
100 & 0.264914 & 0.529828 & 0.735086 \tabularnewline
101 & 0.321398 & 0.642796 & 0.678602 \tabularnewline
102 & 0.374538 & 0.749077 & 0.625462 \tabularnewline
103 & 0.30819 & 0.616381 & 0.69181 \tabularnewline
104 & 0.395278 & 0.790556 & 0.604722 \tabularnewline
105 & 0.499318 & 0.998636 & 0.500682 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269300&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]0.244579[/C][C]0.489159[/C][C]0.755421[/C][/ROW]
[ROW][C]9[/C][C]0.286238[/C][C]0.572476[/C][C]0.713762[/C][/ROW]
[ROW][C]10[/C][C]0.724594[/C][C]0.550812[/C][C]0.275406[/C][/ROW]
[ROW][C]11[/C][C]0.694934[/C][C]0.610132[/C][C]0.305066[/C][/ROW]
[ROW][C]12[/C][C]0.799988[/C][C]0.400024[/C][C]0.200012[/C][/ROW]
[ROW][C]13[/C][C]0.796167[/C][C]0.407667[/C][C]0.203833[/C][/ROW]
[ROW][C]14[/C][C]0.72798[/C][C]0.544041[/C][C]0.27202[/C][/ROW]
[ROW][C]15[/C][C]0.666545[/C][C]0.666911[/C][C]0.333455[/C][/ROW]
[ROW][C]16[/C][C]0.650307[/C][C]0.699386[/C][C]0.349693[/C][/ROW]
[ROW][C]17[/C][C]0.566942[/C][C]0.866116[/C][C]0.433058[/C][/ROW]
[ROW][C]18[/C][C]0.484176[/C][C]0.968353[/C][C]0.515824[/C][/ROW]
[ROW][C]19[/C][C]0.405944[/C][C]0.811889[/C][C]0.594056[/C][/ROW]
[ROW][C]20[/C][C]0.357972[/C][C]0.715944[/C][C]0.642028[/C][/ROW]
[ROW][C]21[/C][C]0.494706[/C][C]0.989412[/C][C]0.505294[/C][/ROW]
[ROW][C]22[/C][C]0.611014[/C][C]0.777971[/C][C]0.388986[/C][/ROW]
[ROW][C]23[/C][C]0.551108[/C][C]0.897785[/C][C]0.448892[/C][/ROW]
[ROW][C]24[/C][C]0.506011[/C][C]0.987979[/C][C]0.493989[/C][/ROW]
[ROW][C]25[/C][C]0.444758[/C][C]0.889516[/C][C]0.555242[/C][/ROW]
[ROW][C]26[/C][C]0.402304[/C][C]0.804609[/C][C]0.597696[/C][/ROW]
[ROW][C]27[/C][C]0.47741[/C][C]0.954819[/C][C]0.52259[/C][/ROW]
[ROW][C]28[/C][C]0.478899[/C][C]0.957798[/C][C]0.521101[/C][/ROW]
[ROW][C]29[/C][C]0.587394[/C][C]0.825211[/C][C]0.412606[/C][/ROW]
[ROW][C]30[/C][C]0.522468[/C][C]0.955063[/C][C]0.477532[/C][/ROW]
[ROW][C]31[/C][C]0.498027[/C][C]0.996054[/C][C]0.501973[/C][/ROW]
[ROW][C]32[/C][C]0.481763[/C][C]0.963526[/C][C]0.518237[/C][/ROW]
[ROW][C]33[/C][C]0.464756[/C][C]0.929511[/C][C]0.535244[/C][/ROW]
[ROW][C]34[/C][C]0.440156[/C][C]0.880312[/C][C]0.559844[/C][/ROW]
[ROW][C]35[/C][C]0.386849[/C][C]0.773698[/C][C]0.613151[/C][/ROW]
[ROW][C]36[/C][C]0.332616[/C][C]0.665232[/C][C]0.667384[/C][/ROW]
[ROW][C]37[/C][C]0.284989[/C][C]0.569977[/C][C]0.715011[/C][/ROW]
[ROW][C]38[/C][C]0.358895[/C][C]0.717789[/C][C]0.641105[/C][/ROW]
[ROW][C]39[/C][C]0.35417[/C][C]0.70834[/C][C]0.64583[/C][/ROW]
