A statistical study of anthropometric data of children
| dc.contributor.advisor | Shukla, A.K. | |
| dc.contributor.author | Pathak, Shreya | |
| dc.date.accessioned | 2019-09-18T10:33:24Z | |
| dc.date.available | 2019-09-18T10:33:24Z | |
| dc.date.issued | 2019-08 | |
| dc.description.abstract | Present work deals withstatistical assessment of anthropometricand body parameters of children randomly selected fromthree different states of India namely Uttarakhand, Arunachal Pradesh and Gujarat. The secondary data on Age, Body Mass Index (BMI), Height (Ht), Weight (Wt) and Mid Upper Arm Circumference (MUAC) was obtained from College of Home Science, G.B. Pant University of Agriculture andTechnology, Pantnagar. From each state data of 500 children (250 boys and 250 girls) wererandomly selected.Following major conclusions were made on the basis of present study: The distributionpatterns of anthropometric parameters were carried out with the help of EasyFit5.6 Professional software and testing of their goodness of fit by Kolmogorov Smirnov and Chi-square tests. It was concluded that the distribution patterns of anthropometric parameters Height, Weight and MUAC were dissimilar and behave differently for different age groups in children.As none of the anthropometric parameters viz. Ht,Wt and MUAC follow normal distribution it was also concluded that the non -parametric test will provide more precise conclusions as compared to parametric test for statistical analysis of these parameters. Spearman’s Rank Correlation Coefficient was used to study inter relationship between anthropometric and body parameters of children from three different states and the significance of these coefficient was tested by t-test.It was concluded that all the parameters viz.Age, Ht, Wt, BMI and MUAC are significantly positively correlated to each other. Linear Regression (LR) and Multiple Linear Regression (MLR) analysis were used to develop prediction models for BMI and MUAC in children using IBM SPSS Statstics20 software. Out of the 14 LR and 7 MLR prediction models developed for BMI, it was concluded that the best fitted LR model to estimate BMI is LR4(r2=0.980) taking Age as the only predictor and the best fitted MLR model to estimate BMI is MLR1 (R2=0.988) taking MUAC and Age as the predictors.Similarly, out of the22 LR and14MLR prediction models developed for MUAC, it was concluded that the best fitted LR model to estimate MUAC is LR12 (r2=0.887) taking Htas the only predictor. Similarly, the best fitted MLR model to estimate MUAC is MLR9 (R2=0.89) taking Ht, Wt and Age as the predictors. Kruskal Wallis, One way ANOVA and Z test were used for age –wise, state- wise and gender - wise comparison of different parameters. It was concluded there is a significant difference with respect to all the parameters Ht, Wt, BMI and MUAC in different age groups of children within the state and between the states. Similarly, gender-wise significant difference was obtained with respect to all the parameters in each state. The findings of the study are expected to provide useful information for health experts and policy makers to initiate healthy intervention programmes at school level. | en_US |
| dc.identifier.uri | http://krishikosh.egranth.ac.in/handle/1/5810128634 | |
| dc.keywords | statistical analysis, anthropometry, children | en_US |
| dc.language.iso | en | en_US |
| dc.pages | 235 | en_US |
| dc.publisher | G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand) | en_US |
| dc.research.problem | Children | en_US |
| dc.sub | Agricultural Statistics and Informatics | en_US |
| dc.subject | null | en_US |
| dc.theme | Anthropometry | en_US |
| dc.these.type | M.Sc | en_US |
| dc.title | A statistical study of anthropometric data of children | en_US |
| dc.type | Thesis | en_US |