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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 13 Dec 2017 08:56:45 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/13/t1513151871xqdak7n6n4tymd2.htm/, Retrieved Wed, 15 May 2024 00:36:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309214, Retrieved Wed, 15 May 2024 00:36:59 +0000

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

-       [Multiple Regression] [Multiple regressi...] [2017-12-13 07:56:45] [10ffd28249f7eed11c347be075080a78] [Current]

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Dataseries X:

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Histogram

Boxplots

62.4 63.2
67.4 68.6
76.1 77.7
67.4 68.1
74.5 75.1
72.6 73.3
60.5 60.5
66.1 65.9
76.5 77.7
76.8 77.1
77 77.7
71 71.3
74.8 76
73.7 75.3
80.5 81.7
71.8 72.5
76.9 77.4
79.9 81.1
65.9 65.1
69.5 68.7
75.1 75.6
79.6 79.7
75.2 75.3
68 67.7
72.8 73.2
71.5 72.2
78.5 79.3
76.8 77.5
75.3 75.6
76.7 77.4
69.7 69.2
67.8 67.1
77.5 77.9
82.5 82.7
75.3 75.7
70.9 70.1
76 76.4
73.7 74.3
79.7 80.5
77.8 78
73.3 73.5
78.3 78.8
71.9 71.2
67 66.2
82 82.7
83.7 83.8
74.8 75
80 80.4
74.3 74.6
76.8 77.7
89 89.8
81.9 82.4
76.8 77
88.9 89.6
75.8 75.7
75.5 75.1
89.1 89.9
88 88.8
85.9 86.5
89.3 90
82.9 84
81.2 82.7
90.5 91.7
86.4 87.5
81.8 82
91.3 92.2
73.4 73.1
76.6 75.6
91 91.6
87 87.5
89.7 90.1
90.7 91.3
86.5 87.6
86.6 88.4
98.8 100.7
84.4 85.3
91.4 92
95.7 96.8
78.5 77.9
81.7 80.9
94.3 95.3
98.5 99.3
95.4 96.1
91.7 92.5
92.8 93.7
90.5 92.1
102.2 103.6
91.8 92.5
95 95.7
102 103.4
88.9 89
89.6 89.1
97.9 98.7
108.6 109.4
100.8 101.1
95.1 95.4
101 101.4
100.9 102.1
102.5 103.6
105.4 106
98.4 98.4
105.3 106.6
96.5 95.8
88.1 87.2
107.9 108.5
107 107
92.5 92
95.7 94.9
85.2 84.4
85.5 85
94.7 94
86.2 84.5
88.8 88.2
93.4 92.1
83.4 81.1
82.9 81.2
96.7 96.1
96.2 95.3
92.8 92.1
92.8 91.7
90 90.3
95.4 96.1
108.3 108.7
96.3 95.9
95 95.1
109 109.4
92 91.2
92.3 91.4
107 107.4
105.5 105.6
105.4 105.3
103.9 103.7
99.2 99.5
102.2 103.2
121.5 123.1
102.3 102.2
110 110
105.9 106.2
91.9 91.3
100 99.3
111.7 111.8
104.9 104.4
103.3 102.4
101.8 101
100.8 100.6
104.2 104.5
116.5 117.4
97.9 97.4
100.7 99.5
107 106.4
96.3 95.2
96 94
104.5 104.1
107.4 105.8
102.4 101.1
94.9 93.5
98.8 97.9
96.8 96.8
108.2 108.4
103.8 103.5
102.3 101.3
107.2 107.4
102 100.7
92.6 91.1
105.2 105
113 112.8
105.6 105.6
101.6 101
101.7 101.9
102.7 103.5
109 109.5
105.5 105
103.3 102.9
108.6 108.5
98.2 96.9
90 88.4
112.4 112.4
111.9 111.3
102.1 101.6
102.4 101.2
101.7 101.8
98.7 98.8
114 114.4
105.1 104.5
98.3 97.6
110 109.1
96.5 94.5
92.2 90.4
112 111.8
111.4 110.5
107.5 106.8
103.4 101.8
103.5 103.7
107.4 107.4
117.6 117.5
110.2 109.6
104.3 102.8
115.9 115.5
98.9 97.8
101.9 100.2
113.5 112.9
109.5 108.7
110 109
114.2 113.9
106.9 106.9
109.2 109.6
124.2 124.5
104.7 104.2
111.9 110.8
119 118.7
102.9 102.1
106.3 105.1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	12 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time12 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309214&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]

12 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309214&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309214&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	12 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
X1[t] = -1.82982 + 0.910351X2[t] + 0.0154065`X1(t-1)`[t] + 0.0303267`X1(t-2)`[t] + 0.00929613`X1(t-3)`[t] + 0.0020855`X1(t-4)`[t] + 0.054083`X1(t-1s)`[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X1[t] =  -1.82982 +  0.910351X2[t] +  0.0154065`X1(t-1)`[t] +  0.0303267`X1(t-2)`[t] +  0.00929613`X1(t-3)`[t] +  0.0020855`X1(t-4)`[t] +  0.054083`X1(t-1s)`[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309214&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X1[t] =  -1.82982 +  0.910351X2[t] +  0.0154065`X1(t-1)`[t] +  0.0303267`X1(t-2)`[t] +  0.00929613`X1(t-3)`[t] +  0.0020855`X1(t-4)`[t] +  0.054083`X1(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309214&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309214&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
X1[t] = -1.82982 + 0.910351X2[t] + 0.0154065`X1(t-1)`[t] + 0.0303267`X1(t-2)`[t] + 0.00929613`X1(t-3)`[t] + 0.0020855`X1(t-4)`[t] + 0.054083`X1(t-1s)`[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-1.83	0.3477	-5.2620e+00	3.835e-07	1.918e-07
X2	+0.9103	0.008063	+1.1290e+02	2.128e-175	1.064e-175
`X1(t-1)`	+0.01541	0.007338	+2.1000e+00	0.03709	0.01854
`X1(t-2)`	+0.03033	0.006346	+4.7790e+00	3.537e-06	1.768e-06
`X1(t-3)`	+0.009296	0.007412	+1.2540e+00	0.2113	0.1057
`X1(t-4)`	+0.002086	0.006809	+3.0630e-01	0.7597	0.3799
`X1(t-1s)`	+0.05408	0.007427	+7.2820e+00	8.642e-12	4.321e-12

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -1.83 &  0.3477 & -5.2620e+00 &  3.835e-07 &  1.918e-07 \tabularnewline
X2 & +0.9103 &  0.008063 & +1.1290e+02 &  2.128e-175 &  1.064e-175 \tabularnewline
`X1(t-1)` & +0.01541 &  0.007338 & +2.1000e+00 &  0.03709 &  0.01854 \tabularnewline
`X1(t-2)` & +0.03033 &  0.006346 & +4.7790e+00 &  3.537e-06 &  1.768e-06 \tabularnewline
`X1(t-3)` & +0.009296 &  0.007412 & +1.2540e+00 &  0.2113 &  0.1057 \tabularnewline
`X1(t-4)` & +0.002086 &  0.006809 & +3.0630e-01 &  0.7597 &  0.3799 \tabularnewline
`X1(t-1s)` & +0.05408 &  0.007427 & +7.2820e+00 &  8.642e-12 &  4.321e-12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309214&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-1.83[/C][C] 0.3477[/C][C]-5.2620e+00[/C][C] 3.835e-07[/C][C] 1.918e-07[/C][/ROW]
[ROW][C]X2[/C][C]+0.9103[/C][C] 0.008063[/C][C]+1.1290e+02[/C][C] 2.128e-175[/C][C] 1.064e-175[/C][/ROW]
[ROW][C]`X1(t-1)`[/C][C]+0.01541[/C][C] 0.007338[/C][C]+2.1000e+00[/C][C] 0.03709[/C][C] 0.01854[/C][/ROW]
[ROW][C]`X1(t-2)`[/C][C]+0.03033[/C][C] 0.006346[/C][C]+4.7790e+00[/C][C] 3.537e-06[/C][C] 1.768e-06[/C][/ROW]
[ROW][C]`X1(t-3)`[/C][C]+0.009296[/C][C] 0.007412[/C][C]+1.2540e+00[/C][C] 0.2113[/C][C] 0.1057[/C][/ROW]
[ROW][C]`X1(t-4)`[/C][C]+0.002086[/C][C] 0.006809[/C][C]+3.0630e-01[/C][C] 0.7597[/C][C] 0.3799[/C][/ROW]
[ROW][C]`X1(t-1s)`[/C][C]+0.05408[/C][C] 0.007427[/C][C]+7.2820e+00[/C][C] 8.642e-12[/C][C] 4.321e-12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309214&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309214&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-1.83	0.3477	-5.2620e+00	3.835e-07	1.918e-07
X2	+0.9103	0.008063	+1.1290e+02	2.128e-175	1.064e-175
`X1(t-1)`	+0.01541	0.007338	+2.1000e+00	0.03709	0.01854
`X1(t-2)`	+0.03033	0.006346	+4.7790e+00	3.537e-06	1.768e-06
`X1(t-3)`	+0.009296	0.007412	+1.2540e+00	0.2113	0.1057
`X1(t-4)`	+0.002086	0.006809	+3.0630e-01	0.7597	0.3799
`X1(t-1s)`	+0.05408	0.007427	+7.2820e+00	8.642e-12	4.321e-12

Multiple Linear Regression - Regression Statistics
Multiple R	0.999
R-squared	0.9979
Adjusted R-squared	0.9979
F-TEST (value)	1.524e+04
F-TEST (DF numerator)	6
F-TEST (DF denominator)	189
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.5944
Sum Squared Residuals	66.77

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.999 \tabularnewline
R-squared &  0.9979 \tabularnewline
Adjusted R-squared &  0.9979 \tabularnewline
F-TEST (value) &  1.524e+04 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 189 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.5944 \tabularnewline
Sum Squared Residuals &  66.77 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309214&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.999[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9979[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9979[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.524e+04[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]189[/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] 0.5944[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 66.77[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309214&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309214&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.999
R-squared	0.9979
Adjusted R-squared	0.9979
F-TEST (value)	1.524e+04
F-TEST (DF numerator)	6
F-TEST (DF denominator)	189
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.5944
Sum Squared Residuals	66.77

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

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309214&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=309214&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309214&T=4

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	76.9	77.05	-0.1491
2	79.9	80.19	-0.2903
3	65.9	65.1	0.7955
4	69.5	68.59	0.9108
5	75.1	75.1	-0.002487
6	79.6	78.92	0.6773
7	75.2	75.17	0.02859
8	68	68.06	-0.05649
9	72.8	73.08	-0.2781
10	71.5	71.93	-0.4323
11	78.5	78.81	-0.313
12	76.8	76.8	-0.001888
13	75.3	75.53	-0.2321
14	76.7	77.32	-0.6206
15	69.7	69.07	0.6265
16	67.8	67.27	0.5264
17	77.5	77.18	0.3235
18	82.5	81.82	0.6807
19	75.3	75.55	-0.2478
20	70.9	70.19	0.7127
21	76	75.96	0.03728
22	73.7	73.87	-0.1693
23	79.7	79.96	-0.2554
24	77.8	77.65	0.1515
25	73.3	73.61	-0.3127
26	78.3	78.44	-0.1373
27	71.9	71.08	0.8245
28	67	66.43	0.5718
29	82	81.74	0.2589
30	83.7	83.05	0.6536
31	74.8	75.07	-0.2681
32	80	79.79	0.2103
33	74.3	74.64	-0.3427
34	76.8	77.33	-0.5311
35	89	88.57	0.4337
36	81.9	81.95	-0.04858
37	76.8	77.06	-0.2613
38	88.9	88.63	0.2732
39	75.8	75.62	0.182
40	75.5	74.91	0.5903
41	89.1	88.89	0.2059
42	88	88.09	-0.08853
43	85.9	85.88	0.02123
44	89.3	89.41	-0.1063
45	82.9	83.64	-0.7428
46	81.2	82.58	-1.377
47	90.5	91.24	-0.7371
48	86.4	87.07	-0.669
49	81.8	81.98	-0.176
50	91.3	91.8	-0.5036
51	73.4	73.7	-0.2956
52	76.6	75.92	0.6837
53	91	90.8	0.1974
54	87	87.18	-0.183
55	89.7	89.8	-0.1038
56	90.7	91.14	-0.4409
57	86.5	87.52	-1.017
58	86.6	88.14	-1.535
59	98.8	99.72	-0.9247
60	84.4	85.64	-1.238
61	91.4	91.63	-0.2285
62	95.7	96.3	-0.5967
63	78.5	78.29	0.2069
64	81.7	81.1	0.6023
65	94.3	94.57	-0.2678
66	98.5	98.13	0.3669
67	95.4	95.81	-0.4067
68	91.7	92.79	-1.087
69	92.8	93.57	-0.7665
70	90.5	92	-1.5
71	102.2	103.1	-0.886
72	91.8	92.32	-0.5153
73	95	95.78	-0.7825
74	102	102.9	-0.8626
75	88.9	88.96	-0.05596
76	89.6	89.24	0.3614
77	97.9	98.34	-0.4446
78	108.6	108.4	0.2455
79	100.8	101	-0.2267
80	95.1	95.92	-0.8205
81	101	101.2	-0.2345
82	100.9	101.6	-0.7152
83	102.5	103.7	-1.222
84	105.4	105.4	-0.008582
85	98.4	98.77	-0.3676
86	105.3	106.6	-1.306
87	96.5	95.99	0.5102
88	88.1	88.21	-0.1133
89	107.9	107.7	0.1941
90	107	106.9	0.09802
91	92.5	93.31	-0.815
92	95.7	95.56	0.1374
93	85.2	85.97	-0.7655
94	85.5	86.3	-0.8049
95	94.7	94.27	0.4297
96	86.2	85.84	0.3613
97	88.8	88.96	-0.1574
98	93.4	92.75	0.6506
99	83.4	82.35	1.051
100	82.9	81.98	0.9219
101	96.7	96.35	0.3496
102	96.2	95.69	0.5125
103	92.8	92.38	0.4245
104	92.8	92.24	0.5559
105	90	90.32	-0.3227
106	95.4	95.54	-0.1432
107	108.3	107.5	0.7976
108	96.3	95.73	0.5733
109	95	95.39	-0.3897
110	109	108.4	0.5963
111	92	91.39	0.6139
112	92.3	91.67	0.6333
113	107	106.6	0.4048
114	105.5	105	0.4637
115	105.4	105	0.4307
116	103.9	103.6	0.297
117	99.2	99.62	-0.4186
118	102.2	103.2	-0.957
119	121.5	121.9	-0.3602
120	102.3	102.5	-0.2264
121	110	109.9	0.1356
122	105.9	106.9	-0.9843
123	91.9	92.43	-0.5327
124	100	99.42	0.5767
125	111.7	111.3	0.4242
126	104.9	104.7	0.1547
127	103.3	103.2	0.08462
128	101.8	101.8	0.04545
129	100.8	101	-0.2258
130	104.2	104.6	-0.4484
131	116.5	117.4	-0.9405
132	97.9	98.48	-0.5753
133	100.7	100.9	-0.2195
134	107	106.6	0.4204
135	96.3	95.66	0.6387
136	96	95.02	0.9796
137	104.5	104.6	-0.08298
138	107.4	105.8	1.602
139	102.4	101.7	0.6895
140	94.9	94.8	0.09998
141	98.8	98.53	0.271
142	96.8	97.5	-0.7037
143	108.2	108.7	-0.5363
144	103.8	103.4	0.3948
145	102.3	101.8	0.4786
146	107.2	107.7	-0.4605
147	102	101	1.005
148	92.6	92.29	0.3149
149	105.2	105.1	0.06143
150	113	112.3	0.7329
151	105.6	105.8	-0.2462
152	101.6	101.5	0.127
153	101.7	102.3	-0.616
154	102.7	103.5	-0.7921
155	109	109.5	-0.5366
156	105.5	105.3	0.178
157	103.3	103.5	-0.1758
158	108.6	108.8	-0.1594
159	98.2	97.91	0.2864
160	90	89.64	0.36
161	112.4	111.8	0.6272
162	111.9	111.2	0.6959
163	102.1	102.5	-0.4472
164	102.4	102	0.4083
165	101.7	102.3	-0.5928
166	98.7	99.52	-0.822
167	114	114	0.02091
168	105.1	104.9	0.1838
169	98.3	98.81	-0.5133
170	110	109.3	0.6697
171	96.5	95.4	1.1
172	92.2	91.29	0.911
173	112	111.6	0.3991
174	111.4	110.5	0.936
175	107.5	107.1	0.4112
176	103.4	102.7	0.7499
177	103.5	104.2	-0.6961
178	107.4	107.2	0.1581
179	117.6	117.3	0.3192
180	110.2	109.9	0.3245
181	104.3	103.5	0.7509
182	115.9	115.5	0.369
183	98.9	98.64	0.2601
184	101.9	100.6	1.288
185	113.5	112.9	0.6297
186	109.5	109.2	0.3498
187	110	109.5	0.505
188	114.2	113.7	0.4655
189	106.9	107.4	-0.5343
190	109.2	110.1	-0.9144
191	124.2	124.1	0.1156
192	104.7	105.4	-0.7458
193	111.9	111.3	0.6044
194	119	118.8	0.2214
195	102.9	102.9	-0.02509
196	106.3	105.8	0.4881

