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 computationMon, 05 Nov 2012 09:17:57 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352125098fh4545yopmxcbks.htm/, Retrieved Mon, 06 Feb 2023 00:22:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186075, Retrieved Mon, 06 Feb 2023 00:22:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact57
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 14:17:57] [564f08b1e01e129faa0d56ace254d273] [Current]
Feedback Forum

Post a new message
Dataseries X:
0	0	1	75.50	78.40	0.00	67.30	0.00	75.30	0.00	106.10	0.00	125.70	0.00	101.60	0
0	0	2	83.20	79.30	0.00	75.20	0.00	83.60	0.00	112.70	0.00	153.80	0.00	113.40	0
0	0	3	94.50	84.30	0.00	91.10	0.00	91.20	0.00	123.20	0.00	134.90	0.00	122.20	0
4	1	4	83.30	81.20	81.20	83.70	83.70	85.20	85.20	101.70	101.70	95.30	95.30	102.20	102.2
5	1	5	92.70	88.40	88.40	105.00	105.00	100.00	100.00	118.70	118.70	96.60	96.60	113.20	113.2
6	1	6	89.80	83.10	83.10	106.20	106.20	89.80	89.80	107.10	107.10	100.50	100.50	115.30	115.3
7	1	7	74.80	76.60	76.60	88.50	88.50	88.90	88.90	93.60	93.60	106.20	106.20	87.40	87.4
8	1	8	81.50	82.60	82.60	100.10	100.10	85.60	85.60	77.50	77.50	153.40	153.40	98.70	98.7
9	1	9	92.80	84.40	84.40	90.30	90.30	83.20	83.20	117.20	117.20	132.10	132.10	117.30	117.3
0	0	10	92.80	94.60	0.00	85.30	0.00	97.10	0.00	124.50	0.00	110.90	0.00	121.20	0
0	0	11	91.70	91.80	0.00	81.90	0.00	85.80	0.00	120.80	0.00	94.30	0.00	118.70	0
0	0	12	83.50	89.30	0.00	77.20	0.00	80.90	0.00	97.00	0.00	91.70	0.00	112.10	0
0	0	13	92.80	87.70	0.00	78.60	0.00	81.30	0.00	115.10	0.00	138.60	0.00	102.90	0
0	0	14	91.30	83.10	0.00	75.10	0.00	83.20	0.00	112.90	0.00	154.30	0.00	108.80	0
0	0	15	99.50	93.60	0.00	90.30	0.00	90.70	0.00	122.70	0.00	149.80	0.00	118.60	0
16	1	16	87.60	85.10	85.10	88.50	88.50	88.40	88.40	106.90	106.90	99.20	99.20	99.20	99.2
17	1	17	95.30	90.80	90.80	112.50	112.50	94.10	94.10	115.00	115.00	97.70	97.70	102.20	102.2
18	1	18	98.50	90.50	90.50	101.10	101.10	92.00	92.00	114.90	114.90	107.70	107.70	108.80	108.8
19	1	19	80.10	86.10	86.10	114.00	114.00	92.00	92.00	103.10	103.10	120.10	120.10	94.00	94
20	1	20	84.20	93.30	93.30	107.70	107.70	89.30	89.30	80.80	80.80	164.50	164.50	96.20	96.2
21	1	21	92.40	94.90	94.90	77.80	77.80	87.00	87.00	118.20	118.20	136.10	136.10	118.40	118.4
0	0	22	98.00	102.60	0.00	101.40	0.00	97.70	0.00	129.60	0.00	117.50	0.00	120.00	0
0	0	23	92.20	98.30	0.00	87.20	0.00	82.50	0.00	118.70	0.00	98.20	0.00	117.50	0
0	0	24	80.00	93.40	0.00	75.90	0.00	96.50	0.00	88.40	0.00	91.90	0.00	102.60	0
0	0	25	88.70	92.80	0.00	78.80	0.00	86.20	0.00	113.10	0.00	141.80	0.00	92.80	0
0	0	26	87.40	86.50	0.00	82.30	0.00	84.90	0.00	109.80	0.00	154.20	0.00	100.30	0
0	0	27	96.10	93.80	0.00	89.10	0.00	100.00	0.00	116.10	0.00	138.60	0.00	106.30	0
28	1	28	94.10	90.40	90.40	100.10	100.10	92.70	92.70	113.60	113.60	97.90	97.90	103.90	103.9
29	1	29	91.90	91.00	91.00	101.80	101.80	96.70	96.70	107.90	107.90	90.30	90.30	102.40	102.4
30	1	30	93.60	89.10	89.10	98.50	98.50	105.80	105.80	107.40	107.40	90.90	90.90	114.50	114.5
31	1	31	83.50	89.60	89.60	106.60	106.60	88.50	88.50	102.70	102.70	127.00	127.00	89.00	89
32	1	32	80.80	89.30	89.30	101.80	101.80	78.70	78.70	78.30	78.30	156.80	156.80	94.30	94.3
33	1	33	96.30	95.30	95.30	92.40	92.40	99.90	99.90	121.00	121.00	127.20	127.20	115.70	115.7
0	0	34	101.50	104.10	0.00	94.40	0.00	107.80	0.00	132.20	0.00	111.30	0.00	120.20	0
0	0	35	91.60	94.70	0.00	81.00	0.00	102.40	0.00	113.20	0.00	93.00	0.00	109.50	0
0	0	36	84.00	97.60	0.00	94.60	0.00	106.00	0.00	89.20	0.00	89.50	0.00	99.40	0
0	0	37	91.80	96.80	0.00	83.80	0.00	87.30	0.00	113.20	0.00	141.80	0.00	86.40	0
0	0	38	90.40	92.80	0.00	79.40	0.00	93.30	0.00	107.60	0.00	152.00	0.00	95.10	0
0	0	39	98.00	94.70	0.00	95.60	0.00	98.20	0.00	107.30	0.00	120.20	0.00	101.50	0
40	1	40	95.50	95.80	95.80	106.00	106.00	102.00	102.00	110.90	110.90	88.80	88.80	92.90	92.9
41	1	41	90.50	88.90	88.90	106.20	106.20	93.90	93.90	96.40	96.40	82.80	82.80	90.80	90.8
42	1	42	97.10	91.20	91.20	115.00	115.00	106.60	106.60	101.20	101.20	82.80	82.80	100.40	100.4
43	1	43	87.90	91.60	91.60	122.40	122.40	92.90	92.90	94.00	94.00	121.70	121.70	82.20	82.2
44	1	44	79.80	87.30	87.30	113.70	113.70	78.00	78.00	70.50	70.50	147.10	147.10	75.30	75.3
45	1	45	102.00	97.80	97.80	98.00	98.00	104.20	104.20	116.40	116.40	132.50	132.50	110.30	110.3
0	0	46	104.30	105.10	0.00	105.80	0.00	115.90	0.00	121.90	0.00	107.50	0.00	113.50	0
0	0	47	92.10	93.80	0.00	88.30	0.00	99.90	0.00	109.50	0.00	77.90	0.00	94.90	0
0	0	48	95.90	99.00	0.00	95.70	0.00	103.90	0.00	91.10	0.00	85.50	0.00	95.70	0
0	0	49	89.10	91.40	0.00	85.80	0.00	93.50	0.00	104.00	0.00	126.50	0.00	85.30	0
0	0	50	92.20	89.00	0.00	83.90	0.00	101.70	0.00	101.20	0.00	135.40	0.00	92.50	0
0	0	51	107.50	101.40	0.00	114.10	0.00	124.60	0.00	118.40	0.00	122.50	0.00	107.70	0
52	1	52	99.70	95.40	95.40	102.00	102.00	124.20	124.20	106.90	106.90	79.20	79.20	97.90	97.9
53	1	53	92.20	90.50	90.50	108.10	108.10	103.30	103.30	95.60	95.60	66.10	66.10	93.90	93.9
54	1	54	108.90	98.70	98.70	125.40	125.40	120.50	120.50	114.20	114.20	77.90	77.90	111.50	111.5
55	1	55	89.80	91.20	91.20	108.10	108.10	98.00	98.00	92.40	92.40	109.60	109.60	88.60	88.6
56	1	56	89.40	91.70	91.70	110.40	110.40	100.40	100.40	75.30	75.30	142.90	142.90	82.50	82.5
57	1	57	107.60	102.90	102.90	102.40	102.40	126.80	126.80	120.40	120.40	120.50	120.50	108.60	108.6
0	0	58	105.60	105.50	0.00	89.60	0.00	120.20	0.00	115.90	0.00	96.30	0.00	113.80	0
0	0	59	100.90	102.60	0.00	95.00	0.00	114.00	0.00	109.80	0.00	82.60	0.00	103.40	0
0	0	60	102.90	107.20	0.00	93.70	0.00	109.10	0.00	94.90	0.00	78.40	0.00	99.00	0
0	0	61	96.20	96.90	0.00	77.70	0.00	94.20	0.00	97.50	0.00	104.50	0.00	89.90	0
0	0	62	94.70	88.90	0.00	80.10	0.00	86.00	0.00	101.30	0.00	137.90	0.00	97.90	0
0	0	63	107.30	99.60	0.00	103.60	0.00	112.90	0.00	108.70	0.00	125.80	0.00	107.80	0
64	1	64	103.00	96.70	96.70	103.10	103.10	99.70	99.70	105.10	105.10	78.00	78.00	103.70	103.7
65	1	65	96.10	93.80	93.80	112.40	112.40	104.50	104.50	94.90	94.90	67.70	67.70	98.20	98.2
66	1	66	109.80	101.90	101.90	119.20	119.20	111.60	111.60	108.90	108.90	78.40	78.40	111.70	111.7
67	1	67	85.40	87.60	87.60	105.30	105.30	99.20	99.20	87.50	87.50	101.70	101.70	82.60	82.6
68	1	68	89.90	100.00	100.00	107.20	107.20	90.90	90.90	73.00	73.00	154.10	154.10	86.10	86.1
69	1	69	109.30	105.80	105.80	108.70	108.70	111.40	111.40	115.20	115.20	107.30	107.30	111.20	111.2
0	0	70	101.20	105.50	0.00	93.70	0.00	98.20	0.00	107.50	0.00	86.50	0.00	105.30	0
0	0	71	104.70	111.30	0.00	96.10	0.00	101.70	0.00	109.80	0.00	82.10	0.00	106.30	0
0	0	72	102.40	112.10	0.00	92.90	0.00	89.70	0.00	90.70	0.00	76.10	0.00	99.40	0
0	0	73	97.70	102.00	0.00	81.10	0.00	89.50	0.00	97.60	0.00	115.50	0.00	91.90	0
0	0	74	98.90	93.20	0.00	83.20	0.00	85.10	0.00	98.70	0.00	129.60	0.00	96.20	0
0	0	75	115.00	108.40	0.00	99.70	0.00	95.90	0.00	113.90	0.00	121.60	0.00	105.40	0
76	1	76	97.50	97.90	97.90	96.80	96.80	88.90	88.90	96.60	96.60	64.00	64.00	95.00	95
77	1	77	107.30	106.40	106.40	108.70	108.70	98.10	98.10	104.40	104.40	58.10	58.10	100.50	100.5
78	1	78	112.30	102.80	102.80	120.90	120.90	109.70	109.70	115.10	115.10	79.70	79.70	111.60	111.6
79	1	79	88.50	96.30	96.30	114.80	114.80	92.00	92.00	91.40	91.40	108.90	108.90	88.50	88.5
80	1	80	92.90	105.70	105.70	108.70	108.70	74.30	74.30	76.20	76.20	138.50	138.50	83.70	83.7
81	1	81	108.80	108.40	108.40	97.40	97.40	96.90	96.90	117.40	117.40	117.90	117.90	113.90	113.9
0	0	82	112.30	115.80	0.00	98.60	0.00	100.30	0.00	122.00	0.00	96.70	0.00	115.20	0
0	0	83	107.30	113.80	0.00	91.70	0.00	97.10	0.00	120.20	0.00	78.60	0.00	111.00	0
0	0	84	101.80	106.40	0.00	91.20	0.00	86.00	0.00	93.60	0.00	64.10	0.00	96.90	0
0	0	85	105.00	107.90	0.00	83.50	0.00	97.30	0.00	106.60	0.00	112.00	0.00	102.10	0
0	0	86	103.40	98.20	0.00	82.40	0.00	86.40	0.00	108.40	0.00	139.40	0.00	101.50	0
0	0	87	116.70	111.10	0.00	103.10	0.00	97.70	0.00	121.40	0.00	116.20	0.00	115.00	0
88	1	88	103.60	99.80	99.80	110.30	110.30	90.60	90.60	104.80	104.80	63.40	63.40	105.00	105
89	1	89	108.80	103.50	103.50	115.80	115.80	99.20	99.20	104.20	104.20	61.10	61.10	105.40	105.4
90	1	90	117.00	105.40	105.40	120.10	120.10	107.40	107.40	115.00	115.00	65.50	65.50	119.70	119.7
91	1	91	100.90	102.60	102.60	105.10	105.10	107.10	107.10	99.00	99.00	90.90	90.90	91.80	91.8
92	1	92	100.80	107.40	107.40	108.60	108.60	78.90	78.90	82.80	82.80	115.30	115.30	89.10	89.1
93	1	93	109.70	108.20	108.20	95.70	95.70	92.80	92.80	112.50	112.50	85.20	85.20	106.20	106.2
0	0	94	121.00	121.70	0.00	103.20	0.00	106.20	0.00	127.90	0.00	87.00	0.00	119.90	0
0	0	95	114.10	118.00	0.00	96.90	0.00	97.20	0.00	114.40	0.00	62.60	0.00	111.60	0
0	0	96	105.50	109.60	0.00	95.70	0.00	80.00	0.00	83.70	0.00	62.70	0.00	95.10	0
0	0	97	112.50	116.70	0.00	92.70	0.00	109.30	0.00	108.50	0.00	91.60	0.00	101.30	0
0	0	98	113.80	110.60	0.00	81.30	0.00	111.30	0.00	109.70	0.00	104.30	0.00	118.30	0
0	0	99	115.30	109.60	0.00	94.50	0.00	119.50	0.00	104.70	0.00	88.10	0.00	126.20	0
100	1	100	120.40	117.40	117.40	105.60	105.60	119.80	119.80	112.20	112.20	62.30	62.30	113.20	113.2
101	1	101	111.10	109.20	109.20	112.90	112.90	112.50	112.50	96.90	96.90	50.30	50.30	103.60	103.6
102	1	102	120.10	110.80	110.80	102.60	102.60	125.60	125.60	103.80	103.80	64.10	64.10	116.20	116.2
103	1	103	106.10	112.80	112.80	116.20	116.20	105.10	105.10	95.10	95.10	75.70	75.70	98.30	98.3
104	1	104	95.90	106.50	106.50	104.90	104.90	91.90	91.90	66.70	66.70	85.50	85.50	84.20	84.2
105	1	105	119.40	119.60	119.60	100.40	100.40	128.20	128.20	103.40	103.40	71.90	71.90	118.30	118.3
0	0	106	117.40	127.20	0.00	97.10	0.00	122.60	0.00	105.40	0.00	66.90	0.00	117.40	0
0	0	107	98.60	113.90	0.00	90.20	0.00	109.60	0.00	89.20	0.00	50.50	0.00	94.50	0
0	0	108	99.70	120.00	0.00	100.50	0.00	120.40	0.00	72.50	0.00	57.90	0.00	93.30	0
0	0	109	87.40	107.60	0.00	81.10	0.00	103.80	0.00	78.00	0.00	84.10	0.00	90.20	0
0	0	110	90.80	105.20	0.00	87.20	0.00	96.60	0.00	77.30	0.00	87.00	0.00	88.50	0
0	0	111	101.30	115.30	0.00	102.00	0.00	110.70	0.00	85.10	0.00	71.90	0.00	101.00	0
112	1	112	93.20	113.90	113.90	107.00	107.00	111.70	111.70	80.90	80.90	45.00	45.00	87.00	87
113	1	113	95.10	106.10	106.10	107.60	107.60	111.90	111.90	72.50	72.50	39.50	39.50	81.20	81.2
114	1	114	101.90	114.30	114.30	123.50	123.50	131.50	131.50	82.10	82.10	53.80	53.80	98.10	98.1
115	1	115	87.00	112.00	112.00	116.60	116.60	122.80	122.80	78.30	78.30	59.50	59.50	75.50	75.5
116	1	116	86.20	109.00	109.00	103.20	103.20	98.30	98.30	57.80	57.80	68.40	68.40	70.70	70.7
117	1	117	105.00	119.10	119.10	103.90	103.90	133.70	133.70	89.30	89.30	56.90	56.90	103.70	103.7
0	0	118	104.10	124.40	0.00	95.40	0.00	120.00	0.00	91.40	0.00	61.90	0.00	100.40	0
0	0	119	99.20	116.60	0.00	93.60	0.00	119.60	0.00	84.20	0.00	40.40	0.00	91.30	0
0	0	120	95.20	118.50	0.00	102.10	0.00	108.70	0.00	72.50	0.00	49.40	0.00	97.20	0
0	0	121	92.70	108.90	0.00	69.00	0.00	112.50	0.00	74.60	0.00	65.20	0.00	85.40	0
0	0	122	99.30	107.50	0.00	88.90	0.00	102.70	0.00	80.30	0.00	82.10	0.00	86.50	0
0	0	123	113.50	125.90	0.00	106.20	0.00	123.40	0.00	92.60	0.00	69.00	0.00	105.30	0
124	1	124	104.70	117.70	117.70	103.00	103.00	116.50	116.50	86.30	86.30	45.90	45.90	97.70	97.7
125	1	125	100.50	109.20	109.20	103.50	103.50	102.30	102.30	80.30	80.30	39.10	39.10	84.30	84.3
126	1	126	116.20	118.80	118.80	124.50	124.50	148.40	148.40	93.60	93.60	56.90	56.90	109.80	109.8
127	1	127	94.10	108.10	108.10	117.90	117.90	126.60	126.60	79.50	79.50	51.60	51.60	79.10	79.1
