#basic linear regression

[data-analysis-and-interpretation]

Testing a Basic Linear Regression Model Assignment

My Program:

Program Output and Summary:

Mean of Life Expectancy(Explanatory variable): 68.758621.

HIV rates and Life Expectancy

The results of the linear regression model indicated that life expectancy (Beta=-0.2416, p<0.0001) was significantly and negatively associated with HIV rates.

The results of the linear regression model indicated that life expectancy (Beta=1.543, p<0.0001) was significantly and positively associated with breast cancer cases.

[asgardian--angels]

EIGHT SEASONS DOWN THE ROAD WHAT

from (x)

[sunlight-shunlight]

i do think the argument of "oh you're being EXTRA harsh on veilguard for being racist/misogynistic/etc! what about all that stuff in previous games??" is actually in very bad faith.

bc firstly: people have already talked about previous games at length. it is weird to expect old arguments to be rehashed when a new thing is available to discuss.

and secondly: yeah, i will actually hold veilguard to a higher standard! i don't think that's unreasonable at all. time is linear, and 10 years passed in between the last game! there have been so many highly public social movements since then, the entire world has changed. most people have had growth in their awareness of the world and how they understand history and politics in that time, just from being alive and reading the news occasionally. they SHOULD have improved! i was never expecting it to be showstoppingly good or like a masterpiece of writing, but... i think "not getting worse, and perhaps being 2% better" is a completely reasonable expectation.

but, it did not improve, and did actually get worse, unfortunately! i do not think it's reasonable for a game published in 2024 to have such a regressive, flat way of depicting women; their implications on the worldbuilding became incredibly similar to irl racist or antisemitic conspiracy theories, while the writers crowed about how they were so proud of ~giving the elves a win~; the portrayal of the qunari only got more flat and islamophobic, somehow; and the entire idea of "i can excuse being nb but i draw the line at being an immigrant" is so stupid that it sends me into space.

i think it's completely reasonable to expect better from Progressive Writers™️ in this modern era! this is a very low bar to exceed, and they still managed to dig under it, on such basic topics as "can women visibly age or be rude and still be major characters". previous games were managing to reach this standard, minor as it was! whether it's a failure of the writing itself, or corporate interference, it is completely valid to expect better.

[comradekarin]

now that I have time to process csm 192-194…. I don’t get the outrage with fujimoto for including the denji-yoru scene. I mean, this entire story is about coercion, grooming, sexual assault and indoctrination (and you can even look at it through a capitalism critical lens as well). being forced to see the assault is where the line gets drawn and not makima grooming denji? what reze did to denji (putting her arc aside for a second)? what the yazuka did to denji? denji’s organs being taken and sold on the black market? denji’s entire conception of sex, intimacy and satisfaction is directly warped due to not just his upbringing but the exploitive situation he’s in currently. even denji’s distrust of men (but his trust in women who take advantage of him more than the men do) is directly rooted in his childhood.

and this scene isn’t just about the violation of denji’s autonomy, but also asa’s. yoru has never really fully valued asa’s autonomy and wellbeing. yoru is prone to immature, childish bursts, willing to doom the people around her—even asa—for the sake of pettiness. so not only has denji been violated, but asa’s consent has not been granted for yoru to commit these acts. it feels as if yoru just weaponized whatever asa feels for denji and is just pushing buttons to see what works. denji didn’t even known it was yoru and not asa during those safe moments he felt he had until recently. you read yoru’s assault and it’s clear this is not a good moment. it’s uncomfortable and almost painful to watch. this thing that he’s been waiting on for a while, and it’s not at all what he expected. you watch denji go through this dissociative state, he’s confused, he’s lost and disappointed. what denji wants is intimacy and basic human decency, he wants to be treated like he actually matters, and the only reason he seems to be “wanted” is either because he’s chainsaw man or through sexual manipulation from women.

I can understand being uncomfortable with a scene like this—that’s the entire point, I won’t take that away from anyone—but I want to pose a question: what made denji’s traumatic experiences with assault more palatable prior to this scene? does it feel unnecessary because you’re finally forced to “see it”? denji HAS made progress and developed from part one (i.e his dynamic with nayuta, who despite embodying the same toxic, manipulative traits as makima, he’s able to love her unconditionally and recognize her childhood innocence, the same naivety makima exploited with him… his refusal to immediately succumb to the whims of the women around him, him wanting to help innocent lives around him, him admitting he wants sex and steak instead of just being content with scraps, etc). and he’s doing it all by himself. he doesn’t have a support system. he’s learning all on his own. I also want to point out that aside from pochita, absolutely no one in denji’s life cared about denji for denji. I love power and aki, but even their beginning relationships with denji rooted in what he could do for them (though, this obviously does evolve into something more genuine and warm). denji has little framework to go with and even less support to combat his experiences.

I also don’t think it’s fair to say “I thought we were over this since makima died”- no, we are not over this. this is not something you just “get over”. denji’s worldview is not going to be suddenly changed because makima died (he sees her in literally everything he does in part two. he can’t let her go… he loves her despite the hell she put him and his loved ones through). progress is slow, it’s not linear at all and often time disappointing when we see regression. I’d even argue that this scene doesn’t immediately ruin asaden, but that’s another convo for a different day.

