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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 14 Dec 2017 14:32:23 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/14/t151325966559uwwc5fm22rg0l.htm/, Retrieved Tue, 14 May 2024 05:49:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309506, Retrieved Tue, 14 May 2024 05:49:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsLBE
Estimated Impact92

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple Regression ] [2017-12-14 13:32:23] [3c189a0c4f7caff37e2cfca896353419] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
46.8	58.5	55.5
52.8	59.8	63
58.3	64.6	77.2
54.5	62.2	71.1
64.7	68	90.1
58.3	64.3	91.5
57.5	58.9	76.1
56.7	64.8	87.8
56	67.5	81
66.2	76.2	77.2
58.2	73.7	73.8
53.9	70.4	68.9
53.1	67.7	68.4
54.4	63.7	65.2
59.2	72.4	78.7
57.8	66	77
61.5	70.1	97.6
60.1	70.4	88.1
60.1	66.6	98.7
58.4	72.6	93.4
56.8	74	68
63.8	79	87.9
53.9	76.1	75.8
63.1	72.3	66.3
55.7	71.6	68.4
54.9	67.2	71.3
64.6	73.8	77.4
60.2	70.8	87.1
63.9	71.4	88.5
69.9	70.4	85.9
58.5	70.7	92.7
52	70.6	88.5
66.7	75.5	80.2
72	82.1	81.8
68.4	74.3	70.4
70.8	76.3	82.2
56.5	74.5	72.8
62.6	71.1	69
66.5	73.3	83
69.2	73.8	92.4
63.7	69	92.3
73.6	71.1	100.5
64.1	71.9	106.9
53.8	69	99.5
72.2	77.3	85.9
80.2	82.8	92.6
69.1	74	77.4
72	77.6	84.1
66.3	72.3	75.3
72.5	70.7	73.8
88.9	81	100.1
88.6	76.4	90.7
73.7	72.3	96.5
86	79.5	111.8
70	73.3	97.4
71.6	74.5	100.8
90.5	82.7	93.7
85.7	83.8	82
84.8	81.6	86
81.1	85.5	84.3
70.8	76.7	73.1
65.7	71.8	75.4
86.2	80.2	97.9
76.1	76.8	97.5
79.8	76.1	106
85.2	80.7	112.8
75.8	71.3	99.5
69.4	80.9	100.8
85	85	102.9
75	84.5	88.8
77.7	87.7	91.3
68.5	87.7	88.3
68.4	80.2	77.4
65	74.4	80.5
73.2	85.8	96.7
67.9	77	93.8
76.5	84.5	105
85.5	83.6	117.1
71.7	77.7	111.1
57.9	85.7	105.8
75.5	87.9	95.7
78.2	93.7	97.1
75.7	92.3	91
67.1	87	90.9
74.6	89.1	83.5
66.2	81.3	82.3
74.9	92.7	101.7
69.5	83.9	108.3
76.1	87.3	114
82.3	89.1	118.2
82.1	86.9	103.4
60.5	91.7	106.8
71.2	93	95.4
81.4	105.3	101.8
74.5	101.6	95.6
61.4	94.2	94.8
83.8	100.5	94
85.4	95.8	82.4
91.6	95.8	95.8
91.9	102.1	106.7
86.3	96	114.1
96.8	96.8	103.9
81	98.9	117.4
70.8	93.4	105.9
98.8	105.5	101.7
94.5	110.9	98.7
84.5	98.6	91.3
92.8	102.6	102.3
81.2	93.5	80.5
75.7	90.8	86.7
86.7	99.7	102.6
87.5	97.8	107.3
87.8	91.1	108
103.1	98.1	124.3
96.4	96	117.1
77.1	93.5	103.9
106.5	101.2	104.7
95.7	105.2	95.9
95.3	98.9	94.2
86.6	101.3	102.7
89.6	92.1	70.3
81.9	90.6	90.2
98.4	105.4	107.3
92.9	98.4	104.6
83.9	92.7	102.7
121.8	101.2	124.5
103.9	93.4	117.8
87.5	98.3	104.2
118.9	104.3	99.9
109	107	91.5
112.2	107.7	95.7
100.1	108.9	91.4
111.3	99.6	86.2
102.7	96.1	91.5
122.6	109	115.5
124.8	99.5	113.9
120.3	104.6	131.9
118.3	99.9	121.2
108.7	94.1	105.2
100.7	105.3	107.5
124	110.4	113.8
103.1	110.5	100.5
115	110	104.8
112.7	108.5	103.8
101.7	104.3	93.1
111.5	101.2	106.2
114.4	109.2	117.5
112.5	99.6	109.9
107.2	105.6	123.6
136.7	106.2	131.7
107.8	102.2	111
94.6	107.5	122
110.7	105.8	110.9
126.6	120.5	108
127.9	113.2	103.6
109.2	104.3	107.3
87.1	107.7	94.4
90.8	99.2	85.2
94.5	105.1	113.2
103.3	104.3	111.7
103.2	106.1	124.3
105.4	100.8	124
103.9	106.7	133.4
79.8	101.6	112.6
105.6	104.4	115.8
113	114.8	112.3
87.7	105.4	103.6
110	104	111.4
90.3	102	95.1
108.9	96.5	93.4
105.1	102.3	117.3
113	105.3	121.5
100.4	101.9	123.1
110.1	102.2	139.3
114.7	102.8	125.8
88.6	100.4	108.6
117.2	110.7	121
127.7	116.4	111.6
107.8	106	99.7
102.8	109.2	116.7
100.2	103	90.3
108.4	99.8	90.4
114.2	109.8	117.3
94.4	107.3	121.6
92.2	101.2	114.6
115.3	111.8	133.3
102	106.9	127.4
86.3	103.5	115
112	113.1	112.6
112.5	119.4	108.3
109.5	113.3	107.6
105.9	115	109
115.3	104.7	89
126.2	107.2	102.5
112.2	116.6	124.5
112.5	111.3	124.2
106.9	111.4	130.8
90.6	115	138.7
75.6	102.4	127.6
78.8	111.4	130.9
101.8	113.2	136.9
93.9	112.9	125.2
100	114.2	131.3
89.2	115.6	124.1
97.7	107.1	103.2
121.1	102.3	118.1
108.8	117.9	136.5
92.9	105.8	117.8
113.6	114.3	145.1
112.6	113.1	158.8
98.8	102.9	136.9
78	112.2	132.7

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time13 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 time13 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309506&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]13 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309506&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309506&T=0

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


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

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R ServerBig Analytics Cloud Computing Center






Multiple Linear Regression - Estimated Regression Equation
Tabacco[t] = -4.71021 + 0.37248Food[t] + 0.0123296beverage[t] + 0.291623`Tabacco(t-1)`[t] + 0.36416`Tabacco(t-1s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Tabacco[t] =  -4.71021 +  0.37248Food[t] +  0.0123296beverage[t] +  0.291623`Tabacco(t-1)`[t] +  0.36416`Tabacco(t-1s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309506&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Tabacco[t] =  -4.71021 +  0.37248Food[t] +  0.0123296beverage[t] +  0.291623`Tabacco(t-1)`[t] +  0.36416`Tabacco(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309506&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309506&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Tabacco[t] = -4.71021 + 0.37248Food[t] + 0.0123296beverage[t] + 0.291623`Tabacco(t-1)`[t] + 0.36416`Tabacco(t-1s)`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-4.71 4.72-9.9800e-01 0.3195 0.1598
Food+0.3725 0.09125+4.0820e+00 6.519e-05 3.259e-05
beverage+0.01233 0.05261+2.3440e-01 0.815 0.4075
`Tabacco(t-1)`+0.2916 0.05489+5.3130e+00 2.944e-07 1.472e-07
`Tabacco(t-1s)`+0.3642 0.05861+6.2140e+00 3.097e-09 1.549e-09

\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) & -4.71 &  4.72 & -9.9800e-01 &  0.3195 &  0.1598 \tabularnewline
Food & +0.3725 &  0.09125 & +4.0820e+00 &  6.519e-05 &  3.259e-05 \tabularnewline
beverage & +0.01233 &  0.05261 & +2.3440e-01 &  0.815 &  0.4075 \tabularnewline
`Tabacco(t-1)` & +0.2916 &  0.05489 & +5.3130e+00 &  2.944e-07 &  1.472e-07 \tabularnewline
`Tabacco(t-1s)` & +0.3642 &  0.05861 & +6.2140e+00 &  3.097e-09 &  1.549e-09 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309506&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]-4.71[/C][C] 4.72[/C][C]-9.9800e-01[/C][C] 0.3195[/C][C] 0.1598[/C][/ROW]
[ROW][C]Food[/C][C]+0.3725[/C][C] 0.09125[/C][C]+4.0820e+00[/C][C] 6.519e-05[/C][C] 3.259e-05[/C][/ROW]
[ROW][C]beverage[/C][C]+0.01233[/C][C] 0.05261[/C][C]+2.3440e-01[/C][C] 0.815[/C][C] 0.4075[/C][/ROW]
[ROW][C]`Tabacco(t-1)`[/C][C]+0.2916[/C][C] 0.05489[/C][C]+5.3130e+00[/C][C] 2.944e-07[/C][C] 1.472e-07[/C][/ROW]
[ROW][C]`Tabacco(t-1s)`[/C][C]+0.3642[/C][C] 0.05861[/C][C]+6.2140e+00[/C][C] 3.097e-09[/C][C] 1.549e-09[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309506&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309506&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-4.71 4.72-9.9800e-01 0.3195 0.1598
Food+0.3725 0.09125+4.0820e+00 6.519e-05 3.259e-05
beverage+0.01233 0.05261+2.3440e-01 0.815 0.4075
`Tabacco(t-1)`+0.2916 0.05489+5.3130e+00 2.944e-07 1.472e-07
`Tabacco(t-1s)`+0.3642 0.05861+6.2140e+00 3.097e-09 1.549e-09






Multiple Linear Regression - Regression Statistics
Multiple R 0.8864
R-squared 0.7858
Adjusted R-squared 0.7813
F-TEST (value) 177.9
F-TEST (DF numerator)4
F-TEST (DF denominator)194
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 9.243
Sum Squared Residuals 1.657e+04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8864 \tabularnewline
R-squared &  0.7858 \tabularnewline
Adjusted R-squared &  0.7813 \tabularnewline
F-TEST (value) &  177.9 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 194 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  9.243 \tabularnewline
Sum Squared Residuals &  1.657e+04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309506&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8864[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7858[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.7813[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 177.9[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]194[/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] 9.243[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.657e+04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309506&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309506&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R 0.8864
R-squared 0.7858
Adjusted R-squared 0.7813
F-TEST (value) 177.9
F-TEST (DF numerator)4
F-TEST (DF denominator)194
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 9.243
Sum Squared Residuals 1.657e+04






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=309506&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=309506&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309506&T=4

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


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

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






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 54.4 54.53-0.1335
2 59.2 60.32-1.122
3 57.8 57.93-0.1336
4 61.5 63.02-1.521
5 60.1 61.76-1.664
6 60.1 59.78 0.3204
7 58.4 61.66-3.258
8 56.8 61.12-4.315
9 63.8 66.47-2.671
10 53.9 64.37-10.47
11 63.1 58.38 4.716
12 55.7 60.54-4.841
13 54.9 57.25-2.353
14 64.6 61.3 3.299
15 60.2 62.62-2.423
16 63.9 62.93 0.9724
17 69.9 63.09 6.808
18 58.5 65.04-6.538
19 52 61-9.005
20 66.7 60.25 6.45
21 72 69.56 2.436
22 68.4 64.46 3.942
23 70.8 67.65 3.151
24 56.5 64.87-8.368
25 62.6 59.09 3.507
26 66.5 65.4 1.104
27 69.2 65.23 3.967
28 63.7 65.58-1.879
29 73.6 67.04 6.557
30 64.1 66.16-2.056
31 53.8 59.85-6.047
32 72.2 65.12 7.08
33 80.2 74.55 5.652
34 69.1 72.1-3.004
35 72 71.16 0.8352
36 66.3 64.72 1.58
37 72.5 64.67 7.835
38 88.9 72.05 16.85
39 88.6 75.99 12.61
40 73.7 72.44 1.255
41 86 74.58 11.42
42 70 72.22-2.216
43 71.6 64.29 7.312
44 90.5 74.42 16.08
45 85.7 83.11 2.588
46 84.8 76.9 7.9
47 81.1 79.13 1.975
48 70.8 72.55-1.755
49 65.7 70.01-4.312
50 86.2 77.9 8.297
51 76.1 82.5-6.401
52 79.8 73.97 5.826
53 85.2 81.33 3.871
54 75.8 73.41 2.388
55 69.4 74.85-5.445
56 85 81.41 3.586
57 75 83.86-8.856
58 77.7 81.83-4.134
59 68.5 81.24-12.74
60 68.4 71.88-3.476
61 65 67.87-2.867
62 73.2 78.79-5.587
63 67.9 74.19-6.287
64 76.5 76.92-0.4201
65 85.5 81.21 4.292
66 71.7 78.14-6.438
67 57.9 74.7-16.8
68 75.5 77.05-1.549
69 78.2 80.72-2.518
70 75.7 81.89-6.192
71 67.1 75.84-8.737
72 74.6 73.98 0.6163
73 66.2 72.01-5.813
74 74.9 77.03-2.135
75 69.5 74.45-4.945
76 76.1 77.34-1.239
77 82.3 83.26-0.9633
78 82.1 79.04 3.056
79 60.5 75.79-15.29
80 71.2 76.24-5.044
81 81.4 85.01-3.608
82 74.5 85.62-11.12
83 61.4 77.71-16.31
84 83.8 78.95 4.845
85 85.4 80.53 4.865
86 91.6 84.33 7.265
87 91.9 86.66 5.243
88 86.3 86.97-0.6674
89 96.8 87.76 9.036
90 81 91.7-10.7
91 70.8 77.04-6.238
92 98.8 82.42 16.38
93 94.5 96.27-1.77
94 84.5 87.83-3.33
95 92.8 81.77 11.03
96 81.2 88.69-7.488
97 75.7 84.96-9.259
98 86.7 89.12-2.424
99 87.5 91.79-4.291
100 87.8 87.5 0.3017
101 103.1 94.22 8.882
102 96.4 92.05 4.345
103 77.1 85.29-8.193
104 106.5 92.74 13.76
105 95.7 101.1-5.428
106 95.3 91.97 3.331
107 86.6 95.87-9.274
108 89.6 85.29 4.314
109 81.9 83.84-1.945
110 98.4 91.33 7.071
111 92.9 93.79-0.8912
112 83.9 90.15-6.25
113 121.8 96.53 25.27
114 103.9 102.2 1.743
115 87.5 91.57-4.066
116 118.9 99.67 19.23
117 109 105.8 3.203
118 112.2 103.1 9.123
119 100.1 101.2-1.136
120 111.3 95.27 16.03
121 102.7 94.5 8.204
122 122.6 103.1 19.5
123 124.8 103.3 21.46
124 120.3 102.8 17.48
125 118.3 113.4 4.868
126 108.7 104 4.728
127 100.7 99.4 1.299
128 124 110.5 13.52
129 103.1 113.5-10.44
130 115 108.5 6.52
131 112.7 107 5.727
132 101.7 108.7-6.984
133 111.5 101.4 10.15
134 114.4 114.6-0.1753
135 112.5 112.6-0.05264
136 107.2 112.8-5.564
137 136.7 110.8 25.89
138 107.8 114.2-6.375
139 94.6 104.9-10.34
140 110.7 108.8 1.891
141 126.6 111.3 15.27
142 127.9 117.5 10.37
143 109.2 113.8-4.602
144 87.1 105.5-18.35
145 90.8 99.29-8.494
146 94.5 104-9.472
147 103.3 104-0.743
148 103.2 105.5-2.305
149 105.4 114.2-8.841
150 103.9 106.7-2.772
151 79.8 99.27-19.47
152 105.6 99.19 6.412
153 113 116.3-3.333
154 87.7 115.4-27.66
155 110 100.7 9.257
156 90.3 98.25-7.952
157 108.9 91.78 17.11
158 105.1 101 4.088
159 113 104.3 8.723
160 100.4 105.3-4.898
161 110.1 102.7 7.364
162 114.7 105.1 9.624
163 88.6 96.53-7.935
164 117.2 102.3 14.89
165 127.7 115.4 12.35
166 107.8 105.2 2.621
167 102.8 108.9-6.098
168 100.2 97.63 2.569
169 108.4 102.5 5.944
170 114.2 107.5 6.681
171 94.4 111.2-16.81
172 92.2 98.49-6.289
173 115.3 105.6 9.742
174 102 112.1-10.07
175 86.3 97.27-10.97
176 112 106.7 5.348
177 112.5 120.3-7.764
178 109.5 110.9-1.382
179 105.9 108.8-2.937
180 115.3 102.8 12.54
181 126.2 109.6 16.62
182 112.2 118.6-6.446
183 112.5 105.4 7.125
184 106.9 104.8 2.12
185 90.6 113-22.4
186 75.6 98.57-22.97
187 78.8 91.87-13.07
188 101.8 102.9-1.108
189 93.9 109.5-15.64
190 100 106.7-6.705
191 89.2 107.6-18.41
192 97.7 104.5-6.755
193 121.1 109.3 11.8
194 108.8 117.1-8.262
195 92.9 108.8-15.95
196 113.6 105.7 7.926
197 112.6 105.5 7.104
198 98.8 95.67 3.127
199 78 96.23-18.23

