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Full-Text Articles in Applied Statistics
Nba Salaries: Assessing True Player Value, Michael Ghirardo
Nba Salaries: Assessing True Player Value, Michael Ghirardo
Statistics
This paper analyzes and calculates an advanced NBA statistic that is becoming more and more widely used in the NBA. The Adjusted plus-minus (APM) statistic measures a player’s contribution, independent of all other players on the court. The most appealing aspect to the APM is that it only attempts to capture how a team’s scoring margin changes with a particular player on and off the court. Scoring margin in basketball effects winning percentage greatly, so it only makes sense that players with high APM’s will increase their team’s scoring margin and, therefore, help win games. The APM statistic is not …
Emirical Assessment Of The Future Performance Of The S&P 500 Losers, Nicholas Powers
Emirical Assessment Of The Future Performance Of The S&P 500 Losers, Nicholas Powers
Statistics
In the Wall Street Journal in early 2013, there was an article posted by Andrew Bary that explored a trend in the previous 3 years of the S&P 500. The article pointed out that the average returns for the top 10 percentage decliners for 2009, 2010, and 2011 outperformed the S&P 500 for the first two weeks of the next year. These top 10 percentage decliners or losers well enough to bet on. This study looks to see if there is statistical evidence that the losers outperformed the S&P 500.
Analysis Of Dietary Patterns Over Freshman Year Of College, Chelsea Lofland
Analysis Of Dietary Patterns Over Freshman Year Of College, Chelsea Lofland
Statistics
This analysis is an investigation of changes in Cal Poly students’ eating habits over freshman year. The motivation behind this was an interest in college students’ lifestyles; college is the first time most students live on their own and it can be an important maturation period. College is stressful, exciting, liberating, and terrifying all at the same time. This distinctive life experience, along with my desire to handle big and messy data, led me to this research question.
The response variable analyzed was food consumption and the explanatory variables were: sex, race, quarter, food group, stress, exercise, BMI, sleep quality …