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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 computationSat, 13 Dec 2014 15:38:04 +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/13/t1418494489nihziccmwecb4lr.htm/, Retrieved Sun, 19 May 2024 15:51:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267254, Retrieved Sun, 19 May 2024 15:51:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact67

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
- RM D    [Multiple Regression] [] [2014-12-13 15:38:04] [a9a71eb17c5e64a5660208fb3c309b9e] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
26	50	3,19047619	1,785714286	12,9
51	68	3,476190476	2	7,4
57	62	3,19047619	2,285714286	12,2
37	54	3	3,357142857	12,8
67	71	3,19047619	1,857142857	7,4
43	54	2,761904762	2,714285714	6,7
52	65	3,095238095	2,642857143	12,6
52	73	3,761904762	2,928571429	14,8
43	52	2,571428571	2,285714286	13,3
84	84	3,19047619	2,5	11,1
67	42	3,666666667	1,785714286	8,2
49	66	2,285714286	1,928571429	11,4
70	65	3,095238095	2,428571429	6,4
52	78	2,761904762	2,357142857	10,6
58	73	3,285714286	1,785714286	12,0
68	75	3,857142857	2,642857143	6,3
62	72	3,428571429	4,071428571	11,3
43	66	3	2,142857143	11,9
56	70	3	2,857142857	9,3
56	61	3,333333333	2,5	9,6
74	81	3,666666667	2,285714286	10,0
63	69	3,19047619	2,571428571	13,8
58	71	3,238095238	2,785714286	10,8
63	68	3,666666667	2,571428571	11,7
53	70	3,666666667	3,214285714	10,9
57	68	3,80952381	3,428571429	16,1
51	61	2,714285714	2,214285714	13,4
64	67	3,285714286	2,285714286	9,9
53	76	2,857142857	1,928571429	11,5
29	70	2,19047619	1,642857143	8,3
54	60	2,80952381	3,071428571	11,7
51	77	3,095238095	2,928571429	6,1
58	72	2,761904762	2,285714286	9,0
43	69	3,285714286	3,571428571	9,7
51	71	3,238095238	2,428571429	10,8
53	62	2,80952381	2,071428571	10,3
54	70	3,238095238	2,642857143	10,4
56	64	3,047619048	2,5	12,7
61	58	3,380952381	2,428571429	9,3
47	76	3,428571429	2,285714286	11,8
39	52	2,238095238	1,5	5,9
48	59	2,952380952	2,285714286	11,4
50	68	3,904761905	3,142857143	13,0
35	76	2,19047619	1,571428571	10,8
30	65	3,238095238	2,5	12,3
68	67	3,333333333	2,5	11,3
49	59	3	1,642857143	11,8
61	69	3,047619048	2	7,9
67	76	4,333333333	4,285714286	12,7
47	63	3,619047619	2,714285714	12,3
56	75	3,142857143	1,714285714	11,6
50	63	2,904761905	2,714285714	6,7
43	60	2,047619048	1,357142857	10,9
67	73	3,428571429	2,5	12,1
62	63	2,952380952	1,642857143	13,3
57	70	3,619047619	2,5	10,1
41	75	3,285714286	2,857142857	5,7
54	66	3,095238095	1,642857143	14,3
45	63	2,857142857	1,928571429	8,0
48	63	3,142857143	2,357142857	13,3
61	64	3,047619048	1,785714286	9,3
56	70	3,428571429	2,071428571	12,5
41	75	3,904761905	3,357142857	7,6
43	61	2,523809524	1,571428571	15,9
53	60	3,238095238	2,714285714	9,2
44	62	2,380952381	1,571428571	9,1
66	73	3,523809524	2,214285714	11,1
58	61	3,857142857	2,071428571	13,0
46	66	2,285714286	2,571428571	14,5
37	64	2,238095238	1,071428571	12,2
51	59	3,666666667	2,5	12,3
51	64	3,333333333	2,571428571	11,4
66	56	2,952380952	2,214285714	14,6
45	66	3,238095238	2,714285714	7,3
37	78	2,619047619	1,785714286	12,6
59	53	3,095238095	2,285714286	5,9
42	67	3,19047619	2,071428571	13,0
38	59	3,523809524	2,428571429	12,6
66	66	3,142857143	2,071428571	13,2
34	68	3	1,214285714	9,9
53	71	2,904761905	1,142857143	7,7
49	66	3,238095238	1,642857143	10,5
55	73	2,523809524	1,714285714	13,4
49	72	3,428571429	2,214285714	10,9
59	71	2,428571429	2,571428571	4,3
40	59	3,095238095	3	10,3
58	64	2,904761905	2,285714286	11,8
60	66	2,666666667	2	11,2
63	78	3,904761905	3,285714286	11,4
56	68	2,666666667	2,214285714	8,6
54	73	3,285714286	2,142857143	13,2
52	62	2,761904762	2,142857143	12,6
34	65	3,142857143	3,285714286	5,6
69	68	3,142857143	2,285714286	9,9
32	65	2,380952381	2,142857143	8,8
48	60	2,904761905	1,928571429	7,7
67	71	2,571428571	2,571428571	9,0
58	65	3,142857143	2	7,3
57	68	2,666666667	1,857142857	11,4
42	64	3,095238095	2,285714286	13,6
64	74	3,904761905	3	7,9
58	69	3,095238095	3,285714286	10,7
66	76	2,523809524	2,214285714	10,3
61	72	3,142857143	2,928571429	9,6
52	67	3,238095238	1,5	14,2
51	63	3,238095238	2,928571429	8,5
55	59	3,238095238	2,357142857	13,5
60	66	3,19047619	2,642857143	6,4
56	62	2,80952381	1,357142857	9,6
63	69	3,428571429	2,214285714	11,6
61	66	3,476190476	1,857142857	11,1

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'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 & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267254&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267254&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267254&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'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.176 -0.0171283AMSI[t] -0.00937862AMSE[t] + 1.08282PERFI[t] -0.583943PERFE[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.176 -0.0171283AMSI[t] -0.00937862AMSE[t] +  1.08282PERFI[t] -0.583943PERFE[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267254&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.176 -0.0171283AMSI[t] -0.00937862AMSE[t] +  1.08282PERFI[t] -0.583943PERFE[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267254&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267254&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] = + 10.176 -0.0171283AMSI[t] -0.00937862AMSE[t] + 1.08282PERFI[t] -0.583943PERFE[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.1762.633273.8640.0001921899.60946e-05
