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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 10 Dec 2016 18:26:03 +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/10/t14813907754koeg48fgx0qf8k.htm/, Retrieved Sun, 05 May 2024 21:01:44 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298737, Retrieved Sun, 05 May 2024 21:01:44 +0000

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

-       [Multiple Regression] [Multiple regressi...] [2016-12-10 17:26:03] [2e11ca31a00cf8de75c33c1af2d59434] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 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 time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298737&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]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298737&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298737&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	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVSUM[t] = + 7.02571 + 0.495243SK1[t] + 1.10411SK2[t] + 0.371856SK4[t] + 0.177647SK5[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVSUM[t] =  +  7.02571 +  0.495243SK1[t] +  1.10411SK2[t] +  0.371856SK4[t] +  0.177647SK5[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298737&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVSUM[t] =  +  7.02571 +  0.495243SK1[t] +  1.10411SK2[t] +  0.371856SK4[t] +  0.177647SK5[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298737&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298737&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
TVSUM[t] = + 7.02571 + 0.495243SK1[t] + 1.10411SK2[t] + 0.371856SK4[t] + 0.177647SK5[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+7.026	1.186	+5.9250e+00	1.851e-08	9.255e-09
SK1	+0.4952	0.1579	+3.1370e+00	0.002031	0.001015
SK2	+1.104	0.1896	+5.8230e+00	3.07e-08	1.535e-08
SK4	+0.3719	0.2039	+1.8240e+00	0.07009	0.03505
SK5	+0.1777	0.1802	+9.8580e-01	0.3257	0.1629

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +7.026 &  1.186 & +5.9250e+00 &  1.851e-08 &  9.255e-09 \tabularnewline
SK1 & +0.4952 &  0.1579 & +3.1370e+00 &  0.002031 &  0.001015 \tabularnewline
SK2 & +1.104 &  0.1896 & +5.8230e+00 &  3.07e-08 &  1.535e-08 \tabularnewline
SK4 & +0.3719 &  0.2039 & +1.8240e+00 &  0.07009 &  0.03505 \tabularnewline
SK5 & +0.1777 &  0.1802 & +9.8580e-01 &  0.3257 &  0.1629 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298737&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+7.026[/C][C] 1.186[/C][C]+5.9250e+00[/C][C] 1.851e-08[/C][C] 9.255e-09[/C][/ROW]
[ROW][C]SK1[/C][C]+0.4952[/C][C] 0.1579[/C][C]+3.1370e+00[/C][C] 0.002031[/C][C] 0.001015[/C][/ROW]
[ROW][C]SK2[/C][C]+1.104[/C][C] 0.1896[/C][C]+5.8230e+00[/C][C] 3.07e-08[/C][C] 1.535e-08[/C][/ROW]
[ROW][C]SK4[/C][C]+0.3719[/C][C] 0.2039[/C][C]+1.8240e+00[/C][C] 0.07009[/C][C] 0.03505[/C][/ROW]
[ROW][C]SK5[/C][C]+0.1777[/C][C] 0.1802[/C][C]+9.8580e-01[/C][C] 0.3257[/C][C] 0.1629[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298737&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298737&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+7.026	1.186	+5.9250e+00	1.851e-08	9.255e-09
SK1	+0.4952	0.1579	+3.1370e+00	0.002031	0.001015
SK2	+1.104	0.1896	+5.8230e+00	3.07e-08	1.535e-08
SK4	+0.3719	0.2039	+1.8240e+00	0.07009	0.03505
SK5	+0.1777	0.1802	+9.8580e-01	0.3257	0.1629

Multiple Linear Regression - Regression Statistics
Multiple R	0.5563
R-squared	0.3094
Adjusted R-squared	0.2922
F-TEST (value)	17.92
F-TEST (DF numerator)	4
F-TEST (DF denominator)	160
p-value	3.527e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.395
Sum Squared Residuals	311.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5563 \tabularnewline
R-squared &  0.3094 \tabularnewline
Adjusted R-squared &  0.2922 \tabularnewline
F-TEST (value) &  17.92 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value &  3.527e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.395 \tabularnewline
Sum Squared Residuals &  311.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298737&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5563[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3094[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2922[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 17.92[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C] 3.527e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.395[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 311.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298737&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298737&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	0.5563
R-squared	0.3094
Adjusted R-squared	0.2922
F-TEST (value)	17.92
F-TEST (DF numerator)	4
F-TEST (DF denominator)	160
p-value	3.527e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.395
Sum Squared Residuals	311.5

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.22	-0.2187
2	16	15.19	0.8101
3	17	15.8	1.201
4	15	14.75	0.246
5	16	15.8	0.2012
6	16	15.3	0.6965
7	17	14.58	2.424
8	16	15.13	0.8741
9	17	16.9	0.0971
10	17	16.9	0.0971
11	17	15.8	1.201
12	15	15.43	-0.4269
13	16	15.25	0.7507
14	14	14.02	-0.02179
15	16	15.27	0.7342
16	17	15.13	1.874
17	16	15.13	0.8741
18	15	16.85	-1.849
19	17	15.8	1.201
20	16	14.75	1.246
21	15	15.8	-0.7988
22	16	15.62	0.3789
23	15	15.62	-0.6211
24	17	15.62	1.379
25	15	15.13	-0.1259
26	16	14.93	1.068
27	15	15.62	-0.6211
28	16	14.81	1.192
29	16	16.12	-0.1164
30	13	14.52	-1.517
31	15	16.9	-1.903
32	17	16.12	0.8836
33	15	14.52	0.483
34	13	13.7	-0.7042
35	17	16.73	0.2747
36	15	15.13	-0.1259
37	14	14.15	-0.1452
38	14	14.52	-0.517
39	18	15.62	2.379
40	15	16.12	-1.116
41	17	16.9	0.0971
42	13	14.02	-1.022
43	16	17.03	-1.026
44	15	15.74	-0.7445
45	15	15.25	-0.2493
46	16	15.62	0.3789
47	15	15.68	-0.6805
48	13	15.8	-2.799
49	17	16.73	0.2747
50	17	17.4	-0.3981
51	17	17.22	-0.2205
52	11	14.69	-3.695
53	14	14.15	-0.1452
54	13	15.62	-2.621
55	15	14.58	0.4236
56	17	15.13	1.874
57	16	15.43	0.5731
58	15	15.8	-0.7988
59	17	17.4	-0.3981
60	16	14.81	1.192
61	16	15.8	0.2012
62	16	14.77	1.229
63	15	15.8	-0.7988
64	12	13.41	-1.413
65	17	15.43	1.573
66	14	15.43	-1.427
67	14	15.57	-1.567
68	16	14.93	1.068
69	15	14.75	0.246
70	15	17.27	-2.275
71	13	15.62	-2.621
72	13	15.62	-2.621
73	17	16.17	0.8294
74	15	15.13	-0.1259
75	16	15.8	0.2012
76	14	14.93	-0.9317
77	15	14.02	0.9782
78	17	14.52	2.483
79	16	15.62	0.3789
80	12	14.02	-2.022
81	16	15.8	0.2012
82	17	15.8	1.201
83	17	15.8	1.201
84	20	16.12	3.884
85	17	16.49	0.5118
86	18	15.8	2.201
87	15	15.13	-0.1259
88	17	15.13	1.874
89	14	13.04	0.9589
90	15	15.62	-0.6211
91	17	15.62	1.379
92	16	15.8	0.2012
93	17	16.9	0.0971
94	15	14.93	0.06831
95	16	15.62	0.3789
96	18	16.12	1.884
97	18	16.49	1.512
98	16	16.9	-0.9029
99	17	15.19	1.81
100	15	15.62	-0.6211
101	13	16.12	-3.116
102	15	14.75	0.246
103	17	16.67	0.3341
104	16	15.25	0.7507
105	16	15.25	0.7507
106	15	15.62	-0.6211
107	16	15.62	0.3789
108	16	15.3	0.6965
109	13	15.62	-2.621
110	15	15.25	-0.2493
111	12	13.83	-1.828
112	19	15.25	3.751
113	16	15.13	0.8741
114	16	15.44	0.5565
115	17	16.67	0.3341
116	16	16.29	-0.294
117	14	15.62	-1.621
118	15	15.25	-0.2493
119	14	14.75	-0.754
120	16	15.62	0.3789
121	15	15.8	-0.7988
122	17	16.73	0.2747
123	15	15.13	-0.1259
124	16	15.25	0.7507
125	16	15.62	0.3789
126	15	14.75	0.246
127	15	15.25	-0.2493
128	11	12.17	-1.174
129	16	15.43	0.5731
130	18	15.92	2.078
131	13	14.08	-1.081
132	11	14.02	-3.022
133	16	15.25	0.7507
134	18	17.4	0.6019
135	15	16.73	-1.725
136	19	17.77	1.23
137	17	16.9	0.0971
138	13	15.25	-2.249
139	14	15.3	-1.304
140	16	15.62	0.3789
141	13	15.62	-2.621
142	17	15.8	1.201
143	14	15.8	-1.799
144	19	15.92	3.078
145	14	14.52	-0.517
146	16	15.62	0.3789
147	12	13.65	-1.65
148	16	16.73	-0.7253
149	16	15.25	0.7507
150	15	15.62	-0.6211
151	12	14.93	-2.932
152	15	15.62	-0.6211
153	17	16.29	0.706
154	13	15.44	-2.443
155	15	13.53	1.473
156	18	15.62	2.379
157	15	14.32	0.6772
158	18	15.62	2.379
159	15	17.1	-2.097
160	15	16.12	-1.116
161	16	15.74	0.2555
162	13	14.2	-1.199
163	16	15.62	0.3789
164	13	15.8	-2.799
165	16	14.08	1.924

