If \( 75 \% \) of the items were sold in birr 45 or less and most items were sold in birr 34 , find the missing frequencies. If \( 25 \% \) of the items were sold in birr 45 or mote nad most items were sold in bir 34 , find the missiog frequencies.

To solve this problem, let's start by analyzing the information provided about the items sold based on their prices. Given: 1. \( 75\% \) of the items were sold for birr 45 or less. 2. The most frequently sold item price is birr 34. From the first statement, if we assume the total number of items sold is \( N \), then the number of items sold for birr 45 or less is \( 0.75N \). This means the remaining \( 25\% \), or \( 0.25N \), were sold for birr 45 or more. Now, let's break this down into missing frequencies for the two scenarios: 1. **Scenario 1:** For items sold at birr 45 or less, we can guess frequencies based on subtotal areas. For birr 34, which sold the most, let's say its frequency is \( f_1 \). Therefore, from the \( 0.75N \), we can express missing frequencies in this bracket to find a plausible distribution. 2. **Scenario 2:** For items sold at birr 45 or more, we need to establish that the majority (over \( 35\% \)) made up by higher frequencies centered around a specific item pricing much above birr 34. Thus, \( 0.25N \) in this case could be composed of frequencies at birr 45 and above – with birr 45 perhaps having its own frequency \( f_2 \). These examples show distributions at both ends of the pricing and direct connections with sales frequency at different price points!