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  1. Mean, median, and mode review (article) | Khan Academy

    Mean, median, and mode are different measures of center in a numerical data set. They each try to summarize a dataset with a single number to represent a "typical" data point from the dataset.

  2. Statistics intro: Mean, median, & mode (video) | Khan Academy

    The mean (average) of a data set is found by adding all numbers in the data set and then dividing by the number of values in the set. The median is the middle value when a data set is ordered …

  3. Mean, median, & mode example (video) | Khan Academy

    Here we give you a set of numbers and then ask you to find the mean, median, and mode. It's your first opportunity to practice with us!

  4. Mean absolute deviation (MAD) review (article) | Khan Academy

    Mean absolute deviation The mean absolute deviation of a dataset is the average distance between each data point and the mean. It gives us an idea about the variability in a dataset. …

  5. Calculating the mean (article) | Khan Academy

    Learn how to calculate the mean by walking through some basic examples & trying practice problems. The mean is used to summarize a data set. It is a measure of the center of a data …

  6. Mean, median, and mode (practice) | Khan Academy

    Calculate the mean, median, or mode of a data set!

  7. Calculating the median (practice) | Khan Academy

    Practice finding the median of a data set. Like the mean, the median gives us a sense of the middle, or center, of the data.

  8. Calculating the mean (practice) | Khan Academy

    Practice calculating the mean (average) of a data set. The mean gives us a sense of the middle, or center, of the data.

  9. Mean and standard deviation of a discrete random variable

    Practice calculating and interpreting the mean and standard deviation of a discrete random variable.

  10. Mean absolute deviation (MAD) (video) | Khan Academy

    To find the Mean Absolute Deviation (MAD), first calculate the mean (average) of your data set. Next, find the absolute difference (distance) between each data point and the mean. Sum up …