统计新手关于repeat purchases类别可视化及观测值平衡性分析的技术问询

Hey there! Let's work through your problem step by step—since you're dealing with a categorical variable (your 6 repeat purchase categories) and need to visualize counts plus assess balance, we can fix this easily.

推荐的可视化方案 #

First off, forget the bell curve for this task—it's not meant for categorical data! Here are the best tools for your needs:

• Bar Chart: This is your go-to. It’ll clearly display the number of products for each repeat purchase category, making it super easy to compare which category has the highest/lowest counts. Just plot your 6 categories on the x-axis and the corresponding product counts on the y-axis; the tallest bar will immediately show you the most frequent category.
• Pie Chart (Optional): If you want to quickly see each category’s share of the total 1500 products, a pie chart works. But keep in mind, bar charts are better for precise numerical comparisons between categories.

Answering Your Core Questions #

a) Identifying the Category with the Most Observations #

Once you’ve made your bar chart, the answer is straightforward: the category with the tallest bar is the one with the most products. If you want to confirm numerically, just pick the maximum value from your 6 category counts.

b) Assessing Balance Across Categories #

Your initial idea of using mean and standard deviation is on track, but applying a bell curve was the wrong fit. For categorical variables, here’s how to judge balance:

1. Calculate the expected average per category: 1500 total products / 6 categories = 250 products per category.
2. Compare each category’s actual count to this 250 benchmark:
   • If all counts are within a tight range around 250 (say, 200–300), your data is balanced.
   • If some categories have way more than 250 (e.g., 400+) and others have way fewer (e.g., 100–), your data is unbalanced.
3. For a more formal check, compute the Coefficient of Variation (CV): divide the standard deviation of your 6 category counts by the mean (250). A small CV (like <0.2) means low variability and good balance; a large CV means high variability and imbalance.

Why Your Bell Curve Attempt Didn’t Work #

Bell curves (normal distribution plots) are designed for continuous numerical data (like heights or test scores), not categorical groups. Your repeat purchase categories are discrete groups, so the bell curve can’t properly represent their counts. Switching to a bar chart will make your data clear and actionable.

备注：内容来源于stack exchange，提问作者Khai