ISE
Notes for the 1st Midterm

# Notes for the 1st Midterm - ISE-410 Midterm Notes Chapter 4...

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ISE-410 Midterm Notes Chapter 4: Forecasting Causal Factor : something that influences the data in a known way and can be helpful in forecasting. Processes : Constant Process : plotted data is roughly level with some small variations. o Should have some reason to assume a process is constant o If the variance changes over time, the assumption of a constant process isn’t valid Trend Process : a process with a growth stage (sales increase) and/or a decline or phase- out stage (sales are decreasing). o Assuming a constant process in either case can be disastrous o Can be linear or nonlinear Seasonal Process : a process where the pattern seems to repeat o Weather is often an underlying cause o Seasonal and cyclical are the same thing There will always be some part that is unexplainable Models : Generally have the form: d t = f(x t-k ) + ε t o d t = dependent variable o x t = independent variable (causal factor) o ε t = noise component at time t Constant: d t = a + ε t o a = constant portion o Constant processes should have a constant mean o Estimates of future demand should be independent of how far in the future we look Linear trend: d t = a + bt + ε t o b = trend Seasonal: d t = ac t + ε t o c t = seasonal factor Simple Linear Regression : d t = a + bh t + ε t t = 1, 2,…, n a = intercept of the straight line relating d t and h t b = slope of the line n = total number of months of data available â = estimate of a o value of the dependent variable when the independent variable is zero not necessarily a meaningful number o if zero is not a possible value of the independent variable, â may still be positive in this case, â calibrates the other values bhat = estimate of b (can be positive or negative)

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o positive: dependent variable increases as the independent variable increases (positive correlation) o magnitude should reflect the amount of change in the dependent variable for a unit change in the independent variable Coefficient of Determination : r 2 (p. 106 in the text) o Coefficient of determination of .85 is considered quite good Regression models are very useful for forecasting when there is a strong relationship and a time lag between the dependent variable and the independent variable(s) o If they occur in the same time period, we can’t forecast future values of the dependent variable unless we use a forecast of the independent variable This introduces error in the forecast of the dependent variable Extrapolating regression results can be dangerous o Statistically, only values in the range of the data used to fit the equation should be used to forecast If causal relationships do not exist, regression is NOT the best forecasting method o Be careful; often the cause and effect relationship isn’t clear Trend Series Methods : time-ordered list of historical data Favored for short-term forecasting History is a reasonable predictor of the future Includes constant, trend, or seasonal models
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• Summer '07
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