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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_One Factor ANOVA.wasp
Title produced by softwareOne-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)
Date of computationSat, 16 Dec 2017 19:35:50 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/16/t15134493603raznd0sv97nlp0.htm/, Retrieved Wed, 15 May 2024 21:44:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309933, Retrieved Wed, 15 May 2024 21:44:03 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact50

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2017-12-16 18:35:50] [ec3e05fa52755d2406325c662ce6a84e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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8224	'H'
52180	'T'
295607	'H'
75000	'H'
161793	'S'
1600	'S'
20200	'M'
300000	'H'
8058	'H'
51483	'T'
13200	'H'
38763	'T'
50635	'H'
31262	'T'
42050	'T'
1339302	'T'
21725	'M'
1300	'S'
24284	'T'
16920	'H'
0	'H'
101769	'M'
96000	'T'
16897	'H'
17000	'T'
45855	'M'
10936	'M'
10149	'M'
13861	'M'
14000	'H'
42038	'T'
30676	'M'
24500	'T'
45785	'E'
1588	'S'
257	'S'
61829	'H'
4063	'H'
200230	'E'
21206	'E'
20000	'M'
14900	'M'
1000	'E'
3000	'T'
182755	'H'
840586	'T'
706972	'T'
7700	'H'
30848	'M'
3545	'H'
49000	'T'
113000	'T'
15000	'H'
88636	'M'
0	'M'
11200	'E'
0	'E'
18000	'H'
986	'H'
9600	'H'
82750	'H'
0	'H'
106000	'T'
36338	'H'
296	'H'
57000	'M'
13927	'H'
9100	'H'
30328	'H'
12032	'H'
13500	'H'
9500	'M'
0	'M'
18349	'S'
69883	'H'
55000	'M'
0	'M'
3500	'T'
0	'T'
16000	'M'
16200	'T'
3100	'T'
40000	'H'
9240	'M'
61352	'S'
11410	'M'
38500	'H'
13015	'H'
5450	'T'
15928	'S'
20350	'H'
9300	'H'
0	'H'
76882	'T'
0	'T'
591	'T'
53811	'H'
7094	'H'
4329	'H'
2825	'H'
21017	'H'
25362	'T'
0	'T'
946052	'T'
40000	'H'
105205	'M'
38861	'M'
18582	'H'
1000	'H'
180000	'M'
27650	'T'
89472	'H'
56378	'H'
16059	'T'
888	'T'
0	'E'
9900	'T'
30000	'M'
24152	'T'
6377	'S'
48693	'T'
500	'T'
159789	'T'
8000	'T'
28000	'T'
10000	'T'
10000	'H'
11300	'H'
9502	'T'
0	'T'
50546	'M'
130000	'T'
250648	'M'
344195	'E'
9000	'T'
0	'H'
1982	'T'
34249	'H'
47766	'M'
23500	'H'
4500	'H'
6000	'H'
6000	'H'
3500	'E'
6000	'H'
6000	'M'
0	'M'
12753	'H'
84434	'E'
300	'H'
18660	'H'
1014	'M'
9804	'S'
2776	'S'
22500	'T'
114910	'S'
62783	'T'
36490	'H'
0	'H'
0	'H'
7301	'H'
6340	'H'
50000	'M'
590491	'H'
1033000	'E'
215000	'M'
3018	'M'
1474	'M'
12705	'E'
8000	'T'
45500	'T'
34253	'H'
0	'H'
1112	'E'
20059	'H'
20059	'H'
25200	'H'
1125	'T'
13000	'E'
180	'T'
21921	'H'
0	'H'
25532	'T'
9852	'T'
30000	'M'
75200	'T'
0	'T'
31138	'T'
13214	'H'
2228845	'E'
64359	'T'
5071	'T'
15000	'M'
0	'M'
9877	'M'
500	'M'
13600	'M'
50	'M'
203963	'T'
11348	'E'
7493	'E'
18500	'H'
0	'H'
600	'H'
10853	'T'