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  54.4 &  54.53 & -0.1335 \tabularnewline
2 &  59.2 &  60.32 & -1.122 \tabularnewline
3 &  57.8 &  57.93 & -0.1336 \tabularnewline
4 &  61.5 &  63.02 & -1.521 \tabularnewline
5 &  60.1 &  61.76 & -1.664 \tabularnewline
6 &  60.1 &  59.78 &  0.3204 \tabularnewline
7 &  58.4 &  61.66 & -3.258 \tabularnewline
8 &  56.8 &  61.12 & -4.315 \tabularnewline
9 &  63.8 &  66.47 & -2.671 \tabularnewline
10 &  53.9 &  64.37 & -10.47 \tabularnewline
11 &  63.1 &  58.38 &  4.716 \tabularnewline
12 &  55.7 &  60.54 & -4.841 \tabularnewline
13 &  54.9 &  57.25 & -2.353 \tabularnewline
14 &  64.6 &  61.3 &  3.299 \tabularnewline
15 &  60.2 &  62.62 & -2.423 \tabularnewline
16 &  63.9 &  62.93 &  0.9724 \tabularnewline
17 &  69.9 &  63.09 &  6.808 \tabularnewline
18 &  58.5 &  65.04 & -6.538 \tabularnewline
19 &  52 &  61 & -9.005 \tabularnewline
20 &  66.7 &  60.25 &  6.45 \tabularnewline
21 &  72 &  69.56 &  2.436 \tabularnewline
22 &  68.4 &  64.46 &  3.942 \tabularnewline
23 &  70.8 &  67.65 &  3.151 \tabularnewline
24 &  56.5 &  64.87 & -8.368 \tabularnewline
25 &  62.6 &  59.09 &  3.507 \tabularnewline
26 &  66.5 &  65.4 &  1.104 \tabularnewline
27 &  69.2 &  65.23 &  3.967 \tabularnewline
28 &  63.7 &  65.58 & -1.879 \tabularnewline
29 &  73.6 &  67.04 &  6.557 \tabularnewline
30 &  64.1 &  66.16 & -2.056 \tabularnewline
31 &  53.8 &  59.85 & -6.047 \tabularnewline
32 &  72.2 &  65.12 &  7.08 \tabularnewline
33 &  80.2 &  74.55 &  5.652 \tabularnewline
34 &  69.1 &  72.1 & -3.004 \tabularnewline
35 &  72 &  71.16 &  0.8352 \tabularnewline
36 &  66.3 &  64.72 &  1.58 \tabularnewline
37 &  72.5 &  64.67 &  7.835 \tabularnewline
38 &  88.9 &  72.05 &  16.85 \tabularnewline
39 &  88.6 &  75.99 &  12.61 \tabularnewline
40 &  73.7 &  72.44 &  1.255 \tabularnewline
41 &  86 &  74.58 &  11.42 \tabularnewline
42 &  70 &  72.22 & -2.216 \tabularnewline
43 &  71.6 &  64.29 &  7.312 \tabularnewline
44 &  90.5 &  74.42 &  16.08 \tabularnewline
45 &  85.7 &  83.11 &  2.588 \tabularnewline
46 &  84.8 &  76.9 &  7.9 \tabularnewline
47 &  81.1 &  79.13 &  1.975 \tabularnewline
48 &  70.8 &  72.55 & -1.755 \tabularnewline
49 &  65.7 &  70.01 & -4.312 \tabularnewline
50 &  86.2 &  77.9 &  8.297 \tabularnewline
51 &  76.1 &  82.5 & -6.401 \tabularnewline
52 &  79.8 &  73.97 &  5.826 \tabularnewline
53 &  85.2 &  81.33 &  3.871 \tabularnewline
54 &  75.8 &  73.41 &  2.388 \tabularnewline
55 &  69.4 &  74.85 & -5.445 \tabularnewline
56 &  85 &  81.41 &  3.586 \tabularnewline
57 &  75 &  83.86 & -8.856 \tabularnewline
58 &  77.7 &  81.83 & -4.134 \tabularnewline
59 &  68.5 &  81.24 & -12.74 \tabularnewline
60 &  68.4 &  71.88 & -3.476 \tabularnewline
61 &  65 &  67.87 & -2.867 \tabularnewline
62 &  73.2 &  78.79 & -5.587 \tabularnewline
63 &  67.9 &  74.19 & -6.287 \tabularnewline
64 &  76.5 &  76.92 & -0.4201 \tabularnewline
65 &  85.5 &  81.21 &  4.292 \tabularnewline
66 &  71.7 &  78.14 & -6.438 \tabularnewline
67 &  57.9 &  74.7 & -16.8 \tabularnewline
68 &  75.5 &  77.05 & -1.549 \tabularnewline
69 &  78.2 &  80.72 & -2.518 \tabularnewline
70 &  75.7 &  81.89 & -6.192 \tabularnewline
71 &  67.1 &  75.84 & -8.737 \tabularnewline
72 &  74.6 &  73.98 &  0.6163 \tabularnewline
73 &  66.2 &  72.01 & -5.813 \tabularnewline
74 &  74.9 &  77.03 & -2.135 \tabularnewline
75 &  69.5 &  74.45 & -4.945 \tabularnewline
76 &  76.1 &  77.34 & -1.239 \tabularnewline
77 &  82.3 &  83.26 & -0.9633 \tabularnewline
78 &  82.1 &  79.04 &  3.056 \tabularnewline
79 &  60.5 &  75.79 & -15.29 \tabularnewline
80 &  71.2 &  76.24 & -5.044 \tabularnewline
81 &  81.4 &  85.01 & -3.608 \tabularnewline
82 &  74.5 &  85.62 & -11.12 \tabularnewline
83 &  61.4 &  77.71 & -16.31 \tabularnewline
84 &  83.8 &  78.95 &  4.845 \tabularnewline
85 &  85.4 &  80.53 &  4.865 \tabularnewline
86 &  91.6 &  84.33 &  7.265 \tabularnewline
87 &  91.9 &  86.66 &  5.243 \tabularnewline
88 &  86.3 &  86.97 & -0.6674 \tabularnewline
89 &  96.8 &  87.76 &  9.036 \tabularnewline
90 &  81 &  91.7 & -10.7 \tabularnewline
91 &  70.8 &  77.04 & -6.238 \tabularnewline
92 &  98.8 &  82.42 &  16.38 \tabularnewline
93 &  94.5 &  96.27 & -1.77 \tabularnewline
94 &  84.5 &  87.83 & -3.33 \tabularnewline
95 &  92.8 &  81.77 &  11.03 \tabularnewline
96 &  81.2 &  88.69 & -7.488 \tabularnewline
97 &  75.7 &  84.96 & -9.259 \tabularnewline
98 &  86.7 &  89.12 & -2.424 \tabularnewline
99 &  87.5 &  91.79 & -4.291 \tabularnewline
100 &  87.8 &  87.5 &  0.3017 \tabularnewline
101 &  103.1 &  94.22 &  8.882 \tabularnewline
102 &  96.4 &  92.05 &  4.345 \tabularnewline
103 &  77.1 &  85.29 & -8.193 \tabularnewline
104 &  106.5 &  92.74 &  13.76 \tabularnewline
105 &  95.7 &  101.1 & -5.428 \tabularnewline
106 &  95.3 &  91.97 &  3.331 \tabularnewline
107 &  86.6 &  95.87 & -9.274 \tabularnewline
108 &  89.6 &  85.29 &  4.314 \tabularnewline
109 &  81.9 &  83.84 & -1.945 \tabularnewline
110 &  98.4 &  91.33 &  7.071 \tabularnewline
111 &  92.9 &  93.79 & -0.8912 \tabularnewline
112 &  83.9 &  90.15 & -6.25 \tabularnewline
113 &  121.8 &  96.53 &  25.27 \tabularnewline
114 &  103.9 &  102.2 &  1.743 \tabularnewline
115 &  87.5 &  91.57 & -4.066 \tabularnewline
116 &  118.9 &  99.67 &  19.23 \tabularnewline
117 &  109 &  105.8 &  3.203 \tabularnewline
118 &  112.2 &  103.1 &  9.123 \tabularnewline
119 &  100.1 &  101.2 & -1.136 \tabularnewline
120 &  111.3 &  95.27 &  16.03 \tabularnewline
121 &  102.7 &  94.5 &  8.204 \tabularnewline
122 &  122.6 &  103.1 &  19.5 \tabularnewline
123 &  124.8 &  103.3 &  21.46 \tabularnewline
124 &  120.3 &  102.8 &  17.48 \tabularnewline
125 &  118.3 &  113.4 &  4.868 \tabularnewline
126 &  108.7 &  104 &  4.728 \tabularnewline
127 &  100.7 &  99.4 &  1.299 \tabularnewline
128 &  124 &  110.5 &  13.52 \tabularnewline
129 &  103.1 &  113.5 & -10.44 \tabularnewline
130 &  115 &  108.5 &  6.52 \tabularnewline
131 &  112.7 &  107 &  5.727 \tabularnewline
132 &  101.7 &  108.7 & -6.984 \tabularnewline
133 &  111.5 &  101.4 &  10.15 \tabularnewline
134 &  114.4 &  114.6 & -0.1753 \tabularnewline
135 &  112.5 &  112.6 & -0.05264 \tabularnewline
136 &  107.2 &  112.8 & -5.564 \tabularnewline
137 &  136.7 &  110.8 &  25.89 \tabularnewline
138 &  107.8 &  114.2 & -6.375 \tabularnewline
139 &  94.6 &  104.9 & -10.34 \tabularnewline
140 &  110.7 &  108.8 &  1.891 \tabularnewline
141 &  126.6 &  111.3 &  15.27 \tabularnewline
142 &  127.9 &  117.5 &  10.37 \tabularnewline
143 &  109.2 &  113.8 & -4.602 \tabularnewline
144 &  87.1 &  105.5 & -18.35 \tabularnewline
145 &  90.8 &  99.29 & -8.494 \tabularnewline
146 &  94.5 &  104 & -9.472 \tabularnewline
147 &  103.3 &  104 & -0.743 \tabularnewline
148 &  103.2 &  105.5 & -2.305 \tabularnewline
149 &  105.4 &  114.2 & -8.841 \tabularnewline
150 &  103.9 &  106.7 & -2.772 \tabularnewline
151 &  79.8 &  99.27 & -19.47 \tabularnewline
152 &  105.6 &  99.19 &  6.412 \tabularnewline
153 &  113 &  116.3 & -3.333 \tabularnewline
154 &  87.7 &  115.4 & -27.66 \tabularnewline
155 &  110 &  100.7 &  9.257 \tabularnewline
156 &  90.3 &  98.25 & -7.952 \tabularnewline
157 &  108.9 &  91.78 &  17.11 \tabularnewline
158 &  105.1 &  101 &  4.088 \tabularnewline
159 &  113 &  104.3 &  8.723 \tabularnewline
160 &  100.4 &  105.3 & -4.898 \tabularnewline
161 &  110.1 &  102.7 &  7.364 \tabularnewline
162 &  114.7 &  105.1 &  9.624 \tabularnewline
163 &  88.6 &  96.53 & -7.935 \tabularnewline
164 &  117.2 &  102.3 &  14.89 \tabularnewline
165 &  127.7 &  115.4 &  12.35 \tabularnewline
166 &  107.8 &  105.2 &  2.621 \tabularnewline
167 &  102.8 &  108.9 & -6.098 \tabularnewline
168 &  100.2 &  97.63 &  2.569 \tabularnewline
169 &  108.4 &  102.5 &  5.944 \tabularnewline
170 &  114.2 &  107.5 &  6.681 \tabularnewline
171 &  94.4 &  111.2 & -16.81 \tabularnewline
172 &  92.2 &  98.49 & -6.289 \tabularnewline
173 &  115.3 &  105.6 &  9.742 \tabularnewline
174 &  102 &  112.1 & -10.07 \tabularnewline
175 &  86.3 &  97.27 & -10.97 \tabularnewline
176 &  112 &  106.7 &  5.348 \tabularnewline
177 &  112.5 &  120.3 & -7.764 \tabularnewline
178 &  109.5 &  110.9 & -1.382 \tabularnewline
179 &  105.9 &  108.8 & -2.937 \tabularnewline
180 &  115.3 &  102.8 &  12.54 \tabularnewline
181 &  126.2 &  109.6 &  16.62 \tabularnewline
182 &  112.2 &  118.6 & -6.446 \tabularnewline
183 &  112.5 &  105.4 &  7.125 \tabularnewline
184 &  106.9 &  104.8 &  2.12 \tabularnewline
185 &  90.6 &  113 & -22.4 \tabularnewline
186 &  75.6 &  98.57 & -22.97 \tabularnewline
187 &  78.8 &  91.87 & -13.07 \tabularnewline
188 &  101.8 &  102.9 & -1.108 \tabularnewline
189 &  93.9 &  109.5 & -15.64 \tabularnewline
190 &  100 &  106.7 & -6.705 \tabularnewline
191 &  89.2 &  107.6 & -18.41 \tabularnewline
192 &  97.7 &  104.5 & -6.755 \tabularnewline
193 &  121.1 &  109.3 &  11.8 \tabularnewline
194 &  108.8 &  117.1 & -8.262 \tabularnewline
195 &  92.9 &  108.8 & -15.95 \tabularnewline
196 &  113.6 &  105.7 &  7.926 \tabularnewline
197 &  112.6 &  105.5 &  7.104 \tabularnewline
198 &  98.8 &  95.67 &  3.127 \tabularnewline
199 &  78 &  96.23 & -18.23 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309506&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] 54.4[/C][C] 54.53[/C][C]-0.1335[/C][/ROW]
[ROW][C]2[/C][C] 59.2[/C][C] 60.32[/C][C]-1.122[/C][/ROW]
[ROW][C]3[/C][C] 57.8[/C][C] 57.93[/C][C]-0.1336[/C][/ROW]
[ROW][C]4[/C][C] 61.5[/C][C] 63.02[/C][C]-1.521[/C][/ROW]
[ROW][C]5[/C][C] 60.1[/C][C] 61.76[/C][C]-1.664[/C][/ROW]
[ROW][C]6[/C][C] 60.1[/C][C] 59.78[/C][C] 0.3204[/C][/ROW]
[ROW][C]7[/C][C] 58.4[/C][C] 61.66[/C][C]-3.258[/C][/ROW]
[ROW][C]8[/C][C] 56.8[/C][C] 61.12[/C][C]-4.315[/C][/ROW]
[ROW][C]9[/C][C] 63.8[/C][C] 66.47[/C][C]-2.671[/C][/ROW]
[ROW][C]10[/C][C] 53.9[/C][C] 64.37[/C][C]-10.47[/C][/ROW]
[ROW][C]11[/C][C] 63.1[/C][C] 58.38[/C][C] 4.716[/C][/ROW]
[ROW][C]12[/C][C] 55.7[/C][C] 60.54[/C][C]-4.841[/C][/ROW]
[ROW][C]13[/C][C] 54.9[/C][C] 57.25[/C][C]-2.353[/C][/ROW]
[ROW][C]14[/C][C] 64.6[/C][C] 61.3[/C][C] 3.299[/C][/ROW]
[ROW][C]15[/C][C] 60.2[/C][C] 62.62[/C][C]-2.423[/C][/ROW]
[ROW][C]16[/C][C] 63.9[/C][C] 62.93[/C][C] 0.9724[/C][/ROW]
[ROW][C]17[/C][C] 69.9[/C][C] 63.09[/C][C] 6.808[/C][/ROW]
[ROW][C]18[/C][C] 58.5[/C][C] 65.04[/C][C]-6.538[/C][/ROW]
[ROW][C]19[/C][C] 52[/C][C] 61[/C][C]-9.005[/C][/ROW]
[ROW][C]20[/C][C] 66.7[/C][C] 60.25[/C][C] 6.45[/C][/ROW]
[ROW][C]21[/C][C] 72[/C][C] 69.56[/C][C] 2.436[/C][/ROW]
[ROW][C]22[/C][C] 68.4[/C][C] 64.46[/C][C] 3.942[/C][/ROW]
[ROW][C]23[/C][C] 70.8[/C][C] 67.65[/C][C] 3.151[/C][/ROW]
[ROW][C]24[/C][C] 56.5[/C][C] 64.87[/C][C]-8.368[/C][/ROW]
[ROW][C]25[/C][C] 62.6[/C][C] 59.09[/C][C] 3.507[/C][/ROW]
[ROW][C]26[/C][C] 66.5[/C][C] 65.4[/C][C] 1.104[/C][/ROW]
[ROW][C]27[/C][C] 69.2[/C][C] 65.23[/C][C] 3.967[/C][/ROW]
[ROW][C]28[/C][C] 63.7[/C][C] 65.58[/C][C]-1.879[/C][/ROW]
[ROW][C]29[/C][C] 73.6[/C][C] 67.04[/C][C] 6.557[/C][/ROW]
[ROW][C]30[/C][C] 64.1[/C][C] 66.16[/C][C]-2.056[/C][/ROW]
[ROW][C]31[/C][C] 53.8[/C][C] 59.85[/C][C]-6.047[/C][/ROW]
[ROW][C]32[/C][C] 72.2[/C][C] 65.12[/C][C] 7.08[/C][/ROW]
[ROW][C]33[/C][C] 80.2[/C][C] 74.55[/C][C] 5.652[/C][/ROW]
[ROW][C]34[/C][C] 69.1[/C][C] 72.1[/C][C]-3.004[/C][/ROW]
[ROW][C]35[/C][C] 72[/C][C] 71.16[/C][C] 0.8352[/C][/ROW]
[ROW][C]36[/C][C] 66.3[/C][C] 64.72[/C][C] 1.58[/C][/ROW]
[ROW][C]37[/C][C] 72.5[/C][C] 64.67[/C][C] 7.835[/C][/ROW]
[ROW][C]38[/C][C] 88.9[/C][C] 72.05[/C][C] 16.85[/C][/ROW]
[ROW][C]39[/C][C] 88.6[/C][C] 75.99[/C][C] 12.61[/C][/ROW]
[ROW][C]40[/C][C] 73.7[/C][C] 72.44[/C][C] 1.255[/C][/ROW]
[ROW][C]41[/C][C] 86[/C][C] 74.58[/C][C] 11.42[/C][/ROW]
[ROW][C]42[/C][C] 70[/C][C] 72.22[/C][C]-2.216[/C][/ROW]
[ROW][C]43[/C][C] 71.6[/C][C] 64.29[/C][C] 7.312[/C][/ROW]
[ROW][C]44[/C][C] 90.5[/C][C] 74.42[/C][C] 16.08[/C][/ROW]
[ROW][C]45[/C][C] 85.7[/C][C] 83.11[/C][C] 2.588[/C][/ROW]
[ROW][C]46[/C][C] 84.8[/C][C] 76.9[/C][C] 7.9[/C][/ROW]
[ROW][C]47[/C][C] 81.1[/C][C] 79.13[/C][C] 1.975[/C][/ROW]
[ROW][C]48[/C][C] 70.8[/C][C] 72.55[/C][C]-1.755[/C][/ROW]
[ROW][C]49[/C][C] 65.7[/C][C] 70.01[/C][C]-4.312[/C][/ROW]
[ROW][C]50[/C][C] 86.2[/C][C] 77.9[/C][C] 8.297[/C][/ROW]
[ROW][C]51[/C][C] 76.1[/C][C] 82.5[/C][C]-6.401[/C][/ROW]
[ROW][C]52[/C][C] 79.8[/C][C] 73.97[/C][C] 5.826[/C][/ROW]
[ROW][C]53[/C][C] 85.2[/C][C] 81.33[/C][C] 3.871[/C][/ROW]
[ROW][C]54[/C][C] 75.8[/C][C] 73.41[/C][C] 2.388[/C][/ROW]
[ROW][C]55[/C][C] 69.4[/C][C] 74.85[/C][C]-5.445[/C][/ROW]
[ROW][C]56[/C][C] 85[/C][C] 81.41[/C][C] 3.586[/C][/ROW]
[ROW][C]57[/C][C] 75[/C][C] 83.86[/C][C]-8.856[/C][/ROW]
[ROW][C]58[/C][C] 77.7[/C][C] 81.83[/C][C]-4.134[/C][/ROW]
[ROW][C]59[/C][C] 68.5[/C][C] 81.24[/C][C]-12.74[/C][/ROW]
[ROW][C]60[/C][C] 68.4[/C][C] 71.88[/C][C]-3.476[/C][/ROW]
[ROW][C]61[/C][C] 65[/C][C] 67.87[/C][C]-2.867[/C][/ROW]
[ROW][C]62[/C][C] 73.2[/C][C] 78.79[/C][C]-5.587[/C][/ROW]
[ROW][C]63[/C][C] 67.9[/C][C] 74.19[/C][C]-6.287[/C][/ROW]
[ROW][C]64[/C][C] 76.5[/C][C] 76.92[/C][C]-0.4201[/C][/ROW]
[ROW][C]65[/C][C] 85.5[/C][C] 81.21[/C][C] 4.292[/C][/ROW]
[ROW][C]66[/C][C] 71.7[/C][C] 78.14[/C][C]-6.438[/C][/ROW]
[ROW][C]67[/C][C] 57.9[/C][C] 74.7[/C][C]-16.8[/C][/ROW]
[ROW][C]68[/C][C] 75.5[/C][C] 77.05[/C][C]-1.549[/C][/ROW]
[ROW][C]69[/C][C] 78.2[/C][C] 80.72[/C][C]-2.518[/C][/ROW]
[ROW][C]70[/C][C] 75.7[/C][C] 81.89[/C][C]-6.192[/C][/ROW]
[ROW][C]71[/C][C] 67.1[/C][C] 75.84[/C][C]-8.737[/C][/ROW]
[ROW][C]72[/C][C] 74.6[/C][C] 73.98[/C][C] 0.6163[/C][/ROW]
[ROW][C]73[/C][C] 66.2[/C][C] 72.01[/C][C]-5.813[/C][/ROW]
[ROW][C]74[/C][C] 74.9[/C][C] 77.03[/C][C]-2.135[/C][/ROW]
[ROW][C]75[/C][C] 69.5[/C][C] 74.45[/C][C]-4.945[/C][/ROW]
[ROW][C]76[/C][C] 76.1[/C][C] 77.34[/C][C]-1.239[/C][/ROW]
[ROW][C]77[/C][C] 82.3[/C][C] 83.26[/C][C]-0.9633[/C][/ROW]
[ROW][C]78[/C][C] 82.1[/C][C] 79.04[/C][C] 3.056[/C][/ROW]
[ROW][C]79[/C][C] 60.5[/C][C] 75.79[/C][C]-15.29[/C][/ROW]
[ROW][C]80[/C][C] 71.2[/C][C] 76.24[/C][C]-5.044[/C][/ROW]
[ROW][C]81[/C][C] 81.4[/C][C] 85.01[/C][C]-3.608[/C][/ROW]
[ROW][C]82[/C][C] 74.5[/C][C] 85.62[/C][C]-11.12[/C][/ROW]
[ROW][C]83[/C][C] 61.4[/C][C] 77.71[/C][C]-16.31[/C][/ROW]
[ROW][C]84[/C][C] 83.8[/C][C] 78.95[/C][C] 4.845[/C][/ROW]
[ROW][C]85[/C][C] 85.4[/C][C] 80.53[/C][C] 4.865[/C][/ROW]
[ROW][C]86[/C][C] 91.6[/C][C] 84.33[/C][C] 7.265[/C][/ROW]
[ROW][C]87[/C][C] 91.9[/C][C] 86.66[/C][C] 5.243[/C][/ROW]
[ROW][C]88[/C][C] 86.3[/C][C] 86.97[/C][C]-0.6674[/C][/ROW]
[ROW][C]89[/C][C] 96.8[/C][C] 87.76[/C][C] 9.036[/C][/ROW]
[ROW][C]90[/C][C] 81[/C][C] 91.7[/C][C]-10.7[/C][/ROW]
[ROW][C]91[/C][C] 70.8[/C][C] 77.04[/C][C]-6.238[/C][/ROW]
[ROW][C]92[/C][C] 98.8[/C][C] 82.42[/C][C] 16.38[/C][/ROW]
[ROW][C]93[/C][C] 94.5[/C][C] 96.27[/C][C]-1.77[/C][/ROW]
[ROW][C]94[/C][C] 84.5[/C][C] 87.83[/C][C]-3.33[/C][/ROW]
[ROW][C]95[/C][C] 92.8[/C][C] 81.77[/C][C] 11.03[/C][/ROW]
[ROW][C]96[/C][C] 81.2[/C][C] 88.69[/C][C]-7.488[/C][/ROW]
[ROW][C]97[/C][C] 75.7[/C][C] 84.96[/C][C]-9.259[/C][/ROW]
[ROW][C]98[/C][C] 86.7[/C][C] 89.12[/C][C]-2.424[/C][/ROW]
[ROW][C]99[/C][C] 87.5[/C][C] 91.79[/C][C]-4.291[/C][/ROW]
[ROW][C]100[/C][C] 87.8[/C][C] 87.5[/C][C] 0.3017[/C][/ROW]
[ROW][C]101[/C][C] 103.1[/C][C] 94.22[/C][C] 8.882[/C][/ROW]
[ROW][C]102[/C][C] 96.4[/C][C] 92.05[/C][C] 4.345[/C][/ROW]
[ROW][C]103[/C][C] 77.1[/C][C] 85.29[/C][C]-8.193[/C][/ROW]
[ROW][C]104[/C][C] 106.5[/C][C] 92.74[/C][C] 13.76[/C][/ROW]
[ROW][C]105[/C][C] 95.7[/C][C] 101.1[/C][C]-5.428[/C][/ROW]
[ROW][C]106[/C][C] 95.3[/C][C] 91.97[/C][C] 3.331[/C][/ROW]
[ROW][C]107[/C][C] 86.6[/C][C] 95.87[/C][C]-9.274[/C][/ROW]
[ROW][C]108[/C][C] 89.6[/C][C] 85.29[/C][C] 4.314[/C][/ROW]
[ROW][C]109[/C][C] 81.9[/C][C] 83.84[/C][C]-1.945[/C][/ROW]
[ROW][C]110[/C][C] 98.4[/C][C] 91.33[/C][C] 7.071[/C][/ROW]
[ROW][C]111[/C][C] 92.9[/C][C] 93.79[/C][C]-0.8912[/C][/ROW]
[ROW][C]112[/C][C] 83.9[/C][C] 90.15[/C][C]-6.25[/C][/ROW]
[ROW][C]113[/C][C] 121.8[/C][C] 96.53[/C][C] 25.27[/C][/ROW]
[ROW][C]114[/C][C] 103.9[/C][C] 102.2[/C][C] 1.743[/C][/ROW]
[ROW][C]115[/C][C] 87.5[/C][C] 91.57[/C][C]-4.066[/C][/ROW]
[ROW][C]116[/C][C] 118.9[/C][C] 99.67[/C][C] 19.23[/C][/ROW]
[ROW][C]117[/C][C] 109[/C][C] 105.8[/C][C] 3.203[/C][/ROW]
[ROW][C]118[/C][C] 112.2[/C][C] 103.1[/C][C] 9.123[/C][/ROW]
[ROW][C]119[/C][C] 100.1[/C][C] 101.2[/C][C]-1.136[/C][/ROW]
[ROW][C]120[/C][C] 111.3[/C][C] 95.27[/C][C] 16.03[/C][/ROW]
[ROW][C]121[/C][C] 102.7[/C][C] 94.5[/C][C] 8.204[/C][/ROW]
[ROW][C]122[/C][C] 122.6[/C][C] 103.1[/C][C] 19.5[/C][/ROW]
[ROW][C]123[/C][C] 124.8[/C][C] 103.3[/C][C] 21.46[/C][/ROW]
[ROW][C]124[/C][C] 120.3[/C][C] 102.8[/C][C] 17.48[/C][/ROW]
[ROW][C]125[/C][C] 118.3[/C][C] 113.4[/C][C] 4.868[/C][/ROW]
[ROW][C]126[/C][C] 108.7[/C][C] 104[/C][C] 4.728[/C][/ROW]
[ROW][C]127[/C][C] 100.7[/C][C] 99.4[/C][C] 1.299[/C][/ROW]
[ROW][C]128[/C][C] 124[/C][C] 110.5[/C][C] 13.52[/C][/ROW]
[ROW][C]129[/C][C] 103.1[/C][C] 113.5[/C][C]-10.44[/C][/ROW]
[ROW][C]130[/C][C] 115[/C][C] 108.5[/C][C] 6.52[/C][/ROW]
[ROW][C]131[/C][C] 112.7[/C][C] 107[/C][C] 5.727[/C][/ROW]
[ROW][C]132[/C][C] 101.7[/C][C] 108.7[/C][C]-6.984[/C][/ROW]
[ROW][C]133[/C][C] 111.5[/C][C] 101.4[/C][C] 10.15[/C][/ROW]
[ROW][C]134[/C][C] 114.4[/C][C] 114.6[/C][C]-0.1753[/C][/ROW]
[ROW][C]135[/C][C] 112.5[/C][C] 112.6[/C][C]-0.05264[/C][/ROW]
[ROW][C]136[/C][C] 107.2[/C][C] 112.8[/C][C]-5.564[/C][/ROW]
[ROW][C]137[/C][C] 136.7[/C][C] 110.8[/C][C] 25.89[/C][/ROW]
[ROW][C]138[/C][C] 107.8[/C][C] 114.2[/C][C]-6.375[/C][/ROW]
[ROW][C]139[/C][C] 94.6[/C][C] 104.9[/C][C]-10.34[/C][/ROW]
[ROW][C]140[/C][C] 110.7[/C][C] 108.8[/C][C] 1.891[/C][/ROW]
[ROW][C]141[/C][C] 126.6[/C][C] 111.3[/C][C] 15.27[/C][/ROW]
[ROW][C]142[/C][C] 127.9[/C][C] 117.5[/C][C] 10.37[/C][/ROW]
[ROW][C]143[/C][C] 109.2[/C][C] 113.8[/C][C]-4.602[/C][/ROW]
[ROW][C]144[/C][C] 87.1[/C][C] 105.5[/C][C]-18.35[/C][/ROW]
[ROW][C]145[/C][C] 90.8[/C][C] 99.29[/C][C]-8.494[/C][/ROW]
[ROW][C]146[/C][C] 94.5[/C][C] 104[/C][C]-9.472[/C][/ROW]
[ROW][C]147[/C][C] 103.3[/C][C] 104[/C][C]-0.743[/C][/ROW]
[ROW][C]148[/C][C] 103.2[/C][C] 105.5[/C][C]-2.305[/C][/ROW]
[ROW][C]149[/C][C] 105.4[/C][C] 114.2[/C][C]-8.841[/C][/ROW]
[ROW][C]150[/C][C] 103.9[/C][C] 106.7[/C][C]-2.772[/C][/ROW]
[ROW][C]151[/C][C] 79.8[/C][C] 99.27[/C][C]-19.47[/C][/ROW]
[ROW][C]152[/C][C] 105.6[/C][C] 99.19[/C][C] 6.412[/C][/ROW]
[ROW][C]153[/C][C] 113[/C][C] 116.3[/C][C]-3.333[/C][/ROW]
[ROW][C]154[/C][C] 87.7[/C][C] 115.4[/C][C]-27.66[/C][/ROW]
[ROW][C]155[/C][C] 110[/C][C] 100.7[/C][C] 9.257[/C][/ROW]
[ROW][C]156[/C][C] 90.3[/C][C] 98.25[/C][C]-7.952[/C][/ROW]
[ROW][C]157[/C][C] 108.9[/C][C] 91.78[/C][C] 17.11[/C][/ROW]
[ROW][C]158[/C][C] 105.1[/C][C] 101[/C][C] 4.088[/C][/ROW]
[ROW][C]159[/C][C] 113[/C][C] 104.3[/C][C] 8.723[/C][/ROW]
[ROW][C]160[/C][C] 100.4[/C][C] 105.3[/C][C]-4.898[/C][/ROW]
[ROW][C]161[/C][C] 110.1[/C][C] 102.7[/C][C] 7.364[/C][/ROW]
[ROW][C]162[/C][C] 114.7[/C][C] 105.1[/C][C] 9.624[/C][/ROW]
[ROW][C]163[/C][C] 88.6[/C][C] 96.53[/C][C]-7.935[/C][/ROW]
[ROW][C]164[/C][C] 117.2[/C][C] 102.3[/C][C] 14.89[/C][/ROW]
[ROW][C]165[/C][C] 127.7[/C][C] 115.4[/C][C] 12.35[/C][/ROW]
[ROW][C]166[/C][C] 107.8[/C][C] 105.2[/C][C] 2.621[/C][/ROW]
[ROW][C]167[/C][C] 102.8[/C][C] 108.9[/C][C]-6.098[/C][/ROW]
[ROW][C]168[/C][C] 100.2[/C][C] 97.63[/C][C] 2.569[/C][/ROW]
[ROW][C]169[/C][C] 108.4[/C][C] 102.5[/C][C] 5.944[/C][/ROW]
[ROW][C]170[/C][C] 114.2[/C][C] 107.5[/C][C] 6.681[/C][/ROW]
[ROW][C]171[/C][C] 94.4[/C][C] 111.2[/C][C]-16.81[/C][/ROW]
[ROW][C]172[/C][C] 92.2[/C][C] 98.49[/C][C]-6.289[/C][/ROW]
[ROW][C]173[/C][C] 115.3[/C][C] 105.6[/C][C] 9.742[/C][/ROW]
[ROW][C]174[/C][C] 102[/C][C] 112.1[/C][C]-10.07[/C][/ROW]
[ROW][C]175[/C][C] 86.3[/C][C] 97.27[/C][C]-10.97[/C][/ROW]
[ROW][C]176[/C][C] 112[/C][C] 106.7[/C][C] 5.348[/C][/ROW]
[ROW][C]177[/C][C] 112.5[/C][C] 120.3[/C][C]-7.764[/C][/ROW]
[ROW][C]178[/C][C] 109.5[/C][C] 110.9[/C][C]-1.382[/C][/ROW]
[ROW][C]179[/C][C] 105.9[/C][C] 108.8[/C][C]-2.937[/C][/ROW]
[ROW][C]180[/C][C] 115.3[/C][C] 102.8[/C][C] 12.54[/C][/ROW]
[ROW][C]181[/C][C] 126.2[/C][C] 109.6[/C][C] 16.62[/C][/ROW]
[ROW][C]182[/C][C] 112.2[/C][C] 118.6[/C][C]-6.446[/C][/ROW]
[ROW][C]183[/C][C] 112.5[/C][C] 105.4[/C][C] 7.125[/C][/ROW]
[ROW][C]184[/C][C] 106.9[/C][C] 104.8[/C][C] 2.12[/C][/ROW]
[ROW][C]185[/C][C] 90.6[/C][C] 113[/C][C]-22.4[/C][/ROW]
[ROW][C]186[/C][C] 75.6[/C][C] 98.57[/C][C]-22.97[/C][/ROW]
[ROW][C]187[/C][C] 78.8[/C][C] 91.87[/C][C]-13.07[/C][/ROW]
[ROW][C]188[/C][C] 101.8[/C][C] 102.9[/C][C]-1.108[/C][/ROW]
[ROW][C]189[/C][C] 93.9[/C][C] 109.5[/C][C]-15.64[/C][/ROW]
[ROW][C]190[/C][C] 100[/C][C] 106.7[/C][C]-6.705[/C][/ROW]
[ROW][C]191[/C][C] 89.2[/C][C] 107.6[/C][C]-18.41[/C][/ROW]
[ROW][C]192[/C][C] 97.7[/C][C] 104.5[/C][C]-6.755[/C][/ROW]
[ROW][C]193[/C][C] 121.1[/C][C] 109.3[/C][C] 11.8[/C][/ROW]
[ROW][C]194[/C][C] 108.8[/C][C] 117.1[/C][C]-8.262[/C][/ROW]
[ROW][C]195[/C][C] 92.9[/C][C] 108.8[/C][C]-15.95[/C][/ROW]
[ROW][C]196[/C][C] 113.6[/C][C] 105.7[/C][C] 7.926[/C][/ROW]
[ROW][C]197[/C][C] 112.6[/C][C] 105.5[/C][C] 7.104[/C][/ROW]
[ROW][C]198[/C][C] 98.8[/C][C] 95.67[/C][C] 3.127[/C][/ROW]
[ROW][C]199[/C][C] 78[/C][C] 96.23[/C][C]-18.23[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309506&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309506&T=5

