Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 20 Nov 2010 15:25:08 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/20/t1290267419gzieprzor9f9plk.htm/, Retrieved Fri, 29 Mar 2024 05:11:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98234, Retrieved Fri, 29 Mar 2024 05:11:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact233
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Multiple linear r...] [2010-11-20 15:25:08] [9ea95e194e0eb2a674315798620d5bc6] [Current]
- R         [Multiple Regression] [WS 7 - Blog 4] [2011-11-21 19:50:21] [69d59b79aaf660457acc70a0ef0bfdab]
- RM        [Multiple Regression] [] [2011-11-22 11:08:23] [74be16979710d4c4e7c6647856088456]
Feedback Forum

Post a new message
Dataseries X:
1	132	132	15	10	10	77	77	5	5	4	4	15	15	11	11	12	12	13	13	6	6
0	24	0	12	20	0	63	0	6	0	4	0	9	0	12	0	7	0	11	0	4	0
0	135	0	15	16	0	73	0	4	0	10	0	12	0	12	0	13	0	14	0	6	0
0	95	0	12	10	0	76	0	6	0	6	0	15	0	11	0	11	0	12	0	5	0
0	122	0	14	8	0	90	0	3	0	5	0	17	0	11	0	16	0	12	0	5	0
0	144	0	8	14	0	67	0	10	0	8	0	14	0	10	0	10	0	6	0	4	0
1	23	23	11	19	19	69	69	8	8	9	9	9	9	11	11	15	15	10	10	5	5
1	42	42	15	15	15	70	70	3	3	6	6	12	12	9	9	5	5	11	11	3	3
0	105	0	4	23	0	54	0	4	0	8	0	11	0	10	0	4	0	10	0	2	0
0	68	0	13	9	0	54	0	3	0	11	0	13	0	12	0	7	0	12	0	5	0
1	139	139	19	12	12	76	76	5	5	6	6	16	16	12	12	15	15	15	15	6	6
1	51	51	10	14	14	75	75	5	5	8	8	16	16	12	12	5	5	13	13	6	6
1	7	7	15	13	13	76	76	6	6	11	11	15	15	13	13	16	16	18	18	8	8
0	52	0	6	11	0	80	0	5	0	5	0	10	0	9	0	15	0	11	0	6	0
1	138	138	7	11	11	89	89	3	3	10	10	16	16	12	12	13	13	12	12	3	3
0	41	0	14	10	0	73	0	4	0	7	0	12	0	12	0	13	0	13	0	6	0
0	125	0	16	12	0	74	0	8	0	7	0	15	0	12	0	15	0	14	0	6	0
1	152	152	16	18	18	78	78	8	8	13	13	13	13	12	12	15	15	16	16	7	7
1	106	106	14	12	12	76	76	8	8	10	10	18	18	13	13	10	10	16	16	8	8
0	26	0	15	10	0	69	0	5	0	8	0	13	0	11	0	17	0	16	0	6	0
1	33	33	14	15	15	74	74	8	8	6	6	17	17	12	12	14	14	15	15	7	7
1	136	136	12	15	15	82	82	2	2	8	8	14	14	12	12	9	9	13	13	4	4
0	48	0	9	12	0	77	0	0	0	7	0	13	0	15	0	6	0	8	0	4	0
1	156	156	12	9	9	84	84	5	5	5	5	13	13	11	11	11	11	14	14	2	2
1	114	114	14	11	11	75	75	2	2	9	9	15	15	12	12	13	13	15	15	6	6
1	75	75	12	15	15	54	54	7	7	9	9	13	13	10	10	12	12	13	13	6	6
1	91	91	14	16	16	79	79	5	5	11	11	15	15	11	11	10	10	16	16	6	6
1	146	146	10	17	17	79	79	2	2	11	11	13	13	13	13	4	4	13	13	6	6
1	82	82	14	12	12	69	69	12	12	11	11	14	14	6	6	13	13	12	12	6	6
1	102	102	16	11	11	88	88	7	7	9	9	13	13	12	12	15	15	15	15	7	7
1	96	96	10	13	13	57	57	0	0	7	7	16	16	12	12	8	8	11	11	4	4
1	109	109	8	9	9	69	69	2	2	6	6	14	14	10	10	10	10	14	14	3	3
1	2	2	12	11	11	86	86	3	3	6	6	18	18	12	12	8	8	13	13	5	5
1	113	113	11	9	9	65	65	0	0	6	6	15	15	12	12	7	7	13	13	6	6
0	123	0	8	20	0	66	0	9	0	5	0	9	0	11	0	9	0	12	0	4	0
0	29	0	13	8	0	54	0	2	0	4	0	16	0	9	0	14	0	14	0	6	0
1	104	104	11	12	12	85	85	3	3	10	10	16	16	10	10	5	5	13	13	3	3
0	6	0	12	10	0	79	0	1	0	8	0	17	0	12	0	7	0	12	0	3	0
0	62	0	16	11	0	84	0	10	0	6	0	13	0	12	0	16	0	14	0	6	0
1	64	64	16	13	13	70	70	1	1	5	5	17	17	11	11	14	14	15	15	6	6
1	50	50	13	13	13	54	54	4	4	9	9	15	15	12	12	16	16	16	16	6	6
1	108	108	14	13	13	70	70	6	6	10	10	14	14	11	11	15	15	15	15	8	8
0	70	0	5	15	0	54	0	6	0	6	0	10	0	14	0	4	0	5	0	2	0
0	154	0	14	12	0	69	0	4	0	9	0	13	0	10	0	12	0	15	0	6	0
1	31	31	13	13	13	68	68	4	4	10	10	11	11	10	10	8	8	8	8	4	4
1	101	101	16	13	13	68	68	7	7	6	6	11	11	11	11	17	17	16	16	7	7
0	149	0	14	9	0	71	0	7	0	6	0	16	0	11	0	15	0	16	0	6	0
0	149	0	15	9	0	71	0	7	0	6	0	16	0	11	0	16	0	14	0	6	0
1	3	3	15	14	14	66	66	0	0	13	13	11	11	10	10	12	12	16	16	6	6
1	111	111	11	9	9	67	67	3	3	8	8	15	15	10	10	12	12	14	14	5	5
1	69	69	15	9	9	71	71	8	8	10	10	15	15	12	12	13	13	13	13	6	6
1	116	116	16	15	15	54	54	8	8	5	5	12	12	11	11	14	14	14	14	6	6
1	28	28	13	10	10	76	76	10	10	8	8	17	17	8	8	14	14	14	14	5	5
0	67	0	11	13	0	77	0	11	0	6	0	15	0	12	0	15	0	12	0	6	0
0	32	0	12	8	0	71	0	6	0	9	0	16	0	10	0	14	0	13	0	7	0
1	88	88	12	15	15	69	69	2	2	9	9	14	14	7	7	11	11	15	15	5	5
1	92	92	10	13	13	73	73	6	6	7	7	17	17	11	11	13	13	15	15	6	6
1	97	97	8	24	24	46	46	1	1	20	20	10	10	7	7	4	4	13	13	6	6
0	87	0	9	11	0	66	0	5	0	8	0	11	0	11	0	8	0	10	0	4	0
1	78	78	12	13	13	77	77	4	4	8	8	15	15	8	8	13	13	13	13	5	5
0	137	0	14	12	0	77	0	6	0	7	0	15	0	11	0	15	0	14	0	6	0
1	76	76	12	22	22	70	70	6	6	7	7	7	7	12	12	15	15	13	13	6	6
0	34	0	11	11	0	86	0	4	0	10	0	17	0	8	0	8	0	13	0	4	0
0	103	0	14	15	0	38	0	1	0	5	0	14	0	14	0	17	0	18	0	6	0
0	14	0	7	7	0	66	0	6	0	8	0	18	0	14	0	12	0	12	0	4	0
0	46	0	16	14	0	75	0	7	0	9	0	14	0	11	0	13	0	14	0	7	0
1	127	127	16	19	19	80	80	7	7	9	9	12	12	12	12	14	14	16	16	8	8
0	15	0	11	10	0	64	0	2	0	20	0	14	0	14	0	7	0	13	0	6	0
1	58	58	16	9	9	80	80	7	7	6	6	9	9	9	9	16	16	16	16	6	6
1	134	134	13	12	12	86	86	8	8	10	10	14	14	13	13	11	11	15	15	6	6
1	129	129	11	16	16	54	54	5	5	11	11	11	11	8	8	10	10	14	14	5	5
1	39	39	13	13	13	74	74	4	4	7	7	16	16	11	11	14	14	13	13	6	6
1	63	63	14	11	11	88	88	2	2	12	12	17	17	9	9	19	19	12	12	6	6
1	143	143	15	12	12	85	85	0	0	12	12	16	16	12	12	14	14	16	16	4	4
0	38	0	10	11	0	63	0	7	0	8	0	12	0	7	0	8	0	9	0	5	0
1	60	60	15	13	13	81	81	0	0	6	6	15	15	11	11	15	15	15	15	8	8
0	118	0	11	13	0	81	0	5	0	6	0	15	0	12	0	8	0	16	0	6	0
1	45	45	11	10	10	74	74	3	3	9	9	15	15	11	11	8	8	12	12	6	6
1	80	80	6	11	11	80	80	3	3	5	5	16	16	12	12	6	6	11	11	2	2
1	21	21	11	9	9	80	80	3	3	11	11	16	16	9	9	7	7	13	13	2	2
0	141	0	12	13	0	60	0	3	0	6	0	11	0	11	0	16	0	13	0	4	0
0	124	0	13	15	0	65	0	7	0	6	0	15	0	13	0	15	0	14	0	6	0
1	37	37	12	14	14	62	62	6	6	10	10	12	12	12	12	10	10	15	15	6	6
0	147	0	8	14	0	63	0	3	0	8	0	14	0	12	0	8	0	14	0	5	0
1	153	153	9	11	11	89	89	0	0	7	7	15	15	11	11	9	9	12	12	4	4
1	133	133	10	10	10	76	76	2	2	8	8	17	17	12	12	8	8	16	16	4	4
1	117	117	16	11	11	81	81	0	0	9	9	19	19	12	12	14	14	14	14	6	6
1	71	71	15	12	12	72	72	9	9	8	8	15	15	11	11	14	14	13	13	5	5
0	155	0	14	14	0	84	0	10	0	10	0	16	0	11	0	14	0	12	0	6	0
1	112	112	12	14	14	76	76	3	3	13	13	14	14	8	8	15	15	13	13	7	7
1	4	4	12	21	21	76	76	7	7	7	7	16	16	9	9	7	7	12	12	6	6
1	19	19	10	14	14	78	78	3	3	7	7	15	15	11	11	7	7	9	9	4	4
1	121	121	12	13	13	72	72	6	6	7	7	15	15	12	12	12	12	13	13	4	4
0	98	0	8	11	0	81	0	5	0	8	0	17	0	13	0	7	0	10	0	3	0
1	150	150	16	12	12	72	72	0	0	9	9	12	12	12	12	12	12	15	15	8	8
1	10	10	11	12	12	78	78	0	0	9	9	18	18	6	6	6	6	9	9	4	4
1	145	145	12	11	11	79	79	4	4	8	8	13	13	12	12	10	10	13	13	4	4
1	49	49	9	14	14	52	52	0	0	7	7	14	14	11	11	12	12	13	13	5	5
0	55	0	14	13	0	67	0	0	0	6	0	14	0	13	0	13	0	13	0	5	0
0	22	0	15	13	0	74	0	7	0	8	0	14	0	11	0	14	0	15	0	7	0
0	54	0	8	12	0	73	0	3	0	8	0	12	0	12	0	8	0	13	0	4	0
1	140	140	12	14	14	69	69	9	9	4	4	14	14	10	10	14	14	14	14	5	5
0	5	0	10	12	0	67	0	4	0	8	0	12	0	10	0	10	0	11	0	5	0
1	89	89	16	12	12	76	76	4	4	10	10	15	15	11	11	14	14	15	15	8	8
1	47	47	17	12	12	77	77	15	15	7	7	11	11	11	11	15	15	14	14	5	5
0	59	0	8	18	0	63	0	7	0	8	0	11	0	11	0	10	0	15	0	2	0
1	65	65	9	11	11	84	84	8	8	7	7	15	15	9	9	6	6	12	12	5	5
1	110	110	8	15	15	90	90	2	2	10	10	14	14	7	7	9	9	15	15	4	4
0	73	0	11	13	0	75	0	8	0	9	0	15	0	11	0	11	0	14	0	5	0
1	93	93	16	11	11	76	76	7	7	8	8	16	16	12	12	16	16	16	16	7	7
0	142	0	13	11	0	75	0	3	0	8	0	12	0	12	0	14	0	14	0	6	0
1	43	43	5	22	22	53	53	3	3	5	5	14	14	15	15	8	8	12	12	3	3
1	13	13	15	10	10	87	87	6	6	8	8	18	18	11	11	16	16	11	11	5	5
1	94	94	15	11	11	78	78	8	8	9	9	14	14	10	10	16	16	13	13	6	6
1	77	77	12	15	15	54	54	5	5	11	11	13	13	13	13	14	14	12	12	5	5
0	86	0	12	14	0	58	0	6	0	7	0	14	0	13	0	12	0	12	0	6	0
1	40	40	16	11	11	80	80	10	10	8	8	14	14	11	11	16	16	16	16	7	7
1	128	128	12	10	10	74	74	0	0	4	4	17	17	12	12	15	15	13	13	6	6
1	17	17	10	14	14	56	56	5	5	16	16	12	12	12	12	11	11	12	12	6	6
1	126	126	12	14	14	82	82	0	0	9	9	16	16	12	12	6	6	14	14	5	5
1	130	130	4	11	11	64	64	0	0	16	16	15	15	8	8	6	6	4	4	4	4
0	11	0	11	15	0	67	0	5	0	12	0	10	0	5	0	16	0	14	0	6	0
0	36	0	16	11	0	75	0	10	0	8	0	13	0	11	0	16	0	15	0	6	0
0	61	0	7	10	0	69	0	0	0	4	0	15	0	12	0	8	0	12	0	3	0
1	35	35	9	10	10	72	72	5	5	11	11	16	16	12	12	11	11	11	11	4	4
0	120	0	14	16	0	71	0	6	0	11	0	15	0	11	0	12	0	12	0	4	0
1	16	16	11	12	12	54	54	1	1	8	8	14	14	12	12	13	13	11	11	4	4
1	84	84	10	14	14	68	68	5	5	8	8	11	11	10	10	11	11	12	12	5	5
0	20	0	6	15	0	54	0	3	0	12	0	13	0	7	0	9	0	11	0	4	0
1	25	25	14	10	10	71	71	3	3	8	8	17	17	12	12	15	15	13	13	6	6
1	12	12	11	12	12	53	53	6	6	6	6	14	14	12	12	11	11	12	12	6	6
1	57	57	11	15	15	54	54	2	2	8	8	16	16	9	9	12	12	12	12	4	4
0	27	0	9	12	0	71	0	5	0	6	0	15	0	11	0	15	0	15	0	7	0
1	30	30	16	11	11	69	69	6	6	14	14	12	12	12	12	8	8	14	14	4	4
0	81	0	7	10	0	30	0	2	0	10	0	16	0	12	0	7	0	12	0	4	0
0	44	0	8	20	0	53	0	3	0	5	0	8	0	11	0	10	0	12	0	4	0
0	90	0	10	19	0	68	0	7	0	8	0	9	0	11	0	9	0	12	0	4	0
1	66	66	14	17	17	69	69	6	6	12	12	13	13	12	12	13	13	13	13	5	5
1	8	8	9	8	8	54	54	3	3	11	11	19	19	12	12	11	11	11	11	4	4
1	151	151	13	17	17	66	66	6	6	8	8	11	11	11	11	12	12	13	13	7	7
0	85	0	13	11	0	79	0	9	0	8	0	15	0	12	0	5	0	12	0	3	0
0	53	0	12	13	0	67	0	2	0	9	0	11	0	12	0	12	0	14	0	5	0
0	99	0	11	9	0	74	0	5	0	6	0	15	0	8	0	14	0	15	0	5	0
0	148	0	10	10	0	86	0	10	0	5	0	16	0	15	0	15	0	15	0	6	0
1	56	56	12	13	13	63	63	9	9	8	8	15	15	11	11	14	14	13	13	5	5
1	83	83	14	16	16	69	69	8	8	7	7	12	12	11	11	13	13	16	16	6	6
0	74	0	11	12	0	73	0	8	0	4	0	16	0	6	0	14	0	17	0	6	0
0	1	0	13	14	0	69	0	5	0	9	0	15	0	13	0	14	0	13	0	3	0
0	119	0	14	11	0	71	0	9	0	5	0	13	0	12	0	15	0	14	0	6	0
1	72	72	13	13	13	77	77	9	9	9	9	14	14	12	12	13	13	13	13	5	5
1	131	131	16	15	15	74	74	14	14	12	12	11	11	12	12	14	14	16	16	8	8
1	100	100	13	14	14	82	82	5	5	6	6	15	15	12	12	11	11	13	13	6	6
1	9	9	12	14	14	54	54	12	12	4	4	16	16	12	12	14	14	14	14	4	4
1	107	107	9	14	14	54	54	6	6	6	6	14	14	10	10	11	11	13	13	3	3
1	79	79	14	10	10	80	80	6	6	7	7	13	13	12	12	8	8	14	14	4	4
0	115	0	15	8	0	76	0	8	0	9	0	15	0	12	0	12	0	16	0	7	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 12 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98234&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98234&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98234&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -5.63418426484257 + 9.18492464973122Gender[t] + 0.00230324704365767trend[t] -0.00927199770983318trend_G[t] -0.0215672275846862Depression[t] -0.121606500243508Depression_G[t] + 0.0485298120141413Belonging[t] + 0.0050992101862968Belonging_G[t] + 0.0331300242551255WeightedPopularity[t] + 0.0446957854911477WeightedPopularity_G[t] + 0.17194203693447ParentalCriticism[t] -0.217763937453644ParentalCriticism_G[t] -0.00789804780864542Happiness[t] -0.189089829189876Happiness_G[t] + 0.259336367937663FindingFriends[t] -0.26414704323834FindingFriends_G[t] + 0.264170480731017KnowingPeople[t] -0.0601889711386116KnowingPeople_G[t] + 0.290831322282456Liked[t] + 0.0569803763954684Liked_G[t] + 0.527764692648575Celebrity[t] + 0.102234815794935Celebrity_G[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  -5.63418426484257 +  9.18492464973122Gender[t] +  0.00230324704365767trend[t] -0.00927199770983318trend_G[t] -0.0215672275846862Depression[t] -0.121606500243508Depression_G[t] +  0.0485298120141413Belonging[t] +  0.0050992101862968Belonging_G[t] +  0.0331300242551255WeightedPopularity[t] +  0.0446957854911477WeightedPopularity_G[t] +  0.17194203693447ParentalCriticism[t] -0.217763937453644ParentalCriticism_G[t] -0.00789804780864542Happiness[t] -0.189089829189876Happiness_G[t] +  0.259336367937663FindingFriends[t] -0.26414704323834FindingFriends_G[t] +  0.264170480731017KnowingPeople[t] -0.0601889711386116KnowingPeople_G[t] +  0.290831322282456Liked[t] +  0.0569803763954684Liked_G[t] +  0.527764692648575Celebrity[t] +  0.102234815794935Celebrity_G[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98234&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  -5.63418426484257 +  9.18492464973122Gender[t] +  0.00230324704365767trend[t] -0.00927199770983318trend_G[t] -0.0215672275846862Depression[t] -0.121606500243508Depression_G[t] +  0.0485298120141413Belonging[t] +  0.0050992101862968Belonging_G[t] +  0.0331300242551255WeightedPopularity[t] +  0.0446957854911477WeightedPopularity_G[t] +  0.17194203693447ParentalCriticism[t] -0.217763937453644ParentalCriticism_G[t] -0.00789804780864542Happiness[t] -0.189089829189876Happiness_G[t] +  0.259336367937663FindingFriends[t] -0.26414704323834FindingFriends_G[t] +  0.264170480731017KnowingPeople[t] -0.0601889711386116KnowingPeople_G[t] +  0.290831322282456Liked[t] +  0.0569803763954684Liked_G[t] +  0.527764692648575Celebrity[t] +  0.102234815794935Celebrity_G[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98234&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98234&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
