Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 17 Dec 2017 14:56:56 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/17/t1513519250153cfbnfjx1cmla.htm/, Retrieved Thu, 16 May 2024 00:59:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309974, Retrieved Thu, 16 May 2024 00:59:43 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

122

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

-       [Multiple Regression] [Dataset 3: Regres...] [2017-12-17 13:56:56] [90753a48488b87cdd376975a1d76550a] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

56.6	90.4	NA	NA	NA	NA
71.5	127	90.4	NA	NA	NA
83.3	139.9	127	90.4	NA	NA
66.9	113.4	139.9	127	90.4	NA
86.8	137.6	113.4	139.9	127	90.4
74.9	122.7	137.6	113.4	139.9	127
60.9	40.3	122.7	137.6	113.4	139.9
72.1	126.5	40.3	122.7	137.6	113.4
84.3	134.6	126.5	40.3	122.7	137.6
88.6	131.1	134.6	126.5	40.3	122.7
82.2	119.1	131.1	134.6	126.5	40.3
51.8	98.7	119.1	131.1	134.6	126.5
80.9	92.2	98.7	119.1	131.1	134.6
76.7	111.2	92.2	98.7	119.1	131.1
82.6	117.9	111.2	92.2	98.7	119.1
74.6	102.4	117.9	111.2	92.2	98.7
78.6	122.1	102.4	117.9	111.2	92.2
79	122.2	122.1	102.4	117.9	111.2
64.4	45.4	122.2	122.1	102.4	117.9
64	118.6	45.4	122.2	122.1	102.4
77.9	109.8	118.6	45.4	122.2	122.1
83.8	127.6	109.8	118.6	45.4	122.2
74.2	106.3	127.6	109.8	118.6	45.4
51.7	74.6	106.3	127.6	109.8	118.6
79.9	82.7	74.6	106.3	127.6	109.8
74.8	86.5	82.7	74.6	106.3	127.6
78	103.6	86.5	82.7	74.6	106.3
78.4	114	103.6	86.5	82.7	74.6
77.3	112	114	103.6	86.5	82.7
77.9	115.3	112	114	103.6	86.5
72	48.1	115.3	112	114	103.6
66.4	100.5	48.1	115.3	112	114
83.5	120.7	100.5	48.1	115.3	112
85.1	122.7	120.7	100.5	48.1	115.3
74.8	107.6	122.7	120.7	100.5	48.1
56.1	70.8	107.6	122.7	120.7	100.5
75.3	82.5	70.8	107.6	122.7	120.7
75.3	91.6	82.5	70.8	107.6	122.7
75.4	115.4	91.6	82.5	70.8	107.6
76.7	108.7	115.4	91.6	82.5	70.8
72.3	101.4	108.7	115.4	91.6	82.5
78.1	114	101.4	108.7	115.4	91.6
69.4	51.4	114	101.4	108.7	115.4
55	96.2	51.4	114	101.4	108.7
79.9	125.5	96.2	51.4	114	101.4
88.6	120.1	125.5	96.2	51.4	114
72.2	96.9	120.1	125.5	96.2	51.4
59.2	77.3	96.9	120.1	125.5	96.2
77.9	86.6	77.3	96.9	120.1	125.5
77.8	98.2	86.6	77.3	96.9	120.1
90.4	121.7	98.2	86.6	77.3	96.9
87.4	106.8	121.7	98.2	86.6	77.3
82.9	100.6	106.8	121.7	98.2	86.6
97.5	124.5	100.6	106.8	121.7	98.2
75.8	42.7	124.5	100.6	106.8	121.7
74	107	42.7	124.5	100.6	106.8
95.5	123.8	107	42.7	124.5	100.6
95.6	117.3	123.8	107	42.7	124.5
95.8	101.9	117.3	123.8	107	42.7
75.5	86.3	101.9	117.3	123.8	107
89.9	78.7	86.3	101.9	117.3	123.8
91.8	92.2	78.7	86.3	101.9	117.3
97	103.6	92.2	78.7	86.3	101.9
95.7	120.8	103.6	92.2	78.7	86.3
86	105.5	120.8	103.6	92.2	78.7
93.3	127.8	105.5	120.8	103.6	92.2
68.7	36.9	127.8	105.5	120.8	103.6
64.5	112.4	36.9	127.8	105.5	120.8
91	127.5	112.4	36.9	127.8	105.5
84.9	111.5	127.5	112.4	36.9	127.8
97.3	108.7	111.5	127.5	112.4	36.9
70.2	87.3	108.7	111.5	127.5	112.4
100.9	84.6	87.3	108.7	111.5	127.5
99.7	96	84.6	87.3	108.7	111.5
121.3	118.3	96	84.6	87.3	108.7
102.8	107.5	118.3	96	84.6	87.3
111.8	121.5	107.5	118.3	96	84.6
117.6	130.4	121.5	107.5	118.3	96
80.7	41.5	130.4	121.5	107.5	118.3
81.6	116.4	41.5	130.4	121.5	107.5
99.5	130.2	116.4	41.5	130.4	121.5
108.3	121.4	130.2	116.4	41.5	130.4
107.5	120.1	121.4	130.2	116.4	41.5
84.4	88.3	120.1	121.4	130.2	116.4
115.6	97.9	88.3	120.1	121.4	130.2
109.8	109.6	97.9	88.3	120.1	121.4
116.9	126	109.6	97.9	88.3	120.1
106.8	112.7	126	109.6	97.9	88.3
112.9	115.7	112.7	126	109.6	97.9
113.9	128.2	115.7	112.7	126	109.6
94.9	47.9	128.2	115.7	112.7	126
85.1	121.4	47.9	128.2	115.7	112.7
101	123.1	121.4	47.9	128.2	115.7
109.7	137.2	123.1	121.4	47.9	128.2
104.1	119	137.2	123.1	121.4	47.9
76.7	81.5	119	137.2	123.1	121.4
116.5	115.3	81.5	119	137.2	123.1
121.7	124.2	115.3	81.5	119	137.2
117.9	102.9	124.2	115.3	81.5	119
133.3	137.6	102.9	124.2	115.3	81.5
117.8	120.7	137.6	102.9	124.2	115.3
129.8	130.6	120.7	137.6	102.9	124.2
109.1	55.8	130.6	120.7	137.6	102.9
88	110.5	55.8	130.6	120.7	137.6
120.1	134.9	110.5	55.8	130.6	120.7
118.4	125.7	134.9	110.5	55.8	130.6
89.7	105	125.7	134.9	110.5	55.8
71.4	82.6	105	125.7	134.9	110.5
75.9	90.8	82.6	105	125.7	134.9
75.2	107.2	90.8	82.6	105	125.7
79.2	124.9	107.2	90.8	82.6	105
70.8	108.7	124.9	107.2	90.8	82.6
73.7	108.5	108.7	124.9	107.2	90.8
79.4	124.5	108.5	108.7	124.9	107.2
68.5	52.1	124.5	108.5	108.7	124.9
66.5	106.8	52.1	124.5	108.5	108.7
93	129.8	106.8	52.1	124.5	108.5
91.9	129.2	129.8	106.8	52.1	124.5
86.1	95.5	129.2	129.8	106.8	52.1
66.2	75.1	95.5	129.2	129.8	106.8
90.4	77.7	75.1	95.5	129.2	129.8
92.4	86.3	77.7	75.1	95.5	129.2
108.8	130.3	86.3	77.7	75.1	95.5
103.6	110.4	130.3	86.3	77.7	75.1
103	100	110.4	130.3	86.3	77.7
117.1	127.2	100	110.4	130.3	86.3
91.9	46.7	127.2	100	110.4	130.3
80.3	109.9	46.7	127.2	100	110.4
111.6	127.7	109.9	46.7	127.2	100
106.6	122.2	127.7	109.9	46.7	127.2
107	100.9	122.2	127.7	109.9	46.7
87.3	60.7	100.9	122.2	127.7	109.9
104.5	86.7	60.7	100.9	122.2	127.7
102.8	112.3	86.7	60.7	100.9	122.2
116.2	134.2	112.3	86.7	60.7	100.9
103.4	105	134.2	112.3	86.7	60.7
112.8	126.5	105	134.2	112.3	86.7
103	114.5	126.5	105	134.2	112.3
85.5	43.6	114.5	126.5	105	134.2
83.2	112.4	43.6	114.5	126.5	105
106.4	129.4	112.4	43.6	114.5	126.5
98.2	116.2	129.4	112.4	43.6	114.5
100.5	115.9	116.2	129.4	112.4	43.6
75.5	85.6	115.9	116.2	129.4	112.4
101.3	92.5	85.6	115.9	116.2	129.4
105.2	91.2	92.5	85.6	115.9	116.2
112.7	128.8	91.2	92.5	85.6	115.9
95.7	103.6	128.8	91.2	92.5	85.6
99.3	113.8	103.6	128.8	91.2	92.5
103	120.9	113.8	103.6	128.8	91.2
88.4	52.5	120.9	113.8	103.6	128.8
78.5	112.8	52.5	120.9	113.8	103.6
97	115.8	112.8	52.5	120.9	113.8
106.4	123.4	115.8	112.8	52.5	120.9
94.7	112.1	123.4	115.8	112.8	52.5
73.7	71.9	112.1	123.4	115.8	112.8
101.5	76.6	71.9	112.1	123.4	115.8
100.5	91.2	76.6	71.9	112.1	123.4
102.1	105.4	91.2	76.6	71.9	112.1
101.4	107.8	105.4	91.2	76.6	71.9
98.6	105.9	107.8	105.4	91.2	76.6
104.7	114.5	105.9	107.8	105.4	91.2
87.6	54.4	114.5	105.9	107.8	105.4
76	97.2	54.4	114.5	105.9	107.8
102.9	116.9	97.2	54.4	114.5	105.9
107.8	121.5	116.9	97.2	54.4	114.5
96	101.2	121.5	116.9	97.2	54.4
69.6	81.6	101.2	121.5	116.9	97.2
105.4	100.4	81.6	101.2	121.5	116.9
100.5	101	100.4	81.6	101.2	121.5
100.4	110.6	101	100.4	81.6	101.2
101.8	100	110.6	101	100.4	81.6
94.9	98.7	100	110.6	101	100.4
100.5	106.2	98.7	100	110.6	101
89.4	51.8	106.2	98.7	100	110.6
75.9	89.8	51.8	106.2	98.7	100
109.1	116.3	89.8	51.8	106.2	98.7
107.4	118.3	116.3	89.8	51.8	106.2
86.6	94.3	118.3	116.3	89.8	51.8
75.7	71.7	94.3	118.3	116.3	89.8
105.3	90.8	71.7	94.3	118.3	116.3
104.4	93.6	90.8	71.7	94.3	118.3
119.5	112.3	93.6	90.8	71.7	94.3
111.6	97	112.3	93.6	90.8	71.7
105.7	90.2	97	112.3	93.6	90.8
122.3	114.6	90.2	97	112.3	93.6
97.7	50.9	114.6	90.2	97	112.3
82.4	94.3	50.9	114.6	90.2	97
113.4	112.2	94.3	50.9	114.6	90.2
113.8	114	112.2	94.3	50.9	114.6
103.1	88.4	114	112.2	94.3	50.9
82.2	67.7	88.4	114	112.2	94.3
104.5	87.6	67.7	88.4	114	112.2
104.8	96.3	87.6	67.7	88.4	114
110.7	97	96.3	87.6	67.7	88.4
110.6	105.8	97	96.3	87.6	67.7
103.9	95.2	105.8	97	96.3	87.6
111.9	119.6	95.2	105.8	97	96.3
82.8	45.4	119.6	95.2	105.8	97
81.4	98.6	45.4	119.6	95.2	105.8
108.3	112.7	98.6	45.4	119.6	95.2
103.9	101.3	112.7	98.6	45.4	119.6
105.3	84.7	101.3	112.7	98.6	45.4
86	78	84.7	101.3	112.7	98.6
109.9	73.6	78	84.7	101.3	112.7
103.9	96.3	73.6	78	84.7	101.3
120.5	113.8	96.3	73.6	78	84.7
102.6	85	113.8	96.3	73.6	78
110.7	103.5	85	113.8	96.3	73.6
116.8	106.4	103.5	85	113.8	96.3
86.7	44.3	106.4	103.5	85	113.8
90.1	95.9	44.3	106.4	103.5	85

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309974&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309974&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309974&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)BM[t] = + 0.0367038 + 0.309554`(1-Bs)(1-B)Build0`[t] + 0.0597578`(1-Bs)(1-B)Build1`[t] + 0.0822989`(1-Bs)(1-B)Build2`[t] + 0.130482`(1-Bs)(1-B)Build3`[t] + 0.00904741`(1-Bs)(1-B)Build4`[t] -0.190772`(1-Bs)(1-B)BM(t-1)`[t] + 0.0631585`(1-Bs)(1-B)BM(t-2)`[t] + 0.154764`(1-Bs)(1-B)BM(t-3)`[t] -0.00286295`(1-Bs)(1-B)BM(t-4)`[t] -0.303524`(1-Bs)(1-B)BM(t-1s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)BM[t] =  +  0.0367038 +  0.309554`(1-Bs)(1-B)Build0`[t] +  0.0597578`(1-Bs)(1-B)Build1`[t] +  0.0822989`(1-Bs)(1-B)Build2`[t] +  0.130482`(1-Bs)(1-B)Build3`[t] +  0.00904741`(1-Bs)(1-B)Build4`[t] -0.190772`(1-Bs)(1-B)BM(t-1)`[t] +  0.0631585`(1-Bs)(1-B)BM(t-2)`[t] +  0.154764`(1-Bs)(1-B)BM(t-3)`[t] -0.00286295`(1-Bs)(1-B)BM(t-4)`[t] -0.303524`(1-Bs)(1-B)BM(t-1s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309974&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)BM[t] =  +  0.0367038 +  0.309554`(1-Bs)(1-B)Build0`[t] +  0.0597578`(1-Bs)(1-B)Build1`[t] +  0.0822989`(1-Bs)(1-B)Build2`[t] +  0.130482`(1-Bs)(1-B)Build3`[t] +  0.00904741`(1-Bs)(1-B)Build4`[t] -0.190772`(1-Bs)(1-B)BM(t-1)`[t] +  0.0631585`(1-Bs)(1-B)BM(t-2)`[t] +  0.154764`(1-Bs)(1-B)BM(t-3)`[t] -0.00286295`(1-Bs)(1-B)BM(t-4)`[t] -0.303524`(1-Bs)(1-B)BM(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309974&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309974&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
(1-Bs)(1-B)BM[t] = + 0.0367038 + 0.309554`(1-Bs)(1-B)Build0`[t] + 0.0597578`(1-Bs)(1-B)Build1`[t] + 0.0822989`(1-Bs)(1-B)Build2`[t] + 0.130482`(1-Bs)(1-B)Build3`[t] + 0.00904741`(1-Bs)(1-B)Build4`[t] -0.190772`(1-Bs)(1-B)BM(t-1)`[t] + 0.0631585`(1-Bs)(1-B)BM(t-2)`[t] + 0.154764`(1-Bs)(1-B)BM(t-3)`[t] -0.00286295`(1-Bs)(1-B)BM(t-4)`[t] -0.303524`(1-Bs)(1-B)BM(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)	+0.0367	0.5152	+7.1250e-02	0.9433	0.4716
`(1-Bs)(1-B)Build0`	+0.3095	0.05103	+6.0660e+00	8.417e-09	4.208e-09
`(1-Bs)(1-B)Build1`	+0.05976	0.06899	+8.6620e-01	0.3876	0.1938
`(1-Bs)(1-B)Build2`	+0.0823	0.07637	+1.0780e+00	0.2828	0.1414
`(1-Bs)(1-B)Build3`	+0.1305	0.06979	+1.8700e+00	0.06328	0.03164
`(1-Bs)(1-B)Build4`	+0.009047	0.05609	+1.6130e-01	0.872	0.436
`(1-Bs)(1-B)BM(t-1)`	-0.1908	0.07373	-2.5870e+00	0.01052	0.00526
`(1-Bs)(1-B)BM(t-2)`	+0.06316	0.0724	+8.7230e-01	0.3843	0.1921
`(1-Bs)(1-B)BM(t-3)`	+0.1548	0.07255	+2.1330e+00	0.03435	0.01718
`(1-Bs)(1-B)BM(t-4)`	-0.002863	0.06957	-4.1150e-02	0.9672	0.4836
`(1-Bs)(1-B)BM(t-1s)`	-0.3035	0.0568	-5.3440e+00	2.933e-07	1.466e-07