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  76.9 &  77.05 & -0.1491 \tabularnewline
2 &  79.9 &  80.19 & -0.2903 \tabularnewline
3 &  65.9 &  65.1 &  0.7955 \tabularnewline
4 &  69.5 &  68.59 &  0.9108 \tabularnewline
5 &  75.1 &  75.1 & -0.002487 \tabularnewline
6 &  79.6 &  78.92 &  0.6773 \tabularnewline
7 &  75.2 &  75.17 &  0.02859 \tabularnewline
8 &  68 &  68.06 & -0.05649 \tabularnewline
9 &  72.8 &  73.08 & -0.2781 \tabularnewline
10 &  71.5 &  71.93 & -0.4323 \tabularnewline
11 &  78.5 &  78.81 & -0.313 \tabularnewline
12 &  76.8 &  76.8 & -0.001888 \tabularnewline
13 &  75.3 &  75.53 & -0.2321 \tabularnewline
14 &  76.7 &  77.32 & -0.6206 \tabularnewline
15 &  69.7 &  69.07 &  0.6265 \tabularnewline
16 &  67.8 &  67.27 &  0.5264 \tabularnewline
17 &  77.5 &  77.18 &  0.3235 \tabularnewline
18 &  82.5 &  81.82 &  0.6807 \tabularnewline
19 &  75.3 &  75.55 & -0.2478 \tabularnewline
20 &  70.9 &  70.19 &  0.7127 \tabularnewline
21 &  76 &  75.96 &  0.03728 \tabularnewline
22 &  73.7 &  73.87 & -0.1693 \tabularnewline
23 &  79.7 &  79.96 & -0.2554 \tabularnewline
24 &  77.8 &  77.65 &  0.1515 \tabularnewline
25 &  73.3 &  73.61 & -0.3127 \tabularnewline
26 &  78.3 &  78.44 & -0.1373 \tabularnewline
27 &  71.9 &  71.08 &  0.8245 \tabularnewline
28 &  67 &  66.43 &  0.5718 \tabularnewline
29 &  82 &  81.74 &  0.2589 \tabularnewline
30 &  83.7 &  83.05 &  0.6536 \tabularnewline
31 &  74.8 &  75.07 & -0.2681 \tabularnewline
32 &  80 &  79.79 &  0.2103 \tabularnewline
33 &  74.3 &  74.64 & -0.3427 \tabularnewline
34 &  76.8 &  77.33 & -0.5311 \tabularnewline
35 &  89 &  88.57 &  0.4337 \tabularnewline
36 &  81.9 &  81.95 & -0.04858 \tabularnewline
37 &  76.8 &  77.06 & -0.2613 \tabularnewline
38 &  88.9 &  88.63 &  0.2732 \tabularnewline
39 &  75.8 &  75.62 &  0.182 \tabularnewline
40 &  75.5 &  74.91 &  0.5903 \tabularnewline
41 &  89.1 &  88.89 &  0.2059 \tabularnewline
42 &  88 &  88.09 & -0.08853 \tabularnewline
43 &  85.9 &  85.88 &  0.02123 \tabularnewline
44 &  89.3 &  89.41 & -0.1063 \tabularnewline
45 &  82.9 &  83.64 & -0.7428 \tabularnewline
46 &  81.2 &  82.58 & -1.377 \tabularnewline
47 &  90.5 &  91.24 & -0.7371 \tabularnewline
48 &  86.4 &  87.07 & -0.669 \tabularnewline
49 &  81.8 &  81.98 & -0.176 \tabularnewline
50 &  91.3 &  91.8 & -0.5036 \tabularnewline
51 &  73.4 &  73.7 & -0.2956 \tabularnewline
52 &  76.6 &  75.92 &  0.6837 \tabularnewline
53 &  91 &  90.8 &  0.1974 \tabularnewline
54 &  87 &  87.18 & -0.183 \tabularnewline
55 &  89.7 &  89.8 & -0.1038 \tabularnewline
56 &  90.7 &  91.14 & -0.4409 \tabularnewline
57 &  86.5 &  87.52 & -1.017 \tabularnewline
58 &  86.6 &  88.14 & -1.535 \tabularnewline
59 &  98.8 &  99.72 & -0.9247 \tabularnewline
60 &  84.4 &  85.64 & -1.238 \tabularnewline
61 &  91.4 &  91.63 & -0.2285 \tabularnewline
62 &  95.7 &  96.3 & -0.5967 \tabularnewline
63 &  78.5 &  78.29 &  0.2069 \tabularnewline
64 &  81.7 &  81.1 &  0.6023 \tabularnewline
65 &  94.3 &  94.57 & -0.2678 \tabularnewline
66 &  98.5 &  98.13 &  0.3669 \tabularnewline
67 &  95.4 &  95.81 & -0.4067 \tabularnewline
68 &  91.7 &  92.79 & -1.087 \tabularnewline
69 &  92.8 &  93.57 & -0.7665 \tabularnewline
70 &  90.5 &  92 & -1.5 \tabularnewline
71 &  102.2 &  103.1 & -0.886 \tabularnewline
72 &  91.8 &  92.32 & -0.5153 \tabularnewline
73 &  95 &  95.78 & -0.7825 \tabularnewline
74 &  102 &  102.9 & -0.8626 \tabularnewline
75 &  88.9 &  88.96 & -0.05596 \tabularnewline
76 &  89.6 &  89.24 &  0.3614 \tabularnewline
77 &  97.9 &  98.34 & -0.4446 \tabularnewline
78 &  108.6 &  108.4 &  0.2455 \tabularnewline
79 &  100.8 &  101 & -0.2267 \tabularnewline
80 &  95.1 &  95.92 & -0.8205 \tabularnewline
81 &  101 &  101.2 & -0.2345 \tabularnewline
82 &  100.9 &  101.6 & -0.7152 \tabularnewline
83 &  102.5 &  103.7 & -1.222 \tabularnewline
84 &  105.4 &  105.4 & -0.008582 \tabularnewline
85 &  98.4 &  98.77 & -0.3676 \tabularnewline
86 &  105.3 &  106.6 & -1.306 \tabularnewline
87 &  96.5 &  95.99 &  0.5102 \tabularnewline
88 &  88.1 &  88.21 & -0.1133 \tabularnewline
89 &  107.9 &  107.7 &  0.1941 \tabularnewline
90 &  107 &  106.9 &  0.09802 \tabularnewline
91 &  92.5 &  93.31 & -0.815 \tabularnewline
92 &  95.7 &  95.56 &  0.1374 \tabularnewline
93 &  85.2 &  85.97 & -0.7655 \tabularnewline
94 &  85.5 &  86.3 & -0.8049 \tabularnewline
95 &  94.7 &  94.27 &  0.4297 \tabularnewline
96 &  86.2 &  85.84 &  0.3613 \tabularnewline
97 &  88.8 &  88.96 & -0.1574 \tabularnewline
98 &  93.4 &  92.75 &  0.6506 \tabularnewline
99 &  83.4 &  82.35 &  1.051 \tabularnewline
100 &  82.9 &  81.98 &  0.9219 \tabularnewline
101 &  96.7 &  96.35 &  0.3496 \tabularnewline
102 &  96.2 &  95.69 &  0.5125 \tabularnewline
103 &  92.8 &  92.38 &  0.4245 \tabularnewline
104 &  92.8 &  92.24 &  0.5559 \tabularnewline
105 &  90 &  90.32 & -0.3227 \tabularnewline
106 &  95.4 &  95.54 & -0.1432 \tabularnewline
107 &  108.3 &  107.5 &  0.7976 \tabularnewline
108 &  96.3 &  95.73 &  0.5733 \tabularnewline
109 &  95 &  95.39 & -0.3897 \tabularnewline
110 &  109 &  108.4 &  0.5963 \tabularnewline
111 &  92 &  91.39 &  0.6139 \tabularnewline
112 &  92.3 &  91.67 &  0.6333 \tabularnewline
113 &  107 &  106.6 &  0.4048 \tabularnewline
114 &  105.5 &  105 &  0.4637 \tabularnewline
115 &  105.4 &  105 &  0.4307 \tabularnewline
116 &  103.9 &  103.6 &  0.297 \tabularnewline
117 &  99.2 &  99.62 & -0.4186 \tabularnewline
118 &  102.2 &  103.2 & -0.957 \tabularnewline
119 &  121.5 &  121.9 & -0.3602 \tabularnewline
120 &  102.3 &  102.5 & -0.2264 \tabularnewline
121 &  110 &  109.9 &  0.1356 \tabularnewline
122 &  105.9 &  106.9 & -0.9843 \tabularnewline
123 &  91.9 &  92.43 & -0.5327 \tabularnewline
124 &  100 &  99.42 &  0.5767 \tabularnewline
125 &  111.7 &  111.3 &  0.4242 \tabularnewline
126 &  104.9 &  104.7 &  0.1547 \tabularnewline
127 &  103.3 &  103.2 &  0.08462 \tabularnewline
128 &  101.8 &  101.8 &  0.04545 \tabularnewline
129 &  100.8 &  101 & -0.2258 \tabularnewline
130 &  104.2 &  104.6 & -0.4484 \tabularnewline
131 &  116.5 &  117.4 & -0.9405 \tabularnewline
132 &  97.9 &  98.48 & -0.5753 \tabularnewline
133 &  100.7 &  100.9 & -0.2195 \tabularnewline
134 &  107 &  106.6 &  0.4204 \tabularnewline
135 &  96.3 &  95.66 &  0.6387 \tabularnewline
136 &  96 &  95.02 &  0.9796 \tabularnewline
137 &  104.5 &  104.6 & -0.08298 \tabularnewline
138 &  107.4 &  105.8 &  1.602 \tabularnewline
139 &  102.4 &  101.7 &  0.6895 \tabularnewline
140 &  94.9 &  94.8 &  0.09998 \tabularnewline
141 &  98.8 &  98.53 &  0.271 \tabularnewline
142 &  96.8 &  97.5 & -0.7037 \tabularnewline
143 &  108.2 &  108.7 & -0.5363 \tabularnewline
144 &  103.8 &  103.4 &  0.3948 \tabularnewline
145 &  102.3 &  101.8 &  0.4786 \tabularnewline
146 &  107.2 &  107.7 & -0.4605 \tabularnewline
147 &  102 &  101 &  1.005 \tabularnewline
148 &  92.6 &  92.29 &  0.3149 \tabularnewline
149 &  105.2 &  105.1 &  0.06143 \tabularnewline
150 &  113 &  112.3 &  0.7329 \tabularnewline
151 &  105.6 &  105.8 & -0.2462 \tabularnewline
152 &  101.6 &  101.5 &  0.127 \tabularnewline
153 &  101.7 &  102.3 & -0.616 \tabularnewline
154 &  102.7 &  103.5 & -0.7921 \tabularnewline
155 &  109 &  109.5 & -0.5366 \tabularnewline
156 &  105.5 &  105.3 &  0.178 \tabularnewline
157 &  103.3 &  103.5 & -0.1758 \tabularnewline
158 &  108.6 &  108.8 & -0.1594 \tabularnewline
159 &  98.2 &  97.91 &  0.2864 \tabularnewline
160 &  90 &  89.64 &  0.36 \tabularnewline
161 &  112.4 &  111.8 &  0.6272 \tabularnewline
162 &  111.9 &  111.2 &  0.6959 \tabularnewline
163 &  102.1 &  102.5 & -0.4472 \tabularnewline
164 &  102.4 &  102 &  0.4083 \tabularnewline
165 &  101.7 &  102.3 & -0.5928 \tabularnewline
166 &  98.7 &  99.52 & -0.822 \tabularnewline
167 &  114 &  114 &  0.02091 \tabularnewline
168 &  105.1 &  104.9 &  0.1838 \tabularnewline
169 &  98.3 &  98.81 & -0.5133 \tabularnewline
170 &  110 &  109.3 &  0.6697 \tabularnewline
171 &  96.5 &  95.4 &  1.1 \tabularnewline
172 &  92.2 &  91.29 &  0.911 \tabularnewline
173 &  112 &  111.6 &  0.3991 \tabularnewline
174 &  111.4 &  110.5 &  0.936 \tabularnewline
175 &  107.5 &  107.1 &  0.4112 \tabularnewline
176 &  103.4 &  102.7 &  0.7499 \tabularnewline
177 &  103.5 &  104.2 & -0.6961 \tabularnewline
178 &  107.4 &  107.2 &  0.1581 \tabularnewline
179 &  117.6 &  117.3 &  0.3192 \tabularnewline
180 &  110.2 &  109.9 &  0.3245 \tabularnewline
181 &  104.3 &  103.5 &  0.7509 \tabularnewline
182 &  115.9 &  115.5 &  0.369 \tabularnewline
183 &  98.9 &  98.64 &  0.2601 \tabularnewline
184 &  101.9 &  100.6 &  1.288 \tabularnewline
185 &  113.5 &  112.9 &  0.6297 \tabularnewline
186 &  109.5 &  109.2 &  0.3498 \tabularnewline
187 &  110 &  109.5 &  0.505 \tabularnewline
188 &  114.2 &  113.7 &  0.4655 \tabularnewline
189 &  106.9 &  107.4 & -0.5343 \tabularnewline
190 &  109.2 &  110.1 & -0.9144 \tabularnewline
191 &  124.2 &  124.1 &  0.1156 \tabularnewline
192 &  104.7 &  105.4 & -0.7458 \tabularnewline
193 &  111.9 &  111.3 &  0.6044 \tabularnewline
194 &  119 &  118.8 &  0.2214 \tabularnewline
195 &  102.9 &  102.9 & -0.02509 \tabularnewline
196 &  106.3 &  105.8 &  0.4881 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309214&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] 76.9[/C][C] 77.05[/C][C]-0.1491[/C][/ROW]
[ROW][C]2[/C][C] 79.9[/C][C] 80.19[/C][C]-0.2903[/C][/ROW]
[ROW][C]3[/C][C] 65.9[/C][C] 65.1[/C][C] 0.7955[/C][/ROW]
[ROW][C]4[/C][C] 69.5[/C][C] 68.59[/C][C] 0.9108[/C][/ROW]
[ROW][C]5[/C][C] 75.1[/C][C] 75.1[/C][C]-0.002487[/C][/ROW]
[ROW][C]6[/C][C] 79.6[/C][C] 78.92[/C][C] 0.6773[/C][/ROW]
[ROW][C]7[/C][C] 75.2[/C][C] 75.17[/C][C] 0.02859[/C][/ROW]
[ROW][C]8[/C][C] 68[/C][C] 68.06[/C][C]-0.05649[/C][/ROW]
[ROW][C]9[/C][C] 72.8[/C][C] 73.08[/C][C]-0.2781[/C][/ROW]
[ROW][C]10[/C][C] 71.5[/C][C] 71.93[/C][C]-0.4323[/C][/ROW]
[ROW][C]11[/C][C] 78.5[/C][C] 78.81[/C][C]-0.313[/C][/ROW]
[ROW][C]12[/C][C] 76.8[/C][C] 76.8[/C][C]-0.001888[/C][/ROW]
[ROW][C]13[/C][C] 75.3[/C][C] 75.53[/C][C]-0.2321[/C][/ROW]
[ROW][C]14[/C][C] 76.7[/C][C] 77.32[/C][C]-0.6206[/C][/ROW]
[ROW][C]15[/C][C] 69.7[/C][C] 69.07[/C][C] 0.6265[/C][/ROW]
[ROW][C]16[/C][C] 67.8[/C][C] 67.27[/C][C] 0.5264[/C][/ROW]
[ROW][C]17[/C][C] 77.5[/C][C] 77.18[/C][C] 0.3235[/C][/ROW]
[ROW][C]18[/C][C] 82.5[/C][C] 81.82[/C][C] 0.6807[/C][/ROW]
[ROW][C]19[/C][C] 75.3[/C][C] 75.55[/C][C]-0.2478[/C][/ROW]
[ROW][C]20[/C][C] 70.9[/C][C] 70.19[/C][C] 0.7127[/C][/ROW]
[ROW][C]21[/C][C] 76[/C][C] 75.96[/C][C] 0.03728[/C][/ROW]
[ROW][C]22[/C][C] 73.7[/C][C] 73.87[/C][C]-0.1693[/C][/ROW]
[ROW][C]23[/C][C] 79.7[/C][C] 79.96[/C][C]-0.2554[/C][/ROW]
