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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 20 Dec 2017 11:44:05 +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/20/t15137666785ab5yiwjuscglfx.htm/, Retrieved Tue, 14 May 2024 05:40:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310469, Retrieved Tue, 14 May 2024 05:40:58 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

118

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

-       [Multiple Regression] [regressie model d...] [2017-12-20 10:44:05] [bc0a1b24d4c8c5bd2fad05813077f37f] [Current]

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63.9	66.8	NA	NA	NA	NA	NA	NA
67.1	75.2	66.8	NA	NA	NA	NA	NA
75.5	86.3	75.2	66.8	NA	NA	NA	NA
68.1	74.1	86.3	75.2	66.8	NA	NA	NA
75	86.5	74.1	86.3	75.2	66.8	NA	NA
71.9	81.3	86.5	74.1	86.3	75.2	66.8	NA
67	67.9	81.3	86.5	74.1	86.3	75.2	66.8
67.9	75.3	67.9	81.3	86.5	74.1	86.3	75.2
72.7	86	75.3	67.9	81.3	86.5	74.1	86.3
73.3	88.6	86	75.3	67.9	81.3	86.5	74.1
71.9	83.9	88.6	86	75.3	67.9	81.3	86.5
67	69.3	83.9	88.6	86	75.3	67.9	81.3
72.5	79.8	69.3	83.9	88.6	86	75.3	67.9
71	79	79.8	69.3	83.9	88.6	86	75.3
76.9	86.3	79	79.8	69.3	83.9	88.6	86
69.1	77.4	86.3	79	79.8	69.3	83.9	88.6
75.2	82.6	77.4	86.3	79	79.8	69.3	83.9
72.2	83.3	82.6	77.4	86.3	79	79.8	69.3
65.7	69	83.3	82.6	77.4	86.3	79	79.8
65	72	69	83.3	82.6	77.4	86.3	79
65.6	79.2	72	69	83.3	82.6	77.4	86.3
68.1	84.6	79.2	72	69	83.3	82.6	77.4
63.5	76.9	84.6	79.2	72	69	83.3	82.6
56.3	62.6	76.9	84.6	79.2	72	69	83.3
65.2	74.5	62.6	76.9	84.6	79.2	72	69
65.5	74.2	74.5	62.6	76.9	84.6	79.2	72
71	81.5	74.2	74.5	62.6	76.9	84.6	79.2
71.6	81.6	81.5	74.2	74.5	62.6	76.9	84.6
71	79.9	81.6	81.5	74.2	74.5	62.6	76.9
70.8	81.6	79.9	81.6	81.5	74.2	74.5	62.6
71.8	73.6	81.6	79.9	81.6	81.5	74.2	74.5
63.9	70.1	73.6	81.6	79.9	81.6	81.5	74.2
71	84.2	70.1	73.6	81.6	79.9	81.6	81.5
72.6	87.3	84.2	70.1	73.6	81.6	79.9	81.6
68.5	78.3	87.3	84.2	70.1	73.6	81.6	79.9
64.3	66.3	78.3	87.3	84.2	70.1	73.6	81.6
74.7	78.6	66.3	78.3	87.3	84.2	70.1	73.6
70.7	78.3	78.6	66.3	78.3	87.3	84.2	70.1
77.1	84.5	78.3	78.6	66.3	78.3	87.3	84.2
76.6	83.6	84.5	78.3	78.6	66.3	78.3	87.3
71.2	78.5	83.6	84.5	78.3	78.6	66.3	78.3
73	83.9	78.5	83.6	84.5	78.3	78.6	66.3
71.8	74.8	83.9	78.5	83.6	84.5	78.3	78.6
63.3	66.3	74.8	83.9	78.5	83.6	84.5	78.3
73.3	86.7	66.3	74.8	83.9	78.5	83.6	84.5
74.7	89.3	86.7	66.3	74.8	83.9	78.5	83.6
68.1	76.8	89.3	86.7	66.3	74.8	83.9	78.5
66.5	71.7	76.8	89.3	86.7	66.3	74.8	83.9
72.3	77.7	71.7	76.8	89.3	86.7	66.3	74.8
73.6	79.8	77.7	71.7	76.8	89.3	86.7	66.3
82.4	92.9	79.8	77.7	71.7	76.8	89.3	86.7
78.4	88.4	92.9	79.8	77.7	71.7	76.8	89.3
73.1	82	88.4	92.9	79.8	77.7	71.7	76.8
85.6	97.2	82	88.4	92.9	79.8	77.7	71.7
80	79.8	97.2	82	88.4	92.9	79.8	77.7
79.4	79.4	79.8	97.2	82	88.4	92.9	79.8
90.1	96.6	79.4	79.8	97.2	82	88.4	92.9
91.1	96.3	96.6	79.4	79.8	97.2	82	88.4
89	92.6	96.3	96.6	79.4	79.8	97.2	82
85.4	83.7	92.6	96.3	96.6	79.4	79.8	97.2
85.7	85.8	83.7	92.6	96.3	96.6	79.4	79.8
82.8	86.8	85.8	83.7	92.6	96.3	96.6	79.4
95.7	96.4	86.8	85.8	83.7	92.6	96.3	96.6
91.5	95.5	96.4	86.8	85.8	83.7	92.6	96.3
87.3	89.8	95.5	96.4	86.8	85.8	83.7	92.6
91.5	99.9	89.8	95.5	96.4	86.8	85.8	83.7
83.5	77.4	99.9	89.8	95.5	96.4	86.8	85.8
84.4	81	77.4	99.9	89.8	95.5	96.4	86.8
92.2	98.2	81	77.4	99.9	89.8	95.5	96.4
91.8	93.9	98.2	81	77.4	99.9	89.8	95.5
92.5	96.3	93.9	98.2	81	77.4	99.9	89.8
84.8	83.3	96.3	93.9	98.2	81	77.4	99.9
94.3	92.8	83.3	96.3	93.9	98.2	81	77.4
91	92.7	92.8	83.3	96.3	93.9	98.2	81
102	108.5	92.7	92.8	83.3	96.3	93.9	98.2
89.8	94.9	108.5	92.7	92.8	83.3	96.3	93.9
97.6	103.9	94.9	108.5	92.7	92.8	83.3	96.3
100.5	109.1	103.9	94.9	108.5	92.7	92.8	83.3
92.9	85.7	109.1	103.9	94.9	108.5	92.7	92.8
95.3	89.9	85.7	109.1	103.9	94.9	108.5	92.7
98.6	104.3	89.9	85.7	109.1	103.9	94.9	108.5
99.2	107.6	104.3	89.9	85.7	109.1	103.9	94.9
97.4	104	107.6	104.3	89.9	85.7	109.1	103.9
89.4	89.3	104	107.6	104.3	89.9	85.7	109.1
99.2	104	89.3	104	107.6	104.3	89.9	85.7
96	102.4	104	89.3	104	107.6	104.3	89.9
101.4	113.9	102.4	104	89.3	104	107.6	104.3
97.8	104.7	113.9	102.4	104	89.3	104	107.6
103.7	110.4	104.7	113.9	102.4	104	89.3	104
100.5	114.4	110.4	104.7	113.9	102.4	104	89.3
98	96.9	114.4	110.4	104.7	113.9	102.4	104
95.6	96.8	96.9	114.4	110.4	104.7	113.9	102.4
92.6	105.7	96.8	96.9	114.4	110.4	104.7	113.9
105.5	117.9	105.7	96.8	96.9	114.4	110.4	104.7
97.1	108.1	117.9	105.7	96.8	96.9	114.4	110.4
88.2	90.3	108.1	117.9	105.7	96.8	96.9	114.4
106.7	110.9	90.3	108.1	117.9	105.7	96.8	96.9
105.6	114.5	110.9	90.3	108.1	117.9	105.7	96.8
107.4	114.1	114.5	110.9	90.3	108.1	117.9	105.7
113.1	122.7	114.1	114.5	110.9	90.3	108.1	117.9
108.4	113.8	122.7	114.1	114.5	110.9	90.3	108.1
112	121.1	113.8	122.7	114.1	114.5	110.9	90.3
114.5	107.8	121.1	113.8	122.7	114.1	114.5	110.9
106.1	97.2	107.8	121.1	113.8	122.7	114.1	114.5
112.9	119.8	97.2	107.8	121.1	113.8	122.7	114.1
111.7	117.6	119.8	97.2	107.8	121.1	113.8	122.7
84.7	92.6	117.6	119.8	97.2	107.8	121.1	113.8
72.8	80.6	92.6	117.6	119.8	97.2	107.8	121.1
74.3	80.6	80.6	92.6	117.6	119.8	97.2	107.8
76.4	82	80.6	80.6	92.6	117.6	119.8	97.2
77.8	89.3	82	80.6	80.6	92.6	117.6	119.8
75.7	84.6	89.3	82	80.6	80.6	92.6	117.6
74.8	81.9	84.6	89.3	82	80.6	80.6	92.6
85	92.5	81.9	84.6	89.3	82	80.6	80.6
87.6	81.4	92.5	81.9	84.6	89.3	82	80.6
81.7	78.7	81.4	92.5	81.9	84.6	89.3	82
94.3	99.7	78.7	81.4	92.5	81.9	84.6	89.3
91.2	98.4	99.7	78.7	81.4	92.5	81.9	84.6
85.4	89.8	98.4	99.7	78.7	81.4	92.5	81.9
80.3	79.6	89.8	98.4	99.7	78.7	81.4	92.5
90.9	86.9	79.6	89.8	98.4	99.7	78.7	81.4
92.3	90.2	86.9	79.6	89.8	98.4	99.7	78.7
101.9	107.1	90.2	86.9	79.6	89.8	98.4	99.7
98.4	102.1	107.1	90.2	86.9	79.6	89.8	98.4
102.7	99.9	102.1	107.1	90.2	86.9	79.6	89.8
105.6	113.2	99.9	102.1	107.1	90.2	86.9	79.6
102.8	93.5	113.2	99.9	102.1	107.1	90.2	86.9
95.7	90.9	93.5	113.2	99.9	102.1	107.1	90.2
106.8	111.1	90.9	93.5	113.2	99.9	102.1	107.1
104.3	109.4	111.1	90.9	93.5	113.2	99.9	102.1
101.5	104.1	109.4	111.1	90.9	93.5	113.2	99.9
97.2	91.5	104.1	109.4	111.1	90.9	93.5	113.2
100.8	99.1	91.5	104.1	109.4	111.1	90.9	93.5
101.8	102.1	99.1	91.5	104.1	109.4	111.1	90.9
117	118.2	102.1	99.1	91.5	104.1	109.4	111.1
104.3	103.7	118.2	102.1	99.1	91.5	104.1	109.4
109	113.1	103.7	118.2	102.1	99.1	91.5	104.1
107.2	107.6	113.1	103.7	118.2	102.1	99.1	91.5
101.7	90.3	107.6	113.1	103.7	118.2	102.1	99.1
103.5	97	90.3	107.6	113.1	103.7	118.2	102.1
103.7	111.7	97	90.3	107.6	113.1	103.7	118.2
100	104.3	111.7	97	90.3	107.6	113.1	103.7
99.8	102.2	104.3	111.7	97	90.3	107.6	113.1
91.4	91.7	102.2	104.3	111.7	97	90.3	107.6
102.2	99.4	91.7	102.2	104.3	111.7	97	90.3
104.2	101.6	99.4	91.7	102.2	104.3	111.7	97
106.3	112.6	101.6	99.4	91.7	102.2	104.3	111.7
98.6	100.3	112.6	101.6	99.4	91.7	102.2	104.3
102.4	103.8	100.3	112.6	101.6	99.4	91.7	102.2
98.4	108.7	103.8	100.3	112.6	101.6	99.4	91.7
105.2	96.1	108.7	103.8	100.3	112.6	101.6	99.4
99	92.8	96.1	108.7	103.8	100.3	112.6	101.6
96.8	101.5	92.8	96.1	108.7	103.8	100.3	112.6
102.7	108.6	101.5	92.8	96.1	108.7	103.8	100.3
98.1	100	108.6	101.5	92.8	96.1	108.7	103.8
86.8	83.3	100	108.6	101.5	92.8	96.1	108.7
101.6	95.7	83.3	100	108.6	101.5	92.8	96.1
95.6	94.1	95.7	83.3	100	108.6	101.5	92.8
98.1	99.2	94.1	95.7	83.3	100	108.6	101.5
99.6	102	99.2	94.1	95.7	83.3	100	108.6
98.1	100.4	102	99.2	94.1	95.7	83.3	100
95.7	102.9	100.4	102	99.2	94.1	95.7	83.3
99.8	94.1	102.9	100.4	102	99.2	94.1	95.7
94.5	87.3	94.1	102.9	100.4	102	99.2	94.1
96	101.9	87.3	94.1	102.9	100.4	102	99.2
101.8	106.6	101.9	87.3	94.1	102.9	100.4	102
92.8	94.4	106.6	101.9	87.3	94.1	102.9	100.4
84.4	80.4	94.4	106.6	101.9	87.3	94.1	102.9
96.9	96.6	80.4	94.4	106.6	101.9	87.3	94.1
89.6	93.6	96.6	80.4	94.4	106.6	101.9	87.3
99.5	101	93.6	96.6	80.4	94.4	106.6	101.9
97	100.5	101	93.6	96.6	80.4	94.4	106.6
90.5	94.1	100.5	101	93.6	96.6	80.4	94.4
91.8	99.6	94.1	100.5	101	93.6	96.6	80.4
102	94.2	99.6	94.1	100.5	101	93.6	96.6
87.4	83.2	94.2	99.6	94.1	100.5	101	93.6
97.6	105.6	83.2	94.2	99.6	94.1	100.5	101
98.6	104.8	105.6	83.2	94.2	99.6	94.1	100.5
92	91.1	104.8	105.6	83.2	94.2	99.6	94.1
88.8	84.5	91.1	104.8	105.6	83.2	94.2	99.6
99.9	96.6	84.5	91.1	104.8	105.6	83.2	94.2
93.7	94.8	96.6	84.5	91.1	104.8	105.6	83.2
100.8	107.6	94.8	96.6	84.5	91.1	104.8	105.6
94.1	100.5	107.6	94.8	96.6	84.5	91.1	104.8
90.9	94.1	100.5	107.6	94.8	96.6	84.5	91.1
94.3	108.5	94.1	100.5	107.6	94.8	96.6	84.5
93.2	92.5	108.5	94.1	100.5	107.6	94.8	96.6
85	84.5	92.5	108.5	94.1	100.5	107.6	94.8
91.4	103.3	84.5	92.5	108.5	94.1	100.5	107.6
91.8	103.1	103.3	84.5	92.5	108.5	94.1	100.5
86.6	94.4	103.1	103.3	84.5	92.5	108.5	94.1
82.7	84.2	94.4	103.1	103.3	84.5	92.5	108.5
90.1	92.9	84.2	94.4	103.1	103.3	84.5	92.5
93.8	96.8	92.9	84.2	94.4	103.1	103.3	84.5
96.2	104.3	96.8	92.9	84.2	94.4	103.1	103.3
91.7	101.1	104.3	96.8	92.9	84.2	94.4	103.1
86.9	96.2	101.1	104.3	96.8	92.9	84.2	94.4
91.6	105.6	96.2	101.1	104.3	96.8	92.9	84.2
85.5	84.7	105.6	96.2	101.1	104.3	96.8	92.9
86.4	87.3	84.7	105.6	96.2	101.1	104.3	96.8
89.2	102.3	87.3	84.7	105.6	96.2	101.1	104.3
89.1	97.9	102.3	87.3	84.7	105.6	96.2	101.1
89.7	98.4	97.9	102.3	87.3	84.7	105.6	96.2
88.1	89.3	98.4	97.9	102.3	87.3	84.7	105.6
94.6	96.4	89.3	98.4	97.9	102.3	87.3	84.7
90.3	96.8	96.4	89.3	98.4	97.9	102.3	87.3
101.4	113	96.8	96.4	89.3	98.4	97.9	102.3
94.3	98.1	113	96.8	96.4	89.3	98.4	97.9
97.8	104.9	98.1	113	96.8	96.4	89.3	98.4
99.5	109.9	104.9	98.1	113	96.8	96.4	89.3
97.5	91	109.9	104.9	98.1	113	96.8	96.4
90.3	91.8	91	109.9	104.9	98.1	113	96.8

