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

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

  3. Calculating the mean (article) - Khan Academy

    Learn how to calculate the mean by walking through some basic examples & trying practice problems.

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

    The mean absolute deviation (MAD) is the mean (average) distance between each data value and the mean of the data set. It can be used to quantify the spread in the data set and also be …

  5. 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 …

  6. 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.

  7. Mean as the balancing point (article) | Khan Academy

    Explore how we can think of the mean as the balancing point of a data distribution. You know how to find the mean by adding up and dividing. In this article, we'll think about the mean as the …

  8. 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!

  9. Center and spread | Lesson (article) | Khan Academy

    How is the mean calculated? The mean is useful for describing the center of data with similar values. The mean is the average value.

  10. Clusters, gaps, peaks & outliers (video) | Khan Academy

    This video explores the features of distributions in data sets, like clusters, gaps, and peaks. We learn how to identify outliers, which are data points far from the rest. We also discuss how to …