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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 21:00:36 +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/t1418766816n6qhtmksnaw70hm.htm/, Retrieved Thu, 16 May 2024 23:24:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269956, Retrieved Thu, 16 May 2024 23:24:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-12-10 11:27:08] [3f7e537763d373cc2ab5235e2041342a]
- R PD    [Multiple Regression] [] [2014-12-16 21:00:36] [6870495cd5e22452491bd29e9b20b7c8] [Current]
- RMPD      [Multiple Regression] [] [2014-12-18 00:37:44] [69bf0eb8b9b38defaaf4848d8c317571]
- RMPD        [Multiple Regression] [] [2014-12-18 13:22:32] [69bf0eb8b9b38defaaf4848d8c317571]
- RMPD          [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2014-12-18 17:04:58] [69bf0eb8b9b38defaaf4848d8c317571]
Feedback Forum

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
26	50	21	12.9
51	68	26	7.4
57	62	22	12.2
37	54	22	12.8
67	71	18	7.4
43	54	23	6.7
52	65	12	12.6
52	73	20	14.8
43	52	22	13.3
84	84	21	11.1
67	42	19	8.2
49	66	22	11.4
70	65	15	6.4
52	78	20	10.6
58	73	19	12.0
68	75	18	6.3
43	66	20	11.9
56	70	21	9.3
74	81	15	10.0
65	71	16	6.4
63	69	23	13.8
58	71	21	10.8
57	72	18	13.8
63	68	25	11.7
53	70	9	10.9
64	67	23	9.9
53	76	16	11.5
29	70	16	8.3
54	60	19	11.7
51	77	25	6.1
58	72	25	9.0
43	69	18	9.7
51	71	23	10.8
53	62	21	10.3
54	70	10	10.4
61	58	22	9.3
47	76	26	11.8
39	52	23	5.9
48	59	23	11.4
50	68	24	13.0
35	76	24	10.8
68	67	23	11.3
49	59	15	11.8
67	76	16	12.7
43	60	19	10.9
62	63	18	13.3
57	70	27	10.1
54	66	13	14.3
61	64	28	9.3
56	70	23	12.5
41	75	21	7.6
43	61	19	15.9
53	60	19	9.2
66	73	18	11.1
58	61	19	13.0
46	66	17	14.5
51	59	25	12.3
51	64	19	11.4
45	66	26	7.3
37	78	14	12.6
59	53	28	NA
42	67	16	13.0
66	66	20	13.2
53	71	24	7.7
52	51	23	4.35
16	56	22	12.7
46	67	21	18.1
56	69	25	17.85
50	55	27	17.1
59	63	23	19.1
60	67	23	16.1
52	65	18	13.35
44	47	18	18.4
67	76	23	14.7
52	64	19	10.6
55	68	15	12.6
37	64	20	16.2
54	65	16	13.6
51	63	25	14.1
48	60	25	14.5
60	68	19	16.15
50	72	19	14.75
63	70	16	14.8
33	61	19	12.45
67	61	19	12.65
46	62	23	17.35
54	71	21	8.6
59	71	22	18.4
61	51	19	16.1
47	70	20	17.75
69	73	3	15.25
52	76	23	17.65
55	59	14	15.6
55	68	23	16.35
41	48	20	17.65
73	52	15	13.6
51	59	13	11.7
52	60	16	14.35
50	59	7	14.75
51	57	24	18.25
60	79	17	9.9
56	60	24	16
56	60	24	18.25
29	59	19	16.85
73	61	28	18.95
55	71	23	15.6
43	58	19	17.1
61	59	23	16.1
56	58	25	15.4
56	60	25	15.4
47	55	20	13.35
25	62	16	19.1
46	69	20	7.6
51	68	25	19.1
48	72	25	14.75
47	19	23	19.25
58	68	17	13.6
51	79	20	12.75
55	71	16	9.85
57	71	23	15.25
60	74	12	11.9
56	75	24	16.35
49	53	11	12.4
43	50	14	14.35
59	70	23	18.15
58	78	18	17.75
53	59	29	12.35
48	72	16	15.6
51	70	19	19.3
59	63	16	17.1
62	74	23	18.4
51	67	19	19.05
64	66	4	18.55
52	62	20	19.1
50	73	20	12.85
54	67	4	9.5
58	61	24	4.5
63	74	16	13.6
31	32	3	11.7
71	69	24	13.35
54	60	23	17.75
43	57	17	17.6
41	60	20	14.05
63	68	22	16.1
63	68	19	13.35
56	73	24	11.85
51	69	19	11.95
41	65	27	13.2
66	81	22	7.7
44	55	23	14.6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269956&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269956&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269956&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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
EX[t] = + 17.1167 -0.0066476AMSI[t] -0.0611956AMSE[t] + 0.0174233NUMTOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
EX[t] =  +  17.1167 -0.0066476AMSI[t] -0.0611956AMSE[t] +  0.0174233NUMTOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269956&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]EX[t] =  +  17.1167 -0.0066476AMSI[t] -0.0611956AMSE[t] +  0.0174233NUMTOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269956&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269956&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] = + 17.1167 -0.0066476AMSI[t] -0.0611956AMSE[t] + 0.0174233NUMTOT[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.11672.533056.7573.17694e-101.58847e-10
AMSI-0.00664760.0300371-0.22130.825160.41258
AMSE-0.06119560.0342327-1.7880.07592340.0379617
NUMTOT0.01742330.06005240.29010.7721270.386063

\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) & 17.1167 & 2.53305 & 6.757 & 3.17694e-10 & 1.58847e-10 \tabularnewline
AMSI & -0.0066476 & 0.0300371 & -0.2213 & 0.82516 & 0.41258 \tabularnewline
AMSE & -0.0611956 & 0.0342327 & -1.788 & 0.0759234 & 0.0379617 \tabularnewline
NUMTOT & 0.0174233 & 0.0600524 & 0.2901 & 0.772127 & 0.386063 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269956&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]17.1167[/C][C]2.53305[/C][C]6.757[/C][C]3.17694e-10[/C][C]1.58847e-10[/C][/ROW]
[ROW][C]AMSI[/C][C]-0.0066476[/C][C]0.0300371[/C][C]-0.2213[/C][C]0.82516[/C][C]0.41258[/C][/ROW]
[ROW][C]AMSE[/C][C]-0.0611956[/C][C]0.0342327[/C][C]-1.788[/C][C]0.0759234[/C][C]0.0379617[/C][/ROW]
[ROW][C]NUMTOT[/C][C]0.0174233[/C][C]0.0600524[/C][C]0.2901[/C][C]0.772127[/C][C]0.386063[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269956&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269956&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)17.11672.533056.7573.17694e-101.58847e-10
AMSI-0.00664760.0300371-0.22130.825160.41258
AMSE-0.06119560.0342327-1.7880.07592340.0379617
NUMTOT0.01742330.06005240.29010.7721270.386063






Multiple Linear Regression - Regression Statistics
Multiple R0.164923
R-squared0.0271996
Adjusted R-squared0.00707272
F-TEST (value)1.35141
F-TEST (DF numerator)3
F-TEST (DF denominator)145
p-value0.26014
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.53273
Sum Squared Residuals1809.62

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.164923 \tabularnewline
R-squared & 0.0271996 \tabularnewline
Adjusted R-squared & 0.00707272 \tabularnewline
F-TEST (value) & 1.35141 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 145 \tabularnewline
p-value & 0.26014 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.53273 \tabularnewline
Sum Squared Residuals & 1809.62 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269956&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.164923[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0271996[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00707272[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.35141[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]145[/C][/ROW]
[ROW][C]p-value[/C][C]0.26014[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.53273[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1809.62[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269956&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269956&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 R0.164923
R-squared0.0271996
Adjusted R-squared0.00707272
F-TEST (value)1.35141
F-TEST (DF numerator)3
F-TEST (DF denominator)145
p-value0.26014
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.53273
