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What measure of central tendency is robust when outliers are present?
Meanlol
Which measure of central tendency is robust when outliers are present?
mean
Is the mean a better measure of location when there are no outliers?
Yes, the mean is generally a better measure of central tendency when there are no outliers, as it takes into account all values in the dataset and provides a mathematically precise average. In the absence of outliers, the mean reflects the true center of the data distribution effectively. However, in the presence of outliers, the median might be preferred since it is less affected by extreme values.
Is the best measure of central tendency always the mean?
Its the one most commonly used but outliers can seriously distort the mean.
Which measure of central tendency would be most?
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.
What measure of central tendency is robust when outliers are present?
Meanlol
Which measure of central tendency is robust when outliers are present?
mean
When is the mean used as a measure of central tendency?
When there aren't extreme values (outliers)
Is the mean a better measure of location when there are no outliers?
Yes, the mean is generally a better measure of central tendency when there are no outliers, as it takes into account all values in the dataset and provides a mathematically precise average. In the absence of outliers, the mean reflects the true center of the data distribution effectively. However, in the presence of outliers, the median might be preferred since it is less affected by extreme values.
Is the best measure of central tendency always the mean?
Its the one most commonly used but outliers can seriously distort the mean.
When given a set of data that appears to contain outliers which measure of central tendency is most appropriate to use?
Coefficient of Determination
What is the appropriate measure of central tendency for age?
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.
How can you determine which measure of central tendency is best for the set if data?
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. :)
When is each measure of central tendency most useful?
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.
Under what conditions might you prefer to use the median rather than the mean as the best measure of central tendency?
the median is perferred when the data is strongly skewed or has outliers. =)
Which measure of central tendency would be most?
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.
Why is arithmetic mean considered as the best measure of central tendency?
The arithmatic mean is not a best measure for central tendency.. It is because any outliers in the dataset would affect its value thus it is considered not a robust measure.. The mode or median however would be better to measure central tendency since outliers wont affect it value.. Consider this example : Arithmatic mean dan mode from 1, 5, 5, 9 is 5.. If we add 30 to the dataset then the arithmatic mean will be 10 but the mode will still same.. Mode is more robust than arithmatic mean..
