Github Rlodetti 1 Multiple Linear Regression Statsmodels Lab V2 5

Leo Migdal

-Dec 4, 2025, 6:39 AM

github rlodetti 1 multiple linear regression statsmodels lab v2 5

In this lab, you'll practice fitting a multiple linear regression model on the Ames Housing dataset! The Ames Housing dataset is a newer (2011) replacement for the classic Boston Housing dataset. Each record represents a residential property sale in Ames, Iowa. It contains many different potential predictors and the target variable is SalePrice. We will focus specifically on a subset of the overall dataset. These features are:

For each feature in the subset, create a scatter plot that shows the feature on the x-axis and SalePrice on the y-axis. Set the dependent variable (y) to be the SalePrice, then choose one of the features shown in the subset above to be the baseline independent variable (X). In this lab, you'll practice fitting a multiple linear regression model on the Ames Housing dataset! The Ames Housing dataset is a newer (2011) replacement for the classic Boston Housing dataset. Each record represents a residential property sale in Ames, Iowa. It contains many different potential predictors and the target variable is SalePrice.

We will focus specifically on a subset of the overall dataset. These features are: For each feature in the subset, create a scatter plot that shows the feature on the x-axis and SalePrice on the y-axis. Set the dependent variable (y) to be the SalePrice, then choose one of the features shown in the subset above to be the baseline independent variable (X). There was an error while loading. Please reload this page.

In this lecture, you'll learn how to run your first multiple linear regression model. This lecture will be more of a code-along, where we will walk through a multiple linear regression model using both Statsmodels and Scikit-Learn. Remember that we introduced single linear regression before, which is known as ordinary least squares. It determines a line of best fit by minimizing the sum of squares of the errors between the models predictions and the actual data. In algebra and statistics classes, this is often limited to the simple 2 variable case of $y=mx+b$, but this process can be generalized to use multiple predictive variables. The code below reiterates the steps we've taken before: we've created dummies for our categorical variables and have log-transformed some of our continuous predictors.

This was the data we had until now. As we want to focus on model interpretation and still don't want to have a massive model for now, let's only inlude "acc", "horse" and the three "orig" categories in our final data. This is the third is a series of excerpts from Elements of Data Science which available from Lulu.com and online booksellers. It’s from Chapter 10, which is about multiple regression. You can read the complete chapter here, or run the Jupyter notebook on Colab. In the previous chapter we used simple linear regression to quantify the relationship between two variables.

In this chapter we’ll get farther into regression, including multiple regression and one of my all-time favorite tools, logistic regression. These tools will allow us to explore relationships among sets of variables. As an example, we will use data from the General Social Survey (GSS) to explore the relationship between education, sex, age, and income. The GSS dataset contains hundreds of columns. We’ll work with an extract that contains just the columns we need, as we did in Chapter 8. Instructions for downloading the extract are in the notebook for this chapter.

We can read the DataFrame like this and display the first few rows. We’ll start with a simple regression, estimating the parameters of real income as a function of years of education. First we’ll select the subset of the data where both variables are valid. Your reproducible lab report: Before you get started, download the R Markdown template for this lab. Remember all of your code and answers go in this document: Many college courses conclude by giving students the opportunity to evaluate the course and the instructor anonymously.

However, the use of these student evaluations as an indicator of course quality and teaching effectiveness is often criticized because these measures may reflect the influence of non-teaching related characteristics, such as the physical... The article titled, “Beauty in the classroom: instructors’ pulchritude and putative pedagogical productivity” by Hamermesh and Parker found that instructors who are viewed to be better looking receive higher instructional ratings. In this lab we will analyze the data from this study in order to learn what goes into a positive professor evaluation. As usual, we’re going to load the tidyverse package for data manipulation. We’ll also be using the GGally package for the ggpairs function that generates pairwise correlation plots. If you don’t have GGally installed yet, you can install it by typing install.packages('GGally').

We’ll also be reading in a dataset to work with just like we usually do. This tutorial comes from datarobot's blog post on multi-regression using statsmodel. I only fixed the broken links to the data. This is part of a series of blog posts showing how to do common statistical learning techniques with Python. We provide only a small amount of background on the concepts and techniques we cover, so if you’d like a more thorough explanation check out Introduction to Statistical Learning or sign up for the... Earlier we covered Ordinary Least Squares regression with a single variable.

In this posting we will build upon that by extending Linear Regression to multiple input variables giving rise to Multiple Regression, the workhorse of statistical learning. We first describe Multiple Regression in an intuitive way by moving from a straight line in a single predictor case to a 2d plane in the case of two predictors. Next we explain how to deal with categorical variables in the context of linear regression. The final section of the post investigates basic extensions. This includes interaction terms and fitting non-linear relationships using polynomial regression. In Ordinary Least Squares Regression with a single variable we described the relationship between the predictor and the response with a straight line.

In the case of multiple regression we extend this idea by fitting a $p$-dimensional hyperplane to our $p$ predictors. Instantly share code, notes, and snippets. There was an error while loading. Please reload this page. You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs.

As with all our labs, you are expected to work through the examples and questions in this lab collaboratively with your partner(s). This means you will work together and discuss each section. You each will receive the same score for your work, so is your responsibility to make sure that both of you are on the same page. Any groups found to be using a “divide and conquer” strategy to rush through the lab’s questions will be penalized. You should record your answers to the lab’s questions in an R Markdown file. When submitting the lab, you should only turn in the compiled .html file created by R Markdown.

You are strongly encouraged to open a separate, blank R script to run and experiment with the example code that is given throughout the lab. Please do not turn-in this code. The previous lab focused on conceptually understanding the role of various combinations of predictors within a multiple regression model. This lab will focus on the statistical inference and how to choose which predictors belong in a model. To begin, let’s review the population-level multiple linear regression model: \[y = \beta_0 + \beta_1x_1 + \beta_2 x_2 + \ldots + \beta_k x_k + \epsilon\]

Github Rlodetti 1 Multiple Linear Regression Statsmodels Lab V2 5

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