Sum Squared Residuals1809.62






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.914.25-1.34996
27.413.0694-5.66936
312.213.327-1.12696
412.813.9495-1.14948
57.412.64-5.24003
66.713.927-7.22701
712.613.0024-0.402377
814.812.65222.1478
913.314.032-0.731981
1011.111.7837-0.683748
118.214.4321-6.23212
1211.413.1354-1.73536
136.412.935-6.53499
1410.612.3462-1.74622
151212.5949-0.59489
166.312.3886-6.0886
1711.913.1404-1.2404
189.312.8266-3.52662
191011.9293-1.92927
206.412.6185-6.21848
2113.812.87610.923872
2210.812.7521-1.95213
2313.812.64531.15469
2411.712.9722-1.27217
2510.912.6375-1.73748
269.912.9919-3.09187
2711.512.3923-0.892272
288.312.919-4.61899
2911.713.417-1.71702
306.112.5012-6.40118
31912.7606-3.76063
329.712.922-3.22196
3310.812.8335-2.03351
3410.313.3361-3.03613
3510.412.6483-2.24826
369.313.5452-4.24515
3711.812.6064-0.80639
385.914.076-8.17599
3911.413.5878-2.1878
401313.0412-0.0411655
4110.812.6513-1.85131
4211.312.9653-1.66528
4311.813.4418-1.64176
4412.712.29920.400795
4510.913.4901-2.59015
4613.313.16280.137168
4710.112.9245-2.82451
4814.312.94531.35469
499.313.2825-3.98252
5012.512.8615-0.361465
517.612.6204-5.02035
5215.913.4292.47105
539.213.4237-4.22367
5411.112.5243-1.42429
551313.3292-0.329237
5614.513.06821.43182
5712.313.6027-1.3027
5811.413.1922-1.79218
597.313.2316-5.93164
6012.612.34140.258605
61NANA-0.0161552
621312.78750.212498
6313.218.3376-5.13764
647.717.4008-9.70077
654.355.61668-1.26668
6612.77.676685.02332
6718.113.20754.89249
6817.8514.6393.21102
6917.111.26995.83011
7019.116.01853.08154
7116.115.85690.243083
7213.359.211624.13838
7318.416.12122.27883
7414.717.2855-2.58554
7510.610.8511-0.251118
7612.69.702672.89733
7716.215.65880.541225
7813.612.85790.742081
7914.113.16140.938551
8014.511.23763.26243
8116.1514.10932.04073
8214.7512.6432.10703
8314.815.8454-1.04543
8412.4513.0694-0.619409
8512.658.717513.93249
8617.3521.5287-4.17872
878.62.96295.6371
8818.416.22122.17875
8916.111.2194.88098
9017.7514.7433.00701
9115.2510.12095.12912
9217.6515.43452.21555
9315.612.24053.3595
9416.3512.95523.39479
9517.6517.7606-0.11059
9613.615.2936-1.69362
9711.710.7280.971952
9814.3512.89571.45427
9914.7510.20774.54233
10018.2520.5296-2.27958
1019.97.390842.50916
1021611.24084.75916
10318.2515.04443.20559
10416.8511.28635.56367
10518.9516.15692.79308
10615.612.11253.48746
10717.114.50142.59862
10816.114.33071.76934
10915.413.50831.89173
11015.415.837-0.436957
11113.357.685145.66486
11219.124.4369-5.33687
1137.61.551946.04806
11419.117.17711.9229
11514.7511.54233.20773
11619.2518.5160.733979
11713.613.14170.458327
11812.7515.585-2.83495
1199.857.393622.45638
12015.2515.7484-0.498436
12111.98.122913.77709
12216.3517.6892-1.33924
12312.412.0650.335015
12414.359.041525.30848
12518.1512.67155.47851
12617.7519.0591-1.3091
12712.359.420292.92971
12815.69.125016.47499
12919.315.34793.95207
13017.111.27685.8232
13118.412.35866.0414
13219.0513.2225.82798
13318.5512.77545.77465
13419.118.91550.184506
13512.8516.0773-3.2273
1369.518.4164-8.91635
1374.53.348191.15181
13813.616.9046-3.30462
13911.711.19040.50963
14013.359.086724.26328
14117.7513.78893.96111
14217.617.07090.529135
14314.0510.86993.1801
14416.115.61760.48237
14513.3514.1953-0.845302
14611.8512.7862-0.936206
14711.9512.0869-0.136851
14813.217.6044-4.40441
1497.76.959170.74083
15014.6NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 14.25 & -1.34996 \tabularnewline
2 & 7.4 & 13.0694 & -5.66936 \tabularnewline
3 & 12.2 & 13.327 & -1.12696 \tabularnewline
4 & 12.8 & 13.9495 & -1.14948 \tabularnewline
5 & 7.4 & 12.64 & -5.24003 \tabularnewline
6 & 6.7 & 13.927 & -7.22701 \tabularnewline
7 & 12.6 & 13.0024 & -0.402377 \tabularnewline
8 & 14.8 & 12.6522 & 2.1478 \tabularnewline
9 & 13.3 & 14.032 & -0.731981 \tabularnewline
10 & 11.1 & 11.7837 & -0.683748 \tabularnewline
11 & 8.2 & 14.4321 & -6.23212 \tabularnewline
12 & 11.4 & 13.1354 & -1.73536 \tabularnewline
13 & 6.4 & 12.935 & -6.53499 \tabularnewline
14 & 10.6 & 12.3462 & -1.74622 \tabularnewline
15 & 12 & 12.5949 & -0.59489 \tabularnewline
16 & 6.3 & 12.3886 & -6.0886 \tabularnewline
17 & 11.9 & 13.1404 & -1.2404 \tabularnewline
18 & 9.3 & 12.8266 & -3.52662 \tabularnewline
19 & 10 & 11.9293 & -1.92927 \tabularnewline
20 & 6.4 & 12.6185 & -6.21848 \tabularnewline
21 & 13.8 & 12.8761 & 0.923872 \tabularnewline
22 & 10.8 & 12.7521 & -1.95213 \tabularnewline
23 & 13.8 & 12.6453 & 1.15469 \tabularnewline
24 & 11.7 & 12.9722 & -1.27217 \tabularnewline
25 & 10.9 & 12.6375 & -1.73748 \tabularnewline
26 & 9.9 & 12.9919 & -3.09187 \tabularnewline
27 & 11.5 & 12.3923 & -0.892272 \tabularnewline
28 & 8.3 & 12.919 & -4.61899 \tabularnewline
29 & 11.7 & 13.417 & -1.71702 \tabularnewline
30 & 6.1 & 12.5012 & -6.40118 \tabularnewline
31 & 9 & 12.7606 & -3.76063 \tabularnewline
32 & 9.7 & 12.922 & -3.22196 \tabularnewline
33 & 10.8 & 12.8335 & -2.03351 \tabularnewline
34 & 10.3 & 13.3361 & -3.03613 \tabularnewline
35 & 10.4 & 12.6483 & -2.24826 \tabularnewline
36 & 9.3 & 13.5452 & -4.24515 \tabularnewline
37 & 11.8 & 12.6064 & -0.80639 \tabularnewline
38 & 5.9 & 14.076 & -8.17599 \tabularnewline
39 & 11.4 & 13.5878 & -2.1878 \tabularnewline
40 & 13 & 13.0412 & -0.0411655 \tabularnewline
41 & 10.8 & 12.6513 & -1.85131 \tabularnewline
42 & 11.3 & 12.9653 & -1.66528 \tabularnewline
43 & 11.8 & 13.4418 & -1.64176 \tabularnewline
44 & 12.7 & 12.2992 & 0.400795 \tabularnewline
45 & 10.9 & 13.4901 & -2.59015 \tabularnewline
46 & 13.3 & 13.1628 & 0.137168 \tabularnewline
47 & 10.1 & 12.9245 & -2.82451 \tabularnewline
48 & 14.3 & 12.9453 & 1.35469 \tabularnewline
49 & 9.3 & 13.2825 & -3.98252 \tabularnewline
50 & 12.5 & 12.8615 & -0.361465 \tabularnewline
51 & 7.6 & 12.6204 & -5.02035 \tabularnewline
52 & 15.9 & 13.429 & 2.47105 \tabularnewline
53 & 9.2 & 13.4237 & -4.22367 \tabularnewline
54 & 11.1 & 12.5243 & -1.42429 \tabularnewline
55 & 13 & 13.3292 & -0.329237 \tabularnewline
56 & 14.5 & 13.0682 & 1.43182 \tabularnewline
57 & 12.3 & 13.6027 & -1.3027 \tabularnewline
58 & 11.4 & 13.1922 & -1.79218 \tabularnewline
59 & 7.3 & 13.2316 & -5.93164 \tabularnewline
60 & 12.6 & 12.3414 & 0.258605 \tabularnewline
61 & NA & NA & -0.0161552 \tabularnewline
62 & 13 & 12.7875 & 0.212498 \tabularnewline
63 & 13.2 & 18.3376 & -5.13764 \tabularnewline
64 & 7.7 & 17.4008 & -9.70077 \tabularnewline
65 & 4.35 & 5.61668 & -1.26668 \tabularnewline
66 & 12.7 & 7.67668 & 5.02332 \tabularnewline
67 & 18.1 & 13.2075 & 4.89249 \tabularnewline
68 & 17.85 & 14.639 & 3.21102 \tabularnewline
69 & 17.1 & 11.2699 & 5.83011 \tabularnewline
70 & 19.1 & 16.0185 & 3.08154 \tabularnewline
71 & 16.1 & 15.8569 & 0.243083 \tabularnewline
72 & 13.35 & 9.21162 & 4.13838 \tabularnewline
73 & 18.4 & 16.1212 & 2.27883 \tabularnewline
74 & 14.7 & 17.2855 & -2.58554 \tabularnewline
75 & 10.6 & 10.8511 & -0.251118 \tabularnewline
76 & 12.6 & 9.70267 & 2.89733 \tabularnewline
77 & 16.2 & 15.6588 & 0.541225 \tabularnewline
