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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 19 Dec 2017 17:54:46 +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/19/t1513702522bf3izrsznke4cfk.htm/, Retrieved Wed, 15 May 2024 12:12:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310376, Retrieved Wed, 15 May 2024 12:12:16 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

102

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

-       [Multiple Regression] [lags] [2017-12-19 16:54:46] [341fa61cadd50fad972999b365b6b694] [Current]

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Dataseries X:

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Histogram

Boxplots

57.7	63.2
60.1	68.6
66.5	77.7
63.4	68.1
71.4	75.1
68.5	73.3
61.6	60.5
68.3	65.9
69.3	77.7
76.1	77.1
73.3	77.7
69.7	71.3
67.4	76
63.7	75.3
73	81.7
67.5	72.5
74.4	77.4
72.9	81.1
71.7	65.1
75.6	68.7
72.5	75.6
80	79.7
75.4	75.3
71	67.7
70.6	73.2
67.5	72.2
74.1	79.3
73.2	77.5
74	75.6
73	77.4
74	69.2
73	67.1
76	77.9
81.7	82.7
73.5	75.7
77	70.1
73.6	76.4
70.4	74.3
74.7	80.5
76.8	78
72.7	73.5
76	78.8
77.5	71.2
73.6	66.2
78.5	82.7
84.3	83.8
74.4	75
78.5	80.4
72.7	74.6
71.3	77.7
84.4	89.8
79.1	82.4
76.2	77
84.9	89.6
77.1	75.7
78.7	75.1
84.7	89.9
83.7	88.8
82.5	86.5
85.2	90
76	84
72.2	82.7
83.2	91.7
80.2	87.5
81.1	82
86	92.2
76	73.1
83.9	75.6
87.9	91.6
85	87.5
88.1	90.1
87.4	91.3
79.5	87.6
75.2	88.4
87.3	100.7
79.5	85.3
87.6	92
89.1	96.8
83	77.9
88.3	80.9
88.9	95.3
93.9	99.3
91.7	96.1
87.2	92.5
87.8	93.7
81	92.1
93.7	103.6
87.5	92.5
91.4	95.7
93.8	103.4
89.5	89
93.3	89.1
92.8	98.7
104.1	109.4
99.9	101.1
93.4	95.4
99	101.4
93.2	102.1
95.7	103.6
102.6	106
98.8	98.4
98	106.6
101.5	95.8
94.9	87.2
104.7	108.5
108.4	107
97	92
102.3	94.9
90.8	84.4
89.6	85
99.9	94
99.2	84.5
94	88.2
103	92.1
99.8	81.1
94.9	81.2
102	96.1
103.2	95.3
98	92.1
101.1	91.7
88.2	90.3
90.3	96.1
105.5	108.7
99.4	95.9
94.3	95.1
105.9	109.4
98	91.2
99	91.4
103.9	107.4
104.3	105.6
105.7	105.3
105.5	103.7
97.4	99.5
95.4	103.2
110.5	123.1
102.8	102.2
110	110
104.3	106.2
96.5	91.3
105.6	99.3
111.3	111.8
108.5	104.4
109.1	102.4
107.7	101
102.3	100.6
102.4	104.5
110.8	117.4
101.7	97.4
108.9	99.5
111.5	106.4
104	95.2
109.9	94
106.8	104.1
118.4	105.8
111.8	101.1
105	93.5
104.9	97.9
96.5	96.8
106.3	108.4
105.6	103.5
109.3	101.3
105.1	107.4
111.5	100.7
103.1	91.1
106.5	105
114.4	112.8
104.7	105.6
105.5	101
100.5	101.9
96.4	103.5
105.1	109.5
108.4	105
105.7	102.9
109	108.5
107.2	96.9
101.6	88.4
112.7	112.4
115.9	111.3
105	101.6
110.4	101.2
100.9	101.8
98.5	98.8
111.3	114.4
109.6	104.5
103.4	97.6
115.7	109.1
110.4	94.5
105.2	90.4
113.2	111.8
117.4	110.5
112.3	106.8
113.9	101.8
102.2	103.7
106.9	107.4
118	117.5
113.8	109.6
114.9	102.8
118.8	115.5
106.3	97.8
114.2	100.2
117.3	112.9
114.7	108.7
117	109
116.6	113.9
106.5	106.9
105.7	109.6
121	124.5
107.8	104.2
119.7	110.8
121	118.7
108.8	102.1
115	105.1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 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 time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310376&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]

11 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310376&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310376&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	11 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
food[t] = -2.74664 + 0.342709nofood[t] + 0.100598`food(t-1)`[t] + 0.183761`food(t-2)`[t] + 0.0363879`food(t-3)`[t] -0.12345`food(t-4)`[t] + 0.507112`food(t-1s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
food[t] =  -2.74664 +  0.342709nofood[t] +  0.100598`food(t-1)`[t] +  0.183761`food(t-2)`[t] +  0.0363879`food(t-3)`[t] -0.12345`food(t-4)`[t] +  0.507112`food(t-1s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310376&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]food[t] =  -2.74664 +  0.342709nofood[t] +  0.100598`food(t-1)`[t] +  0.183761`food(t-2)`[t] +  0.0363879`food(t-3)`[t] -0.12345`food(t-4)`[t] +  0.507112`food(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310376&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310376&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
food[t] = -2.74664 + 0.342709nofood[t] + 0.100598`food(t-1)`[t] + 0.183761`food(t-2)`[t] + 0.0363879`food(t-3)`[t] -0.12345`food(t-4)`[t] + 0.507112`food(t-1s)`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-2.747	1.684	-1.6310e+00	0.1046	0.0523
nofood	+0.3427	0.03205	+1.0690e+01	3.59e-21	1.795e-21
`food(t-1)`	+0.1006	0.04612	+2.1810e+00	0.03039	0.0152
`food(t-2)`	+0.1838	0.04027	+4.5640e+00	9.032e-06	4.516e-06
`food(t-3)`	+0.03639	0.04562	+7.9760e-01	0.4261	0.213
`food(t-4)`	-0.1235	0.04136	-2.9850e+00	0.003214	0.001607
`food(t-1s)`	+0.5071	0.0441	+1.1500e+01	1.539e-23	7.694e-24

\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) & -2.747 &  1.684 & -1.6310e+00 &  0.1046 &  0.0523 \tabularnewline
nofood & +0.3427 &  0.03205 & +1.0690e+01 &  3.59e-21 &  1.795e-21 \tabularnewline
`food(t-1)` & +0.1006 &  0.04612 & +2.1810e+00 &  0.03039 &  0.0152 \tabularnewline
`food(t-2)` & +0.1838 &  0.04027 & +4.5640e+00 &  9.032e-06 &  4.516e-06 \tabularnewline
`food(t-3)` & +0.03639 &  0.04562 & +7.9760e-01 &  0.4261 &  0.213 \tabularnewline
`food(t-4)` & -0.1235 &  0.04136 & -2.9850e+00 &  0.003214 &  0.001607 \tabularnewline
`food(t-1s)` & +0.5071 &  0.0441 & +1.1500e+01 &  1.539e-23 &  7.694e-24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310376&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]-2.747[/C][C] 1.684[/C][C]-1.6310e+00[/C][C] 0.1046[/C][C] 0.0523[/C][/ROW]
[ROW][C]nofood[/C][C]+0.3427[/C][C] 0.03205[/C][C]+1.0690e+01[/C][C] 3.59e-21[/C][C] 1.795e-21[/C][/ROW]
[ROW][C]`food(t-1)`[/C][C]+0.1006[/C][C] 0.04612[/C][C]+2.1810e+00[/C][C] 0.03039[/C][C] 0.0152[/C][/ROW]
[ROW][C]`food(t-2)`[/C][C]+0.1838[/C][C] 0.04027[/C][C]+4.5640e+00[/C][C] 9.032e-06[/C][C] 4.516e-06[/C][/ROW]
[ROW][C]`food(t-3)`[/C][C]+0.03639[/C][C] 0.04562[/C][C]+7.9760e-01[/C][C] 0.4261[/C][C] 0.213[/C][/ROW]
[ROW][C]`food(t-4)`[/C][C]-0.1235[/C][C] 0.04136[/C][C]-2.9850e+00[/C][C] 0.003214[/C][C] 0.001607[/C][/ROW]
[ROW][C]`food(t-1s)`[/C][C]+0.5071[/C][C] 0.0441[/C][C]+1.1500e+01[/C][C] 1.539e-23[/C][C] 7.694e-24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310376&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310376&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)	-2.747	1.684	-1.6310e+00	0.1046	0.0523
nofood	+0.3427	0.03205	+1.0690e+01	3.59e-21	1.795e-21
`food(t-1)`	+0.1006	0.04612	+2.1810e+00	0.03039	0.0152
`food(t-2)`	+0.1838	0.04027	+4.5640e+00	9.032e-06	4.516e-06
`food(t-3)`	+0.03639	0.04562	+7.9760e-01	0.4261	0.213
`food(t-4)`	-0.1235	0.04136	-2.9850e+00	0.003214	0.001607
`food(t-1s)`	+0.5071	0.0441	+1.1500e+01	1.539e-23	7.694e-24

Multiple Linear Regression - Regression Statistics
Multiple R	0.9783
R-squared	0.9571
Adjusted R-squared	0.9558
F-TEST (value)	703.3
F-TEST (DF numerator)	6
F-TEST (DF denominator)	189
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.994
Sum Squared Residuals	1694

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9783 \tabularnewline
R-squared &  0.9571 \tabularnewline
Adjusted R-squared &  0.9558 \tabularnewline
F-TEST (value) &  703.3 \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 &  2.994 \tabularnewline
Sum Squared Residuals &  1694 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310376&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9783[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9571[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9558[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 703.3[/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] 2.994[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1694[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310376&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310376&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	0.9783
R-squared	0.9571
Adjusted R-squared	0.9558
F-TEST (value)	703.3
F-TEST (DF numerator)	6
F-TEST (DF denominator)	189
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.994
Sum Squared Residuals	1694

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=310376&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=310376&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310376&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	74.4	74.19	0.2109
2	72.9	74.47	-1.565
3	71.7	65.25	6.448
4	75.6	70.42	5.183
5	72.5	72.55	-0.05388
6	80	77.95	2.046
7	75.4	75.5	-0.1007
8	71	71.39	-0.3917
9	70.6	71.48	-0.8779
10	67.5	67.32	0.1831
11	74.1	74.49	-0.3887
12	73.2	71.71	1.494
13	74	75.61	-1.612
14	73	76.01	-3.007
15	74	71.79	2.213
16	73	73.1	-0.1018
17	76	75.18	0.821
18	81.7	80.91	0.7948
19	73.5	77.14	-3.638
20	77	73.44	3.557
21	73.6	74.08	-0.4816
22	70.4	71.09	-0.6889
23	74.7	76.75	-2.054
24	76.8	74.73	2.071
25	72.7	74.9	-2.197
26	76	76.73	-0.7316
27	77.5	73.76	3.742
28	73.6	71.89	1.714
29	78.5	79.57	-1.072
30	84.3	82.26	2.037
31	74.4	76.25	-1.846
32	78.5	80.6	-2.101
33	72.7	75.09	-2.388
34	71.3	73.62	-2.322
35	84.4	80.11	4.286
36	79.1	78.99	0.114
37	76.2	77.6	-1.395
38	84.9	82.97	1.929
39	77.1	77.5	-0.4001
40	78.7	76.68	2.02
41	84.7	83.64	1.061
42	83.7	85.74	-2.043
43	82.5	81.96	0.5428
44	85.2	84.95	0.2479
45	76	79.23	-3.229
46	72.2	77.72	-5.524
47	83.2	85.62	-2.425
48	80.2	81.24	-1.038
49	81.1	80.6	0.5007
50	86	88.92	-2.915
51	76	77.61	-1.605
52	83.9	79.57	4.329
53	87.9	87.12	0.7785
54	85	86.09	-1.095
55	88.1	88.34	-0.2423
56	87.4	89.07	-1.672
57	79.5	83.04	-3.538
58	75.2	80.93	-5.733
59	87.3	88.43	-1.134
60	79.5	81.86	-2.361
61	87.6	86.87	0.7287
62	89.1	91.35	-2.254
63	83	79.67	3.333
64	88.3	85.62	2.679
65	88.9	91.05	-2.152
66	93.9	91.58	2.321
67	91.7	93.61	-1.913
68	87.2	92.09	-4.89
69	87.8	87.75	0.05426
70	81	83.55	-2.553
71	93.7	93.16	0.5358
72	87.5	86.01	1.49
73	91.4	92.6	-1.203
74	93.8	96.56	-2.757
75	89.5	87.69	1.807
76	93.3	91.33	1.969
77	92.8	94.12	-1.323
78	104.1	100.5	3.579
79	99.9	98.27	1.625
80	93.4	95.21	-1.806
81	99	96.61	2.386
82	93.2	91.23	1.974
83	95.7	98.91	-3.208
84	102.6	96.78	5.821
85	98.8	96.4	2.397
86	98	102.1	-4.123
87	101.5	95.4	6.095
88	94.9	93.6	1.3
89	104.7	101.1	3.635
90	108.4	106.3	2.12
91	97	100.5	-3.511
92	102.3	98.91	3.387
93	90.8	95.52	-4.717
94	89.6	91.73	-2.127
95	99.9	95.45	4.454
96	99.2	95.43	3.768
97	94	97.97	-3.971
98	103	98.77	4.227
99	99.8	95.43	4.369
100	94.9	93.35	1.552
101	102	103.3	-1.312
102	103.2	103.5	-0.3005
103	98	98.26	-0.2649
104	101.1	101.4	-0.2762
105	88.2	93.59	-5.388
106	90.3	93.9	-3.602
107	105.5	102	3.461
108	99.4	98.36	1.04
109	94.3	99.3	-4.997
110	105.9	107.4	-1.522
111	98	97.69	0.3068
112	99	97.18	1.819
113	103.9	106	-2.066
114	104.3	104.9	-0.6145
115	105.7	104.1	1.573
116	105.5	105.4	0.08007
117	97.4	97.09	0.3144
118	95.4	98.57	-3.169
119	110.5	111.2	-0.7268
120	102.8	101.9	0.9478
121	110	104.9	5.134
122	104.3	109.6	-5.252
123	96.5	99.05	-2.545
124	105.6	101.7	3.926
125	111.3	106.8	4.471
126	108.5	107.2	1.339
127	109.1	109.2	-0.1456
128	107.7	107.3	0.4058
129	102.3	102.2	0.08659
130	102.4	102.1	0.2972
131	110.8	113.1	-2.274
132	101.7	103.2	-1.455
133	108.9	108.8	0.07606
134	111.5	107.6	3.857
135	104	100.1	3.934
136	109.9	105.4	4.522
137	106.8	110.2	-3.351
138	118.4	109.5	8.908
139	111.8	109.9	1.876
140	105	107.2	-2.236
141	104.9	104.9	-0.01326
142	96.5	101.7	-5.155
143	106.3	109.6	-3.294
144	105.6	103.6	2.022
145	109.3	107.9	1.387
146	105.1	113	-7.859
147	111.5	105.9	5.618
148	103.1	105.7	-2.577
149	106.5	108.6	-2.09
150	114.4	116.7	-2.295
151	104.7	111.2	-6.504
152	105.5	107.8	-2.316
153	100.5	106.2	-5.74
154	96.4	100.8	-4.444
155	105.1	107.8	-2.665
156	108.4	105.7	2.691
157	105.7	109.3	-3.565
158	109	110.2	-1.211
159	107.2	108.4	-1.163
160	101.6	101.1	0.4897
161	112.7	110.6	2.081
162	115.9	113.9	2.037
163	105	108	-3
164	110.4	108.9	1.545
165	100.9	103.8	-2.911
166	98.5	99.95	-1.449
167	111.3	109.3	2.038
168	109.6	107.4	2.223
169	103.4	106.9	-3.51
170	115.7	112.3	3.35
171	110.4	104.9	5.51
172	105.2	102.4	2.844
173	113.2	115	-1.835
174	117.4	114.3	3.05
175	112.3	109.9	2.388
176	113.9	112.1	1.771
177	102.2	106.4	-4.152
178	106.9	104.8	2.084
179	118	113.8	4.221
180	113.8	111.6	2.234
181	114.9	109.3	5.576
182	118.8	119.1	-0.2768
183	106.3	109.4	-3.094
184	114.2	107.6	6.602
185	117.3	114.5	2.789
186	114.7	116	-1.329
187	117	115.7	1.316
188	116.6	117.1	-0.4656
189	106.5	108.6	-2.139
190	105.7	111.3	-5.562
191	121	119.8	1.237
192	107.8	111.8	-3.95
193	119.7	117.3	2.429
194	121	121.4	-0.3831
195	108.8	109.3	-0.5037
196	115	115.4	-0.4121

