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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Fri, 15 Dec 2017 16:02:38 +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/t1513350416ng41fufi9jb2t9r.htm/, Retrieved Thu, 16 May 2024 01:27:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309796, Retrieved Thu, 16 May 2024 01:27:34 +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 15:02:38] [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	10 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309796&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309796&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309796&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)Chemicals[t] = + 0.0636788 + 0.198654`(1-Bs)(1-B)Build0`[t] + 0.0897215`(1-Bs)(1-B)Build1`[t] + 0.0975309`(1-Bs)(1-B)Build2`[t] + 0.105182`(1-Bs)(1-B)Build3`[t] -2.07662e-05`(1-Bs)(1-B)Build4`[t] -0.430572`(1-Bs)(1-B)Chemicals(t-1)`[t] -0.322824`(1-Bs)(1-B)Chemicals(t-2)`[t] -0.252134`(1-Bs)(1-B)Chemicals(t-3)`[t] -0.0925362`(1-Bs)(1-B)Chemicals(t-4)`[t] -0.31044`(1-Bs)(1-B)Chemicals(t-1s)`[t] + e[t]
Warning: you did not specify the seasonality. The seasonal period was set to s = 12.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)Chemicals[t] =  +  0.0636788 +  0.198654`(1-Bs)(1-B)Build0`[t] +  0.0897215`(1-Bs)(1-B)Build1`[t] +  0.0975309`(1-Bs)(1-B)Build2`[t] +  0.105182`(1-Bs)(1-B)Build3`[t] -2.07662e-05`(1-Bs)(1-B)Build4`[t] -0.430572`(1-Bs)(1-B)Chemicals(t-1)`[t] -0.322824`(1-Bs)(1-B)Chemicals(t-2)`[t] -0.252134`(1-Bs)(1-B)Chemicals(t-3)`[t] -0.0925362`(1-Bs)(1-B)Chemicals(t-4)`[t] -0.31044`(1-Bs)(1-B)Chemicals(t-1s)`[t]  + e[t] \tabularnewline
Warning: you did not specify the seasonality. The seasonal period was set to s = 12. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309796&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)Chemicals[t] =  +  0.0636788 +  0.198654`(1-Bs)(1-B)Build0`[t] +  0.0897215`(1-Bs)(1-B)Build1`[t] +  0.0975309`(1-Bs)(1-B)Build2`[t] +  0.105182`(1-Bs)(1-B)Build3`[t] -2.07662e-05`(1-Bs)(1-B)Build4`[t] -0.430572`(1-Bs)(1-B)Chemicals(t-1)`[t] -0.322824`(1-Bs)(1-B)Chemicals(t-2)`[t] -0.252134`(1-Bs)(1-B)Chemicals(t-3)`[t] -0.0925362`(1-Bs)(1-B)Chemicals(t-4)`[t] -0.31044`(1-Bs)(1-B)Chemicals(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the seasonality. The seasonal period was set to s = 12.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309796&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309796&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] = + 0.0636788 + 0.198654`(1-Bs)(1-B)Build0`[t] + 0.0897215`(1-Bs)(1-B)Build1`[t] + 0.0975309`(1-Bs)(1-B)Build2`[t] + 0.105182`(1-Bs)(1-B)Build3`[t] -2.07662e-05`(1-Bs)(1-B)Build4`[t] -0.430572`(1-Bs)(1-B)Chemicals(t-1)`[t] -0.322824`(1-Bs)(1-B)Chemicals(t-2)`[t] -0.252134`(1-Bs)(1-B)Chemicals(t-3)`[t] -0.0925362`(1-Bs)(1-B)Chemicals(t-4)`[t] -0.31044`(1-Bs)(1-B)Chemicals(t-1s)`[t] + e[t]
Warning: you did not specify the seasonality. The seasonal period was set to s = 12.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+0.06368	0.6148	+1.0360e-01	0.9176	0.4588
`(1-Bs)(1-B)Build0`	+0.1986	0.06103	+3.2550e+00	0.001371	0.0006854
`(1-Bs)(1-B)Build1`	+0.08972	0.08186	+1.0960e+00	0.2746	0.1373
`(1-Bs)(1-B)Build2`	+0.09753	0.08956	+1.0890e+00	0.2777	0.1389
`(1-Bs)(1-B)Build3`	+0.1052	0.08124	+1.2950e+00	0.1972	0.09858
`(1-Bs)(1-B)Build4`	-2.077e-05	0.06089	-3.4110e-04	0.9997	0.4999
`(1-Bs)(1-B)Chemicals(t-1)`	-0.4306	0.07534	-5.7150e+00	4.888e-08	2.444e-08
`(1-Bs)(1-B)Chemicals(t-2)`	-0.3228	0.08141	-3.9660e+00	0.0001082	5.41e-05
`(1-Bs)(1-B)Chemicals(t-3)`	-0.2521	0.08071	-3.1240e+00	0.002101	0.001051
`(1-Bs)(1-B)Chemicals(t-4)`	-0.09254	0.07429	-1.2460e+00	0.2146	0.1073
`(1-Bs)(1-B)Chemicals(t-1s)`	-0.3104	0.06825	-4.5490e+00	1.03e-05	5.148e-06

\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) & +0.06368 &  0.6148 & +1.0360e-01 &  0.9176 &  0.4588 \tabularnewline
`(1-Bs)(1-B)Build0` & +0.1986 &  0.06103 & +3.2550e+00 &  0.001371 &  0.0006854 \tabularnewline
`(1-Bs)(1-B)Build1` & +0.08972 &  0.08186 & +1.0960e+00 &  0.2746 &  0.1373 \tabularnewline
`(1-Bs)(1-B)Build2` & +0.09753 &  0.08956 & +1.0890e+00 &  0.2777 &  0.1389 \tabularnewline
`(1-Bs)(1-B)Build3` & +0.1052 &  0.08124 & +1.2950e+00 &  0.1972 &  0.09858 \tabularnewline
`(1-Bs)(1-B)Build4` & -2.077e-05 &  0.06089 & -3.4110e-04 &  0.9997 &  0.4999 \tabularnewline
`(1-Bs)(1-B)Chemicals(t-1)` & -0.4306 &  0.07534 & -5.7150e+00 &  4.888e-08 &  2.444e-08 \tabularnewline
`(1-Bs)(1-B)Chemicals(t-2)` & -0.3228 &  0.08141 & -3.9660e+00 &  0.0001082 &  5.41e-05 \tabularnewline
`(1-Bs)(1-B)Chemicals(t-3)` & -0.2521 &  0.08071 & -3.1240e+00 &  0.002101 &  0.001051 \tabularnewline
`(1-Bs)(1-B)Chemicals(t-4)` & -0.09254 &  0.07429 & -1.2460e+00 &  0.2146 &  0.1073 \tabularnewline
`(1-Bs)(1-B)Chemicals(t-1s)` & -0.3104 &  0.06825 & -4.5490e+00 &  1.03e-05 &  5.148e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309796&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]+0.06368[/C][C] 0.6148[/C][C]+1.0360e-01[/C][C] 0.9176[/C][C] 0.4588[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build0`[/C][C]+0.1986[/C][C] 0.06103[/C][C]+3.2550e+00[/C][C] 0.001371[/C][C] 0.0006854[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build1`[/C][C]+0.08972[/C][C] 0.08186[/C][C]+1.0960e+00[/C][C] 0.2746[/C][C] 0.1373[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build2`[/C][C]+0.09753[/C][C] 0.08956[/C][C]+1.0890e+00[/C][C] 0.2777[/C][C] 0.1389[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build3`[/C][C]+0.1052[/C][C] 0.08124[/C][C]+1.2950e+00[/C][C] 0.1972[/C][C] 0.09858[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build4`[/C][C]-2.077e-05[/C][C] 0.06089[/C][C]-3.4110e-04[/C][C] 0.9997[/C][C] 0.4999[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemicals(t-1)`[/C][C]-0.4306[/C][C] 0.07534[/C][C]-5.7150e+00[/C][C] 4.888e-08[/C][C] 2.444e-08[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemicals(t-2)`[/C][C]-0.3228[/C][C] 0.08141[/C][C]-3.9660e+00[/C][C] 0.0001082[/C][C] 5.41e-05[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemicals(t-3)`[/C][C]-0.2521[/C][C] 0.08071[/C][C]-3.1240e+00[/C][C] 0.002101[/C][C] 0.001051[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemicals(t-4)`[/C][C]-0.09254[/C][C] 0.07429[/C][C]-1.2460e+00[/C][C] 0.2146[/C][C] 0.1073[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemicals(t-1s)`[/C][C]-0.3104[/C][C] 0.06825[/C][C]-4.5490e+00[/C][C] 1.03e-05[/C][C] 5.148e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309796&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309796&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)	+0.06368	0.6148	+1.0360e-01	0.9176	0.4588
`(1-Bs)(1-B)Build0`	+0.1986	0.06103	+3.2550e+00	0.001371	0.0006854
`(1-Bs)(1-B)Build1`	+0.08972	0.08186	+1.0960e+00	0.2746	0.1373
`(1-Bs)(1-B)Build2`	+0.09753	0.08956	+1.0890e+00	0.2777	0.1389
`(1-Bs)(1-B)Build3`	+0.1052	0.08124	+1.2950e+00	0.1972	0.09858
`(1-Bs)(1-B)Build4`	-2.077e-05	0.06089	-3.4110e-04	0.9997	0.4999
`(1-Bs)(1-B)Chemicals(t-1)`	-0.4306	0.07534	-5.7150e+00	4.888e-08	2.444e-08
`(1-Bs)(1-B)Chemicals(t-2)`	-0.3228	0.08141	-3.9660e+00	0.0001082	5.41e-05
`(1-Bs)(1-B)Chemicals(t-3)`	-0.2521	0.08071	-3.1240e+00	0.002101	0.001051
`(1-Bs)(1-B)Chemicals(t-4)`	-0.09254	0.07429	-1.2460e+00	0.2146	0.1073
`(1-Bs)(1-B)Chemicals(t-1s)`	-0.3104	0.06825	-4.5490e+00	1.03e-05	5.148e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.6214
R-squared	0.3861
Adjusted R-squared	0.3496
F-TEST (value)	10.57
F-TEST (DF numerator)	10
F-TEST (DF denominator)	168
p-value	8.882e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	8.225
Sum Squared Residuals	1.136e+04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6214 \tabularnewline
R-squared &  0.3861 \tabularnewline
Adjusted R-squared &  0.3496 \tabularnewline
F-TEST (value) &  10.57 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 168 \tabularnewline
p-value &  8.882e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  8.225 \tabularnewline
Sum Squared Residuals &  1.136e+04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309796&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6214[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3861[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3496[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 10.57[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]168[/C][/ROW]
[ROW][C]p-value[/C][C] 8.882e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 8.225[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.136e+04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309796&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309796&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	0.6214
R-squared	0.3861
Adjusted R-squared	0.3496
F-TEST (value)	10.57
F-TEST (DF numerator)	10
F-TEST (DF denominator)	168
p-value	8.882e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	8.225
Sum Squared Residuals	1.136e+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=309796&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=309796&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309796&T=4