[ROW][C]24[/C][C] 77.8[/C][C] 77.65[/C][C] 0.1515[/C][/ROW]
[ROW][C]25[/C][C] 73.3[/C][C] 73.61[/C][C]-0.3127[/C][/ROW]
[ROW][C]26[/C][C] 78.3[/C][C] 78.44[/C][C]-0.1373[/C][/ROW]
[ROW][C]27[/C][C] 71.9[/C][C] 71.08[/C][C] 0.8245[/C][/ROW]
[ROW][C]28[/C][C] 67[/C][C] 66.43[/C][C] 0.5718[/C][/ROW]
[ROW][C]29[/C][C] 82[/C][C] 81.74[/C][C] 0.2589[/C][/ROW]
[ROW][C]30[/C][C] 83.7[/C][C] 83.05[/C][C] 0.6536[/C][/ROW]
[ROW][C]31[/C][C] 74.8[/C][C] 75.07[/C][C]-0.2681[/C][/ROW]
[ROW][C]32[/C][C] 80[/C][C] 79.79[/C][C] 0.2103[/C][/ROW]
[ROW][C]33[/C][C] 74.3[/C][C] 74.64[/C][C]-0.3427[/C][/ROW]
[ROW][C]34[/C][C] 76.8[/C][C] 77.33[/C][C]-0.5311[/C][/ROW]
[ROW][C]35[/C][C] 89[/C][C] 88.57[/C][C] 0.4337[/C][/ROW]
[ROW][C]36[/C][C] 81.9[/C][C] 81.95[/C][C]-0.04858[/C][/ROW]
[ROW][C]37[/C][C] 76.8[/C][C] 77.06[/C][C]-0.2613[/C][/ROW]
[ROW][C]38[/C][C] 88.9[/C][C] 88.63[/C][C] 0.2732[/C][/ROW]
[ROW][C]39[/C][C] 75.8[/C][C] 75.62[/C][C] 0.182[/C][/ROW]
[ROW][C]40[/C][C] 75.5[/C][C] 74.91[/C][C] 0.5903[/C][/ROW]
[ROW][C]41[/C][C] 89.1[/C][C] 88.89[/C][C] 0.2059[/C][/ROW]
[ROW][C]42[/C][C] 88[/C][C] 88.09[/C][C]-0.08853[/C][/ROW]
[ROW][C]43[/C][C] 85.9[/C][C] 85.88[/C][C] 0.02123[/C][/ROW]
[ROW][C]44[/C][C] 89.3[/C][C] 89.41[/C][C]-0.1063[/C][/ROW]
[ROW][C]45[/C][C] 82.9[/C][C] 83.64[/C][C]-0.7428[/C][/ROW]
[ROW][C]46[/C][C] 81.2[/C][C] 82.58[/C][C]-1.377[/C][/ROW]
[ROW][C]47[/C][C] 90.5[/C][C] 91.24[/C][C]-0.7371[/C][/ROW]
[ROW][C]48[/C][C] 86.4[/C][C] 87.07[/C][C]-0.669[/C][/ROW]
[ROW][C]49[/C][C] 81.8[/C][C] 81.98[/C][C]-0.176[/C][/ROW]
[ROW][C]50[/C][C] 91.3[/C][C] 91.8[/C][C]-0.5036[/C][/ROW]
[ROW][C]51[/C][C] 73.4[/C][C] 73.7[/C][C]-0.2956[/C][/ROW]
[ROW][C]52[/C][C] 76.6[/C][C] 75.92[/C][C] 0.6837[/C][/ROW]
[ROW][C]53[/C][C] 91[/C][C] 90.8[/C][C] 0.1974[/C][/ROW]
[ROW][C]54[/C][C] 87[/C][C] 87.18[/C][C]-0.183[/C][/ROW]
[ROW][C]55[/C][C] 89.7[/C][C] 89.8[/C][C]-0.1038[/C][/ROW]
[ROW][C]56[/C][C] 90.7[/C][C] 91.14[/C][C]-0.4409[/C][/ROW]
[ROW][C]57[/C][C] 86.5[/C][C] 87.52[/C][C]-1.017[/C][/ROW]
[ROW][C]58[/C][C] 86.6[/C][C] 88.14[/C][C]-1.535[/C][/ROW]
[ROW][C]59[/C][C] 98.8[/C][C] 99.72[/C][C]-0.9247[/C][/ROW]
[ROW][C]60[/C][C] 84.4[/C][C] 85.64[/C][C]-1.238[/C][/ROW]
[ROW][C]61[/C][C] 91.4[/C][C] 91.63[/C][C]-0.2285[/C][/ROW]
[ROW][C]62[/C][C] 95.7[/C][C] 96.3[/C][C]-0.5967[/C][/ROW]
[ROW][C]63[/C][C] 78.5[/C][C] 78.29[/C][C] 0.2069[/C][/ROW]
[ROW][C]64[/C][C] 81.7[/C][C] 81.1[/C][C] 0.6023[/C][/ROW]
[ROW][C]65[/C][C] 94.3[/C][C] 94.57[/C][C]-0.2678[/C][/ROW]
[ROW][C]66[/C][C] 98.5[/C][C] 98.13[/C][C] 0.3669[/C][/ROW]
[ROW][C]67[/C][C] 95.4[/C][C] 95.81[/C][C]-0.4067[/C][/ROW]
[ROW][C]68[/C][C] 91.7[/C][C] 92.79[/C][C]-1.087[/C][/ROW]
[ROW][C]69[/C][C] 92.8[/C][C] 93.57[/C][C]-0.7665[/C][/ROW]
[ROW][C]70[/C][C] 90.5[/C][C] 92[/C][C]-1.5[/C][/ROW]
[ROW][C]71[/C][C] 102.2[/C][C] 103.1[/C][C]-0.886[/C][/ROW]
[ROW][C]72[/C][C] 91.8[/C][C] 92.32[/C][C]-0.5153[/C][/ROW]
[ROW][C]73[/C][C] 95[/C][C] 95.78[/C][C]-0.7825[/C][/ROW]
[ROW][C]74[/C][C] 102[/C][C] 102.9[/C][C]-0.8626[/C][/ROW]
[ROW][C]75[/C][C] 88.9[/C][C] 88.96[/C][C]-0.05596[/C][/ROW]
[ROW][C]76[/C][C] 89.6[/C][C] 89.24[/C][C] 0.3614[/C][/ROW]
[ROW][C]77[/C][C] 97.9[/C][C] 98.34[/C][C]-0.4446[/C][/ROW]
[ROW][C]78[/C][C] 108.6[/C][C] 108.4[/C][C] 0.2455[/C][/ROW]
[ROW][C]79[/C][C] 100.8[/C][C] 101[/C][C]-0.2267[/C][/ROW]
[ROW][C]80[/C][C] 95.1[/C][C] 95.92[/C][C]-0.8205[/C][/ROW]
[ROW][C]81[/C][C] 101[/C][C] 101.2[/C][C]-0.2345[/C][/ROW]
[ROW][C]82[/C][C] 100.9[/C][C] 101.6[/C][C]-0.7152[/C][/ROW]
[ROW][C]83[/C][C] 102.5[/C][C] 103.7[/C][C]-1.222[/C][/ROW]
[ROW][C]84[/C][C] 105.4[/C][C] 105.4[/C][C]-0.008582[/C][/ROW]
[ROW][C]85[/C][C] 98.4[/C][C] 98.77[/C][C]-0.3676[/C][/ROW]
[ROW][C]86[/C][C] 105.3[/C][C] 106.6[/C][C]-1.306[/C][/ROW]
[ROW][C]87[/C][C] 96.5[/C][C] 95.99[/C][C] 0.5102[/C][/ROW]
[ROW][C]88[/C][C] 88.1[/C][C] 88.21[/C][C]-0.1133[/C][/ROW]
[ROW][C]89[/C][C] 107.9[/C][C] 107.7[/C][C] 0.1941[/C][/ROW]
[ROW][C]90[/C][C] 107[/C][C] 106.9[/C][C] 0.09802[/C][/ROW]
[ROW][C]91[/C][C] 92.5[/C][C] 93.31[/C][C]-0.815[/C][/ROW]
[ROW][C]92[/C][C] 95.7[/C][C] 95.56[/C][C] 0.1374[/C][/ROW]
[ROW][C]93[/C][C] 85.2[/C][C] 85.97[/C][C]-0.7655[/C][/ROW]
[ROW][C]94[/C][C] 85.5[/C][C] 86.3[/C][C]-0.8049[/C][/ROW]
[ROW][C]95[/C][C] 94.7[/C][C] 94.27[/C][C] 0.4297[/C][/ROW]
[ROW][C]96[/C][C] 86.2[/C][C] 85.84[/C][C] 0.3613[/C][/ROW]
[ROW][C]97[/C][C] 88.8[/C][C] 88.96[/C][C]-0.1574[/C][/ROW]
[ROW][C]98[/C][C] 93.4[/C][C] 92.75[/C][C] 0.6506[/C][/ROW]
[ROW][C]99[/C][C] 83.4[/C][C] 82.35[/C][C] 1.051[/C][/ROW]
[ROW][C]100[/C][C] 82.9[/C][C] 81.98[/C][C] 0.9219[/C][/ROW]
[ROW][C]101[/C][C] 96.7[/C][C] 96.35[/C][C] 0.3496[/C][/ROW]
[ROW][C]102[/C][C] 96.2[/C][C] 95.69[/C][C] 0.5125[/C][/ROW]
[ROW][C]103[/C][C] 92.8[/C][C] 92.38[/C][C] 0.4245[/C][/ROW]
[ROW][C]104[/C][C] 92.8[/C][C] 92.24[/C][C] 0.5559[/C][/ROW]
[ROW][C]105[/C][C] 90[/C][C] 90.32[/C][C]-0.3227[/C][/ROW]
[ROW][C]106[/C][C] 95.4[/C][C] 95.54[/C][C]-0.1432[/C][/ROW]
[ROW][C]107[/C][C] 108.3[/C][C] 107.5[/C][C] 0.7976[/C][/ROW]
[ROW][C]108[/C][C] 96.3[/C][C] 95.73[/C][C] 0.5733[/C][/ROW]
[ROW][C]109[/C][C] 95[/C][C] 95.39[/C][C]-0.3897[/C][/ROW]
[ROW][C]110[/C][C] 109[/C][C] 108.4[/C][C] 0.5963[/C][/ROW]
[ROW][C]111[/C][C] 92[/C][C] 91.39[/C][C] 0.6139[/C][/ROW]
[ROW][C]112[/C][C] 92.3[/C][C] 91.67[/C][C] 0.6333[/C][/ROW]
[ROW][C]113[/C][C] 107[/C][C] 106.6[/C][C] 0.4048[/C][/ROW]
[ROW][C]114[/C][C] 105.5[/C][C] 105[/C][C] 0.4637[/C][/ROW]
[ROW][C]115[/C][C] 105.4[/C][C] 105[/C][C] 0.4307[/C][/ROW]
[ROW][C]116[/C][C] 103.9[/C][C] 103.6[/C][C] 0.297[/C][/ROW]
[ROW][C]117[/C][C] 99.2[/C][C] 99.62[/C][C]-0.4186[/C][/ROW]
[ROW][C]118[/C][C] 102.2[/C][C] 103.2[/C][C]-0.957[/C][/ROW]
[ROW][C]119[/C][C] 121.5[/C][C] 121.9[/C][C]-0.3602[/C][/ROW]
[ROW][C]120[/C][C] 102.3[/C][C] 102.5[/C][C]-0.2264[/C][/ROW]
[ROW][C]121[/C][C] 110[/C][C] 109.9[/C][C] 0.1356[/C][/ROW]
[ROW][C]122[/C][C] 105.9[/C][C] 106.9[/C][C]-0.9843[/C][/ROW]
[ROW][C]123[/C][C] 91.9[/C][C] 92.43[/C][C]-0.5327[/C][/ROW]
[ROW][C]124[/C][C] 100[/C][C] 99.42[/C][C] 0.5767[/C][/ROW]
[ROW][C]125[/C][C] 111.7[/C][C] 111.3[/C][C] 0.4242[/C][/ROW]
[ROW][C]126[/C][C] 104.9[/C][C] 104.7[/C][C] 0.1547[/C][/ROW]
[ROW][C]127[/C][C] 103.3[/C][C] 103.2[/C][C] 0.08462[/C][/ROW]
[ROW][C]128[/C][C] 101.8[/C][C] 101.8[/C][C] 0.04545[/C][/ROW]
[ROW][C]129[/C][C] 100.8[/C][C] 101[/C][C]-0.2258[/C][/ROW]
[ROW][C]130[/C][C] 104.2[/C][C] 104.6[/C][C]-0.4484[/C][/ROW]
[ROW][C]131[/C][C] 116.5[/C][C] 117.4[/C][C]-0.9405[/C][/ROW]
[ROW][C]132[/C][C] 97.9[/C][C] 98.48[/C][C]-0.5753[/C][/ROW]
[ROW][C]133[/C][C] 100.7[/C][C] 100.9[/C][C]-0.2195[/C][/ROW]
[ROW][C]134[/C][C] 107[/C][C] 106.6[/C][C] 0.4204[/C][/ROW]
[ROW][C]135[/C][C] 96.3[/C][C] 95.66[/C][C] 0.6387[/C][/ROW]
[ROW][C]136[/C][C] 96[/C][C] 95.02[/C][C] 0.9796[/C][/ROW]
[ROW][C]137[/C][C] 104.5[/C][C] 104.6[/C][C]-0.08298[/C][/ROW]
[ROW][C]138[/C][C] 107.4[/C][C] 105.8[/C][C] 1.602[/C][/ROW]
[ROW][C]139[/C][C] 102.4[/C][C] 101.7[/C][C] 0.6895[/C][/ROW]
[ROW][C]140[/C][C] 94.9[/C][C] 94.8[/C][C] 0.09998[/C][/ROW]
[ROW][C]141[/C][C] 98.8[/C][C] 98.53[/C][C] 0.271[/C][/ROW]
[ROW][C]142[/C][C] 96.8[/C][C] 97.5[/C][C]-0.7037[/C][/ROW]
[ROW][C]143[/C][C] 108.2[/C][C] 108.7[/C][C]-0.5363[/C][/ROW]
[ROW][C]144[/C][C] 103.8[/C][C] 103.4[/C][C] 0.3948[/C][/ROW]
[ROW][C]145[/C][C] 102.3[/C][C] 101.8[/C][C] 0.4786[/C][/ROW]
[ROW][C]146[/C][C] 107.2[/C][C] 107.7[/C][C]-0.4605[/C][/ROW]
[ROW][C]147[/C][C] 102[/C][C] 101[/C][C] 1.005[/C][/ROW]
[ROW][C]148[/C][C] 92.6[/C][C] 92.29[/C][C] 0.3149[/C][/ROW]
[ROW][C]149[/C][C] 105.2[/C][C] 105.1[/C][C] 0.06143[/C][/ROW]
[ROW][C]150[/C][C] 113[/C][C] 112.3[/C][C] 0.7329[/C][/ROW]
[ROW][C]151[/C][C] 105.6[/C][C] 105.8[/C][C]-0.2462[/C][/ROW]
[ROW][C]152[/C][C] 101.6[/C][C] 101.5[/C][C] 0.127[/C][/ROW]
[ROW][C]153[/C][C] 101.7[/C][C] 102.3[/C][C]-0.616[/C][/ROW]
[ROW][C]154[/C][C] 102.7[/C][C] 103.5[/C][C]-0.7921[/C][/ROW]
[ROW][C]155[/C][C] 109[/C][C] 109.5[/C][C]-0.5366[/C][/ROW]
[ROW][C]156[/C][C] 105.5[/C][C] 105.3[/C][C] 0.178[/C][/ROW]
[ROW][C]157[/C][C] 103.3[/C][C] 103.5[/C][C]-0.1758[/C][/ROW]
[ROW][C]158[/C][C] 108.6[/C][C] 108.8[/C][C]-0.1594[/C][/ROW]
[ROW][C]159[/C][C] 98.2[/C][C] 97.91[/C][C] 0.2864[/C][/ROW]
[ROW][C]160[/C][C] 90[/C][C] 89.64[/C][C] 0.36[/C][/ROW]
[ROW][C]161[/C][C] 112.4[/C][C] 111.8[/C][C] 0.6272[/C][/ROW]
[ROW][C]162[/C][C] 111.9[/C][C] 111.2[/C][C] 0.6959[/C][/ROW]
[ROW][C]163[/C][C] 102.1[/C][C] 102.5[/C][C]-0.4472[/C][/ROW]
[ROW][C]164[/C][C] 102.4[/C][C] 102[/C][C] 0.4083[/C][/ROW]
[ROW][C]165[/C][C] 101.7[/C][C] 102.3[/C][C]-0.5928[/C][/ROW]
[ROW][C]166[/C][C] 98.7[/C][C] 99.52[/C][C]-0.822[/C][/ROW]
[ROW][C]167[/C][C] 114[/C][C] 114[/C][C] 0.02091[/C][/ROW]
[ROW][C]168[/C][C] 105.1[/C][C] 104.9[/C][C] 0.1838[/C][/ROW]
[ROW][C]169[/C][C] 98.3[/C][C] 98.81[/C][C]-0.5133[/C][/ROW]
[ROW][C]170[/C][C] 110[/C][C] 109.3[/C][C] 0.6697[/C][/ROW]
[ROW][C]171[/C][C] 96.5[/C][C] 95.4[/C][C] 1.1[/C][/ROW]
[ROW][C]172[/C][C] 92.2[/C][C] 91.29[/C][C] 0.911[/C][/ROW]
[ROW][C]173[/C][C] 112[/C][C] 111.6[/C][C] 0.3991[/C][/ROW]
[ROW][C]174[/C][C] 111.4[/C][C] 110.5[/C][C] 0.936[/C][/ROW]
[ROW][C]175[/C][C] 107.5[/C][C] 107.1[/C][C] 0.4112[/C][/ROW]
[ROW][C]176[/C][C] 103.4[/C][C] 102.7[/C][C] 0.7499[/C][/ROW]
[ROW][C]177[/C][C] 103.5[/C][C] 104.2[/C][C]-0.6961[/C][/ROW]
[ROW][C]178[/C][C] 107.4[/C][C] 107.2[/C][C] 0.1581[/C][/ROW]
[ROW][C]179[/C][C] 117.6[/C][C] 117.3[/C][C] 0.3192[/C][/ROW]
[ROW][C]180[/C][C] 110.2[/C][C] 109.9[/C][C] 0.3245[/C][/ROW]
[ROW][C]181[/C][C] 104.3[/C][C] 103.5[/C][C] 0.7509[/C][/ROW]
[ROW][C]182[/C][C] 115.9[/C][C] 115.5[/C][C] 0.369[/C][/ROW]
[ROW][C]183[/C][C] 98.9[/C][C] 98.64[/C][C] 0.2601[/C][/ROW]
[ROW][C]184[/C][C] 101.9[/C][C] 100.6[/C][C] 1.288[/C][/ROW]
[ROW][C]185[/C][C] 113.5[/C][C] 112.9[/C][C] 0.6297[/C][/ROW]
[ROW][C]186[/C][C] 109.5[/C][C] 109.2[/C][C] 0.3498[/C][/ROW]
[ROW][C]187[/C][C] 110[/C][C] 109.5[/C][C] 0.505[/C][/ROW]
[ROW][C]188[/C][C] 114.2[/C][C] 113.7[/C][C] 0.4655[/C][/ROW]
[ROW][C]189[/C][C] 106.9[/C][C] 107.4[/C][C]-0.5343[/C][/ROW]
[ROW][C]190[/C][C] 109.2[/C][C] 110.1[/C][C]-0.9144[/C][/ROW]
[ROW][C]191[/C][C] 124.2[/C][C] 124.1[/C][C] 0.1156[/C][/ROW]
[ROW][C]192[/C][C] 104.7[/C][C] 105.4[/C][C]-0.7458[/C][/ROW]
[ROW][C]193[/C][C] 111.9[/C][C] 111.3[/C][C] 0.6044[/C][/ROW]
[ROW][C]194[/C][C] 119[/C][C] 118.8[/C][C] 0.2214[/C][/ROW]
[ROW][C]195[/C][C] 102.9[/C][C] 102.9[/C][C]-0.02509[/C][/ROW]
[ROW][C]196[/C][C] 106.3[/C][C] 105.8[/C][C] 0.4881[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309214&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309214&T=5

