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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 14 Dec 2017 16:29:46 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/14/t1513265489mhsm1l6ifb50buc.htm/, Retrieved Mon, 13 May 2024 22:15:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309534, Retrieved Mon, 13 May 2024 22:15:34 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

101

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

-       [Multiple Regression] [poging 2] [2017-12-14 15:29:46] [bc0a1b24d4c8c5bd2fad05813077f37f] [Current]

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Dataseries X:

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

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309534&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309534&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309534&T=0

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)V1[t] = + 0.0091368 + 0.811851`(1-Bs)(1-B)V2`[t] + 0.5589`(1-Bs)(1-B)V3`[t] + 0.410643`(1-Bs)(1-B)V4`[t] + 0.0980931`(1-Bs)(1-B)V5`[t] -0.0216354`(1-Bs)(1-B)V6`[t] -0.0382662`(1-Bs)(1-B)V7`[t] + 0.0903857`(1-Bs)(1-B)V8`[t] -0.446915`(1-Bs)(1-B)V1(t-1)`[t] -0.335667`(1-Bs)(1-B)V1(t-2)`[t] -0.0726488`(1-Bs)(1-B)V1(t-3)`[t] -0.211768`(1-Bs)(1-B)V1(t-1s)`[t] -0.0556532`(1-Bs)(1-B)V1(t-2s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)V1[t] =  +  0.0091368 +  0.811851`(1-Bs)(1-B)V2`[t] +  0.5589`(1-Bs)(1-B)V3`[t] +  0.410643`(1-Bs)(1-B)V4`[t] +  0.0980931`(1-Bs)(1-B)V5`[t] -0.0216354`(1-Bs)(1-B)V6`[t] -0.0382662`(1-Bs)(1-B)V7`[t] +  0.0903857`(1-Bs)(1-B)V8`[t] -0.446915`(1-Bs)(1-B)V1(t-1)`[t] -0.335667`(1-Bs)(1-B)V1(t-2)`[t] -0.0726488`(1-Bs)(1-B)V1(t-3)`[t] -0.211768`(1-Bs)(1-B)V1(t-1s)`[t] -0.0556532`(1-Bs)(1-B)V1(t-2s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309534&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)V1[t] =  +  0.0091368 +  0.811851`(1-Bs)(1-B)V2`[t] +  0.5589`(1-Bs)(1-B)V3`[t] +  0.410643`(1-Bs)(1-B)V4`[t] +  0.0980931`(1-Bs)(1-B)V5`[t] -0.0216354`(1-Bs)(1-B)V6`[t] -0.0382662`(1-Bs)(1-B)V7`[t] +  0.0903857`(1-Bs)(1-B)V8`[t] -0.446915`(1-Bs)(1-B)V1(t-1)`[t] -0.335667`(1-Bs)(1-B)V1(t-2)`[t] -0.0726488`(1-Bs)(1-B)V1(t-3)`[t] -0.211768`(1-Bs)(1-B)V1(t-1s)`[t] -0.0556532`(1-Bs)(1-B)V1(t-2s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309534&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309534&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)V1[t] = + 0.0091368 + 0.811851`(1-Bs)(1-B)V2`[t] + 0.5589`(1-Bs)(1-B)V3`[t] + 0.410643`(1-Bs)(1-B)V4`[t] + 0.0980931`(1-Bs)(1-B)V5`[t] -0.0216354`(1-Bs)(1-B)V6`[t] -0.0382662`(1-Bs)(1-B)V7`[t] + 0.0903857`(1-Bs)(1-B)V8`[t] -0.446915`(1-Bs)(1-B)V1(t-1)`[t] -0.335667`(1-Bs)(1-B)V1(t-2)`[t] -0.0726488`(1-Bs)(1-B)V1(t-3)`[t] -0.211768`(1-Bs)(1-B)V1(t-1s)`[t] -0.0556532`(1-Bs)(1-B)V1(t-2s)`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+0.009137	0.251	+3.6410e-02	0.971	0.4855
`(1-Bs)(1-B)V2`	+0.8118	0.05222	+1.5550e+01	2.69e-33	1.345e-33
`(1-Bs)(1-B)V3`	+0.5589	0.09721	+5.7490e+00	4.711e-08	2.355e-08
`(1-Bs)(1-B)V4`	+0.4106	0.1043	+3.9370e+00	0.0001251	6.256e-05
`(1-Bs)(1-B)V5`	+0.09809	0.09183	+1.0680e+00	0.2871	0.1436
`(1-Bs)(1-B)V6`	-0.02164	0.05477	-3.9500e-01	0.6934	0.3467
`(1-Bs)(1-B)V7`	-0.03827	0.05606	-6.8260e-01	0.4959	0.2479
`(1-Bs)(1-B)V8`	+0.09039	0.04834	+1.8700e+00	0.0634	0.0317
`(1-Bs)(1-B)V1(t-1)`	-0.4469	0.08196	-5.4530e+00	1.946e-07	9.729e-08
`(1-Bs)(1-B)V1(t-2)`	-0.3357	0.09078	-3.6980e+00	0.0003027	0.0001513
`(1-Bs)(1-B)V1(t-3)`	-0.07265	0.08323	-8.7280e-01	0.3841	0.1921
`(1-Bs)(1-B)V1(t-1s)`	-0.2118	0.05217	-4.0590e+00	7.837e-05	3.918e-05
`(1-Bs)(1-B)V1(t-2s)`	-0.05565	0.04954	-1.1230e+00	0.2631	0.1315

\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) & +0.009137 &  0.251 & +3.6410e-02 &  0.971 &  0.4855 \tabularnewline
`(1-Bs)(1-B)V2` & +0.8118 &  0.05222 & +1.5550e+01 &  2.69e-33 &  1.345e-33 \tabularnewline
`(1-Bs)(1-B)V3` & +0.5589 &  0.09721 & +5.7490e+00 &  4.711e-08 &  2.355e-08 \tabularnewline
`(1-Bs)(1-B)V4` & +0.4106 &  0.1043 & +3.9370e+00 &  0.0001251 &  6.256e-05 \tabularnewline
`(1-Bs)(1-B)V5` & +0.09809 &  0.09183 & +1.0680e+00 &  0.2871 &  0.1436 \tabularnewline
`(1-Bs)(1-B)V6` & -0.02164 &  0.05477 & -3.9500e-01 &  0.6934 &  0.3467 \tabularnewline
`(1-Bs)(1-B)V7` & -0.03827 &  0.05606 & -6.8260e-01 &  0.4959 &  0.2479 \tabularnewline
`(1-Bs)(1-B)V8` & +0.09039 &  0.04834 & +1.8700e+00 &  0.0634 &  0.0317 \tabularnewline
`(1-Bs)(1-B)V1(t-1)` & -0.4469 &  0.08196 & -5.4530e+00 &  1.946e-07 &  9.729e-08 \tabularnewline
`(1-Bs)(1-B)V1(t-2)` & -0.3357 &  0.09078 & -3.6980e+00 &  0.0003027 &  0.0001513 \tabularnewline
`(1-Bs)(1-B)V1(t-3)` & -0.07265 &  0.08323 & -8.7280e-01 &  0.3841 &  0.1921 \tabularnewline
`(1-Bs)(1-B)V1(t-1s)` & -0.2118 &  0.05217 & -4.0590e+00 &  7.837e-05 &  3.918e-05 \tabularnewline
`(1-Bs)(1-B)V1(t-2s)` & -0.05565 &  0.04954 & -1.1230e+00 &  0.2631 &  0.1315 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309534&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]+0.009137[/C][C] 0.251[/C][C]+3.6410e-02[/C][C] 0.971[/C][C] 0.4855[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V2`[/C][C]+0.8118[/C][C] 0.05222[/C][C]+1.5550e+01[/C][C] 2.69e-33[/C][C] 1.345e-33[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V3`[/C][C]+0.5589[/C][C] 0.09721[/C][C]+5.7490e+00[/C][C] 4.711e-08[/C][C] 2.355e-08[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V4`[/C][C]+0.4106[/C][C] 0.1043[/C][C]+3.9370e+00[/C][C] 0.0001251[/C][C] 6.256e-05[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V5`[/C][C]+0.09809[/C][C] 0.09183[/C][C]+1.0680e+00[/C][C] 0.2871[/C][C] 0.1436[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V6`[/C][C]-0.02164[/C][C] 0.05477[/C][C]-3.9500e-01[/C][C] 0.6934[/C][C] 0.3467[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V7`[/C][C]-0.03827[/C][C] 0.05606[/C][C]-6.8260e-01[/C][C] 0.4959[/C][C] 0.2479[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V8`[/C][C]+0.09039[/C][C] 0.04834[/C][C]+1.8700e+00[/C][C] 0.0634[/C][C] 0.0317[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V1(t-1)`[/C][C]-0.4469[/C][C] 0.08196[/C][C]-5.4530e+00[/C][C] 1.946e-07[/C][C] 9.729e-08[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V1(t-2)`[/C][C]-0.3357[/C][C] 0.09078[/C][C]-3.6980e+00[/C][C] 0.0003027[/C][C] 0.0001513[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V1(t-3)`[/C][C]-0.07265[/C][C] 0.08323[/C][C]-8.7280e-01[/C][C] 0.3841[/C][C] 0.1921[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V1(t-1s)`[/C][C]-0.2118[/C][C] 0.05217[/C][C]-4.0590e+00[/C][C] 7.837e-05[/C][C] 3.918e-05[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)V1(t-2s)`[/C][C]-0.05565[/C][C] 0.04954[/C][C]-1.1230e+00[/C][C] 0.2631[/C][C] 0.1315[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309534&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309534&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+0.009137	0.251	+3.6410e-02	0.971	0.4855
`(1-Bs)(1-B)V2`	+0.8118	0.05222	+1.5550e+01	2.69e-33	1.345e-33
`(1-Bs)(1-B)V3`	+0.5589	0.09721	+5.7490e+00	4.711e-08	2.355e-08
`(1-Bs)(1-B)V4`	+0.4106	0.1043	+3.9370e+00	0.0001251	6.256e-05
`(1-Bs)(1-B)V5`	+0.09809	0.09183	+1.0680e+00	0.2871	0.1436
`(1-Bs)(1-B)V6`	-0.02164	0.05477	-3.9500e-01	0.6934	0.3467
`(1-Bs)(1-B)V7`	-0.03827	0.05606	-6.8260e-01	0.4959	0.2479
`(1-Bs)(1-B)V8`	+0.09039	0.04834	+1.8700e+00	0.0634	0.0317
`(1-Bs)(1-B)V1(t-1)`	-0.4469	0.08196	-5.4530e+00	1.946e-07	9.729e-08
`(1-Bs)(1-B)V1(t-2)`	-0.3357	0.09078	-3.6980e+00	0.0003027	0.0001513
`(1-Bs)(1-B)V1(t-3)`	-0.07265	0.08323	-8.7280e-01	0.3841	0.1921
`(1-Bs)(1-B)V1(t-1s)`	-0.2118	0.05217	-4.0590e+00	7.837e-05	3.918e-05
`(1-Bs)(1-B)V1(t-2s)`	-0.05565	0.04954	-1.1230e+00	0.2631	0.1315

Multiple Linear Regression - Regression Statistics
Multiple R	0.8562
R-squared	0.7331
Adjusted R-squared	0.7122
F-TEST (value)	35.03
F-TEST (DF numerator)	12
F-TEST (DF denominator)	153
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.233
Sum Squared Residuals	1599

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8562 \tabularnewline
R-squared &  0.7331 \tabularnewline
Adjusted R-squared &  0.7122 \tabularnewline
F-TEST (value) &  35.03 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.233 \tabularnewline
Sum Squared Residuals &  1599 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309534&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8562[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7331[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.7122[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 35.03[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/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] 3.233[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1599[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309534&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309534&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.8562
R-squared	0.7331
Adjusted R-squared	0.7122
F-TEST (value)	35.03
F-TEST (DF numerator)	12
F-TEST (DF denominator)	153
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.233
Sum Squared Residuals	1599

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

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309534&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=309534&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309534&T=4

