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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 30 Nov 2017 15:46:51 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/01/t1512138293wa9gyfkc6okspz0.htm/, Retrieved Wed, 15 May 2024 15:30:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308427, Retrieved Wed, 15 May 2024 15:30:05 +0000

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Estimated Impact

124

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-       [Multiple Regression] [] [2017-11-30 14:46:51] [8329b9b38c877eb1bcf8703660df8d0b] [Current]

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Dataseries X:

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82	97.7
96.5	88.9
104.8	96.5
87.2	89.5
98.6	85.4
98.7	84.3
75	83.7
86.8	86.2
105	90.7
109.8	95.7
108.2	95.6
99	97
89.6	97.2
97.8	86.6
104.8	88.4
87	81.4
87.9	86.9
93.9	84.9
84.3	83.7
84	86.8
104.3	88.3
104.4	92.5
102.3	94.7
89.4	94.5
78.7	98.7
86.9	88.6
93.7	95.2
87	91.3
83.9	91.7
95.3	89.3
73.7	88.7
76.6	91.2
94.7	88.6
97.7	94.6
90	96
82.4	94.3
77.4	102
85	93.4
90.3	96.7
82.1	93.7
79.6	91.6
86.2	89.6
73.4	92.9
66.7	94.1
96.7	92
98.6	97.5
83.2	92.7
84	100.7
75.8	105.9
83.2	95.3
95.7	99.8
87.3	91.3
83.8	90.8
98.7	87.1
80.8	91.4
74.2	86.1
96.1	87.1
99.4	92.6
91.8	96.6
89.7	105.3
82.9	102.4
90	98.2
98.5	98.6
93.4	92.6
89.1	87.9
103	84.1
74.7	86.7
79	84.4
101.3	86
96.7	90.4
99.1	92.9
92.3	105.8
90.6	106
95.2	99.1
107.6	99.9
97.6	88.1
104	87.8
112	87.1
90.6	85.9
84.9	86.5
112.7	84.1
115.2	92.1
110.1	93.3
95.7	98.9
104.2	103
103.3	98.4
116.1	100.7
106.9	92.3
105.9	89
120.2	88.9
96.2	85.5
91.5	90.1
108.3	87
121.1	97.1
111.4	101.5
95.6	103
98.7	106.1
117.7	96.1
124.5	94.2
114.8	89.1
108	85.2
120.7	86.5
95.6	88
84.3	88.4
122.2	87.9
117.1	95.7
97.2	94.8
99.5	105.2
90.1	108.7
87.3	96.1
97.4	98.3
90.1	88.6
83.6	90.8
97.8	88.1
79.7	91.9
75.1	98.5
106.1	98.6
103.5	100.3
94.5	98.7
100.9	110.7
89.7	115.4
91.4	105.4
110.2	108
102.8	94.5
89.8	96.5
112.8	91
84	94.1
86.5	96.4
107.3	93.1
120.2	97.5
105.5	102.5
99.9	105.7
100.4	109.1
99.6	97.2
118.6	100.3
96	91.3
105.3	94.3
105.8	89.5
80.1	89.3
89.3	93.4
120.4	91.9
111.3	92.9
98.1	93.7
102.9	100.1
95.4	105.5
108.7	110.5
123	89.5
107.7	90.4
97.2	89.9
127.7	84.6
100.6	86.2
89.7	83.4
108.3	82.9
110	81.8
105.2	87.6
87.7	94.6
91.4	99.6
92.8	96.7
97.5	99.8
95.7	83.8
93.5	82.4
97.3	86.8
84.1	91
87.8	85.3
96.2	83.6
94.6	94
88.7	100.3
76.5	107.1
83.9	100.7
88.1	95.5
93	92.9
81.8	79.2
84.1	82
89.1	79.3
75.8	81.5
71.4	76
93.8	73.1
88.5	80.4
78.1	82.1
83.6	90.5
78.2	98.1
76.2	89.5
92	86.5
79.5	77
69.5	74.7
86.4	73.4
72.3	72.5
65	69.3
86	75.2
83.4	83.5
87.2	90.5
76.4	92.2
76.3	110.5
76.9	101.8
92.7	107.4
83.3	95.5
73.8	84.5
94	81.1
73.1	86.2
69.8	91.5
86	84.7
78.8	92.2
89.4	99.2
83.8	104.5
74.1	113
77.2	100.4
103.6	101
78	84.8
80.2	86.5
88.8	91.7
72.9	94.8
73.6	95

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308427&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308427&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308427&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Elect.equipment[t] = + 11.0655 + 0.18125Energy[t] + 0.29539`Elect.equipment(t-1)`[t] + 0.102799`Elect.equipment(t-2)`[t] + 0.323177`Elect.equipment(t-3)`[t] + 0.26232`Elect.equipment(t-4)`[t] -0.00638473`Elect.equipment(t-5)`[t] + 0.0708733`Elect.equipment(t-6)`[t] -0.0111728`Elect.equipment(t-7)`[t] -0.127492`Elect.equipment(t-8)`[t] + 0.0453019`Elect.equipment(t-9)`[t] -0.0570579`Elect.equipment(t-10)`[t] + 0.00518098`Elect.equipment(t-1s)`[t] -0.0500016`Elect.equipment(t-2s)`[t] + 0.00466526`Elect.equipment(t-3s)`[t] -0.0536471`Elect.equipment(t-4s)`[t] -1.9766M1[t] -16.6319M2[t] -21.1729M3[t] -10.795M4[t] + 0.818477M5[t] -10.1066M6[t] -9.91948M7[t] + 0.709909M8[t] -25.4871M9[t] -19.9258M10[t] + 3.60962M11[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Elect.equipment[t] =  +  11.0655 +  0.18125Energy[t] +  0.29539`Elect.equipment(t-1)`[t] +  0.102799`Elect.equipment(t-2)`[t] +  0.323177`Elect.equipment(t-3)`[t] +  0.26232`Elect.equipment(t-4)`[t] -0.00638473`Elect.equipment(t-5)`[t] +  0.0708733`Elect.equipment(t-6)`[t] -0.0111728`Elect.equipment(t-7)`[t] -0.127492`Elect.equipment(t-8)`[t] +  0.0453019`Elect.equipment(t-9)`[t] -0.0570579`Elect.equipment(t-10)`[t] +  0.00518098`Elect.equipment(t-1s)`[t] -0.0500016`Elect.equipment(t-2s)`[t] +  0.00466526`Elect.equipment(t-3s)`[t] -0.0536471`Elect.equipment(t-4s)`[t] -1.9766M1[t] -16.6319M2[t] -21.1729M3[t] -10.795M4[t] +  0.818477M5[t] -10.1066M6[t] -9.91948M7[t] +  0.709909M8[t] -25.4871M9[t] -19.9258M10[t] +  3.60962M11[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308427&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Elect.equipment[t] =  +  11.0655 +  0.18125Energy[t] +  0.29539`Elect.equipment(t-1)`[t] +  0.102799`Elect.equipment(t-2)`[t] +  0.323177`Elect.equipment(t-3)`[t] +  0.26232`Elect.equipment(t-4)`[t] -0.00638473`Elect.equipment(t-5)`[t] +  0.0708733`Elect.equipment(t-6)`[t] -0.0111728`Elect.equipment(t-7)`[t] -0.127492`Elect.equipment(t-8)`[t] +  0.0453019`Elect.equipment(t-9)`[t] -0.0570579`Elect.equipment(t-10)`[t] +  0.00518098`Elect.equipment(t-1s)`[t] -0.0500016`Elect.equipment(t-2s)`[t] +  0.00466526`Elect.equipment(t-3s)`[t] -0.0536471`Elect.equipment(t-4s)`[t] -1.9766M1[t] -16.6319M2[t] -21.1729M3[t] -10.795M4[t] +  0.818477M5[t] -10.1066M6[t] -9.91948M7[t] +  0.709909M8[t] -25.4871M9[t] -19.9258M10[t] +  3.60962M11[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308427&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308427&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Elect.equipment[t] = + 11.0655 + 0.18125Energy[t] + 0.29539`Elect.equipment(t-1)`[t] + 0.102799`Elect.equipment(t-2)`[t] + 0.323177`Elect.equipment(t-3)`[t] + 0.26232`Elect.equipment(t-4)`[t] -0.00638473`Elect.equipment(t-5)`[t] + 0.0708733`Elect.equipment(t-6)`[t] -0.0111728`Elect.equipment(t-7)`[t] -0.127492`Elect.equipment(t-8)`[t] + 0.0453019`Elect.equipment(t-9)`[t] -0.0570579`Elect.equipment(t-10)`[t] + 0.00518098`Elect.equipment(t-1s)`[t] -0.0500016`Elect.equipment(t-2s)`[t] + 0.00466526`Elect.equipment(t-3s)`[t] -0.0536471`Elect.equipment(t-4s)`[t] -1.9766M1[t] -16.6319M2[t] -21.1729M3[t] -10.795M4[t] + 0.818477M5[t] -10.1066M6[t] -9.91948M7[t] + 0.709909M8[t] -25.4871M9[t] -19.9258M10[t] + 3.60962M11[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+11.07	12	+9.2240e-01	0.358	0.179
Energy	+0.1812	0.08411	+2.1550e+00	0.03307	0.01653
`Elect.equipment(t-1)`	+0.2954	0.08814	+3.3510e+00	0.001059	0.0005296
`Elect.equipment(t-2)`	+0.1028	0.09099	+1.1300e+00	0.2607	0.1304
`Elect.equipment(t-3)`	+0.3232	0.09295	+3.4770e+00	0.0006955	0.0003478
`Elect.equipment(t-4)`	+0.2623	0.09776	+2.6830e+00	0.008261	0.00413
`Elect.equipment(t-5)`	-0.006385	0.1008	-6.3370e-02	0.9496	0.4748
`Elect.equipment(t-6)`	+0.07087	0.1008	+7.0330e-01	0.4832	0.2416
`Elect.equipment(t-7)`	-0.01117	0.09912	-1.1270e-01	0.9104	0.4552
`Elect.equipment(t-8)`	-0.1275	0.0986	-1.2930e+00	0.1983	0.09916
`Elect.equipment(t-9)`	+0.0453	0.1006	+4.5050e-01	0.6531	0.3265
`Elect.equipment(t-10)`	-0.05706	0.09373	-6.0870e-01	0.5438	0.2719
`Elect.equipment(t-1s)`	+0.005181	0.08973	+5.7740e-02	0.954	0.477
`Elect.equipment(t-2s)`	-0.05	0.0614	-8.1440e-01	0.417	0.2085
`Elect.equipment(t-3s)`	+0.004665	0.06331	+7.3690e-02	0.9414	0.4707
`Elect.equipment(t-4s)`	-0.05365	0.06167	-8.7000e-01	0.386	0.193
M1	-1.977	4.362	-4.5310e-01	0.6513	0.3256
M2	-16.63	5.202	-3.1970e+00	0.001753	0.0008765
M3	-21.17	4.871	-4.3470e+00	2.807e-05	1.403e-05
M4	-10.79	4.916	-2.1960e+00	0.02991	0.01496
M5	+0.8185	4.106	+1.9930e-01	0.8423	0.4212
M6	-10.11	3.689	-2.7400e+00	0.007037	0.003518
M7	-9.919	4.373	-2.2680e+00	0.02501	0.0125
M8	+0.7099	4.39	+1.6170e-01	0.8718	0.4359
M9	-25.49	4.568	-5.5790e+00	1.393e-07	6.966e-08
M10	-19.93	5.482	-3.6340e+00	0.0004032	0.0002016
M11	+3.61	4.27	+8.4530e-01	0.3995	0.1998

