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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 18 Dec 2014 23:08:16 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/18/t1418944160dzyu2wt0h4qhho9.htm/, Retrieved Sun, 19 May 2024 20:26:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271319, Retrieved Sun, 19 May 2024 20:26:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact71

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-18 23:08:16] [4511a3627978005d3fb0dfa0eb9854e9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	12	13	13	12.9
8	8	13	16	12.2
14	11	11	11	12.8
16	13	14	10	7.4
14	11	15	9	6.7
13	10	14	8	12.6
15	7	11	26	14.8
13	10	13	10	13.3
20	15	16	10	11.1
17	12	14	8	8.2
15	12	14	13	11.4
16	10	15	11	6.4
12	10	15	8	10.6
17	14	13	12	12.0
11	6	14	24	6.3
16	12	11	21	11.3
16	14	12	5	11.9
15	11	14	14	9.3
13	8	13	11	9.6
14	12	12	9	10.0
19	15	15	8	6.4
16	13	15	17	13.8
17	11	14	18	10.8
10	12	14	16	13.8
15	7	12	23	11.7
14	11	12	9	10.9
14	7	12	14	16.1
16	12	15	13	13.4
15	12	14	10	9.9
17	13	16	8	11.5
14	9	12	10	8.3
16	11	12	19	11.7
15	12	14	11	9.0
16	15	16	16	9.7
16	12	15	12	10.8
10	6	12	11	10.3
8	5	14	11	10.4
17	13	13	10	12.7
14	11	14	13	9.3
10	6	16	14	11.8
14	12	12	8	5.9
12	10	14	11	11.4
16	6	15	11	13.0
16	12	13	13	10.8
16	11	16	15	12.3
8	6	16	15	11.3
16	12	12	16	11.8
15	12	12	12	7.9
8	8	16	12	12.7
13	10	12	17	12.3
14	11	15	14	11.6
13	7	12	15	6.7
16	12	13	12	10.9
19	13	12	13	12.1
19	14	14	7	13.3
14	12	14	8	10.1
15	6	11	16	5.7
13	14	10	20	14.3
10	10	12	14	8.0
16	12	11	10	13.3
15	11	16	16	9.3
11	10	14	11	12.5
9	7	14	26	7.6
16	12	15	9	15.9
12	7	15	15	9.2
12	12	14	12	9.1
14	12	13	21	11.1
14	10	11	20	13.0
13	10	16	20	14.5
15	12	12	10	12.2
17	12	15	15	12.3
14	12	14	10	11.4
11	8	15	16	8.8
9	10	14	9	14.6
7	5	13	17	12.6
13	10	6	10	NA
15	10	12	19	13.0
12	12	12	13	12.6
15	11	14	8	13.2
14	9	14	11	9.9
16	12	15	9	7.7
14	11	11	12	10.5
13	10	13	10	13.4
16	12	14	9	10.9
13	10	16	14	4.3
16	9	13	14	10.3
16	11	14	10	11.8
16	12	16	8	11.2
10	7	11	13	11.4
12	11	13	9	8.6
12	12	13	14	13.2
12	6	15	8	12.6
12	9	12	16	5.6
19	15	13	14	9.9
14	10	12	14	8.8
13	11	14	8	7.7
16	12	14	11	9.0
15	12	16	11	7.3
12	12	15	13	11.4
8	11	14	12	13.6
10	9	13	13	7.9
16	11	14	9	10.7
16	12	15	10	10.3
10	12	14	12	8.3
18	14	12	11	9.6
12	8	7	13	14.2
16	10	12	17	8.5
10	9	15	15	13.5
14	10	12	15	4.9
12	9	13	14	6.4
11	10	11	10	9.6
15	12	14	15	11.6
7	11	13	14	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 Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271319&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271319&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271319&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 Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
TOT [t] = + 11.4549 -0.0860601CONFSTATTOT[t] + 0.136669CONFSOFTTOT[t] -0.113805STRESSTOT[t] + 0.0393848CESDTOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT
[t] =  +  11.4549 -0.0860601CONFSTATTOT[t] +  0.136669CONFSOFTTOT[t] -0.113805STRESSTOT[t] +  0.0393848CESDTOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271319&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT
[t] =  +  11.4549 -0.0860601CONFSTATTOT[t] +  0.136669CONFSOFTTOT[t] -0.113805STRESSTOT[t] +  0.0393848CESDTOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271319&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271319&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] = + 11.4549 -0.0860601CONFSTATTOT[t] + 0.136669CONFSOFTTOT[t] -0.113805STRESSTOT[t] + 0.0393848CESDTOT[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.45492.730014.1965.62815e-052.81407e-05
CONFSTATTOT-0.08606010.108291-0.79470.4285420.214271
CONFSOFTTOT0.1366690.1360771.0040.3174770.158738
STRESSTOT-0.1138050.149498-0.76120.4481850.224092
CESDTOT0.03938480.06290650.62610.5325930.266296

\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) & 11.4549 & 2.73001 & 4.196 & 5.62815e-05 & 2.81407e-05 \tabularnewline
CONFSTATTOT & -0.0860601 & 0.108291 & -0.7947 & 0.428542 & 0.214271 \tabularnewline
CONFSOFTTOT & 0.136669 & 0.136077 & 1.004 & 0.317477 & 0.158738 \tabularnewline
STRESSTOT & -0.113805 & 0.149498 & -0.7612 & 0.448185 & 0.224092 \tabularnewline
CESDTOT & 0.0393848 & 0.0629065 & 0.6261 & 0.532593 & 0.266296 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271319&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]11.4549[/C][C]2.73001[/C][C]4.196[/C][C]5.62815e-05[/C][C]2.81407e-05[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.0860601[/C][C]0.108291[/C][C]-0.7947[/C][C]0.428542[/C][C]0.214271[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.136669[/C][C]0.136077[/C][C]1.004[/C][C]0.317477[/C][C]0.158738[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]-0.113805[/C][C]0.149498[/C][C]-0.7612[/C][C]0.448185[/C][C]0.224092[/C][/ROW]
[ROW][C]CESDTOT[/C][C]0.0393848[/C][C]0.0629065[/C][C]0.6261[/C][C]0.532593[/C][C]0.266296[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271319&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271319&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)11.45492.730014.1965.62815e-052.81407e-05
CONFSTATTOT-0.08606010.108291-0.79470.4285420.214271
CONFSOFTTOT0.1366690.1360771.0040.3174770.158738
STRESSTOT-0.1138050.149498-0.76120.4481850.224092
CESDTOT0.03938480.06290650.62610.5325930.266296






Multiple Linear Regression - Regression Statistics
Multiple R0.132372
R-squared0.0175224
