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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 23 Jan 2017 10:41:53 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/23/t14851645243saxzjybegww8j5.htm/, Retrieved Wed, 15 May 2024 23:51:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=304574, Retrieved Wed, 15 May 2024 23:51:04 +0000

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Estimated Impact

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

-       [Multiple Regression] [11] [2017-01-23 09:41:53] [94c1b173d9287822f5e2740a4a602bdd] [Current]

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14 22 13 22
19 24 16 24
17 21 17 26
17 21 NA 21
15 24 NA 26
20 20 16 25
15 22 NA 21
19 20 NA 24
15 19 NA 27
15 23 17 28
19 21 17 23
NA 19 15 25
20 19 16 24
18 21 14 24
15 21 16 24
14 22 17 25
20 22 NA 25
NA 19 NA NA
16 21 NA 25
16 21 NA 25
16 21 16 24
10 20 NA 26
19 22 16 26
19 22 NA 25
16 24 NA 26
15 21 NA 23
18 19 16 24
17 19 15 24
19 23 16 25
17 21 16 25
NA 21 13 24
19 19 15 28
20 21 17 27
5 19 NA NA
19 21 13 23
16 21 17 23
15 23 NA 24
16 19 14 24
18 19 14 22
16 19 18 25
15 18 NA 25
17 22 17 28
NA 18 13 22
20 22 16 28
19 18 15 25
7 22 15 24
13 22 NA 24
16 19 15 23
16 22 13 25
NA 25 NA NA
18 19 17 26
18 19 NA 25
16 19 NA 27
17 19 11 26
19 21 14 23
16 21 13 25
19 20 NA 21
13 19 17 22
16 19 16 24
13 22 NA 25
12 26 17 27
17 19 16 24
17 21 16 26
17 21 16 21
16 20 15 27
16 23 12 22
14 22 17 23
16 22 14 24
13 22 14 25
16 21 16 24
14 21 NA 23
20 22 NA 28
12 23 NA NA
13 18 NA 24
18 24 NA 26
14 22 15 22
19 21 16 25
18 21 14 25
14 21 15 24
18 23 17 24
19 21 NA 26
15 23 10 21
14 21 NA 25
17 19 17 25
19 21 NA 26
13 21 20 25
19 21 17 26
18 23 18 27
20 23 NA 25
15 20 17 NA
15 20 14 20
15 19 NA 24
20 23 17 26
15 22 NA 25
19 19 17 25
18 23 NA 24
18 22 16 26
15 22 18 25
20 21 18 28
17 21 16 27
12 21 NA 25
18 21 NA 26
19 22 15 26
20 25 13 26
NA 21 NA NA
17 23 NA 28
15 19 NA NA
16 22 NA 21
18 20 NA 25
18 21 16 25
14 25 NA 24
15 21 NA 24
12 19 NA 24
17 23 12 23
14 22 NA 23
18 21 16 24
17 24 16 24
17 21 NA 25
20 19 16 28
16 18 14 23
14 19 15 24
15 20 14 23
18 19 NA 24
20 22 15 25
17 21 NA 24
17 22 15 23
17 24 16 23
17 28 NA 25
15 19 NA 21
17 18 NA 22
18 23 11 19
17 19 NA 24
20 23 18 25
15 19 NA 21
16 22 11 22
15 21 NA 23
18 19 18 27
11 22 NA NA
15 21 15 26
18 23 19 29
20 22 17 28
19 19 NA 24
14 19 14 25
16 21 NA 25
15 22 13 22
17 21 17 25
18 20 14 26
20 23 19 26
17 22 14 24
18 23 NA 25
15 22 NA 19
16 21 16 25
11 20 16 23
15 18 15 25
18 18 12 25
17 20 NA 26
16 19 17 27
12 21 NA 24
19 24 NA 22
18 19 18 25
15 20 15 24
17 19 18 23
19 23 15 27
18 22 NA 24
19 21 NA 24
16 24 NA 21
16 21 16 25
16 21 NA 25
14 22 16 23

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304574&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]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=304574&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304574&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	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 7.13508 -0.00181701Bevr_Leeftijd[t] -0.0610084TVDC[t] + 0.433477SKEOUSUM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  7.13508 -0.00181701Bevr_Leeftijd[t] -0.0610084TVDC[t] +  0.433477SKEOUSUM[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304574&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  7.13508 -0.00181701Bevr_Leeftijd[t] -0.0610084TVDC[t] +  0.433477SKEOUSUM[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304574&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304574&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
ITHSUM[t] = + 7.13508 -0.00181701Bevr_Leeftijd[t] -0.0610084TVDC[t] + 0.433477SKEOUSUM[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)	+7.135	4.011	+1.7790e+00	0.07849	0.03924
Bevr_Leeftijd	-0.001817	0.1327	-1.3690e-02	0.9891	0.4946
TVDC	-0.06101	0.1334	-4.5740e-01	0.6484	0.3242
SKEOUSUM	+0.4335	0.1331	+3.2560e+00	0.001568	0.0007838

\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) & +7.135 &  4.011 & +1.7790e+00 &  0.07849 &  0.03924 \tabularnewline
Bevr_Leeftijd & -0.001817 &  0.1327 & -1.3690e-02 &  0.9891 &  0.4946 \tabularnewline
TVDC & -0.06101 &  0.1334 & -4.5740e-01 &  0.6484 &  0.3242 \tabularnewline
SKEOUSUM & +0.4335 &  0.1331 & +3.2560e+00 &  0.001568 &  0.0007838 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304574&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]+7.135[/C][C] 4.011[/C][C]+1.7790e+00[/C][C] 0.07849[/C][C] 0.03924[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.001817[/C][C] 0.1327[/C][C]-1.3690e-02[/C][C] 0.9891[/C][C] 0.4946[/C][/ROW]
[ROW][C]TVDC[/C][C]-0.06101[/C][C] 0.1334[/C][C]-4.5740e-01[/C][C] 0.6484[/C][C] 0.3242[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.4335[/C][C] 0.1331[/C][C]+3.2560e+00[/C][C] 0.001568[/C][C] 0.0007838[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304574&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304574&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)	+7.135	4.011	+1.7790e+00	0.07849	0.03924
Bevr_Leeftijd	-0.001817	0.1327	-1.3690e-02	0.9891	0.4946
TVDC	-0.06101	0.1334	-4.5740e-01	0.6484	0.3242
SKEOUSUM	+0.4335	0.1331	+3.2560e+00	0.001568	0.0007838

