To write a regression equation, we need to know the specific details of the variables involved, including the dependent and independent variables, and the coefficients from the regression analysis. A standard linear regression equation is generally written as:

\[ y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \ldots + \beta_n x_n + \varepsilon \]

Where:
- \( y \) is the dependent variable (the outcome we are trying to predict).
- \( \beta_0 \) is the y-intercept (the predicted value of \( y \) when all \( x \) variables are 0).
- \( \beta_1, \beta_2, \ldots, \beta_n \) are the coefficients of the independent variables \( x_1, x_2, \ldots, x_n \) (these coefficients represent the change in \( y \) for a one-unit change in the respective \( x \), holding other variables constant).
- \( \varepsilon \) is the error term (it captures the deviation of the observed values from the values predicted by the model).

If you have specific values for these coefficients from a regression output, you can plug them into this equation format. If you need help interpreting regression output or understanding how to apply these values, feel free to provide more details, and I can guide you through the process.

Learn how to write a regression equation step-by-step with our guide. Understand the components like the dependent variable, coefficients, and error term. Suitable for Grades 10-12 students and beyond.

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