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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 06 Dec 2015 13:20:57 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/06/t1449408741ta32fcag9gfpqfk.htm/, Retrieved Thu, 31 Oct 2024 23:19:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285277, Retrieved Thu, 31 Oct 2024 23:19:24 +0000
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Estimated Impact203
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-       [Multiple Regression] [] [2015-12-06 13:20:57] [63a9f0ea7bb98050796b649e85481845] [Current]
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Dataseries X:
1687 0
1508 0
1507 0
1385 0
1632 0
1511 0
1559 0
1630 0
1579 0
1653 0
2152 0
2148 0
1752 0
1765 0
1717 0
1558 0
1575 0
1520 0
1805 0
1800 0
1719 0
2008 0
2242 0
2478 0
2030 0
1655 0
1693 0
1623 0
1805 0
1746 0
1795 0
1926 0
1619 0
1992 0
2233 0
2192 0
2080 0
1768 0
1835 0
1569 0
1976 0
1853 0
1965 0
1689 0
1778 0
1976 0
2397 0
2654 0
2097 0
1963 0
1677 0
1941 0
2003 0
1813 0
2012 0
1912 0
2084 0
2080 0
2118 0
2150 0
1608 0
1503 0
1548 0
1382 0
1731 0
1798 0
1779 0
1887 0
2004 0
2077 0
2092 0
2051 0
1577 0
1356 0
1652 0
1382 0
1519 0
1421 0
1442 0
1543 0
1656 0
1561 0
1905 0
2199 0
1473 0
1655 0
1407 0
1395 0
1530 0
1309 0
1526 0
1327 0
1627 0
1748 0
1958 0
2274 0
1648 0
1401 0
1411 0
1403 0
1394 0
1520 0
1528 0
1643 0
1515 0
1685 0
2000 0
2215 0
1956 0
1462 0
1563 0
1459 0
1446 0
1622 0
1657 0
1638 0
1643 0
1683 0
2050 0
2262 0
1813 0
1445 0
1762 0
1461 0
1556 0
1431 0
1427 0
1554 0
1645 0
1653 0
2016 0
2207 0
1665 0
1361 0
1506 0
1360 0
1453 0
1522 0
1460 0
1552 0
1548 0
1827 0
1737 0
1941 0
1474 0
1458 0
1542 0
1404 0
1522 0
1385 0
1641 0
1510 0
1681 0
1938 0
1868 0
1726 0
1456 0
1445 0
1456 0
1365 0
1487 0
1558 0
1488 0
1684 0
1594 0
1850 0
1998 0
2079 0
1494 0
1057 1
1218 1
1168 1
1236 1
1076 1
1174 1
1139 1
1427 1
1487 1
1483 1
1513 1
1357 1
1165 1
1282 1
1110 1
1297 1
1185 1
1222 1
1284 1
1444 1
1575 1
1737 1
1763 1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285277&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285277&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285277&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
(1-B12)(1-B)Accidents[t] = -0.590018 -281.524`(1-B12)(1-B)Belt`[t] -0.443272`(1-B12)(1-B)Accidents(t-1)`[t] -0.205127`(1-B12)(1-B)Accidents(t-2)`[t] -0.177881`(1-B12)(1-B)Accidents(t-3)`[t] -0.177537`(1-B12)(1-B)Accidents(t-4)`[t] -0.493227`(1-B12)(1-B)Accidents(t-1s)`[t] -0.242136`(1-B12)(1-B)Accidents(t-2s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-B12)(1-B)Accidents[t] =  -0.590018 -281.524`(1-B12)(1-B)Belt`[t] -0.443272`(1-B12)(1-B)Accidents(t-1)`[t] -0.205127`(1-B12)(1-B)Accidents(t-2)`[t] -0.177881`(1-B12)(1-B)Accidents(t-3)`[t] -0.177537`(1-B12)(1-B)Accidents(t-4)`[t] -0.493227`(1-B12)(1-B)Accidents(t-1s)`[t] -0.242136`(1-B12)(1-B)Accidents(t-2s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285277&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-B12)(1-B)Accidents[t] =  -0.590018 -281.524`(1-B12)(1-B)Belt`[t] -0.443272`(1-B12)(1-B)Accidents(t-1)`[t] -0.205127`(1-B12)(1-B)Accidents(t-2)`[t] -0.177881`(1-B12)(1-B)Accidents(t-3)`[t] -0.177537`(1-B12)(1-B)Accidents(t-4)`[t] -0.493227`(1-B12)(1-B)Accidents(t-1s)`[t] -0.242136`(1-B12)(1-B)Accidents(t-2s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285277&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285277&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
(1-B12)(1-B)Accidents[t] = -0.590018 -281.524`(1-B12)(1-B)Belt`[t] -0.443272`(1-B12)(1-B)Accidents(t-1)`[t] -0.205127`(1-B12)(1-B)Accidents(t-2)`[t] -0.177881`(1-B12)(1-B)Accidents(t-3)`[t] -0.177537`(1-B12)(1-B)Accidents(t-4)`[t] -0.493227`(1-B12)(1-B)Accidents(t-1s)`[t] -0.242136`(1-B12)(1-B)Accidents(t-2s)`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.59 12.51-4.7150e-02 0.9625 0.4812
`(1-B12)(1-B)Belt`-281.5 117.3-2.4000e+00 0.01766 0.008832
`(1-B12)(1-B)Accidents(t-1)`-0.4433 0.07502-5.9090e+00 2.409e-08 1.204e-08
`(1-B12)(1-B)Accidents(t-2)`-0.2051 0.07727-2.6550e+00 0.008841 0.00442
`(1-B12)(1-B)Accidents(t-3)`-0.1779 0.07789-2.2840e+00 0.02385 0.01193
`(1-B12)(1-B)Accidents(t-4)`-0.1775 0.06881-2.5800e+00 0.01088 0.005442
`(1-B12)(1-B)Accidents(t-1s)`-0.4932 0.07205-6.8460e+00 2.071e-10 1.036e-10
`(1-B12)(1-B)Accidents(t-2s)`-0.2421 0.07529-3.2160e+00 0.001606 0.0008031

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.59 &  12.51 & -4.7150e-02 &  0.9625 &  0.4812 \tabularnewline
`(1-B12)(1-B)Belt` & -281.5 &  117.3 & -2.4000e+00 &  0.01766 &  0.008832 \tabularnewline
`(1-B12)(1-B)Accidents(t-1)` & -0.4433 &  0.07502 & -5.9090e+00 &  2.409e-08 &  1.204e-08 \tabularnewline
`(1-B12)(1-B)Accidents(t-2)` & -0.2051 &  0.07727 & -2.6550e+00 &  0.008841 &  0.00442 \tabularnewline
`(1-B12)(1-B)Accidents(t-3)` & -0.1779 &  0.07789 & -2.2840e+00 &  0.02385 &  0.01193 \tabularnewline
`(1-B12)(1-B)Accidents(t-4)` & -0.1775 &  0.06881 & -2.5800e+00 &  0.01088 &  0.005442 \tabularnewline
`(1-B12)(1-B)Accidents(t-1s)` & -0.4932 &  0.07205 & -6.8460e+00 &  2.071e-10 &  1.036e-10 \tabularnewline
`(1-B12)(1-B)Accidents(t-2s)` & -0.2421 &  0.07529 & -3.2160e+00 &  0.001606 &  0.0008031 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285277&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.59[/C][C] 12.51[/C][C]-4.7150e-02[/C][C] 0.9625[/C][C] 0.4812[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Belt`[/C][C]-281.5[/C][C] 117.3[/C][C]-2.4000e+00[/C][C] 0.01766[/C][C] 0.008832[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Accidents(t-1)`[/C][C]-0.4433[/C][C] 0.07502[/C][C]-5.9090e+00[/C][C] 2.409e-08[/C][C] 1.204e-08[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Accidents(t-2)`[/C][C]-0.2051[/C][C] 0.07727[/C][C]-2.6550e+00[/C][C] 0.008841[/C][C] 0.00442[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Accidents(t-3)`[/C][C]-0.1779[/C][C] 0.07789[/C][C]-2.2840e+00[/C][C] 0.02385[/C][C] 0.01193[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Accidents(t-4)`[/C][C]-0.1775[/C][C] 0.06881[/C][C]-2.5800e+00[/C][C] 0.01088[/C][C] 0.005442[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Accidents(t-1s)`[/C][C]-0.4932[/C][C] 0.07205[/C][C]-6.8460e+00[/C][C] 2.071e-10[/C][C] 1.036e-10[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Accidents(t-2s)`[/C][C]-0.2421[/C][C] 0.07529[/C][C]-3.2160e+00[/C][C] 0.001606[/C][C] 0.0008031[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285277&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285277&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.59 12.51-4.7150e-02 0.9625 0.4812
