http://www.arbizostatistics.com/

Arbizo Statistics, Angela Avenue, Pavia (2026)

24/07/2026

𝙒𝙝𝙚𝙧𝙚 𝙨𝙝𝙤𝙪𝙡𝙙 𝙖 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙤𝙥𝙚𝙣 𝙞𝙩𝙨 𝙣𝙚𝙭𝙩 𝙗𝙧𝙖𝙣𝙘𝙝 𝙞𝙣 𝙄𝙡𝙤𝙞𝙡𝙤? 📊

We recently developed a preliminary Business Location Suitability Index using secondary data from from the Philippine Statistics Authority and Department of Trade and Industry's Cities and Municipalities Competitiveness Index (DTI-CMCI).

Instead of looking at population alone, the index considers several indicators such as pop size, pop growth, business activity, purchasing power, and cost-related business environment indicators.

🏆 The goal is simple: which areas may be worth studying further before opening a branch?

To make this more useful for Ilonggo local businesses, we are offering a 𝙁𝙍𝙀𝙀 𝘽𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙇𝙤𝙘𝙖𝙩𝙞𝙤𝙣 𝙎𝙣𝙖𝙥𝙨𝙝𝙤𝙩 to the 𝙛𝙞𝙧𝙨𝙩 3 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙤𝙬𝙣𝙚𝙧𝙨 who message us! 😉

The snapshot includes:

✅ preliminary branch suitability score
✅ ranking / classification
✅ key indicators behind the result
✅ suggested next step for validation

Planning to open or expand a business in Iloilo? 𝙈𝙚𝙨𝙨𝙖𝙜𝙚 𝙪𝙨 to check your target area's suitability score! ☺️

16/07/2026

𝗧𝗵𝗲 𝗙𝗜𝗙𝗔 𝗪𝗼𝗿𝗹𝗱 𝗖𝘂𝗽 𝗙𝗶𝗻𝗮𝗹 𝗶𝘀 𝘀𝗲𝘁: 𝗔𝗿𝗴𝗲𝗻𝘁𝗶𝗻𝗮 𝘃𝘀 𝗦𝗽𝗮𝗶𝗻! ⚽🌏🏆

🇦🇷 Argentina comes in with the stronger attacking profile. They have scored 17 goals from 12.8 xG, which means they are finishing well above expectation.

🇪🇸 Spain has been excellent defensively. They have conceded only 1 goal from 1.8 xGA, showing how well they limit opponents' dangerous chances.

Will Argentina's clinical attack win it? Or will Spain's defensive control decide the final?

𝗪𝗮𝗻𝘁 𝗮 𝘀𝗺𝗼𝗼𝘁𝗵𝗲𝗿 𝘄𝗮𝘆 𝘁𝗼 𝘄𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗙𝗜𝗙𝗔 𝗪𝗼𝗿𝗹𝗱 𝗖𝘂𝗽 𝗙𝗶𝗻𝗮𝗹? Stream the finals more securely with ExpressVPN! 😉

14/07/2026

𝙒𝙝𝙤 𝙞𝙨 𝙧𝙚𝙖𝙡𝙡𝙮 𝙙𝙤𝙢𝙞𝙣𝙖𝙩𝙞𝙣𝙜 𝙩𝙝𝙚 𝙁𝙄𝙁𝘼 𝙒𝙤𝙧𝙡𝙙 𝘾𝙪𝙥 𝙨𝙚𝙢𝙞𝙛𝙞𝙣𝙖𝙡𝙨? ⚽🌏🏆

Using the 𝘅𝗚, or expected goals, we can look deeper into how France, Spain, England, and Argentina are performing. xG estimates the quality of chances a team creates or allows.

Based on the data:

🇪🇸 Spain stands out defensively, allowing the fewest quality chances;

🏴󠁧󠁢󠁥󠁮󠁧󠁿 England remains efficient in attack, but its defensive numbers suggest it has allowed more danger compared with others;

🇦🇷 Argentina is the most clinical finisher, scoring well above expectations;

🇫🇷 France shows the strongest overall balance, creating the highest xG among the semifinalists.

So who is really dominating?