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-1.1	-6.118	5.018
2	0.4	3.456	-3.056
3	-0.6	1.313	-1.913
4	4.7	-1.518	6.218
5	-7.9	0.821	-8.721
6	5.2	3.031	2.169
7	-1.9	-3.524	1.624
8	-4.7	0.5013	-5.201
9	4.9	4.051	0.8494
10	-0.1	-5.072	4.972
11	1.4	1.063	0.3366
12	5.5	0.5935	4.906
13	-3.7	-3.754	0.05431
14	-4	-2.772	-1.228
15	15.8	4.576	11.22
16	-23.4	-6.985	-16.41
17	15.3	9.949	5.351
18	2.4	-2.458	4.858
19	-5	-2.546	-2.454
20	0.6	0.9251	-0.3251
21	7.6	-0.6105	8.21
22	-3.2	-5.888	2.688
23	0.2	2.234	-2.034
24	0.6	-4.086	4.686
25	-2.4	-0.465	-1.935
26	7.7	4.674	3.026
27	-1.8	-7.48	5.68
28	1.3	3.814	-2.514
29	-14.8	-6.312	-8.488
30	0.3	1.552	-1.252
31	4.3	9.802	-5.502
32	2.1	1.633	0.4672
33	-8.6	-2.57	-6.03
34	-2.8	3.508	-6.308
35	3.2	3.351	-0.1507
36	2.3	0.9891	1.311
37	0.1	-1.617	1.717
38	-2.6	-0.9969	-1.603
39	-13	-0.2936	-12.71
40	14.8	6.542	8.258
41	7.5	3.911	3.589
42	0.2	-2.674	2.874
43	-12.4	-10.58	-1.819
44	7.7	5.576	2.124
45	0.6	1.059	-0.4592
46	-3.2	0.4042	-3.604
47	3.2	1.296	1.904
48	-4.8	-3.386	-1.414
49	4.8	3.283	1.517
50	0	0.597	-0.597
51	0.8	2.098	-1.298
52	-7.8	-3.227	-4.573
53	-4.6	0.6979	-5.298
54	-1.7	3.259	-4.959
55	8.3	8.32	-0.01993
56	1	-6.424	7.424
57	0.5	-3.514	4.014
58	9.3	-0.7207	10.02
59	-10.1	-6.428	-3.672
60	-11	1.37	-12.37
61	4.2	8.411	-4.211
62	-0.7	0.8621	-1.562
63	1.8	0.7811	1.019
64	10.8	6.952	3.848
65	2.6	-4.671	7.271
66	-6.1	-10.38	4.283
67	6.9	4.81	2.09
68	-8.9	-6.522	-2.378
69	2.8	1.225	1.575
70	2.8	1.637	1.163
71	-7.1	-2.894	-4.206
72	20.2	8.835	11.36
73	-12.6	-12.46	-0.1423
74	-9.7	-1.635	-8.065
75	10.9	6.204	4.696
76	-28.6	-9.969	-18.63
77	6.5	12.07	-5.567
78	6.7	14.05	-7.353
79	-10.4	-9.433	-0.967
80	4.9	9.493	-4.593
81	0.6	-0.001011	0.601
82	-0.5	-3.345	2.845
83	4.5	4.584	-0.08418
84	-7.1	-7.991	0.8908
85	4.8	7.485	-2.685
86	11.8	0.2644	11.54
87	-6.5	-8.654	2.154
88	22.6	6.082	16.52
89	7.8	-16.23	24.03
90	-5.8	-7.934	2.134
91	-5.7	-1.55	-4.15
92	3.1	-1.802	4.902
93	6.9	4.839	2.061
94	-9	-3.768	-5.232
95	2.5	1.042	1.458
96	-2.9	2.188	-5.088
97	-2.4	-0.8173	-1.583
98	8.3	0.9833	7.317
99	-3.8	-4.063	0.2634
100	-9.1	-3.556	-5.544
101	-3.8	5.766	-9.566
102	26.6	3.963	22.64
103	-14.1	-5.458	-8.642
104	-4.9	3.52	-8.42
105	-12.4	-9.929	-2.471
106	1.9	11.4	-9.496
107	20.5	6.519	13.98
108	-15.6	-7.74	-7.86
109	-4.3	1.531	-5.831
110	-12.6	3.022	-15.62
111	-1.8	13.11	-14.91
112	11.9	8.591	3.309
113	-7.2	-3.608	-3.592
114	-5.3	-7.432	2.132
115	-0.6	3.784	-4.384
116	0.1	1.085	-0.9847
117	1.9	10.89	-8.989
118	19	0.7938	18.21
119	-20.2	-15.85	-4.346
120	11.9	5.691	6.209
121	7.4	0.1389	7.261
122	2.1	-2.296	4.396
123	-3.8	-6.199	2.399
124	1.7	-5.849	7.549
125	-7.5	2.418	-9.918
126	-6.7	0.6757	-7.376
127	30.8	10.18	20.62
128	-7.4	-9.87	2.47
129	3.3	-5.457	8.757
130	-10.6	-8.506	-2.094
131	-6.5	4.99	-11.49
132	-0.2	5.158	-5.358
133	11.9	2.387	9.513
134	-2.1	-5.297	3.197
135	-0.2	3.707	-3.907
136	-12.9	0.7737	-13.67
137	15.6	6.986	8.614
138	-9.6	1.208	-10.81
139	-0.4	-10.01	9.607
140	4.7	-0.07597	4.776
141	4.1	-3.795	7.895
142	-18.6	0.7858	-19.39
143	3.5	7.133	-3.633
144	27.6	4.515	23.09
145	-18	-12.11	-5.891
146	1.5	-0.4154	1.915
147	-0.5	-2.458	1.958
148	4.3	4.94	-0.6398
149	-25.9	-5.4	-20.5
150	19.2	14.51	4.686
151	-10	-0.6269	-9.373
152	-4.7	2.449	-7.149
153	-9.1	4.961	-14.06
154	15.5	10.66	4.844
155	11	-1.34	12.34
156	-20.6	-15.87	-4.732
157	0.4	6.641	-6.241
158	2.6	1.262	1.338
159	-0.3	2.456	-2.756
160	0	-0.0235	0.0235
161	17.2	8.769	8.431
162	-13.9	-16.24	2.336
163	3.8	7.46	-3.66
164	2.2	0.3095	1.89
165	20.9	2.744	18.16
166	-5.1	-14.05	8.95
167	-7.3	-8.204	0.904
168	-5.3	4.876	-10.18
169	-3.1	0.8182	-3.918
170	-6.2	5.879	-12.08
171	38.7	7.74	30.96
172	-23.3	-17.41	-5.892
173	4.6	-2.973	7.573
174	-2.2	4.432	-6.632
175	-8.4	-6.513	-1.887
176	6.6	10.22	-3.623
177	-12.3	-9.986	-2.314
178	8	6.491	1.509
179	2.4	3.702	-1.302

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -1.1 & -6.118 &  5.018 \tabularnewline
2 &  0.4 &  3.456 & -3.056 \tabularnewline
3 & -0.6 &  1.313 & -1.913 \tabularnewline
4 &  4.7 & -1.518 &  6.218 \tabularnewline
5 & -7.9 &  0.821 & -8.721 \tabularnewline
6 &  5.2 &  3.031 &  2.169 \tabularnewline
7 & -1.9 & -3.524 &  1.624 \tabularnewline
8 & -4.7 &  0.5013 & -5.201 \tabularnewline
9 &  4.9 &  4.051 &  0.8494 \tabularnewline
10 & -0.1 & -5.072 &  4.972 \tabularnewline
11 &  1.4 &  1.063 &  0.3366 \tabularnewline
12 &  5.5 &  0.5935 &  4.906 \tabularnewline
13 & -3.7 & -3.754 &  0.05431 \tabularnewline
14 & -4 & -2.772 & -1.228 \tabularnewline
15 &  15.8 &  4.576 &  11.22 \tabularnewline
16 & -23.4 & -6.985 & -16.41 \tabularnewline
17 &  15.3 &  9.949 &  5.351 \tabularnewline
18 &  2.4 & -2.458 &  4.858 \tabularnewline
19 & -5 & -2.546 & -2.454 \tabularnewline
20 &  0.6 &  0.9251 & -0.3251 \tabularnewline
21 &  7.6 & -0.6105 &  8.21 \tabularnewline
22 & -3.2 & -5.888 &  2.688 \tabularnewline
23 &  0.2 &  2.234 & -2.034 \tabularnewline
24 &  0.6 & -4.086 &  4.686 \tabularnewline
25 & -2.4 & -0.465 & -1.935 \tabularnewline
26 &  7.7 &  4.674 &  3.026 \tabularnewline
27 & -1.8 & -7.48 &  5.68 \tabularnewline
28 &  1.3 &  3.814 & -2.514 \tabularnewline
29 & -14.8 & -6.312 & -8.488 \tabularnewline
30 &  0.3 &  1.552 & -1.252 \tabularnewline
31 &  4.3 &  9.802 & -5.502 \tabularnewline
32 &  2.1 &  1.633 &  0.4672 \tabularnewline
33 & -8.6 & -2.57 & -6.03 \tabularnewline
34 & -2.8 &  3.508 & -6.308 \tabularnewline
35 &  3.2 &  3.351 & -0.1507 \tabularnewline
36 &  2.3 &  0.9891 &  1.311 \tabularnewline
37 &  0.1 & -1.617 &  1.717 \tabularnewline
38 & -2.6 & -0.9969 & -1.603 \tabularnewline
39 & -13 & -0.2936 & -12.71 \tabularnewline
40 &  14.8 &  6.542 &  8.258 \tabularnewline
41 &  7.5 &  3.911 &  3.589 \tabularnewline
42 &  0.2 & -2.674 &  2.874 \tabularnewline
43 & -12.4 & -10.58 & -1.819 \tabularnewline
44 &  7.7 &  5.576 &  2.124 \tabularnewline
45 &  0.6 &  1.059 & -0.4592 \tabularnewline
46 & -3.2 &  0.4042 & -3.604 \tabularnewline
47 &  3.2 &  1.296 &  1.904 \tabularnewline
48 & -4.8 & -3.386 & -1.414 \tabularnewline
49 &  4.8 &  3.283 &  1.517 \tabularnewline
50 &  0 &  0.597 & -0.597 \tabularnewline
51 &  0.8 &  2.098 & -1.298 \tabularnewline
52 & -7.8 & -3.227 & -4.573 \tabularnewline
53 & -4.6 &  0.6979 & -5.298 \tabularnewline
54 & -1.7 &  3.259 & -4.959 \tabularnewline
55 &  8.3 &  8.32 & -0.01993 \tabularnewline
56 &  1 & -6.424 &  7.424 \tabularnewline
57 &  0.5 & -3.514 &  4.014 \tabularnewline
58 &  9.3 & -0.7207 &  10.02 \tabularnewline
59 & -10.1 & -6.428 & -3.672 \tabularnewline
60 & -11 &  1.37 & -12.37 \tabularnewline
61 &  4.2 &  8.411 & -4.211 \tabularnewline
62 & -0.7 &  0.8621 & -1.562 \tabularnewline
63 &  1.8 &  0.7811 &  1.019 \tabularnewline
64 &  10.8 &  6.952 &  3.848 \tabularnewline
65 &  2.6 & -4.671 &  7.271 \tabularnewline
66 & -6.1 & -10.38 &  4.283 \tabularnewline
67 &  6.9 &  4.81 &  2.09 \tabularnewline
68 & -8.9 & -6.522 & -2.378 \tabularnewline
69 &  2.8 &  1.225 &  1.575 \tabularnewline
70 &  2.8 &  1.637 &  1.163 \tabularnewline
71 & -7.1 & -2.894 & -4.206 \tabularnewline
72 &  20.2 &  8.835 &  11.36 \tabularnewline
73 & -12.6 & -12.46 & -0.1423 \tabularnewline
74 & -9.7 & -1.635 & -8.065 \tabularnewline
75 &  10.9 &  6.204 &  4.696 \tabularnewline
76 & -28.6 & -9.969 & -18.63 \tabularnewline
77 &  6.5 &  12.07 & -5.567 \tabularnewline
78 &  6.7 &  14.05 & -7.353 \tabularnewline
79 & -10.4 & -9.433 & -0.967 \tabularnewline
80 &  4.9 &  9.493 & -4.593 \tabularnewline
81 &  0.6 & -0.001011 &  0.601 \tabularnewline
82 & -0.5 & -3.345 &  2.845 \tabularnewline
83 &  4.5 &  4.584 & -0.08418 \tabularnewline
84 & -7.1 & -7.991 &  0.8908 \tabularnewline
85 &  4.8 &  7.485 & -2.685 \tabularnewline
86 &  11.8 &  0.2644 &  11.54 \tabularnewline
87 & -6.5 & -8.654 &  2.154 \tabularnewline
88 &  22.6 &  6.082 &  16.52 \tabularnewline
89 &  7.8 & -16.23 &  24.03 \tabularnewline
90 & -5.8 & -7.934 &  2.134 \tabularnewline
91 & -5.7 & -1.55 & -4.15 \tabularnewline
92 &  3.1 & -1.802 &  4.902 \tabularnewline
93 &  6.9 &  4.839 &  2.061 \tabularnewline
94 & -9 & -3.768 & -5.232 \tabularnewline
95 &  2.5 &  1.042 &  1.458 \tabularnewline
96 & -2.9 &  2.188 & -5.088 \tabularnewline
97 & -2.4 & -0.8173 & -1.583 \tabularnewline
98 &  8.3 &  0.9833 &  7.317 \tabularnewline
99 & -3.8 & -4.063 &  0.2634 \tabularnewline
100 & -9.1 & -3.556 & -5.544 \tabularnewline
101 & -3.8 &  5.766 & -9.566 \tabularnewline
102 &  26.6 &  3.963 &  22.64 \tabularnewline
103 & -14.1 & -5.458 & -8.642 \tabularnewline
104 & -4.9 &  3.52 & -8.42 \tabularnewline
105 & -12.4 & -9.929 & -2.471 \tabularnewline
106 &  1.9 &  11.4 & -9.496 \tabularnewline
107 &  20.5 &  6.519 &  13.98 \tabularnewline
108 & -15.6 & -7.74 & -7.86 \tabularnewline
109 & -4.3 &  1.531 & -5.831 \tabularnewline
110 & -12.6 &  3.022 & -15.62 \tabularnewline
111 & -1.8 &  13.11 & -14.91 \tabularnewline
112 &  11.9 &  8.591 &  3.309 \tabularnewline
113 & -7.2 & -3.608 & -3.592 \tabularnewline
114 & -5.3 & -7.432 &  2.132 \tabularnewline
115 & -0.6 &  3.784 & -4.384 \tabularnewline
116 &  0.1 &  1.085 & -0.9847 \tabularnewline
117 &  1.9 &  10.89 & -8.989 \tabularnewline
118 &  19 &  0.7938 &  18.21 \tabularnewline
119 & -20.2 & -15.85 & -4.346 \tabularnewline
120 &  11.9 &  5.691 &  6.209 \tabularnewline
121 &  7.4 &  0.1389 &  7.261 \tabularnewline
122 &  2.1 & -2.296 &  4.396 \tabularnewline
123 & -3.8 & -6.199 &  2.399 \tabularnewline
124 &  1.7 & -5.849 &  7.549 \tabularnewline
125 & -7.5 &  2.418 & -9.918 \tabularnewline
126 & -6.7 &  0.6757 & -7.376 \tabularnewline
127 &  30.8 &  10.18 &  20.62 \tabularnewline
128 & -7.4 & -9.87 &  2.47 \tabularnewline
129 &  3.3 & -5.457 &  8.757 \tabularnewline
130 & -10.6 & -8.506 & -2.094 \tabularnewline
131 & -6.5 &  4.99 & -11.49 \tabularnewline
132 & -0.2 &  5.158 & -5.358 \tabularnewline
133 &  11.9 &  2.387 &  9.513 \tabularnewline
134 & -2.1 & -5.297 &  3.197 \tabularnewline
135 & -0.2 &  3.707 & -3.907 \tabularnewline
136 & -12.9 &  0.7737 & -13.67 \tabularnewline
137 &  15.6 &  6.986 &  8.614 \tabularnewline
138 & -9.6 &  1.208 & -10.81 \tabularnewline
139 & -0.4 & -10.01 &  9.607 \tabularnewline
140 &  4.7 & -0.07597 &  4.776 \tabularnewline
141 &  4.1 & -3.795 &  7.895 \tabularnewline
142 & -18.6 &  0.7858 & -19.39 \tabularnewline
143 &  3.5 &  7.133 & -3.633 \tabularnewline
144 &  27.6 &  4.515 &  23.09 \tabularnewline
145 & -18 & -12.11 & -5.891 \tabularnewline
146 &  1.5 & -0.4154 &  1.915 \tabularnewline
147 & -0.5 & -2.458 &  1.958 \tabularnewline
148 &  4.3 &  4.94 & -0.6398 \tabularnewline
149 & -25.9 & -5.4 & -20.5 \tabularnewline
150 &  19.2 &  14.51 &  4.686 \tabularnewline
151 & -10 & -0.6269 & -9.373 \tabularnewline
152 & -4.7 &  2.449 & -7.149 \tabularnewline
153 & -9.1 &  4.961 & -14.06 \tabularnewline
154 &  15.5 &  10.66 &  4.844 \tabularnewline
155 &  11 & -1.34 &  12.34 \tabularnewline
156 & -20.6 & -15.87 & -4.732 \tabularnewline
157 &  0.4 &  6.641 & -6.241 \tabularnewline
158 &  2.6 &  1.262 &  1.338 \tabularnewline
159 & -0.3 &  2.456 & -2.756 \tabularnewline
160 &  0 & -0.0235 &  0.0235 \tabularnewline
161 &  17.2 &  8.769 &  8.431 \tabularnewline
162 & -13.9 & -16.24 &  2.336 \tabularnewline
163 &  3.8 &  7.46 & -3.66 \tabularnewline
164 &  2.2 &  0.3095 &  1.89 \tabularnewline
165 &  20.9 &  2.744 &  18.16 \tabularnewline
166 & -5.1 & -14.05 &  8.95 \tabularnewline
167 & -7.3 & -8.204 &  0.904 \tabularnewline
168 & -5.3 &  4.876 & -10.18 \tabularnewline
169 & -3.1 &  0.8182 & -3.918 \tabularnewline
170 & -6.2 &  5.879 & -12.08 \tabularnewline
171 &  38.7 &  7.74 &  30.96 \tabularnewline
172 & -23.3 & -17.41 & -5.892 \tabularnewline
173 &  4.6 & -2.973 &  7.573 \tabularnewline
174 & -2.2 &  4.432 & -6.632 \tabularnewline
175 & -8.4 & -6.513 & -1.887 \tabularnewline
176 &  6.6 &  10.22 & -3.623 \tabularnewline
177 & -12.3 & -9.986 & -2.314 \tabularnewline
178 &  8 &  6.491 &  1.509 \tabularnewline
179 &  2.4 &  3.702 & -1.302 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309796&T=5

