What's the worst that could happen?
1. What's the worst that could happen?
Trying to predict the future will always come with risks. We might make the wrong business decision and lose a fortune, or we might pursue a medical treatment that doesn't work. We often think of risks as difficult to quantify, and that's often true. But in this video, we'll offer a couple of tips to attempt to measure risk more systematically.2. A likely story
We can think of risk as the probability that something will occur multiplied by the consequences of that event occurring.3. Who wants to be a millionaire
Take the lottery, for example. The likelihood of winning is very small, but the potential consequence is very positive. But just how long are the odds and how positive the consequence? Currently in the United States, the odds of winning $1 million in the PowerBall are 1 in 11,688,053.52. (For comparison's sake, the odds of being struck by lightning in your lifetime are 1 in 3000.) So if we multiply the potential outcome ($1 million), by the odds (1 in 11,688,053.52), I get 8.5 cents. We wouldn't even recoup 10% of our $1 million investment on average. Note, this will always be the case with the lottery, otherwise it would go out of business.4. Another risk example
So how can risk calculations help us make better decisions and allocate resources more efficiently? Well, we can perform this same risk calculation on the data we have been using in this chapter to see where car crashes cause the most damage and where we should consequently try hardest to prevent them. We can define the consequence as the number of actual or predicted injuries in the dataset. Furthermore, we can consider the likelihood to be the number of injuries per crash in a given precinct. In this way, we can construct a risk measure for each event and precinct to determine what additional resources might be needed.5. Review: useful functions
Before we jump into the exercises, though, let's review a couple useful functions. The FORECAST function allows us to predict values and estimate probabilities. We supply it with a value to use for the prediction, as well as the sets of outcome data and predictors. The SUMIF function is also useful when determining consequences and probability. It calculates a sum of a given range, including only the rows that meet certain criteria. For example, it easily adds up the number of car crashes for a given precinct. It just requires the range to check, the criterion to check against that range, and the cells to add up.6. Let's practice!
Now that you have some tools for quantifying risk, let's dig into the exercises.Create Your Free Account
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