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310469&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Framework error message[/C][C]

The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310469&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310469&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)Chemical_products[t] = -0.0726387 + 0.693961`(1-Bs)(1-B)intermediate0`[t] + 0.499782`(1-Bs)(1-B)intermediate1`[t] + 0.327606`(1-Bs)(1-B)intermediate2`[t] + 0.00796865`(1-Bs)(1-B)intermediate3`[t] -0.0547541`(1-Bs)(1-B)intermediate4`[t] -0.0551273`(1-Bs)(1-B)intermediate5`[t] + 0.0788882`(1-Bs)(1-B)intermediate6\r`[t] -0.354889`(1-Bs)(1-B)Chemical_products(t-1)`[t] -0.237394`(1-Bs)(1-B)Chemical_products(t-2)`[t] -0.352818`(1-Bs)(1-B)Chemical_products(t-1s)`[t] -0.20158`(1-Bs)(1-B)Chemical_products(t-2s)`[t] -0.238693`(1-Bs)(1-B)Chemical_products(t-3s)`[t] -0.18449`(1-Bs)(1-B)Chemical_products(t-4s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)Chemical_products[t] =  -0.0726387 +  0.693961`(1-Bs)(1-B)intermediate0`[t] +  0.499782`(1-Bs)(1-B)intermediate1`[t] +  0.327606`(1-Bs)(1-B)intermediate2`[t] +  0.00796865`(1-Bs)(1-B)intermediate3`[t] -0.0547541`(1-Bs)(1-B)intermediate4`[t] -0.0551273`(1-Bs)(1-B)intermediate5`[t] +  0.0788882`(1-Bs)(1-B)intermediate6\r`[t] -0.354889`(1-Bs)(1-B)Chemical_products(t-1)`[t] -0.237394`(1-Bs)(1-B)Chemical_products(t-2)`[t] -0.352818`(1-Bs)(1-B)Chemical_products(t-1s)`[t] -0.20158`(1-Bs)(1-B)Chemical_products(t-2s)`[t] -0.238693`(1-Bs)(1-B)Chemical_products(t-3s)`[t] -0.18449`(1-Bs)(1-B)Chemical_products(t-4s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310469&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)Chemical_products[t] =  -0.0726387 +  0.693961`(1-Bs)(1-B)intermediate0`[t] +  0.499782`(1-Bs)(1-B)intermediate1`[t] +  0.327606`(1-Bs)(1-B)intermediate2`[t] +  0.00796865`(1-Bs)(1-B)intermediate3`[t] -0.0547541`(1-Bs)(1-B)intermediate4`[t] -0.0551273`(1-Bs)(1-B)intermediate5`[t] +  0.0788882`(1-Bs)(1-B)intermediate6\r`[t] -0.354889`(1-Bs)(1-B)Chemical_products(t-1)`[t] -0.237394`(1-Bs)(1-B)Chemical_products(t-2)`[t] -0.352818`(1-Bs)(1-B)Chemical_products(t-1s)`[t] -0.20158`(1-Bs)(1-B)Chemical_products(t-2s)`[t] -0.238693`(1-Bs)(1-B)Chemical_products(t-3s)`[t] -0.18449`(1-Bs)(1-B)Chemical_products(t-4s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310469&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310469&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)Chemical_products[t] = -0.0726387 + 0.693961`(1-Bs)(1-B)intermediate0`[t] + 0.499782`(1-Bs)(1-B)intermediate1`[t] + 0.327606`(1-Bs)(1-B)intermediate2`[t] + 0.00796865`(1-Bs)(1-B)intermediate3`[t] -0.0547541`(1-Bs)(1-B)intermediate4`[t] -0.0551273`(1-Bs)(1-B)intermediate5`[t] + 0.0788882`(1-Bs)(1-B)intermediate6\r`[t] -0.354889`(1-Bs)(1-B)Chemical_products(t-1)`[t] -0.237394`(1-Bs)(1-B)Chemical_products(t-2)`[t] -0.352818`(1-Bs)(1-B)Chemical_products(t-1s)`[t] -0.20158`(1-Bs)(1-B)Chemical_products(t-2s)`[t] -0.238693`(1-Bs)(1-B)Chemical_products(t-3s)`[t] -0.18449`(1-Bs)(1-B)Chemical_products(t-4s)`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-0.07264	0.2646	-2.7450e-01	0.7841	0.3921
`(1-Bs)(1-B)intermediate0`	+0.694	0.05841	+1.1880e+01	1.899e-22	9.494e-23
`(1-Bs)(1-B)intermediate1`	+0.4998	0.09015	+5.5440e+00	1.601e-07	8.003e-08
`(1-Bs)(1-B)intermediate2`	+0.3276	0.09076	+3.6100e+00	0.0004373	0.0002186
`(1-Bs)(1-B)intermediate3`	+0.007969	0.05869	+1.3580e-01	0.8922	0.4461
`(1-Bs)(1-B)intermediate4`	-0.05475	0.05484	-9.9840e-01	0.3199	0.16
`(1-Bs)(1-B)intermediate5`	-0.05513	0.05655	-9.7480e-01	0.3315	0.1657
`(1-Bs)(1-B)intermediate6\r`	+0.07889	0.04892	+1.6130e+00	0.1093	0.05464
`(1-Bs)(1-B)Chemical_products(t-1)`	-0.3549	0.0827	-4.2910e+00	3.455e-05	1.728e-05
`(1-Bs)(1-B)Chemical_products(t-2)`	-0.2374	0.08221	-2.8880e+00	0.004555	0.002277
`(1-Bs)(1-B)Chemical_products(t-1s)`	-0.3528	0.06427	-5.4900e+00	2.052e-07	1.026e-07
`(1-Bs)(1-B)Chemical_products(t-2s)`	-0.2016	0.06215	-3.2430e+00	0.001505	0.0007523
`(1-Bs)(1-B)Chemical_products(t-3s)`	-0.2387	0.06518	-3.6620e+00	0.0003639	0.000182
`(1-Bs)(1-B)Chemical_products(t-4s)`	-0.1845	0.06212	-2.9700e+00	0.003553	0.001776

\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) & -0.07264 &  0.2646 & -2.7450e-01 &  0.7841 &  0.3921 \tabularnewline
`(1-Bs)(1-B)intermediate0` & +0.694 &  0.05841 & +1.1880e+01 &  1.899e-22 &  9.494e-23 \tabularnewline
`(1-Bs)(1-B)intermediate1` & +0.4998 &  0.09015 & +5.5440e+00 &  1.601e-07 &  8.003e-08 \tabularnewline
`(1-Bs)(1-B)intermediate2` & +0.3276 &  0.09076 & +3.6100e+00 &  0.0004373 &  0.0002186 \tabularnewline
`(1-Bs)(1-B)intermediate3` & +0.007969 &  0.05869 & +1.3580e-01 &  0.8922 &  0.4461 \tabularnewline
`(1-Bs)(1-B)intermediate4` & -0.05475 &  0.05484 & -9.9840e-01 &  0.3199 &  0.16 \tabularnewline
`(1-Bs)(1-B)intermediate5` & -0.05513 &  0.05655 & -9.7480e-01 &  0.3315 &  0.1657 \tabularnewline
`(1-Bs)(1-B)intermediate6\r` & +0.07889 &  0.04892 & +1.6130e+00 &  0.1093 &  0.05464 \tabularnewline
`(1-Bs)(1-B)Chemical_products(t-1)` & -0.3549 &  0.0827 & -4.2910e+00 &  3.455e-05 &  1.728e-05 \tabularnewline
`(1-Bs)(1-B)Chemical_products(t-2)` & -0.2374 &  0.08221 & -2.8880e+00 &  0.004555 &  0.002277 \tabularnewline
`(1-Bs)(1-B)Chemical_products(t-1s)` & -0.3528 &  0.06427 & -5.4900e+00 &  2.052e-07 &  1.026e-07 \tabularnewline
`(1-Bs)(1-B)Chemical_products(t-2s)` & -0.2016 &  0.06215 & -3.2430e+00 &  0.001505 &  0.0007523 \tabularnewline
`(1-Bs)(1-B)Chemical_products(t-3s)` & -0.2387 &  0.06518 & -3.6620e+00 &  0.0003639 &  0.000182 \tabularnewline
`(1-Bs)(1-B)Chemical_products(t-4s)` & -0.1845 &  0.06212 & -2.9700e+00 &  0.003553 &  0.001776 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310469&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]-0.07264[/C][C] 0.2646[/C][C]-2.7450e-01[/C][C] 0.7841[/C][C] 0.3921[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate0`[/C][C]+0.694[/C][C] 0.05841[/C][C]+1.1880e+01[/C][C] 1.899e-22[/C][C] 9.494e-23[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate1`[/C][C]+0.4998[/C][C] 0.09015[/C][C]+5.5440e+00[/C][C] 1.601e-07[/C][C] 8.003e-08[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate2`[/C][C]+0.3276[/C][C] 0.09076[/C][C]+3.6100e+00[/C][C] 0.0004373[/C][C] 0.0002186[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate3`[/C][C]+0.007969[/C][C] 0.05869[/C][C]+1.3580e-01[/C][C] 0.8922[/C][C] 0.4461[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate4`[/C][C]-0.05475[/C][C] 0.05484[/C][C]-9.9840e-01[/C][C] 0.3199[/C][C] 0.16[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate5`[/C][C]-0.05513[/C][C] 0.05655[/C][C]-9.7480e-01[/C][C] 0.3315[/C][C] 0.1657[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate6\r`[/C][C]+0.07889[/C][C] 0.04892[/C][C]+1.6130e+00[/C][C] 0.1093[/C][C] 0.05464[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemical_products(t-1)`[/C][C]-0.3549[/C][C] 0.0827[/C][C]-4.2910e+00[/C][C] 3.455e-05[/C][C] 1.728e-05[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemical_products(t-2)`[/C][C]-0.2374[/C][C] 0.08221[/C][C]-2.8880e+00[/C][C] 0.004555[/C][C] 0.002277[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemical_products(t-1s)`[/C][C]-0.3528[/C][C] 0.06427[/C][C]-5.4900e+00[/C][C] 2.052e-07[/C][C] 1.026e-07[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemical_products(t-2s)`[/C][C]-0.2016[/C][C] 0.06215[/C][C]-3.2430e+00[/C][C] 0.001505[/C][C] 0.0007523[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemical_products(t-3s)`[/C][C]-0.2387[/C][C] 0.06518[/C][C]-3.6620e+00[/C][C] 0.0003639[/C][C] 0.000182[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemical_products(t-4s)`[/C][C]-0.1845[/C][C] 0.06212[/C][C]-2.9700e+00[/C][C] 0.003553[/C][C] 0.001776[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310469&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310469&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)	-0.07264	0.2646	-2.7450e-01	0.7841	0.3921
`(1-Bs)(1-B)intermediate0`	+0.694	0.05841	+1.1880e+01	1.899e-22	9.494e-23
`(1-Bs)(1-B)intermediate1`	+0.4998	0.09015	+5.5440e+00	1.601e-07	8.003e-08
`(1-Bs)(1-B)intermediate2`	+0.3276	0.09076	+3.6100e+00	0.0004373	0.0002186
`(1-Bs)(1-B)intermediate3`	+0.007969	0.05869	+1.3580e-01	0.8922	0.4461
`(1-Bs)(1-B)intermediate4`	-0.05475	0.05484	-9.9840e-01	0.3199	0.16
`(1-Bs)(1-B)intermediate5`	-0.05513	0.05655	-9.7480e-01	0.3315	0.1657
`(1-Bs)(1-B)intermediate6\r`	+0.07889	0.04892	+1.6130e+00	0.1093	0.05464
`(1-Bs)(1-B)Chemical_products(t-1)`	-0.3549	0.0827	-4.2910e+00	3.455e-05	1.728e-05
`(1-Bs)(1-B)Chemical_products(t-2)`	-0.2374	0.08221	-2.8880e+00	0.004555	0.002277
`(1-Bs)(1-B)Chemical_products(t-1s)`	-0.3528	0.06427	-5.4900e+00	2.052e-07	1.026e-07
`(1-Bs)(1-B)Chemical_products(t-2s)`	-0.2016	0.06215	-3.2430e+00	0.001505	0.0007523
`(1-Bs)(1-B)Chemical_products(t-3s)`	-0.2387	0.06518	-3.6620e+00	0.0003639	0.000182
`(1-Bs)(1-B)Chemical_products(t-4s)`	-0.1845	0.06212	-2.9700e+00	0.003553	0.001776