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]-1.1[/C][C]-6.118[/C][C] 5.018[/C][/ROW]
[ROW][C]2[/C][C] 0.4[/C][C] 3.456[/C][C]-3.056[/C][/ROW]
[ROW][C]3[/C][C]-0.6[/C][C] 1.313[/C][C]-1.913[/C][/ROW]
[ROW][C]4[/C][C] 4.7[/C][C]-1.518[/C][C] 6.218[/C][/ROW]
[ROW][C]5[/C][C]-7.9[/C][C] 0.821[/C][C]-8.721[/C][/ROW]
[ROW][C]6[/C][C] 5.2[/C][C] 3.031[/C][C] 2.169[/C][/ROW]
[ROW][C]7[/C][C]-1.9[/C][C]-3.524[/C][C] 1.624[/C][/ROW]
[ROW][C]8[/C][C]-4.7[/C][C] 0.5013[/C][C]-5.201[/C][/ROW]
[ROW][C]9[/C][C] 4.9[/C][C] 4.051[/C][C] 0.8494[/C][/ROW]
[ROW][C]10[/C][C]-0.1[/C][C]-5.072[/C][C] 4.972[/C][/ROW]
[ROW][C]11[/C][C] 1.4[/C][C] 1.063[/C][C] 0.3366[/C][/ROW]
[ROW][C]12[/C][C] 5.5[/C][C] 0.5935[/C][C] 4.906[/C][/ROW]
[ROW][C]13[/C][C]-3.7[/C][C]-3.754[/C][C] 0.05431[/C][/ROW]
[ROW][C]14[/C][C]-4[/C][C]-2.772[/C][C]-1.228[/C][/ROW]
[ROW][C]15[/C][C] 15.8[/C][C] 4.576[/C][C] 11.22[/C][/ROW]
[ROW][C]16[/C][C]-23.4[/C][C]-6.985[/C][C]-16.41[/C][/ROW]
[ROW][C]17[/C][C] 15.3[/C][C] 9.949[/C][C] 5.351[/C][/ROW]
[ROW][C]18[/C][C] 2.4[/C][C]-2.458[/C][C] 4.858[/C][/ROW]
[ROW][C]19[/C][C]-5[/C][C]-2.546[/C][C]-2.454[/C][/ROW]
[ROW][C]20[/C][C] 0.6[/C][C] 0.9251[/C][C]-0.3251[/C][/ROW]
[ROW][C]21[/C][C] 7.6[/C][C]-0.6105[/C][C] 8.21[/C][/ROW]
[ROW][C]22[/C][C]-3.2[/C][C]-5.888[/C][C] 2.688[/C][/ROW]
[ROW][C]23[/C][C] 0.2[/C][C] 2.234[/C][C]-2.034[/C][/ROW]
[ROW][C]24[/C][C] 0.6[/C][C]-4.086[/C][C] 4.686[/C][/ROW]
[ROW][C]25[/C][C]-2.4[/C][C]-0.465[/C][C]-1.935[/C][/ROW]
[ROW][C]26[/C][C] 7.7[/C][C] 4.674[/C][C] 3.026[/C][/ROW]
[ROW][C]27[/C][C]-1.8[/C][C]-7.48[/C][C] 5.68[/C][/ROW]
[ROW][C]28[/C][C] 1.3[/C][C] 3.814[/C][C]-2.514[/C][/ROW]
[ROW][C]29[/C][C]-14.8[/C][C]-6.312[/C][C]-8.488[/C][/ROW]
[ROW][C]30[/C][C] 0.3[/C][C] 1.552[/C][C]-1.252[/C][/ROW]
[ROW][C]31[/C][C] 4.3[/C][C] 9.802[/C][C]-5.502[/C][/ROW]
[ROW][C]32[/C][C] 2.1[/C][C] 1.633[/C][C] 0.4672[/C][/ROW]
[ROW][C]33[/C][C]-8.6[/C][C]-2.57[/C][C]-6.03[/C][/ROW]
[ROW][C]34[/C][C]-2.8[/C][C] 3.508[/C][C]-6.308[/C][/ROW]
[ROW][C]35[/C][C] 3.2[/C][C] 3.351[/C][C]-0.1507[/C][/ROW]
[ROW][C]36[/C][C] 2.3[/C][C] 0.9891[/C][C] 1.311[/C][/ROW]
[ROW][C]37[/C][C] 0.1[/C][C]-1.617[/C][C] 1.717[/C][/ROW]
[ROW][C]38[/C][C]-2.6[/C][C]-0.9969[/C][C]-1.603[/C][/ROW]
[ROW][C]39[/C][C]-13[/C][C]-0.2936[/C][C]-12.71[/C][/ROW]
[ROW][C]40[/C][C] 14.8[/C][C] 6.542[/C][C] 8.258[/C][/ROW]
[ROW][C]41[/C][C] 7.5[/C][C] 3.911[/C][C] 3.589[/C][/ROW]
[ROW][C]42[/C][C] 0.2[/C][C]-2.674[/C][C] 2.874[/C][/ROW]
[ROW][C]43[/C][C]-12.4[/C][C]-10.58[/C][C]-1.819[/C][/ROW]
[ROW][C]44[/C][C] 7.7[/C][C] 5.576[/C][C] 2.124[/C][/ROW]
[ROW][C]45[/C][C] 0.6[/C][C] 1.059[/C][C]-0.4592[/C][/ROW]
[ROW][C]46[/C][C]-3.2[/C][C] 0.4042[/C][C]-3.604[/C][/ROW]
[ROW][C]47[/C][C] 3.2[/C][C] 1.296[/C][C] 1.904[/C][/ROW]
[ROW][C]48[/C][C]-4.8[/C][C]-3.386[/C][C]-1.414[/C][/ROW]
[ROW][C]49[/C][C] 4.8[/C][C] 3.283[/C][C] 1.517[/C][/ROW]
[ROW][C]50[/C][C] 0[/C][C] 0.597[/C][C]-0.597[/C][/ROW]
[ROW][C]51[/C][C] 0.8[/C][C] 2.098[/C][C]-1.298[/C][/ROW]
[ROW][C]52[/C][C]-7.8[/C][C]-3.227[/C][C]-4.573[/C][/ROW]
[ROW][C]53[/C][C]-4.6[/C][C] 0.6979[/C][C]-5.298[/C][/ROW]
[ROW][C]54[/C][C]-1.7[/C][C] 3.259[/C][C]-4.959[/C][/ROW]
[ROW][C]55[/C][C] 8.3[/C][C] 8.32[/C][C]-0.01993[/C][/ROW]
[ROW][C]56[/C][C] 1[/C][C]-6.424[/C][C] 7.424[/C][/ROW]
[ROW][C]57[/C][C] 0.5[/C][C]-3.514[/C][C] 4.014[/C][/ROW]
[ROW][C]58[/C][C] 9.3[/C][C]-0.7207[/C][C] 10.02[/C][/ROW]
[ROW][C]59[/C][C]-10.1[/C][C]-6.428[/C][C]-3.672[/C][/ROW]
[ROW][C]60[/C][C]-11[/C][C] 1.37[/C][C]-12.37[/C][/ROW]
[ROW][C]61[/C][C] 4.2[/C][C] 8.411[/C][C]-4.211[/C][/ROW]
[ROW][C]62[/C][C]-0.7[/C][C] 0.8621[/C][C]-1.562[/C][/ROW]
[ROW][C]63[/C][C] 1.8[/C][C] 0.7811[/C][C] 1.019[/C][/ROW]
[ROW][C]64[/C][C] 10.8[/C][C] 6.952[/C][C] 3.848[/C][/ROW]
[ROW][C]65[/C][C] 2.6[/C][C]-4.671[/C][C] 7.271[/C][/ROW]
[ROW][C]66[/C][C]-6.1[/C][C]-10.38[/C][C] 4.283[/C][/ROW]
[ROW][C]67[/C][C] 6.9[/C][C] 4.81[/C][C] 2.09[/C][/ROW]
[ROW][C]68[/C][C]-8.9[/C][C]-6.522[/C][C]-2.378[/C][/ROW]
[ROW][C]69[/C][C] 2.8[/C][C] 1.225[/C][C] 1.575[/C][/ROW]
[ROW][C]70[/C][C] 2.8[/C][C] 1.637[/C][C] 1.163[/C][/ROW]
[ROW][C]71[/C][C]-7.1[/C][C]-2.894[/C][C]-4.206[/C][/ROW]
[ROW][C]72[/C][C] 20.2[/C][C] 8.835[/C][C] 11.36[/C][/ROW]
[ROW][C]73[/C][C]-12.6[/C][C]-12.46[/C][C]-0.1423[/C][/ROW]
[ROW][C]74[/C][C]-9.7[/C][C]-1.635[/C][C]-8.065[/C][/ROW]
[ROW][C]75[/C][C] 10.9[/C][C] 6.204[/C][C] 4.696[/C][/ROW]
[ROW][C]76[/C][C]-28.6[/C][C]-9.969[/C][C]-18.63[/C][/ROW]
[ROW][C]77[/C][C] 6.5[/C][C] 12.07[/C][C]-5.567[/C][/ROW]
[ROW][C]78[/C][C] 6.7[/C][C] 14.05[/C][C]-7.353[/C][/ROW]
[ROW][C]79[/C][C]-10.4[/C][C]-9.433[/C][C]-0.967[/C][/ROW]
[ROW][C]80[/C][C] 4.9[/C][C] 9.493[/C][C]-4.593[/C][/ROW]
[ROW][C]81[/C][C] 0.6[/C][C]-0.001011[/C][C] 0.601[/C][/ROW]
[ROW][C]82[/C][C]-0.5[/C][C]-3.345[/C][C] 2.845[/C][/ROW]
[ROW][C]83[/C][C] 4.5[/C][C] 4.584[/C][C]-0.08418[/C][/ROW]
[ROW][C]84[/C][C]-7.1[/C][C]-7.991[/C][C] 0.8908[/C][/ROW]
[ROW][C]85[/C][C] 4.8[/C][C] 7.485[/C][C]-2.685[/C][/ROW]
[ROW][C]86[/C][C] 11.8[/C][C] 0.2644[/C][C] 11.54[/C][/ROW]
[ROW][C]87[/C][C]-6.5[/C][C]-8.654[/C][C] 2.154[/C][/ROW]
[ROW][C]88[/C][C] 22.6[/C][C] 6.082[/C][C] 16.52[/C][/ROW]
[ROW][C]89[/C][C] 7.8[/C][C]-16.23[/C][C] 24.03[/C][/ROW]
[ROW][C]90[/C][C]-5.8[/C][C]-7.934[/C][C] 2.134[/C][/ROW]
[ROW][C]91[/C][C]-5.7[/C][C]-1.55[/C][C]-4.15[/C][/ROW]
[ROW][C]92[/C][C] 3.1[/C][C]-1.802[/C][C] 4.902[/C][/ROW]
[ROW][C]93[/C][C] 6.9[/C][C] 4.839[/C][C] 2.061[/C][/ROW]
[ROW][C]94[/C][C]-9[/C][C]-3.768[/C][C]-5.232[/C][/ROW]
[ROW][C]95[/C][C] 2.5[/C][C] 1.042[/C][C] 1.458[/C][/ROW]
[ROW][C]96[/C][C]-2.9[/C][C] 2.188[/C][C]-5.088[/C][/ROW]
[ROW][C]97[/C][C]-2.4[/C][C]-0.8173[/C][C]-1.583[/C][/ROW]
[ROW][C]98[/C][C] 8.3[/C][C] 0.9833[/C][C] 7.317[/C][/ROW]
[ROW][C]99[/C][C]-3.8[/C][C]-4.063[/C][C] 0.2634[/C][/ROW]
[ROW][C]100[/C][C]-9.1[/C][C]-3.556[/C][C]-5.544[/C][/ROW]
[ROW][C]101[/C][C]-3.8[/C][C] 5.766[/C][C]-9.566[/C][/ROW]
[ROW][C]102[/C][C] 26.6[/C][C] 3.963[/C][C] 22.64[/C][/ROW]
[ROW][C]103[/C][C]-14.1[/C][C]-5.458[/C][C]-8.642[/C][/ROW]
[ROW][C]104[/C][C]-4.9[/C][C] 3.52[/C][C]-8.42[/C][/ROW]
[ROW][C]105[/C][C]-12.4[/C][C]-9.929[/C][C]-2.471[/C][/ROW]
[ROW][C]106[/C][C] 1.9[/C][C] 11.4[/C][C]-9.496[/C][/ROW]
[ROW][C]107[/C][C] 20.5[/C][C] 6.519[/C][C] 13.98[/C][/ROW]
[ROW][C]108[/C][C]-15.6[/C][C]-7.74[/C][C]-7.86[/C][/ROW]
[ROW][C]109[/C][C]-4.3[/C][C] 1.531[/C][C]-5.831[/C][/ROW]
[ROW][C]110[/C][C]-12.6[/C][C] 3.022[/C][C]-15.62[/C][/ROW]
[ROW][C]111[/C][C]-1.8[/C][C] 13.11[/C][C]-14.91[/C][/ROW]
[ROW][C]112[/C][C] 11.9[/C][C] 8.591[/C][C] 3.309[/C][/ROW]
[ROW][C]113[/C][C]-7.2[/C][C]-3.608[/C][C]-3.592[/C][/ROW]
[ROW][C]114[/C][C]-5.3[/C][C]-7.432[/C][C] 2.132[/C][/ROW]
[ROW][C]115[/C][C]-0.6[/C][C] 3.784[/C][C]-4.384[/C][/ROW]
[ROW][C]116[/C][C] 0.1[/C][C] 1.085[/C][C]-0.9847[/C][/ROW]
[ROW][C]117[/C][C] 1.9[/C][C] 10.89[/C][C]-8.989[/C][/ROW]
[ROW][C]118[/C][C] 19[/C][C] 0.7938[/C][C] 18.21[/C][/ROW]
[ROW][C]119[/C][C]-20.2[/C][C]-15.85[/C][C]-4.346[/C][/ROW]
[ROW][C]120[/C][C] 11.9[/C][C] 5.691[/C][C] 6.209[/C][/ROW]
[ROW][C]121[/C][C] 7.4[/C][C] 0.1389[/C][C] 7.261[/C][/ROW]
[ROW][C]122[/C][C] 2.1[/C][C]-2.296[/C][C] 4.396[/C][/ROW]
[ROW][C]123[/C][C]-3.8[/C][C]-6.199[/C][C] 2.399[/C][/ROW]
[ROW][C]124[/C][C] 1.7[/C][C]-5.849[/C][C] 7.549[/C][/ROW]
[ROW][C]125[/C][C]-7.5[/C][C] 2.418[/C][C]-9.918[/C][/ROW]
[ROW][C]126[/C][C]-6.7[/C][C] 0.6757[/C][C]-7.376[/C][/ROW]
[ROW][C]127[/C][C] 30.8[/C][C] 10.18[/C][C] 20.62[/C][/ROW]
[ROW][C]128[/C][C]-7.4[/C][C]-9.87[/C][C] 2.47[/C][/ROW]
[ROW][C]129[/C][C] 3.3[/C][C]-5.457[/C][C] 8.757[/C][/ROW]
[ROW][C]130[/C][C]-10.6[/C][C]-8.506[/C][C]-2.094[/C][/ROW]
[ROW][C]131[/C][C]-6.5[/C][C] 4.99[/C][C]-11.49[/C][/ROW]
[ROW][C]132[/C][C]-0.2[/C][C] 5.158[/C][C]-5.358[/C][/ROW]
[ROW][C]133[/C][C] 11.9[/C][C] 2.387[/C][C] 9.513[/C][/ROW]
[ROW][C]134[/C][C]-2.1[/C][C]-5.297[/C][C] 3.197[/C][/ROW]
[ROW][C]135[/C][C]-0.2[/C][C] 3.707[/C][C]-3.907[/C][/ROW]
[ROW][C]136[/C][C]-12.9[/C][C] 0.7737[/C][C]-13.67[/C][/ROW]
[ROW][C]137[/C][C] 15.6[/C][C] 6.986[/C][C] 8.614[/C][/ROW]
[ROW][C]138[/C][C]-9.6[/C][C] 1.208[/C][C]-10.81[/C][/ROW]
[ROW][C]139[/C][C]-0.4[/C][C]-10.01[/C][C] 9.607[/C][/ROW]
[ROW][C]140[/C][C] 4.7[/C][C]-0.07597[/C][C] 4.776[/C][/ROW]
[ROW][C]141[/C][C] 4.1[/C][C]-3.795[/C][C] 7.895[/C][/ROW]
[ROW][C]142[/C][C]-18.6[/C][C] 0.7858[/C][C]-19.39[/C][/ROW]
[ROW][C]143[/C][C] 3.5[/C][C] 7.133[/C][C]-3.633[/C][/ROW]
[ROW][C]144[/C][C] 27.6[/C][C] 4.515[/C][C] 23.09[/C][/ROW]
[ROW][C]145[/C][C]-18[/C][C]-12.11[/C][C]-5.891[/C][/ROW]
[ROW][C]146[/C][C] 1.5[/C][C]-0.4154[/C][C] 1.915[/C][/ROW]
[ROW][C]147[/C][C]-0.5[/C][C]-2.458[/C][C] 1.958[/C][/ROW]
[ROW][C]148[/C][C] 4.3[/C][C] 4.94[/C][C]-0.6398[/C][/ROW]
[ROW][C]149[/C][C]-25.9[/C][C]-5.4[/C][C]-20.5[/C][/ROW]
[ROW][C]150[/C][C] 19.2[/C][C] 14.51[/C][C] 4.686[/C][/ROW]
[ROW][C]151[/C][C]-10[/C][C]-0.6269[/C][C]-9.373[/C][/ROW]
[ROW][C]152[/C][C]-4.7[/C][C] 2.449[/C][C]-7.149[/C][/ROW]
[ROW][C]153[/C][C]-9.1[/C][C] 4.961[/C][C]-14.06[/C][/ROW]
[ROW][C]154[/C][C] 15.5[/C][C] 10.66[/C][C] 4.844[/C][/ROW]
[ROW][C]155[/C][C] 11[/C][C]-1.34[/C][C] 12.34[/C][/ROW]
[ROW][C]156[/C][C]-20.6[/C][C]-15.87[/C][C]-4.732[/C][/ROW]
[ROW][C]157[/C][C] 0.4[/C][C] 6.641[/C][C]-6.241[/C][/ROW]
[ROW][C]158[/C][C] 2.6[/C][C] 1.262[/C][C] 1.338[/C][/ROW]
[ROW][C]159[/C][C]-0.3[/C][C] 2.456[/C][C]-2.756[/C][/ROW]
[ROW][C]160[/C][C] 0[/C][C]-0.0235[/C][C] 0.0235[/C][/ROW]
[ROW][C]161[/C][C] 17.2[/C][C] 8.769[/C][C] 8.431[/C][/ROW]
[ROW][C]162[/C][C]-13.9[/C][C]-16.24[/C][C] 2.336[/C][/ROW]
[ROW][C]163[/C][C] 3.8[/C][C] 7.46[/C][C]-3.66[/C][/ROW]
[ROW][C]164[/C][C] 2.2[/C][C] 0.3095[/C][C] 1.89[/C][/ROW]
[ROW][C]165[/C][C] 20.9[/C][C] 2.744[/C][C] 18.16[/C][/ROW]
[ROW][C]166[/C][C]-5.1[/C][C]-14.05[/C][C] 8.95[/C][/ROW]
[ROW][C]167[/C][C]-7.3[/C][C]-8.204[/C][C] 0.904[/C][/ROW]
[ROW][C]168[/C][C]-5.3[/C][C] 4.876[/C][C]-10.18[/C][/ROW]
[ROW][C]169[/C][C]-3.1[/C][C] 0.8182[/C][C]-3.918[/C][/ROW]
[ROW][C]170[/C][C]-6.2[/C][C] 5.879[/C][C]-12.08[/C][/ROW]
[ROW][C]171[/C][C] 38.7[/C][C] 7.74[/C][C] 30.96[/C][/ROW]
[ROW][C]172[/C][C]-23.3[/C][C]-17.41[/C][C]-5.892[/C][/ROW]
[ROW][C]173[/C][C] 4.6[/C][C]-2.973[/C][C] 7.573[/C][/ROW]
[ROW][C]174[/C][C]-2.2[/C][C] 4.432[/C][C]-6.632[/C][/ROW]
[ROW][C]175[/C][C]-8.4[/C][C]-6.513[/C][C]-1.887[/C][/ROW]
[ROW][C]176[/C][C] 6.6[/C][C] 10.22[/C][C]-3.623[/C][/ROW]
[ROW][C]177[/C][C]-12.3[/C][C]-9.986[/C][C]-2.314[/C][/ROW]
[ROW][C]178[/C][C] 8[/C][C] 6.491[/C][C] 1.509[/C][/ROW]
[ROW][C]179[/C][C] 2.4[/C][C] 3.702[/C][C]-1.302[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309796&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309796&T=5