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.22 & -0.2187 \tabularnewline
2 &  16 &  15.19 &  0.8101 \tabularnewline
3 &  17 &  15.8 &  1.201 \tabularnewline
4 &  15 &  14.75 &  0.246 \tabularnewline
5 &  16 &  15.8 &  0.2012 \tabularnewline
6 &  16 &  15.3 &  0.6965 \tabularnewline
7 &  17 &  14.58 &  2.424 \tabularnewline
8 &  16 &  15.13 &  0.8741 \tabularnewline
9 &  17 &  16.9 &  0.0971 \tabularnewline
10 &  17 &  16.9 &  0.0971 \tabularnewline
11 &  17 &  15.8 &  1.201 \tabularnewline
12 &  15 &  15.43 & -0.4269 \tabularnewline
13 &  16 &  15.25 &  0.7507 \tabularnewline
14 &  14 &  14.02 & -0.02179 \tabularnewline
15 &  16 &  15.27 &  0.7342 \tabularnewline
16 &  17 &  15.13 &  1.874 \tabularnewline
17 &  16 &  15.13 &  0.8741 \tabularnewline
18 &  15 &  16.85 & -1.849 \tabularnewline
19 &  17 &  15.8 &  1.201 \tabularnewline
20 &  16 &  14.75 &  1.246 \tabularnewline
21 &  15 &  15.8 & -0.7988 \tabularnewline
22 &  16 &  15.62 &  0.3789 \tabularnewline
23 &  15 &  15.62 & -0.6211 \tabularnewline
24 &  17 &  15.62 &  1.379 \tabularnewline
25 &  15 &  15.13 & -0.1259 \tabularnewline
26 &  16 &  14.93 &  1.068 \tabularnewline
27 &  15 &  15.62 & -0.6211 \tabularnewline
28 &  16 &  14.81 &  1.192 \tabularnewline
29 &  16 &  16.12 & -0.1164 \tabularnewline
30 &  13 &  14.52 & -1.517 \tabularnewline
31 &  15 &  16.9 & -1.903 \tabularnewline
32 &  17 &  16.12 &  0.8836 \tabularnewline
33 &  15 &  14.52 &  0.483 \tabularnewline
34 &  13 &  13.7 & -0.7042 \tabularnewline
35 &  17 &  16.73 &  0.2747 \tabularnewline
36 &  15 &  15.13 & -0.1259 \tabularnewline
37 &  14 &  14.15 & -0.1452 \tabularnewline
38 &  14 &  14.52 & -0.517 \tabularnewline
39 &  18 &  15.62 &  2.379 \tabularnewline
40 &  15 &  16.12 & -1.116 \tabularnewline
41 &  17 &  16.9 &  0.0971 \tabularnewline
42 &  13 &  14.02 & -1.022 \tabularnewline
43 &  16 &  17.03 & -1.026 \tabularnewline
44 &  15 &  15.74 & -0.7445 \tabularnewline
45 &  15 &  15.25 & -0.2493 \tabularnewline
46 &  16 &  15.62 &  0.3789 \tabularnewline
47 &  15 &  15.68 & -0.6805 \tabularnewline
48 &  13 &  15.8 & -2.799 \tabularnewline
49 &  17 &  16.73 &  0.2747 \tabularnewline
50 &  17 &  17.4 & -0.3981 \tabularnewline
51 &  17 &  17.22 & -0.2205 \tabularnewline
52 &  11 &  14.69 & -3.695 \tabularnewline
53 &  14 &  14.15 & -0.1452 \tabularnewline
54 &  13 &  15.62 & -2.621 \tabularnewline
55 &  15 &  14.58 &  0.4236 \tabularnewline
56 &  17 &  15.13 &  1.874 \tabularnewline
57 &  16 &  15.43 &  0.5731 \tabularnewline
58 &  15 &  15.8 & -0.7988 \tabularnewline
59 &  17 &  17.4 & -0.3981 \tabularnewline
60 &  16 &  14.81 &  1.192 \tabularnewline
61 &  16 &  15.8 &  0.2012 \tabularnewline
62 &  16 &  14.77 &  1.229 \tabularnewline
63 &  15 &  15.8 & -0.7988 \tabularnewline
64 &  12 &  13.41 & -1.413 \tabularnewline
65 &  17 &  15.43 &  1.573 \tabularnewline
66 &  14 &  15.43 & -1.427 \tabularnewline
67 &  14 &  15.57 & -1.567 \tabularnewline
68 &  16 &  14.93 &  1.068 \tabularnewline
69 &  15 &  14.75 &  0.246 \tabularnewline
70 &  15 &  17.27 & -2.275 \tabularnewline
71 &  13 &  15.62 & -2.621 \tabularnewline
72 &  13 &  15.62 & -2.621 \tabularnewline
73 &  17 &  16.17 &  0.8294 \tabularnewline
74 &  15 &  15.13 & -0.1259 \tabularnewline
75 &  16 &  15.8 &  0.2012 \tabularnewline
76 &  14 &  14.93 & -0.9317 \tabularnewline
77 &  15 &  14.02 &  0.9782 \tabularnewline
78 &  17 &  14.52 &  2.483 \tabularnewline
79 &  16 &  15.62 &  0.3789 \tabularnewline
80 &  12 &  14.02 & -2.022 \tabularnewline
81 &  16 &  15.8 &  0.2012 \tabularnewline
82 &  17 &  15.8 &  1.201 \tabularnewline
83 &  17 &  15.8 &  1.201 \tabularnewline
84 &  20 &  16.12 &  3.884 \tabularnewline
85 &  17 &  16.49 &  0.5118 \tabularnewline
86 &  18 &  15.8 &  2.201 \tabularnewline
87 &  15 &  15.13 & -0.1259 \tabularnewline
88 &  17 &  15.13 &  1.874 \tabularnewline
89 &  14 &  13.04 &  0.9589 \tabularnewline
90 &  15 &  15.62 & -0.6211 \tabularnewline
91 &  17 &  15.62 &  1.379 \tabularnewline
92 &  16 &  15.8 &  0.2012 \tabularnewline
93 &  17 &  16.9 &  0.0971 \tabularnewline
94 &  15 &  14.93 &  0.06831 \tabularnewline
95 &  16 &  15.62 &  0.3789 \tabularnewline
96 &  18 &  16.12 &  1.884 \tabularnewline
97 &  18 &  16.49 &  1.512 \tabularnewline
98 &  16 &  16.9 & -0.9029 \tabularnewline
99 &  17 &  15.19 &  1.81 \tabularnewline
100 &  15 &  15.62 & -0.6211 \tabularnewline
101 &  13 &  16.12 & -3.116 \tabularnewline
102 &  15 &  14.75 &  0.246 \tabularnewline
103 &  17 &  16.67 &  0.3341 \tabularnewline
104 &  16 &  15.25 &  0.7507 \tabularnewline
105 &  16 &  15.25 &  0.7507 \tabularnewline
106 &  15 &  15.62 & -0.6211 \tabularnewline
107 &  16 &  15.62 &  0.3789 \tabularnewline
108 &  16 &  15.3 &  0.6965 \tabularnewline
109 &  13 &  15.62 & -2.621 \tabularnewline
110 &  15 &  15.25 & -0.2493 \tabularnewline
111 &  12 &  13.83 & -1.828 \tabularnewline
112 &  19 &  15.25 &  3.751 \tabularnewline
113 &  16 &  15.13 &  0.8741 \tabularnewline
114 &  16 &  15.44 &  0.5565 \tabularnewline
115 &  17 &  16.67 &  0.3341 \tabularnewline
116 &  16 &  16.29 & -0.294 \tabularnewline
117 &  14 &  15.62 & -1.621 \tabularnewline
118 &  15 &  15.25 & -0.2493 \tabularnewline
119 &  14 &  14.75 & -0.754 \tabularnewline
120 &  16 &  15.62 &  0.3789 \tabularnewline
121 &  15 &  15.8 & -0.7988 \tabularnewline
122 &  17 &  16.73 &  0.2747 \tabularnewline
123 &  15 &  15.13 & -0.1259 \tabularnewline
124 &  16 &  15.25 &  0.7507 \tabularnewline
125 &  16 &  15.62 &  0.3789 \tabularnewline
126 &  15 &  14.75 &  0.246 \tabularnewline
127 &  15 &  15.25 & -0.2493 \tabularnewline
128 &  11 &  12.17 & -1.174 \tabularnewline
129 &  16 &  15.43 &  0.5731 \tabularnewline
130 &  18 &  15.92 &  2.078 \tabularnewline
131 &  13 &  14.08 & -1.081 \tabularnewline
132 &  11 &  14.02 & -3.022 \tabularnewline
133 &  16 &  15.25 &  0.7507 \tabularnewline
134 &  18 &  17.4 &  0.6019 \tabularnewline
135 &  15 &  16.73 & -1.725 \tabularnewline
136 &  19 &  17.77 &  1.23 \tabularnewline
137 &  17 &  16.9 &  0.0971 \tabularnewline
138 &  13 &  15.25 & -2.249 \tabularnewline
139 &  14 &  15.3 & -1.304 \tabularnewline
140 &  16 &  15.62 &  0.3789 \tabularnewline
141 &  13 &  15.62 & -2.621 \tabularnewline
142 &  17 &  15.8 &  1.201 \tabularnewline
143 &  14 &  15.8 & -1.799 \tabularnewline
144 &  19 &  15.92 &  3.078 \tabularnewline
145 &  14 &  14.52 & -0.517 \tabularnewline
146 &  16 &  15.62 &  0.3789 \tabularnewline
147 &  12 &  13.65 & -1.65 \tabularnewline
148 &  16 &  16.73 & -0.7253 \tabularnewline
149 &  16 &  15.25 &  0.7507 \tabularnewline
150 &  15 &  15.62 & -0.6211 \tabularnewline
151 &  12 &  14.93 & -2.932 \tabularnewline
152 &  15 &  15.62 & -0.6211 \tabularnewline
153 &  17 &  16.29 &  0.706 \tabularnewline
154 &  13 &  15.44 & -2.443 \tabularnewline
155 &  15 &  13.53 &  1.473 \tabularnewline
156 &  18 &  15.62 &  2.379 \tabularnewline
157 &  15 &  14.32 &  0.6772 \tabularnewline
158 &  18 &  15.62 &  2.379 \tabularnewline
159 &  15 &  17.1 & -2.097 \tabularnewline
160 &  15 &  16.12 & -1.116 \tabularnewline
161 &  16 &  15.74 &  0.2555 \tabularnewline
162 &  13 &  14.2 & -1.199 \tabularnewline
163 &  16 &  15.62 &  0.3789 \tabularnewline
164 &  13 &  15.8 & -2.799 \tabularnewline
165 &  16 &  14.08 &  1.924 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298737&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] 13.22[/C][C]-0.2187[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.19[/C][C] 0.8101[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.8[/C][C] 1.201[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.75[/C][C] 0.246[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.8[/C][C] 0.2012[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.3[/C][C] 0.6965[/C][/ROW]
[ROW][C]7[/C][C] 17[/C][C] 14.58[/C][C] 2.424[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.13[/C][C] 0.8741[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.9[/C][C] 0.0971[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 16.9[/C][C] 0.0971[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.8[/C][C] 1.201[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.43[/C][C]-0.4269[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.25[/C][C] 0.7507[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 14.02[/C][C]-0.02179[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.27[/C][C] 0.7342[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.13[/C][C] 1.874[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.13[/C][C] 0.8741[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 16.85[/C][C]-1.849[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.8[/C][C] 1.201[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.75[/C][C] 1.246[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.8[/C][C]-0.7988[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.62[/C][C] 0.3789[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.62[/C][C]-0.6211[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.62[/C][C] 1.379[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 15.13[/C][C]-0.1259[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.93[/C][C] 1.068[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.62[/C][C]-0.6211[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.81[/C][C] 1.192[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.12[/C][C]-0.1164[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.52[/C][C]-1.517[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 16.9[/C][C]-1.903[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.12[/C][C] 0.8836[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 14.52[/C][C] 0.483[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 13.7[/C][C]-0.7042[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 16.73[/C][C] 0.2747[/C][/ROW]
[ROW][C]36[/C][C] 15[/C][C] 15.13[/C][C]-0.1259[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 14.15[/C][C]-0.1452[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 14.52[/C][C]-0.517[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 15.62[/C][C] 2.379[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 16.12[/C][C]-1.116[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 16.9[/C][C] 0.0971[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 14.02[/C][C]-1.022[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 17.03[/C][C]-1.026[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 15.74[/C][C]-0.7445[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 15.25[/C][C]-0.2493[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.62[/C][C] 0.3789[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 15.68[/C][C]-0.6805[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 15.8[/C][C]-2.799[/C][/ROW]
[ROW][C]49[/C][C] 17[/C][C] 16.73[/C][C] 0.2747[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 17.4[/C][C]-0.3981[/C][/ROW]