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-2.5	-2.209	-0.2914
2	2.6	5.019	-2.419
3	-4.6	-3.595	-1.005
4	5.3	2.42	2.88
5	2.4	4.525	-2.125
6	-3.5	-1.502	-1.998
7	0.1	1.329	-1.229
8	10.7	7.626	3.074
9	-4.4	-7.462	3.062
10	7.9	4.769	3.131
11	0.7	-3.526	4.226
12	-0.4	-4.662	4.262
13	4.5	4.572	-0.07164
14	-2	-1.279	-0.7205
15	-5.5	-2.64	-2.86
16	-4.2	-0.9241	-3.276
17	4.1	-2.48	6.58
18	-0.2	0.4042	-0.6042
19	1.1	1.334	-0.2337
20	-8.3	-5.875	-2.425
21	-2.4	-2.167	-0.2331
22	1.5	0.4763	1.024
23	-2.9	-0.3905	-2.51
24	-1.4	-0.6243	-0.7757
25	2.8	4.163	-1.363
26	-4.1	-2.197	-1.903
27	9.2	7.639	1.561
28	-0.4	0.2704	-0.6704
29	-1.9	3.472	-5.372
30	-8	-6.516	-1.484
31	12	11.84	0.1648
32	-1.3	-2.367	1.067
33	0.4	-0.2054	0.6054
34	1.5	-2.01	3.51
35	-4.5	-2.186	-2.314
36	1	4.965	-3.965
37	-2.5	-0.0584	-2.442
38	-0.3	-0.2115	-0.08848
39	0.3	0.777	-0.477
40	0.1	0.8746	-0.7746
41	-5.6	-2.715	-2.885
42	8.6	6.178	2.422
43	-1.9	-7.287	5.387
44	-6.1	-2.638	-3.462
45	5.1	6.613	-1.513
46	-4.8	-1.562	-3.238
47	-6.3	-3.049	-3.251
48	12.3	7.446	4.854
49	-6.6	-5.771	-0.8295
50	-0.9	-3.528	2.628
51	8.7	3.359	5.341
52	2.1	2.171	-0.07085
53	-3.6	-7.832	4.232
54	9.3	10.53	-1.228
55	-10.6	-10.21	-0.3926
56	6.8	3.293	3.507
57	5	0.3468	4.653
58	-6	-8.519	2.519
59	9.8	7.964	1.836
60	-14.1	-11.22	-2.882
61	-18.6	-12.34	-6.265
62	-3	4.559	-7.559
63	-17	-13.89	-3.107
64	3.2	-4.056	7.256
65	-0.4	4.827	-5.227
66	-7.8	-12.36	4.563
67	3.8	5.143	-1.343
68	6.6	2.656	3.944
69	0.1	-2.065	2.165
70	2.5	8.411	-5.911
71	5.8	2.068	3.732
72	-1.9	0.6575	-2.558
73	21.2	17.36	3.842
74	6.8	2.319	4.481
75	9.1	6.794	2.306
76	-0.7	0.05116	-0.7512
77	8.2	8.555	-0.3545
78	-1.4	3.086	-4.486
79	5.2	3.341	1.859
80	-7.3	-1.085	-6.215
81	-5.4	-3.589	-1.811
82	-1.2	0.5429	-1.743
83	-1.5	-1.877	0.3772
84	0.6	0.9826	-0.3826
85	3	-0.8579	3.858
86	0.8	-2.473	3.273
87	-7	-2.926	-4.074
88	-0.4	1.932	-2.332
89	5.6	-0.2926	5.893
90	-9.2	-9.484	0.2842
91	0.4	5.091	-4.691
92	-4.7	-9.291	4.591
93	-2.7	-0.8897	-1.81
94	8.9	5.398	3.502
95	-10.9	-2.812	-8.088
96	-1.2	-2.475	1.275
97	2.6	1.524	1.076
98	-4.1	-1.885	-2.215
99	7.2	4.022	3.178
100	1	-0.1406	1.141
101	-13.1	-8.888	-4.212
102	5	4.965	0.03494
103	-0.9	-3.801	2.901
104	-2.2	6.838	-9.038
105	12.3	9.375	2.925
106	-8	-8.227	0.2265
107	-2.4	-5.92	3.52
108	9.6	8.067	1.533
109	-4.4	-5.263	0.8627
110	-2.9	-2.573	-0.3272
111	4	0.9905	3.009
112	-8	-5.305	-2.695
113	0.4	-1.633	2.033
114	9.2	11.23	-2.034
115	-5.3	-2.436	-2.864
116	1.6	0.1531	1.447
117	-2.7	-0.2213	-2.479
118	0.9	-0.3853	1.285
119	3.7	4.67	-0.9699
120	-0.1	-1.82	1.72
121	-4.4	-3.102	-1.298
122	2.9	2.069	0.8308
123	-2.3	2.18	-4.48
124	-1.3	3.274	-4.574
125	7.4	5.409	1.991
126	-4	-6.654	2.654
127	-5	-4.865	-0.1347
128	3.7	1.585	2.115
129	6.1	2.701	3.399
130	-9.3	-4.233	-5.067
131	8.7	7.313	1.387
132	-4.8	-3.39	-1.41
133	2.4	-1.037	3.437
134	5.2	3.371	1.829
135	-1.4	-2.556	1.156
136	1.1	0.4508	0.6492
137	-2.8	3.065	-5.865
138	-4.2	-1.521	-2.679
139	3.3	2.416	0.8837
140	2.1	5.12	-3.02
141	-11.3	-7.686	-3.614
142	6.4	6.245	0.1555
143	-3.8	-5.244	1.444
144	-0.6	-0.7198	0.1198
145	1.4	3.924	-2.524
146	-0.7	-0.4914	-0.2086
147	-3.7	-3.335	-0.3648
148	9.9	3.771	6.129
149	-4.7	-6.264	1.564
150	2.2	2.319	-0.1194
151	-1.6	1.907	-3.507
152	1.3	-2.781	4.081
153	-5	-4.326	-0.6741
154	9.1	5.68	3.42
155	-3.6	-2.477	-1.123
156	-0.5	-1.937	1.437
157	5.8	5.384	0.4162
158	2.3	1.149	1.151
159	-0.9	-0.1524	-0.7476
160	-8	-4.127	-3.873
161	8.7	9.047	-0.3466
162	-2.6	-8.346	5.746
163	8.3	6	2.3
164	-3	-4.599	1.599
165	4.1	3.063	1.037
166	-8.1	-5.101	-2.999

