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Bivariate statistical measures regression and linear correlation
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Bivariate statistical measures regression and linear correlation
Linear regression and linear correlation are statistical methods that study the relationship linearity between two variables.
Linear regression
- Study the statistical relationship between two continuous variables known as predictor and response variables.
- Predictor variable
- It is usually denoted as "x" and also is known as the independent variable.
- Response variable
- It is usually denoted as "and" and also is known as the dependent variable
- Predictor variable
- Study the statistical relationship between two continuous variables known as predictor and response variables.
Linear correlation
- Also known as the linear correlation coefficient (of Pearson), is a regression measure that aims to quantify the degree of joint variation between two variables.
- Correlation Types
- Negative perfect correlation
- When the "x" value rises, the "y" value rises, and also with the same intensity
- There is no correlation
- Positive perfect correlation
- When the value "x" goes up, and the value "y" low, and also with the same intensity
- Negative perfect correlation
- Correlation Types
- Also known as the linear correlation coefficient (of Pearson), is a regression measure that aims to quantify the degree of joint variation between two variables.
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- Mind Map on Bivariate statistical measures regression and linear correlation, created by Maria Camila Romero Melendez on 27/11/2022.
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- Maria Camila Romero Melendez
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- Created by Maria Camila Romero Melendez · May 3, 2021
- Copied by Maria Camila Romero Melendez · Nov 27, 2022
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