`(1-B12)(1-B)Belt`-281.5 117.3-2.4000e+00 0.01766 0.008832
`(1-B12)(1-B)Accidents(t-1)`-0.4433 0.07502-5.9090e+00 2.409e-08 1.204e-08
`(1-B12)(1-B)Accidents(t-2)`-0.2051 0.07727-2.6550e+00 0.008841 0.00442
`(1-B12)(1-B)Accidents(t-3)`-0.1779 0.07789-2.2840e+00 0.02385 0.01193
`(1-B12)(1-B)Accidents(t-4)`-0.1775 0.06881-2.5800e+00 0.01088 0.005442
`(1-B12)(1-B)Accidents(t-1s)`-0.4932 0.07205-6.8460e+00 2.071e-10 1.036e-10
`(1-B12)(1-B)Accidents(t-2s)`-0.2421 0.07529-3.2160e+00 0.001606 0.0008031







Multiple Linear Regression - Regression Statistics
Multiple R 0.7147
R-squared 0.5108
Adjusted R-squared 0.4868
F-TEST (value) 21.33
F-TEST (DF numerator)7
F-TEST (DF denominator)143
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 153.7
Sum Squared Residuals 3.379e+06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7147 \tabularnewline
R-squared &  0.5108 \tabularnewline
Adjusted R-squared &  0.4868 \tabularnewline
F-TEST (value) &  21.33 \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 &  153.7 \tabularnewline
Sum Squared Residuals &  3.379e+06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285277&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7147[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.5108[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.4868[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 21.33[/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] 153.7[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 3.379e+06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285277&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285277&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R 0.7147
R-squared 0.5108
Adjusted R-squared 0.4868
F-TEST (value) 21.33
F-TEST (DF numerator)7
F-TEST (DF denominator)143
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 153.7
Sum Squared Residuals 3.379e+06







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-64-90.47 26.47
2 63 70.36-7.357
3-407-69.29-337.7
4 396 257.1 138.9
5-175-186 10.97
6 180 117.7 62.32
7 298 35.85 262.2
8-445-361.9-83.08
9 178 197.5-19.46
10-353-108.3-244.7
11 530 220.7 309.3
12-345-266.7-78.3
13-67 107.3-174.3
14 87 94.34-7.342
15 176 109.7 66.32
16 83-163.9 246.9
17-202-11.09-190.9
18-383-65.3-317.7
19-225 84.7-309.7
20 15 337-322
21 29 39.86-10.86
22 331 258.6 72.41
23-430-329.9-100.1
24 287 230 57.02
25 257-55.09 312.1
26-218-213.8-4.176
27 208 80.36 127.6
28-55-281.6 226.6
29 77 116.3-39.28
30-23 123.6-146.6
31-73 5.486-78.49
32 68 132.9-64.91
33-116-82.74-33.26
34 251-23.83 274.8
35-104-3.437-100.6
36-212-55.43-156.6
37-165-19.87-145.1
38 40 176.4-136.4
39-7-73.51 66.51
40-4 68.33-72.33
41-168 35.73-203.7
42 329 172.9 156.1
43 335-19.52 354.5
44-252-223.2-28.85
45 403 63.89 339.1
46-544-449.5-94.52
47 258 298.6-40.65
48-2 4.759-6.759
49-123-8.253-114.7
50 196 138.1 57.91
51-300-154.6-145.4
52 187 129.7 57.29
53 216 29.25 186.8
54-134-272.8 138.8
55 22-113.1 135.1
56 100 53.35 46.65
57-429-234.6-194.4
58 258 396.5-138.5
59 4-150.7 154.7
60-144 55.59-199.6
61 347 193.3 153.7
62-209-277.7 68.74
63 314 195.4 118.6
64-428-224.3-203.7
65 49 34.43 14.57
66 105 33.16 71.84
67-101-128.8 27.77
68 367 101.6 265.4
69-247-55.92-191.1
70 91 37.41 53.59
71-96-102.1 6.057
72-4 73.59-77.59
73 50-92.83 142.8
74 27 34.61-7.613
75-134-87.29-46.71
76 133 210.9-77.91
77-130-122.2-7.793
78 52 29.45 22.55
79-3 47.65-50.65
80-190-215.6 25.64
81 126 323.8-197.8
82 216-133.5 349.5
83-197-41.47-155.5
84 108 90.59 17.41
85-301-177.5-123.5
86-39 144.7-183.7
87 146 84.27 61.73
88 86 15.1 70.9
89-32 43.97-75.97
90-4-74.17 70.17
91-21-7.536-13.46
92-93 4.812-97.81
93 64 49 15
94-172-134-37.99
95 155 203.2-48.21
96-2-81.19 79.19
97 194 124.1 69.91
98-58-70.51 12.51
99-35-81.4 46.4
100-95-81.95-13.05
101 271 71.84 199.2
102-453-95.32-357.7
103 13 178.8-165.8
104 75 147.1-72.11
105 288-66.11 354.1
106-61-32.99-28.01
107 8-77.03 85.03
108 25-81.33 106.3
109-206-76.4-129.6
110 318 133.1 184.9
111-223-123.3-99.75
112 175 91.27 83.73
113-22-178.3 156.3
114 20 180.9-160.9
115-346 2.192-348.2
116 197 107 89.95
117 5-174.1 179.1
118-73 86.51-159.5
119 47 15.65 31.35
120 4-54.16 58.16
121 208 54.72 153.3
122-326-231.8-94.19
123 327 210.7 116.3
124-261-179.7-81.31
125-1 14.32-15.32
126 218 152.9 65.08
127 223 58.86 164.1
128-315-213-102
129-426-299-127
130 150 225.3-75.26
131 41 11.63 29.37
132-54 74.14-128.1
133-231 11.17-242.2
134 168 162.8 5.248
135-231-132.6-98.36
136 378 204.4 173.6
137-196-103.8-92.18
138-152-92.35-59.65
139-51 54.55-105.6
140 429 128.6 300.4
141 245 372-127
142-44-217.4 173.4
143-122-130.2 8.201
144 119-31.56 150.6
145 48-0.4126 48.41
146-61-20.69-40.31
147 97 51.85 45.15
148-128-184 55.98
149 71 135.5-64.5
150 166 9.954 156
151-4-112 108

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -64 & -90.47 &  26.47 \tabularnewline
2 &  63 &  70.36 & -7.357 \tabularnewline
3 & -407 & -69.29 & -337.7 \tabularnewline
4 &  396 &  257.1 &  138.9 \tabularnewline
5 & -175 & -186 &  10.97 \tabularnewline
6 &  180 &  117.7 &  62.32 \tabularnewline
7 &  298 &  35.85 &  262.2 \tabularnewline
8 & -445 & -361.9 & -83.08 \tabularnewline
9 &  178 &  197.5 & -19.46 \tabularnewline
10 & -353 & -108.3 & -244.7 \tabularnewline
11 &  530 &  220.7 &  309.3 \tabularnewline
12 & -345 & -266.7 & -78.3 \tabularnewline
13 & -67 &  107.3 & -174.3 \tabularnewline
14 &  87 &  94.34 & -7.342 \tabularnewline
15 &  176 &  109.7 &  66.32 \tabularnewline
16 &  83 & -163.9 &  246.9 \tabularnewline
17 & -202 & -11.09 & -190.9 \tabularnewline
18 & -383 & -65.3 & -317.7 \tabularnewline
19 & -225 &  84.7 & -309.7 \tabularnewline
20 &  15 &  337 & -322 \tabularnewline
21 &  29 &  39.86 & -10.86 \tabularnewline
22 &  331 &  258.6 &  72.41 \tabularnewline
23 & -430 & -329.9 & -100.1 \tabularnewline
24 &  287 &  230 &  57.02 \tabularnewline
25 &  257 & -55.09 &  312.1 \tabularnewline
26 & -218 & -213.8 & -4.176 \tabularnewline
27 &  208 &  80.36 &  127.6 \tabularnewline
28 & -55 & -281.6 &  226.6 \tabularnewline
29 &  77 &  116.3 & -39.28 \tabularnewline
30 & -23 &  123.6 & -146.6 \tabularnewline
31 & -73 &  5.486 & -78.49 \tabularnewline
32 &  68 &  132.9 & -64.91 \tabularnewline
33 & -116 & -82.74 & -33.26 \tabularnewline
34 &  251 & -23.83 &  274.8 \tabularnewline
35 & -104 & -3.437 & -100.6 \tabularnewline
36 & -212 & -55.43 & -156.6 \tabularnewline
37 & -165 & -19.87 & -145.1 \tabularnewline
38 &  40 &  176.4 & -136.4 \tabularnewline
39 & -7 & -73.51 &  66.51 \tabularnewline
40 & -4 &  68.33 & -72.33 \tabularnewline
41 & -168 &  35.73 & -203.7 \tabularnewline
