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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 17 Dec 2017 20:35:23 +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/17/t15135404156n34mfz3dwjfzit.htm/, Retrieved Wed, 15 May 2024 01:41:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310056, Retrieved Wed, 15 May 2024 01:41:43 +0000

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Estimated Impact

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

-       [Multiple Regression] [Klassiek decomp i...] [2017-12-17 19:35:23] [b34ea42038cce67d37e0f1b321879014] [Current]

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Dataseries X:

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52	62.4
54.9	67.4
60.5	76.1
54.8	67.4
60.1	74.5
60.3	72.6
49.8	60.5
53.8	66.1
64.8	76.5
62	76.8
65.2	77
60.1	71
61.2	74.8
63.6	73.7
68.6	80.5
63.1	71.8
66.5	76.9
71.9	79.9
58.1	65.9
61.5	69.5
66.2	75.1
72.3	79.6
67	75.2
62.9	68
66.4	72.8
65.6	71.5
70.9	78.5
68.4	76.8
66.4	75.3
67.6	76.7
64.1	69.7
62.1	67.8
70	77.5
74.4	82.5
67	75.3
64.8	70.9
70.7	76
64	73.7
72.5	79.7
70.4	77.8
63.6	73.3
69.8	78.3
67.7	71.9
66.4	67
78.9	82
79.9	83.7
69.1	74.8
81.2	80
66	74.3
71.8	76.8
86.1	89
76.1	81.9
70.5	76.8
83.3	88.9
74.8	75.8
73.4	75.5
86.5	89.1
82	88
80.8	85.9
91.5	89.3
77	82.9
72.3	81.2
83.5	90.5
79	86.4
76.7	81.8
83.1	91.3
71.1	73.4
75.5	76.6
90.9	91
85.4	87
84.8	89.7
83.8	90.7
79.3	86.5
79.9	86.6
93	98.8
78.1	84.4
82.3	91.4
87.3	95.7
74.6	78.5
80	81.7
91.3	94.3
94.2	98.5
90.9	95.4
88	91.7
81.6	92.8
77.4	90.5
91	102.2
79.9	91.8
83.4	95
91.6	102
85.2	88.9
84.1	89.6
87	97.9
92.8	108.6
89.2	100.8
87.3	95.1
89.5	101
86.8	100.9
92	102.5
92.2	105.4
86.4	98.4
92.9	105.3
91.2	96.5
80.3	88.1
102	107.9
99	107
89.2	92.5
103	95.7
80.4	85.2
83.4	85.5
97.6	94.7
87	86.2
84.4	88.8
94.1	93.4
88.9	83.4
82.3	82.9
94.7	96.7
94.5	96.2
91.6	92.8
96.8	92.8
87.9	90
99.9	95.4
109.5	108.3
91.2	96.3
89.4	95
109.7	109
96.9	92
94.1	92.3
104.4	107
100.8	105.5
107.4	105.4
108.9	103.9
95.2	99.2
102.7	102.2
130.9	121.5
104	102.3
106.5	110
106.1	105.9
97.8	91.9
112.2	100
114.5	111.7
105.8	104.9
101	103.3
101.2	101.8
96.5	100.8
99.5	104.2
123.8	116.5
94.6	97.9
95.8	100.7
105.4	107
104.4	96.3
105.2	96
112.7	104.5
114.8	107.4
108.9	102.4
103.8	94.9
102.5	98.8
98.1	96.8
118.2	108.2
114.8	103.8
109.9	102.3
116.7	107.2
116.9	102
104.4	92.6
113.5	105.2
123.8	113
116.4	105.6
114.1	101.6
102.8	101.7
112.7	102.7
121.1	109
120.8	105.5
117.8	103.3
130.4	108.6
110.9	98.2
105.4	90
137.6	112.4
133.3	111.9
123.3	102.1
122.8	102.4
110.2	101.7
101.4	98.7
128.7	114
120.6	105.1
110.1	98.3
121.6	110
113	96.5
115.9	92.2
131.1	112
127.4	111.4
123.9	107.5
120.8	103.4
108.5	103.5
112.9	107.4
129.6	117.6
121.3	110.2
119.1	104.3
140.8	115.9
127.4	98.9
128.1	101.9
136.6	113.5
126.5	109.5
120.8	110
144.3	114.2
116	106.9
123.4	109.2
138.6	124.2
118.3	104.7
124.2	111.9
136	119
127.4	102.9
131.6	106.3

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time12 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310056&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310056&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310056&T=0

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
Consumer[t] = -19.5608 + 0.527769totip[t] + 0.166236`Consumer(t-1)`[t] + 0.0457035`Consumer(t-2)`[t] + 0.173026`Consumer(t-3)`[t] -0.0335128`Consumer(t-4)`[t] + 0.349353`Consumer(t-1s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Consumer[t] =  -19.5608 +  0.527769totip[t] +  0.166236`Consumer(t-1)`[t] +  0.0457035`Consumer(t-2)`[t] +  0.173026`Consumer(t-3)`[t] -0.0335128`Consumer(t-4)`[t] +  0.349353`Consumer(t-1s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310056&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Consumer[t] =  -19.5608 +  0.527769totip[t] +  0.166236`Consumer(t-1)`[t] +  0.0457035`Consumer(t-2)`[t] +  0.173026`Consumer(t-3)`[t] -0.0335128`Consumer(t-4)`[t] +  0.349353`Consumer(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310056&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310056&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
Consumer[t] = -19.5608 + 0.527769totip[t] + 0.166236`Consumer(t-1)`[t] + 0.0457035`Consumer(t-2)`[t] + 0.173026`Consumer(t-3)`[t] -0.0335128`Consumer(t-4)`[t] + 0.349353`Consumer(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)	-19.56	3.187	-6.1380e+00	4.817e-09	2.408e-09
totip	+0.5278	0.06121	+8.6230e+00	2.617e-15	1.308e-15
`Consumer(t-1)`	+0.1662	0.05275	+3.1520e+00	0.001888	0.0009441
`Consumer(t-2)`	+0.0457	0.04378	+1.0440e+00	0.2979	0.149
`Consumer(t-3)`	+0.173	0.05352	+3.2330e+00	0.001447	0.0007235
`Consumer(t-4)`	-0.03351	0.04986	-6.7210e-01	0.5023	0.2512
`Consumer(t-1s)`	+0.3493	0.05418	+6.4480e+00	9.222e-10	4.611e-10

\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) & -19.56 &  3.187 & -6.1380e+00 &  4.817e-09 &  2.408e-09 \tabularnewline
totip & +0.5278 &  0.06121 & +8.6230e+00 &  2.617e-15 &  1.308e-15 \tabularnewline
`Consumer(t-1)` & +0.1662 &  0.05275 & +3.1520e+00 &  0.001888 &  0.0009441 \tabularnewline
`Consumer(t-2)` & +0.0457 &  0.04378 & +1.0440e+00 &  0.2979 &  0.149 \tabularnewline
`Consumer(t-3)` & +0.173 &  0.05352 & +3.2330e+00 &  0.001447 &  0.0007235 \tabularnewline
`Consumer(t-4)` & -0.03351 &  0.04986 & -6.7210e-01 &  0.5023 &  0.2512 \tabularnewline
`Consumer(t-1s)` & +0.3493 &  0.05418 & +6.4480e+00 &  9.222e-10 &  4.611e-10 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310056&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]-19.56[/C][C] 3.187[/C][C]-6.1380e+00[/C][C] 4.817e-09[/C][C] 2.408e-09[/C][/ROW]
[ROW][C]totip[/C][C]+0.5278[/C][C] 0.06121[/C][C]+8.6230e+00[/C][C] 2.617e-15[/C][C] 1.308e-15[/C][/ROW]
[ROW][C]`Consumer(t-1)`[/C][C]+0.1662[/C][C] 0.05275[/C][C]+3.1520e+00[/C][C] 0.001888[/C][C] 0.0009441[/C][/ROW]
[ROW][C]`Consumer(t-2)`[/C][C]+0.0457[/C][C] 0.04378[/C][C]+1.0440e+00[/C][C] 0.2979[/C][C] 0.149[/C][/ROW]
[ROW][C]`Consumer(t-3)`[/C][C]+0.173[/C][C] 0.05352[/C][C]+3.2330e+00[/C][C] 0.001447[/C][C] 0.0007235[/C][/ROW]
[ROW][C]`Consumer(t-4)`[/C][C]-0.03351[/C][C] 0.04986[/C][C]-6.7210e-01[/C][C] 0.5023[/C][C] 0.2512[/C][/ROW]
[ROW][C]`Consumer(t-1s)`[/C][C]+0.3493[/C][C] 0.05418[/C][C]+6.4480e+00[/C][C] 9.222e-10[/C][C] 4.611e-10[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310056&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310056&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)	-19.56	3.187	-6.1380e+00	4.817e-09	2.408e-09
totip	+0.5278	0.06121	+8.6230e+00	2.617e-15	1.308e-15
`Consumer(t-1)`	+0.1662	0.05275	+3.1520e+00	0.001888	0.0009441
`Consumer(t-2)`	+0.0457	0.04378	+1.0440e+00	0.2979	0.149
`Consumer(t-3)`	+0.173	0.05352	+3.2330e+00	0.001447	0.0007235
`Consumer(t-4)`	-0.03351	0.04986	-6.7210e-01	0.5023	0.2512
`Consumer(t-1s)`	+0.3493	0.05418	+6.4480e+00	9.222e-10	4.611e-10

Multiple Linear Regression - Regression Statistics
Multiple R	0.9694
R-squared	0.9398
Adjusted R-squared	0.9379
F-TEST (value)	492
F-TEST (DF numerator)	6
F-TEST (DF denominator)	189
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.123
Sum Squared Residuals	4960

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9694 \tabularnewline
R-squared &  0.9398 \tabularnewline
Adjusted R-squared &  0.9379 \tabularnewline
F-TEST (value) &  492 \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 &  5.123 \tabularnewline
Sum Squared Residuals &  4960 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310056&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9694[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9398[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9379[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 492[/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] 5.123[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 4960[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310056&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310056&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	0.9694
R-squared	0.9398
Adjusted R-squared	0.9379
F-TEST (value)	492
F-TEST (DF numerator)	6
F-TEST (DF denominator)	189
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.123
Sum Squared Residuals	4960

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=310056&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=310056&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310056&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	66.5	64.6	1.901
2	71.9	67.35	4.549
3	58.1	56.23	1.872
4	61.5	58.25	3.25
5	66.2	65.8	0.3964
6	72.3	65.57	6.732
7	67	66.64	0.3563
8	62.9	61.16	1.741
9	66.4	64.05	2.349
10	65.6	63.48	2.124
11	70.9	68.41	2.488
12	68.4	67.18	1.219
13	66.4	67.15	-0.7483
14	67.6	70.27	-2.671
15	64.1	61.25	2.847
16	62.1	60.65	1.451
17	70	67.19	2.807
18	74.4	72.54	1.861
19	67	67.75	-0.7508
20	64.8	64.4	0.3989
21	70.7	68.11	2.592
22	64	66.07	-2.067
23	72.5	70.11	2.391
24	70.4	70.43	-0.03379
25	63.6	66.04	-2.443
26	69.8	69.57	0.2305
27	67.7	65.04	2.659
28	66.4	60.58	5.816
29	78.9	72.25	6.651
30	79.9	76.13	3.769
31	69.1	69.43	-0.3313
32	81.2	71.86	9.336
33	66	72.19	-6.189
34	71.8	67.29	4.508
35	86.1	79.42	6.675
36	76.1	74.55	1.549
37	70.5	69.99	0.5123
38	83.3	79.43	3.868
39	74.8	71.45	3.353
40	73.4	69.37	4.028
41	86.5	82.7	3.802
42	82	82.68	-0.6809
43	80.8	77.69	3.107
44	91.5	85.62	5.877
45	77	77.44	-0.4412
46	72.3	76.59	-4.292
47	83.5	86.94	-3.444
48	79	80.07	-1.066
49	76.7	75.12	1.582
50	83.1	86.11	-3.011
51	71.1	73.5	-2.399
52	75.5	72.75	2.75
53	90.9	86.29	4.606
54	85.4	83.08	2.319
55	84.8	85.04	-0.2395
56	83.8	91.47	-7.671
57	79.3	82.53	-3.228
58	79.9	80.23	-0.3253
59	93	90.32	2.682
60	78.1	82.61	-4.506
61	82.3	83.87	-1.573
62	87.3	90.64	-3.342
63	74.6	75.38	-0.7785
64	80	77.95	2.052
65	91.3	91.02	0.2806
66	94.2	91.07	3.125
67	90.9	91.59	-0.6876
68	88	90.64	-2.644
69	81.6	89.14	-7.542
70	77.4	86.27	-8.873
71	91	95.64	-4.643
72	79.9	86.01	-6.108
73	83.4	87.43	-4.028
74	91.6	95.44	-3.837
75	85.2	83.23	1.966
76	84.1	85.78	-1.678
77	87	94.93	-7.932
78	92.8	100.6	-7.842
79	89.2	96.49	-7.294
80	87.3	92.68	-5.377
81	89.5	93.98	-4.481
82	86.8	91.92	-5.123
83	92	96.96	-4.962
84	92.2	95.8	-3.6
85	86.4	93.06	-6.659
86	92.9	99.6	-6.7
87	91.2	93.4	-2.196
88	80.3	87.58	-7.282
89	102	98.47	3.525
90	99	102.6	-3.623
91	89.2	92.38	-3.177
92	103	95.76	7.244
93	80.4	91.58	-11.18
94	83.4	86.08	-2.676
95	97.6	94.93	2.67
96	87	88.64	-1.639
97	84.4	88.15	-3.748
98	94.1	94.29	-0.1866
99	88.9	87.6	1.301
100	82.3	83.01	-0.7111
101	94.7	98.31	-3.606
102	94.5	97.53	-3.029
103	91.6	91.88	-0.2766
104	96.8	98.57	-1.773
105	87.9	89.48	-1.582
106	99.9	91.64	8.257
107	109.5	106	3.503
108	91.2	96.39	-5.191
109	89.4	94.57	-5.167
110	109.7	105.5	4.232
111	96.9	94.48	2.416
112	94.1	91.44	2.662
113	104.4	106.1	-1.651
114	100.8	103.9	-3.078
115	107.4	102.6	4.771
116	108.9	106.5	2.437
117	95.2	100.5	-5.256
118	102.7	105.3	-2.585
119	130.9	119.5	11.42
120	104	105.6	-1.567
121	106.5	107.6	-1.076
122	106.1	116.3	-10.22
123	97.8	98.91	-1.106
124	112.2	102.1	10.06
125	114.5	113.8	0.7262
126	105.8	108.5	-2.745
127	101	111.4	-10.44
128	101.2	109.9	-8.687
129	96.5	102.8	-6.305
130	99.5	105.9	-6.408
131	123.8	122.7	1.069
132	94.6	106.9	-12.27
133	95.8	106.2	-10.36
134	105.4	112.3	-6.912
135	104.4	99.55	4.851
136	105.2	105.9	-0.6802
137	112.7	112.9	-0.1778
138	114.8	112.2	2.642
139	108.9	108.7	0.1944
140	103.8	105.2	-1.403
141	102.5	104.6	-2.114
142	98.1	103.1	-4.966
143	118.2	116.1	2.103
144	114.8	106.7	8.14
145	109.9	105.9	3.977
146	116.7	114.5	2.182
147	116.9	111.1	5.831
148	104.4	106	-1.597
149	113.5	114.5	-1.039
150	123.8	120.1	3.662
151	116.4	114.1	2.27
152	114.1	111.5	2.629
153	102.8	111.8	-9.026
154	112.7	107.2	5.492
155	121.1	118.5	2.566
156	120.8	115.5	5.33
157	117.8	115	2.777
158	130.4	120.8	9.595
159	110.9	117	-6.11
160	105.4	105.1	0.2597
161	137.6	120.6	16.98
162	133.3	125.3	8.044
163	123.3	118	5.343
164	122.8	121.2	1.591
165	110.2	114.5	-4.329
166	101.4	112.7	-11.3
167	128.7	121.9	6.78
168	120.6	119.1	1.509
169	110.1	113.3	-3.154
170	121.6	126.7	-5.134
171	113	111.9	1.088
172	115.9	105.3	10.63
173	131.1	129.4	1.698
174	127.4	128.4	-0.9687
175	123.9	123.7	0.2135
176	120.8	123.1	-2.33
177	108.5	117	-8.456
178	112.9	113.3	-0.3719
179	129.6	127.9	1.657
180	121.3	122.2	-0.8603
181	119.1	115.9	3.165
182	140.8	128.1	12.73
183	127.4	117.6	9.793
184	128.1	118.9	9.235
185	136.6	133.6	2.971
186	126.5	128.6	-2.125
187	120.8	126.9	-6.146
188	144.3	128.1	16.18
189	116	121.6	-5.581
190	123.4	120.1	3.346
191	138.6	138	0.6013
192	118.3	122	-3.688
193	124.2	124.6	-0.3686
194	136	138.3	-2.332
195	127.4	123.4	4.037
196	131.6	126.2	5.387

