4. The sales (in millions of dollars) for an 18-month period are as follows.MonthSalesMonthSales18401010851225118403805128054910138759801491011201584077701697381085171050840181085a. Compare a three-month moving average forecast with an exponential smoothingforecast. Use a = 0.3. Which provides the better forecasts based on MSE?b. What is the forecast for the next month?

Forecasting of time series data is done to predict the value for the future using the past trends.…

Answered: The sales (in millions of dollars) for…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Use the border crossings data below and a = = 0.3 and ẞ = 0.2. Truck Crossings Month February 113,691 March 139,204 April 121,498 May 127,511 June 131,500 July 105,245 August 126,606 Copy Data Step 2 of 2: Calculate the adjusted exponential smoothing forecast for September 2011. Assume the forecast component for February 2011 to be the actual truck crossing value minus the initial trend. Let the initial trend be 10,000 trucks. Round your answer to two decimal places.
For the data set, perform exponential smoothing with alpha = 0.1 to determine the forecast for week 20. week Sales (1000s of gallons) 1 17 2 21 3 19 4 23 5 18 6 16 7 20 8 18 9 22 10 20 11 15 12 22 13 31 14 34 15 31 16 33 17 28 18 32 19 30
Does this graph have trends and/or seasonality? What forecasting method is suitable for this data?
Suppose seafood price and quantity data for the years 2000 and 2009 follow. Use 2000 as the base period. 2000 2000 Price ($/lb) 2009 Seafood Qty. (Ib) Price ($/lb) Halibut 75,090 2.01 2.33 Lobster 83,180 3.62 3.09 Tuna 50,779 1.87 1.97 (a) Compute a price relative for each type of seafood. (Round your answers to one decimal place.) Seafood Price Relative Halibut Lobster Tuna (b) Compute a weighted aggregate price index for the seafood catch. (Round your answer to one decimal place.) I 2009 - Comment on the change in seafood prices over the nine-year period. (Enter your percentage as a positive value. Round your answer to one decimal place.) Seafood prices have --Select--- v by % over the 9-year period according to the index.
Below is data collected over 6 specific years. The data collected is the Consumer Price Index (CPI) and the cost of a slice of tuna. We would like to build a model using the CPI to predict the cost of a slice of pizza in a given year. Year 1960 1973 1986 1995 2002 2003 CPI (x) 30.2 48.3 112.3 162.2 191.9 197.8 Cost of a slice of tuna(y) 0.15 0.35 1.00 1.25 1.75 2.00 Plot the data. Are we justified in creating a Linear Regression model to predict the price of a pizza slice using the CPI? Why or why not? Calculate the descriptive statistics. Calculate the sums of squares Calculate the slope, intercept, and correlation. What is this correlation value telling us? Assemble the prediction equation, and interpret the slope within the context of the problem. State the hypotheses for the hypothesis test for regression.
Consider the following data: Month Jan-14 Feb-14 Profit ($) 16,113 16,263 Monthly Profit of an Auto Repair Shop Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 15,055 17,117 18,763 17,210 19,141 18,468 20,254 Step 3 of 4: Determine the exponential smoothing forecast for the next time period using a smoothing constant of 0.35. If necessary, round your answer to one decimal place.
The below data represent the advertising expenditure and sales of Liu Liu Ltd from year 2010 to 2019. Table: Advertising Expenditure vs. Sales from 2010 to 2019 Year Advert. Expenditure Sales Growth rate of Ad. Expenditure Growth rate of Sales (x, £000) (y, £000) 2010 19 84 2011 22 108 18% 29% 2012 32 82 46% -24% 2013 42 86 32% 6% 2014 47 84 11% -3% 2015 46 94 -2% 11% 2016 58 103 27% 10% 2017 63 115 8% 12% 2018 70 106 11% -8% 2019 80 142 15% 34% Total 479 1004 a. What is the statistical relationship shown in the table and list out 3 characteristics of this relationship?
the table shows the percent of households with internet access for selected years from 2009 and projected through 2015. Year Percent of households 2009 67 2010 70 2011 72.5 2012 75 2013 76.5 2014 77.2 2015 78 Use the model to predict the percent of households with internet access in 2022.
Month Actual demand Forecasted Demand 1 950 1000 2 1070 1000 3 1100 1000 4 960 1000 5 1090 1000 6 1050 1000 For the dataset above, compute the overall MAPE.
Below is a table containing data on product demand for the most recent three months along with the forecasts that had been made for those three previous months. Calculate the MAPE. Month Demand Forecast 1 308 310 388 390 344 342 2.

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