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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 04 Nov 2012 14:35:33 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/04/t1352058357ikiso54fzbi77ny.htm/, Retrieved Thu, 02 May 2024 16:41:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185903, Retrieved Thu, 02 May 2024 16:41:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2012-11-04 19:35:33] [c52127b355a401c4b5ab4a80e41e35a5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	1901	61	17	56	84	4	21	51	9
2	2509	74	19	73	47	3	15	45	9
3	2114	57	18	62	63	3	17	44	9
4	1331	50	15	42	28	3	20	42	9
5	1399	48	15	59	22	2	12	38	9
6	7333	2	12	27	18	6	4	38	9
7	1170	31	20	78	27	5	11	35	9
8	1507	61	14	56	37	5	12	35	9
9	1107	36	15	59	20	5	9	34	9
10	2051	46	13	51	67	5	14	33	9
11	1290	30	17	47	28	4	11	32	9
12	820	49	10	35	45	3	14	31	9
13	1502	14	13	47	15	5	4	30	9
14	1451	12	12	47	23	6	7	30	9
15	1178	54	16	55	30	6	9	30	9
16	1514	44	15	54	27	2	14	29	9
17	883	40	15	60	43	5	13	29	9
18	1405	57	15	55	36	5	11	29	9
19	927	29	12	48	28	5	9	28	9
20	1352	32	13	47	28	9	8	27	9
21	1314	28	12	47	22	4	9	27	9
22	1307	40	15	52	27	4	11	27	9
23	1243	54	12	48	24	5	7	26	9
24	1232	56	12	48	52	3	15	26	9
25	1097	19	9	27	12	0	4	26	9
26	1100	67	12	12	24	5	10	26	9
27	1316	25	13	51	10	3	10	26	9
28	903	42	16	58	71	4	13	25	9
29	929	28	15	60	12	2	10	25	9
30	1049	57	13	46	24	5	10	25	9
31	1372	28	12	45	22	11	6	24	9
32	1470	35	13	42	21	5	8	24	9
33	821	10	12	41	13	3	7	24	9
34	1239	30	12	47	28	4	11	24	9
35	1384	23	8	32	19	5	10	24	9
36	820	32	15	56	29	5	11	24	9
37	1462	24	12	42	12	2	10	24	9
38	1202	42	12	41	32	6	8	23	9
39	1091	33	12	47	21	3	10	23	9
40	1228	19	14	47	19	4	5	23	9
41	707	17	15	49	15	8	5	23	9
42	868	49	15	52	14	14	5	23	9
43	1165	30	12	42	34	11	9	22	9
44	1106	3	13	55	8	8	2	22	9
45	1429	56	12	48	27	3	9	22	9
46	1671	37	13	48	31	3	13	22	9
47	1579	26	12	38	21	11	7	22	9
48	774	19	12	48	10	3	5	21	10
49	934	22	13	50	21	4	7	21	10
50	825	53	12	39	19	3	8	21	10
51	1375	35	12	48	27	5	8	21	10
52	968	12	9	36	17	6	5	21	10
53	1156	34	13	49	30	8	5	21	10
54	1374	28	13	39	19	3	10	21	10
55	1224	38	12	41	17	3	5	21	10
56	804	38	15	45	24	5	10	21	10
57	998	45	15	60	36	5	10	21	10
58	1112	15	13	45	16	3	7	21	10
59	1153	35	14	41	16	3	10	20	10
60	613	27	14	52	30	3	9	20	10
61	729	23	12	46	18	5	10	20	10
62	813	33	12	39	26	3	10	20	10
63	912	23	9	32	17	3	5	20	10
64	1178	26	14	52	28	6	8	20	10
65	1201	32	16	54	20	4	6	19	10
66	1165	35	15	51	27	3	7	19	10
67	705	18	13	52	13	13	6	18	10
68	814	18	16	57	10	5	3	17	10
69	1082	41	12	47	29	6	9	17	10
70	885	39	12	45	34	5	11	17	10
71	837	56	12	41	30	3	9	17	10
72	586	35	12	43	16	4	10	16	10
73	913	37	10	31	22	4	9	16	10
74	547	26	15	32	22	7	7	15	10
75	758	33	12	41	31	4	6	15	10
76	848	7	9	27	10	5	6	15	10
77	634	16	10	40	7	7	5	15	10
78	501	13	13	46	10	3	5	15	10
79	849	54	12	32	55	6	8	15	10
80	733	30	13	9	25	8	7	15	10
81	634	9	16	64	9	5	5	15	10
82	1010	35	15	30	31	5	10	15	10
83	778	0	12	46	0	0	0	15	10
84	480	40	12	37	24	3	10	15	10
85	848	22	12	22	14	5	6	15	10
86	714	29	12	20	11	3	6	14	10
87	871	25	12	21	8	8	4	14	10
88	776	17	14	44	9	9	3	14	10
89	815	32	12	24	18	9	7	14	10
90	811	40	12	33	14	4	5	14	10
91	529	24	12	45	27	2	8	13	10
92	642	18	13	35	10	0	0	13	10
93	562	15	8	31	16	3	5	13	10
94	626	17	16	20	13	7	5	13	10
95	636	28	12	13	10	5	5	13	11
96	935	18	11	33	16	3	5	13	11
97	473	16	15	58	11	3	6	12	11
98	836	28	13	26	8	3	5	12	11
99	938	17	12	36	29	7	6	12	11
100	656	25	13	32	12	4	4	12	11
101	566	2	13	34	1	0	0	12	11
102	765	10	12	15	26	5	8	12	11
103	705	9	12	40	5	5	2	11	11
104	558	7	12	37	5	5	2	11	11
105	582	27	14	26	24	6	8	11	11
106	608	25	12	31	19	6	3	11	11
107	567	16	16	47	10	5	3	11	11
108	434	28	8	21	6	6	3	11	11
109	479	7	8	21	61	0	3	11	11
110	488	0	5	9	25	25	1	10	11
111	507	16	9	28	7	2	2	10	11
112	394	10	11	24	10	5	2	10	11
113	504	0	4	15	3	3	1	9	11
114	368	2	8	19	1	1	2	9	11
115	386	5	13	35	38	5	7	9	11
116	451	36	13	45	13	4	4	9	11
117	580	10	12	20	2	0	1	9	11
118	565	43	13	1	8	4	6	9	11
119	510	14	12	29	30	10	3	9	11
120	495	12	12	33	11	6	2	8	11
121	596	15	10	32	69	23	3	8	11
122	412	8	12	11	2	0	2	8	11
123	338	39	5	10	23	6	5	7	11
124	446	10	13	18	8	4	4	7	11
125	418	0	12	41	0	0	0	7	11
126	335	7	6	0	2	0	0	6	11
127	349	10	9	10	4	2	3	6	11
128	308	3	12	24	4	4	2	5	11
129	466	8	15	28	0	0	0	5	11
130	228	0	11	38	9	9	1	5	11
131	428	8	3	4	5	5	3	5	11
132	242	1	8	25	0	0	0	5	11
133	352	0	12	40	0	0	0	5	11
134	244	8	0	0	13	4	4	5	11
135	269	3	9	23	1	0	1	5	11
136	242	0	4	13	0	0	0	4	11
137	291	0	14	6	39	0	2	4	11
138	213	0	9	31	10	0	0	4	11
139	135	0	0	0	1	0	1	3	11
140	210	3	1	3	3	3	3	3	11

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185903&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185903&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185903&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Place[t] = 0 0pageviews[t] 0Blogs[t] 0PR[t] 0LFM[t] 0KCS[t] 0SPR[t] 0CH[t] 0Hours[t] 0`Month\r`[t] 0M1[t] 0M2[t] 0M3[t] 0M4[t] 0M5[t] 0M6[t] 0M7[t] 0M8[t] 0M9[t] 0M10[t] 0M11[t] + 1t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Place[t] =  0 0pageviews[t] 0Blogs[t] 0PR[t] 0LFM[t] 0KCS[t] 0SPR[t] 0CH[t] 0Hours[t] 0`Month\r`[t] 0M1[t] 0M2[t] 0M3[t] 0M4[t] 0M5[t] 0M6[t] 0M7[t] 0M8[t] 0M9[t] 0M10[t] 0M11[t] +  1t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185903&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Place[t] =  0 0pageviews[t] 0Blogs[t] 0PR[t] 0LFM[t] 0KCS[t] 0SPR[t] 0CH[t] 0Hours[t] 0`Month\r`[t] 0M1[t] 0M2[t] 0M3[t] 0M4[t] 0M5[t] 0M6[t] 0M7[t] 0M8[t] 0M9[t] 0M10[t] 0M11[t] +  1t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185903&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185903&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Place[t] = 0 0pageviews[t] 0Blogs[t] 0PR[t] 0LFM[t] 0KCS[t] 0SPR[t] 0CH[t] 0Hours[t] 0`Month\r`[t] 0M1[t] 0M2[t] 0M3[t] 0M4[t] 0M5[t] 0M6[t] 0M7[t] 0M8[t] 0M9[t] 0M10[t] 0M11[t] + 1t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)00010.5
pageviews00010.5
Blogs00010.5
PR00010.5
LFM00010.5
KCS00010.5
SPR00010.5
CH00010.5
Hours00010.5
`Month\r`00010.5
M100010.5
M200010.5
M300010.5
M400010.5
M500010.5
M600010.5
M700010.5
M800010.5
M900010.5
M1000010.5
M1100010.5
t1021023451602140006400

\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) & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
pageviews & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
Blogs & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
PR & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
LFM & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
KCS & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
SPR & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
CH & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
Hours & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
`Month\r` & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
M1 & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
M2 & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
M3 & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
M4 & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
M5 & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
M6 & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
M7 & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
M8 & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
M9 & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
M10 & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
M11 & 0 & 0 & 0 & 1 & 0.5 \tabularnewline
