Chapter 4 Part 2

Chapter 4 Part 2 - Demand Forecasting Chapter 4 (Part 2)...

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1 1 Demand Forecasting Chapter 4 (Part 2) 2 Causal Modeling Using Regression Analysis Procedure 1. Identify variables that you “think” might explain what you are trying to forecast – initial (or theoretical) regression model 2. Collect the data (observations) 3. Initial screening of the data • check for problematic data points 4. Build regression model and test • check that data meet necessary conditions 5. Use model to forecast 3 Necessary Conditions for Regression Analysis If the following assumptions are not satisfied, then corrective action must be taken or another method must be used. a) linear relationship exists between the independent variables and the dependent variable b) independent variables are not highly correlated c) residuals ( or errors ) exhibit a constant variance d) independent residuals e) normally distributed residuals 4 Linear Relationship Two ways to check: 1) scatter plots : plots with dependent variable on the „y-axis‟ against each independent variable on the „x-axis‟ a plot should suggest a straight line relationship (with or without a trend) 2) residual plots : plots with residuals on the „y-axis‟ against each independent variable and the predicted values on the „x-axis‟ a plot should exhibit a random pattern … Remedy: eliminate offending independent variable(s) from the analysis or transform the data
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2 5 Scatter Plots: Linear Relationship 0 5 10 15 20 25 30 35 30 35 40 45 50 55 Independent Dependent no slope, high variability 0 10 20 30 40 50 60 0 200 400 600 800 1000 Independent negative slope, low variability 6 Scatter Plots: Nonlinear Relationship 0 1000 2000 3000 4000 5000 0 5 10 15 20 25 Independent exponential curve 400 600 800 1000 1200 1400 1600 200 400 600 800 Independent parabolic curve 7 Residuals Plot: Linear Relationship -100 -50 0 50 100 150 1.00 1.20 1.40 1.60 1.80 2.00 2.20 Independent Variable Residuals residuals tend to be scattered randomly in a horizontal band centered at zero 8 Residuals Plot: Nonlinear Relationship curved pattern -400 -200 0 200 400 600 300 400 500 600 700 800 Independent Variable
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3 9 Correlation Among Independent Variables The independent variables must not be highly correlated. The accepted rule of thumb is that the absolute value of the
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This note was uploaded on 04/11/2011 for the course WCOB 2023 taught by Professor Billthompson during the Spring '07 term at Arkansas.

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Chapter 4 Part 2 - Demand Forecasting Chapter 4 (Part 2)...

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