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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 computationFri, 09 Nov 2012 10:07:52 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/09/t1352473721eoh6pkx7og5q4ee.htm/, Retrieved Mon, 29 Apr 2024 10:39:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=187145, Retrieved Mon, 29 Apr 2024 10:39:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
- RMPD  [Structural Time Series Models] [HPC Retail Sales] [2008-03-06 16:52:55] [74be16979710d4c4e7c6647856088456]
- R  D    [Structural Time Series Models] [HPC Retail Sales] [2008-03-08 11:33:35] [74be16979710d4c4e7c6647856088456]
- RMPD        [Multiple Regression] [] [2012-11-09 15:07:52] [bdee33f3d7ceb254f97215ce68b6a08e] [Current]
- R P           [Multiple Regression] [] [2012-11-09 16:39:52] [83c7ccdb194e46f99f0902896e3c3ab1]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
37
30
47
35
30
43
82
40
47
19
52
136
80
42
54
66
81
63
137
72
107
58
36
52
79
77
54
84
48
96
83
66
61
53
30
74
69
59
42
65
70
100
63
105
82
81
75
102
121
98
76
77
63
37
35
23
40
29
37
51
20
28
13
22
25
13
16
13
16
17
9
17
25
14
8
7
10
7
10
3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187145&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187145&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187145&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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
casualties[t] = + 72 -10.4285714285714M1[t] -22.2857142857143M2[t] -30M3[t] -21.1428571428571M4[t] -25.2857142857143M5[t] -20.7142857142857M6[t] -11.1428571428571M7[t] -26M8[t] -13.1666666666667M9[t] -29.1666666666667M10[t] -32.1666666666667M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
casualties[t] =  +  72 -10.4285714285714M1[t] -22.2857142857143M2[t] -30M3[t] -21.1428571428571M4[t] -25.2857142857143M5[t] -20.7142857142857M6[t] -11.1428571428571M7[t] -26M8[t] -13.1666666666667M9[t] -29.1666666666667M10[t] -32.1666666666667M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187145&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]casualties[t] =  +  72 -10.4285714285714M1[t] -22.2857142857143M2[t] -30M3[t] -21.1428571428571M4[t] -25.2857142857143M5[t] -20.7142857142857M6[t] -11.1428571428571M7[t] -26M8[t] -13.1666666666667M9[t] -29.1666666666667M10[t] -32.1666666666667M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187145&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187145&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
casualties[t] = + 72 -10.4285714285714M1[t] -22.2857142857143M2[t] -30M3[t] -21.1428571428571M4[t] -25.2857142857143M5[t] -20.7142857142857M6[t] -11.1428571428571M7[t] -26M8[t] -13.1666666666667M9[t] -29.1666666666667M10[t] -32.1666666666667M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7213.4702125.34511e-061e-06
M1-10.428571428571418.356804-0.56810.5718360.285918
M2-22.285714285714318.356804-1.2140.2289360.114468
M3-3018.356804-1.63430.1068240.053412
M4-21.142857142857118.356804-1.15180.2534480.126724
M5-25.285714285714318.356804-1.37750.1728880.086444
M6-20.714285714285718.356804-1.12840.2631060.131553
M7-11.142857142857118.356804-0.6070.5458620.272931
M8-2618.356804-1.41640.1612320.080616
M9-13.166666666666719.049756-0.69120.491810.245905
M10-29.166666666666719.049756-1.53110.1303890.065195
M11-32.166666666666719.049756-1.68860.0958850.047942

\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) & 72 & 13.470212 & 5.3451 & 1e-06 & 1e-06 \tabularnewline
M1 & -10.4285714285714 & 18.356804 & -0.5681 & 0.571836 & 0.285918 \tabularnewline
M2 & -22.2857142857143 & 18.356804 & -1.214 & 0.228936 & 0.114468 \tabularnewline
M3 & -30 & 18.356804 & -1.6343 & 0.106824 & 0.053412 \tabularnewline
M4 & -21.1428571428571 & 18.356804 & -1.1518 & 0.253448 & 0.126724 \tabularnewline
M5 & -25.2857142857143 & 18.356804 & -1.3775 & 0.172888 & 0.086444 \tabularnewline
M6 & -20.7142857142857 & 18.356804 & -1.1284 & 0.263106 & 0.131553 \tabularnewline
M7 & -11.1428571428571 & 18.356804 & -0.607 & 0.545862 & 0.272931 \tabularnewline
M8 & -26 & 18.356804 & -1.4164 & 0.161232 & 0.080616 \tabularnewline
M9 & -13.1666666666667 & 19.049756 & -0.6912 & 0.49181 & 0.245905 \tabularnewline
M10 & -29.1666666666667 & 19.049756 & -1.5311 & 0.130389 & 0.065195 \tabularnewline
M11 & -32.1666666666667 & 19.049756 & -1.6886 & 0.095885 & 0.047942 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187145&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]72[/C][C]13.470212[/C][C]5.3451[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M1[/C][C]-10.4285714285714[/C][C]18.356804[/C][C]-0.5681[/C][C]0.571836[/C][C]0.285918[/C][/ROW]
[ROW][C]M2[/C][C]-22.2857142857143[/C][C]18.356804[/C][C]-1.214[/C][C]0.228936[/C][C]0.114468[/C][/ROW]
[ROW][C]M3[/C][C]-30[/C][C]18.356804[/C][C]-1.6343[/C][C]0.106824[/C][C]0.053412[/C][/ROW]
[ROW][C]M4[/C][C]-21.1428571428571[/C][C]18.356804[/C][C]-1.1518[/C][C]0.253448[/C][C]0.126724[/C][/ROW]
[ROW][C]M5[/C][C]-25.2857142857143[/C][C]18.356804[/C][C]-1.3775[/C][C]0.172888[/C][C]0.086444[/C][/ROW]