AMSI-0.01712830.0251823-0.68020.4978770.248939
AMSE-0.009378620.0362938-0.25840.7965930.398297
PERFI1.082820.6740991.6060.1111770.0555884
PERFE-0.5839430.479959-1.2170.2264390.11322

\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) & 10.176 & 2.63327 & 3.864 & 0.000192189 & 9.60946e-05 \tabularnewline
AMSI & -0.0171283 & 0.0251823 & -0.6802 & 0.497877 & 0.248939 \tabularnewline
AMSE & -0.00937862 & 0.0362938 & -0.2584 & 0.796593 & 0.398297 \tabularnewline
PERFI & 1.08282 & 0.674099 & 1.606 & 0.111177 & 0.0555884 \tabularnewline
PERFE & -0.583943 & 0.479959 & -1.217 & 0.226439 & 0.11322 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267254&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]10.176[/C][C]2.63327[/C][C]3.864[/C][C]0.000192189[/C][C]9.60946e-05[/C][/ROW]
[ROW][C]AMSI[/C][C]-0.0171283[/C][C]0.0251823[/C][C]-0.6802[/C][C]0.497877[/C][C]0.248939[/C][/ROW]
[ROW][C]AMSE[/C][C]-0.00937862[/C][C]0.0362938[/C][C]-0.2584[/C][C]0.796593[/C][C]0.398297[/C][/ROW]
[ROW][C]PERFI[/C][C]1.08282[/C][C]0.674099[/C][C]1.606[/C][C]0.111177[/C][C]0.0555884[/C][/ROW]
[ROW][C]PERFE[/C][C]-0.583943[/C][C]0.479959[/C][C]-1.217[/C][C]0.226439[/C][C]0.11322[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267254&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267254&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)10.1762.633273.8640.0001921899.60946e-05
AMSI-0.01712830.0251823-0.68020.4978770.248939
AMSE-0.009378620.0362938-0.25840.7965930.398297
PERFI1.082820.6740991.6060.1111770.0555884
PERFE-0.5839430.479959-1.2170.2264390.11322






Multiple Linear Regression - Regression Statistics
Multiple R0.16648
R-squared0.0277155
Adjusted R-squared-0.00897445
F-TEST (value)0.755398
F-TEST (DF numerator)4
F-TEST (DF denominator)106
p-value0.556615
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.48486
Sum Squared Residuals654.498

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.16648 \tabularnewline
R-squared & 0.0277155 \tabularnewline
Adjusted R-squared & -0.00897445 \tabularnewline
F-TEST (value) & 0.755398 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 0.556615 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.48486 \tabularnewline
Sum Squared Residuals & 654.498 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267254&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.16648[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0277155[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00897445[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.755398[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/C][/ROW]
[ROW][C]p-value[/C][C]0.556615[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.48486[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]654.498[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267254&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267254&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 R0.16648
R-squared0.0277155
Adjusted R-squared-0.00897445
F-TEST (value)0.755398
F-TEST (DF numerator)4
F-TEST (DF denominator)106
p-value0.556615
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.48486
Sum Squared Residuals654.498






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.911.67371.22632
27.411.2609-3.86091
312.210.73821.46181
412.810.32392.47612
57.410.7328-3.33276
66.710.3387-3.63869
712.610.4842.11598
814.810.9643.83597
913.310.40152.89855
1011.19.944261.15574
118.211.5621-3.36208
1211.410.06661.33345
136.410.3008-3.90084
1410.610.1680.432005
151211.0130.987007
166.310.9412-4.64119
1711.39.773821.52618
1811.910.81761.08236
199.310.1404-0.840354
209.610.7943-1.19425
211010.7844-0.784444
2213.810.40293.39707
2310.810.39620.403756
2411.710.92790.772063
2510.910.70510.194929
2616.110.68495.41513
2713.410.37643.02358
289.910.6745-0.774524
2911.510.5230.976989
308.310.4353-2.13532
3111.79.937011.76299
326.110.2218-4.12176
33910.1632-1.16321
349.710.2647-0.564677
3510.810.72470.0753062
3610.310.5193-0.219328
3710.410.5576-0.157557
3812.710.45672.24326
399.310.83-1.53002
4011.811.0360.764013
415.910.5678-4.66784
4211.410.66270.737336
431311.07471.92526
4410.810.3180.482011
4512.311.0991.20105
4611.310.53240.767557
4711.811.07250.727509
487.910.6162-2.71618
4912.710.50522.19477
5012.311.11391.1861
5111.610.91550.684486
526.710.2891-3.58907
5310.910.30150.598538
5412.110.59641.50357
5513.310.76072.53925
5610.111.0021-0.902097
575.710.6598-4.95976
5814.311.02433.27567
59810.782-2.78196
6013.310.78972.51031
619.310.7882-1.4882
6212.511.06321.43677
637.611.0381-3.43811
6415.910.68265.21742
659.210.6268-1.42676
669.110.5014-1.40139
6711.110.88350.216479
681311.57751.42255
6914.59.742554.75745
7012.210.73981.46019
7112.311.25961.04041
7211.410.810.58995
7314.610.42424.1758
747.310.7075-3.40752
7512.610.60391.99609
765.910.6852-4.78521
771311.07341.92665
7812.611.36931.23072
7913.210.62012.57991
809.911.4953-1.59527
817.711.0803-3.38028
8210.511.2647-0.764656
8313.410.28113.11892
8410.911.081-0.180955
854.39.62767-5.32767
8610.310.5373-0.237278
8711.810.39291.40708
8811.210.24890.951063
8911.410.67490.725136
908.610.1736-1.57356
9113.210.8732.32704
9212.610.44322.15682
935.610.4685-4.8685
949.910.4248-0.524814
958.810.3451-1.54511
967.710.8103-3.11027
9799.64534-0.645337
987.310.8082-3.5082
9911.410.3651.03502
10013.610.87322.72677
1017.910.8621-2.96209
10210.79.968340.731659
10310.39.772560.527439
1049.610.1489-0.548934