Popularity[t] = -5.63418426484257 + 9.18492464973122Gender[t] + 0.00230324704365767trend[t] -0.00927199770983318trend_G[t] -0.0215672275846862Depression[t] -0.121606500243508Depression_G[t] + 0.0485298120141413Belonging[t] + 0.0050992101862968Belonging_G[t] + 0.0331300242551255WeightedPopularity[t] + 0.0446957854911477WeightedPopularity_G[t] + 0.17194203693447ParentalCriticism[t] -0.217763937453644ParentalCriticism_G[t] -0.00789804780864542Happiness[t] -0.189089829189876Happiness_G[t] + 0.259336367937663FindingFriends[t] -0.26414704323834FindingFriends_G[t] + 0.264170480731017KnowingPeople[t] -0.0601889711386116KnowingPeople_G[t] + 0.290831322282456Liked[t] + 0.0569803763954684Liked_G[t] + 0.527764692648575Celebrity[t] + 0.102234815794935Celebrity_G[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-5.634184264842574.276467-1.31750.1899240.094962
Gender9.184924649731225.4869191.6740.096470.048235
trend0.002303247043657670.0062020.37130.7109690.355485
trend_G-0.009271997709833180.008071-1.14880.2526730.126337
Depression-0.02156722758468620.118609-0.18180.8559870.427994
Depression_G-0.1216065002435080.145592-0.83530.4050610.20253
Belonging0.04852981201414130.0283171.71380.088880.04444
Belonging_G0.00509921018629680.0363790.14020.8887380.444369
WeightedPopularity0.03313002425512550.114090.29040.7719710.385986
WeightedPopularity_G0.04469578549114770.1355520.32970.7421180.371059
ParentalCriticism0.171942036934470.1133191.51730.1315410.065771
ParentalCriticism_G-0.2177639374536440.140323-1.55190.123050.061525
Happiness-0.007898047808645420.158552-0.04980.9603450.480173
Happiness_G-0.1890898291898760.195098-0.96920.3341890.167095
FindingFriends0.2593363679376630.1409091.84040.0679140.033957
FindingFriends_G-0.264147043238340.194901-1.35530.1776060.088803
KnowingPeople0.2641704807310170.1168162.26140.0253440.012672
KnowingPeople_G-0.06018897113861160.141009-0.42680.6701770.335089
Liked0.2908313222824560.1548271.87840.0624950.031247
Liked_G0.05698037639546840.2018860.28220.7781940.389097
Celebrity0.5277646926485750.315831.6710.0970470.048524
Celebrity_G0.1022348157949350.3692130.27690.7822840.391142

\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) & -5.63418426484257 & 4.276467 & -1.3175 & 0.189924 & 0.094962 \tabularnewline
Gender & 9.18492464973122 & 5.486919 & 1.674 & 0.09647 & 0.048235 \tabularnewline
trend & 0.00230324704365767 & 0.006202 & 0.3713 & 0.710969 & 0.355485 \tabularnewline
trend_G & -0.00927199770983318 & 0.008071 & -1.1488 & 0.252673 & 0.126337 \tabularnewline
Depression & -0.0215672275846862 & 0.118609 & -0.1818 & 0.855987 & 0.427994 \tabularnewline
Depression_G & -0.121606500243508 & 0.145592 & -0.8353 & 0.405061 & 0.20253 \tabularnewline
Belonging & 0.0485298120141413 & 0.028317 & 1.7138 & 0.08888 & 0.04444 \tabularnewline
Belonging_G & 0.0050992101862968 & 0.036379 & 0.1402 & 0.888738 & 0.444369 \tabularnewline
WeightedPopularity & 0.0331300242551255 & 0.11409 & 0.2904 & 0.771971 & 0.385986 \tabularnewline
WeightedPopularity_G & 0.0446957854911477 & 0.135552 & 0.3297 & 0.742118 & 0.371059 \tabularnewline
ParentalCriticism & 0.17194203693447 & 0.113319 & 1.5173 & 0.131541 & 0.065771 \tabularnewline
ParentalCriticism_G & -0.217763937453644 & 0.140323 & -1.5519 & 0.12305 & 0.061525 \tabularnewline
Happiness & -0.00789804780864542 & 0.158552 & -0.0498 & 0.960345 & 0.480173 \tabularnewline
Happiness_G & -0.189089829189876 & 0.195098 & -0.9692 & 0.334189 & 0.167095 \tabularnewline
FindingFriends & 0.259336367937663 & 0.140909 & 1.8404 & 0.067914 & 0.033957 \tabularnewline
FindingFriends_G & -0.26414704323834 & 0.194901 & -1.3553 & 0.177606 & 0.088803 \tabularnewline
KnowingPeople & 0.264170480731017 & 0.116816 & 2.2614 & 0.025344 & 0.012672 \tabularnewline
KnowingPeople_G & -0.0601889711386116 & 0.141009 & -0.4268 & 0.670177 & 0.335089 \tabularnewline
Liked & 0.290831322282456 & 0.154827 & 1.8784 & 0.062495 & 0.031247 \tabularnewline
Liked_G & 0.0569803763954684 & 0.201886 & 0.2822 & 0.778194 & 0.389097 \tabularnewline
Celebrity & 0.527764692648575 & 0.31583 & 1.671 & 0.097047 & 0.048524 \tabularnewline
Celebrity_G & 0.102234815794935 & 0.369213 & 0.2769 & 0.782284 & 0.391142 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98234&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]-5.63418426484257[/C][C]4.276467[/C][C]-1.3175[/C][C]0.189924[/C][C]0.094962[/C][/ROW]
[ROW][C]Gender[/C][C]9.18492464973122[/C][C]5.486919[/C][C]1.674[/C][C]0.09647[/C][C]0.048235[/C][/ROW]
[ROW][C]trend[/C][C]0.00230324704365767[/C][C]0.006202[/C][C]0.3713[/C][C]0.710969[/C][C]0.355485[/C][/ROW]
[ROW][C]trend_G[/C][C]-0.00927199770983318[/C][C]0.008071[/C][C]-1.1488[/C][C]0.252673[/C][C]0.126337[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0215672275846862[/C][C]0.118609[/C][C]-0.1818[/C][C]0.855987[/C][C]0.427994[/C][/ROW]
[ROW][C]Depression_G[/C][C]-0.121606500243508[/C][C]0.145592[/C][C]-0.8353[/C][C]0.405061[/C][C]0.20253[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0485298120141413[/C][C]0.028317[/C][C]1.7138[/C][C]0.08888[/C][C]0.04444[/C][/ROW]
[ROW][C]Belonging_G[/C][C]0.0050992101862968[/C][C]0.036379[/C][C]0.1402[/C][C]0.888738[/C][C]0.444369[/C][/ROW]
[ROW][C]WeightedPopularity[/C][C]0.0331300242551255[/C][C]0.11409[/C][C]0.2904[/C][C]0.771971[/C][C]0.385986[/C][/ROW]
[ROW][C]WeightedPopularity_G[/C][C]0.0446957854911477[/C][C]0.135552[/C][C]0.3297[/C][C]0.742118[/C][C]0.371059[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]0.17194203693447[/C][C]0.113319[/C][C]1.5173[/C][C]0.131541[/C][C]0.065771[/C][/ROW]
[ROW][C]ParentalCriticism_G[/C][C]-0.217763937453644[/C][C]0.140323[/C][C]-1.5519[/C][C]0.12305[/C][C]0.061525[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.00789804780864542[/C][C]0.158552[/C][C]-0.0498[/C][C]0.960345[/C][C]0.480173[/C][/ROW]
[ROW][C]Happiness_G[/C][C]-0.189089829189876[/C][C]0.195098[/C][C]-0.9692[/C][C]0.334189[/C][C]0.167095[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.259336367937663[/C][C]0.140909[/C][C]1.8404[/C][C]0.067914[/C][C]0.033957[/C][/ROW]
[ROW][C]FindingFriends_G[/C][C]-0.26414704323834[/C][C]0.194901[/C][C]-1.3553[/C][C]0.177606[/C][C]0.088803[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.264170480731017[/C][C]0.116816[/C][C]2.2614[/C][C]0.025344[/C][C]0.012672[/C][/ROW]
[ROW][C]KnowingPeople_G[/C][C]-0.0601889711386116[/C][C]0.141009[/C][C]-0.4268[/C][C]0.670177[/C][C]0.335089[/C][/ROW]
[ROW][C]Liked[/C][C]0.290831322282456[/C][C]0.154827[/C][C]1.8784[/C][C]0.062495[/C][C]0.031247[/C][/ROW]
[ROW][C]Liked_G[/C][C]0.0569803763954684[/C][C]0.201886[/C][C]0.2822[/C][C]0.778194[/C][C]0.389097[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.527764692648575[/C][C]0.31583[/C][C]1.671[/C][C]0.097047[/C][C]0.048524[/C][/ROW]
[ROW][C]Celebrity_G[/C][C]0.102234815794935[/C][C]0.369213[/C][C]0.2769[/C][C]0.782284[/C][C]0.391142[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98234&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98234&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-5.634184264842574.276467-1.31750.1899240.094962
Gender9.184924649731225.4869191.6740.096470.048235
trend0.002303247043657670.0062020.37130.7109690.355485
trend_G-0.009271997709833180.008071-1.14880.2526730.126337
Depression-0.02156722758468620.118609-0.18180.8559870.427994
Depression_G-0.1216065002435080.145592-0.83530.4050610.20253
Belonging0.04852981201414130.0283171.71380.088880.04444
Belonging_G0.00509921018629680.0363790.14020.8887380.444369
WeightedPopularity0.03313002425512550.114090.29040.7719710.385986
WeightedPopularity_G0.04469578549114770.1355520.32970.7421180.371059
ParentalCriticism0.171942036934470.1133191.51730.1315410.065771
ParentalCriticism_G-0.2177639374536440.140323-1.55190.123050.061525
Happiness-0.007898047808645420.158552-0.04980.9603450.480173
Happiness_G-0.1890898291898760.195098-0.96920.3341890.167095
FindingFriends0.2593363679376630.1409091.84040.0679140.033957
FindingFriends_G-0.264147043238340.194901-1.35530.1776060.088803
KnowingPeople0.2641704807310170.1168162.26140.0253440.012672
KnowingPeople_G-0.06018897113861160.141009-0.42680.6701770.335089
Liked0.2908313222824560.1548271.87840.0624950.031247
Liked_G0.05698037639546840.2018860.28220.7781940.389097
Celebrity0.5277646926485750.315831.6710.0970470.048524
Celebrity_G0.1022348157949350.3692130.27690.7822840.391142







Multiple Linear Regression - Regression Statistics
Multiple R0.766689208812685
R-squared0.587812342909821
Adjusted R-squared0.523215769783749
F-TEST (value)9.09974499363288
F-TEST (DF numerator)21
F-TEST (DF denominator)134
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.02772835975805
Sum Squared Residuals550.965428329585

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.766689208812685 \tabularnewline
R-squared & 0.587812342909821 \tabularnewline
Adjusted R-squared & 0.523215769783749 \tabularnewline
F-TEST (value) & 9.09974499363288 \tabularnewline
F-TEST (DF numerator) & 21 \tabularnewline
F-TEST (DF denominator) & 134 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.02772835975805 \tabularnewline
Sum Squared Residuals & 550.965428329585 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98234&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.766689208812685[/C][/ROW]
[ROW][C]R-squared[/C][C]0.587812342909821[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.523215769783749[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.09974499363288[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]21[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]134[/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]2.02772835975805[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]550.965428329585[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98234&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98234&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 R0.766689208812685
R-squared0.587812342909821
Adjusted R-squared0.523215769783749
F-TEST (value)9.09974499363288
F-TEST (DF numerator)21
F-TEST (DF denominator)134
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.02772835975805
Sum Squared Residuals550.965428329585