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  66.5 &  64.6 &  1.901 \tabularnewline
2 &  71.9 &  67.35 &  4.549 \tabularnewline
3 &  58.1 &  56.23 &  1.872 \tabularnewline
4 &  61.5 &  58.25 &  3.25 \tabularnewline
5 &  66.2 &  65.8 &  0.3964 \tabularnewline
6 &  72.3 &  65.57 &  6.732 \tabularnewline
7 &  67 &  66.64 &  0.3563 \tabularnewline
8 &  62.9 &  61.16 &  1.741 \tabularnewline
9 &  66.4 &  64.05 &  2.349 \tabularnewline
10 &  65.6 &  63.48 &  2.124 \tabularnewline
11 &  70.9 &  68.41 &  2.488 \tabularnewline
12 &  68.4 &  67.18 &  1.219 \tabularnewline
13 &  66.4 &  67.15 & -0.7483 \tabularnewline
14 &  67.6 &  70.27 & -2.671 \tabularnewline
15 &  64.1 &  61.25 &  2.847 \tabularnewline
16 &  62.1 &  60.65 &  1.451 \tabularnewline
17 &  70 &  67.19 &  2.807 \tabularnewline
18 &  74.4 &  72.54 &  1.861 \tabularnewline
19 &  67 &  67.75 & -0.7508 \tabularnewline
20 &  64.8 &  64.4 &  0.3989 \tabularnewline
21 &  70.7 &  68.11 &  2.592 \tabularnewline
22 &  64 &  66.07 & -2.067 \tabularnewline
23 &  72.5 &  70.11 &  2.391 \tabularnewline
24 &  70.4 &  70.43 & -0.03379 \tabularnewline
25 &  63.6 &  66.04 & -2.443 \tabularnewline
26 &  69.8 &  69.57 &  0.2305 \tabularnewline
27 &  67.7 &  65.04 &  2.659 \tabularnewline
28 &  66.4 &  60.58 &  5.816 \tabularnewline
29 &  78.9 &  72.25 &  6.651 \tabularnewline
30 &  79.9 &  76.13 &  3.769 \tabularnewline
31 &  69.1 &  69.43 & -0.3313 \tabularnewline
32 &  81.2 &  71.86 &  9.336 \tabularnewline
33 &  66 &  72.19 & -6.189 \tabularnewline
34 &  71.8 &  67.29 &  4.508 \tabularnewline
35 &  86.1 &  79.42 &  6.675 \tabularnewline
36 &  76.1 &  74.55 &  1.549 \tabularnewline
37 &  70.5 &  69.99 &  0.5123 \tabularnewline
38 &  83.3 &  79.43 &  3.868 \tabularnewline
39 &  74.8 &  71.45 &  3.353 \tabularnewline
40 &  73.4 &  69.37 &  4.028 \tabularnewline
41 &  86.5 &  82.7 &  3.802 \tabularnewline
42 &  82 &  82.68 & -0.6809 \tabularnewline
43 &  80.8 &  77.69 &  3.107 \tabularnewline
44 &  91.5 &  85.62 &  5.877 \tabularnewline
45 &  77 &  77.44 & -0.4412 \tabularnewline
46 &  72.3 &  76.59 & -4.292 \tabularnewline
47 &  83.5 &  86.94 & -3.444 \tabularnewline
48 &  79 &  80.07 & -1.066 \tabularnewline
49 &  76.7 &  75.12 &  1.582 \tabularnewline
50 &  83.1 &  86.11 & -3.011 \tabularnewline
51 &  71.1 &  73.5 & -2.399 \tabularnewline
52 &  75.5 &  72.75 &  2.75 \tabularnewline
53 &  90.9 &  86.29 &  4.606 \tabularnewline
54 &  85.4 &  83.08 &  2.319 \tabularnewline
55 &  84.8 &  85.04 & -0.2395 \tabularnewline
56 &  83.8 &  91.47 & -7.671 \tabularnewline
57 &  79.3 &  82.53 & -3.228 \tabularnewline
58 &  79.9 &  80.23 & -0.3253 \tabularnewline
59 &  93 &  90.32 &  2.682 \tabularnewline
60 &  78.1 &  82.61 & -4.506 \tabularnewline
61 &  82.3 &  83.87 & -1.573 \tabularnewline
62 &  87.3 &  90.64 & -3.342 \tabularnewline
63 &  74.6 &  75.38 & -0.7785 \tabularnewline
64 &  80 &  77.95 &  2.052 \tabularnewline
65 &  91.3 &  91.02 &  0.2806 \tabularnewline
66 &  94.2 &  91.07 &  3.125 \tabularnewline
67 &  90.9 &  91.59 & -0.6876 \tabularnewline
68 &  88 &  90.64 & -2.644 \tabularnewline
69 &  81.6 &  89.14 & -7.542 \tabularnewline
70 &  77.4 &  86.27 & -8.873 \tabularnewline
71 &  91 &  95.64 & -4.643 \tabularnewline
72 &  79.9 &  86.01 & -6.108 \tabularnewline
73 &  83.4 &  87.43 & -4.028 \tabularnewline
74 &  91.6 &  95.44 & -3.837 \tabularnewline
75 &  85.2 &  83.23 &  1.966 \tabularnewline
76 &  84.1 &  85.78 & -1.678 \tabularnewline
77 &  87 &  94.93 & -7.932 \tabularnewline
78 &  92.8 &  100.6 & -7.842 \tabularnewline
79 &  89.2 &  96.49 & -7.294 \tabularnewline
80 &  87.3 &  92.68 & -5.377 \tabularnewline
81 &  89.5 &  93.98 & -4.481 \tabularnewline
82 &  86.8 &  91.92 & -5.123 \tabularnewline
83 &  92 &  96.96 & -4.962 \tabularnewline
84 &  92.2 &  95.8 & -3.6 \tabularnewline
85 &  86.4 &  93.06 & -6.659 \tabularnewline
86 &  92.9 &  99.6 & -6.7 \tabularnewline
87 &  91.2 &  93.4 & -2.196 \tabularnewline
88 &  80.3 &  87.58 & -7.282 \tabularnewline
89 &  102 &  98.47 &  3.525 \tabularnewline
90 &  99 &  102.6 & -3.623 \tabularnewline
91 &  89.2 &  92.38 & -3.177 \tabularnewline
92 &  103 &  95.76 &  7.244 \tabularnewline
93 &  80.4 &  91.58 & -11.18 \tabularnewline
94 &  83.4 &  86.08 & -2.676 \tabularnewline
95 &  97.6 &  94.93 &  2.67 \tabularnewline
96 &  87 &  88.64 & -1.639 \tabularnewline
97 &  84.4 &  88.15 & -3.748 \tabularnewline
98 &  94.1 &  94.29 & -0.1866 \tabularnewline
99 &  88.9 &  87.6 &  1.301 \tabularnewline
100 &  82.3 &  83.01 & -0.7111 \tabularnewline
101 &  94.7 &  98.31 & -3.606 \tabularnewline
102 &  94.5 &  97.53 & -3.029 \tabularnewline
103 &  91.6 &  91.88 & -0.2766 \tabularnewline
104 &  96.8 &  98.57 & -1.773 \tabularnewline
105 &  87.9 &  89.48 & -1.582 \tabularnewline
106 &  99.9 &  91.64 &  8.257 \tabularnewline
107 &  109.5 &  106 &  3.503 \tabularnewline
108 &  91.2 &  96.39 & -5.191 \tabularnewline
109 &  89.4 &  94.57 & -5.167 \tabularnewline
110 &  109.7 &  105.5 &  4.232 \tabularnewline
111 &  96.9 &  94.48 &  2.416 \tabularnewline
112 &  94.1 &  91.44 &  2.662 \tabularnewline
113 &  104.4 &  106.1 & -1.651 \tabularnewline
114 &  100.8 &  103.9 & -3.078 \tabularnewline
115 &  107.4 &  102.6 &  4.771 \tabularnewline
116 &  108.9 &  106.5 &  2.437 \tabularnewline
117 &  95.2 &  100.5 & -5.256 \tabularnewline
118 &  102.7 &  105.3 & -2.585 \tabularnewline
119 &  130.9 &  119.5 &  11.42 \tabularnewline
120 &  104 &  105.6 & -1.567 \tabularnewline
121 &  106.5 &  107.6 & -1.076 \tabularnewline
122 &  106.1 &  116.3 & -10.22 \tabularnewline
123 &  97.8 &  98.91 & -1.106 \tabularnewline
124 &  112.2 &  102.1 &  10.06 \tabularnewline
125 &  114.5 &  113.8 &  0.7262 \tabularnewline
126 &  105.8 &  108.5 & -2.745 \tabularnewline
127 &  101 &  111.4 & -10.44 \tabularnewline
128 &  101.2 &  109.9 & -8.687 \tabularnewline
129 &  96.5 &  102.8 & -6.305 \tabularnewline
130 &  99.5 &  105.9 & -6.408 \tabularnewline
131 &  123.8 &  122.7 &  1.069 \tabularnewline
132 &  94.6 &  106.9 & -12.27 \tabularnewline
133 &  95.8 &  106.2 & -10.36 \tabularnewline
134 &  105.4 &  112.3 & -6.912 \tabularnewline
135 &  104.4 &  99.55 &  4.851 \tabularnewline
136 &  105.2 &  105.9 & -0.6802 \tabularnewline
137 &  112.7 &  112.9 & -0.1778 \tabularnewline
138 &  114.8 &  112.2 &  2.642 \tabularnewline
139 &  108.9 &  108.7 &  0.1944 \tabularnewline
140 &  103.8 &  105.2 & -1.403 \tabularnewline
141 &  102.5 &  104.6 & -2.114 \tabularnewline
142 &  98.1 &  103.1 & -4.966 \tabularnewline
143 &  118.2 &  116.1 &  2.103 \tabularnewline
144 &  114.8 &  106.7 &  8.14 \tabularnewline
145 &  109.9 &  105.9 &  3.977 \tabularnewline
146 &  116.7 &  114.5 &  2.182 \tabularnewline
147 &  116.9 &  111.1 &  5.831 \tabularnewline
148 &  104.4 &  106 & -1.597 \tabularnewline
149 &  113.5 &  114.5 & -1.039 \tabularnewline
150 &  123.8 &  120.1 &  3.662 \tabularnewline
151 &  116.4 &  114.1 &  2.27 \tabularnewline
152 &  114.1 &  111.5 &  2.629 \tabularnewline
153 &  102.8 &  111.8 & -9.026 \tabularnewline
154 &  112.7 &  107.2 &  5.492 \tabularnewline
155 &  121.1 &  118.5 &  2.566 \tabularnewline
156 &  120.8 &  115.5 &  5.33 \tabularnewline
157 &  117.8 &  115 &  2.777 \tabularnewline
158 &  130.4 &  120.8 &  9.595 \tabularnewline
159 &  110.9 &  117 & -6.11 \tabularnewline
160 &  105.4 &  105.1 &  0.2597 \tabularnewline
161 &  137.6 &  120.6 &  16.98 \tabularnewline
162 &  133.3 &  125.3 &  8.044 \tabularnewline
163 &  123.3 &  118 &  5.343 \tabularnewline
164 &  122.8 &  121.2 &  1.591 \tabularnewline
165 &  110.2 &  114.5 & -4.329 \tabularnewline
166 &  101.4 &  112.7 & -11.3 \tabularnewline
167 &  128.7 &  121.9 &  6.78 \tabularnewline
168 &  120.6 &  119.1 &  1.509 \tabularnewline
169 &  110.1 &  113.3 & -3.154 \tabularnewline
170 &  121.6 &  126.7 & -5.134 \tabularnewline
171 &  113 &  111.9 &  1.088 \tabularnewline
172 &  115.9 &  105.3 &  10.63 \tabularnewline
173 &  131.1 &  129.4 &  1.698 \tabularnewline
174 &  127.4 &  128.4 & -0.9687 \tabularnewline
175 &  123.9 &  123.7 &  0.2135 \tabularnewline
176 &  120.8 &  123.1 & -2.33 \tabularnewline
177 &  108.5 &  117 & -8.456 \tabularnewline
178 &  112.9 &  113.3 & -0.3719 \tabularnewline
179 &  129.6 &  127.9 &  1.657 \tabularnewline
180 &  121.3 &  122.2 & -0.8603 \tabularnewline
181 &  119.1 &  115.9 &  3.165 \tabularnewline
182 &  140.8 &  128.1 &  12.73 \tabularnewline
183 &  127.4 &  117.6 &  9.793 \tabularnewline
184 &  128.1 &  118.9 &  9.235 \tabularnewline
185 &  136.6 &  133.6 &  2.971 \tabularnewline
186 &  126.5 &  128.6 & -2.125 \tabularnewline
187 &  120.8 &  126.9 & -6.146 \tabularnewline
188 &  144.3 &  128.1 &  16.18 \tabularnewline
189 &  116 &  121.6 & -5.581 \tabularnewline
190 &  123.4 &  120.1 &  3.346 \tabularnewline
191 &  138.6 &  138 &  0.6013 \tabularnewline
192 &  118.3 &  122 & -3.688 \tabularnewline
193 &  124.2 &  124.6 & -0.3686 \tabularnewline
194 &  136 &  138.3 & -2.332 \tabularnewline
195 &  127.4 &  123.4 &  4.037 \tabularnewline
196 &  131.6 &  126.2 &  5.387 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310056&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] 66.5[/C][C] 64.6[/C][C] 1.901[/C][/ROW]
[ROW][C]2[/C][C] 71.9[/C][C] 67.35[/C][C] 4.549[/C][/ROW]
[ROW][C]3[/C][C] 58.1[/C][C] 56.23[/C][C] 1.872[/C][/ROW]
[ROW][C]4[/C][C] 61.5[/C][C] 58.25[/C][C] 3.25[/C][/ROW]
[ROW][C]5[/C][C] 66.2[/C][C] 65.8[/C][C] 0.3964[/C][/ROW]
[ROW][C]6[/C][C] 72.3[/C][C] 65.57[/C][C] 6.732[/C][/ROW]
[ROW][C]7[/C][C] 67[/C][C] 66.64[/C][C] 0.3563[/C][/ROW]
[ROW][C]8[/C][C] 62.9[/C][C] 61.16[/C][C] 1.741[/C][/ROW]
[ROW][C]9[/C][C] 66.4[/C][C] 64.05[/C][C] 2.349[/C][/ROW]
[ROW][C]10[/C][C] 65.6[/C][C] 63.48[/C][C] 2.124[/C][/ROW]
[ROW][C]11[/C][C] 70.9[/C][C] 68.41[/C][C] 2.488[/C][/ROW]
[ROW][C]12[/C][C] 68.4[/C][C] 67.18[/C][C] 1.219[/C][/ROW]
[ROW][C]13[/C][C] 66.4[/C][C] 67.15[/C][C]-0.7483[/C][/ROW]
[ROW][C]14[/C][C] 67.6[/C][C] 70.27[/C][C]-2.671[/C][/ROW]
[ROW][C]15[/C][C] 64.1[/C][C] 61.25[/C][C] 2.847[/C][/ROW]
[ROW][C]16[/C][C] 62.1[/C][C] 60.65[/C][C] 1.451[/C][/ROW]
[ROW][C]17[/C][C] 70[/C][C] 67.19[/C][C] 2.807[/C][/ROW]
[ROW][C]18[/C][C] 74.4[/C][C] 72.54[/C][C] 1.861[/C][/ROW]
[ROW][C]19[/C][C] 67[/C][C] 67.75[/C][C]-0.7508[/C][/ROW]
[ROW][C]20[/C][C] 64.8[/C][C] 64.4[/C][C] 0.3989[/C][/ROW]
[ROW][C]21[/C][C] 70.7[/C][C] 68.11[/C][C] 2.592[/C][/ROW]
[ROW][C]22[/C][C] 64[/C][C] 66.07[/C][C]-2.067[/C][/ROW]
[ROW][C]23[/C][C] 72.5[/C][C] 70.11[/C][C] 2.391[/C][/ROW]
[ROW][C]24[/C][C] 70.4[/C][C] 70.43[/C][C]-0.03379[/C][/ROW]
[ROW][C]25[/C][C] 63.6[/C][C] 66.04[/C][C]-2.443[/C][/ROW]
[ROW][C]26[/C][C] 69.8[/C][C] 69.57[/C][C] 0.2305[/C][/ROW]
[ROW][C]27[/C][C] 67.7[/C][C] 65.04[/C][C] 2.659[/C][/ROW]
[ROW][C]28[/C][C] 66.4[/C][C] 60.58[/C][C] 5.816[/C][/ROW]
[ROW][C]29[/C][C] 78.9[/C][C] 72.25[/C][C] 6.651[/C][/ROW]
[ROW][C]30[/C][C] 79.9[/C][C] 76.13[/C][C] 3.769[/C][/ROW]
[ROW][C]31[/C][C] 69.1[/C][C] 69.43[/C][C]-0.3313[/C][/ROW]
[ROW][C]32[/C][C] 81.2[/C][C] 71.86[/C][C] 9.336[/C][/ROW]
[ROW][C]33[/C][C] 66[/C][C] 72.19[/C][C]-6.189[/C][/ROW]
[ROW][C]34[/C][C] 71.8[/C][C] 67.29[/C][C] 4.508[/C][/ROW]
[ROW][C]35[/C][C] 86.1[/C][C] 79.42[/C][C] 6.675[/C][/ROW]
[ROW][C]36[/C][C] 76.1[/C][C] 74.55[/C][C] 1.549[/C][/ROW]
[ROW][C]37[/C][C] 70.5[/C][C] 69.99[/C][C] 0.5123[/C][/ROW]
[ROW][C]38[/C][C] 83.3[/C][C] 79.43[/C][C] 3.868[/C][/ROW]
[ROW][C]39[/C][C] 74.8[/C][C] 71.45[/C][C] 3.353[/C][/ROW]
[ROW][C]40[/C][C] 73.4[/C][C] 69.37[/C][C] 4.028[/C][/ROW]
[ROW][C]41[/C][C] 86.5[/C][C] 82.7[/C][C] 3.802[/C][/ROW]
[ROW][C]42[/C][C] 82[/C][C] 82.68[/C][C]-0.6809[/C][/ROW]
[ROW][C]43[/C][C] 80.8[/C][C] 77.69[/C][C] 3.107[/C][/ROW]
[ROW][C]44[/C][C] 91.5[/C][C] 85.62[/C][C] 5.877[/C][/ROW]
[ROW][C]45[/C][C] 77[/C][C] 77.44[/C][C]-0.4412[/C][/ROW]
[ROW][C]46[/C][C] 72.3[/C][C] 76.59[/C][C]-4.292[/C][/ROW]
[ROW][C]47[/C][C] 83.5[/C][C] 86.94[/C][C]-3.444[/C][/ROW]
[ROW][C]48[/C][C] 79[/C][C] 80.07[/C][C]-1.066[/C][/ROW]
[ROW][C]49[/C][C] 76.7[/C][C] 75.12[/C][C] 1.582[/C][/ROW]
[ROW][C]50[/C][C] 83.1[/C][C] 86.11[/C][C]-3.011[/C][/ROW]
[ROW][C]51[/C][C] 71.1[/C][C] 73.5[/C][C]-2.399[/C][/ROW]
[ROW][C]52[/C][C] 75.5[/C][C] 72.75[/C][C] 2.75[/C][/ROW]
[ROW][C]53[/C][C] 90.9[/C][C] 86.29[/C][C] 4.606[/C][/ROW]
[ROW][C]54[/C][C] 85.4[/C][C] 83.08[/C][C] 2.319[/C][/ROW]
[ROW][C]55[/C][C] 84.8[/C][C] 85.04[/C][C]-0.2395[/C][/ROW]
[ROW][C]56[/C][C] 83.8[/C][C] 91.47[/C][C]-7.671[/C][/ROW]
[ROW][C]57[/C][C] 79.3[/C][C] 82.53[/C][C]-3.228[/C][/ROW]
[ROW][C]58[/C][C] 79.9[/C][C] 80.23[/C][C]-0.3253[/C][/ROW]
[ROW][C]59[/C][C] 93[/C][C] 90.32[/C][C] 2.682[/C][/ROW]