10514.211.28732.91269
1068.510.5078-2.00775
10713.510.81042.68957
1086.410.4407-4.04074
1099.610.885-1.28505
11011.610.86930.730706
11111.111.1918-0.0918006

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 11.6737 & 1.22632 \tabularnewline
2 & 7.4 & 11.2609 & -3.86091 \tabularnewline
3 & 12.2 & 10.7382 & 1.46181 \tabularnewline
4 & 12.8 & 10.3239 & 2.47612 \tabularnewline
5 & 7.4 & 10.7328 & -3.33276 \tabularnewline
6 & 6.7 & 10.3387 & -3.63869 \tabularnewline
7 & 12.6 & 10.484 & 2.11598 \tabularnewline
8 & 14.8 & 10.964 & 3.83597 \tabularnewline
9 & 13.3 & 10.4015 & 2.89855 \tabularnewline
10 & 11.1 & 9.94426 & 1.15574 \tabularnewline
11 & 8.2 & 11.5621 & -3.36208 \tabularnewline
12 & 11.4 & 10.0666 & 1.33345 \tabularnewline
13 & 6.4 & 10.3008 & -3.90084 \tabularnewline
14 & 10.6 & 10.168 & 0.432005 \tabularnewline
15 & 12 & 11.013 & 0.987007 \tabularnewline
16 & 6.3 & 10.9412 & -4.64119 \tabularnewline
17 & 11.3 & 9.77382 & 1.52618 \tabularnewline
18 & 11.9 & 10.8176 & 1.08236 \tabularnewline
19 & 9.3 & 10.1404 & -0.840354 \tabularnewline
20 & 9.6 & 10.7943 & -1.19425 \tabularnewline
21 & 10 & 10.7844 & -0.784444 \tabularnewline
22 & 13.8 & 10.4029 & 3.39707 \tabularnewline
23 & 10.8 & 10.3962 & 0.403756 \tabularnewline
24 & 11.7 & 10.9279 & 0.772063 \tabularnewline
25 & 10.9 & 10.7051 & 0.194929 \tabularnewline
26 & 16.1 & 10.6849 & 5.41513 \tabularnewline
27 & 13.4 & 10.3764 & 3.02358 \tabularnewline
28 & 9.9 & 10.6745 & -0.774524 \tabularnewline
29 & 11.5 & 10.523 & 0.976989 \tabularnewline
30 & 8.3 & 10.4353 & -2.13532 \tabularnewline
31 & 11.7 & 9.93701 & 1.76299 \tabularnewline
32 & 6.1 & 10.2218 & -4.12176 \tabularnewline
33 & 9 & 10.1632 & -1.16321 \tabularnewline
34 & 9.7 & 10.2647 & -0.564677 \tabularnewline
35 & 10.8 & 10.7247 & 0.0753062 \tabularnewline
36 & 10.3 & 10.5193 & -0.219328 \tabularnewline
37 & 10.4 & 10.5576 & -0.157557 \tabularnewline
38 & 12.7 & 10.4567 & 2.24326 \tabularnewline
39 & 9.3 & 10.83 & -1.53002 \tabularnewline
40 & 11.8 & 11.036 & 0.764013 \tabularnewline
41 & 5.9 & 10.5678 & -4.66784 \tabularnewline
42 & 11.4 & 10.6627 & 0.737336 \tabularnewline
43 & 13 & 11.0747 & 1.92526 \tabularnewline
44 & 10.8 & 10.318 & 0.482011 \tabularnewline
45 & 12.3 & 11.099 & 1.20105 \tabularnewline
46 & 11.3 & 10.5324 & 0.767557 \tabularnewline
47 & 11.8 & 11.0725 & 0.727509 \tabularnewline
48 & 7.9 & 10.6162 & -2.71618 \tabularnewline
49 & 12.7 & 10.5052 & 2.19477 \tabularnewline
50 & 12.3 & 11.1139 & 1.1861 \tabularnewline
51 & 11.6 & 10.9155 & 0.684486 \tabularnewline
52 & 6.7 & 10.2891 & -3.58907 \tabularnewline
53 & 10.9 & 10.3015 & 0.598538 \tabularnewline
54 & 12.1 & 10.5964 & 1.50357 \tabularnewline
55 & 13.3 & 10.7607 & 2.53925 \tabularnewline
56 & 10.1 & 11.0021 & -0.902097 \tabularnewline
57 & 5.7 & 10.6598 & -4.95976 \tabularnewline
58 & 14.3 & 11.0243 & 3.27567 \tabularnewline
59 & 8 & 10.782 & -2.78196 \tabularnewline
60 & 13.3 & 10.7897 & 2.51031 \tabularnewline
61 & 9.3 & 10.7882 & -1.4882 \tabularnewline
62 & 12.5 & 11.0632 & 1.43677 \tabularnewline
63 & 7.6 & 11.0381 & -3.43811 \tabularnewline
64 & 15.9 & 10.6826 & 5.21742 \tabularnewline
65 & 9.2 & 10.6268 & -1.42676 \tabularnewline
66 & 9.1 & 10.5014 & -1.40139 \tabularnewline
67 & 11.1 & 10.8835 & 0.216479 \tabularnewline
68 & 13 & 11.5775 & 1.42255 \tabularnewline
69 & 14.5 & 9.74255 & 4.75745 \tabularnewline
70 & 12.2 & 10.7398 & 1.46019 \tabularnewline
71 & 12.3 & 11.2596 & 1.04041 \tabularnewline
72 & 11.4 & 10.81 & 0.58995 \tabularnewline
73 & 14.6 & 10.4242 & 4.1758 \tabularnewline
74 & 7.3 & 10.7075 & -3.40752 \tabularnewline
75 & 12.6 & 10.6039 & 1.99609 \tabularnewline
76 & 5.9 & 10.6852 & -4.78521 \tabularnewline
77 & 13 & 11.0734 & 1.92665 \tabularnewline
78 & 12.6 & 11.3693 & 1.23072 \tabularnewline
79 & 13.2 & 10.6201 & 2.57991 \tabularnewline
80 & 9.9 & 11.4953 & -1.59527 \tabularnewline
81 & 7.7 & 11.0803 & -3.38028 \tabularnewline
82 & 10.5 & 11.2647 & -0.764656 \tabularnewline
83 & 13.4 & 10.2811 & 3.11892 \tabularnewline
84 & 10.9 & 11.081 & -0.180955 \tabularnewline
85 & 4.3 & 9.62767 & -5.32767 \tabularnewline
86 & 10.3 & 10.5373 & -0.237278 \tabularnewline
87 & 11.8 & 10.3929 & 1.40708 \tabularnewline
88 & 11.2 & 10.2489 & 0.951063 \tabularnewline
89 & 11.4 & 10.6749 & 0.725136 \tabularnewline
90 & 8.6 & 10.1736 & -1.57356 \tabularnewline
91 & 13.2 & 10.873 & 2.32704 \tabularnewline
92 & 12.6 & 10.4432 & 2.15682 \tabularnewline
93 & 5.6 & 10.4685 & -4.8685 \tabularnewline
94 & 9.9 & 10.4248 & -0.524814 \tabularnewline
95 & 8.8 & 10.3451 & -1.54511 \tabularnewline
96 & 7.7 & 10.8103 & -3.11027 \tabularnewline
97 & 9 & 9.64534 & -0.645337 \tabularnewline
98 & 7.3 & 10.8082 & -3.5082 \tabularnewline
99 & 11.4 & 10.365 & 1.03502 \tabularnewline
100 & 13.6 & 10.8732 & 2.72677 \tabularnewline
101 & 7.9 & 10.8621 & -2.96209 \tabularnewline
102 & 10.7 & 9.96834 & 0.731659 \tabularnewline
103 & 10.3 & 9.77256 & 0.527439 \tabularnewline
104 & 9.6 & 10.1489 & -0.548934 \tabularnewline
105 & 14.2 & 11.2873 & 2.91269 \tabularnewline
106 & 8.5 & 10.5078 & -2.00775 \tabularnewline
107 & 13.5 & 10.8104 & 2.68957 \tabularnewline
108 & 6.4 & 10.4407 & -4.04074 \tabularnewline
109 & 9.6 & 10.885 & -1.28505 \tabularnewline
110 & 11.6 & 10.8693 & 0.730706 \tabularnewline