𝙒𝙖𝙣𝙩 𝙖 𝙨𝙢𝙤𝙤𝙩𝙝𝙚𝙧 𝙬𝙖𝙮 𝙩𝙤 𝙬𝙖𝙩𝙘𝙝 𝙩𝙝𝙚 𝙁𝙄𝙁𝘼 𝙒𝙤𝙧𝙡𝙙 𝘾𝙪𝙥 𝙡𝙞𝙫𝙚 𝙛𝙧𝙤𝙢 𝙩𝙝𝙚 𝙋𝙝𝙞𝙡𝙞𝙥𝙥𝙞𝙣𝙚𝙨? Stream the semis more securely with ExpressVPN - with a 3-day free trial on iOS and Android! 😉

07/12/2025

📊 𝙉𝙀𝙀𝘿 𝘼 𝘽𝙄𝙊𝙎𝙏𝘼𝙏𝙄𝙎𝙏𝙄𝘾𝙄𝘼𝙉 𝙁𝙊𝙍 𝙔𝙊𝙐𝙍 𝙍𝙀𝙎𝙀𝘼𝙍𝘾𝙃 𝙊𝙍 𝙋𝙍𝙊𝙂𝙍𝘼𝙈❓

Arbizo Statistics specializes in:

✅ Cleaning raw datasets
✅ Running powerful statistical analyses
✅ Writing statistical analysis reports
✅ Designing interactive dashboards that drive decisions

𝘿𝙧𝙤𝙬𝙣𝙞𝙣𝙜 𝙞𝙣 𝙢𝙚𝙨𝙨𝙮 𝙝𝙚𝙖𝙡𝙩𝙝 𝙤𝙧 𝙢𝙚𝙙𝙞𝙘𝙖𝙡 𝙙𝙖𝙩𝙖❓Let's collaborate! 👉 Click "Send Message" button today for inquiries ☺️ Or reach out through the contact details below:

🌐 website: www.arbizostatistics.com
📳 contact number: 09471885588
📩 email: [email protected]

18/11/2025

Had a class on Statistical Inference, and I couldn't help but rant a little about one of my biggest pet peeves: the mentioning of "WE ACCEPT THE NULL HYPOTHESIS" when the data simply failed to support the alternative hypothesis 😄

To make the students feel the irritation, the weight, the danger of that misunderstanding, and for them not to say that phrase ever again, I let them think of the flood control corruption mess as an analogy so they could easily relate:

Let,

1) The accused politicians and contractors be the defendants.

2) The null hypothesis be the default assumption (that they're innocent?).

3) The alternative hypothesis be the accusation that public funds were misused.

4) The investigation becomes the statistical test.

5) The COA reports, testimonies, audit trails, etc., be the sample data.

6) And the significance level alpha? That's the version of reasonable doubt.

Now, if the evidence is strong enough to exceed that threshold, we reject the null hypothesis and conclude guilt, right? But if it doesn't? The verdict is "not guilty". In statistics, we call that "failed to reject the null".

But here's the problem: people hear "not guilty" and assume "innocence". Just like they hear "no sufficient evidence of corruption" and assume "no corruption happened".

That's not what it means. It only means that the available evidence wasn't enough to reject the presumption of innocence. NOT THAT INNOCENCE WAS PROVEN.

Sabi nga nila, the "absence of evidence is not evidence of absence". And no - we don't accept the null hypothesis, ever.

22/10/2025

✅ PH Earthquake Analytics Dashboard, Done!

Just finished a mini project (still far from perfect though) - an interactive earthquake analytics dashboard that visualizes and analyzes seismic activity in and around the Philippines, powered by real-time data from the USGS Earthquake API.

Feel free to check it out and share (best viewed on a computer but also works in mobile):

👉 arbizostatistics.shinyapps.io/phquakewatch/

Key features you can explore:

Hotspots: see where PH earthquakes cluster based on the two methods used (Getis-Ord-Gi* and Local Moran's I). The Getis-Ord-Gi* highlights statistically significant clusters of high activity, while the Moran's I identifies areas where similar magnitudes are grouped. Both methods are helpful for detecting regional clusters.

