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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 14 Dec 2017 15:57:36 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/14/t1513263513xthcblv81ofyju3.htm/, Retrieved Tue, 14 May 2024 06:19:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309521, Retrieved Tue, 14 May 2024 06:19:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Probeersel maar h...] [2017-12-14 14:57:36] [e158d092b4ed09fb9d2052d6342a716b] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
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

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R ServerBig 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 time11 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=309521&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]11 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [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=309521&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309521&T=0

As an alternative you can also use a QR Code:  
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 Outputview raw output of R engine
Computing time11 seconds
R ServerBig 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] = + 0.0145169 + 0.822565`(1-Bs)(1-B)intermediate0`[t] + 0.552842`(1-Bs)(1-B)intermediate1`[t] + 0.417766`(1-Bs)(1-B)intermediate2`[t] + 0.0890115`(1-Bs)(1-B)intermediate3`[t] -0.0288226`(1-Bs)(1-B)intermediate4`[t] -0.0381101`(1-Bs)(1-B)intermediate5`[t] + 0.0952896`(1-Bs)(1-B)intermediate6\r`[t] -0.449411`(1-Bs)(1-B)Chemical_products(t-1)`[t] -0.329001`(1-Bs)(1-B)Chemical_products(t-2)`[t] -0.0694046`(1-Bs)(1-B)Chemical_products(t-3)`[t] -0.194083`(1-Bs)(1-B)Chemical_products(t-1s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)Chemical_products[t] =  +  0.0145169 +  0.822565`(1-Bs)(1-B)intermediate0`[t] +  0.552842`(1-Bs)(1-B)intermediate1`[t] +  0.417766`(1-Bs)(1-B)intermediate2`[t] +  0.0890115`(1-Bs)(1-B)intermediate3`[t] -0.0288226`(1-Bs)(1-B)intermediate4`[t] -0.0381101`(1-Bs)(1-B)intermediate5`[t] +  0.0952896`(1-Bs)(1-B)intermediate6\r`[t] -0.449411`(1-Bs)(1-B)Chemical_products(t-1)`[t] -0.329001`(1-Bs)(1-B)Chemical_products(t-2)`[t] -0.0694046`(1-Bs)(1-B)Chemical_products(t-3)`[t] -0.194083`(1-Bs)(1-B)Chemical_products(t-1s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309521&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)Chemical_products[t] =  +  0.0145169 +  0.822565`(1-Bs)(1-B)intermediate0`[t] +  0.552842`(1-Bs)(1-B)intermediate1`[t] +  0.417766`(1-Bs)(1-B)intermediate2`[t] +  0.0890115`(1-Bs)(1-B)intermediate3`[t] -0.0288226`(1-Bs)(1-B)intermediate4`[t] -0.0381101`(1-Bs)(1-B)intermediate5`[t] +  0.0952896`(1-Bs)(1-B)intermediate6\r`[t] -0.449411`(1-Bs)(1-B)Chemical_products(t-1)`[t] -0.329001`(1-Bs)(1-B)Chemical_products(t-2)`[t] -0.0694046`(1-Bs)(1-B)Chemical_products(t-3)`[t] -0.194083`(1-Bs)(1-B)Chemical_products(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309521&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309521&T=1

As an alternative you can also use a QR Code:  
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] = + 0.0145169 + 0.822565`(1-Bs)(1-B)intermediate0`[t] + 0.552842`(1-Bs)(1-B)intermediate1`[t] + 0.417766`(1-Bs)(1-B)intermediate2`[t] + 0.0890115`(1-Bs)(1-B)intermediate3`[t] -0.0288226`(1-Bs)(1-B)intermediate4`[t] -0.0381101`(1-Bs)(1-B)intermediate5`[t] + 0.0952896`(1-Bs)(1-B)intermediate6\r`[t] -0.449411`(1-Bs)(1-B)Chemical_products(t-1)`[t] -0.329001`(1-Bs)(1-B)Chemical_products(t-2)`[t] -0.0694046`(1-Bs)(1-B)Chemical_products(t-3)`[t] -0.194083`(1-Bs)(1-B)Chemical_products(t-1s)`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+0.01452 0.2359+6.1540e-02 0.951 0.4755
`(1-Bs)(1-B)intermediate0`+0.8226 0.04967+1.6560e+01 5.419e-37 2.71e-37
`(1-Bs)(1-B)intermediate1`+0.5528 0.0937+5.9000e+00 1.981e-08 9.907e-09
`(1-Bs)(1-B)intermediate2`+0.4178 0.1003+4.1650e+00 4.993e-05 2.497e-05
`(1-Bs)(1-B)intermediate3`+0.08901 0.08814+1.0100e+00 0.314 0.157
`(1-Bs)(1-B)intermediate4`-0.02882 0.05225-5.5170e-01 0.5819 0.291
`(1-Bs)(1-B)intermediate5`-0.03811 0.05404-7.0520e-01 0.4817 0.2408
`(1-Bs)(1-B)intermediate6\r`+0.09529 0.04626+2.0600e+00 0.04096 0.02048
`(1-Bs)(1-B)Chemical_products(t-1)`-0.4494 0.0786-5.7170e+00 4.914e-08 2.457e-08
`(1-Bs)(1-B)Chemical_products(t-2)`-0.329 0.08721-3.7730e+00 0.0002245 0.0001123
`(1-Bs)(1-B)Chemical_products(t-3)`-0.0694 0.08007-8.6680e-01 0.3873 0.1936
`(1-Bs)(1-B)Chemical_products(t-1s)`-0.1941 0.04719-4.1130e+00 6.139e-05 3.07e-05

\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) & +0.01452 &  0.2359 & +6.1540e-02 &  0.951 &  0.4755 \tabularnewline
`(1-Bs)(1-B)intermediate0` & +0.8226 &  0.04967 & +1.6560e+01 &  5.419e-37 &  2.71e-37 \tabularnewline
`(1-Bs)(1-B)intermediate1` & +0.5528 &  0.0937 & +5.9000e+00 &  1.981e-08 &  9.907e-09 \tabularnewline
`(1-Bs)(1-B)intermediate2` & +0.4178 &  0.1003 & +4.1650e+00 &  4.993e-05 &  2.497e-05 \tabularnewline
`(1-Bs)(1-B)intermediate3` & +0.08901 &  0.08814 & +1.0100e+00 &  0.314 &  0.157 \tabularnewline
`(1-Bs)(1-B)intermediate4` & -0.02882 &  0.05225 & -5.5170e-01 &  0.5819 &  0.291 \tabularnewline
`(1-Bs)(1-B)intermediate5` & -0.03811 &  0.05404 & -7.0520e-01 &  0.4817 &  0.2408 \tabularnewline
`(1-Bs)(1-B)intermediate6\r` & +0.09529 &  0.04626 & +2.0600e+00 &  0.04096 &  0.02048 \tabularnewline
`(1-Bs)(1-B)Chemical_products(t-1)` & -0.4494 &  0.0786 & -5.7170e+00 &  4.914e-08 &  2.457e-08 \tabularnewline
`(1-Bs)(1-B)Chemical_products(t-2)` & -0.329 &  0.08721 & -3.7730e+00 &  0.0002245 &  0.0001123 \tabularnewline
`(1-Bs)(1-B)Chemical_products(t-3)` & -0.0694 &  0.08007 & -8.6680e-01 &  0.3873 &  0.1936 \tabularnewline
`(1-Bs)(1-B)Chemical_products(t-1s)` & -0.1941 &  0.04719 & -4.1130e+00 &  6.139e-05 &  3.07e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309521&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]+0.01452[/C][C] 0.2359[/C][C]+6.1540e-02[/C][C] 0.951[/C][C] 0.4755[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate0`[/C][C]+0.8226[/C][C] 0.04967[/C][C]+1.6560e+01[/C][C] 5.419e-37[/C][C] 2.71e-37[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate1`[/C][C]+0.5528[/C][C] 0.0937[/C][C]+5.9000e+00[/C][C] 1.981e-08[/C][C] 9.907e-09[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate2`[/C][C]+0.4178[/C][C] 0.1003[/C][C]+4.1650e+00[/C][C] 4.993e-05[/C][C] 2.497e-05[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate3`[/C][C]+0.08901[/C][C] 0.08814[/C][C]+1.0100e+00[/C][C] 0.314[/C][C] 0.157[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate4`[/C][C]-0.02882[/C][C] 0.05225[/C][C]-5.5170e-01[/C][C] 0.5819[/C][C] 0.291[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate5`[/C][C]-0.03811[/C][C] 0.05404[/C][C]-7.0520e-01[/C][C] 0.4817[/C][C] 0.2408[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)intermediate6\r`[/C][C]+0.09529[/C][C] 0.04626[/C][C]+2.0600e+00[/C][C] 0.04096[/C][C] 0.02048[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemical_products(t-1)`[/C][C]-0.4494[/C][C] 0.0786[/C][C]-5.7170e+00[/C][C] 4.914e-08[/C][C] 2.457e-08[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemical_products(t-2)`[/C][C]-0.329[/C][C] 0.08721[/C][C]-3.7730e+00[/C][C] 0.0002245[/C][C] 0.0001123[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemical_products(t-3)`[/C][C]-0.0694[/C][C] 0.08007[/C][C]-8.6680e-01[/C][C] 0.3873[/C][C] 0.1936[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Chemical_products(t-1s)`[/C][C]-0.1941[/C][C] 0.04719[/C][C]-4.1130e+00[/C][C] 6.139e-05[/C][C] 3.07e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309521&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309521&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+0.01452 0.2359+6.1540e-02 0.951 0.4755
`(1-Bs)(1-B)intermediate0`+0.8226 0.04967+1.6560e+01 5.419e-37 2.71e-37
`(1-Bs)(1-B)intermediate1`+0.5528 0.0937+5.9000e+00 1.981e-08 9.907e-09
`(1-Bs)(1-B)intermediate2`+0.4178 0.1003+4.1650e+00 4.993e-05 2.497e-05
`(1-Bs)(1-B)intermediate3`+0.08901 0.08814+1.0100e+00 0.314 0.157
`(1-Bs)(1-B)intermediate4`-0.02882 0.05225-5.5170e-01 0.5819 0.291
`(1-Bs)(1-B)intermediate5`-0.03811 0.05404-7.0520e-01 0.4817 0.2408
`(1-Bs)(1-B)intermediate6\r`+0.09529 0.04626+2.0600e+00 0.04096 0.02048
`(1-Bs)(1-B)Chemical_products(t-1)`-0.4494 0.0786-5.7170e+00 4.914e-08 2.457e-08
`(1-Bs)(1-B)Chemical_products(t-2)`-0.329 0.08721-3.7730e+00 0.0002245 0.0001123
`(1-Bs)(1-B)Chemical_products(t-3)`-0.0694 0.08007-8.6680e-01 0.3873 0.1936
`(1-Bs)(1-B)Chemical_products(t-1s)`-0.1941 0.04719-4.1130e+00 6.139e-05 3.07e-05






Multiple Linear Regression - Regression Statistics
Multiple R 0.8538
R-squared 0.729
Adjusted R-squared 0.711
F-TEST (value) 40.59
F-TEST (DF numerator)11
F-TEST (DF denominator)166
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 3.147
Sum Squared Residuals 1643

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8538 \tabularnewline
R-squared &  0.729 \tabularnewline
Adjusted R-squared &  0.711 \tabularnewline
F-TEST (value) &  40.59 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 166 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.147 \tabularnewline
Sum Squared Residuals &  1643 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309521&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8538[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.729[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.711[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 40.59[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]166[/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] 3.147[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1643[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309521&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309521&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R 0.8538
R-squared 0.729
Adjusted R-squared 0.711
F-TEST (value) 40.59
F-TEST (DF numerator)11
F-TEST (DF denominator)166
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 3.147
Sum Squared Residuals 1643






Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\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=309521&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=309521&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309521&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 0.5-1.513 2.013
2 3 0.9502 2.05
3 1.5-0.5937 2.094
4-4.3-1.78-2.52
5 0.9 1.573-0.6725
6-1.1-2.341 1.241
7-4.8-2.221-2.579
8 2 2.78-0.7798
9-2.2-0.9833-1.217
10-0.6-1.331 0.7314
11 2.9 1.927 0.9733
12-0.2 0.04851-0.2485
13-2.5-2.307-0.1931
14 2.6 5.052-2.452
15-4.6-3.579-1.021
16 5.3 2.595 2.705
17 2.4 4.496-2.096
18-3.5-1.093-2.407
19 0.1 0.9834-0.8834
20 10.7 7.847 2.853
21-4.4-7.321 2.921
22 7.9 4.703 3.197
23 0.7-3.311 4.011
24-0.4-4.64 4.24
25 4.5 4.662-0.1622
26-2-1.17-0.8302
27-5.5-2.545-2.955
28-4.2-1.084-3.116
29 4.1-2.518 6.618
30-0.2 0.3322-0.5322
31 1.1 1.129-0.02929
32-8.3-5.573-2.727
33-2.4-2.38-0.02004
34 1.5 0.5415 0.9585
35-2.9-0.2939-2.606
36-1.4-0.5413-0.8587
37 2.8 4.191-1.391
38-4.1-2.272-1.828
39 9.2 7.475 1.725
40-0.4 0.3624-0.7624
41-1.9 3.851-5.751
42-8-6.915-1.085
43 12 12.14-0.1363
44-1.3-2.302 1.002
45 0.4-0.1984 0.5984
46 1.5-1.579 3.079
47-4.5-2.271-2.229
48 1 5.032-4.032
49-2.5 0.1614-2.661
50-0.3-0.3224 0.02243
51 0.3 0.5964-0.2964
52 0.1 0.571-0.471
53-5.6-2.468-3.132
54 8.6 6.11 2.49
55-1.9-7.213 5.313
56-6.1-2.993-3.107
57 5.1 6.586-1.486
58-4.8-1.606-3.194
59-6.3-3.235-3.065
60 12.3 7.466 4.834
61-6.6-5.921-0.679
62-0.9-3.535 2.635
63 8.7 3.904 4.796
64 2.1 2.075 0.02509
65-3.6-8.052 4.452
66 9.3 10.6-1.303
67-10.6-10.08-0.5223
68 6.8 3.502 3.298
69 5 0.2818 4.718
70-6-8.602 2.602
71 9.8 7.938 1.862
72-14.1-11.28-2.824
73-18.6-12.49-6.111
74-3 4.535-7.535
75-17-14.19-2.808
76 3.2-3.711 6.911
77-0.4 4.396-4.796
78-7.8-11.88 4.08
79 3.8 5.179-1.379
80 6.6 2.253 4.347
81 0.1-1.692 1.792
82 2.5 8.224-5.724
83 5.8 1.824 3.976
84-1.9 1.068-2.968
85 21.2 16.82 4.379
86 6.8 2.076 4.724
87 9.1 7.301 1.799
88-0.7 0.1934-0.8934
89 8.2 8.438-0.2384
90-1.4 3.355-4.755
91 5.2 2.966 2.234
92-7.3-0.6468-6.653
93-5.4-3.391-2.009
94-1.2 0.3211-1.521
95-1.5-1.332-0.168
96 0.6 0.2065 0.3935
97 3-1.432 4.432
98 0.8-2.553 3.353
99-7-3.677-3.323
100-0.4 2.087-2.487
101 5.6-0.218 5.818
102-9.2-10.06 0.863
103 0.4 5.651-5.251
104-4.7-9.433 4.733
105-2.7-0.674-2.026
106 8.9 5.41 3.49
107-10.9-2.582-8.318
108-1.2-2.38 1.18
109 2.6 2.704-0.1037
110-4.1-1.683-2.417
111 7.2 4.549 2.651
112 1-0.1557 1.156
113-13.1-8.414-4.686
114 5 4.798 0.2021
115-0.9-3.686 2.786
116-2.2 6.573-8.773
117 12.3 9.01 3.29
118-8-8.207 0.2073
119-2.4-6.143 3.743
120 9.6 8.09 1.51
121-4.4-5.281 0.8808
122-2.9-2.274-0.6261
123 4 0.7173 3.283
124-8-5.556-2.444
125 0.4-1.442 1.842
126 9.2 11.02-1.817
127-5.3-2.691-2.609
128 1.6 0.09786 1.502
129-2.7-0.2115-2.489
130 0.9-0.1852 1.085
131 3.7 4.154-0.4545
132-0.1-1.754 1.654
133-4.4-3.005-1.395
134 2.9 1.811 1.089
135-2.3 2.616-4.916
136-1.3 3.22-4.52
137 7.4 4.78 2.62
138-4-6.368 2.368
139-5-4.995-0.004505
140 3.7 1.546 2.154
141 6.1 3.304 2.796
142-9.3-4.647-4.653
143 8.7 7.477 1.223
144-4.8-3.122-1.678
145 2.4-1.251 3.651
146 5.2 3.28 1.92
147-1.4-2.471 1.071
148 1.1 0.158 0.9419
149-2.8 3.231-6.031
150-4.2-1.203-2.997
151 3.3 2.125 1.175
152 2.1 5.261-3.161
153-11.3-7.886-3.414
154 6.4 6.402-0.002361
155-3.8-5.151 1.351
156-0.6-0.7398 0.1398
157 1.4 3.798-2.398
158-0.7-0.264-0.436
159-3.7-3.483-0.2165
160 9.9 3.753 6.147
161-4.7-6.085 1.385
162 2.2 2.269-0.06854
163-1.6 1.639-3.239
164 1.3-2.575 3.875
165-5-4.212-0.7877
166 9.1 5.39 3.71
167-3.6-2.237-1.363
168-0.5-2.003 1.503
169 5.8 5.597 0.2033
170 2.3 1.261 1.039
171-0.9-0.1743-0.7257
172-8-3.871-4.129
173 8.7 8.842-0.1421
174-2.6-8.829 6.229
175 8.3 6.546 1.754
176-3-4.714 1.714
177 4.1 2.572 1.528
178-8.1-4.683-3.417