16973	'T'
15731	'S'
0	'S'
1457	'T'
9840	'H'
3595	'H'
0	'H'
327829	'M'
1641447	'T'
1200	'T'
17000	'H'
962	'M'
1000	'T'
23043	'T'
80000	'H'
920	'H'
5400	'M'
1100	'M'
7506	'H'
34600	'H'
2100	'T'
3795	'T'
983	'M'
60100	'E'
24320	'E'
39067	'H'
57842	'H'
16382	'H'
95000	'M'
263	'M'
5429	'M'
214254	'M'
5000	'T'
11300	'T'
36585	'M'
573991	'T'
10179	'S'
491	'S'
80000	'T'
0	'T'
76628	'H'
25000	'H'
219013	'E'
50449	'H'
13991	'H'
0	'H'
15251	'H'
12843	'H'
21000	'E'
14670	'H'
1509	'H'
24500	'H'
9430	'M'
716769	'T'
14042	'S'
8421	'S'
14000	'H'
5200	'H'
50100	'M'
34500	'M'
80000	'T'
10000	'H'
9144	'T'
96503	'T'
200000	'T'
11064	'H'
126737	'E'
300	'E'
94250	'T'
2635	'T'
84455	'M'
0	'M'
953479	'M'
37032	'T'
4997	'M'
872562	'T'
1000000	'M'
4500	'T'
10800	'M'
1600	'T'
14335	'M'
12000	'T'
30385	'T'
5330	'T'
44403	'T'
12003	'M'
12000	'H'
0	'M'
10000	'E'
595	'T'
24922	'H'
38000	'M'
13250	'H'
21800	'S'
28800	'M'
1092	'E'
0	'E'
20386	'H'
10127	'H'
0	'H'
36262	'T'
12000	'M'
97921	'H'
10500	'M'
15014	'T'
49000	'H'
8200	'E'
19631	'S'
5123	'T'
81911	'T'
600	'T'
26831	'M'
33966	'H'
29413	'M'
0	'E'
9400	'H'
9000	'H'
21922	'S'
8500	'T'
200	'M'
95000	'T'
3500	'M'
676333	'T'
394759	'H'
61120	'H'
0	'H'
0	'H'
48000	'T'
5653	'H'
37000	'T'
0	'T'
25000	'M'
0	'M'
65315	'T'
31754	'H'
30000	'E'
7000	'T'
23750	'T'
0	'M'
21689	'H'
34900	'E'
16200	'T'
28846	'T'
23532	'T'
2020904	'T'
287799	'T'
17797	'H'
0	'H'
11700	'H'
10000	'M'
0	'M'
8300	'T'
42817	'E'
100000	'T'
14395	'H'
2203	'H'
26200	'T'
22282	'E'
9456	'E'
825	'H'
0	'H'
23662	'H'
1448	'H'
13154	'H'
31064	'T'
3500	'H'
3850	'T'
25000	'E'
202000	'M'
33463	'T'
640	'E'
25000	'T'
4662	'T'
17500	'E'
15335	'H'
10000	'H'
6348	'T'
21236	'H'
3000	'H'
52997	'H'
0	'H'
15331	'T'
237767	'T'
40000	'T'
18000	'T'
14360	'H'
4500	'H'
888227	'E'
21000	'T'
0	'T'
83412	'M'
0	'M'
3500	'T'
9831	'M'
261337	'E'
9000	'M'
0	'H'
1934	'T'
0	'T'
10000	'H'
9270	'H'
29696	'T'
0	'T'
9251	'T'
10000	'T'
87217	'T'
10500	'T'
1727600	'T'
13877	'T'
2500	'M'
17331	'T'
19928	'T'
13420	'T'
15170	'M'
12000	'H'
2009399	'T'
2703	'H'
5042	'T'
15200	'M'
36092	'H'
14460	'M'
0	'M'
50210	'M'
18000	'M'
200072	'T'
42897	'M'
16000	'M'
338	'T'
95000	'T'
22308	'E'
24900	'M'
80000	'H'
62636	'H'
40265	'M'
9810	'M'
10000	'H'
15905	'H'
193083	'T'
0	'T'
18350	'T'
25000	'H'
3000	'H'
48000	'H'
1800	'T'
0	'T'
22944	'M'
421198	'T'
12000	'T'
8000	'T'
45812	'M'
127466	'H'
73516	'H'
26400	'M'
3212	'H'
16169	'M'
70182	'H'
6375	'H'
144097	'T'
29048	'T'
26000	'T'
1250	'S'
23000	'T'
120530	'E'
89500	'H'
1602	'T'
16161	'E'
10000	'M'
60923	'T'
12718	'M'
74860	'T'
20000	'H'
51584	'M'
336	'T'
20942	'M'
18200	'H'
4620	'T'
5700	'H'
128651	'T'
75201	'T'
20000	'T'
50000	'E'
7869	'T'
32639	'H'
13646	'M'
