Gabriel Fantacone (they/he)

queer socialist, data scientist, policy researcher, organizer, transit nerd! all views my own!

[Chi Ossé @OsseChi]

Who owns the land affects how affordable housing can be. Let’s break down public land vs market rate.

[maryam alwan🇵🇸🇸🇾 @maryamalwan]

From the Gaza Solidarity Encampment to Congress. So beyond proud of you @DarializaforNY :-)

[whatever man @_vectorist]

Pro-Palestine student activists should be heartened by the fact that NYC just elected someone with a Canary Mission profile

[Kate Willett @katewillett]

A champion for public housing is going to Congress

[Courtney Gross @courtneycgross]

Another missed storyline from last night - @newkingsdems say they won a majority of district leader seats in Brooklyn. The whole goal of their campaign was to get a majority of the party’s county committee and vote out @AMBichotte as chair

[Shahana Hanif @ShahanaFromBK]

How are you feeling, @scottmstringer?

[CUFFH Action @CuffhAction]

CUFFH Action is proud to endorse @dianaforqueens for New York’s 36th Assembly District. We’ve seen firsthand Diana’s commitment to immigrant and working-class communities, and she has the backbone to stand up to Trump’s agenda and deliver real wins for Queens families in Albany.

[Phara Souffrant Forrest @phara4assembly]

Can’t imagine the trauma this family has been through. And for what? Who is helped when we invest in imprisoning children instead of providing healthcare?

Our society is deeply broken.

[Jess Coleman @jesskcoleman]

“The FARE Act will raise rents.”

“Congestion pricing will destroy business.”

“If Zohran wins, rich people will flee.”

Almost like we should stop letting right-wing corporate interests dominate our politics and just do more good stuff for New Yorkers!

[CUFFH Action @CuffhAction]

🎉 Endorsement Alert! 🎊

CUFFH Action is proud to announce our endorsement of @cct4nyc for New York State Assembly District 54. Christian is a dedicated organizer and community leader with the experience necessary to fight for Bushwick and East New York at the state level! 🌟

[Jackie Fielder @JackieFielder_]

https://t.co/q7C8uDQuJt

[Hotel and Gaming Trades Council @NYHTC]

We’re deeply appreciative of our partners at @CUFFH whose leadership and members have worked hard to educate, lobby, and mobilize stakeholders in this fight against a sickening amount of money spent by @Airbnb.

[Hotel and Gaming Trades Council @NYHTC]

Thank you to the @NYCCouncil for rejecting Intro 948B, a bill that would have taken housing and job opportunities away from New Yorkers. Tenants, homeowners, and workers deserve real solutions on affordability, but converting homes into tourist rentals hurts everyone.

[Read Jackson Rising @JPHilllllll]

NYU faculty creating a ring around the Gaza solidarity encampment to protect their students after the school threatened mass arrest. So beautiful.

[Robert Komaniecki @Komaniecki_R]

With another semester ending, here's your annual reminder that teaching evaluations systemically disadvantage women. Even when controlling for grades and other factors, students (esp. males) consistently give female professors lower scores. This can have serious ramifications.🧵

[Matt Dancho (Business Science) @mdancho84]

Linear Regression is one of the most important tools in a Data Scientist's toolbox. Here's everything you need to know in 3 minutes.

1. OLS regression aims to find the best-fitting linear equation that describes the relationship between the dependent variable (often denoted as Y) and independent variables (denoted as X1, X2, ..., Xn).

2. OLS does this by minimizing the sum of the squares of the differences between the observed dependent variable values and those predicted by the linear model. These differences are called "residuals."

3. "Best fit" in the context of OLS means that the sum of the squares of the residuals is as small as possible. Mathematically, it's about finding the values of β0, β1, ..., βn that minimize this sum.

4. Slopes (β1, β2, ..., βn): These coefficients represent the change in the dependent variable for a one-unit change in the corresponding independent variable, holding other variables constant.

5. R-squared (R²): This statistic measures the proportion of variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with higher values indicating a better fit of the model to the data.

6. t-Statistics and p-Values: For each coefficient, the t-statistic and its associated p-value test the null hypothesis that the coefficient is equal to zero (no effect). A small p-value (< 0.05) suggests that you can reject the null hypothesis.

7. Confidence Intervals: These intervals provide a range of plausible values for each coefficient (usually at the 95% confidence level).

Understanding and interpreting these outputs is crucial for assessing the quality of the model, understanding the relationships between variables, and making predictions or conclusions based on the model.

====

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[Gabriel Fantacone (they/he) @ggfantacone]

Selling two tickets to the Janelle Monáe show at Kings Theatre Brooklyn on 9/28 (this Thursday)! $100 each! Please share and DM me if interested!

[Matt Dancho (Business Science) @mdancho84]

I love the R ecosystem. It’s amazing for real-world data science.

But it took me 5 years to learn.

So I put a 40-min webinar together that consolidates my 10 secrets from trial &amp; error.

Learn more about what worked for me in my free webinar. https://t.co/39C0Re9xjo

#rstats