128	1	128	94.80	112.10	112.10	104.20	104.20	106.60	106.60	61.80	61.80	62.90	62.90	83.40	83.4
129	1	129	115.10	117.80	117.80	99.90	99.90	144.40	144.40	94.80	94.80	58.30	58.30	101.90	101.9
0	0	130	110.00	121.80	0.00	89.40	0.00	132.40	0.00	91.60	0.00	56.90	0.00	113.00	0
0	0	131	108.40	121.00	0.00	93.50	0.00	136.20	0.00	89.20	0.00	41.30	0.00	98.60	0
0	0	132	103.90	121.70	0.00	89.60	0.00	121.60	0.00	74.10	0.00	46.90	0.00	94.70	0
0	0	133	102.90	114.20	0.00	85.00	0.00	135.10	0.00	78.60	0.00	61.90	0.00	94.50	0
0	0	134	107.70	109.80	0.00	90.00	0.00	124.70	0.00	78.20	0.00	74.80	0.00	90.70	0
0	0	135	126.70	124.10	0.00	113.70	0.00	148.80	0.00	95.10	0.00	67.00	0.00	113.00	0
136	1	136	108.80	112.90	112.90	112.10	112.10	145.60	145.60	78.70	78.70	53.30	53.30	89.90	89.9
137	1	137	117.10	118.70	118.70	129.80	129.80	140.30	140.30	85.90	85.90	51.40	51.40	98.70	98.7
138	1	138	112.20	113.30	113.30	119.10	119.10	138.50	138.50	81.20	81.20	50.30	50.30	102.20	102.2
139	1	139	94.70	106.80	106.80	103.50	103.50	127.30	127.30	73.10	73.10	52.70	52.70	74.30	74.3
140	1	140	102.70	119.30	119.30	105.50	105.50	117.90	117.90	58.70	58.70	70.30	70.30	84.50	84.5
141	1	141	119.10	126.40	126.40	111.70	111.70	145.30	145.30	85.70	85.70	59.70	59.70	110.10	110.1
0	0	142	110.60	126.60	0.00	98.60	0.00	120.70	0.00	81.80	0.00	52.00	0.00	100.40	0
0	0	143	109.10	127.20	0.00	102.80	0.00	134.70	0.00	79.60	0.00	36.10	0.00	92.80	0
0	0	144	105.30	123.80	0.00	101.10	0.00	124.40	0.00	70.70	0.00	39.70	0.00	92.20	0
0	0	145	103.40	116.80	0.00	94.20	0.00	128.30	0.00	74.50	0.00	67.60	0.00	94.00	0
0	0	146	103.70	113.80	0.00	92.60	0.00	128.40	0.00	84.80	0.00	72.80	0.00	100.70	0
0	0	147	117.00	130.40	0.00	112.00	0.00	134.10	0.00	80.70	0.00	53.80	0.00	111.90	0
148	1	148	101.20	112.80	112.80	108.60	108.60	133.30	133.30	69.90	69.90	39.60	39.60	95.90	95.9
149	1	149	105.40	119.40	119.40	125.80	125.80	130.60	130.60	74.10	74.10	39.40	39.40	88.80	88.8
150	1	150	110.30	117.50	117.50	138.70	138.70	165.70	165.70	76.10	76.10	41.20	41.20	102.00	102
151	1	151	97.70	117.50	117.50	115.20	115.20	146.80	146.80	71.30	71.30	49.60	49.60	81.60	81.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=186075&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=186075&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186075&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 time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = -10.6059397600565 -0.041735987522976s_t[t] -3.1774365617245s[t] + 0.203077934796797t + 0.197000832977214voeding[t] + 0.0612229420211562voeding_s[t] + 0.249547417839873dranken[t] -0.117940135739093dranken_s[t] -0.0163843621853488tabak[t] -0.0110643132889445tabak_s[t] + 0.302937607211015textiel[t] -0.0708112500168828textiel_s[t] + 0.0606263900773153kleding[t] -0.0548195277304905kleding_s[t] + 0.173418455257098apparatuur[t] + 0.2385537693664`apparatuur_s\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  -10.6059397600565 -0.041735987522976s_t[t] -3.1774365617245s[t] +  0.203077934796797t +  0.197000832977214voeding[t] +  0.0612229420211562voeding_s[t] +  0.249547417839873dranken[t] -0.117940135739093dranken_s[t] -0.0163843621853488tabak[t] -0.0110643132889445tabak_s[t] +  0.302937607211015textiel[t] -0.0708112500168828textiel_s[t] +  0.0606263900773153kleding[t] -0.0548195277304905kleding_s[t] +  0.173418455257098apparatuur[t] +  0.2385537693664`apparatuur_s\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186075&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  -10.6059397600565 -0.041735987522976s_t[t] -3.1774365617245s[t] +  0.203077934796797t +  0.197000832977214voeding[t] +  0.0612229420211562voeding_s[t] +  0.249547417839873dranken[t] -0.117940135739093dranken_s[t] -0.0163843621853488tabak[t] -0.0110643132889445tabak_s[t] +  0.302937607211015textiel[t] -0.0708112500168828textiel_s[t] +  0.0606263900773153kleding[t] -0.0548195277304905kleding_s[t] +  0.173418455257098apparatuur[t] +  0.2385537693664`apparatuur_s\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186075&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186075&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
Totaal[t] = -10.6059397600565 -0.041735987522976s_t[t] -3.1774365617245s[t] + 0.203077934796797t + 0.197000832977214voeding[t] + 0.0612229420211562voeding_s[t] + 0.249547417839873dranken[t] -0.117940135739093dranken_s[t] -0.0163843621853488tabak[t] -0.0110643132889445tabak_s[t] + 0.302937607211015textiel[t] -0.0708112500168828textiel_s[t] + 0.0606263900773153kleding[t] -0.0548195277304905kleding_s[t] + 0.173418455257098apparatuur[t] + 0.2385537693664`apparatuur_s\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-10.60593976005659.215295-1.15090.2518050.125903
s_t-0.0417359875229760.053354-0.78230.4354370.217719
s-3.177436561724513.136354-0.24190.8092390.40462
t0.2030779347967970.0317696.392200
voeding0.1970008329772140.119671.64620.1020480.051024
voeding_s0.06122294202115620.1716570.35670.7219050.360952
dranken0.2495474178398730.0646873.85780.0001768.8e-05
dranken_s-0.1179401357390930.08016-1.47130.1435360.071768
tabak-0.01638436218534880.042768-0.38310.7022510.351126
tabak_s-0.01106431328894450.056267-0.19660.8444050.422202
textiel0.3029376072110150.0598745.05961e-061e-06
textiel_s-0.07081125001688280.078933-0.89710.3712580.185629
kleding0.06062639007731530.0289352.09530.0380130.019006
kleding_s-0.05481952773049050.036205-1.51410.1323280.066164
apparatuur0.1734184552570980.0642672.69840.0078570.003929
`apparatuur_s\r`0.23855376936640.0916962.60160.0103150.005157

\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) & -10.6059397600565 & 9.215295 & -1.1509 & 0.251805 & 0.125903 \tabularnewline
s_t & -0.041735987522976 & 0.053354 & -0.7823 & 0.435437 & 0.217719 \tabularnewline
s & -3.1774365617245 & 13.136354 & -0.2419 & 0.809239 & 0.40462 \tabularnewline
t & 0.203077934796797 & 0.031769 & 6.3922 & 0 & 0 \tabularnewline
voeding & 0.197000832977214 & 0.11967 & 1.6462 & 0.102048 & 0.051024 \tabularnewline
voeding_s & 0.0612229420211562 & 0.171657 & 0.3567 & 0.721905 & 0.360952 \tabularnewline
dranken & 0.249547417839873 & 0.064687 & 3.8578 & 0.000176 & 8.8e-05 \tabularnewline
dranken_s & -0.117940135739093 & 0.08016 & -1.4713 & 0.143536 & 0.071768 \tabularnewline
tabak & -0.0163843621853488 & 0.042768 & -0.3831 & 0.702251 & 0.351126 \tabularnewline
tabak_s & -0.0110643132889445 & 0.056267 & -0.1966 & 0.844405 & 0.422202 \tabularnewline
textiel & 0.302937607211015 & 0.059874 & 5.0596 & 1e-06 & 1e-06 \tabularnewline
textiel_s & -0.0708112500168828 & 0.078933 & -0.8971 & 0.371258 & 0.185629 \tabularnewline
kleding & 0.0606263900773153 & 0.028935 & 2.0953 & 0.038013 & 0.019006 \tabularnewline
kleding_s & -0.0548195277304905 & 0.036205 & -1.5141 & 0.132328 & 0.066164 \tabularnewline
apparatuur & 0.173418455257098 & 0.064267 & 2.6984 & 0.007857 & 0.003929 \tabularnewline
`apparatuur_s\r` & 0.2385537693664 & 0.091696 & 2.6016 & 0.010315 & 0.005157 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186075&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]-10.6059397600565[/C][C]9.215295[/C][C]-1.1509[/C][C]0.251805[/C][C]0.125903[/C][/ROW]
[ROW][C]s_t[/C][C]-0.041735987522976[/C][C]0.053354[/C][C]-0.7823[/C][C]0.435437[/C][C]0.217719[/C][/ROW]
[ROW][C]s[/C][C]-3.1774365617245[/C][C]13.136354[/C][C]-0.2419[/C][C]0.809239[/C][C]0.40462[/C][/ROW]
[ROW][C]t[/C][C]0.203077934796797[/C][C]0.031769[/C][C]6.3922[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]voeding[/C][C]0.197000832977214[/C][C]0.11967[/C][C]1.6462[/C][C]0.102048[/C][C]0.051024[/C][/ROW]
[ROW][C]voeding_s[/C][C]0.0612229420211562[/C][C]0.171657[/C][C]0.3567[/C][C]0.721905[/C][C]0.360952[/C][/ROW]
[ROW][C]dranken[/C][C]0.249547417839873[/C][C]0.064687[/C][C]3.8578[/C][C]0.000176[/C][C]8.8e-05[/C][/ROW]
[ROW][C]dranken_s[/C][C]-0.117940135739093[/C][C]0.08016[/C][C]-1.4713[/C][C]0.143536[/C][C]0.071768[/C][/ROW]
[ROW][C]tabak[/C][C]-0.0163843621853488[/C][C]0.042768[/C][C]-0.3831[/C][C]0.702251[/C][C]0.351126[/C][/ROW]
[ROW][C]tabak_s[/C][C]-0.0110643132889445[/C][C]0.056267[/C][C]-0.1966[/C][C]0.844405[/C][C]0.422202[/C][/ROW]
[ROW][C]textiel[/C][C]0.302937607211015[/C][C]0.059874[/C][C]5.0596[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]textiel_s[/C][C]-0.0708112500168828[/C][C]0.078933[/C][C]-0.8971[/C][C]0.371258[/C][C]0.185629[/C][/ROW]
[ROW][C]kleding[/C][C]0.0606263900773153[/C][C]0.028935[/C][C]2.0953[/C][C]0.038013[/C][C]0.019006[/C][/ROW]
[ROW][C]kleding_s[/C][C]-0.0548195277304905[/C][C]0.036205[/C][C]-1.5141[/C][C]0.132328[/C][C]0.066164[/C][/ROW]
[ROW][C]apparatuur[/C][C]0.173418455257098[/C][C]0.064267[/C][C]2.6984[/C][C]0.007857[/C][C]0.003929[/C][/ROW]
[ROW][C]`apparatuur_s\r`[/C][C]0.2385537693664[/C][C]0.091696[/C][C]2.6016[/C][C]0.010315[/C][C]0.005157[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186075&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186075&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)-10.60593976005659.215295-1.15090.2518050.125903
s_t-0.0417359875229760.053354-0.78230.4354370.217719
s-3.177436561724513.136354-0.24190.8092390.40462
t0.2030779347967970.0317696.392200
voeding0.1970008329772140.119671.64620.1020480.051024
voeding_s0.06122294202115620.1716570.35670.7219050.360952
dranken0.2495474178398730.0646873.85780.0001768.8e-05
dranken_s-0.1179401357390930.08016-1.47130.1435360.071768
tabak-0.01638436218534880.042768-0.38310.7022510.351126
tabak_s-0.01106431328894450.056267-0.19660.8444050.422202
textiel0.3029376072110150.0598745.05961e-061e-06
textiel_s-0.07081125001688280.078933-0.89710.3712580.185629
kleding0.06062639007731530.0289352.09530.0380130.019006
kleding_s-0.05481952773049050.036205-1.51410.1323280.066164
apparatuur0.1734184552570980.0642672.69840.0078570.003929
`apparatuur_s\r`0.23855376936640.0916962.60160.0103150.005157







Multiple Linear Regression - Regression Statistics
Multiple R0.947303847629336
R-squared0.897384579733344
Adjusted R-squared0.885982866370382
F-TEST (value)78.7061164551355
F-TEST (DF numerator)15
F-TEST (DF denominator)135
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.56147528397684
Sum Squared Residuals1712.35433678102

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.947303847629336 \tabularnewline
R-squared & 0.897384579733344 \tabularnewline
Adjusted R-squared & 0.885982866370382 \tabularnewline
F-TEST (value) & 78.7061164551355 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 135 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.56147528397684 \tabularnewline
Sum Squared Residuals & 1712.35433678102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186075&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.947303847629336[/C][/ROW]
[ROW][C]R-squared[/C][C]0.897384579733344[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.885982866370382[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]78.7061164551355[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]135[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.56147528397684[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1712.35433678102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186075&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186075&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 R0.947303847629336
R-squared0.897384579733344
Adjusted R-squared0.885982866370382
F-TEST (value)78.7061164551355
