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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 06 Dec 2016 17:31:43 +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/06/t14810419474b9ycuncbg0fs3y.htm/, Retrieved Sat, 04 May 2024 16:23:35 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297870, Retrieved Sat, 04 May 2024 16:23:35 +0000

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

-       [Multiple Regression] [Lin Reg TA V2] [2016-12-06 16:31:43] [8b2c6464bd93a4843579a2d15e9e0aeb] [Current]

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4	2	4	3	5	4	13
5	3	3	4	5	4	16
4	4	5	4	5	4	17
3	4	3	3	4	4	15
4	4	5	4	5	4	16
3	4	4	4	5	5	16
3	4	4	3	3	4	18
3	4	5	4	4	4	16
4	5	4	4	5	5	17
4	5	5	4	5	5	17
4	4	2	4	5	4	17
4	4	5	3	5	4	15
4	4	4	3	4	5	16
3	3	5	4	4	5	14
4	4	5	4	2	5	16
3	4	5	4	4	5	17
3	4	5	4	4	5	16
5	5	4	3	4	4	15
4	4	4	4	5	4	17
3	4	5	3	4	5	16
4	4	4	4	5	5	15
4	4	5	4	4	5	16
4	4	5	4	4	4	15
4	4	5	4	4	5	17
3	4	4	4	4	4	14
3	4	4	3	5	5	16
4	4	4	4	4	4	15
2	4	5	4	5	5	16
5	4	4	4	4	4	16
4	3	5	4	4	4	13
4	5	5	4	5	5	15
5	4	5	4	4	5	17
4	3	5	4	4	5	15
2	3	5	4	5	4	13
4	5	2	4	4	4	17
3	4	5	4	4	4	15
4	3	5	3	4	5	14
4	3	3	4	4	4	14
4	4	5	4	4	4	18
5	4	4	4	4	4	15
4	5	5	4	5	5	17
3	3	4	4	4	4	13
5	5	5	3	5	5	16
5	4	5	3	4	4	15
4	4	4	3	4	5	15
4	4	4	4	4	4	16
3	5	5	3	3	4	15
4	4	4	4	5	4	13
4	5	5	4	4	4	17
5	5	2	4	5	4	18
5	5	5	4	4	4	17
4	3	5	4	5	5	11
4	3	4	3	4	5	14
4	4	5	4	4	4	13
3	4	4	3	3	4	15
3	4	4	4	4	3	17
4	4	4	3	5	4	16
4	4	4	4	5	4	15
5	5	3	4	5	5	17
2	4	4	4	5	5	16
4	4	4	4	5	5	16
3	4	4	4	2	4	16
4	4	5	4	5	5	15
4	2	4	4	4	4	12
4	4	4	3	5	3	17
4	4	4	3	5	4	14
5	4	5	3	3	5	14
3	4	4	3	5	5	16
3	4	4	3	4	5	15
4	5	5	5	5	4	15
4	4	3	4	4	4	13
4	4	4	4	4	4	13
4	4	4	5	5	4	17
3	4	3	4	4	4	15
4	4	4	4	5	4	16
3	4	5	3	5	5	14
3	3	5	4	4	5	15
4	3	5	4	4	4	17
4	4	5	4	4	5	16
3	3	3	4	4	4	10
4	4	4	4	5	4	16
4	4	3	4	5	5	17
4	4	4	4	5	5	17
5	4	4	4	4	4	20
5	4	3	5	4	5	17
4	4	5	4	5	5	18
3	4	5	4	4	5	15
3	4	4	4	4	4	17
4	2	3	3	4	4	14
4	4	5	4	4	3	15
4	4	5	4	4	5	17
4	4	4	4	5	4	16
4	5	4	4	5	3	17
3	4	4	3	5	5	15
4	4	5	4	4	5	16
5	4	3	4	4	5	18
5	4	5	5	4	5	18
4	5	4	4	5	5	16
5	3	4	4	5	5	17
4	4	5	4	4	5	15
5	4	4	4	4	5	13
3	4	4	3	4	4	15
5	4	4	5	5	5	17
4	4	5	3	4	5	16
4	4	3	3	4	3	16
4	4	5	4	4	4	15
4	4	5	4	4	4	16
3	4	5	4	5	3	16
4	4	4	4	4	4	13
4	4	4	3	4	5	15
3	3	4	3	5	5	12
4	4	4	3	4	4	19
3	4	5	4	4	4	16
4	4	5	4	3	4	16
5	4	5	1	5	5	17
5	4	5	4	5	5	16
4	4	4	4	4	3	14
4	4	5	3	4	4	15
3	4	4	3	4	5	14
4	4	4	4	4	4	16
4	4	4	4	5	4	15
4	5	3	4	4	4	17
3	4	4	4	4	4	15
4	4	4	3	4	4	16
4	4	4	4	4	5	16
3	4	3	3	4	4	15
4	4	4	3	4	3	15
3	2	4	2	4	4	11
4	4	4	3	5	4	16
5	4	4	3	5	4	18
2	4	4	3	3	5	13
3	3	4	4	4	4	11
5	5	4	4	5	4	18
4	5	5	4	4	4	15
5	5	5	5	5	4	19
4	5	5	4	5	5	17
4	4	4	3	4	5	13
3	4	5	4	5	4	14
4	4	5	4	4	4	16
4	4	2	4	4	4	13
4	4	3	4	5	5	17
4	4	4	4	5	5	14
5	4	5	3	5	4	19
4	3	5	4	4	4	14
4	4	5	4	4	4	16
3	3	2	3	4	4	12
4	5	5	4	4	3	16
4	4	4	3	4	4	16
4	4	4	4	4	5	15
3	4	5	3	5	5	12
4	4	5	4	4	5	15
5	4	5	4	5	4	17
4	4	5	4	3	4	13
2	3	5	4	4	4	15
4	4	4	4	4	5	18
4	3	4	3	5	5	15
4	4	4	4	4	3	18
4	5	5	5	4	4	15
5	4	3	4	4	4	15
5	4	4	3	4	4	16
3	3	1	4	5	5	13
4	4	4	4	4	5	16
4	4	4	4	5	4	13
2	3	4	5	5	4	16

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
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297870&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] [ROW]

R Framework error message[/C][C]

The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297870&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297870&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
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
TVDC [t] = + 6.56847 + 0.546429SK1[t] + 1.14316SK2[t] + 0.0976977SK3[t] + 0.23913SK4[t] + 0.211817SK5[t] + 0.00900298SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC
[t] =  +  6.56847 +  0.546429SK1[t] +  1.14316SK2[t] +  0.0976977SK3[t] +  0.23913SK4[t] +  0.211817SK5[t] +  0.00900298SK6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297870&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC
[t] =  +  6.56847 +  0.546429SK1[t] +  1.14316SK2[t] +  0.0976977SK3[t] +  0.23913SK4[t] +  0.211817SK5[t] +  0.00900298SK6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297870&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297870&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] = + 6.56847 + 0.546429SK1[t] + 1.14316SK2[t] + 0.0976977SK3[t] + 0.23913SK4[t] + 0.211817SK5[t] + 0.00900298SK6[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)	+6.569	1.554	+4.2260e+00	4.028e-05	2.014e-05
SK1	+0.5464	0.1637	+3.3380e+00	0.001053	0.0005264
SK2	+1.143	0.1992	+5.7390e+00	4.765e-08	2.383e-08
SK3	+0.0977	0.1458	+6.7000e-01	0.5038	0.2519
SK4	+0.2391	0.1995	+1.1990e+00	0.2324	0.1162
SK5	+0.2118	0.1894	+1.1180e+00	0.2651	0.1326
SK6	+0.009003	0.1957	+4.6010e-02	0.9634	0.4817

\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) & +6.569 &  1.554 & +4.2260e+00 &  4.028e-05 &  2.014e-05 \tabularnewline
SK1 & +0.5464 &  0.1637 & +3.3380e+00 &  0.001053 &  0.0005264 \tabularnewline
SK2 & +1.143 &  0.1992 & +5.7390e+00 &  4.765e-08 &  2.383e-08 \tabularnewline
SK3 & +0.0977 &  0.1458 & +6.7000e-01 &  0.5038 &  0.2519 \tabularnewline
SK4 & +0.2391 &  0.1995 & +1.1990e+00 &  0.2324 &  0.1162 \tabularnewline
SK5 & +0.2118 &  0.1894 & +1.1180e+00 &  0.2651 &  0.1326 \tabularnewline
SK6 & +0.009003 &  0.1957 & +4.6010e-02 &  0.9634 &  0.4817 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297870&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]+6.569[/C][C] 1.554[/C][C]+4.2260e+00[/C][C] 4.028e-05[/C][C] 2.014e-05[/C][/ROW]
[ROW][C]SK1[/C][C]+0.5464[/C][C] 0.1637[/C][C]+3.3380e+00[/C][C] 0.001053[/C][C] 0.0005264[/C][/ROW]
[ROW][C]SK2[/C][C]+1.143[/C][C] 0.1992[/C][C]+5.7390e+00[/C][C] 4.765e-08[/C][C] 2.383e-08[/C][/ROW]
[ROW][C]SK3[/C][C]+0.0977[/C][C] 0.1458[/C][C]+6.7000e-01[/C][C] 0.5038[/C][C] 0.2519[/C][/ROW]
[ROW][C]SK4[/C][C]+0.2391[/C][C] 0.1995[/C][C]+1.1990e+00[/C][C] 0.2324[/C][C] 0.1162[/C][/ROW]
[ROW][C]SK5[/C][C]+0.2118[/C][C] 0.1894[/C][C]+1.1180e+00[/C][C] 0.2651[/C][C] 0.1326[/C][/ROW]
[ROW][C]SK6[/C][C]+0.009003[/C][C] 0.1957[/C][C]+4.6010e-02[/C][C] 0.9634[/C][C] 0.4817[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297870&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297870&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)	+6.569	1.554	+4.2260e+00	4.028e-05	2.014e-05
SK1	+0.5464	0.1637	+3.3380e+00	0.001053	0.0005264
SK2	+1.143	0.1992	+5.7390e+00	4.765e-08	2.383e-08
SK3	+0.0977	0.1458	+6.7000e-01	0.5038	0.2519
SK4	+0.2391	0.1995	+1.1990e+00	0.2324	0.1162
SK5	+0.2118	0.1894	+1.1180e+00	0.2651	0.1326
SK6	+0.009003	0.1957	+4.6010e-02	0.9634	0.4817