t & 1 & 0 & 210234516021400064 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185903&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]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]pageviews[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]Blogs[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]PR[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]LFM[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]KCS[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]SPR[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]CH[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]Hours[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]`Month\r`[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M1[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M2[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M3[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M4[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M5[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M6[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M7[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M8[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M9[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M10[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]M11[/C][C]0[/C][C]0[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]t[/C][C]1[/C][C]0[/C][C]210234516021400064[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185903&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185903&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)00010.5
pageviews00010.5
Blogs00010.5
PR00010.5
LFM00010.5
KCS00010.5
SPR00010.5
CH00010.5
Hours00010.5
`Month\r`00010.5
M100010.5
M200010.5
M300010.5
M400010.5
M500010.5
M600010.5
M700010.5
M800010.5
M900010.5
M1000010.5
M1100010.5
t1021023451602140006400






Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)7.83779740827324e+34
F-TEST (DF numerator)21
F-TEST (DF denominator)118
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.72720849338448e-16
Sum Squared Residuals1.63926581207257e-29

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
R-squared & 1 \tabularnewline
Adjusted R-squared & 1 \tabularnewline
F-TEST (value) & 7.83779740827324e+34 \tabularnewline
F-TEST (DF numerator) & 21 \tabularnewline
F-TEST (DF denominator) & 118 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.72720849338448e-16 \tabularnewline
Sum Squared Residuals & 1.63926581207257e-29 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185903&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]1[/C][/ROW]
[ROW][C]R-squared[/C][C]1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.83779740827324e+34[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]21[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]118[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.72720849338448e-16[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.63926581207257e-29[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185903&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185903&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)7.83779740827324e+34
F-TEST (DF numerator)21
F-TEST (DF denominator)118
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.72720849338448e-16
Sum Squared Residuals1.63926581207257e-29






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111-7.97634353415857e-17
222-1.44813521381552e-16
3333.30773272771716e-16
444-1.43677547922788e-17
555-1.47694193164958e-16
666-2.57331063588023e-16
777-3.10203894040604e-16
8883.33879398662313e-16
9991.07503249186494e-17
1010106.24051982163887e-16
111111-2.43722916423967e-16
121212-2.44301124629744e-16
131313-3.43558629663781e-17
141414-1.05750285281624e-16
151515-1.9743603976084e-16
1616165.81573453429147e-16
1717175.27048721740276e-16
181818-1.2628849600408e-16
1919195.01637341713209e-16
202020-5.01882576868341e-16
2121215.84429209924293e-16
222222-1.7656789312555e-15
2323231.03627148695093e-16
2424242.17689848463289e-16
2525252.2550777667528e-16
2626261.34553829094619e-16
272727-3.80916479295685e-17
282828-5.53534589998377e-16
292929-1.61334808420687e-17
3030301.12843787104342e-16
313131-2.8495202349558e-16
323232-1.67590876581963e-16
333333-6.87864879526288e-17
3434342.52599494728778e-16
353535-1.71300146698318e-16
363636-6.51928591305154e-17
3737379.15997315999331e-17
3838382.34370694663095e-16
393939-2.71744853231668e-16
4040408.59401429935066e-17
414141-2.6802185115733e-16
4242423.92278353903212e-16
434343-2.94679168330986e-17
444444-1.24277192603127e-16
454545-2.00108401820708e-16
464646-5.01530870036958e-17
4747477.54977192596114e-16
4848481.9098099626685e-16
494949-6.35361757936597e-16
505050-7.57742256548325e-16
515151-2.02900692218031e-16
525252-1.53497779623321e-16
5353533.12825049612134e-16
5454547.7295143456182e-16
5555553.34990325261051e-16
565656-3.96512454639541e-16
575757-5.03210296620342e-16
5858584.55971011883684e-16
5959599.32581969867824e-17
6060601.76606787134555e-16
616161-2.82192549926085e-16
6262624.18073127410164e-16
6363632.25832856466757e-16
646464-1.66444374757115e-16
656565-1.2691953976782e-16
666666-3.59831871381328e-16
676767-1.35007909723549e-16
6868683.3431291870767e-16
6969692.70487337596975e-16
707070-6.62025093708371e-17
717171-1.41521811031577e-16
7272724.48940606336782e-17
7373731.08754238757116e-16
7474744.06527815147739e-17
7575751.63941931222284e-16
7676761.52140166311089e-18
777777-1.26483724738504e-16
7878781.65538981096481e-16
7979792.14414004513489e-16
8080803.65316328291068e-16
8181811.300738812896e-16
8282822.68842822129304e-16
8383833.33241254093858e-16
848484-7.6274477222133e-16
858585-1.17288679016795e-16
8686862.88522608516221e-16
878787-1.75245775852714e-18
8888882.75378668536736e-16
898989-1.5403475253504e-16
9090903.46178680378569e-16
919191-1.36555628473518e-16
929292-4.40751230597616e-17
939393-4.64723602052111e-16
9494941.67070468750056e-16
959595-3.65010374159259e-16
9696962.68531959693792e-16
9797979.97349407986638e-16
989898-3.98216156332618e-16
999999-1.22988090827218e-16
100100100-5.78882382642139e-16
1011011014.56156522118559e-16
1021021024.12138838197608e-17
1031031033.01857130360259e-16
1041041047.81296795721997e-17
1051051051.31078659101693e-16
1061061062.01683309129135e-16
107107107-1.33561788139811e-16
108108108-3.82276625925965e-16
1091091091.49875473366465e-16
110110110-5.85960350533267e-17
111111111-1.22704717710238e-17
1121121121.20261749762166e-16
113113113-1.07083134362561e-17
114114114-6.63887497943285e-16
1151151151.98786247071244e-16
1161161166.83706543356026e-17
117117117-4.70412757118294e-17
1181181182.68656229962784e-16
119119119-2.45119119363858e-16
1201201205.80306568288563e-16
121121121-1.12956513094745e-16
1221221226.88900451690587e-17
1231231231.03688978233035e-16
124124124-6.23810382535117e-17
125125125-1.07403564180906e-16
126126126-1.31620993117056e-16
127127127-2.8825131258986e-16
128128128-3.78047431074876e-17
1291291291.57050651326408e-16
130130130-3.56840791117599e-16
1311311311.51323634449419e-17
132132132-2.44948385731719e-17
133133133-3.11167830103248e-16
1341341342.80055168229514e-16
1351351352.29472148030852e-17
1361361364.64432503682076e-16
137137137-3.38630873648087e-16
138138138-2.92045198830414e-16
139139139-3.67246363763043e-16
1401401409.21339872913681e-17

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 1 & -7.97634353415857e-17 \tabularnewline
2 & 2 & 2 & -1.44813521381552e-16 \tabularnewline
3 & 3 & 3 & 3.30773272771716e-16 \tabularnewline
4 & 4 & 4 & -1.43677547922788e-17 \tabularnewline
5 & 5 & 5 & -1.47694193164958e-16 \tabularnewline
6 & 6 & 6 & -2.57331063588023e-16 \tabularnewline
7 & 7 & 7 & -3.10203894040604e-16 \tabularnewline
8 & 8 & 8 & 3.33879398662313e-16 \tabularnewline
9 & 9 & 9 & 1.07503249186494e-17 \tabularnewline
10 & 10 & 10 & 6.24051982163887e-16 \tabularnewline
11 & 11 & 11 & -2.43722916423967e-16 \tabularnewline
12 & 12 & 12 & -2.44301124629744e-16 \tabularnewline
13 & 13 & 13 & -3.43558629663781e-17 \tabularnewline
14 & 14 & 14 & -1.05750285281624e-16 \tabularnewline
15 & 15 & 15 & -1.9743603976084e-16 \tabularnewline
16 & 16 & 16 & 5.81573453429147e-16 \tabularnewline
17 & 17 & 17 & 5.27048721740276e-16 \tabularnewline
18 & 18 & 18 & -1.2628849600408e-16 \tabularnewline
19 & 19 & 19 & 5.01637341713209e-16 \tabularnewline
20 & 20 & 20 & -5.01882576868341e-16 \tabularnewline
21 & 21 & 21 & 5.84429209924293e-16 \tabularnewline
22 & 22 & 22 & -1.7656789312555e-15 \tabularnewline
23 & 23 & 23 & 1.03627148695093e-16 \tabularnewline