111 & 11.1 & 11.1918 & -0.0918006 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267254&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]11.6737[/C][C]1.22632[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]11.2609[/C][C]-3.86091[/C][/ROW]
[ROW][C]3[/C][C]12.2[/C][C]10.7382[/C][C]1.46181[/C][/ROW]
[ROW][C]4[/C][C]12.8[/C][C]10.3239[/C][C]2.47612[/C][/ROW]
[ROW][C]5[/C][C]7.4[/C][C]10.7328[/C][C]-3.33276[/C][/ROW]
[ROW][C]6[/C][C]6.7[/C][C]10.3387[/C][C]-3.63869[/C][/ROW]
[ROW][C]7[/C][C]12.6[/C][C]10.484[/C][C]2.11598[/C][/ROW]
[ROW][C]8[/C][C]14.8[/C][C]10.964[/C][C]3.83597[/C][/ROW]
[ROW][C]9[/C][C]13.3[/C][C]10.4015[/C][C]2.89855[/C][/ROW]
[ROW][C]10[/C][C]11.1[/C][C]9.94426[/C][C]1.15574[/C][/ROW]
[ROW][C]11[/C][C]8.2[/C][C]11.5621[/C][C]-3.36208[/C][/ROW]
[ROW][C]12[/C][C]11.4[/C][C]10.0666[/C][C]1.33345[/C][/ROW]
[ROW][C]13[/C][C]6.4[/C][C]10.3008[/C][C]-3.90084[/C][/ROW]
[ROW][C]14[/C][C]10.6[/C][C]10.168[/C][C]0.432005[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]11.013[/C][C]0.987007[/C][/ROW]
[ROW][C]16[/C][C]6.3[/C][C]10.9412[/C][C]-4.64119[/C][/ROW]
[ROW][C]17[/C][C]11.3[/C][C]9.77382[/C][C]1.52618[/C][/ROW]
[ROW][C]18[/C][C]11.9[/C][C]10.8176[/C][C]1.08236[/C][/ROW]
[ROW][C]19[/C][C]9.3[/C][C]10.1404[/C][C]-0.840354[/C][/ROW]
[ROW][C]20[/C][C]9.6[/C][C]10.7943[/C][C]-1.19425[/C][/ROW]
[ROW][C]21[/C][C]10[/C][C]10.7844[/C][C]-0.784444[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]10.4029[/C][C]3.39707[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]10.3962[/C][C]0.403756[/C][/ROW]
[ROW][C]24[/C][C]11.7[/C][C]10.9279[/C][C]0.772063[/C][/ROW]
[ROW][C]25[/C][C]10.9[/C][C]10.7051[/C][C]0.194929[/C][/ROW]
[ROW][C]26[/C][C]16.1[/C][C]10.6849[/C][C]5.41513[/C][/ROW]
[ROW][C]27[/C][C]13.4[/C][C]10.3764[/C][C]3.02358[/C][/ROW]
[ROW][C]28[/C][C]9.9[/C][C]10.6745[/C][C]-0.774524[/C][/ROW]
[ROW][C]29[/C][C]11.5[/C][C]10.523[/C][C]0.976989[/C][/ROW]
[ROW][C]30[/C][C]8.3[/C][C]10.4353[/C][C]-2.13532[/C][/ROW]
[ROW][C]31[/C][C]11.7[/C][C]9.93701[/C][C]1.76299[/C][/ROW]
[ROW][C]32[/C][C]6.1[/C][C]10.2218[/C][C]-4.12176[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]10.1632[/C][C]-1.16321[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]10.2647[/C][C]-0.564677[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]10.7247[/C][C]0.0753062[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]10.5193[/C][C]-0.219328[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]10.5576[/C][C]-0.157557[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]10.4567[/C][C]2.24326[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]10.83[/C][C]-1.53002[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]11.036[/C][C]0.764013[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]10.5678[/C][C]-4.66784[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]10.6627[/C][C]0.737336[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]11.0747[/C][C]1.92526[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]10.318[/C][C]0.482011[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]11.099[/C][C]1.20105[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]10.5324[/C][C]0.767557[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]11.0725[/C][C]0.727509[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]10.6162[/C][C]-2.71618[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]10.5052[/C][C]2.19477[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]11.1139[/C][C]1.1861[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.9155[/C][C]0.684486[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]10.2891[/C][C]-3.58907[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]10.3015[/C][C]0.598538[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]10.5964[/C][C]1.50357[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]10.7607[/C][C]2.53925[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]11.0021[/C][C]-0.902097[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]10.6598[/C][C]-4.95976[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]11.0243[/C][C]3.27567[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.782[/C][C]-2.78196[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]10.7897[/C][C]2.51031[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]10.7882[/C][C]-1.4882[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]11.0632[/C][C]1.43677[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]11.0381[/C][C]-3.43811[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]10.6826[/C][C]5.21742[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]10.6268[/C][C]-1.42676[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]10.5014[/C][C]-1.40139[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]10.8835[/C][C]0.216479[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]11.5775[/C][C]1.42255[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]9.74255[/C][C]4.75745[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]10.7398[/C][C]1.46019[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]11.2596[/C][C]1.04041[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]10.81[/C][C]0.58995[/C][/ROW]
[ROW][C]73[/C][C]14.6[/C][C]10.4242[/C][C]4.1758[/C][/ROW]
[ROW][C]74[/C][C]7.3[/C][C]10.7075[/C][C]-3.40752[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]10.6039[/C][C]1.99609[/C][/ROW]
[ROW][C]76[/C][C]5.9[/C][C]10.6852[/C][C]-4.78521[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.0734[/C][C]1.92665[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]11.3693[/C][C]1.23072[/C][/ROW]