Multiple Linear Regression - Regression Statistics
Multiple R	0.876
R-squared	0.7674
Adjusted R-squared	0.744
F-TEST (value)	32.74
F-TEST (DF numerator)	13
F-TEST (DF denominator)	129
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.16
Sum Squared Residuals	1288

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.876 \tabularnewline
R-squared &  0.7674 \tabularnewline
Adjusted R-squared &  0.744 \tabularnewline
F-TEST (value) &  32.74 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 129 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.16 \tabularnewline
Sum Squared Residuals &  1288 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310469&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.876[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7674[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.744[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 32.74[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]129[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 3.16[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1288[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310469&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310469&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	0.876
R-squared	0.7674
Adjusted R-squared	0.744
F-TEST (value)	32.74
F-TEST (DF numerator)	13
F-TEST (DF denominator)	129
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.16
Sum Squared Residuals	1288

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=310469&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=310469&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310469&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	-1.4	-0.3352	-1.065
2	2.8	3.381	-0.5815
3	-4.1	-2.291	-1.809
4	9.2	7.03	2.17
5	-0.4	0.9185	-1.318
6	-1.9	1.845	-3.745
7	-8	-6.258	-1.742
8	12	11.37	0.6305
9	-1.3	-3.491	2.191
10	0.4	0.3612	0.0388
11	1.5	-1.811	3.311
12	-4.5	-3.302	-1.198
13	1	4.477	-3.477
14	-2.5	-0.5486	-1.951
15	-0.3	-0.8279	0.5279
16	0.3	0.2145	0.08555
17	0.1	1.147	-1.047
18	-5.6	-3.429	-2.171
19	8.6	7.27	1.33
20	-1.9	-7.565	5.665
21	-6.1	-3.645	-2.455
22	5.1	7.519	-2.419
23	-4.8	-3.618	-1.182
24	-6.3	-2.553	-3.747
25	12.3	6.025	6.275
26	-6.6	-5.445	-1.155
27	-0.9	-2.283	1.383
28	8.7	3.603	5.097
29	2.1	2.093	0.007459
30	-3.6	-6.282	2.682
31	9.3	9.348	-0.04759
32	-10.6	-10.28	-0.3179
33	6.8	3.778	3.022
34	5	0.2882	4.712
35	-6	-8.017	2.017
36	9.8	9.104	0.6962
37	-14.1	-10.78	-3.317
38	-18.6	-10.97	-7.635
39	-3	4.025	-7.025
40	-17	-15.23	-1.765
41	3.2	-4.056	7.256
42	-0.4	5.172	-5.572
43	-7.8	-10.64	2.841
44	3.8	3.616	0.1843
45	6.6	3.909	2.691
46	0.1	-2.586	2.686
47	2.5	8.617	-6.117
48	5.8	3.024	2.776
49	-1.9	0.6493	-2.549
50	21.2	18.75	2.446
51	6.8	4.357	2.443
52	9.1	6.128	2.972
53	-0.7	-0.06936	-0.6306
54	8.2	9.513	-1.313
55	-1.4	1.823	-3.223
56	5.2	1.665	3.535
57	-7.3	-1.852	-5.448
58	-5.4	-5.562	0.1624
59	-1.2	1.07	-2.27
60	-1.5	-2.607	1.107
61	0.6	0.2246	0.3754
62	3	0.5671	2.433
63	0.8	-2.276	3.076
64	-7	-3.538	-3.462
65	-0.4	0.5596	-0.9596
66	5.6	-0.03666	5.637
67	-9.2	-10.88	1.676
68	0.4	5.557	-5.157
69	-4.7	-7.84	3.14
70	-2.7	-2.671	-0.02922
71	8.9	6.366	2.534
72	-10.9	-3.114	-7.786
73	-1.2	-1.048	-0.1524
74	2.6	2.73	-0.1305
75	-4.1	-2.461	-1.639
76	7.2	5.953	1.247
77	1	-0.9663	1.966
78	-13.1	-9.19	-3.91
79	5	5.989	-0.9887
80	-0.9	-3.419	2.519
81	-2.2	4.512	-6.712
82	12.3	8.892	3.408
83	-8	-7.436	-0.5636
84	-2.4	-6.937	4.537
85	9.6	9.79	-0.19
86	-4.4	-6.666	2.266
87	-2.9	-2.314	-0.5858
88	4	1.236	2.764
89	-8	-5.122	-2.878
90	0.4	-1.993	2.393
91	9.2	11.71	-2.513
92	-5.3	-3.522	-1.778
93	1.6	2.018	-0.418
94	-2.7	-1.117	-1.583
95	0.9	-0.7985	1.698
96	3.7	5.133	-1.433
97	-0.1	-2.767	2.667
98	-4.4	-6.785	2.385
99	2.9	1.045	1.855
100	-2.3	0.02968	-2.33
101	-1.3	4.214	-5.514
102	7.4	3.542	3.858
103	-4	-5.897	1.897
104	-5	-4.292	-0.7079
105	3.7	3.699	0.0006915
106	6.1	2.426	3.674
107	-9.3	-4.051	-5.249
108	8.7	8.914	-0.2143
109	-4.8	-4.104	-0.6961
110	2.4	-1.167	3.567
111	5.2	3.533	1.667
112	-1.4	-2.519	1.119
113	1.1	1.832	-0.732
114	-2.8	3.335	-6.135
115	-4.2	-1.557	-2.643
116	3.3	3.234	0.06562
117	2.1	4.319	-2.219
118	-11.3	-9.293	-2.007
119	6.4	6.665	-0.2647
120	-3.8	-4.504	0.7037
121	-0.6	-2.092	1.492
122	1.4	4.357	-2.957
123	-0.7	-0.1069	-0.5931
124	-3.7	-4.802	1.102
125	9.9	4.525	5.375
126	-4.7	-3.453	-1.247
127	2.2	0.2816	1.918
128	-1.6	3.615	-5.215
129	1.3	-3.383	4.683
130	-5	-4.691	-0.3092
131	9.1	6.08	3.02
132	-3.6	-2.668	-0.9317
133	-0.5	-2.405	1.905
134	5.8	5.631	0.1691
135	2.3	0.2797	2.02
136	-0.9	0.79	-1.69
137	-8	-3.747	-4.253
138	8.7	6.564	2.136
139	-2.6	-7.762	5.162
140	8.3	6.702	1.598
141	-3	-5.064	2.064
142	4.1	4.625	-0.5247
143	-8.1	-4.975	-3.125