24 & 24 & 24 & 2.17689848463289e-16 \tabularnewline
25 & 25 & 25 & 2.2550777667528e-16 \tabularnewline
26 & 26 & 26 & 1.34553829094619e-16 \tabularnewline
27 & 27 & 27 & -3.80916479295685e-17 \tabularnewline
28 & 28 & 28 & -5.53534589998377e-16 \tabularnewline
29 & 29 & 29 & -1.61334808420687e-17 \tabularnewline
30 & 30 & 30 & 1.12843787104342e-16 \tabularnewline
31 & 31 & 31 & -2.8495202349558e-16 \tabularnewline
32 & 32 & 32 & -1.67590876581963e-16 \tabularnewline
33 & 33 & 33 & -6.87864879526288e-17 \tabularnewline
34 & 34 & 34 & 2.52599494728778e-16 \tabularnewline
35 & 35 & 35 & -1.71300146698318e-16 \tabularnewline
36 & 36 & 36 & -6.51928591305154e-17 \tabularnewline
37 & 37 & 37 & 9.15997315999331e-17 \tabularnewline
38 & 38 & 38 & 2.34370694663095e-16 \tabularnewline
39 & 39 & 39 & -2.71744853231668e-16 \tabularnewline
40 & 40 & 40 & 8.59401429935066e-17 \tabularnewline
41 & 41 & 41 & -2.6802185115733e-16 \tabularnewline
42 & 42 & 42 & 3.92278353903212e-16 \tabularnewline
43 & 43 & 43 & -2.94679168330986e-17 \tabularnewline
44 & 44 & 44 & -1.24277192603127e-16 \tabularnewline
45 & 45 & 45 & -2.00108401820708e-16 \tabularnewline
46 & 46 & 46 & -5.01530870036958e-17 \tabularnewline
47 & 47 & 47 & 7.54977192596114e-16 \tabularnewline
48 & 48 & 48 & 1.9098099626685e-16 \tabularnewline
49 & 49 & 49 & -6.35361757936597e-16 \tabularnewline
50 & 50 & 50 & -7.57742256548325e-16 \tabularnewline
51 & 51 & 51 & -2.02900692218031e-16 \tabularnewline
52 & 52 & 52 & -1.53497779623321e-16 \tabularnewline
53 & 53 & 53 & 3.12825049612134e-16 \tabularnewline
54 & 54 & 54 & 7.7295143456182e-16 \tabularnewline
55 & 55 & 55 & 3.34990325261051e-16 \tabularnewline
56 & 56 & 56 & -3.96512454639541e-16 \tabularnewline
57 & 57 & 57 & -5.03210296620342e-16 \tabularnewline
58 & 58 & 58 & 4.55971011883684e-16 \tabularnewline
59 & 59 & 59 & 9.32581969867824e-17 \tabularnewline
60 & 60 & 60 & 1.76606787134555e-16 \tabularnewline
61 & 61 & 61 & -2.82192549926085e-16 \tabularnewline
62 & 62 & 62 & 4.18073127410164e-16 \tabularnewline
63 & 63 & 63 & 2.25832856466757e-16 \tabularnewline
64 & 64 & 64 & -1.66444374757115e-16 \tabularnewline
65 & 65 & 65 & -1.2691953976782e-16 \tabularnewline
66 & 66 & 66 & -3.59831871381328e-16 \tabularnewline
67 & 67 & 67 & -1.35007909723549e-16 \tabularnewline
68 & 68 & 68 & 3.3431291870767e-16 \tabularnewline
69 & 69 & 69 & 2.70487337596975e-16 \tabularnewline
70 & 70 & 70 & -6.62025093708371e-17 \tabularnewline
71 & 71 & 71 & -1.41521811031577e-16 \tabularnewline
72 & 72 & 72 & 4.48940606336782e-17 \tabularnewline
73 & 73 & 73 & 1.08754238757116e-16 \tabularnewline
74 & 74 & 74 & 4.06527815147739e-17 \tabularnewline
75 & 75 & 75 & 1.63941931222284e-16 \tabularnewline
76 & 76 & 76 & 1.52140166311089e-18 \tabularnewline
77 & 77 & 77 & -1.26483724738504e-16 \tabularnewline
78 & 78 & 78 & 1.65538981096481e-16 \tabularnewline
79 & 79 & 79 & 2.14414004513489e-16 \tabularnewline
80 & 80 & 80 & 3.65316328291068e-16 \tabularnewline
81 & 81 & 81 & 1.300738812896e-16 \tabularnewline
82 & 82 & 82 & 2.68842822129304e-16 \tabularnewline
83 & 83 & 83 & 3.33241254093858e-16 \tabularnewline
84 & 84 & 84 & -7.6274477222133e-16 \tabularnewline
85 & 85 & 85 & -1.17288679016795e-16 \tabularnewline
86 & 86 & 86 & 2.88522608516221e-16 \tabularnewline
87 & 87 & 87 & -1.75245775852714e-18 \tabularnewline
88 & 88 & 88 & 2.75378668536736e-16 \tabularnewline
89 & 89 & 89 & -1.5403475253504e-16 \tabularnewline
90 & 90 & 90 & 3.46178680378569e-16 \tabularnewline
91 & 91 & 91 & -1.36555628473518e-16 \tabularnewline
92 & 92 & 92 & -4.40751230597616e-17 \tabularnewline
93 & 93 & 93 & -4.64723602052111e-16 \tabularnewline
94 & 94 & 94 & 1.67070468750056e-16 \tabularnewline
95 & 95 & 95 & -3.65010374159259e-16 \tabularnewline
96 & 96 & 96 & 2.68531959693792e-16 \tabularnewline
97 & 97 & 97 & 9.97349407986638e-16 \tabularnewline
98 & 98 & 98 & -3.98216156332618e-16 \tabularnewline
99 & 99 & 99 & -1.22988090827218e-16 \tabularnewline
100 & 100 & 100 & -5.78882382642139e-16 \tabularnewline
101 & 101 & 101 & 4.56156522118559e-16 \tabularnewline
102 & 102 & 102 & 4.12138838197608e-17 \tabularnewline
103 & 103 & 103 & 3.01857130360259e-16 \tabularnewline
104 & 104 & 104 & 7.81296795721997e-17 \tabularnewline
105 & 105 & 105 & 1.31078659101693e-16 \tabularnewline
106 & 106 & 106 & 2.01683309129135e-16 \tabularnewline
107 & 107 & 107 & -1.33561788139811e-16 \tabularnewline
108 & 108 & 108 & -3.82276625925965e-16 \tabularnewline
109 & 109 & 109 & 1.49875473366465e-16 \tabularnewline
110 & 110 & 110 & -5.85960350533267e-17 \tabularnewline
111 & 111 & 111 & -1.22704717710238e-17 \tabularnewline
112 & 112 & 112 & 1.20261749762166e-16 \tabularnewline
113 & 113 & 113 & -1.07083134362561e-17 \tabularnewline
114 & 114 & 114 & -6.63887497943285e-16 \tabularnewline
115 & 115 & 115 & 1.98786247071244e-16 \tabularnewline
116 & 116 & 116 & 6.83706543356026e-17 \tabularnewline
117 & 117 & 117 & -4.70412757118294e-17 \tabularnewline
118 & 118 & 118 & 2.68656229962784e-16 \tabularnewline
119 & 119 & 119 & -2.45119119363858e-16 \tabularnewline
120 & 120 & 120 & 5.80306568288563e-16 \tabularnewline
121 & 121 & 121 & -1.12956513094745e-16 \tabularnewline
122 & 122 & 122 & 6.88900451690587e-17 \tabularnewline
123 & 123 & 123 & 1.03688978233035e-16 \tabularnewline
124 & 124 & 124 & -6.23810382535117e-17 \tabularnewline
125 & 125 & 125 & -1.07403564180906e-16 \tabularnewline
126 & 126 & 126 & -1.31620993117056e-16 \tabularnewline
127 & 127 & 127 & -2.8825131258986e-16 \tabularnewline
128 & 128 & 128 & -3.78047431074876e-17 \tabularnewline
129 & 129 & 129 & 1.57050651326408e-16 \tabularnewline
130 & 130 & 130 & -3.56840791117599e-16 \tabularnewline
131 & 131 & 131 & 1.51323634449419e-17 \tabularnewline
132 & 132 & 132 & -2.44948385731719e-17 \tabularnewline
133 & 133 & 133 & -3.11167830103248e-16 \tabularnewline
134 & 134 & 134 & 2.80055168229514e-16 \tabularnewline
135 & 135 & 135 & 2.29472148030852e-17 \tabularnewline
136 & 136 & 136 & 4.64432503682076e-16 \tabularnewline
137 & 137 & 137 & -3.38630873648087e-16 \tabularnewline
138 & 138 & 138 & -2.92045198830414e-16 \tabularnewline
139 & 139 & 139 & -3.67246363763043e-16 \tabularnewline
140 & 140 & 140 & 9.21339872913681e-17 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185903&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]1[/C][C]1[/C][C]-7.97634353415857e-17[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]2[/C][C]-1.44813521381552e-16[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]3[/C][C]3.30773272771716e-16[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]4[/C][C]-1.43677547922788e-17[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]5[/C][C]-1.47694193164958e-16[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]6[/C][C]-2.57331063588023e-16[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]7[/C][C]-3.10203894040604e-16[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]8[/C][C]3.33879398662313e-16[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]9[/C][C]1.07503249186494e-17[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]10[/C][C]6.24051982163887e-16[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]11[/C][C]-2.43722916423967e-16[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]12[/C][C]-2.44301124629744e-16[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]13[/C][C]-3.43558629663781e-17[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]14[/C][C]-1.05750285281624e-16[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]15[/C][C]-1.9743603976084e-16[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]16[/C][C]5.81573453429147e-16[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]17[/C][C]5.27048721740276e-16[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]18[/C][C]-1.2628849600408e-16[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]19[/C][C]5.01637341713209e-16[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]20[/C][C]-5.01882576868341e-16[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]21[/C][C]5.84429209924293e-16[/C][/ROW]