\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) & +11.07 &  12 & +9.2240e-01 &  0.358 &  0.179 \tabularnewline
Energy & +0.1812 &  0.08411 & +2.1550e+00 &  0.03307 &  0.01653 \tabularnewline
`Elect.equipment(t-1)` & +0.2954 &  0.08814 & +3.3510e+00 &  0.001059 &  0.0005296 \tabularnewline
`Elect.equipment(t-2)` & +0.1028 &  0.09099 & +1.1300e+00 &  0.2607 &  0.1304 \tabularnewline
`Elect.equipment(t-3)` & +0.3232 &  0.09295 & +3.4770e+00 &  0.0006955 &  0.0003478 \tabularnewline
`Elect.equipment(t-4)` & +0.2623 &  0.09776 & +2.6830e+00 &  0.008261 &  0.00413 \tabularnewline
`Elect.equipment(t-5)` & -0.006385 &  0.1008 & -6.3370e-02 &  0.9496 &  0.4748 \tabularnewline
`Elect.equipment(t-6)` & +0.07087 &  0.1008 & +7.0330e-01 &  0.4832 &  0.2416 \tabularnewline
`Elect.equipment(t-7)` & -0.01117 &  0.09912 & -1.1270e-01 &  0.9104 &  0.4552 \tabularnewline
`Elect.equipment(t-8)` & -0.1275 &  0.0986 & -1.2930e+00 &  0.1983 &  0.09916 \tabularnewline
`Elect.equipment(t-9)` & +0.0453 &  0.1006 & +4.5050e-01 &  0.6531 &  0.3265 \tabularnewline
`Elect.equipment(t-10)` & -0.05706 &  0.09373 & -6.0870e-01 &  0.5438 &  0.2719 \tabularnewline
`Elect.equipment(t-1s)` & +0.005181 &  0.08973 & +5.7740e-02 &  0.954 &  0.477 \tabularnewline
`Elect.equipment(t-2s)` & -0.05 &  0.0614 & -8.1440e-01 &  0.417 &  0.2085 \tabularnewline
`Elect.equipment(t-3s)` & +0.004665 &  0.06331 & +7.3690e-02 &  0.9414 &  0.4707 \tabularnewline
`Elect.equipment(t-4s)` & -0.05365 &  0.06167 & -8.7000e-01 &  0.386 &  0.193 \tabularnewline
M1 & -1.977 &  4.362 & -4.5310e-01 &  0.6513 &  0.3256 \tabularnewline
M2 & -16.63 &  5.202 & -3.1970e+00 &  0.001753 &  0.0008765 \tabularnewline
M3 & -21.17 &  4.871 & -4.3470e+00 &  2.807e-05 &  1.403e-05 \tabularnewline
M4 & -10.79 &  4.916 & -2.1960e+00 &  0.02991 &  0.01496 \tabularnewline
M5 & +0.8185 &  4.106 & +1.9930e-01 &  0.8423 &  0.4212 \tabularnewline
M6 & -10.11 &  3.689 & -2.7400e+00 &  0.007037 &  0.003518 \tabularnewline
M7 & -9.919 &  4.373 & -2.2680e+00 &  0.02501 &  0.0125 \tabularnewline
M8 & +0.7099 &  4.39 & +1.6170e-01 &  0.8718 &  0.4359 \tabularnewline
M9 & -25.49 &  4.568 & -5.5790e+00 &  1.393e-07 &  6.966e-08 \tabularnewline
M10 & -19.93 &  5.482 & -3.6340e+00 &  0.0004032 &  0.0002016 \tabularnewline
M11 & +3.61 &  4.27 & +8.4530e-01 &  0.3995 &  0.1998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308427&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]+11.07[/C][C] 12[/C][C]+9.2240e-01[/C][C] 0.358[/C][C] 0.179[/C][/ROW]
[ROW][C]Energy[/C][C]+0.1812[/C][C] 0.08411[/C][C]+2.1550e+00[/C][C] 0.03307[/C][C] 0.01653[/C][/ROW]
[ROW][C]`Elect.equipment(t-1)`[/C][C]+0.2954[/C][C] 0.08814[/C][C]+3.3510e+00[/C][C] 0.001059[/C][C] 0.0005296[/C][/ROW]
[ROW][C]`Elect.equipment(t-2)`[/C][C]+0.1028[/C][C] 0.09099[/C][C]+1.1300e+00[/C][C] 0.2607[/C][C] 0.1304[/C][/ROW]
[ROW][C]`Elect.equipment(t-3)`[/C][C]+0.3232[/C][C] 0.09295[/C][C]+3.4770e+00[/C][C] 0.0006955[/C][C] 0.0003478[/C][/ROW]
[ROW][C]`Elect.equipment(t-4)`[/C][C]+0.2623[/C][C] 0.09776[/C][C]+2.6830e+00[/C][C] 0.008261[/C][C] 0.00413[/C][/ROW]
[ROW][C]`Elect.equipment(t-5)`[/C][C]-0.006385[/C][C] 0.1008[/C][C]-6.3370e-02[/C][C] 0.9496[/C][C] 0.4748[/C][/ROW]
[ROW][C]`Elect.equipment(t-6)`[/C][C]+0.07087[/C][C] 0.1008[/C][C]+7.0330e-01[/C][C] 0.4832[/C][C] 0.2416[/C][/ROW]
[ROW][C]`Elect.equipment(t-7)`[/C][C]-0.01117[/C][C] 0.09912[/C][C]-1.1270e-01[/C][C] 0.9104[/C][C] 0.4552[/C][/ROW]
[ROW][C]`Elect.equipment(t-8)`[/C][C]-0.1275[/C][C] 0.0986[/C][C]-1.2930e+00[/C][C] 0.1983[/C][C] 0.09916[/C][/ROW]
[ROW][C]`Elect.equipment(t-9)`[/C][C]+0.0453[/C][C] 0.1006[/C][C]+4.5050e-01[/C][C] 0.6531[/C][C] 0.3265[/C][/ROW]
[ROW][C]`Elect.equipment(t-10)`[/C][C]-0.05706[/C][C] 0.09373[/C][C]-6.0870e-01[/C][C] 0.5438[/C][C] 0.2719[/C][/ROW]
[ROW][C]`Elect.equipment(t-1s)`[/C][C]+0.005181[/C][C] 0.08973[/C][C]+5.7740e-02[/C][C] 0.954[/C][C] 0.477[/C][/ROW]
[ROW][C]`Elect.equipment(t-2s)`[/C][C]-0.05[/C][C] 0.0614[/C][C]-8.1440e-01[/C][C] 0.417[/C][C] 0.2085[/C][/ROW]
[ROW][C]`Elect.equipment(t-3s)`[/C][C]+0.004665[/C][C] 0.06331[/C][C]+7.3690e-02[/C][C] 0.9414[/C][C] 0.4707[/C][/ROW]
[ROW][C]`Elect.equipment(t-4s)`[/C][C]-0.05365[/C][C] 0.06167[/C][C]-8.7000e-01[/C][C] 0.386[/C][C] 0.193[/C][/ROW]
[ROW][C]M1[/C][C]-1.977[/C][C] 4.362[/C][C]-4.5310e-01[/C][C] 0.6513[/C][C] 0.3256[/C][/ROW]
[ROW][C]M2[/C][C]-16.63[/C][C] 5.202[/C][C]-3.1970e+00[/C][C] 0.001753[/C][C] 0.0008765[/C][/ROW]
[ROW][C]M3[/C][C]-21.17[/C][C] 4.871[/C][C]-4.3470e+00[/C][C] 2.807e-05[/C][C] 1.403e-05[/C][/ROW]
[ROW][C]M4[/C][C]-10.79[/C][C] 4.916[/C][C]-2.1960e+00[/C][C] 0.02991[/C][C] 0.01496[/C][/ROW]
[ROW][C]M5[/C][C]+0.8185[/C][C] 4.106[/C][C]+1.9930e-01[/C][C] 0.8423[/C][C] 0.4212[/C][/ROW]
[ROW][C]M6[/C][C]-10.11[/C][C] 3.689[/C][C]-2.7400e+00[/C][C] 0.007037[/C][C] 0.003518[/C][/ROW]
[ROW][C]M7[/C][C]-9.919[/C][C] 4.373[/C][C]-2.2680e+00[/C][C] 0.02501[/C][C] 0.0125[/C][/ROW]
[ROW][C]M8[/C][C]+0.7099[/C][C] 4.39[/C][C]+1.6170e-01[/C][C] 0.8718[/C][C] 0.4359[/C][/ROW]
[ROW][C]M9[/C][C]-25.49[/C][C] 4.568[/C][C]-5.5790e+00[/C][C] 1.393e-07[/C][C] 6.966e-08[/C][/ROW]
[ROW][C]M10[/C][C]-19.93[/C][C] 5.482[/C][C]-3.6340e+00[/C][C] 0.0004032[/C][C] 0.0002016[/C][/ROW]
[ROW][C]M11[/C][C]+3.61[/C][C] 4.27[/C][C]+8.4530e-01[/C][C] 0.3995[/C][C] 0.1998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308427&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308427&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+11.07	12	+9.2240e-01	0.358	0.179
Energy	+0.1812	0.08411	+2.1550e+00	0.03307	0.01653
`Elect.equipment(t-1)`	+0.2954	0.08814	+3.3510e+00	0.001059	0.0005296
`Elect.equipment(t-2)`	+0.1028	0.09099	+1.1300e+00	0.2607	0.1304
`Elect.equipment(t-3)`	+0.3232	0.09295	+3.4770e+00	0.0006955	0.0003478
`Elect.equipment(t-4)`	+0.2623	0.09776	+2.6830e+00	0.008261	0.00413
`Elect.equipment(t-5)`	-0.006385	0.1008	-6.3370e-02	0.9496	0.4748
`Elect.equipment(t-6)`	+0.07087	0.1008	+7.0330e-01	0.4832	0.2416
`Elect.equipment(t-7)`	-0.01117	0.09912	-1.1270e-01	0.9104	0.4552
`Elect.equipment(t-8)`	-0.1275	0.0986	-1.2930e+00	0.1983	0.09916
`Elect.equipment(t-9)`	+0.0453	0.1006	+4.5050e-01	0.6531	0.3265
`Elect.equipment(t-10)`	-0.05706	0.09373	-6.0870e-01	0.5438	0.2719
`Elect.equipment(t-1s)`	+0.005181	0.08973	+5.7740e-02	0.954	0.477
`Elect.equipment(t-2s)`	-0.05	0.0614	-8.1440e-01	0.417	0.2085
`Elect.equipment(t-3s)`	+0.004665	0.06331	+7.3690e-02	0.9414	0.4707
`Elect.equipment(t-4s)`	-0.05365	0.06167	-8.7000e-01	0.386	0.193
M1	-1.977	4.362	-4.5310e-01	0.6513	0.3256
M2	-16.63	5.202	-3.1970e+00	0.001753	0.0008765
M3	-21.17	4.871	-4.3470e+00	2.807e-05	1.403e-05
M4	-10.79	4.916	-2.1960e+00	0.02991	0.01496
M5	+0.8185	4.106	+1.9930e-01	0.8423	0.4212
M6	-10.11	3.689	-2.7400e+00	0.007037	0.003518
M7	-9.919	4.373	-2.2680e+00	0.02501	0.0125
M8	+0.7099	4.39	+1.6170e-01	0.8718	0.4359
M9	-25.49	4.568	-5.5790e+00	1.393e-07	6.966e-08
M10	-19.93	5.482	-3.6340e+00	0.0004032	0.0002016
M11	+3.61	4.27	+8.4530e-01	0.3995	0.1998

Multiple Linear Regression - Regression Statistics
Multiple R	0.9232
R-squared	0.8523
Adjusted R-squared	0.8221
F-TEST (value)	28.2
F-TEST (DF numerator)	26
F-TEST (DF denominator)	127
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.746
Sum Squared Residuals	4193

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9232 \tabularnewline
R-squared &  0.8523 \tabularnewline
Adjusted R-squared &  0.8221 \tabularnewline
F-TEST (value) &  28.2 \tabularnewline
F-TEST (DF numerator) & 26 \tabularnewline
F-TEST (DF denominator) & 127 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  5.746 \tabularnewline
Sum Squared Residuals &  4193 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308427&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9232[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.8523[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.8221[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 28.2[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]26[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]127[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 5.746[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 4193[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308427&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308427&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.9232
R-squared	0.8523
Adjusted R-squared	0.8221
F-TEST (value)	28.2
F-TEST (DF numerator)	26
F-TEST (DF denominator)	127
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.746
Sum Squared Residuals	4193