[ROW][C]51[/C][C] 17[/C][C] 17.22[/C][C]-0.2205[/C][/ROW]
[ROW][C]52[/C][C] 11[/C][C] 14.69[/C][C]-3.695[/C][/ROW]
[ROW][C]53[/C][C] 14[/C][C] 14.15[/C][C]-0.1452[/C][/ROW]
[ROW][C]54[/C][C] 13[/C][C] 15.62[/C][C]-2.621[/C][/ROW]
[ROW][C]55[/C][C] 15[/C][C] 14.58[/C][C] 0.4236[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 15.13[/C][C] 1.874[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 15.43[/C][C] 0.5731[/C][/ROW]
[ROW][C]58[/C][C] 15[/C][C] 15.8[/C][C]-0.7988[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 17.4[/C][C]-0.3981[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 14.81[/C][C] 1.192[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 15.8[/C][C] 0.2012[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 14.77[/C][C] 1.229[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.8[/C][C]-0.7988[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 13.41[/C][C]-1.413[/C][/ROW]
[ROW][C]65[/C][C] 17[/C][C] 15.43[/C][C] 1.573[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 15.43[/C][C]-1.427[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 15.57[/C][C]-1.567[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 14.93[/C][C] 1.068[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 14.75[/C][C] 0.246[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 17.27[/C][C]-2.275[/C][/ROW]
[ROW][C]71[/C][C] 13[/C][C] 15.62[/C][C]-2.621[/C][/ROW]
[ROW][C]72[/C][C] 13[/C][C] 15.62[/C][C]-2.621[/C][/ROW]
[ROW][C]73[/C][C] 17[/C][C] 16.17[/C][C] 0.8294[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.13[/C][C]-0.1259[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.8[/C][C] 0.2012[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 14.93[/C][C]-0.9317[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 14.02[/C][C] 0.9782[/C][/ROW]
[ROW][C]78[/C][C] 17[/C][C] 14.52[/C][C] 2.483[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.62[/C][C] 0.3789[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 14.02[/C][C]-2.022[/C][/ROW]
[ROW][C]81[/C][C] 16[/C][C] 15.8[/C][C] 0.2012[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 15.8[/C][C] 1.201[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 15.8[/C][C] 1.201[/C][/ROW]
[ROW][C]84[/C][C] 20[/C][C] 16.12[/C][C] 3.884[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.49[/C][C] 0.5118[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 15.8[/C][C] 2.201[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 15.13[/C][C]-0.1259[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.13[/C][C] 1.874[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 13.04[/C][C] 0.9589[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 15.62[/C][C]-0.6211[/C][/ROW]
[ROW][C]91[/C][C] 17[/C][C] 15.62[/C][C] 1.379[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 15.8[/C][C] 0.2012[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.9[/C][C] 0.0971[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 14.93[/C][C] 0.06831[/C][/ROW]
[ROW][C]95[/C][C] 16[/C][C] 15.62[/C][C] 0.3789[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 16.12[/C][C] 1.884[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 16.49[/C][C] 1.512[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.9[/C][C]-0.9029[/C][/ROW]
[ROW][C]99[/C][C] 17[/C][C] 15.19[/C][C] 1.81[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 15.62[/C][C]-0.6211[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 16.12[/C][C]-3.116[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 14.75[/C][C] 0.246[/C][/ROW]
[ROW][C]103[/C][C] 17[/C][C] 16.67[/C][C] 0.3341[/C][/ROW]
[ROW][C]104[/C][C] 16[/C][C] 15.25[/C][C] 0.7507[/C][/ROW]
[ROW][C]105[/C][C] 16[/C][C] 15.25[/C][C] 0.7507[/C][/ROW]
[ROW][C]106[/C][C] 15[/C][C] 15.62[/C][C]-0.6211[/C][/ROW]
[ROW][C]107[/C][C] 16[/C][C] 15.62[/C][C] 0.3789[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 15.3[/C][C] 0.6965[/C][/ROW]
[ROW][C]109[/C][C] 13[/C][C] 15.62[/C][C]-2.621[/C][/ROW]
[ROW][C]110[/C][C] 15[/C][C] 15.25[/C][C]-0.2493[/C][/ROW]
[ROW][C]111[/C][C] 12[/C][C] 13.83[/C][C]-1.828[/C][/ROW]
[ROW][C]112[/C][C] 19[/C][C] 15.25[/C][C] 3.751[/C][/ROW]
[ROW][C]113[/C][C] 16[/C][C] 15.13[/C][C] 0.8741[/C][/ROW]
[ROW][C]114[/C][C] 16[/C][C] 15.44[/C][C] 0.5565[/C][/ROW]
[ROW][C]115[/C][C] 17[/C][C] 16.67[/C][C] 0.3341[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 16.29[/C][C]-0.294[/C][/ROW]
[ROW][C]117[/C][C] 14[/C][C] 15.62[/C][C]-1.621[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 15.25[/C][C]-0.2493[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 14.75[/C][C]-0.754[/C][/ROW]
[ROW][C]120[/C][C] 16[/C][C] 15.62[/C][C] 0.3789[/C][/ROW]
[ROW][C]121[/C][C] 15[/C][C] 15.8[/C][C]-0.7988[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 16.73[/C][C] 0.2747[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.13[/C][C]-0.1259[/C][/ROW]
[ROW][C]124[/C][C] 16[/C][C] 15.25[/C][C] 0.7507[/C][/ROW]
[ROW][C]125[/C][C] 16[/C][C] 15.62[/C][C] 0.3789[/C][/ROW]
[ROW][C]126[/C][C] 15[/C][C] 14.75[/C][C] 0.246[/C][/ROW]
[ROW][C]127[/C][C] 15[/C][C] 15.25[/C][C]-0.2493[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 12.17[/C][C]-1.174[/C][/ROW]
[ROW][C]129[/C][C] 16[/C][C] 15.43[/C][C] 0.5731[/C][/ROW]
[ROW][C]130[/C][C] 18[/C][C] 15.92[/C][C] 2.078[/C][/ROW]
[ROW][C]131[/C][C] 13[/C][C] 14.08[/C][C]-1.081[/C][/ROW]
[ROW][C]132[/C][C] 11[/C][C] 14.02[/C][C]-3.022[/C][/ROW]
[ROW][C]133[/C][C] 16[/C][C] 15.25[/C][C] 0.7507[/C][/ROW]
[ROW][C]134[/C][C] 18[/C][C] 17.4[/C][C] 0.6019[/C][/ROW]
[ROW][C]135[/C][C] 15[/C][C] 16.73[/C][C]-1.725[/C][/ROW]
[ROW][C]136[/C][C] 19[/C][C] 17.77[/C][C] 1.23[/C][/ROW]
[ROW][C]137[/C][C] 17[/C][C] 16.9[/C][C] 0.0971[/C][/ROW]
[ROW][C]138[/C][C] 13[/C][C] 15.25[/C][C]-2.249[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 15.3[/C][C]-1.304[/C][/ROW]
[ROW][C]140[/C][C] 16[/C][C] 15.62[/C][C] 0.3789[/C][/ROW]
[ROW][C]141[/C][C] 13[/C][C] 15.62[/C][C]-2.621[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 15.8[/C][C] 1.201[/C][/ROW]
[ROW][C]143[/C][C] 14[/C][C] 15.8[/C][C]-1.799[/C][/ROW]
[ROW][C]144[/C][C] 19[/C][C] 15.92[/C][C] 3.078[/C][/ROW]
[ROW][C]145[/C][C] 14[/C][C] 14.52[/C][C]-0.517[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 15.62[/C][C] 0.3789[/C][/ROW]
[ROW][C]147[/C][C] 12[/C][C] 13.65[/C][C]-1.65[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 16.73[/C][C]-0.7253[/C][/ROW]
[ROW][C]149[/C][C] 16[/C][C] 15.25[/C][C] 0.7507[/C][/ROW]
[ROW][C]150[/C][C] 15[/C][C] 15.62[/C][C]-0.6211[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 14.93[/C][C]-2.932[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 15.62[/C][C]-0.6211[/C][/ROW]
[ROW][C]153[/C][C] 17[/C][C] 16.29[/C][C] 0.706[/C][/ROW]
[ROW][C]154[/C][C] 13[/C][C] 15.44[/C][C]-2.443[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 13.53[/C][C] 1.473[/C][/ROW]
[ROW][C]156[/C][C] 18[/C][C] 15.62[/C][C] 2.379[/C][/ROW]
[ROW][C]157[/C][C] 15[/C][C] 14.32[/C][C] 0.6772[/C][/ROW]
[ROW][C]158[/C][C] 18[/C][C] 15.62[/C][C] 2.379[/C][/ROW]
[ROW][C]159[/C][C] 15[/C][C] 17.1[/C][C]-2.097[/C][/ROW]
[ROW][C]160[/C][C] 15[/C][C] 16.12[/C][C]-1.116[/C][/ROW]
[ROW][C]161[/C][C] 16[/C][C] 15.74[/C][C] 0.2555[/C][/ROW]
[ROW][C]162[/C][C] 13[/C][C] 14.2[/C][C]-1.199[/C][/ROW]
[ROW][C]163[/C][C] 16[/C][C] 15.62[/C][C] 0.3789[/C][/ROW]
[ROW][C]164[/C][C] 13[/C][C] 15.8[/C][C]-2.799[/C][/ROW]
[ROW][C]165[/C][C] 16[/C][C] 14.08[/C][C] 1.924[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298737&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298737&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	13.22	-0.2187
2	16	15.19	0.8101
3	17	15.8	1.201
4	15	14.75	0.246
5	16	15.8	0.2012
6	16	15.3	0.6965
7	17	14.58	2.424
8	16	15.13	0.8741
9	17	16.9	0.0971
10	17	16.9	0.0971
11	17	15.8	1.201
12	15	15.43	-0.4269
13	16	15.25	0.7507
14	14	14.02	-0.02179
15	16	15.27	0.7342
16	17	15.13	1.874
17	16	15.13	0.8741
18	15	16.85	-1.849
19	17	15.8	1.201
20	16	14.75	1.246
21	15	15.8	-0.7988
22	16	15.62	0.3789
23	15	15.62	-0.6211
24	17	15.62	1.379
25	15	15.13	-0.1259
26	16	14.93	1.068
27	15	15.62	-0.6211
28	16	14.81	1.192
29	16	16.12	-0.1164
30	13	14.52	-1.517
31	15	16.9	-1.903
32	17	16.12	0.8836
33	15	14.52	0.483
34	13	13.7	-0.7042
35	17	16.73	0.2747
36	15	15.13	-0.1259
37	14	14.15	-0.1452
38	14	14.52	-0.517
39	18	15.62	2.379
40	15	16.12	-1.116
41	17	16.9	0.0971
42	13	14.02	-1.022
43	16	17.03	-1.026
44	15	15.74	-0.7445
45	15	15.25	-0.2493
46	16	15.62	0.3789
47	15	15.68	-0.6805
48	13	15.8	-2.799
49	17	16.73	0.2747
50	17	17.4	-0.3981
51	17	17.22	-0.2205
52	11	14.69	-3.695
53	14	14.15	-0.1452
54	13	15.62	-2.621
55	15	14.58	0.4236
56	17	15.13	1.874
57	16	15.43	0.5731
58	15	15.8	-0.7988
59	17	17.4	-0.3981
60	16	14.81	1.192
61	16	15.8	0.2012
62	16	14.77	1.229
63	15	15.8	-0.7988
64	12	13.41	-1.413
65	17	15.43	1.573
66	14	15.43	-1.427
67	14	15.57	-1.567
68	16	14.93	1.068
69	15	14.75	0.246
70	15	17.27	-2.275
71	13	15.62	-2.621
72	13	15.62	-2.621
73	17	16.17	0.8294
74	15	15.13	-0.1259
75	16	15.8	0.2012
76	14	14.93	-0.9317
77	15	14.02	0.9782
78	17	14.52	2.483
79	16	15.62	0.3789
80	12	14.02	-2.022
81	16	15.8	0.2012
82	17	15.8	1.201
83	17	15.8	1.201
84	20	16.12	3.884
85	17	16.49	0.5118
86	18	15.8	2.201
87	15	15.13	-0.1259
88	17	15.13	1.874
89	14	13.04	0.9589
90	15	15.62	-0.6211
91	17	15.62	1.379
92	16	15.8	0.2012
93	17	16.9	0.0971
94	15	14.93	0.06831
95	16	15.62	0.3789
96	18	16.12	1.884
97	18	16.49	1.512
98	16	16.9	-0.9029
99	17	15.19	1.81
100	15	15.62	-0.6211
101	13	16.12	-3.116
102	15	14.75	0.246
103	17	16.67	0.3341
104	16	15.25	0.7507
105	16	15.25	0.7507
106	15	15.62	-0.6211
107	16	15.62	0.3789
108	16	15.3	0.6965
109	13	15.62	-2.621
110	15	15.25	-0.2493
111	12	13.83	-1.828
112	19	15.25	3.751
113	16	15.13	0.8741
114	16	15.44	0.5565
115	17	16.67	0.3341
116	16	16.29	-0.294
117	14	15.62	-1.621
118	15	15.25	-0.2493
119	14	14.75	-0.754
120	16	15.62	0.3789
121	15	15.8	-0.7988
122	17	16.73	0.2747
123	15	15.13	-0.1259
124	16	15.25	0.7507
125	16	15.62	0.3789
126	15	14.75	0.246
127	15	15.25	-0.2493
128	11	12.17	-1.174
129	16	15.43	0.5731
130	18	15.92	2.078
131	13	14.08	-1.081
132	11	14.02	-3.022
133	16	15.25	0.7507
134	18	17.4	0.6019
135	15	16.73	-1.725
136	19	17.77	1.23
137	17	16.9	0.0971
138	13	15.25	-2.249
139	14	15.3	-1.304
140	16	15.62	0.3789
141	13	15.62	-2.621
142	17	15.8	1.201
143	14	15.8	-1.799
144	19	15.92	3.078
145	14	14.52	-0.517
146	16	15.62	0.3789
147	12	13.65	-1.65
148	16	16.73	-0.7253
149	16	15.25	0.7507
150	15	15.62	-0.6211
151	12	14.93	-2.932
152	15	15.62	-0.6211
153	17	16.29	0.706
154	13	15.44	-2.443
155	15	13.53	1.473
156	18	15.62	2.379
157	15	14.32	0.6772
158	18	15.62	2.379
159	15	17.1	-2.097
160	15	16.12	-1.116
161	16	15.74	0.2555
162	13	14.2	-1.199
163	16	15.62	0.3789
164	13	15.8	-2.799
165	16	14.08	1.924

