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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 computationTue, 28 Jan 2020 12:07:42 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Jan/28/t1580210238o6zzbuv2ckqrrvr.htm/, Retrieved Wed, 21 Apr 2021 09:04:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319053, Retrieved Wed, 21 Apr 2021 09:04:27 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact37
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [vraag 10] [2020-01-28 11:07:42] [43eb2330ebca6ad52336dea971844457] [Current]
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Dataseries X:
10 36 21 10
8 32 22 15
8 33 17 14
9 39 21 14
5 34 19 8
10 39 23 19
8 36 21 17
9 33 22 18
8 30 11 10
7 39 20 15
10 37 18 16
10 37 16 12
9 35 18 13
4 32 13 10
4 36 17 14
8 36 20 15
9 41 20 20
10 36 15 9
8 37 18 12
5 29 15 13
10 39 19 16
8 37 19 12
7 32 19 14
8 36 20 15
8 43 20 19
9 30 16 16
8 33 18 16
6 28 17 14
8 30 18 14
8 28 13 14
5 39 20 13
9 34 21 18
8 34 17 15
8 29 19 15
8 32 20 15
6 33 15 13
6 27 15 14
9 35 19 15
8 38 18 14
9 40 22 19
10 34 20 16
8 34 18 16
8 26 14 12
7 39 15 10
7 34 17 11
10 39 16 13
8 26 17 14
7 30 15 11
10 34 17 11
7 34 18 16
7 29 16 9
9 41 18 16
9 43 22 19
8 31 16 13
6 33 16 15
8 34 20 14
9 30 18 15
2 23 16 11
6 29 16 14
8 35 20 15
8 40 21 17
7 27 18 16
8 30 15 13
6 27 18 15
10 29 18 14
10 33 20 15
10 32 18 14
8 33 16 12
8 36 19 12
7 34 20 15
10 45 22 17
5 30 18 13
3 22 8 5
2 24 13 7
3 25 13 10
4 26 18 15
2 27 12 9
6 27 16 9
8 35 21 15
8 36 20 14
5 32 18 11
10 35 22 18
9 35 23 20
8 36 23 20
9 37 21 16
8 33 16 15
5 25 14 14
7 35 18 13
9 37 22 18
8 36 20 14
4 35 18 12
7 29 12 9
8 35 17 19
7 31 15 13
7 30 18 12
9 37 18 14
6 36 15 6
7 35 16 14
4 32 15 11
6 34 16 11
10 37 19 14
9 36 19 12
10 39 23 19
8 37 20 13
4 31 18 14
8 40 21 17
5 38 19 12
8 35 18 16
9 38 19 15
8 32 17 15
4 41 21 15
8 28 19 16
10 40 24 15
6 25 12 12
7 28 15 13
10 37 18 14
9 37 19 17
8 40 22 14
3 26 19 14
8 30 16 14
7 32 19 15
7 31 18 11
8 28 18 11
8 34 19 16
7 39 21 12
7 33 19 12
9 43 22 19
9 37 23 18
9 31 17 16
4 31 18 16
6 34 19 13
6 32 15 11
6 27 14 10
8 34 18 14
3 28 17 14
8 32 19 14
8 39 16 16
6 28 14 10
10 39 20 16
2 32 16 7
9 36 18 16
6 31 16 15
6 39 21 17
5 23 16 11
4 25 14 11
7 32 16 10
5 32 19 13
8 36 19 14
6 39 19 13
9 31 18 13
6 32 16 12
4 28 14 10
7 34 19 15
2 28 11 6
8 38 18 15
9 35 18 15
6 32 16 11
5 26 20 14
7 32 18 14
8 28 20 16
4 31 16 12
9 33 18 15
9 38 19 20
9 38 19 12
7 36 15 9
5 31 17 13
7 36 21 15
9 43 24 19
8 37 16 11
6 28 13 11
9 35 21 17
8 34 16 15
7 40 17 14
7 31 17 15
7 41 18 11
8 35 18 12
10 38 23 15
6 37 20 16
6 31 20 16




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319053&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
Intention_to_Use[t] = -1.52273 + 0.151696System_Quality[t] + 0.0322698Information_Quality[t] + 0.226814Perceived_Ease_of_Use[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Intention_to_Use[t] =  -1.52273 +  0.151696System_Quality[t] +  0.0322698Information_Quality[t] +  0.226814Perceived_Ease_of_Use[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Intention_to_Use[t] =  -1.52273 +  0.151696System_Quality[t] +  0.0322698Information_Quality[t] +  0.226814Perceived_Ease_of_Use[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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
Intention_to_Use[t] = -1.52273 + 0.151696System_Quality[t] + 0.0322698Information_Quality[t] + 0.226814Perceived_Ease_of_Use[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.523 0.9064-1.6800e+00 0.09473 0.04737
System_Quality+0.1517 0.0325+4.6680e+00 6.029e-06 3.014e-06
Information_Quality+0.03227 0.06818+4.7330e-01 0.6366 0.3183
Perceived_Ease_of_Use+0.2268 0.05703+3.9770e+00 0.0001019 5.093e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -1.523 &  0.9064 & -1.6800e+00 &  0.09473 &  0.04737 \tabularnewline
System_Quality & +0.1517 &  0.0325 & +4.6680e+00 &  6.029e-06 &  3.014e-06 \tabularnewline
Information_Quality & +0.03227 &  0.06818 & +4.7330e-01 &  0.6366 &  0.3183 \tabularnewline
Perceived_Ease_of_Use & +0.2268 &  0.05703 & +3.9770e+00 &  0.0001019 &  5.093e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&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]-1.523[/C][C] 0.9064[/C][C]-1.6800e+00[/C][C] 0.09473[/C][C] 0.04737[/C][/ROW]
[ROW][C]System_Quality[/C][C]+0.1517[/C][C] 0.0325[/C][C]+4.6680e+00[/C][C] 6.029e-06[/C][C] 3.014e-06[/C][/ROW]
[ROW][C]Information_Quality[/C][C]+0.03227[/C][C] 0.06818[/C][C]+4.7330e-01[/C][C] 0.6366[/C][C] 0.3183[/C][/ROW]
[ROW][C]Perceived_Ease_of_Use[/C][C]+0.2268[/C][C] 0.05703[/C][C]+3.9770e+00[/C][C] 0.0001019[/C][C] 5.093e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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)-1.523 0.9064-1.6800e+00 0.09473 0.04737
System_Quality+0.1517 0.0325+4.6680e+00 6.029e-06 3.014e-06
Information_Quality+0.03227 0.06818+4.7330e-01 0.6366 0.3183
Perceived_Ease_of_Use+0.2268 0.05703+3.9770e+00 0.0001019 5.093e-05







Multiple Linear Regression - Regression Statistics
Multiple R 0.6205
R-squared 0.385
Adjusted R-squared 0.3745
F-TEST (value) 36.53
F-TEST (DF numerator)3
F-TEST (DF denominator)175
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.553
Sum Squared Residuals 421.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6205 \tabularnewline
R-squared &  0.385 \tabularnewline
Adjusted R-squared &  0.3745 \tabularnewline
F-TEST (value) &  36.53 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 175 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.553 \tabularnewline
Sum Squared Residuals &  421.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6205[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.385[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3745[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 36.53[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]175[/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] 1.553[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 421.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&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 0.6205
R-squared 0.385
Adjusted R-squared 0.3745
F-TEST (value) 36.53
F-TEST (DF numerator)3
F-TEST (DF denominator)175
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.553
Sum Squared Residuals 421.9







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

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&T=4

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 10 6.884 3.116
2 8 7.444 0.5563
3 8 7.207 0.7928
4 9 8.246 0.7535
5 5 6.063-1.063
6 10 9.445 0.5549
7 8 8.472-0.4718
8 9 8.276 0.7242
9 8 5.651 2.349
10 7 8.441-1.441
11 10 8.3 1.7
12 10 7.328 2.672
13 9 7.316 1.684
14 4 6.019-2.019
15 4 7.662-3.662
16 8 7.986 0.01408
17 9 9.878-0.8785
18 10 6.464 3.536
19 8 7.393 0.6074
20 5 6.309-1.309
21 10 8.636 1.364
22 8 7.425 0.5751
23 7 7.12-0.1201
24 8 7.986 0.01408
25 8 9.955-1.955
26 9 7.173 1.827
27 8 7.693 0.3069
28 6 6.449-0.4487
29 8 6.784 1.216
30 8 6.32 1.68
31 5 7.987-2.987
32 9 8.395 0.6048
33 8 7.586 0.4143
34 8 6.892 1.108
35 8 7.379 0.6209
36 6 6.916-0.9159
37 6 6.232-0.2325
38 9 7.802 1.198
39 8 7.998 0.002045
40 9 9.564-0.5645
41 10 7.909 2.091
42 8 7.845 0.1552
43 8 5.595 2.405
44 7 7.146-0.1456
45 7 6.678 0.3215
46 10 7.858 2.142
47 8 6.145 1.855
48 7 6.007 0.9929
49 10 6.678 3.322
50 7 7.845-0.8448
51 7 5.434 1.566
52 9 8.907 0.09333
53 9 10.02-1.02
54 8 6.645 1.355
55 6 7.402-1.402
56 8 7.456 0.5443
57 9 7.011 1.989
58 2 4.978-2.978
59 6 6.568-0.5682
60 8 7.834 0.1658
61 8 9.079-1.079
62 7 6.783 0.2171
63 8 6.461 1.539
64 6 6.556-0.5561
65 10 6.633 3.367
66 10 7.531 2.469
67 10 7.088 2.912
68 8 6.721 1.279
69 8 7.273 0.7268
70 7 7.683-0.6825
71 10 9.869 0.1307
72 5 6.558-1.558
73 3 3.207-0.2068
74 2 4.125-2.125
75 3 4.957-1.957
76 4 6.404-2.404
77 2 5.002-3.002
78 6 5.131 0.8693
79 8 7.866 0.1335
80 8 7.759 0.2409
81 5 6.407-1.407
82 10 8.579 1.421
83 9 9.065-0.0651
84 8 9.217-1.217
85 9 8.397 0.6033
86 8 7.402 0.5982
87 5 5.897-0.8968
88 7 7.316-0.3161
89 9 8.883 0.1174
90 8 7.759 0.2409
91 4 7.089-3.089
92 7 5.305 1.695
93 8 8.645-0.6447
94 7 6.612 0.3875
95 7 6.331 0.6692
96 9 7.846 1.154
97 6 5.783 0.2168
98 7 7.478-0.4783
99 4 6.311-2.311
100 6 6.646-0.6462
101 10 7.879 2.121
102 9 7.273 1.727
103 10 9.445 0.5549
104 8 7.684 0.316
105 4 6.936-2.936
106 8 9.079-1.079
107 5 7.577-2.577
108 8 7.996 0.003505
109 9 8.257 0.743
110 8 7.282 0.7177
111 4 8.777-4.777
112 8 6.967 1.033
113 10 8.722 1.278
114 6 5.379 0.6213
115 7 6.157 0.8426
116 10 7.846 2.154
117 9 8.559 0.441
118 8 8.43-0.4304
119 3 6.21-3.21
120 8 6.72 1.28
121 7 7.347-0.3469
122 7 6.256 0.7444
123 8 5.801 2.199
124 8 7.877 0.1229
125 7 7.793-0.7928
126 7 6.818 0.1819
127 9 10.02-1.02
128 9 8.915 0.08514
129 9 7.357 1.643
130 4 7.39-3.39
131 6 7.197-1.197
132 6 6.311-0.3105
133 6 5.293 0.707
134 8 7.391 0.6088
135 3 6.449-3.449
136 8 7.12 0.88
137 8 8.539-0.5387
138 6 5.445 0.5553
139 10 8.668 1.332
140 2 5.436-3.436
141 9 8.148 0.8518
142 6 7.098-1.098
143 6 8.927-2.927
144 5 4.978 0.02246
145 4 5.216-1.216
146 7 6.116 0.884
147 5 6.893-1.893
148 8 7.727 0.2732
149 6 7.955-1.955
150 9 6.709 2.291
151 6 6.57-0.5696
152 4 5.445-1.445
153 7 7.65-0.6503
154 2 4.441-2.441
155 8 8.225-0.2248
156 9 7.77 1.23
157 6 6.343-0.3428
158 5 6.242-1.242
159 7 7.088-0.08778
160 8 6.999 1.001
161 4 6.418-2.418
162 9 7.466 1.534
163 9 9.391-0.3911
164 9 7.577 1.423
165 7 6.464 0.5363
166 5 6.677-1.677
167 7 8.018-1.018
168 9 10.08-1.084
169 8 7.101 0.8987
170 6 5.639 0.3608
171 9 8.32 0.6799
172 8 7.553 0.4466
173 7 8.269-1.269
174 7 7.131-0.1306
175 7 7.773-0.7726
176 8 7.089 0.9108
177 10 8.386 1.614
178 6 8.364-2.364
179 6 7.454-1.454