Adjusted R-squared-0.0192057
F-TEST (value)0.477084
F-TEST (DF numerator)4
F-TEST (DF denominator)107
p-value0.752476
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49496
Sum Squared Residuals666.056

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.132372 \tabularnewline
R-squared & 0.0175224 \tabularnewline
Adjusted R-squared & -0.0192057 \tabularnewline
F-TEST (value) & 0.477084 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 107 \tabularnewline
p-value & 0.752476 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.49496 \tabularnewline
Sum Squared Residuals & 666.056 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271319&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.132372[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0175224[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0192057[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.477084[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]107[/C][/ROW]
[ROW][C]p-value[/C][C]0.752476[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.49496[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]666.056[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271319&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271319&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 R0.132372
R-squared0.0175224
Adjusted R-squared-0.0192057
F-TEST (value)0.477084
F-TEST (DF numerator)4
F-TEST (DF denominator)107
p-value0.752476
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49496
Sum Squared Residuals666.056






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.911.00871.89131
212.211.01051.18953
312.810.93481.8652
47.410.6552-3.25522
56.710.4008-3.70081
612.610.42462.17537
714.810.89283.90716
813.310.61722.6828
911.110.35670.743291
108.210.3537-2.15372
1111.410.72280.677233
126.410.1708-3.77079
1310.610.39690.20312
141210.89841.10159
156.310.6802-4.38023
1611.311.29320.00679903
1711.910.82261.07742
189.310.6255-1.32548
199.610.3832-0.783247
201010.8789-0.878898
216.410.4778-4.0778
2213.810.81712.98289
2310.810.61090.189098
2413.811.27122.52878
2511.710.66091.03912
2610.910.74220.157771
2716.110.39255.70752
2813.410.52292.8771
299.910.6046-0.704613
3011.510.26281.23722
318.310.5083-2.20828
3211.710.9640.736043
33910.644-1.644
349.710.9373-1.23726
3510.810.48350.316483
3610.310.4819-0.181894
3710.410.28970.110265
3812.710.6832.01703
399.310.6722-1.37216
4011.810.14481.65517
415.910.8395-4.93951
4211.410.62880.77116
43139.624123.37588
4410.810.75050.0494877
4512.310.35121.9488
4611.310.35630.943667
4711.810.98250.817528
487.910.911-3.01099
4912.710.51152.18848
5012.311.00671.2933
5111.610.59771.00226
526.710.5179-3.81792
5310.910.71110.188873
5412.110.74281.35719
5513.310.41562.88444
5610.110.6119-0.511903
575.710.3623-4.66232
5814.311.89912.40086
59811.1467-3.14672
6013.310.862.44003
619.310.4766-1.17664
6212.510.71491.7851
637.611.0678-3.46779
6415.910.36545.53464
659.210.2626-1.06257
669.110.9416-1.84156
6711.111.2377-0.137711
681311.15261.8474
6914.510.66963.83037
7012.210.83221.36778
7112.310.51561.78439
7211.410.69070.709327
738.810.5247-1.72468
7414.610.80833.79175
7512.610.72591.87409
76NANA2.08665
771311.60861.39144
7812.69.789172.81083
7913.213.6201-0.420051
809.912.5654-2.66536
817.78.17419-0.474189
8210.57.71722.7828
8313.412.97920.420832
8410.917.0333-6.13332
854.34.37989-0.0798899
8610.38.881881.41812
8711.810.81220.987827
8811.210.61110.588862
8911.413.6005-2.20054
908.66.534142.06586
9113.210.45022.7498
9212.617.9167-5.3167
935.66.64172-1.04172
949.911.9025-2.00248
958.811.6613-2.86129
967.79.25794-1.55794
97912.1164-3.11639
987.36.767140.532858
9911.48.949132.45087
10013.616.5569-2.95687
1017.97.54250.357501
10210.710.8047-0.104748
10310.313.1137-2.81368
1048.39.58677-1.28677
1059.66.630912.96909
10614.216.4485-2.24852
1078.55.708032.79197
10813.519.4419-5.94187
1094.99.22413-4.32413
1106.47.81693-1.41693
1119.68.801540.798463
11211.611.9278-0.327769
11311.1NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 11.0087 & 1.89131 \tabularnewline
2 & 12.2 & 11.0105 & 1.18953 \tabularnewline
3 & 12.8 & 10.9348 & 1.8652 \tabularnewline
4 & 7.4 & 10.6552 & -3.25522 \tabularnewline
5 & 6.7 & 10.4008 & -3.70081 \tabularnewline
6 & 12.6 & 10.4246 & 2.17537 \tabularnewline
7 & 14.8 & 10.8928 & 3.90716 \tabularnewline
8 & 13.3 & 10.6172 & 2.6828 \tabularnewline
9 & 11.1 & 10.3567 & 0.743291 \tabularnewline
10 & 8.2 & 10.3537 & -2.15372 \tabularnewline
11 & 11.4 & 10.7228 & 0.677233 \tabularnewline
12 & 6.4 & 10.1708 & -3.77079 \tabularnewline
13 & 10.6 & 10.3969 & 0.20312 \tabularnewline
14 & 12 & 10.8984 & 1.10159 \tabularnewline
15 & 6.3 & 10.6802 & -4.38023 \tabularnewline
16 & 11.3 & 11.2932 & 0.00679903 \tabularnewline
17 & 11.9 & 10.8226 & 1.07742 \tabularnewline
18 & 9.3 & 10.6255 & -1.32548 \tabularnewline
19 & 9.6 & 10.3832 & -0.783247 \tabularnewline
20 & 10 & 10.8789 & -0.878898 \tabularnewline
21 & 6.4 & 10.4778 & -4.0778 \tabularnewline
22 & 13.8 & 10.8171 & 2.98289 \tabularnewline
23 & 10.8 & 10.6109 & 0.189098 \tabularnewline
24 & 13.8 & 11.2712 & 2.52878 \tabularnewline
25 & 11.7 & 10.6609 & 1.03912 \tabularnewline
26 & 10.9 & 10.7422 & 0.157771 \tabularnewline
27 & 16.1 & 10.3925 & 5.70752 \tabularnewline
28 & 13.4 & 10.5229 & 2.8771 \tabularnewline
29 & 9.9 & 10.6046 & -0.704613 \tabularnewline
30 & 11.5 & 10.2628 & 1.23722 \tabularnewline
31 & 8.3 & 10.5083 & -2.20828 \tabularnewline