[ROW][C]22[/C][C]22[/C][C]22[/C][C]-1.7656789312555e-15[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]23[/C][C]1.03627148695093e-16[/C][/ROW]
[ROW][C]24[/C][C]24[/C][C]24[/C][C]2.17689848463289e-16[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]25[/C][C]2.2550777667528e-16[/C][/ROW]
[ROW][C]26[/C][C]26[/C][C]26[/C][C]1.34553829094619e-16[/C][/ROW]
[ROW][C]27[/C][C]27[/C][C]27[/C][C]-3.80916479295685e-17[/C][/ROW]
[ROW][C]28[/C][C]28[/C][C]28[/C][C]-5.53534589998377e-16[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]29[/C][C]-1.61334808420687e-17[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]30[/C][C]1.12843787104342e-16[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]31[/C][C]-2.8495202349558e-16[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]32[/C][C]-1.67590876581963e-16[/C][/ROW]
[ROW][C]33[/C][C]33[/C][C]33[/C][C]-6.87864879526288e-17[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]34[/C][C]2.52599494728778e-16[/C][/ROW]
[ROW][C]35[/C][C]35[/C][C]35[/C][C]-1.71300146698318e-16[/C][/ROW]
[ROW][C]36[/C][C]36[/C][C]36[/C][C]-6.51928591305154e-17[/C][/ROW]
[ROW][C]37[/C][C]37[/C][C]37[/C][C]9.15997315999331e-17[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]38[/C][C]2.34370694663095e-16[/C][/ROW]
[ROW][C]39[/C][C]39[/C][C]39[/C][C]-2.71744853231668e-16[/C][/ROW]
[ROW][C]40[/C][C]40[/C][C]40[/C][C]8.59401429935066e-17[/C][/ROW]
[ROW][C]41[/C][C]41[/C][C]41[/C][C]-2.6802185115733e-16[/C][/ROW]
[ROW][C]42[/C][C]42[/C][C]42[/C][C]3.92278353903212e-16[/C][/ROW]
[ROW][C]43[/C][C]43[/C][C]43[/C][C]-2.94679168330986e-17[/C][/ROW]
[ROW][C]44[/C][C]44[/C][C]44[/C][C]-1.24277192603127e-16[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]45[/C][C]-2.00108401820708e-16[/C][/ROW]
[ROW][C]46[/C][C]46[/C][C]46[/C][C]-5.01530870036958e-17[/C][/ROW]
[ROW][C]47[/C][C]47[/C][C]47[/C][C]7.54977192596114e-16[/C][/ROW]
[ROW][C]48[/C][C]48[/C][C]48[/C][C]1.9098099626685e-16[/C][/ROW]
[ROW][C]49[/C][C]49[/C][C]49[/C][C]-6.35361757936597e-16[/C][/ROW]
[ROW][C]50[/C][C]50[/C][C]50[/C][C]-7.57742256548325e-16[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]51[/C][C]-2.02900692218031e-16[/C][/ROW]
[ROW][C]52[/C][C]52[/C][C]52[/C][C]-1.53497779623321e-16[/C][/ROW]
[ROW][C]53[/C][C]53[/C][C]53[/C][C]3.12825049612134e-16[/C][/ROW]
[ROW][C]54[/C][C]54[/C][C]54[/C][C]7.7295143456182e-16[/C][/ROW]
[ROW][C]55[/C][C]55[/C][C]55[/C][C]3.34990325261051e-16[/C][/ROW]
[ROW][C]56[/C][C]56[/C][C]56[/C][C]-3.96512454639541e-16[/C][/ROW]
[ROW][C]57[/C][C]57[/C][C]57[/C][C]-5.03210296620342e-16[/C][/ROW]
[ROW][C]58[/C][C]58[/C][C]58[/C][C]4.55971011883684e-16[/C][/ROW]
[ROW][C]59[/C][C]59[/C][C]59[/C][C]9.32581969867824e-17[/C][/ROW]
[ROW][C]60[/C][C]60[/C][C]60[/C][C]1.76606787134555e-16[/C][/ROW]
[ROW][C]61[/C][C]61[/C][C]61[/C][C]-2.82192549926085e-16[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]62[/C][C]4.18073127410164e-16[/C][/ROW]
[ROW][C]63[/C][C]63[/C][C]63[/C][C]2.25832856466757e-16[/C][/ROW]
[ROW][C]64[/C][C]64[/C][C]64[/C][C]-1.66444374757115e-16[/C][/ROW]
[ROW][C]65[/C][C]65[/C][C]65[/C][C]-1.2691953976782e-16[/C][/ROW]
[ROW][C]66[/C][C]66[/C][C]66[/C][C]-3.59831871381328e-16[/C][/ROW]
[ROW][C]67[/C][C]67[/C][C]67[/C][C]-1.35007909723549e-16[/C][/ROW]
[ROW][C]68[/C][C]68[/C][C]68[/C][C]3.3431291870767e-16[/C][/ROW]
[ROW][C]69[/C][C]69[/C][C]69[/C][C]2.70487337596975e-16[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]70[/C][C]-6.62025093708371e-17[/C][/ROW]
[ROW][C]71[/C][C]71[/C][C]71[/C][C]-1.41521811031577e-16[/C][/ROW]
[ROW][C]72[/C][C]72[/C][C]72[/C][C]4.48940606336782e-17[/C][/ROW]
[ROW][C]73[/C][C]73[/C][C]73[/C][C]1.08754238757116e-16[/C][/ROW]
[ROW][C]74[/C][C]74[/C][C]74[/C][C]4.06527815147739e-17[/C][/ROW]
[ROW][C]75[/C][C]75[/C][C]75[/C][C]1.63941931222284e-16[/C][/ROW]
[ROW][C]76[/C][C]76[/C][C]76[/C][C]1.52140166311089e-18[/C][/ROW]
[ROW][C]77[/C][C]77[/C][C]77[/C][C]-1.26483724738504e-16[/C][/ROW]
[ROW][C]78[/C][C]78[/C][C]78[/C][C]1.65538981096481e-16[/C][/ROW]
[ROW][C]79[/C][C]79[/C][C]79[/C][C]2.14414004513489e-16[/C][/ROW]
[ROW][C]80[/C][C]80[/C][C]80[/C][C]3.65316328291068e-16[/C][/ROW]
[ROW][C]81[/C][C]81[/C][C]81[/C][C]1.300738812896e-16[/C][/ROW]
[ROW][C]82[/C][C]82[/C][C]82[/C][C]2.68842822129304e-16[/C][/ROW]
[ROW][C]83[/C][C]83[/C][C]83[/C][C]3.33241254093858e-16[/C][/ROW]
[ROW][C]84[/C][C]84[/C][C]84[/C][C]-7.6274477222133e-16[/C][/ROW]
[ROW][C]85[/C][C]85[/C][C]85[/C][C]-1.17288679016795e-16[/C][/ROW]
[ROW][C]86[/C][C]86[/C][C]86[/C][C]2.88522608516221e-16[/C][/ROW]
[ROW][C]87[/C][C]87[/C][C]87[/C][C]-1.75245775852714e-18[/C][/ROW]
[ROW][C]88[/C][C]88[/C][C]88[/C][C]2.75378668536736e-16[/C][/ROW]
[ROW][C]89[/C][C]89[/C][C]89[/C][C]-1.5403475253504e-16[/C][/ROW]
[ROW][C]90[/C][C]90[/C][C]90[/C][C]3.46178680378569e-16[/C][/ROW]
[ROW][C]91[/C][C]91[/C][C]91[/C][C]-1.36555628473518e-16[/C][/ROW]
[ROW][C]92[/C][C]92[/C][C]92[/C][C]-4.40751230597616e-17[/C][/ROW]
[ROW][C]93[/C][C]93[/C][C]93[/C][C]-4.64723602052111e-16[/C][/ROW]
[ROW][C]94[/C][C]94[/C][C]94[/C][C]1.67070468750056e-16[/C][/ROW]
[ROW][C]95[/C][C]95[/C][C]95[/C][C]-3.65010374159259e-16[/C][/ROW]
[ROW][C]96[/C][C]96[/C][C]96[/C][C]2.68531959693792e-16[/C][/ROW]
[ROW][C]97[/C][C]97[/C][C]97[/C][C]9.97349407986638e-16[/C][/ROW]
[ROW][C]98[/C][C]98[/C][C]98[/C][C]-3.98216156332618e-16[/C][/ROW]
[ROW][C]99[/C][C]99[/C][C]99[/C][C]-1.22988090827218e-16[/C][/ROW]
[ROW][C]100[/C][C]100[/C][C]100[/C][C]-5.78882382642139e-16[/C][/ROW]
[ROW][C]101[/C][C]101[/C][C]101[/C][C]4.56156522118559e-16[/C][/ROW]
[ROW][C]102[/C][C]102[/C][C]102[/C][C]4.12138838197608e-17[/C][/ROW]
[ROW][C]103[/C][C]103[/C][C]103[/C][C]3.01857130360259e-16[/C][/ROW]
[ROW][C]104[/C][C]104[/C][C]104[/C][C]7.81296795721997e-17[/C][/ROW]
[ROW][C]105[/C][C]105[/C][C]105[/C][C]1.31078659101693e-16[/C][/ROW]
[ROW][C]106[/C][C]106[/C][C]106[/C][C]2.01683309129135e-16[/C][/ROW]
[ROW][C]107[/C][C]107[/C][C]107[/C][C]-1.33561788139811e-16[/C][/ROW]
[ROW][C]108[/C][C]108[/C][C]108[/C][C]-3.82276625925965e-16[/C][/ROW]
[ROW][C]109[/C][C]109[/C][C]109[/C][C]1.49875473366465e-16[/C][/ROW]
[ROW][C]110[/C][C]110[/C][C]110[/C][C]-5.85960350533267e-17[/C][/ROW]
[ROW][C]111[/C][C]111[/C][C]111[/C][C]-1.22704717710238e-17[/C][/ROW]
[ROW][C]112[/C][C]112[/C][C]112[/C][C]1.20261749762166e-16[/C][/ROW]
[ROW][C]113[/C][C]113[/C][C]113[/C][C]-1.07083134362561e-17[/C][/ROW]
[ROW][C]114[/C][C]114[/C][C]114[/C][C]-6.63887497943285e-16[/C][/ROW]
[ROW][C]115[/C][C]115[/C][C]115[/C][C]1.98786247071244e-16[/C][/ROW]
[ROW][C]116[/C][C]116[/C][C]116[/C][C]6.83706543356026e-17[/C][/ROW]
[ROW][C]117[/C][C]117[/C][C]117[/C][C]-4.70412757118294e-17[/C][/ROW]
[ROW][C]118[/C][C]118[/C][C]118[/C][C]2.68656229962784e-16[/C][/ROW]
[ROW][C]119[/C][C]119[/C][C]119[/C][C]-2.45119119363858e-16[/C][/ROW]
[ROW][C]120[/C][C]120[/C][C]120[/C][C]5.80306568288563e-16[/C][/ROW]
[ROW][C]121[/C][C]121[/C][C]121[/C][C]-1.12956513094745e-16[/C][/ROW]
[ROW][C]122[/C][C]122[/C][C]122[/C][C]6.88900451690587e-17[/C][/ROW]
[ROW][C]123[/C][C]123[/C][C]123[/C][C]1.03688978233035e-16[/C][/ROW]
[ROW][C]124[/C][C]124[/C][C]124[/C][C]-6.23810382535117e-17[/C][/ROW]
[ROW][C]125[/C][C]125[/C][C]125[/C][C]-1.07403564180906e-16[/C][/ROW]
[ROW][C]126[/C][C]126[/C][C]126[/C][C]-1.31620993117056e-16[/C][/ROW]
[ROW][C]127[/C][C]127[/C][C]127[/C][C]-2.8825131258986e-16[/C][/ROW]
[ROW][C]128[/C][C]128[/C][C]128[/C][C]-3.78047431074876e-17[/C][/ROW]
[ROW][C]129[/C][C]129[/C][C]129[/C][C]1.57050651326408e-16[/C][/ROW]
[ROW][C]130[/C][C]130[/C][C]130[/C][C]-3.56840791117599e-16[/C][/ROW]
[ROW][C]131[/C][C]131[/C][C]131[/C][C]1.51323634449419e-17[/C][/ROW]
[ROW][C]132[/C][C]132[/C][C]132[/C][C]-2.44948385731719e-17[/C][/ROW]
[ROW][C]133[/C][C]133[/C][C]133[/C][C]-3.11167830103248e-16[/C][/ROW]
[ROW][C]134[/C][C]134[/C][C]134[/C][C]2.80055168229514e-16[/C][/ROW]
[ROW][C]135[/C][C]135[/C][C]135[/C][C]2.29472148030852e-17[/C][/ROW]