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  6.884 &  3.116 \tabularnewline
2 &  8 &  7.444 &  0.5563 \tabularnewline
3 &  8 &  7.207 &  0.7928 \tabularnewline
4 &  9 &  8.246 &  0.7535 \tabularnewline
5 &  5 &  6.063 & -1.063 \tabularnewline
6 &  10 &  9.445 &  0.5549 \tabularnewline
7 &  8 &  8.472 & -0.4718 \tabularnewline
8 &  9 &  8.276 &  0.7242 \tabularnewline
9 &  8 &  5.651 &  2.349 \tabularnewline
10 &  7 &  8.441 & -1.441 \tabularnewline
11 &  10 &  8.3 &  1.7 \tabularnewline
12 &  10 &  7.328 &  2.672 \tabularnewline
13 &  9 &  7.316 &  1.684 \tabularnewline
14 &  4 &  6.019 & -2.019 \tabularnewline
15 &  4 &  7.662 & -3.662 \tabularnewline
16 &  8 &  7.986 &  0.01408 \tabularnewline
17 &  9 &  9.878 & -0.8785 \tabularnewline
18 &  10 &  6.464 &  3.536 \tabularnewline
19 &  8 &  7.393 &  0.6074 \tabularnewline
20 &  5 &  6.309 & -1.309 \tabularnewline
21 &  10 &  8.636 &  1.364 \tabularnewline
22 &  8 &  7.425 &  0.5751 \tabularnewline
23 &  7 &  7.12 & -0.1201 \tabularnewline
24 &  8 &  7.986 &  0.01408 \tabularnewline
25 &  8 &  9.955 & -1.955 \tabularnewline
26 &  9 &  7.173 &  1.827 \tabularnewline
27 &  8 &  7.693 &  0.3069 \tabularnewline
28 &  6 &  6.449 & -0.4487 \tabularnewline
29 &  8 &  6.784 &  1.216 \tabularnewline
30 &  8 &  6.32 &  1.68 \tabularnewline
31 &  5 &  7.987 & -2.987 \tabularnewline
32 &  9 &  8.395 &  0.6048 \tabularnewline
33 &  8 &  7.586 &  0.4143 \tabularnewline
34 &  8 &  6.892 &  1.108 \tabularnewline
35 &  8 &  7.379 &  0.6209 \tabularnewline
36 &  6 &  6.916 & -0.9159 \tabularnewline
37 &  6 &  6.232 & -0.2325 \tabularnewline
38 &  9 &  7.802 &  1.198 \tabularnewline
39 &  8 &  7.998 &  0.002045 \tabularnewline
40 &  9 &  9.564 & -0.5645 \tabularnewline
41 &  10 &  7.909 &  2.091 \tabularnewline
42 &  8 &  7.845 &  0.1552 \tabularnewline
43 &  8 &  5.595 &  2.405 \tabularnewline
44 &  7 &  7.146 & -0.1456 \tabularnewline
45 &  7 &  6.678 &  0.3215 \tabularnewline
46 &  10 &  7.858 &  2.142 \tabularnewline
47 &  8 &  6.145 &  1.855 \tabularnewline
48 &  7 &  6.007 &  0.9929 \tabularnewline
49 &  10 &  6.678 &  3.322 \tabularnewline
50 &  7 &  7.845 & -0.8448 \tabularnewline
51 &  7 &  5.434 &  1.566 \tabularnewline
52 &  9 &  8.907 &  0.09333 \tabularnewline
53 &  9 &  10.02 & -1.02 \tabularnewline
54 &  8 &  6.645 &  1.355 \tabularnewline
55 &  6 &  7.402 & -1.402 \tabularnewline
56 &  8 &  7.456 &  0.5443 \tabularnewline
57 &  9 &  7.011 &  1.989 \tabularnewline
58 &  2 &  4.978 & -2.978 \tabularnewline
59 &  6 &  6.568 & -0.5682 \tabularnewline
60 &  8 &  7.834 &  0.1658 \tabularnewline
61 &  8 &  9.079 & -1.079 \tabularnewline
62 &  7 &  6.783 &  0.2171 \tabularnewline
63 &  8 &  6.461 &  1.539 \tabularnewline
64 &  6 &  6.556 & -0.5561 \tabularnewline
65 &  10 &  6.633 &  3.367 \tabularnewline
66 &  10 &  7.531 &  2.469 \tabularnewline
67 &  10 &  7.088 &  2.912 \tabularnewline
68 &  8 &  6.721 &  1.279 \tabularnewline
69 &  8 &  7.273 &  0.7268 \tabularnewline
70 &  7 &  7.683 & -0.6825 \tabularnewline
71 &  10 &  9.869 &  0.1307 \tabularnewline
72 &  5 &  6.558 & -1.558 \tabularnewline
73 &  3 &  3.207 & -0.2068 \tabularnewline
74 &  2 &  4.125 & -2.125 \tabularnewline
75 &  3 &  4.957 & -1.957 \tabularnewline
76 &  4 &  6.404 & -2.404 \tabularnewline
77 &  2 &  5.002 & -3.002 \tabularnewline
78 &  6 &  5.131 &  0.8693 \tabularnewline
79 &  8 &  7.866 &  0.1335 \tabularnewline
80 &  8 &  7.759 &  0.2409 \tabularnewline
81 &  5 &  6.407 & -1.407 \tabularnewline
82 &  10 &  8.579 &  1.421 \tabularnewline
83 &  9 &  9.065 & -0.0651 \tabularnewline
84 &  8 &  9.217 & -1.217 \tabularnewline
85 &  9 &  8.397 &  0.6033 \tabularnewline
86 &  8 &  7.402 &  0.5982 \tabularnewline
87 &  5 &  5.897 & -0.8968 \tabularnewline
88 &  7 &  7.316 & -0.3161 \tabularnewline
89 &  9 &  8.883 &  0.1174 \tabularnewline
90 &  8 &  7.759 &  0.2409 \tabularnewline
91 &  4 &  7.089 & -3.089 \tabularnewline
92 &  7 &  5.305 &  1.695 \tabularnewline
93 &  8 &  8.645 & -0.6447 \tabularnewline
94 &  7 &  6.612 &  0.3875 \tabularnewline
95 &  7 &  6.331 &  0.6692 \tabularnewline
96 &  9 &  7.846 &  1.154 \tabularnewline
97 &  6 &  5.783 &  0.2168 \tabularnewline
98 &  7 &  7.478 & -0.4783 \tabularnewline
99 &  4 &  6.311 & -2.311 \tabularnewline
100 &  6 &  6.646 & -0.6462 \tabularnewline
101 &  10 &  7.879 &  2.121 \tabularnewline
102 &  9 &  7.273 &  1.727 \tabularnewline
103 &  10 &  9.445 &  0.5549 \tabularnewline
104 &  8 &  7.684 &  0.316 \tabularnewline
105 &  4 &  6.936 & -2.936 \tabularnewline
106 &  8 &  9.079 & -1.079 \tabularnewline
107 &  5 &  7.577 & -2.577 \tabularnewline
108 &  8 &  7.996 &  0.003505 \tabularnewline
109 &  9 &  8.257 &  0.743 \tabularnewline
110 &  8 &  7.282 &  0.7177 \tabularnewline
111 &  4 &  8.777 & -4.777 \tabularnewline
112 &  8 &  6.967 &  1.033 \tabularnewline
113 &  10 &  8.722 &  1.278 \tabularnewline
114 &  6 &  5.379 &  0.6213 \tabularnewline
115 &  7 &  6.157 &  0.8426 \tabularnewline
116 &  10 &  7.846 &  2.154 \tabularnewline
117 &  9 &  8.559 &  0.441 \tabularnewline
118 &  8 &  8.43 & -0.4304 \tabularnewline
119 &  3 &  6.21 & -3.21 \tabularnewline
120 &  8 &  6.72 &  1.28 \tabularnewline
121 &  7 &  7.347 & -0.3469 \tabularnewline
122 &  7 &  6.256 &  0.7444 \tabularnewline
123 &  8 &  5.801 &  2.199 \tabularnewline
124 &  8 &  7.877 &  0.1229 \tabularnewline
125 &  7 &  7.793 & -0.7928 \tabularnewline
126 &  7 &  6.818 &  0.1819 \tabularnewline
127 &  9 &  10.02 & -1.02 \tabularnewline
128 &  9 &  8.915 &  0.08514 \tabularnewline
129 &  9 &  7.357 &  1.643 \tabularnewline
130 &  4 &  7.39 & -3.39 \tabularnewline
131 &  6 &  7.197 & -1.197 \tabularnewline
132 &  6 &  6.311 & -0.3105 \tabularnewline
133 &  6 &  5.293 &  0.707 \tabularnewline
134 &  8 &  7.391 &  0.6088 \tabularnewline
135 &  3 &  6.449 & -3.449 \tabularnewline
136 &  8 &  7.12 &  0.88 \tabularnewline
137 &  8 &  8.539 & -0.5387 \tabularnewline
138 &  6 &  5.445 &  0.5553 \tabularnewline
139 &  10 &  8.668 &  1.332 \tabularnewline
140 &  2 &  5.436 & -3.436 \tabularnewline
141 &  9 &  8.148 &  0.8518 \tabularnewline
142 &  6 &  7.098 & -1.098 \tabularnewline
143 &  6 &  8.927 & -2.927 \tabularnewline
144 &  5 &  4.978 &  0.02246 \tabularnewline
145 &  4 &  5.216 & -1.216 \tabularnewline
146 &  7 &  6.116 &  0.884 \tabularnewline
147 &  5 &  6.893 & -1.893 \tabularnewline
148 &  8 &  7.727 &  0.2732 \tabularnewline
149 &  6 &  7.955 & -1.955 \tabularnewline
150 &  9 &  6.709 &  2.291 \tabularnewline
151 &  6 &  6.57 & -0.5696 \tabularnewline
152 &  4 &  5.445 & -1.445 \tabularnewline
153 &  7 &  7.65 & -0.6503 \tabularnewline
154 &  2 &  4.441 & -2.441 \tabularnewline
155 &  8 &  8.225 & -0.2248 \tabularnewline
156 &  9 &  7.77 &  1.23 \tabularnewline
157 &  6 &  6.343 & -0.3428 \tabularnewline
158 &  5 &  6.242 & -1.242 \tabularnewline
159 &  7 &  7.088 & -0.08778 \tabularnewline
160 &  8 &  6.999 &  1.001 \tabularnewline
161 &  4 &  6.418 & -2.418 \tabularnewline
162 &  9 &  7.466 &  1.534 \tabularnewline
163 &  9 &  9.391 & -0.3911 \tabularnewline
164 &  9 &  7.577 &  1.423 \tabularnewline
165 &  7 &  6.464 &  0.5363 \tabularnewline
166 &  5 &  6.677 & -1.677 \tabularnewline
167 &  7 &  8.018 & -1.018 \tabularnewline
168 &  9 &  10.08 & -1.084 \tabularnewline
169 &  8 &  7.101 &  0.8987 \tabularnewline
170 &  6 &  5.639 &  0.3608 \tabularnewline
171 &  9 &  8.32 &  0.6799 \tabularnewline
172 &  8 &  7.553 &  0.4466 \tabularnewline
173 &  7 &  8.269 & -1.269 \tabularnewline
174 &  7 &  7.131 & -0.1306 \tabularnewline
175 &  7 &  7.773 & -0.7726 \tabularnewline
176 &  8 &  7.089 &  0.9108 \tabularnewline
177 &  10 &  8.386 &  1.614 \tabularnewline
178 &  6 &  8.364 & -2.364 \tabularnewline
179 &  6 &  7.454 & -1.454 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&T=5

