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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 02 Dec 2016 17:06:07 +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/02/t1480695365t43xpzjktz1ujl4.htm/, Retrieved Wed, 08 May 2024 00:04:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297582, Retrieved Wed, 08 May 2024 00:04:31 +0000

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No (this computation is public)

User-defined keywords

Regressieanalyse

Estimated Impact

125

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

-       [Multiple Regression] [Regressieanalyse] [2016-12-02 16:06:07] [404ac5ee4f7301873f6a96ef36861981] [Current]

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Dataseries X:

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Histogram

Boxplots

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	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=297582&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=297582&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297582&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
TVDC[t] = + 6.51162 + 0.559398SK1[t] + 1.12488SK2[t] + 0.088725SK3[t] + 0.266163SK4[t] + 0.219108SK5[t] + 0.00273889SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  6.51162 +  0.559398SK1[t] +  1.12488SK2[t] +  0.088725SK3[t] +  0.266163SK4[t] +  0.219108SK5[t] +  0.00273889SK6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297582&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  6.51162 +  0.559398SK1[t] +  1.12488SK2[t] +  0.088725SK3[t] +  0.266163SK4[t] +  0.219108SK5[t] +  0.00273889SK6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297582&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297582&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 6.51162 + 0.559398SK1[t] + 1.12488SK2[t] + 0.088725SK3[t] + 0.266163SK4[t] + 0.219108SK5[t] + 0.00273889SK6[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+6.512	1.552	+4.1950e+00	4.614e-05	2.307e-05
SK1	+0.5594	0.1636	+3.4200e+00	0.0008037	0.0004019
SK2	+1.125	0.1993	+5.6440e+00	7.92e-08	3.96e-08
SK3	+0.08872	0.1461	+6.0740e-01	0.5445	0.2722
SK4	+0.2662	0.1997	+1.3330e+00	0.1846	0.09232
SK5	+0.2191	0.1879	+1.1660e+00	0.2454	0.1227
SK6	+0.002739	0.195	+1.4050e-02	0.9888	0.4944

\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) & +6.512 &  1.552 & +4.1950e+00 &  4.614e-05 &  2.307e-05 \tabularnewline
SK1 & +0.5594 &  0.1636 & +3.4200e+00 &  0.0008037 &  0.0004019 \tabularnewline
SK2 & +1.125 &  0.1993 & +5.6440e+00 &  7.92e-08 &  3.96e-08 \tabularnewline
SK3 & +0.08872 &  0.1461 & +6.0740e-01 &  0.5445 &  0.2722 \tabularnewline
SK4 & +0.2662 &  0.1997 & +1.3330e+00 &  0.1846 &  0.09232 \tabularnewline
SK5 & +0.2191 &  0.1879 & +1.1660e+00 &  0.2454 &  0.1227 \tabularnewline
SK6 & +0.002739 &  0.195 & +1.4050e-02 &  0.9888 &  0.4944 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297582&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]+6.512[/C][C] 1.552[/C][C]+4.1950e+00[/C][C] 4.614e-05[/C][C] 2.307e-05[/C][/ROW]
[ROW][C]SK1[/C][C]+0.5594[/C][C] 0.1636[/C][C]+3.4200e+00[/C][C] 0.0008037[/C][C] 0.0004019[/C][/ROW]
[ROW][C]SK2[/C][C]+1.125[/C][C] 0.1993[/C][C]+5.6440e+00[/C][C] 7.92e-08[/C][C] 3.96e-08[/C][/ROW]
[ROW][C]SK3[/C][C]+0.08872[/C][C] 0.1461[/C][C]+6.0740e-01[/C][C] 0.5445[/C][C] 0.2722[/C][/ROW]
[ROW][C]SK4[/C][C]+0.2662[/C][C] 0.1997[/C][C]+1.3330e+00[/C][C] 0.1846[/C][C] 0.09232[/C][/ROW]
[ROW][C]SK5[/C][C]+0.2191[/C][C] 0.1879[/C][C]+1.1660e+00[/C][C] 0.2454[/C][C] 0.1227[/C][/ROW]
[ROW][C]SK6[/C][C]+0.002739[/C][C] 0.195[/C][C]+1.4050e-02[/C][C] 0.9888[/C][C] 0.4944[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297582&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297582&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)	+6.512	1.552	+4.1950e+00	4.614e-05	2.307e-05
SK1	+0.5594	0.1636	+3.4200e+00	0.0008037	0.0004019
SK2	+1.125	0.1993	+5.6440e+00	7.92e-08	3.96e-08
SK3	+0.08872	0.1461	+6.0740e-01	0.5445	0.2722
SK4	+0.2662	0.1997	+1.3330e+00	0.1846	0.09232
SK5	+0.2191	0.1879	+1.1660e+00	0.2454	0.1227
SK6	+0.002739	0.195	+1.4050e-02	0.9888	0.4944

Multiple Linear Regression - Regression Statistics
Multiple R	0.5641
R-squared	0.3182
Adjusted R-squared	0.2913
F-TEST (value)	11.83
F-TEST (DF numerator)	6
F-TEST (DF denominator)	152
p-value	7.293e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.436
Sum Squared Residuals	313.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5641 \tabularnewline
R-squared &  0.3182 \tabularnewline
Adjusted R-squared &  0.2913 \tabularnewline
F-TEST (value) &  11.83 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value &  7.293e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.436 \tabularnewline
Sum Squared Residuals &  313.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297582&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5641[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3182[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2913[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 11.83[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C] 7.293e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.436[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 313.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297582&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297582&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	0.5641
R-squared	0.3182
Adjusted R-squared	0.2913
F-TEST (value)	11.83
F-TEST (DF numerator)	6
F-TEST (DF denominator)	152
p-value	7.293e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.436
Sum Squared Residuals	313.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.26	-0.2589
2	16	15.12	0.8794
3	17	15.86	1.136
4	15	14.64	0.3586
5	16	15.86	0.1365
6	16	15.22	0.7819
7	17	14.51	2.489
8	16	15.09	0.915
9	17	16.9	0.0976
10	17	16.99	0.008872
11	17	15.6	1.403
12	15	15.6	-0.5973
13	16	15.29	0.7077
14	14	13.96	0.03714
15	16	15.21	0.7911
16	17	15.09	1.912
17	16	15.09	0.9123
18	15	16.97	-1.974
19	17	15.77	1.225
20	16	14.82	1.178
21	15	15.78	-0.7775
22	16	15.65	0.3529
23	15	15.64	-0.6444
24	17	15.65	1.353
25	15	15	0.003723
26	16	14.95	1.048
27	15	15.56	-0.5557
28	16	14.75	1.253
29	16	16.12	-0.1151
30	13	14.52	-1.52
31	15	16.99	-1.991
32	17	16.21	0.7935
33	13	13.62	-0.6198
34	17	16.5	0.4969
35	15	15.09	-0.085
36	14	14.26	-0.2561
37	14	14.34	-0.3421
38	18	15.64	2.356
39	15	16.12	-1.115
40	17	16.99	0.008872
41	13	13.87	-0.8714
42	16	17.28	-1.284
43	15	15.94	-0.9376
44	15	15.29	-0.2923
45	16	15.56	0.4443
46	15	15.72	-0.7246
47	13	15.77	-2.775
48	17	16.77	0.2307
49	17	17.28	-0.2816
50	17	17.33	-0.3287
51	11	14.74	-3.741
52	14	14.17	-0.1674
53	13	15.64	-2.644
54	15	14.51	0.489
55	17	14.99	2.006
56	16	15.51	0.4914
57	15	15.77	-0.7748
58	17	17.37	-0.3731
59	16	14.66	1.341
60	16	15.78	0.2225
61	16	14.56	1.442
62	15	15.87	-0.8662
63	12	13.31	-1.306
64	17	15.51	1.494
65	14	15.51	-1.509
66	14	15.72	-1.721
67	16	14.95	1.048
68	15	14.73	0.2671
69	15	17.25	-2.255
70	13	15.56	-2.556
71	17	16.04	0.9591
72	15	14.91	0.09245
73	16	15.77	0.2252
74	14	15.04	-1.041
75	15	13.96	1.037
76	17	14.52	2.48
77	16	15.65	0.3529
78	10	13.78	-3.783
79	16	15.77	0.2252
80	17	15.69	1.311
81	17	15.78	1.222
82	20	16.12	3.885
83	17	16.3	0.7047
84	18	15.87	2.134
85	15	15.09	-0.08774
86	14	12.95	1.049
87	15	15.64	-0.6417
88	17	15.65	1.353
89	16	15.77	0.2252
90	17	16.9	0.1031
91	15	14.95	0.04804
92	16	15.65	0.3529
93	18	16.03	1.971
94	18	16.47	1.527
95	16	16.9	-0.9024
96	17	15.21	1.788
97	15	15.65	-0.6471
98	13	16.12	-3.118
99	17	16.6	0.3969
100	16	15.2	0.802
101	15	15.64	-0.6444
102	16	15.64	0.3556
103	16	15.3	0.6986
104	13	15.56	-2.556
105	15	15.29	-0.2923
106	12	13.83	-1.827
107	19	15.29	3.71
108	16	15.09	0.915
109	16	15.43	0.5747
110	17	15.63	1.373
111	16	16.43	-0.4256
112	14	15.55	-1.553
113	15	15.38	-0.3782
114	14	14.73	-0.7329
115	16	15.56	0.4443
116	15	15.77	-0.7748
117	17	16.59	0.4082
118	15	15	0.003723
119	16	15.29	0.7105
120	16	15.56	0.4416
121	15	14.64	0.3586
122	15	15.29	-0.2868
123	11	12.21	-1.214
124	16	15.51	0.4914
125	18	16.07	1.932
126	13	13.95	-0.9543
127	11	13.87	-2.871
128	18	17.46	0.5409
129	15	16.77	-1.769
130	19	17.81	1.186
131	17	16.99	0.008872
132	13	15.29	-2.292
133	14	15.3	-1.304
134	16	15.64	0.3556
135	13	15.38	-2.378
136	17	15.69	1.311
137	14	15.78	-1.778
138	19	16.16	2.843
139	14	14.52	-0.5195
140	16	15.64	0.3556
141	12	13.43	-1.428
142	16	16.77	-0.7665
143	16	15.29	0.7105
144	15	15.56	-0.5584
145	12	15.04	-3.041
146	15	15.65	-0.6471
147	17	16.42	0.5771
148	13	15.43	-2.425
149	15	13.4	1.599
150	18	15.56	2.442
151	15	14.39	0.6135
152	18	15.55	2.447
153	15	17.04	-2.035
154	15	16.03	-1.026
155	16	15.85	0.1511
156	13	13.83	-0.8271
157	16	15.56	0.4416
158	13	15.77	-2.775
159	16	13.8	2.203

