![]() Looking at the spread of the distribution of data tells us about the amount of variation, or diversity, within the data. The Spread: How is the data distributed around the center? (Measures of Dispersion) Here we have added a reference line marking the average on our chart so we can see how that looks:Ģ. The average value for height is 171.4 centimeters. To calculate the Average height (in cm) we sum up all the values and divide by the total count of observations:Īverage height = (181 + 175 + 159 + 177 + 165) ÷ 5 = 857 ÷ 5 = 171.4 Here we've made a quick chart that plots the height of each animal: The dataset has two variables - name and height - and five observations. ![]() We also have measurements of the height of each animal. In the example dataset below, we have information about the names of some animals. Put another way the average (mean) is the sum divided by the count. To calculate the average, we add up all the numbers for a variable and then divide by how many numbers there are. The average is the most appropriate way to measure the center for interval/continuous data (e.g, numbers of registered voters). In this module, we are going to focus on the average. All three summarize a distribution of the data by describing the typical value of a variable (average), the most frequently repeated number (mode), or the number in the middle of all the other numbers in a data set (median). The three common ways of looking at the center are average (also called mean), mode and median. The Center: What is Typical? (Central Tendencies) These two ways of describing the data are also referred to as descriptive statistics. This is also called a "measure of dispersion". The spread of the values around the center: This describes how densely the data is distributed around the center.This way of describing the center is also called a "measure of central tendency". The typical: This describes the center-or middle-of the data. ![]() The two most useful ways of describing the distribution of data are: The distribution of a variable shows what values the variable takes and how often the variable takes these values. Why do we summarize? We summarize data to "simplify" the data and quickly identify what looks "normal" and what looks odd. For example, an election day observation form may ask for the number of registered voters for each polling station or the number of votes received for each candidate.īy first understanding what type of data a variable is, we can then decide how to best summarize or describe that variable. Continuous or Interval: This kind of data has a continuous range of numbers."Many" is more than "Some", which is more than "None". For example, on many election observation forms, there is a questions that asks "How many people were assisted to vote?" where the answer options are "None", "Few", "Some", or "Many". Ordinal: These are data with categories that go in a specific order or rank.The "position'" variable is likely to be categorical data (e.g., President, Deputy President, and Secretary). ![]() An election management body (EMB) might release a list of officials who are assigned to each polling station, and that list might contain the name and position of the official. For example, an election observation form might ask "Were you permitted to observe all day?" where the answer option is either "Yes" or "No".
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