INTELLIPATH - MATH 301 INTELLIPATH PREDICTIONS FROM LINEAR...

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MATH 301INTELLIPATHPREDICTIONS FROM LINEAR REGRESSIONSLearning MaterialPredictions can be made from the scatterplot of a correlation and the line of best-fit. An error of prediction can occur for observed values that do not fall near the line of best-fit. The value of the point from the observation minus the predicted value is the error of prediction. The formula for the error of prediction is y – y’ where y’ is the predicted value on the line of best-fit. The line of best-fit is the line that minimizes the sum of the squared errors of prediction. The error for each y must be calculated. The regression equation for the best-fit line is Y’= bx + A where b is the slope of the line and A is the y-intercept of the line. The slope or b of the regression equation is:The y-intercept is the difference in the mean of yand the slope times the mean ofx, or:Outliers are extreme data values in a sample. When the data points are plotted in the scatterplot, an outlier will lie well above or below the other values. An outlier tends to

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