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308427&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308427&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308427&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	91.8	93.19	-1.394
2	89.7	86.8	2.902
3	82.9	85.76	-2.863
4	90	88.83	1.166
5	98.5	103.5	-4.964
6	93.4	92.45	0.9484
7	89.1	89.08	0.02272
8	103	102.2	0.7611
9	74.7	81.09	-6.385
10	79	77.16	1.838
11	101.3	102.1	-0.7824
12	96.7	99.21	-2.508
13	99.1	92.68	6.419
14	92.3	91.38	0.9154
15	90.6	88.05	2.55
16	95.2	93.61	1.59
17	107.6	110.1	-2.509
18	97.6	96.35	1.252
19	104	95.38	8.619
20	112	111.6	0.4401
21	90.6	88.17	2.426
22	84.9	89.46	-4.56
23	112.7	111.8	0.9066
24	115.2	110.9	4.262
25	110.1	105.3	4.808
26	95.7	100.6	-4.878
27	104.2	97.12	7.082
28	103.3	105.5	-2.194
29	116.1	116.3	-0.1977
30	106.9	106.3	0.588
31	105.9	103.9	1.975
32	120.2	116.5	3.694
33	96.2	96.05	0.1481
34	91.5	95.4	-3.897
35	108.3	115.9	-7.627
36	121.1	115.3	5.789
37	111.4	110.3	1.147
38	95.6	101.7	-6.111
39	98.7	98.3	0.4017
40	117.7	104.3	13.44
41	124.5	117.6	6.945
42	114.8	106.9	7.919
43	108	109.3	-1.276
44	120.7	121.6	-0.9226
45	95.6	100.3	-4.706
46	84.3	97.84	-13.54
47	122.2	115.1	7.116
48	117.1	115.8	1.316
49	97.2	105.8	-8.584
50	99.5	98.21	1.289
51	90.1	99.37	-9.271
52	87.3	94.8	-7.503
53	97.4	106.6	-9.151
54	90.1	93.69	-3.586
55	83.6	84.78	-1.179
56	97.8	96.88	0.9224
57	79.7	77.14	2.556
58	75.1	74.86	0.2434
59	106.1	99.6	6.504
60	103.5	101.7	1.843
61	94.5	94.46	0.04353
62	100.9	91.7	9.196
63	89.7	94.71	-5.009
64	91.4	93.81	-2.408
65	110.2	109.6	0.5557
66	102.8	99.48	3.316
67	89.8	93.57	-3.772
68	112.8	106.8	5.974
69	84	89.77	-5.77
70	86.5	83.34	3.162
71	107.3	108.5	-1.221
72	120.2	107.3	12.94
73	105.5	103.9	1.596
74	99.9	98.46	1.435
75	100.4	98.91	1.494
76	99.6	102.9	-3.285
77	118.6	115	3.634
78	96	105.2	-9.248
79	105.3	99.36	5.937
80	105.8	113.1	-7.297
81	80.1	88.95	-8.849
82	89.3	84.36	4.939
83	120.4	110.2	10.24
84	111.3	106.7	4.558
85	98.1	100.9	-2.774
86	102.9	99.4	3.499
87	95.4	96.29	-0.8899
88	108.7	100.5	8.235
89	123	113.3	9.713
90	107.7	104.4	3.283
91	97.2	101.9	-4.682
92	127.7	115.8	11.9
93	100.6	98.03	2.572
94	89.7	91.56	-1.857
95	108.3	116.2	-7.921
96	110	112.1	-2.111
97	105.2	103.1	2.1
98	87.7	96.13	-8.425
99	91.4	90.28	1.118
100	92.8	94.51	-1.71
101	97.5	106.1	-8.6
102	95.7	90.19	5.515
103	93.5	89.7	3.805
104	97.3	101.7	-4.39
105	84.1	79.86	4.241
106	87.8	81.38	6.418
107	96.2	101.3	-5.11
108	94.6	100.7	-6.063
109	88.7	98.09	-9.395
110	76.5	86.49	-9.99
111	83.9	78.19	5.708
112	88.1	85.35	2.745
113	93	93.94	-0.942
114	81.8	81.2	0.6047
115	84.1	83.17	0.9343
116	89.1	92.31	-3.21
117	75.8	69.44	6.363
118	71.4	70.43	0.9722
119	93.8	90.34	3.455
120	88.5	90.04	-1.541
121	78.1	84.53	-6.435
122	83.6	76.83	6.771
123	78.2	76.12	2.075
124	76.2	79.02	-2.819
125	92	90.29	1.708
126	79.5	82.09	-2.59
127	69.5	74.29	-4.794
128	86.4	87.46	-1.064
129	72.3	66.39	5.907
130	65	60.63	4.369
131	86	85.34	0.6639
132	83.4	87.81	-4.407
133	87.2	80.88	6.315
134	76.4	76.39	0.0131
135	76.3	75.72	0.581
136	76.9	80.61	-3.711
137	92.7	94.72	-2.024
138	83.3	83.7	-0.3981
139	73.8	79.46	-5.657
140	94	90.07	3.928
141	73.1	71.19	1.91
142	69.8	70.5	-0.7032
143	86	92.23	-6.229
144	78.8	92.88	-14.08
145	89.4	83.25	6.152
146	83.8	80.42	3.384
147	74.1	77.08	-2.977
148	77.2	80.74	-3.542
149	103.6	97.77	5.831
150	78	85.6	-7.604
151	80.2	80.13	0.06716
152	88.8	99.53	-10.73
153	72.9	73.31	-0.4133
154	73.6	70.99	2.613

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  91.8 &  93.19 & -1.394 \tabularnewline
2 &  89.7 &  86.8 &  2.902 \tabularnewline
3 &  82.9 &  85.76 & -2.863 \tabularnewline
4 &  90 &  88.83 &  1.166 \tabularnewline
5 &  98.5 &  103.5 & -4.964 \tabularnewline
6 &  93.4 &  92.45 &  0.9484 \tabularnewline
7 &  89.1 &  89.08 &  0.02272 \tabularnewline
8 &  103 &  102.2 &  0.7611 \tabularnewline
9 &  74.7 &  81.09 & -6.385 \tabularnewline
10 &  79 &  77.16 &  1.838 \tabularnewline
11 &  101.3 &  102.1 & -0.7824 \tabularnewline
12 &  96.7 &  99.21 & -2.508 \tabularnewline
13 &  99.1 &  92.68 &  6.419 \tabularnewline
14 &  92.3 &  91.38 &  0.9154 \tabularnewline
15 &  90.6 &  88.05 &  2.55 \tabularnewline
16 &  95.2 &  93.61 &  1.59 \tabularnewline
17 &  107.6 &  110.1 & -2.509 \tabularnewline
18 &  97.6 &  96.35 &  1.252 \tabularnewline
19 &  104 &  95.38 &  8.619 \tabularnewline
20 &  112 &  111.6 &  0.4401 \tabularnewline
21 &  90.6 &  88.17 &  2.426 \tabularnewline
22 &  84.9 &  89.46 & -4.56 \tabularnewline
23 &  112.7 &  111.8 &  0.9066 \tabularnewline
24 &  115.2 &  110.9 &  4.262 \tabularnewline
25 &  110.1 &  105.3 &  4.808 \tabularnewline
26 &  95.7 &  100.6 & -4.878 \tabularnewline
27 &  104.2 &  97.12 &  7.082 \tabularnewline
28 &  103.3 &  105.5 & -2.194 \tabularnewline
29 &  116.1 &  116.3 & -0.1977 \tabularnewline
30 &  106.9 &  106.3 &  0.588 \tabularnewline
31 &  105.9 &  103.9 &  1.975 \tabularnewline
32 &  120.2 &  116.5 &  3.694 \tabularnewline
33 &  96.2 &  96.05 &  0.1481 \tabularnewline
34 &  91.5 &  95.4 & -3.897 \tabularnewline
35 &  108.3 &  115.9 & -7.627 \tabularnewline
36 &  121.1 &  115.3 &  5.789 \tabularnewline
37 &  111.4 &  110.3 &  1.147 \tabularnewline
38 &  95.6 &  101.7 & -6.111 \tabularnewline
39 &  98.7 &  98.3 &  0.4017 \tabularnewline
40 &  117.7 &  104.3 &  13.44 \tabularnewline
41 &  124.5 &  117.6 &  6.945 \tabularnewline
42 &  114.8 &  106.9 &  7.919 \tabularnewline
43 &  108 &  109.3 & -1.276 \tabularnewline
44 &  120.7 &  121.6 & -0.9226 \tabularnewline
45 &  95.6 &  100.3 & -4.706 \tabularnewline
46 &  84.3 &  97.84 & -13.54 \tabularnewline
47 &  122.2 &  115.1 &  7.116 \tabularnewline
48 &  117.1 &  115.8 &  1.316 \tabularnewline
49 &  97.2 &  105.8 & -8.584 \tabularnewline
50 &  99.5 &  98.21 &  1.289 \tabularnewline
51 &  90.1 &  99.37 & -9.271 \tabularnewline
52 &  87.3 &  94.8 & -7.503 \tabularnewline
53 &  97.4 &  106.6 & -9.151 \tabularnewline
54 &  90.1 &  93.69 & -3.586 \tabularnewline
55 &  83.6 &  84.78 & -1.179 \tabularnewline
56 &  97.8 &  96.88 &  0.9224 \tabularnewline
57 &  79.7 &  77.14 &  2.556 \tabularnewline
58 &  75.1 &  74.86 &  0.2434 \tabularnewline
59 &  106.1 &  99.6 &  6.504 \tabularnewline
60 &  103.5 &  101.7 &  1.843 \tabularnewline
61 &  94.5 &  94.46 &  0.04353 \tabularnewline
62 &  100.9 &  91.7 &  9.196 \tabularnewline
63 &  89.7 &  94.71 & -5.009 \tabularnewline
64 &  91.4 &  93.81 & -2.408 \tabularnewline
65 &  110.2 &  109.6 &  0.5557 \tabularnewline
66 &  102.8 &  99.48 &  3.316 \tabularnewline
67 &  89.8 &  93.57 & -3.772 \tabularnewline
68 &  112.8 &  106.8 &  5.974 \tabularnewline
69 &  84 &  89.77 & -5.77 \tabularnewline
70 &  86.5 &  83.34 &  3.162 \tabularnewline
71 &  107.3 &  108.5 & -1.221 \tabularnewline
72 &  120.2 &  107.3 &  12.94 \tabularnewline
73 &  105.5 &  103.9 &  1.596 \tabularnewline
74 &  99.9 &  98.46 &  1.435 \tabularnewline
75 &  100.4 &  98.91 &  1.494 \tabularnewline
76 &  99.6 &  102.9 & -3.285 \tabularnewline
77 &  118.6 &  115 &  3.634 \tabularnewline
78 &  96 &  105.2 & -9.248 \tabularnewline
79 &  105.3 &  99.36 &  5.937 \tabularnewline
80 &  105.8 &  113.1 & -7.297 \tabularnewline
81 &  80.1 &  88.95 & -8.849 \tabularnewline
82 &  89.3 &  84.36 &  4.939 \tabularnewline
83 &  120.4 &  110.2 &  10.24 \tabularnewline
84 &  111.3 &  106.7 &  4.558 \tabularnewline
85 &  98.1 &  100.9 & -2.774 \tabularnewline
86 &  102.9 &  99.4 &  3.499 \tabularnewline
87 &  95.4 &  96.29 & -0.8899 \tabularnewline
88 &  108.7 &  100.5 &  8.235 \tabularnewline
89 &  123 &  113.3 &  9.713 \tabularnewline
90 &  107.7 &  104.4 &  3.283 \tabularnewline
91 &  97.2 &  101.9 & -4.682 \tabularnewline
92 &  127.7 &  115.8 &  11.9 \tabularnewline
93 &  100.6 &  98.03 &  2.572 \tabularnewline
94 &  89.7 &  91.56 & -1.857 \tabularnewline
95 &  108.3 &  116.2 & -7.921 \tabularnewline
96 &  110 &  112.1 & -2.111 \tabularnewline
97 &  105.2 &  103.1 &  2.1 \tabularnewline
98 &  87.7 &  96.13 & -8.425 \tabularnewline
99 &  91.4 &  90.28 &  1.118 \tabularnewline
100 &  92.8 &  94.51 & -1.71 \tabularnewline
101 &  97.5 &  106.1 & -8.6 \tabularnewline
102 &  95.7 &  90.19 &  5.515 \tabularnewline
103 &  93.5 &  89.7 &  3.805 \tabularnewline
104 &  97.3 &  101.7 & -4.39 \tabularnewline
105 &  84.1 &  79.86 &  4.241 \tabularnewline
106 &  87.8 &  81.38 &  6.418 \tabularnewline
107 &  96.2 &  101.3 & -5.11 \tabularnewline
108 &  94.6 &  100.7 & -6.063 \tabularnewline
109 &  88.7 &  98.09 & -9.395 \tabularnewline
110 &  76.5 &  86.49 & -9.99 \tabularnewline
111 &  83.9 &  78.19 &  5.708 \tabularnewline
112 &  88.1 &  85.35 &  2.745 \tabularnewline
113 &  93 &  93.94 & -0.942 \tabularnewline
114 &  81.8 &  81.2 &  0.6047 \tabularnewline
115 &  84.1 &  83.17 &  0.9343 \tabularnewline
116 &  89.1 &  92.31 & -3.21 \tabularnewline
117 &  75.8 &  69.44 &  6.363 \tabularnewline
118 &  71.4 &  70.43 &  0.9722 \tabularnewline
119 &  93.8 &  90.34 &  3.455 \tabularnewline
120 &  88.5 &  90.04 & -1.541 \tabularnewline
121 &  78.1 &  84.53 & -6.435 \tabularnewline
122 &  83.6 &  76.83 &  6.771 \tabularnewline
123 &  78.2 &  76.12 &  2.075 \tabularnewline
124 &  76.2 &  79.02 & -2.819 \tabularnewline
125 &  92 &  90.29 &  1.708 \tabularnewline
126 &  79.5 &  82.09 & -2.59 \tabularnewline
127 &  69.5 &  74.29 & -4.794 \tabularnewline
128 &  86.4 &  87.46 & -1.064 \tabularnewline
129 &  72.3 &  66.39 &  5.907 \tabularnewline
130 &  65 &  60.63 &  4.369 \tabularnewline
131 &  86 &  85.34 &  0.6639 \tabularnewline
132 &  83.4 &  87.81 & -4.407 \tabularnewline
133 &  87.2 &  80.88 &  6.315 \tabularnewline
134 &  76.4 &  76.39 &  0.0131 \tabularnewline
135 &  76.3 &  75.72 &  0.581 \tabularnewline
136 &  76.9 &  80.61 & -3.711 \tabularnewline
137 &  92.7 &  94.72 & -2.024 \tabularnewline
138 &  83.3 &  83.7 & -0.3981 \tabularnewline
139 &  73.8 &  79.46 & -5.657 \tabularnewline
140 &  94 &  90.07 &  3.928 \tabularnewline
141 &  73.1 &  71.19 &  1.91 \tabularnewline
142 &  69.8 &  70.5 & -0.7032 \tabularnewline
143 &  86 &  92.23 & -6.229 \tabularnewline
144 &  78.8 &  92.88 & -14.08 \tabularnewline
145 &  89.4 &  83.25 &  6.152 \tabularnewline
146 &  83.8 &  80.42 &  3.384 \tabularnewline
147 &  74.1 &  77.08 & -2.977 \tabularnewline
148 &  77.2 &  80.74 & -3.542 \tabularnewline
149 &  103.6 &  97.77 &  5.831 \tabularnewline
150 &  78 &  85.6 & -7.604 \tabularnewline
151 &  80.2 &  80.13 &  0.06716 \tabularnewline
152 &  88.8 &  99.53 & -10.73 \tabularnewline
153 &  72.9 &  73.31 & -0.4133 \tabularnewline
154 &  73.6 &  70.99 &  2.613 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308427&T=5