\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) & +0.0367 &  0.5152 & +7.1250e-02 &  0.9433 &  0.4716 \tabularnewline
`(1-Bs)(1-B)Build0` & +0.3095 &  0.05103 & +6.0660e+00 &  8.417e-09 &  4.208e-09 \tabularnewline
`(1-Bs)(1-B)Build1` & +0.05976 &  0.06899 & +8.6620e-01 &  0.3876 &  0.1938 \tabularnewline
`(1-Bs)(1-B)Build2` & +0.0823 &  0.07637 & +1.0780e+00 &  0.2828 &  0.1414 \tabularnewline
`(1-Bs)(1-B)Build3` & +0.1305 &  0.06979 & +1.8700e+00 &  0.06328 &  0.03164 \tabularnewline
`(1-Bs)(1-B)Build4` & +0.009047 &  0.05609 & +1.6130e-01 &  0.872 &  0.436 \tabularnewline
`(1-Bs)(1-B)BM(t-1)` & -0.1908 &  0.07373 & -2.5870e+00 &  0.01052 &  0.00526 \tabularnewline
`(1-Bs)(1-B)BM(t-2)` & +0.06316 &  0.0724 & +8.7230e-01 &  0.3843 &  0.1921 \tabularnewline
`(1-Bs)(1-B)BM(t-3)` & +0.1548 &  0.07255 & +2.1330e+00 &  0.03435 &  0.01718 \tabularnewline
`(1-Bs)(1-B)BM(t-4)` & -0.002863 &  0.06957 & -4.1150e-02 &  0.9672 &  0.4836 \tabularnewline
`(1-Bs)(1-B)BM(t-1s)` & -0.3035 &  0.0568 & -5.3440e+00 &  2.933e-07 &  1.466e-07 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309974&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]+0.0367[/C][C] 0.5152[/C][C]+7.1250e-02[/C][C] 0.9433[/C][C] 0.4716[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build0`[/C][C]+0.3095[/C][C] 0.05103[/C][C]+6.0660e+00[/C][C] 8.417e-09[/C][C] 4.208e-09[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build1`[/C][C]+0.05976[/C][C] 0.06899[/C][C]+8.6620e-01[/C][C] 0.3876[/C][C] 0.1938[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build2`[/C][C]+0.0823[/C][C] 0.07637[/C][C]+1.0780e+00[/C][C] 0.2828[/C][C] 0.1414[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build3`[/C][C]+0.1305[/C][C] 0.06979[/C][C]+1.8700e+00[/C][C] 0.06328[/C][C] 0.03164[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Build4`[/C][C]+0.009047[/C][C] 0.05609[/C][C]+1.6130e-01[/C][C] 0.872[/C][C] 0.436[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)BM(t-1)`[/C][C]-0.1908[/C][C] 0.07373[/C][C]-2.5870e+00[/C][C] 0.01052[/C][C] 0.00526[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)BM(t-2)`[/C][C]+0.06316[/C][C] 0.0724[/C][C]+8.7230e-01[/C][C] 0.3843[/C][C] 0.1921[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)BM(t-3)`[/C][C]+0.1548[/C][C] 0.07255[/C][C]+2.1330e+00[/C][C] 0.03435[/C][C] 0.01718[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)BM(t-4)`[/C][C]-0.002863[/C][C] 0.06957[/C][C]-4.1150e-02[/C][C] 0.9672[/C][C] 0.4836[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)BM(t-1s)`[/C][C]-0.3035[/C][C] 0.0568[/C][C]-5.3440e+00[/C][C] 2.933e-07[/C][C] 1.466e-07[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309974&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309974&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)	+0.0367	0.5152	+7.1250e-02	0.9433	0.4716
`(1-Bs)(1-B)Build0`	+0.3095	0.05103	+6.0660e+00	8.417e-09	4.208e-09
`(1-Bs)(1-B)Build1`	+0.05976	0.06899	+8.6620e-01	0.3876	0.1938
`(1-Bs)(1-B)Build2`	+0.0823	0.07637	+1.0780e+00	0.2828	0.1414
`(1-Bs)(1-B)Build3`	+0.1305	0.06979	+1.8700e+00	0.06328	0.03164
`(1-Bs)(1-B)Build4`	+0.009047	0.05609	+1.6130e-01	0.872	0.436
`(1-Bs)(1-B)BM(t-1)`	-0.1908	0.07373	-2.5870e+00	0.01052	0.00526
`(1-Bs)(1-B)BM(t-2)`	+0.06316	0.0724	+8.7230e-01	0.3843	0.1921
`(1-Bs)(1-B)BM(t-3)`	+0.1548	0.07255	+2.1330e+00	0.03435	0.01718
`(1-Bs)(1-B)BM(t-4)`	-0.002863	0.06957	-4.1150e-02	0.9672	0.4836
`(1-Bs)(1-B)BM(t-1s)`	-0.3035	0.0568	-5.3440e+00	2.933e-07	1.466e-07

Multiple Linear Regression - Regression Statistics
Multiple R	0.7269
R-squared	0.5284
Adjusted R-squared	0.5003
F-TEST (value)	18.82
F-TEST (DF numerator)	10
F-TEST (DF denominator)	168
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.892
Sum Squared Residuals	7980

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7269 \tabularnewline
R-squared &  0.5284 \tabularnewline
Adjusted R-squared &  0.5003 \tabularnewline
F-TEST (value) &  18.82 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 168 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  6.892 \tabularnewline
Sum Squared Residuals &  7980 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309974&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7269[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.5284[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.5003[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 18.82[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]168[/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] 6.892[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 7980[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309974&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309974&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	0.7269
R-squared	0.5284
Adjusted R-squared	0.5003
F-TEST (value)	18.82
F-TEST (DF numerator)	10
F-TEST (DF denominator)	168
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.892
Sum Squared Residuals	7980

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=309974&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=309974&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309974&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	-4.3	-3.63	-0.6698
2	-0.7	1.935	-2.635
3	3.8	-0.9018	4.702
4	-9	-1.613	-7.387
5	5.1	4.273	0.8271
6	-3.1	1.983	-5.083
7	0.9	-7.036	7.936
8	-3.3	0.6453	-3.945
9	5.2	2.245	2.955
10	-2.8	-4.283	1.483
11	-8.8	-0.1943	-8.606
12	7.8	5.288	2.512
13	7.1	-2.838	9.938
14	-6.1	-5.117	-0.9827
15	5.7	7.078	-1.378
16	-0.5	1.107	-1.607
17	-0.1	-1.098	0.9981
18	12.5	3.895	8.605
19	-4.3	-5.229	0.9292
20	-0.1	2.764	-2.864
21	8.8	3.013	5.787
22	-13	-7.75	-5.25
23	12.6	11.63	0.9714
24	-3.4	-6.993	3.593
25	-8.6	-4.597	-4.003
26	16.6	8.992	7.608
27	-7.3	-5.806	-1.494
28	-4.3	-3.299	-1.001
29	2	3.935	-1.935
30	-7.4	-10.02	2.618
31	1.7	9.436	-7.736
32	-5.2	-2.202	-2.998
33	-7.3	-2.645	-4.655
34	-2.9	5.749	-8.649
35	-2.4	-2.611	0.2106
36	5	-0.6685	5.668
37	-6.2	-2.209	-3.991
38	12.2	0.7055	11.49
39	-6.8	-1.631	-5.169
40	16.3	3.388	12.91
41	-3.1	-1.48	-1.62
42	16.4	5.825	10.57
43	-17.2	-8.864	-8.336
44	18.7	13.47	5.23
45	-1.5	-3.153	1.653
46	-12.3	-1.65	-10.65
47	5.1	8.361	-3.261
48	-8.6	-5.273	-3.327
49	14.9	4.223	10.68
50	-13.2	-5.5	-7.7
51	4	1.502	2.498
52	0.5	0.06027	0.4397
53	-4.6	-0.7178	-3.882
54	-14.5	-5.515	-8.985
55	8.4	8.208	0.192
56	-2.9	-12.76	9.86
57	-4.8	-1.171	-3.629
58	17.9	7.437	10.46
59	-10.7	-6.783	-3.917
60	-2	2.333	-4.333
61	-0.1	5.409	-5.509
62	-4.8	-2.735	-2.065
63	-4.3	-3.028	-1.272
64	8.6	9.031	-0.4306
65	11	-3.11	14.11
66	-10.9	-8.512	-2.388
67	25.5	17.09	8.414
68	-21.6	-9.499	-12.1
69	11	2.518	8.482
70	-1.7	0.9501	-2.65
71	-11.3	-6.98	-4.32
72	16.2	10.29	5.907
73	-10.4	-10.74	0.3409
74	-23.1	0.05549	-23.16
75	9.1	13.03	-3.931
76	-35.3	-17.49	-17.81
77	-5.9	1.959	-7.859
78	7.8	16.08	-8.277
79	-23.8	-31.07	7.265
80	18.4	16.9	1.502
81	-6.3	-3.238	-3.062
82	9.8	-4.596	14.4
83	19.1	6.479	12.62
84	-5.6	-8.222	2.622
85	0.6	9.95	-9.35
86	22.9	5.904	17
87	-1.6	-7.61	6.01
88	19.7	11.04	8.663
89	2.7	-2.692	5.392
90	12.4	5.443	6.957
91	3.2	7.191	-3.991
92	-3.5	-7.296	3.796
93	8.4	10.64	-2.244
94	-14.3	-7.224	-7.076
95	-9.6	-1.348	-8.252
96	4.8	3.575	1.225
97	-3.9	-5.988	2.088
98	6.2	-3.158	9.358
99	0.2	-6.529	6.729
100	-7	0.1878	-7.188
101	-3.7	8.141	-11.84
102	-3	-9.821	6.821
103	-7.6	-1.606	-5.994
104	10	11.74	-1.737
105	-23.9	-19.08	-4.822
106	7.7	10.24	-2.542
107	9.3	4.701	4.599
108	-8.1	-10.38	2.285
109	-3.2	3.541	-6.741
110	1.9	6.463	-4.563
111	-5.3	1.763	-7.063
112	8.6	-1.784	10.38
113	5.6	-6.496	12.1
114	-5.9	2.759	-8.659
115	-4.2	2.729	-6.929
116	-5.8	-7.377	1.577
117	13.5	14.57	-1.075
118	2.9	-4.226	7.126
119	-7.6	-5.72	-1.88
120	-4.7	3.988	-8.688
121	17.6	7.236	10.36
122	-14	-9.78	-4.22
123	4	0.8084	3.192
124	2	-1.074	3.074
125	-4.9	-1.28	-3.62
126	-5.9	-4.318	-1.582
127	16.3	10.5	5.797
128	-6.4	-4.416	-1.984
129	2.4	-3.606	6.006
130	-2.5	5.887	-8.387
131	-1.7	-4.192	2.492
132	8.4	6.913	1.487
133	-4.5	-7.683	3.183
134	-0.1	1.62	-1.72
135	-5.4	7.475	-12.88
136	8	4.348	3.652
137	-3.9	-3.343	-0.5569
138	-1.7	3.748	-5.448
139	2.1	-7.004	9.104
140	-4.1	-1.824	-2.276
141	-0.5	-2.13	1.63
142	6	0.972	5.028
143	-1.9	-2.539	0.6392
144	6.3	0.312	5.988
145	-6.6	0.951	-7.551
146	-9	0.09699	-9.097
147	15.5	3.436	12.06
148	-6.2	-7.625	1.425
149	4	1.954	2.046
150	15.2	4.371	10.83
151	-9.3	-4.97	-4.33
152	1	3.707	-2.707
153	11	7.475	3.525
154	-13.5	-8.155	-5.345
155	-1.8	5.811	-7.611
156	-2.2	-1.635	-0.5653
157	2.1	-0.9668	3.067
158	10.1	1.394	8.706
159	-10	-7.395	-2.605
160	-7.3	4.921	-12.22
161	1.2	2.961	-1.761
162	-9.2	-11.76	2.564
163	7.8	10.58	-2.781
164	-0.8	-2.57	1.77
165	-8.6	-4.623	-3.977
166	-4.5	6.377	-10.88
167	13.9	2.881	11.02
168	-4.1	-5.049	0.9494
169	-4.8	-4.489	-0.3108
170	12.1	2.66	9.44
171	1.6	3.164	-1.564
172	1.6	-5.721	7.321
173	-6.3	6.443	-12.74
174	10.7	10.29	0.4096
175	-17.8	-17.05	-0.746
176	14.8	13.12	1.678
177	-1.9	-5.319	3.419
178	-1	0.01592	-1.016
179	4.8	0.1452	4.655

