Residuals
A residual is a difference in the observed value of the response variable (the actual data point you were given) and the value predicted by the line of best fit (the ‘y’ value you would get if you substituted ‘x’ into the line of the best fit equation).
In other words, it measures how far the data falls from the line of best fit.
Residual= observed y - predicted y
You can calculate residuals by subtracting the predicted value from the actual value.
A residual plot is a scatterplot of all the residual values. They help us assess the fit of a regression line.
If the regression line captures the overall relationship between x and y, the residuals should have no systematic problems. They should always equal zero.
A residual plot shows us if a linear model can be used with the data. You can tell if the plot is linear when it doesn’t have a uniform shape/pattern.
A residual is a difference in the observed value of the response variable (the actual data point you were given) and the value predicted by the line of best fit (the ‘y’ value you would get if you substituted ‘x’ into the line of the best fit equation).
In other words, it measures how far the data falls from the line of best fit.
Residual= observed y - predicted y
You can calculate residuals by subtracting the predicted value from the actual value.
A residual plot is a scatterplot of all the residual values. They help us assess the fit of a regression line.
If the regression line captures the overall relationship between x and y, the residuals should have no systematic problems. They should always equal zero.
A residual plot shows us if a linear model can be used with the data. You can tell if the plot is linear when it doesn’t have a uniform shape/pattern.