[ROW][C]40[/C][C]0.300072[/C][C]0.600143[/C][C]0.699928[/C][/ROW]
[ROW][C]41[/C][C]0.43786[/C][C]0.875721[/C][C]0.56214[/C][/ROW]
[ROW][C]42[/C][C]0.383155[/C][C]0.76631[/C][C]0.616845[/C][/ROW]
[ROW][C]43[/C][C]0.349435[/C][C]0.69887[/C][C]0.650565[/C][/ROW]
[ROW][C]44[/C][C]0.304347[/C][C]0.608694[/C][C]0.695653[/C][/ROW]
[ROW][C]45[/C][C]0.317046[/C][C]0.634091[/C][C]0.682954[/C][/ROW]
[ROW][C]46[/C][C]0.270508[/C][C]0.541016[/C][C]0.729492[/C][/ROW]
[ROW][C]47[/C][C]0.235528[/C][C]0.471056[/C][C]0.764472[/C][/ROW]
[ROW][C]48[/C][C]0.230633[/C][C]0.461266[/C][C]0.769367[/C][/ROW]
[ROW][C]49[/C][C]0.231028[/C][C]0.462056[/C][C]0.768972[/C][/ROW]
[ROW][C]50[/C][C]0.253859[/C][C]0.507719[/C][C]0.746141[/C][/ROW]
[ROW][C]51[/C][C]0.218498[/C][C]0.436996[/C][C]0.781502[/C][/ROW]
[ROW][C]52[/C][C]0.216564[/C][C]0.433128[/C][C]0.783436[/C][/ROW]
[ROW][C]53[/C][C]0.178474[/C][C]0.356948[/C][C]0.821526[/C][/ROW]
[ROW][C]54[/C][C]0.166653[/C][C]0.333306[/C][C]0.833347[/C][/ROW]
[ROW][C]55[/C][C]0.17466[/C][C]0.349321[/C][C]0.82534[/C][/ROW]
[ROW][C]56[/C][C]0.14171[/C][C]0.283421[/C][C]0.85829[/C][/ROW]
[ROW][C]57[/C][C]0.257351[/C][C]0.514701[/C][C]0.742649[/C][/ROW]
[ROW][C]58[/C][C]0.319699[/C][C]0.639398[/C][C]0.680301[/C][/ROW]
[ROW][C]59[/C][C]0.273521[/C][C]0.547042[/C][C]0.726479[/C][/ROW]
[ROW][C]60[/C][C]0.290698[/C][C]0.581395[/C][C]0.709302[/C][/ROW]
[ROW][C]61[/C][C]0.318433[/C][C]0.636867[/C][C]0.681567[/C][/ROW]
[ROW][C]62[/C][C]0.307728[/C][C]0.615455[/C][C]0.692272[/C][/ROW]
[ROW][C]63[/C][C]0.330371[/C][C]0.660742[/C][C]0.669629[/C][/ROW]
[ROW][C]64[/C][C]0.379558[/C][C]0.759115[/C][C]0.620442[/C][/ROW]
[ROW][C]65[/C][C]0.370653[/C][C]0.741306[/C][C]0.629347[/C][/ROW]
[ROW][C]66[/C][C]0.319428[/C][C]0.638856[/C][C]0.680572[/C][/ROW]
[ROW][C]67[/C][C]0.286269[/C][C]0.572538[/C][C]0.713731[/C][/ROW]
[ROW][C]68[/C][C]0.255931[/C][C]0.511862[/C][C]0.744069[/C][/ROW]
[ROW][C]69[/C][C]0.278451[/C][C]0.556902[/C][C]0.721549[/C][/ROW]
[ROW][C]70[/C][C]0.265704[/C][C]0.531408[/C][C]0.734296[/C][/ROW]
[ROW][C]71[/C][C]0.221239[/C][C]0.442479[/C][C]0.778761[/C][/ROW]
[ROW][C]72[/C][C]0.186101[/C][C]0.372203[/C][C]0.813899[/C][/ROW]
[ROW][C]73[/C][C]0.160228[/C][C]0.320456[/C][C]0.839772[/C][/ROW]
[ROW][C]74[/C][C]0.199993[/C][C]0.399986[/C][C]0.800007[/C][/ROW]
[ROW][C]75[/C][C]0.174734[/C][C]0.349468[/C][C]0.825266[/C][/ROW]
[ROW][C]76[/C][C]0.150419[/C][C]0.300838[/C][C]0.849581[/C][/ROW]
[ROW][C]77[/C][C]0.154111[/C][C]0.308221[/C][C]0.845889[/C][/ROW]