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.5 & -1.513 &  2.013 \tabularnewline
2 &  3 &  0.9502 &  2.05 \tabularnewline
3 &  1.5 & -0.5937 &  2.094 \tabularnewline
4 & -4.3 & -1.78 & -2.52 \tabularnewline
5 &  0.9 &  1.573 & -0.6725 \tabularnewline
6 & -1.1 & -2.341 &  1.241 \tabularnewline
7 & -4.8 & -2.221 & -2.579 \tabularnewline
8 &  2 &  2.78 & -0.7798 \tabularnewline
9 & -2.2 & -0.9833 & -1.217 \tabularnewline
10 & -0.6 & -1.331 &  0.7314 \tabularnewline
11 &  2.9 &  1.927 &  0.9733 \tabularnewline
12 & -0.2 &  0.04851 & -0.2485 \tabularnewline
13 & -2.5 & -2.307 & -0.1931 \tabularnewline
14 &  2.6 &  5.052 & -2.452 \tabularnewline
15 & -4.6 & -3.579 & -1.021 \tabularnewline
16 &  5.3 &  2.595 &  2.705 \tabularnewline
17 &  2.4 &  4.496 & -2.096 \tabularnewline
18 & -3.5 & -1.093 & -2.407 \tabularnewline
19 &  0.1 &  0.9834 & -0.8834 \tabularnewline
20 &  10.7 &  7.847 &  2.853 \tabularnewline
21 & -4.4 & -7.321 &  2.921 \tabularnewline
22 &  7.9 &  4.703 &  3.197 \tabularnewline
23 &  0.7 & -3.311 &  4.011 \tabularnewline
24 & -0.4 & -4.64 &  4.24 \tabularnewline
25 &  4.5 &  4.662 & -0.1622 \tabularnewline
26 & -2 & -1.17 & -0.8302 \tabularnewline
27 & -5.5 & -2.545 & -2.955 \tabularnewline
28 & -4.2 & -1.084 & -3.116 \tabularnewline
29 &  4.1 & -2.518 &  6.618 \tabularnewline
30 & -0.2 &  0.3322 & -0.5322 \tabularnewline
31 &  1.1 &  1.129 & -0.02929 \tabularnewline
32 & -8.3 & -5.573 & -2.727 \tabularnewline
33 & -2.4 & -2.38 & -0.02004 \tabularnewline
34 &  1.5 &  0.5415 &  0.9585 \tabularnewline
35 & -2.9 & -0.2939 & -2.606 \tabularnewline
36 & -1.4 & -0.5413 & -0.8587 \tabularnewline
37 &  2.8 &  4.191 & -1.391 \tabularnewline
38 & -4.1 & -2.272 & -1.828 \tabularnewline
39 &  9.2 &  7.475 &  1.725 \tabularnewline
40 & -0.4 &  0.3624 & -0.7624 \tabularnewline
41 & -1.9 &  3.851 & -5.751 \tabularnewline
42 & -8 & -6.915 & -1.085 \tabularnewline
43 &  12 &  12.14 & -0.1363 \tabularnewline
44 & -1.3 & -2.302 &  1.002 \tabularnewline
45 &  0.4 & -0.1984 &  0.5984 \tabularnewline
46 &  1.5 & -1.579 &  3.079 \tabularnewline
47 & -4.5 & -2.271 & -2.229 \tabularnewline
48 &  1 &  5.032 & -4.032 \tabularnewline
49 & -2.5 &  0.1614 & -2.661 \tabularnewline
50 & -0.3 & -0.3224 &  0.02243 \tabularnewline
51 &  0.3 &  0.5964 & -0.2964 \tabularnewline
52 &  0.1 &  0.571 & -0.471 \tabularnewline
53 & -5.6 & -2.468 & -3.132 \tabularnewline
54 &  8.6 &  6.11 &  2.49 \tabularnewline
55 & -1.9 & -7.213 &  5.313 \tabularnewline
56 & -6.1 & -2.993 & -3.107 \tabularnewline
57 &  5.1 &  6.586 & -1.486 \tabularnewline
58 & -4.8 & -1.606 & -3.194 \tabularnewline
59 & -6.3 & -3.235 & -3.065 \tabularnewline
60 &  12.3 &  7.466 &  4.834 \tabularnewline
61 & -6.6 & -5.921 & -0.679 \tabularnewline
62 & -0.9 & -3.535 &  2.635 \tabularnewline
63 &  8.7 &  3.904 &  4.796 \tabularnewline
64 &  2.1 &  2.075 &  0.02509 \tabularnewline
65 & -3.6 & -8.052 &  4.452 \tabularnewline
66 &  9.3 &  10.6 & -1.303 \tabularnewline
67 & -10.6 & -10.08 & -0.5223 \tabularnewline
68 &  6.8 &  3.502 &  3.298 \tabularnewline
69 &  5 &  0.2818 &  4.718 \tabularnewline
70 & -6 & -8.602 &  2.602 \tabularnewline
71 &  9.8 &  7.938 &  1.862 \tabularnewline
72 & -14.1 & -11.28 & -2.824 \tabularnewline
73 & -18.6 & -12.49 & -6.111 \tabularnewline
74 & -3 &  4.535 & -7.535 \tabularnewline
75 & -17 & -14.19 & -2.808 \tabularnewline
76 &  3.2 & -3.711 &  6.911 \tabularnewline
77 & -0.4 &  4.396 & -4.796 \tabularnewline
78 & -7.8 & -11.88 &  4.08 \tabularnewline
79 &  3.8 &  5.179 & -1.379 \tabularnewline
80 &  6.6 &  2.253 &  4.347 \tabularnewline
81 &  0.1 & -1.692 &  1.792 \tabularnewline
82 &  2.5 &  8.224 & -5.724 \tabularnewline
83 &  5.8 &  1.824 &  3.976 \tabularnewline
84 & -1.9 &  1.068 & -2.968 \tabularnewline
85 &  21.2 &  16.82 &  4.379 \tabularnewline
86 &  6.8 &  2.076 &  4.724 \tabularnewline
87 &  9.1 &  7.301 &  1.799 \tabularnewline
88 & -0.7 &  0.1934 & -0.8934 \tabularnewline
89 &  8.2 &  8.438 & -0.2384 \tabularnewline
90 & -1.4 &  3.355 & -4.755 \tabularnewline
91 &  5.2 &  2.966 &  2.234 \tabularnewline
92 & -7.3 & -0.6468 & -6.653 \tabularnewline
93 & -5.4 & -3.391 & -2.009 \tabularnewline
94 & -1.2 &  0.3211 & -1.521 \tabularnewline
95 & -1.5 & -1.332 & -0.168 \tabularnewline
96 &  0.6 &  0.2065 &  0.3935 \tabularnewline
97 &  3 & -1.432 &  4.432 \tabularnewline
98 &  0.8 & -2.553 &  3.353 \tabularnewline
99 & -7 & -3.677 & -3.323 \tabularnewline
100 & -0.4 &  2.087 & -2.487 \tabularnewline
101 &  5.6 & -0.218 &  5.818 \tabularnewline
102 & -9.2 & -10.06 &  0.863 \tabularnewline
103 &  0.4 &  5.651 & -5.251 \tabularnewline
104 & -4.7 & -9.433 &  4.733 \tabularnewline
105 & -2.7 & -0.674 & -2.026 \tabularnewline
106 &  8.9 &  5.41 &  3.49 \tabularnewline
107 & -10.9 & -2.582 & -8.318 \tabularnewline
108 & -1.2 & -2.38 &  1.18 \tabularnewline
109 &  2.6 &  2.704 & -0.1037 \tabularnewline
110 & -4.1 & -1.683 & -2.417 \tabularnewline
111 &  7.2 &  4.549 &  2.651 \tabularnewline
112 &  1 & -0.1557 &  1.156 \tabularnewline
113 & -13.1 & -8.414 & -4.686 \tabularnewline
114 &  5 &  4.798 &  0.2021 \tabularnewline
115 & -0.9 & -3.686 &  2.786 \tabularnewline
116 & -2.2 &  6.573 & -8.773 \tabularnewline
117 &  12.3 &  9.01 &  3.29 \tabularnewline
118 & -8 & -8.207 &  0.2073 \tabularnewline
119 & -2.4 & -6.143 &  3.743 \tabularnewline
120 &  9.6 &  8.09 &  1.51 \tabularnewline
121 & -4.4 & -5.281 &  0.8808 \tabularnewline
122 & -2.9 & -2.274 & -0.6261 \tabularnewline
123 &  4 &  0.7173 &  3.283 \tabularnewline
124 & -8 & -5.556 & -2.444 \tabularnewline
125 &  0.4 & -1.442 &  1.842 \tabularnewline
126 &  9.2 &  11.02 & -1.817 \tabularnewline
127 & -5.3 & -2.691 & -2.609 \tabularnewline
128 &  1.6 &  0.09786 &  1.502 \tabularnewline
129 & -2.7 & -0.2115 & -2.489 \tabularnewline
130 &  0.9 & -0.1852 &  1.085 \tabularnewline
131 &  3.7 &  4.154 & -0.4545 \tabularnewline
132 & -0.1 & -1.754 &  1.654 \tabularnewline
133 & -4.4 & -3.005 & -1.395 \tabularnewline
134 &  2.9 &  1.811 &  1.089 \tabularnewline
135 & -2.3 &  2.616 & -4.916 \tabularnewline
136 & -1.3 &  3.22 & -4.52 \tabularnewline
137 &  7.4 &  4.78 &  2.62 \tabularnewline
138 & -4 & -6.368 &  2.368 \tabularnewline
139 & -5 & -4.995 & -0.004505 \tabularnewline
140 &  3.7 &  1.546 &  2.154 \tabularnewline
141 &  6.1 &  3.304 &  2.796 \tabularnewline
142 & -9.3 & -4.647 & -4.653 \tabularnewline
143 &  8.7 &  7.477 &  1.223 \tabularnewline
144 & -4.8 & -3.122 & -1.678 \tabularnewline
145 &  2.4 & -1.251 &  3.651 \tabularnewline
146 &  5.2 &  3.28 &  1.92 \tabularnewline
147 & -1.4 & -2.471 &  1.071 \tabularnewline
148 &  1.1 &  0.158 &  0.9419 \tabularnewline
149 & -2.8 &  3.231 & -6.031 \tabularnewline
150 & -4.2 & -1.203 & -2.997 \tabularnewline
151 &  3.3 &  2.125 &  1.175 \tabularnewline
152 &  2.1 &  5.261 & -3.161 \tabularnewline
153 & -11.3 & -7.886 & -3.414 \tabularnewline
154 &  6.4 &  6.402 & -0.002361 \tabularnewline
155 & -3.8 & -5.151 &  1.351 \tabularnewline
156 & -0.6 & -0.7398 &  0.1398 \tabularnewline
157 &  1.4 &  3.798 & -2.398 \tabularnewline
158 & -0.7 & -0.264 & -0.436 \tabularnewline
159 & -3.7 & -3.483 & -0.2165 \tabularnewline
160 &  9.9 &  3.753 &  6.147 \tabularnewline
161 & -4.7 & -6.085 &  1.385 \tabularnewline
162 &  2.2 &  2.269 & -0.06854 \tabularnewline
163 & -1.6 &  1.639 & -3.239 \tabularnewline
164 &  1.3 & -2.575 &  3.875 \tabularnewline
165 & -5 & -4.212 & -0.7877 \tabularnewline
166 &  9.1 &  5.39 &  3.71 \tabularnewline
167 & -3.6 & -2.237 & -1.363 \tabularnewline
168 & -0.5 & -2.003 &  1.503 \tabularnewline
169 &  5.8 &  5.597 &  0.2033 \tabularnewline
170 &  2.3 &  1.261 &  1.039 \tabularnewline
171 & -0.9 & -0.1743 & -0.7257 \tabularnewline
172 & -8 & -3.871 & -4.129 \tabularnewline
173 &  8.7 &  8.842 & -0.1421 \tabularnewline
174 & -2.6 & -8.829 &  6.229 \tabularnewline
175 &  8.3 &  6.546 &  1.754 \tabularnewline
176 & -3 & -4.714 &  1.714 \tabularnewline
177 &  4.1 &  2.572 &  1.528 \tabularnewline
178 & -8.1 & -4.683 & -3.417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309521&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] 0.5[/C][C]-1.513[/C][C] 2.013[/C][/ROW]
[ROW][C]2[/C][C] 3[/C][C] 0.9502[/C][C] 2.05[/C][/ROW]
[ROW][C]3[/C][C] 1.5[/C][C]-0.5937[/C][C] 2.094[/C][/ROW]
[ROW][C]4[/C][C]-4.3[/C][C]-1.78[/C][C]-2.52[/C][/ROW]
[ROW][C]5[/C][C] 0.9[/C][C] 1.573[/C][C]-0.6725[/C][/ROW]
[ROW][C]6[/C][C]-1.1[/C][C]-2.341[/C][C] 1.241[/C][/ROW]
[ROW][C]7[/C][C]-4.8[/C][C]-2.221[/C][C]-2.579[/C][/ROW]
[ROW][C]8[/C][C] 2[/C][C] 2.78[/C][C]-0.7798[/C][/ROW]
[ROW][C]9[/C][C]-2.2[/C][C]-0.9833[/C][C]-1.217[/C][/ROW]
[ROW][C]10[/C][C]-0.6[/C][C]-1.331[/C][C] 0.7314[/C][/ROW]
[ROW][C]11[/C][C] 2.9[/C][C] 1.927[/C][C] 0.9733[/C][/ROW]
[ROW][C]12[/C][C]-0.2[/C][C] 0.04851[/C][C]-0.2485[/C][/ROW]
[ROW][C]13[/C][C]-2.5[/C][C]-2.307[/C][C]-0.1931[/C][/ROW]
[ROW][C]14[/C][C] 2.6[/C][C] 5.052[/C][C]-2.452[/C][/ROW]
[ROW][C]15[/C][C]-4.6[/C][C]-3.579[/C][C]-1.021[/C][/ROW]
[ROW][C]16[/C][C] 5.3[/C][C] 2.595[/C][C] 2.705[/C][/ROW]
[ROW][C]17[/C][C] 2.4[/C][C] 4.496[/C][C]-2.096[/C][/ROW]
[ROW][C]18[/C][C]-3.5[/C][C]-1.093[/C][C]-2.407[/C][/ROW]
[ROW][C]19[/C][C] 0.1[/C][C] 0.9834[/C][C]-0.8834[/C][/ROW]
[ROW][C]20[/C][C] 10.7[/C][C] 7.847[/C][C] 2.853[/C][/ROW]
[ROW][C]21[/C][C]-4.4[/C][C]-7.321[/C][C] 2.921[/C][/ROW]
[ROW][C]22[/C][C] 7.9[/C][C] 4.703[/C][C] 3.197[/C][/ROW]
[ROW][C]23[/C][C] 0.7[/C][C]-3.311[/C][C] 4.011[/C][/ROW]
[ROW][C]24[/C][C]-0.4[/C][C]-4.64[/C][C] 4.24[/C][/ROW]
[ROW][C]25[/C][C] 4.5[/C][C] 4.662[/C][C]-0.1622[/C][/ROW]
[ROW][C]26[/C][C]-2[/C][C]-1.17[/C][C]-0.8302[/C][/ROW]
[ROW][C]27[/C][C]-5.5[/C][C]-2.545[/C][C]-2.955[/C][/ROW]
[ROW][C]28[/C][C]-4.2[/C][C]-1.084[/C][C]-3.116[/C][/ROW]
[ROW][C]29[/C][C] 4.1[/C][C]-2.518[/C][C] 6.618[/C][/ROW]
[ROW][C]30[/C][C]-0.2[/C][C] 0.3322[/C][C]-0.5322[/C][/ROW]
[ROW][C]31[/C][C] 1.1[/C][C] 1.129[/C][C]-0.02929[/C][/ROW]
[ROW][C]32[/C][C]-8.3[/C][C]-5.573[/C][C]-2.727[/C][/ROW]
[ROW][C]33[/C][C]-2.4[/C][C]-2.38[/C][C]-0.02004[/C][/ROW]
[ROW][C]34[/C][C] 1.5[/C][C] 0.5415[/C][C] 0.9585[/C][/ROW]
[ROW][C]35[/C][C]-2.9[/C][C]-0.2939[/C][C]-2.606[/C][/ROW]
[ROW][C]36[/C][C]-1.4[/C][C]-0.5413[/C][C]-0.8587[/C][/ROW]
[ROW][C]37[/C][C] 2.8[/C][C] 4.191[/C][C]-1.391[/C][/ROW]
[ROW][C]38[/C][C]-4.1[/C][C]-2.272[/C][C]-1.828[/C][/ROW]
[ROW][C]39[/C][C] 9.2[/C][C] 7.475[/C][C] 1.725[/C][/ROW]
[ROW][C]40[/C][C]-0.4[/C][C] 0.3624[/C][C]-0.7624[/C][/ROW]
[ROW][C]41[/C][C]-1.9[/C][C] 3.851[/C][C]-5.751[/C][/ROW]
[ROW][C]42[/C][C]-8[/C][C]-6.915[/C][C]-1.085[/C][/ROW]
[ROW][C]43[/C][C] 12[/C][C] 12.14[/C][C]-0.1363[/C][/ROW]
[ROW][C]44[/C][C]-1.3[/C][C]-2.302[/C][C] 1.002[/C][/ROW]
[ROW][C]45[/C][C] 0.4[/C][C]-0.1984[/C][C] 0.5984[/C][/ROW]
[ROW][C]46[/C][C] 1.5[/C][C]-1.579[/C][C] 3.079[/C][/ROW]
[ROW][C]47[/C][C]-4.5[/C][C]-2.271[/C][C]-2.229[/C][/ROW]
[ROW][C]48[/C][C] 1[/C][C] 5.032[/C][C]-4.032[/C][/ROW]
[ROW][C]49[/C][C]-2.5[/C][C] 0.1614[/C][C]-2.661[/C][/ROW]
[ROW][C]50[/C][C]-0.3[/C][C]-0.3224[/C][C] 0.02243[/C][/ROW]
[ROW][C]51[/C][C] 0.3[/C][C] 0.5964[/C][C]-0.2964[/C][/ROW]
[ROW][C]52[/C][C] 0.1[/C][C] 0.571[/C][C]-0.471[/C][/ROW]
