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*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 12 Dec 2016 23:22:10 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/12/t14815813791gado5gv33485nr.htm/, Retrieved Fri, 01 Nov 2024 03:38:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299008, Retrieved Fri, 01 Nov 2024 03:38:37 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

126

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

-     [Cronbach Alpha] [Cronbach Alpha] [2016-12-06 14:03:30] [683f400e1b95307fc738e729f07c4fce]
- RMPD  [Multiple Regression] [] [2016-12-12 22:12:07] [683f400e1b95307fc738e729f07c4fce]
-   PD      [Multiple Regression] [MR zonder TVDC4] [2016-12-12 22:22:10] [404ac5ee4f7301873f6a96ef36861981] [Current]
- R           [Multiple Regression] [] [2016-12-12 22:27:50] [683f400e1b95307fc738e729f07c4fce] 
- RMPD        [Cronbach Alpha] [Cronbach Alpha ITH] [2016-12-13 12:54:18] [683f400e1b95307fc738e729f07c4fce] 
-   PD        [Multiple Regression] [Regressieanalyse ITH] [2016-12-13 13:14:55] [683f400e1b95307fc738e729f07c4fce] 
-   PD        [Multiple Regression] [Regressieanalyse ...] [2016-12-13 22:05:17] [683f400e1b95307fc738e729f07c4fce] 

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Dataseries X:

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Histogram

Boxplots

4	2	4	3	5	4	10
5	3	3	4	5	4	13
4	4	5	4	5	4	14
3	4	3	3	4	4	12
4	4	5	4	5	4	12
3	4	4	4	5	5	13
3	4	4	3	3	4	13
3	4	5	4	4	4	13
4	5	4	4	5	5	13
4	5	5	4	5	5	14
4	4	2	4	5	4	14
4	4	5	3	5	4	12
4	4	4	3	4	5	12
3	3	5	4	4	5	11
4	4	5	4	2	5	12
3	4	5	4	4	5	14
3	4	5	4	4	5	12
5	5	4	3	4	4	11
4	4	4	4	5	4	13
3	4	5	3	4	5	13
4	4	4	4	5	5	12
4	4	5	4	4	5	13
4	4	5	4	4	4	12
4	4	5	4	4	5	13
3	4	4	4	4	4	12
3	4	4	3	5	5	12
4	4	4	4	4	4	12
2	4	5	4	5	5	13
5	4	4	4	4	4	13
4	3	5	4	4	4	10
4	5	5	4	5	5	12
5	4	5	4	4	5	13
2	3	5	4	5	4	10
4	5	2	4	4	4	14
3	4	5	4	4	4	12
4	3	5	3	4	5	10
4	3	3	4	4	4	10
4	4	5	4	4	4	14
5	4	4	4	4	4	12
4	5	5	4	5	5	14
3	3	4	4	4	4	10
5	5	5	3	5	5	13
5	4	5	3	4	4	12
4	4	4	3	4	5	12
4	4	4	4	4	4	13
3	5	5	3	3	4	12
4	4	4	4	5	4	10
4	5	5	4	4	4	14
5	5	2	4	5	4	14
5	5	5	4	4	4	13
4	3	5	4	5	5	8
4	3	4	3	4	5	11
4	4	5	4	4	4	10
3	4	4	3	3	4	12
3	4	4	4	4	3	14
4	4	4	3	5	4	12
4	4	4	4	5	4	12
5	5	3	4	5	5	14
2	4	4	4	5	5	13
4	4	4	4	5	5	13
3	4	4	4	2	4	13
4	4	5	4	5	5	12
4	2	4	4	4	4	10
4	4	4	3	5	3	14
4	4	4	3	5	4	11
5	4	5	3	3	5	10
3	4	4	3	5	5	13
3	4	4	3	4	5	12
4	5	5	5	5	4	12
4	4	4	4	4	4	10
4	4	4	5	5	4	13
3	4	3	4	4	4	12
4	4	4	4	5	4	13
3	4	5	3	5	5	11
3	3	5	4	4	5	10
4	3	5	4	4	4	14
4	4	5	4	4	5	13
3	3	3	4	4	4	7
4	4	4	4	5	4	13
4	4	3	4	5	5	13
4	4	4	4	5	5	13
5	4	4	4	4	4	15
5	4	3	5	4	5	13
4	4	5	4	5	5	14
3	4	5	4	4	5	12
4	2	3	3	4	4	11
4	4	5	4	4	3	12
4	4	5	4	4	5	14
4	4	4	4	5	4	13
4	5	4	4	5	3	14
3	4	4	3	5	5	12
4	4	5	4	4	5	12
5	4	3	4	4	5	13
5	4	5	5	4	5	14
4	5	4	4	5	5	13
5	3	4	4	5	5	13
4	4	5	4	4	5	12
5	4	4	4	4	5	10
5	4	4	5	5	5	13
4	4	3	3	4	3	13
4	4	5	4	4	4	12
4	4	5	4	4	4	12
3	4	5	4	5	3	12
4	4	4	4	4	4	10
4	4	4	3	4	5	12
3	3	4	3	5	5	9
4	4	4	3	4	4	14
3	4	5	4	4	4	12
4	4	5	4	3	4	13
5	4	5	1	5	5	13
5	4	5	4	5	5	13
4	4	4	4	4	3	11
4	4	5	3	4	4	12
3	4	4	3	4	5	11
4	4	4	4	4	4	12
4	4	4	4	5	4	12
4	5	3	4	4	4	13
3	4	4	4	4	4	12
4	4	4	3	4	4	13
4	4	4	4	4	5	13
3	4	3	3	4	4	12
4	4	4	3	4	3	12
3	2	4	2	4	4	8
4	4	4	3	5	4	12
5	4	4	3	5	4	13
2	4	4	3	3	5	10
3	3	4	4	4	4	8
5	5	4	4	5	4	13
4	5	5	4	4	4	12
5	5	5	5	5	4	15
4	5	5	4	5	5	14
4	4	4	3	4	5	10
3	4	5	4	5	4	11
4	4	5	4	4	4	12
4	4	2	4	4	4	10
4	4	3	4	5	5	14
4	4	4	4	5	5	10
5	4	5	3	5	4	15
4	3	5	4	4	4	11
4	4	5	4	4	4	12
3	3	2	3	4	4	9
4	5	5	4	4	3	12
4	4	4	3	4	4	13
4	4	4	4	4	5	12
3	4	5	3	5	5	9
4	4	5	4	4	5	12
5	4	5	4	5	4	14
4	4	5	4	3	4	10
2	3	5	4	4	4	12
4	4	4	4	4	5	14
4	3	4	3	5	5	12
4	4	4	4	4	3	15
4	5	5	5	4	4	11
5	4	3	4	4	4	12
5	4	4	3	4	4	12
3	3	1	4	5	5	10
4	4	4	4	4	5	12
4	4	4	4	5	4	10
2	3	4	5	5	4	11

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299008&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=299008&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299008&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	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 4.62089 + 0.373494SK1[t] + 1.09084SK2[t] + 0.0525709SK3[t] + 0.196513SK4[t] + 0.242939SK5[t] -0.0654923SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  4.62089 +  0.373494SK1[t] +  1.09084SK2[t] +  0.0525709SK3[t] +  0.196513SK4[t] +  0.242939SK5[t] -0.0654923SK6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299008&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  4.62089 +  0.373494SK1[t] +  1.09084SK2[t] +  0.0525709SK3[t] +  0.196513SK4[t] +  0.242939SK5[t] -0.0654923SK6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299008&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299008&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
TVDC[t] = + 4.62089 + 0.373494SK1[t] + 1.09084SK2[t] + 0.0525709SK3[t] + 0.196513SK4[t] + 0.242939SK5[t] -0.0654923SK6[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)	+4.621	1.375	+3.3620e+00	0.0009793	0.0004896
SK1	+0.3735	0.1448	+2.5790e+00	0.01087	0.005434
SK2	+1.091	0.1765	+6.1800e+00	5.614e-09	2.807e-09
SK3	+0.05257	0.1294	+4.0640e-01	0.685	0.3425
SK4	+0.1965	0.1769	+1.1110e+00	0.2683	0.1342
SK5	+0.2429	0.1664	+1.4600e+00	0.1463	0.07317
SK6	-0.06549	0.1727	-3.7930e-01	0.705	0.3525

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +4.621 &  1.375 & +3.3620e+00 &  0.0009793 &  0.0004896 \tabularnewline
SK1 & +0.3735 &  0.1448 & +2.5790e+00 &  0.01087 &  0.005434 \tabularnewline
SK2 & +1.091 &  0.1765 & +6.1800e+00 &  5.614e-09 &  2.807e-09 \tabularnewline
SK3 & +0.05257 &  0.1294 & +4.0640e-01 &  0.685 &  0.3425 \tabularnewline
SK4 & +0.1965 &  0.1769 & +1.1110e+00 &  0.2683 &  0.1342 \tabularnewline
SK5 & +0.2429 &  0.1664 & +1.4600e+00 &  0.1463 &  0.07317 \tabularnewline
SK6 & -0.06549 &  0.1727 & -3.7930e-01 &  0.705 &  0.3525 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299008&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+4.621[/C][C] 1.375[/C][C]+3.3620e+00[/C][C] 0.0009793[/C][C] 0.0004896[/C][/ROW]
[ROW][C]SK1[/C][C]+0.3735[/C][C] 0.1448[/C][C]+2.5790e+00[/C][C] 0.01087[/C][C] 0.005434[/C][/ROW]
[ROW][C]SK2[/C][C]+1.091[/C][C] 0.1765[/C][C]+6.1800e+00[/C][C] 5.614e-09[/C][C] 2.807e-09[/C][/ROW]
[ROW][C]SK3[/C][C]+0.05257[/C][C] 0.1294[/C][C]+4.0640e-01[/C][C] 0.685[/C][C] 0.3425[/C][/ROW]
[ROW][C]SK4[/C][C]+0.1965[/C][C] 0.1769[/C][C]+1.1110e+00[/C][C] 0.2683[/C][C] 0.1342[/C][/ROW]
[ROW][C]SK5[/C][C]+0.2429[/C][C] 0.1664[/C][C]+1.4600e+00[/C][C] 0.1463[/C][C] 0.07317[/C][/ROW]
[ROW][C]SK6[/C][C]-0.06549[/C][C] 0.1727[/C][C]-3.7930e-01[/C][C] 0.705[/C][C] 0.3525[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299008&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299008&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)	+4.621	1.375	+3.3620e+00	0.0009793	0.0004896
SK1	+0.3735	0.1448	+2.5790e+00	0.01087	0.005434
SK2	+1.091	0.1765	+6.1800e+00	5.614e-09	2.807e-09
SK3	+0.05257	0.1294	+4.0640e-01	0.685	0.3425
SK4	+0.1965	0.1769	+1.1110e+00	0.2683	0.1342
SK5	+0.2429	0.1664	+1.4600e+00	0.1463	0.07317
SK6	-0.06549	0.1727	-3.7930e-01	0.705	0.3525