55548	'T'
23967	'H'
13685	'M'
3030	'T'
27222	'M'
1047156	'T'
16477	'H'
1000	'H'
24438	'M'
6231	'T'
13200	'H'
0	'H'
6000	'E'
49770	'T'
120000	'E'
5332	'T'
13500	'H'
150	'T'
4425	'M'
3392	'M'
250805	'M'
213863	'M'
38575	'M'
25250	'M'
13550	'T'
34275	'T'
0	'H'
0	'H'
41000	'T'
19446	'S'
47	'S'
10000	'H'
27571	'T'
623888	'T'
37624	'H'
11000	'H'
18765	'H'
19322	'H'
16200	'H'
106728	'E'
7850	'M'
625	'M'
13767	'M'
1371103	'T'
10000	'M'
90421	'E'
0	'M'
21994	'T'
102769	'T'
24477	'E'
50000	'H'
36000	'H'
19000	'H'
39500	'H'
18000	'M'
404000	'H'
49851	'H'
1237	'T'
0	'T'
39563	'M'
46250	'T'
23519	'S'
13500	'H'
402	'T'
28274	'T'
16954	'T'
20150	'M'
266984	'T'
938419	'T'
10000	'M'
0	'M'
10500	'H'
251683	'E'
0	'E'
15000	'H'
6900	'M'
11705	'E'
0	'E'
20738	'E'
1883021	'E'
19885	'S'
15226	'M'
18286	'H'
20000	'H'
0	'H'
16063	'M'
300	'M'
10000	'M'
13148	'T'
50000	'H'
9400	'T'
9600	'H'
40000	'T'
13438	'T'
63182	'H'
1329954	'T'
60125	'T'
4875	'T'
20857	'H'
2423	'H'
7300	'H'
300000	'H'
327800	'M'
6300	'T'
500	'T'
200	'T'
5000	'M'
25669	'E'
100	'H'
30036	'T'
25000	'T'
2900	'T'
129275	'E'
18500	'H'
25000	'M'
295812	'E'
128000	'H'
6000	'H'
11089	'M'
91804	'T'
41366	'T'
58040	'M'
154463	'E'
19376	'T'
1202	'M'
50000	'H'
30000	'H'
41290	'H'
5280	'H'
136726	'M'
0	'M'
71958	'M'
15046	'H'
63469	'T'
0	'T'
6300	'T'
0	'T'
23000	'S'
0	'S'
70668	'H'
3733	'H'
81358	'E'
54470	'T'
46396	'H'
159639	'E'
750	'T'
9800	'H'
8850	'H'
9146	'M'
38000	'M'
114514	'M'
86796	'H'
132000	'H'
0	'H'
1259300	'T'
75000	'M'
44596	'T'
32804	'T'
8219	'T'
500	'M'
12500	'T'
0	'T'
206110	'H'
68897	'H'
0	'H'
25000	'H'
20661	'H'
1600	'T'
180000	'E'
300592	'T'
336	'T'
16869	'M'
11395	'M'
90000	'H'
20500	'H'
500	'H'
40000	'T'
0	'H'
11101	'T'
84896	'E'
35000	'E'
23329	'H'
1782	'E'
60000	'H'
10000	'M'
127600	'M'
27000	'T'
40615	'H'
30000	'H'
50000	'M'
256	'T'
53576	'H'
18249	'M'
35000	'M'
0	'M'
13500	'T'
11130	'T'
60600	'T'
74284	'T'
13998	'E'
0	'E'
12250	'M'
457149	'H'
47919	'H'
16762	'S'
104600	'H'
5200	'E'
200	'H'
35668	'H'
9882	'E'
61772	'H'
1052808	'T'
19396	'H'
114111	'H'
30000	'M'
0	'M'
10930	'H'
22079	'H'
20000	'M'
67190	'E'
10798	'H'
16218	'H'
14557	'H'
22088	'E'
50000	'H'
225000	'T'
224	'T'
5573	'H'
4000	'H'
10000	'M'
19500	'T'
0	'T'
8700	'H'
223914	'T'
30000	'T'
8500	'M'
10133	'H'
500	'H'
7465	'E'
27116	'H'
91772	'T'
11000	'H'
100	'H'
22000	'T'
20000	'H'
1100	'H'
64928	'M'
42150	'H'
15542	'H'
13500	'E'
69030	'T'
9260	'T'
6728	'T'
0	'T'
174330	'H'
63119	'H'
14600	'E'
2505	'T'
40000	'M'
26073	'S'
12700	'H'
2900	'H'
25754	'M'
40021	'M'
150	'M'
200	'E'
100000	'M'
12861	'M'
18000	'T'
10467	'E'
40156	'T'
21405	'M'
0	'M'
19024	'H'
7953	'H'
7747	'H'
232617	'H'
407464	'M'
9400	'T'
34500	'E'