[ROW][C]53[/C][C]-5.6[/C][C]-2.468[/C][C]-3.132[/C][/ROW]
[ROW][C]54[/C][C] 8.6[/C][C] 6.11[/C][C] 2.49[/C][/ROW]
[ROW][C]55[/C][C]-1.9[/C][C]-7.213[/C][C] 5.313[/C][/ROW]
[ROW][C]56[/C][C]-6.1[/C][C]-2.993[/C][C]-3.107[/C][/ROW]
[ROW][C]57[/C][C] 5.1[/C][C] 6.586[/C][C]-1.486[/C][/ROW]
[ROW][C]58[/C][C]-4.8[/C][C]-1.606[/C][C]-3.194[/C][/ROW]
[ROW][C]59[/C][C]-6.3[/C][C]-3.235[/C][C]-3.065[/C][/ROW]
[ROW][C]60[/C][C] 12.3[/C][C] 7.466[/C][C] 4.834[/C][/ROW]
[ROW][C]61[/C][C]-6.6[/C][C]-5.921[/C][C]-0.679[/C][/ROW]
[ROW][C]62[/C][C]-0.9[/C][C]-3.535[/C][C] 2.635[/C][/ROW]
[ROW][C]63[/C][C] 8.7[/C][C] 3.904[/C][C] 4.796[/C][/ROW]
[ROW][C]64[/C][C] 2.1[/C][C] 2.075[/C][C] 0.02509[/C][/ROW]
[ROW][C]65[/C][C]-3.6[/C][C]-8.052[/C][C] 4.452[/C][/ROW]
[ROW][C]66[/C][C] 9.3[/C][C] 10.6[/C][C]-1.303[/C][/ROW]
[ROW][C]67[/C][C]-10.6[/C][C]-10.08[/C][C]-0.5223[/C][/ROW]
[ROW][C]68[/C][C] 6.8[/C][C] 3.502[/C][C] 3.298[/C][/ROW]
[ROW][C]69[/C][C] 5[/C][C] 0.2818[/C][C] 4.718[/C][/ROW]
[ROW][C]70[/C][C]-6[/C][C]-8.602[/C][C] 2.602[/C][/ROW]
[ROW][C]71[/C][C] 9.8[/C][C] 7.938[/C][C] 1.862[/C][/ROW]
[ROW][C]72[/C][C]-14.1[/C][C]-11.28[/C][C]-2.824[/C][/ROW]
[ROW][C]73[/C][C]-18.6[/C][C]-12.49[/C][C]-6.111[/C][/ROW]
[ROW][C]74[/C][C]-3[/C][C] 4.535[/C][C]-7.535[/C][/ROW]
[ROW][C]75[/C][C]-17[/C][C]-14.19[/C][C]-2.808[/C][/ROW]
[ROW][C]76[/C][C] 3.2[/C][C]-3.711[/C][C] 6.911[/C][/ROW]
[ROW][C]77[/C][C]-0.4[/C][C] 4.396[/C][C]-4.796[/C][/ROW]
[ROW][C]78[/C][C]-7.8[/C][C]-11.88[/C][C] 4.08[/C][/ROW]
[ROW][C]79[/C][C] 3.8[/C][C] 5.179[/C][C]-1.379[/C][/ROW]
[ROW][C]80[/C][C] 6.6[/C][C] 2.253[/C][C] 4.347[/C][/ROW]
[ROW][C]81[/C][C] 0.1[/C][C]-1.692[/C][C] 1.792[/C][/ROW]
[ROW][C]82[/C][C] 2.5[/C][C] 8.224[/C][C]-5.724[/C][/ROW]
[ROW][C]83[/C][C] 5.8[/C][C] 1.824[/C][C] 3.976[/C][/ROW]
[ROW][C]84[/C][C]-1.9[/C][C] 1.068[/C][C]-2.968[/C][/ROW]
[ROW][C]85[/C][C] 21.2[/C][C] 16.82[/C][C] 4.379[/C][/ROW]
[ROW][C]86[/C][C] 6.8[/C][C] 2.076[/C][C] 4.724[/C][/ROW]
[ROW][C]87[/C][C] 9.1[/C][C] 7.301[/C][C] 1.799[/C][/ROW]
[ROW][C]88[/C][C]-0.7[/C][C] 0.1934[/C][C]-0.8934[/C][/ROW]
[ROW][C]89[/C][C] 8.2[/C][C] 8.438[/C][C]-0.2384[/C][/ROW]
[ROW][C]90[/C][C]-1.4[/C][C] 3.355[/C][C]-4.755[/C][/ROW]
[ROW][C]91[/C][C] 5.2[/C][C] 2.966[/C][C] 2.234[/C][/ROW]
[ROW][C]92[/C][C]-7.3[/C][C]-0.6468[/C][C]-6.653[/C][/ROW]
[ROW][C]93[/C][C]-5.4[/C][C]-3.391[/C][C]-2.009[/C][/ROW]
[ROW][C]94[/C][C]-1.2[/C][C] 0.3211[/C][C]-1.521[/C][/ROW]
[ROW][C]95[/C][C]-1.5[/C][C]-1.332[/C][C]-0.168[/C][/ROW]
[ROW][C]96[/C][C] 0.6[/C][C] 0.2065[/C][C] 0.3935[/C][/ROW]
[ROW][C]97[/C][C] 3[/C][C]-1.432[/C][C] 4.432[/C][/ROW]
[ROW][C]98[/C][C] 0.8[/C][C]-2.553[/C][C] 3.353[/C][/ROW]
[ROW][C]99[/C][C]-7[/C][C]-3.677[/C][C]-3.323[/C][/ROW]
[ROW][C]100[/C][C]-0.4[/C][C] 2.087[/C][C]-2.487[/C][/ROW]
[ROW][C]101[/C][C] 5.6[/C][C]-0.218[/C][C] 5.818[/C][/ROW]
[ROW][C]102[/C][C]-9.2[/C][C]-10.06[/C][C] 0.863[/C][/ROW]
[ROW][C]103[/C][C] 0.4[/C][C] 5.651[/C][C]-5.251[/C][/ROW]
[ROW][C]104[/C][C]-4.7[/C][C]-9.433[/C][C] 4.733[/C][/ROW]
[ROW][C]105[/C][C]-2.7[/C][C]-0.674[/C][C]-2.026[/C][/ROW]
[ROW][C]106[/C][C] 8.9[/C][C] 5.41[/C][C] 3.49[/C][/ROW]
[ROW][C]107[/C][C]-10.9[/C][C]-2.582[/C][C]-8.318[/C][/ROW]
[ROW][C]108[/C][C]-1.2[/C][C]-2.38[/C][C] 1.18[/C][/ROW]
[ROW][C]109[/C][C] 2.6[/C][C] 2.704[/C][C]-0.1037[/C][/ROW]
[ROW][C]110[/C][C]-4.1[/C][C]-1.683[/C][C]-2.417[/C][/ROW]
[ROW][C]111[/C][C] 7.2[/C][C] 4.549[/C][C] 2.651[/C][/ROW]
[ROW][C]112[/C][C] 1[/C][C]-0.1557[/C][C] 1.156[/C][/ROW]
[ROW][C]113[/C][C]-13.1[/C][C]-8.414[/C][C]-4.686[/C][/ROW]
[ROW][C]114[/C][C] 5[/C][C] 4.798[/C][C] 0.2021[/C][/ROW]
[ROW][C]115[/C][C]-0.9[/C][C]-3.686[/C][C] 2.786[/C][/ROW]
[ROW][C]116[/C][C]-2.2[/C][C] 6.573[/C][C]-8.773[/C][/ROW]
[ROW][C]117[/C][C] 12.3[/C][C] 9.01[/C][C] 3.29[/C][/ROW]
[ROW][C]118[/C][C]-8[/C][C]-8.207[/C][C] 0.2073[/C][/ROW]
[ROW][C]119[/C][C]-2.4[/C][C]-6.143[/C][C] 3.743[/C][/ROW]
[ROW][C]120[/C][C] 9.6[/C][C] 8.09[/C][C] 1.51[/C][/ROW]
[ROW][C]121[/C][C]-4.4[/C][C]-5.281[/C][C] 0.8808[/C][/ROW]
[ROW][C]122[/C][C]-2.9[/C][C]-2.274[/C][C]-0.6261[/C][/ROW]
[ROW][C]123[/C][C] 4[/C][C] 0.7173[/C][C] 3.283[/C][/ROW]
[ROW][C]124[/C][C]-8[/C][C]-5.556[/C][C]-2.444[/C][/ROW]
[ROW][C]125[/C][C] 0.4[/C][C]-1.442[/C][C] 1.842[/C][/ROW]
[ROW][C]126[/C][C] 9.2[/C][C] 11.02[/C][C]-1.817[/C][/ROW]
[ROW][C]127[/C][C]-5.3[/C][C]-2.691[/C][C]-2.609[/C][/ROW]
[ROW][C]128[/C][C] 1.6[/C][C] 0.09786[/C][C] 1.502[/C][/ROW]
[ROW][C]129[/C][C]-2.7[/C][C]-0.2115[/C][C]-2.489[/C][/ROW]
[ROW][C]130[/C][C] 0.9[/C][C]-0.1852[/C][C] 1.085[/C][/ROW]
[ROW][C]131[/C][C] 3.7[/C][C] 4.154[/C][C]-0.4545[/C][/ROW]
[ROW][C]132[/C][C]-0.1[/C][C]-1.754[/C][C] 1.654[/C][/ROW]
[ROW][C]133[/C][C]-4.4[/C][C]-3.005[/C][C]-1.395[/C][/ROW]
[ROW][C]134[/C][C] 2.9[/C][C] 1.811[/C][C] 1.089[/C][/ROW]
[ROW][C]135[/C][C]-2.3[/C][C] 2.616[/C][C]-4.916[/C][/ROW]
[ROW][C]136[/C][C]-1.3[/C][C] 3.22[/C][C]-4.52[/C][/ROW]
[ROW][C]137[/C][C] 7.4[/C][C] 4.78[/C][C] 2.62[/C][/ROW]
[ROW][C]138[/C][C]-4[/C][C]-6.368[/C][C] 2.368[/C][/ROW]
[ROW][C]139[/C][C]-5[/C][C]-4.995[/C][C]-0.004505[/C][/ROW]
[ROW][C]140[/C][C] 3.7[/C][C] 1.546[/C][C] 2.154[/C][/ROW]
[ROW][C]141[/C][C] 6.1[/C][C] 3.304[/C][C] 2.796[/C][/ROW]
[ROW][C]142[/C][C]-9.3[/C][C]-4.647[/C][C]-4.653[/C][/ROW]
[ROW][C]143[/C][C] 8.7[/C][C] 7.477[/C][C] 1.223[/C][/ROW]
[ROW][C]144[/C][C]-4.8[/C][C]-3.122[/C][C]-1.678[/C][/ROW]
[ROW][C]145[/C][C] 2.4[/C][C]-1.251[/C][C] 3.651[/C][/ROW]
[ROW][C]146[/C][C] 5.2[/C][C] 3.28[/C][C] 1.92[/C][/ROW]
[ROW][C]147[/C][C]-1.4[/C][C]-2.471[/C][C] 1.071[/C][/ROW]
[ROW][C]148[/C][C] 1.1[/C][C] 0.158[/C][C] 0.9419[/C][/ROW]
[ROW][C]149[/C][C]-2.8[/C][C] 3.231[/C][C]-6.031[/C][/ROW]
[ROW][C]150[/C][C]-4.2[/C][C]-1.203[/C][C]-2.997[/C][/ROW]
[ROW][C]151[/C][C] 3.3[/C][C] 2.125[/C][C] 1.175[/C][/ROW]
[ROW][C]152[/C][C] 2.1[/C][C] 5.261[/C][C]-3.161[/C][/ROW]
[ROW][C]153[/C][C]-11.3[/C][C]-7.886[/C][C]-3.414[/C][/ROW]
[ROW][C]154[/C][C] 6.4[/C][C] 6.402[/C][C]-0.002361[/C][/ROW]
[ROW][C]155[/C][C]-3.8[/C][C]-5.151[/C][C] 1.351[/C][/ROW]
[ROW][C]156[/C][C]-0.6[/C][C]-0.7398[/C][C] 0.1398[/C][/ROW]
[ROW][C]157[/C][C] 1.4[/C][C] 3.798[/C][C]-2.398[/C][/ROW]
[ROW][C]158[/C][C]-0.7[/C][C]-0.264[/C][C]-0.436[/C][/ROW]
[ROW][C]159[/C][C]-3.7[/C][C]-3.483[/C][C]-0.2165[/C][/ROW]
[ROW][C]160[/C][C] 9.9[/C][C] 3.753[/C][C] 6.147[/C][/ROW]
[ROW][C]161[/C][C]-4.7[/C][C]-6.085[/C][C] 1.385[/C][/ROW]
[ROW][C]162[/C][C] 2.2[/C][C] 2.269[/C][C]-0.06854[/C][/ROW]
[ROW][C]163[/C][C]-1.6[/C][C] 1.639[/C][C]-3.239[/C][/ROW]
[ROW][C]164[/C][C] 1.3[/C][C]-2.575[/C][C] 3.875[/C][/ROW]
[ROW][C]165[/C][C]-5[/C][C]-4.212[/C][C]-0.7877[/C][/ROW]
[ROW][C]166[/C][C] 9.1[/C][C] 5.39[/C][C] 3.71[/C][/ROW]
[ROW][C]167[/C][C]-3.6[/C][C]-2.237[/C][C]-1.363[/C][/ROW]
[ROW][C]168[/C][C]-0.5[/C][C]-2.003[/C][C] 1.503[/C][/ROW]
[ROW][C]169[/C][C] 5.8[/C][C] 5.597[/C][C] 0.2033[/C][/ROW]
[ROW][C]170[/C][C] 2.3[/C][C] 1.261[/C][C] 1.039[/C][/ROW]
[ROW][C]171[/C][C]-0.9[/C][C]-0.1743[/C][C]-0.7257[/C][/ROW]
[ROW][C]172[/C][C]-8[/C][C]-3.871[/C][C]-4.129[/C][/ROW]
[ROW][C]173[/C][C] 8.7[/C][C] 8.842[/C][C]-0.1421[/C][/ROW]
[ROW][C]174[/C][C]-2.6[/C][C]-8.829[/C][C] 6.229[/C][/ROW]
[ROW][C]175[/C][C] 8.3[/C][C] 6.546[/C][C] 1.754[/C][/ROW]
[ROW][C]176[/C][C]-3[/C][C]-4.714[/C][C] 1.714[/C][/ROW]
[ROW][C]177[/C][C] 4.1[/C][C] 2.572[/C][C] 1.528[/C][/ROW]
[ROW][C]178[/C][C]-8.1[/C][C]-4.683[/C][C]-3.417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309521&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309521&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 0.5-1.513 2.013
2 3 0.9502 2.05
3 1.5-0.5937 2.094
4-4.3-1.78-2.52
5 0.9 1.573-0.6725
6-1.1-2.341 1.241
7-4.8-2.221-2.579
8 2 2.78-0.7798
9-2.2-0.9833-1.217
10-0.6-1.331 0.7314
11 2.9 1.927 0.9733
12-0.2 0.04851-0.2485
13-2.5-2.307-0.1931
14 2.6 5.052-2.452
15-4.6-3.579-1.021
16 5.3 2.595 2.705
17 2.4 4.496-2.096
18-3.5-1.093-2.407
19 0.1 0.9834-0.8834
20 10.7 7.847 2.853
21-4.4-7.321 2.921
22 7.9 4.703 3.197
23 0.7-3.311 4.011
24-0.4-4.64 4.24
25 4.5 4.662-0.1622
26-2-1.17-0.8302
27-5.5-2.545-2.955
28-4.2-1.084-3.116
29 4.1-2.518 6.618
30-0.2 0.3322-0.5322
31 1.1 1.129-0.02929
32-8.3-5.573-2.727
33-2.4-2.38-0.02004
34 1.5 0.5415 0.9585
35-2.9-0.2939-2.606
36-1.4-0.5413-0.8587
37 2.8 4.191-1.391
38-4.1-2.272-1.828
39 9.2 7.475 1.725
40-0.4 0.3624-0.7624
41-1.9 3.851-5.751
42-8-6.915-1.085
43 12 12.14-0.1363
44-1.3-2.302 1.002
45 0.4-0.1984 0.5984
46 1.5-1.579 3.079
47-4.5-2.271-2.229
48 1 5.032-4.032
49-2.5 0.1614-2.661
50-0.3-0.3224 0.02243
51 0.3 0.5964-0.2964
52 0.1 0.571-0.471
53-5.6-2.468-3.132
54 8.6 6.11 2.49
55-1.9-7.213 5.313
56-6.1-2.993-3.107
57 5.1 6.586-1.486
58-4.8-1.606-3.194
59-6.3-3.235-3.065
60 12.3 7.466 4.834
61-6.6-5.921-0.679
62-0.9-3.535 2.635
63 8.7 3.904 4.796
64 2.1 2.075 0.02509
65-3.6-8.052 4.452
66 9.3 10.6-1.303
67-10.6-10.08-0.5223
68 6.8 3.502 3.298
69 5 0.2818 4.718
70-6-8.602 2.602
71 9.8 7.938 1.862
72-14.1-11.28-2.824
73-18.6-12.49-6.111
74-3 4.535-7.535
75-17-14.19-2.808
76 3.2-3.711 6.911
77-0.4 4.396-4.796
78-7.8-11.88 4.08
79 3.8 5.179-1.379
80 6.6 2.253 4.347
81 0.1-1.692 1.792
82 2.5 8.224-5.724
83 5.8 1.824 3.976
84-1.9 1.068-2.968
85 21.2 16.82 4.379
86 6.8 2.076 4.724
87 9.1 7.301 1.799
88-0.7 0.1934-0.8934
89 8.2 8.438-0.2384
90-1.4 3.355-4.755
91 5.2 2.966 2.234
92-7.3-0.6468-6.653
93-5.4-3.391-2.009
94-1.2 0.3211-1.521
95-1.5-1.332-0.168
96 0.6 0.2065 0.3935
97 3-1.432 4.432
98 0.8-2.553 3.353
99-7-3.677-3.323
100-0.4 2.087-2.487
101 5.6-0.218 5.818
102-9.2-10.06 0.863
103 0.4 5.651-5.251
104-4.7-9.433 4.733
105-2.7-0.674-2.026
106 8.9 5.41 3.49
107-10.9-2.582-8.318
108-1.2-2.38 1.18
109 2.6 2.704-0.1037
110-4.1-1.683-2.417
111 7.2 4.549 2.651
112 1-0.1557 1.156
113-13.1-8.414-4.686
114 5 4.798 0.2021
115-0.9-3.686 2.786
116-2.2 6.573-8.773
117 12.3 9.01 3.29
118-8-8.207 0.2073
119-2.4-6.143 3.743
120 9.6 8.09 1.51
121-4.4-5.281 0.8808
122-2.9-2.274-0.6261
123 4 0.7173 3.283
124-8-5.556-2.444
125 0.4-1.442 1.842
126 9.2 11.02-1.817
127-5.3-2.691-2.609
128 1.6 0.09786 1.502
129-2.7-0.2115-2.489
130 0.9-0.1852 1.085
131 3.7 4.154-0.4545
132-0.1-1.754 1.654
133-4.4-3.005-1.395
134 2.9 1.811 1.089
135-2.3 2.616-4.916
136-1.3 3.22-4.52
137 7.4 4.78 2.62
138-4-6.368 2.368
139-5-4.995-0.004505
140 3.7 1.546 2.154
141 6.1 3.304 2.796
142-9.3-4.647-4.653
143 8.7 7.477 1.223
144-4.8-3.122-1.678
145 2.4-1.251 3.651
146 5.2 3.28 1.92
147-1.4-2.471 1.071
148 1.1 0.158 0.9419
149-2.8 3.231-6.031
150-4.2-1.203-2.997
151 3.3 2.125 1.175
152 2.1 5.261-3.161
153-11.3-7.886-3.414
154 6.4 6.402-0.002361
155-3.8-5.151 1.351
156-0.6-0.7398 0.1398
157 1.4 3.798-2.398
158-0.7-0.264-0.436
159-3.7-3.483-0.2165
160 9.9 3.753 6.147
161-4.7-6.085 1.385
162 2.2 2.269-0.06854
163-1.6 1.639-3.239
164 1.3-2.575 3.875
165-5-4.212-0.7877
166 9.1 5.39 3.71
167-3.6-2.237-1.363
168-0.5-2.003 1.503
169 5.8 5.597 0.2033
170 2.3 1.261 1.039
171-0.9-0.1743-0.7257
172-8-3.871-4.129
173 8.7 8.842-0.1421
174-2.6-8.829 6.229
175 8.3 6.546 1.754
176-3-4.714 1.714
177 4.1 2.572 1.528
178-8.1-4.683-3.417






