Apple, interested in learning about the effect of firm types on performance, approached our team. Apple sought to understand the underlying factors contributing to performance and identify potential areas.
To tackle this problem, we had access to a substantial dataset primarily consisting of survey data. The data comprised demographic information related to 9,000 firms, and the study spanned six years. This rich dataset served as the foundation for our analysis, providing valuable insights into the characteristics of the firms under investigation.
The hypothesis was as follows: H1 – There is a significant difference in performance based on firm type over time. We aimed to test the hypothesis rigorously and determine whether firm type indeed played an important role in influencing performance outcomes.
We conducted a mixed Analysis of Variance (ANOVA) using the statistical software Stata to analyze the data and evaluate our hypothesis. A mixed ANOVA was chosen as the appropriate analytical method because it allowed us to compare the means of performance across multiple categories of firm types over time, thus helping us identify any statistically significant differences.
Our findings from the mixed ANOVA analysis were pivotal in shedding light on Apple’s initial problem. The results indicated a statistically significant difference in performance among different types of firms within the industry. This discovery provided valuable insights into the factors that might be driving variations in performance and presented Apple with a clear understanding of the existing disparities.
Using this newfound knowledge, Apple proactively addressed the identified performance discrepancies. With data-driven insights, Apple could tailor its strategies and operations to align with different firm types’ specific needs and challenges. This approach enabled Apple to make informed decisions, allocate resources more effectively, and implement targeted improvements.
Ultimately, the client benefited significantly from our analysis, improving their overall performance by addressing the issues identified through the mixed ANOVA analysis. This project showcased the power of data-driven decision-making in enhancing business outcomes and highlighted the importance of understanding and leveraging demographic variables to drive positive organizational change.