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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 30 Oct 2012 12:22:17 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Oct/30/t1351614152ri62e1co0n5zwh8.htm/, Retrieved Fri, 03 May 2024 18:02:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185198, Retrieved Fri, 03 May 2024 18:02:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact101

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-10-30 16:22:17] [564f08b1e01e129faa0d56ace254d273] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
1	75,5	78,4	67,3	75,3	106,1	125,7	101,6
2	83,2	79,3	75,2	83,6	112,7	153,8	113,4
3	94,5	84,3	91,1	91,2	123,2	134,9	122,2
4	83,3	81,2	83,7	85,2	101,7	95,3	102,2
5	92,7	88,4	105,0	100,0	118,7	96,6	113,2
6	89,8	83,1	106,2	89,8	107,1	100,5	115,3
7	74,8	76,6	88,5	88,9	93,6	106,2	87,4
8	81,5	82,6	100,1	85,6	77,5	153,4	98,7
9	92,8	84,4	90,3	83,2	117,2	132,1	117,3
10	92,8	94,6	85,3	97,1	124,5	110,9	121,2
11	91,7	91,8	81,9	85,8	120,8	94,3	118,7
12	83,5	89,3	77,2	80,9	97,0	91,7	112,1
13	92,8	87,7	78,6	81,3	115,1	138,6	102,9
14	91,3	83,1	75,1	83,2	112,9	154,3	108,8
15	99,5	93,6	90,3	90,7	122,7	149,8	118,6
16	87,6	85,1	88,5	88,4	106,9	99,2	99,2
17	95,3	90,8	112,5	94,1	115,0	97,7	102,2
18	98,5	90,5	101,1	92,0	114,9	107,7	108,8
19	80,1	86,1	114,0	92,0	103,1	120,1	94,0
20	84,2	93,3	107,7	89,3	80,8	164,5	96,2
21	92,4	94,9	77,8	87,0	118,2	136,1	118,4
22	98,0	102,6	101,4	97,7	129,6	117,5	120,0
23	92,2	98,3	87,2	82,5	118,7	98,2	117,5
24	80,0	93,4	75,9	96,5	88,4	91,9	102,6
25	88,7	92,8	78,8	86,2	113,1	141,8	92,8
26	87,4	86,5	82,3	84,9	109,8	154,2	100,3
27	96,1	93,8	89,1	100,0	116,1	138,6	106,3
28	94,1	90,4	100,1	92,7	113,6	97,9	103,9
29	91,9	91,0	101,8	96,7	107,9	90,3	102,4
30	93,6	89,1	98,5	105,8	107,4	90,9	114,5
31	83,5	89,6	106,6	88,5	102,7	127,0	89,0
32	80,8	89,3	101,8	78,7	78,3	156,8	94,3
33	96,3	95,3	92,4	99,9	121,0	127,2	115,7
34	101,5	104,1	94,4	107,8	132,2	111,3	120,2
35	91,6	94,7	81,0	102,4	113,2	93,0	109,5
36	84,0	97,6	94,6	106,0	89,2	89,5	99,4
37	91,8	96,8	83,8	87,3	113,2	141,8	86,4
38	90,4	92,8	79,4	93,3	107,6	152,0	95,1
39	98,0	94,7	95,6	98,2	107,3	120,2	101,5
40	95,5	95,8	106,0	102,0	110,9	88,8	92,9
41	90,5	88,9	106,2	93,9	96,4	82,8	90,8
42	97,1	91,2	115,0	106,6	101,2	82,8	100,4
43	87,9	91,6	122,4	92,9	94,0	121,7	82,2
44	79,8	87,3	113,7	78,0	70,5	147,1	75,3
45	102,0	97,8	98,0	104,2	116,4	132,5	110,3
46	104,3	105,1	105,8	115,9	121,9	107,5	113,5
47	92,1	93,8	88,3	99,9	109,5	77,9	94,9
48	95,9	99,0	95,7	103,9	91,1	85,5	95,7
49	89,1	91,4	85,8	93,5	104,0	126,5	85,3
50	92,2	89,0	83,9	101,7	101,2	135,4	92,5
51	107,5	101,4	114,1	124,6	118,4	122,5	107,7
52	99,7	95,4	102,0	124,2	106,9	79,2	97,9
53	92,2	90,5	108,1	103,3	95,6	66,1	93,9
54	108,9	98,7	125,4	120,5	114,2	77,9	111,5
55	89,8	91,2	108,1	98,0	92,4	109,6	88,6
56	89,4	91,7	110,4	100,4	75,3	142,9	82,5
57	107,6	102,9	102,4	126,8	120,4	120,5	108,6
58	105,6	105,5	89,6	120,2	115,9	96,3	113,8
59	100,9	102,6	95,0	114,0	109,8	82,6	103,4
60	102,9	107,2	93,7	109,1	94,9	78,4	99,0
61	96,2	96,9	77,7	94,2	97,5	104,5	89,9
62	94,7	88,9	80,1	86,0	101,3	137,9	97,9
63	107,3	99,6	103,6	112,9	108,7	125,8	107,8
64	103,0	96,7	103,1	99,7	105,1	78,0	103,7
65	96,1	93,8	112,4	104,5	94,9	67,7	98,2
66	109,8	101,9	119,2	111,6	108,9	78,4	111,7
67	85,4	87,6	105,3	99,2	87,5	101,7	82,6
68	89,9	100,0	107,2	90,9	73,0	154,1	86,1
69	109,3	105,8	108,7	111,4	115,2	107,3	111,2
70	101,2	105,5	93,7	98,2	107,5	86,5	105,3
71	104,7	111,3	96,1	101,7	109,8	82,1	106,3
72	102,4	112,1	92,9	89,7	90,7	76,1	99,4
73	97,7	102,0	81,1	89,5	97,6	115,5	91,9
74	98,9	93,2	83,2	85,1	98,7	129,6	96,2
75	115,0	108,4	99,7	95,9	113,9	121,6	105,4
76	97,5	97,9	96,8	88,9	96,6	64,0	95,0
77	107,3	106,4	108,7	98,1	104,4	58,1	100,5
78	112,3	102,8	120,9	109,7	115,1	79,7	111,6
79	88,5	96,3	114,8	92,0	91,4	108,9	88,5
80	92,9	105,7	108,7	74,3	76,2	138,5	83,7
81	108,8	108,4	97,4	96,9	117,4	117,9	113,9
82	112,3	115,8	98,6	100,3	122,0	96,7	115,2
83	107,3	113,8	91,7	97,1	120,2	78,6	111,0
84	101,8	106,4	91,2	86,0	93,6	64,1	96,9
85	105,0	107,9	83,5	97,3	106,6	112,0	102,1
86	103,4	98,2	82,4	86,4	108,4	139,4	101,5
87	116,7	111,1	103,1	97,7	121,4	116,2	115,0
88	103,6	99,8	110,3	90,6	104,8	63,4	105,0
89	108,8	103,5	115,8	99,2	104,2	61,1	105,4
90	117,0	105,4	120,1	107,4	115,0	65,5	119,7
91	100,9	102,6	105,1	107,1	99,0	90,9	91,8
92	100,8	107,4	108,6	78,9	82,8	115,3	89,1
93	109,7	108,2	95,7	92,8	112,5	85,2	106,2
94	121,0	121,7	103,2	106,2	127,9	87,0	119,9
95	114,1	118,0	96,9	97,2	114,4	62,6	111,6
96	105,5	109,6	95,7	80,0	83,7	62,7	95,1
97	112,5	116,7	92,7	109,3	108,5	91,6	101,3
98	113,8	110,6	81,3	111,3	109,7	104,3	118,3
99	115,3	109,6	94,5	119,5	104,7	88,1	126,2
100	120,4	117,4	105,6	119,8	112,2	62,3	113,2
101	111,1	109,2	112,9	112,5	96,9	50,3	103,6
102	120,1	110,8	102,6	125,6	103,8	64,1	116,2
103	106,1	112,8	116,2	105,1	95,1	75,7	98,3
104	95,9	106,5	104,9	91,9	66,7	85,5	84,2
105	119,4	119,6	100,4	128,2	103,4	71,9	118,3
106	117,4	127,2	97,1	122,6	105,4	66,9	117,4
107	98,6	113,9	90,2	109,6	89,2	50,5	94,5
108	99,7	120,0	100,5	120,4	72,5	57,9	93,3
109	87,4	107,6	81,1	103,8	78,0	84,1	90,2
110	90,8	105,2	87,2	96,6	77,3	87,0	88,5
111	101,3	115,3	102,0	110,7	85,1	71,9	101,0
112	93,2	113,9	107,0	111,7	80,9	45,0	87,0
113	95,1	106,1	107,6	111,9	72,5	39,5	81,2
114	101,9	114,3	123,5	131,5	82,1	53,8	98,1
115	87,0	112,0	116,6	122,8	78,3	59,5	75,5
116	86,2	109,0	103,2	98,3	57,8	68,4	70,7
117	105,0	119,1	103,9	133,7	89,3	56,9	103,7
118	104,1	124,4	95,4	120,0	91,4	61,9	100,4
119	99,2	116,6	93,6	119,6	84,2	40,4	91,3
120	95,2	118,5	102,1	108,7	72,5	49,4	97,2
121	92,7	108,9	69,0	112,5	74,6	65,2	85,4
122	99,3	107,5	88,9	102,7	80,3	82,1	86,5
123	113,5	125,9	106,2	123,4	92,6	69,0	105,3
124	104,7	117,7	103,0	116,5	86,3	45,9	97,7
125	100,5	109,2	103,5	102,3	80,3	39,1	84,3
126	116,2	118,8	124,5	148,4	93,6	56,9	109,8
127	94,1	108,1	117,9	126,6	79,5	51,6	79,1
128	94,8	112,1	104,2	106,6	61,8	62,9	83,4
129	115,1	117,8	99,9	144,4	94,8	58,3	101,9
130	110,0	121,8	89,4	132,4	91,6	56,9	113,0
131	108,4	121,0	93,5	136,2	89,2	41,3	98,6
132	103,9	121,7	89,6	121,6	74,1	46,9	94,7
133	102,9	114,2	85,0	135,1	78,6	61,9	94,5
134	107,7	109,8	90,0	124,7	78,2	74,8	90,7
135	126,7	124,1	113,7	148,8	95,1	67,0	113,0
136	108,8	112,9	112,1	145,6	78,7	53,3	89,9
137	117,1	118,7	129,8	140,3	85,9	51,4	98,7
138	112,2	113,3	119,1	138,5	81,2	50,3	102,2
139	94,7	106,8	103,5	127,3	73,1	52,7	74,3
140	102,7	119,3	105,5	117,9	58,7	70,3	84,5
141	119,1	126,4	111,7	145,3	85,7	59,7	110,1
142	110,6	126,6	98,6	120,7	81,8	52,0	100,4
143	109,1	127,2	102,8	134,7	79,6	36,1	92,8
144	105,3	123,8	101,1	124,4	70,7	39,7	92,2
145	103,4	116,8	94,2	128,3	74,5	67,6	94,0
146	103,7	113,8	92,6	128,4	84,8	72,8	100,7
147	117,0	130,4	112,0	134,1	80,7	53,8	111,9
148	101,2	112,8	108,6	133,3	69,9	39,6	95,9
149	105,4	119,4	125,8	130,6	74,1	39,4	88,8
150	110,3	117,5	138,7	165,7	76,1	41,2	102,0
151	97,7	117,5	115,2	146,8	71,3	49,6	81,6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 17 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185198&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]17 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185198&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185198&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = -12.0473600348214 + 0.2007174838492t + 0.190621181423909voeding[t] + 0.149532587564484dranken[t] -0.0112917989525946tabak[t] + 0.304063576941629textiel[t] + 0.0338657309913272kleding[t] + 0.302088320113641`apparatuur\r\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  -12.0473600348214 +  0.2007174838492t +  0.190621181423909voeding[t] +  0.149532587564484dranken[t] -0.0112917989525946tabak[t] +  0.304063576941629textiel[t] +  0.0338657309913272kleding[t] +  0.302088320113641`apparatuur\r\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185198&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  -12.0473600348214 +  0.2007174838492t +  0.190621181423909voeding[t] +  0.149532587564484dranken[t] -0.0112917989525946tabak[t] +  0.304063576941629textiel[t] +  0.0338657309913272kleding[t] +  0.302088320113641`apparatuur\r\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185198&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185198&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = -12.0473600348214 + 0.2007174838492t + 0.190621181423909voeding[t] + 0.149532587564484dranken[t] -0.0112917989525946tabak[t] + 0.304063576941629textiel[t] + 0.0338657309913272kleding[t] + 0.302088320113641`apparatuur\r\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-12.04736003482145.928764-2.0320.0440030.022002
t0.20071748384920.0229218.757100
voeding0.1906211814239090.0657612.89870.0043380.002169
dranken0.1495325875644840.0262655.693300
tabak-0.01129179895259460.028076-0.40220.688150.344075
textiel0.3040635769416290.034938.704900
kleding0.03386573099132720.0145292.33090.0211560.010578
`apparatuur\r\r`0.3020883201136410.0463746.514200

\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) & -12.0473600348214 & 5.928764 & -2.032 & 0.044003 & 0.022002 \tabularnewline
t & 0.2007174838492 & 0.022921 & 8.7571 & 0 & 0 \tabularnewline
voeding & 0.190621181423909 & 0.065761 & 2.8987 & 0.004338 & 0.002169 \tabularnewline
dranken & 0.149532587564484 & 0.026265 & 5.6933 & 0 & 0 \tabularnewline
tabak & -0.0112917989525946 & 0.028076 & -0.4022 & 0.68815 & 0.344075 \tabularnewline
textiel & 0.304063576941629 & 0.03493 & 8.7049 & 0 & 0 \tabularnewline
kleding & 0.0338657309913272 & 0.014529 & 2.3309 & 0.021156 & 0.010578 \tabularnewline
`apparatuur\r\r` & 0.302088320113641 & 0.046374 & 6.5142 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185198&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]-12.0473600348214[/C][C]5.928764[/C][C]-2.032[/C][C]0.044003[/C][C]0.022002[/C][/ROW]
[ROW][C]t[/C][C]0.2007174838492[/C][C]0.022921[/C][C]8.7571[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]voeding[/C][C]0.190621181423909[/C][C]0.065761[/C][C]2.8987[/C][C]0.004338[/C][C]0.002169[/C][/ROW]
[ROW][C]dranken[/C][C]0.149532587564484[/C][C]0.026265[/C][C]5.6933[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]tabak[/C][C]-0.0112917989525946[/C][C]0.028076[/C][C]-0.4022[/C][C]0.68815[/C][C]0.344075[/C][/ROW]
[ROW][C]textiel[/C][C]0.304063576941629[/C][C]0.03493[/C][C]8.7049[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]kleding[/C][C]0.0338657309913272[/C][C]0.014529[/C][C]2.3309[/C][C]0.021156[/C][C]0.010578[/C][/ROW]
[ROW][C]`apparatuur\r\r`[/C][C]0.302088320113641[/C][C]0.046374[/C][C]6.5142[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185198&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185198&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-12.04736003482145.928764-2.0320.0440030.022002