[ROW][C]136[/C][C]136[/C][C]136[/C][C]4.64432503682076e-16[/C][/ROW]
[ROW][C]137[/C][C]137[/C][C]137[/C][C]-3.38630873648087e-16[/C][/ROW]
[ROW][C]138[/C][C]138[/C][C]138[/C][C]-2.92045198830414e-16[/C][/ROW]
[ROW][C]139[/C][C]139[/C][C]139[/C][C]-3.67246363763043e-16[/C][/ROW]
[ROW][C]140[/C][C]140[/C][C]140[/C][C]9.21339872913681e-17[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185903&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185903&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111-7.97634353415857e-17
222-1.44813521381552e-16
3333.30773272771716e-16
444-1.43677547922788e-17
555-1.47694193164958e-16
666-2.57331063588023e-16
777-3.10203894040604e-16
8883.33879398662313e-16
9991.07503249186494e-17
1010106.24051982163887e-16
111111-2.43722916423967e-16
121212-2.44301124629744e-16
131313-3.43558629663781e-17
141414-1.05750285281624e-16
151515-1.9743603976084e-16
1616165.81573453429147e-16
1717175.27048721740276e-16
181818-1.2628849600408e-16
1919195.01637341713209e-16
202020-5.01882576868341e-16
2121215.84429209924293e-16
222222-1.7656789312555e-15
2323231.03627148695093e-16
2424242.17689848463289e-16
2525252.2550777667528e-16
2626261.34553829094619e-16
272727-3.80916479295685e-17
282828-5.53534589998377e-16
292929-1.61334808420687e-17
3030301.12843787104342e-16
313131-2.8495202349558e-16
323232-1.67590876581963e-16
333333-6.87864879526288e-17
3434342.52599494728778e-16
353535-1.71300146698318e-16
363636-6.51928591305154e-17
3737379.15997315999331e-17
3838382.34370694663095e-16
393939-2.71744853231668e-16
4040408.59401429935066e-17
414141-2.6802185115733e-16
4242423.92278353903212e-16
434343-2.94679168330986e-17
444444-1.24277192603127e-16
454545-2.00108401820708e-16
464646-5.01530870036958e-17
4747477.54977192596114e-16
4848481.9098099626685e-16
494949-6.35361757936597e-16
505050-7.57742256548325e-16
515151-2.02900692218031e-16
525252-1.53497779623321e-16
5353533.12825049612134e-16
5454547.7295143456182e-16
5555553.34990325261051e-16
565656-3.96512454639541e-16
575757-5.03210296620342e-16
5858584.55971011883684e-16
5959599.32581969867824e-17
6060601.76606787134555e-16
616161-2.82192549926085e-16
6262624.18073127410164e-16
6363632.25832856466757e-16
646464-1.66444374757115e-16
656565-1.2691953976782e-16
666666-3.59831871381328e-16
676767-1.35007909723549e-16
6868683.3431291870767e-16
6969692.70487337596975e-16
707070-6.62025093708371e-17
717171-1.41521811031577e-16
7272724.48940606336782e-17
7373731.08754238757116e-16
7474744.06527815147739e-17
7575751.63941931222284e-16
7676761.52140166311089e-18
777777-1.26483724738504e-16
7878781.65538981096481e-16
7979792.14414004513489e-16
8080803.65316328291068e-16
8181811.300738812896e-16
8282822.68842822129304e-16
8383833.33241254093858e-16
848484-7.6274477222133e-16
858585-1.17288679016795e-16
8686862.88522608516221e-16
878787-1.75245775852714e-18
8888882.75378668536736e-16
898989-1.5403475253504e-16
9090903.46178680378569e-16
919191-1.36555628473518e-16
929292-4.40751230597616e-17
939393-4.64723602052111e-16
9494941.67070468750056e-16
959595-3.65010374159259e-16
9696962.68531959693792e-16
9797979.97349407986638e-16
989898-3.98216156332618e-16
999999-1.22988090827218e-16
100100100-5.78882382642139e-16
1011011014.56156522118559e-16
1021021024.12138838197608e-17
1031031033.01857130360259e-16
1041041047.81296795721997e-17
1051051051.31078659101693e-16
1061061062.01683309129135e-16
107107107-1.33561788139811e-16
108108108-3.82276625925965e-16
1091091091.49875473366465e-16
110110110-5.85960350533267e-17
111111111-1.22704717710238e-17
1121121121.20261749762166e-16
113113113-1.07083134362561e-17
114114114-6.63887497943285e-16
1151151151.98786247071244e-16
1161161166.83706543356026e-17
117117117-4.70412757118294e-17
1181181182.68656229962784e-16
119119119-2.45119119363858e-16
1201201205.80306568288563e-16
121121121-1.12956513094745e-16
1221221226.88900451690587e-17
1231231231.03688978233035e-16
124124124-6.23810382535117e-17
125125125-1.07403564180906e-16
126126126-1.31620993117056e-16
127127127-2.8825131258986e-16
128128128-3.78047431074876e-17
1291291291.57050651326408e-16
130130130-3.56840791117599e-16
1311311311.51323634449419e-17
132132132-2.44948385731719e-17
133133133-3.11167830103248e-16
1341341342.80055168229514e-16
1351351352.29472148030852e-17
1361361364.64432503682076e-16
137137137-3.38630873648087e-16
138138138-2.92045198830414e-16
139139139-3.67246363763043e-16
1401401409.21339872913681e-17






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
250.1238037214397260.2476074428794510.876196278560274
260.4092810608499550.818562121699910.590718939150045
270.01725321365589680.03450642731179360.982746786344103
280.1893284763226460.3786569526452910.810671523677354
2912.26024889156927e-191.13012444578463e-19
300.99999978791224.24175600776501e-072.1208780038825e-07
3112.76160894145547e-281.38080447072773e-28
320.9993172963771170.001365407245765820.00068270362288291
330.9815710693547810.03685786129043760.0184289306452188
340.9740876325252550.05182473494949010.0259123674747451
3514.48398248768303e-162.24199124384151e-16
3619.10633735094409e-234.55316867547204e-23
3713.76184105753325e-161.88092052876663e-16
380.9999999677721786.44556448637385e-083.22278224318693e-08
3913.77583187432384e-181.88791593716192e-18
400.9999999999998792.41138169404257e-131.20569084702128e-13
410.9973409561287080.005318087742583970.00265904387129198
4212.23269360768023e-391.11634680384012e-39
4311.39622822949522e-326.98114114747612e-33
440.9999999934739981.30520048333921e-086.52600241669604e-09
450.9999476390744570.000104721851086635.2360925543315e-05
460.3191565548986430.6383131097972860.680843445101357
470.9985827592044420.002834481591116330.00141724079555817
4818.93540087582234e-234.46770043791117e-23
4918.52616142741356e-204.26308071370678e-20
500.9999990509566291.89808674237756e-069.49043371188782e-07
510.9995955184202450.0008089631595103030.000404481579755151
5213.14331600470729e-331.57165800235364e-33
5317.08306475052568e-253.54153237526284e-25
5411.31407368717302e-186.57036843586508e-19
550.9999996942784716.11443058749362e-073.05721529374681e-07
5611.40232728734329e-217.01163643671645e-22
570.6086819189599020.7826361620801950.391318081040098
580.08896264889290960.1779252977858190.91103735110709
590.9999733956281265.3208743748578e-052.6604371874289e-05
600.9999999999999872.55098233871617e-141.27549116935809e-14
610.9999999901924291.96151425319049e-089.80757126595243e-09
620.9830281838855820.03394363222883680.0169718161144184
630.9999812378806823.75242386350189e-051.87621193175095e-05
6414.16158305477251e-212.08079152738626e-21
650.0007007523782641470.001401504756528290.999299247621736
6614.19080056020306e-192.09540028010153e-19
670.999846936483580.0003061270328406570.000153063516420329
680.999999994253111.1493780673825e-085.74689033691252e-09
690.9999975875267534.82494649350227e-062.41247324675114e-06
700.9999792507373424.14985253157809e-052.07492626578904e-05
7113.56767355629989e-201.78383677814994e-20
720.9999999908313151.83373707576268e-089.1686853788134e-09
7314.10339123965752e-312.05169561982876e-31
740.9999999955855638.82887432735974e-094.41443716367987e-09
750.9999999999999991.51405769448098e-157.5702884724049e-16
7611.13671200172383e-265.68356000861914e-27
770.9999996759373216.48125357629e-073.240626788145e-07
780.999999999998333.34081507235599e-121.67040753617799e-12
790.9999999999993791.24206722714394e-126.21033613571969e-13
800.9999339729629090.0001320540741818096.60270370909047e-05
810.9999999999960657.86962087637197e-123.93481043818599e-12
820.9831136031626040.03377279367479270.0168863968373964
830.9999999999999992.31041395071117e-151.15520697535558e-15
840.9999999999990071.98513548386677e-129.92567741933386e-13
8512.01186198076286e-211.00593099038143e-21
8615.92754041467751e-282.96377020733876e-28
870.9999992776265451.44474691000114e-067.22373455000569e-07
880.9999999999999539.4255116628118e-144.7127558314059e-14
