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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationThu, 30 Dec 2010 00:50:02 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/30/t1293670092m4x9sv9swkuu8lp.htm/, Retrieved Fri, 03 May 2024 05:53:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117205, Retrieved Fri, 03 May 2024 05:53:09 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact211
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared and McNemar Tests] [] [2010-11-16 14:33:59] [b98453cac15ba1066b407e146608df68]
- RMPD    [Kendall tau Correlation Matrix] [] [2010-12-30 00:50:02] [393d554610c677f923bed472882d0fdb] [Current]
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Dataseries X:
13	8	360	175	3821	11	73	0	0
15	8	390	190	3850	8.5	70	0	0
17	8	304	150	3672	11.5	72	0	0
19.4	6	232	90	3210	17.2	78	0	0
24.3	4	151	90	3003	20.1	80	0	0
18.1	6	258	120	3410	15.1	78	0	0
20.2	6	232	90	3265	18.2	79	0	0
18	6	232	100	2789	15	73	0	0
19	6	232	100	2634	13	71	0	0
20	6	232	100	2914	16	75	0	0
21	6	199	90	2648	15	70	0	0
18	6	199	97	2774	15.5	70	0	0
18	6	232	100	2945	16	73	0	0
19	6	232	100	2901	16	74	0	0
22.5	6	232	90	3085	17.6	76	0	0
18	6	258	110	2962	13.5	71	0	0
14	8	304	150	3672	11.5	73	0	0
15	6	258	110	3730	19	75	0	0
15.5	8	304	120	3962	13.9	76	0	0
16	6	258	110	3632	18	74	0	0
18	6	232	100	3288	15.5	71	0	0
14	8	304	150	4257	15.5	74	0	0
15	8	304	150	3892	12.5	72	0	0
19	6	232	90	3211	17	75	0	0
17.5	6	258	95	3193	17.8	76	0	0
16	8	304	150	3433	12	70	0	0
27.4	4	121	80	2670	15	79	0	0
24	4	107	90	2430	14.5	70	0	1
20	4	114	91	2582	14	73	0	1
23	4	115	95	2694	15	75	0	1
34.3	4	97	78	2188	15.8	80	0	1
20.3	5	131	103	2830	15.9	78	0	1
36.4	5	121	67	2950	19.9	80	0	1
29	4	98	83	2219	16.5	74	0	1
26	4	121	113	2234	12.5	70	0	1
21.5	4	121	110	2600	12.8	77	0	1
17	6	231	110	3907	21	75	0	0
22.4	6	231	110	3415	15.8	81	0	0
13	8	350	175	4100	13	73	0	0
25	6	181	110	2945	16.4	82	0	0
13	8	350	150	4699	14.5	74	0	0
20.6	6	231	105	3380	15.8	78	0	0
12	8	455	225	4951	11	73	0	0
14	8	455	225	3086	10	70	0	0
16.9	8	350	155	4360	14.9	79	0	0
13	8	350	155	4502	13.5	72	0	0
30	4	111	80	2155	14.8	77	0	0
17.7	6	231	165	3445	13.4	78	0	0
21	6	231	110	3039	15	75	0	0
20.5	6	231	105	3425	16.9	77	0	0
26.6	4	151	84	2635	16.4	81	0	0
15	8	350	165	3693	11.5	70	0	0
28.4	4	151	90	2670	16	79	0	0
23	8	350	125	3900	17.4	79	0	0
16.5	8	350	180	4380	12.1	76	0	0
25	4	140	92	2572	14.9	76	0	0
16	6	250	105	3897	18.5	75	0	0
15	8	350	145	4440	14	75	0	0
27	4	151	90	2950	17.3	82	0	0
13	8	400	150	4464	12	73	0	0
17	8	305	130	3840	15.4	79	0	0
17.5	8	305	145	3880	12.5	77	0	0
28	4	112	88	2605	19.6	82	0	0
34	4	112	88	2395	18	82	0	0
27	4	112	88	2640	18.6	82	0	0
13	8	307	130	4098	14	72	0	0
17	6	250	100	3329	15.5	71	0	0
18	8	307	130	3504	12	70	0	0
16	6	250	100	3781	17	74	0	0
17.5	8	305	140	4215	13	76	0	0
29	4	85	52	2035	22.2	76	0	0
30	4	98	68	2155	16.5	78	0	0
30.5	4	98	63	2051	17	77	0	0
32.1	4	98	70	2120	15.5	80	0	0
23.5	6	173	110	2725	12.6	81	0	0
28	4	151	90	2678	16.5	80	0	0
28.8	6	173	115	2595	11.3	79	0	0
17.5	6	250	110	3520	16.4	77	0	0
11	8	400	150	4997	14	73	0	0
13	8	350	165	4274	12	72	0	0
14	8	350	165	4209	12	71	0	0
14	8	454	220	4354	9	70	0	0
13	8	350	145	3988	13	73	0	0
20.5	6	200	95	3155	18.2	78	0	0
19.2	8	267	125	3605	15	79	0	0
15	8	400	150	3761	9.5	70	0	0
15.5	8	350	170	4165	11.4	77	0	0
19.2	8	305	145	3425	13.2	78	0	0
15	8	350	145	4082	13	73	0	0
20	8	262	110	3221	13.5	75	0	0
15	6	250	100	3336	17	74	0	0
18	6	250	105	3459	16	75	0	0
22	6	250	105	3353	14.5	76	0	0
16	6	250	100	3278	18	73	0	0
20	4	140	90	2408	19.5	72	0	0
21	4	140	72	2401	19.5	73	0	0
25	4	140	75	2542	17	74	0	0
22	4	140	72	2408	19	71	0	0
28	4	140	90	2264	15.5	71	0	0
24.5	4	98	60	2164	22.1	76	0	0
