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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 04 Dec 2016 13:25:03 +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/04/t1480854351cncp60aortuxuqj.htm/, Retrieved Fri, 17 May 2024 15:05:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297666, Retrieved Fri, 17 May 2024 15:05:37 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

119

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

-       [Multiple Regression] [Regressie Analyse ] [2016-12-04 12:25:03] [dfff7639a5c2d8e28b3442052a637c76] [Current]

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2	2	3	4	3,250
4	2	1	4	4,000
4	2	5	4	4,250
4	3	4	4	3,667
3	4	3	3	4,000
4	3	2	5	4,000
1	4	4	4	4,333
4	2	5	4	4,000
3	NA	5	2	4,333
4	4	3	4	4,250
2	2	2	4	4,250
4	2	2	3	3,750
4	5	4	3	4,000
5	4	4	4	3,500
4	2	4	4	4,000
1	3	5	4	4,250
2	1	2	5	4,000
4	3	2	4	3,667
5	4	4	4	4,333
5	5	4	4	4,000
4	5	4	4	3,667
1	1	5	4	4,000
4	4	3	4	3,667
2	2	4	4	4,333
4	4	3	4	3,667
5	4	3	3	4,000
3	3	3	3	3,750
5	4	5	5	4,000
3	2	4	4	4,000
5	2	4	4	3,250
2	4	3	4	3,750
1	2	3	4	4,250
NA	4	5	1	3,667
4	2	3	3	3,250
4	4	3	4	4,250
3	3	3	4	3,667
5	3	5	5	3,500
4	4	3	4	3,500
NA	2	3	4	4,500
4	3	3	4	3,667
2	2	4	3	4,250
3	4	3	4	3,250
1	2	1	5	4,000
3	2	4	4	3,750
3	3	4	3	3,750
3	3	3	3	4,000
4	NA	4	5	3,750
4	4	4	4	3,250
4	5	5	1	3,000
4	4	4	4	4,250
4	4	4	4	4,333
2	4	3	4	4,333
5	2	2	4	2,750
3	2	4	3	3,500
3	1	3	4	3,250
4	3	3	3	3,667
4	4	3	4	4,250
4	3	4	2	4,000
3	3	4	4	3,667
4	2	3	4	4,250
4	3	4	4	4,000
4	2	5	3	4,000
4	4	2	4	4,000
4	3	3	3	3,750
2	2	3	4	3,000
4	4	3	3	4,250
4	5	4	4	3,500
4	4	3	4	3,500
4	3	4	4	4,000
4	2	3	4	3,667
5	3	1	3	3,667
3	4	4	3	3,333
2	4	3	2	3,333
4	4	2	4	4,333
5	5	3	5	3,750
4	4	3	4	4,000
5	4	4	5	3,500
5	4	5	2	3,750
2	3	3	4	4,250
4	2	4	4	4,000
4	4	2	4	2,500
4	4	2	4	4,000
3	4	2	5	4,250
4	2	3	4	4,333
2	2	4	4	5,000
5	1	3	4	4,250
3	NA	5	4	4,500
4	4	4	1	3,667
2	4	4	4	4,250
4	4	3	4	3,500
3	3	4	3	3,667
3	4	3	4	4,250
4	4	5	4	4,000
4	4	4	3	4,250
4	2	4	3	3,667
3	4	3	4	4,000
4	4	4	5	4,500
3	1	1	3	4,500
3	4	4	4	4,000
1	2	4	3	4,000
4	3	4	4	4,333
3	3	4	5	3,750
3	4	4	3	3,250
5	3	3	4	3,667
5	4	5	4	4,333
4	4	3	NA	4,000
5	4	5	5	4,000
4	4	4	4	3,667
4	5	4	4	4,000
4	5	4	5	4,000
4	2	4	3	3,333
3	1	3	3	3,667
4	3	4	3	3,000
3	3	3	4	4,667
4	1	3	4	4,000
2	4	3	4	4,000
1	4	3	4	4,333
5	2	2	4	4,000
4	4	4	4	3,500
3	3	3	3	3,750
4	4	2	4	3,500
4	4	4	5	4,000
4	2	4	4	3,750
4	2	3	3	4,333
2	4	4	4	3,750
4	4	5	4	4,000
4	2	4	3	4,000
4	2	NA	3	3,667
4	2	4	4	3,667
3	2	4	2	2,750
4	5	4	4	4,000
5	2	5	3	4,500
2	NA	2	4	3,333
5	2	4	4	2,750
4	4	4	4	4,000
3	5	5	4	4,500
NA	4	4	3	3,000
2	4	4	2	3,750
2	3	5	5	4,750
2	3	2	3	4,250
4	1	4	4	3,333
4	4	5	4	3,500
5	5	3	4	4,000
3	4	4	5	3,250
3	4	4	4	4,250
4	5	3	4	3,500
4	4	5	3	4,750
4	5	5	1	3,500
4	5	3	4	4,000
4	3	2	5	3,000
4	5	4	4	4,000
4	1	5	4	4,000
2	3	3	4	3,750
5	2	3	5	3,000
4	2	4	4	3,667
4	NA	3	4	4,250
4	4	2	4	3,333
4	2	3	4	3,667
4	5	3	4	4,500
2	4	4	3	3,750
3	5	1	5	4,500
3	3	4	3	3,750
4	2	3	4	3,667
4	4	3	4	4,000
4	2	2	5	3,333
4	3	3	4	4,000
3	3	3	4	3,333
3	2	5	2	4,000

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297666&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297666&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297666&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	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 3.56702 -0.0968872IVHB1[t] + 0.0369166IVHB2[t] + 0.0491564IVHB3[t] + 0.0893407IVHB4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  3.56702 -0.0968872IVHB1[t] +  0.0369166IVHB2[t] +  0.0491564IVHB3[t] +  0.0893407IVHB4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297666&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  3.56702 -0.0968872IVHB1[t] +  0.0369166IVHB2[t] +  0.0491564IVHB3[t] +  0.0893407IVHB4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297666&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297666&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 3.56702 -0.0968872IVHB1[t] + 0.0369166IVHB2[t] + 0.0491564IVHB3[t] + 0.0893407IVHB4[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)	+3.567	0.2528	+1.4110e+01	1.713e-29	8.567e-30
IVHB1	-0.09689	0.03342	-2.8990e+00	0.004294	0.002147
IVHB2	+0.03692	0.03025	+1.2200e+00	0.2242	0.1121
IVHB3	+0.04916	0.03551	+1.3840e+00	0.1683	0.08416
IVHB4	+0.08934	0.0437	+2.0440e+00	0.04264	0.02132

\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) & +3.567 &  0.2528 & +1.4110e+01 &  1.713e-29 &  8.567e-30 \tabularnewline
IVHB1 & -0.09689 &  0.03342 & -2.8990e+00 &  0.004294 &  0.002147 \tabularnewline
IVHB2 & +0.03692 &  0.03025 & +1.2200e+00 &  0.2242 &  0.1121 \tabularnewline
IVHB3 & +0.04916 &  0.03551 & +1.3840e+00 &  0.1683 &  0.08416 \tabularnewline
IVHB4 & +0.08934 &  0.0437 & +2.0440e+00 &  0.04264 &  0.02132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297666&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]+3.567[/C][C] 0.2528[/C][C]+1.4110e+01[/C][C] 1.713e-29[/C][C] 8.567e-30[/C][/ROW]
[ROW][C]IVHB1[/C][C]-0.09689[/C][C] 0.03342[/C][C]-2.8990e+00[/C][C] 0.004294[/C][C] 0.002147[/C][/ROW]
[ROW][C]IVHB2[/C][C]+0.03692[/C][C] 0.03025[/C][C]+1.2200e+00[/C][C] 0.2242[/C][C] 0.1121[/C][/ROW]
[ROW][C]IVHB3[/C][C]+0.04916[/C][C] 0.03551[/C][C]+1.3840e+00[/C][C] 0.1683[/C][C] 0.08416[/C][/ROW]
[ROW][C]IVHB4[/C][C]+0.08934[/C][C] 0.0437[/C][C]+2.0440e+00[/C][C] 0.04264[/C][C] 0.02132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297666&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297666&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)	+3.567	0.2528	+1.4110e+01	1.713e-29	8.567e-30
IVHB1	-0.09689	0.03342	-2.8990e+00	0.004294	0.002147
IVHB2	+0.03692	0.03025	+1.2200e+00	0.2242	0.1121
IVHB3	+0.04916	0.03551	+1.3840e+00	0.1683	0.08416
IVHB4	+0.08934	0.0437	+2.0440e+00	0.04264	0.02132

Multiple Linear Regression - Regression Statistics
Multiple R	0.285
R-squared	0.08121
Adjusted R-squared	0.05719
F-TEST (value)	3.381
F-TEST (DF numerator)	4
F-TEST (DF denominator)	153
p-value	0.01107
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.4174
Sum Squared Residuals	26.66

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.285 \tabularnewline
R-squared &  0.08121 \tabularnewline
Adjusted R-squared &  0.05719 \tabularnewline
F-TEST (value) &  3.381 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value &  0.01107 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.4174 \tabularnewline
Sum Squared Residuals &  26.66 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297666&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.285[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.08121[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.05719[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 3.381[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C] 0.01107[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.4174[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 26.66[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297666&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297666&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	0.285
R-squared	0.08121
Adjusted R-squared	0.05719
F-TEST (value)	3.381
F-TEST (DF numerator)	4
F-TEST (DF denominator)	153
p-value	0.01107
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.4174
Sum Squared Residuals	26.66

