SmartPLS4 : How to Create a Regression Model in PLS- SEM?

How to Create a Regression Model in PLS - SEM?
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Welcome to My Easy Statistics, where we make statistics easy! In today's video, we're diving into the world of statistics and regression modeling using Partial Least Squares Structural Equation Modeling (PLSEM). We'll be taking you through the process of creating a regression model in PLSEM and exploring the interpretation of the results step by step.
📊 Regression Analysis Explained: Regression analysis is a powerful statistical technique used to understand the relationship between one dependent variable and multiple independent variables. Imagine you're analyzing student performance - that's exactly what we'll be doing in this tutorial!
📊 The Student Performance Example: We'll be working with a dataset that contains information on student performance. The dependent variable is the final exam score, while the independent variables include the number of practice exams taken, hours of study, previous quiz scores, and class attendance. Our goal is to see how these independent variables impact the final exam score.
📊 Getting Started with PLSEM: We'll be using SmartPLS, a tool that helps us perform Partial Least Squares Analysis. First, we'll import the student performance dataset into the software.
📊 Building the Regression Model: Next, we'll create our regression model on the SmartPLS canvas. We'll link the dependent variable (final exam score) with the independent variables (practice exams, hours studied, quiz scores, and class attendance). Each arrow represents a potential impact of an independent variable on the final exam score.
📊 Calculation and Interpretation: We'll then proceed to calculate the regression analysis results. We'll explore standardized coefficients, unstandardized coefficients, t-values, p-values, and confidence intervals. These values help us understand the impact of each independent variable on the dependent variable. We'll interpret each variable's contribution to the final exam score, taking into account significance levels.
📊 ANOVA and Model Fit: We'll delve into the ANOVA table to test whether the independent variables are significantly related to the dependent variable. We'll also examine the R-square value, which tells us how much of the variance in the final exam score is explained by our model. Additionally, we'll assess the presence of autocorrelation, collinearity, and the distribution of residuals.
📊 Predicted vs. Actual Analysis: We'll compare predicted final exam scores with actual scores and analyze how well our model performs. We'll discuss outliers, deviations from the regression line, and the implications of these findings.
📊 Homoscedasticity and Beyond: Our exploration doesn't stop there. We'll conduct a Breusch Pagan test to check for homoscedasticity - whether the variance of residuals remains constant across different levels of the independent variables.
📊 What's Next? In our next video, we'll take a deep dive into logistic regression, exploring another powerful statistical tool for modeling relationships between variables.
Thank you for joining us on this journey into regression analysis with PLSEM! Don't forget to like this video, subscribe to our channel, and hit the notification bell so you won't miss any of our exciting statistical tutorials. Stay tuned for more insights into the world of statistics!
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[@johnagpgnt]

This is great! This really helped me understand SmartPLS which I'm using now for my Masters Research Paper.
Thank you :)