13	8	350	145	4055	12	76	0	0
10	8	307	200	4376	15	70	0	0
31	4	119	82	2720	19.4	82	0	0
15.5	8	400	190	4325	12.2	77	0	0
26	4	156	92	2585	14.5	82	0	0
17.6	6	225	85	3465	16.6	81	0	0
18.5	8	360	150	3940	13	79	0	0
13	8	440	215	4735	11	73	0	0
13	8	400	190	4422	12.5	72	0	0
35	4	72	69	1613	18	71	1	0
23.9	4	119	97	2405	14.9	78	1	0
32.9	4	119	100	2615	14.8	81	1	0
31.8	4	85	65	2020	19.2	79	1	0
40.8	4	85	65	2110	19.2	80	1	0
37	4	85	65	1975	19.4	81	1	0
32.7	6	168	132	2910	11.4	80	1	0
37.2	4	86	65	2019	16.4	80	1	0
38	4	91	67	1995	16.2	82	1	0
27.2	4	119	97	2300	14.7	78	1	0
28	4	97	92	2288	17	72	1	0
37	4	119	92	2434	15	80	1	0
22	4	108	94	2379	16.5	73	1	0
24	4	119	97	2545	17	75	1	0
32	4	83	61	2003	19	74	1	0
22	6	146	97	2815	14.5	77	1	0
24.2	6	146	120	2930	13.8	81	1	0
32	4	85	70	1990	17	76	1	0
31	4	79	67	1950	19	74	1	0
39.4	4	85	70	2070	18.6	78	1	0
33.5	4	85	70	1945	16.8	77	1	0
27	4	97	88	2130	14.5	70	1	0
27	4	97	88	2130	14.5	71	1	0
29	4	135	84	2525	16	82	0	0
25.8	4	156	92	2620	14.4	81	0	0
18.6	6	225	110	3620	18.7	78	0	0
19.1	6	225	90	3381	18.7	80	0	0
20.6	6	225	110	3360	16.6	79	0	0
20	6	225	100	3651	17.7	76	0	0
15	8	383	170	3563	10	70	0	0
36	4	135	84	2370	13	82	0	0
26	4	98	79	2255	17.7	76	0	0
27.9	4	156	105	2800	14.4	80	0	0
28	4	90	75	2125	14.5	74	0	0
28	4	98	80	2164	15	72	0	0
25	4	97.5	80	2126	17	72	0	0
35.7	4	98	80	1915	14.4	79	0	0
33.5	4	98	83	2075	15.9	77	0	0
16	8	318	150	4190	13	76	0	0
15	8	318	150	3777	12.5	73	0	0
14	8	318	150	4457	13.5	74	0	0
13	8	318	150	3755	14	76	0	0
11	8	318	210	4382	13.5	70	0	0
15	8	318	150	3399	11	73	0	0
19.4	8	318	140	3735	13.2	78	0	0
17.5	8	318	140	4080	13.7	78	0	0
12	8	383	180	4955	11.5	71	0	0
15.5	8	318	145	4140	13.7	77	0	0
30.9	4	105	75	2230	14.5	78	0	0
32	4	135	84	2295	11.6	82	0	0
18.2	8	318	135	3830	15.2	79	0	0
26	4	98	90	2265	15.5	73	0	1
26	4	116	75	2246	14	74	0	1
30	4	88	76	2065	14.5	71	0	1
24	4	90	75	2108	15.5	74	0	1
29	4	68	49	1867	19.5	73	0	1
28	4	107	86	2464	15.5	76	0	1
37.3	4	91	69	2130	14.7	79	0	1
31	4	79	67	2000	16	74	0	1
12	8	400	167	4906	12.5	73	0	0
13	8	400	170	4746	12	71	0	0
15.5	8	351	142	4054	14.3	79	0	0
29.9	4	98	65	2380	20.7	81	0	0
34.4	4	98	65	2045	16.2	81	0	0
13	8	302	130	3870	15	76	0	0
10	8	360	215	4615	14	70	0	0
26.4	4	140	88	2870	18.1	80	0	0
20.2	6	200	85	2965	15.8	78	0	0
25.1	4	140	88	2720	15.4	78	0	0
22.3	4	140	88	2890	17.3	79	0	0
24	4	140	92	2865	16.4	82	0	0
36.1	4	98	66	1800	14.4	78	0	0
18.1	8	302	139	3205	11.2	78	0	0
14	8	351	153	4129	13	72	0	0
14	8	351	153	4154	13.5	71	0	0
15	8	429	198	4341	10	70	0	0
14	8	302	137	4042	14.5	73	0	0
14.5	8	351	152	4215	12.8	76	0	0
16	8	302	140	4141	14	74	0	0
13	8	302	140	4294	16	72	0	0
14	8	302	140	4638	16	74	0	0
18.5	6	250	98	3525	19	77	0	0
18	6	250	78	3574	21	76	0	0
20.2	6	200	88	3060	17.1	81	0	0
22	6	232	112	2835	14.7	82	0	0
13	8	351	158	4363	13	73	0	0
14	8	351	148	4657	13.5	75	0	0
17.6	8	302	129	3725	13.4	79	0	0
15	6	250	72	3158	19.5	75	0	0
18	6	250	88	3021	16.5	73	0	0
21	6	200	85	2587	16	70	0	0
24	6	200	81	3012	17.6	76	0	0
18	6	250	88	3139	14.5	71	0	0
27	4	140	86	2790	15.6	82	0	0
13	8	302	129	3169	12	75	0	0
25.5	4	140	89	2755	15.8	77	0	0
18	6	171	97	2984	14.5	75	0	0
19	4	122	85	2310	18.5	73	0	0
23	4	140	83	2639	17	75	0	0
26	4	122	80	2451	16.5	74	0	0
26.5	4	140	72	2565	13.6	76	0	0
22	4	122	86	2395	16	72	0	0
21	4	122	86	2226	16.5	72	0	0
28	4	120	79	2625	18.6	82	0	0
16	8	351	149	4335	14.5	77	0	0
17	8	302	140	3449	10.5	70	0	0
19	6	250	88	3302	15.5	71	0	0
9	8	304	193	4732	18.5	70	0	0
32.4	4	107	72	2290	17	80	1	0
36	4	107	75	2205	14.5	82	1	0
31.5	4	98	68	2045	18.5	77	1	0
29.5	4	98	68	2135	16.6	78	1	0
24	4	120	97	2489	15	74	1	0
33	4	91	53	1795	17.4	76	1	0
38	4	91	67	1965	15	82	1	0
32	4	91	67	1965	15.7	82	1	0
35.1	4	81	60	1760	16.1	81	1	0