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	3.25	3.952	-0.7019
2	4	3.66	0.3402
3	4.25	3.856	0.3935
4	3.667	3.844	-0.1772
5	4	3.84	0.1605
6	4	3.835	0.1648
7	4.333	4.172	0.1612
8	4	3.856	0.1435
9	4.25	3.832	0.418
10	4.25	3.903	0.3472
11	3.75	3.62	0.1304
12	4	3.829	0.1713
13	3.5	3.784	-0.2842
14	4	3.807	0.1927
15	4.25	4.184	0.06597
16	4	3.955	0.04482
17	3.667	3.746	-0.0789
18	4.333	3.784	0.5488
19	4	3.821	0.1788
20	3.667	3.918	-0.251
21	4	4.11	-0.1102
22	3.667	3.832	-0.165
23	4.333	4.001	0.3319
24	3.667	3.832	-0.165
25	4	3.646	0.3543
26	3.75	3.803	-0.0526
27	4	3.923	0.07726
28	4	3.904	0.09582
29	3.25	3.71	-0.4604
30	3.75	4.026	-0.2757
31	4.25	4.049	0.2012
32	3.25	3.669	-0.4188
33	4.25	3.832	0.418
34	3.667	3.892	-0.2249
35	3.5	3.886	-0.3858
36	3.5	3.832	-0.332
37	3.667	3.795	-0.1281
38	4.25	3.912	0.3383
39	3.25	3.929	-0.6789
40	4	4.04	-0.03983
41	3.75	3.904	-0.1542
42	3.75	3.852	-0.1018
43	4	3.803	0.1974
44	3.25	3.881	-0.6311
45	3	3.699	-0.6992
46	4.25	3.881	0.3689
47	4.333	3.881	0.4519
48	4.333	4.026	0.3073
49	2.75	3.612	-0.8621
50	3.5	3.815	-0.3148
51	3.25	3.818	-0.5681
52	3.667	3.706	-0.03871
53	4.25	3.832	0.418
54	4	3.666	0.3345
55	3.667	3.941	-0.2741
56	4.25	3.758	0.4919
57	4	3.844	0.1558
58	4	3.767	0.2329
59	4	3.783	0.2172
60	3.75	3.706	0.04429
61	3	3.952	-0.9519
62	4.25	3.743	0.5074
63	3.5	3.918	-0.418
64	3.5	3.832	-0.332
65	4	3.844	0.1558
66	3.667	3.758	-0.09114
67	3.667	3.511	0.1565
68	3.333	3.889	-0.5557
69	3.333	3.847	-0.5141
70	4.333	3.783	0.5502
71	3.75	3.861	-0.1113
72	4	3.832	0.168
73	3.5	3.874	-0.3736
74	3.75	3.655	0.09528
75	4.25	3.989	0.2612
76	4	3.807	0.1927
77	2.5	3.783	-1.283
78	4	3.783	0.2172
79	4.25	3.969	0.281
80	4.333	3.758	0.5749
81	5	4.001	0.9989
82	4.25	3.624	0.6257
83	3.667	3.613	0.05389
84	4.25	4.075	0.1751
85	3.5	3.832	-0.332
86	3.667	3.852	-0.1848
87	4.25	3.929	0.3211
88	4	3.93	0.06972
89	4.25	3.792	0.4582
90	3.667	3.718	-0.05095
91	4	3.929	0.07114
92	4.5	3.97	0.5295
93	4.5	3.63	0.8695
94	4	3.978	0.02199
95	4	4.009	-0.008616
96	4.333	3.844	0.4888
97	3.75	4.03	-0.2804
98	3.25	3.889	-0.6387
99	3.667	3.698	-0.03117
100	4.333	3.833	0.4996
101	4	3.923	0.07726
102	3.667	3.881	-0.2141
103	4	3.918	0.08196
104	4	4.007	-0.007385
105	3.333	3.718	-0.385
106	3.667	3.729	-0.06177
107	3	3.755	-0.7549
108	4.667	3.892	0.7751
109	4	3.721	0.2788
110	4	4.026	-0.02575
111	4.333	4.123	0.2104
112	4	3.612	0.3879
113	3.5	3.881	-0.3811
114	3.75	3.803	-0.0526
115	3.5	3.783	-0.2828
116	4	3.97	0.02953
117	3.75	3.807	-0.05729
118	4.333	3.669	0.6642
119	3.75	4.075	-0.3249
120	4	3.93	0.06972
121	4	3.718	0.282
122	3.667	3.807	-0.1403
123	2.75	3.725	-0.9755
124	4	3.918	0.08196
125	4.5	3.67	0.8298
126	2.75	3.71	-0.9604
127	4	3.881	0.1189
128	4.5	4.064	0.4359
129	3.75	3.896	-0.1462
130	4.75	4.176	0.5735
131	4.25	3.85	0.3997
132	3.333	3.77	-0.4374
133	3.5	3.93	-0.4303
134	4	3.772	0.228
135	3.25	4.067	-0.8174
136	4.25	3.978	0.272
137	3.5	3.869	-0.3689
138	4.75	3.841	0.9091
139	3.5	3.699	-0.1992
140	4	3.869	0.1311
141	3	3.835	-0.8352
142	4	3.918	0.08196
143	4	3.82	0.1805
144	3.75	3.989	-0.2388
145	3	3.751	-0.7506
146	3.667	3.807	-0.1403
147	3.333	3.783	-0.4498
148	3.667	3.758	-0.09114
149	4.5	3.869	0.6311
150	3.75	3.986	-0.2356
151	4.5	3.957	0.5432
152	3.75	3.852	-0.1018
153	3.667	3.758	-0.09114
154	4	3.832	0.168
155	3.333	3.798	-0.4653
156	4	3.795	0.2049
157	3.333	3.892	-0.5589
158	4	3.775	0.2253

