# Statistics: Probability – Bayes Theorem

Determining Probability Based on Prior Events With my other probability articles I demonstrated how to determine the likelihood of independent and dependent events. In those examples, I used no prior knowledge of events, but in many scenarios when testing for probability on dependent events, one event has already occurred for which the probability has been…

# Statistics: Probability – Independent & Dependent

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…

# Statistics: Probability

Determine the Likelihood of an Event Similar to my Distribution articles, Probability is another concept which focuses on how likely an event will occur. Single Event Probability The following formula returns the likelihood of an event using how many events qualify for the desired outcome (criteria), divided by the total number of possibilities. To illustrate…

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,…

# Excel: Spread – Standard Deviation

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

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…

# Statistics: Distribution Outliers

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…

# Statistics: Distributions – Conditional

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…

# Statistics: Distributions – Marginal

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…