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  74.4 &  74.19 &  0.2109 \tabularnewline
2 &  72.9 &  74.47 & -1.565 \tabularnewline
3 &  71.7 &  65.25 &  6.448 \tabularnewline
4 &  75.6 &  70.42 &  5.183 \tabularnewline
5 &  72.5 &  72.55 & -0.05388 \tabularnewline
6 &  80 &  77.95 &  2.046 \tabularnewline
7 &  75.4 &  75.5 & -0.1007 \tabularnewline
8 &  71 &  71.39 & -0.3917 \tabularnewline
9 &  70.6 &  71.48 & -0.8779 \tabularnewline
10 &  67.5 &  67.32 &  0.1831 \tabularnewline
11 &  74.1 &  74.49 & -0.3887 \tabularnewline
12 &  73.2 &  71.71 &  1.494 \tabularnewline
13 &  74 &  75.61 & -1.612 \tabularnewline
14 &  73 &  76.01 & -3.007 \tabularnewline
15 &  74 &  71.79 &  2.213 \tabularnewline
16 &  73 &  73.1 & -0.1018 \tabularnewline
17 &  76 &  75.18 &  0.821 \tabularnewline
18 &  81.7 &  80.91 &  0.7948 \tabularnewline
19 &  73.5 &  77.14 & -3.638 \tabularnewline
20 &  77 &  73.44 &  3.557 \tabularnewline
21 &  73.6 &  74.08 & -0.4816 \tabularnewline
22 &  70.4 &  71.09 & -0.6889 \tabularnewline
23 &  74.7 &  76.75 & -2.054 \tabularnewline
24 &  76.8 &  74.73 &  2.071 \tabularnewline
25 &  72.7 &  74.9 & -2.197 \tabularnewline
26 &  76 &  76.73 & -0.7316 \tabularnewline
27 &  77.5 &  73.76 &  3.742 \tabularnewline
28 &  73.6 &  71.89 &  1.714 \tabularnewline
29 &  78.5 &  79.57 & -1.072 \tabularnewline
30 &  84.3 &  82.26 &  2.037 \tabularnewline
31 &  74.4 &  76.25 & -1.846 \tabularnewline
32 &  78.5 &  80.6 & -2.101 \tabularnewline
33 &  72.7 &  75.09 & -2.388 \tabularnewline
34 &  71.3 &  73.62 & -2.322 \tabularnewline
35 &  84.4 &  80.11 &  4.286 \tabularnewline
36 &  79.1 &  78.99 &  0.114 \tabularnewline
37 &  76.2 &  77.6 & -1.395 \tabularnewline
38 &  84.9 &  82.97 &  1.929 \tabularnewline
39 &  77.1 &  77.5 & -0.4001 \tabularnewline
40 &  78.7 &  76.68 &  2.02 \tabularnewline
41 &  84.7 &  83.64 &  1.061 \tabularnewline
42 &  83.7 &  85.74 & -2.043 \tabularnewline
43 &  82.5 &  81.96 &  0.5428 \tabularnewline
44 &  85.2 &  84.95 &  0.2479 \tabularnewline
45 &  76 &  79.23 & -3.229 \tabularnewline
46 &  72.2 &  77.72 & -5.524 \tabularnewline
47 &  83.2 &  85.62 & -2.425 \tabularnewline
48 &  80.2 &  81.24 & -1.038 \tabularnewline
49 &  81.1 &  80.6 &  0.5007 \tabularnewline
50 &  86 &  88.92 & -2.915 \tabularnewline
51 &  76 &  77.61 & -1.605 \tabularnewline
52 &  83.9 &  79.57 &  4.329 \tabularnewline
53 &  87.9 &  87.12 &  0.7785 \tabularnewline
54 &  85 &  86.09 & -1.095 \tabularnewline
55 &  88.1 &  88.34 & -0.2423 \tabularnewline
56 &  87.4 &  89.07 & -1.672 \tabularnewline
57 &  79.5 &  83.04 & -3.538 \tabularnewline
58 &  75.2 &  80.93 & -5.733 \tabularnewline
59 &  87.3 &  88.43 & -1.134 \tabularnewline
60 &  79.5 &  81.86 & -2.361 \tabularnewline
61 &  87.6 &  86.87 &  0.7287 \tabularnewline
62 &  89.1 &  91.35 & -2.254 \tabularnewline
63 &  83 &  79.67 &  3.333 \tabularnewline
64 &  88.3 &  85.62 &  2.679 \tabularnewline
65 &  88.9 &  91.05 & -2.152 \tabularnewline
66 &  93.9 &  91.58 &  2.321 \tabularnewline
67 &  91.7 &  93.61 & -1.913 \tabularnewline
68 &  87.2 &  92.09 & -4.89 \tabularnewline
69 &  87.8 &  87.75 &  0.05426 \tabularnewline
70 &  81 &  83.55 & -2.553 \tabularnewline
71 &  93.7 &  93.16 &  0.5358 \tabularnewline
72 &  87.5 &  86.01 &  1.49 \tabularnewline
73 &  91.4 &  92.6 & -1.203 \tabularnewline
74 &  93.8 &  96.56 & -2.757 \tabularnewline
75 &  89.5 &  87.69 &  1.807 \tabularnewline
76 &  93.3 &  91.33 &  1.969 \tabularnewline
77 &  92.8 &  94.12 & -1.323 \tabularnewline
78 &  104.1 &  100.5 &  3.579 \tabularnewline
79 &  99.9 &  98.27 &  1.625 \tabularnewline
80 &  93.4 &  95.21 & -1.806 \tabularnewline
81 &  99 &  96.61 &  2.386 \tabularnewline
82 &  93.2 &  91.23 &  1.974 \tabularnewline
83 &  95.7 &  98.91 & -3.208 \tabularnewline
84 &  102.6 &  96.78 &  5.821 \tabularnewline
85 &  98.8 &  96.4 &  2.397 \tabularnewline
86 &  98 &  102.1 & -4.123 \tabularnewline
87 &  101.5 &  95.4 &  6.095 \tabularnewline
88 &  94.9 &  93.6 &  1.3 \tabularnewline
89 &  104.7 &  101.1 &  3.635 \tabularnewline
90 &  108.4 &  106.3 &  2.12 \tabularnewline
91 &  97 &  100.5 & -3.511 \tabularnewline
92 &  102.3 &  98.91 &  3.387 \tabularnewline
93 &  90.8 &  95.52 & -4.717 \tabularnewline
94 &  89.6 &  91.73 & -2.127 \tabularnewline
95 &  99.9 &  95.45 &  4.454 \tabularnewline
96 &  99.2 &  95.43 &  3.768 \tabularnewline
97 &  94 &  97.97 & -3.971 \tabularnewline
98 &  103 &  98.77 &  4.227 \tabularnewline
99 &  99.8 &  95.43 &  4.369 \tabularnewline
100 &  94.9 &  93.35 &  1.552 \tabularnewline
101 &  102 &  103.3 & -1.312 \tabularnewline
102 &  103.2 &  103.5 & -0.3005 \tabularnewline
103 &  98 &  98.26 & -0.2649 \tabularnewline
104 &  101.1 &  101.4 & -0.2762 \tabularnewline
105 &  88.2 &  93.59 & -5.388 \tabularnewline
106 &  90.3 &  93.9 & -3.602 \tabularnewline
107 &  105.5 &  102 &  3.461 \tabularnewline
108 &  99.4 &  98.36 &  1.04 \tabularnewline
109 &  94.3 &  99.3 & -4.997 \tabularnewline
110 &  105.9 &  107.4 & -1.522 \tabularnewline
111 &  98 &  97.69 &  0.3068 \tabularnewline
112 &  99 &  97.18 &  1.819 \tabularnewline
113 &  103.9 &  106 & -2.066 \tabularnewline
114 &  104.3 &  104.9 & -0.6145 \tabularnewline
115 &  105.7 &  104.1 &  1.573 \tabularnewline
116 &  105.5 &  105.4 &  0.08007 \tabularnewline
117 &  97.4 &  97.09 &  0.3144 \tabularnewline
118 &  95.4 &  98.57 & -3.169 \tabularnewline
119 &  110.5 &  111.2 & -0.7268 \tabularnewline
120 &  102.8 &  101.9 &  0.9478 \tabularnewline
121 &  110 &  104.9 &  5.134 \tabularnewline
122 &  104.3 &  109.6 & -5.252 \tabularnewline
123 &  96.5 &  99.05 & -2.545 \tabularnewline
124 &  105.6 &  101.7 &  3.926 \tabularnewline
125 &  111.3 &  106.8 &  4.471 \tabularnewline
126 &  108.5 &  107.2 &  1.339 \tabularnewline
127 &  109.1 &  109.2 & -0.1456 \tabularnewline
128 &  107.7 &  107.3 &  0.4058 \tabularnewline
129 &  102.3 &  102.2 &  0.08659 \tabularnewline
130 &  102.4 &  102.1 &  0.2972 \tabularnewline
131 &  110.8 &  113.1 & -2.274 \tabularnewline
132 &  101.7 &  103.2 & -1.455 \tabularnewline
133 &  108.9 &  108.8 &  0.07606 \tabularnewline
134 &  111.5 &  107.6 &  3.857 \tabularnewline
135 &  104 &  100.1 &  3.934 \tabularnewline
136 &  109.9 &  105.4 &  4.522 \tabularnewline
137 &  106.8 &  110.2 & -3.351 \tabularnewline
138 &  118.4 &  109.5 &  8.908 \tabularnewline
139 &  111.8 &  109.9 &  1.876 \tabularnewline
140 &  105 &  107.2 & -2.236 \tabularnewline
141 &  104.9 &  104.9 & -0.01326 \tabularnewline
142 &  96.5 &  101.7 & -5.155 \tabularnewline
143 &  106.3 &  109.6 & -3.294 \tabularnewline
144 &  105.6 &  103.6 &  2.022 \tabularnewline
145 &  109.3 &  107.9 &  1.387 \tabularnewline
146 &  105.1 &  113 & -7.859 \tabularnewline
147 &  111.5 &  105.9 &  5.618 \tabularnewline
148 &  103.1 &  105.7 & -2.577 \tabularnewline
149 &  106.5 &  108.6 & -2.09 \tabularnewline
150 &  114.4 &  116.7 & -2.295 \tabularnewline
151 &  104.7 &  111.2 & -6.504 \tabularnewline
152 &  105.5 &  107.8 & -2.316 \tabularnewline
153 &  100.5 &  106.2 & -5.74 \tabularnewline
154 &  96.4 &  100.8 & -4.444 \tabularnewline
155 &  105.1 &  107.8 & -2.665 \tabularnewline
156 &  108.4 &  105.7 &  2.691 \tabularnewline
157 &  105.7 &  109.3 & -3.565 \tabularnewline
158 &  109 &  110.2 & -1.211 \tabularnewline
159 &  107.2 &  108.4 & -1.163 \tabularnewline
160 &  101.6 &  101.1 &  0.4897 \tabularnewline
161 &  112.7 &  110.6 &  2.081 \tabularnewline
162 &  115.9 &  113.9 &  2.037 \tabularnewline
163 &  105 &  108 & -3 \tabularnewline
164 &  110.4 &  108.9 &  1.545 \tabularnewline
165 &  100.9 &  103.8 & -2.911 \tabularnewline
166 &  98.5 &  99.95 & -1.449 \tabularnewline
167 &  111.3 &  109.3 &  2.038 \tabularnewline
168 &  109.6 &  107.4 &  2.223 \tabularnewline
169 &  103.4 &  106.9 & -3.51 \tabularnewline
170 &  115.7 &  112.3 &  3.35 \tabularnewline
171 &  110.4 &  104.9 &  5.51 \tabularnewline
172 &  105.2 &  102.4 &  2.844 \tabularnewline
173 &  113.2 &  115 & -1.835 \tabularnewline
174 &  117.4 &  114.3 &  3.05 \tabularnewline
175 &  112.3 &  109.9 &  2.388 \tabularnewline
176 &  113.9 &  112.1 &  1.771 \tabularnewline
177 &  102.2 &  106.4 & -4.152 \tabularnewline
178 &  106.9 &  104.8 &  2.084 \tabularnewline
179 &  118 &  113.8 &  4.221 \tabularnewline
180 &  113.8 &  111.6 &  2.234 \tabularnewline
181 &  114.9 &  109.3 &  5.576 \tabularnewline
182 &  118.8 &  119.1 & -0.2768 \tabularnewline