44.6	4	91	67	1850	13.8	80	1	0
33	4	91	53	1795	17.5	75	1	0
36.1	4	91	60	1800	16.4	78	1	0
33.7	4	107	75	2210	14.4	81	1	0
34.1	4	86	65	1975	15.2	79	1	0
18	3	70	90	2124	13.5	73	1	0
31.3	4	120	75	2542	17.5	80	1	0
31.6	4	120	74	2635	18.3	81	1	0
46.6	4	86	65	2110	17.9	80	1	0
34.1	4	91	68	1985	16	81	1	0
31	4	91	68	1970	17.6	82	1	0
37	4	91	68	2025	18.2	82	1	0
32.8	4	78	52	1985	19.4	78	1	0
21.5	3	80	110	2720	13.5	77	1	0
23.7	3	70	100	2420	12.5	80	1	0
19	3	70	97	2330	13.5	72	1	0
25.4	5	183	77	3530	20.1	79	0	1
30	4	146	67	3250	21.8	80	0	1
16.5	6	168	120	3820	16.7	76	0	1
23	4	122	86	2220	14	71	0	0
21	6	155	107	2472	14	73	0	0
15	8	302	130	4295	14.9	77	0	0
16.5	8	351	138	3955	13.2	79	0	0
36	4	98	70	2125	17.3	82	0	0
11	8	429	208	4633	11	72	0	0
12	8	429	198	4952	11.5	73	0	0
15	6	250	72	3432	21	75	0	0
20.2	8	302	139	3570	12.8	78	0	0
20.8	6	200	85	3070	16.7	78	0	0
19.8	6	200	85	2990	18.2	79	0	0
36	4	120	88	2160	14.5	82	1	0
38	6	262	85	3015	17	82	0	0
26.6	8	350	105	3725	19	81	0	0
19.9	8	260	110	3365	15.5	78	0	0
23.9	8	260	90	3420	22.2	79	0	0
17	8	260	110	4060	19	77	0	0
12	8	350	160	4456	13.5	72	0	0
11	8	350	180	3664	11	73	0	0
26.8	6	173	115	2700	12.9	79	0	0
23.8	4	151	85	2855	17.6	78	0	0
12	8	350	180	4499	12.5	73	0	0
25	4	116	81	2220	16.9	76	0	1
28	4	116	90	2123	14	71	0	1
24	4	116	75	2158	15.5	73	0	1
26	4	97	78	2300	14.5	74	0	1
30	4	79	70	2074	19.5	71	0	1
19	4	120	88	3270	21.9	76	0	1
23	4	120	88	2957	17	75	0	1
25	4	110	87	2672	17.5	70	0	1
27.2	4	141	71	3190	24.8	79	0	1
21	4	120	87	2979	19.5	72	0	1
28.1	4	141	80	3230	20.4	81	0	1
16.2	6	163	133	3410	15.8	78	0	1
14	8	340	160	3609	8	70	0	0
25.5	4	122	96	2300	15.5	77	0	0
39	4	86	64	1875	16.4	81	0	0
26	4	91	70	1955	20.5	71	0	0
13	8	360	170	4654	13	73	0	0
20	6	198	95	3102	16.5	74	0	0
22	6	198	95	2833	15.5	70	0	0
23	6	198	95	2904	16	73	0	0
18	6	225	95	3785	19	75	0	0
14	8	318	150	4237	14.5	73	0	0
14	8	318	150	4096	13	71	0	0
14	8	440	215	4312	8.5	70	0	0
15	8	318	150	4135	13.5	72	0	0
16	8	318	150	4498	14.5	75	0	0
34.2	4	105	70	2200	13.2	79	0	0
34.7	4	105	63	2215	14.9	81	0	0
38	4	105	63	2125	14.7	82	0	0
34.5	4	105	70	2150	14.9	79	0	0
27.2	4	135	84	2490	15.7	81	0	0
30	4	135	84	2385	12.9	81	0	0
23.2	4	156	105	2745	16.7	78	0	0
18	8	318	150	3436	11	70	0	0
16	6	225	105	3439	15.5	71	0	0
14	8	318	150	4077	14	72	0	0
18	6	225	105	3613	16.5	74	0	0
18	6	225	105	3121	16.5	73	0	0
22	6	225	100	3233	15.4	76	0	0
19	6	225	95	3264	16	75	0	0
20.5	6	225	100	3430	17.2	78	0	0
19	6	225	100	3630	17.7	77	0	0
13	8	318	150	3940	13.2	76	0	0
23	4	140	78	2592	18.5	75	0	0
14	8	400	175	4385	12	72	0	0
14	8	455	225	4425	10	70	0	0
16	8	400	170	4668	11.5	75	0	0
14	8	400	175	4464	11.5	71	0	0
19	6	250	100	3282	15	71	0	0
16	8	400	230	4278	9.5	73	0	0
16	8	400	180	4220	11.1	77	0	0
31	4	112	85	2575	16.2	82	0	0
21.5	6	231	115	3245	15.4	79	0	0
27	4	151	90	2735	18	82	0	0
33.5	4	151	90	2556	13.2	79	0	0
19.2	6	231	105	3535	19.2	78	0	0
13	8	400	175	5140	12	71	0	0
24.5	4	151	88	2740	16	77	0	0
18.5	6	250	110	3645	16.2	76	0	0
26	4	96	69	2189	18	72	0	1
27	4	101	83	2202	15.3	76	0	1
36	4	79	58	1825	18.6	77	0	1
25	4	104	95	2375	17.5	70	0	1
21.6	4	121	115	2795	15.7	78	0	1
24	4	121	110	2660	14	73	0	1
25	4	121	115	2671	13.5	75	0	1
26	4	108	93	2391	15.5	74	1	0
32.3	4	97	67	2065	17.8	81	1	0
30	4	97	67	1985	16.4	77	1	0
33.8	4	97	67	2145	18	80	1	0
20	4	97	88	2279	19	73	1	0
32	4	144	96	2665	13.9	82	1	0
21.1	4	134	95	2515	14.8	78	1	0
28	4	97	75	2155	16.4	76	1	0
29	4	97	75	2171	16	75	1	0
32.2	4	108	75	2265	15.2	80	1	0
32.4	4	108	75	2350	16.8	81	1	0
34	4	108	70	2245	16.9	82	1	0
31	4	71	65	1773	19	71	1	0
32	4	71	65	1836	21	74	1	0
27	4	97	88	2100	16.5	72	1	0
26	4	97	75	2265	18.2	77	1	0
38.1	4	89	60	1968	18.8	80	1	0
24	4	134	96	2702	13.5	75	1	0