Multiple Linear Regression - Regression Statistics
Multiple R	0.5622
R-squared	0.316
Adjusted R-squared	0.289
F-TEST (value)	11.71
F-TEST (DF numerator)	6
F-TEST (DF denominator)	152
p-value	9.209e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.271
Sum Squared Residuals	245.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5622 \tabularnewline
R-squared &  0.316 \tabularnewline
Adjusted R-squared &  0.289 \tabularnewline
F-TEST (value) &  11.71 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value &  9.209e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.271 \tabularnewline
Sum Squared Residuals &  245.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299008&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5622[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.316[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.289[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 11.71[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C] 9.209e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.271[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 245.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299008&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299008&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	0.5622
R-squared	0.316
Adjusted R-squared	0.289
F-TEST (value)	11.71
F-TEST (DF numerator)	6
F-TEST (DF denominator)	152
p-value	9.209e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.271
Sum Squared Residuals	245.7

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	10	10.05	-0.0491
2	13	11.66	1.343
3	14	12.48	1.52
4	12	11.56	0.4382
5	12	12.48	-0.4799
6	13	11.99	1.012
7	13	11.37	1.629
8	13	11.86	1.137
9	13	13.45	-0.4526
10	14	13.51	0.4948
11	14	12.32	1.678
12	12	12.28	-0.2833
13	12	11.92	0.07766
14	11	10.71	0.2929
15	12	11.69	0.3145
16	14	11.8	2.202
17	12	11.8	0.2021
18	11	13.45	-2.452
19	13	12.43	0.5727
20	13	11.6	1.399
21	12	12.36	-0.3618
22	13	12.17	0.8286
23	12	12.24	-0.2369
24	13	12.17	0.8286
25	12	11.81	0.1891
26	12	11.79	0.2082
27	12	12.18	-0.1843
28	13	11.67	1.333
29	13	12.56	0.4422
30	10	11.15	-1.146
31	12	13.51	-1.505
32	13	12.54	0.4551
33	10	10.64	-0.642
34	14	13.17	0.83
35	12	11.86	0.1366
36	10	10.88	-0.8841
37	10	11.04	-1.041
38	14	12.24	1.763
39	12	12.56	-0.5578
40	14	13.51	0.4948
41	10	10.72	-0.72
42	13	13.68	-0.6822
43	12	12.41	-0.4139
44	12	11.92	0.07766
45	13	12.18	0.8157
46	12	12.51	-0.5148
47	10	12.43	-2.427
48	14	13.33	0.6722
49	14	13.79	0.2135
50	13	13.7	-0.7013
51	8	11.32	-3.324
52	11	10.83	0.1685
53	10	12.24	-2.237
54	12	11.37	0.6286
55	14	11.88	2.124
56	12	12.23	-0.2308
57	12	12.43	-0.4273
58	14	13.77	0.2264
59	13	11.61	1.385
60	13	12.36	0.6382
61	13	11.32	1.675
62	12	12.41	-0.4144
63	10	10	-0.002671
64	14	12.3	1.704
65	11	12.23	-1.231
66	10	12.11	-2.105
67	13	11.79	1.208
68	12	11.55	0.4512
69	12	13.77	-1.767
70	10	12.18	-2.184
71	13	12.62	0.3762
72	12	11.76	0.2417
73	13	12.43	0.5727
74	11	11.84	-0.8444
75	10	10.71	-0.7071
76	14	11.15	2.854
77	13	12.17	0.8286
78	7	10.67	-3.667
79	13	12.43	0.5727
80	13	12.31	0.6908
81	13	12.36	0.6382
82	15	12.56	2.442
83	13	12.64	0.3637
84	14	12.41	1.586
85	12	11.8	0.2021
86	11	9.754	1.246
87	12	12.3	-0.3024
88	14	12.17	1.829
89	13	12.43	0.5727
90	14	13.58	0.4164
91	12	11.79	0.2082
92	12	12.17	-0.1714
93	13	12.44	0.5602
94	14	12.74	1.259
95	13	13.45	-0.4526
96	13	11.64	1.356
97	12	12.17	-0.1714
98	10	12.49	-2.492
99	13	12.93	0.0682
100	13	12	0.9992
101	12	12.24	-0.2369
102	12	12.24	-0.2369
103	12	12.17	-0.1719
104	10	12.18	-2.184
105	12	11.92	0.07766
106	9	10.7	-1.701
107	14	11.99	2.012
108	12	11.86	0.1366
109	13	11.99	1.006
110	13	12.2	0.8017
111	13	12.79	0.2121
112	11	12.25	-1.25
113	12	12.04	-0.04041
114	11	11.55	-0.5488
115	12	12.18	-0.1843
116	12	12.43	-0.4273
117	13	13.22	-0.2226
118	12	11.81	0.1891
119	13	11.99	1.012
120	13	12.12	0.8811
121	12	11.56	0.4382
122	12	12.05	-0.05333
123	8	9.236	-1.236
124	12	12.23	-0.2308
125	13	12.6	0.3957
126	10	10.93	-0.9324
127	8	10.72	-2.72
128	13	13.89	-0.8916
129	12	13.33	-1.328
130	15	14.14	0.8593
131	14	13.51	0.4948
132	10	11.92	-1.922
133	11	12.11	-1.106
134	12	12.24	-0.2369
135	10	12.08	-2.079
136	14	12.31	1.691
137	10	12.36	-2.362
138	15	12.66	2.343
139	11	11.15	-0.1461
140	12	12.24	-0.2369
141	9	10.42	-1.418
142	12	13.39	-1.393
143	13	11.99	1.012
144	12	12.12	-0.1189
145	9	11.84	-2.844
146	12	12.17	-0.1714
147	14	12.85	1.147
148	10	11.99	-1.994
149	12	10.4	1.601
150	14	12.12	1.881
151	12	11.07	0.9256
152	15	12.25	2.75
153	11	13.52	-2.524
154	12	12.51	-0.5053
155	12	12.36	-0.3613
156	10	10.74	-0.7397
157	12	12.12	-0.1189
158	10	12.43	-2.427
159	11	10.79	0.214

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  10.05 & -0.0491 \tabularnewline
2 &  13 &  11.66 &  1.343 \tabularnewline
3 &  14 &  12.48 &  1.52 \tabularnewline
4 &  12 &  11.56 &  0.4382 \tabularnewline
5 &  12 &  12.48 & -0.4799 \tabularnewline
6 &  13 &  11.99 &  1.012 \tabularnewline
7 &  13 &  11.37 &  1.629 \tabularnewline
8 &  13 &  11.86 &  1.137 \tabularnewline
9 &  13 &  13.45 & -0.4526 \tabularnewline
10 &  14 &  13.51 &  0.4948 \tabularnewline
11 &  14 &  12.32 &  1.678 \tabularnewline
12 &  12 &  12.28 & -0.2833 \tabularnewline
13 &  12 &  11.92 &  0.07766 \tabularnewline
14 &  11 &  10.71 &  0.2929 \tabularnewline
15 &  12 &  11.69 &  0.3145 \tabularnewline
16 &  14 &  11.8 &  2.202 \tabularnewline
17 &  12 &  11.8 &  0.2021 \tabularnewline
18 &  11 &  13.45 & -2.452 \tabularnewline
19 &  13 &  12.43 &  0.5727 \tabularnewline
20 &  13 &  11.6 &  1.399 \tabularnewline
21 &  12 &  12.36 & -0.3618 \tabularnewline
22 &  13 &  12.17 &  0.8286 \tabularnewline
23 &  12 &  12.24 & -0.2369 \tabularnewline
24 &  13 &  12.17 &  0.8286 \tabularnewline
25 &  12 &  11.81 &  0.1891 \tabularnewline
26 &  12 &  11.79 &  0.2082 \tabularnewline
27 &  12 &  12.18 & -0.1843 \tabularnewline
28 &  13 &  11.67 &  1.333 \tabularnewline
29 &  13 &  12.56 &  0.4422 \tabularnewline
30 &  10 &  11.15 & -1.146 \tabularnewline
31 &  12 &  13.51 & -1.505 \tabularnewline
32 &  13 &  12.54 &  0.4551 \tabularnewline
33 &  10 &  10.64 & -0.642 \tabularnewline
34 &  14 &  13.17 &  0.83 \tabularnewline
35 &  12 &  11.86 &  0.1366 \tabularnewline
36 &  10 &  10.88 & -0.8841 \tabularnewline
37 &  10 &  11.04 & -1.041 \tabularnewline
38 &  14 &  12.24 &  1.763 \tabularnewline
39 &  12 &  12.56 & -0.5578 \tabularnewline
40 &  14 &  13.51 &  0.4948 \tabularnewline
41 &  10 &  10.72 & -0.72 \tabularnewline
42 &  13 &  13.68 & -0.6822 \tabularnewline
43 &  12 &  12.41 & -0.4139 \tabularnewline
44 &  12 &  11.92 &  0.07766 \tabularnewline
45 &  13 &  12.18 &  0.8157 \tabularnewline
46 &  12 &  12.51 & -0.5148 \tabularnewline
47 &  10 &  12.43 & -2.427 \tabularnewline
48 &  14 &  13.33 &  0.6722 \tabularnewline
49 &  14 &  13.79 &  0.2135 \tabularnewline
50 &  13 &  13.7 & -0.7013 \tabularnewline
51 &  8 &  11.32 & -3.324 \tabularnewline
52 &  11 &  10.83 &  0.1685 \tabularnewline
53 &  10 &  12.24 & -2.237 \tabularnewline
54 &  12 &  11.37 &  0.6286 \tabularnewline
55 &  14 &  11.88 &  2.124 \tabularnewline
56 &  12 &  12.23 & -0.2308 \tabularnewline
57 &  12 &  12.43 & -0.4273 \tabularnewline
58 &  14 &  13.77 &  0.2264 \tabularnewline
59 &  13 &  11.61 &  1.385 \tabularnewline
60 &  13 &  12.36 &  0.6382 \tabularnewline
61 &  13 &  11.32 &  1.675 \tabularnewline
62 &  12 &  12.41 & -0.4144 \tabularnewline
63 &  10 &  10 & -0.002671 \tabularnewline
64 &  14 &  12.3 &  1.704 \tabularnewline
65 &  11 &  12.23 & -1.231 \tabularnewline
66 &  10 &  12.11 & -2.105 \tabularnewline
67 &  13 &  11.79 &  1.208 \tabularnewline
68 &  12 &  11.55 &  0.4512 \tabularnewline
69 &  12 &  13.77 & -1.767 \tabularnewline
70 &  10 &  12.18 & -2.184 \tabularnewline
71 &  13 &  12.62 &  0.3762 \tabularnewline
72 &  12 &  11.76 &  0.2417 \tabularnewline
73 &  13 &  12.43 &  0.5727 \tabularnewline
74 &  11 &  11.84 & -0.8444 \tabularnewline
75 &  10 &  10.71 & -0.7071 \tabularnewline
76 &  14 &  11.15 &  2.854 \tabularnewline
77 &  13 &  12.17 &  0.8286 \tabularnewline
78 &  7 &  10.67 & -3.667 \tabularnewline
79 &  13 &  12.43 &  0.5727 \tabularnewline
80 &  13 &  12.31 &  0.6908 \tabularnewline
81 &  13 &  12.36 &  0.6382 \tabularnewline
82 &  15 &  12.56 &  2.442 \tabularnewline
83 &  13 &  12.64 &  0.3637 \tabularnewline
84 &  14 &  12.41 &  1.586 \tabularnewline
85 &  12 &  11.8 &  0.2021 \tabularnewline
86 &  11 &  9.754 &  1.246 \tabularnewline
87 &  12 &  12.3 & -0.3024 \tabularnewline
88 &  14 &  12.17 &  1.829 \tabularnewline
89 &  13 &  12.43 &  0.5727 \tabularnewline
90 &  14 &  13.58 &  0.4164 \tabularnewline
91 &  12 &  11.79 &  0.2082 \tabularnewline
92 &  12 &  12.17 & -0.1714 \tabularnewline
93 &  13 &  12.44 &  0.5602 \tabularnewline
94 &  14 &  12.74 &  1.259 \tabularnewline
95 &  13 &  13.45 & -0.4526 \tabularnewline
96 &  13 &  11.64 &  1.356 \tabularnewline
97 &  12 &  12.17 & -0.1714 \tabularnewline
98 &  10 &  12.49 & -2.492 \tabularnewline
99 &  13 &  12.93 &  0.0682 \tabularnewline
100 &  13 &  12 &  0.9992 \tabularnewline
101 &  12 &  12.24 & -0.2369 \tabularnewline
102 &  12 &  12.24 & -0.2369 \tabularnewline
103 &  12 &  12.17 & -0.1719 \tabularnewline
104 &  10 &  12.18 & -2.184 \tabularnewline
105 &  12 &  11.92 &  0.07766 \tabularnewline
106 &  9 &  10.7 & -1.701 \tabularnewline
107 &  14 &  11.99 &  2.012 \tabularnewline
108 &  12 &  11.86 &  0.1366 \tabularnewline
109 &  13 &  11.99 &  1.006 \tabularnewline
110 &  13 &  12.2 &  0.8017 \tabularnewline
111 &  13 &  12.79 &  0.2121 \tabularnewline
112 &  11 &  12.25 & -1.25 \tabularnewline
113 &  12 &  12.04 & -0.04041 \tabularnewline
114 &  11 &  11.55 & -0.5488 \tabularnewline
115 &  12 &  12.18 & -0.1843 \tabularnewline
116 &  12 &  12.43 & -0.4273 \tabularnewline
117 &  13 &  13.22 & -0.2226 \tabularnewline
118 &  12 &  11.81 &  0.1891 \tabularnewline
119 &  13 &  11.99 &  1.012 \tabularnewline
120 &  13 &  12.12 &  0.8811 \tabularnewline
121 &  12 &  11.56 &  0.4382 \tabularnewline
122 &  12 &  12.05 & -0.05333 \tabularnewline
123 &  8 &  9.236 & -1.236 \tabularnewline
124 &  12 &  12.23 & -0.2308 \tabularnewline
125 &  13 &  12.6 &  0.3957 \tabularnewline
126 &  10 &  10.93 & -0.9324 \tabularnewline
127 &  8 &  10.72 & -2.72 \tabularnewline
128 &  13 &  13.89 & -0.8916 \tabularnewline
129 &  12 &  13.33 & -1.328 \tabularnewline
130 &  15 &  14.14 &  0.8593 \tabularnewline
131 &  14 &  13.51 &  0.4948 \tabularnewline
132 &  10 &  11.92 & -1.922 \tabularnewline
133 &  11 &  12.11 & -1.106 \tabularnewline
134 &  12 &  12.24 & -0.2369 \tabularnewline
135 &  10 &  12.08 & -2.079 \tabularnewline
136 &  14 &  12.31 &  1.691 \tabularnewline
137 &  10 &  12.36 & -2.362 \tabularnewline
138 &  15 &  12.66 &  2.343 \tabularnewline
139 &  11 &  11.15 & -0.1461 \tabularnewline
140 &  12 &  12.24 & -0.2369 \tabularnewline
141 &  9 &  10.42 & -1.418 \tabularnewline
142 &  12 &  13.39 & -1.393 \tabularnewline
143 &  13 &  11.99 &  1.012 \tabularnewline
144 &  12 &  12.12 & -0.1189 \tabularnewline
145 &  9 &  11.84 & -2.844 \tabularnewline
146 &  12 &  12.17 & -0.1714 \tabularnewline
147 &  14 &  12.85 &  1.147 \tabularnewline
148 &  10 &  11.99 & -1.994 \tabularnewline
149 &  12 &  10.4 &  1.601 \tabularnewline
150 &  14 &  12.12 &  1.881 \tabularnewline
151 &  12 &  11.07 &  0.9256 \tabularnewline
152 &  15 &  12.25 &  2.75 \tabularnewline
153 &  11 &  13.52 & -2.524 \tabularnewline
154 &  12 &  12.51 & -0.5053 \tabularnewline
155 &  12 &  12.36 & -0.3613 \tabularnewline
156 &  10 &  10.74 & -0.7397 \tabularnewline
157 &  12 &  12.12 & -0.1189 \tabularnewline
158 &  10 &  12.43 & -2.427 \tabularnewline
159 &  11 &  10.79 &  0.214 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299008&T=4