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	76.9	77.05	-0.1491
2	79.9	80.19	-0.2903
3	65.9	65.1	0.7955
4	69.5	68.59	0.9108
5	75.1	75.1	-0.002487
6	79.6	78.92	0.6773
7	75.2	75.17	0.02859
8	68	68.06	-0.05649
9	72.8	73.08	-0.2781
10	71.5	71.93	-0.4323
11	78.5	78.81	-0.313
12	76.8	76.8	-0.001888
13	75.3	75.53	-0.2321
14	76.7	77.32	-0.6206
15	69.7	69.07	0.6265
16	67.8	67.27	0.5264
17	77.5	77.18	0.3235
18	82.5	81.82	0.6807
19	75.3	75.55	-0.2478
20	70.9	70.19	0.7127
21	76	75.96	0.03728
22	73.7	73.87	-0.1693
23	79.7	79.96	-0.2554
24	77.8	77.65	0.1515
25	73.3	73.61	-0.3127
26	78.3	78.44	-0.1373
27	71.9	71.08	0.8245
28	67	66.43	0.5718
29	82	81.74	0.2589
30	83.7	83.05	0.6536
31	74.8	75.07	-0.2681
32	80	79.79	0.2103
33	74.3	74.64	-0.3427
34	76.8	77.33	-0.5311
35	89	88.57	0.4337
36	81.9	81.95	-0.04858
37	76.8	77.06	-0.2613
38	88.9	88.63	0.2732
39	75.8	75.62	0.182
40	75.5	74.91	0.5903
41	89.1	88.89	0.2059
42	88	88.09	-0.08853
43	85.9	85.88	0.02123
44	89.3	89.41	-0.1063
45	82.9	83.64	-0.7428
46	81.2	82.58	-1.377
47	90.5	91.24	-0.7371
48	86.4	87.07	-0.669
49	81.8	81.98	-0.176
50	91.3	91.8	-0.5036
51	73.4	73.7	-0.2956
52	76.6	75.92	0.6837
53	91	90.8	0.1974
54	87	87.18	-0.183
55	89.7	89.8	-0.1038
56	90.7	91.14	-0.4409
57	86.5	87.52	-1.017
58	86.6	88.14	-1.535
59	98.8	99.72	-0.9247
60	84.4	85.64	-1.238
61	91.4	91.63	-0.2285
62	95.7	96.3	-0.5967
63	78.5	78.29	0.2069
64	81.7	81.1	0.6023
65	94.3	94.57	-0.2678
66	98.5	98.13	0.3669
67	95.4	95.81	-0.4067
68	91.7	92.79	-1.087
69	92.8	93.57	-0.7665
70	90.5	92	-1.5
71	102.2	103.1	-0.886
72	91.8	92.32	-0.5153
73	95	95.78	-0.7825
74	102	102.9	-0.8626
75	88.9	88.96	-0.05596
76	89.6	89.24	0.3614
77	97.9	98.34	-0.4446
78	108.6	108.4	0.2455
79	100.8	101	-0.2267
80	95.1	95.92	-0.8205
81	101	101.2	-0.2345
82	100.9	101.6	-0.7152
83	102.5	103.7	-1.222
84	105.4	105.4	-0.008582
85	98.4	98.77	-0.3676
86	105.3	106.6	-1.306
87	96.5	95.99	0.5102
88	88.1	88.21	-0.1133
89	107.9	107.7	0.1941
90	107	106.9	0.09802
91	92.5	93.31	-0.815
92	95.7	95.56	0.1374
93	85.2	85.97	-0.7655
94	85.5	86.3	-0.8049
95	94.7	94.27	0.4297
96	86.2	85.84	0.3613
97	88.8	88.96	-0.1574
98	93.4	92.75	0.6506
99	83.4	82.35	1.051
100	82.9	81.98	0.9219
101	96.7	96.35	0.3496
102	96.2	95.69	0.5125
103	92.8	92.38	0.4245
104	92.8	92.24	0.5559
105	90	90.32	-0.3227
106	95.4	95.54	-0.1432
107	108.3	107.5	0.7976
108	96.3	95.73	0.5733
109	95	95.39	-0.3897
110	109	108.4	0.5963
111	92	91.39	0.6139
112	92.3	91.67	0.6333
113	107	106.6	0.4048
114	105.5	105	0.4637
115	105.4	105	0.4307
116	103.9	103.6	0.297
117	99.2	99.62	-0.4186
118	102.2	103.2	-0.957
119	121.5	121.9	-0.3602
120	102.3	102.5	-0.2264
121	110	109.9	0.1356
122	105.9	106.9	-0.9843
123	91.9	92.43	-0.5327
124	100	99.42	0.5767
125	111.7	111.3	0.4242
126	104.9	104.7	0.1547
127	103.3	103.2	0.08462
128	101.8	101.8	0.04545
129	100.8	101	-0.2258
130	104.2	104.6	-0.4484
131	116.5	117.4	-0.9405
132	97.9	98.48	-0.5753
133	100.7	100.9	-0.2195
134	107	106.6	0.4204
135	96.3	95.66	0.6387
136	96	95.02	0.9796
137	104.5	104.6	-0.08298
138	107.4	105.8	1.602
139	102.4	101.7	0.6895
140	94.9	94.8	0.09998
141	98.8	98.53	0.271
142	96.8	97.5	-0.7037
143	108.2	108.7	-0.5363
144	103.8	103.4	0.3948
145	102.3	101.8	0.4786
146	107.2	107.7	-0.4605
147	102	101	1.005
148	92.6	92.29	0.3149
149	105.2	105.1	0.06143
150	113	112.3	0.7329
151	105.6	105.8	-0.2462
152	101.6	101.5	0.127
153	101.7	102.3	-0.616
154	102.7	103.5	-0.7921
155	109	109.5	-0.5366
156	105.5	105.3	0.178
157	103.3	103.5	-0.1758
158	108.6	108.8	-0.1594
159	98.2	97.91	0.2864
160	90	89.64	0.36
161	112.4	111.8	0.6272
162	111.9	111.2	0.6959
163	102.1	102.5	-0.4472
164	102.4	102	0.4083
165	101.7	102.3	-0.5928
166	98.7	99.52	-0.822
167	114	114	0.02091
168	105.1	104.9	0.1838
169	98.3	98.81	-0.5133
170	110	109.3	0.6697
171	96.5	95.4	1.1
172	92.2	91.29	0.911
173	112	111.6	0.3991
174	111.4	110.5	0.936
175	107.5	107.1	0.4112
176	103.4	102.7	0.7499
177	103.5	104.2	-0.6961
178	107.4	107.2	0.1581
179	117.6	117.3	0.3192
180	110.2	109.9	0.3245
181	104.3	103.5	0.7509
182	115.9	115.5	0.369
183	98.9	98.64	0.2601
184	101.9	100.6	1.288
185	113.5	112.9	0.6297
186	109.5	109.2	0.3498
187	110	109.5	0.505
188	114.2	113.7	0.4655
189	106.9	107.4	-0.5343
190	109.2	110.1	-0.9144
191	124.2	124.1	0.1156
192	104.7	105.4	-0.7458
193	111.9	111.3	0.6044
194	119	118.8	0.2214
195	102.9	102.9	-0.02509
196	106.3	105.8	0.4881

