Write an R program to create a lollipop chart comparing the Gross Domestic Product (GDP) of various Indian states using base R and ggplot2

Optimalisasi Keuntungan Penjualan melalui Budget Sales dan Total Expenses: Analisis Regresi Linear Berganda pada Rantai Kopi di Kota NewYork

At Australia Walmart, making data-driven decisions is crucial for maximizing profit and efficiency. We recently examined how residual errors — the difference between predicted and actual sales — impact our Return on Investment (ROI). These residuals can either be too high or too low, both of which hint at model imperfections. If the residual is strongly negative, it means we overestimated sales. If it's strongly positive, we underestimated them. You might think this is just a math issue — but it goes deeper. These errors influence ROI directly. We built a model that calculated ROI for each transaction and grouped the data based on how extreme their residuals were. The results were clear: High residual errors — whether positive or negative — disrupt our ROI. The best ROI came from entries with small or "nominal" residuals. What does this mean for decision-makers? If we keep including high-error data points in our analysis, we risk making flawed investment decisions. However, when we prune out the extreme cases, our model becomes more stable, and ROI predictions become more reliable. This isn't just about cleaning data — it's about protecting profits. By removing high-residual transactions, Australia Walmart can build a smarter, leaner sales strategy. The data shows us the story — we just have to listen. Better models = better decisions = better ROI. And in retail, that’s everything.