[ROW][C]M6[/C][C]-20.7142857142857[/C][C]18.356804[/C][C]-1.1284[/C][C]0.263106[/C][C]0.131553[/C][/ROW]
[ROW][C]M7[/C][C]-11.1428571428571[/C][C]18.356804[/C][C]-0.607[/C][C]0.545862[/C][C]0.272931[/C][/ROW]
[ROW][C]M8[/C][C]-26[/C][C]18.356804[/C][C]-1.4164[/C][C]0.161232[/C][C]0.080616[/C][/ROW]
[ROW][C]M9[/C][C]-13.1666666666667[/C][C]19.049756[/C][C]-0.6912[/C][C]0.49181[/C][C]0.245905[/C][/ROW]
[ROW][C]M10[/C][C]-29.1666666666667[/C][C]19.049756[/C][C]-1.5311[/C][C]0.130389[/C][C]0.065195[/C][/ROW]
[ROW][C]M11[/C][C]-32.1666666666667[/C][C]19.049756[/C][C]-1.6886[/C][C]0.095885[/C][C]0.047942[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187145&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187145&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)7213.4702125.34511e-061e-06
M1-10.428571428571418.356804-0.56810.5718360.285918
M2-22.285714285714318.356804-1.2140.2289360.114468
M3-3018.356804-1.63430.1068240.053412
M4-21.142857142857118.356804-1.15180.2534480.126724
M5-25.285714285714318.356804-1.37750.1728880.086444
M6-20.714285714285718.356804-1.12840.2631060.131553
M7-11.142857142857118.356804-0.6070.5458620.272931
M8-2618.356804-1.41640.1612320.080616
M9-13.166666666666719.049756-0.69120.491810.245905
M10-29.166666666666719.049756-1.53110.1303890.065195
M11-32.166666666666719.049756-1.68860.0958850.047942






Multiple Linear Regression - Regression Statistics
Multiple R0.283229920156028
R-squared0.0802191876715898
Adjusted R-squared-0.0685688849109471
F-TEST (value)0.53915066093144
F-TEST (DF numerator)11
F-TEST (DF denominator)68
p-value0.869724119967224
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation32.9951454285133
Sum Squared Residuals74030.2142857143

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.283229920156028 \tabularnewline
R-squared & 0.0802191876715898 \tabularnewline
Adjusted R-squared & -0.0685688849109471 \tabularnewline
F-TEST (value) & 0.53915066093144 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 68 \tabularnewline
p-value & 0.869724119967224 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 32.9951454285133 \tabularnewline
Sum Squared Residuals & 74030.2142857143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187145&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.283229920156028[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0802191876715898[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0685688849109471[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.53915066093144[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]68[/C][/ROW]
[ROW][C]p-value[/C][C]0.869724119967224[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]32.9951454285133[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]74030.2142857143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187145&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187145&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 R0.283229920156028
R-squared0.0802191876715898
Adjusted R-squared-0.0685688849109471
F-TEST (value)0.53915066093144
F-TEST (DF numerator)11
F-TEST (DF denominator)68
p-value0.869724119967224
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation32.9951454285133
Sum Squared Residuals74030.2142857143






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13761.5714285714286-24.5714285714286
23049.7142857142857-19.7142857142857
347425
43550.8571428571429-15.8571428571429
53046.7142857142857-16.7142857142857
64351.2857142857143-8.28571428571428
78260.857142857142921.1428571428571
84046-6.00000000000001
94758.8333333333333-11.8333333333333
101942.8333333333333-23.8333333333333
115239.833333333333312.1666666666667
121367264
138061.571428571428618.4285714285714
144249.7142857142857-7.71428571428571
15544212
166650.857142857142915.1428571428571
178146.714285714285734.2857142857143
186351.285714285714311.7142857142857
1913760.857142857142976.1428571428571
20724626
2110758.833333333333348.1666666666667
225842.833333333333315.1666666666667
233639.8333333333333-3.83333333333333
245272-20
257961.571428571428617.4285714285714
267749.714285714285727.2857142857143
27544212
288450.857142857142833.1428571428572
294846.71428571428571.28571428571429
309651.285714285714344.7142857142857
318360.857142857142922.1428571428571
32664620
336158.83333333333332.16666666666666
345342.833333333333310.1666666666667
353039.8333333333333-9.83333333333333
3674722
376961.57142857142867.42857142857145
385949.71428571428579.2857142857143
3942425.14909295756816e-15
406550.857142857142914.1428571428571
417046.714285714285723.2857142857143
4210051.285714285714348.7142857142857
436360.85714285714292.14285714285715
441054659
458258.833333333333323.1666666666667
468142.833333333333338.1666666666667
477539.833333333333335.1666666666667
481027230
4912161.571428571428659.4285714285714
509849.714285714285748.2857142857143
51764234
527750.857142857142926.1428571428571
536346.714285714285716.2857142857143
543751.2857142857143-14.2857142857143