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.26 & -0.2589 \tabularnewline
2 &  16 &  15.12 &  0.8794 \tabularnewline
3 &  17 &  15.86 &  1.136 \tabularnewline
4 &  15 &  14.64 &  0.3586 \tabularnewline
5 &  16 &  15.86 &  0.1365 \tabularnewline
6 &  16 &  15.22 &  0.7819 \tabularnewline
7 &  17 &  14.51 &  2.489 \tabularnewline
8 &  16 &  15.09 &  0.915 \tabularnewline
9 &  17 &  16.9 &  0.0976 \tabularnewline
10 &  17 &  16.99 &  0.008872 \tabularnewline
11 &  17 &  15.6 &  1.403 \tabularnewline
12 &  15 &  15.6 & -0.5973 \tabularnewline
13 &  16 &  15.29 &  0.7077 \tabularnewline
14 &  14 &  13.96 &  0.03714 \tabularnewline
15 &  16 &  15.21 &  0.7911 \tabularnewline
16 &  17 &  15.09 &  1.912 \tabularnewline
17 &  16 &  15.09 &  0.9123 \tabularnewline
18 &  15 &  16.97 & -1.974 \tabularnewline
19 &  17 &  15.77 &  1.225 \tabularnewline
20 &  16 &  14.82 &  1.178 \tabularnewline
21 &  15 &  15.78 & -0.7775 \tabularnewline
22 &  16 &  15.65 &  0.3529 \tabularnewline
23 &  15 &  15.64 & -0.6444 \tabularnewline
24 &  17 &  15.65 &  1.353 \tabularnewline
25 &  15 &  15 &  0.003723 \tabularnewline
26 &  16 &  14.95 &  1.048 \tabularnewline
27 &  15 &  15.56 & -0.5557 \tabularnewline
28 &  16 &  14.75 &  1.253 \tabularnewline
29 &  16 &  16.12 & -0.1151 \tabularnewline
30 &  13 &  14.52 & -1.52 \tabularnewline
31 &  15 &  16.99 & -1.991 \tabularnewline
32 &  17 &  16.21 &  0.7935 \tabularnewline
33 &  13 &  13.62 & -0.6198 \tabularnewline
34 &  17 &  16.5 &  0.4969 \tabularnewline
35 &  15 &  15.09 & -0.085 \tabularnewline
36 &  14 &  14.26 & -0.2561 \tabularnewline
37 &  14 &  14.34 & -0.3421 \tabularnewline
38 &  18 &  15.64 &  2.356 \tabularnewline
39 &  15 &  16.12 & -1.115 \tabularnewline
40 &  17 &  16.99 &  0.008872 \tabularnewline
41 &  13 &  13.87 & -0.8714 \tabularnewline
42 &  16 &  17.28 & -1.284 \tabularnewline
43 &  15 &  15.94 & -0.9376 \tabularnewline
44 &  15 &  15.29 & -0.2923 \tabularnewline
45 &  16 &  15.56 &  0.4443 \tabularnewline
46 &  15 &  15.72 & -0.7246 \tabularnewline
47 &  13 &  15.77 & -2.775 \tabularnewline
48 &  17 &  16.77 &  0.2307 \tabularnewline
49 &  17 &  17.28 & -0.2816 \tabularnewline
50 &  17 &  17.33 & -0.3287 \tabularnewline
51 &  11 &  14.74 & -3.741 \tabularnewline
52 &  14 &  14.17 & -0.1674 \tabularnewline
53 &  13 &  15.64 & -2.644 \tabularnewline
54 &  15 &  14.51 &  0.489 \tabularnewline
55 &  17 &  14.99 &  2.006 \tabularnewline
56 &  16 &  15.51 &  0.4914 \tabularnewline
57 &  15 &  15.77 & -0.7748 \tabularnewline
58 &  17 &  17.37 & -0.3731 \tabularnewline
59 &  16 &  14.66 &  1.341 \tabularnewline
60 &  16 &  15.78 &  0.2225 \tabularnewline
61 &  16 &  14.56 &  1.442 \tabularnewline
62 &  15 &  15.87 & -0.8662 \tabularnewline
63 &  12 &  13.31 & -1.306 \tabularnewline
64 &  17 &  15.51 &  1.494 \tabularnewline
65 &  14 &  15.51 & -1.509 \tabularnewline
66 &  14 &  15.72 & -1.721 \tabularnewline
67 &  16 &  14.95 &  1.048 \tabularnewline
68 &  15 &  14.73 &  0.2671 \tabularnewline
69 &  15 &  17.25 & -2.255 \tabularnewline
70 &  13 &  15.56 & -2.556 \tabularnewline
71 &  17 &  16.04 &  0.9591 \tabularnewline
72 &  15 &  14.91 &  0.09245 \tabularnewline
73 &  16 &  15.77 &  0.2252 \tabularnewline
74 &  14 &  15.04 & -1.041 \tabularnewline
75 &  15 &  13.96 &  1.037 \tabularnewline
76 &  17 &  14.52 &  2.48 \tabularnewline
77 &  16 &  15.65 &  0.3529 \tabularnewline
78 &  10 &  13.78 & -3.783 \tabularnewline
79 &  16 &  15.77 &  0.2252 \tabularnewline
80 &  17 &  15.69 &  1.311 \tabularnewline
81 &  17 &  15.78 &  1.222 \tabularnewline
82 &  20 &  16.12 &  3.885 \tabularnewline
83 &  17 &  16.3 &  0.7047 \tabularnewline
84 &  18 &  15.87 &  2.134 \tabularnewline
85 &  15 &  15.09 & -0.08774 \tabularnewline
86 &  14 &  12.95 &  1.049 \tabularnewline
87 &  15 &  15.64 & -0.6417 \tabularnewline
88 &  17 &  15.65 &  1.353 \tabularnewline
89 &  16 &  15.77 &  0.2252 \tabularnewline
90 &  17 &  16.9 &  0.1031 \tabularnewline
91 &  15 &  14.95 &  0.04804 \tabularnewline
92 &  16 &  15.65 &  0.3529 \tabularnewline
93 &  18 &  16.03 &  1.971 \tabularnewline
94 &  18 &  16.47 &  1.527 \tabularnewline
95 &  16 &  16.9 & -0.9024 \tabularnewline
96 &  17 &  15.21 &  1.788 \tabularnewline
97 &  15 &  15.65 & -0.6471 \tabularnewline
98 &  13 &  16.12 & -3.118 \tabularnewline
99 &  17 &  16.6 &  0.3969 \tabularnewline
100 &  16 &  15.2 &  0.802 \tabularnewline
101 &  15 &  15.64 & -0.6444 \tabularnewline
102 &  16 &  15.64 &  0.3556 \tabularnewline
103 &  16 &  15.3 &  0.6986 \tabularnewline
104 &  13 &  15.56 & -2.556 \tabularnewline
105 &  15 &  15.29 & -0.2923 \tabularnewline
106 &  12 &  13.83 & -1.827 \tabularnewline
107 &  19 &  15.29 &  3.71 \tabularnewline
108 &  16 &  15.09 &  0.915 \tabularnewline
109 &  16 &  15.43 &  0.5747 \tabularnewline
110 &  17 &  15.63 &  1.373 \tabularnewline
111 &  16 &  16.43 & -0.4256 \tabularnewline
112 &  14 &  15.55 & -1.553 \tabularnewline
113 &  15 &  15.38 & -0.3782 \tabularnewline
114 &  14 &  14.73 & -0.7329 \tabularnewline
115 &  16 &  15.56 &  0.4443 \tabularnewline
116 &  15 &  15.77 & -0.7748 \tabularnewline
117 &  17 &  16.59 &  0.4082 \tabularnewline
118 &  15 &  15 &  0.003723 \tabularnewline
119 &  16 &  15.29 &  0.7105 \tabularnewline
120 &  16 &  15.56 &  0.4416 \tabularnewline
121 &  15 &  14.64 &  0.3586 \tabularnewline
122 &  15 &  15.29 & -0.2868 \tabularnewline
123 &  11 &  12.21 & -1.214 \tabularnewline
124 &  16 &  15.51 &  0.4914 \tabularnewline
125 &  18 &  16.07 &  1.932 \tabularnewline
126 &  13 &  13.95 & -0.9543 \tabularnewline
127 &  11 &  13.87 & -2.871 \tabularnewline
128 &  18 &  17.46 &  0.5409 \tabularnewline
129 &  15 &  16.77 & -1.769 \tabularnewline
130 &  19 &  17.81 &  1.186 \tabularnewline
131 &  17 &  16.99 &  0.008872 \tabularnewline
132 &  13 &  15.29 & -2.292 \tabularnewline
133 &  14 &  15.3 & -1.304 \tabularnewline
134 &  16 &  15.64 &  0.3556 \tabularnewline
135 &  13 &  15.38 & -2.378 \tabularnewline
136 &  17 &  15.69 &  1.311 \tabularnewline
137 &  14 &  15.78 & -1.778 \tabularnewline
138 &  19 &  16.16 &  2.843 \tabularnewline
139 &  14 &  14.52 & -0.5195 \tabularnewline
140 &  16 &  15.64 &  0.3556 \tabularnewline
141 &  12 &  13.43 & -1.428 \tabularnewline
142 &  16 &  16.77 & -0.7665 \tabularnewline
143 &  16 &  15.29 &  0.7105 \tabularnewline
144 &  15 &  15.56 & -0.5584 \tabularnewline
145 &  12 &  15.04 & -3.041 \tabularnewline
146 &  15 &  15.65 & -0.6471 \tabularnewline
147 &  17 &  16.42 &  0.5771 \tabularnewline
148 &  13 &  15.43 & -2.425 \tabularnewline
149 &  15 &  13.4 &  1.599 \tabularnewline
150 &  18 &  15.56 &  2.442 \tabularnewline
151 &  15 &  14.39 &  0.6135 \tabularnewline
152 &  18 &  15.55 &  2.447 \tabularnewline
153 &  15 &  17.04 & -2.035 \tabularnewline
154 &  15 &  16.03 & -1.026 \tabularnewline
155 &  16 &  15.85 &  0.1511 \tabularnewline
156 &  13 &  13.83 & -0.8271 \tabularnewline
157 &  16 &  15.56 &  0.4416 \tabularnewline
158 &  13 &  15.77 & -2.775 \tabularnewline
159 &  16 &  13.8 &  2.203 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297582&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 13.26[/C][C]-0.2589[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.12[/C][C] 0.8794[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.86[/C][C] 1.136[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.64[/C][C] 0.3586[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.86[/C][C] 0.1365[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.22[/C][C] 0.7819[/C][/ROW]
[ROW][C]7[/C][C] 17[/C][C] 14.51[/C][C] 2.489[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.09[/C][C] 0.915[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.9[/C][C] 0.0976[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 16.99[/C][C] 0.008872[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.6[/C][C] 1.403[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.6[/C][C]-0.5973[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.29[/C][C] 0.7077[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 13.96[/C][C] 0.03714[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.21[/C][C] 0.7911[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.09[/C][C] 1.912[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.09[/C][C] 0.9123[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 16.97[/C][C]-1.974[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.77[/C][C] 1.225[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.82[/C][C] 1.178[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.78[/C][C]-0.7775[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.65[/C][C] 0.3529[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.64[/C][C]-0.6444[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.65[/C][C] 1.353[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 15[/C][C] 0.003723[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.95[/C][C] 1.048[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.56[/C][C]-0.5557[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.75[/C][C] 1.253[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.12[/C][C]-0.1151[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.52[/C][C]-1.52[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 16.99[/C][C]-1.991[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.21[/C][C] 0.7935[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 13.62[/C][C]-0.6198[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 16.5[/C][C] 0.4969[/C][/ROW]
[ROW][C]35[/C][C] 15[/C][C] 15.09[/C][C]-0.085[/C][/ROW]
[ROW][C]36[/C][C] 14[/C][C] 14.26[/C][C]-0.2561[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 14.34[/C][C]-0.3421[/C][/ROW]
[ROW][C]38[/C][C] 18[/C][C] 15.64[/C][C] 2.356[/C][/ROW]
[ROW][C]39[/C][C] 15[/C][C] 16.12[/C][C]-1.115[/C][/ROW]
[ROW][C]40[/C][C] 17[/C][C] 16.99[/C][C] 0.008872[/C][/ROW]
[ROW][C]41[/C][C] 13[/C][C] 13.87[/C][C]-0.8714[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 17.28[/C][C]-1.284[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 15.94[/C][C]-0.9376[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 15.29[/C][C]-0.2923[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 15.56[/C][C] 0.4443[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 15.72[/C][C]-0.7246[/C][/ROW]
