How Do You Calculate Multiple Regression Coefficients?
Least squares regression calculates statistical parameters used to build a predictive model in which the value of a dependent variable y can be estimated using known values of a set of j independent variables x(j). The mathematics used in calculating the j x-coefficients minimize the difference of the sum of the squares between the n observed x and y pairs to determine an a and and set of bs such that y = b(i)x(i) + a provides the best predictive model. Obtain n, the sample size for which you’ll calculate the least squares x-coefficients in the calculated model. Calculate the sum of each of the j pairs of the observed x(i) and y values and call them Sum(X(i)) and Sum(Y). Calculate the square of the sum of each of the j observed x(i) values and call them Sum(X(i))^2. Calculate the sum of the squares of each of the j observed x(i) value deviations from the mean of x (x(i) — x (i)bar)^2 and call it Sum(x(i)^2). Calculate the sum of the cross products of each of the j observed x(i) value