[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] 91.8[/C][C] 93.19[/C][C]-1.394[/C][/ROW]
[ROW][C]2[/C][C] 89.7[/C][C] 86.8[/C][C] 2.902[/C][/ROW]
[ROW][C]3[/C][C] 82.9[/C][C] 85.76[/C][C]-2.863[/C][/ROW]
[ROW][C]4[/C][C] 90[/C][C] 88.83[/C][C] 1.166[/C][/ROW]
[ROW][C]5[/C][C] 98.5[/C][C] 103.5[/C][C]-4.964[/C][/ROW]
[ROW][C]6[/C][C] 93.4[/C][C] 92.45[/C][C] 0.9484[/C][/ROW]
[ROW][C]7[/C][C] 89.1[/C][C] 89.08[/C][C] 0.02272[/C][/ROW]
[ROW][C]8[/C][C] 103[/C][C] 102.2[/C][C] 0.7611[/C][/ROW]
[ROW][C]9[/C][C] 74.7[/C][C] 81.09[/C][C]-6.385[/C][/ROW]
[ROW][C]10[/C][C] 79[/C][C] 77.16[/C][C] 1.838[/C][/ROW]
[ROW][C]11[/C][C] 101.3[/C][C] 102.1[/C][C]-0.7824[/C][/ROW]
[ROW][C]12[/C][C] 96.7[/C][C] 99.21[/C][C]-2.508[/C][/ROW]
[ROW][C]13[/C][C] 99.1[/C][C] 92.68[/C][C] 6.419[/C][/ROW]
[ROW][C]14[/C][C] 92.3[/C][C] 91.38[/C][C] 0.9154[/C][/ROW]
[ROW][C]15[/C][C] 90.6[/C][C] 88.05[/C][C] 2.55[/C][/ROW]
[ROW][C]16[/C][C] 95.2[/C][C] 93.61[/C][C] 1.59[/C][/ROW]
[ROW][C]17[/C][C] 107.6[/C][C] 110.1[/C][C]-2.509[/C][/ROW]
[ROW][C]18[/C][C] 97.6[/C][C] 96.35[/C][C] 1.252[/C][/ROW]
[ROW][C]19[/C][C] 104[/C][C] 95.38[/C][C] 8.619[/C][/ROW]
[ROW][C]20[/C][C] 112[/C][C] 111.6[/C][C] 0.4401[/C][/ROW]
[ROW][C]21[/C][C] 90.6[/C][C] 88.17[/C][C] 2.426[/C][/ROW]
[ROW][C]22[/C][C] 84.9[/C][C] 89.46[/C][C]-4.56[/C][/ROW]
[ROW][C]23[/C][C] 112.7[/C][C] 111.8[/C][C] 0.9066[/C][/ROW]
[ROW][C]24[/C][C] 115.2[/C][C] 110.9[/C][C] 4.262[/C][/ROW]
[ROW][C]25[/C][C] 110.1[/C][C] 105.3[/C][C] 4.808[/C][/ROW]
[ROW][C]26[/C][C] 95.7[/C][C] 100.6[/C][C]-4.878[/C][/ROW]
[ROW][C]27[/C][C] 104.2[/C][C] 97.12[/C][C] 7.082[/C][/ROW]
[ROW][C]28[/C][C] 103.3[/C][C] 105.5[/C][C]-2.194[/C][/ROW]
[ROW][C]29[/C][C] 116.1[/C][C] 116.3[/C][C]-0.1977[/C][/ROW]
[ROW][C]30[/C][C] 106.9[/C][C] 106.3[/C][C] 0.588[/C][/ROW]
[ROW][C]31[/C][C] 105.9[/C][C] 103.9[/C][C] 1.975[/C][/ROW]
[ROW][C]32[/C][C] 120.2[/C][C] 116.5[/C][C] 3.694[/C][/ROW]
[ROW][C]33[/C][C] 96.2[/C][C] 96.05[/C][C] 0.1481[/C][/ROW]
[ROW][C]34[/C][C] 91.5[/C][C] 95.4[/C][C]-3.897[/C][/ROW]
[ROW][C]35[/C][C] 108.3[/C][C] 115.9[/C][C]-7.627[/C][/ROW]
[ROW][C]36[/C][C] 121.1[/C][C] 115.3[/C][C] 5.789[/C][/ROW]
[ROW][C]37[/C][C] 111.4[/C][C] 110.3[/C][C] 1.147[/C][/ROW]
[ROW][C]38[/C][C] 95.6[/C][C] 101.7[/C][C]-6.111[/C][/ROW]
[ROW][C]39[/C][C] 98.7[/C][C] 98.3[/C][C] 0.4017[/C][/ROW]
[ROW][C]40[/C][C] 117.7[/C][C] 104.3[/C][C] 13.44[/C][/ROW]
[ROW][C]41[/C][C] 124.5[/C][C] 117.6[/C][C] 6.945[/C][/ROW]
[ROW][C]42[/C][C] 114.8[/C][C] 106.9[/C][C] 7.919[/C][/ROW]
[ROW][C]43[/C][C] 108[/C][C] 109.3[/C][C]-1.276[/C][/ROW]
[ROW][C]44[/C][C] 120.7[/C][C] 121.6[/C][C]-0.9226[/C][/ROW]
[ROW][C]45[/C][C] 95.6[/C][C] 100.3[/C][C]-4.706[/C][/ROW]
[ROW][C]46[/C][C] 84.3[/C][C] 97.84[/C][C]-13.54[/C][/ROW]
[ROW][C]47[/C][C] 122.2[/C][C] 115.1[/C][C] 7.116[/C][/ROW]
[ROW][C]48[/C][C] 117.1[/C][C] 115.8[/C][C] 1.316[/C][/ROW]
[ROW][C]49[/C][C] 97.2[/C][C] 105.8[/C][C]-8.584[/C][/ROW]
[ROW][C]50[/C][C] 99.5[/C][C] 98.21[/C][C] 1.289[/C][/ROW]
[ROW][C]51[/C][C] 90.1[/C][C] 99.37[/C][C]-9.271[/C][/ROW]
[ROW][C]52[/C][C] 87.3[/C][C] 94.8[/C][C]-7.503[/C][/ROW]
[ROW][C]53[/C][C] 97.4[/C][C] 106.6[/C][C]-9.151[/C][/ROW]
[ROW][C]54[/C][C] 90.1[/C][C] 93.69[/C][C]-3.586[/C][/ROW]
[ROW][C]55[/C][C] 83.6[/C][C] 84.78[/C][C]-1.179[/C][/ROW]
[ROW][C]56[/C][C] 97.8[/C][C] 96.88[/C][C] 0.9224[/C][/ROW]
[ROW][C]57[/C][C] 79.7[/C][C] 77.14[/C][C] 2.556[/C][/ROW]
[ROW][C]58[/C][C] 75.1[/C][C] 74.86[/C][C] 0.2434[/C][/ROW]
[ROW][C]59[/C][C] 106.1[/C][C] 99.6[/C][C] 6.504[/C][/ROW]
[ROW][C]60[/C][C] 103.5[/C][C] 101.7[/C][C] 1.843[/C][/ROW]
[ROW][C]61[/C][C] 94.5[/C][C] 94.46[/C][C] 0.04353[/C][/ROW]
[ROW][C]62[/C][C] 100.9[/C][C] 91.7[/C][C] 9.196[/C][/ROW]
[ROW][C]63[/C][C] 89.7[/C][C] 94.71[/C][C]-5.009[/C][/ROW]
[ROW][C]64[/C][C] 91.4[/C][C] 93.81[/C][C]-2.408[/C][/ROW]
[ROW][C]65[/C][C] 110.2[/C][C] 109.6[/C][C] 0.5557[/C][/ROW]
[ROW][C]66[/C][C] 102.8[/C][C] 99.48[/C][C] 3.316[/C][/ROW]
[ROW][C]67[/C][C] 89.8[/C][C] 93.57[/C][C]-3.772[/C][/ROW]
[ROW][C]68[/C][C] 112.8[/C][C] 106.8[/C][C] 5.974[/C][/ROW]
[ROW][C]69[/C][C] 84[/C][C] 89.77[/C][C]-5.77[/C][/ROW]
[ROW][C]70[/C][C] 86.5[/C][C] 83.34[/C][C] 3.162[/C][/ROW]
[ROW][C]71[/C][C] 107.3[/C][C] 108.5[/C][C]-1.221[/C][/ROW]
[ROW][C]72[/C][C] 120.2[/C][C] 107.3[/C][C] 12.94[/C][/ROW]
[ROW][C]73[/C][C] 105.5[/C][C] 103.9[/C][C] 1.596[/C][/ROW]
[ROW][C]74[/C][C] 99.9[/C][C] 98.46[/C][C] 1.435[/C][/ROW]
[ROW][C]75[/C][C] 100.4[/C][C] 98.91[/C][C] 1.494[/C][/ROW]
[ROW][C]76[/C][C] 99.6[/C][C] 102.9[/C][C]-3.285[/C][/ROW]
[ROW][C]77[/C][C] 118.6[/C][C] 115[/C][C] 3.634[/C][/ROW]
[ROW][C]78[/C][C] 96[/C][C] 105.2[/C][C]-9.248[/C][/ROW]
[ROW][C]79[/C][C] 105.3[/C][C] 99.36[/C][C] 5.937[/C][/ROW]
[ROW][C]80[/C][C] 105.8[/C][C] 113.1[/C][C]-7.297[/C][/ROW]
[ROW][C]81[/C][C] 80.1[/C][C] 88.95[/C][C]-8.849[/C][/ROW]
[ROW][C]82[/C][C] 89.3[/C][C] 84.36[/C][C] 4.939[/C][/ROW]
[ROW][C]83[/C][C] 120.4[/C][C] 110.2[/C][C] 10.24[/C][/ROW]
[ROW][C]84[/C][C] 111.3[/C][C] 106.7[/C][C] 4.558[/C][/ROW]
[ROW][C]85[/C][C] 98.1[/C][C] 100.9[/C][C]-2.774[/C][/ROW]
[ROW][C]86[/C][C] 102.9[/C][C] 99.4[/C][C] 3.499[/C][/ROW]
[ROW][C]87[/C][C] 95.4[/C][C] 96.29[/C][C]-0.8899[/C][/ROW]
[ROW][C]88[/C][C] 108.7[/C][C] 100.5[/C][C] 8.235[/C][/ROW]
[ROW][C]89[/C][C] 123[/C][C] 113.3[/C][C] 9.713[/C][/ROW]
[ROW][C]90[/C][C] 107.7[/C][C] 104.4[/C][C] 3.283[/C][/ROW]
[ROW][C]91[/C][C] 97.2[/C][C] 101.9[/C][C]-4.682[/C][/ROW]
[ROW][C]92[/C][C] 127.7[/C][C] 115.8[/C][C] 11.9[/C][/ROW]
[ROW][C]93[/C][C] 100.6[/C][C] 98.03[/C][C] 2.572[/C][/ROW]
[ROW][C]94[/C][C] 89.7[/C][C] 91.56[/C][C]-1.857[/C][/ROW]
[ROW][C]95[/C][C] 108.3[/C][C] 116.2[/C][C]-7.921[/C][/ROW]
[ROW][C]96[/C][C] 110[/C][C] 112.1[/C][C]-2.111[/C][/ROW]
[ROW][C]97[/C][C] 105.2[/C][C] 103.1[/C][C] 2.1[/C][/ROW]
[ROW][C]98[/C][C] 87.7[/C][C] 96.13[/C][C]-8.425[/C][/ROW]
[ROW][C]99[/C][C] 91.4[/C][C] 90.28[/C][C] 1.118[/C][/ROW]
[ROW][C]100[/C][C] 92.8[/C][C] 94.51[/C][C]-1.71[/C][/ROW]
[ROW][C]101[/C][C] 97.5[/C][C] 106.1[/C][C]-8.6[/C][/ROW]
[ROW][C]102[/C][C] 95.7[/C][C] 90.19[/C][C] 5.515[/C][/ROW]
[ROW][C]103[/C][C] 93.5[/C][C] 89.7[/C][C] 3.805[/C][/ROW]
[ROW][C]104[/C][C] 97.3[/C][C] 101.7[/C][C]-4.39[/C][/ROW]
[ROW][C]105[/C][C] 84.1[/C][C] 79.86[/C][C] 4.241[/C][/ROW]
[ROW][C]106[/C][C] 87.8[/C][C] 81.38[/C][C] 6.418[/C][/ROW]
[ROW][C]107[/C][C] 96.2[/C][C] 101.3[/C][C]-5.11[/C][/ROW]
[ROW][C]108[/C][C] 94.6[/C][C] 100.7[/C][C]-6.063[/C][/ROW]
[ROW][C]109[/C][C] 88.7[/C][C] 98.09[/C][C]-9.395[/C][/ROW]
[ROW][C]110[/C][C] 76.5[/C][C] 86.49[/C][C]-9.99[/C][/ROW]
[ROW][C]111[/C][C] 83.9[/C][C] 78.19[/C][C] 5.708[/C][/ROW]
[ROW][C]112[/C][C] 88.1[/C][C] 85.35[/C][C] 2.745[/C][/ROW]
[ROW][C]113[/C][C] 93[/C][C] 93.94[/C][C]-0.942[/C][/ROW]
[ROW][C]114[/C][C] 81.8[/C][C] 81.2[/C][C] 0.6047[/C][/ROW]
[ROW][C]115[/C][C] 84.1[/C][C] 83.17[/C][C] 0.9343[/C][/ROW]
[ROW][C]116[/C][C] 89.1[/C][C] 92.31[/C][C]-3.21[/C][/ROW]
[ROW][C]117[/C][C] 75.8[/C][C] 69.44[/C][C] 6.363[/C][/ROW]
[ROW][C]118[/C][C] 71.4[/C][C] 70.43[/C][C] 0.9722[/C][/ROW]
[ROW][C]119[/C][C] 93.8[/C][C] 90.34[/C][C] 3.455[/C][/ROW]
[ROW][C]120[/C][C] 88.5[/C][C] 90.04[/C][C]-1.541[/C][/ROW]
[ROW][C]121[/C][C] 78.1[/C][C] 84.53[/C][C]-6.435[/C][/ROW]
[ROW][C]122[/C][C] 83.6[/C][C] 76.83[/C][C] 6.771[/C][/ROW]
[ROW][C]123[/C][C] 78.2[/C][C] 76.12[/C][C] 2.075[/C][/ROW]
[ROW][C]124[/C][C] 76.2[/C][C] 79.02[/C][C]-2.819[/C][/ROW]
[ROW][C]125[/C][C] 92[/C][C] 90.29[/C][C] 1.708[/C][/ROW]
[ROW][C]126[/C][C] 79.5[/C][C] 82.09[/C][C]-2.59[/C][/ROW]
[ROW][C]127[/C][C] 69.5[/C][C] 74.29[/C][C]-4.794[/C][/ROW]
[ROW][C]128[/C][C] 86.4[/C][C] 87.46[/C][C]-1.064[/C][/ROW]
[ROW][C]129[/C][C] 72.3[/C][C] 66.39[/C][C] 5.907[/C][/ROW]
[ROW][C]130[/C][C] 65[/C][C] 60.63[/C][C] 4.369[/C][/ROW]
[ROW][C]131[/C][C] 86[/C][C] 85.34[/C][C] 0.6639[/C][/ROW]
[ROW][C]132[/C][C] 83.4[/C][C] 87.81[/C][C]-4.407[/C][/ROW]
[ROW][C]133[/C][C] 87.2[/C][C] 80.88[/C][C] 6.315[/C][/ROW]
[ROW][C]134[/C][C] 76.4[/C][C] 76.39[/C][C] 0.0131[/C][/ROW]
[ROW][C]135[/C][C] 76.3[/C][C] 75.72[/C][C] 0.581[/C][/ROW]
[ROW][C]136[/C][C] 76.9[/C][C] 80.61[/C][C]-3.711[/C][/ROW]
[ROW][C]137[/C][C] 92.7[/C][C] 94.72[/C][C]-2.024[/C][/ROW]
[ROW][C]138[/C][C] 83.3[/C][C] 83.7[/C][C]-0.3981[/C][/ROW]
[ROW][C]139[/C][C] 73.8[/C][C] 79.46[/C][C]-5.657[/C][/ROW]
[ROW][C]140[/C][C] 94[/C][C] 90.07[/C][C] 3.928[/C][/ROW]
[ROW][C]141[/C][C] 73.1[/C][C] 71.19[/C][C] 1.91[/C][/ROW]
[ROW][C]142[/C][C] 69.8[/C][C] 70.5[/C][C]-0.7032[/C][/ROW]
[ROW][C]143[/C][C] 86[/C][C] 92.23[/C][C]-6.229[/C][/ROW]
[ROW][C]144[/C][C] 78.8[/C][C] 92.88[/C][C]-14.08[/C][/ROW]
[ROW][C]145[/C][C] 89.4[/C][C] 83.25[/C][C] 6.152[/C][/ROW]
[ROW][C]146[/C][C] 83.8[/C][C] 80.42[/C][C] 3.384[/C][/ROW]
[ROW][C]147[/C][C] 74.1[/C][C] 77.08[/C][C]-2.977[/C][/ROW]
[ROW][C]148[/C][C] 77.2[/C][C] 80.74[/C][C]-3.542[/C][/ROW]
[ROW][C]149[/C][C] 103.6[/C][C] 97.77[/C][C] 5.831[/C][/ROW]
[ROW][C]150[/C][C] 78[/C][C] 85.6[/C][C]-7.604[/C][/ROW]
[ROW][C]151[/C][C] 80.2[/C][C] 80.13[/C][C] 0.06716[/C][/ROW]
[ROW][C]152[/C][C] 88.8[/C][C] 99.53[/C][C]-10.73[/C][/ROW]
[ROW][C]153[/C][C] 72.9[/C][C] 73.31[/C][C]-0.4133[/C][/ROW]
[ROW][C]154[/C][C] 73.6[/C][C] 70.99[/C][C] 2.613[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308427&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308427&T=5