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11513.27599584005761.72400415994239
2128.13402622846363.8659737715364
31513.4159979469911.58400205300899
41211.05655395752850.943446042471505
51412.87501764941251.12498235058745
688.3344185878878-0.334418587887804
71112.4427990475475-1.44279904754751
8159.151708676702185.84829132329782
945.7672863660809-1.7672863660809
10139.927048303161183.07295169683882
111913.90136368033315.09863631966693
121011.3475549667815-1.34755496678145
131517.2263741718015-2.2263741718015
14611.7394270903916-5.73942709039158
15711.0683475853148-4.06834758531479
161412.52223865730941.47776134269056
171613.64910500316052.3508949968395
181614.54048456282611.45951543717389
191414.3706390177842-0.370639017784193
201514.16058423881270.839415761187271
211414.5657795741536-0.565779574153627
221210.70386593397221.29613406602777
2398.968057776659530.0319422233404683
241211.599308716090.400691283909992
251413.58320887963260.416791120367408
261212.049207024473-0.0492070244729711
271413.12464906801730.875350931982731
281010.4817468741181-0.48174687411809
291413.21029216911260.789707830887418
301616.1850785065891-0.18507850658912
31108.524827481346021.47517251865398
32811.076946131789-3.07694613178895
331212.2324256249959-0.232425624995913
341111.4025364911055-0.402536491105469
3589.3388051473348-1.33880514733481
361310.77898206833852.22101793166153
37119.673176263874851.32682373612515
38129.306717323377852.69328267662215
391614.18515037506031.81484962493974
401613.29743206950922.70256793049075
411313.7320598473499-0.732059847349922
421415.205770743808-1.20577074380801
4355.1726734230187-0.172673423018699
441412.68005732403571.31994267596432
45139.692678695843.30732130416
461616.1251462063081-0.125146206308121
471413.73285134654890.267148653451069
481513.4153591827151.58464081728496
491514.04702164640130.952978353598736
501112.412906753837-1.41290675383696
511513.69414358706771.30585641293226
521612.97255357854333.02744642145668
531313.8991898151183-0.899189815118311
541112.9853242743521-1.98532427435209
551213.0993986337065-1.09939863370649
561212.0530060783113-0.0530060783113355
571013.3566978555583-3.35669785555826
5888.18084659772967-0.180846597729668
5998.9716700948950.0283299051050059
601212.5500878077745-0.550087807774467
611413.49673698727890.503263012721092
621213.6961549656755-1.69615496567551
631110.17804486766030.821955132339715
641413.42910144355790.570898556442103
65711.2839993319433-4.2839993319433
661613.53128330422772.46871669577234
671615.40725531423320.592744685766772
681113.1124261135569-2.11242611355686
691617.2106617492342-1.21066174923418
701314.0959265231572-1.09592652315721
711110.99587161578840.00412838421159339
721313.3293650328085-0.329365032808534
731414.2992369840814-0.299236984081363
741512.57592059782442.42407940217559
75107.971171453030422.02882854696958
761515.6495348865898-0.64953488658979
771112.4122609001172-1.41226090011715
781112.1728912222776-1.17289122227761
7968.81330165247662-2.81330165247662
801110.14991092717260.850089072827393
811211.33744579831460.662554201685404
821313.1995960719836-0.199596071983586
831213.2376046495221-1.2376046495221
84810.7600459564978-2.76004595649784
85910.8836766200139-1.88367662001395
861011.3673566473549-1.3673566473549
871612.79664344426453.20335655573546
881513.05257621168541.94742378831456
891413.62938469771370.370615302286296
901213.6743621358241-1.67436213582408
911211.00255604191540.997443958084581
92109.58012809561230.4198719043877
931211.33053621889980.669463781100173
9489.40430226823121-1.40430226823121
951614.51960258001841.4803974199816
96118.833181312902512.16681868709748
971211.60957602877160.390423971228418
98910.9813752972926-1.98137529729258
991411.60991700679862.39008299320144
1001513.83210255682941.16789744317058
101810.2714766374347-2.27147663743473
1021212.8573957465154-0.857395746515445
103109.856338958136150.143661041863849
1041615.24650386141270.753496138587303
1051715.34049281393711.65950718606293
10689.60384738462476-1.60384738462476
107911.879064375873-2.87906437587303
108811.9421186017172-3.94211860171718
1091112.0563998316784-1.0563998316784
1101615.61090047397340.389099526026618
1111313.5241728208553-0.524172820855277
11257.81356877600441-2.81356877600441
1131513.43544510058121.56455489941884
1141514.47335617705630.526643822943704
1151211.20369388877110.796306111228929
1161211.56646720154030.433532798459679
1171616.827024206619-0.827024206618975
1181212.9670128268691-0.96701282686913
1191011.8629947303403-1.8629947303403
1201210.68714457279761.31285542720237
12145.91082913335389-1.91082913335389
1221112.2307507294481-1.23075072944806
1231614.06387667201161.93612332798838
12478.50716619499291-1.50716619499291
125911.0016637639058-2.00166376390576
1261411.33819868079592.66180131920409
1271110.51050140705310.489498592946897
1281011.9828216254215-1.98282162542155
12968.27209422333465-2.27209422333465
1301413.5740969060460.425903093953982
1311112.1653679336672-1.16536793366721
132119.63737371699211.36262628300789
133913.5657248941332-4.56572489413315
1341611.89224890624654.10775109375348
13578.01417690154054-1.01417690154054
13688.59924945771092-0.599249457710921
137109.830990908800230.169009091199769
1381411.97908279706252.02091720293752
13999.76422983745289-0.76422983745289
1401312.86394346250530.136056537494722
141139.219601940438383.78039805956162
1421211.97841528906460.0215847109353771
1431111.8441408302318-0.844140830231831
1441015.1108902374318-5.11089023743181
1451212.5312725440459-0.531272544045936
1461414.2637820424306-0.263782042430586
1471112.0116907117981-1.01169071179814
1481311.44325334258891.5567466574111
1491413.21630535844430.783694641555701
1501313.1129526357795-0.112952635779527
1511616.2346039333436-0.234603933343586
1521312.89401007513320.105989924866785
1531212.1657475669748-0.165747566974778
15499.73805271087334-0.738052710873338
1551412.40763852227261.59236147772737
1561514.47020103598030.529798964019667

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 13.2759958400576 & 1.72400415994239 \tabularnewline
2 & 12 & 8.1340262284636 & 3.8659737715364 \tabularnewline
3 & 15 & 13.415997946991 & 1.58400205300899 \tabularnewline
4 & 12 & 11.0565539575285 & 0.943446042471505 \tabularnewline
5 & 14 & 12.8750176494125 & 1.12498235058745 \tabularnewline
6 & 8 & 8.3344185878878 & -0.334418587887804 \tabularnewline
7 & 11 & 12.4427990475475 & -1.44279904754751 \tabularnewline
8 & 15 & 9.15170867670218 & 5.84829132329782 \tabularnewline
9 & 4 & 5.7672863660809 & -1.7672863660809 \tabularnewline
10 & 13 & 9.92704830316118 & 3.07295169683882 \tabularnewline
11 & 19 & 13.9013636803331 & 5.09863631966693 \tabularnewline
12 & 10 & 11.3475549667815 & -1.34755496678145 \tabularnewline
13 & 15 & 17.2263741718015 & -2.2263741718015 \tabularnewline
14 & 6 & 11.7394270903916 & -5.73942709039158 \tabularnewline
15 & 7 & 11.0683475853148 & -4.06834758531479 \tabularnewline
16 & 14 & 12.5222386573094 & 1.47776134269056 \tabularnewline
17 & 16 & 13.6491050031605 & 2.3508949968395 \tabularnewline
18 & 16 & 14.5404845628261 & 1.45951543717389 \tabularnewline
19 & 14 & 14.3706390177842 & -0.370639017784193 \tabularnewline
20 & 15 & 14.1605842388127 & 0.839415761187271 \tabularnewline
21 & 14 & 14.5657795741536 & -0.565779574153627 \tabularnewline
22 & 12 & 10.7038659339722 & 1.29613406602777 \tabularnewline
23 & 9 & 8.96805777665953 & 0.0319422233404683 \tabularnewline
24 & 12 & 11.59930871609 & 0.400691283909992 \tabularnewline
25 & 14 & 13.5832088796326 & 0.416791120367408 \tabularnewline
26 & 12 & 12.049207024473 & -0.0492070244729711 \tabularnewline
27 & 14 & 13.1246490680173 & 0.875350931982731 \tabularnewline
28 & 10 & 10.4817468741181 & -0.48174687411809 \tabularnewline
29 & 14 & 13.2102921691126 & 0.789707830887418 \tabularnewline
30 & 16 & 16.1850785065891 & -0.18507850658912 \tabularnewline
31 & 10 & 8.52482748134602 & 1.47517251865398 \tabularnewline
32 & 8 & 11.076946131789 & -3.07694613178895 \tabularnewline
33 & 12 & 12.2324256249959 & -0.232425624995913 \tabularnewline
34 & 11 & 11.4025364911055 & -0.402536491105469 \tabularnewline
35 & 8 & 9.3388051473348 & -1.33880514733481 \tabularnewline
36 & 13 & 10.7789820683385 & 2.22101793166153 \tabularnewline
37 & 11 & 9.67317626387485 & 1.32682373612515 \tabularnewline
38 & 12 & 9.30671732337785 & 2.69328267662215 \tabularnewline
39 & 16 & 14.1851503750603 & 1.81484962493974 \tabularnewline
40 & 16 & 13.2974320695092 & 2.70256793049075 \tabularnewline
41 & 13 & 13.7320598473499 & -0.732059847349922 \tabularnewline
42 & 14 & 15.205770743808 & -1.20577074380801 \tabularnewline
43 & 5 & 5.1726734230187 & -0.172673423018699 \tabularnewline
44 & 14 & 12.6800573240357 & 1.31994267596432 \tabularnewline
45 & 13 & 9.69267869584 & 3.30732130416 \tabularnewline
46 & 16 & 16.1251462063081 & -0.125146206308121 \tabularnewline
47 & 14 & 13.7328513465489 & 0.267148653451069 \tabularnewline
48 & 15 & 13.415359182715 & 1.58464081728496 \tabularnewline
49 & 15 & 14.0470216464013 & 0.952978353598736 \tabularnewline
50 & 11 & 12.412906753837 & -1.41290675383696 \tabularnewline
51 & 15 & 13.6941435870677 & 1.30585641293226 \tabularnewline
52 & 16 & 12.9725535785433 & 3.02744642145668 \tabularnewline
53 & 13 & 13.8991898151183 & -0.899189815118311 \tabularnewline
54 & 11 & 12.9853242743521 & -1.98532427435209 \tabularnewline
55 & 12 & 13.0993986337065 & -1.09939863370649 \tabularnewline
56 & 12 & 12.0530060783113 & -0.0530060783113355 \tabularnewline
57 & 10 & 13.3566978555583 & -3.35669785555826 \tabularnewline
58 & 8 & 8.18084659772967 & -0.180846597729668 \tabularnewline
59 & 9 & 8.971670094895 & 0.0283299051050059 \tabularnewline
60 & 12 & 12.5500878077745 & -0.550087807774467 \tabularnewline
61 & 14 & 13.4967369872789 & 0.503263012721092 \tabularnewline
62 & 12 & 13.6961549656755 & -1.69615496567551 \tabularnewline
63 & 11 & 10.1780448676603 & 0.821955132339715 \tabularnewline
64 & 14 & 13.4291014435579 & 0.570898556442103 \tabularnewline
65 & 7 & 11.2839993319433 & -4.2839993319433 \tabularnewline
66 & 16 & 13.5312833042277 & 2.46871669577234 \tabularnewline
67 & 16 & 15.4072553142332 & 0.592744685766772 \tabularnewline
68 & 11 & 13.1124261135569 & -2.11242611355686 \tabularnewline
69 & 16 & 17.2106617492342 & -1.21066174923418 \tabularnewline
70 & 13 & 14.0959265231572 & -1.09592652315721 \tabularnewline
71 & 11 & 10.9958716157884 & 0.00412838421159339 \tabularnewline
72 & 13 & 13.3293650328085 & -0.329365032808534 \tabularnewline
73 & 14 & 14.2992369840814 & -0.299236984081363 \tabularnewline
74 & 15 & 12.5759205978244 & 2.42407940217559 \tabularnewline
75 & 10 & 7.97117145303042 & 2.02882854696958 \tabularnewline
76 & 15 & 15.6495348865898 & -0.64953488658979 \tabularnewline
77 & 11 & 12.4122609001172 & -1.41226090011715 \tabularnewline
78 & 11 & 12.1728912222776 & -1.17289122227761 \tabularnewline
79 & 6 & 8.81330165247662 & -2.81330165247662 \tabularnewline
80 & 11 & 10.1499109271726 & 0.850089072827393 \tabularnewline
81 & 12 & 11.3374457983146 & 0.662554201685404 \tabularnewline
82 & 13 & 13.1995960719836 & -0.199596071983586 \tabularnewline
83 & 12 & 13.2376046495221 & -1.2376046495221 \tabularnewline
84 & 8 & 10.7600459564978 & -2.76004595649784 \tabularnewline