[ROW][C]60[/C][C] 78.1[/C][C] 82.61[/C][C]-4.506[/C][/ROW]
[ROW][C]61[/C][C] 82.3[/C][C] 83.87[/C][C]-1.573[/C][/ROW]
[ROW][C]62[/C][C] 87.3[/C][C] 90.64[/C][C]-3.342[/C][/ROW]
[ROW][C]63[/C][C] 74.6[/C][C] 75.38[/C][C]-0.7785[/C][/ROW]
[ROW][C]64[/C][C] 80[/C][C] 77.95[/C][C] 2.052[/C][/ROW]
[ROW][C]65[/C][C] 91.3[/C][C] 91.02[/C][C] 0.2806[/C][/ROW]
[ROW][C]66[/C][C] 94.2[/C][C] 91.07[/C][C] 3.125[/C][/ROW]
[ROW][C]67[/C][C] 90.9[/C][C] 91.59[/C][C]-0.6876[/C][/ROW]
[ROW][C]68[/C][C] 88[/C][C] 90.64[/C][C]-2.644[/C][/ROW]
[ROW][C]69[/C][C] 81.6[/C][C] 89.14[/C][C]-7.542[/C][/ROW]
[ROW][C]70[/C][C] 77.4[/C][C] 86.27[/C][C]-8.873[/C][/ROW]
[ROW][C]71[/C][C] 91[/C][C] 95.64[/C][C]-4.643[/C][/ROW]
[ROW][C]72[/C][C] 79.9[/C][C] 86.01[/C][C]-6.108[/C][/ROW]
[ROW][C]73[/C][C] 83.4[/C][C] 87.43[/C][C]-4.028[/C][/ROW]
[ROW][C]74[/C][C] 91.6[/C][C] 95.44[/C][C]-3.837[/C][/ROW]
[ROW][C]75[/C][C] 85.2[/C][C] 83.23[/C][C] 1.966[/C][/ROW]
[ROW][C]76[/C][C] 84.1[/C][C] 85.78[/C][C]-1.678[/C][/ROW]
[ROW][C]77[/C][C] 87[/C][C] 94.93[/C][C]-7.932[/C][/ROW]
[ROW][C]78[/C][C] 92.8[/C][C] 100.6[/C][C]-7.842[/C][/ROW]
[ROW][C]79[/C][C] 89.2[/C][C] 96.49[/C][C]-7.294[/C][/ROW]
[ROW][C]80[/C][C] 87.3[/C][C] 92.68[/C][C]-5.377[/C][/ROW]
[ROW][C]81[/C][C] 89.5[/C][C] 93.98[/C][C]-4.481[/C][/ROW]
[ROW][C]82[/C][C] 86.8[/C][C] 91.92[/C][C]-5.123[/C][/ROW]
[ROW][C]83[/C][C] 92[/C][C] 96.96[/C][C]-4.962[/C][/ROW]
[ROW][C]84[/C][C] 92.2[/C][C] 95.8[/C][C]-3.6[/C][/ROW]
[ROW][C]85[/C][C] 86.4[/C][C] 93.06[/C][C]-6.659[/C][/ROW]
[ROW][C]86[/C][C] 92.9[/C][C] 99.6[/C][C]-6.7[/C][/ROW]
[ROW][C]87[/C][C] 91.2[/C][C] 93.4[/C][C]-2.196[/C][/ROW]
[ROW][C]88[/C][C] 80.3[/C][C] 87.58[/C][C]-7.282[/C][/ROW]
[ROW][C]89[/C][C] 102[/C][C] 98.47[/C][C] 3.525[/C][/ROW]
[ROW][C]90[/C][C] 99[/C][C] 102.6[/C][C]-3.623[/C][/ROW]
[ROW][C]91[/C][C] 89.2[/C][C] 92.38[/C][C]-3.177[/C][/ROW]
[ROW][C]92[/C][C] 103[/C][C] 95.76[/C][C] 7.244[/C][/ROW]
[ROW][C]93[/C][C] 80.4[/C][C] 91.58[/C][C]-11.18[/C][/ROW]
[ROW][C]94[/C][C] 83.4[/C][C] 86.08[/C][C]-2.676[/C][/ROW]
[ROW][C]95[/C][C] 97.6[/C][C] 94.93[/C][C] 2.67[/C][/ROW]
[ROW][C]96[/C][C] 87[/C][C] 88.64[/C][C]-1.639[/C][/ROW]
[ROW][C]97[/C][C] 84.4[/C][C] 88.15[/C][C]-3.748[/C][/ROW]
[ROW][C]98[/C][C] 94.1[/C][C] 94.29[/C][C]-0.1866[/C][/ROW]
[ROW][C]99[/C][C] 88.9[/C][C] 87.6[/C][C] 1.301[/C][/ROW]
[ROW][C]100[/C][C] 82.3[/C][C] 83.01[/C][C]-0.7111[/C][/ROW]
[ROW][C]101[/C][C] 94.7[/C][C] 98.31[/C][C]-3.606[/C][/ROW]
[ROW][C]102[/C][C] 94.5[/C][C] 97.53[/C][C]-3.029[/C][/ROW]
[ROW][C]103[/C][C] 91.6[/C][C] 91.88[/C][C]-0.2766[/C][/ROW]
[ROW][C]104[/C][C] 96.8[/C][C] 98.57[/C][C]-1.773[/C][/ROW]
[ROW][C]105[/C][C] 87.9[/C][C] 89.48[/C][C]-1.582[/C][/ROW]
[ROW][C]106[/C][C] 99.9[/C][C] 91.64[/C][C] 8.257[/C][/ROW]
[ROW][C]107[/C][C] 109.5[/C][C] 106[/C][C] 3.503[/C][/ROW]
[ROW][C]108[/C][C] 91.2[/C][C] 96.39[/C][C]-5.191[/C][/ROW]
[ROW][C]109[/C][C] 89.4[/C][C] 94.57[/C][C]-5.167[/C][/ROW]
[ROW][C]110[/C][C] 109.7[/C][C] 105.5[/C][C] 4.232[/C][/ROW]
[ROW][C]111[/C][C] 96.9[/C][C] 94.48[/C][C] 2.416[/C][/ROW]
[ROW][C]112[/C][C] 94.1[/C][C] 91.44[/C][C] 2.662[/C][/ROW]
[ROW][C]113[/C][C] 104.4[/C][C] 106.1[/C][C]-1.651[/C][/ROW]
[ROW][C]114[/C][C] 100.8[/C][C] 103.9[/C][C]-3.078[/C][/ROW]
[ROW][C]115[/C][C] 107.4[/C][C] 102.6[/C][C] 4.771[/C][/ROW]
[ROW][C]116[/C][C] 108.9[/C][C] 106.5[/C][C] 2.437[/C][/ROW]
[ROW][C]117[/C][C] 95.2[/C][C] 100.5[/C][C]-5.256[/C][/ROW]
[ROW][C]118[/C][C] 102.7[/C][C] 105.3[/C][C]-2.585[/C][/ROW]
[ROW][C]119[/C][C] 130.9[/C][C] 119.5[/C][C] 11.42[/C][/ROW]
[ROW][C]120[/C][C] 104[/C][C] 105.6[/C][C]-1.567[/C][/ROW]
[ROW][C]121[/C][C] 106.5[/C][C] 107.6[/C][C]-1.076[/C][/ROW]
[ROW][C]122[/C][C] 106.1[/C][C] 116.3[/C][C]-10.22[/C][/ROW]
[ROW][C]123[/C][C] 97.8[/C][C] 98.91[/C][C]-1.106[/C][/ROW]
[ROW][C]124[/C][C] 112.2[/C][C] 102.1[/C][C] 10.06[/C][/ROW]
[ROW][C]125[/C][C] 114.5[/C][C] 113.8[/C][C] 0.7262[/C][/ROW]
[ROW][C]126[/C][C] 105.8[/C][C] 108.5[/C][C]-2.745[/C][/ROW]
[ROW][C]127[/C][C] 101[/C][C] 111.4[/C][C]-10.44[/C][/ROW]
[ROW][C]128[/C][C] 101.2[/C][C] 109.9[/C][C]-8.687[/C][/ROW]
[ROW][C]129[/C][C] 96.5[/C][C] 102.8[/C][C]-6.305[/C][/ROW]
[ROW][C]130[/C][C] 99.5[/C][C] 105.9[/C][C]-6.408[/C][/ROW]
[ROW][C]131[/C][C] 123.8[/C][C] 122.7[/C][C] 1.069[/C][/ROW]
[ROW][C]132[/C][C] 94.6[/C][C] 106.9[/C][C]-12.27[/C][/ROW]
[ROW][C]133[/C][C] 95.8[/C][C] 106.2[/C][C]-10.36[/C][/ROW]
[ROW][C]134[/C][C] 105.4[/C][C] 112.3[/C][C]-6.912[/C][/ROW]
[ROW][C]135[/C][C] 104.4[/C][C] 99.55[/C][C] 4.851[/C][/ROW]
[ROW][C]136[/C][C] 105.2[/C][C] 105.9[/C][C]-0.6802[/C][/ROW]
[ROW][C]137[/C][C] 112.7[/C][C] 112.9[/C][C]-0.1778[/C][/ROW]
[ROW][C]138[/C][C] 114.8[/C][C] 112.2[/C][C] 2.642[/C][/ROW]
[ROW][C]139[/C][C] 108.9[/C][C] 108.7[/C][C] 0.1944[/C][/ROW]
[ROW][C]140[/C][C] 103.8[/C][C] 105.2[/C][C]-1.403[/C][/ROW]
[ROW][C]141[/C][C] 102.5[/C][C] 104.6[/C][C]-2.114[/C][/ROW]
[ROW][C]142[/C][C] 98.1[/C][C] 103.1[/C][C]-4.966[/C][/ROW]
[ROW][C]143[/C][C] 118.2[/C][C] 116.1[/C][C] 2.103[/C][/ROW]
[ROW][C]144[/C][C] 114.8[/C][C] 106.7[/C][C] 8.14[/C][/ROW]
[ROW][C]145[/C][C] 109.9[/C][C] 105.9[/C][C] 3.977[/C][/ROW]
[ROW][C]146[/C][C] 116.7[/C][C] 114.5[/C][C] 2.182[/C][/ROW]
[ROW][C]147[/C][C] 116.9[/C][C] 111.1[/C][C] 5.831[/C][/ROW]
[ROW][C]148[/C][C] 104.4[/C][C] 106[/C][C]-1.597[/C][/ROW]
[ROW][C]149[/C][C] 113.5[/C][C] 114.5[/C][C]-1.039[/C][/ROW]
[ROW][C]150[/C][C] 123.8[/C][C] 120.1[/C][C] 3.662[/C][/ROW]
[ROW][C]151[/C][C] 116.4[/C][C] 114.1[/C][C] 2.27[/C][/ROW]
[ROW][C]152[/C][C] 114.1[/C][C] 111.5[/C][C] 2.629[/C][/ROW]
[ROW][C]153[/C][C] 102.8[/C][C] 111.8[/C][C]-9.026[/C][/ROW]
[ROW][C]154[/C][C] 112.7[/C][C] 107.2[/C][C] 5.492[/C][/ROW]
[ROW][C]155[/C][C] 121.1[/C][C] 118.5[/C][C] 2.566[/C][/ROW]
[ROW][C]156[/C][C] 120.8[/C][C] 115.5[/C][C] 5.33[/C][/ROW]
[ROW][C]157[/C][C] 117.8[/C][C] 115[/C][C] 2.777[/C][/ROW]
[ROW][C]158[/C][C] 130.4[/C][C] 120.8[/C][C] 9.595[/C][/ROW]
[ROW][C]159[/C][C] 110.9[/C][C] 117[/C][C]-6.11[/C][/ROW]
[ROW][C]160[/C][C] 105.4[/C][C] 105.1[/C][C] 0.2597[/C][/ROW]
[ROW][C]161[/C][C] 137.6[/C][C] 120.6[/C][C] 16.98[/C][/ROW]
[ROW][C]162[/C][C] 133.3[/C][C] 125.3[/C][C] 8.044[/C][/ROW]
[ROW][C]163[/C][C] 123.3[/C][C] 118[/C][C] 5.343[/C][/ROW]
[ROW][C]164[/C][C] 122.8[/C][C] 121.2[/C][C] 1.591[/C][/ROW]
[ROW][C]165[/C][C] 110.2[/C][C] 114.5[/C][C]-4.329[/C][/ROW]
[ROW][C]166[/C][C] 101.4[/C][C] 112.7[/C][C]-11.3[/C][/ROW]
[ROW][C]167[/C][C] 128.7[/C][C] 121.9[/C][C] 6.78[/C][/ROW]
[ROW][C]168[/C][C] 120.6[/C][C] 119.1[/C][C] 1.509[/C][/ROW]
[ROW][C]169[/C][C] 110.1[/C][C] 113.3[/C][C]-3.154[/C][/ROW]
[ROW][C]170[/C][C] 121.6[/C][C] 126.7[/C][C]-5.134[/C][/ROW]
[ROW][C]171[/C][C] 113[/C][C] 111.9[/C][C] 1.088[/C][/ROW]
[ROW][C]172[/C][C] 115.9[/C][C] 105.3[/C][C] 10.63[/C][/ROW]
[ROW][C]173[/C][C] 131.1[/C][C] 129.4[/C][C] 1.698[/C][/ROW]
[ROW][C]174[/C][C] 127.4[/C][C] 128.4[/C][C]-0.9687[/C][/ROW]
[ROW][C]175[/C][C] 123.9[/C][C] 123.7[/C][C] 0.2135[/C][/ROW]
[ROW][C]176[/C][C] 120.8[/C][C] 123.1[/C][C]-2.33[/C][/ROW]
[ROW][C]177[/C][C] 108.5[/C][C] 117[/C][C]-8.456[/C][/ROW]
[ROW][C]178[/C][C] 112.9[/C][C] 113.3[/C][C]-0.3719[/C][/ROW]
[ROW][C]179[/C][C] 129.6[/C][C] 127.9[/C][C] 1.657[/C][/ROW]
[ROW][C]180[/C][C] 121.3[/C][C] 122.2[/C][C]-0.8603[/C][/ROW]
[ROW][C]181[/C][C] 119.1[/C][C] 115.9[/C][C] 3.165[/C][/ROW]
[ROW][C]182[/C][C] 140.8[/C][C] 128.1[/C][C] 12.73[/C][/ROW]
[ROW][C]183[/C][C] 127.4[/C][C] 117.6[/C][C] 9.793[/C][/ROW]
[ROW][C]184[/C][C] 128.1[/C][C] 118.9[/C][C] 9.235[/C][/ROW]
[ROW][C]185[/C][C] 136.6[/C][C] 133.6[/C][C] 2.971[/C][/ROW]
[ROW][C]186[/C][C] 126.5[/C][C] 128.6[/C][C]-2.125[/C][/ROW]
[ROW][C]187[/C][C] 120.8[/C][C] 126.9[/C][C]-6.146[/C][/ROW]
[ROW][C]188[/C][C] 144.3[/C][C] 128.1[/C][C] 16.18[/C][/ROW]
[ROW][C]189[/C][C] 116[/C][C] 121.6[/C][C]-5.581[/C][/ROW]
[ROW][C]190[/C][C] 123.4[/C][C] 120.1[/C][C] 3.346[/C][/ROW]
[ROW][C]191[/C][C] 138.6[/C][C] 138[/C][C] 0.6013[/C][/ROW]
[ROW][C]192[/C][C] 118.3[/C][C] 122[/C][C]-3.688[/C][/ROW]
[ROW][C]193[/C][C] 124.2[/C][C] 124.6[/C][C]-0.3686[/C][/ROW]
[ROW][C]194[/C][C] 136[/C][C] 138.3[/C][C]-2.332[/C][/ROW]
[ROW][C]195[/C][C] 127.4[/C][C] 123.4[/C][C] 4.037[/C][/ROW]
[ROW][C]196[/C][C] 131.6[/C][C] 126.2[/C][C] 5.387[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310056&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310056&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	66.5	64.6	1.901
2	71.9	67.35	4.549
3	58.1	56.23	1.872
4	61.5	58.25	3.25
5	66.2	65.8	0.3964
6	72.3	65.57	6.732
7	67	66.64	0.3563
8	62.9	61.16	1.741
9	66.4	64.05	2.349
10	65.6	63.48	2.124
11	70.9	68.41	2.488
12	68.4	67.18	1.219
13	66.4	67.15	-0.7483
14	67.6	70.27	-2.671
15	64.1	61.25	2.847
16	62.1	60.65	1.451
17	70	67.19	2.807
18	74.4	72.54	1.861
19	67	67.75	-0.7508
20	64.8	64.4	0.3989
21	70.7	68.11	2.592
22	64	66.07	-2.067
23	72.5	70.11	2.391
24	70.4	70.43	-0.03379
25	63.6	66.04	-2.443
26	69.8	69.57	0.2305
27	67.7	65.04	2.659
28	66.4	60.58	5.816
29	78.9	72.25	6.651
30	79.9	76.13	3.769
31	69.1	69.43	-0.3313
32	81.2	71.86	9.336
33	66	72.19	-6.189
34	71.8	67.29	4.508
35	86.1	79.42	6.675
36	76.1	74.55	1.549
37	70.5	69.99	0.5123
38	83.3	79.43	3.868
39	74.8	71.45	3.353
40	73.4	69.37	4.028
41	86.5	82.7	3.802
42	82	82.68	-0.6809
43	80.8	77.69	3.107
44	91.5	85.62	5.877
45	77	77.44	-0.4412
46	72.3	76.59	-4.292
47	83.5	86.94	-3.444
48	79	80.07	-1.066
49	76.7	75.12	1.582
50	83.1	86.11	-3.011
51	71.1	73.5	-2.399
52	75.5	72.75	2.75
53	90.9	86.29	4.606
54	85.4	83.08	2.319
55	84.8	85.04	-0.2395
56	83.8	91.47	-7.671
57	79.3	82.53	-3.228
58	79.9	80.23	-0.3253
59	93	90.32	2.682
60	78.1	82.61	-4.506
61	82.3	83.87	-1.573
62	87.3	90.64	-3.342
63	74.6	75.38	-0.7785
64	80	77.95	2.052
65	91.3	91.02	0.2806
66	94.2	91.07	3.125
67	90.9	91.59	-0.6876
68	88	90.64	-2.644
69	81.6	89.14	-7.542
70	77.4	86.27	-8.873
71	91	95.64	-4.643
72	79.9	86.01	-6.108
73	83.4	87.43	-4.028
74	91.6	95.44	-3.837
75	85.2	83.23	1.966
76	84.1	85.78	-1.678
77	87	94.93	-7.932
78	92.8	100.6	-7.842
79	89.2	96.49	-7.294
80	87.3	92.68	-5.377
81	89.5	93.98	-4.481
82	86.8	91.92	-5.123
83	92	96.96	-4.962
84	92.2	95.8	-3.6
85	86.4	93.06	-6.659
86	92.9	99.6	-6.7
87	91.2	93.4	-2.196
88	80.3	87.58	-7.282
89	102	98.47	3.525
90	99	102.6	-3.623
91	89.2	92.38	-3.177
92	103	95.76	7.244
93	80.4	91.58	-11.18
94	83.4	86.08	-2.676
95	97.6	94.93	2.67
96	87	88.64	-1.639
97	84.4	88.15	-3.748
98	94.1	94.29	-0.1866
99	88.9	87.6	1.301
100	82.3	83.01	-0.7111
101	94.7	98.31	-3.606
102	94.5	97.53	-3.029
103	91.6	91.88	-0.2766
104	96.8	98.57	-1.773
105	87.9	89.48	-1.582
106	99.9	91.64	8.257
107	109.5	106	3.503
108	91.2	96.39	-5.191
109	89.4	94.57	-5.167
110	109.7	105.5	4.232
111	96.9	94.48	2.416
112	94.1	91.44	2.662
113	104.4	106.1	-1.651
114	100.8	103.9	-3.078
115	107.4	102.6	4.771
116	108.9	106.5	2.437
117	95.2	100.5	-5.256
118	102.7	105.3	-2.585
119	130.9	119.5	11.42
120	104	105.6	-1.567
121	106.5	107.6	-1.076
122	106.1	116.3	-10.22
123	97.8	98.91	-1.106
124	112.2	102.1	10.06
125	114.5	113.8	0.7262
126	105.8	108.5	-2.745
127	101	111.4	-10.44
128	101.2	109.9	-8.687
129	96.5	102.8	-6.305
130	99.5	105.9	-6.408
131	123.8	122.7	1.069
132	94.6	106.9	-12.27
133	95.8	106.2	-10.36
134	105.4	112.3	-6.912
135	104.4	99.55	4.851
136	105.2	105.9	-0.6802
137	112.7	112.9	-0.1778
138	114.8	112.2	2.642
139	108.9	108.7	0.1944
140	103.8	105.2	-1.403
141	102.5	104.6	-2.114
142	98.1	103.1	-4.966
143	118.2	116.1	2.103
144	114.8	106.7	8.14
145	109.9	105.9	3.977
146	116.7	114.5	2.182
147	116.9	111.1	5.831
148	104.4	106	-1.597
149	113.5	114.5	-1.039
150	123.8	120.1	3.662
151	116.4	114.1	2.27
152	114.1	111.5	2.629
153	102.8	111.8	-9.026
154	112.7	107.2	5.492
155	121.1	118.5	2.566
156	120.8	115.5	5.33
157	117.8	115	2.777
158	130.4	120.8	9.595
159	110.9	117	-6.11
160	105.4	105.1	0.2597
161	137.6	120.6	16.98
162	133.3	125.3	8.044
163	123.3	118	5.343
164	122.8	121.2	1.591
165	110.2	114.5	-4.329
166	101.4	112.7	-11.3
167	128.7	121.9	6.78
168	120.6	119.1	1.509
169	110.1	113.3	-3.154
170	121.6	126.7	-5.134
171	113	111.9	1.088
172	115.9	105.3	10.63
173	131.1	129.4	1.698
174	127.4	128.4	-0.9687
175	123.9	123.7	0.2135
176	120.8	123.1	-2.33
177	108.5	117	-8.456
178	112.9	113.3	-0.3719
179	129.6	127.9	1.657
180	121.3	122.2	-0.8603
181	119.1	115.9	3.165
182	140.8	128.1	12.73
183	127.4	117.6	9.793
184	128.1	118.9	9.235
185	136.6	133.6	2.971
186	126.5	128.6	-2.125
187	120.8	126.9	-6.146
188	144.3	128.1	16.18
189	116	121.6	-5.581
190	123.4	120.1	3.346
191	138.6	138	0.6013
192	118.3	122	-3.688
193	124.2	124.6	-0.3686
194	136	138.3	-2.332
195	127.4	123.4	4.037
196	131.6	126.2	5.387