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	91.8	93.19	-1.394
2	89.7	86.8	2.902
3	82.9	85.76	-2.863
4	90	88.83	1.166
5	98.5	103.5	-4.964
6	93.4	92.45	0.9484
7	89.1	89.08	0.02272
8	103	102.2	0.7611
9	74.7	81.09	-6.385
10	79	77.16	1.838
11	101.3	102.1	-0.7824
12	96.7	99.21	-2.508
13	99.1	92.68	6.419
14	92.3	91.38	0.9154
15	90.6	88.05	2.55
16	95.2	93.61	1.59
17	107.6	110.1	-2.509
18	97.6	96.35	1.252
19	104	95.38	8.619
20	112	111.6	0.4401
21	90.6	88.17	2.426
22	84.9	89.46	-4.56
23	112.7	111.8	0.9066
24	115.2	110.9	4.262
25	110.1	105.3	4.808
26	95.7	100.6	-4.878
27	104.2	97.12	7.082
28	103.3	105.5	-2.194
29	116.1	116.3	-0.1977
30	106.9	106.3	0.588
31	105.9	103.9	1.975
32	120.2	116.5	3.694
33	96.2	96.05	0.1481
34	91.5	95.4	-3.897
35	108.3	115.9	-7.627
36	121.1	115.3	5.789
37	111.4	110.3	1.147
38	95.6	101.7	-6.111
39	98.7	98.3	0.4017
40	117.7	104.3	13.44
41	124.5	117.6	6.945
42	114.8	106.9	7.919
43	108	109.3	-1.276
44	120.7	121.6	-0.9226
45	95.6	100.3	-4.706
46	84.3	97.84	-13.54
47	122.2	115.1	7.116
48	117.1	115.8	1.316
49	97.2	105.8	-8.584
50	99.5	98.21	1.289
51	90.1	99.37	-9.271
52	87.3	94.8	-7.503
53	97.4	106.6	-9.151
54	90.1	93.69	-3.586
55	83.6	84.78	-1.179
56	97.8	96.88	0.9224
57	79.7	77.14	2.556
58	75.1	74.86	0.2434
59	106.1	99.6	6.504
60	103.5	101.7	1.843
61	94.5	94.46	0.04353
62	100.9	91.7	9.196
63	89.7	94.71	-5.009
64	91.4	93.81	-2.408
65	110.2	109.6	0.5557
66	102.8	99.48	3.316
67	89.8	93.57	-3.772
68	112.8	106.8	5.974
69	84	89.77	-5.77
70	86.5	83.34	3.162
71	107.3	108.5	-1.221
72	120.2	107.3	12.94
73	105.5	103.9	1.596
74	99.9	98.46	1.435
75	100.4	98.91	1.494
76	99.6	102.9	-3.285
77	118.6	115	3.634
78	96	105.2	-9.248
79	105.3	99.36	5.937
80	105.8	113.1	-7.297
81	80.1	88.95	-8.849
82	89.3	84.36	4.939
83	120.4	110.2	10.24
84	111.3	106.7	4.558
85	98.1	100.9	-2.774
86	102.9	99.4	3.499
87	95.4	96.29	-0.8899
88	108.7	100.5	8.235
89	123	113.3	9.713
90	107.7	104.4	3.283
91	97.2	101.9	-4.682
92	127.7	115.8	11.9
93	100.6	98.03	2.572
94	89.7	91.56	-1.857
95	108.3	116.2	-7.921
96	110	112.1	-2.111
97	105.2	103.1	2.1
98	87.7	96.13	-8.425
99	91.4	90.28	1.118
100	92.8	94.51	-1.71
101	97.5	106.1	-8.6
102	95.7	90.19	5.515
103	93.5	89.7	3.805
104	97.3	101.7	-4.39
105	84.1	79.86	4.241
106	87.8	81.38	6.418
107	96.2	101.3	-5.11
108	94.6	100.7	-6.063
109	88.7	98.09	-9.395
110	76.5	86.49	-9.99
111	83.9	78.19	5.708
112	88.1	85.35	2.745
113	93	93.94	-0.942
114	81.8	81.2	0.6047
115	84.1	83.17	0.9343
116	89.1	92.31	-3.21
117	75.8	69.44	6.363
118	71.4	70.43	0.9722
119	93.8	90.34	3.455
120	88.5	90.04	-1.541
121	78.1	84.53	-6.435
122	83.6	76.83	6.771
123	78.2	76.12	2.075
124	76.2	79.02	-2.819
125	92	90.29	1.708
126	79.5	82.09	-2.59
127	69.5	74.29	-4.794
128	86.4	87.46	-1.064
129	72.3	66.39	5.907
130	65	60.63	4.369
131	86	85.34	0.6639
132	83.4	87.81	-4.407
133	87.2	80.88	6.315
134	76.4	76.39	0.0131
135	76.3	75.72	0.581
136	76.9	80.61	-3.711
137	92.7	94.72	-2.024
138	83.3	83.7	-0.3981
139	73.8	79.46	-5.657
140	94	90.07	3.928
141	73.1	71.19	1.91
142	69.8	70.5	-0.7032
143	86	92.23	-6.229
144	78.8	92.88	-14.08
145	89.4	83.25	6.152
146	83.8	80.42	3.384
147	74.1	77.08	-2.977
148	77.2	80.74	-3.542
149	103.6	97.77	5.831
150	78	85.6	-7.604
151	80.2	80.13	0.06716
152	88.8	99.53	-10.73
153	72.9	73.31	-0.4133
154	73.6	70.99	2.613