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -4.3 & -3.63 & -0.6698 \tabularnewline
2 & -0.7 &  1.935 & -2.635 \tabularnewline
3 &  3.8 & -0.9018 &  4.702 \tabularnewline
4 & -9 & -1.613 & -7.387 \tabularnewline
5 &  5.1 &  4.273 &  0.8271 \tabularnewline
6 & -3.1 &  1.983 & -5.083 \tabularnewline
7 &  0.9 & -7.036 &  7.936 \tabularnewline
8 & -3.3 &  0.6453 & -3.945 \tabularnewline
9 &  5.2 &  2.245 &  2.955 \tabularnewline
10 & -2.8 & -4.283 &  1.483 \tabularnewline
11 & -8.8 & -0.1943 & -8.606 \tabularnewline
12 &  7.8 &  5.288 &  2.512 \tabularnewline
13 &  7.1 & -2.838 &  9.938 \tabularnewline
14 & -6.1 & -5.117 & -0.9827 \tabularnewline
15 &  5.7 &  7.078 & -1.378 \tabularnewline
16 & -0.5 &  1.107 & -1.607 \tabularnewline
17 & -0.1 & -1.098 &  0.9981 \tabularnewline
18 &  12.5 &  3.895 &  8.605 \tabularnewline
19 & -4.3 & -5.229 &  0.9292 \tabularnewline
20 & -0.1 &  2.764 & -2.864 \tabularnewline
21 &  8.8 &  3.013 &  5.787 \tabularnewline
22 & -13 & -7.75 & -5.25 \tabularnewline
23 &  12.6 &  11.63 &  0.9714 \tabularnewline
24 & -3.4 & -6.993 &  3.593 \tabularnewline
25 & -8.6 & -4.597 & -4.003 \tabularnewline
26 &  16.6 &  8.992 &  7.608 \tabularnewline
27 & -7.3 & -5.806 & -1.494 \tabularnewline
28 & -4.3 & -3.299 & -1.001 \tabularnewline
29 &  2 &  3.935 & -1.935 \tabularnewline
30 & -7.4 & -10.02 &  2.618 \tabularnewline
31 &  1.7 &  9.436 & -7.736 \tabularnewline
32 & -5.2 & -2.202 & -2.998 \tabularnewline
33 & -7.3 & -2.645 & -4.655 \tabularnewline
34 & -2.9 &  5.749 & -8.649 \tabularnewline
35 & -2.4 & -2.611 &  0.2106 \tabularnewline
36 &  5 & -0.6685 &  5.668 \tabularnewline
37 & -6.2 & -2.209 & -3.991 \tabularnewline
38 &  12.2 &  0.7055 &  11.49 \tabularnewline
39 & -6.8 & -1.631 & -5.169 \tabularnewline
40 &  16.3 &  3.388 &  12.91 \tabularnewline
41 & -3.1 & -1.48 & -1.62 \tabularnewline
42 &  16.4 &  5.825 &  10.57 \tabularnewline
43 & -17.2 & -8.864 & -8.336 \tabularnewline
44 &  18.7 &  13.47 &  5.23 \tabularnewline
45 & -1.5 & -3.153 &  1.653 \tabularnewline
46 & -12.3 & -1.65 & -10.65 \tabularnewline
47 &  5.1 &  8.361 & -3.261 \tabularnewline
48 & -8.6 & -5.273 & -3.327 \tabularnewline
49 &  14.9 &  4.223 &  10.68 \tabularnewline
50 & -13.2 & -5.5 & -7.7 \tabularnewline
51 &  4 &  1.502 &  2.498 \tabularnewline
52 &  0.5 &  0.06027 &  0.4397 \tabularnewline
53 & -4.6 & -0.7178 & -3.882 \tabularnewline
54 & -14.5 & -5.515 & -8.985 \tabularnewline
55 &  8.4 &  8.208 &  0.192 \tabularnewline
56 & -2.9 & -12.76 &  9.86 \tabularnewline
57 & -4.8 & -1.171 & -3.629 \tabularnewline
58 &  17.9 &  7.437 &  10.46 \tabularnewline
59 & -10.7 & -6.783 & -3.917 \tabularnewline
60 & -2 &  2.333 & -4.333 \tabularnewline
61 & -0.1 &  5.409 & -5.509 \tabularnewline
62 & -4.8 & -2.735 & -2.065 \tabularnewline
63 & -4.3 & -3.028 & -1.272 \tabularnewline
64 &  8.6 &  9.031 & -0.4306 \tabularnewline
65 &  11 & -3.11 &  14.11 \tabularnewline
66 & -10.9 & -8.512 & -2.388 \tabularnewline
67 &  25.5 &  17.09 &  8.414 \tabularnewline
68 & -21.6 & -9.499 & -12.1 \tabularnewline
69 &  11 &  2.518 &  8.482 \tabularnewline
70 & -1.7 &  0.9501 & -2.65 \tabularnewline
71 & -11.3 & -6.98 & -4.32 \tabularnewline
72 &  16.2 &  10.29 &  5.907 \tabularnewline
73 & -10.4 & -10.74 &  0.3409 \tabularnewline
74 & -23.1 &  0.05549 & -23.16 \tabularnewline
75 &  9.1 &  13.03 & -3.931 \tabularnewline
76 & -35.3 & -17.49 & -17.81 \tabularnewline
77 & -5.9 &  1.959 & -7.859 \tabularnewline
78 &  7.8 &  16.08 & -8.277 \tabularnewline
79 & -23.8 & -31.07 &  7.265 \tabularnewline
80 &  18.4 &  16.9 &  1.502 \tabularnewline
81 & -6.3 & -3.238 & -3.062 \tabularnewline
82 &  9.8 & -4.596 &  14.4 \tabularnewline
83 &  19.1 &  6.479 &  12.62 \tabularnewline
84 & -5.6 & -8.222 &  2.622 \tabularnewline
85 &  0.6 &  9.95 & -9.35 \tabularnewline
86 &  22.9 &  5.904 &  17 \tabularnewline
87 & -1.6 & -7.61 &  6.01 \tabularnewline
88 &  19.7 &  11.04 &  8.663 \tabularnewline
89 &  2.7 & -2.692 &  5.392 \tabularnewline
90 &  12.4 &  5.443 &  6.957 \tabularnewline
91 &  3.2 &  7.191 & -3.991 \tabularnewline
92 & -3.5 & -7.296 &  3.796 \tabularnewline
93 &  8.4 &  10.64 & -2.244 \tabularnewline
94 & -14.3 & -7.224 & -7.076 \tabularnewline
95 & -9.6 & -1.348 & -8.252 \tabularnewline
96 &  4.8 &  3.575 &  1.225 \tabularnewline
97 & -3.9 & -5.988 &  2.088 \tabularnewline
98 &  6.2 & -3.158 &  9.358 \tabularnewline
99 &  0.2 & -6.529 &  6.729 \tabularnewline
100 & -7 &  0.1878 & -7.188 \tabularnewline
101 & -3.7 &  8.141 & -11.84 \tabularnewline
102 & -3 & -9.821 &  6.821 \tabularnewline
103 & -7.6 & -1.606 & -5.994 \tabularnewline
104 &  10 &  11.74 & -1.737 \tabularnewline
105 & -23.9 & -19.08 & -4.822 \tabularnewline
106 &  7.7 &  10.24 & -2.542 \tabularnewline
107 &  9.3 &  4.701 &  4.599 \tabularnewline
108 & -8.1 & -10.38 &  2.285 \tabularnewline
109 & -3.2 &  3.541 & -6.741 \tabularnewline
110 &  1.9 &  6.463 & -4.563 \tabularnewline
111 & -5.3 &  1.763 & -7.063 \tabularnewline
112 &  8.6 & -1.784 &  10.38 \tabularnewline
113 &  5.6 & -6.496 &  12.1 \tabularnewline
114 & -5.9 &  2.759 & -8.659 \tabularnewline
115 & -4.2 &  2.729 & -6.929 \tabularnewline
116 & -5.8 & -7.377 &  1.577 \tabularnewline
117 &  13.5 &  14.57 & -1.075 \tabularnewline
118 &  2.9 & -4.226 &  7.126 \tabularnewline
119 & -7.6 & -5.72 & -1.88 \tabularnewline
120 & -4.7 &  3.988 & -8.688 \tabularnewline
121 &  17.6 &  7.236 &  10.36 \tabularnewline
122 & -14 & -9.78 & -4.22 \tabularnewline
123 &  4 &  0.8084 &  3.192 \tabularnewline
124 &  2 & -1.074 &  3.074 \tabularnewline
125 & -4.9 & -1.28 & -3.62 \tabularnewline
126 & -5.9 & -4.318 & -1.582 \tabularnewline
127 &  16.3 &  10.5 &  5.797 \tabularnewline
128 & -6.4 & -4.416 & -1.984 \tabularnewline
129 &  2.4 & -3.606 &  6.006 \tabularnewline
130 & -2.5 &  5.887 & -8.387 \tabularnewline
131 & -1.7 & -4.192 &  2.492 \tabularnewline
132 &  8.4 &  6.913 &  1.487 \tabularnewline
133 & -4.5 & -7.683 &  3.183 \tabularnewline
134 & -0.1 &  1.62 & -1.72 \tabularnewline
135 & -5.4 &  7.475 & -12.88 \tabularnewline
136 &  8 &  4.348 &  3.652 \tabularnewline
137 & -3.9 & -3.343 & -0.5569 \tabularnewline
138 & -1.7 &  3.748 & -5.448 \tabularnewline
139 &  2.1 & -7.004 &  9.104 \tabularnewline
140 & -4.1 & -1.824 & -2.276 \tabularnewline
141 & -0.5 & -2.13 &  1.63 \tabularnewline
142 &  6 &  0.972 &  5.028 \tabularnewline
143 & -1.9 & -2.539 &  0.6392 \tabularnewline
144 &  6.3 &  0.312 &  5.988 \tabularnewline
145 & -6.6 &  0.951 & -7.551 \tabularnewline
146 & -9 &  0.09699 & -9.097 \tabularnewline
147 &  15.5 &  3.436 &  12.06 \tabularnewline
148 & -6.2 & -7.625 &  1.425 \tabularnewline
149 &  4 &  1.954 &  2.046 \tabularnewline
150 &  15.2 &  4.371 &  10.83 \tabularnewline
151 & -9.3 & -4.97 & -4.33 \tabularnewline
152 &  1 &  3.707 & -2.707 \tabularnewline
153 &  11 &  7.475 &  3.525 \tabularnewline
154 & -13.5 & -8.155 & -5.345 \tabularnewline
155 & -1.8 &  5.811 & -7.611 \tabularnewline
156 & -2.2 & -1.635 & -0.5653 \tabularnewline
157 &  2.1 & -0.9668 &  3.067 \tabularnewline
158 &  10.1 &  1.394 &  8.706 \tabularnewline
159 & -10 & -7.395 & -2.605 \tabularnewline
160 & -7.3 &  4.921 & -12.22 \tabularnewline
161 &  1.2 &  2.961 & -1.761 \tabularnewline
162 & -9.2 & -11.76 &  2.564 \tabularnewline
163 &  7.8 &  10.58 & -2.781 \tabularnewline
164 & -0.8 & -2.57 &  1.77 \tabularnewline
165 & -8.6 & -4.623 & -3.977 \tabularnewline
166 & -4.5 &  6.377 & -10.88 \tabularnewline
167 &  13.9 &  2.881 &  11.02 \tabularnewline
168 & -4.1 & -5.049 &  0.9494 \tabularnewline
169 & -4.8 & -4.489 & -0.3108 \tabularnewline
170 &  12.1 &  2.66 &  9.44 \tabularnewline
171 &  1.6 &  3.164 & -1.564 \tabularnewline
172 &  1.6 & -5.721 &  7.321 \tabularnewline
173 & -6.3 &  6.443 & -12.74 \tabularnewline
174 &  10.7 &  10.29 &  0.4096 \tabularnewline
175 & -17.8 & -17.05 & -0.746 \tabularnewline
176 &  14.8 &  13.12 &  1.678 \tabularnewline
177 & -1.9 & -5.319 &  3.419 \tabularnewline
178 & -1 &  0.01592 & -1.016 \tabularnewline
179 &  4.8 &  0.1452 &  4.655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309974&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]-4.3[/C][C]-3.63[/C][C]-0.6698[/C][/ROW]
[ROW][C]2[/C][C]-0.7[/C][C] 1.935[/C][C]-2.635[/C][/ROW]
[ROW][C]3[/C][C] 3.8[/C][C]-0.9018[/C][C] 4.702[/C][/ROW]
[ROW][C]4[/C][C]-9[/C][C]-1.613[/C][C]-7.387[/C][/ROW]
[ROW][C]5[/C][C] 5.1[/C][C] 4.273[/C][C] 0.8271[/C][/ROW]
[ROW][C]6[/C][C]-3.1[/C][C] 1.983[/C][C]-5.083[/C][/ROW]
[ROW][C]7[/C][C] 0.9[/C][C]-7.036[/C][C] 7.936[/C][/ROW]
[ROW][C]8[/C][C]-3.3[/C][C] 0.6453[/C][C]-3.945[/C][/ROW]
[ROW][C]9[/C][C] 5.2[/C][C] 2.245[/C][C] 2.955[/C][/ROW]
[ROW][C]10[/C][C]-2.8[/C][C]-4.283[/C][C] 1.483[/C][/ROW]
[ROW][C]11[/C][C]-8.8[/C][C]-0.1943[/C][C]-8.606[/C][/ROW]
[ROW][C]12[/C][C] 7.8[/C][C] 5.288[/C][C] 2.512[/C][/ROW]
[ROW][C]13[/C][C] 7.1[/C][C]-2.838[/C][C] 9.938[/C][/ROW]
[ROW][C]14[/C][C]-6.1[/C][C]-5.117[/C][C]-0.9827[/C][/ROW]
[ROW][C]15[/C][C] 5.7[/C][C] 7.078[/C][C]-1.378[/C][/ROW]
[ROW][C]16[/C][C]-0.5[/C][C] 1.107[/C][C]-1.607[/C][/ROW]
[ROW][C]17[/C][C]-0.1[/C][C]-1.098[/C][C] 0.9981[/C][/ROW]
[ROW][C]18[/C][C] 12.5[/C][C] 3.895[/C][C] 8.605[/C][/ROW]
[ROW][C]19[/C][C]-4.3[/C][C]-5.229[/C][C] 0.9292[/C][/ROW]
[ROW][C]20[/C][C]-0.1[/C][C] 2.764[/C][C]-2.864[/C][/ROW]
[ROW][C]21[/C][C] 8.8[/C][C] 3.013[/C][C] 5.787[/C][/ROW]
[ROW][C]22[/C][C]-13[/C][C]-7.75[/C][C]-5.25[/C][/ROW]
[ROW][C]23[/C][C] 12.6[/C][C] 11.63[/C][C] 0.9714[/C][/ROW]
[ROW][C]24[/C][C]-3.4[/C][C]-6.993[/C][C] 3.593[/C][/ROW]
[ROW][C]25[/C][C]-8.6[/C][C]-4.597[/C][C]-4.003[/C][/ROW]
[ROW][C]26[/C][C] 16.6[/C][C] 8.992[/C][C] 7.608[/C][/ROW]
[ROW][C]27[/C][C]-7.3[/C][C]-5.806[/C][C]-1.494[/C][/ROW]
[ROW][C]28[/C][C]-4.3[/C][C]-3.299[/C][C]-1.001[/C][/ROW]
[ROW][C]29[/C][C] 2[/C][C] 3.935[/C][C]-1.935[/C][/ROW]
[ROW][C]30[/C][C]-7.4[/C][C]-10.02[/C][C] 2.618[/C][/ROW]
[ROW][C]31[/C][C] 1.7[/C][C] 9.436[/C][C]-7.736[/C][/ROW]
[ROW][C]32[/C][C]-5.2[/C][C]-2.202[/C][C]-2.998[/C][/ROW]
[ROW][C]33[/C][C]-7.3[/C][C]-2.645[/C][C]-4.655[/C][/ROW]
[ROW][C]34[/C][C]-2.9[/C][C] 5.749[/C][C]-8.649[/C][/ROW]
[ROW][C]35[/C][C]-2.4[/C][C]-2.611[/C][C] 0.2106[/C][/ROW]
[ROW][C]36[/C][C] 5[/C][C]-0.6685[/C][C] 5.668[/C][/ROW]
[ROW][C]37[/C][C]-6.2[/C][C]-2.209[/C][C]-3.991[/C][/ROW]
[ROW][C]38[/C][C] 12.2[/C][C] 0.7055[/C][C] 11.49[/C][/ROW]
[ROW][C]39[/C][C]-6.8[/C][C]-1.631[/C][C]-5.169[/C][/ROW]
[ROW][C]40[/C][C] 16.3[/C][C] 3.388[/C][C] 12.91[/C][/ROW]
[ROW][C]41[/C][C]-3.1[/C][C]-1.48[/C][C]-1.62[/C][/ROW]
[ROW][C]42[/C][C] 16.4[/C][C] 5.825[/C][C] 10.57[/C][/ROW]
[ROW][C]43[/C][C]-17.2[/C][C]-8.864[/C][C]-8.336[/C][/ROW]
[ROW][C]44[/C][C] 18.7[/C][C] 13.47[/C][C] 5.23[/C][/ROW]
[ROW][C]45[/C][C]-1.5[/C][C]-3.153[/C][C] 1.653[/C][/ROW]
[ROW][C]46[/C][C]-12.3[/C][C]-1.65[/C][C]-10.65[/C][/ROW]
[ROW][C]47[/C][C] 5.1[/C][C] 8.361[/C][C]-3.261[/C][/ROW]
[ROW][C]48[/C][C]-8.6[/C][C]-5.273[/C][C]-3.327[/C][/ROW]
[ROW][C]49[/C][C] 14.9[/C][C] 4.223[/C][C] 10.68[/C][/ROW]
[ROW][C]50[/C][C]-13.2[/C][C]-5.5[/C][C]-7.7[/C][/ROW]
[ROW][C]51[/C][C] 4[/C][C] 1.502[/C][C] 2.498[/C][/ROW]
[ROW][C]52[/C][C] 0.5[/C][C] 0.06027[/C][C] 0.4397[/C][/ROW]
[ROW][C]53[/C][C]-4.6[/C][C]-0.7178[/C][C]-3.882[/C][/ROW]
[ROW][C]54[/C][C]-14.5[/C][C]-5.515[/C][C]-8.985[/C][/ROW]
