Develop a three-period moving average forecast for periods 13-15.Period1011121314Demand265359436261462Calculate the forecasts for periods 13-15 by using a three-period moving average model (enter your responses rounded to the nearest whole number).PeriodDemandForecast10265113591243613261

The given table shows the demand for the period of 10 to 14.

Answered: Develop a three-period moving average…

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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a. Using a weighted moving average with weights of 0.60, 0.30, and 0.10, find the July forecast. b. Using a simple 3-month moving average, find the July forecast. c. Using single exponential smoothing with α = 0.2 and a June forecast ≈ 13, find the July forecast. Make whatever assumptions you wish. d. Using simple linear regression analysis, calculate the regression equation for the demand data. e. Using the regression equation in d, calculate the forecast for July.
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Please assist with this Forecasting question. Show all workings and provide explanations. Also, show all input in the table please
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Based on the information in the following table, use the THREE-MONTH MOVING AVERAGE Approach to forecast the demand for periods 4-12 PERIOD MONTH Demand Forecast Jan 57 2. Feb 69 3. Mar 210 Apr 54 May 207 Jun: 108 7. Jul 210 Aug 78 69 Sep 156 10 Oct 114 11 Nov 135 12 Dec 171
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.
Use the gas prices data set below to calculate a 3-month Weighted Moving Average (WMA) forecast for the average gas prices and predict the average retail gasoline price for February 2015. Use the weights of 0.6, 0.3, and 0.1 for the 3-month WMA, where the first weight is used for the most recent month and the last weight is used for the least recent month. Round your answer to two decimal places, if necessary. Month-Year Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Average Gas Price $3.766 $3.688 $3.565 $3.484 $3.255 $2.997 $2.632 $2.208 Copy Data
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.
Describe the patterns in quantity sold and own and rival prices during this time period using basic descriptive statistics. Graphs are welcome as well. Take the logs of the variables, and estimate the demand function. Interpret the R-square. Interpret the coefficients for logP and logPsub Interpret the p-values associated with each independent variable Are consumers price sensitive? Why or why not? (be as precise as you can – you have estimates!). Does this price sensitivity make sense given the good we are examining? How sensitive are our consumers to changes in the rival good’s price? Explain in detail. Suppose we decide to charge a per ounce price of $2, while at the same time our rival charges a price of $2.15. All else equal, what would you expect sales to be? How confident are you in your forecast? Suppose we are charging a price of $2 and our current marginal cost is $1.50 Are we maximizing profits at this price? If not, should we raise or lower price? Why?
Note:- Do not provide handwritten solution. Maintain accuracy and quality in your answer. Take care of plagiarism. Answer completely. You will get up vote for sure.
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.
(b) Table 1 shows consumables usage data from a leading manufacturer. Forecast the demand for August, September and October using: i) a three-month moving average ii) a four-month weighted moving average using 50 percent of the actual usage for the most recent month, 20 percent of two months ago, 15 percent of three months ago and 15 percent of four months ago. Using an example, explain what are the benefits of using the weighted moving average method of forecasting instead? Month February 2023 March 2023 April 2023 May 2023 June 2023 July 2023 Consumables Used 884 892 972 990 956 880

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