Obesity is a common disease that resulted from over-nutrition in adults and children. It rarely causes damage to the centers of food in the brain. Obesity is defined as an increased body weight from its natural limit which is resulted from the accumulation of excessive amounts of fatty tissue incredibly up to 20% in males, 30 % in females unless this increase is not due to an increase in muscles as in athletes or accumulation of water in the body which is resulted from Mesothelioma or the magnitude of the skeleton.Obesity is the increase of the total average of fat in the body compared to other tissues, which causes an increasing body weight, thereby increasing body mass. The fatty child has an increase in the stored fatty layer under the skin, and increased weight 20%more than the normal weight of other children in the same age and height.
This thesis focuses on identifying showing the relationship between obesity and some of the variables. Moreover, the thesis reflects the importance of maintaining the ideal weight of the child and his role in the growth and health. The evidence shows that children who suffer from obesity are more likely to become fat adults, so they are exposed to increased risk of serious health problems. Hence, protecting children from obesity and treating them is possible through changing lifestyle, improving the child's nutrition system, and urging him to exercise to improve their health at the present time and in the future.
Therefore, the thesis aims: to detect significant differences in obesity for kindergarten children depending on certain variables through the testing hypothesis as followed: Statistically there are no significant differences in obesity with kindergarten children that are attributable to the following variables:
(A)Sex variable.
(B) The sequence of the child in the family..
(C) Dietary habits of the family.
To achieve the objectives of this study, 300 children were selected as samples, 166 males and 134 females. One of the most important tools that were used is:
1 - an electronic device for measuring weight.
2 - Length measuring tape.
3 - Measurement of body mass index (BMI)..
The researcher used in data statistical analysis:
(1) One way Anova.
(2) The T- test for two independent samples: Independent Samples Test.
(3) The real bilateral correlation coefficient (Point Bay Cyril).
The most important results that have been reached:
(1) That females suffer from obesity more than males because the body mass index of females is greater than the body mass index (BMI) for males.
(2) There is no statistically significant difference between the averages of body mass index (BMI) scores for kindergarten children due to the variable of the sequence of the child within his brothers.
(3) There is no statistically significant difference between the averages of body mass index (BMI) scores for obese kindergarten children that is attributed to behavioral tendencies variable of the child.
Abstract
In this research provide theoretical aspects of one of the most important statistical distributions which it is Lomax, which has many applications in several areas, set of estimation methods was used(MLE,LSE,GWPM) and compare with (RRE) estimation method ,in order to find out best estimation method set of simulation experiment (36) with many replications in order to get mean square error and used it to make compare , simulation experiment contrast with (estimation method, sample size ,value of location and shape parameter) results show that estimation method effected by simulation experiment factors and ability of using other estimation methods such as(Shrinkage, jackknif
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Abstract
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An Auto Crop method is used for detection and extraction signature, logo and stamp from the document image. This method improves the performance of security system based on signature, logo and stamp images as well as it is extracted images from the original document image and keeping the content information of cropped images. An Auto Crop method reduces the time cost associated with document contents recognition. This method consists of preprocessing, feature extraction and classification. The HSL color space is used to extract color features from cropped image. The k-Nearest Neighbors (KNN) classifier is used for classification.
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