Multiple Linear Regression - Regression Statistics
Multiple R	0.5548
R-squared	0.3078
Adjusted R-squared	0.2814
F-TEST (value)	11.64
F-TEST (DF numerator)	6
F-TEST (DF denominator)	157
p-value	9.19e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.453
Sum Squared Residuals	331.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5548 \tabularnewline
R-squared &  0.3078 \tabularnewline
Adjusted R-squared &  0.2814 \tabularnewline
F-TEST (value) &  11.64 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value &  9.19e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.453 \tabularnewline
Sum Squared Residuals &  331.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297870&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5548[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3078[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2814[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 11.64[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C] 9.19e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.453[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 331.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297870&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297870&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	0.5548
R-squared	0.3078
Adjusted R-squared	0.2814
F-TEST (value)	11.64
F-TEST (DF numerator)	6
F-TEST (DF denominator)	157
p-value	9.19e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.453
Sum Squared Residuals	331.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.24	-0.2438
2	16	15.07	0.9252
3	17	15.87	1.133
4	15	14.67	0.3258
5	16	15.87	0.1331
6	16	15.23	0.7682
7	18	14.56	3.44
8	16	15.11	0.8913
9	17	16.92	0.07859
10	17	17.02	-0.01911
11	17	15.57	1.426
12	15	15.63	-0.6278
13	16	15.33	0.6727
14	14	13.97	0.02546
15	16	15.24	0.7595
16	17	15.12	1.882
17	16	15.12	0.8823
18	15	17.01	-2.008
19	17	15.77	1.231
20	16	14.88	1.121
21	15	15.78	-0.7783
22	16	15.66	0.3359
23	15	15.66	-0.6551
24	17	15.66	1.336
25	14	15.01	-1.011
26	16	14.99	1.007
27	15	15.56	-0.5574
28	16	14.78	1.217
29	16	16.1	-0.1039
30	13	14.51	-1.512
31	15	17.02	-2.019
32	17	16.21	0.7894
33	15	14.52	0.479
34	13	13.63	-0.6309
35	17	16.51	0.4948
36	15	15.11	-0.1087
37	14	14.28	-0.2818
38	14	14.32	-0.3166
39	18	15.66	2.345
40	15	16.1	-1.104
41	17	17.02	-0.01911
42	13	13.87	-0.8678
43	16	17.33	-1.326
44	15	15.96	-0.9624
45	15	15.33	-0.3273
46	16	15.56	0.4426
47	15	15.8	-0.8009
48	13	15.77	-2.769
49	17	16.8	0.2017
50	18	17.26	0.7366
51	17	17.34	-0.3447
52	11	14.73	-3.733
53	14	14.18	-0.1841
54	13	15.66	-2.655
55	15	14.56	0.4399
56	17	15	1.998
57	16	15.53	0.4699
58	15	15.77	-0.7692
59	17	17.37	-0.3701
60	16	14.69	1.315
61	16	15.78	0.2217
62	16	14.59	1.413
63	15	15.88	-0.8759
64	12	13.27	-1.271
65	17	15.52	1.479
66	14	15.53	-1.53
67	14	15.76	-1.76
68	16	14.99	1.007
69	15	14.78	0.2191
70	15	17.25	-2.249
71	13	15.46	-2.46
72	13	15.56	-2.557
73	17	16.01	0.9916
74	15	14.91	0.08669
75	16	15.77	0.2308
76	14	15.09	-1.09
77	15	13.97	1.025
78	17	14.51	2.488
79	16	15.66	0.3359
80	10	13.77	-3.77
81	16	15.77	0.2308
82	17	15.68	1.319
83	17	15.78	1.222
84	20	16.1	3.896
85	17	16.25	0.7457
86	18	15.88	2.124
87	15	15.12	-0.1177
88	17	15.01	1.989
89	14	12.93	1.066
90	15	15.65	-0.6461
91	17	15.66	1.336
92	16	15.77	0.2308
93	17	16.9	0.09659
94	15	14.99	0.007308
95	16	15.66	0.3359
96	18	16.02	1.985
97	18	16.45	1.55
98	16	16.92	-0.9214
99	17	15.18	1.818
100	15	15.66	-0.6641
101	13	16.11	-3.113
102	15	14.77	0.2281
103	17	16.56	0.4362
104	16	15.43	0.575
105	16	15.21	0.7884
106	15	15.66	-0.6551
107	16	15.66	0.3449
108	16	15.31	0.6885
109	13	15.56	-2.557
110	15	15.33	-0.3273
111	12	13.85	-1.85
112	19	15.32	3.682
113	16	15.11	0.8913
114	16	15.44	0.5567
115	17	15.71	1.295
116	16	16.42	-0.4224
117	14	15.55	-1.548
118	15	15.42	-0.416
119	14	14.78	-0.7809
120	16	15.56	0.4426
121	15	15.77	-0.7692
122	17	16.6	0.3971
123	15	15.01	-0.011
124	16	15.32	0.6817
125	16	15.57	0.4336
126	15	14.67	0.3258
127	15	15.31	-0.3093
128	11	12.25	-1.246
129	16	15.53	0.4699
130	18	16.08	1.923
131	13	14.02	-1.023
132	11	13.87	-2.868
133	18	17.46	0.5412
134	15	16.8	-1.798
135	19	17.8	1.204
136	17	17.02	-0.01911
137	13	15.33	-2.327
138	14	15.32	-1.321
139	16	15.66	0.3449
140	13	15.36	-2.362
141	17	15.68	1.319
142	14	15.78	-1.778
143	19	16.17	2.826
144	14	14.51	-0.512
145	16	15.66	0.3449
146	12	13.43	-1.433
147	16	16.79	-0.7893
148	16	15.32	0.6817
149	15	15.57	-0.5664
150	12	15.09	-3.09
151	15	15.66	-0.6641
152	17	16.41	0.5866
153	13	15.44	-2.443
154	15	13.42	1.581
155	18	15.57	2.434
156	15	14.4	0.604
157	18	15.55	2.452
158	15	17.04	-2.037
159	15	16.01	-1.006
160	16	15.86	0.1353
161	13	13.8	-0.7956
162	16	15.57	0.4336
163	13	15.77	-2.769
164	16	13.77	2.228

