Definition:

Statistical Regression refers to a technique in statistics that is used to model the relationship between a dependent variable and one or more independent variables. It aims to understand and predict the behavior or values of the dependent variable based on the values of the independent variables.

Subtitles:

  1. Dependent Variable:

    2. The dependent variable in statistical regression is the outcome variable that is being predicted or explained. It is also known as the response variable, as its value depends on the values of the independent variables.

  2. Independent Variables:

    3. Independent variables are the predictor variables in statistical regression. They are the factors or variables that are used to explain or predict the value of the dependent variable. In regression analysis, multiple independent variables can be used to assess their impact on the dependent variable simultaneously.

  3. Line of Best Fit:

    4. The line of best fit, also known as the regression line or the trendline, is the straight line that represents the relationship between the dependent and independent variables. It is determined by minimizing the differences between the observed values and the predicted values.

  4. Coefficients:

    5. Coefficients are the numerical values that express the relationship between the dependent and independent variables in the regression model. These coefficients are estimated using statistical techniques like ordinary least squares (OLS) and represent the slope or impact of the independent variables on the dependent variable.

  5. Residuals:

    6. Residuals are the differences between the observed values of the dependent variable and the values predicted by the regression equation. They represent the errors or unexplained variability in the model. A good regression model aims to minimize the residuals, indicating a better fit to the data.

  6. Regression Analysis:

    7. Regression analysis is the process of analyzing the relationship between variables using statistical regression techniques. It includes estimating coefficients, evaluating model fit, and assessing the statistical significance of the relationships. Regression analysis is widely used in various fields for prediction, forecasting, and understanding the impact of variables.