Simple Linear Regression Visualizer
Build an R script that fits a straight line through data, a simple linear regression, and explains the result in charts and plain words. Using a dataset like house size versus price, you'll plot the data, fit a model with lm(), read the summary (slope, intercept, R-squared, p-value), draw the fitted line with its confidence band, predict new values, and check whether a straight line was a reasonable choice by looking at the residuals. It's a friendly introduction to both R and statistics: you'll learn data frames, ggplot2 charts built in layers, fitting and interpreting a model, and checking its assumptions.
- Estimate
- ~8h
- Steps
- 6
- Completed by
- 0
- Proposed by
- codeseed.app
ggplot2 · stats
Project roadmap
- 01
Load and explore the data
~1.5hInstall R, RStudio and ggplot2. Load a two-variable dataset, for example the built-in cars dataset (speed and stopping distance) or a house size and price CSV saved in data/. Print summary() and the correlation with cor(), and draw a scatter plot with ggplot2 (geom_point) with clear axis labels and units. Done when: the scatter plot is saved as a PNG, and the correlation is printed.
- 02
Fit the model
~1hFit the model with lm(y ~ x, data = df) and print summary(model). In comments, explain in your own words the intercept, the slope (how much y changes when x increases by 1), R-squared, and the p-value of the slope. Done when: the script prints the summary, and the comments explain each number.
- 03
Plot the fitted line with a confidence band
~1.5hAdd the regression line with geom_smooth(method = "lm"), which draws a 95% confidence band around it, on top of the scatter plot. Add a subtitle with the fitted equation and R-squared, built from coef(model) and summary(model)$r.squared. Done when: the chart shows the points, the line, the band and the equation.
- 04
Predict new values
~1hUse predict() with a new data frame of 3 values of x to get predictions with interval = "confidence" and interval = "prediction". Print both, and explain in a comment why the prediction interval is wider (it covers individual observations, not just the average). Add a warning when a value is outside the range of the data. Done when: the predictions and both intervals are printed and explained.
- 05
Check the assumptions
~1.5hPlot the residuals against the fitted values and a Q-Q plot of the residuals with ggplot2 (or plot(model)). In comments, explain what each chart should look like when the model is appropriate (no pattern, points close to the line) and whether yours does. Done when: both diagnostic charts are saved, with your interpretation written next to them.
- 06
Wrap it in a report
~1.5hMove everything into an R Markdown (or Quarto) report with short sections: the data, the model, the fitted line, predictions and diagnostics, each with its chart and one or two sentences of explanation. Done when: rendering the report produces an HTML file with every chart and explanation.
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