[ROW][C]79[/C][C]13.2[/C][C]10.6201[/C][C]2.57991[/C][/ROW]
[ROW][C]80[/C][C]9.9[/C][C]11.4953[/C][C]-1.59527[/C][/ROW]
[ROW][C]81[/C][C]7.7[/C][C]11.0803[/C][C]-3.38028[/C][/ROW]
[ROW][C]82[/C][C]10.5[/C][C]11.2647[/C][C]-0.764656[/C][/ROW]
[ROW][C]83[/C][C]13.4[/C][C]10.2811[/C][C]3.11892[/C][/ROW]
[ROW][C]84[/C][C]10.9[/C][C]11.081[/C][C]-0.180955[/C][/ROW]
[ROW][C]85[/C][C]4.3[/C][C]9.62767[/C][C]-5.32767[/C][/ROW]
[ROW][C]86[/C][C]10.3[/C][C]10.5373[/C][C]-0.237278[/C][/ROW]
[ROW][C]87[/C][C]11.8[/C][C]10.3929[/C][C]1.40708[/C][/ROW]
[ROW][C]88[/C][C]11.2[/C][C]10.2489[/C][C]0.951063[/C][/ROW]
[ROW][C]89[/C][C]11.4[/C][C]10.6749[/C][C]0.725136[/C][/ROW]
[ROW][C]90[/C][C]8.6[/C][C]10.1736[/C][C]-1.57356[/C][/ROW]
[ROW][C]91[/C][C]13.2[/C][C]10.873[/C][C]2.32704[/C][/ROW]
[ROW][C]92[/C][C]12.6[/C][C]10.4432[/C][C]2.15682[/C][/ROW]
[ROW][C]93[/C][C]5.6[/C][C]10.4685[/C][C]-4.8685[/C][/ROW]
[ROW][C]94[/C][C]9.9[/C][C]10.4248[/C][C]-0.524814[/C][/ROW]
[ROW][C]95[/C][C]8.8[/C][C]10.3451[/C][C]-1.54511[/C][/ROW]
[ROW][C]96[/C][C]7.7[/C][C]10.8103[/C][C]-3.11027[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]9.64534[/C][C]-0.645337[/C][/ROW]
[ROW][C]98[/C][C]7.3[/C][C]10.8082[/C][C]-3.5082[/C][/ROW]
[ROW][C]99[/C][C]11.4[/C][C]10.365[/C][C]1.03502[/C][/ROW]
[ROW][C]100[/C][C]13.6[/C][C]10.8732[/C][C]2.72677[/C][/ROW]
[ROW][C]101[/C][C]7.9[/C][C]10.8621[/C][C]-2.96209[/C][/ROW]
[ROW][C]102[/C][C]10.7[/C][C]9.96834[/C][C]0.731659[/C][/ROW]
[ROW][C]103[/C][C]10.3[/C][C]9.77256[/C][C]0.527439[/C][/ROW]
[ROW][C]104[/C][C]9.6[/C][C]10.1489[/C][C]-0.548934[/C][/ROW]
[ROW][C]105[/C][C]14.2[/C][C]11.2873[/C][C]2.91269[/C][/ROW]
[ROW][C]106[/C][C]8.5[/C][C]10.5078[/C][C]-2.00775[/C][/ROW]
[ROW][C]107[/C][C]13.5[/C][C]10.8104[/C][C]2.68957[/C][/ROW]
[ROW][C]108[/C][C]6.4[/C][C]10.4407[/C][C]-4.04074[/C][/ROW]
[ROW][C]109[/C][C]9.6[/C][C]10.885[/C][C]-1.28505[/C][/ROW]
[ROW][C]110[/C][C]11.6[/C][C]10.8693[/C][C]0.730706[/C][/ROW]
[ROW][C]111[/C][C]11.1[/C][C]11.1918[/C][C]-0.0918006[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267254&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267254&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
112.911.67371.22632
27.411.2609-3.86091
312.210.73821.46181
412.810.32392.47612
57.410.7328-3.33276
66.710.3387-3.63869
712.610.4842.11598
814.810.9643.83597
913.310.40152.89855
1011.19.944261.15574
118.211.5621-3.36208
1211.410.06661.33345
136.410.3008-3.90084
1410.610.1680.432005
151211.0130.987007
166.310.9412-4.64119
1711.39.773821.52618
1811.910.81761.08236
199.310.1404-0.840354
209.610.7943-1.19425
211010.7844-0.784444
2213.810.40293.39707
2310.810.39620.403756
2411.710.92790.772063
2510.910.70510.194929
2616.110.68495.41513
2713.410.37643.02358
289.910.6745-0.774524
2911.510.5230.976989
308.310.4353-2.13532
3111.79.937011.76299
326.110.2218-4.12176
33910.1632-1.16321
349.710.2647-0.564677
3510.810.72470.0753062
3610.310.5193-0.219328
3710.410.5576-0.157557
3812.710.45672.24326
399.310.83-1.53002
4011.811.0360.764013
415.910.5678-4.66784
4211.410.66270.737336
431311.07471.92526
4410.810.3180.482011
4512.311.0991.20105
4611.310.53240.767557
4711.811.07250.727509
487.910.6162-2.71618
4912.710.50522.19477
5012.311.11391.1861
5111.610.91550.684486
526.710.2891-3.58907
5310.910.30150.598538
5412.110.59641.50357
5513.310.76072.53925
5610.111.0021-0.902097
575.710.6598-4.95976
5814.311.02433.27567
59810.782-2.78196
6013.310.78972.51031
619.310.7882-1.4882
6212.511.06321.43677
637.611.0381-3.43811
6415.910.68265.21742
659.210.6268-1.42676
669.110.5014-1.40139
6711.110.88350.216479
681311.57751.42255
6914.59.742554.75745
7012.210.73981.46019
7112.311.25961.04041
7211.410.810.58995
7314.610.42424.1758
747.310.7075-3.40752
7512.610.60391.99609
765.910.6852-4.78521
771311.07341.92665
7812.611.36931.23072
7913.210.62012.57991
809.911.4953-1.59527
817.711.0803-3.38028
8210.511.2647-0.764656
8313.410.28113.11892
8410.911.081-0.180955
854.39.62767-5.32767
8610.310.5373-0.237278
8711.810.39291.40708
8811.210.24890.951063
8911.410.67490.725136
908.610.1736-1.57356
9113.210.8732.32704
9212.610.44322.15682
935.610.4685-4.8685
949.910.4248-0.524814
958.810.3451-1.54511
967.710.8103-3.11027
9799.64534-0.645337
987.310.8082-3.5082
9911.410.3651.03502
10013.610.87322.72677
1017.910.8621-2.96209
10210.79.968340.731659
10310.39.772560.527439
1049.610.1489-0.548934
10514.211.28732.91269
1068.510.5078-2.00775
10713.510.81042.68957
1086.410.4407-4.04074
1099.610.885-1.28505
11011.610.86930.730706
11111.111.1918-0.0918006






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8307150.3385690.169285
90.9344960.1310080.0655038
100.910560.178880.0894398
110.8630620.2738760.136938
120.8104620.3790770.189538
130.8369930.3260140.163007
140.778350.44330.22165
150.7290340.5419310.270966
160.8286940.3426120.171306
170.773440.453120.22656
180.7076870.5846270.292313
190.6596280.6807430.340372
200.5889790.8220420.411021
210.5224770.9550450.477523
220.6176090.7647820.382391
230.5448230.9103540.455177
240.4976430.9952850.502357
250.4308530.8617050.569147
260.6205020.7589950.379498
270.6477290.7045410.352271
280.5860980.8278030.413902
290.5235270.9529450.476473
300.5725590.8548810.427441
310.5290670.9418650.470933
320.7169850.5660290.283015
330.6710250.657950.328975
340.6573410.6853180.342659
350.5986940.8026120.401306
360.5381850.9236290.461815