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.24 & -0.2438 \tabularnewline
2 &  16 &  15.07 &  0.9252 \tabularnewline
3 &  17 &  15.87 &  1.133 \tabularnewline
4 &  15 &  14.67 &  0.3258 \tabularnewline
5 &  16 &  15.87 &  0.1331 \tabularnewline
6 &  16 &  15.23 &  0.7682 \tabularnewline
7 &  18 &  14.56 &  3.44 \tabularnewline
8 &  16 &  15.11 &  0.8913 \tabularnewline
9 &  17 &  16.92 &  0.07859 \tabularnewline
10 &  17 &  17.02 & -0.01911 \tabularnewline
11 &  17 &  15.57 &  1.426 \tabularnewline
12 &  15 &  15.63 & -0.6278 \tabularnewline
13 &  16 &  15.33 &  0.6727 \tabularnewline
14 &  14 &  13.97 &  0.02546 \tabularnewline
15 &  16 &  15.24 &  0.7595 \tabularnewline
16 &  17 &  15.12 &  1.882 \tabularnewline
17 &  16 &  15.12 &  0.8823 \tabularnewline
18 &  15 &  17.01 & -2.008 \tabularnewline
19 &  17 &  15.77 &  1.231 \tabularnewline
20 &  16 &  14.88 &  1.121 \tabularnewline
21 &  15 &  15.78 & -0.7783 \tabularnewline
22 &  16 &  15.66 &  0.3359 \tabularnewline
23 &  15 &  15.66 & -0.6551 \tabularnewline
24 &  17 &  15.66 &  1.336 \tabularnewline
25 &  14 &  15.01 & -1.011 \tabularnewline
26 &  16 &  14.99 &  1.007 \tabularnewline
27 &  15 &  15.56 & -0.5574 \tabularnewline
28 &  16 &  14.78 &  1.217 \tabularnewline
29 &  16 &  16.1 & -0.1039 \tabularnewline
30 &  13 &  14.51 & -1.512 \tabularnewline
31 &  15 &  17.02 & -2.019 \tabularnewline
32 &  17 &  16.21 &  0.7894 \tabularnewline
33 &  15 &  14.52 &  0.479 \tabularnewline
34 &  13 &  13.63 & -0.6309 \tabularnewline
35 &  17 &  16.51 &  0.4948 \tabularnewline
36 &  15 &  15.11 & -0.1087 \tabularnewline
37 &  14 &  14.28 & -0.2818 \tabularnewline
38 &  14 &  14.32 & -0.3166 \tabularnewline
39 &  18 &  15.66 &  2.345 \tabularnewline
40 &  15 &  16.1 & -1.104 \tabularnewline
41 &  17 &  17.02 & -0.01911 \tabularnewline
42 &  13 &  13.87 & -0.8678 \tabularnewline
43 &  16 &  17.33 & -1.326 \tabularnewline
44 &  15 &  15.96 & -0.9624 \tabularnewline
45 &  15 &  15.33 & -0.3273 \tabularnewline
46 &  16 &  15.56 &  0.4426 \tabularnewline
47 &  15 &  15.8 & -0.8009 \tabularnewline
48 &  13 &  15.77 & -2.769 \tabularnewline
49 &  17 &  16.8 &  0.2017 \tabularnewline
50 &  18 &  17.26 &  0.7366 \tabularnewline
51 &  17 &  17.34 & -0.3447 \tabularnewline
52 &  11 &  14.73 & -3.733 \tabularnewline
53 &  14 &  14.18 & -0.1841 \tabularnewline
54 &  13 &  15.66 & -2.655 \tabularnewline
55 &  15 &  14.56 &  0.4399 \tabularnewline
56 &  17 &  15 &  1.998 \tabularnewline
57 &  16 &  15.53 &  0.4699 \tabularnewline
58 &  15 &  15.77 & -0.7692 \tabularnewline
59 &  17 &  17.37 & -0.3701 \tabularnewline
60 &  16 &  14.69 &  1.315 \tabularnewline
61 &  16 &  15.78 &  0.2217 \tabularnewline
62 &  16 &  14.59 &  1.413 \tabularnewline
63 &  15 &  15.88 & -0.8759 \tabularnewline
64 &  12 &  13.27 & -1.271 \tabularnewline
65 &  17 &  15.52 &  1.479 \tabularnewline
66 &  14 &  15.53 & -1.53 \tabularnewline
67 &  14 &  15.76 & -1.76 \tabularnewline
68 &  16 &  14.99 &  1.007 \tabularnewline
69 &  15 &  14.78 &  0.2191 \tabularnewline
70 &  15 &  17.25 & -2.249 \tabularnewline
71 &  13 &  15.46 & -2.46 \tabularnewline
72 &  13 &  15.56 & -2.557 \tabularnewline
73 &  17 &  16.01 &  0.9916 \tabularnewline
74 &  15 &  14.91 &  0.08669 \tabularnewline
75 &  16 &  15.77 &  0.2308 \tabularnewline
76 &  14 &  15.09 & -1.09 \tabularnewline
77 &  15 &  13.97 &  1.025 \tabularnewline
78 &  17 &  14.51 &  2.488 \tabularnewline
79 &  16 &  15.66 &  0.3359 \tabularnewline
80 &  10 &  13.77 & -3.77 \tabularnewline
81 &  16 &  15.77 &  0.2308 \tabularnewline
82 &  17 &  15.68 &  1.319 \tabularnewline
83 &  17 &  15.78 &  1.222 \tabularnewline
84 &  20 &  16.1 &  3.896 \tabularnewline
85 &  17 &  16.25 &  0.7457 \tabularnewline
86 &  18 &  15.88 &  2.124 \tabularnewline
87 &  15 &  15.12 & -0.1177 \tabularnewline
88 &  17 &  15.01 &  1.989 \tabularnewline
89 &  14 &  12.93 &  1.066 \tabularnewline
90 &  15 &  15.65 & -0.6461 \tabularnewline
91 &  17 &  15.66 &  1.336 \tabularnewline
92 &  16 &  15.77 &  0.2308 \tabularnewline
93 &  17 &  16.9 &  0.09659 \tabularnewline
94 &  15 &  14.99 &  0.007308 \tabularnewline
95 &  16 &  15.66 &  0.3359 \tabularnewline
96 &  18 &  16.02 &  1.985 \tabularnewline
97 &  18 &  16.45 &  1.55 \tabularnewline
98 &  16 &  16.92 & -0.9214 \tabularnewline
99 &  17 &  15.18 &  1.818 \tabularnewline
100 &  15 &  15.66 & -0.6641 \tabularnewline
101 &  13 &  16.11 & -3.113 \tabularnewline
102 &  15 &  14.77 &  0.2281 \tabularnewline
103 &  17 &  16.56 &  0.4362 \tabularnewline
104 &  16 &  15.43 &  0.575 \tabularnewline
105 &  16 &  15.21 &  0.7884 \tabularnewline
106 &  15 &  15.66 & -0.6551 \tabularnewline
107 &  16 &  15.66 &  0.3449 \tabularnewline
108 &  16 &  15.31 &  0.6885 \tabularnewline
109 &  13 &  15.56 & -2.557 \tabularnewline
110 &  15 &  15.33 & -0.3273 \tabularnewline
111 &  12 &  13.85 & -1.85 \tabularnewline
112 &  19 &  15.32 &  3.682 \tabularnewline
113 &  16 &  15.11 &  0.8913 \tabularnewline
114 &  16 &  15.44 &  0.5567 \tabularnewline
115 &  17 &  15.71 &  1.295 \tabularnewline
116 &  16 &  16.42 & -0.4224 \tabularnewline
117 &  14 &  15.55 & -1.548 \tabularnewline
118 &  15 &  15.42 & -0.416 \tabularnewline
119 &  14 &  14.78 & -0.7809 \tabularnewline
120 &  16 &  15.56 &  0.4426 \tabularnewline
121 &  15 &  15.77 & -0.7692 \tabularnewline
122 &  17 &  16.6 &  0.3971 \tabularnewline
123 &  15 &  15.01 & -0.011 \tabularnewline
124 &  16 &  15.32 &  0.6817 \tabularnewline
125 &  16 &  15.57 &  0.4336 \tabularnewline
126 &  15 &  14.67 &  0.3258 \tabularnewline
127 &  15 &  15.31 & -0.3093 \tabularnewline
128 &  11 &  12.25 & -1.246 \tabularnewline
129 &  16 &  15.53 &  0.4699 \tabularnewline
130 &  18 &  16.08 &  1.923 \tabularnewline
131 &  13 &  14.02 & -1.023 \tabularnewline
132 &  11 &  13.87 & -2.868 \tabularnewline
133 &  18 &  17.46 &  0.5412 \tabularnewline
134 &  15 &  16.8 & -1.798 \tabularnewline
135 &  19 &  17.8 &  1.204 \tabularnewline
136 &  17 &  17.02 & -0.01911 \tabularnewline
137 &  13 &  15.33 & -2.327 \tabularnewline
138 &  14 &  15.32 & -1.321 \tabularnewline
139 &  16 &  15.66 &  0.3449 \tabularnewline
140 &  13 &  15.36 & -2.362 \tabularnewline
141 &  17 &  15.68 &  1.319 \tabularnewline
142 &  14 &  15.78 & -1.778 \tabularnewline
143 &  19 &  16.17 &  2.826 \tabularnewline
144 &  14 &  14.51 & -0.512 \tabularnewline
145 &  16 &  15.66 &  0.3449 \tabularnewline
146 &  12 &  13.43 & -1.433 \tabularnewline
147 &  16 &  16.79 & -0.7893 \tabularnewline
148 &  16 &  15.32 &  0.6817 \tabularnewline
149 &  15 &  15.57 & -0.5664 \tabularnewline
150 &  12 &  15.09 & -3.09 \tabularnewline
151 &  15 &  15.66 & -0.6641 \tabularnewline
152 &  17 &  16.41 &  0.5866 \tabularnewline
153 &  13 &  15.44 & -2.443 \tabularnewline
154 &  15 &  13.42 &  1.581 \tabularnewline
155 &  18 &  15.57 &  2.434 \tabularnewline
156 &  15 &  14.4 &  0.604 \tabularnewline
157 &  18 &  15.55 &  2.452 \tabularnewline
158 &  15 &  17.04 & -2.037 \tabularnewline
159 &  15 &  16.01 & -1.006 \tabularnewline
160 &  16 &  15.86 &  0.1353 \tabularnewline
161 &  13 &  13.8 & -0.7956 \tabularnewline
162 &  16 &  15.57 &  0.4336 \tabularnewline
163 &  13 &  15.77 & -2.769 \tabularnewline
164 &  16 &  13.77 &  2.228 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297870&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] 13[/C][C] 13.24[/C][C]-0.2438[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.07[/C][C] 0.9252[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.87[/C][C] 1.133[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.67[/C][C] 0.3258[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.87[/C][C] 0.1331[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.23[/C][C] 0.7682[/C][/ROW]
[ROW][C]7[/C][C] 18[/C][C] 14.56[/C][C] 3.44[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.11[/C][C] 0.8913[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.92[/C][C] 0.07859[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 17.02[/C][C]-0.01911[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.57[/C][C] 1.426[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.63[/C][C]-0.6278[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.33[/C][C] 0.6727[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 13.97[/C][C] 0.02546[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.24[/C][C] 0.7595[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.12[/C][C] 1.882[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.12[/C][C] 0.8823[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 17.01[/C][C]-2.008[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.77[/C][C] 1.231[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.88[/C][C] 1.121[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.78[/C][C]-0.7783[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.66[/C][C] 0.3359[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.66[/C][C]-0.6551[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.66[/C][C] 1.336[/C][/ROW]
[ROW][C]25[/C][C] 14[/C][C] 15.01[/C][C]-1.011[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.99[/C][C] 1.007[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.56[/C][C]-0.5574[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.78[/C][C] 1.217[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.1[/C][C]-0.1039[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.51[/C][C]-1.512[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 17.02[/C][C]-2.019[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.21[/C][C] 0.7894[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 14.52[/C][C] 0.479[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 13.63[/C][C]-0.6309[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 16.51[/C][C] 0.4948[/C][/ROW]
[ROW][C]36[/C][C] 15[/C][C] 15.11[/C][C]-0.1087[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 14.28[/C][C]-0.2818[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 14.32[/C][C]-0.3166[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 15.66[/C][C] 2.345[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 16.1[/C][C]-1.104[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 17.02[/C][C]-0.01911[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 13.87[/C][C]-0.8678[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 17.33[/C][C]-1.326[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 15.96[/C][C]-0.9624[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 15.33[/C][C]-0.3273[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.56[/C][C] 0.4426[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 15.8[/C][C]-0.8009[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 15.77[/C][C]-2.769[/C][/ROW]
[ROW][C]49[/C][C] 17[/C][C] 16.8[/C][C] 0.2017[/C][/ROW]