I feel like people want denji to be this “perfect victim” and that’s just not what’s going on here. I don’t think fujimoto has a “grooming” fetish or whatever people are calling it. I genuinely want denji to recover from his assault, to discover a healthy mindset about sex and intimacy, to find someone who understands the extent of his loneliness (I believe that person can be asa, who shares that similar sentiment). I’ll re-evaluate this post as the narrative continues to unfold, of course, I am also very open to seeing what others think on this. my stance isn’t definitive.

[squidwof]

Guys I think I'm just woke by nature.... /hj

Like for basically everything that has had people shit on it due to being weird or a minority, I've been chill with it as a default. The only exception to this is that I used to be against good faith labels, but even then I came around to that eventually.

When I first heard of lgbt I was like "Yeah that makes sense, I don't see why not."

A bunch of complex mental/neurological disorders? Yeah, they're just living. Systems (Special/Specific mention cause they get SO MUCH HATE </3)? Fair enough, sounds logical. Fictives? I'm being deadass when I say I never had a phase where I hated them. Same with Factives and any other type of introject.

When I first heard of Therians I was ON BOARD BRO. (I myself am actually a therian!!) When I saw stuff about otherkin I was very geeked about it, this goes forward to almost any/all types therianthropy, kintype, or anything even remotely similar. (Esp Fictionkin!!!)

I was always open to the idea of other religions as well I think, I personally don't *technically* have a religion but once my life is more sorted out and linear I want to practice hellenic polytheism.

Also, neopronouns, xenogenders, etc. I NEVER questioned that shit, I just accepted it cause it seemed cool (and I do still think it is!!).

Age regression and Pet regression too!!! Idk it always made sense to me I suppose.

TL;DR: I'm too woke for the world and just generally like people and LITERALLY cannot comprehend why so much hate exists. /hj

[swaps55]

Reading your long fics, there's so much setup and payoff that has me thinking about how the story plays out for my Mass Effect universe. Which things bend, and which things break beyond repair, more tailored than the canon beats.

If you wanted a takeaway from your writing to be 'how to wound your characters in a way that makes the story really stick', it's working so well. It's so impressive how deftly you juggle the many characters while keeping the stakes and momentum charged.

Thank you!!!!! That means a lot to hear especially with this fic, given it is an absolute logistical nightmare. I work in constant fear that I am writing checks the story won't be able to cash. There are just too many characters in ME2 and too many siloed story lines that don't connect to a bigger throughline. It works fine in a video game, but in a linear format, ME2 is where narrative tension goes to die.

My approach to Mezzo has largely been:

Let game events that I don't have anything new or different to add fade into the background. (Like Freedom's progress, or Mordin's recruitment.)

Focus on characters who give me the most interesting perspectives on/relationships with Sam and determine what their primary drivers are. (For example, EDI and Jack.)

Find ways to connect those drivers to Sam's character regression/progression and somehow make a satisfying character arc for both. (Neither EDI nor Sam have a voice when it comes to their bodily autonomy and role with Cerberus; Jack and Sam both need to look outside themselves and their anger to heal).

Give characters reasons to talk to each other outside of Sam, for the love of god. (Miranda and Mordin, for instance.)

Set up all the weird shit I'm gonna do with ME3. [eyebrow wiggle]

Some of my beloved ME2 characters are going to get neglected in Mezzo because there just isn't room to do them the full justice they deserve, and some are going to play smaller roles because I just do not want to write a 300k story.

But it has been a tremendous amount of fun digging into characters like EDI, Jack, Garrus, Liara, Jacob, and Miranda in ways I haven't before. Mordin as a character already has a perfect arc in the game, but the biotic mystery I am setting up has been a really excellent way to involve one of my favorite characters of all time in a way that feels really satisfying.

Basically, the payoff for me personally while writing Mezzo has been extremely high and very rewarding, but clawing my way to finding those payoffs has already aged me ten years, lol. If it reads like it's deft and easy, that's great because it means the hard work has been worth it.

(this is so much more than you wanted to know, sorry. XD)

[milesprowerpower]

hot take on sonic forces - I don't mind Tails falling apart after Sonic died (or he thought he died) because, you know, grief is a powerful thing. I think it could even make sense as a sort of culmination of his insecurities. Sonic was desperately asking Tails what was up with Infinite when he was getting beat down and Tails couldn't provide any answers to help him. That probably stuck in his head, especially if we know he battles confidence issues. The self doubt spiral is "I couldn't save Sonic when he needed me, which means I must really be a burden to him, which means I suck, etc."

In the infamous scene with Chaos 0, he's beating himself up for not being "smart enough" to fix Omega. Tails always admired Sonic's athleticism and bravery, but nobody doubted that Tails was their mechanical genius. His inability to succeed in the one field where he's indisputably the best must have pushed him even further down the self-doubt spiral. (Before Infinite defeats Sonic, Tails also fails to interpret the data re:Infinite in time to give Sonic useful information, another example of him failing to be 'the smart guy' in time.) In a situation of despair like this, I don't think it's crazy to think he'd freak out at an enemy that he'd seen before (Chaos 0) and kind of lose control and cower. It's a bad moment for him!

A lot of people say this contradicts or negates his growth in Adventure, but I don't think that's true. Self-doubt isn't something you conquer once and then forget about for the rest of your life. It requires action and mental discipline. Even if you've been doing well, a traumatic incident (like someone who was basically your adoptive big brother dying) can shatter your coping mechanisms. That doesn't mean your growth never happened! It means that you can still fail, and you need to cope with the fact that growth is not linear.