F-TEST (DF numerator)15
F-TEST (DF denominator)135
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.56147528397684
Sum Squared Residuals1712.35433678102







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
175.577.9845346401489-2.48453464014888
283.285.9496752602208-2.74967526022084
394.594.5421286604659-0.042128660465873
483.382.77087022342460.529129776575405
592.795.6738095266234-2.97380952662335
689.893.0995933861746-3.29959338617456
774.874.68310393696260.116896063037444
881.579.20314932585092.29685067414906
992.895.3598331152892-2.55983311528924
1092.895.2141070956056-2.4141070956056
1191.791.64145140999810.0585485900018664
1283.581.74733230269271.75266769730733
1392.888.70912014160784.09087985839223
1491.388.40998846848422.89001153151583
1599.598.74728384115730.752716158842748
1687.686.25171265246171.34828734753829
1795.393.99447731106421.30552268893579
1898.595.38954399864963.11045600135043
1980.187.3481604288127-7.24816042881272
2084.284.601444919562-0.401444919562037
2192.498.9664133806632-6.5664133806632
2298104.971945611874-6.97194561187356
2392.296.1277335515244-3.92773355152444
248080.1713497732358-0.171349773235839
2588.789.9569885519416-1.25698855194158
2687.490.866388419853-3.46638841985303
2796.195.96033697946170.139663020538269
2894.194.4489843229488-0.348984322948767
2991.992.8939874861181-0.993987486118083
3093.696.7529021399017-3.15290213990173
3183.587.1975791694051-3.69757916940506
3280.883.6113502226468-2.81135022264679
3396.3102.059132381775-5.7591323817748
34101.5106.238505947271-4.73850594727051
3591.692.613461262153-1.01346126215299
368487.4884814549071-3.48848145490706
3791.893.3320570392832-1.53205703928316
3890.491.9814959697073-1.58149596970724
399895.6123379084822.38766209151802
4095.593.08942827507072.41057172492926
4190.587.45186687751253.04813312248747
4297.194.08580928216343.01419071783663
4387.986.75706416632621.14293583367383
4479.876.92196235104232.87803764895771
45102101.998111776160.00188822384025417
46104.3107.072027940693-2.77202794069272
4792.192.1675157021276-0.0675157021276568
4895.990.20155476779025.69844523220979
4989.191.1973294935913-2.09732949359129
5092.291.25907601477550.940923985224481
51107.5108.130501335149-0.630501335149437
5299.794.86206222423634.83793777576374
5392.290.78760278071451.41239721928554
54108.9106.5078508180382.39214918196247
5589.888.76286267633581.03713732366417
5689.483.00111367672866.39888632327142
57107.6105.3683586702162.23164132978362
58105.6103.0300268894872.56997311051267
59100.999.62888862781441.27111137218556
60102.994.96259973692277.93740026307728
6196.290.17981602010496.02018397989508
6294.794.10358484186050.596415158139461
63107.3105.0631983464362.23680165356407
6410399.91576028350173.0842397164983
6596.195.72710060355920.372899396440783
66109.8107.5536265109842.24637348901624
6785.485.7127949426441-0.312794942644053
6889.987.93433973616671.96566026383327
69109.3109.092566608170.20743339182994
70101.2102.237691095254-1.03769109525449
71104.7104.728361231988-0.028361231988344
72102.497.14064646283525.25935353716476
7397.795.59094417086282.10905582913725
7498.996.59031837216712.30968162783288
75115109.4434805483285.55651945167208
7697.595.99052353544291.50947646455706
77107.3103.7025387465363.5974612534641
78112.3111.4035512519330.896448748067234
7988.594.721083123021-6.22108312302095
8092.991.65886150828211.24113849171791
81108.8112.295451030277-3.49545103027699
82112.3114.619937269701-2.31993726970112
83107.3110.388583448982-3.08858344898243
84101.897.80852470402613.99147529597388
85105103.9444144240111.05558557598922
86103.4104.243071375069-0.843071375069057
87116.7116.840754101783-0.14075410178315
88103.6106.166961893099-2.56696189309871
89108.8107.7836705424771.01632945752291
90117117.200187500531-0.200187500530643
91100.999.61207577044721.28792422955276
92100.897.51648542167523.28351457832482
93109.7109.5692271538430.130772846156915
94121121.284750425501-0.284750425501495
95114.1112.325921011691.77407898830957
96105.598.70101966354626.79898033645377
97112.5109.4142492015923.0857507984075
98113.8109.6196067894894.18039321051117
99115.3111.6585282780983.64147172190163
100120.4116.3172678872014.08273211279941
101111.1107.9461344001683.15386559983188
102120.1113.678158328376.42184167163017
103106.1107.382062184308-1.28206218430761
10495.992.44746491710783.45253508289226
105119.4116.8912354684592.50876453154101
106117.4114.546014694832.85398530516984
10798.6100.74695641687-2.14695641687047
10899.799.7266018248169-0.0266018248168517
10987.495.6346009852687-8.23460098526869
11090.896.6740324097174-5.87403240971736
111101.3105.944286571593-4.6442865715929
11293.299.596486524363-6.39648652436295
11395.193.44591961466061.65408038533944
114101.9106.553040019788-4.65304001978765
1158795.2916271962443-8.29162719624433
11686.286.902876861707-0.702876861707009
117105109.633005669347-4.63300566934707
118104.1108.557344120698-4.45734412069821
11999.2101.718457849614-2.51845784961391
12095.2102.62001635887-7.42001635887048
12192.792.15733435661290.542665643387148
12299.3100.153262143864-0.853262143863675
123113.5114.15136325435-0.651363254350386
124104.7107.522468774395-2.82246877439478
125100.598.99181084988971.50818915011027
126116.2116.826404450254-0.626404450254007
12794.198.0032197657029-3.90321976570289
12894.895.6718721457394-0.871872145739382
129115.1110.9565627406884.14343725931203
130110110.724154681677-0.724154681676504
131108.4107.5604680887160.839531911284088
132103.9102.2562413049711.6437586950287
133102.9101.8506373734311.04936262656875
134107.7102.6069613581915.09303864180871
135126.7119.6605531501977.03944684980329
136108.8103.6833949471315.11660505286856
137117.1113.1029939726193.99700602738106
138112.2110.8556585910651.34434140893475
13994.794.23258547475390.467414525246121
140102.799.10465464803553.59534535196447
141119.1117.9156046925541.18439530744622
142110.6111.143296780938-0.543296780937884
143109.1109.334890701806-0.234890701806101
144105.3106.030753351608-0.730753351607722
145103.4106.223841669968-2.82384166996776
146103.7110.032421033969-6.33242103396864
147117117.80188293567-0.801882935669587
148101.2105.82023654818-4.62023654817965
149105.4108.072378619276-2.67237861927586
150110.3114.390119255648-4.09011925564789
15197.7102.50780778688-4.80780778687964

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 75.5 & 77.9845346401489 & -2.48453464014888 \tabularnewline
2 & 83.2 & 85.9496752602208 & -2.74967526022084 \tabularnewline
3 & 94.5 & 94.5421286604659 & -0.042128660465873 \tabularnewline
4 & 83.3 & 82.7708702234246 & 0.529129776575405 \tabularnewline
5 & 92.7 & 95.6738095266234 & -2.97380952662335 \tabularnewline
6 & 89.8 & 93.0995933861746 & -3.29959338617456 \tabularnewline
7 & 74.8 & 74.6831039369626 & 0.116896063037444 \tabularnewline
8 & 81.5 & 79.2031493258509 & 2.29685067414906 \tabularnewline
9 & 92.8 & 95.3598331152892 & -2.55983311528924 \tabularnewline
10 & 92.8 & 95.2141070956056 & -2.4141070956056 \tabularnewline
11 & 91.7 & 91.6414514099981 & 0.0585485900018664 \tabularnewline
12 & 83.5 & 81.7473323026927 & 1.75266769730733 \tabularnewline
13 & 92.8 & 88.7091201416078 & 4.09087985839223 \tabularnewline
14 & 91.3 & 88.4099884684842 & 2.89001153151583 \tabularnewline
15 & 99.5 & 98.7472838411573 & 0.752716158842748 \tabularnewline
16 & 87.6 & 86.2517126524617 & 1.34828734753829 \tabularnewline
17 & 95.3 & 93.9944773110642 & 1.30552268893579 \tabularnewline
18 & 98.5 & 95.3895439986496 & 3.11045600135043 \tabularnewline
19 & 80.1 & 87.3481604288127 & -7.24816042881272 \tabularnewline
20 & 84.2 & 84.601444919562 & -0.401444919562037 \tabularnewline
21 & 92.4 & 98.9664133806632 & -6.5664133806632 \tabularnewline
22 & 98 & 104.971945611874 & -6.97194561187356 \tabularnewline
23 & 92.2 & 96.1277335515244 & -3.92773355152444 \tabularnewline
24 & 80 & 80.1713497732358 & -0.171349773235839 \tabularnewline
25 & 88.7 & 89.9569885519416 & -1.25698855194158 \tabularnewline
26 & 87.4 & 90.866388419853 & -3.46638841985303 \tabularnewline
27 & 96.1 & 95.9603369794617 & 0.139663020538269 \tabularnewline
28 & 94.1 & 94.4489843229488 & -0.348984322948767 \tabularnewline
29 & 91.9 & 92.8939874861181 & -0.993987486118083 \tabularnewline
30 & 93.6 & 96.7529021399017 & -3.15290213990173 \tabularnewline
31 & 83.5 & 87.1975791694051 & -3.69757916940506 \tabularnewline
32 & 80.8 & 83.6113502226468 & -2.81135022264679 \tabularnewline
33 & 96.3 & 102.059132381775 & -5.7591323817748 \tabularnewline
34 & 101.5 & 106.238505947271 & -4.73850594727051 \tabularnewline
35 & 91.6 & 92.613461262153 & -1.01346126215299 \tabularnewline
36 & 84 & 87.4884814549071 & -3.48848145490706 \tabularnewline
37 & 91.8 & 93.3320570392832 & -1.53205703928316 \tabularnewline
38 & 90.4 & 91.9814959697073 & -1.58149596970724 \tabularnewline
39 & 98 & 95.612337908482 & 2.38766209151802 \tabularnewline
40 & 95.5 & 93.0894282750707 & 2.41057172492926 \tabularnewline
41 & 90.5 & 87.4518668775125 & 3.04813312248747 \tabularnewline
42 & 97.1 & 94.0858092821634 & 3.01419071783663 \tabularnewline
43 & 87.9 & 86.7570641663262 & 1.14293583367383 \tabularnewline
44 & 79.8 & 76.9219623510423 & 2.87803764895771 \tabularnewline
45 & 102 & 101.99811177616 & 0.00188822384025417 \tabularnewline
46 & 104.3 & 107.072027940693 & -2.77202794069272 \tabularnewline
47 & 92.1 & 92.1675157021276 & -0.0675157021276568 \tabularnewline
48 & 95.9 & 90.2015547677902 & 5.69844523220979 \tabularnewline
49 & 89.1 & 91.1973294935913 & -2.09732949359129 \tabularnewline
50 & 92.2 & 91.2590760147755 & 0.940923985224481 \tabularnewline
51 & 107.5 & 108.130501335149 & -0.630501335149437 \tabularnewline
52 & 99.7 & 94.8620622242363 & 4.83793777576374 \tabularnewline
53 & 92.2 & 90.7876027807145 & 1.41239721928554 \tabularnewline
54 & 108.9 & 106.507850818038 & 2.39214918196247 \tabularnewline
55 & 89.8 & 88.7628626763358 & 1.03713732366417 \tabularnewline
56 & 89.4 & 83.0011136767286 & 6.39888632327142 \tabularnewline
57 & 107.6 & 105.368358670216 & 2.23164132978362 \tabularnewline
58 & 105.6 & 103.030026889487 & 2.56997311051267 \tabularnewline
59 & 100.9 & 99.6288886278144 & 1.27111137218556 \tabularnewline
60 & 102.9 & 94.9625997369227 & 7.93740026307728 \tabularnewline
61 & 96.2 & 90.1798160201049 & 6.02018397989508 \tabularnewline
62 & 94.7 & 94.1035848418605 & 0.596415158139461 \tabularnewline
63 & 107.3 & 105.063198346436 & 2.23680165356407 \tabularnewline
64 & 103 & 99.9157602835017 & 3.0842397164983 \tabularnewline
65 & 96.1 & 95.7271006035592 & 0.372899396440783 \tabularnewline
66 & 109.8 & 107.553626510984 & 2.24637348901624 \tabularnewline
67 & 85.4 & 85.7127949426441 & -0.312794942644053 \tabularnewline
68 & 89.9 & 87.9343397361667 & 1.96566026383327 \tabularnewline
69 & 109.3 & 109.09256660817 & 0.20743339182994 \tabularnewline
70 & 101.2 & 102.237691095254 & -1.03769109525449 \tabularnewline
71 & 104.7 & 104.728361231988 & -0.028361231988344 \tabularnewline
72 & 102.4 & 97.1406464628352 & 5.25935353716476 \tabularnewline
73 & 97.7 & 95.5909441708628 & 2.10905582913725 \tabularnewline
74 & 98.9 & 96.5903183721671 & 2.30968162783288 \tabularnewline
75 & 115 & 109.443480548328 & 5.55651945167208 \tabularnewline
76 & 97.5 & 95.9905235354429 & 1.50947646455706 \tabularnewline
77 & 107.3 & 103.702538746536 & 3.5974612534641 \tabularnewline
78 & 112.3 & 111.403551251933 & 0.896448748067234 \tabularnewline