78 & 13.6 & 12.8579 & 0.742081 \tabularnewline
79 & 14.1 & 13.1614 & 0.938551 \tabularnewline
80 & 14.5 & 11.2376 & 3.26243 \tabularnewline
81 & 16.15 & 14.1093 & 2.04073 \tabularnewline
82 & 14.75 & 12.643 & 2.10703 \tabularnewline
83 & 14.8 & 15.8454 & -1.04543 \tabularnewline
84 & 12.45 & 13.0694 & -0.619409 \tabularnewline
85 & 12.65 & 8.71751 & 3.93249 \tabularnewline
86 & 17.35 & 21.5287 & -4.17872 \tabularnewline
87 & 8.6 & 2.9629 & 5.6371 \tabularnewline
88 & 18.4 & 16.2212 & 2.17875 \tabularnewline
89 & 16.1 & 11.219 & 4.88098 \tabularnewline
90 & 17.75 & 14.743 & 3.00701 \tabularnewline
91 & 15.25 & 10.1209 & 5.12912 \tabularnewline
92 & 17.65 & 15.4345 & 2.21555 \tabularnewline
93 & 15.6 & 12.2405 & 3.3595 \tabularnewline
94 & 16.35 & 12.9552 & 3.39479 \tabularnewline
95 & 17.65 & 17.7606 & -0.11059 \tabularnewline
96 & 13.6 & 15.2936 & -1.69362 \tabularnewline
97 & 11.7 & 10.728 & 0.971952 \tabularnewline
98 & 14.35 & 12.8957 & 1.45427 \tabularnewline
99 & 14.75 & 10.2077 & 4.54233 \tabularnewline
100 & 18.25 & 20.5296 & -2.27958 \tabularnewline
101 & 9.9 & 7.39084 & 2.50916 \tabularnewline
102 & 16 & 11.2408 & 4.75916 \tabularnewline
103 & 18.25 & 15.0444 & 3.20559 \tabularnewline
104 & 16.85 & 11.2863 & 5.56367 \tabularnewline
105 & 18.95 & 16.1569 & 2.79308 \tabularnewline
106 & 15.6 & 12.1125 & 3.48746 \tabularnewline
107 & 17.1 & 14.5014 & 2.59862 \tabularnewline
108 & 16.1 & 14.3307 & 1.76934 \tabularnewline
109 & 15.4 & 13.5083 & 1.89173 \tabularnewline
110 & 15.4 & 15.837 & -0.436957 \tabularnewline
111 & 13.35 & 7.68514 & 5.66486 \tabularnewline
112 & 19.1 & 24.4369 & -5.33687 \tabularnewline
113 & 7.6 & 1.55194 & 6.04806 \tabularnewline
114 & 19.1 & 17.1771 & 1.9229 \tabularnewline
115 & 14.75 & 11.5423 & 3.20773 \tabularnewline
116 & 19.25 & 18.516 & 0.733979 \tabularnewline
117 & 13.6 & 13.1417 & 0.458327 \tabularnewline
118 & 12.75 & 15.585 & -2.83495 \tabularnewline
119 & 9.85 & 7.39362 & 2.45638 \tabularnewline
120 & 15.25 & 15.7484 & -0.498436 \tabularnewline
121 & 11.9 & 8.12291 & 3.77709 \tabularnewline
122 & 16.35 & 17.6892 & -1.33924 \tabularnewline
123 & 12.4 & 12.065 & 0.335015 \tabularnewline
124 & 14.35 & 9.04152 & 5.30848 \tabularnewline
125 & 18.15 & 12.6715 & 5.47851 \tabularnewline
126 & 17.75 & 19.0591 & -1.3091 \tabularnewline
127 & 12.35 & 9.42029 & 2.92971 \tabularnewline
128 & 15.6 & 9.12501 & 6.47499 \tabularnewline
129 & 19.3 & 15.3479 & 3.95207 \tabularnewline
130 & 17.1 & 11.2768 & 5.8232 \tabularnewline
131 & 18.4 & 12.3586 & 6.0414 \tabularnewline
132 & 19.05 & 13.222 & 5.82798 \tabularnewline
133 & 18.55 & 12.7754 & 5.77465 \tabularnewline
134 & 19.1 & 18.9155 & 0.184506 \tabularnewline
135 & 12.85 & 16.0773 & -3.2273 \tabularnewline
136 & 9.5 & 18.4164 & -8.91635 \tabularnewline
137 & 4.5 & 3.34819 & 1.15181 \tabularnewline
138 & 13.6 & 16.9046 & -3.30462 \tabularnewline
139 & 11.7 & 11.1904 & 0.50963 \tabularnewline
140 & 13.35 & 9.08672 & 4.26328 \tabularnewline
141 & 17.75 & 13.7889 & 3.96111 \tabularnewline
142 & 17.6 & 17.0709 & 0.529135 \tabularnewline
143 & 14.05 & 10.8699 & 3.1801 \tabularnewline
144 & 16.1 & 15.6176 & 0.48237 \tabularnewline
145 & 13.35 & 14.1953 & -0.845302 \tabularnewline
146 & 11.85 & 12.7862 & -0.936206 \tabularnewline
147 & 11.95 & 12.0869 & -0.136851 \tabularnewline
148 & 13.2 & 17.6044 & -4.40441 \tabularnewline
149 & 7.7 & 6.95917 & 0.74083 \tabularnewline
150 & 14.6 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269956&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]14.25[/C][C]-1.34996[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]13.0694[/C][C]-5.66936[/C][/ROW]
[ROW][C]3[/C][C]12.2[/C][C]13.327[/C][C]-1.12696[/C][/ROW]
[ROW][C]4[/C][C]12.8[/C][C]13.9495[/C][C]-1.14948[/C][/ROW]
[ROW][C]5[/C][C]7.4[/C][C]12.64[/C][C]-5.24003[/C][/ROW]
[ROW][C]6[/C][C]6.7[/C][C]13.927[/C][C]-7.22701[/C][/ROW]
[ROW][C]7[/C][C]12.6[/C][C]13.0024[/C][C]-0.402377[/C][/ROW]
[ROW][C]8[/C][C]14.8[/C][C]12.6522[/C][C]2.1478[/C][/ROW]
[ROW][C]9[/C][C]13.3[/C][C]14.032[/C][C]-0.731981[/C][/ROW]
[ROW][C]10[/C][C]11.1[/C][C]11.7837[/C][C]-0.683748[/C][/ROW]
[ROW][C]11[/C][C]8.2[/C][C]14.4321[/C][C]-6.23212[/C][/ROW]
[ROW][C]12[/C][C]11.4[/C][C]13.1354[/C][C]-1.73536[/C][/ROW]
[ROW][C]13[/C][C]6.4[/C][C]12.935[/C][C]-6.53499[/C][/ROW]
[ROW][C]14[/C][C]10.6[/C][C]12.3462[/C][C]-1.74622[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]12.5949[/C][C]-0.59489[/C][/ROW]
[ROW][C]16[/C][C]6.3[/C][C]12.3886[/C][C]-6.0886[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]13.1404[/C][C]-1.2404[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]12.8266[/C][C]-3.52662[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]11.9293[/C][C]-1.92927[/C][/ROW]
[ROW][C]20[/C][C]6.4[/C][C]12.6185[/C][C]-6.21848[/C][/ROW]
[ROW][C]21[/C][C]13.8[/C][C]12.8761[/C][C]0.923872[/C][/ROW]
[ROW][C]22[/C][C]10.8[/C][C]12.7521[/C][C]-1.95213[/C][/ROW]
[ROW][C]23[/C][C]13.8[/C][C]12.6453[/C][C]1.15469[/C][/ROW]
[ROW][C]24[/C][C]11.7[/C][C]12.9722[/C][C]-1.27217[/C][/ROW]
[ROW][C]25[/C][C]10.9[/C][C]12.6375[/C][C]-1.73748[/C][/ROW]
[ROW][C]26[/C][C]9.9[/C][C]12.9919[/C][C]-3.09187[/C][/ROW]
[ROW][C]27[/C][C]11.5[/C][C]12.3923[/C][C]-0.892272[/C][/ROW]
[ROW][C]28[/C][C]8.3[/C][C]12.919[/C][C]-4.61899[/C][/ROW]
[ROW][C]29[/C][C]11.7[/C][C]13.417[/C][C]-1.71702[/C][/ROW]
[ROW][C]30[/C][C]6.1[/C][C]12.5012[/C][C]-6.40118[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.7606[/C][C]-3.76063[/C][/ROW]
[ROW][C]32[/C][C]9.7[/C][C]12.922[/C][C]-3.22196[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]12.8335[/C][C]-2.03351[/C][/ROW]
[ROW][C]34[/C][C]10.3[/C][C]13.3361[/C][C]-3.03613[/C][/ROW]
[ROW][C]35[/C][C]10.4[/C][C]12.6483[/C][C]-2.24826[/C][/ROW]
[ROW][C]36[/C][C]9.3[/C][C]13.5452[/C][C]-4.24515[/C][/ROW]
[ROW][C]37[/C][C]11.8[/C][C]12.6064[/C][C]-0.80639[/C][/ROW]
[ROW][C]38[/C][C]5.9[/C][C]14.076[/C][C]-8.17599[/C][/ROW]
[ROW][C]39[/C][C]11.4[/C][C]13.5878[/C][C]-2.1878[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]13.0412[/C][C]-0.0411655[/C][/ROW]
[ROW][C]41[/C][C]10.8[/C][C]12.6513[/C][C]-1.85131[/C][/ROW]
[ROW][C]42[/C][C]11.3[/C][C]12.9653[/C][C]-1.66528[/C][/ROW]
[ROW][C]43[/C][C]11.8[/C][C]13.4418[/C][C]-1.64176[/C][/ROW]
[ROW][C]44[/C][C]12.7[/C][C]12.2992[/C][C]0.400795[/C][/ROW]
[ROW][C]45[/C][C]10.9[/C][C]13.4901[/C][C]-2.59015[/C][/ROW]
[ROW][C]46[/C][C]13.3[/C][C]13.1628[/C][C]0.137168[/C][/ROW]
[ROW][C]47[/C][C]10.1[/C][C]12.9245[/C][C]-2.82451[/C][/ROW]
[ROW][C]48[/C][C]14.3[/C][C]12.9453[/C][C]1.35469[/C][/ROW]
[ROW][C]49[/C][C]9.3[/C][C]13.2825[/C][C]-3.98252[/C][/ROW]
[ROW][C]50[/C][C]12.5[/C][C]12.8615[/C][C]-0.361465[/C][/ROW]
[ROW][C]51[/C][C]7.6[/C][C]12.6204[/C][C]-5.02035[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]13.429[/C][C]2.47105[/C][/ROW]
[ROW][C]53[/C][C]9.2[/C][C]13.4237[/C][C]-4.22367[/C][/ROW]
