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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 19 Dec 2016 15:35:18 +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/19/t1482158161b9os09vqrtz8xgt.htm/, Retrieved Fri, 17 May 2024 16:08:50 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301370, Retrieved Fri, 17 May 2024 16:08:50 +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] [Multiple regression ] [2016-12-19 14:35:18] [9fb47d69755d1f4b66b6f2591280f9e0] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301370&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]

7 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301370&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301370&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	7 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
a[t] = + 6.47204 + 0.653687b[t] + 1.22044d[t] + 0.48177e[t] -0.00265787t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  +  6.47204 +  0.653687b[t] +  1.22044d[t] +  0.48177e[t] -0.00265787t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301370&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  +  6.47204 +  0.653687b[t] +  1.22044d[t] +  0.48177e[t] -0.00265787t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301370&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301370&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
a[t] = + 6.47204 + 0.653687b[t] + 1.22044d[t] + 0.48177e[t] -0.00265787t + 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)	+6.472	1.352	+4.7860e+00	6.081e-06	3.041e-06
b	+0.6537	0.2119	+3.0850e+00	0.002649	0.001325
d	+1.22	0.2308	+5.2880e+00	7.628e-07	3.814e-07
e	+0.4818	0.2834	+1.7000e+00	0.0923	0.04615
t	-0.002658	0.005131	-5.1800e-01	0.6057	0.3028

\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) & +6.472 &  1.352 & +4.7860e+00 &  6.081e-06 &  3.041e-06 \tabularnewline
b & +0.6537 &  0.2119 & +3.0850e+00 &  0.002649 &  0.001325 \tabularnewline
d & +1.22 &  0.2308 & +5.2880e+00 &  7.628e-07 &  3.814e-07 \tabularnewline
e & +0.4818 &  0.2834 & +1.7000e+00 &  0.0923 &  0.04615 \tabularnewline
t & -0.002658 &  0.005131 & -5.1800e-01 &  0.6057 &  0.3028 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301370&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]+6.472[/C][C] 1.352[/C][C]+4.7860e+00[/C][C] 6.081e-06[/C][C] 3.041e-06[/C][/ROW]
[ROW][C]b[/C][C]+0.6537[/C][C] 0.2119[/C][C]+3.0850e+00[/C][C] 0.002649[/C][C] 0.001325[/C][/ROW]
[ROW][C]d[/C][C]+1.22[/C][C] 0.2308[/C][C]+5.2880e+00[/C][C] 7.628e-07[/C][C] 3.814e-07[/C][/ROW]
[ROW][C]e[/C][C]+0.4818[/C][C] 0.2834[/C][C]+1.7000e+00[/C][C] 0.0923[/C][C] 0.04615[/C][/ROW]
[ROW][C]t[/C][C]-0.002658[/C][C] 0.005131[/C][C]-5.1800e-01[/C][C] 0.6057[/C][C] 0.3028[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301370&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301370&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)	+6.472	1.352	+4.7860e+00	6.081e-06	3.041e-06
b	+0.6537	0.2119	+3.0850e+00	0.002649	0.001325
d	+1.22	0.2308	+5.2880e+00	7.628e-07	3.814e-07
e	+0.4818	0.2834	+1.7000e+00	0.0923	0.04615
t	-0.002658	0.005131	-5.1800e-01	0.6057	0.3028

Multiple Linear Regression - Regression Statistics
Multiple R	0.6136
R-squared	0.3764
Adjusted R-squared	0.3507
F-TEST (value)	14.64
F-TEST (DF numerator)	4
F-TEST (DF denominator)	97
p-value	2.168e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.511
Sum Squared Residuals	221.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6136 \tabularnewline
R-squared &  0.3764 \tabularnewline
Adjusted R-squared &  0.3507 \tabularnewline
F-TEST (value) &  14.64 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 97 \tabularnewline
p-value &  2.168e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.511 \tabularnewline
Sum Squared Residuals &  221.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301370&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6136[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3764[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3507[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 14.64[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]97[/C][/ROW]
[ROW][C]p-value[/C][C] 2.168e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.511[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 221.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301370&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301370&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	0.6136
R-squared	0.3764
Adjusted R-squared	0.3507
F-TEST (value)	14.64
F-TEST (DF numerator)	4
F-TEST (DF denominator)	97
p-value	2.168e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.511
Sum Squared Residuals	221.6

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	12.97	0.02969
2	16	15.32	0.6765
3	17	15.89	1.112
4	16	15.23	0.7687
5	17	17.1	-0.1028
6	17	15.88	1.12
7	15	15.4	-0.3952
8	16	15.39	0.6074
9	14	14	0.002433
10	16	15.87	0.131
11	17	15.21	1.787
12	16	14.73	1.272
13	16	15.86	0.1389
14	16	14.72	1.277
15	15	15.86	-0.8557
16	16	14.55	1.454
17	16	16.5	-0.5041
18	13	14.63	-1.627
19	15	17.07	-2.066
20	17	16.5	0.5039
21	13	13.31	-0.312
22	17	17.06	-0.05758
23	14	14.13	-0.1323
24	14	14.61	-0.6114
25	18	15.83	2.171
26	17	17.05	-0.04695
27	13	13.95	-0.9497
28	16	17.21	-1.214
29	15	15.99	-0.9905
30	15	15.33	-0.3341
31	15	15.9	-0.8982
32	13	15.81	-2.811
33	17	17.03	-0.02834
34	11	14.58	-3.585
35	14	14.1	-0.1004
36	13	15.8	-2.8
37	17	15.14	1.856
38	16	15.31	0.6872
39	17	17.67	-0.6661
40	16	14.48	1.518
41	16	15.79	0.2134
42	16	15.13	0.8697
43	15	15.78	-0.7813
44	12	13.34	-1.338
45	17	15.29	1.706
46	14	15.29	-1.292
47	14	15.94	-1.943
48	16	14.63	1.367
49	15	15.11	-0.1117
50	16	15.76	0.2373
51	14	14.62	-0.6246
52	15	13.88	1.117
53	17	14.53	2.466
54	10	13.88	-3.878
55	17	15.75	1.251
56	20	16.4	3.6
57	17	16.88	0.1204
58	18	15.74	2.259
59	14	12.82	1.184
60	17	15.74	1.264
61	17	16.95	0.04608
62	16	15.73	0.2692
63	18	16.38	1.618
64	18	16.86	1.139
65	16	16.94	-0.9433
66	15	15.72	-0.7202
67	13	16.37	-3.371
68	16	15.71	0.2851
69	12	13.36	-1.356
70	16	15.06	0.9441
71	16	15.71	0.2931
72	16	16.36	-0.3579
73	14	15.7	-1.702
74	15	15.22	-0.2172
75	14	14.56	-0.5608
76	15	15.69	-0.6936
77	15	15.04	-0.03727
78	16	15.21	0.7935
79	11	11.63	-0.6275
80	18	15.85	2.145
81	11	13.81	-2.806
82	18	17.55	0.4482
83	15	16.9	-1.895
84	19	18.03	0.9718
85	17	16.89	0.1099
86	14	15.01	-1.013
87	13	15.66	-2.664
88	17	15.66	1.338
89	14	15.66	-1.659
90	19	15.83	3.172
91	14	14.43	-0.4333
92	16	16.87	-0.8715
93	16	15.17	0.8333
94	15	15.65	-0.6458
95	12	14.51	-2.508
96	17	16.29	0.7059
97	18	15.64	2.362
98	15	13.93	1.067
99	18	15.63	2.368
100	15	17.33	-2.332
101	16	15.63	0.3728
102	16	13.58	2.422

