am i done?

thesis.md

@@ -976,16 +976,30 @@ Lastly, improvements can be made to the implementation of the binary logistic re
 
 Future ethical implications of EduAttain include the static nature of the dashboard application as well as the implementation of the confustion matrix. In relation to the nature of EduAttain, as the information presented in the tool uses specific data from specific years, the findings from this project may become outdated in coming years, especially if there are shifts in the rates of educational attainment within the populations studied in this project. A recommendation to resolve this would be to expand the project to include data after 2015.
 
-Additionally, the confusion matrices used in this project to calculate the accuracy of the regressions in this project may result in inaccurate results due to how they were implemented in the project. The recommended implementation of a confusion matrix involves separating the original data into a training and testing set, using the training set for the the regression and the testing set to project the predicted probabilties. Then the training and testing sets are compared against each other to construct the confusion matrix of true positives and negatives, and false positives and negatives, which are then used to compute the accuracy. Instead of following the recommended implementation, due to issues related to working with a sample size within the regression, the confusion matrix was constructed using the sample data only. To resolve this issues, working on a machine with more computing power should allow for a regression to be run using the entirety of the data, so as to be able to divide up this data into testing and training sets for use in the construction of a regression and confusion matrix.
+Additionally, the confusion matrices used in this project to calculate the accuracy of the regressions may result in inaccurate results due to how they were implemented in the project. The recommended implementation of a confusion matrix involves separating the original data into a training and testing set, using the training set for the the regression and the testing set to project the predicted probabilties. Then the training and testing sets are compared against each other to construct the confusion matrix of true positives and negatives, and false positives and negatives, which are then used to compute the accuracy. Instead of following the recommended implementation, due to issues related to working with a sample size within the regression, the confusion matrix was constructed using the sample data only. To resolve this issues, working on a machine with more computing power should allow for a regression to be run using the entirety of the data, so as to be able to divide up this data into testing and training sets for use in the construction of a regression and confusion matrix.
 
 ## Conclusions
 
-reiterate results with focus on insights and briefly describe room for future improvement but that the project was successful in ascribing more detail to the study of racial and ethnic inequalities in education.
+Disparities in educational outcomes based on differences in gender, racial, and Hispanic ethnic identity were explored in detail within EduAttain.
+
+The findings within the descriptive statistics section of the project match much of what has been previously established about gender, racial, and ethnic inequalities in education. Comparisons of piecharts showed that the female, Asian, Non Hispanic, Cuban populations had the highest proportions of high school and college aged individuals, which matches the literature. Outcomes of interest from comparing these plots include that the Mixed Race population had a higher proportion of high school and college educated individuals than the White population. Furthermore, the findings for the Dominican and Salvadorian populations add to the gaps present in the literature for these Hispanic ethnic subgroups. Within the Hispanic population, the highest proportion of individuals with a high school diploma or greater was the Cuban population, followed by the  Puerto Rican, Other
+Hispanic, Dominican, Mexican, and Salvadorian populations, in order.
+
+The statistical analysis presented in this project also contributes new findings to the study of educational inequality. The results produced by the first regression point to a statistically significant relationship between educational attainment and the factors of gender, race, and Hispanic ethnicity. In looking at the odds of attaining a high school education or greater using an odds ratio, the female population had a higher odds of attaining this level of education compared to the male population. The odds for the racial comparisons shows that, when compared to the White population, all other racial groups had a lower odds of having a high school education or greater, which contrasts findings in the literature. The Hispanic comparisons showed that, when compared to the non Hispanic population, all Hispanic ethnic subgroups had a lower odds of having an education of high school or greater, matching the findings in the literature. The second binary logit compared the entire Hispanic population to other racial groups and found that Hispanics had the lowest odds of having an educational attainment at or greater than high school level, supporting the findings presented in prior research.
+
+This project successfully bridges the gap in the study of gender, racial, and ethnic inequality in education by providing new insight into specific populations not previously captured and validating the findings of previous research in this field.
 
 # Acknowledgements
 
-ppl: family esp mom for inspiring project and instilling that the most valuable gift in life is the knowledge gained through the education she was able to provide me; jj, bianco, all the other professors that helped provide guidance: obc and econ dept prof (find his name!!), luman, compsci senior class especially my personal comp buddy adriana, roomies, comp buddy, anyone who listened to me drone about my project, mila, veronica
-things: monster energy, rabbit, cc booths
+First and foremost, I want to thank my family who have always propelled me to believe in myself and aspire to more, and who have given me so much in life to get to where I am. A special thank you to my mom who has always given me the world and more, and for partly inspiring this project with something she always said to me growing up: "Your education is the greatest gift I will ever give you. Your mind is something that no one can take away." Little did she know when she said those words that I would take them to heart the way I did.
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+Thank you to all of the wonderful professors and mentors I have had while at Allegheny, especially Professor Jumadinova and Professor Bianco. This project could not have been completed without your endless support and patience. Special thanks too to Professor Oliver Bonham-Carter and Professor Doug Luman for assisting me throughout the development of my project.
+
+Thank you to all of the graduating Computer Science seniors for struggling alongside me these past four years. I have enjoyed (and almost looked forward to) every week of comp group to have an opportunity to rant to people dealing with the same behemoth of a project.
+
+I want to thank all of my friends turned family, who have been my home away from home while at Allegheny. I especially want to thank my roommates and best friends, Abby, Mimi, Alexa, and Zoey, as well as, my other best friends (apparently I have a few of those) and fellow comp buddies (i.e. I bully them until they provide me company while I work) Adriana, Sam, Mila, and Veronica, who have been my personal entourage of emotional support throughout this whole process.
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+Lastly, I want to acknowledge the things that got me through the nightmare that is comping. Namely, endless flows of Monster Energy and Diet Coke, scheduled cuddle sessions with my pet rabbit, the internet, and comfy booths (specifically in Kins) for me to sit on while I comp away for hours.
 
 # References