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-1.1	-6.118	5.018
2	0.4	3.456	-3.056
3	-0.6	1.313	-1.913
4	4.7	-1.518	6.218
5	-7.9	0.821	-8.721
6	5.2	3.031	2.169
7	-1.9	-3.524	1.624
8	-4.7	0.5013	-5.201
9	4.9	4.051	0.8494
10	-0.1	-5.072	4.972
11	1.4	1.063	0.3366
12	5.5	0.5935	4.906
13	-3.7	-3.754	0.05431
14	-4	-2.772	-1.228
15	15.8	4.576	11.22
16	-23.4	-6.985	-16.41
17	15.3	9.949	5.351
18	2.4	-2.458	4.858
19	-5	-2.546	-2.454
20	0.6	0.9251	-0.3251
21	7.6	-0.6105	8.21
22	-3.2	-5.888	2.688
23	0.2	2.234	-2.034
24	0.6	-4.086	4.686
25	-2.4	-0.465	-1.935
26	7.7	4.674	3.026
27	-1.8	-7.48	5.68
28	1.3	3.814	-2.514
29	-14.8	-6.312	-8.488
30	0.3	1.552	-1.252
31	4.3	9.802	-5.502
32	2.1	1.633	0.4672
33	-8.6	-2.57	-6.03
34	-2.8	3.508	-6.308
35	3.2	3.351	-0.1507
36	2.3	0.9891	1.311
37	0.1	-1.617	1.717
38	-2.6	-0.9969	-1.603
39	-13	-0.2936	-12.71
40	14.8	6.542	8.258
41	7.5	3.911	3.589
42	0.2	-2.674	2.874
43	-12.4	-10.58	-1.819
44	7.7	5.576	2.124
45	0.6	1.059	-0.4592
46	-3.2	0.4042	-3.604
47	3.2	1.296	1.904
48	-4.8	-3.386	-1.414
49	4.8	3.283	1.517
50	0	0.597	-0.597
51	0.8	2.098	-1.298
52	-7.8	-3.227	-4.573
53	-4.6	0.6979	-5.298
54	-1.7	3.259	-4.959
55	8.3	8.32	-0.01993
56	1	-6.424	7.424
57	0.5	-3.514	4.014
58	9.3	-0.7207	10.02
59	-10.1	-6.428	-3.672
60	-11	1.37	-12.37
61	4.2	8.411	-4.211
62	-0.7	0.8621	-1.562
63	1.8	0.7811	1.019
64	10.8	6.952	3.848
65	2.6	-4.671	7.271
66	-6.1	-10.38	4.283
67	6.9	4.81	2.09
68	-8.9	-6.522	-2.378
69	2.8	1.225	1.575
70	2.8	1.637	1.163
71	-7.1	-2.894	-4.206
72	20.2	8.835	11.36
73	-12.6	-12.46	-0.1423
74	-9.7	-1.635	-8.065
75	10.9	6.204	4.696
76	-28.6	-9.969	-18.63
77	6.5	12.07	-5.567
78	6.7	14.05	-7.353
79	-10.4	-9.433	-0.967
80	4.9	9.493	-4.593
81	0.6	-0.001011	0.601
82	-0.5	-3.345	2.845
83	4.5	4.584	-0.08418
84	-7.1	-7.991	0.8908
85	4.8	7.485	-2.685
86	11.8	0.2644	11.54
87	-6.5	-8.654	2.154
88	22.6	6.082	16.52
89	7.8	-16.23	24.03
90	-5.8	-7.934	2.134
91	-5.7	-1.55	-4.15
92	3.1	-1.802	4.902
93	6.9	4.839	2.061
94	-9	-3.768	-5.232
95	2.5	1.042	1.458
96	-2.9	2.188	-5.088
97	-2.4	-0.8173	-1.583
98	8.3	0.9833	7.317
99	-3.8	-4.063	0.2634
100	-9.1	-3.556	-5.544
101	-3.8	5.766	-9.566
102	26.6	3.963	22.64
103	-14.1	-5.458	-8.642
104	-4.9	3.52	-8.42
105	-12.4	-9.929	-2.471
106	1.9	11.4	-9.496
107	20.5	6.519	13.98
108	-15.6	-7.74	-7.86
109	-4.3	1.531	-5.831
110	-12.6	3.022	-15.62
111	-1.8	13.11	-14.91
112	11.9	8.591	3.309
113	-7.2	-3.608	-3.592
114	-5.3	-7.432	2.132
115	-0.6	3.784	-4.384
116	0.1	1.085	-0.9847
117	1.9	10.89	-8.989
118	19	0.7938	18.21
119	-20.2	-15.85	-4.346
120	11.9	5.691	6.209
121	7.4	0.1389	7.261
122	2.1	-2.296	4.396
123	-3.8	-6.199	2.399
124	1.7	-5.849	7.549
125	-7.5	2.418	-9.918
126	-6.7	0.6757	-7.376
127	30.8	10.18	20.62
128	-7.4	-9.87	2.47
129	3.3	-5.457	8.757
130	-10.6	-8.506	-2.094
131	-6.5	4.99	-11.49
132	-0.2	5.158	-5.358
133	11.9	2.387	9.513
134	-2.1	-5.297	3.197
135	-0.2	3.707	-3.907
136	-12.9	0.7737	-13.67
137	15.6	6.986	8.614
138	-9.6	1.208	-10.81
139	-0.4	-10.01	9.607
140	4.7	-0.07597	4.776
141	4.1	-3.795	7.895
142	-18.6	0.7858	-19.39
143	3.5	7.133	-3.633
144	27.6	4.515	23.09
145	-18	-12.11	-5.891
146	1.5	-0.4154	1.915
147	-0.5	-2.458	1.958
148	4.3	4.94	-0.6398
149	-25.9	-5.4	-20.5
150	19.2	14.51	4.686
151	-10	-0.6269	-9.373
152	-4.7	2.449	-7.149
153	-9.1	4.961	-14.06
154	15.5	10.66	4.844
155	11	-1.34	12.34
156	-20.6	-15.87	-4.732
157	0.4	6.641	-6.241
158	2.6	1.262	1.338
159	-0.3	2.456	-2.756
160	0	-0.0235	0.0235
161	17.2	8.769	8.431
162	-13.9	-16.24	2.336
163	3.8	7.46	-3.66
164	2.2	0.3095	1.89
165	20.9	2.744	18.16
166	-5.1	-14.05	8.95
167	-7.3	-8.204	0.904
168	-5.3	4.876	-10.18
169	-3.1	0.8182	-3.918
170	-6.2	5.879	-12.08
171	38.7	7.74	30.96
172	-23.3	-17.41	-5.892
173	4.6	-2.973	7.573
174	-2.2	4.432	-6.632
175	-8.4	-6.513	-1.887
176	6.6	10.22	-3.623
177	-12.3	-9.986	-2.314
178	8	6.491	1.509
179	2.4	3.702	-1.302