[ROW][C]50[/C][C] 18[/C][C] 17.26[/C][C] 0.7366[/C][/ROW]
[ROW][C]51[/C][C] 17[/C][C] 17.34[/C][C]-0.3447[/C][/ROW]
[ROW][C]52[/C][C] 11[/C][C] 14.73[/C][C]-3.733[/C][/ROW]
[ROW][C]53[/C][C] 14[/C][C] 14.18[/C][C]-0.1841[/C][/ROW]
[ROW][C]54[/C][C] 13[/C][C] 15.66[/C][C]-2.655[/C][/ROW]
[ROW][C]55[/C][C] 15[/C][C] 14.56[/C][C] 0.4399[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 15[/C][C] 1.998[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 15.53[/C][C] 0.4699[/C][/ROW]
[ROW][C]58[/C][C] 15[/C][C] 15.77[/C][C]-0.7692[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 17.37[/C][C]-0.3701[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 14.69[/C][C] 1.315[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 15.78[/C][C] 0.2217[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 14.59[/C][C] 1.413[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.88[/C][C]-0.8759[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 13.27[/C][C]-1.271[/C][/ROW]
[ROW][C]65[/C][C] 17[/C][C] 15.52[/C][C] 1.479[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 15.53[/C][C]-1.53[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 15.76[/C][C]-1.76[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 14.99[/C][C] 1.007[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 14.78[/C][C] 0.2191[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 17.25[/C][C]-2.249[/C][/ROW]
[ROW][C]71[/C][C] 13[/C][C] 15.46[/C][C]-2.46[/C][/ROW]
[ROW][C]72[/C][C] 13[/C][C] 15.56[/C][C]-2.557[/C][/ROW]
[ROW][C]73[/C][C] 17[/C][C] 16.01[/C][C] 0.9916[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 14.91[/C][C] 0.08669[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.77[/C][C] 0.2308[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 15.09[/C][C]-1.09[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 13.97[/C][C] 1.025[/C][/ROW]
[ROW][C]78[/C][C] 17[/C][C] 14.51[/C][C] 2.488[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.66[/C][C] 0.3359[/C][/ROW]
[ROW][C]80[/C][C] 10[/C][C] 13.77[/C][C]-3.77[/C][/ROW]
[ROW][C]81[/C][C] 16[/C][C] 15.77[/C][C] 0.2308[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 15.68[/C][C] 1.319[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 15.78[/C][C] 1.222[/C][/ROW]
[ROW][C]84[/C][C] 20[/C][C] 16.1[/C][C] 3.896[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.25[/C][C] 0.7457[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 15.88[/C][C] 2.124[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 15.12[/C][C]-0.1177[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.01[/C][C] 1.989[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 12.93[/C][C] 1.066[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 15.65[/C][C]-0.6461[/C][/ROW]
[ROW][C]91[/C][C] 17[/C][C] 15.66[/C][C] 1.336[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 15.77[/C][C] 0.2308[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.9[/C][C] 0.09659[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 14.99[/C][C] 0.007308[/C][/ROW]
[ROW][C]95[/C][C] 16[/C][C] 15.66[/C][C] 0.3359[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 16.02[/C][C] 1.985[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 16.45[/C][C] 1.55[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.92[/C][C]-0.9214[/C][/ROW]
[ROW][C]99[/C][C] 17[/C][C] 15.18[/C][C] 1.818[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 15.66[/C][C]-0.6641[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 16.11[/C][C]-3.113[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 14.77[/C][C] 0.2281[/C][/ROW]
[ROW][C]103[/C][C] 17[/C][C] 16.56[/C][C] 0.4362[/C][/ROW]
[ROW][C]104[/C][C] 16[/C][C] 15.43[/C][C] 0.575[/C][/ROW]
[ROW][C]105[/C][C] 16[/C][C] 15.21[/C][C] 0.7884[/C][/ROW]
[ROW][C]106[/C][C] 15[/C][C] 15.66[/C][C]-0.6551[/C][/ROW]
[ROW][C]107[/C][C] 16[/C][C] 15.66[/C][C] 0.3449[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 15.31[/C][C] 0.6885[/C][/ROW]
[ROW][C]109[/C][C] 13[/C][C] 15.56[/C][C]-2.557[/C][/ROW]
[ROW][C]110[/C][C] 15[/C][C] 15.33[/C][C]-0.3273[/C][/ROW]
[ROW][C]111[/C][C] 12[/C][C] 13.85[/C][C]-1.85[/C][/ROW]
[ROW][C]112[/C][C] 19[/C][C] 15.32[/C][C] 3.682[/C][/ROW]
[ROW][C]113[/C][C] 16[/C][C] 15.11[/C][C] 0.8913[/C][/ROW]
[ROW][C]114[/C][C] 16[/C][C] 15.44[/C][C] 0.5567[/C][/ROW]
[ROW][C]115[/C][C] 17[/C][C] 15.71[/C][C] 1.295[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 16.42[/C][C]-0.4224[/C][/ROW]
[ROW][C]117[/C][C] 14[/C][C] 15.55[/C][C]-1.548[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 15.42[/C][C]-0.416[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 14.78[/C][C]-0.7809[/C][/ROW]
[ROW][C]120[/C][C] 16[/C][C] 15.56[/C][C] 0.4426[/C][/ROW]
[ROW][C]121[/C][C] 15[/C][C] 15.77[/C][C]-0.7692[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 16.6[/C][C] 0.3971[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.01[/C][C]-0.011[/C][/ROW]
[ROW][C]124[/C][C] 16[/C][C] 15.32[/C][C] 0.6817[/C][/ROW]
[ROW][C]125[/C][C] 16[/C][C] 15.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]126[/C][C] 15[/C][C] 14.67[/C][C] 0.3258[/C][/ROW]
[ROW][C]127[/C][C] 15[/C][C] 15.31[/C][C]-0.3093[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 12.25[/C][C]-1.246[/C][/ROW]
[ROW][C]129[/C][C] 16[/C][C] 15.53[/C][C] 0.4699[/C][/ROW]
[ROW][C]130[/C][C] 18[/C][C] 16.08[/C][C] 1.923[/C][/ROW]
[ROW][C]131[/C][C] 13[/C][C] 14.02[/C][C]-1.023[/C][/ROW]
[ROW][C]132[/C][C] 11[/C][C] 13.87[/C][C]-2.868[/C][/ROW]
[ROW][C]133[/C][C] 18[/C][C] 17.46[/C][C] 0.5412[/C][/ROW]
[ROW][C]134[/C][C] 15[/C][C] 16.8[/C][C]-1.798[/C][/ROW]
[ROW][C]135[/C][C] 19[/C][C] 17.8[/C][C] 1.204[/C][/ROW]
[ROW][C]136[/C][C] 17[/C][C] 17.02[/C][C]-0.01911[/C][/ROW]
[ROW][C]137[/C][C] 13[/C][C] 15.33[/C][C]-2.327[/C][/ROW]
[ROW][C]138[/C][C] 14[/C][C] 15.32[/C][C]-1.321[/C][/ROW]
[ROW][C]139[/C][C] 16[/C][C] 15.66[/C][C] 0.3449[/C][/ROW]
[ROW][C]140[/C][C] 13[/C][C] 15.36[/C][C]-2.362[/C][/ROW]
[ROW][C]141[/C][C] 17[/C][C] 15.68[/C][C] 1.319[/C][/ROW]
[ROW][C]142[/C][C] 14[/C][C] 15.78[/C][C]-1.778[/C][/ROW]
[ROW][C]143[/C][C] 19[/C][C] 16.17[/C][C] 2.826[/C][/ROW]
[ROW][C]144[/C][C] 14[/C][C] 14.51[/C][C]-0.512[/C][/ROW]
[ROW][C]145[/C][C] 16[/C][C] 15.66[/C][C] 0.3449[/C][/ROW]
[ROW][C]146[/C][C] 12[/C][C] 13.43[/C][C]-1.433[/C][/ROW]
[ROW][C]147[/C][C] 16[/C][C] 16.79[/C][C]-0.7893[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 15.32[/C][C] 0.6817[/C][/ROW]
[ROW][C]149[/C][C] 15[/C][C] 15.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]150[/C][C] 12[/C][C] 15.09[/C][C]-3.09[/C][/ROW]
[ROW][C]151[/C][C] 15[/C][C] 15.66[/C][C]-0.6641[/C][/ROW]
[ROW][C]152[/C][C] 17[/C][C] 16.41[/C][C] 0.5866[/C][/ROW]
[ROW][C]153[/C][C] 13[/C][C] 15.44[/C][C]-2.443[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 13.42[/C][C] 1.581[/C][/ROW]
[ROW][C]155[/C][C] 18[/C][C] 15.57[/C][C] 2.434[/C][/ROW]
[ROW][C]156[/C][C] 15[/C][C] 14.4[/C][C] 0.604[/C][/ROW]
[ROW][C]157[/C][C] 18[/C][C] 15.55[/C][C] 2.452[/C][/ROW]
[ROW][C]158[/C][C] 15[/C][C] 17.04[/C][C]-2.037[/C][/ROW]
[ROW][C]159[/C][C] 15[/C][C] 16.01[/C][C]-1.006[/C][/ROW]
[ROW][C]160[/C][C] 16[/C][C] 15.86[/C][C] 0.1353[/C][/ROW]
[ROW][C]161[/C][C] 13[/C][C] 13.8[/C][C]-0.7956[/C][/ROW]
[ROW][C]162[/C][C] 16[/C][C] 15.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]163[/C][C] 13[/C][C] 15.77[/C][C]-2.769[/C][/ROW]
[ROW][C]164[/C][C] 16[/C][C] 13.77[/C][C] 2.228[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297870&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297870&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	13	13.24	-0.2438
2	16	15.07	0.9252
3	17	15.87	1.133
4	15	14.67	0.3258
5	16	15.87	0.1331
6	16	15.23	0.7682
7	18	14.56	3.44
8	16	15.11	0.8913
9	17	16.92	0.07859
10	17	17.02	-0.01911
11	17	15.57	1.426
12	15	15.63	-0.6278
13	16	15.33	0.6727
14	14	13.97	0.02546
15	16	15.24	0.7595
16	17	15.12	1.882
17	16	15.12	0.8823
18	15	17.01	-2.008
19	17	15.77	1.231
20	16	14.88	1.121
21	15	15.78	-0.7783
22	16	15.66	0.3359
23	15	15.66	-0.6551
24	17	15.66	1.336
25	14	15.01	-1.011
26	16	14.99	1.007
27	15	15.56	-0.5574
28	16	14.78	1.217
29	16	16.1	-0.1039
30	13	14.51	-1.512
31	15	17.02	-2.019
32	17	16.21	0.7894
33	15	14.52	0.479
34	13	13.63	-0.6309
35	17	16.51	0.4948
36	15	15.11	-0.1087
37	14	14.28	-0.2818
38	14	14.32	-0.3166
39	18	15.66	2.345
40	15	16.1	-1.104
41	17	17.02	-0.01911
42	13	13.87	-0.8678
43	16	17.33	-1.326
44	15	15.96	-0.9624
45	15	15.33	-0.3273
46	16	15.56	0.4426
47	15	15.8	-0.8009
48	13	15.77	-2.769
49	17	16.8	0.2017
50	18	17.26	0.7366
51	17	17.34	-0.3447
52	11	14.73	-3.733
53	14	14.18	-0.1841
54	13	15.66	-2.655
55	15	14.56	0.4399
56	17	15	1.998
57	16	15.53	0.4699
58	15	15.77	-0.7692
59	17	17.37	-0.3701
60	16	14.69	1.315
61	16	15.78	0.2217
62	16	14.59	1.413
63	15	15.88	-0.8759
64	12	13.27	-1.271
65	17	15.52	1.479
66	14	15.53	-1.53
67	14	15.76	-1.76
68	16	14.99	1.007
69	15	14.78	0.2191
70	15	17.25	-2.249
71	13	15.46	-2.46
72	13	15.56	-2.557
73	17	16.01	0.9916
74	15	14.91	0.08669
75	16	15.77	0.2308
76	14	15.09	-1.09
77	15	13.97	1.025
78	17	14.51	2.488
79	16	15.66	0.3359
80	10	13.77	-3.77
81	16	15.77	0.2308
82	17	15.68	1.319
83	17	15.78	1.222
84	20	16.1	3.896
85	17	16.25	0.7457
86	18	15.88	2.124
87	15	15.12	-0.1177
88	17	15.01	1.989
89	14	12.93	1.066
90	15	15.65	-0.6461
91	17	15.66	1.336
92	16	15.77	0.2308
93	17	16.9	0.09659
94	15	14.99	0.007308
95	16	15.66	0.3359
96	18	16.02	1.985
97	18	16.45	1.55
98	16	16.92	-0.9214
99	17	15.18	1.818
100	15	15.66	-0.6641
101	13	16.11	-3.113
102	15	14.77	0.2281
103	17	16.56	0.4362
104	16	15.43	0.575
105	16	15.21	0.7884
106	15	15.66	-0.6551
107	16	15.66	0.3449
108	16	15.31	0.6885
109	13	15.56	-2.557
110	15	15.33	-0.3273
111	12	13.85	-1.85
112	19	15.32	3.682
113	16	15.11	0.8913
114	16	15.44	0.5567
115	17	15.71	1.295
116	16	16.42	-0.4224
117	14	15.55	-1.548
118	15	15.42	-0.416
119	14	14.78	-0.7809
120	16	15.56	0.4426
121	15	15.77	-0.7692
122	17	16.6	0.3971
123	15	15.01	-0.011
124	16	15.32	0.6817
125	16	15.57	0.4336
126	15	14.67	0.3258
127	15	15.31	-0.3093
128	11	12.25	-1.246
129	16	15.53	0.4699
130	18	16.08	1.923
131	13	14.02	-1.023
132	11	13.87	-2.868
133	18	17.46	0.5412
134	15	16.8	-1.798
135	19	17.8	1.204
136	17	17.02	-0.01911
137	13	15.33	-2.327
138	14	15.32	-1.321
139	16	15.66	0.3449
140	13	15.36	-2.362
141	17	15.68	1.319
142	14	15.78	-1.778
143	19	16.17	2.826
144	14	14.51	-0.512
145	16	15.66	0.3449
146	12	13.43	-1.433
147	16	16.79	-0.7893
148	16	15.32	0.6817
149	15	15.57	-0.5664
150	12	15.09	-3.09
151	15	15.66	-0.6641
152	17	16.41	0.5866
153	13	15.44	-2.443
154	15	13.42	1.581
155	18	15.57	2.434
156	15	14.4	0.604
157	18	15.55	2.452
158	15	17.04	-2.037
159	15	16.01	-1.006
160	16	15.86	0.1353
161	13	13.8	-0.7956
162	16	15.57	0.4336
163	13	15.77	-2.769
164	16	13.77	2.228