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.02011	0.04022	0.9799
11	0.00725	0.0145	0.9928
12	0.002125	0.004249	0.9979
13	0.002527	0.005055	0.9975
14	0.00112	0.002241	0.9989
15	0.0003588	0.0007177	0.9996
16	9.744e-05	0.0001949	0.9999
17	2.675e-05	5.349e-05	1
18	6.589e-06	1.318e-05	1
19	1.625e-06	3.249e-06	1
20	5.968e-07	1.194e-06	1
21	2.419e-07	4.839e-07	1
22	8.419e-07	1.684e-06	1
23	2.497e-07	4.995e-07	1
24	6.927e-08	1.385e-07	1
25	6.206e-08	1.241e-07	1
26	1.849e-08	3.698e-08	1
27	1.644e-08	3.287e-08	1
28	3.383e-07	6.766e-07	1
29	4.345e-06	8.69e-06	1
30	5.046e-06	1.009e-05	1
31	2.017e-06	4.034e-06	1
32	6.669e-06	1.334e-05	1
33	2.054e-05	4.108e-05	1
34	1.206e-05	2.412e-05	1
35	8.219e-06	1.644e-05	1
36	3.831e-06	7.662e-06	1
37	2.592e-06	5.183e-06	1
38	1.889e-06	3.778e-06	1
39	1.666e-06	3.333e-06	1
40	1.094e-06	2.189e-06	1
41	6.143e-07	1.229e-06	1
42	3.744e-07	7.489e-07	1
43	2.084e-07	4.167e-07	1
44	3.834e-07	7.669e-07	1
45	3.75e-07	7.499e-07	1
46	2.093e-06	4.187e-06	1
47	2.483e-06	4.967e-06	1
48	1.499e-06	2.998e-06	1
49	8.077e-07	1.615e-06	1
50	7.759e-07	1.552e-06	1
51	4.578e-07	9.156e-07	1
52	5.09e-07	1.018e-06	1
53	8.934e-07	1.787e-06	1
54	8.108e-07	1.622e-06	1
55	4.478e-07	8.956e-07	1
56	6.471e-07	1.294e-06	1
57	5.869e-07	1.174e-06	1
58	3.87e-07	7.74e-07	1
59	2.209e-07	4.417e-07	1
60	1.379e-07	2.759e-07	1
61	1.271e-07	2.542e-07	1
62	1.181e-07	2.362e-07	1
63	6.544e-08	1.309e-07	1
64	6.153e-08	1.231e-07	1
65	3.742e-08	7.484e-08	1
66	2.422e-08	4.845e-08	1
67	1.224e-08	2.449e-08	1
68	6.004e-09	1.201e-08	1
69	2.381e-08	4.762e-08	1
70	1.327e-07	2.654e-07	1
71	1.432e-07	2.864e-07	1
72	2.25e-07	4.499e-07	1
73	2.302e-07	4.604e-07	1
74	1.867e-07	3.734e-07	1
75	1.617e-07	3.234e-07	1
76	8.875e-08	1.775e-07	1
77	1.211e-07	2.423e-07	1
78	3.153e-07	6.306e-07	1
79	3.725e-07	7.45e-07	1
80	2.351e-07	4.702e-07	1
81	1.719e-07	3.438e-07	1
82	1.613e-07	3.225e-07	1
83	1.086e-07	2.172e-07	1
84	7.571e-08	1.514e-07	1
85	7.376e-08	1.475e-07	1
86	7.477e-08	1.495e-07	1
87	4.794e-08	9.589e-08	1
88	3.596e-08	7.192e-08	1
89	5.423e-08	1.085e-07	1
90	3.727e-08	7.453e-08	1
91	2.544e-08	5.088e-08	1
92	7.842e-07	1.568e-06	1
93	8.391e-07	1.678e-06	1
94	5.95e-07	1.19e-06	1
95	1.255e-06	2.509e-06	1
96	1.263e-06	2.527e-06	1
97	7.567e-07	1.513e-06	1
98	5.848e-07	1.17e-06	1
99	1.028e-06	2.055e-06	1
100	7.405e-07	1.481e-06	1
101	4.321e-07	8.642e-07	1
102	2.638e-07	5.276e-07	1
103	1.958e-07	3.916e-07	1
104	1.295e-07	2.59e-07	1
105	8.211e-08	1.642e-07	1
106	1.258e-06	2.517e-06	1
107	2.171e-06	4.342e-06	1
108	1.54e-06	3.08e-06	1
109	1.065e-06	2.129e-06	1
110	1.743e-06	3.487e-06	1
111	2.932e-06	5.864e-06	1
112	3.584e-06	7.168e-06	1
113	2.214e-06	4.429e-06	1
114	1.584e-06	3.168e-06	1
115	2.726e-06	5.453e-06	1
116	3.011e-06	6.022e-06	1
117	2.353e-06	4.706e-06	1
118	1.466e-06	2.932e-06	1
119	2.322e-05	4.645e-05	1
120	1.746e-05	3.492e-05	1
121	1.424e-05	2.847e-05	1
122	2.239e-05	4.478e-05	1
123	1.661e-05	3.321e-05	1
124	0.0001466	0.0002932	0.9999
125	0.0001141	0.0002282	0.9999
126	9.058e-05	0.0001812	0.9999
127	0.0001821	0.0003642	0.9998
128	0.0002105	0.0004211	0.9998
129	0.0002195	0.0004391	0.9998
130	0.0002505	0.000501	0.9998
131	0.0002372	0.0004745	0.9998
132	0.0009634	0.001927	0.999
133	0.002669	0.005338	0.9973
134	0.00343	0.006861	0.9966
135	0.004061	0.008121	0.9959
136	0.003491	0.006983	0.9965
137	0.003101	0.006202	0.9969
138	0.002958	0.005917	0.997
139	0.002646	0.005291	0.9974
140	0.002064	0.004128	0.9979
141	0.001628	0.003257	0.9984
142	0.001627	0.003254	0.9984
143	0.001385	0.00277	0.9986
144	0.002027	0.004054	0.998
145	0.001876	0.003753	0.9981
146	0.001697	0.003395	0.9983
147	0.002033	0.004065	0.998
148	0.001459	0.002917	0.9985
149	0.001197	0.002393	0.9988
150	0.001013	0.002025	0.999
151	0.0008046	0.001609	0.9992
152	0.0006458	0.001292	0.9994
153	0.002379	0.004757	0.9976
154	0.002054	0.004107	0.9979
155	0.001621	0.003243	0.9984
156	0.001421	0.002842	0.9986
157	0.001219	0.002439	0.9988
158	0.001988	0.003976	0.998
159	0.002065	0.004131	0.9979
160	0.001396	0.002791	0.9986
161	0.01615	0.0323	0.9839
162	0.01893	0.03787	0.9811
163	0.01586	0.03172	0.9841
164	0.01166	0.02333	0.9883
165	0.0101	0.0202	0.9899
166	0.03342	0.06684	0.9666
167	0.03285	0.06571	0.9671
168	0.02375	0.0475	0.9763
169	0.02228	0.04457	0.9777
170	0.03948	0.07895	0.9605
171	0.02939	0.05877	0.9706
172	0.03877	0.07755	0.9612
173	0.02713	0.05426	0.9729
174	0.01859	0.03718	0.9814
175	0.01412	0.02825	0.9859
176	0.01902	0.03805	0.981
177	0.08958	0.1792	0.9104
178	0.3993	0.7987	0.6007
179	0.5513	0.8975	0.4487
180	0.5198	0.9604	0.4802
181	0.6353	0.7294	0.3647
182	0.6797	0.6406	0.3203
183	0.6609	0.6782	0.3391
184	0.5837	0.8325	0.4163
185	0.4742	0.9484	0.5258
186	0.7428	0.5144	0.2572

