Means are often called averages, and variance is just the standard deviation squared.
Quantitative variables are often called continuous variables. However, the median and percentiles often give you a better sense of how the variable is distributed, especially for variables that are not symmetric (like income, which often has a few very high values). Summary Statistics for a Single Quantitative Variableįor a variable that describes quantities (like income) the mean tells you what the expected value of the variable is, and the standard deviation tells you how much it varies.
Frequency tables for a single variable are sometimes called one-way tables. Indicator variables (also called binary or dummy variables) are just categorical variables with two categories. These are examples of univariate statistics, or statistics that describe a single variable.Ĭategorical variables are also sometimes called factor variables. Topics Covered in this Section Frequencies for a Single Categorical Variableįor a variable that describes categories (like sex or race) rather than quantities (like income) frequencies tell you how many observations are in each category. If you are new to Stata we strongly recommend reading all the articles in the Stata Basics section.ĭescriptive statistics give you a basic understanding one or more variables and how they relate to each other. Might we one day witness a sub-\(2\) hour marathon? The study of statistics can help you make reasonable guesses about the answers to these questions.This article is part of the Stata for Students series. When we look at the times within each gender, we also wonder how much they will decrease (if at all) in the next century of the Olympics. This fact leads us to ask whether the gender gap will close or remain constant. We note that Takahashi (the lead female runner in \(2000\)) would have beaten the male runner in \(1956\) and all male runners in the first \(12\) marathons. All we can affirm is that the two means are "suggestive."Įxamining Table 3 leads to many other questions. Does this prove that the fastest men are running faster? Or is the difference just due to chance, no more than what often emerges from chance differences in performance from year to year? We can't answer this question with descriptive statistics alone. This is quite a difference (over half an hour). The mean winning time for the second \(13\) races is \(2:13:18\). To gain insight into the improvement in speed over the years, let us divide the men's times into two pieces, namely, the first \(13\) races (up to \(1952\)) and the second \(13\) (starting from \(1956\)). There are many descriptive statistics that we can compute from the data in the table. \( \newcommand\): Winning Olympic marathon times.
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