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 10[/C][C] 6.884[/C][C] 3.116[/C][/ROW]
[ROW][C]2[/C][C] 8[/C][C] 7.444[/C][C] 0.5563[/C][/ROW]
[ROW][C]3[/C][C] 8[/C][C] 7.207[/C][C] 0.7928[/C][/ROW]
[ROW][C]4[/C][C] 9[/C][C] 8.246[/C][C] 0.7535[/C][/ROW]
[ROW][C]5[/C][C] 5[/C][C] 6.063[/C][C]-1.063[/C][/ROW]
[ROW][C]6[/C][C] 10[/C][C] 9.445[/C][C] 0.5549[/C][/ROW]
[ROW][C]7[/C][C] 8[/C][C] 8.472[/C][C]-0.4718[/C][/ROW]
[ROW][C]8[/C][C] 9[/C][C] 8.276[/C][C] 0.7242[/C][/ROW]
[ROW][C]9[/C][C] 8[/C][C] 5.651[/C][C] 2.349[/C][/ROW]
[ROW][C]10[/C][C] 7[/C][C] 8.441[/C][C]-1.441[/C][/ROW]
[ROW][C]11[/C][C] 10[/C][C] 8.3[/C][C] 1.7[/C][/ROW]
[ROW][C]12[/C][C] 10[/C][C] 7.328[/C][C] 2.672[/C][/ROW]
[ROW][C]13[/C][C] 9[/C][C] 7.316[/C][C] 1.684[/C][/ROW]
[ROW][C]14[/C][C] 4[/C][C] 6.019[/C][C]-2.019[/C][/ROW]
[ROW][C]15[/C][C] 4[/C][C] 7.662[/C][C]-3.662[/C][/ROW]
[ROW][C]16[/C][C] 8[/C][C] 7.986[/C][C] 0.01408[/C][/ROW]
[ROW][C]17[/C][C] 9[/C][C] 9.878[/C][C]-0.8785[/C][/ROW]
[ROW][C]18[/C][C] 10[/C][C] 6.464[/C][C] 3.536[/C][/ROW]
[ROW][C]19[/C][C] 8[/C][C] 7.393[/C][C] 0.6074[/C][/ROW]
[ROW][C]20[/C][C] 5[/C][C] 6.309[/C][C]-1.309[/C][/ROW]
[ROW][C]21[/C][C] 10[/C][C] 8.636[/C][C] 1.364[/C][/ROW]
[ROW][C]22[/C][C] 8[/C][C] 7.425[/C][C] 0.5751[/C][/ROW]
[ROW][C]23[/C][C] 7[/C][C] 7.12[/C][C]-0.1201[/C][/ROW]
[ROW][C]24[/C][C] 8[/C][C] 7.986[/C][C] 0.01408[/C][/ROW]
[ROW][C]25[/C][C] 8[/C][C] 9.955[/C][C]-1.955[/C][/ROW]
[ROW][C]26[/C][C] 9[/C][C] 7.173[/C][C] 1.827[/C][/ROW]
[ROW][C]27[/C][C] 8[/C][C] 7.693[/C][C] 0.3069[/C][/ROW]
[ROW][C]28[/C][C] 6[/C][C] 6.449[/C][C]-0.4487[/C][/ROW]
[ROW][C]29[/C][C] 8[/C][C] 6.784[/C][C] 1.216[/C][/ROW]
[ROW][C]30[/C][C] 8[/C][C] 6.32[/C][C] 1.68[/C][/ROW]
[ROW][C]31[/C][C] 5[/C][C] 7.987[/C][C]-2.987[/C][/ROW]
[ROW][C]32[/C][C] 9[/C][C] 8.395[/C][C] 0.6048[/C][/ROW]
[ROW][C]33[/C][C] 8[/C][C] 7.586[/C][C] 0.4143[/C][/ROW]
[ROW][C]34[/C][C] 8[/C][C] 6.892[/C][C] 1.108[/C][/ROW]
[ROW][C]35[/C][C] 8[/C][C] 7.379[/C][C] 0.6209[/C][/ROW]
[ROW][C]36[/C][C] 6[/C][C] 6.916[/C][C]-0.9159[/C][/ROW]
[ROW][C]37[/C][C] 6[/C][C] 6.232[/C][C]-0.2325[/C][/ROW]
[ROW][C]38[/C][C] 9[/C][C] 7.802[/C][C] 1.198[/C][/ROW]
[ROW][C]39[/C][C] 8[/C][C] 7.998[/C][C] 0.002045[/C][/ROW]
[ROW][C]40[/C][C] 9[/C][C] 9.564[/C][C]-0.5645[/C][/ROW]
[ROW][C]41[/C][C] 10[/C][C] 7.909[/C][C] 2.091[/C][/ROW]
[ROW][C]42[/C][C] 8[/C][C] 7.845[/C][C] 0.1552[/C][/ROW]
[ROW][C]43[/C][C] 8[/C][C] 5.595[/C][C] 2.405[/C][/ROW]
[ROW][C]44[/C][C] 7[/C][C] 7.146[/C][C]-0.1456[/C][/ROW]
[ROW][C]45[/C][C] 7[/C][C] 6.678[/C][C] 0.3215[/C][/ROW]
[ROW][C]46[/C][C] 10[/C][C] 7.858[/C][C] 2.142[/C][/ROW]
[ROW][C]47[/C][C] 8[/C][C] 6.145[/C][C] 1.855[/C][/ROW]
[ROW][C]48[/C][C] 7[/C][C] 6.007[/C][C] 0.9929[/C][/ROW]
[ROW][C]49[/C][C] 10[/C][C] 6.678[/C][C] 3.322[/C][/ROW]
[ROW][C]50[/C][C] 7[/C][C] 7.845[/C][C]-0.8448[/C][/ROW]
[ROW][C]51[/C][C] 7[/C][C] 5.434[/C][C] 1.566[/C][/ROW]
[ROW][C]52[/C][C] 9[/C][C] 8.907[/C][C] 0.09333[/C][/ROW]
[ROW][C]53[/C][C] 9[/C][C] 10.02[/C][C]-1.02[/C][/ROW]
[ROW][C]54[/C][C] 8[/C][C] 6.645[/C][C] 1.355[/C][/ROW]
[ROW][C]55[/C][C] 6[/C][C] 7.402[/C][C]-1.402[/C][/ROW]
[ROW][C]56[/C][C] 8[/C][C] 7.456[/C][C] 0.5443[/C][/ROW]
[ROW][C]57[/C][C] 9[/C][C] 7.011[/C][C] 1.989[/C][/ROW]
[ROW][C]58[/C][C] 2[/C][C] 4.978[/C][C]-2.978[/C][/ROW]
[ROW][C]59[/C][C] 6[/C][C] 6.568[/C][C]-0.5682[/C][/ROW]
[ROW][C]60[/C][C] 8[/C][C] 7.834[/C][C] 0.1658[/C][/ROW]
[ROW][C]61[/C][C] 8[/C][C] 9.079[/C][C]-1.079[/C][/ROW]
[ROW][C]62[/C][C] 7[/C][C] 6.783[/C][C] 0.2171[/C][/ROW]
[ROW][C]63[/C][C] 8[/C][C] 6.461[/C][C] 1.539[/C][/ROW]
[ROW][C]64[/C][C] 6[/C][C] 6.556[/C][C]-0.5561[/C][/ROW]
[ROW][C]65[/C][C] 10[/C][C] 6.633[/C][C] 3.367[/C][/ROW]
[ROW][C]66[/C][C] 10[/C][C] 7.531[/C][C] 2.469[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 7.088[/C][C] 2.912[/C][/ROW]
[ROW][C]68[/C][C] 8[/C][C] 6.721[/C][C] 1.279[/C][/ROW]
[ROW][C]69[/C][C] 8[/C][C] 7.273[/C][C] 0.7268[/C][/ROW]
[ROW][C]70[/C][C] 7[/C][C] 7.683[/C][C]-0.6825[/C][/ROW]
[ROW][C]71[/C][C] 10[/C][C] 9.869[/C][C] 0.1307[/C][/ROW]
[ROW][C]72[/C][C] 5[/C][C] 6.558[/C][C]-1.558[/C][/ROW]
[ROW][C]73[/C][C] 3[/C][C] 3.207[/C][C]-0.2068[/C][/ROW]
[ROW][C]74[/C][C] 2[/C][C] 4.125[/C][C]-2.125[/C][/ROW]
[ROW][C]75[/C][C] 3[/C][C] 4.957[/C][C]-1.957[/C][/ROW]
[ROW][C]76[/C][C] 4[/C][C] 6.404[/C][C]-2.404[/C][/ROW]
[ROW][C]77[/C][C] 2[/C][C] 5.002[/C][C]-3.002[/C][/ROW]
[ROW][C]78[/C][C] 6[/C][C] 5.131[/C][C] 0.8693[/C][/ROW]
[ROW][C]79[/C][C] 8[/C][C] 7.866[/C][C] 0.1335[/C][/ROW]
[ROW][C]80[/C][C] 8[/C][C] 7.759[/C][C] 0.2409[/C][/ROW]
[ROW][C]81[/C][C] 5[/C][C] 6.407[/C][C]-1.407[/C][/ROW]
[ROW][C]82[/C][C] 10[/C][C] 8.579[/C][C] 1.421[/C][/ROW]
[ROW][C]83[/C][C] 9[/C][C] 9.065[/C][C]-0.0651[/C][/ROW]
[ROW][C]84[/C][C] 8[/C][C] 9.217[/C][C]-1.217[/C][/ROW]
[ROW][C]85[/C][C] 9[/C][C] 8.397[/C][C] 0.6033[/C][/ROW]
[ROW][C]86[/C][C] 8[/C][C] 7.402[/C][C] 0.5982[/C][/ROW]
[ROW][C]87[/C][C] 5[/C][C] 5.897[/C][C]-0.8968[/C][/ROW]
[ROW][C]88[/C][C] 7[/C][C] 7.316[/C][C]-0.3161[/C][/ROW]
[ROW][C]89[/C][C] 9[/C][C] 8.883[/C][C] 0.1174[/C][/ROW]
[ROW][C]90[/C][C] 8[/C][C] 7.759[/C][C] 0.2409[/C][/ROW]
[ROW][C]91[/C][C] 4[/C][C] 7.089[/C][C]-3.089[/C][/ROW]
[ROW][C]92[/C][C] 7[/C][C] 5.305[/C][C] 1.695[/C][/ROW]
[ROW][C]93[/C][C] 8[/C][C] 8.645[/C][C]-0.6447[/C][/ROW]
[ROW][C]94[/C][C] 7[/C][C] 6.612[/C][C] 0.3875[/C][/ROW]
[ROW][C]95[/C][C] 7[/C][C] 6.331[/C][C] 0.6692[/C][/ROW]
[ROW][C]96[/C][C] 9[/C][C] 7.846[/C][C] 1.154[/C][/ROW]
[ROW][C]97[/C][C] 6[/C][C] 5.783[/C][C] 0.2168[/C][/ROW]
[ROW][C]98[/C][C] 7[/C][C] 7.478[/C][C]-0.4783[/C][/ROW]
[ROW][C]99[/C][C] 4[/C][C] 6.311[/C][C]-2.311[/C][/ROW]
[ROW][C]100[/C][C] 6[/C][C] 6.646[/C][C]-0.6462[/C][/ROW]
[ROW][C]101[/C][C] 10[/C][C] 7.879[/C][C] 2.121[/C][/ROW]
[ROW][C]102[/C][C] 9[/C][C] 7.273[/C][C] 1.727[/C][/ROW]
[ROW][C]103[/C][C] 10[/C][C] 9.445[/C][C] 0.5549[/C][/ROW]
[ROW][C]104[/C][C] 8[/C][C] 7.684[/C][C] 0.316[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 6.936[/C][C]-2.936[/C][/ROW]
[ROW][C]106[/C][C] 8[/C][C] 9.079[/C][C]-1.079[/C][/ROW]
[ROW][C]107[/C][C] 5[/C][C] 7.577[/C][C]-2.577[/C][/ROW]
[ROW][C]108[/C][C] 8[/C][C] 7.996[/C][C] 0.003505[/C][/ROW]
[ROW][C]109[/C][C] 9[/C][C] 8.257[/C][C] 0.743[/C][/ROW]
[ROW][C]110[/C][C] 8[/C][C] 7.282[/C][C] 0.7177[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C] 8.777[/C][C]-4.777[/C][/ROW]
[ROW][C]112[/C][C] 8[/C][C] 6.967[/C][C] 1.033[/C][/ROW]
[ROW][C]113[/C][C] 10[/C][C] 8.722[/C][C] 1.278[/C][/ROW]
[ROW][C]114[/C][C] 6[/C][C] 5.379[/C][C] 0.6213[/C][/ROW]
[ROW][C]115[/C][C] 7[/C][C] 6.157[/C][C] 0.8426[/C][/ROW]
[ROW][C]116[/C][C] 10[/C][C] 7.846[/C][C] 2.154[/C][/ROW]
[ROW][C]117[/C][C] 9[/C][C] 8.559[/C][C] 0.441[/C][/ROW]
[ROW][C]118[/C][C] 8[/C][C] 8.43[/C][C]-0.4304[/C][/ROW]
[ROW][C]119[/C][C] 3[/C][C] 6.21[/C][C]-3.21[/C][/ROW]
[ROW][C]120[/C][C] 8[/C][C] 6.72[/C][C] 1.28[/C][/ROW]
[ROW][C]121[/C][C] 7[/C][C] 7.347[/C][C]-0.3469[/C][/ROW]
[ROW][C]122[/C][C] 7[/C][C] 6.256[/C][C] 0.7444[/C][/ROW]
[ROW][C]123[/C][C] 8[/C][C] 5.801[/C][C] 2.199[/C][/ROW]
[ROW][C]124[/C][C] 8[/C][C] 7.877[/C][C] 0.1229[/C][/ROW]
[ROW][C]125[/C][C] 7[/C][C] 7.793[/C][C]-0.7928[/C][/ROW]
[ROW][C]126[/C][C] 7[/C][C] 6.818[/C][C] 0.1819[/C][/ROW]
[ROW][C]127[/C][C] 9[/C][C] 10.02[/C][C]-1.02[/C][/ROW]
[ROW][C]128[/C][C] 9[/C][C] 8.915[/C][C] 0.08514[/C][/ROW]
[ROW][C]129[/C][C] 9[/C][C] 7.357[/C][C] 1.643[/C][/ROW]
[ROW][C]130[/C][C] 4[/C][C] 7.39[/C][C]-3.39[/C][/ROW]
[ROW][C]131[/C][C] 6[/C][C] 7.197[/C][C]-1.197[/C][/ROW]
[ROW][C]132[/C][C] 6[/C][C] 6.311[/C][C]-0.3105[/C][/ROW]
[ROW][C]133[/C][C] 6[/C][C] 5.293[/C][C] 0.707[/C][/ROW]
[ROW][C]134[/C][C] 8[/C][C] 7.391[/C][C] 0.6088[/C][/ROW]
[ROW][C]135[/C][C] 3[/C][C] 6.449[/C][C]-3.449[/C][/ROW]
[ROW][C]136[/C][C] 8[/C][C] 7.12[/C][C] 0.88[/C][/ROW]
[ROW][C]137[/C][C] 8[/C][C] 8.539[/C][C]-0.5387[/C][/ROW]
[ROW][C]138[/C][C] 6[/C][C] 5.445[/C][C] 0.5553[/C][/ROW]
[ROW][C]139[/C][C] 10[/C][C] 8.668[/C][C] 1.332[/C][/ROW]
[ROW][C]140[/C][C] 2[/C][C] 5.436[/C][C]-3.436[/C][/ROW]
[ROW][C]141[/C][C] 9[/C][C] 8.148[/C][C] 0.8518[/C][/ROW]
[ROW][C]142[/C][C] 6[/C][C] 7.098[/C][C]-1.098[/C][/ROW]
[ROW][C]143[/C][C] 6[/C][C] 8.927[/C][C]-2.927[/C][/ROW]
[ROW][C]144[/C][C] 5[/C][C] 4.978[/C][C] 0.02246[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 5.216[/C][C]-1.216[/C][/ROW]
[ROW][C]146[/C][C] 7[/C][C] 6.116[/C][C] 0.884[/C][/ROW]
[ROW][C]147[/C][C] 5[/C][C] 6.893[/C][C]-1.893[/C][/ROW]
[ROW][C]148[/C][C] 8[/C][C] 7.727[/C][C] 0.2732[/C][/ROW]
[ROW][C]149[/C][C] 6[/C][C] 7.955[/C][C]-1.955[/C][/ROW]
[ROW][C]150[/C][C] 9[/C][C] 6.709[/C][C] 2.291[/C][/ROW]
[ROW][C]151[/C][C] 6[/C][C] 6.57[/C][C]-0.5696[/C][/ROW]
[ROW][C]152[/C][C] 4[/C][C] 5.445[/C][C]-1.445[/C][/ROW]
[ROW][C]153[/C][C] 7[/C][C] 7.65[/C][C]-0.6503[/C][/ROW]
[ROW][C]154[/C][C] 2[/C][C] 4.441[/C][C]-2.441[/C][/ROW]
[ROW][C]155[/C][C] 8[/C][C] 8.225[/C][C]-0.2248[/C][/ROW]
[ROW][C]156[/C][C] 9[/C][C] 7.77[/C][C] 1.23[/C][/ROW]
[ROW][C]157[/C][C] 6[/C][C] 6.343[/C][C]-0.3428[/C][/ROW]
[ROW][C]158[/C][C] 5[/C][C] 6.242[/C][C]-1.242[/C][/ROW]
[ROW][C]159[/C][C] 7[/C][C] 7.088[/C][C]-0.08778[/C][/ROW]
[ROW][C]160[/C][C] 8[/C][C] 6.999[/C][C] 1.001[/C][/ROW]
[ROW][C]161[/C][C] 4[/C][C] 6.418[/C][C]-2.418[/C][/ROW]
[ROW][C]162[/C][C] 9[/C][C] 7.466[/C][C] 1.534[/C][/ROW]
[ROW][C]163[/C][C] 9[/C][C] 9.391[/C][C]-0.3911[/C][/ROW]
[ROW][C]164[/C][C] 9[/C][C] 7.577[/C][C] 1.423[/C][/ROW]
[ROW][C]165[/C][C] 7[/C][C] 6.464[/C][C] 0.5363[/C][/ROW]
[ROW][C]166[/C][C] 5[/C][C] 6.677[/C][C]-1.677[/C][/ROW]
[ROW][C]167[/C][C] 7[/C][C] 8.018[/C][C]-1.018[/C][/ROW]
[ROW][C]168[/C][C] 9[/C][C] 10.08[/C][C]-1.084[/C][/ROW]
[ROW][C]169[/C][C] 8[/C][C] 7.101[/C][C] 0.8987[/C][/ROW]
[ROW][C]170[/C][C] 6[/C][C] 5.639[/C][C] 0.3608[/C][/ROW]
[ROW][C]171[/C][C] 9[/C][C] 8.32[/C][C] 0.6799[/C][/ROW]
[ROW][C]172[/C][C] 8[/C][C] 7.553[/C][C] 0.4466[/C][/ROW]
[ROW][C]173[/C][C] 7[/C][C] 8.269[/C][C]-1.269[/C][/ROW]
[ROW][C]174[/C][C] 7[/C][C] 7.131[/C][C]-0.1306[/C][/ROW]
[ROW][C]175[/C][C] 7[/C][C] 7.773[/C][C]-0.7726[/C][/ROW]
[ROW][C]176[/C][C] 8[/C][C] 7.089[/C][C] 0.9108[/C][/ROW]
[ROW][C]177[/C][C] 10[/C][C] 8.386[/C][C] 1.614[/C][/ROW]
[ROW][C]178[/C][C] 6[/C][C] 8.364[/C][C]-2.364[/C][/ROW]
[ROW][C]179[/C][C] 6[/C][C] 7.454[/C][C]-1.454[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&T=5

