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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 15 Dec 2017 15:54:48 +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/15/t15133499907qz737d2bhi7f3v.htm/, Retrieved Thu, 31 Oct 2024 22:47:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309795, Retrieved Thu, 31 Oct 2024 22:47:40 +0000

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [] [2017-12-15 14:54:48] [1fb90e819e5b19aec9e872ea972cd63e] [Current]

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52.1	137.6	113.4	139.9	127	90.4
52.6	122.7	137.6	113.4	139.9	127
45.6	40.3	122.7	137.6	113.4	139.9
46.3	126.5	40.3	122.7	137.6	113.4
56.1	134.6	126.5	40.3	122.7	137.6
50.7	131.1	134.6	126.5	40.3	122.7
55	119.1	131.1	134.6	126.5	40.3
50.7	98.7	119.1	131.1	134.6	126.5
54.6	92.2	98.7	119.1	131.1	134.6
58.1	111.2	92.2	98.7	119.1	131.1
60.5	117.9	111.2	92.2	98.7	119.1
56.3	102.4	117.9	111.2	92.2	98.7
57.6	122.1	102.4	117.9	111.2	92.2
63.5	122.2	122.1	102.4	117.9	111.2
49.3	45.4	122.2	122.1	102.4	117.9
50.6	118.6	45.4	122.2	122.1	102.4
54.8	109.8	118.6	45.4	122.2	122.1
58.9	127.6	109.8	118.6	45.4	122.2
54.3	106.3	127.6	109.8	118.6	45.4
50.1	74.6	106.3	127.6	109.8	118.6
57.2	82.7	74.6	106.3	127.6	109.8
57.3	86.5	82.7	74.6	106.3	127.6
61.8	103.6	86.5	82.7	74.6	106.3
60.4	114	103.6	86.5	82.7	74.6
58.1	112	114	103.6	86.5	82.7
59.1	115.3	112	114	103.6	86.5
57.7	48.1	115.3	112	114	103.6
52.8	100.5	48.1	115.3	112	114
59.1	120.7	100.5	48.1	115.3	112
62.1	122.7	120.7	100.5	48.1	115.3
57.9	107.6	122.7	120.7	100.5	48.1
53.1	70.8	107.6	122.7	120.7	100.5
64.9	82.5	70.8	107.6	122.7	120.7
57.1	91.6	82.5	70.8	107.6	122.7
66.8	115.4	91.6	82.5	70.8	107.6
63.5	108.7	115.4	91.6	82.5	70.8
56.5	101.4	108.7	115.4	91.6	82.5
62.4	114	101.4	108.7	115.4	91.6
60.9	51.4	114	101.4	108.7	115.4
57.4	96.2	51.4	114	101.4	108.7
69.2	125.5	96.2	51.4	114	101.4
68.5	120.1	125.5	96.2	51.4	114
60.3	96.9	120.1	125.5	96.2	51.4
71.3	77.3	96.9	120.1	125.5	96.2
59.7	86.6	77.3	96.9	120.1	125.5
67.2	98.2	86.6	77.3	96.9	120.1
79.3	121.7	98.2	86.6	77.3	96.9
71	106.8	121.7	98.2	86.6	77.3
64.6	100.6	106.8	121.7	98.2	86.6
78.1	124.5	100.6	106.8	121.7	98.2
73.4	42.7	124.5	100.6	106.8	121.7
70.1	107	42.7	124.5	100.6	106.8
82.5	123.8	107	42.7	124.5	100.6
79.4	117.3	123.8	107	42.7	124.5
78.9	101.9	117.3	123.8	107	42.7
88.1	86.3	101.9	117.3	123.8	107
77.8	78.7	86.3	101.9	117.3	123.8
70.5	92.2	78.7	86.3	101.9	117.3
82.9	103.6	92.2	78.7	86.3	101.9
78.9	120.8	103.6	92.2	78.7	86.3
74.6	105.5	120.8	103.6	92.2	78.7
79.5	127.8	105.5	120.8	103.6	92.2
72	36.9	127.8	105.5	120.8	103.6
71.9	112.4	36.9	127.8	105.5	120.8
86.6	127.5	112.4	36.9	127.8	105.5
83.6	111.5	127.5	112.4	36.9	127.8
80.5	108.7	111.5	127.5	112.4	36.9
76.7	87.3	108.7	111.5	127.5	112.4
81.2	84.6	87.3	108.7	111.5	127.5
81.4	96	84.6	87.3	108.7	111.5
94	118.3	96	84.6	87.3	108.7
77.6	107.5	118.3	96	84.6	87.3
81	121.5	107.5	118.3	96	84.6
86.5	130.4	121.5	107.5	118.3	96
75.8	41.5	130.4	121.5	107.5	118.3
78.9	116.4	41.5	130.4	121.5	107.5
88.8	130.2	116.4	41.5	130.4	121.5
90.6	121.4	130.2	116.4	41.5	130.4
87.5	120.1	121.4	130.2	116.4	41.5
84.5	88.3	120.1	121.4	130.2	116.4
81.2	97.9	88.3	120.1	121.4	130.2
76.8	109.6	97.9	88.3	120.1	121.4
87.7	126	109.6	97.9	88.3	120.1
79.6	112.7	126	109.6	97.9	88.3
84	115.7	112.7	126	109.6	97.9
90	128.2	115.7	112.7	126	109.6
88.6	47.9	128.2	115.7	112.7	126
81.6	121.4	47.9	128.2	115.7	112.7
80.5	123.1	121.4	47.9	128.2	115.7
86.5	137.2	123.1	121.4	47.9	128.2
82.7	119	137.2	123.1	121.4	47.9
81.5	81.5	119	137.2	123.1	121.4
89	115.3	81.5	119	137.2	123.1
87.2	124.2	115.3	81.5	119	137.2
92	102.9	124.2	115.3	81.5	119
90.8	137.6	102.9	124.2	115.3	81.5
86.3	120.7	137.6	102.9	124.2	115.3
95.1	130.6	120.7	137.6	102.9	124.2
96.5	55.8	130.6	120.7	137.6	102.9
82.4	110.5	55.8	130.6	120.7	137.6
101.5	134.9	110.5	55.8	130.6	120.7
94.9	125.7	134.9	110.5	55.8	130.6
81.4	105	125.7	134.9	110.5	55.8
91.1	82.6	105	125.7	134.9	110.5
70	90.8	82.6	105	125.7	134.9
74.7	107.2	90.8	82.6	105	125.7
86.2	124.9	107.2	90.8	82.6	105
74.6	108.7	124.9	107.2	90.8	82.6
75	108.5	108.7	124.9	107.2	90.8
84.4	124.5	108.5	108.7	124.9	107.2
85.3	52.1	124.5	108.5	108.7	124.9
75.7	106.8	52.1	124.5	108.5	108.7
87.7	129.8	106.8	52.1	124.5	108.5
85.9	129.2	129.8	106.8	52.1	124.5
84.2	95.5	129.2	129.8	106.8	52.1
87.4	75.1	95.5	129.2	129.8	106.8
88.9	77.7	75.1	95.5	129.2	129.8
101.4	86.3	77.7	75.1	95.5	129.2
107.1	130.3	86.3	77.7	75.1	95.5
89.8	110.4	130.3	86.3	77.7	75.1
93.3	100	110.4	130.3	86.3	77.7
109.6	127.2	100	110.4	130.3	86.3
101.5	46.7	127.2	100	110.4	130.3
94.4	109.9	46.7	127.2	100	110.4
103.5	127.7	109.9	46.7	127.2	100
99.3	122.2	127.7	109.9	46.7	127.2
105.9	100.9	122.2	127.7	109.9	46.7
105.3	60.7	100.9	122.2	127.7	109.9
97.7	86.7	60.7	100.9	122.2	127.7
106.4	112.3	86.7	60.7	100.9	122.2
138.7	134.2	112.3	86.7	60.7	100.9
107.3	105	134.2	112.3	86.7	60.7
105.9	126.5	105	134.2	112.3	86.7
109.8	114.5	126.5	105	134.2	112.3
103.6	43.6	114.5	126.5	105	134.2
117	112.4	43.6	114.5	126.5	105
110.5	129.4	112.4	43.6	114.5	126.5
102	116.2	129.4	112.4	43.6	114.5
96	115.9	116.2	129.4	112.4	43.6
93.6	85.6	115.9	116.2	129.4	112.4
97.9	92.5	85.6	115.9	116.2	129.4
99.4	91.2	92.5	85.6	115.9	116.2
126.4	128.8	91.2	92.5	85.6	115.9
94.4	103.6	128.8	91.2	92.5	85.6
93.1	113.8	103.6	128.8	91.2	92.5
98.9	120.9	113.8	103.6	128.8	91.2
111.7	52.5	120.9	113.8	103.6	128.8
104.9	112.8	52.5	120.9	113.8	103.6
110.3	115.8	112.8	52.5	120.9	113.8
109.2	123.4	115.8	112.8	52.5	120.9
105.3	112.1	123.4	115.8	112.8	52.5
99.1	71.9	112.1	123.4	115.8	112.8
105.1	76.6	71.9	112.1	123.4	115.8
99.1	91.2	76.6	71.9	112.1	123.4
119.4	105.4	91.2	76.6	71.9	112.1
118.2	107.8	105.4	91.2	76.6	71.9
109.5	105.9	107.8	105.4	91.2	76.6
118.6	114.5	105.9	107.8	105.4	91.2
120.8	54.4	114.5	105.9	107.8	105.4
107.5	97.2	54.4	114.5	105.9	107.8
112.7	116.9	97.2	54.4	114.5	105.9
123.5	121.5	116.9	97.2	54.4	114.5
117.5	101.2	121.5	116.9	97.2	54.4
111.1	81.6	101.2	121.5	116.9	97.2
104.2	100.4	81.6	101.2	121.5	116.9
113.8	101	100.4	81.6	101.2	121.5
124.5	110.6	101	100.4	81.6	101.2
122.9	100	110.6	101	100.4	81.6
118.9	98.7	100	110.6	101	100.4
132.1	106.2	98.7	100	110.6	101
115.7	51.8	106.2	98.7	100	110.6
105.9	89.8	51.8	106.2	98.7	100
138.7	116.3	89.8	51.8	106.2	98.7
131.5	118.3	116.3	89.8	51.8	106.2
127	94.3	118.3	116.3	89.8	51.8
120.1	71.7	94.3	118.3	116.3	89.8
117.5	90.8	71.7	94.3	118.3	116.3
101.2	93.6	90.8	71.7	94.3	118.3
131.1	112.3	93.6	90.8	71.7	94.3
119.5	97	112.3	93.6	90.8	71.7
110.8	90.2	97	112.3	93.6	90.8
114.9	114.6	90.2	97	112.3	93.6
114	50.9	114.6	90.2	97	112.3
115.2	94.3	50.9	114.6	90.2	97
127.4	112.2	94.3	50.9	114.6	90.2
120.6	114	112.2	94.3	50.9	114.6
118.7	88.4	114	112.2	94.3	50.9
111.5	67.7	88.4	114	112.2	94.3
108.9	87.6	67.7	88.4	114	112.2
109.8	96.3	87.6	67.7	88.4	114
125.8	97	96.3	87.6	67.7	88.4
118	105.8	97	96.3	87.6	67.7
111.5	95.2	105.8	97	96.3	87.6
136.5	119.6	95.2	105.8	97	96.3
130.5	45.4	119.6	95.2	105.8	97
124.4	98.6	45.4	119.6	95.2	105.8
131.3	112.7	98.6	45.4	119.6	95.2
121.4	101.3	112.7	98.6	45.4	119.6
113.3	84.7	101.3	112.7	98.6	45.4
144.8	78	84.7	101.3	112.7	98.6
118.9	73.6	78	84.7	101.3	112.7
124.4	96.3	73.6	78	84.7	101.3
138.2	113.8	96.3	73.6	78	84.7
122	85	113.8	96.3	73.6	78
122.1	103.5	85	113.8	96.3	73.6
134.8	106.4	103.5	85	113.8	96.3
136.8	44.3	106.4	103.5	85	113.8
133.1	95.9	44.3	106.4	103.5	85

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 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 time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309795&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]

11 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309795&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309795&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	11 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)Chemicals[t] = + 0.0251769 + 0.193903`(1-Bs)(1-B)Build0`[t] + 0.108724`(1-Bs)(1-B)Build1`[t] + 0.0983087`(1-Bs)(1-B)Build2`[t] + 0.0704262`(1-Bs)(1-B)Build3`[t] -0.0264919`(1-Bs)(1-B)Build4`[t] -0.515142`(1-Bs)(1-B)Chemicals(t-1)`[t] -0.378904`(1-Bs)(1-B)Chemicals(t-2)`[t] -0.257349`(1-Bs)(1-B)Chemicals(t-3)`[t] -0.075016`(1-Bs)(1-B)Chemicals(t-4)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)Chemicals[t] =  +  0.0251769 +  0.193903`(1-Bs)(1-B)Build0`[t] +  0.108724`(1-Bs)(1-B)Build1`[t] +  0.0983087`(1-Bs)(1-B)Build2`[t] +  0.0704262`(1-Bs)(1-B)Build3`[t] -0.0264919`(1-Bs)(1-B)Build4`[t] -0.515142`(1-Bs)(1-B)Chemicals(t-1)`[t] -0.378904`(1-Bs)(1-B)Chemicals(t-2)`[t] -0.257349`(1-Bs)(1-B)Chemicals(t-3)`[t] -0.075016`(1-Bs)(1-B)Chemicals(t-4)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309795&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)Chemicals[t] =  +  0.0251769 +  0.193903`(1-Bs)(1-B)Build0`[t] +  0.108724`(1-Bs)(1-B)Build1`[t] +  0.0983087`(1-Bs)(1-B)Build2`[t] +  0.0704262`(1-Bs)(1-B)Build3`[t] -0.0264919`(1-Bs)(1-B)Build4`[t] -0.515142`(1-Bs)(1-B)Chemicals(t-1)`[t] -0.378904`(1-Bs)(1-B)Chemicals(t-2)`[t] -0.257349`(1-Bs)(1-B)Chemicals(t-3)`[t] -0.075016`(1-Bs)(1-B)Chemicals(t-4)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309795&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309795&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)Chemicals[t] = + 0.0251769 + 0.193903`(1-Bs)(1-B)Build0`[t] + 0.108724`(1-Bs)(1-B)Build1`[t] + 0.0983087`(1-Bs)(1-B)Build2`[t] + 0.0704262`(1-Bs)(1-B)Build3`[t] -0.0264919`(1-Bs)(1-B)Build4`[t] -0.515142`(1-Bs)(1-B)Chemicals(t-1)`[t] -0.378904`(1-Bs)(1-B)Chemicals(t-2)`[t] -0.257349`(1-Bs)(1-B)Chemicals(t-3)`[t] -0.075016`(1-Bs)(1-B)Chemicals(t-4)`[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)	+0.02518	0.6122	+4.1120e-02	0.9672	0.4836
`(1-Bs)(1-B)Build0`	+0.1939	0.06039	+3.2110e+00	0.001567	0.0007837
`(1-Bs)(1-B)Build1`	+0.1087	0.08052	+1.3500e+00	0.1786	0.08931
`(1-Bs)(1-B)Build2`	+0.09831	0.08785	+1.1190e+00	0.2646	0.1323
`(1-Bs)(1-B)Build3`	+0.07043	0.07881	+8.9360e-01	0.3727	0.1864
`(1-Bs)(1-B)Build4`	-0.02649	0.05942	-4.4580e-01	0.6562	0.3281
`(1-Bs)(1-B)Chemicals(t-1)`	-0.5151	0.07501	-6.8680e+00	1.009e-10	5.043e-11
`(1-Bs)(1-B)Chemicals(t-2)`	-0.3789	0.08239	-4.5990e+00	7.964e-06	3.982e-06
`(1-Bs)(1-B)Chemicals(t-3)`	-0.2574	0.08252	-3.1190e+00	0.002114	0.001057
`(1-Bs)(1-B)Chemicals(t-4)`	-0.07502	0.07573	-9.9060e-01	0.3232	0.1616