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.1422	0.2843	0.8578
9	0.06908	0.1382	0.9309
10	0.02779	0.05558	0.9722
11	0.01954	0.03908	0.9805
12	0.007368	0.01474	0.9926
13	0.00265	0.0053	0.9973
14	0.008523	0.01705	0.9915
15	0.01537	0.03075	0.9846
16	0.01474	0.02948	0.9853
17	0.007537	0.01507	0.9925
18	0.01941	0.03882	0.9806
19	0.01447	0.02895	0.9855
20	0.009653	0.01931	0.9903
21	0.01095	0.02191	0.989
22	0.006232	0.01246	0.9938
23	0.006324	0.01265	0.9937
24	0.005363	0.01073	0.9946
25	0.005908	0.01182	0.9941
26	0.003846	0.007692	0.9962
27	0.003486	0.006972	0.9965
28	0.002181	0.004361	0.9978
29	0.001217	0.002434	0.9988
30	0.003358	0.006715	0.9966
31	0.008403	0.01681	0.9916
32	0.008576	0.01715	0.9914
33	0.005468	0.01094	0.9945
34	0.008628	0.01726	0.9914
35	0.005562	0.01112	0.9944
36	0.004187	0.008374	0.9958
37	0.002799	0.005598	0.9972
38	0.00207	0.00414	0.9979
39	0.005609	0.01122	0.9944
40	0.005027	0.01005	0.995
41	0.003258	0.006516	0.9967
42	0.004047	0.008094	0.996
43	0.003209	0.006418	0.9968
44	0.002285	0.00457	0.9977
45	0.001518	0.003036	0.9985
46	0.0009687	0.001937	0.999
47	0.001021	0.002041	0.999
48	0.005691	0.01138	0.9943
49	0.003879	0.007757	0.9961
50	0.002624	0.005248	0.9974
51	0.001732	0.003464	0.9983
52	0.01985	0.0397	0.9801
53	0.01445	0.02891	0.9855
54	0.03701	0.07403	0.963
55	0.02897	0.05794	0.971
56	0.03351	0.06702	0.9665
57	0.02783	0.05567	0.9722
58	0.02238	0.04475	0.9776
59	0.01693	0.03386	0.9831
60	0.01458	0.02915	0.9854
61	0.01091	0.02181	0.9891
62	0.009986	0.01997	0.99
63	0.007906	0.01581	0.9921
64	0.007728	0.01546	0.9923
65	0.009506	0.01901	0.9905
66	0.01007	0.02015	0.9899
67	0.01068	0.02136	0.9893
68	0.009076	0.01815	0.9909
69	0.006877	0.01375	0.9931
70	0.01179	0.02359	0.9882
71	0.02601	0.05202	0.974
72	0.0507	0.1014	0.9493
73	0.04873	0.09746	0.9513
74	0.03932	0.07863	0.9607
75	0.03125	0.0625	0.9687
76	0.0293	0.0586	0.9707
77	0.02577	0.05155	0.9742
78	0.05208	0.1042	0.9479
79	0.0421	0.08421	0.9579
80	0.05882	0.1176	0.9412
81	0.04748	0.09496	0.9525
82	0.0466	0.09319	0.9534
83	0.04539	0.09077	0.9546
84	0.2018	0.4037	0.7982
85	0.1774	0.3547	0.8226
86	0.2256	0.4512	0.7744
87	0.196	0.3919	0.804
88	0.2313	0.4626	0.7687
89	0.2123	0.4246	0.7877
90	0.1861	0.3722	0.8139
91	0.188	0.376	0.812
92	0.1593	0.3187	0.8407
93	0.1333	0.2666	0.8667
94	0.1109	0.2217	0.8891
95	0.09263	0.1853	0.9074
96	0.1093	0.2187	0.8907
97	0.115	0.23	0.885
98	0.103	0.206	0.897
99	0.1172	0.2344	0.8828
100	0.09937	0.1988	0.9006
101	0.2008	0.4016	0.7992
102	0.1732	0.3463	0.8268
103	0.1461	0.2923	0.8539
104	0.1282	0.2564	0.8718
105	0.112	0.224	0.888
106	0.09424	0.1885	0.9058
107	0.07803	0.1561	0.922
108	0.06795	0.1359	0.9321
109	0.11	0.22	0.89
110	0.08952	0.179	0.9105
111	0.1003	0.2006	0.8997
112	0.3025	0.6051	0.6975
113	0.2963	0.5926	0.7037
114	0.2774	0.5548	0.7226
115	0.2392	0.4784	0.7608
116	0.2107	0.4213	0.7893
117	0.2136	0.4273	0.7864
118	0.1793	0.3586	0.8207
119	0.1528	0.3057	0.8472
120	0.129	0.2581	0.871
121	0.1136	0.2273	0.8864
122	0.09519	0.1904	0.9048
123	0.07855	0.1571	0.9215
124	0.06885	0.1377	0.9311
125	0.05595	0.1119	0.9441
126	0.04855	0.09711	0.9514
127	0.03685	0.0737	0.9631
128	0.03155	0.06309	0.9685
129	0.02406	0.04812	0.9759
130	0.02805	0.0561	0.972
131	0.02615	0.0523	0.9738
132	0.05722	0.1144	0.9428
133	0.05522	0.1104	0.9448
134	0.04323	0.08647	0.9568
135	0.03654	0.07308	0.9635
136	0.03035	0.06071	0.9696
137	0.02493	0.04987	0.9751
138	0.02778	0.05556	0.9722
139	0.02166	0.04331	0.9783
140	0.01579	0.03157	0.9842
141	0.02677	0.05355	0.9732
142	0.02383	0.04766	0.9762
143	0.0258	0.0516	0.9742
144	0.08693	0.1739	0.9131
145	0.08097	0.1619	0.919
146	0.05918	0.1184	0.9408
147	0.07374	0.1475	0.9263
148	0.06292	0.1258	0.9371
149	0.06402	0.128	0.936
150	0.04249	0.08499	0.9575
151	0.04086	0.08172	0.9591
152	0.02541	0.05083	0.9746
153	0.02197	0.04393	0.978
154	0.1806	0.3613	0.8194
155	0.5348	0.9305	0.4652
156	0.4912	0.9823	0.5088
157	0.4182	0.8363	0.5818

