a. Write the equation for the line of fit. \( y=1 \times 5=5-5 \) b. Use the equation for the line of fit to predict the grade of a 13 -year-old.

To derive the equation for the line of fit from your example, you'd typically need a dataset to analyze. However, from your expression, it seems you're simplifying to find an intercept form. You might need to clarify the variables and what they represent, as it appears a little muddled! To predict the grade for a 13-year-old, if we assume the line of fit resolves to a linear relationship based on age, simply plug in \(x = 13\) into the derived equation (e.g., \(y = mx + b\)). If your line of fit was \(y = 1x + 5\), your prediction would be \(y = 1(13) + 5 = 18\). This indicates the grade for a 13-year-old might commonly fall around the 6th-7th grade, depending on the educational system! To ensure accuracy, always check the dataset and how age correlates with grades!