32 & 11.7 & 10.964 & 0.736043 \tabularnewline
33 & 9 & 10.644 & -1.644 \tabularnewline
34 & 9.7 & 10.9373 & -1.23726 \tabularnewline
35 & 10.8 & 10.4835 & 0.316483 \tabularnewline
36 & 10.3 & 10.4819 & -0.181894 \tabularnewline
37 & 10.4 & 10.2897 & 0.110265 \tabularnewline
38 & 12.7 & 10.683 & 2.01703 \tabularnewline
39 & 9.3 & 10.6722 & -1.37216 \tabularnewline
40 & 11.8 & 10.1448 & 1.65517 \tabularnewline
41 & 5.9 & 10.8395 & -4.93951 \tabularnewline
42 & 11.4 & 10.6288 & 0.77116 \tabularnewline
43 & 13 & 9.62412 & 3.37588 \tabularnewline
44 & 10.8 & 10.7505 & 0.0494877 \tabularnewline
45 & 12.3 & 10.3512 & 1.9488 \tabularnewline
46 & 11.3 & 10.3563 & 0.943667 \tabularnewline
47 & 11.8 & 10.9825 & 0.817528 \tabularnewline
48 & 7.9 & 10.911 & -3.01099 \tabularnewline
49 & 12.7 & 10.5115 & 2.18848 \tabularnewline
50 & 12.3 & 11.0067 & 1.2933 \tabularnewline
51 & 11.6 & 10.5977 & 1.00226 \tabularnewline
52 & 6.7 & 10.5179 & -3.81792 \tabularnewline
53 & 10.9 & 10.7111 & 0.188873 \tabularnewline
54 & 12.1 & 10.7428 & 1.35719 \tabularnewline
55 & 13.3 & 10.4156 & 2.88444 \tabularnewline
56 & 10.1 & 10.6119 & -0.511903 \tabularnewline
57 & 5.7 & 10.3623 & -4.66232 \tabularnewline
58 & 14.3 & 11.8991 & 2.40086 \tabularnewline
59 & 8 & 11.1467 & -3.14672 \tabularnewline
60 & 13.3 & 10.86 & 2.44003 \tabularnewline
61 & 9.3 & 10.4766 & -1.17664 \tabularnewline
62 & 12.5 & 10.7149 & 1.7851 \tabularnewline
63 & 7.6 & 11.0678 & -3.46779 \tabularnewline
64 & 15.9 & 10.3654 & 5.53464 \tabularnewline
65 & 9.2 & 10.2626 & -1.06257 \tabularnewline
66 & 9.1 & 10.9416 & -1.84156 \tabularnewline
67 & 11.1 & 11.2377 & -0.137711 \tabularnewline
68 & 13 & 11.1526 & 1.8474 \tabularnewline
69 & 14.5 & 10.6696 & 3.83037 \tabularnewline
70 & 12.2 & 10.8322 & 1.36778 \tabularnewline
71 & 12.3 & 10.5156 & 1.78439 \tabularnewline
72 & 11.4 & 10.6907 & 0.709327 \tabularnewline
73 & 8.8 & 10.5247 & -1.72468 \tabularnewline
74 & 14.6 & 10.8083 & 3.79175 \tabularnewline
75 & 12.6 & 10.7259 & 1.87409 \tabularnewline
76 & NA & NA & 2.08665 \tabularnewline
77 & 13 & 11.6086 & 1.39144 \tabularnewline
78 & 12.6 & 9.78917 & 2.81083 \tabularnewline
79 & 13.2 & 13.6201 & -0.420051 \tabularnewline
80 & 9.9 & 12.5654 & -2.66536 \tabularnewline
81 & 7.7 & 8.17419 & -0.474189 \tabularnewline
82 & 10.5 & 7.7172 & 2.7828 \tabularnewline
83 & 13.4 & 12.9792 & 0.420832 \tabularnewline
84 & 10.9 & 17.0333 & -6.13332 \tabularnewline
85 & 4.3 & 4.37989 & -0.0798899 \tabularnewline
86 & 10.3 & 8.88188 & 1.41812 \tabularnewline
87 & 11.8 & 10.8122 & 0.987827 \tabularnewline
88 & 11.2 & 10.6111 & 0.588862 \tabularnewline
89 & 11.4 & 13.6005 & -2.20054 \tabularnewline
90 & 8.6 & 6.53414 & 2.06586 \tabularnewline
91 & 13.2 & 10.4502 & 2.7498 \tabularnewline
92 & 12.6 & 17.9167 & -5.3167 \tabularnewline
93 & 5.6 & 6.64172 & -1.04172 \tabularnewline
94 & 9.9 & 11.9025 & -2.00248 \tabularnewline
95 & 8.8 & 11.6613 & -2.86129 \tabularnewline
96 & 7.7 & 9.25794 & -1.55794 \tabularnewline
97 & 9 & 12.1164 & -3.11639 \tabularnewline
98 & 7.3 & 6.76714 & 0.532858 \tabularnewline
99 & 11.4 & 8.94913 & 2.45087 \tabularnewline
100 & 13.6 & 16.5569 & -2.95687 \tabularnewline
101 & 7.9 & 7.5425 & 0.357501 \tabularnewline
102 & 10.7 & 10.8047 & -0.104748 \tabularnewline
103 & 10.3 & 13.1137 & -2.81368 \tabularnewline
104 & 8.3 & 9.58677 & -1.28677 \tabularnewline
105 & 9.6 & 6.63091 & 2.96909 \tabularnewline
106 & 14.2 & 16.4485 & -2.24852 \tabularnewline
107 & 8.5 & 5.70803 & 2.79197 \tabularnewline
108 & 13.5 & 19.4419 & -5.94187 \tabularnewline
109 & 4.9 & 9.22413 & -4.32413 \tabularnewline
110 & 6.4 & 7.81693 & -1.41693 \tabularnewline
111 & 9.6 & 8.80154 & 0.798463 \tabularnewline
112 & 11.6 & 11.9278 & -0.327769 \tabularnewline
113 & 11.1 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271319&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]11.0087[/C][C]1.89131[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]11.0105[/C][C]1.18953[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]10.9348[/C][C]1.8652[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]10.6552[/C][C]-3.25522[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.4008[/C][C]-3.70081[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]10.4246[/C][C]2.17537[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]10.8928[/C][C]3.90716[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]10.6172[/C][C]2.6828[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]10.3567[/C][C]0.743291[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.3537[/C][C]-2.15372[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]10.7228[/C][C]0.677233[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.1708[/C][C]-3.77079[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.3969[/C][C]0.20312[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]10.8984[/C][C]1.10159[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]10.6802[/C][C]-4.38023[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]11.2932[/C][C]0.00679903[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]10.8226[/C][C]1.07742[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]10.6255[/C][C]-1.32548[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]10.3832[/C][C]-0.783247[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]10.8789[/C][C]-0.878898[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