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.1422 &  0.2843 &  0.8578 \tabularnewline
9 &  0.06908 &  0.1382 &  0.9309 \tabularnewline
10 &  0.02779 &  0.05558 &  0.9722 \tabularnewline
11 &  0.01954 &  0.03908 &  0.9805 \tabularnewline
12 &  0.007368 &  0.01474 &  0.9926 \tabularnewline
13 &  0.00265 &  0.0053 &  0.9973 \tabularnewline
14 &  0.008523 &  0.01705 &  0.9915 \tabularnewline
15 &  0.01537 &  0.03075 &  0.9846 \tabularnewline
16 &  0.01474 &  0.02948 &  0.9853 \tabularnewline
17 &  0.007537 &  0.01507 &  0.9925 \tabularnewline
18 &  0.01941 &  0.03882 &  0.9806 \tabularnewline
19 &  0.01447 &  0.02895 &  0.9855 \tabularnewline
20 &  0.009653 &  0.01931 &  0.9903 \tabularnewline
21 &  0.01095 &  0.02191 &  0.989 \tabularnewline
22 &  0.006232 &  0.01246 &  0.9938 \tabularnewline
23 &  0.006324 &  0.01265 &  0.9937 \tabularnewline
24 &  0.005363 &  0.01073 &  0.9946 \tabularnewline
25 &  0.005908 &  0.01182 &  0.9941 \tabularnewline
26 &  0.003846 &  0.007692 &  0.9962 \tabularnewline
27 &  0.003486 &  0.006972 &  0.9965 \tabularnewline
28 &  0.002181 &  0.004361 &  0.9978 \tabularnewline
29 &  0.001217 &  0.002434 &  0.9988 \tabularnewline
30 &  0.003358 &  0.006715 &  0.9966 \tabularnewline
31 &  0.008403 &  0.01681 &  0.9916 \tabularnewline
32 &  0.008576 &  0.01715 &  0.9914 \tabularnewline
33 &  0.005468 &  0.01094 &  0.9945 \tabularnewline
34 &  0.008628 &  0.01726 &  0.9914 \tabularnewline
35 &  0.005562 &  0.01112 &  0.9944 \tabularnewline
36 &  0.004187 &  0.008374 &  0.9958 \tabularnewline
37 &  0.002799 &  0.005598 &  0.9972 \tabularnewline
38 &  0.00207 &  0.00414 &  0.9979 \tabularnewline
39 &  0.005609 &  0.01122 &  0.9944 \tabularnewline
40 &  0.005027 &  0.01005 &  0.995 \tabularnewline
41 &  0.003258 &  0.006516 &  0.9967 \tabularnewline
42 &  0.004047 &  0.008094 &  0.996 \tabularnewline
43 &  0.003209 &  0.006418 &  0.9968 \tabularnewline
44 &  0.002285 &  0.00457 &  0.9977 \tabularnewline
45 &  0.001518 &  0.003036 &  0.9985 \tabularnewline
46 &  0.0009687 &  0.001937 &  0.999 \tabularnewline
47 &  0.001021 &  0.002041 &  0.999 \tabularnewline
48 &  0.005691 &  0.01138 &  0.9943 \tabularnewline
49 &  0.003879 &  0.007757 &  0.9961 \tabularnewline
50 &  0.002624 &  0.005248 &  0.9974 \tabularnewline
51 &  0.001732 &  0.003464 &  0.9983 \tabularnewline
52 &  0.01985 &  0.0397 &  0.9801 \tabularnewline
53 &  0.01445 &  0.02891 &  0.9855 \tabularnewline
54 &  0.03701 &  0.07403 &  0.963 \tabularnewline
55 &  0.02897 &  0.05794 &  0.971 \tabularnewline
56 &  0.03351 &  0.06702 &  0.9665 \tabularnewline
57 &  0.02783 &  0.05567 &  0.9722 \tabularnewline
58 &  0.02238 &  0.04475 &  0.9776 \tabularnewline
59 &  0.01693 &  0.03386 &  0.9831 \tabularnewline
60 &  0.01458 &  0.02915 &  0.9854 \tabularnewline
61 &  0.01091 &  0.02181 &  0.9891 \tabularnewline
62 &  0.009986 &  0.01997 &  0.99 \tabularnewline
63 &  0.007906 &  0.01581 &  0.9921 \tabularnewline
64 &  0.007728 &  0.01546 &  0.9923 \tabularnewline
65 &  0.009506 &  0.01901 &  0.9905 \tabularnewline
66 &  0.01007 &  0.02015 &  0.9899 \tabularnewline
67 &  0.01068 &  0.02136 &  0.9893 \tabularnewline
68 &  0.009076 &  0.01815 &  0.9909 \tabularnewline
69 &  0.006877 &  0.01375 &  0.9931 \tabularnewline
70 &  0.01179 &  0.02359 &  0.9882 \tabularnewline
71 &  0.02601 &  0.05202 &  0.974 \tabularnewline
72 &  0.0507 &  0.1014 &  0.9493 \tabularnewline
73 &  0.04873 &  0.09746 &  0.9513 \tabularnewline
74 &  0.03932 &  0.07863 &  0.9607 \tabularnewline
75 &  0.03125 &  0.0625 &  0.9687 \tabularnewline
76 &  0.0293 &  0.0586 &  0.9707 \tabularnewline
77 &  0.02577 &  0.05155 &  0.9742 \tabularnewline
78 &  0.05208 &  0.1042 &  0.9479 \tabularnewline
79 &  0.0421 &  0.08421 &  0.9579 \tabularnewline
80 &  0.05882 &  0.1176 &  0.9412 \tabularnewline
81 &  0.04748 &  0.09496 &  0.9525 \tabularnewline
82 &  0.0466 &  0.09319 &  0.9534 \tabularnewline
83 &  0.04539 &  0.09077 &  0.9546 \tabularnewline
84 &  0.2018 &  0.4037 &  0.7982 \tabularnewline
85 &  0.1774 &  0.3547 &  0.8226 \tabularnewline
86 &  0.2256 &  0.4512 &  0.7744 \tabularnewline
87 &  0.196 &  0.3919 &  0.804 \tabularnewline
88 &  0.2313 &  0.4626 &  0.7687 \tabularnewline
89 &  0.2123 &  0.4246 &  0.7877 \tabularnewline
90 &  0.1861 &  0.3722 &  0.8139 \tabularnewline
91 &  0.188 &  0.376 &  0.812 \tabularnewline
92 &  0.1593 &  0.3187 &  0.8407 \tabularnewline
93 &  0.1333 &  0.2666 &  0.8667 \tabularnewline
94 &  0.1109 &  0.2217 &  0.8891 \tabularnewline
95 &  0.09263 &  0.1853 &  0.9074 \tabularnewline
96 &  0.1093 &  0.2187 &  0.8907 \tabularnewline
97 &  0.115 &  0.23 &  0.885 \tabularnewline
98 &  0.103 &  0.206 &  0.897 \tabularnewline
99 &  0.1172 &  0.2344 &  0.8828 \tabularnewline
100 &  0.09937 &  0.1988 &  0.9006 \tabularnewline
101 &  0.2008 &  0.4016 &  0.7992 \tabularnewline
102 &  0.1732 &  0.3463 &  0.8268 \tabularnewline
103 &  0.1461 &  0.2923 &  0.8539 \tabularnewline
104 &  0.1282 &  0.2564 &  0.8718 \tabularnewline
105 &  0.112 &  0.224 &  0.888 \tabularnewline
106 &  0.09424 &  0.1885 &  0.9058 \tabularnewline
107 &  0.07803 &  0.1561 &  0.922 \tabularnewline
108 &  0.06795 &  0.1359 &  0.9321 \tabularnewline
109 &  0.11 &  0.22 &  0.89 \tabularnewline
110 &  0.08952 &  0.179 &  0.9105 \tabularnewline
111 &  0.1003 &  0.2006 &  0.8997 \tabularnewline
112 &  0.3025 &  0.6051 &  0.6975 \tabularnewline
113 &  0.2963 &  0.5926 &  0.7037 \tabularnewline
114 &  0.2774 &  0.5548 &  0.7226 \tabularnewline
115 &  0.2392 &  0.4784 &  0.7608 \tabularnewline
116 &  0.2107 &  0.4213 &  0.7893 \tabularnewline
117 &  0.2136 &  0.4273 &  0.7864 \tabularnewline
118 &  0.1793 &  0.3586 &  0.8207 \tabularnewline
119 &  0.1528 &  0.3057 &  0.8472 \tabularnewline
120 &  0.129 &  0.2581 &  0.871 \tabularnewline
121 &  0.1136 &  0.2273 &  0.8864 \tabularnewline
122 &  0.09519 &  0.1904 &  0.9048 \tabularnewline
123 &  0.07855 &  0.1571 &  0.9215 \tabularnewline
124 &  0.06885 &  0.1377 &  0.9311 \tabularnewline
125 &  0.05595 &  0.1119 &  0.9441 \tabularnewline
126 &  0.04855 &  0.09711 &  0.9514 \tabularnewline
127 &  0.03685 &  0.0737 &  0.9631 \tabularnewline
128 &  0.03155 &  0.06309 &  0.9685 \tabularnewline
129 &  0.02406 &  0.04812 &  0.9759 \tabularnewline
130 &  0.02805 &  0.0561 &  0.972 \tabularnewline
131 &  0.02615 &  0.0523 &  0.9738 \tabularnewline
132 &  0.05722 &  0.1144 &  0.9428 \tabularnewline
133 &  0.05522 &  0.1104 &  0.9448 \tabularnewline
134 &  0.04323 &  0.08647 &  0.9568 \tabularnewline
135 &  0.03654 &  0.07308 &  0.9635 \tabularnewline
136 &  0.03035 &  0.06071 &  0.9696 \tabularnewline
137 &  0.02493 &  0.04987 &  0.9751 \tabularnewline
138 &  0.02778 &  0.05556 &  0.9722 \tabularnewline
139 &  0.02166 &  0.04331 &  0.9783 \tabularnewline
140 &  0.01579 &  0.03157 &  0.9842 \tabularnewline
141 &  0.02677 &  0.05355 &  0.9732 \tabularnewline
142 &  0.02383 &  0.04766 &  0.9762 \tabularnewline
143 &  0.0258 &  0.0516 &  0.9742 \tabularnewline
144 &  0.08693 &  0.1739 &  0.9131 \tabularnewline
145 &  0.08097 &  0.1619 &  0.919 \tabularnewline
146 &  0.05918 &  0.1184 &  0.9408 \tabularnewline
147 &  0.07374 &  0.1475 &  0.9263 \tabularnewline