What really bothers me are the beginning, where Tails is seen cowering in front of a group of villagers. There isn't any reason for him to be cowering here since Eggman attacks aren't new for him. There's no traumatic event to throw him off balance. He's defeated Eggman's robots before... it's not a novel threat. I know the point is to introduce Sonic being all cool, but I do think it's a bad move. It doesn't make sense with Tails' experience. I also think it cheapens Tails' breakdown by just making him seem cowardly in general? The breakdown would have been more impactful if we showed him being a fighter in the beginning, trying to hold off Eggman's robots while still needing Sonic's help to finish them off. That contrast would have shown just how much the events damaged him. I think that was a lost opportunity.

This isn't to say Forces had great writing in general. I just don't hate Tails having a breakdown! I think it makes sense given the circumstances that he would lose it in an especially tense situation. I think it could have been an opportunity to point out that growth isn't linear, you can regress, but that doesn't mean your earlier growth never happened. (This is what they did in Frontiers, at least.)

Oh yeah, another annoying thing, the "Sonic! Help me!" He does this in SA2 when he dies too, which makes him seem really clingy, but I guess it's not unprecedented. I guess you could interpret this as him being so defeated and desperate that he deludes himself into thinking Sonic can save him, but I don't love it. I think it's unnecessary to show that he's hit rock bottom.

If I cared enough I'd write a fanfic trying to incorporate this idea that you can hit rock bottom and regress but that doesn't mean the growth never happened and you never changed for the better. But I'm lazy and also too busy lol

[sonadukane]

How to Become a Data Scientist in 2025 (Roadmap for Absolute Beginners)

Want to become a data scientist in 2025 but don’t know where to start? You’re not alone. With job roles, tech stacks, and buzzwords changing rapidly, it’s easy to feel lost.

But here’s the good news: you don’t need a PhD or years of coding experience to get started. You just need the right roadmap.

Let’s break down the beginner-friendly path to becoming a data scientist in 2025.

✈️ Step 1: Get Comfortable with Python

Python is the most beginner-friendly programming language in data science.

What to learn:

Variables, loops, functions

Libraries like NumPy, Pandas, and Matplotlib

Why: It’s the backbone of everything you’ll do in data analysis and machine learning.

🔢 Step 2: Learn Basic Math & Stats

You don’t need to be a math genius. But you do need to understand:

Descriptive statistics

Probability

Linear algebra basics

Hypothesis testing

These concepts help you interpret data and build reliable models.

📊 Step 3: Master Data Handling

You’ll spend 70% of your time cleaning and preparing data.

Skills to focus on:

Working with CSV/Excel files

Cleaning missing data

Data transformation with Pandas

Visualizing data with Seaborn/Matplotlib

This is the “real work” most data scientists do daily.

🧬 Step 4: Learn Machine Learning (ML)

Once you’re solid with data handling, dive into ML.

Start with:

Supervised learning (Linear Regression, Decision Trees, KNN)

Unsupervised learning (Clustering)

Model evaluation metrics (accuracy, recall, precision)

Toolkits: Scikit-learn, XGBoost

🚀 Step 5: Work on Real Projects

Projects are what make your resume pop.

Try solving:

Customer churn

Sales forecasting

Sentiment analysis

Fraud detection

Pro tip: Document everything on GitHub and write blogs about your process.

✏️ Step 6: Learn SQL and Databases

Data lives in databases. Knowing how to query it with SQL is a must-have skill.

Focus on:

SELECT, JOIN, GROUP BY

Creating and updating tables

Writing nested queries

🌍 Step 7: Understand the Business Side

Data science isn’t just tech. You need to translate insights into decisions.

Learn to:

Tell stories with data (data storytelling)

Build dashboards with tools like Power BI or Tableau

Align your analysis with business goals

🎥 Want a Structured Way to Learn All This?

Instead of guessing what to learn next, check out Intellipaat’s full Data Science course on YouTube. It covers Python, ML, real projects, and everything you need to build job-ready skills.

https://www.youtube.com/watch?v=rxNDw68XcE4

🔄 Final Thoughts

Becoming a data scientist in 2025 is 100% possible — even for beginners. All you need is consistency, a good learning path, and a little curiosity.

Start simple. Build as you go. And let your projects speak louder than your resume.

Drop a comment if you’re starting your journey. And don’t forget to check out the free Intellipaat course to speed up your progress!

[boar410511]

Mars Crater Study-1

This article was written as a practice exercise with reference to the information provided in the COURSERA course, specifically the Mars Crater Study.