Multiple Linear Regression - Regression Statistics
Multiple R	0.3354
R-squared	0.1125
Adjusted R-squared	0.08445
F-TEST (value)	4.013
F-TEST (DF numerator)	3
F-TEST (DF denominator)	95
p-value	0.009775
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.186
Sum Squared Residuals	453.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3354 \tabularnewline
R-squared &  0.1125 \tabularnewline
Adjusted R-squared &  0.08445 \tabularnewline
F-TEST (value) &  4.013 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 95 \tabularnewline
p-value &  0.009775 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.186 \tabularnewline
Sum Squared Residuals &  453.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304574&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3354[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1125[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.08445[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 4.013[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]95[/C][/ROW]
[ROW][C]p-value[/C][C] 0.009775[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.186[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 453.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304574&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304574&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	0.3354
R-squared	0.1125
Adjusted R-squared	0.08445
F-TEST (value)	4.013
F-TEST (DF numerator)	3
F-TEST (DF denominator)	95
p-value	0.009775
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.186
Sum Squared Residuals	453.9

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	15.84	-1.838
2	19	16.52	2.481
3	17	17.33	-0.3302
4	20	16.96	3.04
5	15	18.19	-3.193
6	19	16.03	2.97
7	20	16.53	3.472
8	18	16.65	1.354
9	15	16.52	-1.524
10	14	16.89	-2.895
11	16	16.52	-0.5242
12	19	17.39	1.611
13	18	16.53	1.472
14	17	16.59	0.4111
15	19	16.95	2.046
16	17	16.96	0.04229
17	19	18.32	0.6772
18	20	17.76	2.236
19	19	16.27	2.726
20	16	16.03	-0.02975
21	16	16.65	-0.6499
22	18	15.78	2.217
23	16	16.84	-0.8393
24	17	18.2	-1.195
25	20	18.26	1.744
26	19	17.02	1.976
27	7	16.58	-9.583
28	16	16.16	-0.1554
29	16	17.14	-1.139
30	18	17.33	0.6662
31	17	17.7	-0.6999
32	19	16.21	2.787
33	16	17.14	-1.141
34	13	15.6	-2.6
35	16	16.53	-0.5279
36	12	17.75	-5.755
37	17	16.53	0.4721
38	17	17.39	-0.3912
39	17	15.22	1.776
40	16	17.89	-1.887
41	16	15.9	0.1023
42	14	16.03	-2.028
43	16	16.64	-0.6444
44	13	17.08	-4.078
45	16	16.52	-0.5242
46	14	15.72	-1.716
47	19	16.96	2.042
48	18	17.08	0.9203
49	14	16.59	-2.585
50	18	16.46	1.54
51	15	15.59	-0.5862
52	17	16.9	0.09966
53	13	16.71	-3.714
54	19	17.33	1.67
55	18	17.7	0.301
56	15	14.91	0.08584
57	20	17.33	2.673
58	19	16.9	2.1
59	18	17.39	0.6106
60	15	16.83	-1.834
61	20	18.14	1.864
62	17	17.82	-0.8247
63	19	17.45	1.55
64	20	17.57	2.433
65	18	16.96	1.042
66	17	16.33	0.6688
67	18	16.52	1.476
68	17	16.52	0.4812
69	20	18.26	1.738
70	16	16.22	-0.2182
71	14	16.59	-2.589
72	15	16.21	-1.215
73	20	17.02	2.983
74	17	16.15	0.8501
75	17	16.09	0.9147
76	18	14.66	3.342
77	20	16.83	3.168
78	16	15.96	0.03949
79	18	17.71	0.2937
80	15	17.45	-2.452
81	18	18.5	-0.505
82	20	18.2	1.805
83	14	17.08	-3.083
84	15	15.84	-0.8385
85	17	16.9	0.1033
86	18	17.52	0.485
87	20	17.2	2.795
88	17	16.64	0.3556
89	16	16.96	-0.9577
90	11	16.09	-5.093
91	15	17.02	-2.024
92	18	17.21	0.7928
93	16	17.77	-1.767
94	18	16.84	1.161
95	15	16.59	-1.587
96	17	15.97	1.028
97	19	17.88	1.118
98	16	16.96	-0.9577
99	14	16.09	-2.089