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -2.5 & -2.209 & -0.2914 \tabularnewline
2 &  2.6 &  5.019 & -2.419 \tabularnewline
3 & -4.6 & -3.595 & -1.005 \tabularnewline
4 &  5.3 &  2.42 &  2.88 \tabularnewline
5 &  2.4 &  4.525 & -2.125 \tabularnewline
6 & -3.5 & -1.502 & -1.998 \tabularnewline
7 &  0.1 &  1.329 & -1.229 \tabularnewline
8 &  10.7 &  7.626 &  3.074 \tabularnewline
9 & -4.4 & -7.462 &  3.062 \tabularnewline
10 &  7.9 &  4.769 &  3.131 \tabularnewline
11 &  0.7 & -3.526 &  4.226 \tabularnewline
12 & -0.4 & -4.662 &  4.262 \tabularnewline
13 &  4.5 &  4.572 & -0.07164 \tabularnewline
14 & -2 & -1.279 & -0.7205 \tabularnewline
15 & -5.5 & -2.64 & -2.86 \tabularnewline
16 & -4.2 & -0.9241 & -3.276 \tabularnewline
17 &  4.1 & -2.48 &  6.58 \tabularnewline
18 & -0.2 &  0.4042 & -0.6042 \tabularnewline
19 &  1.1 &  1.334 & -0.2337 \tabularnewline
20 & -8.3 & -5.875 & -2.425 \tabularnewline
21 & -2.4 & -2.167 & -0.2331 \tabularnewline
22 &  1.5 &  0.4763 &  1.024 \tabularnewline
23 & -2.9 & -0.3905 & -2.51 \tabularnewline
24 & -1.4 & -0.6243 & -0.7757 \tabularnewline
25 &  2.8 &  4.163 & -1.363 \tabularnewline
26 & -4.1 & -2.197 & -1.903 \tabularnewline
27 &  9.2 &  7.639 &  1.561 \tabularnewline
28 & -0.4 &  0.2704 & -0.6704 \tabularnewline
29 & -1.9 &  3.472 & -5.372 \tabularnewline
30 & -8 & -6.516 & -1.484 \tabularnewline
31 &  12 &  11.84 &  0.1648 \tabularnewline
32 & -1.3 & -2.367 &  1.067 \tabularnewline
33 &  0.4 & -0.2054 &  0.6054 \tabularnewline
34 &  1.5 & -2.01 &  3.51 \tabularnewline
35 & -4.5 & -2.186 & -2.314 \tabularnewline
36 &  1 &  4.965 & -3.965 \tabularnewline
37 & -2.5 & -0.0584 & -2.442 \tabularnewline
38 & -0.3 & -0.2115 & -0.08848 \tabularnewline
39 &  0.3 &  0.777 & -0.477 \tabularnewline
40 &  0.1 &  0.8746 & -0.7746 \tabularnewline
41 & -5.6 & -2.715 & -2.885 \tabularnewline
42 &  8.6 &  6.178 &  2.422 \tabularnewline
43 & -1.9 & -7.287 &  5.387 \tabularnewline
44 & -6.1 & -2.638 & -3.462 \tabularnewline
45 &  5.1 &  6.613 & -1.513 \tabularnewline
46 & -4.8 & -1.562 & -3.238 \tabularnewline
47 & -6.3 & -3.049 & -3.251 \tabularnewline
48 &  12.3 &  7.446 &  4.854 \tabularnewline
49 & -6.6 & -5.771 & -0.8295 \tabularnewline
50 & -0.9 & -3.528 &  2.628 \tabularnewline
51 &  8.7 &  3.359 &  5.341 \tabularnewline
52 &  2.1 &  2.171 & -0.07085 \tabularnewline
53 & -3.6 & -7.832 &  4.232 \tabularnewline
54 &  9.3 &  10.53 & -1.228 \tabularnewline
55 & -10.6 & -10.21 & -0.3926 \tabularnewline
56 &  6.8 &  3.293 &  3.507 \tabularnewline
57 &  5 &  0.3468 &  4.653 \tabularnewline
58 & -6 & -8.519 &  2.519 \tabularnewline
59 &  9.8 &  7.964 &  1.836 \tabularnewline
60 & -14.1 & -11.22 & -2.882 \tabularnewline
61 & -18.6 & -12.34 & -6.265 \tabularnewline
62 & -3 &  4.559 & -7.559 \tabularnewline
63 & -17 & -13.89 & -3.107 \tabularnewline
64 &  3.2 & -4.056 &  7.256 \tabularnewline
65 & -0.4 &  4.827 & -5.227 \tabularnewline
66 & -7.8 & -12.36 &  4.563 \tabularnewline
67 &  3.8 &  5.143 & -1.343 \tabularnewline
68 &  6.6 &  2.656 &  3.944 \tabularnewline
69 &  0.1 & -2.065 &  2.165 \tabularnewline
70 &  2.5 &  8.411 & -5.911 \tabularnewline
71 &  5.8 &  2.068 &  3.732 \tabularnewline
72 & -1.9 &  0.6575 & -2.558 \tabularnewline
73 &  21.2 &  17.36 &  3.842 \tabularnewline
74 &  6.8 &  2.319 &  4.481 \tabularnewline
75 &  9.1 &  6.794 &  2.306 \tabularnewline
76 & -0.7 &  0.05116 & -0.7512 \tabularnewline
77 &  8.2 &  8.555 & -0.3545 \tabularnewline
78 & -1.4 &  3.086 & -4.486 \tabularnewline
79 &  5.2 &  3.341 &  1.859 \tabularnewline
80 & -7.3 & -1.085 & -6.215 \tabularnewline
81 & -5.4 & -3.589 & -1.811 \tabularnewline
82 & -1.2 &  0.5429 & -1.743 \tabularnewline
83 & -1.5 & -1.877 &  0.3772 \tabularnewline
84 &  0.6 &  0.9826 & -0.3826 \tabularnewline
85 &  3 & -0.8579 &  3.858 \tabularnewline
86 &  0.8 & -2.473 &  3.273 \tabularnewline
87 & -7 & -2.926 & -4.074 \tabularnewline
88 & -0.4 &  1.932 & -2.332 \tabularnewline
89 &  5.6 & -0.2926 &  5.893 \tabularnewline
90 & -9.2 & -9.484 &  0.2842 \tabularnewline
91 &  0.4 &  5.091 & -4.691 \tabularnewline
92 & -4.7 & -9.291 &  4.591 \tabularnewline
93 & -2.7 & -0.8897 & -1.81 \tabularnewline
94 &  8.9 &  5.398 &  3.502 \tabularnewline
95 & -10.9 & -2.812 & -8.088 \tabularnewline
96 & -1.2 & -2.475 &  1.275 \tabularnewline
97 &  2.6 &  1.524 &  1.076 \tabularnewline
98 & -4.1 & -1.885 & -2.215 \tabularnewline
99 &  7.2 &  4.022 &  3.178 \tabularnewline
100 &  1 & -0.1406 &  1.141 \tabularnewline
101 & -13.1 & -8.888 & -4.212 \tabularnewline
102 &  5 &  4.965 &  0.03494 \tabularnewline
103 & -0.9 & -3.801 &  2.901 \tabularnewline
104 & -2.2 &  6.838 & -9.038 \tabularnewline
105 &  12.3 &  9.375 &  2.925 \tabularnewline
106 & -8 & -8.227 &  0.2265 \tabularnewline
107 & -2.4 & -5.92 &  3.52 \tabularnewline
108 &  9.6 &  8.067 &  1.533 \tabularnewline
109 & -4.4 & -5.263 &  0.8627 \tabularnewline
110 & -2.9 & -2.573 & -0.3272 \tabularnewline
111 &  4 &  0.9905 &  3.009 \tabularnewline
112 & -8 & -5.305 & -2.695 \tabularnewline
113 &  0.4 & -1.633 &  2.033 \tabularnewline
114 &  9.2 &  11.23 & -2.034 \tabularnewline
115 & -5.3 & -2.436 & -2.864 \tabularnewline
116 &  1.6 &  0.1531 &  1.447 \tabularnewline
117 & -2.7 & -0.2213 & -2.479 \tabularnewline
118 &  0.9 & -0.3853 &  1.285 \tabularnewline
119 &  3.7 &  4.67 & -0.9699 \tabularnewline
120 & -0.1 & -1.82 &  1.72 \tabularnewline
121 & -4.4 & -3.102 & -1.298 \tabularnewline
122 &  2.9 &  2.069 &  0.8308 \tabularnewline
123 & -2.3 &  2.18 & -4.48 \tabularnewline
124 & -1.3 &  3.274 & -4.574 \tabularnewline
125 &  7.4 &  5.409 &  1.991 \tabularnewline
126 & -4 & -6.654 &  2.654 \tabularnewline
127 & -5 & -4.865 & -0.1347 \tabularnewline
128 &  3.7 &  1.585 &  2.115 \tabularnewline
129 &  6.1 &  2.701 &  3.399 \tabularnewline
130 & -9.3 & -4.233 & -5.067 \tabularnewline
131 &  8.7 &  7.313 &  1.387 \tabularnewline
132 & -4.8 & -3.39 & -1.41 \tabularnewline
133 &  2.4 & -1.037 &  3.437 \tabularnewline
134 &  5.2 &  3.371 &  1.829 \tabularnewline
135 & -1.4 & -2.556 &  1.156 \tabularnewline
136 &  1.1 &  0.4508 &  0.6492 \tabularnewline
137 & -2.8 &  3.065 & -5.865 \tabularnewline
138 & -4.2 & -1.521 & -2.679 \tabularnewline
139 &  3.3 &  2.416 &  0.8837 \tabularnewline
140 &  2.1 &  5.12 & -3.02 \tabularnewline
141 & -11.3 & -7.686 & -3.614 \tabularnewline
142 &  6.4 &  6.245 &  0.1555 \tabularnewline
143 & -3.8 & -5.244 &  1.444 \tabularnewline
144 & -0.6 & -0.7198 &  0.1198 \tabularnewline
145 &  1.4 &  3.924 & -2.524 \tabularnewline
146 & -0.7 & -0.4914 & -0.2086 \tabularnewline
147 & -3.7 & -3.335 & -0.3648 \tabularnewline
148 &  9.9 &  3.771 &  6.129 \tabularnewline
149 & -4.7 & -6.264 &  1.564 \tabularnewline
150 &  2.2 &  2.319 & -0.1194 \tabularnewline
151 & -1.6 &  1.907 & -3.507 \tabularnewline
152 &  1.3 & -2.781 &  4.081 \tabularnewline
153 & -5 & -4.326 & -0.6741 \tabularnewline
154 &  9.1 &  5.68 &  3.42 \tabularnewline
155 & -3.6 & -2.477 & -1.123 \tabularnewline
156 & -0.5 & -1.937 &  1.437 \tabularnewline
157 &  5.8 &  5.384 &  0.4162 \tabularnewline
158 &  2.3 &  1.149 &  1.151 \tabularnewline
159 & -0.9 & -0.1524 & -0.7476 \tabularnewline
160 & -8 & -4.127 & -3.873 \tabularnewline
161 &  8.7 &  9.047 & -0.3466 \tabularnewline
162 & -2.6 & -8.346 &  5.746 \tabularnewline
163 &  8.3 &  6 &  2.3 \tabularnewline
164 & -3 & -4.599 &  1.599 \tabularnewline
165 &  4.1 &  3.063 &  1.037 \tabularnewline
166 & -8.1 & -5.101 & -2.999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309534&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]-2.5[/C][C]-2.209[/C][C]-0.2914[/C][/ROW]
[ROW][C]2[/C][C] 2.6[/C][C] 5.019[/C][C]-2.419[/C][/ROW]
[ROW][C]3[/C][C]-4.6[/C][C]-3.595[/C][C]-1.005[/C][/ROW]
[ROW][C]4[/C][C] 5.3[/C][C] 2.42[/C][C] 2.88[/C][/ROW]
[ROW][C]5[/C][C] 2.4[/C][C] 4.525[/C][C]-2.125[/C][/ROW]
[ROW][C]6[/C][C]-3.5[/C][C]-1.502[/C][C]-1.998[/C][/ROW]
[ROW][C]7[/C][C] 0.1[/C][C] 1.329[/C][C]-1.229[/C][/ROW]
[ROW][C]8[/C][C] 10.7[/C][C] 7.626[/C][C] 3.074[/C][/ROW]
[ROW][C]9[/C][C]-4.4[/C][C]-7.462[/C][C] 3.062[/C][/ROW]
[ROW][C]10[/C][C] 7.9[/C][C] 4.769[/C][C] 3.131[/C][/ROW]
[ROW][C]11[/C][C] 0.7[/C][C]-3.526[/C][C] 4.226[/C][/ROW]
[ROW][C]12[/C][C]-0.4[/C][C]-4.662[/C][C] 4.262[/C][/ROW]
[ROW][C]13[/C][C] 4.5[/C][C] 4.572[/C][C]-0.07164[/C][/ROW]
[ROW][C]14[/C][C]-2[/C][C]-1.279[/C][C]-0.7205[/C][/ROW]
[ROW][C]15[/C][C]-5.5[/C][C]-2.64[/C][C]-2.86[/C][/ROW]
[ROW][C]16[/C][C]-4.2[/C][C]-0.9241[/C][C]-3.276[/C][/ROW]
[ROW][C]17[/C][C] 4.1[/C][C]-2.48[/C][C] 6.58[/C][/ROW]
[ROW][C]18[/C][C]-0.2[/C][C] 0.4042[/C][C]-0.6042[/C][/ROW]
[ROW][C]19[/C][C] 1.1[/C][C] 1.334[/C][C]-0.2337[/C][/ROW]
[ROW][C]20[/C][C]-8.3[/C][C]-5.875[/C][C]-2.425[/C][/ROW]
[ROW][C]21[/C][C]-2.4[/C][C]-2.167[/C][C]-0.2331[/C][/ROW]
[ROW][C]22[/C][C] 1.5[/C][C] 0.4763[/C][C] 1.024[/C][/ROW]
[ROW][C]23[/C][C]-2.9[/C][C]-0.3905[/C][C]-2.51[/C][/ROW]
[ROW][C]24[/C][C]-1.4[/C][C]-0.6243[/C][C]-0.7757[/C][/ROW]
[ROW][C]25[/C][C] 2.8[/C][C] 4.163[/C][C]-1.363[/C][/ROW]
[ROW][C]26[/C][C]-4.1[/C][C]-2.197[/C][C]-1.903[/C][/ROW]
[ROW][C]27[/C][C] 9.2[/C][C] 7.639[/C][C] 1.561[/C][/ROW]
[ROW][C]28[/C][C]-0.4[/C][C] 0.2704[/C][C]-0.6704[/C][/ROW]
[ROW][C]29[/C][C]-1.9[/C][C] 3.472[/C][C]-5.372[/C][/ROW]
[ROW][C]30[/C][C]-8[/C][C]-6.516[/C][C]-1.484[/C][/ROW]
[ROW][C]31[/C][C] 12[/C][C] 11.84[/C][C] 0.1648[/C][/ROW]
[ROW][C]32[/C][C]-1.3[/C][C]-2.367[/C][C] 1.067[/C][/ROW]
[ROW][C]33[/C][C] 0.4[/C][C]-0.2054[/C][C] 0.6054[/C][/ROW]
[ROW][C]34[/C][C] 1.5[/C][C]-2.01[/C][C] 3.51[/C][/ROW]
[ROW][C]35[/C][C]-4.5[/C][C]-2.186[/C][C]-2.314[/C][/ROW]
[ROW][C]36[/C][C] 1[/C][C] 4.965[/C][C]-3.965[/C][/ROW]
[ROW][C]37[/C][C]-2.5[/C][C]-0.0584[/C][C]-2.442[/C][/ROW]
[ROW][C]38[/C][C]-0.3[/C][C]-0.2115[/C][C]-0.08848[/C][/ROW]
[ROW][C]39[/C][C] 0.3[/C][C] 0.777[/C][C]-0.477[/C][/ROW]
[ROW][C]40[/C][C] 0.1[/C][C] 0.8746[/C][C]-0.7746[/C][/ROW]
[ROW][C]41[/C][C]-5.6[/C][C]-2.715[/C][C]-2.885[/C][/ROW]
[ROW][C]42[/C][C] 8.6[/C][C] 6.178[/C][C] 2.422[/C][/ROW]
[ROW][C]43[/C][C]-1.9[/C][C]-7.287[/C][C] 5.387[/C][/ROW]
[ROW][C]44[/C][C]-6.1[/C][C]-2.638[/C][C]-3.462[/C][/ROW]
[ROW][C]45[/C][C] 5.1[/C][C] 6.613[/C][C]-1.513[/C][/ROW]
[ROW][C]46[/C][C]-4.8[/C][C]-1.562[/C][C]-3.238[/C][/ROW]
[ROW][C]47[/C][C]-6.3[/C][C]-3.049[/C][C]-3.251[/C][/ROW]
[ROW][C]48[/C][C] 12.3[/C][C] 7.446[/C][C] 4.854[/C][/ROW]
[ROW][C]49[/C][C]-6.6[/C][C]-5.771[/C][C]-0.8295[/C][/ROW]
[ROW][C]50[/C][C]-0.9[/C][C]-3.528[/C][C] 2.628[/C][/ROW]
[ROW][C]51[/C][C] 8.7[/C][C] 3.359[/C][C] 5.341[/C][/ROW]
[ROW][C]52[/C][C] 2.1[/C][C] 2.171[/C][C]-0.07085[/C][/ROW]
[ROW][C]53[/C][C]-3.6[/C][C]-7.832[/C][C] 4.232[/C][/ROW]
[ROW][C]54[/C][C] 9.3[/C][C] 10.53[/C][C]-1.228[/C][/ROW]
[ROW][C]55[/C][C]-10.6[/C][C]-10.21[/C][C]-0.3926[/C][/ROW]
[ROW][C]56[/C][C] 6.8[/C][C] 3.293[/C][C] 3.507[/C][/ROW]
[ROW][C]57[/C][C] 5[/C][C] 0.3468[/C][C] 4.653[/C][/ROW]
[ROW][C]58[/C][C]-6[/C][C]-8.519[/C][C] 2.519[/C][/ROW]
[ROW][C]59[/C][C] 9.8[/C][C] 7.964[/C][C] 1.836[/C][/ROW]
[ROW][C]60[/C][C]-14.1[/C][C]-11.22[/C][C]-2.882[/C][/ROW]
[ROW][C]61[/C][C]-18.6[/C][C]-12.34[/C][C]-6.265[/C][/ROW]
[ROW][C]62[/C][C]-3[/C][C] 4.559[/C][C]-7.559[/C][/ROW]
[ROW][C]63[/C][C]-17[/C][C]-13.89[/C][C]-3.107[/C][/ROW]
[ROW][C]64[/C][C] 3.2[/C][C]-4.056[/C][C] 7.256[/C][/ROW]
[ROW][C]65[/C][C]-0.4[/C][C] 4.827[/C][C]-5.227[/C][/ROW]
[ROW][C]66[/C][C]-7.8[/C][C]-12.36[/C][C] 4.563[/C][/ROW]
[ROW][C]67[/C][C] 3.8[/C][C] 5.143[/C][C]-1.343[/C][/ROW]
[ROW][C]68[/C][C] 6.6[/C][C] 2.656[/C][C] 3.944[/C][/ROW]
[ROW][C]69[/C][C] 0.1[/C][C]-2.065[/C][C] 2.165[/C][/ROW]
[ROW][C]70[/C][C] 2.5[/C][C] 8.411[/C][C]-5.911[/C][/ROW]
[ROW][C]71[/C][C] 5.8[/C][C] 2.068[/C][C] 3.732[/C][/ROW]
[ROW][C]72[/C][C]-1.9[/C][C] 0.6575[/C][C]-2.558[/C][/ROW]
[ROW][C]73[/C][C] 21.2[/C][C] 17.36[/C][C] 3.842[/C][/ROW]
[ROW][C]74[/C][C] 6.8[/C][C] 2.319[/C][C] 4.481[/C][/ROW]
[ROW][C]75[/C][C] 9.1[/C][C] 6.794[/C][C] 2.306[/C][/ROW]
[ROW][C]76[/C][C]-0.7[/C][C] 0.05116[/C][C]-0.7512[/C][/ROW]
[ROW][C]77[/C][C] 8.2[/C][C] 8.555[/C][C]-0.3545[/C][/ROW]
[ROW][C]78[/C][C]-1.4[/C][C] 3.086[/C][C]-4.486[/C][/ROW]
[ROW][C]79[/C][C] 5.2[/C][C] 3.341[/C][C] 1.859[/C][/ROW]
[ROW][C]80[/C][C]-7.3[/C][C]-1.085[/C][C]-6.215[/C][/ROW]
[ROW][C]81[/C][C]-5.4[/C][C]-3.589[/C][C]-1.811[/C][/ROW]
[ROW][C]82[/C][C]-1.2[/C][C] 0.5429[/C][C]-1.743[/C][/ROW]
[ROW][C]83[/C][C]-1.5[/C][C]-1.877[/C][C] 0.3772[/C][/ROW]
[ROW][C]84[/C][C] 0.6[/C][C] 0.9826[/C][C]-0.3826[/C][/ROW]
[ROW][C]85[/C][C] 3[/C][C]-0.8579[/C][C] 3.858[/C][/ROW]
[ROW][C]86[/C][C] 0.8[/C][C]-2.473[/C][C] 3.273[/C][/ROW]
[ROW][C]87[/C][C]-7[/C][C]-2.926[/C][C]-4.074[/C][/ROW]
[ROW][C]88[/C][C]-0.4[/C][C] 1.932[/C][C]-2.332[/C][/ROW]
[ROW][C]89[/C][C] 5.6[/C][C]-0.2926[/C][C] 5.893[/C][/ROW]
[ROW][C]90[/C][C]-9.2[/C][C]-9.484[/C][C] 0.2842[/C][/ROW]
[ROW][C]91[/C][C] 0.4[/C][C] 5.091[/C][C]-4.691[/C][/ROW]
[ROW][C]92[/C][C]-4.7[/C][C]-9.291[/C][C] 4.591[/C][/ROW]
[ROW][C]93[/C][C]-2.7[/C][C]-0.8897[/C][C]-1.81[/C][/ROW]
[ROW][C]94[/C][C] 8.9[/C][C] 5.398[/C][C] 3.502[/C][/ROW]
[ROW][C]95[/C][C]-10.9[/C][C]-2.812[/C][C]-8.088[/C][/ROW]
[ROW][C]96[/C][C]-1.2[/C][C]-2.475[/C][C] 1.275[/C][/ROW]
[ROW][C]97[/C][C] 2.6[/C][C] 1.524[/C][C] 1.076[/C][/ROW]
[ROW][C]98[/C][C]-4.1[/C][C]-1.885[/C][C]-2.215[/C][/ROW]
[ROW][C]99[/C][C] 7.2[/C][C] 4.022[/C][C] 3.178[/C][/ROW]
[ROW][C]100[/C][C] 1[/C][C]-0.1406[/C][C] 1.141[/C][/ROW]
[ROW][C]101[/C][C]-13.1[/C][C]-8.888[/C][C]-4.212[/C][/ROW]
[ROW][C]102[/C][C] 5[/C][C] 4.965[/C][C] 0.03494[/C][/ROW]
[ROW][C]103[/C][C]-0.9[/C][C]-3.801[/C][C] 2.901[/C][/ROW]
[ROW][C]104[/C][C]-2.2[/C][C] 6.838[/C][C]-9.038[/C][/ROW]
[ROW][C]105[/C][C] 12.3[/C][C] 9.375[/C][C] 2.925[/C][/ROW]
[ROW][C]106[/C][C]-8[/C][C]-8.227[/C][C] 0.2265[/C][/ROW]
[ROW][C]107[/C][C]-2.4[/C][C]-5.92[/C][C] 3.52[/C][/ROW]
[ROW][C]108[/C][C] 9.6[/C][C] 8.067[/C][C] 1.533[/C][/ROW]
[ROW][C]109[/C][C]-4.4[/C][C]-5.263[/C][C] 0.8627[/C][/ROW]
[ROW][C]110[/C][C]-2.9[/C][C]-2.573[/C][C]-0.3272[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C] 0.9905[/C][C] 3.009[/C][/ROW]
[ROW][C]112[/C][C]-8[/C][C]-5.305[/C][C]-2.695[/C][/ROW]
[ROW][C]113[/C][C] 0.4[/C][C]-1.633[/C][C] 2.033[/C][/ROW]
[ROW][C]114[/C][C] 9.2[/C][C] 11.23[/C][C]-2.034[/C][/ROW]
[ROW][C]115[/C][C]-5.3[/C][C]-2.436[/C][C]-2.864[/C][/ROW]
[ROW][C]116[/C][C] 1.6[/C][C] 0.1531[/C][C] 1.447[/C][/ROW]
[ROW][C]117[/C][C]-2.7[/C][C]-0.2213[/C][C]-2.479[/C][/ROW]
[ROW][C]118[/C][C] 0.9[/C][C]-0.3853[/C][C] 1.285[/C][/ROW]
[ROW][C]119[/C][C] 3.7[/C][C] 4.67[/C][C]-0.9699[/C][/ROW]
[ROW][C]120[/C][C]-0.1[/C][C]-1.82[/C][C] 1.72[/C][/ROW]
[ROW][C]121[/C][C]-4.4[/C][C]-3.102[/C][C]-1.298[/C][/ROW]
[ROW][C]122[/C][C] 2.9[/C][C] 2.069[/C][C] 0.8308[/C][/ROW]
[ROW][C]123[/C][C]-2.3[/C][C] 2.18[/C][C]-4.48[/C][/ROW]
[ROW][C]124[/C][C]-1.3[/C][C] 3.274[/C][C]-4.574[/C][/ROW]
[ROW][C]125[/C][C] 7.4[/C][C] 5.409[/C][C] 1.991[/C][/ROW]
[ROW][C]126[/C][C]-4[/C][C]-6.654[/C][C] 2.654[/C][/ROW]
[ROW][C]127[/C][C]-5[/C][C]-4.865[/C][C]-0.1347[/C][/ROW]
[ROW][C]128[/C][C] 3.7[/C][C] 1.585[/C][C] 2.115[/C][/ROW]
[ROW][C]129[/C][C] 6.1[/C][C] 2.701[/C][C] 3.399[/C][/ROW]
[ROW][C]130[/C][C]-9.3[/C][C]-4.233[/C][C]-5.067[/C][/ROW]
[ROW][C]131[/C][C] 8.7[/C][C] 7.313[/C][C] 1.387[/C][/ROW]
[ROW][C]132[/C][C]-4.8[/C][C]-3.39[/C][C]-1.41[/C][/ROW]
[ROW][C]133[/C][C] 2.4[/C][C]-1.037[/C][C] 3.437[/C][/ROW]
[ROW][C]134[/C][C] 5.2[/C][C] 3.371[/C][C] 1.829[/C][/ROW]
[ROW][C]135[/C][C]-1.4[/C][C]-2.556[/C][C] 1.156[/C][/ROW]
[ROW][C]136[/C][C] 1.1[/C][C] 0.4508[/C][C] 0.6492[/C][/ROW]
[ROW][C]137[/C][C]-2.8[/C][C] 3.065[/C][C]-5.865[/C][/ROW]
[ROW][C]138[/C][C]-4.2[/C][C]-1.521[/C][C]-2.679[/C][/ROW]
[ROW][C]139[/C][C] 3.3[/C][C] 2.416[/C][C] 0.8837[/C][/ROW]
[ROW][C]140[/C][C] 2.1[/C][C] 5.12[/C][C]-3.02[/C][/ROW]
[ROW][C]141[/C][C]-11.3[/C][C]-7.686[/C][C]-3.614[/C][/ROW]
[ROW][C]142[/C][C] 6.4[/C][C] 6.245[/C][C] 0.1555[/C][/ROW]
[ROW][C]143[/C][C]-3.8[/C][C]-5.244[/C][C] 1.444[/C][/ROW]
[ROW][C]144[/C][C]-0.6[/C][C]-0.7198[/C][C] 0.1198[/C][/ROW]
[ROW][C]145[/C][C] 1.4[/C][C] 3.924[/C][C]-2.524[/C][/ROW]
[ROW][C]146[/C][C]-0.7[/C][C]-0.4914[/C][C]-0.2086[/C][/ROW]
[ROW][C]147[/C][C]-3.7[/C][C]-3.335[/C][C]-0.3648[/C][/ROW]
[ROW][C]148[/C][C] 9.9[/C][C] 3.771[/C][C] 6.129[/C][/ROW]
[ROW][C]149[/C][C]-4.7[/C][C]-6.264[/C][C] 1.564[/C][/ROW]
[ROW][C]150[/C][C] 2.2[/C][C] 2.319[/C][C]-0.1194[/C][/ROW]
[ROW][C]151[/C][C]-1.6[/C][C] 1.907[/C][C]-3.507[/C][/ROW]
[ROW][C]152[/C][C] 1.3[/C][C]-2.781[/C][C] 4.081[/C][/ROW]
[ROW][C]153[/C][C]-5[/C][C]-4.326[/C][C]-0.6741[/C][/ROW]
[ROW][C]154[/C][C] 9.1[/C][C] 5.68[/C][C] 3.42[/C][/ROW]
[ROW][C]155[/C][C]-3.6[/C][C]-2.477[/C][C]-1.123[/C][/ROW]
[ROW][C]156[/C][C]-0.5[/C][C]-1.937[/C][C] 1.437[/C][/ROW]
[ROW][C]157[/C][C] 5.8[/C][C] 5.384[/C][C] 0.4162[/C][/ROW]
[ROW][C]158[/C][C] 2.3[/C][C] 1.149[/C][C] 1.151[/C][/ROW]
[ROW][C]159[/C][C]-0.9[/C][C]-0.1524[/C][C]-0.7476[/C][/ROW]
[ROW][C]160[/C][C]-8[/C][C]-4.127[/C][C]-3.873[/C][/ROW]
[ROW][C]161[/C][C] 8.7[/C][C] 9.047[/C][C]-0.3466[/C][/ROW]
[ROW][C]162[/C][C]-2.6[/C][C]-8.346[/C][C] 5.746[/C][/ROW]
[ROW][C]163[/C][C] 8.3[/C][C] 6[/C][C] 2.3[/C][/ROW]
[ROW][C]164[/C][C]-3[/C][C]-4.599[/C][C] 1.599[/C][/ROW]
[ROW][C]165[/C][C] 4.1[/C][C] 3.063[/C][C] 1.037[/C][/ROW]
[ROW][C]166[/C][C]-8.1[/C][C]-5.101[/C][C]-2.999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309534&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309534&T=5