\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) & +0.02518 &  0.6122 & +4.1120e-02 &  0.9672 &  0.4836 \tabularnewline
`(1-Bs)(1-B)Build0` & +0.1939 &  0.06039 & +3.2110e+00 &  0.001567 &  0.0007837 \tabularnewline
`(1-Bs)(1-B)Build1` & +0.1087 &  0.08052 & +1.3500e+00 &  0.1786 &  0.08931 \tabularnewline
`(1-Bs)(1-B)Build2` & +0.09831 &  0.08785 & +1.1190e+00 &  0.2646 &  0.1323 \tabularnewline
`(1-Bs)(1-B)Build3` & +0.07043 &  0.07881 & +8.9360e-01 &  0.3727 &  0.1864 \tabularnewline
`(1-Bs)(1-B)Build4` & -0.02649 &  0.05942 & -4.4580e-01 &  0.6562 &  0.3281 \tabularnewline
`(1-Bs)(1-B)Chemicals(t-1)` & -0.5151 &  0.07501 & -6.8680e+00 &  1.009e-10 &  5.043e-11 \tabularnewline
`(1-Bs)(1-B)Chemicals(t-2)` & -0.3789 &  0.08239 & -4.5990e+00 &  7.964e-06 &  3.982e-06 \tabularnewline
`(1-Bs)(1-B)Chemicals(t-3)` & -0.2574 &  0.08252 & -3.1190e+00 &  0.002114 &  0.001057 \tabularnewline
`(1-Bs)(1-B)Chemicals(t-4)` & -0.07502 &  0.07573 & -9.9060e-01 &  0.3232 &  0.1616 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309795&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]+0.02518[/C][C] 0.6122[/C][C]+4.1120e-02[/C][C] 0.9672[/C][C] 0.4836[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build0`[/C][C]+0.1939[/C][C] 0.06039[/C][C]+3.2110e+00[/C][C] 0.001567[/C][C] 0.0007837[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build1`[/C][C]+0.1087[/C][C] 0.08052[/C][C]+1.3500e+00[/C][C] 0.1786[/C][C] 0.08931[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build2`[/C][C]+0.09831[/C][C] 0.08785[/C][C]+1.1190e+00[/C][C] 0.2646[/C][C] 0.1323[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build3`[/C][C]+0.07043[/C][C] 0.07881[/C][C]+8.9360e-01[/C][C] 0.3727[/C][C] 0.1864[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build4`[/C][C]-0.02649[/C][C] 0.05942[/C][C]-4.4580e-01[/C][C] 0.6562[/C][C] 0.3281[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemicals(t-1)`[/C][C]-0.5151[/C][C] 0.07501[/C][C]-6.8680e+00[/C][C] 1.009e-10[/C][C] 5.043e-11[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemicals(t-2)`[/C][C]-0.3789[/C][C] 0.08239[/C][C]-4.5990e+00[/C][C] 7.964e-06[/C][C] 3.982e-06[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemicals(t-3)`[/C][C]-0.2574[/C][C] 0.08252[/C][C]-3.1190e+00[/C][C] 0.002114[/C][C] 0.001057[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemicals(t-4)`[/C][C]-0.07502[/C][C] 0.07573[/C][C]-9.9060e-01[/C][C] 0.3232[/C][C] 0.1616[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309795&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309795&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)	+0.02518	0.6122	+4.1120e-02	0.9672	0.4836
`(1-Bs)(1-B)Build0`	+0.1939	0.06039	+3.2110e+00	0.001567	0.0007837
`(1-Bs)(1-B)Build1`	+0.1087	0.08052	+1.3500e+00	0.1786	0.08931
`(1-Bs)(1-B)Build2`	+0.09831	0.08785	+1.1190e+00	0.2646	0.1323
`(1-Bs)(1-B)Build3`	+0.07043	0.07881	+8.9360e-01	0.3727	0.1864
`(1-Bs)(1-B)Build4`	-0.02649	0.05942	-4.4580e-01	0.6562	0.3281
`(1-Bs)(1-B)Chemicals(t-1)`	-0.5151	0.07501	-6.8680e+00	1.009e-10	5.043e-11
`(1-Bs)(1-B)Chemicals(t-2)`	-0.3789	0.08239	-4.5990e+00	7.964e-06	3.982e-06
`(1-Bs)(1-B)Chemicals(t-3)`	-0.2574	0.08252	-3.1190e+00	0.002114	0.001057
`(1-Bs)(1-B)Chemicals(t-4)`	-0.07502	0.07573	-9.9060e-01	0.3232	0.1616

Multiple Linear Regression - Regression Statistics
Multiple R	0.5626
R-squared	0.3166
Adjusted R-squared	0.2826
F-TEST (value)	9.315
F-TEST (DF numerator)	9
F-TEST (DF denominator)	181
p-value	1.408e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	8.461
Sum Squared Residuals	1.296e+04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5626 \tabularnewline
R-squared &  0.3166 \tabularnewline
Adjusted R-squared &  0.2826 \tabularnewline
F-TEST (value) &  9.315 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 181 \tabularnewline
p-value &  1.408e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  8.461 \tabularnewline
Sum Squared Residuals &  1.296e+04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309795&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5626[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3166[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2826[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 9.315[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]181[/C][/ROW]
[ROW][C]p-value[/C][C] 1.408e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 8.461[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.296e+04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309795&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309795&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	0.5626
R-squared	0.3166
Adjusted R-squared	0.2826
F-TEST (value)	9.315
F-TEST (DF numerator)	9
F-TEST (DF denominator)	181
p-value	1.408e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	8.461
Sum Squared Residuals	1.296e+04

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=309795&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=309795&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309795&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	9.5	5.142	4.358
2	-8.9	-4.574	-4.326
3	0.1	0.4524	-0.3524
4	3.2	3.957	-0.7571
5	-3.4	-3.773	0.3735
6	2.1	2.456	-0.3559
7	2.8	5.387	-2.587
8	-3.6	-3.405	-0.1954
9	-4.9	2.476	-7.376
10	12.8	4.66	8.14
11	-6.2	-8.885	2.685
12	2.1	5.006	-2.906
13	-1.1	-2.998	1.898
14	0.4	1.048	-0.6476
15	-0.6	0.8859	-1.486
16	4.7	-0.8197	5.52
17	-7.9	-0.4161	-7.484
18	5.2	4.144	1.056
19	-1.9	-2.502	0.6024
20	-4.7	-1.236	-3.464
21	4.9	2.298	2.602
22	-0.1	-0.619	0.519
23	1.4	-0.4074	1.807
24	5.5	0.6192	4.881
25	-3.7	-4.795	1.095
26	-4	-2.643	-1.357
27	15.8	4.536	11.26
28	-23.4	-6.213	-17.19
29	15.3	8.94	6.36
30	2.4	-1.353	3.753
31	-5	-4.173	-0.8268
32	0.6	-0.9586	1.559
33	7.6	1.262	6.338
34	-3.2	-5.966	2.766
35	0.2	2.114	-1.914
36	0.6	-2.291	2.891
37	-2.4	-1.413	-0.9868
38	7.7	3.269	4.431
39	-1.8	-3.083	1.283
40	1.3	-3.214	4.514
41	-14.8	-2.261	-12.54
42	0.3	3.316	-3.016
43	4.3	9.078	-4.778
44	2.1	2.525	-0.4252
45	-8.6	-0.6993	-7.901
46	-2.8	2.279	-5.079
47	3.2	3.397	-0.1969
48	2.3	1.615	0.6848
49	0.1	-2.531	2.631
50	-2.6	0.7617	-3.362
51	-13	-0.6104	-12.39
52	14.8	8.443	6.357
53	7.5	-1.317	8.817
54	0.2	-4.281	4.481
55	-12.4	-9.705	-2.695
56	7.7	6.728	0.9723
57	0.6	-1.199	1.799
58	-3.2	-0.4747	-2.725
59	3.2	1.984	1.216
60	-4.8	-2.983	-1.817
61	4.8	3.755	1.045
62	0	-0.3616	0.3616
63	0.8	-2.02	2.82
64	-7.8	0.6811	-8.481
65	-4.6	3.668	-8.268
66	-1.7	4.47	-6.17
67	8.3	4.636	3.664
68	1	-5.127	6.127
69	0.5	-4.019	4.519
70	9.3	-1.662	10.96
71	-10.1	-5.526	-4.574
72	-11	0.3926	-11.39
73	4.2	10.59	-6.385
74	-0.7	1.628	-2.328
75	1.8	0.8764	0.9236
76	10.8	3.452	7.348
77	2.6	-6.624	9.224
78	-6.1	-11.01	4.906
79	6.9	6.056	0.8435
80	-8.9	-5.882	-3.018
81	2.8	2.84	-0.04047
82	2.8	3.844	-1.044
83	-7.1	-6.681	-0.4186
84	20.2	5.811	14.39
85	-12.6	-12.06	-0.537
86	-9.7	-1.777	-7.923
87	10.9	7.593	3.307
88	-28.6	-5.997	-22.6
89	6.5	14.67	-8.166
90	6.7	12.43	-5.727
91	-10.4	-6.442	-3.958
92	4.9	6.061	-1.161
93	0.6	-0.2277	0.8277
94	-0.5	-1.814	1.314
95	4.5	2.96	1.54
96	-7.1	-2.674	-4.426
97	4.8	3.584	1.216
98	11.8	-2.665	14.46
99	-6.5	-6.661	0.1614
100	22.6	-3.304	25.9
101	7.8	-15.62	23.42
102	-5.8	-7.582	1.782
103	-5.7	-4.343	-1.357
104	3.1	1.261	1.839
105	6.9	4.253	2.647
106	-9	-5.115	-3.885
107	2.5	2.925	-0.4251
108	-2.9	0.3178	-3.218
109	-2.4	0.8233	-3.223
110	8.3	4.566	3.734
111	-3.8	-6.347	2.547
112	-9.1	3.069	-12.17
113	-3.8	9.094	-12.89
114	26.6	3.927	22.67
115	-14.1	-9.973	-4.127
116	-4.9	2.449	-7.349
117	-12.4	-5.722	-6.678
118	1.9	10.57	-8.669
119	20.5	6.832	13.67
120	-15.6	-9.905	-5.695
121	-4.3	1.42	-5.72
122	-12.6	6.029	-18.63
123	-1.8	13.86	-15.66
124	11.9	6.92	4.98
125	-7.2	-6.494	-0.7058
126	-5.3	-0.9835	-4.316
127	-0.6	0.7868	-1.387
128	0.1	1.701	-1.601
129	1.9	6.792	-4.892
130	19	0.8768	18.12
131	-20.2	-10.86	-9.336
132	11.9	0.9869	10.91
133	7.4	-2.138	9.538
134	2.1	-6.435	8.535
135	-3.8	-7.239	3.439
136	1.7	-2.359	4.059
137	-7.5	0.0386	-7.539
138	-6.7	0.6343	-7.334
139	30.8	10.64	20.16
140	-7.4	-11.97	4.568
141	3.3	-5.927	9.227
142	-10.6	-3.148	-7.452
143	-6.5	-0.09757	-6.402
144	-0.2	9.674	-9.874
145	11.9	5.13	6.77
146	-2.1	-5.443	3.343
147	-0.2	1.498	-1.698
148	-12.9	1.312	-14.21
149	15.6	6.683	8.917
150	-9.6	-2.253	-7.347
151	-0.4	-1.555	1.155
152	4.7	-2.287	6.987
153	4.1	-2.323	6.423
154	-18.6	-2.794	-15.81
155	3.5	6.841	-3.341
156	27.6	5.126	22.47
157	-18	-10.85	-7.154
158	1.5	-1.486	2.986
159	-0.5	-1.926	1.426
160	4.3	1.281	3.019
161	-25.9	-1.064	-24.84
162	19.2	13.67	5.526
163	-10	-0.7261	-9.274
164	-4.7	3.709	-8.409
165	-9.1	6.037	-15.14
166	15.5	6.548	8.952
167	11	-1.117	12.12
168	-20.6	-9.479	-11.12
169	0.4	1.617	-1.217
170	2.6	3.08	-0.4802
171	-0.3	2.437	-2.737
172	0	1.055	-1.055
173	17.2	0.7501	16.45
174	-13.9	-11.55	-2.353
175	3.8	3.993	-0.1931
176	2.2	-0.5838	2.784
177	20.9	0.271	20.63
178	-5.1	-11.75	6.646
179	-7.3	-6.265	-1.035
180	-5.3	-0.4284	-4.872
181	-3.1	2.517	-5.617
182	-6.2	6.796	-13
183	38.7	8.173	30.53
184	-23.3	-19.5	-3.8
185	4.6	1.625	2.975
186	-2.2	0.128	-2.328
187	-8.4	-3.661	-4.739
188	6.6	10.59	-3.986
189	-12.3	-3.86	-8.44
190	8	5.964	2.036
191	2.4	1.433	0.9666

