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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 11 Dec 2016 22:24:42 +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/11/t1481491521je43fjvoz2sm474.htm/, Retrieved Fri, 01 Nov 2024 05:24:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298857, Retrieved Fri, 01 Nov 2024 05:24:58 +0000

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114

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

-       [Multiple Regression] [lineair regresssion] [2016-12-11 21:24:42] [e6dc02234f5305f92311fb16bc25f73e] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298857&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
IVHBSUM[t] = + 13.5297 -0.111111KVDD1[t] + 0.0494897KVDD2[t] + 0.233617KVDD3[t] + 0.0092694KVDD4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
IVHBSUM[t] =  +  13.5297 -0.111111KVDD1[t] +  0.0494897KVDD2[t] +  0.233617KVDD3[t] +  0.0092694KVDD4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]IVHBSUM[t] =  +  13.5297 -0.111111KVDD1[t] +  0.0494897KVDD2[t] +  0.233617KVDD3[t] +  0.0092694KVDD4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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
IVHBSUM[t] = + 13.5297 -0.111111KVDD1[t] + 0.0494897KVDD2[t] + 0.233617KVDD3[t] + 0.0092694KVDD4[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)	+13.53	1.147	+1.1800e+01	1.375e-23	6.874e-24
KVDD1	-0.1111	0.2288	-4.8570e-01	0.6279	0.3139
KVDD2	+0.04949	0.1845	+2.6830e-01	0.7888	0.3944
KVDD3	+0.2336	0.1835	+1.2730e+00	0.2049	0.1025
KVDD4	+0.009269	0.1905	+4.8650e-02	0.9613	0.4806

\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) & +13.53 &  1.147 & +1.1800e+01 &  1.375e-23 &  6.874e-24 \tabularnewline
KVDD1 & -0.1111 &  0.2288 & -4.8570e-01 &  0.6279 &  0.3139 \tabularnewline
KVDD2 & +0.04949 &  0.1845 & +2.6830e-01 &  0.7888 &  0.3944 \tabularnewline
KVDD3 & +0.2336 &  0.1835 & +1.2730e+00 &  0.2049 &  0.1025 \tabularnewline
KVDD4 & +0.009269 &  0.1905 & +4.8650e-02 &  0.9613 &  0.4806 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&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]+13.53[/C][C] 1.147[/C][C]+1.1800e+01[/C][C] 1.375e-23[/C][C] 6.874e-24[/C][/ROW]
[ROW][C]KVDD1[/C][C]-0.1111[/C][C] 0.2288[/C][C]-4.8570e-01[/C][C] 0.6279[/C][C] 0.3139[/C][/ROW]
[ROW][C]KVDD2[/C][C]+0.04949[/C][C] 0.1845[/C][C]+2.6830e-01[/C][C] 0.7888[/C][C] 0.3944[/C][/ROW]
[ROW][C]KVDD3[/C][C]+0.2336[/C][C] 0.1835[/C][C]+1.2730e+00[/C][C] 0.2049[/C][C] 0.1025[/C][/ROW]
[ROW][C]KVDD4[/C][C]+0.009269[/C][C] 0.1905[/C][C]+4.8650e-02[/C][C] 0.9613[/C][C] 0.4806[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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)	+13.53	1.147	+1.1800e+01	1.375e-23	6.874e-24
KVDD1	-0.1111	0.2288	-4.8570e-01	0.6279	0.3139
KVDD2	+0.04949	0.1845	+2.6830e-01	0.7888	0.3944
KVDD3	+0.2336	0.1835	+1.2730e+00	0.2049	0.1025
KVDD4	+0.009269	0.1905	+4.8650e-02	0.9613	0.4806

Multiple Linear Regression - Regression Statistics
Multiple R	0.1118
R-squared	0.01251
Adjusted R-squared	-0.01188
F-TEST (value)	0.5129
F-TEST (DF numerator)	4
F-TEST (DF denominator)	162
p-value	0.7263
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.109
Sum Squared Residuals	720.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1118 \tabularnewline
R-squared &  0.01251 \tabularnewline
Adjusted R-squared & -0.01188 \tabularnewline
F-TEST (value) &  0.5129 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 162 \tabularnewline
p-value &  0.7263 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.109 \tabularnewline
Sum Squared Residuals &  720.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1118[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.01251[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.01188[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.5129[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]162[/C][/ROW]
[ROW][C]p-value[/C][C] 0.7263[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.109[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 720.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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	0.1118
R-squared	0.01251
Adjusted R-squared	-0.01188
F-TEST (value)	0.5129
F-TEST (DF numerator)	4
F-TEST (DF denominator)	162
p-value	0.7263
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.109
Sum Squared Residuals	720.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	14.14	-3.144
2	11	14.19	-3.193
3	15	13.95	1.047
4	15	13.96	1.038
5	13	14.13	-1.134
6	14	14.08	-0.08485
7	13	13.67	-0.6671
8	15	13.68	1.324
9	15	13.58	1.423
10	15	13.53	1.472
11	10	14	-4.003
12	11	13.89	-2.891
13	16	14.38	1.623
14	17	14.2	2.798
15	14	14.02	-0.0211
16	13	14	-0.9951
17	10	14.35	-4.347
18	13	13.56	-0.5589
19	17	14.1	2.897
20	18	13.86	4.139
21	17	14.12	2.883
22	11	13.99	-2.993
23	15	13.73	1.271
24	12	13.69	-1.688
25	15	14.02	0.9789
26	15	13.89	1.109
27	12	14.02	-2.021
28	19	14.03	4.974
29	13	14.2	-1.196
30	15	13.91	1.09
31	13	14.25	-1.255
32	10	14.02	-4.021
33	14	14.21	-0.2052
34	12	13.91	-1.91
35	15	13.91	1.09
36	13	13.92	-0.9193
37	18	13.91	4.09
38	15	13.96	1.04
39	11	14.43	-3.43
40	14	14.02	-0.0211
41	11	13.45	-2.455
42	14	13.62	0.3824
43	9	13.66	-4.658
44	13	13.92	-0.9193
45	13	13.74	-0.738
46	12	13.91	-1.913
47	17	14.33	2.672
48	16	14.09	1.906
49	15	14.38	0.6228
50	16	13.73	2.271
51	16	14.21	1.795
52	13	13.86	-0.8605
53	13	13.86	-0.8605
54	12	13.63	-1.627
55	11	14.08	-3.08
56	13	13.68	-0.6764
57	15	14.17	0.8254
58	13	13.97	-0.9716
59	14	14.03	-0.03037
60	13	13.42	-0.4242
61	15	13.29	1.715
62	14	14.02	-0.0211
63	14	13.96	0.03766
64	13	13.63	-0.6269
65	11	14.13	-3.132
66	14	14.27	-0.2668
67	17	13.92	3.081
68	15	14.29	0.7051
69	15	14.25	0.7453
70	13	13.91	-0.91
71	12	14.14	-2.144
72	14	14.25	-0.2547
73	11	13.91	-2.91
74	14	13.92	0.07788
75	18	14.49	3.512
76	15	13.69	1.312
77	18	14.19	3.807
78	16	14.06	1.939
79	12	13.91	-1.913
80	14	14	-0.002562
81	14	14.19	-0.1867
82	14	14.19	-0.1867
83	14	14.18	-0.1846
84	13	14.49	-1.488
85	12	14.02	-2.021
86	13	14.09	-1.094
87	15	13.97	1.028
88	13	14.38	-1.377
89	14	14.25	-0.2547
90	15	14.16	0.8443
91	13	14.3	-1.298
92	14	14.2	-0.196
93	17	13.94	3.058
94	15	14.01	0.9882
95	13	14.14	-1.144
96	14	14.14	-0.1415
97	17	14.02	2.979
98	8	14.44	-6.436
99	15	14.23	0.7659
100	10	13.4	-3.403
101	15	13.91	1.09
102	15	14.15	0.8471
103	14	13.67	0.33
104	15	13.96	1.038
105	18	14.09	3.906
106	14	14.1	-0.1034
107	19	14.25	4.745
108	16	14.26	1.736
109	17	14.15	2.847
110	18	14.15	3.847
111	13	13.86	-0.8605
112	10	13.97	-3.972
113	14	13.86	0.1395
114	13	13.69	-0.6885
115	12	14.09	-2.094
116	13	13.69	-0.6885
117	12	14.39	-2.386
118	13	13.74	-0.7351
119	16	13.75	2.253
120	12	13.74	-1.738
121	14	13.97	0.02839
122	17	13.81	3.189
123	14	14.1	-0.1034
124	12	13.97	-1.972
125	14	14.21	-0.2052
126	17	13.91	3.09
127	13	14.14	-1.144
128	11	14.19	-3.187
129	14	14.02	-0.0211
130	11	13.85	-2.854
131	17	14.54	2.462
132	15	14.13	0.8657
133	10	13.87	-3.87
134	15	14.05	0.9479
135	16	14.02	1.979
136	17	13.96	3.038
137	12	14.12	-2.123
138	15	13.79	1.213
139	10	14.1	-4.103
140	13	14.15	-1.146
141	17	14.24	2.764
142	17	14.34	2.663
143	16	13.85	2.151
144	15	13.79	1.213
145	16	14.24	1.763
146	16	13.87	2.13
147	15	13.78	1.222
148	16	14.13	1.866
149	14	13.84	0.1602
150	17	14.02	2.979
151	14	14.25	-0.2547
152	12	13.97	-1.972
153	15	13.92	1.078
154	14	14.14	-0.1436
155	15	13.58	1.423
156	14	14.34	-0.337
157	13	14.13	-1.134
158	16	13.96	2.038
159	13	14.28	-1.278
160	14	13.68	0.3236
161	13	14.12	-1.116
162	13	14.41	-1.406
163	15	14.25	0.7453
164	13	13.95	-0.9531
165	14	14.39	-0.3865
166	13	14.02	-1.021
167	12	13.96	-1.962

