1. a) When does it make sense to choose a linear function to model a set of data?
- It makes sense when you need to write an equation based on a data table and you can't clearly see slope of the line from looking at the numbers in the table. Instead, you could plot the data points on a graph and draw a line of best fit. Then you could find two points on the line, not the data points, but two points that lay on the line, and use rise over run to find the slope.
b) What steps would you follow to find a line of best fit?
- You would first plot your data points from the table onto a graph, then use a ruler and draw a line that lies closest to the data points. You could also calculate the residuals by measuring how far a data point is from the line. The line of best fit is the predictive model, which means that it predicts what the future data is going to be. To find a residual you must take the predicted value and subtract it from the measured value.
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