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
15 0.2491 0.4981 0.7509
16 0.1258 0.2516 0.8742
17 0.1264 0.2528 0.8736
18 0.07037 0.1407 0.9296
19 0.05366 0.1073 0.9463
20 0.03284 0.06568 0.9672
21 0.027 0.05399 0.973
22 0.02729 0.05458 0.9727
23 0.02507 0.05013 0.9749
24 0.01777 0.03554 0.9822
25 0.009567 0.01913 0.9904
26 0.008006 0.01601 0.992
27 0.01393 0.02787 0.9861
28 0.009968 0.01994 0.99
29 0.01954 0.03908 0.9805
30 0.01533 0.03067 0.9847
31 0.03491 0.06982 0.9651
32 0.03108 0.06216 0.9689
33 0.02138 0.04277 0.9786
34 0.01693 0.03387 0.9831
35 0.01106 0.02212 0.9889
36 0.009082 0.01816 0.9909
37 0.005671 0.01134 0.9943
38 0.00378 0.007561 0.9962
39 0.002349 0.004698 0.9977
40 0.001392 0.002784 0.9986
41 0.004907 0.009813 0.9951
42 0.003342 0.006684 0.9967
43 0.004085 0.008169 0.9959
44 0.003051 0.006102 0.9969
45 0.001895 0.003789 0.9981
46 0.002204 0.004407 0.9978
47 0.009925 0.01985 0.9901
48 0.01262 0.02525 0.9874
49 0.009546 0.01909 0.9905
50 0.006452 0.0129 0.9935
51 0.004323 0.008645 0.9957
52 0.002845 0.00569 0.9972
53 0.00279 0.00558 0.9972
54 0.002346 0.004693 0.9977
55 0.004361 0.008722 0.9956
56 0.004265 0.008531 0.9957
57 0.002983 0.005966 0.997
58 0.002988 0.005976 0.997
59 0.002452 0.004904 0.9975
60 0.003867 0.007733 0.9961
61 0.003092 0.006185 0.9969
62 0.004297 0.008593 0.9957
63 0.004423 0.008847 0.9956
64 0.003029 0.006058 0.997
65 0.007239 0.01448 0.9928
66 0.005949 0.0119 0.9941
67 0.004323 0.008645 0.9957
68 0.003678 0.007356 0.9963
69 0.004743 0.009486 0.9953
70 0.003725 0.007451 0.9963
71 0.002786 0.005571 0.9972
72 0.002768 0.005536 0.9972
73 0.01444 0.02888 0.9856
74 0.0546 0.1092 0.9454
75 0.05733 0.1147 0.9427
76 0.1908 0.3816 0.8092
77 0.2399 0.4799 0.7601
78 0.3044 0.6088 0.6956
79 0.2703 0.5406 0.7297
80 0.3109 0.6218 0.6891
81 0.3004 0.6008 0.6996
82 0.3755 0.7509 0.6245
83 0.4106 0.8212 0.5894
84 0.4052 0.8103 0.5948
85 0.4541 0.9082 0.5459
86 0.5251 0.9497 0.4749
87 0.5272 0.9455 0.4728
88 0.5001 0.9999 0.4999
89 0.4828 0.9657 0.5172
90 0.4989 0.9979 0.5011
91 0.496 0.992 0.504
92 0.6265 0.747 0.3735
93 0.6211 0.7579 0.3789
94 0.6206 0.7589 0.3794
95 0.5978 0.8044 0.4022
96 0.5531 0.8938 0.4469
97 0.6108 0.7783 0.3892
98 0.6496 0.7008 0.3504
99 0.6434 0.7133 0.3566
100 0.6498 0.7004 0.3502
101 0.7438 0.5123 0.2562
102 0.7065 0.5871 0.2935
103 0.7577 0.4846 0.2423
104 0.7819 0.4363 0.2181
105 0.7565 0.487 0.2435
106 0.7768 0.4464 0.2232
107 0.8939 0.2122 0.1061
108 0.8768 0.2465 0.1232
109 0.854 0.292 0.146
110 0.8346 0.3309 0.1654
111 0.8214 0.3571 0.1786
112 0.7944 0.4112 0.2056
113 0.823 0.3539 0.1769
114 0.8013 0.3975 0.1987
115 0.78 0.4399 0.22
116 0.9432 0.1136 0.05679
117 0.9647 0.07055 0.03528
118 0.9561 0.08785 0.04392
119 0.9534 0.0932 0.0466
120 0.9413 0.1174 0.0587
121 0.9287 0.1426 0.07132
122 0.9118 0.1765 0.08823
123 0.9152 0.1697 0.08484
124 0.9186 0.1627 0.08136
125 0.8996 0.2007 0.1004
126 0.8832 0.2335 0.1168
127 0.8606 0.2788 0.1394
128 0.8478 0.3043 0.1522
129 0.8317 0.3367 0.1683
130 0.7973 0.4054 0.2027
131 0.7584 0.4832 0.2416
132 0.7415 0.5171 0.2585
133 0.7124 0.5753 0.2876
134 0.665 0.67 0.335
135 0.7887 0.4225 0.2113
136 0.8001 0.3999 0.1999
137 0.8043 0.3913 0.1957
138 0.7842 0.4315 0.2158
139 0.7499 0.5003 0.2501
140 0.7017 0.5966 0.2983
141 0.6676 0.6648 0.3324
142 0.714 0.572 0.286
143 0.7198 0.5605 0.2802
144 0.6685 0.6631 0.3315
145 0.6478 0.7043 0.3522
146 0.6058 0.7885 0.3942
147 0.5405 0.9191 0.4595
148 0.4856 0.9712 0.5144
149 0.5726 0.8547 0.4274
150 0.5742 0.8517 0.4258
151 0.4983 0.9965 0.5017
152 0.6357 0.7285 0.3643
153 0.6727 0.6547 0.3273
154 0.592 0.8159 0.408
155 0.5182 0.9636 0.4818
156 0.6227 0.7547 0.3773
157 0.5407 0.9187 0.4593
158 0.5078 0.9844 0.4922
159 0.4094 0.8188 0.5906
160 0.5174 0.9653 0.4826
161 0.4162 0.8324 0.5838
162 0.2926 0.5851 0.7074
163 0.1922 0.3844 0.8078