25	4	113	95	2228	14	71	1	0
27.5	4	134	95	2560	14.2	78	1	0
31	4	76	52	1649	16.5	74	1	0
24	4	113	95	2278	15.5	72	1	0
29.8	4	134	90	2711	15.5	80	1	0
24	4	113	95	2372	15	70	1	0
25.4	6	168	116	2900	12.6	81	1	0
19	6	156	108	2930	15.5	76	1	0
20	6	156	122	2807	13.5	73	1	0
39.1	4	79	58	1755	16.9	81	1	0
37.7	4	89	62	2050	17.3	81	1	0
23	4	120	97	2506	14.5	72	1	0
35	4	122	88	2500	15.1	80	0	1
29.8	4	89	62	1845	15.3	80	0	1
26	4	97	46	1835	20.5	70	0	1
22	4	121	76	2511	18	72	0	1
25	4	90	71	2223	16.5	75	0	1
26	4	79	67	1963	15.5	74	0	1
30.5	4	97	78	2190	14.1	77	0	1
33	4	105	74	2190	14.2	81	0	1
27	4	97	60	1834	19	71	0	1
29	4	90	70	1937	14	75	0	1
29.5	4	97	71	1825	12.2	76	0	1
29	4	97	78	1940	14.5	77	0	1
43.1	4	90	48	1985	21.5	78	0	1
36	4	105	74	1980	15.3	82	0	1
31.5	4	89	71	1990	14.9	78	0	1
26	4	97	46	1950	21	73	0	1
23	4	97	54	2254	23.5	72	0	1
19	4	121	112	2868	15.5	73	0	1
18	4	121	112	2933	14.5	72	0	1
22	4	121	98	2945	14.5	75	0	1
20	4	130	102	3150	15.7	76	0	1
17	6	163	125	3140	13.6	78	0	1
30.7	6	145	76	3160	19.6	81	0	1
43.4	4	90	48	2335	23.7	80	0	1
44	4	97	52	2130	24.6	82	0	1
29	4	90	70	1937	14.2	76	0	1
41.5	4	98	76	2144	14.7	80	0	1
44.3	4	90	48	2085	21.7	80	0	1
31.9	4	89	71	1925	14	79	0	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ www.wessa.org

\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ www.wessa.org \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117205&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ www.wessa.org[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117205&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117205&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 time6 seconds
R Server'Gwilym Jenkins' @ www.wessa.org







Correlations for all pairs of data series (method=pearson)
MPGCylinDisplHPWeightAccelYearOriginJapanOriginEurope
MPG1-0.778-0.805-0.778-0.8320.4230.5810.4510.244
Cylin-0.77810.9510.8430.898-0.505-0.346-0.404-0.352
Displ-0.8050.95110.8970.933-0.544-0.37-0.441-0.372
HP-0.7780.8430.89710.865-0.689-0.416-0.322-0.285
Weight-0.8320.8980.9330.8651-0.417-0.309-0.448-0.294
Accel0.423-0.505-0.544-0.689-0.41710.290.1150.208
Year0.581-0.346-0.37-0.416-0.3090.2910.2-0.038
OriginJapan0.451-0.404-0.441-0.322-0.4480.1150.21-0.23
OriginEurope0.244-0.352-0.372-0.285-0.2940.208-0.038-0.231

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & MPG & Cylin & Displ & HP & Weight & Accel & Year & OriginJapan & OriginEurope \tabularnewline
MPG & 1 & -0.778 & -0.805 & -0.778 & -0.832 & 0.423 & 0.581 & 0.451 & 0.244 \tabularnewline
Cylin & -0.778 & 1 & 0.951 & 0.843 & 0.898 & -0.505 & -0.346 & -0.404 & -0.352 \tabularnewline
Displ & -0.805 & 0.951 & 1 & 0.897 & 0.933 & -0.544 & -0.37 & -0.441 & -0.372 \tabularnewline
HP & -0.778 & 0.843 & 0.897 & 1 & 0.865 & -0.689 & -0.416 & -0.322 & -0.285 \tabularnewline
Weight & -0.832 & 0.898 & 0.933 & 0.865 & 1 & -0.417 & -0.309 & -0.448 & -0.294 \tabularnewline
Accel & 0.423 & -0.505 & -0.544 & -0.689 & -0.417 & 1 & 0.29 & 0.115 & 0.208 \tabularnewline
Year & 0.581 & -0.346 & -0.37 & -0.416 & -0.309 & 0.29 & 1 & 0.2 & -0.038 \tabularnewline
OriginJapan & 0.451 & -0.404 & -0.441 & -0.322 & -0.448 & 0.115 & 0.2 & 1 & -0.23 \tabularnewline
OriginEurope & 0.244 & -0.352 & -0.372 & -0.285 & -0.294 & 0.208 & -0.038 & -0.23 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117205&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]MPG[/C][C]Cylin[/C][C]Displ[/C][C]HP[/C][C]Weight[/C][C]Accel[/C][C]Year[/C][C]OriginJapan[/C][C]OriginEurope[/C][/ROW]
[ROW][C]MPG[/C][C]1[/C][C]-0.778[/C][C]-0.805[/C][C]-0.778[/C][C]-0.832[/C][C]0.423[/C][C]0.581[/C][C]0.451[/C][C]0.244[/C][/ROW]