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  15.84 & -1.838 \tabularnewline
2 &  19 &  16.52 &  2.481 \tabularnewline
3 &  17 &  17.33 & -0.3302 \tabularnewline
4 &  20 &  16.96 &  3.04 \tabularnewline
5 &  15 &  18.19 & -3.193 \tabularnewline
6 &  19 &  16.03 &  2.97 \tabularnewline
7 &  20 &  16.53 &  3.472 \tabularnewline
8 &  18 &  16.65 &  1.354 \tabularnewline
9 &  15 &  16.52 & -1.524 \tabularnewline
10 &  14 &  16.89 & -2.895 \tabularnewline
11 &  16 &  16.52 & -0.5242 \tabularnewline
12 &  19 &  17.39 &  1.611 \tabularnewline
13 &  18 &  16.53 &  1.472 \tabularnewline
14 &  17 &  16.59 &  0.4111 \tabularnewline
15 &  19 &  16.95 &  2.046 \tabularnewline
16 &  17 &  16.96 &  0.04229 \tabularnewline
17 &  19 &  18.32 &  0.6772 \tabularnewline
18 &  20 &  17.76 &  2.236 \tabularnewline
19 &  19 &  16.27 &  2.726 \tabularnewline
20 &  16 &  16.03 & -0.02975 \tabularnewline
21 &  16 &  16.65 & -0.6499 \tabularnewline
22 &  18 &  15.78 &  2.217 \tabularnewline
23 &  16 &  16.84 & -0.8393 \tabularnewline
24 &  17 &  18.2 & -1.195 \tabularnewline
25 &  20 &  18.26 &  1.744 \tabularnewline
26 &  19 &  17.02 &  1.976 \tabularnewline
27 &  7 &  16.58 & -9.583 \tabularnewline
28 &  16 &  16.16 & -0.1554 \tabularnewline
29 &  16 &  17.14 & -1.139 \tabularnewline
30 &  18 &  17.33 &  0.6662 \tabularnewline
31 &  17 &  17.7 & -0.6999 \tabularnewline
32 &  19 &  16.21 &  2.787 \tabularnewline
33 &  16 &  17.14 & -1.141 \tabularnewline
34 &  13 &  15.6 & -2.6 \tabularnewline
35 &  16 &  16.53 & -0.5279 \tabularnewline
36 &  12 &  17.75 & -5.755 \tabularnewline
37 &  17 &  16.53 &  0.4721 \tabularnewline
38 &  17 &  17.39 & -0.3912 \tabularnewline
39 &  17 &  15.22 &  1.776 \tabularnewline
40 &  16 &  17.89 & -1.887 \tabularnewline
41 &  16 &  15.9 &  0.1023 \tabularnewline
42 &  14 &  16.03 & -2.028 \tabularnewline
43 &  16 &  16.64 & -0.6444 \tabularnewline
44 &  13 &  17.08 & -4.078 \tabularnewline
45 &  16 &  16.52 & -0.5242 \tabularnewline
46 &  14 &  15.72 & -1.716 \tabularnewline
47 &  19 &  16.96 &  2.042 \tabularnewline
48 &  18 &  17.08 &  0.9203 \tabularnewline
49 &  14 &  16.59 & -2.585 \tabularnewline
50 &  18 &  16.46 &  1.54 \tabularnewline
51 &  15 &  15.59 & -0.5862 \tabularnewline
52 &  17 &  16.9 &  0.09966 \tabularnewline
53 &  13 &  16.71 & -3.714 \tabularnewline
54 &  19 &  17.33 &  1.67 \tabularnewline
55 &  18 &  17.7 &  0.301 \tabularnewline
56 &  15 &  14.91 &  0.08584 \tabularnewline
57 &  20 &  17.33 &  2.673 \tabularnewline
58 &  19 &  16.9 &  2.1 \tabularnewline
59 &  18 &  17.39 &  0.6106 \tabularnewline
60 &  15 &  16.83 & -1.834 \tabularnewline
61 &  20 &  18.14 &  1.864 \tabularnewline
62 &  17 &  17.82 & -0.8247 \tabularnewline
63 &  19 &  17.45 &  1.55 \tabularnewline
64 &  20 &  17.57 &  2.433 \tabularnewline
65 &  18 &  16.96 &  1.042 \tabularnewline
66 &  17 &  16.33 &  0.6688 \tabularnewline
67 &  18 &  16.52 &  1.476 \tabularnewline
68 &  17 &  16.52 &  0.4812 \tabularnewline
69 &  20 &  18.26 &  1.738 \tabularnewline
70 &  16 &  16.22 & -0.2182 \tabularnewline
71 &  14 &  16.59 & -2.589 \tabularnewline
72 &  15 &  16.21 & -1.215 \tabularnewline
73 &  20 &  17.02 &  2.983 \tabularnewline
74 &  17 &  16.15 &  0.8501 \tabularnewline
75 &  17 &  16.09 &  0.9147 \tabularnewline
76 &  18 &  14.66 &  3.342 \tabularnewline
77 &  20 &  16.83 &  3.168 \tabularnewline
78 &  16 &  15.96 &  0.03949 \tabularnewline
79 &  18 &  17.71 &  0.2937 \tabularnewline
80 &  15 &  17.45 & -2.452 \tabularnewline
81 &  18 &  18.5 & -0.505 \tabularnewline
82 &  20 &  18.2 &  1.805 \tabularnewline
83 &  14 &  17.08 & -3.083 \tabularnewline
84 &  15 &  15.84 & -0.8385 \tabularnewline
85 &  17 &  16.9 &  0.1033 \tabularnewline
86 &  18 &  17.52 &  0.485 \tabularnewline
87 &  20 &  17.2 &  2.795 \tabularnewline
88 &  17 &  16.64 &  0.3556 \tabularnewline
89 &  16 &  16.96 & -0.9577 \tabularnewline
90 &  11 &  16.09 & -5.093 \tabularnewline
91 &  15 &  17.02 & -2.024 \tabularnewline
92 &  18 &  17.21 &  0.7928 \tabularnewline
93 &  16 &  17.77 & -1.767 \tabularnewline
94 &  18 &  16.84 &  1.161 \tabularnewline
95 &  15 &  16.59 & -1.587 \tabularnewline
96 &  17 &  15.97 &  1.028 \tabularnewline
97 &  19 &  17.88 &  1.118 \tabularnewline
98 &  16 &  16.96 & -0.9577 \tabularnewline
99 &  14 &  16.09 & -2.089 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304574&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] 14[/C][C] 15.84[/C][C]-1.838[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 16.52[/C][C] 2.481[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 17.33[/C][C]-0.3302[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 16.96[/C][C] 3.04[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 18.19[/C][C]-3.193[/C][/ROW]
[ROW][C]6[/C][C] 19[/C][C] 16.03[/C][C] 2.97[/C][/ROW]
[ROW][C]7[/C][C] 20[/C][C] 16.53[/C][C] 3.472[/C][/ROW]
[ROW][C]8[/C][C] 18[/C][C] 16.65[/C][C] 1.354[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 16.52[/C][C]-1.524[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 16.89[/C][C]-2.895[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 16.52[/C][C]-0.5242[/C][/ROW]
[ROW][C]12[/C][C] 19[/C][C] 17.39[/C][C] 1.611[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 16.53[/C][C] 1.472[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 16.59[/C][C] 0.4111[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 16.95[/C][C] 2.046[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 16.96[/C][C] 0.04229[/C][/ROW]
[ROW][C]17[/C][C] 19[/C][C] 18.32[/C][C] 0.6772[/C][/ROW]
[ROW][C]18[/C][C] 20[/C][C] 17.76[/C][C] 2.236[/C][/ROW]
[ROW][C]19[/C][C] 19[/C][C] 16.27[/C][C] 2.726[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 16.03[/C][C]-0.02975[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 16.65[/C][C]-0.6499[/C][/ROW]
[ROW][C]22[/C][C] 18[/C][C] 15.78[/C][C] 2.217[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 16.84[/C][C]-0.8393[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 18.2[/C][C]-1.195[/C][/ROW]
[ROW][C]25[/C][C] 20[/C][C] 18.26[/C][C] 1.744[/C][/ROW]
[ROW][C]26[/C][C] 19[/C][C] 17.02[/C][C] 1.976[/C][/ROW]
[ROW][C]27[/C][C] 7[/C][C] 16.58[/C][C]-9.583[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 16.16[/C][C]-0.1554[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 17.14[/C][C]-1.139[/C][/ROW]
[ROW][C]30[/C][C] 18[/C][C] 17.33[/C][C] 0.6662[/C][/ROW]
[ROW][C]31[/C][C] 17[/C][C] 17.7[/C][C]-0.6999[/C][/ROW]
