basic_statistics_and_probability_by_shahid_jamal_pdf_124_best__eknjd v1.0.0

Basic Statistics And Probability By Shahid Jamal Pdf 124 ##BEST##

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We generated spatially explicit maps for the classification probabilities of anemia in each LMIC, using the trained RF model (variable model) for the best scale. The result is a map of probability that a location is classified as having the target outcome. To generate uncertainty in the prediction, we generated multiple maps based on a cross validation of the model, using a split-sample validation method in the randomForest package (Liaw and Wiener 2002 ). The cross validation method identifies a subsample of 30% of the dataset (training samples) that was split randomly into a separate test set of 60%. The model was then trained on the training data and prediction was made on the test data. The model was trained five times using randomly chosen samples from the 30% training dataset and each run of the split was used to test the model’s predictive ability on the independent samples. The mean of the five results is the probability value assigned to each location. For interpretation, a value of ‘0.5’ means the location was predicted with 50% certainty based on the RF model. We combined the results of each iteration of the split into a single model map. Maps of anemia in China, India, Iran and Thailand are shown as examples of the map-making methodology. The full set of maps for all 82 LMICs at 10 spatial scales is publicly available as a geospatial pdf of all mapped variables at all spatial scales and as a virtual globes (Supplemental Information 1 ).


We estimated the probability that each second-level unit (for example, province) will have at least one district with a high probability of meeting the WHO GNT for anemia by 2025 and 2030 (Figs 3–5). We defined a high probability of meeting the WHO GNT for anemia at a district-level unit as a probability >95%. In 2015, the first data year we defined the GNT, the WHO recommended that 15.5% of the districts (10.5% of all districts) in LMICs achieve a >50% probability of meeting the WHO GNT (corresponding to 8.3% of the country) by 2025 and, in 2025, 26.1% of the districts (17.6% of all districts) in LMICs achieve a >50% probability of meeting the WHO GNT (corresponding to 12.2% of the country) (WHO, 2015). To determine these probabilities in 2025 and 2030, we simulated the probabilities of meeting the WHO GNT by 2025 and 2030 given the recent trends from 2000–2018 to infer our 2015 projected probabilities. We assumed that the distribution of these probabilities would be similar to those of 2015. Furthermore, a recent systematic analysis of settings with improved anemia management found the expansion in the scale of interventions had a positive association with further decrease in anemia prevalence (Haider et al. 2019). We thus used the posterior distributions of the probability of national-level attainment (‘high’) of the WHO GNT for anemia to estimate the probability that districts, first-level administrative units and second-level administrative units will have achieved the WHO GNT by 2025 and 2030. We estimated the probability that at least one district in a second-level unit in each LMIC will have achieved the WHO GNT by 2025 and 2030. Additionally, we estimated the probability that a district in a second-level unit across a majority of LMICs in sub-Saharan Africa, Southeast Asia and Eastern Mediterranean Region will have achieved the WHO GNT by 2025. 84d34552a1