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.264	0.5279	0.736
11	0.1882	0.3764	0.8118
12	0.09787	0.1957	0.9021
13	0.05128	0.1026	0.9487
14	0.04752	0.09505	0.9525
15	0.06466	0.1293	0.9353
16	0.07049	0.141	0.9295
17	0.04058	0.08115	0.9594
18	0.08627	0.1725	0.9137
19	0.06037	0.1207	0.9396
20	0.04613	0.09226	0.9539
21	0.03774	0.07548	0.9623
22	0.02296	0.04592	0.977
23	0.02368	0.04737	0.9763
24	0.02301	0.04602	0.977
25	0.07179	0.1436	0.9282
26	0.05125	0.1025	0.9488
27	0.0435	0.087	0.9565
28	0.03058	0.06117	0.9694
29	0.02005	0.0401	0.98
30	0.02684	0.05369	0.9732
31	0.03982	0.07963	0.9602
32	0.03766	0.07532	0.9623
33	0.0261	0.0522	0.9739
34	0.02515	0.05031	0.9748
35	0.0179	0.03579	0.9821
36	0.01219	0.02437	0.9878
37	0.009124	0.01825	0.9909
38	0.007572	0.01514	0.9924
39	0.02101	0.04202	0.979
40	0.01784	0.03569	0.9822
41	0.01225	0.0245	0.9878
42	0.01307	0.02613	0.9869
43	0.01094	0.02189	0.9891
44	0.00792	0.01584	0.9921
45	0.005961	0.01192	0.994
46	0.004039	0.008078	0.996
47	0.003609	0.007218	0.9964
48	0.01142	0.02284	0.9886
49	0.008305	0.01661	0.9917
50	0.006573	0.01315	0.9934
51	0.00457	0.009139	0.9954
52	0.03586	0.07172	0.9641
53	0.02711	0.05421	0.9729
54	0.05122	0.1024	0.9488
55	0.04085	0.08171	0.9591
56	0.05086	0.1017	0.9491
57	0.04238	0.08477	0.9576
58	0.03422	0.06844	0.9658
59	0.02623	0.05246	0.9738
60	0.02266	0.04531	0.9773
61	0.01689	0.03379	0.9831
62	0.01612	0.03224	0.9839
63	0.01287	0.02573	0.9871
64	0.01239	0.02479	0.9876
65	0.01496	0.02993	0.985
66	0.01595	0.03189	0.9841
67	0.01681	0.03363	0.9832
68	0.01403	0.02805	0.986
69	0.01105	0.0221	0.989
70	0.01617	0.03235	0.9838
71	0.03592	0.07184	0.9641
72	0.0639	0.1278	0.9361
73	0.06098	0.122	0.939
74	0.05194	0.1039	0.9481
75	0.04157	0.08315	0.9584
76	0.03838	0.07676	0.9616
77	0.03365	0.06729	0.9664
78	0.06347	0.1269	0.9365
79	0.05149	0.103	0.9485
80	0.2037	0.4073	0.7963
81	0.1748	0.3496	0.8252
82	0.1705	0.341	0.8295
83	0.1635	0.327	0.8365
84	0.4111	0.8223	0.5889
85	0.3769	0.7538	0.6231
86	0.4286	0.8573	0.5714
87	0.388	0.776	0.612
88	0.4389	0.8779	0.5611
89	0.4116	0.8232	0.5884
90	0.3763	0.7525	0.6237
91	0.3756	0.7512	0.6244
92	0.3334	0.6668	0.6666
93	0.2929	0.5858	0.7071
94	0.2566	0.5132	0.7434
95	0.2244	0.4489	0.7756
96	0.2593	0.5187	0.7407
97	0.2727	0.5454	0.7273
98	0.2476	0.4953	0.7524
99	0.2692	0.5383	0.7308
100	0.2371	0.4741	0.7629
101	0.3647	0.7294	0.6353
102	0.3249	0.6498	0.6751
103	0.287	0.5739	0.713
104	0.2581	0.5162	0.7419
105	0.2293	0.4587	0.7707
106	0.1992	0.3984	0.8008
107	0.1697	0.3394	0.8303
108	0.1459	0.2917	0.8541
109	0.203	0.406	0.797
110	0.171	0.342	0.829
111	0.185	0.3701	0.815
112	0.423	0.846	0.577
113	0.4061	0.8122	0.5939
114	0.3823	0.7646	0.6177
115	0.3676	0.7351	0.6324
116	0.3303	0.6606	0.6697
117	0.3347	0.6693	0.6653
118	0.2898	0.5796	0.7102
119	0.2556	0.5112	0.7444
120	0.2213	0.4427	0.7787
121	0.1992	0.3984	0.8008
122	0.1776	0.3552	0.8224
123	0.1494	0.2989	0.8506
124	0.1329	0.2659	0.8671
125	0.116	0.2321	0.884
126	0.1095	0.2189	0.8905
127	0.08565	0.1713	0.9143
128	0.08024	0.1605	0.9198
129	0.06231	0.1246	0.9377
130	0.06552	0.131	0.9345
131	0.07264	0.1453	0.9274
132	0.145	0.29	0.855
133	0.1213	0.2427	0.8787
134	0.1034	0.2069	0.8966
135	0.08636	0.1727	0.9136
136	0.07841	0.1568	0.9216
137	0.07551	0.151	0.9245
138	0.0664	0.1328	0.9336
139	0.04806	0.09613	0.9519
140	0.05134	0.1027	0.9487
141	0.06094	0.1219	0.9391
142	0.05402	0.108	0.946
143	0.1046	0.2091	0.8954
144	0.1348	0.2696	0.8652
145	0.09621	0.1924	0.9038
146	0.1006	0.2011	0.8994
147	0.07318	0.1464	0.9268
148	0.07073	0.1415	0.9293
149	0.04455	0.08909	0.9555
150	0.03869	0.07738	0.9613
151	0.02263	0.04527	0.9774
152	0.01248	0.02497	0.9875
153	0.1277	0.2554	0.8723
154	0.6842	0.6316	0.3158