[ROW][C]Cylin[/C][C]-0.778[/C][C]1[/C][C]0.951[/C][C]0.843[/C][C]0.898[/C][C]-0.505[/C][C]-0.346[/C][C]-0.404[/C][C]-0.352[/C][/ROW]
[ROW][C]Displ[/C][C]-0.805[/C][C]0.951[/C][C]1[/C][C]0.897[/C][C]0.933[/C][C]-0.544[/C][C]-0.37[/C][C]-0.441[/C][C]-0.372[/C][/ROW]
[ROW][C]HP[/C][C]-0.778[/C][C]0.843[/C][C]0.897[/C][C]1[/C][C]0.865[/C][C]-0.689[/C][C]-0.416[/C][C]-0.322[/C][C]-0.285[/C][/ROW]
[ROW][C]Weight[/C][C]-0.832[/C][C]0.898[/C][C]0.933[/C][C]0.865[/C][C]1[/C][C]-0.417[/C][C]-0.309[/C][C]-0.448[/C][C]-0.294[/C][/ROW]
[ROW][C]Accel[/C][C]0.423[/C][C]-0.505[/C][C]-0.544[/C][C]-0.689[/C][C]-0.417[/C][C]1[/C][C]0.29[/C][C]0.115[/C][C]0.208[/C][/ROW]
[ROW][C]Year[/C][C]0.581[/C][C]-0.346[/C][C]-0.37[/C][C]-0.416[/C][C]-0.309[/C][C]0.29[/C][C]1[/C][C]0.2[/C][C]-0.038[/C][/ROW]
[ROW][C]OriginJapan[/C][C]0.451[/C][C]-0.404[/C][C]-0.441[/C][C]-0.322[/C][C]-0.448[/C][C]0.115[/C][C]0.2[/C][C]1[/C][C]-0.23[/C][/ROW]
[ROW][C]OriginEurope[/C][C]0.244[/C][C]-0.352[/C][C]-0.372[/C][C]-0.285[/C][C]-0.294[/C][C]0.208[/C][C]-0.038[/C][C]-0.23[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117205&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117205&T=1

As an alternative you can also use a QR Code:  

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

Correlations for all pairs of data series (method=pearson)
MPGCylinDisplHPWeightAccelYearOriginJapanOriginEurope
MPG1-0.778-0.805-0.778-0.8320.4230.5810.4510.244
Cylin-0.77810.9510.8430.898-0.505-0.346-0.404-0.352
Displ-0.8050.95110.8970.933-0.544-0.37-0.441-0.372
HP-0.7780.8430.89710.865-0.689-0.416-0.322-0.285
Weight-0.8320.8980.9330.8651-0.417-0.309-0.448-0.294
Accel0.423-0.505-0.544-0.689-0.41710.290.1150.208
Year0.581-0.346-0.37-0.416-0.3090.2910.2-0.038
OriginJapan0.451-0.404-0.441-0.322-0.4480.1150.21-0.23
OriginEurope0.244-0.352-0.372-0.285-0.2940.208-0.038-0.231







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
MPG;Cylin-0.7776-0.8232-0.6874
p-value(0)(0)(0)
MPG;Displ-0.8051-0.8552-0.6786
p-value(0)(0)(0)
MPG;HP-0.7784-0.8536-0.6792
p-value(0)(0)(0)
MPG;Weight-0.8322-0.8756-0.6942
p-value(0)(0)(0)
MPG;Accel0.42330.44150.3031
p-value(0)(0)(0)
MPG;Year0.58050.57480.4152
p-value(0)(0)(0)
MPG;OriginJapan0.45150.45070.3718
p-value(0)(0)(0)
MPG;OriginEurope0.24430.25340.209
p-value(0)(0)(0)
Cylin;Displ0.95080.91360.7966
p-value(0)(0)(0)
Cylin;HP0.8430.81620.6861
p-value(0)(0)(0)
Cylin;Weight0.89750.8760.738
p-value(0)(0)(0)
Cylin;Accel-0.5047-0.4763-0.3685
p-value(0)(0)(0)
Cylin;Year-0.3456-0.3311-0.2702
p-value(0)(0)(0)
Cylin;OriginJapan-0.4042-0.4196-0.3965
p-value(0)(0)(0)
Cylin;OriginEurope-0.3523-0.3512-0.3319
p-value(0)(0)(0)
Displ;HP0.89730.87620.7185
p-value(0)(0)(0)
Displ;Weight0.9330.94560.7997
p-value(0)(0)(0)
Displ;Accel-0.5438-0.4994-0.3539
p-value(0)(0)(0)
Displ;Year-0.3699-0.3066-0.2199
p-value(0)(0)(0)
Displ;OriginJapan-0.4408-0.5069-0.4189
p-value(0)(0)(0)
Displ;OriginEurope-0.3716-0.3784-0.3127
p-value(0)(0)(0)
HP;Weight0.86450.87880.7037
p-value(0)(0)(0)
HP;Accel-0.6892-0.6581-0.4882
p-value(0)(0)(0)
HP;Year-0.4164-0.3895-0.2778
p-value(0)(0)(0)
HP;OriginJapan-0.3219-0.3484-0.2879
p-value(0)(0)(0)
HP;OriginEurope-0.2849-0.295-0.2438
p-value(0)(0)(0)
Weight;Accel-0.4168-0.4051-0.2686
p-value(0)(0)(0)
Weight;Year-0.3091-0.281-0.1998
p-value(0)(0)(0)
Weight;OriginJapan-0.4479-0.4757-0.389
p-value(0)(0)(0)
Weight;OriginEurope-0.2938-0.2982-0.2439
p-value(0)(0)(0)
Accel;Year0.29030.27830.1984
p-value(0)(0)(0)
Accel;OriginJapan0.1150.14430.1191
p-value(0.0228)(0.0042)(0.0043)
Accel;OriginEurope0.20830.14950.1235
p-value(0)(0.003)(0.0031)
Year;OriginJapan0.19980.19840.1682
p-value(1e-04)(1e-04)(1e-04)
Year;OriginEurope-0.0377-0.0368-0.0312
p-value(0.4562)(0.468)(0.4673)