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 10 6.884 3.116
2 8 7.444 0.5563
3 8 7.207 0.7928
4 9 8.246 0.7535
5 5 6.063-1.063
6 10 9.445 0.5549
7 8 8.472-0.4718
8 9 8.276 0.7242
9 8 5.651 2.349
10 7 8.441-1.441
11 10 8.3 1.7
12 10 7.328 2.672
13 9 7.316 1.684
14 4 6.019-2.019
15 4 7.662-3.662
16 8 7.986 0.01408
17 9 9.878-0.8785
18 10 6.464 3.536
19 8 7.393 0.6074
20 5 6.309-1.309
21 10 8.636 1.364
22 8 7.425 0.5751
23 7 7.12-0.1201
24 8 7.986 0.01408
25 8 9.955-1.955
26 9 7.173 1.827
27 8 7.693 0.3069
28 6 6.449-0.4487
29 8 6.784 1.216
30 8 6.32 1.68
31 5 7.987-2.987
32 9 8.395 0.6048
33 8 7.586 0.4143
34 8 6.892 1.108
35 8 7.379 0.6209
36 6 6.916-0.9159
37 6 6.232-0.2325
38 9 7.802 1.198
39 8 7.998 0.002045
40 9 9.564-0.5645
41 10 7.909 2.091
42 8 7.845 0.1552
43 8 5.595 2.405
44 7 7.146-0.1456
45 7 6.678 0.3215
46 10 7.858 2.142
47 8 6.145 1.855
48 7 6.007 0.9929
49 10 6.678 3.322
50 7 7.845-0.8448
51 7 5.434 1.566
52 9 8.907 0.09333
53 9 10.02-1.02
54 8 6.645 1.355
55 6 7.402-1.402
56 8 7.456 0.5443
57 9 7.011 1.989
58 2 4.978-2.978
59 6 6.568-0.5682
60 8 7.834 0.1658
61 8 9.079-1.079
62 7 6.783 0.2171
63 8 6.461 1.539
64 6 6.556-0.5561
65 10 6.633 3.367
66 10 7.531 2.469
67 10 7.088 2.912
68 8 6.721 1.279
69 8 7.273 0.7268
70 7 7.683-0.6825
71 10 9.869 0.1307
72 5 6.558-1.558
73 3 3.207-0.2068
74 2 4.125-2.125
75 3 4.957-1.957
76 4 6.404-2.404
77 2 5.002-3.002
78 6 5.131 0.8693
79 8 7.866 0.1335
80 8 7.759 0.2409
81 5 6.407-1.407
82 10 8.579 1.421
83 9 9.065-0.0651
84 8 9.217-1.217
85 9 8.397 0.6033
86 8 7.402 0.5982
87 5 5.897-0.8968
88 7 7.316-0.3161
89 9 8.883 0.1174
90 8 7.759 0.2409
91 4 7.089-3.089
92 7 5.305 1.695
93 8 8.645-0.6447
94 7 6.612 0.3875
95 7 6.331 0.6692
96 9 7.846 1.154
97 6 5.783 0.2168
98 7 7.478-0.4783
99 4 6.311-2.311
100 6 6.646-0.6462
101 10 7.879 2.121
102 9 7.273 1.727
103 10 9.445 0.5549
104 8 7.684 0.316
105 4 6.936-2.936
106 8 9.079-1.079
107 5 7.577-2.577
108 8 7.996 0.003505
109 9 8.257 0.743
110 8 7.282 0.7177
111 4 8.777-4.777
112 8 6.967 1.033
113 10 8.722 1.278
114 6 5.379 0.6213
115 7 6.157 0.8426
116 10 7.846 2.154
117 9 8.559 0.441
118 8 8.43-0.4304
119 3 6.21-3.21
120 8 6.72 1.28
121 7 7.347-0.3469
122 7 6.256 0.7444
123 8 5.801 2.199
124 8 7.877 0.1229
125 7 7.793-0.7928
126 7 6.818 0.1819
127 9 10.02-1.02
128 9 8.915 0.08514
129 9 7.357 1.643
130 4 7.39-3.39
131 6 7.197-1.197
132 6 6.311-0.3105
133 6 5.293 0.707
134 8 7.391 0.6088
135 3 6.449-3.449
136 8 7.12 0.88
137 8 8.539-0.5387
138 6 5.445 0.5553
139 10 8.668 1.332
140 2 5.436-3.436
141 9 8.148 0.8518
142 6 7.098-1.098
143 6 8.927-2.927
144 5 4.978 0.02246
145 4 5.216-1.216
146 7 6.116 0.884
147 5 6.893-1.893
148 8 7.727 0.2732
149 6 7.955-1.955
150 9 6.709 2.291
151 6 6.57-0.5696
152 4 5.445-1.445
153 7 7.65-0.6503
154 2 4.441-2.441
155 8 8.225-0.2248
156 9 7.77 1.23
157 6 6.343-0.3428
158 5 6.242-1.242
159 7 7.088-0.08778
160 8 6.999 1.001
161 4 6.418-2.418
162 9 7.466 1.534
163 9 9.391-0.3911
164 9 7.577 1.423
165 7 6.464 0.5363
166 5 6.677-1.677
167 7 8.018-1.018
168 9 10.08-1.084
169 8 7.101 0.8987
170 6 5.639 0.3608
171 9 8.32 0.6799
172 8 7.553 0.4466
173 7 8.269-1.269
174 7 7.131-0.1306
175 7 7.773-0.7726
176 8 7.089 0.9108
177 10 8.386 1.614
178 6 8.364-2.364
179 6 7.454-1.454