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  12.97 &  0.02969 \tabularnewline
2 &  16 &  15.32 &  0.6765 \tabularnewline
3 &  17 &  15.89 &  1.112 \tabularnewline
4 &  16 &  15.23 &  0.7687 \tabularnewline
5 &  17 &  17.1 & -0.1028 \tabularnewline
6 &  17 &  15.88 &  1.12 \tabularnewline
7 &  15 &  15.4 & -0.3952 \tabularnewline
8 &  16 &  15.39 &  0.6074 \tabularnewline
9 &  14 &  14 &  0.002433 \tabularnewline
10 &  16 &  15.87 &  0.131 \tabularnewline
11 &  17 &  15.21 &  1.787 \tabularnewline
12 &  16 &  14.73 &  1.272 \tabularnewline
13 &  16 &  15.86 &  0.1389 \tabularnewline
14 &  16 &  14.72 &  1.277 \tabularnewline
15 &  15 &  15.86 & -0.8557 \tabularnewline
16 &  16 &  14.55 &  1.454 \tabularnewline
17 &  16 &  16.5 & -0.5041 \tabularnewline
18 &  13 &  14.63 & -1.627 \tabularnewline
19 &  15 &  17.07 & -2.066 \tabularnewline
20 &  17 &  16.5 &  0.5039 \tabularnewline
21 &  13 &  13.31 & -0.312 \tabularnewline
22 &  17 &  17.06 & -0.05758 \tabularnewline
23 &  14 &  14.13 & -0.1323 \tabularnewline
24 &  14 &  14.61 & -0.6114 \tabularnewline
25 &  18 &  15.83 &  2.171 \tabularnewline
26 &  17 &  17.05 & -0.04695 \tabularnewline
27 &  13 &  13.95 & -0.9497 \tabularnewline
28 &  16 &  17.21 & -1.214 \tabularnewline
29 &  15 &  15.99 & -0.9905 \tabularnewline
30 &  15 &  15.33 & -0.3341 \tabularnewline
31 &  15 &  15.9 & -0.8982 \tabularnewline
32 &  13 &  15.81 & -2.811 \tabularnewline
33 &  17 &  17.03 & -0.02834 \tabularnewline
34 &  11 &  14.58 & -3.585 \tabularnewline
35 &  14 &  14.1 & -0.1004 \tabularnewline
36 &  13 &  15.8 & -2.8 \tabularnewline
37 &  17 &  15.14 &  1.856 \tabularnewline
38 &  16 &  15.31 &  0.6872 \tabularnewline
39 &  17 &  17.67 & -0.6661 \tabularnewline
40 &  16 &  14.48 &  1.518 \tabularnewline
41 &  16 &  15.79 &  0.2134 \tabularnewline
42 &  16 &  15.13 &  0.8697 \tabularnewline
43 &  15 &  15.78 & -0.7813 \tabularnewline
44 &  12 &  13.34 & -1.338 \tabularnewline
45 &  17 &  15.29 &  1.706 \tabularnewline
46 &  14 &  15.29 & -1.292 \tabularnewline
47 &  14 &  15.94 & -1.943 \tabularnewline
48 &  16 &  14.63 &  1.367 \tabularnewline
49 &  15 &  15.11 & -0.1117 \tabularnewline
50 &  16 &  15.76 &  0.2373 \tabularnewline
51 &  14 &  14.62 & -0.6246 \tabularnewline
52 &  15 &  13.88 &  1.117 \tabularnewline
53 &  17 &  14.53 &  2.466 \tabularnewline
54 &  10 &  13.88 & -3.878 \tabularnewline
55 &  17 &  15.75 &  1.251 \tabularnewline
56 &  20 &  16.4 &  3.6 \tabularnewline
57 &  17 &  16.88 &  0.1204 \tabularnewline
58 &  18 &  15.74 &  2.259 \tabularnewline
59 &  14 &  12.82 &  1.184 \tabularnewline
60 &  17 &  15.74 &  1.264 \tabularnewline
61 &  17 &  16.95 &  0.04608 \tabularnewline
62 &  16 &  15.73 &  0.2692 \tabularnewline
63 &  18 &  16.38 &  1.618 \tabularnewline
64 &  18 &  16.86 &  1.139 \tabularnewline
65 &  16 &  16.94 & -0.9433 \tabularnewline
66 &  15 &  15.72 & -0.7202 \tabularnewline
67 &  13 &  16.37 & -3.371 \tabularnewline
68 &  16 &  15.71 &  0.2851 \tabularnewline
69 &  12 &  13.36 & -1.356 \tabularnewline
70 &  16 &  15.06 &  0.9441 \tabularnewline
71 &  16 &  15.71 &  0.2931 \tabularnewline
72 &  16 &  16.36 & -0.3579 \tabularnewline
73 &  14 &  15.7 & -1.702 \tabularnewline
74 &  15 &  15.22 & -0.2172 \tabularnewline
75 &  14 &  14.56 & -0.5608 \tabularnewline
76 &  15 &  15.69 & -0.6936 \tabularnewline
77 &  15 &  15.04 & -0.03727 \tabularnewline
78 &  16 &  15.21 &  0.7935 \tabularnewline
79 &  11 &  11.63 & -0.6275 \tabularnewline
80 &  18 &  15.85 &  2.145 \tabularnewline
81 &  11 &  13.81 & -2.806 \tabularnewline
82 &  18 &  17.55 &  0.4482 \tabularnewline
83 &  15 &  16.9 & -1.895 \tabularnewline
84 &  19 &  18.03 &  0.9718 \tabularnewline
85 &  17 &  16.89 &  0.1099 \tabularnewline
86 &  14 &  15.01 & -1.013 \tabularnewline
87 &  13 &  15.66 & -2.664 \tabularnewline
88 &  17 &  15.66 &  1.338 \tabularnewline
89 &  14 &  15.66 & -1.659 \tabularnewline
90 &  19 &  15.83 &  3.172 \tabularnewline
91 &  14 &  14.43 & -0.4333 \tabularnewline
92 &  16 &  16.87 & -0.8715 \tabularnewline
93 &  16 &  15.17 &  0.8333 \tabularnewline
94 &  15 &  15.65 & -0.6458 \tabularnewline
95 &  12 &  14.51 & -2.508 \tabularnewline
96 &  17 &  16.29 &  0.7059 \tabularnewline
97 &  18 &  15.64 &  2.362 \tabularnewline
98 &  15 &  13.93 &  1.067 \tabularnewline
99 &  18 &  15.63 &  2.368 \tabularnewline
100 &  15 &  17.33 & -2.332 \tabularnewline
101 &  16 &  15.63 &  0.3728 \tabularnewline
102 &  16 &  13.58 &  2.422 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301370&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] 12.97[/C][C] 0.02969[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.32[/C][C] 0.6765[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.89[/C][C] 1.112[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.23[/C][C] 0.7687[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 17.1[/C][C]-0.1028[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.88[/C][C] 1.12[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.4[/C][C]-0.3952[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.39[/C][C] 0.6074[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 14[/C][C] 0.002433[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 15.87[/C][C] 0.131[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.21[/C][C] 1.787[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 14.73[/C][C] 1.272[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.86[/C][C] 0.1389[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 14.72[/C][C] 1.277[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.86[/C][C]-0.8557[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 14.55[/C][C] 1.454[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.5[/C][C]-0.5041[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 14.63[/C][C]-1.627[/C][/ROW]
[ROW][C]19[/C][C] 15[/C][C] 17.07[/C][C]-2.066[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.5[/C][C] 0.5039[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 13.31[/C][C]-0.312[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 17.06[/C][C]-0.05758[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 14.13[/C][C]-0.1323[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 14.61[/C][C]-0.6114[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 15.83[/C][C] 2.171[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 17.05[/C][C]-0.04695[/C][/ROW]
[ROW][C]27[/C][C] 13[/C][C] 13.95[/C][C]-0.9497[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 17.21[/C][C]-1.214[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 15.99[/C][C]-0.9905[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 15.33[/C][C]-0.3341[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 15.9[/C][C]-0.8982[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 15.81[/C][C]-2.811[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 17.03[/C][C]-0.02834[/C][/ROW]
