Module 4 Simple linear regression

Module overview: simple linear regression with NOAA variables; estimating slope and intercept; residual analysis, sum of squared errors, \(R^2\), and correlation; prediction in a weather context.

4.1 Data source

Use Local Climatological Data (LCD) daily or hourly files for rich paired measurements.

• Core variables: air temperature, relative humidity, dew point, wind speed, precipitation (choose one response and one predictor)
• Access: NOAA Local Climatological Data object store

4.2 Statistics topics

• Simple linear regression structure and estimation
• Slope and intercept interpretation (weather context)
• Correlation, \(R^2\), and explained variation
• Residual plots and core regression assumptions

4.3 Suggested workflow

1. Download one LCD file appropriate to your teaching location or template station.
2. Select one response variable and one predictor.
3. Fit a simple linear regression model.
4. Interpret slope, intercept, and \(R^2\).
5. Show a residual plot and one prediction example.

4.4 Assignment

Replicate the instructor regression walkthrough using NOAA data.