The overall development of China's domestic sports goods manufacturing industry has shown a positive trend. As of October 28, 2014, the industry in East China held the largest share of sales revenue and maintained steady growth over the years. From 2003 to 2014, the sales revenue in this region increased from 1,873 thousand yuan to a significant proportion of the national total, showing a growth of 261.79%. Meanwhile, regions such as North China, Northeast China, Central China, Northwest China, and Southwest China had relatively similar levels of sales revenue, though their growth trajectories varied over the seven-year period. Notably, Central and Southwest China saw remarkable growth rates, with sales revenue increasing by 4,595.75% and 1,379.95%, respectively, despite starting from a lower base. North and Northeast China also experienced consistent growth, while the Northwest region faced a decline of 91.11%. Overall, in 2003, the entire sports goods manufacturing industry saw a 203.83% increase in sales volume, reflecting a strong and healthy development trend across the country. To analyze the long-term relationships between factors such as cultural education levels, national income, and the growth of the sports goods industry, researchers used a panel cointegration model. Before conducting the analysis, it was essential to test for stationarity in the data to avoid spurious regression results. This involved performing unit root tests on the panel data. Four different tests were applied to ensure the robustness of the findings. The variables were often expressed in logarithmic form, as the difference in logarithms approximates the growth rate of the variable, which is typically stationary. This makes them suitable for classical regression models. Additionally, using logarithmic transformations helps capture the long-run elasticity between economic indicators and the development of the sports goods sector. The unit root tests included both level and first-order difference tests. The results indicated that some variables were non-stationary at the level but became stationary after first differencing. The choice of lag order was based on the Schwarz Information Criterion (SIC), ensuring a more accurate model specification. The statistical significance of the results was also reported, with p-values provided in parentheses. The test specifications included only an intercept term, without a trend component, to maintain simplicity and focus on the core relationships.

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