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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 13 Dec 2017 20:34:34 +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/13/t151319374198bzxn5gj04448s.htm/, Retrieved Wed, 15 May 2024 06:19:06 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309401, Retrieved Wed, 15 May 2024 06:19:06 +0000

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-       [Multiple Regression] [] [2017-12-13 19:34:34] [d45155ea4037f62d47a0a82219388c6c] [Current]

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99.5	62.4
89.9	67.4
96	76.1
86.9	67.4
85.6	74.5
82.5	72.6
80.5	60.5
82.7	66.1
87.7	76.5
92.2	76.8
93.9	77
94.5	71
94.8	74.8
85	73.7
87.4	80.5
79.5	71.8
80.5	76.9
79.8	79.9
78.8	65.9
81.5	69.5
82.6	75.1
89.5	79.6
90.7	75.2
90.7	68
95.7	72.8
86.6	71.5
92.4	78.5
86.3	76.8
84.7	75.3
83.1	76.7
82.2	69.7
84.5	67.8
81.2	77.5
88.2	82.5
89.1	75.3
89.1	70.9
98	76
91.7	73.7
90.9	79.7
87.1	77.8
84.5	73.3
83.5	78.3
85.9	71.9
89	67
87.6	82
92.9	83.7
89.1	74.8
96.9	80
104.1	74.3
93	76.8
98	89
85.9	81.9
84.8	76.8
81.5	88.9
85.3	75.8
79.3	75.5
82.3	89.1
87.8	88
95	85.9
104.4	89.3
103.5	82.9
99.5	81.2
96.6	90.5
88.1	86.4
86.4	81.8
83.6	91.3
85.7	73.4
79.8	76.6
81.9	91
87.1	87
92	89.7
106.1	90.7
108.5	86.5
101.4	86.6
100.1	98.8
84.4	84.4
81.6	91.4
81.5	95.7
80.9	78.5
79.9	81.7
81.2	94.3
90.5	98.5
91.7	95.4
102.7	91.7
104.8	92.8
98.7	90.5
100.8	102.2
93.6	91.8
88.1	95
86.8	102
80.8	88.9
84.6	89.6
82	97.9
93.6	108.6
99.7	100.8
102.1	95.1
106.6	101
95.9	100.9
92.1	102.5
85.9	105.4
79.3	98.4
83.7	105.3
84.1	96.5
83.2	88.1
85	107.9
93.1	107
95.4	92.5
107.3	95.7
112.5	85.2
97.8	85.5
99.1	94.7
85.6	86.2
87.2	88.8
86	93.4
92.7	83.4
98.8	82.9
99.2	96.7
101.4	96.2
98.8	92.8
113.2	92.8
119.2	90
107.4	95.4
111.6	108.3
94.8	96.3
97.7	95
87.3	109
91.4	92
93.4	92.3
90.8	107
96.1	105.5
102.6	105.4
107.7	103.9
111.4	99.2
98.9	102.2
100.7	121.5
91	102.3
94.8	110
87.3	105.9
88.8	91.9
92.3	100
90.9	111.7
95.2	104.9
98.2	103.3
103.5	101.8
109.7	100.8
116.4	104.2
87.5	116.5
87.2	97.9
85.5	100.7
79	107
81.8	96.3
78.2	96
78.9	104.5
76.9	107.4
84.4	102.4
93.1	94.9
101.6	98.8
97.1	96.8
99.3	108.2
77.8	103.8
74.3	102.3
80.4	107.2
85.3	102
80.1	92.6
78.8	105.2
91.8	113
100	105.6
108.4	101.6
101.7	101.7
94.4	102.7
89.5	109
69.8	105.5
72.5	103.3
69.1	108.6
71.9	98.2
67	90
63.8	112.4
73.2	111.9
74.2	102.1
84.7	102.4
97.8	101.7
87.4	98.7
81.8	114
68.6	105.1
64.9	98.3
64.1	110
63.6	96.5
59.8	92.2
66.3	112
78.1	111.4
86.8	107.5
89	103.4
111.3	103.5
99.7	107.4
103.7	117.6
90.4	110.2
77.6	104.3
73.9	115.9
81.5	98.9
88.2	101.9
78	113.5
84.7	109.5
94.8	110
101.5	114.2
112.4	106.9
96.6	109.2
96.9	124.2
76.1	104.7
76.9	111.9
83.8	119
89.4	102.9
89.1	106.3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 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 time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309401&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]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309401&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309401&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	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)EnergySupply[t] = + 0.385838 + 0.0261272`(1-Bs)(1-B)Totind`[t] -0.0367814`(1-Bs)(1-B)EnergySupply(t-1)`[t] -0.152222`(1-Bs)(1-B)EnergySupply(t-2)`[t] + 0.0234884`(1-Bs)(1-B)EnergySupply(t-3)`[t] -0.831327`(1-Bs)(1-B)EnergySupply(t-1s)`[t] -0.835232`(1-Bs)(1-B)EnergySupply(t-2s)`[t] -0.557115`(1-Bs)(1-B)EnergySupply(t-3s)`[t] -0.53352`(1-Bs)(1-B)EnergySupply(t-4s)`[t] -0.46032`(1-Bs)(1-B)EnergySupply(t-5s)`[t] -0.312055`(1-Bs)(1-B)EnergySupply(t-6s)`[t] -0.0760142`(1-Bs)(1-B)EnergySupply(t-7s)`[t] + 0.195412`(1-Bs)(1-B)EnergySupply(t-8s)`[t] + 0.659114`(1-Bs)(1-B)EnergySupply(t-9s)`[t] + 0.149573`(1-Bs)(1-B)EnergySupply(t-10s)`[t] + 