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 54.4 54.53-0.1335
2 59.2 60.32-1.122
3 57.8 57.93-0.1336
4 61.5 63.02-1.521
5 60.1 61.76-1.664
6 60.1 59.78 0.3204
7 58.4 61.66-3.258
8 56.8 61.12-4.315
9 63.8 66.47-2.671
10 53.9 64.37-10.47
11 63.1 58.38 4.716
12 55.7 60.54-4.841
13 54.9 57.25-2.353
14 64.6 61.3 3.299
15 60.2 62.62-2.423
16 63.9 62.93 0.9724
17 69.9 63.09 6.808
18 58.5 65.04-6.538
19 52 61-9.005
20 66.7 60.25 6.45
21 72 69.56 2.436
22 68.4 64.46 3.942
23 70.8 67.65 3.151
24 56.5 64.87-8.368
25 62.6 59.09 3.507
26 66.5 65.4 1.104
27 69.2 65.23 3.967
28 63.7 65.58-1.879
29 73.6 67.04 6.557
30 64.1 66.16-2.056
31 53.8 59.85-6.047
32 72.2 65.12 7.08
33 80.2 74.55 5.652
34 69.1 72.1-3.004
35 72 71.16 0.8352
36 66.3 64.72 1.58
37 72.5 64.67 7.835
38 88.9 72.05 16.85
39 88.6 75.99 12.61
40 73.7 72.44 1.255
41 86 74.58 11.42
42 70 72.22-2.216
43 71.6 64.29 7.312
44 90.5 74.42 16.08
45 85.7 83.11 2.588
46 84.8 76.9 7.9
47 81.1 79.13 1.975
48 70.8 72.55-1.755
49 65.7 70.01-4.312
50 86.2 77.9 8.297
51 76.1 82.5-6.401
52 79.8 73.97 5.826
53 85.2 81.33 3.871
54 75.8 73.41 2.388
55 69.4 74.85-5.445
56 85 81.41 3.586
57 75 83.86-8.856
58 77.7 81.83-4.134
59 68.5 81.24-12.74
60 68.4 71.88-3.476
61 65 67.87-2.867
62 73.2 78.79-5.587
63 67.9 74.19-6.287
64 76.5 76.92-0.4201
65 85.5 81.21 4.292
66 71.7 78.14-6.438
67 57.9 74.7-16.8
68 75.5 77.05-1.549
69 78.2 80.72-2.518
70 75.7 81.89-6.192
71 67.1 75.84-8.737
72 74.6 73.98 0.6163
73 66.2 72.01-5.813
74 74.9 77.03-2.135
75 69.5 74.45-4.945
76 76.1 77.34-1.239
77 82.3 83.26-0.9633
78 82.1 79.04 3.056
79 60.5 75.79-15.29
80 71.2 76.24-5.044
81 81.4 85.01-3.608
82 74.5 85.62-11.12
83 61.4 77.71-16.31
84 83.8 78.95 4.845
85 85.4 80.53 4.865
86 91.6 84.33 7.265
87 91.9 86.66 5.243
88 86.3 86.97-0.6674
89 96.8 87.76 9.036
90 81 91.7-10.7
91 70.8 77.04-6.238
92 98.8 82.42 16.38
93 94.5 96.27-1.77
94 84.5 87.83-3.33
95 92.8 81.77 11.03
96 81.2 88.69-7.488
97 75.7 84.96-9.259
98 86.7 89.12-2.424
99 87.5 91.79-4.291
100 87.8 87.5 0.3017
101 103.1 94.22 8.882
102 96.4 92.05 4.345
103 77.1 85.29-8.193
104 106.5 92.74 13.76
105 95.7 101.1-5.428
106 95.3 91.97 3.331
107 86.6 95.87-9.274
108 89.6 85.29 4.314
109 81.9 83.84-1.945
110 98.4 91.33 7.071
111 92.9 93.79-0.8912
112 83.9 90.15-6.25
113 121.8 96.53 25.27
114 103.9 102.2 1.743
115 87.5 91.57-4.066
116 118.9 99.67 19.23
117 109 105.8 3.203
118 112.2 103.1 9.123
119 100.1 101.2-1.136
120 111.3 95.27 16.03
121 102.7 94.5 8.204
122 122.6 103.1 19.5
123 124.8 103.3 21.46
124 120.3 102.8 17.48
125 118.3 113.4 4.868
126 108.7 104 4.728
127 100.7 99.4 1.299
128 124 110.5 13.52
129 103.1 113.5-10.44
130 115 108.5 6.52
131 112.7 107 5.727
132 101.7 108.7-6.984
133 111.5 101.4 10.15
134 114.4 114.6-0.1753
135 112.5 112.6-0.05264
136 107.2 112.8-5.564
137 136.7 110.8 25.89
138 107.8 114.2-6.375
139 94.6 104.9-10.34
140 110.7 108.8 1.891
141 126.6 111.3 15.27
142 127.9 117.5 10.37
143 109.2 113.8-4.602
144 87.1 105.5-18.35
145 90.8 99.29-8.494
146 94.5 104-9.472
147 103.3 104-0.743
148 103.2 105.5-2.305
149 105.4 114.2-8.841
150 103.9 106.7-2.772
151 79.8 99.27-19.47
152 105.6 99.19 6.412
153 113 116.3-3.333
154 87.7 115.4-27.66
155 110 100.7 9.257
156 90.3 98.25-7.952
157 108.9 91.78 17.11
158 105.1 101 4.088
159 113 104.3 8.723
160 100.4 105.3-4.898
161 110.1 102.7 7.364
162 114.7 105.1 9.624
163 88.6 96.53-7.935
164 117.2 102.3 14.89
165 127.7 115.4 12.35
166 107.8 105.2 2.621
167 102.8 108.9-6.098
168 100.2 97.63 2.569
169 108.4 102.5 5.944
170 114.2 107.5 6.681
171 94.4 111.2-16.81
172 92.2 98.49-6.289
173 115.3 105.6 9.742
174 102 112.1-10.07
175 86.3 97.27-10.97
176 112 106.7 5.348
177 112.5 120.3-7.764
178 109.5 110.9-1.382
179 105.9 108.8-2.937
180 115.3 102.8 12.54
181 126.2 109.6 16.62
182 112.2 118.6-6.446
183 112.5 105.4 7.125
184 106.9 104.8 2.12
185 90.6 113-22.4
186 75.6 98.57-22.97
187 78.8 91.87-13.07
188 101.8 102.9-1.108
189 93.9 109.5-15.64
190 100 106.7-6.705
191 89.2 107.6-18.41
192 97.7 104.5-6.755
193 121.1 109.3 11.8
194 108.8 117.1-8.262
195 92.9 108.8-15.95
196 113.6 105.7 7.926
197 112.6 105.5 7.104
198 98.8 95.67 3.127
199 78 96.23-18.23






