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Author

*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 15 Dec 2012 15:40:16 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/15/t13556040492if061f6ri34gqo.htm/, Retrieved Tue, 30 Apr 2024 10:35:30 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200132, Retrieved Tue, 30 Apr 2024 10:35:30 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

130

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

-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
F RMPD  [Univariate Explorative Data Analysis] [Colombia Coffee] [2008-01-07 14:21:11] [74be16979710d4c4e7c6647856088456]
- RMPD    [Univariate Explorative Data Analysis] [Workshop 6, Tutor...] [2010-11-07 12:24:29] [8ffb4cfa64b4677df0d2c448735a40bb]
- R P       [Univariate Explorative Data Analysis] [WS6 2. Technique 2] [2010-11-11 18:06:41] [afe9379cca749d06b3d6872e02cc47ed]
- RMPD        [Multiple Regression] [Apple Inc - Multi...] [2010-12-11 10:33:09] [afe9379cca749d06b3d6872e02cc47ed]
-    D          [Multiple Regression] [WS10 Multiple Reg...] [2010-12-13 13:48:19] [afe9379cca749d06b3d6872e02cc47ed]
- R  D              [Multiple Regression] [] [2012-12-15 20:40:16] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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Histogram

Boxplots

10.81	24563400	-0.2643	24.45	2772.73	0.0373	 115.7	5.98
9.12	14163200	-0.2643	23.62	2151.83	0.0353	 109.2	5.49
11.03	18184800	-0.2643	21.90	1840.26	0.0292	 116.9	5.31
12.74	20810300	-0.1918	27.12	2116.24	0.0327	 109.9	4.8
9.98	12843000	-0.1918	27.70	2110.49	0.0362	 116.1	4.21
11.62	13866700	-0.1918	29.23	2160.54	0.0325	 118.9	3.97
9.40	15119200	-0.2246	26.50	2027.13	0.0272	 116.3	3.77
9.27	8301600	-0.2246	22.84	1805.43	0.0272	 114.0	3.65
7.76	14039600	-0.2246	20.49	1498.80	0.0265	 97.0	3.07
8.78	12139700	0.3654	23.28	1690.20	0.0213	 85.3	2.49
10.65	9649000	0.3654	25.71	1930.58	0.019	 84.9	2.09
10.95	8513600	0.3654	26.52	1950.40	0.0155	 94.6	1.82
12.36	15278600	0.0447	25.51	1934.03	0.0114	 97.8	1.73
10.85	15590900	0.0447	23.36	1731.49	0.0114	 95.0	1.74
11.84	9691100	0.0447	24.15	1845.35	0.0148	 110.7	1.73
12.14	10882700	-0.0312	20.92	1688.23	0.0164	 108.5	1.75
11.65	10294800	-0.0312	20.38	1615.73	0.0118	 110.3	1.75
8.86	16031900	-0.0312	21.90	1463.21	0.0107	 106.3	1.75
7.63	13683600	-0.0048	19.21	1328.26	0.0146	 97.4	1.73
7.38	8677200	-0.0048	19.65	1314.85	0.018	 94.5	1.74
7.25	9874100	-0.0048	17.51	1172.06	0.0151	 93.7	1.75
8.03	10725500	0.0705	26,81	91,517,810	0,3549	112.99	2971.33	0,0157	73.90	0.16
28.24	80,968,650	0,3549	111.99	2992.16	0,0157	73.90	0.17
27,58	46,664,930	0,3549	112.64	3013.81	0,0157	73.90	0.17
27.98	77,035,520	0,3549	113.45	3022.30	0,0157	73.90	0.16
27.84	50,463,630	0,3549	111.74	2986.96	0,0157	73.90	0.16
27,49	51,648,160	0,3549	109.70	2978.04	0,0157	73.90	0.16
26.97	45,933,360	0,3549	109.05	2989.27	0,0157	73.90	0.16