[ROW][C]54[/C][C]11.1[/C][C]12.5243[/C][C]-1.42429[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]13.3292[/C][C]-0.329237[/C][/ROW]
[ROW][C]56[/C][C]14.5[/C][C]13.0682[/C][C]1.43182[/C][/ROW]
[ROW][C]57[/C][C]12.3[/C][C]13.6027[/C][C]-1.3027[/C][/ROW]
[ROW][C]58[/C][C]11.4[/C][C]13.1922[/C][C]-1.79218[/C][/ROW]
[ROW][C]59[/C][C]7.3[/C][C]13.2316[/C][C]-5.93164[/C][/ROW]
[ROW][C]60[/C][C]12.6[/C][C]12.3414[/C][C]0.258605[/C][/ROW]
[ROW][C]61[/C][C]NA[/C][C]NA[/C][C]-0.0161552[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]12.7875[/C][C]0.212498[/C][/ROW]
[ROW][C]63[/C][C]13.2[/C][C]18.3376[/C][C]-5.13764[/C][/ROW]
[ROW][C]64[/C][C]7.7[/C][C]17.4008[/C][C]-9.70077[/C][/ROW]
[ROW][C]65[/C][C]4.35[/C][C]5.61668[/C][C]-1.26668[/C][/ROW]
[ROW][C]66[/C][C]12.7[/C][C]7.67668[/C][C]5.02332[/C][/ROW]
[ROW][C]67[/C][C]18.1[/C][C]13.2075[/C][C]4.89249[/C][/ROW]
[ROW][C]68[/C][C]17.85[/C][C]14.639[/C][C]3.21102[/C][/ROW]
[ROW][C]69[/C][C]17.1[/C][C]11.2699[/C][C]5.83011[/C][/ROW]
[ROW][C]70[/C][C]19.1[/C][C]16.0185[/C][C]3.08154[/C][/ROW]
[ROW][C]71[/C][C]16.1[/C][C]15.8569[/C][C]0.243083[/C][/ROW]
[ROW][C]72[/C][C]13.35[/C][C]9.21162[/C][C]4.13838[/C][/ROW]
[ROW][C]73[/C][C]18.4[/C][C]16.1212[/C][C]2.27883[/C][/ROW]
[ROW][C]74[/C][C]14.7[/C][C]17.2855[/C][C]-2.58554[/C][/ROW]
[ROW][C]75[/C][C]10.6[/C][C]10.8511[/C][C]-0.251118[/C][/ROW]
[ROW][C]76[/C][C]12.6[/C][C]9.70267[/C][C]2.89733[/C][/ROW]
[ROW][C]77[/C][C]16.2[/C][C]15.6588[/C][C]0.541225[/C][/ROW]
[ROW][C]78[/C][C]13.6[/C][C]12.8579[/C][C]0.742081[/C][/ROW]
[ROW][C]79[/C][C]14.1[/C][C]13.1614[/C][C]0.938551[/C][/ROW]
[ROW][C]80[/C][C]14.5[/C][C]11.2376[/C][C]3.26243[/C][/ROW]
[ROW][C]81[/C][C]16.15[/C][C]14.1093[/C][C]2.04073[/C][/ROW]
[ROW][C]82[/C][C]14.75[/C][C]12.643[/C][C]2.10703[/C][/ROW]
[ROW][C]83[/C][C]14.8[/C][C]15.8454[/C][C]-1.04543[/C][/ROW]
[ROW][C]84[/C][C]12.45[/C][C]13.0694[/C][C]-0.619409[/C][/ROW]
[ROW][C]85[/C][C]12.65[/C][C]8.71751[/C][C]3.93249[/C][/ROW]
[ROW][C]86[/C][C]17.35[/C][C]21.5287[/C][C]-4.17872[/C][/ROW]
[ROW][C]87[/C][C]8.6[/C][C]2.9629[/C][C]5.6371[/C][/ROW]
[ROW][C]88[/C][C]18.4[/C][C]16.2212[/C][C]2.17875[/C][/ROW]
[ROW][C]89[/C][C]16.1[/C][C]11.219[/C][C]4.88098[/C][/ROW]
[ROW][C]90[/C][C]17.75[/C][C]14.743[/C][C]3.00701[/C][/ROW]
[ROW][C]91[/C][C]15.25[/C][C]10.1209[/C][C]5.12912[/C][/ROW]
[ROW][C]92[/C][C]17.65[/C][C]15.4345[/C][C]2.21555[/C][/ROW]
[ROW][C]93[/C][C]15.6[/C][C]12.2405[/C][C]3.3595[/C][/ROW]
[ROW][C]94[/C][C]16.35[/C][C]12.9552[/C][C]3.39479[/C][/ROW]
[ROW][C]95[/C][C]17.65[/C][C]17.7606[/C][C]-0.11059[/C][/ROW]
[ROW][C]96[/C][C]13.6[/C][C]15.2936[/C][C]-1.69362[/C][/ROW]
[ROW][C]97[/C][C]11.7[/C][C]10.728[/C][C]0.971952[/C][/ROW]
[ROW][C]98[/C][C]14.35[/C][C]12.8957[/C][C]1.45427[/C][/ROW]
[ROW][C]99[/C][C]14.75[/C][C]10.2077[/C][C]4.54233[/C][/ROW]
[ROW][C]100[/C][C]18.25[/C][C]20.5296[/C][C]-2.27958[/C][/ROW]
[ROW][C]101[/C][C]9.9[/C][C]7.39084[/C][C]2.50916[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]11.2408[/C][C]4.75916[/C][/ROW]
[ROW][C]103[/C][C]18.25[/C][C]15.0444[/C][C]3.20559[/C][/ROW]
[ROW][C]104[/C][C]16.85[/C][C]11.2863[/C][C]5.56367[/C][/ROW]
[ROW][C]105[/C][C]18.95[/C][C]16.1569[/C][C]2.79308[/C][/ROW]
[ROW][C]106[/C][C]15.6[/C][C]12.1125[/C][C]3.48746[/C][/ROW]
[ROW][C]107[/C][C]17.1[/C][C]14.5014[/C][C]2.59862[/C][/ROW]
[ROW][C]108[/C][C]16.1[/C][C]14.3307[/C][C]1.76934[/C][/ROW]
[ROW][C]109[/C][C]15.4[/C][C]13.5083[/C][C]1.89173[/C][/ROW]
[ROW][C]110[/C][C]15.4[/C][C]15.837[/C][C]-0.436957[/C][/ROW]
[ROW][C]111[/C][C]13.35[/C][C]7.68514[/C][C]5.66486[/C][/ROW]
[ROW][C]112[/C][C]19.1[/C][C]24.4369[/C][C]-5.33687[/C][/ROW]
[ROW][C]113[/C][C]7.6[/C][C]1.55194[/C][C]6.04806[/C][/ROW]
[ROW][C]114[/C][C]19.1[/C][C]17.1771[/C][C]1.9229[/C][/ROW]
[ROW][C]115[/C][C]14.75[/C][C]11.5423[/C][C]3.20773[/C][/ROW]
[ROW][C]116[/C][C]19.25[/C][C]18.516[/C][C]0.733979[/C][/ROW]
[ROW][C]117[/C][C]13.6[/C][C]13.1417[/C][C]0.458327[/C][/ROW]
[ROW][C]118[/C][C]12.75[/C][C]15.585[/C][C]-2.83495[/C][/ROW]
[ROW][C]119[/C][C]9.85[/C][C]7.39362[/C][C]2.45638[/C][/ROW]
[ROW][C]120[/C][C]15.25[/C][C]15.7484[/C][C]-0.498436[/C][/ROW]
[ROW][C]121[/C][C]11.9[/C][C]8.12291[/C][C]3.77709[/C][/ROW]
[ROW][C]122[/C][C]16.35[/C][C]17.6892[/C][C]-1.33924[/C][/ROW]
[ROW][C]123[/C][C]12.4[/C][C]12.065[/C][C]0.335015[/C][/ROW]
[ROW][C]124[/C][C]14.35[/C][C]9.04152[/C][C]5.30848[/C][/ROW]
[ROW][C]125[/C][C]18.15[/C][C]12.6715[/C][C]5.47851[/C][/ROW]
[ROW][C]126[/C][C]17.75[/C][C]19.0591[/C][C]-1.3091[/C][/ROW]
[ROW][C]127[/C][C]12.35[/C][C]9.42029[/C][C]2.92971[/C][/ROW]
[ROW][C]128[/C][C]15.6[/C][C]9.12501[/C][C]6.47499[/C][/ROW]
[ROW][C]129[/C][C]19.3[/C][C]15.3479[/C][C]3.95207[/C][/ROW]
[ROW][C]130[/C][C]17.1[/C][C]11.2768[/C][C]5.8232[/C][/ROW]
[ROW][C]131[/C][C]18.4[/C][C]12.3586[/C][C]6.0414[/C][/ROW]
[ROW][C]132[/C][C]19.05[/C][C]13.222[/C][C]5.82798[/C][/ROW]
[ROW][C]133[/C][C]18.55[/C][C]12.7754[/C][C]5.77465[/C][/ROW]
[ROW][C]134[/C][C]19.1[/C][C]18.9155[/C][C]0.184506[/C][/ROW]
[ROW][C]135[/C][C]12.85[/C][C]16.0773[/C][C]-3.2273[/C][/ROW]
[ROW][C]136[/C][C]9.5[/C][C]18.4164[/C][C]-8.91635[/C][/ROW]
[ROW][C]137[/C][C]4.5[/C][C]3.34819[/C][C]1.15181[/C][/ROW]
[ROW][C]138[/C][C]13.6[/C][C]16.9046[/C][C]-3.30462[/C][/ROW]
[ROW][C]139[/C][C]11.7[/C][C]11.1904[/C][C]0.50963[/C][/ROW]
[ROW][C]140[/C][C]13.35[/C][C]9.08672[/C][C]4.26328[/C][/ROW]
[ROW][C]141[/C][C]17.75[/C][C]13.7889[/C][C]3.96111[/C][/ROW]
[ROW][C]142[/C][C]17.6[/C][C]17.0709[/C][C]0.529135[/C][/ROW]
[ROW][C]143[/C][C]14.05[/C][C]10.8699[/C][C]3.1801[/C][/ROW]
[ROW][C]144[/C][C]16.1[/C][C]15.6176[/C][C]0.48237[/C][/ROW]
[ROW][C]145[/C][C]13.35[/C][C]14.1953[/C][C]-0.845302[/C][/ROW]
[ROW][C]146[/C][C]11.85[/C][C]12.7862[/C][C]-0.936206[/C][/ROW]
[ROW][C]147[/C][C]11.95[/C][C]12.0869[/C][C]-0.136851[/C][/ROW]
[ROW][C]148[/C][C]13.2[/C][C]17.6044[/C][C]-4.40441[/C][/ROW]
[ROW][C]149[/C][C]7.7[/C][C]6.95917[/C][C]0.74083[/C][/ROW]
[ROW][C]150[/C][C]14.6[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269956&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269956&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
112.914.25-1.34996
27.413.0694-5.66936
312.213.327-1.12696
412.813.9495-1.14948
57.412.64-5.24003
66.713.927-7.22701
712.613.0024-0.402377
814.812.65222.1478
913.314.032-0.731981
1011.111.7837-0.683748
118.214.4321-6.23212
1211.413.1354-1.73536
136.412.935-6.53499
1410.612.3462-1.74622
151212.5949-0.59489
166.312.3886-6.0886
1711.913.1404-1.2404
189.312.8266-3.52662
191011.9293-1.92927
206.412.6185-6.21848
2113.812.87610.923872
2210.812.7521-1.95213
2313.812.64531.15469
2411.712.9722-1.27217
2510.912.6375-1.73748
269.912.9919-3.09187
2711.512.3923-0.892272
288.312.919-4.61899
2911.713.417-1.71702
306.112.5012-6.40118
31912.7606-3.76063
329.712.922-3.22196
3310.812.8335-2.03351
3410.313.3361-3.03613
3510.412.6483-2.24826
369.313.5452-4.24515
3711.812.6064-0.80639
385.914.076-8.17599
3911.413.5878-2.1878
401313.0412-0.0411655
4110.812.6513-1.85131
4211.312.9653-1.66528