183 &  106.3 &  109.4 & -3.094 \tabularnewline
184 &  114.2 &  107.6 &  6.602 \tabularnewline
185 &  117.3 &  114.5 &  2.789 \tabularnewline
186 &  114.7 &  116 & -1.329 \tabularnewline
187 &  117 &  115.7 &  1.316 \tabularnewline
188 &  116.6 &  117.1 & -0.4656 \tabularnewline
189 &  106.5 &  108.6 & -2.139 \tabularnewline
190 &  105.7 &  111.3 & -5.562 \tabularnewline
191 &  121 &  119.8 &  1.237 \tabularnewline
192 &  107.8 &  111.8 & -3.95 \tabularnewline
193 &  119.7 &  117.3 &  2.429 \tabularnewline
194 &  121 &  121.4 & -0.3831 \tabularnewline
195 &  108.8 &  109.3 & -0.5037 \tabularnewline
196 &  115 &  115.4 & -0.4121 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310376&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] 74.4[/C][C] 74.19[/C][C] 0.2109[/C][/ROW]
[ROW][C]2[/C][C] 72.9[/C][C] 74.47[/C][C]-1.565[/C][/ROW]
[ROW][C]3[/C][C] 71.7[/C][C] 65.25[/C][C] 6.448[/C][/ROW]
[ROW][C]4[/C][C] 75.6[/C][C] 70.42[/C][C] 5.183[/C][/ROW]
[ROW][C]5[/C][C] 72.5[/C][C] 72.55[/C][C]-0.05388[/C][/ROW]
[ROW][C]6[/C][C] 80[/C][C] 77.95[/C][C] 2.046[/C][/ROW]
[ROW][C]7[/C][C] 75.4[/C][C] 75.5[/C][C]-0.1007[/C][/ROW]
[ROW][C]8[/C][C] 71[/C][C] 71.39[/C][C]-0.3917[/C][/ROW]
[ROW][C]9[/C][C] 70.6[/C][C] 71.48[/C][C]-0.8779[/C][/ROW]
[ROW][C]10[/C][C] 67.5[/C][C] 67.32[/C][C] 0.1831[/C][/ROW]
[ROW][C]11[/C][C] 74.1[/C][C] 74.49[/C][C]-0.3887[/C][/ROW]
[ROW][C]12[/C][C] 73.2[/C][C] 71.71[/C][C] 1.494[/C][/ROW]
[ROW][C]13[/C][C] 74[/C][C] 75.61[/C][C]-1.612[/C][/ROW]
[ROW][C]14[/C][C] 73[/C][C] 76.01[/C][C]-3.007[/C][/ROW]
[ROW][C]15[/C][C] 74[/C][C] 71.79[/C][C] 2.213[/C][/ROW]
[ROW][C]16[/C][C] 73[/C][C] 73.1[/C][C]-0.1018[/C][/ROW]
[ROW][C]17[/C][C] 76[/C][C] 75.18[/C][C] 0.821[/C][/ROW]
[ROW][C]18[/C][C] 81.7[/C][C] 80.91[/C][C] 0.7948[/C][/ROW]
[ROW][C]19[/C][C] 73.5[/C][C] 77.14[/C][C]-3.638[/C][/ROW]
[ROW][C]20[/C][C] 77[/C][C] 73.44[/C][C] 3.557[/C][/ROW]
[ROW][C]21[/C][C] 73.6[/C][C] 74.08[/C][C]-0.4816[/C][/ROW]
[ROW][C]22[/C][C] 70.4[/C][C] 71.09[/C][C]-0.6889[/C][/ROW]
[ROW][C]23[/C][C] 74.7[/C][C] 76.75[/C][C]-2.054[/C][/ROW]
[ROW][C]24[/C][C] 76.8[/C][C] 74.73[/C][C] 2.071[/C][/ROW]
[ROW][C]25[/C][C] 72.7[/C][C] 74.9[/C][C]-2.197[/C][/ROW]
[ROW][C]26[/C][C] 76[/C][C] 76.73[/C][C]-0.7316[/C][/ROW]
[ROW][C]27[/C][C] 77.5[/C][C] 73.76[/C][C] 3.742[/C][/ROW]
[ROW][C]28[/C][C] 73.6[/C][C] 71.89[/C][C] 1.714[/C][/ROW]
[ROW][C]29[/C][C] 78.5[/C][C] 79.57[/C][C]-1.072[/C][/ROW]
[ROW][C]30[/C][C] 84.3[/C][C] 82.26[/C][C] 2.037[/C][/ROW]
[ROW][C]31[/C][C] 74.4[/C][C] 76.25[/C][C]-1.846[/C][/ROW]
[ROW][C]32[/C][C] 78.5[/C][C] 80.6[/C][C]-2.101[/C][/ROW]
[ROW][C]33[/C][C] 72.7[/C][C] 75.09[/C][C]-2.388[/C][/ROW]
[ROW][C]34[/C][C] 71.3[/C][C] 73.62[/C][C]-2.322[/C][/ROW]
[ROW][C]35[/C][C] 84.4[/C][C] 80.11[/C][C] 4.286[/C][/ROW]
[ROW][C]36[/C][C] 79.1[/C][C] 78.99[/C][C] 0.114[/C][/ROW]
[ROW][C]37[/C][C] 76.2[/C][C] 77.6[/C][C]-1.395[/C][/ROW]
[ROW][C]38[/C][C] 84.9[/C][C] 82.97[/C][C] 1.929[/C][/ROW]
[ROW][C]39[/C][C] 77.1[/C][C] 77.5[/C][C]-0.4001[/C][/ROW]
[ROW][C]40[/C][C] 78.7[/C][C] 76.68[/C][C] 2.02[/C][/ROW]
[ROW][C]41[/C][C] 84.7[/C][C] 83.64[/C][C] 1.061[/C][/ROW]
[ROW][C]42[/C][C] 83.7[/C][C] 85.74[/C][C]-2.043[/C][/ROW]
[ROW][C]43[/C][C] 82.5[/C][C] 81.96[/C][C] 0.5428[/C][/ROW]
[ROW][C]44[/C][C] 85.2[/C][C] 84.95[/C][C] 0.2479[/C][/ROW]
[ROW][C]45[/C][C] 76[/C][C] 79.23[/C][C]-3.229[/C][/ROW]
[ROW][C]46[/C][C] 72.2[/C][C] 77.72[/C][C]-5.524[/C][/ROW]
[ROW][C]47[/C][C] 83.2[/C][C] 85.62[/C][C]-2.425[/C][/ROW]
[ROW][C]48[/C][C] 80.2[/C][C] 81.24[/C][C]-1.038[/C][/ROW]
[ROW][C]49[/C][C] 81.1[/C][C] 80.6[/C][C] 0.5007[/C][/ROW]
[ROW][C]50[/C][C] 86[/C][C] 88.92[/C][C]-2.915[/C][/ROW]
[ROW][C]51[/C][C] 76[/C][C] 77.61[/C][C]-1.605[/C][/ROW]
[ROW][C]52[/C][C] 83.9[/C][C] 79.57[/C][C] 4.329[/C][/ROW]
[ROW][C]53[/C][C] 87.9[/C][C] 87.12[/C][C] 0.7785[/C][/ROW]
[ROW][C]54[/C][C] 85[/C][C] 86.09[/C][C]-1.095[/C][/ROW]
[ROW][C]55[/C][C] 88.1[/C][C] 88.34[/C][C]-0.2423[/C][/ROW]
[ROW][C]56[/C][C] 87.4[/C][C] 89.07[/C][C]-1.672[/C][/ROW]
[ROW][C]57[/C][C] 79.5[/C][C] 83.04[/C][C]-3.538[/C][/ROW]
[ROW][C]58[/C][C] 75.2[/C][C] 80.93[/C][C]-5.733[/C][/ROW]
[ROW][C]59[/C][C] 87.3[/C][C] 88.43[/C][C]-1.134[/C][/ROW]
[ROW][C]60[/C][C] 79.5[/C][C] 81.86[/C][C]-2.361[/C][/ROW]
[ROW][C]61[/C][C] 87.6[/C][C] 86.87[/C][C] 0.7287[/C][/ROW]
[ROW][C]62[/C][C] 89.1[/C][C] 91.35[/C][C]-2.254[/C][/ROW]
[ROW][C]63[/C][C] 83[/C][C] 79.67[/C][C] 3.333[/C][/ROW]
[ROW][C]64[/C][C] 88.3[/C][C] 85.62[/C][C] 2.679[/C][/ROW]
[ROW][C]65[/C][C] 88.9[/C][C] 91.05[/C][C]-2.152[/C][/ROW]
[ROW][C]66[/C][C] 93.9[/C][C] 91.58[/C][C] 2.321[/C][/ROW]
[ROW][C]67[/C][C] 91.7[/C][C] 93.61[/C][C]-1.913[/C][/ROW]
[ROW][C]68[/C][C] 87.2[/C][C] 92.09[/C][C]-4.89[/C][/ROW]
[ROW][C]69[/C][C] 87.8[/C][C] 87.75[/C][C] 0.05426[/C][/ROW]
[ROW][C]70[/C][C] 81[/C][C] 83.55[/C][C]-2.553[/C][/ROW]
[ROW][C]71[/C][C] 93.7[/C][C] 93.16[/C][C] 0.5358[/C][/ROW]
[ROW][C]72[/C][C] 87.5[/C][C] 86.01[/C][C] 1.49[/C][/ROW]
[ROW][C]73[/C][C] 91.4[/C][C] 92.6[/C][C]-1.203[/C][/ROW]
[ROW][C]74[/C][C] 93.8[/C][C] 96.56[/C][C]-2.757[/C][/ROW]
[ROW][C]75[/C][C] 89.5[/C][C] 87.69[/C][C] 1.807[/C][/ROW]
[ROW][C]76[/C][C] 93.3[/C][C] 91.33[/C][C] 1.969[/C][/ROW]
[ROW][C]77[/C][C] 92.8[/C][C] 94.12[/C][C]-1.323[/C][/ROW]
[ROW][C]78[/C][C] 104.1[/C][C] 100.5[/C][C] 3.579[/C][/ROW]
[ROW][C]79[/C][C] 99.9[/C][C] 98.27[/C][C] 1.625[/C][/ROW]
[ROW][C]80[/C][C] 93.4[/C][C] 95.21[/C][C]-1.806[/C][/ROW]
[ROW][C]81[/C][C] 99[/C][C] 96.61[/C][C] 2.386[/C][/ROW]
[ROW][C]82[/C][C] 93.2[/C][C] 91.23[/C][C] 1.974[/C][/ROW]
[ROW][C]83[/C][C] 95.7[/C][C] 98.91[/C][C]-3.208[/C][/ROW]
[ROW][C]84[/C][C] 102.6[/C][C] 96.78[/C][C] 5.821[/C][/ROW]
[ROW][C]85[/C][C] 98.8[/C][C] 96.4[/C][C] 2.397[/C][/ROW]
[ROW][C]86[/C][C] 98[/C][C] 102.1[/C][C]-4.123[/C][/ROW]
[ROW][C]87[/C][C] 101.5[/C][C] 95.4[/C][C] 6.095[/C][/ROW]
[ROW][C]88[/C][C] 94.9[/C][C] 93.6[/C][C] 1.3[/C][/ROW]
[ROW][C]89[/C][C] 104.7[/C][C] 101.1[/C][C] 3.635[/C][/ROW]
[ROW][C]90[/C][C] 108.4[/C][C] 106.3[/C][C] 2.12[/C][/ROW]
[ROW][C]91[/C][C] 97[/C][C] 100.5[/C][C]-3.511[/C][/ROW]
[ROW][C]92[/C][C] 102.3[/C][C] 98.91[/C][C] 3.387[/C][/ROW]
[ROW][C]93[/C][C] 90.8[/C][C] 95.52[/C][C]-4.717[/C][/ROW]
[ROW][C]94[/C][C] 89.6[/C][C] 91.73[/C][C]-2.127[/C][/ROW]
[ROW][C]95[/C][C] 99.9[/C][C] 95.45[/C][C] 4.454[/C][/ROW]
[ROW][C]96[/C][C] 99.2[/C][C] 95.43[/C][C] 3.768[/C][/ROW]
[ROW][C]97[/C][C] 94[/C][C] 97.97[/C][C]-3.971[/C][/ROW]
[ROW][C]98[/C][C] 103[/C][C] 98.77[/C][C] 4.227[/C][/ROW]
[ROW][C]99[/C][C] 99.8[/C][C] 95.43[/C][C] 4.369[/C][/ROW]
[ROW][C]100[/C][C] 94.9[/C][C] 93.35[/C][C] 1.552[/C][/ROW]
[ROW][C]101[/C][C] 102[/C][C] 103.3[/C][C]-1.312[/C][/ROW]
[ROW][C]102[/C][C] 103.2[/C][C] 103.5[/C][C]-0.3005[/C][/ROW]
[ROW][C]103[/C][C] 98[/C][C] 98.26[/C][C]-0.2649[/C][/ROW]
[ROW][C]104[/C][C] 101.1[/C][C] 101.4[/C][C]-0.2762[/C][/ROW]
[ROW][C]105[/C][C] 88.2[/C][C] 93.59[/C][C]-5.388[/C][/ROW]
[ROW][C]106[/C][C] 90.3[/C][C] 93.9[/C][C]-3.602[/C][/ROW]
[ROW][C]107[/C][C] 105.5[/C][C] 102[/C][C] 3.461[/C][/ROW]
[ROW][C]108[/C][C] 99.4[/C][C] 98.36[/C][C] 1.04[/C][/ROW]
[ROW][C]109[/C][C] 94.3[/C][C] 99.3[/C][C]-4.997[/C][/ROW]
[ROW][C]110[/C][C] 105.9[/C][C] 107.4[/C][C]-1.522[/C][/ROW]
[ROW][C]111[/C][C] 98[/C][C] 97.69[/C][C] 0.3068[/C][/ROW]
[ROW][C]112[/C][C] 99[/C][C] 97.18[/C][C] 1.819[/C][/ROW]
[ROW][C]113[/C][C] 103.9[/C][C] 106[/C][C]-2.066[/C][/ROW]
[ROW][C]114[/C][C] 104.3[/C][C] 104.9[/C][C]-0.6145[/C][/ROW]
[ROW][C]115[/C][C] 105.7[/C][C] 104.1[/C][C] 1.573[/C][/ROW]
[ROW][C]116[/C][C] 105.5[/C][C] 105.4[/C][C] 0.08007[/C][/ROW]
[ROW][C]117[/C][C] 97.4[/C][C] 97.09[/C][C] 0.3144[/C][/ROW]
[ROW][C]118[/C][C] 95.4[/C][C] 98.57[/C][C]-3.169[/C][/ROW]
[ROW][C]119[/C][C] 110.5[/C][C] 111.2[/C][C]-0.7268[/C][/ROW]
[ROW][C]120[/C][C] 102.8[/C][C] 101.9[/C][C] 0.9478[/C][/ROW]
[ROW][C]121[/C][C] 110[/C][C] 104.9[/C][C] 5.134[/C][/ROW]
[ROW][C]122[/C][C] 104.3[/C][C] 109.6[/C][C]-5.252[/C][/ROW]
[ROW][C]123[/C][C] 96.5[/C][C] 99.05[/C][C]-2.545[/C][/ROW]
[ROW][C]124[/C][C] 105.6[/C][C] 101.7[/C][C] 3.926[/C][/ROW]
[ROW][C]125[/C][C] 111.3[/C][C] 106.8[/C][C] 4.471[/C][/ROW]
[ROW][C]126[/C][C] 108.5[/C][C] 107.2[/C][C] 1.339[/C][/ROW]
[ROW][C]127[/C][C] 109.1[/C][C] 109.2[/C][C]-0.1456[/C][/ROW]
[ROW][C]128[/C][C] 107.7[/C][C] 107.3[/C][C] 0.4058[/C][/ROW]
[ROW][C]129[/C][C] 102.3[/C][C] 102.2[/C][C] 0.08659[/C][/ROW]