OriginJapan;OriginEurope-0.2302-0.2302-0.2302
p-value(0)(0)(0)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
MPG;Cylin & -0.7776 & -0.8232 & -0.6874 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
MPG;Displ & -0.8051 & -0.8552 & -0.6786 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
MPG;HP & -0.7784 & -0.8536 & -0.6792 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
MPG;Weight & -0.8322 & -0.8756 & -0.6942 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
MPG;Accel & 0.4233 & 0.4415 & 0.3031 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
MPG;Year & 0.5805 & 0.5748 & 0.4152 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
MPG;OriginJapan & 0.4515 & 0.4507 & 0.3718 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
MPG;OriginEurope & 0.2443 & 0.2534 & 0.209 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Cylin;Displ & 0.9508 & 0.9136 & 0.7966 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Cylin;HP & 0.843 & 0.8162 & 0.6861 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Cylin;Weight & 0.8975 & 0.876 & 0.738 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Cylin;Accel & -0.5047 & -0.4763 & -0.3685 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Cylin;Year & -0.3456 & -0.3311 & -0.2702 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Cylin;OriginJapan & -0.4042 & -0.4196 & -0.3965 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Cylin;OriginEurope & -0.3523 & -0.3512 & -0.3319 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Displ;HP & 0.8973 & 0.8762 & 0.7185 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Displ;Weight & 0.933 & 0.9456 & 0.7997 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Displ;Accel & -0.5438 & -0.4994 & -0.3539 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Displ;Year & -0.3699 & -0.3066 & -0.2199 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Displ;OriginJapan & -0.4408 & -0.5069 & -0.4189 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Displ;OriginEurope & -0.3716 & -0.3784 & -0.3127 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
HP;Weight & 0.8645 & 0.8788 & 0.7037 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
HP;Accel & -0.6892 & -0.6581 & -0.4882 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
HP;Year & -0.4164 & -0.3895 & -0.2778 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
HP;OriginJapan & -0.3219 & -0.3484 & -0.2879 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
HP;OriginEurope & -0.2849 & -0.295 & -0.2438 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Weight;Accel & -0.4168 & -0.4051 & -0.2686 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Weight;Year & -0.3091 & -0.281 & -0.1998 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Weight;OriginJapan & -0.4479 & -0.4757 & -0.389 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Weight;OriginEurope & -0.2938 & -0.2982 & -0.2439 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Accel;Year & 0.2903 & 0.2783 & 0.1984 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Accel;OriginJapan & 0.115 & 0.1443 & 0.1191 \tabularnewline
p-value & (0.0228) & (0.0042) & (0.0043) \tabularnewline
Accel;OriginEurope & 0.2083 & 0.1495 & 0.1235 \tabularnewline
p-value & (0) & (0.003) & (0.0031) \tabularnewline
Year;OriginJapan & 0.1998 & 0.1984 & 0.1682 \tabularnewline
p-value & (1e-04) & (1e-04) & (1e-04) \tabularnewline
Year;OriginEurope & -0.0377 & -0.0368 & -0.0312 \tabularnewline
p-value & (0.4562) & (0.468) & (0.4673) \tabularnewline
OriginJapan;OriginEurope & -0.2302 & -0.2302 & -0.2302 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117205&T=2

[TABLE]