27.71	58,899,760	0,3549	107.81	2973.70	0,0157	73.90	0.17
27.46	72,810,360	0,3549	106.17	2996.69	0,0157	73.90	0.16
27.04	123,384,900	0,3549	107.63	3002.20	0,0157	73.90	0.16
28.00	126,867,400	0,3549	108.14	3010.24	0,0157	73.90	0.16
27.32	88,572,990	0,3549	107.11	3012.03	0,0157	73.90	0.16
26.36	49,113,040	0,3549	107.85	2991.78	0,0157	73.90	0.16
26.15	85,687,430	0,3549	107.59	2967.79	0,0157	73.90	0.16
25.94	123,513,900	0,3338	108.82	2976.78	0,0176	73.70	0.16
24,00	29,520,310	0,3338	107.62	2966.85	0,0176	73.70	0.16
24.32	89,720,920	0,3338	107.59	2926.55	0,0176	73.70	0.16
23.10	46,515,660	0,3338	106.76	2916.68	0,0176	73.70	0.16
22.92	84,944,370	0,3338	105.53	2916.07	0,0176	73.70	0.16
23,56	107,091,400	0,3338	103.13	2853.13	0,0176	73.70	0.16
22.17	78,808,770	0,3338	100.65	2836.94	0,0176	73.70	0.16
22.36	229,081,600	0,3338	98.77	2846.81	0,0176	73.70	0.16
19.86	71,524,510	0,3338	99.26	2883.89	0,0176	73.70	0.16
20.07	67,220,540	0,3338	99.00	2904.26	0,0176	73.70	0.16
19.21	42,257,790	0,3338	95.62	2904.87	0,0176	73.70	0.16
19.99	34,106,840	0,3338	96.35	2895.58	0,0176	73.70	0.16
20.47	33,352,920	0,3338	101.21	2937.29	0,0176	73.70	0.16
21,17	29,030,780	0,3338	104.14	3011.93	0,0176	73.70	0.16
21.25	31,752,420	0,3338	107.29	2999.66	0,0176	73.70	0.17
21.18	38,308,550	0,3338	106.78	2982.13	0,0176	73.70	0.16
21.21	37,631,000	0,3338	106.85	3020.06	0,0176	73.70	0.17
21.11	99,311,480	0,3338	106.93	2977.23	0,0176	73.70	0.18
21,94	9,999,999	0,3338	104.55	2987.95	0,0176	73.70	0.17
22.56	76,050,080	0,3478	105.72	2986.12	0,0216	73.10	0.17
23,23	228,364,700	0,3478	107.55	2981.70	0,0216	73.10	0.16
19.50	72,281,000	0,3478	104.13	2990.46	0,0216	73.10	0.16
19,32	32,269,470	0,3478	107.28	3016.96	0,0216	73.10	0.17
19.00	34,769,600	0,3478	106.72	3005.62	0,0216	73.10	0.15
18,98	52,059,860	0,3478	108.87	3072.87	0,0216	73.10	0.15
19.88	44,020,450	0,3478	109.42	3104.12	0,0216	73.10	0.16
19.48	21,816,100	0,3478	111.18	3101.17	0,0216	73.10	0.15
19.52	20,154,860	0,3478	110.85	3064.18	0,0216	73.10	0.15
19,52	18,743,920	0,3478	110.41	3044.11	0,0216	73.10	0.16
19.75	21,803,710	0,3478	113.77	3049.41	0,0216	73.10	0.16
19.64	39,282,420	0,3478	111.98	3051.78	0,0216	73.10	0.16
20,23	27,125,670	0,3478	113.07	3065.02	0,0216	73.10	0.16
20.40	32,190,200	0,3478	117.97	3112.35	0,0216	73.10	0.16
20.91	40,483,780	0,3299	118.83	3136.19	0,0199	70.30	0.16
21,95	46,549,400	0,3299	121.98	3149.46	0,0199	70.30	0.16
21.83	31,940,720	0,3299	121.68	3135.23	0,0199	70.30	0.15
22.27	29,276,680	0,3299	118.77	3120.04	0,0199	70.30	0.15
21.99	51,198,150	0,3299	117.94	3113.53	0,0199	70.30	0.16
21.66	65,415,370	0,3299	120.40	3116.23	0,0199	70.30	0.16
20.32	30,061,050	0,3299	119.61	3136.60	0,0199	70.30	0.15
20.62	38,212,360	0,3299	118.00	3093.70	0,0199	70.30	0.09
20.28	46,169,460	0,3299	119.89	3117.73	0,0199	70.30	0.14
20.79	78,968,380	0,3299	122.70	3160.78	0,0199	70.30	0.15