8915.95473326931478e-222.97736663465739e-22
9015.2639527086593e-202.63197635432965e-20
910.9189708530042470.1620582939915070.0810291469957533
920.9999999999999991.22111890922733e-156.10559454613663e-16
930.9999999998716822.56636371996363e-101.28318185998181e-10
940.9999999989792322.04153583870287e-091.02076791935144e-09
950.9999649757237417.00485525183595e-053.50242762591798e-05
960.9909412689458480.0181174621083040.00905873105415201
9718.048362730301e-184.0241813651505e-18
980.9999999659834226.80331560175907e-083.40165780087954e-08
990.998324980635020.003350038729959130.00167501936497957
1000.8565477638602250.2869044722795510.143452236139775
1010.963175996550110.07364800689978070.0368240034498903
1020.999999822075253.55849500410809e-071.77924750205404e-07
1030.5846574496900820.8306851006198370.415342550309918
1040.2229675966442940.4459351932885880.777032403355706
1050.9999923487018071.53025963867646e-057.65129819338229e-06
1060.9999999255868261.48826348700765e-077.44131743503823e-08
1070.8960532219973750.2078935560052510.103946778002625
1080.9283873509597070.1432252980805870.0716126490402933
1090.9826673034523510.03466539309529840.0173326965476492
1100.9853396068008270.02932078639834530.0146603931991726
1110.9998412742285870.0003174515428249320.000158725771412466
1120.9972292254535540.005541549092891710.00277077454644585
1130.9708550445808970.05828991083820640.0291449554191032
1140.9290963849722730.1418072300554540.0709036150277272
1150.9980934121786320.003813175642736560.00190658782136828

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
25 & 0.123803721439726 & 0.247607442879451 & 0.876196278560274 \tabularnewline
26 & 0.409281060849955 & 0.81856212169991 & 0.590718939150045 \tabularnewline
27 & 0.0172532136558968 & 0.0345064273117936 & 0.982746786344103 \tabularnewline
28 & 0.189328476322646 & 0.378656952645291 & 0.810671523677354 \tabularnewline
29 & 1 & 2.26024889156927e-19 & 1.13012444578463e-19 \tabularnewline
30 & 0.9999997879122 & 4.24175600776501e-07 & 2.1208780038825e-07 \tabularnewline
31 & 1 & 2.76160894145547e-28 & 1.38080447072773e-28 \tabularnewline
32 & 0.999317296377117 & 0.00136540724576582 & 0.00068270362288291 \tabularnewline
33 & 0.981571069354781 & 0.0368578612904376 & 0.0184289306452188 \tabularnewline
34 & 0.974087632525255 & 0.0518247349494901 & 0.0259123674747451 \tabularnewline
35 & 1 & 4.48398248768303e-16 & 2.24199124384151e-16 \tabularnewline
36 & 1 & 9.10633735094409e-23 & 4.55316867547204e-23 \tabularnewline
37 & 1 & 3.76184105753325e-16 & 1.88092052876663e-16 \tabularnewline
38 & 0.999999967772178 & 6.44556448637385e-08 & 3.22278224318693e-08 \tabularnewline
39 & 1 & 3.77583187432384e-18 & 1.88791593716192e-18 \tabularnewline
40 & 0.999999999999879 & 2.41138169404257e-13 & 1.20569084702128e-13 \tabularnewline
41 & 0.997340956128708 & 0.00531808774258397 & 0.00265904387129198 \tabularnewline
42 & 1 & 2.23269360768023e-39 & 1.11634680384012e-39 \tabularnewline
43 & 1 & 1.39622822949522e-32 & 6.98114114747612e-33 \tabularnewline
44 & 0.999999993473998 & 1.30520048333921e-08 & 6.52600241669604e-09 \tabularnewline
45 & 0.999947639074457 & 0.00010472185108663 & 5.2360925543315e-05 \tabularnewline
46 & 0.319156554898643 & 0.638313109797286 & 0.680843445101357 \tabularnewline
47 & 0.998582759204442 & 0.00283448159111633 & 0.00141724079555817 \tabularnewline
48 & 1 & 8.93540087582234e-23 & 4.46770043791117e-23 \tabularnewline
49 & 1 & 8.52616142741356e-20 & 4.26308071370678e-20 \tabularnewline
50 & 0.999999050956629 & 1.89808674237756e-06 & 9.49043371188782e-07 \tabularnewline
51 & 0.999595518420245 & 0.000808963159510303 & 0.000404481579755151 \tabularnewline
52 & 1 & 3.14331600470729e-33 & 1.57165800235364e-33 \tabularnewline
53 & 1 & 7.08306475052568e-25 & 3.54153237526284e-25 \tabularnewline
54 & 1 & 1.31407368717302e-18 & 6.57036843586508e-19 \tabularnewline
55 & 0.999999694278471 & 6.11443058749362e-07 & 3.05721529374681e-07 \tabularnewline
56 & 1 & 1.40232728734329e-21 & 7.01163643671645e-22 \tabularnewline
57 & 0.608681918959902 & 0.782636162080195 & 0.391318081040098 \tabularnewline
58 & 0.0889626488929096 & 0.177925297785819 & 0.91103735110709 \tabularnewline
59 & 0.999973395628126 & 5.3208743748578e-05 & 2.6604371874289e-05 \tabularnewline
60 & 0.999999999999987 & 2.55098233871617e-14 & 1.27549116935809e-14 \tabularnewline
61 & 0.999999990192429 & 1.96151425319049e-08 & 9.80757126595243e-09 \tabularnewline
62 & 0.983028183885582 & 0.0339436322288368 & 0.0169718161144184 \tabularnewline
63 & 0.999981237880682 & 3.75242386350189e-05 & 1.87621193175095e-05 \tabularnewline
64 & 1 & 4.16158305477251e-21 & 2.08079152738626e-21 \tabularnewline
65 & 0.000700752378264147 & 0.00140150475652829 & 0.999299247621736 \tabularnewline
66 & 1 & 4.19080056020306e-19 & 2.09540028010153e-19 \tabularnewline
67 & 0.99984693648358 & 0.000306127032840657 & 0.000153063516420329 \tabularnewline
68 & 0.99999999425311 & 1.1493780673825e-08 & 5.74689033691252e-09 \tabularnewline
69 & 0.999997587526753 & 4.82494649350227e-06 & 2.41247324675114e-06 \tabularnewline
70 & 0.999979250737342 & 4.14985253157809e-05 & 2.07492626578904e-05 \tabularnewline
71 & 1 & 3.56767355629989e-20 & 1.78383677814994e-20 \tabularnewline
72 & 0.999999990831315 & 1.83373707576268e-08 & 9.1686853788134e-09 \tabularnewline
73 & 1 & 4.10339123965752e-31 & 2.05169561982876e-31 \tabularnewline
74 & 0.999999995585563 & 8.82887432735974e-09 & 4.41443716367987e-09 \tabularnewline
75 & 0.999999999999999 & 1.51405769448098e-15 & 7.5702884724049e-16 \tabularnewline
76 & 1 & 1.13671200172383e-26 & 5.68356000861914e-27 \tabularnewline
77 & 0.999999675937321 & 6.48125357629e-07 & 3.240626788145e-07 \tabularnewline
78 & 0.99999999999833 & 3.34081507235599e-12 & 1.67040753617799e-12 \tabularnewline
79 & 0.999999999999379 & 1.24206722714394e-12 & 6.21033613571969e-13 \tabularnewline
80 & 0.999933972962909 & 0.000132054074181809 & 6.60270370909047e-05 \tabularnewline
81 & 0.999999999996065 & 7.86962087637197e-12 & 3.93481043818599e-12 \tabularnewline
82 & 0.983113603162604 & 0.0337727936747927 & 0.0168863968373964 \tabularnewline
83 & 0.999999999999999 & 2.31041395071117e-15 & 1.15520697535558e-15 \tabularnewline
84 & 0.999999999999007 & 1.98513548386677e-12 & 9.92567741933386e-13 \tabularnewline
85 & 1 & 2.01186198076286e-21 & 1.00593099038143e-21 \tabularnewline
86 & 1 & 5.92754041467751e-28 & 2.96377020733876e-28 \tabularnewline
87 & 0.999999277626545 & 1.44474691000114e-06 & 7.22373455000569e-07 \tabularnewline
88 & 0.999999999999953 & 9.4255116628118e-14 & 4.7127558314059e-14 \tabularnewline
89 & 1 & 5.95473326931478e-22 & 2.97736663465739e-22 \tabularnewline
90 & 1 & 5.2639527086593e-20 & 2.63197635432965e-20 \tabularnewline
91 & 0.918970853004247 & 0.162058293991507 & 0.0810291469957533 \tabularnewline
92 & 0.999999999999999 & 1.22111890922733e-15 & 6.10559454613663e-16 \tabularnewline
93 & 0.999999999871682 & 2.56636371996363e-10 & 1.28318185998181e-10 \tabularnewline
94 & 0.999999998979232 & 2.04153583870287e-09 & 1.02076791935144e-09 \tabularnewline
95 & 0.999964975723741 & 7.00485525183595e-05 & 3.50242762591798e-05 \tabularnewline
96 & 0.990941268945848 & 0.018117462108304 & 0.00905873105415201 \tabularnewline
97 & 1 & 8.048362730301e-18 & 4.0241813651505e-18 \tabularnewline
98 & 0.999999965983422 & 6.80331560175907e-08 & 3.40165780087954e-08 \tabularnewline
99 & 0.99832498063502 & 0.00335003872995913 & 0.00167501936497957 \tabularnewline
100 & 0.856547763860225 & 0.286904472279551 & 0.143452236139775 \tabularnewline
101 & 0.96317599655011 & 0.0736480068997807 & 0.0368240034498903 \tabularnewline
102 & 0.99999982207525 & 3.55849500410809e-07 & 1.77924750205404e-07 \tabularnewline
103 & 0.584657449690082 & 0.830685100619837 & 0.415342550309918 \tabularnewline
104 & 0.222967596644294 & 0.445935193288588 & 0.777032403355706 \tabularnewline
105 & 0.999992348701807 & 1.53025963867646e-05 & 7.65129819338229e-06 \tabularnewline
106 & 0.999999925586826 & 1.48826348700765e-07 & 7.44131743503823e-08 \tabularnewline