370.477970.9559410.52203
380.4681730.9363450.531827
390.4263170.8526350.573683
400.3741610.7483230.625839
410.4816530.9633070.518347
420.4307550.861510.569245
430.3990280.7980550.600972
440.3502160.7004310.649784
450.3117680.6235360.688232
460.2708310.5416620.729169
470.2399850.4799710.760015
480.2409170.4818340.759083
490.247090.4941790.75291
500.2169920.4339840.783008
510.1871490.3742980.812851
520.2373570.4747140.762643
530.2098510.4197020.790149
540.1904280.3808560.809572
550.2096670.4193340.790333
560.1759420.3518850.824058
570.30730.61460.6927
580.3484350.6968690.651565
590.3608150.7216290.639185
600.3650360.7300720.634964
610.3347890.6695770.665211
620.3022810.6045630.697719
630.3322260.6644520.667774
640.5046380.9907230.495362
650.4619790.9239580.538021
660.4253310.8506620.574669
670.3700440.7400880.629956
680.3344780.6689560.665522
690.504840.9903210.49516
700.4651190.9302380.534881
710.4205710.8411420.579429
720.3719050.7438110.628095
730.5106350.978730.489365
740.5366170.9267660.463383
750.5049370.9901260.495063
760.6424290.7151430.357571
770.6328720.7342560.367128
780.6005590.7988820.399441
790.5950530.8098940.404947
800.5515230.8969530.448477
810.6687370.6625260.331263
820.6322670.7354670.367733
830.6498320.7003360.350168
840.5865650.826870.413435
850.7495930.5008140.250407
860.7159870.5680260.284013
870.6911340.6177330.308866
880.6353340.7293310.364666
890.5826510.8346980.417349
900.5303770.9392450.469623
910.5175680.9648640.482432
920.525360.949280.47464
930.5918240.8163520.408176
940.5061230.9877540.493877
950.5377850.924430.462215
960.655090.689820.34491
970.5650040.8699930.434996
980.660560.6788790.33944
990.5513280.8973440.448672
1000.4407930.8815850.559207
1010.4035990.8071970.596401
1020.336560.6731210.66344
1030.305480.6109590.69452

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.830715 & 0.338569 & 0.169285 \tabularnewline
9 & 0.934496 & 0.131008 & 0.0655038 \tabularnewline
10 & 0.91056 & 0.17888 & 0.0894398 \tabularnewline
11 & 0.863062 & 0.273876 & 0.136938 \tabularnewline
12 & 0.810462 & 0.379077 & 0.189538 \tabularnewline
13 & 0.836993 & 0.326014 & 0.163007 \tabularnewline
14 & 0.77835 & 0.4433 & 0.22165 \tabularnewline
15 & 0.729034 & 0.541931 & 0.270966 \tabularnewline
16 & 0.828694 & 0.342612 & 0.171306 \tabularnewline
17 & 0.77344 & 0.45312 & 0.22656 \tabularnewline
18 & 0.707687 & 0.584627 & 0.292313 \tabularnewline
19 & 0.659628 & 0.680743 & 0.340372 \tabularnewline
20 & 0.588979 & 0.822042 & 0.411021 \tabularnewline
21 & 0.522477 & 0.955045 & 0.477523 \tabularnewline
22 & 0.617609 & 0.764782 & 0.382391 \tabularnewline
23 & 0.544823 & 0.910354 & 0.455177 \tabularnewline
24 & 0.497643 & 0.995285 & 0.502357 \tabularnewline
25 & 0.430853 & 0.861705 & 0.569147 \tabularnewline
26 & 0.620502 & 0.758995 & 0.379498 \tabularnewline
27 & 0.647729 & 0.704541 & 0.352271 \tabularnewline
28 & 0.586098 & 0.827803 & 0.413902 \tabularnewline
29 & 0.523527 & 0.952945 & 0.476473 \tabularnewline
30 & 0.572559 & 0.854881 & 0.427441 \tabularnewline
31 & 0.529067 & 0.941865 & 0.470933 \tabularnewline
32 & 0.716985 & 0.566029 & 0.283015 \tabularnewline
33 & 0.671025 & 0.65795 & 0.328975 \tabularnewline
34 & 0.657341 & 0.685318 & 0.342659 \tabularnewline
35 & 0.598694 & 0.802612 & 0.401306 \tabularnewline
36 & 0.538185 & 0.923629 & 0.461815 \tabularnewline
37 & 0.47797 & 0.955941 & 0.52203 \tabularnewline
38 & 0.468173 & 0.936345 & 0.531827 \tabularnewline
39 & 0.426317 & 0.852635 & 0.573683 \tabularnewline
40 & 0.374161 & 0.748323 & 0.625839 \tabularnewline
41 & 0.481653 & 0.963307 & 0.518347 \tabularnewline
42 & 0.430755 & 0.86151 & 0.569245 \tabularnewline
43 & 0.399028 & 0.798055 & 0.600972 \tabularnewline
44 & 0.350216 & 0.700431 & 0.649784 \tabularnewline
45 & 0.311768 & 0.623536 & 0.688232 \tabularnewline
46 & 0.270831 & 0.541662 & 0.729169 \tabularnewline
47 & 0.239985 & 0.479971 & 0.760015 \tabularnewline
48 & 0.240917 & 0.481834 & 0.759083 \tabularnewline
49 & 0.24709 & 0.494179 & 0.75291 \tabularnewline
50 & 0.216992 & 0.433984 & 0.783008 \tabularnewline
51 & 0.187149 & 0.374298 & 0.812851 \tabularnewline
52 & 0.237357 & 0.474714 & 0.762643 \tabularnewline
53 & 0.209851 & 0.419702 & 0.790149 \tabularnewline
54 & 0.190428 & 0.380856 & 0.809572 \tabularnewline
55 & 0.209667 & 0.419334 & 0.790333 \tabularnewline
56 & 0.175942 & 0.351885 & 0.824058 \tabularnewline
57 & 0.3073 & 0.6146 & 0.6927 \tabularnewline
58 & 0.348435 & 0.696869 & 0.651565 \tabularnewline
59 & 0.360815 & 0.721629 & 0.639185 \tabularnewline
60 & 0.365036 & 0.730072 & 0.634964 \tabularnewline
61 & 0.334789 & 0.669577 & 0.665211 \tabularnewline
62 & 0.302281 & 0.604563 & 0.697719 \tabularnewline
63 & 0.332226 & 0.664452 & 0.667774 \tabularnewline
64 & 0.504638 & 0.990723 & 0.495362 \tabularnewline
65 & 0.461979 & 0.923958 & 0.538021 \tabularnewline
66 & 0.425331 & 0.850662 & 0.574669 \tabularnewline
67 & 0.370044 & 0.740088 & 0.629956 \tabularnewline
68 & 0.334478 & 0.668956 & 0.665522 \tabularnewline
69 & 0.50484 & 0.990321 & 0.49516 \tabularnewline
70 & 0.465119 & 0.930238 & 0.534881 \tabularnewline
71 & 0.420571 & 0.841142 & 0.579429 \tabularnewline
72 & 0.371905 & 0.743811 & 0.628095 \tabularnewline
73 & 0.510635 & 0.97873 & 0.489365 \tabularnewline
74 & 0.536617 & 0.926766 & 0.463383 \tabularnewline
75 & 0.504937 & 0.990126 & 0.495063 \tabularnewline
76 & 0.642429 & 0.715143 & 0.357571 \tabularnewline
