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Sagot :
To determine the skewness of a dataset, we can compare the mean and the median:
1. Mean (Average): The sum of all the data points divided by the number of data points.
2. Median (Middle Value): The value that separates the dataset into two equal halves.
Now, let's consider the characteristics of skewness:
- Negative Skew (Left Skew): The mean is less than the median. The left tail (lower values) is longer or fatter than the right tail.
- Positive Skew (Right Skew): The mean is greater than the median. The right tail (higher values) is longer or fatter than the left tail.
- No Skew (Symmetric): The mean is equal to the median.
In the given problem:
- The mean is 15.
- The median is 10.
Since the mean (15) is greater than the median (10), the dataset indicates positive skewness or right skew.
Therefore, the type of skew of the dataset is Positive Skew.
1. Mean (Average): The sum of all the data points divided by the number of data points.
2. Median (Middle Value): The value that separates the dataset into two equal halves.
Now, let's consider the characteristics of skewness:
- Negative Skew (Left Skew): The mean is less than the median. The left tail (lower values) is longer or fatter than the right tail.
- Positive Skew (Right Skew): The mean is greater than the median. The right tail (higher values) is longer or fatter than the left tail.
- No Skew (Symmetric): The mean is equal to the median.
In the given problem:
- The mean is 15.
- The median is 10.
Since the mean (15) is greater than the median (10), the dataset indicates positive skewness or right skew.
Therefore, the type of skew of the dataset is Positive Skew.
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