Our Markov chain model (see Fig 1) requires specification of the parameters λ4G, λ5G, λ6G, and ρ, which are related, respectively, to the four-year graduation, five-year graduation, six-year graduation, and first-year retention rates. These rates must be specified for each year and for each racial/ethnic group. We assess the fit of linear, log-linear, and optimal Box-Cox models on historical data. We choose the preferred model, specified in the table above, and use it to forecast future values. Fig 3 shows various models for four-year graduation rates, corresponding to the top section of the table above. The column labeled Λ is an exponent used in the Box-Cox transformation, and thus is relevant only to those fits.

These proportions specify the enrollment level necessary to reflect a racial/ethnic group’s representation given the institution’s location and available applicant pool; see formula in (18). These proportions are relative to each other as our model accounts for only four racial/ethnic groups. This restriction stems from limitations in the availability of data.

Our Markov chain model (see Fig 1) requires specification of the parameters δ, α, and ϵ, which are probabilities derived from counts of applicants, acceptances, and enrollments. These counts must be specified for each year and for each racial/ethnic group. We assess the fit of linear, log-linear, and optimal Box-Cox models on historical data. We choose the preferred model, specified in the table above, and use it to forecast future values. Fig 3 shows various models for application count, corresponding to the top section of the table. The column labeled Λ is an exponent used in the Box-Cox transformation, and thus is relevant only to those fits.