0.121759`(1-Bs)(1-B)EnergySupply(t-11s)`[t] -0.184984`(1-Bs)(1-B)EnergySupply(t-12s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)EnergySupply[t] =  +  0.385838 +  0.0261272`(1-Bs)(1-B)Totind`[t] -0.0367814`(1-Bs)(1-B)EnergySupply(t-1)`[t] -0.152222`(1-Bs)(1-B)EnergySupply(t-2)`[t] +  0.0234884`(1-Bs)(1-B)EnergySupply(t-3)`[t] -0.831327`(1-Bs)(1-B)EnergySupply(t-1s)`[t] -0.835232`(1-Bs)(1-B)EnergySupply(t-2s)`[t] -0.557115`(1-Bs)(1-B)EnergySupply(t-3s)`[t] -0.53352`(1-Bs)(1-B)EnergySupply(t-4s)`[t] -0.46032`(1-Bs)(1-B)EnergySupply(t-5s)`[t] -0.312055`(1-Bs)(1-B)EnergySupply(t-6s)`[t] -0.0760142`(1-Bs)(1-B)EnergySupply(t-7s)`[t] +  0.195412`(1-Bs)(1-B)EnergySupply(t-8s)`[t] +  0.659114`(1-Bs)(1-B)EnergySupply(t-9s)`[t] +  0.149573`(1-Bs)(1-B)EnergySupply(t-10s)`[t] +  0.121759`(1-Bs)(1-B)EnergySupply(t-11s)`[t] -0.184984`(1-Bs)(1-B)EnergySupply(t-12s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309401&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)EnergySupply[t] =  +  0.385838 +  0.0261272`(1-Bs)(1-B)Totind`[t] -0.0367814`(1-Bs)(1-B)EnergySupply(t-1)`[t] -0.152222`(1-Bs)(1-B)EnergySupply(t-2)`[t] +  0.0234884`(1-Bs)(1-B)EnergySupply(t-3)`[t] -0.831327`(1-Bs)(1-B)EnergySupply(t-1s)`[t] -0.835232`(1-Bs)(1-B)EnergySupply(t-2s)`[t] -0.557115`(1-Bs)(1-B)EnergySupply(t-3s)`[t] -0.53352`(1-Bs)(1-B)EnergySupply(t-4s)`[t] -0.46032`(1-Bs)(1-B)EnergySupply(t-5s)`[t] -0.312055`(1-Bs)(1-B)EnergySupply(t-6s)`[t] -0.0760142`(1-Bs)(1-B)EnergySupply(t-7s)`[t] +  0.195412`(1-Bs)(1-B)EnergySupply(t-8s)`[t] +  0.659114`(1-Bs)(1-B)EnergySupply(t-9s)`[t] +  0.149573`(1-Bs)(1-B)EnergySupply(t-10s)`[t] +  0.121759`(1-Bs)(1-B)EnergySupply(t-11s)`[t] -0.184984`(1-Bs)(1-B)EnergySupply(t-12s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309401&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309401&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
(1-Bs)(1-B)EnergySupply[t] = + 0.385838 + 0.0261272`(1-Bs)(1-B)Totind`[t] -0.0367814`(1-Bs)(1-B)EnergySupply(t-1)`[t] -0.152222`(1-Bs)(1-B)EnergySupply(t-2)`[t] + 0.0234884`(1-Bs)(1-B)EnergySupply(t-3)`[t] -0.831327`(1-Bs)(1-B)EnergySupply(t-1s)`[t] -0.835232`(1-Bs)(1-B)EnergySupply(t-2s)`[t] -0.557115`(1-Bs)(1-B)EnergySupply(t-3s)`[t] -0.53352`(1-Bs)(1-B)EnergySupply(t-4s)`[t] -0.46032`(1-Bs)(1-B)EnergySupply(t-5s)`[t] -0.312055`(1-Bs)(1-B)EnergySupply(t-6s)`[t] -0.0760142`(1-Bs)(1-B)EnergySupply(t-7s)`[t] + 0.195412`(1-Bs)(1-B)EnergySupply(t-8s)`[t] + 0.659114`(1-Bs)(1-B)EnergySupply(t-9s)`[t] + 0.149573`(1-Bs)(1-B)EnergySupply(t-10s)`[t] + 0.121759`(1-Bs)(1-B)EnergySupply(t-11s)`[t] -0.184984`(1-Bs)(1-B)EnergySupply(t-12s)`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+0.3858	0.8541	+4.5170e-01	0.6543	0.3271
`(1-Bs)(1-B)Totind`	+0.02613	0.2312	+1.1300e-01	0.9107	0.4553
`(1-Bs)(1-B)EnergySupply(t-1)`	-0.03678	0.1181	-3.1150e-01	0.7572	0.3786
`(1-Bs)(1-B)EnergySupply(t-2)`	-0.1522	0.1111	-1.3700e+00	0.1795	0.08975
`(1-Bs)(1-B)EnergySupply(t-3)`	+0.02349	0.105	+2.2360e-01	0.8244	0.4122
`(1-Bs)(1-B)EnergySupply(t-1s)`	-0.8313	0.196	-4.2420e+00	0.0001541	7.705e-05
`(1-Bs)(1-B)EnergySupply(t-2s)`	-0.8352	0.2546	-3.2800e+00	0.002353	0.001177
`(1-Bs)(1-B)EnergySupply(t-3s)`	-0.5571	0.2847	-1.9570e+00	0.0584	0.0292
`(1-Bs)(1-B)EnergySupply(t-4s)`	-0.5335	0.2835	-1.8820e+00	0.06815	0.03408
`(1-Bs)(1-B)EnergySupply(t-5s)`	-0.4603	0.3298	-1.3960e+00	0.1715	0.08577
`(1-Bs)(1-B)EnergySupply(t-6s)`	-0.312	0.3842	-8.1220e-01	0.4222	0.2111
`(1-Bs)(1-B)EnergySupply(t-7s)`	-0.07601	0.4104	-1.8520e-01	0.8541	0.4271
`(1-Bs)(1-B)EnergySupply(t-8s)`	+0.1954	0.4455	+4.3870e-01	0.6636	0.3318
`(1-Bs)(1-B)EnergySupply(t-9s)`	+0.6591	0.4284	+1.5390e+00	0.1329	0.06644
`(1-Bs)(1-B)EnergySupply(t-10s)`	+0.1496	0.3612	+4.1410e-01	0.6813	0.3407
`(1-Bs)(1-B)EnergySupply(t-11s)`	+0.1218	0.3534	+3.4450e-01	0.7325	0.3663
`(1-Bs)(1-B)EnergySupply(t-12s)`	-0.185	0.3028	-6.1090e-01	0.5452	0.2726

\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) & +0.3858 &  0.8541 & +4.5170e-01 &  0.6543 &  0.3271 \tabularnewline
`(1-Bs)(1-B)Totind` & +0.02613 &  0.2312 & +1.1300e-01 &  0.9107 &  0.4553 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-1)` & -0.03678 &  0.1181 & -3.1150e-01 &  0.7572 &  0.3786 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-2)` & -0.1522 &  0.1111 & -1.3700e+00 &  0.1795 &  0.08975 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-3)` & +0.02349 &  0.105 & +2.2360e-01 &  0.8244 &  0.4122 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-1s)` & -0.8313 &  0.196 & -4.2420e+00 &  0.0001541 &  7.705e-05 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-2s)` & -0.8352 &  0.2546 & -3.2800e+00 &  0.002353 &  0.001177 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-3s)` & -0.5571 &  0.2847 & -1.9570e+00 &  0.0584 &  0.0292 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-4s)` & -0.5335 &  0.2835 & -1.8820e+00 &  0.06815 &  0.03408 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-5s)` & -0.4603 &  0.3298 & -1.3960e+00 &  0.1715 &  0.08577 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-6s)` & -0.312 &  0.3842 & -8.1220e-01 &  0.4222 &  0.2111 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-7s)` & -0.07601 &  0.4104 & -1.8520e-01 &  0.8541 &  0.4271 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-8s)` & +0.1954 &  0.4455 & +4.3870e-01 &  0.6636 &  0.3318 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-9s)` & +0.6591 &  0.4284 & +1.5390e+00 &  0.1329 &  0.06644 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-10s)` & +0.1496 &  0.3612 & +4.1410e-01 &  0.6813 &  0.3407 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-11s)` & +0.1218 &  0.3534 & +3.4450e-01 &  0.7325 &  0.3663 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-12s)` & -0.185 &  0.3028 & -6.1090e-01 &  0.5452 &  0.2726 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309401&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]+0.3858[/C][C] 0.8541[/C][C]+4.5170e-01[/C][C] 0.6543[/C][C] 0.3271[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind`[/C][C]+0.02613[/C][C] 0.2312[/C][C]+1.1300e-01[/C][C] 0.9107[/C][C] 0.4553[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-1)`[/C][C]-0.03678[/C][C] 0.1181[/C][C]-3.1150e-01[/C][C] 0.7572[/C][C] 0.3786[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-2)`[/C][C]-0.1522[/C][C] 0.1111[/C][C]-1.3700e+00[/C][C] 0.1795[/C][C] 0.08975[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-3)`[/C][C]+0.02349[/C][C] 0.105[/C][C]+2.2360e-01[/C][C] 0.8244[/C][C] 0.4122[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-1s)`[/C][C]-0.8313[/C][C] 0.196[/C][C]-4.2420e+00[/C][C] 0.0001541[/C][C] 7.705e-05[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-2s)`[/C][C]-0.8352[/C][C] 0.2546[/C][C]-3.2800e+00[/C][C] 0.002353[/C][C] 0.001177[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-3s)`[/C][C]-0.5571[/C][C] 0.2847[/C][C]-1.9570e+00[/C][C] 0.0584[/C][C] 0.0292[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-4s)`[/C][C]-0.5335[/C][C] 0.2835[/C][C]-1.8820e+00[/C][C] 0.06815[/C][C] 0.03408[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-5s)`[/C][C]-0.4603[/C][C] 0.3298[/C][C]-1.3960e+00[/C][C] 0.1715[/C][C] 0.08577[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-6s)`[/C][C]-0.312[/C][C] 0.3842[/C][C]-8.1220e-01[/C][C] 0.4222[/C][C] 0.2111[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-7s)`[/C][C]-0.07601[/C][C] 0.4104[/C][C]-1.8520e-01[/C][C] 0.8541[/C][C] 0.4271[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-8s)`[/C][C]+0.1954[/C][C] 0.4455[/C][C]+4.3870e-01[/C][C] 0.6636[/C][C] 0.3318[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-9s)`[/C][C]+0.6591[/C][C] 0.4284[/C][C]+1.5390e+00[/C][C] 0.1329[/C][C] 0.06644[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-10s)`[/C][C]+0.1496[/C][C] 0.3612[/C][C]+4.1410e-01[/C][C] 0.6813[/C][C] 0.3407[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-11s)`[/C][C]+0.1218[/C][C] 0.3534[/C][C]+3.4450e-01[/C][C] 0.7325[/C][C] 0.3663[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-12s)`[/C][C]-0.185[/C][C] 0.3028[/C][C]-6.1090e-01[/C][C] 0.5452[/C][C] 0.2726[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309401&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309401&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)	+0.3858	0.8541	+4.5170e-01	0.6543	0.3271
`(1-Bs)(1-B)Totind`	+0.02613	0.2312	+1.1300e-01	0.9107	0.4553
`(1-Bs)(1-B)EnergySupply(t-1)`	-0.03678	0.1181	-3.1150e-01	0.7572	0.3786
`(1-Bs)(1-B)EnergySupply(t-2)`	-0.1522	0.1111	-1.3700e+00	0.1795	0.08975
`(1-Bs)(1-B)EnergySupply(t-3)`	+0.02349	0.105	+2.2360e-01	0.8244	0.4122
`(1-Bs)(1-B)EnergySupply(t-1s)`	-0.8313	0.196	-4.2420e+00	0.0001541	7.705e-05
`(1-Bs)(1-B)EnergySupply(t-2s)`	-0.8352	0.2546	-3.2800e+00	0.002353	0.001177
`(1-Bs)(1-B)EnergySupply(t-3s)`	-0.5571	0.2847	-1.9570e+00	0.0584	0.0292
`(1-Bs)(1-B)EnergySupply(t-4s)`	-0.5335	0.2835	-1.8820e+00	0.06815	0.03408
`(1-Bs)(1-B)EnergySupply(t-5s)`	-0.4603	0.3298	-1.3960e+00	0.1715	0.08577
`(1-Bs)(1-B)EnergySupply(t-6s)`	-0.312	0.3842	-8.1220e-01	0.4222	0.2111
`(1-Bs)(1-B)EnergySupply(t-7s)`	-0.07601	0.4104	-1.8520e-01	0.8541	0.4271
`(1-Bs)(1-B)EnergySupply(t-8s)`	+0.1954	0.4455	+4.3870e-01	0.6636	0.3318
`(1-Bs)(1-B)EnergySupply(t-9s)`	+0.6591	0.4284	+1.5390e+00	0.1329	0.06644
`(1-Bs)(1-B)EnergySupply(t-10s)`	+0.1496	0.3612	+4.1410e-01	0.6813	0.3407
`(1-Bs)(1-B)EnergySupply(t-11s)`	+0.1218	0.3534	+3.4450e-01	0.7325	0.3663
`(1-Bs)(1-B)EnergySupply(t-12s)`	-0.185	0.3028	-6.1090e-01	0.5452	0.2726

Multiple Linear Regression - Regression Statistics
Multiple R	0.7549
R-squared	0.5699
Adjusted R-squared	0.3732
F-TEST (value)	2.898
F-TEST (DF numerator)	16
F-TEST (DF denominator)	35
p-value	0.004221
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.039
Sum Squared Residuals	1276

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7549 \tabularnewline
R-squared &  0.5699 \tabularnewline
Adjusted R-squared &  0.3732 \tabularnewline
F-TEST (value) &  2.898 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 35 \tabularnewline
p-value &  0.004221 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  6.039 \tabularnewline
Sum Squared Residuals &  1276 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309401&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7549[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.5699[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3732[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.898[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]35[/C][/ROW]
[ROW][C]p-value[/C][C] 0.004221[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 6.039[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1276[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309401&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309401&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	0.7549
R-squared	0.5699
Adjusted R-squared	0.3732
F-TEST (value)	2.898
F-TEST (DF numerator)	16
F-TEST (DF denominator)	35
p-value	0.004221
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.039
Sum Squared Residuals	1276

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=309401&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=309401&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309401&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	-1.8	-3.81	2.01
2	12.6	4.896	7.704
3	2.1	-1.124	3.224
4	-1.6	-4.397	2.797
5	-2	2.645	-4.645
6	15	8.342	6.658
7	0.7	3.42	-2.72
8	-0.3	0.772	-1.072
9	-15.2	-8.993	-6.207
10	-2.8	-0.02926	-2.771
11	-7.1	-3.985	-3.115
12	1.8	10.39	-8.591
13	6.2	2.959	3.241
14	-9.5	-6.647	-2.853
15	-2.1	-5.84	3.74
16	0.3	8.411	-8.111
17	-1.9	2.09	-3.99
18	-3.6	-4.12	0.5198
19	-7.2	-4.535	-2.665
20	2.1	3.746	-1.646
21	19.8	11.06	8.744
22	-3.1	-1.202	-1.898
23	-0.7	-3.933	3.233
24	6.5	8.132	-1.632
25	-6.4	-5.055	-1.345
26	2.6	1.83	0.77
27	-3.3	-2.794	-0.5061
28	1.1	7.292	-6.192
29	9.7	2.524	7.176
30	2.4	-1.585	3.985
31	7.7	1.775	5.925
32	-8.3	-5.136	-3.164
33	9.2	-5.935	15.13
34	-1.2	3.354	-4.554
35	9.6	3.371	6.229
36	-0.1	5.37	-5.47
37	-9.1	-0.9502	-8.15
38	-2.9	2.353	-5.253
39	8.1	2.769	5.331
40	10.5	8.751	1.749
41	-16.7	-10.26	-6.437
42	-5.1	-3.168	-1.932
43	1.4	-0.9386	2.339
44	4.5	3.055	1.445
45	-11.4	-14.17	2.771
46	-4.2	-2.962	-1.238
47	-3.7	-3.593	-0.1066
48	-7.5	-1.791	-5.709
49	13.6	14.49	-0.888
50	10.6	1.686	8.914
51	-2	-1.117	-0.8832
52	-7	-7.107	0.1075

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -1.8 & -3.81 &  2.01 \tabularnewline
2 &  12.6 &  4.896 &  7.704 \tabularnewline
3 &  2.1 & -1.124 &  3.224 \tabularnewline
4 & -1.6 & -4.397 &  2.797 \tabularnewline
5 & -2 &  2.645 & -4.645 \tabularnewline
6 &  15 &  8.342 &  6.658 \tabularnewline
7 &  0.7 &  3.42 & -2.72 \tabularnewline
8 & -0.3 &  0.772 & -1.072 \tabularnewline
9 & -15.2 & -8.993 & -6.207 \tabularnewline
10 & -2.8 & -0.02926 & -2.771 \tabularnewline
11 & -7.1 & -3.985 & -3.115 \tabularnewline
12 &  1.8 &  10.39 & -8.591 \tabularnewline
13 &  6.2 &  2.959 &  3.241 \tabularnewline
14 & -9.5 & -6.647 & -2.853 \tabularnewline
15 & -2.1 & -5.84 &  3.74 \tabularnewline
16 &  0.3 &  8.411 & -8.111 \tabularnewline
17 & -1.9 &  2.09 & -3.99 \tabularnewline
18 & -3.6 & -4.12 &  0.5198 \tabularnewline
19 & -7.2 & -4.535 & -2.665 \tabularnewline
20 &  2.1 &  3.746 & -1.646 \tabularnewline
21 &  19.8 &  11.06 &  8.744 \tabularnewline
22 & -3.1 & -1.202 & -1.898 \tabularnewline
23 & -0.7 & -3.933 &  3.233 \tabularnewline
24 &  6.5 &  8.132 & -1.632 \tabularnewline
25 & -6.4 & -5.055 & -1.345 \tabularnewline
26 &  2.6 &  1.83 &  0.77 \tabularnewline
27 & -3.3 & -2.794 & -0.5061 \tabularnewline
28 &  1.1 &  7.292 & -6.192 \tabularnewline
29 &  9.7 &  2.524 &  7.176 \tabularnewline
30 &  2.4 & -1.585 &  3.985 \tabularnewline
31 &  7.7 &  1.775 &  5.925 \tabularnewline
32 & -8.3 & -5.136 & -3.164 \tabularnewline
33 &  9.2 & -5.935 &  15.13 \tabularnewline
34 & -1.2 &  3.354 & -4.554 \tabularnewline
35 &  9.6 &  3.371 &  6.229 \tabularnewline
36 & -0.1 &  5.37 & -5.47 \tabularnewline
37 & -9.1 & -0.9502 & -8.15 \tabularnewline
38 & -2.9 &  2.353 & -5.253 \tabularnewline
39 &  8.1 &  2.769 &  5.331 \tabularnewline
40 &  10.5 &  8.751 &  1.749 \tabularnewline
41 & -16.7 & -10.26 & -6.437 \tabularnewline
42 & -5.1 & -3.168 & -1.932 \tabularnewline
43 &  1.4 & -0.9386 &  2.339 \tabularnewline
44 &  4.5 &  3.055 &  1.445 \tabularnewline
45 & -11.4 & -14.17 &  2.771 \tabularnewline
46 & -4.2 & -2.962 & -1.238 \tabularnewline
47 & -3.7 & -3.593 & -0.1066 \tabularnewline
48 & -7.5 & -1.791 & -5.709 \tabularnewline
49 &  13.6 &  14.49 & -0.888 \tabularnewline
50 &  10.6 &  1.686 &  8.914 \tabularnewline
51 & -2 & -1.117 & -0.8832 \tabularnewline
52 & -7 & -7.107 &  0.1075 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309401&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]-1.8[/C][C]-3.81[/C][C] 2.01[/C][/ROW]
[ROW][C]2[/C][C] 12.6[/C][C] 4.896[/C][C] 7.704[/C][/ROW]
[ROW][C]3[/C][C] 2.1[/C][C]-1.124[/C][C] 3.224[/C][/ROW]
[ROW][C]4[/C][C]-1.6[/C][C]-4.397[/C][C] 2.797[/C][/ROW]
[ROW][C]5[/C][C]-2[/C][C] 2.645[/C][C]-4.645[/C][/ROW]
[ROW][C]6[/C][C] 15[/C][C] 8.342[/C][C] 6.658[/C][/ROW]
[ROW][C]7[/C][C] 0.7[/C][C] 3.42[/C][C]-2.72[/C][/ROW]
[ROW][C]8[/C][C]-0.3[/C][C] 0.772[/C][C]-1.072[/C][/ROW]
[ROW][C]9[/C][C]-15.2[/C][C]-8.993[/C][C]-6.207[/C][/ROW]
[ROW][C]10[/C][C]-2.8[/C][C]-0.02926[/C][C]-2.771[/C][/ROW]
[ROW][C]11[/C][C]-7.1[/C][C]-3.985[/C][C]-3.115[/C][/ROW]
[ROW][C]12[/C][C] 1.8[/C][C] 10.39[/C][C]-8.591[/C][/ROW]
[ROW][C]13[/C][C] 6.2[/C][C] 2.959[/C][C] 3.241[/C][/ROW]
[ROW][C]14[/C][C]-9.5[/C][C]-6.647[/C][C]-2.853[/C][/ROW]
[ROW][C]15[/C][C]-2.1[/C][C]-5.84[/C][C] 3.74[/C][/ROW]
[ROW][C]16[/C][C] 0.3[/C][C] 8.411[/C][C]-8.111[/C][/ROW]
[ROW][C]17[/C][C]-1.9[/C][C] 2.09[/C][C]-3.99[/C][/ROW]
[ROW][C]18[/C][C]-3.6[/C][C]-4.12[/C][C] 0.5198[/C][/ROW]
[ROW][C]19[/C][C]-7.2[/C][C]-4.535[/C][C]-2.665[/C][/ROW]
[ROW][C]20[/C][C] 2.1[/C][C] 3.746[/C][C]-1.646[/C][/ROW]
[ROW][C]21[/C][C] 19.8[/C][C] 11.06[/C][C] 8.744[/C][/ROW]
[ROW][C]22[/C][C]-3.1[/C][C]-1.202[/C][C]-1.898[/C][/ROW]
[ROW][C]23[/C][C]-0.7[/C][C]-3.933[/C][C] 3.233[/C][/ROW]
[ROW][C]24[/C][C] 6.5[/C][C] 8.132[/C][C]-1.632[/C][/ROW]
[ROW][C]25[/C][C]-6.4[/C][C]-5.055[/C][C]-1.345[/C][/ROW]
[ROW][C]26[/C][C] 2.6[/C][C] 1.83[/C][C] 0.77[/C][/ROW]
[ROW][C]27[/C][C]-3.3[/C][C]-2.794[/C][C]-0.5061[/C][/ROW]
[ROW][C]28[/C][C] 1.1[/C][C] 7.292[/C][C]-6.192[/C][/ROW]
[ROW][C]29[/C][C] 9.7[/C][C] 2.524[/C][C] 7.176[/C][/ROW]
[ROW][C]30[/C][C] 2.4[/C][C]-1.585[/C][C] 3.985[/C][/ROW]
[ROW][C]31[/C][C] 7.7[/C][C] 1.775[/C][C] 5.925[/C][/ROW]
[ROW][C]32[/C][C]-8.3[/C][C]-5.136[/C][C]-3.164[/C][/ROW]
[ROW][C]33[/C][C] 9.2[/C][C]-5.935[/C][C] 15.13[/C][/ROW]
[ROW][C]34[/C][C]-1.2[/C][C] 3.354[/C][C]-4.554[/C][/ROW]
[ROW][C]35[/C][C] 9.6[/C][C] 3.371[/C][C] 6.229[/C][/ROW]
[ROW][C]36[/C][C]-0.1[/C][C] 5.37[/C][C]-5.47[/C][/ROW]
[ROW][C]37[/C][C]-9.1[/C][C]-0.9502[/C][C]-8.15[/C][/ROW]
[ROW][C]38[/C][C]-2.9[/C][C] 2.353[/C][C]-5.253[/C][/ROW]
[ROW][C]39[/C][C] 8.1[/C][C] 2.769[/C][C] 5.331[/C][/ROW]
[ROW][C]40[/C][C] 10.5[/C][C] 8.751[/C][C] 1.749[/C][/ROW]
[ROW][C]41[/C][C]-16.7[/C][C]-10.26[/C][C]-6.437[/C][/ROW]
[ROW][C]42[/C][C]-5.1[/C][C]-3.168[/C][C]-1.932[/C][/ROW]
[ROW][C]43[/C][C] 1.4[/C][C]-0.9386[/C][C] 2.339[/C][/ROW]
[ROW][C]44[/C][C] 4.5[/C][C] 3.055[/C][C] 1.445[/C][/ROW]
[ROW][C]45[/C][C]-11.4[/C][C]-14.17[/C][C] 2.771[/C][/ROW]
[ROW][C]46[/C][C]-4.2[/C][C]-2.962[/C][C]-1.238[/C][/ROW]
[ROW][C]47[/C][C]-3.7[/C][C]-3.593[/C][C]-0.1066[/C][/ROW]
[ROW][C]48[/C][C]-7.5[/C][C]-1.791[/C][C]-5.709[/C][/ROW]
[ROW][C]49[/C][C] 13.6[/C][C] 14.49[/C][C]-0.888[/C][/ROW]
[ROW][C]50[/C][C] 10.6[/C][C] 1.686[/C][C] 8.914[/C][/ROW]
[ROW][C]51[/C][C]-2[/C][C]-1.117[/C][C]-0.8832[/C][/ROW]
[ROW][C]52[/C][C]-7[/C][C]-7.107[/C][C] 0.1075[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309401&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309401&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	-1.8	-3.81	2.01
2	12.6	4.896	7.704
3	2.1	-1.124	3.224
4	-1.6	-4.397	2.797
5	-2	2.645	-4.645
6	15	8.342	6.658
7	0.7	3.42	-2.72
8	-0.3	0.772	-1.072
9	-15.2	-8.993	-6.207
10	-2.8	-0.02926	-2.771
11	-7.1	-3.985	-3.115
12	1.8	10.39	-8.591
13	6.2	2.959	3.241
14	-9.5	-6.647	-2.853
15	-2.1	-5.84	3.74
16	0.3	8.411	-8.111
17	-1.9	2.09	-3.99
18	-3.6	-4.12	0.5198
19	-7.2	-4.535	-2.665
20	2.1	3.746	-1.646
21	19.8	11.06	8.744
22	-3.1	-1.202	-1.898
23	-0.7	-3.933	3.233
24	6.5	8.132	-1.632
25	-6.4	-5.055	-1.345
26	2.6	1.83	0.77
27	-3.3	-2.794	-0.5061
28	1.1	7.292	-6.192
29	9.7	2.524	7.176
30	2.4	-1.585	3.985
31	7.7	1.775	5.925
32	-8.3	-5.136	-3.164
33	9.2	-5.935	15.13
34	-1.2	3.354	-4.554
35	9.6	3.371	6.229
36	-0.1	5.37	-5.47
37	-9.1	-0.9502	-8.15
38	-2.9	2.353	-5.253
39	8.1	2.769	5.331
40	10.5	8.751	1.749
41	-16.7	-10.26	-6.437
42	-5.1	-3.168	-1.932
43	1.4	-0.9386	2.339
44	4.5	3.055	1.445
45	-11.4	-14.17	2.771
46	-4.2	-2.962	-1.238
47	-3.7	-3.593	-0.1066
48	-7.5	-1.791	-5.709
49	13.6	14.49	-0.888
50	10.6	1.686	8.914
51	-2	-1.117	-0.8832
52	-7	-7.107	0.1075

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
20	0.4239	0.8478	0.5761
21	0.4546	0.9091	0.5454
22	0.296	0.592	0.704
23	0.2537	0.5074	0.7463
24	0.1674	0.3348	0.8326
25	0.1076	0.2152	0.8924
26	0.08121	0.1624	0.9188
27	0.0415	0.083	0.9585
28	0.1016	0.2031	0.8984
29	0.07667	0.1533	0.9233
30	0.1599	0.3198	0.8401
31	0.1115	0.2231	0.8885
32	0.06554	0.1311	0.9345

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 &  0.4239 &  0.8478 &  0.5761 \tabularnewline
21 &  0.4546 &  0.9091 &  0.5454 \tabularnewline
22 &  0.296 &  0.592 &  0.704 \tabularnewline
23 &  0.2537 &  0.5074 &  0.7463 \tabularnewline
24 &  0.1674 &  0.3348 &  0.8326 \tabularnewline
25 &  0.1076 &  0.2152 &  0.8924 \tabularnewline
26 &  0.08121 &  0.1624 &  0.9188 \tabularnewline
27 &  0.0415 &  0.083 &  0.9585 \tabularnewline
28 &  0.1016 &  0.2031 &  0.8984 \tabularnewline
29 &  0.07667 &  0.1533 &  0.9233 \tabularnewline
30 &  0.1599 &  0.3198 &  0.8401 \tabularnewline
31 &  0.1115 &  0.2231 &  0.8885 \tabularnewline
32 &  0.06554 &  0.1311 &  0.9345 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309401&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]20[/C][C] 0.4239[/C][C] 0.8478[/C][C] 0.5761[/C][/ROW]
[ROW][C]21[/C][C] 0.4546[/C][C] 0.9091[/C][C] 0.5454[/C][/ROW]
[ROW][C]22[/C][C] 0.296[/C][C] 0.592[/C][C] 0.704[/C][/ROW]
[ROW][C]23[/C][C] 0.2537[/C][C] 0.5074[/C][C] 0.7463[/C][/ROW]
[ROW][C]24[/C][C] 0.1674[/C][C] 0.3348[/C][C] 0.8326[/C][/ROW]
[ROW][C]25[/C][C] 0.1076[/C][C] 0.2152[/C][C] 0.8924[/C][/ROW]
[ROW][C]26[/C][C] 0.08121[/C][C] 0.1624[/C][C] 0.9188[/C][/ROW]
[ROW][C]27[/C][C] 0.0415[/C][C] 0.083[/C][C] 0.9585[/C][/ROW]
[ROW][C]28[/C][C] 0.1016[/C][C] 0.2031[/C][C] 0.8984[/C][/ROW]
[ROW][C]29[/C][C] 0.07667[/C][C] 0.1533[/C][C] 0.9233[/C][/ROW]
[ROW][C]30[/C][C] 0.1599[/C][C] 0.3198[/C][C] 0.8401[/C][/ROW]
[ROW][C]31[/C][C] 0.1115[/C][C] 0.2231[/C][C] 0.8885[/C][/ROW]
[ROW][C]32[/C][C] 0.06554[/C][C] 0.1311[/C][C] 0.9345[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309401&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309401&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
20	0.4239	0.8478	0.5761
21	0.4546	0.9091	0.5454
22	0.296	0.592	0.704
23	0.2537	0.5074	0.7463
24	0.1674	0.3348	0.8326
25	0.1076	0.2152	0.8924
26	0.08121	0.1624	0.9188
27	0.0415	0.083	0.9585
28	0.1016	0.2031	0.8984
29	0.07667	0.1533	0.9233
30	0.1599	0.3198	0.8401
31	0.1115	0.2231	0.8885
32	0.06554	0.1311	0.9345

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	0	0	OK
10% type I error level	1	0.0769231	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 & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0769231 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309401&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0769231[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309401&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309401&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	0	0	OK
10% type I error level	1	0.0769231	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.063038, df1 = 2, df2 = 33, p-value = 0.939
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1449, df1 = 32, df2 = 3, p-value = 0.535
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.68918, df1 = 2, df2 = 33, p-value = 0.5091

\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 = 0.063038, df1 = 2, df2 = 33, p-value = 0.939
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1449, df1 = 32, df2 = 3, p-value = 0.535
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.68918, df1 = 2, df2 = 33, p-value = 0.5091
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309401&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 = 0.063038, df1 = 2, df2 = 33, p-value = 0.939
[/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 = 1.1449, df1 = 32, df2 = 3, p-value = 0.535
[/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 = 0.68918, df1 = 2, df2 = 33, p-value = 0.5091
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309401&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309401&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 = 0.063038, df1 = 2, df2 = 33, p-value = 0.939
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1449, df1 = 32, df2 = 3, p-value = 0.535
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.68918, df1 = 2, df2 = 33, p-value = 0.5091

Variance Inflation Factors (Multicollinearity)