=========================================

My program,

import pandas as pd

import statsmodels.formula.api as smf

# Set display format

pd.set_option('display.float_format', lambda x: '%.2f' % x)

# Read dataset

data = pd.read_csv('marscrater_pds.csv')

# Convert necessary variables to numeric format

data['DIAM_CIRCLE_IMAGE'] = pd.to_numeric(data['DIAM_CIRCLE_IMAGE'], errors='coerce')

data['DEPTH_RIMFLOOR_TOPOG'] = pd.to_numeric(data['DEPTH_RIMFLOOR_TOPOG'], errors='coerce')

# Perform basic linear regression analysis

print("OLS regression model for the association between crater diameter and depth")

reg1 = smf.ols('DEPTH_RIMFLOOR_TOPOG ~ DIAM_CIRCLE_IMAGE', data=data).fit()

print(reg1.summary())

=========================================

Output results,

Dep. Variable:     DEPTH_RIMFLOOR_TOPOG

R-squared:0.344

Model: OLS

Adj. R-squared:0.344

Method:Least Squares  

F-statistic:2.018e+05

Date:Thu, 27 Mar 2025

Prob (F-statistic):0.00

Time:14:58:20

Log-Likelihood:1.1503e+05

No. Observations:384343

AIC:-2.301e+05

Df Residuals:384341

BIC:-2.300e+05

Df Model: 1                                        

Covariance Type:nonrobust                                        

                  coef    std err        t      P>|t|      [0.025      0.975]

Intercept   0.0220   0.000     70.370     0.000     0.021       0.023

DIAM_CIRCLE_IMAGE    

0.0151   3.37e-05   449.169    0.000    0.015    0.015

Omnibus:390327.615

Durbin-Watson:1.276

Prob(Omnibus):0.000  

Jarque-Bera (JB):4086668077.223

Skew: -3.506

Prob(JB):0.00

Kurtosis:508.113

Cond. No.10.1

=========================================

Results Summary:

Regression Model Results:

R-squared: 0.344, indicating that the model explains approximately 34.4% of the variability in crater depth.

Regression Coefficient (DIAMCIRCLEIMAGE): 0.0151, meaning that for each unit increase in crater diameter, the depth increases by an average of 0.0151 units.

p-value: 0.000, indicating that the effect of diameter on depth is statistically significant.

Intercept: 0.0220, which is the predicted crater depth when the diameter is zero.

Conclusion:

The analysis shows a significant positive association between crater diameter and depth. While the model provides some explanatory power, other factors likely influence crater depth, and further exploration is recommended.

[economicshomeworkhelper]

Econometrics Demystified: The Ultimate Compilation of Top 10 Study Aids

Welcome to the world of econometrics, where economic theories meet statistical methods to analyze and interpret data. If you're a student navigating through the complexities of econometrics, you know how challenging it can be to grasp the intricacies of this field. Fear not! This blog is your ultimate guide to the top 10 study aids that will demystify econometrics and make your academic journey smoother.

Economicshomeworkhelper.com – Your Go-To Destination

Let's kick off our list with the go-to destination for all your econometrics homework and exam needs – https://www.economicshomeworkhelper.com/. With a team of experienced experts, this website is dedicated to providing high-quality assistance tailored to your specific requirements. Whether you're struggling with regression analysis or hypothesis testing, the experts at Economicshomeworkhelper.com have got you covered. When in doubt, remember to visit the website and say, "Write My Econometrics Homework."

Econometrics Homework Help: Unraveling the Basics

Before delving into the intricacies, it's crucial to build a strong foundation in the basics of econometrics. Websites offering econometrics homework help, such as Khan Academy and Coursera, provide comprehensive video tutorials and interactive lessons to help you grasp fundamental concepts like linear regression, correlation, and statistical inference.

The Econometrics Academy: Online Courses for In-Depth Learning

For those seeking a more immersive learning experience, The Econometrics Academy offers online courses that cover a wide range of econometrics topics. These courses, often led by seasoned professors, provide in-depth insights into advanced econometric methods, ensuring you gain a deeper understanding of the subject.

"Mastering Metrics" by Joshua D. Angrist and Jörn-Steffen Pischke

No compilation of study aids would be complete without mentioning authoritative books, and "Mastering Metrics" is a must-read for econometrics enthusiasts. Authored by two renowned economists, Joshua D. Angrist and Jörn-Steffen Pischke, this book breaks down complex concepts into digestible chapters, making it an invaluable resource for both beginners and advanced learners.

Econometrics Forums: Join the Conversation

Engaging in discussions with fellow econometrics students and professionals can enhance your understanding of the subject. Platforms like Econometrics Stack Exchange and Reddit's econometrics community provide a space for asking questions, sharing insights, and gaining valuable perspectives. Don't hesitate to join the conversation and expand your econometrics network.

Gretl: Your Free Econometrics Software

Practical application is key in econometrics, and Gretl is the perfect tool for hands-on learning. This free and open-source software allows you to perform a wide range of econometric analyses, from simple regressions to advanced time-series modeling. Download Gretl and take your econometrics skills to the next level.

Econometrics Journal Articles: Stay Updated

Staying abreast of the latest developments in econometrics is essential for academic success. Explore journals such as the "Journal of Econometrics" and "Econometrica" to access cutting-edge research and gain insights from scholars in the field. Reading journal articles not only enriches your knowledge but also equips you with the latest methodologies and approaches.

Econometrics Bloggers: Learn from the Pros

Numerous econometrics bloggers share their expertise and experiences online, offering valuable insights and practical tips. Follow blogs like "The Unassuming Economist" and "Econometrics by Simulation" to benefit from the expertise of professionals who simplify complex econometric concepts through real-world examples and applications.

Econometrics Software Manuals: Master the Tools

While software like STATA, R, and Python are indispensable for econometric analysis, navigating through them can be challenging. Refer to comprehensive manuals and documentation provided by these software platforms to master their functionalities. Understanding the tools at your disposal will empower you to apply econometric techniques with confidence.

