Using Statistics to Spot Racial Bias
“How can statistics be used to spot racial bias?”
Racism exists in every single part of the world, although numerous people will try to dispute this. This can be countered by looking at how different ethnic groups are affected differently by similar quantitative measurements, such as per-capita income or incarceration rate and seeing how it holds true for nearly every phenomenon. This is how we can Use Statistics to Spot Racial Bias.
And don’t forget, #BlackLivesMatter
Ecological Data Analysis
“How can we apply advanced statistical analysis to Ecology?”
Ecology is an academic field that is rich in data. From the largest of ecosystems to the smallest of invertebrates, data can play a central role in how everything operates. To take a deeper dive into its fundamental core, Ecological Data Analysis can be done to quantify these relationships. This can range from seeing what factors lead to evolution to categorizing population characteristics.
“How is it that some losses are to be expected?”
Although we try to make ourselves as resilient as possible to disasters, sometimes losses are bound to occur. For this reason, Expected Losses can be calculated and projected as risk and calculated using statistical methods.
How Risk Analysis Can Be Used to Determine an Area’s Proneness to Wildfires
“How can we use risk analysis techniques to assess an area’s proneness to wildfires?”
With the ascent of climate change, the natural environment is becoming more unstable. In dry areas with sufficient vegetation, this means that wildfires are becoming more common. As such, people will need to understand what areas of liable to become aflame. This can be accomplished using risk analysis techniques which take into account the dryness, lack of rainfall, and temperatures of a particular area to produce a risk assessment. This is How Risk Analysis Can Be Used to Determine an Area’s Proneness to Wildfires.
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How Probability Distributions Can Help with Risk Assessment
“How can we use probability distributions to create risk assessments?”
One of the most important parts of any engineering project is to analyze the risks involved in any process. However, quantifying these issues can prove to be quite difficult. To solve this, statisticians and engineers have worked together to create methods based upon preexisting probability distributions to help with the risk assessments. For example, an exponential decay distribution can be used to model a reliability function or a binomial distribution can be used to analyze failure of a specific component. This is How Probability Distributions Can Help with Risk Assessments.
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Discrete Reliability Function
“How can we have a reliability function in a discrete form?”
Reliability functions are useful for predicting how long a component will last for. However, sometimes real-world data will not give a continuous output. This can be solved by using a Discrete Reliability Function which is more flexible to such needs.
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“How can we assess risk?”
If phenomenon has a possibility of occurring with malignant effects it is classified as a risk. The origin of the risk, its spread, and its malignant effects can all be assessed. However, these all can be brought together under a complete Risk Assessment, which gives a holistic and quantitative overview of the entire picture.
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“How can we analyze risk?”
Every engineering, business, or policy action has some sort of risk attached. Whether it be installing a wind turbine or funding a renewable energy startup, something could go wrong. As a result, before institutions will make a Risk Analysis to see if taking such an action is rational. Risk analysis can be performed in a multitude of different ways, both qualitatively and quantitatively.
Climate Change Attribution
“How can we attribute what is responsible for climate change?”
One of the biggest debates going on about climate change is what is causing it. Many political pundits will claim that everything can be explained entirely through natural factors such as the Earth’s tilt or volcanoes. However, to prove causation, mathematical analysis will need to be done to isolate each component and observe its effects. Using this Climate Change Attribution, we can see that these effects do not contribute greatly to global warming and that carbon emissions is the primary driver behind climate change.