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  11 &  14.14 & -3.144 \tabularnewline
2 &  11 &  14.19 & -3.193 \tabularnewline
3 &  15 &  13.95 &  1.047 \tabularnewline
4 &  15 &  13.96 &  1.038 \tabularnewline
5 &  13 &  14.13 & -1.134 \tabularnewline
6 &  14 &  14.08 & -0.08485 \tabularnewline
7 &  13 &  13.67 & -0.6671 \tabularnewline
8 &  15 &  13.68 &  1.324 \tabularnewline
9 &  15 &  13.58 &  1.423 \tabularnewline
10 &  15 &  13.53 &  1.472 \tabularnewline
11 &  10 &  14 & -4.003 \tabularnewline
12 &  11 &  13.89 & -2.891 \tabularnewline
13 &  16 &  14.38 &  1.623 \tabularnewline
14 &  17 &  14.2 &  2.798 \tabularnewline
15 &  14 &  14.02 & -0.0211 \tabularnewline
16 &  13 &  14 & -0.9951 \tabularnewline
17 &  10 &  14.35 & -4.347 \tabularnewline
18 &  13 &  13.56 & -0.5589 \tabularnewline
19 &  17 &  14.1 &  2.897 \tabularnewline
20 &  18 &  13.86 &  4.139 \tabularnewline
21 &  17 &  14.12 &  2.883 \tabularnewline
22 &  11 &  13.99 & -2.993 \tabularnewline
23 &  15 &  13.73 &  1.271 \tabularnewline
24 &  12 &  13.69 & -1.688 \tabularnewline
25 &  15 &  14.02 &  0.9789 \tabularnewline
26 &  15 &  13.89 &  1.109 \tabularnewline
27 &  12 &  14.02 & -2.021 \tabularnewline
28 &  19 &  14.03 &  4.974 \tabularnewline
29 &  13 &  14.2 & -1.196 \tabularnewline
30 &  15 &  13.91 &  1.09 \tabularnewline
31 &  13 &  14.25 & -1.255 \tabularnewline
32 &  10 &  14.02 & -4.021 \tabularnewline
33 &  14 &  14.21 & -0.2052 \tabularnewline
34 &  12 &  13.91 & -1.91 \tabularnewline
35 &  15 &  13.91 &  1.09 \tabularnewline
36 &  13 &  13.92 & -0.9193 \tabularnewline
37 &  18 &  13.91 &  4.09 \tabularnewline
38 &  15 &  13.96 &  1.04 \tabularnewline
39 &  11 &  14.43 & -3.43 \tabularnewline
40 &  14 &  14.02 & -0.0211 \tabularnewline
41 &  11 &  13.45 & -2.455 \tabularnewline
42 &  14 &  13.62 &  0.3824 \tabularnewline
43 &  9 &  13.66 & -4.658 \tabularnewline
44 &  13 &  13.92 & -0.9193 \tabularnewline
45 &  13 &  13.74 & -0.738 \tabularnewline
46 &  12 &  13.91 & -1.913 \tabularnewline
47 &  17 &  14.33 &  2.672 \tabularnewline
48 &  16 &  14.09 &  1.906 \tabularnewline
49 &  15 &  14.38 &  0.6228 \tabularnewline
50 &  16 &  13.73 &  2.271 \tabularnewline
51 &  16 &  14.21 &  1.795 \tabularnewline
52 &  13 &  13.86 & -0.8605 \tabularnewline
53 &  13 &  13.86 & -0.8605 \tabularnewline
54 &  12 &  13.63 & -1.627 \tabularnewline
55 &  11 &  14.08 & -3.08 \tabularnewline
56 &  13 &  13.68 & -0.6764 \tabularnewline
57 &  15 &  14.17 &  0.8254 \tabularnewline
58 &  13 &  13.97 & -0.9716 \tabularnewline
59 &  14 &  14.03 & -0.03037 \tabularnewline
60 &  13 &  13.42 & -0.4242 \tabularnewline
61 &  15 &  13.29 &  1.715 \tabularnewline
62 &  14 &  14.02 & -0.0211 \tabularnewline
63 &  14 &  13.96 &  0.03766 \tabularnewline
64 &  13 &  13.63 & -0.6269 \tabularnewline
65 &  11 &  14.13 & -3.132 \tabularnewline
66 &  14 &  14.27 & -0.2668 \tabularnewline
67 &  17 &  13.92 &  3.081 \tabularnewline
68 &  15 &  14.29 &  0.7051 \tabularnewline
69 &  15 &  14.25 &  0.7453 \tabularnewline
70 &  13 &  13.91 & -0.91 \tabularnewline
71 &  12 &  14.14 & -2.144 \tabularnewline
72 &  14 &  14.25 & -0.2547 \tabularnewline
73 &  11 &  13.91 & -2.91 \tabularnewline
74 &  14 &  13.92 &  0.07788 \tabularnewline
75 &  18 &  14.49 &  3.512 \tabularnewline
76 &  15 &  13.69 &  1.312 \tabularnewline
77 &  18 &  14.19 &  3.807 \tabularnewline
78 &  16 &  14.06 &  1.939 \tabularnewline
79 &  12 &  13.91 & -1.913 \tabularnewline
80 &  14 &  14 & -0.002562 \tabularnewline
81 &  14 &  14.19 & -0.1867 \tabularnewline
82 &  14 &  14.19 & -0.1867 \tabularnewline
83 &  14 &  14.18 & -0.1846 \tabularnewline
84 &  13 &  14.49 & -1.488 \tabularnewline
85 &  12 &  14.02 & -2.021 \tabularnewline
86 &  13 &  14.09 & -1.094 \tabularnewline
87 &  15 &  13.97 &  1.028 \tabularnewline
88 &  13 &  14.38 & -1.377 \tabularnewline
89 &  14 &  14.25 & -0.2547 \tabularnewline
90 &  15 &  14.16 &  0.8443 \tabularnewline
91 &  13 &  14.3 & -1.298 \tabularnewline
92 &  14 &  14.2 & -0.196 \tabularnewline
93 &  17 &  13.94 &  3.058 \tabularnewline
94 &  15 &  14.01 &  0.9882 \tabularnewline
95 &  13 &  14.14 & -1.144 \tabularnewline
96 &  14 &  14.14 & -0.1415 \tabularnewline
97 &  17 &  14.02 &  2.979 \tabularnewline
98 &  8 &  14.44 & -6.436 \tabularnewline
99 &  15 &  14.23 &  0.7659 \tabularnewline
100 &  10 &  13.4 & -3.403 \tabularnewline
101 &  15 &  13.91 &  1.09 \tabularnewline
102 &  15 &  14.15 &  0.8471 \tabularnewline
103 &  14 &  13.67 &  0.33 \tabularnewline
104 &  15 &  13.96 &  1.038 \tabularnewline
105 &  18 &  14.09 &  3.906 \tabularnewline
106 &  14 &  14.1 & -0.1034 \tabularnewline
107 &  19 &  14.25 &  4.745 \tabularnewline
108 &  16 &  14.26 &  1.736 \tabularnewline
109 &  17 &  14.15 &  2.847 \tabularnewline
110 &  18 &  14.15 &  3.847 \tabularnewline
111 &  13 &  13.86 & -0.8605 \tabularnewline
112 &  10 &  13.97 & -3.972 \tabularnewline
113 &  14 &  13.86 &  0.1395 \tabularnewline
114 &  13 &  13.69 & -0.6885 \tabularnewline
115 &  12 &  14.09 & -2.094 \tabularnewline
116 &  13 &  13.69 & -0.6885 \tabularnewline
117 &  12 &  14.39 & -2.386 \tabularnewline
118 &  13 &  13.74 & -0.7351 \tabularnewline
119 &  16 &  13.75 &  2.253 \tabularnewline
120 &  12 &  13.74 & -1.738 \tabularnewline
121 &  14 &  13.97 &  0.02839 \tabularnewline
122 &  17 &  13.81 &  3.189 \tabularnewline
123 &  14 &  14.1 & -0.1034 \tabularnewline
124 &  12 &  13.97 & -1.972 \tabularnewline
125 &  14 &  14.21 & -0.2052 \tabularnewline
126 &  17 &  13.91 &  3.09 \tabularnewline
127 &  13 &  14.14 & -1.144 \tabularnewline
128 &  11 &  14.19 & -3.187 \tabularnewline
129 &  14 &  14.02 & -0.0211 \tabularnewline
130 &  11 &  13.85 & -2.854 \tabularnewline
131 &  17 &  14.54 &  2.462 \tabularnewline
132 &  15 &  14.13 &  0.8657 \tabularnewline
133 &  10 &  13.87 & -3.87 \tabularnewline
134 &  15 &  14.05 &  0.9479 \tabularnewline
135 &  16 &  14.02 &  1.979 \tabularnewline
136 &  17 &  13.96 &  3.038 \tabularnewline
137 &  12 &  14.12 & -2.123 \tabularnewline
138 &  15 &  13.79 &  1.213 \tabularnewline
139 &  10 &  14.1 & -4.103 \tabularnewline
140 &  13 &  14.15 & -1.146 \tabularnewline
141 &  17 &  14.24 &  2.764 \tabularnewline
142 &  17 &  14.34 &  2.663 \tabularnewline
143 &  16 &  13.85 &  2.151 \tabularnewline
144 &  15 &  13.79 &  1.213 \tabularnewline
145 &  16 &  14.24 &  1.763 \tabularnewline
146 &  16 &  13.87 &  2.13 \tabularnewline
147 &  15 &  13.78 &  1.222 \tabularnewline
148 &  16 &  14.13 &  1.866 \tabularnewline
149 &  14 &  13.84 &  0.1602 \tabularnewline
150 &  17 &  14.02 &  2.979 \tabularnewline
151 &  14 &  14.25 & -0.2547 \tabularnewline
152 &  12 &  13.97 & -1.972 \tabularnewline
153 &  15 &  13.92 &  1.078 \tabularnewline
154 &  14 &  14.14 & -0.1436 \tabularnewline
155 &  15 &  13.58 &  1.423 \tabularnewline
156 &  14 &  14.34 & -0.337 \tabularnewline
157 &  13 &  14.13 & -1.134 \tabularnewline
158 &  16 &  13.96 &  2.038 \tabularnewline
159 &  13 &  14.28 & -1.278 \tabularnewline
160 &  14 &  13.68 &  0.3236 \tabularnewline
161 &  13 &  14.12 & -1.116 \tabularnewline
162 &  13 &  14.41 & -1.406 \tabularnewline
163 &  15 &  14.25 &  0.7453 \tabularnewline
164 &  13 &  13.95 & -0.9531 \tabularnewline
165 &  14 &  14.39 & -0.3865 \tabularnewline
166 &  13 &  14.02 & -1.021 \tabularnewline
167 &  12 &  13.96 & -1.962 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&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] 11[/C][C] 14.14[/C][C]-3.144[/C][/ROW]
[ROW][C]2[/C][C] 11[/C][C] 14.19[/C][C]-3.193[/C][/ROW]
[ROW][C]3[/C][C] 15[/C][C] 13.95[/C][C] 1.047[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 13.96[/C][C] 1.038[/C][/ROW]
[ROW][C]5[/C][C] 13[/C][C] 14.13[/C][C]-1.134[/C][/ROW]
[ROW][C]6[/C][C] 14[/C][C] 14.08[/C][C]-0.08485[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 13.67[/C][C]-0.6671[/C][/ROW]
[ROW][C]8[/C][C] 15[/C][C] 13.68[/C][C] 1.324[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 13.58[/C][C] 1.423[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 13.53[/C][C] 1.472[/C][/ROW]
[ROW][C]11[/C][C] 10[/C][C] 14[/C][C]-4.003[/C][/ROW]
[ROW][C]12[/C][C] 11[/C][C] 13.89[/C][C]-2.891[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 14.38[/C][C] 1.623[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 14.2[/C][C] 2.798[/C][/ROW]
[ROW][C]15[/C][C] 14[/C][C] 14.02[/C][C]-0.0211[/C][/ROW]
[ROW][C]16[/C][C] 13[/C][C] 14[/C][C]-0.9951[/C][/ROW]
[ROW][C]17[/C][C] 10[/C][C] 14.35[/C][C]-4.347[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 13.56[/C][C]-0.5589[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 14.1[/C][C] 2.897[/C][/ROW]
[ROW][C]20[/C][C] 18[/C][C] 13.86[/C][C] 4.139[/C][/ROW]
[ROW][C]21[/C][C] 17[/C][C] 14.12[/C][C] 2.883[/C][/ROW]
[ROW][C]22[/C][C] 11[/C][C] 13.99[/C][C]-2.993[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 13.73[/C][C] 1.271[/C][/ROW]
[ROW][C]24[/C][C] 12[/C][C] 13.69[/C][C]-1.688[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 14.02[/C][C] 0.9789[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 13.89[/C][C] 1.109[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 14.02[/C][C]-2.021[/C][/ROW]
[ROW][C]28[/C][C] 19[/C][C] 14.03[/C][C] 4.974[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 14.2[/C][C]-1.196[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 13.91[/C][C] 1.09[/C][/ROW]
[ROW][C]31[/C][C] 13[/C][C] 14.25[/C][C]-1.255[/C][/ROW]
[ROW][C]32[/C][C] 10[/C][C] 14.02[/C][C]-4.021[/C][/ROW]
[ROW][C]33[/C][C] 14[/C][C] 14.21[/C][C]-0.2052[/C][/ROW]
[ROW][C]34[/C][C] 12[/C][C] 13.91[/C][C]-1.91[/C][/ROW]
[ROW][C]35[/C][C] 15[/C][C] 13.91[/C][C] 1.09[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 13.92[/C][C]-0.9193[/C][/ROW]
[ROW][C]37[/C][C] 18[/C][C] 13.91[/C][C] 4.09[/C][/ROW]
[ROW][C]38[/C][C] 15[/C][C] 13.96[/C][C] 1.04[/C][/ROW]
[ROW][C]39[/C][C] 11[/C][C] 14.43[/C][C]-3.43[/C][/ROW]
[ROW][C]40[/C][C] 14[/C][C] 14.02[/C][C]-0.0211[/C][/ROW]
[ROW][C]41[/C][C] 11[/C][C] 13.45[/C][C]-2.455[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 13.62[/C][C] 0.3824[/C][/ROW]
[ROW][C]43[/C][C] 9[/C][C] 13.66[/C][C]-4.658[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 13.92[/C][C]-0.9193[/C][/ROW]
[ROW][C]45[/C][C] 13[/C][C] 13.74[/C][C]-0.738[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 13.91[/C][C]-1.913[/C][/ROW]
[ROW][C]47[/C][C] 17[/C][C] 14.33[/C][C] 2.672[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 14.09[/C][C] 1.906[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 14.38[/C][C] 0.6228[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 13.73[/C][C] 2.271[/C][/ROW]
