Graduate Students Seminar

Speaker 1: Weiding Fan

Title

Performing univariate data analyses with reports for observational studies: Introduction for SAS macro: UNI_CAT

Abstract

 %UNI_CAT is suitable to compare multiple covariates specified in CLIST and NLIST between two or more cohorts defined by a categorical variable (OUTCOME). For each categorical covariate, frequencies from a contingency table are reported along with row percentages (Row%) or column percentages (Col%), which can be controlled by the ROWPERCENT option based on the desired interpretation. For each numeric covariate, summary statistics will be generated for each level of OUTCOME. The univariate associations can be tested by either parametric or non-parametric tests using the NONPAR option.For categorical variables, the decision to employ a non-parametric test is based on the sample size. If sample size is less than 5, parametric test results will not be highly referential. For Numerical variables, we select whether to employ a non-parametric test based on sample size and the Kolmogorov-Smirnov test. If the sample size is less than twenty or the p-value of the Kolmogorov-Smirnov test is greater than 0.05, the program will utilize a non-parametric test result.

Speaker 2: Gargi Chaudhuri

Title

Data assimilation in Lorenz system

Abstract

jData assimilation is a method which usually takes a forecast and applies a correction to the forecast based on a set of observed data. This method uses the information coming from observations and numerical model. It can be applied to any classical system like earth's atmosphere, ocean, land surface etc. Here we will use this method for Lorenz system of equations. By nudging and insertion method we will discuss about the approximate solution of Lorenz system and its convergence to the reference solution.