While working in math class, it can be easy to forget that reality is somewhat messy. Not all oranges weigh exactly the same amount, beans have different lengths, and even the same person running a race multiple times will probably have different finishing times. We can approximate these messy situations with more precise mathematical tools to better understand what is happening. We can also predict or estimate additional results as long as we continue to keep in mind that reality will vary a little bit from what our mathematical model predicts.[br][br]For example, the data in this scatter plot represents the price of a package of broccoli and its weight. The data can be modeled by a line given by the equation y = 0.46x + 0.92. The data does not all fall on the line because there may be factors other than weight that go into the price, such as: the quality of the broccoli, the region where the package is sold, and any discounts happening in the store.[br][br][img]data:image/png;base64,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[/img][br][br][br]We can interpret the y-intercept of the line as the price for the package without any broccoli (which might include the cost of things like preparing the package and shipping costs for getting the vegetable to the store). In many situations, the data may not follow the same linear model farther away from the given data, especially as one variable gets close to zero. For this reason, the interpretation of the y-intercept should always be considered in context to determine if it is reasonable to make sense of the value in that way.[br][br]We can also interpret the slope as the approximate increase in price of the package for the addition of 1 pound of broccoli to the package.[br][br]The equation also allows us to predict additional values for the price of a package of broccoli for packages that have weights near the weights observed in the data set. For example, even though the data does not include the price of a package that contains 1.7 pounds of broccoli, we can predict the price to be about $1.70 based on the equation of the line, since 0.46*1.7 + 0.92 = 1.70.[br][br][br]On the other hand, it does not make sense to predict the price of 1,000 pounds of broccoli with this data, because there may be many more factors that will influence the pricing of packages that far away from the data presented here.