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1294	0.2589	0.8706
11	0.1069	0.2138	0.8931
12	0.1084	0.2169	0.8916
13	0.05715	0.1143	0.9428
14	0.03566	0.07133	0.9643
15	0.0266	0.0532	0.9734
16	0.01418	0.02836	0.9858
17	0.007541	0.01508	0.9925
18	0.02689	0.05377	0.9731
19	0.01658	0.03316	0.9834
20	0.02387	0.04774	0.9761
21	0.01413	0.02826	0.9859
22	0.01008	0.02015	0.9899
23	0.006294	0.01259	0.9937
24	0.006925	0.01385	0.9931
25	0.003817	0.007634	0.9962
26	0.002246	0.004492	0.9978
27	0.005551	0.0111	0.9944
28	0.003472	0.006943	0.9965
29	0.002205	0.004409	0.9978
30	0.009503	0.01901	0.9905
31	0.006468	0.01294	0.9935
32	0.004153	0.008305	0.9958
33	0.002478	0.004956	0.9975
34	0.002634	0.005267	0.9974
35	0.002917	0.005833	0.9971
36	0.001811	0.003622	0.9982
37	0.001074	0.002148	0.9989
38	0.001029	0.002058	0.999
39	0.0006488	0.001298	0.9994
40	0.0004968	0.0009935	0.9995
41	0.0004033	0.0008066	0.9996
42	0.0002411	0.0004823	0.9998
43	0.0001443	0.0002885	0.9999
44	8.458e-05	0.0001692	0.9999
45	0.000154	0.000308	0.9998
46	0.0005096	0.001019	0.9995
47	0.0004195	0.0008391	0.9996
48	0.0002718	0.0005436	0.9997
49	0.0002082	0.0004164	0.9998
50	0.0002007	0.0004013	0.9998
51	0.0001489	0.0002978	0.9999
52	0.0008781	0.001756	0.9991
53	0.001142	0.002283	0.9989
54	0.0009052	0.00181	0.9991
55	0.0007472	0.001494	0.9993
56	0.0005491	0.001098	0.9995
57	0.0005838	0.001168	0.9994
58	0.002582	0.005165	0.9974
59	0.002279	0.004559	0.9977
60	0.002492	0.004983	0.9975
61	0.002013	0.004026	0.998
62	0.001744	0.003488	0.9983
63	0.00217	0.00434	0.9978
64	0.004422	0.008845	0.9956
65	0.003567	0.007134	0.9964
66	0.0042	0.008399	0.9958
67	0.003589	0.007177	0.9964
68	0.003775	0.007551	0.9962
69	0.003252	0.006503	0.9967
70	0.008184	0.01637	0.9918
71	0.008098	0.0162	0.9919
72	0.00747	0.01494	0.9925
73	0.007231	0.01446	0.9928
74	0.008268	0.01654	0.9917
75	0.007503	0.01501	0.9925
76	0.01125	0.0225	0.9888
77	0.01174	0.02349	0.9883
78	0.01659	0.03319	0.9834
79	0.02089	0.04179	0.9791
80	0.02314	0.04627	0.9769
81	0.02448	0.04895	0.9755
82	0.02578	0.05157	0.9742
83	0.03588	0.07176	0.9641
84	0.0372	0.0744	0.9628
85	0.04195	0.08389	0.9581
86	0.06632	0.1326	0.9337
87	0.1114	0.2227	0.8886
88	0.1158	0.2316	0.8842
89	0.1243	0.2486	0.8757
90	0.1708	0.3416	0.8292
91	0.1819	0.3638	0.8181
92	0.2067	0.4133	0.7933
93	0.2009	0.4018	0.7991
94	0.2048	0.4097	0.7952
95	0.2469	0.4938	0.7531
96	0.2809	0.5619	0.7191
97	0.2732	0.5464	0.7268
98	0.3187	0.6375	0.6813
99	0.4274	0.8548	0.5726
100	0.4802	0.9604	0.5198
101	0.456	0.9121	0.544
102	0.4515	0.903	0.5485
103	0.4438	0.8877	0.5562
104	0.446	0.8921	0.554
105	0.445	0.89	0.555
106	0.4487	0.8975	0.5513
107	0.5035	0.9929	0.4965
108	0.5176	0.9647	0.4824
109	0.5365	0.9271	0.4635
110	0.5567	0.8867	0.4433
111	0.5639	0.8721	0.4361
112	0.5732	0.8536	0.4268
113	0.558	0.884	0.442
114	0.5439	0.9123	0.4561
115	0.5337	0.9327	0.4663
116	0.5137	0.9725	0.4863
117	0.5241	0.9518	0.4759
118	0.6513	0.6973	0.3487
119	0.6734	0.6531	0.3266
120	0.6743	0.6515	0.3257
121	0.6729	0.6542	0.3271
122	0.7169	0.5662	0.2831
123	0.7005	0.5989	0.2995
124	0.6939	0.6122	0.3061
125	0.6747	0.6506	0.3253
126	0.641	0.7181	0.359
127	0.6053	0.7894	0.3947
128	0.5677	0.8647	0.4323
129	0.5403	0.9194	0.4597
130	0.5541	0.8917	0.4459
131	0.5784	0.8433	0.4216
132	0.5864	0.8272	0.4136
133	0.5454	0.9093	0.4546
134	0.5246	0.9508	0.4754
135	0.514	0.972	0.486
136	0.6022	0.7955	0.3978
137	0.5572	0.8857	0.4428
138	0.7874	0.4251	0.2126
139	0.7896	0.4207	0.2104
140	0.7566	0.4868	0.2434
141	0.7266	0.5468	0.2734
142	0.736	0.528	0.264
143	0.7105	0.5791	0.2895
144	0.6878	0.6245	0.3122
145	0.6598	0.6803	0.3402
146	0.6782	0.6435	0.3218
147	0.7116	0.5767	0.2884
148	0.677	0.6461	0.323
149	0.6343	0.7313	0.3657
150	0.6199	0.7602	0.3801
151	0.6087	0.7826	0.3913
152	0.5936	0.8127	0.4064
153	0.665	0.67	0.335
154	0.816	0.368	0.184
155	0.8407	0.3187	0.1593
156	0.8158	0.3684	0.1842
157	0.8032	0.3936	0.1968
158	0.7854	0.4291	0.2146
159	0.7516	0.4968	0.2484
160	0.7102	0.5797	0.2898
161	0.6757	0.6487	0.3243
162	0.6601	0.6798	0.3399
163	0.6495	0.701	0.3505
164	0.6042	0.7916	0.3958
165	0.6516	0.6969	0.3484
166	0.7595	0.4809	0.2405
167	0.775	0.4501	0.225
168	0.7443	0.5114	0.2557
169	0.7881	0.4238	0.2119
170	0.7452	0.5096	0.2548
171	0.8036	0.3928	0.1964
172	0.764	0.472	0.236
173	0.7335	0.533	0.2665
174	0.7578	0.4844	0.2422
175	0.7275	0.545	0.2725
176	0.6667	0.6665	0.3333
177	0.8192	0.3616	0.1808
178	0.8616	0.2768	0.1384
179	0.8048	0.3904	0.1952
180	0.7272	0.5456	0.2728
181	0.6791	0.6417	0.3209
182	0.5742	0.8516	0.4258
183	0.4611	0.9222	0.5389
184	0.3947	0.7894	0.6053
185	0.2793	0.5587	0.7207
186	0.3131	0.6261	0.6869