148 &  0.06292 &  0.1258 &  0.9371 \tabularnewline
149 &  0.06402 &  0.128 &  0.936 \tabularnewline
150 &  0.04249 &  0.08499 &  0.9575 \tabularnewline
151 &  0.04086 &  0.08172 &  0.9591 \tabularnewline
152 &  0.02541 &  0.05083 &  0.9746 \tabularnewline
153 &  0.02197 &  0.04393 &  0.978 \tabularnewline
154 &  0.1806 &  0.3613 &  0.8194 \tabularnewline
155 &  0.5348 &  0.9305 &  0.4652 \tabularnewline
156 &  0.4912 &  0.9823 &  0.5088 \tabularnewline
157 &  0.4182 &  0.8363 &  0.5818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298737&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] 0.1422[/C][C] 0.2843[/C][C] 0.8578[/C][/ROW]
[ROW][C]9[/C][C] 0.06908[/C][C] 0.1382[/C][C] 0.9309[/C][/ROW]
[ROW][C]10[/C][C] 0.02779[/C][C] 0.05558[/C][C] 0.9722[/C][/ROW]
[ROW][C]11[/C][C] 0.01954[/C][C] 0.03908[/C][C] 0.9805[/C][/ROW]
[ROW][C]12[/C][C] 0.007368[/C][C] 0.01474[/C][C] 0.9926[/C][/ROW]
[ROW][C]13[/C][C] 0.00265[/C][C] 0.0053[/C][C] 0.9973[/C][/ROW]
[ROW][C]14[/C][C] 0.008523[/C][C] 0.01705[/C][C] 0.9915[/C][/ROW]
[ROW][C]15[/C][C] 0.01537[/C][C] 0.03075[/C][C] 0.9846[/C][/ROW]
[ROW][C]16[/C][C] 0.01474[/C][C] 0.02948[/C][C] 0.9853[/C][/ROW]
[ROW][C]17[/C][C] 0.007537[/C][C] 0.01507[/C][C] 0.9925[/C][/ROW]
[ROW][C]18[/C][C] 0.01941[/C][C] 0.03882[/C][C] 0.9806[/C][/ROW]
[ROW][C]19[/C][C] 0.01447[/C][C] 0.02895[/C][C] 0.9855[/C][/ROW]
[ROW][C]20[/C][C] 0.009653[/C][C] 0.01931[/C][C] 0.9903[/C][/ROW]
[ROW][C]21[/C][C] 0.01095[/C][C] 0.02191[/C][C] 0.989[/C][/ROW]
[ROW][C]22[/C][C] 0.006232[/C][C] 0.01246[/C][C] 0.9938[/C][/ROW]
[ROW][C]23[/C][C] 0.006324[/C][C] 0.01265[/C][C] 0.9937[/C][/ROW]
[ROW][C]24[/C][C] 0.005363[/C][C] 0.01073[/C][C] 0.9946[/C][/ROW]
[ROW][C]25[/C][C] 0.005908[/C][C] 0.01182[/C][C] 0.9941[/C][/ROW]
[ROW][C]26[/C][C] 0.003846[/C][C] 0.007692[/C][C] 0.9962[/C][/ROW]
[ROW][C]27[/C][C] 0.003486[/C][C] 0.006972[/C][C] 0.9965[/C][/ROW]
[ROW][C]28[/C][C] 0.002181[/C][C] 0.004361[/C][C] 0.9978[/C][/ROW]
[ROW][C]29[/C][C] 0.001217[/C][C] 0.002434[/C][C] 0.9988[/C][/ROW]
[ROW][C]30[/C][C] 0.003358[/C][C] 0.006715[/C][C] 0.9966[/C][/ROW]
[ROW][C]31[/C][C] 0.008403[/C][C] 0.01681[/C][C] 0.9916[/C][/ROW]
[ROW][C]32[/C][C] 0.008576[/C][C] 0.01715[/C][C] 0.9914[/C][/ROW]
[ROW][C]33[/C][C] 0.005468[/C][C] 0.01094[/C][C] 0.9945[/C][/ROW]
[ROW][C]34[/C][C] 0.008628[/C][C] 0.01726[/C][C] 0.9914[/C][/ROW]
[ROW][C]35[/C][C] 0.005562[/C][C] 0.01112[/C][C] 0.9944[/C][/ROW]
[ROW][C]36[/C][C] 0.004187[/C][C] 0.008374[/C][C] 0.9958[/C][/ROW]
[ROW][C]37[/C][C] 0.002799[/C][C] 0.005598[/C][C] 0.9972[/C][/ROW]
[ROW][C]38[/C][C] 0.00207[/C][C] 0.00414[/C][C] 0.9979[/C][/ROW]
[ROW][C]39[/C][C] 0.005609[/C][C] 0.01122[/C][C] 0.9944[/C][/ROW]
[ROW][C]40[/C][C] 0.005027[/C][C] 0.01005[/C][C] 0.995[/C][/ROW]
[ROW][C]41[/C][C] 0.003258[/C][C] 0.006516[/C][C] 0.9967[/C][/ROW]
[ROW][C]42[/C][C] 0.004047[/C][C] 0.008094[/C][C] 0.996[/C][/ROW]
[ROW][C]43[/C][C] 0.003209[/C][C] 0.006418[/C][C] 0.9968[/C][/ROW]
[ROW][C]44[/C][C] 0.002285[/C][C] 0.00457[/C][C] 0.9977[/C][/ROW]
[ROW][C]45[/C][C] 0.001518[/C][C] 0.003036[/C][C] 0.9985[/C][/ROW]
[ROW][C]46[/C][C] 0.0009687[/C][C] 0.001937[/C][C] 0.999[/C][/ROW]
[ROW][C]47[/C][C] 0.001021[/C][C] 0.002041[/C][C] 0.999[/C][/ROW]
[ROW][C]48[/C][C] 0.005691[/C][C] 0.01138[/C][C] 0.9943[/C][/ROW]
[ROW][C]49[/C][C] 0.003879[/C][C] 0.007757[/C][C] 0.9961[/C][/ROW]
[ROW][C]50[/C][C] 0.002624[/C][C] 0.005248[/C][C] 0.9974[/C][/ROW]
[ROW][C]51[/C][C] 0.001732[/C][C] 0.003464[/C][C] 0.9983[/C][/ROW]
[ROW][C]52[/C][C] 0.01985[/C][C] 0.0397[/C][C] 0.9801[/C][/ROW]
[ROW][C]53[/C][C] 0.01445[/C][C] 0.02891[/C][C] 0.9855[/C][/ROW]
[ROW][C]54[/C][C] 0.03701[/C][C] 0.07403[/C][C] 0.963[/C][/ROW]
[ROW][C]55[/C][C] 0.02897[/C][C] 0.05794[/C][C] 0.971[/C][/ROW]
[ROW][C]56[/C][C] 0.03351[/C][C] 0.06702[/C][C] 0.9665[/C][/ROW]
[ROW][C]57[/C][C] 0.02783[/C][C] 0.05567[/C][C] 0.9722[/C][/ROW]
[ROW][C]58[/C][C] 0.02238[/C][C] 0.04475[/C][C] 0.9776[/C][/ROW]
[ROW][C]59[/C][C] 0.01693[/C][C] 0.03386[/C][C] 0.9831[/C][/ROW]
[ROW][C]60[/C][C] 0.01458[/C][C] 0.02915[/C][C] 0.9854[/C][/ROW]
[ROW][C]61[/C][C] 0.01091[/C][C] 0.02181[/C][C] 0.9891[/C][/ROW]
[ROW][C]62[/C][C] 0.009986[/C][C] 0.01997[/C][C] 0.99[/C][/ROW]
[ROW][C]63[/C][C] 0.007906[/C][C] 0.01581[/C][C] 0.9921[/C][/ROW]
[ROW][C]64[/C][C] 0.007728[/C][C] 0.01546[/C][C] 0.9923[/C][/ROW]
[ROW][C]65[/C][C] 0.009506[/C][C] 0.01901[/C][C] 0.9905[/C][/ROW]
[ROW][C]66[/C][C] 0.01007[/C][C] 0.02015[/C][C] 0.9899[/C][/ROW]
[ROW][C]67[/C][C] 0.01068[/C][C] 0.02136[/C][C] 0.9893[/C][/ROW]
[ROW][C]68[/C][C] 0.009076[/C][C] 0.01815[/C][C] 0.9909[/C][/ROW]
[ROW][C]69[/C][C] 0.006877[/C][C] 0.01375[/C][C] 0.9931[/C][/ROW]
[ROW][C]70[/C][C] 0.01179[/C][C] 0.02359[/C][C] 0.9882[/C][/ROW]
[ROW][C]71[/C][C] 0.02601[/C][C] 0.05202[/C][C] 0.974[/C][/ROW]
[ROW][C]72[/C][C] 0.0507[/C][C] 0.1014[/C][C] 0.9493[/C][/ROW]
[ROW][C]73[/C][C] 0.04873[/C][C] 0.09746[/C][C] 0.9513[/C][/ROW]
[ROW][C]74[/C][C] 0.03932[/C][C] 0.07863[/C][C] 0.9607[/C][/ROW]
[ROW][C]75[/C][C] 0.03125[/C][C] 0.0625[/C][C] 0.9687[/C][/ROW]
[ROW][C]76[/C][C] 0.0293[/C][C] 0.0586[/C][C] 0.9707[/C][/ROW]
[ROW][C]77[/C][C] 0.02577[/C][C] 0.05155[/C][C] 0.9742[/C][/ROW]
[ROW][C]78[/C][C] 0.05208[/C][C] 0.1042[/C][C] 0.9479[/C][/ROW]
[ROW][C]79[/C][C] 0.0421[/C][C] 0.08421[/C][C] 0.9579[/C][/ROW]
[ROW][C]80[/C][C] 0.05882[/C][C] 0.1176[/C][C] 0.9412[/C][/ROW]
[ROW][C]81[/C][C] 0.04748[/C][C] 0.09496[/C][C] 0.9525[/C][/ROW]
[ROW][C]82[/C][C] 0.0466[/C][C] 0.09319[/C][C] 0.9534[/C][/ROW]
[ROW][C]83[/C][C] 0.04539[/C][C] 0.09077[/C][C] 0.9546[/C][/ROW]
[ROW][C]84[/C][C] 0.2018[/C][C] 0.4037[/C][C] 0.7982[/C][/ROW]
[ROW][C]85[/C][C] 0.1774[/C][C] 0.3547[/C][C] 0.8226[/C][/ROW]
[ROW][C]86[/C][C] 0.2256[/C][C] 0.4512[/C][C] 0.7744[/C][/ROW]
[ROW][C]87[/C][C] 0.196[/C][C] 0.3919[/C][C] 0.804[/C][/ROW]
[ROW][C]88[/C][C] 0.2313[/C][C] 0.4626[/C][C] 0.7687[/C][/ROW]
[ROW][C]89[/C][C] 0.2123[/C][C] 0.4246[/C][C] 0.7877[/C][/ROW]
[ROW][C]90[/C][C] 0.1861[/C][C] 0.3722[/C][C] 0.8139[/C][/ROW]
[ROW][C]91[/C][C] 0.188[/C][C] 0.376[/C][C] 0.812[/C][/ROW]
[ROW][C]92[/C][C] 0.1593[/C][C] 0.3187[/C][C] 0.8407[/C][/ROW]
[ROW][C]93[/C][C] 0.1333[/C][C] 0.2666[/C][C] 0.8667[/C][/ROW]
[ROW][C]94[/C][C] 0.1109[/C][C] 0.2217[/C][C] 0.8891[/C][/ROW]
[ROW][C]95[/C][C] 0.09263[/C][C] 0.1853[/C][C] 0.9074[/C][/ROW]
[ROW][C]96[/C][C] 0.1093[/C][C] 0.2187[/C][C] 0.8907[/C][/ROW]
[ROW][C]97[/C][C] 0.115[/C][C] 0.23[/C][C] 0.885[/C][/ROW]
[ROW][C]98[/C][C] 0.103[/C][C] 0.206[/C][C] 0.897[/C][/ROW]
[ROW][C]99[/C][C] 0.1172[/C][C] 0.2344[/C][C] 0.8828[/C][/ROW]
[ROW][C]100[/C][C] 0.09937[/C][C] 0.1988[/C][C] 0.9006[/C][/ROW]
[ROW][C]101[/C][C] 0.2008[/C][C] 0.4016[/C][C] 0.7992[/C][/ROW]
[ROW][C]102[/C][C] 0.1732[/C][C] 0.3463[/C][C] 0.8268[/C][/ROW]
[ROW][C]103[/C][C] 0.1461[/C][C] 0.2923[/C][C] 0.8539[/C][/ROW]
[ROW][C]104[/C][C] 0.1282[/C][C] 0.2564[/C][C] 0.8718[/C][/ROW]
[ROW][C]105[/C][C] 0.112[/C][C] 0.224[/C][C] 0.888[/C][/ROW]
[ROW][C]106[/C][C] 0.09424[/C][C] 0.1885[/C][C] 0.9058[/C][/ROW]
[ROW][C]107[/C][C] 0.07803[/C][C] 0.1561[/C][C] 0.922[/C][/ROW]
[ROW][C]108[/C][C] 0.06795[/C][C] 0.1359[/C][C] 0.9321[/C][/ROW]
[ROW][C]109[/C][C] 0.11[/C][C] 0.22[/C][C] 0.89[/C][/ROW]