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -1.4 & -0.3352 & -1.065 \tabularnewline
2 &  2.8 &  3.381 & -0.5815 \tabularnewline
3 & -4.1 & -2.291 & -1.809 \tabularnewline
4 &  9.2 &  7.03 &  2.17 \tabularnewline
5 & -0.4 &  0.9185 & -1.318 \tabularnewline
6 & -1.9 &  1.845 & -3.745 \tabularnewline
7 & -8 & -6.258 & -1.742 \tabularnewline
8 &  12 &  11.37 &  0.6305 \tabularnewline
9 & -1.3 & -3.491 &  2.191 \tabularnewline
10 &  0.4 &  0.3612 &  0.0388 \tabularnewline
11 &  1.5 & -1.811 &  3.311 \tabularnewline
12 & -4.5 & -3.302 & -1.198 \tabularnewline
13 &  1 &  4.477 & -3.477 \tabularnewline
14 & -2.5 & -0.5486 & -1.951 \tabularnewline
15 & -0.3 & -0.8279 &  0.5279 \tabularnewline
16 &  0.3 &  0.2145 &  0.08555 \tabularnewline
17 &  0.1 &  1.147 & -1.047 \tabularnewline
18 & -5.6 & -3.429 & -2.171 \tabularnewline
19 &  8.6 &  7.27 &  1.33 \tabularnewline
20 & -1.9 & -7.565 &  5.665 \tabularnewline
21 & -6.1 & -3.645 & -2.455 \tabularnewline
22 &  5.1 &  7.519 & -2.419 \tabularnewline
23 & -4.8 & -3.618 & -1.182 \tabularnewline
24 & -6.3 & -2.553 & -3.747 \tabularnewline
25 &  12.3 &  6.025 &  6.275 \tabularnewline
26 & -6.6 & -5.445 & -1.155 \tabularnewline
27 & -0.9 & -2.283 &  1.383 \tabularnewline
28 &  8.7 &  3.603 &  5.097 \tabularnewline
29 &  2.1 &  2.093 &  0.007459 \tabularnewline
30 & -3.6 & -6.282 &  2.682 \tabularnewline
31 &  9.3 &  9.348 & -0.04759 \tabularnewline
32 & -10.6 & -10.28 & -0.3179 \tabularnewline
33 &  6.8 &  3.778 &  3.022 \tabularnewline
34 &  5 &  0.2882 &  4.712 \tabularnewline
35 & -6 & -8.017 &  2.017 \tabularnewline
36 &  9.8 &  9.104 &  0.6962 \tabularnewline
37 & -14.1 & -10.78 & -3.317 \tabularnewline
38 & -18.6 & -10.97 & -7.635 \tabularnewline
39 & -3 &  4.025 & -7.025 \tabularnewline
40 & -17 & -15.23 & -1.765 \tabularnewline
41 &  3.2 & -4.056 &  7.256 \tabularnewline
42 & -0.4 &  5.172 & -5.572 \tabularnewline
43 & -7.8 & -10.64 &  2.841 \tabularnewline
44 &  3.8 &  3.616 &  0.1843 \tabularnewline
45 &  6.6 &  3.909 &  2.691 \tabularnewline
46 &  0.1 & -2.586 &  2.686 \tabularnewline
47 &  2.5 &  8.617 & -6.117 \tabularnewline
48 &  5.8 &  3.024 &  2.776 \tabularnewline
49 & -1.9 &  0.6493 & -2.549 \tabularnewline
50 &  21.2 &  18.75 &  2.446 \tabularnewline
51 &  6.8 &  4.357 &  2.443 \tabularnewline
52 &  9.1 &  6.128 &  2.972 \tabularnewline
53 & -0.7 & -0.06936 & -0.6306 \tabularnewline
54 &  8.2 &  9.513 & -1.313 \tabularnewline
55 & -1.4 &  1.823 & -3.223 \tabularnewline
56 &  5.2 &  1.665 &  3.535 \tabularnewline
57 & -7.3 & -1.852 & -5.448 \tabularnewline
58 & -5.4 & -5.562 &  0.1624 \tabularnewline
59 & -1.2 &  1.07 & -2.27 \tabularnewline
60 & -1.5 & -2.607 &  1.107 \tabularnewline
61 &  0.6 &  0.2246 &  0.3754 \tabularnewline
62 &  3 &  0.5671 &  2.433 \tabularnewline
63 &  0.8 & -2.276 &  3.076 \tabularnewline
64 & -7 & -3.538 & -3.462 \tabularnewline
65 & -0.4 &  0.5596 & -0.9596 \tabularnewline
66 &  5.6 & -0.03666 &  5.637 \tabularnewline
67 & -9.2 & -10.88 &  1.676 \tabularnewline
68 &  0.4 &  5.557 & -5.157 \tabularnewline
69 & -4.7 & -7.84 &  3.14 \tabularnewline
70 & -2.7 & -2.671 & -0.02922 \tabularnewline
71 &  8.9 &  6.366 &  2.534 \tabularnewline
72 & -10.9 & -3.114 & -7.786 \tabularnewline
73 & -1.2 & -1.048 & -0.1524 \tabularnewline
74 &  2.6 &  2.73 & -0.1305 \tabularnewline
75 & -4.1 & -2.461 & -1.639 \tabularnewline
76 &  7.2 &  5.953 &  1.247 \tabularnewline
77 &  1 & -0.9663 &  1.966 \tabularnewline
78 & -13.1 & -9.19 & -3.91 \tabularnewline
79 &  5 &  5.989 & -0.9887 \tabularnewline
80 & -0.9 & -3.419 &  2.519 \tabularnewline
81 & -2.2 &  4.512 & -6.712 \tabularnewline
82 &  12.3 &  8.892 &  3.408 \tabularnewline
83 & -8 & -7.436 & -0.5636 \tabularnewline
84 & -2.4 & -6.937 &  4.537 \tabularnewline
85 &  9.6 &  9.79 & -0.19 \tabularnewline
86 & -4.4 & -6.666 &  2.266 \tabularnewline
87 & -2.9 & -2.314 & -0.5858 \tabularnewline
88 &  4 &  1.236 &  2.764 \tabularnewline
89 & -8 & -5.122 & -2.878 \tabularnewline
90 &  0.4 & -1.993 &  2.393 \tabularnewline
91 &  9.2 &  11.71 & -2.513 \tabularnewline
92 & -5.3 & -3.522 & -1.778 \tabularnewline
93 &  1.6 &  2.018 & -0.418 \tabularnewline
94 & -2.7 & -1.117 & -1.583 \tabularnewline
95 &  0.9 & -0.7985 &  1.698 \tabularnewline
96 &  3.7 &  5.133 & -1.433 \tabularnewline
97 & -0.1 & -2.767 &  2.667 \tabularnewline
98 & -4.4 & -6.785 &  2.385 \tabularnewline
99 &  2.9 &  1.045 &  1.855 \tabularnewline
100 & -2.3 &  0.02968 & -2.33 \tabularnewline
101 & -1.3 &  4.214 & -5.514 \tabularnewline
102 &  7.4 &  3.542 &  3.858 \tabularnewline
103 & -4 & -5.897 &  1.897 \tabularnewline
104 & -5 & -4.292 & -0.7079 \tabularnewline
105 &  3.7 &  3.699 &  0.0006915 \tabularnewline
106 &  6.1 &  2.426 &  3.674 \tabularnewline
107 & -9.3 & -4.051 & -5.249 \tabularnewline
108 &  8.7 &  8.914 & -0.2143 \tabularnewline
109 & -4.8 & -4.104 & -0.6961 \tabularnewline
110 &  2.4 & -1.167 &  3.567 \tabularnewline
111 &  5.2 &  3.533 &  1.667 \tabularnewline
112 & -1.4 & -2.519 &  1.119 \tabularnewline
113 &  1.1 &  1.832 & -0.732 \tabularnewline
114 & -2.8 &  3.335 & -6.135 \tabularnewline
115 & -4.2 & -1.557 & -2.643 \tabularnewline
116 &  3.3 &  3.234 &  0.06562 \tabularnewline
117 &  2.1 &  4.319 & -2.219 \tabularnewline
118 & -11.3 & -9.293 & -2.007 \tabularnewline
119 &  6.4 &  6.665 & -0.2647 \tabularnewline
120 & -3.8 & -4.504 &  0.7037 \tabularnewline
121 & -0.6 & -2.092 &  1.492 \tabularnewline
122 &  1.4 &  4.357 & -2.957 \tabularnewline
123 & -0.7 & -0.1069 & -0.5931 \tabularnewline
124 & -3.7 & -4.802 &  1.102 \tabularnewline
125 &  9.9 &  4.525 &  5.375 \tabularnewline
126 & -4.7 & -3.453 & -1.247 \tabularnewline
127 &  2.2 &  0.2816 &  1.918 \tabularnewline
128 & -1.6 &  3.615 & -5.215 \tabularnewline
129 &  1.3 & -3.383 &  4.683 \tabularnewline
130 & -5 & -4.691 & -0.3092 \tabularnewline
131 &  9.1 &  6.08 &  3.02 \tabularnewline
132 & -3.6 & -2.668 & -0.9317 \tabularnewline
133 & -0.5 & -2.405 &  1.905 \tabularnewline
134 &  5.8 &  5.631 &  0.1691 \tabularnewline
135 &  2.3 &  0.2797 &  2.02 \tabularnewline
136 & -0.9 &  0.79 & -1.69 \tabularnewline
137 & -8 & -3.747 & -4.253 \tabularnewline
138 &  8.7 &  6.564 &  2.136 \tabularnewline
139 & -2.6 & -7.762 &  5.162 \tabularnewline
140 &  8.3 &  6.702 &  1.598 \tabularnewline
141 & -3 & -5.064 &  2.064 \tabularnewline
142 &  4.1 &  4.625 & -0.5247 \tabularnewline
143 & -8.1 & -4.975 & -3.125 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310469&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]-1.4[/C][C]-0.3352[/C][C]-1.065[/C][/ROW]
[ROW][C]2[/C][C] 2.8[/C][C] 3.381[/C][C]-0.5815[/C][/ROW]
[ROW][C]3[/C][C]-4.1[/C][C]-2.291[/C][C]-1.809[/C][/ROW]
[ROW][C]4[/C][C] 9.2[/C][C] 7.03[/C][C] 2.17[/C][/ROW]
[ROW][C]5[/C][C]-0.4[/C][C] 0.9185[/C][C]-1.318[/C][/ROW]
[ROW][C]6[/C][C]-1.9[/C][C] 1.845[/C][C]-3.745[/C][/ROW]
[ROW][C]7[/C][C]-8[/C][C]-6.258[/C][C]-1.742[/C][/ROW]
[ROW][C]8[/C][C] 12[/C][C] 11.37[/C][C] 0.6305[/C][/ROW]
[ROW][C]9[/C][C]-1.3[/C][C]-3.491[/C][C] 2.191[/C][/ROW]
[ROW][C]10[/C][C] 0.4[/C][C] 0.3612[/C][C] 0.0388[/C][/ROW]
[ROW][C]11[/C][C] 1.5[/C][C]-1.811[/C][C] 3.311[/C][/ROW]
[ROW][C]12[/C][C]-4.5[/C][C]-3.302[/C][C]-1.198[/C][/ROW]
[ROW][C]13[/C][C] 1[/C][C] 4.477[/C][C]-3.477[/C][/ROW]
[ROW][C]14[/C][C]-2.5[/C][C]-0.5486[/C][C]-1.951[/C][/ROW]
[ROW][C]15[/C][C]-0.3[/C][C]-0.8279[/C][C] 0.5279[/C][/ROW]
[ROW][C]16[/C][C] 0.3[/C][C] 0.2145[/C][C] 0.08555[/C][/ROW]
[ROW][C]17[/C][C] 0.1[/C][C] 1.147[/C][C]-1.047[/C][/ROW]
[ROW][C]18[/C][C]-5.6[/C][C]-3.429[/C][C]-2.171[/C][/ROW]
[ROW][C]19[/C][C] 8.6[/C][C] 7.27[/C][C] 1.33[/C][/ROW]
[ROW][C]20[/C][C]-1.9[/C][C]-7.565[/C][C] 5.665[/C][/ROW]
[ROW][C]21[/C][C]-6.1[/C][C]-3.645[/C][C]-2.455[/C][/ROW]
[ROW][C]22[/C][C] 5.1[/C][C] 7.519[/C][C]-2.419[/C][/ROW]
[ROW][C]23[/C][C]-4.8[/C][C]-3.618[/C][C]-1.182[/C][/ROW]
[ROW][C]24[/C][C]-6.3[/C][C]-2.553[/C][C]-3.747[/C][/ROW]
[ROW][C]25[/C][C] 12.3[/C][C] 6.025[/C][C] 6.275[/C][/ROW]
[ROW][C]26[/C][C]-6.6[/C][C]-5.445[/C][C]-1.155[/C][/ROW]
[ROW][C]27[/C][C]-0.9[/C][C]-2.283[/C][C] 1.383[/C][/ROW]
[ROW][C]28[/C][C] 8.7[/C][C] 3.603[/C][C] 5.097[/C][/ROW]
[ROW][C]29[/C][C] 2.1[/C][C] 2.093[/C][C] 0.007459[/C][/ROW]
[ROW][C]30[/C][C]-3.6[/C][C]-6.282[/C][C] 2.682[/C][/ROW]
[ROW][C]31[/C][C] 9.3[/C][C] 9.348[/C][C]-0.04759[/C][/ROW]
[ROW][C]32[/C][C]-10.6[/C][C]-10.28[/C][C]-0.3179[/C][/ROW]
[ROW][C]33[/C][C] 6.8[/C][C] 3.778[/C][C] 3.022[/C][/ROW]
[ROW][C]34[/C][C] 5[/C][C] 0.2882[/C][C] 4.712[/C][/ROW]
[ROW][C]35[/C][C]-6[/C][C]-8.017[/C][C] 2.017[/C][/ROW]
[ROW][C]36[/C][C] 9.8[/C][C] 9.104[/C][C] 0.6962[/C][/ROW]
[ROW][C]37[/C][C]-14.1[/C][C]-10.78[/C][C]-3.317[/C][/ROW]
[ROW][C]38[/C][C]-18.6[/C][C]-10.97[/C][C]-7.635[/C][/ROW]
[ROW][C]39[/C][C]-3[/C][C] 4.025[/C][C]-7.025[/C][/ROW]
[ROW][C]40[/C][C]-17[/C][C]-15.23[/C][C]-1.765[/C][/ROW]
[ROW][C]41[/C][C] 3.2[/C][C]-4.056[/C][C] 7.256[/C][/ROW]
[ROW][C]42[/C][C]-0.4[/C][C] 5.172[/C][C]-5.572[/C][/ROW]
[ROW][C]43[/C][C]-7.8[/C][C]-10.64[/C][C] 2.841[/C][/ROW]