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.264 &  0.5279 &  0.736 \tabularnewline
11 &  0.1882 &  0.3764 &  0.8118 \tabularnewline
12 &  0.09787 &  0.1957 &  0.9021 \tabularnewline
13 &  0.05128 &  0.1026 &  0.9487 \tabularnewline
14 &  0.04752 &  0.09505 &  0.9525 \tabularnewline
15 &  0.06466 &  0.1293 &  0.9353 \tabularnewline
16 &  0.07049 &  0.141 &  0.9295 \tabularnewline
17 &  0.04058 &  0.08115 &  0.9594 \tabularnewline
18 &  0.08627 &  0.1725 &  0.9137 \tabularnewline
19 &  0.06037 &  0.1207 &  0.9396 \tabularnewline
20 &  0.04613 &  0.09226 &  0.9539 \tabularnewline
21 &  0.03774 &  0.07548 &  0.9623 \tabularnewline
22 &  0.02296 &  0.04592 &  0.977 \tabularnewline
23 &  0.02368 &  0.04737 &  0.9763 \tabularnewline
24 &  0.02301 &  0.04602 &  0.977 \tabularnewline
25 &  0.07179 &  0.1436 &  0.9282 \tabularnewline
26 &  0.05125 &  0.1025 &  0.9488 \tabularnewline
27 &  0.0435 &  0.087 &  0.9565 \tabularnewline
28 &  0.03058 &  0.06117 &  0.9694 \tabularnewline
29 &  0.02005 &  0.0401 &  0.98 \tabularnewline
30 &  0.02684 &  0.05369 &  0.9732 \tabularnewline
31 &  0.03982 &  0.07963 &  0.9602 \tabularnewline
32 &  0.03766 &  0.07532 &  0.9623 \tabularnewline
33 &  0.0261 &  0.0522 &  0.9739 \tabularnewline
34 &  0.02515 &  0.05031 &  0.9748 \tabularnewline
35 &  0.0179 &  0.03579 &  0.9821 \tabularnewline
36 &  0.01219 &  0.02437 &  0.9878 \tabularnewline
37 &  0.009124 &  0.01825 &  0.9909 \tabularnewline
38 &  0.007572 &  0.01514 &  0.9924 \tabularnewline
39 &  0.02101 &  0.04202 &  0.979 \tabularnewline
40 &  0.01784 &  0.03569 &  0.9822 \tabularnewline
41 &  0.01225 &  0.0245 &  0.9878 \tabularnewline
42 &  0.01307 &  0.02613 &  0.9869 \tabularnewline
43 &  0.01094 &  0.02189 &  0.9891 \tabularnewline
44 &  0.00792 &  0.01584 &  0.9921 \tabularnewline
45 &  0.005961 &  0.01192 &  0.994 \tabularnewline
46 &  0.004039 &  0.008078 &  0.996 \tabularnewline
47 &  0.003609 &  0.007218 &  0.9964 \tabularnewline
48 &  0.01142 &  0.02284 &  0.9886 \tabularnewline
49 &  0.008305 &  0.01661 &  0.9917 \tabularnewline
50 &  0.006573 &  0.01315 &  0.9934 \tabularnewline
51 &  0.00457 &  0.009139 &  0.9954 \tabularnewline
52 &  0.03586 &  0.07172 &  0.9641 \tabularnewline
53 &  0.02711 &  0.05421 &  0.9729 \tabularnewline
54 &  0.05122 &  0.1024 &  0.9488 \tabularnewline
55 &  0.04085 &  0.08171 &  0.9591 \tabularnewline
56 &  0.05086 &  0.1017 &  0.9491 \tabularnewline
57 &  0.04238 &  0.08477 &  0.9576 \tabularnewline
58 &  0.03422 &  0.06844 &  0.9658 \tabularnewline
59 &  0.02623 &  0.05246 &  0.9738 \tabularnewline
60 &  0.02266 &  0.04531 &  0.9773 \tabularnewline
61 &  0.01689 &  0.03379 &  0.9831 \tabularnewline
62 &  0.01612 &  0.03224 &  0.9839 \tabularnewline
63 &  0.01287 &  0.02573 &  0.9871 \tabularnewline
64 &  0.01239 &  0.02479 &  0.9876 \tabularnewline
65 &  0.01496 &  0.02993 &  0.985 \tabularnewline
66 &  0.01595 &  0.03189 &  0.9841 \tabularnewline
67 &  0.01681 &  0.03363 &  0.9832 \tabularnewline
68 &  0.01403 &  0.02805 &  0.986 \tabularnewline
69 &  0.01105 &  0.0221 &  0.989 \tabularnewline
70 &  0.01617 &  0.03235 &  0.9838 \tabularnewline
71 &  0.03592 &  0.07184 &  0.9641 \tabularnewline
72 &  0.0639 &  0.1278 &  0.9361 \tabularnewline
73 &  0.06098 &  0.122 &  0.939 \tabularnewline
74 &  0.05194 &  0.1039 &  0.9481 \tabularnewline
75 &  0.04157 &  0.08315 &  0.9584 \tabularnewline
76 &  0.03838 &  0.07676 &  0.9616 \tabularnewline
77 &  0.03365 &  0.06729 &  0.9664 \tabularnewline
78 &  0.06347 &  0.1269 &  0.9365 \tabularnewline
79 &  0.05149 &  0.103 &  0.9485 \tabularnewline
80 &  0.2037 &  0.4073 &  0.7963 \tabularnewline
81 &  0.1748 &  0.3496 &  0.8252 \tabularnewline
82 &  0.1705 &  0.341 &  0.8295 \tabularnewline
83 &  0.1635 &  0.327 &  0.8365 \tabularnewline
84 &  0.4111 &  0.8223 &  0.5889 \tabularnewline
85 &  0.3769 &  0.7538 &  0.6231 \tabularnewline
86 &  0.4286 &  0.8573 &  0.5714 \tabularnewline
87 &  0.388 &  0.776 &  0.612 \tabularnewline
88 &  0.4389 &  0.8779 &  0.5611 \tabularnewline
89 &  0.4116 &  0.8232 &  0.5884 \tabularnewline
90 &  0.3763 &  0.7525 &  0.6237 \tabularnewline
91 &  0.3756 &  0.7512 &  0.6244 \tabularnewline
92 &  0.3334 &  0.6668 &  0.6666 \tabularnewline
93 &  0.2929 &  0.5858 &  0.7071 \tabularnewline
94 &  0.2566 &  0.5132 &  0.7434 \tabularnewline
95 &  0.2244 &  0.4489 &  0.7756 \tabularnewline
96 &  0.2593 &  0.5187 &  0.7407 \tabularnewline
97 &  0.2727 &  0.5454 &  0.7273 \tabularnewline
98 &  0.2476 &  0.4953 &  0.7524 \tabularnewline
99 &  0.2692 &  0.5383 &  0.7308 \tabularnewline
100 &  0.2371 &  0.4741 &  0.7629 \tabularnewline
101 &  0.3647 &  0.7294 &  0.6353 \tabularnewline
102 &  0.3249 &  0.6498 &  0.6751 \tabularnewline
103 &  0.287 &  0.5739 &  0.713 \tabularnewline
104 &  0.2581 &  0.5162 &  0.7419 \tabularnewline
105 &  0.2293 &  0.4587 &  0.7707 \tabularnewline
106 &  0.1992 &  0.3984 &  0.8008 \tabularnewline
107 &  0.1697 &  0.3394 &  0.8303 \tabularnewline
108 &  0.1459 &  0.2917 &  0.8541 \tabularnewline
109 &  0.203 &  0.406 &  0.797 \tabularnewline
110 &  0.171 &  0.342 &  0.829 \tabularnewline
111 &  0.185 &  0.3701 &  0.815 \tabularnewline
112 &  0.423 &  0.846 &  0.577 \tabularnewline
113 &  0.4061 &  0.8122 &  0.5939 \tabularnewline
114 &  0.3823 &  0.7646 &  0.6177 \tabularnewline
115 &  0.3676 &  0.7351 &  0.6324 \tabularnewline
116 &  0.3303 &  0.6606 &  0.6697 \tabularnewline
117 &  0.3347 &  0.6693 &  0.6653 \tabularnewline
118 &  0.2898 &  0.5796 &  0.7102 \tabularnewline
119 &  0.2556 &  0.5112 &  0.7444 \tabularnewline
120 &  0.2213 &  0.4427 &  0.7787 \tabularnewline
121 &  0.1992 &  0.3984 &  0.8008 \tabularnewline
122 &  0.1776 &  0.3552 &  0.8224 \tabularnewline
123 &  0.1494 &  0.2989 &  0.8506 \tabularnewline
124 &  0.1329 &  0.2659 &  0.8671 \tabularnewline
125 &  0.116 &  0.2321 &  0.884 \tabularnewline
126 &  0.1095 &  0.2189 &  0.8905 \tabularnewline
127 &  0.08565 &  0.1713 &  0.9143 \tabularnewline
128 &  0.08024 &  0.1605 &  0.9198 \tabularnewline
129 &  0.06231 &  0.1246 &  0.9377 \tabularnewline
130 &  0.06552 &  0.131 &  0.9345 \tabularnewline
131 &  0.07264 &  0.1453 &  0.9274 \tabularnewline
132 &  0.145 &  0.29 &  0.855 \tabularnewline
133 &  0.1213 &  0.2427 &  0.8787 \tabularnewline
134 &  0.1034 &  0.2069 &  0.8966 \tabularnewline
135 &  0.08636 &  0.1727 &  0.9136 \tabularnewline
136 &  0.07841 &  0.1568 &  0.9216 \tabularnewline
137 &  0.07551 &  0.151 &  0.9245 \tabularnewline
138 &  0.0664 &  0.1328 &  0.9336 \tabularnewline
139 &  0.04806 &  0.09613 &  0.9519 \tabularnewline
140 &  0.05134 &  0.1027 &  0.9487 \tabularnewline
141 &  0.06094 &  0.1219 &  0.9391 \tabularnewline
142 &  0.05402 &  0.108 &  0.946 \tabularnewline
143 &  0.1046 &  0.2091 &  0.8954 \tabularnewline
144 &  0.1348 &  0.2696 &  0.8652 \tabularnewline
145 &  0.09621 &  0.1924 &  0.9038 \tabularnewline
146 &  0.1006 &  0.2011 &  0.8994 \tabularnewline
147 &  0.07318 &  0.1464 &  0.9268 \tabularnewline
148 &  0.07073 &  0.1415 &  0.9293 \tabularnewline
149 &  0.04455 &  0.08909 &  0.9555 \tabularnewline
150 &  0.03869 &  0.07738 &  0.9613 \tabularnewline
151 &  0.02263 &  0.04527 &  0.9774 \tabularnewline
152 &  0.01248 &  0.02497 &  0.9875 \tabularnewline
153 &  0.1277 &  0.2554 &  0.8723 \tabularnewline
154 &  0.6842 &  0.6316 &  0.3158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297870&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] 0.264[/C][C] 0.5279[/C][C] 0.736[/C][/ROW]
[ROW][C]11[/C][C] 0.1882[/C][C] 0.3764[/C][C] 0.8118[/C][/ROW]
[ROW][C]12[/C][C] 0.09787[/C][C] 0.1957[/C][C] 0.9021[/C][/ROW]
[ROW][C]13[/C][C] 0.05128[/C][C] 0.1026[/C][C] 0.9487[/C][/ROW]
[ROW][C]14[/C][C] 0.04752[/C][C] 0.09505[/C][C] 0.9525[/C][/ROW]
[ROW][C]15[/C][C] 0.06466[/C][C] 0.1293[/C][C] 0.9353[/C][/ROW]
[ROW][C]16[/C][C] 0.07049[/C][C] 0.141[/C][C] 0.9295[/C][/ROW]
[ROW][C]17[/C][C] 0.04058[/C][C] 0.08115[/C][C] 0.9594[/C][/ROW]
[ROW][C]18[/C][C] 0.08627[/C][C] 0.1725[/C][C] 0.9137[/C][/ROW]
[ROW][C]19[/C][C] 0.06037[/C][C] 0.1207[/C][C] 0.9396[/C][/ROW]
[ROW][C]20[/C][C] 0.04613[/C][C] 0.09226[/C][C] 0.9539[/C][/ROW]
[ROW][C]21[/C][C] 0.03774[/C][C] 0.07548[/C][C] 0.9623[/C][/ROW]
[ROW][C]22[/C][C] 0.02296[/C][C] 0.04592[/C][C] 0.977[/C][/ROW]
[ROW][C]23[/C][C] 0.02368[/C][C] 0.04737[/C][C] 0.9763[/C][/ROW]
[ROW][C]24[/C][C] 0.02301[/C][C] 0.04602[/C][C] 0.977[/C][/ROW]
[ROW][C]25[/C][C] 0.07179[/C][C] 0.1436[/C][C] 0.9282[/C][/ROW]
[ROW][C]26[/C][C] 0.05125[/C][C] 0.1025[/C][C] 0.9488[/C][/ROW]
[ROW][C]27[/C][C] 0.0435[/C][C] 0.087[/C][C] 0.9565[/C][/ROW]
[ROW][C]28[/C][C] 0.03058[/C][C] 0.06117[/C][C] 0.9694[/C][/ROW]
[ROW][C]29[/C][C] 0.02005[/C][C] 0.0401[/C][C] 0.98[/C][/ROW]
[ROW][C]30[/C][C] 0.02684[/C][C] 0.05369[/C][C] 0.9732[/C][/ROW]
[ROW][C]31[/C][C] 0.03982[/C][C] 0.07963[/C][C] 0.9602[/C][/ROW]
[ROW][C]32[/C][C] 0.03766[/C][C] 0.07532[/C][C] 0.9623[/C][/ROW]
[ROW][C]33[/C][C] 0.0261[/C][C] 0.0522[/C][C] 0.9739[/C][/ROW]
[ROW][C]34[/C][C] 0.02515[/C][C] 0.05031[/C][C] 0.9748[/C][/ROW]
[ROW][C]35[/C][C] 0.0179[/C][C] 0.03579[/C][C] 0.9821[/C][/ROW]
[ROW][C]36[/C][C] 0.01219[/C][C] 0.02437[/C][C] 0.9878[/C][/ROW]
[ROW][C]37[/C][C] 0.009124[/C][C] 0.01825[/C][C] 0.9909[/C][/ROW]
[ROW][C]38[/C][C] 0.007572[/C][C] 0.01514[/C][C] 0.9924[/C][/ROW]
[ROW][C]39[/C][C] 0.02101[/C][C] 0.04202[/C][C] 0.979[/C][/ROW]
[ROW][C]40[/C][C] 0.01784[/C][C] 0.03569[/C][C] 0.9822[/C][/ROW]
[ROW][C]41[/C][C] 0.01225[/C][C] 0.0245[/C][C] 0.9878[/C][/ROW]
[ROW][C]42[/C][C] 0.01307[/C][C] 0.02613[/C][C] 0.9869[/C][/ROW]
[ROW][C]43[/C][C] 0.01094[/C][C] 0.02189[/C][C] 0.9891[/C][/ROW]
[ROW][C]44[/C][C] 0.00792[/C][C] 0.01584[/C][C] 0.9921[/C][/ROW]
[ROW][C]45[/C][C] 0.005961[/C][C] 0.01192[/C][C] 0.994[/C][/ROW]
[ROW][C]46[/C][C] 0.004039[/C][C] 0.008078[/C][C] 0.996[/C][/ROW]
[ROW][C]47[/C][C] 0.003609[/C][C] 0.007218[/C][C] 0.9964[/C][/ROW]
[ROW][C]48[/C][C] 0.01142[/C][C] 0.02284[/C][C] 0.9886[/C][/ROW]
[ROW][C]49[/C][C] 0.008305[/C][C] 0.01661[/C][C] 0.9917[/C][/ROW]
[ROW][C]50[/C][C] 0.006573[/C][C] 0.01315[/C][C] 0.9934[/C][/ROW]
[ROW][C]51[/C][C] 0.00457[/C][C] 0.009139[/C][C] 0.9954[/C][/ROW]
[ROW][C]52[/C][C] 0.03586[/C][C] 0.07172[/C][C] 0.9641[/C][/ROW]
[ROW][C]53[/C][C] 0.02711[/C][C] 0.05421[/C][C] 0.9729[/C][/ROW]