200485	'T'
20000	'E'
50000	'T'
13260	'M'
52400	'T'
0	'T'
6056	'E'
2498	'E'
30705	'T'
190	'T'
125670	'H'
0	'H'
51062	'H'
22200	'E'
18400	'E'
6611	'T'
3067	'T'
14866	'M'
10000	'M'
12750	'H'
297986	'T'
0	'T'
0	'T'
29000	'H'
16664	'E'
9830	'T'
148245	'M'
18000	'M'
17563	'E'
29213	'H'
2919	'H'
20062	'T'
300	'T'
11000	'E'
5708	'T'
16300	'H'
11000	'H'
9000	'H'
0	'T'
361200	'H'
59000	'E'
45740	'T'
0	'T'
253875	'T'
33243	'M'
1544	'M'
11394	'T'
29200	'H'
39215	'H'
26338	'H'
293	'E'
10000	'M'
12000	'H'
7507	'T'
61288	'M'
48000	'M'
21686	'H'
134897	'H'
12000	'H'
7500	'M'
58960	'H'
32000	'H'
8224	'H'
14677	'H'
2472	'H'
2595780	'T'
8849	'T'
10602	'T'
11926	'S'
3153	'T'
16225	'H'
0	'H'
2617	'T'
9816	'E'
9816	'E'
4880	'E'
80000	'H'
10543	'S'
25758	'T'
819118	'T'
0	'T'
13000	'E'
45000	'M'
60000	'H'
13510	'H'
50000	'T'
45602	'T'
2485296	'T'
383552	'T'
9458	'H'
0	'H'
17241	'M'
0	'H'
10000	'T'
25848	'T'
2412174	'H'
26061	'H'
24047	'M'
0	'M'
7763	'M'
11000	'H'
30000	'H'
19946	'M'
47103	'T'
15000	'T'
0	'T'
10650	'E'
104533	'H'
11500	'T'
7052	'S'
1880	'S'
10270	'H'
4500	'T'
1342565	'E'
61846	'T'
57766	'T'
15892	'H'
75045	'T'
0	'T'
9000	'T'
9809	'H'
6700	'H'
2200	'H'
600	'H'
17506	'M'
156000	'T'
12000	'H'
3000	'H'
21800	'T'
7948	'H'
2424	'H'
8096	'H'
10000	'T'
44500	'T'
12888	'H'
100491	'M'
23198	'T'
5000	'T'
10075	'M'
7900	'E'
13058	'H'
0	'H'
9450	'H'
3295	'T'
135638	'H'
67068	'T'
42118	'T'
1050	'T'
11794	'H'
2000	'H'
9237	'H'
416	'H'
14025	'T'
9000	'T'
1290	'H'
0	'H'
37279	'E'
100000	'E'
39000	'T'
662076	'E'
35081	'T'
22253	'H'
8017	'H'
30000	'H'
23152	'H'
27205	'H'
612	'H'
12859	'E'
15815	'H'
300	'T'
123913	'T'
30000	'M'
40578	'H'
12000	'H'
42870	'H'
13500	'H'
22068	'H'
124293	'H'
42308	'H'
7000	'T'
0	'T'
0	'T'
10700	'T'
500000	'T'
17101	'T'
9077	'H'
3200	'H'
31000	'T'
13420	'T'
40291	'M'
0	'M'
336	'M'
20744	'M'
30000	'H'
13200	'S'
7233	'E'
42793	'H'
13650	'H'
10544	'S'
5376	'H'
1902	'H'
71300	'E'
40000	'H'
21369	'H'
0	'T'
224	'H'
105000	'T'
100	'M'
1575	'E'
178591	'E'
15000	'T'
4664	'H'
11000	'M'
230874	'H'
55399	'H'
8000	'H'
400	'H'
15000	'E'
1400	'E'
13420	'T'
11499	'M'
12267	'H'
2793	'M'
545	'M'
1181910	'H'
454735	'H'
10065	'H'
0	'H'
5600	'M'
111335	'M'
14601	'E'
0	'E'
59388	'T'
16092	'T'
38569	'T'
52198	'T'
20372	'H'
4500	'H'
382	'M'
49	'M'
15663	'M'
24654	'T'
70000	'T'
12352	'T'
10000	'T'
464370	'T'
13681	'T'
88907	'T'
28100	'H'
86872	'T'
17644	'T'
18096	'E'
190000	'T'
10500	'M'
42548	'E'
79338	'T'
36000	'M'
20000	'H'
0	'H'
13526	'H'
0	'H'
0	'H'
14000	'M'
11468	'T'
18415	'M'
35000	'M'
14500	'T'
62000	'M'
8250	'M'
4500	'M'
40447	'T'
15000	'M'
28200	'T'
43109	'E'
126845	'H'
59024	'H'
61463	'H'
7000	'H'
118000	'H'
20000	'H'
20255	'M'
4500	'M'
0	'M'