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
7 0.7195 0.561 0.2805
8 0.5766 0.8468 0.4234
9 0.5462 0.9077 0.4538
10 0.5939 0.8122 0.4061
11 0.5434 0.9132 0.4566
12 0.54 0.92 0.46
13 0.4566 0.9133 0.5434
14 0.7585 0.483 0.2415
15 0.9561 0.08785 0.04392
16 0.9346 0.1307 0.06535
17 0.9093 0.1814 0.09071
18 0.9511 0.09786 0.04893
19 0.9303 0.1393 0.06965
20 0.9225 0.1549 0.07747
21 0.9066 0.1869 0.09344
22 0.8771 0.2459 0.1229
23 0.8398 0.3204 0.1602
24 0.7972 0.4056 0.2028
25 0.8118 0.3765 0.1882
26 0.8319 0.3362 0.1681
27 0.7902 0.4195 0.2098
28 0.7542 0.4915 0.2458
29 0.7204 0.5592 0.2796
30 0.7057 0.5887 0.2943
31 0.838 0.324 0.162
32 0.8054 0.3891 0.1946
33 0.7644 0.4711 0.2356
34 0.7265 0.547 0.2735
35 0.68 0.6399 0.32
36 0.6653 0.6695 0.3347
37 0.6301 0.7398 0.3699
38 0.6003 0.7994 0.3997
39 0.5475 0.905 0.4525
40 0.4962 0.9925 0.5038
41 0.5213 0.9573 0.4787
42 0.469 0.938 0.531
43 0.4787 0.9575 0.5213
44 0.4295 0.859 0.5705
45 0.3823 0.7647 0.6177
46 0.4278 0.8556 0.5722
47 0.4095 0.819 0.5905
48 0.3689 0.7378 0.6311
49 0.4886 0.9772 0.5114
50 0.4629 0.9257 0.5371
51 0.4356 0.8713 0.5644
52 0.3902 0.7804 0.6098
53 0.3529 0.7058 0.6471
54 0.3263 0.6527 0.6737
55 0.3369 0.6737 0.6631
56 0.2973 0.5945 0.7027
57 0.3027 0.6054 0.6973
58 0.5662 0.8676 0.4338
59 0.5376 0.9248 0.4624
60 0.4922 0.9844 0.5078
61 0.4662 0.9324 0.5338
62 0.4219 0.8438 0.5781
63 0.4087 0.8173 0.5913
64 0.3791 0.7583 0.6209
65 0.5217 0.9566 0.4783
66 0.5805 0.839 0.4195
67 0.6735 0.6531 0.3265
68 0.6541 0.6918 0.3459
69 0.6202 0.7596 0.3798
70 0.5929 0.8143 0.4071
71 0.5511 0.8979 0.4489
72 0.5794 0.8412 0.4206
73 0.573 0.854 0.427
74 0.6555 0.6889 0.3445
75 0.6942 0.6116 0.3058
76 0.7515 0.4971 0.2485
77 0.8409 0.3183 0.1591
78 0.8222 0.3556 0.1778
79 0.7939 0.4122 0.2061
80 0.7636 0.4729 0.2364
81 0.7626 0.4748 0.2374
82 0.7567 0.4866 0.2433
83 0.7223 0.5555 0.2777
84 0.7084 0.5831 0.2916
85 0.6767 0.6466 0.3233
86 0.6434 0.7133 0.3566
87 0.6145 0.7709 0.3855
88 0.5764 0.8473 0.4236
89 0.5347 0.9306 0.4653
90 0.4944 0.9889 0.5056
91 0.625 0.75 0.375
92 0.6341 0.7317 0.3659
93 0.5987 0.8026 0.4013
94 0.56 0.8799 0.44
95 0.5273 0.9454 0.4727
96 0.5097 0.9805 0.4903
97 0.4737 0.9474 0.5263
98 0.4347 0.8694 0.5653
99 0.4835 0.9671 0.5165
100 0.4478 0.8955 0.5522
101 0.4907 0.9813 0.5093
102 0.5104 0.9792 0.4896
103 0.4742 0.9485 0.5258
104 0.4379 0.8758 0.5621
105 0.5456 0.9088 0.4544
106 0.521 0.9581 0.479
107 0.5867 0.8266 0.4133
108 0.5432 0.9136 0.4568
109 0.5129 0.9743 0.4871
110 0.4799 0.9598 0.5201
111 0.8044 0.3912 0.1956
112 0.791 0.4181 0.209
113 0.7869 0.4263 0.2131
114 0.7607 0.4787 0.2393
115 0.741 0.518 0.259
116 0.7843 0.4313 0.2157
117 0.7547 0.4906 0.2453
118 0.7189 0.5621 0.2811
119 0.8239 0.3522 0.1761
120 0.8229 0.3542 0.1771
121 0.7915 0.417 0.2085
122 0.7697 0.4605 0.2303
123 0.8249 0.3502 0.1751
124 0.7942 0.4116 0.2058
125 0.7642 0.4716 0.2358
126 0.7305 0.5389 0.2695
127 0.706 0.588 0.294
128 0.6636 0.6728 0.3364
129 0.6895 0.6209 0.3105
130 0.8169 0.3662 0.1831
131 0.7962 0.4076 0.2038
132 0.7598 0.4804 0.2402
133 0.7456 0.5087 0.2544
134 0.7177 0.5647 0.2823
135 0.8469 0.3062 0.1531
136 0.8327 0.3345 0.1673
137 0.8004 0.3991 0.1996
138 0.7818 0.4364 0.2182
139 0.7811 0.4377 0.2189
140 0.8834 0.2331 0.1166
141 0.8754 0.2493 0.1246
142 0.8506 0.2987 0.1494
143 0.9197 0.1606 0.08029
144 0.8983 0.2034 0.1017
145 0.8773 0.2454 0.1227
146 0.8688 0.2624 0.1312
147 0.8798 0.2405 0.1202
148 0.8491 0.3018 0.1509
149 0.8706 0.2588 0.1294
150 0.9304 0.1392 0.0696
151 0.9061 0.1878 0.09389
152 0.8872 0.2256 0.1128
153 0.8551 0.2899 0.1449
154 0.8981 0.2038 0.1019
155 0.8627 0.2745 0.1373
156 0.8644 0.2713 0.1356
157 0.8218 0.3564 0.1782
158 0.8112 0.3776 0.1888
159 0.7541 0.4918 0.2459
160 0.7298 0.5403 0.2702
161 0.8397 0.3206 0.1603
162 0.8694 0.2613 0.1306
163 0.8536 0.2927 0.1464
164 0.8356 0.3287 0.1644
165 0.7678 0.4644 0.2322
166 0.8112 0.3775 0.1888
167 0.778 0.4439 0.222
168 0.6855 0.6289 0.3145
169 0.6057 0.7886 0.3943
170 0.4746 0.9492 0.5254
171 0.4236 0.8471 0.5764
172 0.4825 0.9649 0.5175