[ROW][C]47[/C][C] 13[/C][C] 15.77[/C][C]-2.775[/C][/ROW]
[ROW][C]48[/C][C] 17[/C][C] 16.77[/C][C] 0.2307[/C][/ROW]
[ROW][C]49[/C][C] 17[/C][C] 17.28[/C][C]-0.2816[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 17.33[/C][C]-0.3287[/C][/ROW]
[ROW][C]51[/C][C] 11[/C][C] 14.74[/C][C]-3.741[/C][/ROW]
[ROW][C]52[/C][C] 14[/C][C] 14.17[/C][C]-0.1674[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 15.64[/C][C]-2.644[/C][/ROW]
[ROW][C]54[/C][C] 15[/C][C] 14.51[/C][C] 0.489[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 14.99[/C][C] 2.006[/C][/ROW]
[ROW][C]56[/C][C] 16[/C][C] 15.51[/C][C] 0.4914[/C][/ROW]
[ROW][C]57[/C][C] 15[/C][C] 15.77[/C][C]-0.7748[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 17.37[/C][C]-0.3731[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 14.66[/C][C] 1.341[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 15.78[/C][C] 0.2225[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 14.56[/C][C] 1.442[/C][/ROW]
[ROW][C]62[/C][C] 15[/C][C] 15.87[/C][C]-0.8662[/C][/ROW]
[ROW][C]63[/C][C] 12[/C][C] 13.31[/C][C]-1.306[/C][/ROW]
[ROW][C]64[/C][C] 17[/C][C] 15.51[/C][C] 1.494[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 15.51[/C][C]-1.509[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 15.72[/C][C]-1.721[/C][/ROW]
[ROW][C]67[/C][C] 16[/C][C] 14.95[/C][C] 1.048[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 14.73[/C][C] 0.2671[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 17.25[/C][C]-2.255[/C][/ROW]
[ROW][C]70[/C][C] 13[/C][C] 15.56[/C][C]-2.556[/C][/ROW]
[ROW][C]71[/C][C] 17[/C][C] 16.04[/C][C] 0.9591[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 14.91[/C][C] 0.09245[/C][/ROW]
[ROW][C]73[/C][C] 16[/C][C] 15.77[/C][C] 0.2252[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 15.04[/C][C]-1.041[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 13.96[/C][C] 1.037[/C][/ROW]
[ROW][C]76[/C][C] 17[/C][C] 14.52[/C][C] 2.48[/C][/ROW]
[ROW][C]77[/C][C] 16[/C][C] 15.65[/C][C] 0.3529[/C][/ROW]
[ROW][C]78[/C][C] 10[/C][C] 13.78[/C][C]-3.783[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.77[/C][C] 0.2252[/C][/ROW]
[ROW][C]80[/C][C] 17[/C][C] 15.69[/C][C] 1.311[/C][/ROW]
[ROW][C]81[/C][C] 17[/C][C] 15.78[/C][C] 1.222[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 16.12[/C][C] 3.885[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 16.3[/C][C] 0.7047[/C][/ROW]
[ROW][C]84[/C][C] 18[/C][C] 15.87[/C][C] 2.134[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 15.09[/C][C]-0.08774[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 12.95[/C][C] 1.049[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 15.64[/C][C]-0.6417[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.65[/C][C] 1.353[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 15.77[/C][C] 0.2252[/C][/ROW]
[ROW][C]90[/C][C] 17[/C][C] 16.9[/C][C] 0.1031[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 14.95[/C][C] 0.04804[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 15.65[/C][C] 0.3529[/C][/ROW]
[ROW][C]93[/C][C] 18[/C][C] 16.03[/C][C] 1.971[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 16.47[/C][C] 1.527[/C][/ROW]
[ROW][C]95[/C][C] 16[/C][C] 16.9[/C][C]-0.9024[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 15.21[/C][C] 1.788[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 15.65[/C][C]-0.6471[/C][/ROW]
[ROW][C]98[/C][C] 13[/C][C] 16.12[/C][C]-3.118[/C][/ROW]
[ROW][C]99[/C][C] 17[/C][C] 16.6[/C][C] 0.3969[/C][/ROW]
[ROW][C]100[/C][C] 16[/C][C] 15.2[/C][C] 0.802[/C][/ROW]
[ROW][C]101[/C][C] 15[/C][C] 15.64[/C][C]-0.6444[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 15.64[/C][C] 0.3556[/C][/ROW]
[ROW][C]103[/C][C] 16[/C][C] 15.3[/C][C] 0.6986[/C][/ROW]
[ROW][C]104[/C][C] 13[/C][C] 15.56[/C][C]-2.556[/C][/ROW]
[ROW][C]105[/C][C] 15[/C][C] 15.29[/C][C]-0.2923[/C][/ROW]
[ROW][C]106[/C][C] 12[/C][C] 13.83[/C][C]-1.827[/C][/ROW]
[ROW][C]107[/C][C] 19[/C][C] 15.29[/C][C] 3.71[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 15.09[/C][C] 0.915[/C][/ROW]
[ROW][C]109[/C][C] 16[/C][C] 15.43[/C][C] 0.5747[/C][/ROW]
[ROW][C]110[/C][C] 17[/C][C] 15.63[/C][C] 1.373[/C][/ROW]
[ROW][C]111[/C][C] 16[/C][C] 16.43[/C][C]-0.4256[/C][/ROW]
[ROW][C]112[/C][C] 14[/C][C] 15.55[/C][C]-1.553[/C][/ROW]
[ROW][C]113[/C][C] 15[/C][C] 15.38[/C][C]-0.3782[/C][/ROW]
[ROW][C]114[/C][C] 14[/C][C] 14.73[/C][C]-0.7329[/C][/ROW]
[ROW][C]115[/C][C] 16[/C][C] 15.56[/C][C] 0.4443[/C][/ROW]
[ROW][C]116[/C][C] 15[/C][C] 15.77[/C][C]-0.7748[/C][/ROW]
[ROW][C]117[/C][C] 17[/C][C] 16.59[/C][C] 0.4082[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 15[/C][C] 0.003723[/C][/ROW]
[ROW][C]119[/C][C] 16[/C][C] 15.29[/C][C] 0.7105[/C][/ROW]
[ROW][C]120[/C][C] 16[/C][C] 15.56[/C][C] 0.4416[/C][/ROW]
[ROW][C]121[/C][C] 15[/C][C] 14.64[/C][C] 0.3586[/C][/ROW]
[ROW][C]122[/C][C] 15[/C][C] 15.29[/C][C]-0.2868[/C][/ROW]
[ROW][C]123[/C][C] 11[/C][C] 12.21[/C][C]-1.214[/C][/ROW]
[ROW][C]124[/C][C] 16[/C][C] 15.51[/C][C] 0.4914[/C][/ROW]
[ROW][C]125[/C][C] 18[/C][C] 16.07[/C][C] 1.932[/C][/ROW]
[ROW][C]126[/C][C] 13[/C][C] 13.95[/C][C]-0.9543[/C][/ROW]
[ROW][C]127[/C][C] 11[/C][C] 13.87[/C][C]-2.871[/C][/ROW]
[ROW][C]128[/C][C] 18[/C][C] 17.46[/C][C] 0.5409[/C][/ROW]
[ROW][C]129[/C][C] 15[/C][C] 16.77[/C][C]-1.769[/C][/ROW]
[ROW][C]130[/C][C] 19[/C][C] 17.81[/C][C] 1.186[/C][/ROW]
[ROW][C]131[/C][C] 17[/C][C] 16.99[/C][C] 0.008872[/C][/ROW]
[ROW][C]132[/C][C] 13[/C][C] 15.29[/C][C]-2.292[/C][/ROW]
[ROW][C]133[/C][C] 14[/C][C] 15.3[/C][C]-1.304[/C][/ROW]
[ROW][C]134[/C][C] 16[/C][C] 15.64[/C][C] 0.3556[/C][/ROW]
[ROW][C]135[/C][C] 13[/C][C] 15.38[/C][C]-2.378[/C][/ROW]
[ROW][C]136[/C][C] 17[/C][C] 15.69[/C][C] 1.311[/C][/ROW]
[ROW][C]137[/C][C] 14[/C][C] 15.78[/C][C]-1.778[/C][/ROW]
[ROW][C]138[/C][C] 19[/C][C] 16.16[/C][C] 2.843[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 14.52[/C][C]-0.5195[/C][/ROW]
[ROW][C]140[/C][C] 16[/C][C] 15.64[/C][C] 0.3556[/C][/ROW]
[ROW][C]141[/C][C] 12[/C][C] 13.43[/C][C]-1.428[/C][/ROW]
[ROW][C]142[/C][C] 16[/C][C] 16.77[/C][C]-0.7665[/C][/ROW]
[ROW][C]143[/C][C] 16[/C][C] 15.29[/C][C] 0.7105[/C][/ROW]
[ROW][C]144[/C][C] 15[/C][C] 15.56[/C][C]-0.5584[/C][/ROW]
[ROW][C]145[/C][C] 12[/C][C] 15.04[/C][C]-3.041[/C][/ROW]
[ROW][C]146[/C][C] 15[/C][C] 15.65[/C][C]-0.6471[/C][/ROW]
[ROW][C]147[/C][C] 17[/C][C] 16.42[/C][C] 0.5771[/C][/ROW]
[ROW][C]148[/C][C] 13[/C][C] 15.43[/C][C]-2.425[/C][/ROW]
[ROW][C]149[/C][C] 15[/C][C] 13.4[/C][C] 1.599[/C][/ROW]
[ROW][C]150[/C][C] 18[/C][C] 15.56[/C][C] 2.442[/C][/ROW]
[ROW][C]151[/C][C] 15[/C][C] 14.39[/C][C] 0.6135[/C][/ROW]
[ROW][C]152[/C][C] 18[/C][C] 15.55[/C][C] 2.447[/C][/ROW]
[ROW][C]153[/C][C] 15[/C][C] 17.04[/C][C]-2.035[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 16.03[/C][C]-1.026[/C][/ROW]
[ROW][C]155[/C][C] 16[/C][C] 15.85[/C][C] 0.1511[/C][/ROW]
[ROW][C]156[/C][C] 13[/C][C] 13.83[/C][C]-0.8271[/C][/ROW]
[ROW][C]157[/C][C] 16[/C][C] 15.56[/C][C] 0.4416[/C][/ROW]
[ROW][C]158[/C][C] 13[/C][C] 15.77[/C][C]-2.775[/C][/ROW]
[ROW][C]159[/C][C] 16[/C][C] 13.8[/C][C] 2.203[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297582&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297582&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.26	-0.2589
2	16	15.12	0.8794
3	17	15.86	1.136
4	15	14.64	0.3586
5	16	15.86	0.1365
6	16	15.22	0.7819
7	17	14.51	2.489
8	16	15.09	0.915
9	17	16.9	0.0976
10	17	16.99	0.008872
11	17	15.6	1.403
12	15	15.6	-0.5973
13	16	15.29	0.7077
14	14	13.96	0.03714
15	16	15.21	0.7911
16	17	15.09	1.912
17	16	15.09	0.9123
18	15	16.97	-1.974
19	17	15.77	1.225
20	16	14.82	1.178
21	15	15.78	-0.7775
22	16	15.65	0.3529
23	15	15.64	-0.6444
24	17	15.65	1.353
25	15	15	0.003723
26	16	14.95	1.048
27	15	15.56	-0.5557
28	16	14.75	1.253
29	16	16.12	-0.1151
30	13	14.52	-1.52
31	15	16.99	-1.991
32	17	16.21	0.7935
33	13	13.62	-0.6198
34	17	16.5	0.4969
35	15	15.09	-0.085
36	14	14.26	-0.2561
37	14	14.34	-0.3421
38	18	15.64	2.356
39	15	16.12	-1.115
40	17	16.99	0.008872
41	13	13.87	-0.8714
42	16	17.28	-1.284
43	15	15.94	-0.9376
44	15	15.29	-0.2923
45	16	15.56	0.4443
46	15	15.72	-0.7246
47	13	15.77	-2.775
48	17	16.77	0.2307
49	17	17.28	-0.2816
50	17	17.33	-0.3287
51	11	14.74	-3.741
52	14	14.17	-0.1674
53	13	15.64	-2.644
54	15	14.51	0.489
55	17	14.99	2.006
56	16	15.51	0.4914
57	15	15.77	-0.7748
58	17	17.37	-0.3731
59	16	14.66	1.341
60	16	15.78	0.2225
61	16	14.56	1.442
62	15	15.87	-0.8662
63	12	13.31	-1.306
64	17	15.51	1.494
65	14	15.51	-1.509
66	14	15.72	-1.721
67	16	14.95	1.048
68	15	14.73	0.2671
69	15	17.25	-2.255
70	13	15.56	-2.556
71	17	16.04	0.9591
72	15	14.91	0.09245
73	16	15.77	0.2252
74	14	15.04	-1.041
75	15	13.96	1.037
76	17	14.52	2.48
77	16	15.65	0.3529
78	10	13.78	-3.783
79	16	15.77	0.2252
80	17	15.69	1.311
81	17	15.78	1.222
82	20	16.12	3.885
83	17	16.3	0.7047
84	18	15.87	2.134
85	15	15.09	-0.08774
86	14	12.95	1.049
87	15	15.64	-0.6417
88	17	15.65	1.353
89	16	15.77	0.2252
90	17	16.9	0.1031
91	15	14.95	0.04804
92	16	15.65	0.3529
93	18	16.03	1.971
94	18	16.47	1.527
95	16	16.9	-0.9024
96	17	15.21	1.788
97	15	15.65	-0.6471
98	13	16.12	-3.118
99	17	16.6	0.3969
100	16	15.2	0.802
101	15	15.64	-0.6444
102	16	15.64	0.3556
103	16	15.3	0.6986
104	13	15.56	-2.556
105	15	15.29	-0.2923
106	12	13.83	-1.827
107	19	15.29	3.71
108	16	15.09	0.915
109	16	15.43	0.5747
110	17	15.63	1.373
111	16	16.43	-0.4256
112	14	15.55	-1.553
113	15	15.38	-0.3782
114	14	14.73	-0.7329
115	16	15.56	0.4443
116	15	15.77	-0.7748
117	17	16.59	0.4082
118	15	15	0.003723
119	16	15.29	0.7105
120	16	15.56	0.4416
121	15	14.64	0.3586
122	15	15.29	-0.2868
123	11	12.21	-1.214
124	16	15.51	0.4914
125	18	16.07	1.932
126	13	13.95	-0.9543
127	11	13.87	-2.871
128	18	17.46	0.5409
129	15	16.77	-1.769
130	19	17.81	1.186
131	17	16.99	0.008872
132	13	15.29	-2.292
133	14	15.3	-1.304
134	16	15.64	0.3556
135	13	15.38	-2.378
136	17	15.69	1.311
137	14	15.78	-1.778
138	19	16.16	2.843
139	14	14.52	-0.5195
140	16	15.64	0.3556
141	12	13.43	-1.428
142	16	16.77	-0.7665
143	16	15.29	0.7105
144	15	15.56	-0.5584
145	12	15.04	-3.041
146	15	15.65	-0.6471
147	17	16.42	0.5771
148	13	15.43	-2.425
149	15	13.4	1.599
150	18	15.56	2.442
151	15	14.39	0.6135
152	18	15.55	2.447
153	15	17.04	-2.035
154	15	16.03	-1.026
155	16	15.85	0.1511
156	13	13.83	-0.8271
157	16	15.56	0.4416
158	13	15.77	-2.775
159	16	13.8	2.203