84280	'H'
52184	'E'
1402005	'T'
12950	'H'
12900	'H'
30000	'T'
5000	'T'
0	'E'
0	'H'
0	'H'
520905	'T'
16549	'E'
59801	'H'
11186	'H'
15000	'M'
0	'M'
2568	'E'
0	'H'
337	'H'
180920	'H'
15000	'M'
8478	'T'
0	'T'
10000	'T'
9200	'T'
7997	'E'
0	'E'
26613	'T'
5000	'H'
4022	'H'
10342	'H'
13203	'H'
10137	'H'
2848	'M'
14873	'E'
115000	'H'
17000	'T'
14000	'T'
0	'T'
12000	'H'
1000	'H'
9680	'H'
7750	'E'
14458	'M'
6550	'T'
881978	'M'
6200	'T'
15725	'T'
113	'H'
30288	'H'
2676	'H'
0	'H'
70000	'T'
17488	'H'
1100	'H'
12000	'M'
3018	'M'
116400	'S'
10622	'E'
70127	'T'
13185	'T'
8800	'H'
10000	'H'
16000	'M'
20983	'T'
0	'T'
33000	'S'
76000	'T'
0	'T'
22817	'M'
18464	'H'
15722	'H'
10408	'H'
11750	'H'
17000	'T'
2600	'E'
40000	'H'
500	'H'
31000	'M'
8500	'M'
0	'M'
7683	'H'
465923	'M'
50000	'H'
0	'H'
12000	'H'
48514	'T'
24112	'M'
12000	'M'
351231	'T'
9502	'M'
11750	'M'
0	'M'
12000	'M'
4500	'M'
26877	'H'
14095	'M'
10859	'E'
13635	'S'
2655	'S'
36000	'M'
19953	'T'
14800	'M'
50918	'E'
30602	'T'
30350	'H'
110	'H'
2283887	'E'
8561	'T'
2054	'T'
63537	'T'
2555	'T'
0	'H'
65000	'H'
0	'H'
82892	'T'
90389	'M'
5628	'E'
5030	'E'
0	'E'
59000	'T'
16800	'T'
0	'T'
202000	'M'
0	'M'
20000	'T'
4000	'T'
16958	'H'
17304	'M'
8765	'T'
5300	'M'
18950	'H'
175496	'H'
4618	'H'
20000	'H'
36000	'H'
8505	'M'
26460	'T'
0	'E'
14000	'M'
20112	'S'
14714	'S'
5000	'S'
20000	'T'
17800	'H'
10050	'H'
9827	'M'
3953	'M'
95149	'T'
44211	'T'
12900	'T'
44805	'T'
0	'T'
71136	'H'
43217	'H'
23100	'H'
17500	'M'
8395	'T'
45970	'E'
60100	'H'
9200	'T'
12500	'T'
60000	'T'
18150	'H'
11000	'H'
49770	'T'
20000	'E'
74658	'T'
8617	'T'
145420	'H'
4357	'T'
60000	'M'
43540	'S'
1617133	'T'
1500	'M'
17522	'H'
10424	'H'
1000	'M'
10000	'H'
5000	'H'
19900	'H'
17000	'T'
4858	'H'
2927	'H'
21341	'T'
3000	'E'
10690	'H'
10690	'H'
1000	'M'
85546	'T'
23695	'M'
75000	'E'
35000	'H'
0	'H'
7483	'H'
2273	'H'
10200	'E'
77382	'T'
0	'T'
10000	'M'
72946	'H'
365332	'H'
11000	'H'
371977	'T'
5168	'T'
17929	'T'
16390	'T'
17929	'T'
100000	'H'
4000	'T'
61969	'H'
15578	'H'
20694	'H'
835	'H'
13858	'T'
50	'T'
18217	'H'
250	'H'
44138	'H'
0	'H'
643434	'T'
26650	'E'
0	'H'
3000	'M'
350000	'H'
254069	'H'
18000	'H'
0	'H'
119112	'T'
82000	'T'
2000	'M'
4500	'T'
25134	'H'
60000	'M'
10847	'H'
45000	'T'
21900	'E'
0	'E'
35000	'T'
0	'T'
10000	'E'
0	'E'
27433	'H'
0	'H'
0	'E'
14500	'T'
30305	'H'
27082	'H'
14632	'S'
0	'S'
16681	'H'
224	'H'
7600	'H'
2100	'H'
11600	'E'
0	'E'
3730	'H'
8201	'T'
40000	'H'
0	'H'
27200	'T'
6000	'T'
0	'T'
10050	'H'
11200	'T'
0	'T'
25000	'H'
5000	'H'
6000	'M'
5500	'M'
9450	'T'
5800	'T'
0	'T'
11120	'H'
16370	'H'
3260	'H'
21350	'H'
0	'H'
500	'T'
110000	'T'
61756	'E'
65895	'T'
72705	'T'
11862	'H'
12211	'M'
17200	'T'
95612	'H'
10000	'H'
1150	'T'