[ROW][C]130[/C][C] 102.4[/C][C] 102.1[/C][C] 0.2972[/C][/ROW]
[ROW][C]131[/C][C] 110.8[/C][C] 113.1[/C][C]-2.274[/C][/ROW]
[ROW][C]132[/C][C] 101.7[/C][C] 103.2[/C][C]-1.455[/C][/ROW]
[ROW][C]133[/C][C] 108.9[/C][C] 108.8[/C][C] 0.07606[/C][/ROW]
[ROW][C]134[/C][C] 111.5[/C][C] 107.6[/C][C] 3.857[/C][/ROW]
[ROW][C]135[/C][C] 104[/C][C] 100.1[/C][C] 3.934[/C][/ROW]
[ROW][C]136[/C][C] 109.9[/C][C] 105.4[/C][C] 4.522[/C][/ROW]
[ROW][C]137[/C][C] 106.8[/C][C] 110.2[/C][C]-3.351[/C][/ROW]
[ROW][C]138[/C][C] 118.4[/C][C] 109.5[/C][C] 8.908[/C][/ROW]
[ROW][C]139[/C][C] 111.8[/C][C] 109.9[/C][C] 1.876[/C][/ROW]
[ROW][C]140[/C][C] 105[/C][C] 107.2[/C][C]-2.236[/C][/ROW]
[ROW][C]141[/C][C] 104.9[/C][C] 104.9[/C][C]-0.01326[/C][/ROW]
[ROW][C]142[/C][C] 96.5[/C][C] 101.7[/C][C]-5.155[/C][/ROW]
[ROW][C]143[/C][C] 106.3[/C][C] 109.6[/C][C]-3.294[/C][/ROW]
[ROW][C]144[/C][C] 105.6[/C][C] 103.6[/C][C] 2.022[/C][/ROW]
[ROW][C]145[/C][C] 109.3[/C][C] 107.9[/C][C] 1.387[/C][/ROW]
[ROW][C]146[/C][C] 105.1[/C][C] 113[/C][C]-7.859[/C][/ROW]
[ROW][C]147[/C][C] 111.5[/C][C] 105.9[/C][C] 5.618[/C][/ROW]
[ROW][C]148[/C][C] 103.1[/C][C] 105.7[/C][C]-2.577[/C][/ROW]
[ROW][C]149[/C][C] 106.5[/C][C] 108.6[/C][C]-2.09[/C][/ROW]
[ROW][C]150[/C][C] 114.4[/C][C] 116.7[/C][C]-2.295[/C][/ROW]
[ROW][C]151[/C][C] 104.7[/C][C] 111.2[/C][C]-6.504[/C][/ROW]
[ROW][C]152[/C][C] 105.5[/C][C] 107.8[/C][C]-2.316[/C][/ROW]
[ROW][C]153[/C][C] 100.5[/C][C] 106.2[/C][C]-5.74[/C][/ROW]
[ROW][C]154[/C][C] 96.4[/C][C] 100.8[/C][C]-4.444[/C][/ROW]
[ROW][C]155[/C][C] 105.1[/C][C] 107.8[/C][C]-2.665[/C][/ROW]
[ROW][C]156[/C][C] 108.4[/C][C] 105.7[/C][C] 2.691[/C][/ROW]
[ROW][C]157[/C][C] 105.7[/C][C] 109.3[/C][C]-3.565[/C][/ROW]
[ROW][C]158[/C][C] 109[/C][C] 110.2[/C][C]-1.211[/C][/ROW]
[ROW][C]159[/C][C] 107.2[/C][C] 108.4[/C][C]-1.163[/C][/ROW]
[ROW][C]160[/C][C] 101.6[/C][C] 101.1[/C][C] 0.4897[/C][/ROW]
[ROW][C]161[/C][C] 112.7[/C][C] 110.6[/C][C] 2.081[/C][/ROW]
[ROW][C]162[/C][C] 115.9[/C][C] 113.9[/C][C] 2.037[/C][/ROW]
[ROW][C]163[/C][C] 105[/C][C] 108[/C][C]-3[/C][/ROW]
[ROW][C]164[/C][C] 110.4[/C][C] 108.9[/C][C] 1.545[/C][/ROW]
[ROW][C]165[/C][C] 100.9[/C][C] 103.8[/C][C]-2.911[/C][/ROW]
[ROW][C]166[/C][C] 98.5[/C][C] 99.95[/C][C]-1.449[/C][/ROW]
[ROW][C]167[/C][C] 111.3[/C][C] 109.3[/C][C] 2.038[/C][/ROW]
[ROW][C]168[/C][C] 109.6[/C][C] 107.4[/C][C] 2.223[/C][/ROW]
[ROW][C]169[/C][C] 103.4[/C][C] 106.9[/C][C]-3.51[/C][/ROW]
[ROW][C]170[/C][C] 115.7[/C][C] 112.3[/C][C] 3.35[/C][/ROW]
[ROW][C]171[/C][C] 110.4[/C][C] 104.9[/C][C] 5.51[/C][/ROW]
[ROW][C]172[/C][C] 105.2[/C][C] 102.4[/C][C] 2.844[/C][/ROW]
[ROW][C]173[/C][C] 113.2[/C][C] 115[/C][C]-1.835[/C][/ROW]
[ROW][C]174[/C][C] 117.4[/C][C] 114.3[/C][C] 3.05[/C][/ROW]
[ROW][C]175[/C][C] 112.3[/C][C] 109.9[/C][C] 2.388[/C][/ROW]
[ROW][C]176[/C][C] 113.9[/C][C] 112.1[/C][C] 1.771[/C][/ROW]
[ROW][C]177[/C][C] 102.2[/C][C] 106.4[/C][C]-4.152[/C][/ROW]
[ROW][C]178[/C][C] 106.9[/C][C] 104.8[/C][C] 2.084[/C][/ROW]
[ROW][C]179[/C][C] 118[/C][C] 113.8[/C][C] 4.221[/C][/ROW]
[ROW][C]180[/C][C] 113.8[/C][C] 111.6[/C][C] 2.234[/C][/ROW]
[ROW][C]181[/C][C] 114.9[/C][C] 109.3[/C][C] 5.576[/C][/ROW]
[ROW][C]182[/C][C] 118.8[/C][C] 119.1[/C][C]-0.2768[/C][/ROW]
[ROW][C]183[/C][C] 106.3[/C][C] 109.4[/C][C]-3.094[/C][/ROW]
[ROW][C]184[/C][C] 114.2[/C][C] 107.6[/C][C] 6.602[/C][/ROW]
[ROW][C]185[/C][C] 117.3[/C][C] 114.5[/C][C] 2.789[/C][/ROW]
[ROW][C]186[/C][C] 114.7[/C][C] 116[/C][C]-1.329[/C][/ROW]
[ROW][C]187[/C][C] 117[/C][C] 115.7[/C][C] 1.316[/C][/ROW]
[ROW][C]188[/C][C] 116.6[/C][C] 117.1[/C][C]-0.4656[/C][/ROW]
[ROW][C]189[/C][C] 106.5[/C][C] 108.6[/C][C]-2.139[/C][/ROW]
[ROW][C]190[/C][C] 105.7[/C][C] 111.3[/C][C]-5.562[/C][/ROW]
[ROW][C]191[/C][C] 121[/C][C] 119.8[/C][C] 1.237[/C][/ROW]
[ROW][C]192[/C][C] 107.8[/C][C] 111.8[/C][C]-3.95[/C][/ROW]
[ROW][C]193[/C][C] 119.7[/C][C] 117.3[/C][C] 2.429[/C][/ROW]
[ROW][C]194[/C][C] 121[/C][C] 121.4[/C][C]-0.3831[/C][/ROW]
[ROW][C]195[/C][C] 108.8[/C][C] 109.3[/C][C]-0.5037[/C][/ROW]
[ROW][C]196[/C][C] 115[/C][C] 115.4[/C][C]-0.4121[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310376&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310376&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	74.4	74.19	0.2109
2	72.9	74.47	-1.565
3	71.7	65.25	6.448
4	75.6	70.42	5.183
5	72.5	72.55	-0.05388
6	80	77.95	2.046
7	75.4	75.5	-0.1007
8	71	71.39	-0.3917
9	70.6	71.48	-0.8779
10	67.5	67.32	0.1831
11	74.1	74.49	-0.3887
12	73.2	71.71	1.494
13	74	75.61	-1.612
14	73	76.01	-3.007
15	74	71.79	2.213
16	73	73.1	-0.1018
17	76	75.18	0.821
18	81.7	80.91	0.7948
19	73.5	77.14	-3.638
20	77	73.44	3.557
21	73.6	74.08	-0.4816
22	70.4	71.09	-0.6889
23	74.7	76.75	-2.054
24	76.8	74.73	2.071
25	72.7	74.9	-2.197
26	76	76.73	-0.7316
27	77.5	73.76	3.742
28	73.6	71.89	1.714
29	78.5	79.57	-1.072
30	84.3	82.26	2.037
31	74.4	76.25	-1.846
32	78.5	80.6	-2.101
33	72.7	75.09	-2.388
34	71.3	73.62	-2.322
35	84.4	80.11	4.286
36	79.1	78.99	0.114
37	76.2	77.6	-1.395
38	84.9	82.97	1.929
39	77.1	77.5	-0.4001
40	78.7	76.68	2.02
41	84.7	83.64	1.061
42	83.7	85.74	-2.043
43	82.5	81.96	0.5428
44	85.2	84.95	0.2479
45	76	79.23	-3.229
46	72.2	77.72	-5.524
47	83.2	85.62	-2.425
48	80.2	81.24	-1.038
49	81.1	80.6	0.5007
50	86	88.92	-2.915
51	76	77.61	-1.605
52	83.9	79.57	4.329
53	87.9	87.12	0.7785
54	85	86.09	-1.095
55	88.1	88.34	-0.2423
56	87.4	89.07	-1.672
57	79.5	83.04	-3.538
58	75.2	80.93	-5.733
59	87.3	88.43	-1.134
60	79.5	81.86	-2.361
61	87.6	86.87	0.7287
62	89.1	91.35	-2.254
63	83	79.67	3.333
64	88.3	85.62	2.679
65	88.9	91.05	-2.152
66	93.9	91.58	2.321
67	91.7	93.61	-1.913
68	87.2	92.09	-4.89
69	87.8	87.75	0.05426
70	81	83.55	-2.553
71	93.7	93.16	0.5358
72	87.5	86.01	1.49
73	91.4	92.6	-1.203
74	93.8	96.56	-2.757
75	89.5	87.69	1.807
76	93.3	91.33	1.969
77	92.8	94.12	-1.323
78	104.1	100.5	3.579
79	99.9	98.27	1.625
80	93.4	95.21	-1.806
81	99	96.61	2.386
82	93.2	91.23	1.974
83	95.7	98.91	-3.208
84	102.6	96.78	5.821
85	98.8	96.4	2.397
86	98	102.1	-4.123
87	101.5	95.4	6.095
88	94.9	93.6	1.3
89	104.7	101.1	3.635
90	108.4	106.3	2.12
91	97	100.5	-3.511
92	102.3	98.91	3.387
93	90.8	95.52	-4.717
94	89.6	91.73	-2.127
95	99.9	95.45	4.454
96	99.2	95.43	3.768
97	94	97.97	-3.971
98	103	98.77	4.227
99	99.8	95.43	4.369
100	94.9	93.35	1.552
101	102	103.3	-1.312
102	103.2	103.5	-0.3005
103	98	98.26	-0.2649
104	101.1	101.4	-0.2762
105	88.2	93.59	-5.388
106	90.3	93.9	-3.602
107	105.5	102	3.461
108	99.4	98.36	1.04
109	94.3	99.3	-4.997
110	105.9	107.4	-1.522
111	98	97.69	0.3068
112	99	97.18	1.819
113	103.9	106	-2.066
114	104.3	104.9	-0.6145
115	105.7	104.1	1.573
116	105.5	105.4	0.08007
117	97.4	97.09	0.3144
118	95.4	98.57	-3.169
119	110.5	111.2	-0.7268
120	102.8	101.9	0.9478
121	110	104.9	5.134
122	104.3	109.6	-5.252
123	96.5	99.05	-2.545
124	105.6	101.7	3.926
125	111.3	106.8	4.471
126	108.5	107.2	1.339
127	109.1	109.2	-0.1456
128	107.7	107.3	0.4058
129	102.3	102.2	0.08659
130	102.4	102.1	0.2972
131	110.8	113.1	-2.274
132	101.7	103.2	-1.455
133	108.9	108.8	0.07606
134	111.5	107.6	3.857
135	104	100.1	3.934
136	109.9	105.4	4.522
137	106.8	110.2	-3.351
138	118.4	109.5	8.908
139	111.8	109.9	1.876
140	105	107.2	-2.236
141	104.9	104.9	-0.01326
142	96.5	101.7	-5.155
143	106.3	109.6	-3.294
144	105.6	103.6	2.022
145	109.3	107.9	1.387
146	105.1	113	-7.859
147	111.5	105.9	5.618
148	103.1	105.7	-2.577
149	106.5	108.6	-2.09
150	114.4	116.7	-2.295
151	104.7	111.2	-6.504
152	105.5	107.8	-2.316
153	100.5	106.2	-5.74
154	96.4	100.8	-4.444
155	105.1	107.8	-2.665
156	108.4	105.7	2.691
157	105.7	109.3	-3.565
158	109	110.2	-1.211
159	107.2	108.4	-1.163
160	101.6	101.1	0.4897
161	112.7	110.6	2.081
162	115.9	113.9	2.037
163	105	108	-3
164	110.4	108.9	1.545
165	100.9	103.8	-2.911
166	98.5	99.95	-1.449
167	111.3	109.3	2.038
168	109.6	107.4	2.223
169	103.4	106.9	-3.51
170	115.7	112.3	3.35
171	110.4	104.9	5.51
172	105.2	102.4	2.844
173	113.2	115	-1.835
174	117.4	114.3	3.05
175	112.3	109.9	2.388
176	113.9	112.1	1.771
177	102.2	106.4	-4.152
178	106.9	104.8	2.084
179	118	113.8	4.221
180	113.8	111.6	2.234
181	114.9	109.3	5.576
182	118.8	119.1	-0.2768
183	106.3	109.4	-3.094
184	114.2	107.6	6.602
185	117.3	114.5	2.789
186	114.7	116	-1.329
187	117	115.7	1.316
188	116.6	117.1	-0.4656
189	106.5	108.6	-2.139
190	105.7	111.3	-5.562
191	121	119.8	1.237
192	107.8	111.8	-3.95
193	119.7	117.3	2.429
194	121	121.4	-0.3831
195	108.8	109.3	-0.5037
196	115	115.4	-0.4121

