The hottest Statistical Methods Substack posts right now

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Nobel Prize to the Statistical Physics of artificial neural networks

Complexity Thoughts • 379 implied HN points • 08 Oct 24
🔬 Science Physics Machine Learning Artificial Intelligence Computational Models Statistical Methods
  1. John J. Hopfield and Geoffrey E. Hinton won the Nobel Prize for their work on artificial neural networks. Their research helps us understand how machines can learn from data using ideas from physics.
  2. Hopfield's networks use energy minimization to recall memories, similar to how physical systems find stable states. This shows a connection between physics and how machines learn.
  3. Boltzmann machines, developed by Hinton, introduce randomness to help networks explore different configurations. This randomness allows for better learning from data, making these models more effective.

Does education increase intelligence and does it matter?

The Infinitesimal • 539 implied HN points • 27 Jul 24
🚌 Education Cognitive skills Statistical Methods
  1. Education seems to improve specific skills rather than overall intelligence. This means going to school might help you get better at certain subjects instead of making you smarter in a general way.
  2. The study raising these points had some issues in how it was set up. This makes us wonder about the validity of its conclusions regarding education and intelligence.
  3. A strong theory behind how education impacts intelligence is important for clear understanding. Without it, we might misinterpret results and make broad claims that don’t hold up.

Data Science Weekly - Issue 558

Data Science Weekly Newsletter • 219 implied HN points • 01 Aug 24
🕹 Technology Data science Machine Learning AI Data Visualization Statistical Methods
  1. Data science and AI are rapidly evolving fields with plenty of interesting developments. Staying updated with the latest articles and news can really help you understand these changes better.
  2. Effective communication is key in data science. Using intuitive methods and visuals can make complex concepts easier to grasp for everyone.
  3. Using tools and methods like quantization can help make large models more accessible. It's important to find efficient ways to work with vast amounts of data to improve performance.

Are the Polls too Close to Be Trusted?

Richard Hanania's Newsletter • 1755 implied HN points • 27 Oct 24
🇺🇸 U.S. Politics Polling Elections Voter Behavior Political Analysis Statistical Methods
  1. Polls can often show very similar results, especially in tight races. This might indicate that pollsters are playing it safe and not reporting outlier results.
  2. There is a concern called 'herding' where polling companies avoid reporting unusual findings to not seem wrong. This can lead to less information available to the public.
  3. The lack of variation in polls today is unusual and might mean real voter sentiment is being missed, setting the stage for a surprise outcome in elections.

Correlation is not a percentage

inexactscience • 39 implied HN points • 22 Jul 23
🚌 Education Statistical Methods Data interpretation Social Sciences
  1. Correlation does not mean one thing causes another. Just because two things are related doesn't mean one causes the other.
  2. Many people mistakenly think the correlation coefficient is a percentage. This can be misleading and lead to wrong conclusions.
  3. To understand how much one thing explains another, use the coefficient of determination, not the correlation. Squaring the correlation gives you a clearer picture of the relationship.
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Data Science Weekly - Issue 347

Data Science Weekly Newsletter • 19 implied HN points • 16 Jul 20
🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Methods Data Infrastructure
  1. Netflix is working on making its data usage more efficient. They have created a dashboard that helps their team understand data costs and trends better.
  2. Using meta-augmentation in machine learning can improve performance more than just changing the model. It's important to focus on enhancing the data we use.
  3. When building robots, the goal should be to assist humans, not replace them. This approach considers the future of robotics in various fields like transportation and healthcare.

Data Science Weekly - Issue 49

Data Science Weekly Newsletter • 19 implied HN points • 30 Oct 14
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Statistical Methods
  1. Getting into data science can be tricky, especially for those coming from academia. It's helpful to have guidance on how to make that transition.
  2. Machine learning can be used to identify negative behaviors online, which demonstrates the power of data science in addressing social issues.
  3. Trusting data sources too much can lead to problems. It's important to be skeptical and question how the data is collected and used.

[in case you missed it] Data Science Weekly - Issue 371

Data Science Weekly Newsletter • 0 implied HN points • 03 Jan 21
🕹 Technology Machine Learning Data science AI Tools Statistical Methods Programming
  1. Real-time machine learning is becoming important for many companies, with some investing heavily in the necessary infrastructure. This has led to positive financial returns for them.
  2. There is a growing list of tools for machine learning operations, with many new entries improving how developers can manage their ML projects.
  3. Different techniques like Markov models can help in planning and optimizing tasks, like workout routines, by predicting the next steps based on previous actions.

Alternatives to Regression Betas

Musings on Markets • 0 implied HN points • 15 Feb 09
💰 Finance Risk Assessment Investment strategy Market Analysis Statistical Methods Investment Risk Assessment Market Analysis Valuation Methods Financial Metrics
  1. You can use relative standard deviations instead of regression betas to measure risk. This method looks at how a stock's volatility compares to the average volatility of other stocks.
  2. Option-based methods provide a forward-looking estimate of risk by using prices from traded options. However, this approach only works for companies with those options and bonds available.
  3. Accounting betas are calculated by looking at changes in a company's earnings compared to the overall market. They can be a stable alternative, especially for private companies, but their lagging nature can be a drawback.

We really need better data

Something to Consider • 0 implied HN points • 06 Aug 24
🚌 Education Data Analysis Educational Policy Statistical Methods
  1. We need better data to answer important questions about education and healthcare. Good data helps us understand what really works and what doesn't.
  2. There are big gaps in our knowledge, especially in poorer countries. Without accurate information, we can't properly assess living standards or make informed decisions.
  3. Collecting reliable data should be a priority. New technologies, like satellite data, hold promise for improving how we gather and analyze information.

The problem with regression betas

Musings on Markets • 0 implied HN points • 11 Feb 09
💰 Finance Investing Economics Risk management Market Analysis Statistical Methods Investments Risk management Market Analysis Economics Quantitative finance
  1. Regression betas can be unreliable because they come with a standard error, meaning the estimated beta can vary widely.
  2. Using different time frames or market indices can give you different beta values for the same company, and there's no one 'correct' beta.
  3. Regression betas are based on past data, so they may not accurately reflect a company's future risk as its business model or debt levels change.

Financial Management: Modelling & Analysis

Logos • 0 implied HN points • 18 Aug 20
💰 Finance Financial Analysis Investment strategy Risk management Statistical Methods
  1. Only create financial models when necessary. If the decision is clear, don't waste time building a model just to check a box.
  2. Focus on the key variables that have the biggest impact. It's often just a couple of factors that make the most difference in the results.
  3. Use tools like Monte Carlo simulations and sensitivity analysis to understand risks and potential outcomes better. They can help you see how different situations might play out.