[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] 10[/C][C] 10.05[/C][C]-0.0491[/C][/ROW]
[ROW][C]2[/C][C] 13[/C][C] 11.66[/C][C] 1.343[/C][/ROW]
[ROW][C]3[/C][C] 14[/C][C] 12.48[/C][C] 1.52[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 11.56[/C][C] 0.4382[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 12.48[/C][C]-0.4799[/C][/ROW]
[ROW][C]6[/C][C] 13[/C][C] 11.99[/C][C] 1.012[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 11.37[/C][C] 1.629[/C][/ROW]
[ROW][C]8[/C][C] 13[/C][C] 11.86[/C][C] 1.137[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 13.45[/C][C]-0.4526[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 13.51[/C][C] 0.4948[/C][/ROW]
[ROW][C]11[/C][C] 14[/C][C] 12.32[/C][C] 1.678[/C][/ROW]
[ROW][C]12[/C][C] 12[/C][C] 12.28[/C][C]-0.2833[/C][/ROW]
[ROW][C]13[/C][C] 12[/C][C] 11.92[/C][C] 0.07766[/C][/ROW]
[ROW][C]14[/C][C] 11[/C][C] 10.71[/C][C] 0.2929[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 11.69[/C][C] 0.3145[/C][/ROW]
[ROW][C]16[/C][C] 14[/C][C] 11.8[/C][C] 2.202[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 11.8[/C][C] 0.2021[/C][/ROW]
[ROW][C]18[/C][C] 11[/C][C] 13.45[/C][C]-2.452[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 12.43[/C][C] 0.5727[/C][/ROW]
[ROW][C]20[/C][C] 13[/C][C] 11.6[/C][C] 1.399[/C][/ROW]
[ROW][C]21[/C][C] 12[/C][C] 12.36[/C][C]-0.3618[/C][/ROW]
[ROW][C]22[/C][C] 13[/C][C] 12.17[/C][C] 0.8286[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 12.24[/C][C]-0.2369[/C][/ROW]
[ROW][C]24[/C][C] 13[/C][C] 12.17[/C][C] 0.8286[/C][/ROW]
[ROW][C]25[/C][C] 12[/C][C] 11.81[/C][C] 0.1891[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 11.79[/C][C] 0.2082[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 12.18[/C][C]-0.1843[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 11.67[/C][C] 1.333[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 12.56[/C][C] 0.4422[/C][/ROW]
[ROW][C]30[/C][C] 10[/C][C] 11.15[/C][C]-1.146[/C][/ROW]
[ROW][C]31[/C][C] 12[/C][C] 13.51[/C][C]-1.505[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 12.54[/C][C] 0.4551[/C][/ROW]
[ROW][C]33[/C][C] 10[/C][C] 10.64[/C][C]-0.642[/C][/ROW]
[ROW][C]34[/C][C] 14[/C][C] 13.17[/C][C] 0.83[/C][/ROW]
[ROW][C]35[/C][C] 12[/C][C] 11.86[/C][C] 0.1366[/C][/ROW]
[ROW][C]36[/C][C] 10[/C][C] 10.88[/C][C]-0.8841[/C][/ROW]
[ROW][C]37[/C][C] 10[/C][C] 11.04[/C][C]-1.041[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 12.24[/C][C] 1.763[/C][/ROW]
[ROW][C]39[/C][C] 12[/C][C] 12.56[/C][C]-0.5578[/C][/ROW]
[ROW][C]40[/C][C] 14[/C][C] 13.51[/C][C] 0.4948[/C][/ROW]
[ROW][C]41[/C][C] 10[/C][C] 10.72[/C][C]-0.72[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 13.68[/C][C]-0.6822[/C][/ROW]
[ROW][C]43[/C][C] 12[/C][C] 12.41[/C][C]-0.4139[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 11.92[/C][C] 0.07766[/C][/ROW]
[ROW][C]45[/C][C] 13[/C][C] 12.18[/C][C] 0.8157[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 12.51[/C][C]-0.5148[/C][/ROW]
[ROW][C]47[/C][C] 10[/C][C] 12.43[/C][C]-2.427[/C][/ROW]
[ROW][C]48[/C][C] 14[/C][C] 13.33[/C][C] 0.6722[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 13.79[/C][C] 0.2135[/C][/ROW]
[ROW][C]50[/C][C] 13[/C][C] 13.7[/C][C]-0.7013[/C][/ROW]
[ROW][C]51[/C][C] 8[/C][C] 11.32[/C][C]-3.324[/C][/ROW]
[ROW][C]52[/C][C] 11[/C][C] 10.83[/C][C] 0.1685[/C][/ROW]
[ROW][C]53[/C][C] 10[/C][C] 12.24[/C][C]-2.237[/C][/ROW]
[ROW][C]54[/C][C] 12[/C][C] 11.37[/C][C] 0.6286[/C][/ROW]
[ROW][C]55[/C][C] 14[/C][C] 11.88[/C][C] 2.124[/C][/ROW]
[ROW][C]56[/C][C] 12[/C][C] 12.23[/C][C]-0.2308[/C][/ROW]
[ROW][C]57[/C][C] 12[/C][C] 12.43[/C][C]-0.4273[/C][/ROW]
[ROW][C]58[/C][C] 14[/C][C] 13.77[/C][C] 0.2264[/C][/ROW]
[ROW][C]59[/C][C] 13[/C][C] 11.61[/C][C] 1.385[/C][/ROW]
[ROW][C]60[/C][C] 13[/C][C] 12.36[/C][C] 0.6382[/C][/ROW]
[ROW][C]61[/C][C] 13[/C][C] 11.32[/C][C] 1.675[/C][/ROW]
[ROW][C]62[/C][C] 12[/C][C] 12.41[/C][C]-0.4144[/C][/ROW]
[ROW][C]63[/C][C] 10[/C][C] 10[/C][C]-0.002671[/C][/ROW]
[ROW][C]64[/C][C] 14[/C][C] 12.3[/C][C] 1.704[/C][/ROW]
[ROW][C]65[/C][C] 11[/C][C] 12.23[/C][C]-1.231[/C][/ROW]
[ROW][C]66[/C][C] 10[/C][C] 12.11[/C][C]-2.105[/C][/ROW]
[ROW][C]67[/C][C] 13[/C][C] 11.79[/C][C] 1.208[/C][/ROW]
[ROW][C]68[/C][C] 12[/C][C] 11.55[/C][C] 0.4512[/C][/ROW]
[ROW][C]69[/C][C] 12[/C][C] 13.77[/C][C]-1.767[/C][/ROW]
[ROW][C]70[/C][C] 10[/C][C] 12.18[/C][C]-2.184[/C][/ROW]
[ROW][C]71[/C][C] 13[/C][C] 12.62[/C][C] 0.3762[/C][/ROW]
[ROW][C]72[/C][C] 12[/C][C] 11.76[/C][C] 0.2417[/C][/ROW]
[ROW][C]73[/C][C] 13[/C][C] 12.43[/C][C] 0.5727[/C][/ROW]
[ROW][C]74[/C][C] 11[/C][C] 11.84[/C][C]-0.8444[/C][/ROW]
[ROW][C]75[/C][C] 10[/C][C] 10.71[/C][C]-0.7071[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 11.15[/C][C] 2.854[/C][/ROW]
[ROW][C]77[/C][C] 13[/C][C] 12.17[/C][C] 0.8286[/C][/ROW]
[ROW][C]78[/C][C] 7[/C][C] 10.67[/C][C]-3.667[/C][/ROW]
[ROW][C]79[/C][C] 13[/C][C] 12.43[/C][C] 0.5727[/C][/ROW]
[ROW][C]80[/C][C] 13[/C][C] 12.31[/C][C] 0.6908[/C][/ROW]
[ROW][C]81[/C][C] 13[/C][C] 12.36[/C][C] 0.6382[/C][/ROW]
[ROW][C]82[/C][C] 15[/C][C] 12.56[/C][C] 2.442[/C][/ROW]
[ROW][C]83[/C][C] 13[/C][C] 12.64[/C][C] 0.3637[/C][/ROW]
[ROW][C]84[/C][C] 14[/C][C] 12.41[/C][C] 1.586[/C][/ROW]
[ROW][C]85[/C][C] 12[/C][C] 11.8[/C][C] 0.2021[/C][/ROW]
[ROW][C]86[/C][C] 11[/C][C] 9.754[/C][C] 1.246[/C][/ROW]
[ROW][C]87[/C][C] 12[/C][C] 12.3[/C][C]-0.3024[/C][/ROW]
[ROW][C]88[/C][C] 14[/C][C] 12.17[/C][C] 1.829[/C][/ROW]
[ROW][C]89[/C][C] 13[/C][C] 12.43[/C][C] 0.5727[/C][/ROW]
[ROW][C]90[/C][C] 14[/C][C] 13.58[/C][C] 0.4164[/C][/ROW]
[ROW][C]91[/C][C] 12[/C][C] 11.79[/C][C] 0.2082[/C][/ROW]
[ROW][C]92[/C][C] 12[/C][C] 12.17[/C][C]-0.1714[/C][/ROW]
[ROW][C]93[/C][C] 13[/C][C] 12.44[/C][C] 0.5602[/C][/ROW]
[ROW][C]94[/C][C] 14[/C][C] 12.74[/C][C] 1.259[/C][/ROW]
[ROW][C]95[/C][C] 13[/C][C] 13.45[/C][C]-0.4526[/C][/ROW]
[ROW][C]96[/C][C] 13[/C][C] 11.64[/C][C] 1.356[/C][/ROW]
[ROW][C]97[/C][C] 12[/C][C] 12.17[/C][C]-0.1714[/C][/ROW]
[ROW][C]98[/C][C] 10[/C][C] 12.49[/C][C]-2.492[/C][/ROW]
[ROW][C]99[/C][C] 13[/C][C] 12.93[/C][C] 0.0682[/C][/ROW]
[ROW][C]100[/C][C] 13[/C][C] 12[/C][C] 0.9992[/C][/ROW]
[ROW][C]101[/C][C] 12[/C][C] 12.24[/C][C]-0.2369[/C][/ROW]
[ROW][C]102[/C][C] 12[/C][C] 12.24[/C][C]-0.2369[/C][/ROW]
[ROW][C]103[/C][C] 12[/C][C] 12.17[/C][C]-0.1719[/C][/ROW]
[ROW][C]104[/C][C] 10[/C][C] 12.18[/C][C]-2.184[/C][/ROW]
[ROW][C]105[/C][C] 12[/C][C] 11.92[/C][C] 0.07766[/C][/ROW]
[ROW][C]106[/C][C] 9[/C][C] 10.7[/C][C]-1.701[/C][/ROW]
[ROW][C]107[/C][C] 14[/C][C] 11.99[/C][C] 2.012[/C][/ROW]
[ROW][C]108[/C][C] 12[/C][C] 11.86[/C][C] 0.1366[/C][/ROW]
[ROW][C]109[/C][C] 13[/C][C] 11.99[/C][C] 1.006[/C][/ROW]
[ROW][C]110[/C][C] 13[/C][C] 12.2[/C][C] 0.8017[/C][/ROW]
[ROW][C]111[/C][C] 13[/C][C] 12.79[/C][C] 0.2121[/C][/ROW]
[ROW][C]112[/C][C] 11[/C][C] 12.25[/C][C]-1.25[/C][/ROW]
[ROW][C]113[/C][C] 12[/C][C] 12.04[/C][C]-0.04041[/C][/ROW]
[ROW][C]114[/C][C] 11[/C][C] 11.55[/C][C]-0.5488[/C][/ROW]
[ROW][C]115[/C][C] 12[/C][C] 12.18[/C][C]-0.1843[/C][/ROW]
[ROW][C]116[/C][C] 12[/C][C] 12.43[/C][C]-0.4273[/C][/ROW]
[ROW][C]117[/C][C] 13[/C][C] 13.22[/C][C]-0.2226[/C][/ROW]
[ROW][C]118[/C][C] 12[/C][C] 11.81[/C][C] 0.1891[/C][/ROW]
[ROW][C]119[/C][C] 13[/C][C] 11.99[/C][C] 1.012[/C][/ROW]
[ROW][C]120[/C][C] 13[/C][C] 12.12[/C][C] 0.8811[/C][/ROW]
[ROW][C]121[/C][C] 12[/C][C] 11.56[/C][C] 0.4382[/C][/ROW]
[ROW][C]122[/C][C] 12[/C][C] 12.05[/C][C]-0.05333[/C][/ROW]
[ROW][C]123[/C][C] 8[/C][C] 9.236[/C][C]-1.236[/C][/ROW]
[ROW][C]124[/C][C] 12[/C][C] 12.23[/C][C]-0.2308[/C][/ROW]
[ROW][C]125[/C][C] 13[/C][C] 12.6[/C][C] 0.3957[/C][/ROW]
[ROW][C]126[/C][C] 10[/C][C] 10.93[/C][C]-0.9324[/C][/ROW]
[ROW][C]127[/C][C] 8[/C][C] 10.72[/C][C]-2.72[/C][/ROW]
[ROW][C]128[/C][C] 13[/C][C] 13.89[/C][C]-0.8916[/C][/ROW]
[ROW][C]129[/C][C] 12[/C][C] 13.33[/C][C]-1.328[/C][/ROW]
[ROW][C]130[/C][C] 15[/C][C] 14.14[/C][C] 0.8593[/C][/ROW]
[ROW][C]131[/C][C] 14[/C][C] 13.51[/C][C] 0.4948[/C][/ROW]
[ROW][C]132[/C][C] 10[/C][C] 11.92[/C][C]-1.922[/C][/ROW]
[ROW][C]133[/C][C] 11[/C][C] 12.11[/C][C]-1.106[/C][/ROW]
[ROW][C]134[/C][C] 12[/C][C] 12.24[/C][C]-0.2369[/C][/ROW]
[ROW][C]135[/C][C] 10[/C][C] 12.08[/C][C]-2.079[/C][/ROW]
[ROW][C]136[/C][C] 14[/C][C] 12.31[/C][C] 1.691[/C][/ROW]
[ROW][C]137[/C][C] 10[/C][C] 12.36[/C][C]-2.362[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 12.66[/C][C] 2.343[/C][/ROW]
[ROW][C]139[/C][C] 11[/C][C] 11.15[/C][C]-0.1461[/C][/ROW]
[ROW][C]140[/C][C] 12[/C][C] 12.24[/C][C]-0.2369[/C][/ROW]
[ROW][C]141[/C][C] 9[/C][C] 10.42[/C][C]-1.418[/C][/ROW]
[ROW][C]142[/C][C] 12[/C][C] 13.39[/C][C]-1.393[/C][/ROW]
[ROW][C]143[/C][C] 13[/C][C] 11.99[/C][C] 1.012[/C][/ROW]
[ROW][C]144[/C][C] 12[/C][C] 12.12[/C][C]-0.1189[/C][/ROW]
[ROW][C]145[/C][C] 9[/C][C] 11.84[/C][C]-2.844[/C][/ROW]
[ROW][C]146[/C][C] 12[/C][C] 12.17[/C][C]-0.1714[/C][/ROW]
[ROW][C]147[/C][C] 14[/C][C] 12.85[/C][C] 1.147[/C][/ROW]
[ROW][C]148[/C][C] 10[/C][C] 11.99[/C][C]-1.994[/C][/ROW]
[ROW][C]149[/C][C] 12[/C][C] 10.4[/C][C] 1.601[/C][/ROW]
[ROW][C]150[/C][C] 14[/C][C] 12.12[/C][C] 1.881[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 11.07[/C][C] 0.9256[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 12.25[/C][C] 2.75[/C][/ROW]
[ROW][C]153[/C][C] 11[/C][C] 13.52[/C][C]-2.524[/C][/ROW]
[ROW][C]154[/C][C] 12[/C][C] 12.51[/C][C]-0.5053[/C][/ROW]
[ROW][C]155[/C][C] 12[/C][C] 12.36[/C][C]-0.3613[/C][/ROW]
[ROW][C]156[/C][C] 10[/C][C] 10.74[/C][C]-0.7397[/C][/ROW]
[ROW][C]157[/C][C] 12[/C][C] 12.12[/C][C]-0.1189[/C][/ROW]
[ROW][C]158[/C][C] 10[/C][C] 12.43[/C][C]-2.427[/C][/ROW]
[ROW][C]159[/C][C] 11[/C][C] 10.79[/C][C] 0.214[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299008&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299008&T=4

