Regression Analysis Is the Technique

Regression analysis is the technique used to express the linear relationship between two variables. With this technique we can estimate the value of the dependent variable (Y) based on a selected value of the independent variable (X). This kind of analysis is mostly used to determine if sales numbers of a product a are related to the number of TV advertisements for that particular product during a specific timeframe. The mathematical equation for the line used to estimate the value of variable Y on the basis of variable X is also known as the regression equation (general form of linear regression equation: Y1 = a + bX) (Lind, Marchal, & Wathen, 2005).

Y1

= read Y prime, is the predicted value of Y for a selected X value a = is the Y-intercept, or the estimated value of Y when X = 0

b

= is the slope of the line, or the average change in Y1

X

= any value of the independent variable that is selected

Linear regression is used to make predictions about a single variables value. In other words, it is used for discovering the equation for a line that most closely fits the given data (Lind, Marchal, & Wathen, 2005). That linear equation is then used to predict values for the data. For example: A sales manager wants to know if more sales calls will sell more copy machines, and by how much the number of calls have to increase to reach a certain number of sales for those machines. The main components are the slope and the intercept, also the standard errors and confidence intervals. Furthermore the correlation coefficient, and standard deviation are components of the linear regression analysis.

Simple regression - the simple linear regression equation is also called the least squares regression equation. Its name tells us the criterion used to select the best fitting line, namely that the sum of the squares of the residuals should be least. In other words, the least squares regression equation is the line for which the sum of squared residuals is a minimum (Dallal, 2008).

Multiple regression - the general purpose of multiple is to learn more about the relationship between several independent variables and a dependent variable. For example, a real estate agent might record for each listing the size of the house in square feet, the number of bedrooms, the average income in the respective neighborhood, and a subjective rating of appeal of the house. As soon as this information is compiled for different houses it would be exciting to see whether these measures relate to the price for which a house is sold.