85 & 9 & 10.8836766200139 & -1.88367662001395 \tabularnewline
86 & 10 & 11.3673566473549 & -1.3673566473549 \tabularnewline
87 & 16 & 12.7966434442645 & 3.20335655573546 \tabularnewline
88 & 15 & 13.0525762116854 & 1.94742378831456 \tabularnewline
89 & 14 & 13.6293846977137 & 0.370615302286296 \tabularnewline
90 & 12 & 13.6743621358241 & -1.67436213582408 \tabularnewline
91 & 12 & 11.0025560419154 & 0.997443958084581 \tabularnewline
92 & 10 & 9.5801280956123 & 0.4198719043877 \tabularnewline
93 & 12 & 11.3305362188998 & 0.669463781100173 \tabularnewline
94 & 8 & 9.40430226823121 & -1.40430226823121 \tabularnewline
95 & 16 & 14.5196025800184 & 1.4803974199816 \tabularnewline
96 & 11 & 8.83318131290251 & 2.16681868709748 \tabularnewline
97 & 12 & 11.6095760287716 & 0.390423971228418 \tabularnewline
98 & 9 & 10.9813752972926 & -1.98137529729258 \tabularnewline
99 & 14 & 11.6099170067986 & 2.39008299320144 \tabularnewline
100 & 15 & 13.8321025568294 & 1.16789744317058 \tabularnewline
101 & 8 & 10.2714766374347 & -2.27147663743473 \tabularnewline
102 & 12 & 12.8573957465154 & -0.857395746515445 \tabularnewline
103 & 10 & 9.85633895813615 & 0.143661041863849 \tabularnewline
104 & 16 & 15.2465038614127 & 0.753496138587303 \tabularnewline
105 & 17 & 15.3404928139371 & 1.65950718606293 \tabularnewline
106 & 8 & 9.60384738462476 & -1.60384738462476 \tabularnewline
107 & 9 & 11.879064375873 & -2.87906437587303 \tabularnewline
108 & 8 & 11.9421186017172 & -3.94211860171718 \tabularnewline
109 & 11 & 12.0563998316784 & -1.0563998316784 \tabularnewline
110 & 16 & 15.6109004739734 & 0.389099526026618 \tabularnewline
111 & 13 & 13.5241728208553 & -0.524172820855277 \tabularnewline
112 & 5 & 7.81356877600441 & -2.81356877600441 \tabularnewline
113 & 15 & 13.4354451005812 & 1.56455489941884 \tabularnewline
114 & 15 & 14.4733561770563 & 0.526643822943704 \tabularnewline
115 & 12 & 11.2036938887711 & 0.796306111228929 \tabularnewline
116 & 12 & 11.5664672015403 & 0.433532798459679 \tabularnewline
117 & 16 & 16.827024206619 & -0.827024206618975 \tabularnewline
118 & 12 & 12.9670128268691 & -0.96701282686913 \tabularnewline
119 & 10 & 11.8629947303403 & -1.8629947303403 \tabularnewline
120 & 12 & 10.6871445727976 & 1.31285542720237 \tabularnewline
121 & 4 & 5.91082913335389 & -1.91082913335389 \tabularnewline
122 & 11 & 12.2307507294481 & -1.23075072944806 \tabularnewline
123 & 16 & 14.0638766720116 & 1.93612332798838 \tabularnewline
124 & 7 & 8.50716619499291 & -1.50716619499291 \tabularnewline
125 & 9 & 11.0016637639058 & -2.00166376390576 \tabularnewline
126 & 14 & 11.3381986807959 & 2.66180131920409 \tabularnewline
127 & 11 & 10.5105014070531 & 0.489498592946897 \tabularnewline
128 & 10 & 11.9828216254215 & -1.98282162542155 \tabularnewline
129 & 6 & 8.27209422333465 & -2.27209422333465 \tabularnewline
130 & 14 & 13.574096906046 & 0.425903093953982 \tabularnewline
131 & 11 & 12.1653679336672 & -1.16536793366721 \tabularnewline
132 & 11 & 9.6373737169921 & 1.36262628300789 \tabularnewline
133 & 9 & 13.5657248941332 & -4.56572489413315 \tabularnewline
134 & 16 & 11.8922489062465 & 4.10775109375348 \tabularnewline
135 & 7 & 8.01417690154054 & -1.01417690154054 \tabularnewline
136 & 8 & 8.59924945771092 & -0.599249457710921 \tabularnewline
137 & 10 & 9.83099090880023 & 0.169009091199769 \tabularnewline
138 & 14 & 11.9790827970625 & 2.02091720293752 \tabularnewline
139 & 9 & 9.76422983745289 & -0.76422983745289 \tabularnewline
140 & 13 & 12.8639434625053 & 0.136056537494722 \tabularnewline
141 & 13 & 9.21960194043838 & 3.78039805956162 \tabularnewline
142 & 12 & 11.9784152890646 & 0.0215847109353771 \tabularnewline
143 & 11 & 11.8441408302318 & -0.844140830231831 \tabularnewline
144 & 10 & 15.1108902374318 & -5.11089023743181 \tabularnewline
145 & 12 & 12.5312725440459 & -0.531272544045936 \tabularnewline
146 & 14 & 14.2637820424306 & -0.263782042430586 \tabularnewline
147 & 11 & 12.0116907117981 & -1.01169071179814 \tabularnewline
148 & 13 & 11.4432533425889 & 1.5567466574111 \tabularnewline
149 & 14 & 13.2163053584443 & 0.783694641555701 \tabularnewline
150 & 13 & 13.1129526357795 & -0.112952635779527 \tabularnewline
151 & 16 & 16.2346039333436 & -0.234603933343586 \tabularnewline
152 & 13 & 12.8940100751332 & 0.105989924866785 \tabularnewline
153 & 12 & 12.1657475669748 & -0.165747566974778 \tabularnewline
154 & 9 & 9.73805271087334 & -0.738052710873338 \tabularnewline
155 & 14 & 12.4076385222726 & 1.59236147772737 \tabularnewline
156 & 15 & 14.4702010359803 & 0.529798964019667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98234&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]15[/C][C]13.2759958400576[/C][C]1.72400415994239[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]8.1340262284636[/C][C]3.8659737715364[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.415997946991[/C][C]1.58400205300899[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.0565539575285[/C][C]0.943446042471505[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]12.8750176494125[/C][C]1.12498235058745[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]8.3344185878878[/C][C]-0.334418587887804[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]12.4427990475475[/C][C]-1.44279904754751[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]9.15170867670218[/C][C]5.84829132329782[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]5.7672863660809[/C][C]-1.7672863660809[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]9.92704830316118[/C][C]3.07295169683882[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]13.9013636803331[/C][C]5.09863631966693[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]11.3475549667815[/C][C]-1.34755496678145[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]17.2263741718015[/C][C]-2.2263741718015[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]11.7394270903916[/C][C]-5.73942709039158[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]11.0683475853148[/C][C]-4.06834758531479[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.5222386573094[/C][C]1.47776134269056[/C][/ROW]
[ROW][C]17[/C][C]16[/C][C]13.6491050031605[/C][C]2.3508949968395[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]14.5404845628261[/C][C]1.45951543717389[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14.3706390177842[/C][C]-0.370639017784193[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]14.1605842388127[/C][C]0.839415761187271[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]14.5657795741536[/C][C]-0.565779574153627[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]10.7038659339722[/C][C]1.29613406602777[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]8.96805777665953[/C][C]0.0319422233404683[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.59930871609[/C][C]0.400691283909992[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.5832088796326[/C][C]0.416791120367408[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]12.049207024473[/C][C]-0.0492070244729711[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]13.1246490680173[/C][C]0.875350931982731[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]10.4817468741181[/C][C]-0.48174687411809[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]13.2102921691126[/C][C]0.789707830887418[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]16.1850785065891[/C][C]-0.18507850658912[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]8.52482748134602[/C][C]1.47517251865398[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]11.076946131789[/C][C]-3.07694613178895[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]12.2324256249959[/C][C]-0.232425624995913[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.4025364911055[/C][C]-0.402536491105469[/C][/ROW]
[ROW][C]35[/C][C]8[/C][C]9.3388051473348[/C][C]-1.33880514733481[/C][/ROW]
[ROW][C]36[/C][C]13[/C][C]10.7789820683385[/C][C]2.22101793166153[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]9.67317626387485[/C][C]1.32682373612515[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]9.30671732337785[/C][C]2.69328267662215[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.1851503750603[/C][C]1.81484962493974[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]13.2974320695092[/C][C]2.70256793049075[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]13.7320598473499[/C][C]-0.732059847349922[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.205770743808[/C][C]-1.20577074380801[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]5.1726734230187[/C][C]-0.172673423018699[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]12.6800573240357[/C][C]1.31994267596432[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]9.69267869584[/C][C]3.30732130416[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]16.1251462063081[/C][C]-0.125146206308121[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]13.7328513465489[/C][C]0.267148653451069[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]13.415359182715[/C][C]1.58464081728496[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]14.0470216464013[/C][C]0.952978353598736[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.412906753837[/C][C]-1.41290675383696[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]13.6941435870677[/C][C]1.30585641293226[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]12.9725535785433[/C][C]3.02744642145668[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]13.8991898151183[/C][C]-0.899189815118311[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]12.9853242743521[/C][C]-1.98532427435209[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.0993986337065[/C][C]-1.09939863370649[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.0530060783113[/C][C]-0.0530060783113355[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]13.3566978555583[/C][C]-3.35669785555826[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]8.18084659772967[/C][C]-0.180846597729668[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]8.971670094895[/C][C]0.0283299051050059[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]12.5500878077745[/C][C]-0.550087807774467[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]13.4967369872789[/C][C]0.503263012721092[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.6961549656755[/C][C]-1.69615496567551[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]10.1780448676603[/C][C]0.821955132339715[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.4291014435579[/C][C]0.570898556442103[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]11.2839993319433[/C][C]-4.2839993319433[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]13.5312833042277[/C][C]2.46871669577234[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]15.4072553142332[/C][C]0.592744685766772[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]13.1124261135569[/C][C]-2.11242611355686[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]17.2106617492342[/C][C]-1.21066174923418[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]14.0959265231572[/C][C]-1.09592652315721[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.9958716157884[/C][C]0.00412838421159339[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]13.3293650328085[/C][C]-0.329365032808534[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]14.2992369840814[/C][C]-0.299236984081363[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]12.5759205978244[/C][C]2.42407940217559[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]7.97117145303042[/C][C]2.02882854696958[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]15.6495348865898[/C][C]-0.64953488658979[/C][/ROW]
