Wednesday, May 3, 2017

2017 Spring Awards Ceremony

Congratulations to all our AWESOME students!

Pi Mu Epsilon Inductees

Mu Sigma Rho Inductees

Departmental Scholarship Recipients

Outstanding Departmental Graduates

University Distinguished Graduates
Le Tang -- Mathematics
Emily Robinson -- Statistics and Math Education
Shane Will -- Data Science

Wednesday, April 26, 2017

Student Seminar

12:00 - 12:50 PM, Friday, April 28, Gildemeister 155

Refreshments served beforehand Gildemeister 135. 

 

Modeling/Predicting Home Runs in Major League Baseball  

Tyler Kelemen 

What goes in to predicting a given players’ home run total for an upcoming season? How much of this can actually be modeled instead of being left up to chance/injury? I am most interested in determining which metrics best predict home run totals for 2017. You may have heard the phrase “pitcher’s league” being said within the last few years. This phrase carries the notion that baseball is becoming more of an offensive struggle with pitching now dominating America’s pastime. What if I told you that last year was the second most prolific home run hitting season in the history of baseball? In order to investigate further, I first looked at predicting 2016 home run totals with all 32 2015 metrics to get a better idea of what variables influence home runs. The only players I was interested in were those that were qualifying players in both the 2015 and 2016 seasons. A qualifying player is one whom has at least 502 plate appearances and there were 89 players whom qualified in both. From there, I fit forward and backward selection models which eventually culminated into fitting a decision tree using the variables remaining from the backward selection model. The prediction formula from the decision tree was then used on the 2016 metrics to predict 2017 home run totals for the 87 remaining players as two players retired.


Dakota County Recidivism 

Andy Hansen 

Re-entry into society presents a wide array of complex challenges for individuals leaving jails and prisons. There are many basic needs that are commonly left unmet for these former inmates upon their release into society. To address the challenges that inmates experience, Dakota County government agencies have create a re-entry assistance program that will provide services tailored to each individual’s needs. The goal of the program are to increase self-sufficiency and assist individuals in setting goals to succeed in re-entry. Dakota County corrections hypothesizes that the new re-entry assistance program is effective at creating smaller rates of recidivism compared to those who are not receiving the assistance. Data was gathered on inmates who received the re-entry program (RAP) and those who did not receive the assistance. A formal statistical analysis has been performed and found that there is no evidence to conclude that there is a significant difference among recidivism rates among those receiving re-entry assistance and those who are not. Other variables were collected for suggestions on whom to provide/award the Re-entry assistance program to.