[ROW][C]44[/C][C] 3.8[/C][C] 3.616[/C][C] 0.1843[/C][/ROW]
[ROW][C]45[/C][C] 6.6[/C][C] 3.909[/C][C] 2.691[/C][/ROW]
[ROW][C]46[/C][C] 0.1[/C][C]-2.586[/C][C] 2.686[/C][/ROW]
[ROW][C]47[/C][C] 2.5[/C][C] 8.617[/C][C]-6.117[/C][/ROW]
[ROW][C]48[/C][C] 5.8[/C][C] 3.024[/C][C] 2.776[/C][/ROW]
[ROW][C]49[/C][C]-1.9[/C][C] 0.6493[/C][C]-2.549[/C][/ROW]
[ROW][C]50[/C][C] 21.2[/C][C] 18.75[/C][C] 2.446[/C][/ROW]
[ROW][C]51[/C][C] 6.8[/C][C] 4.357[/C][C] 2.443[/C][/ROW]
[ROW][C]52[/C][C] 9.1[/C][C] 6.128[/C][C] 2.972[/C][/ROW]
[ROW][C]53[/C][C]-0.7[/C][C]-0.06936[/C][C]-0.6306[/C][/ROW]
[ROW][C]54[/C][C] 8.2[/C][C] 9.513[/C][C]-1.313[/C][/ROW]
[ROW][C]55[/C][C]-1.4[/C][C] 1.823[/C][C]-3.223[/C][/ROW]
[ROW][C]56[/C][C] 5.2[/C][C] 1.665[/C][C] 3.535[/C][/ROW]
[ROW][C]57[/C][C]-7.3[/C][C]-1.852[/C][C]-5.448[/C][/ROW]
[ROW][C]58[/C][C]-5.4[/C][C]-5.562[/C][C] 0.1624[/C][/ROW]
[ROW][C]59[/C][C]-1.2[/C][C] 1.07[/C][C]-2.27[/C][/ROW]
[ROW][C]60[/C][C]-1.5[/C][C]-2.607[/C][C] 1.107[/C][/ROW]
[ROW][C]61[/C][C] 0.6[/C][C] 0.2246[/C][C] 0.3754[/C][/ROW]
[ROW][C]62[/C][C] 3[/C][C] 0.5671[/C][C] 2.433[/C][/ROW]
[ROW][C]63[/C][C] 0.8[/C][C]-2.276[/C][C] 3.076[/C][/ROW]
[ROW][C]64[/C][C]-7[/C][C]-3.538[/C][C]-3.462[/C][/ROW]
[ROW][C]65[/C][C]-0.4[/C][C] 0.5596[/C][C]-0.9596[/C][/ROW]
[ROW][C]66[/C][C] 5.6[/C][C]-0.03666[/C][C] 5.637[/C][/ROW]
[ROW][C]67[/C][C]-9.2[/C][C]-10.88[/C][C] 1.676[/C][/ROW]
[ROW][C]68[/C][C] 0.4[/C][C] 5.557[/C][C]-5.157[/C][/ROW]
[ROW][C]69[/C][C]-4.7[/C][C]-7.84[/C][C] 3.14[/C][/ROW]
[ROW][C]70[/C][C]-2.7[/C][C]-2.671[/C][C]-0.02922[/C][/ROW]
[ROW][C]71[/C][C] 8.9[/C][C] 6.366[/C][C] 2.534[/C][/ROW]
[ROW][C]72[/C][C]-10.9[/C][C]-3.114[/C][C]-7.786[/C][/ROW]
[ROW][C]73[/C][C]-1.2[/C][C]-1.048[/C][C]-0.1524[/C][/ROW]
[ROW][C]74[/C][C] 2.6[/C][C] 2.73[/C][C]-0.1305[/C][/ROW]
[ROW][C]75[/C][C]-4.1[/C][C]-2.461[/C][C]-1.639[/C][/ROW]
[ROW][C]76[/C][C] 7.2[/C][C] 5.953[/C][C] 1.247[/C][/ROW]
[ROW][C]77[/C][C] 1[/C][C]-0.9663[/C][C] 1.966[/C][/ROW]
[ROW][C]78[/C][C]-13.1[/C][C]-9.19[/C][C]-3.91[/C][/ROW]
[ROW][C]79[/C][C] 5[/C][C] 5.989[/C][C]-0.9887[/C][/ROW]
[ROW][C]80[/C][C]-0.9[/C][C]-3.419[/C][C] 2.519[/C][/ROW]
[ROW][C]81[/C][C]-2.2[/C][C] 4.512[/C][C]-6.712[/C][/ROW]
[ROW][C]82[/C][C] 12.3[/C][C] 8.892[/C][C] 3.408[/C][/ROW]
[ROW][C]83[/C][C]-8[/C][C]-7.436[/C][C]-0.5636[/C][/ROW]
[ROW][C]84[/C][C]-2.4[/C][C]-6.937[/C][C] 4.537[/C][/ROW]
[ROW][C]85[/C][C] 9.6[/C][C] 9.79[/C][C]-0.19[/C][/ROW]
[ROW][C]86[/C][C]-4.4[/C][C]-6.666[/C][C] 2.266[/C][/ROW]
[ROW][C]87[/C][C]-2.9[/C][C]-2.314[/C][C]-0.5858[/C][/ROW]
[ROW][C]88[/C][C] 4[/C][C] 1.236[/C][C] 2.764[/C][/ROW]
[ROW][C]89[/C][C]-8[/C][C]-5.122[/C][C]-2.878[/C][/ROW]
[ROW][C]90[/C][C] 0.4[/C][C]-1.993[/C][C] 2.393[/C][/ROW]
[ROW][C]91[/C][C] 9.2[/C][C] 11.71[/C][C]-2.513[/C][/ROW]
[ROW][C]92[/C][C]-5.3[/C][C]-3.522[/C][C]-1.778[/C][/ROW]
[ROW][C]93[/C][C] 1.6[/C][C] 2.018[/C][C]-0.418[/C][/ROW]
[ROW][C]94[/C][C]-2.7[/C][C]-1.117[/C][C]-1.583[/C][/ROW]
[ROW][C]95[/C][C] 0.9[/C][C]-0.7985[/C][C] 1.698[/C][/ROW]
[ROW][C]96[/C][C] 3.7[/C][C] 5.133[/C][C]-1.433[/C][/ROW]
[ROW][C]97[/C][C]-0.1[/C][C]-2.767[/C][C] 2.667[/C][/ROW]
[ROW][C]98[/C][C]-4.4[/C][C]-6.785[/C][C] 2.385[/C][/ROW]
[ROW][C]99[/C][C] 2.9[/C][C] 1.045[/C][C] 1.855[/C][/ROW]
[ROW][C]100[/C][C]-2.3[/C][C] 0.02968[/C][C]-2.33[/C][/ROW]
[ROW][C]101[/C][C]-1.3[/C][C] 4.214[/C][C]-5.514[/C][/ROW]
[ROW][C]102[/C][C] 7.4[/C][C] 3.542[/C][C] 3.858[/C][/ROW]
[ROW][C]103[/C][C]-4[/C][C]-5.897[/C][C] 1.897[/C][/ROW]
[ROW][C]104[/C][C]-5[/C][C]-4.292[/C][C]-0.7079[/C][/ROW]
[ROW][C]105[/C][C] 3.7[/C][C] 3.699[/C][C] 0.0006915[/C][/ROW]
[ROW][C]106[/C][C] 6.1[/C][C] 2.426[/C][C] 3.674[/C][/ROW]
[ROW][C]107[/C][C]-9.3[/C][C]-4.051[/C][C]-5.249[/C][/ROW]
[ROW][C]108[/C][C] 8.7[/C][C] 8.914[/C][C]-0.2143[/C][/ROW]
[ROW][C]109[/C][C]-4.8[/C][C]-4.104[/C][C]-0.6961[/C][/ROW]
[ROW][C]110[/C][C] 2.4[/C][C]-1.167[/C][C] 3.567[/C][/ROW]
[ROW][C]111[/C][C] 5.2[/C][C] 3.533[/C][C] 1.667[/C][/ROW]
[ROW][C]112[/C][C]-1.4[/C][C]-2.519[/C][C] 1.119[/C][/ROW]
[ROW][C]113[/C][C] 1.1[/C][C] 1.832[/C][C]-0.732[/C][/ROW]
[ROW][C]114[/C][C]-2.8[/C][C] 3.335[/C][C]-6.135[/C][/ROW]
[ROW][C]115[/C][C]-4.2[/C][C]-1.557[/C][C]-2.643[/C][/ROW]
[ROW][C]116[/C][C] 3.3[/C][C] 3.234[/C][C] 0.06562[/C][/ROW]
[ROW][C]117[/C][C] 2.1[/C][C] 4.319[/C][C]-2.219[/C][/ROW]
[ROW][C]118[/C][C]-11.3[/C][C]-9.293[/C][C]-2.007[/C][/ROW]
[ROW][C]119[/C][C] 6.4[/C][C] 6.665[/C][C]-0.2647[/C][/ROW]
[ROW][C]120[/C][C]-3.8[/C][C]-4.504[/C][C] 0.7037[/C][/ROW]
[ROW][C]121[/C][C]-0.6[/C][C]-2.092[/C][C] 1.492[/C][/ROW]
[ROW][C]122[/C][C] 1.4[/C][C] 4.357[/C][C]-2.957[/C][/ROW]
[ROW][C]123[/C][C]-0.7[/C][C]-0.1069[/C][C]-0.5931[/C][/ROW]
[ROW][C]124[/C][C]-3.7[/C][C]-4.802[/C][C] 1.102[/C][/ROW]
[ROW][C]125[/C][C] 9.9[/C][C] 4.525[/C][C] 5.375[/C][/ROW]
[ROW][C]126[/C][C]-4.7[/C][C]-3.453[/C][C]-1.247[/C][/ROW]
[ROW][C]127[/C][C] 2.2[/C][C] 0.2816[/C][C] 1.918[/C][/ROW]
[ROW][C]128[/C][C]-1.6[/C][C] 3.615[/C][C]-5.215[/C][/ROW]
[ROW][C]129[/C][C] 1.3[/C][C]-3.383[/C][C] 4.683[/C][/ROW]
[ROW][C]130[/C][C]-5[/C][C]-4.691[/C][C]-0.3092[/C][/ROW]
[ROW][C]131[/C][C] 9.1[/C][C] 6.08[/C][C] 3.02[/C][/ROW]
[ROW][C]132[/C][C]-3.6[/C][C]-2.668[/C][C]-0.9317[/C][/ROW]
[ROW][C]133[/C][C]-0.5[/C][C]-2.405[/C][C] 1.905[/C][/ROW]
[ROW][C]134[/C][C] 5.8[/C][C] 5.631[/C][C] 0.1691[/C][/ROW]
[ROW][C]135[/C][C] 2.3[/C][C] 0.2797[/C][C] 2.02[/C][/ROW]
[ROW][C]136[/C][C]-0.9[/C][C] 0.79[/C][C]-1.69[/C][/ROW]
[ROW][C]137[/C][C]-8[/C][C]-3.747[/C][C]-4.253[/C][/ROW]
[ROW][C]138[/C][C] 8.7[/C][C] 6.564[/C][C] 2.136[/C][/ROW]
[ROW][C]139[/C][C]-2.6[/C][C]-7.762[/C][C] 5.162[/C][/ROW]
[ROW][C]140[/C][C] 8.3[/C][C] 6.702[/C][C] 1.598[/C][/ROW]
[ROW][C]141[/C][C]-3[/C][C]-5.064[/C][C] 2.064[/C][/ROW]
[ROW][C]142[/C][C] 4.1[/C][C] 4.625[/C][C]-0.5247[/C][/ROW]
[ROW][C]143[/C][C]-8.1[/C][C]-4.975[/C][C]-3.125[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310469&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310469&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	-1.4	-0.3352	-1.065
2	2.8	3.381	-0.5815
3	-4.1	-2.291	-1.809
4	9.2	7.03	2.17
5	-0.4	0.9185	-1.318
6	-1.9	1.845	-3.745
7	-8	-6.258	-1.742
8	12	11.37	0.6305
9	-1.3	-3.491	2.191
10	0.4	0.3612	0.0388
11	1.5	-1.811	3.311
12	-4.5	-3.302	-1.198
13	1	4.477	-3.477
14	-2.5	-0.5486	-1.951
15	-0.3	-0.8279	0.5279
16	0.3	0.2145	0.08555
17	0.1	1.147	-1.047
18	-5.6	-3.429	-2.171
19	8.6	7.27	1.33
20	-1.9	-7.565	5.665
21	-6.1	-3.645	-2.455
22	5.1	7.519	-2.419
23	-4.8	-3.618	-1.182
24	-6.3	-2.553	-3.747
25	12.3	6.025	6.275
26	-6.6	-5.445	-1.155
27	-0.9	-2.283	1.383
28	8.7	3.603	5.097
29	2.1	2.093	0.007459
30	-3.6	-6.282	2.682
31	9.3	9.348	-0.04759
32	-10.6	-10.28	-0.3179
33	6.8	3.778	3.022
34	5	0.2882	4.712
35	-6	-8.017	2.017
36	9.8	9.104	0.6962
37	-14.1	-10.78	-3.317
38	-18.6	-10.97	-7.635
39	-3	4.025	-7.025
40	-17	-15.23	-1.765
41	3.2	-4.056	7.256
42	-0.4	5.172	-5.572
43	-7.8	-10.64	2.841
44	3.8	3.616	0.1843
45	6.6	3.909	2.691
46	0.1	-2.586	2.686
47	2.5	8.617	-6.117
48	5.8	3.024	2.776
49	-1.9	0.6493	-2.549
50	21.2	18.75	2.446
51	6.8	4.357	2.443
52	9.1	6.128	2.972
53	-0.7	-0.06936	-0.6306
54	8.2	9.513	-1.313
55	-1.4	1.823	-3.223
56	5.2	1.665	3.535
57	-7.3	-1.852	-5.448
58	-5.4	-5.562	0.1624
59	-1.2	1.07	-2.27
60	-1.5	-2.607	1.107
61	0.6	0.2246	0.3754
62	3	0.5671	2.433
63	0.8	-2.276	3.076
64	-7	-3.538	-3.462
65	-0.4	0.5596	-0.9596
66	5.6	-0.03666	5.637
67	-9.2	-10.88	1.676
68	0.4	5.557	-5.157
69	-4.7	-7.84	3.14
70	-2.7	-2.671	-0.02922
71	8.9	6.366	2.534
72	-10.9	-3.114	-7.786
73	-1.2	-1.048	-0.1524
74	2.6	2.73	-0.1305
75	-4.1	-2.461	-1.639
76	7.2	5.953	1.247
77	1	-0.9663	1.966
78	-13.1	-9.19	-3.91
79	5	5.989	-0.9887
80	-0.9	-3.419	2.519
81	-2.2	4.512	-6.712
82	12.3	8.892	3.408
83	-8	-7.436	-0.5636
84	-2.4	-6.937	4.537
85	9.6	9.79	-0.19
86	-4.4	-6.666	2.266
87	-2.9	-2.314	-0.5858
88	4	1.236	2.764
89	-8	-5.122	-2.878
90	0.4	-1.993	2.393
91	9.2	11.71	-2.513
92	-5.3	-3.522	-1.778
93	1.6	2.018	-0.418
94	-2.7	-1.117	-1.583
95	0.9	-0.7985	1.698
96	3.7	5.133	-1.433
97	-0.1	-2.767	2.667
98	-4.4	-6.785	2.385
99	2.9	1.045	1.855
100	-2.3	0.02968	-2.33
101	-1.3	4.214	-5.514
102	7.4	3.542	3.858
103	-4	-5.897	1.897
104	-5	-4.292	-0.7079
105	3.7	3.699	0.0006915
106	6.1	2.426	3.674
107	-9.3	-4.051	-5.249
108	8.7	8.914	-0.2143
109	-4.8	-4.104	-0.6961
110	2.4	-1.167	3.567
111	5.2	3.533	1.667
112	-1.4	-2.519	1.119
113	1.1	1.832	-0.732
114	-2.8	3.335	-6.135
115	-4.2	-1.557	-2.643
116	3.3	3.234	0.06562
117	2.1	4.319	-2.219
118	-11.3	-9.293	-2.007
119	6.4	6.665	-0.2647
120	-3.8	-4.504	0.7037
121	-0.6	-2.092	1.492
122	1.4	4.357	-2.957
123	-0.7	-0.1069	-0.5931
124	-3.7	-4.802	1.102
125	9.9	4.525	5.375
126	-4.7	-3.453	-1.247
127	2.2	0.2816	1.918
128	-1.6	3.615	-5.215
129	1.3	-3.383	4.683
130	-5	-4.691	-0.3092
131	9.1	6.08	3.02
132	-3.6	-2.668	-0.9317
133	-0.5	-2.405	1.905
134	5.8	5.631	0.1691
135	2.3	0.2797	2.02
136	-0.9	0.79	-1.69
137	-8	-3.747	-4.253
138	8.7	6.564	2.136
139	-2.6	-7.762	5.162
140	8.3	6.702	1.598
141	-3	-5.064	2.064
142	4.1	4.625	-0.5247
143	-8.1	-4.975	-3.125