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 &  0.2491 &  0.4981 &  0.7509 \tabularnewline
16 &  0.1258 &  0.2516 &  0.8742 \tabularnewline
17 &  0.1264 &  0.2528 &  0.8736 \tabularnewline
18 &  0.07037 &  0.1407 &  0.9296 \tabularnewline
19 &  0.05366 &  0.1073 &  0.9463 \tabularnewline
20 &  0.03284 &  0.06568 &  0.9672 \tabularnewline
21 &  0.027 &  0.05399 &  0.973 \tabularnewline
22 &  0.02729 &  0.05458 &  0.9727 \tabularnewline
23 &  0.02507 &  0.05013 &  0.9749 \tabularnewline
24 &  0.01777 &  0.03554 &  0.9822 \tabularnewline
25 &  0.009567 &  0.01913 &  0.9904 \tabularnewline
26 &  0.008006 &  0.01601 &  0.992 \tabularnewline
27 &  0.01393 &  0.02787 &  0.9861 \tabularnewline
28 &  0.009968 &  0.01994 &  0.99 \tabularnewline
29 &  0.01954 &  0.03908 &  0.9805 \tabularnewline
30 &  0.01533 &  0.03067 &  0.9847 \tabularnewline
31 &  0.03491 &  0.06982 &  0.9651 \tabularnewline
32 &  0.03108 &  0.06216 &  0.9689 \tabularnewline
33 &  0.02138 &  0.04277 &  0.9786 \tabularnewline
34 &  0.01693 &  0.03387 &  0.9831 \tabularnewline
35 &  0.01106 &  0.02212 &  0.9889 \tabularnewline
36 &  0.009082 &  0.01816 &  0.9909 \tabularnewline
37 &  0.005671 &  0.01134 &  0.9943 \tabularnewline
38 &  0.00378 &  0.007561 &  0.9962 \tabularnewline
39 &  0.002349 &  0.004698 &  0.9977 \tabularnewline
40 &  0.001392 &  0.002784 &  0.9986 \tabularnewline
41 &  0.004907 &  0.009813 &  0.9951 \tabularnewline
42 &  0.003342 &  0.006684 &  0.9967 \tabularnewline
43 &  0.004085 &  0.008169 &  0.9959 \tabularnewline
44 &  0.003051 &  0.006102 &  0.9969 \tabularnewline
45 &  0.001895 &  0.003789 &  0.9981 \tabularnewline
46 &  0.002204 &  0.004407 &  0.9978 \tabularnewline
47 &  0.009925 &  0.01985 &  0.9901 \tabularnewline
48 &  0.01262 &  0.02525 &  0.9874 \tabularnewline
49 &  0.009546 &  0.01909 &  0.9905 \tabularnewline
50 &  0.006452 &  0.0129 &  0.9935 \tabularnewline
51 &  0.004323 &  0.008645 &  0.9957 \tabularnewline
52 &  0.002845 &  0.00569 &  0.9972 \tabularnewline
53 &  0.00279 &  0.00558 &  0.9972 \tabularnewline
54 &  0.002346 &  0.004693 &  0.9977 \tabularnewline
55 &  0.004361 &  0.008722 &  0.9956 \tabularnewline
56 &  0.004265 &  0.008531 &  0.9957 \tabularnewline
57 &  0.002983 &  0.005966 &  0.997 \tabularnewline
58 &  0.002988 &  0.005976 &  0.997 \tabularnewline
59 &  0.002452 &  0.004904 &  0.9975 \tabularnewline
60 &  0.003867 &  0.007733 &  0.9961 \tabularnewline
61 &  0.003092 &  0.006185 &  0.9969 \tabularnewline
62 &  0.004297 &  0.008593 &  0.9957 \tabularnewline
63 &  0.004423 &  0.008847 &  0.9956 \tabularnewline
64 &  0.003029 &  0.006058 &  0.997 \tabularnewline
65 &  0.007239 &  0.01448 &  0.9928 \tabularnewline
66 &  0.005949 &  0.0119 &  0.9941 \tabularnewline
67 &  0.004323 &  0.008645 &  0.9957 \tabularnewline
68 &  0.003678 &  0.007356 &  0.9963 \tabularnewline
69 &  0.004743 &  0.009486 &  0.9953 \tabularnewline
70 &  0.003725 &  0.007451 &  0.9963 \tabularnewline
71 &  0.002786 &  0.005571 &  0.9972 \tabularnewline
72 &  0.002768 &  0.005536 &  0.9972 \tabularnewline
73 &  0.01444 &  0.02888 &  0.9856 \tabularnewline
74 &  0.0546 &  0.1092 &  0.9454 \tabularnewline
75 &  0.05733 &  0.1147 &  0.9427 \tabularnewline
76 &  0.1908 &  0.3816 &  0.8092 \tabularnewline
77 &  0.2399 &  0.4799 &  0.7601 \tabularnewline
78 &  0.3044 &  0.6088 &  0.6956 \tabularnewline
79 &  0.2703 &  0.5406 &  0.7297 \tabularnewline
80 &  0.3109 &  0.6218 &  0.6891 \tabularnewline
81 &  0.3004 &  0.6008 &  0.6996 \tabularnewline
82 &  0.3755 &  0.7509 &  0.6245 \tabularnewline
83 &  0.4106 &  0.8212 &  0.5894 \tabularnewline
84 &  0.4052 &  0.8103 &  0.5948 \tabularnewline
85 &  0.4541 &  0.9082 &  0.5459 \tabularnewline
86 &  0.5251 &  0.9497 &  0.4749 \tabularnewline
87 &  0.5272 &  0.9455 &  0.4728 \tabularnewline
88 &  0.5001 &  0.9999 &  0.4999 \tabularnewline
89 &  0.4828 &  0.9657 &  0.5172 \tabularnewline
90 &  0.4989 &  0.9979 &  0.5011 \tabularnewline
91 &  0.496 &  0.992 &  0.504 \tabularnewline
92 &  0.6265 &  0.747 &  0.3735 \tabularnewline
93 &  0.6211 &  0.7579 &  0.3789 \tabularnewline
94 &  0.6206 &  0.7589 &  0.3794 \tabularnewline
95 &  0.5978 &  0.8044 &  0.4022 \tabularnewline
96 &  0.5531 &  0.8938 &  0.4469 \tabularnewline
97 &  0.6108 &  0.7783 &  0.3892 \tabularnewline
98 &  0.6496 &  0.7008 &  0.3504 \tabularnewline
99 &  0.6434 &  0.7133 &  0.3566 \tabularnewline
100 &  0.6498 &  0.7004 &  0.3502 \tabularnewline
101 &  0.7438 &  0.5123 &  0.2562 \tabularnewline
102 &  0.7065 &  0.5871 &  0.2935 \tabularnewline
103 &  0.7577 &  0.4846 &  0.2423 \tabularnewline
104 &  0.7819 &  0.4363 &  0.2181 \tabularnewline
105 &  0.7565 &  0.487 &  0.2435 \tabularnewline
106 &  0.7768 &  0.4464 &  0.2232 \tabularnewline
107 &  0.8939 &  0.2122 &  0.1061 \tabularnewline
108 &  0.8768 &  0.2465 &  0.1232 \tabularnewline
109 &  0.854 &  0.292 &  0.146 \tabularnewline
110 &  0.8346 &  0.3309 &  0.1654 \tabularnewline
111 &  0.8214 &  0.3571 &  0.1786 \tabularnewline
112 &  0.7944 &  0.4112 &  0.2056 \tabularnewline
113 &  0.823 &  0.3539 &  0.1769 \tabularnewline
114 &  0.8013 &  0.3975 &  0.1987 \tabularnewline
115 &  0.78 &  0.4399 &  0.22 \tabularnewline
116 &  0.9432 &  0.1136 &  0.05679 \tabularnewline
117 &  0.9647 &  0.07055 &  0.03528 \tabularnewline
118 &  0.9561 &  0.08785 &  0.04392 \tabularnewline
119 &  0.9534 &  0.0932 &  0.0466 \tabularnewline
120 &  0.9413 &  0.1174 &  0.0587 \tabularnewline
121 &  0.9287 &  0.1426 &  0.07132 \tabularnewline
122 &  0.9118 &  0.1765 &  0.08823 \tabularnewline
123 &  0.9152 &  0.1697 &  0.08484 \tabularnewline
124 &  0.9186 &  0.1627 &  0.08136 \tabularnewline
125 &  0.8996 &  0.2007 &  0.1004 \tabularnewline
126 &  0.8832 &  0.2335 &  0.1168 \tabularnewline
127 &  0.8606 &  0.2788 &  0.1394 \tabularnewline
128 &  0.8478 &  0.3043 &  0.1522 \tabularnewline
129 &  0.8317 &  0.3367 &  0.1683 \tabularnewline
130 &  0.7973 &  0.4054 &  0.2027 \tabularnewline
131 &  0.7584 &  0.4832 &  0.2416 \tabularnewline
132 &  0.7415 &  0.5171 &  0.2585 \tabularnewline
133 &  0.7124 &  0.5753 &  0.2876 \tabularnewline
134 &  0.665 &  0.67 &  0.335 \tabularnewline
135 &  0.7887 &  0.4225 &  0.2113 \tabularnewline
136 &  0.8001 &  0.3999 &  0.1999 \tabularnewline
137 &  0.8043 &  0.3913 &  0.1957 \tabularnewline
138 &  0.7842 &  0.4315 &  0.2158 \tabularnewline
139 &  0.7499 &  0.5003 &  0.2501 \tabularnewline
140 &  0.7017 &  0.5966 &  0.2983 \tabularnewline
141 &  0.6676 &  0.6648 &  0.3324 \tabularnewline
142 &  0.714 &  0.572 &  0.286 \tabularnewline
143 &  0.7198 &  0.5605 &  0.2802 \tabularnewline
144 &  0.6685 &  0.6631 &  0.3315 \tabularnewline
145 &  0.6478 &  0.7043 &  0.3522 \tabularnewline
146 &  0.6058 &  0.7885 &  0.3942 \tabularnewline
147 &  0.5405 &  0.9191 &  0.4595 \tabularnewline
148 &  0.4856 &  0.9712 &  0.5144 \tabularnewline
149 &  0.5726 &  0.8547 &  0.4274 \tabularnewline
150 &  0.5742 &  0.8517 &  0.4258 \tabularnewline
151 &  0.4983 &  0.9965 &  0.5017 \tabularnewline
152 &  0.6357 &  0.7285 &  0.3643 \tabularnewline
153 &  0.6727 &  0.6547 &  0.3273 \tabularnewline
154 &  0.592 &  0.8159 &  0.408 \tabularnewline
155 &  0.5182 &  0.9636 &  0.4818 \tabularnewline
156 &  0.6227 &  0.7547 &  0.3773 \tabularnewline
157 &  0.5407 &  0.9187 &  0.4593 \tabularnewline
158 &  0.5078 &  0.9844 &  0.4922 \tabularnewline
159 &  0.4094 &  0.8188 &  0.5906 \tabularnewline
160 &  0.5174 &  0.9653 &  0.4826 \tabularnewline
161 &  0.4162 &  0.8324 &  0.5838 \tabularnewline
162 &  0.2926 &  0.5851 &  0.7074 \tabularnewline
163 &  0.1922 &  0.3844 &  0.8078 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309521&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]15[/C][C] 0.2491[/C][C] 0.4981[/C][C] 0.7509[/C][/ROW]
[ROW][C]16[/C][C] 0.1258[/C][C] 0.2516[/C][C] 0.8742[/C][/ROW]
[ROW][C]17[/C][C] 0.1264[/C][C] 0.2528[/C][C] 0.8736[/C][/ROW]
[ROW][C]18[/C][C] 0.07037[/C][C] 0.1407[/C][C] 0.9296[/C][/ROW]
[ROW][C]19[/C][C] 0.05366[/C][C] 0.1073[/C][C] 0.9463[/C][/ROW]
[ROW][C]20[/C][C] 0.03284[/C][C] 0.06568[/C][C] 0.9672[/C][/ROW]
[ROW][C]21[/C][C] 0.027[/C][C] 0.05399[/C][C] 0.973[/C][/ROW]
[ROW][C]22[/C][C] 0.02729[/C][C] 0.05458[/C][C] 0.9727[/C][/ROW]
[ROW][C]23[/C][C] 0.02507[/C][C] 0.05013[/C][C] 0.9749[/C][/ROW]
[ROW][C]24[/C][C] 0.01777[/C][C] 0.03554[/C][C] 0.9822[/C][/ROW]
[ROW][C]25[/C][C] 0.009567[/C][C] 0.01913[/C][C] 0.9904[/C][/ROW]
[ROW][C]26[/C][C] 0.008006[/C][C] 0.01601[/C][C] 0.992[/C][/ROW]
[ROW][C]27[/C][C] 0.01393[/C][C] 0.02787[/C][C] 0.9861[/C][/ROW]
[ROW][C]28[/C][C] 0.009968[/C][C] 0.01994[/C][C] 0.99[/C][/ROW]
[ROW][C]29[/C][C] 0.01954[/C][C] 0.03908[/C][C] 0.9805[/C][/ROW]
[ROW][C]30[/C][C] 0.01533[/C][C] 0.03067[/C][C] 0.9847[/C][/ROW]
[ROW][C]31[/C][C] 0.03491[/C][C] 0.06982[/C][C] 0.9651[/C][/ROW]
[ROW][C]32[/C][C] 0.03108[/C][C] 0.06216[/C][C] 0.9689[/C][/ROW]
[ROW][C]33[/C][C] 0.02138[/C][C] 0.04277[/C][C] 0.9786[/C][/ROW]
[ROW][C]34[/C][C] 0.01693[/C][C] 0.03387[/C][C] 0.9831[/C][/ROW]
[ROW][C]35[/C][C] 0.01106[/C][C] 0.02212[/C][C] 0.9889[/C][/ROW]
[ROW][C]36[/C][C] 0.009082[/C][C] 0.01816[/C][C] 0.9909[/C][/ROW]
[ROW][C]37[/C][C] 0.005671[/C][C] 0.01134[/C][C] 0.9943[/C][/ROW]
[ROW][C]38[/C][C] 0.00378[/C][C] 0.007561[/C][C] 0.9962[/C][/ROW]
[ROW][C]39[/C][C] 0.002349[/C][C] 0.004698[/C][C] 0.9977[/C][/ROW]
[ROW][C]40[/C][C] 0.001392[/C][C] 0.002784[/C][C] 0.9986[/C][/ROW]
[ROW][C]41[/C][C] 0.004907[/C][C] 0.009813[/C][C] 0.9951[/C][/ROW]
[ROW][C]42[/C][C] 0.003342[/C][C] 0.006684[/C][C] 0.9967[/C][/ROW]
[ROW][C]43[/C][C] 0.004085[/C][C] 0.008169[/C][C] 0.9959[/C][/ROW]
[ROW][C]44[/C][C] 0.003051[/C][C] 0.006102[/C][C] 0.9969[/C][/ROW]
[ROW][C]45[/C][C] 0.001895[/C][C] 0.003789[/C][C] 0.9981[/C][/ROW]
[ROW][C]46[/C][C] 0.002204[/C][C] 0.004407[/C][C] 0.9978[/C][/ROW]
[ROW][C]47[/C][C] 0.009925[/C][C] 0.01985[/C][C] 0.9901[/C][/ROW]
[ROW][C]48[/C][C] 0.01262[/C][C] 0.02525[/C][C] 0.9874[/C][/ROW]
[ROW][C]49[/C][C] 0.009546[/C][C] 0.01909[/C][C] 0.9905[/C][/ROW]
[ROW][C]50[/C][C] 0.006452[/C][C] 0.0129[/C][C] 0.9935[/C][/ROW]
[ROW][C]51[/C][C] 0.004323[/C][C] 0.008645[/C][C] 0.9957[/C][/ROW]
[ROW][C]52[/C][C] 0.002845[/C][C] 0.00569[/C][C] 0.9972[/C][/ROW]
[ROW][C]53[/C][C] 0.00279[/C][C] 0.00558[/C][C] 0.9972[/C][/ROW]
[ROW][C]54[/C][C] 0.002346[/C][C] 0.004693[/C][C] 0.9977[/C][/ROW]
[ROW][C]55[/C][C] 0.004361[/C][C] 0.008722[/C][C] 0.9956[/C][/ROW]
[ROW][C]56[/C][C] 0.004265[/C][C] 0.008531[/C][C] 0.9957[/C][/ROW]
[ROW][C]57[/C][C] 0.002983[/C][C] 0.005966[/C][C] 0.997[/C][/ROW]
[ROW][C]58[/C][C] 0.002988[/C][C] 0.005976[/C][C] 0.997[/C][/ROW]
[ROW][C]59[/C][C] 0.002452[/C][C] 0.004904[/C][C] 0.9975[/C][/ROW]
[ROW][C]60[/C][C] 0.003867[/C][C] 0.007733[/C][C] 0.9961[/C][/ROW]
[ROW][C]61[/C][C] 0.003092[/C][C] 0.006185[/C][C] 0.9969[/C][/ROW]
[ROW][C]62[/C][C] 0.004297[/C][C] 0.008593[/C][C] 0.9957[/C][/ROW]
[ROW][C]63[/C][C] 0.004423[/C][C] 0.008847[/C][C] 0.9956[/C][/ROW]
[ROW][C]64[/C][C] 0.003029[/C][C] 0.006058[/C][C] 0.997[/C][/ROW]
[ROW][C]65[/C][C] 0.007239[/C][C] 0.01448[/C][C] 0.9928[/C][/ROW]
[ROW][C]66[/C][C] 0.005949[/C][C] 0.0119[/C][C] 0.9941[/C][/ROW]
[ROW][C]67[/C][C] 0.004323[/C][C] 0.008645[/C][C] 0.9957[/C][/ROW]
[ROW][C]68[/C][C] 0.003678[/C][C] 0.007356[/C][C] 0.9963[/C][/ROW]
[ROW][C]69[/C][C] 0.004743[/C][C] 0.009486[/C][C] 0.9953[/C][/ROW]
[ROW][C]70[/C][C] 0.003725[/C][C] 0.007451[/C][C] 0.9963[/C][/ROW]
[ROW][C]71[/C][C] 0.002786[/C][C] 0.005571[/C][C] 0.9972[/C][/ROW]
[ROW][C]72[/C][C] 0.002768[/C][C] 0.005536[/C][C] 0.9972[/C][/ROW]
[ROW][C]73[/C][C] 0.01444[/C][C] 0.02888[/C][C] 0.9856[/C][/ROW]
[ROW][C]74[/C][C] 0.0546[/C][C] 0.1092[/C][C] 0.9454[/C][/ROW]
[ROW][C]75[/C][C] 0.05733[/C][C] 0.1147[/C][C] 0.9427[/C][/ROW]
[ROW][C]76[/C][C] 0.1908[/C][C] 0.3816[/C][C] 0.8092[/C][/ROW]
[ROW][C]77[/C][C] 0.2399[/C][C] 0.4799[/C][C] 0.7601[/C][/ROW]
[ROW][C]78[/C][C] 0.3044[/C][C] 0.6088[/C][C] 0.6956[/C][/ROW]
[ROW][C]79[/C][C] 0.2703[/C][C] 0.5406[/C][C] 0.7297[/C][/ROW]
[ROW][C]80[/C][C] 0.3109[/C][C] 0.6218[/C][C] 0.6891[/C][/ROW]
[ROW][C]81[/C][C] 0.3004[/C][C] 0.6008[/C][C] 0.6996[/C][/ROW]
[ROW][C]82[/C][C] 0.3755[/C][C] 0.7509[/C][C] 0.6245[/C][/ROW]
[ROW][C]83[/C][C] 0.4106[/C][C] 0.8212[/C][C] 0.5894[/C][/ROW]
[ROW][C]84[/C][C] 0.4052[/C][C] 0.8103[/C][C] 0.5948[/C][/ROW]
[ROW][C]85[/C][C] 0.4541[/C][C] 0.9082[/C][C] 0.5459[/C][/ROW]
[ROW][C]86[/C][C] 0.5251[/C][C] 0.9497[/C][C] 0.4749[/C][/ROW]
[ROW][C]87[/C][C] 0.5272[/C][C] 0.9455[/C][C] 0.4728[/C][/ROW]
[ROW][C]88[/C][C] 0.5001[/C][C] 0.9999[/C][C] 0.4999[/C][/ROW]
[ROW][C]89[/C][C] 0.4828[/C][C] 0.9657[/C][C] 0.5172[/C][/ROW]
[ROW][C]90[/C][C] 0.4989[/C][C] 0.9979[/C][C] 0.5011[/C][/ROW]
[ROW][C]91[/C][C] 0.496[/C][C] 0.992[/C][C] 0.504[/C][/ROW]
[ROW][C]92[/C][C] 0.6265[/C][C] 0.747[/C][C] 0.3735[/C][/ROW]
[ROW][C]93[/C][C] 0.6211[/C][C] 0.7579[/C][C] 0.3789[/C][/ROW]
[ROW][C]94[/C][C] 0.6206[/C][C] 0.7589[/C][C] 0.3794[/C][/ROW]
[ROW][C]95[/C][C] 0.5978[/C][C] 0.8044[/C][C] 0.4022[/C][/ROW]
[ROW][C]96[/C][C] 0.5531[/C][C] 0.8938[/C][C] 0.4469[/C][/ROW]
[ROW][C]97[/C][C] 0.6108[/C][C] 0.7783[/C][C] 0.3892[/C][/ROW]
[ROW][C]98[/C][C] 0.6496[/C][C] 0.7008[/C][C] 0.3504[/C][/ROW]
[ROW][C]99[/C][C] 0.6434[/C][C] 0.7133[/C][C] 0.3566[/C][/ROW]
[ROW][C]100[/C][C] 0.6498[/C][C] 0.7004[/C][C] 0.3502[/C][/ROW]
[ROW][C]101[/C][C] 0.7438[/C][C] 0.5123[/C][C] 0.2562[/C][/ROW]
[ROW][C]102[/C][C] 0.7065[/C][C] 0.5871[/C][C] 0.2935[/C][/ROW]
[ROW][C]103[/C][C] 0.7577[/C][C] 0.4846[/C][C] 0.2423[/C][/ROW]
[ROW][C]104[/C][C] 0.7819[/C][C] 0.4363[/C][C] 0.2181[/C][/ROW]
[ROW][C]105[/C][C] 0.7565[/C][C] 0.487[/C][C] 0.2435[/C][/ROW]
[ROW][C]106[/C][C] 0.7768[/C][C] 0.4464[/C][C] 0.2232[/C][/ROW]
[ROW][C]107[/C][C] 0.8939[/C][C] 0.2122[/C][C] 0.1061[/C][/ROW]
[ROW][C]108[/C][C] 0.8768[/C][C] 0.2465[/C][C] 0.1232[/C][/ROW]
[ROW][C]109[/C][C] 0.854[/C][C] 0.292[/C][C] 0.146[/C][/ROW]
[ROW][C]110[/C][C] 0.8346[/C][C] 0.3309[/C][C] 0.1654[/C][/ROW]
[ROW][C]111[/C][C] 0.8214[/C][C] 0.3571[/C][C] 0.1786[/C][/ROW]
[ROW][C]112[/C][C] 0.7944[/C][C] 0.4112[/C][C] 0.2056[/C][/ROW]
[ROW][C]113[/C][C] 0.823[/C][C] 0.3539[/C][C] 0.1769[/C][/ROW]
[ROW][C]114[/C][C] 0.8013[/C][C] 0.3975[/C][C] 0.1987[/C][/ROW]
[ROW][C]115[/C][C] 0.78[/C][C] 0.4399[/C][C] 0.22[/C][/ROW]
[ROW][C]116[/C][C] 0.9432[/C][C] 0.1136[/C][C] 0.05679[/C][/ROW]
[ROW][C]117[/C][C] 0.9647[/C][C] 0.07055[/C][C] 0.03528[/C][/ROW]
[ROW][C]118[/C][C] 0.9561[/C][C] 0.08785[/C][C] 0.04392[/C][/ROW]
[ROW][C]119[/C][C] 0.9534[/C][C] 0.0932[/C][C] 0.0466[/C][/ROW]
[ROW][C]120[/C][C] 0.9413[/C][C] 0.1174[/C][C] 0.0587[/C][/ROW]
[ROW][C]121[/C][C] 0.9287[/C][C] 0.1426[/C][C] 0.07132[/C][/ROW]
[ROW][C]122[/C][C] 0.9118[/C][C] 0.1765[/C][C] 0.08823[/C][/ROW]
[ROW][C]123[/C][C] 0.9152[/C][C] 0.1697[/C][C] 0.08484[/C][/ROW]
[ROW][C]124[/C][C] 0.9186[/C][C] 0.1627[/C][C] 0.08136[/C][/ROW]
[ROW][C]125[/C][C] 0.8996[/C][C] 0.2007[/C][C] 0.1004[/C][/ROW]
[ROW][C]126[/C][C] 0.8832[/C][C] 0.2335[/C][C] 0.1168[/C][/ROW]
[ROW][C]127[/C][C] 0.8606[/C][C] 0.2788[/C][C] 0.1394[/C][/ROW]
[ROW][C]128[/C][C] 0.8478[/C][C] 0.3043[/C][C] 0.1522[/C][/ROW]
[ROW][C]129[/C][C] 0.8317[/C][C] 0.3367[/C][C] 0.1683[/C][/ROW]
[ROW][C]130[/C][C] 0.7973[/C][C] 0.4054[/C][C] 0.2027[/C][/ROW]
[ROW][C]131[/C][C] 0.7584[/C][C] 0.4832[/C][C] 0.2416[/C][/ROW]
[ROW][C]132[/C][C] 0.7415[/C][C] 0.5171[/C][C] 0.2585[/C][/ROW]
[ROW][C]133[/C][C] 0.7124[/C][C] 0.5753[/C][C] 0.2876[/C][/ROW]
[ROW][C]134[/C][C] 0.665[/C][C] 0.67[/C][C] 0.335[/C][/ROW]
[ROW][C]135[/C][C] 0.7887[/C][C] 0.4225[/C][C] 0.2113[/C][/ROW]
[ROW][C]136[/C][C] 0.8001[/C][C] 0.3999[/C][C] 0.1999[/C][/ROW]
[ROW][C]137[/C][C] 0.8043[/C][C] 0.3913[/C][C] 0.1957[/C][/ROW]
[ROW][C]138[/C][C] 0.7842[/C][C] 0.4315[/C][C] 0.2158[/C][/ROW]
[ROW][C]139[/C][C] 0.7499[/C][C] 0.5003[/C][C] 0.2501[/C][/ROW]
[ROW][C]140[/C][C] 0.7017[/C][C] 0.5966[/C][C] 0.2983[/C][/ROW]
[ROW][C]141[/C][C] 0.6676[/C][C] 0.6648[/C][C] 0.3324[/C][/ROW]
[ROW][C]142[/C][C] 0.714[/C][C] 0.572[/C][C] 0.286[/C][/ROW]
[ROW][C]143[/C][C] 0.7198[/C][C] 0.5605[/C][C] 0.2802[/C][/ROW]
[ROW][C]144[/C][C] 0.6685[/C][C] 0.6631[/C][C] 0.3315[/C][/ROW]
[ROW][C]145[/C][C] 0.6478[/C][C] 0.7043[/C][C] 0.3522[/C][/ROW]
[ROW][C]146[/C][C] 0.6058[/C][C] 0.7885[/C][C] 0.3942[/C][/ROW]
[ROW][C]147[/C][C] 0.5405[/C][C] 0.9191[/C][C] 0.4595[/C][/ROW]
[ROW][C]148[/C][C] 0.4856[/C][C] 0.9712[/C][C] 0.5144[/C][/ROW]
[ROW][C]149[/C][C] 0.5726[/C][C] 0.8547[/C][C] 0.4274[/C][/ROW]
[ROW][C]150[/C][C] 0.5742[/C][C] 0.8517[/C][C] 0.4258[/C][/ROW]
[ROW][C]151[/C][C] 0.4983[/C][C] 0.9965[/C][C] 0.5017[/C][/ROW]
[ROW][C]152[/C][C] 0.6357[/C][C] 0.7285[/C][C] 0.3643[/C][/ROW]
[ROW][C]153[/C][C] 0.6727[/C][C] 0.6547[/C][C] 0.3273[/C][/ROW]
[ROW][C]154[/C][C] 0.592[/C][C] 0.8159[/C][C] 0.408[/C][/ROW]
[ROW][C]155[/C][C] 0.5182[/C][C] 0.9636[/C][C] 0.4818[/C][/ROW]
[ROW][C]156[/C][C] 0.6227[/C][C] 0.7547[/C][C] 0.3773[/C][/ROW]
[ROW][C]157[/C][C] 0.5407[/C][C] 0.9187[/C][C] 0.4593[/C][/ROW]
[ROW][C]158[/C][C] 0.5078[/C][C] 0.9844[/C][C] 0.4922[/C][/ROW]
[ROW][C]159[/C][C] 0.4094[/C][C] 0.8188[/C][C] 0.5906[/C][/ROW]
[ROW][C]160[/C][C] 0.5174[/C][C] 0.9653[/C][C] 0.4826[/C][/ROW]
[ROW][C]161[/C][C] 0.4162[/C][C] 0.8324[/C][C] 0.5838[/C][/ROW]
[ROW][C]162[/C][C] 0.2926[/C][C] 0.5851[/C][C] 0.7074[/C][/ROW]
[ROW][C]163[/C][C] 0.1922[/C][C] 0.3844[/C][C] 0.8078[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309521&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309521&T=6

