This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: 1 1 Demand Forecasting Chapter 4 (Part 3) 2 independent variables Build Regression Model: Basic Structure F t (or Å· t ) ~ dependent variable representing forecast for observation t b ~ intercept k ~ number of independent variables b i ~ regression coefficient of the i th independent variable x i,t ~ independent variable data (e.g., price, advertising, etc.) dependent variable After (a) initial screening of the data and checking for linearity and (b) taking appropriate steps to remedy these problems, then (c) build the regression model including the issue of highly correlated independent variables. Finally, verify that the model meets the remaining conditions of normality, constant variance, and independent residuals. For example: demand = f (price, previous advertising, consumer confidence, etc.) t k k t t t x b x b b y F , , 1 1 ... Ë† 2 3 Example: Building The Model and Forecasting You are a concrete producer who wants to forecast the demand for concrete as a function of price per cubic yard and the number of building permits issued the previous month. Use the historical data in the table below to run a regression analysis and obtain the summary output from EXCEL....
View Full Document
- Spring '07
- Regression Analysis, #, $5, $10.00, $9.25, $11.25