]10.4778[/C][C]-4.0778[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]10.8171[/C][C]2.98289[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]10.6109[/C][C]0.189098[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]11.2712[/C][C]2.52878[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]10.6609[/C][C]1.03912[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]10.7422[/C][C]0.157771[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]10.3925[/C][C]5.70752[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]10.5229[/C][C]2.8771[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]10.6046[/C][C]-0.704613[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]10.2628[/C][C]1.23722[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]10.5083[/C][C]-2.20828[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]10.964[/C][C]0.736043[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]10.644[/C][C]-1.644[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]10.9373[/C][C]-1.23726[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]10.4835[/C][C]0.316483[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]10.4819[/C][C]-0.181894[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]10.2897[/C][C]0.110265[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]10.683[/C][C]2.01703[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]10.6722[/C][C]-1.37216[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]10.1448[/C][C]1.65517[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]10.8395[/C][C]-4.93951[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]10.6288[/C][C]0.77116[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]9.62412[/C][C]3.37588[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]10.7505[/C][C]0.0494877[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]10.3512[/C][C]1.9488[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]10.3563[/C][C]0.943667[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]10.9825[/C][C]0.817528[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]10.911[/C][C]-3.01099[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]10.5115[/C][C]2.18848[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]11.0067[/C][C]1.2933[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.5977[/C][C]1.00226[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]10.5179[/C][C]-3.81792[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]10.7111[/C][C]0.188873[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]10.7428[/C][C]1.35719[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]10.4156[/C][C]2.88444[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.6119[/C][C]-0.511903[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]10.3623[/C][C]-4.66232[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]11.8991[/C][C]2.40086[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]11.1467[/C][C]-3.14672[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]10.86[/C][C]2.44003[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]10.4766[/C][C]-1.17664[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]10.7149[/C][C]1.7851[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]11.0678[/C][C]-3.46779[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]10.3654[/C][C]5.53464[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]10.2626[/C][C]-1.06257[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]10.9416[/C][C]-1.84156[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]11.2377[/C][C]-0.137711[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]11.1526[/C][C]1.8474[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]10.6696[/C][C]3.83037[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]10.8322[/C][C]1.36778[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]10.5156[/C][C]1.78439[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]10.6907[/C][C]0.709327[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]10.5247[/C][C]-1.72468[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]10.8083[/C][C]3.79175[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]10.7259[/C][C]1.87409[/C][/ROW]
[ROW][C]76[/C][C]NA[/C][C]NA[/C][C]2.08665[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.6086[/C][C]1.39144[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]9.78917[/C][C]2.81083[/C][/ROW]
[ROW][C]79[/C][C]13.2[/C][C]13.6201[/C][C]-0.420051[/C][/ROW]
[ROW][C]80[/C][C]9.9[/C][C]12.5654[/C][C]-2.66536[/C][/ROW]
[ROW][C]81[/C][C]7.7[/C][C]8.17419[/C][C]-0.474189[/C][/ROW]
[ROW][C]82[/C][C]10.5[/C][C]7.7172[/C][C]2.7828[/C][/ROW]
[ROW][C]83[/C][C]13.4[/C][C]12.9792[/C][C]0.420832[/C][/ROW]
[ROW][C]84[/C][C]10.9[/C][C]17.0333[/C][C]-6.13332[/C][/ROW]
[ROW][C]85[/C][C]4.3[/C][C]4.37989[/C][C]-0.0798899[/C][/ROW]
[ROW][C]86[/C][C]10.3[/C][C]8.88188[/C][C]1.41812[/C][/ROW]
[ROW][C]87[/C][C]11.8[/C][C]10.8122[/C][C]0.987827[/C][/ROW]
[ROW][C]88[/C][C]11.2[/C][C]10.6111[/C][C]0.588862[/C][/ROW]
[ROW][C]89[/C][C]11.4[/C][C]13.6005[/C][C]-2.20054[/C][/ROW]
[ROW][C]90[/C][C]8.6[/C][C]6.53414[/C][C]2.06586[/C][/ROW]
[ROW][C]91[/C][C]13.2[/C][C]10.4502[/C][C]2.7498[/C][/ROW]
[ROW][C]92[/C][C]12.6[/C][C]17.9167[/C][C]-5.3167[/C][/ROW]
[ROW][C]93[/C][C]5.6[/C][C]6.64172[/C][C]-1.04172[/C][/ROW]
[ROW][C]94[/C][C]9.9[/C][C]11.9025[/C][C]-2.00248[/C][/ROW]
[ROW][C]95[/C][C]8.8[/C][C]11.6613[/C][C]-2.86129[/C][/ROW]
