Respuesta :
Answer:
a. mean and median
Step-by-step explanation:
To calculate skewness, you typically need to calculate the mean and median of the distribution. Skewness involves comparing the distribution of data points around the mean and median to determine the asymmetry.
Final answer:
To calculate skewness, it is important to have an understanding of the shape and spread of the data distribution you're working with, since skewness measures the asymmetry of that distribution relative to the normal distribution. So the correct answer to the question is: a. mean and median
Explanation:
To calculate skewness, it is important to have an understanding of the shape and spread of the data distribution you're working with, since skewness measures the asymmetry of that distribution relative to the normal distribution.
Now, let's analyze the options to see which one is necessary for calculating skewness:
a. mean and median - Skewness involves measuring the extent to which the data are not symmetrical. The mean is used as a comparison point to see if more values fall below or above it, while the median could give us an indication of where the center of the data lies. These two measures can be compared to assess whether the data are skewed to the left or right.
b. median and mode - While the median and mode can tell us something about the data distribution, they are less directly used in the calculation of skewness. Skewness is more precisely defined in terms of moments, and neither the median nor the mode is used in calculating the first or second moments (mean or variance).
c. mode and mean - The mode is the most frequent value in the data set, and the mean is the average. Although comparing the mean and mode can give some insight into the skewness, this isn't the formal method for calculating skewness.
d. mode and variance - Variance is a measure of the dispersion of the data, or how spread out it is, and it is indeed used in the calculation of the second moment about the mean; however, the mode does not contribute to the calculation of skewness in a formal statistical sense.
The calculation of skewness typically uses the third moment about the mean and normalizes it by the standard deviation (square root of variance) raised to the third power. This can also be done using the formula:
skewness = (mean - median) / standard deviation
Given that, we can deduce that the mean and median (option a) are the necessary statistical measures you need to calculate before calculating skewness because skewness is fundamentally a comparison of the mean to the rest of the distribution, and the median helps assess where the center of the data lies relative to the mean in a non-parametric way. The exact calculations typically involve moments, but at a basic level, understanding the relationship between the mean and median is key to understanding skewness.
So the correct answer to the question is: a. mean and median
