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statistics – Urban Analytics Institute https://urbananalyticsinstitute.com Mon, 02 Nov 2020 14:29:22 +0000 en-CA hourly 1 https://wordpress.org/?v=6.7.5 https://urbananalyticsinstitute.com/wp-content/uploads/2024/08/cropped-1-1-modified-removebg-preview-150x150.png statistics – Urban Analytics Institute https://urbananalyticsinstitute.com 32 32 Statistics with Python https://urbananalyticsinstitute.com/statistics-with-python/ Mon, 02 Nov 2020 14:29:22 +0000 https://urbananalyticsinstitute.com/?p=345 UAI’s Murtaza Haider is now offering a course on Statistics for Data Science with Python on Coursera. The course is a collaboration with IBM’s Data Science Team and is part of the Data Science certification available from IBM.

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 https://urbananalyticsinstitute.com/causal-inference-for-complex-observational-data-with-stata/ Tue, 20 Oct 2020 20:04:53 +0000 https://urbananalyticsinstitute.com/?p=330 Ryerson Urban Analytics Institute is pleased to invite you to a webinar on:

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.

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