[ROW][C]51[/C][C] 16[/C][C] 14.21[/C][C] 1.795[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 13.86[/C][C]-0.8605[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 13.86[/C][C]-0.8605[/C][/ROW]
[ROW][C]54[/C][C] 12[/C][C] 13.63[/C][C]-1.627[/C][/ROW]
[ROW][C]55[/C][C] 11[/C][C] 14.08[/C][C]-3.08[/C][/ROW]
[ROW][C]56[/C][C] 13[/C][C] 13.68[/C][C]-0.6764[/C][/ROW]
[ROW][C]57[/C][C] 15[/C][C] 14.17[/C][C] 0.8254[/C][/ROW]
[ROW][C]58[/C][C] 13[/C][C] 13.97[/C][C]-0.9716[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 14.03[/C][C]-0.03037[/C][/ROW]
[ROW][C]60[/C][C] 13[/C][C] 13.42[/C][C]-0.4242[/C][/ROW]
[ROW][C]61[/C][C] 15[/C][C] 13.29[/C][C] 1.715[/C][/ROW]
[ROW][C]62[/C][C] 14[/C][C] 14.02[/C][C]-0.0211[/C][/ROW]
[ROW][C]63[/C][C] 14[/C][C] 13.96[/C][C] 0.03766[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 13.63[/C][C]-0.6269[/C][/ROW]
[ROW][C]65[/C][C] 11[/C][C] 14.13[/C][C]-3.132[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 14.27[/C][C]-0.2668[/C][/ROW]
[ROW][C]67[/C][C] 17[/C][C] 13.92[/C][C] 3.081[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 14.29[/C][C] 0.7051[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 14.25[/C][C] 0.7453[/C][/ROW]
[ROW][C]70[/C][C] 13[/C][C] 13.91[/C][C]-0.91[/C][/ROW]
[ROW][C]71[/C][C] 12[/C][C] 14.14[/C][C]-2.144[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 14.25[/C][C]-0.2547[/C][/ROW]
[ROW][C]73[/C][C] 11[/C][C] 13.91[/C][C]-2.91[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 13.92[/C][C] 0.07788[/C][/ROW]
[ROW][C]75[/C][C] 18[/C][C] 14.49[/C][C] 3.512[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 13.69[/C][C] 1.312[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 14.19[/C][C] 3.807[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 14.06[/C][C] 1.939[/C][/ROW]
[ROW][C]79[/C][C] 12[/C][C] 13.91[/C][C]-1.913[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 14[/C][C]-0.002562[/C][/ROW]
[ROW][C]81[/C][C] 14[/C][C] 14.19[/C][C]-0.1867[/C][/ROW]
[ROW][C]82[/C][C] 14[/C][C] 14.19[/C][C]-0.1867[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 14.18[/C][C]-0.1846[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 14.49[/C][C]-1.488[/C][/ROW]
[ROW][C]85[/C][C] 12[/C][C] 14.02[/C][C]-2.021[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 14.09[/C][C]-1.094[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 13.97[/C][C] 1.028[/C][/ROW]
[ROW][C]88[/C][C] 13[/C][C] 14.38[/C][C]-1.377[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 14.25[/C][C]-0.2547[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 14.16[/C][C] 0.8443[/C][/ROW]
[ROW][C]91[/C][C] 13[/C][C] 14.3[/C][C]-1.298[/C][/ROW]
[ROW][C]92[/C][C] 14[/C][C] 14.2[/C][C]-0.196[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 13.94[/C][C] 3.058[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 14.01[/C][C] 0.9882[/C][/ROW]
[ROW][C]95[/C][C] 13[/C][C] 14.14[/C][C]-1.144[/C][/ROW]
[ROW][C]96[/C][C] 14[/C][C] 14.14[/C][C]-0.1415[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 14.02[/C][C] 2.979[/C][/ROW]
[ROW][C]98[/C][C] 8[/C][C] 14.44[/C][C]-6.436[/C][/ROW]
[ROW][C]99[/C][C] 15[/C][C] 14.23[/C][C] 0.7659[/C][/ROW]
[ROW][C]100[/C][C] 10[/C][C] 13.4[/C][C]-3.403[/C][/ROW]
[ROW][C]101[/C][C] 15[/C][C] 13.91[/C][C] 1.09[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 14.15[/C][C] 0.8471[/C][/ROW]
[ROW][C]103[/C][C] 14[/C][C] 13.67[/C][C] 0.33[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 13.96[/C][C] 1.038[/C][/ROW]
[ROW][C]105[/C][C] 18[/C][C] 14.09[/C][C] 3.906[/C][/ROW]
[ROW][C]106[/C][C] 14[/C][C] 14.1[/C][C]-0.1034[/C][/ROW]
[ROW][C]107[/C][C] 19[/C][C] 14.25[/C][C] 4.745[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 14.26[/C][C] 1.736[/C][/ROW]
[ROW][C]109[/C][C] 17[/C][C] 14.15[/C][C] 2.847[/C][/ROW]
[ROW][C]110[/C][C] 18[/C][C] 14.15[/C][C] 3.847[/C][/ROW]
[ROW][C]111[/C][C] 13[/C][C] 13.86[/C][C]-0.8605[/C][/ROW]
[ROW][C]112[/C][C] 10[/C][C] 13.97[/C][C]-3.972[/C][/ROW]
[ROW][C]113[/C][C] 14[/C][C] 13.86[/C][C] 0.1395[/C][/ROW]
[ROW][C]114[/C][C] 13[/C][C] 13.69[/C][C]-0.6885[/C][/ROW]
[ROW][C]115[/C][C] 12[/C][C] 14.09[/C][C]-2.094[/C][/ROW]
[ROW][C]116[/C][C] 13[/C][C] 13.69[/C][C]-0.6885[/C][/ROW]
[ROW][C]117[/C][C] 12[/C][C] 14.39[/C][C]-2.386[/C][/ROW]
[ROW][C]118[/C][C] 13[/C][C] 13.74[/C][C]-0.7351[/C][/ROW]
[ROW][C]119[/C][C] 16[/C][C] 13.75[/C][C] 2.253[/C][/ROW]
[ROW][C]120[/C][C] 12[/C][C] 13.74[/C][C]-1.738[/C][/ROW]
[ROW][C]121[/C][C] 14[/C][C] 13.97[/C][C] 0.02839[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 13.81[/C][C] 3.189[/C][/ROW]
[ROW][C]123[/C][C] 14[/C][C] 14.1[/C][C]-0.1034[/C][/ROW]
[ROW][C]124[/C][C] 12[/C][C] 13.97[/C][C]-1.972[/C][/ROW]
[ROW][C]125[/C][C] 14[/C][C] 14.21[/C][C]-0.2052[/C][/ROW]
[ROW][C]126[/C][C] 17[/C][C] 13.91[/C][C] 3.09[/C][/ROW]
[ROW][C]127[/C][C] 13[/C][C] 14.14[/C][C]-1.144[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 14.19[/C][C]-3.187[/C][/ROW]
[ROW][C]129[/C][C] 14[/C][C] 14.02[/C][C]-0.0211[/C][/ROW]
[ROW][C]130[/C][C] 11[/C][C] 13.85[/C][C]-2.854[/C][/ROW]
[ROW][C]131[/C][C] 17[/C][C] 14.54[/C][C] 2.462[/C][/ROW]
[ROW][C]132[/C][C] 15[/C][C] 14.13[/C][C] 0.8657[/C][/ROW]
[ROW][C]133[/C][C] 10[/C][C] 13.87[/C][C]-3.87[/C][/ROW]
[ROW][C]134[/C][C] 15[/C][C] 14.05[/C][C] 0.9479[/C][/ROW]
[ROW][C]135[/C][C] 16[/C][C] 14.02[/C][C] 1.979[/C][/ROW]
[ROW][C]136[/C][C] 17[/C][C] 13.96[/C][C] 3.038[/C][/ROW]
[ROW][C]137[/C][C] 12[/C][C] 14.12[/C][C]-2.123[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 13.79[/C][C] 1.213[/C][/ROW]
[ROW][C]139[/C][C] 10[/C][C] 14.1[/C][C]-4.103[/C][/ROW]
[ROW][C]140[/C][C] 13[/C][C] 14.15[/C][C]-1.146[/C][/ROW]
[ROW][C]141[/C][C] 17[/C][C] 14.24[/C][C] 2.764[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 14.34[/C][C] 2.663[/C][/ROW]
[ROW][C]143[/C][C] 16[/C][C] 13.85[/C][C] 2.151[/C][/ROW]
[ROW][C]144[/C][C] 15[/C][C] 13.79[/C][C] 1.213[/C][/ROW]
[ROW][C]145[/C][C] 16[/C][C] 14.24[/C][C] 1.763[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 13.87[/C][C] 2.13[/C][/ROW]
[ROW][C]147[/C][C] 15[/C][C] 13.78[/C][C] 1.222[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 14.13[/C][C] 1.866[/C][/ROW]
[ROW][C]149[/C][C] 14[/C][C] 13.84[/C][C] 0.1602[/C][/ROW]
[ROW][C]150[/C][C] 17[/C][C] 14.02[/C][C] 2.979[/C][/ROW]
[ROW][C]151[/C][C] 14[/C][C] 14.25[/C][C]-0.2547[/C][/ROW]
[ROW][C]152[/C][C] 12[/C][C] 13.97[/C][C]-1.972[/C][/ROW]
[ROW][C]153[/C][C] 15[/C][C] 13.92[/C][C] 1.078[/C][/ROW]
[ROW][C]154[/C][C] 14[/C][C] 14.14[/C][C]-0.1436[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 13.58[/C][C] 1.423[/C][/ROW]
[ROW][C]156[/C][C] 14[/C][C] 14.34[/C][C]-0.337[/C][/ROW]
[ROW][C]157[/C][C] 13[/C][C] 14.13[/C][C]-1.134[/C][/ROW]
[ROW][C]158[/C][C] 16[/C][C] 13.96[/C][C] 2.038[/C][/ROW]
[ROW][C]159[/C][C] 13[/C][C] 14.28[/C][C]-1.278[/C][/ROW]
[ROW][C]160[/C][C] 14[/C][C] 13.68[/C][C] 0.3236[/C][/ROW]
[ROW][C]161[/C][C] 13[/C][C] 14.12[/C][C]-1.116[/C][/ROW]
[ROW][C]162[/C][C] 13[/C][C] 14.41[/C][C]-1.406[/C][/ROW]
[ROW][C]163[/C][C] 15[/C][C] 14.25[/C][C] 0.7453[/C][/ROW]
[ROW][C]164[/C][C] 13[/C][C] 13.95[/C][C]-0.9531[/C][/ROW]
[ROW][C]165[/C][C] 14[/C][C] 14.39[/C][C]-0.3865[/C][/ROW]
[ROW][C]166[/C][C] 13[/C][C] 14.02[/C][C]-1.021[/C][/ROW]
[ROW][C]167[/C][C] 12[/C][C] 13.96[/C][C]-1.962[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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	11	14.14	-3.144
2	11	14.19	-3.193
3	15	13.95	1.047
4	15	13.96	1.038
5	13	14.13	-1.134
6	14	14.08	-0.08485
7	13	13.67	-0.6671
8	15	13.68	1.324
9	15	13.58	1.423
10	15	13.53	1.472
11	10	14	-4.003
12	11	13.89	-2.891
13	16	14.38	1.623
14	17	14.2	2.798
15	14	14.02	-0.0211
16	13	14	-0.9951
17	10	14.35	-4.347
18	13	13.56	-0.5589
19	17	14.1	2.897
20	18	13.86	4.139
21	17	14.12	2.883
22	11	13.99	-2.993
23	15	13.73	1.271
24	12	13.69	-1.688
25	15	14.02	0.9789
26	15	13.89	1.109
27	12	14.02	-2.021
28	19	14.03	4.974
29	13	14.2	-1.196
30	15	13.91	1.09
31	13	14.25	-1.255
32	10	14.02	-4.021
33	14	14.21	-0.2052
34	12	13.91	-1.91
35	15	13.91	1.09
36	13	13.92	-0.9193
37	18	13.91	4.09
38	15	13.96	1.04
39	11	14.43	-3.43
40	14	14.02	-0.0211
41	11	13.45	-2.455
42	14	13.62	0.3824
43	9	13.66	-4.658
44	13	13.92	-0.9193
45	13	13.74	-0.738
46	12	13.91	-1.913
47	17	14.33	2.672
48	16	14.09	1.906
49	15	14.38	0.6228
50	16	13.73	2.271
51	16	14.21	1.795
52	13	13.86	-0.8605
53	13	13.86	-0.8605
54	12	13.63	-1.627
55	11	14.08	-3.08
56	13	13.68	-0.6764
57	15	14.17	0.8254
58	13	13.97	-0.9716
59	14	14.03	-0.03037
60	13	13.42	-0.4242
61	15	13.29	1.715
62	14	14.02	-0.0211
63	14	13.96	0.03766
64	13	13.63	-0.6269
65	11	14.13	-3.132
66	14	14.27	-0.2668
67	17	13.92	3.081
68	15	14.29	0.7051
69	15	14.25	0.7453
70	13	13.91	-0.91
71	12	14.14	-2.144
72	14	14.25	-0.2547
73	11	13.91	-2.91
74	14	13.92	0.07788
75	18	14.49	3.512
76	15	13.69	1.312
77	18	14.19	3.807
78	16	14.06	1.939
79	12	13.91	-1.913
80	14	14	-0.002562
81	14	14.19	-0.1867
82	14	14.19	-0.1867
83	14	14.18	-0.1846
84	13	14.49	-1.488
85	12	14.02	-2.021
86	13	14.09	-1.094
87	15	13.97	1.028
88	13	14.38	-1.377
89	14	14.25	-0.2547
90	15	14.16	0.8443
91	13	14.3	-1.298
92	14	14.2	-0.196
93	17	13.94	3.058
94	15	14.01	0.9882
95	13	14.14	-1.144
96	14	14.14	-0.1415
97	17	14.02	2.979
98	8	14.44	-6.436
99	15	14.23	0.7659
100	10	13.4	-3.403
101	15	13.91	1.09
102	15	14.15	0.8471
103	14	13.67	0.33
104	15	13.96	1.038
105	18	14.09	3.906
106	14	14.1	-0.1034
107	19	14.25	4.745
108	16	14.26	1.736
109	17	14.15	2.847
110	18	14.15	3.847
111	13	13.86	-0.8605
112	10	13.97	-3.972
113	14	13.86	0.1395
114	13	13.69	-0.6885
115	12	14.09	-2.094
116	13	13.69	-0.6885
117	12	14.39	-2.386
118	13	13.74	-0.7351
119	16	13.75	2.253
120	12	13.74	-1.738
121	14	13.97	0.02839
122	17	13.81	3.189
123	14	14.1	-0.1034
124	12	13.97	-1.972
125	14	14.21	-0.2052
126	17	13.91	3.09
127	13	14.14	-1.144
128	11	14.19	-3.187
129	14	14.02	-0.0211
130	11	13.85	-2.854
131	17	14.54	2.462
132	15	14.13	0.8657
133	10	13.87	-3.87
134	15	14.05	0.9479
135	16	14.02	1.979
136	17	13.96	3.038
137	12	14.12	-2.123
138	15	13.79	1.213
139	10	14.1	-4.103
140	13	14.15	-1.146
141	17	14.24	2.764
142	17	14.34	2.663
143	16	13.85	2.151
144	15	13.79	1.213
145	16	14.24	1.763
146	16	13.87	2.13
147	15	13.78	1.222
148	16	14.13	1.866
149	14	13.84	0.1602
150	17	14.02	2.979
151	14	14.25	-0.2547
152	12	13.97	-1.972
153	15	13.92	1.078
154	14	14.14	-0.1436
155	15	13.58	1.423
156	14	14.34	-0.337
157	13	14.13	-1.134
158	16	13.96	2.038
159	13	14.28	-1.278
160	14	13.68	0.3236
161	13	14.12	-1.116
162	13	14.41	-1.406
163	15	14.25	0.7453
164	13	13.95	-0.9531
165	14	14.39	-0.3865
166	13	14.02	-1.021
167	12	13.96	-1.962