42 &  329 &  172.9 &  156.1 \tabularnewline
43 &  335 & -19.52 &  354.5 \tabularnewline
44 & -252 & -223.2 & -28.85 \tabularnewline
45 &  403 &  63.89 &  339.1 \tabularnewline
46 & -544 & -449.5 & -94.52 \tabularnewline
47 &  258 &  298.6 & -40.65 \tabularnewline
48 & -2 &  4.759 & -6.759 \tabularnewline
49 & -123 & -8.253 & -114.7 \tabularnewline
50 &  196 &  138.1 &  57.91 \tabularnewline
51 & -300 & -154.6 & -145.4 \tabularnewline
52 &  187 &  129.7 &  57.29 \tabularnewline
53 &  216 &  29.25 &  186.8 \tabularnewline
54 & -134 & -272.8 &  138.8 \tabularnewline
55 &  22 & -113.1 &  135.1 \tabularnewline
56 &  100 &  53.35 &  46.65 \tabularnewline
57 & -429 & -234.6 & -194.4 \tabularnewline
58 &  258 &  396.5 & -138.5 \tabularnewline
59 &  4 & -150.7 &  154.7 \tabularnewline
60 & -144 &  55.59 & -199.6 \tabularnewline
61 &  347 &  193.3 &  153.7 \tabularnewline
62 & -209 & -277.7 &  68.74 \tabularnewline
63 &  314 &  195.4 &  118.6 \tabularnewline
64 & -428 & -224.3 & -203.7 \tabularnewline
65 &  49 &  34.43 &  14.57 \tabularnewline
66 &  105 &  33.16 &  71.84 \tabularnewline
67 & -101 & -128.8 &  27.77 \tabularnewline
68 &  367 &  101.6 &  265.4 \tabularnewline
69 & -247 & -55.92 & -191.1 \tabularnewline
70 &  91 &  37.41 &  53.59 \tabularnewline
71 & -96 & -102.1 &  6.057 \tabularnewline
72 & -4 &  73.59 & -77.59 \tabularnewline
73 &  50 & -92.83 &  142.8 \tabularnewline
74 &  27 &  34.61 & -7.613 \tabularnewline
75 & -134 & -87.29 & -46.71 \tabularnewline
76 &  133 &  210.9 & -77.91 \tabularnewline
77 & -130 & -122.2 & -7.793 \tabularnewline
78 &  52 &  29.45 &  22.55 \tabularnewline
79 & -3 &  47.65 & -50.65 \tabularnewline
80 & -190 & -215.6 &  25.64 \tabularnewline
81 &  126 &  323.8 & -197.8 \tabularnewline
82 &  216 & -133.5 &  349.5 \tabularnewline
83 & -197 & -41.47 & -155.5 \tabularnewline
84 &  108 &  90.59 &  17.41 \tabularnewline
85 & -301 & -177.5 & -123.5 \tabularnewline
86 & -39 &  144.7 & -183.7 \tabularnewline
87 &  146 &  84.27 &  61.73 \tabularnewline
88 &  86 &  15.1 &  70.9 \tabularnewline
89 & -32 &  43.97 & -75.97 \tabularnewline
90 & -4 & -74.17 &  70.17 \tabularnewline
91 & -21 & -7.536 & -13.46 \tabularnewline
92 & -93 &  4.812 & -97.81 \tabularnewline
93 &  64 &  49 &  15 \tabularnewline
94 & -172 & -134 & -37.99 \tabularnewline
95 &  155 &  203.2 & -48.21 \tabularnewline
96 & -2 & -81.19 &  79.19 \tabularnewline
97 &  194 &  124.1 &  69.91 \tabularnewline
98 & -58 & -70.51 &  12.51 \tabularnewline
99 & -35 & -81.4 &  46.4 \tabularnewline
100 & -95 & -81.95 & -13.05 \tabularnewline
101 &  271 &  71.84 &  199.2 \tabularnewline
102 & -453 & -95.32 & -357.7 \tabularnewline
103 &  13 &  178.8 & -165.8 \tabularnewline
104 &  75 &  147.1 & -72.11 \tabularnewline
105 &  288 & -66.11 &  354.1 \tabularnewline
106 & -61 & -32.99 & -28.01 \tabularnewline
107 &  8 & -77.03 &  85.03 \tabularnewline
108 &  25 & -81.33 &  106.3 \tabularnewline
109 & -206 & -76.4 & -129.6 \tabularnewline
110 &  318 &  133.1 &  184.9 \tabularnewline
111 & -223 & -123.3 & -99.75 \tabularnewline
112 &  175 &  91.27 &  83.73 \tabularnewline
113 & -22 & -178.3 &  156.3 \tabularnewline
114 &  20 &  180.9 & -160.9 \tabularnewline
115 & -346 &  2.192 & -348.2 \tabularnewline
116 &  197 &  107 &  89.95 \tabularnewline
117 &  5 & -174.1 &  179.1 \tabularnewline
118 & -73 &  86.51 & -159.5 \tabularnewline
119 &  47 &  15.65 &  31.35 \tabularnewline
120 &  4 & -54.16 &  58.16 \tabularnewline
121 &  208 &  54.72 &  153.3 \tabularnewline
122 & -326 & -231.8 & -94.19 \tabularnewline
123 &  327 &  210.7 &  116.3 \tabularnewline
124 & -261 & -179.7 & -81.31 \tabularnewline
125 & -1 &  14.32 & -15.32 \tabularnewline
126 &  218 &  152.9 &  65.08 \tabularnewline
127 &  223 &  58.86 &  164.1 \tabularnewline
128 & -315 & -213 & -102 \tabularnewline
129 & -426 & -299 & -127 \tabularnewline
130 &  150 &  225.3 & -75.26 \tabularnewline
131 &  41 &  11.63 &  29.37 \tabularnewline
132 & -54 &  74.14 & -128.1 \tabularnewline
133 & -231 &  11.17 & -242.2 \tabularnewline
134 &  168 &  162.8 &  5.248 \tabularnewline
135 & -231 & -132.6 & -98.36 \tabularnewline
136 &  378 &  204.4 &  173.6 \tabularnewline
137 & -196 & -103.8 & -92.18 \tabularnewline
138 & -152 & -92.35 & -59.65 \tabularnewline
139 & -51 &  54.55 & -105.6 \tabularnewline
140 &  429 &  128.6 &  300.4 \tabularnewline
141 &  245 &  372 & -127 \tabularnewline
142 & -44 & -217.4 &  173.4 \tabularnewline
143 & -122 & -130.2 &  8.201 \tabularnewline
144 &  119 & -31.56 &  150.6 \tabularnewline
145 &  48 & -0.4126 &  48.41 \tabularnewline
146 & -61 & -20.69 & -40.31 \tabularnewline
147 &  97 &  51.85 &  45.15 \tabularnewline
148 & -128 & -184 &  55.98 \tabularnewline
149 &  71 &  135.5 & -64.5 \tabularnewline
150 &  166 &  9.954 &  156 \tabularnewline
151 & -4 & -112 &  108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285277&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]-64[/C][C]-90.47[/C][C] 26.47[/C][/ROW]
[ROW][C]2[/C][C] 63[/C][C] 70.36[/C][C]-7.357[/C][/ROW]
[ROW][C]3[/C][C]-407[/C][C]-69.29[/C][C]-337.7[/C][/ROW]
[ROW][C]4[/C][C] 396[/C][C] 257.1[/C][C] 138.9[/C][/ROW]
[ROW][C]5[/C][C]-175[/C][C]-186[/C][C] 10.97[/C][/ROW]
[ROW][C]6[/C][C] 180[/C][C] 117.7[/C][C] 62.32[/C][/ROW]
[ROW][C]7[/C][C] 298[/C][C] 35.85[/C][C] 262.2[/C][/ROW]
[ROW][C]8[/C][C]-445[/C][C]-361.9[/C][C]-83.08[/C][/ROW]
[ROW][C]9[/C][C] 178[/C][C] 197.5[/C][C]-19.46[/C][/ROW]
[ROW][C]10[/C][C]-353[/C][C]-108.3[/C][C]-244.7[/C][/ROW]
[ROW][C]11[/C][C] 530[/C][C] 220.7[/C][C] 309.3[/C][/ROW]
[ROW][C]12[/C][C]-345[/C][C]-266.7[/C][C]-78.3[/C][/ROW]
[ROW][C]13[/C][C]-67[/C][C] 107.3[/C][C]-174.3[/C][/ROW]
[ROW][C]14[/C][C] 87[/C][C] 94.34[/C][C]-7.342[/C][/ROW]
[ROW][C]15[/C][C] 176[/C][C] 109.7[/C][C] 66.32[/C][/ROW]
[ROW][C]16[/C][C] 83[/C][C]-163.9[/C][C] 246.9[/C][/ROW]
[ROW][C]17[/C][C]-202[/C][C]-11.09[/C][C]-190.9[/C][/ROW]
[ROW][C]18[/C][C]-383[/C][C]-65.3[/C][C]-317.7[/C][/ROW]
[ROW][C]19[/C][C]-225[/C][C] 84.7[/C][C]-309.7[/C][/ROW]
[ROW][C]20[/C][C] 15[/C][C] 337[/C][C]-322[/C][/ROW]
[ROW][C]21[/C][C] 29[/C][C] 39.86[/C][C]-10.86[/C][/ROW]
[ROW][C]22[/C][C] 331[/C][C] 258.6[/C][C] 72.41[/C][/ROW]
[ROW][C]23[/C][C]-430[/C][C]-329.9[/C][C]-100.1[/C][/ROW]
[ROW][C]24[/C][C] 287[/C][C] 230[/C][C] 57.02[/C][/ROW]
[ROW][C]25[/C][C] 257[/C][C]-55.09[/C][C] 312.1[/C][/ROW]
[ROW][C]26[/C][C]-218[/C][C]-213.8[/C][C]-4.176[/C][/ROW]
[ROW][C]27[/C][C] 208[/C][C] 80.36[/C][C] 127.6[/C][/ROW]
[ROW][C]28[/C][C]-55[/C][C]-281.6[/C][C] 226.6[/C][/ROW]
[ROW][C]29[/C][C] 77[/C][C] 116.3[/C][C]-39.28[/C][/ROW]
[ROW][C]30[/C][C]-23[/C][C] 123.6[/C][C]-146.6[/C][/ROW]
