# Shared Flashcard Set

## Details

Goods and Services Test #2
N/A
37
03/10/2013

Term
 Causal Modeling Using Regression Analysis
Definition
 1. Identify variables that ou think might explain what you are trying to forecast-initial regression model 2. Collect the data (observations) 3.Check for problematic data points 4. Build regression model and test 5. Use model to forecast
Term
 Necessary Conditions for Regression Analysis
Definition
 a. linear relationship exists between the independent variables and the dependent variables b. independent variables are not highly correlated c. residuals (errors) exhibit a constant variable d. independent residuals e. normally distributed residuals
Term
 Linear Relationship
Definition
 Two ways to check Scatter Plots: a plot should suggest straight line relationship Residual Plots: a plot should exhibit a random pattern
Term
 Correlation Among Independent Variables
Definition
 The independent variables must not be highly correlated. The accepted rule of thumb is that the absolute value of the correlation, r, does not exceed .90 Excessive correlations among two (collinearity) or more (multicollinearity) independent variables creates several adverse effects -unstable regression coefficients -inflated standard errors which leads to type II error
Term
 Constant Variance
Definition
 Heteroscedasticity: the condition where the variance of the residuals is not constant Homoscedasticity: the condition where the variance of teh residuals is constant To check: scatterplot: plot should exhibit a random pattern with a relatively uniform variance
Term
 Independent Residuals
Definition
 The residuals must be independent of each other. To check: first, order the data in time sequence from oldest to most recent Scatter plot: plot should not exhibit a trend or any special pattern
Term
 Normally Distributed Residuals
Definition
 The residuals must be normally distributed, although regression analysis is fairly robust with regard to violation of this condition. To check: Histogram: should exhibit a bell shaped curve about zero
Term
 P-Values
Definition
 A P-Value represents the probablility of obtaining a value of the test statistic equal to or more extreme than the value obtained for the sample if the null hypothesis is true If null hypothesis is rejected it is significant If null hypothesis is not rejected it is not significant A statistical decision is made by comparing the p-value and the significance level (alpha) P value < a then reject Ho P value > then fail to reject Ho
Term
 Hypothesis Testing: The Model
Definition
 Regression Model the significance of the overal regression model is determined using Excel by the "Significance F" The null hypothesis (Ho) is that the set of independent variables cannot be used to predict the dependent variables (the model is not significant) The alternative hypothesis (Ha) is that one, or more of the independent variables can be used to predict the dependent variable (the model is significant)
Term
 Hypothesis Model: The Variables
Definition
 Independent Variables The significance of a specific independent variable (xi) is determined using Excel by the "p-value" associated with the variable The null hypothesis (Ho) is that this independent variable cannot be used to predict the dependent variable (this variable is not significant) The alternatve hypothesis (Ha) is that this independent variable can be used to predict the dependent variable (this variable is significant)
Term
 Coefficient of Determination
Definition
 The coefficiant of determination,R2, is used to assess how well the regression model fits the data. The coefficient of determination represents the percentage of the variation in the dependent variable that is explained, or accounted for, by the regression model R2 varies between 0 (no fit) to 1 (perfect fit) adjusted R2 is a refinement of R2 which considers the relative values of the sample size (n) and the number of independent variables (k)
Term
 Time Series Forecasting Models
Definition
 Require historical data -forecasts extrapolate past data into the future Some of the most commonly used methods -simple moving average -weighted moving average -exponential smoothing Time series forecasts have been shown to typically yield more accurate short-term forecasts
Term
 Developing a Time Series Forecast
Definition
 1.Identify time series data components 2.Choose appropriate time series methods3.Evaluate different methods -calculate forecasts using historical data -evaluate forecasting errors for each method -choose method which performs best 4.Implement method of choice 5. Monitor forecast performance
Term
 Naive Method
Definition
 The naive forecast for the next period equals the demand for the current period. Ft + 1= At Period 1 demand =15 Period 2 Forecast = 15
Term
 Simple Moving Average
Definition
 Simple moving average places the same weight on each time period Method works well when the demnad is fairly stable over time Method does not do a good job of forecasting when a trend is present The forecast lags the actual demand because of averaging effect. Decreasing the number of periods in forecast, creates a more responsive forecast
Term
 SMA Example
Definition
 Ft+1= At + At-1/2 Period 1 Demand = 15 Period 2 Demand =28 Period 3 Forecast = 21.5
Term
 Simple Moving Average: Choosing n
Definition
 The larger n, the smoother the forecast and the less responsive it is to changes in demand. The smaller n, the forecasts is more responsive to changes in demand