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.185	0.37	0.815
9	0.1089	0.2178	0.8911
10	0.06138	0.1228	0.9386
11	0.3331	0.6663	0.6669
12	0.2692	0.5383	0.7308
13	0.4029	0.8058	0.5971
14	0.4729	0.9458	0.5271
15	0.4029	0.8058	0.5971
16	0.4369	0.8739	0.5631
17	0.47	0.9401	0.53
18	0.39	0.78	0.61
19	0.3599	0.7198	0.6401
20	0.4914	0.9829	0.5086
21	0.6603	0.6793	0.3397
22	0.921	0.158	0.079
23	0.9001	0.1998	0.09991
24	0.9018	0.1964	0.0982
25	0.8782	0.2436	0.1218
26	0.8609	0.2782	0.1391
27	0.8486	0.3027	0.1514
28	0.9337	0.1326	0.06632
29	0.9168	0.1664	0.08321
30	0.8978	0.2045	0.1022
31	0.8723	0.2553	0.1277
32	0.9073	0.1854	0.09271
33	0.8812	0.2377	0.1188
34	0.8707	0.2587	0.1293
35	0.8494	0.3012	0.1506
36	0.8181	0.3639	0.1819
37	0.8979	0.2042	0.1021
38	0.9035	0.193	0.09652
39	0.9213	0.1573	0.07867
40	0.9011	0.1978	0.0989
41	0.9175	0.165	0.0825
42	0.8964	0.2073	0.1036
43	0.9533	0.09349	0.04674
44	0.9418	0.1163	0.05815
45	0.9267	0.1466	0.0733
46	0.9225	0.155	0.07748
47	0.9266	0.1467	0.07337
48	0.9191	0.1618	0.08092
49	0.9001	0.1999	0.09993
50	0.9099	0.1802	0.09012
51	0.904	0.192	0.09602
52	0.8877	0.2246	0.1123
53	0.8693	0.2614	0.1307
54	0.859	0.2819	0.141
55	0.872	0.2561	0.128
56	0.8477	0.3046	0.1523
57	0.8286	0.3427	0.1714
58	0.8018	0.3965	0.1982
59	0.7706	0.4589	0.2294
60	0.7381	0.5237	0.2619
61	0.7174	0.5652	0.2826
62	0.6809	0.6382	0.3191
63	0.6387	0.7226	0.3613
64	0.5989	0.8022	0.4011
65	0.631	0.7381	0.369
66	0.5864	0.8271	0.4136
67	0.6387	0.7225	0.3613
68	0.6151	0.7697	0.3849
69	0.5803	0.8394	0.4197
70	0.5439	0.9122	0.4561
71	0.5504	0.8992	0.4496
72	0.5068	0.9864	0.4932
73	0.5513	0.8973	0.4487
74	0.5065	0.9869	0.4935
75	0.5933	0.8135	0.4067
76	0.5678	0.8643	0.4322
77	0.6652	0.6696	0.3348
78	0.6735	0.653	0.3265
79	0.665	0.67	0.335
80	0.6259	0.7483	0.3741
81	0.5824	0.8352	0.4176
82	0.5379	0.9242	0.4621
83	0.5015	0.9971	0.4985
84	0.4804	0.9608	0.5196
85	0.4786	0.9572	0.5214
86	0.4499	0.8998	0.5501
87	0.4167	0.8333	0.5833
88	0.3954	0.7908	0.6046
89	0.3541	0.7081	0.6459
90	0.3208	0.6415	0.6792
91	0.296	0.592	0.704
92	0.2584	0.5167	0.7417
93	0.3127	0.6254	0.6873
94	0.2823	0.5645	0.7177
95	0.2569	0.5139	0.7431
96	0.2248	0.4495	0.7752
97	0.2554	0.5109	0.7446
98	0.6493	0.7014	0.3507
99	0.6131	0.7738	0.3869
100	0.6863	0.6274	0.3137
101	0.6529	0.6943	0.3471
102	0.6143	0.7713	0.3857
103	0.5732	0.8535	0.4268
104	0.5382	0.9237	0.4618
105	0.6561	0.6877	0.3439
106	0.6117	0.7767	0.3883
107	0.7647	0.4707	0.2353
108	0.746	0.5081	0.254
109	0.7705	0.459	0.2295
110	0.8471	0.3058	0.1529
111	0.8212	0.3575	0.1788
112	0.8966	0.2067	0.1034
113	0.8727	0.2547	0.1273
114	0.8484	0.3033	0.1516
115	0.8444	0.3111	0.1556
116	0.8172	0.3656	0.1828
117	0.8253	0.3495	0.1747
118	0.8171	0.3658	0.1829
119	0.8105	0.379	0.1895
120	0.8129	0.3741	0.1871
121	0.776	0.4481	0.224
122	0.8428	0.3143	0.1572
123	0.8089	0.3822	0.1911
124	0.8111	0.3778	0.1889
125	0.7723	0.4553	0.2277
126	0.806	0.3881	0.194
127	0.7803	0.4395	0.2197
128	0.8262	0.3477	0.1738
129	0.791	0.418	0.209
130	0.8139	0.3722	0.1861
131	0.8278	0.3444	0.1722
132	0.7918	0.4165	0.2082
133	0.8962	0.2076	0.1038
134	0.8676	0.2647	0.1324
135	0.855	0.29	0.145
136	0.8816	0.2369	0.1184
137	0.9045	0.191	0.09552
138	0.8758	0.2484	0.1242
139	0.9617	0.07654	0.03827
140	0.9514	0.09722	0.04861
141	0.974	0.05202	0.02601
142	0.9878	0.02444	0.01222
143	0.9831	0.03372	0.01686
144	0.9734	0.05322	0.02661
145	0.984	0.03204	0.01602
146	0.9792	0.04158	0.02079
147	0.9674	0.06528	0.03264
148	0.9758	0.04842	0.02421
149	0.9593	0.0815	0.04075
150	0.9862	0.02754	0.01377
151	0.9752	0.0496	0.0248
152	0.9809	0.03829	0.01915
153	0.9726	0.05485	0.02743
154	0.9474	0.1051	0.05257
155	0.9202	0.1595	0.07976
156	0.8599	0.2802	0.1401
157	0.7773	0.4454	0.2227
158	0.9433	0.1135	0.05674
159	0.8646	0.2709	0.1354