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	10	10.05	-0.0491
2	13	11.66	1.343
3	14	12.48	1.52
4	12	11.56	0.4382
5	12	12.48	-0.4799
6	13	11.99	1.012
7	13	11.37	1.629
8	13	11.86	1.137
9	13	13.45	-0.4526
10	14	13.51	0.4948
11	14	12.32	1.678
12	12	12.28	-0.2833
13	12	11.92	0.07766
14	11	10.71	0.2929
15	12	11.69	0.3145
16	14	11.8	2.202
17	12	11.8	0.2021
18	11	13.45	-2.452
19	13	12.43	0.5727
20	13	11.6	1.399
21	12	12.36	-0.3618
22	13	12.17	0.8286
23	12	12.24	-0.2369
24	13	12.17	0.8286
25	12	11.81	0.1891
26	12	11.79	0.2082
27	12	12.18	-0.1843
28	13	11.67	1.333
29	13	12.56	0.4422
30	10	11.15	-1.146
31	12	13.51	-1.505
32	13	12.54	0.4551
33	10	10.64	-0.642
34	14	13.17	0.83
35	12	11.86	0.1366
36	10	10.88	-0.8841
37	10	11.04	-1.041
38	14	12.24	1.763
39	12	12.56	-0.5578
40	14	13.51	0.4948
41	10	10.72	-0.72
42	13	13.68	-0.6822
43	12	12.41	-0.4139
44	12	11.92	0.07766
45	13	12.18	0.8157
46	12	12.51	-0.5148
47	10	12.43	-2.427
48	14	13.33	0.6722
49	14	13.79	0.2135
50	13	13.7	-0.7013
51	8	11.32	-3.324
52	11	10.83	0.1685
53	10	12.24	-2.237
54	12	11.37	0.6286
55	14	11.88	2.124
56	12	12.23	-0.2308
57	12	12.43	-0.4273
58	14	13.77	0.2264
59	13	11.61	1.385
60	13	12.36	0.6382
61	13	11.32	1.675
62	12	12.41	-0.4144
63	10	10	-0.002671
64	14	12.3	1.704
65	11	12.23	-1.231
66	10	12.11	-2.105
67	13	11.79	1.208
68	12	11.55	0.4512
69	12	13.77	-1.767
70	10	12.18	-2.184
71	13	12.62	0.3762
72	12	11.76	0.2417
73	13	12.43	0.5727
74	11	11.84	-0.8444
75	10	10.71	-0.7071
76	14	11.15	2.854
77	13	12.17	0.8286
78	7	10.67	-3.667
79	13	12.43	0.5727
80	13	12.31	0.6908
81	13	12.36	0.6382
82	15	12.56	2.442
83	13	12.64	0.3637
84	14	12.41	1.586
85	12	11.8	0.2021
86	11	9.754	1.246
87	12	12.3	-0.3024
88	14	12.17	1.829
89	13	12.43	0.5727
90	14	13.58	0.4164
91	12	11.79	0.2082
92	12	12.17	-0.1714
93	13	12.44	0.5602
94	14	12.74	1.259
95	13	13.45	-0.4526
96	13	11.64	1.356
97	12	12.17	-0.1714
98	10	12.49	-2.492
99	13	12.93	0.0682
100	13	12	0.9992
101	12	12.24	-0.2369
102	12	12.24	-0.2369
103	12	12.17	-0.1719
104	10	12.18	-2.184
105	12	11.92	0.07766
106	9	10.7	-1.701
107	14	11.99	2.012
108	12	11.86	0.1366
109	13	11.99	1.006
110	13	12.2	0.8017
111	13	12.79	0.2121
112	11	12.25	-1.25
113	12	12.04	-0.04041
114	11	11.55	-0.5488
115	12	12.18	-0.1843
116	12	12.43	-0.4273
117	13	13.22	-0.2226
118	12	11.81	0.1891
119	13	11.99	1.012
120	13	12.12	0.8811
121	12	11.56	0.4382
122	12	12.05	-0.05333
123	8	9.236	-1.236
124	12	12.23	-0.2308
125	13	12.6	0.3957
126	10	10.93	-0.9324
127	8	10.72	-2.72
128	13	13.89	-0.8916
129	12	13.33	-1.328
130	15	14.14	0.8593
131	14	13.51	0.4948
132	10	11.92	-1.922
133	11	12.11	-1.106
134	12	12.24	-0.2369
135	10	12.08	-2.079
136	14	12.31	1.691
137	10	12.36	-2.362
138	15	12.66	2.343
139	11	11.15	-0.1461
140	12	12.24	-0.2369
141	9	10.42	-1.418
142	12	13.39	-1.393
143	13	11.99	1.012
144	12	12.12	-0.1189
145	9	11.84	-2.844
146	12	12.17	-0.1714
147	14	12.85	1.147
148	10	11.99	-1.994
149	12	10.4	1.601
150	14	12.12	1.881
151	12	11.07	0.9256
152	15	12.25	2.75
153	11	13.52	-2.524
154	12	12.51	-0.5053
155	12	12.36	-0.3613
156	10	10.74	-0.7397
157	12	12.12	-0.1189
158	10	12.43	-2.427
159	11	10.79	0.214