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-2.5	-2.209	-0.2914
2	2.6	5.019	-2.419
3	-4.6	-3.595	-1.005
4	5.3	2.42	2.88
5	2.4	4.525	-2.125
6	-3.5	-1.502	-1.998
7	0.1	1.329	-1.229
8	10.7	7.626	3.074
9	-4.4	-7.462	3.062
10	7.9	4.769	3.131
11	0.7	-3.526	4.226
12	-0.4	-4.662	4.262
13	4.5	4.572	-0.07164
14	-2	-1.279	-0.7205
15	-5.5	-2.64	-2.86
16	-4.2	-0.9241	-3.276
17	4.1	-2.48	6.58
18	-0.2	0.4042	-0.6042
19	1.1	1.334	-0.2337
20	-8.3	-5.875	-2.425
21	-2.4	-2.167	-0.2331
22	1.5	0.4763	1.024
23	-2.9	-0.3905	-2.51
24	-1.4	-0.6243	-0.7757
25	2.8	4.163	-1.363
26	-4.1	-2.197	-1.903
27	9.2	7.639	1.561
28	-0.4	0.2704	-0.6704
29	-1.9	3.472	-5.372
30	-8	-6.516	-1.484
31	12	11.84	0.1648
32	-1.3	-2.367	1.067
33	0.4	-0.2054	0.6054
34	1.5	-2.01	3.51
35	-4.5	-2.186	-2.314
36	1	4.965	-3.965
37	-2.5	-0.0584	-2.442
38	-0.3	-0.2115	-0.08848
39	0.3	0.777	-0.477
40	0.1	0.8746	-0.7746
41	-5.6	-2.715	-2.885
42	8.6	6.178	2.422
43	-1.9	-7.287	5.387
44	-6.1	-2.638	-3.462
45	5.1	6.613	-1.513
46	-4.8	-1.562	-3.238
47	-6.3	-3.049	-3.251
48	12.3	7.446	4.854
49	-6.6	-5.771	-0.8295
50	-0.9	-3.528	2.628
51	8.7	3.359	5.341
52	2.1	2.171	-0.07085
53	-3.6	-7.832	4.232
54	9.3	10.53	-1.228
55	-10.6	-10.21	-0.3926
56	6.8	3.293	3.507
57	5	0.3468	4.653
58	-6	-8.519	2.519
59	9.8	7.964	1.836
60	-14.1	-11.22	-2.882
61	-18.6	-12.34	-6.265
62	-3	4.559	-7.559
63	-17	-13.89	-3.107
64	3.2	-4.056	7.256
65	-0.4	4.827	-5.227
66	-7.8	-12.36	4.563
67	3.8	5.143	-1.343
68	6.6	2.656	3.944
69	0.1	-2.065	2.165
70	2.5	8.411	-5.911
71	5.8	2.068	3.732
72	-1.9	0.6575	-2.558
73	21.2	17.36	3.842
74	6.8	2.319	4.481
75	9.1	6.794	2.306
76	-0.7	0.05116	-0.7512
77	8.2	8.555	-0.3545
78	-1.4	3.086	-4.486
79	5.2	3.341	1.859
80	-7.3	-1.085	-6.215
81	-5.4	-3.589	-1.811
82	-1.2	0.5429	-1.743
83	-1.5	-1.877	0.3772
84	0.6	0.9826	-0.3826
85	3	-0.8579	3.858
86	0.8	-2.473	3.273
87	-7	-2.926	-4.074
88	-0.4	1.932	-2.332
89	5.6	-0.2926	5.893
90	-9.2	-9.484	0.2842
91	0.4	5.091	-4.691
92	-4.7	-9.291	4.591
93	-2.7	-0.8897	-1.81
94	8.9	5.398	3.502
95	-10.9	-2.812	-8.088
96	-1.2	-2.475	1.275
97	2.6	1.524	1.076
98	-4.1	-1.885	-2.215
99	7.2	4.022	3.178
100	1	-0.1406	1.141
101	-13.1	-8.888	-4.212
102	5	4.965	0.03494
103	-0.9	-3.801	2.901
104	-2.2	6.838	-9.038
105	12.3	9.375	2.925
106	-8	-8.227	0.2265
107	-2.4	-5.92	3.52
108	9.6	8.067	1.533
109	-4.4	-5.263	0.8627
110	-2.9	-2.573	-0.3272
111	4	0.9905	3.009
112	-8	-5.305	-2.695
113	0.4	-1.633	2.033
114	9.2	11.23	-2.034
115	-5.3	-2.436	-2.864
116	1.6	0.1531	1.447
117	-2.7	-0.2213	-2.479
118	0.9	-0.3853	1.285
119	3.7	4.67	-0.9699
120	-0.1	-1.82	1.72
121	-4.4	-3.102	-1.298
122	2.9	2.069	0.8308
123	-2.3	2.18	-4.48
124	-1.3	3.274	-4.574
125	7.4	5.409	1.991
126	-4	-6.654	2.654
127	-5	-4.865	-0.1347
128	3.7	1.585	2.115
129	6.1	2.701	3.399
130	-9.3	-4.233	-5.067
131	8.7	7.313	1.387
132	-4.8	-3.39	-1.41
133	2.4	-1.037	3.437
134	5.2	3.371	1.829
135	-1.4	-2.556	1.156
136	1.1	0.4508	0.6492
137	-2.8	3.065	-5.865
138	-4.2	-1.521	-2.679
139	3.3	2.416	0.8837
140	2.1	5.12	-3.02
141	-11.3	-7.686	-3.614
142	6.4	6.245	0.1555
143	-3.8	-5.244	1.444
144	-0.6	-0.7198	0.1198
145	1.4	3.924	-2.524
146	-0.7	-0.4914	-0.2086
147	-3.7	-3.335	-0.3648
148	9.9	3.771	6.129
149	-4.7	-6.264	1.564
150	2.2	2.319	-0.1194
151	-1.6	1.907	-3.507
152	1.3	-2.781	4.081
153	-5	-4.326	-0.6741
154	9.1	5.68	3.42
155	-3.6	-2.477	-1.123
156	-0.5	-1.937	1.437
157	5.8	5.384	0.4162
158	2.3	1.149	1.151
159	-0.9	-0.1524	-0.7476
160	-8	-4.127	-3.873
161	8.7	9.047	-0.3466
162	-2.6	-8.346	5.746
163	8.3	6	2.3
164	-3	-4.599	1.599
165	4.1	3.063	1.037
166	-8.1	-5.101	-2.999