[ROW][C]31[/C][C]-73[/C][C] 5.486[/C][C]-78.49[/C][/ROW]
[ROW][C]32[/C][C] 68[/C][C] 132.9[/C][C]-64.91[/C][/ROW]
[ROW][C]33[/C][C]-116[/C][C]-82.74[/C][C]-33.26[/C][/ROW]
[ROW][C]34[/C][C] 251[/C][C]-23.83[/C][C] 274.8[/C][/ROW]
[ROW][C]35[/C][C]-104[/C][C]-3.437[/C][C]-100.6[/C][/ROW]
[ROW][C]36[/C][C]-212[/C][C]-55.43[/C][C]-156.6[/C][/ROW]
[ROW][C]37[/C][C]-165[/C][C]-19.87[/C][C]-145.1[/C][/ROW]
[ROW][C]38[/C][C] 40[/C][C] 176.4[/C][C]-136.4[/C][/ROW]
[ROW][C]39[/C][C]-7[/C][C]-73.51[/C][C] 66.51[/C][/ROW]
[ROW][C]40[/C][C]-4[/C][C] 68.33[/C][C]-72.33[/C][/ROW]
[ROW][C]41[/C][C]-168[/C][C] 35.73[/C][C]-203.7[/C][/ROW]
[ROW][C]42[/C][C] 329[/C][C] 172.9[/C][C] 156.1[/C][/ROW]
[ROW][C]43[/C][C] 335[/C][C]-19.52[/C][C] 354.5[/C][/ROW]
[ROW][C]44[/C][C]-252[/C][C]-223.2[/C][C]-28.85[/C][/ROW]
[ROW][C]45[/C][C] 403[/C][C] 63.89[/C][C] 339.1[/C][/ROW]
[ROW][C]46[/C][C]-544[/C][C]-449.5[/C][C]-94.52[/C][/ROW]
[ROW][C]47[/C][C] 258[/C][C] 298.6[/C][C]-40.65[/C][/ROW]
[ROW][C]48[/C][C]-2[/C][C] 4.759[/C][C]-6.759[/C][/ROW]
[ROW][C]49[/C][C]-123[/C][C]-8.253[/C][C]-114.7[/C][/ROW]
[ROW][C]50[/C][C] 196[/C][C] 138.1[/C][C] 57.91[/C][/ROW]
[ROW][C]51[/C][C]-300[/C][C]-154.6[/C][C]-145.4[/C][/ROW]
[ROW][C]52[/C][C] 187[/C][C] 129.7[/C][C] 57.29[/C][/ROW]
[ROW][C]53[/C][C] 216[/C][C] 29.25[/C][C] 186.8[/C][/ROW]
[ROW][C]54[/C][C]-134[/C][C]-272.8[/C][C] 138.8[/C][/ROW]
[ROW][C]55[/C][C] 22[/C][C]-113.1[/C][C] 135.1[/C][/ROW]
[ROW][C]56[/C][C] 100[/C][C] 53.35[/C][C] 46.65[/C][/ROW]
[ROW][C]57[/C][C]-429[/C][C]-234.6[/C][C]-194.4[/C][/ROW]
[ROW][C]58[/C][C] 258[/C][C] 396.5[/C][C]-138.5[/C][/ROW]
[ROW][C]59[/C][C] 4[/C][C]-150.7[/C][C] 154.7[/C][/ROW]
[ROW][C]60[/C][C]-144[/C][C] 55.59[/C][C]-199.6[/C][/ROW]
[ROW][C]61[/C][C] 347[/C][C] 193.3[/C][C] 153.7[/C][/ROW]
[ROW][C]62[/C][C]-209[/C][C]-277.7[/C][C] 68.74[/C][/ROW]
[ROW][C]63[/C][C] 314[/C][C] 195.4[/C][C] 118.6[/C][/ROW]
[ROW][C]64[/C][C]-428[/C][C]-224.3[/C][C]-203.7[/C][/ROW]
[ROW][C]65[/C][C] 49[/C][C] 34.43[/C][C] 14.57[/C][/ROW]
[ROW][C]66[/C][C] 105[/C][C] 33.16[/C][C] 71.84[/C][/ROW]
[ROW][C]67[/C][C]-101[/C][C]-128.8[/C][C] 27.77[/C][/ROW]
[ROW][C]68[/C][C] 367[/C][C] 101.6[/C][C] 265.4[/C][/ROW]
[ROW][C]69[/C][C]-247[/C][C]-55.92[/C][C]-191.1[/C][/ROW]
[ROW][C]70[/C][C] 91[/C][C] 37.41[/C][C] 53.59[/C][/ROW]
[ROW][C]71[/C][C]-96[/C][C]-102.1[/C][C] 6.057[/C][/ROW]
[ROW][C]72[/C][C]-4[/C][C] 73.59[/C][C]-77.59[/C][/ROW]
[ROW][C]73[/C][C] 50[/C][C]-92.83[/C][C] 142.8[/C][/ROW]
[ROW][C]74[/C][C] 27[/C][C] 34.61[/C][C]-7.613[/C][/ROW]
[ROW][C]75[/C][C]-134[/C][C]-87.29[/C][C]-46.71[/C][/ROW]
[ROW][C]76[/C][C] 133[/C][C] 210.9[/C][C]-77.91[/C][/ROW]
[ROW][C]77[/C][C]-130[/C][C]-122.2[/C][C]-7.793[/C][/ROW]
[ROW][C]78[/C][C] 52[/C][C] 29.45[/C][C] 22.55[/C][/ROW]
[ROW][C]79[/C][C]-3[/C][C] 47.65[/C][C]-50.65[/C][/ROW]
[ROW][C]80[/C][C]-190[/C][C]-215.6[/C][C] 25.64[/C][/ROW]
[ROW][C]81[/C][C] 126[/C][C] 323.8[/C][C]-197.8[/C][/ROW]
[ROW][C]82[/C][C] 216[/C][C]-133.5[/C][C] 349.5[/C][/ROW]
[ROW][C]83[/C][C]-197[/C][C]-41.47[/C][C]-155.5[/C][/ROW]
[ROW][C]84[/C][C] 108[/C][C] 90.59[/C][C] 17.41[/C][/ROW]
[ROW][C]85[/C][C]-301[/C][C]-177.5[/C][C]-123.5[/C][/ROW]
[ROW][C]86[/C][C]-39[/C][C] 144.7[/C][C]-183.7[/C][/ROW]
[ROW][C]87[/C][C] 146[/C][C] 84.27[/C][C] 61.73[/C][/ROW]
[ROW][C]88[/C][C] 86[/C][C] 15.1[/C][C] 70.9[/C][/ROW]
[ROW][C]89[/C][C]-32[/C][C] 43.97[/C][C]-75.97[/C][/ROW]
[ROW][C]90[/C][C]-4[/C][C]-74.17[/C][C] 70.17[/C][/ROW]
[ROW][C]91[/C][C]-21[/C][C]-7.536[/C][C]-13.46[/C][/ROW]
[ROW][C]92[/C][C]-93[/C][C] 4.812[/C][C]-97.81[/C][/ROW]
[ROW][C]93[/C][C] 64[/C][C] 49[/C][C] 15[/C][/ROW]
[ROW][C]94[/C][C]-172[/C][C]-134[/C][C]-37.99[/C][/ROW]
[ROW][C]95[/C][C] 155[/C][C] 203.2[/C][C]-48.21[/C][/ROW]
[ROW][C]96[/C][C]-2[/C][C]-81.19[/C][C] 79.19[/C][/ROW]
[ROW][C]97[/C][C] 194[/C][C] 124.1[/C][C] 69.91[/C][/ROW]
[ROW][C]98[/C][C]-58[/C][C]-70.51[/C][C] 12.51[/C][/ROW]
[ROW][C]99[/C][C]-35[/C][C]-81.4[/C][C] 46.4[/C][/ROW]
[ROW][C]100[/C][C]-95[/C][C]-81.95[/C][C]-13.05[/C][/ROW]
[ROW][C]101[/C][C] 271[/C][C] 71.84[/C][C] 199.2[/C][/ROW]
[ROW][C]102[/C][C]-453[/C][C]-95.32[/C][C]-357.7[/C][/ROW]
[ROW][C]103[/C][C] 13[/C][C] 178.8[/C][C]-165.8[/C][/ROW]
[ROW][C]104[/C][C] 75[/C][C] 147.1[/C][C]-72.11[/C][/ROW]
[ROW][C]105[/C][C] 288[/C][C]-66.11[/C][C] 354.1[/C][/ROW]
[ROW][C]106[/C][C]-61[/C][C]-32.99[/C][C]-28.01[/C][/ROW]
[ROW][C]107[/C][C] 8[/C][C]-77.03[/C][C] 85.03[/C][/ROW]
[ROW][C]108[/C][C] 25[/C][C]-81.33[/C][C] 106.3[/C][/ROW]
[ROW][C]109[/C][C]-206[/C][C]-76.4[/C][C]-129.6[/C][/ROW]
[ROW][C]110[/C][C] 318[/C][C] 133.1[/C][C] 184.9[/C][/ROW]
[ROW][C]111[/C][C]-223[/C][C]-123.3[/C][C]-99.75[/C][/ROW]
[ROW][C]112[/C][C] 175[/C][C] 91.27[/C][C] 83.73[/C][/ROW]
[ROW][C]113[/C][C]-22[/C][C]-178.3[/C][C] 156.3[/C][/ROW]
[ROW][C]114[/C][C] 20[/C][C] 180.9[/C][C]-160.9[/C][/ROW]
[ROW][C]115[/C][C]-346[/C][C] 2.192[/C][C]-348.2[/C][/ROW]
[ROW][C]116[/C][C] 197[/C][C] 107[/C][C] 89.95[/C][/ROW]
[ROW][C]117[/C][C] 5[/C][C]-174.1[/C][C] 179.1[/C][/ROW]
[ROW][C]118[/C][C]-73[/C][C] 86.51[/C][C]-159.5[/C][/ROW]
[ROW][C]119[/C][C] 47[/C][C] 15.65[/C][C] 31.35[/C][/ROW]
[ROW][C]120[/C][C] 4[/C][C]-54.16[/C][C] 58.16[/C][/ROW]
[ROW][C]121[/C][C] 208[/C][C] 54.72[/C][C] 153.3[/C][/ROW]
[ROW][C]122[/C][C]-326[/C][C]-231.8[/C][C]-94.19[/C][/ROW]
[ROW][C]123[/C][C] 327[/C][C] 210.7[/C][C] 116.3[/C][/ROW]
[ROW][C]124[/C][C]-261[/C][C]-179.7[/C][C]-81.31[/C][/ROW]
[ROW][C]125[/C][C]-1[/C][C] 14.32[/C][C]-15.32[/C][/ROW]
[ROW][C]126[/C][C] 218[/C][C] 152.9[/C][C] 65.08[/C][/ROW]
[ROW][C]127[/C][C] 223[/C][C] 58.86[/C][C] 164.1[/C][/ROW]
[ROW][C]128[/C][C]-315[/C][C]-213[/C][C]-102[/C][/ROW]
[ROW][C]129[/C][C]-426[/C][C]-299[/C][C]-127[/C][/ROW]
[ROW][C]130[/C][C] 150[/C][C] 225.3[/C][C]-75.26[/C][/ROW]
[ROW][C]131[/C][C] 41[/C][C] 11.63[/C][C] 29.37[/C][/ROW]
[ROW][C]132[/C][C]-54[/C][C] 74.14[/C][C]-128.1[/C][/ROW]
[ROW][C]133[/C][C]-231[/C][C] 11.17[/C][C]-242.2[/C][/ROW]
[ROW][C]134[/C][C] 168[/C][C] 162.8[/C][C] 5.248[/C][/ROW]
[ROW][C]135[/C][C]-231[/C][C]-132.6[/C][C]-98.36[/C][/ROW]
[ROW][C]136[/C][C] 378[/C][C] 204.4[/C][C] 173.6[/C][/ROW]
[ROW][C]137[/C][C]-196[/C][C]-103.8[/C][C]-92.18[/C][/ROW]
[ROW][C]138[/C][C]-152[/C][C]-92.35[/C][C]-59.65[/C][/ROW]
[ROW][C]139[/C][C]-51[/C][C] 54.55[/C][C]-105.6[/C][/ROW]
[ROW][C]140[/C][C] 429[/C][C] 128.6[/C][C] 300.4[/C][/ROW]
[ROW][C]141[/C][C] 245[/C][C] 372[/C][C]-127[/C][/ROW]
[ROW][C]142[/C][C]-44[/C][C]-217.4[/C][C] 173.4[/C][/ROW]
[ROW][C]143[/C][C]-122[/C][C]-130.2[/C][C] 8.201[/C][/ROW]
[ROW][C]144[/C][C] 119[/C][C]-31.56[/C][C] 150.6[/C][/ROW]