4311.813.4418-1.64176
4412.712.29920.400795
4510.913.4901-2.59015
4613.313.16280.137168
4710.112.9245-2.82451
4814.312.94531.35469
499.313.2825-3.98252
5012.512.8615-0.361465
517.612.6204-5.02035
5215.913.4292.47105
539.213.4237-4.22367
5411.112.5243-1.42429
551313.3292-0.329237
5614.513.06821.43182
5712.313.6027-1.3027
5811.413.1922-1.79218
597.313.2316-5.93164
6012.612.34140.258605
61NANA-0.0161552
621312.78750.212498
6313.218.3376-5.13764
647.717.4008-9.70077
654.355.61668-1.26668
6612.77.676685.02332
6718.113.20754.89249
6817.8514.6393.21102
6917.111.26995.83011
7019.116.01853.08154
7116.115.85690.243083
7213.359.211624.13838
7318.416.12122.27883
7414.717.2855-2.58554
7510.610.8511-0.251118
7612.69.702672.89733
7716.215.65880.541225
7813.612.85790.742081
7914.113.16140.938551
8014.511.23763.26243
8116.1514.10932.04073
8214.7512.6432.10703
8314.815.8454-1.04543
8412.4513.0694-0.619409
8512.658.717513.93249
8617.3521.5287-4.17872
878.62.96295.6371
8818.416.22122.17875
8916.111.2194.88098
9017.7514.7433.00701
9115.2510.12095.12912
9217.6515.43452.21555
9315.612.24053.3595
9416.3512.95523.39479
9517.6517.7606-0.11059
9613.615.2936-1.69362
9711.710.7280.971952
9814.3512.89571.45427
9914.7510.20774.54233
10018.2520.5296-2.27958
1019.97.390842.50916
1021611.24084.75916
10318.2515.04443.20559
10416.8511.28635.56367
10518.9516.15692.79308
10615.612.11253.48746
10717.114.50142.59862
10816.114.33071.76934
10915.413.50831.89173
11015.415.837-0.436957
11113.357.685145.66486
11219.124.4369-5.33687
1137.61.551946.04806
11419.117.17711.9229
11514.7511.54233.20773
11619.2518.5160.733979
11713.613.14170.458327
11812.7515.585-2.83495
1199.857.393622.45638
12015.2515.7484-0.498436
12111.98.122913.77709
12216.3517.6892-1.33924
12312.412.0650.335015
12414.359.041525.30848
12518.1512.67155.47851
12617.7519.0591-1.3091
12712.359.420292.92971
12815.69.125016.47499
12919.315.34793.95207
13017.111.27685.8232
13118.412.35866.0414
13219.0513.2225.82798
13318.5512.77545.77465
13419.118.91550.184506
13512.8516.0773-3.2273
1369.518.4164-8.91635
1374.53.348191.15181
13813.616.9046-3.30462
13911.711.19040.50963
14013.359.086724.26328
14117.7513.78893.96111
14217.617.07090.529135
14314.0510.86993.1801
14416.115.61760.48237
14513.3514.1953-0.845302
14611.8512.7862-0.936206
14711.9512.0869-0.136851
14813.217.6044-4.40441
1497.76.959170.74083
15014.6NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.4926520.9853050.507348
80.4810620.9621240.518938
90.4731490.9462980.526851
100.4169610.8339220.583039
110.320610.641220.67939
120.2248990.4497990.775101
130.2689320.5378650.731068
140.2086740.4173470.791326
150.1495770.2991530.850423
160.1822480.3644960.817752
170.1283730.2567450.871627
180.09578220.1915640.904218
190.06535390.1307080.934646
200.08231060.1646210.917689
210.1117240.2234480.888276
220.08043710.1608740.919563
230.07950860.1590170.920491
240.06206910.1241380.937931
250.04340630.08681270.956594
260.03103720.06207440.968963
270.0205990.0411980.979401
280.03596410.07192820.964036
290.02717040.05434080.97283
300.05942380.1188480.940576
310.04918150.0983630.950818
320.03934270.07868540.960657
330.028780.05755990.97122
340.02149110.04298220.978509
350.01550440.03100880.984496
360.01260120.02520240.987399
370.008926340.01785270.991074
380.02532810.05065620.974672
390.01986020.03972040.98014
400.01753760.03507520.982462
410.01299580.02599160.987004
420.01041510.02083010.989585
430.00808440.01616880.991916
440.007138670.01427730.992861
450.005318850.01063770.994681
460.005404630.01080930.994595
470.004234940.008469870.995765
480.004689680.009379360.99531
490.004280740.008561470.995719
500.003529780.007059560.99647
510.005501810.01100360.994498
520.009444930.01888990.990555
530.009520070.01904010.99048
540.00739970.01479940.9926
550.006480810.01296160.993519
560.006615620.01323120.993384
570.005609510.0112190.99439
580.004345750.00869150.995654
590.008441420.01688280.991559
600.006148320.01229660.993852
610.00469690.00939380.995303
620.004333990.008667970.995666
630.007296370.01459270.992704
640.0663570.1327140.933643
650.05936290.1187260.940637
660.1326780.2653560.867322
670.2453580.4907150.754642
680.3270720.6541450.672928
690.5113650.977270.488635
700.5386560.9226880.461344
710.5061590.9876810.493841
720.5840730.8318550.415927
730.5755870.8488260.424413
740.5732910.8534180.426709
750.535320.929360.46468
760.535670.928660.46433
770.4990020.9980040.500998
780.4726730.9453450.527327
790.4470810.8941610.552919
800.4563750.9127490.543625
810.4306690.8613380.569331
820.4081660.8163330.591834
830.3821540.7643070.617846
840.3507790.7015580.649221
850.372090.744180.62791
860.440850.8817010.55915
870.5213230.9573530.478677
880.5028640.9942720.497136
890.5418030.9163940.458197
900.5352040.9295920.464796
910.5750360.8499270.424964
920.546360.907280.45364
930.5358790.9282420.464121
940.5301470.9397070.469853
950.4852770.9705540.514723
960.4584410.9168820.541559
970.4137080.8274150.586292
980.3708090.7416170.629191
990.3889880.7779760.611012
1000.3827420.7654850.617258
1010.3537690.7075390.646231
1020.372880.745760.62712
1030.351150.70230.64885
1040.3946230.7892470.605377
1050.3631250.726250.636875
1060.3453940.6907880.654606
1070.3125490.6250980.687451
1080.2721360.5442720.727864
1090.2347960.4695930.765204
1100.2011930.4023850.798807
1110.2468220.4936450.753178
1120.3426970.6853950.657303
1130.3963060.7926120.603694
1140.3486470.6972930.651353
1150.3464210.6928430.653579
1160.2966590.5933180.703341
1170.2611440.5222880.738856
1180.2844850.568970.715515
1190.2435790.4871580.756421
1200.2222750.4445510.777725
1210.196150.39230.80385
1220.1642140.3284280.835786
1230.1280010.2560010.871999
1240.1389470.2778950.861053
1250.1386570.2773140.861343
1260.1095880.2191760.890412
1270.08583490.171670.914165
1280.1178830.2357660.882117
1290.1072420.2144850.892758
1300.1345350.269070.865465
1310.1901680.3803370.809832
1320.2998960.5997930.700104
1330.4192060.8384120.580794
1340.3363460.6726930.663654
1350.2777740.5555480.722226
1360.8670820.2658350.132918
1370.8711270.2577450.128873
1380.9809130.03817320.0190866
1390.9664710.06705860.0335293
1400.9403820.1192350.0596176
1410.9248080.1503840.075192
1420.8334060.3331890.166594
1430.8431240.3137530.156876

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.492652 & 0.985305 & 0.507348 \tabularnewline
8 & 0.481062 & 0.962124 & 0.518938 \tabularnewline
9 & 0.473149 & 0.946298 & 0.526851 \tabularnewline
10 & 0.416961 & 0.833922 & 0.583039 \tabularnewline
11 & 0.32061 & 0.64122 & 0.67939 \tabularnewline
12 & 0.224899 & 0.449799 & 0.775101 \tabularnewline
13 & 0.268932 & 0.537865 & 0.731068 \tabularnewline
14 & 0.208674 & 0.417347 & 0.791326 \tabularnewline
15 & 0.149577 & 0.299153 & 0.850423 \tabularnewline
16 & 0.182248 & 0.364496 & 0.817752 \tabularnewline
17 & 0.128373 & 0.256745 & 0.871627 \tabularnewline
18 & 0.0957822 & 0.191564 & 0.904218 \tabularnewline
19 & 0.0653539 & 0.130708 & 0.934646 \tabularnewline
