Regression and correlation

Regression and correlation

1. There are numerous examples that can be worked with two variables that are relate; income and expense, hours worked and accidents

2. Regression, a statical method developed to investigate such relationships

3. Regression analysis gives rise to the mathematical equation that allows us to describe the relationship between two variables, that is to obtain a regression line, which describes the relationship or dependency between two variables.

4. - This line or mathematical function in the case of a single independent or explanatory variable can be expressed, through one.

5. Just as the correlation analysis allows us to measure the strength of association between two variables, the regression analysis allows the prediction, that is, the estimation of a variable called slope, based on an average value supposedly known for another variable. So- called independent.

6. The regression analysis, in addiction to explaining the relationship between the variables, of cause and effect, tells us if the mathematical relationship can be linear, parabolic, or exponential. It also allows us to estimate the values of a variable, assuming a known value of the other variable.

7. Regression and linear correlation

   • The mathematical mode describing a linear relationship when the value of y is estimated as a function of x is represented indistantly by:

8. Method least squares

   • It is a deep estimation method by which the quantities to be estimated are determined by minimizing the sum of the squares of the differences between he observed values and the estimated values.

9. Prediction or point estimated

   • it can be said that one of the purposes of establishing the function that adequately represents the set of pairs of observations that have been taken for analysis, is to be able to perform interpolations since it is not indicated for making extrapolations.

10. Residual variance and standard error of estimate

    • Arithmetic mean of the square of the differences between the observed values and the estimated values the formulas that are used for their calculation.

11. Explained variance

    • It is measure that allows us to calculate the degree of dispersion of the values that were explained by the regression line, that is, the dispersion of the tip in which the graph is confused with the tip of the lines or estimated values.

12. Pearson correlation coefficient

    • Is a variable of interdependence of two random variables

13. Squared correlation or coefficient of determination

    • Measures the proportion of the variance that is explained by the regression equation that helps to analyze the relationship established between two variables

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