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QMET 352 Notes for Feb. 11th

by: Alexandra Casey

QMET 352 Notes for Feb. 11th QMET 352

Alexandra Casey
University of Louisiana at Lafayette
GPA 3.9

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Important: The entire objective part on the next exam consist of these notes! If you missed class after Mardi Gras, you will need these notes. The next exam will not be open book.
Advanced Business Statistics
Dr. John Tanner
Class Notes
QMET, business, Statistics
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This 2 page Class Notes was uploaded by Alexandra Casey on Sunday February 14, 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 78 views. For similar materials see Advanced Business Statistics in Business at University of Louisiana at Lafayette.

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Date Created: 02/14/16
Key Box: Feb. 11 2016 ***: Know for test EXAM 2 (NOT OPEN BOOK) will consist of: The entireobjective part on the exam ***: Definition  9 Multiple Choice consistofthese notes! ***: Titles  3 Fill-in-the-blank On exam, know: names,ranks,  1 True/ False  3 problems to work definitions,and examples! ALL DEFINITIONS IN THESE NOTES WILL BE ON THE NEXT EXAM!  4 Types of Data o Types of measurement scales= Data (same thing) o Ranked from weakest to strongest  Below is the correct order! 1. Nominal (weakest): Numbers are used to represent names (names/classification, etc.) a. Ex: Social Security (most used version of this type of data) b. Weak  Calculate mean- doesn’t really tell you anything 2. Original: Data which has been ranked OR put into order a. Can’t do much with it, but you do use it to put things in rank b. Ex: Sport rankings, movie rankings, etc. 3. Interval: Data where the numbers mean something, but there is NO ONE TRUE ZERO POINT a. You can find the mean, but no true zero point b. Ex: Temperature  each version (Fahrenheit or Celsius) has their own different zero point 4. Ratio (strongest): Data where the numbers mean something and there is a true zero a. Ex: GPA—The numbers tell you something b. MOST OF WHAT WE DO IS RATIO DATA!  Time Series  A set of values/data measured at successive points in time over a given time period  Reference example problems given in class o 1 column= Time nd o 2 column= Data  We will use it to measure future data o What we will be doing: Taking what is given and forecasting future data A. Components of a time series: 1. Trend – the gradual shifting of the time series value usually because of long term factors. a. Long-term = more than a year b. Long- term factors that affect trends include: i. Changes in the population  change in size ii. Changes in the demographic characteristics of population iii. Changes in technology iv. Changes in consumer preferences v. Economic changes mainly inflation 2. Cyclical – an up and down movement over a long time period  usually last MORE THAN a year a. Like a legit cycle of events: 3. Seasonal – shows changes in the historical data within ONE YEAR usually because of seasonal influences a. Seasonal change  weather 4. Irregular – explains changes in actual time series values which cannot be explained by any other components. a. Irregular components CANNOT be predicted using historical data. Usually due to acts of God: unanticipated, nonrecurring factors. i. Ex: tornado, hurricane, flood, etc. 1. One can see it coming, cannot predict what it will do or when another will come B. Time Series Forecasting using SMOOTHING method: a. Smoothing – smoothing out irregular factors because smoothed values have less variation than the raw data b. Smoothing techniques: i. Simple moving average (simplest) ii. Weighted moving average 1. Problem will give “weight”  check problem #2 for example iii. Exponential smoothing (hardest to grasp)  If the problem sates just “moving average”, go with the simple moving method  check prob. #1 for example  After working all, we can determine which way to work it is best NOTE FROM DR.TANNER: PLEASE ONLY USE THESE NOTES AS A REFERENCE. DO NOT MISS CLASS JUST BECAUSE YOU CAN BUY THE NOTES.


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