To find the predicted price of a car that is 10 years old using a linear regression model based on the provided data, you'll need to input the values into a statistical calculator or software like Desmos.
However, I can guide you through the general approach:
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Input Data: First, input the age (independent variable) and sale price (dependent variable) data into the calculator.
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Run Linear Regression: Use the linear regression function to compute the best-fit line for the data. This will provide you with an equation of the form: \[ y = mx + b \] where \(y\) is the sale price, \(x\) is the age, \(m\) is the slope of the line, and \(b\) is the y-intercept.
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Predict for Age 10: Once you have the regression equation, substitute \(x = 10\) into the equation to predict the sale price for a car that is 10 years old.
Since I can't use Desmos directly, you can go ahead and input the data. After performing the regression, you'll find the values for \(m\) and \(b\) that will allow you to calculate the predicted price.
If you have already done this and calculated, let me know which option aligns with your findings!