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
15 0.2491 0.4981 0.7509
16 0.1258 0.2516 0.8742
17 0.1264 0.2528 0.8736
18 0.07037 0.1407 0.9296
19 0.05366 0.1073 0.9463
20 0.03284 0.06568 0.9672
21 0.027 0.05399 0.973
22 0.02729 0.05458 0.9727
23 0.02507 0.05013 0.9749
24 0.01777 0.03554 0.9822
25 0.009567 0.01913 0.9904
26 0.008006 0.01601 0.992
27 0.01393 0.02787 0.9861
28 0.009968 0.01994 0.99
29 0.01954 0.03908 0.9805
30 0.01533 0.03067 0.9847
31 0.03491 0.06982 0.9651
32 0.03108 0.06216 0.9689
33 0.02138 0.04277 0.9786
34 0.01693 0.03387 0.9831
35 0.01106 0.02212 0.9889
36 0.009082 0.01816 0.9909
37 0.005671 0.01134 0.9943
38 0.00378 0.007561 0.9962
39 0.002349 0.004698 0.9977
40 0.001392 0.002784 0.9986
41 0.004907 0.009813 0.9951
42 0.003342 0.006684 0.9967
43 0.004085 0.008169 0.9959
44 0.003051 0.006102 0.9969
45 0.001895 0.003789 0.9981
46 0.002204 0.004407 0.9978
47 0.009925 0.01985 0.9901
48 0.01262 0.02525 0.9874
49 0.009546 0.01909 0.9905
50 0.006452 0.0129 0.9935
51 0.004323 0.008645 0.9957
52 0.002845 0.00569 0.9972
53 0.00279 0.00558 0.9972
54 0.002346 0.004693 0.9977
55 0.004361 0.008722 0.9956
56 0.004265 0.008531 0.9957
57 0.002983 0.005966 0.997
58 0.002988 0.005976 0.997
59 0.002452 0.004904 0.9975
60 0.003867 0.007733 0.9961
61 0.003092 0.006185 0.9969
62 0.004297 0.008593 0.9957
63 0.004423 0.008847 0.9956
64 0.003029 0.006058 0.997
65 0.007239 0.01448 0.9928
66 0.005949 0.0119 0.9941
67 0.004323 0.008645 0.9957
68 0.003678 0.007356 0.9963
69 0.004743 0.009486 0.9953
70 0.003725 0.007451 0.9963
71 0.002786 0.005571 0.9972
72 0.002768 0.005536 0.9972
73 0.01444 0.02888 0.9856
74 0.0546 0.1092 0.9454
75 0.05733 0.1147 0.9427
76 0.1908 0.3816 0.8092
77 0.2399 0.4799 0.7601
78 0.3044 0.6088 0.6956
79 0.2703 0.5406 0.7297
80 0.3109 0.6218 0.6891
81 0.3004 0.6008 0.6996
82 0.3755 0.7509 0.6245
83 0.4106 0.8212 0.5894
84 0.4052 0.8103 0.5948
85 0.4541 0.9082 0.5459
86 0.5251 0.9497 0.4749
87 0.5272 0.9455 0.4728
88 0.5001 0.9999 0.4999
89 0.4828 0.9657 0.5172
90 0.4989 0.9979 0.5011
91 0.496 0.992 0.504
92 0.6265 0.747 0.3735
93 0.6211 0.7579 0.3789
94 0.6206 0.7589 0.3794
95 0.5978 0.8044 0.4022
96 0.5531 0.8938 0.4469
97 0.6108 0.7783 0.3892
98 0.6496 0.7008 0.3504
99 0.6434 0.7133 0.3566
100 0.6498 0.7004 0.3502
101 0.7438 0.5123 0.2562
102 0.7065 0.5871 0.2935
103 0.7577 0.4846 0.2423
104 0.7819 0.4363 0.2181
105 0.7565 0.487 0.2435
106 0.7768 0.4464 0.2232
107 0.8939 0.2122 0.1061
108 0.8768 0.2465 0.1232
109 0.854 0.292 0.146
110 0.8346 0.3309 0.1654
111 0.8214 0.3571 0.1786
112 0.7944 0.4112 0.2056
113 0.823 0.3539 0.1769
114 0.8013 0.3975 0.1987
115 0.78 0.4399 0.22
116 0.9432 0.1136 0.05679
117 0.9647 0.07055 0.03528
118 0.9561 0.08785 0.04392
119 0.9534 0.0932 0.0466
120 0.9413 0.1174 0.0587
121 0.9287 0.1426 0.07132
122 0.9118 0.1765 0.08823
123 0.9152 0.1697 0.08484
124 0.9186 0.1627 0.08136
125 0.8996 0.2007 0.1004
126 0.8832 0.2335 0.1168
127 0.8606 0.2788 0.1394
128 0.8478 0.3043 0.1522
129 0.8317 0.3367 0.1683
130 0.7973 0.4054 0.2027
131 0.7584 0.4832 0.2416
132 0.7415 0.5171 0.2585
133 0.7124 0.5753 0.2876
134 0.665 0.67 0.335
135 0.7887 0.4225 0.2113
136 0.8001 0.3999 0.1999
137 0.8043 0.3913 0.1957
138 0.7842 0.4315 0.2158
139 0.7499 0.5003 0.2501
140 0.7017 0.5966 0.2983
141 0.6676 0.6648 0.3324
142 0.714 0.572 0.286
143 0.7198 0.5605 0.2802
144 0.6685 0.6631 0.3315
145 0.6478 0.7043 0.3522
146 0.6058 0.7885 0.3942
147 0.5405 0.9191 0.4595
148 0.4856 0.9712 0.5144
149 0.5726 0.8547 0.4274
150 0.5742 0.8517 0.4258
151 0.4983 0.9965 0.5017
152 0.6357 0.7285 0.3643
153 0.6727 0.6547 0.3273
154 0.592 0.8159 0.408
155 0.5182 0.9636 0.4818
156 0.6227 0.7547 0.3773
157 0.5407 0.9187 0.4593
158 0.5078 0.9844 0.4922
159 0.4094 0.8188 0.5906
160 0.5174 0.9653 0.4826
161 0.4162 0.8324 0.5838
162 0.2926 0.5851 0.7074
163 0.1922 0.3844 0.8078






