Simple Linear Regression Visualizer
Build an R script that fits a linear regression model to a two-variable dataset, prints model coefficients and diagnostics, and plots the fitted line with confidence bands. Learners practice lm(), model summary interpretation, and ggplot2 layering.
- Estimate
- ~5.5h
- Steps
- 4
- Completed by
- 0
- Proposed by
- codeseed.app
ggplot2 · stats
Project roadmap
- 01
Load and explore the dataset
~1hLoad a numeric dataset (e.g., house size vs. price) and create a scatter plot to check for a linear relationship.
- 02
Fit the regression model
~1.5hUse lm() to fit a simple linear regression and print the summary including R-squared and p-values.
- 03
Plot the fitted line with confidence bands
~1.5hUse ggplot2's geom_smooth or manual prediction intervals to overlay the regression line and confidence band on the scatter plot.
- 04
Check residual diagnostics
~1.5hPlot residuals vs fitted values and a Q-Q plot to assess whether linear regression assumptions hold.
Resources
- DocsR Manuals
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Tech stack