[ROW][C]34[/C][C] 11[/C][C] 14.58[/C][C]-3.585[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 14.1[/C][C]-0.1004[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 15.8[/C][C]-2.8[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 15.14[/C][C] 1.856[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.31[/C][C] 0.6872[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 17.67[/C][C]-0.6661[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 14.48[/C][C] 1.518[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 15.79[/C][C] 0.2134[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 15.13[/C][C] 0.8697[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 15.78[/C][C]-0.7813[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 13.34[/C][C]-1.338[/C][/ROW]
[ROW][C]45[/C][C] 17[/C][C] 15.29[/C][C] 1.706[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.29[/C][C]-1.292[/C][/ROW]
[ROW][C]47[/C][C] 14[/C][C] 15.94[/C][C]-1.943[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 14.63[/C][C] 1.367[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 15.11[/C][C]-0.1117[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 15.76[/C][C] 0.2373[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 14.62[/C][C]-0.6246[/C][/ROW]
[ROW][C]52[/C][C] 15[/C][C] 13.88[/C][C] 1.117[/C][/ROW]
[ROW][C]53[/C][C] 17[/C][C] 14.53[/C][C] 2.466[/C][/ROW]
[ROW][C]54[/C][C] 10[/C][C] 13.88[/C][C]-3.878[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 15.75[/C][C] 1.251[/C][/ROW]
[ROW][C]56[/C][C] 20[/C][C] 16.4[/C][C] 3.6[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 16.88[/C][C] 0.1204[/C][/ROW]
[ROW][C]58[/C][C] 18[/C][C] 15.74[/C][C] 2.259[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 12.82[/C][C] 1.184[/C][/ROW]
[ROW][C]60[/C][C] 17[/C][C] 15.74[/C][C] 1.264[/C][/ROW]
[ROW][C]61[/C][C] 17[/C][C] 16.95[/C][C] 0.04608[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 15.73[/C][C] 0.2692[/C][/ROW]
[ROW][C]63[/C][C] 18[/C][C] 16.38[/C][C] 1.618[/C][/ROW]
[ROW][C]64[/C][C] 18[/C][C] 16.86[/C][C] 1.139[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 16.94[/C][C]-0.9433[/C][/ROW]
[ROW][C]66[/C][C] 15[/C][C] 15.72[/C][C]-0.7202[/C][/ROW]
[ROW][C]67[/C][C] 13[/C][C] 16.37[/C][C]-3.371[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.71[/C][C] 0.2851[/C][/ROW]
[ROW][C]69[/C][C] 12[/C][C] 13.36[/C][C]-1.356[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 15.06[/C][C] 0.9441[/C][/ROW]
[ROW][C]71[/C][C] 16[/C][C] 15.71[/C][C] 0.2931[/C][/ROW]
[ROW][C]72[/C][C] 16[/C][C] 16.36[/C][C]-0.3579[/C][/ROW]
[ROW][C]73[/C][C] 14[/C][C] 15.7[/C][C]-1.702[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.22[/C][C]-0.2172[/C][/ROW]
[ROW][C]75[/C][C] 14[/C][C] 14.56[/C][C]-0.5608[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 15.69[/C][C]-0.6936[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 15.04[/C][C]-0.03727[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 15.21[/C][C] 0.7935[/C][/ROW]
[ROW][C]79[/C][C] 11[/C][C] 11.63[/C][C]-0.6275[/C][/ROW]
[ROW][C]80[/C][C] 18[/C][C] 15.85[/C][C] 2.145[/C][/ROW]
[ROW][C]81[/C][C] 11[/C][C] 13.81[/C][C]-2.806[/C][/ROW]
[ROW][C]82[/C][C] 18[/C][C] 17.55[/C][C] 0.4482[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 16.9[/C][C]-1.895[/C][/ROW]
[ROW][C]84[/C][C] 19[/C][C] 18.03[/C][C] 0.9718[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.89[/C][C] 0.1099[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 15.01[/C][C]-1.013[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 15.66[/C][C]-2.664[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.66[/C][C] 1.338[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 15.66[/C][C]-1.659[/C][/ROW]
[ROW][C]90[/C][C] 19[/C][C] 15.83[/C][C] 3.172[/C][/ROW]
[ROW][C]91[/C][C] 14[/C][C] 14.43[/C][C]-0.4333[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 16.87[/C][C]-0.8715[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 15.17[/C][C] 0.8333[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 15.65[/C][C]-0.6458[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 14.51[/C][C]-2.508[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 16.29[/C][C] 0.7059[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 15.64[/C][C] 2.362[/C][/ROW]
[ROW][C]98[/C][C] 15[/C][C] 13.93[/C][C] 1.067[/C][/ROW]
[ROW][C]99[/C][C] 18[/C][C] 15.63[/C][C] 2.368[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 17.33[/C][C]-2.332[/C][/ROW]
[ROW][C]101[/C][C] 16[/C][C] 15.63[/C][C] 0.3728[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 13.58[/C][C] 2.422[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301370&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301370&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	12.97	0.02969
2	16	15.32	0.6765
3	17	15.89	1.112
4	16	15.23	0.7687
5	17	17.1	-0.1028
6	17	15.88	1.12
7	15	15.4	-0.3952
8	16	15.39	0.6074
9	14	14	0.002433
10	16	15.87	0.131
11	17	15.21	1.787
12	16	14.73	1.272
13	16	15.86	0.1389
14	16	14.72	1.277
15	15	15.86	-0.8557
16	16	14.55	1.454
17	16	16.5	-0.5041
18	13	14.63	-1.627
19	15	17.07	-2.066
20	17	16.5	0.5039
21	13	13.31	-0.312
22	17	17.06	-0.05758
23	14	14.13	-0.1323
24	14	14.61	-0.6114
25	18	15.83	2.171
26	17	17.05	-0.04695
27	13	13.95	-0.9497
28	16	17.21	-1.214
29	15	15.99	-0.9905
30	15	15.33	-0.3341
31	15	15.9	-0.8982
32	13	15.81	-2.811
33	17	17.03	-0.02834
34	11	14.58	-3.585
35	14	14.1	-0.1004
36	13	15.8	-2.8
37	17	15.14	1.856
38	16	15.31	0.6872
39	17	17.67	-0.6661
40	16	14.48	1.518
41	16	15.79	0.2134
42	16	15.13	0.8697
43	15	15.78	-0.7813
44	12	13.34	-1.338
45	17	15.29	1.706
46	14	15.29	-1.292
47	14	15.94	-1.943
48	16	14.63	1.367
49	15	15.11	-0.1117
50	16	15.76	0.2373
51	14	14.62	-0.6246
52	15	13.88	1.117
53	17	14.53	2.466
54	10	13.88	-3.878
55	17	15.75	1.251
56	20	16.4	3.6
57	17	16.88	0.1204
58	18	15.74	2.259
59	14	12.82	1.184
60	17	15.74	1.264
61	17	16.95	0.04608
62	16	15.73	0.2692
63	18	16.38	1.618
64	18	16.86	1.139
65	16	16.94	-0.9433
66	15	15.72	-0.7202
67	13	16.37	-3.371
68	16	15.71	0.2851
69	12	13.36	-1.356
70	16	15.06	0.9441
71	16	15.71	0.2931
72	16	16.36	-0.3579
73	14	15.7	-1.702
74	15	15.22	-0.2172
75	14	14.56	-0.5608
76	15	15.69	-0.6936
77	15	15.04	-0.03727
78	16	15.21	0.7935
79	11	11.63	-0.6275
80	18	15.85	2.145
81	11	13.81	-2.806
82	18	17.55	0.4482
83	15	16.9	-1.895
84	19	18.03	0.9718
85	17	16.89	0.1099
86	14	15.01	-1.013
87	13	15.66	-2.664
88	17	15.66	1.338
89	14	15.66	-1.659
90	19	15.83	3.172
91	14	14.43	-0.4333
92	16	16.87	-0.8715
93	16	15.17	0.8333
94	15	15.65	-0.6458
95	12	14.51	-2.508
96	17	16.29	0.7059
97	18	15.64	2.362
98	15	13.93	1.067
99	18	15.63	2.368
100	15	17.33	-2.332
101	16	15.63	0.3728
102	16	13.58	2.422