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.7195 &  0.561 &  0.2805 \tabularnewline
8 &  0.5766 &  0.8468 &  0.4234 \tabularnewline
9 &  0.5462 &  0.9077 &  0.4538 \tabularnewline
10 &  0.5939 &  0.8122 &  0.4061 \tabularnewline
11 &  0.5434 &  0.9132 &  0.4566 \tabularnewline
12 &  0.54 &  0.92 &  0.46 \tabularnewline
13 &  0.4566 &  0.9133 &  0.5434 \tabularnewline
14 &  0.7585 &  0.483 &  0.2415 \tabularnewline
15 &  0.9561 &  0.08785 &  0.04392 \tabularnewline
16 &  0.9346 &  0.1307 &  0.06535 \tabularnewline
17 &  0.9093 &  0.1814 &  0.09071 \tabularnewline
18 &  0.9511 &  0.09786 &  0.04893 \tabularnewline
19 &  0.9303 &  0.1393 &  0.06965 \tabularnewline
20 &  0.9225 &  0.1549 &  0.07747 \tabularnewline
21 &  0.9066 &  0.1869 &  0.09344 \tabularnewline
22 &  0.8771 &  0.2459 &  0.1229 \tabularnewline
23 &  0.8398 &  0.3204 &  0.1602 \tabularnewline
24 &  0.7972 &  0.4056 &  0.2028 \tabularnewline
25 &  0.8118 &  0.3765 &  0.1882 \tabularnewline
26 &  0.8319 &  0.3362 &  0.1681 \tabularnewline
27 &  0.7902 &  0.4195 &  0.2098 \tabularnewline
28 &  0.7542 &  0.4915 &  0.2458 \tabularnewline
29 &  0.7204 &  0.5592 &  0.2796 \tabularnewline
30 &  0.7057 &  0.5887 &  0.2943 \tabularnewline
31 &  0.838 &  0.324 &  0.162 \tabularnewline
32 &  0.8054 &  0.3891 &  0.1946 \tabularnewline
33 &  0.7644 &  0.4711 &  0.2356 \tabularnewline
34 &  0.7265 &  0.547 &  0.2735 \tabularnewline
35 &  0.68 &  0.6399 &  0.32 \tabularnewline
36 &  0.6653 &  0.6695 &  0.3347 \tabularnewline
37 &  0.6301 &  0.7398 &  0.3699 \tabularnewline
38 &  0.6003 &  0.7994 &  0.3997 \tabularnewline
39 &  0.5475 &  0.905 &  0.4525 \tabularnewline
40 &  0.4962 &  0.9925 &  0.5038 \tabularnewline
41 &  0.5213 &  0.9573 &  0.4787 \tabularnewline
42 &  0.469 &  0.938 &  0.531 \tabularnewline
43 &  0.4787 &  0.9575 &  0.5213 \tabularnewline
44 &  0.4295 &  0.859 &  0.5705 \tabularnewline
45 &  0.3823 &  0.7647 &  0.6177 \tabularnewline
46 &  0.4278 &  0.8556 &  0.5722 \tabularnewline
47 &  0.4095 &  0.819 &  0.5905 \tabularnewline
48 &  0.3689 &  0.7378 &  0.6311 \tabularnewline
49 &  0.4886 &  0.9772 &  0.5114 \tabularnewline
50 &  0.4629 &  0.9257 &  0.5371 \tabularnewline
51 &  0.4356 &  0.8713 &  0.5644 \tabularnewline
52 &  0.3902 &  0.7804 &  0.6098 \tabularnewline
53 &  0.3529 &  0.7058 &  0.6471 \tabularnewline
54 &  0.3263 &  0.6527 &  0.6737 \tabularnewline
55 &  0.3369 &  0.6737 &  0.6631 \tabularnewline
56 &  0.2973 &  0.5945 &  0.7027 \tabularnewline
57 &  0.3027 &  0.6054 &  0.6973 \tabularnewline
58 &  0.5662 &  0.8676 &  0.4338 \tabularnewline
59 &  0.5376 &  0.9248 &  0.4624 \tabularnewline
60 &  0.4922 &  0.9844 &  0.5078 \tabularnewline
61 &  0.4662 &  0.9324 &  0.5338 \tabularnewline
62 &  0.4219 &  0.8438 &  0.5781 \tabularnewline
63 &  0.4087 &  0.8173 &  0.5913 \tabularnewline
64 &  0.3791 &  0.7583 &  0.6209 \tabularnewline
65 &  0.5217 &  0.9566 &  0.4783 \tabularnewline
66 &  0.5805 &  0.839 &  0.4195 \tabularnewline
67 &  0.6735 &  0.6531 &  0.3265 \tabularnewline
68 &  0.6541 &  0.6918 &  0.3459 \tabularnewline
69 &  0.6202 &  0.7596 &  0.3798 \tabularnewline
70 &  0.5929 &  0.8143 &  0.4071 \tabularnewline
71 &  0.5511 &  0.8979 &  0.4489 \tabularnewline
72 &  0.5794 &  0.8412 &  0.4206 \tabularnewline
73 &  0.573 &  0.854 &  0.427 \tabularnewline
74 &  0.6555 &  0.6889 &  0.3445 \tabularnewline
75 &  0.6942 &  0.6116 &  0.3058 \tabularnewline
76 &  0.7515 &  0.4971 &  0.2485 \tabularnewline
77 &  0.8409 &  0.3183 &  0.1591 \tabularnewline
78 &  0.8222 &  0.3556 &  0.1778 \tabularnewline
79 &  0.7939 &  0.4122 &  0.2061 \tabularnewline
80 &  0.7636 &  0.4729 &  0.2364 \tabularnewline
81 &  0.7626 &  0.4748 &  0.2374 \tabularnewline
82 &  0.7567 &  0.4866 &  0.2433 \tabularnewline
83 &  0.7223 &  0.5555 &  0.2777 \tabularnewline
84 &  0.7084 &  0.5831 &  0.2916 \tabularnewline
85 &  0.6767 &  0.6466 &  0.3233 \tabularnewline
86 &  0.6434 &  0.7133 &  0.3566 \tabularnewline
87 &  0.6145 &  0.7709 &  0.3855 \tabularnewline
88 &  0.5764 &  0.8473 &  0.4236 \tabularnewline
89 &  0.5347 &  0.9306 &  0.4653 \tabularnewline
90 &  0.4944 &  0.9889 &  0.5056 \tabularnewline
91 &  0.625 &  0.75 &  0.375 \tabularnewline
92 &  0.6341 &  0.7317 &  0.3659 \tabularnewline
93 &  0.5987 &  0.8026 &  0.4013 \tabularnewline
94 &  0.56 &  0.8799 &  0.44 \tabularnewline
95 &  0.5273 &  0.9454 &  0.4727 \tabularnewline
96 &  0.5097 &  0.9805 &  0.4903 \tabularnewline
97 &  0.4737 &  0.9474 &  0.5263 \tabularnewline
98 &  0.4347 &  0.8694 &  0.5653 \tabularnewline
99 &  0.4835 &  0.9671 &  0.5165 \tabularnewline
100 &  0.4478 &  0.8955 &  0.5522 \tabularnewline
101 &  0.4907 &  0.9813 &  0.5093 \tabularnewline
102 &  0.5104 &  0.9792 &  0.4896 \tabularnewline
103 &  0.4742 &  0.9485 &  0.5258 \tabularnewline
104 &  0.4379 &  0.8758 &  0.5621 \tabularnewline
105 &  0.5456 &  0.9088 &  0.4544 \tabularnewline
106 &  0.521 &  0.9581 &  0.479 \tabularnewline
107 &  0.5867 &  0.8266 &  0.4133 \tabularnewline
108 &  0.5432 &  0.9136 &  0.4568 \tabularnewline
109 &  0.5129 &  0.9743 &  0.4871 \tabularnewline
110 &  0.4799 &  0.9598 &  0.5201 \tabularnewline
111 &  0.8044 &  0.3912 &  0.1956 \tabularnewline
112 &  0.791 &  0.4181 &  0.209 \tabularnewline
113 &  0.7869 &  0.4263 &  0.2131 \tabularnewline
114 &  0.7607 &  0.4787 &  0.2393 \tabularnewline
115 &  0.741 &  0.518 &  0.259 \tabularnewline
116 &  0.7843 &  0.4313 &  0.2157 \tabularnewline
117 &  0.7547 &  0.4906 &  0.2453 \tabularnewline
118 &  0.7189 &  0.5621 &  0.2811 \tabularnewline
119 &  0.8239 &  0.3522 &  0.1761 \tabularnewline
120 &  0.8229 &  0.3542 &  0.1771 \tabularnewline
121 &  0.7915 &  0.417 &  0.2085 \tabularnewline
122 &  0.7697 &  0.4605 &  0.2303 \tabularnewline
123 &  0.8249 &  0.3502 &  0.1751 \tabularnewline
124 &  0.7942 &  0.4116 &  0.2058 \tabularnewline
125 &  0.7642 &  0.4716 &  0.2358 \tabularnewline
126 &  0.7305 &  0.5389 &  0.2695 \tabularnewline
127 &  0.706 &  0.588 &  0.294 \tabularnewline
128 &  0.6636 &  0.6728 &  0.3364 \tabularnewline
129 &  0.6895 &  0.6209 &  0.3105 \tabularnewline
130 &  0.8169 &  0.3662 &  0.1831 \tabularnewline
131 &  0.7962 &  0.4076 &  0.2038 \tabularnewline
132 &  0.7598 &  0.4804 &  0.2402 \tabularnewline
133 &  0.7456 &  0.5087 &  0.2544 \tabularnewline
134 &  0.7177 &  0.5647 &  0.2823 \tabularnewline
135 &  0.8469 &  0.3062 &  0.1531 \tabularnewline
136 &  0.8327 &  0.3345 &  0.1673 \tabularnewline
137 &  0.8004 &  0.3991 &  0.1996 \tabularnewline
138 &  0.7818 &  0.4364 &  0.2182 \tabularnewline
139 &  0.7811 &  0.4377 &  0.2189 \tabularnewline
140 &  0.8834 &  0.2331 &  0.1166 \tabularnewline
141 &  0.8754 &  0.2493 &  0.1246 \tabularnewline
142 &  0.8506 &  0.2987 &  0.1494 \tabularnewline
143 &  0.9197 &  0.1606 &  0.08029 \tabularnewline
144 &  0.8983 &  0.2034 &  0.1017 \tabularnewline
145 &  0.8773 &  0.2454 &  0.1227 \tabularnewline
146 &  0.8688 &  0.2624 &  0.1312 \tabularnewline
147 &  0.8798 &  0.2405 &  0.1202 \tabularnewline
148 &  0.8491 &  0.3018 &  0.1509 \tabularnewline
149 &  0.8706 &  0.2588 &  0.1294 \tabularnewline
150 &  0.9304 &  0.1392 &  0.0696 \tabularnewline
151 &  0.9061 &  0.1878 &  0.09389 \tabularnewline
152 &  0.8872 &  0.2256 &  0.1128 \tabularnewline
153 &  0.8551 &  0.2899 &  0.1449 \tabularnewline
154 &  0.8981 &  0.2038 &  0.1019 \tabularnewline
155 &  0.8627 &  0.2745 &  0.1373 \tabularnewline
156 &  0.8644 &  0.2713 &  0.1356 \tabularnewline
157 &  0.8218 &  0.3564 &  0.1782 \tabularnewline
158 &  0.8112 &  0.3776 &  0.1888 \tabularnewline
159 &  0.7541 &  0.4918 &  0.2459 \tabularnewline
160 &  0.7298 &  0.5403 &  0.2702 \tabularnewline
161 &  0.8397 &  0.3206 &  0.1603 \tabularnewline
162 &  0.8694 &  0.2613 &  0.1306 \tabularnewline
163 &  0.8536 &  0.2927 &  0.1464 \tabularnewline
164 &  0.8356 &  0.3287 &  0.1644 \tabularnewline
165 &  0.7678 &  0.4644 &  0.2322 \tabularnewline
166 &  0.8112 &  0.3775 &  0.1888 \tabularnewline
167 &  0.778 &  0.4439 &  0.222 \tabularnewline
168 &  0.6855 &  0.6289 &  0.3145 \tabularnewline
169 &  0.6057 &  0.7886 &  0.3943 \tabularnewline
170 &  0.4746 &  0.9492 &  0.5254 \tabularnewline
171 &  0.4236 &  0.8471 &  0.5764 \tabularnewline
172 &  0.4825 &  0.9649 &  0.5175 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C] 0.7195[/C][C] 0.561[/C][C] 0.2805[/C][/ROW]
[ROW][C]8[/C][C] 0.5766[/C][C] 0.8468[/C][C] 0.4234[/C][/ROW]
[ROW][C]9[/C][C] 0.5462[/C][C] 0.9077[/C][C] 0.4538[/C][/ROW]
[ROW][C]10[/C][C] 0.5939[/C][C] 0.8122[/C][C] 0.4061[/C][/ROW]
[ROW][C]11[/C][C] 0.5434[/C][C] 0.9132[/C][C] 0.4566[/C][/ROW]
[ROW][C]12[/C][C] 0.54[/C][C] 0.92[/C][C] 0.46[/C][/ROW]
[ROW][C]13[/C][C] 0.4566[/C][C] 0.9133[/C][C] 0.5434[/C][/ROW]
[ROW][C]14[/C][C] 0.7585[/C][C] 0.483[/C][C] 0.2415[/C][/ROW]
[ROW][C]15[/C][C] 0.9561[/C][C] 0.08785[/C][C] 0.04392[/C][/ROW]
[ROW][C]16[/C][C] 0.9346[/C][C] 0.1307[/C][C] 0.06535[/C][/ROW]
[ROW][C]17[/C][C] 0.9093[/C][C] 0.1814[/C][C] 0.09071[/C][/ROW]
[ROW][C]18[/C][C] 0.9511[/C][C] 0.09786[/C][C] 0.04893[/C][/ROW]
[ROW][C]19[/C][C] 0.9303[/C][C] 0.1393[/C][C] 0.06965[/C][/ROW]
[ROW][C]20[/C][C] 0.9225[/C][C] 0.1549[/C][C] 0.07747[/C][/ROW]
[ROW][C]21[/C][C] 0.9066[/C][C] 0.1869[/C][C] 0.09344[/C][/ROW]
[ROW][C]22[/C][C] 0.8771[/C][C] 0.2459[/C][C] 0.1229[/C][/ROW]
[ROW][C]23[/C][C] 0.8398[/C][C] 0.3204[/C][C] 0.1602[/C][/ROW]
[ROW][C]24[/C][C] 0.7972[/C][C] 0.4056[/C][C] 0.2028[/C][/ROW]
[ROW][C]25[/C][C] 0.8118[/C][C] 0.3765[/C][C] 0.1882[/C][/ROW]
[ROW][C]26[/C][C] 0.8319[/C][C] 0.3362[/C][C] 0.1681[/C][/ROW]
[ROW][C]27[/C][C] 0.7902[/C][C] 0.4195[/C][C] 0.2098[/C][/ROW]
[ROW][C]28[/C][C] 0.7542[/C][C] 0.4915[/C][C] 0.2458[/C][/ROW]
[ROW][C]29[/C][C] 0.7204[/C][C] 0.5592[/C][C] 0.2796[/C][/ROW]
[ROW][C]30[/C][C] 0.7057[/C][C] 0.5887[/C][C] 0.2943[/C][/ROW]
[ROW][C]31[/C][C] 0.838[/C][C] 0.324[/C][C] 0.162[/C][/ROW]
[ROW][C]32[/C][C] 0.8054[/C][C] 0.3891[/C][C] 0.1946[/C][/ROW]
[ROW][C]33[/C][C] 0.7644[/C][C] 0.4711[/C][C] 0.2356[/C][/ROW]
[ROW][C]34[/C][C] 0.7265[/C][C] 0.547[/C][C] 0.2735[/C][/ROW]
[ROW][C]35[/C][C] 0.68[/C][C] 0.6399[/C][C] 0.32[/C][/ROW]
[ROW][C]36[/C][C] 0.6653[/C][C] 0.6695[/C][C] 0.3347[/C][/ROW]
[ROW][C]37[/C][C] 0.6301[/C][C] 0.7398[/C][C] 0.3699[/C][/ROW]
[ROW][C]38[/C][C] 0.6003[/C][C] 0.7994[/C][C] 0.3997[/C][/ROW]
[ROW][C]39[/C][C] 0.5475[/C][C] 0.905[/C][C] 0.4525[/C][/ROW]
[ROW][C]40[/C][C] 0.4962[/C][C] 0.9925[/C][C] 0.5038[/C][/ROW]
[ROW][C]41[/C][C] 0.5213[/C][C] 0.9573[/C][C] 0.4787[/C][/ROW]
[ROW][C]42[/C][C] 0.469[/C][C] 0.938[/C][C] 0.531[/C][/ROW]
[ROW][C]43[/C][C] 0.4787[/C][C] 0.9575[/C][C] 0.5213[/C][/ROW]
[ROW][C]44[/C][C] 0.4295[/C][C] 0.859[/C][C] 0.5705[/C][/ROW]
[ROW][C]45[/C][C] 0.3823[/C][C] 0.7647[/C][C] 0.6177[/C][/ROW]
[ROW][C]46[/C][C] 0.4278[/C][C] 0.8556[/C][C] 0.5722[/C][/ROW]
[ROW][C]47[/C][C] 0.4095[/C][C] 0.819[/C][C] 0.5905[/C][/ROW]
[ROW][C]48[/C][C] 0.3689[/C][C] 0.7378[/C][C] 0.6311[/C][/ROW]
[ROW][C]49[/C][C] 0.4886[/C][C] 0.9772[/C][C] 0.5114[/C][/ROW]
[ROW][C]50[/C][C] 0.4629[/C][C] 0.9257[/C][C] 0.5371[/C][/ROW]
[ROW][C]51[/C][C] 0.4356[/C][C] 0.8713[/C][C] 0.5644[/C][/ROW]
[ROW][C]52[/C][C] 0.3902[/C][C] 0.7804[/C][C] 0.6098[/C][/ROW]
[ROW][C]53[/C][C] 0.3529[/C][C] 0.7058[/C][C] 0.6471[/C][/ROW]
[ROW][C]54[/C][C] 0.3263[/C][C] 0.6527[/C][C] 0.6737[/C][/ROW]
[ROW][C]55[/C][C] 0.3369[/C][C] 0.6737[/C][C] 0.6631[/C][/ROW]
[ROW][C]56[/C][C] 0.2973[/C][C] 0.5945[/C][C] 0.7027[/C][/ROW]
[ROW][C]57[/C][C] 0.3027[/C][C] 0.6054[/C][C] 0.6973[/C][/ROW]
[ROW][C]58[/C][C] 0.5662[/C][C] 0.8676[/C][C] 0.4338[/C][/ROW]
[ROW][C]59[/C][C] 0.5376[/C][C] 0.9248[/C][C] 0.4624[/C][/ROW]
[ROW][C]60[/C][C] 0.4922[/C][C] 0.9844[/C][C] 0.5078[/C][/ROW]
[ROW][C]61[/C][C] 0.4662[/C][C] 0.9324[/C][C] 0.5338[/C][/ROW]
[ROW][C]62[/C][C] 0.4219[/C][C] 0.8438[/C][C] 0.5781[/C][/ROW]
[ROW][C]63[/C][C] 0.4087[/C][C] 0.8173[/C][C] 0.5913[/C][/ROW]
[ROW][C]64[/C][C] 0.3791[/C][C] 0.7583[/C][C] 0.6209[/C][/ROW]
[ROW][C]65[/C][C] 0.5217[/C][C] 0.9566[/C][C] 0.4783[/C][/ROW]
[ROW][C]66[/C][C] 0.5805[/C][C] 0.839[/C][C] 0.4195[/C][/ROW]
[ROW][C]67[/C][C] 0.6735[/C][C] 0.6531[/C][C] 0.3265[/C][/ROW]
[ROW][C]68[/C][C] 0.6541[/C][C] 0.6918[/C][C] 0.3459[/C][/ROW]
[ROW][C]69[/C][C] 0.6202[/C][C] 0.7596[/C][C] 0.3798[/C][/ROW]
[ROW][C]70[/C][C] 0.5929[/C][C] 0.8143[/C][C] 0.4071[/C][/ROW]
[ROW][C]71[/C][C] 0.5511[/C][C] 0.8979[/C][C] 0.4489[/C][/ROW]
[ROW][C]72[/C][C] 0.5794[/C][C] 0.8412[/C][C] 0.4206[/C][/ROW]
[ROW][C]73[/C][C] 0.573[/C][C] 0.854[/C][C] 0.427[/C][/ROW]
[ROW][C]74[/C][C] 0.6555[/C][C] 0.6889[/C][C] 0.3445[/C][/ROW]
[ROW][C]75[/C][C] 0.6942[/C][C] 0.6116[/C][C] 0.3058[/C][/ROW]
[ROW][C]76[/C][C] 0.7515[/C][C] 0.4971[/C][C] 0.2485[/C][/ROW]
[ROW][C]77[/C][C] 0.8409[/C][C] 0.3183[/C][C] 0.1591[/C][/ROW]
[ROW][C]78[/C][C] 0.8222[/C][C] 0.3556[/C][C] 0.1778[/C][/ROW]
[ROW][C]79[/C][C] 0.7939[/C][C] 0.4122[/C][C] 0.2061[/C][/ROW]
[ROW][C]80[/C][C] 0.7636[/C][C] 0.4729[/C][C] 0.2364[/C][/ROW]
[ROW][C]81[/C][C] 0.7626[/C][C] 0.4748[/C][C] 0.2374[/C][/ROW]
[ROW][C]82[/C][C] 0.7567[/C][C] 0.4866[/C][C] 0.2433[/C][/ROW]
[ROW][C]83[/C][C] 0.7223[/C][C] 0.5555[/C][C] 0.2777[/C][/ROW]
[ROW][C]84[/C][C] 0.7084[/C][C] 0.5831[/C][C] 0.2916[/C][/ROW]
[ROW][C]85[/C][C] 0.6767[/C][C] 0.6466[/C][C] 0.3233[/C][/ROW]
[ROW][C]86[/C][C] 0.6434[/C][C] 0.7133[/C][C] 0.3566[/C][/ROW]
[ROW][C]87[/C][C] 0.6145[/C][C] 0.7709[/C][C] 0.3855[/C][/ROW]
[ROW][C]88[/C][C] 0.5764[/C][C] 0.8473[/C][C] 0.4236[/C][/ROW]
[ROW][C]89[/C][C] 0.5347[/C][C] 0.9306[/C][C] 0.4653[/C][/ROW]
[ROW][C]90[/C][C] 0.4944[/C][C] 0.9889[/C][C] 0.5056[/C][/ROW]
[ROW][C]91[/C][C] 0.625[/C][C] 0.75[/C][C] 0.375[/C][/ROW]
[ROW][C]92[/C][C] 0.6341[/C][C] 0.7317[/C][C] 0.3659[/C][/ROW]
[ROW][C]93[/C][C] 0.5987[/C][C] 0.8026[/C][C] 0.4013[/C][/ROW]
[ROW][C]94[/C][C] 0.56[/C][C] 0.8799[/C][C] 0.44[/C][/ROW]
[ROW][C]95[/C][C] 0.5273[/C][C] 0.9454[/C][C] 0.4727[/C][/ROW]
[ROW][C]96[/C][C] 0.5097[/C][C] 0.9805[/C][C] 0.4903[/C][/ROW]
[ROW][C]97[/C][C] 0.4737[/C][C] 0.9474[/C][C] 0.5263[/C][/ROW]
[ROW][C]98[/C][C] 0.4347[/C][C] 0.8694[/C][C] 0.5653[/C][/ROW]
[ROW][C]99[/C][C] 0.4835[/C][C] 0.9671[/C][C] 0.5165[/C][/ROW]
[ROW][C]100[/C][C] 0.4478[/C][C] 0.8955[/C][C] 0.5522[/C][/ROW]
[ROW][C]101[/C][C] 0.4907[/C][C] 0.9813[/C][C] 0.5093[/C][/ROW]
[ROW][C]102[/C][C] 0.5104[/C][C] 0.9792[/C][C] 0.4896[/C][/ROW]
[ROW][C]103[/C][C] 0.4742[/C][C] 0.9485[/C][C] 0.5258[/C][/ROW]
[ROW][C]104[/C][C] 0.4379[/C][C] 0.8758[/C][C] 0.5621[/C][/ROW]
[ROW][C]105[/C][C] 0.5456[/C][C] 0.9088[/C][C] 0.4544[/C][/ROW]
[ROW][C]106[/C][C] 0.521[/C][C] 0.9581[/C][C] 0.479[/C][/ROW]
[ROW][C]107[/C][C] 0.5867[/C][C] 0.8266[/C][C] 0.4133[/C][/ROW]
[ROW][C]108[/C][C] 0.5432[/C][C] 0.9136[/C][C] 0.4568[/C][/ROW]
[ROW][C]109[/C][C] 0.5129[/C][C] 0.9743[/C][C] 0.4871[/C][/ROW]
[ROW][C]110[/C][C] 0.4799[/C][C] 0.9598[/C][C] 0.5201[/C][/ROW]
[ROW][C]111[/C][C] 0.8044[/C][C] 0.3912[/C][C] 0.1956[/C][/ROW]
[ROW][C]112[/C][C] 0.791[/C][C] 0.4181[/C][C] 0.209[/C][/ROW]
[ROW][C]113[/C][C] 0.7869[/C][C] 0.4263[/C][C] 0.2131[/C][/ROW]
[ROW][C]114[/C][C] 0.7607[/C][C] 0.4787[/C][C] 0.2393[/C][/ROW]
[ROW][C]115[/C][C] 0.741[/C][C] 0.518[/C][C] 0.259[/C][/ROW]
[ROW][C]116[/C][C] 0.7843[/C][C] 0.4313[/C][C] 0.2157[/C][/ROW]
[ROW][C]117[/C][C] 0.7547[/C][C] 0.4906[/C][C] 0.2453[/C][/ROW]
[ROW][C]118[/C][C] 0.7189[/C][C] 0.5621[/C][C] 0.2811[/C][/ROW]
[ROW][C]119[/C][C] 0.8239[/C][C] 0.3522[/C][C] 0.1761[/C][/ROW]
[ROW][C]120[/C][C] 0.8229[/C][C] 0.3542[/C][C] 0.1771[/C][/ROW]
[ROW][C]121[/C][C] 0.7915[/C][C] 0.417[/C][C] 0.2085[/C][/ROW]
[ROW][C]122[/C][C] 0.7697[/C][C] 0.4605[/C][C] 0.2303[/C][/ROW]
[ROW][C]123[/C][C] 0.8249[/C][C] 0.3502[/C][C] 0.1751[/C][/ROW]
[ROW][C]124[/C][C] 0.7942[/C][C] 0.4116[/C][C] 0.2058[/C][/ROW]
[ROW][C]125[/C][C] 0.7642[/C][C] 0.4716[/C][C] 0.2358[/C][/ROW]
[ROW][C]126[/C][C] 0.7305[/C][C] 0.5389[/C][C] 0.2695[/C][/ROW]
[ROW][C]127[/C][C] 0.706[/C][C] 0.588[/C][C] 0.294[/C][/ROW]
[ROW][C]128[/C][C] 0.6636[/C][C] 0.6728[/C][C] 0.3364[/C][/ROW]
[ROW][C]129[/C][C] 0.6895[/C][C] 0.6209[/C][C] 0.3105[/C][/ROW]
[ROW][C]130[/C][C] 0.8169[/C][C] 0.3662[/C][C] 0.1831[/C][/ROW]
[ROW][C]131[/C][C] 0.7962[/C][C] 0.4076[/C][C] 0.2038[/C][/ROW]
[ROW][C]132[/C][C] 0.7598[/C][C] 0.4804[/C][C] 0.2402[/C][/ROW]
[ROW][C]133[/C][C] 0.7456[/C][C] 0.5087[/C][C] 0.2544[/C][/ROW]
[ROW][C]134[/C][C] 0.7177[/C][C] 0.5647[/C][C] 0.2823[/C][/ROW]
[ROW][C]135[/C][C] 0.8469[/C][C] 0.3062[/C][C] 0.1531[/C][/ROW]
[ROW][C]136[/C][C] 0.8327[/C][C] 0.3345[/C][C] 0.1673[/C][/ROW]
[ROW][C]137[/C][C] 0.8004[/C][C] 0.3991[/C][C] 0.1996[/C][/ROW]
[ROW][C]138[/C][C] 0.7818[/C][C] 0.4364[/C][C] 0.2182[/C][/ROW]
[ROW][C]139[/C][C] 0.7811[/C][C] 0.4377[/C][C] 0.2189[/C][/ROW]
[ROW][C]140[/C][C] 0.8834[/C][C] 0.2331[/C][C] 0.1166[/C][/ROW]
[ROW][C]141[/C][C] 0.8754[/C][C] 0.2493[/C][C] 0.1246[/C][/ROW]
[ROW][C]142[/C][C] 0.8506[/C][C] 0.2987[/C][C] 0.1494[/C][/ROW]
[ROW][C]143[/C][C] 0.9197[/C][C] 0.1606[/C][C] 0.08029[/C][/ROW]
[ROW][C]144[/C][C] 0.8983[/C][C] 0.2034[/C][C] 0.1017[/C][/ROW]
[ROW][C]145[/C][C] 0.8773[/C][C] 0.2454[/C][C] 0.1227[/C][/ROW]
[ROW][C]146[/C][C] 0.8688[/C][C] 0.2624[/C][C] 0.1312[/C][/ROW]
[ROW][C]147[/C][C] 0.8798[/C][C] 0.2405[/C][C] 0.1202[/C][/ROW]
[ROW][C]148[/C][C] 0.8491[/C][C] 0.3018[/C][C] 0.1509[/C][/ROW]
[ROW][C]149[/C][C] 0.8706[/C][C] 0.2588[/C][C] 0.1294[/C][/ROW]
[ROW][C]150[/C][C] 0.9304[/C][C] 0.1392[/C][C] 0.0696[/C][/ROW]
[ROW][C]151[/C][C] 0.9061[/C][C] 0.1878[/C][C] 0.09389[/C][/ROW]
[ROW][C]152[/C][C] 0.8872[/C][C] 0.2256[/C][C] 0.1128[/C][/ROW]
[ROW][C]153[/C][C] 0.8551[/C][C] 0.2899[/C][C] 0.1449[/C][/ROW]
[ROW][C]154[/C][C] 0.8981[/C][C] 0.2038[/C][C] 0.1019[/C][/ROW]
[ROW][C]155[/C][C] 0.8627[/C][C] 0.2745[/C][C] 0.1373[/C][/ROW]
[ROW][C]156[/C][C] 0.8644[/C][C] 0.2713[/C][C] 0.1356[/C][/ROW]
[ROW][C]157[/C][C] 0.8218[/C][C] 0.3564[/C][C] 0.1782[/C][/ROW]
[ROW][C]158[/C][C] 0.8112[/C][C] 0.3776[/C][C] 0.1888[/C][/ROW]
[ROW][C]159[/C][C] 0.7541[/C][C] 0.4918[/C][C] 0.2459[/C][/ROW]
[ROW][C]160[/C][C] 0.7298[/C][C] 0.5403[/C][C] 0.2702[/C][/ROW]
[ROW][C]161[/C][C] 0.8397[/C][C] 0.3206[/C][C] 0.1603[/C][/ROW]
[ROW][C]162[/C][C] 0.8694[/C][C] 0.2613[/C][C] 0.1306[/C][/ROW]
[ROW][C]163[/C][C] 0.8536[/C][C] 0.2927[/C][C] 0.1464[/C][/ROW]
[ROW][C]164[/C][C] 0.8356[/C][C] 0.3287[/C][C] 0.1644[/C][/ROW]
[ROW][C]165[/C][C] 0.7678[/C][C] 0.4644[/C][C] 0.2322[/C][/ROW]
[ROW][C]166[/C][C] 0.8112[/C][C] 0.3775[/C][C] 0.1888[/C][/ROW]
[ROW][C]167[/C][C] 0.778[/C][C] 0.4439[/C][C] 0.222[/C][/ROW]
[ROW][C]168[/C][C] 0.6855[/C][C] 0.6289[/C][C] 0.3145[/C][/ROW]
[ROW][C]169[/C][C] 0.6057[/C][C] 0.7886[/C][C] 0.3943[/C][/ROW]
[ROW][C]170[/C][C] 0.4746[/C][C] 0.9492[/C][C] 0.5254[/C][/ROW]
[ROW][C]171[/C][C] 0.4236[/C][C] 0.8471[/C][C] 0.5764[/C][/ROW]
[ROW][C]172[/C][C] 0.4825[/C][C] 0.9649[/C][C] 0.5175[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&T=6