Econometrics Webinars and Workshops: Continuous Learning

Finally, take advantage of webinars and workshops hosted by academic institutions and industry experts. These events provide opportunities to deepen your knowledge, ask questions, and engage with professionals in the field. Check out platforms like Econometric Society and DataCamp for upcoming events tailored to econometrics enthusiasts.

Conclusion

Embarking on your econometrics journey doesn't have to be daunting. With the right study aids, you can demystify the complexities of this field and excel in your academic pursuits. Remember to leverage online resources, engage with the econometrics community, and seek assistance when needed. And when the workload becomes overwhelming, don't hesitate to visit Economicshomeworkhelper.com and say, "Write My Econometrics Homework" – your trusted partner in mastering econometrics. Happy studying!

[joltai-showa]

broooooo I HATE Decembers with fucking passion

good thing is I finished checking all the students' works during the weekend and that only took like 10 hours of my life that I'm not getting back (checking programming projects that also include students' writing about theoretical concepts is HELL because not only will they copypaste the paragraphs off the Internet/ChatGPT regardless if they got the results that the text they put in talks about, but they will also write something equivalent of eldritch horrors for DS majors instead of code and you need a bottle of vodka and 40 minutes to understand if they did things correctly and it's a bunch of bullshit thrown into the wall in hopes that something sticks. I've seen a guy calculate R2 for linear regression by the FUCKING DEFINITION. YOU ARE USING SKLEARN. r2_score IS A BASIC FUCKING FUNCTION IN THERE. BRO WHAT ARE YOU DOING)

bad thing is I went to a dentist appointment and had the wonderful experience of getting to hear a doctor giddily say "If you hadn't come with complaints about this, I wouldn't have ever believed that such thing could ever happen. I've never seen anything like that in my career😃" so now I gotta cough up quite the sum to fix something that should have been impossible to happen. that's on top of me spending quite the sum on the presents for my parents' birthday and just general New Year's stuff, so fuck you December for taking all of my hard earned money

and even worse thing is that my Chinese written exam is in two days and I just finished filling up my own personal vocabulary to train 汉字. It's got over 750 words. And I need to remember all of the grammar, especially the psychotic alchemy that are the comparison structures. I am cooked.

My friend is now back to sending me videos of that one girl who was studying to become a dentist and recorded her all-nighters before her finals and I guess I am indeed back to that exam/thesis preparation regiment when I drank at least 5 coffees per day cuz no way I am learning all that with a normal sleep schedule

anyway I liked the new Pure Fiction in HSR!

I struggled to get 8-9 stars on the previous ones and finished it fully on the first try with this one😊honestly the buffs currently might be a bit too busted, but it's certainly a great improvement to how it used to be! Now my Jade actually feels like the T0 unit she is supposed to be in this mode😘

p.s. yeah no fanfiction work is being done rn I am fighting for my life here. hopefully by Friday I'll get some time to do the notes on "Eclipse"🙏

[augerer]

@girderednerve replied to your post coming out on tumblr as someone whose taught "AI bootcamp" courses to middle school students AMA:

did they like it? what kinds of durable skills did you want them to walk away with? do you feel bullish on "AI"?

It was an extracurricular thing so the students were quite self-selecting and all were already interested in the topic or in doing well in the class. Probably what most interested me about the demographic of students taking the courses (they were online) was the number who were international students outside of the imperial core probably eventually looking to go abroad for college, like watching/participating in the cogs of brain drain.

I'm sure my perspective is influenced because my background is in statistics and not computer science. But I hope that they walked away with a greater understanding and familiarity with data and basic statistical concepts. Things like sample bias, types of data (categorical/quantitative/qualitative), correlation (and correlation not being causation), ways to plot and examine data. Lots of students weren't familiar before we started the course with like, what a csv file is/tabular data in general. I also tried to really emphasize that data doesn't appear in a vacuum and might not represent an "absolute truth" about the world and there are many many ways that data can become biased especially when its on topics where people's existing demographic biases are already influencing reality.

Maybe a bit tangential but there was a part of the course material that was teaching logistic regression using the example of lead pipes in flint, like, can you believe the water in this town was undrinkable until it got Fixed using the power of AI to Predict Where The Lead Pipes Would Be? it was definitely a trip to ask my students if they'd heard of the flint water crisis and none of them had. also obviously it was a trip for the course material to present the flint water crisis as something that got "fixed by AI". added in extra information for my students like, by the way this is actually still happening and was a major protest event especially due to the socioeconomic and racial demographics of flint.