[ROW][C]Correlations for all pairs of data series with p-values[/C][/ROW]
[ROW][C]pair[/C][C]Pearson r[/C][C]Spearman rho[/C][C]Kendall tau[/C][/ROW]
[ROW][C]MPG;Cylin[/C][C]-0.7776[/C][C]-0.8232[/C][C]-0.6874[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]MPG;Displ[/C][C]-0.8051[/C][C]-0.8552[/C][C]-0.6786[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]MPG;HP[/C][C]-0.7784[/C][C]-0.8536[/C][C]-0.6792[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]MPG;Weight[/C][C]-0.8322[/C][C]-0.8756[/C][C]-0.6942[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]MPG;Accel[/C][C]0.4233[/C][C]0.4415[/C][C]0.3031[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]MPG;Year[/C][C]0.5805[/C][C]0.5748[/C][C]0.4152[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]MPG;OriginJapan[/C][C]0.4515[/C][C]0.4507[/C][C]0.3718[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]MPG;OriginEurope[/C][C]0.2443[/C][C]0.2534[/C][C]0.209[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Cylin;Displ[/C][C]0.9508[/C][C]0.9136[/C][C]0.7966[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Cylin;HP[/C][C]0.843[/C][C]0.8162[/C][C]0.6861[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Cylin;Weight[/C][C]0.8975[/C][C]0.876[/C][C]0.738[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Cylin;Accel[/C][C]-0.5047[/C][C]-0.4763[/C][C]-0.3685[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Cylin;Year[/C][C]-0.3456[/C][C]-0.3311[/C][C]-0.2702[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Cylin;OriginJapan[/C][C]-0.4042[/C][C]-0.4196[/C][C]-0.3965[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Cylin;OriginEurope[/C][C]-0.3523[/C][C]-0.3512[/C][C]-0.3319[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Displ;HP[/C][C]0.8973[/C][C]0.8762[/C][C]0.7185[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Displ;Weight[/C][C]0.933[/C][C]0.9456[/C][C]0.7997[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Displ;Accel[/C][C]-0.5438[/C][C]-0.4994[/C][C]-0.3539[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Displ;Year[/C][C]-0.3699[/C][C]-0.3066[/C][C]-0.2199[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Displ;OriginJapan[/C][C]-0.4408[/C][C]-0.5069[/C][C]-0.4189[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Displ;OriginEurope[/C][C]-0.3716[/C][C]-0.3784[/C][C]-0.3127[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]HP;Weight[/C][C]0.8645[/C][C]0.8788[/C][C]0.7037[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]HP;Accel[/C][C]-0.6892[/C][C]-0.6581[/C][C]-0.4882[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]HP;Year[/C][C]-0.4164[/C][C]-0.3895[/C][C]-0.2778[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]HP;OriginJapan[/C][C]-0.3219[/C][C]-0.3484[/C][C]-0.2879[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]HP;OriginEurope[/C][C]-0.2849[/C][C]-0.295[/C][C]-0.2438[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Weight;Accel[/C][C]-0.4168[/C][C]-0.4051[/C][C]-0.2686[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Weight;Year[/C][C]-0.3091[/C][C]-0.281[/C][C]-0.1998[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Weight;OriginJapan[/C][C]-0.4479[/C][C]-0.4757[/C][C]-0.389[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Weight;OriginEurope[/C][C]-0.2938[/C][C]-0.2982[/C][C]-0.2439[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Accel;Year[/C][C]0.2903[/C][C]0.2783[/C][C]0.1984[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Accel;OriginJapan[/C][C]0.115[/C][C]0.1443[/C][C]0.1191[/C][/ROW]
[ROW][C]p-value[/C][C](0.0228)[/C][C](0.0042)[/C][C](0.0043)[/C][/ROW]
[ROW][C]Accel;OriginEurope[/C][C]0.2083[/C][C]0.1495[/C][C]0.1235[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0.003)[/C][C](0.0031)[/C][/ROW]
