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    Creating suitable regression model

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    Using any data of your choice or use the attached: build an optimized regression model eliminating non-significant Betas. Provide the Betas for the significant variables.

    How can a institution use these results to optimize its cost and labor in acquiring new students (enrollments)?  How can one use these results to sharpen the institution brand image against other institutions or known competitors?

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    1. Among the list of variables, we take the following steps to clean the dataset, making them suitable for regression analysis.
    - Create a binary dependent variable "Enrollment," which equals 1 when "Enroll" is "TRUE;" and 0 otherwise.
    - Create a "Women" binary variable for those whose "Gender" is "F"
    - Create a "Non-White" binary variable for those whose "Ethnicity" is NOT "White"
    - Create an "Age 19 or above" binary variable for those whose age is 19 or above

    - Create a "Suburb" binary variable for those whose "Region description" is in an Suburban area
    - Create a "Other area" binary variable for those whose "Region description" is outside of Chicago
    - This makes "Chicago" the default group in the regression analysis

    - Create three binary variables for "Act category description":
    "24-27"; "28-32", and "Unknown."
    Therefore, the "16-23" category becomes the default group

    - Create six binary variables for "Colleges":
    "Communication", "Computer Science", "Liberal Arts & Social Sciences",
    "Science and Health", "Music", "Theatre Arts"
    Therefore, "Commerce" becomes the default group.
    - Create "No_first_aid" for those whose "Fin_aid_interest" is ...

    Solution Summary

    The solution provides detailed explanation about regression analysis. It shows the steps to building regression models: cleaning the raw data; creating new variables; selecting the model; making decision based on statistical significance; and interpreting the economic meaning of regression results.