553560.8571428571429-25.8571428571429
562346-23
574058.8333333333333-18.8333333333333
582942.8333333333333-13.8333333333333
593739.8333333333333-2.83333333333333
605172-21
612061.5714285714286-41.5714285714286
622849.7142857142857-21.7142857142857
631342-29
642250.8571428571429-28.8571428571429
652546.7142857142857-21.7142857142857
661351.2857142857143-38.2857142857143
671660.8571428571429-44.8571428571429
681346-33
691658.8333333333333-42.8333333333333
701742.8333333333333-25.8333333333333
71939.8333333333333-30.8333333333333
721772-55
732561.5714285714286-36.5714285714286
741449.7142857142857-35.7142857142857
75842-34
76750.8571428571428-43.8571428571428
771046.7142857142857-36.7142857142857
78751.2857142857143-44.2857142857143
791060.8571428571429-50.8571428571429
80346-43

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 37 & 61.5714285714286 & -24.5714285714286 \tabularnewline
2 & 30 & 49.7142857142857 & -19.7142857142857 \tabularnewline
3 & 47 & 42 & 5 \tabularnewline
4 & 35 & 50.8571428571429 & -15.8571428571429 \tabularnewline
5 & 30 & 46.7142857142857 & -16.7142857142857 \tabularnewline
6 & 43 & 51.2857142857143 & -8.28571428571428 \tabularnewline
7 & 82 & 60.8571428571429 & 21.1428571428571 \tabularnewline
8 & 40 & 46 & -6.00000000000001 \tabularnewline
9 & 47 & 58.8333333333333 & -11.8333333333333 \tabularnewline
10 & 19 & 42.8333333333333 & -23.8333333333333 \tabularnewline
11 & 52 & 39.8333333333333 & 12.1666666666667 \tabularnewline
12 & 136 & 72 & 64 \tabularnewline
13 & 80 & 61.5714285714286 & 18.4285714285714 \tabularnewline
14 & 42 & 49.7142857142857 & -7.71428571428571 \tabularnewline
15 & 54 & 42 & 12 \tabularnewline
16 & 66 & 50.8571428571429 & 15.1428571428571 \tabularnewline
17 & 81 & 46.7142857142857 & 34.2857142857143 \tabularnewline
18 & 63 & 51.2857142857143 & 11.7142857142857 \tabularnewline
19 & 137 & 60.8571428571429 & 76.1428571428571 \tabularnewline
20 & 72 & 46 & 26 \tabularnewline
21 & 107 & 58.8333333333333 & 48.1666666666667 \tabularnewline
22 & 58 & 42.8333333333333 & 15.1666666666667 \tabularnewline
23 & 36 & 39.8333333333333 & -3.83333333333333 \tabularnewline
24 & 52 & 72 & -20 \tabularnewline
25 & 79 & 61.5714285714286 & 17.4285714285714 \tabularnewline
26 & 77 & 49.7142857142857 & 27.2857142857143 \tabularnewline
27 & 54 & 42 & 12 \tabularnewline
28 & 84 & 50.8571428571428 & 33.1428571428572 \tabularnewline
29 & 48 & 46.7142857142857 & 1.28571428571429 \tabularnewline
30 & 96 & 51.2857142857143 & 44.7142857142857 \tabularnewline
31 & 83 & 60.8571428571429 & 22.1428571428571 \tabularnewline
32 & 66 & 46 & 20 \tabularnewline
33 & 61 & 58.8333333333333 & 2.16666666666666 \tabularnewline
34 & 53 & 42.8333333333333 & 10.1666666666667 \tabularnewline
35 & 30 & 39.8333333333333 & -9.83333333333333 \tabularnewline
36 & 74 & 72 & 2 \tabularnewline
37 & 69 & 61.5714285714286 & 7.42857142857145 \tabularnewline
38 & 59 & 49.7142857142857 & 9.2857142857143 \tabularnewline
39 & 42 & 42 & 5.14909295756816e-15 \tabularnewline
40 & 65 & 50.8571428571429 & 14.1428571428571 \tabularnewline
41 & 70 & 46.7142857142857 & 23.2857142857143 \tabularnewline
42 & 100 & 51.2857142857143 & 48.7142857142857 \tabularnewline
43 & 63 & 60.8571428571429 & 2.14285714285715 \tabularnewline
44 & 105 & 46 & 59 \tabularnewline
45 & 82 & 58.8333333333333 & 23.1666666666667 \tabularnewline
46 & 81 & 42.8333333333333 & 38.1666666666667 \tabularnewline
47 & 75 & 39.8333333333333 & 35.1666666666667 \tabularnewline
48 & 102 & 72 & 30 \tabularnewline
49 & 121 & 61.5714285714286 & 59.4285714285714 \tabularnewline
50 & 98 & 49.7142857142857 & 48.2857142857143 \tabularnewline
51 & 76 & 42 & 34 \tabularnewline
52 & 77 & 50.8571428571429 & 26.1428571428571 \tabularnewline
53 & 63 & 46.7142857142857 & 16.2857142857143 \tabularnewline
54 & 37 & 51.2857142857143 & -14.2857142857143 \tabularnewline
55 & 35 & 60.8571428571429 & -25.8571428571429 \tabularnewline
56 & 23 & 46 & -23 \tabularnewline
57 & 40 & 58.8333333333333 & -18.8333333333333 \tabularnewline
58 & 29 & 42.8333333333333 & -13.8333333333333 \tabularnewline
59 & 37 & 39.8333333333333 & -2.83333333333333 \tabularnewline
60 & 51 & 72 & -21 \tabularnewline
61 & 20 & 61.5714285714286 & -41.5714285714286 \tabularnewline
62 & 28 & 49.7142857142857 & -21.7142857142857 \tabularnewline
63 & 13 & 42 & -29 \tabularnewline
64 & 22 & 50.8571428571429 & -28.8571428571429 \tabularnewline
65 & 25 & 46.7142857142857 & -21.7142857142857 \tabularnewline
66 & 13 & 51.2857142857143 & -38.2857142857143 \tabularnewline
67 & 16 & 60.8571428571429 & -44.8571428571429 \tabularnewline
68 & 13 & 46 & -33 \tabularnewline
69 & 16 & 58.8333333333333 & -42.8333333333333 \tabularnewline
70 & 17 & 42.8333333333333 & -25.8333333333333 \tabularnewline
71 & 9 & 39.8333333333333 & -30.8333333333333 \tabularnewline
72 & 17 & 72 & -55 \tabularnewline
73 & 25 & 61.5714285714286 & -36.5714285714286 \tabularnewline
74 & 14 & 49.7142857142857 & -35.7142857142857 \tabularnewline
75 & 8 & 42 & -34 \tabularnewline