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.3272	0.6545	0.6728
18	0.2069	0.4138	0.7931
19	0.1817	0.3635	0.8183
20	0.4153	0.8306	0.5847
21	0.3462	0.6923	0.6538
22	0.2549	0.5098	0.7451
23	0.175	0.3501	0.825
24	0.1936	0.3872	0.8064
25	0.3957	0.7913	0.6043
26	0.3596	0.7191	0.6404
27	0.3668	0.7335	0.6332
28	0.327	0.6539	0.673
29	0.2555	0.5109	0.7445
30	0.3871	0.7742	0.6129
31	0.3178	0.6357	0.6822
32	0.2528	0.5056	0.7472
33	0.223	0.4459	0.777
34	0.1961	0.3923	0.8039
35	0.1587	0.3175	0.8413
36	0.1209	0.2418	0.8791
37	0.1293	0.2587	0.8707
38	0.3621	0.7241	0.6379
39	0.5136	0.9728	0.4864
40	0.558	0.884	0.442
41	0.7037	0.5926	0.2963
42	0.794	0.412	0.206
43	0.7657	0.4687	0.2343
44	0.7329	0.5343	0.2671
45	0.7455	0.509	0.2545
46	0.7603	0.4794	0.2397
47	0.8236	0.3528	0.1764
48	0.814	0.3719	0.186
49	0.8271	0.3458	0.1729
50	0.8428	0.3145	0.1572
51	0.8262	0.3476	0.1738
52	0.8436	0.3129	0.1564
53	0.8094	0.3813	0.1906
54	0.7834	0.4332	0.2166
55	0.7764	0.4472	0.2236
56	0.8075	0.3849	0.1925
57	0.8884	0.2232	0.1116
58	0.8767	0.2467	0.1233
59	0.8778	0.2444	0.1222
60	0.8486	0.3028	0.1514
61	0.8213	0.3574	0.1787
62	0.816	0.368	0.184
63	0.8267	0.3466	0.1733
64	0.8116	0.3768	0.1884
65	0.7885	0.4229	0.2115
66	0.855	0.2899	0.145
67	0.849	0.3021	0.151
68	0.8944	0.2112	0.1056
69	0.887	0.226	0.113
70	0.8598	0.2804	0.1402
71	0.8616	0.2768	0.1384
72	0.9527	0.09452	0.04726
73	0.9436	0.1128	0.0564
74	0.9278	0.1443	0.07216
75	0.91	0.1801	0.09003
76	0.8997	0.2006	0.1003
77	0.8815	0.2371	0.1185
78	0.8851	0.2298	0.1149
79	0.8668	0.2664	0.1332
80	0.8441	0.3118	0.1559
81	0.9311	0.1379	0.06894
82	0.9527	0.09452	0.04726
83	0.9417	0.1166	0.05832
84	0.9522	0.09561	0.04781
85	0.9413	0.1175	0.05873
86	0.9331	0.1339	0.06693
87	0.9156	0.1689	0.08444
88	0.9066	0.1869	0.09344
89	0.9276	0.1449	0.07243
90	0.9183	0.1634	0.0817
91	0.911	0.178	0.08902
92	0.8877	0.2247	0.1123
93	0.8627	0.2747	0.1373
94	0.8376	0.3247	0.1624
95	0.8076	0.3849	0.1924
96	0.7733	0.4533	0.2267
97	0.786	0.428	0.214
98	0.7663	0.4674	0.2337
99	0.7251	0.5497	0.2749
100	0.8169	0.3662	0.1831
101	0.8274	0.3453	0.1726
102	0.8606	0.2788	0.1394
103	0.8262	0.3476	0.1738
104	0.8434	0.3132	0.1566
105	0.8078	0.3844	0.1922
106	0.8112	0.3777	0.1888
107	0.8468	0.3064	0.1532
108	0.8164	0.3672	0.1836
109	0.7658	0.4684	0.2342
110	0.7266	0.5468	0.2734
111	0.6827	0.6347	0.3173
112	0.622	0.756	0.378
113	0.5481	0.9038	0.4519
114	0.8613	0.2773	0.1387
115	0.815	0.3701	0.185
116	0.8081	0.3838	0.1919
117	0.8326	0.3349	0.1674
118	0.7993	0.4015	0.2007
119	0.747	0.5059	0.253
120	0.6745	0.6509	0.3255
121	0.7583	0.4834	0.2417
122	0.6633	0.6733	0.3367
123	0.554	0.892	0.446
124	0.424	0.848	0.576
125	0.4132	0.8265	0.5868
126	0.2786	0.5572	0.7214