[ROW][C]55[/C][C] 8.4[/C][C] 8.208[/C][C] 0.192[/C][/ROW]
[ROW][C]56[/C][C]-2.9[/C][C]-12.76[/C][C] 9.86[/C][/ROW]
[ROW][C]57[/C][C]-4.8[/C][C]-1.171[/C][C]-3.629[/C][/ROW]
[ROW][C]58[/C][C] 17.9[/C][C] 7.437[/C][C] 10.46[/C][/ROW]
[ROW][C]59[/C][C]-10.7[/C][C]-6.783[/C][C]-3.917[/C][/ROW]
[ROW][C]60[/C][C]-2[/C][C] 2.333[/C][C]-4.333[/C][/ROW]
[ROW][C]61[/C][C]-0.1[/C][C] 5.409[/C][C]-5.509[/C][/ROW]
[ROW][C]62[/C][C]-4.8[/C][C]-2.735[/C][C]-2.065[/C][/ROW]
[ROW][C]63[/C][C]-4.3[/C][C]-3.028[/C][C]-1.272[/C][/ROW]
[ROW][C]64[/C][C] 8.6[/C][C] 9.031[/C][C]-0.4306[/C][/ROW]
[ROW][C]65[/C][C] 11[/C][C]-3.11[/C][C] 14.11[/C][/ROW]
[ROW][C]66[/C][C]-10.9[/C][C]-8.512[/C][C]-2.388[/C][/ROW]
[ROW][C]67[/C][C] 25.5[/C][C] 17.09[/C][C] 8.414[/C][/ROW]
[ROW][C]68[/C][C]-21.6[/C][C]-9.499[/C][C]-12.1[/C][/ROW]
[ROW][C]69[/C][C] 11[/C][C] 2.518[/C][C] 8.482[/C][/ROW]
[ROW][C]70[/C][C]-1.7[/C][C] 0.9501[/C][C]-2.65[/C][/ROW]
[ROW][C]71[/C][C]-11.3[/C][C]-6.98[/C][C]-4.32[/C][/ROW]
[ROW][C]72[/C][C] 16.2[/C][C] 10.29[/C][C] 5.907[/C][/ROW]
[ROW][C]73[/C][C]-10.4[/C][C]-10.74[/C][C] 0.3409[/C][/ROW]
[ROW][C]74[/C][C]-23.1[/C][C] 0.05549[/C][C]-23.16[/C][/ROW]
[ROW][C]75[/C][C] 9.1[/C][C] 13.03[/C][C]-3.931[/C][/ROW]
[ROW][C]76[/C][C]-35.3[/C][C]-17.49[/C][C]-17.81[/C][/ROW]
[ROW][C]77[/C][C]-5.9[/C][C] 1.959[/C][C]-7.859[/C][/ROW]
[ROW][C]78[/C][C] 7.8[/C][C] 16.08[/C][C]-8.277[/C][/ROW]
[ROW][C]79[/C][C]-23.8[/C][C]-31.07[/C][C] 7.265[/C][/ROW]
[ROW][C]80[/C][C] 18.4[/C][C] 16.9[/C][C] 1.502[/C][/ROW]
[ROW][C]81[/C][C]-6.3[/C][C]-3.238[/C][C]-3.062[/C][/ROW]
[ROW][C]82[/C][C] 9.8[/C][C]-4.596[/C][C] 14.4[/C][/ROW]
[ROW][C]83[/C][C] 19.1[/C][C] 6.479[/C][C] 12.62[/C][/ROW]
[ROW][C]84[/C][C]-5.6[/C][C]-8.222[/C][C] 2.622[/C][/ROW]
[ROW][C]85[/C][C] 0.6[/C][C] 9.95[/C][C]-9.35[/C][/ROW]
[ROW][C]86[/C][C] 22.9[/C][C] 5.904[/C][C] 17[/C][/ROW]
[ROW][C]87[/C][C]-1.6[/C][C]-7.61[/C][C] 6.01[/C][/ROW]
[ROW][C]88[/C][C] 19.7[/C][C] 11.04[/C][C] 8.663[/C][/ROW]
[ROW][C]89[/C][C] 2.7[/C][C]-2.692[/C][C] 5.392[/C][/ROW]
[ROW][C]90[/C][C] 12.4[/C][C] 5.443[/C][C] 6.957[/C][/ROW]
[ROW][C]91[/C][C] 3.2[/C][C] 7.191[/C][C]-3.991[/C][/ROW]
[ROW][C]92[/C][C]-3.5[/C][C]-7.296[/C][C] 3.796[/C][/ROW]
[ROW][C]93[/C][C] 8.4[/C][C] 10.64[/C][C]-2.244[/C][/ROW]
[ROW][C]94[/C][C]-14.3[/C][C]-7.224[/C][C]-7.076[/C][/ROW]
[ROW][C]95[/C][C]-9.6[/C][C]-1.348[/C][C]-8.252[/C][/ROW]
[ROW][C]96[/C][C] 4.8[/C][C] 3.575[/C][C] 1.225[/C][/ROW]
[ROW][C]97[/C][C]-3.9[/C][C]-5.988[/C][C] 2.088[/C][/ROW]
[ROW][C]98[/C][C] 6.2[/C][C]-3.158[/C][C] 9.358[/C][/ROW]
[ROW][C]99[/C][C] 0.2[/C][C]-6.529[/C][C] 6.729[/C][/ROW]
[ROW][C]100[/C][C]-7[/C][C] 0.1878[/C][C]-7.188[/C][/ROW]
[ROW][C]101[/C][C]-3.7[/C][C] 8.141[/C][C]-11.84[/C][/ROW]
[ROW][C]102[/C][C]-3[/C][C]-9.821[/C][C] 6.821[/C][/ROW]
[ROW][C]103[/C][C]-7.6[/C][C]-1.606[/C][C]-5.994[/C][/ROW]
[ROW][C]104[/C][C] 10[/C][C] 11.74[/C][C]-1.737[/C][/ROW]
[ROW][C]105[/C][C]-23.9[/C][C]-19.08[/C][C]-4.822[/C][/ROW]
[ROW][C]106[/C][C] 7.7[/C][C] 10.24[/C][C]-2.542[/C][/ROW]
[ROW][C]107[/C][C] 9.3[/C][C] 4.701[/C][C] 4.599[/C][/ROW]
[ROW][C]108[/C][C]-8.1[/C][C]-10.38[/C][C] 2.285[/C][/ROW]
[ROW][C]109[/C][C]-3.2[/C][C] 3.541[/C][C]-6.741[/C][/ROW]
[ROW][C]110[/C][C] 1.9[/C][C] 6.463[/C][C]-4.563[/C][/ROW]
[ROW][C]111[/C][C]-5.3[/C][C] 1.763[/C][C]-7.063[/C][/ROW]
[ROW][C]112[/C][C] 8.6[/C][C]-1.784[/C][C] 10.38[/C][/ROW]
[ROW][C]113[/C][C] 5.6[/C][C]-6.496[/C][C] 12.1[/C][/ROW]
[ROW][C]114[/C][C]-5.9[/C][C] 2.759[/C][C]-8.659[/C][/ROW]
[ROW][C]115[/C][C]-4.2[/C][C] 2.729[/C][C]-6.929[/C][/ROW]
[ROW][C]116[/C][C]-5.8[/C][C]-7.377[/C][C] 1.577[/C][/ROW]
[ROW][C]117[/C][C] 13.5[/C][C] 14.57[/C][C]-1.075[/C][/ROW]
[ROW][C]118[/C][C] 2.9[/C][C]-4.226[/C][C] 7.126[/C][/ROW]
[ROW][C]119[/C][C]-7.6[/C][C]-5.72[/C][C]-1.88[/C][/ROW]
[ROW][C]120[/C][C]-4.7[/C][C] 3.988[/C][C]-8.688[/C][/ROW]
[ROW][C]121[/C][C] 17.6[/C][C] 7.236[/C][C] 10.36[/C][/ROW]
[ROW][C]122[/C][C]-14[/C][C]-9.78[/C][C]-4.22[/C][/ROW]
[ROW][C]123[/C][C] 4[/C][C] 0.8084[/C][C] 3.192[/C][/ROW]
[ROW][C]124[/C][C] 2[/C][C]-1.074[/C][C] 3.074[/C][/ROW]
[ROW][C]125[/C][C]-4.9[/C][C]-1.28[/C][C]-3.62[/C][/ROW]
[ROW][C]126[/C][C]-5.9[/C][C]-4.318[/C][C]-1.582[/C][/ROW]
[ROW][C]127[/C][C] 16.3[/C][C] 10.5[/C][C] 5.797[/C][/ROW]
[ROW][C]128[/C][C]-6.4[/C][C]-4.416[/C][C]-1.984[/C][/ROW]
[ROW][C]129[/C][C] 2.4[/C][C]-3.606[/C][C] 6.006[/C][/ROW]
[ROW][C]130[/C][C]-2.5[/C][C] 5.887[/C][C]-8.387[/C][/ROW]
[ROW][C]131[/C][C]-1.7[/C][C]-4.192[/C][C] 2.492[/C][/ROW]
[ROW][C]132[/C][C] 8.4[/C][C] 6.913[/C][C] 1.487[/C][/ROW]
[ROW][C]133[/C][C]-4.5[/C][C]-7.683[/C][C] 3.183[/C][/ROW]
[ROW][C]134[/C][C]-0.1[/C][C] 1.62[/C][C]-1.72[/C][/ROW]
[ROW][C]135[/C][C]-5.4[/C][C] 7.475[/C][C]-12.88[/C][/ROW]
[ROW][C]136[/C][C] 8[/C][C] 4.348[/C][C] 3.652[/C][/ROW]
[ROW][C]137[/C][C]-3.9[/C][C]-3.343[/C][C]-0.5569[/C][/ROW]
[ROW][C]138[/C][C]-1.7[/C][C] 3.748[/C][C]-5.448[/C][/ROW]
[ROW][C]139[/C][C] 2.1[/C][C]-7.004[/C][C] 9.104[/C][/ROW]
[ROW][C]140[/C][C]-4.1[/C][C]-1.824[/C][C]-2.276[/C][/ROW]
[ROW][C]141[/C][C]-0.5[/C][C]-2.13[/C][C] 1.63[/C][/ROW]
[ROW][C]142[/C][C] 6[/C][C] 0.972[/C][C] 5.028[/C][/ROW]
[ROW][C]143[/C][C]-1.9[/C][C]-2.539[/C][C] 0.6392[/C][/ROW]
[ROW][C]144[/C][C] 6.3[/C][C] 0.312[/C][C] 5.988[/C][/ROW]
[ROW][C]145[/C][C]-6.6[/C][C] 0.951[/C][C]-7.551[/C][/ROW]
[ROW][C]146[/C][C]-9[/C][C] 0.09699[/C][C]-9.097[/C][/ROW]
[ROW][C]147[/C][C] 15.5[/C][C] 3.436[/C][C] 12.06[/C][/ROW]
[ROW][C]148[/C][C]-6.2[/C][C]-7.625[/C][C] 1.425[/C][/ROW]
[ROW][C]149[/C][C] 4[/C][C] 1.954[/C][C] 2.046[/C][/ROW]
[ROW][C]150[/C][C] 15.2[/C][C] 4.371[/C][C] 10.83[/C][/ROW]
[ROW][C]151[/C][C]-9.3[/C][C]-4.97[/C][C]-4.33[/C][/ROW]
[ROW][C]152[/C][C] 1[/C][C] 3.707[/C][C]-2.707[/C][/ROW]
[ROW][C]153[/C][C] 11[/C][C] 7.475[/C][C] 3.525[/C][/ROW]
[ROW][C]154[/C][C]-13.5[/C][C]-8.155[/C][C]-5.345[/C][/ROW]
[ROW][C]155[/C][C]-1.8[/C][C] 5.811[/C][C]-7.611[/C][/ROW]
[ROW][C]156[/C][C]-2.2[/C][C]-1.635[/C][C]-0.5653[/C][/ROW]
[ROW][C]157[/C][C] 2.1[/C][C]-0.9668[/C][C] 3.067[/C][/ROW]
[ROW][C]158[/C][C] 10.1[/C][C] 1.394[/C][C] 8.706[/C][/ROW]
[ROW][C]159[/C][C]-10[/C][C]-7.395[/C][C]-2.605[/C][/ROW]
[ROW][C]160[/C][C]-7.3[/C][C] 4.921[/C][C]-12.22[/C][/ROW]
[ROW][C]161[/C][C] 1.2[/C][C] 2.961[/C][C]-1.761[/C][/ROW]
[ROW][C]162[/C][C]-9.2[/C][C]-11.76[/C][C] 2.564[/C][/ROW]
[ROW][C]163[/C][C] 7.8[/C][C] 10.58[/C][C]-2.781[/C][/ROW]
[ROW][C]164[/C][C]-0.8[/C][C]-2.57[/C][C] 1.77[/C][/ROW]
[ROW][C]165[/C][C]-8.6[/C][C]-4.623[/C][C]-3.977[/C][/ROW]
[ROW][C]166[/C][C]-4.5[/C][C] 6.377[/C][C]-10.88[/C][/ROW]
[ROW][C]167[/C][C] 13.9[/C][C] 2.881[/C][C] 11.02[/C][/ROW]
[ROW][C]168[/C][C]-4.1[/C][C]-5.049[/C][C] 0.9494[/C][/ROW]
[ROW][C]169[/C][C]-4.8[/C][C]-4.489[/C][C]-0.3108[/C][/ROW]
[ROW][C]170[/C][C] 12.1[/C][C] 2.66[/C][C] 9.44[/C][/ROW]
[ROW][C]171[/C][C] 1.6[/C][C] 3.164[/C][C]-1.564[/C][/ROW]
[ROW][C]172[/C][C] 1.6[/C][C]-5.721[/C][C] 7.321[/C][/ROW]
[ROW][C]173[/C][C]-6.3[/C][C] 6.443[/C][C]-12.74[/C][/ROW]
[ROW][C]174[/C][C] 10.7[/C][C] 10.29[/C][C] 0.4096[/C][/ROW]
[ROW][C]175[/C][C]-17.8[/C][C]-17.05[/C][C]-0.746[/C][/ROW]
[ROW][C]176[/C][C] 14.8[/C][C] 13.12[/C][C] 1.678[/C][/ROW]
[ROW][C]177[/C][C]-1.9[/C][C]-5.319[/C][C] 3.419[/C][/ROW]
[ROW][C]178[/C][C]-1[/C][C] 0.01592[/C][C]-1.016[/C][/ROW]
[ROW][C]179[/C][C] 4.8[/C][C] 0.1452[/C][C] 4.655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309974&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309974&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	-4.3	-3.63	-0.6698
2	-0.7	1.935	-2.635
3	3.8	-0.9018	4.702
4	-9	-1.613	-7.387
5	5.1	4.273	0.8271
6	-3.1	1.983	-5.083
7	0.9	-7.036	7.936
8	-3.3	0.6453	-3.945
9	5.2	2.245	2.955
10	-2.8	-4.283	1.483
11	-8.8	-0.1943	-8.606
12	7.8	5.288	2.512
13	7.1	-2.838	9.938
14	-6.1	-5.117	-0.9827
15	5.7	7.078	-1.378
16	-0.5	1.107	-1.607
17	-0.1	-1.098	0.9981
18	12.5	3.895	8.605
19	-4.3	-5.229	0.9292
20	-0.1	2.764	-2.864
21	8.8	3.013	5.787
22	-13	-7.75	-5.25
23	12.6	11.63	0.9714
24	-3.4	-6.993	3.593
25	-8.6	-4.597	-4.003
26	16.6	8.992	7.608
27	-7.3	-5.806	-1.494
28	-4.3	-3.299	-1.001
29	2	3.935	-1.935
30	-7.4	-10.02	2.618
31	1.7	9.436	-7.736
32	-5.2	-2.202	-2.998
33	-7.3	-2.645	-4.655
34	-2.9	5.749	-8.649
35	-2.4	-2.611	0.2106
36	5	-0.6685	5.668
37	-6.2	-2.209	-3.991
38	12.2	0.7055	11.49
39	-6.8	-1.631	-5.169
40	16.3	3.388	12.91
41	-3.1	-1.48	-1.62
42	16.4	5.825	10.57
43	-17.2	-8.864	-8.336
44	18.7	13.47	5.23
45	-1.5	-3.153	1.653
46	-12.3	-1.65	-10.65
47	5.1	8.361	-3.261
48	-8.6	-5.273	-3.327
49	14.9	4.223	10.68
50	-13.2	-5.5	-7.7
51	4	1.502	2.498
52	0.5	0.06027	0.4397
53	-4.6	-0.7178	-3.882
54	-14.5	-5.515	-8.985
55	8.4	8.208	0.192
56	-2.9	-12.76	9.86
57	-4.8	-1.171	-3.629
58	17.9	7.437	10.46
59	-10.7	-6.783	-3.917
60	-2	2.333	-4.333
61	-0.1	5.409	-5.509
62	-4.8	-2.735	-2.065
63	-4.3	-3.028	-1.272
64	8.6	9.031	-0.4306
65	11	-3.11	14.11
66	-10.9	-8.512	-2.388
67	25.5	17.09	8.414
68	-21.6	-9.499	-12.1
69	11	2.518	8.482
70	-1.7	0.9501	-2.65
71	-11.3	-6.98	-4.32
72	16.2	10.29	5.907
73	-10.4	-10.74	0.3409
74	-23.1	0.05549	-23.16
75	9.1	13.03	-3.931
76	-35.3	-17.49	-17.81
77	-5.9	1.959	-7.859
78	7.8	16.08	-8.277
79	-23.8	-31.07	7.265
80	18.4	16.9	1.502
81	-6.3	-3.238	-3.062
82	9.8	-4.596	14.4
83	19.1	6.479	12.62
84	-5.6	-8.222	2.622
85	0.6	9.95	-9.35
86	22.9	5.904	17
87	-1.6	-7.61	6.01
88	19.7	11.04	8.663
89	2.7	-2.692	5.392
90	12.4	5.443	6.957
91	3.2	7.191	-3.991
92	-3.5	-7.296	3.796
93	8.4	10.64	-2.244
94	-14.3	-7.224	-7.076
95	-9.6	-1.348	-8.252
96	4.8	3.575	1.225
97	-3.9	-5.988	2.088
98	6.2	-3.158	9.358
99	0.2	-6.529	6.729
100	-7	0.1878	-7.188
101	-3.7	8.141	-11.84
102	-3	-9.821	6.821
103	-7.6	-1.606	-5.994
104	10	11.74	-1.737
105	-23.9	-19.08	-4.822
106	7.7	10.24	-2.542
107	9.3	4.701	4.599
108	-8.1	-10.38	2.285
109	-3.2	3.541	-6.741
110	1.9	6.463	-4.563
111	-5.3	1.763	-7.063
112	8.6	-1.784	10.38
113	5.6	-6.496	12.1
114	-5.9	2.759	-8.659
115	-4.2	2.729	-6.929
116	-5.8	-7.377	1.577
117	13.5	14.57	-1.075
118	2.9	-4.226	7.126
119	-7.6	-5.72	-1.88
120	-4.7	3.988	-8.688
121	17.6	7.236	10.36
122	-14	-9.78	-4.22
123	4	0.8084	3.192
124	2	-1.074	3.074
125	-4.9	-1.28	-3.62
126	-5.9	-4.318	-1.582
127	16.3	10.5	5.797
128	-6.4	-4.416	-1.984
129	2.4	-3.606	6.006
130	-2.5	5.887	-8.387
131	-1.7	-4.192	2.492
132	8.4	6.913	1.487
133	-4.5	-7.683	3.183
134	-0.1	1.62	-1.72
135	-5.4	7.475	-12.88
136	8	4.348	3.652
137	-3.9	-3.343	-0.5569
138	-1.7	3.748	-5.448
139	2.1	-7.004	9.104
140	-4.1	-1.824	-2.276
141	-0.5	-2.13	1.63
142	6	0.972	5.028
143	-1.9	-2.539	0.6392
144	6.3	0.312	5.988
145	-6.6	0.951	-7.551
146	-9	0.09699	-9.097
147	15.5	3.436	12.06
148	-6.2	-7.625	1.425
149	4	1.954	2.046
150	15.2	4.371	10.83
151	-9.3	-4.97	-4.33
152	1	3.707	-2.707
153	11	7.475	3.525
154	-13.5	-8.155	-5.345
155	-1.8	5.811	-7.611
156	-2.2	-1.635	-0.5653
157	2.1	-0.9668	3.067
158	10.1	1.394	8.706
159	-10	-7.395	-2.605
160	-7.3	4.921	-12.22
161	1.2	2.961	-1.761
162	-9.2	-11.76	2.564
163	7.8	10.58	-2.781
164	-0.8	-2.57	1.77
165	-8.6	-4.623	-3.977
166	-4.5	6.377	-10.88
167	13.9	2.881	11.02
168	-4.1	-5.049	0.9494
169	-4.8	-4.489	-0.3108
170	12.1	2.66	9.44
171	1.6	3.164	-1.564
172	1.6	-5.721	7.321
173	-6.3	6.443	-12.74
174	10.7	10.29	0.4096
175	-17.8	-17.05	-0.746
176	14.8	13.12	1.678
177	-1.9	-5.319	3.419
178	-1	0.01592	-1.016
179	4.8	0.1452	4.655

