Review of Interpreting and Visualizing Regression Models Using Stata by Michael N. Mitchell (original) (raw)

Interpretation of Regression Output: Diagnostics, Graphs and the Bottom Line

2002

A standard approach in presenting the results of a statistical analysis of regression data in scientific journals is to focus on the question of statistical significance of regression coefficients. The reporting of p-values in conjunction with a description of the various positive and negative associations between the response and the factors in question ensues. The real question of interest beyond these initial assessments ought to be, "how well does the treatment work?" The point of view taken here will be that this standard presentation, while important, constitutes only a first order approximation to a complete analysis, and that the bottom line ought to involve the quantification of regression effects on the scale of observable quantities. This will mainly be accomplished graphically. It is also emphasized that diagnostic assessment of the compatibility of the data to the model should be based on similar considerations.

Basic Stata Graphics for Economics Students

SSRN Electronic Journal, 2018

This paper provides an introduction to the main types of graph in Stata that economics students might need. It covers univariate discrete and continuous variables, bivariate distributions, some simple time plots and methods of visualising the output from estimating models. It shows a small number of the many options available and includes references to further resources.

ICOTS6, 2002: Johnson and Watnik 1 INTERPRETATION OF REGRESSION OUTPUT: DIAGNOSTICS, GRAPHS AND THE BOTTOM LINE

2002

A standard approach in presenting the results of a statistical analysis of regression data in scientific journals is to focus on the question of statistical significance of regression coefficients. The reporting of p-values in conjunction with a description of the various positive and negative associations between the response and the factors in question ensues. The real question of interest beyond these initial assessments ought to be, “how well does the treatment work?” The point of view taken here will be that this standard presentation, while important, constitutes only a first order approximation to a complete analysis, and that the bottom line ought to involve the quantification of regression effects on the scale of observable quantities. This will mainly be accomplished graphically. It is also emphasized that diagnostic assessment of the compatibility of the data to the model should be based on similar considerations.

Data Analysis using STATA

ASA Publications, 2022

Anyone can download it from the links, print it out for personal use, and share it with others, but it is strictly prohibited to use it for any kind of profit-making venture without the written permission of the first author. Its contents may be used and incorporated into other materials with proper acknowledgements and citations. The datasets provided in the links and used in this book are hypothetical and can be used for practice.

Visual Assessment of Residual Plots in Multiple Linear Regression: A Model-Based Simulation Perspective

This article follows a recommendation from the regression literature to help regression learners become more experienced with residual plots for identifying assumption violations in linear regression. The article goes beyond the usual approach to residual displays in standard regression texts by taking a model-based simulation perspective: simulating the data from a generating model and using them to estimate an analytical model. The analytical model is a first order linear regression model; whereas the generating model violates the assumptions of the analytical model. The residuals from the analytical model are plotted to demonstrate assumption violations to provide experience for regression learners with characterized residual patterns. The article also briefly discusses remedial measures.

REGRESSION ANALYSIS AND RELEVANCE TO RESEARCH IN SOCIAL SCIENCES

Academic Journal of Accounting and Business Management, 2021

The study seeks to review regression analysis and its relevance to research in social sciences, the study relied on a review of various regression analyses being used in social sciences and the significance of regression analysis as a tool in the analysis of data sets. The study adopted a systematic exploratory research design, reviewing related articles, journals, and other prior studies in relation to regression analysis and its relevance in social sciences. After a careful systematic and contextual review, the study revealed that regression analysis is significant in providing a measure of coefficients of the determination which explains the effect of the independent variable (explanatory variable) on the explained variable otherwise known as regressed variables that give the idea of the prediction values of the regression analysis. Regression analysis provides a practical and strong tool for statistical analysis that can enhance investment decisions, business projections in manufacturing, production, stock price movement, sales, and revenue estimations, and generally in making future predictions. This review provides originality in a clear understanding of a comprehensive review of the relevance of regression analysis in social sciences, contributing to knowledge in this regard. The study recommends that researchers should adopt the required pragmatic and methodological steps when using regression analysis, unethical torturing of data should be avoided as this could lead to false results and wrong statistical predictions.