EDA

Data are being released that show significant variation across the country and within communities in what providers charge for common services. These data include information comparing the charges for the 100 most common inpatient services and 30 common outpatient services. Providers determine what they will charge for items and services provided to patients and these charges are the amount the providers bills for an item or service

Berdasarkan hasil akhirnya ditemukan bahwa terdapat hubungan positif yang signifikan antara investasi modal dan produksi pada industri manufaktur. Koefisien regresi sebesar 2,65 menunjukkan bahwa setiap tambahan $1 juta dalam investasi modal di sektor manufaktur diperkirakan akan meningkatkan output produksi sebesar 2,65 juta unit. Nilai R-squared sebesar 0,99 mengindikasikan bahwa 99% variasi dalam output produksi dapat dijelaskan oleh perubahan dalam investasi modal. Dengan kata lain, investasi modal merupakan faktor yang sangat berpengaruh terhadap peningkatan produksi di industri manufaktur. Model regresi linear menunjukkan hubungan positif antara investasi dan output dalam industri manufaktur. Ini berarti bahwa dengan peningkatan investasi, produksi dalam industri juga meningkat secara signifikan. Model ini dapat digunakan untuk merencanakan strategi investasi yang optimal dalam industri tersebut.

This project focuses on exploring the U.S. National Oceanic and Atmospheric Administration (NOAA) storm database, which provides detailed information on significant storms and weather events across the United States. The database records the location and timing of these events, as well as their impact in terms of fatalities, injuries, and property damage. Severe weather events, such as storms, can lead to major public health and economic challenges for communities and local governments. Many of these events result in significant loss of life, injury, and damage to property, making it crucial to mitigate these impacts as much as possible. The objective of this analysis is to investigate the NOAA Storm Database and answer key questions about severe weather events: Which types of events are most harmful to population health across the United States? Which types of events have the most significant economic consequences nationwide? Data was analyzed using R, with a focus on identifying the types of events that are most harmful to public health and those that result in the greatest economic losses. Graphs and tables were used to illustrate the findings

Nestor Flores background

A collection of four maps; from left to right, row-wise: 1) Cameron County ZCTAs, cropped to include -- and be bounded by -- the U.S. Census Tracts for Cameron County 2) Adjusted Population Estimates for fractionated U.S. Census Tracts arbitrarily divided by ZCTAs (adjusted population estimates sum to 421,110; original 2022 population estimates equal 421,854 -- 99.8% of county reflected) 3) The estimated population estimate by ZCTA based on the adjusted population estimates generated for Map 2. 4) The original population estimates as presented by the ACS-5 2022 population estimates (table B01001) .. **Be sure to adjust the map's usability by playing with the layers! ** ** the gray icon of stacked squares! ** To use: use the cursor to click and select the fragments in each map. Use the hovering red circle in each other map section to compare and contrast as desired. Thanks! .. library("easypackages") libraries(c("tidyverse", "tmap", "sf", "tidycensus", "tigris", "readxl")) cam_pop_tracts<- get_acs("tract", table = "B01001", state = "TX", county = "Cameron", year= 2022, survey = "acs5", geometry = TRUE) national_zctas<- zctas() crosswalk_2022 <- read_excel("C:/Users/daniel.pinon/Downloads/zip_tract_122022.xlsx") // note: you'll need to download the crosswalk file here: // https://www.huduser.gov/portal/datasets/usps_crosswalk.html cam_ZCTAs_22<- left_join(crosswalk_2022 %>% filter(USPS_ZIP_PREF_STATE == "TX") %>% filter(grepl("48061", .$TRACT)), national_zctas, by=c("ZIP"="ZCTA5CE20")) unique_intersected_zcta_tract_areas<- st_intersection(st_as_sf(cam_pop_tracts %>% filter(variable == "B01001_001")), st_as_sf(left_join(crosswalk_2022 %>% filter(USPS_ZIP_PREF_STATE == "TX") %>% filter(grepl("48061", .$TRACT)), national_zctas, by=c("ZIP"="ZCTA5CE20")) , crs = st_crs(cam_pop_tracts %>% filter(variable == "B01001_001")))) %>% dplyr::select(ZIP, GEOID, NAME, estimate, geometry) %>% cbind(.,st_area(.)) %>% unique(.) cam_pop_ests_across_ZCTA<- st_as_sf(cam_pop_tracts %>% filter(variable == "B01001_001")) %>% dplyr::select(GEOID, NAME, estimate, geometry) %>% cbind(.,st_area(.)) %>% st_drop_geometry(.) %>% left_join(., as.data.frame(unique_intersected_zcta_tract_areas), by=c("GEOID"="GEOID")) %>% unique(.) %>% dplyr::rename(., orig_area=4, intersected_area=8) %>% drop_na(.) %>% mutate(diff = intersected_area/orig_area, new_est = as.numeric(diff*estimate.x), new_est = round(new_est)) %>% group_by(ZIP) %>% mutate(zip_pop = sum(new_est)) %>% st_as_sf(.) tmap_mode("view") cam_zips_maps<- tm_shape(cam_pop_ests_across_ZCTA %>% dplyr::rename(., "New Population Estimate"=11, "New Zip Population Estimate"=12, "Original Population Estimate"=3) %>% .[,-1]) + tm_polygons(col=c("ZIP","New Population Estimate", "New Zip Population Estimate", "Original Population Estimate"), alpha=0.7) + tm_facets(nrow=2, sync=TRUE)

Using Temperature datasets analyze the comparison with histogram, scatterplot and boxplot