79 & 88.5 & 94.721083123021 & -6.22108312302095 \tabularnewline
80 & 92.9 & 91.6588615082821 & 1.24113849171791 \tabularnewline
81 & 108.8 & 112.295451030277 & -3.49545103027699 \tabularnewline
82 & 112.3 & 114.619937269701 & -2.31993726970112 \tabularnewline
83 & 107.3 & 110.388583448982 & -3.08858344898243 \tabularnewline
84 & 101.8 & 97.8085247040261 & 3.99147529597388 \tabularnewline
85 & 105 & 103.944414424011 & 1.05558557598922 \tabularnewline
86 & 103.4 & 104.243071375069 & -0.843071375069057 \tabularnewline
87 & 116.7 & 116.840754101783 & -0.14075410178315 \tabularnewline
88 & 103.6 & 106.166961893099 & -2.56696189309871 \tabularnewline
89 & 108.8 & 107.783670542477 & 1.01632945752291 \tabularnewline
90 & 117 & 117.200187500531 & -0.200187500530643 \tabularnewline
91 & 100.9 & 99.6120757704472 & 1.28792422955276 \tabularnewline
92 & 100.8 & 97.5164854216752 & 3.28351457832482 \tabularnewline
93 & 109.7 & 109.569227153843 & 0.130772846156915 \tabularnewline
94 & 121 & 121.284750425501 & -0.284750425501495 \tabularnewline
95 & 114.1 & 112.32592101169 & 1.77407898830957 \tabularnewline
96 & 105.5 & 98.7010196635462 & 6.79898033645377 \tabularnewline
97 & 112.5 & 109.414249201592 & 3.0857507984075 \tabularnewline
98 & 113.8 & 109.619606789489 & 4.18039321051117 \tabularnewline
99 & 115.3 & 111.658528278098 & 3.64147172190163 \tabularnewline
100 & 120.4 & 116.317267887201 & 4.08273211279941 \tabularnewline
101 & 111.1 & 107.946134400168 & 3.15386559983188 \tabularnewline
102 & 120.1 & 113.67815832837 & 6.42184167163017 \tabularnewline
103 & 106.1 & 107.382062184308 & -1.28206218430761 \tabularnewline
104 & 95.9 & 92.4474649171078 & 3.45253508289226 \tabularnewline
105 & 119.4 & 116.891235468459 & 2.50876453154101 \tabularnewline
106 & 117.4 & 114.54601469483 & 2.85398530516984 \tabularnewline
107 & 98.6 & 100.74695641687 & -2.14695641687047 \tabularnewline
108 & 99.7 & 99.7266018248169 & -0.0266018248168517 \tabularnewline
109 & 87.4 & 95.6346009852687 & -8.23460098526869 \tabularnewline
110 & 90.8 & 96.6740324097174 & -5.87403240971736 \tabularnewline
111 & 101.3 & 105.944286571593 & -4.6442865715929 \tabularnewline
112 & 93.2 & 99.596486524363 & -6.39648652436295 \tabularnewline
113 & 95.1 & 93.4459196146606 & 1.65408038533944 \tabularnewline
114 & 101.9 & 106.553040019788 & -4.65304001978765 \tabularnewline
115 & 87 & 95.2916271962443 & -8.29162719624433 \tabularnewline
116 & 86.2 & 86.902876861707 & -0.702876861707009 \tabularnewline
117 & 105 & 109.633005669347 & -4.63300566934707 \tabularnewline
118 & 104.1 & 108.557344120698 & -4.45734412069821 \tabularnewline
119 & 99.2 & 101.718457849614 & -2.51845784961391 \tabularnewline
120 & 95.2 & 102.62001635887 & -7.42001635887048 \tabularnewline
121 & 92.7 & 92.1573343566129 & 0.542665643387148 \tabularnewline
122 & 99.3 & 100.153262143864 & -0.853262143863675 \tabularnewline
123 & 113.5 & 114.15136325435 & -0.651363254350386 \tabularnewline
124 & 104.7 & 107.522468774395 & -2.82246877439478 \tabularnewline
125 & 100.5 & 98.9918108498897 & 1.50818915011027 \tabularnewline
126 & 116.2 & 116.826404450254 & -0.626404450254007 \tabularnewline
127 & 94.1 & 98.0032197657029 & -3.90321976570289 \tabularnewline
128 & 94.8 & 95.6718721457394 & -0.871872145739382 \tabularnewline
129 & 115.1 & 110.956562740688 & 4.14343725931203 \tabularnewline
130 & 110 & 110.724154681677 & -0.724154681676504 \tabularnewline
131 & 108.4 & 107.560468088716 & 0.839531911284088 \tabularnewline
132 & 103.9 & 102.256241304971 & 1.6437586950287 \tabularnewline
133 & 102.9 & 101.850637373431 & 1.04936262656875 \tabularnewline
134 & 107.7 & 102.606961358191 & 5.09303864180871 \tabularnewline
135 & 126.7 & 119.660553150197 & 7.03944684980329 \tabularnewline
136 & 108.8 & 103.683394947131 & 5.11660505286856 \tabularnewline
137 & 117.1 & 113.102993972619 & 3.99700602738106 \tabularnewline
138 & 112.2 & 110.855658591065 & 1.34434140893475 \tabularnewline
139 & 94.7 & 94.2325854747539 & 0.467414525246121 \tabularnewline
140 & 102.7 & 99.1046546480355 & 3.59534535196447 \tabularnewline
141 & 119.1 & 117.915604692554 & 1.18439530744622 \tabularnewline
142 & 110.6 & 111.143296780938 & -0.543296780937884 \tabularnewline
143 & 109.1 & 109.334890701806 & -0.234890701806101 \tabularnewline
144 & 105.3 & 106.030753351608 & -0.730753351607722 \tabularnewline
145 & 103.4 & 106.223841669968 & -2.82384166996776 \tabularnewline
146 & 103.7 & 110.032421033969 & -6.33242103396864 \tabularnewline
147 & 117 & 117.80188293567 & -0.801882935669587 \tabularnewline
148 & 101.2 & 105.82023654818 & -4.62023654817965 \tabularnewline
149 & 105.4 & 108.072378619276 & -2.67237861927586 \tabularnewline
150 & 110.3 & 114.390119255648 & -4.09011925564789 \tabularnewline
151 & 97.7 & 102.50780778688 & -4.80780778687964 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186075&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]75.5[/C][C]77.9845346401489[/C][C]-2.48453464014888[/C][/ROW]
[ROW][C]2[/C][C]83.2[/C][C]85.9496752602208[/C][C]-2.74967526022084[/C][/ROW]
[ROW][C]3[/C][C]94.5[/C][C]94.5421286604659[/C][C]-0.042128660465873[/C][/ROW]
[ROW][C]4[/C][C]83.3[/C][C]82.7708702234246[/C][C]0.529129776575405[/C][/ROW]
[ROW][C]5[/C][C]92.7[/C][C]95.6738095266234[/C][C]-2.97380952662335[/C][/ROW]
[ROW][C]6[/C][C]89.8[/C][C]93.0995933861746[/C][C]-3.29959338617456[/C][/ROW]
[ROW][C]7[/C][C]74.8[/C][C]74.6831039369626[/C][C]0.116896063037444[/C][/ROW]
[ROW][C]8[/C][C]81.5[/C][C]79.2031493258509[/C][C]2.29685067414906[/C][/ROW]
[ROW][C]9[/C][C]92.8[/C][C]95.3598331152892[/C][C]-2.55983311528924[/C][/ROW]
[ROW][C]10[/C][C]92.8[/C][C]95.2141070956056[/C][C]-2.4141070956056[/C][/ROW]
[ROW][C]11[/C][C]91.7[/C][C]91.6414514099981[/C][C]0.0585485900018664[/C][/ROW]
[ROW][C]12[/C][C]83.5[/C][C]81.7473323026927[/C][C]1.75266769730733[/C][/ROW]
[ROW][C]13[/C][C]92.8[/C][C]88.7091201416078[/C][C]4.09087985839223[/C][/ROW]
[ROW][C]14[/C][C]91.3[/C][C]88.4099884684842[/C][C]2.89001153151583[/C][/ROW]
[ROW][C]15[/C][C]99.5[/C][C]98.7472838411573[/C][C]0.752716158842748[/C][/ROW]
[ROW][C]16[/C][C]87.6[/C][C]86.2517126524617[/C][C]1.34828734753829[/C][/ROW]
[ROW][C]17[/C][C]95.3[/C][C]93.9944773110642[/C][C]1.30552268893579[/C][/ROW]
[ROW][C]18[/C][C]98.5[/C][C]95.3895439986496[/C][C]3.11045600135043[/C][/ROW]
[ROW][C]19[/C][C]80.1[/C][C]87.3481604288127[/C][C]-7.24816042881272[/C][/ROW]
[ROW][C]20[/C][C]84.2[/C][C]84.601444919562[/C][C]-0.401444919562037[/C][/ROW]
[ROW][C]21[/C][C]92.4[/C][C]98.9664133806632[/C][C]-6.5664133806632[/C][/ROW]
[ROW][C]22[/C][C]98[/C][C]104.971945611874[/C][C]-6.97194561187356[/C][/ROW]
[ROW][C]23[/C][C]92.2[/C][C]96.1277335515244[/C][C]-3.92773355152444[/C][/ROW]
[ROW][C]24[/C][C]80[/C][C]80.1713497732358[/C][C]-0.171349773235839[/C][/ROW]
[ROW][C]25[/C][C]88.7[/C][C]89.9569885519416[/C][C]-1.25698855194158[/C][/ROW]
[ROW][C]26[/C][C]87.4[/C][C]90.866388419853[/C][C]-3.46638841985303[/C][/ROW]
[ROW][C]27[/C][C]96.1[/C][C]95.9603369794617[/C][C]0.139663020538269[/C][/ROW]
[ROW][C]28[/C][C]94.1[/C][C]94.4489843229488[/C][C]-0.348984322948767[/C][/ROW]
[ROW][C]29[/C][C]91.9[/C][C]92.8939874861181[/C][C]-0.993987486118083[/C][/ROW]
[ROW][C]30[/C][C]93.6[/C][C]96.7529021399017[/C][C]-3.15290213990173[/C][/ROW]
[ROW][C]31[/C][C]83.5[/C][C]87.1975791694051[/C][C]-3.69757916940506[/C][/ROW]
[ROW][C]32[/C][C]80.8[/C][C]83.6113502226468[/C][C]-2.81135022264679[/C][/ROW]
[ROW][C]33[/C][C]96.3[/C][C]102.059132381775[/C][C]-5.7591323817748[/C][/ROW]
[ROW][C]34[/C][C]101.5[/C][C]106.238505947271[/C][C]-4.73850594727051[/C][/ROW]
[ROW][C]35[/C][C]91.6[/C][C]92.613461262153[/C][C]-1.01346126215299[/C][/ROW]
[ROW][C]36[/C][C]84[/C][C]87.4884814549071[/C][C]-3.48848145490706[/C][/ROW]
[ROW][C]37[/C][C]91.8[/C][C]93.3320570392832[/C][C]-1.53205703928316[/C][/ROW]
[ROW][C]38[/C][C]90.4[/C][C]91.9814959697073[/C][C]-1.58149596970724[/C][/ROW]
[ROW][C]39[/C][C]98[/C][C]95.612337908482[/C][C]2.38766209151802[/C][/ROW]
[ROW][C]40[/C][C]95.5[/C][C]93.0894282750707[/C][C]2.41057172492926[/C][/ROW]
[ROW][C]41[/C][C]90.5[/C][C]87.4518668775125[/C][C]3.04813312248747[/C][/ROW]
[ROW][C]42[/C][C]97.1[/C][C]94.0858092821634[/C][C]3.01419071783663[/C][/ROW]
[ROW][C]43[/C][C]87.9[/C][C]86.7570641663262[/C][C]1.14293583367383[/C][/ROW]
[ROW][C]44[/C][C]79.8[/C][C]76.9219623510423[/C][C]2.87803764895771[/C][/ROW]
[ROW][C]45[/C][C]102[/C][C]101.99811177616[/C][C]0.00188822384025417[/C][/ROW]
[ROW][C]46[/C][C]104.3[/C][C]107.072027940693[/C][C]-2.77202794069272[/C][/ROW]
[ROW][C]47[/C][C]92.1[/C][C]92.1675157021276[/C][C]-0.0675157021276568[/C][/ROW]
[ROW][C]48[/C][C]95.9[/C][C]90.2015547677902[/C][C]5.69844523220979[/C][/ROW]
[ROW][C]49[/C][C]89.1[/C][C]91.1973294935913[/C][C]-2.09732949359129[/C][/ROW]
[ROW][C]50[/C][C]92.2[/C][C]91.2590760147755[/C][C]0.940923985224481[/C][/ROW]
[ROW][C]51[/C][C]107.5[/C][C]108.130501335149[/C][C]-0.630501335149437[/C][/ROW]
[ROW][C]52[/C][C]99.7[/C][C]94.8620622242363[/C][C]4.83793777576374[/C][/ROW]
[ROW][C]53[/C][C]92.2[/C][C]90.7876027807145[/C][C]1.41239721928554[/C][/ROW]
[ROW][C]54[/C][C]108.9[/C][C]106.507850818038[/C][C]2.39214918196247[/C][/ROW]
[ROW][C]55[/C][C]89.8[/C][C]88.7628626763358[/C][C]1.03713732366417[/C][/ROW]
[ROW][C]56[/C][C]89.4[/C][C]83.0011136767286[/C][C]6.39888632327142[/C][/ROW]
[ROW][C]57[/C][C]107.6[/C][C]105.368358670216[/C][C]2.23164132978362[/C][/ROW]
[ROW][C]58[/C][C]105.6[/C][C]103.030026889487[/C][C]2.56997311051267[/C][/ROW]
[ROW][C]59[/C][C]100.9[/C][C]99.6288886278144[/C][C]1.27111137218556[/C][/ROW]
[ROW][C]60[/C][C]102.9[/C][C]94.9625997369227[/C][C]7.93740026307728[/C][/ROW]
[ROW][C]61[/C][C]96.2[/C][C]90.1798160201049[/C][C]6.02018397989508[/C][/ROW]
[ROW][C]62[/C][C]94.7[/C][C]94.1035848418605[/C][C]0.596415158139461[/C][/ROW]
[ROW][C]63[/C][C]107.3[/C][C]105.063198346436[/C][C]2.23680165356407[/C][/ROW]
[ROW][C]64[/C][C]103[/C][C]99.9157602835017[/C][C]3.0842397164983[/C][/ROW]
[ROW][C]65[/C][C]96.1[/C][C]95.7271006035592[/C][C]0.372899396440783[/C][/ROW]
[ROW][C]66[/C][C]109.8[/C][C]107.553626510984[/C][C]2.24637348901624[/C][/ROW]
[ROW][C]67[/C][C]85.4[/C][C]85.7127949426441[/C][C]-0.312794942644053[/C][/ROW]
[ROW][C]68[/C][C]89.9[/C][C]87.9343397361667[/C][C]1.96566026383327[/C][/ROW]
[ROW][C]69[/C][C]109.3[/C][C]109.09256660817[/C][C]0.20743339182994[/C][/ROW]
[ROW][C]70[/C][C]101.2[/C][C]102.237691095254[/C][C]-1.03769109525449[/C][/ROW]
[ROW][C]71[/C][C]104.7[/C][C]104.728361231988[/C][C]-0.028361231988344[/C][/ROW]
[ROW][C]72[/C][C]102.4[/C][C]97.1406464628352[/C][C]5.25935353716476[/C][/ROW]
[ROW][C]73[/C][C]97.7[/C][C]95.5909441708628[/C][C]2.10905582913725[/C][/ROW]
[ROW][C]74[/C][C]98.9[/C][C]96.5903183721671[/C][C]2.30968162783288[/C][/ROW]
[ROW][C]75[/C][C]115[/C][C]109.443480548328[/C][C]5.55651945167208[/C][/ROW]
[ROW][C]76[/C][C]97.5[/C][C]95.9905235354429[/C][C]1.50947646455706[/C][/ROW]
[ROW][C]77[/C][C]107.3[/C][C]103.702538746536[/C][C]3.5974612534641[/C][/ROW]
[ROW][C]78[/C][C]112.3[/C][C]111.403551251933[/C][C]0.896448748067234[/C][/ROW]
[ROW][C]79[/C][C]88.5[/C][C]94.721083123021[/C][C]-6.22108312302095[/C][/ROW]
[ROW][C]80[/C][C]92.9[/C][C]91.6588615082821[/C][C]1.24113849171791[/C][/ROW]
[ROW][C]81[/C][C]108.8[/C][C]112.295451030277[/C][C]-3.49545103027699[/C][/ROW]
[ROW][C]82[/C][C]112.3[/C][C]114.619937269701[/C][C]-2.31993726970112[/C][/ROW]
[ROW][C]83[/C][C]107.3[/C][C]110.388583448982[/C][C]-3.08858344898243[/C][/ROW]