Term
 Weighted Moving Average
Definition
 Weighted moving average allows different emphasis to be placed on different time periods Method works well when the demand is fairly stable over time Method does not do a good job of forecasting when a trend is present The forecast lags the actual demand because of averaging effect Weights tend to be based on the forecaster's experience Decreasing the number of periods in forecast and/or increasing the size of the weights for more recent demand creates a more responsive forecast
Term
 WMA Example
Definition
 Ft+1= 3At + 2At-1/3+2 Period 1 Demand= 15 Period 2 Demand= 28 Period 3 Forecast = 22.8
Term
 Weighted Moving Average: Choosing n
Definition
 The smaller the eights wi for more recent demand and the more weights (larger n), the smoother the forecast and less responsive it is to changes in demand.
Term
 Exponential Smoothing
Definition
 Exponential smoothing is suitable for data without a trend. Exponential smoothing forecasting is a sophisticated weighterd moving average forecasting. This forecast lags the actual demand because of averaging effect.
Term
 Exponential Smoothing Example
Definition
 Ft+1= Ft + 0.3 (At-Ft) Period 1 Demand=15 Period 1 Forecast= 15 Period 2 Demand = 28 Period 2 Forecast= 15 Smoothing parameter = 0.3 Period 3 Forecast=18.9
Term
 Choosing the Smoothing Parameter
Definition
 The exponential smoothing parameter equation places exponetially larger weights on more recent data A larger smoothing parameter emphasizes recent demand and yields a forecast which is more responsive to changes in actual demand A smaller smoothing parameter places more uniform empahsis on demand and yields a forecast which is more stable and less responsive to changes in actual demand.
Term
 Linear Regression Formula
Definition
 y=mx+b
Term
 Forecasting Accuracy
Definition
 The ultimate goal of any forecasting endeavor is to have an accurate and unbiased forecast. The cost associated with prediction error can be substanial and include the cost of lost sales, safety stock, unsatisfied customers, and loss of goodwill.
Term
 Forecast Error
Definition
 Forecast Error, which is the difference between actual demand and the forecast, is used to evaluate the accuracy of a forecasting model. Error (Et)= Actualt-Forecastt For all measures of forecast accuracy, the closer the measure is to 0, the better the forecast.
Term
 Measures of Forecast Accuracy
Definition
 1. Actual measures of forecast accuracy -These measures are expressed in the same unit of measurement as the data. Relative measures of forecast accuracy -These measures provide a perspective of the magnitude of the forecast error relative to actual demand
Term
 Absolute Measures of Forecast Accuracy
Definition
 Common absolute measures to evaluate forecast accuracy are: 1. Running Sum of Forecast Error (RSFE) -Mean Forecast Error (MFE) 2. Mean Absolute Deviation (MAD) 3. Mean Squared Error (MSE)
Term
 Running Sum of Forecast Error and Mean Forecast Error Formulas
Definition
 RSFE=Σ (At-Ft)= Σ Et MFE= Σ Et/n= RSFE/n -Measures the average magnitude or size of the forecast errors (central tendency) or bias. -Bias represents the tendency of a forecast to be consistently higher or lower than the actual demand.
Term
 RSFE and MFE
Definition
 A positive RSFE (and MFE) indicates that the forecasts generally are low-the forecasts underestimate demand and stock outs may occur. A negative RSFE (and MFE) indicates that the forecasts generally are high- the forecasts overestimate demand resulting in higher inventory carrying costs. A zero RSFE (and MFE) indicates that the forecast is unbiased. This does not imply that the forecast was necessarily accurate.
Term
 Mean Absolute Deviation Formula
Definition
 MAD = Σ lEtl /n -Measures the average magnitude of the forecast errors without regard to direction of error.
Term
 Mean Absolute Deviation
Definition
 The MAD is a widely used indicator of forecast accuracy. It provides a simple way to compare different forecast methods. A MAD value greater than zero indicates the forecast either overestimates or underestimates demand. A zero MAD indicates that the forecast exactly predicted demand over the entire evaluation period.
Term
 Mean Squared Error
Definition
 MSE = Σ Et2/n -It is sensitive to large errors. -In general, models that yield forecasts with many small errors and a few very large ones are not desirable.
Term
 Mean Absolute Percent Error
Definition
 MAPE= (Σ lEt/Atl) (100/n) -Measures the average relative magnitude of the forecast errors, expressed as a percentage.
Term
 Tracking Signal
Definition
 Tracking Signal = RSFE / MAD Used to monitor the performance of a forecast over time. Tracks forecast bias relative to the average magnitude of the forecast error. The tracking signal is updated after every period.
Term
 Monitoring Forecast Errors
Definition
 A "control chart" is often used to monitor the tracking signal. If the tracking signal falls outside present control limits, there is a bias problem with the forecast method and reevaluation of the forecast method is warranted.
Term
 Tracking Signal Guidelines
Definition
 Some inventory experts suggest using tracking signal control limits of ±4 for high-volume items and ±8 for low-volume items Note: As higher limits are instituted, there is a greater probability of finding exceptions that require no action, but this also means catching changes in demand sooner.
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