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QMET Feb. 16-18th notes

by: Alexandra Casey

QMET Feb. 16-18th notes QMET 352

Alexandra Casey
University of Louisiana at Lafayette
GPA 3.9

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About this Document

This is the explanation of the problems we worked in class.
Advanced Business Statistics
Dr. John Tanner
Class Notes
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This 2 page Class Notes was uploaded by Alexandra Casey on Monday February 22, 2016. The Class Notes belongs to QMET 352 at University of Louisiana at Lafayette taught by Dr. John Tanner in Spring 2016. Since its upload, it has received 73 views. For similar materials see Advanced Business Statistics in Business at University of Louisiana at Lafayette.

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Date Created: 02/22/16
th Feb. 16 Handed out classroom examples “Moving”- moves “f”- Forecast PAGE 1: Note** If you are doing a three week forecast, the 1 week forecast will begin in the 4 period ** Same instance with a 4 week forecast, start in the 5 period  Check hand out for problems being worked out  Instead of the problem saying “3 week moving average” suppose it said “calculate a 3-week moving average for week 8”, you’d be looking at a blank forecast… Do same thing, but just for one week o DO NOT DO MORE THAN YOU ARE ASKED TO DO  Read the questions thoroughly PAGE 2: “weighted moving average”- the weight is given always in terms of the oldest st *** Find weight 1 and then solve for “f”  Weighting example: Weight Period 1/13= 0.08 Oldest 1 Oldest 1 1/13= 0.08 4 most recent 2 2/13= 0.15 3 most recent 2.5 2.5/13= 0.19 nd 2.75 2.75/13= 0.21 2 most recent Most recent 3.75 3.75/13= 0.29 th Avg. = 13 Feb. 18 Exponential Smoothing-  no matter what they ask for, you have to start at the beginning 1 forecast begins at the 2d period  Forecast come into play/ actually has to be worked out at period 3- when you can use it Smoothing Constant (alpha “α”)  it will always be given  Use this constant to calculate the forecast  = Some decimal figure between 0 and 1 *** The period 2 forecast is called “Naïve Forecast” – the naïve forecast will be sales/value from period 1 Formula to solve for the following forecasts: F (t+1) = αY + (t-α) F t given The forecast we The actual time Forecast from are looking for series of the previous period previous value- pull from column So  example: Check back at problem given in class For period 3:(3)= .202Y ) + (1-2) F How to determine which method to find a forecast is best: Find Mean Squared Error (MSE): Formula: MSE= Σd 2 # of forecasted values  Whole purpose is to have the mean with the lowest error- ALWAYS choose the one with the least amount of error, the lowest MSE!  MSE is the same method for every type of forecasting method!


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