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.185 &  0.37 &  0.815 \tabularnewline
9 &  0.1089 &  0.2178 &  0.8911 \tabularnewline
10 &  0.06138 &  0.1228 &  0.9386 \tabularnewline
11 &  0.3331 &  0.6663 &  0.6669 \tabularnewline
12 &  0.2692 &  0.5383 &  0.7308 \tabularnewline
13 &  0.4029 &  0.8058 &  0.5971 \tabularnewline
14 &  0.4729 &  0.9458 &  0.5271 \tabularnewline
15 &  0.4029 &  0.8058 &  0.5971 \tabularnewline
16 &  0.4369 &  0.8739 &  0.5631 \tabularnewline
17 &  0.47 &  0.9401 &  0.53 \tabularnewline
18 &  0.39 &  0.78 &  0.61 \tabularnewline
19 &  0.3599 &  0.7198 &  0.6401 \tabularnewline
20 &  0.4914 &  0.9829 &  0.5086 \tabularnewline
21 &  0.6603 &  0.6793 &  0.3397 \tabularnewline
22 &  0.921 &  0.158 &  0.079 \tabularnewline
23 &  0.9001 &  0.1998 &  0.09991 \tabularnewline
24 &  0.9018 &  0.1964 &  0.0982 \tabularnewline
25 &  0.8782 &  0.2436 &  0.1218 \tabularnewline
26 &  0.8609 &  0.2782 &  0.1391 \tabularnewline
27 &  0.8486 &  0.3027 &  0.1514 \tabularnewline
28 &  0.9337 &  0.1326 &  0.06632 \tabularnewline
29 &  0.9168 &  0.1664 &  0.08321 \tabularnewline
30 &  0.8978 &  0.2045 &  0.1022 \tabularnewline
31 &  0.8723 &  0.2553 &  0.1277 \tabularnewline
32 &  0.9073 &  0.1854 &  0.09271 \tabularnewline
33 &  0.8812 &  0.2377 &  0.1188 \tabularnewline
34 &  0.8707 &  0.2587 &  0.1293 \tabularnewline
35 &  0.8494 &  0.3012 &  0.1506 \tabularnewline
36 &  0.8181 &  0.3639 &  0.1819 \tabularnewline
37 &  0.8979 &  0.2042 &  0.1021 \tabularnewline
38 &  0.9035 &  0.193 &  0.09652 \tabularnewline
39 &  0.9213 &  0.1573 &  0.07867 \tabularnewline
40 &  0.9011 &  0.1978 &  0.0989 \tabularnewline
41 &  0.9175 &  0.165 &  0.0825 \tabularnewline
42 &  0.8964 &  0.2073 &  0.1036 \tabularnewline
43 &  0.9533 &  0.09349 &  0.04674 \tabularnewline
44 &  0.9418 &  0.1163 &  0.05815 \tabularnewline
45 &  0.9267 &  0.1466 &  0.0733 \tabularnewline
46 &  0.9225 &  0.155 &  0.07748 \tabularnewline
47 &  0.9266 &  0.1467 &  0.07337 \tabularnewline
48 &  0.9191 &  0.1618 &  0.08092 \tabularnewline
49 &  0.9001 &  0.1999 &  0.09993 \tabularnewline
50 &  0.9099 &  0.1802 &  0.09012 \tabularnewline
51 &  0.904 &  0.192 &  0.09602 \tabularnewline
52 &  0.8877 &  0.2246 &  0.1123 \tabularnewline
53 &  0.8693 &  0.2614 &  0.1307 \tabularnewline
54 &  0.859 &  0.2819 &  0.141 \tabularnewline
55 &  0.872 &  0.2561 &  0.128 \tabularnewline
56 &  0.8477 &  0.3046 &  0.1523 \tabularnewline
57 &  0.8286 &  0.3427 &  0.1714 \tabularnewline
58 &  0.8018 &  0.3965 &  0.1982 \tabularnewline
59 &  0.7706 &  0.4589 &  0.2294 \tabularnewline
60 &  0.7381 &  0.5237 &  0.2619 \tabularnewline
61 &  0.7174 &  0.5652 &  0.2826 \tabularnewline
62 &  0.6809 &  0.6382 &  0.3191 \tabularnewline
63 &  0.6387 &  0.7226 &  0.3613 \tabularnewline
64 &  0.5989 &  0.8022 &  0.4011 \tabularnewline
65 &  0.631 &  0.7381 &  0.369 \tabularnewline
66 &  0.5864 &  0.8271 &  0.4136 \tabularnewline
67 &  0.6387 &  0.7225 &  0.3613 \tabularnewline
68 &  0.6151 &  0.7697 &  0.3849 \tabularnewline
69 &  0.5803 &  0.8394 &  0.4197 \tabularnewline
70 &  0.5439 &  0.9122 &  0.4561 \tabularnewline
71 &  0.5504 &  0.8992 &  0.4496 \tabularnewline
72 &  0.5068 &  0.9864 &  0.4932 \tabularnewline
73 &  0.5513 &  0.8973 &  0.4487 \tabularnewline
74 &  0.5065 &  0.9869 &  0.4935 \tabularnewline
75 &  0.5933 &  0.8135 &  0.4067 \tabularnewline
76 &  0.5678 &  0.8643 &  0.4322 \tabularnewline
77 &  0.6652 &  0.6696 &  0.3348 \tabularnewline
78 &  0.6735 &  0.653 &  0.3265 \tabularnewline
79 &  0.665 &  0.67 &  0.335 \tabularnewline
80 &  0.6259 &  0.7483 &  0.3741 \tabularnewline
81 &  0.5824 &  0.8352 &  0.4176 \tabularnewline
82 &  0.5379 &  0.9242 &  0.4621 \tabularnewline
83 &  0.5015 &  0.9971 &  0.4985 \tabularnewline
84 &  0.4804 &  0.9608 &  0.5196 \tabularnewline
85 &  0.4786 &  0.9572 &  0.5214 \tabularnewline
86 &  0.4499 &  0.8998 &  0.5501 \tabularnewline
87 &  0.4167 &  0.8333 &  0.5833 \tabularnewline
88 &  0.3954 &  0.7908 &  0.6046 \tabularnewline
89 &  0.3541 &  0.7081 &  0.6459 \tabularnewline
90 &  0.3208 &  0.6415 &  0.6792 \tabularnewline
91 &  0.296 &  0.592 &  0.704 \tabularnewline
92 &  0.2584 &  0.5167 &  0.7417 \tabularnewline
93 &  0.3127 &  0.6254 &  0.6873 \tabularnewline
94 &  0.2823 &  0.5645 &  0.7177 \tabularnewline
95 &  0.2569 &  0.5139 &  0.7431 \tabularnewline
96 &  0.2248 &  0.4495 &  0.7752 \tabularnewline
97 &  0.2554 &  0.5109 &  0.7446 \tabularnewline
98 &  0.6493 &  0.7014 &  0.3507 \tabularnewline
99 &  0.6131 &  0.7738 &  0.3869 \tabularnewline
100 &  0.6863 &  0.6274 &  0.3137 \tabularnewline
101 &  0.6529 &  0.6943 &  0.3471 \tabularnewline
102 &  0.6143 &  0.7713 &  0.3857 \tabularnewline
103 &  0.5732 &  0.8535 &  0.4268 \tabularnewline
104 &  0.5382 &  0.9237 &  0.4618 \tabularnewline
105 &  0.6561 &  0.6877 &  0.3439 \tabularnewline
106 &  0.6117 &  0.7767 &  0.3883 \tabularnewline
107 &  0.7647 &  0.4707 &  0.2353 \tabularnewline
108 &  0.746 &  0.5081 &  0.254 \tabularnewline
109 &  0.7705 &  0.459 &  0.2295 \tabularnewline
110 &  0.8471 &  0.3058 &  0.1529 \tabularnewline
111 &  0.8212 &  0.3575 &  0.1788 \tabularnewline
112 &  0.8966 &  0.2067 &  0.1034 \tabularnewline
113 &  0.8727 &  0.2547 &  0.1273 \tabularnewline
114 &  0.8484 &  0.3033 &  0.1516 \tabularnewline
115 &  0.8444 &  0.3111 &  0.1556 \tabularnewline
116 &  0.8172 &  0.3656 &  0.1828 \tabularnewline
117 &  0.8253 &  0.3495 &  0.1747 \tabularnewline
118 &  0.8171 &  0.3658 &  0.1829 \tabularnewline
119 &  0.8105 &  0.379 &  0.1895 \tabularnewline
120 &  0.8129 &  0.3741 &  0.1871 \tabularnewline
121 &  0.776 &  0.4481 &  0.224 \tabularnewline
122 &  0.8428 &  0.3143 &  0.1572 \tabularnewline
123 &  0.8089 &  0.3822 &  0.1911 \tabularnewline
124 &  0.8111 &  0.3778 &  0.1889 \tabularnewline
125 &  0.7723 &  0.4553 &  0.2277 \tabularnewline
126 &  0.806 &  0.3881 &  0.194 \tabularnewline
127 &  0.7803 &  0.4395 &  0.2197 \tabularnewline
128 &  0.8262 &  0.3477 &  0.1738 \tabularnewline
129 &  0.791 &  0.418 &  0.209 \tabularnewline
130 &  0.8139 &  0.3722 &  0.1861 \tabularnewline
131 &  0.8278 &  0.3444 &  0.1722 \tabularnewline
132 &  0.7918 &  0.4165 &  0.2082 \tabularnewline
133 &  0.8962 &  0.2076 &  0.1038 \tabularnewline
134 &  0.8676 &  0.2647 &  0.1324 \tabularnewline
135 &  0.855 &  0.29 &  0.145 \tabularnewline
136 &  0.8816 &  0.2369 &  0.1184 \tabularnewline
137 &  0.9045 &  0.191 &  0.09552 \tabularnewline
138 &  0.8758 &  0.2484 &  0.1242 \tabularnewline
139 &  0.9617 &  0.07654 &  0.03827 \tabularnewline
140 &  0.9514 &  0.09722 &  0.04861 \tabularnewline
141 &  0.974 &  0.05202 &  0.02601 \tabularnewline
142 &  0.9878 &  0.02444 &  0.01222 \tabularnewline
143 &  0.9831 &  0.03372 &  0.01686 \tabularnewline
144 &  0.9734 &  0.05322 &  0.02661 \tabularnewline
145 &  0.984 &  0.03204 &  0.01602 \tabularnewline
146 &  0.9792 &  0.04158 &  0.02079 \tabularnewline
147 &  0.9674 &  0.06528 &  0.03264 \tabularnewline
148 &  0.9758 &  0.04842 &  0.02421 \tabularnewline
149 &  0.9593 &  0.0815 &  0.04075 \tabularnewline