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
14	0.1179	0.2358	0.8821
15	0.2445	0.4891	0.7555
16	0.261	0.5219	0.739
17	0.1688	0.3377	0.8312
18	0.1012	0.2023	0.8988
19	0.1165	0.233	0.8835
20	0.07117	0.1423	0.9288
21	0.04142	0.08283	0.9586
22	0.05922	0.1184	0.9408
23	0.0358	0.07159	0.9642
24	0.02081	0.04162	0.9792
25	0.01267	0.02534	0.9873
26	0.007521	0.01504	0.9925
27	0.004723	0.009446	0.9953
28	0.004672	0.009345	0.9953
29	0.009877	0.01975	0.9901
30	0.01282	0.02565	0.9872
31	0.01479	0.02958	0.9852
32	0.009231	0.01846	0.9908
33	0.006478	0.01296	0.9935
34	0.009438	0.01888	0.9906
35	0.006013	0.01203	0.994
36	0.003967	0.007934	0.996
37	0.003236	0.006471	0.9968
38	0.001934	0.003868	0.9981
39	0.009682	0.01936	0.9903
40	0.007133	0.01427	0.9929
41	0.00706	0.01412	0.9929
42	0.006915	0.01383	0.9931
43	0.004465	0.008929	0.9955
44	0.002849	0.005699	0.9972
45	0.0018	0.003599	0.9982
46	0.001137	0.002273	0.9989
47	0.0006941	0.001388	0.9993
48	0.0004145	0.0008291	0.9996
49	0.0002429	0.0004859	0.9998
50	0.0001414	0.0002828	0.9999
51	8.075e-05	0.0001615	0.9999
52	6.064e-05	0.0001213	0.9999
53	6.93e-05	0.0001386	0.9999
54	5.848e-05	0.000117	0.9999
55	3.332e-05	6.665e-05	1
56	5.381e-05	0.0001076	0.9999
57	3.817e-05	7.635e-05	1
58	4.744e-05	9.488e-05	1
59	3.087e-05	6.173e-05	1
60	6.717e-05	0.0001343	0.9999
61	5.44e-05	0.0001088	0.9999
62	3.186e-05	6.371e-05	1
63	1.918e-05	3.836e-05	1
64	1.134e-05	2.269e-05	1
65	1.023e-05	2.046e-05	1
66	1.05e-05	2.101e-05	1
67	6.088e-06	1.218e-05	1
68	3.936e-06	7.872e-06	1
69	2.299e-06	4.598e-06	1
70	1.283e-06	2.566e-06	1
71	7.546e-07	1.509e-06	1
72	9.634e-07	1.927e-06	1
73	5.186e-07	1.037e-06	1
74	5.587e-07	1.117e-06	1
75	3.251e-07	6.501e-07	1
76	2.722e-06	5.444e-06	1
77	2.011e-06	4.021e-06	1
78	4.097e-06	8.194e-06	1
79	3.212e-06	6.423e-06	1
80	2.342e-06	4.684e-06	1
81	1.326e-06	2.652e-06	1
82	7.966e-07	1.593e-06	1
83	4.679e-07	9.358e-07	1
84	2.811e-07	5.622e-07	1
85	1.661e-07	3.323e-07	1
86	4.24e-07	8.48e-07	1
87	2.559e-07	5.118e-07	1
88	1.057e-06	2.114e-06	1
89	3.295e-05	6.591e-05	1
90	2.517e-05	5.035e-05	1
91	2.983e-05	5.965e-05	1
92	2.49e-05	4.981e-05	1
93	1.737e-05	3.475e-05	1
94	1.277e-05	2.554e-05	1
95	7.79e-06	1.558e-05	1
96	6.265e-06	1.253e-05	1
97	3.832e-06	7.665e-06	1
98	3.837e-06	7.674e-06	1
99	2.235e-06	4.47e-06	1
100	1.616e-06	3.232e-06	1
101	1.695e-06	3.39e-06	1
102	7.998e-05	0.00016	0.9999
103	8.373e-05	0.0001675	0.9999
104	7.352e-05	0.000147	0.9999
105	5.944e-05	0.0001189	0.9999
106	7.128e-05	0.0001426	0.9999
107	0.0001967	0.0003933	0.9998
108	0.0002078	0.0004155	0.9998
109	0.0001574	0.0003147	0.9998
110	0.0005543	0.001109	0.9994
111	0.001461	0.002922	0.9985
112	0.001059	0.002118	0.9989
113	0.0007393	0.001479	0.9993
114	0.0005163	0.001033	0.9995
115	0.0004299	0.0008599	0.9996
116	0.0002825	0.000565	0.9997
117	0.0003167	0.0006335	0.9997
118	0.001311	0.002621	0.9987
119	0.001041	0.002083	0.999
120	0.0009974	0.001995	0.999
121	0.0008466	0.001693	0.9992
122	0.0006723	0.001345	0.9993
123	0.0005027	0.001005	0.9995
124	0.0004861	0.0009721	0.9995
125	0.0006007	0.001201	0.9994
126	0.0004753	0.0009505	0.9995
127	0.002152	0.004303	0.9978
128	0.001579	0.003158	0.9984
129	0.001559	0.003118	0.9984
130	0.001087	0.002173	0.9989
131	0.001541	0.003081	0.9985
132	0.001299	0.002597	0.9987
133	0.00131	0.002619	0.9987
134	0.0009174	0.001835	0.9991
135	0.0006265	0.001253	0.9994
136	0.002021	0.004043	0.998
137	0.001709	0.003418	0.9983
138	0.001929	0.003857	0.9981
139	0.002189	0.004379	0.9978
140	0.004683	0.009365	0.9953
141	0.008615	0.01723	0.9914
142	0.01593	0.03185	0.9841
143	0.01177	0.02353	0.9882
144	0.05287	0.1057	0.9471
145	0.05477	0.1095	0.9452
146	0.04373	0.08746	0.9563
147	0.04802	0.09604	0.952
148	0.03584	0.07169	0.9642
149	0.1228	0.2457	0.8772
150	0.09442	0.1888	0.9056
151	0.1193	0.2385	0.8807
152	0.1534	0.3067	0.8466
153	0.3239	0.6478	0.6761
154	0.2719	0.5438	0.7281
155	0.229	0.4579	0.771
156	0.2591	0.5182	0.7409
157	0.2358	0.4715	0.7642
158	0.3707	0.7414	0.6293
159	0.3227	0.6454	0.6773
160	0.3314	0.6628	0.6686
161	0.2924	0.5847	0.7076
162	0.2175	0.435	0.7825
163	0.2032	0.4065	0.7968
164	0.1578	0.3155	0.8422
165	0.09578	0.1916	0.9042

