1. The problem involves interpreting a histogram showing frequency density versus song length in seconds. 2. Frequency density is the height of each bar in the histogram, representing how often songs of certain lengths occur per unit length interval. 3. The histogram bars are given for intervals of 100 seconds each: 0-100, 100-200, 200-300, 300-400, 400-500, and 500-600 seconds. 4. The frequency densities for these intervals are approximately: 0.05, 0.15, 0.5, 0.25, 0, and 0.05 respectively. 5. To find the relative frequency (proportion) of songs in each interval, multiply the frequency density by the width of the interval (100 seconds): - For 0-100 seconds: $0.05 \times 100 = 5$ - For 100-200 seconds: $0.15 \times 100 = 15$ - For 200-300 seconds: $0.5 \times 100 = 50$ - For 300-400 seconds: $0.25 \times 100 = 25$ - For 400-500 seconds: $0 \times 100 = 0$ - For 500-600 seconds: $0.05 \times 100 = 5$ 6. These values represent the relative frequencies or counts proportional to the number of songs in each length interval. 7. The total relative frequency sums to $5 + 15 + 50 + 25 + 0 + 5 = 100$, which can be interpreted as 100% of the songs. 8. This histogram shows that most songs have lengths between 200 and 300 seconds, with a frequency density peak at 0.5. 9. The intervals 400-500 seconds have no songs, as indicated by frequency density 0. 10. This analysis helps understand the distribution of song lengths in the dataset.