15000	'H'
10000	'H'
433698	'T'
1127	'H'
100	'H'
40350	'T'
38581	'H'
20000	'T'
30000	'H'
994	'T'
11600	'M'
1554	'T'
15000	'M'
78226	'T'
0	'T'
36348	'M'
2000	'M'
700	'E'
12895	'M'
103700	'H'
58231	'T'
35500	'H'
0	'H'
225000	'T'
11271	'T'
7456	'H'
5000	'M'
2500	'H'
11300	'E'
2050800	'T'
12882	'M'
0	'M'
50000	'T'
2000	'T'
65000	'T'
41620	'M'
500000	'T'
2275	'H'
68598	'T'
346079	'E'
33532	'T'
107585	'T'
0	'T'
10000	'M'
266050	'M'
28000	'H'
15500	'H'
4591	'T'
777	'H'
102265	'M'
23301	'H'
7220	'H'
110000	'T'
5000	'H'
0	'H'
0	'H'
1835742	'E'
26400	'T'
0	'T'
17000	'E'
24500	'H'
24500	'H'
10000	'E'
67150	'T'
52884	'H'
5700	'T'
2508	'H'
25000	'T'
0	'H'
15568	'T'
8669	'T'
313	'T'
30560	'E'
0	'E'
40352	'T'
0	'T'
10000	'T'
0	'T'
30000	'T'
0	'T'
12157	'E'
500000	'E'
12000	'M'
5000	'M'
4800	'E'
32399	'T'
41145	'T'
301540	'E'
12000	'M'
41300	'T'
700	'T'
11900	'T'
216	'T'
6057	'T'
15000	'M'
500	'M'
10000	'M'
0	'M'
10200	'M'
0	'M'
10171	'E'
257	'T'
15000	'H'
5000	'H'
0	'E'
96303	'S'
17583	'T'
25000	'H'
0	'H'
800	'M'
28335	'M'
739	'M'
84143	'M'
35958	'T'
15759	'E'
2544	'E'
171192	'M'
43556	'M'
48336	'M'
13918	'H'
0	'H'
1659	'H'
19000	'T'
47360	'H'
225	'S'
26864	'H'
16476	'H'
17750	'M'
42000	'T'
0	'T'
9850	'H'
15000	'H'
171000	'E'
0	'E'
18000	'M'
19000	'E'
0	'E'
41626	'E'
2000	'M'
15000	'H'
1824	'H'
60028	'H'
10000	'H'
6255	'T'
14000	'T'
0	'T'
11000	'T'
0	'T'
13989	'M'
35111	'E'
9300	'T'
20647	'S'
225	'S'
219988	'T'
29072	'E'
38000	'M'
42997	'S'
15000	'S'
11735	'M'
12650	'H'
3000	'H'
5546	'M'
3300	'M'
0	'H'
895947	'E'
9540	'M'
120400	'H'
50	'E'
15767	'H'
68435	'T'
95000	'H'
13560	'E'
0	'E'
20331	'T'
339	'T'
12500	'T'
84035	'T'
32000	'M'
31038	'T'
167	'T'
119000	'T'
23316	'T'
0	'T'
506000	'M'
0	'E'
6200	'T'
524656	'E'
11546	'H'
1681	'H'
9000	'H'
16355	'E'
5000	'M'
0	'M'
52947	'T'
17000	'H'
15357	'M'
0	'M'
25000	'M'
16714	'H'
82995	'H'
4000	'H'
361100	'H'
22336	'H'
5174	'H'
81582	'H'
3258	'H'
19250	'H'
40	'H'
24725	'H'
15000	'H'
117851	'T'
0	'H'
23361	'T'
524112	'T'
62599	'T'
20000	'T'
2000	'E'
15230	'H'
29505	'S'
1000	'S'
32000	'H'
7000	'H'
17000	'T'
170963	'H'
0	'H'
12702	'S'
7349	'S'
1000	'M'
20148	'S'
15790	'S'
102565	'T'
36000	'E'
18000	'H'
95000	'M'
3000	'H'
30000	'E'
1300	'E'
25572	'E'
130746	'E'
2846	'H'
30787	'E'
49000	'T'
994355	'M'
0	'M'
2000	'M'
8419	'T'
6198	'M'
0	'M'
6646	'T'
0	'H'
2800	'T'
2671079	'T'
81829	'H'
20792	'H'
100000	'T'
14560	'M'
50000	'T'
20949	'M'
11592	'H'
3200	'M'
215000	'M'
24736	'H'
7771	'H'
2954	'H'
21913	'T'
15586	'S'
10000	'M'
11919	'S'
15000	'H'
3000	'H'
0	'H'
45000	'T'
135200	'T'
55656	'T'
35000	'S'
4206	'T'
17237	'H'
3619	'H'
21502	'H'
23000	'M'
66607	'H'
27450	'H'
269	'H'
51052	'H'
1275	'M'
142069	'E'
115000	'M'

