jetpack domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home/urbanana/public_html/wp-includes/functions.php on line 6114Meta-analysis is a statistical technique for combining the results from multiple similar studies. The talk will provide a brief introduction to meta-analysis and will demonstrate how to perform meta-analysis in Stata. The -meta- command offers full support for meta-analysis, from computing various effect sizes and producing basic meta-analytic summaries and forest plots to accounting for between-study heterogeneity and potential publication bias. Examples demonstrating how to conduct meta-analysis within Stata will be provided. These examples will focus on the interpretation of meta-analysis under various models, meta-regression, subgroup analysis, small-study effects and publication bias, and various types of forest, funnel, and other plots.
]]>This Statistics for Data Science course is designed to introduce learners to the basic principles of statistical methods and procedures used for data analysis. After completing this course learners will have practical knowledge of crucial topics in statistics including – data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts.
]]>Causal Inference for Complex Observational Data with Stata
When: November 02, 2020. 1:30 PM (EST)
To Register, please visit https://tinyurl.com/causal-inference-webinar.
Abstract:
Observational data often present unique challenges. The treatment status or exposure of interest is often not assigned randomly. Data are sometimes missing not at random (MNAR) which can lead to sample selection bias. And many statistical models for MNAR data must account for unobserved confounding. This talk will demonstrate how to use standard maximum likelihood estimation to fit extended regression models (ERMs) that deal with the common issues either alone or simultaneously.
Presenter: Chuck Huber, Ph.D. [StataCorp]
Chuck Huber is Director of Statistical Outreach at StataCorp and Adjunct Associate Professor of Biostatistics at the Texas A&M School of Public Health. In addition to working with Stata’s team of software developers, he produces instructional videos for the Stata YouTube channel, writes blog entries, develops online Net Courses and gives talks about Stata at conferences and universities.
Most of his current work is focused on statistical methods used by behavioural and health scientists. He has published in the areas of neurology, human and animal genetics, alcohol and drug abuse prevention, nutrition and birth defects. Dr. Huber currently teaches introductory biostatistics at Texas A&M where he previously taught categorical data analysis, survey data analysis, and statistical genetics.
]]>Fetching, visualizing, and analyzing Statistics Canada’s data using R.
October 19, 2020. 6:30 – 8:00 PM (EST)
The webinar will demonstrate how to:
Presenter: Jens von Bergmann, Ph.D. [MountainMath Software and Analytics]
Jens von Bergmann holds undergraduate degrees in Physics and Computer Sciences and a Ph.D. in Mathematics. He taught for several years at the University of Calgary, University of Notre Dame and Michigan State University before founding MountainMath to work on his passion for data analysis and visualization.
To Register, please email liam.donaldson@ryerson.ca.
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