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  9.5 &  5.142 &  4.358 \tabularnewline
2 & -8.9 & -4.574 & -4.326 \tabularnewline
3 &  0.1 &  0.4524 & -0.3524 \tabularnewline
4 &  3.2 &  3.957 & -0.7571 \tabularnewline
5 & -3.4 & -3.773 &  0.3735 \tabularnewline
6 &  2.1 &  2.456 & -0.3559 \tabularnewline
7 &  2.8 &  5.387 & -2.587 \tabularnewline
8 & -3.6 & -3.405 & -0.1954 \tabularnewline
9 & -4.9 &  2.476 & -7.376 \tabularnewline
10 &  12.8 &  4.66 &  8.14 \tabularnewline
11 & -6.2 & -8.885 &  2.685 \tabularnewline
12 &  2.1 &  5.006 & -2.906 \tabularnewline
13 & -1.1 & -2.998 &  1.898 \tabularnewline
14 &  0.4 &  1.048 & -0.6476 \tabularnewline
15 & -0.6 &  0.8859 & -1.486 \tabularnewline
16 &  4.7 & -0.8197 &  5.52 \tabularnewline
17 & -7.9 & -0.4161 & -7.484 \tabularnewline
18 &  5.2 &  4.144 &  1.056 \tabularnewline
19 & -1.9 & -2.502 &  0.6024 \tabularnewline
20 & -4.7 & -1.236 & -3.464 \tabularnewline
21 &  4.9 &  2.298 &  2.602 \tabularnewline
22 & -0.1 & -0.619 &  0.519 \tabularnewline
23 &  1.4 & -0.4074 &  1.807 \tabularnewline
24 &  5.5 &  0.6192 &  4.881 \tabularnewline
25 & -3.7 & -4.795 &  1.095 \tabularnewline
26 & -4 & -2.643 & -1.357 \tabularnewline
27 &  15.8 &  4.536 &  11.26 \tabularnewline
28 & -23.4 & -6.213 & -17.19 \tabularnewline
29 &  15.3 &  8.94 &  6.36 \tabularnewline
30 &  2.4 & -1.353 &  3.753 \tabularnewline
31 & -5 & -4.173 & -0.8268 \tabularnewline
32 &  0.6 & -0.9586 &  1.559 \tabularnewline
33 &  7.6 &  1.262 &  6.338 \tabularnewline
34 & -3.2 & -5.966 &  2.766 \tabularnewline
35 &  0.2 &  2.114 & -1.914 \tabularnewline
36 &  0.6 & -2.291 &  2.891 \tabularnewline
37 & -2.4 & -1.413 & -0.9868 \tabularnewline
38 &  7.7 &  3.269 &  4.431 \tabularnewline
39 & -1.8 & -3.083 &  1.283 \tabularnewline
40 &  1.3 & -3.214 &  4.514 \tabularnewline
41 & -14.8 & -2.261 & -12.54 \tabularnewline
42 &  0.3 &  3.316 & -3.016 \tabularnewline
43 &  4.3 &  9.078 & -4.778 \tabularnewline
44 &  2.1 &  2.525 & -0.4252 \tabularnewline
45 & -8.6 & -0.6993 & -7.901 \tabularnewline
46 & -2.8 &  2.279 & -5.079 \tabularnewline
47 &  3.2 &  3.397 & -0.1969 \tabularnewline
48 &  2.3 &  1.615 &  0.6848 \tabularnewline
49 &  0.1 & -2.531 &  2.631 \tabularnewline
50 & -2.6 &  0.7617 & -3.362 \tabularnewline
51 & -13 & -0.6104 & -12.39 \tabularnewline
52 &  14.8 &  8.443 &  6.357 \tabularnewline
53 &  7.5 & -1.317 &  8.817 \tabularnewline
54 &  0.2 & -4.281 &  4.481 \tabularnewline
55 & -12.4 & -9.705 & -2.695 \tabularnewline
56 &  7.7 &  6.728 &  0.9723 \tabularnewline
57 &  0.6 & -1.199 &  1.799 \tabularnewline
58 & -3.2 & -0.4747 & -2.725 \tabularnewline
59 &  3.2 &  1.984 &  1.216 \tabularnewline
60 & -4.8 & -2.983 & -1.817 \tabularnewline
61 &  4.8 &  3.755 &  1.045 \tabularnewline
62 &  0 & -0.3616 &  0.3616 \tabularnewline
63 &  0.8 & -2.02 &  2.82 \tabularnewline
64 & -7.8 &  0.6811 & -8.481 \tabularnewline
65 & -4.6 &  3.668 & -8.268 \tabularnewline
66 & -1.7 &  4.47 & -6.17 \tabularnewline
67 &  8.3 &  4.636 &  3.664 \tabularnewline
68 &  1 & -5.127 &  6.127 \tabularnewline
69 &  0.5 & -4.019 &  4.519 \tabularnewline
70 &  9.3 & -1.662 &  10.96 \tabularnewline
71 & -10.1 & -5.526 & -4.574 \tabularnewline
72 & -11 &  0.3926 & -11.39 \tabularnewline
73 &  4.2 &  10.59 & -6.385 \tabularnewline
74 & -0.7 &  1.628 & -2.328 \tabularnewline
75 &  1.8 &  0.8764 &  0.9236 \tabularnewline
76 &  10.8 &  3.452 &  7.348 \tabularnewline
77 &  2.6 & -6.624 &  9.224 \tabularnewline
78 & -6.1 & -11.01 &  4.906 \tabularnewline
79 &  6.9 &  6.056 &  0.8435 \tabularnewline
80 & -8.9 & -5.882 & -3.018 \tabularnewline
81 &  2.8 &  2.84 & -0.04047 \tabularnewline
82 &  2.8 &  3.844 & -1.044 \tabularnewline
83 & -7.1 & -6.681 & -0.4186 \tabularnewline
84 &  20.2 &  5.811 &  14.39 \tabularnewline
85 & -12.6 & -12.06 & -0.537 \tabularnewline
86 & -9.7 & -1.777 & -7.923 \tabularnewline
87 &  10.9 &  7.593 &  3.307 \tabularnewline
88 & -28.6 & -5.997 & -22.6 \tabularnewline
89 &  6.5 &  14.67 & -8.166 \tabularnewline
90 &  6.7 &  12.43 & -5.727 \tabularnewline
91 & -10.4 & -6.442 & -3.958 \tabularnewline
92 &  4.9 &  6.061 & -1.161 \tabularnewline
93 &  0.6 & -0.2277 &  0.8277 \tabularnewline
94 & -0.5 & -1.814 &  1.314 \tabularnewline
95 &  4.5 &  2.96 &  1.54 \tabularnewline
96 & -7.1 & -2.674 & -4.426 \tabularnewline
97 &  4.8 &  3.584 &  1.216 \tabularnewline
98 &  11.8 & -2.665 &  14.46 \tabularnewline
99 & -6.5 & -6.661 &  0.1614 \tabularnewline
100 &  22.6 & -3.304 &  25.9 \tabularnewline
101 &  7.8 & -15.62 &  23.42 \tabularnewline
102 & -5.8 & -7.582 &  1.782 \tabularnewline
103 & -5.7 & -4.343 & -1.357 \tabularnewline
104 &  3.1 &  1.261 &  1.839 \tabularnewline
105 &  6.9 &  4.253 &  2.647 \tabularnewline
106 & -9 & -5.115 & -3.885 \tabularnewline
107 &  2.5 &  2.925 & -0.4251 \tabularnewline
108 & -2.9 &  0.3178 & -3.218 \tabularnewline
109 & -2.4 &  0.8233 & -3.223 \tabularnewline
110 &  8.3 &  4.566 &  3.734 \tabularnewline
111 & -3.8 & -6.347 &  2.547 \tabularnewline
112 & -9.1 &  3.069 & -12.17 \tabularnewline
113 & -3.8 &  9.094 & -12.89 \tabularnewline
114 &  26.6 &  3.927 &  22.67 \tabularnewline
115 & -14.1 & -9.973 & -4.127 \tabularnewline
116 & -4.9 &  2.449 & -7.349 \tabularnewline
117 & -12.4 & -5.722 & -6.678 \tabularnewline
118 &  1.9 &  10.57 & -8.669 \tabularnewline
119 &  20.5 &  6.832 &  13.67 \tabularnewline
120 & -15.6 & -9.905 & -5.695 \tabularnewline
121 & -4.3 &  1.42 & -5.72 \tabularnewline
122 & -12.6 &  6.029 & -18.63 \tabularnewline
123 & -1.8 &  13.86 & -15.66 \tabularnewline
124 &  11.9 &  6.92 &  4.98 \tabularnewline
125 & -7.2 & -6.494 & -0.7058 \tabularnewline
126 & -5.3 & -0.9835 & -4.316 \tabularnewline
127 & -0.6 &  0.7868 & -1.387 \tabularnewline
128 &  0.1 &  1.701 & -1.601 \tabularnewline
129 &  1.9 &  6.792 & -4.892 \tabularnewline
130 &  19 &  0.8768 &  18.12 \tabularnewline
131 & -20.2 & -10.86 & -9.336 \tabularnewline
132 &  11.9 &  0.9869 &  10.91 \tabularnewline
133 &  7.4 & -2.138 &  9.538 \tabularnewline
134 &  2.1 & -6.435 &  8.535 \tabularnewline
135 & -3.8 & -7.239 &  3.439 \tabularnewline
136 &  1.7 & -2.359 &  4.059 \tabularnewline
137 & -7.5 &  0.0386 & -7.539 \tabularnewline
138 & -6.7 &  0.6343 & -7.334 \tabularnewline
139 &  30.8 &  10.64 &  20.16 \tabularnewline
140 & -7.4 & -11.97 &  4.568 \tabularnewline
141 &  3.3 & -5.927 &  9.227 \tabularnewline
142 & -10.6 & -3.148 & -7.452 \tabularnewline
143 & -6.5 & -0.09757 & -6.402 \tabularnewline
144 & -0.2 &  9.674 & -9.874 \tabularnewline
145 &  11.9 &  5.13 &  6.77 \tabularnewline
146 & -2.1 & -5.443 &  3.343 \tabularnewline
147 & -0.2 &  1.498 & -1.698 \tabularnewline
148 & -12.9 &  1.312 & -14.21 \tabularnewline
149 &  15.6 &  6.683 &  8.917 \tabularnewline
150 & -9.6 & -2.253 & -7.347 \tabularnewline
151 & -0.4 & -1.555 &  1.155 \tabularnewline
152 &  4.7 & -2.287 &  6.987 \tabularnewline
153 &  4.1 & -2.323 &  6.423 \tabularnewline
154 & -18.6 & -2.794 & -15.81 \tabularnewline
155 &  3.5 &  6.841 & -3.341 \tabularnewline
156 &  27.6 &  5.126 &  22.47 \tabularnewline
157 & -18 & -10.85 & -7.154 \tabularnewline
158 &  1.5 & -1.486 &  2.986 \tabularnewline
159 & -0.5 & -1.926 &  1.426 \tabularnewline
160 &  4.3 &  1.281 &  3.019 \tabularnewline
161 & -25.9 & -1.064 & -24.84 \tabularnewline
162 &  19.2 &  13.67 &  5.526 \tabularnewline
163 & -10 & -0.7261 & -9.274 \tabularnewline
164 & -4.7 &  3.709 & -8.409 \tabularnewline
165 & -9.1 &  6.037 & -15.14 \tabularnewline
166 &  15.5 &  6.548 &  8.952 \tabularnewline
167 &  11 & -1.117 &  12.12 \tabularnewline
168 & -20.6 & -9.479 & -11.12 \tabularnewline
169 &  0.4 &  1.617 & -1.217 \tabularnewline
170 &  2.6 &  3.08 & -0.4802 \tabularnewline
171 & -0.3 &  2.437 & -2.737 \tabularnewline
172 &  0 &  1.055 & -1.055 \tabularnewline
173 &  17.2 &  0.7501 &  16.45 \tabularnewline
174 & -13.9 & -11.55 & -2.353 \tabularnewline
175 &  3.8 &  3.993 & -0.1931 \tabularnewline
176 &  2.2 & -0.5838 &  2.784 \tabularnewline
177 &  20.9 &  0.271 &  20.63 \tabularnewline
178 & -5.1 & -11.75 &  6.646 \tabularnewline
179 & -7.3 & -6.265 & -1.035 \tabularnewline
180 & -5.3 & -0.4284 & -4.872 \tabularnewline
181 & -3.1 &  2.517 & -5.617 \tabularnewline
182 & -6.2 &  6.796 & -13 \tabularnewline
183 &  38.7 &  8.173 &  30.53 \tabularnewline
184 & -23.3 & -19.5 & -3.8 \tabularnewline
185 &  4.6 &  1.625 &  2.975 \tabularnewline
186 & -2.2 &  0.128 & -2.328 \tabularnewline
187 & -8.4 & -3.661 & -4.739 \tabularnewline
188 &  6.6 &  10.59 & -3.986 \tabularnewline
189 & -12.3 & -3.86 & -8.44 \tabularnewline
190 &  8 &  5.964 &  2.036 \tabularnewline
191 &  2.4 &  1.433 &  0.9666 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309795&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] 9.5[/C][C] 5.142[/C][C] 4.358[/C][/ROW]
[ROW][C]2[/C][C]-8.9[/C][C]-4.574[/C][C]-4.326[/C][/ROW]
[ROW][C]3[/C][C] 0.1[/C][C] 0.4524[/C][C]-0.3524[/C][/ROW]
[ROW][C]4[/C][C] 3.2[/C][C] 3.957[/C][C]-0.7571[/C][/ROW]
[ROW][C]5[/C][C]-3.4[/C][C]-3.773[/C][C] 0.3735[/C][/ROW]
[ROW][C]6[/C][C] 2.1[/C][C] 2.456[/C][C]-0.3559[/C][/ROW]
[ROW][C]7[/C][C] 2.8[/C][C] 5.387[/C][C]-2.587[/C][/ROW]
[ROW][C]8[/C][C]-3.6[/C][C]-3.405[/C][C]-0.1954[/C][/ROW]
[ROW][C]9[/C][C]-4.9[/C][C] 2.476[/C][C]-7.376[/C][/ROW]
[ROW][C]10[/C][C] 12.8[/C][C] 4.66[/C][C] 8.14[/C][/ROW]
[ROW][C]11[/C][C]-6.2[/C][C]-8.885[/C][C] 2.685[/C][/ROW]
[ROW][C]12[/C][C] 2.1[/C][C] 5.006[/C][C]-2.906[/C][/ROW]
[ROW][C]13[/C][C]-1.1[/C][C]-2.998[/C][C] 1.898[/C][/ROW]
[ROW][C]14[/C][C] 0.4[/C][C] 1.048[/C][C]-0.6476[/C][/ROW]
[ROW][C]15[/C][C]-0.6[/C][C] 0.8859[/C][C]-1.486[/C][/ROW]
[ROW][C]16[/C][C] 4.7[/C][C]-0.8197[/C][C] 5.52[/C][/ROW]
[ROW][C]17[/C][C]-7.9[/C][C]-0.4161[/C][C]-7.484[/C][/ROW]
[ROW][C]18[/C][C] 5.2[/C][C] 4.144[/C][C] 1.056[/C][/ROW]
[ROW][C]19[/C][C]-1.9[/C][C]-2.502[/C][C] 0.6024[/C][/ROW]
[ROW][C]20[/C][C]-4.7[/C][C]-1.236[/C][C]-3.464[/C][/ROW]
[ROW][C]21[/C][C] 4.9[/C][C] 2.298[/C][C] 2.602[/C][/ROW]
[ROW][C]22[/C][C]-0.1[/C][C]-0.619[/C][C] 0.519[/C][/ROW]
[ROW][C]23[/C][C] 1.4[/C][C]-0.4074[/C][C] 1.807[/C][/ROW]
[ROW][C]24[/C][C] 5.5[/C][C] 0.6192[/C][C] 4.881[/C][/ROW]
[ROW][C]25[/C][C]-3.7[/C][C]-4.795[/C][C] 1.095[/C][/ROW]
[ROW][C]26[/C][C]-4[/C][C]-2.643[/C][C]-1.357[/C][/ROW]
[ROW][C]27[/C][C] 15.8[/C][C] 4.536[/C][C] 11.26[/C][/ROW]
[ROW][C]28[/C][C]-23.4[/C][C]-6.213[/C][C]-17.19[/C][/ROW]
[ROW][C]29[/C][C] 15.3[/C][C] 8.94[/C][C] 6.36[/C][/ROW]
[ROW][C]30[/C][C] 2.4[/C][C]-1.353[/C][C] 3.753[/C][/ROW]
[ROW][C]31[/C][C]-5[/C][C]-4.173[/C][C]-0.8268[/C][/ROW]
[ROW][C]32[/C][C] 0.6[/C][C]-0.9586[/C][C] 1.559[/C][/ROW]
[ROW][C]33[/C][C] 7.6[/C][C] 1.262[/C][C] 6.338[/C][/ROW]
[ROW][C]34[/C][C]-3.2[/C][C]-5.966[/C][C] 2.766[/C][/ROW]
[ROW][C]35[/C][C] 0.2[/C][C] 2.114[/C][C]-1.914[/C][/ROW]
[ROW][C]36[/C][C] 0.6[/C][C]-2.291[/C][C] 2.891[/C][/ROW]
[ROW][C]37[/C][C]-2.4[/C][C]-1.413[/C][C]-0.9868[/C][/ROW]
[ROW][C]38[/C][C] 7.7[/C][C] 3.269[/C][C] 4.431[/C][/ROW]
[ROW][C]39[/C][C]-1.8[/C][C]-3.083[/C][C] 1.283[/C][/ROW]
[ROW][C]40[/C][C] 1.3[/C][C]-3.214[/C][C] 4.514[/C][/ROW]
[ROW][C]41[/C][C]-14.8[/C][C]-2.261[/C][C]-12.54[/C][/ROW]
[ROW][C]42[/C][C] 0.3[/C][C] 3.316[/C][C]-3.016[/C][/ROW]
[ROW][C]43[/C][C] 4.3[/C][C] 9.078[/C][C]-4.778[/C][/ROW]
[ROW][C]44[/C][C] 2.1[/C][C] 2.525[/C][C]-0.4252[/C][/ROW]
[ROW][C]45[/C][C]-8.6[/C][C]-0.6993[/C][C]-7.901[/C][/ROW]
[ROW][C]46[/C][C]-2.8[/C][C] 2.279[/C][C]-5.079[/C][/ROW]
[ROW][C]47[/C][C] 3.2[/C][C] 3.397[/C][C]-0.1969[/C][/ROW]
[ROW][C]48[/C][C] 2.3[/C][C] 1.615[/C][C] 0.6848[/C][/ROW]
[ROW][C]49[/C][C] 0.1[/C][C]-2.531[/C][C] 2.631[/C][/ROW]
[ROW][C]50[/C][C]-2.6[/C][C] 0.7617[/C][C]-3.362[/C][/ROW]
[ROW][C]51[/C][C]-13[/C][C]-0.6104[/C][C]-12.39[/C][/ROW]
[ROW][C]52[/C][C] 14.8[/C][C] 8.443[/C][C] 6.357[/C][/ROW]
[ROW][C]53[/C][C] 7.5[/C][C]-1.317[/C][C] 8.817[/C][/ROW]
[ROW][C]54[/C][C] 0.2[/C][C]-4.281[/C][C] 4.481[/C][/ROW]
[ROW][C]55[/C][C]-12.4[/C][C]-9.705[/C][C]-2.695[/C][/ROW]
[ROW][C]56[/C][C] 7.7[/C][C] 6.728[/C][C] 0.9723[/C][/ROW]
[ROW][C]57[/C][C] 0.6[/C][C]-1.199[/C][C] 1.799[/C][/ROW]
[ROW][C]58[/C][C]-3.2[/C][C]-0.4747[/C][C]-2.725[/C][/ROW]
[ROW][C]59[/C][C] 3.2[/C][C] 1.984[/C][C] 1.216[/C][/ROW]
[ROW][C]60[/C][C]-4.8[/C][C]-2.983[/C][C]-1.817[/C][/ROW]
[ROW][C]61[/C][C] 4.8[/C][C] 3.755[/C][C] 1.045[/C][/ROW]
[ROW][C]62[/C][C] 0[/C][C]-0.3616[/C][C] 0.3616[/C][/ROW]
[ROW][C]63[/C][C] 0.8[/C][C]-2.02[/C][C] 2.82[/C][/ROW]
[ROW][C]64[/C][C]-7.8[/C][C] 0.6811[/C][C]-8.481[/C][/ROW]
[ROW][C]65[/C][C]-4.6[/C][C] 3.668[/C][C]-8.268[/C][/ROW]
[ROW][C]66[/C][C]-1.7[/C][C] 4.47[/C][C]-6.17[/C][/ROW]
[ROW][C]67[/C][C] 8.3[/C][C] 4.636[/C][C] 3.664[/C][/ROW]
[ROW][C]68[/C][C] 1[/C][C]-5.127[/C][C] 6.127[/C][/ROW]
[ROW][C]69[/C][C] 0.5[/C][C]-4.019[/C][C] 4.519[/C][/ROW]
[ROW][C]70[/C][C] 9.3[/C][C]-1.662[/C][C] 10.96[/C][/ROW]
[ROW][C]71[/C][C]-10.1[/C][C]-5.526[/C][C]-4.574[/C][/ROW]
[ROW][C]72[/C][C]-11[/C][C] 0.3926[/C][C]-11.39[/C][/ROW]
[ROW][C]73[/C][C] 4.2[/C][C] 10.59[/C][C]-6.385[/C][/ROW]
[ROW][C]74[/C][C]-0.7[/C][C] 1.628[/C][C]-2.328[/C][/ROW]
[ROW][C]75[/C][C] 1.8[/C][C] 0.8764[/C][C] 0.9236[/C][/ROW]
[ROW][C]76[/C][C] 10.8[/C][C] 3.452[/C][C] 7.348[/C][/ROW]
[ROW][C]77[/C][C] 2.6[/C][C]-6.624[/C][C] 9.224[/C][/ROW]
[ROW][C]78[/C][C]-6.1[/C][C]-11.01[/C][C] 4.906[/C][/ROW]
[ROW][C]79[/C][C] 6.9[/C][C] 6.056[/C][C] 0.8435[/C][/ROW]
[ROW][C]80[/C][C]-8.9[/C][C]-5.882[/C][C]-3.018[/C][/ROW]
[ROW][C]81[/C][C] 2.8[/C][C] 2.84[/C][C]-0.04047[/C][/ROW]
[ROW][C]82[/C][C] 2.8[/C][C] 3.844[/C][C]-1.044[/C][/ROW]
[ROW][C]83[/C][C]-7.1[/C][C]-6.681[/C][C]-0.4186[/C][/ROW]
[ROW][C]84[/C][C] 20.2[/C][C] 5.811[/C][C] 14.39[/C][/ROW]
[ROW][C]85[/C][C]-12.6[/C][C]-12.06[/C][C]-0.537[/C][/ROW]
[ROW][C]86[/C][C]-9.7[/C][C]-1.777[/C][C]-7.923[/C][/ROW]
[ROW][C]87[/C][C] 10.9[/C][C] 7.593[/C][C] 3.307[/C][/ROW]
[ROW][C]88[/C][C]-28.6[/C][C]-5.997[/C][C]-22.6[/C][/ROW]
[ROW][C]89[/C][C] 6.5[/C][C] 14.67[/C][C]-8.166[/C][/ROW]
[ROW][C]90[/C][C] 6.7[/C][C] 12.43[/C][C]-5.727[/C][/ROW]
[ROW][C]91[/C][C]-10.4[/C][C]-6.442[/C][C]-3.958[/C][/ROW]
[ROW][C]92[/C][C] 4.9[/C][C] 6.061[/C][C]-1.161[/C][/ROW]
[ROW][C]93[/C][C] 0.6[/C][C]-0.2277[/C][C] 0.8277[/C][/ROW]
[ROW][C]94[/C][C]-0.5[/C][C]-1.814[/C][C] 1.314[/C][/ROW]
[ROW][C]95[/C][C] 4.5[/C][C] 2.96[/C][C] 1.54[/C][/ROW]
[ROW][C]96[/C][C]-7.1[/C][C]-2.674[/C][C]-4.426[/C][/ROW]
[ROW][C]97[/C][C] 4.8[/C][C] 3.584[/C][C] 1.216[/C][/ROW]
[ROW][C]98[/C][C] 11.8[/C][C]-2.665[/C][C] 14.46[/C][/ROW]
[ROW][C]99[/C][C]-6.5[/C][C]-6.661[/C][C] 0.1614[/C][/ROW]
[ROW][C]100[/C][C] 22.6[/C][C]-3.304[/C][C] 25.9[/C][/ROW]
[ROW][C]101[/C][C] 7.8[/C][C]-15.62[/C][C] 23.42[/C][/ROW]
[ROW][C]102[/C][C]-5.8[/C][C]-7.582[/C][C] 1.782[/C][/ROW]
[ROW][C]103[/C][C]-5.7[/C][C]-4.343[/C][C]-1.357[/C][/ROW]
[ROW][C]104[/C][C] 3.1[/C][C] 1.261[/C][C] 1.839[/C][/ROW]
[ROW][C]105[/C][C] 6.9[/C][C] 4.253[/C][C] 2.647[/C][/ROW]
[ROW][C]106[/C][C]-9[/C][C]-5.115[/C][C]-3.885[/C][/ROW]
[ROW][C]107[/C][C] 2.5[/C][C] 2.925[/C][C]-0.4251[/C][/ROW]
[ROW][C]108[/C][C]-2.9[/C][C] 0.3178[/C][C]-3.218[/C][/ROW]
[ROW][C]109[/C][C]-2.4[/C][C] 0.8233[/C][C]-3.223[/C][/ROW]
[ROW][C]110[/C][C] 8.3[/C][C] 4.566[/C][C] 3.734[/C][/ROW]
[ROW][C]111[/C][C]-3.8[/C][C]-6.347[/C][C] 2.547[/C][/ROW]
[ROW][C]112[/C][C]-9.1[/C][C] 3.069[/C][C]-12.17[/C][/ROW]
[ROW][C]113[/C][C]-3.8[/C][C] 9.094[/C][C]-12.89[/C][/ROW]
[ROW][C]114[/C][C] 26.6[/C][C] 3.927[/C][C] 22.67[/C][/ROW]
[ROW][C]115[/C][C]-14.1[/C][C]-9.973[/C][C]-4.127[/C][/ROW]
[ROW][C]116[/C][C]-4.9[/C][C] 2.449[/C][C]-7.349[/C][/ROW]
[ROW][C]117[/C][C]-12.4[/C][C]-5.722[/C][C]-6.678[/C][/ROW]
[ROW][C]118[/C][C] 1.9[/C][C] 10.57[/C][C]-8.669[/C][/ROW]
[ROW][C]119[/C][C] 20.5[/C][C] 6.832[/C][C] 13.67[/C][/ROW]
[ROW][C]120[/C][C]-15.6[/C][C]-9.905[/C][C]-5.695[/C][/ROW]
[ROW][C]121[/C][C]-4.3[/C][C] 1.42[/C][C]-5.72[/C][/ROW]
[ROW][C]122[/C][C]-12.6[/C][C] 6.029[/C][C]-18.63[/C][/ROW]
[ROW][C]123[/C][C]-1.8[/C][C] 13.86[/C][C]-15.66[/C][/ROW]
[ROW][C]124[/C][C] 11.9[/C][C] 6.92[/C][C] 4.98[/C][/ROW]
[ROW][C]125[/C][C]-7.2[/C][C]-6.494[/C][C]-0.7058[/C][/ROW]
[ROW][C]126[/C][C]-5.3[/C][C]-0.9835[/C][C]-4.316[/C][/ROW]
[ROW][C]127[/C][C]-0.6[/C][C] 0.7868[/C][C]-1.387[/C][/ROW]
[ROW][C]128[/C][C] 0.1[/C][C] 1.701[/C][C]-1.601[/C][/ROW]
[ROW][C]129[/C][C] 1.9[/C][C] 6.792[/C][C]-4.892[/C][/ROW]
[ROW][C]130[/C][C] 19[/C][C] 0.8768[/C][C] 18.12[/C][/ROW]
[ROW][C]131[/C][C]-20.2[/C][C]-10.86[/C][C]-9.336[/C][/ROW]
[ROW][C]132[/C][C] 11.9[/C][C] 0.9869[/C][C] 10.91[/C][/ROW]
[ROW][C]133[/C][C] 7.4[/C][C]-2.138[/C][C] 9.538[/C][/ROW]
[ROW][C]134[/C][C] 2.1[/C][C]-6.435[/C][C] 8.535[/C][/ROW]
[ROW][C]135[/C][C]-3.8[/C][C]-7.239[/C][C] 3.439[/C][/ROW]
[ROW][C]136[/C][C] 1.7[/C][C]-2.359[/C][C] 4.059[/C][/ROW]
[ROW][C]137[/C][C]-7.5[/C][C] 0.0386[/C][C]-7.539[/C][/ROW]
[ROW][C]138[/C][C]-6.7[/C][C] 0.6343[/C][C]-7.334[/C][/ROW]
[ROW][C]139[/C][C] 30.8[/C][C] 10.64[/C][C] 20.16[/C][/ROW]
[ROW][C]140[/C][C]-7.4[/C][C]-11.97[/C][C] 4.568[/C][/ROW]
[ROW][C]141[/C][C] 3.3[/C][C]-5.927[/C][C] 9.227[/C][/ROW]
[ROW][C]142[/C][C]-10.6[/C][C]-3.148[/C][C]-7.452[/C][/ROW]
[ROW][C]143[/C][C]-6.5[/C][C]-0.09757[/C][C]-6.402[/C][/ROW]
[ROW][C]144[/C][C]-0.2[/C][C] 9.674[/C][C]-9.874[/C][/ROW]
[ROW][C]145[/C][C] 11.9[/C][C] 5.13[/C][C] 6.77[/C][/ROW]
[ROW][C]146[/C][C]-2.1[/C][C]-5.443[/C][C] 3.343[/C][/ROW]
[ROW][C]147[/C][C]-0.2[/C][C] 1.498[/C][C]-1.698[/C][/ROW]
[ROW][C]148[/C][C]-12.9[/C][C] 1.312[/C][C]-14.21[/C][/ROW]
[ROW][C]149[/C][C] 15.6[/C][C] 6.683[/C][C] 8.917[/C][/ROW]
[ROW][C]150[/C][C]-9.6[/C][C]-2.253[/C][C]-7.347[/C][/ROW]
[ROW][C]151[/C][C]-0.4[/C][C]-1.555[/C][C] 1.155[/C][/ROW]
[ROW][C]152[/C][C] 4.7[/C][C]-2.287[/C][C] 6.987[/C][/ROW]
[ROW][C]153[/C][C] 4.1[/C][C]-2.323[/C][C] 6.423[/C][/ROW]
[ROW][C]154[/C][C]-18.6[/C][C]-2.794[/C][C]-15.81[/C][/ROW]
[ROW][C]155[/C][C] 3.5[/C][C] 6.841[/C][C]-3.341[/C][/ROW]
[ROW][C]156[/C][C] 27.6[/C][C] 5.126[/C][C] 22.47[/C][/ROW]
[ROW][C]157[/C][C]-18[/C][C]-10.85[/C][C]-7.154[/C][/ROW]
[ROW][C]158[/C][C] 1.5[/C][C]-1.486[/C][C] 2.986[/C][/ROW]
[ROW][C]159[/C][C]-0.5[/C][C]-1.926[/C][C] 1.426[/C][/ROW]
[ROW][C]160[/C][C] 4.3[/C][C] 1.281[/C][C] 3.019[/C][/ROW]
[ROW][C]161[/C][C]-25.9[/C][C]-1.064[/C][C]-24.84[/C][/ROW]
[ROW][C]162[/C][C] 19.2[/C][C] 13.67[/C][C] 5.526[/C][/ROW]
[ROW][C]163[/C][C]-10[/C][C]-0.7261[/C][C]-9.274[/C][/ROW]
[ROW][C]164[/C][C]-4.7[/C][C] 3.709[/C][C]-8.409[/C][/ROW]
[ROW][C]165[/C][C]-9.1[/C][C] 6.037[/C][C]-15.14[/C][/ROW]
[ROW][C]166[/C][C] 15.5[/C][C] 6.548[/C][C] 8.952[/C][/ROW]
[ROW][C]167[/C][C] 11[/C][C]-1.117[/C][C] 12.12[/C][/ROW]
[ROW][C]168[/C][C]-20.6[/C][C]-9.479[/C][C]-11.12[/C][/ROW]
[ROW][C]169[/C][C] 0.4[/C][C] 1.617[/C][C]-1.217[/C][/ROW]
[ROW][C]170[/C][C] 2.6[/C][C] 3.08[/C][C]-0.4802[/C][/ROW]
[ROW][C]171[/C][C]-0.3[/C][C] 2.437[/C][C]-2.737[/C][/ROW]
[ROW][C]172[/C][C] 0[/C][C] 1.055[/C][C]-1.055[/C][/ROW]
[ROW][C]173[/C][C] 17.2[/C][C] 0.7501[/C][C] 16.45[/C][/ROW]
[ROW][C]174[/C][C]-13.9[/C][C]-11.55[/C][C]-2.353[/C][/ROW]
[ROW][C]175[/C][C] 3.8[/C][C] 3.993[/C][C]-0.1931[/C][/ROW]
[ROW][C]176[/C][C] 2.2[/C][C]-0.5838[/C][C] 2.784[/C][/ROW]
[ROW][C]177[/C][C] 20.9[/C][C] 0.271[/C][C] 20.63[/C][/ROW]
[ROW][C]178[/C][C]-5.1[/C][C]-11.75[/C][C] 6.646[/C][/ROW]
[ROW][C]179[/C][C]-7.3[/C][C]-6.265[/C][C]-1.035[/C][/ROW]
[ROW][C]180[/C][C]-5.3[/C][C]-0.4284[/C][C]-4.872[/C][/ROW]
[ROW][C]181[/C][C]-3.1[/C][C] 2.517[/C][C]-5.617[/C][/ROW]
[ROW][C]182[/C][C]-6.2[/C][C] 6.796[/C][C]-13[/C][/ROW]
[ROW][C]183[/C][C] 38.7[/C][C] 8.173[/C][C] 30.53[/C][/ROW]
[ROW][C]184[/C][C]-23.3[/C][C]-19.5[/C][C]-3.8[/C][/ROW]
[ROW][C]185[/C][C] 4.6[/C][C] 1.625[/C][C] 2.975[/C][/ROW]
[ROW][C]186[/C][C]-2.2[/C][C] 0.128[/C][C]-2.328[/C][/ROW]
[ROW][C]187[/C][C]-8.4[/C][C]-3.661[/C][C]-4.739[/C][/ROW]
[ROW][C]188[/C][C] 6.6[/C][C] 10.59[/C][C]-3.986[/C][/ROW]
[ROW][C]189[/C][C]-12.3[/C][C]-3.86[/C][C]-8.44[/C][/ROW]
[ROW][C]190[/C][C] 8[/C][C] 5.964[/C][C] 2.036[/C][/ROW]
[ROW][C]191[/C][C] 2.4[/C][C] 1.433[/C][C] 0.9666[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309795&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309795&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	9.5	5.142	4.358
2	-8.9	-4.574	-4.326
3	0.1	0.4524	-0.3524
4	3.2	3.957	-0.7571
5	-3.4	-3.773	0.3735
6	2.1	2.456	-0.3559
7	2.8	5.387	-2.587
8	-3.6	-3.405	-0.1954
9	-4.9	2.476	-7.376
10	12.8	4.66	8.14
11	-6.2	-8.885	2.685
12	2.1	5.006	-2.906
13	-1.1	-2.998	1.898
14	0.4	1.048	-0.6476
15	-0.6	0.8859	-1.486
16	4.7	-0.8197	5.52
17	-7.9	-0.4161	-7.484
18	5.2	4.144	1.056
19	-1.9	-2.502	0.6024
20	-4.7	-1.236	-3.464
21	4.9	2.298	2.602
22	-0.1	-0.619	0.519
23	1.4	-0.4074	1.807
24	5.5	0.6192	4.881
25	-3.7	-4.795	1.095
26	-4	-2.643	-1.357
27	15.8	4.536	11.26
28	-23.4	-6.213	-17.19
29	15.3	8.94	6.36
30	2.4	-1.353	3.753
31	-5	-4.173	-0.8268
32	0.6	-0.9586	1.559
33	7.6	1.262	6.338
34	-3.2	-5.966	2.766
35	0.2	2.114	-1.914
36	0.6	-2.291	2.891
37	-2.4	-1.413	-0.9868
38	7.7	3.269	4.431
39	-1.8	-3.083	1.283
40	1.3	-3.214	4.514
41	-14.8	-2.261	-12.54
42	0.3	3.316	-3.016
43	4.3	9.078	-4.778
44	2.1	2.525	-0.4252
45	-8.6	-0.6993	-7.901
46	-2.8	2.279	-5.079
47	3.2	3.397	-0.1969
48	2.3	1.615	0.6848
49	0.1	-2.531	2.631
50	-2.6	0.7617	-3.362
51	-13	-0.6104	-12.39
52	14.8	8.443	6.357
53	7.5	-1.317	8.817
54	0.2	-4.281	4.481
55	-12.4	-9.705	-2.695
56	7.7	6.728	0.9723
57	0.6	-1.199	1.799
58	-3.2	-0.4747	-2.725
59	3.2	1.984	1.216
60	-4.8	-2.983	-1.817
61	4.8	3.755	1.045
62	0	-0.3616	0.3616
63	0.8	-2.02	2.82
64	-7.8	0.6811	-8.481
65	-4.6	3.668	-8.268
66	-1.7	4.47	-6.17
67	8.3	4.636	3.664
68	1	-5.127	6.127
69	0.5	-4.019	4.519
70	9.3	-1.662	10.96
71	-10.1	-5.526	-4.574
72	-11	0.3926	-11.39
73	4.2	10.59	-6.385
74	-0.7	1.628	-2.328
75	1.8	0.8764	0.9236
76	10.8	3.452	7.348
77	2.6	-6.624	9.224
78	-6.1	-11.01	4.906
79	6.9	6.056	0.8435
80	-8.9	-5.882	-3.018
81	2.8	2.84	-0.04047
82	2.8	3.844	-1.044
83	-7.1	-6.681	-0.4186
84	20.2	5.811	14.39
85	-12.6	-12.06	-0.537
86	-9.7	-1.777	-7.923
87	10.9	7.593	3.307
88	-28.6	-5.997	-22.6
89	6.5	14.67	-8.166
90	6.7	12.43	-5.727
91	-10.4	-6.442	-3.958
92	4.9	6.061	-1.161
93	0.6	-0.2277	0.8277
94	-0.5	-1.814	1.314
95	4.5	2.96	1.54
96	-7.1	-2.674	-4.426
97	4.8	3.584	1.216
98	11.8	-2.665	14.46
99	-6.5	-6.661	0.1614
100	22.6	-3.304	25.9
101	7.8	-15.62	23.42
102	-5.8	-7.582	1.782
103	-5.7	-4.343	-1.357
104	3.1	1.261	1.839
105	6.9	4.253	2.647
106	-9	-5.115	-3.885
107	2.5	2.925	-0.4251
108	-2.9	0.3178	-3.218
109	-2.4	0.8233	-3.223
110	8.3	4.566	3.734
111	-3.8	-6.347	2.547
112	-9.1	3.069	-12.17
113	-3.8	9.094	-12.89
114	26.6	3.927	22.67
115	-14.1	-9.973	-4.127
116	-4.9	2.449	-7.349
117	-12.4	-5.722	-6.678
118	1.9	10.57	-8.669
119	20.5	6.832	13.67
120	-15.6	-9.905	-5.695
121	-4.3	1.42	-5.72
122	-12.6	6.029	-18.63
123	-1.8	13.86	-15.66
124	11.9	6.92	4.98
125	-7.2	-6.494	-0.7058
126	-5.3	-0.9835	-4.316
127	-0.6	0.7868	-1.387
128	0.1	1.701	-1.601
129	1.9	6.792	-4.892
130	19	0.8768	18.12
131	-20.2	-10.86	-9.336
132	11.9	0.9869	10.91
133	7.4	-2.138	9.538
134	2.1	-6.435	8.535
135	-3.8	-7.239	3.439
136	1.7	-2.359	4.059
137	-7.5	0.0386	-7.539
138	-6.7	0.6343	-7.334
139	30.8	10.64	20.16
140	-7.4	-11.97	4.568
141	3.3	-5.927	9.227
142	-10.6	-3.148	-7.452
143	-6.5	-0.09757	-6.402
144	-0.2	9.674	-9.874
145	11.9	5.13	6.77
146	-2.1	-5.443	3.343
147	-0.2	1.498	-1.698
148	-12.9	1.312	-14.21
149	15.6	6.683	8.917
150	-9.6	-2.253	-7.347
151	-0.4	-1.555	1.155
152	4.7	-2.287	6.987
153	4.1	-2.323	6.423
154	-18.6	-2.794	-15.81
155	3.5	6.841	-3.341
156	27.6	5.126	22.47
157	-18	-10.85	-7.154
158	1.5	-1.486	2.986
159	-0.5	-1.926	1.426
160	4.3	1.281	3.019
161	-25.9	-1.064	-24.84
162	19.2	13.67	5.526
163	-10	-0.7261	-9.274
164	-4.7	3.709	-8.409
165	-9.1	6.037	-15.14
166	15.5	6.548	8.952
167	11	-1.117	12.12
168	-20.6	-9.479	-11.12
169	0.4	1.617	-1.217
170	2.6	3.08	-0.4802
171	-0.3	2.437	-2.737
172	0	1.055	-1.055
173	17.2	0.7501	16.45
174	-13.9	-11.55	-2.353
175	3.8	3.993	-0.1931
176	2.2	-0.5838	2.784
177	20.9	0.271	20.63
178	-5.1	-11.75	6.646
179	-7.3	-6.265	-1.035
180	-5.3	-0.4284	-4.872
181	-3.1	2.517	-5.617
182	-6.2	6.796	-13
183	38.7	8.173	30.53
184	-23.3	-19.5	-3.8
185	4.6	1.625	2.975
186	-2.2	0.128	-2.328
187	-8.4	-3.661	-4.739
188	6.6	10.59	-3.986
189	-12.3	-3.86	-8.44
190	8	5.964	2.036
191	2.4	1.433	0.9666