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.2722	0.5443	0.7278
17	0.2528	0.5055	0.7472
18	0.1793	0.3587	0.8207
19	0.6214	0.7573	0.3786
20	0.5081	0.9839	0.4919
21	0.3969	0.7938	0.6031
22	0.325	0.65	0.675
23	0.2409	0.4818	0.7591
24	0.2204	0.4408	0.7796
25	0.1643	0.3286	0.8357
26	0.1261	0.2522	0.8739
27	0.08612	0.1722	0.9139
28	0.05783	0.1157	0.9422
29	0.08216	0.1643	0.9178
30	0.06058	0.1212	0.9394
31	0.05968	0.1194	0.9403
32	0.0463	0.09259	0.9537
33	0.03098	0.06196	0.969
34	0.03443	0.06886	0.9656
35	0.1221	0.2442	0.8779
36	0.1506	0.3012	0.8494
37	0.1223	0.2446	0.8777
38	0.09203	0.1841	0.908
39	0.06941	0.1388	0.9306
40	0.05062	0.1012	0.9494
41	0.04768	0.09537	0.9523
42	0.03843	0.07685	0.9616
43	0.06415	0.1283	0.9358
44	0.06486	0.1297	0.9351
45	0.04917	0.09834	0.9508
46	0.0449	0.0898	0.9551
47	0.03802	0.07605	0.962
48	0.0501	0.1002	0.9499
49	0.04069	0.08139	0.9593
50	0.04708	0.09416	0.9529
51	0.04548	0.09096	0.9545
52	0.03415	0.06831	0.9658
53	0.0616	0.1232	0.9384
54	0.04912	0.09824	0.9509
55	0.03857	0.07714	0.9614
56	0.03259	0.06518	0.9674
57	0.0377	0.0754	0.9623
58	0.03012	0.06024	0.9699
59	0.02347	0.04694	0.9765
60	0.02197	0.04395	0.978
61	0.08509	0.1702	0.9149
62	0.2254	0.4508	0.7746
63	0.2387	0.4774	0.7613
64	0.4674	0.9349	0.5326
65	0.527	0.946	0.473
66	0.5774	0.8453	0.4226
67	0.5315	0.937	0.4685
68	0.6142	0.7716	0.3858
69	0.6273	0.7453	0.3727
70	0.6954	0.6091	0.3046
71	0.7395	0.521	0.2605
72	0.731	0.538	0.269
73	0.7676	0.4647	0.2324
74	0.8063	0.3875	0.1937
75	0.8197	0.3606	0.1803
76	0.8004	0.3992	0.1996
77	0.7836	0.4327	0.2163
78	0.7902	0.4197	0.2098
79	0.7743	0.4515	0.2257
80	0.8457	0.3085	0.1543
81	0.8409	0.3181	0.1591
82	0.8448	0.3104	0.1552
83	0.8226	0.3548	0.1774
84	0.7926	0.4148	0.2074
85	0.8108	0.3784	0.1892
86	0.8297	0.3406	0.1703
87	0.8342	0.3316	0.1658
88	0.834	0.332	0.166
89	0.8906	0.2188	0.1094
90	0.8698	0.2604	0.1302
91	0.8888	0.2224	0.1112
92	0.8982	0.2035	0.1018
93	0.8803	0.2394	0.1197
94	0.8906	0.2189	0.1094
95	0.9545	0.09095	0.04547
96	0.9462	0.1075	0.05376
97	0.9328	0.1345	0.06724
98	0.9203	0.1593	0.07966
99	0.9199	0.1602	0.08012
100	0.9023	0.1953	0.09767
101	0.9074	0.1852	0.09261
102	0.8923	0.2155	0.1077
103	0.8775	0.245	0.1225
104	0.9797	0.04052	0.02026
105	0.9863	0.02735	0.01367
106	0.9822	0.03566	0.01783
107	0.9801	0.03973	0.01986
108	0.9738	0.05231	0.02615
109	0.9671	0.0657	0.03285
110	0.9566	0.08676	0.04338
111	0.9543	0.09147	0.04574
112	0.9592	0.08152	0.04076
113	0.9489	0.1021	0.05107
114	0.9427	0.1146	0.05731
115	0.9318	0.1364	0.06818
116	0.9273	0.1454	0.07269
117	0.9184	0.1631	0.08156
118	0.8962	0.2076	0.1038
119	0.8749	0.2501	0.1251
120	0.8594	0.2811	0.1406
121	0.8331	0.3337	0.1669
122	0.7943	0.4115	0.2057
123	0.8545	0.2911	0.1455
124	0.866	0.268	0.134
125	0.8461	0.3078	0.1539
126	0.8334	0.3331	0.1666
127	0.8008	0.3984	0.1992
128	0.7562	0.4875	0.2438
129	0.7419	0.5162	0.2581
130	0.8468	0.3063	0.1532
131	0.8626	0.2748	0.1374
132	0.8223	0.3553	0.1777
133	0.7857	0.4285	0.2143
134	0.7555	0.489	0.2445
135	0.6965	0.6069	0.3035
136	0.6324	0.7351	0.3676
137	0.7031	0.5938	0.2969
138	0.6527	0.6946	0.3473
139	0.5791	0.8418	0.4209
140	0.8032	0.3936	0.1968
141	0.8104	0.3792	0.1896
142	0.7833	0.4334	0.2167
143	0.7556	0.4887	0.2444
144	0.7878	0.4245	0.2122
145	0.707	0.5861	0.293
146	0.6671	0.6658	0.3329
147	0.7002	0.5997	0.2998
148	0.8108	0.3784	0.1892
149	0.6871	0.6257	0.3129
150	0.5268	0.9463	0.4732