[ROW][C]96[/C][C]7.7[/C][C]9.25794[/C][C]-1.55794[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]12.1164[/C][C]-3.11639[/C][/ROW]
[ROW][C]98[/C][C]7.3[/C][C]6.76714[/C][C]0.532858[/C][/ROW]
[ROW][C]99[/C][C]11.4[/C][C]8.94913[/C][C]2.45087[/C][/ROW]
[ROW][C]100[/C][C]13.6[/C][C]16.5569[/C][C]-2.95687[/C][/ROW]
[ROW][C]101[/C][C]7.9[/C][C]7.5425[/C][C]0.357501[/C][/ROW]
[ROW][C]102[/C][C]10.7[/C][C]10.8047[/C][C]-0.104748[/C][/ROW]
[ROW][C]103[/C][C]10.3[/C][C]13.1137[/C][C]-2.81368[/C][/ROW]
[ROW][C]104[/C][C]8.3[/C][C]9.58677[/C][C]-1.28677[/C][/ROW]
[ROW][C]105[/C][C]9.6[/C][C]6.63091[/C][C]2.96909[/C][/ROW]
[ROW][C]106[/C][C]14.2[/C][C]16.4485[/C][C]-2.24852[/C][/ROW]
[ROW][C]107[/C][C]8.5[/C][C]5.70803[/C][C]2.79197[/C][/ROW]
[ROW][C]108[/C][C]13.5[/C][C]19.4419[/C][C]-5.94187[/C][/ROW]
[ROW][C]109[/C][C]4.9[/C][C]9.22413[/C][C]-4.32413[/C][/ROW]
[ROW][C]110[/C][C]6.4[/C][C]7.81693[/C][C]-1.41693[/C][/ROW]
[ROW][C]111[/C][C]9.6[/C][C]8.80154[/C][C]0.798463[/C][/ROW]
[ROW][C]112[/C][C]11.6[/C][C]11.9278[/C][C]-0.327769[/C][/ROW]
[ROW][C]113[/C][C]11.1[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271319&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271319&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
112.911.00871.89131
212.211.01051.18953
312.810.93481.8652
47.410.6552-3.25522
56.710.4008-3.70081
612.610.42462.17537
714.810.89283.90716
813.310.61722.6828
911.110.35670.743291
108.210.3537-2.15372
1111.410.72280.677233
126.410.1708-3.77079
1310.610.39690.20312
141210.89841.10159
156.310.6802-4.38023
1611.311.29320.00679903
1711.910.82261.07742
189.310.6255-1.32548
199.610.3832-0.783247
201010.8789-0.878898
216.410.4778-4.0778
2213.810.81712.98289
2310.810.61090.189098
2413.811.27122.52878
2511.710.66091.03912
2610.910.74220.157771
2716.110.39255.70752
2813.410.52292.8771
299.910.6046-0.704613
3011.510.26281.23722
318.310.5083-2.20828
3211.710.9640.736043
33910.644-1.644
349.710.9373-1.23726
3510.810.48350.316483
3610.310.4819-0.181894
3710.410.28970.110265
3812.710.6832.01703
399.310.6722-1.37216
4011.810.14481.65517
415.910.8395-4.93951
4211.410.62880.77116
43139.624123.37588
4410.810.75050.0494877
4512.310.35121.9488
4611.310.35630.943667
4711.810.98250.817528
487.910.911-3.01099
4912.710.51152.18848
5012.311.00671.2933
5111.610.59771.00226
526.710.5179-3.81792
5310.910.71110.188873
5412.110.74281.35719
5513.310.41562.88444
5610.110.6119-0.511903
575.710.3623-4.66232
5814.311.89912.40086
59811.1467-3.14672
6013.310.862.44003
619.310.4766-1.17664
6212.510.71491.7851
637.611.0678-3.46779
6415.910.36545.53464
659.210.2626-1.06257
669.110.9416-1.84156
6711.111.2377-0.137711
681311.15261.8474
6914.510.66963.83037
7012.210.83221.36778
7112.310.51561.78439
7211.410.69070.709327
738.810.5247-1.72468
7414.610.80833.79175
7512.610.72591.87409
76NANA2.08665
771311.60861.39144
7812.69.789172.81083
7913.213.6201-0.420051
809.912.5654-2.66536
817.78.17419-0.474189
8210.57.71722.7828
8313.412.97920.420832
8410.917.0333-6.13332
854.34.37989-0.0798899
8610.38.881881.41812
8711.810.81220.987827
8811.210.61110.588862
8911.413.6005-2.20054
908.66.534142.06586
9113.210.45022.7498
9212.617.9167-5.3167
935.66.64172-1.04172
949.911.9025-2.00248
958.811.6613-2.86129
967.79.25794-1.55794
97912.1164-3.11639
987.36.767140.532858
9911.48.949132.45087
10013.616.5569-2.95687
1017.97.54250.357501
10210.710.8047-0.104748
10310.313.1137-2.81368
1048.39.58677-1.28677
1059.66.630912.96909
10614.216.4485-2.24852
1078.55.708032.79197
10813.519.4419-5.94187
1094.99.22413-4.32413
1106.47.81693-1.41693
1119.68.801540.798463
11211.611.9278-0.327769
11311.1NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5836120.8327760.416388
90.7456920.5086160.254308
100.6938810.6122380.306119
110.5771140.8457720.422886
120.5718890.8562230.428111
130.5048460.9903090.495154
140.400850.8016990.59915
150.537020.925960.46298
160.5371820.9256360.462818
170.4708970.9417940.529103
180.3914760.7829510.608524
190.3205240.6410480.679476
200.3133240.6266470.686676
210.3421590.6843180.657841
220.4588070.9176140.541193
230.3928440.7856880.607156
240.3431670.6863340.656833
250.2900540.5801090.709946
260.2328250.4656510.767175
270.5146650.9706690.485335
280.5870770.8258450.412923
290.5249770.9500470.475023
300.5222770.9554450.477723
310.5278180.9443640.472182
320.4706010.9412030.529399
330.4348560.8697120.565144
340.3850350.770070.614965
350.3329470.6658940.667053
360.2814810.5629620.718519
370.2327030.4654060.767297
380.2121350.4242710.787865
390.1836930.3673870.816307
400.1796350.359270.820365
410.348080.6961610.65192
420.2994850.5989690.700515
430.3554760.7109520.644524
440.30260.60520.6974
450.2848370.5696740.715163
460.245770.491540.75423
470.2062160.4124320.793784
480.2308040.4616080.769196
490.2203450.4406910.779655
500.1904430.3808860.809557
510.1596810.3193620.840319
520.2194430.4388860.780557
530.1801450.3602910.819855
540.1562690.3125380.843731
550.1708970.3417940.829103
560.1394490.2788990.860551
570.2198110.4396230.780189
580.2159410.4318810.784059
590.2431090.4862190.756891
600.2428510.4857030.757149
610.2109320.4218650.789068
620.1888520.3777050.811148
630.2198190.4396390.780181
640.4068640.8137280.593136
650.362210.724420.63779
660.3343770.6687530.665623
670.285090.570180.71491
680.271190.5423810.72881
690.3904030.7808070.609597
700.3508170.7016340.649183
710.3721590.7443190.627841
720.3239790.6479570.676021