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 &  0.3272 &  0.6545 &  0.6728 \tabularnewline
18 &  0.2069 &  0.4138 &  0.7931 \tabularnewline
19 &  0.1817 &  0.3635 &  0.8183 \tabularnewline
20 &  0.4153 &  0.8306 &  0.5847 \tabularnewline
21 &  0.3462 &  0.6923 &  0.6538 \tabularnewline
22 &  0.2549 &  0.5098 &  0.7451 \tabularnewline
23 &  0.175 &  0.3501 &  0.825 \tabularnewline
24 &  0.1936 &  0.3872 &  0.8064 \tabularnewline
25 &  0.3957 &  0.7913 &  0.6043 \tabularnewline
26 &  0.3596 &  0.7191 &  0.6404 \tabularnewline
27 &  0.3668 &  0.7335 &  0.6332 \tabularnewline
28 &  0.327 &  0.6539 &  0.673 \tabularnewline
29 &  0.2555 &  0.5109 &  0.7445 \tabularnewline
30 &  0.3871 &  0.7742 &  0.6129 \tabularnewline
31 &  0.3178 &  0.6357 &  0.6822 \tabularnewline
32 &  0.2528 &  0.5056 &  0.7472 \tabularnewline
33 &  0.223 &  0.4459 &  0.777 \tabularnewline
34 &  0.1961 &  0.3923 &  0.8039 \tabularnewline
35 &  0.1587 &  0.3175 &  0.8413 \tabularnewline
36 &  0.1209 &  0.2418 &  0.8791 \tabularnewline
37 &  0.1293 &  0.2587 &  0.8707 \tabularnewline
38 &  0.3621 &  0.7241 &  0.6379 \tabularnewline
39 &  0.5136 &  0.9728 &  0.4864 \tabularnewline
40 &  0.558 &  0.884 &  0.442 \tabularnewline
41 &  0.7037 &  0.5926 &  0.2963 \tabularnewline
42 &  0.794 &  0.412 &  0.206 \tabularnewline
43 &  0.7657 &  0.4687 &  0.2343 \tabularnewline
44 &  0.7329 &  0.5343 &  0.2671 \tabularnewline
45 &  0.7455 &  0.509 &  0.2545 \tabularnewline
46 &  0.7603 &  0.4794 &  0.2397 \tabularnewline
47 &  0.8236 &  0.3528 &  0.1764 \tabularnewline
48 &  0.814 &  0.3719 &  0.186 \tabularnewline
49 &  0.8271 &  0.3458 &  0.1729 \tabularnewline
50 &  0.8428 &  0.3145 &  0.1572 \tabularnewline
51 &  0.8262 &  0.3476 &  0.1738 \tabularnewline
52 &  0.8436 &  0.3129 &  0.1564 \tabularnewline
53 &  0.8094 &  0.3813 &  0.1906 \tabularnewline
54 &  0.7834 &  0.4332 &  0.2166 \tabularnewline
55 &  0.7764 &  0.4472 &  0.2236 \tabularnewline
56 &  0.8075 &  0.3849 &  0.1925 \tabularnewline
57 &  0.8884 &  0.2232 &  0.1116 \tabularnewline
58 &  0.8767 &  0.2467 &  0.1233 \tabularnewline
59 &  0.8778 &  0.2444 &  0.1222 \tabularnewline
60 &  0.8486 &  0.3028 &  0.1514 \tabularnewline
61 &  0.8213 &  0.3574 &  0.1787 \tabularnewline
62 &  0.816 &  0.368 &  0.184 \tabularnewline
63 &  0.8267 &  0.3466 &  0.1733 \tabularnewline
64 &  0.8116 &  0.3768 &  0.1884 \tabularnewline
65 &  0.7885 &  0.4229 &  0.2115 \tabularnewline
66 &  0.855 &  0.2899 &  0.145 \tabularnewline
67 &  0.849 &  0.3021 &  0.151 \tabularnewline
68 &  0.8944 &  0.2112 &  0.1056 \tabularnewline
69 &  0.887 &  0.226 &  0.113 \tabularnewline
70 &  0.8598 &  0.2804 &  0.1402 \tabularnewline
71 &  0.8616 &  0.2768 &  0.1384 \tabularnewline
72 &  0.9527 &  0.09452 &  0.04726 \tabularnewline
73 &  0.9436 &  0.1128 &  0.0564 \tabularnewline
74 &  0.9278 &  0.1443 &  0.07216 \tabularnewline
75 &  0.91 &  0.1801 &  0.09003 \tabularnewline
76 &  0.8997 &  0.2006 &  0.1003 \tabularnewline
77 &  0.8815 &  0.2371 &  0.1185 \tabularnewline
78 &  0.8851 &  0.2298 &  0.1149 \tabularnewline
79 &  0.8668 &  0.2664 &  0.1332 \tabularnewline
80 &  0.8441 &  0.3118 &  0.1559 \tabularnewline
81 &  0.9311 &  0.1379 &  0.06894 \tabularnewline
82 &  0.9527 &  0.09452 &  0.04726 \tabularnewline
83 &  0.9417 &  0.1166 &  0.05832 \tabularnewline
84 &  0.9522 &  0.09561 &  0.04781 \tabularnewline
85 &  0.9413 &  0.1175 &  0.05873 \tabularnewline
86 &  0.9331 &  0.1339 &  0.06693 \tabularnewline
87 &  0.9156 &  0.1689 &  0.08444 \tabularnewline
88 &  0.9066 &  0.1869 &  0.09344 \tabularnewline
89 &  0.9276 &  0.1449 &  0.07243 \tabularnewline
90 &  0.9183 &  0.1634 &  0.0817 \tabularnewline
91 &  0.911 &  0.178 &  0.08902 \tabularnewline
92 &  0.8877 &  0.2247 &  0.1123 \tabularnewline
93 &  0.8627 &  0.2747 &  0.1373 \tabularnewline
94 &  0.8376 &  0.3247 &  0.1624 \tabularnewline
95 &  0.8076 &  0.3849 &  0.1924 \tabularnewline
96 &  0.7733 &  0.4533 &  0.2267 \tabularnewline
97 &  0.786 &  0.428 &  0.214 \tabularnewline
98 &  0.7663 &  0.4674 &  0.2337 \tabularnewline
99 &  0.7251 &  0.5497 &  0.2749 \tabularnewline
100 &  0.8169 &  0.3662 &  0.1831 \tabularnewline
101 &  0.8274 &  0.3453 &  0.1726 \tabularnewline
102 &  0.8606 &  0.2788 &  0.1394 \tabularnewline
103 &  0.8262 &  0.3476 &  0.1738 \tabularnewline
104 &  0.8434 &  0.3132 &  0.1566 \tabularnewline
105 &  0.8078 &  0.3844 &  0.1922 \tabularnewline
106 &  0.8112 &  0.3777 &  0.1888 \tabularnewline
107 &  0.8468 &  0.3064 &  0.1532 \tabularnewline
108 &  0.8164 &  0.3672 &  0.1836 \tabularnewline
109 &  0.7658 &  0.4684 &  0.2342 \tabularnewline
110 &  0.7266 &  0.5468 &  0.2734 \tabularnewline
111 &  0.6827 &  0.6347 &  0.3173 \tabularnewline
112 &  0.622 &  0.756 &  0.378 \tabularnewline
113 &  0.5481 &  0.9038 &  0.4519 \tabularnewline
114 &  0.8613 &  0.2773 &  0.1387 \tabularnewline
115 &  0.815 &  0.3701 &  0.185 \tabularnewline
116 &  0.8081 &  0.3838 &  0.1919 \tabularnewline
117 &  0.8326 &  0.3349 &  0.1674 \tabularnewline
118 &  0.7993 &  0.4015 &  0.2007 \tabularnewline
119 &  0.747 &  0.5059 &  0.253 \tabularnewline
120 &  0.6745 &  0.6509 &  0.3255 \tabularnewline
121 &  0.7583 &  0.4834 &  0.2417 \tabularnewline
122 &  0.6633 &  0.6733 &  0.3367 \tabularnewline
123 &  0.554 &  0.892 &  0.446 \tabularnewline
124 &  0.424 &  0.848 &  0.576 \tabularnewline
125 &  0.4132 &  0.8265 &  0.5868 \tabularnewline
126 &  0.2786 &  0.5572 &  0.7214 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310469&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]17[/C][C] 0.3272[/C][C] 0.6545[/C][C] 0.6728[/C][/ROW]
[ROW][C]18[/C][C] 0.2069[/C][C] 0.4138[/C][C] 0.7931[/C][/ROW]
[ROW][C]19[/C][C] 0.1817[/C][C] 0.3635[/C][C] 0.8183[/C][/ROW]
[ROW][C]20[/C][C] 0.4153[/C][C] 0.8306[/C][C] 0.5847[/C][/ROW]
[ROW][C]21[/C][C] 0.3462[/C][C] 0.6923[/C][C] 0.6538[/C][/ROW]
[ROW][C]22[/C][C] 0.2549[/C][C] 0.5098[/C][C] 0.7451[/C][/ROW]
[ROW][C]23[/C][C] 0.175[/C][C] 0.3501[/C][C] 0.825[/C][/ROW]
[ROW][C]24[/C][C] 0.1936[/C][C] 0.3872[/C][C] 0.8064[/C][/ROW]
[ROW][C]25[/C][C] 0.3957[/C][C] 0.7913[/C][C] 0.6043[/C][/ROW]
[ROW][C]26[/C][C] 0.3596[/C][C] 0.7191[/C][C] 0.6404[/C][/ROW]
[ROW][C]27[/C][C] 0.3668[/C][C] 0.7335[/C][C] 0.6332[/C][/ROW]
[ROW][C]28[/C][C] 0.327[/C][C] 0.6539[/C][C] 0.673[/C][/ROW]
[ROW][C]29[/C][C] 0.2555[/C][C] 0.5109[/C][C] 0.7445[/C][/ROW]
[ROW][C]30[/C][C] 0.3871[/C][C] 0.7742[/C][C] 0.6129[/C][/ROW]
[ROW][C]31[/C][C] 0.3178[/C][C] 0.6357[/C][C] 0.6822[/C][/ROW]
[ROW][C]32[/C][C] 0.2528[/C][C] 0.5056[/C][C] 0.7472[/C][/ROW]
[ROW][C]33[/C][C] 0.223[/C][C] 0.4459[/C][C] 0.777[/C][/ROW]
[ROW][C]34[/C][C] 0.1961[/C][C] 0.3923[/C][C] 0.8039[/C][/ROW]
[ROW][C]35[/C][C] 0.1587[/C][C] 0.3175[/C][C] 0.8413[/C][/ROW]
[ROW][C]36[/C][C] 0.1209[/C][C] 0.2418[/C][C] 0.8791[/C][/ROW]
[ROW][C]37[/C][C] 0.1293[/C][C] 0.2587[/C][C] 0.8707[/C][/ROW]
[ROW][C]38[/C][C] 0.3621[/C][C] 0.7241[/C][C] 0.6379[/C][/ROW]
[ROW][C]39[/C][C] 0.5136[/C][C] 0.9728[/C][C] 0.4864[/C][/ROW]
[ROW][C]40[/C][C] 0.558[/C][C] 0.884[/C][C] 0.442[/C][/ROW]
[ROW][C]41[/C][C] 0.7037[/C][C] 0.5926[/C][C] 0.2963[/C][/ROW]
[ROW][C]42[/C][C] 0.794[/C][C] 0.412[/C][C] 0.206[/C][/ROW]
[ROW][C]43[/C][C] 0.7657[/C][C] 0.4687[/C][C] 0.2343[/C][/ROW]
[ROW][C]44[/C][C] 0.7329[/C][C] 0.5343[/C][C] 0.2671[/C][/ROW]
[ROW][C]45[/C][C] 0.7455[/C][C] 0.509[/C][C] 0.2545[/C][/ROW]
[ROW][C]46[/C][C] 0.7603[/C][C] 0.4794[/C][C] 0.2397[/C][/ROW]
[ROW][C]47[/C][C] 0.8236[/C][C] 0.3528[/C][C] 0.1764[/C][/ROW]
[ROW][C]48[/C][C] 0.814[/C][C] 0.3719[/C][C] 0.186[/C][/ROW]
[ROW][C]49[/C][C] 0.8271[/C][C] 0.3458[/C][C] 0.1729[/C][/ROW]
[ROW][C]50[/C][C] 0.8428[/C][C] 0.3145[/C][C] 0.1572[/C][/ROW]
[ROW][C]51[/C][C] 0.8262[/C][C] 0.3476[/C][C] 0.1738[/C][/ROW]
[ROW][C]52[/C][C] 0.8436[/C][C] 0.3129[/C][C] 0.1564[/C][/ROW]
[ROW][C]53[/C][C] 0.8094[/C][C] 0.3813[/C][C] 0.1906[/C][/ROW]
[ROW][C]54[/C][C] 0.7834[/C][C] 0.4332[/C][C] 0.2166[/C][/ROW]
[ROW][C]55[/C][C] 0.7764[/C][C] 0.4472[/C][C] 0.2236[/C][/ROW]
[ROW][C]56[/C][C] 0.8075[/C][C] 0.3849[/C][C] 0.1925[/C][/ROW]
[ROW][C]57[/C][C] 0.8884[/C][C] 0.2232[/C][C] 0.1116[/C][/ROW]
[ROW][C]58[/C][C] 0.8767[/C][C] 0.2467[/C][C] 0.1233[/C][/ROW]
[ROW][C]59[/C][C] 0.8778[/C][C] 0.2444[/C][C] 0.1222[/C][/ROW]
[ROW][C]60[/C][C] 0.8486[/C][C] 0.3028[/C][C] 0.1514[/C][/ROW]
[ROW][C]61[/C][C] 0.8213[/C][C] 0.3574[/C][C] 0.1787[/C][/ROW]
[ROW][C]62[/C][C] 0.816[/C][C] 0.368[/C][C] 0.184[/C][/ROW]
[ROW][C]63[/C][C] 0.8267[/C][C] 0.3466[/C][C] 0.1733[/C][/ROW]
[ROW][C]64[/C][C] 0.8116[/C][C] 0.3768[/C][C] 0.1884[/C][/ROW]
[ROW][C]65[/C][C] 0.7885[/C][C] 0.4229[/C][C] 0.2115[/C][/ROW]
[ROW][C]66[/C][C] 0.855[/C][C] 0.2899[/C][C] 0.145[/C][/ROW]
[ROW][C]67[/C][C] 0.849[/C][C] 0.3021[/C][C] 0.151[/C][/ROW]
[ROW][C]68[/C][C] 0.8944[/C][C] 0.2112[/C][C] 0.1056[/C][/ROW]
[ROW][C]69[/C][C] 0.887[/C][C] 0.226[/C][C] 0.113[/C][/ROW]
[ROW][C]70[/C][C] 0.8598[/C][C] 0.2804[/C][C] 0.1402[/C][/ROW]
[ROW][C]71[/C][C] 0.8616[/C][C] 0.2768[/C][C] 0.1384[/C][/ROW]
[ROW][C]72[/C][C] 0.9527[/C][C] 0.09452[/C][C] 0.04726[/C][/ROW]
[ROW][C]73[/C][C] 0.9436[/C][C] 0.1128[/C][C] 0.0564[/C][/ROW]
[ROW][C]74[/C][C] 0.9278[/C][C] 0.1443[/C][C] 0.07216[/C][/ROW]
[ROW][C]75[/C][C] 0.91[/C][C] 0.1801[/C][C] 0.09003[/C][/ROW]
[ROW][C]76[/C][C] 0.8997[/C][C] 0.2006[/C][C] 0.1003[/C][/ROW]
[ROW][C]77[/C][C] 0.8815[/C][C] 0.2371[/C][C] 0.1185[/C][/ROW]
[ROW][C]78[/C][C] 0.8851[/C][C] 0.2298[/C][C] 0.1149[/C][/ROW]
[ROW][C]79[/C][C] 0.8668[/C][C] 0.2664[/C][C] 0.1332[/C][/ROW]
[ROW][C]80[/C][C] 0.8441[/C][C] 0.3118[/C][C] 0.1559[/C][/ROW]
[ROW][C]81[/C][C] 0.9311[/C][C] 0.1379[/C][C] 0.06894[/C][/ROW]
[ROW][C]82[/C][C] 0.9527[/C][C] 0.09452[/C][C] 0.04726[/C][/ROW]
[ROW][C]83[/C][C] 0.9417[/C][C] 0.1166[/C][C] 0.05832[/C][/ROW]
[ROW][C]84[/C][C] 0.9522[/C][C] 0.09561[/C][C] 0.04781[/C][/ROW]
[ROW][C]85[/C][C] 0.9413[/C][C] 0.1175[/C][C] 0.05873[/C][/ROW]
[ROW][C]86[/C][C] 0.9331[/C][C] 0.1339[/C][C] 0.06693[/C][/ROW]
[ROW][C]87[/C][C] 0.9156[/C][C] 0.1689[/C][C] 0.08444[/C][/ROW]
[ROW][C]88[/C][C] 0.9066[/C][C] 0.1869[/C][C] 0.09344[/C][/ROW]
[ROW][C]89[/C][C] 0.9276[/C][C] 0.1449[/C][C] 0.07243[/C][/ROW]
[ROW][C]90[/C][C] 0.9183[/C][C] 0.1634[/C][C] 0.0817[/C][/ROW]
[ROW][C]91[/C][C] 0.911[/C][C] 0.178[/C][C] 0.08902[/C][/ROW]
[ROW][C]92[/C][C] 0.8877[/C][C] 0.2247[/C][C] 0.1123[/C][/ROW]
[ROW][C]93[/C][C] 0.8627[/C][C] 0.2747[/C][C] 0.1373[/C][/ROW]
[ROW][C]94[/C][C] 0.8376[/C][C] 0.3247[/C][C] 0.1624[/C][/ROW]
[ROW][C]95[/C][C] 0.8076[/C][C] 0.3849[/C][C] 0.1924[/C][/ROW]
[ROW][C]96[/C][C] 0.7733[/C][C] 0.4533[/C][C] 0.2267[/C][/ROW]
[ROW][C]97[/C][C] 0.786[/C][C] 0.428[/C][C] 0.214[/C][/ROW]
[ROW][C]98[/C][C] 0.7663[/C][C] 0.4674[/C][C] 0.2337[/C][/ROW]
[ROW][C]99[/C][C] 0.7251[/C][C] 0.5497[/C][C] 0.2749[/C][/ROW]
[ROW][C]100[/C][C] 0.8169[/C][C] 0.3662[/C][C] 0.1831[/C][/ROW]
[ROW][C]101[/C][C] 0.8274[/C][C] 0.3453[/C][C] 0.1726[/C][/ROW]
[ROW][C]102[/C][C] 0.8606[/C][C] 0.2788[/C][C] 0.1394[/C][/ROW]
[ROW][C]103[/C][C] 0.8262[/C][C] 0.3476[/C][C] 0.1738[/C][/ROW]
[ROW][C]104[/C][C] 0.8434[/C][C] 0.3132[/C][C] 0.1566[/C][/ROW]
[ROW][C]105[/C][C] 0.8078[/C][C] 0.3844[/C][C] 0.1922[/C][/ROW]
[ROW][C]106[/C][C] 0.8112[/C][C] 0.3777[/C][C] 0.1888[/C][/ROW]
[ROW][C]107[/C][C] 0.8468[/C][C] 0.3064[/C][C] 0.1532[/C][/ROW]
[ROW][C]108[/C][C] 0.8164[/C][C] 0.3672[/C][C] 0.1836[/C][/ROW]
[ROW][C]109[/C][C] 0.7658[/C][C] 0.4684[/C][C] 0.2342[/C][/ROW]
[ROW][C]110[/C][C] 0.7266[/C][C] 0.5468[/C][C] 0.2734[/C][/ROW]
[ROW][C]111[/C][C] 0.6827[/C][C] 0.6347[/C][C] 0.3173[/C][/ROW]
[ROW][C]112[/C][C] 0.622[/C][C] 0.756[/C][C] 0.378[/C][/ROW]
[ROW][C]113[/C][C] 0.5481[/C][C] 0.9038[/C][C] 0.4519[/C][/ROW]
[ROW][C]114[/C][C] 0.8613[/C][C] 0.2773[/C][C] 0.1387[/C][/ROW]
[ROW][C]115[/C][C] 0.815[/C][C] 0.3701[/C][C] 0.185[/C][/ROW]
[ROW][C]116[/C][C] 0.8081[/C][C] 0.3838[/C][C] 0.1919[/C][/ROW]
[ROW][C]117[/C][C] 0.8326[/C][C] 0.3349[/C][C] 0.1674[/C][/ROW]
[ROW][C]118[/C][C] 0.7993[/C][C] 0.4015[/C][C] 0.2007[/C][/ROW]
[ROW][C]119[/C][C] 0.747[/C][C] 0.5059[/C][C] 0.253[/C][/ROW]
[ROW][C]120[/C][C] 0.6745[/C][C] 0.6509[/C][C] 0.3255[/C][/ROW]
[ROW][C]121[/C][C] 0.7583[/C][C] 0.4834[/C][C] 0.2417[/C][/ROW]
[ROW][C]122[/C][C] 0.6633[/C][C] 0.6733[/C][C] 0.3367[/C][/ROW]
[ROW][C]123[/C][C] 0.554[/C][C] 0.892[/C][C] 0.446[/C][/ROW]
[ROW][C]124[/C][C] 0.424[/C][C] 0.848[/C][C] 0.576[/C][/ROW]
[ROW][C]125[/C][C] 0.4132[/C][C] 0.8265[/C][C] 0.5868[/C][/ROW]
[ROW][C]126[/C][C] 0.2786[/C][C] 0.5572[/C][C] 0.7214[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310469&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310469&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
17	0.3272	0.6545	0.6728
18	0.2069	0.4138	0.7931
19	0.1817	0.3635	0.8183
20	0.4153	0.8306	0.5847
21	0.3462	0.6923	0.6538
22	0.2549	0.5098	0.7451
23	0.175	0.3501	0.825
24	0.1936	0.3872	0.8064
25	0.3957	0.7913	0.6043
26	0.3596	0.7191	0.6404
27	0.3668	0.7335	0.6332
28	0.327	0.6539	0.673
29	0.2555	0.5109	0.7445
30	0.3871	0.7742	0.6129
31	0.3178	0.6357	0.6822
32	0.2528	0.5056	0.7472
33	0.223	0.4459	0.777
34	0.1961	0.3923	0.8039
35	0.1587	0.3175	0.8413
36	0.1209	0.2418	0.8791
37	0.1293	0.2587	0.8707
38	0.3621	0.7241	0.6379
39	0.5136	0.9728	0.4864
40	0.558	0.884	0.442
41	0.7037	0.5926	0.2963
42	0.794	0.412	0.206
43	0.7657	0.4687	0.2343
44	0.7329	0.5343	0.2671
45	0.7455	0.509	0.2545
46	0.7603	0.4794	0.2397
47	0.8236	0.3528	0.1764
48	0.814	0.3719	0.186
49	0.8271	0.3458	0.1729
50	0.8428	0.3145	0.1572
51	0.8262	0.3476	0.1738
52	0.8436	0.3129	0.1564
53	0.8094	0.3813	0.1906
54	0.7834	0.4332	0.2166
55	0.7764	0.4472	0.2236
56	0.8075	0.3849	0.1925
57	0.8884	0.2232	0.1116
58	0.8767	0.2467	0.1233
59	0.8778	0.2444	0.1222
60	0.8486	0.3028	0.1514
61	0.8213	0.3574	0.1787
62	0.816	0.368	0.184
63	0.8267	0.3466	0.1733
64	0.8116	0.3768	0.1884
65	0.7885	0.4229	0.2115
66	0.855	0.2899	0.145
67	0.849	0.3021	0.151
68	0.8944	0.2112	0.1056
69	0.887	0.226	0.113
70	0.8598	0.2804	0.1402
71	0.8616	0.2768	0.1384
72	0.9527	0.09452	0.04726
73	0.9436	0.1128	0.0564
74	0.9278	0.1443	0.07216
75	0.91	0.1801	0.09003
76	0.8997	0.2006	0.1003
77	0.8815	0.2371	0.1185
78	0.8851	0.2298	0.1149
79	0.8668	0.2664	0.1332
80	0.8441	0.3118	0.1559
81	0.9311	0.1379	0.06894
82	0.9527	0.09452	0.04726
83	0.9417	0.1166	0.05832
84	0.9522	0.09561	0.04781
85	0.9413	0.1175	0.05873
86	0.9331	0.1339	0.06693
87	0.9156	0.1689	0.08444
88	0.9066	0.1869	0.09344
89	0.9276	0.1449	0.07243
90	0.9183	0.1634	0.0817
91	0.911	0.178	0.08902
92	0.8877	0.2247	0.1123
93	0.8627	0.2747	0.1373
94	0.8376	0.3247	0.1624
95	0.8076	0.3849	0.1924
96	0.7733	0.4533	0.2267
97	0.786	0.428	0.214
98	0.7663	0.4674	0.2337
99	0.7251	0.5497	0.2749
100	0.8169	0.3662	0.1831
101	0.8274	0.3453	0.1726
102	0.8606	0.2788	0.1394
103	0.8262	0.3476	0.1738
104	0.8434	0.3132	0.1566
105	0.8078	0.3844	0.1922
106	0.8112	0.3777	0.1888
107	0.8468	0.3064	0.1532
108	0.8164	0.3672	0.1836
109	0.7658	0.4684	0.2342
110	0.7266	0.5468	0.2734
111	0.6827	0.6347	0.3173
112	0.622	0.756	0.378
113	0.5481	0.9038	0.4519
114	0.8613	0.2773	0.1387
115	0.815	0.3701	0.185
116	0.8081	0.3838	0.1919
117	0.8326	0.3349	0.1674
118	0.7993	0.4015	0.2007
119	0.747	0.5059	0.253
120	0.6745	0.6509	0.3255
121	0.7583	0.4834	0.2417
122	0.6633	0.6733	0.3367
123	0.554	0.892	0.446
124	0.424	0.848	0.576
125	0.4132	0.8265	0.5868
126	0.2786	0.5572	0.7214

