![]() What is the percentage chance that a review is actually fake when an algorithm detects it as fake? Would our estimate still be the max value of 30? 8. Let’s say our N sample is 5 again and our values are instead: (20,30,28,26,16). If we’re given N samples and we have to estimate what d is with zero context of statistics and based on intuition, what value would we choose?įor example, if our N sample is 5 and our values are: (1,4,6,2,3), what value would we guess as d? Probably the max value of 6 right?īut, let’s look at another example. ![]() So, let’s make this easy to understand practically. What does a uniform distribution look like? Just a straight line over the range of values from 0 to d, where any value between 0 to d is equally likely to be randomly sampled. Given N samples from a uniform distribution how would you estimate d? For example, a plane wreck is a non-continuous data point, there are either 0 or 1 crashes. Having a certain mean or standard deviation is not enough to make a distribution non-normal.Īlthough this answer has multiple possible solutions, one solution could be that the data is non-continuous. Remember that normal distributions can have any mean or standard deviation (so long as the standard deviation is positive, obviously). ![]() Explain how a probability distribution could not be normal and give an example scenario. If you are confused, think about how both X and Y are random variables across the same distribution, and that 2X as being on average double positive or double negative the value that Y is. You can create a chart to map out the likelihood of each scenario (there are 6 scenarios) - for example X > Y: both positive - you then can simulate a random sampling and equate that all six are likely to occur. Given two standard normal random variables X and Y, what’s the probability that 2X > Y? Then using n choose k principles you can calculate:ĥ. You will need to use binomial distribution in which there exists n independent experiments. With this probability events question, keep in mind that the biased coin is heads 30% of the time. What is the probability of the coining coming up heads 5 times out of 6? You have a biased coin that comes up heads 30% of the time. The key thing to remember: Covariance can take on any numeric value, while correlation can only be between -1 and 1.Ĥ. Your goal should be to convey these concepts quickly and in layman’s terms. What’s the difference between covariance and correlation?Ī definition-based question like this is generally asked early in the interview process. 08, and by March, you need to take the total customer base divided by 2 to get total churn rate.ģ. For example, in February churn rate will be 20% less than initial churn rate of 10%, so the new churn rate will be. ![]() This is a statistical analysis case question, and because the 10% is compiling, you need to calculate a new churn rate for February and so on. Assume that your new customer acquisition is uniform and that customer churn goes down by 20% month-over-month. You notice that of all customers who bought subscriptions in January 2020 about 10% cancel their subscriptions before the next cycle on February 1. What’s different when we’re using N dice? 2.What’s the expected churn rate in March for all customers who bought a product in January? So the probability of not rolling a three with two dice is 1 - 25⁄ 36 = 11⁄ 36. How does that change with 2 dice? To not roll a three would be ⅚ * ⅚ = 25⁄ 36. To not roll a three, we can do 1 - ⅙ = ⅚. The probability of not rolling a three is ⅙. To solve this conceptual probability problem, it’s easiest to find out the probability of never rolling a three. What’s the probability of rolling at least one 3? What’s the probability of rolling at least one 3 given N dice? Calculating the probability of both of them occurring, or the product of their probabilities individually occurring.Įxpected value: Expected values are key to probability distributions and are the average random variable.Ĭonfidence intervals: The calculation of not just the specific outcome but the range of outcomes and the expectedness of those outcomes.ġ. Independence: Independence is the study of two outcomes that are unrelated. Probability outcomes: This concept involves both distinct outcomes that are mutually exclusive and not exclusive-often involving a sum of probabilities. It’s using math to predict events like dice rolls, one-off events, or just specific unknowns.Ĭounting: Counting is using probability to figure out the number of outcomes possible in a finite situation. Randomness: Randomness questions are calculations of random events. Probability questions in trading interviews cover many different subjects and concepts in statistics, probability, and econometrics, including:
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