open TeachingEval

# create and save graph
g1 <- gnuplot course_eval beauty

# estimate and save model
m1 <- ols course_eval 0 beauty

# compute predicted evaluations
watson = mean(beauty)
stock = watson + sd(beauty)
ev_watson = $coeff(const) + watson * $coeff(beauty)
ev_stock = $coeff(const) + stock * $coeff(beauty)

# find range for beauty effect
b_eff0 = $coeff(const) + $coeff(beauty) * min(beauty)
b_eff1 = $coeff(const) + $coeff(beauty) * max(beauty)