730.285110.5702210.71489
740.3283860.6567710.671614
750.3159420.6318840.684058
760.3971020.7942030.602898
770.3697380.7394760.630262
780.3721090.7442180.627891
790.3160540.6321080.683946
800.3150970.6301950.684903
810.2621450.524290.737855
820.2716260.5432520.728374
830.2234370.4468740.776563
840.4081870.8163750.591813
850.3554110.7108230.644589
860.330130.6602610.66987
870.2883980.5767960.711602
880.2395090.4790190.760491
890.2241580.4483160.775842
900.2468870.4937740.753113
910.2927450.5854890.707255
920.4060280.8120560.593972
930.3348330.6696660.665167
940.2742960.5485930.725704
950.2578840.5157670.742116
960.1964740.3929470.803526
970.1756220.3512430.824378
980.1319430.2638860.868057
990.1331370.2662740.866863
1000.118520.237040.88148
1010.0761870.1523740.923813
1020.05370230.1074050.946298
1030.03738750.0747750.962613
1040.01948260.03896520.980517
1050.167740.3354790.83226

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.583612 & 0.832776 & 0.416388 \tabularnewline
9 & 0.745692 & 0.508616 & 0.254308 \tabularnewline
10 & 0.693881 & 0.612238 & 0.306119 \tabularnewline
11 & 0.577114 & 0.845772 & 0.422886 \tabularnewline
12 & 0.571889 & 0.856223 & 0.428111 \tabularnewline
13 & 0.504846 & 0.990309 & 0.495154 \tabularnewline
14 & 0.40085 & 0.801699 & 0.59915 \tabularnewline
15 & 0.53702 & 0.92596 & 0.46298 \tabularnewline
16 & 0.537182 & 0.925636 & 0.462818 \tabularnewline
17 & 0.470897 & 0.941794 & 0.529103 \tabularnewline
18 & 0.391476 & 0.782951 & 0.608524 \tabularnewline
19 & 0.320524 & 0.641048 & 0.679476 \tabularnewline
20 & 0.313324 & 0.626647 & 0.686676 \tabularnewline
21 & 0.342159 & 0.684318 & 0.657841 \tabularnewline
22 & 0.458807 & 0.917614 & 0.541193 \tabularnewline
23 & 0.392844 & 0.785688 & 0.607156 \tabularnewline
24 & 0.343167 & 0.686334 & 0.656833 \tabularnewline
25 & 0.290054 & 0.580109 & 0.709946 \tabularnewline
26 & 0.232825 & 0.465651 & 0.767175 \tabularnewline
27 & 0.514665 & 0.970669 & 0.485335 \tabularnewline
28 & 0.587077 & 0.825845 & 0.412923 \tabularnewline
29 & 0.524977 & 0.950047 & 0.475023 \tabularnewline
30 & 0.522277 & 0.955445 & 0.477723 \tabularnewline
31 & 0.527818 & 0.944364 & 0.472182 \tabularnewline
32 & 0.470601 & 0.941203 & 0.529399 \tabularnewline
33 & 0.434856 & 0.869712 & 0.565144 \tabularnewline
34 & 0.385035 & 0.77007 & 0.614965 \tabularnewline
35 & 0.332947 & 0.665894 & 0.667053 \tabularnewline
36 & 0.281481 & 0.562962 & 0.718519 \tabularnewline
37 & 0.232703 & 0.465406 & 0.767297 \tabularnewline
38 & 0.212135 & 0.424271 & 0.787865 \tabularnewline
39 & 0.183693 & 0.367387 & 0.816307 \tabularnewline
40 & 0.179635 & 0.35927 & 0.820365 \tabularnewline
41 & 0.34808 & 0.696161 & 0.65192 \tabularnewline
42 & 0.299485 & 0.598969 & 0.700515 \tabularnewline
43 & 0.355476 & 0.710952 & 0.644524 \tabularnewline
44 & 0.3026 & 0.6052 & 0.6974 \tabularnewline
45 & 0.284837 & 0.569674 & 0.715163 \tabularnewline
46 & 0.24577 & 0.49154 & 0.75423 \tabularnewline
47 & 0.206216 & 0.412432 & 0.793784 \tabularnewline
48 & 0.230804 & 0.461608 & 0.769196 \tabularnewline
49 & 0.220345 & 0.440691 & 0.779655 \tabularnewline
50 & 0.190443 & 0.380886 & 0.809557 \tabularnewline
51 & 0.159681 & 0.319362 & 0.840319 \tabularnewline
52 & 0.219443 & 0.438886 & 0.780557 \tabularnewline
53 & 0.180145 & 0.360291 & 0.819855 \tabularnewline
54 & 0.156269 & 0.312538 & 0.843731 \tabularnewline
55 & 0.170897 & 0.341794 & 0.829103 \tabularnewline
56 & 0.139449 & 0.278899 & 0.860551 \tabularnewline
57 & 0.219811 & 0.439623 & 0.780189 \tabularnewline
58 & 0.215941 & 0.431881 & 0.784059 \tabularnewline
59 & 0.243109 & 0.486219 & 0.756891 \tabularnewline
60 & 0.242851 & 0.485703 & 0.757149 \tabularnewline
61 & 0.210932 & 0.421865 & 0.789068 \tabularnewline
62 & 0.188852 & 0.377705 & 0.811148 \tabularnewline
63 & 0.219819 & 0.439639 & 0.780181 \tabularnewline
64 & 0.406864 & 0.813728 & 0.593136 \tabularnewline
65 & 0.36221 & 0.72442 & 0.63779 \tabularnewline
66 & 0.334377 & 0.668753 & 0.665623 \tabularnewline
67 & 0.28509 & 0.57018 & 0.71491 \tabularnewline
68 & 0.27119 & 0.542381 & 0.72881 \tabularnewline
69 & 0.390403 & 0.780807 & 0.609597 \tabularnewline
70 & 0.350817 & 0.701634 & 0.649183 \tabularnewline
71 & 0.372159 & 0.744319 & 0.627841 \tabularnewline
72 & 0.323979 & 0.647957 & 0.676021 \tabularnewline
73 & 0.28511 & 0.570221 & 0.71489 \tabularnewline
74 & 0.328386 & 0.656771 & 0.671614 \tabularnewline
75 & 0.315942 & 0.631884 & 0.684058 \tabularnewline
76 & 0.397102 & 0.794203 & 0.602898 \tabularnewline
77 & 0.369738 & 0.739476 & 0.630262 \tabularnewline
78 & 0.372109 & 0.744218 & 0.627891 \tabularnewline
79 & 0.316054 & 0.632108 & 0.683946 \tabularnewline
80 & 0.315097 & 0.630195 & 0.684903 \tabularnewline
81 & 0.262145 & 0.52429 & 0.737855 \tabularnewline
82 & 0.271626 & 0.543252 & 0.728374 \tabularnewline
83 & 0.223437 & 0.446874 & 0.776563 \tabularnewline
84 & 0.408187 & 0.816375 & 0.591813 \tabularnewline
85 & 0.355411 & 0.710823 & 0.644589 \tabularnewline
86 & 0.33013 & 0.660261 & 0.66987 \tabularnewline
87 & 0.288398 & 0.576796 & 0.711602 \tabularnewline
88 & 0.239509 & 0.479019 & 0.760491 \tabularnewline
89 & 0.224158 & 0.448316 & 0.775842 \tabularnewline
90 & 0.246887 & 0.493774 & 0.753113 \tabularnewline
91 & 0.292745 & 0.585489 & 0.707255 \tabularnewline
92 & 0.406028 & 0.812056 & 0.593972 \tabularnewline
93 & 0.334833 & 0.669666 & 0.665167 \tabularnewline