107 & 0.896053221997375 & 0.207893556005251 & 0.103946778002625 \tabularnewline
108 & 0.928387350959707 & 0.143225298080587 & 0.0716126490402933 \tabularnewline
109 & 0.982667303452351 & 0.0346653930952984 & 0.0173326965476492 \tabularnewline
110 & 0.985339606800827 & 0.0293207863983453 & 0.0146603931991726 \tabularnewline
111 & 0.999841274228587 & 0.000317451542824932 & 0.000158725771412466 \tabularnewline
112 & 0.997229225453554 & 0.00554154909289171 & 0.00277077454644585 \tabularnewline
113 & 0.970855044580897 & 0.0582899108382064 & 0.0291449554191032 \tabularnewline
114 & 0.929096384972273 & 0.141807230055454 & 0.0709036150277272 \tabularnewline
115 & 0.998093412178632 & 0.00381317564273656 & 0.00190658782136828 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185903&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]25[/C][C]0.123803721439726[/C][C]0.247607442879451[/C][C]0.876196278560274[/C][/ROW]
[ROW][C]26[/C][C]0.409281060849955[/C][C]0.81856212169991[/C][C]0.590718939150045[/C][/ROW]
[ROW][C]27[/C][C]0.0172532136558968[/C][C]0.0345064273117936[/C][C]0.982746786344103[/C][/ROW]
[ROW][C]28[/C][C]0.189328476322646[/C][C]0.378656952645291[/C][C]0.810671523677354[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]2.26024889156927e-19[/C][C]1.13012444578463e-19[/C][/ROW]
[ROW][C]30[/C][C]0.9999997879122[/C][C]4.24175600776501e-07[/C][C]2.1208780038825e-07[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]2.76160894145547e-28[/C][C]1.38080447072773e-28[/C][/ROW]
[ROW][C]32[/C][C]0.999317296377117[/C][C]0.00136540724576582[/C][C]0.00068270362288291[/C][/ROW]
[ROW][C]33[/C][C]0.981571069354781[/C][C]0.0368578612904376[/C][C]0.0184289306452188[/C][/ROW]
[ROW][C]34[/C][C]0.974087632525255[/C][C]0.0518247349494901[/C][C]0.0259123674747451[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]4.48398248768303e-16[/C][C]2.24199124384151e-16[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]9.10633735094409e-23[/C][C]4.55316867547204e-23[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]3.76184105753325e-16[/C][C]1.88092052876663e-16[/C][/ROW]
[ROW][C]38[/C][C]0.999999967772178[/C][C]6.44556448637385e-08[/C][C]3.22278224318693e-08[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]3.77583187432384e-18[/C][C]1.88791593716192e-18[/C][/ROW]
[ROW][C]40[/C][C]0.999999999999879[/C][C]2.41138169404257e-13[/C][C]1.20569084702128e-13[/C][/ROW]
[ROW][C]41[/C][C]0.997340956128708[/C][C]0.00531808774258397[/C][C]0.00265904387129198[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]2.23269360768023e-39[/C][C]1.11634680384012e-39[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.39622822949522e-32[/C][C]6.98114114747612e-33[/C][/ROW]
[ROW][C]44[/C][C]0.999999993473998[/C][C]1.30520048333921e-08[/C][C]6.52600241669604e-09[/C][/ROW]
[ROW][C]45[/C][C]0.999947639074457[/C][C]0.00010472185108663[/C][C]5.2360925543315e-05[/C][/ROW]
[ROW][C]46[/C][C]0.319156554898643[/C][C]0.638313109797286[/C][C]0.680843445101357[/C][/ROW]
[ROW][C]47[/C][C]0.998582759204442[/C][C]0.00283448159111633[/C][C]0.00141724079555817[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]8.93540087582234e-23[/C][C]4.46770043791117e-23[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]8.52616142741356e-20[/C][C]4.26308071370678e-20[/C][/ROW]
[ROW][C]50[/C][C]0.999999050956629[/C][C]1.89808674237756e-06[/C][C]9.49043371188782e-07[/C][/ROW]
[ROW][C]51[/C][C]0.999595518420245[/C][C]0.000808963159510303[/C][C]0.000404481579755151[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]3.14331600470729e-33[/C][C]1.57165800235364e-33[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]7.08306475052568e-25[/C][C]3.54153237526284e-25[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.31407368717302e-18[/C][C]6.57036843586508e-19[/C][/ROW]
[ROW][C]55[/C][C]0.999999694278471[/C][C]6.11443058749362e-07[/C][C]3.05721529374681e-07[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.40232728734329e-21[/C][C]7.01163643671645e-22[/C][/ROW]
[ROW][C]57[/C][C]0.608681918959902[/C][C]0.782636162080195[/C][C]0.391318081040098[/C][/ROW]
[ROW][C]58[/C][C]0.0889626488929096[/C][C]0.177925297785819[/C][C]0.91103735110709[/C][/ROW]
[ROW][C]59[/C][C]0.999973395628126[/C][C]5.3208743748578e-05[/C][C]2.6604371874289e-05[/C][/ROW]
[ROW][C]60[/C][C]0.999999999999987[/C][C]2.55098233871617e-14[/C][C]1.27549116935809e-14[/C][/ROW]
[ROW][C]61[/C][C]0.999999990192429[/C][C]1.96151425319049e-08[/C][C]9.80757126595243e-09[/C][/ROW]
[ROW][C]62[/C][C]0.983028183885582[/C][C]0.0339436322288368[/C][C]0.0169718161144184[/C][/ROW]
[ROW][C]63[/C][C]0.999981237880682[/C][C]3.75242386350189e-05[/C][C]1.87621193175095e-05[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]4.16158305477251e-21[/C][C]2.08079152738626e-21[/C][/ROW]
[ROW][C]65[/C][C]0.000700752378264147[/C][C]0.00140150475652829[/C][C]0.999299247621736[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]4.19080056020306e-19[/C][C]2.09540028010153e-19[/C][/ROW]
[ROW][C]67[/C][C]0.99984693648358[/C][C]0.000306127032840657[/C][C]0.000153063516420329[/C][/ROW]
[ROW][C]68[/C][C]0.99999999425311[/C][C]1.1493780673825e-08[/C][C]5.74689033691252e-09[/C][/ROW]
[ROW][C]69[/C][C]0.999997587526753[/C][C]4.82494649350227e-06[/C][C]2.41247324675114e-06[/C][/ROW]
[ROW][C]70[/C][C]0.999979250737342[/C][C]4.14985253157809e-05[/C][C]2.07492626578904e-05[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]3.56767355629989e-20[/C][C]1.78383677814994e-20[/C][/ROW]
[ROW][C]72[/C][C]0.999999990831315[/C][C]1.83373707576268e-08[/C][C]9.1686853788134e-09[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]4.10339123965752e-31[/C][C]2.05169561982876e-31[/C][/ROW]
[ROW][C]74[/C][C]0.999999995585563[/C][C]8.82887432735974e-09[/C][C]4.41443716367987e-09[/C][/ROW]
[ROW][C]75[/C][C]0.999999999999999[/C][C]1.51405769448098e-15[/C][C]7.5702884724049e-16[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.13671200172383e-26[/C][C]5.68356000861914e-27[/C][/ROW]
[ROW][C]77[/C][C]0.999999675937321[/C][C]6.48125357629e-07[/C][C]3.240626788145e-07[/C][/ROW]
[ROW][C]78[/C][C]0.99999999999833[/C][C]3.34081507235599e-12[/C][C]1.67040753617799e-12[/C][/ROW]
[ROW][C]79[/C][C]0.999999999999379[/C][C]1.24206722714394e-12[/C][C]6.21033613571969e-13[/C][/ROW]
[ROW][C]80[/C][C]0.999933972962909[/C][C]0.000132054074181809[/C][C]6.60270370909047e-05[/C][/ROW]
[ROW][C]81[/C][C]0.999999999996065[/C][C]7.86962087637197e-12[/C][C]3.93481043818599e-12[/C][/ROW]
[ROW][C]82[/C][C]0.983113603162604[/C][C]0.0337727936747927[/C][C]0.0168863968373964[/C][/ROW]
[ROW][C]83[/C][C]0.999999999999999[/C][C]2.31041395071117e-15[/C][C]1.15520697535558e-15[/C][/ROW]
[ROW][C]84[/C][C]0.999999999999007[/C][C]1.98513548386677e-12[/C][C]9.92567741933386e-13[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]2.01186198076286e-21[/C][C]1.00593099038143e-21[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]5.92754041467751e-28[/C][C]2.96377020733876e-28[/C][/ROW]
[ROW][C]87[/C][C]0.999999277626545[/C][C]1.44474691000114e-06[/C][C]7.22373455000569e-07[/C][/ROW]
[ROW][C]88[/C][C]0.999999999999953[/C][C]9.4255116628118e-14[/C][C]4.7127558314059e-14[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]5.95473326931478e-22[/C][C]2.97736663465739e-22[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]5.2639527086593e-20[/C][C]2.63197635432965e-20[/C][/ROW]
[ROW][C]91[/C][C]0.918970853004247[/C][C]0.162058293991507[/C][C]0.0810291469957533[/C][/ROW]
[ROW][C]92[/C][C]0.999999999999999[/C][C]1.22111890922733e-15[/C][C]6.10559454613663e-16[/C][/ROW]
[ROW][C]93[/C][C]0.999999999871682[/C][C]2.56636371996363e-10[/C][C]1.28318185998181e-10[/C][/ROW]
[ROW][C]94[/C][C]0.999999998979232[/C][C]2.04153583870287e-09[/C][C]1.02076791935144e-09[/C][/ROW]
[ROW][C]95[/C][C]0.999964975723741[/C][C]7.00485525183595e-05[/C][C]3.50242762591798e-05[/C][/ROW]
[ROW][C]96[/C][C]0.990941268945848[/C][C]0.018117462108304[/C][C]0.00905873105415201[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]8.048362730301e-18[/C][C]4.0241813651505e-18[/C][/ROW]
