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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.
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. :)
Can there be 2 outliers in a set of data?
There is no limit to the number of outliers there can be in a set of data.
What are disadvantages of median?
One disadvantage of using the median is that it may not accurately represent the entire dataset if there are extreme outliers present, as the median is not influenced by the magnitude of these outliers. Additionally, the median may not be as intuitive to interpret as the mean for some individuals, as it does not provide a direct measure of the total value of the dataset. Finally, calculating the median can be more computationally intensive compared to other measures of central tendency, especially with large datasets.
Is the mean or median more accurate?
None of them is "more accurate". They are answers to two different questions.
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.
When the median and mean are substantially different what does that tell you about the data?
I think it means that our data includes outliers.
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. :)
In general the median of a data set is more resistant to outliers than the mean.?
Yes, it is.
When an observation in a data is abnormally more than and less than the remaining observations in the data does it affect Mean Median Mode?
Yes, an observation that is abnormally larger or smaller than the rest of the data can significantly affect the mean, as it will pull the average towards that extreme value. However, the median and mode are less influenced by outliers, as they are not as sensitive to extreme values. The median is the middle value when the data is arranged in order, so outliers have less impact on its value. The mode is the most frequently occurring value, so unless the outlier is the most common value, it will not affect the mode.
Why do they invented the stem and the leaf plot?
to organize your data set and figure out mean, median, mode, range, and outliers.
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. =)
Advantages and disadvantages of median in statistics?
The median is advantageous because it is not influenced by extreme values, making it a robust measure of central tendency for skewed data sets. It is also easy to interpret and calculate. However, the median may not accurately represent the true center of a dataset if the data is heavily skewed or if there are outliers present. Additionally, the median may not be as efficient as the mean for certain statistical calculations due to its lack of sensitivity to all data points.
What is the primary disadvantage of using the range to compare the variability of data sets?
The range is very sensitive to outliers. Indeed if there are outliers then the range will be unrelated to any other elements of the sample.
What are outliers and how do they affect data?
Outliers are observations that are unusually large or unusually small. There is no universally agreed definition but values smaller than Q1 - 1.5*IQR or larger than Q3 + 1.5IQR are normally considered outliers. Q1 and Q3 are the lower and upper quartiles and Q3-Q1 is the inter quartile range, IQR. Outliers distort the mean but cannot affect the median. If it distorts the median, then most of the data are rubbish and the data set should be examined thoroughly. Outliers will distort measures of dispersion, and higher moments, such as the variance, standard deviation, skewness, kurtosis etc but again, will not affect the IQR except in very extreme conditions.
How can the median and mean be different?
You will notice a difference in the data if you have outliers. The mean of a set is going to be heavily influenced by outliers due to the mean being dependant on the quantity of each unit (i.e. 2 cats, 7 cats, 300 cats, etc.) The median, however, is not influenced by outliers because it accounts for the number of units rather than the quantity associated with the units.
Can there be 2 outliers in a set of data?
There is no limit to the number of outliers there can be in a set of data.