76 & 7 & 50.8571428571428 & -43.8571428571428 \tabularnewline
77 & 10 & 46.7142857142857 & -36.7142857142857 \tabularnewline
78 & 7 & 51.2857142857143 & -44.2857142857143 \tabularnewline
79 & 10 & 60.8571428571429 & -50.8571428571429 \tabularnewline
80 & 3 & 46 & -43 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187145&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]37[/C][C]61.5714285714286[/C][C]-24.5714285714286[/C][/ROW]
[ROW][C]2[/C][C]30[/C][C]49.7142857142857[/C][C]-19.7142857142857[/C][/ROW]
[ROW][C]3[/C][C]47[/C][C]42[/C][C]5[/C][/ROW]
[ROW][C]4[/C][C]35[/C][C]50.8571428571429[/C][C]-15.8571428571429[/C][/ROW]
[ROW][C]5[/C][C]30[/C][C]46.7142857142857[/C][C]-16.7142857142857[/C][/ROW]
[ROW][C]6[/C][C]43[/C][C]51.2857142857143[/C][C]-8.28571428571428[/C][/ROW]
[ROW][C]7[/C][C]82[/C][C]60.8571428571429[/C][C]21.1428571428571[/C][/ROW]
[ROW][C]8[/C][C]40[/C][C]46[/C][C]-6.00000000000001[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]58.8333333333333[/C][C]-11.8333333333333[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]42.8333333333333[/C][C]-23.8333333333333[/C][/ROW]
[ROW][C]11[/C][C]52[/C][C]39.8333333333333[/C][C]12.1666666666667[/C][/ROW]
[ROW][C]12[/C][C]136[/C][C]72[/C][C]64[/C][/ROW]
[ROW][C]13[/C][C]80[/C][C]61.5714285714286[/C][C]18.4285714285714[/C][/ROW]
[ROW][C]14[/C][C]42[/C][C]49.7142857142857[/C][C]-7.71428571428571[/C][/ROW]
[ROW][C]15[/C][C]54[/C][C]42[/C][C]12[/C][/ROW]
[ROW][C]16[/C][C]66[/C][C]50.8571428571429[/C][C]15.1428571428571[/C][/ROW]
[ROW][C]17[/C][C]81[/C][C]46.7142857142857[/C][C]34.2857142857143[/C][/ROW]
[ROW][C]18[/C][C]63[/C][C]51.2857142857143[/C][C]11.7142857142857[/C][/ROW]
[ROW][C]19[/C][C]137[/C][C]60.8571428571429[/C][C]76.1428571428571[/C][/ROW]
[ROW][C]20[/C][C]72[/C][C]46[/C][C]26[/C][/ROW]
[ROW][C]21[/C][C]107[/C][C]58.8333333333333[/C][C]48.1666666666667[/C][/ROW]
[ROW][C]22[/C][C]58[/C][C]42.8333333333333[/C][C]15.1666666666667[/C][/ROW]
[ROW][C]23[/C][C]36[/C][C]39.8333333333333[/C][C]-3.83333333333333[/C][/ROW]
[ROW][C]24[/C][C]52[/C][C]72[/C][C]-20[/C][/ROW]
[ROW][C]25[/C][C]79[/C][C]61.5714285714286[/C][C]17.4285714285714[/C][/ROW]
[ROW][C]26[/C][C]77[/C][C]49.7142857142857[/C][C]27.2857142857143[/C][/ROW]
[ROW][C]27[/C][C]54[/C][C]42[/C][C]12[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]50.8571428571428[/C][C]33.1428571428572[/C][/ROW]
[ROW][C]29[/C][C]48[/C][C]46.7142857142857[/C][C]1.28571428571429[/C][/ROW]
[ROW][C]30[/C][C]96[/C][C]51.2857142857143[/C][C]44.7142857142857[/C][/ROW]
[ROW][C]31[/C][C]83[/C][C]60.8571428571429[/C][C]22.1428571428571[/C][/ROW]
[ROW][C]32[/C][C]66[/C][C]46[/C][C]20[/C][/ROW]
[ROW][C]33[/C][C]61[/C][C]58.8333333333333[/C][C]2.16666666666666[/C][/ROW]
[ROW][C]34[/C][C]53[/C][C]42.8333333333333[/C][C]10.1666666666667[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]39.8333333333333[/C][C]-9.83333333333333[/C][/ROW]
[ROW][C]36[/C][C]74[/C][C]72[/C][C]2[/C][/ROW]
[ROW][C]37[/C][C]69[/C][C]61.5714285714286[/C][C]7.42857142857145[/C][/ROW]
[ROW][C]38[/C][C]59[/C][C]49.7142857142857[/C][C]9.2857142857143[/C][/ROW]
[ROW][C]39[/C][C]42[/C][C]42[/C][C]5.14909295756816e-15[/C][/ROW]
[ROW][C]40[/C][C]65[/C][C]50.8571428571429[/C][C]14.1428571428571[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]46.7142857142857[/C][C]23.2857142857143[/C][/ROW]
[ROW][C]42[/C][C]100[/C][C]51.2857142857143[/C][C]48.7142857142857[/C][/ROW]
[ROW][C]43[/C][C]63[/C][C]60.8571428571429[/C][C]2.14285714285715[/C][/ROW]
[ROW][C]44[/C][C]105[/C][C]46[/C][C]59[/C][/ROW]
[ROW][C]45[/C][C]82[/C][C]58.8333333333333[/C][C]23.1666666666667[/C][/ROW]
[ROW][C]46[/C][C]81[/C][C]42.8333333333333[/C][C]38.1666666666667[/C][/ROW]
[ROW][C]47[/C][C]75[/C][C]39.8333333333333[/C][C]35.1666666666667[/C][/ROW]
[ROW][C]48[/C][C]102[/C][C]72[/C][C]30[/C][/ROW]
[ROW][C]49[/C][C]121[/C][C]61.5714285714286[/C][C]59.4285714285714[/C][/ROW]
[ROW][C]50[/C][C]98[/C][C]49.7142857142857[/C][C]48.2857142857143[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]42[/C][C]34[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]50.8571428571429[/C][C]26.1428571428571[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]46.7142857142857[/C][C]16.2857142857143[/C][/ROW]
[ROW][C]54[/C][C]37[/C][C]51.2857142857143[/C][C]-14.2857142857143[/C][/ROW]
[ROW][C]55[/C][C]35[/C][C]60.8571428571429[/C][C]-25.8571428571429[/C][/ROW]
[ROW][C]56[/C][C]23[/C][C]46[/C][C]-23[/C][/ROW]
[ROW][C]57[/C][C]40[/C][C]58.8333333333333[/C][C]-18.8333333333333[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]42.8333333333333[/C][C]-13.8333333333333[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]39.8333333333333[/C][C]-2.83333333333333[/C][/ROW]
[ROW][C]60[/C][C]51[/C][C]72[/C][C]-21[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]61.5714285714286[/C][C]-41.5714285714286[/C][/ROW]
[ROW][C]62[/C][C]28[/C][C]49.7142857142857[/C][C]-21.7142857142857[/C][/ROW]
[ROW][C]63[/C][C]13[/C][C]42[/C][C]-29[/C][/ROW]
[ROW][C]64[/C][C]22[/C][C]50.8571428571429[/C][C]-28.8571428571429[/C][/ROW]