[ROW][C]98[/C][C]0.999999965983422[/C][C]6.80331560175907e-08[/C][C]3.40165780087954e-08[/C][/ROW]
[ROW][C]99[/C][C]0.99832498063502[/C][C]0.00335003872995913[/C][C]0.00167501936497957[/C][/ROW]
[ROW][C]100[/C][C]0.856547763860225[/C][C]0.286904472279551[/C][C]0.143452236139775[/C][/ROW]
[ROW][C]101[/C][C]0.96317599655011[/C][C]0.0736480068997807[/C][C]0.0368240034498903[/C][/ROW]
[ROW][C]102[/C][C]0.99999982207525[/C][C]3.55849500410809e-07[/C][C]1.77924750205404e-07[/C][/ROW]
[ROW][C]103[/C][C]0.584657449690082[/C][C]0.830685100619837[/C][C]0.415342550309918[/C][/ROW]
[ROW][C]104[/C][C]0.222967596644294[/C][C]0.445935193288588[/C][C]0.777032403355706[/C][/ROW]
[ROW][C]105[/C][C]0.999992348701807[/C][C]1.53025963867646e-05[/C][C]7.65129819338229e-06[/C][/ROW]
[ROW][C]106[/C][C]0.999999925586826[/C][C]1.48826348700765e-07[/C][C]7.44131743503823e-08[/C][/ROW]
[ROW][C]107[/C][C]0.896053221997375[/C][C]0.207893556005251[/C][C]0.103946778002625[/C][/ROW]
[ROW][C]108[/C][C]0.928387350959707[/C][C]0.143225298080587[/C][C]0.0716126490402933[/C][/ROW]
[ROW][C]109[/C][C]0.982667303452351[/C][C]0.0346653930952984[/C][C]0.0173326965476492[/C][/ROW]
[ROW][C]110[/C][C]0.985339606800827[/C][C]0.0293207863983453[/C][C]0.0146603931991726[/C][/ROW]
[ROW][C]111[/C][C]0.999841274228587[/C][C]0.000317451542824932[/C][C]0.000158725771412466[/C][/ROW]
[ROW][C]112[/C][C]0.997229225453554[/C][C]0.00554154909289171[/C][C]0.00277077454644585[/C][/ROW]
[ROW][C]113[/C][C]0.970855044580897[/C][C]0.0582899108382064[/C][C]0.0291449554191032[/C][/ROW]
[ROW][C]114[/C][C]0.929096384972273[/C][C]0.141807230055454[/C][C]0.0709036150277272[/C][/ROW]
[ROW][C]115[/C][C]0.998093412178632[/C][C]0.00381317564273656[/C][C]0.00190658782136828[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185903&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185903&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
250.1238037214397260.2476074428794510.876196278560274
260.4092810608499550.818562121699910.590718939150045
270.01725321365589680.03450642731179360.982746786344103
280.1893284763226460.3786569526452910.810671523677354
2912.26024889156927e-191.13012444578463e-19
300.99999978791224.24175600776501e-072.1208780038825e-07
3112.76160894145547e-281.38080447072773e-28
320.9993172963771170.001365407245765820.00068270362288291
330.9815710693547810.03685786129043760.0184289306452188
340.9740876325252550.05182473494949010.0259123674747451
3514.48398248768303e-162.24199124384151e-16
3619.10633735094409e-234.55316867547204e-23
3713.76184105753325e-161.88092052876663e-16
380.9999999677721786.44556448637385e-083.22278224318693e-08
3913.77583187432384e-181.88791593716192e-18
400.9999999999998792.41138169404257e-131.20569084702128e-13
410.9973409561287080.005318087742583970.00265904387129198
4212.23269360768023e-391.11634680384012e-39
4311.39622822949522e-326.98114114747612e-33
440.9999999934739981.30520048333921e-086.52600241669604e-09
450.9999476390744570.000104721851086635.2360925543315e-05
460.3191565548986430.6383131097972860.680843445101357
470.9985827592044420.002834481591116330.00141724079555817
4818.93540087582234e-234.46770043791117e-23
4918.52616142741356e-204.26308071370678e-20
500.9999990509566291.89808674237756e-069.49043371188782e-07
510.9995955184202450.0008089631595103030.000404481579755151
5213.14331600470729e-331.57165800235364e-33
5317.08306475052568e-253.54153237526284e-25
5411.31407368717302e-186.57036843586508e-19
550.9999996942784716.11443058749362e-073.05721529374681e-07
5611.40232728734329e-217.01163643671645e-22
570.6086819189599020.7826361620801950.391318081040098
580.08896264889290960.1779252977858190.91103735110709
590.9999733956281265.3208743748578e-052.6604371874289e-05
600.9999999999999872.55098233871617e-141.27549116935809e-14
610.9999999901924291.96151425319049e-089.80757126595243e-09
620.9830281838855820.03394363222883680.0169718161144184
630.9999812378806823.75242386350189e-051.87621193175095e-05
6414.16158305477251e-212.08079152738626e-21
650.0007007523782641470.001401504756528290.999299247621736
6614.19080056020306e-192.09540028010153e-19
670.999846936483580.0003061270328406570.000153063516420329
680.999999994253111.1493780673825e-085.74689033691252e-09
690.9999975875267534.82494649350227e-062.41247324675114e-06
700.9999792507373424.14985253157809e-052.07492626578904e-05
7113.56767355629989e-201.78383677814994e-20
720.9999999908313151.83373707576268e-089.1686853788134e-09
7314.10339123965752e-312.05169561982876e-31
740.9999999955855638.82887432735974e-094.41443716367987e-09
750.9999999999999991.51405769448098e-157.5702884724049e-16
7611.13671200172383e-265.68356000861914e-27
770.9999996759373216.48125357629e-073.240626788145e-07
780.999999999998333.34081507235599e-121.67040753617799e-12
790.9999999999993791.24206722714394e-126.21033613571969e-13
800.9999339729629090.0001320540741818096.60270370909047e-05
810.9999999999960657.86962087637197e-123.93481043818599e-12
820.9831136031626040.03377279367479270.0168863968373964
830.9999999999999992.31041395071117e-151.15520697535558e-15
840.9999999999990071.98513548386677e-129.92567741933386e-13
8512.01186198076286e-211.00593099038143e-21
8615.92754041467751e-282.96377020733876e-28
870.9999992776265451.44474691000114e-067.22373455000569e-07
880.9999999999999539.4255116628118e-144.7127558314059e-14
8915.95473326931478e-222.97736663465739e-22
9015.2639527086593e-202.63197635432965e-20
910.9189708530042470.1620582939915070.0810291469957533
920.9999999999999991.22111890922733e-156.10559454613663e-16
930.9999999998716822.56636371996363e-101.28318185998181e-10
940.9999999989792322.04153583870287e-091.02076791935144e-09
950.9999649757237417.00485525183595e-053.50242762591798e-05
960.9909412689458480.0181174621083040.00905873105415201
9718.048362730301e-184.0241813651505e-18
980.9999999659834226.80331560175907e-083.40165780087954e-08
990.998324980635020.003350038729959130.00167501936497957
1000.8565477638602250.2869044722795510.143452236139775
1010.963175996550110.07364800689978070.0368240034498903
1020.999999822075253.55849500410809e-071.77924750205404e-07
1030.5846574496900820.8306851006198370.415342550309918
1040.2229675966442940.4459351932885880.777032403355706
1050.9999923487018071.53025963867646e-057.65129819338229e-06
1060.9999999255868261.48826348700765e-077.44131743503823e-08
1070.8960532219973750.2078935560052510.103946778002625
1080.9283873509597070.1432252980805870.0716126490402933
1090.9826673034523510.03466539309529840.0173326965476492
1100.9853396068008270.02932078639834530.0146603931991726
1110.9998412742285870.0003174515428249320.000158725771412466
1120.9972292254535540.005541549092891710.00277077454644585
1130.9708550445808970.05828991083820640.0291449554191032
1140.9290963849722730.1418072300554540.0709036150277272
1150.9980934121786320.003813175642736560.00190658782136828






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level680.747252747252747NOK
5% type I error level750.824175824175824NOK
10% type I error level780.857142857142857NOK

\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 & 68 & 0.747252747252747 & NOK \tabularnewline
5% type I error level & 75 & 0.824175824175824 & NOK \tabularnewline
10% type I error level & 78 & 0.857142857142857 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185903&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]68[/C][C]0.747252747252747[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]75[/C][C]0.824175824175824[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]78[/C][C]0.857142857142857[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185903&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185903&T=6

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level680.747252747252747NOK
5% type I error level750.824175824175824NOK
10% type I error level780.857142857142857NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}