1.6.1: Project Introduction
- Page ID
- 58860
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Semester Project: Investigating Housing Affordability
Throughout this course, you will work on a semester-long project that gives you the opportunity to apply what you’re learning in a meaningful, hands-on way. The focus of the project is a topic you’ve likely heard a lot about: housing affordability.
You’ll take on the role of a data analyst investigating a key question about housing in your own region. Along the way, you’ll learn the statistical skills needed to collect, describe, analyze, and communicate your results step by step, as they’re introduced in each chapter. By the end of the course, you’ll produce either a written report or a short presentation that brings all your findings together.
Project Theme: Housing Affordability
Each student (or team) will choose one foundational focus area:
- Single-family home prices
- Rental prices
- Condominium (condo) prices
This will define the type of properties you’ll be collecting data about in your region. You’ll identify sources of housing data online (public listings, real estate websites, government resources, etc.) and gather data to analyze over the course of the semester.
Project Roadmap
This project will evolve in stages throughout the course. The full study includes:
- Statistical question: Define a focused, researchable question related to housing affordability. For example:
- What is the average cost of renting a 2-bedroom apartment in my city?
- Have condo listing prices increased more in downtown neighborhoods vs. suburbs?
- What is the variation in home prices across ZIP codes?
- Data collection plan: Identify where and how you'll collect your data. Consider:
- What websites or databases can you gather data from?
- What information (variables) should you record? Examples: location, price, size (sq. ft), number of bedrooms, zip code, listing date, etc.
- How can you avoid introducing bias into the sample?
- Descriptive statistics: As we cover these in Chapter 2, you will describe your dataset by summarizing key variables numerically (e.g., average prices, ranges, medians) and noting patterns or outliers.
- Data visualization: In Chapter 3, you'll create graphs and plots to help visualize your housing data (such as histograms, box plots, or scatterplots).
- Inference and confidence intervals: Later in the course, you’ll estimate parameters like average price using confidence intervals to reflect uncertainty about the population.
- Hypothesis testing: You’ll form a testable claim (e.g., “rents are higher in zip code A than zip code B”) and test it using appropriate methods (t-tests, etc.). We’ll provide step-by-step guidance when we reach those chapters.
- Final report or presentation: At the end of the semester, you’ll compile your findings — including cleaned and summarized data, visuals, written interpretation, and conclusions — into a report or shared class presentation.
This Week's Assignment: Start Collecting Data
To get started, you’ll complete an initial project assignment:
- Choose your focus area: home sales, rental listings, or condos.
- Define your geographic area: local city, county, region, or ZIP code.
- Find at least 15–20 listings from a trusted source (e.g. Zillow, Realtor.com, Rentals.com, Craigslist, or government housing data).
- Record the following variables for each listing (as available):
- Price
- Location (address or ZIP)
- Property type
- Number of bedrooms
- Square footage
- Year built (if available)
- Listing date
- Any other variables of interest
- Save your data into a spreadsheet or table (Google Sheets, Excel, CSV).
Submission Instructions:
Your instructor will provide submission details (how/where to upload your spreadsheet or report this information).
What to Expect
You do not need to clean, summarize, or analyze your data yet. We’ll do this together, one skill at a time, starting in Chapter 2.
As the course continues, we’ll use your dataset in:
- Examples and case studies in class
- Practice problems and assignments
- Midterm and final assessments
By the end of the course, you’ll have completed a full statistical investigation with real-world data and have something meaningful to show for it.