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  3.25 &  3.952 & -0.7019 \tabularnewline
2 &  4 &  3.66 &  0.3402 \tabularnewline
3 &  4.25 &  3.856 &  0.3935 \tabularnewline
4 &  3.667 &  3.844 & -0.1772 \tabularnewline
5 &  4 &  3.84 &  0.1605 \tabularnewline
6 &  4 &  3.835 &  0.1648 \tabularnewline
7 &  4.333 &  4.172 &  0.1612 \tabularnewline
8 &  4 &  3.856 &  0.1435 \tabularnewline
9 &  4.25 &  3.832 &  0.418 \tabularnewline
10 &  4.25 &  3.903 &  0.3472 \tabularnewline
11 &  3.75 &  3.62 &  0.1304 \tabularnewline
12 &  4 &  3.829 &  0.1713 \tabularnewline
13 &  3.5 &  3.784 & -0.2842 \tabularnewline
14 &  4 &  3.807 &  0.1927 \tabularnewline
15 &  4.25 &  4.184 &  0.06597 \tabularnewline
16 &  4 &  3.955 &  0.04482 \tabularnewline
17 &  3.667 &  3.746 & -0.0789 \tabularnewline
18 &  4.333 &  3.784 &  0.5488 \tabularnewline
19 &  4 &  3.821 &  0.1788 \tabularnewline
20 &  3.667 &  3.918 & -0.251 \tabularnewline
21 &  4 &  4.11 & -0.1102 \tabularnewline
22 &  3.667 &  3.832 & -0.165 \tabularnewline
23 &  4.333 &  4.001 &  0.3319 \tabularnewline
24 &  3.667 &  3.832 & -0.165 \tabularnewline
25 &  4 &  3.646 &  0.3543 \tabularnewline
26 &  3.75 &  3.803 & -0.0526 \tabularnewline
27 &  4 &  3.923 &  0.07726 \tabularnewline
28 &  4 &  3.904 &  0.09582 \tabularnewline
29 &  3.25 &  3.71 & -0.4604 \tabularnewline
30 &  3.75 &  4.026 & -0.2757 \tabularnewline
31 &  4.25 &  4.049 &  0.2012 \tabularnewline
32 &  3.25 &  3.669 & -0.4188 \tabularnewline
33 &  4.25 &  3.832 &  0.418 \tabularnewline
34 &  3.667 &  3.892 & -0.2249 \tabularnewline
35 &  3.5 &  3.886 & -0.3858 \tabularnewline
36 &  3.5 &  3.832 & -0.332 \tabularnewline
37 &  3.667 &  3.795 & -0.1281 \tabularnewline
38 &  4.25 &  3.912 &  0.3383 \tabularnewline
39 &  3.25 &  3.929 & -0.6789 \tabularnewline
40 &  4 &  4.04 & -0.03983 \tabularnewline
41 &  3.75 &  3.904 & -0.1542 \tabularnewline
42 &  3.75 &  3.852 & -0.1018 \tabularnewline
43 &  4 &  3.803 &  0.1974 \tabularnewline
44 &  3.25 &  3.881 & -0.6311 \tabularnewline
45 &  3 &  3.699 & -0.6992 \tabularnewline
46 &  4.25 &  3.881 &  0.3689 \tabularnewline
47 &  4.333 &  3.881 &  0.4519 \tabularnewline
48 &  4.333 &  4.026 &  0.3073 \tabularnewline
49 &  2.75 &  3.612 & -0.8621 \tabularnewline
50 &  3.5 &  3.815 & -0.3148 \tabularnewline
51 &  3.25 &  3.818 & -0.5681 \tabularnewline
52 &  3.667 &  3.706 & -0.03871 \tabularnewline
53 &  4.25 &  3.832 &  0.418 \tabularnewline
54 &  4 &  3.666 &  0.3345 \tabularnewline
55 &  3.667 &  3.941 & -0.2741 \tabularnewline
56 &  4.25 &  3.758 &  0.4919 \tabularnewline
57 &  4 &  3.844 &  0.1558 \tabularnewline
58 &  4 &  3.767 &  0.2329 \tabularnewline
59 &  4 &  3.783 &  0.2172 \tabularnewline
60 &  3.75 &  3.706 &  0.04429 \tabularnewline
61 &  3 &  3.952 & -0.9519 \tabularnewline
62 &  4.25 &  3.743 &  0.5074 \tabularnewline
63 &  3.5 &  3.918 & -0.418 \tabularnewline
64 &  3.5 &  3.832 & -0.332 \tabularnewline
65 &  4 &  3.844 &  0.1558 \tabularnewline
66 &  3.667 &  3.758 & -0.09114 \tabularnewline
67 &  3.667 &  3.511 &  0.1565 \tabularnewline
68 &  3.333 &  3.889 & -0.5557 \tabularnewline
69 &  3.333 &  3.847 & -0.5141 \tabularnewline
70 &  4.333 &  3.783 &  0.5502 \tabularnewline
71 &  3.75 &  3.861 & -0.1113 \tabularnewline
72 &  4 &  3.832 &  0.168 \tabularnewline
73 &  3.5 &  3.874 & -0.3736 \tabularnewline
74 &  3.75 &  3.655 &  0.09528 \tabularnewline
75 &  4.25 &  3.989 &  0.2612 \tabularnewline
76 &  4 &  3.807 &  0.1927 \tabularnewline
77 &  2.5 &  3.783 & -1.283 \tabularnewline
78 &  4 &  3.783 &  0.2172 \tabularnewline
79 &  4.25 &  3.969 &  0.281 \tabularnewline
80 &  4.333 &  3.758 &  0.5749 \tabularnewline
81 &  5 &  4.001 &  0.9989 \tabularnewline
82 &  4.25 &  3.624 &  0.6257 \tabularnewline
83 &  3.667 &  3.613 &  0.05389 \tabularnewline
84 &  4.25 &  4.075 &  0.1751 \tabularnewline
85 &  3.5 &  3.832 & -0.332 \tabularnewline
86 &  3.667 &  3.852 & -0.1848 \tabularnewline
87 &  4.25 &  3.929 &  0.3211 \tabularnewline
88 &  4 &  3.93 &  0.06972 \tabularnewline
89 &  4.25 &  3.792 &  0.4582 \tabularnewline
90 &  3.667 &  3.718 & -0.05095 \tabularnewline
91 &  4 &  3.929 &  0.07114 \tabularnewline
92 &  4.5 &  3.97 &  0.5295 \tabularnewline
93 &  4.5 &  3.63 &  0.8695 \tabularnewline
94 &  4 &  3.978 &  0.02199 \tabularnewline
95 &  4 &  4.009 & -0.008616 \tabularnewline
96 &  4.333 &  3.844 &  0.4888 \tabularnewline
97 &  3.75 &  4.03 & -0.2804 \tabularnewline
98 &  3.25 &  3.889 & -0.6387 \tabularnewline
99 &  3.667 &  3.698 & -0.03117 \tabularnewline
100 &  4.333 &  3.833 &  0.4996 \tabularnewline
101 &  4 &  3.923 &  0.07726 \tabularnewline
102 &  3.667 &  3.881 & -0.2141 \tabularnewline
103 &  4 &  3.918 &  0.08196 \tabularnewline
104 &  4 &  4.007 & -0.007385 \tabularnewline
105 &  3.333 &  3.718 & -0.385 \tabularnewline
106 &  3.667 &  3.729 & -0.06177 \tabularnewline
107 &  3 &  3.755 & -0.7549 \tabularnewline
108 &  4.667 &  3.892 &  0.7751 \tabularnewline
109 &  4 &  3.721 &  0.2788 \tabularnewline
110 &  4 &  4.026 & -0.02575 \tabularnewline
111 &  4.333 &  4.123 &  0.2104 \tabularnewline
112 &  4 &  3.612 &  0.3879 \tabularnewline
113 &  3.5 &  3.881 & -0.3811 \tabularnewline
114 &  3.75 &  3.803 & -0.0526 \tabularnewline
115 &  3.5 &  3.783 & -0.2828 \tabularnewline
116 &  4 &  3.97 &  0.02953 \tabularnewline
117 &  3.75 &  3.807 & -0.05729 \tabularnewline
118 &  4.333 &  3.669 &  0.6642 \tabularnewline
119 &  3.75 &  4.075 & -0.3249 \tabularnewline
120 &  4 &  3.93 &  0.06972 \tabularnewline
121 &  4 &  3.718 &  0.282 \tabularnewline
122 &  3.667 &  3.807 & -0.1403 \tabularnewline
123 &  2.75 &  3.725 & -0.9755 \tabularnewline
124 &  4 &  3.918 &  0.08196 \tabularnewline
125 &  4.5 &  3.67 &  0.8298 \tabularnewline
126 &  2.75 &  3.71 & -0.9604 \tabularnewline
127 &  4 &  3.881 &  0.1189 \tabularnewline
128 &  4.5 &  4.064 &  0.4359 \tabularnewline
129 &  3.75 &  3.896 & -0.1462 \tabularnewline
130 &  4.75 &  4.176 &  0.5735 \tabularnewline
131 &  4.25 &  3.85 &  0.3997 \tabularnewline
132 &  3.333 &  3.77 & -0.4374 \tabularnewline
133 &  3.5 &  3.93 & -0.4303 \tabularnewline
134 &  4 &  3.772 &  0.228 \tabularnewline
135 &  3.25 &  4.067 & -0.8174 \tabularnewline
136 &  4.25 &  3.978 &  0.272 \tabularnewline
137 &  3.5 &  3.869 & -0.3689 \tabularnewline
138 &  4.75 &  3.841 &  0.9091 \tabularnewline
139 &  3.5 &  3.699 & -0.1992 \tabularnewline
140 &  4 &  3.869 &  0.1311 \tabularnewline
141 &  3 &  3.835 & -0.8352 \tabularnewline
142 &  4 &  3.918 &  0.08196 \tabularnewline
143 &  4 &  3.82 &  0.1805 \tabularnewline
144 &  3.75 &  3.989 & -0.2388 \tabularnewline
145 &  3 &  3.751 & -0.7506 \tabularnewline
146 &  3.667 &  3.807 & -0.1403 \tabularnewline
147 &  3.333 &  3.783 & -0.4498 \tabularnewline
148 &  3.667 &  3.758 & -0.09114 \tabularnewline
149 &  4.5 &  3.869 &  0.6311 \tabularnewline
150 &  3.75 &  3.986 & -0.2356 \tabularnewline
151 &  4.5 &  3.957 &  0.5432 \tabularnewline
152 &  3.75 &  3.852 & -0.1018 \tabularnewline
153 &  3.667 &  3.758 & -0.09114 \tabularnewline
154 &  4 &  3.832 &  0.168 \tabularnewline
155 &  3.333 &  3.798 & -0.4653 \tabularnewline
156 &  4 &  3.795 &  0.2049 \tabularnewline
157 &  3.333 &  3.892 & -0.5589 \tabularnewline
158 &  4 &  3.775 &  0.2253 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297666&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] 3.25[/C][C] 3.952[/C][C]-0.7019[/C][/ROW]
[ROW][C]2[/C][C] 4[/C][C] 3.66[/C][C] 0.3402[/C][/ROW]
[ROW][C]3[/C][C] 4.25[/C][C] 3.856[/C][C] 0.3935[/C][/ROW]
[ROW][C]4[/C][C] 3.667[/C][C] 3.844[/C][C]-0.1772[/C][/ROW]
[ROW][C]5[/C][C] 4[/C][C] 3.84[/C][C] 0.1605[/C][/ROW]
[ROW][C]6[/C][C] 4[/C][C] 3.835[/C][C] 0.1648[/C][/ROW]
[ROW][C]7[/C][C] 4.333[/C][C] 4.172[/C][C] 0.1612[/C][/ROW]
[ROW][C]8[/C][C] 4[/C][C] 3.856[/C][C] 0.1435[/C][/ROW]
[ROW][C]9[/C][C] 4.25[/C][C] 3.832[/C][C] 0.418[/C][/ROW]
[ROW][C]10[/C][C] 4.25[/C][C] 3.903[/C][C] 0.3472[/C][/ROW]
[ROW][C]11[/C][C] 3.75[/C][C] 3.62[/C][C] 0.1304[/C][/ROW]
[ROW][C]12[/C][C] 4[/C][C] 3.829[/C][C] 0.1713[/C][/ROW]
[ROW][C]13[/C][C] 3.5[/C][C] 3.784[/C][C]-0.2842[/C][/ROW]
[ROW][C]14[/C][C] 4[/C][C] 3.807[/C][C] 0.1927[/C][/ROW]
[ROW][C]15[/C][C] 4.25[/C][C] 4.184[/C][C] 0.06597[/C][/ROW]
[ROW][C]16[/C][C] 4[/C][C] 3.955[/C][C] 0.04482[/C][/ROW]
[ROW][C]17[/C][C] 3.667[/C][C] 3.746[/C][C]-0.0789[/C][/ROW]
[ROW][C]18[/C][C] 4.333[/C][C] 3.784[/C][C] 0.5488[/C][/ROW]
[ROW][C]19[/C][C] 4[/C][C] 3.821[/C][C] 0.1788[/C][/ROW]
[ROW][C]20[/C][C] 3.667[/C][C] 3.918[/C][C]-0.251[/C][/ROW]
[ROW][C]21[/C][C] 4[/C][C] 4.11[/C][C]-0.1102[/C][/ROW]
[ROW][C]22[/C][C] 3.667[/C][C] 3.832[/C][C]-0.165[/C][/ROW]
[ROW][C]23[/C][C] 4.333[/C][C] 4.001[/C][C] 0.3319[/C][/ROW]
[ROW][C]24[/C][C] 3.667[/C][C] 3.832[/C][C]-0.165[/C][/ROW]
[ROW][C]25[/C][C] 4[/C][C] 3.646[/C][C] 0.3543[/C][/ROW]
[ROW][C]26[/C][C] 3.75[/C][C] 3.803[/C][C]-0.0526[/C][/ROW]
[ROW][C]27[/C][C] 4[/C][C] 3.923[/C][C] 0.07726[/C][/ROW]
[ROW][C]28[/C][C] 4[/C][C] 3.904[/C][C] 0.09582[/C][/ROW]
[ROW][C]29[/C][C] 3.25[/C][C] 3.71[/C][C]-0.4604[/C][/ROW]
[ROW][C]30[/C][C] 3.75[/C][C] 4.026[/C][C]-0.2757[/C][/ROW]
[ROW][C]31[/C][C] 4.25[/C][C] 4.049[/C][C] 0.2012[/C][/ROW]
[ROW][C]32[/C][C] 3.25[/C][C] 3.669[/C][C]-0.4188[/C][/ROW]
[ROW][C]33[/C][C] 4.25[/C][C] 3.832[/C][C] 0.418[/C][/ROW]
[ROW][C]34[/C][C] 3.667[/C][C] 3.892[/C][C]-0.2249[/C][/ROW]
[ROW][C]35[/C][C] 3.5[/C][C] 3.886[/C][C]-0.3858[/C][/ROW]
[ROW][C]36[/C][C] 3.5[/C][C] 3.832[/C][C]-0.332[/C][/ROW]
[ROW][C]37[/C][C] 3.667[/C][C] 3.795[/C][C]-0.1281[/C][/ROW]
[ROW][C]38[/C][C] 4.25[/C][C] 3.912[/C][C] 0.3383[/C][/ROW]
[ROW][C]39[/C][C] 3.25[/C][C] 3.929[/C][C]-0.6789[/C][/ROW]
[ROW][C]40[/C][C] 4[/C][C] 4.04[/C][C]-0.03983[/C][/ROW]
[ROW][C]41[/C][C] 3.75[/C][C] 3.904[/C][C]-0.1542[/C][/ROW]
[ROW][C]42[/C][C] 3.75[/C][C] 3.852[/C][C]-0.1018[/C][/ROW]
[ROW][C]43[/C][C] 4[/C][C] 3.803[/C][C] 0.1974[/C][/ROW]
[ROW][C]44[/C][C] 3.25[/C][C] 3.881[/C][C]-0.6311[/C][/ROW]
[ROW][C]45[/C][C] 3[/C][C] 3.699[/C][C]-0.6992[/C][/ROW]
[ROW][C]46[/C][C] 4.25[/C][C] 3.881[/C][C] 0.3689[/C][/ROW]
[ROW][C]47[/C][C] 4.333[/C][C] 3.881[/C][C] 0.4519[/C][/ROW]
