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Visual Guides/Simple Linear Regression
UNIT 10: REGRESSION FOUNDATIONS

Simple Linear Regression: Fit the Line

Place points on the scatter plot, drag them, and watch the OLS regression line, residuals, R², and prediction intervals update in real time.

Interact with points (0/3)
Switch preset
View residuals
Use prediction tool
Open diagnostics

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024681012510152025Predictor (x)Response (y)ŷ = 4.57 + 1.82x

Click to add point · Drag to move · Right-click to remove

Goodness of FitStrong

R² = 0.91

90.5% of variance in y explained by x

91% explained
Explained (R²)Unexplained (1−R²)

Model Coefficients

Slope (b)1.8210

For each unit ↑ in x, y changes by 1.821

Intercept (a)4.5744
Residual SE±1.911

Typical residual size: ±1.911 (sqrt of SSE/(n−2))

Pearson r0.9514
n15 data points

Prediction Tool

Enter an x value to see predictions

Residual Diagnostics

Inspect residual patterns to check OLS assumptions

OLS Principle

Ordinary Least Squares minimises the sum of squared residuals: the total vertical distance² between each point and the fitted line.

R² Explained

R² = 1 − SSE/SST. It measures the proportion of variance in y explained by x. A value near 1 means the model fits well.

PI vs CI

The 95% CI covers the true mean response at x*. The 95% PI covers where a new individual observation will fall, and is always wider.

← Simpson's ParadoxMultiple Regression →