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develop a least squares estimated regression line. (round your numerical values to five decimal places.)

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The least squares estimated regression line is  0.5x+1.667

If your data shows a linear relationship between the X and Y variables, you will want to find the line that best fits that relationship. That line is called a Regression Line and has the equation ŷ= a + b x. The Least Squares Regression Line is the line that makes the vertical distance from the data points to the regression line as small as possible. It’s called a “least squares” because the best line of fit is one that minimizes the variance (the sum of squares of the errors).

Data rarely fit a straight line exactly. Usually, it is satisfactory with rough predictions. Typically, you have a set of data whose scatter plot appears to “fit” a straight line. This is called a Line of Best Fit or Least-Squares Line.

To know more about Least-Squares Line visit: brainly.com/question/2141008

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