20 & 0.0823106 & 0.164621 & 0.917689 \tabularnewline
21 & 0.111724 & 0.223448 & 0.888276 \tabularnewline
22 & 0.0804371 & 0.160874 & 0.919563 \tabularnewline
23 & 0.0795086 & 0.159017 & 0.920491 \tabularnewline
24 & 0.0620691 & 0.124138 & 0.937931 \tabularnewline
25 & 0.0434063 & 0.0868127 & 0.956594 \tabularnewline
26 & 0.0310372 & 0.0620744 & 0.968963 \tabularnewline
27 & 0.020599 & 0.041198 & 0.979401 \tabularnewline
28 & 0.0359641 & 0.0719282 & 0.964036 \tabularnewline
29 & 0.0271704 & 0.0543408 & 0.97283 \tabularnewline
30 & 0.0594238 & 0.118848 & 0.940576 \tabularnewline
31 & 0.0491815 & 0.098363 & 0.950818 \tabularnewline
32 & 0.0393427 & 0.0786854 & 0.960657 \tabularnewline
33 & 0.02878 & 0.0575599 & 0.97122 \tabularnewline
34 & 0.0214911 & 0.0429822 & 0.978509 \tabularnewline
35 & 0.0155044 & 0.0310088 & 0.984496 \tabularnewline
36 & 0.0126012 & 0.0252024 & 0.987399 \tabularnewline
37 & 0.00892634 & 0.0178527 & 0.991074 \tabularnewline
38 & 0.0253281 & 0.0506562 & 0.974672 \tabularnewline
39 & 0.0198602 & 0.0397204 & 0.98014 \tabularnewline
40 & 0.0175376 & 0.0350752 & 0.982462 \tabularnewline
41 & 0.0129958 & 0.0259916 & 0.987004 \tabularnewline
42 & 0.0104151 & 0.0208301 & 0.989585 \tabularnewline
43 & 0.0080844 & 0.0161688 & 0.991916 \tabularnewline
44 & 0.00713867 & 0.0142773 & 0.992861 \tabularnewline
45 & 0.00531885 & 0.0106377 & 0.994681 \tabularnewline
46 & 0.00540463 & 0.0108093 & 0.994595 \tabularnewline
47 & 0.00423494 & 0.00846987 & 0.995765 \tabularnewline
48 & 0.00468968 & 0.00937936 & 0.99531 \tabularnewline
49 & 0.00428074 & 0.00856147 & 0.995719 \tabularnewline
50 & 0.00352978 & 0.00705956 & 0.99647 \tabularnewline
51 & 0.00550181 & 0.0110036 & 0.994498 \tabularnewline
52 & 0.00944493 & 0.0188899 & 0.990555 \tabularnewline
53 & 0.00952007 & 0.0190401 & 0.99048 \tabularnewline
54 & 0.0073997 & 0.0147994 & 0.9926 \tabularnewline
55 & 0.00648081 & 0.0129616 & 0.993519 \tabularnewline
56 & 0.00661562 & 0.0132312 & 0.993384 \tabularnewline
57 & 0.00560951 & 0.011219 & 0.99439 \tabularnewline
58 & 0.00434575 & 0.0086915 & 0.995654 \tabularnewline
59 & 0.00844142 & 0.0168828 & 0.991559 \tabularnewline
60 & 0.00614832 & 0.0122966 & 0.993852 \tabularnewline
61 & 0.0046969 & 0.0093938 & 0.995303 \tabularnewline
62 & 0.00433399 & 0.00866797 & 0.995666 \tabularnewline
63 & 0.00729637 & 0.0145927 & 0.992704 \tabularnewline
64 & 0.066357 & 0.132714 & 0.933643 \tabularnewline
65 & 0.0593629 & 0.118726 & 0.940637 \tabularnewline
66 & 0.132678 & 0.265356 & 0.867322 \tabularnewline
67 & 0.245358 & 0.490715 & 0.754642 \tabularnewline
68 & 0.327072 & 0.654145 & 0.672928 \tabularnewline
69 & 0.511365 & 0.97727 & 0.488635 \tabularnewline
70 & 0.538656 & 0.922688 & 0.461344 \tabularnewline
71 & 0.506159 & 0.987681 & 0.493841 \tabularnewline
72 & 0.584073 & 0.831855 & 0.415927 \tabularnewline
73 & 0.575587 & 0.848826 & 0.424413 \tabularnewline
74 & 0.573291 & 0.853418 & 0.426709 \tabularnewline
75 & 0.53532 & 0.92936 & 0.46468 \tabularnewline
76 & 0.53567 & 0.92866 & 0.46433 \tabularnewline
77 & 0.499002 & 0.998004 & 0.500998 \tabularnewline
78 & 0.472673 & 0.945345 & 0.527327 \tabularnewline
79 & 0.447081 & 0.894161 & 0.552919 \tabularnewline
80 & 0.456375 & 0.912749 & 0.543625 \tabularnewline
81 & 0.430669 & 0.861338 & 0.569331 \tabularnewline
82 & 0.408166 & 0.816333 & 0.591834 \tabularnewline
83 & 0.382154 & 0.764307 & 0.617846 \tabularnewline
84 & 0.350779 & 0.701558 & 0.649221 \tabularnewline
85 & 0.37209 & 0.74418 & 0.62791 \tabularnewline
86 & 0.44085 & 0.881701 & 0.55915 \tabularnewline
87 & 0.521323 & 0.957353 & 0.478677 \tabularnewline
88 & 0.502864 & 0.994272 & 0.497136 \tabularnewline
89 & 0.541803 & 0.916394 & 0.458197 \tabularnewline
90 & 0.535204 & 0.929592 & 0.464796 \tabularnewline
91 & 0.575036 & 0.849927 & 0.424964 \tabularnewline
92 & 0.54636 & 0.90728 & 0.45364 \tabularnewline
93 & 0.535879 & 0.928242 & 0.464121 \tabularnewline
94 & 0.530147 & 0.939707 & 0.469853 \tabularnewline
95 & 0.485277 & 0.970554 & 0.514723 \tabularnewline
96 & 0.458441 & 0.916882 & 0.541559 \tabularnewline
97 & 0.413708 & 0.827415 & 0.586292 \tabularnewline
98 & 0.370809 & 0.741617 & 0.629191 \tabularnewline
99 & 0.388988 & 0.777976 & 0.611012 \tabularnewline
100 & 0.382742 & 0.765485 & 0.617258 \tabularnewline
101 & 0.353769 & 0.707539 & 0.646231 \tabularnewline
102 & 0.37288 & 0.74576 & 0.62712 \tabularnewline
103 & 0.35115 & 0.7023 & 0.64885 \tabularnewline
104 & 0.394623 & 0.789247 & 0.605377 \tabularnewline
105 & 0.363125 & 0.72625 & 0.636875 \tabularnewline
106 & 0.345394 & 0.690788 & 0.654606 \tabularnewline
107 & 0.312549 & 0.625098 & 0.687451 \tabularnewline
108 & 0.272136 & 0.544272 & 0.727864 \tabularnewline
109 & 0.234796 & 0.469593 & 0.765204 \tabularnewline
110 & 0.201193 & 0.402385 & 0.798807 \tabularnewline
111 & 0.246822 & 0.493645 & 0.753178 \tabularnewline
112 & 0.342697 & 0.685395 & 0.657303 \tabularnewline
113 & 0.396306 & 0.792612 & 0.603694 \tabularnewline
114 & 0.348647 & 0.697293 & 0.651353 \tabularnewline
115 & 0.346421 & 0.692843 & 0.653579 \tabularnewline
116 & 0.296659 & 0.593318 & 0.703341 \tabularnewline
117 & 0.261144 & 0.522288 & 0.738856 \tabularnewline
118 & 0.284485 & 0.56897 & 0.715515 \tabularnewline
119 & 0.243579 & 0.487158 & 0.756421 \tabularnewline
120 & 0.222275 & 0.444551 & 0.777725 \tabularnewline
121 & 0.19615 & 0.3923 & 0.80385 \tabularnewline
122 & 0.164214 & 0.328428 & 0.835786 \tabularnewline
123 & 0.128001 & 0.256001 & 0.871999 \tabularnewline
124 & 0.138947 & 0.277895 & 0.861053 \tabularnewline
125 & 0.138657 & 0.277314 & 0.861343 \tabularnewline
126 & 0.109588 & 0.219176 & 0.890412 \tabularnewline
127 & 0.0858349 & 0.17167 & 0.914165 \tabularnewline
128 & 0.117883 & 0.235766 & 0.882117 \tabularnewline
129 & 0.107242 & 0.214485 & 0.892758 \tabularnewline
130 & 0.134535 & 0.26907 & 0.865465 \tabularnewline
131 & 0.190168 & 0.380337 & 0.809832 \tabularnewline
132 & 0.299896 & 0.599793 & 0.700104 \tabularnewline
133 & 0.419206 & 0.838412 & 0.580794 \tabularnewline
134 & 0.336346 & 0.672693 & 0.663654 \tabularnewline
135 & 0.277774 & 0.555548 & 0.722226 \tabularnewline
136 & 0.867082 & 0.265835 & 0.132918 \tabularnewline
137 & 0.871127 & 0.257745 & 0.128873 \tabularnewline
138 & 0.980913 & 0.0381732 & 0.0190866 \tabularnewline
139 & 0.966471 & 0.0670586 & 0.0335293 \tabularnewline
140 & 0.940382 & 0.119235 & 0.0596176 \tabularnewline
141 & 0.924808 & 0.150384 & 0.075192 \tabularnewline
142 & 0.833406 & 0.333189 & 0.166594 \tabularnewline
143 & 0.843124 & 0.313753 & 0.156876 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269956&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]7[/C][C]0.492652[/C][C]0.985305[/C][C]0.507348[/C][/ROW]
[ROW][C]8[/C][C]0.481062[/C][C]0.962124[/C][C]0.518938[/C][/ROW]
[ROW][C]9[/C][C]0.473149[/C][C]0.946298[/C][C]0.526851[/C][/ROW]
[ROW][C]10[/C][C]0.416961[/C][C]0.833922[/C][C]0.583039[/C][/ROW]