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.1294 &  0.2589 &  0.8706 \tabularnewline
11 &  0.1069 &  0.2138 &  0.8931 \tabularnewline
12 &  0.1084 &  0.2169 &  0.8916 \tabularnewline
13 &  0.05715 &  0.1143 &  0.9428 \tabularnewline
14 &  0.03566 &  0.07133 &  0.9643 \tabularnewline
15 &  0.0266 &  0.0532 &  0.9734 \tabularnewline
16 &  0.01418 &  0.02836 &  0.9858 \tabularnewline
17 &  0.007541 &  0.01508 &  0.9925 \tabularnewline
18 &  0.02689 &  0.05377 &  0.9731 \tabularnewline
19 &  0.01658 &  0.03316 &  0.9834 \tabularnewline
20 &  0.02387 &  0.04774 &  0.9761 \tabularnewline
21 &  0.01413 &  0.02826 &  0.9859 \tabularnewline
22 &  0.01008 &  0.02015 &  0.9899 \tabularnewline
23 &  0.006294 &  0.01259 &  0.9937 \tabularnewline
24 &  0.006925 &  0.01385 &  0.9931 \tabularnewline
25 &  0.003817 &  0.007634 &  0.9962 \tabularnewline
26 &  0.002246 &  0.004492 &  0.9978 \tabularnewline
27 &  0.005551 &  0.0111 &  0.9944 \tabularnewline
28 &  0.003472 &  0.006943 &  0.9965 \tabularnewline
29 &  0.002205 &  0.004409 &  0.9978 \tabularnewline
30 &  0.009503 &  0.01901 &  0.9905 \tabularnewline
31 &  0.006468 &  0.01294 &  0.9935 \tabularnewline
32 &  0.004153 &  0.008305 &  0.9958 \tabularnewline
33 &  0.002478 &  0.004956 &  0.9975 \tabularnewline
34 &  0.002634 &  0.005267 &  0.9974 \tabularnewline
35 &  0.002917 &  0.005833 &  0.9971 \tabularnewline
36 &  0.001811 &  0.003622 &  0.9982 \tabularnewline
37 &  0.001074 &  0.002148 &  0.9989 \tabularnewline
38 &  0.001029 &  0.002058 &  0.999 \tabularnewline
39 &  0.0006488 &  0.001298 &  0.9994 \tabularnewline
40 &  0.0004968 &  0.0009935 &  0.9995 \tabularnewline
41 &  0.0004033 &  0.0008066 &  0.9996 \tabularnewline
42 &  0.0002411 &  0.0004823 &  0.9998 \tabularnewline
43 &  0.0001443 &  0.0002885 &  0.9999 \tabularnewline
44 &  8.458e-05 &  0.0001692 &  0.9999 \tabularnewline
45 &  0.000154 &  0.000308 &  0.9998 \tabularnewline
46 &  0.0005096 &  0.001019 &  0.9995 \tabularnewline
47 &  0.0004195 &  0.0008391 &  0.9996 \tabularnewline
48 &  0.0002718 &  0.0005436 &  0.9997 \tabularnewline
49 &  0.0002082 &  0.0004164 &  0.9998 \tabularnewline
50 &  0.0002007 &  0.0004013 &  0.9998 \tabularnewline
51 &  0.0001489 &  0.0002978 &  0.9999 \tabularnewline
52 &  0.0008781 &  0.001756 &  0.9991 \tabularnewline
53 &  0.001142 &  0.002283 &  0.9989 \tabularnewline
54 &  0.0009052 &  0.00181 &  0.9991 \tabularnewline
55 &  0.0007472 &  0.001494 &  0.9993 \tabularnewline
56 &  0.0005491 &  0.001098 &  0.9995 \tabularnewline
57 &  0.0005838 &  0.001168 &  0.9994 \tabularnewline
58 &  0.002582 &  0.005165 &  0.9974 \tabularnewline
59 &  0.002279 &  0.004559 &  0.9977 \tabularnewline
60 &  0.002492 &  0.004983 &  0.9975 \tabularnewline
61 &  0.002013 &  0.004026 &  0.998 \tabularnewline
62 &  0.001744 &  0.003488 &  0.9983 \tabularnewline
63 &  0.00217 &  0.00434 &  0.9978 \tabularnewline
64 &  0.004422 &  0.008845 &  0.9956 \tabularnewline
65 &  0.003567 &  0.007134 &  0.9964 \tabularnewline
66 &  0.0042 &  0.008399 &  0.9958 \tabularnewline
67 &  0.003589 &  0.007177 &  0.9964 \tabularnewline
68 &  0.003775 &  0.007551 &  0.9962 \tabularnewline
69 &  0.003252 &  0.006503 &  0.9967 \tabularnewline
70 &  0.008184 &  0.01637 &  0.9918 \tabularnewline
71 &  0.008098 &  0.0162 &  0.9919 \tabularnewline
72 &  0.00747 &  0.01494 &  0.9925 \tabularnewline
73 &  0.007231 &  0.01446 &  0.9928 \tabularnewline
74 &  0.008268 &  0.01654 &  0.9917 \tabularnewline
75 &  0.007503 &  0.01501 &  0.9925 \tabularnewline
76 &  0.01125 &  0.0225 &  0.9888 \tabularnewline
77 &  0.01174 &  0.02349 &  0.9883 \tabularnewline
78 &  0.01659 &  0.03319 &  0.9834 \tabularnewline
79 &  0.02089 &  0.04179 &  0.9791 \tabularnewline
80 &  0.02314 &  0.04627 &  0.9769 \tabularnewline
81 &  0.02448 &  0.04895 &  0.9755 \tabularnewline
82 &  0.02578 &  0.05157 &  0.9742 \tabularnewline
83 &  0.03588 &  0.07176 &  0.9641 \tabularnewline
84 &  0.0372 &  0.0744 &  0.9628 \tabularnewline
85 &  0.04195 &  0.08389 &  0.9581 \tabularnewline
86 &  0.06632 &  0.1326 &  0.9337 \tabularnewline
87 &  0.1114 &  0.2227 &  0.8886 \tabularnewline
88 &  0.1158 &  0.2316 &  0.8842 \tabularnewline
89 &  0.1243 &  0.2486 &  0.8757 \tabularnewline
90 &  0.1708 &  0.3416 &  0.8292 \tabularnewline
91 &  0.1819 &  0.3638 &  0.8181 \tabularnewline
92 &  0.2067 &  0.4133 &  0.7933 \tabularnewline
93 &  0.2009 &  0.4018 &  0.7991 \tabularnewline
94 &  0.2048 &  0.4097 &  0.7952 \tabularnewline
95 &  0.2469 &  0.4938 &  0.7531 \tabularnewline
96 &  0.2809 &  0.5619 &  0.7191 \tabularnewline
97 &  0.2732 &  0.5464 &  0.7268 \tabularnewline
98 &  0.3187 &  0.6375 &  0.6813 \tabularnewline
99 &  0.4274 &  0.8548 &  0.5726 \tabularnewline
100 &  0.4802 &  0.9604 &  0.5198 \tabularnewline
101 &  0.456 &  0.9121 &  0.544 \tabularnewline
102 &  0.4515 &  0.903 &  0.5485 \tabularnewline
103 &  0.4438 &  0.8877 &  0.5562 \tabularnewline
104 &  0.446 &  0.8921 &  0.554 \tabularnewline
105 &  0.445 &  0.89 &  0.555 \tabularnewline
106 &  0.4487 &  0.8975 &  0.5513 \tabularnewline
107 &  0.5035 &  0.9929 &  0.4965 \tabularnewline
108 &  0.5176 &  0.9647 &  0.4824 \tabularnewline
109 &  0.5365 &  0.9271 &  0.4635 \tabularnewline
110 &  0.5567 &  0.8867 &  0.4433 \tabularnewline
111 &  0.5639 &  0.8721 &  0.4361 \tabularnewline
112 &  0.5732 &  0.8536 &  0.4268 \tabularnewline
113 &  0.558 &  0.884 &  0.442 \tabularnewline
114 &  0.5439 &  0.9123 &  0.4561 \tabularnewline
115 &  0.5337 &  0.9327 &  0.4663 \tabularnewline
116 &  0.5137 &  0.9725 &  0.4863 \tabularnewline
117 &  0.5241 &  0.9518 &  0.4759 \tabularnewline
118 &  0.6513 &  0.6973 &  0.3487 \tabularnewline
119 &  0.6734 &  0.6531 &  0.3266 \tabularnewline
120 &  0.6743 &  0.6515 &  0.3257 \tabularnewline
121 &  0.6729 &  0.6542 &  0.3271 \tabularnewline
122 &  0.7169 &  0.5662 &  0.2831 \tabularnewline
123 &  0.7005 &  0.5989 &  0.2995 \tabularnewline
124 &  0.6939 &  0.6122 &  0.3061 \tabularnewline
125 &  0.6747 &  0.6506 &  0.3253 \tabularnewline
126 &  0.641 &  0.7181 &  0.359 \tabularnewline
127 &  0.6053 &  0.7894 &  0.3947 \tabularnewline
128 &  0.5677 &  0.8647 &  0.4323 \tabularnewline
129 &  0.5403 &  0.9194 &  0.4597 \tabularnewline
130 &  0.5541 &  0.8917 &  0.4459 \tabularnewline
131 &  0.5784 &  0.8433 &  0.4216 \tabularnewline
132 &  0.5864 &  0.8272 &  0.4136 \tabularnewline
133 &  0.5454 &  0.9093 &  0.4546 \tabularnewline
134 &  0.5246 &  0.9508 &  0.4754 \tabularnewline
135 &  0.514 &  0.972 &  0.486 \tabularnewline
136 &  0.6022 &  0.7955 &  0.3978 \tabularnewline
137 &  0.5572 &  0.8857 &  0.4428 \tabularnewline
138 &  0.7874 &  0.4251 &  0.2126 \tabularnewline
139 &  0.7896 &  0.4207 &  0.2104 \tabularnewline
140 &  0.7566 &  0.4868 &  0.2434 \tabularnewline
141 &  0.7266 &  0.5468 &  0.2734 \tabularnewline
142 &  0.736 &  0.528 &  0.264 \tabularnewline
143 &  0.7105 &  0.5791 &  0.2895 \tabularnewline
144 &  0.6878 &  0.6245 &  0.3122 \tabularnewline
145 &  0.6598 &  0.6803 &  0.3402 \tabularnewline
146 &  0.6782 &  0.6435 &  0.3218 \tabularnewline
147 &  0.7116 &  0.5767 &  0.2884 \tabularnewline
148 &  0.677 &  0.6461 &  0.323 \tabularnewline
149 &  0.6343 &  0.7313 &  0.3657 \tabularnewline
150 &  0.6199 &  0.7602 &  0.3801 \tabularnewline
151 &  0.6087 &  0.7826 &  0.3913 \tabularnewline
152 &  0.5936 &  0.8127 &  0.4064 \tabularnewline
153 &  0.665 &  0.67 &  0.335 \tabularnewline
154 &  0.816 &  0.368 &  0.184 \tabularnewline
155 &  0.8407 &  0.3187 &  0.1593 \tabularnewline
156 &  0.8158 &  0.3684 &  0.1842 \tabularnewline
157 &  0.8032 &  0.3936 &  0.1968 \tabularnewline
158 &  0.7854 &  0.4291 &  0.2146 \tabularnewline
159 &  0.7516 &  0.4968 &  0.2484 \tabularnewline
160 &  0.7102 &  0.5797 &  0.2898 \tabularnewline
161 &  0.6757 &  0.6487 &  0.3243 \tabularnewline
162 &  0.6601 &  0.6798 &  0.3399 \tabularnewline
163 &  0.6495 &  0.701 &  0.3505 \tabularnewline
164 &  0.6042 &  0.7916 &  0.3958 \tabularnewline
165 &  0.6516 &  0.6969 &  0.3484 \tabularnewline
166 &  0.7595 &  0.4809 &  0.2405 \tabularnewline
167 &  0.775 &  0.4501 &  0.225 \tabularnewline
168 &  0.7443 &  0.5114 &  0.2557 \tabularnewline
169 &  0.7881 &  0.4238 &  0.2119 \tabularnewline
170 &  0.7452 &  0.5096 &  0.2548 \tabularnewline
171 &  0.8036 &  0.3928 &  0.1964 \tabularnewline
172 &  0.764 &  0.472 &  0.236 \tabularnewline
173 &  0.7335 &  0.533 &  0.2665 \tabularnewline
174 &  0.7578 &  0.4844 &  0.2422 \tabularnewline
175 &  0.7275 &  0.545 &  0.2725 \tabularnewline
176 &  0.6667 &  0.6665 &  0.3333 \tabularnewline
177 &  0.8192 &  0.3616 &  0.1808 \tabularnewline
178 &  0.8616 &  0.2768 &  0.1384 \tabularnewline
179 &  0.8048 &  0.3904 &  0.1952 \tabularnewline
180 &  0.7272 &  0.5456 &  0.2728 \tabularnewline
181 &  0.6791 &  0.6417 &  0.3209 \tabularnewline
182 &  0.5742 &  0.8516 &  0.4258 \tabularnewline
183 &  0.4611 &  0.9222 &  0.5389 \tabularnewline
184 &  0.3947 &  0.7894 &  0.6053 \tabularnewline
185 &  0.2793 &  0.5587 &  0.7207 \tabularnewline
186 &  0.3131 &  0.6261 &  0.6869 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309214&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]10[/C][C] 0.1294[/C][C] 0.2589[/C][C] 0.8706[/C][/ROW]
[ROW][C]11[/C][C] 0.1069[/C][C] 0.2138[/C][C] 0.8931[/C][/ROW]
[ROW][C]12[/C][C] 0.1084[/C][C] 0.2169[/C][C] 0.8916[/C][/ROW]
[ROW][C]13[/C][C] 0.05715[/C][C] 0.1143[/C][C] 0.9428[/C][/ROW]
[ROW][C]14[/C][C] 0.03566[/C][C] 0.07133[/C][C] 0.9643[/C][/ROW]
[ROW][C]15[/C][C] 0.0266[/C][C] 0.0532[/C][C] 0.9734[/C][/ROW]
[ROW][C]16[/C][C] 0.01418[/C][C] 0.02836[/C][C] 0.9858[/C][/ROW]
[ROW][C]17[/C][C] 0.007541[/C][C] 0.01508[/C][C] 0.9925[/C][/ROW]
[ROW][C]18[/C][C] 0.02689[/C][C] 0.05377[/C][C] 0.9731[/C][/ROW]
[ROW][C]19[/C][C] 0.01658[/C][C] 0.03316[/C][C] 0.9834[/C][/ROW]
[ROW][C]20[/C][C] 0.02387[/C][C] 0.04774[/C][C] 0.9761[/C][/ROW]
[ROW][C]21[/C][C] 0.01413[/C][C] 0.02826[/C][C] 0.9859[/C][/ROW]
[ROW][C]22[/C][C] 0.01008[/C][C] 0.02015[/C][C] 0.9899[/C][/ROW]
[ROW][C]23[/C][C] 0.006294[/C][C] 0.01259[/C][C] 0.9937[/C][/ROW]
[ROW][C]24[/C][C] 0.006925[/C][C] 0.01385[/C][C] 0.9931[/C][/ROW]
[ROW][C]25[/C][C] 0.003817[/C][C] 0.007634[/C][C] 0.9962[/C][/ROW]
[ROW][C]26[/C][C] 0.002246[/C][C] 0.004492[/C][C] 0.9978[/C][/ROW]
[ROW][C]27[/C][C] 0.005551[/C][C] 0.0111[/C][C] 0.9944[/C][/ROW]
[ROW][C]28[/C][C] 0.003472[/C][C] 0.006943[/C][C] 0.9965[/C][/ROW]
[ROW][C]29[/C][C] 0.002205[/C][C] 0.004409[/C][C] 0.9978[/C][/ROW]
[ROW][C]30[/C][C] 0.009503[/C][C] 0.01901[/C][C] 0.9905[/C][/ROW]
[ROW][C]31[/C][C] 0.006468[/C][C] 0.01294[/C][C] 0.9935[/C][/ROW]
[ROW][C]32[/C][C] 0.004153[/C][C] 0.008305[/C][C] 0.9958[/C][/ROW]
[ROW][C]33[/C][C] 0.002478[/C][C] 0.004956[/C][C] 0.9975[/C][/ROW]
[ROW][C]34[/C][C] 0.002634[/C][C] 0.005267[/C][C] 0.9974[/C][/ROW]
[ROW][C]35[/C][C] 0.002917[/C][C] 0.005833[/C][C] 0.9971[/C][/ROW]
[ROW][C]36[/C][C] 0.001811[/C][C] 0.003622[/C][C] 0.9982[/C][/ROW]
[ROW][C]37[/C][C] 0.001074[/C][C] 0.002148[/C][C] 0.9989[/C][/ROW]
[ROW][C]38[/C][C] 0.001029[/C][C] 0.002058[/C][C] 0.999[/C][/ROW]
[ROW][C]39[/C][C] 0.0006488[/C][C] 0.001298[/C][C] 0.9994[/C][/ROW]
[ROW][C]40[/C][C] 0.0004968[/C][C] 0.0009935[/C][C] 0.9995[/C][/ROW]
[ROW][C]41[/C][C] 0.0004033[/C][C] 0.0008066[/C][C] 0.9996[/C][/ROW]
[ROW][C]42[/C][C] 0.0002411[/C][C] 0.0004823[/C][C] 0.9998[/C][/ROW]
[ROW][C]43[/C][C] 0.0001443[/C][C] 0.0002885[/C][C] 0.9999[/C][/ROW]
[ROW][C]44[/C][C] 8.458e-05[/C][C] 0.0001692[/C][C] 0.9999[/C][/ROW]
[ROW][C]45[/C][C] 0.000154[/C][C] 0.000308[/C][C] 0.9998[/C][/ROW]
[ROW][C]46[/C][C] 0.0005096[/C][C] 0.001019[/C][C] 0.9995[/C][/ROW]
[ROW][C]47[/C][C] 0.0004195[/C][C] 0.0008391[/C][C] 0.9996[/C][/ROW]
[ROW][C]48[/C][C] 0.0002718[/C][C] 0.0005436[/C][C] 0.9997[/C][/ROW]
[ROW][C]49[/C][C] 0.0002082[/C][C] 0.0004164[/C][C] 0.9998[/C][/ROW]
[ROW][C]50[/C][C] 0.0002007[/C][C] 0.0004013[/C][C] 0.9998[/C][/ROW]
[ROW][C]51[/C][C] 0.0001489[/C][C] 0.0002978[/C][C] 0.9999[/C][/ROW]
[ROW][C]52[/C][C] 0.0008781[/C][C] 0.001756[/C][C] 0.9991[/C][/ROW]
[ROW][C]53[/C][C] 0.001142[/C][C] 0.002283[/C][C] 0.9989[/C][/ROW]
[ROW][C]54[/C][C] 0.0009052[/C][C] 0.00181[/C][C] 0.9991[/C][/ROW]
[ROW][C]55[/C][C] 0.0007472[/C][C] 0.001494[/C][C] 0.9993[/C][/ROW]
[ROW][C]56[/C][C] 0.0005491[/C][C] 0.001098[/C][C] 0.9995[/C][/ROW]
[ROW][C]57[/C][C] 0.0005838[/C][C] 0.001168[/C][C] 0.9994[/C][/ROW]
[ROW][C]58[/C][C] 0.002582[/C][C] 0.005165[/C][C] 0.9974[/C][/ROW]
[ROW][C]59[/C][C] 0.002279[/C][C] 0.004559[/C][C] 0.9977[/C][/ROW]
[ROW][C]60[/C][C] 0.002492[/C][C] 0.004983[/C][C] 0.9975[/C][/ROW]
[ROW][C]61[/C][C] 0.002013[/C][C] 0.004026[/C][C] 0.998[/C][/ROW]
[ROW][C]62[/C][C] 0.001744[/C][C] 0.003488[/C][C] 0.9983[/C][/ROW]
[ROW][C]63[/C][C] 0.00217[/C][C] 0.00434[/C][C] 0.9978[/C][/ROW]
[ROW][C]64[/C][C] 0.004422[/C][C] 0.008845[/C][C] 0.9956[/C][/ROW]
[ROW][C]65[/C][C] 0.003567[/C][C] 0.007134[/C][C] 0.9964[/C][/ROW]
[ROW][C]66[/C][C] 0.0042[/C][C] 0.008399[/C][C] 0.9958[/C][/ROW]
[ROW][C]67[/C][C] 0.003589[/C][C] 0.007177[/C][C] 0.9964[/C][/ROW]
[ROW][C]68[/C][C] 0.003775[/C][C] 0.007551[/C][C] 0.9962[/C][/ROW]
[ROW][C]69[/C][C] 0.003252[/C][C] 0.006503[/C][C] 0.9967[/C][/ROW]
[ROW][C]70[/C][C] 0.008184[/C][C] 0.01637[/C][C] 0.9918[/C][/ROW]
[ROW][C]71[/C][C] 0.008098[/C][C] 0.0162[/C][C] 0.9919[/C][/ROW]
[ROW][C]72[/C][C] 0.00747[/C][C] 0.01494[/C][C] 0.9925[/C][/ROW]
[ROW][C]73[/C][C] 0.007231[/C][C] 0.01446[/C][C] 0.9928[/C][/ROW]
[ROW][C]74[/C][C] 0.008268[/C][C] 0.01654[/C][C] 0.9917[/C][/ROW]
[ROW][C]75[/C][C] 0.007503[/C][C] 0.01501[/C][C] 0.9925[/C][/ROW]
[ROW][C]76[/C][C] 0.01125[/C][C] 0.0225[/C][C] 0.9888[/C][/ROW]
[ROW][C]77[/C][C] 0.01174[/C][C] 0.02349[/C][C] 0.9883[/C][/ROW]
[ROW][C]78[/C][C] 0.01659[/C][C] 0.03319[/C][C] 0.9834[/C][/ROW]
[ROW][C]79[/C][C] 0.02089[/C][C] 0.04179[/C][C] 0.9791[/C][/ROW]
[ROW][C]80[/C][C] 0.02314[/C][C] 0.04627[/C][C] 0.9769[/C][/ROW]
[ROW][C]81[/C][C] 0.02448[/C][C] 0.04895[/C][C] 0.9755[/C][/ROW]
[ROW][C]82[/C][C] 0.02578[/C][C] 0.05157[/C][C] 0.9742[/C][/ROW]
[ROW][C]83[/C][C] 0.03588[/C][C] 0.07176[/C][C] 0.9641[/C][/ROW]
[ROW][C]84[/C][C] 0.0372[/C][C] 0.0744[/C][C] 0.9628[/C][/ROW]
[ROW][C]85[/C][C] 0.04195[/C][C] 0.08389[/C][C] 0.9581[/C][/ROW]
[ROW][C]86[/C][C] 0.06632[/C][C] 0.1326[/C][C] 0.9337[/C][/ROW]
[ROW][C]87[/C][C] 0.1114[/C][C] 0.2227[/C][C] 0.8886[/C][/ROW]
[ROW][C]88[/C][C] 0.1158[/C][C] 0.2316[/C][C] 0.8842[/C][/ROW]
[ROW][C]89[/C][C] 0.1243[/C][C] 0.2486[/C][C] 0.8757[/C][/ROW]
[ROW][C]90[/C][C] 0.1708[/C][C] 0.3416[/C][C] 0.8292[/C][/ROW]
[ROW][C]91[/C][C] 0.1819[/C][C] 0.3638[/C][C] 0.8181[/C][/ROW]
[ROW][C]92[/C][C] 0.2067[/C][C] 0.4133[/C][C] 0.7933[/C][/ROW]
[ROW][C]93[/C][C] 0.2009[/C][C] 0.4018[/C][C] 0.7991[/C][/ROW]
[ROW][C]94[/C][C] 0.2048[/C][C] 0.4097[/C][C] 0.7952[/C][/ROW]
[ROW][C]95[/C][C] 0.2469[/C][C] 0.4938[/C][C] 0.7531[/C][/ROW]
[ROW][C]96[/C][C] 0.2809[/C][C] 0.5619[/C][C] 0.7191[/C][/ROW]
[ROW][C]97[/C][C] 0.2732[/C][C] 0.5464[/C][C] 0.7268[/C][/ROW]
[ROW][C]98[/C][C] 0.3187[/C][C] 0.6375[/C][C] 0.6813[/C][/ROW]
[ROW][C]99[/C][C] 0.4274[/C][C] 0.8548[/C][C] 0.5726[/C][/ROW]
[ROW][C]100[/C][C] 0.4802[/C][C] 0.9604[/C][C] 0.5198[/C][/ROW]
[ROW][C]101[/C][C] 0.456[/C][C] 0.9121[/C][C] 0.544[/C][/ROW]
[ROW][C]102[/C][C] 0.4515[/C][C] 0.903[/C][C] 0.5485[/C][/ROW]
[ROW][C]103[/C][C] 0.4438[/C][C] 0.8877[/C][C] 0.5562[/C][/ROW]
[ROW][C]104[/C][C] 0.446[/C][C] 0.8921[/C][C] 0.554[/C][/ROW]
[ROW][C]105[/C][C] 0.445[/C][C] 0.89[/C][C] 0.555[/C][/ROW]
[ROW][C]106[/C][C] 0.4487[/C][C] 0.8975[/C][C] 0.5513[/C][/ROW]
[ROW][C]107[/C][C] 0.5035[/C][C] 0.9929[/C][C] 0.4965[/C][/ROW]
[ROW][C]108[/C][C] 0.5176[/C][C] 0.9647[/C][C] 0.4824[/C][/ROW]
[ROW][C]109[/C][C] 0.5365[/C][C] 0.9271[/C][C] 0.4635[/C][/ROW]
[ROW][C]110[/C][C] 0.5567[/C][C] 0.8867[/C][C] 0.4433[/C][/ROW]
[ROW][C]111[/C][C] 0.5639[/C][C] 0.8721[/C][C] 0.4361[/C][/ROW]
[ROW][C]112[/C][C] 0.5732[/C][C] 0.8536[/C][C] 0.4268[/C][/ROW]
[ROW][C]113[/C][C] 0.558[/C][C] 0.884[/C][C] 0.442[/C][/ROW]
[ROW][C]114[/C][C] 0.5439[/C][C] 0.9123[/C][C] 0.4561[/C][/ROW]
[ROW][C]115[/C][C] 0.5337[/C][C] 0.9327[/C][C] 0.4663[/C][/ROW]
[ROW][C]116[/C][C] 0.5137[/C][C] 0.9725[/C][C] 0.4863[/C][/ROW]
[ROW][C]117[/C][C] 0.5241[/C][C] 0.9518[/C][C] 0.4759[/C][/ROW]
[ROW][C]118[/C][C] 0.6513[/C][C] 0.6973[/C][C] 0.3487[/C][/ROW]
[ROW][C]119[/C][C] 0.6734[/C][C] 0.6531[/C][C] 0.3266[/C][/ROW]
[ROW][C]120[/C][C] 0.6743[/C][C] 0.6515[/C][C] 0.3257[/C][/ROW]
[ROW][C]121[/C][C] 0.6729[/C][C] 0.6542[/C][C] 0.3271[/C][/ROW]
[ROW][C]122[/C][C] 0.7169[/C][C] 0.5662[/C][C] 0.2831[/C][/ROW]
[ROW][C]123[/C][C] 0.7005[/C][C] 0.5989[/C][C] 0.2995[/C][/ROW]
[ROW][C]124[/C][C] 0.6939[/C][C] 0.6122[/C][C] 0.3061[/C][/ROW]
[ROW][C]125[/C][C] 0.6747[/C][C] 0.6506[/C][C] 0.3253[/C][/ROW]
[ROW][C]126[/C][C] 0.641[/C][C] 0.7181[/C][C] 0.359[/C][/ROW]
[ROW][C]127[/C][C] 0.6053[/C][C] 0.7894[/C][C] 0.3947[/C][/ROW]
[ROW][C]128[/C][C] 0.5677[/C][C] 0.8647[/C][C] 0.4323[/C][/ROW]
[ROW][C]129[/C][C] 0.5403[/C][C] 0.9194[/C][C] 0.4597[/C][/ROW]
[ROW][C]130[/C][C] 0.5541[/C][C] 0.8917[/C][C] 0.4459[/C][/ROW]
[ROW][C]131[/C][C] 0.5784[/C][C] 0.8433[/C][C] 0.4216[/C][/ROW]
[ROW][C]132[/C][C] 0.5864[/C][C] 0.8272[/C][C] 0.4136[/C][/ROW]
[ROW][C]133[/C][C] 0.5454[/C][C] 0.9093[/C][C] 0.4546[/C][/ROW]
[ROW][C]134[/C][C] 0.5246[/C][C] 0.9508[/C][C] 0.4754[/C][/ROW]
[ROW][C]135[/C][C] 0.514[/C][C] 0.972[/C][C] 0.486[/C][/ROW]
[ROW][C]136[/C][C] 0.6022[/C][C] 0.7955[/C][C] 0.3978[/C][/ROW]
[ROW][C]137[/C][C] 0.5572[/C][C] 0.8857[/C][C] 0.4428[/C][/ROW]
[ROW][C]138[/C][C] 0.7874[/C][C] 0.4251[/C][C] 0.2126[/C][/ROW]
[ROW][C]139[/C][C] 0.7896[/C][C] 0.4207[/C][C] 0.2104[/C][/ROW]
[ROW][C]140[/C][C] 0.7566[/C][C] 0.4868[/C][C] 0.2434[/C][/ROW]
[ROW][C]141[/C][C] 0.7266[/C][C] 0.5468[/C][C] 0.2734[/C][/ROW]
[ROW][C]142[/C][C] 0.736[/C][C] 0.528[/C][C] 0.264[/C][/ROW]
[ROW][C]143[/C][C] 0.7105[/C][C] 0.5791[/C][C] 0.2895[/C][/ROW]
[ROW][C]144[/C][C] 0.6878[/C][C] 0.6245[/C][C] 0.3122[/C][/ROW]
[ROW][C]145[/C][C] 0.6598[/C][C] 0.6803[/C][C] 0.3402[/C][/ROW]
[ROW][C]146[/C][C] 0.6782[/C][C] 0.6435[/C][C] 0.3218[/C][/ROW]
[ROW][C]147[/C][C] 0.7116[/C][C] 0.5767[/C][C] 0.2884[/C][/ROW]
[ROW][C]148[/C][C] 0.677[/C][C] 0.6461[/C][C] 0.323[/C][/ROW]
[ROW][C]149[/C][C] 0.6343[/C][C] 0.7313[/C][C] 0.3657[/C][/ROW]
[ROW][C]150[/C][C] 0.6199[/C][C] 0.7602[/C][C] 0.3801[/C][/ROW]
[ROW][C]151[/C][C] 0.6087[/C][C] 0.7826[/C][C] 0.3913[/C][/ROW]
[ROW][C]152[/C][C] 0.5936[/C][C] 0.8127[/C][C] 0.4064[/C][/ROW]
[ROW][C]153[/C][C] 0.665[/C][C] 0.67[/C][C] 0.335[/C][/ROW]
[ROW][C]154[/C][C] 0.816[/C][C] 0.368[/C][C] 0.184[/C][/ROW]
[ROW][C]155[/C][C] 0.8407[/C][C] 0.3187[/C][C] 0.1593[/C][/ROW]
[ROW][C]156[/C][C] 0.8158[/C][C] 0.3684[/C][C] 0.1842[/C][/ROW]
[ROW][C]157[/C][C] 0.8032[/C][C] 0.3936[/C][C] 0.1968[/C][/ROW]
[ROW][C]158[/C][C] 0.7854[/C][C] 0.4291[/C][C] 0.2146[/C][/ROW]
[ROW][C]159[/C][C] 0.7516[/C][C] 0.4968[/C][C] 0.2484[/C][/ROW]
[ROW][C]160[/C][C] 0.7102[/C][C] 0.5797[/C][C] 0.2898[/C][/ROW]
[ROW][C]161[/C][C] 0.6757[/C][C] 0.6487[/C][C] 0.3243[/C][/ROW]
[ROW][C]162[/C][C] 0.6601[/C][C] 0.6798[/C][C] 0.3399[/C][/ROW]
[ROW][C]163[/C][C] 0.6495[/C][C] 0.701[/C][C] 0.3505[/C][/ROW]
[ROW][C]164[/C][C] 0.6042[/C][C] 0.7916[/C][C] 0.3958[/C][/ROW]
[ROW][C]165[/C][C] 0.6516[/C][C] 0.6969[/C][C] 0.3484[/C][/ROW]
[ROW][C]166[/C][C] 0.7595[/C][C] 0.4809[/C][C] 0.2405[/C][/ROW]
[ROW][C]167[/C][C] 0.775[/C][C] 0.4501[/C][C] 0.225[/C][/ROW]
[ROW][C]168[/C][C] 0.7443[/C][C] 0.5114[/C][C] 0.2557[/C][/ROW]
[ROW][C]169[/C][C] 0.7881[/C][C] 0.4238[/C][C] 0.2119[/C][/ROW]
[ROW][C]170[/C][C] 0.7452[/C][C] 0.5096[/C][C] 0.2548[/C][/ROW]
[ROW][C]171[/C][C] 0.8036[/C][C] 0.3928[/C][C] 0.1964[/C][/ROW]
[ROW][C]172[/C][C] 0.764[/C][C] 0.472[/C][C] 0.236[/C][/ROW]
[ROW][C]173[/C][C] 0.7335[/C][C] 0.533[/C][C] 0.2665[/C][/ROW]
[ROW][C]174[/C][C] 0.7578[/C][C] 0.4844[/C][C] 0.2422[/C][/ROW]
[ROW][C]175[/C][C] 0.7275[/C][C] 0.545[/C][C] 0.2725[/C][/ROW]
[ROW][C]176[/C][C] 0.6667[/C][C] 0.6665[/C][C] 0.3333[/C][/ROW]
[ROW][C]177[/C][C] 0.8192[/C][C] 0.3616[/C][C] 0.1808[/C][/ROW]
[ROW][C]178[/C][C] 0.8616[/C][C] 0.2768[/C][C] 0.1384[/C][/ROW]
[ROW][C]179[/C][C] 0.8048[/C][C] 0.3904[/C][C] 0.1952[/C][/ROW]
[ROW][C]180[/C][C] 0.7272[/C][C] 0.5456[/C][C] 0.2728[/C][/ROW]
[ROW][C]181[/C][C] 0.6791[/C][C] 0.6417[/C][C] 0.3209[/C][/ROW]
[ROW][C]182[/C][C] 0.5742[/C][C] 0.8516[/C][C] 0.4258[/C][/ROW]
[ROW][C]183[/C][C] 0.4611[/C][C] 0.9222[/C][C] 0.5389[/C][/ROW]
[ROW][C]184[/C][C] 0.3947[/C][C] 0.7894[/C][C] 0.6053[/C][/ROW]
[ROW][C]185[/C][C] 0.2793[/C][C] 0.5587[/C][C] 0.7207[/C][/ROW]
[ROW][C]186[/C][C] 0.3131[/C][C] 0.6261[/C][C] 0.6869[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309214&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309214&T=6