[ROW][C]48[/C][C] 4.333[/C][C] 4.026[/C][C] 0.3073[/C][/ROW]
[ROW][C]49[/C][C] 2.75[/C][C] 3.612[/C][C]-0.8621[/C][/ROW]
[ROW][C]50[/C][C] 3.5[/C][C] 3.815[/C][C]-0.3148[/C][/ROW]
[ROW][C]51[/C][C] 3.25[/C][C] 3.818[/C][C]-0.5681[/C][/ROW]
[ROW][C]52[/C][C] 3.667[/C][C] 3.706[/C][C]-0.03871[/C][/ROW]
[ROW][C]53[/C][C] 4.25[/C][C] 3.832[/C][C] 0.418[/C][/ROW]
[ROW][C]54[/C][C] 4[/C][C] 3.666[/C][C] 0.3345[/C][/ROW]
[ROW][C]55[/C][C] 3.667[/C][C] 3.941[/C][C]-0.2741[/C][/ROW]
[ROW][C]56[/C][C] 4.25[/C][C] 3.758[/C][C] 0.4919[/C][/ROW]
[ROW][C]57[/C][C] 4[/C][C] 3.844[/C][C] 0.1558[/C][/ROW]
[ROW][C]58[/C][C] 4[/C][C] 3.767[/C][C] 0.2329[/C][/ROW]
[ROW][C]59[/C][C] 4[/C][C] 3.783[/C][C] 0.2172[/C][/ROW]
[ROW][C]60[/C][C] 3.75[/C][C] 3.706[/C][C] 0.04429[/C][/ROW]
[ROW][C]61[/C][C] 3[/C][C] 3.952[/C][C]-0.9519[/C][/ROW]
[ROW][C]62[/C][C] 4.25[/C][C] 3.743[/C][C] 0.5074[/C][/ROW]
[ROW][C]63[/C][C] 3.5[/C][C] 3.918[/C][C]-0.418[/C][/ROW]
[ROW][C]64[/C][C] 3.5[/C][C] 3.832[/C][C]-0.332[/C][/ROW]
[ROW][C]65[/C][C] 4[/C][C] 3.844[/C][C] 0.1558[/C][/ROW]
[ROW][C]66[/C][C] 3.667[/C][C] 3.758[/C][C]-0.09114[/C][/ROW]
[ROW][C]67[/C][C] 3.667[/C][C] 3.511[/C][C] 0.1565[/C][/ROW]
[ROW][C]68[/C][C] 3.333[/C][C] 3.889[/C][C]-0.5557[/C][/ROW]
[ROW][C]69[/C][C] 3.333[/C][C] 3.847[/C][C]-0.5141[/C][/ROW]
[ROW][C]70[/C][C] 4.333[/C][C] 3.783[/C][C] 0.5502[/C][/ROW]
[ROW][C]71[/C][C] 3.75[/C][C] 3.861[/C][C]-0.1113[/C][/ROW]
[ROW][C]72[/C][C] 4[/C][C] 3.832[/C][C] 0.168[/C][/ROW]
[ROW][C]73[/C][C] 3.5[/C][C] 3.874[/C][C]-0.3736[/C][/ROW]
[ROW][C]74[/C][C] 3.75[/C][C] 3.655[/C][C] 0.09528[/C][/ROW]
[ROW][C]75[/C][C] 4.25[/C][C] 3.989[/C][C] 0.2612[/C][/ROW]
[ROW][C]76[/C][C] 4[/C][C] 3.807[/C][C] 0.1927[/C][/ROW]
[ROW][C]77[/C][C] 2.5[/C][C] 3.783[/C][C]-1.283[/C][/ROW]
[ROW][C]78[/C][C] 4[/C][C] 3.783[/C][C] 0.2172[/C][/ROW]
[ROW][C]79[/C][C] 4.25[/C][C] 3.969[/C][C] 0.281[/C][/ROW]
[ROW][C]80[/C][C] 4.333[/C][C] 3.758[/C][C] 0.5749[/C][/ROW]
[ROW][C]81[/C][C] 5[/C][C] 4.001[/C][C] 0.9989[/C][/ROW]
[ROW][C]82[/C][C] 4.25[/C][C] 3.624[/C][C] 0.6257[/C][/ROW]
[ROW][C]83[/C][C] 3.667[/C][C] 3.613[/C][C] 0.05389[/C][/ROW]
[ROW][C]84[/C][C] 4.25[/C][C] 4.075[/C][C] 0.1751[/C][/ROW]
[ROW][C]85[/C][C] 3.5[/C][C] 3.832[/C][C]-0.332[/C][/ROW]
[ROW][C]86[/C][C] 3.667[/C][C] 3.852[/C][C]-0.1848[/C][/ROW]
[ROW][C]87[/C][C] 4.25[/C][C] 3.929[/C][C] 0.3211[/C][/ROW]
[ROW][C]88[/C][C] 4[/C][C] 3.93[/C][C] 0.06972[/C][/ROW]
[ROW][C]89[/C][C] 4.25[/C][C] 3.792[/C][C] 0.4582[/C][/ROW]
[ROW][C]90[/C][C] 3.667[/C][C] 3.718[/C][C]-0.05095[/C][/ROW]
[ROW][C]91[/C][C] 4[/C][C] 3.929[/C][C] 0.07114[/C][/ROW]
[ROW][C]92[/C][C] 4.5[/C][C] 3.97[/C][C] 0.5295[/C][/ROW]
[ROW][C]93[/C][C] 4.5[/C][C] 3.63[/C][C] 0.8695[/C][/ROW]
[ROW][C]94[/C][C] 4[/C][C] 3.978[/C][C] 0.02199[/C][/ROW]
[ROW][C]95[/C][C] 4[/C][C] 4.009[/C][C]-0.008616[/C][/ROW]
[ROW][C]96[/C][C] 4.333[/C][C] 3.844[/C][C] 0.4888[/C][/ROW]
[ROW][C]97[/C][C] 3.75[/C][C] 4.03[/C][C]-0.2804[/C][/ROW]
[ROW][C]98[/C][C] 3.25[/C][C] 3.889[/C][C]-0.6387[/C][/ROW]
[ROW][C]99[/C][C] 3.667[/C][C] 3.698[/C][C]-0.03117[/C][/ROW]
[ROW][C]100[/C][C] 4.333[/C][C] 3.833[/C][C] 0.4996[/C][/ROW]
[ROW][C]101[/C][C] 4[/C][C] 3.923[/C][C] 0.07726[/C][/ROW]
[ROW][C]102[/C][C] 3.667[/C][C] 3.881[/C][C]-0.2141[/C][/ROW]
[ROW][C]103[/C][C] 4[/C][C] 3.918[/C][C] 0.08196[/C][/ROW]
[ROW][C]104[/C][C] 4[/C][C] 4.007[/C][C]-0.007385[/C][/ROW]
[ROW][C]105[/C][C] 3.333[/C][C] 3.718[/C][C]-0.385[/C][/ROW]
[ROW][C]106[/C][C] 3.667[/C][C] 3.729[/C][C]-0.06177[/C][/ROW]
[ROW][C]107[/C][C] 3[/C][C] 3.755[/C][C]-0.7549[/C][/ROW]
[ROW][C]108[/C][C] 4.667[/C][C] 3.892[/C][C] 0.7751[/C][/ROW]
[ROW][C]109[/C][C] 4[/C][C] 3.721[/C][C] 0.2788[/C][/ROW]
[ROW][C]110[/C][C] 4[/C][C] 4.026[/C][C]-0.02575[/C][/ROW]
[ROW][C]111[/C][C] 4.333[/C][C] 4.123[/C][C] 0.2104[/C][/ROW]
[ROW][C]112[/C][C] 4[/C][C] 3.612[/C][C] 0.3879[/C][/ROW]
[ROW][C]113[/C][C] 3.5[/C][C] 3.881[/C][C]-0.3811[/C][/ROW]
[ROW][C]114[/C][C] 3.75[/C][C] 3.803[/C][C]-0.0526[/C][/ROW]
[ROW][C]115[/C][C] 3.5[/C][C] 3.783[/C][C]-0.2828[/C][/ROW]
[ROW][C]116[/C][C] 4[/C][C] 3.97[/C][C] 0.02953[/C][/ROW]
[ROW][C]117[/C][C] 3.75[/C][C] 3.807[/C][C]-0.05729[/C][/ROW]
[ROW][C]118[/C][C] 4.333[/C][C] 3.669[/C][C] 0.6642[/C][/ROW]
[ROW][C]119[/C][C] 3.75[/C][C] 4.075[/C][C]-0.3249[/C][/ROW]
[ROW][C]120[/C][C] 4[/C][C] 3.93[/C][C] 0.06972[/C][/ROW]
[ROW][C]121[/C][C] 4[/C][C] 3.718[/C][C] 0.282[/C][/ROW]
[ROW][C]122[/C][C] 3.667[/C][C] 3.807[/C][C]-0.1403[/C][/ROW]
[ROW][C]123[/C][C] 2.75[/C][C] 3.725[/C][C]-0.9755[/C][/ROW]
[ROW][C]124[/C][C] 4[/C][C] 3.918[/C][C] 0.08196[/C][/ROW]
[ROW][C]125[/C][C] 4.5[/C][C] 3.67[/C][C] 0.8298[/C][/ROW]
[ROW][C]126[/C][C] 2.75[/C][C] 3.71[/C][C]-0.9604[/C][/ROW]
[ROW][C]127[/C][C] 4[/C][C] 3.881[/C][C] 0.1189[/C][/ROW]
[ROW][C]128[/C][C] 4.5[/C][C] 4.064[/C][C] 0.4359[/C][/ROW]
[ROW][C]129[/C][C] 3.75[/C][C] 3.896[/C][C]-0.1462[/C][/ROW]
[ROW][C]130[/C][C] 4.75[/C][C] 4.176[/C][C] 0.5735[/C][/ROW]
[ROW][C]131[/C][C] 4.25[/C][C] 3.85[/C][C] 0.3997[/C][/ROW]
[ROW][C]132[/C][C] 3.333[/C][C] 3.77[/C][C]-0.4374[/C][/ROW]
[ROW][C]133[/C][C] 3.5[/C][C] 3.93[/C][C]-0.4303[/C][/ROW]
[ROW][C]134[/C][C] 4[/C][C] 3.772[/C][C] 0.228[/C][/ROW]
[ROW][C]135[/C][C] 3.25[/C][C] 4.067[/C][C]-0.8174[/C][/ROW]
[ROW][C]136[/C][C] 4.25[/C][C] 3.978[/C][C] 0.272[/C][/ROW]
[ROW][C]137[/C][C] 3.5[/C][C] 3.869[/C][C]-0.3689[/C][/ROW]
[ROW][C]138[/C][C] 4.75[/C][C] 3.841[/C][C] 0.9091[/C][/ROW]
[ROW][C]139[/C][C] 3.5[/C][C] 3.699[/C][C]-0.1992[/C][/ROW]
[ROW][C]140[/C][C] 4[/C][C] 3.869[/C][C] 0.1311[/C][/ROW]
[ROW][C]141[/C][C] 3[/C][C] 3.835[/C][C]-0.8352[/C][/ROW]
[ROW][C]142[/C][C] 4[/C][C] 3.918[/C][C] 0.08196[/C][/ROW]
[ROW][C]143[/C][C] 4[/C][C] 3.82[/C][C] 0.1805[/C][/ROW]
[ROW][C]144[/C][C] 3.75[/C][C] 3.989[/C][C]-0.2388[/C][/ROW]
[ROW][C]145[/C][C] 3[/C][C] 3.751[/C][C]-0.7506[/C][/ROW]
[ROW][C]146[/C][C] 3.667[/C][C] 3.807[/C][C]-0.1403[/C][/ROW]
[ROW][C]147[/C][C] 3.333[/C][C] 3.783[/C][C]-0.4498[/C][/ROW]
[ROW][C]148[/C][C] 3.667[/C][C] 3.758[/C][C]-0.09114[/C][/ROW]
[ROW][C]149[/C][C] 4.5[/C][C] 3.869[/C][C] 0.6311[/C][/ROW]
[ROW][C]150[/C][C] 3.75[/C][C] 3.986[/C][C]-0.2356[/C][/ROW]
[ROW][C]151[/C][C] 4.5[/C][C] 3.957[/C][C] 0.5432[/C][/ROW]
[ROW][C]152[/C][C] 3.75[/C][C] 3.852[/C][C]-0.1018[/C][/ROW]
[ROW][C]153[/C][C] 3.667[/C][C] 3.758[/C][C]-0.09114[/C][/ROW]
[ROW][C]154[/C][C] 4[/C][C] 3.832[/C][C] 0.168[/C][/ROW]
[ROW][C]155[/C][C] 3.333[/C][C] 3.798[/C][C]-0.4653[/C][/ROW]
[ROW][C]156[/C][C] 4[/C][C] 3.795[/C][C] 0.2049[/C][/ROW]
[ROW][C]157[/C][C] 3.333[/C][C] 3.892[/C][C]-0.5589[/C][/ROW]
[ROW][C]158[/C][C] 4[/C][C] 3.775[/C][C] 0.2253[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297666&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297666&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	3.25	3.952	-0.7019
2	4	3.66	0.3402
3	4.25	3.856	0.3935
4	3.667	3.844	-0.1772
5	4	3.84	0.1605
6	4	3.835	0.1648
7	4.333	4.172	0.1612
8	4	3.856	0.1435
9	4.25	3.832	0.418
10	4.25	3.903	0.3472
11	3.75	3.62	0.1304
12	4	3.829	0.1713
13	3.5	3.784	-0.2842
14	4	3.807	0.1927
15	4.25	4.184	0.06597
16	4	3.955	0.04482
17	3.667	3.746	-0.0789
18	4.333	3.784	0.5488
19	4	3.821	0.1788
20	3.667	3.918	-0.251
21	4	4.11	-0.1102
22	3.667	3.832	-0.165
23	4.333	4.001	0.3319
24	3.667	3.832	-0.165
25	4	3.646	0.3543
26	3.75	3.803	-0.0526
27	4	3.923	0.07726
28	4	3.904	0.09582
29	3.25	3.71	-0.4604
30	3.75	4.026	-0.2757
31	4.25	4.049	0.2012
32	3.25	3.669	-0.4188
33	4.25	3.832	0.418
34	3.667	3.892	-0.2249
35	3.5	3.886	-0.3858
36	3.5	3.832	-0.332
37	3.667	3.795	-0.1281
38	4.25	3.912	0.3383
39	3.25	3.929	-0.6789
40	4	4.04	-0.03983
41	3.75	3.904	-0.1542
42	3.75	3.852	-0.1018
43	4	3.803	0.1974
44	3.25	3.881	-0.6311
45	3	3.699	-0.6992
46	4.25	3.881	0.3689
47	4.333	3.881	0.4519
48	4.333	4.026	0.3073
49	2.75	3.612	-0.8621
50	3.5	3.815	-0.3148
51	3.25	3.818	-0.5681
52	3.667	3.706	-0.03871
53	4.25	3.832	0.418
54	4	3.666	0.3345
55	3.667	3.941	-0.2741
56	4.25	3.758	0.4919
57	4	3.844	0.1558
58	4	3.767	0.2329
59	4	3.783	0.2172
60	3.75	3.706	0.04429
61	3	3.952	-0.9519
62	4.25	3.743	0.5074
63	3.5	3.918	-0.418
64	3.5	3.832	-0.332
65	4	3.844	0.1558
66	3.667	3.758	-0.09114
67	3.667	3.511	0.1565
68	3.333	3.889	-0.5557
69	3.333	3.847	-0.5141
70	4.333	3.783	0.5502
71	3.75	3.861	-0.1113
72	4	3.832	0.168
73	3.5	3.874	-0.3736
74	3.75	3.655	0.09528
75	4.25	3.989	0.2612
76	4	3.807	0.1927
77	2.5	3.783	-1.283
78	4	3.783	0.2172
79	4.25	3.969	0.281
80	4.333	3.758	0.5749
81	5	4.001	0.9989
82	4.25	3.624	0.6257
83	3.667	3.613	0.05389
84	4.25	4.075	0.1751
85	3.5	3.832	-0.332
86	3.667	3.852	-0.1848
87	4.25	3.929	0.3211
88	4	3.93	0.06972
89	4.25	3.792	0.4582
90	3.667	3.718	-0.05095
91	4	3.929	0.07114
92	4.5	3.97	0.5295
93	4.5	3.63	0.8695
94	4	3.978	0.02199
95	4	4.009	-0.008616
96	4.333	3.844	0.4888
97	3.75	4.03	-0.2804
98	3.25	3.889	-0.6387
99	3.667	3.698	-0.03117
100	4.333	3.833	0.4996
101	4	3.923	0.07726
102	3.667	3.881	-0.2141
103	4	3.918	0.08196
104	4	4.007	-0.007385
105	3.333	3.718	-0.385
106	3.667	3.729	-0.06177
107	3	3.755	-0.7549
108	4.667	3.892	0.7751
109	4	3.721	0.2788
110	4	4.026	-0.02575
111	4.333	4.123	0.2104
112	4	3.612	0.3879
113	3.5	3.881	-0.3811
114	3.75	3.803	-0.0526
115	3.5	3.783	-0.2828
116	4	3.97	0.02953
117	3.75	3.807	-0.05729
118	4.333	3.669	0.6642
119	3.75	4.075	-0.3249
120	4	3.93	0.06972
121	4	3.718	0.282
122	3.667	3.807	-0.1403
123	2.75	3.725	-0.9755
124	4	3.918	0.08196
125	4.5	3.67	0.8298
126	2.75	3.71	-0.9604
127	4	3.881	0.1189
128	4.5	4.064	0.4359
129	3.75	3.896	-0.1462
130	4.75	4.176	0.5735
131	4.25	3.85	0.3997
132	3.333	3.77	-0.4374
133	3.5	3.93	-0.4303
134	4	3.772	0.228
135	3.25	4.067	-0.8174
136	4.25	3.978	0.272
137	3.5	3.869	-0.3689
138	4.75	3.841	0.9091
139	3.5	3.699	-0.1992
140	4	3.869	0.1311
141	3	3.835	-0.8352
142	4	3.918	0.08196
143	4	3.82	0.1805
144	3.75	3.989	-0.2388
145	3	3.751	-0.7506
146	3.667	3.807	-0.1403
147	3.333	3.783	-0.4498
148	3.667	3.758	-0.09114
149	4.5	3.869	0.6311
150	3.75	3.986	-0.2356
151	4.5	3.957	0.5432
152	3.75	3.852	-0.1018
153	3.667	3.758	-0.09114
154	4	3.832	0.168
155	3.333	3.798	-0.4653
156	4	3.795	0.2049
157	3.333	3.892	-0.5589
158	4	3.775	0.2253