[ROW][C]65[/C][C]25[/C][C]46.7142857142857[/C][C]-21.7142857142857[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]51.2857142857143[/C][C]-38.2857142857143[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]60.8571428571429[/C][C]-44.8571428571429[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]46[/C][C]-33[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]58.8333333333333[/C][C]-42.8333333333333[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]42.8333333333333[/C][C]-25.8333333333333[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]39.8333333333333[/C][C]-30.8333333333333[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]72[/C][C]-55[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]61.5714285714286[/C][C]-36.5714285714286[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]49.7142857142857[/C][C]-35.7142857142857[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]42[/C][C]-34[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]50.8571428571428[/C][C]-43.8571428571428[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]46.7142857142857[/C][C]-36.7142857142857[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]51.2857142857143[/C][C]-44.2857142857143[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]60.8571428571429[/C][C]-50.8571428571429[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]46[/C][C]-43[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187145&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187145&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
13761.5714285714286-24.5714285714286
23049.7142857142857-19.7142857142857
347425
43550.8571428571429-15.8571428571429
53046.7142857142857-16.7142857142857
64351.2857142857143-8.28571428571428
78260.857142857142921.1428571428571
84046-6.00000000000001
94758.8333333333333-11.8333333333333
101942.8333333333333-23.8333333333333
115239.833333333333312.1666666666667
121367264
138061.571428571428618.4285714285714
144249.7142857142857-7.71428571428571
15544212
166650.857142857142915.1428571428571
178146.714285714285734.2857142857143
186351.285714285714311.7142857142857
1913760.857142857142976.1428571428571
20724626
2110758.833333333333348.1666666666667
225842.833333333333315.1666666666667
233639.8333333333333-3.83333333333333
245272-20
257961.571428571428617.4285714285714
267749.714285714285727.2857142857143
27544212
288450.857142857142833.1428571428572
294846.71428571428571.28571428571429
309651.285714285714344.7142857142857
318360.857142857142922.1428571428571
32664620
336158.83333333333332.16666666666666
345342.833333333333310.1666666666667
353039.8333333333333-9.83333333333333
3674722
376961.57142857142867.42857142857145
385949.71428571428579.2857142857143
3942425.14909295756816e-15
406550.857142857142914.1428571428571
417046.714285714285723.2857142857143
4210051.285714285714348.7142857142857
436360.85714285714292.14285714285715
441054659
458258.833333333333323.1666666666667
468142.833333333333338.1666666666667
477539.833333333333335.1666666666667
481027230
4912161.571428571428659.4285714285714
509849.714285714285748.2857142857143
51764234
527750.857142857142926.1428571428571
536346.714285714285716.2857142857143
543751.2857142857143-14.2857142857143
553560.8571428571429-25.8571428571429
562346-23
574058.8333333333333-18.8333333333333
582942.8333333333333-13.8333333333333
593739.8333333333333-2.83333333333333
605172-21
612061.5714285714286-41.5714285714286
622849.7142857142857-21.7142857142857
631342-29
642250.8571428571429-28.8571428571429
652546.7142857142857-21.7142857142857
661351.2857142857143-38.2857142857143
671660.8571428571429-44.8571428571429
681346-33
691658.8333333333333-42.8333333333333
701742.8333333333333-25.8333333333333
71939.8333333333333-30.8333333333333
721772-55
732561.5714285714286-36.5714285714286
741449.7142857142857-35.7142857142857
75842-34
76750.8571428571428-43.8571428571428
771046.7142857142857-36.7142857142857
78751.2857142857143-44.2857142857143
791060.8571428571429-50.8571428571429
80346-43






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.1584755616301260.3169511232602510.841524438369874
160.1239932557634590.2479865115269170.876006744236541
170.1878874231819790.3757748463639570.812112576818021
180.1203930221128060.2407860442256120.879606977887194
190.2195159181557380.4390318363114770.780484081844262
200.1846239733807250.3692479467614490.815376026619275
210.2757078667170270.5514157334340530.724292133282973
220.2504321315134880.5008642630269760.749567868486512
230.1831058510013760.3662117020027520.816894148998624
240.3397238278776630.6794476557553250.660276172122337
250.2782893468882320.5565786937764650.721710653111768
260.2744656898834260.5489313797668510.725534310116574
270.2082863919188630.4165727838377260.791713608081137
280.1978969085784110.3957938171568210.802103091421589
290.1445955968773950.289191193754790.855404403122605
300.1702318256103180.3404636512206350.829768174389683
310.1633993931081310.3267987862162620.836600606891869