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1488	0.2976	0.8512
11	0.08174	0.1635	0.9183
12	0.03336	0.06672	0.9666
13	0.01247	0.02493	0.9875
14	0.01188	0.02375	0.9881
15	0.01252	0.02505	0.9875
16	0.01957	0.03914	0.9804
17	0.009441	0.01888	0.9906
18	0.02644	0.05288	0.9736
19	0.01695	0.03391	0.983
20	0.01393	0.02787	0.9861
21	0.01318	0.02636	0.9868
22	0.007185	0.01437	0.9928
23	0.007547	0.01509	0.9925
24	0.007449	0.0149	0.9926
25	0.009478	0.01896	0.9905
26	0.005974	0.01195	0.994
27	0.005276	0.01055	0.9947
28	0.003283	0.006565	0.9967
29	0.001827	0.003653	0.9982
30	0.003488	0.006977	0.9965
31	0.007586	0.01517	0.9924
32	0.007628	0.01526	0.9924
33	0.00834	0.01668	0.9917
34	0.005619	0.01124	0.9944
35	0.003568	0.007136	0.9964
36	0.002439	0.004878	0.9976
37	0.002065	0.004129	0.9979
38	0.008039	0.01608	0.992
39	0.006806	0.01361	0.9932
40	0.00439	0.008781	0.9956
41	0.005049	0.0101	0.995
42	0.00403	0.008061	0.996
43	0.002739	0.005478	0.9973
44	0.001924	0.003847	0.9981
45	0.00123	0.002459	0.9988
46	0.00103	0.00206	0.999
47	0.0047	0.009401	0.9953
48	0.003278	0.006555	0.9967
49	0.002157	0.004314	0.9978
50	0.001429	0.002857	0.9986
51	0.01884	0.03768	0.9812
52	0.01361	0.02721	0.9864
53	0.02952	0.05903	0.9705
54	0.02284	0.04567	0.9772
55	0.02978	0.05956	0.9702
56	0.02466	0.04931	0.9753
57	0.01944	0.03889	0.9806
58	0.01437	0.02874	0.9856
59	0.01247	0.02493	0.9875
60	0.009039	0.01808	0.991
61	0.008797	0.01759	0.9912
62	0.006866	0.01373	0.9931
63	0.006656	0.01331	0.9933
64	0.008469	0.01694	0.9915
65	0.009096	0.01819	0.9909
66	0.009414	0.01883	0.9906
67	0.007912	0.01582	0.9921
68	0.006139	0.01228	0.9939
69	0.009827	0.01965	0.9902
70	0.02225	0.04451	0.9777
71	0.02076	0.04153	0.9792
72	0.01779	0.03559	0.9822
73	0.01353	0.02706	0.9865
74	0.01213	0.02426	0.9879
75	0.01052	0.02104	0.9895
76	0.02473	0.04947	0.9753
77	0.0193	0.03861	0.9807
78	0.1213	0.2425	0.8787
79	0.1007	0.2013	0.8993
80	0.09832	0.1966	0.9017
81	0.09442	0.1888	0.9056
82	0.3047	0.6095	0.6953
83	0.2727	0.5454	0.7273
84	0.3255	0.6509	0.6745
85	0.2897	0.5794	0.7103
86	0.2661	0.5321	0.7339
87	0.2362	0.4724	0.7638
88	0.2375	0.4749	0.7625
89	0.2033	0.4067	0.7967
90	0.1722	0.3444	0.8278
91	0.1458	0.2915	0.8542
92	0.1234	0.2469	0.8766
93	0.1478	0.2957	0.8522
94	0.157	0.314	0.843
95	0.1391	0.2781	0.8609
96	0.1541	0.3083	0.8459
97	0.1314	0.2629	0.8686
98	0.2331	0.4662	0.7669
99	0.2008	0.4015	0.7992
100	0.1761	0.3523	0.8239
101	0.1505	0.3011	0.8495
102	0.126	0.252	0.874
103	0.1067	0.2135	0.8933
104	0.1572	0.3143	0.8428
105	0.1299	0.2598	0.8701
106	0.1423	0.2845	0.8577
107	0.3666	0.7331	0.6334
108	0.3504	0.7007	0.6496
109	0.3276	0.6551	0.6724
110	0.3186	0.6373	0.6814
111	0.2833	0.5666	0.7167
112	0.2887	0.5774	0.7113
113	0.2467	0.4934	0.7533
114	0.2153	0.4306	0.7847
115	0.1843	0.3685	0.8157
116	0.1646	0.3293	0.8354
117	0.1454	0.2909	0.8546
118	0.1208	0.2417	0.8792
119	0.1067	0.2135	0.8933
120	0.09228	0.1846	0.9077
121	0.08667	0.1733	0.9133
122	0.06675	0.1335	0.9333
123	0.06242	0.1248	0.9376
124	0.04782	0.09563	0.9522
125	0.05111	0.1022	0.9489
126	0.05703	0.1141	0.943
127	0.121	0.242	0.879
128	0.1003	0.2006	0.8997
129	0.08509	0.1702	0.9149
130	0.07057	0.1411	0.9294
131	0.0638	0.1276	0.9362
132	0.06184	0.1237	0.9382
133	0.05419	0.1084	0.9458
134	0.03871	0.07743	0.9613
135	0.04226	0.08451	0.9577
136	0.05071	0.1014	0.9493
137	0.04507	0.09014	0.9549
138	0.09117	0.1823	0.9088
139	0.1191	0.2382	0.8809
140	0.08406	0.1681	0.9159
141	0.08846	0.1769	0.9115
142	0.06379	0.1276	0.9362
143	0.06189	0.1238	0.9381
144	0.03852	0.07704	0.9615
145	0.03366	0.06732	0.9663
146	0.01948	0.03897	0.9805
147	0.01064	0.02128	0.9894
148	0.1182	0.2364	0.8818
149	0.6746	0.6508	0.3254