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.02011 &  0.04022 &  0.9799 \tabularnewline
11 &  0.00725 &  0.0145 &  0.9928 \tabularnewline
12 &  0.002125 &  0.004249 &  0.9979 \tabularnewline
13 &  0.002527 &  0.005055 &  0.9975 \tabularnewline
14 &  0.00112 &  0.002241 &  0.9989 \tabularnewline
15 &  0.0003588 &  0.0007177 &  0.9996 \tabularnewline
16 &  9.744e-05 &  0.0001949 &  0.9999 \tabularnewline
17 &  2.675e-05 &  5.349e-05 &  1 \tabularnewline
18 &  6.589e-06 &  1.318e-05 &  1 \tabularnewline
19 &  1.625e-06 &  3.249e-06 &  1 \tabularnewline
20 &  5.968e-07 &  1.194e-06 &  1 \tabularnewline
21 &  2.419e-07 &  4.839e-07 &  1 \tabularnewline
22 &  8.419e-07 &  1.684e-06 &  1 \tabularnewline
23 &  2.497e-07 &  4.995e-07 &  1 \tabularnewline
24 &  6.927e-08 &  1.385e-07 &  1 \tabularnewline
25 &  6.206e-08 &  1.241e-07 &  1 \tabularnewline
26 &  1.849e-08 &  3.698e-08 &  1 \tabularnewline
27 &  1.644e-08 &  3.287e-08 &  1 \tabularnewline
28 &  3.383e-07 &  6.766e-07 &  1 \tabularnewline
29 &  4.345e-06 &  8.69e-06 &  1 \tabularnewline
30 &  5.046e-06 &  1.009e-05 &  1 \tabularnewline
31 &  2.017e-06 &  4.034e-06 &  1 \tabularnewline
32 &  6.669e-06 &  1.334e-05 &  1 \tabularnewline
33 &  2.054e-05 &  4.108e-05 &  1 \tabularnewline
34 &  1.206e-05 &  2.412e-05 &  1 \tabularnewline
35 &  8.219e-06 &  1.644e-05 &  1 \tabularnewline
36 &  3.831e-06 &  7.662e-06 &  1 \tabularnewline
37 &  2.592e-06 &  5.183e-06 &  1 \tabularnewline
38 &  1.889e-06 &  3.778e-06 &  1 \tabularnewline
39 &  1.666e-06 &  3.333e-06 &  1 \tabularnewline
40 &  1.094e-06 &  2.189e-06 &  1 \tabularnewline
41 &  6.143e-07 &  1.229e-06 &  1 \tabularnewline
42 &  3.744e-07 &  7.489e-07 &  1 \tabularnewline
43 &  2.084e-07 &  4.167e-07 &  1 \tabularnewline
44 &  3.834e-07 &  7.669e-07 &  1 \tabularnewline
45 &  3.75e-07 &  7.499e-07 &  1 \tabularnewline
46 &  2.093e-06 &  4.187e-06 &  1 \tabularnewline
47 &  2.483e-06 &  4.967e-06 &  1 \tabularnewline
48 &  1.499e-06 &  2.998e-06 &  1 \tabularnewline
49 &  8.077e-07 &  1.615e-06 &  1 \tabularnewline
50 &  7.759e-07 &  1.552e-06 &  1 \tabularnewline
51 &  4.578e-07 &  9.156e-07 &  1 \tabularnewline
52 &  5.09e-07 &  1.018e-06 &  1 \tabularnewline
53 &  8.934e-07 &  1.787e-06 &  1 \tabularnewline
54 &  8.108e-07 &  1.622e-06 &  1 \tabularnewline
55 &  4.478e-07 &  8.956e-07 &  1 \tabularnewline
56 &  6.471e-07 &  1.294e-06 &  1 \tabularnewline
57 &  5.869e-07 &  1.174e-06 &  1 \tabularnewline
58 &  3.87e-07 &  7.74e-07 &  1 \tabularnewline
59 &  2.209e-07 &  4.417e-07 &  1 \tabularnewline
60 &  1.379e-07 &  2.759e-07 &  1 \tabularnewline
61 &  1.271e-07 &  2.542e-07 &  1 \tabularnewline
62 &  1.181e-07 &  2.362e-07 &  1 \tabularnewline
63 &  6.544e-08 &  1.309e-07 &  1 \tabularnewline
64 &  6.153e-08 &  1.231e-07 &  1 \tabularnewline
65 &  3.742e-08 &  7.484e-08 &  1 \tabularnewline
66 &  2.422e-08 &  4.845e-08 &  1 \tabularnewline
67 &  1.224e-08 &  2.449e-08 &  1 \tabularnewline
68 &  6.004e-09 &  1.201e-08 &  1 \tabularnewline
69 &  2.381e-08 &  4.762e-08 &  1 \tabularnewline
70 &  1.327e-07 &  2.654e-07 &  1 \tabularnewline
71 &  1.432e-07 &  2.864e-07 &  1 \tabularnewline
72 &  2.25e-07 &  4.499e-07 &  1 \tabularnewline
73 &  2.302e-07 &  4.604e-07 &  1 \tabularnewline
74 &  1.867e-07 &  3.734e-07 &  1 \tabularnewline
75 &  1.617e-07 &  3.234e-07 &  1 \tabularnewline
76 &  8.875e-08 &  1.775e-07 &  1 \tabularnewline
77 &  1.211e-07 &  2.423e-07 &  1 \tabularnewline
78 &  3.153e-07 &  6.306e-07 &  1 \tabularnewline
79 &  3.725e-07 &  7.45e-07 &  1 \tabularnewline
80 &  2.351e-07 &  4.702e-07 &  1 \tabularnewline
81 &  1.719e-07 &  3.438e-07 &  1 \tabularnewline
82 &  1.613e-07 &  3.225e-07 &  1 \tabularnewline
83 &  1.086e-07 &  2.172e-07 &  1 \tabularnewline
84 &  7.571e-08 &  1.514e-07 &  1 \tabularnewline
85 &  7.376e-08 &  1.475e-07 &  1 \tabularnewline
86 &  7.477e-08 &  1.495e-07 &  1 \tabularnewline
87 &  4.794e-08 &  9.589e-08 &  1 \tabularnewline
88 &  3.596e-08 &  7.192e-08 &  1 \tabularnewline
89 &  5.423e-08 &  1.085e-07 &  1 \tabularnewline
90 &  3.727e-08 &  7.453e-08 &  1 \tabularnewline
91 &  2.544e-08 &  5.088e-08 &  1 \tabularnewline
92 &  7.842e-07 &  1.568e-06 &  1 \tabularnewline
93 &  8.391e-07 &  1.678e-06 &  1 \tabularnewline
94 &  5.95e-07 &  1.19e-06 &  1 \tabularnewline
95 &  1.255e-06 &  2.509e-06 &  1 \tabularnewline
96 &  1.263e-06 &  2.527e-06 &  1 \tabularnewline
97 &  7.567e-07 &  1.513e-06 &  1 \tabularnewline
98 &  5.848e-07 &  1.17e-06 &  1 \tabularnewline
99 &  1.028e-06 &  2.055e-06 &  1 \tabularnewline
100 &  7.405e-07 &  1.481e-06 &  1 \tabularnewline
101 &  4.321e-07 &  8.642e-07 &  1 \tabularnewline
102 &  2.638e-07 &  5.276e-07 &  1 \tabularnewline
103 &  1.958e-07 &  3.916e-07 &  1 \tabularnewline
104 &  1.295e-07 &  2.59e-07 &  1 \tabularnewline
105 &  8.211e-08 &  1.642e-07 &  1 \tabularnewline
106 &  1.258e-06 &  2.517e-06 &  1 \tabularnewline
107 &  2.171e-06 &  4.342e-06 &  1 \tabularnewline
108 &  1.54e-06 &  3.08e-06 &  1 \tabularnewline
109 &  1.065e-06 &  2.129e-06 &  1 \tabularnewline
110 &  1.743e-06 &  3.487e-06 &  1 \tabularnewline
111 &  2.932e-06 &  5.864e-06 &  1 \tabularnewline
112 &  3.584e-06 &  7.168e-06 &  1 \tabularnewline
113 &  2.214e-06 &  4.429e-06 &  1 \tabularnewline
114 &  1.584e-06 &  3.168e-06 &  1 \tabularnewline
115 &  2.726e-06 &  5.453e-06 &  1 \tabularnewline
116 &  3.011e-06 &  6.022e-06 &  1 \tabularnewline
117 &  2.353e-06 &  4.706e-06 &  1 \tabularnewline
118 &  1.466e-06 &  2.932e-06 &  1 \tabularnewline
119 &  2.322e-05 &  4.645e-05 &  1 \tabularnewline
120 &  1.746e-05 &  3.492e-05 &  1 \tabularnewline
121 &  1.424e-05 &  2.847e-05 &  1 \tabularnewline
122 &  2.239e-05 &  4.478e-05 &  1 \tabularnewline
123 &  1.661e-05 &  3.321e-05 &  1 \tabularnewline
124 &  0.0001466 &  0.0002932 &  0.9999 \tabularnewline
125 &  0.0001141 &  0.0002282 &  0.9999 \tabularnewline
126 &  9.058e-05 &  0.0001812 &  0.9999 \tabularnewline
127 &  0.0001821 &  0.0003642 &  0.9998 \tabularnewline
128 &  0.0002105 &  0.0004211 &  0.9998 \tabularnewline
129 &  0.0002195 &  0.0004391 &  0.9998 \tabularnewline
130 &  0.0002505 &  0.000501 &  0.9998 \tabularnewline
131 &  0.0002372 &  0.0004745 &  0.9998 \tabularnewline
132 &  0.0009634 &  0.001927 &  0.999 \tabularnewline
133 &  0.002669 &  0.005338 &  0.9973 \tabularnewline
134 &  0.00343 &  0.006861 &  0.9966 \tabularnewline
135 &  0.004061 &  0.008121 &  0.9959 \tabularnewline
136 &  0.003491 &  0.006983 &  0.9965 \tabularnewline
137 &  0.003101 &  0.006202 &  0.9969 \tabularnewline
138 &  0.002958 &  0.005917 &  0.997 \tabularnewline
139 &  0.002646 &  0.005291 &  0.9974 \tabularnewline
140 &  0.002064 &  0.004128 &  0.9979 \tabularnewline
141 &  0.001628 &  0.003257 &  0.9984 \tabularnewline
142 &  0.001627 &  0.003254 &  0.9984 \tabularnewline
143 &  0.001385 &  0.00277 &  0.9986 \tabularnewline
144 &  0.002027 &  0.004054 &  0.998 \tabularnewline
145 &  0.001876 &  0.003753 &  0.9981 \tabularnewline
146 &  0.001697 &  0.003395 &  0.9983 \tabularnewline
147 &  0.002033 &  0.004065 &  0.998 \tabularnewline
148 &  0.001459 &  0.002917 &  0.9985 \tabularnewline
149 &  0.001197 &  0.002393 &  0.9988 \tabularnewline
150 &  0.001013 &  0.002025 &  0.999 \tabularnewline
151 &  0.0008046 &  0.001609 &  0.9992 \tabularnewline
152 &  0.0006458 &  0.001292 &  0.9994 \tabularnewline
153 &  0.002379 &  0.004757 &  0.9976 \tabularnewline
154 &  0.002054 &  0.004107 &  0.9979 \tabularnewline
155 &  0.001621 &  0.003243 &  0.9984 \tabularnewline
156 &  0.001421 &  0.002842 &  0.9986 \tabularnewline
157 &  0.001219 &  0.002439 &  0.9988 \tabularnewline
158 &  0.001988 &  0.003976 &  0.998 \tabularnewline
159 &  0.002065 &  0.004131 &  0.9979 \tabularnewline
160 &  0.001396 &  0.002791 &  0.9986 \tabularnewline
161 &  0.01615 &  0.0323 &  0.9839 \tabularnewline
162 &  0.01893 &  0.03787 &  0.9811 \tabularnewline
163 &  0.01586 &  0.03172 &  0.9841 \tabularnewline
164 &  0.01166 &  0.02333 &  0.9883 \tabularnewline
165 &  0.0101 &  0.0202 &  0.9899 \tabularnewline
166 &  0.03342 &  0.06684 &  0.9666 \tabularnewline
167 &  0.03285 &  0.06571 &  0.9671 \tabularnewline
168 &  0.02375 &  0.0475 &  0.9763 \tabularnewline
169 &  0.02228 &  0.04457 &  0.9777 \tabularnewline
170 &  0.03948 &  0.07895 &  0.9605 \tabularnewline
171 &  0.02939 &  0.05877 &  0.9706 \tabularnewline
172 &  0.03877 &  0.07755 &  0.9612 \tabularnewline
173 &  0.02713 &  0.05426 &  0.9729 \tabularnewline
174 &  0.01859 &  0.03718 &  0.9814 \tabularnewline