[ROW][C]11[/C][C]0.32061[/C][C]0.64122[/C][C]0.67939[/C][/ROW]
[ROW][C]12[/C][C]0.224899[/C][C]0.449799[/C][C]0.775101[/C][/ROW]
[ROW][C]13[/C][C]0.268932[/C][C]0.537865[/C][C]0.731068[/C][/ROW]
[ROW][C]14[/C][C]0.208674[/C][C]0.417347[/C][C]0.791326[/C][/ROW]
[ROW][C]15[/C][C]0.149577[/C][C]0.299153[/C][C]0.850423[/C][/ROW]
[ROW][C]16[/C][C]0.182248[/C][C]0.364496[/C][C]0.817752[/C][/ROW]
[ROW][C]17[/C][C]0.128373[/C][C]0.256745[/C][C]0.871627[/C][/ROW]
[ROW][C]18[/C][C]0.0957822[/C][C]0.191564[/C][C]0.904218[/C][/ROW]
[ROW][C]19[/C][C]0.0653539[/C][C]0.130708[/C][C]0.934646[/C][/ROW]
[ROW][C]20[/C][C]0.0823106[/C][C]0.164621[/C][C]0.917689[/C][/ROW]
[ROW][C]21[/C][C]0.111724[/C][C]0.223448[/C][C]0.888276[/C][/ROW]
[ROW][C]22[/C][C]0.0804371[/C][C]0.160874[/C][C]0.919563[/C][/ROW]
[ROW][C]23[/C][C]0.0795086[/C][C]0.159017[/C][C]0.920491[/C][/ROW]
[ROW][C]24[/C][C]0.0620691[/C][C]0.124138[/C][C]0.937931[/C][/ROW]
[ROW][C]25[/C][C]0.0434063[/C][C]0.0868127[/C][C]0.956594[/C][/ROW]
[ROW][C]26[/C][C]0.0310372[/C][C]0.0620744[/C][C]0.968963[/C][/ROW]
[ROW][C]27[/C][C]0.020599[/C][C]0.041198[/C][C]0.979401[/C][/ROW]
[ROW][C]28[/C][C]0.0359641[/C][C]0.0719282[/C][C]0.964036[/C][/ROW]
[ROW][C]29[/C][C]0.0271704[/C][C]0.0543408[/C][C]0.97283[/C][/ROW]
[ROW][C]30[/C][C]0.0594238[/C][C]0.118848[/C][C]0.940576[/C][/ROW]
[ROW][C]31[/C][C]0.0491815[/C][C]0.098363[/C][C]0.950818[/C][/ROW]
[ROW][C]32[/C][C]0.0393427[/C][C]0.0786854[/C][C]0.960657[/C][/ROW]
[ROW][C]33[/C][C]0.02878[/C][C]0.0575599[/C][C]0.97122[/C][/ROW]
[ROW][C]34[/C][C]0.0214911[/C][C]0.0429822[/C][C]0.978509[/C][/ROW]
[ROW][C]35[/C][C]0.0155044[/C][C]0.0310088[/C][C]0.984496[/C][/ROW]
[ROW][C]36[/C][C]0.0126012[/C][C]0.0252024[/C][C]0.987399[/C][/ROW]
[ROW][C]37[/C][C]0.00892634[/C][C]0.0178527[/C][C]0.991074[/C][/ROW]
[ROW][C]38[/C][C]0.0253281[/C][C]0.0506562[/C][C]0.974672[/C][/ROW]
[ROW][C]39[/C][C]0.0198602[/C][C]0.0397204[/C][C]0.98014[/C][/ROW]
[ROW][C]40[/C][C]0.0175376[/C][C]0.0350752[/C][C]0.982462[/C][/ROW]
[ROW][C]41[/C][C]0.0129958[/C][C]0.0259916[/C][C]0.987004[/C][/ROW]
[ROW][C]42[/C][C]0.0104151[/C][C]0.0208301[/C][C]0.989585[/C][/ROW]
[ROW][C]43[/C][C]0.0080844[/C][C]0.0161688[/C][C]0.991916[/C][/ROW]
[ROW][C]44[/C][C]0.00713867[/C][C]0.0142773[/C][C]0.992861[/C][/ROW]
[ROW][C]45[/C][C]0.00531885[/C][C]0.0106377[/C][C]0.994681[/C][/ROW]
[ROW][C]46[/C][C]0.00540463[/C][C]0.0108093[/C][C]0.994595[/C][/ROW]
[ROW][C]47[/C][C]0.00423494[/C][C]0.00846987[/C][C]0.995765[/C][/ROW]
[ROW][C]48[/C][C]0.00468968[/C][C]0.00937936[/C][C]0.99531[/C][/ROW]
[ROW][C]49[/C][C]0.00428074[/C][C]0.00856147[/C][C]0.995719[/C][/ROW]
[ROW][C]50[/C][C]0.00352978[/C][C]0.00705956[/C][C]0.99647[/C][/ROW]
[ROW][C]51[/C][C]0.00550181[/C][C]0.0110036[/C][C]0.994498[/C][/ROW]
[ROW][C]52[/C][C]0.00944493[/C][C]0.0188899[/C][C]0.990555[/C][/ROW]
[ROW][C]53[/C][C]0.00952007[/C][C]0.0190401[/C][C]0.99048[/C][/ROW]
[ROW][C]54[/C][C]0.0073997[/C][C]0.0147994[/C][C]0.9926[/C][/ROW]
[ROW][C]55[/C][C]0.00648081[/C][C]0.0129616[/C][C]0.993519[/C][/ROW]
[ROW][C]56[/C][C]0.00661562[/C][C]0.0132312[/C][C]0.993384[/C][/ROW]
[ROW][C]57[/C][C]0.00560951[/C][C]0.011219[/C][C]0.99439[/C][/ROW]
[ROW][C]58[/C][C]0.00434575[/C][C]0.0086915[/C][C]0.995654[/C][/ROW]
[ROW][C]59[/C][C]0.00844142[/C][C]0.0168828[/C][C]0.991559[/C][/ROW]
[ROW][C]60[/C][C]0.00614832[/C][C]0.0122966[/C][C]0.993852[/C][/ROW]
[ROW][C]61[/C][C]0.0046969[/C][C]0.0093938[/C][C]0.995303[/C][/ROW]
[ROW][C]62[/C][C]0.00433399[/C][C]0.00866797[/C][C]0.995666[/C][/ROW]
[ROW][C]63[/C][C]0.00729637[/C][C]0.0145927[/C][C]0.992704[/C][/ROW]
[ROW][C]64[/C][C]0.066357[/C][C]0.132714[/C][C]0.933643[/C][/ROW]
[ROW][C]65[/C][C]0.0593629[/C][C]0.118726[/C][C]0.940637[/C][/ROW]
[ROW][C]66[/C][C]0.132678[/C][C]0.265356[/C][C]0.867322[/C][/ROW]
[ROW][C]67[/C][C]0.245358[/C][C]0.490715[/C][C]0.754642[/C][/ROW]
[ROW][C]68[/C][C]0.327072[/C][C]0.654145[/C][C]0.672928[/C][/ROW]
[ROW][C]69[/C][C]0.511365[/C][C]0.97727[/C][C]0.488635[/C][/ROW]
[ROW][C]70[/C][C]0.538656[/C][C]0.922688[/C][C]0.461344[/C][/ROW]
[ROW][C]71[/C][C]0.506159[/C][C]0.987681[/C][C]0.493841[/C][/ROW]
[ROW][C]72[/C][C]0.584073[/C][C]0.831855[/C][C]0.415927[/C][/ROW]
[ROW][C]73[/C][C]0.575587[/C][C]0.848826[/C][C]0.424413[/C][/ROW]
[ROW][C]74[/C][C]0.573291[/C][C]0.853418[/C][C]0.426709[/C][/ROW]
[ROW][C]75[/C][C]0.53532[/C][C]0.92936[/C][C]0.46468[/C][/ROW]
[ROW][C]76[/C][C]0.53567[/C][C]0.92866[/C][C]0.46433[/C][/ROW]
[ROW][C]77[/C][C]0.499002[/C][C]0.998004[/C][C]0.500998[/C][/ROW]
[ROW][C]78[/C][C]0.472673[/C][C]0.945345[/C][C]0.527327[/C][/ROW]
[ROW][C]79[/C][C]0.447081[/C][C]0.894161[/C][C]0.552919[/C][/ROW]
[ROW][C]80[/C][C]0.456375[/C][C]0.912749[/C][C]0.543625[/C][/ROW]
[ROW][C]81[/C][C]0.430669[/C][C]0.861338[/C][C]0.569331[/C][/ROW]
[ROW][C]82[/C][C]0.408166[/C][C]0.816333[/C][C]0.591834[/C][/ROW]
[ROW][C]83[/C][C]0.382154[/C][C]0.764307[/C][C]0.617846[/C][/ROW]
[ROW][C]84[/C][C]0.350779[/C][C]0.701558[/C][C]0.649221[/C][/ROW]
[ROW][C]85[/C][C]0.37209[/C][C]0.74418[/C][C]0.62791[/C][/ROW]
[ROW][C]86[/C][C]0.44085[/C][C]0.881701[/C][C]0.55915[/C][/ROW]
[ROW][C]87[/C][C]0.521323[/C][C]0.957353[/C][C]0.478677[/C][/ROW]
[ROW][C]88[/C][C]0.502864[/C][C]0.994272[/C][C]0.497136[/C][/ROW]
[ROW][C]89[/C][C]0.541803[/C][C]0.916394[/C][C]0.458197[/C][/ROW]
[ROW][C]90[/C][C]0.535204[/C][C]0.929592[/C][C]0.464796[/C][/ROW]
[ROW][C]91[/C][C]0.575036[/C][C]0.849927[/C][C]0.424964[/C][/ROW]
[ROW][C]92[/C][C]0.54636[/C][C]0.90728[/C][C]0.45364[/C][/ROW]
[ROW][C]93[/C][C]0.535879[/C][C]0.928242[/C][C]0.464121[/C][/ROW]
[ROW][C]94[/C][C]0.530147[/C][C]0.939707[/C][C]0.469853[/C][/ROW]
[ROW][C]95[/C][C]0.485277[/C][C]0.970554[/C][C]0.514723[/C][/ROW]
[ROW][C]96[/C][C]0.458441[/C][C]0.916882[/C][C]0.541559[/C][/ROW]
[ROW][C]97[/C][C]0.413708[/C][C]0.827415[/C][C]0.586292[/C][/ROW]
[ROW][C]98[/C][C]0.370809[/C][C]0.741617[/C][C]0.629191[/C][/ROW]
[ROW][C]99[/C][C]0.388988[/C][C]0.777976[/C][C]0.611012[/C][/ROW]
[ROW][C]100[/C][C]0.382742[/C][C]0.765485[/C][C]0.617258[/C][/ROW]
[ROW][C]101[/C][C]0.353769[/C][C]0.707539[/C][C]0.646231[/C][/ROW]
[ROW][C]102[/C][C]0.37288[/C][C]0.74576[/C][C]0.62712[/C][/ROW]
[ROW][C]103[/C][C]0.35115[/C][C]0.7023[/C][C]0.64885[/C][/ROW]
[ROW][C]104[/C][C]0.394623[/C][C]0.789247[/C][C]0.605377[/C][/ROW]
[ROW][C]105[/C][C]0.363125[/C][C]0.72625[/C][C]0.636875[/C][/ROW]
[ROW][C]106[/C][C]0.345394[/C][C]0.690788[/C][C]0.654606[/C][/ROW]
[ROW][C]107[/C][C]0.312549[/C][C]0.625098[/C][C]0.687451[/C][/ROW]
[ROW][C]108[/C][C]0.272136[/C][C]0.544272[/C][C]0.727864[/C][/ROW]
[ROW][C]109[/C][C]0.234796[/C][C]0.469593[/C][C]0.765204[/C][/ROW]
[ROW][C]110[/C][C]0.201193[/C][C]0.402385[/C][C]0.798807[/C][/ROW]
[ROW][C]111[/C][C]0.246822[/C][C]0.493645[/C][C]0.753178[/C][/ROW]
[ROW][C]112[/C][C]0.342697[/C][C]0.685395[/C][C]0.657303[/C][/ROW]
[ROW][C]113[/C][C]0.396306[/C][C]0.792612[/C][C]0.603694[/C][/ROW]
