Final answer:
The value that appears as if it might be an outlier in the given data set is 35.
Step-by-step explanation:
The value that appears as if it might be an outlier in the given data set is d) 35.
To determine if a value is an outlier, you can look at the shape of the data. In this case, it is not explicitly mentioned what kind of data it is, but assuming it is a unimodal distribution, you can use the Interquartile Range (IQR) method to identify outliers.
Calculate the IQR by finding the the difference between the first quartile and the third quartile. Any value that falls more than 1.5 times the IQR above the third quartile or below the first quartile is considered an outlier. In this data set, the first quartile is 25 and the third quartile is 33. The IQR is 8.
Therefore, any value greater than 33 + (1.5 * 8) = 45 or less than 25 - (1.5 * 8) = 13 is considered an outlier. The value 35 falls within this range, so it appears to be an outlier.