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.04758	0.09517	0.9524
9	0.05267	0.1053	0.9473
10	0.01975	0.0395	0.9803
11	0.03445	0.06891	0.9655
12	0.0237	0.0474	0.9763
13	0.01226	0.02452	0.9877
14	0.006716	0.01343	0.9933
15	0.00744	0.01488	0.9926
16	0.00366	0.00732	0.9963
17	0.001574	0.003148	0.9984
18	0.001944	0.003888	0.9981
19	0.005085	0.01017	0.9949
20	0.01207	0.02413	0.9879
21	0.007539	0.01508	0.9925
22	0.004288	0.008576	0.9957
23	0.002475	0.00495	0.9975
24	0.001277	0.002554	0.9987
25	0.01104	0.02208	0.989
26	0.006496	0.01299	0.9935
27	0.0045	0.008999	0.9955
28	0.003075	0.006151	0.9969
29	0.001764	0.003528	0.9982
30	0.000969	0.001938	0.999
31	0.0006513	0.001303	0.9993
32	0.001672	0.003343	0.9983
33	0.001181	0.002363	0.9988
34	0.004345	0.008691	0.9957
35	0.004187	0.008374	0.9958
36	0.006023	0.01205	0.994
37	0.02295	0.0459	0.977
38	0.02442	0.04884	0.9756
39	0.01915	0.0383	0.9808
40	0.02408	0.04817	0.9759
41	0.02086	0.04172	0.9791
42	0.01943	0.03885	0.9806
43	0.01361	0.02722	0.9864
44	0.01166	0.02331	0.9883
45	0.01986	0.03972	0.9801
46	0.01629	0.03259	0.9837
47	0.019	0.038	0.981
48	0.02052	0.04103	0.9795
49	0.01445	0.0289	0.9855
50	0.01168	0.02336	0.9883
51	0.008484	0.01697	0.9915
52	0.009122	0.01825	0.9909
53	0.03018	0.06036	0.9698
54	0.1349	0.2698	0.8651
55	0.143	0.286	0.857
56	0.3957	0.7915	0.6043
57	0.3457	0.6914	0.6543
58	0.4345	0.8689	0.5655
59	0.4081	0.8162	0.5919
60	0.4077	0.8153	0.5923
61	0.3677	0.7354	0.6323
62	0.3244	0.6487	0.6756
63	0.3378	0.6756	0.6622
64	0.3296	0.6591	0.6704
65	0.2942	0.5885	0.7058
66	0.2521	0.5042	0.7479
67	0.4551	0.9102	0.5449
68	0.4063	0.8126	0.5937
69	0.3688	0.7376	0.6312
70	0.4205	0.8411	0.5795
71	0.3845	0.769	0.6155
72	0.3263	0.6527	0.6737
73	0.3073	0.6146	0.6927
74	0.2514	0.5028	0.7486
75	0.2157	0.4315	0.7843
76	0.1708	0.3416	0.8292
77	0.1689	0.3378	0.8311
78	0.1651	0.3303	0.8349
79	0.1245	0.2491	0.8755
80	0.1548	0.3096	0.8452
81	0.1903	0.3805	0.8097
82	0.1547	0.3093	0.8453
83	0.1266	0.2532	0.8734
84	0.1162	0.2325	0.8838
85	0.1285	0.2569	0.8715
86	0.09909	0.1982	0.9009
87	0.125	0.2501	0.875
88	0.1375	0.275	0.8625
89	0.1119	0.2238	0.8881
90	0.2665	0.5331	0.7335
91	0.3266	0.6532	0.6734
92	0.261	0.522	0.739
93	0.2679	0.5358	0.7321
94	0.1778	0.3557	0.8222