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.6732	0.6535	0.3268
9	0.5173	0.9655	0.4827
10	0.5653	0.8694	0.4347
11	0.437	0.874	0.563
12	0.3336	0.6673	0.6664
13	0.3877	0.7755	0.6123
14	0.2982	0.5964	0.7018
15	0.2195	0.4391	0.7805
16	0.1533	0.3067	0.8467
17	0.1177	0.2354	0.8823
18	0.1235	0.2471	0.8765
19	0.08466	0.1693	0.9153
20	0.08526	0.1705	0.9147
21	0.05841	0.1168	0.9416
22	0.04777	0.09554	0.9522
23	0.0415	0.083	0.9585
24	0.03261	0.06521	0.9674
25	0.02387	0.04774	0.9761
26	0.0168	0.03359	0.9832
27	0.01048	0.02096	0.9895
28	0.006393	0.01279	0.9936
29	0.01378	0.02755	0.9862
30	0.01205	0.0241	0.988
31	0.008694	0.01739	0.9913
32	0.0123	0.0246	0.9877
33	0.01186	0.02371	0.9881
34	0.009783	0.01957	0.9902
35	0.01041	0.02082	0.9896
36	0.01062	0.02124	0.9894
37	0.007488	0.01498	0.9925
38	0.006352	0.0127	0.9936
39	0.01645	0.0329	0.9835
40	0.01129	0.02258	0.9887
41	0.008208	0.01642	0.9918
42	0.005856	0.01171	0.9941
43	0.00411	0.00822	0.9959
44	0.007896	0.01579	0.9921
45	0.01612	0.03224	0.9839
46	0.0168	0.0336	0.9832
47	0.0198	0.0396	0.9802
48	0.01744	0.03488	0.9826
49	0.05094	0.1019	0.9491
50	0.04329	0.08658	0.9567
51	0.05148	0.103	0.9485
52	0.03952	0.07904	0.9605
53	0.04004	0.08008	0.96
54	0.04196	0.08392	0.958
55	0.03605	0.07211	0.9639
56	0.04421	0.08842	0.9558
57	0.03535	0.0707	0.9646
58	0.03069	0.06139	0.9693
59	0.02476	0.04952	0.9752
60	0.01851	0.03702	0.9815
61	0.06104	0.1221	0.939
62	0.06861	0.1372	0.9314
63	0.07125	0.1425	0.9288
64	0.06588	0.1318	0.9341
65	0.0539	0.1078	0.9461
66	0.04233	0.08467	0.9577
67	0.03382	0.06764	0.9662
68	0.04139	0.08277	0.9586
69	0.04564	0.09127	0.9544
70	0.05422	0.1084	0.9458
71	0.04391	0.08781	0.9561
72	0.03541	0.07083	0.9646
73	0.03399	0.06798	0.966
74	0.02671	0.05341	0.9733
75	0.02278	0.04555	0.9772
76	0.01837	0.03674	0.9816
77	0.1351	0.2701	0.8649
78	0.1176	0.2351	0.8824
79	0.1052	0.2103	0.8948
80	0.1238	0.2477	0.8762
81	0.2688	0.5376	0.7312
82	0.3126	0.6252	0.6874
83	0.2745	0.5489	0.7255
84	0.2447	0.4894	0.7553
85	0.2309	0.4618	0.7691
86	0.2031	0.4061	0.7969
87	0.19	0.38	0.81
88	0.1606	0.3213	0.8394
89	0.164	0.328	0.836
90	0.1372	0.2743	0.8628
91	0.1137	0.2274	0.8863
92	0.1276	0.2552	0.8724
93	0.234	0.4681	0.766
94	0.1994	0.3989	0.8006
95	0.1685	0.337	0.8315
96	0.1799	0.3598	0.8201
97	0.1609	0.3218	0.8391
98	0.2006	0.4013	0.7994
99	0.1695	0.3389	0.8305
100	0.1784	0.3568	0.8216
101	0.1498	0.2997	0.8502
102	0.1293	0.2587	0.8707
103	0.1063	0.2127	0.8937
104	0.08564	0.1713	0.9144
105	0.08106	0.1621	0.9189
106	0.06477	0.1295	0.9352
107	0.1042	0.2083	0.8958
108	0.1736	0.3473	0.8264
109	0.1693	0.3387	0.8307
110	0.1394	0.2788	0.8606
111	0.1236	0.2471	0.8764
112	0.1361	0.2721	0.8639
113	0.1319	0.2638	0.8681
114	0.1064	0.2128	0.8936
115	0.09023	0.1805	0.9098
116	0.0709	0.1418	0.9291
117	0.0554	0.1108	0.9446
118	0.09986	0.1997	0.9001
119	0.09253	0.1851	0.9075
120	0.07273	0.1455	0.9273
121	0.07086	0.1417	0.9291
122	0.05526	0.1105	0.9447
123	0.1289	0.2579	0.8711
124	0.1021	0.2041	0.8979
125	0.2388	0.4776	0.7612
126	0.3519	0.7039	0.6481
127	0.3013	0.6025	0.6987
128	0.2747	0.5494	0.7253
129	0.2557	0.5113	0.7443
130	0.3414	0.6827	0.6586
131	0.3366	0.6731	0.6634
132	0.297	0.5939	0.703
133	0.3057	0.6113	0.6943
134	0.255	0.51	0.745
135	0.4975	0.9951	0.5025
136	0.4293	0.8587	0.5707
137	0.4605	0.9209	0.5395
138	0.589	0.822	0.411
139	0.6432	0.7136	0.3568
140	0.5665	0.867	0.4335
141	0.6614	0.6772	0.3386
142	0.6319	0.7361	0.3681
143	0.6786	0.6427	0.3214
144	0.5886	0.8229	0.4114
145	0.6647	0.6706	0.3353
146	0.5595	0.8811	0.4405
147	0.9982	0.003548	0.001774
148	0.9933	0.01341	0.006706
149	0.9865	0.02701	0.0135
150	0.9911	0.01782	0.008912

