The research aims to identify the level of emotional reassurance and level of family climate at
the students of kindergarten Department.
Search tool :
The two researchers built a measure of emotional reassurance and consisting of (26) items and
the scale of family climate consisting of (30 ) items .
The applied Statistical methods:
The two researchers used the statistical bag spss and they used t-test and Pearson correlation
coefficient and analysis of variance and Alpha -Cronbach .
The results showed : The students of kindergarten Department have a high level of
emotional reassurance and good family climate and there is a relationship between emotional
reassurance and family climate .
The two researchers have got some recommendations and proposals .
Background: The aim of this study was for estimation and comparism of masticatory efficiency in patient wearing heat cured acrylic and flexible base partial denture, finding out the role of peanuts and carrots on the measurement of chewing efficiency, and to find out whether the types of P.D. (being single or two opposing each other) has any effect on the masticatory performance. Materials and methods: Twenty partially edentulous patients were selected. Five of these patients were selected having Kennedy class I with no modification against natural dentition, other 5patients having Cl.I against Cl.I . The other 5 patients having Cl.III against natural dentition and the last 5 patients were with Cl.III against Cl.III. several ways were used
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In order to obtain a mixed model with high significance and accurate alertness, it is necessary to search for the method that performs the task of selecting the most important variables to be included in the model, especially when the data under study suffers from the problem of multicollinearity as well as the problem of high dimensions. The research aims to compare some methods of choosing the explanatory variables and the estimation of the parameters of the regression model, which are Bayesian Ridge Regression (unbiased) and the adaptive Lasso regression model, using simulation. MSE was used to compare the methods.
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