[ROW][C]145[/C][C] 48[/C][C]-0.4126[/C][C] 48.41[/C][/ROW]
[ROW][C]146[/C][C]-61[/C][C]-20.69[/C][C]-40.31[/C][/ROW]
[ROW][C]147[/C][C] 97[/C][C] 51.85[/C][C] 45.15[/C][/ROW]
[ROW][C]148[/C][C]-128[/C][C]-184[/C][C] 55.98[/C][/ROW]
[ROW][C]149[/C][C] 71[/C][C] 135.5[/C][C]-64.5[/C][/ROW]
[ROW][C]150[/C][C] 166[/C][C] 9.954[/C][C] 156[/C][/ROW]
[ROW][C]151[/C][C]-4[/C][C]-112[/C][C] 108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285277&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285277&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-64-90.47 26.47
2 63 70.36-7.357
3-407-69.29-337.7
4 396 257.1 138.9
5-175-186 10.97
6 180 117.7 62.32
7 298 35.85 262.2
8-445-361.9-83.08
9 178 197.5-19.46
10-353-108.3-244.7
11 530 220.7 309.3
12-345-266.7-78.3
13-67 107.3-174.3
14 87 94.34-7.342
15 176 109.7 66.32
16 83-163.9 246.9
17-202-11.09-190.9
18-383-65.3-317.7
19-225 84.7-309.7
20 15 337-322
21 29 39.86-10.86
22 331 258.6 72.41
23-430-329.9-100.1
24 287 230 57.02
25 257-55.09 312.1
26-218-213.8-4.176
27 208 80.36 127.6
28-55-281.6 226.6
29 77 116.3-39.28
30-23 123.6-146.6
31-73 5.486-78.49
32 68 132.9-64.91
33-116-82.74-33.26
34 251-23.83 274.8
35-104-3.437-100.6
36-212-55.43-156.6
37-165-19.87-145.1
38 40 176.4-136.4
39-7-73.51 66.51
40-4 68.33-72.33
41-168 35.73-203.7
42 329 172.9 156.1
43 335-19.52 354.5
44-252-223.2-28.85
45 403 63.89 339.1
46-544-449.5-94.52
47 258 298.6-40.65
48-2 4.759-6.759
49-123-8.253-114.7
50 196 138.1 57.91
51-300-154.6-145.4
52 187 129.7 57.29
53 216 29.25 186.8
54-134-272.8 138.8
55 22-113.1 135.1
56 100 53.35 46.65
57-429-234.6-194.4
58 258 396.5-138.5
59 4-150.7 154.7
60-144 55.59-199.6
61 347 193.3 153.7
62-209-277.7 68.74
63 314 195.4 118.6
64-428-224.3-203.7
65 49 34.43 14.57
66 105 33.16 71.84
67-101-128.8 27.77
68 367 101.6 265.4
69-247-55.92-191.1
70 91 37.41 53.59
71-96-102.1 6.057
72-4 73.59-77.59
73 50-92.83 142.8
74 27 34.61-7.613
75-134-87.29-46.71
76 133 210.9-77.91
77-130-122.2-7.793
78 52 29.45 22.55
79-3 47.65-50.65
80-190-215.6 25.64
81 126 323.8-197.8
82 216-133.5 349.5
83-197-41.47-155.5
84 108 90.59 17.41
85-301-177.5-123.5
86-39 144.7-183.7
87 146 84.27 61.73
88 86 15.1 70.9
89-32 43.97-75.97
90-4-74.17 70.17
91-21-7.536-13.46
92-93 4.812-97.81
93 64 49 15
94-172-134-37.99
95 155 203.2-48.21
96-2-81.19 79.19
97 194 124.1 69.91
98-58-70.51 12.51
99-35-81.4 46.4
100-95-81.95-13.05
101 271 71.84 199.2
102-453-95.32-357.7
103 13 178.8-165.8
104 75 147.1-72.11
105 288-66.11 354.1
106-61-32.99-28.01
107 8-77.03 85.03
108 25-81.33 106.3
109-206-76.4-129.6
110 318 133.1 184.9
111-223-123.3-99.75
112 175 91.27 83.73
113-22-178.3 156.3
114 20 180.9-160.9
115-346 2.192-348.2
116 197 107 89.95
117 5-174.1 179.1
118-73 86.51-159.5
119 47 15.65 31.35
120 4-54.16 58.16
121 208 54.72 153.3
122-326-231.8-94.19
123 327 210.7 116.3
124-261-179.7-81.31
125-1 14.32-15.32
126 218 152.9 65.08
127 223 58.86 164.1
128-315-213-102
129-426-299-127
130 150 225.3-75.26
131 41 11.63 29.37
132-54 74.14-128.1
133-231 11.17-242.2
134 168 162.8 5.248
135-231-132.6-98.36
136 378 204.4 173.6
137-196-103.8-92.18
138-152-92.35-59.65
139-51 54.55-105.6
140 429 128.6 300.4
141 245 372-127
142-44-217.4 173.4
143-122-130.2 8.201
144 119-31.56 150.6
145 48-0.4126 48.41
146-61-20.69-40.31
147 97 51.85 45.15
148-128-184 55.98
149 71 135.5-64.5
150 166 9.954 156
151-4-112 108







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
11 0.9189 0.1622 0.08112
12 0.8575 0.2849 0.1425
13 0.8591 0.2818 0.1409
14 0.7901 0.4198 0.2099
15 0.8173 0.3655 0.1827
16 0.8873 0.2253 0.1127
17 0.868 0.264 0.132
18 0.8437 0.3125 0.1563
19 0.8705 0.259 0.1295
20 0.981 0.03805 0.01902
21 0.9703 0.05947 0.02974
22 0.9709 0.05816 0.02908
23 0.9595 0.08108 0.04054
24 0.9524 0.0951 0.04755
25 0.9808 0.0383 0.01915
26 0.9716 0.05683 0.02842
27 0.9627 0.07454 0.03727
28 0.9922 0.01568 0.007839
29 0.9897 0.02064 0.01032
30 0.9914 0.01721 0.008603
31 0.9878 0.02442 0.01221
32 0.9835 0.03305 0.01653
33 0.9764 0.04718 0.02359
34 0.9895 0.02092 0.01046
35 0.9865 0.02694 0.01347
36 0.9858 0.02846 0.01423
37 0.9825 0.03499 0.0175
38 0.9791 0.04173 0.02087
39 0.9746 0.05084 0.02542
40 0.9673 0.06533 0.03267
41 0.9724 0.05515 0.02758
42 0.9713 0.05738 0.02869
43 0.9885 0.023 0.0115
44 0.9847 0.03051 0.01525
45 0.9964 0.00711 0.003555
46 0.9953 0.009499 0.004749
47 0.9933 0.0134 0.006701
48 0.9905 0.0191 0.009549
49 0.9884 0.02316 0.01158
50 0.9846 0.0308 0.0154
51 0.9834 0.03325 0.01663
52 0.9789 0.04216 0.02108
53 0.9808 0.03836 0.01918
54 0.9803 0.0394 0.0197
55 0.9798 0.04042 0.02021
56 0.9734 0.05312 0.02656
57 0.977 0.04608 0.02304
58 0.9752 0.04955 0.02478
59 0.9756 0.04885 0.02442
60 0.9812 0.03754 0.01877
61 0.982 0.03593 0.01796
62 0.9771 0.04579 0.02289
63 0.9744 0.05121 0.0256
64 0.9797 0.04055 0.02027
65 0.9751 0.04988 0.02494
66 0.9699 0.0603 0.03015
67 0.9635 0.07296 0.03648
68 0.983 0.03406 0.01703
69 0.9864 0.02712 0.01356
70 0.983 0.03398 0.01699
71 0.9775 0.04508 0.02254
72 0.9739 0.05218 0.02609
73 0.975 0.05003 0.02502
74 0.9673 0.06533 0.03266
75 0.9585 0.08298 0.04149
76 0.9499 0.1002 0.05011
77 0.9377 0.1245 0.06227
78 0.9219 0.1563 0.07813
79 0.9044 0.1912 0.09562
80 0.8861 0.2278 0.1139
81 0.8927 0.2146 0.1073
82 0.9557 0.08853 0.04427
83 0.9571 0.08587 0.04293
84 0.9466 0.1067 0.05335
85 0.9513 0.09745 0.04873
86 0.959 0.08195 0.04097
87 0.949 0.1019 0.05097
88 0.9396 0.1207 0.06037
89 0.9265 0.147 0.07348
90 0.9109 0.1782 0.08911
91 0.8897 0.2205 0.1103
92 0.8901 0.2199 0.1099
93 0.8665 0.2669 0.1335
94 0.8425 0.315 0.1575
95 0.8107 0.3786 0.1893
96 0.7856 0.4288 0.2144
97 0.7541 0.4918 0.2459
98 0.715 0.5699 0.285
99 0.6719 0.6562 0.3281
100 0.6279 0.7441 0.3721
101 0.6578 0.6843 0.3422
102 0.8566 0.2868 0.1434
103 0.8614 0.2771 0.1386
104 0.8318 0.3364 0.1682
105 0.9205 0.159 0.07952
106 0.8993 0.2015 0.1007
107 0.8879 0.2242 0.1121
108 0.8657 0.2686 0.1343
109 0.8591 0.2819 0.1409
110 0.8843 0.2314 0.1157
111 0.8803 0.2394 0.1197
112 0.8622 0.2757 0.1378
113 0.8727 0.2547 0.1273
114 0.9201 0.1598 0.07989
115 0.9794 0.04127 0.02063
116 0.9742 0.05156 0.02578
117 0.9865 0.02702 0.01351
118 0.9913 0.01739 0.008697
119 0.9921 0.01581 0.007903
120 0.9875 0.02508 0.01254
121 0.9866 0.02685 0.01342
122 0.9795 0.04109 0.02055
123 0.9714 0.05724 0.02862
124 0.9616 0.07674 0.03837
125 0.9435 0.1131 0.05654
126 0.9622 0.0756 0.0378
127 0.9463 0.1074 0.05371
128 0.9247 0.1506 0.07529
129 0.9717 0.05663 0.02831
130 0.9543 0.09148 0.04574
131 0.9266 0.1469 0.07345
132 0.8983 0.2034 0.1017
133 0.8764 0.2472 0.1236
134 0.8498 0.3004 0.1502
