Linear Regression using R
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This document presents a linear regression analysis performed in R. It defines dependent and independent variables for a linear regression model using data from a CSV file. It generates an ANOVA table, correlation plots, scatter plots with and without regression lines, normal probability plots of residuals, a residuals histogram, and calculates errors. It also produces confidence and prediction intervals for predicted mean responses at new x-values.
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How to Build a Great Product
Being a tech entrepreneur is about providing a remarkable product or service that serves the needs of its customers better, faster, and cheaper than anything else. The goal is to "make something people want" which we call, product market fit.
But how do we get there? We'll explore the process of taking an idea to product market fit (PMF), how you know you have true PMF, and how your product strategies differ pre-PMF from post-PMF.
Brandon is a 3x founder, 1x exit, ex-banker & corporate strategist, car dealership owner, and alumnus of Techstars & Y Combinator. He enjoys building products and services that impact people for the better.
Brandon has had 3 different careers (banking, corporate finance & strategy, technology) in 7 different industries; Investment Banking, CPG, Media & Entertainment, Telecommunications, Consumer application, Automotive, & Fintech/Insuretech.
He's an idea to revenue leader and entrepreneur that helps organizations build products and processes, hire talent, test & iterate quickly, collect feedback, and grow in unregulated and heavily regulated industries.Mastering ChatGPT & LLMs for Practical Applications: Tips, Tricks, and Use Cases
Mastering ChatGPT & LLMs for Practical Applications: Tips, Tricks, and Use CasesSanjay Willie
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Our latest session with Astiostech covered how to unlock the full potential of ChatGPT and LLMs for real-world use!
Key Takeaways:
Effective Prompting: Crafting context-specific, high-quality prompts for optimal AI responses.
Advanced ChatGPT Features: Managing system prompts, conversation memory, and file uploads.
Optimizing AI Outputs: Refining responses, handling large texts, and knowing when fine-tuning is needed.
Competitive Insights: Exploring how ChatGPT compares with other AI tools.
Business & Content Use Cases: From summarization to SEO, sales, and audience targeting.
The session provided hands-on strategies to make AI a powerful tool for content creation, decision-making, and business growth.
Are you using AI effectively in your workflow? Lets discuss how it can improve efficiency and creativity!
#AI #ChatGPT #PromptEngineering #ArtificialIntelligence #LLM #Productivity #Astiostech
Revolutionizing Field Service: How LLMs Are Powering Smarter Knowledge Access...
Revolutionizing Field Service: How LLMs Are Powering Smarter Knowledge Access...Earley Information Science
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What is Blockchain and How Can Blockchain Consulting Help Businesses.pdf
What is Blockchain and How Can Blockchain Consulting Help Businesses.pdf Yodaplus Technologies Private Limited
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Linear Regression using R
- 1. Linear Regression using R Presented by Sunil Kumar P (A13020) Maruthi Nataraj K (A13009) Praxis Business School, Kolkata
- 2. READING CSV FILE FROM SPECIFIED LOCATION Contents display
- 3. DEFINING DEPENDENT (Y) AND INDEPEDENT (X) VARIABLES FOR LINEAR REGRESSION MODEL ANOVA Correlation
- 4. SCATTER PLOT OF Y AND X WITHOUT & WITH REGRESSION LINE
- 5. NORMAL PROBABILITY PLOTS OF RESIDUALS
- 6. NORMAL PROBABILITY PLOTS OF RESIDUALS
- 7. RESIDUALS HISTOGRAM AND NORMALITY TESTS
- 8. MEAN ABSOLUTE PERCENTAGE ERROR CALCULATION
- 9. CONFIDENCE AND PREDICTION INTERVAL FOR THE MEAN RESPONSE AT NEW X VALUE (95% default) Confidence Interval for the mean response at new x value and desired confidence interval (level) Confidence Interval for the mean response at various new x values
- 10. SCATTER PLOT WITH FITTED LINE ,CONFIDENCE BANDS AND PREDICTION BANDS(95% CI default)