94 & 0.274296 & 0.548593 & 0.725704 \tabularnewline
95 & 0.257884 & 0.515767 & 0.742116 \tabularnewline
96 & 0.196474 & 0.392947 & 0.803526 \tabularnewline
97 & 0.175622 & 0.351243 & 0.824378 \tabularnewline
98 & 0.131943 & 0.263886 & 0.868057 \tabularnewline
99 & 0.133137 & 0.266274 & 0.866863 \tabularnewline
100 & 0.11852 & 0.23704 & 0.88148 \tabularnewline
101 & 0.076187 & 0.152374 & 0.923813 \tabularnewline
102 & 0.0537023 & 0.107405 & 0.946298 \tabularnewline
103 & 0.0373875 & 0.074775 & 0.962613 \tabularnewline
104 & 0.0194826 & 0.0389652 & 0.980517 \tabularnewline
105 & 0.16774 & 0.335479 & 0.83226 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271319&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]0.583612[/C][C]0.832776[/C][C]0.416388[/C][/ROW]
[ROW][C]9[/C][C]0.745692[/C][C]0.508616[/C][C]0.254308[/C][/ROW]
[ROW][C]10[/C][C]0.693881[/C][C]0.612238[/C][C]0.306119[/C][/ROW]
[ROW][C]11[/C][C]0.577114[/C][C]0.845772[/C][C]0.422886[/C][/ROW]
[ROW][C]12[/C][C]0.571889[/C][C]0.856223[/C][C]0.428111[/C][/ROW]
[ROW][C]13[/C][C]0.504846[/C][C]0.990309[/C][C]0.495154[/C][/ROW]
[ROW][C]14[/C][C]0.40085[/C][C]0.801699[/C][C]0.59915[/C][/ROW]
[ROW][C]15[/C][C]0.53702[/C][C]0.92596[/C][C]0.46298[/C][/ROW]
[ROW][C]16[/C][C]0.537182[/C][C]0.925636[/C][C]0.462818[/C][/ROW]
[ROW][C]17[/C][C]0.470897[/C][C]0.941794[/C][C]0.529103[/C][/ROW]
[ROW][C]18[/C][C]0.391476[/C][C]0.782951[/C][C]0.608524[/C][/ROW]
[ROW][C]19[/C][C]0.320524[/C][C]0.641048[/C][C]0.679476[/C][/ROW]
[ROW][C]20[/C][C]0.313324[/C][C]0.626647[/C][C]0.686676[/C][/ROW]
[ROW][C]21[/C][C]0.342159[/C][C]0.684318[/C][C]0.657841[/C][/ROW]
[ROW][C]22[/C][C]0.458807[/C][C]0.917614[/C][C]0.541193[/C][/ROW]
[ROW][C]23[/C][C]0.392844[/C][C]0.785688[/C][C]0.607156[/C][/ROW]
[ROW][C]24[/C][C]0.343167[/C][C]0.686334[/C][C]0.656833[/C][/ROW]
[ROW][C]25[/C][C]0.290054[/C][C]0.580109[/C][C]0.709946[/C][/ROW]
[ROW][C]26[/C][C]0.232825[/C][C]0.465651[/C][C]0.767175[/C][/ROW]
[ROW][C]27[/C][C]0.514665[/C][C]0.970669[/C][C]0.485335[/C][/ROW]
[ROW][C]28[/C][C]0.587077[/C][C]0.825845[/C][C]0.412923[/C][/ROW]
[ROW][C]29[/C][C]0.524977[/C][C]0.950047[/C][C]0.475023[/C][/ROW]
[ROW][C]30[/C][C]0.522277[/C][C]0.955445[/C][C]0.477723[/C][/ROW]
[ROW][C]31[/C][C]0.527818[/C][C]0.944364[/C][C]0.472182[/C][/ROW]
[ROW][C]32[/C][C]0.470601[/C][C]0.941203[/C][C]0.529399[/C][/ROW]
[ROW][C]33[/C][C]0.434856[/C][C]0.869712[/C][C]0.565144[/C][/ROW]
[ROW][C]34[/C][C]0.385035[/C][C]0.77007[/C][C]0.614965[/C][/ROW]
[ROW][C]35[/C][C]0.332947[/C][C]0.665894[/C][C]0.667053[/C][/ROW]
[ROW][C]36[/C][C]0.281481[/C][C]0.562962[/C][C]0.718519[/C][/ROW]
[ROW][C]37[/C][C]0.232703[/C][C]0.465406[/C][C]0.767297[/C][/ROW]
[ROW][C]38[/C][C]0.212135[/C][C]0.424271[/C][C]0.787865[/C][/ROW]
[ROW][C]39[/C][C]0.183693[/C][C]0.367387[/C][C]0.816307[/C][/ROW]
[ROW][C]40[/C][C]0.179635[/C][C]0.35927[/C][C]0.820365[/C][/ROW]
[ROW][C]41[/C][C]0.34808[/C][C]0.696161[/C][C]0.65192[/C][/ROW]
[ROW][C]42[/C][C]0.299485[/C][C]0.598969[/C][C]0.700515[/C][/ROW]
[ROW][C]43[/C][C]0.355476[/C][C]0.710952[/C][C]0.644524[/C][/ROW]
[ROW][C]44[/C][C]0.3026[/C][C]0.6052[/C][C]0.6974[/C][/ROW]
[ROW][C]45[/C][C]0.284837[/C][C]0.569674[/C][C]0.715163[/C][/ROW]
[ROW][C]46[/C][C]0.24577[/C][C]0.49154[/C][C]0.75423[/C][/ROW]
[ROW][C]47[/C][C]0.206216[/C][C]0.412432[/C][C]0.793784[/C][/ROW]
[ROW][C]48[/C][C]0.230804[/C][C]0.461608[/C][C]0.769196[/C][/ROW]
[ROW][C]49[/C][C]0.220345[/C][C]0.440691[/C][C]0.779655[/C][/ROW]
[ROW][C]50[/C][C]0.190443[/C][C]0.380886[/C][C]0.809557[/C][/ROW]
[ROW][C]51[/C][C]0.159681[/C][C]0.319362[/C][C]0.840319[/C][/ROW]
[ROW][C]52[/C][C]0.219443[/C][C]0.438886[/C][C]0.780557[/C][/ROW]
[ROW][C]53[/C][C]0.180145[/C][C]0.360291[/C][C]0.819855[/C][/ROW]
[ROW][C]54[/C][C]0.156269[/C][C]0.312538[/C][C]0.843731[/C][/ROW]
[ROW][C]55[/C][C]0.170897[/C][C]0.341794[/C][C]0.829103[/C][/ROW]
[ROW][C]56[/C][C]0.139449[/C][C]0.278899[/C][C]0.860551[/C][/ROW]
[ROW][C]57[/C][C]0.219811[/C][C]0.439623[/C][C]0.780189[/C][/ROW]
[ROW][C]58[/C][C]0.215941[/C][C]0.431881[/C][C]0.784059[/C][/ROW]
[ROW][C]59[/C][C]0.243109[/C][C]0.486219[/C][C]0.756891[/C][/ROW]
[ROW][C]60[/C][C]0.242851[/C][C]0.485703[/C][C]0.757149[/C][/ROW]
[ROW][C]61[/C][C]0.210932[/C][C]0.421865[/C][C]0.789068[/C][/ROW]
[ROW][C]62[/C][C]0.188852[/C][C]0.377705[/C][C]0.811148[/C][/ROW]
[ROW][C]63[/C][C]0.219819[/C][C]0.439639[/C][C]0.780181[/C][/ROW]
[ROW][C]64[/C][C]0.406864[/C][C]0.813728[/C][C]0.593136[/C][/ROW]
[ROW][C]65[/C][C]0.36221[/C][C]0.72442[/C][C]0.63779[/C][/ROW]
[ROW][C]66[/C][C]0.334377[/C][C]0.668753[/C][C]0.665623[/C][/ROW]
[ROW][C]67[/C][C]0.28509[/C][C]0.57018[/C][C]0.71491[/C][/ROW]
[ROW][C]68[/C][C]0.27119[/C][C]0.542381[/C][C]0.72881[/C][/ROW]
[ROW][C]69[/C][C]0.390403[/C][C]0.780807[/C][C]0.609597[/C][/ROW]
[ROW][C]70[/C][C]0.350817[/C][C]0.701634[/C][C]0.649183[/C][/ROW]
[ROW][C]71[/C][C]0.372159[/C][C]0.744319[/C][C]0.627841[/C][/ROW]
[ROW][C]72[/C][C]0.323979[/C][C]0.647957[/C][C]0.676021[/C][/ROW]
[ROW][C]73[/C][C]0.28511[/C][C]0.570221[/C][C]0.71489[/C][/ROW]
[ROW][C]74[/C][C]0.328386[/C][C]0.656771[/C][C]0.671614[/C][/ROW]
[ROW][C]75[/C][C]0.315942[/C][C]0.631884[/C][C]0.684058[/C][/ROW]
[ROW][C]76[/C][C]0.397102[/C][C]0.794203[/C][C]0.602898[/C][/ROW]
[ROW][C]77[/C][C]0.369738[/C][C]0.739476[/C][C]0.630262[/C][/ROW]
[ROW][C]78[/C][C]0.372109[/C][C]0.744218[/C][C]0.627891[/C][/ROW]