Magnitude-Frequency Distribution: injected here also is the Gutenberg-Richter Law, which describes the inverse relationship between earthquake magnitude and frequency, where smaller quakes occur more often than larger ones.

Forecast: displays short-term earthquake probability by municipality or province, using an OEF-approach based on a Poisson process (take note that this is a basic statistical model that assumes quakes occur independently at a constant average rate - not a physics-based forecast).

Feel free to explore the analytics dashboard but interpret the results with care 😉

For more info, send a message at 👉 [email protected]

15/05/2025

While most people and media narratives highlight how SWS and Pulse Asia were off the mark with their pre-election surveys, here's my unpopular opinion:

these pollsters were ACCURATE DURING THE TIME THEY CONDUCTED THE SURVEYS.

What if candidates like Bam, Kiko, and Marcoleta took the low rankings seriously and used them to recalibrate? If they had access to the internal data breakdowns (by region, age, socioeconomic class, etc) from SWS and Pulse, they likely pinpointed where their support was weakest and refined their messaging to resonate with quieter segments of the electorate. In other words, they didn't just hope for a miracle, instead they read the data and moved.

So instead of blaming the polls, maybe we should look at how strategy and listening to the data shifted the momentum.

Surveys were not wrong - they were a diagnosis, and the campaign was the treatment.

23/03/2025

𝘿𝙤 𝙮𝙤𝙪 𝙣𝙚𝙚𝙙 𝙝𝙚𝙡𝙥 𝙖𝙣𝙖𝙡𝙮𝙯𝙞𝙣𝙜 𝙮𝙤𝙪𝙧 𝙩𝙝𝙚𝙨𝙞𝙨, 𝙙𝙞𝙨𝙨𝙚𝙧𝙩𝙖𝙩𝙞𝙤𝙣, 𝙤𝙧 𝙧𝙚𝙨𝙚𝙖𝙧𝙘𝙝 𝙙𝙖𝙩𝙖? 📊📈

Let Arbizo Statistics guide you - from data cleaning to interpretation! 😉

✅ Affordable
✅ Accurate
✅ Fast turnaround

Work with a reliable statistician who understands your needs!

𝙎𝙚𝙣𝙙 𝙢𝙚 𝙖 𝙢𝙚𝙨𝙨𝙖𝙜𝙚 𝙣𝙤𝙬!

📱 09471885588
📩 [email protected]
🌐 www.arbizostatistics.com

15/03/2025

Congrats to my client for a successful defense! 🎉 I'm glad to have played a small role in this journey ☺️

If anyone needs statistics help, I guess you know where to find me 😉

📱 09471885588

📩 [email protected]

🌐 www.arbizostatistics.com/research

🌐 www.arbizostatistics.com

Get a FREE quotation now! ☺️

21/02/2025

Exactly 2 weeks ago, I had the privilege of being one of the panelists for the defense of students presenting their research on WASH (water, sanitation, and hygiene) in Iloilo. Their study is very promising as they will be collecting primary data 👏👏👍

Since they'll be conducting primary data collection, the sampling methodology became one of the hot topics during the defense. This made me think - what if they approached WASH research using secondary data instead?

If given the chance, I would explore WASH deprivation patterns using the secondary data (from the DHS dataset perhaps) to uncover hidden household risk groups. Here's how I would structure my statistical framework 😉

1️⃣) Data preprocessing and selection
2️⃣) Perform Latent Class Analysis (LCA) to identify hidden WASH patterns (of course nanjan si Akaike or BIC to determine the optimal number of classes)
3️⃣) Use Random Forest or XGBoost Model to predict which households are at the highest risk of WASH deprivation

And kung kaya pa ng data:

4️⃣) Apply Kulldorff's SaTScan or Moran's I to identify geographic hotspots of WASH deprivation
5️⃣) Test health outcomes using causal mediation analysis

My statistical juices were just flowing that time 😄

Need help with data analysis for your research? Message me now! 👍

📱 09471885588

📩 [email protected]

🌐 www.arbizostatistics.com for more info

Arbizo Statistics

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07/12/2025

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