Dr Behrooz Hassani-M
3.7K posts
Dr Behrooz Hassani-M
@behrooz_hm
Personal Views, that being said: Chief Analytics Officer @ Monash University. Top 25 Australian Analytics Leader.
Melbourne, Victoria Katılım Şubat 2017
922 Takip Edilen4.9K Takipçiler
@BenPhillips_ANU There are some good data sources like PLIDA available, but I am not sure if they are used effectively abs.gov.au/about/data-ser…
English
Seems odd that in a ‘cost of living crisis’ Australia has no appropriate survey/data from which to guide govt decisions as to who is actually struggling. Next ABS data out in mid/late 2025. Last data 2019/20.
English
English
A very useful source on #AI / #GenAI #Goverance, #Readiness, and #Operations: The UK government has released guidance on the use of GenAI by departments and agencies. Link: gov.uk/government/pub…
English
English
Cell phone data could be the key to an accurate and timely definition of what a city is, argues a Perspective in @NatCities. The authors contend that an accurate and global definition of a city is crucial for the implementation of legislation and policies. go.nature.com/4aWfFJA
English
The only constant of living in Melbourne over the past 15 years…
English
Australians, like many around the world, seem to be becoming more civically and politically detached. We don’t join or participate in local sports clubs, religious organisations or political parties at anything like the rate we used to.
theguardian.com/australia-news…
English
Emerging trends in AI skill demand across 14 OECD countries. AU ~ average levels in terms of the proportion of AI-related online vacancies. However, when it comes to manufacturing, AU finds itself positioned at the lower end of the list. Link: oecd-ilibrary.org/science-and-te…
English
@PlayContra This may help a bit
Dr Behrooz Hassani-M@behrooz_hm
With more data in, Age and Education of residents still explain ~70% of the variations in the Yes Vote % across the electorates. Here is a cleaner table, including the univariate models, and also one with almost everything (removed income, it was causing multicollinearity).
English
@clinton_roy @dbRaevn Oh, thank you! That would be great, I try everything that emerges, hoping to find a new place and leave this website!
English
@behrooz_hm @dbRaevn Blue sky. I can possibly rustle up a code if interested
English
@TrotStats I did, but I was not sure how to interpret it. You can think of a mechanism for age or education or income, but with distance, it is hard to think of it in the context of the outcome, other than the fact that it correlates with other measures.
English
@behrooz_hm Have you considered distance from CBD as a covariate?
English
With more data in, Age and Education of residents still explain ~70% of the variations in the Yes Vote % across the electorates. Here is a cleaner table, including the univariate models, and also one with almost everything (removed income, it was causing multicollinearity).
English
Updated: Yes Vote vs Sum of ALP/Greens Vote in 2022 Election. You can use the dashed line to find the electorates where the Yes Vote % was higher than the 2022 ALP/GRN vote %. #Referendum2023
English