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	0.07417	0.1483	0.9258
14	0.03194	0.06387	0.9681
15	0.01543	0.03086	0.9846
16	0.004901	0.009801	0.9951
17	0.001939	0.003877	0.9981
18	0.0007269	0.001454	0.9993
19	0.0003887	0.0007773	0.9996
20	0.0002237	0.0004474	0.9998
21	8.393e-05	0.0001679	0.9999
22	2.516e-05	5.032e-05	1
23	6.08e-05	0.0001216	0.9999
24	0.0001222	0.0002443	0.9999
25	4.574e-05	9.147e-05	1
26	2.045e-05	4.09e-05	1
27	0.0001177	0.0002354	0.9999
28	0.0008552	0.00171	0.9991
29	0.0004503	0.0009006	0.9996
30	0.0002604	0.0005208	0.9997
31	0.000259	0.0005181	0.9997
32	0.0001226	0.0002453	0.9999
33	6.403e-05	0.0001281	0.9999
34	7.326e-05	0.0001465	0.9999
35	3.478e-05	6.956e-05	1
36	1.628e-05	3.256e-05	1
37	8.579e-06	1.716e-05	1
38	5.762e-06	1.152e-05	1
39	3.137e-06	6.275e-06	1
40	2.909e-06	5.818e-06	1
41	2.962e-05	5.925e-05	1
42	0.0001154	0.0002307	0.9999
43	0.0001136	0.0002272	0.9999
44	6.139e-05	0.0001228	0.9999
45	4.71e-05	9.419e-05	1
46	5.93e-05	0.0001186	0.9999
47	3.611e-05	7.222e-05	1
48	1.991e-05	3.981e-05	1
49	1.518e-05	3.037e-05	1
50	8.492e-06	1.698e-05	1
51	6.304e-05	0.0001261	0.9999
52	3.998e-05	7.995e-05	1
53	7.118e-05	0.0001424	0.9999
54	7.597e-05	0.0001519	0.9999
55	4.417e-05	8.835e-05	1
56	2.513e-05	5.026e-05	1
57	1.409e-05	2.818e-05	1
58	8.053e-06	1.611e-05	1
59	4.421e-06	8.843e-06	1
60	2.446e-06	4.893e-06	1
61	1.3e-06	2.6e-06	1
62	6.803e-07	1.361e-06	1
63	4.061e-07	8.122e-07	1
64	4.732e-07	9.464e-07	1
65	9e-07	1.8e-06	1
66	9.155e-07	1.831e-06	1
67	5.167e-07	1.033e-06	1
68	4.191e-07	8.381e-07	1
69	3.09e-07	6.181e-07	1
70	8.008e-07	1.602e-06	1
71	4.86e-07	9.719e-07	1
72	9.868e-07	1.974e-06	1
73	9.039e-07	1.808e-06	1
74	5.489e-07	1.098e-06	1
75	2.985e-07	5.971e-07	1
76	2.389e-07	4.778e-07	1
77	3.63e-07	7.26e-07	1
78	3.537e-07	7.073e-07	1
79	1.919e-07	3.838e-07	1
80	1.224e-07	2.447e-07	1
81	6.921e-08	1.384e-07	1
82	3.715e-08	7.43e-08	1
83	1.947e-08	3.894e-08	1
84	7.437e-08	1.487e-07	1
85	3.979e-08	7.958e-08	1
86	4.474e-08	8.948e-08	1
87	2.519e-08	5.038e-08	1
88	1.171e-06	2.341e-06	1
89	1.394e-06	2.787e-06	1
90	1.263e-06	2.527e-06	1
91	8.176e-07	1.635e-06	1
92	4.792e-07	9.583e-07	1
93	2.731e-07	5.462e-07	1
94	1.581e-07	3.161e-07	1
95	8.941e-08	1.788e-07	1
96	5.688e-08	1.138e-07	1
97	3.11e-08	6.22e-08	1
98	1.689e-07	3.378e-07	1
99	9.356e-08	1.871e-07	1
100	7.263e-06	1.453e-05	1
101	0.0001166	0.0002332	0.9999
102	9.349e-05	0.000187	0.9999
103	7.184e-05	0.0001437	0.9999
104	5.465e-05	0.0001093	0.9999
105	3.955e-05	7.909e-05	1
106	2.876e-05	5.752e-05	1
107	1.81e-05	3.62e-05	1
108	1.197e-05	2.394e-05	1
109	7.841e-06	1.568e-05	1
110	5.283e-06	1.057e-05	1
111	3.288e-06	6.576e-06	1
112	6.185e-06	1.237e-05	1
113	1.03e-05	2.059e-05	1
114	0.0002717	0.0005434	0.9997
115	0.0001992	0.0003984	0.9998
116	0.0001729	0.0003458	0.9998
117	0.0001723	0.0003447	0.9998
118	0.0001655	0.0003311	0.9998
119	0.0003344	0.0006689	0.9997
120	0.0002845	0.000569	0.9997
121	0.0002194	0.0004388	0.9998
122	0.001016	0.002031	0.999
123	0.002774	0.005548	0.9972
124	0.002191	0.004383	0.9978
125	0.001619	0.003238	0.9984
126	0.00123	0.00246	0.9988
127	0.0008585	0.001717	0.9991
128	0.0005884	0.001177	0.9994
129	0.0004814	0.0009629	0.9995
130	0.001582	0.003165	0.9984
131	0.001857	0.003715	0.9981
132	0.002781	0.005562	0.9972
133	0.002746	0.005492	0.9973
134	0.002679	0.005358	0.9973
135	0.002119	0.004238	0.9979
136	0.001774	0.003547	0.9982
137	0.001805	0.003609	0.9982
138	0.001431	0.002862	0.9986
139	0.004507	0.009014	0.9955
140	0.003695	0.00739	0.9963
141	0.003524	0.007048	0.9965
142	0.002942	0.005884	0.9971
143	0.002757	0.005514	0.9972
144	0.003304	0.006608	0.9967
145	0.002793	0.005586	0.9972
146	0.002037	0.004074	0.998
147	0.001378	0.002756	0.9986
148	0.005018	0.01004	0.995
149	0.0043	0.008599	0.9957
150	0.003628	0.007256	0.9964
151	0.003318	0.006637	0.9967
152	0.009018	0.01804	0.991
153	0.01395	0.0279	0.986
154	0.01948	0.03896	0.9805
155	0.01485	0.0297	0.9851
156	0.05271	0.1054	0.9473
157	0.05523	0.1105	0.9448
158	0.04777	0.09554	0.9522
159	0.04773	0.09547	0.9523
160	0.03479	0.06957	0.9652
161	0.1703	0.3406	0.8297
162	0.1383	0.2765	0.8617
163	0.1751	0.3502	0.8249
164	0.2065	0.413	0.7935
165	0.4303	0.8606	0.5697
166	0.3739	0.7478	0.6261
167	0.3212	0.6425	0.6788
168	0.3776	0.7552	0.6224
169	0.342	0.684	0.658
170	0.4838	0.9675	0.5162
171	0.4398	0.8797	0.5602
172	0.4434	0.8868	0.5566
173	0.439	0.878	0.561
174	0.3584	0.7168	0.6416
175	0.3175	0.6349	0.6825
176	0.2883	0.5766	0.7117
177	0.2083	0.4166	0.7917
178	0.3692	0.7385	0.6308