320.1273040420698940.2546080841397890.872695957930105
330.09534918209166040.1906983641833210.90465081790834
340.06874606497841840.1374921299568370.931253935021582
350.04825566293610120.09651132587220240.951744337063899
360.0352706484903670.07054129698073390.964729351509633
370.02287672591160070.04575345182320130.977123274088399
380.01467487899597440.02934975799194880.985325121004026
390.009121177836867590.01824235567373520.990878822163132
400.005912626620499260.01182525324099850.994087373379501
410.00442938471156380.00885876942312760.995570615288436
420.008795546431614550.01759109286322910.991204453568385
430.01026954205828980.02053908411657950.98973045794171
440.03722467121358520.07444934242717040.962775328786415
450.0371004250117080.07420085002341610.962899574988292
460.05303122090175220.1060624418035040.946968779098248
470.06720220392008920.1344044078401780.932797796079911
480.09678371735895760.1935674347179150.903216282641042
490.4068779455407420.8137558910814830.593122054459258
500.7142012601486870.5715974797026260.285798739851313
510.8881073960652750.2237852078694510.111892603934725
520.9794274969531610.04114500609367870.0205725030468393
530.9952001772766940.009599645446612380.00479982272330619
540.9971415128209240.005716974358152590.00285848717907629
550.9981515398626330.003696920274733620.00184846013736681
560.9978010940565230.004397811886954130.00219890594347706
570.9979349989890990.00413000202180150.00206500101090075
580.9960773413430510.007845317313897680.00392265865694884
590.9972564989407330.005487002118533120.00274350105926656
600.9996894756134050.0006210487731893140.000310524386594657
610.9991494794442320.001701041111536680.000850520555768341
620.998573343750010.002853312499979830.00142665624998991
630.9952140876378350.009571824724329040.00478591236216452
640.9927509229193340.01449815416133280.0072490770806664
650.9909145475418410.01817090491631880.00908545245815941

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.158475561630126 & 0.316951123260251 & 0.841524438369874 \tabularnewline
16 & 0.123993255763459 & 0.247986511526917 & 0.876006744236541 \tabularnewline
17 & 0.187887423181979 & 0.375774846363957 & 0.812112576818021 \tabularnewline
18 & 0.120393022112806 & 0.240786044225612 & 0.879606977887194 \tabularnewline
19 & 0.219515918155738 & 0.439031836311477 & 0.780484081844262 \tabularnewline
20 & 0.184623973380725 & 0.369247946761449 & 0.815376026619275 \tabularnewline
21 & 0.275707866717027 & 0.551415733434053 & 0.724292133282973 \tabularnewline
22 & 0.250432131513488 & 0.500864263026976 & 0.749567868486512 \tabularnewline
23 & 0.183105851001376 & 0.366211702002752 & 0.816894148998624 \tabularnewline
24 & 0.339723827877663 & 0.679447655755325 & 0.660276172122337 \tabularnewline
25 & 0.278289346888232 & 0.556578693776465 & 0.721710653111768 \tabularnewline
26 & 0.274465689883426 & 0.548931379766851 & 0.725534310116574 \tabularnewline
27 & 0.208286391918863 & 0.416572783837726 & 0.791713608081137 \tabularnewline
28 & 0.197896908578411 & 0.395793817156821 & 0.802103091421589 \tabularnewline
29 & 0.144595596877395 & 0.28919119375479 & 0.855404403122605 \tabularnewline
30 & 0.170231825610318 & 0.340463651220635 & 0.829768174389683 \tabularnewline
31 & 0.163399393108131 & 0.326798786216262 & 0.836600606891869 \tabularnewline
32 & 0.127304042069894 & 0.254608084139789 & 0.872695957930105 \tabularnewline
33 & 0.0953491820916604 & 0.190698364183321 & 0.90465081790834 \tabularnewline
34 & 0.0687460649784184 & 0.137492129956837 & 0.931253935021582 \tabularnewline
35 & 0.0482556629361012 & 0.0965113258722024 & 0.951744337063899 \tabularnewline
36 & 0.035270648490367 & 0.0705412969807339 & 0.964729351509633 \tabularnewline
37 & 0.0228767259116007 & 0.0457534518232013 & 0.977123274088399 \tabularnewline
38 & 0.0146748789959744 & 0.0293497579919488 & 0.985325121004026 \tabularnewline
39 & 0.00912117783686759 & 0.0182423556737352 & 0.990878822163132 \tabularnewline
40 & 0.00591262662049926 & 0.0118252532409985 & 0.994087373379501 \tabularnewline
41 & 0.0044293847115638 & 0.0088587694231276 & 0.995570615288436 \tabularnewline
42 & 0.00879554643161455 & 0.0175910928632291 & 0.991204453568385 \tabularnewline
43 & 0.0102695420582898 & 0.0205390841165795 & 0.98973045794171 \tabularnewline
44 & 0.0372246712135852 & 0.0744493424271704 & 0.962775328786415 \tabularnewline
45 & 0.037100425011708 & 0.0742008500234161 & 0.962899574988292 \tabularnewline
46 & 0.0530312209017522 & 0.106062441803504 & 0.946968779098248 \tabularnewline
47 & 0.0672022039200892 & 0.134404407840178 & 0.932797796079911 \tabularnewline
48 & 0.0967837173589576 & 0.193567434717915 & 0.903216282641042 \tabularnewline
49 & 0.406877945540742 & 0.813755891081483 & 0.593122054459258 \tabularnewline
50 & 0.714201260148687 & 0.571597479702626 & 0.285798739851313 \tabularnewline
51 & 0.888107396065275 & 0.223785207869451 & 0.111892603934725 \tabularnewline
52 & 0.979427496953161 & 0.0411450060936787 & 0.0205725030468393 \tabularnewline