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.6732 &  0.6535 &  0.3268 \tabularnewline
9 &  0.5173 &  0.9655 &  0.4827 \tabularnewline
10 &  0.5653 &  0.8694 &  0.4347 \tabularnewline
11 &  0.437 &  0.874 &  0.563 \tabularnewline
12 &  0.3336 &  0.6673 &  0.6664 \tabularnewline
13 &  0.3877 &  0.7755 &  0.6123 \tabularnewline
14 &  0.2982 &  0.5964 &  0.7018 \tabularnewline
15 &  0.2195 &  0.4391 &  0.7805 \tabularnewline
16 &  0.1533 &  0.3067 &  0.8467 \tabularnewline
17 &  0.1177 &  0.2354 &  0.8823 \tabularnewline
18 &  0.1235 &  0.2471 &  0.8765 \tabularnewline
19 &  0.08466 &  0.1693 &  0.9153 \tabularnewline
20 &  0.08526 &  0.1705 &  0.9147 \tabularnewline
21 &  0.05841 &  0.1168 &  0.9416 \tabularnewline
22 &  0.04777 &  0.09554 &  0.9522 \tabularnewline
23 &  0.0415 &  0.083 &  0.9585 \tabularnewline
24 &  0.03261 &  0.06521 &  0.9674 \tabularnewline
25 &  0.02387 &  0.04774 &  0.9761 \tabularnewline
26 &  0.0168 &  0.03359 &  0.9832 \tabularnewline
27 &  0.01048 &  0.02096 &  0.9895 \tabularnewline
28 &  0.006393 &  0.01279 &  0.9936 \tabularnewline
29 &  0.01378 &  0.02755 &  0.9862 \tabularnewline
30 &  0.01205 &  0.0241 &  0.988 \tabularnewline
31 &  0.008694 &  0.01739 &  0.9913 \tabularnewline
32 &  0.0123 &  0.0246 &  0.9877 \tabularnewline
33 &  0.01186 &  0.02371 &  0.9881 \tabularnewline
34 &  0.009783 &  0.01957 &  0.9902 \tabularnewline
35 &  0.01041 &  0.02082 &  0.9896 \tabularnewline
36 &  0.01062 &  0.02124 &  0.9894 \tabularnewline
37 &  0.007488 &  0.01498 &  0.9925 \tabularnewline
38 &  0.006352 &  0.0127 &  0.9936 \tabularnewline
39 &  0.01645 &  0.0329 &  0.9835 \tabularnewline
40 &  0.01129 &  0.02258 &  0.9887 \tabularnewline
41 &  0.008208 &  0.01642 &  0.9918 \tabularnewline
42 &  0.005856 &  0.01171 &  0.9941 \tabularnewline
43 &  0.00411 &  0.00822 &  0.9959 \tabularnewline
44 &  0.007896 &  0.01579 &  0.9921 \tabularnewline
45 &  0.01612 &  0.03224 &  0.9839 \tabularnewline
46 &  0.0168 &  0.0336 &  0.9832 \tabularnewline
47 &  0.0198 &  0.0396 &  0.9802 \tabularnewline
48 &  0.01744 &  0.03488 &  0.9826 \tabularnewline
49 &  0.05094 &  0.1019 &  0.9491 \tabularnewline
50 &  0.04329 &  0.08658 &  0.9567 \tabularnewline
51 &  0.05148 &  0.103 &  0.9485 \tabularnewline
52 &  0.03952 &  0.07904 &  0.9605 \tabularnewline
53 &  0.04004 &  0.08008 &  0.96 \tabularnewline
54 &  0.04196 &  0.08392 &  0.958 \tabularnewline
55 &  0.03605 &  0.07211 &  0.9639 \tabularnewline
56 &  0.04421 &  0.08842 &  0.9558 \tabularnewline
57 &  0.03535 &  0.0707 &  0.9646 \tabularnewline
58 &  0.03069 &  0.06139 &  0.9693 \tabularnewline
59 &  0.02476 &  0.04952 &  0.9752 \tabularnewline
60 &  0.01851 &  0.03702 &  0.9815 \tabularnewline
61 &  0.06104 &  0.1221 &  0.939 \tabularnewline
62 &  0.06861 &  0.1372 &  0.9314 \tabularnewline
63 &  0.07125 &  0.1425 &  0.9288 \tabularnewline
64 &  0.06588 &  0.1318 &  0.9341 \tabularnewline
65 &  0.0539 &  0.1078 &  0.9461 \tabularnewline
66 &  0.04233 &  0.08467 &  0.9577 \tabularnewline
67 &  0.03382 &  0.06764 &  0.9662 \tabularnewline
68 &  0.04139 &  0.08277 &  0.9586 \tabularnewline
69 &  0.04564 &  0.09127 &  0.9544 \tabularnewline
70 &  0.05422 &  0.1084 &  0.9458 \tabularnewline
71 &  0.04391 &  0.08781 &  0.9561 \tabularnewline
72 &  0.03541 &  0.07083 &  0.9646 \tabularnewline
73 &  0.03399 &  0.06798 &  0.966 \tabularnewline
74 &  0.02671 &  0.05341 &  0.9733 \tabularnewline
75 &  0.02278 &  0.04555 &  0.9772 \tabularnewline
76 &  0.01837 &  0.03674 &  0.9816 \tabularnewline
77 &  0.1351 &  0.2701 &  0.8649 \tabularnewline
78 &  0.1176 &  0.2351 &  0.8824 \tabularnewline
79 &  0.1052 &  0.2103 &  0.8948 \tabularnewline
80 &  0.1238 &  0.2477 &  0.8762 \tabularnewline
81 &  0.2688 &  0.5376 &  0.7312 \tabularnewline
82 &  0.3126 &  0.6252 &  0.6874 \tabularnewline
83 &  0.2745 &  0.5489 &  0.7255 \tabularnewline
84 &  0.2447 &  0.4894 &  0.7553 \tabularnewline
85 &  0.2309 &  0.4618 &  0.7691 \tabularnewline
86 &  0.2031 &  0.4061 &  0.7969 \tabularnewline
87 &  0.19 &  0.38 &  0.81 \tabularnewline
88 &  0.1606 &  0.3213 &  0.8394 \tabularnewline
89 &  0.164 &  0.328 &  0.836 \tabularnewline
90 &  0.1372 &  0.2743 &  0.8628 \tabularnewline
91 &  0.1137 &  0.2274 &  0.8863 \tabularnewline
92 &  0.1276 &  0.2552 &  0.8724 \tabularnewline
93 &  0.234 &  0.4681 &  0.766 \tabularnewline
94 &  0.1994 &  0.3989 &  0.8006 \tabularnewline
95 &  0.1685 &  0.337 &  0.8315 \tabularnewline
96 &  0.1799 &  0.3598 &  0.8201 \tabularnewline
97 &  0.1609 &  0.3218 &  0.8391 \tabularnewline
98 &  0.2006 &  0.4013 &  0.7994 \tabularnewline
99 &  0.1695 &  0.3389 &  0.8305 \tabularnewline
100 &  0.1784 &  0.3568 &  0.8216 \tabularnewline
101 &  0.1498 &  0.2997 &  0.8502 \tabularnewline
102 &  0.1293 &  0.2587 &  0.8707 \tabularnewline
103 &  0.1063 &  0.2127 &  0.8937 \tabularnewline
104 &  0.08564 &  0.1713 &  0.9144 \tabularnewline
105 &  0.08106 &  0.1621 &  0.9189 \tabularnewline
106 &  0.06477 &  0.1295 &  0.9352 \tabularnewline
107 &  0.1042 &  0.2083 &  0.8958 \tabularnewline
108 &  0.1736 &  0.3473 &  0.8264 \tabularnewline
109 &  0.1693 &  0.3387 &  0.8307 \tabularnewline
110 &  0.1394 &  0.2788 &  0.8606 \tabularnewline
111 &  0.1236 &  0.2471 &  0.8764 \tabularnewline
112 &  0.1361 &  0.2721 &  0.8639 \tabularnewline
113 &  0.1319 &  0.2638 &  0.8681 \tabularnewline
114 &  0.1064 &  0.2128 &  0.8936 \tabularnewline
115 &  0.09023 &  0.1805 &  0.9098 \tabularnewline
116 &  0.0709 &  0.1418 &  0.9291 \tabularnewline
117 &  0.0554 &  0.1108 &  0.9446 \tabularnewline
118 &  0.09986 &  0.1997 &  0.9001 \tabularnewline
119 &  0.09253 &  0.1851 &  0.9075 \tabularnewline
120 &  0.07273 &  0.1455 &  0.9273 \tabularnewline
121 &  0.07086 &  0.1417 &  0.9291 \tabularnewline
122 &  0.05526 &  0.1105 &  0.9447 \tabularnewline
123 &  0.1289 &  0.2579 &  0.8711 \tabularnewline
124 &  0.1021 &  0.2041 &  0.8979 \tabularnewline
125 &  0.2388 &  0.4776 &  0.7612 \tabularnewline
126 &  0.3519 &  0.7039 &  0.6481 \tabularnewline
127 &  0.3013 &  0.6025 &  0.6987 \tabularnewline
128 &  0.2747 &  0.5494 &  0.7253 \tabularnewline
129 &  0.2557 &  0.5113 &  0.7443 \tabularnewline
130 &  0.3414 &  0.6827 &  0.6586 \tabularnewline
131 &  0.3366 &  0.6731 &  0.6634 \tabularnewline
132 &  0.297 &  0.5939 &  0.703 \tabularnewline
133 &  0.3057 &  0.6113 &  0.6943 \tabularnewline
134 &  0.255 &  0.51 &  0.745 \tabularnewline
135 &  0.4975 &  0.9951 &  0.5025 \tabularnewline
136 &  0.4293 &  0.8587 &  0.5707 \tabularnewline
137 &  0.4605 &  0.9209 &  0.5395 \tabularnewline
138 &  0.589 &  0.822 &  0.411 \tabularnewline
139 &  0.6432 &  0.7136 &  0.3568 \tabularnewline
140 &  0.5665 &  0.867 &  0.4335 \tabularnewline
141 &  0.6614 &  0.6772 &  0.3386 \tabularnewline
142 &  0.6319 &  0.7361 &  0.3681 \tabularnewline
143 &  0.6786 &  0.6427 &  0.3214 \tabularnewline
144 &  0.5886 &  0.8229 &  0.4114 \tabularnewline
145 &  0.6647 &  0.6706 &  0.3353 \tabularnewline
146 &  0.5595 &  0.8811 &  0.4405 \tabularnewline
147 &  0.9982 &  0.003548 &  0.001774 \tabularnewline
148 &  0.9933 &  0.01341 &  0.006706 \tabularnewline
149 &  0.9865 &  0.02701 &  0.0135 \tabularnewline
150 &  0.9911 &  0.01782 &  0.008912 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297666&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] 0.6732[/C][C] 0.6535[/C][C] 0.3268[/C][/ROW]
[ROW][C]9[/C][C] 0.5173[/C][C] 0.9655[/C][C] 0.4827[/C][/ROW]
[ROW][C]10[/C][C] 0.5653[/C][C] 0.8694[/C][C] 0.4347[/C][/ROW]
[ROW][C]11[/C][C] 0.437[/C][C] 0.874[/C][C] 0.563[/C][/ROW]
[ROW][C]12[/C][C] 0.3336[/C][C] 0.6673[/C][C] 0.6664[/C][/ROW]
[ROW][C]13[/C][C] 0.3877[/C][C] 0.7755[/C][C] 0.6123[/C][/ROW]
[ROW][C]14[/C][C] 0.2982[/C][C] 0.5964[/C][C] 0.7018[/C][/ROW]
[ROW][C]15[/C][C] 0.2195[/C][C] 0.4391[/C][C] 0.7805[/C][/ROW]
[ROW][C]16[/C][C] 0.1533[/C][C] 0.3067[/C][C] 0.8467[/C][/ROW]
[ROW][C]17[/C][C] 0.1177[/C][C] 0.2354[/C][C] 0.8823[/C][/ROW]
[ROW][C]18[/C][C] 0.1235[/C][C] 0.2471[/C][C] 0.8765[/C][/ROW]
[ROW][C]19[/C][C] 0.08466[/C][C] 0.1693[/C][C] 0.9153[/C][/ROW]
[ROW][C]20[/C][C] 0.08526[/C][C] 0.1705[/C][C] 0.9147[/C][/ROW]
[ROW][C]21[/C][C] 0.05841[/C][C] 0.1168[/C][C] 0.9416[/C][/ROW]
[ROW][C]22[/C][C] 0.04777[/C][C] 0.09554[/C][C] 0.9522[/C][/ROW]
[ROW][C]23[/C][C] 0.0415[/C][C] 0.083[/C][C] 0.9585[/C][/ROW]
[ROW][C]24[/C][C] 0.03261[/C][C] 0.06521[/C][C] 0.9674[/C][/ROW]
[ROW][C]25[/C][C] 0.02387[/C][C] 0.04774[/C][C] 0.9761[/C][/ROW]
[ROW][C]26[/C][C] 0.0168[/C][C] 0.03359[/C][C] 0.9832[/C][/ROW]
[ROW][C]27[/C][C] 0.01048[/C][C] 0.02096[/C][C] 0.9895[/C][/ROW]
[ROW][C]28[/C][C] 0.006393[/C][C] 0.01279[/C][C] 0.9936[/C][/ROW]
[ROW][C]29[/C][C] 0.01378[/C][C] 0.02755[/C][C] 0.9862[/C][/ROW]
[ROW][C]30[/C][C] 0.01205[/C][C] 0.0241[/C][C] 0.988[/C][/ROW]
[ROW][C]31[/C][C] 0.008694[/C][C] 0.01739[/C][C] 0.9913[/C][/ROW]
[ROW][C]32[/C][C] 0.0123[/C][C] 0.0246[/C][C] 0.9877[/C][/ROW]
[ROW][C]33[/C][C] 0.01186[/C][C] 0.02371[/C][C] 0.9881[/C][/ROW]
[ROW][C]34[/C][C] 0.009783[/C][C] 0.01957[/C][C] 0.9902[/C][/ROW]
[ROW][C]35[/C][C] 0.01041[/C][C] 0.02082[/C][C] 0.9896[/C][/ROW]
[ROW][C]36[/C][C] 0.01062[/C][C] 0.02124[/C][C] 0.9894[/C][/ROW]
[ROW][C]37[/C][C] 0.007488[/C][C] 0.01498[/C][C] 0.9925[/C][/ROW]
[ROW][C]38[/C][C] 0.006352[/C][C] 0.0127[/C][C] 0.9936[/C][/ROW]
[ROW][C]39[/C][C] 0.01645[/C][C] 0.0329[/C][C] 0.9835[/C][/ROW]
[ROW][C]40[/C][C] 0.01129[/C][C] 0.02258[/C][C] 0.9887[/C][/ROW]
[ROW][C]41[/C][C] 0.008208[/C][C] 0.01642[/C][C] 0.9918[/C][/ROW]
[ROW][C]42[/C][C] 0.005856[/C][C] 0.01171[/C][C] 0.9941[/C][/ROW]
[ROW][C]43[/C][C] 0.00411[/C][C] 0.00822[/C][C] 0.9959[/C][/ROW]
[ROW][C]44[/C][C] 0.007896[/C][C] 0.01579[/C][C] 0.9921[/C][/ROW]
[ROW][C]45[/C][C] 0.01612[/C][C] 0.03224[/C][C] 0.9839[/C][/ROW]
[ROW][C]46[/C][C] 0.0168[/C][C] 0.0336[/C][C] 0.9832[/C][/ROW]
[ROW][C]47[/C][C] 0.0198[/C][C] 0.0396[/C][C] 0.9802[/C][/ROW]
[ROW][C]48[/C][C] 0.01744[/C][C] 0.03488[/C][C] 0.9826[/C][/ROW]
[ROW][C]49[/C][C] 0.05094[/C][C] 0.1019[/C][C] 0.9491[/C][/ROW]
[ROW][C]50[/C][C] 0.04329[/C][C] 0.08658[/C][C] 0.9567[/C][/ROW]
[ROW][C]51[/C][C] 0.05148[/C][C] 0.103[/C][C] 0.9485[/C][/ROW]
