Regression Analysis is a very effective quantitative forecasting technique for short, medium and long range time horizons and can be easily updated and changed. Regression Analysis: presupposes that a linear relationship exists between one or more independent (casual) variables, which are predicted to affect the dependent(target) variable. Linearity: The observed relationship between the independent and dependent variables Example: A HR can use regression analysis to predict the number of personnel required to perform the work.` Regression projects the future based on the past historical relationship between the independent and dependent variables
Simple Regression Prediction model
General form of a simple LINEAR FUNCTION
This equation describes any straight line. The slope of the linear relationship between X and Y is represented by the letter b. The constant(y intercept) is represented in the equation by the letter a. The dependent(tatget)vatiable is represented by Y. The independent(casual) variable is represented by X. By using this formula we can determine the values of Y Warning: When you use a regression equation, do not use values for the independent variable that are outside the range of values used to create the equation. That is called extrapolation, and it can produce unreasonable estimates..
Example: SIMULATION MODEL/REGRESSION FORECAST
TARGET STORES STAFFING FORECAST MODEL
Y = 8 + .0011(X1) + .00004(X2) + .02(X3)
Y = Number of employees needed to staff the store
X1 = Square feet of sales space
X2 = Population of metropolitan area
X3 = Projected annual disposable income in millions of dollars Y = 8 + .0011(50,000sq ft) + .00004(150,000popul) + .00000002($850 million) Y = 8 + 55 + 6 + 17
Y = 86 employees needed at this store
Regression is a valuable forecasting technique and it enables us to plan and execute recruitment, selection, training and development programs in a planned, proactive fashion to ensure the trained marketing staff are on hand exactly when required by the organization.
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