[ROW][C]77[/C][C]11[/C][C]12.4122609001172[/C][C]-1.41226090011715[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]12.1728912222776[/C][C]-1.17289122227761[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]8.81330165247662[/C][C]-2.81330165247662[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]10.1499109271726[/C][C]0.850089072827393[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.3374457983146[/C][C]0.662554201685404[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]13.1995960719836[/C][C]-0.199596071983586[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]13.2376046495221[/C][C]-1.2376046495221[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.7600459564978[/C][C]-2.76004595649784[/C][/ROW]
[ROW][C]85[/C][C]9[/C][C]10.8836766200139[/C][C]-1.88367662001395[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.3673566473549[/C][C]-1.3673566473549[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]12.7966434442645[/C][C]3.20335655573546[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]13.0525762116854[/C][C]1.94742378831456[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]13.6293846977137[/C][C]0.370615302286296[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.6743621358241[/C][C]-1.67436213582408[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]11.0025560419154[/C][C]0.997443958084581[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]9.5801280956123[/C][C]0.4198719043877[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.3305362188998[/C][C]0.669463781100173[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]9.40430226823121[/C][C]-1.40430226823121[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.5196025800184[/C][C]1.4803974199816[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]8.83318131290251[/C][C]2.16681868709748[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]11.6095760287716[/C][C]0.390423971228418[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]10.9813752972926[/C][C]-1.98137529729258[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.6099170067986[/C][C]2.39008299320144[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]13.8321025568294[/C][C]1.16789744317058[/C][/ROW]
[ROW][C]101[/C][C]8[/C][C]10.2714766374347[/C][C]-2.27147663743473[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.8573957465154[/C][C]-0.857395746515445[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]9.85633895813615[/C][C]0.143661041863849[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]15.2465038614127[/C][C]0.753496138587303[/C][/ROW]
[ROW][C]105[/C][C]17[/C][C]15.3404928139371[/C][C]1.65950718606293[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]9.60384738462476[/C][C]-1.60384738462476[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]11.879064375873[/C][C]-2.87906437587303[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]11.9421186017172[/C][C]-3.94211860171718[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]12.0563998316784[/C][C]-1.0563998316784[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.6109004739734[/C][C]0.389099526026618[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]13.5241728208553[/C][C]-0.524172820855277[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]7.81356877600441[/C][C]-2.81356877600441[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]13.4354451005812[/C][C]1.56455489941884[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]14.4733561770563[/C][C]0.526643822943704[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]11.2036938887711[/C][C]0.796306111228929[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]11.5664672015403[/C][C]0.433532798459679[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]16.827024206619[/C][C]-0.827024206618975[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]12.9670128268691[/C][C]-0.96701282686913[/C][/ROW]
[ROW][C]119[/C][C]10[/C][C]11.8629947303403[/C][C]-1.8629947303403[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]10.6871445727976[/C][C]1.31285542720237[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]5.91082913335389[/C][C]-1.91082913335389[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]12.2307507294481[/C][C]-1.23075072944806[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]14.0638766720116[/C][C]1.93612332798838[/C][/ROW]
[ROW][C]124[/C][C]7[/C][C]8.50716619499291[/C][C]-1.50716619499291[/C][/ROW]
[ROW][C]125[/C][C]9[/C][C]11.0016637639058[/C][C]-2.00166376390576[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]11.3381986807959[/C][C]2.66180131920409[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]10.5105014070531[/C][C]0.489498592946897[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]11.9828216254215[/C][C]-1.98282162542155[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]8.27209422333465[/C][C]-2.27209422333465[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]13.574096906046[/C][C]0.425903093953982[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]12.1653679336672[/C][C]-1.16536793366721[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]9.6373737169921[/C][C]1.36262628300789[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]13.5657248941332[/C][C]-4.56572489413315[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]11.8922489062465[/C][C]4.10775109375348[/C][/ROW]
[ROW][C]135[/C][C]7[/C][C]8.01417690154054[/C][C]-1.01417690154054[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]8.59924945771092[/C][C]-0.599249457710921[/C][/ROW]
[ROW][C]137[/C][C]10[/C][C]9.83099090880023[/C][C]0.169009091199769[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]11.9790827970625[/C][C]2.02091720293752[/C][/ROW]
[ROW][C]139[/C][C]9[/C][C]9.76422983745289[/C][C]-0.76422983745289[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]12.8639434625053[/C][C]0.136056537494722[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]9.21960194043838[/C][C]3.78039805956162[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.9784152890646[/C][C]0.0215847109353771[/C][/ROW]
[ROW][C]143[/C][C]11[/C][C]11.8441408302318[/C][C]-0.844140830231831[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]15.1108902374318[/C][C]-5.11089023743181[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.5312725440459[/C][C]-0.531272544045936[/C][/ROW]
[ROW][C]146[/C][C]14[/C][C]14.2637820424306[/C][C]-0.263782042430586[/C][/ROW]
[ROW][C]147[/C][C]11[/C][C]12.0116907117981[/C][C]-1.01169071179814[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]11.4432533425889[/C][C]1.5567466574111[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]13.2163053584443[/C][C]0.783694641555701[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.1129526357795[/C][C]-0.112952635779527[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]16.2346039333436[/C][C]-0.234603933343586[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.8940100751332[/C][C]0.105989924866785[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]12.1657475669748[/C][C]-0.165747566974778[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]9.73805271087334[/C][C]-0.738052710873338[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.4076385222726[/C][C]1.59236147772737[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]14.4702010359803[/C][C]0.529798964019667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98234&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98234&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 IndexActualsInterpolationForecastResidualsPrediction Error
11513.27599584005761.72400415994239
2128.13402622846363.8659737715364
31513.4159979469911.58400205300899
41211.05655395752850.943446042471505
51412.87501764941251.12498235058745
688.3344185878878-0.334418587887804
71112.4427990475475-1.44279904754751
8159.151708676702185.84829132329782
945.7672863660809-1.7672863660809
10139.927048303161183.07295169683882
111913.90136368033315.09863631966693
121011.3475549667815-1.34755496678145
131517.2263741718015-2.2263741718015
14611.7394270903916-5.73942709039158
15711.0683475853148-4.06834758531479
161412.52223865730941.47776134269056
171613.64910500316052.3508949968395
181614.54048456282611.45951543717389
191414.3706390177842-0.370639017784193
201514.16058423881270.839415761187271
211414.5657795741536-0.565779574153627
221210.70386593397221.29613406602777
2398.968057776659530.0319422233404683
241211.599308716090.400691283909992
251413.58320887963260.416791120367408
261212.049207024473-0.0492070244729711
271413.12464906801730.875350931982731
281010.4817468741181-0.48174687411809
291413.21029216911260.789707830887418
301616.1850785065891-0.18507850658912
31108.524827481346021.47517251865398
32811.076946131789-3.07694613178895
331212.2324256249959-0.232425624995913
341111.4025364911055-0.402536491105469
3589.3388051473348-1.33880514733481
361310.77898206833852.22101793166153
37119.673176263874851.32682373612515
38129.306717323377852.69328267662215
391614.18515037506031.81484962493974
401613.29743206950922.70256793049075
411313.7320598473499-0.732059847349922
421415.205770743808-1.20577074380801
4355.1726734230187-0.172673423018699
441412.68005732403571.31994267596432
45139.692678695843.30732130416
461616.1251462063081-0.125146206308121
471413.73285134654890.267148653451069
481513.4153591827151.58464081728496
491514.04702164640130.952978353598736
501112.412906753837-1.41290675383696
511513.69414358706771.30585641293226
521612.97255357854333.02744642145668
531313.8991898151183-0.899189815118311
541112.9853242743521-1.98532427435209
551213.0993986337065-1.09939863370649
561212.0530060783113-0.0530060783113355
571013.3566978555583-3.35669785555826
5888.18084659772967-0.180846597729668
5998.9716700948950.0283299051050059
601212.5500878077745-0.550087807774467
611413.49673698727890.503263012721092
621213.6961549656755-1.69615496567551
631110.17804486766030.821955132339715
641413.42910144355790.570898556442103
65711.2839993319433-4.2839993319433
661613.53128330422772.46871669577234
671615.40725531423320.592744685766772
681113.1124261135569-2.11242611355686
691617.2106617492342-1.21066174923418
701314.0959265231572-1.09592652315721
711110.99587161578840.00412838421159339
721313.3293650328085-0.329365032808534
731414.2992369840814-0.299236984081363
741512.57592059782442.42407940217559
75107.971171453030422.02882854696958
761515.6495348865898-0.64953488658979
771112.4122609001172-1.41226090011715
781112.1728912222776-1.17289122227761
7968.81330165247662-2.81330165247662
801110.14991092717260.850089072827393
811211.33744579831460.662554201685404
821313.1995960719836-0.199596071983586
831213.2376046495221-1.2376046495221
84810.7600459564978-2.76004595649784
85910.8836766200139-1.88367662001395
861011.3673566473549-1.3673566473549
871612.79664344426453.20335655573546
881513.05257621168541.94742378831456