[ROW][C]32[/C][C] 19[/C][C] 16.21[/C][C] 2.787[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 17.14[/C][C]-1.141[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 15.6[/C][C]-2.6[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 16.53[/C][C]-0.5279[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 17.75[/C][C]-5.755[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 16.53[/C][C] 0.4721[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 17.39[/C][C]-0.3912[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 15.22[/C][C] 1.776[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 17.89[/C][C]-1.887[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 15.9[/C][C] 0.1023[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 16.03[/C][C]-2.028[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 16.64[/C][C]-0.6444[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 17.08[/C][C]-4.078[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 16.52[/C][C]-0.5242[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.72[/C][C]-1.716[/C][/ROW]
[ROW][C]47[/C][C] 19[/C][C] 16.96[/C][C] 2.042[/C][/ROW]
[ROW][C]48[/C][C] 18[/C][C] 17.08[/C][C] 0.9203[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 16.59[/C][C]-2.585[/C][/ROW]
[ROW][C]50[/C][C] 18[/C][C] 16.46[/C][C] 1.54[/C][/ROW]
[ROW][C]51[/C][C] 15[/C][C] 15.59[/C][C]-0.5862[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 16.9[/C][C] 0.09966[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 16.71[/C][C]-3.714[/C][/ROW]
[ROW][C]54[/C][C] 19[/C][C] 17.33[/C][C] 1.67[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 17.7[/C][C] 0.301[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 14.91[/C][C] 0.08584[/C][/ROW]
[ROW][C]57[/C][C] 20[/C][C] 17.33[/C][C] 2.673[/C][/ROW]
[ROW][C]58[/C][C] 19[/C][C] 16.9[/C][C] 2.1[/C][/ROW]
[ROW][C]59[/C][C] 18[/C][C] 17.39[/C][C] 0.6106[/C][/ROW]
[ROW][C]60[/C][C] 15[/C][C] 16.83[/C][C]-1.834[/C][/ROW]
[ROW][C]61[/C][C] 20[/C][C] 18.14[/C][C] 1.864[/C][/ROW]
[ROW][C]62[/C][C] 17[/C][C] 17.82[/C][C]-0.8247[/C][/ROW]
[ROW][C]63[/C][C] 19[/C][C] 17.45[/C][C] 1.55[/C][/ROW]
[ROW][C]64[/C][C] 20[/C][C] 17.57[/C][C] 2.433[/C][/ROW]
[ROW][C]65[/C][C] 18[/C][C] 16.96[/C][C] 1.042[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 16.33[/C][C] 0.6688[/C][/ROW]
[ROW][C]67[/C][C] 18[/C][C] 16.52[/C][C] 1.476[/C][/ROW]
[ROW][C]68[/C][C] 17[/C][C] 16.52[/C][C] 0.4812[/C][/ROW]
[ROW][C]69[/C][C] 20[/C][C] 18.26[/C][C] 1.738[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 16.22[/C][C]-0.2182[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 16.59[/C][C]-2.589[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 16.21[/C][C]-1.215[/C][/ROW]
[ROW][C]73[/C][C] 20[/C][C] 17.02[/C][C] 2.983[/C][/ROW]
[ROW][C]74[/C][C] 17[/C][C] 16.15[/C][C] 0.8501[/C][/ROW]
[ROW][C]75[/C][C] 17[/C][C] 16.09[/C][C] 0.9147[/C][/ROW]
[ROW][C]76[/C][C] 18[/C][C] 14.66[/C][C] 3.342[/C][/ROW]
[ROW][C]77[/C][C] 20[/C][C] 16.83[/C][C] 3.168[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 15.96[/C][C] 0.03949[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 17.71[/C][C] 0.2937[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 17.45[/C][C]-2.452[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 18.5[/C][C]-0.505[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 18.2[/C][C] 1.805[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 17.08[/C][C]-3.083[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 15.84[/C][C]-0.8385[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.9[/C][C] 0.1033[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 17.52[/C][C] 0.485[/C][/ROW]
[ROW][C]87[/C][C] 20[/C][C] 17.2[/C][C] 2.795[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 16.64[/C][C] 0.3556[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 16.96[/C][C]-0.9577[/C][/ROW]
[ROW][C]90[/C][C] 11[/C][C] 16.09[/C][C]-5.093[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 17.02[/C][C]-2.024[/C][/ROW]
[ROW][C]92[/C][C] 18[/C][C] 17.21[/C][C] 0.7928[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 17.77[/C][C]-1.767[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 16.84[/C][C] 1.161[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 16.59[/C][C]-1.587[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 15.97[/C][C] 1.028[/C][/ROW]
[ROW][C]97[/C][C] 19[/C][C] 17.88[/C][C] 1.118[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.96[/C][C]-0.9577[/C][/ROW]
[ROW][C]99[/C][C] 14[/C][C] 16.09[/C][C]-2.089[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304574&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304574&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	14	15.84	-1.838
2	19	16.52	2.481
3	17	17.33	-0.3302
4	20	16.96	3.04
5	15	18.19	-3.193
6	19	16.03	2.97
7	20	16.53	3.472
8	18	16.65	1.354
9	15	16.52	-1.524
10	14	16.89	-2.895
11	16	16.52	-0.5242
12	19	17.39	1.611
13	18	16.53	1.472
14	17	16.59	0.4111
15	19	16.95	2.046
16	17	16.96	0.04229
17	19	18.32	0.6772
18	20	17.76	2.236
19	19	16.27	2.726
20	16	16.03	-0.02975
21	16	16.65	-0.6499
22	18	15.78	2.217
23	16	16.84	-0.8393
24	17	18.2	-1.195
25	20	18.26	1.744
26	19	17.02	1.976
27	7	16.58	-9.583
28	16	16.16	-0.1554
29	16	17.14	-1.139
30	18	17.33	0.6662
31	17	17.7	-0.6999
32	19	16.21	2.787
33	16	17.14	-1.141
34	13	15.6	-2.6
35	16	16.53	-0.5279
36	12	17.75	-5.755
37	17	16.53	0.4721
38	17	17.39	-0.3912
39	17	15.22	1.776
40	16	17.89	-1.887
41	16	15.9	0.1023
42	14	16.03	-2.028
43	16	16.64	-0.6444
44	13	17.08	-4.078
45	16	16.52	-0.5242
46	14	15.72	-1.716
47	19	16.96	2.042
48	18	17.08	0.9203
49	14	16.59	-2.585
50	18	16.46	1.54
51	15	15.59	-0.5862
52	17	16.9	0.09966
53	13	16.71	-3.714
54	19	17.33	1.67
55	18	17.7	0.301
56	15	14.91	0.08584
57	20	17.33	2.673
58	19	16.9	2.1
59	18	17.39	0.6106
60	15	16.83	-1.834
61	20	18.14	1.864
62	17	17.82	-0.8247
63	19	17.45	1.55
64	20	17.57	2.433
65	18	16.96	1.042
66	17	16.33	0.6688
67	18	16.52	1.476
68	17	16.52	0.4812
69	20	18.26	1.738
70	16	16.22	-0.2182
71	14	16.59	-2.589
72	15	16.21	-1.215
73	20	17.02	2.983
74	17	16.15	0.8501
75	17	16.09	0.9147
76	18	14.66	3.342
77	20	16.83	3.168
78	16	15.96	0.03949
79	18	17.71	0.2937
80	15	17.45	-2.452
81	18	18.5	-0.505
82	20	18.2	1.805
83	14	17.08	-3.083
84	15	15.84	-0.8385
85	17	16.9	0.1033
86	18	17.52	0.485
87	20	17.2	2.795
88	17	16.64	0.3556
89	16	16.96	-0.9577
90	11	16.09	-5.093
91	15	17.02	-2.024
92	18	17.21	0.7928
93	16	17.77	-1.767
94	18	16.84	1.161
95	15	16.59	-1.587
96	17	15.97	1.028
97	19	17.88	1.118
98	16	16.96	-0.9577
99	14	16.09	-2.089

