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A linear regression equation describes the relationship between the independent variables (IVs) and the dependent variable (DV). It can also predict new values of the DV for the IV values you specify. In this post, we’ll explore the various parts of the regression line equation and understand how to interpret it using an example.
A regression line, or a line of best fit, can be drawn on a scatter plot and used to predict outcomes for the \(x\) and \(y\) variables in a given data set or sample data. There are several ways to find a regression line, but usually the least-squares regression line is used because it creates a uniform line.
A regression line, or a line of best fit, can be drawn on a scatter plot and used to predict outcomes for the \(x\) and \(y\) variables in a given data set or sample data. There are several ways to find a regression line, but usually the least-squares regression line is used because it creates a uniform line.
y = a + bx. where, x is Independent Variable, Plotted along X-axis. y is Dependent Variable, Plotted along Y-axis.
Revised on June 22, 2023. Simple linear regression is used to estimate the relationship between two quantitative variables. You can use simple linear regression when you want to know: How strong the relationship is between two variables (e.g., the relationship between rainfall and soil erosion).
How to find a regression line? The formula of the regression line for Y on X is as follows: Y = a + bX + ɛ Here Y is the dependent variable, a is the Y-intercept, b is the slope of the regression line, X is the independent variable, and ɛ is the residual (error).
12.3 The Regression Equation. 1.1 Definitions of Statistics, Probability, and Key Terms. 1.3 Frequency, Frequency Tables, and Levels of Measurement. 2.1 Stem-and-Leaf Graphs (Stemplots), Line Graphs, and Bar Graphs. 2.2 Histograms, Frequency Polygons, and Time Series Graphs.