22.86	51,163,120	0,3299	122.84	3179.96	0,0199	70.30	0.15
22.59	57,115,160	0,3299	122.78	3175.96	0,0199	70.30	0.16
23.29	78,525,460	0,3299	122.51	3182.62	0,0199	70.30	0.15
21.87	36,696,330	0,3299	121.43	3177.80	0,0199	70.30	0.15
21.52	50,593,510	0,3299	121.40	3178.67	0,0199	70.30	0.15
22.00	72,707,430	0,3299	123.23	3183.95	0,0199	70.30	0.1621.41	1329.75	0.0203	 79.6	1.75
7.75	8348400	0.0705	23.09	1478.78	0.022	 84.9	1.34
7.16	8046200	0.0705	20.70	1335.51	0.0238	 80.7	1.24
7.18	10862300	-0.0134	19.00	1320.91	0.026	 78.8	1.24
7.51	8100300	-0.0134	19.04	1337.52	0.0298	 64.8	1.26
7.07	7287500	-0.0134	19.45	1341.17	0.0302	 61.4	1.25
7.11	14002500	0.0812	20.54	1464.31	0.0222	 81.0	1.26
8.98	19037900	0.0812	19.77	1595.91	0.0206	 83.6	1.26
9.53	10774600	0.0812	20.60	1622.80	0.0211	 83.5	1.22
10.54	8960600	0.1885	21.21	1735.02	0.0211	 77.0	1.01
11.31	7773300	0.1885	21.30	1810.45	0.0216	 81.7	1.03
10.36	9579700	0.1885	22.33	1786.94	0.0232	 77.0	1.01
11.44	11270700	0.3628	21.12	1932.21	0.0204	 81.7	1.01
10.45	9492800	0.3628	20.77	1960.26	0.0177	 92.5	1
10.69	9136800	0.3628	22.11	2003.37	0.0188	 91.7	0.98
11.28	14487600	0.2942	22.34	2066.15	0.0193	 96.4	1
11.96	10133200	0.2942	21.43	2029.82	0.0169	 88.5	1.01
13.52	18659700	0.2942	20.14	1994.22	0.0174	 88.5	1
12.89	15980700	0.3036	21.11	1920.15	0.0229	 93.0	1
14.03	9732100	0.3036	21.19	1986.74	0.0305	 93.1	1
16.27	14626300	0.3036	23.07	2047.79	0.0327	 102.8	1.03
16.17	16904000	0.3703	23.01	1887.36	0.0299	 105.7	1.26
17.25	13616700	0.3703	22.12	1838.10	0.0265	 98.7	1.43
19.38	13772900	0.3703	22.40	1896.84	0.0254	 96.7	1.61
26.20	28749200	0.7398	22.66	1974.99	0.0319	 92.9	1.76
33.53	31408300	0.7398	24.21	2096.81	0.0352	 92.6	1.93
32.20	26342800	0.7398	24.13	2175.44	0.0326	 102.7	2.16
38.45	48909500	0.6988	23.73	2062.41	0.0297	 105.1	2.28
44.86	41542400	0.6988	22.79	2051.72	0.0301	 104.4	2.5
41.67	24857200	0.6988	21.89	1999.23	0.0315	 103.0	2.63
36.06	34093700	0.7478	22.92	1921.65	0.0351	 97.5	2.79
39.76	22555200	0.7478	23.44	2068.22	0.028	 103.1	3
36.81	19067500	0.7478	22.57	2056.96	0.0253	 106.2	3.04
42.65	19029100	0.5651	23.27	2184.83	0.0317	 103.6	3.26
46.89	15223200	0.5651	24.95	2152.09	0.0364	 105.5	3.5
53.61	21903700	0.5651	23.45	2151.69	0.0469	 87.5	3.62
57.59	33306600	0.6473	23.42	2120.30	0.0435	 85.2	3.78
67.82	23898100	0.6473	25.30	2232.82	0.0346	 98.3	4
71.89	23279600	0.6473	23.90	2205.32	0.0342	 103.8	4.16
75.51	40699800	0.3441	25.73	2305.82	0.0399	 106.8	4.29
68.49	37646000	0.3441	24.64	2281.39	0.036	 102.7	4.49
62.72	37277000	0.3441	24.95	2339.79	0.0336	 107.5	4.59
70.39	39246800	0.2415	22.15	2322.57	0.0355	 109.8	4.79
59.77	27418400	0.2415	20.85	2178.88	0.0417	 104.7	4.94
57.27	30318700	0.2415	21.45	2172.09	0.0432	 105.7	4.99
67.96	32808100	0.3151	22.15	2091.47	0.0415	 107.0	5.24
67.85	28668200	0.3151	23.75	2183.75	0.0382	 100.2	5.25
76.98	32370300	0.3151	25.27	2258.43	0.0206	 105.9	5.25