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.3859	0.7718	0.6141
11	0.2617	0.5234	0.7383
12	0.1662	0.3324	0.8338
13	0.09038	0.1808	0.9096
14	0.06034	0.1207	0.9397
15	0.04254	0.08509	0.9575
16	0.09013	0.1803	0.9099
17	0.06354	0.1271	0.9365
18	0.1232	0.2464	0.8768
19	0.0824	0.1648	0.9176
20	0.09835	0.1967	0.9016
21	0.07986	0.1597	0.9201
22	0.06077	0.1215	0.9392
23	0.04579	0.09158	0.9542
24	0.03372	0.06744	0.9663
25	0.03527	0.07055	0.9647
26	0.02359	0.04717	0.9764
27	0.0177	0.03539	0.9823
28	0.01235	0.0247	0.9877
29	0.008651	0.0173	0.9913
30	0.01575	0.0315	0.9842
31	0.0202	0.0404	0.9798
32	0.01678	0.03355	0.9832
33	0.02737	0.05475	0.9726
34	0.01909	0.03818	0.9809
35	0.01286	0.02571	0.9871
36	0.01126	0.02252	0.9887
37	0.02051	0.04103	0.9795
38	0.03313	0.06627	0.9669
39	0.02498	0.04997	0.975
40	0.01828	0.03656	0.9817
41	0.02023	0.04047	0.9798
42	0.0144	0.02879	0.9856
43	0.01025	0.0205	0.9898
44	0.006908	0.01382	0.9931
45	0.005148	0.0103	0.9949
46	0.003823	0.007645	0.9962
47	0.0162	0.03241	0.9838
48	0.01291	0.02583	0.9871
49	0.009006	0.01801	0.991
50	0.00658	0.01316	0.9934
51	0.05539	0.1108	0.9446
52	0.04263	0.08526	0.9574
53	0.07548	0.151	0.9245
54	0.06124	0.1225	0.9388
55	0.0862	0.1724	0.9138
56	0.06811	0.1362	0.9319
57	0.05449	0.109	0.9455
58	0.04208	0.08416	0.9579
59	0.04006	0.08012	0.9599
60	0.03247	0.06494	0.9675
61	0.03634	0.07268	0.9637
62	0.02815	0.05629	0.9719
63	0.02141	0.04283	0.9786
64	0.0328	0.0656	0.9672
65	0.03269	0.06539	0.9673
66	0.04548	0.09097	0.9545
67	0.04486	0.08973	0.9551
68	0.03723	0.07446	0.9628
69	0.05092	0.1018	0.9491
70	0.09291	0.1858	0.9071
71	0.07587	0.1517	0.9241
72	0.07008	0.1402	0.9299
73	0.05874	0.1175	0.9413
74	0.05199	0.104	0.948
75	0.04519	0.09037	0.9548
76	0.1317	0.2635	0.8683
77	0.1211	0.2421	0.8789
78	0.4501	0.9003	0.5499
79	0.4135	0.8269	0.5865
80	0.3835	0.767	0.6165
81	0.3522	0.7045	0.6478
82	0.4781	0.9562	0.5219
83	0.4346	0.8692	0.5654
84	0.4658	0.9316	0.5342
85	0.4282	0.8563	0.5718
86	0.4175	0.8349	0.5825
87	0.3746	0.7492	0.6254
88	0.4348	0.8696	0.5652
89	0.3985	0.797	0.6015
90	0.3611	0.7222	0.6389
91	0.3269	0.6539	0.6731
92	0.2858	0.5716	0.7142
93	0.254	0.508	0.746
94	0.2546	0.5092	0.7454
95	0.2227	0.4454	0.7773
96	0.2253	0.4507	0.7747
97	0.1924	0.3848	0.8076
98	0.2949	0.5897	0.7051
99	0.2546	0.5092	0.7454
100	0.2411	0.4823	0.7589
101	0.2056	0.4111	0.7944
102	0.1732	0.3463	0.8268
103	0.1452	0.2903	0.8548
104	0.2006	0.4011	0.7994
105	0.1683	0.3367	0.8317
106	0.1849	0.3697	0.8151
107	0.248	0.4961	0.752
108	0.2166	0.4331	0.7834
109	0.2107	0.4215	0.7893
110	0.1896	0.3791	0.8104
111	0.1584	0.3167	0.8416
112	0.1513	0.3025	0.8487
113	0.1229	0.2459	0.8771
114	0.1026	0.2051	0.8974
115	0.08148	0.163	0.9185
116	0.06477	0.1295	0.9352
117	0.05218	0.1044	0.9478
118	0.04365	0.08731	0.9563
119	0.0425	0.085	0.9575
120	0.04024	0.08048	0.9598
121	0.04119	0.08237	0.9588
122	0.03144	0.06287	0.9686
123	0.03192	0.06384	0.9681
124	0.02307	0.04614	0.9769
125	0.01661	0.03322	0.9834
126	0.01806	0.03611	0.9819
127	0.04924	0.09847	0.9508
128	0.03849	0.07698	0.9615
129	0.0301	0.06021	0.9699
130	0.02361	0.04722	0.9764
131	0.03027	0.06054	0.9697
132	0.0291	0.0582	0.9709
133	0.02179	0.04357	0.9782
134	0.01462	0.02924	0.9854
135	0.01575	0.0315	0.9843
136	0.03618	0.07235	0.9638
137	0.04473	0.08947	0.9553
138	0.06483	0.1297	0.9352
139	0.0872	0.1744	0.9128
140	0.06209	0.1242	0.9379
141	0.07627	0.1525	0.9237
142	0.05347	0.1069	0.9465
143	0.0548	0.1096	0.9452
144	0.03577	0.07153	0.9642
145	0.03075	0.06149	0.9693
146	0.01686	0.03372	0.9831
147	0.01345	0.0269	0.9865
148	0.1214	0.2427	0.8786
149	0.2418	0.4836	0.7582

