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  3. Introduction to Linear Modeling in Python
  • 1

    Exploring Linear Trends

    Gratuito

    We start the course with an initial exploration of linear relationships, including some motivating examples of how linear models are used, and demonstrations of data visualization methods from matplotlib. We then use descriptive statistics to quantify the shape of our data and use correlation to quantify the strength of linear relationships between two variables.

  • 2

    Building Linear Models

    Here we look at the parts that go into building a linear model. Using the concept of a Taylor Series, we focus on the parameters slope and intercept, how they define the model, and how to interpret the them in several applied contexts. We apply a variety of python modules to find the model that best fits the data, by computing the optimal values of slope and intercept, using least-squares, numpy, statsmodels, and scikit-learn.

  • 3

    Making Model Predictions

    Next we will apply models to real data and make predictions. We will explore some of the most common pit-falls and limitations of predictions, and we evaluate and compare models by quantifying and contrasting several measures of goodness-of-fit, including RMSE and R-squared.

  • 4

    Estimating Model Parameters

    In our final chapter, we introduce concepts from inferential statistics, and use them to explore how maximum likelihood estimation and bootstrap resampling can be used to estimate linear model parameters. We then apply these methods to make probabilistic statements about our confidence in the model parameters.

Initializing

Ejercicio

Plotting the Data

Everything in python is an object, even modules. Your goal in this exercise is to review the use of the object oriented interfaces to the python library matplotlib in order to visualize measured data in a more flexible and extendable work flow. The general plotting work flow looks like this:

import matplotlib.pyplot as plt 
fig, axis = plt.subplots()
axis.plot(x, y, color="green", linestyle="--", marker="s")
plt.show()

context figure

Instrucciones

100 XP
  • Use plt.subplots() to create figure and axis objects.
  • Data have been provided in two predefined numpy arrays, times and distances.
  • Use axis.plot() to plot times on the horizontal and distances on the vertical.
  • Use the input key word args linestyle=" ", marker="o", and color="red" when calling axis.plot().