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 &  0.07417 &  0.1483 &  0.9258 \tabularnewline
14 &  0.03194 &  0.06387 &  0.9681 \tabularnewline
15 &  0.01543 &  0.03086 &  0.9846 \tabularnewline
16 &  0.004901 &  0.009801 &  0.9951 \tabularnewline
17 &  0.001939 &  0.003877 &  0.9981 \tabularnewline
18 &  0.0007269 &  0.001454 &  0.9993 \tabularnewline
19 &  0.0003887 &  0.0007773 &  0.9996 \tabularnewline
20 &  0.0002237 &  0.0004474 &  0.9998 \tabularnewline
21 &  8.393e-05 &  0.0001679 &  0.9999 \tabularnewline
22 &  2.516e-05 &  5.032e-05 &  1 \tabularnewline
23 &  6.08e-05 &  0.0001216 &  0.9999 \tabularnewline
24 &  0.0001222 &  0.0002443 &  0.9999 \tabularnewline
25 &  4.574e-05 &  9.147e-05 &  1 \tabularnewline
26 &  2.045e-05 &  4.09e-05 &  1 \tabularnewline
27 &  0.0001177 &  0.0002354 &  0.9999 \tabularnewline
28 &  0.0008552 &  0.00171 &  0.9991 \tabularnewline
29 &  0.0004503 &  0.0009006 &  0.9996 \tabularnewline
30 &  0.0002604 &  0.0005208 &  0.9997 \tabularnewline
31 &  0.000259 &  0.0005181 &  0.9997 \tabularnewline
32 &  0.0001226 &  0.0002453 &  0.9999 \tabularnewline
33 &  6.403e-05 &  0.0001281 &  0.9999 \tabularnewline
34 &  7.326e-05 &  0.0001465 &  0.9999 \tabularnewline
35 &  3.478e-05 &  6.956e-05 &  1 \tabularnewline
36 &  1.628e-05 &  3.256e-05 &  1 \tabularnewline
37 &  8.579e-06 &  1.716e-05 &  1 \tabularnewline
38 &  5.762e-06 &  1.152e-05 &  1 \tabularnewline
39 &  3.137e-06 &  6.275e-06 &  1 \tabularnewline
40 &  2.909e-06 &  5.818e-06 &  1 \tabularnewline
41 &  2.962e-05 &  5.925e-05 &  1 \tabularnewline
42 &  0.0001154 &  0.0002307 &  0.9999 \tabularnewline
43 &  0.0001136 &  0.0002272 &  0.9999 \tabularnewline
44 &  6.139e-05 &  0.0001228 &  0.9999 \tabularnewline
45 &  4.71e-05 &  9.419e-05 &  1 \tabularnewline
46 &  5.93e-05 &  0.0001186 &  0.9999 \tabularnewline
47 &  3.611e-05 &  7.222e-05 &  1 \tabularnewline
48 &  1.991e-05 &  3.981e-05 &  1 \tabularnewline
49 &  1.518e-05 &  3.037e-05 &  1 \tabularnewline
50 &  8.492e-06 &  1.698e-05 &  1 \tabularnewline
51 &  6.304e-05 &  0.0001261 &  0.9999 \tabularnewline
52 &  3.998e-05 &  7.995e-05 &  1 \tabularnewline
53 &  7.118e-05 &  0.0001424 &  0.9999 \tabularnewline
54 &  7.597e-05 &  0.0001519 &  0.9999 \tabularnewline
55 &  4.417e-05 &  8.835e-05 &  1 \tabularnewline
56 &  2.513e-05 &  5.026e-05 &  1 \tabularnewline
57 &  1.409e-05 &  2.818e-05 &  1 \tabularnewline
58 &  8.053e-06 &  1.611e-05 &  1 \tabularnewline
59 &  4.421e-06 &  8.843e-06 &  1 \tabularnewline
60 &  2.446e-06 &  4.893e-06 &  1 \tabularnewline
61 &  1.3e-06 &  2.6e-06 &  1 \tabularnewline
62 &  6.803e-07 &  1.361e-06 &  1 \tabularnewline
63 &  4.061e-07 &  8.122e-07 &  1 \tabularnewline
64 &  4.732e-07 &  9.464e-07 &  1 \tabularnewline
65 &  9e-07 &  1.8e-06 &  1 \tabularnewline
66 &  9.155e-07 &  1.831e-06 &  1 \tabularnewline
67 &  5.167e-07 &  1.033e-06 &  1 \tabularnewline
68 &  4.191e-07 &  8.381e-07 &  1 \tabularnewline
69 &  3.09e-07 &  6.181e-07 &  1 \tabularnewline
70 &  8.008e-07 &  1.602e-06 &  1 \tabularnewline
71 &  4.86e-07 &  9.719e-07 &  1 \tabularnewline
72 &  9.868e-07 &  1.974e-06 &  1 \tabularnewline
73 &  9.039e-07 &  1.808e-06 &  1 \tabularnewline
74 &  5.489e-07 &  1.098e-06 &  1 \tabularnewline
75 &  2.985e-07 &  5.971e-07 &  1 \tabularnewline
76 &  2.389e-07 &  4.778e-07 &  1 \tabularnewline
77 &  3.63e-07 &  7.26e-07 &  1 \tabularnewline
78 &  3.537e-07 &  7.073e-07 &  1 \tabularnewline
79 &  1.919e-07 &  3.838e-07 &  1 \tabularnewline
80 &  1.224e-07 &  2.447e-07 &  1 \tabularnewline
81 &  6.921e-08 &  1.384e-07 &  1 \tabularnewline
82 &  3.715e-08 &  7.43e-08 &  1 \tabularnewline
83 &  1.947e-08 &  3.894e-08 &  1 \tabularnewline
84 &  7.437e-08 &  1.487e-07 &  1 \tabularnewline
85 &  3.979e-08 &  7.958e-08 &  1 \tabularnewline
86 &  4.474e-08 &  8.948e-08 &  1 \tabularnewline
87 &  2.519e-08 &  5.038e-08 &  1 \tabularnewline
88 &  1.171e-06 &  2.341e-06 &  1 \tabularnewline
89 &  1.394e-06 &  2.787e-06 &  1 \tabularnewline
90 &  1.263e-06 &  2.527e-06 &  1 \tabularnewline
91 &  8.176e-07 &  1.635e-06 &  1 \tabularnewline
92 &  4.792e-07 &  9.583e-07 &  1 \tabularnewline
93 &  2.731e-07 &  5.462e-07 &  1 \tabularnewline
94 &  1.581e-07 &  3.161e-07 &  1 \tabularnewline
95 &  8.941e-08 &  1.788e-07 &  1 \tabularnewline
96 &  5.688e-08 &  1.138e-07 &  1 \tabularnewline
97 &  3.11e-08 &  6.22e-08 &  1 \tabularnewline
98 &  1.689e-07 &  3.378e-07 &  1 \tabularnewline
99 &  9.356e-08 &  1.871e-07 &  1 \tabularnewline
100 &  7.263e-06 &  1.453e-05 &  1 \tabularnewline
101 &  0.0001166 &  0.0002332 &  0.9999 \tabularnewline
102 &  9.349e-05 &  0.000187 &  0.9999 \tabularnewline
103 &  7.184e-05 &  0.0001437 &  0.9999 \tabularnewline
104 &  5.465e-05 &  0.0001093 &  0.9999 \tabularnewline
105 &  3.955e-05 &  7.909e-05 &  1 \tabularnewline
106 &  2.876e-05 &  5.752e-05 &  1 \tabularnewline
107 &  1.81e-05 &  3.62e-05 &  1 \tabularnewline
108 &  1.197e-05 &  2.394e-05 &  1 \tabularnewline
109 &  7.841e-06 &  1.568e-05 &  1 \tabularnewline
110 &  5.283e-06 &  1.057e-05 &  1 \tabularnewline
111 &  3.288e-06 &  6.576e-06 &  1 \tabularnewline
112 &  6.185e-06 &  1.237e-05 &  1 \tabularnewline
113 &  1.03e-05 &  2.059e-05 &  1 \tabularnewline
114 &  0.0002717 &  0.0005434 &  0.9997 \tabularnewline
115 &  0.0001992 &  0.0003984 &  0.9998 \tabularnewline
116 &  0.0001729 &  0.0003458 &  0.9998 \tabularnewline
117 &  0.0001723 &  0.0003447 &  0.9998 \tabularnewline
118 &  0.0001655 &  0.0003311 &  0.9998 \tabularnewline
119 &  0.0003344 &  0.0006689 &  0.9997 \tabularnewline
120 &  0.0002845 &  0.000569 &  0.9997 \tabularnewline
121 &  0.0002194 &  0.0004388 &  0.9998 \tabularnewline
122 &  0.001016 &  0.002031 &  0.999 \tabularnewline
123 &  0.002774 &  0.005548 &  0.9972 \tabularnewline
124 &  0.002191 &  0.004383 &  0.9978 \tabularnewline
125 &  0.001619 &  0.003238 &  0.9984 \tabularnewline
126 &  0.00123 &  0.00246 &  0.9988 \tabularnewline
127 &  0.0008585 &  0.001717 &  0.9991 \tabularnewline
128 &  0.0005884 &  0.001177 &  0.9994 \tabularnewline
129 &  0.0004814 &  0.0009629 &  0.9995 \tabularnewline
130 &  0.001582 &  0.003165 &  0.9984 \tabularnewline
131 &  0.001857 &  0.003715 &  0.9981 \tabularnewline
132 &  0.002781 &  0.005562 &  0.9972 \tabularnewline
133 &  0.002746 &  0.005492 &  0.9973 \tabularnewline
134 &  0.002679 &  0.005358 &  0.9973 \tabularnewline
135 &  0.002119 &  0.004238 &  0.9979 \tabularnewline
136 &  0.001774 &  0.003547 &  0.9982 \tabularnewline
137 &  0.001805 &  0.003609 &  0.9982 \tabularnewline
138 &  0.001431 &  0.002862 &  0.9986 \tabularnewline
139 &  0.004507 &  0.009014 &  0.9955 \tabularnewline
140 &  0.003695 &  0.00739 &  0.9963 \tabularnewline
141 &  0.003524 &  0.007048 &  0.9965 \tabularnewline
142 &  0.002942 &  0.005884 &  0.9971 \tabularnewline
143 &  0.002757 &  0.005514 &  0.9972 \tabularnewline
144 &  0.003304 &  0.006608 &  0.9967 \tabularnewline
145 &  0.002793 &  0.005586 &  0.9972 \tabularnewline
146 &  0.002037 &  0.004074 &  0.998 \tabularnewline
147 &  0.001378 &  0.002756 &  0.9986 \tabularnewline
148 &  0.005018 &  0.01004 &  0.995 \tabularnewline
149 &  0.0043 &  0.008599 &  0.9957 \tabularnewline
150 &  0.003628 &  0.007256 &  0.9964 \tabularnewline
151 &  0.003318 &  0.006637 &  0.9967 \tabularnewline
152 &  0.009018 &  0.01804 &  0.991 \tabularnewline
153 &  0.01395 &  0.0279 &  0.986 \tabularnewline
154 &  0.01948 &  0.03896 &  0.9805 \tabularnewline
155 &  0.01485 &  0.0297 &  0.9851 \tabularnewline
156 &  0.05271 &  0.1054 &  0.9473 \tabularnewline
157 &  0.05523 &  0.1105 &  0.9448 \tabularnewline
158 &  0.04777 &  0.09554 &  0.9522 \tabularnewline
159 &  0.04773 &  0.09547 &  0.9523 \tabularnewline
160 &  0.03479 &  0.06957 &  0.9652 \tabularnewline
161 &  0.1703 &  0.3406 &  0.8297 \tabularnewline
162 &  0.1383 &  0.2765 &  0.8617 \tabularnewline
163 &  0.1751 &  0.3502 &  0.8249 \tabularnewline
164 &  0.2065 &  0.413 &  0.7935 \tabularnewline
165 &  0.4303 &  0.8606 &  0.5697 \tabularnewline
166 &  0.3739 &  0.7478 &  0.6261 \tabularnewline
167 &  0.3212 &  0.6425 &  0.6788 \tabularnewline
168 &  0.3776 &  0.7552 &  0.6224 \tabularnewline
169 &  0.342 &  0.684 &  0.658 \tabularnewline
170 &  0.4838 &  0.9675 &  0.5162 \tabularnewline
171 &  0.4398 &  0.8797 &  0.5602 \tabularnewline
172 &  0.4434 &  0.8868 &  0.5566 \tabularnewline
173 &  0.439 &  0.878 &  0.561 \tabularnewline
174 &  0.3584 &  0.7168 &  0.6416 \tabularnewline
175 &  0.3175 &  0.6349 &  0.6825 \tabularnewline
176 &  0.2883 &  0.5766 &  0.7117 \tabularnewline
177 &  0.2083 &  0.4166 &  0.7917 \tabularnewline
178 &  0.3692 &  0.7385 &  0.6308 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309795&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]13[/C][C] 0.07417[/C][C] 0.1483[/C][C] 0.9258[/C][/ROW]
[ROW][C]14[/C][C] 0.03194[/C][C] 0.06387[/C][C] 0.9681[/C][/ROW]
[ROW][C]15[/C][C] 0.01543[/C][C] 0.03086[/C][C] 0.9846[/C][/ROW]
[ROW][C]16[/C][C] 0.004901[/C][C] 0.009801[/C][C] 0.9951[/C][/ROW]
[ROW][C]17[/C][C] 0.001939[/C][C] 0.003877[/C][C] 0.9981[/C][/ROW]
[ROW][C]18[/C][C] 0.0007269[/C][C] 0.001454[/C][C] 0.9993[/C][/ROW]
[ROW][C]19[/C][C] 0.0003887[/C][C] 0.0007773[/C][C] 0.9996[/C][/ROW]
[ROW][C]20[/C][C] 0.0002237[/C][C] 0.0004474[/C][C] 0.9998[/C][/ROW]
[ROW][C]21[/C][C] 8.393e-05[/C][C] 0.0001679[/C][C] 0.9999[/C][/ROW]
[ROW][C]22[/C][C] 2.516e-05[/C][C] 5.032e-05[/C][C] 1[/C][/ROW]
[ROW][C]23[/C][C] 6.08e-05[/C][C] 0.0001216[/C][C] 0.9999[/C][/ROW]
[ROW][C]24[/C][C] 0.0001222[/C][C] 0.0002443[/C][C] 0.9999[/C][/ROW]
[ROW][C]25[/C][C] 4.574e-05[/C][C] 9.147e-05[/C][C] 1[/C][/ROW]
[ROW][C]26[/C][C] 2.045e-05[/C][C] 4.09e-05[/C][C] 1[/C][/ROW]
[ROW][C]27[/C][C] 0.0001177[/C][C] 0.0002354[/C][C] 0.9999[/C][/ROW]
[ROW][C]28[/C][C] 0.0008552[/C][C] 0.00171[/C][C] 0.9991[/C][/ROW]
[ROW][C]29[/C][C] 0.0004503[/C][C] 0.0009006[/C][C] 0.9996[/C][/ROW]
[ROW][C]30[/C][C] 0.0002604[/C][C] 0.0005208[/C][C] 0.9997[/C][/ROW]
[ROW][C]31[/C][C] 0.000259[/C][C] 0.0005181[/C][C] 0.9997[/C][/ROW]
[ROW][C]32[/C][C] 0.0001226[/C][C] 0.0002453[/C][C] 0.9999[/C][/ROW]
[ROW][C]33[/C][C] 6.403e-05[/C][C] 0.0001281[/C][C] 0.9999[/C][/ROW]
[ROW][C]34[/C][C] 7.326e-05[/C][C] 0.0001465[/C][C] 0.9999[/C][/ROW]
[ROW][C]35[/C][C] 3.478e-05[/C][C] 6.956e-05[/C][C] 1[/C][/ROW]
[ROW][C]36[/C][C] 1.628e-05[/C][C] 3.256e-05[/C][C] 1[/C][/ROW]
[ROW][C]37[/C][C] 8.579e-06[/C][C] 1.716e-05[/C][C] 1[/C][/ROW]
[ROW][C]38[/C][C] 5.762e-06[/C][C] 1.152e-05[/C][C] 1[/C][/ROW]
[ROW][C]39[/C][C] 3.137e-06[/C][C] 6.275e-06[/C][C] 1[/C][/ROW]
[ROW][C]40[/C][C] 2.909e-06[/C][C] 5.818e-06[/C][C] 1[/C][/ROW]
[ROW][C]41[/C][C] 2.962e-05[/C][C] 5.925e-05[/C][C] 1[/C][/ROW]
[ROW][C]42[/C][C] 0.0001154[/C][C] 0.0002307[/C][C] 0.9999[/C][/ROW]
[ROW][C]43[/C][C] 0.0001136[/C][C] 0.0002272[/C][C] 0.9999[/C][/ROW]
[ROW][C]44[/C][C] 6.139e-05[/C][C] 0.0001228[/C][C] 0.9999[/C][/ROW]
[ROW][C]45[/C][C] 4.71e-05[/C][C] 9.419e-05[/C][C] 1[/C][/ROW]
[ROW][C]46[/C][C] 5.93e-05[/C][C] 0.0001186[/C][C] 0.9999[/C][/ROW]
[ROW][C]47[/C][C] 3.611e-05[/C][C] 7.222e-05[/C][C] 1[/C][/ROW]
[ROW][C]48[/C][C] 1.991e-05[/C][C] 3.981e-05[/C][C] 1[/C][/ROW]
[ROW][C]49[/C][C] 1.518e-05[/C][C] 3.037e-05[/C][C] 1[/C][/ROW]
[ROW][C]50[/C][C] 8.492e-06[/C][C] 1.698e-05[/C][C] 1[/C][/ROW]
[ROW][C]51[/C][C] 6.304e-05[/C][C] 0.0001261[/C][C] 0.9999[/C][/ROW]
[ROW][C]52[/C][C] 3.998e-05[/C][C] 7.995e-05[/C][C] 1[/C][/ROW]
[ROW][C]53[/C][C] 7.118e-05[/C][C] 0.0001424[/C][C] 0.9999[/C][/ROW]
[ROW][C]54[/C][C] 7.597e-05[/C][C] 0.0001519[/C][C] 0.9999[/C][/ROW]
[ROW][C]55[/C][C] 4.417e-05[/C][C] 8.835e-05[/C][C] 1[/C][/ROW]
[ROW][C]56[/C][C] 2.513e-05[/C][C] 5.026e-05[/C][C] 1[/C][/ROW]
[ROW][C]57[/C][C] 1.409e-05[/C][C] 2.818e-05[/C][C] 1[/C][/ROW]
[ROW][C]58[/C][C] 8.053e-06[/C][C] 1.611e-05[/C][C] 1[/C][/ROW]
[ROW][C]59[/C][C] 4.421e-06[/C][C] 8.843e-06[/C][C] 1[/C][/ROW]
[ROW][C]60[/C][C] 2.446e-06[/C][C] 4.893e-06[/C][C] 1[/C][/ROW]
[ROW][C]61[/C][C] 1.3e-06[/C][C] 2.6e-06[/C][C] 1[/C][/ROW]
[ROW][C]62[/C][C] 6.803e-07[/C][C] 1.361e-06[/C][C] 1[/C][/ROW]
[ROW][C]63[/C][C] 4.061e-07[/C][C] 8.122e-07[/C][C] 1[/C][/ROW]