t0.20071748384920.0229218.757100
voeding0.1906211814239090.0657612.89870.0043380.002169
dranken0.1495325875644840.0262655.693300
tabak-0.01129179895259460.028076-0.40220.688150.344075
textiel0.3040635769416290.034938.704900
kleding0.03386573099132720.0145292.33090.0211560.010578
`apparatuur\r\r`0.3020883201136410.0463746.514200






Multiple Linear Regression - Regression Statistics
Multiple R0.94032098696102
R-squared0.884203558519347
Adjusted R-squared0.87853520124407
F-TEST (value)155.989383798356
F-TEST (DF numerator)7
F-TEST (DF denominator)143
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.67595420984379
Sum Squared Residuals1932.30742746017

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.94032098696102 \tabularnewline
R-squared & 0.884203558519347 \tabularnewline
Adjusted R-squared & 0.87853520124407 \tabularnewline
F-TEST (value) & 155.989383798356 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 143 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.67595420984379 \tabularnewline
Sum Squared Residuals & 1932.30742746017 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185198&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.94032098696102[/C][/ROW]
[ROW][C]R-squared[/C][C]0.884203558519347[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.87853520124407[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]155.989383798356[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]143[/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]3.67595420984379[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1932.30742746017[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185198&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185198&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.94032098696102
R-squared0.884203558519347
Adjusted R-squared0.87853520124407
F-TEST (value)155.989383798356
F-TEST (DF numerator)7
F-TEST (DF denominator)143
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.67595420984379
Sum Squared Residuals1932.30742746017






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
175.579.5215699772843-4.02156997728427
283.287.5045208608799-4.3045208608799
394.596.1610771812353-1.6610771812353
483.380.81186239463442.48813760536563
592.793.9390556549073-1.23905565490728
689.890.6634206621855-0.86342066218554
774.874.64844853271330.15155146728672
881.577.88137098871213.61862901128793
992.893.9557142470597-1.15571424705973
1092.897.8760039012703-5.07600390127028
1191.794.7197414381534-3.01974143815344
1283.584.4778856772192-0.97788567721925
1392.888.89107915489073.90892084510933
1491.389.3151939256671.984806074333
1599.599.49353345498360.00646654501638756
1687.685.4524594626612.14754053733905
1795.393.58251786522541.71748213477462
1898.594.34712413918194.15287586081811
1980.187.9991565230471-7.89915652304707
2084.284.04839406313080.151605936869223
2192.497.2256039306572-4.82560393065721
2298105.622014822077-7.62201482207727
2392.298.328211429387-6.12821142938696
248081.819485243174-1.81948524317404
2588.788.69558484112940.0044151588706099
2687.489.9156199383594-2.51561993835942
2796.195.55401252980690.545987470193116
2894.193.97040043040420.129599569595769
2991.992.0508544018852-0.150854401885177
3093.694.8167350550966-1.2167350550966
3183.588.6095271250733-5.10952712507335
3280.883.3370770666894-2.53707706668938
3396.3101.482308326677-5.18230832667709
34101.5107.796796549956-6.29679654995568
3591.694.6336181051519-3.03361810515187
368486.9129817915615-2.91298179156152
3791.890.69896244095691.10103755904314
3890.490.6823438303382-0.282343830338209
399894.37755559269043.62244440730962
4095.593.73347182164151.76652817835847
4190.587.49377151885443.00622848114556
4297.193.66495168625893.43504831374108
4387.988.83326819182-0.933268191820021
4479.878.71191498839641.08808501160363
45102102.305817833001-0.305817833001088
46104.3106.724699099262-2.42469909926187
4792.191.94358898835070.156411011649262
4895.989.10119096367016.79880903632992
4989.188.65944614492570.440553855074265
5092.289.65103102077822.54896897922178
51107.5105.8575211620521.64247883794786
5299.795.18610114354184.51389885645822
5392.290.5130094447851.68699055521496
54108.9106.0414680300032.85853196999671
5589.890.0068135292362-0.206813529236155
5689.484.70516916132554.69483083867449
57107.6106.3856597850861.21434021491408
58105.6104.6255235712620.974476428737499
59100.999.69045789222971.20954210777034
60102.994.62699828656898.27300171343114
6196.289.56550117100156.63449882899854
6294.793.39598373342311.30401626657693
63107.3103.6775837679473.62241623205328
6410399.44781234721743.55218765278264
6596.195.32042955967420.779570440325837
66109.8106.6992741562563.10072584374374
6785.487.7269649558495-2.32696495584949
6889.989.09217049572390.807829504276113
69109.3109.2202914060450.079708593954661
70101.2102.441867632434-1.24186763243372
71104.7104.91997021308-0.219970213079813
72102.496.83496383497575.56503616502427
7397.794.51486729407633.18513270592372
7498.995.51277724877413.38722275122592
75115108.086326023026.9136739769797
7697.595.57823467527711.92176532472288
77107.3102.9081592908044.39184070919606
78112.3111.4541136379130.845886362086815
7988.596.5078418766296-8.00784187662958
8092.992.71874985446610.181250145533862
81108.8112.442084211358-3.64208421135835
82112.3115.267899199446-2.96789919944611
83107.3111.662678108986-4.36267810898561
84101.897.66478196526554.13521803473447
85105104.0182872481560.981712751843576
86103.4103.622556510045-0.222556510045169
87116.7116.495348331460.204651668540165
88103.6105.842403693537-2.24240369353704
89108.8107.3342453098671.46575469013252
90117116.2002992384950.799700761505388
91100.9101.194584345518-0.294584345518433
92100.898.23693171256842.56306828743157
93109.7109.6812597628050.0187402371954519
94121122.307694882888-1.30769488288775
95114.1113.5241697025420.575830297458006
96105.597.82262657845367.67737342154637
97112.5108.9897508969143.51024910308555
98113.8112.2308965957891.56910340421078
99115.3114.4397853047850.860214695215459
100120.4115.2633649920295.13663500797115
101111.1107.1163974375833.98360256241713
102120.1112.3056991955277.79430080447341
103106.1107.492892541702-1.39289254170182
10495.992.38906335424643.51093664575363
105119.4115.003900416884.39609958311968
106117.4116.3900344255471.00996557445269
10798.6100.771458263333-2.17145826333277
10899.798.36343786736821.33656213263179
10987.495.1101223982225-7.71012239822247
11090.895.2186147550788-4.41861475507885
111101.3105.034901465628-3.73490146562849
11293.299.2878287659415-6.08782876594152
11395.193.59665440401131.5033455959887
114101.9106.025297360077-4.12529736007713
1158797.0644469630481-10.0644469630481
11686.287.584291045574-1.38429104557396
117105108.572686921189-3.57268692118915
118104.1108.478338028097-4.37833802809655
11999.2101.261193675477-2.06119367547681
12095.2101.747767824288-6.54776782428821
12192.792.735054365963-0.0350543659630296
12299.398.39305071253310.906949287466863
123113.5113.4299727996610.0700272003387706
124104.7106.673235574905-1.97323557490533
125100.599.38613093364681.1138690663532
126116.2116.386551914172-0.186551914172217
12794.1100.065972659407-5.96597265940678
12894.895.5061516231939-0.706151623193895
129115.1111.190539312843.90946068715996
130110113.051915824049-3.05191582404928
131108.4108.0621813379930.337818662007183
132103.9102.3981604553351.50183954466522
133102.9102.1447842869330.75521571306748
134107.7101.5391731020266.16082689797444
135126.7119.3486547378737.35134526212722
136108.8104.782453235274.01754676472968
137117.1113.5786369878153.52136301218544
138112.2110.7612846478311.43871535216904
13994.796.7068228846693-2.00682288466926
140102.798.99433544424773.70566455575228
141119.1116.7503708916312.34962910836947
142110.6110.931243185096-0.331243185095704
143109.1108.2130088353360.886991164663607
144105.3104.8622122374170.437787762583501
145103.4105.296823044429-1.89682304442904
146103.7110.017244052423-6.31724405242316
147117117.712121723607-0.712121723607345
148101.2105.460335922974-4.26033592297387
149105.4108.647068371652-3.24706837165172
150110.3114.674885142309-4.37488514230898
15197.7104.237367059286-6.53736705928589

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 75.5 & 79.5215699772843 & -4.02156997728427 \tabularnewline
2 & 83.2 & 87.5045208608799 & -4.3045208608799 \tabularnewline
3 & 94.5 & 96.1610771812353 & -1.6610771812353 \tabularnewline
4 & 83.3 & 80.8118623946344 & 2.48813760536563 \tabularnewline
5 & 92.7 & 93.9390556549073 & -1.23905565490728 \tabularnewline
6 & 89.8 & 90.6634206621855 & -0.86342066218554 \tabularnewline
7 & 74.8 & 74.6484485327133 & 0.15155146728672 \tabularnewline
8 & 81.5 & 77.8813709887121 & 3.61862901128793 \tabularnewline
9 & 92.8 & 93.9557142470597 & -1.15571424705973 \tabularnewline
10 & 92.8 & 97.8760039012703 & -5.07600390127028 \tabularnewline
11 & 91.7 & 94.7197414381534 & -3.01974143815344 \tabularnewline
12 & 83.5 & 84.4778856772192 & -0.97788567721925 \tabularnewline
13 & 92.8 & 88.8910791548907 & 3.90892084510933 \tabularnewline
14 & 91.3 & 89.315193925667 & 1.984806074333 \tabularnewline
15 & 99.5 & 99.4935334549836 & 0.00646654501638756 \tabularnewline
16 & 87.6 & 85.452459462661 & 2.14754053733905 \tabularnewline
17 & 95.3 & 93.5825178652254 & 1.71748213477462 \tabularnewline
18 & 98.5 & 94.3471241391819 & 4.15287586081811 \tabularnewline
19 & 80.1 & 87.9991565230471 & -7.89915652304707 \tabularnewline
20 & 84.2 & 84.0483940631308 & 0.151605936869223 \tabularnewline
21 & 92.4 & 97.2256039306572 & -4.82560393065721 \tabularnewline
22 & 98 & 105.622014822077 & -7.62201482207727 \tabularnewline
23 & 92.2 & 98.328211429387 & -6.12821142938696 \tabularnewline
24 & 80 & 81.819485243174 & -1.81948524317404 \tabularnewline
25 & 88.7 & 88.6955848411294 & 0.0044151588706099 \tabularnewline
26 & 87.4 & 89.9156199383594 & -2.51561993835942 \tabularnewline
27 & 96.1 & 95.5540125298069 & 0.545987470193116 \tabularnewline
28 & 94.1 & 93.9704004304042 & 0.129599569595769 \tabularnewline
29 & 91.9 & 92.0508544018852 & -0.150854401885177 \tabularnewline
30 & 93.6 & 94.8167350550966 & -1.2167350550966 \tabularnewline
31 & 83.5 & 88.6095271250733 & -5.10952712507335 \tabularnewline
32 & 80.8 & 83.3370770666894 & -2.53707706668938 \tabularnewline
33 & 96.3 & 101.482308326677 & -5.18230832667709 \tabularnewline
34 & 101.5 & 107.796796549956 & -6.29679654995568 \tabularnewline
35 & 91.6 & 94.6336181051519 & -3.03361810515187 \tabularnewline
36 & 84 & 86.9129817915615 & -2.91298179156152 \tabularnewline
37 & 91.8 & 90.6989624409569 & 1.10103755904314 \tabularnewline
38 & 90.4 & 90.6823438303382 & -0.282343830338209 \tabularnewline