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8 0.001903 0.003807 0.9981
9 0.000329 0.000658 0.9997
10 0.0008031 0.001606 0.9992
11 0.001891 0.003781 0.9981
12 0.0005734 0.001147 0.9994
13 0.0002247 0.0004495 0.9998
14 0.000135 0.0002701 0.9999
15 8.167e-05 0.0001633 0.9999
16 5.346e-05 0.0001069 0.9999
17 0.0007927 0.001585 0.9992
18 0.0003623 0.0007245 0.9996
19 0.0007325 0.001465 0.9993
20 0.0007722 0.001544 0.9992
21 0.0008729 0.001746 0.9991
22 0.001195 0.002389 0.9988
23 0.0007746 0.001549 0.9992
24 0.0007156 0.001431 0.9993
25 0.000424 0.0008481 0.9996
26 0.0002057 0.0004114 0.9998
27 0.0001814 0.0003629 0.9998
28 8.619e-05 0.0001724 0.9999
29 6.615e-05 0.0001323 0.9999
30 3.29e-05 6.579e-05 1
31 1.629e-05 3.257e-05 1
32 9.403e-06 1.881e-05 1
33 4.819e-06 9.637e-06 1
34 2.733e-06 5.466e-06 1
35 1.284e-06 2.568e-06 1
36 8.524e-07 1.705e-06 1
37 1.003e-06 2.007e-06 1
38 2.396e-05 4.792e-05 1
39 4.475e-05 8.949e-05 1
40 2.346e-05 4.692e-05 1
41 1.803e-05 3.607e-05 1
42 1.133e-05 2.266e-05 1
43 1.453e-05 2.906e-05 1
44 2.11e-05 4.22e-05 1
45 1.861e-05 3.722e-05 1
46 1.16e-05 2.321e-05 1
47 8.52e-06 1.704e-05 1
48 5.113e-06 1.023e-05 1
49 3.861e-06 7.722e-06 1
50 2.27e-06 4.539e-06 1
51 4.24e-06 8.48e-06 1
52 2.511e-06 5.023e-06 1
53 1.545e-06 3.089e-06 1
54 9.093e-07 1.819e-06 1
55 1.929e-06 3.858e-06 1
56 1.268e-06 2.535e-06 1
57 3.755e-06 7.51e-06 1
58 4.482e-06 8.963e-06 1
59 2.443e-05 4.886e-05 1
60 1.588e-05 3.177e-05 1
61 9.653e-06 1.931e-05 1
62 9.1e-06 1.82e-05 1
63 7.542e-06 1.508e-05 1
64 4.755e-06 9.511e-06 1
65 2.889e-06 5.777e-06 1
66 3.061e-06 6.122e-06 1
67 3.743e-05 7.486e-05 1
68 2.323e-05 4.647e-05 1
69 1.48e-05 2.96e-05 1
70 1.116e-05 2.232e-05 1
71 1.08e-05 2.16e-05 1
72 6.671e-06 1.334e-05 1
73 4.756e-06 9.512e-06 1
74 2.819e-06 5.638e-06 1
75 1.986e-06 3.972e-06 1
76 1.154e-06 2.308e-06 1
77 6.604e-07 1.321e-06 1
78 4.258e-07 8.516e-07 1
79 9.839e-07 1.968e-06 1
80 6.229e-07 1.246e-06 1
81 3.804e-07 7.608e-07 1
82 3.454e-07 6.908e-07 1
83 8.254e-07 1.651e-06 1
84 9.835e-07 1.967e-06 1
85 1.211e-06 2.422e-06 1
86 1.545e-06 3.09e-06 1
87 1.59e-06 3.179e-06 1
88 9.504e-07 1.901e-06 1
89 1.184e-06 2.367e-06 1
90 1.434e-06 2.867e-06 1
91 1.057e-06 2.114e-06 1
92 6.215e-06 1.243e-05 1
93 3.86e-06 7.72e-06 1
94 2.483e-06 4.966e-06 1
95 3.887e-06 7.774e-06 1
96 3.287e-06 6.575e-06 1
97 3.564e-06 7.128e-06 1
98 2.29e-06 4.579e-06 1
99 1.565e-06 3.13e-06 1
100 9.63e-07 1.926e-06 1
101 9.736e-07 1.947e-06 1
102 6.61e-07 1.322e-06 1
103 6.723e-07 1.345e-06 1
104 1.307e-06 2.613e-06 1
105 9.001e-07 1.8e-06 1
106 6.05e-07 1.21e-06 1
107 6.509e-07 1.302e-06 1
108 4.799e-07 9.598e-07 1
109 3.213e-07 6.427e-07 1
110 2.478e-07 4.956e-07 1
111 1.493e-07 2.987e-07 1
112 1.334e-07 2.667e-07 1
113 2.957e-06 5.915e-06 1
114 1.861e-06 3.721e-06 1
115 1.35e-06 2.701e-06 1
116 5.171e-06 1.034e-05 1
117 3.494e-06 6.988e-06 1
118 3.32e-06 6.639e-06 1
119 2.083e-06 4.166e-06 1
120 4.58e-06 9.16e-06 1
121 3.923e-06 7.847e-06 1
122 1.483e-05 2.965e-05 1
123 7.09e-05 0.0001418 0.9999
124 0.0001701 0.0003402 0.9998
125 0.0001305 0.000261 0.9999
126 9.422e-05 0.0001884 0.9999
127 6.298e-05 0.000126 0.9999
128 7.623e-05 0.0001525 0.9999
129 0.0001143 0.0002285 0.9999
130 8.584e-05 0.0001717 0.9999
131 6.328e-05 0.0001266 0.9999
132 6.216e-05 0.0001243 0.9999
133 5.998e-05 0.00012 0.9999
134 4.438e-05 8.877e-05 1
135 3.155e-05 6.309e-05 1
136 2.873e-05 5.745e-05 1
137 0.000411 0.000822 0.9996
138 0.0003459 0.0006918 0.9997
139 0.0004282 0.0008564 0.9996
140 0.000314 0.0006281 0.9997
141 0.0005327 0.001065 0.9995
142 0.000661 0.001322 0.9993
143 0.0005058 0.001012 0.9995
144 0.001478 0.002956 0.9985
145 0.001547 0.003094 0.9985
146 0.001714 0.003428 0.9983
147 0.001215 0.00243 0.9988
148 0.0008897 0.001779 0.9991
149 0.0008312 0.001662 0.9992
150 0.0006076 0.001215 0.9994
151 0.002278 0.004555 0.9977
152 0.001732 0.003464 0.9983
153 0.001235 0.002471 0.9988
154 0.01461 0.02922 0.9854
155 0.01314 0.02628 0.9869
156 0.01341 0.02683 0.9866
157 0.01924 0.03849 0.9808
158 0.01476 0.02952 0.9852
159 0.01412 0.02823 0.9859
160 0.01117 0.02235 0.9888
161 0.01077 0.02153 0.9892
162 0.01213 0.02425 0.9879
163 0.01093 0.02185 0.9891
164 0.02171 0.04341 0.9783
165 0.02856 0.05712 0.9714
166 0.02084 0.04168 0.9792
167 0.01626 0.03251 0.9837
168 0.01145 0.0229 0.9885
169 0.008677 0.01735 0.9913
170 0.00796 0.01592 0.992
171 0.01354 0.02709 0.9865
172 0.01044 0.02088 0.9896
173 0.01665 0.0333 0.9834
174 0.01616 0.03232 0.9838
175 0.01808 0.03616 0.9819
176 0.02191 0.04382 0.9781
177 0.01547 0.03094 0.9845
178 0.01008 0.02015 0.9899
179 0.006439 0.01288 0.9936
180 0.008169 0.01634 0.9918
181 0.03797 0.07595 0.962
182 0.0258 0.0516 0.9742
183 0.05339 0.1068 0.9466
184 0.06893 0.1379 0.9311
185 0.2116 0.4231 0.7884
186 0.8715 0.257 0.1285
187 0.8175 0.365 0.1825
188 0.7829 0.4343 0.2172
189 0.7525 0.4951 0.2475
190 0.6332 0.7336 0.3668
191 0.4898 0.9797 0.5102