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.1488 &  0.2976 &  0.8512 \tabularnewline
11 &  0.08174 &  0.1635 &  0.9183 \tabularnewline
12 &  0.03336 &  0.06672 &  0.9666 \tabularnewline
13 &  0.01247 &  0.02493 &  0.9875 \tabularnewline
14 &  0.01188 &  0.02375 &  0.9881 \tabularnewline
15 &  0.01252 &  0.02505 &  0.9875 \tabularnewline
16 &  0.01957 &  0.03914 &  0.9804 \tabularnewline
17 &  0.009441 &  0.01888 &  0.9906 \tabularnewline
18 &  0.02644 &  0.05288 &  0.9736 \tabularnewline
19 &  0.01695 &  0.03391 &  0.983 \tabularnewline
20 &  0.01393 &  0.02787 &  0.9861 \tabularnewline
21 &  0.01318 &  0.02636 &  0.9868 \tabularnewline
22 &  0.007185 &  0.01437 &  0.9928 \tabularnewline
23 &  0.007547 &  0.01509 &  0.9925 \tabularnewline
24 &  0.007449 &  0.0149 &  0.9926 \tabularnewline
25 &  0.009478 &  0.01896 &  0.9905 \tabularnewline
26 &  0.005974 &  0.01195 &  0.994 \tabularnewline
27 &  0.005276 &  0.01055 &  0.9947 \tabularnewline
28 &  0.003283 &  0.006565 &  0.9967 \tabularnewline
29 &  0.001827 &  0.003653 &  0.9982 \tabularnewline
30 &  0.003488 &  0.006977 &  0.9965 \tabularnewline
31 &  0.007586 &  0.01517 &  0.9924 \tabularnewline
32 &  0.007628 &  0.01526 &  0.9924 \tabularnewline
33 &  0.00834 &  0.01668 &  0.9917 \tabularnewline
34 &  0.005619 &  0.01124 &  0.9944 \tabularnewline
35 &  0.003568 &  0.007136 &  0.9964 \tabularnewline
36 &  0.002439 &  0.004878 &  0.9976 \tabularnewline
37 &  0.002065 &  0.004129 &  0.9979 \tabularnewline
38 &  0.008039 &  0.01608 &  0.992 \tabularnewline
39 &  0.006806 &  0.01361 &  0.9932 \tabularnewline
40 &  0.00439 &  0.008781 &  0.9956 \tabularnewline
41 &  0.005049 &  0.0101 &  0.995 \tabularnewline
42 &  0.00403 &  0.008061 &  0.996 \tabularnewline
43 &  0.002739 &  0.005478 &  0.9973 \tabularnewline
44 &  0.001924 &  0.003847 &  0.9981 \tabularnewline
45 &  0.00123 &  0.002459 &  0.9988 \tabularnewline
46 &  0.00103 &  0.00206 &  0.999 \tabularnewline
47 &  0.0047 &  0.009401 &  0.9953 \tabularnewline
48 &  0.003278 &  0.006555 &  0.9967 \tabularnewline
49 &  0.002157 &  0.004314 &  0.9978 \tabularnewline
50 &  0.001429 &  0.002857 &  0.9986 \tabularnewline
51 &  0.01884 &  0.03768 &  0.9812 \tabularnewline
52 &  0.01361 &  0.02721 &  0.9864 \tabularnewline
53 &  0.02952 &  0.05903 &  0.9705 \tabularnewline
54 &  0.02284 &  0.04567 &  0.9772 \tabularnewline
55 &  0.02978 &  0.05956 &  0.9702 \tabularnewline
56 &  0.02466 &  0.04931 &  0.9753 \tabularnewline
57 &  0.01944 &  0.03889 &  0.9806 \tabularnewline
58 &  0.01437 &  0.02874 &  0.9856 \tabularnewline
59 &  0.01247 &  0.02493 &  0.9875 \tabularnewline
60 &  0.009039 &  0.01808 &  0.991 \tabularnewline
61 &  0.008797 &  0.01759 &  0.9912 \tabularnewline
62 &  0.006866 &  0.01373 &  0.9931 \tabularnewline
63 &  0.006656 &  0.01331 &  0.9933 \tabularnewline
64 &  0.008469 &  0.01694 &  0.9915 \tabularnewline
65 &  0.009096 &  0.01819 &  0.9909 \tabularnewline
66 &  0.009414 &  0.01883 &  0.9906 \tabularnewline
67 &  0.007912 &  0.01582 &  0.9921 \tabularnewline
68 &  0.006139 &  0.01228 &  0.9939 \tabularnewline
69 &  0.009827 &  0.01965 &  0.9902 \tabularnewline
70 &  0.02225 &  0.04451 &  0.9777 \tabularnewline
71 &  0.02076 &  0.04153 &  0.9792 \tabularnewline
72 &  0.01779 &  0.03559 &  0.9822 \tabularnewline
73 &  0.01353 &  0.02706 &  0.9865 \tabularnewline
74 &  0.01213 &  0.02426 &  0.9879 \tabularnewline
75 &  0.01052 &  0.02104 &  0.9895 \tabularnewline
76 &  0.02473 &  0.04947 &  0.9753 \tabularnewline
77 &  0.0193 &  0.03861 &  0.9807 \tabularnewline
78 &  0.1213 &  0.2425 &  0.8787 \tabularnewline
79 &  0.1007 &  0.2013 &  0.8993 \tabularnewline
80 &  0.09832 &  0.1966 &  0.9017 \tabularnewline
81 &  0.09442 &  0.1888 &  0.9056 \tabularnewline
82 &  0.3047 &  0.6095 &  0.6953 \tabularnewline
83 &  0.2727 &  0.5454 &  0.7273 \tabularnewline
84 &  0.3255 &  0.6509 &  0.6745 \tabularnewline
85 &  0.2897 &  0.5794 &  0.7103 \tabularnewline
86 &  0.2661 &  0.5321 &  0.7339 \tabularnewline
87 &  0.2362 &  0.4724 &  0.7638 \tabularnewline
88 &  0.2375 &  0.4749 &  0.7625 \tabularnewline
89 &  0.2033 &  0.4067 &  0.7967 \tabularnewline
90 &  0.1722 &  0.3444 &  0.8278 \tabularnewline
91 &  0.1458 &  0.2915 &  0.8542 \tabularnewline
92 &  0.1234 &  0.2469 &  0.8766 \tabularnewline
93 &  0.1478 &  0.2957 &  0.8522 \tabularnewline
94 &  0.157 &  0.314 &  0.843 \tabularnewline
95 &  0.1391 &  0.2781 &  0.8609 \tabularnewline
96 &  0.1541 &  0.3083 &  0.8459 \tabularnewline
97 &  0.1314 &  0.2629 &  0.8686 \tabularnewline
98 &  0.2331 &  0.4662 &  0.7669 \tabularnewline
99 &  0.2008 &  0.4015 &  0.7992 \tabularnewline
100 &  0.1761 &  0.3523 &  0.8239 \tabularnewline
101 &  0.1505 &  0.3011 &  0.8495 \tabularnewline
102 &  0.126 &  0.252 &  0.874 \tabularnewline
103 &  0.1067 &  0.2135 &  0.8933 \tabularnewline
104 &  0.1572 &  0.3143 &  0.8428 \tabularnewline
105 &  0.1299 &  0.2598 &  0.8701 \tabularnewline
106 &  0.1423 &  0.2845 &  0.8577 \tabularnewline
107 &  0.3666 &  0.7331 &  0.6334 \tabularnewline
108 &  0.3504 &  0.7007 &  0.6496 \tabularnewline
109 &  0.3276 &  0.6551 &  0.6724 \tabularnewline
110 &  0.3186 &  0.6373 &  0.6814 \tabularnewline
111 &  0.2833 &  0.5666 &  0.7167 \tabularnewline
112 &  0.2887 &  0.5774 &  0.7113 \tabularnewline
113 &  0.2467 &  0.4934 &  0.7533 \tabularnewline
114 &  0.2153 &  0.4306 &  0.7847 \tabularnewline
115 &  0.1843 &  0.3685 &  0.8157 \tabularnewline
116 &  0.1646 &  0.3293 &  0.8354 \tabularnewline
117 &  0.1454 &  0.2909 &  0.8546 \tabularnewline
118 &  0.1208 &  0.2417 &  0.8792 \tabularnewline
119 &  0.1067 &  0.2135 &  0.8933 \tabularnewline
120 &  0.09228 &  0.1846 &  0.9077 \tabularnewline
121 &  0.08667 &  0.1733 &  0.9133 \tabularnewline
122 &  0.06675 &  0.1335 &  0.9333 \tabularnewline
123 &  0.06242 &  0.1248 &  0.9376 \tabularnewline
124 &  0.04782 &  0.09563 &  0.9522 \tabularnewline
125 &  0.05111 &  0.1022 &  0.9489 \tabularnewline
126 &  0.05703 &  0.1141 &  0.943 \tabularnewline
127 &  0.121 &  0.242 &  0.879 \tabularnewline
128 &  0.1003 &  0.2006 &  0.8997 \tabularnewline
129 &  0.08509 &  0.1702 &  0.9149 \tabularnewline
130 &  0.07057 &  0.1411 &  0.9294 \tabularnewline
131 &  0.0638 &  0.1276 &  0.9362 \tabularnewline
132 &  0.06184 &  0.1237 &  0.9382 \tabularnewline
133 &  0.05419 &  0.1084 &  0.9458 \tabularnewline
134 &  0.03871 &  0.07743 &  0.9613 \tabularnewline
135 &  0.04226 &  0.08451 &  0.9577 \tabularnewline
136 &  0.05071 &  0.1014 &  0.9493 \tabularnewline
137 &  0.04507 &  0.09014 &  0.9549 \tabularnewline
138 &  0.09117 &  0.1823 &  0.9088 \tabularnewline
139 &  0.1191 &  0.2382 &  0.8809 \tabularnewline
140 &  0.08406 &  0.1681 &  0.9159 \tabularnewline
