Курсова робота «Statistic study of crop yield and factors», 2009 рік

З предмету Статистика · додано 24.03.2011 20:37 · від Cлава · Додати в закладки
35 грн Вартість завантаження

Зміст

Introduction 4 Chapter 1. System performance statistics of crops and methods of their calculation 5 1.1The subject and purpose of statistics of plant 5 1.2. System performance of crop statistics 8 Chapter 2. Statistical estimation of indexes of products of stock-raising and factors, that on it 14 2.1. Statistical groupings and their kinds 14 2.2. Distributing rows and them graphic image 16 2.3. Summarizing the indexes of distributing rows 22 2.4. Summarizing the indexes of distributing rows 27 2.5. Variation of signs and indexes of their measuring 33 2.6. The sample method 45 2.7. Statistics control in accordance with a number of empirical distributions 50 Chapter 3. Correlation regressive analysis 56 3.1. Simple correlation 56 3.2. Multiple correlation 64 3.3. Range correlation 70 Chapter 4. Problems and prospects of Agrarian statistics at the present stage 73 Conclusions 76 Use literature 79

Висновок

This course work, I tried to explore the dependence of productivity on factors such as organic fertilizers and made part of high-grade crops. After grouping the data and analytical features vyrahuvavshy volume and average structural and performance variations and after correlation-regression analysis, we can say that the greatest impact on our data to yield a distribution of organic fertilizers.

Statistical together, we inquired at the level of productivity is uniform (coefficient of variation is 19, 51%, while the aggregate is homogeneous ratio variation less than 33%), the level of making organic fertilizers and share high-grade crops - not homogeneous (coefficient variation is under 39,26 and 39,67%).

Calculating the average, we checked the data received method of moments. All true value. Since the research carried out selectively, data received on average had to expand beyond a certain limit, given the size of the general population. Adopting the 0.954 level of probability, we determined the average level for each of the signs and boundary limits.

Reviewing conjecture concerning the empirical distribution theoretically, we came to this conclusion that the differences between actual and theoretical frequencies are not due to random and significant reasons why we rejected this hypothesis. In doing so, we began the correlation-regression analysis to determine how close the relationship between the study and effective two factorial bases is.

First, we conducted an analysis of each factor and effective feature separately (pair correlations).

For research, we chose a linear type of equation, because there are signs of straightforward communication. First of all, we count up, expected value of productivity, ie a value that is theoretical and does not reflect fully the impact factor characteristics. As we mention, to determine the theoretical value of effective features using a linear equation. Defining the parameters of equations, we checked them on materiality by Student t-criterion. Option A1 equation that describes the relationship between yields and make organic fertilizer is essential, while the same parameter equation that describes the relationship between yields and the share of high-grade crops, is insignificant. According to the parameters we can say that changing the ball fertilizers, crop changes, while between yields and the share of high-grade crops there a direct link, but if you change the share of high-grade crops per unit, yield change, and between the attributes no connection ' communication.

Count up, parameters that characterize the straits relation, calculating the difference between the expected value of productivity and intermediate productivity, we saw that and made between the yields of organic fertilizers is the average relationship, while between yields and the share of high-grade crops is poor communication. As the coefficients of determination, variation in productivity 43, 56% due to a variation made organic fertilizers and only 54, 5% - a variation of high-grade crops. These coefficients of determination were tested for essentiality using Fisher F-criterion. According to the results of this test, coefficient of determination, which describes the relationship between yields and appied organic manure is essential, and determination coefficient, which describes the relationship between yields and the part of crops is not essential (as the figures show).

After the pair correlation, we started multiple analyses, in order to study the effect of two factors on yield. To do this, we also identified the expected value of productivity, but under the influence of two factors simultaneously.

The value of multiple correlation coefficient equal to the value of the pair correlation coefficient, which describes the straits relationship between yields and amended with organic fertilizers, because among all the companies do not close connection.

Calculating the coefficient of multiple determinations showed that the variation of productivity of crops to 54, 5% depending on variations made organic fertilizers and the share of high-grade crops. This ratio was tested for essentiality by the Fisher F-criterion, which showed that this factor is not likely. Identifying coefficients of partial determination and summ them, we received a coefficient of determination is equal to 54, 5%.

Then we defined the criteria neparametrychni correlation: the correlation coefficient of ranks (the formula Spirmena) and coefficient of Fechner.

Rank correlation coefficient showed that yields of grain and made with organic fertilizers is a direct and close relationship, but between the cost of production and share of grade crops - also direct and close.

Given all the above said, we can make such a conclusion: grain yield depends on the deposited organic fertilizers, and the share of high-grade crops, but between yields and applied fertilizers there is a close and direct communication (43,56%), and between yields and the share of high-grade crops is poor communication (about 28,623%) aur totality are homogenous(τ = 2,624

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