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.001903 &  0.003807 &  0.9981 \tabularnewline
9 &  0.000329 &  0.000658 &  0.9997 \tabularnewline
10 &  0.0008031 &  0.001606 &  0.9992 \tabularnewline
11 &  0.001891 &  0.003781 &  0.9981 \tabularnewline
12 &  0.0005734 &  0.001147 &  0.9994 \tabularnewline
13 &  0.0002247 &  0.0004495 &  0.9998 \tabularnewline
14 &  0.000135 &  0.0002701 &  0.9999 \tabularnewline
15 &  8.167e-05 &  0.0001633 &  0.9999 \tabularnewline
16 &  5.346e-05 &  0.0001069 &  0.9999 \tabularnewline
17 &  0.0007927 &  0.001585 &  0.9992 \tabularnewline
18 &  0.0003623 &  0.0007245 &  0.9996 \tabularnewline
19 &  0.0007325 &  0.001465 &  0.9993 \tabularnewline
20 &  0.0007722 &  0.001544 &  0.9992 \tabularnewline
21 &  0.0008729 &  0.001746 &  0.9991 \tabularnewline
22 &  0.001195 &  0.002389 &  0.9988 \tabularnewline
23 &  0.0007746 &  0.001549 &  0.9992 \tabularnewline
24 &  0.0007156 &  0.001431 &  0.9993 \tabularnewline
25 &  0.000424 &  0.0008481 &  0.9996 \tabularnewline
26 &  0.0002057 &  0.0004114 &  0.9998 \tabularnewline
27 &  0.0001814 &  0.0003629 &  0.9998 \tabularnewline
28 &  8.619e-05 &  0.0001724 &  0.9999 \tabularnewline
29 &  6.615e-05 &  0.0001323 &  0.9999 \tabularnewline
30 &  3.29e-05 &  6.579e-05 &  1 \tabularnewline
31 &  1.629e-05 &  3.257e-05 &  1 \tabularnewline
32 &  9.403e-06 &  1.881e-05 &  1 \tabularnewline
33 &  4.819e-06 &  9.637e-06 &  1 \tabularnewline
34 &  2.733e-06 &  5.466e-06 &  1 \tabularnewline
35 &  1.284e-06 &  2.568e-06 &  1 \tabularnewline
36 &  8.524e-07 &  1.705e-06 &  1 \tabularnewline
37 &  1.003e-06 &  2.007e-06 &  1 \tabularnewline
38 &  2.396e-05 &  4.792e-05 &  1 \tabularnewline
39 &  4.475e-05 &  8.949e-05 &  1 \tabularnewline
40 &  2.346e-05 &  4.692e-05 &  1 \tabularnewline
41 &  1.803e-05 &  3.607e-05 &  1 \tabularnewline
42 &  1.133e-05 &  2.266e-05 &  1 \tabularnewline
43 &  1.453e-05 &  2.906e-05 &  1 \tabularnewline
44 &  2.11e-05 &  4.22e-05 &  1 \tabularnewline
45 &  1.861e-05 &  3.722e-05 &  1 \tabularnewline
46 &  1.16e-05 &  2.321e-05 &  1 \tabularnewline
47 &  8.52e-06 &  1.704e-05 &  1 \tabularnewline
48 &  5.113e-06 &  1.023e-05 &  1 \tabularnewline
49 &  3.861e-06 &  7.722e-06 &  1 \tabularnewline
50 &  2.27e-06 &  4.539e-06 &  1 \tabularnewline
51 &  4.24e-06 &  8.48e-06 &  1 \tabularnewline
52 &  2.511e-06 &  5.023e-06 &  1 \tabularnewline
53 &  1.545e-06 &  3.089e-06 &  1 \tabularnewline
54 &  9.093e-07 &  1.819e-06 &  1 \tabularnewline
55 &  1.929e-06 &  3.858e-06 &  1 \tabularnewline
56 &  1.268e-06 &  2.535e-06 &  1 \tabularnewline
57 &  3.755e-06 &  7.51e-06 &  1 \tabularnewline
58 &  4.482e-06 &  8.963e-06 &  1 \tabularnewline
59 &  2.443e-05 &  4.886e-05 &  1 \tabularnewline
60 &  1.588e-05 &  3.177e-05 &  1 \tabularnewline
61 &  9.653e-06 &  1.931e-05 &  1 \tabularnewline
62 &  9.1e-06 &  1.82e-05 &  1 \tabularnewline
63 &  7.542e-06 &  1.508e-05 &  1 \tabularnewline
64 &  4.755e-06 &  9.511e-06 &  1 \tabularnewline
65 &  2.889e-06 &  5.777e-06 &  1 \tabularnewline
66 &  3.061e-06 &  6.122e-06 &  1 \tabularnewline
67 &  3.743e-05 &  7.486e-05 &  1 \tabularnewline
68 &  2.323e-05 &  4.647e-05 &  1 \tabularnewline
69 &  1.48e-05 &  2.96e-05 &  1 \tabularnewline
70 &  1.116e-05 &  2.232e-05 &  1 \tabularnewline
71 &  1.08e-05 &  2.16e-05 &  1 \tabularnewline
72 &  6.671e-06 &  1.334e-05 &  1 \tabularnewline
73 &  4.756e-06 &  9.512e-06 &  1 \tabularnewline
74 &  2.819e-06 &  5.638e-06 &  1 \tabularnewline
75 &  1.986e-06 &  3.972e-06 &  1 \tabularnewline
76 &  1.154e-06 &  2.308e-06 &  1 \tabularnewline
77 &  6.604e-07 &  1.321e-06 &  1 \tabularnewline
78 &  4.258e-07 &  8.516e-07 &  1 \tabularnewline
79 &  9.839e-07 &  1.968e-06 &  1 \tabularnewline
80 &  6.229e-07 &  1.246e-06 &  1 \tabularnewline
81 &  3.804e-07 &  7.608e-07 &  1 \tabularnewline
82 &  3.454e-07 &  6.908e-07 &  1 \tabularnewline
83 &  8.254e-07 &  1.651e-06 &  1 \tabularnewline
84 &  9.835e-07 &  1.967e-06 &  1 \tabularnewline
85 &  1.211e-06 &  2.422e-06 &  1 \tabularnewline
86 &  1.545e-06 &  3.09e-06 &  1 \tabularnewline
87 &  1.59e-06 &  3.179e-06 &  1 \tabularnewline
88 &  9.504e-07 &  1.901e-06 &  1 \tabularnewline
89 &  1.184e-06 &  2.367e-06 &  1 \tabularnewline
90 &  1.434e-06 &  2.867e-06 &  1 \tabularnewline
91 &  1.057e-06 &  2.114e-06 &  1 \tabularnewline
92 &  6.215e-06 &  1.243e-05 &  1 \tabularnewline
93 &  3.86e-06 &  7.72e-06 &  1 \tabularnewline
94 &  2.483e-06 &  4.966e-06 &  1 \tabularnewline
95 &  3.887e-06 &  7.774e-06 &  1 \tabularnewline
96 &  3.287e-06 &  6.575e-06 &  1 \tabularnewline
97 &  3.564e-06 &  7.128e-06 &  1 \tabularnewline
98 &  2.29e-06 &  4.579e-06 &  1 \tabularnewline
99 &  1.565e-06 &  3.13e-06 &  1 \tabularnewline
100 &  9.63e-07 &  1.926e-06 &  1 \tabularnewline
101 &  9.736e-07 &  1.947e-06 &  1 \tabularnewline
102 &  6.61e-07 &  1.322e-06 &  1 \tabularnewline
103 &  6.723e-07 &  1.345e-06 &  1 \tabularnewline
104 &  1.307e-06 &  2.613e-06 &  1 \tabularnewline
105 &  9.001e-07 &  1.8e-06 &  1 \tabularnewline
106 &  6.05e-07 &  1.21e-06 &  1 \tabularnewline
107 &  6.509e-07 &  1.302e-06 &  1 \tabularnewline
108 &  4.799e-07 &  9.598e-07 &  1 \tabularnewline
109 &  3.213e-07 &  6.427e-07 &  1 \tabularnewline
110 &  2.478e-07 &  4.956e-07 &  1 \tabularnewline
111 &  1.493e-07 &  2.987e-07 &  1 \tabularnewline
112 &  1.334e-07 &  2.667e-07 &  1 \tabularnewline
113 &  2.957e-06 &  5.915e-06 &  1 \tabularnewline
114 &  1.861e-06 &  3.721e-06 &  1 \tabularnewline
115 &  1.35e-06 &  2.701e-06 &  1 \tabularnewline
116 &  5.171e-06 &  1.034e-05 &  1 \tabularnewline
117 &  3.494e-06 &  6.988e-06 &  1 \tabularnewline
118 &  3.32e-06 &  6.639e-06 &  1 \tabularnewline
119 &  2.083e-06 &  4.166e-06 &  1 \tabularnewline
120 &  4.58e-06 &  9.16e-06 &  1 \tabularnewline
121 &  3.923e-06 &  7.847e-06 &  1 \tabularnewline
122 &  1.483e-05 &  2.965e-05 &  1 \tabularnewline
123 &  7.09e-05 &  0.0001418 &  0.9999 \tabularnewline
124 &  0.0001701 &  0.0003402 &  0.9998 \tabularnewline
125 &  0.0001305 &  0.000261 &  0.9999 \tabularnewline
126 &  9.422e-05 &  0.0001884 &  0.9999 \tabularnewline
127 &  6.298e-05 &  0.000126 &  0.9999 \tabularnewline
128 &  7.623e-05 &  0.0001525 &  0.9999 \tabularnewline
129 &  0.0001143 &  0.0002285 &  0.9999 \tabularnewline
130 &  8.584e-05 &  0.0001717 &  0.9999 \tabularnewline
131 &  6.328e-05 &  0.0001266 &  0.9999 \tabularnewline
132 &  6.216e-05 &  0.0001243 &  0.9999 \tabularnewline
133 &  5.998e-05 &  0.00012 &  0.9999 \tabularnewline
134 &  4.438e-05 &  8.877e-05 &  1 \tabularnewline
135 &  3.155e-05 &  6.309e-05 &  1 \tabularnewline
136 &  2.873e-05 &  5.745e-05 &  1 \tabularnewline
137 &  0.000411 &  0.000822 &  0.9996 \tabularnewline
138 &  0.0003459 &  0.0006918 &  0.9997 \tabularnewline
139 &  0.0004282 &  0.0008564 &  0.9996 \tabularnewline
140 &  0.000314 &  0.0006281 &  0.9997 \tabularnewline
141 &  0.0005327 &  0.001065 &  0.9995 \tabularnewline
142 &  0.000661 &  0.001322 &  0.9993 \tabularnewline
143 &  0.0005058 &  0.001012 &  0.9995 \tabularnewline
144 &  0.001478 &  0.002956 &  0.9985 \tabularnewline
145 &  0.001547 &  0.003094 &  0.9985 \tabularnewline
146 &  0.001714 &  0.003428 &  0.9983 \tabularnewline
147 &  0.001215 &  0.00243 &  0.9988 \tabularnewline
148 &  0.0008897 &  0.001779 &  0.9991 \tabularnewline
149 &  0.0008312 &  0.001662 &  0.9992 \tabularnewline
150 &  0.0006076 &  0.001215 &  0.9994 \tabularnewline
151 &  0.002278 &  0.004555 &  0.9977 \tabularnewline
152 &  0.001732 &  0.003464 &  0.9983 \tabularnewline
153 &  0.001235 &  0.002471 &  0.9988 \tabularnewline
154 &  0.01461 &  0.02922 &  0.9854 \tabularnewline
155 &  0.01314 &  0.02628 &  0.9869 \tabularnewline
156 &  0.01341 &  0.02683 &  0.9866 \tabularnewline
157 &  0.01924 &  0.03849 &  0.9808 \tabularnewline
158 &  0.01476 &  0.02952 &  0.9852 \tabularnewline
159 &  0.01412 &  0.02823 &  0.9859 \tabularnewline
160 &  0.01117 &  0.02235 &  0.9888 \tabularnewline
161 &  0.01077 &  0.02153 &  0.9892 \tabularnewline
162 &  0.01213 &  0.02425 &  0.9879 \tabularnewline
163 &  0.01093 &  0.02185 &  0.9891 \tabularnewline
164 &  0.02171 &  0.04341 &  0.9783 \tabularnewline
165 &  0.02856 &  0.05712 &  0.9714 \tabularnewline
166 &  0.02084 &  0.04168 &  0.9792 \tabularnewline
167 &  0.01626 &  0.03251 &  0.9837 \tabularnewline
168 &  0.01145 &  0.0229 &  0.9885 \tabularnewline
169 &  0.008677 &  0.01735 &  0.9913 \tabularnewline
170 &  0.00796 &  0.01592 &  0.992 \tabularnewline
171 &  0.01354 &  0.02709 &  0.9865 \tabularnewline
172 &  0.01044 &  0.02088 &  0.9896 \tabularnewline
173 &  0.01665 &  0.0333 &  0.9834 \tabularnewline
174 &  0.01616 &  0.03232 &  0.9838 \tabularnewline
175 &  0.01808 &  0.03616 &  0.9819 \tabularnewline
176 &  0.02191 &  0.04382 &  0.9781 \tabularnewline
177 &  0.01547 &  0.03094 &  0.9845 \tabularnewline
178 &  0.01008 &  0.02015 &  0.9899 \tabularnewline
179 &  0.006439 &  0.01288 &  0.9936 \tabularnewline
180 &  0.008169 &  0.01634 &  0.9918 \tabularnewline
181 &  0.03797 &  0.07595 &  0.962 \tabularnewline
182 &  0.0258 &  0.0516 &  0.9742 \tabularnewline
183 &  0.05339 &  0.1068 &  0.9466 \tabularnewline
184 &  0.06893 &  0.1379 &  0.9311 \tabularnewline
185 &  0.2116 &  0.4231 &  0.7884 \tabularnewline
186 &  0.8715 &  0.257 &  0.1285 \tabularnewline
187 &  0.8175 &  0.365 &  0.1825 \tabularnewline
188 &  0.7829 &  0.4343 &  0.2172 \tabularnewline
189 &  0.7525 &  0.4951 &  0.2475 \tabularnewline
190 &  0.6332 &  0.7336 &  0.3668 \tabularnewline
191 &  0.4898 &  0.9797 &  0.5102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309506&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]8[/C][C] 0.001903[/C][C] 0.003807[/C][C] 0.9981[/C][/ROW]
[ROW][C]9[/C][C] 0.000329[/C][C] 0.000658[/C][C] 0.9997[/C][/ROW]
[ROW][C]10[/C][C] 0.0008031[/C][C] 0.001606[/C][C] 0.9992[/C][/ROW]
[ROW][C]11[/C][C] 0.001891[/C][C] 0.003781[/C][C] 0.9981[/C][/ROW]
[ROW][C]12[/C][C] 0.0005734[/C][C] 0.001147[/C][C] 0.9994[/C][/ROW]
[ROW][C]13[/C][C] 0.0002247[/C][C] 0.0004495[/C][C] 0.9998[/C][/ROW]
[ROW][C]14[/C][C] 0.000135[/C][C] 0.0002701[/C][C] 0.9999[/C][/ROW]
[ROW][C]15[/C][C] 8.167e-05[/C][C] 0.0001633[/C][C] 0.9999[/C][/ROW]
[ROW][C]16[/C][C] 5.346e-05[/C][C] 0.0001069[/C][C] 0.9999[/C][/ROW]
[ROW][C]17[/C][C] 0.0007927[/C][C] 0.001585[/C][C] 0.9992[/C][/ROW]
[ROW][C]18[/C][C] 0.0003623[/C][C] 0.0007245[/C][C] 0.9996[/C][/ROW]
[ROW][C]19[/C][C] 0.0007325[/C][C] 0.001465[/C][C] 0.9993[/C][/ROW]
[ROW][C]20[/C][C] 0.0007722[/C][C] 0.001544[/C][C] 0.9992[/C][/ROW]
[ROW][C]21[/C][C] 0.0008729[/C][C] 0.001746[/C][C] 0.9991[/C][/ROW]
[ROW][C]22[/C][C] 0.001195[/C][C] 0.002389[/C][C] 0.9988[/C][/ROW]
[ROW][C]23[/C][C] 0.0007746[/C][C] 0.001549[/C][C] 0.9992[/C][/ROW]
[ROW][C]24[/C][C] 0.0007156[/C][C] 0.001431[/C][C] 0.9993[/C][/ROW]
[ROW][C]25[/C][C] 0.000424[/C][C] 0.0008481[/C][C] 0.9996[/C][/ROW]
[ROW][C]26[/C][C] 0.0002057[/C][C] 0.0004114[/C][C] 0.9998[/C][/ROW]
