Stata Tutorial

Learning Stata: A Tutorial on Creating and Customizing Histograms for Data Visualization

A histogram is an indispensable graphical tool within statistical analysis, serving as the foundational method for visualizing the empirical distribution of a continuous dataset. Through the use of connected rectangular bars, this chart effectively depicts the frequency, count, or proportion of data observations that fall within specific, predetermined numerical intervals, commonly referred to as bins. […]

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A Comprehensive Guide to Correlation Coefficients: Pearson, Spearman, and Kendall using Stata

In the realm of statistics and data analysis, the concept of correlation is absolutely fundamental. It quantifies the statistical relationship between two variables, specifically detailing both the strength and the direction of that association. This relationship is summarized by a correlation coefficient, a standardized metric that always ranges between -1 and 1. A coefficient of

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A Comprehensive Guide to Welch’s t-test in Stata: Comparing Means with Unequal Variances

The comparison of means between two distinct and independent groups is a cornerstone of statistical inference. Typically, researchers rely on the independent two-sample t-test (often called Student’s t-test). However, this procedure relies on a critical assumption: homogeneity of variance (or homoscedasticity). This assumption mandates that the spread or variability of the outcome variable must be

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A Step-by-Step Guide to the Kruskal-Wallis Test in Stata

The Kruskal-Wallis Test stands as a cornerstone in statistical methodology, essential for determining whether statistically significant differences exist among the medians of three or more independent groups. Its utility stems from its role as the direct non-parametric alternative to the standard one-way analysis of variance (ANOVA), making it invaluable in situations where parametric assumptions are

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Chi-Square Goodness of Fit Test in Stata: A Step-by-Step Guide

The Chi-Square Goodness of Fit Test represents a fundamental and indispensable statistical procedure utilized across various empirical disciplines, ranging from social sciences to bioinformatics. Its primary function is to rigorously assess whether the observed distribution of frequencies for a specific categorical variable within a collected sample deviates significantly from a theoretical, predetermined, or previously established

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Chi-Square Test of Independence with Stata: A Tutorial for Analyzing Categorical Data

The Chi-Square Test of Independence is a foundational tool in inferential statistics, widely applied across fields from social research to medical epidemiology. Its primary purpose is to determine whether there is a statistically significant association between two factors, both of which are measured as categorical variables. When researchers classify data into discrete, non-overlapping groups—such as

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A Comprehensive Guide to Linear Regression in Stata: Prediction and Residual Analysis

The Foundation of Linear Regression and Diagnostic Tools Linear regression stands as a cornerstone in statistical modeling, offering a robust framework for understanding and quantifying the relationship between variables. This technique allows analysts to define a linear mathematical relationship between one or more explanatory variables (or predictors) and a single continuous response variable. The fundamental

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Learning to Create and Modify Pie Charts with Stata: A Step-by-Step Guide

The Role of Pie Charts in Statistical Analysis A pie chart is a fundamental graphical representation tool in statistics, specifically designed to display the proportional distribution of categorical data. This intuitive circular chart divides a dataset into “slices,” where the area of each slice is mathematically proportional to the quantity it represents. By illustrating the

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Learning Guide: Understanding and Generating Q-Q Plots in Stata

The Quantile-Quantile plot (or Q-Q plot) is a fundamental graphical technique in statistical diagnostics, serving as an indispensable tool for comparing the probability distribution of a specific dataset against a theoretical distribution. In the vast majority of cases, particularly within the framework of linear modeling, this comparison is made against the normal distribution. Within the

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