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
30	0.239	0.478	0.761
31	0.1587	0.3173	0.8413
32	0.0793	0.1586	0.9207
33	0.07801	0.156	0.922
34	0.04131	0.08263	0.9587
35	0.0901	0.1802	0.9099
36	0.05359	0.1072	0.9464
37	0.02995	0.0599	0.9701
38	0.02191	0.04382	0.9781
39	0.01301	0.02601	0.987
40	0.03866	0.07732	0.9613
41	0.1128	0.2257	0.8872
42	0.1429	0.2858	0.8571
43	0.1251	0.2503	0.8749
44	0.1354	0.2708	0.8646
45	0.1798	0.3596	0.8202
46	0.195	0.39	0.805
47	0.3313	0.6626	0.6687
48	0.3143	0.6286	0.6857
49	0.5193	0.9614	0.4807
50	0.4585	0.9171	0.5415
51	0.4466	0.8932	0.5534
52	0.4644	0.9287	0.5356
53	0.4555	0.9111	0.5445
54	0.4122	0.8245	0.5878
55	0.3528	0.7056	0.6472
56	0.3686	0.7372	0.6314
57	0.414	0.828	0.586
58	0.3841	0.7682	0.6159
59	0.3676	0.7352	0.6324
60	0.3176	0.6352	0.6824
61	0.2656	0.5312	0.7344
62	0.3437	0.6874	0.6563
63	0.3058	0.6115	0.6942
64	0.2561	0.5121	0.7439
65	0.2166	0.4332	0.7834
66	0.1999	0.3999	0.8001
67	0.1646	0.3291	0.8354
68	0.1759	0.3517	0.8241
69	0.1632	0.3264	0.8368
70	0.1422	0.2843	0.8578
71	0.1137	0.2274	0.8863
72	0.2685	0.5369	0.7315
73	0.2323	0.4646	0.7677
74	0.2138	0.4276	0.7862
75	0.1845	0.369	0.8155
76	0.1732	0.3464	0.8268
77	0.1545	0.309	0.8455
78	0.1879	0.3757	0.8121
79	0.1875	0.3749	0.8125
80	0.1872	0.3743	0.8128
81	0.3191	0.6383	0.6809
82	0.3252	0.6503	0.6748
83	0.4659	0.9318	0.5341
84	0.5443	0.9114	0.4557
85	0.4984	0.9969	0.5016
86	0.4652	0.9304	0.5348
87	0.4424	0.8848	0.5576
88	0.5019	0.9961	0.4981
89	0.7054	0.5892	0.2946
90	0.7779	0.4442	0.2221
91	0.7583	0.4834	0.2417
92	0.8826	0.2349	0.1174
93	0.8648	0.2704	0.1352
94	0.8403	0.3194	0.1597
95	0.8247	0.3506	0.1753
96	0.8543	0.2914	0.1457
97	0.868	0.264	0.132
98	0.84	0.32	0.16
99	0.8008	0.3983	0.1992
100	0.7538	0.4925	0.2462
101	0.8527	0.2947	0.1473
102	0.8226	0.3547	0.1774
103	0.8291	0.3418	0.1709
104	0.7865	0.4269	0.2135
105	0.7545	0.491	0.2455
106	0.8258	0.3484	0.1742
107	0.8094	0.3813	0.1906
108	0.8203	0.3593	0.1797
109	0.795	0.4099	0.205
110	0.866	0.268	0.134
111	0.8524	0.2951	0.1476
112	0.8713	0.2574	0.1287
113	0.8227	0.3546	0.1773
114	0.7637	0.4726	0.2363
115	0.7062	0.5875	0.2938
116	0.6388	0.7223	0.3612
117	0.6022	0.7957	0.3978
118	0.5981	0.8037	0.4019
119	0.6621	0.6758	0.3379
120	0.7524	0.4951	0.2476
121	0.7323	0.5354	0.2677
122	0.6279	0.7441	0.3721
123	0.6505	0.6989	0.3495
124	0.6655	0.6691	0.3345

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
30 &  0.239 &  0.478 &  0.761 \tabularnewline
31 &  0.1587 &  0.3173 &  0.8413 \tabularnewline
32 &  0.0793 &  0.1586 &  0.9207 \tabularnewline
33 &  0.07801 &  0.156 &  0.922 \tabularnewline
34 &  0.04131 &  0.08263 &  0.9587 \tabularnewline
35 &  0.0901 &  0.1802 &  0.9099 \tabularnewline
36 &  0.05359 &  0.1072 &  0.9464 \tabularnewline
37 &  0.02995 &  0.0599 &  0.9701 \tabularnewline
38 &  0.02191 &  0.04382 &  0.9781 \tabularnewline
39 &  0.01301 &  0.02601 &  0.987 \tabularnewline
40 &  0.03866 &  0.07732 &  0.9613 \tabularnewline
41 &  0.1128 &  0.2257 &  0.8872 \tabularnewline
42 &  0.1429 &  0.2858 &  0.8571 \tabularnewline
43 &  0.1251 &  0.2503 &  0.8749 \tabularnewline
44 &  0.1354 &  0.2708 &  0.8646 \tabularnewline
45 &  0.1798 &  0.3596 &  0.8202 \tabularnewline
46 &  0.195 &  0.39 &  0.805 \tabularnewline
47 &  0.3313 &  0.6626 &  0.6687 \tabularnewline
48 &  0.3143 &  0.6286 &  0.6857 \tabularnewline
49 &  0.5193 &  0.9614 &  0.4807 \tabularnewline
50 &  0.4585 &  0.9171 &  0.5415 \tabularnewline
51 &  0.4466 &  0.8932 &  0.5534 \tabularnewline
52 &  0.4644 &  0.9287 &  0.5356 \tabularnewline
53 &  0.4555 &  0.9111 &  0.5445 \tabularnewline
54 &  0.4122 &  0.8245 &  0.5878 \tabularnewline
55 &  0.3528 &  0.7056 &  0.6472 \tabularnewline
56 &  0.3686 &  0.7372 &  0.6314 \tabularnewline
57 &  0.414 &  0.828 &  0.586 \tabularnewline
58 &  0.3841 &  0.7682 &  0.6159 \tabularnewline
59 &  0.3676 &  0.7352 &  0.6324 \tabularnewline
60 &  0.3176 &  0.6352 &  0.6824 \tabularnewline
61 &  0.2656 &  0.5312 &  0.7344 \tabularnewline
62 &  0.3437 &  0.6874 &  0.6563 \tabularnewline
63 &  0.3058 &  0.6115 &  0.6942 \tabularnewline
64 &  0.2561 &  0.5121 &  0.7439 \tabularnewline
65 &  0.2166 &  0.4332 &  0.7834 \tabularnewline
66 &  0.1999 &  0.3999 &  0.8001 \tabularnewline
67 &  0.1646 &  0.3291 &  0.8354 \tabularnewline
68 &  0.1759 &  0.3517 &  0.8241 \tabularnewline
69 &  0.1632 &  0.3264 &  0.8368 \tabularnewline
70 &  0.1422 &  0.2843 &  0.8578 \tabularnewline
71 &  0.1137 &  0.2274 &  0.8863 \tabularnewline
72 &  0.2685 &  0.5369 &  0.7315 \tabularnewline
73 &  0.2323 &  0.4646 &  0.7677 \tabularnewline
74 &  0.2138 &  0.4276 &  0.7862 \tabularnewline
75 &  0.1845 &  0.369 &  0.8155 \tabularnewline
76 &  0.1732 &  0.3464 &  0.8268 \tabularnewline
77 &  0.1545 &  0.309 &  0.8455 \tabularnewline
78 &  0.1879 &  0.3757 &  0.8121 \tabularnewline
79 &  0.1875 &  0.3749 &  0.8125 \tabularnewline
80 &  0.1872 &  0.3743 &  0.8128 \tabularnewline
81 &  0.3191 &  0.6383 &  0.6809 \tabularnewline
82 &  0.3252 &  0.6503 &  0.6748 \tabularnewline
83 &  0.4659 &  0.9318 &  0.5341 \tabularnewline
84 &  0.5443 &  0.9114 &  0.4557 \tabularnewline
85 &  0.4984 &  0.9969 &  0.5016 \tabularnewline
86 &  0.4652 &  0.9304 &  0.5348 \tabularnewline
87 &  0.4424 &  0.8848 &  0.5576 \tabularnewline
88 &  0.5019 &  0.9961 &  0.4981 \tabularnewline
89 &  0.7054 &  0.5892 &  0.2946 \tabularnewline
90 &  0.7779 &  0.4442 &  0.2221 \tabularnewline
91 &  0.7583 &  0.4834 &  0.2417 \tabularnewline
92 &  0.8826 &  0.2349 &  0.1174 \tabularnewline
93 &  0.8648 &  0.2704 &  0.1352 \tabularnewline
94 &  0.8403 &  0.3194 &  0.1597 \tabularnewline
95 &  0.8247 &  0.3506 &  0.1753 \tabularnewline
96 &  0.8543 &  0.2914 &  0.1457 \tabularnewline
97 &  0.868 &  0.264 &  0.132 \tabularnewline
98 &  0.84 &  0.32 &  0.16 \tabularnewline
99 &  0.8008 &  0.3983 &  0.1992 \tabularnewline
100 &  0.7538 &  0.4925 &  0.2462 \tabularnewline
101 &  0.8527 &  0.2947 &  0.1473 \tabularnewline
102 &  0.8226 &  0.3547 &  0.1774 \tabularnewline
103 &  0.8291 &  0.3418 &  0.1709 \tabularnewline
104 &  0.7865 &  0.4269 &  0.2135 \tabularnewline
105 &  0.7545 &  0.491 &  0.2455 \tabularnewline
106 &  0.8258 &  0.3484 &  0.1742 \tabularnewline
107 &  0.8094 &  0.3813 &  0.1906 \tabularnewline
108 &  0.8203 &  0.3593 &  0.1797 \tabularnewline
109 &  0.795 &  0.4099 &  0.205 \tabularnewline
110 &  0.866 &  0.268 &  0.134 \tabularnewline
111 &  0.8524 &  0.2951 &  0.1476 \tabularnewline
112 &  0.8713 &  0.2574 &  0.1287 \tabularnewline
113 &  0.8227 &  0.3546 &  0.1773 \tabularnewline
114 &  0.7637 &  0.4726 &  0.2363 \tabularnewline
115 &  0.7062 &  0.5875 &  0.2938 \tabularnewline
116 &  0.6388 &  0.7223 &  0.3612 \tabularnewline
117 &  0.6022 &  0.7957 &  0.3978 \tabularnewline
118 &  0.5981 &  0.8037 &  0.4019 \tabularnewline
119 &  0.6621 &  0.6758 &  0.3379 \tabularnewline
120 &  0.7524 &  0.4951 &  0.2476 \tabularnewline
121 &  0.7323 &  0.5354 &  0.2677 \tabularnewline
122 &  0.6279 &  0.7441 &  0.3721 \tabularnewline
123 &  0.6505 &  0.6989 &  0.3495 \tabularnewline
124 &  0.6655 &  0.6691 &  0.3345 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308427&T=6