[ROW][C]64[/C][C] 4.732e-07[/C][C] 9.464e-07[/C][C] 1[/C][/ROW]
[ROW][C]65[/C][C] 9e-07[/C][C] 1.8e-06[/C][C] 1[/C][/ROW]
[ROW][C]66[/C][C] 9.155e-07[/C][C] 1.831e-06[/C][C] 1[/C][/ROW]
[ROW][C]67[/C][C] 5.167e-07[/C][C] 1.033e-06[/C][C] 1[/C][/ROW]
[ROW][C]68[/C][C] 4.191e-07[/C][C] 8.381e-07[/C][C] 1[/C][/ROW]
[ROW][C]69[/C][C] 3.09e-07[/C][C] 6.181e-07[/C][C] 1[/C][/ROW]
[ROW][C]70[/C][C] 8.008e-07[/C][C] 1.602e-06[/C][C] 1[/C][/ROW]
[ROW][C]71[/C][C] 4.86e-07[/C][C] 9.719e-07[/C][C] 1[/C][/ROW]
[ROW][C]72[/C][C] 9.868e-07[/C][C] 1.974e-06[/C][C] 1[/C][/ROW]
[ROW][C]73[/C][C] 9.039e-07[/C][C] 1.808e-06[/C][C] 1[/C][/ROW]
[ROW][C]74[/C][C] 5.489e-07[/C][C] 1.098e-06[/C][C] 1[/C][/ROW]
[ROW][C]75[/C][C] 2.985e-07[/C][C] 5.971e-07[/C][C] 1[/C][/ROW]
[ROW][C]76[/C][C] 2.389e-07[/C][C] 4.778e-07[/C][C] 1[/C][/ROW]
[ROW][C]77[/C][C] 3.63e-07[/C][C] 7.26e-07[/C][C] 1[/C][/ROW]
[ROW][C]78[/C][C] 3.537e-07[/C][C] 7.073e-07[/C][C] 1[/C][/ROW]
[ROW][C]79[/C][C] 1.919e-07[/C][C] 3.838e-07[/C][C] 1[/C][/ROW]
[ROW][C]80[/C][C] 1.224e-07[/C][C] 2.447e-07[/C][C] 1[/C][/ROW]
[ROW][C]81[/C][C] 6.921e-08[/C][C] 1.384e-07[/C][C] 1[/C][/ROW]
[ROW][C]82[/C][C] 3.715e-08[/C][C] 7.43e-08[/C][C] 1[/C][/ROW]
[ROW][C]83[/C][C] 1.947e-08[/C][C] 3.894e-08[/C][C] 1[/C][/ROW]
[ROW][C]84[/C][C] 7.437e-08[/C][C] 1.487e-07[/C][C] 1[/C][/ROW]
[ROW][C]85[/C][C] 3.979e-08[/C][C] 7.958e-08[/C][C] 1[/C][/ROW]
[ROW][C]86[/C][C] 4.474e-08[/C][C] 8.948e-08[/C][C] 1[/C][/ROW]
[ROW][C]87[/C][C] 2.519e-08[/C][C] 5.038e-08[/C][C] 1[/C][/ROW]
[ROW][C]88[/C][C] 1.171e-06[/C][C] 2.341e-06[/C][C] 1[/C][/ROW]
[ROW][C]89[/C][C] 1.394e-06[/C][C] 2.787e-06[/C][C] 1[/C][/ROW]
[ROW][C]90[/C][C] 1.263e-06[/C][C] 2.527e-06[/C][C] 1[/C][/ROW]
[ROW][C]91[/C][C] 8.176e-07[/C][C] 1.635e-06[/C][C] 1[/C][/ROW]
[ROW][C]92[/C][C] 4.792e-07[/C][C] 9.583e-07[/C][C] 1[/C][/ROW]
[ROW][C]93[/C][C] 2.731e-07[/C][C] 5.462e-07[/C][C] 1[/C][/ROW]
[ROW][C]94[/C][C] 1.581e-07[/C][C] 3.161e-07[/C][C] 1[/C][/ROW]
[ROW][C]95[/C][C] 8.941e-08[/C][C] 1.788e-07[/C][C] 1[/C][/ROW]
[ROW][C]96[/C][C] 5.688e-08[/C][C] 1.138e-07[/C][C] 1[/C][/ROW]
[ROW][C]97[/C][C] 3.11e-08[/C][C] 6.22e-08[/C][C] 1[/C][/ROW]
[ROW][C]98[/C][C] 1.689e-07[/C][C] 3.378e-07[/C][C] 1[/C][/ROW]
[ROW][C]99[/C][C] 9.356e-08[/C][C] 1.871e-07[/C][C] 1[/C][/ROW]
[ROW][C]100[/C][C] 7.263e-06[/C][C] 1.453e-05[/C][C] 1[/C][/ROW]
[ROW][C]101[/C][C] 0.0001166[/C][C] 0.0002332[/C][C] 0.9999[/C][/ROW]
[ROW][C]102[/C][C] 9.349e-05[/C][C] 0.000187[/C][C] 0.9999[/C][/ROW]
[ROW][C]103[/C][C] 7.184e-05[/C][C] 0.0001437[/C][C] 0.9999[/C][/ROW]
[ROW][C]104[/C][C] 5.465e-05[/C][C] 0.0001093[/C][C] 0.9999[/C][/ROW]
[ROW][C]105[/C][C] 3.955e-05[/C][C] 7.909e-05[/C][C] 1[/C][/ROW]
[ROW][C]106[/C][C] 2.876e-05[/C][C] 5.752e-05[/C][C] 1[/C][/ROW]
[ROW][C]107[/C][C] 1.81e-05[/C][C] 3.62e-05[/C][C] 1[/C][/ROW]
[ROW][C]108[/C][C] 1.197e-05[/C][C] 2.394e-05[/C][C] 1[/C][/ROW]
[ROW][C]109[/C][C] 7.841e-06[/C][C] 1.568e-05[/C][C] 1[/C][/ROW]
[ROW][C]110[/C][C] 5.283e-06[/C][C] 1.057e-05[/C][C] 1[/C][/ROW]
[ROW][C]111[/C][C] 3.288e-06[/C][C] 6.576e-06[/C][C] 1[/C][/ROW]
[ROW][C]112[/C][C] 6.185e-06[/C][C] 1.237e-05[/C][C] 1[/C][/ROW]
[ROW][C]113[/C][C] 1.03e-05[/C][C] 2.059e-05[/C][C] 1[/C][/ROW]
[ROW][C]114[/C][C] 0.0002717[/C][C] 0.0005434[/C][C] 0.9997[/C][/ROW]
[ROW][C]115[/C][C] 0.0001992[/C][C] 0.0003984[/C][C] 0.9998[/C][/ROW]
[ROW][C]116[/C][C] 0.0001729[/C][C] 0.0003458[/C][C] 0.9998[/C][/ROW]
[ROW][C]117[/C][C] 0.0001723[/C][C] 0.0003447[/C][C] 0.9998[/C][/ROW]
[ROW][C]118[/C][C] 0.0001655[/C][C] 0.0003311[/C][C] 0.9998[/C][/ROW]
[ROW][C]119[/C][C] 0.0003344[/C][C] 0.0006689[/C][C] 0.9997[/C][/ROW]
[ROW][C]120[/C][C] 0.0002845[/C][C] 0.000569[/C][C] 0.9997[/C][/ROW]
[ROW][C]121[/C][C] 0.0002194[/C][C] 0.0004388[/C][C] 0.9998[/C][/ROW]
[ROW][C]122[/C][C] 0.001016[/C][C] 0.002031[/C][C] 0.999[/C][/ROW]
[ROW][C]123[/C][C] 0.002774[/C][C] 0.005548[/C][C] 0.9972[/C][/ROW]
[ROW][C]124[/C][C] 0.002191[/C][C] 0.004383[/C][C] 0.9978[/C][/ROW]
[ROW][C]125[/C][C] 0.001619[/C][C] 0.003238[/C][C] 0.9984[/C][/ROW]
[ROW][C]126[/C][C] 0.00123[/C][C] 0.00246[/C][C] 0.9988[/C][/ROW]
[ROW][C]127[/C][C] 0.0008585[/C][C] 0.001717[/C][C] 0.9991[/C][/ROW]
[ROW][C]128[/C][C] 0.0005884[/C][C] 0.001177[/C][C] 0.9994[/C][/ROW]
[ROW][C]129[/C][C] 0.0004814[/C][C] 0.0009629[/C][C] 0.9995[/C][/ROW]
[ROW][C]130[/C][C] 0.001582[/C][C] 0.003165[/C][C] 0.9984[/C][/ROW]
[ROW][C]131[/C][C] 0.001857[/C][C] 0.003715[/C][C] 0.9981[/C][/ROW]
[ROW][C]132[/C][C] 0.002781[/C][C] 0.005562[/C][C] 0.9972[/C][/ROW]
[ROW][C]133[/C][C] 0.002746[/C][C] 0.005492[/C][C] 0.9973[/C][/ROW]
[ROW][C]134[/C][C] 0.002679[/C][C] 0.005358[/C][C] 0.9973[/C][/ROW]
[ROW][C]135[/C][C] 0.002119[/C][C] 0.004238[/C][C] 0.9979[/C][/ROW]
[ROW][C]136[/C][C] 0.001774[/C][C] 0.003547[/C][C] 0.9982[/C][/ROW]
[ROW][C]137[/C][C] 0.001805[/C][C] 0.003609[/C][C] 0.9982[/C][/ROW]
[ROW][C]138[/C][C] 0.001431[/C][C] 0.002862[/C][C] 0.9986[/C][/ROW]
[ROW][C]139[/C][C] 0.004507[/C][C] 0.009014[/C][C] 0.9955[/C][/ROW]
[ROW][C]140[/C][C] 0.003695[/C][C] 0.00739[/C][C] 0.9963[/C][/ROW]
[ROW][C]141[/C][C] 0.003524[/C][C] 0.007048[/C][C] 0.9965[/C][/ROW]
[ROW][C]142[/C][C] 0.002942[/C][C] 0.005884[/C][C] 0.9971[/C][/ROW]
[ROW][C]143[/C][C] 0.002757[/C][C] 0.005514[/C][C] 0.9972[/C][/ROW]
[ROW][C]144[/C][C] 0.003304[/C][C] 0.006608[/C][C] 0.9967[/C][/ROW]
[ROW][C]145[/C][C] 0.002793[/C][C] 0.005586[/C][C] 0.9972[/C][/ROW]
[ROW][C]146[/C][C] 0.002037[/C][C] 0.004074[/C][C] 0.998[/C][/ROW]
[ROW][C]147[/C][C] 0.001378[/C][C] 0.002756[/C][C] 0.9986[/C][/ROW]
[ROW][C]148[/C][C] 0.005018[/C][C] 0.01004[/C][C] 0.995[/C][/ROW]
[ROW][C]149[/C][C] 0.0043[/C][C] 0.008599[/C][C] 0.9957[/C][/ROW]
[ROW][C]150[/C][C] 0.003628[/C][C] 0.007256[/C][C] 0.9964[/C][/ROW]
[ROW][C]151[/C][C] 0.003318[/C][C] 0.006637[/C][C] 0.9967[/C][/ROW]
[ROW][C]152[/C][C] 0.009018[/C][C] 0.01804[/C][C] 0.991[/C][/ROW]
[ROW][C]153[/C][C] 0.01395[/C][C] 0.0279[/C][C] 0.986[/C][/ROW]
[ROW][C]154[/C][C] 0.01948[/C][C] 0.03896[/C][C] 0.9805[/C][/ROW]
[ROW][C]155[/C][C] 0.01485[/C][C] 0.0297[/C][C] 0.9851[/C][/ROW]
[ROW][C]156[/C][C] 0.05271[/C][C] 0.1054[/C][C] 0.9473[/C][/ROW]
[ROW][C]157[/C][C] 0.05523[/C][C] 0.1105[/C][C] 0.9448[/C][/ROW]
[ROW][C]158[/C][C] 0.04777[/C][C] 0.09554[/C][C] 0.9522[/C][/ROW]
[ROW][C]159[/C][C] 0.04773[/C][C] 0.09547[/C][C] 0.9523[/C][/ROW]
[ROW][C]160[/C][C] 0.03479[/C][C] 0.06957[/C][C] 0.9652[/C][/ROW]
[ROW][C]161[/C][C] 0.1703[/C][C] 0.3406[/C][C] 0.8297[/C][/ROW]
[ROW][C]162[/C][C] 0.1383[/C][C] 0.2765[/C][C] 0.8617[/C][/ROW]
[ROW][C]163[/C][C] 0.1751[/C][C] 0.3502[/C][C] 0.8249[/C][/ROW]
[ROW][C]164[/C][C] 0.2065[/C][C] 0.413[/C][C] 0.7935[/C][/ROW]
[ROW][C]165[/C][C] 0.4303[/C][C] 0.8606[/C][C] 0.5697[/C][/ROW]
[ROW][C]166[/C][C] 0.3739[/C][C] 0.7478[/C][C] 0.6261[/C][/ROW]
[ROW][C]167[/C][C] 0.3212[/C][C] 0.6425[/C][C] 0.6788[/C][/ROW]
[ROW][C]168[/C][C] 0.3776[/C][C] 0.7552[/C][C] 0.6224[/C][/ROW]
[ROW][C]169[/C][C] 0.342[/C][C] 0.684[/C][C] 0.658[/C][/ROW]
[ROW][C]170[/C][C] 0.4838[/C][C] 0.9675[/C][C] 0.5162[/C][/ROW]
[ROW][C]171[/C][C] 0.4398[/C][C] 0.8797[/C][C] 0.5602[/C][/ROW]
[ROW][C]172[/C][C] 0.4434[/C][C] 0.8868[/C][C] 0.5566[/C][/ROW]
[ROW][C]173[/C][C] 0.439[/C][C] 0.878[/C][C] 0.561[/C][/ROW]
[ROW][C]174[/C][C] 0.3584[/C][C] 0.7168[/C][C] 0.6416[/C][/ROW]
[ROW][C]175[/C][C] 0.3175[/C][C] 0.6349[/C][C] 0.6825[/C][/ROW]
[ROW][C]176[/C][C] 0.2883[/C][C] 0.5766[/C][C] 0.7117[/C][/ROW]
[ROW][C]177[/C][C] 0.2083[/C][C] 0.4166[/C][C] 0.7917[/C][/ROW]
[ROW][C]178[/C][C] 0.3692[/C][C] 0.7385[/C][C] 0.6308[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309795&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309795&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
13	0.07417	0.1483	0.9258
14	0.03194	0.06387	0.9681
15	0.01543	0.03086	0.9846
16	0.004901	0.009801	0.9951
17	0.001939	0.003877	0.9981
18	0.0007269	0.001454	0.9993
19	0.0003887	0.0007773	0.9996
20	0.0002237	0.0004474	0.9998
21	8.393e-05	0.0001679	0.9999
22	2.516e-05	5.032e-05	1
23	6.08e-05	0.0001216	0.9999
24	0.0001222	0.0002443	0.9999
25	4.574e-05	9.147e-05	1
26	2.045e-05	4.09e-05	1
27	0.0001177	0.0002354	0.9999
28	0.0008552	0.00171	0.9991
29	0.0004503	0.0009006	0.9996
30	0.0002604	0.0005208	0.9997
31	0.000259	0.0005181	0.9997
32	0.0001226	0.0002453	0.9999
33	6.403e-05	0.0001281	0.9999
34	7.326e-05	0.0001465	0.9999
35	3.478e-05	6.956e-05	1
36	1.628e-05	3.256e-05	1
37	8.579e-06	1.716e-05	1
38	5.762e-06	1.152e-05	1
39	3.137e-06	6.275e-06	1
40	2.909e-06	5.818e-06	1
41	2.962e-05	5.925e-05	1
42	0.0001154	0.0002307	0.9999
43	0.0001136	0.0002272	0.9999
44	6.139e-05	0.0001228	0.9999
45	4.71e-05	9.419e-05	1
46	5.93e-05	0.0001186	0.9999
47	3.611e-05	7.222e-05	1
48	1.991e-05	3.981e-05	1
49	1.518e-05	3.037e-05	1
50	8.492e-06	1.698e-05	1
51	6.304e-05	0.0001261	0.9999
52	3.998e-05	7.995e-05	1
53	7.118e-05	0.0001424	0.9999
54	7.597e-05	0.0001519	0.9999
55	4.417e-05	8.835e-05	1
56	2.513e-05	5.026e-05	1
57	1.409e-05	2.818e-05	1
58	8.053e-06	1.611e-05	1
59	4.421e-06	8.843e-06	1
60	2.446e-06	4.893e-06	1
61	1.3e-06	2.6e-06	1
62	6.803e-07	1.361e-06	1
63	4.061e-07	8.122e-07	1
64	4.732e-07	9.464e-07	1
65	9e-07	1.8e-06	1
66	9.155e-07	1.831e-06	1
67	5.167e-07	1.033e-06	1
68	4.191e-07	8.381e-07	1
69	3.09e-07	6.181e-07	1
70	8.008e-07	1.602e-06	1
71	4.86e-07	9.719e-07	1
72	9.868e-07	1.974e-06	1
73	9.039e-07	1.808e-06	1
74	5.489e-07	1.098e-06	1
75	2.985e-07	5.971e-07	1
76	2.389e-07	4.778e-07	1
77	3.63e-07	7.26e-07	1
78	3.537e-07	7.073e-07	1
79	1.919e-07	3.838e-07	1
80	1.224e-07	2.447e-07	1
81	6.921e-08	1.384e-07	1
82	3.715e-08	7.43e-08	1
83	1.947e-08	3.894e-08	1
84	7.437e-08	1.487e-07	1
85	3.979e-08	7.958e-08	1
86	4.474e-08	8.948e-08	1
87	2.519e-08	5.038e-08	1
88	1.171e-06	2.341e-06	1
89	1.394e-06	2.787e-06	1
90	1.263e-06	2.527e-06	1
91	8.176e-07	1.635e-06	1
92	4.792e-07	9.583e-07	1
93	2.731e-07	5.462e-07	1
94	1.581e-07	3.161e-07	1
95	8.941e-08	1.788e-07	1
96	5.688e-08	1.138e-07	1
97	3.11e-08	6.22e-08	1
98	1.689e-07	3.378e-07	1
99	9.356e-08	1.871e-07	1
100	7.263e-06	1.453e-05	1
101	0.0001166	0.0002332	0.9999
102	9.349e-05	0.000187	0.9999
103	7.184e-05	0.0001437	0.9999
104	5.465e-05	0.0001093	0.9999
105	3.955e-05	7.909e-05	1
106	2.876e-05	5.752e-05	1
107	1.81e-05	3.62e-05	1
108	1.197e-05	2.394e-05	1
109	7.841e-06	1.568e-05	1
110	5.283e-06	1.057e-05	1
111	3.288e-06	6.576e-06	1
112	6.185e-06	1.237e-05	1
113	1.03e-05	2.059e-05	1
114	0.0002717	0.0005434	0.9997
115	0.0001992	0.0003984	0.9998
116	0.0001729	0.0003458	0.9998
117	0.0001723	0.0003447	0.9998
118	0.0001655	0.0003311	0.9998
119	0.0003344	0.0006689	0.9997
120	0.0002845	0.000569	0.9997
121	0.0002194	0.0004388	0.9998
122	0.001016	0.002031	0.999
123	0.002774	0.005548	0.9972
124	0.002191	0.004383	0.9978
125	0.001619	0.003238	0.9984
126	0.00123	0.00246	0.9988
127	0.0008585	0.001717	0.9991
128	0.0005884	0.001177	0.9994
129	0.0004814	0.0009629	0.9995
130	0.001582	0.003165	0.9984
131	0.001857	0.003715	0.9981
132	0.002781	0.005562	0.9972
133	0.002746	0.005492	0.9973
134	0.002679	0.005358	0.9973
135	0.002119	0.004238	0.9979
136	0.001774	0.003547	0.9982
137	0.001805	0.003609	0.9982
138	0.001431	0.002862	0.9986
139	0.004507	0.009014	0.9955
140	0.003695	0.00739	0.9963
141	0.003524	0.007048	0.9965
142	0.002942	0.005884	0.9971
143	0.002757	0.005514	0.9972
144	0.003304	0.006608	0.9967
145	0.002793	0.005586	0.9972
146	0.002037	0.004074	0.998
147	0.001378	0.002756	0.9986
148	0.005018	0.01004	0.995
149	0.0043	0.008599	0.9957
150	0.003628	0.007256	0.9964
151	0.003318	0.006637	0.9967
152	0.009018	0.01804	0.991
153	0.01395	0.0279	0.986
154	0.01948	0.03896	0.9805
155	0.01485	0.0297	0.9851
156	0.05271	0.1054	0.9473
157	0.05523	0.1105	0.9448
158	0.04777	0.09554	0.9522
159	0.04773	0.09547	0.9523
160	0.03479	0.06957	0.9652
161	0.1703	0.3406	0.8297
162	0.1383	0.2765	0.8617
163	0.1751	0.3502	0.8249
164	0.2065	0.413	0.7935
165	0.4303	0.8606	0.5697
166	0.3739	0.7478	0.6261
167	0.3212	0.6425	0.6788
168	0.3776	0.7552	0.6224
169	0.342	0.684	0.658
170	0.4838	0.9675	0.5162
171	0.4398	0.8797	0.5602
172	0.4434	0.8868	0.5566
173	0.439	0.878	0.561
174	0.3584	0.7168	0.6416
175	0.3175	0.6349	0.6825
176	0.2883	0.5766	0.7117
177	0.2083	0.4166	0.7917
178	0.3692	0.7385	0.6308