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.04758 &  0.09517 &  0.9524 \tabularnewline
9 &  0.05267 &  0.1053 &  0.9473 \tabularnewline
10 &  0.01975 &  0.0395 &  0.9803 \tabularnewline
11 &  0.03445 &  0.06891 &  0.9655 \tabularnewline
12 &  0.0237 &  0.0474 &  0.9763 \tabularnewline
13 &  0.01226 &  0.02452 &  0.9877 \tabularnewline
14 &  0.006716 &  0.01343 &  0.9933 \tabularnewline
15 &  0.00744 &  0.01488 &  0.9926 \tabularnewline
16 &  0.00366 &  0.00732 &  0.9963 \tabularnewline
17 &  0.001574 &  0.003148 &  0.9984 \tabularnewline
18 &  0.001944 &  0.003888 &  0.9981 \tabularnewline
19 &  0.005085 &  0.01017 &  0.9949 \tabularnewline
20 &  0.01207 &  0.02413 &  0.9879 \tabularnewline
21 &  0.007539 &  0.01508 &  0.9925 \tabularnewline
22 &  0.004288 &  0.008576 &  0.9957 \tabularnewline
23 &  0.002475 &  0.00495 &  0.9975 \tabularnewline
24 &  0.001277 &  0.002554 &  0.9987 \tabularnewline
25 &  0.01104 &  0.02208 &  0.989 \tabularnewline
26 &  0.006496 &  0.01299 &  0.9935 \tabularnewline
27 &  0.0045 &  0.008999 &  0.9955 \tabularnewline
28 &  0.003075 &  0.006151 &  0.9969 \tabularnewline
29 &  0.001764 &  0.003528 &  0.9982 \tabularnewline
30 &  0.000969 &  0.001938 &  0.999 \tabularnewline
31 &  0.0006513 &  0.001303 &  0.9993 \tabularnewline
32 &  0.001672 &  0.003343 &  0.9983 \tabularnewline
33 &  0.001181 &  0.002363 &  0.9988 \tabularnewline
34 &  0.004345 &  0.008691 &  0.9957 \tabularnewline
35 &  0.004187 &  0.008374 &  0.9958 \tabularnewline
36 &  0.006023 &  0.01205 &  0.994 \tabularnewline
37 &  0.02295 &  0.0459 &  0.977 \tabularnewline
38 &  0.02442 &  0.04884 &  0.9756 \tabularnewline
39 &  0.01915 &  0.0383 &  0.9808 \tabularnewline
40 &  0.02408 &  0.04817 &  0.9759 \tabularnewline
41 &  0.02086 &  0.04172 &  0.9791 \tabularnewline
42 &  0.01943 &  0.03885 &  0.9806 \tabularnewline
43 &  0.01361 &  0.02722 &  0.9864 \tabularnewline
44 &  0.01166 &  0.02331 &  0.9883 \tabularnewline
45 &  0.01986 &  0.03972 &  0.9801 \tabularnewline
46 &  0.01629 &  0.03259 &  0.9837 \tabularnewline
47 &  0.019 &  0.038 &  0.981 \tabularnewline
48 &  0.02052 &  0.04103 &  0.9795 \tabularnewline
49 &  0.01445 &  0.0289 &  0.9855 \tabularnewline
50 &  0.01168 &  0.02336 &  0.9883 \tabularnewline
51 &  0.008484 &  0.01697 &  0.9915 \tabularnewline
52 &  0.009122 &  0.01825 &  0.9909 \tabularnewline
53 &  0.03018 &  0.06036 &  0.9698 \tabularnewline
54 &  0.1349 &  0.2698 &  0.8651 \tabularnewline
55 &  0.143 &  0.286 &  0.857 \tabularnewline
56 &  0.3957 &  0.7915 &  0.6043 \tabularnewline
57 &  0.3457 &  0.6914 &  0.6543 \tabularnewline
58 &  0.4345 &  0.8689 &  0.5655 \tabularnewline
59 &  0.4081 &  0.8162 &  0.5919 \tabularnewline
60 &  0.4077 &  0.8153 &  0.5923 \tabularnewline
61 &  0.3677 &  0.7354 &  0.6323 \tabularnewline
62 &  0.3244 &  0.6487 &  0.6756 \tabularnewline
63 &  0.3378 &  0.6756 &  0.6622 \tabularnewline
64 &  0.3296 &  0.6591 &  0.6704 \tabularnewline
65 &  0.2942 &  0.5885 &  0.7058 \tabularnewline
66 &  0.2521 &  0.5042 &  0.7479 \tabularnewline
67 &  0.4551 &  0.9102 &  0.5449 \tabularnewline
68 &  0.4063 &  0.8126 &  0.5937 \tabularnewline
69 &  0.3688 &  0.7376 &  0.6312 \tabularnewline
70 &  0.4205 &  0.8411 &  0.5795 \tabularnewline
71 &  0.3845 &  0.769 &  0.6155 \tabularnewline
72 &  0.3263 &  0.6527 &  0.6737 \tabularnewline
73 &  0.3073 &  0.6146 &  0.6927 \tabularnewline
74 &  0.2514 &  0.5028 &  0.7486 \tabularnewline
75 &  0.2157 &  0.4315 &  0.7843 \tabularnewline
76 &  0.1708 &  0.3416 &  0.8292 \tabularnewline
77 &  0.1689 &  0.3378 &  0.8311 \tabularnewline
78 &  0.1651 &  0.3303 &  0.8349 \tabularnewline
79 &  0.1245 &  0.2491 &  0.8755 \tabularnewline
80 &  0.1548 &  0.3096 &  0.8452 \tabularnewline
81 &  0.1903 &  0.3805 &  0.8097 \tabularnewline
82 &  0.1547 &  0.3093 &  0.8453 \tabularnewline
83 &  0.1266 &  0.2532 &  0.8734 \tabularnewline
84 &  0.1162 &  0.2325 &  0.8838 \tabularnewline
85 &  0.1285 &  0.2569 &  0.8715 \tabularnewline
86 &  0.09909 &  0.1982 &  0.9009 \tabularnewline
87 &  0.125 &  0.2501 &  0.875 \tabularnewline
88 &  0.1375 &  0.275 &  0.8625 \tabularnewline
89 &  0.1119 &  0.2238 &  0.8881 \tabularnewline