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.5505	0.899	0.4495
11	0.4045	0.809	0.5955
12	0.2738	0.5476	0.7262
13	0.3012	0.6023	0.6988
14	0.2725	0.545	0.7275
15	0.1913	0.3825	0.8087
16	0.1829	0.3657	0.8171
17	0.1474	0.2948	0.8526
18	0.1739	0.3478	0.8261
19	0.1766	0.3533	0.8234
20	0.1762	0.3523	0.8238
21	0.1267	0.2533	0.8733
22	0.09382	0.1876	0.9062
23	0.06886	0.1377	0.9311
24	0.07209	0.1442	0.9279
25	0.06659	0.1332	0.9334
26	0.04524	0.09047	0.9548
27	0.05137	0.1027	0.9486
28	0.03568	0.07135	0.9643
29	0.02523	0.05047	0.9748
30	0.04326	0.08652	0.9567
31	0.03043	0.06087	0.9696
32	0.02086	0.04172	0.9791
33	0.01895	0.03791	0.981
34	0.01445	0.0289	0.9855
35	0.05792	0.1158	0.9421
36	0.04734	0.09467	0.9527
37	0.03444	0.06888	0.9656
38	0.03982	0.07965	0.9602
39	0.02916	0.05833	0.9708
40	0.02861	0.05721	0.9714
41	0.02338	0.04676	0.9766
42	0.01675	0.0335	0.9833
43	0.01337	0.02675	0.9866
44	0.009663	0.01933	0.9903
45	0.009121	0.01824	0.9909
46	0.019	0.038	0.981
47	0.01502	0.03005	0.985
48	0.0108	0.02159	0.9892
49	0.008286	0.01657	0.9917
50	0.006943	0.01389	0.9931
51	0.004908	0.009817	0.9951
52	0.01043	0.02087	0.9896
53	0.009087	0.01817	0.9909
54	0.006664	0.01333	0.9933
55	0.004829	0.009658	0.9952
56	0.003381	0.006763	0.9966
57	0.002762	0.005524	0.9972
58	0.004357	0.008714	0.9956
59	0.003296	0.006592	0.9967
60	0.002472	0.004943	0.9975
61	0.002082	0.004164	0.9979
62	0.00155	0.003099	0.9984
63	0.003106	0.006211	0.9969
64	0.003037	0.006075	0.997
65	0.002242	0.004483	0.9978
66	0.003733	0.007467	0.9963
67	0.002767	0.005534	0.9972
68	0.003835	0.00767	0.9962
69	0.003331	0.006661	0.9967
70	0.002674	0.005348	0.9973
71	0.00231	0.00462	0.9977
72	0.00263	0.005261	0.9974
73	0.001918	0.003835	0.9981
74	0.001651	0.003301	0.9983
75	0.001687	0.003374	0.9983
76	0.001512	0.003024	0.9985
77	0.001129	0.002258	0.9989
78	0.002155	0.004311	0.9978
79	0.002017	0.004035	0.998
80	0.00159	0.00318	0.9984
81	0.001734	0.003468	0.9983
82	0.001824	0.003649	0.9982
83	0.001931	0.003863	0.9981
84	0.00616	0.01232	0.9938
85	0.005572	0.01114	0.9944
86	0.007505	0.01501	0.9925
87	0.01638	0.03276	0.9836
88	0.01273	0.02546	0.9873
89	0.01461	0.02922	0.9854
90	0.01206	0.02412	0.9879
91	0.01545	0.03091	0.9845
92	0.01492	0.02985	0.9851
93	0.02614	0.05227	0.9739
94	0.02286	0.04572	0.9771
95	0.03042	0.06084	0.9696
96	0.03498	0.06996	0.965
97	0.04218	0.08436	0.9578
98	0.04988	0.09976	0.9501
99	0.06214	0.1243	0.9379
100	0.05393	0.1079	0.9461
101	0.04726	0.09453	0.9527
102	0.03847	0.07694	0.9615
103	0.03055	0.0611	0.9694
104	0.02462	0.04924	0.9754
105	0.04136	0.08272	0.9586
106	0.04493	0.08986	0.9551
107	0.04783	0.09565	0.9522
108	0.03961	0.07923	0.9604
109	0.05821	0.1164	0.9418
110	0.05038	0.1008	0.9496
111	0.04082	0.08164	0.9592
112	0.0355	0.07101	0.9645
113	0.03179	0.06358	0.9682
114	0.02529	0.05059	0.9747
115	0.02098	0.04195	0.979
116	0.01621	0.03242	0.9838
117	0.01251	0.02501	0.9875
118	0.01399	0.02797	0.986
119	0.01126	0.02253	0.9887
120	0.0086	0.0172	0.9914
121	0.01234	0.02469	0.9877
122	0.02237	0.04473	0.9776
123	0.02046	0.04093	0.9795
124	0.0234	0.04681	0.9766
125	0.02917	0.05835	0.9708
126	0.02339	0.04678	0.9766
127	0.01822	0.03643	0.9818
128	0.01392	0.02784	0.9861
129	0.01048	0.02097	0.9895
130	0.007819	0.01564	0.9922
131	0.006955	0.01391	0.993
132	0.005464	0.01093	0.9945
133	0.003963	0.007926	0.996
134	0.004484	0.008968	0.9955
135	0.005246	0.01049	0.9948
136	0.007836	0.01567	0.9922
137	0.007902	0.0158	0.9921
138	0.05233	0.1047	0.9477
139	0.04511	0.09023	0.9549
140	0.03939	0.07879	0.9606
141	0.03076	0.06151	0.9692
142	0.04278	0.08557	0.9572
143	0.04393	0.08786	0.9561
144	0.03986	0.07972	0.9601
145	0.03273	0.06546	0.9673
146	0.1072	0.2144	0.8928
147	0.1714	0.3428	0.8286
148	0.1544	0.3089	0.8456
149	0.1377	0.2754	0.8623
150	0.1299	0.2599	0.8701
151	0.2432	0.4863	0.7568
152	0.2277	0.4554	0.7723
153	0.3326	0.6651	0.6674
154	0.383	0.7661	0.617
155	0.4189	0.8377	0.5811
156	0.3861	0.7723	0.6139
157	0.4567	0.9134	0.5433
158	0.4481	0.8963	0.5519
159	0.4297	0.8593	0.5703
160	0.3787	0.7574	0.6213
161	0.3317	0.6634	0.6683
162	0.2867	0.5735	0.7133
163	0.308	0.616	0.692
164	0.2606	0.5211	0.7394
165	0.2624	0.5247	0.7376
166	0.2597	0.5194	0.7403
167	0.2246	0.4493	0.7754
168	0.1853	0.3705	0.8147
169	0.4717	0.9434	0.5283
170	0.4377	0.8753	0.5623
171	0.4721	0.9442	0.5279
172	0.4082	0.8164	0.5918
173	0.556	0.888	0.444
174	0.5175	0.9649	0.4825
175	0.4456	0.8912	0.5544
176	0.3701	0.7402	0.6299
177	0.4607	0.9214	0.5393
178	0.3915	0.7829	0.6085
179	0.3224	0.6449	0.6776
180	0.2475	0.495	0.7525
181	0.2138	0.4276	0.7862
182	0.1478	0.2957	0.8522
183	0.1105	0.2211	0.8894
184	0.4197	0.8394	0.5803
185	0.7679	0.4643	0.2321
186	0.7137	0.5726	0.2863