[ROW][C]114[/C][C]0.348647[/C][C]0.697293[/C][C]0.651353[/C][/ROW]
[ROW][C]115[/C][C]0.346421[/C][C]0.692843[/C][C]0.653579[/C][/ROW]
[ROW][C]116[/C][C]0.296659[/C][C]0.593318[/C][C]0.703341[/C][/ROW]
[ROW][C]117[/C][C]0.261144[/C][C]0.522288[/C][C]0.738856[/C][/ROW]
[ROW][C]118[/C][C]0.284485[/C][C]0.56897[/C][C]0.715515[/C][/ROW]
[ROW][C]119[/C][C]0.243579[/C][C]0.487158[/C][C]0.756421[/C][/ROW]
[ROW][C]120[/C][C]0.222275[/C][C]0.444551[/C][C]0.777725[/C][/ROW]
[ROW][C]121[/C][C]0.19615[/C][C]0.3923[/C][C]0.80385[/C][/ROW]
[ROW][C]122[/C][C]0.164214[/C][C]0.328428[/C][C]0.835786[/C][/ROW]
[ROW][C]123[/C][C]0.128001[/C][C]0.256001[/C][C]0.871999[/C][/ROW]
[ROW][C]124[/C][C]0.138947[/C][C]0.277895[/C][C]0.861053[/C][/ROW]
[ROW][C]125[/C][C]0.138657[/C][C]0.277314[/C][C]0.861343[/C][/ROW]
[ROW][C]126[/C][C]0.109588[/C][C]0.219176[/C][C]0.890412[/C][/ROW]
[ROW][C]127[/C][C]0.0858349[/C][C]0.17167[/C][C]0.914165[/C][/ROW]
[ROW][C]128[/C][C]0.117883[/C][C]0.235766[/C][C]0.882117[/C][/ROW]
[ROW][C]129[/C][C]0.107242[/C][C]0.214485[/C][C]0.892758[/C][/ROW]
[ROW][C]130[/C][C]0.134535[/C][C]0.26907[/C][C]0.865465[/C][/ROW]
[ROW][C]131[/C][C]0.190168[/C][C]0.380337[/C][C]0.809832[/C][/ROW]
[ROW][C]132[/C][C]0.299896[/C][C]0.599793[/C][C]0.700104[/C][/ROW]
[ROW][C]133[/C][C]0.419206[/C][C]0.838412[/C][C]0.580794[/C][/ROW]
[ROW][C]134[/C][C]0.336346[/C][C]0.672693[/C][C]0.663654[/C][/ROW]
[ROW][C]135[/C][C]0.277774[/C][C]0.555548[/C][C]0.722226[/C][/ROW]
[ROW][C]136[/C][C]0.867082[/C][C]0.265835[/C][C]0.132918[/C][/ROW]
[ROW][C]137[/C][C]0.871127[/C][C]0.257745[/C][C]0.128873[/C][/ROW]
[ROW][C]138[/C][C]0.980913[/C][C]0.0381732[/C][C]0.0190866[/C][/ROW]
[ROW][C]139[/C][C]0.966471[/C][C]0.0670586[/C][C]0.0335293[/C][/ROW]
[ROW][C]140[/C][C]0.940382[/C][C]0.119235[/C][C]0.0596176[/C][/ROW]
[ROW][C]141[/C][C]0.924808[/C][C]0.150384[/C][C]0.075192[/C][/ROW]
[ROW][C]142[/C][C]0.833406[/C][C]0.333189[/C][C]0.166594[/C][/ROW]
[ROW][C]143[/C][C]0.843124[/C][C]0.313753[/C][C]0.156876[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269956&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269956&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
70.4926520.9853050.507348
80.4810620.9621240.518938
90.4731490.9462980.526851
100.4169610.8339220.583039
110.320610.641220.67939
120.2248990.4497990.775101
130.2689320.5378650.731068
140.2086740.4173470.791326
150.1495770.2991530.850423
160.1822480.3644960.817752
170.1283730.2567450.871627
180.09578220.1915640.904218
190.06535390.1307080.934646
200.08231060.1646210.917689
210.1117240.2234480.888276
220.08043710.1608740.919563
230.07950860.1590170.920491
240.06206910.1241380.937931
250.04340630.08681270.956594
260.03103720.06207440.968963
270.0205990.0411980.979401
280.03596410.07192820.964036
290.02717040.05434080.97283
300.05942380.1188480.940576
310.04918150.0983630.950818
320.03934270.07868540.960657
330.028780.05755990.97122
340.02149110.04298220.978509
350.01550440.03100880.984496
360.01260120.02520240.987399
370.008926340.01785270.991074
380.02532810.05065620.974672
390.01986020.03972040.98014
400.01753760.03507520.982462
410.01299580.02599160.987004
420.01041510.02083010.989585
430.00808440.01616880.991916
440.007138670.01427730.992861
450.005318850.01063770.994681
460.005404630.01080930.994595
470.004234940.008469870.995765
480.004689680.009379360.99531
490.004280740.008561470.995719
500.003529780.007059560.99647
510.005501810.01100360.994498
520.009444930.01888990.990555
530.009520070.01904010.99048
540.00739970.01479940.9926
550.006480810.01296160.993519
560.006615620.01323120.993384
570.005609510.0112190.99439
580.004345750.00869150.995654
590.008441420.01688280.991559
600.006148320.01229660.993852
610.00469690.00939380.995303
620.004333990.008667970.995666
630.007296370.01459270.992704
640.0663570.1327140.933643
650.05936290.1187260.940637
660.1326780.2653560.867322
670.2453580.4907150.754642
680.3270720.6541450.672928
690.5113650.977270.488635
700.5386560.9226880.461344
710.5061590.9876810.493841
720.5840730.8318550.415927
730.5755870.8488260.424413
740.5732910.8534180.426709
750.535320.929360.46468
760.535670.928660.46433
770.4990020.9980040.500998
780.4726730.9453450.527327
790.4470810.8941610.552919
800.4563750.9127490.543625
810.4306690.8613380.569331
820.4081660.8163330.591834
830.3821540.7643070.617846
840.3507790.7015580.649221
850.372090.744180.62791
860.440850.8817010.55915
870.5213230.9573530.478677
880.5028640.9942720.497136
890.5418030.9163940.458197
900.5352040.9295920.464796
910.5750360.8499270.424964
920.546360.907280.45364
930.5358790.9282420.464121
940.5301470.9397070.469853
950.4852770.9705540.514723
960.4584410.9168820.541559
970.4137080.8274150.586292
980.3708090.7416170.629191
990.3889880.7779760.611012
1000.3827420.7654850.617258
1010.3537690.7075390.646231
1020.372880.745760.62712
1030.351150.70230.64885
1040.3946230.7892470.605377
1050.3631250.726250.636875
1060.3453940.6907880.654606
1070.3125490.6250980.687451
1080.2721360.5442720.727864
1090.2347960.4695930.765204
1100.2011930.4023850.798807
1110.2468220.4936450.753178
1120.3426970.6853950.657303
1130.3963060.7926120.603694
1140.3486470.6972930.651353
1150.3464210.6928430.653579
1160.2966590.5933180.703341
1170.2611440.5222880.738856
1180.2844850.568970.715515
1190.2435790.4871580.756421
1200.2222750.4445510.777725
1210.196150.39230.80385
1220.1642140.3284280.835786
1230.1280010.2560010.871999
1240.1389470.2778950.861053
1250.1386570.2773140.861343
1260.1095880.2191760.890412
1270.08583490.171670.914165
1280.1178830.2357660.882117
1290.1072420.2144850.892758
1300.1345350.269070.865465
1310.1901680.3803370.809832
1320.2998960.5997930.700104
1330.4192060.8384120.580794
1340.3363460.6726930.663654
1350.2777740.5555480.722226
1360.8670820.2658350.132918
1370.8711270.2577450.128873
1380.9809130.03817320.0190866
1390.9664710.06705860.0335293
1400.9403820.1192350.0596176
1410.9248080.1503840.075192
1420.8334060.3331890.166594
1430.8431240.3137530.156876






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.0510949NOK
5% type I error level310.226277NOK
10% type I error level400.291971NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 7 & 0.0510949 & NOK \tabularnewline
5% type I error level & 31 & 0.226277 & NOK \tabularnewline
10% type I error level & 40 & 0.291971 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269956&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]7[/C][C]0.0510949[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.226277[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]40[/C][C]0.291971[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269956&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269956&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 level70.0510949NOK
5% type I error level310.226277NOK
10% type I error level400.291971NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '4'
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')
}