90 &  0.2665 &  0.5331 &  0.7335 \tabularnewline
91 &  0.3266 &  0.6532 &  0.6734 \tabularnewline
92 &  0.261 &  0.522 &  0.739 \tabularnewline
93 &  0.2679 &  0.5358 &  0.7321 \tabularnewline
94 &  0.1778 &  0.3557 &  0.8222 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301370&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]8[/C][C] 0.04758[/C][C] 0.09517[/C][C] 0.9524[/C][/ROW]
[ROW][C]9[/C][C] 0.05267[/C][C] 0.1053[/C][C] 0.9473[/C][/ROW]
[ROW][C]10[/C][C] 0.01975[/C][C] 0.0395[/C][C] 0.9803[/C][/ROW]
[ROW][C]11[/C][C] 0.03445[/C][C] 0.06891[/C][C] 0.9655[/C][/ROW]
[ROW][C]12[/C][C] 0.0237[/C][C] 0.0474[/C][C] 0.9763[/C][/ROW]
[ROW][C]13[/C][C] 0.01226[/C][C] 0.02452[/C][C] 0.9877[/C][/ROW]
[ROW][C]14[/C][C] 0.006716[/C][C] 0.01343[/C][C] 0.9933[/C][/ROW]
[ROW][C]15[/C][C] 0.00744[/C][C] 0.01488[/C][C] 0.9926[/C][/ROW]
[ROW][C]16[/C][C] 0.00366[/C][C] 0.00732[/C][C] 0.9963[/C][/ROW]
[ROW][C]17[/C][C] 0.001574[/C][C] 0.003148[/C][C] 0.9984[/C][/ROW]
[ROW][C]18[/C][C] 0.001944[/C][C] 0.003888[/C][C] 0.9981[/C][/ROW]
[ROW][C]19[/C][C] 0.005085[/C][C] 0.01017[/C][C] 0.9949[/C][/ROW]
[ROW][C]20[/C][C] 0.01207[/C][C] 0.02413[/C][C] 0.9879[/C][/ROW]
[ROW][C]21[/C][C] 0.007539[/C][C] 0.01508[/C][C] 0.9925[/C][/ROW]
[ROW][C]22[/C][C] 0.004288[/C][C] 0.008576[/C][C] 0.9957[/C][/ROW]
[ROW][C]23[/C][C] 0.002475[/C][C] 0.00495[/C][C] 0.9975[/C][/ROW]
[ROW][C]24[/C][C] 0.001277[/C][C] 0.002554[/C][C] 0.9987[/C][/ROW]
[ROW][C]25[/C][C] 0.01104[/C][C] 0.02208[/C][C] 0.989[/C][/ROW]
[ROW][C]26[/C][C] 0.006496[/C][C] 0.01299[/C][C] 0.9935[/C][/ROW]
[ROW][C]27[/C][C] 0.0045[/C][C] 0.008999[/C][C] 0.9955[/C][/ROW]
[ROW][C]28[/C][C] 0.003075[/C][C] 0.006151[/C][C] 0.9969[/C][/ROW]
[ROW][C]29[/C][C] 0.001764[/C][C] 0.003528[/C][C] 0.9982[/C][/ROW]
[ROW][C]30[/C][C] 0.000969[/C][C] 0.001938[/C][C] 0.999[/C][/ROW]
[ROW][C]31[/C][C] 0.0006513[/C][C] 0.001303[/C][C] 0.9993[/C][/ROW]
[ROW][C]32[/C][C] 0.001672[/C][C] 0.003343[/C][C] 0.9983[/C][/ROW]
[ROW][C]33[/C][C] 0.001181[/C][C] 0.002363[/C][C] 0.9988[/C][/ROW]
[ROW][C]34[/C][C] 0.004345[/C][C] 0.008691[/C][C] 0.9957[/C][/ROW]
[ROW][C]35[/C][C] 0.004187[/C][C] 0.008374[/C][C] 0.9958[/C][/ROW]
[ROW][C]36[/C][C] 0.006023[/C][C] 0.01205[/C][C] 0.994[/C][/ROW]
[ROW][C]37[/C][C] 0.02295[/C][C] 0.0459[/C][C] 0.977[/C][/ROW]
[ROW][C]38[/C][C] 0.02442[/C][C] 0.04884[/C][C] 0.9756[/C][/ROW]
[ROW][C]39[/C][C] 0.01915[/C][C] 0.0383[/C][C] 0.9808[/C][/ROW]
[ROW][C]40[/C][C] 0.02408[/C][C] 0.04817[/C][C] 0.9759[/C][/ROW]
[ROW][C]41[/C][C] 0.02086[/C][C] 0.04172[/C][C] 0.9791[/C][/ROW]
[ROW][C]42[/C][C] 0.01943[/C][C] 0.03885[/C][C] 0.9806[/C][/ROW]
[ROW][C]43[/C][C] 0.01361[/C][C] 0.02722[/C][C] 0.9864[/C][/ROW]
[ROW][C]44[/C][C] 0.01166[/C][C] 0.02331[/C][C] 0.9883[/C][/ROW]
[ROW][C]45[/C][C] 0.01986[/C][C] 0.03972[/C][C] 0.9801[/C][/ROW]
[ROW][C]46[/C][C] 0.01629[/C][C] 0.03259[/C][C] 0.9837[/C][/ROW]
[ROW][C]47[/C][C] 0.019[/C][C] 0.038[/C][C] 0.981[/C][/ROW]
[ROW][C]48[/C][C] 0.02052[/C][C] 0.04103[/C][C] 0.9795[/C][/ROW]
[ROW][C]49[/C][C] 0.01445[/C][C] 0.0289[/C][C] 0.9855[/C][/ROW]
[ROW][C]50[/C][C] 0.01168[/C][C] 0.02336[/C][C] 0.9883[/C][/ROW]
[ROW][C]51[/C][C] 0.008484[/C][C] 0.01697[/C][C] 0.9915[/C][/ROW]
[ROW][C]52[/C][C] 0.009122[/C][C] 0.01825[/C][C] 0.9909[/C][/ROW]
[ROW][C]53[/C][C] 0.03018[/C][C] 0.06036[/C][C] 0.9698[/C][/ROW]
[ROW][C]54[/C][C] 0.1349[/C][C] 0.2698[/C][C] 0.8651[/C][/ROW]
[ROW][C]55[/C][C] 0.143[/C][C] 0.286[/C][C] 0.857[/C][/ROW]
[ROW][C]56[/C][C] 0.3957[/C][C] 0.7915[/C][C] 0.6043[/C][/ROW]
[ROW][C]57[/C][C] 0.3457[/C][C] 0.6914[/C][C] 0.6543[/C][/ROW]
[ROW][C]58[/C][C] 0.4345[/C][C] 0.8689[/C][C] 0.5655[/C][/ROW]
[ROW][C]59[/C][C] 0.4081[/C][C] 0.8162[/C][C] 0.5919[/C][/ROW]
[ROW][C]60[/C][C] 0.4077[/C][C] 0.8153[/C][C] 0.5923[/C][/ROW]
[ROW][C]61[/C][C] 0.3677[/C][C] 0.7354[/C][C] 0.6323[/C][/ROW]
[ROW][C]62[/C][C] 0.3244[/C][C] 0.6487[/C][C] 0.6756[/C][/ROW]
[ROW][C]63[/C][C] 0.3378[/C][C] 0.6756[/C][C] 0.6622[/C][/ROW]
[ROW][C]64[/C][C] 0.3296[/C][C] 0.6591[/C][C] 0.6704[/C][/ROW]
[ROW][C]65[/C][C] 0.2942[/C][C] 0.5885[/C][C] 0.7058[/C][/ROW]