141 &  0.08846 &  0.1769 &  0.9115 \tabularnewline
142 &  0.06379 &  0.1276 &  0.9362 \tabularnewline
143 &  0.06189 &  0.1238 &  0.9381 \tabularnewline
144 &  0.03852 &  0.07704 &  0.9615 \tabularnewline
145 &  0.03366 &  0.06732 &  0.9663 \tabularnewline
146 &  0.01948 &  0.03897 &  0.9805 \tabularnewline
147 &  0.01064 &  0.02128 &  0.9894 \tabularnewline
148 &  0.1182 &  0.2364 &  0.8818 \tabularnewline
149 &  0.6746 &  0.6508 &  0.3254 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297582&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C] 0.1488[/C][C] 0.2976[/C][C] 0.8512[/C][/ROW]
[ROW][C]11[/C][C] 0.08174[/C][C] 0.1635[/C][C] 0.9183[/C][/ROW]
[ROW][C]12[/C][C] 0.03336[/C][C] 0.06672[/C][C] 0.9666[/C][/ROW]
[ROW][C]13[/C][C] 0.01247[/C][C] 0.02493[/C][C] 0.9875[/C][/ROW]
[ROW][C]14[/C][C] 0.01188[/C][C] 0.02375[/C][C] 0.9881[/C][/ROW]
[ROW][C]15[/C][C] 0.01252[/C][C] 0.02505[/C][C] 0.9875[/C][/ROW]
[ROW][C]16[/C][C] 0.01957[/C][C] 0.03914[/C][C] 0.9804[/C][/ROW]
[ROW][C]17[/C][C] 0.009441[/C][C] 0.01888[/C][C] 0.9906[/C][/ROW]
[ROW][C]18[/C][C] 0.02644[/C][C] 0.05288[/C][C] 0.9736[/C][/ROW]
[ROW][C]19[/C][C] 0.01695[/C][C] 0.03391[/C][C] 0.983[/C][/ROW]
[ROW][C]20[/C][C] 0.01393[/C][C] 0.02787[/C][C] 0.9861[/C][/ROW]
[ROW][C]21[/C][C] 0.01318[/C][C] 0.02636[/C][C] 0.9868[/C][/ROW]
[ROW][C]22[/C][C] 0.007185[/C][C] 0.01437[/C][C] 0.9928[/C][/ROW]
[ROW][C]23[/C][C] 0.007547[/C][C] 0.01509[/C][C] 0.9925[/C][/ROW]
[ROW][C]24[/C][C] 0.007449[/C][C] 0.0149[/C][C] 0.9926[/C][/ROW]
[ROW][C]25[/C][C] 0.009478[/C][C] 0.01896[/C][C] 0.9905[/C][/ROW]
[ROW][C]26[/C][C] 0.005974[/C][C] 0.01195[/C][C] 0.994[/C][/ROW]
[ROW][C]27[/C][C] 0.005276[/C][C] 0.01055[/C][C] 0.9947[/C][/ROW]
[ROW][C]28[/C][C] 0.003283[/C][C] 0.006565[/C][C] 0.9967[/C][/ROW]
[ROW][C]29[/C][C] 0.001827[/C][C] 0.003653[/C][C] 0.9982[/C][/ROW]
[ROW][C]30[/C][C] 0.003488[/C][C] 0.006977[/C][C] 0.9965[/C][/ROW]
[ROW][C]31[/C][C] 0.007586[/C][C] 0.01517[/C][C] 0.9924[/C][/ROW]
[ROW][C]32[/C][C] 0.007628[/C][C] 0.01526[/C][C] 0.9924[/C][/ROW]
[ROW][C]33[/C][C] 0.00834[/C][C] 0.01668[/C][C] 0.9917[/C][/ROW]
[ROW][C]34[/C][C] 0.005619[/C][C] 0.01124[/C][C] 0.9944[/C][/ROW]
[ROW][C]35[/C][C] 0.003568[/C][C] 0.007136[/C][C] 0.9964[/C][/ROW]
[ROW][C]36[/C][C] 0.002439[/C][C] 0.004878[/C][C] 0.9976[/C][/ROW]
[ROW][C]37[/C][C] 0.002065[/C][C] 0.004129[/C][C] 0.9979[/C][/ROW]
[ROW][C]38[/C][C] 0.008039[/C][C] 0.01608[/C][C] 0.992[/C][/ROW]
[ROW][C]39[/C][C] 0.006806[/C][C] 0.01361[/C][C] 0.9932[/C][/ROW]
[ROW][C]40[/C][C] 0.00439[/C][C] 0.008781[/C][C] 0.9956[/C][/ROW]
[ROW][C]41[/C][C] 0.005049[/C][C] 0.0101[/C][C] 0.995[/C][/ROW]
[ROW][C]42[/C][C] 0.00403[/C][C] 0.008061[/C][C] 0.996[/C][/ROW]
[ROW][C]43[/C][C] 0.002739[/C][C] 0.005478[/C][C] 0.9973[/C][/ROW]
[ROW][C]44[/C][C] 0.001924[/C][C] 0.003847[/C][C] 0.9981[/C][/ROW]
[ROW][C]45[/C][C] 0.00123[/C][C] 0.002459[/C][C] 0.9988[/C][/ROW]
[ROW][C]46[/C][C] 0.00103[/C][C] 0.00206[/C][C] 0.999[/C][/ROW]
[ROW][C]47[/C][C] 0.0047[/C][C] 0.009401[/C][C] 0.9953[/C][/ROW]
[ROW][C]48[/C][C] 0.003278[/C][C] 0.006555[/C][C] 0.9967[/C][/ROW]
[ROW][C]49[/C][C] 0.002157[/C][C] 0.004314[/C][C] 0.9978[/C][/ROW]
[ROW][C]50[/C][C] 0.001429[/C][C] 0.002857[/C][C] 0.9986[/C][/ROW]
[ROW][C]51[/C][C] 0.01884[/C][C] 0.03768[/C][C] 0.9812[/C][/ROW]
[ROW][C]52[/C][C] 0.01361[/C][C] 0.02721[/C][C] 0.9864[/C][/ROW]
[ROW][C]53[/C][C] 0.02952[/C][C] 0.05903[/C][C] 0.9705[/C][/ROW]
[ROW][C]54[/C][C] 0.02284[/C][C] 0.04567[/C][C] 0.9772[/C][/ROW]
[ROW][C]55[/C][C] 0.02978[/C][C] 0.05956[/C][C] 0.9702[/C][/ROW]
[ROW][C]56[/C][C] 0.02466[/C][C] 0.04931[/C][C] 0.9753[/C][/ROW]
[ROW][C]57[/C][C] 0.01944[/C][C] 0.03889[/C][C] 0.9806[/C][/ROW]
[ROW][C]58[/C][C] 0.01437[/C][C] 0.02874[/C][C] 0.9856[/C][/ROW]
[ROW][C]59[/C][C] 0.01247[/C][C] 0.02493[/C][C] 0.9875[/C][/ROW]
[ROW][C]60[/C][C] 0.009039[/C][C] 0.01808[/C][C] 0.991[/C][/ROW]
[ROW][C]61[/C][C] 0.008797[/C][C] 0.01759[/C][C] 0.9912[/C][/ROW]
[ROW][C]62[/C][C] 0.006866[/C][C] 0.01373[/C][C] 0.9931[/C][/ROW]
[ROW][C]63[/C][C] 0.006656[/C][C] 0.01331[/C][C] 0.9933[/C][/ROW]
[ROW][C]64[/C][C] 0.008469[/C][C] 0.01694[/C][C] 0.9915[/C][/ROW]
[ROW][C]65[/C][C] 0.009096[/C][C] 0.01819[/C][C] 0.9909[/C][/ROW]
[ROW][C]66[/C][C] 0.009414[/C][C] 0.01883[/C][C] 0.9906[/C][/ROW]
[ROW][C]67[/C][C] 0.007912[/C][C] 0.01582[/C][C] 0.9921[/C][/ROW]
[ROW][C]68[/C][C] 0.006139[/C][C] 0.01228[/C][C] 0.9939[/C][/ROW]
[ROW][C]69[/C][C] 0.009827[/C][C] 0.01965[/C][C] 0.9902[/C][/ROW]
[ROW][C]70[/C][C] 0.02225[/C][C] 0.04451[/C][C] 0.9777[/C][/ROW]
[ROW][C]71[/C][C] 0.02076[/C][C] 0.04153[/C][C] 0.9792[/C][/ROW]
[ROW][C]72[/C][C] 0.01779[/C][C] 0.03559[/C][C] 0.9822[/C][/ROW]
[ROW][C]73[/C][C] 0.01353[/C][C] 0.02706[/C][C] 0.9865[/C][/ROW]
[ROW][C]74[/C][C] 0.01213[/C][C] 0.02426[/C][C] 0.9879[/C][/ROW]
[ROW][C]75[/C][C] 0.01052[/C][C] 0.02104[/C][C] 0.9895[/C][/ROW]
[ROW][C]76[/C][C] 0.02473[/C][C] 0.04947[/C][C] 0.9753[/C][/ROW]
[ROW][C]77[/C][C] 0.0193[/C][C] 0.03861[/C][C] 0.9807[/C][/ROW]
[ROW][C]78[/C][C] 0.1213[/C][C] 0.2425[/C][C] 0.8787[/C][/ROW]
[ROW][C]79[/C][C] 0.1007[/C][C] 0.2013[/C][C] 0.8993[/C][/ROW]
[ROW][C]80[/C][C] 0.09832[/C][C] 0.1966[/C][C] 0.9017[/C][/ROW]
[ROW][C]81[/C][C] 0.09442[/C][C] 0.1888[/C][C] 0.9056[/C][/ROW]
[ROW][C]82[/C][C] 0.3047[/C][C] 0.6095[/C][C] 0.6953[/C][/ROW]
[ROW][C]83[/C][C] 0.2727[/C][C] 0.5454[/C][C] 0.7273[/C][/ROW]
[ROW][C]84[/C][C] 0.3255[/C][C] 0.6509[/C][C] 0.6745[/C][/ROW]
[ROW][C]85[/C][C] 0.2897[/C][C] 0.5794[/C][C] 0.7103[/C][/ROW]
[ROW][C]86[/C][C] 0.2661[/C][C] 0.5321[/C][C] 0.7339[/C][/ROW]
[ROW][C]87[/C][C] 0.2362[/C][C] 0.4724[/C][C] 0.7638[/C][/ROW]
[ROW][C]88[/C][C] 0.2375[/C][C] 0.4749[/C][C] 0.7625[/C][/ROW]
[ROW][C]89[/C][C] 0.2033[/C][C] 0.4067[/C][C] 0.7967[/C][/ROW]
[ROW][C]90[/C][C] 0.1722[/C][C] 0.3444[/C][C] 0.8278[/C][/ROW]
[ROW][C]91[/C][C] 0.1458[/C][C] 0.2915[/C][C] 0.8542[/C][/ROW]
[ROW][C]92[/C][C] 0.1234[/C][C] 0.2469[/C][C] 0.8766[/C][/ROW]
[ROW][C]93[/C][C] 0.1478[/C][C] 0.2957[/C][C] 0.8522[/C][/ROW]
[ROW][C]94[/C][C] 0.157[/C][C] 0.314[/C][C] 0.843[/C][/ROW]
[ROW][C]95[/C][C] 0.1391[/C][C] 0.2781[/C][C] 0.8609[/C][/ROW]
[ROW][C]96[/C][C] 0.1541[/C][C] 0.3083[/C][C] 0.8459[/C][/ROW]
[ROW][C]97[/C][C] 0.1314[/C][C] 0.2629[/C][C] 0.8686[/C][/ROW]
[ROW][C]98[/C][C] 0.2331[/C][C] 0.4662[/C][C] 0.7669[/C][/ROW]
[ROW][C]99[/C][C] 0.2008[/C][C] 0.4015[/C][C] 0.7992[/C][/ROW]
[ROW][C]100[/C][C] 0.1761[/C][C] 0.3523[/C][C] 0.8239[/C][/ROW]
[ROW][C]101[/C][C] 0.1505[/C][C] 0.3011[/C][C] 0.8495[/C][/ROW]
[ROW][C]102[/C][C] 0.126[/C][C] 0.252[/C][C] 0.874[/C][/ROW]
[ROW][C]103[/C][C] 0.1067[/C][C] 0.2135[/C][C] 0.8933[/C][/ROW]
[ROW][C]104[/C][C] 0.1572[/C][C] 0.3143[/C][C] 0.8428[/C][/ROW]