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1294	0.2589	0.8706
11	0.1069	0.2138	0.8931
12	0.1084	0.2169	0.8916
13	0.05715	0.1143	0.9428
14	0.03566	0.07133	0.9643
15	0.0266	0.0532	0.9734
16	0.01418	0.02836	0.9858
17	0.007541	0.01508	0.9925
18	0.02689	0.05377	0.9731
19	0.01658	0.03316	0.9834
20	0.02387	0.04774	0.9761
21	0.01413	0.02826	0.9859
22	0.01008	0.02015	0.9899
23	0.006294	0.01259	0.9937
24	0.006925	0.01385	0.9931
25	0.003817	0.007634	0.9962
26	0.002246	0.004492	0.9978
27	0.005551	0.0111	0.9944
28	0.003472	0.006943	0.9965
29	0.002205	0.004409	0.9978
30	0.009503	0.01901	0.9905
31	0.006468	0.01294	0.9935
32	0.004153	0.008305	0.9958
33	0.002478	0.004956	0.9975
34	0.002634	0.005267	0.9974
35	0.002917	0.005833	0.9971
36	0.001811	0.003622	0.9982
37	0.001074	0.002148	0.9989
38	0.001029	0.002058	0.999
39	0.0006488	0.001298	0.9994
40	0.0004968	0.0009935	0.9995
41	0.0004033	0.0008066	0.9996
42	0.0002411	0.0004823	0.9998
43	0.0001443	0.0002885	0.9999
44	8.458e-05	0.0001692	0.9999
45	0.000154	0.000308	0.9998
46	0.0005096	0.001019	0.9995
47	0.0004195	0.0008391	0.9996
48	0.0002718	0.0005436	0.9997
49	0.0002082	0.0004164	0.9998
50	0.0002007	0.0004013	0.9998
51	0.0001489	0.0002978	0.9999
52	0.0008781	0.001756	0.9991
53	0.001142	0.002283	0.9989
54	0.0009052	0.00181	0.9991
55	0.0007472	0.001494	0.9993
56	0.0005491	0.001098	0.9995
57	0.0005838	0.001168	0.9994
58	0.002582	0.005165	0.9974
59	0.002279	0.004559	0.9977
60	0.002492	0.004983	0.9975
61	0.002013	0.004026	0.998
62	0.001744	0.003488	0.9983
63	0.00217	0.00434	0.9978
64	0.004422	0.008845	0.9956
65	0.003567	0.007134	0.9964
66	0.0042	0.008399	0.9958
67	0.003589	0.007177	0.9964
68	0.003775	0.007551	0.9962
69	0.003252	0.006503	0.9967
70	0.008184	0.01637	0.9918
71	0.008098	0.0162	0.9919
72	0.00747	0.01494	0.9925
73	0.007231	0.01446	0.9928
74	0.008268	0.01654	0.9917
75	0.007503	0.01501	0.9925
76	0.01125	0.0225	0.9888
77	0.01174	0.02349	0.9883
78	0.01659	0.03319	0.9834
79	0.02089	0.04179	0.9791
80	0.02314	0.04627	0.9769
81	0.02448	0.04895	0.9755
82	0.02578	0.05157	0.9742
83	0.03588	0.07176	0.9641
84	0.0372	0.0744	0.9628
85	0.04195	0.08389	0.9581
86	0.06632	0.1326	0.9337
87	0.1114	0.2227	0.8886
88	0.1158	0.2316	0.8842
89	0.1243	0.2486	0.8757
90	0.1708	0.3416	0.8292
91	0.1819	0.3638	0.8181
92	0.2067	0.4133	0.7933
93	0.2009	0.4018	0.7991
94	0.2048	0.4097	0.7952
95	0.2469	0.4938	0.7531
96	0.2809	0.5619	0.7191
97	0.2732	0.5464	0.7268
98	0.3187	0.6375	0.6813
99	0.4274	0.8548	0.5726
100	0.4802	0.9604	0.5198
101	0.456	0.9121	0.544
102	0.4515	0.903	0.5485
103	0.4438	0.8877	0.5562
104	0.446	0.8921	0.554
105	0.445	0.89	0.555
106	0.4487	0.8975	0.5513
107	0.5035	0.9929	0.4965
108	0.5176	0.9647	0.4824
109	0.5365	0.9271	0.4635
110	0.5567	0.8867	0.4433
111	0.5639	0.8721	0.4361
112	0.5732	0.8536	0.4268
113	0.558	0.884	0.442
114	0.5439	0.9123	0.4561
115	0.5337	0.9327	0.4663
116	0.5137	0.9725	0.4863
117	0.5241	0.9518	0.4759
118	0.6513	0.6973	0.3487
119	0.6734	0.6531	0.3266
120	0.6743	0.6515	0.3257
121	0.6729	0.6542	0.3271
122	0.7169	0.5662	0.2831
123	0.7005	0.5989	0.2995
124	0.6939	0.6122	0.3061
125	0.6747	0.6506	0.3253
126	0.641	0.7181	0.359
127	0.6053	0.7894	0.3947
128	0.5677	0.8647	0.4323
129	0.5403	0.9194	0.4597
130	0.5541	0.8917	0.4459
131	0.5784	0.8433	0.4216
132	0.5864	0.8272	0.4136
133	0.5454	0.9093	0.4546
134	0.5246	0.9508	0.4754
135	0.514	0.972	0.486
136	0.6022	0.7955	0.3978
137	0.5572	0.8857	0.4428
138	0.7874	0.4251	0.2126
139	0.7896	0.4207	0.2104
140	0.7566	0.4868	0.2434
141	0.7266	0.5468	0.2734
142	0.736	0.528	0.264
143	0.7105	0.5791	0.2895
144	0.6878	0.6245	0.3122
145	0.6598	0.6803	0.3402
146	0.6782	0.6435	0.3218
147	0.7116	0.5767	0.2884
148	0.677	0.6461	0.323
149	0.6343	0.7313	0.3657
150	0.6199	0.7602	0.3801
151	0.6087	0.7826	0.3913
152	0.5936	0.8127	0.4064
153	0.665	0.67	0.335
154	0.816	0.368	0.184
155	0.8407	0.3187	0.1593
156	0.8158	0.3684	0.1842
157	0.8032	0.3936	0.1968
158	0.7854	0.4291	0.2146
159	0.7516	0.4968	0.2484
160	0.7102	0.5797	0.2898
161	0.6757	0.6487	0.3243
162	0.6601	0.6798	0.3399
163	0.6495	0.701	0.3505
164	0.6042	0.7916	0.3958
165	0.6516	0.6969	0.3484
166	0.7595	0.4809	0.2405
167	0.775	0.4501	0.225
168	0.7443	0.5114	0.2557
169	0.7881	0.4238	0.2119
170	0.7452	0.5096	0.2548
171	0.8036	0.3928	0.1964
172	0.764	0.472	0.236
173	0.7335	0.533	0.2665
174	0.7578	0.4844	0.2422
175	0.7275	0.545	0.2725
176	0.6667	0.6665	0.3333
177	0.8192	0.3616	0.1808
178	0.8616	0.2768	0.1384
179	0.8048	0.3904	0.1952
180	0.7272	0.5456	0.2728
181	0.6791	0.6417	0.3209
182	0.5742	0.8516	0.4258
183	0.4611	0.9222	0.5389
184	0.3947	0.7894	0.6053
185	0.2793	0.5587	0.7207
186	0.3131	0.6261	0.6869