[ROW][C]66[/C][C] 0.2521[/C][C] 0.5042[/C][C] 0.7479[/C][/ROW]
[ROW][C]67[/C][C] 0.4551[/C][C] 0.9102[/C][C] 0.5449[/C][/ROW]
[ROW][C]68[/C][C] 0.4063[/C][C] 0.8126[/C][C] 0.5937[/C][/ROW]
[ROW][C]69[/C][C] 0.3688[/C][C] 0.7376[/C][C] 0.6312[/C][/ROW]
[ROW][C]70[/C][C] 0.4205[/C][C] 0.8411[/C][C] 0.5795[/C][/ROW]
[ROW][C]71[/C][C] 0.3845[/C][C] 0.769[/C][C] 0.6155[/C][/ROW]
[ROW][C]72[/C][C] 0.3263[/C][C] 0.6527[/C][C] 0.6737[/C][/ROW]
[ROW][C]73[/C][C] 0.3073[/C][C] 0.6146[/C][C] 0.6927[/C][/ROW]
[ROW][C]74[/C][C] 0.2514[/C][C] 0.5028[/C][C] 0.7486[/C][/ROW]
[ROW][C]75[/C][C] 0.2157[/C][C] 0.4315[/C][C] 0.7843[/C][/ROW]
[ROW][C]76[/C][C] 0.1708[/C][C] 0.3416[/C][C] 0.8292[/C][/ROW]
[ROW][C]77[/C][C] 0.1689[/C][C] 0.3378[/C][C] 0.8311[/C][/ROW]
[ROW][C]78[/C][C] 0.1651[/C][C] 0.3303[/C][C] 0.8349[/C][/ROW]
[ROW][C]79[/C][C] 0.1245[/C][C] 0.2491[/C][C] 0.8755[/C][/ROW]
[ROW][C]80[/C][C] 0.1548[/C][C] 0.3096[/C][C] 0.8452[/C][/ROW]
[ROW][C]81[/C][C] 0.1903[/C][C] 0.3805[/C][C] 0.8097[/C][/ROW]
[ROW][C]82[/C][C] 0.1547[/C][C] 0.3093[/C][C] 0.8453[/C][/ROW]
[ROW][C]83[/C][C] 0.1266[/C][C] 0.2532[/C][C] 0.8734[/C][/ROW]
[ROW][C]84[/C][C] 0.1162[/C][C] 0.2325[/C][C] 0.8838[/C][/ROW]
[ROW][C]85[/C][C] 0.1285[/C][C] 0.2569[/C][C] 0.8715[/C][/ROW]
[ROW][C]86[/C][C] 0.09909[/C][C] 0.1982[/C][C] 0.9009[/C][/ROW]
[ROW][C]87[/C][C] 0.125[/C][C] 0.2501[/C][C] 0.875[/C][/ROW]
[ROW][C]88[/C][C] 0.1375[/C][C] 0.275[/C][C] 0.8625[/C][/ROW]
[ROW][C]89[/C][C] 0.1119[/C][C] 0.2238[/C][C] 0.8881[/C][/ROW]
[ROW][C]90[/C][C] 0.2665[/C][C] 0.5331[/C][C] 0.7335[/C][/ROW]
[ROW][C]91[/C][C] 0.3266[/C][C] 0.6532[/C][C] 0.6734[/C][/ROW]
[ROW][C]92[/C][C] 0.261[/C][C] 0.522[/C][C] 0.739[/C][/ROW]
[ROW][C]93[/C][C] 0.2679[/C][C] 0.5358[/C][C] 0.7321[/C][/ROW]
[ROW][C]94[/C][C] 0.1778[/C][C] 0.3557[/C][C] 0.8222[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301370&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301370&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
8	0.04758	0.09517	0.9524
9	0.05267	0.1053	0.9473
10	0.01975	0.0395	0.9803
11	0.03445	0.06891	0.9655
12	0.0237	0.0474	0.9763
13	0.01226	0.02452	0.9877
14	0.006716	0.01343	0.9933
15	0.00744	0.01488	0.9926
16	0.00366	0.00732	0.9963
17	0.001574	0.003148	0.9984
18	0.001944	0.003888	0.9981
19	0.005085	0.01017	0.9949
20	0.01207	0.02413	0.9879
21	0.007539	0.01508	0.9925
22	0.004288	0.008576	0.9957
23	0.002475	0.00495	0.9975
24	0.001277	0.002554	0.9987
25	0.01104	0.02208	0.989
26	0.006496	0.01299	0.9935
27	0.0045	0.008999	0.9955
28	0.003075	0.006151	0.9969
29	0.001764	0.003528	0.9982
30	0.000969	0.001938	0.999
31	0.0006513	0.001303	0.9993
32	0.001672	0.003343	0.9983
33	0.001181	0.002363	0.9988
34	0.004345	0.008691	0.9957
35	0.004187	0.008374	0.9958
36	0.006023	0.01205	0.994
37	0.02295	0.0459	0.977
38	0.02442	0.04884	0.9756
39	0.01915	0.0383	0.9808
40	0.02408	0.04817	0.9759
41	0.02086	0.04172	0.9791
42	0.01943	0.03885	0.9806
43	0.01361	0.02722	0.9864
44	0.01166	0.02331	0.9883
45	0.01986	0.03972	0.9801
46	0.01629	0.03259	0.9837
47	0.019	0.038	0.981
48	0.02052	0.04103	0.9795
49	0.01445	0.0289	0.9855
50	0.01168	0.02336	0.9883
51	0.008484	0.01697	0.9915
52	0.009122	0.01825	0.9909
53	0.03018	0.06036	0.9698
54	0.1349	0.2698	0.8651
55	0.143	0.286	0.857
56	0.3957	0.7915	0.6043
57	0.3457	0.6914	0.6543
58	0.4345	0.8689	0.5655
59	0.4081	0.8162	0.5919
60	0.4077	0.8153	0.5923
61	0.3677	0.7354	0.6323
62	0.3244	0.6487	0.6756
63	0.3378	0.6756	0.6622
64	0.3296	0.6591	0.6704
65	0.2942	0.5885	0.7058
66	0.2521	0.5042	0.7479
67	0.4551	0.9102	0.5449
68	0.4063	0.8126	0.5937
69	0.3688	0.7376	0.6312
70	0.4205	0.8411	0.5795
71	0.3845	0.769	0.6155
72	0.3263	0.6527	0.6737
73	0.3073	0.6146	0.6927
74	0.2514	0.5028	0.7486
75	0.2157	0.4315	0.7843
76	0.1708	0.3416	0.8292
77	0.1689	0.3378	0.8311
78	0.1651	0.3303	0.8349
79	0.1245	0.2491	0.8755
80	0.1548	0.3096	0.8452
81	0.1903	0.3805	0.8097
82	0.1547	0.3093	0.8453
83	0.1266	0.2532	0.8734
84	0.1162	0.2325	0.8838
85	0.1285	0.2569	0.8715
86	0.09909	0.1982	0.9009
87	0.125	0.2501	0.875
88	0.1375	0.275	0.8625
89	0.1119	0.2238	0.8881
90	0.2665	0.5331	0.7335
91	0.3266	0.6532	0.6734
92	0.261	0.522	0.739
93	0.2679	0.5358	0.7321
94	0.1778	0.3557	0.8222