[ROW][C]54[/C][C] 0.05122[/C][C] 0.1024[/C][C] 0.9488[/C][/ROW]
[ROW][C]55[/C][C] 0.04085[/C][C] 0.08171[/C][C] 0.9591[/C][/ROW]
[ROW][C]56[/C][C] 0.05086[/C][C] 0.1017[/C][C] 0.9491[/C][/ROW]
[ROW][C]57[/C][C] 0.04238[/C][C] 0.08477[/C][C] 0.9576[/C][/ROW]
[ROW][C]58[/C][C] 0.03422[/C][C] 0.06844[/C][C] 0.9658[/C][/ROW]
[ROW][C]59[/C][C] 0.02623[/C][C] 0.05246[/C][C] 0.9738[/C][/ROW]
[ROW][C]60[/C][C] 0.02266[/C][C] 0.04531[/C][C] 0.9773[/C][/ROW]
[ROW][C]61[/C][C] 0.01689[/C][C] 0.03379[/C][C] 0.9831[/C][/ROW]
[ROW][C]62[/C][C] 0.01612[/C][C] 0.03224[/C][C] 0.9839[/C][/ROW]
[ROW][C]63[/C][C] 0.01287[/C][C] 0.02573[/C][C] 0.9871[/C][/ROW]
[ROW][C]64[/C][C] 0.01239[/C][C] 0.02479[/C][C] 0.9876[/C][/ROW]
[ROW][C]65[/C][C] 0.01496[/C][C] 0.02993[/C][C] 0.985[/C][/ROW]
[ROW][C]66[/C][C] 0.01595[/C][C] 0.03189[/C][C] 0.9841[/C][/ROW]
[ROW][C]67[/C][C] 0.01681[/C][C] 0.03363[/C][C] 0.9832[/C][/ROW]
[ROW][C]68[/C][C] 0.01403[/C][C] 0.02805[/C][C] 0.986[/C][/ROW]
[ROW][C]69[/C][C] 0.01105[/C][C] 0.0221[/C][C] 0.989[/C][/ROW]
[ROW][C]70[/C][C] 0.01617[/C][C] 0.03235[/C][C] 0.9838[/C][/ROW]
[ROW][C]71[/C][C] 0.03592[/C][C] 0.07184[/C][C] 0.9641[/C][/ROW]
[ROW][C]72[/C][C] 0.0639[/C][C] 0.1278[/C][C] 0.9361[/C][/ROW]
[ROW][C]73[/C][C] 0.06098[/C][C] 0.122[/C][C] 0.939[/C][/ROW]
[ROW][C]74[/C][C] 0.05194[/C][C] 0.1039[/C][C] 0.9481[/C][/ROW]
[ROW][C]75[/C][C] 0.04157[/C][C] 0.08315[/C][C] 0.9584[/C][/ROW]
[ROW][C]76[/C][C] 0.03838[/C][C] 0.07676[/C][C] 0.9616[/C][/ROW]
[ROW][C]77[/C][C] 0.03365[/C][C] 0.06729[/C][C] 0.9664[/C][/ROW]
[ROW][C]78[/C][C] 0.06347[/C][C] 0.1269[/C][C] 0.9365[/C][/ROW]
[ROW][C]79[/C][C] 0.05149[/C][C] 0.103[/C][C] 0.9485[/C][/ROW]
[ROW][C]80[/C][C] 0.2037[/C][C] 0.4073[/C][C] 0.7963[/C][/ROW]
[ROW][C]81[/C][C] 0.1748[/C][C] 0.3496[/C][C] 0.8252[/C][/ROW]
[ROW][C]82[/C][C] 0.1705[/C][C] 0.341[/C][C] 0.8295[/C][/ROW]
[ROW][C]83[/C][C] 0.1635[/C][C] 0.327[/C][C] 0.8365[/C][/ROW]
[ROW][C]84[/C][C] 0.4111[/C][C] 0.8223[/C][C] 0.5889[/C][/ROW]
[ROW][C]85[/C][C] 0.3769[/C][C] 0.7538[/C][C] 0.6231[/C][/ROW]
[ROW][C]86[/C][C] 0.4286[/C][C] 0.8573[/C][C] 0.5714[/C][/ROW]
[ROW][C]87[/C][C] 0.388[/C][C] 0.776[/C][C] 0.612[/C][/ROW]
[ROW][C]88[/C][C] 0.4389[/C][C] 0.8779[/C][C] 0.5611[/C][/ROW]
[ROW][C]89[/C][C] 0.4116[/C][C] 0.8232[/C][C] 0.5884[/C][/ROW]
[ROW][C]90[/C][C] 0.3763[/C][C] 0.7525[/C][C] 0.6237[/C][/ROW]
[ROW][C]91[/C][C] 0.3756[/C][C] 0.7512[/C][C] 0.6244[/C][/ROW]
[ROW][C]92[/C][C] 0.3334[/C][C] 0.6668[/C][C] 0.6666[/C][/ROW]
[ROW][C]93[/C][C] 0.2929[/C][C] 0.5858[/C][C] 0.7071[/C][/ROW]
[ROW][C]94[/C][C] 0.2566[/C][C] 0.5132[/C][C] 0.7434[/C][/ROW]
[ROW][C]95[/C][C] 0.2244[/C][C] 0.4489[/C][C] 0.7756[/C][/ROW]
[ROW][C]96[/C][C] 0.2593[/C][C] 0.5187[/C][C] 0.7407[/C][/ROW]
[ROW][C]97[/C][C] 0.2727[/C][C] 0.5454[/C][C] 0.7273[/C][/ROW]
[ROW][C]98[/C][C] 0.2476[/C][C] 0.4953[/C][C] 0.7524[/C][/ROW]
[ROW][C]99[/C][C] 0.2692[/C][C] 0.5383[/C][C] 0.7308[/C][/ROW]
[ROW][C]100[/C][C] 0.2371[/C][C] 0.4741[/C][C] 0.7629[/C][/ROW]
[ROW][C]101[/C][C] 0.3647[/C][C] 0.7294[/C][C] 0.6353[/C][/ROW]
[ROW][C]102[/C][C] 0.3249[/C][C] 0.6498[/C][C] 0.6751[/C][/ROW]
[ROW][C]103[/C][C] 0.287[/C][C] 0.5739[/C][C] 0.713[/C][/ROW]
[ROW][C]104[/C][C] 0.2581[/C][C] 0.5162[/C][C] 0.7419[/C][/ROW]
[ROW][C]105[/C][C] 0.2293[/C][C] 0.4587[/C][C] 0.7707[/C][/ROW]
[ROW][C]106[/C][C] 0.1992[/C][C] 0.3984[/C][C] 0.8008[/C][/ROW]
[ROW][C]107[/C][C] 0.1697[/C][C] 0.3394[/C][C] 0.8303[/C][/ROW]
[ROW][C]108[/C][C] 0.1459[/C][C] 0.2917[/C][C] 0.8541[/C][/ROW]
[ROW][C]109[/C][C] 0.203[/C][C] 0.406[/C][C] 0.797[/C][/ROW]
[ROW][C]110[/C][C] 0.171[/C][C] 0.342[/C][C] 0.829[/C][/ROW]
[ROW][C]111[/C][C] 0.185[/C][C] 0.3701[/C][C] 0.815[/C][/ROW]
[ROW][C]112[/C][C] 0.423[/C][C] 0.846[/C][C] 0.577[/C][/ROW]
[ROW][C]113[/C][C] 0.4061[/C][C] 0.8122[/C][C] 0.5939[/C][/ROW]
[ROW][C]114[/C][C] 0.3823[/C][C] 0.7646[/C][C] 0.6177[/C][/ROW]
[ROW][C]115[/C][C] 0.3676[/C][C] 0.7351[/C][C] 0.6324[/C][/ROW]
[ROW][C]116[/C][C] 0.3303[/C][C] 0.6606[/C][C] 0.6697[/C][/ROW]
[ROW][C]117[/C][C] 0.3347[/C][C] 0.6693[/C][C] 0.6653[/C][/ROW]
[ROW][C]118[/C][C] 0.2898[/C][C] 0.5796[/C][C] 0.7102[/C][/ROW]
[ROW][C]119[/C][C] 0.2556[/C][C] 0.5112[/C][C] 0.7444[/C][/ROW]
[ROW][C]120[/C][C] 0.2213[/C][C] 0.4427[/C][C] 0.7787[/C][/ROW]
[ROW][C]121[/C][C] 0.1992[/C][C] 0.3984[/C][C] 0.8008[/C][/ROW]
[ROW][C]122[/C][C] 0.1776[/C][C] 0.3552[/C][C] 0.8224[/C][/ROW]
[ROW][C]123[/C][C] 0.1494[/C][C] 0.2989[/C][C] 0.8506[/C][/ROW]
[ROW][C]124[/C][C] 0.1329[/C][C] 0.2659[/C][C] 0.8671[/C][/ROW]
[ROW][C]125[/C][C] 0.116[/C][C] 0.2321[/C][C] 0.884[/C][/ROW]
[ROW][C]126[/C][C] 0.1095[/C][C] 0.2189[/C][C] 0.8905[/C][/ROW]
[ROW][C]127[/C][C] 0.08565[/C][C] 0.1713[/C][C] 0.9143[/C][/ROW]
[ROW][C]128[/C][C] 0.08024[/C][C] 0.1605[/C][C] 0.9198[/C][/ROW]
[ROW][C]129[/C][C] 0.06231[/C][C] 0.1246[/C][C] 0.9377[/C][/ROW]
[ROW][C]130[/C][C] 0.06552[/C][C] 0.131[/C][C] 0.9345[/C][/ROW]
[ROW][C]131[/C][C] 0.07264[/C][C] 0.1453[/C][C] 0.9274[/C][/ROW]
[ROW][C]132[/C][C] 0.145[/C][C] 0.29[/C][C] 0.855[/C][/ROW]
[ROW][C]133[/C][C] 0.1213[/C][C] 0.2427[/C][C] 0.8787[/C][/ROW]
[ROW][C]134[/C][C] 0.1034[/C][C] 0.2069[/C][C] 0.8966[/C][/ROW]
[ROW][C]135[/C][C] 0.08636[/C][C] 0.1727[/C][C] 0.9136[/C][/ROW]
[ROW][C]136[/C][C] 0.07841[/C][C] 0.1568[/C][C] 0.9216[/C][/ROW]
[ROW][C]137[/C][C] 0.07551[/C][C] 0.151[/C][C] 0.9245[/C][/ROW]
[ROW][C]138[/C][C] 0.0664[/C][C] 0.1328[/C][C] 0.9336[/C][/ROW]
[ROW][C]139[/C][C] 0.04806[/C][C] 0.09613[/C][C] 0.9519[/C][/ROW]
[ROW][C]140[/C][C] 0.05134[/C][C] 0.1027[/C][C] 0.9487[/C][/ROW]
[ROW][C]141[/C][C] 0.06094[/C][C] 0.1219[/C][C] 0.9391[/C][/ROW]
[ROW][C]142[/C][C] 0.05402[/C][C] 0.108[/C][C] 0.946[/C][/ROW]
[ROW][C]143[/C][C] 0.1046[/C][C] 0.2091[/C][C] 0.8954[/C][/ROW]
[ROW][C]144[/C][C] 0.1348[/C][C] 0.2696[/C][C] 0.8652[/C][/ROW]
[ROW][C]145[/C][C] 0.09621[/C][C] 0.1924[/C][C] 0.9038[/C][/ROW]
[ROW][C]146[/C][C] 0.1006[/C][C] 0.2011[/C][C] 0.8994[/C][/ROW]
[ROW][C]147[/C][C] 0.07318[/C][C] 0.1464[/C][C] 0.9268[/C][/ROW]
[ROW][C]148[/C][C] 0.07073[/C][C] 0.1415[/C][C] 0.9293[/C][/ROW]
[ROW][C]149[/C][C] 0.04455[/C][C] 0.08909[/C][C] 0.9555[/C][/ROW]
[ROW][C]150[/C][C] 0.03869[/C][C] 0.07738[/C][C] 0.9613[/C][/ROW]
[ROW][C]151[/C][C] 0.02263[/C][C] 0.04527[/C][C] 0.9774[/C][/ROW]
[ROW][C]152[/C][C] 0.01248[/C][C] 0.02497[/C][C] 0.9875[/C][/ROW]
[ROW][C]153[/C][C] 0.1277[/C][C] 0.2554[/C][C] 0.8723[/C][/ROW]
[ROW][C]154[/C][C] 0.6842[/C][C] 0.6316[/C][C] 0.3158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297870&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297870&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	0.264	0.5279	0.736
11	0.1882	0.3764	0.8118
12	0.09787	0.1957	0.9021
13	0.05128	0.1026	0.9487
14	0.04752	0.09505	0.9525
15	0.06466	0.1293	0.9353
16	0.07049	0.141	0.9295
17	0.04058	0.08115	0.9594
18	0.08627	0.1725	0.9137
19	0.06037	0.1207	0.9396
20	0.04613	0.09226	0.9539
21	0.03774	0.07548	0.9623
22	0.02296	0.04592	0.977
23	0.02368	0.04737	0.9763
24	0.02301	0.04602	0.977
25	0.07179	0.1436	0.9282
26	0.05125	0.1025	0.9488
27	0.0435	0.087	0.9565
28	0.03058	0.06117	0.9694
29	0.02005	0.0401	0.98
30	0.02684	0.05369	0.9732
31	0.03982	0.07963	0.9602
32	0.03766	0.07532	0.9623
33	0.0261	0.0522	0.9739
34	0.02515	0.05031	0.9748
35	0.0179	0.03579	0.9821
36	0.01219	0.02437	0.9878
37	0.009124	0.01825	0.9909
38	0.007572	0.01514	0.9924
39	0.02101	0.04202	0.979
40	0.01784	0.03569	0.9822
41	0.01225	0.0245	0.9878
42	0.01307	0.02613	0.9869
43	0.01094	0.02189	0.9891
44	0.00792	0.01584	0.9921
45	0.005961	0.01192	0.994
46	0.004039	0.008078	0.996
47	0.003609	0.007218	0.9964
48	0.01142	0.02284	0.9886
49	0.008305	0.01661	0.9917
50	0.006573	0.01315	0.9934
51	0.00457	0.009139	0.9954
52	0.03586	0.07172	0.9641
53	0.02711	0.05421	0.9729
54	0.05122	0.1024	0.9488
55	0.04085	0.08171	0.9591
56	0.05086	0.1017	0.9491
57	0.04238	0.08477	0.9576
58	0.03422	0.06844	0.9658
59	0.02623	0.05246	0.9738
60	0.02266	0.04531	0.9773
61	0.01689	0.03379	0.9831
62	0.01612	0.03224	0.9839
63	0.01287	0.02573	0.9871
64	0.01239	0.02479	0.9876
65	0.01496	0.02993	0.985
66	0.01595	0.03189	0.9841
67	0.01681	0.03363	0.9832
68	0.01403	0.02805	0.986
69	0.01105	0.0221	0.989
70	0.01617	0.03235	0.9838
71	0.03592	0.07184	0.9641
72	0.0639	0.1278	0.9361
73	0.06098	0.122	0.939
74	0.05194	0.1039	0.9481
75	0.04157	0.08315	0.9584
76	0.03838	0.07676	0.9616
77	0.03365	0.06729	0.9664
78	0.06347	0.1269	0.9365
79	0.05149	0.103	0.9485
80	0.2037	0.4073	0.7963
81	0.1748	0.3496	0.8252
82	0.1705	0.341	0.8295
83	0.1635	0.327	0.8365
84	0.4111	0.8223	0.5889
85	0.3769	0.7538	0.6231
86	0.4286	0.8573	0.5714
87	0.388	0.776	0.612
88	0.4389	0.8779	0.5611
89	0.4116	0.8232	0.5884
90	0.3763	0.7525	0.6237
91	0.3756	0.7512	0.6244
92	0.3334	0.6668	0.6666
93	0.2929	0.5858	0.7071
94	0.2566	0.5132	0.7434
95	0.2244	0.4489	0.7756
96	0.2593	0.5187	0.7407
97	0.2727	0.5454	0.7273
98	0.2476	0.4953	0.7524
99	0.2692	0.5383	0.7308
100	0.2371	0.4741	0.7629
101	0.3647	0.7294	0.6353
102	0.3249	0.6498	0.6751
103	0.287	0.5739	0.713
104	0.2581	0.5162	0.7419
105	0.2293	0.4587	0.7707
106	0.1992	0.3984	0.8008
107	0.1697	0.3394	0.8303
108	0.1459	0.2917	0.8541
109	0.203	0.406	0.797
110	0.171	0.342	0.829
111	0.185	0.3701	0.815
112	0.423	0.846	0.577
113	0.4061	0.8122	0.5939
114	0.3823	0.7646	0.6177
115	0.3676	0.7351	0.6324
116	0.3303	0.6606	0.6697
117	0.3347	0.6693	0.6653
118	0.2898	0.5796	0.7102
119	0.2556	0.5112	0.7444
120	0.2213	0.4427	0.7787
121	0.1992	0.3984	0.8008
122	0.1776	0.3552	0.8224
123	0.1494	0.2989	0.8506
124	0.1329	0.2659	0.8671
125	0.116	0.2321	0.884
126	0.1095	0.2189	0.8905
127	0.08565	0.1713	0.9143
128	0.08024	0.1605	0.9198
129	0.06231	0.1246	0.9377
130	0.06552	0.131	0.9345
131	0.07264	0.1453	0.9274
132	0.145	0.29	0.855
133	0.1213	0.2427	0.8787
134	0.1034	0.2069	0.8966
135	0.08636	0.1727	0.9136
136	0.07841	0.1568	0.9216
137	0.07551	0.151	0.9245
138	0.0664	0.1328	0.9336
139	0.04806	0.09613	0.9519
140	0.05134	0.1027	0.9487
141	0.06094	0.1219	0.9391
142	0.05402	0.108	0.946
143	0.1046	0.2091	0.8954
144	0.1348	0.2696	0.8652
145	0.09621	0.1924	0.9038
146	0.1006	0.2011	0.8994
147	0.07318	0.1464	0.9268
148	0.07073	0.1415	0.9293
149	0.04455	0.08909	0.9555
150	0.03869	0.07738	0.9613
151	0.02263	0.04527	0.9774
152	0.01248	0.02497	0.9875
153	0.1277	0.2554	0.8723
154	0.6842	0.6316	0.3158

