Table of Contents
Ordinary Least Squares (OLS) Regression is a statistical method used to analyze the relationship between a dependent variable and one or more independent variables. It is commonly used in Mplus, a statistical software program, to estimate the parameters of a linear regression model. OLS Regression works by minimizing the sum of squared residuals, which are the differences between the actual values of the dependent variable and the predicted values from the regression model. This method is used to determine the best fitting line or curve that represents the relationship between the variables. OLS Regression in Mplus is useful for analyzing data and making predictions in various fields such as social sciences, economics, and psychology. It allows researchers to identify significant predictors and determine the strength and direction of their effects on the dependent variable. Overall, OLS Regression is a powerful tool for analyzing and understanding the relationships between variables in a dataset.
Ordinary Least Squares Regression | Mplus Annotated Output
This page was created using Mplus 5.1.
Below is an example of ordinary least squares (OLS) regression with footnotes
explaining the output. To summarize the output, both predictors in this model, x1 and
x3, are
significantly related to the outcome variable, y1.
Here is the same example illustrated in Mplus based on the ex3.1.dat data file.
TITLE:
this is an example of a simple linear
regression for a continuous observed
dependent variable with two covariates
DATA:
FILE IS ex3.1.dat;
VARIABLE:
NAMES ARE y1 x1 x3;
MODEL:
y1 ON x1 x3;
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 1
Number of independent variables 2
Number of continuous latent variables 0
<output omitted>
TESTS OF MODEL FIT
Chi-Square Test of Model Fita
Value 0.000
Degrees of Freedom 0
P-Value 0.0000
Chi-Square Test of Model Fit for the Baseline Modelb
Value 469.585
Degrees of Freedom 2
P-Value 0.0000
CFI/TLIa
CFI 1.000
TLI 1.000
Loglikelihoodc
H0 Value -2124.388
H1 Value -2124.388
Information Criteriad
Number of Free Parameters 4
Akaike (AIC) 4256.776
Bayesian (BIC) 4273.634
Sample-Size Adjusted BIC 4260.938
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)a
Estimate 0.000
90 Percent C.I. 0.000 0.000
Probability RMSEA <= .05 0.000
SRMR (Standardized Root Mean Square Residual)a
Value 0.000
Model Results
MODEL RESULTS
Two-Tailed
Estimatef S.E.g Est./S.E.h P-Valuei
Y1e ON
X1 0.969 0.042 23.357 0.000
X3 0.649 0.044 14.626 0.000
Interceptsj
Y1 0.511 0.043 11.765 0.000
Residual Variancesk
Y1 0.941 0.060 15.811 0.000
Cite this article
stats writer (2024). What is Ordinary Least Squares Regression and how is it used in Mplus?. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/stats/what-is-ordinary-least-squares-regression-and-how-is-it-used-in-mplus/
stats writer. "What is Ordinary Least Squares Regression and how is it used in Mplus?." PSYCHOLOGICAL SCALES, 29 Jun. 2024, https://scales.arabpsychology.com/stats/what-is-ordinary-least-squares-regression-and-how-is-it-used-in-mplus/.
stats writer. "What is Ordinary Least Squares Regression and how is it used in Mplus?." PSYCHOLOGICAL SCALES, 2024. https://scales.arabpsychology.com/stats/what-is-ordinary-least-squares-regression-and-how-is-it-used-in-mplus/.
stats writer (2024) 'What is Ordinary Least Squares Regression and how is it used in Mplus?', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/stats/what-is-ordinary-least-squares-regression-and-how-is-it-used-in-mplus/.
[1] stats writer, "What is Ordinary Least Squares Regression and how is it used in Mplus?," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, June, 2024.
stats writer. What is Ordinary Least Squares Regression and how is it used in Mplus?. PSYCHOLOGICAL SCALES. 2024;vol(issue):pages.
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