[ROW][C]84[/C][C]101.8[/C][C]97.8085247040261[/C][C]3.99147529597388[/C][/ROW]
[ROW][C]85[/C][C]105[/C][C]103.944414424011[/C][C]1.05558557598922[/C][/ROW]
[ROW][C]86[/C][C]103.4[/C][C]104.243071375069[/C][C]-0.843071375069057[/C][/ROW]
[ROW][C]87[/C][C]116.7[/C][C]116.840754101783[/C][C]-0.14075410178315[/C][/ROW]
[ROW][C]88[/C][C]103.6[/C][C]106.166961893099[/C][C]-2.56696189309871[/C][/ROW]
[ROW][C]89[/C][C]108.8[/C][C]107.783670542477[/C][C]1.01632945752291[/C][/ROW]
[ROW][C]90[/C][C]117[/C][C]117.200187500531[/C][C]-0.200187500530643[/C][/ROW]
[ROW][C]91[/C][C]100.9[/C][C]99.6120757704472[/C][C]1.28792422955276[/C][/ROW]
[ROW][C]92[/C][C]100.8[/C][C]97.5164854216752[/C][C]3.28351457832482[/C][/ROW]
[ROW][C]93[/C][C]109.7[/C][C]109.569227153843[/C][C]0.130772846156915[/C][/ROW]
[ROW][C]94[/C][C]121[/C][C]121.284750425501[/C][C]-0.284750425501495[/C][/ROW]
[ROW][C]95[/C][C]114.1[/C][C]112.32592101169[/C][C]1.77407898830957[/C][/ROW]
[ROW][C]96[/C][C]105.5[/C][C]98.7010196635462[/C][C]6.79898033645377[/C][/ROW]
[ROW][C]97[/C][C]112.5[/C][C]109.414249201592[/C][C]3.0857507984075[/C][/ROW]
[ROW][C]98[/C][C]113.8[/C][C]109.619606789489[/C][C]4.18039321051117[/C][/ROW]
[ROW][C]99[/C][C]115.3[/C][C]111.658528278098[/C][C]3.64147172190163[/C][/ROW]
[ROW][C]100[/C][C]120.4[/C][C]116.317267887201[/C][C]4.08273211279941[/C][/ROW]
[ROW][C]101[/C][C]111.1[/C][C]107.946134400168[/C][C]3.15386559983188[/C][/ROW]
[ROW][C]102[/C][C]120.1[/C][C]113.67815832837[/C][C]6.42184167163017[/C][/ROW]
[ROW][C]103[/C][C]106.1[/C][C]107.382062184308[/C][C]-1.28206218430761[/C][/ROW]
[ROW][C]104[/C][C]95.9[/C][C]92.4474649171078[/C][C]3.45253508289226[/C][/ROW]
[ROW][C]105[/C][C]119.4[/C][C]116.891235468459[/C][C]2.50876453154101[/C][/ROW]
[ROW][C]106[/C][C]117.4[/C][C]114.54601469483[/C][C]2.85398530516984[/C][/ROW]
[ROW][C]107[/C][C]98.6[/C][C]100.74695641687[/C][C]-2.14695641687047[/C][/ROW]
[ROW][C]108[/C][C]99.7[/C][C]99.7266018248169[/C][C]-0.0266018248168517[/C][/ROW]
[ROW][C]109[/C][C]87.4[/C][C]95.6346009852687[/C][C]-8.23460098526869[/C][/ROW]
[ROW][C]110[/C][C]90.8[/C][C]96.6740324097174[/C][C]-5.87403240971736[/C][/ROW]
[ROW][C]111[/C][C]101.3[/C][C]105.944286571593[/C][C]-4.6442865715929[/C][/ROW]
[ROW][C]112[/C][C]93.2[/C][C]99.596486524363[/C][C]-6.39648652436295[/C][/ROW]
[ROW][C]113[/C][C]95.1[/C][C]93.4459196146606[/C][C]1.65408038533944[/C][/ROW]
[ROW][C]114[/C][C]101.9[/C][C]106.553040019788[/C][C]-4.65304001978765[/C][/ROW]
[ROW][C]115[/C][C]87[/C][C]95.2916271962443[/C][C]-8.29162719624433[/C][/ROW]
[ROW][C]116[/C][C]86.2[/C][C]86.902876861707[/C][C]-0.702876861707009[/C][/ROW]
[ROW][C]117[/C][C]105[/C][C]109.633005669347[/C][C]-4.63300566934707[/C][/ROW]
[ROW][C]118[/C][C]104.1[/C][C]108.557344120698[/C][C]-4.45734412069821[/C][/ROW]
[ROW][C]119[/C][C]99.2[/C][C]101.718457849614[/C][C]-2.51845784961391[/C][/ROW]
[ROW][C]120[/C][C]95.2[/C][C]102.62001635887[/C][C]-7.42001635887048[/C][/ROW]
[ROW][C]121[/C][C]92.7[/C][C]92.1573343566129[/C][C]0.542665643387148[/C][/ROW]
[ROW][C]122[/C][C]99.3[/C][C]100.153262143864[/C][C]-0.853262143863675[/C][/ROW]
[ROW][C]123[/C][C]113.5[/C][C]114.15136325435[/C][C]-0.651363254350386[/C][/ROW]
[ROW][C]124[/C][C]104.7[/C][C]107.522468774395[/C][C]-2.82246877439478[/C][/ROW]
[ROW][C]125[/C][C]100.5[/C][C]98.9918108498897[/C][C]1.50818915011027[/C][/ROW]
[ROW][C]126[/C][C]116.2[/C][C]116.826404450254[/C][C]-0.626404450254007[/C][/ROW]
[ROW][C]127[/C][C]94.1[/C][C]98.0032197657029[/C][C]-3.90321976570289[/C][/ROW]
[ROW][C]128[/C][C]94.8[/C][C]95.6718721457394[/C][C]-0.871872145739382[/C][/ROW]
[ROW][C]129[/C][C]115.1[/C][C]110.956562740688[/C][C]4.14343725931203[/C][/ROW]
[ROW][C]130[/C][C]110[/C][C]110.724154681677[/C][C]-0.724154681676504[/C][/ROW]
[ROW][C]131[/C][C]108.4[/C][C]107.560468088716[/C][C]0.839531911284088[/C][/ROW]
[ROW][C]132[/C][C]103.9[/C][C]102.256241304971[/C][C]1.6437586950287[/C][/ROW]
[ROW][C]133[/C][C]102.9[/C][C]101.850637373431[/C][C]1.04936262656875[/C][/ROW]
[ROW][C]134[/C][C]107.7[/C][C]102.606961358191[/C][C]5.09303864180871[/C][/ROW]
[ROW][C]135[/C][C]126.7[/C][C]119.660553150197[/C][C]7.03944684980329[/C][/ROW]
[ROW][C]136[/C][C]108.8[/C][C]103.683394947131[/C][C]5.11660505286856[/C][/ROW]
[ROW][C]137[/C][C]117.1[/C][C]113.102993972619[/C][C]3.99700602738106[/C][/ROW]
[ROW][C]138[/C][C]112.2[/C][C]110.855658591065[/C][C]1.34434140893475[/C][/ROW]
[ROW][C]139[/C][C]94.7[/C][C]94.2325854747539[/C][C]0.467414525246121[/C][/ROW]
[ROW][C]140[/C][C]102.7[/C][C]99.1046546480355[/C][C]3.59534535196447[/C][/ROW]
[ROW][C]141[/C][C]119.1[/C][C]117.915604692554[/C][C]1.18439530744622[/C][/ROW]
[ROW][C]142[/C][C]110.6[/C][C]111.143296780938[/C][C]-0.543296780937884[/C][/ROW]
[ROW][C]143[/C][C]109.1[/C][C]109.334890701806[/C][C]-0.234890701806101[/C][/ROW]
[ROW][C]144[/C][C]105.3[/C][C]106.030753351608[/C][C]-0.730753351607722[/C][/ROW]
[ROW][C]145[/C][C]103.4[/C][C]106.223841669968[/C][C]-2.82384166996776[/C][/ROW]
[ROW][C]146[/C][C]103.7[/C][C]110.032421033969[/C][C]-6.33242103396864[/C][/ROW]
[ROW][C]147[/C][C]117[/C][C]117.80188293567[/C][C]-0.801882935669587[/C][/ROW]
[ROW][C]148[/C][C]101.2[/C][C]105.82023654818[/C][C]-4.62023654817965[/C][/ROW]
[ROW][C]149[/C][C]105.4[/C][C]108.072378619276[/C][C]-2.67237861927586[/C][/ROW]
[ROW][C]150[/C][C]110.3[/C][C]114.390119255648[/C][C]-4.09011925564789[/C][/ROW]
[ROW][C]151[/C][C]97.7[/C][C]102.50780778688[/C][C]-4.80780778687964[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186075&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186075&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
175.577.9845346401489-2.48453464014888
283.285.9496752602208-2.74967526022084
394.594.5421286604659-0.042128660465873
483.382.77087022342460.529129776575405
592.795.6738095266234-2.97380952662335
689.893.0995933861746-3.29959338617456
774.874.68310393696260.116896063037444
881.579.20314932585092.29685067414906
992.895.3598331152892-2.55983311528924
1092.895.2141070956056-2.4141070956056
1191.791.64145140999810.0585485900018664
1283.581.74733230269271.75266769730733
1392.888.70912014160784.09087985839223
1491.388.40998846848422.89001153151583
1599.598.74728384115730.752716158842748
1687.686.25171265246171.34828734753829
1795.393.99447731106421.30552268893579
1898.595.38954399864963.11045600135043
1980.187.3481604288127-7.24816042881272
2084.284.601444919562-0.401444919562037
2192.498.9664133806632-6.5664133806632
2298104.971945611874-6.97194561187356
2392.296.1277335515244-3.92773355152444
248080.1713497732358-0.171349773235839
2588.789.9569885519416-1.25698855194158
2687.490.866388419853-3.46638841985303
2796.195.96033697946170.139663020538269
2894.194.4489843229488-0.348984322948767
2991.992.8939874861181-0.993987486118083
3093.696.7529021399017-3.15290213990173
3183.587.1975791694051-3.69757916940506
3280.883.6113502226468-2.81135022264679
3396.3102.059132381775-5.7591323817748
34101.5106.238505947271-4.73850594727051
3591.692.613461262153-1.01346126215299
368487.4884814549071-3.48848145490706
3791.893.3320570392832-1.53205703928316
3890.491.9814959697073-1.58149596970724
399895.6123379084822.38766209151802
4095.593.08942827507072.41057172492926
4190.587.45186687751253.04813312248747
4297.194.08580928216343.01419071783663
4387.986.75706416632621.14293583367383
4479.876.92196235104232.87803764895771
45102101.998111776160.00188822384025417
46104.3107.072027940693-2.77202794069272
4792.192.1675157021276-0.0675157021276568
4895.990.20155476779025.69844523220979
4989.191.1973294935913-2.09732949359129
5092.291.25907601477550.940923985224481
51107.5108.130501335149-0.630501335149437
5299.794.86206222423634.83793777576374
5392.290.78760278071451.41239721928554
54108.9106.5078508180382.39214918196247
5589.888.76286267633581.03713732366417
5689.483.00111367672866.39888632327142
57107.6105.3683586702162.23164132978362
58105.6103.0300268894872.56997311051267
59100.999.62888862781441.27111137218556
60102.994.96259973692277.93740026307728
6196.290.17981602010496.02018397989508
6294.794.10358484186050.596415158139461
63107.3105.0631983464362.23680165356407
6410399.91576028350173.0842397164983
6596.195.72710060355920.372899396440783
66109.8107.5536265109842.24637348901624
6785.485.7127949426441-0.312794942644053
6889.987.93433973616671.96566026383327
69109.3109.092566608170.20743339182994
70101.2102.237691095254-1.03769109525449
71104.7104.728361231988-0.028361231988344
72102.497.14064646283525.25935353716476
7397.795.59094417086282.10905582913725
7498.996.59031837216712.30968162783288
75115109.4434805483285.55651945167208
7697.595.99052353544291.50947646455706
77107.3103.7025387465363.5974612534641
78112.3111.4035512519330.896448748067234
7988.594.721083123021-6.22108312302095
8092.991.65886150828211.24113849171791
81108.8112.295451030277-3.49545103027699
82112.3114.619937269701-2.31993726970112
83107.3110.388583448982-3.08858344898243
84101.897.80852470402613.99147529597388
85105103.9444144240111.05558557598922
86103.4104.243071375069-0.843071375069057
87116.7116.840754101783-0.14075410178315
88103.6106.166961893099-2.56696189309871
89108.8107.7836705424771.01632945752291
90117117.200187500531-0.200187500530643
91100.999.61207577044721.28792422955276
92100.897.51648542167523.28351457832482
93109.7109.5692271538430.130772846156915
94121121.284750425501-0.284750425501495
95114.1112.325921011691.77407898830957
96105.598.70101966354626.79898033645377
97112.5109.4142492015923.0857507984075
98113.8109.6196067894894.18039321051117
99115.3111.6585282780983.64147172190163
100120.4116.3172678872014.08273211279941
101111.1107.9461344001683.15386559983188
102120.1113.678158328376.42184167163017
103106.1107.382062184308-1.28206218430761
10495.992.44746491710783.45253508289226
105119.4116.8912354684592.50876453154101
106117.4114.546014694832.85398530516984
10798.6100.74695641687-2.14695641687047
10899.799.7266018248169-0.0266018248168517
10987.495.6346009852687-8.23460098526869
11090.896.6740324097174-5.87403240971736
111101.3105.944286571593-4.6442865715929
11293.299.596486524363-6.39648652436295
11395.193.44591961466061.65408038533944
114101.9106.553040019788-4.65304001978765
1158795.2916271962443-8.29162719624433
11686.286.902876861707-0.702876861707009
117105109.633005669347-4.63300566934707
118104.1108.557344120698-4.45734412069821
11999.2101.718457849614-2.51845784961391
12095.2102.62001635887-7.42001635887048
12192.792.15733435661290.542665643387148
12299.3100.153262143864-0.853262143863675
123113.5114.15136325435-0.651363254350386
124104.7107.522468774395-2.82246877439478
125100.598.99181084988971.50818915011027
126116.2116.826404450254-0.626404450254007
12794.198.0032197657029-3.90321976570289
12894.895.6718721457394-0.871872145739382
129115.1110.9565627406884.14343725931203
130110110.724154681677-0.724154681676504
131108.4107.5604680887160.839531911284088
132103.9102.2562413049711.6437586950287
133102.9101.8506373734311.04936262656875
134107.7102.6069613581915.09303864180871
135126.7119.6605531501977.03944684980329
136108.8103.6833949471315.11660505286856
137117.1113.1029939726193.99700602738106
138112.2110.8556585910651.34434140893475
13994.794.23258547475390.467414525246121
140102.799.10465464803553.59534535196447
141119.1117.9156046925541.18439530744622
142110.6111.143296780938-0.543296780937884
143109.1109.334890701806-0.234890701806101
