Someone already calculated the 5-number summary and IQR for you. \( \begin{array}{lllllll}52 & 74 & 78 & 79 & 85 & 87 & 88 \\ 8\end{array} \) Min: 52 Q1: 76 Median: 85 Q3: 88 Max: 90 IQR: 12 The low end cutoff is The high end cutoff is

To calculate the low and high end cutoffs for identifying potential outliers using the IQR method, you first need to establish the lower and upper bounds. The lower cutoff is found by subtracting 1.5 times the IQR from Q1, which is \( 76 - (1.5 \times 12) = 76 - 18 = 58 \). The high cutoff is established by adding 1.5 times the IQR to Q3, resulting in \( 88 + (1.5 \times 12) = 88 + 18 = 106 \). Therefore, any data point below 58 or above 106 would be considered an outlier. Consider the 5-number summary as part of a larger context for understanding data distributions. It’s great for quickly summarizing your data, but diving deeper into how this summary reflects data skewness or presence of outliers is even better. Various graphical methods like box plots can vividly showcase the 5-number summary, making your data story much more engaging and interpretable!