150 &  0.9862 &  0.02754 &  0.01377 \tabularnewline
151 &  0.9752 &  0.0496 &  0.0248 \tabularnewline
152 &  0.9809 &  0.03829 &  0.01915 \tabularnewline
153 &  0.9726 &  0.05485 &  0.02743 \tabularnewline
154 &  0.9474 &  0.1051 &  0.05257 \tabularnewline
155 &  0.9202 &  0.1595 &  0.07976 \tabularnewline
156 &  0.8599 &  0.2802 &  0.1401 \tabularnewline
157 &  0.7773 &  0.4454 &  0.2227 \tabularnewline
158 &  0.9433 &  0.1135 &  0.05674 \tabularnewline
159 &  0.8646 &  0.2709 &  0.1354 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&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] 0.185[/C][C] 0.37[/C][C] 0.815[/C][/ROW]
[ROW][C]9[/C][C] 0.1089[/C][C] 0.2178[/C][C] 0.8911[/C][/ROW]
[ROW][C]10[/C][C] 0.06138[/C][C] 0.1228[/C][C] 0.9386[/C][/ROW]
[ROW][C]11[/C][C] 0.3331[/C][C] 0.6663[/C][C] 0.6669[/C][/ROW]
[ROW][C]12[/C][C] 0.2692[/C][C] 0.5383[/C][C] 0.7308[/C][/ROW]
[ROW][C]13[/C][C] 0.4029[/C][C] 0.8058[/C][C] 0.5971[/C][/ROW]
[ROW][C]14[/C][C] 0.4729[/C][C] 0.9458[/C][C] 0.5271[/C][/ROW]
[ROW][C]15[/C][C] 0.4029[/C][C] 0.8058[/C][C] 0.5971[/C][/ROW]
[ROW][C]16[/C][C] 0.4369[/C][C] 0.8739[/C][C] 0.5631[/C][/ROW]
[ROW][C]17[/C][C] 0.47[/C][C] 0.9401[/C][C] 0.53[/C][/ROW]
[ROW][C]18[/C][C] 0.39[/C][C] 0.78[/C][C] 0.61[/C][/ROW]
[ROW][C]19[/C][C] 0.3599[/C][C] 0.7198[/C][C] 0.6401[/C][/ROW]
[ROW][C]20[/C][C] 0.4914[/C][C] 0.9829[/C][C] 0.5086[/C][/ROW]
[ROW][C]21[/C][C] 0.6603[/C][C] 0.6793[/C][C] 0.3397[/C][/ROW]
[ROW][C]22[/C][C] 0.921[/C][C] 0.158[/C][C] 0.079[/C][/ROW]
[ROW][C]23[/C][C] 0.9001[/C][C] 0.1998[/C][C] 0.09991[/C][/ROW]
[ROW][C]24[/C][C] 0.9018[/C][C] 0.1964[/C][C] 0.0982[/C][/ROW]
[ROW][C]25[/C][C] 0.8782[/C][C] 0.2436[/C][C] 0.1218[/C][/ROW]
[ROW][C]26[/C][C] 0.8609[/C][C] 0.2782[/C][C] 0.1391[/C][/ROW]
[ROW][C]27[/C][C] 0.8486[/C][C] 0.3027[/C][C] 0.1514[/C][/ROW]
[ROW][C]28[/C][C] 0.9337[/C][C] 0.1326[/C][C] 0.06632[/C][/ROW]
[ROW][C]29[/C][C] 0.9168[/C][C] 0.1664[/C][C] 0.08321[/C][/ROW]
[ROW][C]30[/C][C] 0.8978[/C][C] 0.2045[/C][C] 0.1022[/C][/ROW]
[ROW][C]31[/C][C] 0.8723[/C][C] 0.2553[/C][C] 0.1277[/C][/ROW]
[ROW][C]32[/C][C] 0.9073[/C][C] 0.1854[/C][C] 0.09271[/C][/ROW]
[ROW][C]33[/C][C] 0.8812[/C][C] 0.2377[/C][C] 0.1188[/C][/ROW]
[ROW][C]34[/C][C] 0.8707[/C][C] 0.2587[/C][C] 0.1293[/C][/ROW]
[ROW][C]35[/C][C] 0.8494[/C][C] 0.3012[/C][C] 0.1506[/C][/ROW]
[ROW][C]36[/C][C] 0.8181[/C][C] 0.3639[/C][C] 0.1819[/C][/ROW]
[ROW][C]37[/C][C] 0.8979[/C][C] 0.2042[/C][C] 0.1021[/C][/ROW]
[ROW][C]38[/C][C] 0.9035[/C][C] 0.193[/C][C] 0.09652[/C][/ROW]
[ROW][C]39[/C][C] 0.9213[/C][C] 0.1573[/C][C] 0.07867[/C][/ROW]
[ROW][C]40[/C][C] 0.9011[/C][C] 0.1978[/C][C] 0.0989[/C][/ROW]
[ROW][C]41[/C][C] 0.9175[/C][C] 0.165[/C][C] 0.0825[/C][/ROW]
[ROW][C]42[/C][C] 0.8964[/C][C] 0.2073[/C][C] 0.1036[/C][/ROW]
[ROW][C]43[/C][C] 0.9533[/C][C] 0.09349[/C][C] 0.04674[/C][/ROW]
[ROW][C]44[/C][C] 0.9418[/C][C] 0.1163[/C][C] 0.05815[/C][/ROW]
[ROW][C]45[/C][C] 0.9267[/C][C] 0.1466[/C][C] 0.0733[/C][/ROW]
[ROW][C]46[/C][C] 0.9225[/C][C] 0.155[/C][C] 0.07748[/C][/ROW]
[ROW][C]47[/C][C] 0.9266[/C][C] 0.1467[/C][C] 0.07337[/C][/ROW]
[ROW][C]48[/C][C] 0.9191[/C][C] 0.1618[/C][C] 0.08092[/C][/ROW]
[ROW][C]49[/C][C] 0.9001[/C][C] 0.1999[/C][C] 0.09993[/C][/ROW]
[ROW][C]50[/C][C] 0.9099[/C][C] 0.1802[/C][C] 0.09012[/C][/ROW]
[ROW][C]51[/C][C] 0.904[/C][C] 0.192[/C][C] 0.09602[/C][/ROW]
[ROW][C]52[/C][C] 0.8877[/C][C] 0.2246[/C][C] 0.1123[/C][/ROW]
[ROW][C]53[/C][C] 0.8693[/C][C] 0.2614[/C][C] 0.1307[/C][/ROW]
[ROW][C]54[/C][C] 0.859[/C][C] 0.2819[/C][C] 0.141[/C][/ROW]
[ROW][C]55[/C][C] 0.872[/C][C] 0.2561[/C][C] 0.128[/C][/ROW]
[ROW][C]56[/C][C] 0.8477[/C][C] 0.3046[/C][C] 0.1523[/C][/ROW]
[ROW][C]57[/C][C] 0.8286[/C][C] 0.3427[/C][C] 0.1714[/C][/ROW]
[ROW][C]58[/C][C] 0.8018[/C][C] 0.3965[/C][C] 0.1982[/C][/ROW]
[ROW][C]59[/C][C] 0.7706[/C][C] 0.4589[/C][C] 0.2294[/C][/ROW]
[ROW][C]60[/C][C] 0.7381[/C][C] 0.5237[/C][C] 0.2619[/C][/ROW]
[ROW][C]61[/C][C] 0.7174[/C][C] 0.5652[/C][C] 0.2826[/C][/ROW]
[ROW][C]62[/C][C] 0.6809[/C][C] 0.6382[/C][C] 0.3191[/C][/ROW]
[ROW][C]63[/C][C] 0.6387[/C][C] 0.7226[/C][C] 0.3613[/C][/ROW]
[ROW][C]64[/C][C] 0.5989[/C][C] 0.8022[/C][C] 0.4011[/C][/ROW]
[ROW][C]65[/C][C] 0.631[/C][C] 0.7381[/C][C] 0.369[/C][/ROW]
[ROW][C]66[/C][C] 0.5864[/C][C] 0.8271[/C][C] 0.4136[/C][/ROW]
[ROW][C]67[/C][C] 0.6387[/C][C] 0.7225[/C][C] 0.3613[/C][/ROW]
[ROW][C]68[/C][C] 0.6151[/C][C] 0.7697[/C][C] 0.3849[/C][/ROW]
[ROW][C]69[/C][C] 0.5803[/C][C] 0.8394[/C][C] 0.4197[/C][/ROW]
[ROW][C]70[/C][C] 0.5439[/C][C] 0.9122[/C][C] 0.4561[/C][/ROW]
[ROW][C]71[/C][C] 0.5504[/C][C] 0.8992[/C][C] 0.4496[/C][/ROW]
[ROW][C]72[/C][C] 0.5068[/C][C] 0.9864[/C][C] 0.4932[/C][/ROW]
[ROW][C]73[/C][C] 0.5513[/C][C] 0.8973[/C][C] 0.4487[/C][/ROW]
[ROW][C]74[/C][C] 0.5065[/C][C] 0.9869[/C][C] 0.4935[/C][/ROW]
[ROW][C]75[/C][C] 0.5933[/C][C] 0.8135[/C][C] 0.4067[/C][/ROW]
[ROW][C]76[/C][C] 0.5678[/C][C] 0.8643[/C][C] 0.4322[/C][/ROW]
[ROW][C]77[/C][C] 0.6652[/C][C] 0.6696[/C][C] 0.3348[/C][/ROW]
[ROW][C]78[/C][C] 0.6735[/C][C] 0.653[/C][C] 0.3265[/C][/ROW]
[ROW][C]79[/C][C] 0.665[/C][C] 0.67[/C][C] 0.335[/C][/ROW]
[ROW][C]80[/C][C] 0.6259[/C][C] 0.7483[/C][C] 0.3741[/C][/ROW]
[ROW][C]81[/C][C] 0.5824[/C][C] 0.8352[/C][C] 0.4176[/C][/ROW]
[ROW][C]82[/C][C] 0.5379[/C][C] 0.9242[/C][C] 0.4621[/C][/ROW]
[ROW][C]83[/C][C] 0.5015[/C][C] 0.9971[/C][C] 0.4985[/C][/ROW]
[ROW][C]84[/C][C] 0.4804[/C][C] 0.9608[/C][C] 0.5196[/C][/ROW]
[ROW][C]85[/C][C] 0.4786[/C][C] 0.9572[/C][C] 0.5214[/C][/ROW]
[ROW][C]86[/C][C] 0.4499[/C][C] 0.8998[/C][C] 0.5501[/C][/ROW]
[ROW][C]87[/C][C] 0.4167[/C][C] 0.8333[/C][C] 0.5833[/C][/ROW]
[ROW][C]88[/C][C] 0.3954[/C][C] 0.7908[/C][C] 0.6046[/C][/ROW]
[ROW][C]89[/C][C] 0.3541[/C][C] 0.7081[/C][C] 0.6459[/C][/ROW]
[ROW][C]90[/C][C] 0.3208[/C][C] 0.6415[/C][C] 0.6792[/C][/ROW]
[ROW][C]91[/C][C] 0.296[/C][C] 0.592[/C][C] 0.704[/C][/ROW]
[ROW][C]92[/C][C] 0.2584[/C][C] 0.5167[/C][C] 0.7417[/C][/ROW]
[ROW][C]93[/C][C] 0.3127[/C][C] 0.6254[/C][C] 0.6873[/C][/ROW]
[ROW][C]94[/C][C] 0.2823[/C][C] 0.5645[/C][C] 0.7177[/C][/ROW]
[ROW][C]95[/C][C] 0.2569[/C][C] 0.5139[/C][C] 0.7431[/C][/ROW]
[ROW][C]96[/C][C] 0.2248[/C][C] 0.4495[/C][C] 0.7752[/C][/ROW]
[ROW][C]97[/C][C] 0.2554[/C][C] 0.5109[/C][C] 0.7446[/C][/ROW]
[ROW][C]98[/C][C] 0.6493[/C][C] 0.7014[/C][C] 0.3507[/C][/ROW]
[ROW][C]99[/C][C] 0.6131[/C][C] 0.7738[/C][C] 0.3869[/C][/ROW]
[ROW][C]100[/C][C] 0.6863[/C][C] 0.6274[/C][C] 0.3137[/C][/ROW]
[ROW][C]101[/C][C] 0.6529[/C][C] 0.6943[/C][C] 0.3471[/C][/ROW]
[ROW][C]102[/C][C] 0.6143[/C][C] 0.7713[/C][C] 0.3857[/C][/ROW]
[ROW][C]103[/C][C] 0.5732[/C][C] 0.8535[/C][C] 0.4268[/C][/ROW]
[ROW][C]104[/C][C] 0.5382[/C][C] 0.9237[/C][C] 0.4618[/C][/ROW]
[ROW][C]105[/C][C] 0.6561[/C][C] 0.6877[/C][C] 0.3439[/C][/ROW]
[ROW][C]106[/C][C] 0.6117[/C][C] 0.7767[/C][C] 0.3883[/C][/ROW]
[ROW][C]107[/C][C] 0.7647[/C][C] 0.4707[/C][C] 0.2353[/C][/ROW]
[ROW][C]108[/C][C] 0.746[/C][C] 0.5081[/C][C] 0.254[/C][/ROW]
[ROW][C]109[/C][C] 0.7705[/C][C] 0.459[/C][C] 0.2295[/C][/ROW]