[ROW][C]27[/C][C] 0.0001814[/C][C] 0.0003629[/C][C] 0.9998[/C][/ROW]
[ROW][C]28[/C][C] 8.619e-05[/C][C] 0.0001724[/C][C] 0.9999[/C][/ROW]
[ROW][C]29[/C][C] 6.615e-05[/C][C] 0.0001323[/C][C] 0.9999[/C][/ROW]
[ROW][C]30[/C][C] 3.29e-05[/C][C] 6.579e-05[/C][C] 1[/C][/ROW]
[ROW][C]31[/C][C] 1.629e-05[/C][C] 3.257e-05[/C][C] 1[/C][/ROW]
[ROW][C]32[/C][C] 9.403e-06[/C][C] 1.881e-05[/C][C] 1[/C][/ROW]
[ROW][C]33[/C][C] 4.819e-06[/C][C] 9.637e-06[/C][C] 1[/C][/ROW]
[ROW][C]34[/C][C] 2.733e-06[/C][C] 5.466e-06[/C][C] 1[/C][/ROW]
[ROW][C]35[/C][C] 1.284e-06[/C][C] 2.568e-06[/C][C] 1[/C][/ROW]
[ROW][C]36[/C][C] 8.524e-07[/C][C] 1.705e-06[/C][C] 1[/C][/ROW]
[ROW][C]37[/C][C] 1.003e-06[/C][C] 2.007e-06[/C][C] 1[/C][/ROW]
[ROW][C]38[/C][C] 2.396e-05[/C][C] 4.792e-05[/C][C] 1[/C][/ROW]
[ROW][C]39[/C][C] 4.475e-05[/C][C] 8.949e-05[/C][C] 1[/C][/ROW]
[ROW][C]40[/C][C] 2.346e-05[/C][C] 4.692e-05[/C][C] 1[/C][/ROW]
[ROW][C]41[/C][C] 1.803e-05[/C][C] 3.607e-05[/C][C] 1[/C][/ROW]
[ROW][C]42[/C][C] 1.133e-05[/C][C] 2.266e-05[/C][C] 1[/C][/ROW]
[ROW][C]43[/C][C] 1.453e-05[/C][C] 2.906e-05[/C][C] 1[/C][/ROW]
[ROW][C]44[/C][C] 2.11e-05[/C][C] 4.22e-05[/C][C] 1[/C][/ROW]
[ROW][C]45[/C][C] 1.861e-05[/C][C] 3.722e-05[/C][C] 1[/C][/ROW]
[ROW][C]46[/C][C] 1.16e-05[/C][C] 2.321e-05[/C][C] 1[/C][/ROW]
[ROW][C]47[/C][C] 8.52e-06[/C][C] 1.704e-05[/C][C] 1[/C][/ROW]
[ROW][C]48[/C][C] 5.113e-06[/C][C] 1.023e-05[/C][C] 1[/C][/ROW]
[ROW][C]49[/C][C] 3.861e-06[/C][C] 7.722e-06[/C][C] 1[/C][/ROW]
[ROW][C]50[/C][C] 2.27e-06[/C][C] 4.539e-06[/C][C] 1[/C][/ROW]
[ROW][C]51[/C][C] 4.24e-06[/C][C] 8.48e-06[/C][C] 1[/C][/ROW]
[ROW][C]52[/C][C] 2.511e-06[/C][C] 5.023e-06[/C][C] 1[/C][/ROW]
[ROW][C]53[/C][C] 1.545e-06[/C][C] 3.089e-06[/C][C] 1[/C][/ROW]
[ROW][C]54[/C][C] 9.093e-07[/C][C] 1.819e-06[/C][C] 1[/C][/ROW]
[ROW][C]55[/C][C] 1.929e-06[/C][C] 3.858e-06[/C][C] 1[/C][/ROW]
[ROW][C]56[/C][C] 1.268e-06[/C][C] 2.535e-06[/C][C] 1[/C][/ROW]
[ROW][C]57[/C][C] 3.755e-06[/C][C] 7.51e-06[/C][C] 1[/C][/ROW]
[ROW][C]58[/C][C] 4.482e-06[/C][C] 8.963e-06[/C][C] 1[/C][/ROW]
[ROW][C]59[/C][C] 2.443e-05[/C][C] 4.886e-05[/C][C] 1[/C][/ROW]
[ROW][C]60[/C][C] 1.588e-05[/C][C] 3.177e-05[/C][C] 1[/C][/ROW]
[ROW][C]61[/C][C] 9.653e-06[/C][C] 1.931e-05[/C][C] 1[/C][/ROW]
[ROW][C]62[/C][C] 9.1e-06[/C][C] 1.82e-05[/C][C] 1[/C][/ROW]
[ROW][C]63[/C][C] 7.542e-06[/C][C] 1.508e-05[/C][C] 1[/C][/ROW]
[ROW][C]64[/C][C] 4.755e-06[/C][C] 9.511e-06[/C][C] 1[/C][/ROW]
[ROW][C]65[/C][C] 2.889e-06[/C][C] 5.777e-06[/C][C] 1[/C][/ROW]
[ROW][C]66[/C][C] 3.061e-06[/C][C] 6.122e-06[/C][C] 1[/C][/ROW]
[ROW][C]67[/C][C] 3.743e-05[/C][C] 7.486e-05[/C][C] 1[/C][/ROW]
[ROW][C]68[/C][C] 2.323e-05[/C][C] 4.647e-05[/C][C] 1[/C][/ROW]
[ROW][C]69[/C][C] 1.48e-05[/C][C] 2.96e-05[/C][C] 1[/C][/ROW]
[ROW][C]70[/C][C] 1.116e-05[/C][C] 2.232e-05[/C][C] 1[/C][/ROW]
[ROW][C]71[/C][C] 1.08e-05[/C][C] 2.16e-05[/C][C] 1[/C][/ROW]
[ROW][C]72[/C][C] 6.671e-06[/C][C] 1.334e-05[/C][C] 1[/C][/ROW]
[ROW][C]73[/C][C] 4.756e-06[/C][C] 9.512e-06[/C][C] 1[/C][/ROW]
[ROW][C]74[/C][C] 2.819e-06[/C][C] 5.638e-06[/C][C] 1[/C][/ROW]
[ROW][C]75[/C][C] 1.986e-06[/C][C] 3.972e-06[/C][C] 1[/C][/ROW]
[ROW][C]76[/C][C] 1.154e-06[/C][C] 2.308e-06[/C][C] 1[/C][/ROW]
[ROW][C]77[/C][C] 6.604e-07[/C][C] 1.321e-06[/C][C] 1[/C][/ROW]
[ROW][C]78[/C][C] 4.258e-07[/C][C] 8.516e-07[/C][C] 1[/C][/ROW]
[ROW][C]79[/C][C] 9.839e-07[/C][C] 1.968e-06[/C][C] 1[/C][/ROW]
[ROW][C]80[/C][C] 6.229e-07[/C][C] 1.246e-06[/C][C] 1[/C][/ROW]
[ROW][C]81[/C][C] 3.804e-07[/C][C] 7.608e-07[/C][C] 1[/C][/ROW]
[ROW][C]82[/C][C] 3.454e-07[/C][C] 6.908e-07[/C][C] 1[/C][/ROW]
[ROW][C]83[/C][C] 8.254e-07[/C][C] 1.651e-06[/C][C] 1[/C][/ROW]
[ROW][C]84[/C][C] 9.835e-07[/C][C] 1.967e-06[/C][C] 1[/C][/ROW]
[ROW][C]85[/C][C] 1.211e-06[/C][C] 2.422e-06[/C][C] 1[/C][/ROW]
[ROW][C]86[/C][C] 1.545e-06[/C][C] 3.09e-06[/C][C] 1[/C][/ROW]
[ROW][C]87[/C][C] 1.59e-06[/C][C] 3.179e-06[/C][C] 1[/C][/ROW]
[ROW][C]88[/C][C] 9.504e-07[/C][C] 1.901e-06[/C][C] 1[/C][/ROW]
[ROW][C]89[/C][C] 1.184e-06[/C][C] 2.367e-06[/C][C] 1[/C][/ROW]
[ROW][C]90[/C][C] 1.434e-06[/C][C] 2.867e-06[/C][C] 1[/C][/ROW]
[ROW][C]91[/C][C] 1.057e-06[/C][C] 2.114e-06[/C][C] 1[/C][/ROW]
[ROW][C]92[/C][C] 6.215e-06[/C][C] 1.243e-05[/C][C] 1[/C][/ROW]
[ROW][C]93[/C][C] 3.86e-06[/C][C] 7.72e-06[/C][C] 1[/C][/ROW]
[ROW][C]94[/C][C] 2.483e-06[/C][C] 4.966e-06[/C][C] 1[/C][/ROW]
[ROW][C]95[/C][C] 3.887e-06[/C][C] 7.774e-06[/C][C] 1[/C][/ROW]
[ROW][C]96[/C][C] 3.287e-06[/C][C] 6.575e-06[/C][C] 1[/C][/ROW]
[ROW][C]97[/C][C] 3.564e-06[/C][C] 7.128e-06[/C][C] 1[/C][/ROW]
[ROW][C]98[/C][C] 2.29e-06[/C][C] 4.579e-06[/C][C] 1[/C][/ROW]
[ROW][C]99[/C][C] 1.565e-06[/C][C] 3.13e-06[/C][C] 1[/C][/ROW]
[ROW][C]100[/C][C] 9.63e-07[/C][C] 1.926e-06[/C][C] 1[/C][/ROW]
[ROW][C]101[/C][C] 9.736e-07[/C][C] 1.947e-06[/C][C] 1[/C][/ROW]
[ROW][C]102[/C][C] 6.61e-07[/C][C] 1.322e-06[/C][C] 1[/C][/ROW]
[ROW][C]103[/C][C] 6.723e-07[/C][C] 1.345e-06[/C][C] 1[/C][/ROW]
[ROW][C]104[/C][C] 1.307e-06[/C][C] 2.613e-06[/C][C] 1[/C][/ROW]
[ROW][C]105[/C][C] 9.001e-07[/C][C] 1.8e-06[/C][C] 1[/C][/ROW]
[ROW][C]106[/C][C] 6.05e-07[/C][C] 1.21e-06[/C][C] 1[/C][/ROW]
[ROW][C]107[/C][C] 6.509e-07[/C][C] 1.302e-06[/C][C] 1[/C][/ROW]
[ROW][C]108[/C][C] 4.799e-07[/C][C] 9.598e-07[/C][C] 1[/C][/ROW]
[ROW][C]109[/C][C] 3.213e-07[/C][C] 6.427e-07[/C][C] 1[/C][/ROW]
[ROW][C]110[/C][C] 2.478e-07[/C][C] 4.956e-07[/C][C] 1[/C][/ROW]
[ROW][C]111[/C][C] 1.493e-07[/C][C] 2.987e-07[/C][C] 1[/C][/ROW]
[ROW][C]112[/C][C] 1.334e-07[/C][C] 2.667e-07[/C][C] 1[/C][/ROW]
[ROW][C]113[/C][C] 2.957e-06[/C][C] 5.915e-06[/C][C] 1[/C][/ROW]
[ROW][C]114[/C][C] 1.861e-06[/C][C] 3.721e-06[/C][C] 1[/C][/ROW]
[ROW][C]115[/C][C] 1.35e-06[/C][C] 2.701e-06[/C][C] 1[/C][/ROW]
[ROW][C]116[/C][C] 5.171e-06[/C][C] 1.034e-05[/C][C] 1[/C][/ROW]
[ROW][C]117[/C][C] 3.494e-06[/C][C] 6.988e-06[/C][C] 1[/C][/ROW]
[ROW][C]118[/C][C] 3.32e-06[/C][C] 6.639e-06[/C][C] 1[/C][/ROW]
[ROW][C]119[/C][C] 2.083e-06[/C][C] 4.166e-06[/C][C] 1[/C][/ROW]
[ROW][C]120[/C][C] 4.58e-06[/C][C] 9.16e-06[/C][C] 1[/C][/ROW]
[ROW][C]121[/C][C] 3.923e-06[/C][C] 7.847e-06[/C][C] 1[/C][/ROW]
[ROW][C]122[/C][C] 1.483e-05[/C][C] 2.965e-05[/C][C] 1[/C][/ROW]
[ROW][C]123[/C][C] 7.09e-05[/C][C] 0.0001418[/C][C] 0.9999[/C][/ROW]
[ROW][C]124[/C][C] 0.0001701[/C][C] 0.0003402[/C][C] 0.9998[/C][/ROW]
[ROW][C]125[/C][C] 0.0001305[/C][C] 0.000261[/C][C] 0.9999[/C][/ROW]
[ROW][C]126[/C][C] 9.422e-05[/C][C] 0.0001884[/C][C] 0.9999[/C][/ROW]
[ROW][C]127[/C][C] 6.298e-05[/C][C] 0.000126[/C][C] 0.9999[/C][/ROW]
[ROW][C]128[/C][C] 7.623e-05[/C][C] 0.0001525[/C][C] 0.9999[/C][/ROW]
[ROW][C]129[/C][C] 0.0001143[/C][C] 0.0002285[/C][C] 0.9999[/C][/ROW]
[ROW][C]130[/C][C] 8.584e-05[/C][C] 0.0001717[/C][C] 0.9999[/C][/ROW]
[ROW][C]131[/C][C] 6.328e-05[/C][C] 0.0001266[/C][C] 0.9999[/C][/ROW]
[ROW][C]132[/C][C] 6.216e-05[/C][C] 0.0001243[/C][C] 0.9999[/C][/ROW]
[ROW][C]133[/C][C] 5.998e-05[/C][C] 0.00012[/C][C] 0.9999[/C][/ROW]
[ROW][C]134[/C][C] 4.438e-05[/C][C] 8.877e-05[/C][C] 1[/C][/ROW]
[ROW][C]135[/C][C] 3.155e-05[/C][C] 6.309e-05[/C][C] 1[/C][/ROW]
[ROW][C]136[/C][C] 2.873e-05[/C][C] 5.745e-05[/C][C] 1[/C][/ROW]
[ROW][C]137[/C][C] 0.000411[/C][C] 0.000822[/C][C] 0.9996[/C][/ROW]
[ROW][C]138[/C][C] 0.0003459[/C][C] 0.0006918[/C][C] 0.9997[/C][/ROW]
[ROW][C]139[/C][C] 0.0004282[/C][C] 0.0008564[/C][C] 0.9996[/C][/ROW]
[ROW][C]140[/C][C] 0.000314[/C][C] 0.0006281[/C][C] 0.9997[/C][/ROW]
[ROW][C]141[/C][C] 0.0005327[/C][C] 0.001065[/C][C] 0.9995[/C][/ROW]
[ROW][C]142[/C][C] 0.000661[/C][C] 0.001322[/C][C] 0.9993[/C][/ROW]
[ROW][C]143[/C][C] 0.0005058[/C][C] 0.001012[/C][C] 0.9995[/C][/ROW]
[ROW][C]144[/C][C] 0.001478[/C][C] 0.002956[/C][C] 0.9985[/C][/ROW]
[ROW][C]145[/C][C] 0.001547[/C][C] 0.003094[/C][C] 0.9985[/C][/ROW]
[ROW][C]146[/C][C] 0.001714[/C][C] 0.003428[/C][C] 0.9983[/C][/ROW]
[ROW][C]147[/C][C] 0.001215[/C][C] 0.00243[/C][C] 0.9988[/C][/ROW]
[ROW][C]148[/C][C] 0.0008897[/C][C] 0.001779[/C][C] 0.9991[/C][/ROW]
[ROW][C]149[/C][C] 0.0008312[/C][C] 0.001662[/C][C] 0.9992[/C][/ROW]
[ROW][C]150[/C][C] 0.0006076[/C][C] 0.001215[/C][C] 0.9994[/C][/ROW]
[ROW][C]151[/C][C] 0.002278[/C][C] 0.004555[/C][C] 0.9977[/C][/ROW]
[ROW][C]152[/C][C] 0.001732[/C][C] 0.003464[/C][C] 0.9983[/C][/ROW]
[ROW][C]153[/C][C] 0.001235[/C][C] 0.002471[/C][C] 0.9988[/C][/ROW]
[ROW][C]154[/C][C] 0.01461[/C][C] 0.02922[/C][C] 0.9854[/C][/ROW]
[ROW][C]155[/C][C] 0.01314[/C][C] 0.02628[/C][C] 0.9869[/C][/ROW]
[ROW][C]156[/C][C] 0.01341[/C][C] 0.02683[/C][C] 0.9866[/C][/ROW]
[ROW][C]157[/C][C] 0.01924[/C][C] 0.03849[/C][C] 0.9808[/C][/ROW]
[ROW][C]158[/C][C] 0.01476[/C][C] 0.02952[/C][C] 0.9852[/C][/ROW]
[ROW][C]159[/C][C] 0.01412[/C][C] 0.02823[/C][C] 0.9859[/C][/ROW]
[ROW][C]160[/C][C] 0.01117[/C][C] 0.02235[/C][C] 0.9888[/C][/ROW]
[ROW][C]161[/C][C] 0.01077[/C][C] 0.02153[/C][C] 0.9892[/C][/ROW]
[ROW][C]162[/C][C] 0.01213[/C][C] 0.02425[/C][C] 0.9879[/C][/ROW]
[ROW][C]163[/C][C] 0.01093[/C][C] 0.02185[/C][C] 0.9891[/C][/ROW]
[ROW][C]164[/C][C] 0.02171[/C][C] 0.04341[/C][C] 0.9783[/C][/ROW]
[ROW][C]165[/C][C] 0.02856[/C][C] 0.05712[/C][C] 0.9714[/C][/ROW]
[ROW][C]166[/C][C] 0.02084[/C][C] 0.04168[/C][C] 0.9792[/C][/ROW]
[ROW][C]167[/C][C] 0.01626[/C][C] 0.03251[/C][C] 0.9837[/C][/ROW]
[ROW][C]168[/C][C] 0.01145[/C][C] 0.0229[/C][C] 0.9885[/C][/ROW]
[ROW][C]169[/C][C] 0.008677[/C][C] 0.01735[/C][C] 0.9913[/C][/ROW]
[ROW][C]170[/C][C] 0.00796[/C][C] 0.01592[/C][C] 0.992[/C][/ROW]
[ROW][C]171[/C][C] 0.01354[/C][C] 0.02709[/C][C] 0.9865[/C][/ROW]
[ROW][C]172[/C][C] 0.01044[/C][C] 0.02088[/C][C] 0.9896[/C][/ROW]
[ROW][C]173[/C][C] 0.01665[/C][C] 0.0333[/C][C] 0.9834[/C][/ROW]
[ROW][C]174[/C][C] 0.01616[/C][C] 0.03232[/C][C] 0.9838[/C][/ROW]
[ROW][C]175[/C][C] 0.01808[/C][C] 0.03616[/C][C] 0.9819[/C][/ROW]
[ROW][C]176[/C][C] 0.02191[/C][C] 0.04382[/C][C] 0.9781[/C][/ROW]
[ROW][C]177[/C][C] 0.01547[/C][C] 0.03094[/C][C] 0.9845[/C][/ROW]
[ROW][C]178[/C][C] 0.01008[/C][C] 0.02015[/C][C] 0.9899[/C][/ROW]
[ROW][C]179[/C][C] 0.006439[/C][C] 0.01288[/C][C] 0.9936[/C][/ROW]
[ROW][C]180[/C][C] 0.008169[/C][C] 0.01634[/C][C] 0.9918[/C][/ROW]
[ROW][C]181[/C][C] 0.03797[/C][C] 0.07595[/C][C] 0.962[/C][/ROW]
[ROW][C]182[/C][C] 0.0258[/C][C] 0.0516[/C][C] 0.9742[/C][/ROW]
[ROW][C]183[/C][C] 0.05339[/C][C] 0.1068[/C][C] 0.9466[/C][/ROW]
[ROW][C]184[/C][C] 0.06893[/C][C] 0.1379[/C][C] 0.9311[/C][/ROW]
[ROW][C]185[/C][C] 0.2116[/C][C] 0.4231[/C][C] 0.7884[/C][/ROW]
[ROW][C]186[/C][C] 0.8715[/C][C] 0.257[/C][C] 0.1285[/C][/ROW]
[ROW][C]187[/C][C] 0.8175[/C][C] 0.365[/C][C] 0.1825[/C][/ROW]
[ROW][C]188[/C][C] 0.7829[/C][C] 0.4343[/C][C] 0.2172[/C][/ROW]
[ROW][C]189[/C][C] 0.7525[/C][C] 0.4951[/C][C] 0.2475[/C][/ROW]
[ROW][C]190[/C][C] 0.6332[/C][C] 0.7336[/C][C] 0.3668[/C][/ROW]
[ROW][C]191[/C][C] 0.4898[/C][C] 0.9797[/C][C] 0.5102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309506&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309506&T=6