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
7 0.7195 0.561 0.2805
8 0.5766 0.8468 0.4234
9 0.5462 0.9077 0.4538
10 0.5939 0.8122 0.4061
11 0.5434 0.9132 0.4566
12 0.54 0.92 0.46
13 0.4566 0.9133 0.5434
14 0.7585 0.483 0.2415
15 0.9561 0.08785 0.04392
16 0.9346 0.1307 0.06535
17 0.9093 0.1814 0.09071
18 0.9511 0.09786 0.04893
19 0.9303 0.1393 0.06965
20 0.9225 0.1549 0.07747
21 0.9066 0.1869 0.09344
22 0.8771 0.2459 0.1229
23 0.8398 0.3204 0.1602
24 0.7972 0.4056 0.2028
25 0.8118 0.3765 0.1882
26 0.8319 0.3362 0.1681
27 0.7902 0.4195 0.2098
28 0.7542 0.4915 0.2458
29 0.7204 0.5592 0.2796
30 0.7057 0.5887 0.2943
31 0.838 0.324 0.162
32 0.8054 0.3891 0.1946
33 0.7644 0.4711 0.2356
34 0.7265 0.547 0.2735
35 0.68 0.6399 0.32
36 0.6653 0.6695 0.3347
37 0.6301 0.7398 0.3699
38 0.6003 0.7994 0.3997
39 0.5475 0.905 0.4525
40 0.4962 0.9925 0.5038
41 0.5213 0.9573 0.4787
42 0.469 0.938 0.531
43 0.4787 0.9575 0.5213
44 0.4295 0.859 0.5705
45 0.3823 0.7647 0.6177
46 0.4278 0.8556 0.5722
47 0.4095 0.819 0.5905
48 0.3689 0.7378 0.6311
49 0.4886 0.9772 0.5114
50 0.4629 0.9257 0.5371
51 0.4356 0.8713 0.5644
52 0.3902 0.7804 0.6098
53 0.3529 0.7058 0.6471
54 0.3263 0.6527 0.6737
55 0.3369 0.6737 0.6631
56 0.2973 0.5945 0.7027
57 0.3027 0.6054 0.6973
58 0.5662 0.8676 0.4338
59 0.5376 0.9248 0.4624
60 0.4922 0.9844 0.5078
61 0.4662 0.9324 0.5338
62 0.4219 0.8438 0.5781
63 0.4087 0.8173 0.5913
64 0.3791 0.7583 0.6209
65 0.5217 0.9566 0.4783
66 0.5805 0.839 0.4195
67 0.6735 0.6531 0.3265
68 0.6541 0.6918 0.3459
69 0.6202 0.7596 0.3798
70 0.5929 0.8143 0.4071
71 0.5511 0.8979 0.4489
72 0.5794 0.8412 0.4206
73 0.573 0.854 0.427
74 0.6555 0.6889 0.3445
75 0.6942 0.6116 0.3058
76 0.7515 0.4971 0.2485
77 0.8409 0.3183 0.1591
78 0.8222 0.3556 0.1778
79 0.7939 0.4122 0.2061
80 0.7636 0.4729 0.2364
81 0.7626 0.4748 0.2374
82 0.7567 0.4866 0.2433
83 0.7223 0.5555 0.2777
84 0.7084 0.5831 0.2916
85 0.6767 0.6466 0.3233
86 0.6434 0.7133 0.3566
87 0.6145 0.7709 0.3855
88 0.5764 0.8473 0.4236
89 0.5347 0.9306 0.4653
90 0.4944 0.9889 0.5056
91 0.625 0.75 0.375
92 0.6341 0.7317 0.3659
93 0.5987 0.8026 0.4013
94 0.56 0.8799 0.44
95 0.5273 0.9454 0.4727
96 0.5097 0.9805 0.4903
97 0.4737 0.9474 0.5263
98 0.4347 0.8694 0.5653
99 0.4835 0.9671 0.5165
100 0.4478 0.8955 0.5522
101 0.4907 0.9813 0.5093
102 0.5104 0.9792 0.4896
103 0.4742 0.9485 0.5258
104 0.4379 0.8758 0.5621
105 0.5456 0.9088 0.4544
106 0.521 0.9581 0.479
107 0.5867 0.8266 0.4133
108 0.5432 0.9136 0.4568
109 0.5129 0.9743 0.4871
110 0.4799 0.9598 0.5201
111 0.8044 0.3912 0.1956
112 0.791 0.4181 0.209
113 0.7869 0.4263 0.2131
114 0.7607 0.4787 0.2393
115 0.741 0.518 0.259
116 0.7843 0.4313 0.2157
117 0.7547 0.4906 0.2453
118 0.7189 0.5621 0.2811
119 0.8239 0.3522 0.1761
120 0.8229 0.3542 0.1771
121 0.7915 0.417 0.2085
122 0.7697 0.4605 0.2303
123 0.8249 0.3502 0.1751
124 0.7942 0.4116 0.2058
125 0.7642 0.4716 0.2358
126 0.7305 0.5389 0.2695
127 0.706 0.588 0.294
128 0.6636 0.6728 0.3364
129 0.6895 0.6209 0.3105
130 0.8169 0.3662 0.1831
131 0.7962 0.4076 0.2038
132 0.7598 0.4804 0.2402
133 0.7456 0.5087 0.2544
134 0.7177 0.5647 0.2823
135 0.8469 0.3062 0.1531
136 0.8327 0.3345 0.1673
137 0.8004 0.3991 0.1996
138 0.7818 0.4364 0.2182
139 0.7811 0.4377 0.2189
140 0.8834 0.2331 0.1166
141 0.8754 0.2493 0.1246
142 0.8506 0.2987 0.1494
143 0.9197 0.1606 0.08029
144 0.8983 0.2034 0.1017
145 0.8773 0.2454 0.1227
146 0.8688 0.2624 0.1312
147 0.8798 0.2405 0.1202
148 0.8491 0.3018 0.1509
149 0.8706 0.2588 0.1294
150 0.9304 0.1392 0.0696
151 0.9061 0.1878 0.09389
152 0.8872 0.2256 0.1128
153 0.8551 0.2899 0.1449
154 0.8981 0.2038 0.1019
155 0.8627 0.2745 0.1373
156 0.8644 0.2713 0.1356
157 0.8218 0.3564 0.1782
158 0.8112 0.3776 0.1888
159 0.7541 0.4918 0.2459
160 0.7298 0.5403 0.2702
161 0.8397 0.3206 0.1603
162 0.8694 0.2613 0.1306
163 0.8536 0.2927 0.1464
164 0.8356 0.3287 0.1644
165 0.7678 0.4644 0.2322
166 0.8112 0.3775 0.1888
167 0.778 0.4439 0.222
168 0.6855 0.6289 0.3145
169 0.6057 0.7886 0.3943
170 0.4746 0.9492 0.5254
171 0.4236 0.8471 0.5764
172 0.4825 0.9649 0.5175