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 &  0.2722 &  0.5443 &  0.7278 \tabularnewline
17 &  0.2528 &  0.5055 &  0.7472 \tabularnewline
18 &  0.1793 &  0.3587 &  0.8207 \tabularnewline
19 &  0.6214 &  0.7573 &  0.3786 \tabularnewline
20 &  0.5081 &  0.9839 &  0.4919 \tabularnewline
21 &  0.3969 &  0.7938 &  0.6031 \tabularnewline
22 &  0.325 &  0.65 &  0.675 \tabularnewline
23 &  0.2409 &  0.4818 &  0.7591 \tabularnewline
24 &  0.2204 &  0.4408 &  0.7796 \tabularnewline
25 &  0.1643 &  0.3286 &  0.8357 \tabularnewline
26 &  0.1261 &  0.2522 &  0.8739 \tabularnewline
27 &  0.08612 &  0.1722 &  0.9139 \tabularnewline
28 &  0.05783 &  0.1157 &  0.9422 \tabularnewline
29 &  0.08216 &  0.1643 &  0.9178 \tabularnewline
30 &  0.06058 &  0.1212 &  0.9394 \tabularnewline
31 &  0.05968 &  0.1194 &  0.9403 \tabularnewline
32 &  0.0463 &  0.09259 &  0.9537 \tabularnewline
33 &  0.03098 &  0.06196 &  0.969 \tabularnewline
34 &  0.03443 &  0.06886 &  0.9656 \tabularnewline
35 &  0.1221 &  0.2442 &  0.8779 \tabularnewline
36 &  0.1506 &  0.3012 &  0.8494 \tabularnewline
37 &  0.1223 &  0.2446 &  0.8777 \tabularnewline
38 &  0.09203 &  0.1841 &  0.908 \tabularnewline
39 &  0.06941 &  0.1388 &  0.9306 \tabularnewline
40 &  0.05062 &  0.1012 &  0.9494 \tabularnewline
41 &  0.04768 &  0.09537 &  0.9523 \tabularnewline
42 &  0.03843 &  0.07685 &  0.9616 \tabularnewline
43 &  0.06415 &  0.1283 &  0.9358 \tabularnewline
44 &  0.06486 &  0.1297 &  0.9351 \tabularnewline
45 &  0.04917 &  0.09834 &  0.9508 \tabularnewline
46 &  0.0449 &  0.0898 &  0.9551 \tabularnewline
47 &  0.03802 &  0.07605 &  0.962 \tabularnewline
48 &  0.0501 &  0.1002 &  0.9499 \tabularnewline
49 &  0.04069 &  0.08139 &  0.9593 \tabularnewline
50 &  0.04708 &  0.09416 &  0.9529 \tabularnewline
51 &  0.04548 &  0.09096 &  0.9545 \tabularnewline
52 &  0.03415 &  0.06831 &  0.9658 \tabularnewline
53 &  0.0616 &  0.1232 &  0.9384 \tabularnewline
54 &  0.04912 &  0.09824 &  0.9509 \tabularnewline
55 &  0.03857 &  0.07714 &  0.9614 \tabularnewline
56 &  0.03259 &  0.06518 &  0.9674 \tabularnewline
57 &  0.0377 &  0.0754 &  0.9623 \tabularnewline
58 &  0.03012 &  0.06024 &  0.9699 \tabularnewline
59 &  0.02347 &  0.04694 &  0.9765 \tabularnewline
60 &  0.02197 &  0.04395 &  0.978 \tabularnewline
61 &  0.08509 &  0.1702 &  0.9149 \tabularnewline
62 &  0.2254 &  0.4508 &  0.7746 \tabularnewline
63 &  0.2387 &  0.4774 &  0.7613 \tabularnewline
64 &  0.4674 &  0.9349 &  0.5326 \tabularnewline
65 &  0.527 &  0.946 &  0.473 \tabularnewline
66 &  0.5774 &  0.8453 &  0.4226 \tabularnewline
67 &  0.5315 &  0.937 &  0.4685 \tabularnewline
68 &  0.6142 &  0.7716 &  0.3858 \tabularnewline
69 &  0.6273 &  0.7453 &  0.3727 \tabularnewline
70 &  0.6954 &  0.6091 &  0.3046 \tabularnewline
71 &  0.7395 &  0.521 &  0.2605 \tabularnewline
72 &  0.731 &  0.538 &  0.269 \tabularnewline
73 &  0.7676 &  0.4647 &  0.2324 \tabularnewline
74 &  0.8063 &  0.3875 &  0.1937 \tabularnewline
75 &  0.8197 &  0.3606 &  0.1803 \tabularnewline
76 &  0.8004 &  0.3992 &  0.1996 \tabularnewline
77 &  0.7836 &  0.4327 &  0.2163 \tabularnewline
78 &  0.7902 &  0.4197 &  0.2098 \tabularnewline
79 &  0.7743 &  0.4515 &  0.2257 \tabularnewline
80 &  0.8457 &  0.3085 &  0.1543 \tabularnewline
81 &  0.8409 &  0.3181 &  0.1591 \tabularnewline
82 &  0.8448 &  0.3104 &  0.1552 \tabularnewline
83 &  0.8226 &  0.3548 &  0.1774 \tabularnewline
84 &  0.7926 &  0.4148 &  0.2074 \tabularnewline
85 &  0.8108 &  0.3784 &  0.1892 \tabularnewline
86 &  0.8297 &  0.3406 &  0.1703 \tabularnewline
87 &  0.8342 &  0.3316 &  0.1658 \tabularnewline
88 &  0.834 &  0.332 &  0.166 \tabularnewline
89 &  0.8906 &  0.2188 &  0.1094 \tabularnewline
90 &  0.8698 &  0.2604 &  0.1302 \tabularnewline
91 &  0.8888 &  0.2224 &  0.1112 \tabularnewline
92 &  0.8982 &  0.2035 &  0.1018 \tabularnewline
93 &  0.8803 &  0.2394 &  0.1197 \tabularnewline
94 &  0.8906 &  0.2189 &  0.1094 \tabularnewline
95 &  0.9545 &  0.09095 &  0.04547 \tabularnewline
96 &  0.9462 &  0.1075 &  0.05376 \tabularnewline
97 &  0.9328 &  0.1345 &  0.06724 \tabularnewline
98 &  0.9203 &  0.1593 &  0.07966 \tabularnewline
99 &  0.9199 &  0.1602 &  0.08012 \tabularnewline
100 &  0.9023 &  0.1953 &  0.09767 \tabularnewline
101 &  0.9074 &  0.1852 &  0.09261 \tabularnewline
102 &  0.8923 &  0.2155 &  0.1077 \tabularnewline
103 &  0.8775 &  0.245 &  0.1225 \tabularnewline
104 &  0.9797 &  0.04052 &  0.02026 \tabularnewline
105 &  0.9863 &  0.02735 &  0.01367 \tabularnewline
106 &  0.9822 &  0.03566 &  0.01783 \tabularnewline
107 &  0.9801 &  0.03973 &  0.01986 \tabularnewline
108 &  0.9738 &  0.05231 &  0.02615 \tabularnewline
109 &  0.9671 &  0.0657 &  0.03285 \tabularnewline
110 &  0.9566 &  0.08676 &  0.04338 \tabularnewline
111 &  0.9543 &  0.09147 &  0.04574 \tabularnewline
112 &  0.9592 &  0.08152 &  0.04076 \tabularnewline
113 &  0.9489 &  0.1021 &  0.05107 \tabularnewline
114 &  0.9427 &  0.1146 &  0.05731 \tabularnewline
115 &  0.9318 &  0.1364 &  0.06818 \tabularnewline
116 &  0.9273 &  0.1454 &  0.07269 \tabularnewline
117 &  0.9184 &  0.1631 &  0.08156 \tabularnewline
118 &  0.8962 &  0.2076 &  0.1038 \tabularnewline
119 &  0.8749 &  0.2501 &  0.1251 \tabularnewline
120 &  0.8594 &  0.2811 &  0.1406 \tabularnewline
121 &  0.8331 &  0.3337 &  0.1669 \tabularnewline
122 &  0.7943 &  0.4115 &  0.2057 \tabularnewline
123 &  0.8545 &  0.2911 &  0.1455 \tabularnewline
124 &  0.866 &  0.268 &  0.134 \tabularnewline
125 &  0.8461 &  0.3078 &  0.1539 \tabularnewline
126 &  0.8334 &  0.3331 &  0.1666 \tabularnewline
127 &  0.8008 &  0.3984 &  0.1992 \tabularnewline
128 &  0.7562 &  0.4875 &  0.2438 \tabularnewline
129 &  0.7419 &  0.5162 &  0.2581 \tabularnewline
130 &  0.8468 &  0.3063 &  0.1532 \tabularnewline
131 &  0.8626 &  0.2748 &  0.1374 \tabularnewline
132 &  0.8223 &  0.3553 &  0.1777 \tabularnewline
133 &  0.7857 &  0.4285 &  0.2143 \tabularnewline
134 &  0.7555 &  0.489 &  0.2445 \tabularnewline
135 &  0.6965 &  0.6069 &  0.3035 \tabularnewline
136 &  0.6324 &  0.7351 &  0.3676 \tabularnewline
137 &  0.7031 &  0.5938 &  0.2969 \tabularnewline
138 &  0.6527 &  0.6946 &  0.3473 \tabularnewline
139 &  0.5791 &  0.8418 &  0.4209 \tabularnewline
140 &  0.8032 &  0.3936 &  0.1968 \tabularnewline
141 &  0.8104 &  0.3792 &  0.1896 \tabularnewline
142 &  0.7833 &  0.4334 &  0.2167 \tabularnewline
143 &  0.7556 &  0.4887 &  0.2444 \tabularnewline
144 &  0.7878 &  0.4245 &  0.2122 \tabularnewline
145 &  0.707 &  0.5861 &  0.293 \tabularnewline
146 &  0.6671 &  0.6658 &  0.3329 \tabularnewline
147 &  0.7002 &  0.5997 &  0.2998 \tabularnewline
148 &  0.8108 &  0.3784 &  0.1892 \tabularnewline
149 &  0.6871 &  0.6257 &  0.3129 \tabularnewline
150 &  0.5268 &  0.9463 &  0.4732 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309534&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]16[/C][C] 0.2722[/C][C] 0.5443[/C][C] 0.7278[/C][/ROW]
[ROW][C]17[/C][C] 0.2528[/C][C] 0.5055[/C][C] 0.7472[/C][/ROW]
[ROW][C]18[/C][C] 0.1793[/C][C] 0.3587[/C][C] 0.8207[/C][/ROW]
[ROW][C]19[/C][C] 0.6214[/C][C] 0.7573[/C][C] 0.3786[/C][/ROW]
[ROW][C]20[/C][C] 0.5081[/C][C] 0.9839[/C][C] 0.4919[/C][/ROW]
[ROW][C]21[/C][C] 0.3969[/C][C] 0.7938[/C][C] 0.6031[/C][/ROW]
[ROW][C]22[/C][C] 0.325[/C][C] 0.65[/C][C] 0.675[/C][/ROW]
[ROW][C]23[/C][C] 0.2409[/C][C] 0.4818[/C][C] 0.7591[/C][/ROW]
[ROW][C]24[/C][C] 0.2204[/C][C] 0.4408[/C][C] 0.7796[/C][/ROW]
[ROW][C]25[/C][C] 0.1643[/C][C] 0.3286[/C][C] 0.8357[/C][/ROW]
[ROW][C]26[/C][C] 0.1261[/C][C] 0.2522[/C][C] 0.8739[/C][/ROW]
[ROW][C]27[/C][C] 0.08612[/C][C] 0.1722[/C][C] 0.9139[/C][/ROW]
[ROW][C]28[/C][C] 0.05783[/C][C] 0.1157[/C][C] 0.9422[/C][/ROW]
[ROW][C]29[/C][C] 0.08216[/C][C] 0.1643[/C][C] 0.9178[/C][/ROW]
[ROW][C]30[/C][C] 0.06058[/C][C] 0.1212[/C][C] 0.9394[/C][/ROW]
[ROW][C]31[/C][C] 0.05968[/C][C] 0.1194[/C][C] 0.9403[/C][/ROW]
[ROW][C]32[/C][C] 0.0463[/C][C] 0.09259[/C][C] 0.9537[/C][/ROW]
[ROW][C]33[/C][C] 0.03098[/C][C] 0.06196[/C][C] 0.969[/C][/ROW]
[ROW][C]34[/C][C] 0.03443[/C][C] 0.06886[/C][C] 0.9656[/C][/ROW]
[ROW][C]35[/C][C] 0.1221[/C][C] 0.2442[/C][C] 0.8779[/C][/ROW]
[ROW][C]36[/C][C] 0.1506[/C][C] 0.3012[/C][C] 0.8494[/C][/ROW]
[ROW][C]37[/C][C] 0.1223[/C][C] 0.2446[/C][C] 0.8777[/C][/ROW]
[ROW][C]38[/C][C] 0.09203[/C][C] 0.1841[/C][C] 0.908[/C][/ROW]
[ROW][C]39[/C][C] 0.06941[/C][C] 0.1388[/C][C] 0.9306[/C][/ROW]
[ROW][C]40[/C][C] 0.05062[/C][C] 0.1012[/C][C] 0.9494[/C][/ROW]
[ROW][C]41[/C][C] 0.04768[/C][C] 0.09537[/C][C] 0.9523[/C][/ROW]
[ROW][C]42[/C][C] 0.03843[/C][C] 0.07685[/C][C] 0.9616[/C][/ROW]
[ROW][C]43[/C][C] 0.06415[/C][C] 0.1283[/C][C] 0.9358[/C][/ROW]
[ROW][C]44[/C][C] 0.06486[/C][C] 0.1297[/C][C] 0.9351[/C][/ROW]
[ROW][C]45[/C][C] 0.04917[/C][C] 0.09834[/C][C] 0.9508[/C][/ROW]
[ROW][C]46[/C][C] 0.0449[/C][C] 0.0898[/C][C] 0.9551[/C][/ROW]
[ROW][C]47[/C][C] 0.03802[/C][C] 0.07605[/C][C] 0.962[/C][/ROW]
[ROW][C]48[/C][C] 0.0501[/C][C] 0.1002[/C][C] 0.9499[/C][/ROW]
[ROW][C]49[/C][C] 0.04069[/C][C] 0.08139[/C][C] 0.9593[/C][/ROW]
[ROW][C]50[/C][C] 0.04708[/C][C] 0.09416[/C][C] 0.9529[/C][/ROW]
[ROW][C]51[/C][C] 0.04548[/C][C] 0.09096[/C][C] 0.9545[/C][/ROW]
[ROW][C]52[/C][C] 0.03415[/C][C] 0.06831[/C][C] 0.9658[/C][/ROW]
[ROW][C]53[/C][C] 0.0616[/C][C] 0.1232[/C][C] 0.9384[/C][/ROW]
[ROW][C]54[/C][C] 0.04912[/C][C] 0.09824[/C][C] 0.9509[/C][/ROW]
[ROW][C]55[/C][C] 0.03857[/C][C] 0.07714[/C][C] 0.9614[/C][/ROW]
[ROW][C]56[/C][C] 0.03259[/C][C] 0.06518[/C][C] 0.9674[/C][/ROW]
[ROW][C]57[/C][C] 0.0377[/C][C] 0.0754[/C][C] 0.9623[/C][/ROW]
[ROW][C]58[/C][C] 0.03012[/C][C] 0.06024[/C][C] 0.9699[/C][/ROW]
[ROW][C]59[/C][C] 0.02347[/C][C] 0.04694[/C][C] 0.9765[/C][/ROW]
[ROW][C]60[/C][C] 0.02197[/C][C] 0.04395[/C][C] 0.978[/C][/ROW]
[ROW][C]61[/C][C] 0.08509[/C][C] 0.1702[/C][C] 0.9149[/C][/ROW]
[ROW][C]62[/C][C] 0.2254[/C][C] 0.4508[/C][C] 0.7746[/C][/ROW]
[ROW][C]63[/C][C] 0.2387[/C][C] 0.4774[/C][C] 0.7613[/C][/ROW]
[ROW][C]64[/C][C] 0.4674[/C][C] 0.9349[/C][C] 0.5326[/C][/ROW]
[ROW][C]65[/C][C] 0.527[/C][C] 0.946[/C][C] 0.473[/C][/ROW]
[ROW][C]66[/C][C] 0.5774[/C][C] 0.8453[/C][C] 0.4226[/C][/ROW]
[ROW][C]67[/C][C] 0.5315[/C][C] 0.937[/C][C] 0.4685[/C][/ROW]
[ROW][C]68[/C][C] 0.6142[/C][C] 0.7716[/C][C] 0.3858[/C][/ROW]
[ROW][C]69[/C][C] 0.6273[/C][C] 0.7453[/C][C] 0.3727[/C][/ROW]
[ROW][C]70[/C][C] 0.6954[/C][C] 0.6091[/C][C] 0.3046[/C][/ROW]
[ROW][C]71[/C][C] 0.7395[/C][C] 0.521[/C][C] 0.2605[/C][/ROW]
[ROW][C]72[/C][C] 0.731[/C][C] 0.538[/C][C] 0.269[/C][/ROW]
[ROW][C]73[/C][C] 0.7676[/C][C] 0.4647[/C][C] 0.2324[/C][/ROW]
[ROW][C]74[/C][C] 0.8063[/C][C] 0.3875[/C][C] 0.1937[/C][/ROW]
[ROW][C]75[/C][C] 0.8197[/C][C] 0.3606[/C][C] 0.1803[/C][/ROW]
[ROW][C]76[/C][C] 0.8004[/C][C] 0.3992[/C][C] 0.1996[/C][/ROW]
[ROW][C]77[/C][C] 0.7836[/C][C] 0.4327[/C][C] 0.2163[/C][/ROW]
[ROW][C]78[/C][C] 0.7902[/C][C] 0.4197[/C][C] 0.2098[/C][/ROW]
[ROW][C]79[/C][C] 0.7743[/C][C] 0.4515[/C][C] 0.2257[/C][/ROW]
[ROW][C]80[/C][C] 0.8457[/C][C] 0.3085[/C][C] 0.1543[/C][/ROW]
[ROW][C]81[/C][C] 0.8409[/C][C] 0.3181[/C][C] 0.1591[/C][/ROW]
[ROW][C]82[/C][C] 0.8448[/C][C] 0.3104[/C][C] 0.1552[/C][/ROW]
[ROW][C]83[/C][C] 0.8226[/C][C] 0.3548[/C][C] 0.1774[/C][/ROW]
[ROW][C]84[/C][C] 0.7926[/C][C] 0.4148[/C][C] 0.2074[/C][/ROW]
[ROW][C]85[/C][C] 0.8108[/C][C] 0.3784[/C][C] 0.1892[/C][/ROW]
[ROW][C]86[/C][C] 0.8297[/C][C] 0.3406[/C][C] 0.1703[/C][/ROW]
[ROW][C]87[/C][C] 0.8342[/C][C] 0.3316[/C][C] 0.1658[/C][/ROW]
[ROW][C]88[/C][C] 0.834[/C][C] 0.332[/C][C] 0.166[/C][/ROW]
[ROW][C]89[/C][C] 0.8906[/C][C] 0.2188[/C][C] 0.1094[/C][/ROW]
[ROW][C]90[/C][C] 0.8698[/C][C] 0.2604[/C][C] 0.1302[/C][/ROW]
[ROW][C]91[/C][C] 0.8888[/C][C] 0.2224[/C][C] 0.1112[/C][/ROW]
[ROW][C]92[/C][C] 0.8982[/C][C] 0.2035[/C][C] 0.1018[/C][/ROW]
[ROW][C]93[/C][C] 0.8803[/C][C] 0.2394[/C][C] 0.1197[/C][/ROW]
[ROW][C]94[/C][C] 0.8906[/C][C] 0.2189[/C][C] 0.1094[/C][/ROW]
[ROW][C]95[/C][C] 0.9545[/C][C] 0.09095[/C][C] 0.04547[/C][/ROW]
[ROW][C]96[/C][C] 0.9462[/C][C] 0.1075[/C][C] 0.05376[/C][/ROW]
[ROW][C]97[/C][C] 0.9328[/C][C] 0.1345[/C][C] 0.06724[/C][/ROW]
[ROW][C]98[/C][C] 0.9203[/C][C] 0.1593[/C][C] 0.07966[/C][/ROW]
[ROW][C]99[/C][C] 0.9199[/C][C] 0.1602[/C][C] 0.08012[/C][/ROW]
[ROW][C]100[/C][C] 0.9023[/C][C] 0.1953[/C][C] 0.09767[/C][/ROW]
[ROW][C]101[/C][C] 0.9074[/C][C] 0.1852[/C][C] 0.09261[/C][/ROW]
[ROW][C]102[/C][C] 0.8923[/C][C] 0.2155[/C][C] 0.1077[/C][/ROW]
[ROW][C]103[/C][C] 0.8775[/C][C] 0.245[/C][C] 0.1225[/C][/ROW]
[ROW][C]104[/C][C] 0.9797[/C][C] 0.04052[/C][C] 0.02026[/C][/ROW]
[ROW][C]105[/C][C] 0.9863[/C][C] 0.02735[/C][C] 0.01367[/C][/ROW]
[ROW][C]106[/C][C] 0.9822[/C][C] 0.03566[/C][C] 0.01783[/C][/ROW]
[ROW][C]107[/C][C] 0.9801[/C][C] 0.03973[/C][C] 0.01986[/C][/ROW]
[ROW][C]108[/C][C] 0.9738[/C][C] 0.05231[/C][C] 0.02615[/C][/ROW]
[ROW][C]109[/C][C] 0.9671[/C][C] 0.0657[/C][C] 0.03285[/C][/ROW]
[ROW][C]110[/C][C] 0.9566[/C][C] 0.08676[/C][C] 0.04338[/C][/ROW]
[ROW][C]111[/C][C] 0.9543[/C][C] 0.09147[/C][C] 0.04574[/C][/ROW]
[ROW][C]112[/C][C] 0.9592[/C][C] 0.08152[/C][C] 0.04076[/C][/ROW]
[ROW][C]113[/C][C] 0.9489[/C][C] 0.1021[/C][C] 0.05107[/C][/ROW]
[ROW][C]114[/C][C] 0.9427[/C][C] 0.1146[/C][C] 0.05731[/C][/ROW]
[ROW][C]115[/C][C] 0.9318[/C][C] 0.1364[/C][C] 0.06818[/C][/ROW]
[ROW][C]116[/C][C] 0.9273[/C][C] 0.1454[/C][C] 0.07269[/C][/ROW]
[ROW][C]117[/C][C] 0.9184[/C][C] 0.1631[/C][C] 0.08156[/C][/ROW]
[ROW][C]118[/C][C] 0.8962[/C][C] 0.2076[/C][C] 0.1038[/C][/ROW]
[ROW][C]119[/C][C] 0.8749[/C][C] 0.2501[/C][C] 0.1251[/C][/ROW]
[ROW][C]120[/C][C] 0.8594[/C][C] 0.2811[/C][C] 0.1406[/C][/ROW]
[ROW][C]121[/C][C] 0.8331[/C][C] 0.3337[/C][C] 0.1669[/C][/ROW]
[ROW][C]122[/C][C] 0.7943[/C][C] 0.4115[/C][C] 0.2057[/C][/ROW]
[ROW][C]123[/C][C] 0.8545[/C][C] 0.2911[/C][C] 0.1455[/C][/ROW]
[ROW][C]124[/C][C] 0.866[/C][C] 0.268[/C][C] 0.134[/C][/ROW]
[ROW][C]125[/C][C] 0.8461[/C][C] 0.3078[/C][C] 0.1539[/C][/ROW]
[ROW][C]126[/C][C] 0.8334[/C][C] 0.3331[/C][C] 0.1666[/C][/ROW]
[ROW][C]127[/C][C] 0.8008[/C][C] 0.3984[/C][C] 0.1992[/C][/ROW]
[ROW][C]128[/C][C] 0.7562[/C][C] 0.4875[/C][C] 0.2438[/C][/ROW]
[ROW][C]129[/C][C] 0.7419[/C][C] 0.5162[/C][C] 0.2581[/C][/ROW]
[ROW][C]130[/C][C] 0.8468[/C][C] 0.3063[/C][C] 0.1532[/C][/ROW]
[ROW][C]131[/C][C] 0.8626[/C][C] 0.2748[/C][C] 0.1374[/C][/ROW]
[ROW][C]132[/C][C] 0.8223[/C][C] 0.3553[/C][C] 0.1777[/C][/ROW]
[ROW][C]133[/C][C] 0.7857[/C][C] 0.4285[/C][C] 0.2143[/C][/ROW]
[ROW][C]134[/C][C] 0.7555[/C][C] 0.489[/C][C] 0.2445[/C][/ROW]
[ROW][C]135[/C][C] 0.6965[/C][C] 0.6069[/C][C] 0.3035[/C][/ROW]
[ROW][C]136[/C][C] 0.6324[/C][C] 0.7351[/C][C] 0.3676[/C][/ROW]
[ROW][C]137[/C][C] 0.7031[/C][C] 0.5938[/C][C] 0.2969[/C][/ROW]
[ROW][C]138[/C][C] 0.6527[/C][C] 0.6946[/C][C] 0.3473[/C][/ROW]
[ROW][C]139[/C][C] 0.5791[/C][C] 0.8418[/C][C] 0.4209[/C][/ROW]
[ROW][C]140[/C][C] 0.8032[/C][C] 0.3936[/C][C] 0.1968[/C][/ROW]
[ROW][C]141[/C][C] 0.8104[/C][C] 0.3792[/C][C] 0.1896[/C][/ROW]
[ROW][C]142[/C][C] 0.7833[/C][C] 0.4334[/C][C] 0.2167[/C][/ROW]
[ROW][C]143[/C][C] 0.7556[/C][C] 0.4887[/C][C] 0.2444[/C][/ROW]
[ROW][C]144[/C][C] 0.7878[/C][C] 0.4245[/C][C] 0.2122[/C][/ROW]
[ROW][C]145[/C][C] 0.707[/C][C] 0.5861[/C][C] 0.293[/C][/ROW]
[ROW][C]146[/C][C] 0.6671[/C][C] 0.6658[/C][C] 0.3329[/C][/ROW]
[ROW][C]147[/C][C] 0.7002[/C][C] 0.5997[/C][C] 0.2998[/C][/ROW]
[ROW][C]148[/C][C] 0.8108[/C][C] 0.3784[/C][C] 0.1892[/C][/ROW]
[ROW][C]149[/C][C] 0.6871[/C][C] 0.6257[/C][C] 0.3129[/C][/ROW]
[ROW][C]150[/C][C] 0.5268[/C][C] 0.9463[/C][C] 0.4732[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309534&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309534&T=6