[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]30[/C][C] 0.239[/C][C] 0.478[/C][C] 0.761[/C][/ROW]
[ROW][C]31[/C][C] 0.1587[/C][C] 0.3173[/C][C] 0.8413[/C][/ROW]
[ROW][C]32[/C][C] 0.0793[/C][C] 0.1586[/C][C] 0.9207[/C][/ROW]
[ROW][C]33[/C][C] 0.07801[/C][C] 0.156[/C][C] 0.922[/C][/ROW]
[ROW][C]34[/C][C] 0.04131[/C][C] 0.08263[/C][C] 0.9587[/C][/ROW]
[ROW][C]35[/C][C] 0.0901[/C][C] 0.1802[/C][C] 0.9099[/C][/ROW]
[ROW][C]36[/C][C] 0.05359[/C][C] 0.1072[/C][C] 0.9464[/C][/ROW]
[ROW][C]37[/C][C] 0.02995[/C][C] 0.0599[/C][C] 0.9701[/C][/ROW]
[ROW][C]38[/C][C] 0.02191[/C][C] 0.04382[/C][C] 0.9781[/C][/ROW]
[ROW][C]39[/C][C] 0.01301[/C][C] 0.02601[/C][C] 0.987[/C][/ROW]
[ROW][C]40[/C][C] 0.03866[/C][C] 0.07732[/C][C] 0.9613[/C][/ROW]
[ROW][C]41[/C][C] 0.1128[/C][C] 0.2257[/C][C] 0.8872[/C][/ROW]
[ROW][C]42[/C][C] 0.1429[/C][C] 0.2858[/C][C] 0.8571[/C][/ROW]
[ROW][C]43[/C][C] 0.1251[/C][C] 0.2503[/C][C] 0.8749[/C][/ROW]
[ROW][C]44[/C][C] 0.1354[/C][C] 0.2708[/C][C] 0.8646[/C][/ROW]
[ROW][C]45[/C][C] 0.1798[/C][C] 0.3596[/C][C] 0.8202[/C][/ROW]
[ROW][C]46[/C][C] 0.195[/C][C] 0.39[/C][C] 0.805[/C][/ROW]
[ROW][C]47[/C][C] 0.3313[/C][C] 0.6626[/C][C] 0.6687[/C][/ROW]
[ROW][C]48[/C][C] 0.3143[/C][C] 0.6286[/C][C] 0.6857[/C][/ROW]
[ROW][C]49[/C][C] 0.5193[/C][C] 0.9614[/C][C] 0.4807[/C][/ROW]
[ROW][C]50[/C][C] 0.4585[/C][C] 0.9171[/C][C] 0.5415[/C][/ROW]
[ROW][C]51[/C][C] 0.4466[/C][C] 0.8932[/C][C] 0.5534[/C][/ROW]
[ROW][C]52[/C][C] 0.4644[/C][C] 0.9287[/C][C] 0.5356[/C][/ROW]
[ROW][C]53[/C][C] 0.4555[/C][C] 0.9111[/C][C] 0.5445[/C][/ROW]
[ROW][C]54[/C][C] 0.4122[/C][C] 0.8245[/C][C] 0.5878[/C][/ROW]
[ROW][C]55[/C][C] 0.3528[/C][C] 0.7056[/C][C] 0.6472[/C][/ROW]
[ROW][C]56[/C][C] 0.3686[/C][C] 0.7372[/C][C] 0.6314[/C][/ROW]
[ROW][C]57[/C][C] 0.414[/C][C] 0.828[/C][C] 0.586[/C][/ROW]
[ROW][C]58[/C][C] 0.3841[/C][C] 0.7682[/C][C] 0.6159[/C][/ROW]
[ROW][C]59[/C][C] 0.3676[/C][C] 0.7352[/C][C] 0.6324[/C][/ROW]
[ROW][C]60[/C][C] 0.3176[/C][C] 0.6352[/C][C] 0.6824[/C][/ROW]
[ROW][C]61[/C][C] 0.2656[/C][C] 0.5312[/C][C] 0.7344[/C][/ROW]
[ROW][C]62[/C][C] 0.3437[/C][C] 0.6874[/C][C] 0.6563[/C][/ROW]
[ROW][C]63[/C][C] 0.3058[/C][C] 0.6115[/C][C] 0.6942[/C][/ROW]
[ROW][C]64[/C][C] 0.2561[/C][C] 0.5121[/C][C] 0.7439[/C][/ROW]
[ROW][C]65[/C][C] 0.2166[/C][C] 0.4332[/C][C] 0.7834[/C][/ROW]
[ROW][C]66[/C][C] 0.1999[/C][C] 0.3999[/C][C] 0.8001[/C][/ROW]
[ROW][C]67[/C][C] 0.1646[/C][C] 0.3291[/C][C] 0.8354[/C][/ROW]
[ROW][C]68[/C][C] 0.1759[/C][C] 0.3517[/C][C] 0.8241[/C][/ROW]
[ROW][C]69[/C][C] 0.1632[/C][C] 0.3264[/C][C] 0.8368[/C][/ROW]
[ROW][C]70[/C][C] 0.1422[/C][C] 0.2843[/C][C] 0.8578[/C][/ROW]
[ROW][C]71[/C][C] 0.1137[/C][C] 0.2274[/C][C] 0.8863[/C][/ROW]
[ROW][C]72[/C][C] 0.2685[/C][C] 0.5369[/C][C] 0.7315[/C][/ROW]
[ROW][C]73[/C][C] 0.2323[/C][C] 0.4646[/C][C] 0.7677[/C][/ROW]
[ROW][C]74[/C][C] 0.2138[/C][C] 0.4276[/C][C] 0.7862[/C][/ROW]
[ROW][C]75[/C][C] 0.1845[/C][C] 0.369[/C][C] 0.8155[/C][/ROW]
[ROW][C]76[/C][C] 0.1732[/C][C] 0.3464[/C][C] 0.8268[/C][/ROW]
[ROW][C]77[/C][C] 0.1545[/C][C] 0.309[/C][C] 0.8455[/C][/ROW]
[ROW][C]78[/C][C] 0.1879[/C][C] 0.3757[/C][C] 0.8121[/C][/ROW]
[ROW][C]79[/C][C] 0.1875[/C][C] 0.3749[/C][C] 0.8125[/C][/ROW]
[ROW][C]80[/C][C] 0.1872[/C][C] 0.3743[/C][C] 0.8128[/C][/ROW]
[ROW][C]81[/C][C] 0.3191[/C][C] 0.6383[/C][C] 0.6809[/C][/ROW]
[ROW][C]82[/C][C] 0.3252[/C][C] 0.6503[/C][C] 0.6748[/C][/ROW]
[ROW][C]83[/C][C] 0.4659[/C][C] 0.9318[/C][C] 0.5341[/C][/ROW]
[ROW][C]84[/C][C] 0.5443[/C][C] 0.9114[/C][C] 0.4557[/C][/ROW]
[ROW][C]85[/C][C] 0.4984[/C][C] 0.9969[/C][C] 0.5016[/C][/ROW]
[ROW][C]86[/C][C] 0.4652[/C][C] 0.9304[/C][C] 0.5348[/C][/ROW]
[ROW][C]87[/C][C] 0.4424[/C][C] 0.8848[/C][C] 0.5576[/C][/ROW]
[ROW][C]88[/C][C] 0.5019[/C][C] 0.9961[/C][C] 0.4981[/C][/ROW]
[ROW][C]89[/C][C] 0.7054[/C][C] 0.5892[/C][C] 0.2946[/C][/ROW]
[ROW][C]90[/C][C] 0.7779[/C][C] 0.4442[/C][C] 0.2221[/C][/ROW]
[ROW][C]91[/C][C] 0.7583[/C][C] 0.4834[/C][C] 0.2417[/C][/ROW]
[ROW][C]92[/C][C] 0.8826[/C][C] 0.2349[/C][C] 0.1174[/C][/ROW]
[ROW][C]93[/C][C] 0.8648[/C][C] 0.2704[/C][C] 0.1352[/C][/ROW]
[ROW][C]94[/C][C] 0.8403[/C][C] 0.3194[/C][C] 0.1597[/C][/ROW]
[ROW][C]95[/C][C] 0.8247[/C][C] 0.3506[/C][C] 0.1753[/C][/ROW]
[ROW][C]96[/C][C] 0.8543[/C][C] 0.2914[/C][C] 0.1457[/C][/ROW]
[ROW][C]97[/C][C] 0.868[/C][C] 0.264[/C][C] 0.132[/C][/ROW]
[ROW][C]98[/C][C] 0.84[/C][C] 0.32[/C][C] 0.16[/C][/ROW]
[ROW][C]99[/C][C] 0.8008[/C][C] 0.3983[/C][C] 0.1992[/C][/ROW]
[ROW][C]100[/C][C] 0.7538[/C][C] 0.4925[/C][C] 0.2462[/C][/ROW]
[ROW][C]101[/C][C] 0.8527[/C][C] 0.2947[/C][C] 0.1473[/C][/ROW]
[ROW][C]102[/C][C] 0.8226[/C][C] 0.3547[/C][C] 0.1774[/C][/ROW]
[ROW][C]103[/C][C] 0.8291[/C][C] 0.3418[/C][C] 0.1709[/C][/ROW]
[ROW][C]104[/C][C] 0.7865[/C][C] 0.4269[/C][C] 0.2135[/C][/ROW]
[ROW][C]105[/C][C] 0.7545[/C][C] 0.491[/C][C] 0.2455[/C][/ROW]
[ROW][C]106[/C][C] 0.8258[/C][C] 0.3484[/C][C] 0.1742[/C][/ROW]
[ROW][C]107[/C][C] 0.8094[/C][C] 0.3813[/C][C] 0.1906[/C][/ROW]
[ROW][C]108[/C][C] 0.8203[/C][C] 0.3593[/C][C] 0.1797[/C][/ROW]
[ROW][C]109[/C][C] 0.795[/C][C] 0.4099[/C][C] 0.205[/C][/ROW]
[ROW][C]110[/C][C] 0.866[/C][C] 0.268[/C][C] 0.134[/C][/ROW]
[ROW][C]111[/C][C] 0.8524[/C][C] 0.2951[/C][C] 0.1476[/C][/ROW]
[ROW][C]112[/C][C] 0.8713[/C][C] 0.2574[/C][C] 0.1287[/C][/ROW]
[ROW][C]113[/C][C] 0.8227[/C][C] 0.3546[/C][C] 0.1773[/C][/ROW]
[ROW][C]114[/C][C] 0.7637[/C][C] 0.4726[/C][C] 0.2363[/C][/ROW]
[ROW][C]115[/C][C] 0.7062[/C][C] 0.5875[/C][C] 0.2938[/C][/ROW]
[ROW][C]116[/C][C] 0.6388[/C][C] 0.7223[/C][C] 0.3612[/C][/ROW]
[ROW][C]117[/C][C] 0.6022[/C][C] 0.7957[/C][C] 0.3978[/C][/ROW]
[ROW][C]118[/C][C] 0.5981[/C][C] 0.8037[/C][C] 0.4019[/C][/ROW]
[ROW][C]119[/C][C] 0.6621[/C][C] 0.6758[/C][C] 0.3379[/C][/ROW]
[ROW][C]120[/C][C] 0.7524[/C][C] 0.4951[/C][C] 0.2476[/C][/ROW]
[ROW][C]121[/C][C] 0.7323[/C][C] 0.5354[/C][C] 0.2677[/C][/ROW]
[ROW][C]122[/C][C] 0.6279[/C][C] 0.7441[/C][C] 0.3721[/C][/ROW]
[ROW][C]123[/C][C] 0.6505[/C][C] 0.6989[/C][C] 0.3495[/C][/ROW]
[ROW][C]124[/C][C] 0.6655[/C][C] 0.6691[/C][C] 0.3345[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308427&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308427&T=6

