DateBWL SalesDPI                   
Jan-200321348251.3  1. Find the appropriate univariate model for the sale of Beer, Wine, and Liquor data (BWL Sales) and justify your answer using data from Jan-2003 to Dec-2006 
Feb-200320598268.1   Calculate the MAPE and RMSE for the data used to run the model and for the hold-up period (7 months)     
Mar-200322898317.6  2. Run a bivariate model using BWL Sales and DPI using data from Jan-2003 to Dec-2006        
Apr-200323488356.8   Compare the results to the univariate model in terms of RMSE and/or MAPE for the data used to run the model and for the hold-up period  
May-200325938412.0  3. Account for the trend and seasonality of BWL sales by adding a time index variable and dummies for months 2-12. Run this new model using data from Jan-2003 to Dec-2006
Jun-200324508449.6   Compare the results to the univariate model in terms of RMSE and/or MAPE for the data used to run the model and for the hold-up period  
Jul-200326298567.8                   
Aug-200326788648.0                   
Sep-200324788572.4                   
Oct-200326598606.2                   
Nov-200326788678.3                   
Dec-200336818712.4                   
Jan-200423088753.6                   
Feb-200422328792.7                   
Mar-200424118839.9                   
Apr-200425678884.2                   
May-200427028960.1                   
Jun-200426608989.2                   
Jul-200428869015.5                   
Aug-200426419049.3                   
Sep-200426399066.9                   
Oct-200427539110.0                   
Nov-200427929119.1                   
Dec-200438439458.4                   
Jan-200522549148.5                   
Feb-200523559179.0                   
Mar-200525769235.1                   
Apr-200526919279.7                   
May-200527679326.7                   
Jun-200528469359.1                   
Jul-200529959422.6                   
Aug-200528659476.0                   
Sep-200528619518.7                   
Oct-200528849578.4                   
Nov-200530259622.2                   
Dec-200542679675.3                   
Jan-200625629848.2                   
Feb-200626679894.7                   
Mar-200629189929.2                   
Apr-200629639957.7                   
May-200632079971.2                   
Jun-2006325210017.0                   
Jul-2006332210049.7                   
Aug-2006322810079.7                   
Sep-2006321210116.6                   
Oct-2006312010147.8                   
Nov-2006335910186.3                   
Dec-2006458810254.7                   
Jan-2007271010295.7                   
Feb-2007274810356.6                   
Mar-2007317610424.2                   
Apr-2007303710442.3                   
May-2007345910466.5                   
Jun-2007357810476.0                   
Jul-20073541

10515.3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

QTRACCFUEL   1.QTR: Quarter (Quarters 1-29 = Control, Quarters 30-50 = Experimental)           
119232.592   2.ACC: Injuries and fatalities from Wednesday to Saturday nighttime accidents          
223837.25    3.FUEL: Fuel consumption (million gallons) in Albuquerque            
323240.032                    
424635.852   The Police Department in Alburquerque, New Mexico introduced a van that housed a Blood Alcohol Testing (BAT) device to try to reduce DWI related accidents 
518538.226   This BATmobile was introduced in Quarter 30 of your data, so you have 29 observations before the program (Control) and 21 during the program (Experimental) 
627438.711   Part of your job is to decide whether or not the program was effective in reducing DWI related accidents      
726643.139                    
819640.434   1. Using the number of accidents (ACC) and the fuel consumption (FUEL), calculate the average number of injuries before and after the program.   
917035.898   Does it look like the program was effective? Explain           
1023437.111   2. Run a multiple-regression model using ACC, FUEL, Quarter dummies (Q2, Q3, Q4), and a dummy variable for whether or not the program was in effect (BAT) 
1127238.944   Describe the results for the FUEL and quarter dummies and comment on the evidence related to the efficacy of the BATmobile program.   
1223437.717   3. Programs like the BATmobile usually take time to catch on (ramping up). To account for this, modify the BAT dummy variable so that the zero values remain unchanged but
1321037.861   the 1's are modified so that the new values are 1, 2, 3, 4, etc. Having this new variable, run the multiple-regression model and comment on whether or not there is  
1428042.524   evidence of a ramping up effect of the BATmobile program in reducing DWI related accidents.       
1524643.965                    
1624841.976                    
1726942.918                    
1832649.789                    
1934248.454                    
2025745.056                    
2128049.385                    
2229042.524                    
2335651.224                    
2429548.562                    
2527948.167                    
2633051.362                    
2735454.646                    
2833153.398                    
2929150.584                    
3037751.32                    
3132750.81                    
3230146.272                    
3326948.664                    
3431448.122                    
3531847.483                    
3628844.732                    
3724246.143                    
3826844.129                    
3932746.258                    
4025348.23                    
4121546.459                    
4226350.686                    
4331949.681                    
4426351.029                    
4520647.236                    
4628651.717                    
4732351.824                    
4830649.38                    
4923047.961                    
5030446.039                    
                       
                       
                   
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FIN 484 2 Exercises 1
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