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
14	0.4474	0.8947	0.5526
15	0.39	0.78	0.61
16	0.2825	0.565	0.7175
17	0.1777	0.3554	0.8223
18	0.2364	0.4728	0.7636
19	0.1572	0.3145	0.8428
20	0.09858	0.1972	0.9014
21	0.09862	0.1972	0.9014
22	0.07959	0.1592	0.9204
23	0.06	0.12	0.94
24	0.03717	0.07435	0.9628
25	0.02419	0.04839	0.9758
26	0.02656	0.05311	0.9734
27	0.01808	0.03617	0.9819
28	0.01086	0.02171	0.9891
29	0.006325	0.01265	0.9937
30	0.003603	0.007205	0.9964
31	0.002875	0.005749	0.9971
32	0.001964	0.003928	0.998
33	0.001443	0.002885	0.9986
34	0.007701	0.0154	0.9923
35	0.004654	0.009308	0.9953
36	0.007638	0.01528	0.9924
37	0.004976	0.009952	0.995
38	0.004505	0.00901	0.9955
39	0.00315	0.0063	0.9969
40	0.02461	0.04921	0.9754
41	0.01898	0.03795	0.981
42	0.01995	0.03991	0.98
43	0.01631	0.03262	0.9837
44	0.01237	0.02474	0.9876
45	0.009556	0.01911	0.9904
46	0.01465	0.0293	0.9853
47	0.01319	0.02639	0.9868
48	0.009598	0.0192	0.9904
49	0.01374	0.02749	0.9863
50	0.01561	0.03122	0.9844
51	0.01135	0.0227	0.9887
52	0.008053	0.01611	0.9919
53	0.01369	0.02737	0.9863
54	0.01294	0.02589	0.9871
55	0.009145	0.01829	0.9909
56	0.01481	0.02962	0.9852
57	0.01207	0.02415	0.9879
58	0.03175	0.06351	0.9682
59	0.02558	0.05116	0.9744
60	0.02023	0.04045	0.9798
61	0.0222	0.0444	0.9778
62	0.01805	0.0361	0.982
63	0.01504	0.03007	0.985
64	0.01539	0.03078	0.9846
65	0.04389	0.08778	0.9561
66	0.04134	0.08269	0.9587
67	0.03955	0.0791	0.9604
68	0.07001	0.14	0.93
69	0.09793	0.1959	0.9021
70	0.08209	0.1642	0.9179
71	0.07166	0.1433	0.9283
72	0.06486	0.1297	0.9351
73	0.05206	0.1041	0.9479
74	0.3206	0.6411	0.6794
75	0.3151	0.6303	0.6849
76	0.542	0.916	0.458
77	0.5505	0.899	0.4495
78	0.5825	0.835	0.4175
79	0.595	0.81	0.405
80	0.5683	0.8634	0.4317
81	0.5414	0.9172	0.4586
82	0.6757	0.6485	0.3243
83	0.7667	0.4666	0.2333
84	0.7367	0.5265	0.2633
85	0.7767	0.4466	0.2233
86	0.9246	0.1508	0.07542
87	0.9188	0.1624	0.08121
88	0.9441	0.1118	0.05589
89	0.9418	0.1164	0.0582
90	0.9719	0.05624	0.02812
91	0.9685	0.0631	0.03155
92	0.9754	0.04918	0.02459
93	0.9716	0.05679	0.0284
94	0.9705	0.05904	0.02952
95	0.9742	0.05153	0.02576
96	0.9679	0.0641	0.03205
97	0.9624	0.07513	0.03756
98	0.9647	0.07065	0.03532
99	0.9636	0.07282	0.03641
100	0.9637	0.07254	0.03627
101	0.9769	0.04616	0.02308
102	0.975	0.05006	0.02503
103	0.9776	0.04473	0.02236
104	0.9765	0.04706	0.02353
105	0.9839	0.03228	0.01614
106	0.9804	0.03925	0.01963
107	0.9755	0.0491	0.02455
108	0.9683	0.06341	0.0317
109	0.966	0.06796	0.03398
110	0.9629	0.07421	0.03711
111	0.9665	0.06693	0.03347
112	0.9709	0.0581	0.02905
113	0.9877	0.02466	0.01233
114	0.9882	0.02361	0.0118
115	0.9912	0.01761	0.008807
116	0.9879	0.02411	0.01205
117	0.9856	0.02872	0.01436
118	0.9826	0.03472	0.01736
119	0.977	0.04599	0.023
120	0.9752	0.0495	0.02475
121	0.9797	0.04059	0.02029
122	0.9768	0.04634	0.02317
123	0.9733	0.05347	0.02674
124	0.9655	0.06904	0.03452
125	0.9632	0.07357	0.03679
126	0.9562	0.08753	0.04377
127	0.9575	0.08494	0.04247
128	0.9445	0.111	0.05552
129	0.9375	0.1251	0.06254
130	0.934	0.132	0.06599
131	0.9191	0.1619	0.08095
132	0.8974	0.2053	0.1026
133	0.8718	0.2563	0.1282
134	0.8403	0.3194	0.1597
135	0.9099	0.1801	0.09006
136	0.8863	0.2274	0.1137
137	0.8604	0.2793	0.1396
138	0.8363	0.3275	0.1637
139	0.8254	0.3493	0.1746
140	0.8199	0.3601	0.1801
141	0.8265	0.347	0.1735
142	0.7954	0.4092	0.2046
143	0.7483	0.5035	0.2517
144	0.7706	0.4587	0.2294
145	0.7479	0.5041	0.2521
146	0.7651	0.4698	0.2349
147	0.7992	0.4016	0.2008
148	0.7504	0.4991	0.2495
149	0.6933	0.6134	0.3067
150	0.7967	0.4066	0.2033
151	0.7619	0.4763	0.2381
152	0.7261	0.5478	0.2739
153	0.7416	0.5169	0.2584
154	0.6852	0.6297	0.3148
155	0.6241	0.7518	0.3759
156	0.5421	0.9158	0.4579
157	0.4564	0.9128	0.5436
158	0.4275	0.8551	0.5725
159	0.4281	0.8561	0.5719
160	0.7143	0.5713	0.2857
161	0.6945	0.6109	0.3055
162	0.643	0.7141	0.357
163	0.5221	0.9558	0.4779
164	0.3801	0.7601	0.6199
165	0.2407	0.4814	0.7593

