Bivariate measures of regression and correlation

1. Regression Problems

1.1. Inhomogeneous variance

1.1.1. Nonlinear relationship

1.1.1.1. Correlation errors

2. Regression Analysis

2.1. Study the relationship between two quantitative variables

2.1.1. Statistical technique used to derive an equation that relates a criterion variable to one or more predictor variables.

2.1.1.1. It studies the strength of the association through a measure of association called the coefficient of influence.

3. Bivariate Statistical Measures

3.1. Coefficient of determination R2

3.1.1. Simple linear correlation

3.1.1.1. Scatter plot

3.1.1.1.1. Regression - Simple Regression

4. Correlation

4.1. Analysis of two quantitative variables

4.1.1. It can be represented by a scatter diagram.

4.2. Mutual relationship, this measures and indicates the degree and values ​​of a variable when related to another variable.

5. Simple regression model

5.1. The Y^ rarely coincides even if the regression model is good with respect to quantity, otherwise residual error will appear.

5.2. A very simple - linear function of X is found, which allows us to approximate -y^=b0+b1x

6. Simple linear regression

6.1. Pearson's linear correlation coefficient

6.2. R is useful for determining if there is a linear relationship between two variables, but it will not work for quadratic or logarithmic variables.

6.3. The dots tend to line up

6.3.1. They have the same sign as S (XY)