def case_3D():
    """
    solar = data("solar")
    model_y1 = lm("y1 ~ A*B*C*D", data=solar)
    summary(model_y1)
    pareto_plot(model_y1)

    model_y2 = lm("y2 ~ A*B*C*D", data=solar)
    summary(model_y2)
    pareto_plot(model_y2)
    pareto_plot(model_y2, main="Pareto plot for Energy Delivery Efficiency")

    # 4H
    A = B = C = c(-1, +1)
    A, B, C = expand.grid(A=A, B=B, C=C)

    # These 4 factors are generated, using the trade-off table relationships
    D = A*B
    E = A*C
    F = B*C
    G = A*B*C

    # These are the 8 experimental outcomes, corresponding to the 8 entries
    # in each of the vectors above
    y = c(320, 276, 306, 290, 272, 274, 290, 255)

    expt = gather(A=A, B=B, C=C, D=D, E=E, F=F, G=G, y=y)

    # And finally, the linear model
    mod_ff = lm("y ~ A*B*C*D*E*F*G", expt)
    pareto_plot(mod_ff)

    # Now rebuild the linear model with only the 4 important terms
    mod_res4 = lm("y ~ A*C*E*G", expt)
    pareto_plot(mod_res4)
    """
    pass