39 & 98 & 94.3775555926904 & 3.62244440730962 \tabularnewline
40 & 95.5 & 93.7334718216415 & 1.76652817835847 \tabularnewline
41 & 90.5 & 87.4937715188544 & 3.00622848114556 \tabularnewline
42 & 97.1 & 93.6649516862589 & 3.43504831374108 \tabularnewline
43 & 87.9 & 88.83326819182 & -0.933268191820021 \tabularnewline
44 & 79.8 & 78.7119149883964 & 1.08808501160363 \tabularnewline
45 & 102 & 102.305817833001 & -0.305817833001088 \tabularnewline
46 & 104.3 & 106.724699099262 & -2.42469909926187 \tabularnewline
47 & 92.1 & 91.9435889883507 & 0.156411011649262 \tabularnewline
48 & 95.9 & 89.1011909636701 & 6.79880903632992 \tabularnewline
49 & 89.1 & 88.6594461449257 & 0.440553855074265 \tabularnewline
50 & 92.2 & 89.6510310207782 & 2.54896897922178 \tabularnewline
51 & 107.5 & 105.857521162052 & 1.64247883794786 \tabularnewline
52 & 99.7 & 95.1861011435418 & 4.51389885645822 \tabularnewline
53 & 92.2 & 90.513009444785 & 1.68699055521496 \tabularnewline
54 & 108.9 & 106.041468030003 & 2.85853196999671 \tabularnewline
55 & 89.8 & 90.0068135292362 & -0.206813529236155 \tabularnewline
56 & 89.4 & 84.7051691613255 & 4.69483083867449 \tabularnewline
57 & 107.6 & 106.385659785086 & 1.21434021491408 \tabularnewline
58 & 105.6 & 104.625523571262 & 0.974476428737499 \tabularnewline
59 & 100.9 & 99.6904578922297 & 1.20954210777034 \tabularnewline
60 & 102.9 & 94.6269982865689 & 8.27300171343114 \tabularnewline
61 & 96.2 & 89.5655011710015 & 6.63449882899854 \tabularnewline
62 & 94.7 & 93.3959837334231 & 1.30401626657693 \tabularnewline
63 & 107.3 & 103.677583767947 & 3.62241623205328 \tabularnewline
64 & 103 & 99.4478123472174 & 3.55218765278264 \tabularnewline
65 & 96.1 & 95.3204295596742 & 0.779570440325837 \tabularnewline
66 & 109.8 & 106.699274156256 & 3.10072584374374 \tabularnewline
67 & 85.4 & 87.7269649558495 & -2.32696495584949 \tabularnewline
68 & 89.9 & 89.0921704957239 & 0.807829504276113 \tabularnewline
69 & 109.3 & 109.220291406045 & 0.079708593954661 \tabularnewline
70 & 101.2 & 102.441867632434 & -1.24186763243372 \tabularnewline
71 & 104.7 & 104.91997021308 & -0.219970213079813 \tabularnewline
72 & 102.4 & 96.8349638349757 & 5.56503616502427 \tabularnewline
73 & 97.7 & 94.5148672940763 & 3.18513270592372 \tabularnewline
74 & 98.9 & 95.5127772487741 & 3.38722275122592 \tabularnewline
75 & 115 & 108.08632602302 & 6.9136739769797 \tabularnewline
76 & 97.5 & 95.5782346752771 & 1.92176532472288 \tabularnewline
77 & 107.3 & 102.908159290804 & 4.39184070919606 \tabularnewline
78 & 112.3 & 111.454113637913 & 0.845886362086815 \tabularnewline
79 & 88.5 & 96.5078418766296 & -8.00784187662958 \tabularnewline
80 & 92.9 & 92.7187498544661 & 0.181250145533862 \tabularnewline
81 & 108.8 & 112.442084211358 & -3.64208421135835 \tabularnewline
82 & 112.3 & 115.267899199446 & -2.96789919944611 \tabularnewline
83 & 107.3 & 111.662678108986 & -4.36267810898561 \tabularnewline
84 & 101.8 & 97.6647819652655 & 4.13521803473447 \tabularnewline
85 & 105 & 104.018287248156 & 0.981712751843576 \tabularnewline
86 & 103.4 & 103.622556510045 & -0.222556510045169 \tabularnewline
87 & 116.7 & 116.49534833146 & 0.204651668540165 \tabularnewline
88 & 103.6 & 105.842403693537 & -2.24240369353704 \tabularnewline
89 & 108.8 & 107.334245309867 & 1.46575469013252 \tabularnewline
90 & 117 & 116.200299238495 & 0.799700761505388 \tabularnewline
91 & 100.9 & 101.194584345518 & -0.294584345518433 \tabularnewline
92 & 100.8 & 98.2369317125684 & 2.56306828743157 \tabularnewline
93 & 109.7 & 109.681259762805 & 0.0187402371954519 \tabularnewline
94 & 121 & 122.307694882888 & -1.30769488288775 \tabularnewline
95 & 114.1 & 113.524169702542 & 0.575830297458006 \tabularnewline
96 & 105.5 & 97.8226265784536 & 7.67737342154637 \tabularnewline
97 & 112.5 & 108.989750896914 & 3.51024910308555 \tabularnewline
98 & 113.8 & 112.230896595789 & 1.56910340421078 \tabularnewline
99 & 115.3 & 114.439785304785 & 0.860214695215459 \tabularnewline
100 & 120.4 & 115.263364992029 & 5.13663500797115 \tabularnewline
101 & 111.1 & 107.116397437583 & 3.98360256241713 \tabularnewline
102 & 120.1 & 112.305699195527 & 7.79430080447341 \tabularnewline
103 & 106.1 & 107.492892541702 & -1.39289254170182 \tabularnewline
104 & 95.9 & 92.3890633542464 & 3.51093664575363 \tabularnewline
105 & 119.4 & 115.00390041688 & 4.39609958311968 \tabularnewline
106 & 117.4 & 116.390034425547 & 1.00996557445269 \tabularnewline
107 & 98.6 & 100.771458263333 & -2.17145826333277 \tabularnewline
108 & 99.7 & 98.3634378673682 & 1.33656213263179 \tabularnewline
109 & 87.4 & 95.1101223982225 & -7.71012239822247 \tabularnewline
110 & 90.8 & 95.2186147550788 & -4.41861475507885 \tabularnewline
111 & 101.3 & 105.034901465628 & -3.73490146562849 \tabularnewline
112 & 93.2 & 99.2878287659415 & -6.08782876594152 \tabularnewline
113 & 95.1 & 93.5966544040113 & 1.5033455959887 \tabularnewline
114 & 101.9 & 106.025297360077 & -4.12529736007713 \tabularnewline
115 & 87 & 97.0644469630481 & -10.0644469630481 \tabularnewline
116 & 86.2 & 87.584291045574 & -1.38429104557396 \tabularnewline
117 & 105 & 108.572686921189 & -3.57268692118915 \tabularnewline
118 & 104.1 & 108.478338028097 & -4.37833802809655 \tabularnewline
119 & 99.2 & 101.261193675477 & -2.06119367547681 \tabularnewline
120 & 95.2 & 101.747767824288 & -6.54776782428821 \tabularnewline
121 & 92.7 & 92.735054365963 & -0.0350543659630296 \tabularnewline
122 & 99.3 & 98.3930507125331 & 0.906949287466863 \tabularnewline
123 & 113.5 & 113.429972799661 & 0.0700272003387706 \tabularnewline
124 & 104.7 & 106.673235574905 & -1.97323557490533 \tabularnewline
125 & 100.5 & 99.3861309336468 & 1.1138690663532 \tabularnewline
126 & 116.2 & 116.386551914172 & -0.186551914172217 \tabularnewline
127 & 94.1 & 100.065972659407 & -5.96597265940678 \tabularnewline
128 & 94.8 & 95.5061516231939 & -0.706151623193895 \tabularnewline
129 & 115.1 & 111.19053931284 & 3.90946068715996 \tabularnewline
130 & 110 & 113.051915824049 & -3.05191582404928 \tabularnewline
131 & 108.4 & 108.062181337993 & 0.337818662007183 \tabularnewline
132 & 103.9 & 102.398160455335 & 1.50183954466522 \tabularnewline
133 & 102.9 & 102.144784286933 & 0.75521571306748 \tabularnewline
134 & 107.7 & 101.539173102026 & 6.16082689797444 \tabularnewline
135 & 126.7 & 119.348654737873 & 7.35134526212722 \tabularnewline
136 & 108.8 & 104.78245323527 & 4.01754676472968 \tabularnewline
137 & 117.1 & 113.578636987815 & 3.52136301218544 \tabularnewline
138 & 112.2 & 110.761284647831 & 1.43871535216904 \tabularnewline
139 & 94.7 & 96.7068228846693 & -2.00682288466926 \tabularnewline
140 & 102.7 & 98.9943354442477 & 3.70566455575228 \tabularnewline
141 & 119.1 & 116.750370891631 & 2.34962910836947 \tabularnewline
142 & 110.6 & 110.931243185096 & -0.331243185095704 \tabularnewline
143 & 109.1 & 108.213008835336 & 0.886991164663607 \tabularnewline
144 & 105.3 & 104.862212237417 & 0.437787762583501 \tabularnewline
145 & 103.4 & 105.296823044429 & -1.89682304442904 \tabularnewline
146 & 103.7 & 110.017244052423 & -6.31724405242316 \tabularnewline
147 & 117 & 117.712121723607 & -0.712121723607345 \tabularnewline
148 & 101.2 & 105.460335922974 & -4.26033592297387 \tabularnewline
149 & 105.4 & 108.647068371652 & -3.24706837165172 \tabularnewline
150 & 110.3 & 114.674885142309 & -4.37488514230898 \tabularnewline
151 & 97.7 & 104.237367059286 & -6.53736705928589 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185198&T=4

[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]75.5[/C][C]79.5215699772843[/C][C]-4.02156997728427[/C][/ROW]
[ROW][C]2[/C][C]83.2[/C][C]87.5045208608799[/C][C]-4.3045208608799[/C][/ROW]
[ROW][C]3[/C][C]94.5[/C][C]96.1610771812353[/C][C]-1.6610771812353[/C][/ROW]
[ROW][C]4[/C][C]83.3[/C][C]80.8118623946344[/C][C]2.48813760536563[/C][/ROW]
[ROW][C]5[/C][C]92.7[/C][C]93.9390556549073[/C][C]-1.23905565490728[/C][/ROW]
[ROW][C]6[/C][C]89.8[/C][C]90.6634206621855[/C][C]-0.86342066218554[/C][/ROW]
[ROW][C]7[/C][C]74.8[/C][C]74.6484485327133[/C][C]0.15155146728672[/C][/ROW]
[ROW][C]8[/C][C]81.5[/C][C]77.8813709887121[/C][C]3.61862901128793[/C][/ROW]
[ROW][C]9[/C][C]92.8[/C][C]93.9557142470597[/C][C]-1.15571424705973[/C][/ROW]
[ROW][C]10[/C][C]92.8[/C][C]97.8760039012703[/C][C]-5.07600390127028[/C][/ROW]
[ROW][C]11[/C][C]91.7[/C][C]94.7197414381534[/C][C]-3.01974143815344[/C][/ROW]
[ROW][C]12[/C][C]83.5[/C][C]84.4778856772192[/C][C]-0.97788567721925[/C][/ROW]
[ROW][C]13[/C][C]92.8[/C][C]88.8910791548907[/C][C]3.90892084510933[/C][/ROW]
[ROW][C]14[/C][C]91.3[/C][C]89.315193925667[/C][C]1.984806074333[/C][/ROW]
[ROW][C]15[/C][C]99.5[/C][C]99.4935334549836[/C][C]0.00646654501638756[/C][/ROW]
[ROW][C]16[/C][C]87.6[/C][C]85.452459462661[/C][C]2.14754053733905[/C][/ROW]
[ROW][C]17[/C][C]95.3[/C][C]93.5825178652254[/C][C]1.71748213477462[/C][/ROW]
[ROW][C]18[/C][C]98.5[/C][C]94.3471241391819[/C][C]4.15287586081811[/C][/ROW]
[ROW][C]19[/C][C]80.1[/C][C]87.9991565230471[/C][C]-7.89915652304707[/C][/ROW]
[ROW][C]20[/C][C]84.2[/C][C]84.0483940631308[/C][C]0.151605936869223[/C][/ROW]
[ROW][C]21[/C][C]92.4[/C][C]97.2256039306572[/C][C]-4.82560393065721[/C][/ROW]
[ROW][C]22[/C][C]98[/C][C]105.622014822077[/C][C]-7.62201482207727[/C][/ROW]
[ROW][C]23[/C][C]92.2[/C][C]98.328211429387[/C][C]-6.12821142938696[/C][/ROW]
[ROW][C]24[/C][C]80[/C][C]81.819485243174[/C][C]-1.81948524317404[/C][/ROW]
[ROW][C]25[/C][C]88.7[/C][C]88.6955848411294[/C][C]0.0044151588706099[/C][/ROW]
[ROW][C]26[/C][C]87.4[/C][C]89.9156199383594[/C][C]-2.51561993835942[/C][/ROW]
[ROW][C]27[/C][C]96.1[/C][C]95.5540125298069[/C][C]0.545987470193116[/C][/ROW]
[ROW][C]28[/C][C]94.1[/C][C]93.9704004304042[/C][C]0.129599569595769[/C][/ROW]
[ROW][C]29[/C][C]91.9[/C][C]92.0508544018852[/C][C]-0.150854401885177[/C][/ROW]
[ROW][C]30[/C][C]93.6[/C][C]94.8167350550966[/C][C]-1.2167350550966[/C][/ROW]
[ROW][C]31[/C][C]83.5[/C][C]88.6095271250733[/C][C]-5.10952712507335[/C][/ROW]
[ROW][C]32[/C][C]80.8[/C][C]83.3370770666894[/C][C]-2.53707706668938[/C][/ROW]
[ROW][C]33[/C][C]96.3[/C][C]101.482308326677[/C][C]-5.18230832667709[/C][/ROW]
[ROW][C]34[/C][C]101.5[/C][C]107.796796549956[/C][C]-6.29679654995568[/C][/ROW]
[ROW][C]35[/C][C]91.6[/C][C]94.6336181051519[/C][C]-3.03361810515187[/C][/ROW]
[ROW][C]36[/C][C]84[/C][C]86.9129817915615[/C][C]-2.91298179156152[/C][/ROW]
[ROW][C]37[/C][C]91.8[/C][C]90.6989624409569[/C][C]1.10103755904314[/C][/ROW]
[ROW][C]38[/C][C]90.4[/C][C]90.6823438303382[/C][C]-0.282343830338209[/C][/ROW]
[ROW][C]39[/C][C]98[/C][C]94.3775555926904[/C][C]3.62244440730962[/C][/ROW]
[ROW][C]40[/C][C]95.5[/C][C]93.7334718216415[/C][C]1.76652817835847[/C][/ROW]
[ROW][C]41[/C][C]90.5[/C][C]87.4937715188544[/C][C]3.00622848114556[/C][/ROW]
