Python Tutorial : Introduction to Linear Modeling in Python

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-linear-modeling-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hello! My name is Jason Vestuto, and I'll be your instructor for this course. I'm a Scientist in the Space and Geophysics Lab of The University of Texas at Austin. In this course, we'll see how to use python to build, evaluate, and apply linear models. To do this, we'll use many tools from the python data science ecosystem, including matplotlib, numpy, scipy, statsmodels, and scikit learn. Before we build models, we'll use exploratory data analysis, including visualization and descriptive statistics, to characterize the data to be modeled. Then, we'll build models and use them to make predictions, quantifying the confidence we can have in those predictions. Finally, we'll explore how linear regression relates to inferential statistics, with an introduction to model parameter estimation. In the end, you'll be well prepared to move on to more advanced forms of regression, statistical modeling, and machine learning. In this first chapter, we start with an introductory exploration of linear relationships. First, we'll introduce some example applications of linear models, such as interpolation and extrapolation. Next, we'll start exploring our data with visualization methods. This is a great first step to see trends that may be harder to find or interpret had you just jumped straight to quantitative methods. Finally, we'll introduce some "descriptive" statistics, and see how they can help you prepare a more quantitative basis for building a model. Let's start by looking at some data from a road trip. Here we have Distance Traveled, plotted on the vertical y-axis, and Elapse Time, plotted on the horizontal x-axis. Consider the total "range" of values of x and y. The range of the data is the difference between the smallest and l