[ROW][C]79[/C][C]0.316054[/C][C]0.632108[/C][C]0.683946[/C][/ROW]
[ROW][C]80[/C][C]0.315097[/C][C]0.630195[/C][C]0.684903[/C][/ROW]
[ROW][C]81[/C][C]0.262145[/C][C]0.52429[/C][C]0.737855[/C][/ROW]
[ROW][C]82[/C][C]0.271626[/C][C]0.543252[/C][C]0.728374[/C][/ROW]
[ROW][C]83[/C][C]0.223437[/C][C]0.446874[/C][C]0.776563[/C][/ROW]
[ROW][C]84[/C][C]0.408187[/C][C]0.816375[/C][C]0.591813[/C][/ROW]
[ROW][C]85[/C][C]0.355411[/C][C]0.710823[/C][C]0.644589[/C][/ROW]
[ROW][C]86[/C][C]0.33013[/C][C]0.660261[/C][C]0.66987[/C][/ROW]
[ROW][C]87[/C][C]0.288398[/C][C]0.576796[/C][C]0.711602[/C][/ROW]
[ROW][C]88[/C][C]0.239509[/C][C]0.479019[/C][C]0.760491[/C][/ROW]
[ROW][C]89[/C][C]0.224158[/C][C]0.448316[/C][C]0.775842[/C][/ROW]
[ROW][C]90[/C][C]0.246887[/C][C]0.493774[/C][C]0.753113[/C][/ROW]
[ROW][C]91[/C][C]0.292745[/C][C]0.585489[/C][C]0.707255[/C][/ROW]
[ROW][C]92[/C][C]0.406028[/C][C]0.812056[/C][C]0.593972[/C][/ROW]
[ROW][C]93[/C][C]0.334833[/C][C]0.669666[/C][C]0.665167[/C][/ROW]
[ROW][C]94[/C][C]0.274296[/C][C]0.548593[/C][C]0.725704[/C][/ROW]
[ROW][C]95[/C][C]0.257884[/C][C]0.515767[/C][C]0.742116[/C][/ROW]
[ROW][C]96[/C][C]0.196474[/C][C]0.392947[/C][C]0.803526[/C][/ROW]
[ROW][C]97[/C][C]0.175622[/C][C]0.351243[/C][C]0.824378[/C][/ROW]
[ROW][C]98[/C][C]0.131943[/C][C]0.263886[/C][C]0.868057[/C][/ROW]
[ROW][C]99[/C][C]0.133137[/C][C]0.266274[/C][C]0.866863[/C][/ROW]
[ROW][C]100[/C][C]0.11852[/C][C]0.23704[/C][C]0.88148[/C][/ROW]
[ROW][C]101[/C][C]0.076187[/C][C]0.152374[/C][C]0.923813[/C][/ROW]
[ROW][C]102[/C][C]0.0537023[/C][C]0.107405[/C][C]0.946298[/C][/ROW]
[ROW][C]103[/C][C]0.0373875[/C][C]0.074775[/C][C]0.962613[/C][/ROW]
[ROW][C]104[/C][C]0.0194826[/C][C]0.0389652[/C][C]0.980517[/C][/ROW]
[ROW][C]105[/C][C]0.16774[/C][C]0.335479[/C][C]0.83226[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271319&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271319&T=5

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5836120.8327760.416388
90.7456920.5086160.254308
100.6938810.6122380.306119
110.5771140.8457720.422886
120.5718890.8562230.428111
130.5048460.9903090.495154
140.400850.8016990.59915
150.537020.925960.46298
160.5371820.9256360.462818
170.4708970.9417940.529103
180.3914760.7829510.608524
190.3205240.6410480.679476
200.3133240.6266470.686676
210.3421590.6843180.657841
220.4588070.9176140.541193
230.3928440.7856880.607156
240.3431670.6863340.656833
250.2900540.5801090.709946
260.2328250.4656510.767175
270.5146650.9706690.485335
280.5870770.8258450.412923
290.5249770.9500470.475023
300.5222770.9554450.477723
310.5278180.9443640.472182
320.4706010.9412030.529399
330.4348560.8697120.565144
340.3850350.770070.614965
350.3329470.6658940.667053
360.2814810.5629620.718519
370.2327030.4654060.767297
380.2121350.4242710.787865
390.1836930.3673870.816307
400.1796350.359270.820365
410.348080.6961610.65192
420.2994850.5989690.700515
430.3554760.7109520.644524
440.30260.60520.6974
450.2848370.5696740.715163
460.245770.491540.75423
470.2062160.4124320.793784
480.2308040.4616080.769196
490.2203450.4406910.779655
500.1904430.3808860.809557
510.1596810.3193620.840319
520.2194430.4388860.780557
530.1801450.3602910.819855
540.1562690.3125380.843731
550.1708970.3417940.829103
560.1394490.2788990.860551
570.2198110.4396230.780189
580.2159410.4318810.784059
590.2431090.4862190.756891
600.2428510.4857030.757149
610.2109320.4218650.789068
620.1888520.3777050.811148
630.2198190.4396390.780181
640.4068640.8137280.593136
650.362210.724420.63779
660.3343770.6687530.665623
670.285090.570180.71491
680.271190.5423810.72881
690.3904030.7808070.609597
700.3508170.7016340.649183
710.3721590.7443190.627841
720.3239790.6479570.676021
730.285110.5702210.71489
740.3283860.6567710.671614
750.3159420.6318840.684058
760.3971020.7942030.602898
770.3697380.7394760.630262
780.3721090.7442180.627891
790.3160540.6321080.683946
800.3150970.6301950.684903
810.2621450.524290.737855
820.2716260.5432520.728374
830.2234370.4468740.776563
840.4081870.8163750.591813
850.3554110.7108230.644589
860.330130.6602610.66987
870.2883980.5767960.711602
880.2395090.4790190.760491
890.2241580.4483160.775842
900.2468870.4937740.753113
910.2927450.5854890.707255
920.4060280.8120560.593972
930.3348330.6696660.665167
940.2742960.5485930.725704
950.2578840.5157670.742116
960.1964740.3929470.803526
970.1756220.3512430.824378
980.1319430.2638860.868057
990.1331370.2662740.866863
1000.118520.237040.88148
1010.0761870.1523740.923813
1020.05370230.1074050.946298
1030.03738750.0747750.962613
1040.01948260.03896520.980517
1050.167740.3354790.83226






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0102041OK
10% type I error level20.0204082OK

\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 & 1 & 0.0102041 & OK \tabularnewline
10% type I error level & 2 & 0.0204082 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271319&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]1[/C][C]0.0102041[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0204082[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271319&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271319&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 level10.0102041OK
10% type I error level20.0204082OK


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):
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '5'
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')
}