Final answer:
The shape of each data set and the appropriate measure of center depend on the distribution of the data. Without more information, it is difficult to determine the most appropriate measure of center for the women's data sets and whether there are outliers or not.
Step-by-step explanation:
The shape of each data set can be described by examining the distribution of the data. In this case, the data sets are the numbers of minutes spent working out for 10 men and 10 women. Without the actual data, we cannot determine the exact shape, but we can make some general observations.
If the data sets follow a bell-shaped curve, also known as a normal distribution, then the most appropriate measure of center would be the mean. However, if the data sets contain outliers or extreme values, the median would be a better measure of center.
Since we don't have any information about the distribution of the data, it is difficult to determine whether the better measure of center for the women is the median or not. Coraline's statement cannot be confirmed without more information.
To determine if there are outliers in the women's data sets, we can use the interquartile range (IQR) method. The IQR is the range between the first quartile (Q1) and the third quartile (Q3). Any data point that falls below Q1 - 1.5 * IQR or above Q3 + 1.5 * IQR can be considered an outlier.
Without the actual data, it is not possible to calculate the IQR or identify any outliers. Therefore, we cannot determine whether Coraline is correct in stating that there are no outliers in the women's data sets.