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.3859 &  0.7718 &  0.6141 \tabularnewline
11 &  0.2617 &  0.5234 &  0.7383 \tabularnewline
12 &  0.1662 &  0.3324 &  0.8338 \tabularnewline
13 &  0.09038 &  0.1808 &  0.9096 \tabularnewline
14 &  0.06034 &  0.1207 &  0.9397 \tabularnewline
15 &  0.04254 &  0.08509 &  0.9575 \tabularnewline
16 &  0.09013 &  0.1803 &  0.9099 \tabularnewline
17 &  0.06354 &  0.1271 &  0.9365 \tabularnewline
18 &  0.1232 &  0.2464 &  0.8768 \tabularnewline
19 &  0.0824 &  0.1648 &  0.9176 \tabularnewline
20 &  0.09835 &  0.1967 &  0.9016 \tabularnewline
21 &  0.07986 &  0.1597 &  0.9201 \tabularnewline
22 &  0.06077 &  0.1215 &  0.9392 \tabularnewline
23 &  0.04579 &  0.09158 &  0.9542 \tabularnewline
24 &  0.03372 &  0.06744 &  0.9663 \tabularnewline
25 &  0.03527 &  0.07055 &  0.9647 \tabularnewline
26 &  0.02359 &  0.04717 &  0.9764 \tabularnewline
27 &  0.0177 &  0.03539 &  0.9823 \tabularnewline
28 &  0.01235 &  0.0247 &  0.9877 \tabularnewline
29 &  0.008651 &  0.0173 &  0.9913 \tabularnewline
30 &  0.01575 &  0.0315 &  0.9842 \tabularnewline
31 &  0.0202 &  0.0404 &  0.9798 \tabularnewline
32 &  0.01678 &  0.03355 &  0.9832 \tabularnewline
33 &  0.02737 &  0.05475 &  0.9726 \tabularnewline
34 &  0.01909 &  0.03818 &  0.9809 \tabularnewline
35 &  0.01286 &  0.02571 &  0.9871 \tabularnewline
36 &  0.01126 &  0.02252 &  0.9887 \tabularnewline
37 &  0.02051 &  0.04103 &  0.9795 \tabularnewline
38 &  0.03313 &  0.06627 &  0.9669 \tabularnewline
39 &  0.02498 &  0.04997 &  0.975 \tabularnewline
40 &  0.01828 &  0.03656 &  0.9817 \tabularnewline
41 &  0.02023 &  0.04047 &  0.9798 \tabularnewline
42 &  0.0144 &  0.02879 &  0.9856 \tabularnewline
43 &  0.01025 &  0.0205 &  0.9898 \tabularnewline
44 &  0.006908 &  0.01382 &  0.9931 \tabularnewline
45 &  0.005148 &  0.0103 &  0.9949 \tabularnewline
46 &  0.003823 &  0.007645 &  0.9962 \tabularnewline
47 &  0.0162 &  0.03241 &  0.9838 \tabularnewline
48 &  0.01291 &  0.02583 &  0.9871 \tabularnewline
49 &  0.009006 &  0.01801 &  0.991 \tabularnewline
50 &  0.00658 &  0.01316 &  0.9934 \tabularnewline
51 &  0.05539 &  0.1108 &  0.9446 \tabularnewline
52 &  0.04263 &  0.08526 &  0.9574 \tabularnewline
53 &  0.07548 &  0.151 &  0.9245 \tabularnewline
54 &  0.06124 &  0.1225 &  0.9388 \tabularnewline
55 &  0.0862 &  0.1724 &  0.9138 \tabularnewline
56 &  0.06811 &  0.1362 &  0.9319 \tabularnewline
57 &  0.05449 &  0.109 &  0.9455 \tabularnewline
58 &  0.04208 &  0.08416 &  0.9579 \tabularnewline
59 &  0.04006 &  0.08012 &  0.9599 \tabularnewline
60 &  0.03247 &  0.06494 &  0.9675 \tabularnewline
61 &  0.03634 &  0.07268 &  0.9637 \tabularnewline
62 &  0.02815 &  0.05629 &  0.9719 \tabularnewline
63 &  0.02141 &  0.04283 &  0.9786 \tabularnewline
64 &  0.0328 &  0.0656 &  0.9672 \tabularnewline
65 &  0.03269 &  0.06539 &  0.9673 \tabularnewline
66 &  0.04548 &  0.09097 &  0.9545 \tabularnewline
67 &  0.04486 &  0.08973 &  0.9551 \tabularnewline
68 &  0.03723 &  0.07446 &  0.9628 \tabularnewline
69 &  0.05092 &  0.1018 &  0.9491 \tabularnewline
70 &  0.09291 &  0.1858 &  0.9071 \tabularnewline
71 &  0.07587 &  0.1517 &  0.9241 \tabularnewline
72 &  0.07008 &  0.1402 &  0.9299 \tabularnewline
73 &  0.05874 &  0.1175 &  0.9413 \tabularnewline
74 &  0.05199 &  0.104 &  0.948 \tabularnewline
75 &  0.04519 &  0.09037 &  0.9548 \tabularnewline
76 &  0.1317 &  0.2635 &  0.8683 \tabularnewline
77 &  0.1211 &  0.2421 &  0.8789 \tabularnewline
78 &  0.4501 &  0.9003 &  0.5499 \tabularnewline
79 &  0.4135 &  0.8269 &  0.5865 \tabularnewline
80 &  0.3835 &  0.767 &  0.6165 \tabularnewline
81 &  0.3522 &  0.7045 &  0.6478 \tabularnewline
82 &  0.4781 &  0.9562 &  0.5219 \tabularnewline
83 &  0.4346 &  0.8692 &  0.5654 \tabularnewline
84 &  0.4658 &  0.9316 &  0.5342 \tabularnewline
85 &  0.4282 &  0.8563 &  0.5718 \tabularnewline
86 &  0.4175 &  0.8349 &  0.5825 \tabularnewline
87 &  0.3746 &  0.7492 &  0.6254 \tabularnewline
88 &  0.4348 &  0.8696 &  0.5652 \tabularnewline
89 &  0.3985 &  0.797 &  0.6015 \tabularnewline
90 &  0.3611 &  0.7222 &  0.6389 \tabularnewline
91 &  0.3269 &  0.6539 &  0.6731 \tabularnewline
92 &  0.2858 &  0.5716 &  0.7142 \tabularnewline
93 &  0.254 &  0.508 &  0.746 \tabularnewline
94 &  0.2546 &  0.5092 &  0.7454 \tabularnewline
95 &  0.2227 &  0.4454 &  0.7773 \tabularnewline
96 &  0.2253 &  0.4507 &  0.7747 \tabularnewline
97 &  0.1924 &  0.3848 &  0.8076 \tabularnewline
98 &  0.2949 &  0.5897 &  0.7051 \tabularnewline
99 &  0.2546 &  0.5092 &  0.7454 \tabularnewline
100 &  0.2411 &  0.4823 &  0.7589 \tabularnewline
101 &  0.2056 &  0.4111 &  0.7944 \tabularnewline
102 &  0.1732 &  0.3463 &  0.8268 \tabularnewline
103 &  0.1452 &  0.2903 &  0.8548 \tabularnewline
104 &  0.2006 &  0.4011 &  0.7994 \tabularnewline
105 &  0.1683 &  0.3367 &  0.8317 \tabularnewline
106 &  0.1849 &  0.3697 &  0.8151 \tabularnewline
107 &  0.248 &  0.4961 &  0.752 \tabularnewline
108 &  0.2166 &  0.4331 &  0.7834 \tabularnewline
109 &  0.2107 &  0.4215 &  0.7893 \tabularnewline
110 &  0.1896 &  0.3791 &  0.8104 \tabularnewline
111 &  0.1584 &  0.3167 &  0.8416 \tabularnewline
112 &  0.1513 &  0.3025 &  0.8487 \tabularnewline
113 &  0.1229 &  0.2459 &  0.8771 \tabularnewline
114 &  0.1026 &  0.2051 &  0.8974 \tabularnewline
115 &  0.08148 &  0.163 &  0.9185 \tabularnewline
116 &  0.06477 &  0.1295 &  0.9352 \tabularnewline
117 &  0.05218 &  0.1044 &  0.9478 \tabularnewline
118 &  0.04365 &  0.08731 &  0.9563 \tabularnewline
119 &  0.0425 &  0.085 &  0.9575 \tabularnewline
120 &  0.04024 &  0.08048 &  0.9598 \tabularnewline
121 &  0.04119 &  0.08237 &  0.9588 \tabularnewline
122 &  0.03144 &  0.06287 &  0.9686 \tabularnewline
123 &  0.03192 &  0.06384 &  0.9681 \tabularnewline
124 &  0.02307 &  0.04614 &  0.9769 \tabularnewline
125 &  0.01661 &  0.03322 &  0.9834 \tabularnewline
126 &  0.01806 &  0.03611 &  0.9819 \tabularnewline
127 &  0.04924 &  0.09847 &  0.9508 \tabularnewline
128 &  0.03849 &  0.07698 &  0.9615 \tabularnewline
129 &  0.0301 &  0.06021 &  0.9699 \tabularnewline
130 &  0.02361 &  0.04722 &  0.9764 \tabularnewline
131 &  0.03027 &  0.06054 &  0.9697 \tabularnewline
132 &  0.0291 &  0.0582 &  0.9709 \tabularnewline
133 &  0.02179 &  0.04357 &  0.9782 \tabularnewline
134 &  0.01462 &  0.02924 &  0.9854 \tabularnewline
135 &  0.01575 &  0.0315 &  0.9843 \tabularnewline
136 &  0.03618 &  0.07235 &  0.9638 \tabularnewline
137 &  0.04473 &  0.08947 &  0.9553 \tabularnewline
138 &  0.06483 &  0.1297 &  0.9352 \tabularnewline
139 &  0.0872 &  0.1744 &  0.9128 \tabularnewline
140 &  0.06209 &  0.1242 &  0.9379 \tabularnewline
141 &  0.07627 &  0.1525 &  0.9237 \tabularnewline
142 &  0.05347 &  0.1069 &  0.9465 \tabularnewline
143 &  0.0548 &  0.1096 &  0.9452 \tabularnewline
144 &  0.03577 &  0.07153 &  0.9642 \tabularnewline
145 &  0.03075 &  0.06149 &  0.9693 \tabularnewline
146 &  0.01686 &  0.03372 &  0.9831 \tabularnewline
147 &  0.01345 &  0.0269 &  0.9865 \tabularnewline
148 &  0.1214 &  0.2427 &  0.8786 \tabularnewline
149 &  0.2418 &  0.4836 &  0.7582 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299008&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C] 0.3859[/C][C] 0.7718[/C][C] 0.6141[/C][/ROW]
[ROW][C]11[/C][C] 0.2617[/C][C] 0.5234[/C][C] 0.7383[/C][/ROW]
[ROW][C]12[/C][C] 0.1662[/C][C] 0.3324[/C][C] 0.8338[/C][/ROW]
[ROW][C]13[/C][C] 0.09038[/C][C] 0.1808[/C][C] 0.9096[/C][/ROW]
[ROW][C]14[/C][C] 0.06034[/C][C] 0.1207[/C][C] 0.9397[/C][/ROW]
[ROW][C]15[/C][C] 0.04254[/C][C] 0.08509[/C][C] 0.9575[/C][/ROW]
[ROW][C]16[/C][C] 0.09013[/C][C] 0.1803[/C][C] 0.9099[/C][/ROW]
[ROW][C]17[/C][C] 0.06354[/C][C] 0.1271[/C][C] 0.9365[/C][/ROW]
[ROW][C]18[/C][C] 0.1232[/C][C] 0.2464[/C][C] 0.8768[/C][/ROW]
[ROW][C]19[/C][C] 0.0824[/C][C] 0.1648[/C][C] 0.9176[/C][/ROW]
[ROW][C]20[/C][C] 0.09835[/C][C] 0.1967[/C][C] 0.9016[/C][/ROW]
[ROW][C]21[/C][C] 0.07986[/C][C] 0.1597[/C][C] 0.9201[/C][/ROW]
[ROW][C]22[/C][C] 0.06077[/C][C] 0.1215[/C][C] 0.9392[/C][/ROW]
[ROW][C]23[/C][C] 0.04579[/C][C] 0.09158[/C][C] 0.9542[/C][/ROW]
[ROW][C]24[/C][C] 0.03372[/C][C] 0.06744[/C][C] 0.9663[/C][/ROW]
[ROW][C]25[/C][C] 0.03527[/C][C] 0.07055[/C][C] 0.9647[/C][/ROW]
[ROW][C]26[/C][C] 0.02359[/C][C] 0.04717[/C][C] 0.9764[/C][/ROW]
[ROW][C]27[/C][C] 0.0177[/C][C] 0.03539[/C][C] 0.9823[/C][/ROW]
[ROW][C]28[/C][C] 0.01235[/C][C] 0.0247[/C][C] 0.9877[/C][/ROW]
[ROW][C]29[/C][C] 0.008651[/C][C] 0.0173[/C][C] 0.9913[/C][/ROW]
[ROW][C]30[/C][C] 0.01575[/C][C] 0.0315[/C][C] 0.9842[/C][/ROW]
[ROW][C]31[/C][C] 0.0202[/C][C] 0.0404[/C][C] 0.9798[/C][/ROW]
[ROW][C]32[/C][C] 0.01678[/C][C] 0.03355[/C][C] 0.9832[/C][/ROW]
[ROW][C]33[/C][C] 0.02737[/C][C] 0.05475[/C][C] 0.9726[/C][/ROW]
[ROW][C]34[/C][C] 0.01909[/C][C] 0.03818[/C][C] 0.9809[/C][/ROW]
[ROW][C]35[/C][C] 0.01286[/C][C] 0.02571[/C][C] 0.9871[/C][/ROW]
[ROW][C]36[/C][C] 0.01126[/C][C] 0.02252[/C][C] 0.9887[/C][/ROW]
[ROW][C]37[/C][C] 0.02051[/C][C] 0.04103[/C][C] 0.9795[/C][/ROW]
[ROW][C]38[/C][C] 0.03313[/C][C] 0.06627[/C][C] 0.9669[/C][/ROW]
[ROW][C]39[/C][C] 0.02498[/C][C] 0.04997[/C][C] 0.975[/C][/ROW]
[ROW][C]40[/C][C] 0.01828[/C][C] 0.03656[/C][C] 0.9817[/C][/ROW]
[ROW][C]41[/C][C] 0.02023[/C][C] 0.04047[/C][C] 0.9798[/C][/ROW]
[ROW][C]42[/C][C] 0.0144[/C][C] 0.02879[/C][C] 0.9856[/C][/ROW]
[ROW][C]43[/C][C] 0.01025[/C][C] 0.0205[/C][C] 0.9898[/C][/ROW]
[ROW][C]44[/C][C] 0.006908[/C][C] 0.01382[/C][C] 0.9931[/C][/ROW]
[ROW][C]45[/C][C] 0.005148[/C][C] 0.0103[/C][C] 0.9949[/C][/ROW]
[ROW][C]46[/C][C] 0.003823[/C][C] 0.007645[/C][C] 0.9962[/C][/ROW]
[ROW][C]47[/C][C] 0.0162[/C][C] 0.03241[/C][C] 0.9838[/C][/ROW]
[ROW][C]48[/C][C] 0.01291[/C][C] 0.02583[/C][C] 0.9871[/C][/ROW]
[ROW][C]49[/C][C] 0.009006[/C][C] 0.01801[/C][C] 0.991[/C][/ROW]
[ROW][C]50[/C][C] 0.00658[/C][C] 0.01316[/C][C] 0.9934[/C][/ROW]
[ROW][C]51[/C][C] 0.05539[/C][C] 0.1108[/C][C] 0.9446[/C][/ROW]
[ROW][C]52[/C][C] 0.04263[/C][C] 0.08526[/C][C] 0.9574[/C][/ROW]
