This analysis aims to evaluate the accuracy of sales predictions by analyzing residual errors — the difference between actual and expected sales. We used a statistical diagnostic plot to visualize how data points deviate from model expectations. Each point represents an individual sales observation, with its position on the graph determined by leverage (influence) and standardized residuals (error magnitude). We categorized the residuals into two types: positive (model underestimated sales) and negative (model overestimated sales). Positive residuals are marked in blue and negative ones in red, providing a clear visual separation. From the visualization, it's apparent that the model tends to overestimate sales more frequently, as indicated by the density of red points. The tooltip data also reveals specific buyer-level deviations, helping us trace systemic prediction flaws back to individuals. For example, even within the same buyer, such as Slade Farris, there are both under- and overestimations, indicating potential volatility in the sales data or model inconsistencies. Leverage values are low across the dataset, suggesting no single point is disproportionately influencing the model. However, some residual errors exceed ±5 units, which may point to possible data issues or outliers. This residual segregation offers insight into where our sales prediction model succeeds or fails. It helps identify whether the model has a consistent bias, such as overpredicting across multiple buyers. Such an approach is crucial for improving forecasting reliability, supporting data-driven business decisions, and reducing financial misestimates. In summary, the chart enables intuitive yet rigorous quality checks of our prediction logic, making the findings accessible and actionable for technical teams and decision-makers alike.

Use area charts to visualize monthly rainfall trends in 5 Indian cities, highlighting seasonal patterns

Laporan ini menyajikan analisis mendalam terhadap faktor-faktor yang memengaruhi total penjualan produk minuman dalam jaringan Coffee Chain. Fokus utama diarahkan pada perbandingan antar wilayah dan tipe toko, untuk mengidentifikasi pola performa penjualan serta profitabilitas. Hasil analisis menunjukkan bahwa beberapa wilayah secara konsisten mencetak profit tertinggi, memberikan wawasan penting untuk pengambilan keputusan bisnis strategis. Visualisasi dan pengujian statistik digunakan untuk mendukung temuan dan rekomendasi berdasarkan data.

Menggunakan Metode Uji T

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