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
30	0.239	0.478	0.761
31	0.1587	0.3173	0.8413
32	0.0793	0.1586	0.9207
33	0.07801	0.156	0.922
34	0.04131	0.08263	0.9587
35	0.0901	0.1802	0.9099
36	0.05359	0.1072	0.9464
37	0.02995	0.0599	0.9701
38	0.02191	0.04382	0.9781
39	0.01301	0.02601	0.987
40	0.03866	0.07732	0.9613
41	0.1128	0.2257	0.8872
42	0.1429	0.2858	0.8571
43	0.1251	0.2503	0.8749
44	0.1354	0.2708	0.8646
45	0.1798	0.3596	0.8202
46	0.195	0.39	0.805
47	0.3313	0.6626	0.6687
48	0.3143	0.6286	0.6857
49	0.5193	0.9614	0.4807
50	0.4585	0.9171	0.5415
51	0.4466	0.8932	0.5534
52	0.4644	0.9287	0.5356
53	0.4555	0.9111	0.5445
54	0.4122	0.8245	0.5878
55	0.3528	0.7056	0.6472
56	0.3686	0.7372	0.6314
57	0.414	0.828	0.586
58	0.3841	0.7682	0.6159
59	0.3676	0.7352	0.6324
60	0.3176	0.6352	0.6824
61	0.2656	0.5312	0.7344
62	0.3437	0.6874	0.6563
63	0.3058	0.6115	0.6942
64	0.2561	0.5121	0.7439
65	0.2166	0.4332	0.7834
66	0.1999	0.3999	0.8001
67	0.1646	0.3291	0.8354
68	0.1759	0.3517	0.8241
69	0.1632	0.3264	0.8368
70	0.1422	0.2843	0.8578
71	0.1137	0.2274	0.8863
72	0.2685	0.5369	0.7315
73	0.2323	0.4646	0.7677
74	0.2138	0.4276	0.7862
75	0.1845	0.369	0.8155
76	0.1732	0.3464	0.8268
77	0.1545	0.309	0.8455
78	0.1879	0.3757	0.8121
79	0.1875	0.3749	0.8125
80	0.1872	0.3743	0.8128
81	0.3191	0.6383	0.6809
82	0.3252	0.6503	0.6748
83	0.4659	0.9318	0.5341
84	0.5443	0.9114	0.4557
85	0.4984	0.9969	0.5016
86	0.4652	0.9304	0.5348
87	0.4424	0.8848	0.5576
88	0.5019	0.9961	0.4981
89	0.7054	0.5892	0.2946
90	0.7779	0.4442	0.2221
91	0.7583	0.4834	0.2417
92	0.8826	0.2349	0.1174
93	0.8648	0.2704	0.1352
94	0.8403	0.3194	0.1597
95	0.8247	0.3506	0.1753
96	0.8543	0.2914	0.1457
97	0.868	0.264	0.132
98	0.84	0.32	0.16
99	0.8008	0.3983	0.1992
100	0.7538	0.4925	0.2462
101	0.8527	0.2947	0.1473
102	0.8226	0.3547	0.1774
103	0.8291	0.3418	0.1709
104	0.7865	0.4269	0.2135
105	0.7545	0.491	0.2455
106	0.8258	0.3484	0.1742
107	0.8094	0.3813	0.1906
108	0.8203	0.3593	0.1797
109	0.795	0.4099	0.205
110	0.866	0.268	0.134
111	0.8524	0.2951	0.1476
112	0.8713	0.2574	0.1287
113	0.8227	0.3546	0.1773
114	0.7637	0.4726	0.2363
115	0.7062	0.5875	0.2938
116	0.6388	0.7223	0.3612
117	0.6022	0.7957	0.3978
118	0.5981	0.8037	0.4019
119	0.6621	0.6758	0.3379
120	0.7524	0.4951	0.2476
121	0.7323	0.5354	0.2677
122	0.6279	0.7441	0.3721
123	0.6505	0.6989	0.3495
124	0.6655	0.6691	0.3345

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	2	0.0210526	OK
10% type I error level	5	0.0526316	OK

\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 & 2 & 0.0210526 & OK \tabularnewline
10% type I error level & 5 & 0.0526316 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308427&T=7

[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]2[/C][C]0.0210526[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.0526316[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308427&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308427&T=7

As an alternative you can also use a 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 tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	2	0.0210526	OK
10% type I error level	5	0.0526316	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 4.3509, df1 = 2, df2 = 125, p-value = 0.0149
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.57445, df1 = 52, df2 = 75, p-value = 0.9821
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.6823, df1 = 2, df2 = 125, p-value = 0.07234

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.3509, df1 = 2, df2 = 125, p-value = 0.0149
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.57445, df1 = 52, df2 = 75, p-value = 0.9821
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.6823, df1 = 2, df2 = 125, p-value = 0.07234
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308427&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.3509, df1 = 2, df2 = 125, p-value = 0.0149
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.57445, df1 = 52, df2 = 75, p-value = 0.9821
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.6823, df1 = 2, df2 = 125, p-value = 0.07234
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308427&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308427&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 4.3509, df1 = 2, df2 = 125, p-value = 0.0149
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.57445, df1 = 52, df2 = 75, p-value = 0.9821
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.6823, df1 = 2, df2 = 125, p-value = 0.07234

Variance Inflation Factors (Multicollinearity)

> vif
                 Energy  `Elect.equipment(t-1)`  `Elect.equipment(t-2)` 
               2.631594                6.582814                6.896825 
 `Elect.equipment(t-3)`  `Elect.equipment(t-4)`  `Elect.equipment(t-5)` 
               7.299033                8.068077                8.487436 
 `Elect.equipment(t-6)`  `Elect.equipment(t-7)`  `Elect.equipment(t-8)` 
               8.505313                8.152339                7.938733 
 `Elect.equipment(t-9)` `Elect.equipment(t-10)` `Elect.equipment(t-1s)` 
               8.260676                7.268197                6.640223 
`Elect.equipment(t-2s)` `Elect.equipment(t-3s)` `Elect.equipment(t-4s)` 
               3.037417                2.966441                2.670900 
                     M1                      M2                      M3 
               6.860536                9.756861                8.553967 
                     M4                      M5                      M6 
               8.711894                6.078110                4.906072 
                     M7                      M8                      M9 
               6.894749                6.946762                7.523166 
                    M10                     M11 
              10.835742                6.111140

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
                 Energy  `Elect.equipment(t-1)`  `Elect.equipment(t-2)` 
               2.631594                6.582814                6.896825 
 `Elect.equipment(t-3)`  `Elect.equipment(t-4)`  `Elect.equipment(t-5)` 
               7.299033                8.068077                8.487436 
 `Elect.equipment(t-6)`  `Elect.equipment(t-7)`  `Elect.equipment(t-8)` 
               8.505313                8.152339                7.938733 
 `Elect.equipment(t-9)` `Elect.equipment(t-10)` `Elect.equipment(t-1s)` 
               8.260676                7.268197                6.640223 
`Elect.equipment(t-2s)` `Elect.equipment(t-3s)` `Elect.equipment(t-4s)` 
               3.037417                2.966441                2.670900 
                     M1                      M2                      M3 
               6.860536                9.756861                8.553967 
                     M4                      M5                      M6 
               8.711894                6.078110                4.906072 
                     M7                      M8                      M9 
               6.894749                6.946762                7.523166 
                    M10                     M11 
              10.835742                6.111140 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308427&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
                 Energy  `Elect.equipment(t-1)`  `Elect.equipment(t-2)` 
               2.631594                6.582814                6.896825 
 `Elect.equipment(t-3)`  `Elect.equipment(t-4)`  `Elect.equipment(t-5)` 
               7.299033                8.068077                8.487436 
 `Elect.equipment(t-6)`  `Elect.equipment(t-7)`  `Elect.equipment(t-8)` 
               8.505313                8.152339                7.938733 
 `Elect.equipment(t-9)` `Elect.equipment(t-10)` `Elect.equipment(t-1s)` 
               8.260676                7.268197                6.640223 
`Elect.equipment(t-2s)` `Elect.equipment(t-3s)` `Elect.equipment(t-4s)` 
               3.037417                2.966441                2.670900 
                     M1                      M2                      M3 
               6.860536                9.756861                8.553967 
                     M4                      M5                      M6 
               8.711894                6.078110                4.906072 
                     M7                      M8                      M9 
               6.894749                6.946762                7.523166 
                    M10                     M11 
              10.835742                6.111140 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308427&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308427&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)

> vif
                 Energy  `Elect.equipment(t-1)`  `Elect.equipment(t-2)` 
               2.631594                6.582814                6.896825 
 `Elect.equipment(t-3)`  `Elect.equipment(t-4)`  `Elect.equipment(t-5)` 
               7.299033                8.068077                8.487436 
 `Elect.equipment(t-6)`  `Elect.equipment(t-7)`  `Elect.equipment(t-8)` 
               8.505313                8.152339                7.938733 
 `Elect.equipment(t-9)` `Elect.equipment(t-10)` `Elect.equipment(t-1s)` 
               8.260676                7.268197                6.640223 
`Elect.equipment(t-2s)` `Elect.equipment(t-3s)` `Elect.equipment(t-4s)` 
               3.037417                2.966441                2.670900 
                     M1                      M2                      M3 
               6.860536                9.756861                8.553967 
                     M4                      M5                      M6 
               8.711894                6.078110                4.906072 
                     M7                      M8                      M9 
               6.894749                6.946762                7.523166 
                    M10                     M11 
              10.835742                6.111140

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = ; par2 = Include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 10 ; par5 = 4 ; par6 = 12 ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
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,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code