77 & 0.632872 & 0.734256 & 0.367128 \tabularnewline
78 & 0.600559 & 0.798882 & 0.399441 \tabularnewline
79 & 0.595053 & 0.809894 & 0.404947 \tabularnewline
80 & 0.551523 & 0.896953 & 0.448477 \tabularnewline
81 & 0.668737 & 0.662526 & 0.331263 \tabularnewline
82 & 0.632267 & 0.735467 & 0.367733 \tabularnewline
83 & 0.649832 & 0.700336 & 0.350168 \tabularnewline
84 & 0.586565 & 0.82687 & 0.413435 \tabularnewline
85 & 0.749593 & 0.500814 & 0.250407 \tabularnewline
86 & 0.715987 & 0.568026 & 0.284013 \tabularnewline
87 & 0.691134 & 0.617733 & 0.308866 \tabularnewline
88 & 0.635334 & 0.729331 & 0.364666 \tabularnewline
89 & 0.582651 & 0.834698 & 0.417349 \tabularnewline
90 & 0.530377 & 0.939245 & 0.469623 \tabularnewline
91 & 0.517568 & 0.964864 & 0.482432 \tabularnewline
92 & 0.52536 & 0.94928 & 0.47464 \tabularnewline
93 & 0.591824 & 0.816352 & 0.408176 \tabularnewline
94 & 0.506123 & 0.987754 & 0.493877 \tabularnewline
95 & 0.537785 & 0.92443 & 0.462215 \tabularnewline
96 & 0.65509 & 0.68982 & 0.34491 \tabularnewline
97 & 0.565004 & 0.869993 & 0.434996 \tabularnewline
98 & 0.66056 & 0.678879 & 0.33944 \tabularnewline
99 & 0.551328 & 0.897344 & 0.448672 \tabularnewline
100 & 0.440793 & 0.881585 & 0.559207 \tabularnewline
101 & 0.403599 & 0.807197 & 0.596401 \tabularnewline
102 & 0.33656 & 0.673121 & 0.66344 \tabularnewline
103 & 0.30548 & 0.610959 & 0.69452 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267254&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]0.830715[/C][C]0.338569[/C][C]0.169285[/C][/ROW]
[ROW][C]9[/C][C]0.934496[/C][C]0.131008[/C][C]0.0655038[/C][/ROW]
[ROW][C]10[/C][C]0.91056[/C][C]0.17888[/C][C]0.0894398[/C][/ROW]
[ROW][C]11[/C][C]0.863062[/C][C]0.273876[/C][C]0.136938[/C][/ROW]
[ROW][C]12[/C][C]0.810462[/C][C]0.379077[/C][C]0.189538[/C][/ROW]
[ROW][C]13[/C][C]0.836993[/C][C]0.326014[/C][C]0.163007[/C][/ROW]
[ROW][C]14[/C][C]0.77835[/C][C]0.4433[/C][C]0.22165[/C][/ROW]
[ROW][C]15[/C][C]0.729034[/C][C]0.541931[/C][C]0.270966[/C][/ROW]
[ROW][C]16[/C][C]0.828694[/C][C]0.342612[/C][C]0.171306[/C][/ROW]
[ROW][C]17[/C][C]0.77344[/C][C]0.45312[/C][C]0.22656[/C][/ROW]
[ROW][C]18[/C][C]0.707687[/C][C]0.584627[/C][C]0.292313[/C][/ROW]
[ROW][C]19[/C][C]0.659628[/C][C]0.680743[/C][C]0.340372[/C][/ROW]
[ROW][C]20[/C][C]0.588979[/C][C]0.822042[/C][C]0.411021[/C][/ROW]
[ROW][C]21[/C][C]0.522477[/C][C]0.955045[/C][C]0.477523[/C][/ROW]
[ROW][C]22[/C][C]0.617609[/C][C]0.764782[/C][C]0.382391[/C][/ROW]
[ROW][C]23[/C][C]0.544823[/C][C]0.910354[/C][C]0.455177[/C][/ROW]
[ROW][C]24[/C][C]0.497643[/C][C]0.995285[/C][C]0.502357[/C][/ROW]
[ROW][C]25[/C][C]0.430853[/C][C]0.861705[/C][C]0.569147[/C][/ROW]
[ROW][C]26[/C][C]0.620502[/C][C]0.758995[/C][C]0.379498[/C][/ROW]
[ROW][C]27[/C][C]0.647729[/C][C]0.704541[/C][C]0.352271[/C][/ROW]
[ROW][C]28[/C][C]0.586098[/C][C]0.827803[/C][C]0.413902[/C][/ROW]
[ROW][C]29[/C][C]0.523527[/C][C]0.952945[/C][C]0.476473[/C][/ROW]
[ROW][C]30[/C][C]0.572559[/C][C]0.854881[/C][C]0.427441[/C][/ROW]
[ROW][C]31[/C][C]0.529067[/C][C]0.941865[/C][C]0.470933[/C][/ROW]
[ROW][C]32[/C][C]0.716985[/C][C]0.566029[/C][C]0.283015[/C][/ROW]
[ROW][C]33[/C][C]0.671025[/C][C]0.65795[/C][C]0.328975[/C][/ROW]
[ROW][C]34[/C][C]0.657341[/C][C]0.685318[/C][C]0.342659[/C][/ROW]
[ROW][C]35[/C][C]0.598694[/C][C]0.802612[/C][C]0.401306[/C][/ROW]
[ROW][C]36[/C][C]0.538185[/C][C]0.923629[/C][C]0.461815[/C][/ROW]
[ROW][C]37[/C][C]0.47797[/C][C]0.955941[/C][C]0.52203[/C][/ROW]
[ROW][C]38[/C][C]0.468173[/C][C]0.936345[/C][C]0.531827[/C][/ROW]
[ROW][C]39[/C][C]0.426317[/C][C]0.852635[/C][C]0.573683[/C][/ROW]
[ROW][C]40[/C][C]0.374161[/C][C]0.748323[/C][C]0.625839[/C][/ROW]
[ROW][C]41[/C][C]0.481653[/C][C]0.963307[/C][C]0.518347[/C][/ROW]
[ROW][C]42[/C][C]0.430755[/C][C]0.86151[/C][C]0.569245[/C][/ROW]
[ROW][C]43[/C][C]0.399028[/C][C]0.798055[/C][C]0.600972[/C][/ROW]
[ROW][C]44[/C][C]0.350216[/C][C]0.700431[/C][C]0.649784[/C][/ROW]
[ROW][C]45[/C][C]0.311768[/C][C]0.623536[/C][C]0.688232[/C][/ROW]
[ROW][C]46[/C][C]0.270831[/C][C]0.541662[/C][C]0.729169[/C][/ROW]
[ROW][C]47[/C][C]0.239985[/C][C]0.479971[/C][C]0.760015[/C][/ROW]
[ROW][C]48[/C][C]0.240917[/C][C]0.481834[/C][C]0.759083[/C][/ROW]
[ROW][C]49[/C][C]0.24709[/C][C]0.494179[/C][C]0.75291[/C][/ROW]
[ROW][C]50[/C][C]0.216992[/C][C]0.433984[/C][C]0.783008[/C][/ROW]
[ROW][C]51[/C][C]0.187149[/C][C]0.374298[/C][C]0.812851[/C][/ROW]
[ROW][C]52[/C][C]0.237357[/C][C]0.474714[/C][C]0.762643[/C][/ROW]
[ROW][C]53[/C][C]0.209851[/C][C]0.419702[/C][C]0.790149[/C][/ROW]
[ROW][C]54[/C][C]0.190428[/C][C]0.380856[/C][C]0.809572[/C][/ROW]
[ROW][C]55[/C][C]0.209667[/C][C]0.419334[/C][C]0.790333[/C][/ROW]
[ROW][C]56[/C][C]0.175942[/C][C]0.351885[/C][C]0.824058[/C][/ROW]
[ROW][C]57[/C][C]0.3073[/C][C]0.6146[/C][C]0.6927[/C][/ROW]
[ROW][C]58[/C][C]0.348435[/C][C]0.696869[/C][C]0.651565[/C][/ROW]
[ROW][C]59[/C][C]0.360815[/C][C]0.721629[/C][C]0.639185[/C][/ROW]
[ROW][C]60[/C][C]0.365036[/C][C]0.730072[/C][C]0.634964[/C][/ROW]
[ROW][C]61[/C][C]0.334789[/C][C]0.669577[/C][C]0.665211[/C][/ROW]
[ROW][C]62[/C][C]0.302281[/C][C]0.604563[/C][C]0.697719[/C][/ROW]
[ROW][C]63[/C][C]0.332226[/C][C]0.664452[/C][C]0.667774[/C][/ROW]
[ROW][C]64[/C][C]0.504638[/C][C]0.990723[/C][C]0.495362[/C][/ROW]
[ROW][C]65[/C][C]0.461979[/C][C]0.923958[/C][C]0.538021[/C][/ROW]
[ROW][C]66[/C][C]0.425331[/C][C]0.850662[/C][C]0.574669[/C][/ROW]
[ROW][C]67[/C][C]0.370044[/C][C]0.740088[/C][C]0.629956[/C][/ROW]