144105.3106.030753351608-0.730753351607722
145103.4106.223841669968-2.82384166996776
146103.7110.032421033969-6.33242103396864
147117117.80188293567-0.801882935669587
148101.2105.82023654818-4.62023654817965
149105.4108.072378619276-2.67237861927586
150110.3114.390119255648-4.09011925564789
15197.7102.50780778688-4.80780778687964







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.4904856247870250.9809712495740510.509514375212975
200.5206793856091510.9586412287816990.479320614390849
210.707558400494510.5848831990109810.29244159950549
220.8902935670621460.2194128658757080.109706432937854
230.8865405751695950.2269188496608110.113459424830405
240.8393089868281860.3213820263436290.160691013171814
250.7763075785156280.4473848429687440.223692421484372
260.74945931676180.50108136647640.2505406832382
270.7050160106938810.5899679786122380.294983989306119
280.640512957764050.7189740844718990.35948704223595
290.5632972406893280.8734055186213450.436702759310673
300.5106319633574680.9787360732850630.489368036642532
310.4541839507641570.9083679015283140.545816049235843
320.4316183535979820.8632367071959630.568381646402018
330.4380909091277160.8761818182554320.561909090872284
340.4093852256245370.8187704512490750.590614774375463
350.3630640224494940.7261280448989880.636935977550506
360.3393079774075050.678615954815010.660692022592495
370.2810383372137070.5620766744274130.718961662786293
380.2425270899813810.4850541799627620.757472910018619
390.281141960348520.5622839206970410.71885803965148
400.2613122698219570.5226245396439130.738687730178043
410.220494592795310.4409891855906210.77950540720469
420.2056669500419170.4113339000838340.794333049958083
430.1744185590496160.3488371180992320.825581440950384
440.1424070439976130.2848140879952260.857592956002387
450.1790251956034470.3580503912068950.820974804396553
460.1654936539318720.3309873078637440.834506346068128
470.1331981695981460.2663963391962920.866801830401854
480.1854157380511390.3708314761022780.814584261948861
490.182330124371460.364660248742920.81766987562854
500.1476139003228690.2952278006457380.852386099677131
510.1310504706787680.2621009413575350.868949529321232
520.112679187348940.225358374697880.88732081265106
530.09868599002718130.1973719800543630.901314009972819
540.08055133926926370.1611026785385270.919448660730736
550.06131422757183430.1226284551436690.938685772428166
560.07170134944321270.1434026988864250.928298650556787
570.05613891752563120.1122778350512620.943861082474369
580.056578555110760.113157110221520.94342144488924
590.04806793776316380.09613587552632750.951932062236836
600.08200227062942390.1640045412588480.917997729370576
610.07537276493331760.1507455298666350.924627235066682
620.08082591830976030.1616518366195210.91917408169024
630.06494248776420830.1298849755284170.935057512235792
640.05239825931533110.1047965186306620.947601740684669
650.04712695940344870.09425391880689750.952873040596551
660.03674936869937010.07349873739874010.96325063130063
670.03024970368648740.06049940737297480.969750296313513
680.02271914948912750.0454382989782550.977280850510872
690.01645364894236740.03290729788473480.983546351057633
700.01710783803161510.03421567606323020.982892161968385
710.01436237296363460.02872474592726910.985637627036365
720.01298254601596630.02596509203193270.987017453984034
730.009578306416910520.0191566128338210.99042169358309
740.00684024842917380.01368049685834760.993159751570826
750.009798723996908380.01959744799381680.990201276003092
760.006885933244758150.01377186648951630.993114066755242
770.006178882312292090.01235776462458420.993821117687708
780.004621037729084480.009242075458168950.995378962270915
790.01067365896964060.02134731793928130.989326341030359
800.007518737285351740.01503747457070350.992481262714648
810.01240242062224790.02480484124449590.987597579377752
820.0130497913185240.0260995826370480.986950208681476
830.01681362352381520.03362724704763040.983186376476185
840.01439404635363250.02878809270726490.985605953646367
850.01067805671473840.02135611342947680.989321943285262
860.008571544687025710.01714308937405140.991428455312974
870.00616198827858160.01232397655716320.993838011721418
880.005703239944135760.01140647988827150.994296760055864
890.003946954267182890.007893908534365780.996053045732817
900.003271196940287080.006542393880574160.996728803059713
910.002293249588259840.004586499176519680.99770675041174
920.001700508637081160.003401017274162330.998299491362919
930.002919256140619890.005838512281239790.99708074385938
940.002591129066673030.005182258133346050.997408870933327
950.001822670472720.003645340945440.99817732952728
960.024713455467330.049426910934660.97528654453267
970.01951220782002440.03902441564004880.980487792179976
980.01776590284052610.03553180568105210.982234097159474
990.01899089606449930.03798179212899870.981009103935501
1000.01548972828917980.03097945657835950.98451027171082
1010.01450602416132090.02901204832264180.985493975838679
1020.01228068170355530.02456136340711060.987719318296445
1030.01869931781525660.03739863563051330.981300682184743
1040.01418126798259790.02836253596519590.985818732017402
1050.01191957652667980.02383915305335960.98808042347332
1060.0126774592962930.02535491859258610.987322540703707
1070.0188324200561880.0376648401123760.981167579943812
1080.02160232859051610.04320465718103220.978397671409484
1090.1125402468399870.2250804936799730.887459753160013
1100.1459234741765830.2918469483531660.854076525823417
1110.1513737485105180.3027474970210360.848626251489482
1120.2517085747335930.5034171494671860.748291425266407
1130.5085175045319390.9829649909361230.491482495468061
1140.5287215922405260.9425568155189480.471278407759474
1150.6368156349805210.7263687300389580.363184365019479
1160.5770776321303320.8458447357393360.422922367869668
1170.5589355833629420.8821288332741150.441064416637058
1180.649390427236530.701219145526940.35060957276347
1190.5831393852730610.8337212294538770.416860614726939
1200.7478363640844480.5043272718311040.252163635915552
1210.67997808765970.6400438246805990.3200219123403
1220.6110806045477670.7778387909044670.388919395452233
1230.817731792138660.3645364157226810.18226820786134
1240.7842867850804790.4314264298390410.215713214919521
1250.8072771352178020.3854457295643950.192722864782198
1260.8126494511986970.3747010976026050.187350548801303
1270.9222814784368020.1554370431263970.0777185215631983
1280.9982014083324310.003597183335137060.00179859166756853
1290.999647063836180.0007058723276405980.000352936163820299
1300.9986117005902120.002776598819575770.00138829940978789
1310.9936076412635050.01278471747298980.00639235873649489
1320.9975194337137340.004961132572533030.00248056628626651

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.490485624787025 & 0.980971249574051 & 0.509514375212975 \tabularnewline
20 & 0.520679385609151 & 0.958641228781699 & 0.479320614390849 \tabularnewline
21 & 0.70755840049451 & 0.584883199010981 & 0.29244159950549 \tabularnewline
22 & 0.890293567062146 & 0.219412865875708 & 0.109706432937854 \tabularnewline
23 & 0.886540575169595 & 0.226918849660811 & 0.113459424830405 \tabularnewline
24 & 0.839308986828186 & 0.321382026343629 & 0.160691013171814 \tabularnewline
25 & 0.776307578515628 & 0.447384842968744 & 0.223692421484372 \tabularnewline
26 & 0.7494593167618 & 0.5010813664764 & 0.2505406832382 \tabularnewline
27 & 0.705016010693881 & 0.589967978612238 & 0.294983989306119 \tabularnewline
28 & 0.64051295776405 & 0.718974084471899 & 0.35948704223595 \tabularnewline
29 & 0.563297240689328 & 0.873405518621345 & 0.436702759310673 \tabularnewline
30 & 0.510631963357468 & 0.978736073285063 & 0.489368036642532 \tabularnewline
31 & 0.454183950764157 & 0.908367901528314 & 0.545816049235843 \tabularnewline
32 & 0.431618353597982 & 0.863236707195963 & 0.568381646402018 \tabularnewline
33 & 0.438090909127716 & 0.876181818255432 & 0.561909090872284 \tabularnewline
34 & 0.409385225624537 & 0.818770451249075 & 0.590614774375463 \tabularnewline
35 & 0.363064022449494 & 0.726128044898988 & 0.636935977550506 \tabularnewline
36 & 0.339307977407505 & 0.67861595481501 & 0.660692022592495 \tabularnewline
37 & 0.281038337213707 & 0.562076674427413 & 0.718961662786293 \tabularnewline
38 & 0.242527089981381 & 0.485054179962762 & 0.757472910018619 \tabularnewline
39 & 0.28114196034852 & 0.562283920697041 & 0.71885803965148 \tabularnewline
40 & 0.261312269821957 & 0.522624539643913 & 0.738687730178043 \tabularnewline
41 & 0.22049459279531 & 0.440989185590621 & 0.77950540720469 \tabularnewline
42 & 0.205666950041917 & 0.411333900083834 & 0.794333049958083 \tabularnewline
43 & 0.174418559049616 & 0.348837118099232 & 0.825581440950384 \tabularnewline
44 & 0.142407043997613 & 0.284814087995226 & 0.857592956002387 \tabularnewline
45 & 0.179025195603447 & 0.358050391206895 & 0.820974804396553 \tabularnewline
46 & 0.165493653931872 & 0.330987307863744 & 0.834506346068128 \tabularnewline
47 & 0.133198169598146 & 0.266396339196292 & 0.866801830401854 \tabularnewline
48 & 0.185415738051139 & 0.370831476102278 & 0.814584261948861 \tabularnewline
49 & 0.18233012437146 & 0.36466024874292 & 0.81766987562854 \tabularnewline
50 & 0.147613900322869 & 0.295227800645738 & 0.852386099677131 \tabularnewline
51 & 0.131050470678768 & 0.262100941357535 & 0.868949529321232 \tabularnewline
52 & 0.11267918734894 & 0.22535837469788 & 0.88732081265106 \tabularnewline
53 & 0.0986859900271813 & 0.197371980054363 & 0.901314009972819 \tabularnewline
54 & 0.0805513392692637 & 0.161102678538527 & 0.919448660730736 \tabularnewline
55 & 0.0613142275718343 & 0.122628455143669 & 0.938685772428166 \tabularnewline
56 & 0.0717013494432127 & 0.143402698886425 & 0.928298650556787 \tabularnewline
57 & 0.0561389175256312 & 0.112277835051262 & 0.943861082474369 \tabularnewline
58 & 0.05657855511076 & 0.11315711022152 & 0.94342144488924 \tabularnewline
59 & 0.0480679377631638 & 0.0961358755263275 & 0.951932062236836 \tabularnewline
60 & 0.0820022706294239 & 0.164004541258848 & 0.917997729370576 \tabularnewline
61 & 0.0753727649333176 & 0.150745529866635 & 0.924627235066682 \tabularnewline
62 & 0.0808259183097603 & 0.161651836619521 & 0.91917408169024 \tabularnewline
63 & 0.0649424877642083 & 0.129884975528417 & 0.935057512235792 \tabularnewline
64 & 0.0523982593153311 & 0.104796518630662 & 0.947601740684669 \tabularnewline
65 & 0.0471269594034487 & 0.0942539188068975 & 0.952873040596551 \tabularnewline
66 & 0.0367493686993701 & 0.0734987373987401 & 0.96325063130063 \tabularnewline
67 & 0.0302497036864874 & 0.0604994073729748 & 0.969750296313513 \tabularnewline
68 & 0.0227191494891275 & 0.045438298978255 & 0.977280850510872 \tabularnewline
69 & 0.0164536489423674 & 0.0329072978847348 & 0.983546351057633 \tabularnewline
70 & 0.0171078380316151 & 0.0342156760632302 & 0.982892161968385 \tabularnewline
71 & 0.0143623729636346 & 0.0287247459272691 & 0.985637627036365 \tabularnewline
72 & 0.0129825460159663 & 0.0259650920319327 & 0.987017453984034 \tabularnewline
73 & 0.00957830641691052 & 0.019156612833821 & 0.99042169358309 \tabularnewline
74 & 0.0068402484291738 & 0.0136804968583476 & 0.993159751570826 \tabularnewline
75 & 0.00979872399690838 & 0.0195974479938168 & 0.990201276003092 \tabularnewline
76 & 0.00688593324475815 & 0.0137718664895163 & 0.993114066755242 \tabularnewline
77 & 0.00617888231229209 & 0.0123577646245842 & 0.993821117687708 \tabularnewline
