The Influence of Environment to Students’ Motivation and The Effect to Student Achievement Grade Audio Video Department SMK Muh. Kutowinangun Kebumen

This research aims to determine: 1) the influence of school environment to student achievement. 2) the influence of family environment to student achievement. 3) the influence of communities to student achievement. 4) the influence of industrial environments to student achievement. 5) the influences of students’ motivation to student achievement. (6) the influence of school environment, family environment, communities, industrial environments and students’ motivation to student achievement from student XII grade Audio Video department SMK Muh. Kutowinangun Kebumen. This research is an Ex-post facto with quantitative approach. The population is a class XII student of Audio Video department SMK Muh. Kutowinangun Kebumen school year 2011/2012 which amounts to 36 students. Methods of data collection using questionnaires Likert scale models for all variables. The validity of research instruments performed by analysis of the items calculated by the formula Product moment correlation. Reliability of the instrument calculated using Cronbach Alpha. Prior to the first data analysis conducted descriptive analysis and testing requirements analysis including tests of normality, linearity tests, and multicollinearity test. Data analysis techniques are used to test the hypothesis is a technical product moment regression analysis.. The results showed that: (1) there is a positive relationship between school environment (X1) with student achievement (Y) are indicated coefficient R = 0,335. The coefficient of determination (R2) = 0,112. (2) there is a positive relationship between family environment (X2) with student achievement (Y) are indicated coefficient R = 0,578. The coefficient of determination (R2) = 0,334. (3) there is a positive relationship between communities (X3) with student achievement (Y) are indicated coefficient R = 0,485. The coefficient of determination (R2) = 0,235. 4) there is a positive relationship between industrial environments (X4) with student achievement (Y) are indicated coefficient R = 0,367. coefficient of determination (R2) = 0,135. (5) there is a positive relationship between students’ motivation (X5) with student achievement (Y), are indicated coefficient R = 0,658. coefficient of determination (R2) = 0,434. (6) there is a positive relationship between school environment (X1), family environment (X2), between communities (X3), industrial environments (X4) and students’ motivation (X5) together in the readiness of student achievement (Y), are indicated coefficient R = 0,725. coefficient of determination (R2) = 0,526

The Entrepreneurship Readiness of Student XII Grade Department of Audio Video SMK Piri 1 Yogyakarta School Year 2011/2012 Reviewed by Knowledge Entrepreneurship, Family Support, Soft Skills and Learning Achievment.

This research aims to determine:1) the influence of knowledge entrepreneurship to the readiness entrepreneurship. 2) the influence of family support to the readiness entrepreneurship 3) The infulence soft skill to the readiness entrepreneurship 4) the influence of student achievment to the readiness entrepreneurship 5) the influences of knowledge entrepreneurship, family support, soft skills dan student achievment to the readiness entrepreneurship together from student XII grade Audio Video department SMK Piri 1 Yogyakarta school year 2011/2012. This research is an Ex-post facto with quantitative approach. The population is a class XII student of Audio Video department SMK Piri 1 Yogyakarta school year 2011/2012 which amounts to 24 students. Methods of data collection using questionnaires Likert scale models for all variables. The validity of research instruments performed by analysis of the items calculated by the formula Product moment correlation. Reliability of the instrument calculated using Cronbach Alpha. Prior to the first data analysis conducted descriptive analysis and testing requirements analysis including tests of normality, linearity tests, and multicollinearity test. Data analysis techniques are used to test the hypothesis is a technical product moment correlation analysis and multiple regression analysis techniques. The results showed that: (1) there is a positive relationship between entrepreneurial knowledge (X1) with the readiness of student entrepreneurship indicated coefficient R = 0.639. The coefficient of determination (R2) = 0.408 and is shown by the equation Y = 27.099 + 0.877 X1. (2) there is a positive relationship between family support (X2) with the readiness of entrepreneurship students (Y) are indicated coefficient R = 0.644. The coefficient of determination (R2) = 0.415 and is shown by the equation Y = 42.00 + 0.777 X2 . (3) there is a positive relationship between soft skills (X3) with the readiness of entrepreneurship students (Y) are indicated coefficient R = 0.344. The coefficient of determination (R2) = 0.118 and is shown by the equation Y = 20.217 + 0.160 X3. (4) there is a positive relationship between learning achievement (X4) with entrepreneurship student readiness (Y) are indicated coefficient R = 0.237. coefficient of determination (R2) = 0.056 and is shown by the equation Y = 18.889 + 0.188 X4. (5) there is a positive relationship between entrepreneurial knowledge (X1), family support (X2), soft skills (X3) and learning achievement (X4) together on the readiness of entrepreneurship students (Y), which indicated multiple regression coefficient Rx(1,2,3,4) y of 0.921. coefficient of determination (r2) = 0.848 and is shown by the equation Y = 13.402 + 0.746 X1 + 0.471 X2 + 0.122 X3 - 0483X