135 0.7924 0.4152 0.2076
136 0.7866 0.4268 0.2134
137 0.8386 0.3228 0.1614
138 0.7531 0.4938 0.2469
139 0.6325 0.7351 0.3675
140 0.768 0.464 0.232

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.9189 &  0.1622 &  0.08112 \tabularnewline
12 &  0.8575 &  0.2849 &  0.1425 \tabularnewline
13 &  0.8591 &  0.2818 &  0.1409 \tabularnewline
14 &  0.7901 &  0.4198 &  0.2099 \tabularnewline
15 &  0.8173 &  0.3655 &  0.1827 \tabularnewline
16 &  0.8873 &  0.2253 &  0.1127 \tabularnewline
17 &  0.868 &  0.264 &  0.132 \tabularnewline
18 &  0.8437 &  0.3125 &  0.1563 \tabularnewline
19 &  0.8705 &  0.259 &  0.1295 \tabularnewline
20 &  0.981 &  0.03805 &  0.01902 \tabularnewline
21 &  0.9703 &  0.05947 &  0.02974 \tabularnewline
22 &  0.9709 &  0.05816 &  0.02908 \tabularnewline
23 &  0.9595 &  0.08108 &  0.04054 \tabularnewline
24 &  0.9524 &  0.0951 &  0.04755 \tabularnewline
25 &  0.9808 &  0.0383 &  0.01915 \tabularnewline
26 &  0.9716 &  0.05683 &  0.02842 \tabularnewline
27 &  0.9627 &  0.07454 &  0.03727 \tabularnewline
28 &  0.9922 &  0.01568 &  0.007839 \tabularnewline
29 &  0.9897 &  0.02064 &  0.01032 \tabularnewline
30 &  0.9914 &  0.01721 &  0.008603 \tabularnewline
31 &  0.9878 &  0.02442 &  0.01221 \tabularnewline
32 &  0.9835 &  0.03305 &  0.01653 \tabularnewline
33 &  0.9764 &  0.04718 &  0.02359 \tabularnewline
34 &  0.9895 &  0.02092 &  0.01046 \tabularnewline
35 &  0.9865 &  0.02694 &  0.01347 \tabularnewline
36 &  0.9858 &  0.02846 &  0.01423 \tabularnewline
37 &  0.9825 &  0.03499 &  0.0175 \tabularnewline
38 &  0.9791 &  0.04173 &  0.02087 \tabularnewline
39 &  0.9746 &  0.05084 &  0.02542 \tabularnewline
40 &  0.9673 &  0.06533 &  0.03267 \tabularnewline
41 &  0.9724 &  0.05515 &  0.02758 \tabularnewline
42 &  0.9713 &  0.05738 &  0.02869 \tabularnewline
43 &  0.9885 &  0.023 &  0.0115 \tabularnewline
44 &  0.9847 &  0.03051 &  0.01525 \tabularnewline
45 &  0.9964 &  0.00711 &  0.003555 \tabularnewline
46 &  0.9953 &  0.009499 &  0.004749 \tabularnewline
47 &  0.9933 &  0.0134 &  0.006701 \tabularnewline
48 &  0.9905 &  0.0191 &  0.009549 \tabularnewline
49 &  0.9884 &  0.02316 &  0.01158 \tabularnewline
50 &  0.9846 &  0.0308 &  0.0154 \tabularnewline
51 &  0.9834 &  0.03325 &  0.01663 \tabularnewline
52 &  0.9789 &  0.04216 &  0.02108 \tabularnewline
53 &  0.9808 &  0.03836 &  0.01918 \tabularnewline
54 &  0.9803 &  0.0394 &  0.0197 \tabularnewline
55 &  0.9798 &  0.04042 &  0.02021 \tabularnewline
56 &  0.9734 &  0.05312 &  0.02656 \tabularnewline
57 &  0.977 &  0.04608 &  0.02304 \tabularnewline
58 &  0.9752 &  0.04955 &  0.02478 \tabularnewline
59 &  0.9756 &  0.04885 &  0.02442 \tabularnewline
60 &  0.9812 &  0.03754 &  0.01877 \tabularnewline
61 &  0.982 &  0.03593 &  0.01796 \tabularnewline
62 &  0.9771 &  0.04579 &  0.02289 \tabularnewline
63 &  0.9744 &  0.05121 &  0.0256 \tabularnewline
64 &  0.9797 &  0.04055 &  0.02027 \tabularnewline
65 &  0.9751 &  0.04988 &  0.02494 \tabularnewline
66 &  0.9699 &  0.0603 &  0.03015 \tabularnewline
67 &  0.9635 &  0.07296 &  0.03648 \tabularnewline
68 &  0.983 &  0.03406 &  0.01703 \tabularnewline
69 &  0.9864 &  0.02712 &  0.01356 \tabularnewline
70 &  0.983 &  0.03398 &  0.01699 \tabularnewline
71 &  0.9775 &  0.04508 &  0.02254 \tabularnewline
72 &  0.9739 &  0.05218 &  0.02609 \tabularnewline
73 &  0.975 &  0.05003 &  0.02502 \tabularnewline
74 &  0.9673 &  0.06533 &  0.03266 \tabularnewline
75 &  0.9585 &  0.08298 &  0.04149 \tabularnewline
76 &  0.9499 &  0.1002 &  0.05011 \tabularnewline
77 &  0.9377 &  0.1245 &  0.06227 \tabularnewline
78 &  0.9219 &  0.1563 &  0.07813 \tabularnewline
79 &  0.9044 &  0.1912 &  0.09562 \tabularnewline
80 &  0.8861 &  0.2278 &  0.1139 \tabularnewline
81 &  0.8927 &  0.2146 &  0.1073 \tabularnewline
82 &  0.9557 &  0.08853 &  0.04427 \tabularnewline
83 &  0.9571 &  0.08587 &  0.04293 \tabularnewline
84 &  0.9466 &  0.1067 &  0.05335 \tabularnewline
85 &  0.9513 &  0.09745 &  0.04873 \tabularnewline
86 &  0.959 &  0.08195 &  0.04097 \tabularnewline
87 &  0.949 &  0.1019 &  0.05097 \tabularnewline
88 &  0.9396 &  0.1207 &  0.06037 \tabularnewline
89 &  0.9265 &  0.147 &  0.07348 \tabularnewline
90 &  0.9109 &  0.1782 &  0.08911 \tabularnewline
91 &  0.8897 &  0.2205 &  0.1103 \tabularnewline
92 &  0.8901 &  0.2199 &  0.1099 \tabularnewline
93 &  0.8665 &  0.2669 &  0.1335 \tabularnewline
94 &  0.8425 &  0.315 &  0.1575 \tabularnewline
95 &  0.8107 &  0.3786 &  0.1893 \tabularnewline
96 &  0.7856 &  0.4288 &  0.2144 \tabularnewline
97 &  0.7541 &  0.4918 &  0.2459 \tabularnewline
98 &  0.715 &  0.5699 &  0.285 \tabularnewline
99 &  0.6719 &  0.6562 &  0.3281 \tabularnewline
100 &  0.6279 &  0.7441 &  0.3721 \tabularnewline
101 &  0.6578 &  0.6843 &  0.3422 \tabularnewline
102 &  0.8566 &  0.2868 &  0.1434 \tabularnewline
103 &  0.8614 &  0.2771 &  0.1386 \tabularnewline
104 &  0.8318 &  0.3364 &  0.1682 \tabularnewline
105 &  0.9205 &  0.159 &  0.07952 \tabularnewline
106 &  0.8993 &  0.2015 &  0.1007 \tabularnewline
107 &  0.8879 &  0.2242 &  0.1121 \tabularnewline
108 &  0.8657 &  0.2686 &  0.1343 \tabularnewline
109 &  0.8591 &  0.2819 &  0.1409 \tabularnewline
110 &  0.8843 &  0.2314 &  0.1157 \tabularnewline
111 &  0.8803 &  0.2394 &  0.1197 \tabularnewline
112 &  0.8622 &  0.2757 &  0.1378 \tabularnewline
113 &  0.8727 &  0.2547 &  0.1273 \tabularnewline
114 &  0.9201 &  0.1598 &  0.07989 \tabularnewline
115 &  0.9794 &  0.04127 &  0.02063 \tabularnewline
116 &  0.9742 &  0.05156 &  0.02578 \tabularnewline
117 &  0.9865 &  0.02702 &  0.01351 \tabularnewline
118 &  0.9913 &  0.01739 &  0.008697 \tabularnewline
119 &  0.9921 &  0.01581 &  0.007903 \tabularnewline
120 &  0.9875 &  0.02508 &  0.01254 \tabularnewline
121 &  0.9866 &  0.02685 &  0.01342 \tabularnewline
122 &  0.9795 &  0.04109 &  0.02055 \tabularnewline
123 &  0.9714 &  0.05724 &  0.02862 \tabularnewline
124 &  0.9616 &  0.07674 &  0.03837 \tabularnewline
125 &  0.9435 &  0.1131 &  0.05654 \tabularnewline
126 &  0.9622 &  0.0756 &  0.0378 \tabularnewline
127 &  0.9463 &  0.1074 &  0.05371 \tabularnewline
128 &  0.9247 &  0.1506 &  0.07529 \tabularnewline
129 &  0.9717 &  0.05663 &  0.02831 \tabularnewline
130 &  0.9543 &  0.09148 &  0.04574 \tabularnewline
131 &  0.9266 &  0.1469 &  0.07345 \tabularnewline
132 &  0.8983 &  0.2034 &  0.1017 \tabularnewline
133 &  0.8764 &  0.2472 &  0.1236 \tabularnewline
134 &  0.8498 &  0.3004 &  0.1502 \tabularnewline
135 &  0.7924 &  0.4152 &  0.2076 \tabularnewline
136 &  0.7866 &  0.4268 &  0.2134 \tabularnewline
137 &  0.8386 &  0.3228 &  0.1614 \tabularnewline