[ROW][C]52[/C][C] 0.03952[/C][C] 0.07904[/C][C] 0.9605[/C][/ROW]
[ROW][C]53[/C][C] 0.04004[/C][C] 0.08008[/C][C] 0.96[/C][/ROW]
[ROW][C]54[/C][C] 0.04196[/C][C] 0.08392[/C][C] 0.958[/C][/ROW]
[ROW][C]55[/C][C] 0.03605[/C][C] 0.07211[/C][C] 0.9639[/C][/ROW]
[ROW][C]56[/C][C] 0.04421[/C][C] 0.08842[/C][C] 0.9558[/C][/ROW]
[ROW][C]57[/C][C] 0.03535[/C][C] 0.0707[/C][C] 0.9646[/C][/ROW]
[ROW][C]58[/C][C] 0.03069[/C][C] 0.06139[/C][C] 0.9693[/C][/ROW]
[ROW][C]59[/C][C] 0.02476[/C][C] 0.04952[/C][C] 0.9752[/C][/ROW]
[ROW][C]60[/C][C] 0.01851[/C][C] 0.03702[/C][C] 0.9815[/C][/ROW]
[ROW][C]61[/C][C] 0.06104[/C][C] 0.1221[/C][C] 0.939[/C][/ROW]
[ROW][C]62[/C][C] 0.06861[/C][C] 0.1372[/C][C] 0.9314[/C][/ROW]
[ROW][C]63[/C][C] 0.07125[/C][C] 0.1425[/C][C] 0.9288[/C][/ROW]
[ROW][C]64[/C][C] 0.06588[/C][C] 0.1318[/C][C] 0.9341[/C][/ROW]
[ROW][C]65[/C][C] 0.0539[/C][C] 0.1078[/C][C] 0.9461[/C][/ROW]
[ROW][C]66[/C][C] 0.04233[/C][C] 0.08467[/C][C] 0.9577[/C][/ROW]
[ROW][C]67[/C][C] 0.03382[/C][C] 0.06764[/C][C] 0.9662[/C][/ROW]
[ROW][C]68[/C][C] 0.04139[/C][C] 0.08277[/C][C] 0.9586[/C][/ROW]
[ROW][C]69[/C][C] 0.04564[/C][C] 0.09127[/C][C] 0.9544[/C][/ROW]
[ROW][C]70[/C][C] 0.05422[/C][C] 0.1084[/C][C] 0.9458[/C][/ROW]
[ROW][C]71[/C][C] 0.04391[/C][C] 0.08781[/C][C] 0.9561[/C][/ROW]
[ROW][C]72[/C][C] 0.03541[/C][C] 0.07083[/C][C] 0.9646[/C][/ROW]
[ROW][C]73[/C][C] 0.03399[/C][C] 0.06798[/C][C] 0.966[/C][/ROW]
[ROW][C]74[/C][C] 0.02671[/C][C] 0.05341[/C][C] 0.9733[/C][/ROW]
[ROW][C]75[/C][C] 0.02278[/C][C] 0.04555[/C][C] 0.9772[/C][/ROW]
[ROW][C]76[/C][C] 0.01837[/C][C] 0.03674[/C][C] 0.9816[/C][/ROW]
[ROW][C]77[/C][C] 0.1351[/C][C] 0.2701[/C][C] 0.8649[/C][/ROW]
[ROW][C]78[/C][C] 0.1176[/C][C] 0.2351[/C][C] 0.8824[/C][/ROW]
[ROW][C]79[/C][C] 0.1052[/C][C] 0.2103[/C][C] 0.8948[/C][/ROW]
[ROW][C]80[/C][C] 0.1238[/C][C] 0.2477[/C][C] 0.8762[/C][/ROW]
[ROW][C]81[/C][C] 0.2688[/C][C] 0.5376[/C][C] 0.7312[/C][/ROW]
[ROW][C]82[/C][C] 0.3126[/C][C] 0.6252[/C][C] 0.6874[/C][/ROW]
[ROW][C]83[/C][C] 0.2745[/C][C] 0.5489[/C][C] 0.7255[/C][/ROW]
[ROW][C]84[/C][C] 0.2447[/C][C] 0.4894[/C][C] 0.7553[/C][/ROW]
[ROW][C]85[/C][C] 0.2309[/C][C] 0.4618[/C][C] 0.7691[/C][/ROW]
[ROW][C]86[/C][C] 0.2031[/C][C] 0.4061[/C][C] 0.7969[/C][/ROW]
[ROW][C]87[/C][C] 0.19[/C][C] 0.38[/C][C] 0.81[/C][/ROW]
[ROW][C]88[/C][C] 0.1606[/C][C] 0.3213[/C][C] 0.8394[/C][/ROW]
[ROW][C]89[/C][C] 0.164[/C][C] 0.328[/C][C] 0.836[/C][/ROW]
[ROW][C]90[/C][C] 0.1372[/C][C] 0.2743[/C][C] 0.8628[/C][/ROW]
[ROW][C]91[/C][C] 0.1137[/C][C] 0.2274[/C][C] 0.8863[/C][/ROW]
[ROW][C]92[/C][C] 0.1276[/C][C] 0.2552[/C][C] 0.8724[/C][/ROW]
[ROW][C]93[/C][C] 0.234[/C][C] 0.4681[/C][C] 0.766[/C][/ROW]
[ROW][C]94[/C][C] 0.1994[/C][C] 0.3989[/C][C] 0.8006[/C][/ROW]
[ROW][C]95[/C][C] 0.1685[/C][C] 0.337[/C][C] 0.8315[/C][/ROW]
[ROW][C]96[/C][C] 0.1799[/C][C] 0.3598[/C][C] 0.8201[/C][/ROW]
[ROW][C]97[/C][C] 0.1609[/C][C] 0.3218[/C][C] 0.8391[/C][/ROW]
[ROW][C]98[/C][C] 0.2006[/C][C] 0.4013[/C][C] 0.7994[/C][/ROW]
[ROW][C]99[/C][C] 0.1695[/C][C] 0.3389[/C][C] 0.8305[/C][/ROW]
[ROW][C]100[/C][C] 0.1784[/C][C] 0.3568[/C][C] 0.8216[/C][/ROW]
[ROW][C]101[/C][C] 0.1498[/C][C] 0.2997[/C][C] 0.8502[/C][/ROW]
[ROW][C]102[/C][C] 0.1293[/C][C] 0.2587[/C][C] 0.8707[/C][/ROW]
[ROW][C]103[/C][C] 0.1063[/C][C] 0.2127[/C][C] 0.8937[/C][/ROW]
[ROW][C]104[/C][C] 0.08564[/C][C] 0.1713[/C][C] 0.9144[/C][/ROW]
[ROW][C]105[/C][C] 0.08106[/C][C] 0.1621[/C][C] 0.9189[/C][/ROW]
[ROW][C]106[/C][C] 0.06477[/C][C] 0.1295[/C][C] 0.9352[/C][/ROW]
[ROW][C]107[/C][C] 0.1042[/C][C] 0.2083[/C][C] 0.8958[/C][/ROW]
[ROW][C]108[/C][C] 0.1736[/C][C] 0.3473[/C][C] 0.8264[/C][/ROW]
[ROW][C]109[/C][C] 0.1693[/C][C] 0.3387[/C][C] 0.8307[/C][/ROW]
[ROW][C]110[/C][C] 0.1394[/C][C] 0.2788[/C][C] 0.8606[/C][/ROW]
[ROW][C]111[/C][C] 0.1236[/C][C] 0.2471[/C][C] 0.8764[/C][/ROW]
[ROW][C]112[/C][C] 0.1361[/C][C] 0.2721[/C][C] 0.8639[/C][/ROW]
[ROW][C]113[/C][C] 0.1319[/C][C] 0.2638[/C][C] 0.8681[/C][/ROW]
[ROW][C]114[/C][C] 0.1064[/C][C] 0.2128[/C][C] 0.8936[/C][/ROW]
[ROW][C]115[/C][C] 0.09023[/C][C] 0.1805[/C][C] 0.9098[/C][/ROW]
[ROW][C]116[/C][C] 0.0709[/C][C] 0.1418[/C][C] 0.9291[/C][/ROW]
[ROW][C]117[/C][C] 0.0554[/C][C] 0.1108[/C][C] 0.9446[/C][/ROW]
[ROW][C]118[/C][C] 0.09986[/C][C] 0.1997[/C][C] 0.9001[/C][/ROW]
[ROW][C]119[/C][C] 0.09253[/C][C] 0.1851[/C][C] 0.9075[/C][/ROW]
[ROW][C]120[/C][C] 0.07273[/C][C] 0.1455[/C][C] 0.9273[/C][/ROW]
[ROW][C]121[/C][C] 0.07086[/C][C] 0.1417[/C][C] 0.9291[/C][/ROW]
[ROW][C]122[/C][C] 0.05526[/C][C] 0.1105[/C][C] 0.9447[/C][/ROW]
[ROW][C]123[/C][C] 0.1289[/C][C] 0.2579[/C][C] 0.8711[/C][/ROW]
[ROW][C]124[/C][C] 0.1021[/C][C] 0.2041[/C][C] 0.8979[/C][/ROW]
[ROW][C]125[/C][C] 0.2388[/C][C] 0.4776[/C][C] 0.7612[/C][/ROW]
[ROW][C]126[/C][C] 0.3519[/C][C] 0.7039[/C][C] 0.6481[/C][/ROW]
[ROW][C]127[/C][C] 0.3013[/C][C] 0.6025[/C][C] 0.6987[/C][/ROW]
[ROW][C]128[/C][C] 0.2747[/C][C] 0.5494[/C][C] 0.7253[/C][/ROW]
[ROW][C]129[/C][C] 0.2557[/C][C] 0.5113[/C][C] 0.7443[/C][/ROW]
[ROW][C]130[/C][C] 0.3414[/C][C] 0.6827[/C][C] 0.6586[/C][/ROW]
[ROW][C]131[/C][C] 0.3366[/C][C] 0.6731[/C][C] 0.6634[/C][/ROW]
[ROW][C]132[/C][C] 0.297[/C][C] 0.5939[/C][C] 0.703[/C][/ROW]
[ROW][C]133[/C][C] 0.3057[/C][C] 0.6113[/C][C] 0.6943[/C][/ROW]
[ROW][C]134[/C][C] 0.255[/C][C] 0.51[/C][C] 0.745[/C][/ROW]
[ROW][C]135[/C][C] 0.4975[/C][C] 0.9951[/C][C] 0.5025[/C][/ROW]
[ROW][C]136[/C][C] 0.4293[/C][C] 0.8587[/C][C] 0.5707[/C][/ROW]
[ROW][C]137[/C][C] 0.4605[/C][C] 0.9209[/C][C] 0.5395[/C][/ROW]
[ROW][C]138[/C][C] 0.589[/C][C] 0.822[/C][C] 0.411[/C][/ROW]
[ROW][C]139[/C][C] 0.6432[/C][C] 0.7136[/C][C] 0.3568[/C][/ROW]
[ROW][C]140[/C][C] 0.5665[/C][C] 0.867[/C][C] 0.4335[/C][/ROW]
[ROW][C]141[/C][C] 0.6614[/C][C] 0.6772[/C][C] 0.3386[/C][/ROW]
[ROW][C]142[/C][C] 0.6319[/C][C] 0.7361[/C][C] 0.3681[/C][/ROW]
[ROW][C]143[/C][C] 0.6786[/C][C] 0.6427[/C][C] 0.3214[/C][/ROW]
[ROW][C]144[/C][C] 0.5886[/C][C] 0.8229[/C][C] 0.4114[/C][/ROW]
[ROW][C]145[/C][C] 0.6647[/C][C] 0.6706[/C][C] 0.3353[/C][/ROW]
[ROW][C]146[/C][C] 0.5595[/C][C] 0.8811[/C][C] 0.4405[/C][/ROW]
[ROW][C]147[/C][C] 0.9982[/C][C] 0.003548[/C][C] 0.001774[/C][/ROW]
[ROW][C]148[/C][C] 0.9933[/C][C] 0.01341[/C][C] 0.006706[/C][/ROW]
[ROW][C]149[/C][C] 0.9865[/C][C] 0.02701[/C][C] 0.0135[/C][/ROW]
[ROW][C]150[/C][C] 0.9911[/C][C] 0.01782[/C][C] 0.008912[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297666&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297666&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	0.6732	0.6535	0.3268
9	0.5173	0.9655	0.4827
10	0.5653	0.8694	0.4347
11	0.437	0.874	0.563
12	0.3336	0.6673	0.6664
13	0.3877	0.7755	0.6123
14	0.2982	0.5964	0.7018
15	0.2195	0.4391	0.7805
16	0.1533	0.3067	0.8467
17	0.1177	0.2354	0.8823
18	0.1235	0.2471	0.8765
19	0.08466	0.1693	0.9153
20	0.08526	0.1705	0.9147
21	0.05841	0.1168	0.9416
22	0.04777	0.09554	0.9522
23	0.0415	0.083	0.9585
24	0.03261	0.06521	0.9674
25	0.02387	0.04774	0.9761
26	0.0168	0.03359	0.9832
27	0.01048	0.02096	0.9895
28	0.006393	0.01279	0.9936
29	0.01378	0.02755	0.9862
30	0.01205	0.0241	0.988
31	0.008694	0.01739	0.9913
32	0.0123	0.0246	0.9877
33	0.01186	0.02371	0.9881
34	0.009783	0.01957	0.9902
35	0.01041	0.02082	0.9896
36	0.01062	0.02124	0.9894
37	0.007488	0.01498	0.9925
38	0.006352	0.0127	0.9936
39	0.01645	0.0329	0.9835
40	0.01129	0.02258	0.9887
41	0.008208	0.01642	0.9918
42	0.005856	0.01171	0.9941
43	0.00411	0.00822	0.9959
44	0.007896	0.01579	0.9921
45	0.01612	0.03224	0.9839
46	0.0168	0.0336	0.9832
47	0.0198	0.0396	0.9802
48	0.01744	0.03488	0.9826
49	0.05094	0.1019	0.9491
50	0.04329	0.08658	0.9567
51	0.05148	0.103	0.9485
52	0.03952	0.07904	0.9605
53	0.04004	0.08008	0.96
54	0.04196	0.08392	0.958
55	0.03605	0.07211	0.9639
56	0.04421	0.08842	0.9558
57	0.03535	0.0707	0.9646
58	0.03069	0.06139	0.9693
59	0.02476	0.04952	0.9752
60	0.01851	0.03702	0.9815
61	0.06104	0.1221	0.939
62	0.06861	0.1372	0.9314
63	0.07125	0.1425	0.9288
64	0.06588	0.1318	0.9341
65	0.0539	0.1078	0.9461
66	0.04233	0.08467	0.9577
67	0.03382	0.06764	0.9662
68	0.04139	0.08277	0.9586
69	0.04564	0.09127	0.9544
70	0.05422	0.1084	0.9458
71	0.04391	0.08781	0.9561
72	0.03541	0.07083	0.9646
73	0.03399	0.06798	0.966
74	0.02671	0.05341	0.9733
75	0.02278	0.04555	0.9772
76	0.01837	0.03674	0.9816
77	0.1351	0.2701	0.8649
78	0.1176	0.2351	0.8824
79	0.1052	0.2103	0.8948
80	0.1238	0.2477	0.8762
81	0.2688	0.5376	0.7312
82	0.3126	0.6252	0.6874
83	0.2745	0.5489	0.7255
84	0.2447	0.4894	0.7553
85	0.2309	0.4618	0.7691
86	0.2031	0.4061	0.7969
87	0.19	0.38	0.81
88	0.1606	0.3213	0.8394
89	0.164	0.328	0.836
90	0.1372	0.2743	0.8628
91	0.1137	0.2274	0.8863
92	0.1276	0.2552	0.8724
93	0.234	0.4681	0.766
94	0.1994	0.3989	0.8006
95	0.1685	0.337	0.8315
96	0.1799	0.3598	0.8201
97	0.1609	0.3218	0.8391
98	0.2006	0.4013	0.7994
99	0.1695	0.3389	0.8305
100	0.1784	0.3568	0.8216
101	0.1498	0.2997	0.8502
102	0.1293	0.2587	0.8707
103	0.1063	0.2127	0.8937
104	0.08564	0.1713	0.9144
105	0.08106	0.1621	0.9189
106	0.06477	0.1295	0.9352
107	0.1042	0.2083	0.8958
108	0.1736	0.3473	0.8264
109	0.1693	0.3387	0.8307
110	0.1394	0.2788	0.8606
111	0.1236	0.2471	0.8764
112	0.1361	0.2721	0.8639
113	0.1319	0.2638	0.8681
114	0.1064	0.2128	0.8936
115	0.09023	0.1805	0.9098
116	0.0709	0.1418	0.9291
117	0.0554	0.1108	0.9446
118	0.09986	0.1997	0.9001
119	0.09253	0.1851	0.9075
120	0.07273	0.1455	0.9273
121	0.07086	0.1417	0.9291
122	0.05526	0.1105	0.9447
123	0.1289	0.2579	0.8711
124	0.1021	0.2041	0.8979
125	0.2388	0.4776	0.7612
126	0.3519	0.7039	0.6481
127	0.3013	0.6025	0.6987
128	0.2747	0.5494	0.7253
129	0.2557	0.5113	0.7443
130	0.3414	0.6827	0.6586
131	0.3366	0.6731	0.6634
132	0.297	0.5939	0.703
133	0.3057	0.6113	0.6943
134	0.255	0.51	0.745
135	0.4975	0.9951	0.5025
136	0.4293	0.8587	0.5707
137	0.4605	0.9209	0.5395
138	0.589	0.822	0.411
139	0.6432	0.7136	0.3568
140	0.5665	0.867	0.4335
141	0.6614	0.6772	0.3386
142	0.6319	0.7361	0.3681
143	0.6786	0.6427	0.3214
144	0.5886	0.8229	0.4114
145	0.6647	0.6706	0.3353
146	0.5595	0.8811	0.4405
147	0.9982	0.003548	0.001774
148	0.9933	0.01341	0.006706
149	0.9865	0.02701	0.0135
150	0.9911	0.01782	0.008912