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.624	0.7519	0.376
8	0.5756	0.8488	0.4244
9	0.698	0.604	0.302
10	0.8014	0.3971	0.1986
11	0.746	0.5081	0.254
12	0.7518	0.4963	0.2482
13	0.6755	0.649	0.3245
14	0.6009	0.7981	0.3991
15	0.6119	0.7762	0.3881
16	0.5248	0.9505	0.4752
17	0.4583	0.9166	0.5417
18	0.4446	0.8892	0.5554
19	0.4489	0.8979	0.5511
20	0.3917	0.7833	0.6083
21	0.372	0.7441	0.628
22	0.3225	0.645	0.6775
23	0.2991	0.5982	0.7009
24	0.2471	0.4942	0.7529
25	0.2372	0.4744	0.7628
26	0.2017	0.4033	0.7983
27	0.9863	0.02747	0.01374
28	0.9808	0.03835	0.01917
29	0.9735	0.05295	0.02648
30	0.9634	0.07311	0.03655
31	0.9508	0.09847	0.04924
32	0.9588	0.08237	0.04118
33	0.9463	0.1074	0.05372
34	0.9597	0.08059	0.0403
35	0.9469	0.1061	0.05307
36	0.9923	0.01535	0.007675
37	0.9893	0.02134	0.01067
38	0.9844	0.03121	0.01561
39	0.9839	0.03218	0.01609
40	0.9827	0.03465	0.01733
41	0.9757	0.04868	0.02434
42	0.9736	0.05274	0.02637
43	0.9643	0.07138	0.03569
44	0.9876	0.0247	0.01235
45	0.9822	0.03555	0.01778
46	0.9794	0.04117	0.02058
47	0.9797	0.04058	0.02029
48	0.9731	0.05375	0.02687
49	0.9778	0.04441	0.0222
50	0.9752	0.04967	0.02483
51	0.9685	0.06303	0.03152
52	0.9583	0.08338	0.04169
53	0.98	0.0399	0.01995
54	0.9775	0.045	0.0225
55	0.9717	0.05667	0.02833
56	0.9619	0.07617	0.03808
57	0.9668	0.0663	0.03315
58	0.9732	0.0535	0.02675
59	0.9631	0.07378	0.03689
60	0.965	0.07005	0.03503
61	0.9604	0.07925	0.03963
62	0.9494	0.1012	0.05061
63	0.9383	0.1235	0.06174
64	0.9348	0.1304	0.06522
65	0.9172	0.1657	0.08283
66	0.8909	0.2182	0.1091
67	0.8757	0.2485	0.1243
68	0.8469	0.3061	0.1531
69	0.8577	0.2845	0.1423
70	0.8381	0.3238	0.1619
71	0.8308	0.3384	0.1692
72	0.7907	0.4186	0.2093
73	0.8242	0.3515	0.1758
74	0.7783	0.4434	0.2217
75	0.7283	0.5434	0.2717
76	0.8236	0.3529	0.1764
77	0.8638	0.2724	0.1362
78	0.8376	0.3249	0.1624
79	0.7852	0.4296	0.2148
80	0.7948	0.4104	0.2052
81	0.8175	0.365	0.1825
82	0.7598	0.4804	0.2402
83	0.7729	0.4543	0.2271
84	0.7139	0.5722	0.2861
85	0.6255	0.749	0.3745
86	0.5351	0.9299	0.4649
87	0.5456	0.9088	0.4544
88	0.4885	0.9771	0.5115
89	0.3731	0.7461	0.6269
90	0.647	0.7059	0.353
91	0.612	0.776	0.388
92	0.6172	0.7656	0.3828