138 &  0.7531 &  0.4938 &  0.2469 \tabularnewline
139 &  0.6325 &  0.7351 &  0.3675 \tabularnewline
140 &  0.768 &  0.464 &  0.232 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285277&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] 0.9189[/C][C] 0.1622[/C][C] 0.08112[/C][/ROW]
[ROW][C]12[/C][C] 0.8575[/C][C] 0.2849[/C][C] 0.1425[/C][/ROW]
[ROW][C]13[/C][C] 0.8591[/C][C] 0.2818[/C][C] 0.1409[/C][/ROW]
[ROW][C]14[/C][C] 0.7901[/C][C] 0.4198[/C][C] 0.2099[/C][/ROW]
[ROW][C]15[/C][C] 0.8173[/C][C] 0.3655[/C][C] 0.1827[/C][/ROW]
[ROW][C]16[/C][C] 0.8873[/C][C] 0.2253[/C][C] 0.1127[/C][/ROW]
[ROW][C]17[/C][C] 0.868[/C][C] 0.264[/C][C] 0.132[/C][/ROW]
[ROW][C]18[/C][C] 0.8437[/C][C] 0.3125[/C][C] 0.1563[/C][/ROW]
[ROW][C]19[/C][C] 0.8705[/C][C] 0.259[/C][C] 0.1295[/C][/ROW]
[ROW][C]20[/C][C] 0.981[/C][C] 0.03805[/C][C] 0.01902[/C][/ROW]
[ROW][C]21[/C][C] 0.9703[/C][C] 0.05947[/C][C] 0.02974[/C][/ROW]
[ROW][C]22[/C][C] 0.9709[/C][C] 0.05816[/C][C] 0.02908[/C][/ROW]
[ROW][C]23[/C][C] 0.9595[/C][C] 0.08108[/C][C] 0.04054[/C][/ROW]
[ROW][C]24[/C][C] 0.9524[/C][C] 0.0951[/C][C] 0.04755[/C][/ROW]
[ROW][C]25[/C][C] 0.9808[/C][C] 0.0383[/C][C] 0.01915[/C][/ROW]
[ROW][C]26[/C][C] 0.9716[/C][C] 0.05683[/C][C] 0.02842[/C][/ROW]
[ROW][C]27[/C][C] 0.9627[/C][C] 0.07454[/C][C] 0.03727[/C][/ROW]
[ROW][C]28[/C][C] 0.9922[/C][C] 0.01568[/C][C] 0.007839[/C][/ROW]
[ROW][C]29[/C][C] 0.9897[/C][C] 0.02064[/C][C] 0.01032[/C][/ROW]
[ROW][C]30[/C][C] 0.9914[/C][C] 0.01721[/C][C] 0.008603[/C][/ROW]
[ROW][C]31[/C][C] 0.9878[/C][C] 0.02442[/C][C] 0.01221[/C][/ROW]
[ROW][C]32[/C][C] 0.9835[/C][C] 0.03305[/C][C] 0.01653[/C][/ROW]
[ROW][C]33[/C][C] 0.9764[/C][C] 0.04718[/C][C] 0.02359[/C][/ROW]
[ROW][C]34[/C][C] 0.9895[/C][C] 0.02092[/C][C] 0.01046[/C][/ROW]
[ROW][C]35[/C][C] 0.9865[/C][C] 0.02694[/C][C] 0.01347[/C][/ROW]
[ROW][C]36[/C][C] 0.9858[/C][C] 0.02846[/C][C] 0.01423[/C][/ROW]
[ROW][C]37[/C][C] 0.9825[/C][C] 0.03499[/C][C] 0.0175[/C][/ROW]
[ROW][C]38[/C][C] 0.9791[/C][C] 0.04173[/C][C] 0.02087[/C][/ROW]
[ROW][C]39[/C][C] 0.9746[/C][C] 0.05084[/C][C] 0.02542[/C][/ROW]
[ROW][C]40[/C][C] 0.9673[/C][C] 0.06533[/C][C] 0.03267[/C][/ROW]
[ROW][C]41[/C][C] 0.9724[/C][C] 0.05515[/C][C] 0.02758[/C][/ROW]
[ROW][C]42[/C][C] 0.9713[/C][C] 0.05738[/C][C] 0.02869[/C][/ROW]
[ROW][C]43[/C][C] 0.9885[/C][C] 0.023[/C][C] 0.0115[/C][/ROW]
[ROW][C]44[/C][C] 0.9847[/C][C] 0.03051[/C][C] 0.01525[/C][/ROW]
[ROW][C]45[/C][C] 0.9964[/C][C] 0.00711[/C][C] 0.003555[/C][/ROW]
[ROW][C]46[/C][C] 0.9953[/C][C] 0.009499[/C][C] 0.004749[/C][/ROW]
[ROW][C]47[/C][C] 0.9933[/C][C] 0.0134[/C][C] 0.006701[/C][/ROW]
[ROW][C]48[/C][C] 0.9905[/C][C] 0.0191[/C][C] 0.009549[/C][/ROW]
[ROW][C]49[/C][C] 0.9884[/C][C] 0.02316[/C][C] 0.01158[/C][/ROW]
[ROW][C]50[/C][C] 0.9846[/C][C] 0.0308[/C][C] 0.0154[/C][/ROW]
[ROW][C]51[/C][C] 0.9834[/C][C] 0.03325[/C][C] 0.01663[/C][/ROW]
[ROW][C]52[/C][C] 0.9789[/C][C] 0.04216[/C][C] 0.02108[/C][/ROW]
[ROW][C]53[/C][C] 0.9808[/C][C] 0.03836[/C][C] 0.01918[/C][/ROW]
[ROW][C]54[/C][C] 0.9803[/C][C] 0.0394[/C][C] 0.0197[/C][/ROW]
[ROW][C]55[/C][C] 0.9798[/C][C] 0.04042[/C][C] 0.02021[/C][/ROW]
[ROW][C]56[/C][C] 0.9734[/C][C] 0.05312[/C][C] 0.02656[/C][/ROW]
[ROW][C]57[/C][C] 0.977[/C][C] 0.04608[/C][C] 0.02304[/C][/ROW]
[ROW][C]58[/C][C] 0.9752[/C][C] 0.04955[/C][C] 0.02478[/C][/ROW]
[ROW][C]59[/C][C] 0.9756[/C][C] 0.04885[/C][C] 0.02442[/C][/ROW]
[ROW][C]60[/C][C] 0.9812[/C][C] 0.03754[/C][C] 0.01877[/C][/ROW]
[ROW][C]61[/C][C] 0.982[/C][C] 0.03593[/C][C] 0.01796[/C][/ROW]
[ROW][C]62[/C][C] 0.9771[/C][C] 0.04579[/C][C] 0.02289[/C][/ROW]
[ROW][C]63[/C][C] 0.9744[/C][C] 0.05121[/C][C] 0.0256[/C][/ROW]
[ROW][C]64[/C][C] 0.9797[/C][C] 0.04055[/C][C] 0.02027[/C][/ROW]
[ROW][C]65[/C][C] 0.9751[/C][C] 0.04988[/C][C] 0.02494[/C][/ROW]
[ROW][C]66[/C][C] 0.9699[/C][C] 0.0603[/C][C] 0.03015[/C][/ROW]
[ROW][C]67[/C][C] 0.9635[/C][C] 0.07296[/C][C] 0.03648[/C][/ROW]
[ROW][C]68[/C][C] 0.983[/C][C] 0.03406[/C][C] 0.01703[/C][/ROW]
[ROW][C]69[/C][C] 0.9864[/C][C] 0.02712[/C][C] 0.01356[/C][/ROW]
[ROW][C]70[/C][C] 0.983[/C][C] 0.03398[/C][C] 0.01699[/C][/ROW]
[ROW][C]71[/C][C] 0.9775[/C][C] 0.04508[/C][C] 0.02254[/C][/ROW]
[ROW][C]72[/C][C] 0.9739[/C][C] 0.05218[/C][C] 0.02609[/C][/ROW]
[ROW][C]73[/C][C] 0.975[/C][C] 0.05003[/C][C] 0.02502[/C][/ROW]
[ROW][C]74[/C][C] 0.9673[/C][C] 0.06533[/C][C] 0.03266[/C][/ROW]
[ROW][C]75[/C][C] 0.9585[/C][C] 0.08298[/C][C] 0.04149[/C][/ROW]
[ROW][C]76[/C][C] 0.9499[/C][C] 0.1002[/C][C] 0.05011[/C][/ROW]
[ROW][C]77[/C][C] 0.9377[/C][C] 0.1245[/C][C] 0.06227[/C][/ROW]
[ROW][C]78[/C][C] 0.9219[/C][C] 0.1563[/C][C] 0.07813[/C][/ROW]
[ROW][C]79[/C][C] 0.9044[/C][C] 0.1912[/C][C] 0.09562[/C][/ROW]
[ROW][C]80[/C][C] 0.8861[/C][C] 0.2278[/C][C] 0.1139[/C][/ROW]
[ROW][C]81[/C][C] 0.8927[/C][C] 0.2146[/C][C] 0.1073[/C][/ROW]
[ROW][C]82[/C][C] 0.9557[/C][C] 0.08853[/C][C] 0.04427[/C][/ROW]
[ROW][C]83[/C][C] 0.9571[/C][C] 0.08587[/C][C] 0.04293[/C][/ROW]
[ROW][C]84[/C][C] 0.9466[/C][C] 0.1067[/C][C] 0.05335[/C][/ROW]
[ROW][C]85[/C][C] 0.9513[/C][C] 0.09745[/C][C] 0.04873[/C][/ROW]
[ROW][C]86[/C][C] 0.959[/C][C] 0.08195[/C][C] 0.04097[/C][/ROW]
[ROW][C]87[/C][C] 0.949[/C][C] 0.1019[/C][C] 0.05097[/C][/ROW]
[ROW][C]88[/C][C] 0.9396[/C][C] 0.1207[/C][C] 0.06037[/C][/ROW]
[ROW][C]89[/C][C] 0.9265[/C][C] 0.147[/C][C] 0.07348[/C][/ROW]
[ROW][C]90[/C][C] 0.9109[/C][C] 0.1782[/C][C] 0.08911[/C][/ROW]
[ROW][C]91[/C][C] 0.8897[/C][C] 0.2205[/C][C] 0.1103[/C][/ROW]
[ROW][C]92[/C][C] 0.8901[/C][C] 0.2199[/C][C] 0.1099[/C][/ROW]
[ROW][C]93[/C][C] 0.8665[/C][C] 0.2669[/C][C] 0.1335[/C][/ROW]
[ROW][C]94[/C][C] 0.8425[/C][C] 0.315[/C][C] 0.1575[/C][/ROW]
[ROW][C]95[/C][C] 0.8107[/C][C] 0.3786[/C][C] 0.1893[/C][/ROW]
[ROW][C]96[/C][C] 0.7856[/C][C] 0.4288[/C][C] 0.2144[/C][/ROW]
[ROW][C]97[/C][C] 0.7541[/C][C] 0.4918[/C][C] 0.2459[/C][/ROW]
[ROW][C]98[/C][C] 0.715[/C][C] 0.5699[/C][C] 0.285[/C][/ROW]
[ROW][C]99[/C][C] 0.6719[/C][C] 0.6562[/C][C] 0.3281[/C][/ROW]
[ROW][C]100[/C][C] 0.6279[/C][C] 0.7441[/C][C] 0.3721[/C][/ROW]
[ROW][C]101[/C][C] 0.6578[/C][C] 0.6843[/C][C] 0.3422[/C][/ROW]
[ROW][C]102[/C][C] 0.8566[/C][C] 0.2868[/C][C] 0.1434[/C][/ROW]
[ROW][C]103[/C][C] 0.8614[/C][C] 0.2771[/C][C] 0.1386[/C][/ROW]
[ROW][C]104[/C][C] 0.8318[/C][C] 0.3364[/C][C] 0.1682[/C][/ROW]
[ROW][C]105[/C][C] 0.9205[/C][C] 0.159[/C][C] 0.07952[/C][/ROW]
[ROW][C]106[/C][C] 0.8993[/C][C] 0.2015[/C][C] 0.1007[/C][/ROW]
[ROW][C]107[/C][C] 0.8879[/C][C] 0.2242[/C][C] 0.1121[/C][/ROW]
[ROW][C]108[/C][C] 0.8657[/C][C] 0.2686[/C][C] 0.1343[/C][/ROW]
[ROW][C]109[/C][C] 0.8591[/C][C] 0.2819[/C][C] 0.1409[/C][/ROW]