[ROW][C]110[/C][C] 0.08952[/C][C] 0.179[/C][C] 0.9105[/C][/ROW]
[ROW][C]111[/C][C] 0.1003[/C][C] 0.2006[/C][C] 0.8997[/C][/ROW]
[ROW][C]112[/C][C] 0.3025[/C][C] 0.6051[/C][C] 0.6975[/C][/ROW]
[ROW][C]113[/C][C] 0.2963[/C][C] 0.5926[/C][C] 0.7037[/C][/ROW]
[ROW][C]114[/C][C] 0.2774[/C][C] 0.5548[/C][C] 0.7226[/C][/ROW]
[ROW][C]115[/C][C] 0.2392[/C][C] 0.4784[/C][C] 0.7608[/C][/ROW]
[ROW][C]116[/C][C] 0.2107[/C][C] 0.4213[/C][C] 0.7893[/C][/ROW]
[ROW][C]117[/C][C] 0.2136[/C][C] 0.4273[/C][C] 0.7864[/C][/ROW]
[ROW][C]118[/C][C] 0.1793[/C][C] 0.3586[/C][C] 0.8207[/C][/ROW]
[ROW][C]119[/C][C] 0.1528[/C][C] 0.3057[/C][C] 0.8472[/C][/ROW]
[ROW][C]120[/C][C] 0.129[/C][C] 0.2581[/C][C] 0.871[/C][/ROW]
[ROW][C]121[/C][C] 0.1136[/C][C] 0.2273[/C][C] 0.8864[/C][/ROW]
[ROW][C]122[/C][C] 0.09519[/C][C] 0.1904[/C][C] 0.9048[/C][/ROW]
[ROW][C]123[/C][C] 0.07855[/C][C] 0.1571[/C][C] 0.9215[/C][/ROW]
[ROW][C]124[/C][C] 0.06885[/C][C] 0.1377[/C][C] 0.9311[/C][/ROW]
[ROW][C]125[/C][C] 0.05595[/C][C] 0.1119[/C][C] 0.9441[/C][/ROW]
[ROW][C]126[/C][C] 0.04855[/C][C] 0.09711[/C][C] 0.9514[/C][/ROW]
[ROW][C]127[/C][C] 0.03685[/C][C] 0.0737[/C][C] 0.9631[/C][/ROW]
[ROW][C]128[/C][C] 0.03155[/C][C] 0.06309[/C][C] 0.9685[/C][/ROW]
[ROW][C]129[/C][C] 0.02406[/C][C] 0.04812[/C][C] 0.9759[/C][/ROW]
[ROW][C]130[/C][C] 0.02805[/C][C] 0.0561[/C][C] 0.972[/C][/ROW]
[ROW][C]131[/C][C] 0.02615[/C][C] 0.0523[/C][C] 0.9738[/C][/ROW]
[ROW][C]132[/C][C] 0.05722[/C][C] 0.1144[/C][C] 0.9428[/C][/ROW]
[ROW][C]133[/C][C] 0.05522[/C][C] 0.1104[/C][C] 0.9448[/C][/ROW]
[ROW][C]134[/C][C] 0.04323[/C][C] 0.08647[/C][C] 0.9568[/C][/ROW]
[ROW][C]135[/C][C] 0.03654[/C][C] 0.07308[/C][C] 0.9635[/C][/ROW]
[ROW][C]136[/C][C] 0.03035[/C][C] 0.06071[/C][C] 0.9696[/C][/ROW]
[ROW][C]137[/C][C] 0.02493[/C][C] 0.04987[/C][C] 0.9751[/C][/ROW]
[ROW][C]138[/C][C] 0.02778[/C][C] 0.05556[/C][C] 0.9722[/C][/ROW]
[ROW][C]139[/C][C] 0.02166[/C][C] 0.04331[/C][C] 0.9783[/C][/ROW]
[ROW][C]140[/C][C] 0.01579[/C][C] 0.03157[/C][C] 0.9842[/C][/ROW]
[ROW][C]141[/C][C] 0.02677[/C][C] 0.05355[/C][C] 0.9732[/C][/ROW]
[ROW][C]142[/C][C] 0.02383[/C][C] 0.04766[/C][C] 0.9762[/C][/ROW]
[ROW][C]143[/C][C] 0.0258[/C][C] 0.0516[/C][C] 0.9742[/C][/ROW]
[ROW][C]144[/C][C] 0.08693[/C][C] 0.1739[/C][C] 0.9131[/C][/ROW]
[ROW][C]145[/C][C] 0.08097[/C][C] 0.1619[/C][C] 0.919[/C][/ROW]
[ROW][C]146[/C][C] 0.05918[/C][C] 0.1184[/C][C] 0.9408[/C][/ROW]
[ROW][C]147[/C][C] 0.07374[/C][C] 0.1475[/C][C] 0.9263[/C][/ROW]
[ROW][C]148[/C][C] 0.06292[/C][C] 0.1258[/C][C] 0.9371[/C][/ROW]
[ROW][C]149[/C][C] 0.06402[/C][C] 0.128[/C][C] 0.936[/C][/ROW]
[ROW][C]150[/C][C] 0.04249[/C][C] 0.08499[/C][C] 0.9575[/C][/ROW]
[ROW][C]151[/C][C] 0.04086[/C][C] 0.08172[/C][C] 0.9591[/C][/ROW]
[ROW][C]152[/C][C] 0.02541[/C][C] 0.05083[/C][C] 0.9746[/C][/ROW]
[ROW][C]153[/C][C] 0.02197[/C][C] 0.04393[/C][C] 0.978[/C][/ROW]
[ROW][C]154[/C][C] 0.1806[/C][C] 0.3613[/C][C] 0.8194[/C][/ROW]
[ROW][C]155[/C][C] 0.5348[/C][C] 0.9305[/C][C] 0.4652[/C][/ROW]
[ROW][C]156[/C][C] 0.4912[/C][C] 0.9823[/C][C] 0.5088[/C][/ROW]
[ROW][C]157[/C][C] 0.4182[/C][C] 0.8363[/C][C] 0.5818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298737&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298737&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	0.1422	0.2843	0.8578
9	0.06908	0.1382	0.9309
10	0.02779	0.05558	0.9722
11	0.01954	0.03908	0.9805
12	0.007368	0.01474	0.9926
13	0.00265	0.0053	0.9973
14	0.008523	0.01705	0.9915
15	0.01537	0.03075	0.9846
16	0.01474	0.02948	0.9853
17	0.007537	0.01507	0.9925
18	0.01941	0.03882	0.9806
19	0.01447	0.02895	0.9855
20	0.009653	0.01931	0.9903
21	0.01095	0.02191	0.989
22	0.006232	0.01246	0.9938
23	0.006324	0.01265	0.9937
24	0.005363	0.01073	0.9946
25	0.005908	0.01182	0.9941
26	0.003846	0.007692	0.9962
27	0.003486	0.006972	0.9965
28	0.002181	0.004361	0.9978
29	0.001217	0.002434	0.9988
30	0.003358	0.006715	0.9966
31	0.008403	0.01681	0.9916
32	0.008576	0.01715	0.9914
33	0.005468	0.01094	0.9945
34	0.008628	0.01726	0.9914
35	0.005562	0.01112	0.9944
36	0.004187	0.008374	0.9958
37	0.002799	0.005598	0.9972
38	0.00207	0.00414	0.9979
39	0.005609	0.01122	0.9944
40	0.005027	0.01005	0.995
41	0.003258	0.006516	0.9967
42	0.004047	0.008094	0.996
43	0.003209	0.006418	0.9968
44	0.002285	0.00457	0.9977
45	0.001518	0.003036	0.9985
46	0.0009687	0.001937	0.999
47	0.001021	0.002041	0.999
48	0.005691	0.01138	0.9943
49	0.003879	0.007757	0.9961
50	0.002624	0.005248	0.9974
51	0.001732	0.003464	0.9983
52	0.01985	0.0397	0.9801
53	0.01445	0.02891	0.9855
54	0.03701	0.07403	0.963
55	0.02897	0.05794	0.971
56	0.03351	0.06702	0.9665
57	0.02783	0.05567	0.9722
58	0.02238	0.04475	0.9776
59	0.01693	0.03386	0.9831
60	0.01458	0.02915	0.9854
61	0.01091	0.02181	0.9891
62	0.009986	0.01997	0.99
63	0.007906	0.01581	0.9921
64	0.007728	0.01546	0.9923
65	0.009506	0.01901	0.9905
66	0.01007	0.02015	0.9899
67	0.01068	0.02136	0.9893
68	0.009076	0.01815	0.9909
69	0.006877	0.01375	0.9931
70	0.01179	0.02359	0.9882
71	0.02601	0.05202	0.974
72	0.0507	0.1014	0.9493
73	0.04873	0.09746	0.9513
74	0.03932	0.07863	0.9607
75	0.03125	0.0625	0.9687
76	0.0293	0.0586	0.9707
77	0.02577	0.05155	0.9742
78	0.05208	0.1042	0.9479
79	0.0421	0.08421	0.9579
80	0.05882	0.1176	0.9412
81	0.04748	0.09496	0.9525
82	0.0466	0.09319	0.9534
83	0.04539	0.09077	0.9546
84	0.2018	0.4037	0.7982
85	0.1774	0.3547	0.8226
86	0.2256	0.4512	0.7744
87	0.196	0.3919	0.804
88	0.2313	0.4626	0.7687
89	0.2123	0.4246	0.7877
90	0.1861	0.3722	0.8139
91	0.188	0.376	0.812
92	0.1593	0.3187	0.8407
93	0.1333	0.2666	0.8667
94	0.1109	0.2217	0.8891
95	0.09263	0.1853	0.9074
96	0.1093	0.2187	0.8907
97	0.115	0.23	0.885
98	0.103	0.206	0.897
99	0.1172	0.2344	0.8828
100	0.09937	0.1988	0.9006
101	0.2008	0.4016	0.7992
102	0.1732	0.3463	0.8268
103	0.1461	0.2923	0.8539
104	0.1282	0.2564	0.8718
105	0.112	0.224	0.888
106	0.09424	0.1885	0.9058
107	0.07803	0.1561	0.922
108	0.06795	0.1359	0.9321
109	0.11	0.22	0.89
110	0.08952	0.179	0.9105
111	0.1003	0.2006	0.8997
112	0.3025	0.6051	0.6975
113	0.2963	0.5926	0.7037
114	0.2774	0.5548	0.7226
115	0.2392	0.4784	0.7608
116	0.2107	0.4213	0.7893
117	0.2136	0.4273	0.7864
118	0.1793	0.3586	0.8207
119	0.1528	0.3057	0.8472
120	0.129	0.2581	0.871
121	0.1136	0.2273	0.8864
122	0.09519	0.1904	0.9048
123	0.07855	0.1571	0.9215
124	0.06885	0.1377	0.9311
125	0.05595	0.1119	0.9441
126	0.04855	0.09711	0.9514
127	0.03685	0.0737	0.9631
128	0.03155	0.06309	0.9685
129	0.02406	0.04812	0.9759
130	0.02805	0.0561	0.972
131	0.02615	0.0523	0.9738
132	0.05722	0.1144	0.9428
133	0.05522	0.1104	0.9448
134	0.04323	0.08647	0.9568
135	0.03654	0.07308	0.9635
136	0.03035	0.06071	0.9696
137	0.02493	0.04987	0.9751
138	0.02778	0.05556	0.9722
139	0.02166	0.04331	0.9783
140	0.01579	0.03157	0.9842
141	0.02677	0.05355	0.9732
142	0.02383	0.04766	0.9762
143	0.0258	0.0516	0.9742
144	0.08693	0.1739	0.9131
145	0.08097	0.1619	0.919
146	0.05918	0.1184	0.9408
147	0.07374	0.1475	0.9263
148	0.06292	0.1258	0.9371
149	0.06402	0.128	0.936
150	0.04249	0.08499	0.9575
151	0.04086	0.08172	0.9591
152	0.02541	0.05083	0.9746
153	0.02197	0.04393	0.978
154	0.1806	0.3613	0.8194
155	0.5348	0.9305	0.4652
156	0.4912	0.9823	0.5088
157	0.4182	0.8363	0.5818