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	15	0.1724	NOK
5% type I error level	42	0.482759	NOK
10% type I error level	45	0.517241	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 & 15 &  0.1724 & NOK \tabularnewline
5% type I error level & 42 & 0.482759 & NOK \tabularnewline
10% type I error level & 45 & 0.517241 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301370&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]15[/C][C] 0.1724[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.482759[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]45[/C][C]0.517241[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301370&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301370&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	15	0.1724	NOK
5% type I error level	42	0.482759	NOK
10% type I error level	45	0.517241	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.83478, df1 = 2, df2 = 95, p-value = 0.4371
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7692, df1 = 8, df2 = 89, p-value = 0.09374
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.75636, df1 = 2, df2 = 95, p-value = 0.4722

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.83478, df1 = 2, df2 = 95, p-value = 0.4371
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7692, df1 = 8, df2 = 89, p-value = 0.09374
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.75636, df1 = 2, df2 = 95, p-value = 0.4722
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301370&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 = 0.83478, df1 = 2, df2 = 95, p-value = 0.4371
[/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 = 1.7692, df1 = 8, df2 = 89, p-value = 0.09374
[/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 = 0.75636, df1 = 2, df2 = 95, p-value = 0.4722
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301370&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301370&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 = 0.83478, df1 = 2, df2 = 95, p-value = 0.4371
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7692, df1 = 8, df2 = 89, p-value = 0.09374
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.75636, df1 = 2, df2 = 95, p-value = 0.4722

Variance Inflation Factors (Multicollinearity)
> vif b d e t 1.072689 1.123955 1.066935 1.019301

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       b        d        e        t 
1.072689 1.123955 1.066935 1.019301 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301370&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       b        d        e        t 
1.072689 1.123955 1.066935 1.019301 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301370&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301370&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 b d e t 1.072689 1.123955 1.066935 1.019301

Figure 1

PNG link

Postscript link

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

PNG link

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Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 0 ; par5 = 0 ;

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

par5 <- '0'
par4 <- '0'
par3 <- 'Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
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