It seems there was an issue with the calculation of total revenue by product category, resulting in incorrect output. We'll correct this and provide the accurate results.

There seems to be an issue with the data, possibly due to incorrect formatting or the presence of non-numeric values in the Revenue column. This has led to incorrect calculations of total revenue by product category. It's recommended to check the data for inconsistencies or formatting errors and correct them for accurate analysis.

The investigation into the Revenue column revealed the presence of non-numeric values, including NaN and #VALUE!, which are causing issues with calculations. Here's a summary of findings:

To proceed with accurate calculations, these non-numeric values need to be addressed, either by removing these rows or converting them to a numeric format where possible.

Rows with non-numeric values in the Revenue column have been successfully removed, and the Revenue column has been converted to numeric in the cleaned dataset. This prepares the data for accurate financial calculations and analysis.

After cleaning the data by removing rows with non-numeric values in the Revenue column, we recalculated the total revenue by product category. Here are the results:

This provides a clearer picture of the revenue generated by each product category.

We visualized the total revenue by product category using a bar chart. This visualization clearly shows the revenue distribution across different categories, highlighting which categories generate the most revenue. The Eyewear (Sports) category stands out as the top revenue generator, followed by Apparels (Sports), and Eyewear (Lifestyle). Other categories contribute to the total revenue to varying degrees, with Helmets and Accessories generating the least revenue among the categories listed.

To answer the research question about the significance of the difference in average sales revenue between different product categories, we'll conduct an ANOVA (Analysis of Variance) test. This statistical test helps determine if there are any statistically significant differences between the means of three or more independent groups. In this context, the independent groups are the different product categories.

We'll first ensure the data is suitable for an ANOVA test, then proceed with the test to analyze the differences in average sales revenue across product categories.

The ANOVA test resulted in a statistic of approximately 161.25 and a p-value of approximately $3.91 \times 10^{-222}$. This very small p-value indicates that there is a statistically significant difference in the average sales revenue between different product categories. Therefore, we can conclude that not all product categories generate the same amount of revenue on average, supporting the need for further analysis to identify specific differences between categories.

The hypothesis you're referring to is typically tested using an ANOVA test, which we just performed. The null hypothesis for an ANOVA test states that there are no significant differences among the group means, which in this context refers to the average sales revenue among different product categories.

Given the results of the ANOVA test, with a p-value of approximately $3.91 \times 10^{-222}$, we reject the null hypothesis. This means there is a statistically significant difference in the average sales revenue among different product categories, contradicting the stated hypothesis.