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 &  0.4474 &  0.8947 &  0.5526 \tabularnewline
15 &  0.39 &  0.78 &  0.61 \tabularnewline
16 &  0.2825 &  0.565 &  0.7175 \tabularnewline
17 &  0.1777 &  0.3554 &  0.8223 \tabularnewline
18 &  0.2364 &  0.4728 &  0.7636 \tabularnewline
19 &  0.1572 &  0.3145 &  0.8428 \tabularnewline
20 &  0.09858 &  0.1972 &  0.9014 \tabularnewline
21 &  0.09862 &  0.1972 &  0.9014 \tabularnewline
22 &  0.07959 &  0.1592 &  0.9204 \tabularnewline
23 &  0.06 &  0.12 &  0.94 \tabularnewline
24 &  0.03717 &  0.07435 &  0.9628 \tabularnewline
25 &  0.02419 &  0.04839 &  0.9758 \tabularnewline
26 &  0.02656 &  0.05311 &  0.9734 \tabularnewline
27 &  0.01808 &  0.03617 &  0.9819 \tabularnewline
28 &  0.01086 &  0.02171 &  0.9891 \tabularnewline
29 &  0.006325 &  0.01265 &  0.9937 \tabularnewline
30 &  0.003603 &  0.007205 &  0.9964 \tabularnewline
31 &  0.002875 &  0.005749 &  0.9971 \tabularnewline
32 &  0.001964 &  0.003928 &  0.998 \tabularnewline
33 &  0.001443 &  0.002885 &  0.9986 \tabularnewline
34 &  0.007701 &  0.0154 &  0.9923 \tabularnewline
35 &  0.004654 &  0.009308 &  0.9953 \tabularnewline
36 &  0.007638 &  0.01528 &  0.9924 \tabularnewline
37 &  0.004976 &  0.009952 &  0.995 \tabularnewline
38 &  0.004505 &  0.00901 &  0.9955 \tabularnewline
39 &  0.00315 &  0.0063 &  0.9969 \tabularnewline
40 &  0.02461 &  0.04921 &  0.9754 \tabularnewline
41 &  0.01898 &  0.03795 &  0.981 \tabularnewline
42 &  0.01995 &  0.03991 &  0.98 \tabularnewline
43 &  0.01631 &  0.03262 &  0.9837 \tabularnewline
44 &  0.01237 &  0.02474 &  0.9876 \tabularnewline
45 &  0.009556 &  0.01911 &  0.9904 \tabularnewline
46 &  0.01465 &  0.0293 &  0.9853 \tabularnewline
47 &  0.01319 &  0.02639 &  0.9868 \tabularnewline
48 &  0.009598 &  0.0192 &  0.9904 \tabularnewline
49 &  0.01374 &  0.02749 &  0.9863 \tabularnewline
50 &  0.01561 &  0.03122 &  0.9844 \tabularnewline
51 &  0.01135 &  0.0227 &  0.9887 \tabularnewline
52 &  0.008053 &  0.01611 &  0.9919 \tabularnewline
53 &  0.01369 &  0.02737 &  0.9863 \tabularnewline
54 &  0.01294 &  0.02589 &  0.9871 \tabularnewline
55 &  0.009145 &  0.01829 &  0.9909 \tabularnewline
56 &  0.01481 &  0.02962 &  0.9852 \tabularnewline
57 &  0.01207 &  0.02415 &  0.9879 \tabularnewline
58 &  0.03175 &  0.06351 &  0.9682 \tabularnewline
59 &  0.02558 &  0.05116 &  0.9744 \tabularnewline
60 &  0.02023 &  0.04045 &  0.9798 \tabularnewline
61 &  0.0222 &  0.0444 &  0.9778 \tabularnewline
62 &  0.01805 &  0.0361 &  0.982 \tabularnewline
63 &  0.01504 &  0.03007 &  0.985 \tabularnewline
64 &  0.01539 &  0.03078 &  0.9846 \tabularnewline
65 &  0.04389 &  0.08778 &  0.9561 \tabularnewline
66 &  0.04134 &  0.08269 &  0.9587 \tabularnewline
67 &  0.03955 &  0.0791 &  0.9604 \tabularnewline
68 &  0.07001 &  0.14 &  0.93 \tabularnewline
69 &  0.09793 &  0.1959 &  0.9021 \tabularnewline
70 &  0.08209 &  0.1642 &  0.9179 \tabularnewline
71 &  0.07166 &  0.1433 &  0.9283 \tabularnewline
72 &  0.06486 &  0.1297 &  0.9351 \tabularnewline
73 &  0.05206 &  0.1041 &  0.9479 \tabularnewline
74 &  0.3206 &  0.6411 &  0.6794 \tabularnewline
75 &  0.3151 &  0.6303 &  0.6849 \tabularnewline
76 &  0.542 &  0.916 &  0.458 \tabularnewline
77 &  0.5505 &  0.899 &  0.4495 \tabularnewline
78 &  0.5825 &  0.835 &  0.4175 \tabularnewline
79 &  0.595 &  0.81 &  0.405 \tabularnewline
80 &  0.5683 &  0.8634 &  0.4317 \tabularnewline
81 &  0.5414 &  0.9172 &  0.4586 \tabularnewline
82 &  0.6757 &  0.6485 &  0.3243 \tabularnewline
83 &  0.7667 &  0.4666 &  0.2333 \tabularnewline
84 &  0.7367 &  0.5265 &  0.2633 \tabularnewline
85 &  0.7767 &  0.4466 &  0.2233 \tabularnewline
86 &  0.9246 &  0.1508 &  0.07542 \tabularnewline
87 &  0.9188 &  0.1624 &  0.08121 \tabularnewline
88 &  0.9441 &  0.1118 &  0.05589 \tabularnewline
89 &  0.9418 &  0.1164 &  0.0582 \tabularnewline
90 &  0.9719 &  0.05624 &  0.02812 \tabularnewline
91 &  0.9685 &  0.0631 &  0.03155 \tabularnewline
92 &  0.9754 &  0.04918 &  0.02459 \tabularnewline
93 &  0.9716 &  0.05679 &  0.0284 \tabularnewline
94 &  0.9705 &  0.05904 &  0.02952 \tabularnewline
95 &  0.9742 &  0.05153 &  0.02576 \tabularnewline
96 &  0.9679 &  0.0641 &  0.03205 \tabularnewline
97 &  0.9624 &  0.07513 &  0.03756 \tabularnewline
98 &  0.9647 &  0.07065 &  0.03532 \tabularnewline
99 &  0.9636 &  0.07282 &  0.03641 \tabularnewline
100 &  0.9637 &  0.07254 &  0.03627 \tabularnewline
101 &  0.9769 &  0.04616 &  0.02308 \tabularnewline
102 &  0.975 &  0.05006 &  0.02503 \tabularnewline
103 &  0.9776 &  0.04473 &  0.02236 \tabularnewline
104 &  0.9765 &  0.04706 &  0.02353 \tabularnewline
105 &  0.9839 &  0.03228 &  0.01614 \tabularnewline
106 &  0.9804 &  0.03925 &  0.01963 \tabularnewline
107 &  0.9755 &  0.0491 &  0.02455 \tabularnewline
108 &  0.9683 &  0.06341 &  0.0317 \tabularnewline
109 &  0.966 &  0.06796 &  0.03398 \tabularnewline
110 &  0.9629 &  0.07421 &  0.03711 \tabularnewline
111 &  0.9665 &  0.06693 &  0.03347 \tabularnewline
112 &  0.9709 &  0.0581 &  0.02905 \tabularnewline
113 &  0.9877 &  0.02466 &  0.01233 \tabularnewline
114 &  0.9882 &  0.02361 &  0.0118 \tabularnewline
115 &  0.9912 &  0.01761 &  0.008807 \tabularnewline
116 &  0.9879 &  0.02411 &  0.01205 \tabularnewline
117 &  0.9856 &  0.02872 &  0.01436 \tabularnewline
118 &  0.9826 &  0.03472 &  0.01736 \tabularnewline
119 &  0.977 &  0.04599 &  0.023 \tabularnewline
120 &  0.9752 &  0.0495 &  0.02475 \tabularnewline
121 &  0.9797 &  0.04059 &  0.02029 \tabularnewline
122 &  0.9768 &  0.04634 &  0.02317 \tabularnewline
123 &  0.9733 &  0.05347 &  0.02674 \tabularnewline
124 &  0.9655 &  0.06904 &  0.03452 \tabularnewline
125 &  0.9632 &  0.07357 &  0.03679 \tabularnewline
126 &  0.9562 &  0.08753 &  0.04377 \tabularnewline
127 &  0.9575 &  0.08494 &  0.04247 \tabularnewline
128 &  0.9445 &  0.111 &  0.05552 \tabularnewline
129 &  0.9375 &  0.1251 &  0.06254 \tabularnewline
130 &  0.934 &  0.132 &  0.06599 \tabularnewline
131 &  0.9191 &  0.1619 &  0.08095 \tabularnewline
132 &  0.8974 &  0.2053 &  0.1026 \tabularnewline
133 &  0.8718 &  0.2563 &  0.1282 \tabularnewline
134 &  0.8403 &  0.3194 &  0.1597 \tabularnewline
135 &  0.9099 &  0.1801 &  0.09006 \tabularnewline
136 &  0.8863 &  0.2274 &  0.1137 \tabularnewline
137 &  0.8604 &  0.2793 &  0.1396 \tabularnewline
138 &  0.8363 &  0.3275 &  0.1637 \tabularnewline
139 &  0.8254 &  0.3493 &  0.1746 \tabularnewline
140 &  0.8199 &  0.3601 &  0.1801 \tabularnewline
141 &  0.8265 &  0.347 &  0.1735 \tabularnewline
142 &  0.7954 &  0.4092 &  0.2046 \tabularnewline
143 &  0.7483 &  0.5035 &  0.2517 \tabularnewline
144 &  0.7706 &  0.4587 &  0.2294 \tabularnewline
145 &  0.7479 &  0.5041 &  0.2521 \tabularnewline
146 &  0.7651 &  0.4698 &  0.2349 \tabularnewline
147 &  0.7992 &  0.4016 &  0.2008 \tabularnewline
148 &  0.7504 &  0.4991 &  0.2495 \tabularnewline
149 &  0.6933 &  0.6134 &  0.3067 \tabularnewline
150 &  0.7967 &  0.4066 &  0.2033 \tabularnewline
151 &  0.7619 &  0.4763 &  0.2381 \tabularnewline
152 &  0.7261 &  0.5478 &  0.2739 \tabularnewline
153 &  0.7416 &  0.5169 &  0.2584 \tabularnewline
154 &  0.6852 &  0.6297 &  0.3148 \tabularnewline
155 &  0.6241 &  0.7518 &  0.3759 \tabularnewline
156 &  0.5421 &  0.9158 &  0.4579 \tabularnewline
157 &  0.4564 &  0.9128 &  0.5436 \tabularnewline
158 &  0.4275 &  0.8551 &  0.5725 \tabularnewline
159 &  0.4281 &  0.8561 &  0.5719 \tabularnewline
160 &  0.7143 &  0.5713 &  0.2857 \tabularnewline
161 &  0.6945 &  0.6109 &  0.3055 \tabularnewline
162 &  0.643 &  0.7141 &  0.357 \tabularnewline
163 &  0.5221 &  0.9558 &  0.4779 \tabularnewline
164 &  0.3801 &  0.7601 &  0.6199 \tabularnewline
165 &  0.2407 &  0.4814 &  0.7593 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309974&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]14[/C][C] 0.4474[/C][C] 0.8947[/C][C] 0.5526[/C][/ROW]
[ROW][C]15[/C][C] 0.39[/C][C] 0.78[/C][C] 0.61[/C][/ROW]
[ROW][C]16[/C][C] 0.2825[/C][C] 0.565[/C][C] 0.7175[/C][/ROW]
[ROW][C]17[/C][C] 0.1777[/C][C] 0.3554[/C][C] 0.8223[/C][/ROW]
[ROW][C]18[/C][C] 0.2364[/C][C] 0.4728[/C][C] 0.7636[/C][/ROW]
[ROW][C]19[/C][C] 0.1572[/C][C] 0.3145[/C][C] 0.8428[/C][/ROW]
[ROW][C]20[/C][C] 0.09858[/C][C] 0.1972[/C][C] 0.9014[/C][/ROW]
[ROW][C]21[/C][C] 0.09862[/C][C] 0.1972[/C][C] 0.9014[/C][/ROW]
[ROW][C]22[/C][C] 0.07959[/C][C] 0.1592[/C][C] 0.9204[/C][/ROW]
[ROW][C]23[/C][C] 0.06[/C][C] 0.12[/C][C] 0.94[/C][/ROW]
[ROW][C]24[/C][C] 0.03717[/C][C] 0.07435[/C][C] 0.9628[/C][/ROW]
[ROW][C]25[/C][C] 0.02419[/C][C] 0.04839[/C][C] 0.9758[/C][/ROW]
[ROW][C]26[/C][C] 0.02656[/C][C] 0.05311[/C][C] 0.9734[/C][/ROW]
[ROW][C]27[/C][C] 0.01808[/C][C] 0.03617[/C][C] 0.9819[/C][/ROW]
[ROW][C]28[/C][C] 0.01086[/C][C] 0.02171[/C][C] 0.9891[/C][/ROW]
[ROW][C]29[/C][C] 0.006325[/C][C] 0.01265[/C][C] 0.9937[/C][/ROW]
[ROW][C]30[/C][C] 0.003603[/C][C] 0.007205[/C][C] 0.9964[/C][/ROW]
[ROW][C]31[/C][C] 0.002875[/C][C] 0.005749[/C][C] 0.9971[/C][/ROW]
[ROW][C]32[/C][C] 0.001964[/C][C] 0.003928[/C][C] 0.998[/C][/ROW]
[ROW][C]33[/C][C] 0.001443[/C][C] 0.002885[/C][C] 0.9986[/C][/ROW]
[ROW][C]34[/C][C] 0.007701[/C][C] 0.0154[/C][C] 0.9923[/C][/ROW]
[ROW][C]35[/C][C] 0.004654[/C][C] 0.009308[/C][C] 0.9953[/C][/ROW]
[ROW][C]36[/C][C] 0.007638[/C][C] 0.01528[/C][C] 0.9924[/C][/ROW]
[ROW][C]37[/C][C] 0.004976[/C][C] 0.009952[/C][C] 0.995[/C][/ROW]
[ROW][C]38[/C][C] 0.004505[/C][C] 0.00901[/C][C] 0.9955[/C][/ROW]
[ROW][C]39[/C][C] 0.00315[/C][C] 0.0063[/C][C] 0.9969[/C][/ROW]
[ROW][C]40[/C][C] 0.02461[/C][C] 0.04921[/C][C] 0.9754[/C][/ROW]
[ROW][C]41[/C][C] 0.01898[/C][C] 0.03795[/C][C] 0.981[/C][/ROW]
[ROW][C]42[/C][C] 0.01995[/C][C] 0.03991[/C][C] 0.98[/C][/ROW]
[ROW][C]43[/C][C] 0.01631[/C][C] 0.03262[/C][C] 0.9837[/C][/ROW]
[ROW][C]44[/C][C] 0.01237[/C][C] 0.02474[/C][C] 0.9876[/C][/ROW]
[ROW][C]45[/C][C] 0.009556[/C][C] 0.01911[/C][C] 0.9904[/C][/ROW]
[ROW][C]46[/C][C] 0.01465[/C][C] 0.0293[/C][C] 0.9853[/C][/ROW]
[ROW][C]47[/C][C] 0.01319[/C][C] 0.02639[/C][C] 0.9868[/C][/ROW]
[ROW][C]48[/C][C] 0.009598[/C][C] 0.0192[/C][C] 0.9904[/C][/ROW]
[ROW][C]49[/C][C] 0.01374[/C][C] 0.02749[/C][C] 0.9863[/C][/ROW]
[ROW][C]50[/C][C] 0.01561[/C][C] 0.03122[/C][C] 0.9844[/C][/ROW]
[ROW][C]51[/C][C] 0.01135[/C][C] 0.0227[/C][C] 0.9887[/C][/ROW]
[ROW][C]52[/C][C] 0.008053[/C][C] 0.01611[/C][C] 0.9919[/C][/ROW]
[ROW][C]53[/C][C] 0.01369[/C][C] 0.02737[/C][C] 0.9863[/C][/ROW]
[ROW][C]54[/C][C] 0.01294[/C][C] 0.02589[/C][C] 0.9871[/C][/ROW]
[ROW][C]55[/C][C] 0.009145[/C][C] 0.01829[/C][C] 0.9909[/C][/ROW]
[ROW][C]56[/C][C] 0.01481[/C][C] 0.02962[/C][C] 0.9852[/C][/ROW]
[ROW][C]57[/C][C] 0.01207[/C][C] 0.02415[/C][C] 0.9879[/C][/ROW]
[ROW][C]58[/C][C] 0.03175[/C][C] 0.06351[/C][C] 0.9682[/C][/ROW]
[ROW][C]59[/C][C] 0.02558[/C][C] 0.05116[/C][C] 0.9744[/C][/ROW]
[ROW][C]60[/C][C] 0.02023[/C][C] 0.04045[/C][C] 0.9798[/C][/ROW]
[ROW][C]61[/C][C] 0.0222[/C][C] 0.0444[/C][C] 0.9778[/C][/ROW]
[ROW][C]62[/C][C] 0.01805[/C][C] 0.0361[/C][C] 0.982[/C][/ROW]
[ROW][C]63[/C][C] 0.01504[/C][C] 0.03007[/C][C] 0.985[/C][/ROW]
[ROW][C]64[/C][C] 0.01539[/C][C] 0.03078[/C][C] 0.9846[/C][/ROW]
[ROW][C]65[/C][C] 0.04389[/C][C] 0.08778[/C][C] 0.9561[/C][/ROW]
[ROW][C]66[/C][C] 0.04134[/C][C] 0.08269[/C][C] 0.9587[/C][/ROW]
[ROW][C]67[/C][C] 0.03955[/C][C] 0.0791[/C][C] 0.9604[/C][/ROW]
[ROW][C]68[/C][C] 0.07001[/C][C] 0.14[/C][C] 0.93[/C][/ROW]
[ROW][C]69[/C][C] 0.09793[/C][C] 0.1959[/C][C] 0.9021[/C][/ROW]
[ROW][C]70[/C][C] 0.08209[/C][C] 0.1642[/C][C] 0.9179[/C][/ROW]
[ROW][C]71[/C][C] 0.07166[/C][C] 0.1433[/C][C] 0.9283[/C][/ROW]
[ROW][C]72[/C][C] 0.06486[/C][C] 0.1297[/C][C] 0.9351[/C][/ROW]
[ROW][C]73[/C][C] 0.05206[/C][C] 0.1041[/C][C] 0.9479[/C][/ROW]
[ROW][C]74[/C][C] 0.3206[/C][C] 0.6411[/C][C] 0.6794[/C][/ROW]
[ROW][C]75[/C][C] 0.3151[/C][C] 0.6303[/C][C] 0.6849[/C][/ROW]
[ROW][C]76[/C][C] 0.542[/C][C] 0.916[/C][C] 0.458[/C][/ROW]
[ROW][C]77[/C][C] 0.5505[/C][C] 0.899[/C][C] 0.4495[/C][/ROW]
[ROW][C]78[/C][C] 0.5825[/C][C] 0.835[/C][C] 0.4175[/C][/ROW]
[ROW][C]79[/C][C] 0.595[/C][C] 0.81[/C][C] 0.405[/C][/ROW]
[ROW][C]80[/C][C] 0.5683[/C][C] 0.8634[/C][C] 0.4317[/C][/ROW]
[ROW][C]81[/C][C] 0.5414[/C][C] 0.9172[/C][C] 0.4586[/C][/ROW]
[ROW][C]82[/C][C] 0.6757[/C][C] 0.6485[/C][C] 0.3243[/C][/ROW]
[ROW][C]83[/C][C] 0.7667[/C][C] 0.4666[/C][C] 0.2333[/C][/ROW]
[ROW][C]84[/C][C] 0.7367[/C][C] 0.5265[/C][C] 0.2633[/C][/ROW]
[ROW][C]85[/C][C] 0.7767[/C][C] 0.4466[/C][C] 0.2233[/C][/ROW]
[ROW][C]86[/C][C] 0.9246[/C][C] 0.1508[/C][C] 0.07542[/C][/ROW]
[ROW][C]87[/C][C] 0.9188[/C][C] 0.1624[/C][C] 0.08121[/C][/ROW]
[ROW][C]88[/C][C] 0.9441[/C][C] 0.1118[/C][C] 0.05589[/C][/ROW]
[ROW][C]89[/C][C] 0.9418[/C][C] 0.1164[/C][C] 0.0582[/C][/ROW]
[ROW][C]90[/C][C] 0.9719[/C][C] 0.05624[/C][C] 0.02812[/C][/ROW]
[ROW][C]91[/C][C] 0.9685[/C][C] 0.0631[/C][C] 0.03155[/C][/ROW]
[ROW][C]92[/C][C] 0.9754[/C][C] 0.04918[/C][C] 0.02459[/C][/ROW]
[ROW][C]93[/C][C] 0.9716[/C][C] 0.05679[/C][C] 0.0284[/C][/ROW]
[ROW][C]94[/C][C] 0.9705[/C][C] 0.05904[/C][C] 0.02952[/C][/ROW]
[ROW][C]95[/C][C] 0.9742[/C][C] 0.05153[/C][C] 0.02576[/C][/ROW]
[ROW][C]96[/C][C] 0.9679[/C][C] 0.0641[/C][C] 0.03205[/C][/ROW]
[ROW][C]97[/C][C] 0.9624[/C][C] 0.07513[/C][C] 0.03756[/C][/ROW]
[ROW][C]98[/C][C] 0.9647[/C][C] 0.07065[/C][C] 0.03532[/C][/ROW]
[ROW][C]99[/C][C] 0.9636[/C][C] 0.07282[/C][C] 0.03641[/C][/ROW]
[ROW][C]100[/C][C] 0.9637[/C][C] 0.07254[/C][C] 0.03627[/C][/ROW]
[ROW][C]101[/C][C] 0.9769[/C][C] 0.04616[/C][C] 0.02308[/C][/ROW]
[ROW][C]102[/C][C] 0.975[/C][C] 0.05006[/C][C] 0.02503[/C][/ROW]
[ROW][C]103[/C][C] 0.9776[/C][C] 0.04473[/C][C] 0.02236[/C][/ROW]
[ROW][C]104[/C][C] 0.9765[/C][C] 0.04706[/C][C] 0.02353[/C][/ROW]
[ROW][C]105[/C][C] 0.9839[/C][C] 0.03228[/C][C] 0.01614[/C][/ROW]
[ROW][C]106[/C][C] 0.9804[/C][C] 0.03925[/C][C] 0.01963[/C][/ROW]
[ROW][C]107[/C][C] 0.9755[/C][C] 0.0491[/C][C] 0.02455[/C][/ROW]
[ROW][C]108[/C][C] 0.9683[/C][C] 0.06341[/C][C] 0.0317[/C][/ROW]
[ROW][C]109[/C][C] 0.966[/C][C] 0.06796[/C][C] 0.03398[/C][/ROW]
[ROW][C]110[/C][C] 0.9629[/C][C] 0.07421[/C][C] 0.03711[/C][/ROW]
[ROW][C]111[/C][C] 0.9665[/C][C] 0.06693[/C][C] 0.03347[/C][/ROW]
[ROW][C]112[/C][C] 0.9709[/C][C] 0.0581[/C][C] 0.02905[/C][/ROW]
[ROW][C]113[/C][C] 0.9877[/C][C] 0.02466[/C][C] 0.01233[/C][/ROW]
[ROW][C]114[/C][C] 0.9882[/C][C] 0.02361[/C][C] 0.0118[/C][/ROW]
[ROW][C]115[/C][C] 0.9912[/C][C] 0.01761[/C][C] 0.008807[/C][/ROW]
[ROW][C]116[/C][C] 0.9879[/C][C] 0.02411[/C][C] 0.01205[/C][/ROW]
[ROW][C]117[/C][C] 0.9856[/C][C] 0.02872[/C][C] 0.01436[/C][/ROW]
[ROW][C]118[/C][C] 0.9826[/C][C] 0.03472[/C][C] 0.01736[/C][/ROW]
[ROW][C]119[/C][C] 0.977[/C][C] 0.04599[/C][C] 0.023[/C][/ROW]
[ROW][C]120[/C][C] 0.9752[/C][C] 0.0495[/C][C] 0.02475[/C][/ROW]
[ROW][C]121[/C][C] 0.9797[/C][C] 0.04059[/C][C] 0.02029[/C][/ROW]
[ROW][C]122[/C][C] 0.9768[/C][C] 0.04634[/C][C] 0.02317[/C][/ROW]
[ROW][C]123[/C][C] 0.9733[/C][C] 0.05347[/C][C] 0.02674[/C][/ROW]
[ROW][C]124[/C][C] 0.9655[/C][C] 0.06904[/C][C] 0.03452[/C][/ROW]
[ROW][C]125[/C][C] 0.9632[/C][C] 0.07357[/C][C] 0.03679[/C][/ROW]
[ROW][C]126[/C][C] 0.9562[/C][C] 0.08753[/C][C] 0.04377[/C][/ROW]
[ROW][C]127[/C][C] 0.9575[/C][C] 0.08494[/C][C] 0.04247[/C][/ROW]
[ROW][C]128[/C][C] 0.9445[/C][C] 0.111[/C][C] 0.05552[/C][/ROW]
[ROW][C]129[/C][C] 0.9375[/C][C] 0.1251[/C][C] 0.06254[/C][/ROW]
[ROW][C]130[/C][C] 0.934[/C][C] 0.132[/C][C] 0.06599[/C][/ROW]
[ROW][C]131[/C][C] 0.9191[/C][C] 0.1619[/C][C] 0.08095[/C][/ROW]
[ROW][C]132[/C][C] 0.8974[/C][C] 0.2053[/C][C] 0.1026[/C][/ROW]
[ROW][C]133[/C][C] 0.8718[/C][C] 0.2563[/C][C] 0.1282[/C][/ROW]
[ROW][C]134[/C][C] 0.8403[/C][C] 0.3194[/C][C] 0.1597[/C][/ROW]
[ROW][C]135[/C][C] 0.9099[/C][C] 0.1801[/C][C] 0.09006[/C][/ROW]
[ROW][C]136[/C][C] 0.8863[/C][C] 0.2274[/C][C] 0.1137[/C][/ROW]
[ROW][C]137[/C][C] 0.8604[/C][C] 0.2793[/C][C] 0.1396[/C][/ROW]
[ROW][C]138[/C][C] 0.8363[/C][C] 0.3275[/C][C] 0.1637[/C][/ROW]
[ROW][C]139[/C][C] 0.8254[/C][C] 0.3493[/C][C] 0.1746[/C][/ROW]
[ROW][C]140[/C][C] 0.8199[/C][C] 0.3601[/C][C] 0.1801[/C][/ROW]
[ROW][C]141[/C][C] 0.8265[/C][C] 0.347[/C][C] 0.1735[/C][/ROW]
[ROW][C]142[/C][C] 0.7954[/C][C] 0.4092[/C][C] 0.2046[/C][/ROW]
[ROW][C]143[/C][C] 0.7483[/C][C] 0.5035[/C][C] 0.2517[/C][/ROW]
[ROW][C]144[/C][C] 0.7706[/C][C] 0.4587[/C][C] 0.2294[/C][/ROW]
[ROW][C]145[/C][C] 0.7479[/C][C] 0.5041[/C][C] 0.2521[/C][/ROW]
[ROW][C]146[/C][C] 0.7651[/C][C] 0.4698[/C][C] 0.2349[/C][/ROW]
[ROW][C]147[/C][C] 0.7992[/C][C] 0.4016[/C][C] 0.2008[/C][/ROW]
[ROW][C]148[/C][C] 0.7504[/C][C] 0.4991[/C][C] 0.2495[/C][/ROW]
[ROW][C]149[/C][C] 0.6933[/C][C] 0.6134[/C][C] 0.3067[/C][/ROW]
[ROW][C]150[/C][C] 0.7967[/C][C] 0.4066[/C][C] 0.2033[/C][/ROW]
[ROW][C]151[/C][C] 0.7619[/C][C] 0.4763[/C][C] 0.2381[/C][/ROW]
[ROW][C]152[/C][C] 0.7261[/C][C] 0.5478[/C][C] 0.2739[/C][/ROW]
[ROW][C]153[/C][C] 0.7416[/C][C] 0.5169[/C][C] 0.2584[/C][/ROW]
[ROW][C]154[/C][C] 0.6852[/C][C] 0.6297[/C][C] 0.3148[/C][/ROW]
[ROW][C]155[/C][C] 0.6241[/C][C] 0.7518[/C][C] 0.3759[/C][/ROW]
[ROW][C]156[/C][C] 0.5421[/C][C] 0.9158[/C][C] 0.4579[/C][/ROW]
[ROW][C]157[/C][C] 0.4564[/C][C] 0.9128[/C][C] 0.5436[/C][/ROW]
[ROW][C]158[/C][C] 0.4275[/C][C] 0.8551[/C][C] 0.5725[/C][/ROW]
[ROW][C]159[/C][C] 0.4281[/C][C] 0.8561[/C][C] 0.5719[/C][/ROW]
[ROW][C]160[/C][C] 0.7143[/C][C] 0.5713[/C][C] 0.2857[/C][/ROW]
[ROW][C]161[/C][C] 0.6945[/C][C] 0.6109[/C][C] 0.3055[/C][/ROW]
[ROW][C]162[/C][C] 0.643[/C][C] 0.7141[/C][C] 0.357[/C][/ROW]
[ROW][C]163[/C][C] 0.5221[/C][C] 0.9558[/C][C] 0.4779[/C][/ROW]
[ROW][C]164[/C][C] 0.3801[/C][C] 0.7601[/C][C] 0.6199[/C][/ROW]
[ROW][C]165[/C][C] 0.2407[/C][C] 0.4814[/C][C] 0.7593[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309974&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309974&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
14	0.4474	0.8947	0.5526
15	0.39	0.78	0.61
16	0.2825	0.565	0.7175
17	0.1777	0.3554	0.8223
18	0.2364	0.4728	0.7636
19	0.1572	0.3145	0.8428
20	0.09858	0.1972	0.9014
21	0.09862	0.1972	0.9014
22	0.07959	0.1592	0.9204
23	0.06	0.12	0.94
24	0.03717	0.07435	0.9628
25	0.02419	0.04839	0.9758
26	0.02656	0.05311	0.9734
27	0.01808	0.03617	0.9819
28	0.01086	0.02171	0.9891
29	0.006325	0.01265	0.9937
30	0.003603	0.007205	0.9964
31	0.002875	0.005749	0.9971
32	0.001964	0.003928	0.998
33	0.001443	0.002885	0.9986
34	0.007701	0.0154	0.9923
35	0.004654	0.009308	0.9953
36	0.007638	0.01528	0.9924
37	0.004976	0.009952	0.995
38	0.004505	0.00901	0.9955
39	0.00315	0.0063	0.9969
40	0.02461	0.04921	0.9754
41	0.01898	0.03795	0.981
42	0.01995	0.03991	0.98
43	0.01631	0.03262	0.9837
44	0.01237	0.02474	0.9876
45	0.009556	0.01911	0.9904
46	0.01465	0.0293	0.9853
47	0.01319	0.02639	0.9868
48	0.009598	0.0192	0.9904
49	0.01374	0.02749	0.9863
50	0.01561	0.03122	0.9844
51	0.01135	0.0227	0.9887
52	0.008053	0.01611	0.9919
53	0.01369	0.02737	0.9863
54	0.01294	0.02589	0.9871
55	0.009145	0.01829	0.9909
56	0.01481	0.02962	0.9852
57	0.01207	0.02415	0.9879
58	0.03175	0.06351	0.9682
59	0.02558	0.05116	0.9744
60	0.02023	0.04045	0.9798
61	0.0222	0.0444	0.9778
62	0.01805	0.0361	0.982
63	0.01504	0.03007	0.985
64	0.01539	0.03078	0.9846
65	0.04389	0.08778	0.9561
66	0.04134	0.08269	0.9587
67	0.03955	0.0791	0.9604
68	0.07001	0.14	0.93
69	0.09793	0.1959	0.9021
70	0.08209	0.1642	0.9179
71	0.07166	0.1433	0.9283
72	0.06486	0.1297	0.9351
73	0.05206	0.1041	0.9479
74	0.3206	0.6411	0.6794
75	0.3151	0.6303	0.6849
76	0.542	0.916	0.458
77	0.5505	0.899	0.4495
78	0.5825	0.835	0.4175
79	0.595	0.81	0.405
80	0.5683	0.8634	0.4317
81	0.5414	0.9172	0.4586
82	0.6757	0.6485	0.3243
83	0.7667	0.4666	0.2333
84	0.7367	0.5265	0.2633
85	0.7767	0.4466	0.2233
86	0.9246	0.1508	0.07542
87	0.9188	0.1624	0.08121
88	0.9441	0.1118	0.05589
89	0.9418	0.1164	0.0582
90	0.9719	0.05624	0.02812
91	0.9685	0.0631	0.03155
92	0.9754	0.04918	0.02459
93	0.9716	0.05679	0.0284
94	0.9705	0.05904	0.02952
95	0.9742	0.05153	0.02576
96	0.9679	0.0641	0.03205
97	0.9624	0.07513	0.03756
98	0.9647	0.07065	0.03532
99	0.9636	0.07282	0.03641
100	0.9637	0.07254	0.03627
101	0.9769	0.04616	0.02308
102	0.975	0.05006	0.02503
103	0.9776	0.04473	0.02236
104	0.9765	0.04706	0.02353
105	0.9839	0.03228	0.01614
106	0.9804	0.03925	0.01963
107	0.9755	0.0491	0.02455
108	0.9683	0.06341	0.0317
109	0.966	0.06796	0.03398
110	0.9629	0.07421	0.03711
111	0.9665	0.06693	0.03347
112	0.9709	0.0581	0.02905
113	0.9877	0.02466	0.01233
114	0.9882	0.02361	0.0118
115	0.9912	0.01761	0.008807
116	0.9879	0.02411	0.01205
117	0.9856	0.02872	0.01436
118	0.9826	0.03472	0.01736
119	0.977	0.04599	0.023
120	0.9752	0.0495	0.02475
121	0.9797	0.04059	0.02029
122	0.9768	0.04634	0.02317
123	0.9733	0.05347	0.02674
124	0.9655	0.06904	0.03452
125	0.9632	0.07357	0.03679
126	0.9562	0.08753	0.04377
127	0.9575	0.08494	0.04247
128	0.9445	0.111	0.05552
129	0.9375	0.1251	0.06254
130	0.934	0.132	0.06599
131	0.9191	0.1619	0.08095
132	0.8974	0.2053	0.1026
133	0.8718	0.2563	0.1282
134	0.8403	0.3194	0.1597
135	0.9099	0.1801	0.09006
136	0.8863	0.2274	0.1137
137	0.8604	0.2793	0.1396
138	0.8363	0.3275	0.1637
139	0.8254	0.3493	0.1746
140	0.8199	0.3601	0.1801
141	0.8265	0.347	0.1735
142	0.7954	0.4092	0.2046
143	0.7483	0.5035	0.2517
144	0.7706	0.4587	0.2294
145	0.7479	0.5041	0.2521
146	0.7651	0.4698	0.2349
147	0.7992	0.4016	0.2008
148	0.7504	0.4991	0.2495
149	0.6933	0.6134	0.3067
150	0.7967	0.4066	0.2033
151	0.7619	0.4763	0.2381
152	0.7261	0.5478	0.2739
153	0.7416	0.5169	0.2584
154	0.6852	0.6297	0.3148
155	0.6241	0.7518	0.3759
156	0.5421	0.9158	0.4579
157	0.4564	0.9128	0.5436
158	0.4275	0.8551	0.5725
159	0.4281	0.8561	0.5719
160	0.7143	0.5713	0.2857
161	0.6945	0.6109	0.3055
162	0.643	0.7141	0.357
163	0.5221	0.9558	0.4779
164	0.3801	0.7601	0.6199
165	0.2407	0.4814	0.7593