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.5505 &  0.899 &  0.4495 \tabularnewline
11 &  0.4045 &  0.809 &  0.5955 \tabularnewline
12 &  0.2738 &  0.5476 &  0.7262 \tabularnewline
13 &  0.3012 &  0.6023 &  0.6988 \tabularnewline
14 &  0.2725 &  0.545 &  0.7275 \tabularnewline
15 &  0.1913 &  0.3825 &  0.8087 \tabularnewline
16 &  0.1829 &  0.3657 &  0.8171 \tabularnewline
17 &  0.1474 &  0.2948 &  0.8526 \tabularnewline
18 &  0.1739 &  0.3478 &  0.8261 \tabularnewline
19 &  0.1766 &  0.3533 &  0.8234 \tabularnewline
20 &  0.1762 &  0.3523 &  0.8238 \tabularnewline
21 &  0.1267 &  0.2533 &  0.8733 \tabularnewline
22 &  0.09382 &  0.1876 &  0.9062 \tabularnewline
23 &  0.06886 &  0.1377 &  0.9311 \tabularnewline
24 &  0.07209 &  0.1442 &  0.9279 \tabularnewline
25 &  0.06659 &  0.1332 &  0.9334 \tabularnewline
26 &  0.04524 &  0.09047 &  0.9548 \tabularnewline
27 &  0.05137 &  0.1027 &  0.9486 \tabularnewline
28 &  0.03568 &  0.07135 &  0.9643 \tabularnewline
29 &  0.02523 &  0.05047 &  0.9748 \tabularnewline
30 &  0.04326 &  0.08652 &  0.9567 \tabularnewline
31 &  0.03043 &  0.06087 &  0.9696 \tabularnewline
32 &  0.02086 &  0.04172 &  0.9791 \tabularnewline
33 &  0.01895 &  0.03791 &  0.981 \tabularnewline
34 &  0.01445 &  0.0289 &  0.9855 \tabularnewline
35 &  0.05792 &  0.1158 &  0.9421 \tabularnewline
36 &  0.04734 &  0.09467 &  0.9527 \tabularnewline
37 &  0.03444 &  0.06888 &  0.9656 \tabularnewline
38 &  0.03982 &  0.07965 &  0.9602 \tabularnewline
39 &  0.02916 &  0.05833 &  0.9708 \tabularnewline
40 &  0.02861 &  0.05721 &  0.9714 \tabularnewline
41 &  0.02338 &  0.04676 &  0.9766 \tabularnewline
42 &  0.01675 &  0.0335 &  0.9833 \tabularnewline
43 &  0.01337 &  0.02675 &  0.9866 \tabularnewline
44 &  0.009663 &  0.01933 &  0.9903 \tabularnewline
45 &  0.009121 &  0.01824 &  0.9909 \tabularnewline
46 &  0.019 &  0.038 &  0.981 \tabularnewline
47 &  0.01502 &  0.03005 &  0.985 \tabularnewline
48 &  0.0108 &  0.02159 &  0.9892 \tabularnewline
49 &  0.008286 &  0.01657 &  0.9917 \tabularnewline
50 &  0.006943 &  0.01389 &  0.9931 \tabularnewline
51 &  0.004908 &  0.009817 &  0.9951 \tabularnewline
52 &  0.01043 &  0.02087 &  0.9896 \tabularnewline
53 &  0.009087 &  0.01817 &  0.9909 \tabularnewline
54 &  0.006664 &  0.01333 &  0.9933 \tabularnewline
55 &  0.004829 &  0.009658 &  0.9952 \tabularnewline
56 &  0.003381 &  0.006763 &  0.9966 \tabularnewline
57 &  0.002762 &  0.005524 &  0.9972 \tabularnewline
58 &  0.004357 &  0.008714 &  0.9956 \tabularnewline
59 &  0.003296 &  0.006592 &  0.9967 \tabularnewline
60 &  0.002472 &  0.004943 &  0.9975 \tabularnewline
61 &  0.002082 &  0.004164 &  0.9979 \tabularnewline
62 &  0.00155 &  0.003099 &  0.9984 \tabularnewline
63 &  0.003106 &  0.006211 &  0.9969 \tabularnewline
64 &  0.003037 &  0.006075 &  0.997 \tabularnewline
65 &  0.002242 &  0.004483 &  0.9978 \tabularnewline
66 &  0.003733 &  0.007467 &  0.9963 \tabularnewline
67 &  0.002767 &  0.005534 &  0.9972 \tabularnewline
68 &  0.003835 &  0.00767 &  0.9962 \tabularnewline
69 &  0.003331 &  0.006661 &  0.9967 \tabularnewline
70 &  0.002674 &  0.005348 &  0.9973 \tabularnewline
71 &  0.00231 &  0.00462 &  0.9977 \tabularnewline
72 &  0.00263 &  0.005261 &  0.9974 \tabularnewline
73 &  0.001918 &  0.003835 &  0.9981 \tabularnewline
74 &  0.001651 &  0.003301 &  0.9983 \tabularnewline
75 &  0.001687 &  0.003374 &  0.9983 \tabularnewline
76 &  0.001512 &  0.003024 &  0.9985 \tabularnewline
77 &  0.001129 &  0.002258 &  0.9989 \tabularnewline
78 &  0.002155 &  0.004311 &  0.9978 \tabularnewline
79 &  0.002017 &  0.004035 &  0.998 \tabularnewline
80 &  0.00159 &  0.00318 &  0.9984 \tabularnewline
81 &  0.001734 &  0.003468 &  0.9983 \tabularnewline
82 &  0.001824 &  0.003649 &  0.9982 \tabularnewline
83 &  0.001931 &  0.003863 &  0.9981 \tabularnewline
84 &  0.00616 &  0.01232 &  0.9938 \tabularnewline
85 &  0.005572 &  0.01114 &  0.9944 \tabularnewline
86 &  0.007505 &  0.01501 &  0.9925 \tabularnewline
87 &  0.01638 &  0.03276 &  0.9836 \tabularnewline
88 &  0.01273 &  0.02546 &  0.9873 \tabularnewline
89 &  0.01461 &  0.02922 &  0.9854 \tabularnewline
90 &  0.01206 &  0.02412 &  0.9879 \tabularnewline
91 &  0.01545 &  0.03091 &  0.9845 \tabularnewline
92 &  0.01492 &  0.02985 &  0.9851 \tabularnewline
93 &  0.02614 &  0.05227 &  0.9739 \tabularnewline
94 &  0.02286 &  0.04572 &  0.9771 \tabularnewline
95 &  0.03042 &  0.06084 &  0.9696 \tabularnewline
96 &  0.03498 &  0.06996 &  0.965 \tabularnewline
97 &  0.04218 &  0.08436 &  0.9578 \tabularnewline
98 &  0.04988 &  0.09976 &  0.9501 \tabularnewline
99 &  0.06214 &  0.1243 &  0.9379 \tabularnewline
100 &  0.05393 &  0.1079 &  0.9461 \tabularnewline
101 &  0.04726 &  0.09453 &  0.9527 \tabularnewline
102 &  0.03847 &  0.07694 &  0.9615 \tabularnewline
103 &  0.03055 &  0.0611 &  0.9694 \tabularnewline
104 &  0.02462 &  0.04924 &  0.9754 \tabularnewline
105 &  0.04136 &  0.08272 &  0.9586 \tabularnewline
106 &  0.04493 &  0.08986 &  0.9551 \tabularnewline
107 &  0.04783 &  0.09565 &  0.9522 \tabularnewline
108 &  0.03961 &  0.07923 &  0.9604 \tabularnewline
109 &  0.05821 &  0.1164 &  0.9418 \tabularnewline
110 &  0.05038 &  0.1008 &  0.9496 \tabularnewline
111 &  0.04082 &  0.08164 &  0.9592 \tabularnewline
112 &  0.0355 &  0.07101 &  0.9645 \tabularnewline
113 &  0.03179 &  0.06358 &  0.9682 \tabularnewline
114 &  0.02529 &  0.05059 &  0.9747 \tabularnewline
115 &  0.02098 &  0.04195 &  0.979 \tabularnewline
116 &  0.01621 &  0.03242 &  0.9838 \tabularnewline
117 &  0.01251 &  0.02501 &  0.9875 \tabularnewline
118 &  0.01399 &  0.02797 &  0.986 \tabularnewline
119 &  0.01126 &  0.02253 &  0.9887 \tabularnewline
120 &  0.0086 &  0.0172 &  0.9914 \tabularnewline
121 &  0.01234 &  0.02469 &  0.9877 \tabularnewline
122 &  0.02237 &  0.04473 &  0.9776 \tabularnewline
123 &  0.02046 &  0.04093 &  0.9795 \tabularnewline
124 &  0.0234 &  0.04681 &  0.9766 \tabularnewline
125 &  0.02917 &  0.05835 &  0.9708 \tabularnewline
126 &  0.02339 &  0.04678 &  0.9766 \tabularnewline
127 &  0.01822 &  0.03643 &  0.9818 \tabularnewline
128 &  0.01392 &  0.02784 &  0.9861 \tabularnewline
129 &  0.01048 &  0.02097 &  0.9895 \tabularnewline
130 &  0.007819 &  0.01564 &  0.9922 \tabularnewline
131 &  0.006955 &  0.01391 &  0.993 \tabularnewline
132 &  0.005464 &  0.01093 &  0.9945 \tabularnewline
133 &  0.003963 &  0.007926 &  0.996 \tabularnewline
134 &  0.004484 &  0.008968 &  0.9955 \tabularnewline
135 &  0.005246 &  0.01049 &  0.9948 \tabularnewline
136 &  0.007836 &  0.01567 &  0.9922 \tabularnewline
137 &  0.007902 &  0.0158 &  0.9921 \tabularnewline
138 &  0.05233 &  0.1047 &  0.9477 \tabularnewline
139 &  0.04511 &  0.09023 &  0.9549 \tabularnewline
140 &  0.03939 &  0.07879 &  0.9606 \tabularnewline
141 &  0.03076 &  0.06151 &  0.9692 \tabularnewline
142 &  0.04278 &  0.08557 &  0.9572 \tabularnewline
143 &  0.04393 &  0.08786 &  0.9561 \tabularnewline
144 &  0.03986 &  0.07972 &  0.9601 \tabularnewline
145 &  0.03273 &  0.06546 &  0.9673 \tabularnewline
146 &  0.1072 &  0.2144 &  0.8928 \tabularnewline
147 &  0.1714 &  0.3428 &  0.8286 \tabularnewline
148 &  0.1544 &  0.3089 &  0.8456 \tabularnewline
149 &  0.1377 &  0.2754 &  0.8623 \tabularnewline
150 &  0.1299 &  0.2599 &  0.8701 \tabularnewline
151 &  0.2432 &  0.4863 &  0.7568 \tabularnewline
152 &  0.2277 &  0.4554 &  0.7723 \tabularnewline
153 &  0.3326 &  0.6651 &  0.6674 \tabularnewline
154 &  0.383 &  0.7661 &  0.617 \tabularnewline
155 &  0.4189 &  0.8377 &  0.5811 \tabularnewline
156 &  0.3861 &  0.7723 &  0.6139 \tabularnewline
157 &  0.4567 &  0.9134 &  0.5433 \tabularnewline
158 &  0.4481 &  0.8963 &  0.5519 \tabularnewline
159 &  0.4297 &  0.8593 &  0.5703 \tabularnewline
160 &  0.3787 &  0.7574 &  0.6213 \tabularnewline
161 &  0.3317 &  0.6634 &  0.6683 \tabularnewline
162 &  0.2867 &  0.5735 &  0.7133 \tabularnewline
163 &  0.308 &  0.616 &  0.692 \tabularnewline
164 &  0.2606 &  0.5211 &  0.7394 \tabularnewline
165 &  0.2624 &  0.5247 &  0.7376 \tabularnewline
166 &  0.2597 &  0.5194 &  0.7403 \tabularnewline
167 &  0.2246 &  0.4493 &  0.7754 \tabularnewline
168 &  0.1853 &  0.3705 &  0.8147 \tabularnewline
169 &  0.4717 &  0.9434 &  0.5283 \tabularnewline
170 &  0.4377 &  0.8753 &  0.5623 \tabularnewline
171 &  0.4721 &  0.9442 &  0.5279 \tabularnewline
172 &  0.4082 &  0.8164 &  0.5918 \tabularnewline
173 &  0.556 &  0.888 &  0.444 \tabularnewline
174 &  0.5175 &  0.9649 &  0.4825 \tabularnewline
175 &  0.4456 &  0.8912 &  0.5544 \tabularnewline
176 &  0.3701 &  0.7402 &  0.6299 \tabularnewline
177 &  0.4607 &  0.9214 &  0.5393 \tabularnewline
178 &  0.3915 &  0.7829 &  0.6085 \tabularnewline
179 &  0.3224 &  0.6449 &  0.6776 \tabularnewline
180 &  0.2475 &  0.495 &  0.7525 \tabularnewline
181 &  0.2138 &  0.4276 &  0.7862 \tabularnewline
182 &  0.1478 &  0.2957 &  0.8522 \tabularnewline
183 &  0.1105 &  0.2211 &  0.8894 \tabularnewline
184 &  0.4197 &  0.8394 &  0.5803 \tabularnewline
185 &  0.7679 &  0.4643 &  0.2321 \tabularnewline
186 &  0.7137 &  0.5726 &  0.2863 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310376&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] 0.5505[/C][C] 0.899[/C][C] 0.4495[/C][/ROW]
[ROW][C]11[/C][C] 0.4045[/C][C] 0.809[/C][C] 0.5955[/C][/ROW]
[ROW][C]12[/C][C] 0.2738[/C][C] 0.5476[/C][C] 0.7262[/C][/ROW]
[ROW][C]13[/C][C] 0.3012[/C][C] 0.6023[/C][C] 0.6988[/C][/ROW]
[ROW][C]14[/C][C] 0.2725[/C][C] 0.545[/C][C] 0.7275[/C][/ROW]
[ROW][C]15[/C][C] 0.1913[/C][C] 0.3825[/C][C] 0.8087[/C][/ROW]
[ROW][C]16[/C][C] 0.1829[/C][C] 0.3657[/C][C] 0.8171[/C][/ROW]
[ROW][C]17[/C][C] 0.1474[/C][C] 0.2948[/C][C] 0.8526[/C][/ROW]
[ROW][C]18[/C][C] 0.1739[/C][C] 0.3478[/C][C] 0.8261[/C][/ROW]
[ROW][C]19[/C][C] 0.1766[/C][C] 0.3533[/C][C] 0.8234[/C][/ROW]
[ROW][C]20[/C][C] 0.1762[/C][C] 0.3523[/C][C] 0.8238[/C][/ROW]
[ROW][C]21[/C][C] 0.1267[/C][C] 0.2533[/C][C] 0.8733[/C][/ROW]
[ROW][C]22[/C][C] 0.09382[/C][C] 0.1876[/C][C] 0.9062[/C][/ROW]
[ROW][C]23[/C][C] 0.06886[/C][C] 0.1377[/C][C] 0.9311[/C][/ROW]
[ROW][C]24[/C][C] 0.07209[/C][C] 0.1442[/C][C] 0.9279[/C][/ROW]
[ROW][C]25[/C][C] 0.06659[/C][C] 0.1332[/C][C] 0.9334[/C][/ROW]
[ROW][C]26[/C][C] 0.04524[/C][C] 0.09047[/C][C] 0.9548[/C][/ROW]
[ROW][C]27[/C][C] 0.05137[/C][C] 0.1027[/C][C] 0.9486[/C][/ROW]
[ROW][C]28[/C][C] 0.03568[/C][C] 0.07135[/C][C] 0.9643[/C][/ROW]
[ROW][C]29[/C][C] 0.02523[/C][C] 0.05047[/C][C] 0.9748[/C][/ROW]
[ROW][C]30[/C][C] 0.04326[/C][C] 0.08652[/C][C] 0.9567[/C][/ROW]
[ROW][C]31[/C][C] 0.03043[/C][C] 0.06087[/C][C] 0.9696[/C][/ROW]
[ROW][C]32[/C][C] 0.02086[/C][C] 0.04172[/C][C] 0.9791[/C][/ROW]
[ROW][C]33[/C][C] 0.01895[/C][C] 0.03791[/C][C] 0.981[/C][/ROW]
[ROW][C]34[/C][C] 0.01445[/C][C] 0.0289[/C][C] 0.9855[/C][/ROW]
[ROW][C]35[/C][C] 0.05792[/C][C] 0.1158[/C][C] 0.9421[/C][/ROW]
[ROW][C]36[/C][C] 0.04734[/C][C] 0.09467[/C][C] 0.9527[/C][/ROW]
[ROW][C]37[/C][C] 0.03444[/C][C] 0.06888[/C][C] 0.9656[/C][/ROW]
[ROW][C]38[/C][C] 0.03982[/C][C] 0.07965[/C][C] 0.9602[/C][/ROW]
[ROW][C]39[/C][C] 0.02916[/C][C] 0.05833[/C][C] 0.9708[/C][/ROW]
[ROW][C]40[/C][C] 0.02861[/C][C] 0.05721[/C][C] 0.9714[/C][/ROW]
[ROW][C]41[/C][C] 0.02338[/C][C] 0.04676[/C][C] 0.9766[/C][/ROW]
[ROW][C]42[/C][C] 0.01675[/C][C] 0.0335[/C][C] 0.9833[/C][/ROW]
[ROW][C]43[/C][C] 0.01337[/C][C] 0.02675[/C][C] 0.9866[/C][/ROW]
[ROW][C]44[/C][C] 0.009663[/C][C] 0.01933[/C][C] 0.9903[/C][/ROW]
[ROW][C]45[/C][C] 0.009121[/C][C] 0.01824[/C][C] 0.9909[/C][/ROW]
[ROW][C]46[/C][C] 0.019[/C][C] 0.038[/C][C] 0.981[/C][/ROW]
[ROW][C]47[/C][C] 0.01502[/C][C] 0.03005[/C][C] 0.985[/C][/ROW]
[ROW][C]48[/C][C] 0.0108[/C][C] 0.02159[/C][C] 0.9892[/C][/ROW]
[ROW][C]49[/C][C] 0.008286[/C][C] 0.01657[/C][C] 0.9917[/C][/ROW]
[ROW][C]50[/C][C] 0.006943[/C][C] 0.01389[/C][C] 0.9931[/C][/ROW]
[ROW][C]51[/C][C] 0.004908[/C][C] 0.009817[/C][C] 0.9951[/C][/ROW]
[ROW][C]52[/C][C] 0.01043[/C][C] 0.02087[/C][C] 0.9896[/C][/ROW]
[ROW][C]53[/C][C] 0.009087[/C][C] 0.01817[/C][C] 0.9909[/C][/ROW]
[ROW][C]54[/C][C] 0.006664[/C][C] 0.01333[/C][C] 0.9933[/C][/ROW]
[ROW][C]55[/C][C] 0.004829[/C][C] 0.009658[/C][C] 0.9952[/C][/ROW]
[ROW][C]56[/C][C] 0.003381[/C][C] 0.006763[/C][C] 0.9966[/C][/ROW]
[ROW][C]57[/C][C] 0.002762[/C][C] 0.005524[/C][C] 0.9972[/C][/ROW]
[ROW][C]58[/C][C] 0.004357[/C][C] 0.008714[/C][C] 0.9956[/C][/ROW]
[ROW][C]59[/C][C] 0.003296[/C][C] 0.006592[/C][C] 0.9967[/C][/ROW]
[ROW][C]60[/C][C] 0.002472[/C][C] 0.004943[/C][C] 0.9975[/C][/ROW]
[ROW][C]61[/C][C] 0.002082[/C][C] 0.004164[/C][C] 0.9979[/C][/ROW]
[ROW][C]62[/C][C] 0.00155[/C][C] 0.003099[/C][C] 0.9984[/C][/ROW]
[ROW][C]63[/C][C] 0.003106[/C][C] 0.006211[/C][C] 0.9969[/C][/ROW]
[ROW][C]64[/C][C] 0.003037[/C][C] 0.006075[/C][C] 0.997[/C][/ROW]