[ROW][C]78[/C][C]0.144441[/C][C]0.288882[/C][C]0.855559[/C][/ROW]
[ROW][C]79[/C][C]0.112248[/C][C]0.224495[/C][C]0.887752[/C][/ROW]
[ROW][C]80[/C][C]0.123484[/C][C]0.246967[/C][C]0.876516[/C][/ROW]
[ROW][C]81[/C][C]0.0949243[/C][C]0.189849[/C][C]0.905076[/C][/ROW]
[ROW][C]82[/C][C]0.121033[/C][C]0.242066[/C][C]0.878967[/C][/ROW]
[ROW][C]83[/C][C]0.0925295[/C][C]0.185059[/C][C]0.907471[/C][/ROW]
[ROW][C]84[/C][C]0.255039[/C][C]0.510079[/C][C]0.744961[/C][/ROW]
[ROW][C]85[/C][C]0.21353[/C][C]0.427059[/C][C]0.78647[/C][/ROW]
[ROW][C]86[/C][C]0.17731[/C][C]0.354621[/C][C]0.82269[/C][/ROW]
[ROW][C]87[/C][C]0.145102[/C][C]0.290203[/C][C]0.854898[/C][/ROW]
[ROW][C]88[/C][C]0.127957[/C][C]0.255914[/C][C]0.872043[/C][/ROW]
[ROW][C]89[/C][C]0.10263[/C][C]0.20526[/C][C]0.89737[/C][/ROW]
[ROW][C]90[/C][C]0.120182[/C][C]0.240364[/C][C]0.879818[/C][/ROW]
[ROW][C]91[/C][C]0.128289[/C][C]0.256578[/C][C]0.871711[/C][/ROW]
[ROW][C]92[/C][C]0.24054[/C][C]0.48108[/C][C]0.75946[/C][/ROW]
[ROW][C]93[/C][C]0.186709[/C][C]0.373417[/C][C]0.813291[/C][/ROW]
[ROW][C]94[/C][C]0.161672[/C][C]0.323345[/C][C]0.838328[/C][/ROW]
[ROW][C]95[/C][C]0.240299[/C][C]0.480597[/C][C]0.759701[/C][/ROW]
[ROW][C]96[/C][C]0.28729[/C][C]0.57458[/C][C]0.71271[/C][/ROW]
[ROW][C]97[/C][C]0.409141[/C][C]0.818283[/C][C]0.590859[/C][/ROW]
[ROW][C]98[/C][C]0.343341[/C][C]0.686682[/C][C]0.656659[/C][/ROW]
[ROW][C]99[/C][C]0.320346[/C][C]0.640691[/C][C]0.679654[/C][/ROW]
[ROW][C]100[/C][C]0.264914[/C][C]0.529828[/C][C]0.735086[/C][/ROW]
[ROW][C]101[/C][C]0.321398[/C][C]0.642796[/C][C]0.678602[/C][/ROW]
[ROW][C]102[/C][C]0.374538[/C][C]0.749077[/C][C]0.625462[/C][/ROW]
[ROW][C]103[/C][C]0.30819[/C][C]0.616381[/C][C]0.69181[/C][/ROW]
[ROW][C]104[/C][C]0.395278[/C][C]0.790556[/C][C]0.604722[/C][/ROW]
[ROW][C]105[/C][C]0.499318[/C][C]0.998636[/C][C]0.500682[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269300&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269300&T=5

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.2445790.4891590.755421
90.2862380.5724760.713762
100.7245940.5508120.275406
110.6949340.6101320.305066
120.7999880.4000240.200012
130.7961670.4076670.203833
140.727980.5440410.27202
150.6665450.6669110.333455
160.6503070.6993860.349693
170.5669420.8661160.433058
180.4841760.9683530.515824
190.4059440.8118890.594056
200.3579720.7159440.642028
210.4947060.9894120.505294
220.6110140.7779710.388986
230.5511080.8977850.448892
240.5060110.9879790.493989
250.4447580.8895160.555242