891413.62938469771370.370615302286296
901213.6743621358241-1.67436213582408
911211.00255604191540.997443958084581
92109.58012809561230.4198719043877
931211.33053621889980.669463781100173
9489.40430226823121-1.40430226823121
951614.51960258001841.4803974199816
96118.833181312902512.16681868709748
971211.60957602877160.390423971228418
98910.9813752972926-1.98137529729258
991411.60991700679862.39008299320144
1001513.83210255682941.16789744317058
101810.2714766374347-2.27147663743473
1021212.8573957465154-0.857395746515445
103109.856338958136150.143661041863849
1041615.24650386141270.753496138587303
1051715.34049281393711.65950718606293
10689.60384738462476-1.60384738462476
107911.879064375873-2.87906437587303
108811.9421186017172-3.94211860171718
1091112.0563998316784-1.0563998316784
1101615.61090047397340.389099526026618
1111313.5241728208553-0.524172820855277
11257.81356877600441-2.81356877600441
1131513.43544510058121.56455489941884
1141514.47335617705630.526643822943704
1151211.20369388877110.796306111228929
1161211.56646720154030.433532798459679
1171616.827024206619-0.827024206618975
1181212.9670128268691-0.96701282686913
1191011.8629947303403-1.8629947303403
1201210.68714457279761.31285542720237
12145.91082913335389-1.91082913335389
1221112.2307507294481-1.23075072944806
1231614.06387667201161.93612332798838
12478.50716619499291-1.50716619499291
125911.0016637639058-2.00166376390576
1261411.33819868079592.66180131920409
1271110.51050140705310.489498592946897
1281011.9828216254215-1.98282162542155
12968.27209422333465-2.27209422333465
1301413.5740969060460.425903093953982
1311112.1653679336672-1.16536793366721
132119.63737371699211.36262628300789
133913.5657248941332-4.56572489413315
1341611.89224890624654.10775109375348
13578.01417690154054-1.01417690154054
13688.59924945771092-0.599249457710921
137109.830990908800230.169009091199769
1381411.97908279706252.02091720293752
13999.76422983745289-0.76422983745289
1401312.86394346250530.136056537494722
141139.219601940438383.78039805956162
1421211.97841528906460.0215847109353771
1431111.8441408302318-0.844140830231831
1441015.1108902374318-5.11089023743181
1451212.5312725440459-0.531272544045936
1461414.2637820424306-0.263782042430586
1471112.0116907117981-1.01169071179814
1481311.44325334258891.5567466574111
1491413.21630535844430.783694641555701
1501313.1129526357795-0.112952635779527
1511616.2346039333436-0.234603933343586
1521312.89401007513320.105989924866785
1531212.1657475669748-0.165747566974778
15499.73805271087334-0.738052710873338
1551412.40763852227261.59236147772737
1561514.47020103598030.529798964019667







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
250.6213374543913420.7573250912173160.378662545608658
260.8270474617181520.3459050765636960.172952538281848
270.7976678788448550.4046642423102890.202332121155145
280.7425207442917280.5149585114165440.257479255708272
290.6474001980290160.7051996039419670.352599801970984
300.5549261534585110.8901476930829770.445073846541489
310.5086389173569920.9827221652860170.491361082643008
320.8321708604146970.3356582791706060.167829139585303
330.7816416640743720.4367166718512550.218358335925628
340.7253754721984670.5492490556030670.274624527801533
350.832129436296850.3357411274062990.16787056370315
360.8411048655484230.3177902689031530.158895134451577
370.8212606783845570.3574786432308850.178739321615443
380.7992705608707020.4014588782585970.200729439129298
390.7546185387697440.4907629224605120.245381461230256
400.7457054480797680.5085891038404650.254294551920232
410.6964130887952980.6071738224094040.303586911204702
420.6736986717346620.6526026565306750.326301328265338
430.6103490457957920.7793019084084160.389650954204208
440.5700899106066890.8598201787866210.429910089393311
450.8303469571695210.3393060856609580.169653042830479
460.7976899693079220.4046200613841560.202310030692078
470.8074939790526880.3850120418946240.192506020947312
480.7735312054951040.4529375890097920.226468794504896
490.7307089488693920.5385821022612150.269291051130608
500.7053799382972980.5892401234054050.294620061702702
510.7945079865160160.4109840269679680.205492013483984
520.817871031007390.364257937985220.18212896899261
530.786953736032750.4260925279344990.213046263967249
540.8842150651150590.2315698697698820.115784934884941
550.8690610135951330.2618779728097350.130938986404867
560.8608851794536130.2782296410927740.139114820546387
570.9169188488480880.1661623023038240.0830811511519121
580.8982418145678680.2035163708642640.101758185432132
590.8716523746179990.2566952507640030.128347625382001
600.8459541257440730.3080917485118540.154045874255927
610.8122209346284970.3755581307430060.187779065371503
620.8361622524889110.3276754950221770.163837747511089
630.8186270634444730.3627458731110530.181372936555527
640.8390479400578580.3219041198842850.160952059942142
650.9430807718787780.1138384562424440.0569192281212218
660.9454604820011390.1090790359977230.0545395179988613
670.9293842239082220.1412315521835550.0706157760917776
680.9237468355778010.1525063288443990.0762531644221993
690.908255011692880.1834899766142380.0917449883071192
700.8923171844151880.2153656311696230.107682815584812
710.8714119244393880.2571761511212240.128588075560612
720.8426131930498640.3147736139002710.157386806950136
730.8231061931441380.3537876137117240.176893806855862
740.8411035406356820.3177929187286360.158896459364318
750.8272168375915680.3455663248168650.172783162408432
760.799406385976240.4011872280475190.200593614023759
770.8690054868784940.2619890262430120.130994513121506
780.8468581352941780.3062837294116440.153141864705822
790.8718763847154820.2562472305690360.128123615284518
800.8497539346186620.3004921307626770.150246065381338
810.8198463359533160.3603073280933680.180153664046684
820.7847397271511150.430520545697770.215260272848885
830.753670100265570.4926597994688610.24632989973443
840.7695118579113480.4609762841773030.230488142088652
850.7661535098613610.4676929802772780.233846490138639
860.746907189496880.506185621006240.25309281050312
870.7734587356049030.4530825287901950.226541264395097
880.7669595735766450.4660808528467090.233040426423355
890.7261015590917650.547796881816470.273898440908235
900.7069090582458160.5861818835083680.293090941754184
910.6845335506715830.6309328986568330.315466449328417
920.6399408258246530.7201183483506930.360059174175347
930.5917436005826110.8165127988347770.408256399417389
940.5843829825706660.8312340348586690.415617017429334
950.5575091887440140.8849816225119730.442490811255986
960.6727847073458960.6544305853082090.327215292654104
970.6258641346454220.7482717307091550.374135865354578
980.608424126433920.783151747132160.39157587356608
990.7166190467222640.5667619065554730.283380953277736
1000.7364828068903510.5270343862192980.263517193109649
1010.7337693227245340.5324613545509320.266230677275466
1020.6902882598478920.6194234803042160.309711740152108
1030.6384511092494610.7230977815010780.361548890750539
1040.5964722435297690.8070555129404630.403527756470231
1050.5639283259694560.8721433480610890.436071674030544
1060.5937397495739550.812520500852090.406260250426045
1070.5707165957640320.8585668084719360.429283404235968
1080.7864741080937640.4270517838124730.213525891906236
1090.756637037477390.4867259250452210.243362962522611
1100.7035755482192020.5928489035615970.296424451780798
1110.6603686604056940.6792626791886110.339631339594306
1120.7063897996248080.5872204007503840.293610200375192
1130.6803383102672570.6393233794654860.319661689732743
1140.644593603665850.71081279266830.35540639633415
1150.5800631268102050.839873746379590.419936873189795
1160.6824734578430850.6350530843138290.317526542156915
1170.6165275930966310.7669448138067380.383472406903369
1180.5483201744591540.9033596510816910.451679825540846
1190.6224107693757040.7551784612485920.377589230624296
1200.5885216222536580.8229567554926850.411478377746342
1210.5561395283757850.887720943248430.443860471624215
1220.5154939515005840.9690120969988310.484506048499416
1230.4560980636263980.9121961272527960.543901936373602
1240.386965643346810.7739312866936190.61303435665319
1250.369536066781090.7390721335621810.63046393321891
1260.9764545335732830.04709093285343320.0235454664267166
1270.9536946975398990.0926106049202030.0463053024601015
1280.973894074538410.0522118509231790.0261059254615895
1290.9485499973635880.1029000052728240.0514500026364122
1300.983706389617160.03258722076568160.0162936103828408
1310.991980726290930.01603854741813970.00801927370906985

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
25 & 0.621337454391342 & 0.757325091217316 & 0.378662545608658 \tabularnewline
26 & 0.827047461718152 & 0.345905076563696 & 0.172952538281848 \tabularnewline
27 & 0.797667878844855 & 0.404664242310289 & 0.202332121155145 \tabularnewline
28 & 0.742520744291728 & 0.514958511416544 & 0.257479255708272 \tabularnewline
29 & 0.647400198029016 & 0.705199603941967 & 0.352599801970984 \tabularnewline
30 & 0.554926153458511 & 0.890147693082977 & 0.445073846541489 \tabularnewline
31 & 0.508638917356992 & 0.982722165286017 & 0.491361082643008 \tabularnewline
32 & 0.832170860414697 & 0.335658279170606 & 0.167829139585303 \tabularnewline
33 & 0.781641664074372 & 0.436716671851255 & 0.218358335925628 \tabularnewline
34 & 0.725375472198467 & 0.549249055603067 & 0.274624527801533 \tabularnewline
35 & 0.83212943629685 & 0.335741127406299 & 0.16787056370315 \tabularnewline
36 & 0.841104865548423 & 0.317790268903153 & 0.158895134451577 \tabularnewline
37 & 0.821260678384557 & 0.357478643230885 & 0.178739321615443 \tabularnewline
38 & 0.799270560870702 & 0.401458878258597 & 0.200729439129298 \tabularnewline
39 & 0.754618538769744 & 0.490762922460512 & 0.245381461230256 \tabularnewline
40 & 0.745705448079768 & 0.508589103840465 & 0.254294551920232 \tabularnewline
41 & 0.696413088795298 & 0.607173822409404 & 0.303586911204702 \tabularnewline
42 & 0.673698671734662 & 0.652602656530675 & 0.326301328265338 \tabularnewline
43 & 0.610349045795792 & 0.779301908408416 & 0.389650954204208 \tabularnewline
44 & 0.570089910606689 & 0.859820178786621 & 0.429910089393311 \tabularnewline
45 & 0.830346957169521 & 0.339306085660958 & 0.169653042830479 \tabularnewline
46 & 0.797689969307922 & 0.404620061384156 & 0.202310030692078 \tabularnewline
47 & 0.807493979052688 & 0.385012041894624 & 0.192506020947312 \tabularnewline
48 & 0.773531205495104 & 0.452937589009792 & 0.226468794504896 \tabularnewline
49 & 0.730708948869392 & 0.538582102261215 & 0.269291051130608 \tabularnewline
50 & 0.705379938297298 & 0.589240123405405 & 0.294620061702702 \tabularnewline
51 & 0.794507986516016 & 0.410984026967968 & 0.205492013483984 \tabularnewline
52 & 0.81787103100739 & 0.36425793798522 & 0.18212896899261 \tabularnewline
53 & 0.78695373603275 & 0.426092527934499 & 0.213046263967249 \tabularnewline
54 & 0.884215065115059 & 0.231569869769882 & 0.115784934884941 \tabularnewline
55 & 0.869061013595133 & 0.261877972809735 & 0.130938986404867 \tabularnewline
56 & 0.860885179453613 & 0.278229641092774 & 0.139114820546387 \tabularnewline
57 & 0.916918848848088 & 0.166162302303824 & 0.0830811511519121 \tabularnewline
58 & 0.898241814567868 & 0.203516370864264 & 0.101758185432132 \tabularnewline
59 & 0.871652374617999 & 0.256695250764003 & 0.128347625382001 \tabularnewline