Aside from that, python is a really useful general programming language so if any of the students go on to do any more CS stuff which is probably a decent chunk of them I'd hope that their coding problemsolving skills and familiarity with it would be improved.

do i feel bullish on "AI"? broad question. . . once again remember my disclaimer bias statement on how i have a stats degree but i definitely came away from after teaching classes on it feeling that a lot of machine learning is like if you repackaged statistics and replaced the theoretical/scientific aspects where you confirm that a certain model is appropriate for the data and test to see if it meets your assumptions with computational power via mass guessing and seeing if your mass guessing was accurate or not lol. as i mentioned in my tags i also really don't think things like linear regression which were getting taught as "AI" should be considered "ML" or "AI" anyways, but the larger issue there is that "AI" is a buzzy catchword that can really mean anything. i definitely think relatedly that there will be a bit of an AI bubble in that people are randomly applying AI to tasks that have no business getting done that way and they will eventually reap the pointlessness of these projects.

besides that though, i'm pretty frustrated with a lot of AI hysteria which assumes that anything that is labeled as "AI" must be evil/useless/bad and also which lacks any actual labor-based understanding of the evils of capitalism. . . like AI (as badly formed as I feel the term is) isn't just people writing chatGPT essays or whatever, it's also used for i.e. lots of cutting edge medical research. if insanely we are going to include "linear regression" as an AI thing that's probably half of social science research too. i occasionally use copilot or an LLM for my work which is in public health data affiliated with a university. last week i got driven batty by a post that was like conspiratorially speculating "spotify must have used AI for wrapped this year and thats why its so bad and also why it took a second longer to load, that was the ai generating everything behind the scenes." im saying this as someone who doesnt use spotify, 1) the ship on spotify using algorithms sailed like a decade ago, how do you think your weekly mixes are made? 2) like truly what is the alternative did you think that previously a guy from minnesota was doing your spotify wrapped for you ahead of time by hand like a fucking christmas elf and loading it personally into your account the night before so it would be ready for you? of course it did turned out that spotify had major layoffs so i think the culprit here is really understaffing.

like not to say that AI like can't have a deleterious effect on workers, like i literally know people who were fired through the logic that AI could be used to obviate their jobs. which usually turned out not to be true, but hasn't the goal of stretching more productivity from a single worker whether its effective or not been a central axiom of the capitalist project this whole time? i just don't think that this is spiritually different from retail ceos discovering that they could chronically understaff all of their stores.

[ensafomer]

a Basic Linear Regression Model

What is linear regression?

Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.

This form of analysis estimates the coefficients of the linear equation, involving one or more independent variables that best predict the value of the dependent variable. Linear regression fits a straight line or surface that minimizes the discrepancies between predicted and actual output values. There are simple linear regression calculators that use a “least squares” method to discover the best-fit line for a set of paired data. You then estimate the value of X (dependent variable)

n statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable.[1][2][3][4][5] That is, it concerns two-dimensional sample points with one independent variable and one dependent variable (conventionally, the x and y coordinates in a Cartesian coordinate system) and finds a linear function (a non-vertical straight line) that, as accurately as possible, predicts the dependent variable values as a function of the independent variable. The adjective simple refers to the fact that the outcome variable is related to a single predictor.

It is common to make the additional stipulation that the ordinary least squares (OLS) method should be used: the accuracy of each predicted value is measured by its squared residual (vertical distance between the point of the data set and the fitted line), and the goal is to make the sum of these squared deviations as small as possible. In this case, the slope of the fitted line is equal to the correlation between y and x corrected by the ratio of standard deviations of these variables. The intercept of the fitted line is such that the line passes through the center of mass (x, y) of the data points.

Formulation and computation

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This relationship between the true (but unobserved) underlying parameters α and β and the data points is called a linear regression model.

Here we have introduced

x¯ and y¯ as the average of the xi and yi, respectively

Δxi and Δyi as the deviations in xi and yi with respect to their respective means.

Expanded formulas

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Interpretation

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Relationship with the sample covariance matrix

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where

rxy is the sample correlation coefficient between x and y

sx and sy are the uncorrected sample standard deviations of x and y

sx2 and sx,y are the sample variance and sample covariance, respectively

Interpretation about the slope

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Interpretation about the intercept

Interpretation about the correlation

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Numerical properties

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The regression line goes through the center of mass point, (x¯,y¯), if the model includes an intercept term (i.e., not forced through the origin).

The sum of the residuals is zero if the model includes an intercept term:∑i=1nε^i=0.

The residuals and x values are uncorrelated (whether or not there is an intercept term in the model), meaning:∑i=1nxiε^i=0

The relationship between ρxy (the correlation coefficient for the population) and the population variances of y (σy2) and the error term of ϵ (σϵ2) is:[10]: 401 σϵ2=(1−ρxy2)σy2For extreme values of ρxy this is self evident. Since when ρxy=0 then σϵ2=σy2. And when ρxy=1 then σϵ2=0.

Statistical properties

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Description of the statistical properties of estimators from the simple linear regression estimates requires the use of a statistical model. The following is based on assuming the validity of a model under which the estimates are optimal. It is also possible to evaluate the properties under other assumptions, such as inhomogeneity, but this is discussed elsewhere.[clarification needed]

Unbiasedness

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Variance of the mean response

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where m is the number of data points.

Variance of the predicted response

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Further information: Prediction interval

Confidence intervals

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The standard method of constructing confidence intervals for linear regression coefficients relies on the normality assumption, which is justified if either:

the errors in the regression are normally distributed (the so-called classic regression assumption), or

the number of observations n is sufficiently large, in which case the estimator is approximately normally distributed.

The latter case is justified by the central limit theorem.

Normality assumption

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Asymptotic assumption

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The alternative second assumption states that when the number of points in the dataset is "large enough", the law of large numbers and the central limit theorem become applicable, and then the distribution of the estimators is approximately normal. Under this assumption all formulas derived in the previous section remain valid, with the only exception that the quantile t*n−2 of Student's t distribution is replaced with the quantile q* of the standard normal distribution. Occasionally the fraction ⁠1/n−2⁠ is replaced with ⁠1/n⁠. When n is large such a change does not alter the results appreciably.