53 & 0.995200177276694 & 0.00959964544661238 & 0.00479982272330619 \tabularnewline
54 & 0.997141512820924 & 0.00571697435815259 & 0.00285848717907629 \tabularnewline
55 & 0.998151539862633 & 0.00369692027473362 & 0.00184846013736681 \tabularnewline
56 & 0.997801094056523 & 0.00439781188695413 & 0.00219890594347706 \tabularnewline
57 & 0.997934998989099 & 0.0041300020218015 & 0.00206500101090075 \tabularnewline
58 & 0.996077341343051 & 0.00784531731389768 & 0.00392265865694884 \tabularnewline
59 & 0.997256498940733 & 0.00548700211853312 & 0.00274350105926656 \tabularnewline
60 & 0.999689475613405 & 0.000621048773189314 & 0.000310524386594657 \tabularnewline
61 & 0.999149479444232 & 0.00170104111153668 & 0.000850520555768341 \tabularnewline
62 & 0.99857334375001 & 0.00285331249997983 & 0.00142665624998991 \tabularnewline
63 & 0.995214087637835 & 0.00957182472432904 & 0.00478591236216452 \tabularnewline
64 & 0.992750922919334 & 0.0144981541613328 & 0.0072490770806664 \tabularnewline
65 & 0.990914547541841 & 0.0181709049163188 & 0.00908545245815941 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187145&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]15[/C][C]0.158475561630126[/C][C]0.316951123260251[/C][C]0.841524438369874[/C][/ROW]
[ROW][C]16[/C][C]0.123993255763459[/C][C]0.247986511526917[/C][C]0.876006744236541[/C][/ROW]
[ROW][C]17[/C][C]0.187887423181979[/C][C]0.375774846363957[/C][C]0.812112576818021[/C][/ROW]
[ROW][C]18[/C][C]0.120393022112806[/C][C]0.240786044225612[/C][C]0.879606977887194[/C][/ROW]
[ROW][C]19[/C][C]0.219515918155738[/C][C]0.439031836311477[/C][C]0.780484081844262[/C][/ROW]
[ROW][C]20[/C][C]0.184623973380725[/C][C]0.369247946761449[/C][C]0.815376026619275[/C][/ROW]
[ROW][C]21[/C][C]0.275707866717027[/C][C]0.551415733434053[/C][C]0.724292133282973[/C][/ROW]
[ROW][C]22[/C][C]0.250432131513488[/C][C]0.500864263026976[/C][C]0.749567868486512[/C][/ROW]
[ROW][C]23[/C][C]0.183105851001376[/C][C]0.366211702002752[/C][C]0.816894148998624[/C][/ROW]
[ROW][C]24[/C][C]0.339723827877663[/C][C]0.679447655755325[/C][C]0.660276172122337[/C][/ROW]
[ROW][C]25[/C][C]0.278289346888232[/C][C]0.556578693776465[/C][C]0.721710653111768[/C][/ROW]
[ROW][C]26[/C][C]0.274465689883426[/C][C]0.548931379766851[/C][C]0.725534310116574[/C][/ROW]
[ROW][C]27[/C][C]0.208286391918863[/C][C]0.416572783837726[/C][C]0.791713608081137[/C][/ROW]
[ROW][C]28[/C][C]0.197896908578411[/C][C]0.395793817156821[/C][C]0.802103091421589[/C][/ROW]
[ROW][C]29[/C][C]0.144595596877395[/C][C]0.28919119375479[/C][C]0.855404403122605[/C][/ROW]
[ROW][C]30[/C][C]0.170231825610318[/C][C]0.340463651220635[/C][C]0.829768174389683[/C][/ROW]
[ROW][C]31[/C][C]0.163399393108131[/C][C]0.326798786216262[/C][C]0.836600606891869[/C][/ROW]
[ROW][C]32[/C][C]0.127304042069894[/C][C]0.254608084139789[/C][C]0.872695957930105[/C][/ROW]
[ROW][C]33[/C][C]0.0953491820916604[/C][C]0.190698364183321[/C][C]0.90465081790834[/C][/ROW]
[ROW][C]34[/C][C]0.0687460649784184[/C][C]0.137492129956837[/C][C]0.931253935021582[/C][/ROW]
[ROW][C]35[/C][C]0.0482556629361012[/C][C]0.0965113258722024[/C][C]0.951744337063899[/C][/ROW]
[ROW][C]36[/C][C]0.035270648490367[/C][C]0.0705412969807339[/C][C]0.964729351509633[/C][/ROW]
[ROW][C]37[/C][C]0.0228767259116007[/C][C]0.0457534518232013[/C][C]0.977123274088399[/C][/ROW]
[ROW][C]38[/C][C]0.0146748789959744[/C][C]0.0293497579919488[/C][C]0.985325121004026[/C][/ROW]
[ROW][C]39[/C][C]0.00912117783686759[/C][C]0.0182423556737352[/C][C]0.990878822163132[/C][/ROW]
[ROW][C]40[/C][C]0.00591262662049926[/C][C]0.0118252532409985[/C][C]0.994087373379501[/C][/ROW]
[ROW][C]41[/C][C]0.0044293847115638[/C][C]0.0088587694231276[/C][C]0.995570615288436[/C][/ROW]
[ROW][C]42[/C][C]0.00879554643161455[/C][C]0.0175910928632291[/C][C]0.991204453568385[/C][/ROW]
[ROW][C]43[/C][C]0.0102695420582898[/C][C]0.0205390841165795[/C][C]0.98973045794171[/C][/ROW]
[ROW][C]44[/C][C]0.0372246712135852[/C][C]0.0744493424271704[/C][C]0.962775328786415[/C][/ROW]
[ROW][C]45[/C][C]0.037100425011708[/C][C]0.0742008500234161[/C][C]0.962899574988292[/C][/ROW]
[ROW][C]46[/C][C]0.0530312209017522[/C][C]0.106062441803504[/C][C]0.946968779098248[/C][/ROW]
[ROW][C]47[/C][C]0.0672022039200892[/C][C]0.134404407840178[/C][C]0.932797796079911[/C][/ROW]
[ROW][C]48[/C][C]0.0967837173589576[/C][C]0.193567434717915[/C][C]0.903216282641042[/C][/ROW]
[ROW][C]49[/C][C]0.406877945540742[/C][C]0.813755891081483[/C][C]0.593122054459258[/C][/ROW]
[ROW][C]50[/C][C]0.714201260148687[/C][C]0.571597479702626[/C][C]0.285798739851313[/C][/ROW]
[ROW][C]51[/C][C]0.888107396065275[/C][C]0.223785207869451[/C][C]0.111892603934725[/C][/ROW]
[ROW][C]52[/C][C]0.979427496953161[/C][C]0.0411450060936787[/C][C]0.0205725030468393[/C][/ROW]
[ROW][C]53[/C][C]0.995200177276694[/C][C]0.00959964544661238[/C][C]0.00479982272330619[/C][/ROW]
[ROW][C]54[/C][C]0.997141512820924[/C][C]0.00571697435815259[/C][C]0.00285848717907629[/C][/ROW]
[ROW][C]55[/C][C]0.998151539862633[/C][C]0.00369692027473362[/C][C]0.00184846013736681[/C][/ROW]