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309933&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309933&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309933&T=0

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


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

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center






ANOVA Model
eqpdamg ~ Acctype
means120237.488-81282.082-59539.003-101046.458-17968.738

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
eqpdamg  ~  Acctype \tabularnewline
means & 120237.488 & -81282.082 & -59539.003 & -101046.458 & -17968.738 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309933&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]eqpdamg  ~  Acctype[/C][/ROW]
[ROW][C]means[/C][C]120237.488[/C][C]-81282.082[/C][C]-59539.003[/C][C]-101046.458[/C][C]-17968.738[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309933&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309933&T=1

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


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

ANOVA Model
eqpdamg ~ Acctype
means120237.488-81282.082-59539.003-101046.458-17968.738






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Acctype42715495079620.41678873769905.10311.1570
Residuals261615917701297236060847482023.074 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
Acctype & 4 & 2715495079620.41 & 678873769905.103 & 11.157 & 0 \tabularnewline
Residuals & 2616 & 159177012972360 & 60847482023.074 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309933&T=2

[TABLE]
[ROW][C]ANOVA Statistics[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]Sum Sq[/C][C]Mean Sq[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]Acctype[/C][C]4[/C][C]2715495079620.41[/C][C]678873769905.103[/C][C]11.157[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]2616[/C][C]159177012972360[/C][C]60847482023.074[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309933&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309933&T=2

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


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

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Acctype42715495079620.41678873769905.10311.1570
Residuals261615917701297236060847482023.074






Tukey Honest Significant Difference Comparisons
difflwruprp adj
H-E-81282.082-126872.71-35691.4530
M-E-59539.003-108616.375-10461.6320.008
S-E-101046.458-179235.774-22857.1420.004
T-E-17968.738-64471.30628533.830.83
M-H21743.079-14693.53158179.6880.479
S-H-19764.376-90705.24851176.4960.942
T-H63313.34330426.76696199.9210
S-M-41507.454-114737.84631722.9370.532
T-M41570.2653998.86779141.6630.021
T-S83077.71911547.371154608.0680.013

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
H-E & -81282.082 & -126872.71 & -35691.453 & 0 \tabularnewline
M-E & -59539.003 & -108616.375 & -10461.632 & 0.008 \tabularnewline
S-E & -101046.458 & -179235.774 & -22857.142 & 0.004 \tabularnewline
T-E & -17968.738 & -64471.306 & 28533.83 & 0.83 \tabularnewline
M-H & 21743.079 & -14693.531 & 58179.688 & 0.479 \tabularnewline
S-H & -19764.376 & -90705.248 & 51176.496 & 0.942 \tabularnewline
T-H & 63313.343 & 30426.766 & 96199.921 & 0 \tabularnewline
S-M & -41507.454 & -114737.846 & 31722.937 & 0.532 \tabularnewline
T-M & 41570.265 & 3998.867 & 79141.663 & 0.021 \tabularnewline
T-S & 83077.719 & 11547.371 & 154608.068 & 0.013 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309933&T=3