175 &  0.01412 &  0.02825 &  0.9859 \tabularnewline
176 &  0.01902 &  0.03805 &  0.981 \tabularnewline
177 &  0.08958 &  0.1792 &  0.9104 \tabularnewline
178 &  0.3993 &  0.7987 &  0.6007 \tabularnewline
179 &  0.5513 &  0.8975 &  0.4487 \tabularnewline
180 &  0.5198 &  0.9604 &  0.4802 \tabularnewline
181 &  0.6353 &  0.7294 &  0.3647 \tabularnewline
182 &  0.6797 &  0.6406 &  0.3203 \tabularnewline
183 &  0.6609 &  0.6782 &  0.3391 \tabularnewline
184 &  0.5837 &  0.8325 &  0.4163 \tabularnewline
185 &  0.4742 &  0.9484 &  0.5258 \tabularnewline
186 &  0.7428 &  0.5144 &  0.2572 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310056&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] 0.02011[/C][C] 0.04022[/C][C] 0.9799[/C][/ROW]
[ROW][C]11[/C][C] 0.00725[/C][C] 0.0145[/C][C] 0.9928[/C][/ROW]
[ROW][C]12[/C][C] 0.002125[/C][C] 0.004249[/C][C] 0.9979[/C][/ROW]
[ROW][C]13[/C][C] 0.002527[/C][C] 0.005055[/C][C] 0.9975[/C][/ROW]
[ROW][C]14[/C][C] 0.00112[/C][C] 0.002241[/C][C] 0.9989[/C][/ROW]
[ROW][C]15[/C][C] 0.0003588[/C][C] 0.0007177[/C][C] 0.9996[/C][/ROW]
[ROW][C]16[/C][C] 9.744e-05[/C][C] 0.0001949[/C][C] 0.9999[/C][/ROW]
[ROW][C]17[/C][C] 2.675e-05[/C][C] 5.349e-05[/C][C] 1[/C][/ROW]
[ROW][C]18[/C][C] 6.589e-06[/C][C] 1.318e-05[/C][C] 1[/C][/ROW]
[ROW][C]19[/C][C] 1.625e-06[/C][C] 3.249e-06[/C][C] 1[/C][/ROW]
[ROW][C]20[/C][C] 5.968e-07[/C][C] 1.194e-06[/C][C] 1[/C][/ROW]
[ROW][C]21[/C][C] 2.419e-07[/C][C] 4.839e-07[/C][C] 1[/C][/ROW]
[ROW][C]22[/C][C] 8.419e-07[/C][C] 1.684e-06[/C][C] 1[/C][/ROW]
[ROW][C]23[/C][C] 2.497e-07[/C][C] 4.995e-07[/C][C] 1[/C][/ROW]
[ROW][C]24[/C][C] 6.927e-08[/C][C] 1.385e-07[/C][C] 1[/C][/ROW]
[ROW][C]25[/C][C] 6.206e-08[/C][C] 1.241e-07[/C][C] 1[/C][/ROW]
[ROW][C]26[/C][C] 1.849e-08[/C][C] 3.698e-08[/C][C] 1[/C][/ROW]
[ROW][C]27[/C][C] 1.644e-08[/C][C] 3.287e-08[/C][C] 1[/C][/ROW]
[ROW][C]28[/C][C] 3.383e-07[/C][C] 6.766e-07[/C][C] 1[/C][/ROW]
[ROW][C]29[/C][C] 4.345e-06[/C][C] 8.69e-06[/C][C] 1[/C][/ROW]
[ROW][C]30[/C][C] 5.046e-06[/C][C] 1.009e-05[/C][C] 1[/C][/ROW]
[ROW][C]31[/C][C] 2.017e-06[/C][C] 4.034e-06[/C][C] 1[/C][/ROW]
[ROW][C]32[/C][C] 6.669e-06[/C][C] 1.334e-05[/C][C] 1[/C][/ROW]
[ROW][C]33[/C][C] 2.054e-05[/C][C] 4.108e-05[/C][C] 1[/C][/ROW]
[ROW][C]34[/C][C] 1.206e-05[/C][C] 2.412e-05[/C][C] 1[/C][/ROW]
[ROW][C]35[/C][C] 8.219e-06[/C][C] 1.644e-05[/C][C] 1[/C][/ROW]
[ROW][C]36[/C][C] 3.831e-06[/C][C] 7.662e-06[/C][C] 1[/C][/ROW]
[ROW][C]37[/C][C] 2.592e-06[/C][C] 5.183e-06[/C][C] 1[/C][/ROW]
[ROW][C]38[/C][C] 1.889e-06[/C][C] 3.778e-06[/C][C] 1[/C][/ROW]
[ROW][C]39[/C][C] 1.666e-06[/C][C] 3.333e-06[/C][C] 1[/C][/ROW]
[ROW][C]40[/C][C] 1.094e-06[/C][C] 2.189e-06[/C][C] 1[/C][/ROW]
[ROW][C]41[/C][C] 6.143e-07[/C][C] 1.229e-06[/C][C] 1[/C][/ROW]
[ROW][C]42[/C][C] 3.744e-07[/C][C] 7.489e-07[/C][C] 1[/C][/ROW]
[ROW][C]43[/C][C] 2.084e-07[/C][C] 4.167e-07[/C][C] 1[/C][/ROW]
[ROW][C]44[/C][C] 3.834e-07[/C][C] 7.669e-07[/C][C] 1[/C][/ROW]
[ROW][C]45[/C][C] 3.75e-07[/C][C] 7.499e-07[/C][C] 1[/C][/ROW]
[ROW][C]46[/C][C] 2.093e-06[/C][C] 4.187e-06[/C][C] 1[/C][/ROW]
[ROW][C]47[/C][C] 2.483e-06[/C][C] 4.967e-06[/C][C] 1[/C][/ROW]
[ROW][C]48[/C][C] 1.499e-06[/C][C] 2.998e-06[/C][C] 1[/C][/ROW]
[ROW][C]49[/C][C] 8.077e-07[/C][C] 1.615e-06[/C][C] 1[/C][/ROW]
[ROW][C]50[/C][C] 7.759e-07[/C][C] 1.552e-06[/C][C] 1[/C][/ROW]
[ROW][C]51[/C][C] 4.578e-07[/C][C] 9.156e-07[/C][C] 1[/C][/ROW]
[ROW][C]52[/C][C] 5.09e-07[/C][C] 1.018e-06[/C][C] 1[/C][/ROW]
[ROW][C]53[/C][C] 8.934e-07[/C][C] 1.787e-06[/C][C] 1[/C][/ROW]
[ROW][C]54[/C][C] 8.108e-07[/C][C] 1.622e-06[/C][C] 1[/C][/ROW]
[ROW][C]55[/C][C] 4.478e-07[/C][C] 8.956e-07[/C][C] 1[/C][/ROW]
[ROW][C]56[/C][C] 6.471e-07[/C][C] 1.294e-06[/C][C] 1[/C][/ROW]
[ROW][C]57[/C][C] 5.869e-07[/C][C] 1.174e-06[/C][C] 1[/C][/ROW]
[ROW][C]58[/C][C] 3.87e-07[/C][C] 7.74e-07[/C][C] 1[/C][/ROW]
[ROW][C]59[/C][C] 2.209e-07[/C][C] 4.417e-07[/C][C] 1[/C][/ROW]
[ROW][C]60[/C][C] 1.379e-07[/C][C] 2.759e-07[/C][C] 1[/C][/ROW]
[ROW][C]61[/C][C] 1.271e-07[/C][C] 2.542e-07[/C][C] 1[/C][/ROW]
[ROW][C]62[/C][C] 1.181e-07[/C][C] 2.362e-07[/C][C] 1[/C][/ROW]
[ROW][C]63[/C][C] 6.544e-08[/C][C] 1.309e-07[/C][C] 1[/C][/ROW]
[ROW][C]64[/C][C] 6.153e-08[/C][C] 1.231e-07[/C][C] 1[/C][/ROW]
[ROW][C]65[/C][C] 3.742e-08[/C][C] 7.484e-08[/C][C] 1[/C][/ROW]
[ROW][C]66[/C][C] 2.422e-08[/C][C] 4.845e-08[/C][C] 1[/C][/ROW]
[ROW][C]67[/C][C] 1.224e-08[/C][C] 2.449e-08[/C][C] 1[/C][/ROW]
[ROW][C]68[/C][C] 6.004e-09[/C][C] 1.201e-08[/C][C] 1[/C][/ROW]
[ROW][C]69[/C][C] 2.381e-08[/C][C] 4.762e-08[/C][C] 1[/C][/ROW]
[ROW][C]70[/C][C] 1.327e-07[/C][C] 2.654e-07[/C][C] 1[/C][/ROW]
[ROW][C]71[/C][C] 1.432e-07[/C][C] 2.864e-07[/C][C] 1[/C][/ROW]
[ROW][C]72[/C][C] 2.25e-07[/C][C] 4.499e-07[/C][C] 1[/C][/ROW]
[ROW][C]73[/C][C] 2.302e-07[/C][C] 4.604e-07[/C][C] 1[/C][/ROW]
[ROW][C]74[/C][C] 1.867e-07[/C][C] 3.734e-07[/C][C] 1[/C][/ROW]
[ROW][C]75[/C][C] 1.617e-07[/C][C] 3.234e-07[/C][C] 1[/C][/ROW]
[ROW][C]76[/C][C] 8.875e-08[/C][C] 1.775e-07[/C][C] 1[/C][/ROW]
[ROW][C]77[/C][C] 1.211e-07[/C][C] 2.423e-07[/C][C] 1[/C][/ROW]
[ROW][C]78[/C][C] 3.153e-07[/C][C] 6.306e-07[/C][C] 1[/C][/ROW]
[ROW][C]79[/C][C] 3.725e-07[/C][C] 7.45e-07[/C][C] 1[/C][/ROW]
[ROW][C]80[/C][C] 2.351e-07[/C][C] 4.702e-07[/C][C] 1[/C][/ROW]
[ROW][C]81[/C][C] 1.719e-07[/C][C] 3.438e-07[/C][C] 1[/C][/ROW]
[ROW][C]82[/C][C] 1.613e-07[/C][C] 3.225e-07[/C][C] 1[/C][/ROW]
[ROW][C]83[/C][C] 1.086e-07[/C][C] 2.172e-07[/C][C] 1[/C][/ROW]
[ROW][C]84[/C][C] 7.571e-08[/C][C] 1.514e-07[/C][C] 1[/C][/ROW]
[ROW][C]85[/C][C] 7.376e-08[/C][C] 1.475e-07[/C][C] 1[/C][/ROW]
[ROW][C]86[/C][C] 7.477e-08[/C][C] 1.495e-07[/C][C] 1[/C][/ROW]
[ROW][C]87[/C][C] 4.794e-08[/C][C] 9.589e-08[/C][C] 1[/C][/ROW]
[ROW][C]88[/C][C] 3.596e-08[/C][C] 7.192e-08[/C][C] 1[/C][/ROW]
[ROW][C]89[/C][C] 5.423e-08[/C][C] 1.085e-07[/C][C] 1[/C][/ROW]
[ROW][C]90[/C][C] 3.727e-08[/C][C] 7.453e-08[/C][C] 1[/C][/ROW]
[ROW][C]91[/C][C] 2.544e-08[/C][C] 5.088e-08[/C][C] 1[/C][/ROW]
[ROW][C]92[/C][C] 7.842e-07[/C][C] 1.568e-06[/C][C] 1[/C][/ROW]
[ROW][C]93[/C][C] 8.391e-07[/C][C] 1.678e-06[/C][C] 1[/C][/ROW]
[ROW][C]94[/C][C] 5.95e-07[/C][C] 1.19e-06[/C][C] 1[/C][/ROW]
[ROW][C]95[/C][C] 1.255e-06[/C][C] 2.509e-06[/C][C] 1[/C][/ROW]
[ROW][C]96[/C][C] 1.263e-06[/C][C] 2.527e-06[/C][C] 1[/C][/ROW]
[ROW][C]97[/C][C] 7.567e-07[/C][C] 1.513e-06[/C][C] 1[/C][/ROW]
[ROW][C]98[/C][C] 5.848e-07[/C][C] 1.17e-06[/C][C] 1[/C][/ROW]
[ROW][C]99[/C][C] 1.028e-06[/C][C] 2.055e-06[/C][C] 1[/C][/ROW]
[ROW][C]100[/C][C] 7.405e-07[/C][C] 1.481e-06[/C][C] 1[/C][/ROW]
[ROW][C]101[/C][C] 4.321e-07[/C][C] 8.642e-07[/C][C] 1[/C][/ROW]
[ROW][C]102[/C][C] 2.638e-07[/C][C] 5.276e-07[/C][C] 1[/C][/ROW]
[ROW][C]103[/C][C] 1.958e-07[/C][C] 3.916e-07[/C][C] 1[/C][/ROW]
[ROW][C]104[/C][C] 1.295e-07[/C][C] 2.59e-07[/C][C] 1[/C][/ROW]
[ROW][C]105[/C][C] 8.211e-08[/C][C] 1.642e-07[/C][C] 1[/C][/ROW]
[ROW][C]106[/C][C] 1.258e-06[/C][C] 2.517e-06[/C][C] 1[/C][/ROW]
[ROW][C]107[/C][C] 2.171e-06[/C][C] 4.342e-06[/C][C] 1[/C][/ROW]
[ROW][C]108[/C][C] 1.54e-06[/C][C] 3.08e-06[/C][C] 1[/C][/ROW]
[ROW][C]109[/C][C] 1.065e-06[/C][C] 2.129e-06[/C][C] 1[/C][/ROW]
[ROW][C]110[/C][C] 1.743e-06[/C][C] 3.487e-06[/C][C] 1[/C][/ROW]
[ROW][C]111[/C][C] 2.932e-06[/C][C] 5.864e-06[/C][C] 1[/C][/ROW]
[ROW][C]112[/C][C] 3.584e-06[/C][C] 7.168e-06[/C][C] 1[/C][/ROW]
[ROW][C]113[/C][C] 2.214e-06[/C][C] 4.429e-06[/C][C] 1[/C][/ROW]
[ROW][C]114[/C][C] 1.584e-06[/C][C] 3.168e-06[/C][C] 1[/C][/ROW]
[ROW][C]115[/C][C] 2.726e-06[/C][C] 5.453e-06[/C][C] 1[/C][/ROW]
[ROW][C]116[/C][C] 3.011e-06[/C][C] 6.022e-06[/C][C] 1[/C][/ROW]
[ROW][C]117[/C][C] 2.353e-06[/C][C] 4.706e-06[/C][C] 1[/C][/ROW]
[ROW][C]118[/C][C] 1.466e-06[/C][C] 2.932e-06[/C][C] 1[/C][/ROW]
[ROW][C]119[/C][C] 2.322e-05[/C][C] 4.645e-05[/C][C] 1[/C][/ROW]
[ROW][C]120[/C][C] 1.746e-05[/C][C] 3.492e-05[/C][C] 1[/C][/ROW]
[ROW][C]121[/C][C] 1.424e-05[/C][C] 2.847e-05[/C][C] 1[/C][/ROW]
[ROW][C]122[/C][C] 2.239e-05[/C][C] 4.478e-05[/C][C] 1[/C][/ROW]
[ROW][C]123[/C][C] 1.661e-05[/C][C] 3.321e-05[/C][C] 1[/C][/ROW]
[ROW][C]124[/C][C] 0.0001466[/C][C] 0.0002932[/C][C] 0.9999[/C][/ROW]
[ROW][C]125[/C][C] 0.0001141[/C][C] 0.0002282[/C][C] 0.9999[/C][/ROW]
[ROW][C]126[/C][C] 9.058e-05[/C][C] 0.0001812[/C][C] 0.9999[/C][/ROW]
[ROW][C]127[/C][C] 0.0001821[/C][C] 0.0003642[/C][C] 0.9998[/C][/ROW]
[ROW][C]128[/C][C] 0.0002105[/C][C] 0.0004211[/C][C] 0.9998[/C][/ROW]
[ROW][C]129[/C][C] 0.0002195[/C][C] 0.0004391[/C][C] 0.9998[/C][/ROW]
[ROW][C]130[/C][C] 0.0002505[/C][C] 0.000501[/C][C] 0.9998[/C][/ROW]
