Correlation and linear regression

1. simple linear regression

1.1. Linearly relates two variables.

1.2. Analyze quantitative variables.

1.3. Dependent variable.

1.4. A single independent variable.

1.5. Mathematical model, with a single coefficient, slope and intercept.

2. Correlation coefficient

2.1. Degree of dependence of the related variables.

2.2. Quantifies the intensity of the linear relationship between two variables.

2.3. Quantifies the intensity of the linear relationship between two variables.Data dispersion.

3. Positive correlation.

3.1. The independent variable is proportional to the dependent variable.

3.2. Slope is positive or increasing.

4. multiple linear regression

4.1. Relates a variable (Y) with more than two variables (X).

4.2. Analyze quantitative variables.

4.3. Dependent variable (Y).

4.4. More than two independent variables (X).

4.5. Mathematical model, with two or more coefficients, slopes and a single intercep.

5. Determination coefficient

5.1. predicts the behavior of an event.

5.2. The closer R2 is to 1, the probability of predicting the behavior of the dependent variable is much greater.

6. Negative correlation.

6.1. The independent variable is inversely proportional to the dependent variable.

6.2. As the independent variable increases, the dependent variable decreases.

6.3. Slope is negative or decreasing.