* Change next line to the appropriate directory. GET FILE='D:\SOC63993\homework\nonlinhw.sav'. ********** Part 1. graph Title = "Plot of X1 with Y1" /scatterplot = x1 with y1. * Curve Estimation. TSET NEWVAR=NONE . CURVEFIT /VARIABLES=y1 WITH x1 /CONSTANT /MODEL=LINEAR quadratic /PLOT FIT. * Based on the above, we do the following compute. compute x2 = x1**2. REGRESSION /STATISTICS DEF cha /DEPENDENT y1 /METHOD=ENTER x1/ Enter x2. ********** Part 2. graph Title = "Plot of X1 with Y2" /scatterplot = x1 with y2. * Curve Estimation. TSET NEWVAR=NONE . CURVEFIT /VARIABLES=y2 WITH x1 /CONSTANT /MODEL=LINEAR growth /PLOT FIT. * Based on the above, we compute the following. compute lny2 = ln(y2). REGRESSION /DEPENDENT y2 /METHOD=ENTER x1 . REGRESSION /DEPENDENT lny2 /METHOD=ENTER x1 . ********** Part 3. graph Title = "Plot of X1 with Y3" /scatterplot = x1 with y3. * Curve Estimation. TSET NEWVAR=NONE . CURVEFIT /VARIABLES=y3 WITH x1 /CONSTANT /MODEL=LINEAR /PLOT FIT. * Based on the above, we compute the following. if (x1 le 0) breakpt = 0. if (x1 gt 0) breakpt = 1. compute breakx1 = breakpt * x1. REGRESSION /STATISTICS DEF CHA /DEPENDENT y3 /METHOD=ENTER x1/ Enter breakpt breakx1 . ******** Part 4. * As this shows, a polynomial model would also be plausible for y3. * In practice, it is often hard to tell just from the scatterplot what * transformation is best, so theory is important. CURVEFIT /VARIABLES=y3 WITH x1 /CONSTANT /MODEL=LINEAR Quadratic /PLOT FIT.