[ROW][C]68[/C][C]0.334478[/C][C]0.668956[/C][C]0.665522[/C][/ROW]
[ROW][C]69[/C][C]0.50484[/C][C]0.990321[/C][C]0.49516[/C][/ROW]
[ROW][C]70[/C][C]0.465119[/C][C]0.930238[/C][C]0.534881[/C][/ROW]
[ROW][C]71[/C][C]0.420571[/C][C]0.841142[/C][C]0.579429[/C][/ROW]
[ROW][C]72[/C][C]0.371905[/C][C]0.743811[/C][C]0.628095[/C][/ROW]
[ROW][C]73[/C][C]0.510635[/C][C]0.97873[/C][C]0.489365[/C][/ROW]
[ROW][C]74[/C][C]0.536617[/C][C]0.926766[/C][C]0.463383[/C][/ROW]
[ROW][C]75[/C][C]0.504937[/C][C]0.990126[/C][C]0.495063[/C][/ROW]
[ROW][C]76[/C][C]0.642429[/C][C]0.715143[/C][C]0.357571[/C][/ROW]
[ROW][C]77[/C][C]0.632872[/C][C]0.734256[/C][C]0.367128[/C][/ROW]
[ROW][C]78[/C][C]0.600559[/C][C]0.798882[/C][C]0.399441[/C][/ROW]
[ROW][C]79[/C][C]0.595053[/C][C]0.809894[/C][C]0.404947[/C][/ROW]
[ROW][C]80[/C][C]0.551523[/C][C]0.896953[/C][C]0.448477[/C][/ROW]
[ROW][C]81[/C][C]0.668737[/C][C]0.662526[/C][C]0.331263[/C][/ROW]
[ROW][C]82[/C][C]0.632267[/C][C]0.735467[/C][C]0.367733[/C][/ROW]
[ROW][C]83[/C][C]0.649832[/C][C]0.700336[/C][C]0.350168[/C][/ROW]
[ROW][C]84[/C][C]0.586565[/C][C]0.82687[/C][C]0.413435[/C][/ROW]
[ROW][C]85[/C][C]0.749593[/C][C]0.500814[/C][C]0.250407[/C][/ROW]
[ROW][C]86[/C][C]0.715987[/C][C]0.568026[/C][C]0.284013[/C][/ROW]
[ROW][C]87[/C][C]0.691134[/C][C]0.617733[/C][C]0.308866[/C][/ROW]
[ROW][C]88[/C][C]0.635334[/C][C]0.729331[/C][C]0.364666[/C][/ROW]
[ROW][C]89[/C][C]0.582651[/C][C]0.834698[/C][C]0.417349[/C][/ROW]
[ROW][C]90[/C][C]0.530377[/C][C]0.939245[/C][C]0.469623[/C][/ROW]
[ROW][C]91[/C][C]0.517568[/C][C]0.964864[/C][C]0.482432[/C][/ROW]
[ROW][C]92[/C][C]0.52536[/C][C]0.94928[/C][C]0.47464[/C][/ROW]
[ROW][C]93[/C][C]0.591824[/C][C]0.816352[/C][C]0.408176[/C][/ROW]
[ROW][C]94[/C][C]0.506123[/C][C]0.987754[/C][C]0.493877[/C][/ROW]
[ROW][C]95[/C][C]0.537785[/C][C]0.92443[/C][C]0.462215[/C][/ROW]
[ROW][C]96[/C][C]0.65509[/C][C]0.68982[/C][C]0.34491[/C][/ROW]
[ROW][C]97[/C][C]0.565004[/C][C]0.869993[/C][C]0.434996[/C][/ROW]
[ROW][C]98[/C][C]0.66056[/C][C]0.678879[/C][C]0.33944[/C][/ROW]
[ROW][C]99[/C][C]0.551328[/C][C]0.897344[/C][C]0.448672[/C][/ROW]
[ROW][C]100[/C][C]0.440793[/C][C]0.881585[/C][C]0.559207[/C][/ROW]
[ROW][C]101[/C][C]0.403599[/C][C]0.807197[/C][C]0.596401[/C][/ROW]
[ROW][C]102[/C][C]0.33656[/C][C]0.673121[/C][C]0.66344[/C][/ROW]
[ROW][C]103[/C][C]0.30548[/C][C]0.610959[/C][C]0.69452[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267254&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267254&T=5

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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
80.8307150.3385690.169285
90.9344960.1310080.0655038
100.910560.178880.0894398
110.8630620.2738760.136938
120.8104620.3790770.189538
130.8369930.3260140.163007
140.778350.44330.22165
150.7290340.5419310.270966
160.8286940.3426120.171306
170.773440.453120.22656
180.7076870.5846270.292313
190.6596280.6807430.340372
200.5889790.8220420.411021
210.5224770.9550450.477523
220.6176090.7647820.382391
230.5448230.9103540.455177
240.4976430.9952850.502357
250.4308530.8617050.569147
260.6205020.7589950.379498
270.6477290.7045410.352271
280.5860980.8278030.413902
290.5235270.9529450.476473
300.5725590.8548810.427441
310.5290670.9418650.470933
320.7169850.5660290.283015
330.6710250.657950.328975
340.6573410.6853180.342659
350.5986940.8026120.401306
360.5381850.9236290.461815
370.477970.9559410.52203
380.4681730.9363450.531827
390.4263170.8526350.573683
400.3741610.7483230.625839
410.4816530.9633070.518347
420.4307550.861510.569245
430.3990280.7980550.600972
440.3502160.7004310.649784
450.3117680.6235360.688232
460.2708310.5416620.729169
470.2399850.4799710.760015
480.2409170.4818340.759083
490.247090.4941790.75291
500.2169920.4339840.783008
510.1871490.3742980.812851
520.2373570.4747140.762643
530.2098510.4197020.790149
540.1904280.3808560.809572
550.2096670.4193340.790333
560.1759420.3518850.824058
570.30730.61460.6927
580.3484350.6968690.651565
590.3608150.7216290.639185
600.3650360.7300720.634964
610.3347890.6695770.665211
620.3022810.6045630.697719
630.3322260.6644520.667774
640.5046380.9907230.495362
650.4619790.9239580.538021
660.4253310.8506620.574669
670.3700440.7400880.629956
680.3344780.6689560.665522
690.504840.9903210.49516
700.4651190.9302380.534881
710.4205710.8411420.579429
720.3719050.7438110.628095
730.5106350.978730.489365
740.5366170.9267660.463383
750.5049370.9901260.495063
760.6424290.7151430.357571
770.6328720.7342560.367128
780.6005590.7988820.399441
790.5950530.8098940.404947
800.5515230.8969530.448477
810.6687370.6625260.331263
820.6322670.7354670.367733
830.6498320.7003360.350168
840.5865650.826870.413435
850.7495930.5008140.250407
860.7159870.5680260.284013
870.6911340.6177330.308866
880.6353340.7293310.364666
890.5826510.8346980.417349
900.5303770.9392450.469623
910.5175680.9648640.482432
920.525360.949280.47464
930.5918240.8163520.408176
940.5061230.9877540.493877
950.5377850.924430.462215
960.655090.689820.34491
970.5650040.8699930.434996
980.660560.6788790.33944
990.5513280.8973440.448672
1000.4407930.8815850.559207
1010.4035990.8071970.596401
1020.336560.6731210.66344
1030.305480.6109590.69452






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=267254&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=267254&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267254&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')
}