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.624 &  0.7519 &  0.376 \tabularnewline
8 &  0.5756 &  0.8488 &  0.4244 \tabularnewline
9 &  0.698 &  0.604 &  0.302 \tabularnewline
10 &  0.8014 &  0.3971 &  0.1986 \tabularnewline
11 &  0.746 &  0.5081 &  0.254 \tabularnewline
12 &  0.7518 &  0.4963 &  0.2482 \tabularnewline
13 &  0.6755 &  0.649 &  0.3245 \tabularnewline
14 &  0.6009 &  0.7981 &  0.3991 \tabularnewline
15 &  0.6119 &  0.7762 &  0.3881 \tabularnewline
16 &  0.5248 &  0.9505 &  0.4752 \tabularnewline
17 &  0.4583 &  0.9166 &  0.5417 \tabularnewline
18 &  0.4446 &  0.8892 &  0.5554 \tabularnewline
19 &  0.4489 &  0.8979 &  0.5511 \tabularnewline
20 &  0.3917 &  0.7833 &  0.6083 \tabularnewline
21 &  0.372 &  0.7441 &  0.628 \tabularnewline
22 &  0.3225 &  0.645 &  0.6775 \tabularnewline
23 &  0.2991 &  0.5982 &  0.7009 \tabularnewline
24 &  0.2471 &  0.4942 &  0.7529 \tabularnewline
25 &  0.2372 &  0.4744 &  0.7628 \tabularnewline
26 &  0.2017 &  0.4033 &  0.7983 \tabularnewline
27 &  0.9863 &  0.02747 &  0.01374 \tabularnewline
28 &  0.9808 &  0.03835 &  0.01917 \tabularnewline
29 &  0.9735 &  0.05295 &  0.02648 \tabularnewline
30 &  0.9634 &  0.07311 &  0.03655 \tabularnewline
31 &  0.9508 &  0.09847 &  0.04924 \tabularnewline
32 &  0.9588 &  0.08237 &  0.04118 \tabularnewline
33 &  0.9463 &  0.1074 &  0.05372 \tabularnewline
34 &  0.9597 &  0.08059 &  0.0403 \tabularnewline
35 &  0.9469 &  0.1061 &  0.05307 \tabularnewline
36 &  0.9923 &  0.01535 &  0.007675 \tabularnewline
37 &  0.9893 &  0.02134 &  0.01067 \tabularnewline
38 &  0.9844 &  0.03121 &  0.01561 \tabularnewline
39 &  0.9839 &  0.03218 &  0.01609 \tabularnewline
40 &  0.9827 &  0.03465 &  0.01733 \tabularnewline
41 &  0.9757 &  0.04868 &  0.02434 \tabularnewline
42 &  0.9736 &  0.05274 &  0.02637 \tabularnewline
43 &  0.9643 &  0.07138 &  0.03569 \tabularnewline
44 &  0.9876 &  0.0247 &  0.01235 \tabularnewline
45 &  0.9822 &  0.03555 &  0.01778 \tabularnewline
46 &  0.9794 &  0.04117 &  0.02058 \tabularnewline
47 &  0.9797 &  0.04058 &  0.02029 \tabularnewline
48 &  0.9731 &  0.05375 &  0.02687 \tabularnewline
49 &  0.9778 &  0.04441 &  0.0222 \tabularnewline
50 &  0.9752 &  0.04967 &  0.02483 \tabularnewline
51 &  0.9685 &  0.06303 &  0.03152 \tabularnewline
52 &  0.9583 &  0.08338 &  0.04169 \tabularnewline
53 &  0.98 &  0.0399 &  0.01995 \tabularnewline
54 &  0.9775 &  0.045 &  0.0225 \tabularnewline
55 &  0.9717 &  0.05667 &  0.02833 \tabularnewline
56 &  0.9619 &  0.07617 &  0.03808 \tabularnewline
57 &  0.9668 &  0.0663 &  0.03315 \tabularnewline
58 &  0.9732 &  0.0535 &  0.02675 \tabularnewline
59 &  0.9631 &  0.07378 &  0.03689 \tabularnewline
60 &  0.965 &  0.07005 &  0.03503 \tabularnewline
61 &  0.9604 &  0.07925 &  0.03963 \tabularnewline
62 &  0.9494 &  0.1012 &  0.05061 \tabularnewline
63 &  0.9383 &  0.1235 &  0.06174 \tabularnewline
64 &  0.9348 &  0.1304 &  0.06522 \tabularnewline
65 &  0.9172 &  0.1657 &  0.08283 \tabularnewline
66 &  0.8909 &  0.2182 &  0.1091 \tabularnewline
67 &  0.8757 &  0.2485 &  0.1243 \tabularnewline
68 &  0.8469 &  0.3061 &  0.1531 \tabularnewline
69 &  0.8577 &  0.2845 &  0.1423 \tabularnewline
70 &  0.8381 &  0.3238 &  0.1619 \tabularnewline
71 &  0.8308 &  0.3384 &  0.1692 \tabularnewline
72 &  0.7907 &  0.4186 &  0.2093 \tabularnewline
73 &  0.8242 &  0.3515 &  0.1758 \tabularnewline
74 &  0.7783 &  0.4434 &  0.2217 \tabularnewline
75 &  0.7283 &  0.5434 &  0.2717 \tabularnewline
76 &  0.8236 &  0.3529 &  0.1764 \tabularnewline
77 &  0.8638 &  0.2724 &  0.1362 \tabularnewline
78 &  0.8376 &  0.3249 &  0.1624 \tabularnewline
79 &  0.7852 &  0.4296 &  0.2148 \tabularnewline
80 &  0.7948 &  0.4104 &  0.2052 \tabularnewline
81 &  0.8175 &  0.365 &  0.1825 \tabularnewline
82 &  0.7598 &  0.4804 &  0.2402 \tabularnewline
83 &  0.7729 &  0.4543 &  0.2271 \tabularnewline
84 &  0.7139 &  0.5722 &  0.2861 \tabularnewline
85 &  0.6255 &  0.749 &  0.3745 \tabularnewline
86 &  0.5351 &  0.9299 &  0.4649 \tabularnewline
87 &  0.5456 &  0.9088 &  0.4544 \tabularnewline
88 &  0.4885 &  0.9771 &  0.5115 \tabularnewline
89 &  0.3731 &  0.7461 &  0.6269 \tabularnewline
90 &  0.647 &  0.7059 &  0.353 \tabularnewline
91 &  0.612 &  0.776 &  0.388 \tabularnewline
92 &  0.6172 &  0.7656 &  0.3828 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304574&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]7[/C][C] 0.624[/C][C] 0.7519[/C][C] 0.376[/C][/ROW]
[ROW][C]8[/C][C] 0.5756[/C][C] 0.8488[/C][C] 0.4244[/C][/ROW]
[ROW][C]9[/C][C] 0.698[/C][C] 0.604[/C][C] 0.302[/C][/ROW]
[ROW][C]10[/C][C] 0.8014[/C][C] 0.3971[/C][C] 0.1986[/C][/ROW]
[ROW][C]11[/C][C] 0.746[/C][C] 0.5081[/C][C] 0.254[/C][/ROW]
[ROW][C]12[/C][C] 0.7518[/C][C] 0.4963[/C][C] 0.2482[/C][/ROW]
[ROW][C]13[/C][C] 0.6755[/C][C] 0.649[/C][C] 0.3245[/C][/ROW]
[ROW][C]14[/C][C] 0.6009[/C][C] 0.7981[/C][C] 0.3991[/C][/ROW]
[ROW][C]15[/C][C] 0.6119[/C][C] 0.7762[/C][C] 0.3881[/C][/ROW]
[ROW][C]16[/C][C] 0.5248[/C][C] 0.9505[/C][C] 0.4752[/C][/ROW]
[ROW][C]17[/C][C] 0.4583[/C][C] 0.9166[/C][C] 0.5417[/C][/ROW]
[ROW][C]18[/C][C] 0.4446[/C][C] 0.8892[/C][C] 0.5554[/C][/ROW]
[ROW][C]19[/C][C] 0.4489[/C][C] 0.8979[/C][C] 0.5511[/C][/ROW]
[ROW][C]20[/C][C] 0.3917[/C][C] 0.7833[/C][C] 0.6083[/C][/ROW]
[ROW][C]21[/C][C] 0.372[/C][C] 0.7441[/C][C] 0.628[/C][/ROW]
[ROW][C]22[/C][C] 0.3225[/C][C] 0.645[/C][C] 0.6775[/C][/ROW]
[ROW][C]23[/C][C] 0.2991[/C][C] 0.5982[/C][C] 0.7009[/C][/ROW]
[ROW][C]24[/C][C] 0.2471[/C][C] 0.4942[/C][C] 0.7529[/C][/ROW]
[ROW][C]25[/C][C] 0.2372[/C][C] 0.4744[/C][C] 0.7628[/C][/ROW]
[ROW][C]26[/C][C] 0.2017[/C][C] 0.4033[/C][C] 0.7983[/C][/ROW]
[ROW][C]27[/C][C] 0.9863[/C][C] 0.02747[/C][C] 0.01374[/C][/ROW]
[ROW][C]28[/C][C] 0.9808[/C][C] 0.03835[/C][C] 0.01917[/C][/ROW]
[ROW][C]29[/C][C] 0.9735[/C][C] 0.05295[/C][C] 0.02648[/C][/ROW]
[ROW][C]30[/C][C] 0.9634[/C][C] 0.07311[/C][C] 0.03655[/C][/ROW]
[ROW][C]31[/C][C] 0.9508[/C][C] 0.09847[/C][C] 0.04924[/C][/ROW]
[ROW][C]32[/C][C] 0.9588[/C][C] 0.08237[/C][C] 0.04118[/C][/ROW]
