![]() ![]() The image above shows the pumping of water out of New Orleans. All of the health concerns for New Orleans came from the amount of flood water because there was so much of it, that it was an optimal breeding ground for mosquitoes and the water covered everything making nothing truly safe. The concern that people were going to get sick because of contaminated food or water also weighed heavily on people's minds. The medical centers were either destroyed or in utter disarray and power was lost for quite awhile. ![]() With the flooding came all new types of bacteria from the open water, leaving New Orleans with little to defend itself. Outbreaks of West Nile, mold, and endotoxin levels rising were the biggest concerns. ![]() The main health effects of Hurricane Katrina had to deal with the amount of water left behind in New Orleans. It continued up to the Great Lakes, weakening until it became a frontal zone (August 31). It was still a hurricane near Laurel, Mississippi, but became a tropical depression over the Tennessee Valley (August 30). The center made landfall near Buras, Louisiana (August 29) and continued north. It then moved southwest across southern Florida and into the eastern Gulf of Mexico (August 26). Tropical Storm Katrina became Hurricane Katrina just before it made landfall near the Miami-Dade/Broward county line (August 25). It continued through the northwestern Bahamas (August 24-25) and then went westward towards southern Florida. It then moved northwest, becoming Tropical Storm Katrina. Katrina began about 200 miles southeast of Nassau in the Bahamas. You can visibly see the eye of the storm. The colors in the image so the the intensity of the storm. This led to the integrity of the buildings to be compromised, leaving people homeless and worries to arise about the places refugees were going to stay when the water was all pumped out of the city.Ī look at Hurricane Katrina before it hits land near New Orleans. The pumps used to rid the city of the water, were not working and because they couldn't be replaced, had to be repaired. Many structures were completely destroyed and those that weren't destroyed by the hurricane, most likely had to be destroyed because of how long the flood waters were there. ![]() Close to 90 percent of the city was flooded, some parts of the city under 20 feet of water. New Orleans, Louisiana was devastated by Hurricane Katrina. This in turn caused molds to grow, endotoxin levels to rise, little clean drinking water, spoiled food, West Nile virus concerns, and many other causes for a person to be sick. Hurricane Katrina brought with it flood waters, the loss of power, little livable space left, and a breeding ground for mosquitoes. With any natural disaster, comes concerns for human health. Hurricanes are natural disasters that have unfortunately been on the rise as the years have gone on. Nothing can truly stop these types of storms, all one can do is know what to look out for and how to protect themselves as best as they can. The water left from the storm left little clean water to use, buildings completely destroyed, and the public at a loss for words. It devastated New Orleans and caused many health concerns for the public. Hurricane Katrina was one of the strongest storms to hit the United States coast within the last 100 years. ![]()
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![]() ![]() I created this example because there don’t seem to be many r packages with flexible outputs for tree diagrams. Taking the sum of all probabilities multiplied against their associated business outcome, Gracie calculates expected values for revenue, cost, and profit for her lemonade stand operations. She then uses her demand function to calculate revenue, cost, and profit expectations for each scenario based on: The least likely outcome is rain with a temperature of 95☏ (p=0.014). There is a probability of 0.396 associated with this. The most probable outcome is to have no rain and a temperature of 85☏. Gracie translates these probabilities into a tree diagram to get a better sense of all potential outcomes and their respective likelihoods. Probability of no rain: p(no rain) = 0.28įurther, she knows the temperature fluctuates widely depending on if it rains or not.When it rains, demand falls an additional 20% across the temperature spectrum. To generate a more realistic view of her business, and to inform ingredient purchasing decisions, Gracie collected historic data to help her better anticipate weather conditions. Glasses of Lemonade =−100+1.7×Temperature She has even estimated a demand equation based on temperature. Not surprisingly, people buy more lemonade on hot days with no rain than they do on wet, cold days. It didn’t take Gracie long to realize that weather has a huge impact on potential sales. It is a lot of work to prepare the stand and bring the right quantity of ingredients, for which she shops for every Friday after school for optimal freshness. Each Saturday, she sells lemonade on the bike path behind her house during peak cycling hours. You can find the single-function solution on GitHub. We start with a simple example and then look at R code used to dynamically build a tree diagram visualization using the ee library to display probabilities associated with each sequential outcome. A tree diagram can effectively illustrate conditional probabilities. ![]() |
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