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.2722	0.5443	0.7278
17	0.2528	0.5055	0.7472
18	0.1793	0.3587	0.8207
19	0.6214	0.7573	0.3786
20	0.5081	0.9839	0.4919
21	0.3969	0.7938	0.6031
22	0.325	0.65	0.675
23	0.2409	0.4818	0.7591
24	0.2204	0.4408	0.7796
25	0.1643	0.3286	0.8357
26	0.1261	0.2522	0.8739
27	0.08612	0.1722	0.9139
28	0.05783	0.1157	0.9422
29	0.08216	0.1643	0.9178
30	0.06058	0.1212	0.9394
31	0.05968	0.1194	0.9403
32	0.0463	0.09259	0.9537
33	0.03098	0.06196	0.969
34	0.03443	0.06886	0.9656
35	0.1221	0.2442	0.8779
36	0.1506	0.3012	0.8494
37	0.1223	0.2446	0.8777
38	0.09203	0.1841	0.908
39	0.06941	0.1388	0.9306
40	0.05062	0.1012	0.9494
41	0.04768	0.09537	0.9523
42	0.03843	0.07685	0.9616
43	0.06415	0.1283	0.9358
44	0.06486	0.1297	0.9351
45	0.04917	0.09834	0.9508
46	0.0449	0.0898	0.9551
47	0.03802	0.07605	0.962
48	0.0501	0.1002	0.9499
49	0.04069	0.08139	0.9593
50	0.04708	0.09416	0.9529
51	0.04548	0.09096	0.9545
52	0.03415	0.06831	0.9658
53	0.0616	0.1232	0.9384
54	0.04912	0.09824	0.9509
55	0.03857	0.07714	0.9614
56	0.03259	0.06518	0.9674
57	0.0377	0.0754	0.9623
58	0.03012	0.06024	0.9699
59	0.02347	0.04694	0.9765
60	0.02197	0.04395	0.978
61	0.08509	0.1702	0.9149
62	0.2254	0.4508	0.7746
63	0.2387	0.4774	0.7613
64	0.4674	0.9349	0.5326
65	0.527	0.946	0.473
66	0.5774	0.8453	0.4226
67	0.5315	0.937	0.4685
68	0.6142	0.7716	0.3858
69	0.6273	0.7453	0.3727
70	0.6954	0.6091	0.3046
71	0.7395	0.521	0.2605
72	0.731	0.538	0.269
73	0.7676	0.4647	0.2324
74	0.8063	0.3875	0.1937
75	0.8197	0.3606	0.1803
76	0.8004	0.3992	0.1996
77	0.7836	0.4327	0.2163
78	0.7902	0.4197	0.2098
79	0.7743	0.4515	0.2257
80	0.8457	0.3085	0.1543
81	0.8409	0.3181	0.1591
82	0.8448	0.3104	0.1552
83	0.8226	0.3548	0.1774
84	0.7926	0.4148	0.2074
85	0.8108	0.3784	0.1892
86	0.8297	0.3406	0.1703
87	0.8342	0.3316	0.1658
88	0.834	0.332	0.166
89	0.8906	0.2188	0.1094
90	0.8698	0.2604	0.1302
91	0.8888	0.2224	0.1112
92	0.8982	0.2035	0.1018
93	0.8803	0.2394	0.1197
94	0.8906	0.2189	0.1094
95	0.9545	0.09095	0.04547
96	0.9462	0.1075	0.05376
97	0.9328	0.1345	0.06724
98	0.9203	0.1593	0.07966
99	0.9199	0.1602	0.08012
100	0.9023	0.1953	0.09767
101	0.9074	0.1852	0.09261
102	0.8923	0.2155	0.1077
103	0.8775	0.245	0.1225
104	0.9797	0.04052	0.02026
105	0.9863	0.02735	0.01367
106	0.9822	0.03566	0.01783
107	0.9801	0.03973	0.01986
108	0.9738	0.05231	0.02615
109	0.9671	0.0657	0.03285
110	0.9566	0.08676	0.04338
111	0.9543	0.09147	0.04574
112	0.9592	0.08152	0.04076
113	0.9489	0.1021	0.05107
114	0.9427	0.1146	0.05731
115	0.9318	0.1364	0.06818
116	0.9273	0.1454	0.07269
117	0.9184	0.1631	0.08156
118	0.8962	0.2076	0.1038
119	0.8749	0.2501	0.1251
120	0.8594	0.2811	0.1406
121	0.8331	0.3337	0.1669
122	0.7943	0.4115	0.2057
123	0.8545	0.2911	0.1455
124	0.866	0.268	0.134
125	0.8461	0.3078	0.1539
126	0.8334	0.3331	0.1666
127	0.8008	0.3984	0.1992
128	0.7562	0.4875	0.2438
129	0.7419	0.5162	0.2581
130	0.8468	0.3063	0.1532
131	0.8626	0.2748	0.1374
132	0.8223	0.3553	0.1777
133	0.7857	0.4285	0.2143
134	0.7555	0.489	0.2445
135	0.6965	0.6069	0.3035
136	0.6324	0.7351	0.3676
137	0.7031	0.5938	0.2969
138	0.6527	0.6946	0.3473
139	0.5791	0.8418	0.4209
140	0.8032	0.3936	0.1968
141	0.8104	0.3792	0.1896
142	0.7833	0.4334	0.2167
143	0.7556	0.4887	0.2444
144	0.7878	0.4245	0.2122
145	0.707	0.5861	0.293
146	0.6671	0.6658	0.3329
147	0.7002	0.5997	0.2998
148	0.8108	0.3784	0.1892
149	0.6871	0.6257	0.3129
150	0.5268	0.9463	0.4732