[ROW][C]53[/C][C] 0.07548[/C][C] 0.151[/C][C] 0.9245[/C][/ROW]
[ROW][C]54[/C][C] 0.06124[/C][C] 0.1225[/C][C] 0.9388[/C][/ROW]
[ROW][C]55[/C][C] 0.0862[/C][C] 0.1724[/C][C] 0.9138[/C][/ROW]
[ROW][C]56[/C][C] 0.06811[/C][C] 0.1362[/C][C] 0.9319[/C][/ROW]
[ROW][C]57[/C][C] 0.05449[/C][C] 0.109[/C][C] 0.9455[/C][/ROW]
[ROW][C]58[/C][C] 0.04208[/C][C] 0.08416[/C][C] 0.9579[/C][/ROW]
[ROW][C]59[/C][C] 0.04006[/C][C] 0.08012[/C][C] 0.9599[/C][/ROW]
[ROW][C]60[/C][C] 0.03247[/C][C] 0.06494[/C][C] 0.9675[/C][/ROW]
[ROW][C]61[/C][C] 0.03634[/C][C] 0.07268[/C][C] 0.9637[/C][/ROW]
[ROW][C]62[/C][C] 0.02815[/C][C] 0.05629[/C][C] 0.9719[/C][/ROW]
[ROW][C]63[/C][C] 0.02141[/C][C] 0.04283[/C][C] 0.9786[/C][/ROW]
[ROW][C]64[/C][C] 0.0328[/C][C] 0.0656[/C][C] 0.9672[/C][/ROW]
[ROW][C]65[/C][C] 0.03269[/C][C] 0.06539[/C][C] 0.9673[/C][/ROW]
[ROW][C]66[/C][C] 0.04548[/C][C] 0.09097[/C][C] 0.9545[/C][/ROW]
[ROW][C]67[/C][C] 0.04486[/C][C] 0.08973[/C][C] 0.9551[/C][/ROW]
[ROW][C]68[/C][C] 0.03723[/C][C] 0.07446[/C][C] 0.9628[/C][/ROW]
[ROW][C]69[/C][C] 0.05092[/C][C] 0.1018[/C][C] 0.9491[/C][/ROW]
[ROW][C]70[/C][C] 0.09291[/C][C] 0.1858[/C][C] 0.9071[/C][/ROW]
[ROW][C]71[/C][C] 0.07587[/C][C] 0.1517[/C][C] 0.9241[/C][/ROW]
[ROW][C]72[/C][C] 0.07008[/C][C] 0.1402[/C][C] 0.9299[/C][/ROW]
[ROW][C]73[/C][C] 0.05874[/C][C] 0.1175[/C][C] 0.9413[/C][/ROW]
[ROW][C]74[/C][C] 0.05199[/C][C] 0.104[/C][C] 0.948[/C][/ROW]
[ROW][C]75[/C][C] 0.04519[/C][C] 0.09037[/C][C] 0.9548[/C][/ROW]
[ROW][C]76[/C][C] 0.1317[/C][C] 0.2635[/C][C] 0.8683[/C][/ROW]
[ROW][C]77[/C][C] 0.1211[/C][C] 0.2421[/C][C] 0.8789[/C][/ROW]
[ROW][C]78[/C][C] 0.4501[/C][C] 0.9003[/C][C] 0.5499[/C][/ROW]
[ROW][C]79[/C][C] 0.4135[/C][C] 0.8269[/C][C] 0.5865[/C][/ROW]
[ROW][C]80[/C][C] 0.3835[/C][C] 0.767[/C][C] 0.6165[/C][/ROW]
[ROW][C]81[/C][C] 0.3522[/C][C] 0.7045[/C][C] 0.6478[/C][/ROW]
[ROW][C]82[/C][C] 0.4781[/C][C] 0.9562[/C][C] 0.5219[/C][/ROW]
[ROW][C]83[/C][C] 0.4346[/C][C] 0.8692[/C][C] 0.5654[/C][/ROW]
[ROW][C]84[/C][C] 0.4658[/C][C] 0.9316[/C][C] 0.5342[/C][/ROW]
[ROW][C]85[/C][C] 0.4282[/C][C] 0.8563[/C][C] 0.5718[/C][/ROW]
[ROW][C]86[/C][C] 0.4175[/C][C] 0.8349[/C][C] 0.5825[/C][/ROW]
[ROW][C]87[/C][C] 0.3746[/C][C] 0.7492[/C][C] 0.6254[/C][/ROW]
[ROW][C]88[/C][C] 0.4348[/C][C] 0.8696[/C][C] 0.5652[/C][/ROW]
[ROW][C]89[/C][C] 0.3985[/C][C] 0.797[/C][C] 0.6015[/C][/ROW]
[ROW][C]90[/C][C] 0.3611[/C][C] 0.7222[/C][C] 0.6389[/C][/ROW]
[ROW][C]91[/C][C] 0.3269[/C][C] 0.6539[/C][C] 0.6731[/C][/ROW]
[ROW][C]92[/C][C] 0.2858[/C][C] 0.5716[/C][C] 0.7142[/C][/ROW]
[ROW][C]93[/C][C] 0.254[/C][C] 0.508[/C][C] 0.746[/C][/ROW]
[ROW][C]94[/C][C] 0.2546[/C][C] 0.5092[/C][C] 0.7454[/C][/ROW]
[ROW][C]95[/C][C] 0.2227[/C][C] 0.4454[/C][C] 0.7773[/C][/ROW]
[ROW][C]96[/C][C] 0.2253[/C][C] 0.4507[/C][C] 0.7747[/C][/ROW]
[ROW][C]97[/C][C] 0.1924[/C][C] 0.3848[/C][C] 0.8076[/C][/ROW]
[ROW][C]98[/C][C] 0.2949[/C][C] 0.5897[/C][C] 0.7051[/C][/ROW]
[ROW][C]99[/C][C] 0.2546[/C][C] 0.5092[/C][C] 0.7454[/C][/ROW]
[ROW][C]100[/C][C] 0.2411[/C][C] 0.4823[/C][C] 0.7589[/C][/ROW]
[ROW][C]101[/C][C] 0.2056[/C][C] 0.4111[/C][C] 0.7944[/C][/ROW]
[ROW][C]102[/C][C] 0.1732[/C][C] 0.3463[/C][C] 0.8268[/C][/ROW]
[ROW][C]103[/C][C] 0.1452[/C][C] 0.2903[/C][C] 0.8548[/C][/ROW]
[ROW][C]104[/C][C] 0.2006[/C][C] 0.4011[/C][C] 0.7994[/C][/ROW]
[ROW][C]105[/C][C] 0.1683[/C][C] 0.3367[/C][C] 0.8317[/C][/ROW]
[ROW][C]106[/C][C] 0.1849[/C][C] 0.3697[/C][C] 0.8151[/C][/ROW]
[ROW][C]107[/C][C] 0.248[/C][C] 0.4961[/C][C] 0.752[/C][/ROW]
[ROW][C]108[/C][C] 0.2166[/C][C] 0.4331[/C][C] 0.7834[/C][/ROW]
[ROW][C]109[/C][C] 0.2107[/C][C] 0.4215[/C][C] 0.7893[/C][/ROW]
[ROW][C]110[/C][C] 0.1896[/C][C] 0.3791[/C][C] 0.8104[/C][/ROW]
[ROW][C]111[/C][C] 0.1584[/C][C] 0.3167[/C][C] 0.8416[/C][/ROW]
[ROW][C]112[/C][C] 0.1513[/C][C] 0.3025[/C][C] 0.8487[/C][/ROW]
[ROW][C]113[/C][C] 0.1229[/C][C] 0.2459[/C][C] 0.8771[/C][/ROW]
[ROW][C]114[/C][C] 0.1026[/C][C] 0.2051[/C][C] 0.8974[/C][/ROW]
[ROW][C]115[/C][C] 0.08148[/C][C] 0.163[/C][C] 0.9185[/C][/ROW]
[ROW][C]116[/C][C] 0.06477[/C][C] 0.1295[/C][C] 0.9352[/C][/ROW]
[ROW][C]117[/C][C] 0.05218[/C][C] 0.1044[/C][C] 0.9478[/C][/ROW]
[ROW][C]118[/C][C] 0.04365[/C][C] 0.08731[/C][C] 0.9563[/C][/ROW]
[ROW][C]119[/C][C] 0.0425[/C][C] 0.085[/C][C] 0.9575[/C][/ROW]
[ROW][C]120[/C][C] 0.04024[/C][C] 0.08048[/C][C] 0.9598[/C][/ROW]
[ROW][C]121[/C][C] 0.04119[/C][C] 0.08237[/C][C] 0.9588[/C][/ROW]
[ROW][C]122[/C][C] 0.03144[/C][C] 0.06287[/C][C] 0.9686[/C][/ROW]
[ROW][C]123[/C][C] 0.03192[/C][C] 0.06384[/C][C] 0.9681[/C][/ROW]
[ROW][C]124[/C][C] 0.02307[/C][C] 0.04614[/C][C] 0.9769[/C][/ROW]
[ROW][C]125[/C][C] 0.01661[/C][C] 0.03322[/C][C] 0.9834[/C][/ROW]
[ROW][C]126[/C][C] 0.01806[/C][C] 0.03611[/C][C] 0.9819[/C][/ROW]
[ROW][C]127[/C][C] 0.04924[/C][C] 0.09847[/C][C] 0.9508[/C][/ROW]
[ROW][C]128[/C][C] 0.03849[/C][C] 0.07698[/C][C] 0.9615[/C][/ROW]
[ROW][C]129[/C][C] 0.0301[/C][C] 0.06021[/C][C] 0.9699[/C][/ROW]
[ROW][C]130[/C][C] 0.02361[/C][C] 0.04722[/C][C] 0.9764[/C][/ROW]
[ROW][C]131[/C][C] 0.03027[/C][C] 0.06054[/C][C] 0.9697[/C][/ROW]
[ROW][C]132[/C][C] 0.0291[/C][C] 0.0582[/C][C] 0.9709[/C][/ROW]
[ROW][C]133[/C][C] 0.02179[/C][C] 0.04357[/C][C] 0.9782[/C][/ROW]
[ROW][C]134[/C][C] 0.01462[/C][C] 0.02924[/C][C] 0.9854[/C][/ROW]
[ROW][C]135[/C][C] 0.01575[/C][C] 0.0315[/C][C] 0.9843[/C][/ROW]
[ROW][C]136[/C][C] 0.03618[/C][C] 0.07235[/C][C] 0.9638[/C][/ROW]
[ROW][C]137[/C][C] 0.04473[/C][C] 0.08947[/C][C] 0.9553[/C][/ROW]
[ROW][C]138[/C][C] 0.06483[/C][C] 0.1297[/C][C] 0.9352[/C][/ROW]
[ROW][C]139[/C][C] 0.0872[/C][C] 0.1744[/C][C] 0.9128[/C][/ROW]
[ROW][C]140[/C][C] 0.06209[/C][C] 0.1242[/C][C] 0.9379[/C][/ROW]
[ROW][C]141[/C][C] 0.07627[/C][C] 0.1525[/C][C] 0.9237[/C][/ROW]
[ROW][C]142[/C][C] 0.05347[/C][C] 0.1069[/C][C] 0.9465[/C][/ROW]
[ROW][C]143[/C][C] 0.0548[/C][C] 0.1096[/C][C] 0.9452[/C][/ROW]
[ROW][C]144[/C][C] 0.03577[/C][C] 0.07153[/C][C] 0.9642[/C][/ROW]
[ROW][C]145[/C][C] 0.03075[/C][C] 0.06149[/C][C] 0.9693[/C][/ROW]
[ROW][C]146[/C][C] 0.01686[/C][C] 0.03372[/C][C] 0.9831[/C][/ROW]
[ROW][C]147[/C][C] 0.01345[/C][C] 0.0269[/C][C] 0.9865[/C][/ROW]
[ROW][C]148[/C][C] 0.1214[/C][C] 0.2427[/C][C] 0.8786[/C][/ROW]
[ROW][C]149[/C][C] 0.2418[/C][C] 0.4836[/C][C] 0.7582[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299008&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299008&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.3859	0.7718	0.6141
11	0.2617	0.5234	0.7383
12	0.1662	0.3324	0.8338
13	0.09038	0.1808	0.9096
14	0.06034	0.1207	0.9397
15	0.04254	0.08509	0.9575
16	0.09013	0.1803	0.9099
17	0.06354	0.1271	0.9365
18	0.1232	0.2464	0.8768
19	0.0824	0.1648	0.9176
20	0.09835	0.1967	0.9016
21	0.07986	0.1597	0.9201
22	0.06077	0.1215	0.9392
23	0.04579	0.09158	0.9542
24	0.03372	0.06744	0.9663
25	0.03527	0.07055	0.9647
26	0.02359	0.04717	0.9764
27	0.0177	0.03539	0.9823
28	0.01235	0.0247	0.9877
29	0.008651	0.0173	0.9913
30	0.01575	0.0315	0.9842
31	0.0202	0.0404	0.9798
32	0.01678	0.03355	0.9832
33	0.02737	0.05475	0.9726
34	0.01909	0.03818	0.9809
35	0.01286	0.02571	0.9871
36	0.01126	0.02252	0.9887
37	0.02051	0.04103	0.9795
38	0.03313	0.06627	0.9669
39	0.02498	0.04997	0.975
40	0.01828	0.03656	0.9817
41	0.02023	0.04047	0.9798
42	0.0144	0.02879	0.9856
43	0.01025	0.0205	0.9898
44	0.006908	0.01382	0.9931
45	0.005148	0.0103	0.9949
46	0.003823	0.007645	0.9962
47	0.0162	0.03241	0.9838
48	0.01291	0.02583	0.9871
49	0.009006	0.01801	0.991
50	0.00658	0.01316	0.9934
51	0.05539	0.1108	0.9446
52	0.04263	0.08526	0.9574
53	0.07548	0.151	0.9245
54	0.06124	0.1225	0.9388
55	0.0862	0.1724	0.9138
56	0.06811	0.1362	0.9319
57	0.05449	0.109	0.9455
58	0.04208	0.08416	0.9579
59	0.04006	0.08012	0.9599
60	0.03247	0.06494	0.9675
61	0.03634	0.07268	0.9637
62	0.02815	0.05629	0.9719
63	0.02141	0.04283	0.9786
64	0.0328	0.0656	0.9672
65	0.03269	0.06539	0.9673
66	0.04548	0.09097	0.9545
67	0.04486	0.08973	0.9551
68	0.03723	0.07446	0.9628
69	0.05092	0.1018	0.9491
70	0.09291	0.1858	0.9071
71	0.07587	0.1517	0.9241
72	0.07008	0.1402	0.9299
73	0.05874	0.1175	0.9413
74	0.05199	0.104	0.948
75	0.04519	0.09037	0.9548
76	0.1317	0.2635	0.8683
77	0.1211	0.2421	0.8789
78	0.4501	0.9003	0.5499
79	0.4135	0.8269	0.5865
80	0.3835	0.767	0.6165
81	0.3522	0.7045	0.6478
82	0.4781	0.9562	0.5219
83	0.4346	0.8692	0.5654
84	0.4658	0.9316	0.5342
85	0.4282	0.8563	0.5718
86	0.4175	0.8349	0.5825
87	0.3746	0.7492	0.6254
88	0.4348	0.8696	0.5652
89	0.3985	0.797	0.6015
90	0.3611	0.7222	0.6389
91	0.3269	0.6539	0.6731
92	0.2858	0.5716	0.7142
93	0.254	0.508	0.746
94	0.2546	0.5092	0.7454
95	0.2227	0.4454	0.7773
96	0.2253	0.4507	0.7747
97	0.1924	0.3848	0.8076
98	0.2949	0.5897	0.7051
99	0.2546	0.5092	0.7454
100	0.2411	0.4823	0.7589
101	0.2056	0.4111	0.7944
102	0.1732	0.3463	0.8268
103	0.1452	0.2903	0.8548
104	0.2006	0.4011	0.7994
105	0.1683	0.3367	0.8317
106	0.1849	0.3697	0.8151
107	0.248	0.4961	0.752
108	0.2166	0.4331	0.7834
109	0.2107	0.4215	0.7893
110	0.1896	0.3791	0.8104
111	0.1584	0.3167	0.8416
112	0.1513	0.3025	0.8487
113	0.1229	0.2459	0.8771
114	0.1026	0.2051	0.8974
115	0.08148	0.163	0.9185
116	0.06477	0.1295	0.9352
117	0.05218	0.1044	0.9478
118	0.04365	0.08731	0.9563
119	0.0425	0.085	0.9575
120	0.04024	0.08048	0.9598
121	0.04119	0.08237	0.9588
122	0.03144	0.06287	0.9686
123	0.03192	0.06384	0.9681
124	0.02307	0.04614	0.9769
125	0.01661	0.03322	0.9834
126	0.01806	0.03611	0.9819
127	0.04924	0.09847	0.9508
128	0.03849	0.07698	0.9615
129	0.0301	0.06021	0.9699
130	0.02361	0.04722	0.9764
131	0.03027	0.06054	0.9697
132	0.0291	0.0582	0.9709
133	0.02179	0.04357	0.9782
134	0.01462	0.02924	0.9854
135	0.01575	0.0315	0.9843
136	0.03618	0.07235	0.9638
137	0.04473	0.08947	0.9553
138	0.06483	0.1297	0.9352
139	0.0872	0.1744	0.9128
140	0.06209	0.1242	0.9379
141	0.07627	0.1525	0.9237
142	0.05347	0.1069	0.9465
143	0.0548	0.1096	0.9452
144	0.03577	0.07153	0.9642
145	0.03075	0.06149	0.9693
146	0.01686	0.03372	0.9831
147	0.01345	0.0269	0.9865
148	0.1214	0.2427	0.8786
149	0.2418	0.4836	0.7582