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level29 0.1946NOK
5% type I error level480.322148NOK
10% type I error level570.38255NOK

\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 & 29 &  0.1946 & NOK \tabularnewline
5% type I error level & 48 & 0.322148 & NOK \tabularnewline
10% type I error level & 57 & 0.38255 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309521&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]29[/C][C] 0.1946[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]48[/C][C]0.322148[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]57[/C][C]0.38255[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309521&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309521&T=7

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level29 0.1946NOK
5% type I error level480.322148NOK
10% type I error level570.38255NOK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.5256, df1 = 2, df2 = 164, p-value = 0.2206
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0911, df1 = 22, df2 = 144, p-value = 0.363
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.542, df1 = 2, df2 = 164, p-value = 0.217


\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline

> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.5256, df1 = 2, df2 = 164, p-value = 0.2206

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline

> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0911, df1 = 22, df2 = 144, p-value = 0.363

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.542, df1 = 2, df2 = 164, p-value = 0.217

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309521&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.5256, df1 = 2, df2 = 164, p-value = 0.2206

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0911, df1 = 22, df2 = 144, p-value = 0.363

[/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 = 1.542, df1 = 2, df2 = 164, p-value = 0.217

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309521&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309521&T=8

As an alternative you can also use a QR Code:  
QR Code


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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.5256, df1 = 2, df2 = 164, p-value = 0.2206
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0911, df1 = 22, df2 = 144, p-value = 0.363
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.542, df1 = 2, df2 = 164, p-value = 0.217






Variance Inflation Factors (Multicollinearity)
> vif
          `(1-Bs)(1-B)intermediate0`           `(1-Bs)(1-B)intermediate1` 
                            1.907354                             6.788802 
          `(1-Bs)(1-B)intermediate2`           `(1-Bs)(1-B)intermediate3` 
                            7.824222                             6.059201 
          `(1-Bs)(1-B)intermediate4`           `(1-Bs)(1-B)intermediate5` 
                            2.102507                             2.209332 
       `(1-Bs)(1-B)intermediate6\\r`  `(1-Bs)(1-B)Chemical_products(t-1)` 
                            1.612536                             3.743471 
 `(1-Bs)(1-B)Chemical_products(t-2)`  `(1-Bs)(1-B)Chemical_products(t-3)` 
                            4.627445                             3.928473 
`(1-Bs)(1-B)Chemical_products(t-1s)` 
                            1.351533 


\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline

> vif
          `(1-Bs)(1-B)intermediate0`           `(1-Bs)(1-B)intermediate1` 
                            1.907354                             6.788802 
          `(1-Bs)(1-B)intermediate2`           `(1-Bs)(1-B)intermediate3` 
                            7.824222                             6.059201 
          `(1-Bs)(1-B)intermediate4`           `(1-Bs)(1-B)intermediate5` 
                            2.102507                             2.209332 
       `(1-Bs)(1-B)intermediate6\\r`  `(1-Bs)(1-B)Chemical_products(t-1)` 
                            1.612536                             3.743471 
 `(1-Bs)(1-B)Chemical_products(t-2)`  `(1-Bs)(1-B)Chemical_products(t-3)` 
                            4.627445                             3.928473 
`(1-Bs)(1-B)Chemical_products(t-1s)` 
                            1.351533 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309521&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
          `(1-Bs)(1-B)intermediate0`           `(1-Bs)(1-B)intermediate1` 
                            1.907354                             6.788802 
          `(1-Bs)(1-B)intermediate2`           `(1-Bs)(1-B)intermediate3` 
                            7.824222                             6.059201 
          `(1-Bs)(1-B)intermediate4`           `(1-Bs)(1-B)intermediate5` 
                            2.102507                             2.209332 
       `(1-Bs)(1-B)intermediate6\\r`  `(1-Bs)(1-B)Chemical_products(t-1)` 
                            1.612536                             3.743471 
 `(1-Bs)(1-B)Chemical_products(t-2)`  `(1-Bs)(1-B)Chemical_products(t-3)` 
                            4.627445                             3.928473 
`(1-Bs)(1-B)Chemical_products(t-1s)` 
                            1.351533 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309521&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309521&T=9

As an alternative you can also use a QR Code:  
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` 
                            1.907354                             6.788802 
          `(1-Bs)(1-B)intermediate2`           `(1-Bs)(1-B)intermediate3` 
                            7.824222                             6.059201 
          `(1-Bs)(1-B)intermediate4`           `(1-Bs)(1-B)intermediate5` 
                            2.102507                             2.209332 
       `(1-Bs)(1-B)intermediate6\\r`  `(1-Bs)(1-B)Chemical_products(t-1)` 
                            1.612536                             3.743471 
 `(1-Bs)(1-B)Chemical_products(t-2)`  `(1-Bs)(1-B)Chemical_products(t-3)` 
                            4.627445                             3.928473 
`(1-Bs)(1-B)Chemical_products(t-1s)` 
                            1.351533


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 6
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Figure 7
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Figure 9
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Figure 10
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Input Parameters & R Code

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