[ROW][C]Year;OriginJapan[/C][C]0.1998[/C][C]0.1984[/C][C]0.1682[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[ROW][C]Year;OriginEurope[/C][C]-0.0377[/C][C]-0.0368[/C][C]-0.0312[/C][/ROW]
[ROW][C]p-value[/C][C](0.4562)[/C][C](0.468)[/C][C](0.4673)[/C][/ROW]
[ROW][C]OriginJapan;OriginEurope[/C][C]-0.2302[/C][C]-0.2302[/C][C]-0.2302[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117205&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117205&T=2

As an alternative you can also use a QR Code:  

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

Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
MPG;Cylin-0.7776-0.8232-0.6874
p-value(0)(0)(0)
MPG;Displ-0.8051-0.8552-0.6786
p-value(0)(0)(0)
MPG;HP-0.7784-0.8536-0.6792
p-value(0)(0)(0)
MPG;Weight-0.8322-0.8756-0.6942
p-value(0)(0)(0)
MPG;Accel0.42330.44150.3031
p-value(0)(0)(0)
MPG;Year0.58050.57480.4152
p-value(0)(0)(0)
MPG;OriginJapan0.45150.45070.3718
p-value(0)(0)(0)
MPG;OriginEurope0.24430.25340.209
p-value(0)(0)(0)
Cylin;Displ0.95080.91360.7966
p-value(0)(0)(0)
Cylin;HP0.8430.81620.6861
p-value(0)(0)(0)
Cylin;Weight0.89750.8760.738
p-value(0)(0)(0)
Cylin;Accel-0.5047-0.4763-0.3685
p-value(0)(0)(0)
Cylin;Year-0.3456-0.3311-0.2702
p-value(0)(0)(0)
Cylin;OriginJapan-0.4042-0.4196-0.3965
p-value(0)(0)(0)
Cylin;OriginEurope-0.3523-0.3512-0.3319
p-value(0)(0)(0)
Displ;HP0.89730.87620.7185
p-value(0)(0)(0)
Displ;Weight0.9330.94560.7997
p-value(0)(0)(0)
Displ;Accel-0.5438-0.4994-0.3539
p-value(0)(0)(0)
Displ;Year-0.3699-0.3066-0.2199
p-value(0)(0)(0)
Displ;OriginJapan-0.4408-0.5069-0.4189
p-value(0)(0)(0)
Displ;OriginEurope-0.3716-0.3784-0.3127
p-value(0)(0)(0)
HP;Weight0.86450.87880.7037
p-value(0)(0)(0)
HP;Accel-0.6892-0.6581-0.4882
p-value(0)(0)(0)
HP;Year-0.4164-0.3895-0.2778
p-value(0)(0)(0)
HP;OriginJapan-0.3219-0.3484-0.2879
p-value(0)(0)(0)
HP;OriginEurope-0.2849-0.295-0.2438
p-value(0)(0)(0)
Weight;Accel-0.4168-0.4051-0.2686
p-value(0)(0)(0)
Weight;Year-0.3091-0.281-0.1998
p-value(0)(0)(0)
Weight;OriginJapan-0.4479-0.4757-0.389
p-value(0)(0)(0)
Weight;OriginEurope-0.2938-0.2982-0.2439
p-value(0)(0)(0)
Accel;Year0.29030.27830.1984
p-value(0)(0)(0)
Accel;OriginJapan0.1150.14430.1191
p-value(0.0228)(0.0042)(0.0043)
Accel;OriginEurope0.20830.14950.1235
p-value(0)(0.003)(0.0031)
Year;OriginJapan0.19980.19840.1682
p-value(1e-04)(1e-04)(1e-04)
Year;OriginEurope-0.0377-0.0368-0.0312
p-value(0.4562)(0.468)(0.4673)
OriginJapan;OriginEurope-0.2302-0.2302-0.2302
p-value(0)(0)(0)



Parameters (Session):
par1 = pearson ;
Parameters (R input):
par1 = pearson ;
R code (references can be found in the software module):
panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
rr <- cor.test(x, y, method=par1)
r <- round(rr$p.value,2)
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep='')
if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
text(0.5, 0.5, txt, cex = cex)
}
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
cor.test(y[1,],y[2,],method=par1)
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='')
a<-table.element(a,dum,header=TRUE)
rp <- cor.test(y[i,],y[j,],method='pearson')
a<-table.element(a,round(rp$estimate,4))
rs <- cor.test(y[i,],y[j,],method='spearman')
a<-table.element(a,round(rs$estimate,4))
rk <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,round(rk$estimate,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=T)
a<-table.element(a,paste('(',round(rp$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rs$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rk$p.value,4),')',sep=''))
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')