Lingxuan Gao - MUSA 5080 Portfolio
  • Home
  • Weekly Notes
    • Weekly Notes 01: Introduction to R and dplyr
    • Weekly Notes 02: Algorithmic Decision Making & The Census
    • Weekly Notes 03: Data Visualization & Exploratory Analysis
    • Weekly Notes 04: Spatial Data & GIS Operations in R
    • Weekly Notes 05: Introduction to Linear Regression
    • Weekly Notes 11: Space-Time Prediction
  • Labs
    • Lab 0: dplyr Basics
    • Lab 1: Census Data Quality for Policy Decisions
    • Lab 2: Spatial Analysis and Visualization-Healthcare Access and Equity in Pennsylvania
    • Lab 4: Spatial Predictive Analysis
    • Lab 5: Space-Time Prediction
  • Midterm
    • Appendix
    • Presentation
  • Final
    • Eviction Risk Prediction in Philadelphia

On this page

  • Introduction to Linear Regression
    • P1 The Statistical Learning Framework
      • Parametric Approach: Linear Regression
    • P2 Two Different Goals
    • P3 Building Model

week-05-notes

Introduction to Linear Regression

Relationships & Predictions

P1 The Statistical Learning Framework

Statistical learning = a set of approaches for estimating that relationship

Parametric Approach: Linear Regression

The assumption: Relationship between X and Y is linear

Y≈β0+β1X1+β2X2+…+βpXp

The task: Estimate the β coefficients using our sample data

The method: Ordinary Least Squares (OLS)

P2 Two Different Goals

Prediction vs Inference

P3 Building Model