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.01399	NOK
5% type I error level	32	0.223776	NOK
10% type I error level	51	0.356643	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 & 2 &  0.01399 & NOK \tabularnewline
5% type I error level & 32 & 0.223776 & NOK \tabularnewline
10% type I error level & 51 & 0.356643 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297666&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]2[/C][C] 0.01399[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.223776[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]51[/C][C]0.356643[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297666&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297666&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	2	0.01399	NOK
5% type I error level	32	0.223776	NOK
10% type I error level	51	0.356643	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.2781, df1 = 2, df2 = 151, p-value = 0.2816
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7441, df1 = 8, df2 = 145, p-value = 0.09299
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.48112, df1 = 2, df2 = 151, p-value = 0.619

\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 = 1.2781, df1 = 2, df2 = 151, p-value = 0.2816
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7441, df1 = 8, df2 = 145, p-value = 0.09299
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.48112, df1 = 2, df2 = 151, p-value = 0.619
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297666&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 = 1.2781, df1 = 2, df2 = 151, p-value = 0.2816
[/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 = 1.7441, df1 = 8, df2 = 145, p-value = 0.09299
[/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 = 0.48112, df1 = 2, df2 = 151, p-value = 0.619
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297666&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297666&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 = 1.2781, df1 = 2, df2 = 151, p-value = 0.2816
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7441, df1 = 8, df2 = 145, p-value = 0.09299
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.48112, df1 = 2, df2 = 151, p-value = 0.619

Variance Inflation Factors (Multicollinearity)
> vif IVHB1 IVHB2 IVHB3 IVHB4 1.031300 1.032837 1.052855 1.044114

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   IVHB1    IVHB2    IVHB3    IVHB4 
1.031300 1.032837 1.052855 1.044114 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297666&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   IVHB1    IVHB2    IVHB3    IVHB4 
1.031300 1.032837 1.052855 1.044114 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297666&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297666&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 IVHB1 IVHB2 IVHB3 IVHB4 1.031300 1.032837 1.052855 1.044114

Figure 1

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Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '5'
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