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	135	0.8133	NOK
5% type I error level	141	0.849398	NOK
10% type I error level	145	0.873494	NOK

\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 & 135 &  0.8133 & NOK \tabularnewline
5% type I error level & 141 & 0.849398 & NOK \tabularnewline
10% type I error level & 145 & 0.873494 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309795&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]135[/C][C] 0.8133[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]141[/C][C]0.849398[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]145[/C][C]0.873494[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309795&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309795&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	135	0.8133	NOK
5% type I error level	141	0.849398	NOK
10% type I error level	145	0.873494	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.3742, df1 = 2, df2 = 179, p-value = 0.2557
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.4705, df1 = 18, df2 = 163, p-value = 0.9672
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.6027, df1 = 2, df2 = 179, p-value = 0.2042

\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 = 1.3742, df1 = 2, df2 = 179, p-value = 0.2557
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.4705, df1 = 18, df2 = 163, p-value = 0.9672
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.6027, df1 = 2, df2 = 179, p-value = 0.2042
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309795&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 = 1.3742, df1 = 2, df2 = 179, p-value = 0.2557
[/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 = 0.4705, df1 = 18, df2 = 163, p-value = 0.9672
[/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 = 1.6027, df1 = 2, df2 = 179, p-value = 0.2042
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309795&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309795&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 = 1.3742, df1 = 2, df2 = 179, p-value = 0.2557
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.4705, df1 = 18, df2 = 163, p-value = 0.9672
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.6027, df1 = 2, df2 = 179, p-value = 0.2042

Variance Inflation Factors (Multicollinearity)

> vif
        `(1-Bs)(1-B)Build0`         `(1-Bs)(1-B)Build1` 
                   2.175024                    3.891967 
        `(1-Bs)(1-B)Build2`         `(1-Bs)(1-B)Build3` 
                   4.635208                    3.693300 
        `(1-Bs)(1-B)Build4` `(1-Bs)(1-B)Chemicals(t-1)` 
                   2.068742                    1.491988 
`(1-Bs)(1-B)Chemicals(t-2)` `(1-Bs)(1-B)Chemicals(t-3)` 
                   1.794305                    1.790372 
`(1-Bs)(1-B)Chemicals(t-4)` 
                   1.506600

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
        `(1-Bs)(1-B)Build0`         `(1-Bs)(1-B)Build1` 
                   2.175024                    3.891967 
        `(1-Bs)(1-B)Build2`         `(1-Bs)(1-B)Build3` 
                   4.635208                    3.693300 
        `(1-Bs)(1-B)Build4` `(1-Bs)(1-B)Chemicals(t-1)` 
                   2.068742                    1.491988 
`(1-Bs)(1-B)Chemicals(t-2)` `(1-Bs)(1-B)Chemicals(t-3)` 
                   1.794305                    1.790372 
`(1-Bs)(1-B)Chemicals(t-4)` 
                   1.506600 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309795&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
        `(1-Bs)(1-B)Build0`         `(1-Bs)(1-B)Build1` 
                   2.175024                    3.891967 
        `(1-Bs)(1-B)Build2`         `(1-Bs)(1-B)Build3` 
                   4.635208                    3.693300 
        `(1-Bs)(1-B)Build4` `(1-Bs)(1-B)Chemicals(t-1)` 
                   2.068742                    1.491988 
`(1-Bs)(1-B)Chemicals(t-2)` `(1-Bs)(1-B)Chemicals(t-3)` 
                   1.794305                    1.790372 
`(1-Bs)(1-B)Chemicals(t-4)` 
                   1.506600 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309795&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309795&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)Build0`         `(1-Bs)(1-B)Build1` 
                   2.175024                    3.891967 
        `(1-Bs)(1-B)Build2`         `(1-Bs)(1-B)Build3` 
                   4.635208                    3.693300 
        `(1-Bs)(1-B)Build4` `(1-Bs)(1-B)Chemicals(t-1)` 
                   2.068742                    1.491988 
`(1-Bs)(1-B)Chemicals(t-2)` `(1-Bs)(1-B)Chemicals(t-3)` 
                   1.794305                    1.790372 
`(1-Bs)(1-B)Chemicals(t-4)` 
                   1.506600

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):

par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 4 ; par5 = 0 ; 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