[ROW][C]56[/C][C]0.997801094056523[/C][C]0.00439781188695413[/C][C]0.00219890594347706[/C][/ROW]
[ROW][C]57[/C][C]0.997934998989099[/C][C]0.0041300020218015[/C][C]0.00206500101090075[/C][/ROW]
[ROW][C]58[/C][C]0.996077341343051[/C][C]0.00784531731389768[/C][C]0.00392265865694884[/C][/ROW]
[ROW][C]59[/C][C]0.997256498940733[/C][C]0.00548700211853312[/C][C]0.00274350105926656[/C][/ROW]
[ROW][C]60[/C][C]0.999689475613405[/C][C]0.000621048773189314[/C][C]0.000310524386594657[/C][/ROW]
[ROW][C]61[/C][C]0.999149479444232[/C][C]0.00170104111153668[/C][C]0.000850520555768341[/C][/ROW]
[ROW][C]62[/C][C]0.99857334375001[/C][C]0.00285331249997983[/C][C]0.00142665624998991[/C][/ROW]
[ROW][C]63[/C][C]0.995214087637835[/C][C]0.00957182472432904[/C][C]0.00478591236216452[/C][/ROW]
[ROW][C]64[/C][C]0.992750922919334[/C][C]0.0144981541613328[/C][C]0.0072490770806664[/C][/ROW]
[ROW][C]65[/C][C]0.990914547541841[/C][C]0.0181709049163188[/C][C]0.00908545245815941[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187145&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187145&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.1584755616301260.3169511232602510.841524438369874
160.1239932557634590.2479865115269170.876006744236541
170.1878874231819790.3757748463639570.812112576818021
180.1203930221128060.2407860442256120.879606977887194
190.2195159181557380.4390318363114770.780484081844262
200.1846239733807250.3692479467614490.815376026619275
210.2757078667170270.5514157334340530.724292133282973
220.2504321315134880.5008642630269760.749567868486512
230.1831058510013760.3662117020027520.816894148998624
240.3397238278776630.6794476557553250.660276172122337
250.2782893468882320.5565786937764650.721710653111768
260.2744656898834260.5489313797668510.725534310116574
270.2082863919188630.4165727838377260.791713608081137
280.1978969085784110.3957938171568210.802103091421589
290.1445955968773950.289191193754790.855404403122605
300.1702318256103180.3404636512206350.829768174389683
310.1633993931081310.3267987862162620.836600606891869
320.1273040420698940.2546080841397890.872695957930105
330.09534918209166040.1906983641833210.90465081790834
340.06874606497841840.1374921299568370.931253935021582
350.04825566293610120.09651132587220240.951744337063899
360.0352706484903670.07054129698073390.964729351509633
370.02287672591160070.04575345182320130.977123274088399
380.01467487899597440.02934975799194880.985325121004026
390.009121177836867590.01824235567373520.990878822163132
400.005912626620499260.01182525324099850.994087373379501
410.00442938471156380.00885876942312760.995570615288436
420.008795546431614550.01759109286322910.991204453568385
430.01026954205828980.02053908411657950.98973045794171
440.03722467121358520.07444934242717040.962775328786415
450.0371004250117080.07420085002341610.962899574988292
460.05303122090175220.1060624418035040.946968779098248
470.06720220392008920.1344044078401780.932797796079911
480.09678371735895760.1935674347179150.903216282641042
490.4068779455407420.8137558910814830.593122054459258
500.7142012601486870.5715974797026260.285798739851313
510.8881073960652750.2237852078694510.111892603934725
520.9794274969531610.04114500609367870.0205725030468393
530.9952001772766940.009599645446612380.00479982272330619
540.9971415128209240.005716974358152590.00285848717907629
550.9981515398626330.003696920274733620.00184846013736681
560.9978010940565230.004397811886954130.00219890594347706
570.9979349989890990.00413000202180150.00206500101090075
580.9960773413430510.007845317313897680.00392265865694884
590.9972564989407330.005487002118533120.00274350105926656
600.9996894756134050.0006210487731893140.000310524386594657
610.9991494794442320.001701041111536680.000850520555768341
620.998573343750010.002853312499979830.00142665624998991
630.9952140876378350.009571824724329040.00478591236216452
640.9927509229193340.01449815416133280.0072490770806664
650.9909145475418410.01817090491631880.00908545245815941






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.235294117647059NOK
5% type I error level210.411764705882353NOK
10% type I error level250.490196078431373NOK

\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 & 12 & 0.235294117647059 & NOK \tabularnewline
5% type I error level & 21 & 0.411764705882353 & NOK \tabularnewline
10% type I error level & 25 & 0.490196078431373 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187145&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]12[/C][C]0.235294117647059[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.411764705882353[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.490196078431373[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187145&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187145&T=6

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.235294117647059NOK
5% type I error level210.411764705882353NOK
10% type I error level250.490196078431373NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 6
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}