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.02069	NOK
5% type I error level	34	0.234483	NOK
10% type I error level	58	0.4	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 & 3 &  0.02069 & NOK \tabularnewline
5% type I error level & 34 & 0.234483 & NOK \tabularnewline
10% type I error level & 58 & 0.4 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297870&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]3[/C][C] 0.02069[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]0.234483[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]58[/C][C]0.4[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297870&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297870&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	3	0.02069	NOK
5% type I error level	34	0.234483	NOK
10% type I error level	58	0.4	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.6445, df1 = 2, df2 = 155, p-value = 0.1965
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2242, df1 = 12, df2 = 145, p-value = 0.2719
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.98483, df1 = 2, df2 = 155, p-value = 0.3758

\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 = 1.6445, df1 = 2, df2 = 155, p-value = 0.1965
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2242, df1 = 12, df2 = 145, p-value = 0.2719
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.98483, df1 = 2, df2 = 155, p-value = 0.3758
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297870&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 = 1.6445, df1 = 2, df2 = 155, p-value = 0.1965
[/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 = 1.2242, df1 = 12, df2 = 145, p-value = 0.2719
[/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 = 0.98483, df1 = 2, df2 = 155, p-value = 0.3758
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297870&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297870&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 = 1.6445, df1 = 2, df2 = 155, p-value = 0.1965
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2242, df1 = 12, df2 = 145, p-value = 0.2719
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.98483, df1 = 2, df2 = 155, p-value = 0.3758

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 1.088929 1.120502 1.044907 1.041363 1.045446 1.037390

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.088929 1.120502 1.044907 1.041363 1.045446 1.037390 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297870&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.088929 1.120502 1.044907 1.041363 1.045446 1.037390 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297870&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297870&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 1.088929 1.120502 1.044907 1.041363 1.045446 1.037390

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