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	3	0.0272727	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 3 & 0.0272727 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310469&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0272727[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310469&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310469&T=7

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	3	0.0272727	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.89407, df1 = 2, df2 = 127, p-value = 0.4115
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.98682, df1 = 26, df2 = 103, p-value = 0.4922
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.205, df1 = 2, df2 = 127, p-value = 0.3031

\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 = 0.89407, df1 = 2, df2 = 127, p-value = 0.4115
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.98682, df1 = 26, df2 = 103, p-value = 0.4922
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.205, df1 = 2, df2 = 127, p-value = 0.3031
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310469&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 = 0.89407, df1 = 2, df2 = 127, p-value = 0.4115
[/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 = 0.98682, df1 = 26, df2 = 103, p-value = 0.4922
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.205, df1 = 2, df2 = 127, p-value = 0.3031
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310469&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310469&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 = 0.89407, df1 = 2, df2 = 127, p-value = 0.4115
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.98682, df1 = 26, df2 = 103, p-value = 0.4922
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.205, df1 = 2, df2 = 127, p-value = 0.3031

Variance Inflation Factors (Multicollinearity)

> vif
          `(1-Bs)(1-B)intermediate0`           `(1-Bs)(1-B)intermediate1` 
                            2.369483                             5.641114 
          `(1-Bs)(1-B)intermediate2`           `(1-Bs)(1-B)intermediate3` 
                            5.727194                             2.397014 
          `(1-Bs)(1-B)intermediate4`           `(1-Bs)(1-B)intermediate5` 
                            2.059653                             2.146653 
       `(1-Bs)(1-B)intermediate6\\r`  `(1-Bs)(1-B)Chemical_products(t-1)` 
                            1.591371                             3.753481 
 `(1-Bs)(1-B)Chemical_products(t-2)` `(1-Bs)(1-B)Chemical_products(t-1s)` 
                            3.700138                             2.213831 
`(1-Bs)(1-B)Chemical_products(t-2s)` `(1-Bs)(1-B)Chemical_products(t-3s)` 
                            2.074824                             2.166331 
`(1-Bs)(1-B)Chemical_products(t-4s)` 
                            1.978042

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
          `(1-Bs)(1-B)intermediate0`           `(1-Bs)(1-B)intermediate1` 
                            2.369483                             5.641114 
          `(1-Bs)(1-B)intermediate2`           `(1-Bs)(1-B)intermediate3` 
                            5.727194                             2.397014 
          `(1-Bs)(1-B)intermediate4`           `(1-Bs)(1-B)intermediate5` 
                            2.059653                             2.146653 
       `(1-Bs)(1-B)intermediate6\\r`  `(1-Bs)(1-B)Chemical_products(t-1)` 
                            1.591371                             3.753481 
 `(1-Bs)(1-B)Chemical_products(t-2)` `(1-Bs)(1-B)Chemical_products(t-1s)` 
                            3.700138                             2.213831 
`(1-Bs)(1-B)Chemical_products(t-2s)` `(1-Bs)(1-B)Chemical_products(t-3s)` 
                            2.074824                             2.166331 
`(1-Bs)(1-B)Chemical_products(t-4s)` 
                            1.978042 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310469&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
          `(1-Bs)(1-B)intermediate0`           `(1-Bs)(1-B)intermediate1` 
                            2.369483                             5.641114 
          `(1-Bs)(1-B)intermediate2`           `(1-Bs)(1-B)intermediate3` 
                            5.727194                             2.397014 
          `(1-Bs)(1-B)intermediate4`           `(1-Bs)(1-B)intermediate5` 
                            2.059653                             2.146653 
       `(1-Bs)(1-B)intermediate6\\r`  `(1-Bs)(1-B)Chemical_products(t-1)` 
                            1.591371                             3.753481 
 `(1-Bs)(1-B)Chemical_products(t-2)` `(1-Bs)(1-B)Chemical_products(t-1s)` 
                            3.700138                             2.213831 
`(1-Bs)(1-B)Chemical_products(t-2s)` `(1-Bs)(1-B)Chemical_products(t-3s)` 
                            2.074824                             2.166331 
`(1-Bs)(1-B)Chemical_products(t-4s)` 
                            1.978042 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310469&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310469&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)intermediate0`           `(1-Bs)(1-B)intermediate1` 
                            2.369483                             5.641114 
          `(1-Bs)(1-B)intermediate2`           `(1-Bs)(1-B)intermediate3` 
                            5.727194                             2.397014 
          `(1-Bs)(1-B)intermediate4`           `(1-Bs)(1-B)intermediate5` 
                            2.059653                             2.146653 
       `(1-Bs)(1-B)intermediate6\\r`  `(1-Bs)(1-B)Chemical_products(t-1)` 
                            1.591371                             3.753481 
 `(1-Bs)(1-B)Chemical_products(t-2)` `(1-Bs)(1-B)Chemical_products(t-1s)` 
                            3.700138                             2.213831 
`(1-Bs)(1-B)Chemical_products(t-2s)` `(1-Bs)(1-B)Chemical_products(t-3s)` 
                            2.074824                             2.166331 
`(1-Bs)(1-B)Chemical_products(t-4s)` 
                            1.978042

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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 2 ; par5 = 4 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 2 ; par5 = 4 ; par6 = 12 ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code