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 &  0.1179 &  0.2358 &  0.8821 \tabularnewline
15 &  0.2445 &  0.4891 &  0.7555 \tabularnewline
16 &  0.261 &  0.5219 &  0.739 \tabularnewline
17 &  0.1688 &  0.3377 &  0.8312 \tabularnewline
18 &  0.1012 &  0.2023 &  0.8988 \tabularnewline
19 &  0.1165 &  0.233 &  0.8835 \tabularnewline
20 &  0.07117 &  0.1423 &  0.9288 \tabularnewline
21 &  0.04142 &  0.08283 &  0.9586 \tabularnewline
22 &  0.05922 &  0.1184 &  0.9408 \tabularnewline
23 &  0.0358 &  0.07159 &  0.9642 \tabularnewline
24 &  0.02081 &  0.04162 &  0.9792 \tabularnewline
25 &  0.01267 &  0.02534 &  0.9873 \tabularnewline
26 &  0.007521 &  0.01504 &  0.9925 \tabularnewline
27 &  0.004723 &  0.009446 &  0.9953 \tabularnewline
28 &  0.004672 &  0.009345 &  0.9953 \tabularnewline
29 &  0.009877 &  0.01975 &  0.9901 \tabularnewline
30 &  0.01282 &  0.02565 &  0.9872 \tabularnewline
31 &  0.01479 &  0.02958 &  0.9852 \tabularnewline
32 &  0.009231 &  0.01846 &  0.9908 \tabularnewline
33 &  0.006478 &  0.01296 &  0.9935 \tabularnewline
34 &  0.009438 &  0.01888 &  0.9906 \tabularnewline
35 &  0.006013 &  0.01203 &  0.994 \tabularnewline
36 &  0.003967 &  0.007934 &  0.996 \tabularnewline
37 &  0.003236 &  0.006471 &  0.9968 \tabularnewline
38 &  0.001934 &  0.003868 &  0.9981 \tabularnewline
39 &  0.009682 &  0.01936 &  0.9903 \tabularnewline
40 &  0.007133 &  0.01427 &  0.9929 \tabularnewline
41 &  0.00706 &  0.01412 &  0.9929 \tabularnewline
42 &  0.006915 &  0.01383 &  0.9931 \tabularnewline
43 &  0.004465 &  0.008929 &  0.9955 \tabularnewline
44 &  0.002849 &  0.005699 &  0.9972 \tabularnewline
45 &  0.0018 &  0.003599 &  0.9982 \tabularnewline
46 &  0.001137 &  0.002273 &  0.9989 \tabularnewline
47 &  0.0006941 &  0.001388 &  0.9993 \tabularnewline
48 &  0.0004145 &  0.0008291 &  0.9996 \tabularnewline
49 &  0.0002429 &  0.0004859 &  0.9998 \tabularnewline
50 &  0.0001414 &  0.0002828 &  0.9999 \tabularnewline
51 &  8.075e-05 &  0.0001615 &  0.9999 \tabularnewline
52 &  6.064e-05 &  0.0001213 &  0.9999 \tabularnewline
53 &  6.93e-05 &  0.0001386 &  0.9999 \tabularnewline
54 &  5.848e-05 &  0.000117 &  0.9999 \tabularnewline
55 &  3.332e-05 &  6.665e-05 &  1 \tabularnewline
56 &  5.381e-05 &  0.0001076 &  0.9999 \tabularnewline
57 &  3.817e-05 &  7.635e-05 &  1 \tabularnewline
58 &  4.744e-05 &  9.488e-05 &  1 \tabularnewline
59 &  3.087e-05 &  6.173e-05 &  1 \tabularnewline
60 &  6.717e-05 &  0.0001343 &  0.9999 \tabularnewline
61 &  5.44e-05 &  0.0001088 &  0.9999 \tabularnewline
62 &  3.186e-05 &  6.371e-05 &  1 \tabularnewline
63 &  1.918e-05 &  3.836e-05 &  1 \tabularnewline
64 &  1.134e-05 &  2.269e-05 &  1 \tabularnewline
65 &  1.023e-05 &  2.046e-05 &  1 \tabularnewline
66 &  1.05e-05 &  2.101e-05 &  1 \tabularnewline
67 &  6.088e-06 &  1.218e-05 &  1 \tabularnewline
68 &  3.936e-06 &  7.872e-06 &  1 \tabularnewline
69 &  2.299e-06 &  4.598e-06 &  1 \tabularnewline
70 &  1.283e-06 &  2.566e-06 &  1 \tabularnewline
71 &  7.546e-07 &  1.509e-06 &  1 \tabularnewline
72 &  9.634e-07 &  1.927e-06 &  1 \tabularnewline
73 &  5.186e-07 &  1.037e-06 &  1 \tabularnewline
74 &  5.587e-07 &  1.117e-06 &  1 \tabularnewline
75 &  3.251e-07 &  6.501e-07 &  1 \tabularnewline
76 &  2.722e-06 &  5.444e-06 &  1 \tabularnewline
77 &  2.011e-06 &  4.021e-06 &  1 \tabularnewline
78 &  4.097e-06 &  8.194e-06 &  1 \tabularnewline
79 &  3.212e-06 &  6.423e-06 &  1 \tabularnewline
80 &  2.342e-06 &  4.684e-06 &  1 \tabularnewline
81 &  1.326e-06 &  2.652e-06 &  1 \tabularnewline
82 &  7.966e-07 &  1.593e-06 &  1 \tabularnewline
83 &  4.679e-07 &  9.358e-07 &  1 \tabularnewline
84 &  2.811e-07 &  5.622e-07 &  1 \tabularnewline
85 &  1.661e-07 &  3.323e-07 &  1 \tabularnewline
86 &  4.24e-07 &  8.48e-07 &  1 \tabularnewline
87 &  2.559e-07 &  5.118e-07 &  1 \tabularnewline
88 &  1.057e-06 &  2.114e-06 &  1 \tabularnewline
89 &  3.295e-05 &  6.591e-05 &  1 \tabularnewline
90 &  2.517e-05 &  5.035e-05 &  1 \tabularnewline
91 &  2.983e-05 &  5.965e-05 &  1 \tabularnewline
92 &  2.49e-05 &  4.981e-05 &  1 \tabularnewline
93 &  1.737e-05 &  3.475e-05 &  1 \tabularnewline
94 &  1.277e-05 &  2.554e-05 &  1 \tabularnewline
95 &  7.79e-06 &  1.558e-05 &  1 \tabularnewline
96 &  6.265e-06 &  1.253e-05 &  1 \tabularnewline
97 &  3.832e-06 &  7.665e-06 &  1 \tabularnewline
98 &  3.837e-06 &  7.674e-06 &  1 \tabularnewline
99 &  2.235e-06 &  4.47e-06 &  1 \tabularnewline
100 &  1.616e-06 &  3.232e-06 &  1 \tabularnewline
101 &  1.695e-06 &  3.39e-06 &  1 \tabularnewline
102 &  7.998e-05 &  0.00016 &  0.9999 \tabularnewline
103 &  8.373e-05 &  0.0001675 &  0.9999 \tabularnewline
104 &  7.352e-05 &  0.000147 &  0.9999 \tabularnewline
105 &  5.944e-05 &  0.0001189 &  0.9999 \tabularnewline
106 &  7.128e-05 &  0.0001426 &  0.9999 \tabularnewline
107 &  0.0001967 &  0.0003933 &  0.9998 \tabularnewline
108 &  0.0002078 &  0.0004155 &  0.9998 \tabularnewline
109 &  0.0001574 &  0.0003147 &  0.9998 \tabularnewline
110 &  0.0005543 &  0.001109 &  0.9994 \tabularnewline
111 &  0.001461 &  0.002922 &  0.9985 \tabularnewline
112 &  0.001059 &  0.002118 &  0.9989 \tabularnewline
113 &  0.0007393 &  0.001479 &  0.9993 \tabularnewline
114 &  0.0005163 &  0.001033 &  0.9995 \tabularnewline
115 &  0.0004299 &  0.0008599 &  0.9996 \tabularnewline
116 &  0.0002825 &  0.000565 &  0.9997 \tabularnewline
117 &  0.0003167 &  0.0006335 &  0.9997 \tabularnewline
118 &  0.001311 &  0.002621 &  0.9987 \tabularnewline
119 &  0.001041 &  0.002083 &  0.999 \tabularnewline
120 &  0.0009974 &  0.001995 &  0.999 \tabularnewline
121 &  0.0008466 &  0.001693 &  0.9992 \tabularnewline
122 &  0.0006723 &  0.001345 &  0.9993 \tabularnewline
123 &  0.0005027 &  0.001005 &  0.9995 \tabularnewline
124 &  0.0004861 &  0.0009721 &  0.9995 \tabularnewline
125 &  0.0006007 &  0.001201 &  0.9994 \tabularnewline
126 &  0.0004753 &  0.0009505 &  0.9995 \tabularnewline
127 &  0.002152 &  0.004303 &  0.9978 \tabularnewline
128 &  0.001579 &  0.003158 &  0.9984 \tabularnewline
129 &  0.001559 &  0.003118 &  0.9984 \tabularnewline
130 &  0.001087 &  0.002173 &  0.9989 \tabularnewline
131 &  0.001541 &  0.003081 &  0.9985 \tabularnewline
132 &  0.001299 &  0.002597 &  0.9987 \tabularnewline
133 &  0.00131 &  0.002619 &  0.9987 \tabularnewline
134 &  0.0009174 &  0.001835 &  0.9991 \tabularnewline
135 &  0.0006265 &  0.001253 &  0.9994 \tabularnewline
136 &  0.002021 &  0.004043 &  0.998 \tabularnewline
137 &  0.001709 &  0.003418 &  0.9983 \tabularnewline
138 &  0.001929 &  0.003857 &  0.9981 \tabularnewline
139 &  0.002189 &  0.004379 &  0.9978 \tabularnewline
140 &  0.004683 &  0.009365 &  0.9953 \tabularnewline
141 &  0.008615 &  0.01723 &  0.9914 \tabularnewline
142 &  0.01593 &  0.03185 &  0.9841 \tabularnewline
143 &  0.01177 &  0.02353 &  0.9882 \tabularnewline
144 &  0.05287 &  0.1057 &  0.9471 \tabularnewline
145 &  0.05477 &  0.1095 &  0.9452 \tabularnewline
146 &  0.04373 &  0.08746 &  0.9563 \tabularnewline
147 &  0.04802 &  0.09604 &  0.952 \tabularnewline
148 &  0.03584 &  0.07169 &  0.9642 \tabularnewline
149 &  0.1228 &  0.2457 &  0.8772 \tabularnewline
150 &  0.09442 &  0.1888 &  0.9056 \tabularnewline
151 &  0.1193 &  0.2385 &  0.8807 \tabularnewline
152 &  0.1534 &  0.3067 &  0.8466 \tabularnewline
153 &  0.3239 &  0.6478 &  0.6761 \tabularnewline
154 &  0.2719 &  0.5438 &  0.7281 \tabularnewline
155 &  0.229 &  0.4579 &  0.771 \tabularnewline
156 &  0.2591 &  0.5182 &  0.7409 \tabularnewline
157 &  0.2358 &  0.4715 &  0.7642 \tabularnewline
158 &  0.3707 &  0.7414 &  0.6293 \tabularnewline
159 &  0.3227 &  0.6454 &  0.6773 \tabularnewline
160 &  0.3314 &  0.6628 &  0.6686 \tabularnewline
161 &  0.2924 &  0.5847 &  0.7076 \tabularnewline
162 &  0.2175 &  0.435 &  0.7825 \tabularnewline
163 &  0.2032 &  0.4065 &  0.7968 \tabularnewline
164 &  0.1578 &  0.3155 &  0.8422 \tabularnewline
165 &  0.09578 &  0.1916 &  0.9042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309796&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]14[/C][C] 0.1179[/C][C] 0.2358[/C][C] 0.8821[/C][/ROW]
[ROW][C]15[/C][C] 0.2445[/C][C] 0.4891[/C][C] 0.7555[/C][/ROW]
[ROW][C]16[/C][C] 0.261[/C][C] 0.5219[/C][C] 0.739[/C][/ROW]
[ROW][C]17[/C][C] 0.1688[/C][C] 0.3377[/C][C] 0.8312[/C][/ROW]
[ROW][C]18[/C][C] 0.1012[/C][C] 0.2023[/C][C] 0.8988[/C][/ROW]
[ROW][C]19[/C][C] 0.1165[/C][C] 0.233[/C][C] 0.8835[/C][/ROW]
[ROW][C]20[/C][C] 0.07117[/C][C] 0.1423[/C][C] 0.9288[/C][/ROW]
[ROW][C]21[/C][C] 0.04142[/C][C] 0.08283[/C][C] 0.9586[/C][/ROW]
[ROW][C]22[/C][C] 0.05922[/C][C] 0.1184[/C][C] 0.9408[/C][/ROW]
[ROW][C]23[/C][C] 0.0358[/C][C] 0.07159[/C][C] 0.9642[/C][/ROW]
[ROW][C]24[/C][C] 0.02081[/C][C] 0.04162[/C][C] 0.9792[/C][/ROW]
[ROW][C]25[/C][C] 0.01267[/C][C] 0.02534[/C][C] 0.9873[/C][/ROW]
[ROW][C]26[/C][C] 0.007521[/C][C] 0.01504[/C][C] 0.9925[/C][/ROW]
[ROW][C]27[/C][C] 0.004723[/C][C] 0.009446[/C][C] 0.9953[/C][/ROW]
[ROW][C]28[/C][C] 0.004672[/C][C] 0.009345[/C][C] 0.9953[/C][/ROW]
[ROW][C]29[/C][C] 0.009877[/C][C] 0.01975[/C][C] 0.9901[/C][/ROW]
[ROW][C]30[/C][C] 0.01282[/C][C] 0.02565[/C][C] 0.9872[/C][/ROW]
[ROW][C]31[/C][C] 0.01479[/C][C] 0.02958[/C][C] 0.9852[/C][/ROW]
[ROW][C]32[/C][C] 0.009231[/C][C] 0.01846[/C][C] 0.9908[/C][/ROW]
[ROW][C]33[/C][C] 0.006478[/C][C] 0.01296[/C][C] 0.9935[/C][/ROW]
[ROW][C]34[/C][C] 0.009438[/C][C] 0.01888[/C][C] 0.9906[/C][/ROW]
[ROW][C]35[/C][C] 0.006013[/C][C] 0.01203[/C][C] 0.994[/C][/ROW]
[ROW][C]36[/C][C] 0.003967[/C][C] 0.007934[/C][C] 0.996[/C][/ROW]
[ROW][C]37[/C][C] 0.003236[/C][C] 0.006471[/C][C] 0.9968[/C][/ROW]
[ROW][C]38[/C][C] 0.001934[/C][C] 0.003868[/C][C] 0.9981[/C][/ROW]
[ROW][C]39[/C][C] 0.009682[/C][C] 0.01936[/C][C] 0.9903[/C][/ROW]
[ROW][C]40[/C][C] 0.007133[/C][C] 0.01427[/C][C] 0.9929[/C][/ROW]
[ROW][C]41[/C][C] 0.00706[/C][C] 0.01412[/C][C] 0.9929[/C][/ROW]
[ROW][C]42[/C][C] 0.006915[/C][C] 0.01383[/C][C] 0.9931[/C][/ROW]
[ROW][C]43[/C][C] 0.004465[/C][C] 0.008929[/C][C] 0.9955[/C][/ROW]
[ROW][C]44[/C][C] 0.002849[/C][C] 0.005699[/C][C] 0.9972[/C][/ROW]
[ROW][C]45[/C][C] 0.0018[/C][C] 0.003599[/C][C] 0.9982[/C][/ROW]
[ROW][C]46[/C][C] 0.001137[/C][C] 0.002273[/C][C] 0.9989[/C][/ROW]
[ROW][C]47[/C][C] 0.0006941[/C][C] 0.001388[/C][C] 0.9993[/C][/ROW]
[ROW][C]48[/C][C] 0.0004145[/C][C] 0.0008291[/C][C] 0.9996[/C][/ROW]
[ROW][C]49[/C][C] 0.0002429[/C][C] 0.0004859[/C][C] 0.9998[/C][/ROW]
[ROW][C]50[/C][C] 0.0001414[/C][C] 0.0002828[/C][C] 0.9999[/C][/ROW]
[ROW][C]51[/C][C] 8.075e-05[/C][C] 0.0001615[/C][C] 0.9999[/C][/ROW]
[ROW][C]52[/C][C] 6.064e-05[/C][C] 0.0001213[/C][C] 0.9999[/C][/ROW]
[ROW][C]53[/C][C] 6.93e-05[/C][C] 0.0001386[/C][C] 0.9999[/C][/ROW]
[ROW][C]54[/C][C] 5.848e-05[/C][C] 0.000117[/C][C] 0.9999[/C][/ROW]
[ROW][C]55[/C][C] 3.332e-05[/C][C] 6.665e-05[/C][C] 1[/C][/ROW]
[ROW][C]56[/C][C] 5.381e-05[/C][C] 0.0001076[/C][C] 0.9999[/C][/ROW]
[ROW][C]57[/C][C] 3.817e-05[/C][C] 7.635e-05[/C][C] 1[/C][/ROW]
[ROW][C]58[/C][C] 4.744e-05[/C][C] 9.488e-05[/C][C] 1[/C][/ROW]
[ROW][C]59[/C][C] 3.087e-05[/C][C] 6.173e-05[/C][C] 1[/C][/ROW]
[ROW][C]60[/C][C] 6.717e-05[/C][C] 0.0001343[/C][C] 0.9999[/C][/ROW]
