This week we take a look at the interesting statistical problem of false positives, which naturally arise when you do lots of diagnostic tests or hypothesis tests. Our course spotlight deals with another aspect of multiple statistical studies – how to combine them into a single conclusion:
False positives generated by detection systems (e.g. fire alarms) and statistical and machine learning algorithms are a huge problem. The key decision factor is the estimated probability that the alarm is real. In today’s Briefing, we look at how you might be led astray in this regard by the common statistic: false-positive rate […]
Industry Spotlight
Digital Advertising
Google is back in the news, with executives testifying on Capitol Hill in defense of Google’s position in digital advertising; it captures close to 40% of the digital advertising dollar. Digital ads generated $125 billion, globally, in 2019. This chart shows dramatically (1) how rapid the growth has been, and (2) how almost all that growth in the last 10 years has been from mobile ads.
There is perhaps no business that is so purely algorithmically driven as digital ads. Did you know that Google has published its algorithm for showing ads? Hint: it’s not a black-box, it’s based on good old-fashioned logistic regression. Read more here.
The Weekly Brief looked at the interesting statistical problem of false positives, which naturally arise when you do lots of diagnostic tests or hypothesis tests. Our course spotlight deals with another aspect of multiple statistical studies – how to combine them into a single conclusion
You will learn how to:
Define the outcome and effect type
Combine disparate studies on the same subject for a common conclusion
Distinguish and handle fixed and random effects models
Visualize and interpret results
Conduct meta regression
Deal with missing data and rare events
Your instructor, Dr. Stephanie Kovalchik, is currently a Research Fellow within ISEAL (International Social and Environmental Accreditation and Labelling Alliance) and holds a joint appointment at Tennis Australia, where she works as a data scientist for the Game Intelligence Group.
See you in class!
This course is an elective course in the Social Science Certificate Program (below).
Our Social Science Statistics Certificate provides the skills necessary to gather, analyze, and assess data for activities like making policy decisions, answering cultural questions, studying behavioral changes, and informing business decisions.This ten (10) course program – including six required courses and four electives – cover the principal statistical concepts used to design, sample, collect, interpret, and present data as it applies to behaviors of groups of people in their environment and special situations.
The core curriculum, taught by leading experts in this industry, covers everything from categorical data analysis, designing valid statistical studies to regression analysis and advanced statistics. The program also includes a comprehensive exploration of survey design, sample size determination, and related processes and mixes theory and practical application so you can apply your skills immediately at your current job or leverage them as you seek a new one.
At the completion of the Social Science Statistics Certificate Program you will have learned:
The design and analysis principles for research studies
The fundamental concepts behind statistical modeling
How to fit linear and logistic regression models, interpret output, and conduct diagnostics
Appropriate designs and sampling plans for surveys
How to determine sample size for a study, and how to calculate power
Techniques for analyzing categorical data (electives)
Bayesian methods for analyzing data (electives)
Need to brush up on your stats? Enrollees in the certificate program can take our intro stats course, offered monthly, at no charge. We offer rolling admissions year-round so you can start right away.
See you in class!
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This week we take a look at the interesting statistical problem of false positives, which naturally arise when you do lots of diagnostic tests or hypothesis tests. Our course spotlight deals with another aspect of multiple statistical studies – how to combine them into a single conclusion:
