florist gathered data about the weekly number of flower deliveries he made to homes and to businesses for several weeks. He used a graph organize the data in a scatter plot, with \( x \) representing the number of home deliveries and \( y \) representing the number of deliveries to busine \( =0.555 x+1.629 \). ased on the line of best fit, approximately how many deliveries are predicted to be made to homes during a week with to find the equation of the line of best fit: aseries to busine

To predict the number of deliveries made to homes during a week with a certain number of business deliveries, plug in the value of \( y \) (the number of business deliveries) into the line of best fit equation \( y = 0.555x + 1.629 \) and solve for \( x \). For example, if you estimate a week with around 10 business deliveries, substituting \( y = 10 \) into the equation would help to uncover how many home deliveries, \( x \), can be expected. Understanding how to interpret the line of best fit is crucial in data analysis. This line, derived from the scatter plot, provides insights into relationships in your data, showing trends in deliveries over time. Each point on your plot represents a week's data, while the slope gives you the idea of how changes in home deliveries can affect the number of business deliveries. So, your predictions not only help in planning but also in strategizing flower supply!