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	19	0.1267	NOK
5% type I error level	62	0.413333	NOK
10% type I error level	91	0.606667	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 & 19 &  0.1267 & NOK \tabularnewline
5% type I error level & 62 & 0.413333 & NOK \tabularnewline
10% type I error level & 91 & 0.606667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298737&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]19[/C][C] 0.1267[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]62[/C][C]0.413333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]91[/C][C]0.606667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298737&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298737&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	19	0.1267	NOK
5% type I error level	62	0.413333	NOK
10% type I error level	91	0.606667	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.6596, df1 = 2, df2 = 158, p-value = 0.1935
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7196, df1 = 8, df2 = 152, p-value = 0.09801
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.56387, df1 = 2, df2 = 158, p-value = 0.5701

\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 = 1.6596, df1 = 2, df2 = 158, p-value = 0.1935
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7196, df1 = 8, df2 = 152, p-value = 0.09801
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.56387, df1 = 2, df2 = 158, p-value = 0.5701
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298737&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 = 1.6596, df1 = 2, df2 = 158, p-value = 0.1935
[/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 = 1.7196, df1 = 8, df2 = 152, p-value = 0.09801
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.56387, df1 = 2, df2 = 158, p-value = 0.5701
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298737&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298737&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 = 1.6596, df1 = 2, df2 = 158, p-value = 0.1935
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7196, df1 = 8, df2 = 152, p-value = 0.09801
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.56387, df1 = 2, df2 = 158, p-value = 0.5701

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK4 SK5 1.098182 1.100719 1.063354 1.027663

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK4      SK5 
1.098182 1.100719 1.063354 1.027663 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298737&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK4      SK5 
1.098182 1.100719 1.063354 1.027663 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298737&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298737&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 SK4 SK5 1.098182 1.100719 1.063354 1.027663

Figure 1

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

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

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