260.4023040.8046090.597696
270.477410.9548190.52259
280.4788990.9577980.521101
290.5873940.8252110.412606
300.5224680.9550630.477532
310.4980270.9960540.501973
320.4817630.9635260.518237
330.4647560.9295110.535244
340.4401560.8803120.559844
350.3868490.7736980.613151
360.3326160.6652320.667384
370.2849890.5699770.715011
380.3588950.7177890.641105
390.354170.708340.64583
400.3000720.6001430.699928
410.437860.8757210.56214
420.3831550.766310.616845
430.3494350.698870.650565
440.3043470.6086940.695653
450.3170460.6340910.682954
460.2705080.5410160.729492
470.2355280.4710560.764472
480.2306330.4612660.769367
490.2310280.4620560.768972
500.2538590.5077190.746141
510.2184980.4369960.781502
520.2165640.4331280.783436
530.1784740.3569480.821526
540.1666530.3333060.833347
550.174660.3493210.82534
560.141710.2834210.85829
570.2573510.5147010.742649
580.3196990.6393980.680301
590.2735210.5470420.726479
600.2906980.5813950.709302
610.3184330.6368670.681567
620.3077280.6154550.692272
630.3303710.6607420.669629
640.3795580.7591150.620442
650.3706530.7413060.629347
660.3194280.6388560.680572
670.2862690.5725380.713731
680.2559310.5118620.744069
690.2784510.5569020.721549
700.2657040.5314080.734296
710.2212390.4424790.778761
720.1861010.3722030.813899
730.1602280.3204560.839772
740.1999930.3999860.800007
750.1747340.3494680.825266
760.1504190.3008380.849581
770.1541110.3082210.845889
780.1444410.2888820.855559
790.1122480.2244950.887752
800.1234840.2469670.876516
810.09492430.1898490.905076
820.1210330.2420660.878967
830.09252950.1850590.907471
840.2550390.5100790.744961
850.213530.4270590.78647
860.177310.3546210.82269
870.1451020.2902030.854898
880.1279570.2559140.872043
890.102630.205260.89737
900.1201820.2403640.879818
910.1282890.2565780.871711
920.240540.481080.75946
930.1867090.3734170.813291
940.1616720.3233450.838328
950.2402990.4805970.759701
960.287290.574580.71271
970.4091410.8182830.590859
980.3433410.6866820.656659
990.3203460.6406910.679654
1000.2649140.5298280.735086
1010.3213980.6427960.678602
1020.3745380.7490770.625462
1030.308190.6163810.69181
1040.3952780.7905560.604722
1050.4993180.9986360.500682






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269300&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269300&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269300&T=6

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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 level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}