Least squares line minimizes the sum of squared

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Least-squares line minimizes the sum of squared residuals. When looking at a regression line the goal is to find one that has the minimum sum of squared residuals. Residual is a prediction error. It is the difference between an observed Y and the predicated Y from the regression line.
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BIAM400 WEEK 7 Fitted value is a predicated value of Y found by substituting given X values into the regression equation. Standard error of estimate is a measure of the magnitude of the predication errors when the regression equation is used for prediction. R-square is when one squares the multiple R. when one does this you get a measure that has a more direct interpretation. R-square represents the percentage if the variation of the Y values that is explained by the explanatory variables included in the regression equation. Linear trend is based on least squares regression analysis of the number of storm or surge events with time. Trends are only calculated when there are at least 10 seasons of data available. Exponential trend line is a curved line that is useful when data values rise or fall at increasingly higher rates. An exponential trend line cannot be created if the data
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