[ROW][C]65[/C][C] 0.002242[/C][C] 0.004483[/C][C] 0.9978[/C][/ROW]
[ROW][C]66[/C][C] 0.003733[/C][C] 0.007467[/C][C] 0.9963[/C][/ROW]
[ROW][C]67[/C][C] 0.002767[/C][C] 0.005534[/C][C] 0.9972[/C][/ROW]
[ROW][C]68[/C][C] 0.003835[/C][C] 0.00767[/C][C] 0.9962[/C][/ROW]
[ROW][C]69[/C][C] 0.003331[/C][C] 0.006661[/C][C] 0.9967[/C][/ROW]
[ROW][C]70[/C][C] 0.002674[/C][C] 0.005348[/C][C] 0.9973[/C][/ROW]
[ROW][C]71[/C][C] 0.00231[/C][C] 0.00462[/C][C] 0.9977[/C][/ROW]
[ROW][C]72[/C][C] 0.00263[/C][C] 0.005261[/C][C] 0.9974[/C][/ROW]
[ROW][C]73[/C][C] 0.001918[/C][C] 0.003835[/C][C] 0.9981[/C][/ROW]
[ROW][C]74[/C][C] 0.001651[/C][C] 0.003301[/C][C] 0.9983[/C][/ROW]
[ROW][C]75[/C][C] 0.001687[/C][C] 0.003374[/C][C] 0.9983[/C][/ROW]
[ROW][C]76[/C][C] 0.001512[/C][C] 0.003024[/C][C] 0.9985[/C][/ROW]
[ROW][C]77[/C][C] 0.001129[/C][C] 0.002258[/C][C] 0.9989[/C][/ROW]
[ROW][C]78[/C][C] 0.002155[/C][C] 0.004311[/C][C] 0.9978[/C][/ROW]
[ROW][C]79[/C][C] 0.002017[/C][C] 0.004035[/C][C] 0.998[/C][/ROW]
[ROW][C]80[/C][C] 0.00159[/C][C] 0.00318[/C][C] 0.9984[/C][/ROW]
[ROW][C]81[/C][C] 0.001734[/C][C] 0.003468[/C][C] 0.9983[/C][/ROW]
[ROW][C]82[/C][C] 0.001824[/C][C] 0.003649[/C][C] 0.9982[/C][/ROW]
[ROW][C]83[/C][C] 0.001931[/C][C] 0.003863[/C][C] 0.9981[/C][/ROW]
[ROW][C]84[/C][C] 0.00616[/C][C] 0.01232[/C][C] 0.9938[/C][/ROW]
[ROW][C]85[/C][C] 0.005572[/C][C] 0.01114[/C][C] 0.9944[/C][/ROW]
[ROW][C]86[/C][C] 0.007505[/C][C] 0.01501[/C][C] 0.9925[/C][/ROW]
[ROW][C]87[/C][C] 0.01638[/C][C] 0.03276[/C][C] 0.9836[/C][/ROW]
[ROW][C]88[/C][C] 0.01273[/C][C] 0.02546[/C][C] 0.9873[/C][/ROW]
[ROW][C]89[/C][C] 0.01461[/C][C] 0.02922[/C][C] 0.9854[/C][/ROW]
[ROW][C]90[/C][C] 0.01206[/C][C] 0.02412[/C][C] 0.9879[/C][/ROW]
[ROW][C]91[/C][C] 0.01545[/C][C] 0.03091[/C][C] 0.9845[/C][/ROW]
[ROW][C]92[/C][C] 0.01492[/C][C] 0.02985[/C][C] 0.9851[/C][/ROW]
[ROW][C]93[/C][C] 0.02614[/C][C] 0.05227[/C][C] 0.9739[/C][/ROW]
[ROW][C]94[/C][C] 0.02286[/C][C] 0.04572[/C][C] 0.9771[/C][/ROW]
[ROW][C]95[/C][C] 0.03042[/C][C] 0.06084[/C][C] 0.9696[/C][/ROW]
[ROW][C]96[/C][C] 0.03498[/C][C] 0.06996[/C][C] 0.965[/C][/ROW]
[ROW][C]97[/C][C] 0.04218[/C][C] 0.08436[/C][C] 0.9578[/C][/ROW]
[ROW][C]98[/C][C] 0.04988[/C][C] 0.09976[/C][C] 0.9501[/C][/ROW]
[ROW][C]99[/C][C] 0.06214[/C][C] 0.1243[/C][C] 0.9379[/C][/ROW]
[ROW][C]100[/C][C] 0.05393[/C][C] 0.1079[/C][C] 0.9461[/C][/ROW]
[ROW][C]101[/C][C] 0.04726[/C][C] 0.09453[/C][C] 0.9527[/C][/ROW]
[ROW][C]102[/C][C] 0.03847[/C][C] 0.07694[/C][C] 0.9615[/C][/ROW]
[ROW][C]103[/C][C] 0.03055[/C][C] 0.0611[/C][C] 0.9694[/C][/ROW]
[ROW][C]104[/C][C] 0.02462[/C][C] 0.04924[/C][C] 0.9754[/C][/ROW]
[ROW][C]105[/C][C] 0.04136[/C][C] 0.08272[/C][C] 0.9586[/C][/ROW]
[ROW][C]106[/C][C] 0.04493[/C][C] 0.08986[/C][C] 0.9551[/C][/ROW]
[ROW][C]107[/C][C] 0.04783[/C][C] 0.09565[/C][C] 0.9522[/C][/ROW]
[ROW][C]108[/C][C] 0.03961[/C][C] 0.07923[/C][C] 0.9604[/C][/ROW]
[ROW][C]109[/C][C] 0.05821[/C][C] 0.1164[/C][C] 0.9418[/C][/ROW]
[ROW][C]110[/C][C] 0.05038[/C][C] 0.1008[/C][C] 0.9496[/C][/ROW]
[ROW][C]111[/C][C] 0.04082[/C][C] 0.08164[/C][C] 0.9592[/C][/ROW]
[ROW][C]112[/C][C] 0.0355[/C][C] 0.07101[/C][C] 0.9645[/C][/ROW]
[ROW][C]113[/C][C] 0.03179[/C][C] 0.06358[/C][C] 0.9682[/C][/ROW]
[ROW][C]114[/C][C] 0.02529[/C][C] 0.05059[/C][C] 0.9747[/C][/ROW]
[ROW][C]115[/C][C] 0.02098[/C][C] 0.04195[/C][C] 0.979[/C][/ROW]
[ROW][C]116[/C][C] 0.01621[/C][C] 0.03242[/C][C] 0.9838[/C][/ROW]
[ROW][C]117[/C][C] 0.01251[/C][C] 0.02501[/C][C] 0.9875[/C][/ROW]
[ROW][C]118[/C][C] 0.01399[/C][C] 0.02797[/C][C] 0.986[/C][/ROW]
[ROW][C]119[/C][C] 0.01126[/C][C] 0.02253[/C][C] 0.9887[/C][/ROW]
[ROW][C]120[/C][C] 0.0086[/C][C] 0.0172[/C][C] 0.9914[/C][/ROW]
[ROW][C]121[/C][C] 0.01234[/C][C] 0.02469[/C][C] 0.9877[/C][/ROW]
[ROW][C]122[/C][C] 0.02237[/C][C] 0.04473[/C][C] 0.9776[/C][/ROW]
[ROW][C]123[/C][C] 0.02046[/C][C] 0.04093[/C][C] 0.9795[/C][/ROW]
[ROW][C]124[/C][C] 0.0234[/C][C] 0.04681[/C][C] 0.9766[/C][/ROW]
[ROW][C]125[/C][C] 0.02917[/C][C] 0.05835[/C][C] 0.9708[/C][/ROW]
[ROW][C]126[/C][C] 0.02339[/C][C] 0.04678[/C][C] 0.9766[/C][/ROW]
[ROW][C]127[/C][C] 0.01822[/C][C] 0.03643[/C][C] 0.9818[/C][/ROW]
[ROW][C]128[/C][C] 0.01392[/C][C] 0.02784[/C][C] 0.9861[/C][/ROW]
[ROW][C]129[/C][C] 0.01048[/C][C] 0.02097[/C][C] 0.9895[/C][/ROW]
[ROW][C]130[/C][C] 0.007819[/C][C] 0.01564[/C][C] 0.9922[/C][/ROW]
[ROW][C]131[/C][C] 0.006955[/C][C] 0.01391[/C][C] 0.993[/C][/ROW]
[ROW][C]132[/C][C] 0.005464[/C][C] 0.01093[/C][C] 0.9945[/C][/ROW]
[ROW][C]133[/C][C] 0.003963[/C][C] 0.007926[/C][C] 0.996[/C][/ROW]
[ROW][C]134[/C][C] 0.004484[/C][C] 0.008968[/C][C] 0.9955[/C][/ROW]
[ROW][C]135[/C][C] 0.005246[/C][C] 0.01049[/C][C] 0.9948[/C][/ROW]
[ROW][C]136[/C][C] 0.007836[/C][C] 0.01567[/C][C] 0.9922[/C][/ROW]
[ROW][C]137[/C][C] 0.007902[/C][C] 0.0158[/C][C] 0.9921[/C][/ROW]
[ROW][C]138[/C][C] 0.05233[/C][C] 0.1047[/C][C] 0.9477[/C][/ROW]
[ROW][C]139[/C][C] 0.04511[/C][C] 0.09023[/C][C] 0.9549[/C][/ROW]
[ROW][C]140[/C][C] 0.03939[/C][C] 0.07879[/C][C] 0.9606[/C][/ROW]
[ROW][C]141[/C][C] 0.03076[/C][C] 0.06151[/C][C] 0.9692[/C][/ROW]
[ROW][C]142[/C][C] 0.04278[/C][C] 0.08557[/C][C] 0.9572[/C][/ROW]
[ROW][C]143[/C][C] 0.04393[/C][C] 0.08786[/C][C] 0.9561[/C][/ROW]
[ROW][C]144[/C][C] 0.03986[/C][C] 0.07972[/C][C] 0.9601[/C][/ROW]
[ROW][C]145[/C][C] 0.03273[/C][C] 0.06546[/C][C] 0.9673[/C][/ROW]
[ROW][C]146[/C][C] 0.1072[/C][C] 0.2144[/C][C] 0.8928[/C][/ROW]
[ROW][C]147[/C][C] 0.1714[/C][C] 0.3428[/C][C] 0.8286[/C][/ROW]
[ROW][C]148[/C][C] 0.1544[/C][C] 0.3089[/C][C] 0.8456[/C][/ROW]
[ROW][C]149[/C][C] 0.1377[/C][C] 0.2754[/C][C] 0.8623[/C][/ROW]
[ROW][C]150[/C][C] 0.1299[/C][C] 0.2599[/C][C] 0.8701[/C][/ROW]
[ROW][C]151[/C][C] 0.2432[/C][C] 0.4863[/C][C] 0.7568[/C][/ROW]
[ROW][C]152[/C][C] 0.2277[/C][C] 0.4554[/C][C] 0.7723[/C][/ROW]
[ROW][C]153[/C][C] 0.3326[/C][C] 0.6651[/C][C] 0.6674[/C][/ROW]
[ROW][C]154[/C][C] 0.383[/C][C] 0.7661[/C][C] 0.617[/C][/ROW]
[ROW][C]155[/C][C] 0.4189[/C][C] 0.8377[/C][C] 0.5811[/C][/ROW]
[ROW][C]156[/C][C] 0.3861[/C][C] 0.7723[/C][C] 0.6139[/C][/ROW]
[ROW][C]157[/C][C] 0.4567[/C][C] 0.9134[/C][C] 0.5433[/C][/ROW]
[ROW][C]158[/C][C] 0.4481[/C][C] 0.8963[/C][C] 0.5519[/C][/ROW]
[ROW][C]159[/C][C] 0.4297[/C][C] 0.8593[/C][C] 0.5703[/C][/ROW]
[ROW][C]160[/C][C] 0.3787[/C][C] 0.7574[/C][C] 0.6213[/C][/ROW]
[ROW][C]161[/C][C] 0.3317[/C][C] 0.6634[/C][C] 0.6683[/C][/ROW]
[ROW][C]162[/C][C] 0.2867[/C][C] 0.5735[/C][C] 0.7133[/C][/ROW]
[ROW][C]163[/C][C] 0.308[/C][C] 0.616[/C][C] 0.692[/C][/ROW]
[ROW][C]164[/C][C] 0.2606[/C][C] 0.5211[/C][C] 0.7394[/C][/ROW]
[ROW][C]165[/C][C] 0.2624[/C][C] 0.5247[/C][C] 0.7376[/C][/ROW]
[ROW][C]166[/C][C] 0.2597[/C][C] 0.5194[/C][C] 0.7403[/C][/ROW]
[ROW][C]167[/C][C] 0.2246[/C][C] 0.4493[/C][C] 0.7754[/C][/ROW]
[ROW][C]168[/C][C] 0.1853[/C][C] 0.3705[/C][C] 0.8147[/C][/ROW]
[ROW][C]169[/C][C] 0.4717[/C][C] 0.9434[/C][C] 0.5283[/C][/ROW]
[ROW][C]170[/C][C] 0.4377[/C][C] 0.8753[/C][C] 0.5623[/C][/ROW]
[ROW][C]171[/C][C] 0.4721[/C][C] 0.9442[/C][C] 0.5279[/C][/ROW]
[ROW][C]172[/C][C] 0.4082[/C][C] 0.8164[/C][C] 0.5918[/C][/ROW]
[ROW][C]173[/C][C] 0.556[/C][C] 0.888[/C][C] 0.444[/C][/ROW]
[ROW][C]174[/C][C] 0.5175[/C][C] 0.9649[/C][C] 0.4825[/C][/ROW]
[ROW][C]175[/C][C] 0.4456[/C][C] 0.8912[/C][C] 0.5544[/C][/ROW]
[ROW][C]176[/C][C] 0.3701[/C][C] 0.7402[/C][C] 0.6299[/C][/ROW]
[ROW][C]177[/C][C] 0.4607[/C][C] 0.9214[/C][C] 0.5393[/C][/ROW]
[ROW][C]178[/C][C] 0.3915[/C][C] 0.7829[/C][C] 0.6085[/C][/ROW]
[ROW][C]179[/C][C] 0.3224[/C][C] 0.6449[/C][C] 0.6776[/C][/ROW]
[ROW][C]180[/C][C] 0.2475[/C][C] 0.495[/C][C] 0.7525[/C][/ROW]
[ROW][C]181[/C][C] 0.2138[/C][C] 0.4276[/C][C] 0.7862[/C][/ROW]
[ROW][C]182[/C][C] 0.1478[/C][C] 0.2957[/C][C] 0.8522[/C][/ROW]
[ROW][C]183[/C][C] 0.1105[/C][C] 0.2211[/C][C] 0.8894[/C][/ROW]
[ROW][C]184[/C][C] 0.4197[/C][C] 0.8394[/C][C] 0.5803[/C][/ROW]
[ROW][C]185[/C][C] 0.7679[/C][C] 0.4643[/C][C] 0.2321[/C][/ROW]
[ROW][C]186[/C][C] 0.7137[/C][C] 0.5726[/C][C] 0.2863[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310376&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310376&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	0.5505	0.899	0.4495
11	0.4045	0.809	0.5955
12	0.2738	0.5476	0.7262
13	0.3012	0.6023	0.6988
14	0.2725	0.545	0.7275
15	0.1913	0.3825	0.8087
16	0.1829	0.3657	0.8171
17	0.1474	0.2948	0.8526
18	0.1739	0.3478	0.8261
19	0.1766	0.3533	0.8234
20	0.1762	0.3523	0.8238
21	0.1267	0.2533	0.8733
22	0.09382	0.1876	0.9062
23	0.06886	0.1377	0.9311
24	0.07209	0.1442	0.9279
25	0.06659	0.1332	0.9334
26	0.04524	0.09047	0.9548
27	0.05137	0.1027	0.9486
28	0.03568	0.07135	0.9643
29	0.02523	0.05047	0.9748
30	0.04326	0.08652	0.9567
31	0.03043	0.06087	0.9696
32	0.02086	0.04172	0.9791
33	0.01895	0.03791	0.981
34	0.01445	0.0289	0.9855
35	0.05792	0.1158	0.9421
36	0.04734	0.09467	0.9527
37	0.03444	0.06888	0.9656
38	0.03982	0.07965	0.9602
39	0.02916	0.05833	0.9708
40	0.02861	0.05721	0.9714
41	0.02338	0.04676	0.9766
42	0.01675	0.0335	0.9833
43	0.01337	0.02675	0.9866
44	0.009663	0.01933	0.9903
45	0.009121	0.01824	0.9909
46	0.019	0.038	0.981
47	0.01502	0.03005	0.985
48	0.0108	0.02159	0.9892
49	0.008286	0.01657	0.9917
50	0.006943	0.01389	0.9931
51	0.004908	0.009817	0.9951
52	0.01043	0.02087	0.9896
53	0.009087	0.01817	0.9909
54	0.006664	0.01333	0.9933
55	0.004829	0.009658	0.9952
56	0.003381	0.006763	0.9966
57	0.002762	0.005524	0.9972
58	0.004357	0.008714	0.9956
59	0.003296	0.006592	0.9967
60	0.002472	0.004943	0.9975
61	0.002082	0.004164	0.9979
62	0.00155	0.003099	0.9984
63	0.003106	0.006211	0.9969
64	0.003037	0.006075	0.997
65	0.002242	0.004483	0.9978
66	0.003733	0.007467	0.9963
67	0.002767	0.005534	0.9972
68	0.003835	0.00767	0.9962
69	0.003331	0.006661	0.9967
70	0.002674	0.005348	0.9973
71	0.00231	0.00462	0.9977
72	0.00263	0.005261	0.9974
73	0.001918	0.003835	0.9981
74	0.001651	0.003301	0.9983
75	0.001687	0.003374	0.9983
76	0.001512	0.003024	0.9985
77	0.001129	0.002258	0.9989
78	0.002155	0.004311	0.9978
79	0.002017	0.004035	0.998
80	0.00159	0.00318	0.9984
81	0.001734	0.003468	0.9983
82	0.001824	0.003649	0.9982
83	0.001931	0.003863	0.9981
84	0.00616	0.01232	0.9938
85	0.005572	0.01114	0.9944
86	0.007505	0.01501	0.9925
87	0.01638	0.03276	0.9836
88	0.01273	0.02546	0.9873
89	0.01461	0.02922	0.9854
90	0.01206	0.02412	0.9879
91	0.01545	0.03091	0.9845
92	0.01492	0.02985	0.9851
93	0.02614	0.05227	0.9739
94	0.02286	0.04572	0.9771
95	0.03042	0.06084	0.9696
96	0.03498	0.06996	0.965
97	0.04218	0.08436	0.9578
98	0.04988	0.09976	0.9501
99	0.06214	0.1243	0.9379
100	0.05393	0.1079	0.9461
101	0.04726	0.09453	0.9527
102	0.03847	0.07694	0.9615
103	0.03055	0.0611	0.9694
104	0.02462	0.04924	0.9754
105	0.04136	0.08272	0.9586
106	0.04493	0.08986	0.9551
107	0.04783	0.09565	0.9522
108	0.03961	0.07923	0.9604
109	0.05821	0.1164	0.9418
110	0.05038	0.1008	0.9496
111	0.04082	0.08164	0.9592
112	0.0355	0.07101	0.9645
113	0.03179	0.06358	0.9682
114	0.02529	0.05059	0.9747
115	0.02098	0.04195	0.979
116	0.01621	0.03242	0.9838
117	0.01251	0.02501	0.9875
118	0.01399	0.02797	0.986
119	0.01126	0.02253	0.9887
120	0.0086	0.0172	0.9914
121	0.01234	0.02469	0.9877
122	0.02237	0.04473	0.9776
123	0.02046	0.04093	0.9795
124	0.0234	0.04681	0.9766
125	0.02917	0.05835	0.9708
126	0.02339	0.04678	0.9766
127	0.01822	0.03643	0.9818
128	0.01392	0.02784	0.9861
129	0.01048	0.02097	0.9895
130	0.007819	0.01564	0.9922
131	0.006955	0.01391	0.993
132	0.005464	0.01093	0.9945
133	0.003963	0.007926	0.996
134	0.004484	0.008968	0.9955
135	0.005246	0.01049	0.9948
136	0.007836	0.01567	0.9922
137	0.007902	0.0158	0.9921
138	0.05233	0.1047	0.9477
139	0.04511	0.09023	0.9549
140	0.03939	0.07879	0.9606
141	0.03076	0.06151	0.9692
142	0.04278	0.08557	0.9572
143	0.04393	0.08786	0.9561
144	0.03986	0.07972	0.9601
145	0.03273	0.06546	0.9673
146	0.1072	0.2144	0.8928
147	0.1714	0.3428	0.8286
148	0.1544	0.3089	0.8456
149	0.1377	0.2754	0.8623
150	0.1299	0.2599	0.8701
151	0.2432	0.4863	0.7568
152	0.2277	0.4554	0.7723
153	0.3326	0.6651	0.6674
154	0.383	0.7661	0.617
155	0.4189	0.8377	0.5811
156	0.3861	0.7723	0.6139
157	0.4567	0.9134	0.5433
158	0.4481	0.8963	0.5519
159	0.4297	0.8593	0.5703
160	0.3787	0.7574	0.6213
161	0.3317	0.6634	0.6683
162	0.2867	0.5735	0.7133
163	0.308	0.616	0.692
164	0.2606	0.5211	0.7394
165	0.2624	0.5247	0.7376
166	0.2597	0.5194	0.7403
167	0.2246	0.4493	0.7754
168	0.1853	0.3705	0.8147
169	0.4717	0.9434	0.5283
170	0.4377	0.8753	0.5623
171	0.4721	0.9442	0.5279
172	0.4082	0.8164	0.5918
173	0.556	0.888	0.444
174	0.5175	0.9649	0.4825
175	0.4456	0.8912	0.5544
176	0.3701	0.7402	0.6299
177	0.4607	0.9214	0.5393
178	0.3915	0.7829	0.6085
179	0.3224	0.6449	0.6776
180	0.2475	0.495	0.7525
181	0.2138	0.4276	0.7862
182	0.1478	0.2957	0.8522
183	0.1105	0.2211	0.8894
184	0.4197	0.8394	0.5803
185	0.7679	0.4643	0.2321
186	0.7137	0.5726	0.2863

