1. **State the problem:** We want to understand how weather variables (temperature $X_1$, humidity $X_2$, rainfall $X_3$) affect household electricity consumption $Y$ using a regression model. 2. **Regression formula:** The model is given by: $$Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \beta_3 X_3 + \epsilon$$ where $\beta_0$ is the base consumption, $\beta_1, \beta_2, \beta_3$ are coefficients showing the effect of each weather variable, and $\epsilon$ is the error term. 3. **Calculate mean electricity consumption:** Given monthly consumption values: $$\text{Mean} = \frac{215 + 250 + 290 + 325 + 340 + 355 + 370 + 395 + 420 + 450 + 475 + 495}{12} = 340.85 \text{ kWh}$$ This shows the average monthly electricity use. 4. **Predict consumption for Month 1:** Using the formula and given values: $$Y = 50 + 8(25.1) + 2(63) + 0.1(20)$$ Calculate step-by-step: $$Y = 50 + 200.8 + 126 + 2 = 378.8 \text{ kWh}$$ 5. **Calculate error term for Month 1:** $$\epsilon = \text{Actual} - \text{Predicted} = 215 - 378.8 = -163.8 \text{ kWh}$$ A negative error means the model overestimated consumption. 6. **Repeat for all months:** The same steps apply to each month to find predicted consumption and errors. 7. **Analysis:** By comparing predicted and actual values, and examining coefficients $\beta_1, \beta_2, \beta_3$, we understand which weather factors most influence electricity use, helping optimize energy planning. This method transforms weather and consumption data into actionable insights.