Coefficient of Determination
When there aren't extreme values (outliers)
median
mode
Accident srry meep!
the median is perferred when the data is strongly skewed or has outliers. =)
The appropriate measure of central tendency for age is the median. This is because age is a continuous variable and can have outliers or extreme values, which can skew the mean. The median provides a more robust estimate of the center of the distribution.
Meanlol
mean
When there aren't extreme values (outliers)
The most appropriate measure of central tendency depends on the nature of the data. The mean is useful for normally distributed data without outliers, while the median is better for skewed distributions or when outliers are present, as it provides a more accurate representation of the central point. The mode is ideal for categorical data where we want to identify the most frequently occurring value. Therefore, the context and characteristics of the data should guide the choice of measure.
Its the one most commonly used but outliers can seriously distort the mean.
median
mode
Mean- If there are no outliers. A really low number or really high number will mess up the mean. Median- If there are outliers. The outliers will not mess up the median. Mode- If the most of one number is centrally located in the data. :)
mean
Mode: Data are qualitative or categoric. Median: Quantitative data with outliers - particularly if the distribution is skew. Mean: Quantitative data without outliers, or else approx symmetrical.
The measure of central tendency that refers to the value that appears most frequently in a data set is the mode. It represents the data point with the highest frequency of occurrence.