Fundamental concept Independent and Dependent Union of Events Exclusive and exhaustive Binomial Experiment Binomial Distribution

HW 9

This report investigates whether hardware performance characteristics meaningfully influence the hourly rental prices of GPU instances in a simulated Lambda AI cloud computing environment. Using a dataset of 300 GPU instances—including variables such as GPU model, VRAM capacity, number of GPUs, benchmark performance score, power consumption, and hourly price—the analysis focuses on determining whether technical specifications are statistically associated with cloud pricing. The project is organized using standard statistical methodology. Data exploration includes descriptive summaries and a correlation heatmap to examine relationships among numeric variables. Statistical inference begins with a Pearson correlation test and a simple linear regression model to evaluate whether higher GPU benchmark scores predict higher hourly rental prices. A Welch two-sample t-test further compares mean prices between two commonly used GPU models (A100 and L40S) to assess whether hardware tier alone drives pricing differences. Results show that benchmark performance and other hardware variables have no statistically significant relationship with hourly price. The regression slope is not significant, the correlation between benchmark score and price is near zero, and the t-test reveals no meaningful difference in mean pricing between A100 and L40S GPUs. These findings suggest that Lambda’s pricing model is not primarily determined by raw hardware performance. Instead, pricing is likely influenced by operational factors such as geographic region, utilization rates, reliability metrics, or customer-specific demand patterns. This study contributes to understanding how cloud GPU resources are priced in modern AI infrastructure. The findings highlight the importance of considering broader market and system-level variables beyond hardware specifications when analyzing or modeling cloud pricing behavior.

An analytical examination of global country data through the application of linear and multiple linear regression models.