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	6	0.0444444	OK
10% type I error level	29	0.214815	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 6 & 0.0444444 & OK \tabularnewline
10% type I error level & 29 & 0.214815 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309534&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.0444444[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]29[/C][C]0.214815[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309534&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309534&T=7

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	6	0.0444444	OK
10% type I error level	29	0.214815	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.1801, df1 = 2, df2 = 151, p-value = 0.3101
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0112, df1 = 24, df2 = 129, p-value = 0.4572
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.1504, df1 = 2, df2 = 151, p-value = 0.12

\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 = 1.1801, df1 = 2, df2 = 151, p-value = 0.3101
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0112, df1 = 24, df2 = 129, p-value = 0.4572
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.1504, df1 = 2, df2 = 151, p-value = 0.12
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309534&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 = 1.1801, df1 = 2, df2 = 151, p-value = 0.3101
[/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 = 1.0112, df1 = 24, df2 = 129, p-value = 0.4572
[/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 = 2.1504, df1 = 2, df2 = 151, p-value = 0.12
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309534&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309534&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.1801, df1 = 2, df2 = 151, p-value = 0.3101
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0112, df1 = 24, df2 = 129, p-value = 0.4572
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.1504, df1 = 2, df2 = 151, p-value = 0.12

Variance Inflation Factors (Multicollinearity)

> vif
      `(1-Bs)(1-B)V2`       `(1-Bs)(1-B)V3`       `(1-Bs)(1-B)V4` 
             1.971396              6.828118              7.899393 
      `(1-Bs)(1-B)V5`       `(1-Bs)(1-B)V6`       `(1-Bs)(1-B)V7` 
             6.126211              2.140430              2.204990 
      `(1-Bs)(1-B)V8`  `(1-Bs)(1-B)V1(t-1)`  `(1-Bs)(1-B)V1(t-2)` 
             1.625200              3.808446              4.665818 
 `(1-Bs)(1-B)V1(t-3)` `(1-Bs)(1-B)V1(t-1s)` `(1-Bs)(1-B)V1(t-2s)` 
             3.916688              1.485751              1.348549

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      `(1-Bs)(1-B)V2`       `(1-Bs)(1-B)V3`       `(1-Bs)(1-B)V4` 
             1.971396              6.828118              7.899393 
      `(1-Bs)(1-B)V5`       `(1-Bs)(1-B)V6`       `(1-Bs)(1-B)V7` 
             6.126211              2.140430              2.204990 
      `(1-Bs)(1-B)V8`  `(1-Bs)(1-B)V1(t-1)`  `(1-Bs)(1-B)V1(t-2)` 
             1.625200              3.808446              4.665818 
 `(1-Bs)(1-B)V1(t-3)` `(1-Bs)(1-B)V1(t-1s)` `(1-Bs)(1-B)V1(t-2s)` 
             3.916688              1.485751              1.348549 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309534&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      `(1-Bs)(1-B)V2`       `(1-Bs)(1-B)V3`       `(1-Bs)(1-B)V4` 
             1.971396              6.828118              7.899393 
      `(1-Bs)(1-B)V5`       `(1-Bs)(1-B)V6`       `(1-Bs)(1-B)V7` 
             6.126211              2.140430              2.204990 
      `(1-Bs)(1-B)V8`  `(1-Bs)(1-B)V1(t-1)`  `(1-Bs)(1-B)V1(t-2)` 
             1.625200              3.808446              4.665818 
 `(1-Bs)(1-B)V1(t-3)` `(1-Bs)(1-B)V1(t-1s)` `(1-Bs)(1-B)V1(t-2s)` 
             3.916688              1.485751              1.348549 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309534&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309534&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)

> vif
      `(1-Bs)(1-B)V2`       `(1-Bs)(1-B)V3`       `(1-Bs)(1-B)V4` 
             1.971396              6.828118              7.899393 
      `(1-Bs)(1-B)V5`       `(1-Bs)(1-B)V6`       `(1-Bs)(1-B)V7` 
             6.126211              2.140430              2.204990 
      `(1-Bs)(1-B)V8`  `(1-Bs)(1-B)V1(t-1)`  `(1-Bs)(1-B)V1(t-2)` 
             1.625200              3.808446              4.665818 
 `(1-Bs)(1-B)V1(t-3)` `(1-Bs)(1-B)V1(t-1s)` `(1-Bs)(1-B)V1(t-2s)` 
             3.916688              1.485751              1.348549

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 3 ; par5 = 1 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 3 ; par5 = 2 ; par6 = 12 ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code