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.007143	OK
5% type I error level	33	0.235714	NOK
10% type I error level	66	0.471429	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 & 1 &  0.007143 & OK \tabularnewline
5% type I error level & 33 & 0.235714 & NOK \tabularnewline
10% type I error level & 66 & 0.471429 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299008&T=6

[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]1[/C][C] 0.007143[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]33[/C][C]0.235714[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]66[/C][C]0.471429[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299008&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299008&T=6

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	1	0.007143	OK
5% type I error level	33	0.235714	NOK
10% type I error level	66	0.471429	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.3909, df1 = 2, df2 = 150, p-value = 0.09503
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3654, df1 = 12, df2 = 140, p-value = 0.1895
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.79988, df1 = 2, df2 = 150, p-value = 0.4513

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.3909, df1 = 2, df2 = 150, p-value = 0.09503
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3654, df1 = 12, df2 = 140, p-value = 0.1895
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.79988, df1 = 2, df2 = 150, p-value = 0.4513
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299008&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.3909, df1 = 2, df2 = 150, p-value = 0.09503
[/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 = 1.3654, df1 = 12, df2 = 140, p-value = 0.1895
[/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 = 0.79988, df1 = 2, df2 = 150, p-value = 0.4513
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299008&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299008&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.3909, df1 = 2, df2 = 150, p-value = 0.09503
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3654, df1 = 12, df2 = 140, p-value = 0.1895
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.79988, df1 = 2, df2 = 150, p-value = 0.4513

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 1.093289 1.131316 1.044801 1.044030 1.044830 1.032498

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.093289 1.131316 1.044801 1.044030 1.044830 1.032498 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299008&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.093289 1.131316 1.044801 1.044030 1.044830 1.032498 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299008&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299008&T=8

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 SK1 SK2 SK3 SK4 SK5 SK6 1.093289 1.131316 1.044801 1.044030 1.044830 1.032498

Figure 1

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Parameters (Session):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(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 - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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