[ROW][C]61[/C][C] 5.44e-05[/C][C] 0.0001088[/C][C] 0.9999[/C][/ROW]
[ROW][C]62[/C][C] 3.186e-05[/C][C] 6.371e-05[/C][C] 1[/C][/ROW]
[ROW][C]63[/C][C] 1.918e-05[/C][C] 3.836e-05[/C][C] 1[/C][/ROW]
[ROW][C]64[/C][C] 1.134e-05[/C][C] 2.269e-05[/C][C] 1[/C][/ROW]
[ROW][C]65[/C][C] 1.023e-05[/C][C] 2.046e-05[/C][C] 1[/C][/ROW]
[ROW][C]66[/C][C] 1.05e-05[/C][C] 2.101e-05[/C][C] 1[/C][/ROW]
[ROW][C]67[/C][C] 6.088e-06[/C][C] 1.218e-05[/C][C] 1[/C][/ROW]
[ROW][C]68[/C][C] 3.936e-06[/C][C] 7.872e-06[/C][C] 1[/C][/ROW]
[ROW][C]69[/C][C] 2.299e-06[/C][C] 4.598e-06[/C][C] 1[/C][/ROW]
[ROW][C]70[/C][C] 1.283e-06[/C][C] 2.566e-06[/C][C] 1[/C][/ROW]
[ROW][C]71[/C][C] 7.546e-07[/C][C] 1.509e-06[/C][C] 1[/C][/ROW]
[ROW][C]72[/C][C] 9.634e-07[/C][C] 1.927e-06[/C][C] 1[/C][/ROW]
[ROW][C]73[/C][C] 5.186e-07[/C][C] 1.037e-06[/C][C] 1[/C][/ROW]
[ROW][C]74[/C][C] 5.587e-07[/C][C] 1.117e-06[/C][C] 1[/C][/ROW]
[ROW][C]75[/C][C] 3.251e-07[/C][C] 6.501e-07[/C][C] 1[/C][/ROW]
[ROW][C]76[/C][C] 2.722e-06[/C][C] 5.444e-06[/C][C] 1[/C][/ROW]
[ROW][C]77[/C][C] 2.011e-06[/C][C] 4.021e-06[/C][C] 1[/C][/ROW]
[ROW][C]78[/C][C] 4.097e-06[/C][C] 8.194e-06[/C][C] 1[/C][/ROW]
[ROW][C]79[/C][C] 3.212e-06[/C][C] 6.423e-06[/C][C] 1[/C][/ROW]
[ROW][C]80[/C][C] 2.342e-06[/C][C] 4.684e-06[/C][C] 1[/C][/ROW]
[ROW][C]81[/C][C] 1.326e-06[/C][C] 2.652e-06[/C][C] 1[/C][/ROW]
[ROW][C]82[/C][C] 7.966e-07[/C][C] 1.593e-06[/C][C] 1[/C][/ROW]
[ROW][C]83[/C][C] 4.679e-07[/C][C] 9.358e-07[/C][C] 1[/C][/ROW]
[ROW][C]84[/C][C] 2.811e-07[/C][C] 5.622e-07[/C][C] 1[/C][/ROW]
[ROW][C]85[/C][C] 1.661e-07[/C][C] 3.323e-07[/C][C] 1[/C][/ROW]
[ROW][C]86[/C][C] 4.24e-07[/C][C] 8.48e-07[/C][C] 1[/C][/ROW]
[ROW][C]87[/C][C] 2.559e-07[/C][C] 5.118e-07[/C][C] 1[/C][/ROW]
[ROW][C]88[/C][C] 1.057e-06[/C][C] 2.114e-06[/C][C] 1[/C][/ROW]
[ROW][C]89[/C][C] 3.295e-05[/C][C] 6.591e-05[/C][C] 1[/C][/ROW]
[ROW][C]90[/C][C] 2.517e-05[/C][C] 5.035e-05[/C][C] 1[/C][/ROW]
[ROW][C]91[/C][C] 2.983e-05[/C][C] 5.965e-05[/C][C] 1[/C][/ROW]
[ROW][C]92[/C][C] 2.49e-05[/C][C] 4.981e-05[/C][C] 1[/C][/ROW]
[ROW][C]93[/C][C] 1.737e-05[/C][C] 3.475e-05[/C][C] 1[/C][/ROW]
[ROW][C]94[/C][C] 1.277e-05[/C][C] 2.554e-05[/C][C] 1[/C][/ROW]
[ROW][C]95[/C][C] 7.79e-06[/C][C] 1.558e-05[/C][C] 1[/C][/ROW]
[ROW][C]96[/C][C] 6.265e-06[/C][C] 1.253e-05[/C][C] 1[/C][/ROW]
[ROW][C]97[/C][C] 3.832e-06[/C][C] 7.665e-06[/C][C] 1[/C][/ROW]
[ROW][C]98[/C][C] 3.837e-06[/C][C] 7.674e-06[/C][C] 1[/C][/ROW]
[ROW][C]99[/C][C] 2.235e-06[/C][C] 4.47e-06[/C][C] 1[/C][/ROW]
[ROW][C]100[/C][C] 1.616e-06[/C][C] 3.232e-06[/C][C] 1[/C][/ROW]
[ROW][C]101[/C][C] 1.695e-06[/C][C] 3.39e-06[/C][C] 1[/C][/ROW]
[ROW][C]102[/C][C] 7.998e-05[/C][C] 0.00016[/C][C] 0.9999[/C][/ROW]
[ROW][C]103[/C][C] 8.373e-05[/C][C] 0.0001675[/C][C] 0.9999[/C][/ROW]
[ROW][C]104[/C][C] 7.352e-05[/C][C] 0.000147[/C][C] 0.9999[/C][/ROW]
[ROW][C]105[/C][C] 5.944e-05[/C][C] 0.0001189[/C][C] 0.9999[/C][/ROW]
[ROW][C]106[/C][C] 7.128e-05[/C][C] 0.0001426[/C][C] 0.9999[/C][/ROW]
[ROW][C]107[/C][C] 0.0001967[/C][C] 0.0003933[/C][C] 0.9998[/C][/ROW]
[ROW][C]108[/C][C] 0.0002078[/C][C] 0.0004155[/C][C] 0.9998[/C][/ROW]
[ROW][C]109[/C][C] 0.0001574[/C][C] 0.0003147[/C][C] 0.9998[/C][/ROW]
[ROW][C]110[/C][C] 0.0005543[/C][C] 0.001109[/C][C] 0.9994[/C][/ROW]
[ROW][C]111[/C][C] 0.001461[/C][C] 0.002922[/C][C] 0.9985[/C][/ROW]
[ROW][C]112[/C][C] 0.001059[/C][C] 0.002118[/C][C] 0.9989[/C][/ROW]
[ROW][C]113[/C][C] 0.0007393[/C][C] 0.001479[/C][C] 0.9993[/C][/ROW]
[ROW][C]114[/C][C] 0.0005163[/C][C] 0.001033[/C][C] 0.9995[/C][/ROW]
[ROW][C]115[/C][C] 0.0004299[/C][C] 0.0008599[/C][C] 0.9996[/C][/ROW]
[ROW][C]116[/C][C] 0.0002825[/C][C] 0.000565[/C][C] 0.9997[/C][/ROW]
[ROW][C]117[/C][C] 0.0003167[/C][C] 0.0006335[/C][C] 0.9997[/C][/ROW]
[ROW][C]118[/C][C] 0.001311[/C][C] 0.002621[/C][C] 0.9987[/C][/ROW]
[ROW][C]119[/C][C] 0.001041[/C][C] 0.002083[/C][C] 0.999[/C][/ROW]
[ROW][C]120[/C][C] 0.0009974[/C][C] 0.001995[/C][C] 0.999[/C][/ROW]
[ROW][C]121[/C][C] 0.0008466[/C][C] 0.001693[/C][C] 0.9992[/C][/ROW]
[ROW][C]122[/C][C] 0.0006723[/C][C] 0.001345[/C][C] 0.9993[/C][/ROW]
[ROW][C]123[/C][C] 0.0005027[/C][C] 0.001005[/C][C] 0.9995[/C][/ROW]
[ROW][C]124[/C][C] 0.0004861[/C][C] 0.0009721[/C][C] 0.9995[/C][/ROW]
[ROW][C]125[/C][C] 0.0006007[/C][C] 0.001201[/C][C] 0.9994[/C][/ROW]
[ROW][C]126[/C][C] 0.0004753[/C][C] 0.0009505[/C][C] 0.9995[/C][/ROW]
[ROW][C]127[/C][C] 0.002152[/C][C] 0.004303[/C][C] 0.9978[/C][/ROW]
[ROW][C]128[/C][C] 0.001579[/C][C] 0.003158[/C][C] 0.9984[/C][/ROW]
[ROW][C]129[/C][C] 0.001559[/C][C] 0.003118[/C][C] 0.9984[/C][/ROW]
[ROW][C]130[/C][C] 0.001087[/C][C] 0.002173[/C][C] 0.9989[/C][/ROW]
[ROW][C]131[/C][C] 0.001541[/C][C] 0.003081[/C][C] 0.9985[/C][/ROW]
[ROW][C]132[/C][C] 0.001299[/C][C] 0.002597[/C][C] 0.9987[/C][/ROW]
[ROW][C]133[/C][C] 0.00131[/C][C] 0.002619[/C][C] 0.9987[/C][/ROW]
[ROW][C]134[/C][C] 0.0009174[/C][C] 0.001835[/C][C] 0.9991[/C][/ROW]
[ROW][C]135[/C][C] 0.0006265[/C][C] 0.001253[/C][C] 0.9994[/C][/ROW]
[ROW][C]136[/C][C] 0.002021[/C][C] 0.004043[/C][C] 0.998[/C][/ROW]
[ROW][C]137[/C][C] 0.001709[/C][C] 0.003418[/C][C] 0.9983[/C][/ROW]
[ROW][C]138[/C][C] 0.001929[/C][C] 0.003857[/C][C] 0.9981[/C][/ROW]
[ROW][C]139[/C][C] 0.002189[/C][C] 0.004379[/C][C] 0.9978[/C][/ROW]
[ROW][C]140[/C][C] 0.004683[/C][C] 0.009365[/C][C] 0.9953[/C][/ROW]
[ROW][C]141[/C][C] 0.008615[/C][C] 0.01723[/C][C] 0.9914[/C][/ROW]
[ROW][C]142[/C][C] 0.01593[/C][C] 0.03185[/C][C] 0.9841[/C][/ROW]
[ROW][C]143[/C][C] 0.01177[/C][C] 0.02353[/C][C] 0.9882[/C][/ROW]
[ROW][C]144[/C][C] 0.05287[/C][C] 0.1057[/C][C] 0.9471[/C][/ROW]
[ROW][C]145[/C][C] 0.05477[/C][C] 0.1095[/C][C] 0.9452[/C][/ROW]
[ROW][C]146[/C][C] 0.04373[/C][C] 0.08746[/C][C] 0.9563[/C][/ROW]
[ROW][C]147[/C][C] 0.04802[/C][C] 0.09604[/C][C] 0.952[/C][/ROW]
[ROW][C]148[/C][C] 0.03584[/C][C] 0.07169[/C][C] 0.9642[/C][/ROW]
[ROW][C]149[/C][C] 0.1228[/C][C] 0.2457[/C][C] 0.8772[/C][/ROW]
[ROW][C]150[/C][C] 0.09442[/C][C] 0.1888[/C][C] 0.9056[/C][/ROW]
[ROW][C]151[/C][C] 0.1193[/C][C] 0.2385[/C][C] 0.8807[/C][/ROW]
[ROW][C]152[/C][C] 0.1534[/C][C] 0.3067[/C][C] 0.8466[/C][/ROW]
[ROW][C]153[/C][C] 0.3239[/C][C] 0.6478[/C][C] 0.6761[/C][/ROW]
[ROW][C]154[/C][C] 0.2719[/C][C] 0.5438[/C][C] 0.7281[/C][/ROW]
[ROW][C]155[/C][C] 0.229[/C][C] 0.4579[/C][C] 0.771[/C][/ROW]
[ROW][C]156[/C][C] 0.2591[/C][C] 0.5182[/C][C] 0.7409[/C][/ROW]
[ROW][C]157[/C][C] 0.2358[/C][C] 0.4715[/C][C] 0.7642[/C][/ROW]
[ROW][C]158[/C][C] 0.3707[/C][C] 0.7414[/C][C] 0.6293[/C][/ROW]
[ROW][C]159[/C][C] 0.3227[/C][C] 0.6454[/C][C] 0.6773[/C][/ROW]
[ROW][C]160[/C][C] 0.3314[/C][C] 0.6628[/C][C] 0.6686[/C][/ROW]
[ROW][C]161[/C][C] 0.2924[/C][C] 0.5847[/C][C] 0.7076[/C][/ROW]
[ROW][C]162[/C][C] 0.2175[/C][C] 0.435[/C][C] 0.7825[/C][/ROW]
[ROW][C]163[/C][C] 0.2032[/C][C] 0.4065[/C][C] 0.7968[/C][/ROW]
[ROW][C]164[/C][C] 0.1578[/C][C] 0.3155[/C][C] 0.8422[/C][/ROW]
[ROW][C]165[/C][C] 0.09578[/C][C] 0.1916[/C][C] 0.9042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309796&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309796&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
14	0.1179	0.2358	0.8821
15	0.2445	0.4891	0.7555
16	0.261	0.5219	0.739
17	0.1688	0.3377	0.8312
18	0.1012	0.2023	0.8988
19	0.1165	0.233	0.8835
20	0.07117	0.1423	0.9288
21	0.04142	0.08283	0.9586
22	0.05922	0.1184	0.9408
23	0.0358	0.07159	0.9642
24	0.02081	0.04162	0.9792
25	0.01267	0.02534	0.9873
26	0.007521	0.01504	0.9925
27	0.004723	0.009446	0.9953
28	0.004672	0.009345	0.9953
29	0.009877	0.01975	0.9901
30	0.01282	0.02565	0.9872
31	0.01479	0.02958	0.9852
32	0.009231	0.01846	0.9908
33	0.006478	0.01296	0.9935
34	0.009438	0.01888	0.9906
35	0.006013	0.01203	0.994
36	0.003967	0.007934	0.996
37	0.003236	0.006471	0.9968
38	0.001934	0.003868	0.9981
39	0.009682	0.01936	0.9903
40	0.007133	0.01427	0.9929
41	0.00706	0.01412	0.9929
42	0.006915	0.01383	0.9931
43	0.004465	0.008929	0.9955
44	0.002849	0.005699	0.9972
45	0.0018	0.003599	0.9982
46	0.001137	0.002273	0.9989
47	0.0006941	0.001388	0.9993
48	0.0004145	0.0008291	0.9996
49	0.0002429	0.0004859	0.9998
50	0.0001414	0.0002828	0.9999
51	8.075e-05	0.0001615	0.9999
52	6.064e-05	0.0001213	0.9999
53	6.93e-05	0.0001386	0.9999
54	5.848e-05	0.000117	0.9999
55	3.332e-05	6.665e-05	1
56	5.381e-05	0.0001076	0.9999
57	3.817e-05	7.635e-05	1
58	4.744e-05	9.488e-05	1
59	3.087e-05	6.173e-05	1
60	6.717e-05	0.0001343	0.9999
61	5.44e-05	0.0001088	0.9999
62	3.186e-05	6.371e-05	1
63	1.918e-05	3.836e-05	1
64	1.134e-05	2.269e-05	1
65	1.023e-05	2.046e-05	1
66	1.05e-05	2.101e-05	1
67	6.088e-06	1.218e-05	1
68	3.936e-06	7.872e-06	1
69	2.299e-06	4.598e-06	1
70	1.283e-06	2.566e-06	1
71	7.546e-07	1.509e-06	1
72	9.634e-07	1.927e-06	1
73	5.186e-07	1.037e-06	1
74	5.587e-07	1.117e-06	1
75	3.251e-07	6.501e-07	1
76	2.722e-06	5.444e-06	1
77	2.011e-06	4.021e-06	1
78	4.097e-06	8.194e-06	1
79	3.212e-06	6.423e-06	1
80	2.342e-06	4.684e-06	1
81	1.326e-06	2.652e-06	1
82	7.966e-07	1.593e-06	1
83	4.679e-07	9.358e-07	1
84	2.811e-07	5.622e-07	1
85	1.661e-07	3.323e-07	1
86	4.24e-07	8.48e-07	1
87	2.559e-07	5.118e-07	1
88	1.057e-06	2.114e-06	1
89	3.295e-05	6.591e-05	1
90	2.517e-05	5.035e-05	1
91	2.983e-05	5.965e-05	1
92	2.49e-05	4.981e-05	1
93	1.737e-05	3.475e-05	1
94	1.277e-05	2.554e-05	1
95	7.79e-06	1.558e-05	1
96	6.265e-06	1.253e-05	1
97	3.832e-06	7.665e-06	1
98	3.837e-06	7.674e-06	1
99	2.235e-06	4.47e-06	1
100	1.616e-06	3.232e-06	1
101	1.695e-06	3.39e-06	1
102	7.998e-05	0.00016	0.9999
103	8.373e-05	0.0001675	0.9999
104	7.352e-05	0.000147	0.9999
105	5.944e-05	0.0001189	0.9999
106	7.128e-05	0.0001426	0.9999
107	0.0001967	0.0003933	0.9998
108	0.0002078	0.0004155	0.9998
109	0.0001574	0.0003147	0.9998
110	0.0005543	0.001109	0.9994
111	0.001461	0.002922	0.9985
112	0.001059	0.002118	0.9989
113	0.0007393	0.001479	0.9993
114	0.0005163	0.001033	0.9995
115	0.0004299	0.0008599	0.9996
116	0.0002825	0.000565	0.9997
117	0.0003167	0.0006335	0.9997
118	0.001311	0.002621	0.9987
119	0.001041	0.002083	0.999
120	0.0009974	0.001995	0.999
121	0.0008466	0.001693	0.9992
122	0.0006723	0.001345	0.9993
123	0.0005027	0.001005	0.9995
124	0.0004861	0.0009721	0.9995
125	0.0006007	0.001201	0.9994
126	0.0004753	0.0009505	0.9995
127	0.002152	0.004303	0.9978
128	0.001579	0.003158	0.9984
129	0.001559	0.003118	0.9984
130	0.001087	0.002173	0.9989
131	0.001541	0.003081	0.9985
132	0.001299	0.002597	0.9987
133	0.00131	0.002619	0.9987
134	0.0009174	0.001835	0.9991
135	0.0006265	0.001253	0.9994
136	0.002021	0.004043	0.998
137	0.001709	0.003418	0.9983
138	0.001929	0.003857	0.9981
139	0.002189	0.004379	0.9978
140	0.004683	0.009365	0.9953
141	0.008615	0.01723	0.9914
142	0.01593	0.03185	0.9841
143	0.01177	0.02353	0.9882
144	0.05287	0.1057	0.9471
145	0.05477	0.1095	0.9452
146	0.04373	0.08746	0.9563
147	0.04802	0.09604	0.952
148	0.03584	0.07169	0.9642
149	0.1228	0.2457	0.8772
150	0.09442	0.1888	0.9056
151	0.1193	0.2385	0.8807
152	0.1534	0.3067	0.8466
153	0.3239	0.6478	0.6761
154	0.2719	0.5438	0.7281
155	0.229	0.4579	0.771
156	0.2591	0.5182	0.7409
157	0.2358	0.4715	0.7642
158	0.3707	0.7414	0.6293
159	0.3227	0.6454	0.6773
160	0.3314	0.6628	0.6686
161	0.2924	0.5847	0.7076
162	0.2175	0.435	0.7825
163	0.2032	0.4065	0.7968
164	0.1578	0.3155	0.8422
165	0.09578	0.1916	0.9042