[ROW][C]42[/C][C]97.1[/C][C]93.6649516862589[/C][C]3.43504831374108[/C][/ROW]
[ROW][C]43[/C][C]87.9[/C][C]88.83326819182[/C][C]-0.933268191820021[/C][/ROW]
[ROW][C]44[/C][C]79.8[/C][C]78.7119149883964[/C][C]1.08808501160363[/C][/ROW]
[ROW][C]45[/C][C]102[/C][C]102.305817833001[/C][C]-0.305817833001088[/C][/ROW]
[ROW][C]46[/C][C]104.3[/C][C]106.724699099262[/C][C]-2.42469909926187[/C][/ROW]
[ROW][C]47[/C][C]92.1[/C][C]91.9435889883507[/C][C]0.156411011649262[/C][/ROW]
[ROW][C]48[/C][C]95.9[/C][C]89.1011909636701[/C][C]6.79880903632992[/C][/ROW]
[ROW][C]49[/C][C]89.1[/C][C]88.6594461449257[/C][C]0.440553855074265[/C][/ROW]
[ROW][C]50[/C][C]92.2[/C][C]89.6510310207782[/C][C]2.54896897922178[/C][/ROW]
[ROW][C]51[/C][C]107.5[/C][C]105.857521162052[/C][C]1.64247883794786[/C][/ROW]
[ROW][C]52[/C][C]99.7[/C][C]95.1861011435418[/C][C]4.51389885645822[/C][/ROW]
[ROW][C]53[/C][C]92.2[/C][C]90.513009444785[/C][C]1.68699055521496[/C][/ROW]
[ROW][C]54[/C][C]108.9[/C][C]106.041468030003[/C][C]2.85853196999671[/C][/ROW]
[ROW][C]55[/C][C]89.8[/C][C]90.0068135292362[/C][C]-0.206813529236155[/C][/ROW]
[ROW][C]56[/C][C]89.4[/C][C]84.7051691613255[/C][C]4.69483083867449[/C][/ROW]
[ROW][C]57[/C][C]107.6[/C][C]106.385659785086[/C][C]1.21434021491408[/C][/ROW]
[ROW][C]58[/C][C]105.6[/C][C]104.625523571262[/C][C]0.974476428737499[/C][/ROW]
[ROW][C]59[/C][C]100.9[/C][C]99.6904578922297[/C][C]1.20954210777034[/C][/ROW]
[ROW][C]60[/C][C]102.9[/C][C]94.6269982865689[/C][C]8.27300171343114[/C][/ROW]
[ROW][C]61[/C][C]96.2[/C][C]89.5655011710015[/C][C]6.63449882899854[/C][/ROW]
[ROW][C]62[/C][C]94.7[/C][C]93.3959837334231[/C][C]1.30401626657693[/C][/ROW]
[ROW][C]63[/C][C]107.3[/C][C]103.677583767947[/C][C]3.62241623205328[/C][/ROW]
[ROW][C]64[/C][C]103[/C][C]99.4478123472174[/C][C]3.55218765278264[/C][/ROW]
[ROW][C]65[/C][C]96.1[/C][C]95.3204295596742[/C][C]0.779570440325837[/C][/ROW]
[ROW][C]66[/C][C]109.8[/C][C]106.699274156256[/C][C]3.10072584374374[/C][/ROW]
[ROW][C]67[/C][C]85.4[/C][C]87.7269649558495[/C][C]-2.32696495584949[/C][/ROW]
[ROW][C]68[/C][C]89.9[/C][C]89.0921704957239[/C][C]0.807829504276113[/C][/ROW]
[ROW][C]69[/C][C]109.3[/C][C]109.220291406045[/C][C]0.079708593954661[/C][/ROW]
[ROW][C]70[/C][C]101.2[/C][C]102.441867632434[/C][C]-1.24186763243372[/C][/ROW]
[ROW][C]71[/C][C]104.7[/C][C]104.91997021308[/C][C]-0.219970213079813[/C][/ROW]
[ROW][C]72[/C][C]102.4[/C][C]96.8349638349757[/C][C]5.56503616502427[/C][/ROW]
[ROW][C]73[/C][C]97.7[/C][C]94.5148672940763[/C][C]3.18513270592372[/C][/ROW]
[ROW][C]74[/C][C]98.9[/C][C]95.5127772487741[/C][C]3.38722275122592[/C][/ROW]
[ROW][C]75[/C][C]115[/C][C]108.08632602302[/C][C]6.9136739769797[/C][/ROW]
[ROW][C]76[/C][C]97.5[/C][C]95.5782346752771[/C][C]1.92176532472288[/C][/ROW]
[ROW][C]77[/C][C]107.3[/C][C]102.908159290804[/C][C]4.39184070919606[/C][/ROW]
[ROW][C]78[/C][C]112.3[/C][C]111.454113637913[/C][C]0.845886362086815[/C][/ROW]
[ROW][C]79[/C][C]88.5[/C][C]96.5078418766296[/C][C]-8.00784187662958[/C][/ROW]
[ROW][C]80[/C][C]92.9[/C][C]92.7187498544661[/C][C]0.181250145533862[/C][/ROW]
[ROW][C]81[/C][C]108.8[/C][C]112.442084211358[/C][C]-3.64208421135835[/C][/ROW]
[ROW][C]82[/C][C]112.3[/C][C]115.267899199446[/C][C]-2.96789919944611[/C][/ROW]
[ROW][C]83[/C][C]107.3[/C][C]111.662678108986[/C][C]-4.36267810898561[/C][/ROW]
[ROW][C]84[/C][C]101.8[/C][C]97.6647819652655[/C][C]4.13521803473447[/C][/ROW]
[ROW][C]85[/C][C]105[/C][C]104.018287248156[/C][C]0.981712751843576[/C][/ROW]
[ROW][C]86[/C][C]103.4[/C][C]103.622556510045[/C][C]-0.222556510045169[/C][/ROW]
[ROW][C]87[/C][C]116.7[/C][C]116.49534833146[/C][C]0.204651668540165[/C][/ROW]
[ROW][C]88[/C][C]103.6[/C][C]105.842403693537[/C][C]-2.24240369353704[/C][/ROW]
[ROW][C]89[/C][C]108.8[/C][C]107.334245309867[/C][C]1.46575469013252[/C][/ROW]
[ROW][C]90[/C][C]117[/C][C]116.200299238495[/C][C]0.799700761505388[/C][/ROW]
[ROW][C]91[/C][C]100.9[/C][C]101.194584345518[/C][C]-0.294584345518433[/C][/ROW]
[ROW][C]92[/C][C]100.8[/C][C]98.2369317125684[/C][C]2.56306828743157[/C][/ROW]
[ROW][C]93[/C][C]109.7[/C][C]109.681259762805[/C][C]0.0187402371954519[/C][/ROW]
[ROW][C]94[/C][C]121[/C][C]122.307694882888[/C][C]-1.30769488288775[/C][/ROW]
[ROW][C]95[/C][C]114.1[/C][C]113.524169702542[/C][C]0.575830297458006[/C][/ROW]
[ROW][C]96[/C][C]105.5[/C][C]97.8226265784536[/C][C]7.67737342154637[/C][/ROW]
[ROW][C]97[/C][C]112.5[/C][C]108.989750896914[/C][C]3.51024910308555[/C][/ROW]
[ROW][C]98[/C][C]113.8[/C][C]112.230896595789[/C][C]1.56910340421078[/C][/ROW]
[ROW][C]99[/C][C]115.3[/C][C]114.439785304785[/C][C]0.860214695215459[/C][/ROW]
[ROW][C]100[/C][C]120.4[/C][C]115.263364992029[/C][C]5.13663500797115[/C][/ROW]
[ROW][C]101[/C][C]111.1[/C][C]107.116397437583[/C][C]3.98360256241713[/C][/ROW]
[ROW][C]102[/C][C]120.1[/C][C]112.305699195527[/C][C]7.79430080447341[/C][/ROW]
[ROW][C]103[/C][C]106.1[/C][C]107.492892541702[/C][C]-1.39289254170182[/C][/ROW]
[ROW][C]104[/C][C]95.9[/C][C]92.3890633542464[/C][C]3.51093664575363[/C][/ROW]
[ROW][C]105[/C][C]119.4[/C][C]115.00390041688[/C][C]4.39609958311968[/C][/ROW]
[ROW][C]106[/C][C]117.4[/C][C]116.390034425547[/C][C]1.00996557445269[/C][/ROW]
[ROW][C]107[/C][C]98.6[/C][C]100.771458263333[/C][C]-2.17145826333277[/C][/ROW]
[ROW][C]108[/C][C]99.7[/C][C]98.3634378673682[/C][C]1.33656213263179[/C][/ROW]
[ROW][C]109[/C][C]87.4[/C][C]95.1101223982225[/C][C]-7.71012239822247[/C][/ROW]
[ROW][C]110[/C][C]90.8[/C][C]95.2186147550788[/C][C]-4.41861475507885[/C][/ROW]
[ROW][C]111[/C][C]101.3[/C][C]105.034901465628[/C][C]-3.73490146562849[/C][/ROW]
[ROW][C]112[/C][C]93.2[/C][C]99.2878287659415[/C][C]-6.08782876594152[/C][/ROW]
[ROW][C]113[/C][C]95.1[/C][C]93.5966544040113[/C][C]1.5033455959887[/C][/ROW]
[ROW][C]114[/C][C]101.9[/C][C]106.025297360077[/C][C]-4.12529736007713[/C][/ROW]
[ROW][C]115[/C][C]87[/C][C]97.0644469630481[/C][C]-10.0644469630481[/C][/ROW]
[ROW][C]116[/C][C]86.2[/C][C]87.584291045574[/C][C]-1.38429104557396[/C][/ROW]
[ROW][C]117[/C][C]105[/C][C]108.572686921189[/C][C]-3.57268692118915[/C][/ROW]
[ROW][C]118[/C][C]104.1[/C][C]108.478338028097[/C][C]-4.37833802809655[/C][/ROW]
[ROW][C]119[/C][C]99.2[/C][C]101.261193675477[/C][C]-2.06119367547681[/C][/ROW]
[ROW][C]120[/C][C]95.2[/C][C]101.747767824288[/C][C]-6.54776782428821[/C][/ROW]
[ROW][C]121[/C][C]92.7[/C][C]92.735054365963[/C][C]-0.0350543659630296[/C][/ROW]
[ROW][C]122[/C][C]99.3[/C][C]98.3930507125331[/C][C]0.906949287466863[/C][/ROW]
[ROW][C]123[/C][C]113.5[/C][C]113.429972799661[/C][C]0.0700272003387706[/C][/ROW]
[ROW][C]124[/C][C]104.7[/C][C]106.673235574905[/C][C]-1.97323557490533[/C][/ROW]
[ROW][C]125[/C][C]100.5[/C][C]99.3861309336468[/C][C]1.1138690663532[/C][/ROW]
[ROW][C]126[/C][C]116.2[/C][C]116.386551914172[/C][C]-0.186551914172217[/C][/ROW]
[ROW][C]127[/C][C]94.1[/C][C]100.065972659407[/C][C]-5.96597265940678[/C][/ROW]
[ROW][C]128[/C][C]94.8[/C][C]95.5061516231939[/C][C]-0.706151623193895[/C][/ROW]
[ROW][C]129[/C][C]115.1[/C][C]111.19053931284[/C][C]3.90946068715996[/C][/ROW]
[ROW][C]130[/C][C]110[/C][C]113.051915824049[/C][C]-3.05191582404928[/C][/ROW]
[ROW][C]131[/C][C]108.4[/C][C]108.062181337993[/C][C]0.337818662007183[/C][/ROW]
[ROW][C]132[/C][C]103.9[/C][C]102.398160455335[/C][C]1.50183954466522[/C][/ROW]
[ROW][C]133[/C][C]102.9[/C][C]102.144784286933[/C][C]0.75521571306748[/C][/ROW]
[ROW][C]134[/C][C]107.7[/C][C]101.539173102026[/C][C]6.16082689797444[/C][/ROW]
[ROW][C]135[/C][C]126.7[/C][C]119.348654737873[/C][C]7.35134526212722[/C][/ROW]
[ROW][C]136[/C][C]108.8[/C][C]104.78245323527[/C][C]4.01754676472968[/C][/ROW]
[ROW][C]137[/C][C]117.1[/C][C]113.578636987815[/C][C]3.52136301218544[/C][/ROW]
[ROW][C]138[/C][C]112.2[/C][C]110.761284647831[/C][C]1.43871535216904[/C][/ROW]
[ROW][C]139[/C][C]94.7[/C][C]96.7068228846693[/C][C]-2.00682288466926[/C][/ROW]
[ROW][C]140[/C][C]102.7[/C][C]98.9943354442477[/C][C]3.70566455575228[/C][/ROW]
[ROW][C]141[/C][C]119.1[/C][C]116.750370891631[/C][C]2.34962910836947[/C][/ROW]
[ROW][C]142[/C][C]110.6[/C][C]110.931243185096[/C][C]-0.331243185095704[/C][/ROW]
[ROW][C]143[/C][C]109.1[/C][C]108.213008835336[/C][C]0.886991164663607[/C][/ROW]
[ROW][C]144[/C][C]105.3[/C][C]104.862212237417[/C][C]0.437787762583501[/C][/ROW]
[ROW][C]145[/C][C]103.4[/C][C]105.296823044429[/C][C]-1.89682304442904[/C][/ROW]
[ROW][C]146[/C][C]103.7[/C][C]110.017244052423[/C][C]-6.31724405242316[/C][/ROW]
[ROW][C]147[/C][C]117[/C][C]117.712121723607[/C][C]-0.712121723607345[/C][/ROW]
[ROW][C]148[/C][C]101.2[/C][C]105.460335922974[/C][C]-4.26033592297387[/C][/ROW]
[ROW][C]149[/C][C]105.4[/C][C]108.647068371652[/C][C]-3.24706837165172[/C][/ROW]
[ROW][C]150[/C][C]110.3[/C][C]114.674885142309[/C][C]-4.37488514230898[/C][/ROW]
[ROW][C]151[/C][C]97.7[/C][C]104.237367059286[/C][C]-6.53736705928589[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185198&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185198&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
175.579.5215699772843-4.02156997728427
283.287.5045208608799-4.3045208608799
394.596.1610771812353-1.6610771812353
483.380.81186239463442.48813760536563
592.793.9390556549073-1.23905565490728
689.890.6634206621855-0.86342066218554
774.874.64844853271330.15155146728672
881.577.88137098871213.61862901128793
992.893.9557142470597-1.15571424705973
1092.897.8760039012703-5.07600390127028
1191.794.7197414381534-3.01974143815344
1283.584.4778856772192-0.97788567721925
1392.888.89107915489073.90892084510933
1491.389.3151939256671.984806074333
1599.599.49353345498360.00646654501638756
1687.685.4524594626612.14754053733905
1795.393.58251786522541.71748213477462
1898.594.34712413918194.15287586081811
1980.187.9991565230471-7.89915652304707
2084.284.04839406313080.151605936869223
2192.497.2256039306572-4.82560393065721
2298105.622014822077-7.62201482207727
2392.298.328211429387-6.12821142938696
248081.819485243174-1.81948524317404
2588.788.69558484112940.0044151588706099
2687.489.9156199383594-2.51561993835942
2796.195.55401252980690.545987470193116
2894.193.97040043040420.129599569595769
2991.992.0508544018852-0.150854401885177
3093.694.8167350550966-1.2167350550966
3183.588.6095271250733-5.10952712507335
3280.883.3370770666894-2.53707706668938
3396.3101.482308326677-5.18230832667709
34101.5107.796796549956-6.29679654995568
3591.694.6336181051519-3.03361810515187
368486.9129817915615-2.91298179156152
3791.890.69896244095691.10103755904314
3890.490.6823438303382-0.282343830338209
399894.37755559269043.62244440730962
4095.593.73347182164151.76652817835847
4190.587.49377151885443.00622848114556
4297.193.66495168625893.43504831374108
4387.988.83326819182-0.933268191820021
4479.878.71191498839641.08808501160363
45102102.305817833001-0.305817833001088
