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Critical Thinking Module

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Critical Thinking Module
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  Time Series and Forecasting Data Sets   Part I (Use time series data sets)  i.   Choose one data set. Graph the data and briefly discuss the behavior. Which forecasting method do you think will perform better? Why? -   I choose the Auto Monthly Retail Sales in Millions of Dollars, other Motor vehicles from January 1992 until Jan 2017. o   In January 1992 auto sales goes up. But the auto sales start to drop sometime around in August 2007 and it continues to drop up to November of 2008, but then auto sales starts to go up again around February 2009 and then it continues to increase until December of 2016! o   I think Single Exponential Smoothing will do better, because Single Exponential Smoothing gives more relevance to what actually happen versus what we forecasted. ii.   Use the data from Jan’92 to Dec’16 to run three forecasting methods: MA (choose the number of  periods you consider adequate), exponential smoothing (choose the alpha you consider adequate), and a simple regression (with X representing the month). What model performs  better? Why? o   Single Exponential Smoothing performs better. o   The accuracy measures (MAPE = 6, MAD = 3807, and MSD = 26092172) of the Single Exponential Smoothing data with an alpha of 0.8 is lower. Using an alpha of 0.8 is a  better representation of the actual behavior of the data. Because we are giving more weight to what actually happen versus what was forecasted. Additionally, on alpha of 0.8 is giving more relevance to what actually happen versus what we forecasted. iii.   Split your data in two: from Jan’92 to Dec’07 and from Jan’10 to Jan’17. Run a regression analysis for both data sets. What can you conclude? What do the parameters tell you? Auto sales was higher from Jan’10 to Jan’17  (after the recession) than Jan’92 to Dec’07  (before the recession). A possible explanation of the increase of auto sales post-recession can be attributed to the government contribution to the auto industry which issued a lot of incentives to the potential buyers to boost the customers of getting rid of their older cars and buying more fuel-efficient cars. The accuracy measures for the data set of Jan’10 to Jan’17  (MAPE = 7, MAD = 4700, and MSD = 30804448) are lower than the data set of Jan’92 to Dec’07  (MAPE = 9, MAD = 4867, and MSD = 34106030). The parameters are saying that the Linear Trend Model  before the recession is 34945+211.24xMonth and the Linear Trend Model after the recession is 53342+435.7xMonth. The latter Linear Trend Model is lower. iv.   If a regression model for the data from Jan’08 to Dec’09 were run, how will the parameters look?   o   It will reflect the decrease in auto sales due to the recession that we went through. Additionally, the monthly retail sales regression equation is = 64645  –   816xMonth. Part II (Use the New Era Organic Foods Stores Franchise data set)    i.   Using the franchise data for New Era Organic Foods Stores, to produce a model to predict the annual net sales from the other variables. Please discuss the significance of the model and the  predictors. Make any additional observations that you consider pertinent regarding the model you run. o   X1 = annual net sales The number of sq. ft., inventory, amount spent on advertising, size of sales district, and number of competing stores in the district are all The errors are normally distributed. There is a linear behavior. All predictors are appropriate  because al p values are less than 0.05. Deliverable:  Provide your answers in a 2- to 4-page response, including your Excel Files.
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