[ROW][C]105[/C][C] 0.1299[/C][C] 0.2598[/C][C] 0.8701[/C][/ROW]
[ROW][C]106[/C][C] 0.1423[/C][C] 0.2845[/C][C] 0.8577[/C][/ROW]
[ROW][C]107[/C][C] 0.3666[/C][C] 0.7331[/C][C] 0.6334[/C][/ROW]
[ROW][C]108[/C][C] 0.3504[/C][C] 0.7007[/C][C] 0.6496[/C][/ROW]
[ROW][C]109[/C][C] 0.3276[/C][C] 0.6551[/C][C] 0.6724[/C][/ROW]
[ROW][C]110[/C][C] 0.3186[/C][C] 0.6373[/C][C] 0.6814[/C][/ROW]
[ROW][C]111[/C][C] 0.2833[/C][C] 0.5666[/C][C] 0.7167[/C][/ROW]
[ROW][C]112[/C][C] 0.2887[/C][C] 0.5774[/C][C] 0.7113[/C][/ROW]
[ROW][C]113[/C][C] 0.2467[/C][C] 0.4934[/C][C] 0.7533[/C][/ROW]
[ROW][C]114[/C][C] 0.2153[/C][C] 0.4306[/C][C] 0.7847[/C][/ROW]
[ROW][C]115[/C][C] 0.1843[/C][C] 0.3685[/C][C] 0.8157[/C][/ROW]
[ROW][C]116[/C][C] 0.1646[/C][C] 0.3293[/C][C] 0.8354[/C][/ROW]
[ROW][C]117[/C][C] 0.1454[/C][C] 0.2909[/C][C] 0.8546[/C][/ROW]
[ROW][C]118[/C][C] 0.1208[/C][C] 0.2417[/C][C] 0.8792[/C][/ROW]
[ROW][C]119[/C][C] 0.1067[/C][C] 0.2135[/C][C] 0.8933[/C][/ROW]
[ROW][C]120[/C][C] 0.09228[/C][C] 0.1846[/C][C] 0.9077[/C][/ROW]
[ROW][C]121[/C][C] 0.08667[/C][C] 0.1733[/C][C] 0.9133[/C][/ROW]
[ROW][C]122[/C][C] 0.06675[/C][C] 0.1335[/C][C] 0.9333[/C][/ROW]
[ROW][C]123[/C][C] 0.06242[/C][C] 0.1248[/C][C] 0.9376[/C][/ROW]
[ROW][C]124[/C][C] 0.04782[/C][C] 0.09563[/C][C] 0.9522[/C][/ROW]
[ROW][C]125[/C][C] 0.05111[/C][C] 0.1022[/C][C] 0.9489[/C][/ROW]
[ROW][C]126[/C][C] 0.05703[/C][C] 0.1141[/C][C] 0.943[/C][/ROW]
[ROW][C]127[/C][C] 0.121[/C][C] 0.242[/C][C] 0.879[/C][/ROW]
[ROW][C]128[/C][C] 0.1003[/C][C] 0.2006[/C][C] 0.8997[/C][/ROW]
[ROW][C]129[/C][C] 0.08509[/C][C] 0.1702[/C][C] 0.9149[/C][/ROW]
[ROW][C]130[/C][C] 0.07057[/C][C] 0.1411[/C][C] 0.9294[/C][/ROW]
[ROW][C]131[/C][C] 0.0638[/C][C] 0.1276[/C][C] 0.9362[/C][/ROW]
[ROW][C]132[/C][C] 0.06184[/C][C] 0.1237[/C][C] 0.9382[/C][/ROW]
[ROW][C]133[/C][C] 0.05419[/C][C] 0.1084[/C][C] 0.9458[/C][/ROW]
[ROW][C]134[/C][C] 0.03871[/C][C] 0.07743[/C][C] 0.9613[/C][/ROW]
[ROW][C]135[/C][C] 0.04226[/C][C] 0.08451[/C][C] 0.9577[/C][/ROW]
[ROW][C]136[/C][C] 0.05071[/C][C] 0.1014[/C][C] 0.9493[/C][/ROW]
[ROW][C]137[/C][C] 0.04507[/C][C] 0.09014[/C][C] 0.9549[/C][/ROW]
[ROW][C]138[/C][C] 0.09117[/C][C] 0.1823[/C][C] 0.9088[/C][/ROW]
[ROW][C]139[/C][C] 0.1191[/C][C] 0.2382[/C][C] 0.8809[/C][/ROW]
[ROW][C]140[/C][C] 0.08406[/C][C] 0.1681[/C][C] 0.9159[/C][/ROW]
[ROW][C]141[/C][C] 0.08846[/C][C] 0.1769[/C][C] 0.9115[/C][/ROW]
[ROW][C]142[/C][C] 0.06379[/C][C] 0.1276[/C][C] 0.9362[/C][/ROW]
[ROW][C]143[/C][C] 0.06189[/C][C] 0.1238[/C][C] 0.9381[/C][/ROW]
[ROW][C]144[/C][C] 0.03852[/C][C] 0.07704[/C][C] 0.9615[/C][/ROW]
[ROW][C]145[/C][C] 0.03366[/C][C] 0.06732[/C][C] 0.9663[/C][/ROW]
[ROW][C]146[/C][C] 0.01948[/C][C] 0.03897[/C][C] 0.9805[/C][/ROW]
[ROW][C]147[/C][C] 0.01064[/C][C] 0.02128[/C][C] 0.9894[/C][/ROW]
[ROW][C]148[/C][C] 0.1182[/C][C] 0.2364[/C][C] 0.8818[/C][/ROW]
[ROW][C]149[/C][C] 0.6746[/C][C] 0.6508[/C][C] 0.3254[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297582&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297582&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1488	0.2976	0.8512
11	0.08174	0.1635	0.9183
12	0.03336	0.06672	0.9666
13	0.01247	0.02493	0.9875
14	0.01188	0.02375	0.9881
15	0.01252	0.02505	0.9875
16	0.01957	0.03914	0.9804
17	0.009441	0.01888	0.9906
18	0.02644	0.05288	0.9736
19	0.01695	0.03391	0.983
20	0.01393	0.02787	0.9861
21	0.01318	0.02636	0.9868
22	0.007185	0.01437	0.9928
23	0.007547	0.01509	0.9925
24	0.007449	0.0149	0.9926
25	0.009478	0.01896	0.9905
26	0.005974	0.01195	0.994
27	0.005276	0.01055	0.9947
28	0.003283	0.006565	0.9967
29	0.001827	0.003653	0.9982
30	0.003488	0.006977	0.9965
31	0.007586	0.01517	0.9924
32	0.007628	0.01526	0.9924
33	0.00834	0.01668	0.9917
34	0.005619	0.01124	0.9944
35	0.003568	0.007136	0.9964
36	0.002439	0.004878	0.9976
37	0.002065	0.004129	0.9979
38	0.008039	0.01608	0.992
39	0.006806	0.01361	0.9932
40	0.00439	0.008781	0.9956
41	0.005049	0.0101	0.995
42	0.00403	0.008061	0.996
43	0.002739	0.005478	0.9973
44	0.001924	0.003847	0.9981
45	0.00123	0.002459	0.9988
46	0.00103	0.00206	0.999
47	0.0047	0.009401	0.9953
48	0.003278	0.006555	0.9967
49	0.002157	0.004314	0.9978
50	0.001429	0.002857	0.9986
51	0.01884	0.03768	0.9812
52	0.01361	0.02721	0.9864
53	0.02952	0.05903	0.9705
54	0.02284	0.04567	0.9772
55	0.02978	0.05956	0.9702
56	0.02466	0.04931	0.9753
57	0.01944	0.03889	0.9806
58	0.01437	0.02874	0.9856
59	0.01247	0.02493	0.9875
60	0.009039	0.01808	0.991
61	0.008797	0.01759	0.9912
62	0.006866	0.01373	0.9931
63	0.006656	0.01331	0.9933
64	0.008469	0.01694	0.9915
65	0.009096	0.01819	0.9909
66	0.009414	0.01883	0.9906
67	0.007912	0.01582	0.9921
68	0.006139	0.01228	0.9939
69	0.009827	0.01965	0.9902
70	0.02225	0.04451	0.9777
71	0.02076	0.04153	0.9792
72	0.01779	0.03559	0.9822
73	0.01353	0.02706	0.9865
74	0.01213	0.02426	0.9879
75	0.01052	0.02104	0.9895
76	0.02473	0.04947	0.9753
77	0.0193	0.03861	0.9807
78	0.1213	0.2425	0.8787
79	0.1007	0.2013	0.8993
80	0.09832	0.1966	0.9017
81	0.09442	0.1888	0.9056
82	0.3047	0.6095	0.6953
83	0.2727	0.5454	0.7273
84	0.3255	0.6509	0.6745
85	0.2897	0.5794	0.7103
86	0.2661	0.5321	0.7339
87	0.2362	0.4724	0.7638
88	0.2375	0.4749	0.7625
89	0.2033	0.4067	0.7967
90	0.1722	0.3444	0.8278
91	0.1458	0.2915	0.8542
92	0.1234	0.2469	0.8766
93	0.1478	0.2957	0.8522
94	0.157	0.314	0.843
95	0.1391	0.2781	0.8609
96	0.1541	0.3083	0.8459
97	0.1314	0.2629	0.8686
98	0.2331	0.4662	0.7669
99	0.2008	0.4015	0.7992
100	0.1761	0.3523	0.8239
101	0.1505	0.3011	0.8495
102	0.126	0.252	0.874
103	0.1067	0.2135	0.8933
104	0.1572	0.3143	0.8428
105	0.1299	0.2598	0.8701
106	0.1423	0.2845	0.8577
107	0.3666	0.7331	0.6334
108	0.3504	0.7007	0.6496
109	0.3276	0.6551	0.6724
110	0.3186	0.6373	0.6814
111	0.2833	0.5666	0.7167
112	0.2887	0.5774	0.7113
113	0.2467	0.4934	0.7533
114	0.2153	0.4306	0.7847
115	0.1843	0.3685	0.8157
116	0.1646	0.3293	0.8354
117	0.1454	0.2909	0.8546
118	0.1208	0.2417	0.8792
119	0.1067	0.2135	0.8933
120	0.09228	0.1846	0.9077
121	0.08667	0.1733	0.9133
122	0.06675	0.1335	0.9333
123	0.06242	0.1248	0.9376
124	0.04782	0.09563	0.9522
125	0.05111	0.1022	0.9489
126	0.05703	0.1141	0.943
127	0.121	0.242	0.879
128	0.1003	0.2006	0.8997
129	0.08509	0.1702	0.9149
130	0.07057	0.1411	0.9294
131	0.0638	0.1276	0.9362
132	0.06184	0.1237	0.9382
133	0.05419	0.1084	0.9458
134	0.03871	0.07743	0.9613
135	0.04226	0.08451	0.9577
136	0.05071	0.1014	0.9493
137	0.04507	0.09014	0.9549
138	0.09117	0.1823	0.9088
139	0.1191	0.2382	0.8809
140	0.08406	0.1681	0.9159
141	0.08846	0.1769	0.9115
142	0.06379	0.1276	0.9362
143	0.06189	0.1238	0.9381
144	0.03852	0.07704	0.9615
145	0.03366	0.06732	0.9663
146	0.01948	0.03897	0.9805
147	0.01064	0.02128	0.9894
148	0.1182	0.2364	0.8818
149	0.6746	0.6508	0.3254