Numerical example

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See also: Ordinary least squares § Example, and Linear least squares § Example

Alternatives

[edit]Calculating the parameters of a linear model by minimizing the squared error.

In SLR, there is an underlying assumption that only the dependent variable contains measurement error; if the explanatory variable is also measured with error, then simple regression is not appropriate for estimating the underlying relationship because it will be biased due to regression dilution.

Other estimation methods that can be used in place of ordinary least squares include least absolute deviations (minimizing the sum of absolute values of residuals) and the Theil–Sen estimator (which chooses a line whose slope is the median of the slopes determined by pairs of sample points).

Deming regression (total least squares) also finds a line that fits a set of two-dimensional sample points, but (unlike ordinary least squares, least absolute deviations, and median slope regression) it is not really an instance of simple linear regression, because it does not separate the coordinates into one dependent and one independent variable and could potentially return a vertical line as its fit. can lead to a model that attempts to fit the outliers more than the data.

Line fitting

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This section is an excerpt from Line fitting.[edit]

Line fitting is the process of constructing a straight line that has the best fit to a series of data points.

Several methods exist, considering:

Vertical distance: Simple linear regression

Resistance to outliers: Robust simple linear regression

Perpendicular distance: Orthogonal regression (this is not scale-invariant i.e. changing the measurement units leads to a different line.)

Weighted geometric distance: Deming regression

Scale invariant approach: Major axis regression This allows for measurement error in both variables, and gives an equivalent equation if the measurement units are altered.

Simple linear regression without the intercept term (single regressor)

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[quinntik]

The Quinntik Macro-Narrative Home Page

It's becoming a running gag in my circles to mention some of the insane shit going on in my Sims game just to confuse my friends. The problem is that some of those friends started showing genuine interest in my storytelling. The problem with that problem is that I never played these games or wrote these stories with any intention to earnestly share their contents. And if I start fully sharing, then I'm going to regress to the version of myself that content-ified everything I do.

So instead I'm finding a balance. Rather than publishing the tens of thousands of words I have stashed away for my own enjoyment, I'm going to be summarizing each chapter of the story on my blog. I'll be cleaning up some of the weirder or more personal details but I'll leave the spirit of the story intact alongside some reflections on the older content. That way this project remains something just for me, but I'm given the option to share.

I'll be editing this post to link to all future posts within the story. You can use this to sort through the story as it gets more and more complex.

The Pre-Integration Arc

Chapter I - The Pursuit of Immortality

Chapter II - The Butler of Goth Manor

Chapter III - What Stares From the Water

Chapter IV - The Lunar Lakes Alien Integration Program

Here's the context you need to approach this story:

I've been playing the same The Sims 3 file since 2015. It has devolved into an insane soap opera.

I started writing the narrative for that file in 2023 as my playstyle became more story-centric and sophisticated.

It turns out I have the most fun when my gameplay causes me to think like an author, doing research into cultures, backgrounds, philosophy, etc. This story has become the springboard to encourage me to read more, consume media more critically, and seek new experiences.

As the story tying each chapter together got more and more complicated, it started extending into my experience with RPGs, TTRPGs, and the general concept of daydreaming and fantasy - so suddenly it's not just a Sims story.

The target audience for this story consists of mainly non-Sims players, so I'm going to be explaining some really basic gameplay elements at times. Please be patient.

Because this was originally just for me, the story sometimes gets into some questionable or violent content that helped me to understand myself and the world around me. I will not be censoring this heavily but I will use my discretion to make some elements more palatable.

In the same vein, at some points the story takes a vague, allegorical, or chronologically non-linear turn. I will not be explaining these.

Thank you for reading. I hope you enjoy the ride.

[borderline-gays-club]

12/17/23 12:16 AM

I feel like I'm always playing catch up. And this has been an ongoing problem for a very very long time. At least since highschool. possibly before. And I now know its because of all my fun mental problems that work together to make my life a chaotic mess (less so now). But now I'm in a place where Im asking myself: how do I just exist without feeling like I need to get everything done right now. Like i cant fucking relax. And the problem is that there is some level of reasoning to it, but at the same time I need to find a balance, cus either way working crazy all the time is just gonna burn me out and cause me more problems in the long run. I have gone through so many periods in my life where I'm severely depressed and incapacitated and then hypo mania kicks in and I can do everything. So in the time periods of mania I would "make up" for all the time I lost and it was a vicious cycle. Even though my bipolar symptoms are currently managed with medication, I look back the past 10 years or so and I'm like wow. I lost a lot of myself bc of my BPD. and I guess thats where alll my current urgency is coming from. BPD took a lot away from me. Time that I can never get back. And I can't say I regret it, because with the tools I had back then, it just wouldn't have been possible to have made different choices. I will say, with every step of this journey in my life, I really have always been trying. Even at my worst I was trying to find ways to not be so miserable. I really wouldn't give up. Its in these moments of reflection that I can really say that my will to live has actually been very strong this whole time. Even when i wanted to die. I still tried. The times that i basically gave up on myself was when my miserableness was being blanketed by obsession/FP shit. That was the perfect way to completely lose hold of myself and dedicate every fiber of my being to that other person. and it would feel euphoric and would get me out of depression. Its terrible. literal addiction shit. These are the times that I mourn because it really does feel like regression. And as much as i dont want to treat myself like a project that must always get better over time, it really just feels tragic how much of my 20s I've lost to losing myself in other people. But this is all time i cannot get back. There is no point in regret. And also, I cant regret decisions I couldn't really make. A lot of this lost time is really due to lack of care that I needed. From my parents, from doctors. I was simply emotionally and medically neglected, and I did my best with what I did have. All I can do now is to strive to live in my truth. but first i must figure out what my truths are. Because I still don't really know what self means. Im figuring it out tho. *last note: mayb i just need to view time differently. I'm viewing it as a linear thing, but I know that time is not really linear. Life is certainly not linear. I think I still view my value with what I do and what I create. This is something I'm working through. I think the question is also: Who am i if I did not make things? Where would I place my self worth without being able to make things? theres always a lot to work thru.

