Articles with this tag assume a beginner knowledge within this category.
Understanding Distribution Shapes and Central Tendency In many of my BI articles I discuss data and its measurements, one of the most commonly used being the mean (link) or average of all values within a data set. A data set contains many data points, their arrangement around the mean and their probably of occurrence is…
Please see me my other Excel articles. Leveraging Z-Score to Understand Data Dispersion In my Standard Deviation article I demonstrated how to use the squared variance to show the average distance each data point resides from the mean. While variance and standard deviation illustrate data dispersion from the mean in terms of their precise location…
Please see me my other Excel articles. Using Variance to Determine Dispersion In my distributions articles (stats link) I illustrated distributions and how their shapes may be understood in terms of data points and their relation to the mean. Since the mean represents the average of all values, understanding the spread of all data points,…
Please see me my other Excel articles. Leveraging Standard Deviation to Understand Data Dispersion In my Variance article I illustrated how to use variance to understand the average of all variances squared from the mean. In this article I will demonstrate how to determine standard deviation, which is simply the variance, squared. Excel provides the…
Statistics: Distributions – The Empirical Rule In my Distribution Shapes article, I discussed the different shapes most distributions of data would visually utilized, with symmetrical being the most common. These bell-shaped distributions demonstrate the “empirical rule” which demonstrates a relationship between data points and standard deviations placement in relation to the mean. The distribution below…
Understanding Causes of Skewed Distribution Shapes In my Distribution Shapes article I illustrates the three most common shapes founds with distributions – symmetrical, left-skewed, and right-skewed. Symmetrical distributions display data points equally on both sides because most of the values reside near the mean (average). This near-uniformity of data points with the average prevents the…
Isolating Patterns Based on Conditions In my Marginal Distributions article, I illustrated how to leverage a data set to isolate two marginal distributions in comparison with the grand total of all distributions, to arrive at specific distributions. This concept illustrates the concept of a conditional distribution in that a distribution is isolated based on a…
Isolating a Distribution’s Subset In my Joint Distributions article, I illustrated how to provide a data set using two (joint) distributions, with the Age Groups and Years distributions complementing each other. In this article I will demonstrate how to create a marginal distribution, which isolates one of the available distributions and measures it against the…
Please see my other Statistics articles. Delivering Multidimensional Understanding In this article I will demonstrate how to use a data set to communicate joint (or two) distributions of data. First, I will convert the table into a relative cumulative table, beginning with the first age group (0-8). In this formula, I’m instructing Excel to divide…
Understanding Distribution Shapes and Central Tendency In many of my BI articles I discuss data and its measurements, one of the most commonly used being the mean (link) or average of all values within a data set. A data set contains many data points, their arrangement around the mean and their probably of occurrence is…
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