> vif
             `(1-Bs)(1-B)Totind`   `(1-Bs)(1-B)EnergySupply(t-1)` 
                        2.147595                         1.287960 
  `(1-Bs)(1-B)EnergySupply(t-2)`   `(1-Bs)(1-B)EnergySupply(t-3)` 
                        1.461918                         1.314608 
 `(1-Bs)(1-B)EnergySupply(t-1s)`  `(1-Bs)(1-B)EnergySupply(t-2s)` 
                        3.960747                         8.082962 
 `(1-Bs)(1-B)EnergySupply(t-3s)`  `(1-Bs)(1-B)EnergySupply(t-4s)` 
                        9.602395                         8.717156 
 `(1-Bs)(1-B)EnergySupply(t-5s)`  `(1-Bs)(1-B)EnergySupply(t-6s)` 
                        7.432279                         4.976869 
 `(1-Bs)(1-B)EnergySupply(t-7s)`  `(1-Bs)(1-B)EnergySupply(t-8s)` 
                        5.213723                         5.587424 
 `(1-Bs)(1-B)EnergySupply(t-9s)` `(1-Bs)(1-B)EnergySupply(t-10s)` 
                        5.221040                         3.542027 
`(1-Bs)(1-B)EnergySupply(t-11s)` `(1-Bs)(1-B)EnergySupply(t-12s)` 
                        3.090292                         2.087229

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
             `(1-Bs)(1-B)Totind`   `(1-Bs)(1-B)EnergySupply(t-1)` 
                        2.147595                         1.287960 
  `(1-Bs)(1-B)EnergySupply(t-2)`   `(1-Bs)(1-B)EnergySupply(t-3)` 
                        1.461918                         1.314608 
 `(1-Bs)(1-B)EnergySupply(t-1s)`  `(1-Bs)(1-B)EnergySupply(t-2s)` 
                        3.960747                         8.082962 
 `(1-Bs)(1-B)EnergySupply(t-3s)`  `(1-Bs)(1-B)EnergySupply(t-4s)` 
                        9.602395                         8.717156 
 `(1-Bs)(1-B)EnergySupply(t-5s)`  `(1-Bs)(1-B)EnergySupply(t-6s)` 
                        7.432279                         4.976869 
 `(1-Bs)(1-B)EnergySupply(t-7s)`  `(1-Bs)(1-B)EnergySupply(t-8s)` 
                        5.213723                         5.587424 
 `(1-Bs)(1-B)EnergySupply(t-9s)` `(1-Bs)(1-B)EnergySupply(t-10s)` 
                        5.221040                         3.542027 
`(1-Bs)(1-B)EnergySupply(t-11s)` `(1-Bs)(1-B)EnergySupply(t-12s)` 
                        3.090292                         2.087229 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309401&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
             `(1-Bs)(1-B)Totind`   `(1-Bs)(1-B)EnergySupply(t-1)` 
                        2.147595                         1.287960 
  `(1-Bs)(1-B)EnergySupply(t-2)`   `(1-Bs)(1-B)EnergySupply(t-3)` 
                        1.461918                         1.314608 
 `(1-Bs)(1-B)EnergySupply(t-1s)`  `(1-Bs)(1-B)EnergySupply(t-2s)` 
                        3.960747                         8.082962 
 `(1-Bs)(1-B)EnergySupply(t-3s)`  `(1-Bs)(1-B)EnergySupply(t-4s)` 
                        9.602395                         8.717156 
 `(1-Bs)(1-B)EnergySupply(t-5s)`  `(1-Bs)(1-B)EnergySupply(t-6s)` 
                        7.432279                         4.976869 
 `(1-Bs)(1-B)EnergySupply(t-7s)`  `(1-Bs)(1-B)EnergySupply(t-8s)` 
                        5.213723                         5.587424 
 `(1-Bs)(1-B)EnergySupply(t-9s)` `(1-Bs)(1-B)EnergySupply(t-10s)` 
                        5.221040                         3.542027 
`(1-Bs)(1-B)EnergySupply(t-11s)` `(1-Bs)(1-B)EnergySupply(t-12s)` 
                        3.090292                         2.087229 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309401&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309401&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
             `(1-Bs)(1-B)Totind`   `(1-Bs)(1-B)EnergySupply(t-1)` 
                        2.147595                         1.287960 
  `(1-Bs)(1-B)EnergySupply(t-2)`   `(1-Bs)(1-B)EnergySupply(t-3)` 
                        1.461918                         1.314608 
 `(1-Bs)(1-B)EnergySupply(t-1s)`  `(1-Bs)(1-B)EnergySupply(t-2s)` 
                        3.960747                         8.082962 
 `(1-Bs)(1-B)EnergySupply(t-3s)`  `(1-Bs)(1-B)EnergySupply(t-4s)` 
                        9.602395                         8.717156 
 `(1-Bs)(1-B)EnergySupply(t-5s)`  `(1-Bs)(1-B)EnergySupply(t-6s)` 
                        7.432279                         4.976869 
 `(1-Bs)(1-B)EnergySupply(t-7s)`  `(1-Bs)(1-B)EnergySupply(t-8s)` 
                        5.213723                         5.587424 
 `(1-Bs)(1-B)EnergySupply(t-9s)` `(1-Bs)(1-B)EnergySupply(t-10s)` 
                        5.221040                         3.542027 
`(1-Bs)(1-B)EnergySupply(t-11s)` `(1-Bs)(1-B)EnergySupply(t-12s)` 
                        3.090292                         2.087229

Figure 1

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 3 ; par5 = 12 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 3 ; par5 = 12 ; par6 = 12 ;

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

par6 <- '12'
par5 <- '12'
par4 <- '2'
par3 <- 'First and Seasonal Differences (s)'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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