learn-co-students / dsc-distributions-section-intro-chicago-ds-120919

Statistical Distributions - Introduction

Introduction

In this section, you'll learn about different probability distributions!

It's all Stats!

You've already seen the value of descriptive statistics when doing exploratory data analysis. In this section, we're going to dive deeper into a range of statistical concepts. We're going to start by looking at discrete and continuous distributions and how you can use stem and leaf plots for visualizing distributions.

We're then going to look at a range of techniques for representing distributions - the Probability Mass Function, the Cumulative Distribution Function, and the Probability Density Function.

We're then going to dig a little deeper into the Normal/Gaussian distribution and the Standard Normal Distribution, before introducing the ideas of skewness and kurtosis that can be used to quantify how "un-normal" a given distribution is.

We'll then introduce the use cases for z-tables and p-values for describing statistical significance, and we'll finish off the section by looking at the "one-sample z test" - the most basic type of hypothesis test.

We'll finish up the distributions section by introducing the Exponential distribution. In some ways it can be thought of as the inverse of a Poisson distribution, allowing us to answer questions about the amount of time it might take before a given event occurs.

Summary

In this section, we're going to take a deeper dive into a range of foundational statistical concepts that we'll need as we start to dig into machine learning later in the course.