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level20.0120482OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0120482 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319053&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0120482[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319053&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&T=7

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level20.0120482OK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.6851, df1 = 2, df2 = 173, p-value = 0.01043
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.3794, df1 = 6, df2 = 169, p-value = 0.03117
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.6541, df1 = 2, df2 = 173, p-value = 0.01075

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.6851, df1 = 2, df2 = 173, p-value = 0.01043
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.3794, df1 = 6, df2 = 169, p-value = 0.03117
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.6541, df1 = 2, df2 = 173, p-value = 0.01075
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319053&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.6851, df1 = 2, df2 = 173, p-value = 0.01043
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW] [ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.3794, df1 = 6, df2 = 169, p-value = 0.03117
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW] [ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.6541, df1 = 2, df2 = 173, p-value = 0.01075
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319053&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&T=8

As an alternative you can also use a QR Code:  

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.6851, df1 = 2, df2 = 173, p-value = 0.01043
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.3794, df1 = 6, df2 = 169, p-value = 0.03117
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.6541, df1 = 2, df2 = 173, p-value = 0.01075







Variance Inflation Factors (Multicollinearity)
> vif
       System_Quality   Information_Quality Perceived_Ease_of_Use 
             1.647271              2.586342              1.972540 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       System_Quality   Information_Quality Perceived_Ease_of_Use 
             1.647271              2.586342              1.972540 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319053&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
       System_Quality   Information_Quality Perceived_Ease_of_Use 
             1.647271              2.586342              1.972540 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319053&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319053&T=9

As an alternative you can also use a QR Code:  

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

Variance Inflation Factors (Multicollinearity)
> vif
       System_Quality   Information_Quality Perceived_Ease_of_Use 
             1.647271              2.586342              1.972540 



Parameters (Session):
par2 = grey ; par3 = FALSE ; par4 = 7-point Likert ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')