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AI-generated Abstract
This paper discusses the application of statistical concepts such as Pearson and Spearman correlation coefficients and regression analysis using PAST software. It illustrates how to perform linear and polynomial regression based on provided datasets, along with the process of creating graphs to visualize relationships between variables. The examples provided demonstrate practical applications in predicting age based on humeral length and other dimensions, highlighting the importance of determining the right model for accurate predictions.
2020
Module 18: Linear Correlation and Regression "Correlation is not causation but it sure is a hint." 1-Edward Tufte The term "regression" is not a particularly happy one from the etymological point of view, but it is so firmly embedded in statistical literature that we make no attempt to replace it by an expression which would more suitably express its essential properties.
Once we've acquired data with multiple variables, one very important question is how the variables are related. For example, we could ask for the relationship between people's weights and heights, or study time and test scores, or two animal populations. Regression is a set of techniques for estimating relationships, and we'll focus on them for the next two chapters.
Statistics, 2024
In this Module, we study the relationship between the variables. Also, the interest lies in establishing the actual relationship between two or more variables. This problem is dealt with regression. On the other hand, we are often not interested to know the actual relationship but are only interested in knowing the degree of relationship between two or more variables. This problem is dealt with correlation analysis. Linear relationship between two variables is represented by a straight line which is known as regression line. In the study of linear relationship between two variables and , suppose the variable is such that it depends on , then we call it as the regression line of on. If depends on , then it is called as the regression line of on .
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