To calculate the average price of a product sold by the factory last month, we need to set up a mean-frequency table using the given information. Let's summarize the data provided:

1. Number of Products Sold:

   • Classic: 37 products
   • Regular: 104 products
   • Premium: 42 products
   • Total: 183 products

2. Price Ranges (assuming hypothetical ranges for the calculation, as the exact price ranges are not provided):

   • Classic: Let's assume the average price is $P_1$.
   • Regular: Let's assume the average price is $P_2$.
   • Premium: Let's assume the average price is $P_3$.

3. Frequency of Each Product Category:

   • Classic: 37
   • Regular: 104
   • Premium: 42

4. Mean-Frequency Table:

Product Type	Price ($)	Frequency	Total Price Contribution ($)
Classic	$P_1$	37	$37 \times P_1$
Regular	$P_2$	104	$104 \times P_2$
Premium	$P_3$	42	$42 \times P_3$
Total	-	183	$37P_1 + 104P_2 + 42P_3$

To calculate the average price of the products sold last month, use the formula for the weighted mean:

$\text{Average Price} = \frac{\text{Total Price Contribution}}{\text{Total Frequency}} = \frac{37P_1 + 104P_2 + 42P_3}{183}$

To complete the table and calculate the exact average, we need the actual average prices ($P_1$, $P_2$, and $P_3$) for each product category. If you have this data, please provide it, and I can compute the exact average price for you.

Would you like to provide those prices, or do you need more help on this calculation?

Here are 5 follow-up questions to consider:

1. What are the exact price values for the Classic, Regular, and Premium products?
2. How does the frequency of sales affect the overall average price calculation?
3. What if one category had significantly higher sales? How would that impact the average?
4. Are there any other factors besides the price and frequency that could affect the average price estimation?
5. How would the calculation change if there were additional product categories?

Tip: When calculating a weighted average, always ensure that the weights (frequencies) accurately represent the proportion of each category in the total dataset.