[uthra-krish]

The Skills I Acquired on My Path to Becoming a Data Scientist

Data science has emerged as one of the most sought-after fields in recent years, and my journey into this exciting discipline has been nothing short of transformative. As someone with a deep curiosity for extracting insights from data, I was naturally drawn to the world of data science. In this blog post, I will share the skills I acquired on my path to becoming a data scientist, highlighting the importance of a diverse skill set in this field.

The Foundation — Mathematics and Statistics

At the core of data science lies a strong foundation in mathematics and statistics. Concepts such as probability, linear algebra, and statistical inference form the building blocks of data analysis and modeling. Understanding these principles is crucial for making informed decisions and drawing meaningful conclusions from data. Throughout my learning journey, I immersed myself in these mathematical concepts, applying them to real-world problems and honing my analytical skills.

Programming Proficiency

Proficiency in programming languages like Python or R is indispensable for a data scientist. These languages provide the tools and frameworks necessary for data manipulation, analysis, and modeling. I embarked on a journey to learn these languages, starting with the basics and gradually advancing to more complex concepts. Writing efficient and elegant code became second nature to me, enabling me to tackle large datasets and build sophisticated models.

Data Handling and Preprocessing

Working with real-world data is often messy and requires careful handling and preprocessing. This involves techniques such as data cleaning, transformation, and feature engineering. I gained valuable experience in navigating the intricacies of data preprocessing, learning how to deal with missing values, outliers, and inconsistent data formats. These skills allowed me to extract valuable insights from raw data and lay the groundwork for subsequent analysis.

Data Visualization and Communication

Data visualization plays a pivotal role in conveying insights to stakeholders and decision-makers. I realized the power of effective visualizations in telling compelling stories and making complex information accessible. I explored various tools and libraries, such as Matplotlib and Tableau, to create visually appealing and informative visualizations. Sharing these visualizations with others enhanced my ability to communicate data-driven insights effectively.

Machine Learning and Predictive Modeling

Machine learning is a cornerstone of data science, enabling us to build predictive models and make data-driven predictions. I delved into the realm of supervised and unsupervised learning, exploring algorithms such as linear regression, decision trees, and clustering techniques. Through hands-on projects, I gained practical experience in building models, fine-tuning their parameters, and evaluating their performance.

Database Management and SQL

Data science often involves working with large datasets stored in databases. Understanding database management and SQL (Structured Query Language) is essential for extracting valuable information from these repositories. I embarked on a journey to learn SQL, mastering the art of querying databases, joining tables, and aggregating data. These skills allowed me to harness the power of databases and efficiently retrieve the data required for analysis.

Domain Knowledge and Specialization

While technical skills are crucial, domain knowledge adds a unique dimension to data science projects. By specializing in specific industries or domains, data scientists can better understand the context and nuances of the problems they are solving. I explored various domains and acquired specialized knowledge, whether it be healthcare, finance, or marketing. This expertise complemented my technical skills, enabling me to provide insights that were not only data-driven but also tailored to the specific industry.

Soft Skills — Communication and Problem-Solving

In addition to technical skills, soft skills play a vital role in the success of a data scientist. Effective communication allows us to articulate complex ideas and findings to non-technical stakeholders, bridging the gap between data science and business. Problem-solving skills help us navigate challenges and find innovative solutions in a rapidly evolving field. Throughout my journey, I honed these skills, collaborating with teams, presenting findings, and adapting my approach to different audiences.

Continuous Learning and Adaptation

Data science is a field that is constantly evolving, with new tools, technologies, and trends emerging regularly. To stay at the forefront of this ever-changing landscape, continuous learning is essential. I dedicated myself to staying updated by following industry blogs, attending conferences, and participating in courses. This commitment to lifelong learning allowed me to adapt to new challenges, acquire new skills, and remain competitive in the field.

In conclusion, the journey to becoming a data scientist is an exciting and dynamic one, requiring a diverse set of skills. From mathematics and programming to data handling and communication, each skill plays a crucial role in unlocking the potential of data. Aspiring data scientists should embrace this multidimensional nature of the field and embark on their own learning journey. If you want to learn more about Data science, I highly recommend that you contact ACTE Technologies because they offer Data Science courses and job placement opportunities. Experienced teachers can help you learn better. You can find these services both online and offline. Take things step by step and consider enrolling in a course if you’re interested. By acquiring these skills and continuously adapting to new developments, they can make a meaningful impact in the world of data science.