60 & 0.845954125744073 & 0.308091748511854 & 0.154045874255927 \tabularnewline
61 & 0.812220934628497 & 0.375558130743006 & 0.187779065371503 \tabularnewline
62 & 0.836162252488911 & 0.327675495022177 & 0.163837747511089 \tabularnewline
63 & 0.818627063444473 & 0.362745873111053 & 0.181372936555527 \tabularnewline
64 & 0.839047940057858 & 0.321904119884285 & 0.160952059942142 \tabularnewline
65 & 0.943080771878778 & 0.113838456242444 & 0.0569192281212218 \tabularnewline
66 & 0.945460482001139 & 0.109079035997723 & 0.0545395179988613 \tabularnewline
67 & 0.929384223908222 & 0.141231552183555 & 0.0706157760917776 \tabularnewline
68 & 0.923746835577801 & 0.152506328844399 & 0.0762531644221993 \tabularnewline
69 & 0.90825501169288 & 0.183489976614238 & 0.0917449883071192 \tabularnewline
70 & 0.892317184415188 & 0.215365631169623 & 0.107682815584812 \tabularnewline
71 & 0.871411924439388 & 0.257176151121224 & 0.128588075560612 \tabularnewline
72 & 0.842613193049864 & 0.314773613900271 & 0.157386806950136 \tabularnewline
73 & 0.823106193144138 & 0.353787613711724 & 0.176893806855862 \tabularnewline
74 & 0.841103540635682 & 0.317792918728636 & 0.158896459364318 \tabularnewline
75 & 0.827216837591568 & 0.345566324816865 & 0.172783162408432 \tabularnewline
76 & 0.79940638597624 & 0.401187228047519 & 0.200593614023759 \tabularnewline
77 & 0.869005486878494 & 0.261989026243012 & 0.130994513121506 \tabularnewline
78 & 0.846858135294178 & 0.306283729411644 & 0.153141864705822 \tabularnewline
79 & 0.871876384715482 & 0.256247230569036 & 0.128123615284518 \tabularnewline
80 & 0.849753934618662 & 0.300492130762677 & 0.150246065381338 \tabularnewline
81 & 0.819846335953316 & 0.360307328093368 & 0.180153664046684 \tabularnewline
82 & 0.784739727151115 & 0.43052054569777 & 0.215260272848885 \tabularnewline
83 & 0.75367010026557 & 0.492659799468861 & 0.24632989973443 \tabularnewline
84 & 0.769511857911348 & 0.460976284177303 & 0.230488142088652 \tabularnewline
85 & 0.766153509861361 & 0.467692980277278 & 0.233846490138639 \tabularnewline
86 & 0.74690718949688 & 0.50618562100624 & 0.25309281050312 \tabularnewline
87 & 0.773458735604903 & 0.453082528790195 & 0.226541264395097 \tabularnewline
88 & 0.766959573576645 & 0.466080852846709 & 0.233040426423355 \tabularnewline
89 & 0.726101559091765 & 0.54779688181647 & 0.273898440908235 \tabularnewline
90 & 0.706909058245816 & 0.586181883508368 & 0.293090941754184 \tabularnewline
91 & 0.684533550671583 & 0.630932898656833 & 0.315466449328417 \tabularnewline
92 & 0.639940825824653 & 0.720118348350693 & 0.360059174175347 \tabularnewline
93 & 0.591743600582611 & 0.816512798834777 & 0.408256399417389 \tabularnewline
94 & 0.584382982570666 & 0.831234034858669 & 0.415617017429334 \tabularnewline
95 & 0.557509188744014 & 0.884981622511973 & 0.442490811255986 \tabularnewline
96 & 0.672784707345896 & 0.654430585308209 & 0.327215292654104 \tabularnewline
97 & 0.625864134645422 & 0.748271730709155 & 0.374135865354578 \tabularnewline
98 & 0.60842412643392 & 0.78315174713216 & 0.39157587356608 \tabularnewline
99 & 0.716619046722264 & 0.566761906555473 & 0.283380953277736 \tabularnewline
100 & 0.736482806890351 & 0.527034386219298 & 0.263517193109649 \tabularnewline
101 & 0.733769322724534 & 0.532461354550932 & 0.266230677275466 \tabularnewline
102 & 0.690288259847892 & 0.619423480304216 & 0.309711740152108 \tabularnewline
103 & 0.638451109249461 & 0.723097781501078 & 0.361548890750539 \tabularnewline
104 & 0.596472243529769 & 0.807055512940463 & 0.403527756470231 \tabularnewline
105 & 0.563928325969456 & 0.872143348061089 & 0.436071674030544 \tabularnewline
106 & 0.593739749573955 & 0.81252050085209 & 0.406260250426045 \tabularnewline
107 & 0.570716595764032 & 0.858566808471936 & 0.429283404235968 \tabularnewline
108 & 0.786474108093764 & 0.427051783812473 & 0.213525891906236 \tabularnewline
109 & 0.75663703747739 & 0.486725925045221 & 0.243362962522611 \tabularnewline
110 & 0.703575548219202 & 0.592848903561597 & 0.296424451780798 \tabularnewline
111 & 0.660368660405694 & 0.679262679188611 & 0.339631339594306 \tabularnewline
112 & 0.706389799624808 & 0.587220400750384 & 0.293610200375192 \tabularnewline
113 & 0.680338310267257 & 0.639323379465486 & 0.319661689732743 \tabularnewline
114 & 0.64459360366585 & 0.7108127926683 & 0.35540639633415 \tabularnewline
115 & 0.580063126810205 & 0.83987374637959 & 0.419936873189795 \tabularnewline
116 & 0.682473457843085 & 0.635053084313829 & 0.317526542156915 \tabularnewline
117 & 0.616527593096631 & 0.766944813806738 & 0.383472406903369 \tabularnewline
118 & 0.548320174459154 & 0.903359651081691 & 0.451679825540846 \tabularnewline
119 & 0.622410769375704 & 0.755178461248592 & 0.377589230624296 \tabularnewline
120 & 0.588521622253658 & 0.822956755492685 & 0.411478377746342 \tabularnewline
121 & 0.556139528375785 & 0.88772094324843 & 0.443860471624215 \tabularnewline
122 & 0.515493951500584 & 0.969012096998831 & 0.484506048499416 \tabularnewline
123 & 0.456098063626398 & 0.912196127252796 & 0.543901936373602 \tabularnewline
124 & 0.38696564334681 & 0.773931286693619 & 0.61303435665319 \tabularnewline
125 & 0.36953606678109 & 0.739072133562181 & 0.63046393321891 \tabularnewline
126 & 0.976454533573283 & 0.0470909328534332 & 0.0235454664267166 \tabularnewline
127 & 0.953694697539899 & 0.092610604920203 & 0.0463053024601015 \tabularnewline
128 & 0.97389407453841 & 0.052211850923179 & 0.0261059254615895 \tabularnewline
129 & 0.948549997363588 & 0.102900005272824 & 0.0514500026364122 \tabularnewline
130 & 0.98370638961716 & 0.0325872207656816 & 0.0162936103828408 \tabularnewline
131 & 0.99198072629093 & 0.0160385474181397 & 0.00801927370906985 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98234&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]0.621337454391342[/C][C]0.757325091217316[/C][C]0.378662545608658[/C][/ROW]
[ROW][C]26[/C][C]0.827047461718152[/C][C]0.345905076563696[/C][C]0.172952538281848[/C][/ROW]
[ROW][C]27[/C][C]0.797667878844855[/C][C]0.404664242310289[/C][C]0.202332121155145[/C][/ROW]
[ROW][C]28[/C][C]0.742520744291728[/C][C]0.514958511416544[/C][C]0.257479255708272[/C][/ROW]
[ROW][C]29[/C][C]0.647400198029016[/C][C]0.705199603941967[/C][C]0.352599801970984[/C][/ROW]
[ROW][C]30[/C][C]0.554926153458511[/C][C]0.890147693082977[/C][C]0.445073846541489[/C][/ROW]
[ROW][C]31[/C][C]0.508638917356992[/C][C]0.982722165286017[/C][C]0.491361082643008[/C][/ROW]
[ROW][C]32[/C][C]0.832170860414697[/C][C]0.335658279170606[/C][C]0.167829139585303[/C][/ROW]
[ROW][C]33[/C][C]0.781641664074372[/C][C]0.436716671851255[/C][C]0.218358335925628[/C][/ROW]
[ROW][C]34[/C][C]0.725375472198467[/C][C]0.549249055603067[/C][C]0.274624527801533[/C][/ROW]
[ROW][C]35[/C][C]0.83212943629685[/C][C]0.335741127406299[/C][C]0.16787056370315[/C][/ROW]
[ROW][C]36[/C][C]0.841104865548423[/C][C]0.317790268903153[/C][C]0.158895134451577[/C][/ROW]
[ROW][C]37[/C][C]0.821260678384557[/C][C]0.357478643230885[/C][C]0.178739321615443[/C][/ROW]
[ROW][C]38[/C][C]0.799270560870702[/C][C]0.401458878258597[/C][C]0.200729439129298[/C][/ROW]
[ROW][C]39[/C][C]0.754618538769744[/C][C]0.490762922460512[/C][C]0.245381461230256[/C][/ROW]
[ROW][C]40[/C][C]0.745705448079768[/C][C]0.508589103840465[/C][C]0.254294551920232[/C][/ROW]
[ROW][C]41[/C][C]0.696413088795298[/C][C]0.607173822409404[/C][C]0.303586911204702[/C][/ROW]
[ROW][C]42[/C][C]0.673698671734662[/C][C]0.652602656530675[/C][C]0.326301328265338[/C][/ROW]
[ROW][C]43[/C][C]0.610349045795792[/C][C]0.779301908408416[/C][C]0.389650954204208[/C][/ROW]
[ROW][C]44[/C][C]0.570089910606689[/C][C]0.859820178786621[/C][C]0.429910089393311[/C][/ROW]
[ROW][C]45[/C][C]0.830346957169521[/C][C]0.339306085660958[/C][C]0.169653042830479[/C][/ROW]
[ROW][C]46[/C][C]0.797689969307922[/C][C]0.404620061384156[/C][C]0.202310030692078[/C][/ROW]
[ROW][C]47[/C][C]0.807493979052688[/C][C]0.385012041894624[/C][C]0.192506020947312[/C][/ROW]
[ROW][C]48[/C][C]0.773531205495104[/C][C]0.452937589009792[/C][C]0.226468794504896[/C][/ROW]
[ROW][C]49[/C][C]0.730708948869392[/C][C]0.538582102261215[/C][C]0.269291051130608[/C][/ROW]
[ROW][C]50[/C][C]0.705379938297298[/C][C]0.589240123405405[/C][C]0.294620061702702[/C][/ROW]
[ROW][C]51[/C][C]0.794507986516016[/C][C]0.410984026967968[/C][C]0.205492013483984[/C][/ROW]
[ROW][C]52[/C][C]0.81787103100739[/C][C]0.36425793798522[/C][C]0.18212896899261[/C][/ROW]
[ROW][C]53[/C][C]0.78695373603275[/C][C]0.426092527934499[/C][C]0.213046263967249[/C][/ROW]
[ROW][C]54[/C][C]0.884215065115059[/C][C]0.231569869769882[/C][C]0.115784934884941[/C][/ROW]
[ROW][C]55[/C][C]0.869061013595133[/C][C]0.261877972809735[/C][C]0.130938986404867[/C][/ROW]
[ROW][C]56[/C][C]0.860885179453613[/C][C]0.278229641092774[/C][C]0.139114820546387[/C][/ROW]
[ROW][C]57[/C][C]0.916918848848088[/C][C]0.166162302303824[/C][C]0.0830811511519121[/C][/ROW]
[ROW][C]58[/C][C]0.898241814567868[/C][C]0.203516370864264[/C][C]0.101758185432132[/C][/ROW]
[ROW][C]59[/C][C]0.871652374617999[/C][C]0.256695250764003[/C][C]0.128347625382001[/C][/ROW]
[ROW][C]60[/C][C]0.845954125744073[/C][C]0.308091748511854[/C][C]0.154045874255927[/C][/ROW]
[ROW][C]61[/C][C]0.812220934628497[/C][C]0.375558130743006[/C][C]0.187779065371503[/C][/ROW]
[ROW][C]62[/C][C]0.836162252488911[/C][C]0.327675495022177[/C][C]0.163837747511089[/C][/ROW]
[ROW][C]63[/C][C]0.818627063444473[/C][C]0.362745873111053[/C][C]0.181372936555527[/C][/ROW]
[ROW][C]64[/C][C]0.839047940057858[/C][C]0.321904119884285[/C][C]0.160952059942142[/C][/ROW]
[ROW][C]65[/C][C]0.943080771878778[/C][C]0.113838456242444[/C][C]0.0569192281212218[/C][/ROW]
[ROW][C]66[/C][C]0.945460482001139[/C][C]0.109079035997723[/C][C]0.0545395179988613[/C][/ROW]
[ROW][C]67[/C][C]0.929384223908222[/C][C]0.141231552183555[/C][C]0.0706157760917776[/C][/ROW]
[ROW][C]68[/C][C]0.923746835577801[/C][C]0.152506328844399[/C][C]0.0762531644221993[/C][/ROW]
[ROW][C]69[/C][C]0.90825501169288[/C][C]0.183489976614238[/C][C]0.0917449883071192[/C][/ROW]
[ROW][C]70[/C][C]0.892317184415188[/C][C]0.215365631169623[/C][C]0.107682815584812[/C][/ROW]
[ROW][C]71[/C][C]0.871411924439388[/C][C]0.257176151121224[/C][C]0.128588075560612[/C][/ROW]
[ROW][C]72[/C][C]0.842613193049864[/C][C]0.314773613900271[/C][C]0.157386806950136[/C][/ROW]
[ROW][C]73[/C][C]0.823106193144138[/C][C]0.353787613711724[/C][C]0.176893806855862[/C][/ROW]
[ROW][C]74[/C][C]0.841103540635682[/C][C]0.317792918728636[/C][C]0.158896459364318[/C][/ROW]
[ROW][C]75[/C][C]0.827216837591568[/C][C]0.345566324816865[/C][C]0.172783162408432[/C][/ROW]
[ROW][C]76[/C][C]0.79940638597624[/C][C]0.401187228047519[/C][C]0.200593614023759[/C][/ROW]
[ROW][C]77[/C][C]0.869005486878494[/C][C]0.261989026243012[/C][C]0.130994513121506[/C][/ROW]
[ROW][C]78[/C][C]0.846858135294178[/C][C]0.306283729411644[/C][C]0.153141864705822[/C][/ROW]
[ROW][C]79[/C][C]0.871876384715482[/C][C]0.256247230569036[/C][C]0.128123615284518[/C][/ROW]
[ROW][C]80[/C][C]0.849753934618662[/C][C]0.300492130762677[/C][C]0.150246065381338[/C][/ROW]
[ROW][C]81[/C][C]0.819846335953316[/C][C]0.360307328093368[/C][C]0.180153664046684[/C][/ROW]
[ROW][C]82[/C][C]0.784739727151115[/C][C]0.43052054569777[/C][C]0.215260272848885[/C][/ROW]
[ROW][C]83[/C][C]0.75367010026557[/C][C]0.492659799468861[/C][C]0.24632989973443[/C][/ROW]
