Pearson correlation • A correlation is a statistical method used to measure and describe the relationship between two variables. • A relationship exists when changes in one variable tend to be accompanied by consistent and predictable changes in the other variable. • The magnitude of the Pearson correlation ranges from 0 (indicating no linear relationship between X and ) to 1.00 (indicating a perfect straight-line relationship between X and Y). • The correlation can be either positive or negative depending on the direction of the relationship. Formulas for Pearson correlation: ; where r – is a correlation coefficient calculated for the sample; Coefficient of det ermination (effect size forr) R =r²; where r is a coefficient correlation calculated for the sample You have two variables X and Y. Calculate the Pearson correlation r-test and the coefficient of determination R2. 4. x = 4 y = 6 SS, = 40; SS, = 54 2 1. 8 10 E(Ti – x) (yi – y) = 40 6 9. N=5 4 6. Paragrani

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Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related. 

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.