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Reasons why Likert scale variables need to undergo validity and reliability testing

A solid understanding of statistics, I believe, is crucial for researchers to master. Having a good grasp of statistics will lead us to choose the appropriate statistical methods in research.

Understanding the Difference between Residual and Error in Regression Analysis

When expressing a linear regression equation, the terms residual or error often appear at the end of the equation. But what exactly do residual and error mean? And what is the fundamental difference between the two?

The Difference Between Simultaneous Equation System Model and Linear Regression Equation

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We might all be familiar with linear regression equations, but how many of us have delved deeper into the simultaneous equation system model? It’s worth noting that the simultaneous equation system model is far more complex than linear regression equations.

Understanding the Importance of the Coefficient of Determination in Linear Regression Analysis

In linear regression analysis, one important parameter often encountered is the coefficient of determination. The value of this coefficient provides an indication of how well the linear regression model can explain the variation in the data.

Understanding the Essence of the Difference Between Descriptive Statistics and Inferential Statistics in Research

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In conducting research, understanding the basic theory of statistics becomes crucial for researchers. Why is this so important? Because to extract accurate conclusions from research data, careful analysis using statistical tools is needed.