Creating the best fit straight line through a set of points on a graph is called?
Creating the best fit straight line through a set of points on a graph is called linear regression. Linear regression is a statistical technique that is used to model the relationship between a dependent variable and one or more independent variables by fitting the best possible straight line that closely represents the data points.
To perform linear regression and calculate the best fit straight line, you can follow these steps:
1. Collect your data: Gather a set of points with corresponding values for both the dependent variable (usually denoted as Y) and the independent variable (usually denoted as X).
2. Plot the data: Create a scatter plot on a graph, with the x-axis representing the independent variable (X) and the y-axis representing the dependent variable (Y). Each point will represent the value pair (X, Y).
3. Determine the equation of the line: The equation of a straight line is typically represented as Y = mX + b, where m is the slope of the line, and b is the y-intercept. To find these values, you need to find the best fit line that minimizes the overall distance between the line and the data points.
4. Calculate the slope (m): The slope (m) is determined using the formula: m = (nΣXY - ΣXΣY) / (nΣX^2 - (ΣX)^2), where n is the number of data points, ΣX is the sum of all X values, ΣY is the sum of all Y values, ΣXY is the sum of the product of X and Y values, and ΣX^2 is the sum of the squared X values.
5. Calculate the y-intercept (b): The y-intercept (b) can be calculated using the formula: b = (ΣY - mΣX) / n.
6. Determine the best fit line: Once you have the values of m and b, you can plug them into the equation Y = mX + b to get the equation of the best fit line. This line represents the linear relationship between the dependent variable and the independent variable.
By following these steps, you can create the best fit straight line through a set of points on a graph using linear regression.