Information for calculating multiple linear correlation

№	y
	1625,2	2298,9	55,7	1132,16	83,69	7003,81
	1530,3	1463,3	52,5	1060,11	11,64	135,53
	885,7	1589,5		1131,12	82,65	6830,73
	482,3	1250,6	59,5	1100,38	51,91	2694,80
	1300,8	1617,5	50,7	1055,48	7,01	49,12
	1241,5	1209,6	49,3	1021,52	-26,95	726,09
	822,3	988,3	54,9	1050,80	2,33	5,43
		1400,3	43,6	989,74	-58,73	3449,30
	884,7		68,5	1142,66	94,19	8872,13
	762,9	1665,6	56,5	1101,74	53,27	2838,12
	1620,1	1369,4	45,9	1005,21	-43,26	1871,21
		1234,6		1005,71	-42,76	1828,29
	689,1	1635,6	48,2	1037,77	-10,70	114,51
		1974,7		1063,27	14,80	218,96
	666,6	822,3	53,5	1030,75	-17,72	313,85
	1126,8	1071,5	45,8	987,33	-61,14	3737,70
	495,6	1022,4	49,7	1013,76	-34,71	1204,78
	1114,4	1288,1	47,5	1012,54	-35,93	1291,13
	1298,8	1522,6	56,2	1091,27	42,80	1831,97
	778,7	1350,5	53,6	1061,87	13,41	179,71
		1176,2	49,8	1023,35	-25,12	630,84
	878,6	820,5	52,8	1025,40	-23,07	532,19
	1485,7	1433,5	44,9	1001,40	-47,07	2215,75
		2093,5	49,5	1073,85	25,38	644,06
	524,6	1124,1	46,1	992,61	-55,86	3120,59
∑	26211,7	34235,1	1291,7

Instead of multiple linear correlation coefficient can be calculated multiple correlation index:

So, with the linear relation: .

Analysis of rank correlation

TABLE 25.

Information for calculating rank correlation

№	y			Rank	Difference ranks, d	d2	+/-
y	x1	x2	x1	x2	x1	x2
	1625,2	2298,9	55,7				-10
	1530,3	1463,3	52,5				-8
	885,7	1589,5					-1
	482,3	1250,6	59,5				-4
	1300,8	1617,5	50,7
	1241,5	1209,6	49,3				-8
	822,3	988,3	54,9					-6
		1400,3	43,6				-10
	884,7		68,5				-8
	762,9	1665,6	56,5				-6	-7
	1620,1	1369,4	45,9
		1234,6					-1
	689,1	1635,6	48,2					-5
		1974,7					-10	-6
	666,6	822,3	53,5					-7
	1126,8	1071,5	45,8				-5
	495,6	1022,4	49,7				-5
	1114,4	1288,1	47,5				-1
	1298,8	1522,6	56,2
	778,7	1350,5	53,6					-5
		1176,2	49,8
	878,6	820,5	52,8
	1485,7	1433,5	44,9
		2093,5	49,5
	524,6	1124,1	46,1
∑	-	-	-	-	-		-				-

The value of the correlation coefficient ranks suggests a close relationship between the sign and its effective factor attributes.

The value of the correlation coefficient ranks suggests a close relationship between yield and influencing factors:

CONCLUSIONS

The aim of this work is the study and analysis of data on the profitability of sunflower production in total 24 regions and the Autonomous Republic of Crimea, to find the average, to grouping regions according to various criteria, to identify the main internal laws of the installation profitability of sunflower, find out the extent of influence on the profitability factors such as the selling price and direct material costs. During the execution of work will be required to use basic methods of statistical analysis, learn to practice the theoretical principles of science. In addition, you should also familiarize yourself with the objectives and performance statistics plants and identify the main trends in the development of statistics in modern Ukraine.

The theoretic part of work engages exploration thing, assignments and the systems of indexes the statisticians of animal husbandry, further to this got acquainted with base normative documents.

Having conducted base practical accounts we can affirm that average level of level of profitability equal to 24,02 and it fluctuation is ±10,75 or on 44,76%; average selling price equal to 295,7836 and it fluctuation is ±25,07 or on 8,48%; average part of direct material costs equal to 163,0812 and it fluctuation is ±31,54 or on 19,34%.

Then by constructing a model of multiple correlation, we can say that the variation of the level of profitability of sunflower on due to the influence factors and , and the remaining of the unaccounted influence of random factors.

So, calculated the rank coefficient of correlation we can say, that between resulting and first factor sign observed straight correlation, but between resulting and second factor sign observed no straight correlation.

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