learn-co-students/dsc-distributions-section-recap-online-ds-pt-061019

Statistical Distributions - Recap

Introduction

This short lesson summarizes the topics we covered in this section and why they'll be important to you as a data scientist.

Key Takeaways

In this section, we really dug into statistical distributions. Key takeaways include:

• There are two types of distributions - continuous, where (subject to measurement and/or storage precision) there are effectively an infinite number of possible values, and discrete, where there are a distinct, non-infinite number of options. For example, a person's height is continuous - assuming a suitably precise tape measure - whereas the number of bedrooms in a house is discrete
• How to describe the distribution of data sets using Probability Mass Functions, Cumulative Distribution Functions, and Probability Density Functions
• One type of discrete distribution deals with a series of boolean events or trials - often called Bernoulli Trials
• A Normal distribution is the classic "bell curve" with 68% of the probability mass within 1 SD of the mean, 95% within 2 SDs and 99.7% within 3 SDs
• Differences between the normal and the standard normal distribution
• How skewness and kurtosis can be used to measure how different a given distribution is from a normal distribution
• The uses of $z$-scores and p-values for describing a distribution
• How a one sample $z$-test is a very simple form of hypothesis testing.
• A uniform distribution represents a process where each outcome is equally likely. A typical example is a dice roll.
• The Poisson distribution can be used to display the likelihood of a given number of successes over a given time period - e.g. "how likely is it that 25 people walk into a store in a given hour?"
• The Exponential distribution can be used to describe the probability distribution of the amount of time it may take before a given event to occur