46104.3106.724699099262-2.42469909926187
4792.191.94358898835070.156411011649262
4895.989.10119096367016.79880903632992
4989.188.65944614492570.440553855074265
5092.289.65103102077822.54896897922178
51107.5105.8575211620521.64247883794786
5299.795.18610114354184.51389885645822
5392.290.5130094447851.68699055521496
54108.9106.0414680300032.85853196999671
5589.890.0068135292362-0.206813529236155
5689.484.70516916132554.69483083867449
57107.6106.3856597850861.21434021491408
58105.6104.6255235712620.974476428737499
59100.999.69045789222971.20954210777034
60102.994.62699828656898.27300171343114
6196.289.56550117100156.63449882899854
6294.793.39598373342311.30401626657693
63107.3103.6775837679473.62241623205328
6410399.44781234721743.55218765278264
6596.195.32042955967420.779570440325837
66109.8106.6992741562563.10072584374374
6785.487.7269649558495-2.32696495584949
6889.989.09217049572390.807829504276113
69109.3109.2202914060450.079708593954661
70101.2102.441867632434-1.24186763243372
71104.7104.91997021308-0.219970213079813
72102.496.83496383497575.56503616502427
7397.794.51486729407633.18513270592372
7498.995.51277724877413.38722275122592
75115108.086326023026.9136739769797
7697.595.57823467527711.92176532472288
77107.3102.9081592908044.39184070919606
78112.3111.4541136379130.845886362086815
7988.596.5078418766296-8.00784187662958
8092.992.71874985446610.181250145533862
81108.8112.442084211358-3.64208421135835
82112.3115.267899199446-2.96789919944611
83107.3111.662678108986-4.36267810898561
84101.897.66478196526554.13521803473447
85105104.0182872481560.981712751843576
86103.4103.622556510045-0.222556510045169
87116.7116.495348331460.204651668540165
88103.6105.842403693537-2.24240369353704
89108.8107.3342453098671.46575469013252
90117116.2002992384950.799700761505388
91100.9101.194584345518-0.294584345518433
92100.898.23693171256842.56306828743157
93109.7109.6812597628050.0187402371954519
94121122.307694882888-1.30769488288775
95114.1113.5241697025420.575830297458006
96105.597.82262657845367.67737342154637
97112.5108.9897508969143.51024910308555
98113.8112.2308965957891.56910340421078
99115.3114.4397853047850.860214695215459
100120.4115.2633649920295.13663500797115
101111.1107.1163974375833.98360256241713
102120.1112.3056991955277.79430080447341
103106.1107.492892541702-1.39289254170182
10495.992.38906335424643.51093664575363
105119.4115.003900416884.39609958311968
106117.4116.3900344255471.00996557445269
10798.6100.771458263333-2.17145826333277
10899.798.36343786736821.33656213263179
10987.495.1101223982225-7.71012239822247
11090.895.2186147550788-4.41861475507885
111101.3105.034901465628-3.73490146562849
11293.299.2878287659415-6.08782876594152
11395.193.59665440401131.5033455959887
114101.9106.025297360077-4.12529736007713
1158797.0644469630481-10.0644469630481
11686.287.584291045574-1.38429104557396
117105108.572686921189-3.57268692118915
118104.1108.478338028097-4.37833802809655
11999.2101.261193675477-2.06119367547681
12095.2101.747767824288-6.54776782428821
12192.792.735054365963-0.0350543659630296
12299.398.39305071253310.906949287466863
123113.5113.4299727996610.0700272003387706
124104.7106.673235574905-1.97323557490533
125100.599.38613093364681.1138690663532
126116.2116.386551914172-0.186551914172217
12794.1100.065972659407-5.96597265940678
12894.895.5061516231939-0.706151623193895
129115.1111.190539312843.90946068715996
130110113.051915824049-3.05191582404928
131108.4108.0621813379930.337818662007183
132103.9102.3981604553351.50183954466522
133102.9102.1447842869330.75521571306748
134107.7101.5391731020266.16082689797444
135126.7119.3486547378737.35134526212722
136108.8104.782453235274.01754676472968
137117.1113.5786369878153.52136301218544
138112.2110.7612846478311.43871535216904
13994.796.7068228846693-2.00682288466926
140102.798.99433544424773.70566455575228
141119.1116.7503708916312.34962910836947
142110.6110.931243185096-0.331243185095704
143109.1108.2130088353360.886991164663607
144105.3104.8622122374170.437787762583501
145103.4105.296823044429-1.89682304442904
146103.7110.017244052423-6.31724405242316
147117117.712121723607-0.712121723607345
148101.2105.460335922974-4.26033592297387
149105.4108.647068371652-3.24706837165172
150110.3114.674885142309-4.37488514230898
15197.7104.237367059286-6.53736705928589






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.291417148763760.5828342975275190.70858285123624
120.1561182681774170.3122365363548340.843881731822583
130.1886857988183340.3773715976366680.811314201181666
140.1341575724260810.2683151448521630.865842427573918
150.07869754484192170.1573950896838430.921302455158078
160.04281097307831210.08562194615662420.957189026921688
170.03665448198639150.07330896397278310.963345518013608
180.02503967034728320.05007934069456650.974960329652717
190.6601652685404520.6796694629190960.339834731459548
200.5945854113533130.8108291772933740.405414588646687
210.6234137400067240.7531725199865530.376586259993276
220.6829998408376260.6340003183247470.317000159162374
230.6974048648982910.6051902702034180.302595135101709
240.6431820566126550.713635886774690.356817943387345
250.5765793339199620.8468413321600750.423420666080038
260.530397450330120.9392050993397610.46960254966988
270.4877331405091130.9754662810182260.512266859490887
280.4260779571201380.8521559142402770.573922042879861
290.3618630382679010.7237260765358020.638136961732099
300.3113213703442170.6226427406884340.688678629655783
310.3744102189325210.7488204378650410.625589781067479
320.3412562042282710.6825124084565430.658743795771729
330.3511475746728750.7022951493457490.648852425327125
340.3751668330808720.7503336661617440.624833166919128
350.3598667199217290.7197334398434580.640133280078271
360.368227068257030.736454136514060.63177293174297
370.3483614739788080.6967229479576150.651638526021192
380.3059881405071210.6119762810142430.694011859492879
390.3785567879847090.7571135759694180.621443212015291
400.3506252595002120.7012505190004250.649374740499788
410.3209481482459620.6418962964919250.679051851754038
420.303675231444810.6073504628896210.69632476855519
430.2724063322995970.5448126645991940.727593667700403
440.2284530990835790.4569061981671570.771546900916421
450.2030096118687860.4060192237375720.796990388131214
460.1903472295931180.3806944591862370.809652770406882
470.1590126191632540.3180252383265080.840987380836746
480.2424283336379750.4848566672759490.757571666362025
490.2048970725850720.4097941451701440.795102927414928
500.1694559358628080.3389118717256150.830544064137192
510.1441956533068290.2883913066136570.855804346693171
520.1222809899974050.2445619799948110.877719010002595
530.09856418790379560.1971283758075910.901435812096204
540.08382718244072330.1676543648814470.916172817559277
550.07264737648947910.1452947529789580.927352623510521
560.06212814805667780.1242562961133560.937871851943322
570.04792396990899120.09584793981798250.952076030091009
580.03940833989440830.07881667978881670.960591660105592
590.03050375283671840.06100750567343680.969496247163282
600.06784937494342570.1356987498868510.932150625056574
610.08368525066275760.1673705013255150.916314749337242
620.06596942521936590.1319388504387320.934030574780634
630.056884816311640.113769632623280.94311518368836
640.04742968094546220.09485936189092440.952570319054538
650.04051366393249530.08102732786499050.959486336067505
660.03339781536178920.06679563072357830.966602184638211
670.05227805086583360.1045561017316670.947721949134166
680.04131472706568580.08262945413137170.958685272934314
690.03196116571860590.06392233143721170.968038834281394
700.02753628064085040.05507256128170070.97246371935915
710.02143905608002610.04287811216005210.978560943919974
720.02459822648221730.04919645296443460.975401773517783
730.01994556892481160.03989113784962320.980054431075188
740.01662929968854320.03325859937708650.983370700311457
750.03930928737646380.07861857475292750.960690712623536
760.03110972654407920.06221945308815830.968890273455921
770.03027807924336360.06055615848672720.969721920756636
780.02307841542622340.04615683085244680.976921584573777
790.101408965999020.2028179319980390.89859103400098
800.08264201507988240.1652840301597650.917357984920118
810.0886446599650490.1772893199300980.911355340034951
820.08591604679045310.1718320935809060.914083953209547
830.1047676427956780.2095352855913560.895232357204322
840.09932802095383430.1986560419076690.900671979046166
850.07969324093363860.1593864818672770.920306759066361
860.0639889895451930.1279779790903860.936011010454807
870.05309019169892110.1061803833978420.946909808301079
880.04884466184398950.0976893236879790.95115533815601
890.0379207739530660.07584154790613210.962079226046934
900.03035140619510740.06070281239021480.969648593804893
910.02558360842267960.05116721684535910.97441639157732
920.02156961582997960.04313923165995920.97843038417002
930.01617540395400090.03235080790800180.983824596045999
940.01416362454744590.02832724909489190.985836375452554
950.01072836942716850.02145673885433710.989271630572832
960.02430473822429060.04860947644858110.975695261775709
970.02043610304307390.04087220608614780.979563896956926
980.01537446874780120.03074893749560240.984625531252199
990.01366500136781920.02733000273563840.986334998632181
1000.01338792198782670.02677584397565340.986612078012173
1010.01267414654540890.02534829309081770.987325853454591
1020.02343235388296290.04686470776592580.976567646117037
1030.02085946085201580.04171892170403160.979140539147984
1040.02829397326391040.05658794652782080.97170602673609
1050.02882041768996310.05764083537992620.971179582310037
1060.02380348819856170.04760697639712330.976196511801438
1070.03020134149977470.06040268299954930.969798658500225
1080.03274236243881870.06548472487763730.967257637561181
1090.1205942368006150.241188473601230.879405763199385
1100.1455547383842110.2911094767684210.854445261615789
1110.1546844770664650.309368954132930.845315522933535
1120.2014770840279070.4029541680558150.798522915972093
1130.2265788464185760.4531576928371520.773421153581424
1140.2272258867437810.4544517734875620.772774113256219
1150.5129238246939120.9741523506121760.487076175306088
1160.459785517336380.919571034672760.54021448266362
1170.4782517134236940.9565034268473890.521748286576306
1180.5851938465537040.8296123068925920.414806153446296
1190.538732428596380.9225351428072390.46126757140362
1200.7045431775819830.5909136448360340.295456822418017
1210.6577151773020390.6845696453959230.342284822697961
1220.5936832263820430.8126335472359150.406316773617957
1230.6156082130962920.7687835738074150.384391786903708
1240.5991063656138840.8017872687722330.400893634386116
1250.5917416782481830.8165166435036330.408258321751817