78 & 0.00462103772908448 & 0.00924207545816895 & 0.995378962270915 \tabularnewline
79 & 0.0106736589696406 & 0.0213473179392813 & 0.989326341030359 \tabularnewline
80 & 0.00751873728535174 & 0.0150374745707035 & 0.992481262714648 \tabularnewline
81 & 0.0124024206222479 & 0.0248048412444959 & 0.987597579377752 \tabularnewline
82 & 0.013049791318524 & 0.026099582637048 & 0.986950208681476 \tabularnewline
83 & 0.0168136235238152 & 0.0336272470476304 & 0.983186376476185 \tabularnewline
84 & 0.0143940463536325 & 0.0287880927072649 & 0.985605953646367 \tabularnewline
85 & 0.0106780567147384 & 0.0213561134294768 & 0.989321943285262 \tabularnewline
86 & 0.00857154468702571 & 0.0171430893740514 & 0.991428455312974 \tabularnewline
87 & 0.0061619882785816 & 0.0123239765571632 & 0.993838011721418 \tabularnewline
88 & 0.00570323994413576 & 0.0114064798882715 & 0.994296760055864 \tabularnewline
89 & 0.00394695426718289 & 0.00789390853436578 & 0.996053045732817 \tabularnewline
90 & 0.00327119694028708 & 0.00654239388057416 & 0.996728803059713 \tabularnewline
91 & 0.00229324958825984 & 0.00458649917651968 & 0.99770675041174 \tabularnewline
92 & 0.00170050863708116 & 0.00340101727416233 & 0.998299491362919 \tabularnewline
93 & 0.00291925614061989 & 0.00583851228123979 & 0.99708074385938 \tabularnewline
94 & 0.00259112906667303 & 0.00518225813334605 & 0.997408870933327 \tabularnewline
95 & 0.00182267047272 & 0.00364534094544 & 0.99817732952728 \tabularnewline
96 & 0.02471345546733 & 0.04942691093466 & 0.97528654453267 \tabularnewline
97 & 0.0195122078200244 & 0.0390244156400488 & 0.980487792179976 \tabularnewline
98 & 0.0177659028405261 & 0.0355318056810521 & 0.982234097159474 \tabularnewline
99 & 0.0189908960644993 & 0.0379817921289987 & 0.981009103935501 \tabularnewline
100 & 0.0154897282891798 & 0.0309794565783595 & 0.98451027171082 \tabularnewline
101 & 0.0145060241613209 & 0.0290120483226418 & 0.985493975838679 \tabularnewline
102 & 0.0122806817035553 & 0.0245613634071106 & 0.987719318296445 \tabularnewline
103 & 0.0186993178152566 & 0.0373986356305133 & 0.981300682184743 \tabularnewline
104 & 0.0141812679825979 & 0.0283625359651959 & 0.985818732017402 \tabularnewline
105 & 0.0119195765266798 & 0.0238391530533596 & 0.98808042347332 \tabularnewline
106 & 0.012677459296293 & 0.0253549185925861 & 0.987322540703707 \tabularnewline
107 & 0.018832420056188 & 0.037664840112376 & 0.981167579943812 \tabularnewline
108 & 0.0216023285905161 & 0.0432046571810322 & 0.978397671409484 \tabularnewline
109 & 0.112540246839987 & 0.225080493679973 & 0.887459753160013 \tabularnewline
110 & 0.145923474176583 & 0.291846948353166 & 0.854076525823417 \tabularnewline
111 & 0.151373748510518 & 0.302747497021036 & 0.848626251489482 \tabularnewline
112 & 0.251708574733593 & 0.503417149467186 & 0.748291425266407 \tabularnewline
113 & 0.508517504531939 & 0.982964990936123 & 0.491482495468061 \tabularnewline
114 & 0.528721592240526 & 0.942556815518948 & 0.471278407759474 \tabularnewline
115 & 0.636815634980521 & 0.726368730038958 & 0.363184365019479 \tabularnewline
116 & 0.577077632130332 & 0.845844735739336 & 0.422922367869668 \tabularnewline
117 & 0.558935583362942 & 0.882128833274115 & 0.441064416637058 \tabularnewline
118 & 0.64939042723653 & 0.70121914552694 & 0.35060957276347 \tabularnewline
119 & 0.583139385273061 & 0.833721229453877 & 0.416860614726939 \tabularnewline
120 & 0.747836364084448 & 0.504327271831104 & 0.252163635915552 \tabularnewline
121 & 0.6799780876597 & 0.640043824680599 & 0.3200219123403 \tabularnewline
122 & 0.611080604547767 & 0.777838790904467 & 0.388919395452233 \tabularnewline
123 & 0.81773179213866 & 0.364536415722681 & 0.18226820786134 \tabularnewline
124 & 0.784286785080479 & 0.431426429839041 & 0.215713214919521 \tabularnewline
125 & 0.807277135217802 & 0.385445729564395 & 0.192722864782198 \tabularnewline
126 & 0.812649451198697 & 0.374701097602605 & 0.187350548801303 \tabularnewline
127 & 0.922281478436802 & 0.155437043126397 & 0.0777185215631983 \tabularnewline
128 & 0.998201408332431 & 0.00359718333513706 & 0.00179859166756853 \tabularnewline
129 & 0.99964706383618 & 0.000705872327640598 & 0.000352936163820299 \tabularnewline
130 & 0.998611700590212 & 0.00277659881957577 & 0.00138829940978789 \tabularnewline
131 & 0.993607641263505 & 0.0127847174729898 & 0.00639235873649489 \tabularnewline
132 & 0.997519433713734 & 0.00496113257253303 & 0.00248056628626651 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186075&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]19[/C][C]0.490485624787025[/C][C]0.980971249574051[/C][C]0.509514375212975[/C][/ROW]
[ROW][C]20[/C][C]0.520679385609151[/C][C]0.958641228781699[/C][C]0.479320614390849[/C][/ROW]
[ROW][C]21[/C][C]0.70755840049451[/C][C]0.584883199010981[/C][C]0.29244159950549[/C][/ROW]
[ROW][C]22[/C][C]0.890293567062146[/C][C]0.219412865875708[/C][C]0.109706432937854[/C][/ROW]
[ROW][C]23[/C][C]0.886540575169595[/C][C]0.226918849660811[/C][C]0.113459424830405[/C][/ROW]
[ROW][C]24[/C][C]0.839308986828186[/C][C]0.321382026343629[/C][C]0.160691013171814[/C][/ROW]
[ROW][C]25[/C][C]0.776307578515628[/C][C]0.447384842968744[/C][C]0.223692421484372[/C][/ROW]
[ROW][C]26[/C][C]0.7494593167618[/C][C]0.5010813664764[/C][C]0.2505406832382[/C][/ROW]
[ROW][C]27[/C][C]0.705016010693881[/C][C]0.589967978612238[/C][C]0.294983989306119[/C][/ROW]
[ROW][C]28[/C][C]0.64051295776405[/C][C]0.718974084471899[/C][C]0.35948704223595[/C][/ROW]
[ROW][C]29[/C][C]0.563297240689328[/C][C]0.873405518621345[/C][C]0.436702759310673[/C][/ROW]
[ROW][C]30[/C][C]0.510631963357468[/C][C]0.978736073285063[/C][C]0.489368036642532[/C][/ROW]
[ROW][C]31[/C][C]0.454183950764157[/C][C]0.908367901528314[/C][C]0.545816049235843[/C][/ROW]
[ROW][C]32[/C][C]0.431618353597982[/C][C]0.863236707195963[/C][C]0.568381646402018[/C][/ROW]
[ROW][C]33[/C][C]0.438090909127716[/C][C]0.876181818255432[/C][C]0.561909090872284[/C][/ROW]
[ROW][C]34[/C][C]0.409385225624537[/C][C]0.818770451249075[/C][C]0.590614774375463[/C][/ROW]
[ROW][C]35[/C][C]0.363064022449494[/C][C]0.726128044898988[/C][C]0.636935977550506[/C][/ROW]
[ROW][C]36[/C][C]0.339307977407505[/C][C]0.67861595481501[/C][C]0.660692022592495[/C][/ROW]
[ROW][C]37[/C][C]0.281038337213707[/C][C]0.562076674427413[/C][C]0.718961662786293[/C][/ROW]
[ROW][C]38[/C][C]0.242527089981381[/C][C]0.485054179962762[/C][C]0.757472910018619[/C][/ROW]
[ROW][C]39[/C][C]0.28114196034852[/C][C]0.562283920697041[/C][C]0.71885803965148[/C][/ROW]
[ROW][C]40[/C][C]0.261312269821957[/C][C]0.522624539643913[/C][C]0.738687730178043[/C][/ROW]
[ROW][C]41[/C][C]0.22049459279531[/C][C]0.440989185590621[/C][C]0.77950540720469[/C][/ROW]
[ROW][C]42[/C][C]0.205666950041917[/C][C]0.411333900083834[/C][C]0.794333049958083[/C][/ROW]
[ROW][C]43[/C][C]0.174418559049616[/C][C]0.348837118099232[/C][C]0.825581440950384[/C][/ROW]
[ROW][C]44[/C][C]0.142407043997613[/C][C]0.284814087995226[/C][C]0.857592956002387[/C][/ROW]
[ROW][C]45[/C][C]0.179025195603447[/C][C]0.358050391206895[/C][C]0.820974804396553[/C][/ROW]
[ROW][C]46[/C][C]0.165493653931872[/C][C]0.330987307863744[/C][C]0.834506346068128[/C][/ROW]
[ROW][C]47[/C][C]0.133198169598146[/C][C]0.266396339196292[/C][C]0.866801830401854[/C][/ROW]
[ROW][C]48[/C][C]0.185415738051139[/C][C]0.370831476102278[/C][C]0.814584261948861[/C][/ROW]
[ROW][C]49[/C][C]0.18233012437146[/C][C]0.36466024874292[/C][C]0.81766987562854[/C][/ROW]
[ROW][C]50[/C][C]0.147613900322869[/C][C]0.295227800645738[/C][C]0.852386099677131[/C][/ROW]
[ROW][C]51[/C][C]0.131050470678768[/C][C]0.262100941357535[/C][C]0.868949529321232[/C][/ROW]
[ROW][C]52[/C][C]0.11267918734894[/C][C]0.22535837469788[/C][C]0.88732081265106[/C][/ROW]
[ROW][C]53[/C][C]0.0986859900271813[/C][C]0.197371980054363[/C][C]0.901314009972819[/C][/ROW]
[ROW][C]54[/C][C]0.0805513392692637[/C][C]0.161102678538527[/C][C]0.919448660730736[/C][/ROW]
[ROW][C]55[/C][C]0.0613142275718343[/C][C]0.122628455143669[/C][C]0.938685772428166[/C][/ROW]
[ROW][C]56[/C][C]0.0717013494432127[/C][C]0.143402698886425[/C][C]0.928298650556787[/C][/ROW]
[ROW][C]57[/C][C]0.0561389175256312[/C][C]0.112277835051262[/C][C]0.943861082474369[/C][/ROW]
[ROW][C]58[/C][C]0.05657855511076[/C][C]0.11315711022152[/C][C]0.94342144488924[/C][/ROW]
[ROW][C]59[/C][C]0.0480679377631638[/C][C]0.0961358755263275[/C][C]0.951932062236836[/C][/ROW]
[ROW][C]60[/C][C]0.0820022706294239[/C][C]0.164004541258848[/C][C]0.917997729370576[/C][/ROW]
[ROW][C]61[/C][C]0.0753727649333176[/C][C]0.150745529866635[/C][C]0.924627235066682[/C][/ROW]
[ROW][C]62[/C][C]0.0808259183097603[/C][C]0.161651836619521[/C][C]0.91917408169024[/C][/ROW]
[ROW][C]63[/C][C]0.0649424877642083[/C][C]0.129884975528417[/C][C]0.935057512235792[/C][/ROW]
[ROW][C]64[/C][C]0.0523982593153311[/C][C]0.104796518630662[/C][C]0.947601740684669[/C][/ROW]
[ROW][C]65[/C][C]0.0471269594034487[/C][C]0.0942539188068975[/C][C]0.952873040596551[/C][/ROW]
[ROW][C]66[/C][C]0.0367493686993701[/C][C]0.0734987373987401[/C][C]0.96325063130063[/C][/ROW]
[ROW][C]67[/C][C]0.0302497036864874[/C][C]0.0604994073729748[/C][C]0.969750296313513[/C][/ROW]
[ROW][C]68[/C][C]0.0227191494891275[/C][C]0.045438298978255[/C][C]0.977280850510872[/C][/ROW]
[ROW][C]69[/C][C]0.0164536489423674[/C][C]0.0329072978847348[/C][C]0.983546351057633[/C][/ROW]
[ROW][C]70[/C][C]0.0171078380316151[/C][C]0.0342156760632302[/C][C]0.982892161968385[/C][/ROW]
[ROW][C]71[/C][C]0.0143623729636346[/C][C]0.0287247459272691[/C][C]0.985637627036365[/C][/ROW]
[ROW][C]72[/C][C]0.0129825460159663[/C][C]0.0259650920319327[/C][C]0.987017453984034[/C][/ROW]
[ROW][C]73[/C][C]0.00957830641691052[/C][C]0.019156612833821[/C][C]0.99042169358309[/C][/ROW]
[ROW][C]74[/C][C]0.0068402484291738[/C][C]0.0136804968583476[/C][C]0.993159751570826[/C][/ROW]
[ROW][C]75[/C][C]0.00979872399690838[/C][C]0.0195974479938168[/C][C]0.990201276003092[/C][/ROW]
[ROW][C]76[/C][C]0.00688593324475815[/C][C]0.0137718664895163[/C][C]0.993114066755242[/C][/ROW]
[ROW][C]77[/C][C]0.00617888231229209[/C][C]0.0123577646245842[/C][C]0.993821117687708[/C][/ROW]
[ROW][C]78[/C][C]0.00462103772908448[/C][C]0.00924207545816895[/C][C]0.995378962270915[/C][/ROW]
[ROW][C]79[/C][C]0.0106736589696406[/C][C]0.0213473179392813[/C][C]0.989326341030359[/C][/ROW]
[ROW][C]80[/C][C]0.00751873728535174[/C][C]0.0150374745707035[/C][C]0.992481262714648[/C][/ROW]
[ROW][C]81[/C][C]0.0124024206222479[/C][C]0.0248048412444959[/C][C]0.987597579377752[/C][/ROW]
[ROW][C]82[/C][C]0.013049791318524[/C][C]0.026099582637048[/C][C]0.986950208681476[/C][/ROW]
[ROW][C]83[/C][C]0.0168136235238152[/C][C]0.0336272470476304[/C][C]0.983186376476185[/C][/ROW]
[ROW][C]84[/C][C]0.0143940463536325[/C][C]0.0287880927072649[/C][C]0.985605953646367[/C][/ROW]
[ROW][C]85[/C][C]0.0106780567147384[/C][C]0.0213561134294768[/C][C]0.989321943285262[/C][/ROW]
[ROW][C]86[/C][C]0.00857154468702571[/C][C]0.0171430893740514[/C][C]0.991428455312974[/C][/ROW]
[ROW][C]87[/C][C]0.0061619882785816[/C][C]0.0123239765571632[/C][C]0.993838011721418[/C][/ROW]
[ROW][C]88[/C][C]0.00570323994413576[/C][C]0.0114064798882715[/C][C]0.994296760055864[/C][/ROW]
[ROW][C]89[/C][C]0.00394695426718289[/C][C]0.00789390853436578[/C][C]0.996053045732817[/C][/ROW]
[ROW][C]90[/C][C]0.00327119694028708[/C][C]0.00654239388057416[/C][C]0.996728803059713[/C][/ROW]
[ROW][C]91[/C][C]0.00229324958825984[/C][C]0.00458649917651968[/C][C]0.99770675041174[/C][/ROW]