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8 0.001903 0.003807 0.9981
9 0.000329 0.000658 0.9997
10 0.0008031 0.001606 0.9992
11 0.001891 0.003781 0.9981
12 0.0005734 0.001147 0.9994
13 0.0002247 0.0004495 0.9998
14 0.000135 0.0002701 0.9999
15 8.167e-05 0.0001633 0.9999
16 5.346e-05 0.0001069 0.9999
17 0.0007927 0.001585 0.9992
18 0.0003623 0.0007245 0.9996
19 0.0007325 0.001465 0.9993
20 0.0007722 0.001544 0.9992
21 0.0008729 0.001746 0.9991
22 0.001195 0.002389 0.9988
23 0.0007746 0.001549 0.9992
24 0.0007156 0.001431 0.9993
25 0.000424 0.0008481 0.9996
26 0.0002057 0.0004114 0.9998
27 0.0001814 0.0003629 0.9998
28 8.619e-05 0.0001724 0.9999
29 6.615e-05 0.0001323 0.9999
30 3.29e-05 6.579e-05 1
31 1.629e-05 3.257e-05 1
32 9.403e-06 1.881e-05 1
33 4.819e-06 9.637e-06 1
34 2.733e-06 5.466e-06 1
35 1.284e-06 2.568e-06 1
36 8.524e-07 1.705e-06 1
37 1.003e-06 2.007e-06 1
38 2.396e-05 4.792e-05 1
39 4.475e-05 8.949e-05 1
40 2.346e-05 4.692e-05 1
41 1.803e-05 3.607e-05 1
42 1.133e-05 2.266e-05 1
43 1.453e-05 2.906e-05 1
44 2.11e-05 4.22e-05 1
45 1.861e-05 3.722e-05 1
46 1.16e-05 2.321e-05 1
47 8.52e-06 1.704e-05 1
48 5.113e-06 1.023e-05 1
49 3.861e-06 7.722e-06 1
50 2.27e-06 4.539e-06 1
51 4.24e-06 8.48e-06 1
52 2.511e-06 5.023e-06 1
53 1.545e-06 3.089e-06 1
54 9.093e-07 1.819e-06 1
55 1.929e-06 3.858e-06 1
56 1.268e-06 2.535e-06 1
57 3.755e-06 7.51e-06 1
58 4.482e-06 8.963e-06 1
59 2.443e-05 4.886e-05 1
60 1.588e-05 3.177e-05 1
61 9.653e-06 1.931e-05 1
62 9.1e-06 1.82e-05 1
63 7.542e-06 1.508e-05 1
64 4.755e-06 9.511e-06 1
65 2.889e-06 5.777e-06 1
66 3.061e-06 6.122e-06 1
67 3.743e-05 7.486e-05 1
68 2.323e-05 4.647e-05 1
69 1.48e-05 2.96e-05 1
70 1.116e-05 2.232e-05 1
71 1.08e-05 2.16e-05 1
72 6.671e-06 1.334e-05 1
73 4.756e-06 9.512e-06 1
74 2.819e-06 5.638e-06 1
75 1.986e-06 3.972e-06 1
76 1.154e-06 2.308e-06 1
77 6.604e-07 1.321e-06 1
78 4.258e-07 8.516e-07 1
79 9.839e-07 1.968e-06 1
80 6.229e-07 1.246e-06 1
81 3.804e-07 7.608e-07 1
82 3.454e-07 6.908e-07 1
83 8.254e-07 1.651e-06 1
84 9.835e-07 1.967e-06 1
85 1.211e-06 2.422e-06 1
86 1.545e-06 3.09e-06 1
87 1.59e-06 3.179e-06 1
88 9.504e-07 1.901e-06 1
89 1.184e-06 2.367e-06 1
90 1.434e-06 2.867e-06 1
91 1.057e-06 2.114e-06 1
92 6.215e-06 1.243e-05 1
93 3.86e-06 7.72e-06 1
94 2.483e-06 4.966e-06 1
95 3.887e-06 7.774e-06 1
96 3.287e-06 6.575e-06 1
97 3.564e-06 7.128e-06 1
98 2.29e-06 4.579e-06 1
99 1.565e-06 3.13e-06 1
100 9.63e-07 1.926e-06 1
101 9.736e-07 1.947e-06 1
102 6.61e-07 1.322e-06 1
103 6.723e-07 1.345e-06 1
104 1.307e-06 2.613e-06 1
105 9.001e-07 1.8e-06 1
106 6.05e-07 1.21e-06 1
107 6.509e-07 1.302e-06 1
108 4.799e-07 9.598e-07 1
109 3.213e-07 6.427e-07 1
110 2.478e-07 4.956e-07 1
111 1.493e-07 2.987e-07 1
112 1.334e-07 2.667e-07 1
113 2.957e-06 5.915e-06 1
114 1.861e-06 3.721e-06 1
115 1.35e-06 2.701e-06 1
116 5.171e-06 1.034e-05 1
117 3.494e-06 6.988e-06 1
118 3.32e-06 6.639e-06 1
119 2.083e-06 4.166e-06 1
120 4.58e-06 9.16e-06 1
121 3.923e-06 7.847e-06 1
122 1.483e-05 2.965e-05 1
123 7.09e-05 0.0001418 0.9999
124 0.0001701 0.0003402 0.9998
125 0.0001305 0.000261 0.9999
126 9.422e-05 0.0001884 0.9999
127 6.298e-05 0.000126 0.9999
128 7.623e-05 0.0001525 0.9999
129 0.0001143 0.0002285 0.9999
130 8.584e-05 0.0001717 0.9999
131 6.328e-05 0.0001266 0.9999
132 6.216e-05 0.0001243 0.9999
133 5.998e-05 0.00012 0.9999
134 4.438e-05 8.877e-05 1
135 3.155e-05 6.309e-05 1
136 2.873e-05 5.745e-05 1
137 0.000411 0.000822 0.9996
138 0.0003459 0.0006918 0.9997
139 0.0004282 0.0008564 0.9996
140 0.000314 0.0006281 0.9997
141 0.0005327 0.001065 0.9995
142 0.000661 0.001322 0.9993
143 0.0005058 0.001012 0.9995
144 0.001478 0.002956 0.9985
145 0.001547 0.003094 0.9985
146 0.001714 0.003428 0.9983
147 0.001215 0.00243 0.9988
148 0.0008897 0.001779 0.9991
149 0.0008312 0.001662 0.9992
150 0.0006076 0.001215 0.9994
151 0.002278 0.004555 0.9977
152 0.001732 0.003464 0.9983
153 0.001235 0.002471 0.9988
154 0.01461 0.02922 0.9854
155 0.01314 0.02628 0.9869
156 0.01341 0.02683 0.9866
157 0.01924 0.03849 0.9808
158 0.01476 0.02952 0.9852
159 0.01412 0.02823 0.9859
160 0.01117 0.02235 0.9888
161 0.01077 0.02153 0.9892
162 0.01213 0.02425 0.9879
163 0.01093 0.02185 0.9891
164 0.02171 0.04341 0.9783
165 0.02856 0.05712 0.9714
166 0.02084 0.04168 0.9792
167 0.01626 0.03251 0.9837
168 0.01145 0.0229 0.9885
169 0.008677 0.01735 0.9913
170 0.00796 0.01592 0.992
171 0.01354 0.02709 0.9865
172 0.01044 0.02088 0.9896
173 0.01665 0.0333 0.9834
174 0.01616 0.03232 0.9838
175 0.01808 0.03616 0.9819
176 0.02191 0.04382 0.9781
177 0.01547 0.03094 0.9845
178 0.01008 0.02015 0.9899
179 0.006439 0.01288 0.9936
180 0.008169 0.01634 0.9918
181 0.03797 0.07595 0.962
182 0.0258 0.0516 0.9742
183 0.05339 0.1068 0.9466
184 0.06893 0.1379 0.9311
185 0.2116 0.4231 0.7884
186 0.8715 0.257 0.1285
187 0.8175 0.365 0.1825
188 0.7829 0.4343 0.2172
189 0.7525 0.4951 0.2475
190 0.6332 0.7336 0.3668
191 0.4898 0.9797 0.5102