[ROW][C]131[/C][C] 0.0002372[/C][C] 0.0004745[/C][C] 0.9998[/C][/ROW]
[ROW][C]132[/C][C] 0.0009634[/C][C] 0.001927[/C][C] 0.999[/C][/ROW]
[ROW][C]133[/C][C] 0.002669[/C][C] 0.005338[/C][C] 0.9973[/C][/ROW]
[ROW][C]134[/C][C] 0.00343[/C][C] 0.006861[/C][C] 0.9966[/C][/ROW]
[ROW][C]135[/C][C] 0.004061[/C][C] 0.008121[/C][C] 0.9959[/C][/ROW]
[ROW][C]136[/C][C] 0.003491[/C][C] 0.006983[/C][C] 0.9965[/C][/ROW]
[ROW][C]137[/C][C] 0.003101[/C][C] 0.006202[/C][C] 0.9969[/C][/ROW]
[ROW][C]138[/C][C] 0.002958[/C][C] 0.005917[/C][C] 0.997[/C][/ROW]
[ROW][C]139[/C][C] 0.002646[/C][C] 0.005291[/C][C] 0.9974[/C][/ROW]
[ROW][C]140[/C][C] 0.002064[/C][C] 0.004128[/C][C] 0.9979[/C][/ROW]
[ROW][C]141[/C][C] 0.001628[/C][C] 0.003257[/C][C] 0.9984[/C][/ROW]
[ROW][C]142[/C][C] 0.001627[/C][C] 0.003254[/C][C] 0.9984[/C][/ROW]
[ROW][C]143[/C][C] 0.001385[/C][C] 0.00277[/C][C] 0.9986[/C][/ROW]
[ROW][C]144[/C][C] 0.002027[/C][C] 0.004054[/C][C] 0.998[/C][/ROW]
[ROW][C]145[/C][C] 0.001876[/C][C] 0.003753[/C][C] 0.9981[/C][/ROW]
[ROW][C]146[/C][C] 0.001697[/C][C] 0.003395[/C][C] 0.9983[/C][/ROW]
[ROW][C]147[/C][C] 0.002033[/C][C] 0.004065[/C][C] 0.998[/C][/ROW]
[ROW][C]148[/C][C] 0.001459[/C][C] 0.002917[/C][C] 0.9985[/C][/ROW]
[ROW][C]149[/C][C] 0.001197[/C][C] 0.002393[/C][C] 0.9988[/C][/ROW]
[ROW][C]150[/C][C] 0.001013[/C][C] 0.002025[/C][C] 0.999[/C][/ROW]
[ROW][C]151[/C][C] 0.0008046[/C][C] 0.001609[/C][C] 0.9992[/C][/ROW]
[ROW][C]152[/C][C] 0.0006458[/C][C] 0.001292[/C][C] 0.9994[/C][/ROW]
[ROW][C]153[/C][C] 0.002379[/C][C] 0.004757[/C][C] 0.9976[/C][/ROW]
[ROW][C]154[/C][C] 0.002054[/C][C] 0.004107[/C][C] 0.9979[/C][/ROW]
[ROW][C]155[/C][C] 0.001621[/C][C] 0.003243[/C][C] 0.9984[/C][/ROW]
[ROW][C]156[/C][C] 0.001421[/C][C] 0.002842[/C][C] 0.9986[/C][/ROW]
[ROW][C]157[/C][C] 0.001219[/C][C] 0.002439[/C][C] 0.9988[/C][/ROW]
[ROW][C]158[/C][C] 0.001988[/C][C] 0.003976[/C][C] 0.998[/C][/ROW]
[ROW][C]159[/C][C] 0.002065[/C][C] 0.004131[/C][C] 0.9979[/C][/ROW]
[ROW][C]160[/C][C] 0.001396[/C][C] 0.002791[/C][C] 0.9986[/C][/ROW]
[ROW][C]161[/C][C] 0.01615[/C][C] 0.0323[/C][C] 0.9839[/C][/ROW]
[ROW][C]162[/C][C] 0.01893[/C][C] 0.03787[/C][C] 0.9811[/C][/ROW]
[ROW][C]163[/C][C] 0.01586[/C][C] 0.03172[/C][C] 0.9841[/C][/ROW]
[ROW][C]164[/C][C] 0.01166[/C][C] 0.02333[/C][C] 0.9883[/C][/ROW]
[ROW][C]165[/C][C] 0.0101[/C][C] 0.0202[/C][C] 0.9899[/C][/ROW]
[ROW][C]166[/C][C] 0.03342[/C][C] 0.06684[/C][C] 0.9666[/C][/ROW]
[ROW][C]167[/C][C] 0.03285[/C][C] 0.06571[/C][C] 0.9671[/C][/ROW]
[ROW][C]168[/C][C] 0.02375[/C][C] 0.0475[/C][C] 0.9763[/C][/ROW]
[ROW][C]169[/C][C] 0.02228[/C][C] 0.04457[/C][C] 0.9777[/C][/ROW]
[ROW][C]170[/C][C] 0.03948[/C][C] 0.07895[/C][C] 0.9605[/C][/ROW]
[ROW][C]171[/C][C] 0.02939[/C][C] 0.05877[/C][C] 0.9706[/C][/ROW]
[ROW][C]172[/C][C] 0.03877[/C][C] 0.07755[/C][C] 0.9612[/C][/ROW]
[ROW][C]173[/C][C] 0.02713[/C][C] 0.05426[/C][C] 0.9729[/C][/ROW]
[ROW][C]174[/C][C] 0.01859[/C][C] 0.03718[/C][C] 0.9814[/C][/ROW]
[ROW][C]175[/C][C] 0.01412[/C][C] 0.02825[/C][C] 0.9859[/C][/ROW]
[ROW][C]176[/C][C] 0.01902[/C][C] 0.03805[/C][C] 0.981[/C][/ROW]
[ROW][C]177[/C][C] 0.08958[/C][C] 0.1792[/C][C] 0.9104[/C][/ROW]
[ROW][C]178[/C][C] 0.3993[/C][C] 0.7987[/C][C] 0.6007[/C][/ROW]
[ROW][C]179[/C][C] 0.5513[/C][C] 0.8975[/C][C] 0.4487[/C][/ROW]
[ROW][C]180[/C][C] 0.5198[/C][C] 0.9604[/C][C] 0.4802[/C][/ROW]
[ROW][C]181[/C][C] 0.6353[/C][C] 0.7294[/C][C] 0.3647[/C][/ROW]
[ROW][C]182[/C][C] 0.6797[/C][C] 0.6406[/C][C] 0.3203[/C][/ROW]
[ROW][C]183[/C][C] 0.6609[/C][C] 0.6782[/C][C] 0.3391[/C][/ROW]
[ROW][C]184[/C][C] 0.5837[/C][C] 0.8325[/C][C] 0.4163[/C][/ROW]
[ROW][C]185[/C][C] 0.4742[/C][C] 0.9484[/C][C] 0.5258[/C][/ROW]
[ROW][C]186[/C][C] 0.7428[/C][C] 0.5144[/C][C] 0.2572[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310056&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310056&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	0.02011	0.04022	0.9799
11	0.00725	0.0145	0.9928
12	0.002125	0.004249	0.9979
13	0.002527	0.005055	0.9975
14	0.00112	0.002241	0.9989
15	0.0003588	0.0007177	0.9996
16	9.744e-05	0.0001949	0.9999
17	2.675e-05	5.349e-05	1
18	6.589e-06	1.318e-05	1
19	1.625e-06	3.249e-06	1
20	5.968e-07	1.194e-06	1
21	2.419e-07	4.839e-07	1
22	8.419e-07	1.684e-06	1
23	2.497e-07	4.995e-07	1
24	6.927e-08	1.385e-07	1
25	6.206e-08	1.241e-07	1
26	1.849e-08	3.698e-08	1
27	1.644e-08	3.287e-08	1
28	3.383e-07	6.766e-07	1
29	4.345e-06	8.69e-06	1
30	5.046e-06	1.009e-05	1
31	2.017e-06	4.034e-06	1
32	6.669e-06	1.334e-05	1
33	2.054e-05	4.108e-05	1
34	1.206e-05	2.412e-05	1
35	8.219e-06	1.644e-05	1
36	3.831e-06	7.662e-06	1
37	2.592e-06	5.183e-06	1
38	1.889e-06	3.778e-06	1
39	1.666e-06	3.333e-06	1
40	1.094e-06	2.189e-06	1
41	6.143e-07	1.229e-06	1
42	3.744e-07	7.489e-07	1
43	2.084e-07	4.167e-07	1
44	3.834e-07	7.669e-07	1
45	3.75e-07	7.499e-07	1
46	2.093e-06	4.187e-06	1
47	2.483e-06	4.967e-06	1
48	1.499e-06	2.998e-06	1
49	8.077e-07	1.615e-06	1
50	7.759e-07	1.552e-06	1
51	4.578e-07	9.156e-07	1
52	5.09e-07	1.018e-06	1
53	8.934e-07	1.787e-06	1
54	8.108e-07	1.622e-06	1
55	4.478e-07	8.956e-07	1
56	6.471e-07	1.294e-06	1
57	5.869e-07	1.174e-06	1
58	3.87e-07	7.74e-07	1
59	2.209e-07	4.417e-07	1
60	1.379e-07	2.759e-07	1
61	1.271e-07	2.542e-07	1
62	1.181e-07	2.362e-07	1
63	6.544e-08	1.309e-07	1
64	6.153e-08	1.231e-07	1
65	3.742e-08	7.484e-08	1
66	2.422e-08	4.845e-08	1
67	1.224e-08	2.449e-08	1
68	6.004e-09	1.201e-08	1
69	2.381e-08	4.762e-08	1
70	1.327e-07	2.654e-07	1
71	1.432e-07	2.864e-07	1
72	2.25e-07	4.499e-07	1
73	2.302e-07	4.604e-07	1
74	1.867e-07	3.734e-07	1
75	1.617e-07	3.234e-07	1
76	8.875e-08	1.775e-07	1
77	1.211e-07	2.423e-07	1
78	3.153e-07	6.306e-07	1
79	3.725e-07	7.45e-07	1
80	2.351e-07	4.702e-07	1
81	1.719e-07	3.438e-07	1
82	1.613e-07	3.225e-07	1
83	1.086e-07	2.172e-07	1
84	7.571e-08	1.514e-07	1
85	7.376e-08	1.475e-07	1
86	7.477e-08	1.495e-07	1
87	4.794e-08	9.589e-08	1
88	3.596e-08	7.192e-08	1
89	5.423e-08	1.085e-07	1
90	3.727e-08	7.453e-08	1
91	2.544e-08	5.088e-08	1
92	7.842e-07	1.568e-06	1
93	8.391e-07	1.678e-06	1
94	5.95e-07	1.19e-06	1
95	1.255e-06	2.509e-06	1
96	1.263e-06	2.527e-06	1
97	7.567e-07	1.513e-06	1
98	5.848e-07	1.17e-06	1
99	1.028e-06	2.055e-06	1
100	7.405e-07	1.481e-06	1
101	4.321e-07	8.642e-07	1
102	2.638e-07	5.276e-07	1
103	1.958e-07	3.916e-07	1
104	1.295e-07	2.59e-07	1
105	8.211e-08	1.642e-07	1
106	1.258e-06	2.517e-06	1
107	2.171e-06	4.342e-06	1
108	1.54e-06	3.08e-06	1
109	1.065e-06	2.129e-06	1
110	1.743e-06	3.487e-06	1
111	2.932e-06	5.864e-06	1
112	3.584e-06	7.168e-06	1
113	2.214e-06	4.429e-06	1
114	1.584e-06	3.168e-06	1
115	2.726e-06	5.453e-06	1
116	3.011e-06	6.022e-06	1
117	2.353e-06	4.706e-06	1
118	1.466e-06	2.932e-06	1
119	2.322e-05	4.645e-05	1
120	1.746e-05	3.492e-05	1
121	1.424e-05	2.847e-05	1
122	2.239e-05	4.478e-05	1
123	1.661e-05	3.321e-05	1
124	0.0001466	0.0002932	0.9999
125	0.0001141	0.0002282	0.9999
126	9.058e-05	0.0001812	0.9999
127	0.0001821	0.0003642	0.9998
128	0.0002105	0.0004211	0.9998
129	0.0002195	0.0004391	0.9998
130	0.0002505	0.000501	0.9998
131	0.0002372	0.0004745	0.9998
132	0.0009634	0.001927	0.999
133	0.002669	0.005338	0.9973
134	0.00343	0.006861	0.9966
135	0.004061	0.008121	0.9959
136	0.003491	0.006983	0.9965
137	0.003101	0.006202	0.9969
138	0.002958	0.005917	0.997
139	0.002646	0.005291	0.9974
140	0.002064	0.004128	0.9979
141	0.001628	0.003257	0.9984
142	0.001627	0.003254	0.9984
143	0.001385	0.00277	0.9986
144	0.002027	0.004054	0.998
145	0.001876	0.003753	0.9981
146	0.001697	0.003395	0.9983
147	0.002033	0.004065	0.998
148	0.001459	0.002917	0.9985
149	0.001197	0.002393	0.9988
150	0.001013	0.002025	0.999
151	0.0008046	0.001609	0.9992
152	0.0006458	0.001292	0.9994
153	0.002379	0.004757	0.9976
154	0.002054	0.004107	0.9979
155	0.001621	0.003243	0.9984
156	0.001421	0.002842	0.9986
157	0.001219	0.002439	0.9988
158	0.001988	0.003976	0.998
159	0.002065	0.004131	0.9979
160	0.001396	0.002791	0.9986
161	0.01615	0.0323	0.9839
162	0.01893	0.03787	0.9811
163	0.01586	0.03172	0.9841
164	0.01166	0.02333	0.9883
165	0.0101	0.0202	0.9899
166	0.03342	0.06684	0.9666
167	0.03285	0.06571	0.9671
168	0.02375	0.0475	0.9763
169	0.02228	0.04457	0.9777
170	0.03948	0.07895	0.9605
171	0.02939	0.05877	0.9706
172	0.03877	0.07755	0.9612
173	0.02713	0.05426	0.9729
174	0.01859	0.03718	0.9814
175	0.01412	0.02825	0.9859
176	0.01902	0.03805	0.981
177	0.08958	0.1792	0.9104
178	0.3993	0.7987	0.6007
179	0.5513	0.8975	0.4487
180	0.5198	0.9604	0.4802
181	0.6353	0.7294	0.3647
182	0.6797	0.6406	0.3203
183	0.6609	0.6782	0.3391
184	0.5837	0.8325	0.4163
185	0.4742	0.9484	0.5258
186	0.7428	0.5144	0.2572