[ROW][C]92[/C][C]0.00170050863708116[/C][C]0.00340101727416233[/C][C]0.998299491362919[/C][/ROW]
[ROW][C]93[/C][C]0.00291925614061989[/C][C]0.00583851228123979[/C][C]0.99708074385938[/C][/ROW]
[ROW][C]94[/C][C]0.00259112906667303[/C][C]0.00518225813334605[/C][C]0.997408870933327[/C][/ROW]
[ROW][C]95[/C][C]0.00182267047272[/C][C]0.00364534094544[/C][C]0.99817732952728[/C][/ROW]
[ROW][C]96[/C][C]0.02471345546733[/C][C]0.04942691093466[/C][C]0.97528654453267[/C][/ROW]
[ROW][C]97[/C][C]0.0195122078200244[/C][C]0.0390244156400488[/C][C]0.980487792179976[/C][/ROW]
[ROW][C]98[/C][C]0.0177659028405261[/C][C]0.0355318056810521[/C][C]0.982234097159474[/C][/ROW]
[ROW][C]99[/C][C]0.0189908960644993[/C][C]0.0379817921289987[/C][C]0.981009103935501[/C][/ROW]
[ROW][C]100[/C][C]0.0154897282891798[/C][C]0.0309794565783595[/C][C]0.98451027171082[/C][/ROW]
[ROW][C]101[/C][C]0.0145060241613209[/C][C]0.0290120483226418[/C][C]0.985493975838679[/C][/ROW]
[ROW][C]102[/C][C]0.0122806817035553[/C][C]0.0245613634071106[/C][C]0.987719318296445[/C][/ROW]
[ROW][C]103[/C][C]0.0186993178152566[/C][C]0.0373986356305133[/C][C]0.981300682184743[/C][/ROW]
[ROW][C]104[/C][C]0.0141812679825979[/C][C]0.0283625359651959[/C][C]0.985818732017402[/C][/ROW]
[ROW][C]105[/C][C]0.0119195765266798[/C][C]0.0238391530533596[/C][C]0.98808042347332[/C][/ROW]
[ROW][C]106[/C][C]0.012677459296293[/C][C]0.0253549185925861[/C][C]0.987322540703707[/C][/ROW]
[ROW][C]107[/C][C]0.018832420056188[/C][C]0.037664840112376[/C][C]0.981167579943812[/C][/ROW]
[ROW][C]108[/C][C]0.0216023285905161[/C][C]0.0432046571810322[/C][C]0.978397671409484[/C][/ROW]
[ROW][C]109[/C][C]0.112540246839987[/C][C]0.225080493679973[/C][C]0.887459753160013[/C][/ROW]
[ROW][C]110[/C][C]0.145923474176583[/C][C]0.291846948353166[/C][C]0.854076525823417[/C][/ROW]
[ROW][C]111[/C][C]0.151373748510518[/C][C]0.302747497021036[/C][C]0.848626251489482[/C][/ROW]
[ROW][C]112[/C][C]0.251708574733593[/C][C]0.503417149467186[/C][C]0.748291425266407[/C][/ROW]
[ROW][C]113[/C][C]0.508517504531939[/C][C]0.982964990936123[/C][C]0.491482495468061[/C][/ROW]
[ROW][C]114[/C][C]0.528721592240526[/C][C]0.942556815518948[/C][C]0.471278407759474[/C][/ROW]
[ROW][C]115[/C][C]0.636815634980521[/C][C]0.726368730038958[/C][C]0.363184365019479[/C][/ROW]
[ROW][C]116[/C][C]0.577077632130332[/C][C]0.845844735739336[/C][C]0.422922367869668[/C][/ROW]
[ROW][C]117[/C][C]0.558935583362942[/C][C]0.882128833274115[/C][C]0.441064416637058[/C][/ROW]
[ROW][C]118[/C][C]0.64939042723653[/C][C]0.70121914552694[/C][C]0.35060957276347[/C][/ROW]
[ROW][C]119[/C][C]0.583139385273061[/C][C]0.833721229453877[/C][C]0.416860614726939[/C][/ROW]
[ROW][C]120[/C][C]0.747836364084448[/C][C]0.504327271831104[/C][C]0.252163635915552[/C][/ROW]
[ROW][C]121[/C][C]0.6799780876597[/C][C]0.640043824680599[/C][C]0.3200219123403[/C][/ROW]
[ROW][C]122[/C][C]0.611080604547767[/C][C]0.777838790904467[/C][C]0.388919395452233[/C][/ROW]
[ROW][C]123[/C][C]0.81773179213866[/C][C]0.364536415722681[/C][C]0.18226820786134[/C][/ROW]
[ROW][C]124[/C][C]0.784286785080479[/C][C]0.431426429839041[/C][C]0.215713214919521[/C][/ROW]
[ROW][C]125[/C][C]0.807277135217802[/C][C]0.385445729564395[/C][C]0.192722864782198[/C][/ROW]
[ROW][C]126[/C][C]0.812649451198697[/C][C]0.374701097602605[/C][C]0.187350548801303[/C][/ROW]
[ROW][C]127[/C][C]0.922281478436802[/C][C]0.155437043126397[/C][C]0.0777185215631983[/C][/ROW]
[ROW][C]128[/C][C]0.998201408332431[/C][C]0.00359718333513706[/C][C]0.00179859166756853[/C][/ROW]
[ROW][C]129[/C][C]0.99964706383618[/C][C]0.000705872327640598[/C][C]0.000352936163820299[/C][/ROW]
[ROW][C]130[/C][C]0.998611700590212[/C][C]0.00277659881957577[/C][C]0.00138829940978789[/C][/ROW]
[ROW][C]131[/C][C]0.993607641263505[/C][C]0.0127847174729898[/C][C]0.00639235873649489[/C][/ROW]
[ROW][C]132[/C][C]0.997519433713734[/C][C]0.00496113257253303[/C][C]0.00248056628626651[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186075&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186075&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
190.4904856247870250.9809712495740510.509514375212975
200.5206793856091510.9586412287816990.479320614390849
210.707558400494510.5848831990109810.29244159950549
220.8902935670621460.2194128658757080.109706432937854
230.8865405751695950.2269188496608110.113459424830405
240.8393089868281860.3213820263436290.160691013171814
250.7763075785156280.4473848429687440.223692421484372
260.74945931676180.50108136647640.2505406832382
270.7050160106938810.5899679786122380.294983989306119
280.640512957764050.7189740844718990.35948704223595
290.5632972406893280.8734055186213450.436702759310673
300.5106319633574680.9787360732850630.489368036642532
310.4541839507641570.9083679015283140.545816049235843
320.4316183535979820.8632367071959630.568381646402018
330.4380909091277160.8761818182554320.561909090872284
340.4093852256245370.8187704512490750.590614774375463
350.3630640224494940.7261280448989880.636935977550506
360.3393079774075050.678615954815010.660692022592495
370.2810383372137070.5620766744274130.718961662786293
380.2425270899813810.4850541799627620.757472910018619
390.281141960348520.5622839206970410.71885803965148
400.2613122698219570.5226245396439130.738687730178043
410.220494592795310.4409891855906210.77950540720469
420.2056669500419170.4113339000838340.794333049958083
430.1744185590496160.3488371180992320.825581440950384
440.1424070439976130.2848140879952260.857592956002387
450.1790251956034470.3580503912068950.820974804396553
460.1654936539318720.3309873078637440.834506346068128
470.1331981695981460.2663963391962920.866801830401854
480.1854157380511390.3708314761022780.814584261948861
490.182330124371460.364660248742920.81766987562854
500.1476139003228690.2952278006457380.852386099677131
510.1310504706787680.2621009413575350.868949529321232
520.112679187348940.225358374697880.88732081265106
530.09868599002718130.1973719800543630.901314009972819
540.08055133926926370.1611026785385270.919448660730736
550.06131422757183430.1226284551436690.938685772428166
560.07170134944321270.1434026988864250.928298650556787
570.05613891752563120.1122778350512620.943861082474369
580.056578555110760.113157110221520.94342144488924
590.04806793776316380.09613587552632750.951932062236836
600.08200227062942390.1640045412588480.917997729370576
610.07537276493331760.1507455298666350.924627235066682
620.08082591830976030.1616518366195210.91917408169024
630.06494248776420830.1298849755284170.935057512235792
640.05239825931533110.1047965186306620.947601740684669
650.04712695940344870.09425391880689750.952873040596551
660.03674936869937010.07349873739874010.96325063130063
670.03024970368648740.06049940737297480.969750296313513
680.02271914948912750.0454382989782550.977280850510872
690.01645364894236740.03290729788473480.983546351057633
700.01710783803161510.03421567606323020.982892161968385
710.01436237296363460.02872474592726910.985637627036365
720.01298254601596630.02596509203193270.987017453984034
730.009578306416910520.0191566128338210.99042169358309
740.00684024842917380.01368049685834760.993159751570826
750.009798723996908380.01959744799381680.990201276003092
760.006885933244758150.01377186648951630.993114066755242
770.006178882312292090.01235776462458420.993821117687708
780.004621037729084480.009242075458168950.995378962270915
790.01067365896964060.02134731793928130.989326341030359
800.007518737285351740.01503747457070350.992481262714648
810.01240242062224790.02480484124449590.987597579377752
820.0130497913185240.0260995826370480.986950208681476
830.01681362352381520.03362724704763040.983186376476185
840.01439404635363250.02878809270726490.985605953646367
850.01067805671473840.02135611342947680.989321943285262
860.008571544687025710.01714308937405140.991428455312974
870.00616198827858160.01232397655716320.993838011721418
880.005703239944135760.01140647988827150.994296760055864
890.003946954267182890.007893908534365780.996053045732817
900.003271196940287080.006542393880574160.996728803059713
910.002293249588259840.004586499176519680.99770675041174
920.001700508637081160.003401017274162330.998299491362919
930.002919256140619890.005838512281239790.99708074385938
940.002591129066673030.005182258133346050.997408870933327
950.001822670472720.003645340945440.99817732952728
960.024713455467330.049426910934660.97528654453267
970.01951220782002440.03902441564004880.980487792179976
980.01776590284052610.03553180568105210.982234097159474
990.01899089606449930.03798179212899870.981009103935501
1000.01548972828917980.03097945657835950.98451027171082
1010.01450602416132090.02901204832264180.985493975838679
1020.01228068170355530.02456136340711060.987719318296445
1030.01869931781525660.03739863563051330.981300682184743
1040.01418126798259790.02836253596519590.985818732017402
1050.01191957652667980.02383915305335960.98808042347332
1060.0126774592962930.02535491859258610.987322540703707
1070.0188324200561880.0376648401123760.981167579943812
1080.02160232859051610.04320465718103220.978397671409484
1090.1125402468399870.2250804936799730.887459753160013
1100.1459234741765830.2918469483531660.854076525823417
1110.1513737485105180.3027474970210360.848626251489482
1120.2517085747335930.5034171494671860.748291425266407
1130.5085175045319390.9829649909361230.491482495468061
1140.5287215922405260.9425568155189480.471278407759474
1150.6368156349805210.7263687300389580.363184365019479
1160.5770776321303320.8458447357393360.422922367869668
1170.5589355833629420.8821288332741150.441064416637058
1180.649390427236530.701219145526940.35060957276347
1190.5831393852730610.8337212294538770.416860614726939
1200.7478363640844480.5043272718311040.252163635915552
1210.67997808765970.6400438246805990.3200219123403
1220.6110806045477670.7778387909044670.388919395452233
1230.817731792138660.3645364157226810.18226820786134
1240.7842867850804790.4314264298390410.215713214919521
1250.8072771352178020.3854457295643950.192722864782198
1260.8126494511986970.3747010976026050.187350548801303
1270.9222814784368020.1554370431263970.0777185215631983
1280.9982014083324310.003597183335137060.00179859166756853
1290.999647063836180.0007058723276405980.000352936163820299
1300.9986117005902120.002776598819575770.00138829940978789
1310.9936076412635050.01278471747298980.00639235873649489
1320.9975194337137340.004961132572533030.00248056628626651







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.105263157894737NOK
5% type I error level460.403508771929825NOK
10% type I error level500.43859649122807NOK

\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 & 12 & 0.105263157894737 & NOK \tabularnewline
5% type I error level & 46 & 0.403508771929825 & NOK \tabularnewline
10% type I error level & 50 & 0.43859649122807 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186075&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]12[/C][C]0.105263157894737[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]46[/C][C]0.403508771929825[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]50[/C][C]0.43859649122807[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186075&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186075&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 level120.105263157894737NOK
5% type I error level460.403508771929825NOK
10% type I error level500.43859649122807NOK



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')
}