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	16	0.1143	NOK
5% type I error level	64	0.457143	NOK
10% type I error level	74	0.528571	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 & 16 &  0.1143 & NOK \tabularnewline
5% type I error level & 64 & 0.457143 & NOK \tabularnewline
10% type I error level & 74 & 0.528571 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297582&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]16[/C][C] 0.1143[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]64[/C][C]0.457143[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]74[/C][C]0.528571[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297582&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297582&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	16	0.1143	NOK
5% type I error level	64	0.457143	NOK
10% type I error level	74	0.528571	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.8522, df1 = 2, df2 = 150, p-value = 0.1605
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3634, df1 = 12, df2 = 140, p-value = 0.1905
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.2002, df1 = 2, df2 = 150, p-value = 0.304

\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 = 1.8522, df1 = 2, df2 = 150, p-value = 0.1605
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3634, df1 = 12, df2 = 140, p-value = 0.1905
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.2002, df1 = 2, df2 = 150, p-value = 0.304
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297582&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 = 1.8522, df1 = 2, df2 = 150, p-value = 0.1605
[/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 = 1.3634, df1 = 12, df2 = 140, p-value = 0.1905
[/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 = 1.2002, df1 = 2, df2 = 150, p-value = 0.304
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297582&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297582&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 = 1.8522, df1 = 2, df2 = 150, p-value = 0.1605
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3634, df1 = 12, df2 = 140, p-value = 0.1905
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.2002, df1 = 2, df2 = 150, p-value = 0.304

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

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.093289 1.131316 1.044801 1.044030 1.044830 1.032498 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297582&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.093289 1.131316 1.044801 1.044030 1.044830 1.032498 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297582&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297582&T=8

As an alternative you can also use a QR Code:

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

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

Figure 1

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Parameters (Session):

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

Parameters (R input):

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

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

par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '7'
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