![]() Here we use linear interpolation to estimate the sales at 21 ☌. Interpolation is where we find a value inside our set of data points. Example: Sea Level RiseĪnd here I have drawn on a "Line of Best Fit". Try to have the line as close as possible to all points, and as many points above the line as below.īut for better accuracy we can calculate the line using Least Squares Regression and the Least Squares Calculator. We can also draw a "Line of Best Fit" (also called a "Trend Line") on our scatter plot: It is now easy to see that warmer weather leads to more sales, but the relationship is not perfect. line can be used to create an equation that will approximate the relationship. Here are their figures for the last 12 days: Ice Cream Sales vs TemperatureĪnd here is the same data as a Scatter Plot: The local ice cream shop keeps track of how much ice cream they sell versus the noon temperature on that day. (The data is plotted on the graph as " Cartesian (x,y) Coordinates") Example: ![]() In this example, each dot shows one person's weight versus their height. The line of best fit is plotted within a scatter plot showing a collection of points from the given data. A Scatter (XY) Plot has points that show the relationship between two sets of data. This line shows an overall view or trend of a given set of data points.
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