#Sort stata code#

Of then the question of interest is whether the distribution of the quantitative variable is different for different categories. Summary Statistics for One Quantitative Variable over One Categorical VariableДЇor a quantitative variable and a categorical variable, the mean value of the quantitative variable for those observations that fall in each category of the categorical variable can give you a sense of how the two variables are related. Tables of frequencies for two variables are often called two-way tables, contingency tables, or crosstabs. These are examples of bivariate statistics, or statistics that describe the joint distribution of the two variables. Frequencies for Two Categorical VariablesДЇor two categorical variables, frequencies tell you how many observations fall in each combination of the two categorical variables (like black women or hispanic men) and can give you a sense of the relationship between the two variables. 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 VariableДЇor 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.Д¬ategorical variables are also sometimes called factor variables. Topics Covered in this Section Frequencies for a Single Categorical VariableДЇor 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.Дescriptive statistics give you a basic understanding one or more variables and how they relate to each other. This means that there is no statistically significant relationship between the variables sexВ and languageВ in this dataset.This article is part of the Stata for Students series. Since we are usingВ 0.05 or below as ourВ cutoff point for the significance level, we can see that 0.885 is very much above 0.05 and we then conclude there is no statistical significance of the Chi-squared test. We can see that the Chi-squared value is 0.244, the degrees of freedom is 2 and the significance level is 0.885. We can see StataВ usesВ the Pearson Chi-squared test ( Pearson chi2)В which includes the degrees of freedom in parentheses, the calculated Chi-squared value, and the Pearson rВ coefficientВ ( Pr) which is the two tailed significance level. On the bottom of the crosstabulation chart Stata gives us the results from the Chi-squared test. In the code, we also specified the cells to include row and col which are the percentages of the observations of the total sample size for this analysis.

#Sort stata code#

We can see that sexВ is first in the code and appears in rows while languageВ is written second and appears in the columns. In the output chart Stata shows the crosstabulation of sexВ by language.