Other examples of continuous variables are height, time, and temperature. We’re talking about a set of infinitely many possibilities. Rather than just one, two, or three seconds, we can have values like 3.45 seconds or 6.98457 seconds. Instead of counting hits, our random variable could be the time the baseball is in the air. The continuous case follows naturally from the discrete case. Common discrete distributions include Bernoulli, binomial, and Poisson. Replace P with f and we’ve got our probability function! Let’s graph it.įrom the graph, we see that it is more likely for John to get 1 or 2 hits than it is for him to get 0 or 3, because the graph is taller for those values of X. If all eight of the above outcomes are equally likely, we have: The probability of John getting n hits is represented by P(X= n). Let X be our random variable, the number of times John gets a hit in the three-pitch experiment. Here is a list of all the possible outcomes: Let’s throw John three pitches and see how many times he hits the ball. John is a baseball player who has a 50% random chance of hitting the ball each time it is pitched to him. Random variables, and therefore distributions, can be either discrete or continuous. It shows, at a glance, how the values of a random variable are dispersed. If you’re picturing a bell curve, you’re on the right track. Other descriptive statistics include skewness, kurtosis, and quartiles.Ī probability distribution is a function that gives the probability of occurrence for every possible outcome of an experiment. The standard deviation measures overall spread and is calculated by taking the square root of the variance. Finally, calculate the mean of those resulting numbers. To calculate the variance, subtract the mean from each value. The variance measures the spread of a dataset with respect to the mean. The mode is the most frequent value(s) in your dataset. If there are two middle numbers, the median is the mean of these. The median is the point that divides the data in half. List your values in ascending (or descending) order. The mean (also known as “expected value” or “average”) is the sum of values divided by the number of values. Let’s take a look at some of the most common descriptive stats. These will quickly identify key features of your dataset and inform your approach no matter the task. You’ve probably heard of some of these: mean, median, mode, variance, standard deviation … How can you get a high-level description of what you’ve got? Descriptive statistics is the answer. Here are the top five statistical concepts every data scientist should know: descriptive statistics, probability distributions, dimensionality reduction, over- and under-sampling, and Bayesian statistics. It’s impossible to perform quality data science without it.īut statistics is a huge field! Where do I start? Essential pillars of statistics and data scienceĪny data scientist can glean information from a dataset - any good data scientist will know that it takes a solid statistical underpinning to glean useful and reliable information.
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