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	32	0.1808	NOK
5% type I error level	79	0.446328	NOK
10% type I error level	113	0.638418	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 & 32 &  0.1808 & NOK \tabularnewline
5% type I error level & 79 & 0.446328 & NOK \tabularnewline
10% type I error level & 113 & 0.638418 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310376&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]32[/C][C] 0.1808[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]79[/C][C]0.446328[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]113[/C][C]0.638418[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310376&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310376&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	32	0.1808	NOK
5% type I error level	79	0.446328	NOK
10% type I error level	113	0.638418	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.2151, df1 = 2, df2 = 187, p-value = 0.112
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7283, df1 = 12, df2 = 177, p-value = 0.06416
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.195, df1 = 2, df2 = 187, p-value = 0.1142

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.2151, df1 = 2, df2 = 187, p-value = 0.112
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7283, df1 = 12, df2 = 177, p-value = 0.06416
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.195, df1 = 2, df2 = 187, p-value = 0.1142
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310376&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.2151, df1 = 2, df2 = 187, p-value = 0.112
[/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 = 1.7283, df1 = 12, df2 = 177, p-value = 0.06416
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.195, df1 = 2, df2 = 187, p-value = 0.1142
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310376&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310376&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 = 2.2151, df1 = 2, df2 = 187, p-value = 0.112
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7283, df1 = 12, df2 = 177, p-value = 0.06416
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.195, df1 = 2, df2 = 187, p-value = 0.1142

Variance Inflation Factors (Multicollinearity)
> vif nofood `food(t-1)` `food(t-2)` `food(t-3)` `food(t-4)` `food(t-1s)` 3.678916 9.471882 7.276842 9.411546 7.759879 9.124188

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      nofood  `food(t-1)`  `food(t-2)`  `food(t-3)`  `food(t-4)` `food(t-1s)` 
    3.678916     9.471882     7.276842     9.411546     7.759879     9.124188 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310376&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      nofood  `food(t-1)`  `food(t-2)`  `food(t-3)`  `food(t-4)` `food(t-1s)` 
    3.678916     9.471882     7.276842     9.411546     7.759879     9.124188 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310376&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310376&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 nofood `food(t-1)` `food(t-2)` `food(t-3)` `food(t-4)` `food(t-1s)` 3.678916 9.471882 7.276842 9.411546 7.759879 9.124188

Figure 1

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; 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