81.08	24171100	0.239	26.53	2366.71	0.0131	 105.1	5.25
91.66	25009100	0.239	27.22	2431.77	0.0197	 105.3	5.25
84.84	32084300	0.239	27.69	2415.29	0.0254	 110.0	5.24
85.73	50117500	0.2127	28.61	2463.93	0.0208	 110.2	5.25
84.61	27522200	0.2127	26.21	2416.15	0.0242	 111.2	5.26
92.91	26816800	0.2127	25.93	2421.64	0.0278	 108.2	5.26
99.80	25136100	0.273	27.86	2525.09	0.0257	 106.3	5.25
121.19	30295600	0.273	28.65	2604.52	0.0269	 108.5	5.25
122.04	41526100	0.273	27.51	2603.23	0.0269	 105.3	5.25
131.76	43845100	0.3657	27.06	2546.27	0.0236	 111.9	5.26
138.48	39188900	0.3657	26.91	2596.36	0.0197	 105.6	5.02
153.47	40496400	0.3657	27.60	2701.50	0.0276	 99.5	4.94
189.95	37438400	0.4643	34.48	2859.12	0.0354	 95.2	4.76
182.22	46553700	0.4643	31.58	2660.96	0.0431	 87.8	4.49
198.08	31771400	0.4643	33.46	2652.28	0.0408	 90.6	4.24
135.36	62108100	0.5096	30.64	2389.86	0.0428	 87.9	3.94
125.02	46645400	0.5096	25.66	2271.48	0.0403	 76.4	2.98
143.50	42313100	0.5096	26.78	2279.10	0.0398	 65.9	2.61
173.95	38841700	0.3592	26.91	2412.80	0.0394	 62.3	2.28
188.75	32650300	0.3592	26.82	2522.66	0.0418	 57.2	1.98
167.44	34281100	0.3592	26.05	2292.98	0.0502	 50.4	2
158.95	33096200	0.7439	24.36	2325.55	0.056	 51.9	2.01
169.53	23273800	0.7439	25.94	2367.52	0.0537	 58.5	2
113.66	43697600	0.7439	25.37	2091.88	0.0494	 61.4	1.81
107.59	66902300	0.139	21.23	1720.95	0.0366	 38.8	0.97
92.67	44957200	0.139	19.35	1535.57	0.0107	 44.9	0.39
85.35	33800900	0.139	18.61	1577.03	0.0009	 38.6	0.16
90.13	33487900	0.1383	16.37	1476.42	0.0003	 4.0	0.15
89.31	27394900	0.1383	15.56	1377.84	0.0024	 25.3	0.22
105.12	25963400	0.1383	17.70	1528.59	-0.0038	 26.9	0.18
125.83	20952600	0.2874	19.52	1717.30	-0.0074	 40.8	0.15
135.81	17702900	0.2874	20.26	1774.33	-0.0128	 54.8	0.18
142.43	21282100	0.2874	23.05	1835.04	-0.0143	 49.3	0.21
163.39	18449100	0.0596	22.81	1978.50	-0.021	 47.4	0.16
168.21	14415700	0.0596	24.04	2009.06	-0.0148	 54.5	0.16
185.35	17906300	0.0596	25.08	2122.42	-0.0129	 53.4	0.15
188.50	22197500	0.3201	27.04	2045.11	-0.0018	 48.7	0.12
199.91	15856500	0.3201	28.81	2144.60	0.0184	 50.6	0.12
210.73	19068700	0.3201	29.86	2269.15	0.0272	 53.6	0.12
192.06	30855100	0.486	27.61	2147.35	0.0263	 56.5	0.11
204.62	21209000	0.486	28.22	2238.26	0.0214	 46.4	0.13
235.00	19541600	0.486	28.83	2397.96	0.0231	 52.3	0.16
261.09	21955000	0.6129	30.06	2461.19	0.0224	 57.7	0.2
256.88	33725900	0.6129	25.51	2257.04	0.0202	 62.7	0.2
251.53	28192800	0.6129	22.75	2109.24	0.0105	 54.3	0.18
257.25	27377000	0.6665	25.52	2254.70	0.0124	 51.0	0.18
243.10	16228100	0.6665	23.33	2114.03	0.0115	 53.2	0.19
283.75	21278900	0.6665	24.34	2368.62	0.0114	 48.6	0.19

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200132&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200132&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200132&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code