1260.6028509336453620.7942981327092750.397149066354638
1270.8517285424471720.2965429151056570.148271457552828
1280.9506193066167320.09876138676653550.0493806933832678
1290.9265402180499540.1469195639000920.0734597819500462
1300.9873454865983460.02530902680330720.0126545134016536
1310.9873279575570390.02534408488592220.0126720424429611
1320.9955278172527910.008944365494417160.00447218274720858
1330.9995405033126630.0009189933746749760.000459496687337488
1340.9998450065343880.0003099869312233640.000154993465611682
1350.9999230879748060.0001538240503875777.69120251937885e-05
1360.9998749709400230.0002500581199543490.000125029059977175
1370.9995297295704570.0009405408590858240.000470270429542912
1380.9977593892309840.004481221538032690.00224061076901635
1390.9917153191850390.01656936162992240.00828468081496121
1400.9763730995790180.04725380084196330.0236269004209816

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.29141714876376 & 0.582834297527519 & 0.70858285123624 \tabularnewline
12 & 0.156118268177417 & 0.312236536354834 & 0.843881731822583 \tabularnewline
13 & 0.188685798818334 & 0.377371597636668 & 0.811314201181666 \tabularnewline
14 & 0.134157572426081 & 0.268315144852163 & 0.865842427573918 \tabularnewline
15 & 0.0786975448419217 & 0.157395089683843 & 0.921302455158078 \tabularnewline
16 & 0.0428109730783121 & 0.0856219461566242 & 0.957189026921688 \tabularnewline
17 & 0.0366544819863915 & 0.0733089639727831 & 0.963345518013608 \tabularnewline
18 & 0.0250396703472832 & 0.0500793406945665 & 0.974960329652717 \tabularnewline
19 & 0.660165268540452 & 0.679669462919096 & 0.339834731459548 \tabularnewline
20 & 0.594585411353313 & 0.810829177293374 & 0.405414588646687 \tabularnewline
21 & 0.623413740006724 & 0.753172519986553 & 0.376586259993276 \tabularnewline
22 & 0.682999840837626 & 0.634000318324747 & 0.317000159162374 \tabularnewline
23 & 0.697404864898291 & 0.605190270203418 & 0.302595135101709 \tabularnewline
24 & 0.643182056612655 & 0.71363588677469 & 0.356817943387345 \tabularnewline
25 & 0.576579333919962 & 0.846841332160075 & 0.423420666080038 \tabularnewline
26 & 0.53039745033012 & 0.939205099339761 & 0.46960254966988 \tabularnewline
27 & 0.487733140509113 & 0.975466281018226 & 0.512266859490887 \tabularnewline
28 & 0.426077957120138 & 0.852155914240277 & 0.573922042879861 \tabularnewline
29 & 0.361863038267901 & 0.723726076535802 & 0.638136961732099 \tabularnewline
30 & 0.311321370344217 & 0.622642740688434 & 0.688678629655783 \tabularnewline
31 & 0.374410218932521 & 0.748820437865041 & 0.625589781067479 \tabularnewline
32 & 0.341256204228271 & 0.682512408456543 & 0.658743795771729 \tabularnewline
33 & 0.351147574672875 & 0.702295149345749 & 0.648852425327125 \tabularnewline
34 & 0.375166833080872 & 0.750333666161744 & 0.624833166919128 \tabularnewline
35 & 0.359866719921729 & 0.719733439843458 & 0.640133280078271 \tabularnewline
36 & 0.36822706825703 & 0.73645413651406 & 0.63177293174297 \tabularnewline
37 & 0.348361473978808 & 0.696722947957615 & 0.651638526021192 \tabularnewline
38 & 0.305988140507121 & 0.611976281014243 & 0.694011859492879 \tabularnewline
39 & 0.378556787984709 & 0.757113575969418 & 0.621443212015291 \tabularnewline
40 & 0.350625259500212 & 0.701250519000425 & 0.649374740499788 \tabularnewline
41 & 0.320948148245962 & 0.641896296491925 & 0.679051851754038 \tabularnewline
42 & 0.30367523144481 & 0.607350462889621 & 0.69632476855519 \tabularnewline
43 & 0.272406332299597 & 0.544812664599194 & 0.727593667700403 \tabularnewline
44 & 0.228453099083579 & 0.456906198167157 & 0.771546900916421 \tabularnewline
45 & 0.203009611868786 & 0.406019223737572 & 0.796990388131214 \tabularnewline
46 & 0.190347229593118 & 0.380694459186237 & 0.809652770406882 \tabularnewline
47 & 0.159012619163254 & 0.318025238326508 & 0.840987380836746 \tabularnewline
48 & 0.242428333637975 & 0.484856667275949 & 0.757571666362025 \tabularnewline
49 & 0.204897072585072 & 0.409794145170144 & 0.795102927414928 \tabularnewline
50 & 0.169455935862808 & 0.338911871725615 & 0.830544064137192 \tabularnewline
51 & 0.144195653306829 & 0.288391306613657 & 0.855804346693171 \tabularnewline
52 & 0.122280989997405 & 0.244561979994811 & 0.877719010002595 \tabularnewline
53 & 0.0985641879037956 & 0.197128375807591 & 0.901435812096204 \tabularnewline
54 & 0.0838271824407233 & 0.167654364881447 & 0.916172817559277 \tabularnewline
55 & 0.0726473764894791 & 0.145294752978958 & 0.927352623510521 \tabularnewline
56 & 0.0621281480566778 & 0.124256296113356 & 0.937871851943322 \tabularnewline
57 & 0.0479239699089912 & 0.0958479398179825 & 0.952076030091009 \tabularnewline
58 & 0.0394083398944083 & 0.0788166797888167 & 0.960591660105592 \tabularnewline
59 & 0.0305037528367184 & 0.0610075056734368 & 0.969496247163282 \tabularnewline
60 & 0.0678493749434257 & 0.135698749886851 & 0.932150625056574 \tabularnewline
61 & 0.0836852506627576 & 0.167370501325515 & 0.916314749337242 \tabularnewline
62 & 0.0659694252193659 & 0.131938850438732 & 0.934030574780634 \tabularnewline
63 & 0.05688481631164 & 0.11376963262328 & 0.94311518368836 \tabularnewline
64 & 0.0474296809454622 & 0.0948593618909244 & 0.952570319054538 \tabularnewline
65 & 0.0405136639324953 & 0.0810273278649905 & 0.959486336067505 \tabularnewline
66 & 0.0333978153617892 & 0.0667956307235783 & 0.966602184638211 \tabularnewline
67 & 0.0522780508658336 & 0.104556101731667 & 0.947721949134166 \tabularnewline
68 & 0.0413147270656858 & 0.0826294541313717 & 0.958685272934314 \tabularnewline
69 & 0.0319611657186059 & 0.0639223314372117 & 0.968038834281394 \tabularnewline
70 & 0.0275362806408504 & 0.0550725612817007 & 0.97246371935915 \tabularnewline
71 & 0.0214390560800261 & 0.0428781121600521 & 0.978560943919974 \tabularnewline
72 & 0.0245982264822173 & 0.0491964529644346 & 0.975401773517783 \tabularnewline
73 & 0.0199455689248116 & 0.0398911378496232 & 0.980054431075188 \tabularnewline
74 & 0.0166292996885432 & 0.0332585993770865 & 0.983370700311457 \tabularnewline
75 & 0.0393092873764638 & 0.0786185747529275 & 0.960690712623536 \tabularnewline
76 & 0.0311097265440792 & 0.0622194530881583 & 0.968890273455921 \tabularnewline
77 & 0.0302780792433636 & 0.0605561584867272 & 0.969721920756636 \tabularnewline
78 & 0.0230784154262234 & 0.0461568308524468 & 0.976921584573777 \tabularnewline
79 & 0.10140896599902 & 0.202817931998039 & 0.89859103400098 \tabularnewline
80 & 0.0826420150798824 & 0.165284030159765 & 0.917357984920118 \tabularnewline
81 & 0.088644659965049 & 0.177289319930098 & 0.911355340034951 \tabularnewline
82 & 0.0859160467904531 & 0.171832093580906 & 0.914083953209547 \tabularnewline
83 & 0.104767642795678 & 0.209535285591356 & 0.895232357204322 \tabularnewline
84 & 0.0993280209538343 & 0.198656041907669 & 0.900671979046166 \tabularnewline
85 & 0.0796932409336386 & 0.159386481867277 & 0.920306759066361 \tabularnewline
86 & 0.063988989545193 & 0.127977979090386 & 0.936011010454807 \tabularnewline
87 & 0.0530901916989211 & 0.106180383397842 & 0.946909808301079 \tabularnewline
88 & 0.0488446618439895 & 0.097689323687979 & 0.95115533815601 \tabularnewline
89 & 0.037920773953066 & 0.0758415479061321 & 0.962079226046934 \tabularnewline
90 & 0.0303514061951074 & 0.0607028123902148 & 0.969648593804893 \tabularnewline
91 & 0.0255836084226796 & 0.0511672168453591 & 0.97441639157732 \tabularnewline
92 & 0.0215696158299796 & 0.0431392316599592 & 0.97843038417002 \tabularnewline
93 & 0.0161754039540009 & 0.0323508079080018 & 0.983824596045999 \tabularnewline
94 & 0.0141636245474459 & 0.0283272490948919 & 0.985836375452554 \tabularnewline
95 & 0.0107283694271685 & 0.0214567388543371 & 0.989271630572832 \tabularnewline
96 & 0.0243047382242906 & 0.0486094764485811 & 0.975695261775709 \tabularnewline
97 & 0.0204361030430739 & 0.0408722060861478 & 0.979563896956926 \tabularnewline
98 & 0.0153744687478012 & 0.0307489374956024 & 0.984625531252199 \tabularnewline
99 & 0.0136650013678192 & 0.0273300027356384 & 0.986334998632181 \tabularnewline
100 & 0.0133879219878267 & 0.0267758439756534 & 0.986612078012173 \tabularnewline
101 & 0.0126741465454089 & 0.0253482930908177 & 0.987325853454591 \tabularnewline
102 & 0.0234323538829629 & 0.0468647077659258 & 0.976567646117037 \tabularnewline
103 & 0.0208594608520158 & 0.0417189217040316 & 0.979140539147984 \tabularnewline
104 & 0.0282939732639104 & 0.0565879465278208 & 0.97170602673609 \tabularnewline
105 & 0.0288204176899631 & 0.0576408353799262 & 0.971179582310037 \tabularnewline
106 & 0.0238034881985617 & 0.0476069763971233 & 0.976196511801438 \tabularnewline
107 & 0.0302013414997747 & 0.0604026829995493 & 0.969798658500225 \tabularnewline
108 & 0.0327423624388187 & 0.0654847248776373 & 0.967257637561181 \tabularnewline
109 & 0.120594236800615 & 0.24118847360123 & 0.879405763199385 \tabularnewline
110 & 0.145554738384211 & 0.291109476768421 & 0.854445261615789 \tabularnewline
111 & 0.154684477066465 & 0.30936895413293 & 0.845315522933535 \tabularnewline
112 & 0.201477084027907 & 0.402954168055815 & 0.798522915972093 \tabularnewline
113 & 0.226578846418576 & 0.453157692837152 & 0.773421153581424 \tabularnewline
114 & 0.227225886743781 & 0.454451773487562 & 0.772774113256219 \tabularnewline
115 & 0.512923824693912 & 0.974152350612176 & 0.487076175306088 \tabularnewline
116 & 0.45978551733638 & 0.91957103467276 & 0.54021448266362 \tabularnewline
117 & 0.478251713423694 & 0.956503426847389 & 0.521748286576306 \tabularnewline
118 & 0.585193846553704 & 0.829612306892592 & 0.414806153446296 \tabularnewline
119 & 0.53873242859638 & 0.922535142807239 & 0.46126757140362 \tabularnewline
120 & 0.704543177581983 & 0.590913644836034 & 0.295456822418017 \tabularnewline
121 & 0.657715177302039 & 0.684569645395923 & 0.342284822697961 \tabularnewline
122 & 0.593683226382043 & 0.812633547235915 & 0.406316773617957 \tabularnewline
123 & 0.615608213096292 & 0.768783573807415 & 0.384391786903708 \tabularnewline
124 & 0.599106365613884 & 0.801787268772233 & 0.400893634386116 \tabularnewline
125 & 0.591741678248183 & 0.816516643503633 & 0.408258321751817 \tabularnewline
126 & 0.602850933645362 & 0.794298132709275 & 0.397149066354638 \tabularnewline
127 & 0.851728542447172 & 0.296542915105657 & 0.148271457552828 \tabularnewline
128 & 0.950619306616732 & 0.0987613867665355 & 0.0493806933832678 \tabularnewline
129 & 0.926540218049954 & 0.146919563900092 & 0.0734597819500462 \tabularnewline
130 & 0.987345486598346 & 0.0253090268033072 & 0.0126545134016536 \tabularnewline
131 & 0.987327957557039 & 0.0253440848859222 & 0.0126720424429611 \tabularnewline
132 & 0.995527817252791 & 0.00894436549441716 & 0.00447218274720858 \tabularnewline
