Articles with this tag assume a beginner knowledge within this category.
Probability: Independent and Dependent In my Probability article, I demonstrated determining the likelihood of events related to students and their grades using a single event. In this article, I will demonstrate the nature of events used in probability determination – either they are independent or dependent events. Independent Events Returning to the example from the…
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…
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 in relation to the mean, Z-Score provides…
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, or the average distance data points from…
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 STDEV.P() function to use the data set…
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…
