Demand forecasting results in an estimate of future demand and gives an organization a basis for planning and making sound business decisions. Since the future is unknown, it is expected that some errors between a forecast and actual demand will exist, so the goal of a good forecasting technique would be to minimize the difference between the forecast and the actual demand.  Address the following requirements: Articulate the difference in short and long-term forecasts, forecasting techniques, and the benefits and challenges of each technique.  Create a forecast for a situation with which you are familiar (personal or professional) explaining the situation and why you chose the method of forecasting that you did. Embed course material concepts, principles, and theories, which require supporting citations along with at least two scholarly, peer-reviewed references in supporting your answer. Keep in mind that these scholarly references can be found in the Saudi Digital Library by conducting an advanced search specific to scholarly references.

Purpose This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.Resources: Microsoft Excel®, DAT565_v3_Wk5_Data_FileInstructions: The Excel file for this assignment contains a database with information about the tax assessment value assigned to medical office buildings in a city. The following is a list of the variables in the database:FloorArea: square feet of floor spaceOffices: number of offices in the buildingEntrances: number of customer entrancesAge: age of the building (years)AssessedValue: tax assessment value (thousands of dollars)Use the data to construct a model that predicts the tax assessment value assigned to medical office buildings with specific characteristics.Construct a scatter plot in Excel with FloorArea as the independent variable and AssessmentValue as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables?Use Excel’s Analysis ToolPak to conduct a regression analysis of FloorArea and AssessmentValue. Is FloorArea a significant predictor of AssessmentValue?Construct a scatter plot in Excel with Age as the independent variable and AssessmentValue as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables?Use Excel’s Analysis ToolPak to conduct a regression analysis of Age and Assessment Value. Is Age a significant predictor of AssessmentValue?Construct a multiple regression model.Use Excel’s Analysis ToolPak to conduct a regression analysis with AssessmentValue as the dependent variable and FloorArea, Offices, Entrances, and Age as independent variables. What is the overall fit r^2? What is the adjusted r^2?Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated?What is the final model if we only use FloorArea and Offices as predictors?Suppose our final model is:AssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x OfficesWhat wouldbe the assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database?