In the field of statistics, regression analysis uses the techniques of modeling and analyzing multiple variables by focusing on the relationships between dependent variables and independent, helping the analyst understand how changing the criterion in one independent variable affects the criterion of other dependent variables. In doing so, it determines the average values ​​of the dependent variables. The target is the regression function and the probability distribution. Widely used for prediction and forecasting purposes, regression analysis is also used to explore relationships. Several techniques have been developed, including linear regression, ordinary least squares regression, and nonparametric regression. According to Lane D, when two variables are related, predicting a person's score on one of the variables from the score on the second variable is likely to be accurate. The assumption adopted by Lane was that the relationship between the two variables was linear in nature. "Since the relationship is linear, the prediction problem is to find the straight line that best fits the data. Since the terms "regression" and "prediction" are synonymous, this line is called a regression line." The methods of simple regression and linear regression have been clearly explained in the work of Waner S who also discussed the regression calculator. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay Regression Analysis In the field of statistics, regression analysis refers to the techniques of modeling and analyzing multiple variables. The analysis always focuses on the relationships that exist between the dependent and independent variables. Such analysis helps the researcher to clearly understand changes in the values ​​of dependent variables when the values ​​of one or more dependent variables fluctuate. For example, the methodology used in the analysis of cardiac study data is "a standard analysis of the Framingham Heart Study data is a generalized person-year approach in which risk factors or covariates are measured every two years with follow-up between these measurement times to observe the occurrence of events such as cardiovascular disease. (Source: RB D'Agostino, M Lee, AJ Belanger, LA Cupples, Statistics - How many subjects does it take to do a regression analysis "- 1990) According to Lane D, when two variables are linked, the prediction of the score d The assumption Lane adopted was that the relationship between the two variables was linear in nature: "Since the relationship is linear, the prediction problem becomes one of finding the straight line that best fits the data. . Since the terms “regression” and “prediction” are synonymous, this line is called regression line To explain the mathematical representation of the regression line predicting Y from X, we obtain Y'=bX + A; is the variable represented on the abscissa (X axis), b is the slope of the line, A is the Y intercept and Y' consists of the predicted values ​​of Y for different values ​​of For illustration, Lane puts forward the following example where the relationships between the identical block tests measuring spatial ability and the Wonderlic test measuring general intelligence are analyzed and represented. It appears that the relationship is quite strong in this case at 0.677. In the process, it also displays the best-fit line with a slope of 0.481 and Y. the intercept of 15.8468 and the regression line can be."