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level146 0.7935NOK
5% type I error level1720.934783NOK
10% type I error level1750.951087NOK

\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 & 146 &  0.7935 & NOK \tabularnewline
5% type I error level & 172 & 0.934783 & NOK \tabularnewline
10% type I error level & 175 & 0.951087 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309506&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]146[/C][C] 0.7935[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]172[/C][C]0.934783[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]175[/C][C]0.951087[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309506&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309506&T=7

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level146 0.7935NOK
5% type I error level1720.934783NOK
10% type I error level1750.951087NOK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.3771, df1 = 2, df2 = 192, p-value = 0.09554
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.63899, df1 = 8, df2 = 186, p-value = 0.7443
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.0239, df1 = 2, df2 = 192, p-value = 0.1349


\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 = 2.3771, df1 = 2, df2 = 192, p-value = 0.09554

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

> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.63899, df1 = 8, df2 = 186, p-value = 0.7443

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

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.0239, df1 = 2, df2 = 192, p-value = 0.1349

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309506&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 = 2.3771, df1 = 2, df2 = 192, p-value = 0.09554

[/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 = 0.63899, df1 = 8, df2 = 186, p-value = 0.7443

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.0239, df1 = 2, df2 = 192, p-value = 0.1349

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309506&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309506&T=8

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


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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.3771, df1 = 2, df2 = 192, p-value = 0.09554
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.63899, df1 = 8, df2 = 186, p-value = 0.7443
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.0239, df1 = 2, df2 = 192, p-value = 0.1349






Variance Inflation Factors (Multicollinearity)
> vif
           Food        beverage  `Tabacco(t-1)` `Tabacco(t-1s)` 
       4.173189        1.947844        2.768644        3.397647 


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

> vif
           Food        beverage  `Tabacco(t-1)` `Tabacco(t-1s)` 
       4.173189        1.947844        2.768644        3.397647 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309506&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
           Food        beverage  `Tabacco(t-1)` `Tabacco(t-1s)` 
       4.173189        1.947844        2.768644        3.397647 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309506&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309506&T=9

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


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

Variance Inflation Factors (Multicollinearity)
> vif
           Food        beverage  `Tabacco(t-1)` `Tabacco(t-1s)` 
       4.173189        1.947844        2.768644        3.397647


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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