[ROW][C]110[/C][C] 0.8471[/C][C] 0.3058[/C][C] 0.1529[/C][/ROW]
[ROW][C]111[/C][C] 0.8212[/C][C] 0.3575[/C][C] 0.1788[/C][/ROW]
[ROW][C]112[/C][C] 0.8966[/C][C] 0.2067[/C][C] 0.1034[/C][/ROW]
[ROW][C]113[/C][C] 0.8727[/C][C] 0.2547[/C][C] 0.1273[/C][/ROW]
[ROW][C]114[/C][C] 0.8484[/C][C] 0.3033[/C][C] 0.1516[/C][/ROW]
[ROW][C]115[/C][C] 0.8444[/C][C] 0.3111[/C][C] 0.1556[/C][/ROW]
[ROW][C]116[/C][C] 0.8172[/C][C] 0.3656[/C][C] 0.1828[/C][/ROW]
[ROW][C]117[/C][C] 0.8253[/C][C] 0.3495[/C][C] 0.1747[/C][/ROW]
[ROW][C]118[/C][C] 0.8171[/C][C] 0.3658[/C][C] 0.1829[/C][/ROW]
[ROW][C]119[/C][C] 0.8105[/C][C] 0.379[/C][C] 0.1895[/C][/ROW]
[ROW][C]120[/C][C] 0.8129[/C][C] 0.3741[/C][C] 0.1871[/C][/ROW]
[ROW][C]121[/C][C] 0.776[/C][C] 0.4481[/C][C] 0.224[/C][/ROW]
[ROW][C]122[/C][C] 0.8428[/C][C] 0.3143[/C][C] 0.1572[/C][/ROW]
[ROW][C]123[/C][C] 0.8089[/C][C] 0.3822[/C][C] 0.1911[/C][/ROW]
[ROW][C]124[/C][C] 0.8111[/C][C] 0.3778[/C][C] 0.1889[/C][/ROW]
[ROW][C]125[/C][C] 0.7723[/C][C] 0.4553[/C][C] 0.2277[/C][/ROW]
[ROW][C]126[/C][C] 0.806[/C][C] 0.3881[/C][C] 0.194[/C][/ROW]
[ROW][C]127[/C][C] 0.7803[/C][C] 0.4395[/C][C] 0.2197[/C][/ROW]
[ROW][C]128[/C][C] 0.8262[/C][C] 0.3477[/C][C] 0.1738[/C][/ROW]
[ROW][C]129[/C][C] 0.791[/C][C] 0.418[/C][C] 0.209[/C][/ROW]
[ROW][C]130[/C][C] 0.8139[/C][C] 0.3722[/C][C] 0.1861[/C][/ROW]
[ROW][C]131[/C][C] 0.8278[/C][C] 0.3444[/C][C] 0.1722[/C][/ROW]
[ROW][C]132[/C][C] 0.7918[/C][C] 0.4165[/C][C] 0.2082[/C][/ROW]
[ROW][C]133[/C][C] 0.8962[/C][C] 0.2076[/C][C] 0.1038[/C][/ROW]
[ROW][C]134[/C][C] 0.8676[/C][C] 0.2647[/C][C] 0.1324[/C][/ROW]
[ROW][C]135[/C][C] 0.855[/C][C] 0.29[/C][C] 0.145[/C][/ROW]
[ROW][C]136[/C][C] 0.8816[/C][C] 0.2369[/C][C] 0.1184[/C][/ROW]
[ROW][C]137[/C][C] 0.9045[/C][C] 0.191[/C][C] 0.09552[/C][/ROW]
[ROW][C]138[/C][C] 0.8758[/C][C] 0.2484[/C][C] 0.1242[/C][/ROW]
[ROW][C]139[/C][C] 0.9617[/C][C] 0.07654[/C][C] 0.03827[/C][/ROW]
[ROW][C]140[/C][C] 0.9514[/C][C] 0.09722[/C][C] 0.04861[/C][/ROW]
[ROW][C]141[/C][C] 0.974[/C][C] 0.05202[/C][C] 0.02601[/C][/ROW]
[ROW][C]142[/C][C] 0.9878[/C][C] 0.02444[/C][C] 0.01222[/C][/ROW]
[ROW][C]143[/C][C] 0.9831[/C][C] 0.03372[/C][C] 0.01686[/C][/ROW]
[ROW][C]144[/C][C] 0.9734[/C][C] 0.05322[/C][C] 0.02661[/C][/ROW]
[ROW][C]145[/C][C] 0.984[/C][C] 0.03204[/C][C] 0.01602[/C][/ROW]
[ROW][C]146[/C][C] 0.9792[/C][C] 0.04158[/C][C] 0.02079[/C][/ROW]
[ROW][C]147[/C][C] 0.9674[/C][C] 0.06528[/C][C] 0.03264[/C][/ROW]
[ROW][C]148[/C][C] 0.9758[/C][C] 0.04842[/C][C] 0.02421[/C][/ROW]
[ROW][C]149[/C][C] 0.9593[/C][C] 0.0815[/C][C] 0.04075[/C][/ROW]
[ROW][C]150[/C][C] 0.9862[/C][C] 0.02754[/C][C] 0.01377[/C][/ROW]
[ROW][C]151[/C][C] 0.9752[/C][C] 0.0496[/C][C] 0.0248[/C][/ROW]
[ROW][C]152[/C][C] 0.9809[/C][C] 0.03829[/C][C] 0.01915[/C][/ROW]
[ROW][C]153[/C][C] 0.9726[/C][C] 0.05485[/C][C] 0.02743[/C][/ROW]
[ROW][C]154[/C][C] 0.9474[/C][C] 0.1051[/C][C] 0.05257[/C][/ROW]
[ROW][C]155[/C][C] 0.9202[/C][C] 0.1595[/C][C] 0.07976[/C][/ROW]
[ROW][C]156[/C][C] 0.8599[/C][C] 0.2802[/C][C] 0.1401[/C][/ROW]
[ROW][C]157[/C][C] 0.7773[/C][C] 0.4454[/C][C] 0.2227[/C][/ROW]
[ROW][C]158[/C][C] 0.9433[/C][C] 0.1135[/C][C] 0.05674[/C][/ROW]
[ROW][C]159[/C][C] 0.8646[/C][C] 0.2709[/C][C] 0.1354[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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	0.185	0.37	0.815
9	0.1089	0.2178	0.8911
10	0.06138	0.1228	0.9386
11	0.3331	0.6663	0.6669
12	0.2692	0.5383	0.7308
13	0.4029	0.8058	0.5971
14	0.4729	0.9458	0.5271
15	0.4029	0.8058	0.5971
16	0.4369	0.8739	0.5631
17	0.47	0.9401	0.53
18	0.39	0.78	0.61
19	0.3599	0.7198	0.6401
20	0.4914	0.9829	0.5086
21	0.6603	0.6793	0.3397
22	0.921	0.158	0.079
23	0.9001	0.1998	0.09991
24	0.9018	0.1964	0.0982
25	0.8782	0.2436	0.1218
26	0.8609	0.2782	0.1391
27	0.8486	0.3027	0.1514
28	0.9337	0.1326	0.06632
29	0.9168	0.1664	0.08321
30	0.8978	0.2045	0.1022
31	0.8723	0.2553	0.1277
32	0.9073	0.1854	0.09271
33	0.8812	0.2377	0.1188
34	0.8707	0.2587	0.1293
35	0.8494	0.3012	0.1506
36	0.8181	0.3639	0.1819
37	0.8979	0.2042	0.1021
38	0.9035	0.193	0.09652
39	0.9213	0.1573	0.07867
40	0.9011	0.1978	0.0989
41	0.9175	0.165	0.0825
42	0.8964	0.2073	0.1036
43	0.9533	0.09349	0.04674
44	0.9418	0.1163	0.05815
45	0.9267	0.1466	0.0733
46	0.9225	0.155	0.07748
47	0.9266	0.1467	0.07337
48	0.9191	0.1618	0.08092
49	0.9001	0.1999	0.09993
50	0.9099	0.1802	0.09012
51	0.904	0.192	0.09602
52	0.8877	0.2246	0.1123
53	0.8693	0.2614	0.1307
54	0.859	0.2819	0.141
55	0.872	0.2561	0.128
56	0.8477	0.3046	0.1523
57	0.8286	0.3427	0.1714
58	0.8018	0.3965	0.1982
59	0.7706	0.4589	0.2294
60	0.7381	0.5237	0.2619
61	0.7174	0.5652	0.2826
62	0.6809	0.6382	0.3191
63	0.6387	0.7226	0.3613
64	0.5989	0.8022	0.4011
65	0.631	0.7381	0.369
66	0.5864	0.8271	0.4136
67	0.6387	0.7225	0.3613
68	0.6151	0.7697	0.3849
69	0.5803	0.8394	0.4197
70	0.5439	0.9122	0.4561
71	0.5504	0.8992	0.4496
72	0.5068	0.9864	0.4932
73	0.5513	0.8973	0.4487
74	0.5065	0.9869	0.4935
75	0.5933	0.8135	0.4067
76	0.5678	0.8643	0.4322
77	0.6652	0.6696	0.3348
78	0.6735	0.653	0.3265
79	0.665	0.67	0.335
80	0.6259	0.7483	0.3741
81	0.5824	0.8352	0.4176
82	0.5379	0.9242	0.4621
83	0.5015	0.9971	0.4985
84	0.4804	0.9608	0.5196
85	0.4786	0.9572	0.5214
86	0.4499	0.8998	0.5501
87	0.4167	0.8333	0.5833
88	0.3954	0.7908	0.6046
89	0.3541	0.7081	0.6459
90	0.3208	0.6415	0.6792
91	0.296	0.592	0.704
92	0.2584	0.5167	0.7417
93	0.3127	0.6254	0.6873
94	0.2823	0.5645	0.7177
95	0.2569	0.5139	0.7431
96	0.2248	0.4495	0.7752
97	0.2554	0.5109	0.7446
98	0.6493	0.7014	0.3507
99	0.6131	0.7738	0.3869
100	0.6863	0.6274	0.3137
101	0.6529	0.6943	0.3471
102	0.6143	0.7713	0.3857
103	0.5732	0.8535	0.4268
104	0.5382	0.9237	0.4618
105	0.6561	0.6877	0.3439
106	0.6117	0.7767	0.3883
107	0.7647	0.4707	0.2353
108	0.746	0.5081	0.254
109	0.7705	0.459	0.2295
110	0.8471	0.3058	0.1529
111	0.8212	0.3575	0.1788
112	0.8966	0.2067	0.1034
113	0.8727	0.2547	0.1273
114	0.8484	0.3033	0.1516
115	0.8444	0.3111	0.1556
116	0.8172	0.3656	0.1828
117	0.8253	0.3495	0.1747
118	0.8171	0.3658	0.1829
119	0.8105	0.379	0.1895
120	0.8129	0.3741	0.1871
121	0.776	0.4481	0.224
122	0.8428	0.3143	0.1572
123	0.8089	0.3822	0.1911
124	0.8111	0.3778	0.1889
125	0.7723	0.4553	0.2277
126	0.806	0.3881	0.194
127	0.7803	0.4395	0.2197
128	0.8262	0.3477	0.1738
129	0.791	0.418	0.209
130	0.8139	0.3722	0.1861
131	0.8278	0.3444	0.1722
132	0.7918	0.4165	0.2082
133	0.8962	0.2076	0.1038
134	0.8676	0.2647	0.1324
135	0.855	0.29	0.145
136	0.8816	0.2369	0.1184
137	0.9045	0.191	0.09552
138	0.8758	0.2484	0.1242
139	0.9617	0.07654	0.03827
140	0.9514	0.09722	0.04861
141	0.974	0.05202	0.02601
142	0.9878	0.02444	0.01222
143	0.9831	0.03372	0.01686
144	0.9734	0.05322	0.02661
145	0.984	0.03204	0.01602
146	0.9792	0.04158	0.02079
147	0.9674	0.06528	0.03264
148	0.9758	0.04842	0.02421
149	0.9593	0.0815	0.04075
150	0.9862	0.02754	0.01377
151	0.9752	0.0496	0.0248
152	0.9809	0.03829	0.01915
153	0.9726	0.05485	0.02743
154	0.9474	0.1051	0.05257
155	0.9202	0.1595	0.07976
156	0.8599	0.2802	0.1401
157	0.7773	0.4454	0.2227
158	0.9433	0.1135	0.05674
159	0.8646	0.2709	0.1354