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	42	0.2373	NOK
5% type I error level	65	0.367232	NOK
10% type I error level	72	0.40678	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 42 &  0.2373 & NOK \tabularnewline
5% type I error level & 65 & 0.367232 & NOK \tabularnewline
10% type I error level & 72 & 0.40678 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309214&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]42[/C][C] 0.2373[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]65[/C][C]0.367232[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]72[/C][C]0.40678[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309214&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309214&T=7

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	42	0.2373	NOK
5% type I error level	65	0.367232	NOK
10% type I error level	72	0.40678	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 5.1811, df1 = 2, df2 = 187, p-value = 0.006457
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.8661, df1 = 12, df2 = 177, p-value = 0.04137
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 5.3134, df1 = 2, df2 = 187, p-value = 0.005697

\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 = 5.1811, df1 = 2, df2 = 187, p-value = 0.006457
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.8661, df1 = 12, df2 = 177, p-value = 0.04137
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 5.3134, df1 = 2, df2 = 187, p-value = 0.005697
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309214&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 = 5.1811, df1 = 2, df2 = 187, p-value = 0.006457
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.8661, df1 = 12, df2 = 177, p-value = 0.04137
[/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 = 5.3134, df1 = 2, df2 = 187, p-value = 0.005697
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309214&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309214&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 5.1811, df1 = 2, df2 = 187, p-value = 0.006457
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.8661, df1 = 12, df2 = 177, p-value = 0.04137
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 5.3134, df1 = 2, df2 = 187, p-value = 0.005697

Variance Inflation Factors (Multicollinearity)
> vif X2 `X1(t-1)` `X1(t-2)` `X1(t-3)` `X1(t-4)` `X1(t-1s)` 5.907920 4.989725 3.746404 5.079818 4.292988 5.247075

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
        X2  `X1(t-1)`  `X1(t-2)`  `X1(t-3)`  `X1(t-4)` `X1(t-1s)` 
  5.907920   4.989725   3.746404   5.079818   4.292988   5.247075 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309214&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
        X2  `X1(t-1)`  `X1(t-2)`  `X1(t-3)`  `X1(t-4)` `X1(t-1s)` 
  5.907920   4.989725   3.746404   5.079818   4.292988   5.247075 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309214&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309214&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif X2 `X1(t-1)` `X1(t-2)` `X1(t-3)` `X1(t-4)` `X1(t-1s)` 5.907920 4.989725 3.746404 5.079818 4.292988 5.247075

Figure 1

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Figure 3

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 4 ; par5 = 1 ; par6 = 12 ;

Parameters (R input):

par1 = ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 4 ; par5 = 1 ; par6 = 12 ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code