Jennifer Rose

 

 


 

Jennifer Rose is a Research Associate Professor in the Quantitative Analysis Center at Wesleyan University with extensive training in statistical methods. At Indiana University, Dr. Rose received postdoctoral training in categorical data analysis, longitudinal data analysis, and multilevel modeling. At Brown University, Dr. Rose gained extensive experience in the design, implementation, and analysis of randomized controlled trials. She has experience with measurement scale development and psychometric evaluation using structural equation modeling techniques. In addition, Dr. Rose has successfully implemented and published numerous analyses including OLS and logistic regression modeling, growth curve modeling of both continuous and categorical outcomes, multilevel modeling, latent class analysis and pattern mixture modeling of missing data. Dr. Rose has similarly strong teaching credentials at the undergraduate level having offered courses at Wesleyan University, Indiana University and Rhode Island College.

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E.g., 2016-12-06
E.g., 2016-12-06
E.g., 2016-12-06
Dec 2nd 2016

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

Average: 6 (30 votes)
Nov 28th 2016

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering.

Average: 6.9 (7 votes)
Nov 28th 2016

In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.

Average: 8.5 (4 votes)