Stata descriptive statistics
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# $ Species : Factor w/ 3 levels "setosa","versicolor".: 1 1 1 1 1 1 1 1 1 1. This dataset is imported by default in R, you only need to load it by running iris: dat <- iris # load the iris dataset and renamed it datīelow a preview of this dataset and its structure: head(dat) # first 6 observations # Sepal.Length Sepal.Width Petal.Length Petal.Width Species We use the dataset iris throughout the article. See online or in the above mentioned article for more information about the purpose and usage of each measure. In this article, we focus only on the implementation in R of the most common descriptive statistics and their visualizations (when deemed appropriate). Location measures give an understanding about the central tendency of the data, whereas dispersion measures give an understanding about the spread of the data. There exists many measures to summarize a dataset.
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If well presented, descriptive statistics is already a good starting point for further analyses. It allows to check the quality of the data and it helps to “understand” the data by having a clear overview of it. Descriptive statistics is often the first step and an important part in any statistical analysis.
Stata descriptive statistics series#
To briefly recap what have been said in that article, descriptive statistics (in the broad sense of the term) is a branch of statistics aiming at summarizing, describing and presenting a series of values or a dataset.
Stata descriptive statistics how to#
To learn more about the reasoning behind each descriptive statistics, how to compute them by hand and how to interpret them, read the article “ Descriptive statistics by hand”. Since the matrix has only one column (3x1), you may have one variable var1 with three observations for N, mean, and standard deviation.This article explains how to compute the main descriptive statistics in R and how to present them graphically. You may clear memory before converting the matrix. If you want to save the matrix as a variable, enter the. matrix stats=r(StatTotal)įinally, to convert a scalar, for example, the mean of the variable, into a macro, enter. To convert the special matrix into a typical matrix using the You can also list the values of the matrix by running matrix list You may check for the presence of the matrix using the return list command. N (the number of valid observations), mean, and standard deviation are stored in a matrix The stat() option specifies the aggregate statistics to be computed. For example, to get the N, mean, and standard deviation of personal income, enter. tabstat command computes aggregate statistics of variables such as mean and standard deviation, and its save option stores these statistics in a matrix. Store the descriptive statistics of a variable in a macro in Stata