[ROW][C]33[/C][C] 0.9463[/C][C] 0.1074[/C][C] 0.05372[/C][/ROW]
[ROW][C]34[/C][C] 0.9597[/C][C] 0.08059[/C][C] 0.0403[/C][/ROW]
[ROW][C]35[/C][C] 0.9469[/C][C] 0.1061[/C][C] 0.05307[/C][/ROW]
[ROW][C]36[/C][C] 0.9923[/C][C] 0.01535[/C][C] 0.007675[/C][/ROW]
[ROW][C]37[/C][C] 0.9893[/C][C] 0.02134[/C][C] 0.01067[/C][/ROW]
[ROW][C]38[/C][C] 0.9844[/C][C] 0.03121[/C][C] 0.01561[/C][/ROW]
[ROW][C]39[/C][C] 0.9839[/C][C] 0.03218[/C][C] 0.01609[/C][/ROW]
[ROW][C]40[/C][C] 0.9827[/C][C] 0.03465[/C][C] 0.01733[/C][/ROW]
[ROW][C]41[/C][C] 0.9757[/C][C] 0.04868[/C][C] 0.02434[/C][/ROW]
[ROW][C]42[/C][C] 0.9736[/C][C] 0.05274[/C][C] 0.02637[/C][/ROW]
[ROW][C]43[/C][C] 0.9643[/C][C] 0.07138[/C][C] 0.03569[/C][/ROW]
[ROW][C]44[/C][C] 0.9876[/C][C] 0.0247[/C][C] 0.01235[/C][/ROW]
[ROW][C]45[/C][C] 0.9822[/C][C] 0.03555[/C][C] 0.01778[/C][/ROW]
[ROW][C]46[/C][C] 0.9794[/C][C] 0.04117[/C][C] 0.02058[/C][/ROW]
[ROW][C]47[/C][C] 0.9797[/C][C] 0.04058[/C][C] 0.02029[/C][/ROW]
[ROW][C]48[/C][C] 0.9731[/C][C] 0.05375[/C][C] 0.02687[/C][/ROW]
[ROW][C]49[/C][C] 0.9778[/C][C] 0.04441[/C][C] 0.0222[/C][/ROW]
[ROW][C]50[/C][C] 0.9752[/C][C] 0.04967[/C][C] 0.02483[/C][/ROW]
[ROW][C]51[/C][C] 0.9685[/C][C] 0.06303[/C][C] 0.03152[/C][/ROW]
[ROW][C]52[/C][C] 0.9583[/C][C] 0.08338[/C][C] 0.04169[/C][/ROW]
[ROW][C]53[/C][C] 0.98[/C][C] 0.0399[/C][C] 0.01995[/C][/ROW]
[ROW][C]54[/C][C] 0.9775[/C][C] 0.045[/C][C] 0.0225[/C][/ROW]
[ROW][C]55[/C][C] 0.9717[/C][C] 0.05667[/C][C] 0.02833[/C][/ROW]
[ROW][C]56[/C][C] 0.9619[/C][C] 0.07617[/C][C] 0.03808[/C][/ROW]
[ROW][C]57[/C][C] 0.9668[/C][C] 0.0663[/C][C] 0.03315[/C][/ROW]
[ROW][C]58[/C][C] 0.9732[/C][C] 0.0535[/C][C] 0.02675[/C][/ROW]
[ROW][C]59[/C][C] 0.9631[/C][C] 0.07378[/C][C] 0.03689[/C][/ROW]
[ROW][C]60[/C][C] 0.965[/C][C] 0.07005[/C][C] 0.03503[/C][/ROW]
[ROW][C]61[/C][C] 0.9604[/C][C] 0.07925[/C][C] 0.03963[/C][/ROW]
[ROW][C]62[/C][C] 0.9494[/C][C] 0.1012[/C][C] 0.05061[/C][/ROW]
[ROW][C]63[/C][C] 0.9383[/C][C] 0.1235[/C][C] 0.06174[/C][/ROW]
[ROW][C]64[/C][C] 0.9348[/C][C] 0.1304[/C][C] 0.06522[/C][/ROW]
[ROW][C]65[/C][C] 0.9172[/C][C] 0.1657[/C][C] 0.08283[/C][/ROW]
[ROW][C]66[/C][C] 0.8909[/C][C] 0.2182[/C][C] 0.1091[/C][/ROW]
[ROW][C]67[/C][C] 0.8757[/C][C] 0.2485[/C][C] 0.1243[/C][/ROW]
[ROW][C]68[/C][C] 0.8469[/C][C] 0.3061[/C][C] 0.1531[/C][/ROW]
[ROW][C]69[/C][C] 0.8577[/C][C] 0.2845[/C][C] 0.1423[/C][/ROW]
[ROW][C]70[/C][C] 0.8381[/C][C] 0.3238[/C][C] 0.1619[/C][/ROW]
[ROW][C]71[/C][C] 0.8308[/C][C] 0.3384[/C][C] 0.1692[/C][/ROW]
[ROW][C]72[/C][C] 0.7907[/C][C] 0.4186[/C][C] 0.2093[/C][/ROW]
[ROW][C]73[/C][C] 0.8242[/C][C] 0.3515[/C][C] 0.1758[/C][/ROW]
[ROW][C]74[/C][C] 0.7783[/C][C] 0.4434[/C][C] 0.2217[/C][/ROW]
[ROW][C]75[/C][C] 0.7283[/C][C] 0.5434[/C][C] 0.2717[/C][/ROW]
[ROW][C]76[/C][C] 0.8236[/C][C] 0.3529[/C][C] 0.1764[/C][/ROW]
[ROW][C]77[/C][C] 0.8638[/C][C] 0.2724[/C][C] 0.1362[/C][/ROW]
[ROW][C]78[/C][C] 0.8376[/C][C] 0.3249[/C][C] 0.1624[/C][/ROW]
[ROW][C]79[/C][C] 0.7852[/C][C] 0.4296[/C][C] 0.2148[/C][/ROW]
[ROW][C]80[/C][C] 0.7948[/C][C] 0.4104[/C][C] 0.2052[/C][/ROW]
[ROW][C]81[/C][C] 0.8175[/C][C] 0.365[/C][C] 0.1825[/C][/ROW]
[ROW][C]82[/C][C] 0.7598[/C][C] 0.4804[/C][C] 0.2402[/C][/ROW]
[ROW][C]83[/C][C] 0.7729[/C][C] 0.4543[/C][C] 0.2271[/C][/ROW]
[ROW][C]84[/C][C] 0.7139[/C][C] 0.5722[/C][C] 0.2861[/C][/ROW]
[ROW][C]85[/C][C] 0.6255[/C][C] 0.749[/C][C] 0.3745[/C][/ROW]
[ROW][C]86[/C][C] 0.5351[/C][C] 0.9299[/C][C] 0.4649[/C][/ROW]
[ROW][C]87[/C][C] 0.5456[/C][C] 0.9088[/C][C] 0.4544[/C][/ROW]
[ROW][C]88[/C][C] 0.4885[/C][C] 0.9771[/C][C] 0.5115[/C][/ROW]
[ROW][C]89[/C][C] 0.3731[/C][C] 0.7461[/C][C] 0.6269[/C][/ROW]
[ROW][C]90[/C][C] 0.647[/C][C] 0.7059[/C][C] 0.353[/C][/ROW]
[ROW][C]91[/C][C] 0.612[/C][C] 0.776[/C][C] 0.388[/C][/ROW]
[ROW][C]92[/C][C] 0.6172[/C][C] 0.7656[/C][C] 0.3828[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304574&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304574&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
7	0.624	0.7519	0.376
8	0.5756	0.8488	0.4244
9	0.698	0.604	0.302
10	0.8014	0.3971	0.1986
11	0.746	0.5081	0.254
12	0.7518	0.4963	0.2482
13	0.6755	0.649	0.3245
14	0.6009	0.7981	0.3991
15	0.6119	0.7762	0.3881
16	0.5248	0.9505	0.4752
17	0.4583	0.9166	0.5417
18	0.4446	0.8892	0.5554
19	0.4489	0.8979	0.5511
20	0.3917	0.7833	0.6083
21	0.372	0.7441	0.628
22	0.3225	0.645	0.6775
23	0.2991	0.5982	0.7009
24	0.2471	0.4942	0.7529
25	0.2372	0.4744	0.7628
26	0.2017	0.4033	0.7983
27	0.9863	0.02747	0.01374
28	0.9808	0.03835	0.01917
29	0.9735	0.05295	0.02648
30	0.9634	0.07311	0.03655
31	0.9508	0.09847	0.04924
32	0.9588	0.08237	0.04118
33	0.9463	0.1074	0.05372
34	0.9597	0.08059	0.0403
35	0.9469	0.1061	0.05307
36	0.9923	0.01535	0.007675
37	0.9893	0.02134	0.01067
38	0.9844	0.03121	0.01561
39	0.9839	0.03218	0.01609
40	0.9827	0.03465	0.01733
41	0.9757	0.04868	0.02434
42	0.9736	0.05274	0.02637
43	0.9643	0.07138	0.03569
44	0.9876	0.0247	0.01235
45	0.9822	0.03555	0.01778
46	0.9794	0.04117	0.02058
47	0.9797	0.04058	0.02029
48	0.9731	0.05375	0.02687
49	0.9778	0.04441	0.0222
50	0.9752	0.04967	0.02483
51	0.9685	0.06303	0.03152
52	0.9583	0.08338	0.04169
53	0.98	0.0399	0.01995
54	0.9775	0.045	0.0225
55	0.9717	0.05667	0.02833
56	0.9619	0.07617	0.03808
57	0.9668	0.0663	0.03315
58	0.9732	0.0535	0.02675
59	0.9631	0.07378	0.03689
60	0.965	0.07005	0.03503
61	0.9604	0.07925	0.03963
62	0.9494	0.1012	0.05061
63	0.9383	0.1235	0.06174
64	0.9348	0.1304	0.06522
65	0.9172	0.1657	0.08283
66	0.8909	0.2182	0.1091
67	0.8757	0.2485	0.1243
68	0.8469	0.3061	0.1531
69	0.8577	0.2845	0.1423
70	0.8381	0.3238	0.1619
71	0.8308	0.3384	0.1692
72	0.7907	0.4186	0.2093
73	0.8242	0.3515	0.1758
74	0.7783	0.4434	0.2217
75	0.7283	0.5434	0.2717
76	0.8236	0.3529	0.1764
77	0.8638	0.2724	0.1362
78	0.8376	0.3249	0.1624
79	0.7852	0.4296	0.2148
80	0.7948	0.4104	0.2052
81	0.8175	0.365	0.1825
82	0.7598	0.4804	0.2402
83	0.7729	0.4543	0.2271
84	0.7139	0.5722	0.2861
85	0.6255	0.749	0.3745
86	0.5351	0.9299	0.4649
87	0.5456	0.9088	0.4544
88	0.4885	0.9771	0.5115
89	0.3731	0.7461	0.6269
90	0.647	0.7059	0.353
91	0.612	0.776	0.388
92	0.6172	0.7656	0.3828