Determination of digestibility coefficient

Nutrients present in the feedstuffs are not completely available to the animal body. Large portions of the nutrients are excreted In the faeces because of being not digested In the alimentary tract. Therefore, the digestibility of the feedstuff is defined as the portion of a feed or nutrient of feed which is not recovered In faeces, i.e., the portion which has been absorbed by the animal. When the digestibility is expressed in percentage it is known as digestibility coefficient. Digestibility coefficients are calculated for dry matter, crude protein, crude fibre, ether extract and nitrogen-free extract. Digestibility of gross energy present in the food can also be determined. The digestibility coefficients normally determined are the apparent digestibility coefficients since the nutrients found in the faeces contain small proportion of nutrients from the previously utilized food In the form of mucosal debris, unspent enzymes ate

On association in regression: the coefficient of determination revisited

Universal coefficients of determination are investigated which quantify the strength of the relation between a vector of dependent variables Y and a vector of independent covariates X. They are defined as measures of dependence between Y and X through theta(x), with theta(x) parameterizing the conditional distribution of Y given X=x. If theta(x) involves unknown coefficients gamma the definition is conditional on gamma, and in practice gamma, respectively the coefficient of determination has to be estimated. The estimates of quantities we propose generalize R^2 in classical linear regression and are also related to other definitions previously suggested. Our definitions apply to generalized regression models with arbitrary link functions as well as multivariate and nonparametric regression. The definition and use of the proposed coefficients of determination is illustrated for several regression problems with simulated and real data sets

Stable Determination of the Discontinuous Conductivity Coefficient of a Parabolic Equation

We deal with the problem of determining a time varying inclusion within a thermal conductor. In particular we study the continuous dependance of the inclusion from the Dirichlet-to-Neumann map. Under a priori regularity assumptions on the unknown defect we establish logarithmic stability estimates.Comment: 36 page

Calculation of Contraction Coefficient under Sluice Gates and Application to Discharge Measurement

The contraction coefficient under sluice gates on flat beds is studied for both free flow and submerged conditions based on the principle of momentum conservation, relying on an analytical determination of the pressure force exerted on the upstream face of the gate together with the energy equation. The contraction coefficient varies with the relative gate opening and the relative submergence, especially at large gate openings. The contraction coefficient may be similar in submerged flow and free flow at small openings but not at large openings, as shown by some experimental results. An application to discharge measurement is also presented

Study of plasmasphere dynamics using incoherent scatter data from Chatanika, Alaska radar facility

Results of the study of Chatanika incoherent scatter radar data and Lockheed Palo Alto Research Laboratory satellite data are reported. Specific topics covered include: determination of the effective recombination coefficient in the auroral E region; determination of the location of the auroral oval; auroral boundary characteristics; and the relationship of auroral current systems, particle precipitation, visual aurora, and radar aurora

Total variation regularization of multi-material topology optimization

This work is concerned with the determination of the diffusion coefficient from distributed data of the state. This problem is related to homogenization theory on the one hand and to regularization theory on the other hand. An approach is proposed which involves total variation regularization combined with a suitably chosen cost functional that promotes the diffusion coefficient assuming prespecified values at each point of the domain. The main difficulty lies in the delicate functional-analytic structure of the resulting nondifferentiable optimization problem with pointwise constraints for functions of bounded variation, which makes the derivation of useful pointwise optimality conditions challenging. To cope with this difficulty, a novel reparametrization technique is introduced. Numerical examples using a regularized semismooth Newton method illustrate the structure of the obtained diffusion coefficient.