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	8	0.0526316	NOK
10% type I error level	16	0.105263	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 8 & 0.0526316 & NOK \tabularnewline
10% type I error level & 16 & 0.105263 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.0526316[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.105263[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	8	0.0526316	NOK
10% type I error level	16	0.105263	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.29018, df1 = 2, df2 = 160, p-value = 0.7485
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.77631, df1 = 8, df2 = 154, p-value = 0.6242
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.329, df1 = 2, df2 = 160, p-value = 0.01476

\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 = 0.29018, df1 = 2, df2 = 160, p-value = 0.7485
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.77631, df1 = 8, df2 = 154, p-value = 0.6242
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.329, df1 = 2, df2 = 160, p-value = 0.01476
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&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 = 0.29018, df1 = 2, df2 = 160, p-value = 0.7485
[/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 = 0.77631, df1 = 8, df2 = 154, p-value = 0.6242
[/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 = 4.329, df1 = 2, df2 = 160, p-value = 0.01476
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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 = 0.29018, df1 = 2, df2 = 160, p-value = 0.7485
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.77631, df1 = 8, df2 = 154, p-value = 0.6242
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.329, df1 = 2, df2 = 160, p-value = 0.01476

Variance Inflation Factors (Multicollinearity)
> vif KVDD1 KVDD2 KVDD3 KVDD4 1.145933 1.078140 1.086082 1.137640

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   KVDD1    KVDD2    KVDD3    KVDD4 
1.145933 1.078140 1.086082 1.137640 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   KVDD1    KVDD2    KVDD3    KVDD4 
1.145933 1.078140 1.086082 1.137640 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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 KVDD1 KVDD2 KVDD3 KVDD4 1.145933 1.078140 1.086082 1.137640

Figure 1

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Parameters (Session):

par1 = 1 ; par2 = 5 ; par3 = TRUE ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code