Transformations.  Although linear regression might produce a ‘good’ fit (high r value) to a set of data, the data set may still be non-linear. To remove.

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Presentation transcript:

Transformations

 Although linear regression might produce a ‘good’ fit (high r value) to a set of data, the data set may still be non-linear. To remove (as much as is possible) such non-linearity, the data can be transformed.  Either the x-values, y-values, or both may be transformed in some way so that the transformed data are more linear. This enables more accurate predictions (extrapolations and interpolations) from the regression equation.

 To decide on an appropriate transformation, examine the points on a scatterplot with high values of x and or y (that is, away from the origin) and decide for each axis whether it needs to be stretched or compressed to make the points line up. The best way to see which of the transformations to use is to look at a number of ‘data patterns’.

 Example:  The seeds in the sunflower are arranged in spirals for a compact head. Counting the number of seeds in the successive circles starting from the centre and moving outwards, the following number of seeds were counted. Regression equation is: y = x Or Number of seeds = 18.77(circle) – 49.73

 Fit a least-squares regression line and plot the data.

 Find the correlation coefficient. What does this mean? r = 0.88 Strong, positive and linear correlation

 Using the regression line for the original data, predict the number of seeds in the 11th circle. (note: 11 th circle is an extrapolation analysis)  y = x  When x = 11  y = 18.77(11) –  y = (round it off to the nearest no. of seeds, y = 157)

 Find the residuals.

 Plot the residuals on a separate graph. Are the data linear?

 What type of transformation could be applied to:  i) the x-values?  Ii) the y-values?

 Apply the log10 y transformation to the data used in the previous question.

 Fit a least-squares regression line to the transformed data and plot it with the data.

 Find the correlation coefficient. Is there an improvement? Why?  Complete the least-squares regression for the transformation.  Calculate the coefficient of determination. How does this explain variation?

 Using the regression line for the transformed data, predict the number of seeds for the 11th circle.  How does this compare with the prediction from the previous question?