133 & 0.999540503312663 & 0.000918993374674976 & 0.000459496687337488 \tabularnewline
134 & 0.999845006534388 & 0.000309986931223364 & 0.000154993465611682 \tabularnewline
135 & 0.999923087974806 & 0.000153824050387577 & 7.69120251937885e-05 \tabularnewline
136 & 0.999874970940023 & 0.000250058119954349 & 0.000125029059977175 \tabularnewline
137 & 0.999529729570457 & 0.000940540859085824 & 0.000470270429542912 \tabularnewline
138 & 0.997759389230984 & 0.00448122153803269 & 0.00224061076901635 \tabularnewline
139 & 0.991715319185039 & 0.0165693616299224 & 0.00828468081496121 \tabularnewline
140 & 0.976373099579018 & 0.0472538008419633 & 0.0236269004209816 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185198&T=5

[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]11[/C][C]0.29141714876376[/C][C]0.582834297527519[/C][C]0.70858285123624[/C][/ROW]
[ROW][C]12[/C][C]0.156118268177417[/C][C]0.312236536354834[/C][C]0.843881731822583[/C][/ROW]
[ROW][C]13[/C][C]0.188685798818334[/C][C]0.377371597636668[/C][C]0.811314201181666[/C][/ROW]
[ROW][C]14[/C][C]0.134157572426081[/C][C]0.268315144852163[/C][C]0.865842427573918[/C][/ROW]
[ROW][C]15[/C][C]0.0786975448419217[/C][C]0.157395089683843[/C][C]0.921302455158078[/C][/ROW]
[ROW][C]16[/C][C]0.0428109730783121[/C][C]0.0856219461566242[/C][C]0.957189026921688[/C][/ROW]
[ROW][C]17[/C][C]0.0366544819863915[/C][C]0.0733089639727831[/C][C]0.963345518013608[/C][/ROW]
[ROW][C]18[/C][C]0.0250396703472832[/C][C]0.0500793406945665[/C][C]0.974960329652717[/C][/ROW]
[ROW][C]19[/C][C]0.660165268540452[/C][C]0.679669462919096[/C][C]0.339834731459548[/C][/ROW]
[ROW][C]20[/C][C]0.594585411353313[/C][C]0.810829177293374[/C][C]0.405414588646687[/C][/ROW]
[ROW][C]21[/C][C]0.623413740006724[/C][C]0.753172519986553[/C][C]0.376586259993276[/C][/ROW]
[ROW][C]22[/C][C]0.682999840837626[/C][C]0.634000318324747[/C][C]0.317000159162374[/C][/ROW]
[ROW][C]23[/C][C]0.697404864898291[/C][C]0.605190270203418[/C][C]0.302595135101709[/C][/ROW]
[ROW][C]24[/C][C]0.643182056612655[/C][C]0.71363588677469[/C][C]0.356817943387345[/C][/ROW]
[ROW][C]25[/C][C]0.576579333919962[/C][C]0.846841332160075[/C][C]0.423420666080038[/C][/ROW]
[ROW][C]26[/C][C]0.53039745033012[/C][C]0.939205099339761[/C][C]0.46960254966988[/C][/ROW]
[ROW][C]27[/C][C]0.487733140509113[/C][C]0.975466281018226[/C][C]0.512266859490887[/C][/ROW]
[ROW][C]28[/C][C]0.426077957120138[/C][C]0.852155914240277[/C][C]0.573922042879861[/C][/ROW]
[ROW][C]29[/C][C]0.361863038267901[/C][C]0.723726076535802[/C][C]0.638136961732099[/C][/ROW]
[ROW][C]30[/C][C]0.311321370344217[/C][C]0.622642740688434[/C][C]0.688678629655783[/C][/ROW]
[ROW][C]31[/C][C]0.374410218932521[/C][C]0.748820437865041[/C][C]0.625589781067479[/C][/ROW]
[ROW][C]32[/C][C]0.341256204228271[/C][C]0.682512408456543[/C][C]0.658743795771729[/C][/ROW]
[ROW][C]33[/C][C]0.351147574672875[/C][C]0.702295149345749[/C][C]0.648852425327125[/C][/ROW]
[ROW][C]34[/C][C]0.375166833080872[/C][C]0.750333666161744[/C][C]0.624833166919128[/C][/ROW]
[ROW][C]35[/C][C]0.359866719921729[/C][C]0.719733439843458[/C][C]0.640133280078271[/C][/ROW]
[ROW][C]36[/C][C]0.36822706825703[/C][C]0.73645413651406[/C][C]0.63177293174297[/C][/ROW]
[ROW][C]37[/C][C]0.348361473978808[/C][C]0.696722947957615[/C][C]0.651638526021192[/C][/ROW]
[ROW][C]38[/C][C]0.305988140507121[/C][C]0.611976281014243[/C][C]0.694011859492879[/C][/ROW]
[ROW][C]39[/C][C]0.378556787984709[/C][C]0.757113575969418[/C][C]0.621443212015291[/C][/ROW]
[ROW][C]40[/C][C]0.350625259500212[/C][C]0.701250519000425[/C][C]0.649374740499788[/C][/ROW]
[ROW][C]41[/C][C]0.320948148245962[/C][C]0.641896296491925[/C][C]0.679051851754038[/C][/ROW]
[ROW][C]42[/C][C]0.30367523144481[/C][C]0.607350462889621[/C][C]0.69632476855519[/C][/ROW]
[ROW][C]43[/C][C]0.272406332299597[/C][C]0.544812664599194[/C][C]0.727593667700403[/C][/ROW]
[ROW][C]44[/C][C]0.228453099083579[/C][C]0.456906198167157[/C][C]0.771546900916421[/C][/ROW]
[ROW][C]45[/C][C]0.203009611868786[/C][C]0.406019223737572[/C][C]0.796990388131214[/C][/ROW]
[ROW][C]46[/C][C]0.190347229593118[/C][C]0.380694459186237[/C][C]0.809652770406882[/C][/ROW]
[ROW][C]47[/C][C]0.159012619163254[/C][C]0.318025238326508[/C][C]0.840987380836746[/C][/ROW]
[ROW][C]48[/C][C]0.242428333637975[/C][C]0.484856667275949[/C][C]0.757571666362025[/C][/ROW]
[ROW][C]49[/C][C]0.204897072585072[/C][C]0.409794145170144[/C][C]0.795102927414928[/C][/ROW]
[ROW][C]50[/C][C]0.169455935862808[/C][C]0.338911871725615[/C][C]0.830544064137192[/C][/ROW]
[ROW][C]51[/C][C]0.144195653306829[/C][C]0.288391306613657[/C][C]0.855804346693171[/C][/ROW]
[ROW][C]52[/C][C]0.122280989997405[/C][C]0.244561979994811[/C][C]0.877719010002595[/C][/ROW]
[ROW][C]53[/C][C]0.0985641879037956[/C][C]0.197128375807591[/C][C]0.901435812096204[/C][/ROW]
[ROW][C]54[/C][C]0.0838271824407233[/C][C]0.167654364881447[/C][C]0.916172817559277[/C][/ROW]
[ROW][C]55[/C][C]0.0726473764894791[/C][C]0.145294752978958[/C][C]0.927352623510521[/C][/ROW]
[ROW][C]56[/C][C]0.0621281480566778[/C][C]0.124256296113356[/C][C]0.937871851943322[/C][/ROW]
[ROW][C]57[/C][C]0.0479239699089912[/C][C]0.0958479398179825[/C][C]0.952076030091009[/C][/ROW]
[ROW][C]58[/C][C]0.0394083398944083[/C][C]0.0788166797888167[/C][C]0.960591660105592[/C][/ROW]
[ROW][C]59[/C][C]0.0305037528367184[/C][C]0.0610075056734368[/C][C]0.969496247163282[/C][/ROW]
[ROW][C]60[/C][C]0.0678493749434257[/C][C]0.135698749886851[/C][C]0.932150625056574[/C][/ROW]
[ROW][C]61[/C][C]0.0836852506627576[/C][C]0.167370501325515[/C][C]0.916314749337242[/C][/ROW]
[ROW][C]62[/C][C]0.0659694252193659[/C][C]0.131938850438732[/C][C]0.934030574780634[/C][/ROW]
[ROW][C]63[/C][C]0.05688481631164[/C][C]0.11376963262328[/C][C]0.94311518368836[/C][/ROW]
[ROW][C]64[/C][C]0.0474296809454622[/C][C]0.0948593618909244[/C][C]0.952570319054538[/C][/ROW]
[ROW][C]65[/C][C]0.0405136639324953[/C][C]0.0810273278649905[/C][C]0.959486336067505[/C][/ROW]
[ROW][C]66[/C][C]0.0333978153617892[/C][C]0.0667956307235783[/C][C]0.966602184638211[/C][/ROW]
[ROW][C]67[/C][C]0.0522780508658336[/C][C]0.104556101731667[/C][C]0.947721949134166[/C][/ROW]
[ROW][C]68[/C][C]0.0413147270656858[/C][C]0.0826294541313717[/C][C]0.958685272934314[/C][/ROW]
[ROW][C]69[/C][C]0.0319611657186059[/C][C]0.0639223314372117[/C][C]0.968038834281394[/C][/ROW]
[ROW][C]70[/C][C]0.0275362806408504[/C][C]0.0550725612817007[/C][C]0.97246371935915[/C][/ROW]
[ROW][C]71[/C][C]0.0214390560800261[/C][C]0.0428781121600521[/C][C]0.978560943919974[/C][/ROW]
[ROW][C]72[/C][C]0.0245982264822173[/C][C]0.0491964529644346[/C][C]0.975401773517783[/C][/ROW]
[ROW][C]73[/C][C]0.0199455689248116[/C][C]0.0398911378496232[/C][C]0.980054431075188[/C][/ROW]
[ROW][C]74[/C][C]0.0166292996885432[/C][C]0.0332585993770865[/C][C]0.983370700311457[/C][/ROW]
[ROW][C]75[/C][C]0.0393092873764638[/C][C]0.0786185747529275[/C][C]0.960690712623536[/C][/ROW]
[ROW][C]76[/C][C]0.0311097265440792[/C][C]0.0622194530881583[/C][C]0.968890273455921[/C][/ROW]
[ROW][C]77[/C][C]0.0302780792433636[/C][C]0.0605561584867272[/C][C]0.969721920756636[/C][/ROW]
[ROW][C]78[/C][C]0.0230784154262234[/C][C]0.0461568308524468[/C][C]0.976921584573777[/C][/ROW]
[ROW][C]79[/C][C]0.10140896599902[/C][C]0.202817931998039[/C][C]0.89859103400098[/C][/ROW]
[ROW][C]80[/C][C]0.0826420150798824[/C][C]0.165284030159765[/C][C]0.917357984920118[/C][/ROW]
[ROW][C]81[/C][C]0.088644659965049[/C][C]0.177289319930098[/C][C]0.911355340034951[/C][/ROW]
[ROW][C]82[/C][C]0.0859160467904531[/C][C]0.171832093580906[/C][C]0.914083953209547[/C][/ROW]
[ROW][C]83[/C][C]0.104767642795678[/C][C]0.209535285591356[/C][C]0.895232357204322[/C][/ROW]
[ROW][C]84[/C][C]0.0993280209538343[/C][C]0.198656041907669[/C][C]0.900671979046166[/C][/ROW]
[ROW][C]85[/C][C]0.0796932409336386[/C][C]0.159386481867277[/C][C]0.920306759066361[/C][/ROW]
[ROW][C]86[/C][C]0.063988989545193[/C][C]0.127977979090386[/C][C]0.936011010454807[/C][/ROW]
[ROW][C]87[/C][C]0.0530901916989211[/C][C]0.106180383397842[/C][C]0.946909808301079[/C][/ROW]
[ROW][C]88[/C][C]0.0488446618439895[/C][C]0.097689323687979[/C][C]0.95115533815601[/C][/ROW]
[ROW][C]89[/C][C]0.037920773953066[/C][C]0.0758415479061321[/C][C]0.962079226046934[/C][/ROW]
[ROW][C]90[/C][C]0.0303514061951074[/C][C]0.0607028123902148[/C][C]0.969648593804893[/C][/ROW]
[ROW][C]91[/C][C]0.0255836084226796[/C][C]0.0511672168453591[/C][C]0.97441639157732[/C][/ROW]
[ROW][C]92[/C][C]0.0215696158299796[/C][C]0.0431392316599592[/C][C]0.97843038417002[/C][/ROW]
[ROW][C]93[/C][C]0.0161754039540009[/C][C]0.0323508079080018[/C][C]0.983824596045999[/C][/ROW]
[ROW][C]94[/C][C]0.0141636245474459[/C][C]0.0283272490948919[/C][C]0.985836375452554[/C][/ROW]
[ROW][C]95[/C][C]0.0107283694271685[/C][C]0.0214567388543371[/C][C]0.989271630572832[/C][/ROW]
[ROW][C]96[/C][C]0.0243047382242906[/C][C]0.0486094764485811[/C][C]0.975695261775709[/C][/ROW]
[ROW][C]97[/C][C]0.0204361030430739[/C][C]0.0408722060861478[/C][C]0.979563896956926[/C][/ROW]
[ROW][C]98[/C][C]0.0153744687478012[/C][C]0.0307489374956024[/C][C]0.984625531252199[/C][/ROW]
[ROW][C]99[/C][C]0.0136650013678192[/C][C]0.0273300027356384[/C][C]0.986334998632181[/C][/ROW]
[ROW][C]100[/C][C]0.0133879219878267[/C][C]0.0267758439756534[/C][C]0.986612078012173[/C][/ROW]
[ROW][C]101[/C][C]0.0126741465454089[/C][C]0.0253482930908177[/C][C]0.987325853454591[/C][/ROW]
[ROW][C]102[/C][C]0.0234323538829629[/C][C]0.0468647077659258[/C][C]0.976567646117037[/C][/ROW]
[ROW][C]103[/C][C]0.0208594608520158[/C][C]0.0417189217040316[/C][C]0.979140539147984[/C][/ROW]
[ROW][C]104[/C][C]0.0282939732639104[/C][C]0.0565879465278208[/C][C]0.97170602673609[/C][/ROW]
[ROW][C]105[/C][C]0.0288204176899631[/C][C]0.0576408353799262[/C][C]0.971179582310037[/C][/ROW]
[ROW][C]106[/C][C]0.0238034881985617[/C][C]0.0476069763971233[/C][C]0.976196511801438[/C][/ROW]
[ROW][C]107[/C][C]0.0302013414997747[/C][C]0.0604026829995493[/C][C]0.969798658500225[/C][/ROW]
[ROW][C]108[/C][C]0.0327423624388187[/C][C]0.0654847248776373[/C][C]0.967257637561181[/C][/ROW]
[ROW][C]109[/C][C]0.120594236800615[/C][C]0.24118847360123[/C][C]0.879405763199385[/C][/ROW]
[ROW][C]110[/C][C]0.145554738384211[/C][C]0.291109476768421[/C][C]0.854445261615789[/C][/ROW]
[ROW][C]111[/C][C]0.154684477066465[/C][C]0.30936895413293[/C][C]0.845315522933535[/C][/ROW]
[ROW][C]112[/C][C]0.201477084027907[/C][C]0.402954168055815[/C][C]0.798522915972093[/C][/ROW]
[ROW][C]113[/C][C]0.226578846418576[/C][C]0.453157692837152[/C][C]0.773421153581424[/C][/ROW]
[ROW][C]114[/C][C]0.227225886743781[/C][C]0.454451773487562[/C][C]0.772774113256219[/C][/ROW]
[ROW][C]115[/C][C]0.512923824693912[/C][C]0.974152350612176[/C][C]0.487076175306088[/C][/ROW]
[ROW][C]116[/C][C]0.45978551733638[/C][C]0.91957103467276[/C][C]0.54021448266362[/C][/ROW]