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	149	0.8418	NOK
5% type I error level	161	0.909605	NOK
10% type I error level	167	0.943503	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 & 149 &  0.8418 & NOK \tabularnewline
5% type I error level & 161 & 0.909605 & NOK \tabularnewline
10% type I error level & 167 & 0.943503 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310056&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]149[/C][C] 0.8418[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]161[/C][C]0.909605[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]167[/C][C]0.943503[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310056&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310056&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	149	0.8418	NOK
5% type I error level	161	0.909605	NOK
10% type I error level	167	0.943503	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 12.467, df1 = 2, df2 = 187, p-value = 8.269e-06
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 3.5685, df1 = 12, df2 = 177, p-value = 9.015e-05
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 13.397, df1 = 2, df2 = 187, p-value = 3.651e-06

\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 = 12.467, df1 = 2, df2 = 187, p-value = 8.269e-06
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 3.5685, df1 = 12, df2 = 177, p-value = 9.015e-05
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 13.397, df1 = 2, df2 = 187, p-value = 3.651e-06
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310056&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 = 12.467, df1 = 2, df2 = 187, p-value = 8.269e-06
[/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 = 3.5685, df1 = 12, df2 = 177, p-value = 9.015e-05
[/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 = 13.397, df1 = 2, df2 = 187, p-value = 3.651e-06
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310056&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310056&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 = 12.467, df1 = 2, df2 = 187, p-value = 8.269e-06
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 3.5685, df1 = 12, df2 = 177, p-value = 9.015e-05
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 13.397, df1 = 2, df2 = 187, p-value = 3.651e-06

Variance Inflation Factors (Multicollinearity)
> vif totip `Consumer(t-1)` `Consumer(t-2)` `Consumer(t-3)` 4.620084 8.715615 5.984091 8.868670 `Consumer(t-4)` `Consumer(t-1s)` 7.721407 8.985612

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
           totip  `Consumer(t-1)`  `Consumer(t-2)`  `Consumer(t-3)` 
        4.620084         8.715615         5.984091         8.868670 
 `Consumer(t-4)` `Consumer(t-1s)` 
        7.721407         8.985612 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310056&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
           totip  `Consumer(t-1)`  `Consumer(t-2)`  `Consumer(t-3)` 
        4.620084         8.715615         5.984091         8.868670 
 `Consumer(t-4)` `Consumer(t-1s)` 
        7.721407         8.985612 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310056&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310056&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 totip `Consumer(t-1)` `Consumer(t-2)` `Consumer(t-3)` 4.620084 8.715615 5.984091 8.868670 `Consumer(t-4)` `Consumer(t-1s)` 7.721407 8.985612

Figure 1

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 12 ; par2 = Triple ; par3 = additive ; par4 = 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):

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code