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	103	0.6776	NOK
5% type I error level	120	0.789474	NOK
10% type I error level	125	0.822368	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 & 103 &  0.6776 & NOK \tabularnewline
5% type I error level & 120 & 0.789474 & NOK \tabularnewline
10% type I error level & 125 & 0.822368 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309796&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]103[/C][C] 0.6776[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]120[/C][C]0.789474[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]125[/C][C]0.822368[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309796&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309796&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	103	0.6776	NOK
5% type I error level	120	0.789474	NOK
10% type I error level	125	0.822368	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.73516, df1 = 2, df2 = 166, p-value = 0.481
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.6225, df1 = 20, df2 = 148, p-value = 0.8912
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.5782, df1 = 2, df2 = 166, p-value = 0.562

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.73516, df1 = 2, df2 = 166, p-value = 0.481
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.6225, df1 = 20, df2 = 148, p-value = 0.8912
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.5782, df1 = 2, df2 = 166, p-value = 0.562
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309796&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.73516, df1 = 2, df2 = 166, p-value = 0.481
[/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 = 0.6225, df1 = 20, df2 = 148, p-value = 0.8912
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.5782, df1 = 2, df2 = 166, p-value = 0.562
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309796&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309796&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.73516, df1 = 2, df2 = 166, p-value = 0.481
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.6225, df1 = 20, df2 = 148, p-value = 0.8912
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.5782, df1 = 2, df2 = 166, p-value = 0.562

Variance Inflation Factors (Multicollinearity)

> vif
         `(1-Bs)(1-B)Build0`          `(1-Bs)(1-B)Build1` 
                    2.144602                     3.941301 
         `(1-Bs)(1-B)Build2`          `(1-Bs)(1-B)Build3` 
                    4.752047                     3.873409 
         `(1-Bs)(1-B)Build4`  `(1-Bs)(1-B)Chemicals(t-1)` 
                    2.130096                     1.553090 
 `(1-Bs)(1-B)Chemicals(t-2)`  `(1-Bs)(1-B)Chemicals(t-3)` 
                    1.810885                     1.780966 
 `(1-Bs)(1-B)Chemicals(t-4)` `(1-Bs)(1-B)Chemicals(t-1s)` 
                    1.507355                     1.134724

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
         `(1-Bs)(1-B)Build0`          `(1-Bs)(1-B)Build1` 
                    2.144602                     3.941301 
         `(1-Bs)(1-B)Build2`          `(1-Bs)(1-B)Build3` 
                    4.752047                     3.873409 
         `(1-Bs)(1-B)Build4`  `(1-Bs)(1-B)Chemicals(t-1)` 
                    2.130096                     1.553090 
 `(1-Bs)(1-B)Chemicals(t-2)`  `(1-Bs)(1-B)Chemicals(t-3)` 
                    1.810885                     1.780966 
 `(1-Bs)(1-B)Chemicals(t-4)` `(1-Bs)(1-B)Chemicals(t-1s)` 
                    1.507355                     1.134724 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309796&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
         `(1-Bs)(1-B)Build0`          `(1-Bs)(1-B)Build1` 
                    2.144602                     3.941301 
         `(1-Bs)(1-B)Build2`          `(1-Bs)(1-B)Build3` 
                    4.752047                     3.873409 
         `(1-Bs)(1-B)Build4`  `(1-Bs)(1-B)Chemicals(t-1)` 
                    2.130096                     1.553090 
 `(1-Bs)(1-B)Chemicals(t-2)`  `(1-Bs)(1-B)Chemicals(t-3)` 
                    1.810885                     1.780966 
 `(1-Bs)(1-B)Chemicals(t-4)` `(1-Bs)(1-B)Chemicals(t-1s)` 
                    1.507355                     1.134724 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309796&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309796&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` 
                    2.144602                     3.941301 
         `(1-Bs)(1-B)Build2`          `(1-Bs)(1-B)Build3` 
                    4.752047                     3.873409 
         `(1-Bs)(1-B)Build4`  `(1-Bs)(1-B)Chemicals(t-1)` 
                    2.130096                     1.553090 
 `(1-Bs)(1-B)Chemicals(t-2)`  `(1-Bs)(1-B)Chemicals(t-3)` 
                    1.810885                     1.780966 
 `(1-Bs)(1-B)Chemicals(t-4)` `(1-Bs)(1-B)Chemicals(t-1s)` 
                    1.507355                     1.134724

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 = 1 ; par6 = White Noise ;

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

par6 <- 'White Noise'
par5 <- '1'
par4 <- '4'
par3 <- 'First and Seasonal Differences (s)'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '48'
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