11.4:
Regression Analysis
Regression analysis is a statistical method of developing a mathematical model to estimate a relationship between the variables. It is used to predict the value of a dependent variable based on another independent variable.
For example, consider a data set having a strong linear relationship with a correlation coefficient of 0.892.
A best-fit line passing through the scatter plot is the regression line.
The algebraic equation of the regression line is known as the regression equation.
It expresses the relationship between the carbon dioxide levels, x, the independent variable, and the annual temperature, y, the dependent variable.
Here, b0 is the y-intercept, and b1 is the slope of the regression line.
As the regression line shows a good fit, the regression equation can be used to predict the annual temperature for, say, a carbon dioxide level of 380 ppm.
This value is put in the regression equation to obtain the predicted annual temperature of 14.7 degrees Celsius.
11.4:
Regression Analysis
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In the equation, 
is the dependent variable, x is the independent variable, b0 is the y-intercept, and b1 is the slope of the regression line. is the estimated value of y. It is the value of y obtained using the regression line. It is not generally equal to y from data.
The regression equation can be used to calculate the dependent variable for a specific value of the independent variable.
This text is adapted from Openstax, Introductory Statistics, Section 12.3 The Regression Equation.