[ROW][C]110[/C][C] 0.8843[/C][C] 0.2314[/C][C] 0.1157[/C][/ROW]
[ROW][C]111[/C][C] 0.8803[/C][C] 0.2394[/C][C] 0.1197[/C][/ROW]
[ROW][C]112[/C][C] 0.8622[/C][C] 0.2757[/C][C] 0.1378[/C][/ROW]
[ROW][C]113[/C][C] 0.8727[/C][C] 0.2547[/C][C] 0.1273[/C][/ROW]
[ROW][C]114[/C][C] 0.9201[/C][C] 0.1598[/C][C] 0.07989[/C][/ROW]
[ROW][C]115[/C][C] 0.9794[/C][C] 0.04127[/C][C] 0.02063[/C][/ROW]
[ROW][C]116[/C][C] 0.9742[/C][C] 0.05156[/C][C] 0.02578[/C][/ROW]
[ROW][C]117[/C][C] 0.9865[/C][C] 0.02702[/C][C] 0.01351[/C][/ROW]
[ROW][C]118[/C][C] 0.9913[/C][C] 0.01739[/C][C] 0.008697[/C][/ROW]
[ROW][C]119[/C][C] 0.9921[/C][C] 0.01581[/C][C] 0.007903[/C][/ROW]
[ROW][C]120[/C][C] 0.9875[/C][C] 0.02508[/C][C] 0.01254[/C][/ROW]
[ROW][C]121[/C][C] 0.9866[/C][C] 0.02685[/C][C] 0.01342[/C][/ROW]
[ROW][C]122[/C][C] 0.9795[/C][C] 0.04109[/C][C] 0.02055[/C][/ROW]
[ROW][C]123[/C][C] 0.9714[/C][C] 0.05724[/C][C] 0.02862[/C][/ROW]
[ROW][C]124[/C][C] 0.9616[/C][C] 0.07674[/C][C] 0.03837[/C][/ROW]
[ROW][C]125[/C][C] 0.9435[/C][C] 0.1131[/C][C] 0.05654[/C][/ROW]
[ROW][C]126[/C][C] 0.9622[/C][C] 0.0756[/C][C] 0.0378[/C][/ROW]
[ROW][C]127[/C][C] 0.9463[/C][C] 0.1074[/C][C] 0.05371[/C][/ROW]
[ROW][C]128[/C][C] 0.9247[/C][C] 0.1506[/C][C] 0.07529[/C][/ROW]
[ROW][C]129[/C][C] 0.9717[/C][C] 0.05663[/C][C] 0.02831[/C][/ROW]
[ROW][C]130[/C][C] 0.9543[/C][C] 0.09148[/C][C] 0.04574[/C][/ROW]
[ROW][C]131[/C][C] 0.9266[/C][C] 0.1469[/C][C] 0.07345[/C][/ROW]
[ROW][C]132[/C][C] 0.8983[/C][C] 0.2034[/C][C] 0.1017[/C][/ROW]
[ROW][C]133[/C][C] 0.8764[/C][C] 0.2472[/C][C] 0.1236[/C][/ROW]
[ROW][C]134[/C][C] 0.8498[/C][C] 0.3004[/C][C] 0.1502[/C][/ROW]
[ROW][C]135[/C][C] 0.7924[/C][C] 0.4152[/C][C] 0.2076[/C][/ROW]
[ROW][C]136[/C][C] 0.7866[/C][C] 0.4268[/C][C] 0.2134[/C][/ROW]
[ROW][C]137[/C][C] 0.8386[/C][C] 0.3228[/C][C] 0.1614[/C][/ROW]
[ROW][C]138[/C][C] 0.7531[/C][C] 0.4938[/C][C] 0.2469[/C][/ROW]
[ROW][C]139[/C][C] 0.6325[/C][C] 0.7351[/C][C] 0.3675[/C][/ROW]
[ROW][C]140[/C][C] 0.768[/C][C] 0.464[/C][C] 0.232[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285277&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285277&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
11 0.9189 0.1622 0.08112
12 0.8575 0.2849 0.1425
13 0.8591 0.2818 0.1409
14 0.7901 0.4198 0.2099
15 0.8173 0.3655 0.1827
16 0.8873 0.2253 0.1127
17 0.868 0.264 0.132
18 0.8437 0.3125 0.1563
19 0.8705 0.259 0.1295
20 0.981 0.03805 0.01902
21 0.9703 0.05947 0.02974
22 0.9709 0.05816 0.02908
23 0.9595 0.08108 0.04054
24 0.9524 0.0951 0.04755
25 0.9808 0.0383 0.01915
26 0.9716 0.05683 0.02842
27 0.9627 0.07454 0.03727
28 0.9922 0.01568 0.007839
29 0.9897 0.02064 0.01032
30 0.9914 0.01721 0.008603
31 0.9878 0.02442 0.01221
32 0.9835 0.03305 0.01653
33 0.9764 0.04718 0.02359
34 0.9895 0.02092 0.01046
35 0.9865 0.02694 0.01347
36 0.9858 0.02846 0.01423
37 0.9825 0.03499 0.0175
38 0.9791 0.04173 0.02087
39 0.9746 0.05084 0.02542
40 0.9673 0.06533 0.03267
41 0.9724 0.05515 0.02758
42 0.9713 0.05738 0.02869
43 0.9885 0.023 0.0115
44 0.9847 0.03051 0.01525
45 0.9964 0.00711 0.003555
46 0.9953 0.009499 0.004749
47 0.9933 0.0134 0.006701
48 0.9905 0.0191 0.009549
49 0.9884 0.02316 0.01158
50 0.9846 0.0308 0.0154
51 0.9834 0.03325 0.01663
52 0.9789 0.04216 0.02108
53 0.9808 0.03836 0.01918
54 0.9803 0.0394 0.0197
55 0.9798 0.04042 0.02021
56 0.9734 0.05312 0.02656
57 0.977 0.04608 0.02304
58 0.9752 0.04955 0.02478
59 0.9756 0.04885 0.02442
60 0.9812 0.03754 0.01877
61 0.982 0.03593 0.01796
62 0.9771 0.04579 0.02289
63 0.9744 0.05121 0.0256
64 0.9797 0.04055 0.02027
65 0.9751 0.04988 0.02494
66 0.9699 0.0603 0.03015
67 0.9635 0.07296 0.03648
68 0.983 0.03406 0.01703
69 0.9864 0.02712 0.01356
70 0.983 0.03398 0.01699
71 0.9775 0.04508 0.02254
72 0.9739 0.05218 0.02609
73 0.975 0.05003 0.02502
74 0.9673 0.06533 0.03266
75 0.9585 0.08298 0.04149
76 0.9499 0.1002 0.05011
77 0.9377 0.1245 0.06227
78 0.9219 0.1563 0.07813
79 0.9044 0.1912 0.09562
80 0.8861 0.2278 0.1139
81 0.8927 0.2146 0.1073
82 0.9557 0.08853 0.04427
83 0.9571 0.08587 0.04293
84 0.9466 0.1067 0.05335
85 0.9513 0.09745 0.04873
86 0.959 0.08195 0.04097
87 0.949 0.1019 0.05097
88 0.9396 0.1207 0.06037
89 0.9265 0.147 0.07348
90 0.9109 0.1782 0.08911
91 0.8897 0.2205 0.1103
92 0.8901 0.2199 0.1099
93 0.8665 0.2669 0.1335
94 0.8425 0.315 0.1575
95 0.8107 0.3786 0.1893
96 0.7856 0.4288 0.2144
97 0.7541 0.4918 0.2459
98 0.715 0.5699 0.285
99 0.6719 0.6562 0.3281
100 0.6279 0.7441 0.3721
101 0.6578 0.6843 0.3422
102 0.8566 0.2868 0.1434
103 0.8614 0.2771 0.1386
104 0.8318 0.3364 0.1682
105 0.9205 0.159 0.07952
106 0.8993 0.2015 0.1007
107 0.8879 0.2242 0.1121
108 0.8657 0.2686 0.1343
109 0.8591 0.2819 0.1409
110 0.8843 0.2314 0.1157
111 0.8803 0.2394 0.1197
112 0.8622 0.2757 0.1378
113 0.8727 0.2547 0.1273
114 0.9201 0.1598 0.07989
115 0.9794 0.04127 0.02063
116 0.9742 0.05156 0.02578
117 0.9865 0.02702 0.01351
118 0.9913 0.01739 0.008697
119 0.9921 0.01581 0.007903
120 0.9875 0.02508 0.01254
121 0.9866 0.02685 0.01342
122 0.9795 0.04109 0.02055
123 0.9714 0.05724 0.02862
124 0.9616 0.07674 0.03837
125 0.9435 0.1131 0.05654
126 0.9622 0.0756 0.0378
127 0.9463 0.1074 0.05371
128 0.9247 0.1506 0.07529
129 0.9717 0.05663 0.02831
130 0.9543 0.09148 0.04574
131 0.9266 0.1469 0.07345
132 0.8983 0.2034 0.1017
133 0.8764 0.2472 0.1236
134 0.8498 0.3004 0.1502
135 0.7924 0.4152 0.2076
136 0.7866 0.4268 0.2134
137 0.8386 0.3228 0.1614
138 0.7531 0.4938 0.2469
139 0.6325 0.7351 0.3675
140 0.768 0.464 0.232







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level2 0.01538NOK
5% type I error level450.346154NOK
10% type I error level730.561538NOK

\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 & 2 &  0.01538 & NOK \tabularnewline
5% type I error level & 45 & 0.346154 & NOK \tabularnewline
10% type I error level & 73 & 0.561538 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285277&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]2[/C][C] 0.01538[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]45[/C][C]0.346154[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]73[/C][C]0.561538[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285277&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285277&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level2 0.01538NOK
5% type I error level450.346154NOK
10% type I error level730.561538NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s=12) ; par4 = 4 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s=12) ; par4 = 4 ; par5 = 2 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}