[ROW][C]84[/C][C]0.769511857911348[/C][C]0.460976284177303[/C][C]0.230488142088652[/C][/ROW]
[ROW][C]85[/C][C]0.766153509861361[/C][C]0.467692980277278[/C][C]0.233846490138639[/C][/ROW]
[ROW][C]86[/C][C]0.74690718949688[/C][C]0.50618562100624[/C][C]0.25309281050312[/C][/ROW]
[ROW][C]87[/C][C]0.773458735604903[/C][C]0.453082528790195[/C][C]0.226541264395097[/C][/ROW]
[ROW][C]88[/C][C]0.766959573576645[/C][C]0.466080852846709[/C][C]0.233040426423355[/C][/ROW]
[ROW][C]89[/C][C]0.726101559091765[/C][C]0.54779688181647[/C][C]0.273898440908235[/C][/ROW]
[ROW][C]90[/C][C]0.706909058245816[/C][C]0.586181883508368[/C][C]0.293090941754184[/C][/ROW]
[ROW][C]91[/C][C]0.684533550671583[/C][C]0.630932898656833[/C][C]0.315466449328417[/C][/ROW]
[ROW][C]92[/C][C]0.639940825824653[/C][C]0.720118348350693[/C][C]0.360059174175347[/C][/ROW]
[ROW][C]93[/C][C]0.591743600582611[/C][C]0.816512798834777[/C][C]0.408256399417389[/C][/ROW]
[ROW][C]94[/C][C]0.584382982570666[/C][C]0.831234034858669[/C][C]0.415617017429334[/C][/ROW]
[ROW][C]95[/C][C]0.557509188744014[/C][C]0.884981622511973[/C][C]0.442490811255986[/C][/ROW]
[ROW][C]96[/C][C]0.672784707345896[/C][C]0.654430585308209[/C][C]0.327215292654104[/C][/ROW]
[ROW][C]97[/C][C]0.625864134645422[/C][C]0.748271730709155[/C][C]0.374135865354578[/C][/ROW]
[ROW][C]98[/C][C]0.60842412643392[/C][C]0.78315174713216[/C][C]0.39157587356608[/C][/ROW]
[ROW][C]99[/C][C]0.716619046722264[/C][C]0.566761906555473[/C][C]0.283380953277736[/C][/ROW]
[ROW][C]100[/C][C]0.736482806890351[/C][C]0.527034386219298[/C][C]0.263517193109649[/C][/ROW]
[ROW][C]101[/C][C]0.733769322724534[/C][C]0.532461354550932[/C][C]0.266230677275466[/C][/ROW]
[ROW][C]102[/C][C]0.690288259847892[/C][C]0.619423480304216[/C][C]0.309711740152108[/C][/ROW]
[ROW][C]103[/C][C]0.638451109249461[/C][C]0.723097781501078[/C][C]0.361548890750539[/C][/ROW]
[ROW][C]104[/C][C]0.596472243529769[/C][C]0.807055512940463[/C][C]0.403527756470231[/C][/ROW]
[ROW][C]105[/C][C]0.563928325969456[/C][C]0.872143348061089[/C][C]0.436071674030544[/C][/ROW]
[ROW][C]106[/C][C]0.593739749573955[/C][C]0.81252050085209[/C][C]0.406260250426045[/C][/ROW]
[ROW][C]107[/C][C]0.570716595764032[/C][C]0.858566808471936[/C][C]0.429283404235968[/C][/ROW]
[ROW][C]108[/C][C]0.786474108093764[/C][C]0.427051783812473[/C][C]0.213525891906236[/C][/ROW]
[ROW][C]109[/C][C]0.75663703747739[/C][C]0.486725925045221[/C][C]0.243362962522611[/C][/ROW]
[ROW][C]110[/C][C]0.703575548219202[/C][C]0.592848903561597[/C][C]0.296424451780798[/C][/ROW]
[ROW][C]111[/C][C]0.660368660405694[/C][C]0.679262679188611[/C][C]0.339631339594306[/C][/ROW]
[ROW][C]112[/C][C]0.706389799624808[/C][C]0.587220400750384[/C][C]0.293610200375192[/C][/ROW]
[ROW][C]113[/C][C]0.680338310267257[/C][C]0.639323379465486[/C][C]0.319661689732743[/C][/ROW]
[ROW][C]114[/C][C]0.64459360366585[/C][C]0.7108127926683[/C][C]0.35540639633415[/C][/ROW]
[ROW][C]115[/C][C]0.580063126810205[/C][C]0.83987374637959[/C][C]0.419936873189795[/C][/ROW]
[ROW][C]116[/C][C]0.682473457843085[/C][C]0.635053084313829[/C][C]0.317526542156915[/C][/ROW]
[ROW][C]117[/C][C]0.616527593096631[/C][C]0.766944813806738[/C][C]0.383472406903369[/C][/ROW]
[ROW][C]118[/C][C]0.548320174459154[/C][C]0.903359651081691[/C][C]0.451679825540846[/C][/ROW]
[ROW][C]119[/C][C]0.622410769375704[/C][C]0.755178461248592[/C][C]0.377589230624296[/C][/ROW]
[ROW][C]120[/C][C]0.588521622253658[/C][C]0.822956755492685[/C][C]0.411478377746342[/C][/ROW]
[ROW][C]121[/C][C]0.556139528375785[/C][C]0.88772094324843[/C][C]0.443860471624215[/C][/ROW]
[ROW][C]122[/C][C]0.515493951500584[/C][C]0.969012096998831[/C][C]0.484506048499416[/C][/ROW]
[ROW][C]123[/C][C]0.456098063626398[/C][C]0.912196127252796[/C][C]0.543901936373602[/C][/ROW]
[ROW][C]124[/C][C]0.38696564334681[/C][C]0.773931286693619[/C][C]0.61303435665319[/C][/ROW]
[ROW][C]125[/C][C]0.36953606678109[/C][C]0.739072133562181[/C][C]0.63046393321891[/C][/ROW]
[ROW][C]126[/C][C]0.976454533573283[/C][C]0.0470909328534332[/C][C]0.0235454664267166[/C][/ROW]
[ROW][C]127[/C][C]0.953694697539899[/C][C]0.092610604920203[/C][C]0.0463053024601015[/C][/ROW]
[ROW][C]128[/C][C]0.97389407453841[/C][C]0.052211850923179[/C][C]0.0261059254615895[/C][/ROW]
[ROW][C]129[/C][C]0.948549997363588[/C][C]0.102900005272824[/C][C]0.0514500026364122[/C][/ROW]
[ROW][C]130[/C][C]0.98370638961716[/C][C]0.0325872207656816[/C][C]0.0162936103828408[/C][/ROW]
[ROW][C]131[/C][C]0.99198072629093[/C][C]0.0160385474181397[/C][C]0.00801927370906985[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98234&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98234&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
250.6213374543913420.7573250912173160.378662545608658
260.8270474617181520.3459050765636960.172952538281848
270.7976678788448550.4046642423102890.202332121155145
280.7425207442917280.5149585114165440.257479255708272
290.6474001980290160.7051996039419670.352599801970984
300.5549261534585110.8901476930829770.445073846541489
310.5086389173569920.9827221652860170.491361082643008
320.8321708604146970.3356582791706060.167829139585303
330.7816416640743720.4367166718512550.218358335925628
340.7253754721984670.5492490556030670.274624527801533
350.832129436296850.3357411274062990.16787056370315
360.8411048655484230.3177902689031530.158895134451577
370.8212606783845570.3574786432308850.178739321615443
380.7992705608707020.4014588782585970.200729439129298
390.7546185387697440.4907629224605120.245381461230256
400.7457054480797680.5085891038404650.254294551920232
410.6964130887952980.6071738224094040.303586911204702
420.6736986717346620.6526026565306750.326301328265338
430.6103490457957920.7793019084084160.389650954204208
440.5700899106066890.8598201787866210.429910089393311
450.8303469571695210.3393060856609580.169653042830479
460.7976899693079220.4046200613841560.202310030692078
470.8074939790526880.3850120418946240.192506020947312
480.7735312054951040.4529375890097920.226468794504896
490.7307089488693920.5385821022612150.269291051130608
500.7053799382972980.5892401234054050.294620061702702
510.7945079865160160.4109840269679680.205492013483984
520.817871031007390.364257937985220.18212896899261
530.786953736032750.4260925279344990.213046263967249
540.8842150651150590.2315698697698820.115784934884941
550.8690610135951330.2618779728097350.130938986404867
560.8608851794536130.2782296410927740.139114820546387
570.9169188488480880.1661623023038240.0830811511519121
580.8982418145678680.2035163708642640.101758185432132
590.8716523746179990.2566952507640030.128347625382001
600.8459541257440730.3080917485118540.154045874255927
610.8122209346284970.3755581307430060.187779065371503
620.8361622524889110.3276754950221770.163837747511089
630.8186270634444730.3627458731110530.181372936555527
640.8390479400578580.3219041198842850.160952059942142
650.9430807718787780.1138384562424440.0569192281212218
660.9454604820011390.1090790359977230.0545395179988613
670.9293842239082220.1412315521835550.0706157760917776
680.9237468355778010.1525063288443990.0762531644221993
690.908255011692880.1834899766142380.0917449883071192
700.8923171844151880.2153656311696230.107682815584812
710.8714119244393880.2571761511212240.128588075560612
720.8426131930498640.3147736139002710.157386806950136
730.8231061931441380.3537876137117240.176893806855862
740.8411035406356820.3177929187286360.158896459364318
750.8272168375915680.3455663248168650.172783162408432
760.799406385976240.4011872280475190.200593614023759
770.8690054868784940.2619890262430120.130994513121506
780.8468581352941780.3062837294116440.153141864705822
790.8718763847154820.2562472305690360.128123615284518
800.8497539346186620.3004921307626770.150246065381338
810.8198463359533160.3603073280933680.180153664046684
820.7847397271511150.430520545697770.215260272848885
830.753670100265570.4926597994688610.24632989973443
840.7695118579113480.4609762841773030.230488142088652
850.7661535098613610.4676929802772780.233846490138639
860.746907189496880.506185621006240.25309281050312
870.7734587356049030.4530825287901950.226541264395097
880.7669595735766450.4660808528467090.233040426423355
890.7261015590917650.547796881816470.273898440908235
900.7069090582458160.5861818835083680.293090941754184
910.6845335506715830.6309328986568330.315466449328417
920.6399408258246530.7201183483506930.360059174175347
930.5917436005826110.8165127988347770.408256399417389
940.5843829825706660.8312340348586690.415617017429334
950.5575091887440140.8849816225119730.442490811255986
960.6727847073458960.6544305853082090.327215292654104
970.6258641346454220.7482717307091550.374135865354578
980.608424126433920.783151747132160.39157587356608
990.7166190467222640.5667619065554730.283380953277736
1000.7364828068903510.5270343862192980.263517193109649
1010.7337693227245340.5324613545509320.266230677275466
1020.6902882598478920.6194234803042160.309711740152108
1030.6384511092494610.7230977815010780.361548890750539
1040.5964722435297690.8070555129404630.403527756470231
1050.5639283259694560.8721433480610890.436071674030544
1060.5937397495739550.812520500852090.406260250426045
1070.5707165957640320.8585668084719360.429283404235968
1080.7864741080937640.4270517838124730.213525891906236
1090.756637037477390.4867259250452210.243362962522611
1100.7035755482192020.5928489035615970.296424451780798
1110.6603686604056940.6792626791886110.339631339594306
1120.7063897996248080.5872204007503840.293610200375192
1130.6803383102672570.6393233794654860.319661689732743
1140.644593603665850.71081279266830.35540639633415
1150.5800631268102050.839873746379590.419936873189795
1160.6824734578430850.6350530843138290.317526542156915
1170.6165275930966310.7669448138067380.383472406903369
1180.5483201744591540.9033596510816910.451679825540846
1190.6224107693757040.7551784612485920.377589230624296
1200.5885216222536580.8229567554926850.411478377746342
1210.5561395283757850.887720943248430.443860471624215
1220.5154939515005840.9690120969988310.484506048499416
1230.4560980636263980.9121961272527960.543901936373602
1240.386965643346810.7739312866936190.61303435665319
1250.369536066781090.7390721335621810.63046393321891
1260.9764545335732830.04709093285343320.0235454664267166
1270.9536946975398990.0926106049202030.0463053024601015
1280.973894074538410.0522118509231790.0261059254615895
1290.9485499973635880.1029000052728240.0514500026364122
1300.983706389617160.03258722076568160.0162936103828408
1310.991980726290930.01603854741813970.00801927370906985







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0280373831775701OK
10% type I error level50.0467289719626168OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0280373831775701 & OK \tabularnewline
10% type I error level & 5 & 0.0467289719626168 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98234&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0280373831775701[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.0467289719626168[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98234&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98234&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 testsOK/NOK
1% type I error level00OK
5% type I error level30.0280373831775701OK
10% type I error level50.0467289719626168OK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}