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3.5.1: Project Check-In 3

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    Project Check-In: Visualizing Your Housing Data

    By this point in the semester, you’ve built a strong foundation for analyzing the housing dataset you’ve collected or been assigned:

    1. Data Overview: You wrote a short summary describing where your data came from, what it represents, and what region or question you’re exploring.
    2. Summary Statistics: You calculated basic descriptive statistics (mean, median, standard deviation, five-number summary) for at least one continuous variable.
    3. Visual Representations: Now it's time to deepen your understanding of that data by graphing it!

    Goal of This Check-In

    In this step, you’ll create a few appropriate visualizations based on the variables in your housing dataset. This is your chance to turn numbers into a story — showing patterns, trends, clusters, and outliers that might influence how people understand prices in a neighborhood, square footage across home types, or rental fluctuations over time.

    What to Include

    1. Create 2–3 Visualizations
      Choose visuals appropriate for your data:
      • A bar chart or pie chart for a categorical variable (e.g., housing type, region, style)
      • A histogram, boxplot, or dot plot for a quantitative variable (e.g., price, square footage)
      • If collected over time, consider a time plot
    2. Label and Caption Each Chart
      For each graph you create, include:
      • Clear title and labeled axes
      • A brief caption (1–2 sentences) explaining what it shows and why it's useful
      • Optional: annotate or highlight any interesting features (peaks, gaps, trends, etc.)
    3. Add These to Your Project Overview
      Update your project write-up or slide deck so your visualizations follow your summary statistics section. You're starting to shape a narrative for your reader!

    What to Consider as You Visualize

    • Do my visuals reinforce or challenge what I saw in the summary statistics?
    • Are there any trends, clusters, or potential outliers that are now more obvious?
    • Does it look like my data is symmetric or skewed?
    • What questions do these visualizations raise?

    Project Builder: Where You Should Be Now

    • Executive Summary (Project description + how data was collected)
    • Summary Statistics Table (for at least one continuous variable)
    • Visualizations added with captions + variable context

    You’ll continue building and refining this throughout the next chapters — especially once we explore relationships between variables, begin comparing groups, and get into deeper analysis.

    Optional Extension

    If working in Google Sheets or Excel: try sorting your dataset by one variable before visualizing it. Does that sorting reveal new shapes or gaps that weren’t as visible before?


    This page titled 3.5.1: Project Check-In 3 is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Mathematics Department.

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