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	8	0.05263	NOK
5% type I error level	54	0.355263	NOK
10% type I error level	82	0.539474	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 & 8 &  0.05263 & NOK \tabularnewline
5% type I error level & 54 & 0.355263 & NOK \tabularnewline
10% type I error level & 82 & 0.539474 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309974&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]8[/C][C] 0.05263[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]54[/C][C]0.355263[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]82[/C][C]0.539474[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309974&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309974&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	8	0.05263	NOK
5% type I error level	54	0.355263	NOK
10% type I error level	82	0.539474	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.1441, df1 = 2, df2 = 166, p-value = 0.8659
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.6656, df1 = 20, df2 = 148, p-value = 0.04526
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.95627, df1 = 2, df2 = 166, p-value = 0.3864

\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 = 0.1441, df1 = 2, df2 = 166, p-value = 0.8659
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6656, df1 = 20, df2 = 148, p-value = 0.04526
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.95627, df1 = 2, df2 = 166, p-value = 0.3864
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309974&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 = 0.1441, df1 = 2, df2 = 166, p-value = 0.8659
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6656, df1 = 20, df2 = 148, p-value = 0.04526
[/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 = 0.95627, df1 = 2, df2 = 166, p-value = 0.3864
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309974&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309974&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 = 0.1441, df1 = 2, df2 = 166, p-value = 0.8659
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.6656, df1 = 20, df2 = 148, p-value = 0.04526
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.95627, df1 = 2, df2 = 166, p-value = 0.3864

