Sales prediction
- Input
- X = investimento em publicidade (R$), Y = vendas
- Expected output
- Ŷ = 500 + 3.2 × X
Each additional R$1 in advertising generates R$3.20 in predicted sales.
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linear regression for prediction with OLS
OLS minimizes the sum of squared residuals. The resulting equation Ŷ = β₀ + β₁X lets you predict Y for any X in the sampled domain.
Each additional R$1 in advertising generates R$3.20 in predicted sales.
For 175cm: Ŷ = −105 + 175 = 70kg expected.
Simple linear regression is a statistical model that describes the relationship between an independent variable X and a dependent variable Y as a line, Ŷ = β₀ + β₁X. The slope β₁ tells you how much Y changes for each one-unit increase in X, and the intercept β₀ is the predicted value of Y when X is zero. It is the foundation of much of data analysis, used both to understand a relationship and to predict values.
Not recommended. Extrapolation beyond the sampled range assumes the linear relationship continues, which is often invalid. The model is only reliable within the observed domain.
No. It helps explain the scenario and use the tool more safely, but real decisions should consider official sources, full context and qualified guidance when needed.
One x, y pair per line. Maximum 200 pairs.
All data stays in your browser. No data is sent to any server.