[TABLE]
[ROW][C]Tukey Honest Significant Difference Comparisons[/C][/ROW]
[ROW][C] [/C][C]diff[/C][C]lwr[/C][C]upr[/C][C]p adj[/C][/ROW]
[ROW][C]H-E[/C][C]-81282.082[/C][C]-126872.71[/C][C]-35691.453[/C][C]0[/C][/ROW]
[ROW][C]M-E[/C][C]-59539.003[/C][C]-108616.375[/C][C]-10461.632[/C][C]0.008[/C][/ROW]
[ROW][C]S-E[/C][C]-101046.458[/C][C]-179235.774[/C][C]-22857.142[/C][C]0.004[/C][/ROW]
[ROW][C]T-E[/C][C]-17968.738[/C][C]-64471.306[/C][C]28533.83[/C][C]0.83[/C][/ROW]
[ROW][C]M-H[/C][C]21743.079[/C][C]-14693.531[/C][C]58179.688[/C][C]0.479[/C][/ROW]
[ROW][C]S-H[/C][C]-19764.376[/C][C]-90705.248[/C][C]51176.496[/C][C]0.942[/C][/ROW]
[ROW][C]T-H[/C][C]63313.343[/C][C]30426.766[/C][C]96199.921[/C][C]0[/C][/ROW]
[ROW][C]S-M[/C][C]-41507.454[/C][C]-114737.846[/C][C]31722.937[/C][C]0.532[/C][/ROW]
[ROW][C]T-M[/C][C]41570.265[/C][C]3998.867[/C][C]79141.663[/C][C]0.021[/C][/ROW]
[ROW][C]T-S[/C][C]83077.719[/C][C]11547.371[/C][C]154608.068[/C][C]0.013[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309933&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309933&T=3

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


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

Tukey Honest Significant Difference Comparisons
difflwruprp adj
H-E-81282.082-126872.71-35691.4530
M-E-59539.003-108616.375-10461.6320.008
S-E-101046.458-179235.774-22857.1420.004
T-E-17968.738-64471.30628533.830.83
M-H21743.079-14693.53158179.6880.479
S-H-19764.376-90705.24851176.4960.942
T-H63313.34330426.76696199.9210
S-M-41507.454-114737.84631722.9370.532
T-M41570.2653998.86779141.6630.021
T-S83077.71911547.371154608.0680.013






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group411.2920
2616 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 4 & 11.292 & 0 \tabularnewline
  & 2616 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309933&T=4

[TABLE]
[ROW][C]Levenes Test for Homogeneity of Variance[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]Group[/C][C]4[/C][C]11.292[/C][C]0[/C][/ROW]
[ROW][C] [/C][C]2616[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309933&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309933&T=4

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


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

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group411.2920
2616


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
R code (references can be found in the software module):
cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
intercept<-as.logical(par3)
x <- t(x)
x1<-as.numeric(x[,cat1])
f1<-as.character(x[,cat2])
xdf<-data.frame(x1,f1)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
names(xdf)<-c('Response', 'Treatment')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment - 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment, data = xdf) )
(aov.xdf<-aov(lmxdf) )
(anova.xdf<-anova(lmxdf) )
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Model', length(lmxdf$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, paste(V1, ' ~ ', V2), length(lmxdf$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'means',,TRUE)
for(i in 1:length(lmxdf$coefficients)){
a<-table.element(a, round(lmxdf$coefficients[i], digits=3),,FALSE)
}
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,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',,TRUE)
a<-table.element(a, 'Df',,FALSE)
a<-table.element(a, 'Sum Sq',,FALSE)
a<-table.element(a, 'Mean Sq',,FALSE)
a<-table.element(a, 'F value',,FALSE)
a<-table.element(a, 'Pr(>F)',,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, V2,,TRUE)
a<-table.element(a, anova.xdf$Df[1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3),,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',,TRUE)
a<-table.element(a, anova.xdf$Df[2],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3),,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='anovaplot.png')
boxplot(Response ~ Treatment, data=xdf, xlab=V2, ylab=V1)
dev.off()
if(intercept==TRUE){
'Tukey Plot'
thsd<-TukeyHSD(aov.xdf)
bitmap(file='TukeyHSDPlot.png')
plot(thsd)
dev.off()
}
if(intercept==TRUE){
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Tukey Honest Significant Difference Comparisons', 5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ', 1, TRUE)
for(i in 1:4){
a<-table.element(a,colnames(thsd[[1]])[i], 1, TRUE)
}
a<-table.row.end(a)
for(i in 1:length(rownames(thsd[[1]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[1]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,round(thsd[[1]][i,j], digits=3), 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
if(intercept==FALSE){
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'TukeyHSD Message', 1,TRUE)
a<-table.row.end(a)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Must Include Intercept to use Tukey Test ', 1, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
library(car)
lt.lmxdf<-leveneTest(lmxdf)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Levenes Test for Homogeneity of Variance', 4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ', 1, TRUE)
for (i in 1:3){
a<-table.element(a,names(lt.lmxdf)[i], 1, FALSE)
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Group', 1, TRUE)
for (i in 1:3){
a<-table.element(a,round(lt.lmxdf[[i]][1], digits=3), 1, FALSE)
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ', 1, TRUE)
a<-table.element(a,lt.lmxdf[[1]][2], 1, FALSE)
a<-table.element(a,' ', 1, FALSE)
a<-table.element(a,' ', 1, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')