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	16	0.186047	NOK
10% type I error level	33	0.383721	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 & 0 &  0 & OK \tabularnewline
5% type I error level & 16 & 0.186047 & NOK \tabularnewline
10% type I error level & 33 & 0.383721 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304574&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]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.186047[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.383721[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304574&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304574&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	0	0	OK
5% type I error level	16	0.186047	NOK
10% type I error level	33	0.383721	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.0868, df1 = 2, df2 = 93, p-value = 0.3415
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3994, df1 = 6, df2 = 89, p-value = 0.2237
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.2832, df1 = 2, df2 = 93, p-value = 0.282

\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 = 1.0868, df1 = 2, df2 = 93, p-value = 0.3415
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3994, df1 = 6, df2 = 89, p-value = 0.2237
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.2832, df1 = 2, df2 = 93, p-value = 0.282
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304574&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 = 1.0868, df1 = 2, df2 = 93, p-value = 0.3415
[/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 = 1.3994, df1 = 6, df2 = 89, p-value = 0.2237
[/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 = 1.2832, df1 = 2, df2 = 93, p-value = 0.282
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304574&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304574&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 = 1.0868, df1 = 2, df2 = 93, p-value = 0.3415
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3994, df1 = 6, df2 = 89, p-value = 0.2237
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.2832, df1 = 2, df2 = 93, p-value = 0.282

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd TVDC SKEOUSUM 1.002095 1.276005 1.277438

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd          TVDC      SKEOUSUM 
     1.002095      1.276005      1.277438 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304574&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Bevr_Leeftijd          TVDC      SKEOUSUM 
     1.002095      1.276005      1.277438 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304574&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304574&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 Bevr_Leeftijd TVDC SKEOUSUM 1.002095 1.276005 1.277438

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Parameters (Session):

Parameters (R input):

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

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