[ROW][C]117[/C][C]0.478251713423694[/C][C]0.956503426847389[/C][C]0.521748286576306[/C][/ROW]
[ROW][C]118[/C][C]0.585193846553704[/C][C]0.829612306892592[/C][C]0.414806153446296[/C][/ROW]
[ROW][C]119[/C][C]0.53873242859638[/C][C]0.922535142807239[/C][C]0.46126757140362[/C][/ROW]
[ROW][C]120[/C][C]0.704543177581983[/C][C]0.590913644836034[/C][C]0.295456822418017[/C][/ROW]
[ROW][C]121[/C][C]0.657715177302039[/C][C]0.684569645395923[/C][C]0.342284822697961[/C][/ROW]
[ROW][C]122[/C][C]0.593683226382043[/C][C]0.812633547235915[/C][C]0.406316773617957[/C][/ROW]
[ROW][C]123[/C][C]0.615608213096292[/C][C]0.768783573807415[/C][C]0.384391786903708[/C][/ROW]
[ROW][C]124[/C][C]0.599106365613884[/C][C]0.801787268772233[/C][C]0.400893634386116[/C][/ROW]
[ROW][C]125[/C][C]0.591741678248183[/C][C]0.816516643503633[/C][C]0.408258321751817[/C][/ROW]
[ROW][C]126[/C][C]0.602850933645362[/C][C]0.794298132709275[/C][C]0.397149066354638[/C][/ROW]
[ROW][C]127[/C][C]0.851728542447172[/C][C]0.296542915105657[/C][C]0.148271457552828[/C][/ROW]
[ROW][C]128[/C][C]0.950619306616732[/C][C]0.0987613867665355[/C][C]0.0493806933832678[/C][/ROW]
[ROW][C]129[/C][C]0.926540218049954[/C][C]0.146919563900092[/C][C]0.0734597819500462[/C][/ROW]
[ROW][C]130[/C][C]0.987345486598346[/C][C]0.0253090268033072[/C][C]0.0126545134016536[/C][/ROW]
[ROW][C]131[/C][C]0.987327957557039[/C][C]0.0253440848859222[/C][C]0.0126720424429611[/C][/ROW]
[ROW][C]132[/C][C]0.995527817252791[/C][C]0.00894436549441716[/C][C]0.00447218274720858[/C][/ROW]
[ROW][C]133[/C][C]0.999540503312663[/C][C]0.000918993374674976[/C][C]0.000459496687337488[/C][/ROW]
[ROW][C]134[/C][C]0.999845006534388[/C][C]0.000309986931223364[/C][C]0.000154993465611682[/C][/ROW]
[ROW][C]135[/C][C]0.999923087974806[/C][C]0.000153824050387577[/C][C]7.69120251937885e-05[/C][/ROW]
[ROW][C]136[/C][C]0.999874970940023[/C][C]0.000250058119954349[/C][C]0.000125029059977175[/C][/ROW]
[ROW][C]137[/C][C]0.999529729570457[/C][C]0.000940540859085824[/C][C]0.000470270429542912[/C][/ROW]
[ROW][C]138[/C][C]0.997759389230984[/C][C]0.00448122153803269[/C][C]0.00224061076901635[/C][/ROW]
[ROW][C]139[/C][C]0.991715319185039[/C][C]0.0165693616299224[/C][C]0.00828468081496121[/C][/ROW]
[ROW][C]140[/C][C]0.976373099579018[/C][C]0.0472538008419633[/C][C]0.0236269004209816[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185198&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185198&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.291417148763760.5828342975275190.70858285123624
120.1561182681774170.3122365363548340.843881731822583
130.1886857988183340.3773715976366680.811314201181666
140.1341575724260810.2683151448521630.865842427573918
150.07869754484192170.1573950896838430.921302455158078
160.04281097307831210.08562194615662420.957189026921688
170.03665448198639150.07330896397278310.963345518013608
180.02503967034728320.05007934069456650.974960329652717
190.6601652685404520.6796694629190960.339834731459548
200.5945854113533130.8108291772933740.405414588646687
210.6234137400067240.7531725199865530.376586259993276
220.6829998408376260.6340003183247470.317000159162374
230.6974048648982910.6051902702034180.302595135101709
240.6431820566126550.713635886774690.356817943387345
250.5765793339199620.8468413321600750.423420666080038
260.530397450330120.9392050993397610.46960254966988
270.4877331405091130.9754662810182260.512266859490887
280.4260779571201380.8521559142402770.573922042879861
290.3618630382679010.7237260765358020.638136961732099
300.3113213703442170.6226427406884340.688678629655783
310.3744102189325210.7488204378650410.625589781067479
320.3412562042282710.6825124084565430.658743795771729
330.3511475746728750.7022951493457490.648852425327125
340.3751668330808720.7503336661617440.624833166919128
350.3598667199217290.7197334398434580.640133280078271
360.368227068257030.736454136514060.63177293174297
370.3483614739788080.6967229479576150.651638526021192
380.3059881405071210.6119762810142430.694011859492879
390.3785567879847090.7571135759694180.621443212015291
400.3506252595002120.7012505190004250.649374740499788
410.3209481482459620.6418962964919250.679051851754038
420.303675231444810.6073504628896210.69632476855519
430.2724063322995970.5448126645991940.727593667700403
440.2284530990835790.4569061981671570.771546900916421
450.2030096118687860.4060192237375720.796990388131214
460.1903472295931180.3806944591862370.809652770406882
470.1590126191632540.3180252383265080.840987380836746
480.2424283336379750.4848566672759490.757571666362025
490.2048970725850720.4097941451701440.795102927414928
500.1694559358628080.3389118717256150.830544064137192
510.1441956533068290.2883913066136570.855804346693171
520.1222809899974050.2445619799948110.877719010002595
530.09856418790379560.1971283758075910.901435812096204
540.08382718244072330.1676543648814470.916172817559277
550.07264737648947910.1452947529789580.927352623510521
560.06212814805667780.1242562961133560.937871851943322
570.04792396990899120.09584793981798250.952076030091009
580.03940833989440830.07881667978881670.960591660105592
590.03050375283671840.06100750567343680.969496247163282
600.06784937494342570.1356987498868510.932150625056574
610.08368525066275760.1673705013255150.916314749337242
620.06596942521936590.1319388504387320.934030574780634
630.056884816311640.113769632623280.94311518368836
640.04742968094546220.09485936189092440.952570319054538
650.04051366393249530.08102732786499050.959486336067505
660.03339781536178920.06679563072357830.966602184638211
670.05227805086583360.1045561017316670.947721949134166
680.04131472706568580.08262945413137170.958685272934314
690.03196116571860590.06392233143721170.968038834281394
700.02753628064085040.05507256128170070.97246371935915
710.02143905608002610.04287811216005210.978560943919974
720.02459822648221730.04919645296443460.975401773517783
730.01994556892481160.03989113784962320.980054431075188
740.01662929968854320.03325859937708650.983370700311457
750.03930928737646380.07861857475292750.960690712623536
760.03110972654407920.06221945308815830.968890273455921
770.03027807924336360.06055615848672720.969721920756636
780.02307841542622340.04615683085244680.976921584573777
790.101408965999020.2028179319980390.89859103400098
800.08264201507988240.1652840301597650.917357984920118
810.0886446599650490.1772893199300980.911355340034951
820.08591604679045310.1718320935809060.914083953209547
830.1047676427956780.2095352855913560.895232357204322
840.09932802095383430.1986560419076690.900671979046166
850.07969324093363860.1593864818672770.920306759066361
860.0639889895451930.1279779790903860.936011010454807
870.05309019169892110.1061803833978420.946909808301079
880.04884466184398950.0976893236879790.95115533815601
890.0379207739530660.07584154790613210.962079226046934
900.03035140619510740.06070281239021480.969648593804893
910.02558360842267960.05116721684535910.97441639157732
920.02156961582997960.04313923165995920.97843038417002
930.01617540395400090.03235080790800180.983824596045999
940.01416362454744590.02832724909489190.985836375452554
950.01072836942716850.02145673885433710.989271630572832
960.02430473822429060.04860947644858110.975695261775709
970.02043610304307390.04087220608614780.979563896956926
980.01537446874780120.03074893749560240.984625531252199
990.01366500136781920.02733000273563840.986334998632181
1000.01338792198782670.02677584397565340.986612078012173
1010.01267414654540890.02534829309081770.987325853454591
1020.02343235388296290.04686470776592580.976567646117037
1030.02085946085201580.04171892170403160.979140539147984
1040.02829397326391040.05658794652782080.97170602673609
1050.02882041768996310.05764083537992620.971179582310037
1060.02380348819856170.04760697639712330.976196511801438
1070.03020134149977470.06040268299954930.969798658500225
1080.03274236243881870.06548472487763730.967257637561181
1090.1205942368006150.241188473601230.879405763199385
1100.1455547383842110.2911094767684210.854445261615789
1110.1546844770664650.309368954132930.845315522933535
1120.2014770840279070.4029541680558150.798522915972093
1130.2265788464185760.4531576928371520.773421153581424
1140.2272258867437810.4544517734875620.772774113256219
1150.5129238246939120.9741523506121760.487076175306088
1160.459785517336380.919571034672760.54021448266362
1170.4782517134236940.9565034268473890.521748286576306
1180.5851938465537040.8296123068925920.414806153446296
1190.538732428596380.9225351428072390.46126757140362
1200.7045431775819830.5909136448360340.295456822418017
1210.6577151773020390.6845696453959230.342284822697961
1220.5936832263820430.8126335472359150.406316773617957
1230.6156082130962920.7687835738074150.384391786903708
1240.5991063656138840.8017872687722330.400893634386116
1250.5917416782481830.8165166435036330.408258321751817
1260.6028509336453620.7942981327092750.397149066354638
1270.8517285424471720.2965429151056570.148271457552828
1280.9506193066167320.09876138676653550.0493806933832678
1290.9265402180499540.1469195639000920.0734597819500462
1300.9873454865983460.02530902680330720.0126545134016536
1310.9873279575570390.02534408488592220.0126720424429611
1320.9955278172527910.008944365494417160.00447218274720858
1330.9995405033126630.0009189933746749760.000459496687337488
1340.9998450065343880.0003099869312233640.000154993465611682
1350.9999230879748060.0001538240503875777.69120251937885e-05
1360.9998749709400230.0002500581199543490.000125029059977175
1370.9995297295704570.0009405408590858240.000470270429542912
1380.9977593892309840.004481221538032690.00224061076901635
1390.9917153191850390.01656936162992240.00828468081496121
1400.9763730995790180.04725380084196330.0236269004209816






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.0538461538461538NOK
5% type I error level290.223076923076923NOK
10% type I error level530.407692307692308NOK

\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 & 7 & 0.0538461538461538 & NOK \tabularnewline
5% type I error level & 29 & 0.223076923076923 & NOK \tabularnewline
10% type I error level & 53 & 0.407692307692308 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185198&T=6

[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]7[/C][C]0.0538461538461538[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.223076923076923[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]53[/C][C]0.407692307692308[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185198&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185198&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.0538461538461538NOK
5% type I error level290.223076923076923NOK
10% type I error level530.407692307692308NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}