Predicting Student Success: A Logistic Regression Analysis of Data From Multiple SIU-C Courses

The objective of this report is to improve prediction techniques regarding the future performance of students in select university courses through the utilization of multiple logistic regressions. This is achieved with the aid of statistical computing software which applies forward step-wise variable selection methods that identify influential variables sufficient to accurately predict student success. Once a logit model is constructed with the required parameters and predictors, the inverse logit function outputs a probability of student success. In all cases, logistic prediction models matched or exceeded the performance of current prediction methods while using an equal or lesser number of explanatory variables. These findings show that current prediction methods can improve by using a statistically justified procedure. It also suggests the inefficacy of some predictors used to currently estimate student performance

Beta Regression in R

The class of beta regression models is commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). It is based on the assumption that the dependent variable is beta-distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. The model also includes a precision parameter which may be constant or depend on a (potentially different) set of regressors through a link function as well. This approach naturally incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions. This paper describes the betareg package which provides the class of beta regressions in the R system for statistical computing. The underlying theory is briefly outlined, the implementation discussed and illustrated in various replication exercises.Series: Research Report Series / Department of Statistics and Mathematic

Beta Regression in R

The class of beta regression models is commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). It is based on the assumption that the dependent variable is beta-distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. The model also includes a precision parameter which may be constant or depend on a (potentially different) set of regressors through a link function as well. This approach naturally incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions. This paper describes the betareg package which provides the class of beta regressions in the R system for statistical computing. The underlying theory is briefly outlined, the implementation discussed and illustrated in various replication exercises.

Measuring Effectiveness of Quantitative Equity Portfolio Management Methods

In this paper, I use quantitative computer models to measure the effectiveness of Quantitative Equity Portfolio Management in predicting future stock returns using commonly accepted industry valuation factors. Industry knowledge and practices are first examined in order to determine strengths and weaknesses, as well as to build a foundation for the modeling. In order to assess the accuracy of the model and its inherent concepts, I employ up to ten years of historical data for a sample of stocks. The analysis examines the historical data to determine if there is any correlation between returns and the valuation factors. Results suggest that the price to cash flow and price to EBITDA exhibited significant predictors of future returns, while the price to earnings ratio is an insignificant predictor

ppmlhdfe: Fast Poisson Estimation with High-Dimensional Fixed Effects

In this paper we present ppmlhdfe, a new Stata command for estimation of (pseudo) Poisson regression models with multiple high-dimensional fixed effects (HDFE). Estimation is implemented using a modified version of the iteratively reweighted least-squares (IRLS) algorithm that allows for fast estimation in the presence of HDFE. Because the code is built around the reghdfe package, it has similar syntax, supports many of the same functionalities, and benefits from reghdfe's fast convergence properties for computing high-dimensional least squares problems. Performance is further enhanced by some new techniques we introduce for accelerating HDFE-IRLS estimation specifically. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo) maximum likelihood estimates.Comment: For associated code and data repository, see https://github.com/sergiocorreia/ppmlhdf

mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data

We present the R-package mgm for the estimation of k-order Mixed Graphical Models (MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since data sets consisting of mixed types of variables (continuous, count, categorical) are ubiquitous. In addition, we allow to relax the stationarity assumption of both models by introducing time-varying versions MGMs and mVAR models based on a kernel weighting approach. Time-varying models offer a rich description of temporally evolving systems and allow to identify external influences on the model structure such as the impact of interventions. We provide the background of all implemented methods and provide fully reproducible examples that illustrate how to use the package

Innovation, skills and performance in the downturn: an analysis of the UK innovation survey 2011

The link between firms’ innovation performance and economic cycles, especially major downturns such as that of 2008-10, is a matter of great policy significance, but is relatively under-researched at least at the level of micro data on business behaviour. It is, for example, often argued that economies need to ‘innovate out of recessions’ since innovation is positively associated with improvements in productivity that then lead to growth and better employment (Nesta, 2009). The issues of how individual firms respond to downturns through their investment in innovation, and how this impacts on innovation outputs and ultimately business performance and growth during and after downturns, has been less studied because relevant data has not been readily available. The UK Innovation Survey (UKIS) 2011 now makes this possible. The UKIS 2011 with reference period 2008 to 2010 covers the downturn in economic activity generated by the global financial crash. The build-up of panels over the life of the UKIS also supports analysis of the longer-term interactions between innovation and the business cycle. This report analyses the last four waves of the surveys. Further, the latest survey includes questions on whether firms employ a specific set of skills, which adds materially to the ability to research the role of skills and human capital in innovation at the micro level