Variance Inflation Factors (Multicollinearity)

> vif
  `(1-Bs)(1-B)Build0`   `(1-Bs)(1-B)Build1`   `(1-Bs)(1-B)Build2` 
             2.135471              3.986951              4.921407 
  `(1-Bs)(1-B)Build3`   `(1-Bs)(1-B)Build4`  `(1-Bs)(1-B)BM(t-1)` 
             4.071543              2.574284              1.935348 
 `(1-Bs)(1-B)BM(t-2)`  `(1-Bs)(1-B)BM(t-3)`  `(1-Bs)(1-B)BM(t-4)` 
             1.869031              1.884327              1.710493 
`(1-Bs)(1-B)BM(t-1s)` 
             1.107583

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
  `(1-Bs)(1-B)Build0`   `(1-Bs)(1-B)Build1`   `(1-Bs)(1-B)Build2` 
             2.135471              3.986951              4.921407 
  `(1-Bs)(1-B)Build3`   `(1-Bs)(1-B)Build4`  `(1-Bs)(1-B)BM(t-1)` 
             4.071543              2.574284              1.935348 
 `(1-Bs)(1-B)BM(t-2)`  `(1-Bs)(1-B)BM(t-3)`  `(1-Bs)(1-B)BM(t-4)` 
             1.869031              1.884327              1.710493 
`(1-Bs)(1-B)BM(t-1s)` 
             1.107583 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309974&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
  `(1-Bs)(1-B)Build0`   `(1-Bs)(1-B)Build1`   `(1-Bs)(1-B)Build2` 
             2.135471              3.986951              4.921407 
  `(1-Bs)(1-B)Build3`   `(1-Bs)(1-B)Build4`  `(1-Bs)(1-B)BM(t-1)` 
             4.071543              2.574284              1.935348 
 `(1-Bs)(1-B)BM(t-2)`  `(1-Bs)(1-B)BM(t-3)`  `(1-Bs)(1-B)BM(t-4)` 
             1.869031              1.884327              1.710493 
`(1-Bs)(1-B)BM(t-1s)` 
             1.107583 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309974&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309974&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
  `(1-Bs)(1-B)Build0`   `(1-Bs)(1-B)Build1`   `(1-Bs)(1-B)Build2` 
             2.135471              3.986951              4.921407 
  `(1-Bs)(1-B)Build3`   `(1-Bs)(1-B)Build4`  `(1-Bs)(1-B)BM(t-1)` 
             4.071543              2.574284              1.935348 
 `(1-Bs)(1-B)BM(t-2)`  `(1-Bs)(1-B)BM(t-3)`  `(1-Bs)(1-B)BM(t-4)` 
             1.869031              1.884327              1.710493 
`(1-Bs)(1-B)BM(t-1s)` 
             1.107583

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 4 ; par5 = 1 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 4 ; par5 = 1 ; par6 = 12 ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code